diff --git "a/Stacking/QuietML.ipynb" "b/Stacking/QuietML.ipynb"
new file mode 100644--- /dev/null
+++ "b/Stacking/QuietML.ipynb"
@@ -0,0 +1,2896 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "colab_type": "text",
+        "id": "view-in-github"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/Apaulgithub/oibsip_taskno4/blob/main/Email_Spam_Detection_with_Machine_Learning.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Hello world\n"
+          ]
+        }
+      ],
+      "source": [
+        "# testing the kernel of this virtual env\n",
+        "print(\"Hello world\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "vncDsAP0Gaoa"
+      },
+      "source": [
+        "# **Project Name**    - QuietML\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Sections \n",
+        "```\n",
+        " 1. EDA\n",
+        " 2. Data Cleaning\n",
+        " 3. Data Vizualization, Storytelling & Experimenting with charts : Understand the relationships between variables\n",
+        " 4. Data Splitting\n",
+        " 5. Model building\n",
+        " 6. Stack\n",
+        "\n",
+        " ```"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "HhfV-JJviCcP"
+      },
+      "source": [
+        "## ***1. EDA (Know Your Data)*** "
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Y3lxredqlCYt"
+      },
+      "source": [
+        "### Import Libraries"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "id": "M8Vqi-pPk-HR"
+      },
+      "outputs": [],
+      "source": [
+        "# Import Libraries\n",
+        "# Importing Numpy & Pandas for data processing & data wrangling\n",
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "\n",
+        "# Importing  tools for visualization\n",
+        "import matplotlib.pyplot as plt\n",
+        "import seaborn as sns\n",
+        "\n",
+        "# Import evaluation metric libraries\n",
+        "from sklearn.metrics import confusion_matrix, accuracy_score, precision_score, recall_score, f1_score, roc_auc_score, roc_curve, classification_report\n",
+        "\n",
+        "# Word Cloud library\n",
+        "from wordcloud import WordCloud, STOPWORDS\n",
+        "\n",
+        "# Library used for data preprocessing\n",
+        "from sklearn.feature_extraction.text import CountVectorizer\n",
+        "\n",
+        "# Import model selection libraries\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "\n",
+        "# Library used for ML Model implementation\n",
+        "from sklearn.naive_bayes import MultinomialNB\n",
+        "\n",
+        "# Importing the Pipeline class from scikit-learn\n",
+        "from sklearn.pipeline import Pipeline\n",
+        "\n",
+        "# Library used for ignore warnings\n",
+        "import warnings\n",
+        "warnings.filterwarnings('ignore')\n",
+        "%matplotlib inline"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "3RnN4peoiCZX"
+      },
+      "source": [
+        "### Dataset Loading"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {
+        "id": "4CkvbW_SlZ_R"
+      },
+      "outputs": [],
+      "source": [
+        "# Load Dataset from github repository\n",
+        "df = pd.read_csv(\"../data/spam.csv\", encoding='ISO-8859-1')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "x71ZqKXriCWQ"
+      },
+      "source": [
+        "### Dataset First View"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "LWNFOSvLl09H",
+        "outputId": "0eab3bb8-ea92-43b6-841f-9917e1795ec1"
+      },
+      "outputs": [
+        {
+          "data": {
+            "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>v1</th>\n",
+              "      <th>v2</th>\n",
+              "      <th>Unnamed: 2</th>\n",
+              "      <th>Unnamed: 3</th>\n",
+              "      <th>Unnamed: 4</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>ham</td>\n",
+              "      <td>Go until jurong point, crazy.. Available only ...</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>ham</td>\n",
+              "      <td>Ok lar... Joking wif u oni...</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>spam</td>\n",
+              "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>ham</td>\n",
+              "      <td>U dun say so early hor... U c already then say...</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>ham</td>\n",
+              "      <td>Nah I don't think he goes to usf, he lives aro...</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "     v1                                                 v2 Unnamed: 2  \\\n",
+              "0   ham  Go until jurong point, crazy.. Available only ...        NaN   \n",
+              "1   ham                      Ok lar... Joking wif u oni...        NaN   \n",
+              "2  spam  Free entry in 2 a wkly comp to win FA Cup fina...        NaN   \n",