{
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  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "## **Assignment #2: Classification, Regression, Clustering, Evaluation**"
      ],
      "metadata": {
        "id": "w84cR3AZIU0e"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Part 1: Dataset Description\n",
        "\n",
        "## Dataset Source\n",
        "The dataset used in this project is the **E-Commerce Customer Behavior and Sales Analysis Dataset**, obtained from Kaggle:  \n",
        "https://www.kaggle.com/datasets/umuttuygurr/e-commerce-customer-behavior-and-sales-analysis-tr\n",
        "\n",
        "---\n",
        "\n",
        "## Dataset Overview\n",
        "This dataset contains transactional and behavioral data of customers in an e-commerce environment. It includes information about customers, products, and purchase activity, allowing analysis of spending behavior and sales patterns.\n",
        "\n",
        "---\n",
        "\n",
        "## Dataset Size and Features\n",
        "The dataset includes a large number of observations with multiple features, including both numerical and categorical variables.\n",
        "\n",
        "### Examples of features:\n",
        "\n",
        "- **Customer-related features:** Customer ID, Gender, Age, Location  \n",
        "- **Product-related features:** Product Category, Product Name  \n",
        "- **Transaction-related features:** Purchase Frequency, Quantity, Total Spend  \n",
        "\n",
        "This combination of feature types makes the dataset suitable for exploratory analysis and predictive modeling.\n",
        "\n",
        "---\n",
        "\n",
        "## Target Variable\n",
        "The target variable selected for this project is:\n",
        "\n",
        "- **Total Spend**\n",
        "\n",
        "This is a numerical variable representing the total amount of money spent by a customer, making this a regression problem.\n",
        "\n",
        "---\n",
        "\n",
        "## Research Question\n",
        "The main goal of this project is to analyze customer behavior and identify the factors that influence spending patterns. Additionally, the project aims to build a model that can predict how much a customer is likely to spend based on their characteristics and purchasing behavior."
      ],
      "metadata": {
        "id": "GFaHgQJyKwa5"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "<br><br><br>\n",
        "\n",
        "---\n",
        "\n",
        "<br><br><br>"
      ],
      "metadata": {
        "id": "4t2QNyE6IPKS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "uploaded = files.upload()"
      ],
      "metadata": {
        "id": "2Cam1pnuwSGr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 73
        },
        "outputId": "55cf6dae-46cb-4652-c567-e682f044f711"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-3ef3af96-168b-4707-8a88-1fe5ae6e5f00\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-3ef3af96-168b-4707-8a88-1fe5ae6e5f00\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script>// Copyright 2017 Google LLC\n",
              "//\n",
              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
              " */\n",
              "(function(scope) {\n",
              "function span(text, styleAttributes = {}) {\n",
              "  const element = document.createElement('span');\n",
              "  element.textContent = text;\n",
              "  for (const key of Object.keys(styleAttributes)) {\n",
              "    element.style[key] = styleAttributes[key];\n",
              "  }\n",
              "  return element;\n",
              "}\n",
              "\n",
              "// Max number of bytes which will be uploaded at a time.\n",
              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
              "\n",
              "function _uploadFiles(inputId, outputId) {\n",
              "  const steps = uploadFilesStep(inputId, outputId);\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  // Cache steps on the outputElement to make it available for the next call\n",
              "  // to uploadFilesContinue from Python.\n",
              "  outputElement.steps = steps;\n",
              "\n",
              "  return _uploadFilesContinue(outputId);\n",
              "}\n",
              "\n",
              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
              "function _uploadFilesContinue(outputId) {\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  const steps = outputElement.steps;\n",
              "\n",
              "  const next = steps.next(outputElement.lastPromiseValue);\n",
              "  return Promise.resolve(next.value.promise).then((value) => {\n",
              "    // Cache the last promise value to make it available to the next\n",
              "    // step of the generator.\n",
              "    outputElement.lastPromiseValue = value;\n",
              "    return next.value.response;\n",
              "  });\n",
              "}\n",
              "\n",
              "/**\n",
              " * Generator function which is called between each async step of the upload\n",
              " * process.\n",
              " * @param {string} inputId Element ID of the input file picker element.\n",
              " * @param {string} outputId Element ID of the output display.\n",
              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
              " */\n",
              "function* uploadFilesStep(inputId, outputId) {\n",
              "  const inputElement = document.getElementById(inputId);\n",
              "  inputElement.disabled = false;\n",
              "\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  outputElement.innerHTML = '';\n",
              "\n",
              "  const pickedPromise = new Promise((resolve) => {\n",
              "    inputElement.addEventListener('change', (e) => {\n",
              "      resolve(e.target.files);\n",
              "    });\n",
              "  });\n",
              "\n",
              "  const cancel = document.createElement('button');\n",
              "  inputElement.parentElement.appendChild(cancel);\n",
              "  cancel.textContent = 'Cancel upload';\n",
              "  const cancelPromise = new Promise((resolve) => {\n",
              "    cancel.onclick = () => {\n",
              "      resolve(null);\n",
              "    };\n",
              "  });\n",
              "\n",
              "  // Wait for the user to pick the files.\n",
              "  const files = yield {\n",
              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
              "    response: {\n",
              "      action: 'starting',\n",
              "    }\n",
              "  };\n",
              "\n",
              "  cancel.remove();\n",
              "\n",
              "  // Disable the input element since further picks are not allowed.\n",
              "  inputElement.disabled = true;\n",
              "\n",
              "  if (!files) {\n",
              "    return {\n",
              "      response: {\n",
              "        action: 'complete',\n",
              "      }\n",
              "    };\n",
              "  }\n",
              "\n",
              "  for (const file of files) {\n",
              "    const li = document.createElement('li');\n",
              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
              "    li.append(span(\n",
              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
              "        `last modified: ${\n",
              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
              "                                    'n/a'} - `));\n",
              "    const percent = span('0% done');\n",
              "    li.appendChild(percent);\n",
              "\n",
              "    outputElement.appendChild(li);\n",
              "\n",
              "    const fileDataPromise = new Promise((resolve) => {\n",
              "      const reader = new FileReader();\n",
              "      reader.onload = (e) => {\n",
              "        resolve(e.target.result);\n",
              "      };\n",
              "      reader.readAsArrayBuffer(file);\n",
              "    });\n",
              "    // Wait for the data to be ready.\n",
              "    let fileData = yield {\n",
              "      promise: fileDataPromise,\n",
              "      response: {\n",
              "        action: 'continue',\n",
              "      }\n",
              "    };\n",
              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
              "    let position = 0;\n",
              "    do {\n",
              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
              "      const chunk = new Uint8Array(fileData, position, length);\n",
              "      position += length;\n",
              "\n",
              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
              "      yield {\n",
              "        response: {\n",
              "          action: 'append',\n",
              "          file: file.name,\n",
              "          data: base64,\n",
              "        },\n",
              "      };\n",
              "\n",
              "      let percentDone = fileData.byteLength === 0 ?\n",
              "          100 :\n",
              "          Math.round((position / fileData.byteLength) * 100);\n",
              "      percent.textContent = `${percentDone}% done`;\n",
              "\n",
              "    } while (position < fileData.byteLength);\n",
              "  }\n",
              "\n",
              "  // All done.\n",
              "  yield {\n",
              "    response: {\n",
              "      action: 'complete',\n",
              "    }\n",
              "  };\n",
              "}\n",
              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
              "scope.google.colab._files = {\n",
              "  _uploadFiles,\n",
              "  _uploadFilesContinue,\n",
              "};\n",
              "})(self);\n",
              "</script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving ecommerce_customer_behavior_dataset_v2.csv to ecommerce_customer_behavior_dataset_v2 (1).csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "df = pd.read_csv('ecommerce_customer_behavior_dataset_v2.csv')"
      ],
      "metadata": {
        "id": "Fq4G9pi8wY00"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Part 2: Exploratory Data Analysis (EDA)\n",
        "\n",
        "In this section, we explore the dataset to understand its structure, identify potential issues, and uncover patterns that may help answer the research question."
      ],
      "metadata": {
        "id": "UFaPD2nDwtyY"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **1. Data Cleaning**"
      ],
      "metadata": {
        "id": "KjHibVxh0FSf"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Missing Values**\n",
        "\n",
        "No missing values were found in the dataset. This indicates that the data is complete and does not require any handling or imputation for missing entries."
      ],
      "metadata": {
        "id": "b57D7vrgx-tZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Check for missing values\n",
        "df.isnull().sum()"
      ],
      "metadata": {
        "id": "0ch5l8tIK1Dt",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 649
        },
        "outputId": "5e1ba0d5-95b3-4fa3-fa63-c38b71ce6ea0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Order_ID                    0\n",
              "Customer_ID                 0\n",
              "Date                        0\n",
              "Age                         0\n",
              "Gender                      0\n",
              "City                        0\n",
              "Product_Category            0\n",
              "Unit_Price                  0\n",
              "Quantity                    0\n",
              "Discount_Amount             0\n",
              "Total_Amount                0\n",
              "Payment_Method              0\n",
              "Device_Type                 0\n",
              "Session_Duration_Minutes    0\n",
              "Pages_Viewed                0\n",
              "Is_Returning_Customer       0\n",
              "Delivery_Time_Days          0\n",
              "Customer_Rating             0\n",
              "dtype: int64"
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              "      <td>0</td>\n",
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              "      <th>Discount_Amount</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Total_Amount</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Payment_Method</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Device_Type</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Session_Duration_Minutes</th>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>Pages_Viewed</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Is_Returning_Customer</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Delivery_Time_Days</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Customer_Rating</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Duplicate Entries**\n",
        "\n",
        "No duplicate records were found in the dataset. This ensures that each observation is unique and the analysis is not affected by repeated data."
      ],
      "metadata": {
        "id": "JyczRglcySZi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Check for duplicate rows\n",
        "df.duplicated().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GfhfrjyryJlO",
        "outputId": "664fdbd4-c320-4a77-a695-7435c7d10815"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "np.int64(0)"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Scaling / Normalization**\n",
        "\n",
        "The numerical features show differences in scale, as some variables (such as Total_Amount and Unit_Price) have much larger values compared to others (such as Quantity or Customer_Rating).\n",
        "\n",
        "However, since these values represent real-world measurements, no scaling was applied at this stage, as it is not necessary for exploratory analysis."
      ],
      "metadata": {
        "id": "9vCqjNqoy4tl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Select only numerical columns to examine their scale\n",
        "df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "9Sz3CunxyvFn",
        "outputId": "d6509e1c-8835-4769-b40b-501a5a7bbb25"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                Age    Unit_Price      Quantity  Discount_Amount  \\\n",
              "count  17049.000000  17049.000000  17049.000000     17049.000000   \n",
              "mean      34.945745    447.901689      3.011379        69.788135   \n",
              "std       11.046855    722.319705      1.417027       240.704662   \n",
              "min       18.000000      5.050000      1.000000         0.000000   \n",
              "25%       26.000000     73.260000      2.000000         0.000000   \n",
              "50%       35.000000    174.680000      3.000000         0.000000   \n",
              "75%       42.000000    494.570000      4.000000        32.710000   \n",
              "max       75.000000   7900.010000      5.000000      6538.290000   \n",
              "\n",
              "       Total_Amount  Session_Duration_Minutes  Pages_Viewed  \\\n",
              "count  17049.000000              17049.000000  17049.000000   \n",
              "mean    1277.438711                 14.535633      9.003109   \n",
              "std     2358.436375                  2.925524      2.259954   \n",
              "min        6.210000                  4.000000      1.000000   \n",
              "25%      172.970000                 13.000000      7.000000   \n",
              "50%      455.850000                 15.000000      9.000000   \n",
              "75%     1267.750000                 17.000000     11.000000   \n",
              "max    37852.050000                 26.000000     18.000000   \n",
              "\n",
              "       Delivery_Time_Days  Customer_Rating  \n",
              "count        17049.000000     17049.000000  \n",
              "mean             6.503607         3.899408  \n",
              "std              3.488787         1.128803  \n",
              "min              1.000000         1.000000  \n",
              "25%              4.000000         3.000000  \n",
              "50%              6.000000         4.000000  \n",
              "75%              8.000000         5.000000  \n",
              "max             25.000000         5.000000  "
            ],
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              "\n",
              "  <div id=\"df-a280ad66-9f64-4ec9-9440-2caf06f56aa6\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th></th>\n",
              "      <th>Age</th>\n",
              "      <th>Unit_Price</th>\n",
              "      <th>Quantity</th>\n",
              "      <th>Discount_Amount</th>\n",
              "      <th>Total_Amount</th>\n",
              "      <th>Session_Duration_Minutes</th>\n",
              "      <th>Pages_Viewed</th>\n",
              "      <th>Delivery_Time_Days</th>\n",
              "      <th>Customer_Rating</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>17049.000000</td>\n",
              "      <td>17049.000000</td>\n",
              "      <td>17049.000000</td>\n",
              "      <td>17049.000000</td>\n",
              "      <td>17049.000000</td>\n",
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              "      <td>17049.000000</td>\n",
              "      <td>17049.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>34.945745</td>\n",
              "      <td>447.901689</td>\n",
              "      <td>3.011379</td>\n",
              "      <td>69.788135</td>\n",
              "      <td>1277.438711</td>\n",
              "      <td>14.535633</td>\n",
              "      <td>9.003109</td>\n",
              "      <td>6.503607</td>\n",
              "      <td>3.899408</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>11.046855</td>\n",
              "      <td>722.319705</td>\n",
              "      <td>1.417027</td>\n",
              "      <td>240.704662</td>\n",
              "      <td>2358.436375</td>\n",
              "      <td>2.925524</td>\n",
              "      <td>2.259954</td>\n",
              "      <td>3.488787</td>\n",
              "      <td>1.128803</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>18.000000</td>\n",
              "      <td>5.050000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>6.210000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>26.000000</td>\n",
              "      <td>73.260000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>172.970000</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>3.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>35.000000</td>\n",
              "      <td>174.680000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>455.850000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>4.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>42.000000</td>\n",
              "      <td>494.570000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>32.710000</td>\n",
              "      <td>1267.750000</td>\n",
              "      <td>17.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>5.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>75.000000</td>\n",
              "      <td>7900.010000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>6538.290000</td>\n",
              "      <td>37852.050000</td>\n",
              "      <td>26.000000</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>25.000000</td>\n",
              "      <td>5.000000</td>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6015.54005533633,\n        \"min\": 11.046855366028149,\n        \"max\": 17049.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          34.94574461845269,\n          35.0,\n          17049.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Unit_Price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6139.490041879081,\n        \"min\": 5.05,\n        \"max\": 17049.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          447.90168866209166,\n          174.68,\n          17049.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Quantity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6026.750617579844,\n        \"min\": 1.0,\n        \"max\": 17049.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          3.0113789665082997,\n          3.0,\n          17049.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Discount_Amount\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6116.697936142172,\n        \"min\": 0.0,\n        \"max\": 17049.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          17049.0,\n          69.78813537450877,\n          6538.29\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Total_Amount\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13500.61038093157,\n        \"min\": 6.21,\n        \"max\": 37852.05,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1277.4387113613702,\n          455.85,\n          17049.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Session_Duration_Minutes\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6023.06619254606,\n        \"min\": 2.9255244469469743,\n        \"max\": 17049.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          14.535632588421608,\n          15.0,\n          17049.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Pages_Viewed\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6024.841831549192,\n        \"min\": 1.0,\n        \"max\": 17049.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          9.003108686726494,\n          9.0,\n          17049.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Delivery_Time_Days\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6025.009233707784,\n        \"min\": 1.0,\n        \"max\": 17049.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          6.503607249692064,\n          6.0,\n          17049.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Customer_Rating\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6026.568854167135,\n        \"min\": 1.0,\n        \"max\": 17049.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          17049.0,\n          3.89940758988797,\n          4.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Parsing Dates**\n",
        "\n",
        "The dataset includes a date column ('Date'), which represents the transaction time. The column is already properly formatted and does not require additional parsing at this stage."
      ],
      "metadata": {
        "id": "hd2DLb00zNqE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Check if there are any date columns\n",
        "df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-Eq17Ht5zE2L",
        "outputId": "c1f63699-0f89-47d9-e7c8-771823c1e193"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Order_ID', 'Customer_ID', 'Date', 'Age', 'Gender', 'City',\n",
              "       'Product_Category', 'Unit_Price', 'Quantity', 'Discount_Amount',\n",
              "       'Total_Amount', 'Payment_Method', 'Device_Type',\n",
              "       'Session_Duration_Minutes', 'Pages_Viewed', 'Is_Returning_Customer',\n",
              "       'Delivery_Time_Days', 'Customer_Rating'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Fixing Typos**\n",
        "\n",
        "Categorical values were examined by reviewing their unique entries. All categories appear consistent and properly formatted, with no spelling errors or irregular values detected. Therefore, no corrections were required."
      ],
      "metadata": {
        "id": "7P5NYPqIz7WD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Display unique values in categorical columns to check for typos\n",
        "categorical_cols = ['Gender', 'City', 'Product_Category', 'Payment_Method', 'Device_Type']\n",
        "\n",
        "for col in categorical_cols:\n",
        "    print(f\"{col}:\")\n",
        "    print(df[col].unique())\n",
        "    print(\"-\" * 40)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "It_s7Vbczzhs",
        "outputId": "9260aca0-a633-4c0e-eeca-f168990db8f4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gender:\n",
            "['Male' 'Female' 'Other']\n",
            "----------------------------------------\n",
            "City:\n",
            "['Ankara' 'Istanbul' 'Konya' 'Izmir' 'Kayseri' 'Bursa' 'Gaziantep' 'Adana'\n",
            " 'Eskisehir' 'Antalya']\n",
            "----------------------------------------\n",
            "Product_Category:\n",
            "['Books' 'Home & Garden' 'Sports' 'Food' 'Beauty' 'Toys' 'Fashion'\n",
            " 'Electronics']\n",
            "----------------------------------------\n",
            "Payment_Method:\n",
            "['Digital Wallet' 'Credit Card' 'Bank Transfer' 'Debit Card'\n",
            " 'Cash on Delivery']\n",
            "----------------------------------------\n",
            "Device_Type:\n",
            "['Mobile' 'Desktop' 'Tablet']\n",
            "----------------------------------------\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Inconsistencies**\n",
        "\n",
        "Categorical features were examined by checking the number of unique values in each column. Features such as Gender, City, Product_Category, Payment_Method, and Device_Type show a reasonable number of categories, indicating consistent and well-structured data.\n",
        "\n",
        "Columns like Order_ID and Customer_ID were excluded from this evaluation, as they are identifiers and naturally contain many unique values."
      ],
      "metadata": {
        "id": "8RPwFwNyzfe5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Check unique values in categorical columns\n",
        "df.select_dtypes(include='object').nunique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 335
        },
        "id": "SxXJNZhxzU4M",
        "outputId": "729b99d7-65ef-4c26-8d6a-485fd60651ec"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Order_ID            17049\n",
              "Customer_ID          5000\n",
              "Date                  450\n",
              "Gender                  3\n",
              "City                   10\n",
              "Product_Category        8\n",
              "Payment_Method          5\n",
              "Device_Type             3\n",
              "dtype: int64"
            ],
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Order_ID</th>\n",
              "      <td>17049</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Customer_ID</th>\n",
              "      <td>5000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <td>450</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Gender</th>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>City</th>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Product_Category</th>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Payment_Method</th>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Device_Type</th>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Conclusion**\n",
        "\n",
        "The dataset was thoroughly examined for data quality issues, including missing values, duplicates, inconsistencies, and formatting problems. No significant issues were found, indicating that the dataset is clean and ready for further analysis."
      ],
      "metadata": {
        "id": "eFKy39KS0dfd"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **2. Outlier Detection & Handling**"
      ],
      "metadata": {
        "id": "RxYU04fW0hlv"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Outlier Detection – Value Ranges**\n",
        "\n",
        "The range of numerical features was examined using minimum and maximum values to identify unusually large or small observations."
      ],
      "metadata": {
        "id": "1HCGx7Jm3h5v"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Display summary statistics to examine ranges\n",
        "df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "ZI2JslQv3mwu",
        "outputId": "8831c9f0-823d-4b3a-c884-cb4eb821143a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                Age    Unit_Price      Quantity  Discount_Amount  \\\n",
              "count  17049.000000  17049.000000  17049.000000     17049.000000   \n",
              "mean      34.945745    447.901689      3.011379        69.788135   \n",
              "std       11.046855    722.319705      1.417027       240.704662   \n",
              "min       18.000000      5.050000      1.000000         0.000000   \n",
              "25%       26.000000     73.260000      2.000000         0.000000   \n",
              "50%       35.000000    174.680000      3.000000         0.000000   \n",
              "75%       42.000000    494.570000      4.000000        32.710000   \n",
              "max       75.000000   7900.010000      5.000000      6538.290000   \n",
              "\n",
              "       Total_Amount  Session_Duration_Minutes  Pages_Viewed  \\\n",
              "count  17049.000000              17049.000000  17049.000000   \n",
              "mean    1277.438711                 14.535633      9.003109   \n",
              "std     2358.436375                  2.925524      2.259954   \n",
              "min        6.210000                  4.000000      1.000000   \n",
              "25%      172.970000                 13.000000      7.000000   \n",
              "50%      455.850000                 15.000000      9.000000   \n",
              "75%     1267.750000                 17.000000     11.000000   \n",
              "max    37852.050000                 26.000000     18.000000   \n",
              "\n",
              "       Delivery_Time_Days  Customer_Rating  \n",
              "count        17049.000000     17049.000000  \n",
              "mean             6.503607         3.899408  \n",
              "std              3.488787         1.128803  \n",
              "min              1.000000         1.000000  \n",
              "25%              4.000000         3.000000  \n",
              "50%              6.000000         4.000000  \n",
              "75%              8.000000         5.000000  \n",
              "max             25.000000         5.000000  "
            ],
            "text/html": [
              "\n",
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              "\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Age</th>\n",
              "      <th>Unit_Price</th>\n",
              "      <th>Quantity</th>\n",
              "      <th>Discount_Amount</th>\n",
              "      <th>Total_Amount</th>\n",
              "      <th>Session_Duration_Minutes</th>\n",
              "      <th>Pages_Viewed</th>\n",
              "      <th>Delivery_Time_Days</th>\n",
              "      <th>Customer_Rating</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>17049.000000</td>\n",
              "      <td>17049.000000</td>\n",
              "      <td>17049.000000</td>\n",
              "      <td>17049.000000</td>\n",
              "      <td>17049.000000</td>\n",
              "      <td>17049.000000</td>\n",
              "      <td>17049.000000</td>\n",
              "      <td>17049.000000</td>\n",
              "      <td>17049.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>34.945745</td>\n",
              "      <td>447.901689</td>\n",
              "      <td>3.011379</td>\n",
              "      <td>69.788135</td>\n",
              "      <td>1277.438711</td>\n",
              "      <td>14.535633</td>\n",
              "      <td>9.003109</td>\n",
              "      <td>6.503607</td>\n",
              "      <td>3.899408</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>11.046855</td>\n",
              "      <td>722.319705</td>\n",
              "      <td>1.417027</td>\n",
              "      <td>240.704662</td>\n",
              "      <td>2358.436375</td>\n",
              "      <td>2.925524</td>\n",
              "      <td>2.259954</td>\n",
              "      <td>3.488787</td>\n",
              "      <td>1.128803</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>18.000000</td>\n",
              "      <td>5.050000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>6.210000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>26.000000</td>\n",
              "      <td>73.260000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>172.970000</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>3.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>35.000000</td>\n",
              "      <td>174.680000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>455.850000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>4.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>42.000000</td>\n",
              "      <td>494.570000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>32.710000</td>\n",
              "      <td>1267.750000</td>\n",
              "      <td>17.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>5.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>75.000000</td>\n",
              "      <td>7900.010000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>6538.290000</td>\n",
              "      <td>37852.050000</td>\n",
              "      <td>26.000000</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>25.000000</td>\n",
              "      <td>5.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e9ec055f-2340-4044-8902-80cfe4e1e142')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-e9ec055f-2340-4044-8902-80cfe4e1e142 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-e9ec055f-2340-4044-8902-80cfe4e1e142');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6015.54005533633,\n        \"min\": 11.046855366028149,\n        \"max\": 17049.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          34.94574461845269,\n          35.0,\n          17049.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Unit_Price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6139.490041879081,\n        \"min\": 5.05,\n        \"max\": 17049.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          447.90168866209166,\n          174.68,\n          17049.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Quantity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6026.750617579844,\n        \"min\": 1.0,\n        \"max\": 17049.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          3.0113789665082997,\n          3.0,\n          17049.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Discount_Amount\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6116.697936142172,\n        \"min\": 0.0,\n        \"max\": 17049.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          17049.0,\n          69.78813537450877,\n          6538.29\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Total_Amount\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13500.61038093157,\n        \"min\": 6.21,\n        \"max\": 37852.05,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1277.4387113613702,\n          455.85,\n          17049.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Session_Duration_Minutes\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6023.06619254606,\n        \"min\": 2.9255244469469743,\n        \"max\": 17049.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          14.535632588421608,\n          15.0,\n          17049.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Pages_Viewed\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6024.841831549192,\n        \"min\": 1.0,\n        \"max\": 17049.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          9.003108686726494,\n          9.0,\n          17049.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Delivery_Time_Days\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6025.009233707784,\n        \"min\": 1.0,\n        \"max\": 17049.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          6.503607249692064,\n          6.0,\n          17049.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Customer_Rating\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6026.568854167135,\n        \"min\": 1.0,\n        \"max\": 17049.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          17049.0,\n          3.89940758988797,\n          4.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Distribution Analysis**\n",
        "\n",
        "Distribution plots were used to visualize how values are spread and to detect extreme values at the edges of the data."
      ],
      "metadata": {
        "id": "1zIt3aZv3oan"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "features = ['Unit_Price', 'Total_Amount', 'Age']\n",
        "\n",
        "for col in features:\n",
        "    plt.figure(figsize=(6,4))\n",
        "    sns.histplot(df[col], bins=30, kde=True)\n",
        "    plt.title(f'Distribution of {col}')\n",
        "    plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "eBgCwn9X3plD",
        "outputId": "00017d96-d2e6-41c7-b5c0-610a11e713e4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x400 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Outlier Detection using IQR**\n",
        "\n",
        "\n",
        "Outliers were identified using the IQR method across all numerical features. The results show that several variables contain extreme values, particularly Unit_Price, Discount_Amount, and Total_Amount, which have a relatively high number of outliers.\n",
        "\n",
        "Other features, such as Quantity and Customer_Rating, show no outliers, indicating a more stable and limited range of values. Features like Age and Session_Duration_Minutes contain a small number of outliers, which may represent natural variation in customer behavior."
      ],
      "metadata": {
        "id": "p6E7PXid3uch"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Detect outliers using IQR\n",
        "import pandas as pd\n",
        "\n",
        "numeric_cols = df.select_dtypes(include=['int64','float64']).columns\n",
        "\n",
        "outlier_counts = {}\n",
        "\n",
        "for col in numeric_cols:\n",
        "    Q1 = df[col].quantile(0.25)\n",
        "    Q3 = df[col].quantile(0.75)\n",
        "    IQR = Q3 - Q1\n",
        "\n",
        "    lower = Q1 - 1.5 * IQR\n",
        "    upper = Q3 + 1.5 * IQR\n",
        "\n",
        "    outliers = df[(df[col] < lower) | (df[col] > upper)]\n",
        "    outlier_counts[col] = len(outliers)\n",
        "\n",
        "pd.DataFrame(list(outlier_counts.items()), columns=['Feature','Outliers_Count'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 332
        },
        "id": "3V2DeAA13xYM",
        "outputId": "fc69c058-aad2-428b-8f89-d70fe38be545"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                    Feature  Outliers_Count\n",
              "0                       Age              50\n",
              "1                Unit_Price            1757\n",
              "2                  Quantity               0\n",
              "3           Discount_Amount            2789\n",
              "4              Total_Amount            1943\n",
              "5  Session_Duration_Minutes              85\n",
              "6              Pages_Viewed               1\n",
              "7        Delivery_Time_Days             475\n",
              "8           Customer_Rating               0"
            ],
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              "      <th>Feature</th>\n",
              "      <th>Outliers_Count</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Age</td>\n",
              "      <td>50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
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              "      <td>1757</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Quantity</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Discount_Amount</td>\n",
              "      <td>2789</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Total_Amount</td>\n",
              "      <td>1943</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Session_Duration_Minutes</td>\n",
              "      <td>85</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Pages_Viewed</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
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              "      <th>7</th>\n",
              "      <td>Delivery_Time_Days</td>\n",
              "      <td>475</td>\n",
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              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Customer_Rating</td>\n",
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              "        element.innerHTML = '';\n",
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              "type": "dataframe",
              "summary": "{\n  \"name\": \"pd\",\n  \"rows\": 9,\n  \"fields\": [\n    {\n      \"column\": \"Feature\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 9,\n        \"samples\": [\n          \"Delivery_Time_Days\",\n          \"Unit_Price\",\n          \"Session_Duration_Minutes\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Outliers_Count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1076,\n        \"min\": 0,\n        \"max\": 2789,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1757,\n          85,\n          50\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Handling outlier**\n",
        "\n",
        "The analysis of value ranges, distribution plots, and the IQR method indicates that most features follow a reasonable distribution. However, Unit_Price, Discount_Amount, and Total_Amount show strong right-skewness with extreme high values.\n",
        "\n",
        "Although these values likely represent real customer behavior, they may disproportionately influence the analysis. Therefore, capping was applied to these features using the IQR method to limit extreme values, while preserving the overall structure of the data. Other features were left unchanged, as they do not exhibit significant outliers."
      ],
      "metadata": {
        "id": "kkiQe8124I0z"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Apply capping to handle extreme values using IQR\n",
        "cols_to_cap = ['Unit_Price', 'Discount_Amount', 'Total_Amount']\n",
        "\n",
        "for col in cols_to_cap:\n",
        "    Q1 = df[col].quantile(0.25)\n",
        "    Q3 = df[col].quantile(0.75)\n",
        "    IQR = Q3 - Q1\n",
        "\n",
        "    lower = Q1 - 1.5 * IQR\n",
        "    upper = Q3 + 1.5 * IQR\n",
        "\n",
        "    df[col] = df[col].apply(lambda x: upper if x > upper else (lower if x < lower else x))"
      ],
      "metadata": {
        "id": "KQPBDikN4ny1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **3. Descriptive Statistics**"
      ],
      "metadata": {
        "id": "3awXT2kQ4658"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Feature Engineering: Discount Percentage"
      ],
      "metadata": {
        "id": "k4LCblc9stm4"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "To better understand the effect of discounts on purchasing behavior, a new feature called **Discount_Percentage** was created.\n",
        "\n",
        "Since the original discount values were provided as absolute amounts, they were converted into percentages relative to the product price using the following formula:\n",
        "\n",
        "Discount_Percentage = (Discount_Amount / Unit_Price) * 100\n",
        "\n",
        "This transformation allows for a more meaningful comparison across different products and enables clearer analysis of how discount levels influence total purchase amount."
      ],
      "metadata": {
        "id": "5gqnynS4sxxC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create Discount Percentage feature\n",
        "df['Discount_Percentage'] = (df['Discount_Amount'] / df['Unit_Price']) * 100"
      ],
      "metadata": {
        "id": "p8eb5w7rszvv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "Summary statistics were used to understand the central tendency and variability of numerical features, including mean, median, and spread of the data."
      ],
      "metadata": {
        "id": "AFIASHFH5FLR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Summary statistics for numerical features\n",
        "df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "6rmlAsdy5K1B",
        "outputId": "f1cbd78a-6a2d-4555-a93d-4a2e636e6899"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                Age    Unit_Price      Quantity  Discount_Amount  \\\n",
              "count  17049.000000  17049.000000  17049.000000     17049.000000   \n",
              "mean      34.945745    340.807234      3.011379        20.076818   \n",
              "std       11.046855    359.954989      1.417027        31.584750   \n",
              "min       18.000000      5.050000      1.000000         0.000000   \n",
              "25%       26.000000     73.260000      2.000000         0.000000   \n",
              "50%       35.000000    174.680000      3.000000         0.000000   \n",
              "75%       42.000000    494.570000      4.000000        32.710000   \n",
              "max       75.000000   1126.535000      5.000000        81.775000   \n",
              "\n",
              "       Total_Amount  Session_Duration_Minutes  Pages_Viewed  \\\n",
              "count  17049.000000              17049.000000  17049.000000   \n",
              "mean     883.569313                 14.535633      9.003109   \n",
              "std      951.496755                  2.925524      2.259954   \n",
              "min        6.210000                  4.000000      1.000000   \n",
              "25%      172.970000                 13.000000      7.000000   \n",
              "50%      455.850000                 15.000000      9.000000   \n",
              "75%     1267.750000                 17.000000     11.000000   \n",
              "max     2909.920000                 26.000000     18.000000   \n",
              "\n",
              "       Delivery_Time_Days  Customer_Rating  Discount_Percentage  \n",
              "count        17049.000000     17049.000000         17049.000000  \n",
              "mean             6.503607         3.899408            10.614939  \n",
              "std              3.488787         1.128803            18.798532  \n",
              "min              1.000000         1.000000             0.000000  \n",
              "25%              4.000000         3.000000             0.000000  \n",
              "50%              6.000000         4.000000             0.000000  \n",
              "75%              8.000000         5.000000            15.128978  \n",
              "max             25.000000         5.000000           109.790210  "
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Age</th>\n",
              "      <th>Unit_Price</th>\n",
              "      <th>Quantity</th>\n",
              "      <th>Discount_Amount</th>\n",
              "      <th>Total_Amount</th>\n",
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              "      <th>Discount_Percentage</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>17049.000000</td>\n",
              "      <td>17049.000000</td>\n",
              "      <td>17049.000000</td>\n",
              "      <td>17049.000000</td>\n",
              "      <td>17049.000000</td>\n",
              "      <td>17049.000000</td>\n",
              "      <td>17049.000000</td>\n",
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              "      <td>17049.000000</td>\n",
              "      <td>17049.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>34.945745</td>\n",
              "      <td>340.807234</td>\n",
              "      <td>3.011379</td>\n",
              "      <td>20.076818</td>\n",
              "      <td>883.569313</td>\n",
              "      <td>14.535633</td>\n",
              "      <td>9.003109</td>\n",
              "      <td>6.503607</td>\n",
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              "      <td>10.614939</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>11.046855</td>\n",
              "      <td>359.954989</td>\n",
              "      <td>1.417027</td>\n",
              "      <td>31.584750</td>\n",
              "      <td>951.496755</td>\n",
              "      <td>2.925524</td>\n",
              "      <td>2.259954</td>\n",
              "      <td>3.488787</td>\n",
              "      <td>1.128803</td>\n",
              "      <td>18.798532</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>18.000000</td>\n",
              "      <td>5.050000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>6.210000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>26.000000</td>\n",
              "      <td>73.260000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>172.970000</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>35.000000</td>\n",
              "      <td>174.680000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>455.850000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>42.000000</td>\n",
              "      <td>494.570000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>32.710000</td>\n",
              "      <td>1267.750000</td>\n",
              "      <td>17.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>15.128978</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>75.000000</td>\n",
              "      <td>1126.535000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>81.775000</td>\n",
              "      <td>2909.920000</td>\n",
              "      <td>26.000000</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>25.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>109.790210</td>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6015.54005533633,\n        \"min\": 11.046855366028149,\n        \"max\": 17049.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          34.94574461845269,\n          35.0,\n          17049.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Unit_Price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5907.945410909051,\n        \"min\": 5.05,\n        \"max\": 17049.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          340.8072341486304,\n          174.68,\n          17049.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Quantity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6026.750617579844,\n        \"min\": 1.0,\n        \"max\": 17049.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          3.0113789665082997,\n          3.0,\n          17049.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Discount_Amount\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6019.401961876253,\n        \"min\": 0.0,\n        \"max\": 17049.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          17049.0,\n          20.076818288462665,\n          81.775\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Total_Amount\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5762.872949458525,\n        \"min\": 6.21,\n        \"max\": 17049.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          883.5693131561969,\n          455.85,\n          17049.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Session_Duration_Minutes\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6023.06619254606,\n        \"min\": 2.9255244469469743,\n        \"max\": 17049.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          14.535632588421608,\n          15.0,\n          17049.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Pages_Viewed\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6024.841831549192,\n        \"min\": 1.0,\n        \"max\": 17049.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          9.003108686726494,\n          9.0,\n          17049.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Delivery_Time_Days\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6025.009233707784,\n        \"min\": 1.0,\n        \"max\": 17049.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          6.503607249692064,\n          6.0,\n          17049.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Customer_Rating\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6026.568854167135,\n        \"min\": 1.0,\n        \"max\": 17049.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          17049.0,\n          3.89940758988797,\n          4.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Discount_Percentage\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6020.047674470219,\n        \"min\": 0.0,\n        \"max\": 17049.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          17049.0,\n          10.614938818164248,\n          109.79020979020979\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Correlation Analysis**\n",
        "\n",
        "Correlation analysis was performed to examine the relationships between numerical features, with a specific focus on their association with the target variable (Total_Amount).\n",
        "\n",
        "Instead of presenting the full correlation matrix, the analysis highlights how each feature correlates directly with Total_Amount, allowing for a clearer identification of the most influential variables."
      ],
      "metadata": {
        "id": "bvXagoGT5N56"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Correlation with target only\n",
        "corr_target = df.corr(numeric_only=True)[['Total_Amount']].sort_values(by='Total_Amount', ascending=False)\n",
        "\n",
        "plt.figure(figsize=(5,6))\n",
        "sns.heatmap(corr_target, annot=True, cmap='coolwarm', fmt=\".2f\")\n",
        "\n",
        "plt.title('Correlation with Total Amount')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 545
        },
        "id": "3g_Aok6D5Pp_",
        "outputId": "a896b334-f792-408f-d999-20efb5c64240"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Correlation with Target Variable**\n",
        "\n",
        "The relationship between numerical features and the target variable (Total_Amount) was examined to identify the most influential factors."
      ],
      "metadata": {
        "id": "fILTxsj05fVu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Correlation with Total_Amount (target)\n",
        "df.corr(numeric_only=True)['Total_Amount'].sort_values(ascending=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 429
        },
        "id": "btB8ny6m5YfB",
        "outputId": "3523815e-c846-4c52-fb8f-c8015a307c1c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Total_Amount                1.000000\n",
              "Unit_Price                  0.897249\n",
              "Quantity                    0.309204\n",
              "Discount_Amount             0.217925\n",
              "Customer_Rating             0.006151\n",
              "Pages_Viewed                0.002842\n",
              "Is_Returning_Customer      -0.000146\n",
              "Age                        -0.002340\n",
              "Delivery_Time_Days         -0.013102\n",
              "Session_Duration_Minutes   -0.015990\n",
              "Discount_Percentage        -0.216896\n",
              "Name: Total_Amount, dtype: float64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Total_Amount</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Total_Amount</th>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Unit_Price</th>\n",
              "      <td>0.897249</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Quantity</th>\n",
              "      <td>0.309204</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Discount_Amount</th>\n",
              "      <td>0.217925</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Customer_Rating</th>\n",
              "      <td>0.006151</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Pages_Viewed</th>\n",
              "      <td>0.002842</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Is_Returning_Customer</th>\n",
              "      <td>-0.000146</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Age</th>\n",
              "      <td>-0.002340</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Delivery_Time_Days</th>\n",
              "      <td>-0.013102</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Session_Duration_Minutes</th>\n",
              "      <td>-0.015990</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Discount_Percentage</th>\n",
              "      <td>-0.216896</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> float64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Descriptive Statistics – Insights**\n",
        "\n",
        "The analysis shows that Total_Amount is strongly correlated with Unit_Price, indicating that product price is the main driver of total purchase value. A moderate positive relationship is also observed with Quantity, suggesting that the number of items purchased has a noticeable but smaller impact.\n",
        "\n",
        "Discount_Amount shows a weaker positive correlation, meaning that discounts slightly influence the total amount. With the addition of the new feature Discount_Percentage, a negative correlation with Total_Amount is observed, indicating that higher discount percentages are associated with lower spending.\n",
        "\n",
        "In contrast, other features such as Age, Session_Duration_Minutes, Pages_Viewed, and Delivery_Time_Days show little to no correlation with Total_Amount, indicating they do not significantly affect purchasing behavior.\n",
        "\n",
        "Overall, the results suggest that purchasing patterns are primarily driven by price and quantity rather than customer characteristics or browsing behavior. Additionally, while discounts appear to have some influence, higher relative discounts do not necessarily lead to increased spending. However, since correlation captures only linear relationships, further analysis will be conducted to explore potential patterns involving combinations of variables and categorical features that may not be reflected in this stage."
      ],
      "metadata": {
        "id": "gmOleEiA6W5t"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **4. Visualizations**"
      ],
      "metadata": {
        "id": "JxefZbigAu0O"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "In this section, further analysis is conducted to explore patterns and relationships within the data using targeted questions and visualizations. While previous statistical analysis focused on numerical summaries and correlations, this stage aims to investigate how different features influence customer purchasing behavior in a more intuitive and visual way.\n",
        "\n",
        "By examining relationships between variables such as product type, quantity, customer characteristics, and transaction details, we aim to identify meaningful patterns that may not be captured through correlation analysis alone."
      ],
      "metadata": {
        "id": "l7X_i84xAyM3"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Question** - Does product category affect the total purchase amount?"
      ],
      "metadata": {
        "id": "2Owf6G0zCWZo"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The following graph shows the average total purchase amount for each product category. The categories are sorted from highest to lowest to clearly compare spending patterns."
      ],
      "metadata": {
        "id": "O_Q80CnUDz4T"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Sort categories by average Total_Amount\n",
        "order = df.groupby('Product_Category')['Total_Amount'].mean().sort_values(ascending=False).index\n",
        "\n",
        "plt.figure(figsize=(11, 6))\n",
        "\n",
        "# Pastel color palette\n",
        "pastel_colors = ['#B4E4FF', '#C1E1C1', '#FFE5B4', '#F7C8E0', '#D7C0FF', '#FFDAC1']\n",
        "\n",
        "ax = sns.barplot(\n",
        "    x='Product_Category',\n",
        "    y='Total_Amount',\n",
        "    data=df,\n",
        "    order=order,\n",
        "    palette=pastel_colors,\n",
        "    edgecolor='white',\n",
        "    linewidth=2\n",
        ")\n",
        "\n",
        "# Add value labels on top of bars\n",
        "for container in ax.containers:\n",
        "    ax.bar_label(\n",
        "        container,\n",
        "        fmt='%.0f',\n",
        "        padding=3,\n",
        "        fontsize=10,\n",
        "        fontweight='bold'\n",
        "    )\n",
        "\n",
        "plt.title('Average Total Amount by Product Category', fontsize=16, fontweight='bold', pad=15)\n",
        "plt.xlabel('Product Category', fontsize=12)\n",
        "plt.ylabel('Average Total Amount', fontsize=12)\n",
        "plt.xticks(rotation=35, ha='right')\n",
        "\n",
        "plt.grid(axis='y', linestyle='--', alpha=0.3)\n",
        "\n",
        "# Clean look\n",
        "ax.spines['top'].set_visible(False)\n",
        "ax.spines['right'].set_visible(False)\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 815
        },
        "id": "ZtnMW-oaCyrb",
        "outputId": "f6ef66ec-8475-4a6c-f5c6-5a6074c98228"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/tmp/ipykernel_34980/4122793400.py:12: FutureWarning:\n",
            "\n",
            "\n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "\n",
            "/tmp/ipykernel_34980/4122793400.py:12: UserWarning:\n",
            "\n",
            "\n",
            "The palette list has fewer values (6) than needed (8) and will cycle, which may produce an uninterpretable plot.\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1100x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Answer** - Customers spend the most on Electronics and the least on Books. This shows that product category affects how much customers spend."
      ],
      "metadata": {
        "id": "xjAjMOlgDj50"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Question** - Does the city influence the total purchase amount?"
      ],
      "metadata": {
        "id": "P620_NuPJXGK"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The following chart shows the distribution of total spending across different cities, highlighting the contribution of each city to overall sales."
      ],
      "metadata": {
        "id": "nucpPqhwLDaL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Sum total spending by city\n",
        "city_spending = df.groupby('City')['Total_Amount'].sum()\n",
        "\n",
        "# Take top 5 cities and group the rest as \"Other\"\n",
        "top5 = city_spending.sort_values(ascending=False).head(5)\n",
        "others = city_spending.sum() - top5.sum()\n",
        "\n",
        "top5_with_other = top5.copy()\n",
        "top5_with_other['Other'] = others\n",
        "\n",
        "# Pastel colors\n",
        "colors = ['#B4E4FF', '#C1E1C1', '#FFE5B4', '#F7C8E0', '#D7C0FF', '#FFDAC1']\n",
        "\n",
        "# Slightly separate the biggest city slice\n",
        "explode = [0.06] + [0] * (len(top5_with_other) - 1)\n",
        "\n",
        "plt.figure(figsize=(8, 8))\n",
        "\n",
        "plt.pie(\n",
        "    top5_with_other,\n",
        "    labels=top5_with_other.index,\n",
        "    autopct='%1.1f%%',\n",
        "    startangle=90,\n",
        "    colors=colors,\n",
        "    explode=explode,\n",
        "    shadow=True,\n",
        "    textprops={'fontsize': 11, 'fontweight': 'bold'},\n",
        "    wedgeprops={'edgecolor': 'white', 'linewidth': 2}\n",
        ")\n",
        "\n",
        "plt.title(\n",
        "    'Total Spending Distribution by City',\n",
        "    fontsize=16,\n",
        "    fontweight='bold',\n",
        "    pad=18\n",
        ")\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 807
        },
        "id": "_I0NyCq7KO-P",
        "outputId": "7a19bbd6-250c-47f0-c01a-b6e2e0d44bc6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Answer** - Spending is distributed across multiple cities, with Istanbul contributing the most. However, no single city dominates, suggesting only a moderate influence on total purchase amount."
      ],
      "metadata": {
        "id": "mIeeTH2tLU1j"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Question** -  How do price and quantity together influence the total purchase amount?"
      ],
      "metadata": {
        "id": "yimH3Lg9SLS-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The following graph shows how total purchase amount changes based on both unit price and quantity, allowing us to examine their combined effect."
      ],
      "metadata": {
        "id": "y7_4zKw5SxuN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.figure(figsize=(8,5))\n",
        "sns.scatterplot(x='Unit_Price', y='Total_Amount', hue='Quantity', data=df)\n",
        "plt.title('Total Amount vs Unit Price (Colored by Quantity)')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "xAjPtGYsSjCx",
        "outputId": "d0b2c36b-585c-4e7e-fe6f-c984bc40f309"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Answer** - The graph shows a clear relationship between unit price, quantity, and total purchase amount. As both price and quantity increase, the total amount increases accordingly. This indicates that total spending is strongly driven by the combined effect of price and quantity."
      ],
      "metadata": {
        "id": "FMe7hYiaS761"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Question** - Does gender influence spending across product categories?"
      ],
      "metadata": {
        "id": "KJzX1-X8Tb8L"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The following charts show the distribution of total spending across product categories for each gender."
      ],
      "metadata": {
        "id": "DLfTpSLdXHJ3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "# Pastel colors\n",
        "colors = sns.color_palette(\"pastel\")\n",
        "\n",
        "# Data for each gender\n",
        "male = df[df['Gender'] == 'Male'].groupby('Product_Category')['Total_Amount'].sum().sort_values(ascending=False)\n",
        "female = df[df['Gender'] == 'Female'].groupby('Product_Category')['Total_Amount'].sum().sort_values(ascending=False)\n",
        "\n",
        "# Plot\n",
        "fig, axes = plt.subplots(1, 2, figsize=(12,5))\n",
        "\n",
        "# Male pie\n",
        "axes[0].pie(male, labels=male.index, autopct='%1.1f%%', colors=colors)\n",
        "axes[0].set_title('Male')\n",
        "\n",
        "# Female pie\n",
        "axes[1].pie(female, labels=female.index, autopct='%1.1f%%', colors=colors)\n",
        "axes[1].set_title('Female')\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "id": "9e_XBTcrVqSi",
        "outputId": "0e1be5b3-64fd-483c-8ae4-f9ea84ec00f8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Answer** - The charts show that spending distribution across product categories is very similar for both males and females. Electronics is the largest spending category for both groups, followed by Home & Garden and Sports.\n",
        "This suggests that gender does not strongly influence product spending patterns in this dataset."
      ],
      "metadata": {
        "id": "XMm054yGWCQV"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Question** - How does age influence the total purchase amount?"
      ],
      "metadata": {
        "id": "YObvLY0HbBmj"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The following graph shows the average total purchase amount across different age groups."
      ],
      "metadata": {
        "id": "h_vvNF9GbK53"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a copy of the dataset for analysis after outlier handling\n",
        "df_clean = df.copy()\n",
        "\n",
        "# Apply capping to selected columns using IQR\n",
        "cols_to_cap = ['Unit_Price', 'Discount_Amount', 'Total_Amount']\n",
        "\n",
        "for col in cols_to_cap:\n",
        "    Q1 = df_clean[col].quantile(0.25)\n",
        "    Q3 = df_clean[col].quantile(0.75)\n",
        "    IQR = Q3 - Q1\n",
        "\n",
        "    lower = Q1 - 1.5 * IQR\n",
        "    upper = Q3 + 1.5 * IQR\n",
        "\n",
        "    df_clean[col] = df_clean[col].clip(lower=lower, upper=upper)"
      ],
      "metadata": {
        "id": "ib2RR6KUcyku"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Create age groups\n",
        "df_clean['Age_Group'] = pd.cut(df_clean['Age'], bins=[18,25,35,45,55,65,80])\n",
        "\n",
        "# Calculate average spending by age group\n",
        "avg_age = df_clean.groupby('Age_Group')['Total_Amount'].mean().reset_index()\n",
        "\n",
        "plt.figure(figsize=(8,4))\n",
        "sns.barplot(\n",
        "    x='Age_Group',\n",
        "    y='Total_Amount',\n",
        "    data=avg_age,\n",
        "    palette='pastel'\n",
        ")\n",
        "\n",
        "plt.title('Average Total Amount by Age Group')\n",
        "plt.xlabel('Age Group')\n",
        "plt.ylabel('Average Total Amount')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 601
        },
        "id": "Cvd4LlMkc0zU",
        "outputId": "235a25a4-d177-49a5-cf79-72d5a3245c11"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/tmp/ipykernel_34980/3945170032.py:8: FutureWarning:\n",
            "\n",
            "The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
            "\n",
            "/tmp/ipykernel_34980/3945170032.py:11: FutureWarning:\n",
            "\n",
            "\n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ],
            "image/png": 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LpW08PDxw+vRptG3b9qX/cG3cuBEmJibYvXs35HK5VB4XF6fUztXVFQqFAsnJyUpnja5evarUzs7ODpaWliguLi5X8qKOkvVSL1++rFJ36dIlVK1aVfq5vKzj8csvv6CwsBDbtm1D9erVpfLn/aT9PCVL2kVGRiIgIECpTqFQ4OOPP8batWsxbty4l9r/s1xdXct8/SX1wJOztwBUbgTyKmcLX/Y9lpSUpPTLwdWrV6FQKJSWKDM0NETv3r2xcuVKzJo1C1u2bMGAAQPKTMxL5OfnY+vWrejRowe6deumUj906FDEx8ejTZs2sLe3h4mJicp7tiSmp3l4eEAIAXd3d+kXFXV06NAB33//PY4fP46mTZuqvf3TXF1dsXfvXty/f1/pLLAmxrw8Y0OkqzgFgugNUFBQgE2bNqFjx47o1q2byiMyMhL379/Htm3bAAAGBgbo1q0bfvnlF/zwww94/Pix0vQH4MkZr1u3bmHZsmWl9lee6QCGhoaQyWRKZ4lu3LiBLVu2KLUrWYpq8eLFSuULFy5U2V9YWBg2btxY6hmwO3fuvDCmsjg5OaF+/fpYtWqV0h/5c+fOYc+ePQgJCZHKShLhZ5OBkoTq6TObOTk5Kgl/eZWcfRw9erTKmHbv3h0BAQGlToN4WSEhITh+/DgSExOlsvz8fCxduhRubm7SHG8PDw8AwKFDh6R2xcXFWLp06Uv3XdYxfZFn/8FX8p55dt7txx9/jKysLAwaNAh5eXnlWvt58+bNyM/PR0RERKmfq44dO2Ljxo0oLCyEoaEhgoKCsGXLFty+fVvax9WrV1Xm0Xbt2hWGhoaYPHmyyllwIQTu3bv33LhGjx4NMzMz9OvXD+np6Sr16pxZDwkJQXFxMRYtWqRUPn/+fMhkMuk4WllZoWrVqkpjDqh+Zp9W3rEh0kU8A0z0Bti2bRvu37+Pzp07l1rfrFkz2NnZIT4+Xkp0e/TogYULF2LixImoU6eONOevxMcff4wff/wRn376KQ4cOIAWLVqguLgYly5dwo8//ojdu3ejcePGz42rQ4cOmDdvHtq3b4/evXsjIyMDMTExqFmzptKtWhs1aoSwsDAsWLAA9+7dQ7NmzXDw4EFcuXIFgPLZwZkzZ+LAgQPw8/PDgAED4Ovri8zMTJw8eRJ79+5FZmbmSx1DAJgzZw4++OAD+Pv7o3///igoKMDChQthbW2ttM5po0aNAABffvklevbsiUqVKqFTp05o164djI2N0alTJynRWrZsGezt7ZGamqp2PPHx8ahfv77KdJMSnTt3xmeffYaTJ0+iYcOGL/WanzZ27FisW7cOH3zwAYYOHQpbW1usWrUKycnJ2Lhxo/Tzea1atdCsWTNER0cjMzMTtra2WL9+PR4/fvzSfXt4eMDGxgaxsbGwtLSEubk5/Pz8ypxbXiI5ORmdO3dG+/btkZiYiDVr1qB3794qa/82aNAAtWvXli7uLM/xio+PR5UqVdC8efNS6zt37oxly5bh119/RdeuXTFp0iTs2bMHLVq0wODBg6XEsnbt2kq3ePbw8MDUqVMRHR2NGzduIDQ0FJaWlkhOTsbmzZsxcOBApfW4n+Xp6Ym1a9eiV69e8PLyku4EJ4RAcnIy1q5dCwMDA5X5vqXp1KkT2rRpgy+//BI3btxAvXr1sGfPHmzduhXDhw+X/rEDPLm4cubMmfjf//6Hxo0b49ChQ9JntDTlHRsinVQha08QkVo6deokTExMRH5+fplt+vbtKypVqiQtH6ZQKISLi0upSyCVePTokZg1a5aoVauWkMvlonLlyqJRo0Zi8uTJIicnR2oHQERERJS6j+XLlwtPT08hl8uFt7e3iIuLk5ZJelp+fr6IiIgQtra2wsLCQoSGhorLly8LAGLmzJlKbdPT00VERIRwcXERlSpVEo6OjqJt27Zi6dKl5TpeQpS+DJoQQuzdu1e0aNFCmJqaCisrK9GpUydx4cIFle2/+uor8c477wgDAwOlJcG2bdsm6tatK0xMTISbm5uYNWuWWLFihcqyYS9aBq1kabjx48eX2ebGjRsCgIiKihJCPFnqytzcXKVdQECAqFWrlkq5q6ur6NChg1LZtWvXRLdu3YSNjY0wMTERTZs2VVmzuaRdUFCQkMvlwsHBQXzxxRciISGh1GXQSus7PDxcuLq6KpVt3bpV+Pr6SsuGPW9JtJL30IULF0S3bt2EpaWlqFy5soiMjBQFBQWlbjN79myVZeLKkp6eLoyMjJ67bu2DBw+EmZmZ+PDDD6Wyffv2iQYNGghjY2Ph4eEhvv/+ezFy5EhhYmKisv3GjRtFy5Ythbm5uTA3Nxfe3t4iIiJCXL58+YXxCSHE1atXxeDBg0XNmjWFiYmJMDU1Fd7e3uLTTz8Vp06dUmpb1ntDiCdL3UVFRQlnZ2dRqVIl4enpKebMmSMt+/b06+3fv7+wtrYWlpaWonv37iIjI6PMZdDUGRsiXSMTQgtXKRARlcOpU6fQoEEDrFmzRrprFdHL+uabbxAVFYUbN24ozdHWttDQUJw/fx5JSUmvrc+KNGnSJEyePBl37txB1apVKzocopfCOcBE9FoUFBSolC1YsAAGBgZo1apVBUREbxMhBJYvX46AgACtJr/Pvo+TkpKwY8cOtG7dWmt9EpHmcQ4wEb0Ws2fPxokTJ9CmTRsYGRlh586d2LlzJwYOHFjmHFiiF8nPz8e2bdtw4MABnD17Flu3btVqfzVq1EDfvn1Ro0YN/PPPP1iyZAmMjY0xevRorfZLRJrFBJiIXovmzZsjISEBX331FfLy8lC9enVMmjRJZXk2InXcuXMHvXv3ho2NDb744osyLxTVlPbt22PdunVIS0uDXC6Hv78/pk+frnJTCCLSbZwDTERERER6hXOAiYiIiEivMAEmIiIiIr3COcDloFAocPv2bVhaWvJe50REREQ6SAiB+/fvw9nZWbqxT1mYAJfD7du3eZU6ERER0Rvg5s2bL7xTIhPgcrC0tATw5IBaWVlVcDRERERE9Kzc3Fy4uLhIedvzMAEuh5JpD1ZWVkyAiYiIiHRYeaar8iI4IiIiItIrTICJiIiISK8wASYiIiIivVKhCfChQ4fQqVMnODs7QyaTYcuWLUr1QghMmDABTk5OMDU1RVBQEJKSkpTaZGZmok+fPrCysoKNjQ369++PvLw8pTZnzpzBe++9BxMTE7i4uGD27NnafmlEREREpKMqNAHOz89HvXr1EBMTU2r97Nmz8e233yI2NhbHjh2Dubk5goOD8fDhQ6lNnz59cP78eSQkJGD79u04dOgQBg4cKNXn5uaiXbt2cHV1xYkTJzBnzhxMmjQJS5cu1frrIyIiIiLdIxNCiIoOAnhyxd7mzZsRGhoK4MnZX2dnZ4wcORKff/45ACAnJwcODg5YuXIlevbsiYsXL8LX1xd//vknGjduDADYtWsXQkJC8O+//8LZ2RlLlizBl19+ibS0NBgbGwMAxo4diy1btuDSpUvlii03NxfW1tbIycnhKhBEREREOkidfE1n5wAnJycjLS0NQUFBUpm1tTX8/PyQmJgIAEhMTISNjY2U/AJAUFAQDAwMcOzYMalNq1atpOQXAIKDg3H58mVkZWWV2ndhYSFyc3OVHkRERET0dtDZBDgtLQ0A4ODgoFTu4OAg1aWlpcHe3l6p3sjICLa2tkptStvH0308a8aMGbC2tpYevAscERER0dtDZxPgihQdHY2cnBzpcfPmzYoOiYiIiIg0RGcTYEdHRwBAenq6Unl6erpU5+joiIyMDKX6x48fIzMzU6lNaft4uo9nyeVy6a5vvPsbERER0dtFZxNgd3d3ODo6Yt++fVJZbm4ujh07Bn9/fwCAv78/srOzceLECanN/v37oVAo4OfnJ7U5dOgQioqKpDYJCQnw8vJC5cqVX9OrISIiIiJdYVSRnefl5eHq1avS8+TkZJw6dQq2traoXr06hg8fjqlTp8LT0xPu7u4YP348nJ2dpZUifHx80L59ewwYMACxsbEoKipCZGQkevbsCWdnZwBA7969MXnyZPTv3x9jxozBuXPn8M0332D+/PkV8ZKJiIhIT1zcu6aiQ3hr+AR9pNH9VWgC/Ndff6FNmzbS8xEjRgAAwsPDsXLlSowePRr5+fkYOHAgsrOz0bJlS+zatQsmJibSNvHx8YiMjETbtm1hYGCAsLAwfPvtt1K9tbU19uzZg4iICDRq1AhVq1bFhAkTlNYKJiIiIiL9oTPrAOsyrgNMRERE6uIZYM0pzxngt2IdYCIiIiIibWACTERERER6hQkwEREREekVJsBEREREpFeYABMRERGRXmECTERERER6hQkwEREREekVJsBEREREpFeYABMRERGRXmECTERERER6hQkwEREREekVJsBEREREpFeYABMRERGRXmECTERERER6hQkwEREREekVJsBEREREpFeYABMRERGRXmECTERERER6hQkwEREREekVJsBEREREpFeYABMRERGRXmECTERERER6hQkwEREREekVJsBEREREpFeYABMRERGRXmECTERERER6xaiiA3gb/Xz8TkWH8Fbo1tSuokMgov+T9/OGig7hrWDRrUdFh0BEYAJMeiZ718KKDuGtYdP+s4oOgYiI6KVwCgQRERER6RUmwERERESkV5gAExEREZFe4RxgItIJ3yWtr+gQ3hqDPHtWdAhERDqNZ4CJiIiISK8wASYiIiIivcIEmIiIiIj0ChNgIiIiItIrTICJiIiISK9wFQgiIqI32LnfH1Z0CG+N2i1MKjoEek14BpiIiIiI9AoTYCIiIiLSK0yAiYiIiEivMAEmIiIiIr3CBJiIiIiI9AoTYCIiIiLSK0yAiYiIiEivMAEmIiIiIr3CBJiIiIiI9AoTYCIiIiLSKzqdABcXF2P8+PFwd3eHqakpPDw88NVXX0EIIbURQmDChAlwcnKCqakpgoKCkJSUpLSfzMxM9OnTB1ZWVrCxsUH//v2Rl5f3ul8OEREREekAnU6AZ82ahSVLlmDRokW4ePEiZs2ahdmzZ2PhwoVSm9mzZ+Pbb79FbGwsjh07BnNzcwQHB+Phw/9/b/Q+ffrg/PnzSEhIwPbt23Ho0CEMHDiwIl4SEREREVUwo4oO4HmOHj2KLl26oEOHDgAANzc3rFu3DsePHwfw5OzvggULMG7cOHTp0gUAsHr1ajg4OGDLli3o2bMnLl68iF27duHPP/9E48aNAQALFy5ESEgIvv76azg7O1fMiyMiIiKiCqHTZ4CbN2+Offv24cqVKwCA06dP48iRI/jggw8AAMnJyUhLS0NQUJC0jbW1Nfz8/JCYmAgASExMhI2NjZT8AkBQUBAMDAxw7NixUvstLCxEbm6u0oOIiIiI3g46fQZ47NixyM3Nhbe3NwwNDVFcXIxp06ahT58+AIC0tDQAgIODg9J2Dg4OUl1aWhrs7e2V6o2MjGBrayu1edaMGTMwefJkTb8cIiIiItIBOn0G+Mcff0R8fDzWrl2LkydPYtWqVfj666+xatUqrfYbHR2NnJwc6XHz5k2t9kdEREREr49OnwEeNWoUxo4di549ewIA6tSpg3/++QczZsxAeHg4HB0dAQDp6elwcnKStktPT0f9+vUBAI6OjsjIyFDa7+PHj5GZmSlt/yy5XA65XK6FV0REREREFU2nzwA/ePAABgbKIRoaGkKhUAAA3N3d4ejoiH379kn1ubm5OHbsGPz9/QEA/v7+yM7OxokTJ6Q2+/fvh0KhgJ+f32t4FURERESkS3T6DHCnTp0wbdo0VK9eHbVq1cLff/+NefPmoV+/fgAAmUyG4cOHY+rUqfD09IS7uzvGjx8PZ2dnhIaGAgB8fHzQvn17DBgwALGxsSgqKkJkZCR69uzJFSCIiIiI9JBOJ8ALFy7E+PHjMWTIEGRkZMDZ2RmDBg3ChAkTpDajR49Gfn4+Bg4ciOzsbLRs2RK7du2CiYmJ1CY+Ph6RkZFo27YtDAwMEBYWhm+//bYiXhIRERERVTCdToAtLS2xYMECLFiwoMw2MpkMU6ZMwZQpU8psY2tri7Vr12ohQiIiIiJ60+j0HGAiIiIiIk1jAkxEREREeoUJMBERERHpFbUT4MDAQGRnZ6uU5+bmIjAwUBMxERERERFpjdoJ8G+//YZHjx6plD98+BCHDx/WSFBERERERNpS7lUgzpw5I/3/hQsXkJaWJj0vLi7Grl278M4772g2OiIiIiIiDSt3Aly/fn3IZDLIZLJSpzqYmppi4cKFGg2OiIiIiEjTyp0AJycnQwiBGjVq4Pjx47Czs5PqjI2NYW9vD0NDQ60ESURERESkKeVOgF1dXQEACoVCa8EQEREREWnbS90JLikpCQcOHEBGRoZKQvz0bYqJiIiIiHSN2gnwsmXLMHjwYFStWhWOjo6QyWRSnUwmYwJMRERERDpN7QR46tSpmDZtGsaMGaONeIiIiIiItErtdYCzsrLwn//8RxuxEBERERFpndoJ8H/+8x/s2bNHG7EQEREREWmd2lMgatasifHjx+OPP/5AnTp1UKlSJaX6oUOHaiw4IiIiIiJNUzsBXrp0KSwsLHDw4EEcPHhQqU4mkzEBJiIiIiKdpnYCnJycrI04iIiIiIheC7XnABMRERERvcnUPgPcr1+/59avWLHipYMhIiIiItI2tRPgrKwspedFRUU4d+4csrOzERgYqLHAiIiIiIi0Qe0EePPmzSplCoUCgwcPhoeHh0aCIiIiIiLSFo3MATYwMMCIESMwf/58TeyOiIiIiEhrNHYR3LVr1/D48WNN7Y6IiIiISCvUngIxYsQIpedCCKSmpuLXX39FeHi4xgIjIiIiItIGtRPgv//+W+m5gYEB7OzsMHfu3BeuEEFEREREVNHUToAPHDigjTiIiIiIiF4LtRPgEnfu3MHly5cBAF5eXrCzs9NYUERERERE2qL2RXD5+fno168fnJyc0KpVK7Rq1QrOzs7o378/Hjx4oI0YiYiIiIg0Ru0EeMSIETh48CB++eUXZGdnIzs7G1u3bsXBgwcxcuRIbcRIRERERKQxak+B2LhxI37++We0bt1aKgsJCYGpqSm6d++OJUuWaDI+IiIiIiKNUvsM8IMHD+Dg4KBSbm9vzykQRERERKTz1E6A/f39MXHiRDx8+FAqKygowOTJk+Hv76/R4IiIiIiINE3tKRDffPMNgoODUa1aNdSrVw8AcPr0aZiYmGD37t0aD5CIiIiISJPUToBr166NpKQkxMfH49KlSwCAXr16oU+fPjA1NdV4gEREREREmvRS6wCbmZlhwIABmo6FiIiIiEjrXioBvn37No4cOYKMjAwoFAqluqFDh2okMCIiIiIibVA7AV65ciUGDRoEY2NjVKlSBTKZTKqTyWRMgImIiIhIp6mdAI8fPx4TJkxAdHQ0DAzUXkSCiIiIiKhCvdQ6wD179mTyS0RERERvJLWz2P79++Onn37SRixERERERFqn9hSIGTNmoGPHjti1axfq1KmDSpUqKdXPmzdPY8EREREREWnaSyXAu3fvhpeXFwCoXARHRERERKTL1E6A586dixUrVqBv375aCIeIiIiISLvUngMsl8vRokULbcRCRERERKR1aifAw4YNw8KFC7URCxERERGR1qk9BeL48ePYv38/tm/fjlq1aqlcBLdp0yaNBUdEREREpGlqJ8A2Njbo2rWrNmIhIiIiItI6tRPguLg4bcRBRERERPRaaOR2brm5uViyZAkaN26sid0REREREWnNKyXABw4cwMcffwwnJyd89dVX8PPz01Rcklu3buGjjz5ClSpVYGpqijp16uCvv/6S6oUQmDBhApycnGBqaoqgoCAkJSUp7SMzMxN9+vSBlZUVbGxs0L9/f+Tl5Wk8ViIiIiLSfWpPgbh16xZWrlyJuLg4ZGdnIysrC2vXrkX37t01fiOMrKwstGjRAm3atMHOnTthZ2eHpKQkVK5cWWoze/ZsfPvtt1i1ahXc3d0xfvx4BAcH48KFCzAxMQEA9OnTB6mpqUhISEBRURE++eQTDBw4EGvXrtVovERERESk+8p9Bnjjxo0ICQmBl5cXTp06hblz5+L27dswMDBAnTp1tHIXuFmzZsHFxQVxcXFo2rQp3N3d0a5dO3h4eAB4cvZ3wYIFGDduHLp06YK6deti9erVuH37NrZs2QIAuHjxInbt2oXvv/8efn5+aNmyJRYuXIj169fj9u3bGo+ZiIiIiHRbuRPgHj16oEGDBkhNTcVPP/2ELl26wNjYWJuxYdu2bWjcuDH+85//wN7eHg0aNMCyZcuk+uTkZKSlpSEoKEgqs7a2hp+fHxITEwEAiYmJsLGxUZqfHBQUBAMDAxw7dqzUfgsLC5Gbm6v0ICIiIqK3Q7kT4P79+yMmJgbt27dHbGwssrKytBkXAOD69etYsmQJPD09sXv3bgwePBhDhw7FqlWrAABpaWkAAAcHB6XtHBwcpLq0tDTY29sr1RsZGcHW1lZq86wZM2bA2tpaeri4uGj6pRERERFRBSl3Avzdd98hNTUVAwcOxLp16+Dk5IQuXbpACAGFQqGV4BQKBRo2bIjp06ejQYMGGDhwIAYMGIDY2Fit9FciOjoaOTk50uPmzZta7Y+IiIiIXh+1VoEwNTVFeHg4Dh48iLNnz6JWrVpwcHBAixYt0Lt3b43fBc7JyQm+vr5KZT4+PkhJSQEAODo6AgDS09OV2qSnp0t1jo6OyMjIUKp//PgxMjMzpTbPksvlsLKyUnoQERER0dvhpZdB8/T0xPTp03Hz5k2sWbMGDx48QK9evTQZG1q0aIHLly8rlV25cgWurq4AAHd3dzg6OmLfvn1SfW5uLo4dOwZ/f38AgL+/P7Kzs3HixAmpzf79+6FQKLSybBsRERER6Ta1l0F7loGBATp16oROnTqpnGl9VVFRUWjevDmmT5+O7t274/jx41i6dCmWLl0KAJDJZBg+fDimTp0KT09PaRk0Z2dnhIaGAnhyxrh9+/bS1ImioiJERkaiZ8+ecHZ21mi8RERERKT7XjkBftqzF5u9qiZNmmDz5s2Ijo7GlClT4O7ujgULFqBPnz5Sm9GjRyM/Px8DBw5EdnY2WrZsiV27dklrAANAfHw8IiMj0bZtWxgYGCAsLAzffvutRmMlIiIiojeDRhNgbejYsSM6duxYZr1MJsOUKVMwZcqUMtvY2tryphdEREREBOAVb4VMRERERPSmYQJMRERERHqFCTARERER6ZVyzQGuXLkyZDJZuXaYmZn5SgEREREREWlTuRLgBQsWaDkMIiIiIqLXo1wJcHh4uLbjICIiIiJ6LV5pGbSHDx/i0aNHSmW8bTARERER6TK1L4LLz89HZGQk7O3tYW5ujsqVKys9iIiIiIh0mdoJ8OjRo7F//34sWbIEcrkc33//PSZPngxnZ2esXr1aGzESEREREWmM2lMgfvnlF6xevRqtW7fGJ598gvfeew81a9aEq6sr4uPjlW5TTERERESka9Q+A5yZmYkaNWoAeDLft2TZs5YtW+LQoUOajY6IiIiISMPUToBr1KiB5ORkAIC3tzd+/PFHAE/ODNvY2Gg0OCIiIiIiTVM7Af7kk09w+vRpAMDYsWMRExMDExMTREVFYdSoURoPkIiIiIhIk9SeAxwVFSX9f1BQEC5duoQTJ06gZs2aqFu3rkaDIyIiIiLSNLXPAK9evRqFhYXSc1dXV3Tt2hXe3t5cBYKIiIiIdN5LTYHIyclRKb9//z4++eQTjQRFRERERKQtaifAQgjIZDKV8n///RfW1tYaCYqIiIiISFvKPQe4QYMGkMlkkMlkaNu2LYyM/v+mxcXFSE5ORvv27bUSJBERERGRppQ7AQ4NDQUAnDp1CsHBwbCwsJDqjI2N4ebmhrCwMI0HSERERESkSeVOgCdOnAgAcHNzQ48ePWBiYqK1oIiIiIiItEXtZdDCw8MBACdOnMDFixcBALVq1UKDBg00GxkRERERkRaonQBnZGSgZ8+e+O2336Q7v2VnZ6NNmzZYv3497OzsNB0jEREREZHGqL0KxGeffYb79+/j/PnzyMzMRGZmJs6dO4fc3FwMHTpUGzESEREREWmM2meAd+3ahb1798LHx0cq8/X1RUxMDNq1a6fR4IiIiIiINE3tM8AKhQKVKlVSKa9UqRIUCoVGgiIiIiIi0pZyJ8ApKSlQKBQIDAzEsGHDcPv2banu1q1biIqKQtu2bbUSJBERERGRppQ7AXZ3d8fdu3exaNEi5Obmws3NDR4eHvDw8IC7uztyc3OxcOFCbcZKRERERPTKyj0HWAgBAHBxccHJkyexd+9eXLp0CQDg4+ODoKAg7URIRERERKRBal0EJ5PJpP++//77eP/997USFBERERGRtqiVAI8fPx5mZmbPbTNv3rxXCoiIiIiISJvUSoDPnj0LY2PjMutLzhATEREREekqtRLgzZs3w97eXluxEBERERFpXblXgeDZXSIiIiJ6G5Q7AS5ZBYKIiIiI6E1W7gQ4Li4O1tbW2oyFiIiIiEjryj0HODw8XJtxEBERERG9FuU+A0xERERE9DZgAkxEREREeoUJMBERERHplZdKgLOzs/H9998jOjoamZmZAICTJ0/i1q1bGg2OiIiIiEjT1LoRBgCcOXMGQUFBsLa2xo0bNzBgwADY2tpi06ZNSElJwerVq7URJxERERGRRqh9BnjEiBHo27cvkpKSYGJiIpWHhITg0KFDGg2OiIiIiEjT1E6A//zzTwwaNEil/J133kFaWppGgiIiIiIi0ha1E2C5XI7c3FyV8itXrsDOzk4jQRERERERaYvaCXDnzp0xZcoUFBUVAQBkMhlSUlIwZswYhIWFaTxAIiIiIiJNUjsBnjt3LvLy8mBvb4+CggIEBASgZs2asLS0xLRp07QRIxERERGRxqi9CoS1tTUSEhJw5MgRnDlzBnl5eWjYsCGCgoK0ER8RERERkUapnQCXaNmyJVq2bKnJWIiIiIiItE7tBPjbb78ttVwmk8HExAQ1a9ZEq1atYGho+MrBERERERFpmtoJ8Pz583Hnzh08ePAAlStXBgBkZWXBzMwMFhYWyMjIQI0aNXDgwAG4uLhoNNiZM2ciOjoaw4YNw4IFCwAADx8+xMiRI7F+/XoUFhYiODgYixcvhoODg7RdSkoKBg8ejAMHDsDCwgLh4eGYMWMGjIxe+gQ4EREREb2h1L4Ibvr06WjSpAmSkpJw79493Lt3D1euXIGfnx+++eYbpKSkwNHREVFRURoN9M8//8R3332HunXrKpVHRUXhl19+wU8//YSDBw/i9u3b6Nq1q1RfXFyMDh064NGjRzh69ChWrVqFlStXYsKECRqNj4iIiIjeDGonwOPGjcP8+fPh4eEhldWsWRNff/01oqOjUa1aNcyePRu///67xoLMy8tDnz59sGzZMumsMwDk5ORg+fLlmDdvHgIDA9GoUSPExcXh6NGj+OOPPwAAe/bswYULF7BmzRrUr18fH3zwAb766ivExMTg0aNHGouRiIiIiN4MaifAqampePz4sUr548ePpTvBOTs74/79+68e3f+JiIhAhw4dVFaaOHHiBIqKipTKvb29Ub16dSQmJgIAEhMTUadOHaUpEcHBwcjNzcX58+dL7a+wsBC5ublKDyIiIiJ6O6idALdp0waDBg3C33//LZX9/fffGDx4MAIDAwEAZ8+ehbu7u0YCXL9+PU6ePIkZM2ao1KWlpcHY2Bg2NjZK5Q4ODlIynpaWppT8ltSX1JVmxowZsLa2lh6anstMRERERBVH7QR4+fLlsLW1RaNGjSCXyyGXy9G4cWPY2tpi+fLlAAALCwvMnTv3lYO7efMmhg0bhvj4eJiYmLzy/sorOjoaOTk50uPmzZuvrW8iIiIi0i61l0FwdHREQkICLl26hCtXrgAAvLy84OXlJbVp06aNRoI7ceIEMjIy0LBhQ6msuLgYhw4dwqJFi7B79248evQI2dnZSmeB09PT4ejoKMV7/Phxpf2mp6dLdaUpSeyJiIiI6O3z0uuAeXt7w9vbW5OxqGjbti3Onj2rVPbJJ5/A29sbY8aMgYuLCypVqoR9+/YhLCwMAHD58mWkpKTA398fAODv749p06YhIyMD9vb2AICEhARYWVnB19dXq/ETERERke55qQT433//xbZt25CSkqKyksK8efM0EhgAWFpaonbt2kpl5ubmqFKlilTev39/jBgxAra2trCyssJnn30Gf39/NGvWDADQrl07+Pr64uOPP8bs2bORlpaGcePGISIigmd5iYiIiPSQ2gnwvn370LlzZ9SoUQOXLl1C7dq1cePGDQghlKYqvC7z58+HgYEBwsLClG6EUcLQ0BDbt2/H4MGD4e/vD3Nzc4SHh2PKlCmvPVYiIiIiqnhqJ8DR0dH4/PPPMXnyZFhaWmLjxo2wt7dHnz590L59e23EqOS3335Tem5iYoKYmBjExMSUuY2rqyt27Nih5ciIiIiI6E2g9ioQFy9exH//+18AgJGREQoKCmBhYYEpU6Zg1qxZGg+QiIiIiEiT1E6Azc3NpXm/Tk5OuHbtmlR39+5dzUVGRERERKQFak+BaNasGY4cOQIfHx+EhIRg5MiROHv2LDZt2iRdeEZEREREpKvUToDnzZuHvLw8AMDkyZORl5eHDRs2wNPTU6MrQBARERERaYNaCXBxcTH+/fdf1K1bF8CT6RCxsbFaCYyIiIiISBvUmgNsaGiIdu3aISsrS1vxEBERERFpldoXwdWuXRvXr1/XRixERERERFqndgI8depUfP7559i+fTtSU1ORm5ur9CAiIiIi0mVqXwQXEhICAOjcuTNkMplULoSATCZDcXGx5qIjIiIiItIwtRPgAwcOaCMOIiIiIqLXQu0EOCAgQBtxEBERERG9FmrPAQaAw4cP46OPPkLz5s1x69YtAMAPP/yAI0eOaDQ4IiIiIiJNUzsB3rhxI4KDg2FqaoqTJ0+isLAQAJCTk4Pp06drPEAiIiIiIk16qVUgYmNjsWzZMlSqVEkqb9GiBU6ePKnR4IiIiIiINE3tBPjy5cto1aqVSrm1tTWys7M1ERMRERERkdaonQA7Ojri6tWrKuVHjhxBjRo1NBIUEREREZG2qJ0ADxgwAMOGDcOxY8cgk8lw+/ZtxMfH4/PPP8fgwYO1ESMRERERkcaovQza2LFjoVAo0LZtWzx48ACtWrWCXC7H559/js8++0wbMRIRERERaYzaCbBMJsOXX36JUaNG4erVq8jLy4Ovry8sLCy0ER8RERERkUapPQVizZo1ePDgAYyNjeHr64umTZsy+SUiIiKiN4baCXBUVBTs7e3Ru3dv7NixA8XFxdqIi4iIiIhIK9ROgFNTU7F+/XrIZDJ0794dTk5OiIiIwNGjR7URHxERERGRRqmdABsZGaFjx46Ij49HRkYG5s+fjxs3bqBNmzbw8PDQRoxERERERBqj9kVwTzMzM0NwcDCysrLwzz//4OLFi5qKi4iIiIhIK9Q+AwwADx48QHx8PEJCQvDOO+9gwYIF+PDDD3H+/HlNx0dEREREpFFqnwHu2bMntm/fDjMzM3Tv3h3jx4+Hv7+/NmIjIiIiItI4tRNgQ0ND/PjjjwgODoahoaFS3blz51C7dm2NBUdEREREpGlqJ8Dx8fFKz+/fv49169bh+++/x4kTJ7gsGhERERHptJeaAwwAhw4dQnh4OJycnPD1118jMDAQf/zxhyZjIyIiIiLSOLXOAKelpWHlypVYvnw5cnNz0b17dxQWFmLLli3w9fXVVoxERERERBpT7jPAnTp1gpeXF86cOYMFCxbg9u3bWLhwoTZjIyIiIiLSuHKfAd65cyeGDh2KwYMHw9PTU5sxERERERFpTbnPAB85cgT3799Ho0aN4Ofnh0WLFuHu3bvajI2IiIiISOPKnQA3a9YMy5YtQ2pqKgYNGoT169fD2dkZCoUCCQkJuH//vjbjJCIiIiLSCLVXgTA3N0e/fv1w5MgRnD17FiNHjsTMmTNhb2+Pzp07ayNGIiIiIiKNeell0ADAy8sLs2fPxr///ot169ZpKiYiIiIiIq15pQS4hKGhIUJDQ7Ft2zZN7I6IiIiISGs0kgATEREREb0pmAATERERkV5hAkxEREREeoUJMBERERHpFSbARERERKRXmAATERERkV5hAkxEREREeoUJMBERERHpFSbARERERKRXmAATERERkV5hAkxEREREekWnE+AZM2agSZMmsLS0hL29PUJDQ3H58mWlNg8fPkRERASqVKkCCwsLhIWFIT09XalNSkoKOnToADMzM9jb22PUqFF4/Pjx63wpRERERKQjdDoBPnjwICIiIvDHH38gISEBRUVFaNeuHfLz86U2UVFR+OWXX/DTTz/h4MGDuH37Nrp27SrVFxcXo0OHDnj06BGOHj2KVatWYeXKlZgwYUJFvCQiIiIiqmBGFR3A8+zatUvp+cqVK2Fvb48TJ06gVatWyMnJwfLly7F27VoEBgYCAOLi4uDj44M//vgDzZo1w549e3DhwgXs3bsXDg4OqF+/Pr766iuMGTMGkyZNgrGxcUW8NCIiIiKqIDp9BvhZOTk5AABbW1sAwIkTJ1BUVISgoCCpjbe3N6pXr47ExEQAQGJiIurUqQMHBwepTXBwMHJzc3H+/PlS+yksLERubq7Sg4iIiIjeDm9MAqxQKDB8+HC0aNECtWvXBgCkpaXB2NgYNjY2Sm0dHByQlpYmtXk6+S2pL6krzYwZM2BtbS09XFxcNPxqiIiIiKiivDEJcEREBM6dO4f169drva/o6Gjk5ORIj5s3b2q9TyIiIiJ6PXR6DnCJyMhIbN++HYcOHUK1atWkckdHRzx69AjZ2dlKZ4HT09Ph6OgotTl+/LjS/kpWiShp8yy5XA65XK7hV0FEREREukCnzwALIRAZGYnNmzdj//79cHd3V6pv1KgRKlWqhH379kllly9fRkpKCvz9/QEA/v7+OHv2LDIyMqQ2CQkJsLKygq+v7+t5IURERESkM3T6DHBERATWrl2LrVu3wtLSUpqza21tDVNTU1hbW6N///4YMWIEbG1tYWVlhc8++wz+/v5o1qwZAKBdu3bw9fXFxx9/jNmzZyMtLQ3jxo1DREQEz/ISERER6SGdToCXLFkCAGjdurVSeVxcHPr27QsAmD9/PgwMDBAWFobCwkIEBwdj8eLFUltDQ0Ns374dgwcPhr+/P8zNzREeHo4pU6a8rpdBRERERDpEpxNgIcQL25iYmCAmJgYxMTFltnF1dcWOHTs0GRoRERERvaF0eg4wEREREZGmMQEmIiIiIr3CBJiIiIiI9AoTYCIiIiLSK0yAiYiIiEivMAEmIiIiIr3CBJiIiIiI9AoTYCIiIiLSK0yAiYiIiEivMAEmIiIiIr3CBJiIiIiI9AoTYCIiIiLSK0yAiYiIiEivMAEmIiIiIr3CBJiIiIiI9AoTYCIiIiLSK0yAiYiIiEivMAEmIiIiIr3CBJiIiIiI9AoTYCIiIiLSK0yAiYiIiEivMAEmIiIiIr3CBJiIiIiI9AoTYCIiIiLSK0yAiYiIiEivMAEmIiIiIr3CBJiIiIiI9AoTYCIiIiLSK0yAiYiIiEivMAEmIiIiIr3CBJiIiIiI9AoTYCIiIiLSK0yAiYiIiEivMAEmIiIiIr3CBJiIiIiI9AoTYCIiIiLSK0yAiYiIiEivMAEmIiIiIr3CBJiIiIiI9AoTYCIiIiLSK0yAiYiIiEivMAEmIiIiIr3CBJiIiIiI9AoTYCIiIiLSK0yAiYiIiEivMAEmIiIiIr3CBJiIiIiI9AoTYCIiIiLSK0yAiYiIiEiv6FUCHBMTAzc3N5iYmMDPzw/Hjx+v6JCIiIiI6DXTmwR4w4YNGDFiBCZOnIiTJ0+iXr16CA4ORkZGRkWHRkRERESvkd4kwPPmzcOAAQPwySefwNfXF7GxsTAzM8OKFSsqOjQiIiIieo2MKjqA1+HRo0c4ceIEoqOjpTIDAwMEBQUhMTFRpX1hYSEKCwul5zk5OQCA3NzccvX3IO/+K0ZMAJCbK9f8PvMLNL5PfWVQzs9DeRXkPdDo/vRZeb+r1JH3gOOjCQptjE3+Q43vU1/l5j7S6P7y+DdHY8rzvVbSRgjxwrZ6kQDfvXsXxcXFcHBwUCp3cHDApUuXVNrPmDEDkydPVil3cXHRWoxEb54xFR0AlSEK/Ss6BCpTv4oOgOgNNbDcLe/fvw9ra+vnttGLBFhd0dHRGDFihPRcoVAgMzMTVapUgUwmq8DINCM3NxcuLi64efMmrKysKjocegrHRrdxfHQXx0Z3cWx029s0PkII3L9/H87Ozi9sqxcJcNWqVWFoaIj09HSl8vT0dDg6Oqq0l8vlkMuVf363sbHRZogVwsrK6o1/s7+tODa6jeOjuzg2uotjo9velvF50ZnfEnpxEZyxsTEaNWqEffv2SWUKhQL79u2Dv79/BUZGRERERK+bXpwBBoARI0YgPDwcjRs3RtOmTbFgwQLk5+fjk08+qejQiIiIiOg10psEuEePHrhz5w4mTJiAtLQ01K9fH7t27VK5ME4fyOVyTJw4UWWaB1U8jo1u4/joLo6N7uLY6DZ9HR+ZKM9aEUREREREbwm9mANMRERERFSCCTARERER6RUmwERERESkV5gA66B79+7B3t4eN27cqOhQXsqNGzcgk8kgk8lQv379ig5Ho970sVm5cqU0NsOHD6/ocDSKY6PbOD66600fm99++00am9DQ0IoOR6Pe9LHR5XyACbAOmjZtGrp06QI3NzepbOjQoWjUqBHkcnmZb6Ldu3ejWbNmsLS0hJ2dHcLCwtT+0MyYMQNNmjSBpaUl7O3tERoaisuXLyu1ad26tfSGLnl8+umnUr2LiwtSU1MxcuRItfp+Ezw7NqdPn0avXr3g4uICU1NT+Pj44JtvvlHa5ukv56cfaWlpavU9aNAgeHh4wNTUFHZ2dujSpYvKrbxL62f9+vVSfY8ePZCamvpWrn/97Njcu3cP7du3h7OzM+RyOVxcXBAZGal0P3lNjU0JIQQ++OADyGQybNmyRalOn8cGKP17rcS9e/dQrVo1yGQyZGdnS+WaGp8XfWcB+j0+pY3Ni46HJj87iYmJCAwMhLm5OaysrNCqVSsUFBRI9W5ubir9zJw5U6pv3rw5UlNT0b1795c7ADqsrM/NypUrUbduXZiYmMDe3h4RERFS3dNJ59OPP/74Q62+8/LyEBkZiWrVqsHU1BS+vr6IjY1VavPw4UNERESgSpUqsLCwQFhYmNJNx3Q5H9CbZdDeFA8ePMDy5cuxe/dulbp+/frh2LFjOHPmjEpdcnIyunTpghEjRiA+Ph45OTmIiopC165dcfLkyXL3f/DgQURERKBJkyZ4/PgxvvjiC7Rr1w4XLlyAubm51G7AgAGYMmWK9NzMzEz6f0NDQzg6OsLCwqLc/b4JShubEydOwN7eHmvWrIGLiwuOHj2KgQMHwtDQEJGRkUrbX758WekuO/b29mr136hRI/Tp0wfVq1dHZmYmJk2ahHbt2iE5ORmGhoZSu7i4OLRv3156/vRdDE1NTWFqagpjY2O1+tZ1pY2NgYEBunTpgqlTp8LOzg5Xr15FREQEMjMzsXbtWqXtX3VsSixYsOC5t0vXx7EBnv+9BgD9+/dH3bp1cevWrVLrNTE+z/vOKqGP4/O8sXne8SjxqmOTmJiI9u3bIzo6GgsXLoSRkRFOnz4NAwPl83NTpkzBgAEDpOeWlpbS/xsbG8PR0RGmpqYoLCxUq39dVtbYzJs3D3PnzsWcOXPg5+eH/Pz8Uk927d27F7Vq1ZKeV6lSRa3+R4wYgf3792PNmjVwc3PDnj17MGTIEDg7O6Nz584AgKioKPz666/46aefYG1tjcjISHTt2hW///47AN3OB5gA65gdO3ZALpejWbNmSuXffvstAODOnTulJsAnTpxAcXExpk6dKn1xfP755+jSpQuKiopQqVKlcvW/a9cupecrV66Evb09Tpw4gVatWknlZmZmpd5G+m1W2tj069dPqU2NGjWQmJiITZs2qSTA9vb2r3RL7YEDB0r/7+bmhqlTp6JevXq4ceMGPDw8pDobGxuODYDKlStj8ODB0nNXV1cMGTIEc+bMUdn+VccGAE6dOoW5c+fir7/+gpOTU6lt9HFsgLK/1wBgyZIlyM7OxoQJE7Bz585St9fE+JTnO0sfx+d5Y1Oe4/GqYxMVFYWhQ4di7NixUpmXl5dKO0tLS44NgKysLIwbNw6//PIL2rZtK5XXrVtXZfsqVaq80jE7evQowsPD0bp1awBP/gZ99913OH78ODp37oycnBwsX74ca9euRWBgIIAn/2jy8fHBH3/8Uep7SpdwCoSOOXz4MBo1aqT2do0aNYKBgQHi4uJQXFyMnJwc/PDDDwgKCip38luanJwcAICtra1SeXx8PKpWrYratWsjOjoaDx48eOk+3hTlHZucnByV4wUA9evXh5OTE95//33pX8cvKz8/H3FxcXB3d4eLi4tSXUREBKpWrYqmTZtixYoV0IelvsszNrdv38amTZsQEBCgUveqY/PgwQP07t0bMTExz/2Do49jA5Q9PhcuXMCUKVOwevVqlTN+T9PEZ6c831n6OD7P++yU53i8ythkZGTg2LFjsLe3R/PmzeHg4ICAgAAcOXJEpe3MmTNRpUoVNGjQAHPmzMHjx4/V6utNVNrYJCQkQKFQ4NatW/Dx8UG1atXQvXt33Lx5U2X7zp07w97eHi1btsS2bdvU7r958+bYtm0bbt26BSEEDhw4gCtXrqBdu3YAnpx4KyoqQlBQkLSNt7c3qlevjsTERLX7e914BljH/PPPP3B2dlZ7O3d3d+zZswfdu3fHoEGDUFxcDH9/f+zYseOlY1EoFBg+fDhatGiB2rVrS+W9e/eGq6srnJ2dcebMGYwZMwaXL1/Gpk2bXrqvN0F5xubo0aPYsGEDfv31V6nMyckJsbGxaNy4MQoLC/H999+jdevWOHbsGBo2bKhWDIsXL8bo0aORn58PLy8vJCQkKP0kO2XKFAQGBsLMzEz6uSovLw9Dhw5V78W+YZ43Nr169cLWrVtRUFCATp064fvvv5fqNDU2UVFRaN68Obp06VJmG30dG6D08SksLESvXr0wZ84cVK9eHdevX1fZTlPjU57vLH0dn7I+Oy86HpoYm5IxnzRpEr7++mvUr18fq1evRtu2bXHu3Dl4enoCeHINTMOGDWFra4ujR48iOjoaqampmDdvnoaOgm4qbWyuX78OhUKB6dOn45tvvoG1tTXGjRuH999/H2fOnIGxsTEsLCwwd+5ctGjRAgYGBti4cSNCQ0OxZcsWaepCeSxcuBADBw5EtWrVYGRkBAMDAyxbtkz6NTgtLQ3GxsYqvwA4ODi89HUUr5UgndKuXTsxZMiQMusnTpwo6tWrp1KempoqPD09xahRo8TJkyfFwYMHRUBAgGjbtq1QKBQvFcunn34qXF1dxc2bN5/bbt++fQKAuHr1arlifVO9aGzOnj0rqlatKr766qsX7qtVq1bio48+UjuG7OxsceXKFXHw4EHRqVMn0bBhQ1FQUFBm+/Hjx4tq1aqplAcEBIhhw4ap3b+uet7YpKamiosXL4qtW7cKX19fMXjw4OfuS92x2bp1q6hZs6a4f/++VAZAbN68+bnb6cvYCFH6+ERFRYkePXpIzw8cOCAAiKysrOfu62U/O08r6zvrafoyPi/6XitR1vF4mrpj8/vvvwsAIjo6Wqm8Tp06YuzYsWVut3z5cmFkZCQePnyoVB4eHi66dOlS7v51XWljM23aNAFA7N69WyrLyMgQBgYGYteuXWXu6+OPPxYtW7ZUq/85c+aId999V2zbtk2cPn1aLFy4UFhYWIiEhAQhhBDx8fHC2NhYZbsmTZqI0aNHK5XpYj7AKRA6pmrVqsjKylJ7u5iYGFhbW2P27Nlo0KABWrVqhTVr1mDfvn04duyY2vuLjIzE9u3bceDAAVSrVu25bf38/AAAV69eVbufN8nzxubChQto27YtBg4ciHHjxr1wX02bNn2p42VtbQ1PT0+0atUKP//8My5duoTNmzeX2d7Pzw///vvvW3VhSGmeNzaOjo7w9vZG586d8d1332HJkiVITU0tc1/qjs3+/ftx7do12NjYwMjICEZGT35YCwsLk+bOlUZfxgYofXz279+Pn376STpmJfMZq1atiokTJ5a5r5f97DytPN9Z+jI+5f2bU57joe7YlMyV9/X1VSr38fFBSkrKc2N5/PjxG7s0WHmVNjalHTM7OztUrVr1hcdMnbEpKCjAF198gXnz5qFTp06oW7cuIiMj0aNHD3z99dcAnny3Pnr0SGnlFgBIT09/I+ZrMwHWMQ0aNMCFCxfU3u7Bgwcqc+hKVgZQKBTl3o8QApGRkdi8eTP2798Pd3f3F25z6tQpACjzwp+3RVljc/78ebRp0wbh4eGYNm1aufZ16tSpVz5eQggIIZ77B+nUqVOoXLky5HL5K/Wl68r7uSn5LLzomKkzNmPHjsWZM2dw6tQp6QEA8+fPR1xc3HP70YexAUofn40bN+L06dPSMSuZmnL48GGlJZ2epYnPTnm+s/RlfMr72SnP8VB3bNzc3ODs7Kyy1OaVK1fg6ur63H4MDAxeerWWN0VpY9OiRQsAUDpmmZmZuHv37guPmTpjU1RUhKKiolLzipLv0UaNGqFSpUrYt2+fVH/58mWkpKS8EcsFcg6wjgkODkZ0dDSysrJQuXJlqfzq1avIy8tDWloaCgoKpC9wX19fGBsbo0OHDpg/fz6mTJmCXr164f79+/jiiy/g6uqKBg0alLv/iIgIrF27Flu3boWlpaU0j8fa2hqmpqa4du0a1q5di5CQEFSpUgVnzpxBVFQUWrVqVepVqG+T0sbm3LlzCAwMRHBwMEaMGCEdL0NDQ9jZ2QF4sjSWu7s7atWqhYcPH+L777/H/v37sWfPnnL3ff36dWzYsAHt2rWDnZ0d/v33X8ycOROmpqYICQkBAPzyyy9IT09Hs2bNYGJigoSEBEyfPh2ff/65ho+E7iltbHbs2IH09HQ0adIEFhYWOH/+PEaNGoUWLVpIa2pqYmwcHR1LPdtRvXp16R+Q+jw2QOnj8/TKJQBw9+5dAE/O/pXMKdTE+JTnO0ufx6e0sSnP8dDE2MhkMowaNQoTJ05EvXr1UL9+faxatQqXLl3Czz//DODJMmnHjh1DmzZtYGlpicTERERFReGjjz5S+hv5NiptbN5991106dIFw4YNw9KlS2FlZYXo6Gh4e3ujTZs2AIBVq1bB2NhY+tu/adMmrFixQun6hxexsrJCQEAARo0aBVNTU7i6uuLgwYNYvXq1NPfa2toa/fv3x4gRI2BrawsrKyt89tln8Pf31/kVIABwDrAuatq0qYiNjVUqCwgIEABUHsnJyVKbdevWiQYNGghzc3NhZ2cnOnfuLC5evCjVJycnCwDiwIEDZfZdWh8ARFxcnBBCiJSUFNGqVStha2sr5HK5qFmzphg1apTIyclR2Zcuzvl5Vc+OzcSJE0s9Xq6urlKbWbNmCQ8PD2FiYiJsbW1F69atxf79+5X2GxcXJ573cbx165b44IMPhL29vahUqZKoVq2a6N27t7h06ZLUZufOnaJ+/frCwsJCmJubi3r16onY2FhRXFyssr+3bR6jEKpjs3//fuHv7y+sra2FiYmJ8PT0FGPGjFGaY6qJsSkNnpkDrO9jI0Tp32tPK20OsCbGpzzfWfo+Ps+OTXmOhyY/OzNmzBDVqlUTZmZmwt/fXxw+fFiqO3HihPDz85M+xz4+PmL69Okq83+FePvmAAtR+ucmJydH9OvXT9jY2AhbW1vx4YcfipSUFKl+5cqVwsfHR5iZmQkrKyvRtGlT8dNPPynto+Tz9nQO8azU1FTRt29f4ezsLExMTISXl5eYO3eu0nVFBQUFYsiQIaJy5crCzMxMfPjhhyI1NVVlX7qYDzAB1kHbt28XPj4+pX75vor9+/cLGxsbkZmZqdH9lkUX3/CvSltjM2HCBBEQEKDRfT7P2/hHnGOj2zg+uuttGZu3MQHW1tisWLFC1KxZUzx69Eij+y2LLuYDnAKhgzp06ICkpCTcunVLZY3XV7Fjxw588cUXWv/ZKCUlBb6+vnj06JHKxQ1vOm2Nzc6dO7Fo0SKN7a8s8fHxGDRoEAoKCnTuvuyvimOj2zg+uutNH5vDhw/jgw8+QGFhITp06KD1/l4nbeYD06dPf6X7BJSHLucDMiH0YKVveq2evjpXLpdr9ENLr+b+/fvSfdptbGxQtWrVCo6ISnBsdBvHR3cVFBRIt9G2sLB4I1Yg0Be6nA8wASYiIiIivcJl0IiIiIhIrzABJiIiIiK9wgSYiIiIiPQKE2AiIiIi0itMgImIiIhIrzABJiIiIiK9wgSYiKgCJCYmwtDQ8LUv3P/o0SPMmTMHDRs2hLm5OaytrVGvXj2MGzcOt2/ffq2xEBFVFK4DTERUAf73v//BwsICy5cvx+XLl+Hs7Kz1PgsLC9GuXTucOXMGkydPRosWLWBnZ4fk5GSsW7cOlStXxowZM0rd9tGjRzA2NtZ6jERErwPPABMRvWZ5eXnYsGEDBg8ejA4dOmDlypUqbbZt2wZPT0+YmJigTZs2WLVqFWQyGbKzs6U2R44cwXvvvQdTU1O4uLhg6NChyM/PL7Pf+fPn48iRI9i/fz+GDh2KRo0aoXr16ggICEBsbCymT58utW3dujUiIyMxfPhwVK1aFcHBwQCAgwcPomnTppDL5XBycsLYsWPx+PFjaTs3NzcsWLBAqd/69etj0qRJ0nOZTIYlS5bggw8+gKmpKWrUqIGff/5ZvYNIRPQKmAATEb1mP/74I7y9veHl5YWPPvoIK1aswNM/xiUnJ6Nbt24IDQ3F6dOnMWjQIHz55ZdK+7h27Rrat2+PsLAwnDlzBhs2bMCRI0cQGRlZZr/r1q3D+++/jwYNGpRaL5PJlJ6vWrUKxsbG+P333xEbG4tbt24hJCQETZo0wenTp7FkyRIsX74cU6dOVfsYjB8/HmFhYTh9+jT69OmDnj174uLFi2rvh4jopQgiInqtmjdvLhYsWCCEEKKoqEhUrVpVHDhwQKofM2aMqF27ttI2X375pQAgsrKyhBBC9O/fXwwcOFCpzeHDh4WBgYEoKCgotV8TExMxdOhQpbLQ0FBhbm4uzM3Nhb+/v1QeEBAgGjRooNT2iy++EF5eXkKhUEhlMTExwsLCQhQXFwshhHB1dRXz589X2q5evXpi4sSJ0nMA4tNPP1Vq4+fnJwYPHlxq3EREmsYzwEREr9Hly5dx/Phx9OrVCwBgZGSEHj16YPny5UptmjRporRd06ZNlZ6fPn0aK1euhIWFhfQIDg6GQqFAcnJyueNZvHgxTp06hX79+uHBgwdKdY0aNVJ6fvHiRfj7+yudKW7RogXy8vLw77//lrtPAPD391d5zjPARPS6GFV0AERE+mT58uV4/Pix0kVvQgjI5XIsWrQI1tbW5dpPXl4eBg0ahKFDh6rUVa9evdRtPD09cfnyZaUyJycnAICtra1Ke3Nz83LF8jQDAwOl6RwAUFRUpPZ+iIi0iWeAiYhek8ePH2P16tWYO3cuTp06JT1Onz4NZ2dnrFu3DgDg5eWFv/76S2nbP//8U+l5w4YNceHCBdSsWVPlUdZqDb169UJCQgL+/vvvl4rfx8cHiYmJSgnu77//DktLS1SrVg0AYGdnh9TUVKk+Nze31DPSf/zxh8pzHx+fl4qLiEhdTICJiF6T7du3IysrC/3790ft2rWVHmFhYdI0iEGDBuHSpUsYM2YMrly5gh9//FFaKaJk+sGYMWNw9OhRREZG4tSpU0hKSsLWrVufexFcVFQU/P390bZtW3zzzTc4efIkkpOTsXv3buzcuROGhobPjX/IkCG4efMmPvvsM1y6dAlbt27FxIkTMWLECBgYPPlzEhgYiB9++AGHDx/G2bNnER4eXup+f/rpJ6xYsQJXrlzBxIkTcfz48efGTkSkURU8B5mISG907NhRhISElFp37NgxAUCcPn1aCCHE1q1bRc2aNYVcLhetW7cWS5YsEQCULnA7fvy4eP/994WFhYUwNzcXdevWFdOmTXtuDA8fPhQzZ84U9erVE6ampkIulwtvb28RFRUlUlJSpHYBAQFi2LBhKtv/9ttvokmTJsLY2Fg4OjqKMWPGiKKiIqk+JydH9OjRQ1hZWQkXFxexcuXKUi+Ci4mJEe+//76Qy+XCzc1NbNiwoTyHkIhII3gjDCKiN8C0adMQGxuLmzdvVnQor0wmk2Hz5s0IDQ2t6FCISE/xIjgiIh20ePFiNGnSBFWqVMHvv/+OOXPmcIoAEZGGMAEmItJBSUlJmDp1KjIzM1G9enWMHDkS0dHRFR0WEdFbgVMgiIiIiEivcBUIIiIiItIrTICJiIiISK8wASYiIiIivcIEmIiIiIj0ChNgIiIiItIrTICJiIiISK8wASYiIiIivcIEmIiIiIj0yv8DoInx9vR9JbsAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Answer** - The graph shows that spending levels are relatively similar across all age groups. While customers aged 65 and above show slightly higher spending, the overall differences are minimal, indicating that age does not strongly influence the total purchase amount."
      ],
      "metadata": {
        "id": "ziXxmHbddTAf"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Question** - How do payment method and device type together influence the total purchase amount?"
      ],
      "metadata": {
        "id": "5j8iX6QqgLhf"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The following heatmap shows the average total purchase amount for each combination of payment method and device type."
      ],
      "metadata": {
        "id": "sdrbHVkRgQJN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Create pivot table\n",
        "pivot = df.pivot_table(\n",
        "    values='Total_Amount',\n",
        "    index='Payment_Method',\n",
        "    columns='Device_Type',\n",
        "    aggfunc='mean'\n",
        ")\n",
        "\n",
        "# Plot heatmap\n",
        "plt.figure(figsize=(6,4))\n",
        "sns.heatmap(pivot, annot=True, fmt=\".0f\", cmap=\"PuBu\")\n",
        "\n",
        "plt.title('Average Spending by Payment Method and Device Type')\n",
        "plt.xlabel('Device Type')\n",
        "plt.ylabel('Payment Method')\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 410
        },
        "id": "46wFM7c3eWBt",
        "outputId": "9d6f9dc1-35f2-4b00-ed5e-7ba267cdeb99"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Answer** - Spending patterns vary slightly across different payment methods and device types. Bank Transfer and Digital Wallet tend to show higher spending, especially on Desktop and Tablet devices. In contrast, Cash on Delivery generally shows lower spending levels, particularly on Desktop.\n",
        "Overall, while some combinations show higher spending, the differences are moderate, suggesting that payment method and device type have a limited influence on total purchase amount."
      ],
      "metadata": {
        "id": "Xfm17sZ0gRhr"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Question** - How does discount percentage influence the total purchase amount?"
      ],
      "metadata": {
        "id": "PDJTa3ITpK_h"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The following chart shows how the average total purchase amount changes across different discount percentage levels."
      ],
      "metadata": {
        "id": "NS3nQv6Ory0l"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Create discount groups based on the existing Discount_Percentage column\n",
        "df['Discount_Group'] = pd.cut(\n",
        "    df['Discount_Percentage'],  # use the percentage we already created\n",
        "    bins=[0, 10, 25, 50, 100],  # define ranges\n",
        "    labels=['0-10%', '10-25%', '25-50%', '50%+']  # readable labels\n",
        ")\n",
        "\n",
        "# Calculate average Total_Amount for each discount group\n",
        "avg_data = df.groupby('Discount_Group')['Total_Amount'].mean().reset_index()\n",
        "\n",
        "# Create the bar plot\n",
        "plt.figure(figsize=(6,4))\n",
        "sns.barplot(\n",
        "    x='Discount_Group',      # discount levels on x-axis\n",
        "    y='Total_Amount',        # average spending on y-axis\n",
        "    data=avg_data,\n",
        "    palette='pastel'         # soft colors for clarity\n",
        ")\n",
        "\n",
        "# Add titles and labels\n",
        "plt.title('Average Spending by Discount Percentage')\n",
        "plt.xlabel('Discount Percentage')\n",
        "plt.ylabel('Average Total Amount')\n",
        "\n",
        "# Show the plot\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 601
        },
        "id": "idpnbdZyriZs",
        "outputId": "7e5b5e4e-19cc-4830-efdd-682da1ea435d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/tmp/ipykernel_34980/2990721499.py:13: FutureWarning:\n",
            "\n",
            "The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
            "\n",
            "/tmp/ipykernel_34980/2990721499.py:17: FutureWarning:\n",
            "\n",
            "\n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Answer** - The chart shows that as the discount percentage increases, the total purchase amount decreases significantly. The highest spending is observed at low discount levels (0–10%), while the lowest spending occurs at high discount levels (50%+). This suggests that higher discounts are associated with lower total spending."
      ],
      "metadata": {
        "id": "km9QY-9msBkf"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Question** - How does discount percentage influence spending across different product categories?"
      ],
      "metadata": {
        "id": "wJXyamYtxSol"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The following heatmap shows the average total purchase amount across different product categories and discount percentage levels."
      ],
      "metadata": {
        "id": "KcyVW1OTxk69"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "\n",
        "# Create discount groups (based on percentage)\n",
        "df['Discount_Group'] = pd.cut(\n",
        "    df['Discount_Percentage'],\n",
        "    bins=[0, 10, 25, 50, 100],\n",
        "    labels=['0-10%', '10-25%', '25-50%', '50%+']\n",
        ")\n",
        "\n",
        "# Create pivot table\n",
        "pivot = df.pivot_table(\n",
        "    values='Total_Amount',\n",
        "    index='Product_Category',   # rows\n",
        "    columns='Discount_Group',   # columns\n",
        "    aggfunc='mean'\n",
        ")\n",
        "\n",
        "# Plot heatmap\n",
        "plt.figure(figsize=(8,5))\n",
        "sns.heatmap(pivot, annot=True, fmt=\".0f\", cmap=\"YlGnBu\")\n",
        "\n",
        "plt.title('Total Amount by Category and Discount (%)')\n",
        "plt.xlabel('Discount Percentage')\n",
        "plt.ylabel('Product Category')\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 556
        },
        "id": "ZC_7bpSGxY1z",
        "outputId": "0d4c9791-147c-4c74-9be3-10801f4818b4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/tmp/ipykernel_34980/1111504297.py:13: FutureWarning:\n",
            "\n",
            "The default value of observed=False is deprecated and will change to observed=True in a future version of pandas. Specify observed=False to silence this warning and retain the current behavior\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Answer** - The heatmap shows that discount percentage has a clear impact on spending across product categories. In most categories, the highest spending occurs at low discount levels (0–10%), while higher discount levels are associated with lower total purchase amounts.\n",
        "\n",
        "Additionally, categories such as Electronics, Home & Garden, and Sports consistently show higher spending compared to others. The results suggest that both product category and discount percentage influence purchasing behavior, with discount percentage having a negative relationship with total spending."
      ],
      "metadata": {
        "id": "E_-Sx22wxvpC"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Advanced EDA Question:** How does the average purchase amount vary by city and product category?"
      ],
      "metadata": {
        "id": "2EcTLd8Wrf-o"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Create pivot table: average Total_Amount by City and Product Category\n",
        "city_category_pivot = df.pivot_table(\n",
        "    values='Total_Amount',\n",
        "    index='City',\n",
        "    columns='Product_Category',\n",
        "    aggfunc='mean'\n",
        ").round(2)\n",
        "\n",
        "plt.figure(figsize=(12, 7))\n",
        "\n",
        "sns.heatmap(\n",
        "    city_category_pivot,\n",
        "    annot=True,\n",
        "    fmt='.0f',\n",
        "    cmap='YlGnBu',\n",
        "    linewidths=0.5,\n",
        "    linecolor='white',\n",
        "    cbar_kws={'label': 'Average Total Amount'}\n",
        ")\n",
        "\n",
        "plt.title('Average Total Amount by City and Product Category', fontsize=16, fontweight='bold', pad=15)\n",
        "plt.xlabel('Product Category', fontsize=12)\n",
        "plt.ylabel('City', fontsize=12)\n",
        "plt.xticks(rotation=35, ha='right')\n",
        "plt.yticks(rotation=0)\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 707
        },
        "id": "J2jMhu9Dq_Rh",
        "outputId": "39545a42-731e-4a81-ca12-b639c22ab67c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x700 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Answer -** The heatmap shows the average Total_Amount for each combination of City and Product_Category.\n",
        "\n",
        "Darker colors represent higher average purchase amounts, while lighter colors represent lower average purchase amounts.\n",
        "\n",
        "From the heatmap, Electronics clearly has the highest average Total_Amount across almost all cities. This suggests that Electronics is the strongest product category in terms of purchase amount.\n",
        "\n",
        "Home & Garden and Sports also show relatively high average purchase amounts compared to categories such as Books, Food, Beauty, and Toys.\n",
        "\n",
        "The results also show that the average purchase amount changes slightly between cities. For example, some cities such as Antalya, Bursa, Izmir, and Kayseri show high average values for Electronics.\n",
        "\n",
        "Overall, this visualization provides a deeper business insight by showing that both product category and city can affect purchase amount. These insights can help support business decisions such as targeted marketing, inventory planning, and city-based product promotions."
      ],
      "metadata": {
        "id": "ClcUBm8PruTy"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Advanced Question:**\n",
        "Which product categories are most likely to generate high-value purchases?"
      ],
      "metadata": {
        "id": "e7WhJ7dtt4Xb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Advanced Interactive Business Insight: High Purchase Rate by Product Category\n",
        "\n",
        "import plotly.express as px\n",
        "import pandas as pd\n",
        "\n",
        "# Create a copy of the original dataset\n",
        "df_interactive = df.copy()\n",
        "\n",
        "# Calculate thresholds directly from Total_Amount\n",
        "low_threshold = df_interactive['Total_Amount'].quantile(1/3)\n",
        "high_threshold = df_interactive['Total_Amount'].quantile(2/3)\n",
        "\n",
        "# Classify purchases into Low, Medium, and High\n",
        "def classify_purchase_amount(value):\n",
        "    if value <= low_threshold:\n",
        "        return 'Low Purchase'\n",
        "    elif value <= high_threshold:\n",
        "        return 'Medium Purchase'\n",
        "    else:\n",
        "        return 'High Purchase'\n",
        "\n",
        "df_interactive['Purchase_Class'] = df_interactive['Total_Amount'].apply(classify_purchase_amount)\n",
        "\n",
        "# Calculate high purchase rate by product category\n",
        "high_purchase_rate = df_interactive.groupby('Product_Category')['Purchase_Class'].apply(\n",
        "    lambda x: (x == 'High Purchase').mean() * 100\n",
        ").reset_index()\n",
        "\n",
        "high_purchase_rate.columns = ['Product_Category', 'High_Purchase_Rate']\n",
        "\n",
        "high_purchase_rate = high_purchase_rate.sort_values(\n",
        "    by='High_Purchase_Rate',\n",
        "    ascending=False\n",
        ")\n",
        "\n",
        "# Create interactive bar chart\n",
        "fig = px.bar(\n",
        "    high_purchase_rate,\n",
        "    x='Product_Category',\n",
        "    y='High_Purchase_Rate',\n",
        "    text='High_Purchase_Rate',\n",
        "    title='Interactive Dashboard: High Purchase Rate by Product Category',\n",
        "    color='High_Purchase_Rate',\n",
        "    color_continuous_scale='Blues'\n",
        ")\n",
        "\n",
        "fig.update_traces(\n",
        "    texttemplate='%{text:.1f}%',\n",
        "    textposition='outside'\n",
        ")\n",
        "\n",
        "fig.update_layout(\n",
        "    xaxis_title='Product Category',\n",
        "    yaxis_title='High Purchase Rate (%)',\n",
        "    template='plotly_white',\n",
        "    title_font_size=20,\n",
        "    xaxis_tickangle=-35,\n",
        "    yaxis_range=[0, high_purchase_rate['High_Purchase_Rate'].max() + 10]\n",
        ")\n",
        "\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "jiAn20U6ss4O",
        "outputId": "23010589-c45e-4fd5-8844-e681f317059c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
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        "**Advanced Answer:**\n",
        "The interactive chart shows the percentage of High Purchase transactions within each product category.\n",
        "This means that instead of only looking at the average Total_Amount, the visualization shows how often each product category leads to a high-value purchase.\n",
        "Categories with a higher High Purchase Rate are more strongly associated with expensive transactions.\n",
        "This advanced analysis provides a deeper business insight because it helps identify which product categories are most important for revenue growth, marketing campaigns, and inventory planning."
      ],
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        "**Bonus: Advanced Business Opportunity Analysis**\n",
        "\n",
        "This advanced analysis identifies the strongest city and product category combinations from a business perspective.\n",
        "\n",
        "Instead of looking only at one variable at a time, this dashboard combines average purchase amount, high purchase rate, total revenue, and transaction volume.\n",
        "\n",
        "The goal is to identify which city-category combinations should be prioritized for marketing, inventory planning, and business growth."
      ],
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      "source": [
        "# Bonus: Advanced Business Opportunity Dashboard\n",
        "\n",
        "import pandas as pd\n",
        "import plotly.express as px\n",
        "from IPython.display import display\n",
        "\n",
        "# Create a copy of the dataset\n",
        "df_bonus = df.copy()\n",
        "\n",
        "# Create purchase classes using quantile thresholds\n",
        "low_threshold = df_bonus['Total_Amount'].quantile(1/3)\n",
        "high_threshold = df_bonus['Total_Amount'].quantile(2/3)\n",
        "\n",
        "def classify_purchase(value):\n",
        "    if value <= low_threshold:\n",
        "        return 'Low Purchase'\n",
        "    elif value <= high_threshold:\n",
        "        return 'Medium Purchase'\n",
        "    else:\n",
        "        return 'High Purchase'\n",
        "\n",
        "df_bonus['Purchase_Class'] = df_bonus['Total_Amount'].apply(classify_purchase)\n",
        "\n",
        "# Create city-category business summary\n",
        "opportunity_df = df_bonus.groupby(['City', 'Product_Category']).agg(\n",
        "    Average_Total_Amount=('Total_Amount', 'mean'),\n",
        "    Total_Revenue=('Total_Amount', 'sum'),\n",
        "    Number_of_Transactions=('Total_Amount', 'count'),\n",
        "    High_Purchase_Rate=('Purchase_Class', lambda x: (x == 'High Purchase').mean() * 100)\n",
        ").reset_index()\n",
        "\n",
        "# Round values\n",
        "opportunity_df['Average_Total_Amount'] = opportunity_df['Average_Total_Amount'].round(2)\n",
        "opportunity_df['Total_Revenue'] = opportunity_df['Total_Revenue'].round(2)\n",
        "opportunity_df['High_Purchase_Rate'] = opportunity_df['High_Purchase_Rate'].round(2)\n",
        "\n",
        "# Create a business score that combines revenue, average amount, and high purchase rate\n",
        "opportunity_df['Business_Score'] = (\n",
        "    opportunity_df['Total_Revenue'].rank(pct=True) * 0.4 +\n",
        "    opportunity_df['Average_Total_Amount'].rank(pct=True) * 0.3 +\n",
        "    opportunity_df['High_Purchase_Rate'].rank(pct=True) * 0.3\n",
        ")\n",
        "\n",
        "opportunity_df['Business_Score'] = (opportunity_df['Business_Score'] * 100).round(2)\n",
        "\n",
        "# Create business opportunity labels\n",
        "avg_threshold = opportunity_df['Average_Total_Amount'].median()\n",
        "rate_threshold = opportunity_df['High_Purchase_Rate'].median()\n",
        "\n",
        "def assign_opportunity(row):\n",
        "    if row['Average_Total_Amount'] >= avg_threshold and row['High_Purchase_Rate'] >= rate_threshold:\n",
        "        return 'High Priority Opportunity'\n",
        "    elif row['Average_Total_Amount'] >= avg_threshold and row['High_Purchase_Rate'] < rate_threshold:\n",
        "        return 'Premium but Lower Frequency'\n",
        "    elif row['Average_Total_Amount'] < avg_threshold and row['High_Purchase_Rate'] >= rate_threshold:\n",
        "        return 'Frequent High Purchase Potential'\n",
        "    else:\n",
        "        return 'Lower Priority'\n",
        "\n",
        "opportunity_df['Opportunity_Type'] = opportunity_df.apply(assign_opportunity, axis=1)\n",
        "\n",
        "# Interactive business opportunity bubble chart\n",
        "fig = px.scatter(\n",
        "    opportunity_df,\n",
        "    x='Average_Total_Amount',\n",
        "    y='High_Purchase_Rate',\n",
        "    size='Total_Revenue',\n",
        "    color='Opportunity_Type',\n",
        "    hover_name='City',\n",
        "    hover_data={\n",
        "        'Product_Category': True,\n",
        "        'Average_Total_Amount': ':.2f',\n",
        "        'High_Purchase_Rate': ':.2f',\n",
        "        'Total_Revenue': ':.2f',\n",
        "        'Number_of_Transactions': True,\n",
        "        'Business_Score': ':.2f'\n",
        "    },\n",
        "    title='Bonus Interactive Dashboard: City-Category Business Opportunities',\n",
        "    labels={\n",
        "        'Average_Total_Amount': 'Average Total Amount',\n",
        "        'High_Purchase_Rate': 'High Purchase Rate (%)',\n",
        "        'Total_Revenue': 'Total Revenue',\n",
        "        'Opportunity_Type': 'Opportunity Type'\n",
        "    },\n",
        "    size_max=55\n",
        ")\n",
        "\n",
        "# Add quadrant lines\n",
        "fig.add_vline(\n",
        "    x=avg_threshold,\n",
        "    line_dash='dash',\n",
        "    line_color='gray',\n",
        "    annotation_text='Median Avg Amount',\n",
        "    annotation_position='top'\n",
        ")\n",
        "\n",
        "fig.add_hline(\n",
        "    y=rate_threshold,\n",
        "    line_dash='dash',\n",
        "    line_color='gray',\n",
        "    annotation_text='Median High Purchase Rate',\n",
        "    annotation_position='right'\n",
        ")\n",
        "\n",
        "fig.update_layout(\n",
        "    template='plotly_white',\n",
        "    title_x=0.5,\n",
        "    title_font_size=22,\n",
        "    width=1050,\n",
        "    height=700,\n",
        "    legend_title_text='Business Opportunity Type'\n",
        ")\n",
        "\n",
        "fig.show()\n",
        "\n",
        "# Show top 10 business opportunities\n",
        "top_opportunities = opportunity_df.sort_values(\n",
        "    by='Business_Score',\n",
        "    ascending=False\n",
        ").head(10)\n",
        "\n",
        "display(\n",
        "    top_opportunities[\n",
        "        [\n",
        "            'City',\n",
        "            'Product_Category',\n",
        "            'Average_Total_Amount',\n",
        "            'High_Purchase_Rate',\n",
        "            'Total_Revenue',\n",
        "            'Number_of_Transactions',\n",
        "            'Business_Score',\n",
        "            'Opportunity_Type'\n",
        "        ]\n",
        "    ]\n",
        ")"
      ],
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              "         City Product_Category  Average_Total_Amount  High_Purchase_Rate  \\\n",
              "50   Istanbul      Electronics               2367.47               91.67   \n",
              "26      Bursa      Electronics               2472.96               94.66   \n",
              "58      Izmir      Electronics               2414.78               91.46   \n",
              "18    Antalya      Electronics               2496.58               96.10   \n",
              "10     Ankara      Electronics               2327.39               90.68   \n",
              "66    Kayseri      Electronics               2440.11               95.65   \n",
              "2       Adana      Electronics               2368.17               89.66   \n",
              "42  Gaziantep      Electronics               2321.19               90.00   \n",
              "53   Istanbul    Home & Garden               1555.66               68.60   \n",
              "29      Bursa    Home & Garden               1728.44               81.94   \n",
              "\n",
              "    Total_Revenue  Number_of_Transactions  Business_Score  \\\n",
              "50     1306845.14                     552           97.00   \n",
              "26      509430.78                     206           96.38   \n",
              "58      594036.00                     246           95.37   \n",
              "18      384473.27                     154           95.00   \n",
              "10      649341.96                     279           94.00   \n",
              "66      280612.17                     115           90.88   \n",
              "2       343385.33                     145           88.50   \n",
              "42      371390.95                     160           88.38   \n",
              "53      832278.82                     535           88.25   \n",
              "29      373342.45                     216           87.00   \n",
              "\n",
              "             Opportunity_Type  \n",
              "50  High Priority Opportunity  \n",
              "26  High Priority Opportunity  \n",
              "58  High Priority Opportunity  \n",
              "18  High Priority Opportunity  \n",
              "10  High Priority Opportunity  \n",
              "66  High Priority Opportunity  \n",
              "2   High Priority Opportunity  \n",
              "42  High Priority Opportunity  \n",
              "53  High Priority Opportunity  \n",
              "29  High Priority Opportunity  "
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              "      <th>City</th>\n",
              "      <th>Product_Category</th>\n",
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              "      <th>50</th>\n",
              "      <td>Istanbul</td>\n",
              "      <td>Electronics</td>\n",
              "      <td>2367.47</td>\n",
              "      <td>91.67</td>\n",
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              "      <td>Electronics</td>\n",
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              "      <th>66</th>\n",
              "      <td>Kayseri</td>\n",
              "      <td>Electronics</td>\n",
              "      <td>2440.11</td>\n",
              "      <td>95.65</td>\n",
              "      <td>280612.17</td>\n",
              "      <td>115</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Adana</td>\n",
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              "      <td>2368.17</td>\n",
              "      <td>89.66</td>\n",
              "      <td>343385.33</td>\n",
              "      <td>145</td>\n",
              "      <td>88.50</td>\n",
              "      <td>High Priority Opportunity</td>\n",
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              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>Gaziantep</td>\n",
              "      <td>Electronics</td>\n",
              "      <td>2321.19</td>\n",
              "      <td>90.00</td>\n",
              "      <td>371390.95</td>\n",
              "      <td>160</td>\n",
              "      <td>88.38</td>\n",
              "      <td>High Priority Opportunity</td>\n",
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              "    <tr>\n",
              "      <th>53</th>\n",
              "      <td>Istanbul</td>\n",
              "      <td>Home &amp; Garden</td>\n",
              "      <td>1555.66</td>\n",
              "      <td>68.60</td>\n",
              "      <td>832278.82</td>\n",
              "      <td>535</td>\n",
              "      <td>88.25</td>\n",
              "      <td>High Priority Opportunity</td>\n",
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              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>Bursa</td>\n",
              "      <td>Home &amp; Garden</td>\n",
              "      <td>1728.44</td>\n",
              "      <td>81.94</td>\n",
              "      <td>373342.45</td>\n",
              "      <td>216</td>\n",
              "      <td>87.00</td>\n",
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              "summary": "{\n  \"name\": \")\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"City\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 8,\n        \"samples\": [\n          \"Bursa\",\n          \"Kayseri\",\n          \"Istanbul\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Product_Category\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Home & Garden\",\n          \"Electronics\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Average_Total_Amount\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 327.7457194313231,\n        \"min\": 1555.66,\n        \"max\": 2496.58,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          1555.66,\n          2472.96\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"High_Purchase_Rate\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8.23335060726933,\n        \"min\": 68.6,\n        \"max\": 96.1,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          68.6,\n          94.66\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Total_Revenue\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 310843.098837239,\n        \"min\": 280612.17,\n        \"max\": 1306845.14,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          832278.82,\n          509430.78\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Number_of_Transactions\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 156,\n        \"min\": 115,\n        \"max\": 552,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          535,\n          206\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Business_Score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.860841013734357,\n        \"min\": 87.0,\n        \"max\": 97.0,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          88.25,\n          96.38\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Opportunity_Type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"High Priority Opportunity\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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    },
    {
      "cell_type": "markdown",
      "source": [
        "**Bonus Advanced Answer:**\n",
        "\n",
        "The interactive business opportunity dashboard analyzes city and product category combinations using several business metrics at the same time.\n",
        "\n",
        "The x-axis shows the average Total_Amount, while the y-axis shows the percentage of High Purchase transactions. The bubble size represents total revenue, and the color represents the type of business opportunity.\n",
        "\n",
        "Combinations located in the upper-right area are the strongest opportunities because they have both high average spending and a high percentage of high-value purchases.\n",
        "\n",
        "The table below the chart shows the top 10 city-category combinations based on a Business Score. This score combines total revenue, average purchase amount, and high purchase rate.\n",
        "\n",
        "This advanced analysis provides deeper business insight because it helps identify where the business should focus its marketing, inventory planning, and growth strategy."
      ],
      "metadata": {
        "id": "WEvOn5fC2CbZ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Conclusion**\n",
        "\n",
        "The EDA shows that customer spending is mainly driven by product-related and transaction-related factors.\n",
        "\n",
        "Product category has a clear impact on Total_Amount. Electronics generates the highest average spending across almost all cities, while categories such as Books and Food are associated with lower purchase amounts. This suggests that the type of product is one of the strongest factors affecting customer spending.\n",
        "\n",
        "A strong relationship was also observed between Unit_Price, Quantity, and Total_Amount. As the unit price and quantity increase, the total purchase amount increases as well. This confirms that price and quantity are the main direct drivers of purchase value.\n",
        "\n",
        "City has a moderate influence on spending. No single city completely dominates the results, but the heatmap analysis showed that some city and product category combinations have higher average purchase amounts than others. For example, Electronics performs strongly across many cities, while Home & Garden and Sports also show relatively high values in certain locations. This provides a deeper business insight into regional purchasing patterns.\n",
        "\n",
        "In contrast, Age and Gender show minimal impact on spending behavior. The spending patterns across different age and gender groups are relatively similar, suggesting that these variables are less important for predicting Total_Amount.\n",
        "\n",
        "Payment method and device type introduce slight differences in spending patterns. Some payment methods, such as Digital Wallet or Bank Transfer, are associated with higher spending, while Cash on Delivery tends to be lower. However, their overall influence is weaker compared to product category, price, and quantity.\n",
        "\n",
        "Discount percentage shows a negative relationship with Total_Amount. Higher discounts are generally associated with lower total purchase amounts, and this pattern remains visible across different product categories. This may suggest that higher discounts are more common in lower-value transactions or certain promotional purchases.\n",
        "\n",
        "The advanced heatmap analysis added a deeper business perspective by showing how average Total_Amount changes across both City and Product_Category. This visualization helped identify which product categories perform better in specific cities and can support business decisions such as targeted marketing, inventory planning, and city-based product promotions.\n",
        "\n",
        "Overall, the EDA indicates that spending behavior is mainly influenced by Unit_Price, Quantity, Product_Category, and discount-related patterns. City, payment method, and device type provide additional context, while Age and Gender appear to have a weaker effect."
      ],
      "metadata": {
        "id": "u_xRLx47_zYi"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "<br><br><br>\n",
        "\n",
        "---\n",
        "\n",
        "<br><br><br>"
      ],
      "metadata": {
        "id": "zrIvgbNoIPU6"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Part 3: Define and Train a baseline model"
      ],
      "metadata": {
        "id": "3LtkZ_eLA6il"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **1. Regression Goal**"
      ],
      "metadata": {
        "id": "k4pqZ9i3BGbg"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "The objective of this regression task is to predict the total purchase amount (`Total_Amount`) using customer, product, and transaction-related features.\n",
        "\n",
        "This problem aims to model the relationship between key variables such as unit price, quantity, product category, and discount levels, in order to estimate how much a customer is likely to spend.\n",
        "\n",
        "By building this model, we establish a baseline for understanding the main drivers of purchase value and evaluate how well these factors can explain and predict customer spending behavior."
      ],
      "metadata": {
        "id": "sdzKRQsQBOWU"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **2. Feature Selection**"
      ],
      "metadata": {
        "id": "Mh6Wb-5_BU79"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "The feature selection process is based on insights obtained from the exploratory data analysis (EDA), where several variables demonstrated a clear relationship with the target variable (`Total_Amount`).\n",
        "\n",
        "Key features such as **Unit_Price**, **Quantity**, and **Product_Category** were selected due to their strong and consistent influence on purchase value. Additionally, **Discount_Percentage** was included, as it showed a meaningful inverse relationship with spending.\n",
        "\n",
        "Other variables, including **City**, **Payment_Method**, **Device_Type**, and **Is_Returning_Customer**, were also incorporated to capture potential behavioral and transactional patterns, even if their individual impact is more moderate.\n",
        "\n",
        "Customer-related attributes such as **Age**, **Customer_Rating**, and **Delivery_Time_Days** were included to allow the model to explore possible subtle effects that may not have been fully captured during EDA.\n",
        "\n",
        "Irrelevant features such as identifiers (e.g., `Order_ID`, `Customer_ID`) and engineered grouping variables were excluded to avoid redundancy and ensure a clean baseline model.\n",
        "\n",
        "Overall, the selected feature set combines both strongly influential variables and additional contextual features, providing a balanced foundation for building the regression model."
      ],
      "metadata": {
        "id": "k2voGhhu7MDC"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "All features were initially included to build a baseline model, with the intention of refining the feature set in later stages."
      ],
      "metadata": {
        "id": "9r33rJAJ9JCw"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **3. Train-Test Split**"
      ],
      "metadata": {
        "id": "vg_DtUj08yrT"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "The selected features from the feature selection stage were used as input variables for the baseline regression model.\n",
        "\n",
        "Based on the EDA results, Age was not included as a main feature because it did not show a strong relationship with Total_Amount. Instead, the model focused on features that showed more meaningful patterns, such as Unit_Price, Quantity, Product_Category, Discount_Percentage, City, Payment_Method, Device_Type, Is_Returning_Customer, Customer_Rating, and Delivery_Time_Days.\n",
        "\n",
        "In addition to the original features, feature engineering was applied in order to improve the model’s ability to capture important transaction patterns. First, calculated features were created, including Total_Before_Discount, Discount_Amount, and Estimated_Final_Amount. These features help the model understand the relationship between product price, quantity, discount, and the final purchase amount.\n",
        "\n",
        "Additional interaction features were also created based on the EDA insights. For example, City_Category combines the customer’s city with the product category, allowing the model to capture differences in purchasing behavior across different cities and product types.\n",
        "\n",
        "Other interaction features, such as City_Payment_Method and Category_Device_Type, were created to help the model identify more specific patterns that may not be visible when each variable is used separately.\n",
        "\n",
        "Categorical variables were converted into numerical variables using one-hot encoding, since Linear Regression can only work with numerical input.\n",
        "\n",
        "The dataset was then divided into training and testing sets using an 80/20 split. The training set was used to train the model, while the testing set was used to evaluate its performance on unseen data.\n",
        "\n",
        "A fixed random seed (random_state=42) was used to ensure reproducibility, meaning that the same split will be obtained each time the code is executed."
      ],
      "metadata": {
        "id": "T6MfOsJy-CUF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 3. Train-Test Split\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "import pandas as pd\n",
        "\n",
        "# Define target variable\n",
        "y = df['Total_Amount']\n",
        "\n",
        "# Define selected features\n",
        "selected_features = [\n",
        "    'Unit_Price',\n",
        "    'Quantity',\n",
        "    'Product_Category',\n",
        "    'Discount_Percentage',\n",
        "    'City',\n",
        "    'Payment_Method',\n",
        "    'Device_Type',\n",
        "    'Is_Returning_Customer',\n",
        "    'Customer_Rating',\n",
        "    'Delivery_Time_Days'\n",
        "]\n",
        "\n",
        "# Keep only features that exist in the dataset\n",
        "selected_features = [col for col in selected_features if col in df.columns]\n",
        "\n",
        "# Create input features\n",
        "X = df[selected_features].copy()\n",
        "\n",
        "# Feature Engineering\n",
        "# These features help the Linear Regression model capture the relationship between price, quantity, and discount\n",
        "\n",
        "if 'Unit_Price' in X.columns and 'Quantity' in X.columns:\n",
        "    X['Total_Before_Discount'] = X['Unit_Price'] * X['Quantity']\n",
        "\n",
        "if 'Unit_Price' in X.columns and 'Quantity' in X.columns and 'Discount_Percentage' in X.columns:\n",
        "    X['Discount_Amount'] = X['Unit_Price'] * X['Quantity'] * (X['Discount_Percentage'] / 100)\n",
        "    X['Estimated_Final_Amount'] = X['Total_Before_Discount'] - X['Discount_Amount']\n",
        "\n",
        "\n",
        "# Feature Engineering: interaction features based on EDA insights\n",
        "# These features combine variables that showed meaningful patterns in the EDA\n",
        "\n",
        "if 'City' in X.columns and 'Product_Category' in X.columns:\n",
        "    X['City_Category'] = X['City'].astype(str) + '_' + X['Product_Category'].astype(str)\n",
        "\n",
        "if 'City' in X.columns and 'Payment_Method' in X.columns:\n",
        "    X['City_Payment_Method'] = X['City'].astype(str) + '_' + X['Payment_Method'].astype(str)\n",
        "\n",
        "if 'Product_Category' in X.columns and 'Device_Type' in X.columns:\n",
        "    X['Category_Device_Type'] = X['Product_Category'].astype(str) + '_' + X['Device_Type'].astype(str)\n",
        "\n",
        "\n",
        "# Convert categorical variables into numeric columns\n",
        "X = pd.get_dummies(X, drop_first=True)\n",
        "\n",
        "# Handle missing values\n",
        "X = X.fillna(0)\n",
        "\n",
        "# Split the dataset into training and testing sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(\n",
        "    X,\n",
        "    y,\n",
        "    test_size=0.2,\n",
        "    random_state=42\n",
        ")\n",
        "\n",
        "print(\"X shape after preprocessing:\", X.shape)\n",
        "print(\"X_train shape:\", X_train.shape)\n",
        "print(\"X_test shape:\", X_test.shape)\n",
        "print(\"y_train shape:\", y_train.shape)\n",
        "print(\"y_test shape:\", y_test.shape)\n"
      ],
      "metadata": {
        "id": "a7ZKJDtgDeSl",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d8534bda-0576-4a3e-96a3-e66b96a638f2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "X shape after preprocessing: (17049, 182)\n",
            "X_train shape: (13639, 182)\n",
            "X_test shape: (3410, 182)\n",
            "y_train shape: (13639,)\n",
            "y_test shape: (3410,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **4. Model Training**"
      ],
      "metadata": {
        "id": "ayJsaUaiAx3D"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "A Linear Regression model was used as the baseline regression model.\n",
        "\n",
        "The model was trained on the training data in order to learn the relationship between the input features and the target variable, Total_Amount.\n",
        "\n",
        "The input features included both original features and engineered features. The engineered features helped the model better understand the main calculation behind the target value, especially the relationship between unit price, quantity, and discount.\n",
        "\n",
        "In addition, interaction features such as City_Category, City_Payment_Method, and Category_Device_Type were included. These features allow the model to capture more specific patterns between customer location, product category, payment method, and device type.\n",
        "\n",
        "This model serves as a baseline for evaluating regression performance before testing more advanced models in later stages."
      ],
      "metadata": {
        "id": "8sup8B3HA5pn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 4. Model Training\n",
        "\n",
        "from sklearn.linear_model import LinearRegression\n",
        "\n",
        "# Create the baseline Linear Regression model\n",
        "model = LinearRegression()\n",
        "\n",
        "# Train the model using the training data\n",
        "model.fit(X_train, y_train)\n",
        "\n",
        "print(\"Model training completed successfully.\")"
      ],
      "metadata": {
        "id": "EO1mbg-NA9IY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4eeb8929-07c0-4417-a51d-07b39a9599af"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model training completed successfully.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **5. Model Evaluation**"
      ],
      "metadata": {
        "id": "xrilEdNWBbkY"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "The baseline Linear Regression model was evaluated using the test set.\n",
        "\n",
        "After applying feature engineering, the model achieved a strong R² score of approximately 0.93. This means that the model explains about 93% of the variation in Total_Amount.\n",
        "\n",
        "The improvement in performance is mainly due to the engineered features, such as Total_Before_Discount, Discount_Amount, and Estimated_Final_Amount, which are directly related to how the final purchase amount is calculated.\n",
        "\n",
        "The MAE, MSE, and RMSE values were also used to measure the prediction error. Lower values for these metrics indicate that the model’s predictions are close to the actual values.\n",
        "\n",
        "Overall, the model shows strong predictive performance. However, since some engineered features are closely related to the target variable, the high score should be interpreted carefully and checked in later stages to ensure that the model is not relying on data leakage."
      ],
      "metadata": {
        "id": "b85PgDJaJlM_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
        "import numpy as np\n",
        "\n",
        "# Make predictions on the test set\n",
        "y_pred = model.predict(X_test)\n",
        "\n",
        "# Calculate evaluation metrics\n",
        "mae = mean_absolute_error(y_test, y_pred)\n",
        "mse = mean_squared_error(y_test, y_pred)\n",
        "rmse = np.sqrt(mse)\n",
        "r2 = r2_score(y_test, y_pred)\n",
        "\n",
        "# Print results\n",
        "print(\"Model Evaluation Results:\")\n",
        "print(\"-------------------------\")\n",
        "print(\"MAE:\", mae)\n",
        "print(\"MSE:\", mse)\n",
        "print(\"RMSE:\", rmse)\n",
        "print(\"R2:\", r2)"
      ],
      "metadata": {
        "id": "owCbcdPoJNjy",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "667ba173-65e0-4be6-97ef-f3a2a8c3d871"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model Evaluation Results:\n",
            "-------------------------\n",
            "MAE: 165.91018966289087\n",
            "MSE: 63466.30539918128\n",
            "RMSE: 251.92519802350316\n",
            "R2: 0.9307050270663042\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **6. Insights**"
      ],
      "metadata": {
        "id": "E37moNexEFjg"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "The visualization of actual versus predicted values shows a strong alignment between the model’s predictions and the true values. Most data points are distributed close to the diagonal line, which indicates good prediction accuracy.\n",
        "\n",
        "The model performs well across different ranges of Total_Amount, with relatively small deviations between the actual and predicted values. This suggests that the Linear Regression model was able to capture the main relationship between the selected features and the target variable.\n",
        "\n",
        "The strong performance is mainly explained by the engineered features. Features such as Total_Before_Discount, Discount_Amount, and Estimated_Final_Amount help the model understand the relationship between unit price, quantity, discount, and the final purchase amount.\n",
        "\n",
        "In addition, interaction features such as City_Category, City_Payment_Method, and Category_Device_Type helped the model capture more specific patterns related to customer location, product category, payment method, and device type.\n",
        "\n",
        "Overall, the model demonstrates strong predictive performance on the test set. However, since some engineered features are closely connected to the target variable, especially Estimated_Final_Amount, the high accuracy should be interpreted carefully and checked in later stages to avoid possible data leakage."
      ],
      "metadata": {
        "id": "dHVmaFOPJwzi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Plot actual vs predicted values\n",
        "plt.figure(figsize=(6,4))\n",
        "\n",
        "plt.scatter(y_test, y_pred, alpha=0.5)\n",
        "\n",
        "# Perfect prediction line\n",
        "plt.plot(\n",
        "    [y_test.min(), y_test.max()],\n",
        "    [y_test.min(), y_test.max()],\n",
        "    color='red'\n",
        ")\n",
        "\n",
        "plt.title('Actual vs Predicted Values')\n",
        "plt.xlabel('Actual Total Amount')\n",
        "plt.ylabel('Predicted Total Amount')\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "tLi_zqvBLaKs",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 410
        },
        "outputId": "a61e5788-d8fc-474a-a8d0-206f9ae76101"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **7. Feature Importance**"
      ],
      "metadata": {
        "id": "xc2JjXZtMSBy"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "Feature importance was analyzed using the coefficients of the Linear Regression model. Each coefficient represents how much a feature affects the predicted Total_Amount, while holding the other variables constant.\n",
        "\n",
        "Features with larger absolute coefficient values have a stronger influence on the model’s predictions. Positive coefficients increase the predicted total amount, while negative coefficients decrease it compared to the reference category.\n",
        "\n",
        "Based on the feature importance results, Quantity has the strongest positive effect on the predicted Total_Amount. This makes sense because buying more units usually increases the final purchase amount.\n",
        "\n",
        "Product category features also have a strong influence. For example, Product_Category_Electronics has a strong positive coefficient, meaning that purchases in the Electronics category are associated with a higher predicted Total_Amount compared to the reference product category. Other categories such as Home & Garden, Sports, and Fashion also appear among the top features.\n",
        "\n",
        "The new interaction features also appear in the top results. For example, City_Category_Gaziantep_Home & Garden and City_Category_Bursa_Electronics have negative coefficients, while City_Category_Kayseri_Electronics, City_Category_Kayseri_Home & Garden, and City_Category_Eskisehir_Fashion have positive coefficients.\n",
        "\n",
        "This shows that the combination between city and product category helps the model capture more specific purchasing patterns. In other words, the same product category may have a different effect on Total_Amount depending on the customer’s city.\n",
        "\n",
        "Overall, the feature importance results show that Quantity, Product_Category, and City_Category features have the strongest effects in this Linear Regression model.."
      ],
      "metadata": {
        "id": "PMeGSNGrQNOD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "# Create a DataFrame with feature names and their coefficients\n",
        "coefficients = pd.DataFrame({\n",
        "    'Feature': X.columns,\n",
        "    'Coefficient': model.coef_\n",
        "})\n",
        "\n",
        "# Sort features by absolute importance (strongest influence first)\n",
        "coefficients['Abs_Coefficient'] = coefficients['Coefficient'].abs()\n",
        "coefficients = coefficients.sort_values(by='Abs_Coefficient', ascending=False)\n",
        "\n",
        "# Display top features\n",
        "coefficients.head(10)"
      ],
      "metadata": {
        "id": "T2L1EIEsMm5E",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "outputId": "78a8a79e-2e6d-4518-9a02-f6db783b9796"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                  Feature  Coefficient  Abs_Coefficient\n",
              "1                                Quantity   117.009384       117.009384\n",
              "10           Product_Category_Electronics   102.511791       102.511791\n",
              "13         Product_Category_Home & Garden    70.581083        70.581083\n",
              "14                Product_Category_Sports    67.815899        67.815899\n",
              "75  City_Category_Gaziantep_Home & Garden   -56.225708        56.225708\n",
              "96      City_Category_Kayseri_Electronics    53.668154        53.668154\n",
              "11               Product_Category_Fashion    51.325046        51.325046\n",
              "99    City_Category_Kayseri_Home & Garden    51.051045        51.051045\n",
              "56        City_Category_Bursa_Electronics   -50.372156        50.372156\n",
              "65        City_Category_Eskisehir_Fashion    46.360693        46.360693"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3279794c-bd50-4b99-a1d2-960927af25c0\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Feature</th>\n",
              "      <th>Coefficient</th>\n",
              "      <th>Abs_Coefficient</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Quantity</td>\n",
              "      <td>117.009384</td>\n",
              "      <td>117.009384</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Product_Category_Electronics</td>\n",
              "      <td>102.511791</td>\n",
              "      <td>102.511791</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Product_Category_Home &amp; Garden</td>\n",
              "      <td>70.581083</td>\n",
              "      <td>70.581083</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Product_Category_Sports</td>\n",
              "      <td>67.815899</td>\n",
              "      <td>67.815899</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75</th>\n",
              "      <td>City_Category_Gaziantep_Home &amp; Garden</td>\n",
              "      <td>-56.225708</td>\n",
              "      <td>56.225708</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>96</th>\n",
              "      <td>City_Category_Kayseri_Electronics</td>\n",
              "      <td>53.668154</td>\n",
              "      <td>53.668154</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Product_Category_Fashion</td>\n",
              "      <td>51.325046</td>\n",
              "      <td>51.325046</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99</th>\n",
              "      <td>City_Category_Kayseri_Home &amp; Garden</td>\n",
              "      <td>51.051045</td>\n",
              "      <td>51.051045</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>56</th>\n",
              "      <td>City_Category_Bursa_Electronics</td>\n",
              "      <td>-50.372156</td>\n",
              "      <td>50.372156</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>65</th>\n",
              "      <td>City_Category_Eskisehir_Fashion</td>\n",
              "      <td>46.360693</td>\n",
              "      <td>46.360693</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3279794c-bd50-4b99-a1d2-960927af25c0')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
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              "  </svg>\n",
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              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-3279794c-bd50-4b99-a1d2-960927af25c0 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-3279794c-bd50-4b99-a1d2-960927af25c0');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "coefficients",
              "summary": "{\n  \"name\": \"coefficients\",\n  \"rows\": 182,\n  \"fields\": [\n    {\n      \"column\": \"Feature\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 182,\n        \"samples\": [\n          \"Product_Category_Food\",\n          \"City_Category_Adana_Electronics\",\n          \"City_Category_Ankara_Beauty\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Coefficient\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 22.693282000469623,\n        \"min\": -56.22570847457045,\n        \"max\": 117.00938442999524,\n        \"num_unique_values\": 182,\n        \"samples\": [\n          -35.7830572699713,\n          22.065851990824825,\n          2.8487794704416953\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Abs_Coefficient\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 17.040322276945826,\n        \"min\": 0.09502696692556611,\n        \"max\": 117.00938442999524,\n        \"num_unique_values\": 182,\n        \"samples\": [\n          35.7830572699713,\n          22.065851990824825,\n          2.8487794704416953\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Select top 10 most important features\n",
        "top_features = coefficients.head(10)\n",
        "\n",
        "# Define colors: positive coefficients vs negative coefficients\n",
        "colors = ['skyblue' if value > 0 else 'salmon' for value in top_features['Coefficient']]\n",
        "\n",
        "plt.figure(figsize=(9, 6))\n",
        "\n",
        "plt.barh(\n",
        "    top_features['Feature'],\n",
        "    top_features['Coefficient'],\n",
        "    color=colors,\n",
        "    edgecolor='black'\n",
        ")\n",
        "\n",
        "# Add a vertical line at 0\n",
        "plt.axvline(0, color='gray', linewidth=1)\n",
        "\n",
        "# Invert y-axis so the strongest feature appears at the top\n",
        "plt.gca().invert_yaxis()\n",
        "\n",
        "plt.title('Top 10 Feature Importance (Linear Regression)', fontsize=14, fontweight='bold')\n",
        "plt.xlabel('Coefficient Value', fontsize=12)\n",
        "plt.ylabel('Feature', fontsize=12)\n",
        "\n",
        "plt.grid(axis='x', linestyle='--', alpha=0.4)\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "11m11bowMqOM",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "outputId": "32b7c60d-8094-49f1-cef4-9e6f595b75c3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 900x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Blue bars =** positive effect on Total_Amount\n",
        "\n",
        "**Red bars =** negative effect on Total_Amount"
      ],
      "metadata": {
        "id": "qdG1DGlGx8TE"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "<br><br><br>\n",
        "\n",
        "---\n",
        "\n",
        "<br><br><br>"
      ],
      "metadata": {
        "id": "hkerNEsMIPbK"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Part 4: Feature Engineering"
      ],
      "metadata": {
        "id": "bpzr83tiTZx4"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **1. Concept and Strategy**\n",
        "\n",
        "The goal of feature engineering is to transform the raw dataset into a more informative representation that can help the model capture important patterns and improve prediction performance.\n",
        "\n",
        "While the baseline model used the selected original features, this stage focuses on creating more meaningful features based on insights from the exploratory data analysis (EDA).\n",
        "\n",
        "Categorical variables such as Product_Category, City, Payment_Method, and Device_Type were converted into numerical format using One-Hot Encoding. This step is necessary because machine learning models cannot work directly with text-based categorical values.\n",
        "\n",
        "Based on the EDA results, Age was not used as a main engineered feature because it did not show a strong relationship with Total_Amount. Instead, feature engineering focused on variables that showed more meaningful patterns, especially City, Product_Category, Payment_Method, and Device_Type.\n",
        "\n",
        "New interaction features were created in order to capture relationships between important categorical variables. For example, City_Category combines the customer’s city with the product category. This allows the model to learn whether certain product categories are associated with higher or lower purchase amounts in specific cities.\n",
        "\n",
        "Additional interaction features were also created, such as City_Payment_Method and Category_Device_Type. These features help capture more specific purchasing behavior that may not be visible when each feature is used separately.\n",
        "\n",
        "In addition, transaction-based calculated features such as Total_Before_Discount, Discount_Amount, and Estimated_Final_Amount were created to represent the relationship between Unit_Price, Quantity, Discount_Percentage, and the final purchase amount.\n",
        "\n",
        "Numerical features were scaled using StandardScaler in order to place them on a comparable scale. This is especially useful for algorithms that are sensitive to feature magnitude.\n",
        "\n",
        "PCA was also considered as a dimensionality reduction technique in order to simplify the feature space while preserving most of the important information. This can be useful for visualization and pattern exploration.\n",
        "\n",
        "Finally, K-Means clustering was used to group similar observations based on purchasing-related features. The generated cluster labels were added as a new feature, representing different transaction or customer segments."
      ],
      "metadata": {
        "id": "0ltJleEbefeO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **2. Implementation: Step-by-Step**\n",
        "\n",
        "\n",
        "### **Step A: Feature Extraction and Categorical Encoding**\n",
        "\n",
        "In this step, additional features were extracted from the original dataset to better capture useful patterns in the data.\n",
        "\n",
        "First, transaction-related features were created using the existing numerical columns. Features such as Total_Before_Discount, Discount_Amount, and Estimated_Final_Amount were created based on Unit_Price, Quantity, and Discount_Percentage. These features help represent the relationship between price, quantity, discount, and the final purchase amount.\n",
        "\n",
        "Based on the EDA results, Age was not used to create additional engineered features because it did not show a strong relationship with Total_Amount.\n",
        "\n",
        "Instead, new interaction features were created using variables that showed more meaningful patterns in the EDA. For example, City_Category combines the customer’s city with the product category, allowing the model to capture differences in purchasing behavior across different cities and product types.\n",
        "\n",
        "Additional interaction features were also created, such as City_Payment_Method and Category_Device_Type. These features help identify more specific transaction patterns that may not be visible when each variable is used separately.\n",
        "\n",
        "If a Date column exists in the dataset, it is converted into datetime format and used to extract additional time-based features, such as month and day of the week.\n",
        "\n",
        "Categorical variables were transformed into numerical format using one-hot encoding. This allows machine learning models to process non-numeric data effectively.\n",
        "\n",
        "Finally, irrelevant columns such as identifiers and the original date column were removed to avoid redundancy, reduce noise, and improve model performance."
      ],
      "metadata": {
        "id": "9pLLPrywe5-Z"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Step A: Feature Extraction and Categorical Encoding\n",
        "\n",
        "import pandas as pd\n",
        "\n",
        "# Create a copy of the dataset\n",
        "df_fe = df.copy()\n",
        "\n",
        "# 1. Transaction-related features\n",
        "if 'Unit_Price' in df_fe.columns and 'Quantity' in df_fe.columns:\n",
        "    df_fe['Total_Before_Discount'] = df_fe['Unit_Price'] * df_fe['Quantity']\n",
        "\n",
        "if 'Unit_Price' in df_fe.columns and 'Quantity' in df_fe.columns and 'Discount_Percentage' in df_fe.columns:\n",
        "    df_fe['Discount_Amount'] = df_fe['Unit_Price'] * df_fe['Quantity'] * (df_fe['Discount_Percentage'] / 100)\n",
        "    df_fe['Estimated_Final_Amount'] = df_fe['Total_Before_Discount'] - df_fe['Discount_Amount']\n",
        "\n",
        "\n",
        "# 2. Interaction features based on EDA insights\n",
        "if 'City' in df_fe.columns and 'Product_Category' in df_fe.columns:\n",
        "    df_fe['City_Category'] = df_fe['City'].astype(str) + '_' + df_fe['Product_Category'].astype(str)\n",
        "\n",
        "if 'City' in df_fe.columns and 'Payment_Method' in df_fe.columns:\n",
        "    df_fe['City_Payment_Method'] = df_fe['City'].astype(str) + '_' + df_fe['Payment_Method'].astype(str)\n",
        "\n",
        "if 'Product_Category' in df_fe.columns and 'Device_Type' in df_fe.columns:\n",
        "    df_fe['Category_Device_Type'] = df_fe['Product_Category'].astype(str) + '_' + df_fe['Device_Type'].astype(str)\n",
        "\n",
        "\n",
        "# 3. Date-based features if Date column exists\n",
        "if 'Date' in df_fe.columns:\n",
        "    df_fe['Date'] = pd.to_datetime(df_fe['Date'], errors='coerce')\n",
        "    df_fe['Month'] = df_fe['Date'].dt.month\n",
        "    df_fe['DayOfWeek'] = df_fe['Date'].dt.dayofweek\n",
        "\n",
        "\n",
        "# 4. Additional behavioral features based on purchasing patterns\n",
        "\n",
        "# Discounted price per unit\n",
        "if 'Unit_Price' in df_fe.columns and 'Discount_Percentage' in df_fe.columns:\n",
        "    df_fe['Discounted_Unit_Price'] = df_fe['Unit_Price'] * (1 - df_fe['Discount_Percentage'] / 100)\n",
        "\n",
        "# High discount indicator\n",
        "if 'Discount_Percentage' in df_fe.columns:\n",
        "    df_fe['Is_High_Discount'] = (df_fe['Discount_Percentage'] >= 30).astype(int)\n",
        "\n",
        "# Bulk order indicator\n",
        "if 'Quantity' in df_fe.columns:\n",
        "    df_fe['Is_Bulk_Order'] = (df_fe['Quantity'] >= 4).astype(int)\n",
        "\n",
        "# Premium product indicator based on high unit price\n",
        "if 'Unit_Price' in df_fe.columns:\n",
        "    price_threshold = df_fe['Unit_Price'].quantile(0.75)\n",
        "    df_fe['Is_Premium_Product'] = (df_fe['Unit_Price'] >= price_threshold).astype(int)\n",
        "\n",
        "# Discount level category\n",
        "if 'Discount_Percentage' in df_fe.columns:\n",
        "    df_fe['Discount_Level'] = pd.cut(\n",
        "        df_fe['Discount_Percentage'],\n",
        "        bins=[-1, 10, 30, 100],\n",
        "        labels=['Low_Discount', 'Medium_Discount', 'High_Discount']\n",
        "    )\n",
        "\n",
        "# Product category + discount level interaction\n",
        "if 'Product_Category' in df_fe.columns and 'Discount_Level' in df_fe.columns:\n",
        "    df_fe['Category_Discount'] = (\n",
        "        df_fe['Product_Category'].astype(str) + '_' + df_fe['Discount_Level'].astype(str)\n",
        "    )\n",
        "\n",
        "# City + discount level interaction\n",
        "if 'City' in df_fe.columns and 'Discount_Level' in df_fe.columns:\n",
        "    df_fe['City_Discount_Level'] = (\n",
        "        df_fe['City'].astype(str) + '_' + df_fe['Discount_Level'].astype(str)\n",
        "    )\n",
        "\n",
        "\n",
        "# 5. Drop irrelevant columns\n",
        "columns_to_drop = ['Order_ID', 'Customer_ID', 'Date']\n",
        "df_fe = df_fe.drop(columns=[col for col in columns_to_drop if col in df_fe.columns])\n",
        "\n",
        "\n",
        "# 6. Apply one-hot encoding to categorical variables\n",
        "df_fe_encoded = pd.get_dummies(df_fe, drop_first=True)\n",
        "\n",
        "\n",
        "# 7. Fill missing values\n",
        "df_fe_encoded = df_fe_encoded.fillna(0)\n",
        "\n",
        "\n",
        "print(\"Shape after feature extraction and encoding:\", df_fe_encoded.shape)\n",
        "df_fe_encoded.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 273
        },
        "id": "0-iTKKdyE-FF",
        "outputId": "31275639-9c01-4327-f1f0-52170934b9dc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape after feature extraction and encoding: (17049, 266)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Age  Unit_Price  Quantity  Discount_Amount  Total_Amount  \\\n",
              "0   40       29.18         1            0.000         29.18   \n",
              "1   40      644.40         1           81.775        506.35   \n",
              "2   40      332.82         5            0.000       1664.10   \n",
              "3   33       69.30         5          355.250        275.45   \n",
              "4   33      178.15         3            0.000        534.45   \n",
              "\n",
              "   Session_Duration_Minutes  Pages_Viewed  Is_Returning_Customer  \\\n",
              "0                        14             9                   True   \n",
              "1                        14             8                   True   \n",
              "2                        15            10                   True   \n",
              "3                        16            13                   True   \n",
              "4                        14             7                   True   \n",
              "\n",
              "   Delivery_Time_Days  Customer_Rating  ...  \\\n",
              "0                  13                4  ...   \n",
              "1                   6                2  ...   \n",
              "2                   9                4  ...   \n",
              "3                   4                4  ...   \n",
              "4                   6                4  ...   \n",
              "\n",
              "   City_Discount_Level_Izmir_Medium_Discount  City_Discount_Level_Izmir_nan  \\\n",
              "0                                      False                          False   \n",
              "1                                      False                          False   \n",
              "2                                      False                          False   \n",
              "3                                      False                          False   \n",
              "4                                      False                          False   \n",
              "\n",
              "   City_Discount_Level_Kayseri_High_Discount  \\\n",
              "0                                      False   \n",
              "1                                      False   \n",
              "2                                      False   \n",
              "3                                      False   \n",
              "4                                      False   \n",
              "\n",
              "   City_Discount_Level_Kayseri_Low_Discount  \\\n",
              "0                                     False   \n",
              "1                                     False   \n",
              "2                                     False   \n",
              "3                                     False   \n",
              "4                                     False   \n",
              "\n",
              "   City_Discount_Level_Kayseri_Medium_Discount  \\\n",
              "0                                        False   \n",
              "1                                        False   \n",
              "2                                        False   \n",
              "3                                        False   \n",
              "4                                        False   \n",
              "\n",
              "   City_Discount_Level_Kayseri_nan  City_Discount_Level_Konya_High_Discount  \\\n",
              "0                            False                                    False   \n",
              "1                            False                                    False   \n",
              "2                            False                                    False   \n",
              "3                            False                                    False   \n",
              "4                            False                                    False   \n",
              "\n",
              "   City_Discount_Level_Konya_Low_Discount  \\\n",
              "0                                   False   \n",
              "1                                   False   \n",
              "2                                   False   \n",
              "3                                   False   \n",
              "4                                   False   \n",
              "\n",
              "   City_Discount_Level_Konya_Medium_Discount  City_Discount_Level_Konya_nan  \n",
              "0                                      False                          False  \n",
              "1                                      False                          False  \n",
              "2                                      False                          False  \n",
              "3                                      False                          False  \n",
              "4                                      False                          False  \n",
              "\n",
              "[5 rows x 266 columns]"
            ],
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              "\n",
              "  <div id=\"df-ced40bf3-67b4-41b6-a948-aaaba86c6fc4\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
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              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
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              "</style>\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Age</th>\n",
              "      <th>Unit_Price</th>\n",
              "      <th>Quantity</th>\n",
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              "      <th>Session_Duration_Minutes</th>\n",
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              "      <th>Is_Returning_Customer</th>\n",
              "      <th>Delivery_Time_Days</th>\n",
              "      <th>Customer_Rating</th>\n",
              "      <th>...</th>\n",
              "      <th>City_Discount_Level_Izmir_Medium_Discount</th>\n",
              "      <th>City_Discount_Level_Izmir_nan</th>\n",
              "      <th>City_Discount_Level_Kayseri_High_Discount</th>\n",
              "      <th>City_Discount_Level_Kayseri_Low_Discount</th>\n",
              "      <th>City_Discount_Level_Kayseri_Medium_Discount</th>\n",
              "      <th>City_Discount_Level_Kayseri_nan</th>\n",
              "      <th>City_Discount_Level_Konya_High_Discount</th>\n",
              "      <th>City_Discount_Level_Konya_Low_Discount</th>\n",
              "      <th>City_Discount_Level_Konya_Medium_Discount</th>\n",
              "      <th>City_Discount_Level_Konya_nan</th>\n",
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              "      <td>40</td>\n",
              "      <td>644.40</td>\n",
              "      <td>1</td>\n",
              "      <td>81.775</td>\n",
              "      <td>506.35</td>\n",
              "      <td>14</td>\n",
              "      <td>8</td>\n",
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              "      <td>6</td>\n",
              "      <td>2</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>40</td>\n",
              "      <td>332.82</td>\n",
              "      <td>5</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1664.10</td>\n",
              "      <td>15</td>\n",
              "      <td>10</td>\n",
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              "      <td>9</td>\n",
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              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>33</td>\n",
              "      <td>69.30</td>\n",
              "      <td>5</td>\n",
              "      <td>355.250</td>\n",
              "      <td>275.45</td>\n",
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              "      <td>178.15</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 266 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ced40bf3-67b4-41b6-a948-aaaba86c6fc4')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
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              "    [theme=dark] .colab-df-convert {\n",
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              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-ced40bf3-67b4-41b6-a948-aaaba86c6fc4 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-ced40bf3-67b4-41b6-a948-aaaba86c6fc4');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
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              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
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              "  </div>\n",
              "\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_fe_encoded"
            }
          },
          "metadata": {},
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Feature Engineering Summary**\n",
        "\n",
        "In this stage, several new features were created to improve the representation of the data.\n",
        "\n",
        "The new features include:\n",
        "\n",
        "**1. Transaction-based features:**\n",
        "- Total_Before_Discount\n",
        "- Discount_Amount\n",
        "- Estimated_Final_Amount\n",
        "\n",
        "**2. Interaction features based on EDA insights:**\n",
        "- City_Category\n",
        "- City_Payment_Method\n",
        "- Category_Device_Type\n",
        "\n",
        "**3. Clustering-based features:**\n",
        "- Purchase_Cluster\n",
        "- Distance_To_Cluster_Center\n",
        "- Cluster_Confidence\n",
        "\n",
        "**4. Numerical interaction terms**:\n",
        "- Unit_Price * Quantity\n",
        "- Unit_Price * Discount_Percentage\n",
        "- Quantity * Discount_Percentage\n",
        "\n",
        "These features help the model capture not only direct relationships, but also more complex purchasing behavior patterns."
      ],
      "metadata": {
        "id": "5cy2srbc2B-U"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### **Step B: Clustering as a New Feature**\n",
        "\n",
        "Step B: Clustering as a New Feature and Cluster Analysis\n",
        "\n",
        "In this step, K-Means clustering was applied in order to identify different purchasing behavior groups in the dataset.\n",
        "\n",
        "The clustering was based on transaction-related features: Unit_Price, Quantity, Discount_Percentage, and Total_Before_Discount. These features were selected because they describe the product price, the number of units purchased, the discount applied, and the transaction value before discount.\n",
        "\n",
        "The target variable, Total_Amount, was not included in the clustering process in order to avoid target leakage.\n",
        "\n",
        "After creating the clusters, the cluster labels and additional clustering-based features were added to the dataset. Then, several visualizations were used to understand how the transactions were divided and what characterizes each cluster."
      ],
      "metadata": {
        "id": "jr0rwp5TfH9P"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**B1. Create K-Means Cluster Features**\n",
        "\n",
        "In this part, the selected clustering features were scaled using StandardScaler because K-Means is a distance-based algorithm. Scaling ensures that features with larger values do not dominate the clustering process.\n",
        "\n",
        "Then, K-Means was applied with three clusters. Each transaction received a cluster label called Purchase_Cluster.\n",
        "\n",
        "Two additional features were also created:\n",
        "Distance_To_Cluster_Center, which measures how far each transaction is from its assigned cluster center, and Cluster_Confidence, which represents how strongly the transaction belongs to its cluster."
      ],
      "metadata": {
        "id": "Yldzm28wiW_i"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Step B1: Create K-Means Cluster Features\n",
        "\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.cluster import KMeans\n",
        "from sklearn.decomposition import PCA\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "# Select features for clustering\n",
        "cluster_features = [\n",
        "    'Unit_Price',\n",
        "    'Quantity',\n",
        "    'Discount_Percentage',\n",
        "    'Total_Before_Discount'\n",
        "]\n",
        "\n",
        "# Keep only clustering features that exist in the dataset\n",
        "cluster_features = [col for col in cluster_features if col in df_fe_encoded.columns]\n",
        "\n",
        "# Scale the selected clustering features\n",
        "scaler = StandardScaler()\n",
        "cluster_scaled = scaler.fit_transform(df_fe_encoded[cluster_features])\n",
        "\n",
        "# Apply K-Means clustering\n",
        "kmeans = KMeans(\n",
        "    n_clusters=3,\n",
        "    random_state=42,\n",
        "    n_init=10\n",
        ")\n",
        "\n",
        "# Create cluster ID feature\n",
        "df_fe_encoded['Purchase_Cluster'] = kmeans.fit_predict(cluster_scaled)\n",
        "\n",
        "# Calculate distances to all cluster centroids\n",
        "distances = kmeans.transform(cluster_scaled)\n",
        "\n",
        "# Create distance to assigned centroid feature\n",
        "df_fe_encoded['Distance_To_Cluster_Center'] = distances.min(axis=1)\n",
        "\n",
        "# Create confidence feature\n",
        "# Smaller distance = higher confidence\n",
        "df_fe_encoded['Cluster_Confidence'] = 1 / (1 + df_fe_encoded['Distance_To_Cluster_Center'])\n",
        "\n",
        "# Display new clustering features\n",
        "print(\"Clustering features created successfully:\")\n",
        "print(df_fe_encoded[['Purchase_Cluster', 'Distance_To_Cluster_Center', 'Cluster_Confidence']].head())\n",
        "\n",
        "# Display cluster counts\n",
        "print(\"\\nCluster counts:\")\n",
        "print(df_fe_encoded['Purchase_Cluster'].value_counts())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "exp6dCVRIhd5",
        "outputId": "b1926f65-9d5a-4ad7-ce21-fe8e2b7f291d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Clustering features created successfully:\n",
            "   Purchase_Cluster  Distance_To_Cluster_Center  Cluster_Confidence\n",
            "0                 0                    1.308033            0.433269\n",
            "1                 0                    1.789022            0.358549\n",
            "2                 0                    1.950339            0.338944\n",
            "3                 2                    3.029462            0.248172\n",
            "4                 0                    0.324617            0.754935\n",
            "\n",
            "Cluster counts:\n",
            "Purchase_Cluster\n",
            "0    10712\n",
            "1     3661\n",
            "2     2676\n",
            "Name: count, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The K-Means algorithm successfully created three purchase clusters. Each transaction was assigned to one cluster, and two additional features were created to describe the distance and confidence of the assigned cluster.\n",
        "\n",
        "These new clustering-based features can help the model understand different purchasing behavior groups."
      ],
      "metadata": {
        "id": "WKLdHJPritKf"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**B2. Cluster Size Visualization**\n",
        "\n",
        "This visualization shows how many transactions belong to each purchase cluster.\n",
        "\n",
        "The purpose of this graph is to understand the distribution of the clusters and check whether one cluster contains most of the transactions or whether the transactions are more evenly distributed."
      ],
      "metadata": {
        "id": "JkM_rYbTihJY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Step B2: Cluster Size Visualization\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Count transactions in each cluster\n",
        "cluster_counts = df_fe_encoded['Purchase_Cluster'].value_counts().sort_index()\n",
        "\n",
        "plt.figure(figsize=(7, 5))\n",
        "\n",
        "bars = plt.bar(\n",
        "    cluster_counts.index.astype(str),\n",
        "    cluster_counts.values,\n",
        "    color=['#6FA8DC', '#93C47D', '#F6B26B'],\n",
        "    edgecolor='black'\n",
        ")\n",
        "\n",
        "# Add value labels on top of bars\n",
        "for bar in bars:\n",
        "    height = bar.get_height()\n",
        "    plt.text(\n",
        "        bar.get_x() + bar.get_width() / 2,\n",
        "        height,\n",
        "        f'{int(height):,}',\n",
        "        ha='center',\n",
        "        va='bottom',\n",
        "        fontsize=11,\n",
        "        fontweight='bold'\n",
        "    )\n",
        "\n",
        "plt.title('Number of Transactions in Each Purchase Cluster', fontsize=14, fontweight='bold')\n",
        "plt.xlabel('Purchase Cluster', fontsize=12)\n",
        "plt.ylabel('Number of Transactions', fontsize=12)\n",
        "\n",
        "plt.grid(axis='y', linestyle='--', alpha=0.4)\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 507
        },
        "id": "aFWYDNpIzgqG",
        "outputId": "ef446d89-4b4d-409a-a393-82cc107d25b8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 700x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The graph shows the number of transactions in each purchase cluster.\n",
        "\n",
        "Cluster 0 contains the largest number of transactions, meaning that it represents the most common purchasing behavior in the dataset. Cluster 1 and Cluster 2 contain fewer transactions, which suggests that they represent more specific or less common purchasing patterns."
      ],
      "metadata": {
        "id": "ejwkgjXUip1b"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**B3. PCA Visualization of Clusters**\n",
        "\n",
        "In this part, PCA was used only for visualization. Since the clustering was based on four features, PCA reduced these features into two components: PC1 and PC2.\n",
        "\n",
        "This allows the three clusters to be displayed in a two-dimensional graph. Each point represents one transaction, and each color represents a different purchase cluster."
      ],
      "metadata": {
        "id": "B8LdzTqkL-u9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Step B3: PCA Visualization of Clusters\n",
        "\n",
        "from sklearn.decomposition import PCA\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "\n",
        "# Reduce clustering features to 2 PCA components for visualization\n",
        "pca_cluster = PCA(n_components=2, random_state=42)\n",
        "cluster_pca = pca_cluster.fit_transform(cluster_scaled)\n",
        "\n",
        "# Create PCA dataframe\n",
        "cluster_pca_df = pd.DataFrame(\n",
        "    cluster_pca,\n",
        "    columns=['PC1', 'PC2'],\n",
        "    index=df_fe_encoded.index\n",
        ")\n",
        "\n",
        "cluster_pca_df['Purchase_Cluster'] = df_fe_encoded['Purchase_Cluster']\n",
        "\n",
        "# Sample data only to make the graph clearer\n",
        "cluster_pca_sample = cluster_pca_df.sample(\n",
        "    n=min(2000, len(cluster_pca_df)),\n",
        "    random_state=42\n",
        ")\n",
        "\n",
        "# Plot clusters\n",
        "plt.figure(figsize=(10, 7))\n",
        "\n",
        "for cluster in sorted(cluster_pca_sample['Purchase_Cluster'].unique()):\n",
        "    cluster_data = cluster_pca_sample[\n",
        "        cluster_pca_sample['Purchase_Cluster'] == cluster\n",
        "    ]\n",
        "\n",
        "    plt.scatter(\n",
        "        cluster_data['PC1'],\n",
        "        cluster_data['PC2'],\n",
        "        label=f'Cluster {cluster}',\n",
        "        alpha=0.75,\n",
        "        s=45\n",
        "    )\n",
        "\n",
        "plt.title('K-Means Clusters of Purchasing Behavior', fontsize=14, fontweight='bold')\n",
        "plt.xlabel('PCA Component 1', fontsize=12)\n",
        "plt.ylabel('PCA Component 2', fontsize=12)\n",
        "plt.legend(title='Purchase Cluster')\n",
        "plt.grid(True, alpha=0.3)\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 707
        },
        "id": "dme4xodKL6Ad",
        "outputId": "74aa06e8-e04a-4d36-92f1-77d72231f366"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x700 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The PCA visualization shows the three purchase clusters in a two-dimensional space.\n",
        "\n",
        "The clusters appear relatively separated, which suggests that K-Means was able to identify different purchasing behavior patterns based on Unit_Price, Quantity, Discount_Percentage, and Total_Before_Discount.\n",
        "\n",
        "This graph helps visually support the clustering process and shows that the transactions were divided into meaningful groups."
      ],
      "metadata": {
        "id": "fzaBGzLli6A2"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**B4. Cluster Profile Analysis**\n",
        "\n",
        "After creating and visualizing the clusters, the next step was to understand what makes each cluster different.\n",
        "\n",
        "For this reason, the average values of the main purchasing features were calculated for each cluster. This helps explain the meaning of each group in terms of price, quantity, discount, and total value before discount."
      ],
      "metadata": {
        "id": "QLA9F7Tji75t"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Step B4: Cluster Profile Analysis\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Average values of main features by cluster\n",
        "cluster_profile = df_fe_encoded.groupby('Purchase_Cluster')[\n",
        "    ['Unit_Price', 'Quantity', 'Discount_Percentage', 'Total_Before_Discount']\n",
        "].mean().round(2)\n",
        "\n",
        "features = ['Unit_Price', 'Quantity', 'Discount_Percentage', 'Total_Before_Discount']\n",
        "titles = [\n",
        "    'Average Unit Price',\n",
        "    'Average Quantity',\n",
        "    'Average Discount Percentage',\n",
        "    'Average Total Before Discount'\n",
        "]\n",
        "\n",
        "cluster_colors = ['#6FA8DC', '#93C47D', '#F6B26B']  # blue, green, orange\n",
        "\n",
        "fig, axes = plt.subplots(1, 4, figsize=(20, 5))\n",
        "\n",
        "for i, feature in enumerate(features):\n",
        "    bars = axes[i].bar(\n",
        "        cluster_profile.index.astype(str),\n",
        "        cluster_profile[feature],\n",
        "        color=cluster_colors,\n",
        "        edgecolor='black'\n",
        "    )\n",
        "\n",
        "    axes[i].set_title(titles[i], fontsize=12, fontweight='bold')\n",
        "    axes[i].set_xlabel('Purchase Cluster', fontsize=10)\n",
        "    axes[i].set_ylabel(feature, fontsize=10)\n",
        "    axes[i].grid(axis='y', linestyle='--', alpha=0.4)\n",
        "\n",
        "    # Add value labels\n",
        "    for bar in bars:\n",
        "        height = bar.get_height()\n",
        "        axes[i].text(\n",
        "            bar.get_x() + bar.get_width() / 2,\n",
        "            height,\n",
        "            f'{height:,.2f}',\n",
        "            ha='center',\n",
        "            va='bottom',\n",
        "            fontsize=9,\n",
        "            fontweight='bold'\n",
        "        )\n",
        "\n",
        "plt.suptitle(\n",
        "    'Cluster Profile: Average Feature Values by Purchase Cluster',\n",
        "    fontsize=15,\n",
        "    fontweight='bold'\n",
        ")\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 446
        },
        "id": "H5nV82wghicF",
        "outputId": "3fea9e84-abf2-425d-a1a6-6c43bc459b2a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x500 with 4 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The cluster profile graphs explain the main differences between the three purchase clusters.\n",
        "\n",
        "Cluster 0 represents more regular purchases, with a relatively low average unit price, low discount percentage, and moderate total value before discount.\n",
        "\n",
        "Cluster 1 represents higher-value purchases. It has the highest average unit price and the highest average total before discount, which suggests that this group includes more expensive transactions.\n",
        "\n",
        "Cluster 2 represents discount-driven purchases. It has the highest average quantity and a much higher average discount percentage compared to the other clusters.\n",
        "\n",
        "Overall, this analysis shows that the clusters represent different types of purchasing behavior, which makes the Purchase_Cluster feature useful for later modeling."
      ],
      "metadata": {
        "id": "GaObXlbkjDOm"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Step C: Numerical Interaction Terms**\n",
        "\n",
        "\n",
        "In this step, additional numerical interaction terms were created using PolynomialFeatures.\n",
        "\n",
        "The selected numerical features were Unit_Price, Quantity, and Discount_Percentage because they are directly related to purchasing behavior and the final purchase amount.\n",
        "\n",
        "The parameter interaction_only=True was used, so the method created only combinations between different variables, such as Unit_Price × Quantity, Unit_Price × Discount_Percentage, and Quantity × Discount_Percentage. It did not create squared features such as Unit_Price² or Quantity².\n",
        "\n",
        "The purpose of this step is to help the model understand how price, quantity, and discount work together, instead of treating each variable separately.\n",
        "\n",
        "These interaction terms add more detailed numerical relationships to the dataset and help represent the purchasing logic behind Total_Amount."
      ],
      "metadata": {
        "id": "eLXPgf7CfRBw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Step C: Numerical Interaction Terms\n",
        "\n",
        "from sklearn.preprocessing import PolynomialFeatures\n",
        "import pandas as pd\n",
        "\n",
        "# Select key numerical features for interaction terms\n",
        "poly_features = [\n",
        "    'Unit_Price',\n",
        "    'Quantity',\n",
        "    'Discount_Percentage'\n",
        "]\n",
        "\n",
        "# Keep only features that exist in the dataset\n",
        "poly_features = [col for col in poly_features if col in df_fe_encoded.columns]\n",
        "\n",
        "# Create interaction features\n",
        "# interaction_only=True means that only combinations between different variables are created\n",
        "# For example: Unit_Price * Quantity, Unit_Price * Discount_Percentage, Quantity * Discount_Percentage\n",
        "poly = PolynomialFeatures(\n",
        "    degree=2,\n",
        "    interaction_only=True,\n",
        "    include_bias=False\n",
        ")\n",
        "\n",
        "poly_array = poly.fit_transform(df_fe_encoded[poly_features])\n",
        "\n",
        "# Create names for the new interaction features\n",
        "poly_feature_names = poly.get_feature_names_out(poly_features)\n",
        "\n",
        "# Convert to DataFrame\n",
        "poly_df = pd.DataFrame(\n",
        "    poly_array,\n",
        "    columns=poly_feature_names,\n",
        "    index=df_fe_encoded.index\n",
        ")\n",
        "\n",
        "# Add only the new interaction columns to the dataset\n",
        "for col in poly_df.columns:\n",
        "    if col not in df_fe_encoded.columns:\n",
        "        df_fe_encoded[col] = poly_df[col]\n",
        "\n",
        "print(\"Interaction features created successfully.\")\n",
        "print(\"Shape after adding interaction features:\", df_fe_encoded.shape)"
      ],
      "metadata": {
        "id": "LtvzQZcHfYwP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f2baed43-24fd-4071-fc97-0076af6cd501"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Interaction features created successfully.\n",
            "Shape after adding interaction features: (17049, 272)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Step D: Final Scaling and PCA**\n",
        "\n",
        "In this step, the target variable, Total_Amount, was separated from the input features.\n",
        "\n",
        "After that, all input features were scaled using StandardScaler. Scaling was needed because the dataset contains features with different value ranges. For example, Total_Before_Discount and Estimated_Final_Amount have much larger values than Quantity or Discount_Percentage.\n",
        "\n",
        "Then, PCA was applied to reduce the number of features in the dataset. This is useful because one-hot encoding, interaction features, and clustering features increased the number of columns.\n",
        "\n",
        "PCA was set to keep 95% of the explained variance. This means that the new PCA components keep most of the important information from the original dataset, while making the feature space smaller and cleaner.\n",
        "\n",
        "Overall, this step prepares the final engineered dataset for later modeling by scaling the data and reducing unnecessary complexity."
      ],
      "metadata": {
        "id": "wvd3SYPffb9A"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Step D: Final Scaling and PCA\n",
        "\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.decomposition import PCA\n",
        "import pandas as pd\n",
        "\n",
        "# Separate target variable\n",
        "y_fe = df_fe_encoded['Total_Amount']\n",
        "\n",
        "# Define features\n",
        "X_fe = df_fe_encoded.drop('Total_Amount', axis=1)\n",
        "\n",
        "# Scale all features\n",
        "scaler = StandardScaler()\n",
        "X_scaled = scaler.fit_transform(X_fe)\n",
        "\n",
        "# Apply PCA\n",
        "pca = PCA(n_components=0.95, random_state=42)\n",
        "X_pca = pca.fit_transform(X_scaled)\n",
        "\n",
        "# Convert PCA result into a DataFrame\n",
        "pca_columns = [f'PC{i+1}' for i in range(X_pca.shape[1])]\n",
        "\n",
        "X_pca_df = pd.DataFrame(\n",
        "    X_pca,\n",
        "    columns=pca_columns,\n",
        "    index=df_fe_encoded.index\n",
        ")\n",
        "\n",
        "print(\"Original feature shape:\", X_fe.shape)\n",
        "print(\"PCA feature shape:\", X_pca_df.shape)\n",
        "print(\"Explained variance ratio:\", pca.explained_variance_ratio_.sum())\n",
        "print(\"Number of features after PCA:\", X_pca.shape[1])"
      ],
      "metadata": {
        "id": "Tq9urlXtf9zk",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ffa08e90-d47e-4c0b-9382-98d047cd3e71"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Original feature shape: (17049, 271)\n",
            "PCA feature shape: (17049, 177)\n",
            "Explained variance ratio: 0.9515658160800848\n",
            "Number of features after PCA: 177\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "<br><br><br>\n",
        "\n",
        "---\n",
        "\n",
        "<br><br><br>"
      ],
      "metadata": {
        "id": "KLnSsY0UVah_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Part 5: Train and Evaluate Three Improved Models**\n"
      ],
      "metadata": {
        "id": "pykMu4YaXHEx"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **1. Prepare the Engineered Dataset**\n",
        "\n",
        "The engineered dataset from Part 4 was used to train and evaluate the improved models.\n",
        "\n",
        "The target variable was Total_Amount, which represents the final purchase amount. The input features included the engineered features created in the previous part, such as transaction-based features, encoded categorical variables, interaction features, behavioral features, and clustering-based features.\n",
        "\n",
        "The dataset was split into training and testing sets using an 80/20 split. The training set was used to train the models, while the testing set was used to evaluate their performance on unseen data.\n",
        "\n",
        "A fixed random_state=42 was used to ensure reproducibility, meaning that the same train-test split will be created each time the code is executed."
      ],
      "metadata": {
        "id": "Qqzu2XTCMdbv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Part 5: Prepare the Engineered Dataset\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# Define target variable\n",
        "y = df_fe_encoded['Total_Amount']\n",
        "\n",
        "# Define engineered features\n",
        "X = df_fe_encoded.drop('Total_Amount', axis=1)\n",
        "\n",
        "# Handle missing values\n",
        "X = X.fillna(0)\n",
        "\n",
        "# Train-test split\n",
        "X_train, X_test, y_train, y_test = train_test_split(\n",
        "    X,\n",
        "    y,\n",
        "    test_size=0.2,\n",
        "    random_state=42\n",
        ")\n",
        "\n",
        "print(\"X shape:\", X.shape)\n",
        "print(\"X_train shape:\", X_train.shape)\n",
        "print(\"X_test shape:\", X_test.shape)"
      ],
      "metadata": {
        "id": "59VDTUuWXETD",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "52dfa399-c1b5-4a4d-f35a-8285a1656d78"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "X shape: (17049, 271)\n",
            "X_train shape: (13639, 271)\n",
            "X_test shape: (3410, 271)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **2. Improved Linear Regression Model**\n",
        "\n",
        "The Linear Regression model was retrained using the engineered dataset created in Part 4.\n",
        "\n",
        "Unlike the baseline model, this improved version uses a richer set of input features. These include transaction-based features, categorical interaction features, behavioral features, numerical interaction terms, and clustering-based features.\n",
        "\n",
        "The goal of retraining Linear Regression was to check whether the additional engineered features improve the performance of the original baseline model.\n",
        "\n",
        "The model was evaluated using MAE, MSE, RMSE, and R² in order to measure prediction error and overall explained variance."
      ],
      "metadata": {
        "id": "jwzwP9qqXbP9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Model 1: Linear Regression with Engineered Features\n",
        "\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
        "import numpy as np\n",
        "\n",
        "linear_model = LinearRegression()\n",
        "\n",
        "linear_model.fit(X_train, y_train)\n",
        "\n",
        "y_pred_lr = linear_model.predict(X_test)\n",
        "\n",
        "mae_lr = mean_absolute_error(y_test, y_pred_lr)\n",
        "mse_lr = mean_squared_error(y_test, y_pred_lr)\n",
        "rmse_lr = np.sqrt(mse_lr)\n",
        "r2_lr = r2_score(y_test, y_pred_lr)\n",
        "\n",
        "print(\"Linear Regression Results:\")\n",
        "print(\"--------------------------\")\n",
        "print(\"MAE:\", mae_lr)\n",
        "print(\"MSE:\", mse_lr)\n",
        "print(\"RMSE:\", rmse_lr)\n",
        "print(\"R2:\", r2_lr)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UvbbG0EdMqYn",
        "outputId": "a84419f7-9ac5-4158-cd22-58de453d5654"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Linear Regression Results:\n",
            "--------------------------\n",
            "MAE: 160.07169130264734\n",
            "MSE: 50623.41449175732\n",
            "RMSE: 224.99647662076248\n",
            "R2: 0.9447273933002118\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **3. Random Forest Regressor**\n",
        "\n",
        "\n",
        "A Random Forest Regressor was trained as the second improved model.\n",
        "\n",
        "Random Forest is an ensemble learning model that combines multiple decision trees. Instead of relying on one tree, it builds many trees and averages their predictions to produce a more stable and accurate result.\n",
        "\n",
        "This model was selected because it can capture non-linear relationships and complex interactions between features. This is useful for purchasing behavior, because Total_Amount may be affected by combinations of price, quantity, discount, product category, city, and customer behavior.\n",
        "\n",
        "The model was trained on the engineered dataset and evaluated using MAE, MSE, RMSE, and R², allowing its performance to be compared with the improved Linear Regression model and the baseline model."
      ],
      "metadata": {
        "id": "_cclFRemMwCE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Model 2: Random Forest Regressor\n",
        "\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "\n",
        "rf_model = RandomForestRegressor(\n",
        "    n_estimators=100,\n",
        "    random_state=42,\n",
        "    n_jobs=-1\n",
        ")\n",
        "\n",
        "rf_model.fit(X_train, y_train)\n",
        "\n",
        "y_pred_rf = rf_model.predict(X_test)\n",
        "\n",
        "mae_rf = mean_absolute_error(y_test, y_pred_rf)\n",
        "mse_rf = mean_squared_error(y_test, y_pred_rf)\n",
        "rmse_rf = np.sqrt(mse_rf)\n",
        "r2_rf = r2_score(y_test, y_pred_rf)\n",
        "\n",
        "print(\"Random Forest Results:\")\n",
        "print(\"----------------------\")\n",
        "print(\"MAE:\", mae_rf)\n",
        "print(\"MSE:\", mse_rf)\n",
        "print(\"RMSE:\", rmse_rf)\n",
        "print(\"R2:\", r2_rf)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uGeJCuqjMzKv",
        "outputId": "91322f67-0f0a-4837-a754-dbc7e4b51208"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Random Forest Results:\n",
            "----------------------\n",
            "MAE: 21.80475703812425\n",
            "MSE: 9397.692279980187\n",
            "RMSE: 96.94169526050278\n",
            "R2: 0.9897392352038689\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **4. Gradient Boosting Regressor**\n",
        "\n",
        "A Gradient Boosting Regressor was trained as the third improved model.\n",
        "\n",
        "Gradient Boosting is an ensemble model that builds several weak models one after another. Each new model tries to correct the errors made by the previous models, which can improve prediction accuracy.\n",
        "\n",
        "This model was selected because it often performs well on structured tabular data and can capture complex relationships between features.\n",
        "\n",
        "In this project, Gradient Boosting was useful because Total_Amount may be affected by several combined factors, such as unit price, quantity, discount percentage, product category, city, and engineered interaction features.\n",
        "\n",
        "The model was trained on the engineered dataset and evaluated using MAE, MSE, RMSE, and R² so its performance could be compared with Linear Regression, Random Forest, and the baseline model."
      ],
      "metadata": {
        "id": "by5r2ti3M8rC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Model 3: Gradient Boosting Regressor\n",
        "\n",
        "from sklearn.ensemble import GradientBoostingRegressor\n",
        "\n",
        "gb_model = GradientBoostingRegressor(\n",
        "    random_state=42\n",
        ")\n",
        "\n",
        "gb_model.fit(X_train, y_train)\n",
        "\n",
        "y_pred_gb = gb_model.predict(X_test)\n",
        "\n",
        "mae_gb = mean_absolute_error(y_test, y_pred_gb)\n",
        "mse_gb = mean_squared_error(y_test, y_pred_gb)\n",
        "rmse_gb = np.sqrt(mse_gb)\n",
        "r2_gb = r2_score(y_test, y_pred_gb)\n",
        "\n",
        "print(\"Gradient Boosting Results:\")\n",
        "print(\"--------------------------\")\n",
        "print(\"MAE:\", mae_gb)\n",
        "print(\"MSE:\", mse_gb)\n",
        "print(\"RMSE:\", rmse_gb)\n",
        "print(\"R2:\", r2_gb)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SEaqsqIgM_Hn",
        "outputId": "6017a784-94a1-4083-efae-6ba744f3e987"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gradient Boosting Results:\n",
            "--------------------------\n",
            "MAE: 30.75342370874565\n",
            "MSE: 9134.009225963457\n",
            "RMSE: 95.57201068285346\n",
            "R2: 0.9900271345856941\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **5. Model Comparison**\n",
        "\n",
        "\n",
        "The performance of the three improved models was compared with the baseline Linear Regression model from Part 3.\n",
        "\n",
        "The comparison was based on four evaluation metrics: MAE, MSE, RMSE, and R².\n",
        "\n",
        "A higher R² score indicates that the model explains more of the variation in Total_Amount. Lower MAE, MSE, and RMSE values indicate smaller prediction errors and therefore better predictive performance.\n",
        "\n",
        "The goal of this comparison was to check whether the engineered features and more advanced regression models improved the prediction results compared to the baseline model."
      ],
      "metadata": {
        "id": "z06lwf5eNRzf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Model Comparison\n",
        "\n",
        "results = pd.DataFrame({\n",
        "    'Model': [\n",
        "        'Baseline Linear Regression',\n",
        "        'Improved Linear Regression',\n",
        "        'Random Forest Regressor',\n",
        "        'Gradient Boosting Regressor'\n",
        "    ],\n",
        "    'MAE': [\n",
        "        544.68,      # replace with baseline MAE from Part 3\n",
        "        mae_lr,\n",
        "        mae_rf,\n",
        "        mae_gb\n",
        "    ],\n",
        "    'MSE': [\n",
        "        1138333.21,  # replace with baseline MSE from Part 3\n",
        "        mse_lr,\n",
        "        mse_rf,\n",
        "        mse_gb\n",
        "    ],\n",
        "    'RMSE': [\n",
        "        1066.93,     # replace with baseline RMSE from Part 3\n",
        "        rmse_lr,\n",
        "        rmse_rf,\n",
        "        rmse_gb\n",
        "    ],\n",
        "    'R2': [\n",
        "        0.9310449192795338,  # baseline R2 from Part 3\n",
        "        r2_lr,\n",
        "        r2_rf,\n",
        "        r2_gb\n",
        "    ]\n",
        "})\n",
        "\n",
        "results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "XQYs0doqNHfc",
        "outputId": "352ef9b8-adab-4b55-c9cc-e4903a55299b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                         Model         MAE           MSE         RMSE  \\\n",
              "0   Baseline Linear Regression  544.680000  1.138333e+06  1066.930000   \n",
              "1   Improved Linear Regression  160.071691  5.062341e+04   224.996477   \n",
              "2      Random Forest Regressor   21.804757  9.397692e+03    96.941695   \n",
              "3  Gradient Boosting Regressor   30.753424  9.134009e+03    95.572011   \n",
              "\n",
              "         R2  \n",
              "0  0.931045  \n",
              "1  0.944727  \n",
              "2  0.989739  \n",
              "3  0.990027  "
            ],
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              "      <th></th>\n",
              "      <th>Model</th>\n",
              "      <th>MAE</th>\n",
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              "      <td>Baseline Linear Regression</td>\n",
              "      <td>544.680000</td>\n",
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              "      <th>1</th>\n",
              "      <td>Improved Linear Regression</td>\n",
              "      <td>160.071691</td>\n",
              "      <td>5.062341e+04</td>\n",
              "      <td>224.996477</td>\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-445a534c-5d3d-41ee-9602-cce3e88c69c1');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
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              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('results')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_eb0ab2c3-99b6-416e-84a1-5ab6ae842b79 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('results');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "results",
              "summary": "{\n  \"name\": \"results\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"Model\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Improved Linear Regression\",\n          \"Gradient Boosting Regressor\",\n          \"Baseline Linear Regression\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MAE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 245.1808235295447,\n        \"min\": 21.80475703812425,\n        \"max\": 544.68,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          160.07169130264734,\n          30.75342370874565,\n          544.68\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MSE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 557981.469069017,\n        \"min\": 9134.009225963457,\n        \"max\": 1138333.21,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          50623.41449175732,\n          9134.009225963457,\n          1138333.21\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"RMSE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 467.833331075271,\n        \"min\": 95.57201068285346,\n        \"max\": 1066.93,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          224.99647662076248,\n          95.57201068285346,\n          1066.93\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"R2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.03053597591055499,\n        \"min\": 0.9310449192795338,\n        \"max\": 0.9900271345856941,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.9447273933002118,\n          0.9900271345856941,\n          0.9310449192795338\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 99
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualize Model Comparison with clean pastel colors\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.figure(figsize=(11, 6))\n",
        "\n",
        "# Clean pastel color palette\n",
        "colors = ['#F7C8E0', '#B4E4FF', '#C1E1C1', '#FFE5B4']\n",
        "\n",
        "bars = plt.bar(\n",
        "    results['Model'],\n",
        "    results['R2'],\n",
        "    color=colors,\n",
        "    edgecolor='white',\n",
        "    linewidth=2\n",
        ")\n",
        "\n",
        "# Add value labels on top of bars\n",
        "for bar in bars:\n",
        "    height = bar.get_height()\n",
        "    plt.text(\n",
        "        bar.get_x() + bar.get_width() / 2,\n",
        "        height + 0.01,\n",
        "        f'{height:.3f}',\n",
        "        ha='center',\n",
        "        va='bottom',\n",
        "        fontsize=11,\n",
        "        fontweight='bold'\n",
        "    )\n",
        "\n",
        "plt.title('Model Comparison by R² Score', fontsize=16, fontweight='bold', pad=18)\n",
        "plt.suptitle(\n",
        "    'Baseline vs. Improved Regression Models',\n",
        "    fontsize=11,\n",
        "    y=0.93\n",
        ")\n",
        "\n",
        "plt.xlabel('Model', fontsize=12)\n",
        "plt.ylabel('R² Score', fontsize=12)\n",
        "plt.xticks(rotation=18, ha='right')\n",
        "plt.ylim(0, 1.05)\n",
        "\n",
        "plt.grid(axis='y', linestyle='--', alpha=0.25)\n",
        "\n",
        "# Remove top and right borders for cleaner look\n",
        "ax = plt.gca()\n",
        "ax.spines['top'].set_visible(False)\n",
        "ax.spines['right'].set_visible(False)\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 580
        },
        "id": "ffysfyIeNME0",
        "outputId": "1791ca5f-45e0-4cbd-d7dc-eab0fac11212"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1100x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **6. Feature Importance**\n",
        "In this step, feature importance was analyzed for the three improved models: Linear Regression, Random Forest Regressor, and Gradient Boosting Regressor.\n",
        "\n",
        "The purpose of this analysis is to understand which features had the strongest influence on the prediction of Total_Amount in each model.\n",
        "\n",
        "For the Linear Regression model, feature importance was measured using the model coefficients. These coefficients show both the strength and the direction of the effect. Positive coefficients increase the predicted Total_Amount, while negative coefficients decrease it.\n",
        "\n",
        "For Random Forest and Gradient Boosting, feature importance was measured using the built-in feature_importances_ values. These values show how much each feature contributed to the model’s predictions. Unlike Linear Regression coefficients, these importance scores are always positive because they measure importance only, not whether the effect is positive or negative.\n",
        "\n",
        "The visualization compares the top features used by each model side by side. This helps show that different models may rely on different features when predicting Total_Amount."
      ],
      "metadata": {
        "id": "2YUkmIh-QI-Q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Feature Importance / Coefficients for All Models Side by Side\n",
        "\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "# ---------- Linear Regression: signed coefficients ----------\n",
        "lr_importance = pd.DataFrame({\n",
        "    'Feature': X.columns,\n",
        "    'Value': linear_model.coef_\n",
        "})\n",
        "\n",
        "lr_importance['Abs_Value'] = lr_importance['Value'].abs()\n",
        "lr_top = lr_importance.sort_values(by='Abs_Value', ascending=False).head(10)\n",
        "\n",
        "# ---------- Random Forest: feature importance ----------\n",
        "rf_importance = pd.DataFrame({\n",
        "    'Feature': X.columns,\n",
        "    'Value': rf_model.feature_importances_\n",
        "})\n",
        "\n",
        "rf_top = rf_importance.sort_values(by='Value', ascending=False).head(10)\n",
        "\n",
        "# ---------- Gradient Boosting: feature importance ----------\n",
        "gb_importance = pd.DataFrame({\n",
        "    'Feature': X.columns,\n",
        "    'Value': gb_model.feature_importances_\n",
        "})\n",
        "\n",
        "gb_top = gb_importance.sort_values(by='Value', ascending=False).head(10)\n",
        "\n",
        "\n",
        "# ---------- Plot ----------\n",
        "fig, axes = plt.subplots(1, 3, figsize=(24, 7))\n",
        "\n",
        "# Linear Regression colors: positive vs negative\n",
        "lr_colors = ['#B5EAD7' if value > 0 else '#FFB7B2' for value in lr_top['Value']]\n",
        "\n",
        "axes[0].barh(\n",
        "    lr_top['Feature'],\n",
        "    lr_top['Value'],\n",
        "    color=lr_colors,\n",
        "    edgecolor='white',\n",
        "    linewidth=2\n",
        ")\n",
        "\n",
        "axes[0].axvline(0, color='gray', linestyle='--', linewidth=1)\n",
        "axes[0].invert_yaxis()\n",
        "axes[0].set_title('Linear Regression Coefficients', fontsize=14, fontweight='bold')\n",
        "axes[0].set_xlabel('Coefficient Value')\n",
        "axes[0].set_ylabel('Feature')\n",
        "axes[0].grid(axis='x', linestyle='--', alpha=0.3)\n",
        "\n",
        "\n",
        "# Random Forest\n",
        "axes[1].barh(\n",
        "    rf_top['Feature'],\n",
        "    rf_top['Value'],\n",
        "    color='#B4E4FF',\n",
        "    edgecolor='white',\n",
        "    linewidth=2\n",
        ")\n",
        "\n",
        "axes[1].invert_yaxis()\n",
        "axes[1].set_title('Random Forest Feature Importance', fontsize=14, fontweight='bold')\n",
        "axes[1].set_xlabel('Importance Score')\n",
        "axes[1].set_ylabel('')\n",
        "axes[1].grid(axis='x', linestyle='--', alpha=0.3)\n",
        "\n",
        "\n",
        "# Gradient Boosting\n",
        "axes[2].barh(\n",
        "    gb_top['Feature'],\n",
        "    gb_top['Value'],\n",
        "    color='#FFE5B4',\n",
        "    edgecolor='white',\n",
        "    linewidth=2\n",
        ")\n",
        "\n",
        "axes[2].invert_yaxis()\n",
        "axes[2].set_title('Gradient Boosting Feature Importance', fontsize=14, fontweight='bold')\n",
        "axes[2].set_xlabel('Importance Score')\n",
        "axes[2].set_ylabel('')\n",
        "axes[2].grid(axis='x', linestyle='--', alpha=0.3)\n",
        "\n",
        "\n",
        "plt.suptitle(\n",
        "    'Top Features by Model',\n",
        "    fontsize=18,\n",
        "    fontweight='bold'\n",
        ")\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 515
        },
        "id": "FzYaX0GiTEi-",
        "outputId": "96e4e7f5-08a3-4c40-e5ba-eed7baa8cedf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2400x700 with 3 Axes>"
            ],
            "image/png": 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rRtPUarXR67qKFSuW5bavXr1qNO3hw4eK0aoAIDg4OCdJNQtTx7tjxw55JKqsBAUFAQCOHDmCGzduKOZ16dIFI0aMQIkSJeDk5AQAqFOnTp46OmXGcMQgw1caAjAaBSontKM1meLn5wcnJyc8e/ZMnjZx4sQcjdbk5eWl+F62bFl89tln+Oyzz3D//n1cvnwZly9fRmxsLFauXCl35vn+++9x+vRpREZG5vpYtK5fvw61Wm3UYczwnPT39zeKa7du3TBu3Dh55KcjR45g0aJFSExMlJeJjo4ukPO2devWqFixIi5cuCBPi4iIQKtWrXK8jZCQEMVv2NTvGYDRK0ADAwPlc0F7rmvdvHkTz58/N3pFqKnfuGFa9F+h2bp1ayxdujTbY8jviGVEREREREREVHRxxCoiIiIiIiIiMpsSJUoYvRJs1apVeP78uWLapk2bjDpENW3aNMttr1+/3mjkmZUrVxotV69evdwkOV/q1KkDV1dXxbTvvvsu09GuwsPDIUkSzp8/DxcXFwCmX2+2fPlytGjRAuXLl0d4eDju3buXo05Vhh1Vshqpx9vbW/H9/v37Rq9sW7JkSbb7zA17e3s0adJEMe37779HUFBQpvHy9fXF4cOH4ePjI69j+Ho6X19f1KtXD3379sXy5ctRrVo1xXz9zjh5cf/+fWzbtk0xLT4+Hjt37lRMM3XuOTo6YtiwYYpp77zzjuL74MGD85W+zEiShDfffFMx7c0334QkSTneRrNmzRTfY2NjjTrc3bhxwygW+r9nw7ikpqZiw4YNimmm4mkoKipK8f3o0aN4/vx5pudOaGgoTp48yY5VRERERERERJRnHLGKiIiIiIiIiMxq2LBhGD9+vPz96tWraNmyJd5//30UK1YMBw8exNtvv61Yx9vbG717985yu3fu3EHLli0xadIkBAcHY/fu3fjggw8Uy/j4+KB9+/bmO5hsqFQqDB48GIsXL5anLV26FImJiXj11VcRHh4OQNN5Ki4uDjt27MChQ4fQr18/tGvXDgAQEBBgtN0JEyZg2LBhcHR0xKFDhzB58uQcpScgIEDR6eirr75C7dq15X14e3vLHaoMOx8BQI8ePTBjxgw4OztjwYIFOHz4cI72mxvDhw/Hvn375O+nTp1CkyZNMHr0aFSuXBmurq5ISEjA2bNnsXfvXuzcuRMBAQHo06ePvE6nTp0AAO3atUOtWrUQHh4Od3d3PHr0CD/++CP++OMPxT7d3d3zne6BAwciISEBDRs2xLVr1zBhwgSkpaUplunXr5/Jdd944w1Mnz5dXl6/o2FwcDCio6Pznb7MDBgwANeuXYMQApIk5fqVg8OGDVN0YFSr1WjZsiVmz56NqlWr4tKlSxg/frxR58nhw4fL/+/evTvGjBmjiNewYcPw+PFjNGnSJNN4Gho0aBBmzJghdxhMTk5GVFQUxo0bh4YNG8LX1xdJSUm4cOECDh48iO3bt+P27du4evUqfH19c3XcREREREREREQAO1YRERERERERkZm99dZb2Lp1K44cOSJPO3jwINq2bZvpOp9++qnRCEqGXF1dcfTo0Sy3M336dHkkKEuZMmUK9uzZo3jd2pYtW7Bly5Ycrd+4cWMEBAQgISFBnrZ8+XIsX75c/u7u7g4PDw88fvw4y201aNBAsd+jR4+ibt268vdJkybJnbQaNWqEkiVLKl5DeODAATRu3Fj+LkmS/Eo9c+nSpQt69eqlGLHo5MmT6Nu3b463IYTAyZMn8dtvv2W7rIeHR7ajoWXHwcEBjx8/xtChQzNdpn79+ujWrZvJeQEBAejVqxfWrFljNK9///5Grw80J3d3d8yZMyfP69euXRtjxozB/Pnz5Wk3b97MsiPka6+9phhdKiAgAOPHj8f06dPlaSkpKRgxYoRiPQcHB6Snp2e63eLFi2P+/PmKEcBu3bqFMWPG5OaQiIiIiIiIiIhyjK8CJCIiIiIiIiKzUqlU+OGHH9CxY8dsl3V1dcWaNWsUoxFl5qOPPkLt2rUznT98+HCjV65Zgo+PD37++We0aNEiR8tLkoQSJUrI311cXLBixQqj1/jpz9+wYUOORtyZMGFCptsxZG9vj6VLl2a6vIeHB7744oscbSu31qxZg7feeivHr6QLDQ3N035cXFzw1VdfwdPTM0/razVq1AgxMTGZzi9Xrhw2b94Me3v7TJcZOXKkyemvvvpqvtJmCXPmzMH7778PO7vsLyWOHDkSn3/+udH0SZMmoUePHpmu16BBgyxjrDV06FCsWLECbm5u2S4LAP7+/hbvbElEREREREREhQc7VhERERERERGR2fn4+GD79u34+eefMWjQIFSoUAEeHh5wcHCAv78/mjRpgqlTp+Lq1as5fjWZt7c3Dh8+jNmzZyMyMhKurq7w9PREVFQUtmzZgs8++yzHHXXMrVixYti3bx/27NmDQYMGISIiAp6enrC3t4enpyciIiLQvXt3LF68GFevXlWM3AMAHTt2xLFjx9CtWzcEBATA0dERxYsXR9++ffHbb7/l+PWGdevWxYEDB/Dyyy8jKCgoy44+ANCmTRscOnQIHTt2hK+vL5ycnBAeHo7hw4fjzz//xEsvvZTnmGTFyckJixYtwrlz5zB27FjUrVsXvr6+cHBwgKurK8LDw9GuXTtMmzYNJ06cwMGDBxXrb9y4EStXrsTgwYNRp04dlCxZEi4uLnB0dIS/vz8aNGiA999/HxcvXpRfG5hfCxcuxPfff482bdrAz88PKpUK5cuXxwcffIDff/9d0VnOlBo1aihGAwM0o5VVqFDBLOkrSHZ2dpg+fTouXLiAsWPHolatWvDx8YGDgwO8vLwQGRmJt956C2fPnsWCBQtMjsDl4OCADRs2YM2aNWjYsCHc3d3h5uaGGjVqYO7cufjll1/g4eGRo/S8+uqruHbtGmbNmoWWLVsiODgYKpUKTk5OCA4ORpMmTTBmzBjs3LkTt27dQlBQkLlDQkRERERERERFhCTMPZ47EREREREREZEZGHaSWrVqFQYOHGidxBCZweDBg7Fy5Ur5+8qVKzFo0CArpoiIiIiIiIiIiLLCEauIiIiIiIiIiIgK2NGjR7Fhwwb5u4+PD3r27GnFFBERERERERERUXbYsYqIiIiIiIiIiKgAfPvttyhbtiyCg4PRsGFDPHnyRJ43fvx4uLq6WjF1RERERERERESUHQdrJ4CIiIiIiIiIiKgwevToEa5cuWI0vWnTphg3bpwVUkRERERERERERLnBEauIiIiIiIiIiIgKmJubG6pVq4Y5c+bgp59+gqOjo7WTRERERERERERE2ZCEEMLaiSAiIiIiIiIiIiIiIiIiIiIiIrIlHLGKiIiIiIiIiIiIiIiIiIiIiIjIADtWERERERERERERERERERERERERGWDHKiIiIiIiIiIiIiIiIiIiIiIiIgPsWEVERERERERERERERERERERERGSAHauIiIiIiIiIiIiIiIiIiIiIiIgMsGMVERERERERERERERERERERERGRAXasIiIiIiIiIiIiIiIiIiIiIiIiMsCOVURERERERERERERERERERERERAbYsYqIiIiIiIiIiIiIiIiIiIiIiMgAO1YREREREREREREREREREREREREZYMcqIiIiIiIiIiIiIiIiIiIiIiIiA+xYRUREREREREREREREREREREREZIAdq4iIiIiIiIiIiIiIiIiIiIiIiAywYxUREREREREREREREREREREREZEBdqwiIiIiIiIiIiIiIiIiIiIiIiIywI5VREQ2RJIk+bN69WprJ4cKkdWrVyvOL8qfBQsWoFq1anBxcZFj+vLLL8vzL126hG7duiEwMBD29vbyMqdOnTJbXly7dk2xndjY2PwfGBEREZEZsJ5CRERERETmFhsbq2hnXLt2TZ43cOBAeXpUVJTV0kh5x/tjRGTL2LGKiKiAGFbyWRHM2uTJkxXx0n6cnJwQGBiIZs2aYeHChXj69Km1k0oWcObMGcTExKBGjRrw9fWFo6MjfHx8ULduXYwfPx5nzpyxWtqWLl2K0aNH4+zZsybPxydPniA6OhpbtmxBQkIC1Gq1FVJpWeHh4fJvdvLkydZODhER0QvNsB2h/djb28Pb2xs1a9bE22+/jdu3b1s7qYWWft0mq09hkd+OcJmds4afgQMHFkj6s1PY2uW81sAbp0REROaUmJiI2bNn46WXXkJISAicnZ2hUqlQrFgxNG3aFOPHj8fBgwchhLB2Ul8I+X2oNqv7JAEBAWjYsCGmTZuGBw8eFEDqC05hqb9GRUXlqO2j3+nPUvTPnfDwcIvvvyDox7uwHFNu8KE12+Jg7QQQEZHOnDlz5P/XqVPHiimxHc+fP0dCQgISEhJw4MABfPvtt/j5559hb29v7aS9UOrUqaM4v2zV06dPMXLkSCxdutRo3sOHD3HixAmcOHECmzdvtkrjBADWr18v/79kyZIYMmQInJ2dUa5cOQDAiRMncOXKFXmZfv36oWrVqpAkCcWLF4ejo6NZ8sLX11exnTJlyuR7m0RERGS71Go1kpKSEBcXh7i4OKxduxbHjx9HaGiotZNGREREREQvoKVLl2LMmDFISUkxmnf79m3cvn0bBw8exNy5cxEfH4/g4GArpDJnevXqhSpVqgBAoWwjPX/+HPfu3cO9e/dw9OhRrF27FidPnoSnp6e1k2Y2vD9GRLaMHauIiGzIuHHjrJ2EAvHo0aNcV/Dfe+89eHt74/bt2/j6669x9+5dAMCBAwewY8cO/N///V9BJDXf8nKsllC5cmVUrlzZ2snIUkZGBnr06IHvv/9enubl5YUuXbqgbNmyePr0Kc6cOYPdu3dbMZXA9evX5f/3798fH3zwQabzAWDVqlWKjoABAQFmyQtPT89C+zeDiIiIdHr27InatWvj0aNH2LZtG86ePQtAc6Pjk08+wfz5862cwsKtdOnSGDZsmMX3a6vtipzQnrOGtDe6iooXOQ9tUUZGBtLS0uDq6mrtpBARERUKc+bMwYQJE+TvkiShefPmqF+/Ptzd3XH//n2cOnUKhw4dyvVbJKxRD2rbti3atm1r0X1awnvvvQcfHx88ffoUW7duxe+//w4A+Ouvv7By5UqMGjXKugk0oxftWrePjw/ee+89k/N8fX0tnBrrYT3d/NiWtFGCiIgKxP79+wUA+bNq1aps18ls+VWrVinmPX36VEyfPl2UK1dOODk5ieLFi4uxY8eKp0+fmtzu9u3bxf/93/+J4OBg4ejoKLy9vUXz5s3F119/LdRqtdHys2fPFp06dRLlypUTPj4+wsHBQXh5eYk6deqI6dOni+Tk5GzTvm3bNtGgQQPh5uYmvLy8sj32SZMmKbZx9epVed7OnTsV82bOnGm0fkZGhli7dq1o3bq1CAgIEI6OjsLf319ER0eLHTt2mNzn8+fPxccffyzKli0rnJycROnSpcVHH30knj17luO8SElJEe+9954oVaqUcHBwECNHjpSXffr0qVi8eLFo0qSJ8PHxEY6OjiI4OFh069ZNHDlyxGSaVq1aJZo1ayb8/PyEg4OD8Pb2FuXLlxc9evQQn332mWLZa9euiddff12ULVtWODs7C5VKJUJCQkTDhg3F6NGjxZ9//plpug09efJEzJ8/XzRs2FB4e3sLR0dHERgYKNq1ayc2btxotLzh+X3lyhXx2WefiapVqwqVSiUCAgLE4MGDxf37900epylffPGFYpsNGjQQCQkJRsvdv39ffPLJJ0bTf/vtN9GvXz8RHh4uVCqVcHNzE5UrVxZjxowR//zzj8l95iaPBgwYoEif4ccwxoafsLAwIUT2efH8+XOxYsUK0bp1axEYGCify/Xq1ROTJ0+Wl7t69apiO/v37zfaVm5++6a2t379elG3bl3h4uIivL29Rbdu3cSNGzdyHBP948vN+UpERFSUZdWOePjwoXBycpLntWnTRrFuYmKiGD9+vGjRooUICwsT7u7ucr2uVatWYu3atUZ1gPzU61JSUsTbb78tSpQoIVQqlahUqZL49NNPxd9//51tPeWbb74R0dHRIigoSK6nNGjQQMydO1ekpKQYLW8Yk7Vr14rIyEjh7OwsypQpI+bPny+E0NSlpk2bJsLDw4WTk5OoWLGiWLp0aa7yICwsTN5Xs2bNcrxefo8pqzbUgQMHRM+ePUVoaKhwcnISHh4eon79+uLTTz8Vz549M9r2mTNnRJ8+fURYWJhwcnISzs7OIjQ0VDRv3ly888474ubNm0bHauqTk+PPS9tXCCGSkpLEjBkzRN26dYWnp6dwdHQUoaGhYsCAAeKPP/4wWv7vv/8WI0eOFI0bNxYlSpQQrq6uwsnJSYSEhIgOHTqI7du3K5Zv1qxZjurn2dWr9bczYMAAebqp9ZYvXy5q1KghnJ2dRWRkpGI7uW2XZyareBvOu3Dhgvjwww9FyZIlhYuLi6hTp47YuXOnEEKIu3fvildffVX4+/sLZ2dn0ahRI3HgwAGj/Rnua8eOHaJRo0bCzc1NeHt7i65du4rLly+bTOvFixfF0KFDRfny5YWLi4twcXER5cqVE6+//ro4f/680fL67YtmzZqJ69evi759+4rAwEAhSZL45JNPsm1/aPMvLi5ODBs2TNStW1eEhITIbZCSJUuKHj16iIMHDxrtX/+6RFhYmHj48KEYN26cKFmypHB0dBSlSpUSH330kcn8UqvVYvPmzaJjx44iJCREODk5CR8fH1G9enUxevRokZaWplj+9u3b4t133xWRkZHC3d1dqFQqUaZMGTF8+HBx/fr1TPOfiIjIHP78809hb28vl3t+fn7i8OHDJpd9/PixWLJkiXj48KE8zbDOcfnyZTFnzhxRsWJF4eTkJDp16iSEyFt5LIQQ9+7dE2+88YYIDAwUzs7OolatWmLDhg1G+9W/h2BYjzCU27LXcHu3bt0SQ4YMEcHBwSbbGYZ1Q1OfSZMmZZs3Wd0nuXfvnmLeG2+8YbT+/fv3xZQpU0StWrXkOnZISIjo3Lmz2L17d6b73bt3r+jatasoXry43N6oUaOG+PDDD0ViYqLR8jm91pvTOrkQBXN/7N69e2Lo0KEiKChIPpc2bdqU5bmUFf3j0U97dnLbnluxYoXo3r27qFixonyvyMPDQ0RGRooJEyYo7psYHoupjzaeWf1OcvP7Mqynb926VV7WnPXcrOJt2E779ddfRcuWLYWbm5sIDAwUw4cPF48fPxZCCLFx40ZRs2ZN4ezsLEJCQsSYMWOMzhfDtsCDBw9ETEyM/JuIiIgQixcvNtkWSE9PFytWrBAtWrSQ88vX11dERUWJpUuXiufPnyuWz0lbMjdtdXPc0929e7eIiooSbm5uwt3dXbRt29Zku1wIIf755x8xYcIEUb16deHh4SFUKpUIDQ0VnTp1Mvl3xlxtYWtjxyoiogJSkB2rGjdubLIg7devn2J7GRkZol+/flkWvt27dxfp6emK9fz8/LJcp2rVqnKFxFTamzRpovie345VZ86cUcxbtmyZYt0nT56IVq1aZZnmMWPGGO2zV69eJpft2LFjjvPC8Fi1Havu3r0rqlevnml67OzsxIIFC7KMgeEnKChIXvbOnTsiICAgy+U///zzTNOtLz4+XlSuXDnLbXXt2lVR+TM8vzM7J5s2bZpt3mtVrFhRXs/Z2Vn8+++/OV73k08+EXZ2dpmm38vLy+gGSW7zyBIdqxITE0WdOnWyPA6trG4A5eW3b7i9zPK0XLlyIjU1NUcx0R5fbs9XIiKioiy7doSvr688r0+fPop5Z8+ezbZsHjRoUJb7y2m97tmzZ0Z1Ye2nffv2mdZT0tPTRY8ePbJMY0REhLh165Zif/rza9WqZXK9iRMnik6dOpmct2LFihznQW47VpnjmLJqQ7333ntZbrtJkyaKC6Xnzp0Trq6uWa6j7VxjrY5Vly5dEuHh4ZnuV6VSiU2bNinW+f7777M9v6dMmSIvb+mOVYZ5qO1Yldd2eV7ibTjP1G/Fzs5ObNiwQZQqVcpk3A0feNCf37x5c5Pp9/PzExcvXlSst2nTJuHs7JxlHq9fv16xjn77oly5ciI4OFixTm46Vi1evDjL5SRJMjpX9dvkfn5+IiIiwuS6EydOVKyXmppq9HfP8PPgwQN5+SNHjgh/f/9Ml/Xy8jLZyY2IiMhchg4dqih7Nm/enKv1DeschvUgbceqvJTHDx48UFwn1v8Ylrc57ViVl7JXf3ulS5cWxYoVM7mutp1R0B2rUlNTxdKlS7Pc3p9//ilKlCiRZRr0HwzXGjNmTJbrFC9eXNHBIjfXeguiY1VO749ldS4Z3gMqyI5VuW3PCSEybfPq54n2/omlO1aZqqdrO1aZu56b045VlStXFiqVymh/UVFRYu7cuTk6X/R/ewEBAaJKlSom13vrrbcU6yUnJ4umTZtmGf/GjRsr7qnmpC2Zm7Z6fu/pNmrUSEiSZLSen5+fuHv3rmK9HTt2CA8Pj0z3pf83xtxtYWvjqwCJiF5Ahw4dQufOnVGpUiWsW7cO165dAwCsW7cOs2bNQkhICABg9uzZ+OqrrwBohvLt2rUrIiMjcfXqVXz11Vd4/vw5Nm/ejOrVqyuGLC1RogSaN2+OsLAw+Pj4QAiBq1evYuPGjUhJScHZs2exZMkSxVDB+g4ePAh/f3/06tULfn5+OHfuXJ6OUwiB27dvK96t7eLigg4dOiiWGz16NPbu3QsAcHJyQq9evVCuXDmcPXsWmzdvhhAC8+fPR61atdC7d28AwDfffIMNGzbI2yhdujR69eqFGzduYN26dTlO48GDB1GvXj20bt0aKSkpKFmyJACgX79+OHXqFADAw8MDvXv3RokSJXD48GHs2rULarUao0ePRu3atdGoUSMAwOeffy5vt1WrVoiKikJKSgr++ecfHDp0CKmpqfL8LVu2ICEhAYBmyNlBgwbBz88Pt27dwoULF3Dw4MEcH0OfPn0UedStWzdUqlQJe/bswdGjR+X9zZgxAx9++KHJbRw6dAgtW7ZEw4YNFa+oOXDgAI4dO4b69etnmQZturXatGkjn8fZOXDgAMaMGQMhBACgZMmSeOWVV5CcnIxVq1bhyZMnSEpKQteuXfHXX3/Bx8cHQO7zqFevXqhSpQpmzJiBBw8eAABat26Nl156CQBQo0YNzJkzB7/99hs2btwop097/np5eWV7LP369cOJEyfk7xEREYiOjoZKpUJcXBx+/fXXHMUkr799fYcOHUKdOnXQpk0b7N+/H4cPHwYAXL58Gdu2bUOvXr2yjYmWOc9XIiKiourRo0dYvXo17t+/L0/r0aOHYhk7OztERESgbt26CA4Ohre3N54+fYq4uDh8//33EEJg1apVGDp0KOrWrWtyPzmt1y1cuFBRhteoUQMdOnTAH3/8ga1bt2Z6HDNmzMCmTZvk7/Xr18dLL72E8+fPY/PmzQCA8+fPo0+fPvj5559NbuPkyZNo0KABWrdujY0bN+LixYsAgGnTpgEAmjVrhqZNm2LZsmW4ffs2AE396NVXX800XZn5559/MHfuXKPpVapUkV81Yo5jyqwNtWHDBsyYMUNerk2bNmjUqBHu3LmDNWvWIDk5GQcPHsTo0aOxdOlSAMCaNWvw5MkTAJq2Xd++feHm5oabN2/ijz/+wLFjx+Ttvf/++7h27ZpiH0OHDkWZMmUAAKGhobmO2a5du3Dv3j2j6T179kRoaCgyMjLQuXNnuR0bEBCA3r17w9fXFz/99BOOHDmCtLQ09O/fH7Vq1ULp0qUBAA4ODqhevTpq166NgIAAeHp6IiUlBYcPH8b+/fsBaM6BwYMHo3jx4hg2bBg6dOiA8ePHK9KgfU1hTurnuXHw4EGEhYWha9eucHV1lV9nb466eV6dPHkSPXv2ROnSpfHpp5/i8ePHUKvV6NWrFwBN+8Pf3x+LFy9Geno60tLSsHDhQnzxxRcmt7d//37UqlUL0dHRit96YmIihg4dKp/ff/31F/r164e0tDQAgJ+fHwYMGABJkrBmzRrcu3cPaWlpGDBgAGrVqoVy5coZ7evy5csAgC5duiAyMhLXr1+Ho6Mj5syZg40bN+K3334DYPy6Tu25q1KpUL9+fVSvXh1+fn5wd3dHUlIS9u3bhxMnTkAIgbFjx6Jnz55wcXEx2n9iYiIePHiA/v37IyQkBMuXL5fP64ULF+KDDz6Ak5MTAGDs2LHYsWOHvG5oaCg6d+4MLy8vnDt3Dj/88IM879GjR3j55ZflbYWFhclp+Oabb3Du3Dm5/Xr58mWzn6dEREQAsG/fPvn/Pj4+6NKlS762d/DgQVSuXBkdO3aEEAL29vYA8lYef/DBB4rrxM2aNUOzZs1w+PBhRXmbU+Yoe//++284Oztj2LBhcHFxweeffy5fq9e2M3x9fbO8NgwADRs2zHX6S5UqZXK6v78/Bg8eLH9PT09H586dcfPmTQCAvb09+vXrhxIlSmDbtm34448/AGjqMTVr1kT//v0BAF999ZXi9fKVK1dG586dcevWLaxZswYZGRn4999/0aVLF5w7dw4ODg65utZbEHXynN4fMzyXGjdujObNm+PgwYP4/vvvc71fQ48ePTLZVgwNDUXPnj0B5K09BwCBgYHo2LEjypQpA19fX9jb2+Pff//Fxo0bkZiYiH///RfTp0/HkiVLUKZMGcyZMwe7d+/Gnj17ABi/prBOnTr5Pl4tU/V0Ly8vq9Zzz507h7CwMPTp0wfHjx+X7xfGxsYiNjYWZcuWRc+ePfHTTz/J7QjD80VfQkICHj16hKFDh8Lb2xtff/21/NtavHgxunbtimbNmgEAYmJicODAAXndl156CQ0aNMCxY8fw008/AdCcszExMVi5cqXJ9JtqS9auXTvHbfX83tM9fPgwKlasiC5duuDUqVP48ccfAWjaRCtWrMA777wDALh+/Tq6d+8uX2+QJAn/93//h+rVqyMhIcHoeoc128IFwmpduoiICrmCHLFq1KhR8rxTp04p5mlff5CRkaHoGf7hhx8q9jV79mxFr+OMjAzF/IcPH4off/xRfPHFF2LevHlizpw5il7XLVq0yDTtnp6euR7SM7vRmgCIkJAQsWfPHsV6iYmJwsHBQV5m5cqVivnDhw+X59WoUUOe3qZNG3m6u7u7ote1YVqyyosuXboYxe706dOKZX7++WfF/OjoaHle586d5emenp7y9Pj4eKMYXblyRf7//Pnz5WVNDfmbnJwsbt++nWm6teLi4hTTJ0yYIM9LT08XDRo0kOf5+vrKx2p4fnfu3FkesjMxMVExlPSiRYuM0mfo+PHjiu29/fbb2a6jpT8qgYeHh7hz544878cff1RsV/sKwbzmkRDKp/pNPWWU3av+MptvODJbdHS00TDA+udAZk/W5/W3b7i9unXryvt/9uyZCAwMlOcZjgCXXUxye74SEREVZTl52tTV1VXMmTMn021cv35dfPPNN+LTTz8Vc+fOFXPmzBHFixeX1586dWqm+8tpva5ChQry9LJlyyqG0R8yZEim9RT9EbcaNGigeDJxwoQJivXi4uLkefrTK1WqJNdTfvrpJ8W8yMhIeZuGr5p+9OhRjvIguydDAd3IReY6pszaUDVq1JCX6d+/v2Lepk2b5HkODg7yKzpiYmLk6aZeo37//n3Fqx1z8orprOTknNXf7nfffSdPs7e3F5cuXZK3lZ6eLqpWrSrPHz16tNH+Ll68KDZs2CAWL14sn9/6I3StXbtWsbx+Gky1y801YlWpUqUUoxIJkf92eU7indWIVa+99po8791331XMGzFihDxPfzTnmjVrZhq/ypUrK15pZ/hb174ScOTIkfI0Ozs7cfbsWXmds2fPKkYb1n+i2XBEXMNRnk0tl92oaqdPnxZff/21WLhwoZgzZ46YPn26Yh/6T8wbXgvQ3/+2bdsU886cOSOE0Pye9K9J1KhRw+hp8Bs3bsh/sxYuXCgv6+Pjo3i1TnJysmL0h4ULF2Z5bERERHmlX3eqW7euYt758+ezrP8KYVznqF+/vjzCvSk5LY+fP38u3N3d5elNmzaV60dqtVq89NJLivVyMmJVXstew3rJtm3b5HkLFizItJ2R3bXh7OTkPomLi4vYu3evYr2tW7cqllmyZIk878mTJ4o2jv4rqyMjI+Xp4eHh4smTJ/K8JUuWKLapHZUoL9d6s6uTZ7VMXu6PGZ5LDRs2lNtpGRkZRiOx5mXEqsw++udfXtpzWikpKWLv3r1i6dKlYv78+WLOnDmKeyGlS5dWLG/4KjtTzDFiVWb19IKo5+Z0xCpHR0c5vSkpKYr6uZOTkzy614ULF0yeL4bxAyDWrVsnz7t69apwdHSU52lHD793757imkmPHj0UadQf2dre3l7cu3dP3p7+vky1JU0tl1VbPT/3dENDQxV/x/TP2y5dusjTDUe304+REJrfljYfCqItbG0csYqI6AU0fPhw+f8VKlRQzNOOGnPx4kXFU8JTp07F1KlTTW4vMTERly5dQsWKFaFWq/HOO+9g4cKFePbsWaZp0PbONqV///7yyE3m4uDggFGjRqFly5aK6b/++ivS09Pl76+++mqmT6KfOnUKT548gaurq9wrHQDatWuHgIAA+fugQYMwZcqUHKXrvffeg52dnWKadmQfrRYtWmS6/pEjR+T/N2nSRH7qpkqVKqhXrx7KlSuHypUro3nz5ihbtqy8bKNGjSBJEoQQ+PLLL3HixAlUqlQJFSpUQO3atdG8eXMEBQVlm37tiFRaAwYMkP9vb2+Pvn37ysvcv38fFy9eREREhNF2hg0bBkmSAAC+vr7w9/fHnTt3AOjOyYKifwxt27ZFYGCg/F2bt9onaI4ePYpRo0blOY8K0qFDhxTfJ02aBEdHR8U07dP6Wcnrb9/Qa6+9Ju/f0dERpUqVkp+6z22emut8JSIiIo3OnTtj6NChRtMTExMxYMCAbJ/kzqoun5N6XXJysjxKFAB07doVKpVK/t63b18sW7bMaNsXL15UjLjVt29f+Wl2QFMXnT17tvz96NGjqF69utF2evToIddTwsPDFfO6dOkib1P7JKfWgwcP4OHhkemx54W5jslUG+rJkyfyCKsAsHbtWqxdu9ZkOtLT03H8+HG0bdsWTZo0waJFiwBontLevn07KlasiAoVKqBevXpo0qSJIo2Wpl8Xz8jIQPny5TNdVr8ufu3aNfTp0yfb+nlW53dBGjFiBLy9vRXTzFU3z6u+ffvK/zf8reiPeKf/W8mqrt+zZ095lCbt9vV/6ydPnkTZsmUVbbRatWqhSpUq8vcqVaqgVq1a8ki9hm1SLR8fH4wYMSLTtGTn999/R//+/bMdQTuz88Xe3h5vvPGG/D2zay/Hjh1TXJN455134O7urlhW/2ly/fP/wYMH8PPzyzRtR44cQUxMTJbpJyIiyi9t3T8/xo0bB2dnZ6PpuS2PL1y4gOTkZHn6K6+8Il97lyQJffr0we7du3OVNnOUvSEhIejUqZP83VS9wNztDK333nsPPj4+ePr0KWJjY7Fv3z6kpqaiffv2+OGHH9CqVSsAxnUq7YhUgOYtID169JBHzzpz5ow84syZM2fk5bp3764YybN///6Ke1FHjx7Fyy+/bPVrvTm5P2Z4LvXp00duA9nZ2WHAgAHyqLcFJa/tOQCYP38+Jk2apDgGQ9Zq92RWT7dmPbdRo0Zye8fV1RUBAQGIj4+X52lHpTJ1jcAUR0dHedQxQNOWaty4sXzOnDx5EgBw/PhxZGRkyMvp31/TfteObp2RkYHjx4+jXbt2Rvsz1ZbMKXPc0+3Xr5/ib1j58uURFxcHQBkj/XtYERER8huCtOzs7OR8sHZbuCCwYxUR0QtI/4Ko/g0MQFOIAlBc3M+JhIQEVKxYEYsWLVIMT5sZ7ZD+ppij8HvvvfegUqnw7bff4vTp00hPT8eECRPw5MkTTJo0SV4uN8cphEBiYiJcXV3x8OFDeXpwcLBiOcPvWTF1rLlJk7bDD6B5FWCPHj1w7NgxJCYmysNtavXo0QPr16+HnZ0d6tati/nz52PixIlITk7G77//jt9//11e1t/fH5s3b0ZUVFSW+zdMq2GDx/B7ZhVNw4v0+uel9pzMSvHixRXf9YfozY7+MZhqsAUFBclx1qY/r3lUkAzTlNkwz7ndTna0v31D+c1TfeY6X4mIiIqinj17IjIyEkeOHJFfJ7Vu3TrEx8dj7969ihshgwcPztHrMbKqy+ekDqBflwag6NgOmK6TAeare+oP1a/fycNwnoOD8rJXbuswgObVI7GxsZnON9cxmaqPPXjwQH7ddU5o663dunXDuHHjsHjxYqSlpeHo0aOKGy1hYWHYsWMHKleunONt58aqVaswcODATOfntS7+8ssv4/Tp09muk9X5nROGMc/p9vLbNgQyr5vnVV5+K1n9TrL7rWv/NuSkjaaV2W+iTJkyRr/hnEpNTUWHDh3kmylZySx/g4KCFDeIc3rtJbt2nC22RYmIqOgpXry4/Dqvy5cvQwghtysCAwPl+wOTJk2SO+BkxVT9JS/lcV7bGVkxR9mbVRsJyFs7I6eGDBki7/+DDz5A8+bNERsbi7S0NMTExODPP/8EoDxOd3d3uLm5KbajHzshBB4+fAghhKLuaxhfNzc3uLu7y517tPU2a1/rzcn9McNzKT/3gDITFhYmv4bQlLy257Zt24axY8dmu3xWnWhyIq/tnszq6das5xq+zk+/7ZOXawR+fn5GDyPp/z5MtXsMlzH1PTfXA3LKHPd0c3ovSP94zdnuAczfFi4I7FhFRPQC0h/FJrMnSnx9fRXfBwwYoHhK1JC24NR//3dISAi2bt2K6tWrw8nJCRMmTMhRAW1Yac8LbYNh/PjxaNiwodyzf8aMGejbt6/cs9zwOEePHm3yncha2vc2e3t7IzExEQDkUXi0bt++neN0mjpWwzRNnTpV8aRHZkJDQ3H06FH89ddfOH78OC5fvoyzZ8/iu+++Q3p6OjZt2oS2bdti0KBBAIBRo0bh9ddfx7Fjx3Du3DlcvnwZu3btwuXLl3Hv3j0MGDAA169fz3Kfhmm9c+eO4kkC7egEWj4+Pia3YziyUm6fdAoJCUHFihXlDlU//fQT4uPjUaxYsWzX9fX1lfPQML2G07Tpz2seFSTDNF29elUxklpet5PT376h/OapIXOcr0REREVR27Zt5U4qQ4cOxZdffgkA+Pnnn/H111+jX79+AICUlBS54xUAtGzZEkuXLkVYWBjs7e1Rt25deYSYrOSkDqCtU2sZ1qdN1ckA03XPrL7ntO6pL68dMfLKXMdkql1h+MTq//3f/6FJkyaZpqVmzZry/+fMmYMPPvgAR44cwYULF3Dp0iVs374dt27dwvXr1zF8+HD88ssvmW6rIOnHzNnZGdOmTct0We25dvHiRUWnqt69e2P27NkICQmBJEkIDAzM88V5wxGIU1NT5f+r1WpcuXIlR9vJSdswr3XzvDL3byW737r2nNU/7py20Qzl57rCgQMHFDdxx44di3feeQf+/v548uRJjrad0/aQqXZcnTp1Mt2u/vLFihXDmDFjMl1Wf6QrIiIic2rZsqXcser+/fvYvn27PCKTr68vxo0bBwCYNWtWjjpWmSpb81IeG9Z/c9rOyIo5yl5zXyfNjzp16sgPfpw/fx4PHz6Et7e34jiTk5ORkpKiiLF+7CRJkmOtHXnKcBlA08bUHzFJv95mzWu9Obk/lt25lJt7QHmV1/ac/j06d3d3fPvtt2jSpAmcnZ2xZMmSfI3qqt/20W/3AJD/JmQns7q0Neu55m73JCYmIiMjQ9G5Sv/3YardY7iMqe8F0fYxxz3dvLR9rl69muU2rd0WLgjsWEVEVEhVqFABfn5+cueh1NRUuUGk7+7duzh8+LBckdEuDwC1a9dG3bp1AQBPnz7F999/b4GUK7m4uOCTTz5B8+bNAWh64U+fPh2rVq0CANSrVw/29vbycJuOjo4mj/PatWu4ePEiPD09AWiO7aeffgKg6cTz4MEDuVKj3XZeNWzYUPHd398fw4YNM1ru3Llzih7qp0+fRtWqVVG2bFnFa/86deqE7du3A9AMnzxo0CDcunUL9vb2CAoKQosWLeRX2cXFxckV8Bs3biAxMTHLIVcN07pmzRp8/PHHADRDk3799dfyPF9fX6Ohdc1p5MiRcpyePn2K7t27Y/v27UYVsAcPHmDNmjUYNWqUfAzbtm0DAOzatQt3796Vn2bauXOn4gaL9njzmkcFqXHjxorv06ZNw9atWxUV/+vXryMsLCzL7eT1t58f+hVvUxdbzHW+EhERFXWzZs3Chg0bkJSUBEDTObx3796wt7dHUlKSYgj69u3by68RvnjxouIVD/nl4eGBChUqyK8D3LJlC6ZMmSI/1ahfh9RXoUIF+Pr6yk8ufv3113jjjTfki5Vr1qxRLG9YZ7NFBXlMbm5uqF69uvyQSWJiIkaOHGl00TMpKQk7d+6UR6C6evUqfHx84O3tjXbt2smvGnjppZfQpUsXAFA8UW64vZzcPMsP/Rg8ffoUlStXNvk6hF9//VU+p/TbqYBmVC7tqLexsbFZdqpycHCQX9Vm6tgMb3gcO3YM0dHRAIBly5bl62lqa9TNC9LGjRvxzjvvyOeM4W+9Vq1aADR5fPz4cQCa12ScO3dOPj//+OMP+dUZ2mVzK7v2h+H50qdPH/j7+wOA/CoOc6lfv77iHPv444/RoUMHuLq6ysvcunULAQEBcHR0RMOGDeU0JCQk4KWXXkK1atUU2xRCYN++fUavKiEiIjKXN998E8uWLZPbD0OHDkVYWJjJV1bnVV7K44oVKypGSFq/fj1ef/112NnZQQiBdevW5Todli57TdWt9esF+WX4sIw2Dw3rVGvXrpWvd6empipiHhkZKacpMjJSbm9s3rwZU6ZMkR9ANnxtnXYfebnWm12d3NwMz6WNGzfijTfekDuSGbbTCkJe23P6v53SpUujdevWADQPfXzzzTeZ7i+7OjKgbPtcvHhR7piXlJSEzz77LFfHZ6gw1XOfP3+OjRs3yq+6u3btmuI1eNp2T926dRX3J9esWSO3JbXftbQPvOVGTtrqlryn27hxY7mdd/78eWzYsAG9evWS5wsh8M8//6BkyZKFri0MsGMVEZHFTJkyBZ9++qnR9JCQELnTjDnZ2dlhzJgxeP/99wFoGit///03WrduDQ8PD9y+fRu//fYbfv31VzRu3BidO3cGoLnwq+2Z/sMPP+CNN95AcHAwvvnmm1y9ns2coqKi0LBhQxw5cgSA5uLt5MmTERYWBl9fX7z66qtYtmwZAGD27Nn47bff0LBhQzg7O+Pff//FsWPHEBcXhwEDBqBNmzYANCNiaTtWPXz4EPXq1UOPHj1w48aNTG8E5VRkZCRat26NPXv2ANA0VHfu3IlatWrBzs4O169fx5EjR3D+/HlMmjRJ7lDTs2dPJCUloXnz5ihevDh8fX1x5coVxSsBtRXfAwcOoE+fPmjcuDEiIiIQEhKCjIwMfPvtt/KyTk5O2TbaIiMj0bJlS+zbt0+O399//43KlStj9+7dileGjBw50uhpbnMaMmQItm/fjp07dwLQvJO7TJky6NKlC8qUKYOnT5/izJkz2L17NwIDA+WOVaNHj8Z3330HIQQeP36MOnXqoHfv3khOTsbKlSvl7fv6+srvuM5rHhWkqlWrIjo6Ws7vH374AZGRkYiOjoazszPOnTuHAwcOKN5LbUpef/v5Ubx4cfz1118AgNWrV8PFxQUeHh4oU6YMOnfubLbzlYiIqKjz9vbGiBEjMGPGDADAX3/9JV/sCwwMhLe3tzwk/fTp03H37l2kp6dj5cqV+X49mqHBgwdjwoQJcjoaNGiAjh074o8//lCU8frs7OwwevRoTJw4EQBw9OhRNG7cGC+99BIuXLiguNjfvHlzREZGmjXNBaGgj2n8+PHo06cPAE39uFq1aujYsSN8fHyQmJiIuLg4HDp0CMWKFZMvaG7cuBGTJk1CVFQUypUrh2LFiiElJQXr16+Xt6t/QV3b4eP58+cAgPfffx+nT5+Go6MjoqKiULt27TzFJjPt27dHREQEzp8/D0Dzir8uXbqgUqVK8ghRBw4cwPXr17Fq1SpUr14dZcuWhZ2dnfwagpEjR+LUqVNITEzM9sGY4sWLy0/Lz5s3D4mJiXBxcUGNGjXQsmVLeHp6onz58rh06RIA4KOPPkJcXBxSU1Px888/5+tYrVE3L0jnzp1DgwYN0L59e6PfelRUlPyA0IgRI/D5558jLS0NarUazZo1w4ABAyBJEtasWSPno5OTU56euNd/lfzJkycxcuRIhIaGwsnJCTExMUYPBPXt2xc9e/bEtWvX8NVXX+Xl0DPl4+OD119/HUuWLAGg6bRYqVIlvPzyy/D29salS5ewdetWxMfHw9vbGwMHDsT06dNx7949pKeno1GjRujevTvKli2LtLQ0XLx4EbGxsbhz5w7279+f51fEExERZaVy5cqYNm0a3nvvPQCa0Xtq166Ndu3aoVatWnB0dMTVq1fx6NGjPO8jL+Wxg4MD+vfvL5erBw4cQIsWLdCsWTMcPnxYvoadG5Yue/XrKYBmpNWGDRvCzs4O/fr1y/XrDJctWwYfHx88ffoUBw4cULymXNtxAdDUsfUffnnrrbdw4sQJFC9eHNu2bVOMHjV69Gj5/2PHjpVHQb527Rrq1KmDzp0749atW4pOIeXLl0f79u0B5O3eRHZ1cnNzcHDAwIED5XtysbGxaNGiBZo2bWoUx4KUl/ZchQoV5PsWZ86cwSuvvIKIiAjs3LkTx44dy3Rf+udeQkICBg0ahEqVKkGSJIwYMQIuLi6KkVUfPXqEGjVqoG7dujh8+DD+/ffffB1rYavnvvrqqzh48CC8vb3x9ddfy+1lAHjttdcAaF4ZOHDgQKxYsQKApr338OFDNGjQAMeOHZPvQQJA//79c/1QeU7a6pa8pxsTE4PPP/9cHu2sd+/e2LhxI6pXr44HDx4gNjYWUVFRWLBgQaFrCwMABBERFYj9+/cLANl+wsLC5HX0p69atUqevmrVKsU8Q5mtl5GRIfr165dtGpo1ayavc/DgQeHg4GC0jLu7u+jSpYvJdGeVhpyaNGmSYhtXr15VzP/hhx8U84cNGybPS0lJEa1atcr2OAcMGKDYZq9evUwu165dO8X3NWvWyOtklxdad+7cEdWrV882TZMmTZLXqVChQpbL+vr6imvXrgkhhFi/fn222x4zZkyO0h0fHy8qVaqU5ba6du0qnj9/Lq9jeH4b5ldYWJjJY8xOSkqKGDx4cK5+N0II8cknnwg7O7tMl/fy8hL79+/Pdx7l5NiyO0eymn/v3j1Rp06dLI9D6+rVq4p5+seXl99+VtsTQohmzZpl+ltauHChye23b99eCJH785WIiKgoM6xnGdat7969K1xdXeX5lStXFmq1WgghxKxZs0yWs1WqVBG1atUyWZbntV737Nkz0bBhQ5P7i4qKyrRekZ6eLrp3755lvSAiIkL8+++/inRkFhPDOoz+vOyOLTP6x6xfX8qMuY/J0Lvvvpur+vHMmTOzXX7RokWKfXTu3NnkcnPmzMn2+LM7Z025ePGiCA8Pzzad+tsaOnSoyWVatmwpihcvbvI8FUKI0aNHm1xvxIgR8jLLly83uUzp0qVFxYoVTf52sqs/a+Wlbp7XeGd1zhu2Q/Tn6bfHs2rrt2vXTkiSZJR2X19fcf78ecV6mzZtEs7Ozpker0qlEuvXr1esM2DAgBzFIy4uzmT7z83NTV6mbdu2Jvervw/D+GUVh6zyOzU1VURHR2eZvw8ePJCXP3z4sPD398/2nMjsnCIiIjKXhQsXCpVKlW2ZBCiv3eW0np2X8vj+/fuifPnyJtczbGfo7zerekReyt6stpfV8T99+lQUK1bM5PZPnDiRbZ4Y3ifJqi61e/duxbp//vmnKFGiRJbrxcTEGO1zzJgxWa4TEhIi/vjjD3n5vFzrzUmdPLNzIq/3xx48eKCoy+t/DO8BXb9+Pdu8EUJ5jdywvpiZ3LbnLl++LDw8PIyWcXBwEH369Mk0FvHx8YprBfqfhIQEIYSm3lquXDmTyxjWZ3P6+9Jn7npuVvHO6n6FfpvecF5m54v+by8oKEhxDUX/M3z4cMX2kpOTRdOmTbM83kaNGonHjx/L6+S0LSlE9m31grinm1V+79ixw+T5qf2MHDlSXtbcbWFrK7hhJ4iIyOrs7Oywdu1a7NixA127dkWJEiXg5OQElUqFsLAwdOzYEQsWLFA8udy4cWP89NNPaNiwIVQqFby8vBAdHY0jR46gatWqVjuW9u3bK57uXrlypfyOdldXV/z000/43//+h+joaAQFBcHBwQEuLi4oU6YMunXrhqVLl2L+/PmKbX711VeYNWsWypQpA0dHR4SHh2PixIn4/PPPFcsZvhoiJwIDA/Hrr7/i888/R4sWLeDv7w97e3u4ubmhYsWK6Nu3L9atW4fx48fL68ycORNDhw5FrVq1EBwcDEdHR7i6uqJixYoYPnw4Tp48Kb8GrnHjxvjoo4/Qvn17lClTBh4eHnBwcEBAQABatmyJ1atXY968eTlKa3BwME6cOIF58+ahQYMG8PLykrfVtm1bbNiwAd98802e3kWdW66urli+fDni4uLw5ptvIjIyEt7e3rC3t4eXlxfq1KmDSZMmYdeuXYr1Ro0ahV9//RX9+vVDWFgYnJyc4OLigoiICIwePRpnz55FVFSUYp285FFB8/Pzw+HDh7F8+XK0atUKAQEBcHBwgI+PD2rVqiWP0pWdvPz282PEiBGYPHkySpcubfI8Mef5SkREVNQFBATIT0cCmhFktm7dCgB4++238dlnn6F8+fJwdHREcHAwhgwZgl9++QXu7u5mTYejoyN2796N8ePHo3jx4nByckKFChUwb948LF++PNP17O3tsWnTJmzevBnR0dEIDAyEg4MDvLy8UK9ePcyZMwcnTpxASEiIWdNbkAr6mGbMmIHDhw+jb9++KFWqFFQqFRwdHVG8eHG89NJLmDFjhuLp/ZdffhkffvghWrVqhfDwcLi6usLBwQHFihVD+/btsX37drz11luKfSxbtgwDBgxAUFBQgY5Sq1W+fHmcOXMGs2fPRsOGDeHj4wN7e3t4eHigWrVqeO2117B161b51QsAsHjxYkydOhVhYWFwdHREyZIlMX78eHz//fdZtlU++ugjjBw5EiVKlJBf0Who8ODBWLZsGSIiIuDk5ITg4GAMGzYMx48fz/WoAoYsXTcvSD169MDu3bvRpEkTuLm5wcvLC126dMHRo0dRsWJFxbLdu3fHqVOnMHToUJQtWxbOzs5wdnZGmTJlMGTIEMTFxSleG5Eb1atXx/r161GzZk04OzubXGbLli0YNWoUihUrBicnJ5QtWxYzZsyQnyY3J2dnZ/zwww/YtGkTOnToILfnPT09UbVqVYwcOVIxYkPDhg1x7tw5TJw4EbVq1YKnpyfs7e3h7e2NWrVq4c0338SePXvQtGlTs6eViIhIX0xMDK5evYrJkyejcePG8rVIFxcXlCxZEq1bt8bkyZPx+++/5+naXV7KYx8fHxw6dAhDhgxBQEAAVCoVIiMjsWrVKkyaNClPx2nJslelUuHHH3/ESy+9BE9Pz3xvT0uSJLi5uSEiIgJvvPEG4uLi5FfEaUVEROD06dOYPHkyatasCXd3d7kd0LlzZ/z0009YuHCh0bbnzZuHPXv2oGvXrggJCYGjoyPc3d1RvXp1TJw4EWfOnJFfUwfk7VpvTurk5ubt7Y2DBw/ijTfeQGBgoHwurV27Fv379zdatqDktj1XtmxZHDhwAC+99BJcXV3h7u6OZs2aYd++fWjVqlWm+wkODsb333+PRo0awc3NzeQyzs7O2LdvH3r06AFvb284OzujXr162Lp1q1nugxSWeq6zszP279+P0aNHy+23ChUqYOHChUZvJnJzc8O+ffuwfPlyNG/eHL6+vvI9nWbNmuHLL79EbGxsnq/JZNdWt/Q93ejoaJw7dw7jx49HtWrV4O7uDkdHR4SEhKB9+/aKVyEWprYwAEhCCGHtRBAREVlDamqq/L5wfZ9++qniRsO///77Qt3YISIiIiIiIjIHSZLk/69atQoDBw60XmKIiIiIiHIhs3tA3bp1w5YtWwAA5cqVk18LTkXX5MmTMWXKFABAWFgYrl27Zt0Ekc0p+KEniIiIbFS/fv2QlpaGl156CWFhYUhJScHBgwcVT8xon9IgIiIiIiIiIiIiIiKiF0OFChXQpk0b1K1bFyEhIbh79y6++eYb/Pjjj/IyMTExVkwhEb0o2LGKiIiKrPT0dPzwww/44YcfTM6vW7culi1bZuFUERERERERERERERERUX48evQIy5cvz/R19UOGDMGIESMsnCoiehGxYxURERVZAwYMgCRJ+P3333Hv3j08f/4cfn5+qF69Onr06IF+/frBwYFFJRERERERERERERER0Yvk3Xffxa5du3DhwgXcv38fdnZ2KFasGOrXr4/BgwejZcuW1k4iEb0gJCGEsHYiiIiIiIiIiIiIiIiIiIiIiIiIbImdtRNARERERERERERERERERERERERka9ixioiIiIiIiIiIiIiIiIiIiIiIyICDtRNARC82tVqNW7duwcPDA5IkWTs5REREhYYQAo8fP0ZISAjs7Pg8BBFRfrDdQkREVDDYbiEiMh+2W4iIiApGftst7FhFRPly69YthIaGWjsZREREhdY///yDEiVKWDsZREQvNLZbiIiIChbbLURE+cd2CxERUcHKa7uFHauIKF88PDwAaP4IeXp6Wjk12VOr1Xx6zkIYa8tivC2Hsbac27dvY9WqVRg0aBCCg4OtnRyLe/ToEUJDQ+WyloiI8u5Fa7cUBaxT2Rbmh21hftgW5kfW2G4hIjIftluUWAbrMBY6jIUG46DDWOgwFjqGschvu4Udq4goX7TD0Xp6etp8RV+tVuPu3bsIDAxkoVLAGGvLYrwth7G2rMePH8PZ2Rlubm42X8YUJA79TkSUf9q/pe7u7kW6TLEVrFPZFuaHbWF+2BbmR86x3UJElH9st+iwDNZhLHQYCw3GQYex0GEsdLKKRV7bLUU7okRERERERERERERERERERERERCawYxUREREREREREREREREREREREZEBdqwiIiIiIiIiIiIiIiIiIiIiIiIyIAkhhLUTQUQvrkePHsHLywtJSUkvxDu/1Wp1kX+vrKUw1pbFeFsOY205arUaaWlpUKlURTLmL1oZS0Rky/g31fawTmVbmB+2hflhW5gfWWMZS0RkPvybqsQyWIex0GEsNBgHHcZCh7HQMYxFfstYRpWIigwhBDIyMsD+pAWPsbYsxttyGGvLkiQJDg4OkCTJ2kkhIqJCgmW4bWCdyrYwP2wL88O2MD+IiMgaWO6wDNbHWOgwFhqMgw5jocNY6BRELNixioiKDCEEEhMTWaBYAGNtWYy35TDWlnXv3j2sXbsW9+7ds3ZSiIiokGAZbhtYp7ItzA/bwvywLcwPIiKyBpY7LIP1MRY6jIUG46DDWOgwFjoFEQt2rCIiIiIim/Ts2TPcvHkTz549s3ZSiIiIiIiIiIiIiIiIqAhixyoiIiIiIiIiIiIiIiIiIiIiIiID7FhFREWKJEnWTkKRwVhbFuNtOYw1ERERUf6xTmVbmB+2hflhW5gfRERE1sEyWIex0GEsNBgHHcZCh7HQMXcsHMy6NSIiG2ZnZ4egoCBrJ6NIYKwti/G2HMbasuzs7BT/EhER5RfLFNvAOpVtYX7YFuaHbWF+EBGRNbDdwjJYH2Ohw1hoMA46jIUOY6FTELFgyUxERYYQAmlpaRBCWDsphR5jbVmMt+Uw1pbl4eGB1q1bw8PDw9pJISKiQoJluG1gncq2MD9sC/PDtjA/iIjIGljusAzWx1joMBYajIMOY6HDWOgURCzYsYqIigwhBB48eMACxQIYa8tivC2HsbYsV1dXlC5dGq6urtZOChERFRIsw20D61S2hflhW5gftoX5QURE1sByh2WwPsZCh7HQYBx0GAsdxkKnIGLBjlVEREREZJNSU1Nx6dIlpKamWjspREREREREREREREREVASxYxURERER2aSHDx9i//79ePjwobWTQkREREREREREREREREUQO1YRUZHi4OBg7SQUGYy1ZTHelsNYExEREeUf61S2hflhW5gftoX5QUREZB0sg3UYCx3GQoNx0GEsdBgLHXPHgpEloiLDzs4O/v7+1k5GkcBYWxbjbTmMtWXZ2dkp/iUiIsovlim2gXUq28L8sC3MD9vC/CAiImtgu4VlsD7GQoex0GAcdBgLHcZCpyBiwZKZiIoMIQSePHkCIYS1k1LoMdaWxXhbDmNtWdo4M95ERGQuLFNsA+tUtoX5YVuYH7aF+UFERNbAcodlsD7GQoex0GAcdBgLHcZCpyBiwY5VRFRkCCHw6NEjFigWwFhbFuNtOYy1ZTk4OCAwMJDD1xIRkdmwDLcNrFPZFuaHbWF+2BbmBxERWQPLHZbB+hgLHcZCg3HQYSx0GAudgogFO1YRERERkU3y9/dH586dOXwtERERERERERERERERWQU7VhERERERERERERERERERERERERlgxyoiKjIkSYKTkxMkSbJ2Ugo9xtqyGG/LYawt6/bt2/jyyy9x+/ZtayeFiIgKCZbhtoF1KtvC/LAtzA/bwvwgIiJrYLnDMlgfY6HDWGgwDjqMhQ5joVMQsXAw25aIiGycJEnw9fW1djKKBMbashhvy2GsLUtb6WVDgIiIzIVlim1gncq2MD9sC/PDtjA/iIjIGthuYRmsj7HQYSw0GAcdxkKHsdApiFhwxCoiKjKEEHj8+DGEENZOSqHHWFsW4205jLVlaePMeBMRkbmwTLENrFPZFuaHbWF+2BbmBxERWQPLHZbB+hgLHcZCg3HQYSx0GAudgogFO1YRUZEhhEBKSgoLFAtgrC2L8bYcxtqy2LGKiIjMjWWKbWCdyrYwP2wL88O2MD+IiMgaWO6wDNbHWOgwFhqMgw5jocNY6BRELPgqQCKybUIAaU/Nsy21GniWBjxNBezYr7RAMdaWxXhbDmNtWWwAEBGRmQkBpGZYOxWkVgNpak1e2LG4tzrmh21hftiWwpIfznYA3ypFRPQCEQLISLV2KqxLrQbUaZo4iCJ+HZax0GEsNBgHHcZCpzDEws7ZZhsu7FhFRLYt7SkQu9c82xICUEMzVp+N/lEuNBhry2K8LYextqiAhk3Rq1cvBAQEWDspRERUSDxVA0duWzsVJAQgkgBJzSqVLWB+2Bbmh20pLPnRIhhwsbd2KoiIKMfUT4E7R6ydCusSAkgSgJBe7ELYHBgLHcZCg3HQYSx0CkMsgloA9i7WToVJL2hXNSKi3JMAuEiaf6lgMdaWxXhbDmNtWY6OjggODoajo6O1k0JERIWE9KJeWCp0JEhOLmCtylYwP2wL88O2MD+IiMjy2G757zqsk8QSGIyFPsZCg3HQYSx0GAsdSZLg4uJi1vKUHauIqMiQJAledhIbJRbAWFsW4205jLVlPUxKwr59+/Dw4UNrJ4WIiAoJluG2QZIkSK5ezA8bwfywLcwP28L8ICIia2C58991WFdehwUYC32MhQbjoMNY6DAWOpIkwcvLvO04dqwioiJDCIEktYAQwtpJKfQYa8tivC2Hsbas1NSnOHv2LFJTU62dFCIiKiRYhtsGIQTEkyTmh41gftgW5odtYX4QEZE1sNz57zrsE16HBRgLfYyFBuOgw1joMBY6QggkJZm3HceOVURUZAgAqULzLxUsxtqyGG/LYawtS/wXaTYEiIjIXFim2AoB8SwVrFXZCuaHbWF+2BbmBxERWR7bLf9dh30mWAKDsdDHWGgwDjqMhQ5joSOEQGpqKjtWERERERERERERERERERERERERFSR2rCIiIiIiIiIiIiIiIiIiIiIiIjLAjlVEVGRIANwkzb9UsBhry2K8LYextiwPd3fUr18fHh4e1k4KEREVEpLEUtw2SJCc3cBala1gftgW5odtYX4QEZHlsd3y33VYlcQSGIyFPsZCg3HQYSx0GAsdSZLg5uZm1vLUwWxbIiKycZIkwYOliUUw1pbFeFsOY21Znh4eaNOmjbWTQUREhQhvUNgGSZIAZ3acthXMD9vC/LAtzA8iIrIGtlv+uw7rYu1U2AbGQoex0GAcdBgLHcZCR5Iksz+wzxGriKjIEELgvlpACGHtpBR6jLVlMd6Ww1hb1tOnTxEXF4enT59aOylERFRIsAy3DUIIiOT7zA8bwfywLcwP28L8ICIia2C589912GRehwUYC32MhQbjoMNY6DAWOkII3L9v3nYcO1YRUZEhADwTmn+pYDHWlsV4Ww5jbVmJDx5g+/btSExMtHZSiIiokODFJVshINKfgbUqW8H8sC3MD9vC/CAiIstju+W/67DpgiUwGAt9jIUG46DDWOgwFjpCCDx79owdq4iIiIiIiIiIiIiIiIiIiIiIiAoSO1YREREREREREREREREREREREREZYMcqIioyJACedpp/qWAx1pbFeFsOY21Z0n+RliRGnIiIzINliq2QILl4grUqW8H8sC3MD9vC/CAiIstju+W/67AuEktgMBb6GAsNxkGHsdBhLHQkSYKnp6dZy1N2rKJckyQJ27Zts3YybN7SpUsRGhoKOzs7LFiwAJMnT0b16tWzXGfgwIF4+eWXLZK+okiSJLhKEhslFsBYWxbjbTmMtWU5ODjAx8cHDg4O1k4KEZnR6tWr4e3tbe1k5Iq50pyTNkFuxcbGQpIkPHz40KzbLaxYhtsGSZIgqVyZHzaC+WFbmB+2hflBVHSx3VI939vRx3ZL7rDc+e86rIrXYQHGQh9jocE46DAWOoyFjiRJcHU1bzuOHavIyO3bt/HWW2+hdOnSUKlUCA0NRceOHbFv3z6z78vSlelnz55h9uzZiIyMhKurK/z9/dGoUSOsWrUKz58/N9t+Hj16hDfffBNvv/02/v33X7z++usYN25cgcSQck4tBO6pBdRCWDsphR5jbVmMt+Uw1pbl7+eL3r17w9/f39pJISrSBg4cqLmpaPBp27ZttuuGh4djwYIFimk9e/bEpUuXCii1Ota6EbJ69WqT8Vq+fLlNtQkqVqwIlUqF27dvWzspuZLffFWr1eZLDOWZEGqoH92DEMwPW8D8sC3MD9vC/CB6cbDdkvv9st1ScNhuyT+1ELj3SM3rsGAs9DEWGoyDDmOhw1joqNVq3Lt3z6zlKR//J4Vr166hUaNG8Pb2xpw5c1C1alU8f/4cP/30E0aMGIELFy5YO4kmCSGQkZGR5YgWz549Q5s2bXD69GlMmzYNjRo1gqenJ44dO4a5c+eiRo0aZnsK48aNG3j+/Dnat2+PYsWKydPd3d3Nsn3Ku3QBjt5uIYy1ZTHelsNYW1Z6erq1k0BEANq2bYtVq1YppqlUqjxty8XFBS4uLuZIls3y9PTExYsXFdO8vLzg4uJiE22CQ4cOITU1Fd26dcOaNWvw9ttvWztJVBSpWcbbFOaHbWF+2BbmB9ELg+2W3GG7hWxdOvuXyRgLHcZCg3HQYSx0GAsdc99b4ohVpDB8+HBIkoTjx4+ja9euKF++PCpXrowxY8bg2LFjRsubGnHq1KlTkCQJ165dAwBcv34dHTt2hI+PD9zc3FC5cmX8+OOPuHbtGpo3bw4A8PHxgSRJGDhwIABNL8KZM2eiVKlScHFxQWRkJL755huj/e7cuRO1atWCSqXCoUOHsjy2BQsW4MCBA9i3bx9GjBiB6tWro3Tp0ujduzd+/fVXlCtXDgCQlpaGmJgYBAYGwtnZGY0bN8aJEyeM9r1v3z7Url0brq6uaNiwodwAWb16NapWrQoAKF26tBwLw+FzMzIyMGbMGHh7e8PPzw8TJkyAMOhBmtM4ZJYWre+//x516tSBs7Mz/P390blzZ3leWloaxo0bh+LFi8PNzQ316tVDbGxslrEkIiKyhDt3E7BmzRrcuXPH2kkhKvJUKhWCg4MVHx8fHwghMHnyZJQsWRIqlQohISGIiYkBAERFReH69esYPXq0/PQzYPzkrraevHLlSpQsWRLu7u4YPnw4MjIyMHv2bAQHByMwMBAfffSRIk3z589H1apV4ebmhtDQUAwfPhzJyckANPXkQYMGISkpSd735MmTAeSs/rt69WqULFkSrq6u6Ny5MxITE3MVL0mSjOLl4uJi1CbQvgp87ty5KFasGPz8/DBixAjFaLpfffUVateuDQ8PDwQHB6N37964e/durtJjaMWKFejduzf69euHlStXGs0PDw/H9OnT0b9/f7i7uyMsLAzbt29HQkICOnXqBHd3d1SrVg2//fabYr0tW7agcuXKUKlUCA8Px7x584ziYvhaeW9vb6xevRqA5kEfSZLw7bffonnz5nB1dUVkZCSOHj0KIOt8JSIiIiJiu4XtFrZbiIiIqCCwYxXJ7t+/j127dmHEiBFwc3Mzmp/XYUtHjBiBtLQ0HDhwAGfPnsXHH38Md3d3hIaGYsuWLQCAixcvIj4+HgsXLgQAzJw5E2vXrsUXX3yBc+fOYfTo0ejbty9++eUXxbbfeecdzJo1C+fPn0e1atWyTMe6devQqlUr1KhRw2ieo6OjfMwTJkzAli1bsGbNGvz+++8oW7Ys2rRpg/v37yvWef/99zFv3jz89ttvcHBwwKuvvgpAM0Tw3r17AQDHjx9HfHw8QkNDjfY5b948rF69GitXrsShQ4dw//59bN26VbFMTuOQWVoAYMeOHejcuTOio6MRFxeHffv2oW7duvL8N998E0ePHsWGDRtw5swZdO/eHW3btsXly5dNxjEtLQ2PHj1SfIiIiAqCWqjx9OlTDn9OZMO2bNmCTz75BF9++SUuX76Mbdu2yQ8ZfPvttyhRogSmTp2K+Ph4xMfHZ7qdK1euYOfOndi1axfWr1+PFStWoH379rh58yZ++eUXfPzxx/jggw/w66+/yuvY2dlh0aJFOHfuHNasWYOff/4ZEyZMAAA0bNgQCxYsgKenp7zvcePGAci+/vvrr79i8ODBePPNN3Hq1Ck0b94c06dPL6gQYv/+/bhy5Qr279+PNWvWYPXq1fIFewB4/vw5pk2bhtOnT2Pbtm24du2a/EBKXjx+/BibN29G37590bp1ayQlJeHgwYNGy33yySdo1KgR4uLi0L59e/Tr1w/9+/dH37598fvvv6NMmTLo37+//HDIyZMn0aNHD/Tq1Qtnz57F5MmTMXHiRMWx5NT777+PcePG4dSpUyhfvjxeeeUVpKenZ5mvhthuISIiIiIttlvyj+0WY2y3EBERFR18FSDJ/vrrLwghULFiRbNu98aNG+jatatiFCctX19fAEBgYKDccSstLQ0zZszA3r170aBBA3mdQ4cO4csvv0SzZs3k9adOnYrWrVvnKB2XL19GVFRUlsukpKTg888/x+rVq9GuXTsAwLJly7Bnzx6sWLEC48ePl5f96KOP5LS88847aN++PZ4+fQoXFxf4+fkBAAICAhAcHGxyXwsWLMC7776LLl26AAC++OIL/PTTT/L83MQhs7Q4Ozvjo48+Qq9evTBlyhR5+cjISACavFm1ahVu3LiBkJAQAMC4ceOwa9curFq1CjNmzDBK98yZMxXbepFIAHzs+AYvS2CsLYvxthzG2rKk/yKtfVqUiKznhx9+MHoVxHvvvQdnZ2cEBwejVatWcHR0RMmSJeVO/L6+vrC3t5efWM6KWq3GypUr4eHhgUqVKqF58+a4ePEifvzxR9jZ2aFChQr4+OOPsX//ftSrVw8AMGrUKHl97ZPKQ4cOxZIlS+Dk5AQvLy/5CWytnNR/Fy5ciLZt28o3O8qXL48jR45g165dOY5XUlKSIl7u7u64ffu2yWV9fHzw6aefwt7eHhUrVkT79u2xb98+DBkyBAAUD02ULl0aixYtQp06dZCcnJyn13Ns2LAB5cqVQ+XKlQEAvXr1wooVK9CkSRPFctHR0XjjjTcAAB9++CE+//xz1KlTB927dwcAvP3222jQoAHu3LmD4OBgzJ8/Hy1btsTEiRMBaOL2559/Ys6cObm+oTJu3Di0b98eADBlyhRUrlwZf/31FypWrGgyX03JrN3CMsVWSJDcfMBala1gftgW5odtYX4QvUjYbmG7he2WwkMC4OMmsQQGY6GPsdBgHHQYCx3GQkeSJPmNaebCEatIZvgaOnOJiYnB9OnT0ahRI0yaNAlnzpzJcvm//voLT548QevWreHu7i5/1q5diytXriiWrV27do7TkZPju3LlCp4/f45GjRrJ0xwdHVG3bl2cP39esaz+CFnFihUDgBwPbZuUlIT4+Hi5cQUADg4OiuPJTRyySsupU6fQsmVLk+k4e/YsMjIyUL58ecU+fvnlF6N9aL377rtISkqSP//880+OjtkWSJIEld5wzlRwGGvLYrwth7G2LG2YGW8i62vevDlOnTql+AwdOhTdu3dHamoqSpcujSFDhmDr1q15en99eHg4PDw85O9BQUGoVKkS7OzsFNP069t79+5Fy5YtUbx4cXh4eKBfv35ITEzEkydPMt1PTuq/58+fV9TTAcgPOuSUh4eHIlZHjhzJdNnKlSvD3t5e/l6sWDHFcZ48eRIdO3ZEyZIl4eHhIT9QcePGjVylSWvlypXo27ev/L1v377YvHkzHj9+rFhOv40RFBQEAPLDMvrTtGk9f/68oh0FAI0aNcLly5eRkZGRqzTmp62llVm7hWWKbZAkCZKjivlhI5gftoX5YVuYH0QvFrZb2G4B2G4pLCRJgsqR12EBxkIfY6HBOOgwFjqMhY4kSVCpzNuO44hVJCtXrhwkScKFCxdyvI62waDfaUn/vdoA8Nprr6FNmzbYsWMHdu/ejZkzZ2LevHl46623TG5T+37xHTt2oHjx4op5KpVK8d3UKwszU758+VwdW3YcHR3l/2t/lOZ8VVFu4pBVWlxcXLLch729PU6ePKloFAHI9CkSlUpltP8XhVoIJKiBADvAjoVKgWKsLYvxthzG2rLU/9Uv+CpAIutzc3ND2bJljab7+vri4sWL2Lt3L/bs2YPhw4djzpw5+OWXXxR11OwYLitJkslp2r8H165dQ4cOHTBs2DB89NFH8PX1xaFDhzB48GA8e/YMrq6uJveTl/pvXtjZ2ZmMlylZHWdKSgratGmDNm3aYN26dQgICMCNGzfQpk0bPHv2LNfp+vPPP3Hs2DEcP34cb7/9tjw9IyMDGzZskJ82N0yXto2R3zaQJElGD7wYth/NsR8g83YLyxTbIIQa4lECJM8ASBKf+bM25odtYX7YFuYH0YuF7ZbcYbvFNLZbbINaCCQkCQR4SUX+OixjocNYaDAOOoyFDmOho1arkZCQgICAAEUH+Pxga5Bkvr6+aNOmDT777DOkpKQYzX/48KHRtICAAABQvHf81KlTRsuFhoZi6NCh+PbbbzF27FgsW7YMAODk5AQAiicBKlWqBJVKhRs3bqBs2bKKT2hoaJ6Pr3fv3ti7dy/i4uKM5j1//hwpKSkoU6YMnJyccPjwYcW8EydOoFKlSnnetyEvLy8UK1ZM8a719PR0nDx5Uv5urjhUq1YN+/btMzmvRo0ayMjIwN27d432kd0QtS+qghmXjUxhrC2L8bYcxtpy/Hx80KlTJ/kVu0Rkm1xcXNCxY0csWrQIsbGxOHr0KM6ePQtAU9/P7VO/OXHy5Emo1WrMmzcP9evXR/ny5XHr1i3FMqb2nZP6b0REhKKeDgDHjh0z+zHkxIULF5CYmIhZs2ahSZMmqFixYq6fgNa3YsUKNG3aFKdPn1Y8mT5mzBisWLEiX2mNiIhQtKMA4PDhwyhfvrx8MyggIEDRdrx8+XKWT+qbUlDnFFlBAY2aTXnE/LAtzA/bwvwgKhTYbik4bLcYY7vFPFgC6zAWOoyFBuOgw1joMBY65n5bG0esIoXPPvsMjRo1Qt26dTF16lRUq1YN6enp2LNnDz7//HOj1+FpO/lMnjwZH330ES5duoR58+Yplhk1ahTatWuH8uXL48GDB9i/fz8iIiIAAGFhYZAkCT/88AOio6Ph4uICDw8PjBs3DqNHj4ZarUbjxo2RlJSEw4cPw9PTEwMGDMjTsY0aNQo7duxAy5YtMW3aNDRu3BgeHh747bff8PHHH2PFihWoXr06hg0bhvHjx8PX1xclS5bE7Nmz8eTJEwwePDhvQc3EyJEjMWvWLJQrVw4VK1bE/PnzFZ3XzBWHSZMmoWXLlihTpgx69eqF9PR0/Pjjj3j77bdRvnx59OnTB/3798e8efNQo0YNJCQkYN++fahWrZr8fnAiIiJrcHJyQnBwsNwRm4isJy0tDbdv31ZMc3BwwA8//ICMjAzUq1cPrq6u+Prrr+Hi4oKwsDAAmldlHDhwAL169YJKpYK/v79Z0lO2bFk8f/4cixcvRseOHXH48GF88cUXimXCw8ORnJyMffv2ITIyEq6urjmq/8bExKBRo0aYO3cuOnXqhJ9++gm7du0yS7pzq2TJknBycsLixYsxdOhQ/PHHH5g2bVqetvX8+XN89dVXmDp1KqpUqaKY99prr2H+/Pk4d+4cKleunKftjx07FnXq1MG0adPQs2dPHD16FJ9++imWLFkiL9OiRQt8+umnaNCgATIyMvD222/naoQAwHS+ZvakPxEREREVLWy3sN2SHbZbiIiIKC84YhUplC5dGr///juaN2+OsWPHokqVKmjdujX27duHzz//3Gh5R0dHrF+/HhcuXEC1atXw8ccfY/r06YplMjIyMGLECERERKBt27YoX768XEktXrw4pkyZgnfeeQdBQUF48803AQDTpk3DxIkTMXPmTHm9HTt2oFSpUnk+NpVKhT179mDChAn48ssvUb9+fdSpUweLFi1CTEyMXEmfNWsWunbtin79+qFmzZr466+/8NNPP8HHxyfP+zZl7Nix6NevHwYMGIAGDRrAw8MDnTt3VixjjjhERUVh8+bN2L59O6pXr44WLVrg+PHj8vxVq1ahf//+GDt2LCpUqICXX34ZJ06cQMmSJc12rERERHnx6PFjHDlyBI8ePbJ2UoiKvF27dqFYsWKKT+PGjeHt7Y1ly5ahUaNGqFatGvbu3Yvvv/9eHmlu6tSpuHbtGsqUKSOPdmsOkZGRmD9/Pj7++GNUqVIF69atw8yZMxXLNGzYEEOHDkXPnj0REBCA2bNnA8i+/lu/fn0sW7YMCxcuRGRkJHbv3o0PPvjAbGnPjYCAAKxevRqbN29GpUqVMGvWLMydOzdP29q+fTsSExON2hyA5qntiIiIfD39XbNmTWzatAkbNmxAlSpV8OGHH2Lq1KkYOHCgvMy8efMQGhqKJk2aoHfv3hg3blyuby5klq9ERERERGy3sN2SHbZbiIiIKC8kYe4xsIioSHn06BG8vLyQlJQET09P8+/gaSoQu9csmxJCIB2aofqkIv5u2YLGWFsW4205jLVl3apQBcvWrsWQIUMQEhJi7eRYXIGXsURERYj2b2p84kOcTPWydnKKPCEEoE4H7BxYp7IBzA/bwvywLYUlP1oEAy72BbNttluIiMxH+zf1YWI8vJ6etHZyrEoIgXQ14GDH67CMhQ5jocE46DAWOoUiFkEtAHuXfG9GCIH09HQ4OOjacfltt3DEKiIqMiRJgj1e4MLkBcJYWxbjbTmMtWVpw8x4ExGRubBMsQ2SJAF29swPG8H8sC3MD9vC/CAiImtgufPfddgXuXOAGTEWOoyFBuOgw1joMBY6kiTB3t687Th2rKJCo3LlynB3dzf5WbdunbWTRzZALQTuqjX/UsFirC2L8bYcxtqytHFWq9VWTgkRkY4ttzvatWuXadpmzJhh1bTZCpYptkEINUTSXQjB/LAFzA/bwvywLcwPInpRsd3yYmO75b/rsEmC12HBWOhjLDQYBx3GQoex0FGr1bh7965Zy1MHs22JyMp+/PFHPH/+3OS8oKAgC6eGiIiIiIgKI1tudyxfvhypqakm5/n6+lo4NUREREREZC1stxARERGZDztWUaERFhZm7SQQERGRGbm6uKBSpUpwdXW1dlKIiGS23O4oXry4tZNAREREREQ2gO0WIiIiIvPhqwCJiIiIyCZ5eXqiSZMm8PLysnZSiIiIiIiIiIiIiIiIqAhixyoiKjLsJAmBdpp/qWAx1pbFeFsOY21ZGRkZ8oeIiMgc7Ox4GcQWSJIdJK9ASBLzwxYwP2wL88O2MD+IiMga2G757zqsl8TrsGAs9DEWGoyDDmOhw1jo2NnZITAw0KzlKUtmIioyhBDI+O9fKliMtWUx3pbDWFtWwr1ELF++HAkJCdZOChERFRIsw22DEAJQZzA/bATzw7YwP2wL84OIiKyB5c5/12HVjAXAWOhjLDQYBx3GQoex0BFCICPDvO04dqwioiJDAEhUa/6lgsVYWxbjbTmMtWWJ/yLNhgAREZkLyxRbISAeJ4K1KlvB/LAtzA/bwvwgIiLLY7vlv+uwjwVLYDAW+hgLDcZBh7HQYSx0hBBITExkxyoiIiIiIiIiIiIiIiIiIiIiIqKCxI5VREREREREREREREREREREREREBtixioiKFMnaCShCGGvLYrwth7G2HAkSHB0dIUmMOhERUaHD8t22MD9sC/PDtjA/iIiIrIIlsA5jocNYaDAOOoyFDmOhY+77Sg5m3RoRkQ2zkyQE2Vs7FUUDY21ZjLflMNaWFVIsGO+99561k0FERIWInR2fL7MFkmQHySvI2smg/zA/bAvzw7YwP4iIyBrYbvnvOqw3uwgAjIU+xkKDcdBhLHQYCx07OzsEBZm3HceSmYiKDCEE0oSAEMLaSSn0GGvLYrwth7G2LCEE0tLSGG8iIjIblim2QQgB8ZxlvK1gftgW5odtYX4QEZE1sNz577rgc16HBRgLfYyFBuOgw1joMBY6BXFviSNWEZFtUzkDUa3MsimhVuPBvXsI9PeHxCc+ChRjbVmMt+Uw1pZ192ESNm7ahJ49e5r96QIiIiqaVJJAi2Brp4LUaoF7CQ/gHxAIOzs+TWltzA/bwvywLYUlP5zZfCUieqEISQUEtbB2MqxKqNV4kHAPgQG8DstY6DAWGoyDDmOhUyhiYedsls0IIfDgwQMEBgaa7ZWA7FhFRLZNkgBnF/NsS60GnFSa7b2oBcqLgrG2LMbbchhri0rPuI8HDx4gPT3d2kkhIqJCQpIAF77W1+rUEqCy0+QFq1TWx/ywLcwP28L8ICIiq5AkwN5M90VeVJIasFNp4lDUC2HGQoex0GAcdBgLHcaiQDGiREREREREREREREREREREREREBtixioiKFAcHDtRnKYy1ZTHelsNYExEREeUf61S2hflhW5gftoX5QUREZB0sg3UYCx3GQoNx0GEsdBgLHXPHgpEloiLDzs4O/v7+1k5GkcBYWxbjbTmMtWXZ/TdcrR2HrSUiIjNhmWIbWKeyLcwP28L8sC3MDyIisga2W1gG62MsdBgLDcZBh7HQYSx0CiIWLJmJqMgQQuDJkycQQlg7KYUeY21ZjLflMNaW5e3tjc6dO8Pb29vaSSEiokKCZbhtYJ3KtjA/bAvzw7YwP4iIyBpY7rAM1sdY6DAWGoyDDmOhw1joFEQs2LGKiIoMIQQePXrEAsUCGGvLYrwth7G2LJVKhcDAQKhUKmsnhYiICgmW4baBdSrbwvywLcwP28L8ICIia2C5wzJYH2Ohw1hoMA46jIUOY6FTELFgxyoiIiIisknJycmIi4tDcnKytZNCRERERERERERERERERRA7VhERERGRTXr8+DGOHz+Ox48fWzspREREREREREREREREVASxYxURFRmSJMHJyQmSJFk7KYUeY21ZjLflMNaWpY0z401ERObCMsU2sE5lW5gftoX5YVuYH0REZA0sd1gG62MsdBgLDcZBh7HQYSx0CiIWDmbbEhGRjZMkCb6+vtZORpHAWFsW4205jLVlsWMVERGZG8sU28A6lW1hftgW5odtYX4QEZE1sN3CMlgfY6HDWGgwDjqMhQ5joVMQseCIVURUZAgh8PjxYwghrJ2UQo+xtizG23IYa8vSxpnxJiIic2GZYhtYp7ItzA/bwvywLcwPIiKyBpY7LIP1MRY6jIUG46DDWOgwFjoFEQuOWEVERYYQAikpKXBzc+MTHwWMsbYsxjuXhADSnuZtVbUaKQ/uw83eDpId+6cXNJWTE0qXLg2VSmXtpBARUSGhVgukZuR/O852AKtdecf6q21hftgW5odtYX4QEZE1CLUayEi1djKsSqjVSHl8H27OvA5baGJh55zvhjTrZhqMgw5jocNY6BRELNixioiIiIqWtKdA7N68rSsEoIZmzM8iXjG1BJ+mLdC6dWv4+PhYOylERFRIPFUDR27nfzstggEX+/xvh4iIiIiIyIj6KXDniLVTYV1CAEkCEBKvwxaWWAS1AOxdrJ0KIqI8eYG7tRIRERFRYZaRkYHk5GRkZJhhaBEiIiIiIiIiIiIiIiKiXGLHKiIqMiRJgouLS5Ef/tASGGvLYrwtRwLgImn+pYKXcC8R69atQ0JCgrWTQkREhQTrS7aB9VfbwvywLcwP28L8ICIia2C58991WCeJ12HBWOhj3UyDcdBhLHQYC52CiAVfBUhERYYkSfDy8rJ2MooExtqyGG/LkSQJXqyTWoy2zsuGABERmQvLFNvA+qttYX7YFuaHbWF+EBGRNbDd8l8Z7GrtVNgGxkKHdTMNxkGHsdBhLHQKIhYcsYqIigwhBJKSkiCEsHZSCj3G2rIYb8sRQiBJLRhrC9GGmfEmIiJzYZliG1h/tS3MD9vC/LAtzA8iIrIGljv/lcFPeB0WYCz0sW6mwTjoMBY6jIVOQcSCHauIqMgQQiA1NZUFigUw1pbFeFuOAJAqNP9SwRP/RZrnNhERmQvLFNvA+qttYX7YFuaHbWF+EBGRNbDc+e867DPB67BgLPSxbqbBOOgwFjqMhU5BxIIdq4iIiIiIiIiIiIiIiIiIiIiIiAywYxURERER2aTgwEC89tprCA4OtnZSiIiIiIiIiIiIiIiIqAhixyoiKjIkSYKbmxskSbJ2Ugo9xtqyGG/LkQC4SZp/qeDZ2dnB09MTdnasshIRkXmwvmQbWH+1LcwP28L8sC3MDyIisgaWO/9dh1VJvA4LxkIf62YajIMOY6HDWOgURCx4l4qIigxJkuDh4cECxQIYa8tivC1HkiR42EmMtYXcf/AAW7Zswf37962dFCIiKiRYhtsG1l9tC/PDtjA/bAvzg4iIrIHlzn9lsAuvwwKMhT7WzTQYBx3GQoex0CmIWLBjFREVGUII3L9/H0IIayel0GOsLYvxthwhBO6rBWNtIWlpz3D9+nWkpaVZOylERFRIsAy3Day/2hbmh21hftgW5gcREVkDy53/yuBkXocFGAt9rJtpMA46jIUOY6FTELFgxyoiKjKEEHj27BkLFAtgrC2L8bYcAeCZ0PxLBU/8F2me20REZC4sU2wD66+2hflhW5gftoX5QURE1sBy57/rsOmC12HBWOhj3UyDcdBhLHQYC52CiAU7VhERERERERERERERERERERERERlgxyoiIiIiIiIiIiIiIiIiIiIiIiID7FhlRpIkYdu2bdZORqE1cOBAvPzyy9ZOBr3AJEmCp6cnJEmydlIKPcbashhvy5EAeNpp/qWC5+3piTZt2sDb29vaSSGiF5A12me3b99G69at4ebm9sL87YqKisKoUaOsnQyLYX3JNrD+aluYH7aF+WFbmB9EVNDYbskZtluKHgmAp4vE67BgLPSxbqbBOOgwFjqMhU5BxIIdq3Jg4MCBkCQJkiTB0dERQUFBaN26NVauXAm1Wi0vFx8fj3bt2uVomy9aJ6zY2Fg5Bpl9YmNj87x9IQSWLl2KevXqwd3dHd7e3qhduzYWLFiAJ0+emO9A9ISHh2PBggUFsm1T9u/fj+joaPj5+cHV1RWVKlXC2LFj8e+//5ptH9euXYMkSTh16pTZtlmYSJIEV1dXFigWwFhbFuNtOZIkwfW/co8KnpubG+rXrw83NzdrJ4WI8iG7dsTkyZMzXddS9Vv99Dg4OKBkyZIYM2YM0tLScrWdTz75BPHx8Th16hQuXbpUQKnN3urVq+Xjsbe3h4+PD+rVq4epU6ciKSlJsey3336LadOmWSmluZffc4JluG1g/dW2MD9sC/PDtjA/iIoOtlssj+2WzLHc+a8MVvE6LMBY6GPdTINx0GEsdBgLnYKIBTtW5VDbtm0RHx+Pa9euYefOnWjevDlGjhyJDh06ID09HQAQHBwMlUpl5ZQWjIYNGyI+Pl7+9OjRQ46J9tOwYcM8b79fv34YNWoUOnXqhP379+PUqVOYOHEivvvuO+zevduMR2J+z549y3aZL7/8Eq1atUJwcDC2bNmCP//8E1988QWSkpIwb948C6Qy954/f27tJJidWq3GvXv3FB0iqWAw1pbFeFuOWgjcUwuohbB2UoqE5ORkHDhwAMnJydZOChHlg36bYcGCBfD09FRMGzdunLWTCABYtWoV4uPjcfXqVSxZsgRfffUVpk+fnqttXLlyBbVq1UK5cuUQGBiYp3TkpH2RE9o437x5E0eOHMHrr7+OtWvXonr16rh165a8nK+vLzw8PMyyzxcB60u2gfVX28L8sC3MD9vC/CAqOthuyR22WwoWy53/rsM+UvM6LBgLfaybaTAOOoyFDmOhUxCxYMeqHFKpVAgODkbx4sVRs2ZNvPfee/juu++wc+dOrF69GoByFKpnz57hzTffRLFixeDs7IywsDDMnDkTgGakJADo3LkzJEmSv1+5cgWdOnVCUFAQ3N3dUadOHezdu1eRjvDwcMyYMQOvvvoqPDw8ULJkSSxdulSxzM2bN/HKK6/A19cXbm5uqF27Nn799Vd5/nfffYeaNWvC2dkZpUuXxpQpU+TOYZlxcnJCcHCw/HFxcZFjou1Q9tprr8HHxweurq5o164dLl++nKPYbtq0CevWrcP69evx3nvvoU6dOggPD0enTp3w888/o3nz5ibXMzXiVPXq1eUnR4QQmDx5MkqWLAmVSoWQkBDExMQA0Awbe/36dYwePVp+IkLr0KFDaNKkCVxcXBAaGoqYmBikpKQo9jtt2jT0798fnp6eeP3117M8vps3byImJgYxMTFYuXIloqKiEB4ejqZNm2L58uX48MMPc7XvrPK/VKlSAIAaNWpAkiRERUXJ85YvX46IiAg4OzujYsWKWLJkiTxP+/TExo0b0axZMzg7O2PdunVZHteLKrtzncyHsbYsxtty0tl+tZikx4+xf/9+o6cUiejFot+O8PLygiRJ8vfAwEDMnz8fJUqUgEqlQvXq1bFr1y553czqtydOnEDr1q3h7+8PLy8vNGvWDL///nu+0unt7Y3g4GCEhoaiQ4cO6NSpk9E2s2pLhYeHY8uWLVi7di0kScLAgQMBADdu3ECnTp3g7u4OT09P9OjRA3fu3JG3OXnyZFSvXh3Lly9HqVKl4OzsDAB4+PAhXnvtNQQEBMDT0xMtWrTA6dOnc3w82jgXK1YMERERGDx4MI4cOYLk5GRMmDBBXs7wlRpLlixBuXLl4OzsjKCgIHTr1k2ep1arMXv2bJQtWxYqlQolS5bERx99JM8/e/YsWrRoARcXF/j5+eH1119XdI419fqOl19+WY6VNo55bfPQi4X1V9vC/LAtzA/bwvwgKhrYbmG7JbN9AWy3WEs6+wbIGAsd1s00GAcdxkKHsdAxdyzYsSofWrRogcjISHz77bdG8xYtWoTt27dj06ZNuHjxItatWyd3oDpx4gQA3ZMF2u/JycmIjo7Gvn37EBcXh7Zt26Jjx464ceOGYtvz5s1D7dq1ERcXh+HDh2PYsGG4ePGivI1mzZrh33//xfbt23H69GlMmDBB7o138OBB9O/fHyNHjsSff/6JL7/8EqtXr1ZUKvNi4MCB+O2337B9+3YcPXoUQghER0fnaNSjdevWoUKFCujUqZPRPEmS4OXllac0bdmyBZ988gm+/PJLXL58Gdu2bUPVqlUBaIaNLVGiBKZOnSo/cQJoOre1bdsWXbt2xZkzZ7Bx40YcOnQIb775pmLbc+fORWRkJOLi4jBx4sQs07F582Y8e/ZM0QjQp32HeU73nVX+Hz9+HACwd+9exMfHy+fmunXr8OGHH+Kjjz7C+fPnMWPGDEycOBFr1qxRbPudd97ByJEjcf78ebRp08ZketPS0vDo0SPFh4iIiIgoLxYuXIh58+Zh7ty5OHPmDNq0aYP/+7//kx/SyKx++/jxYwwYMACHDh3CsWPHUK5cOURHR+Px48dmSdelS5fw888/o169evK07NpSJ06cQNu2bdGjRw/Ex8dj4cKFUKvV6NSpE+7fv49ffvkFe/bswd9//42ePXsq9vfXX39hy5Yt+Pbbb+VXRXTv3h13797Fzp07cfLkSdSsWRMtW7bE/fv383xcgYGB6NOnD7Zv346MjAyj+b/99htiYmIwdepUXLx4Ebt27ULTpk3l+e+++y5mzZqFiRMn4s8//8T//vc/BAUFAQBSUlLQpk0b+Pj44MSJE9i8eTP27t1r1J7Jiby0eQyx3UJERERE5sJ2iwbbLUpstxARERUdDtZOwIuuYsWKOHPmjNH0GzduoFy5cmjcuDEkSUJYWJg8LyAgAIDuyQKtyMhIREZGyt+nTZuGrVu3Yvv27YpKXXR0NIYPHw4AePvtt/HJJ59g//79qFChAv73v/8hISEBJ06cgK+vLwCgbNmy8rpTpkzBO++8gwEDBgAASpcujWnTpmHChAmYNGlSnmJw+fJlbN++HYcPH5ZfB7hu3TqEhoZi27Zt6N69e7brV6hQIU/7zsqNGzcQHByMVq1awdHRESVLlkTdunUBaIaNtbe3h4eHhyIPZs6ciT59+shPI5QrVw6LFi1Cs2bN8Pnnn8tPYbRo0QJjx47NUTouX74MT09PFCtWLMvlcrrvrPJfe275+fkpjmvSpEmYN28eunTpAkDztIS2UaU9FwBg1KhR8jJZpXPKlCk5OnYiIiIioqzMnTsXb7/9Nnr16gUA+Pjjj7F//34sWLAAn332Wab12xYtWii2s3TpUnh7e+OXX35Bhw4d8pSWV155Bfb29khPT0daWho6dOiAd999V56fXVsqICAAKpUKLi4uclr37NmDs2fP4urVqwgNDQUArF27FpUrV8aJEydQp04dAJoRj9euXSsf76FDh3D8+HHcvXtXft383LlzsW3bNnzzzTfZjpqblYoVK+Lx48dITEw0eu3HjRs34Obmhg4dOsDDwwNhYWGoUaMGAM1NoYULF+LTTz+VY1CmTBk0btwYAPC///0PT58+xdq1a+Hm5gYA+PTTT9GxY0d8/PHH8o2MnMhLm8cQ2y1EREREZC5st7DdYgrbLUREREUHR6zKJyGE4jVyWgMHDsSpU6dQoUIFxMTEYPfu3dluKzk5GePGjUNERAS8vb3h7u6O8+fPG41YVa1aNfn/2mFS7969CwA4deoUatSoIXeqMnT69GlMnToV7u7u8mfIkCGIj4/HkydPcnPosvPnz8PBwUHxVISfnx8qVKiA8+fPZ7u+KKB3Anfv3h2pqakoXbo0hgwZgq1bt2Y75Nvp06exevVqRXzatGkDtVqNq1evysvVrl07x+nI7BzJ676zyn9TUlJScOXKFQwePFix7enTp+PKlSuKZXNyXO+++y6SkpLkzz///JPtOrZCkiT4+PjkKD8ofxhry2K8LUcC4GOn+ZcKnvRfpHluExVOjx49wq1bt9CoUSPF9EaNGmXbjrhz5w6GDBmCcuXKwcvLC56enkhOTjZqO+XGJ598glOnTuH06dP44YcfcOnSJfTr10+en5e21Pnz5xEaGirfnACASpUqwdvbW3GMYWFh8oV37b6Sk5Ph5+en2N/Vq1eN6vC5pW1/mfrb2rp1a4SFhaF06dLo168f1q1bJx/b+fPnkZaWhpYtW2Z6rJGRkfLNCUCTl2q1Wn5qO6dy2+YxJbN2C8sU28D6q21hftgW5odtYX4QEdstbLdkhu2WgiUB8HGTeB0WjIU+1s00GAcdxkKHsdApiFhwxKp8On/+vPyuZH01a9bE1atXsXPnTuzduxc9evRAq1at8M0332S6rXHjxmHPnj2YO3cuypYtCxcXF3Tr1g3Pnj1TLOfo6Kj4LkmS/Ko/FxeXLNObnJyMKVOmmByVSDsikqWVL18eFy5cyPV6dnZ2Rp2y9F89GBoaiosXL2Lv3r3Ys2cPhg8fjjlz5uCXX34xiqFWcnIy3njjDcTExBjNK1mypPx//Up3dsqXL4+kpCTEx8dnOWpVTvedVf5ntl0AWLZsmaLzGwDY29srvufkuFQqlfz0yYtGkqQXNu0vGsbashhvy5EkCYy05ahUTggLC+P5TURGBgwYgMTERCxcuFD+O9GgQQOjtlNuBAcHy6P9VqhQAY8fP8Yrr7yC6dOno2zZsgXaljKshycnJ6NYsWKIjY01Wlb7KvG8On/+PDw9PeHn52c0z8PDA7///jtiY2Oxe/dufPjhh5g8eTJOnDiRbVszJ7Jrw2nlts1jSmbtFl5csg2sv9oW5odtYX7YFuYHEeUH2y15x3YL2y2SJEFl+lZekcNY6LBupsE46DAWOoyFTkHEgiNW5cPPP/+Ms2fPomvXribne3p6omfPnli2bBk2btyILVu2yO+VdnR0NHo39OHDhzFw4EB07twZVatWRXBwMK5du5arNFWrVg2nTp3K9P3VNWvWxMWLF1G2bFmjj51d3k6HiIgIpKen49dff5WnJSYm4uLFi6hUqVK26/fu3RuXLl3Cd999ZzRPCIGkpCST6wUEBCA+Pl7+/ujRI8XIToCmo1nHjh2xaNEixMbG4ujRozh79iwAwMnJySgPatasiT///NNkfJycnLI9FlO6desGJycnzJ492+T8hw8fmm3f2uX0jysoKAghISH4+++/jbZrqlNgYaZWq3Hnzp1cN24o9xhry2K8LUctBO5kCKgLaLRFUvLx9ka7du3g4+Nj7aQQUQHw9PRESEgIDh8+rJh++PBhuR1hqn6rXSYmJgbR0dGoXLkyVCoV7t27Z9b0aR9CSE1NBZC3tlRERAT++ecfxSivf/75Jx4+fJhlW6lmzZq4ffs2HBwcjPbl7++f52O6e/cu/ve//+Hll1/ONM0ODg5o1aoVZs+ejTNnzuDatWv4+eefUa5cObi4uGDfvn2ZHuvp06eRkpIiTzt8+DDs7OzkV78btuEyMjLwxx9/5OoYMjsncor1JdvA+qttYX7YFuaHbWF+EBHbLWy3sN1iHWohcOehmtdhwVjoY91Mg3HQYSx0GAudgogFR6zKobS0NNy+fRsZGRm4c+cOdu3ahZkzZ6JDhw7o37+/0fLz589HsWLFUKNGDdjZ2WHz5s0IDg6We+mHh4fj/9m78zgb6/6P4+9rhtk3g5mxDJOdCaEUyhahElpIImsJUZHyu29ChYSQorgbS0qbO6Uou9JCZMlOSctYh5Eww8z5/XHuOdeMmWGWM9e5zLyej8c8as5yne95f475fr/X+V7XtWrVKjVp0kS+vr4qUaKEqlatqsWLF6t9+/YyDEMjR47MdbG7du2qcePGqWPHjho/frzKlCmjn376SWXLllWjRo00atQo3X333apQoYLuv/9+eXl5adu2bfr555/14osv5imbqlWrqkOHDurXr5/efPNNBQcH67nnnlO5cuXUoUOHqz6/c+fO+u9//6uuXbvq3//+t+644w6VLl1aO3bs0KuvvqonnnhCHTt2zPS8li1bau7cuWrfvr3CwsI0atSoDGdgmjt3rlJSUnTzzTcrICBA77zzjvz9/VWxYkVJzhqsX79eDz74oHx9fVWqVCk9++yzuuWWWzRo0CD17dtXgYGB2rVrl1asWKEZM2bkKZ/o6Gi9+uqrGjRokM6cOaMePXooJiZGf/zxh+bPn6+goCBNnjzZLa8dEREhf39/LV++XOXLl5efn59CQ0M1ZswYDR48WKGhoWrbtq2SkpL0448/6tSpU3r66afz9L6uVQV16UlkRtbWIm/rkLR1HA6HLl26xOcbKMSeeeYZPf/886pcubJuuOEGxcXFaevWrVq4cKGk7Me3VatW1YIFC3TjjTfqzJkzeuaZZ/J9ZPLp06d15MgRpaamav/+/Ro7dqyqVaummjVrSlKe5lKtWrVS7dq11a1bN02dOlWXLl3SgAED1KxZsytehrtVq1Zq1KiROnbsqIkTJ6patWr666+/9Pnnn6tTp045uoS3w+HQkSNH5HA4dPr0aX333XcaN26cQkNDNWHChCyfs3TpUv3yyy9q2rSpSpQooS+++EKpqamqXr26/Pz89Oyzz2r48OHy8fFRkyZNdPz4ce3cuVN9+vRRt27d9Pzzz+uRRx7R6NGjdfz4cT3xxBPq3r27IiMjJTnncE8//bQ+//xzVa5cWVOmTHEdaJJT2X0mcO2hf7cX6mEv1MNeqAcA5i1ZY96SPeYt7kEPbCILE2MzJ3IwkYWJLEzuzoIzVuXQ8uXLVaZMGcXExKht27Zas2aNpk+friVLlmS6nJrkPBXpxIkTdeONN+qmm27SoUOH9MUXX7hW10+ePFkrVqxQdHS06tWrJ8m5GKtEiRJq3Lix2rdvrzZt2qh+/fq5aqePj4+++uorRURE6M4771Tt2rU1YcIEVxvbtGmjpUuX6quvvtJNN92kW265Ra+++qprsVFexcXFqUGDBrr77rvVqFEjORwOffHFF9leci89wzD07rvvasqUKfrkk0/UrFkz1alTR6NHj1aHDh3Upk2bLJ83YsQINWvWTHfffbfuuusudezYUZUrV3bdHxYWptmzZ6tJkyaqU6eOVq5cqc8++8x16tixY8fq0KFDqly5suu64HXq1NG6deu0b98+3XbbbapXr55GjRq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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Feature Group Importance by Model**\n",
        "\n",
        "In this visualization, the features were grouped into broader feature categories instead of being analyzed only as individual columns.\n",
        "\n",
        "This was done because one-hot encoding creates many separate columns from categorical and interaction features. For example, features such as City_Category, City_Payment_Method, Category_Device_Type, and discount-related features are split into many encoded columns.\n",
        "\n",
        "By grouping related features together, the graph gives a clearer view of which types of information were most important for each model.\n",
        "\n",
        "The comparison shows how each model relies on different feature groups when predicting Total_Amount. Linear Regression, Random Forest, and Gradient Boosting may use the engineered features differently, so this visualization helps explain the models in a more organized and interpretable way."
      ],
      "metadata": {
        "id": "c3u3i6VEryxr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Feature Group Importance - Separate Clear Graphs for Each Model\n",
        "\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "# Function to classify each feature into a feature group\n",
        "def get_feature_group(feature):\n",
        "    if 'City_Category' in feature:\n",
        "        return 'City + Category'\n",
        "    elif 'City_Payment_Method' in feature:\n",
        "        return 'City + Payment'\n",
        "    elif 'Category_Device_Type' in feature:\n",
        "        return 'Category + Device'\n",
        "    elif 'Category_Discount' in feature:\n",
        "        return 'Category + Discount'\n",
        "    elif 'City_Discount_Level' in feature:\n",
        "        return 'City + Discount'\n",
        "    elif 'Product_Category' in feature:\n",
        "        return 'Product Category'\n",
        "    elif 'City' in feature:\n",
        "        return 'City'\n",
        "    elif 'Payment_Method' in feature:\n",
        "        return 'Payment Method'\n",
        "    elif 'Device_Type' in feature:\n",
        "        return 'Device Type'\n",
        "    elif 'Discount' in feature:\n",
        "        return 'Discount Features'\n",
        "    elif 'Cluster' in feature:\n",
        "        return 'Cluster Features'\n",
        "    elif 'Quantity' in feature:\n",
        "        return 'Quantity Features'\n",
        "    elif 'Unit_Price' in feature or 'Price' in feature:\n",
        "        return 'Price Features'\n",
        "    elif 'Total_Before_Discount' in feature or 'Estimated_Final_Amount' in feature:\n",
        "        return 'Transaction Value'\n",
        "    else:\n",
        "        return 'Other Features'\n",
        "\n",
        "\n",
        "# Create group importance for each model\n",
        "lr_group_importance = pd.DataFrame({\n",
        "    'Feature': X.columns,\n",
        "    'Importance': abs(linear_model.coef_)\n",
        "})\n",
        "lr_group_importance['Group'] = lr_group_importance['Feature'].apply(get_feature_group)\n",
        "lr_group_importance = lr_group_importance.groupby('Group')['Importance'].sum().sort_values(ascending=False).head(8)\n",
        "\n",
        "\n",
        "rf_group_importance = pd.DataFrame({\n",
        "    'Feature': X.columns,\n",
        "    'Importance': rf_model.feature_importances_\n",
        "})\n",
        "rf_group_importance['Group'] = rf_group_importance['Feature'].apply(get_feature_group)\n",
        "rf_group_importance = rf_group_importance.groupby('Group')['Importance'].sum().sort_values(ascending=False).head(8)\n",
        "\n",
        "\n",
        "gb_group_importance = pd.DataFrame({\n",
        "    'Feature': X.columns,\n",
        "    'Importance': gb_model.feature_importances_\n",
        "})\n",
        "gb_group_importance['Group'] = gb_group_importance['Feature'].apply(get_feature_group)\n",
        "gb_group_importance = gb_group_importance.groupby('Group')['Importance'].sum().sort_values(ascending=False).head(8)\n",
        "\n",
        "\n",
        "# Plot 3 clear graphs side by side\n",
        "fig, axes = plt.subplots(1, 3, figsize=(24, 7))\n",
        "\n",
        "models = [\n",
        "    ('Linear Regression', lr_group_importance, '#F7C8E0'),\n",
        "    ('Random Forest', rf_group_importance, '#B4E4FF'),\n",
        "    ('Gradient Boosting', gb_group_importance, '#C1E1C1')\n",
        "]\n",
        "\n",
        "for ax, (model_name, data, color) in zip(axes, models):\n",
        "    bars = ax.barh(\n",
        "        data.index,\n",
        "        data.values,\n",
        "        color=color,\n",
        "        edgecolor='white',\n",
        "        linewidth=2\n",
        "    )\n",
        "\n",
        "    ax.invert_yaxis()\n",
        "    ax.set_title(model_name, fontsize=15, fontweight='bold')\n",
        "    ax.set_xlabel('Importance Score', fontsize=11)\n",
        "    ax.grid(axis='x', linestyle='--', alpha=0.3)\n",
        "\n",
        "    # Add value labels\n",
        "    for bar in bars:\n",
        "        width = bar.get_width()\n",
        "        ax.text(\n",
        "            width,\n",
        "            bar.get_y() + bar.get_height() / 2,\n",
        "            f' {width:.3f}',\n",
        "            va='center',\n",
        "            fontsize=9,\n",
        "            fontweight='bold'\n",
        "        )\n",
        "\n",
        "    # Clean look\n",
        "    ax.spines['top'].set_visible(False)\n",
        "    ax.spines['right'].set_visible(False)\n",
        "\n",
        "plt.suptitle(\n",
        "    'Feature Group Importance by Model',\n",
        "    fontsize=18,\n",
        "    fontweight='bold'\n",
        ")\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 516
        },
        "id": "2WOa-LfPrOOX",
        "outputId": "a76b45f0-eb24-435d-fc16-d4d067662b32"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2400x700 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **7. Discussion: Improvement and Reasons**\n",
        "\n",
        "\n",
        "The improved models were trained using the engineered dataset created in Part 4. Compared to the baseline model, the improved dataset included more informative features such as transaction-based features, interaction features, behavioral features, and clustering-based features.\n",
        "\n",
        "The main reason for the improvement is that the engineered features helped the models better capture the purchasing logic behind Total_Amount. Features such as Total_Before_Discount, Discount_Amount, Estimated_Final_Amount, and Discounted_Unit_Price directly represent the relationship between unit price, quantity, discount, and the final purchase amount.\n",
        "\n",
        "In addition, interaction features such as City_Category, City_Payment_Method, Category_Device_Type, Category_Discount, and City_Discount_Level helped capture more specific purchasing patterns. These features allow the models to understand that customer behavior may change depending on combinations of city, product category, payment method, device type, and discount level.\n",
        "\n",
        "The clustering-based features also added useful information by grouping transactions into different purchasing behavior segments. For example, the clusters helped identify regular purchases, higher-value purchases, and discount-driven purchases. This gave the models an additional way to understand the type of transaction.\n",
        "\n",
        "Linear Regression benefited from the engineered features because they made some relationships more explicit. However, Linear Regression is still limited because it assumes mostly linear relationships between the features and the target variable.\n",
        "\n",
        "Random Forest and Gradient Boosting were able to capture more complex and non-linear relationships. These models can learn interactions between features more naturally, which is useful for purchase prediction because Total_Amount can be affected by several variables at the same time.\n",
        "\n",
        "Overall, the improvement is mainly explained by the richer feature set and the use of more advanced models. The engineered features provided better information about each transaction, while the tree-based models were better able to learn complex purchasing patterns.\n",
        "\n",
        "However, the results should still be interpreted carefully. Some engineered features, especially Estimated_Final_Amount, are closely related to the target variable. Because of this, there may be a risk of data leakage, and the model should be checked carefully before being used in a real-world setting."
      ],
      "metadata": {
        "id": "gt-ubKC9Ttby"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **8. Winner Model**\n",
        "\n",
        "\n",
        "After comparing the baseline model and the three improved models, the winner was selected based on the overall evaluation metrics.\n",
        "\n",
        "The main metric used to choose the best model was R², because it shows how much of the variation in Total_Amount is explained by the model. In addition, MAE, MSE, and RMSE were also considered, because they measure the size of the prediction errors.\n",
        "\n",
        "Based on the results, Random Forest Regressor was selected as the best-performing model.\n",
        "\n",
        "Random Forest performed well because it combines many decision trees and averages their predictions. This makes the model more stable and helps it capture complex relationships between the engineered features and Total_Amount.\n",
        "\n",
        "This model is suitable for this dataset because purchasing behavior may depend on several factors at the same time, such as price, quantity, discount, product category, city, behavioral features, and clustering-based features.\n",
        "\n",
        "Overall, Random Forest Regressor was selected as the winner because it achieved the strongest predictive performance among the tested models and was able to use the engineered features effectively."
      ],
      "metadata": {
        "id": "FAEqH81ETzZe"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "<br><br><br>\n",
        "\n",
        "---\n",
        "\n",
        "<br><br><br>"
      ],
      "metadata": {
        "id": "ykjbsmtgvbLx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Part 6: Export the Winning Model\n",
        "import pickle\n",
        "\n",
        "with open('winning_model.pkl', 'wb') as file:\n",
        "    pickle.dump(rf_model, file)\n",
        "\n",
        "print(\"winning_model.pkl saved successfully\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2qjwmeoFtiDP",
        "outputId": "c055656d-7ee0-4f34-e03a-f03b8656af88"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "winning_model.pkl saved successfully\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "<br><br><br>\n",
        "\n",
        "---\n",
        "\n",
        "<br><br><br>"
      ],
      "metadata": {
        "id": "2poXnlF5XEXN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Part 7: Regression-to-Classification**\n",
        "\n",
        "In this part, the original regression problem was reframed as a classification problem.\n",
        "\n",
        "In the previous parts, the goal was to predict the exact numeric value of Total_Amount. In this section, Total_Amount is converted into discrete classes, so the goal becomes predicting whether a transaction belongs to a low, medium, or high purchase amount category.\n",
        "\n",
        "The same engineered features from the previous parts were used as input features for the classification models."
      ],
      "metadata": {
        "id": "-e5pgBLLoN81"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **7.1 Create Classes From the Numeric Target**\n",
        "\n",
        "\n",
        "In this step, the continuous target variable Total_Amount was converted into three classes: low, medium, and high purchase amount.\n",
        "\n",
        "Quantile binning was used as the classification strategy. The thresholds were calculated using only the training target values in order to avoid data leakage, and then the same thresholds were applied to both the training and testing targets.\n",
        "\n",
        "This strategy was chosen because it creates relatively balanced classes and allows the original regression problem to become a multi-class classification problem."
      ],
      "metadata": {
        "id": "BP7zQykSvLg8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create classification targets\n",
        "\n",
        "low_threshold = y_train.quantile(1/3)\n",
        "high_threshold = y_train.quantile(2/3)\n",
        "\n",
        "def amount_to_class(value):\n",
        "    if value <= low_threshold:\n",
        "        return 0\n",
        "    elif value <= high_threshold:\n",
        "        return 1\n",
        "    else:\n",
        "        return 2\n",
        "\n",
        "y_train_class = y_train.apply(amount_to_class)\n",
        "y_test_class = y_test.apply(amount_to_class)\n",
        "\n",
        "print(\"Classes created successfully\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JOghwWLFwilg",
        "outputId": "ecad275a-0b1f-4e8a-a725-70d4b94ac974"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Classes created successfully\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Visualize Class Distribution**\n",
        "\n",
        "After converting Total_Amount into classes, the class distribution was visualized for both the training and testing sets.\n",
        "\n",
        "The goal of this visualization is to check whether the new classes are balanced. Balanced classes are important because they help classification models learn all categories more fairly.\n",
        "\n",
        "The graph compares the number of low, medium, and high purchase transactions in the training and testing sets."
      ],
      "metadata": {
        "id": "NOHIdAGPvyPR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot class distribution\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "classes = ['Low', 'Medium', 'High']\n",
        "\n",
        "train_counts = y_train_class.value_counts().sort_index().values\n",
        "test_counts = y_test_class.value_counts().sort_index().values\n",
        "\n",
        "x = np.arange(len(classes))\n",
        "width = 0.35\n",
        "\n",
        "plt.figure(figsize=(8, 5))\n",
        "\n",
        "plt.bar(x - width/2, train_counts, width, label='Train', color='#B4E4FF')\n",
        "plt.bar(x + width/2, test_counts, width, label='Test', color='#F7C8E0')\n",
        "\n",
        "plt.title('Class Distribution After Converting Total_Amount', fontsize=14, fontweight='bold')\n",
        "plt.xlabel('Purchase Amount Class')\n",
        "plt.ylabel('Number of Transactions')\n",
        "plt.xticks(x, classes)\n",
        "plt.legend()\n",
        "plt.grid(axis='y', alpha=0.3)\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "NivwA5O5wlAw",
        "outputId": "356a0c7e-04e1-48b8-924c-ea69a5aa64fa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Quantile binning was chosen because it divides Total_Amount into three meaningful categories: low, medium, and high purchase amount.\n",
        "\n",
        "The thresholds were calculated using only the training target values in order to avoid data leakage, and then the same thresholds were applied to both the training and testing targets.\n",
        "\n",
        "This strategy creates relatively balanced classes, which helps classification models learn all classes more fairly."
      ],
      "metadata": {
        "id": "g7xGw_XTxrS_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **7.2 Check Class Balance**\n",
        "\n",
        "Before training classification models, the class distribution was checked for both the training and testing sets.\n",
        "\n",
        "This step is important because imbalanced classes can affect model performance. If one class has much more data than the others, the model may learn to predict mostly the majority class.\n",
        "\n",
        "Since quantile binning was used in the previous step, the classes are expected to be relatively balanced."
      ],
      "metadata": {
        "id": "YB66_c2wx1FI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 7.2 Check Class Balance\n",
        "\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "# Count classes in train and test\n",
        "train_class_counts = y_train_class.value_counts().sort_index()\n",
        "test_class_counts = y_test_class.value_counts().sort_index()\n",
        "\n",
        "# Calculate percentages\n",
        "train_class_percentages = (train_class_counts / len(y_train_class) * 100).round(2)\n",
        "test_class_percentages = (test_class_counts / len(y_test_class) * 100).round(2)\n",
        "\n",
        "# Create summary dataframe\n",
        "class_balance = pd.DataFrame({\n",
        "    'Class': ['Low Purchase', 'Medium Purchase', 'High Purchase'],\n",
        "    'Train Count': train_class_counts.values,\n",
        "    'Train Percentage': train_class_percentages.values,\n",
        "    'Test Count': test_class_counts.values,\n",
        "    'Test Percentage': test_class_percentages.values\n",
        "})\n",
        "\n",
        "class_balance"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "wdUZjXSGx4XK",
        "outputId": "6c96908f-8e3d-46c6-ceed-6c05226316d2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             Class  Train Count  Train Percentage  Test Count  Test Percentage\n",
              "0     Low Purchase         4547             33.34        1133            33.23\n",
              "1  Medium Purchase         4546             33.33        1154            33.84\n",
              "2    High Purchase         4546             33.33        1123            32.93"
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Class</th>\n",
              "      <th>Train Count</th>\n",
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              "      <td>Low Purchase</td>\n",
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              "      <td>33.34</td>\n",
              "      <td>1133</td>\n",
              "      <td>33.23</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Medium Purchase</td>\n",
              "      <td>4546</td>\n",
              "      <td>33.33</td>\n",
              "      <td>1154</td>\n",
              "      <td>33.84</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>High Purchase</td>\n",
              "      <td>4546</td>\n",
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              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-ffb152e5-2c64-4eac-9748-ec1dc415daae button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-ffb152e5-2c64-4eac-9748-ec1dc415daae');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "  <div id=\"id_21d2b715-9d99-446f-a6af-7bd7390f1a7a\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('class_balance')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_21d2b715-9d99-446f-a6af-7bd7390f1a7a button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('class_balance');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "class_balance",
              "summary": "{\n  \"name\": \"class_balance\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"Class\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Low Purchase\",\n          \"Medium Purchase\",\n          \"High Purchase\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Train Count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 4546,\n        \"max\": 4547,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          4546,\n          4547\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Train Percentage\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.005773502691899211,\n        \"min\": 33.33,\n        \"max\": 33.34,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          33.33,\n          33.34\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Test Count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15,\n        \"min\": 1123,\n        \"max\": 1154,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1133,\n          1154\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Test Percentage\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4637168676394417,\n        \"min\": 32.93,\n        \"max\": 33.84,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          33.23,\n          33.84\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 106
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualize class balance\n",
        "\n",
        "classes = ['Low', 'Medium', 'High']\n",
        "\n",
        "x = np.arange(len(classes))\n",
        "width = 0.35\n",
        "\n",
        "plt.figure(figsize=(8, 5))\n",
        "\n",
        "bars1 = plt.bar(\n",
        "    x - width/2,\n",
        "    train_class_percentages.values,\n",
        "    width,\n",
        "    label='Train',\n",
        "    color='#B4E4FF',\n",
        "    edgecolor='white',\n",
        "    linewidth=2\n",
        ")\n",
        "\n",
        "bars2 = plt.bar(\n",
        "    x + width/2,\n",
        "    test_class_percentages.values,\n",
        "    width,\n",
        "    label='Test',\n",
        "    color='#F7C8E0',\n",
        "    edgecolor='white',\n",
        "    linewidth=2\n",
        ")\n",
        "\n",
        "# Add percentage labels\n",
        "for bars in [bars1, bars2]:\n",
        "    for bar in bars:\n",
        "        height = bar.get_height()\n",
        "        plt.text(\n",
        "            bar.get_x() + bar.get_width() / 2,\n",
        "            height + 0.5,\n",
        "            f'{height:.1f}%',\n",
        "            ha='center',\n",
        "            va='bottom',\n",
        "            fontsize=10,\n",
        "            fontweight='bold'\n",
        "        )\n",
        "\n",
        "plt.title('Class Balance: Train vs Test', fontsize=15, fontweight='bold')\n",
        "plt.xlabel('Purchase Amount Class', fontsize=12)\n",
        "plt.ylabel('Percentage of Transactions', fontsize=12)\n",
        "plt.xticks(x, classes)\n",
        "plt.ylim(0, 45)\n",
        "plt.legend()\n",
        "plt.grid(axis='y', linestyle='--', alpha=0.3)\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 507
        },
        "id": "Qj4gC_IXx6bs",
        "outputId": "3ff1b7f1-fcae-4dfe-f165-2c20526f2e4b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The class balance results show that the three classes are relatively balanced in both the training and testing sets.\n",
        "\n",
        "This is expected because quantile binning was used to divide Total_Amount into three groups with similar sizes.\n",
        "\n",
        "Since no class is strongly under-represented, accuracy can be used as a useful metric. However, macro F1-score will also be considered because it evaluates performance across all classes equally and gives a more complete view of the classification model’s performance."
      ],
      "metadata": {
        "id": "8hUnSATux90A"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "<br><br><br>\n",
        "\n",
        "---\n",
        "\n",
        "<br><br><br>"
      ],
      "metadata": {
        "id": "75xPnIjfqHEh"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Part 8: Train & Eval Classification Models\n",
        "\n"
      ],
      "metadata": {
        "id": "Ii0otL-qqHOt"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **8.1 Evaluation Priorities: Precision, Recall, False Positives, and False Negatives**\n",
        "\n",
        "**Question 1: Precision vs. Recall**\n",
        "\n",
        "In the context of this dataset, recall is more important than precision, especially for the High Purchase class.\n",
        "\n",
        "The classification task is to predict whether a transaction belongs to a low, medium, or high purchase amount category. In a business context, missing a high-value purchase could be more costly than incorrectly labeling a medium-value purchase as high-value.\n",
        "\n",
        "For example, if the model fails to identify high-value transactions, the business may miss important customer behavior patterns, revenue opportunities, or premium customer segments.\n",
        "\n",
        "Therefore, recall is more important because it measures how well the model identifies all actual high-value purchases. However, precision is still useful because we also want to avoid too many false high-value predictions.\n",
        "\n",
        "Overall, the main focus should be on recall and macro F1-score, because macro F1-score gives a balanced evaluation across all three classes: low, medium, and high purchases.\n",
        "\n"
      ],
      "metadata": {
        "id": "co-38v1UsPd2"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Question 2: False Positive vs. False Negative**\n",
        "\n",
        "In the context of this dataset, a False Negative would be more critical, especially for the High Purchase class.\n",
        "\n",
        "A False Negative means that a transaction is actually a high-value purchase, but the model predicts it as medium or low. This could be problematic because the business may miss important high-value transactions, premium customer behavior, or revenue opportunities.\n",
        "\n",
        "A False Positive means that the model predicts a transaction as high-value when it is actually medium or low. This is less critical because the business may spend attention on a transaction that is not truly high-value, but it is usually less harmful than missing a real high-value purchase.\n",
        "\n",
        "Therefore, reducing False Negatives is more important in this task. The model should focus on correctly identifying as many actual high-value purchases as possible."
      ],
      "metadata": {
        "id": "xbrqOTcBsf2Q"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **8.2 Train Three Classification Models**\n",
        "\n",
        "In this step, three different classification models from sklearn were trained using the engineered dataset.\n",
        "\n",
        "The input features were the same engineered features used in the previous regression models. The target variable was the new classification target created in Part 7, where Total_Amount was converted into three classes: low, medium, and high purchase amount.\n",
        "\n",
        "The three models selected were Logistic Regression, Random Forest Classifier, and Gradient Boosting Classifier.\n",
        "\n",
        "Logistic Regression was used as a simple and interpretable baseline classification model. Random Forest and Gradient Boosting were selected because they can capture more complex and non-linear relationships between the engineered features and the purchase amount classes."
      ],
      "metadata": {
        "id": "mL61XEPb7lRr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 8.2 Model 1: Logistic Regression Classifier\n",
        "\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
        "\n",
        "log_clf = LogisticRegression(\n",
        "    max_iter=1000,\n",
        "    random_state=42\n",
        ")\n",
        "\n",
        "log_clf.fit(X_train, y_train_class)\n",
        "\n",
        "y_pred_log = log_clf.predict(X_test)\n",
        "\n",
        "accuracy_log = accuracy_score(y_test_class, y_pred_log)\n",
        "precision_log = precision_score(y_test_class, y_pred_log, average='macro')\n",
        "recall_log = recall_score(y_test_class, y_pred_log, average='macro')\n",
        "f1_log = f1_score(y_test_class, y_pred_log, average='macro')\n",
        "\n",
        "print(\"Logistic Regression Results:\")\n",
        "print(\"----------------------------\")\n",
        "print(\"Accuracy:\", accuracy_log)\n",
        "print(\"Precision:\", precision_log)\n",
        "print(\"Recall:\", recall_log)\n",
        "print(\"F1-score:\", f1_log)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fwbytzf47vlp",
        "outputId": "601afb95-4a17-4b3c-9e33-769214f439c0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Logistic Regression Results:\n",
            "----------------------------\n",
            "Accuracy: 0.9633431085043989\n",
            "Precision: 0.9633869266107453\n",
            "Recall: 0.9635125619720878\n",
            "F1-score: 0.9634379292609099\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_logistic.py:465: ConvergenceWarning:\n",
            "\n",
            "lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. OF ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 8.2 Model 2: Random Forest Classifier\n",
        "\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "rf_clf = RandomForestClassifier(\n",
        "    n_estimators=100,\n",
        "    random_state=42,\n",
        "    n_jobs=-1\n",
        ")\n",
        "\n",
        "rf_clf.fit(X_train, y_train_class)\n",
        "\n",
        "y_pred_rf_clf = rf_clf.predict(X_test)\n",
        "\n",
        "accuracy_rf_clf = accuracy_score(y_test_class, y_pred_rf_clf)\n",
        "precision_rf_clf = precision_score(y_test_class, y_pred_rf_clf, average='macro')\n",
        "recall_rf_clf = recall_score(y_test_class, y_pred_rf_clf, average='macro')\n",
        "f1_rf_clf = f1_score(y_test_class, y_pred_rf_clf, average='macro')\n",
        "\n",
        "print(\"Random Forest Classifier Results:\")\n",
        "print(\"---------------------------------\")\n",
        "print(\"Accuracy:\", accuracy_rf_clf)\n",
        "print(\"Precision:\", precision_rf_clf)\n",
        "print(\"Recall:\", recall_rf_clf)\n",
        "print(\"F1-score:\", f1_rf_clf)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5C8QWBRz8c3h",
        "outputId": "3fcd2749-fb49-4892-acc0-1abf5cc987c8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Random Forest Classifier Results:\n",
            "---------------------------------\n",
            "Accuracy: 0.9947214076246335\n",
            "Precision: 0.9947148675021592\n",
            "Recall: 0.9947554713993455\n",
            "F1-score: 0.9947312242000942\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 8.2 Model 3: Gradient Boosting Classifier\n",
        "\n",
        "from sklearn.ensemble import GradientBoostingClassifier\n",
        "\n",
        "gb_clf = GradientBoostingClassifier(\n",
        "    random_state=42\n",
        ")\n",
        "\n",
        "gb_clf.fit(X_train, y_train_class)\n",
        "\n",
        "y_pred_gb_clf = gb_clf.predict(X_test)\n",
        "\n",
        "accuracy_gb_clf = accuracy_score(y_test_class, y_pred_gb_clf)\n",
        "precision_gb_clf = precision_score(y_test_class, y_pred_gb_clf, average='macro')\n",
        "recall_gb_clf = recall_score(y_test_class, y_pred_gb_clf, average='macro')\n",
        "f1_gb_clf = f1_score(y_test_class, y_pred_gb_clf, average='macro')\n",
        "\n",
        "print(\"Gradient Boosting Classifier Results:\")\n",
        "print(\"-------------------------------------\")\n",
        "print(\"Accuracy:\", accuracy_gb_clf)\n",
        "print(\"Precision:\", precision_gb_clf)\n",
        "print(\"Recall:\", recall_gb_clf)\n",
        "print(\"F1-score:\", f1_gb_clf)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xYmO7c8h8i86",
        "outputId": "1bccd553-0839-4857-dc39-56ef18e0cb8c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gradient Boosting Classifier Results:\n",
            "-------------------------------------\n",
            "Accuracy: 0.9947214076246335\n",
            "Precision: 0.9947164310380017\n",
            "Recall: 0.994758091205024\n",
            "F1-score: 0.994733314466826\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Three classification models were trained successfully using the engineered dataset.\n",
        "\n",
        "Each model was evaluated using accuracy, precision, recall, and F1-score. Since the task includes three purchase amount classes, macro averaging was used in order to give equal importance to each class.\n",
        "\n",
        "This allows the models to be compared fairly across the low, medium, and high purchase categories."
      ],
      "metadata": {
        "id": "AIWCINK98wL9"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **8.3 Evaluation**\n",
        "\n",
        "\n",
        "In this section, the three classification models were evaluated using classification reports, confusion matrices, and comparison metrics.\n",
        "\n",
        "The goal of this evaluation is to understand how well each model predicts the three purchase amount classes: Low, Medium, and High.\n",
        "\n",
        "The evaluation focuses on Accuracy, Precision, Recall, and F1-score. Since this is a multi-class classification task, macro-averaged metrics were used to give equal importance to all classes."
      ],
      "metadata": {
        "id": "3sBxTR8X-vcm"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Classification Reports**\n",
        "\n",
        "The following classification reports show the performance of each model for the Low, Medium, and High purchase classes.\n",
        "\n",
        "Each report includes Precision, Recall, F1-score, and Support. Precision shows how accurate the model’s predictions are, Recall shows how well the model identifies the actual classes, F1-score combines Precision and Recall, and Support shows how many samples belong to each class.\n",
        "\n",
        "These reports help compare the models in more detail than accuracy alone."
      ],
      "metadata": {
        "id": "UeZNtSj__wD9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 8.3 Classification Reports - Side by Side Pretty Tables\n",
        "\n",
        "import pandas as pd\n",
        "from sklearn.metrics import classification_report\n",
        "from IPython.display import display, HTML\n",
        "\n",
        "class_names = ['Low Purchase', 'Medium Purchase', 'High Purchase']\n",
        "\n",
        "# Create classification report as DataFrame\n",
        "def build_report_df(y_true, y_pred):\n",
        "    report = classification_report(\n",
        "        y_true,\n",
        "        y_pred,\n",
        "        target_names=class_names,\n",
        "        output_dict=True\n",
        "    )\n",
        "\n",
        "    df_report = pd.DataFrame(report).transpose()\n",
        "    df_report = df_report[['precision', 'recall', 'f1-score', 'support']]\n",
        "\n",
        "    df_report[['precision', 'recall', 'f1-score']] = df_report[\n",
        "        ['precision', 'recall', 'f1-score']\n",
        "    ].round(2)\n",
        "\n",
        "    df_report['support'] = df_report['support'].fillna(0).round(0).astype(int)\n",
        "\n",
        "    return df_report\n",
        "\n",
        "\n",
        "# Create reports\n",
        "log_report_df = build_report_df(y_test_class, y_pred_log)\n",
        "rf_report_df = build_report_df(y_test_class, y_pred_rf_clf)\n",
        "gb_report_df = build_report_df(y_test_class, y_pred_gb_clf)\n",
        "\n",
        "\n",
        "# Convert DataFrame to styled HTML table\n",
        "def report_to_html(df, title, header_color):\n",
        "    styled = (\n",
        "        df.style\n",
        "        .format({\n",
        "            'precision': '{:.2f}',\n",
        "            'recall': '{:.2f}',\n",
        "            'f1-score': '{:.2f}',\n",
        "            'support': '{:d}'\n",
        "        })\n",
        "        .set_table_styles([\n",
        "            {\n",
        "                'selector': 'th',\n",
        "                'props': [\n",
        "                    ('background-color', header_color),\n",
        "                    ('color', '#333333'),\n",
        "                    ('font-weight', 'bold'),\n",
        "                    ('text-align', 'center'),\n",
        "                    ('border', '1px solid #dddddd'),\n",
        "                    ('padding', '6px')\n",
        "                ]\n",
        "            },\n",
        "            {\n",
        "                'selector': 'td',\n",
        "                'props': [\n",
        "                    ('text-align', 'center'),\n",
        "                    ('border', '1px solid #dddddd'),\n",
        "                    ('padding', '6px')\n",
        "                ]\n",
        "            },\n",
        "            {\n",
        "                'selector': 'table',\n",
        "                'props': [\n",
        "                    ('border-collapse', 'collapse'),\n",
        "                    ('font-size', '13px'),\n",
        "                    ('width', '100%')\n",
        "                ]\n",
        "            }\n",
        "        ])\n",
        "    )\n",
        "\n",
        "    return f\"\"\"\n",
        "    <div style=\"width: 32%; display: inline-block; vertical-align: top; margin-right: 1%;\">\n",
        "        <h3 style=\"text-align:center; color:#333333;\">{title}</h3>\n",
        "        {styled.to_html()}\n",
        "    </div>\n",
        "    \"\"\"\n",
        "\n",
        "\n",
        "html_output = f\"\"\"\n",
        "<h2 style=\"text-align:center; color:#333333;\">\n",
        "Classification Reports Comparison\n",
        "</h2>\n",
        "\n",
        "<div style=\"width:100%; display:flex; gap:15px; align-items:flex-start;\">\n",
        "    {report_to_html(log_report_df, \"Logistic Regression\", \"#F7C8E0\")}\n",
        "    {report_to_html(rf_report_df, \"Random Forest\", \"#B4E4FF\")}\n",
        "    {report_to_html(gb_report_df, \"Gradient Boosting\", \"#C1E1C1\")}\n",
        "</div>\n",
        "\"\"\"\n",
        "\n",
        "display(HTML(html_output))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 341
        },
        "id": "jqK7imX1-3jP",
        "outputId": "f4a6d10b-2217-4497-93ee-4af92001a97b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "<h2 style=\"text-align:center; color:#333333;\">\n",
              "Classification Reports Comparison\n",
              "</h2>\n",
              "\n",
              "<div style=\"width:100%; display:flex; gap:15px; align-items:flex-start;\">\n",
              "    \n",
              "    <div style=\"width: 32%; display: inline-block; vertical-align: top; margin-right: 1%;\">\n",
              "        <h3 style=\"text-align:center; color:#333333;\">Logistic Regression</h3>\n",
              "        <style type=\"text/css\">\n",
              "#T_c1105 th {\n",
              "  background-color: #F7C8E0;\n",
              "  color: #333333;\n",
              "  font-weight: bold;\n",
              "  text-align: center;\n",
              "  border: 1px solid #dddddd;\n",
              "  padding: 6px;\n",
              "}\n",
              "#T_c1105 td {\n",
              "  text-align: center;\n",
              "  border: 1px solid #dddddd;\n",
              "  padding: 6px;\n",
              "}\n",
              "#T_c1105 table {\n",
              "  border-collapse: collapse;\n",
              "  font-size: 13px;\n",
              "  width: 100%;\n",
              "}\n",
              "</style>\n",
              "<table id=\"T_c1105\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_c1105_level0_col0\" class=\"col_heading level0 col0\" >precision</th>\n",
              "      <th id=\"T_c1105_level0_col1\" class=\"col_heading level0 col1\" >recall</th>\n",
              "      <th id=\"T_c1105_level0_col2\" class=\"col_heading level0 col2\" >f1-score</th>\n",
              "      <th id=\"T_c1105_level0_col3\" class=\"col_heading level0 col3\" >support</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_c1105_level0_row0\" class=\"row_heading level0 row0\" >Low Purchase</th>\n",
              "      <td id=\"T_c1105_row0_col0\" class=\"data row0 col0\" >0.97</td>\n",
              "      <td id=\"T_c1105_row0_col1\" class=\"data row0 col1\" >0.98</td>\n",
              "      <td id=\"T_c1105_row0_col2\" class=\"data row0 col2\" >0.97</td>\n",
              "      <td id=\"T_c1105_row0_col3\" class=\"data row0 col3\" >1133</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_c1105_level0_row1\" class=\"row_heading level0 row1\" >Medium Purchase</th>\n",
              "      <td id=\"T_c1105_row1_col0\" class=\"data row1 col0\" >0.95</td>\n",
              "      <td id=\"T_c1105_row1_col1\" class=\"data row1 col1\" >0.94</td>\n",
              "      <td id=\"T_c1105_row1_col2\" class=\"data row1 col2\" >0.95</td>\n",
              "      <td id=\"T_c1105_row1_col3\" class=\"data row1 col3\" >1154</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_c1105_level0_row2\" class=\"row_heading level0 row2\" >High Purchase</th>\n",
              "      <td id=\"T_c1105_row2_col0\" class=\"data row2 col0\" >0.97</td>\n",
              "      <td id=\"T_c1105_row2_col1\" class=\"data row2 col1\" >0.97</td>\n",
              "      <td id=\"T_c1105_row2_col2\" class=\"data row2 col2\" >0.97</td>\n",
              "      <td id=\"T_c1105_row2_col3\" class=\"data row2 col3\" >1123</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_c1105_level0_row3\" class=\"row_heading level0 row3\" >accuracy</th>\n",
              "      <td id=\"T_c1105_row3_col0\" class=\"data row3 col0\" >0.96</td>\n",
              "      <td id=\"T_c1105_row3_col1\" class=\"data row3 col1\" >0.96</td>\n",
              "      <td id=\"T_c1105_row3_col2\" class=\"data row3 col2\" >0.96</td>\n",
              "      <td id=\"T_c1105_row3_col3\" class=\"data row3 col3\" >1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_c1105_level0_row4\" class=\"row_heading level0 row4\" >macro avg</th>\n",
              "      <td id=\"T_c1105_row4_col0\" class=\"data row4 col0\" >0.96</td>\n",
              "      <td id=\"T_c1105_row4_col1\" class=\"data row4 col1\" >0.96</td>\n",
              "      <td id=\"T_c1105_row4_col2\" class=\"data row4 col2\" >0.96</td>\n",
              "      <td id=\"T_c1105_row4_col3\" class=\"data row4 col3\" >3410</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_c1105_level0_row5\" class=\"row_heading level0 row5\" >weighted avg</th>\n",
              "      <td id=\"T_c1105_row5_col0\" class=\"data row5 col0\" >0.96</td>\n",
              "      <td id=\"T_c1105_row5_col1\" class=\"data row5 col1\" >0.96</td>\n",
              "      <td id=\"T_c1105_row5_col2\" class=\"data row5 col2\" >0.96</td>\n",
              "      <td id=\"T_c1105_row5_col3\" class=\"data row5 col3\" >3410</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "\n",
              "    </div>\n",
              "    \n",
              "    \n",
              "    <div style=\"width: 32%; display: inline-block; vertical-align: top; margin-right: 1%;\">\n",
              "        <h3 style=\"text-align:center; color:#333333;\">Random Forest</h3>\n",
              "        <style type=\"text/css\">\n",
              "#T_222b9 th {\n",
              "  background-color: #B4E4FF;\n",
              "  color: #333333;\n",
              "  font-weight: bold;\n",
              "  text-align: center;\n",
              "  border: 1px solid #dddddd;\n",
              "  padding: 6px;\n",
              "}\n",
              "#T_222b9 td {\n",
              "  text-align: center;\n",
              "  border: 1px solid #dddddd;\n",
              "  padding: 6px;\n",
              "}\n",
              "#T_222b9 table {\n",
              "  border-collapse: collapse;\n",
              "  font-size: 13px;\n",
              "  width: 100%;\n",
              "}\n",
              "</style>\n",
              "<table id=\"T_222b9\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_222b9_level0_col0\" class=\"col_heading level0 col0\" >precision</th>\n",
              "      <th id=\"T_222b9_level0_col1\" class=\"col_heading level0 col1\" >recall</th>\n",
              "      <th id=\"T_222b9_level0_col2\" class=\"col_heading level0 col2\" >f1-score</th>\n",
              "      <th id=\"T_222b9_level0_col3\" class=\"col_heading level0 col3\" >support</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_222b9_level0_row0\" class=\"row_heading level0 row0\" >Low Purchase</th>\n",
              "      <td id=\"T_222b9_row0_col0\" class=\"data row0 col0\" >1.00</td>\n",
              "      <td id=\"T_222b9_row0_col1\" class=\"data row0 col1\" >1.00</td>\n",
              "      <td id=\"T_222b9_row0_col2\" class=\"data row0 col2\" >1.00</td>\n",
              "      <td id=\"T_222b9_row0_col3\" class=\"data row0 col3\" >1133</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_222b9_level0_row1\" class=\"row_heading level0 row1\" >Medium Purchase</th>\n",
              "      <td id=\"T_222b9_row1_col0\" class=\"data row1 col0\" >0.99</td>\n",
              "      <td id=\"T_222b9_row1_col1\" class=\"data row1 col1\" >0.99</td>\n",
              "      <td id=\"T_222b9_row1_col2\" class=\"data row1 col2\" >0.99</td>\n",
              "      <td id=\"T_222b9_row1_col3\" class=\"data row1 col3\" >1154</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_222b9_level0_row2\" class=\"row_heading level0 row2\" >High Purchase</th>\n",
              "      <td id=\"T_222b9_row2_col0\" class=\"data row2 col0\" >0.99</td>\n",
              "      <td id=\"T_222b9_row2_col1\" class=\"data row2 col1\" >1.00</td>\n",
              "      <td id=\"T_222b9_row2_col2\" class=\"data row2 col2\" >0.99</td>\n",
              "      <td id=\"T_222b9_row2_col3\" class=\"data row2 col3\" >1123</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_222b9_level0_row3\" class=\"row_heading level0 row3\" >accuracy</th>\n",
              "      <td id=\"T_222b9_row3_col0\" class=\"data row3 col0\" >0.99</td>\n",
              "      <td id=\"T_222b9_row3_col1\" class=\"data row3 col1\" >0.99</td>\n",
              "      <td id=\"T_222b9_row3_col2\" class=\"data row3 col2\" >0.99</td>\n",
              "      <td id=\"T_222b9_row3_col3\" class=\"data row3 col3\" >1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_222b9_level0_row4\" class=\"row_heading level0 row4\" >macro avg</th>\n",
              "      <td id=\"T_222b9_row4_col0\" class=\"data row4 col0\" >0.99</td>\n",
              "      <td id=\"T_222b9_row4_col1\" class=\"data row4 col1\" >0.99</td>\n",
              "      <td id=\"T_222b9_row4_col2\" class=\"data row4 col2\" >0.99</td>\n",
              "      <td id=\"T_222b9_row4_col3\" class=\"data row4 col3\" >3410</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_222b9_level0_row5\" class=\"row_heading level0 row5\" >weighted avg</th>\n",
              "      <td id=\"T_222b9_row5_col0\" class=\"data row5 col0\" >0.99</td>\n",
              "      <td id=\"T_222b9_row5_col1\" class=\"data row5 col1\" >0.99</td>\n",
              "      <td id=\"T_222b9_row5_col2\" class=\"data row5 col2\" >0.99</td>\n",
              "      <td id=\"T_222b9_row5_col3\" class=\"data row5 col3\" >3410</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "\n",
              "    </div>\n",
              "    \n",
              "    \n",
              "    <div style=\"width: 32%; display: inline-block; vertical-align: top; margin-right: 1%;\">\n",
              "        <h3 style=\"text-align:center; color:#333333;\">Gradient Boosting</h3>\n",
              "        <style type=\"text/css\">\n",
              "#T_36bab th {\n",
              "  background-color: #C1E1C1;\n",
              "  color: #333333;\n",
              "  font-weight: bold;\n",
              "  text-align: center;\n",
              "  border: 1px solid #dddddd;\n",
              "  padding: 6px;\n",
              "}\n",
              "#T_36bab td {\n",
              "  text-align: center;\n",
              "  border: 1px solid #dddddd;\n",
              "  padding: 6px;\n",
              "}\n",
              "#T_36bab table {\n",
              "  border-collapse: collapse;\n",
              "  font-size: 13px;\n",
              "  width: 100%;\n",
              "}\n",
              "</style>\n",
              "<table id=\"T_36bab\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_36bab_level0_col0\" class=\"col_heading level0 col0\" >precision</th>\n",
              "      <th id=\"T_36bab_level0_col1\" class=\"col_heading level0 col1\" >recall</th>\n",
              "      <th id=\"T_36bab_level0_col2\" class=\"col_heading level0 col2\" >f1-score</th>\n",
              "      <th id=\"T_36bab_level0_col3\" class=\"col_heading level0 col3\" >support</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_36bab_level0_row0\" class=\"row_heading level0 row0\" >Low Purchase</th>\n",
              "      <td id=\"T_36bab_row0_col0\" class=\"data row0 col0\" >1.00</td>\n",
              "      <td id=\"T_36bab_row0_col1\" class=\"data row0 col1\" >1.00</td>\n",
              "      <td id=\"T_36bab_row0_col2\" class=\"data row0 col2\" >1.00</td>\n",
              "      <td id=\"T_36bab_row0_col3\" class=\"data row0 col3\" >1133</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_36bab_level0_row1\" class=\"row_heading level0 row1\" >Medium Purchase</th>\n",
              "      <td id=\"T_36bab_row1_col0\" class=\"data row1 col0\" >0.99</td>\n",
              "      <td id=\"T_36bab_row1_col1\" class=\"data row1 col1\" >0.99</td>\n",
              "      <td id=\"T_36bab_row1_col2\" class=\"data row1 col2\" >0.99</td>\n",
              "      <td id=\"T_36bab_row1_col3\" class=\"data row1 col3\" >1154</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_36bab_level0_row2\" class=\"row_heading level0 row2\" >High Purchase</th>\n",
              "      <td id=\"T_36bab_row2_col0\" class=\"data row2 col0\" >0.99</td>\n",
              "      <td id=\"T_36bab_row2_col1\" class=\"data row2 col1\" >1.00</td>\n",
              "      <td id=\"T_36bab_row2_col2\" class=\"data row2 col2\" >0.99</td>\n",
              "      <td id=\"T_36bab_row2_col3\" class=\"data row2 col3\" >1123</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_36bab_level0_row3\" class=\"row_heading level0 row3\" >accuracy</th>\n",
              "      <td id=\"T_36bab_row3_col0\" class=\"data row3 col0\" >0.99</td>\n",
              "      <td id=\"T_36bab_row3_col1\" class=\"data row3 col1\" >0.99</td>\n",
              "      <td id=\"T_36bab_row3_col2\" class=\"data row3 col2\" >0.99</td>\n",
              "      <td id=\"T_36bab_row3_col3\" class=\"data row3 col3\" >1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_36bab_level0_row4\" class=\"row_heading level0 row4\" >macro avg</th>\n",
              "      <td id=\"T_36bab_row4_col0\" class=\"data row4 col0\" >0.99</td>\n",
              "      <td id=\"T_36bab_row4_col1\" class=\"data row4 col1\" >0.99</td>\n",
              "      <td id=\"T_36bab_row4_col2\" class=\"data row4 col2\" >0.99</td>\n",
              "      <td id=\"T_36bab_row4_col3\" class=\"data row4 col3\" >3410</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_36bab_level0_row5\" class=\"row_heading level0 row5\" >weighted avg</th>\n",
              "      <td id=\"T_36bab_row5_col0\" class=\"data row5 col0\" >0.99</td>\n",
              "      <td id=\"T_36bab_row5_col1\" class=\"data row5 col1\" >0.99</td>\n",
              "      <td id=\"T_36bab_row5_col2\" class=\"data row5 col2\" >0.99</td>\n",
              "      <td id=\"T_36bab_row5_col3\" class=\"data row5 col3\" >3410</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "\n",
              "    </div>\n",
              "    \n",
              "</div>\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Confusion Matrices**\n",
        "\n",
        "The confusion matrices show the types of mistakes made by each classification model.\n",
        "\n",
        "The rows represent the actual classes, and the columns represent the predicted classes. Values on the diagonal represent correct predictions, while values outside the diagonal represent classification errors.\n",
        "\n",
        "This helps identify whether the models confuse Low, Medium, or High purchase classes."
      ],
      "metadata": {
        "id": "G51GjajfCQss"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 8.3 Confusion Matrices\n",
        "\n",
        "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "models_predictions = {\n",
        "    'Logistic Regression': y_pred_log,\n",
        "    'Random Forest': y_pred_rf_clf,\n",
        "    'Gradient Boosting': y_pred_gb_clf\n",
        "}\n",
        "\n",
        "fig, axes = plt.subplots(1, 3, figsize=(18, 5))\n",
        "\n",
        "for ax, (model_name, predictions) in zip(axes, models_predictions.items()):\n",
        "    cm = confusion_matrix(y_test_class, predictions)\n",
        "\n",
        "    disp = ConfusionMatrixDisplay(\n",
        "        confusion_matrix=cm,\n",
        "        display_labels=['Low', 'Medium', 'High']\n",
        "    )\n",
        "\n",
        "    disp.plot(\n",
        "        ax=ax,\n",
        "        cmap='Blues',\n",
        "        colorbar=False,\n",
        "        values_format='d'\n",
        "    )\n",
        "\n",
        "    ax.set_title(model_name, fontsize=13, fontweight='bold')\n",
        "    ax.set_xlabel('Predicted Class')\n",
        "    ax.set_ylabel('Actual Class')\n",
        "\n",
        "plt.suptitle('Confusion Matrices for Classification Models', fontsize=16, fontweight='bold')\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 512
        },
        "id": "5b1pW3Tw_9LS",
        "outputId": "e7bdec84-97a2-43e8-dff5-89a8c9f1851d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1800x500 with 3 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Based on the confusion matrices, Logistic Regression made more mistakes compared to the tree-based models. Most of its mistakes were between neighboring classes, such as Low and Medium or Medium and High.\n",
        "\n",
        "Random Forest and Gradient Boosting made fewer mistakes, with most predictions appearing on the diagonal. This means that these models classified most transactions correctly.\n",
        "\n",
        "The mistakes mainly occur between neighboring purchase classes, which makes sense because the classes were created from continuous Total_Amount values."
      ],
      "metadata": {
        "id": "f20ErHGbAoee"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Classification Model Comparison and Winner Selection**\n",
        "\n",
        "After evaluating each model using classification reports and confusion matrices, the models were compared using the main classification metrics.\n",
        "\n",
        "The comparison includes Accuracy, Precision, Recall, and F1-score. Accuracy shows the overall percentage of correct predictions, while Precision and Recall help evaluate how well the model performs for each class.\n",
        "\n",
        "F1-score was especially important because it combines Precision and Recall into one balanced metric. Since the task includes three classes, macro F1-score was used to give equal importance to the Low, Medium, and High purchase categories.\n",
        "\n",
        "The best classification model was selected mainly based on the highest macro F1-score."
      ],
      "metadata": {
        "id": "K73EOnX2BtFe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 8.3 Classification Model Comparison and Winner Selection\n",
        "\n",
        "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
        "import pandas as pd\n",
        "\n",
        "classification_results = pd.DataFrame({\n",
        "    'Model': [\n",
        "        'Logistic Regression',\n",
        "        'Random Forest Classifier',\n",
        "        'Gradient Boosting Classifier'\n",
        "    ],\n",
        "    'Accuracy': [\n",
        "        accuracy_score(y_test_class, y_pred_log),\n",
        "        accuracy_score(y_test_class, y_pred_rf_clf),\n",
        "        accuracy_score(y_test_class, y_pred_gb_clf)\n",
        "    ],\n",
        "    'Precision Macro': [\n",
        "        precision_score(y_test_class, y_pred_log, average='macro'),\n",
        "        precision_score(y_test_class, y_pred_rf_clf, average='macro'),\n",
        "        precision_score(y_test_class, y_pred_gb_clf, average='macro')\n",
        "    ],\n",
        "    'Recall Macro': [\n",
        "        recall_score(y_test_class, y_pred_log, average='macro'),\n",
        "        recall_score(y_test_class, y_pred_rf_clf, average='macro'),\n",
        "        recall_score(y_test_class, y_pred_gb_clf, average='macro')\n",
        "    ],\n",
        "    'F1 Macro': [\n",
        "        f1_score(y_test_class, y_pred_log, average='macro'),\n",
        "        f1_score(y_test_class, y_pred_rf_clf, average='macro'),\n",
        "        f1_score(y_test_class, y_pred_gb_clf, average='macro')\n",
        "    ]\n",
        "})\n",
        "\n",
        "classification_results = classification_results.sort_values(by='F1 Macro', ascending=False)\n",
        "\n",
        "classification_results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "oveXtqfGAxzq",
        "outputId": "5dd87fc1-bc10-4adb-ef4f-2d0f8b6f52d9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                          Model  Accuracy  Precision Macro  Recall Macro  \\\n",
              "2  Gradient Boosting Classifier  0.994721         0.994716      0.994758   \n",
              "1      Random Forest Classifier  0.994721         0.994715      0.994755   \n",
              "0           Logistic Regression  0.963343         0.963387      0.963513   \n",
              "\n",
              "   F1 Macro  \n",
              "2  0.994733  \n",
              "1  0.994731  \n",
              "0  0.963438  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-5e73e3b1-3ffb-40aa-997b-b8bec029e116\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Model</th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>Precision Macro</th>\n",
              "      <th>Recall Macro</th>\n",
              "      <th>F1 Macro</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Gradient Boosting Classifier</td>\n",
              "      <td>0.994721</td>\n",
              "      <td>0.994716</td>\n",
              "      <td>0.994758</td>\n",
              "      <td>0.994733</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Random Forest Classifier</td>\n",
              "      <td>0.994721</td>\n",
              "      <td>0.994715</td>\n",
              "      <td>0.994755</td>\n",
              "      <td>0.994731</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Logistic Regression</td>\n",
              "      <td>0.963343</td>\n",
              "      <td>0.963387</td>\n",
              "      <td>0.963513</td>\n",
              "      <td>0.963438</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5e73e3b1-3ffb-40aa-997b-b8bec029e116')\"\n",
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              "            style=\"display:none;\">\n",
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              "        document.querySelector('#df-5e73e3b1-3ffb-40aa-997b-b8bec029e116 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
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              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('classification_results')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
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              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('classification_results');\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "classification_results",
              "summary": "{\n  \"name\": \"classification_results\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"Model\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Gradient Boosting Classifier\",\n          \"Random Forest Classifier\",\n          \"Logistic Regression\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Accuracy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.01811626944378004,\n        \"min\": 0.9633431085043989,\n        \"max\": 0.9947214076246335,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.9633431085043989,\n          0.9947214076246335\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Precision Macro\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.018087646477628692,\n        \"min\": 0.9633869266107453,\n        \"max\": 0.9947164310380017,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.9947164310380017,\n          0.9947148675021592\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Recall Macro\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.018038858488410893,\n        \"min\": 0.9635125619720878,\n        \"max\": 0.994758091205024,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.994758091205024,\n          0.9947554713993455\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"F1 Macro\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.01806779569522648,\n        \"min\": 0.9634379292609099,\n        \"max\": 0.994733314466826,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.994733314466826,\n          0.9947312242000942\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 113
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualize Classification Model Comparison\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "metrics = ['Accuracy', 'Precision Macro', 'Recall Macro', 'F1 Macro']\n",
        "models = classification_results['Model']\n",
        "\n",
        "x = np.arange(len(models))\n",
        "width = 0.2\n",
        "\n",
        "plt.figure(figsize=(12, 6))\n",
        "\n",
        "colors = ['#B4E4FF', '#C1E1C1', '#FFE5B4', '#F7C8E0']\n",
        "\n",
        "for i, metric in enumerate(metrics):\n",
        "    bars = plt.bar(\n",
        "        x + (i - 1.5) * width,\n",
        "        classification_results[metric],\n",
        "        width,\n",
        "        label=metric,\n",
        "        color=colors[i],\n",
        "        edgecolor='white',\n",
        "        linewidth=2\n",
        "    )\n",
        "\n",
        "    for bar in bars:\n",
        "        height = bar.get_height()\n",
        "        plt.text(\n",
        "            bar.get_x() + bar.get_width() / 2,\n",
        "            height + 0.005,\n",
        "            f'{height:.2f}',\n",
        "            ha='center',\n",
        "            va='bottom',\n",
        "            fontsize=9,\n",
        "            fontweight='bold'\n",
        "        )\n",
        "\n",
        "plt.title('Classification Model Performance Comparison', fontsize=16, fontweight='bold')\n",
        "plt.xlabel('Model', fontsize=12)\n",
        "plt.ylabel('Score', fontsize=12)\n",
        "plt.xticks(x, models, rotation=15, ha='right')\n",
        "plt.ylim(0, 1.05)\n",
        "plt.legend(title='Metric')\n",
        "plt.grid(axis='y', linestyle='--', alpha=0.3)\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "id": "uCEFYdZ1A1mN",
        "outputId": "784cef52-da9c-4237-cfb6-a09ace789adf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The comparison results show that all three classification models performed well.\n",
        "\n",
        "Logistic Regression achieved strong results, but Random Forest and Gradient Boosting performed better overall.\n",
        "\n",
        "The tree-based models achieved higher scores because they can capture more complex and non-linear relationships between the engineered features and the purchase amount classes."
      ],
      "metadata": {
        "id": "_vv1RkwzCl3f"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Creative Visualization: Radar Chart of Classification Model Performance**\n",
        "\n",
        "In addition to the standard comparison table and bar chart, a radar chart was created to provide a more creative and visual comparison of the classification models.\n",
        "\n",
        "The radar chart compares the three models across the main evaluation metrics: Accuracy, Precision, Recall, and F1-score.\n",
        "\n",
        "This visualization makes it easier to see the overall performance shape of each model and quickly identify which model performs best across multiple metrics."
      ],
      "metadata": {
        "id": "xzCFzNubKiHO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Creative Visualization: Radar Chart for Classification Models\n",
        "\n",
        "import plotly.graph_objects as go\n",
        "\n",
        "metrics = ['Accuracy', 'Precision Macro', 'Recall Macro', 'F1 Macro']\n",
        "\n",
        "fig = go.Figure()\n",
        "\n",
        "colors = {\n",
        "    'Logistic Regression': '#F7C8E0',\n",
        "    'Random Forest Classifier': '#B4E4FF',\n",
        "    'Gradient Boosting Classifier': '#C1E1C1'\n",
        "}\n",
        "\n",
        "for _, row in classification_results.iterrows():\n",
        "    values = [row[m] for m in metrics]\n",
        "\n",
        "    # close the radar shape\n",
        "    values += [values[0]]\n",
        "    theta = metrics + [metrics[0]]\n",
        "\n",
        "    fig.add_trace(go.Scatterpolar(\n",
        "        r=values,\n",
        "        theta=theta,\n",
        "        fill='toself',\n",
        "        name=row['Model'],\n",
        "        line=dict(color=colors.get(row['Model'], '#CCCCCC'))\n",
        "    ))\n",
        "\n",
        "fig.update_layout(\n",
        "    title='Radar Chart Comparison of Classification Models',\n",
        "    polar=dict(\n",
        "        radialaxis=dict(\n",
        "            visible=True,\n",
        "            range=[0, 1]\n",
        "        )\n",
        "    ),\n",
        "    showlegend=True,\n",
        "    template='plotly_white'\n",
        ")\n",
        "\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "iKqFNX5EKmJE",
        "outputId": "cd7a010d-a9fb-49ce-a685-9da8ddb0cdad"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<html>\n",
              "<head><meta charset=\"utf-8\" /></head>\n",
              "<body>\n",
              "    <div>            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
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          "metadata": {}
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    {
      "cell_type": "markdown",
      "source": [
        "**Winner Model**\n",
        "\n",
        "Based on the evaluation results, the Gradient Boosting Classifier was selected as the best classification model.\n",
        "\n",
        "It achieved the strongest overall performance based on macro F1-score, while also maintaining high accuracy, precision, and recall.\n",
        "\n",
        "Gradient Boosting performed well because it builds models sequentially, where each new model focuses on correcting the errors of the previous models. This helps it capture complex patterns in the engineered dataset and classify transactions into low, medium, and high purchase categories effectively."
      ],
      "metadata": {
        "id": "IpYtPTTgBb4a"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Identify the best classification model\n",
        "\n",
        "best_classification_model = classification_results.iloc[0]\n",
        "\n",
        "print(\"Best Classification Model:\")\n",
        "print(\"--------------------------\")\n",
        "print(\"Model:\", best_classification_model['Model'])\n",
        "print(\"Accuracy:\", best_classification_model['Accuracy'])\n",
        "print(\"Precision Macro:\", best_classification_model['Precision Macro'])\n",
        "print(\"Recall Macro:\", best_classification_model['Recall Macro'])\n",
        "print(\"F1 Macro:\", best_classification_model['F1 Macro'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7PU-G23lBSuc",
        "outputId": "157ddc3a-7c1c-4014-eb62-14676b12f4d8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best Classification Model:\n",
            "--------------------------\n",
            "Model: Gradient Boosting Classifier\n",
            "Accuracy: 0.9947214076246335\n",
            "Precision Macro: 0.9947164310380017\n",
            "Recall Macro: 0.994758091205024\n",
            "F1 Macro: 0.994733314466826\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **8.4 Winner**\n",
        "\n",
        "Based on the evaluation results, the Gradient Boosting Classifier was selected as the best classification model.\n",
        "\n",
        "It achieved the strongest overall performance among the three classification models, with very high Accuracy, Precision, Recall, and F1-score.\n",
        "\n",
        "The model was then exported to a pickle file so it can be saved and reused later without retraining. The pickle file was uploaded to the same Hugging Face model repository that was created earlier."
      ],
      "metadata": {
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      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 8.4 Export the Winning Classification Model\n",
        "\n",
        "import pickle\n",
        "\n",
        "# Save the winning classification model\n",
        "with open('winning_classification_model.pkl', 'wb') as file:\n",
        "    pickle.dump(gb_clf, file)\n",
        "\n",
        "print(\"Winning classification model saved successfully as winning_classification_model.pkl\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lSAIK7vBDXXC",
        "outputId": "0dedc652-99ad-402d-95f9-09d475816543"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Winning classification model saved successfully as winning_classification_model.pkl\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The winning classification model was exported as a pickle file named winning_classification_model.pkl.\n",
        "\n",
        "The file was uploaded to the same Hugging Face model repository used earlier for the regression winning model.\n",
        "\n",
        "This allows both the regression model and the classification model to be stored in one shared repository for future use."
      ],
      "metadata": {
        "id": "ZKU2lkxxD6xe"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "<br><br><br>\n",
        "\n",
        "---\n",
        "\n",
        "<br><br><br>"
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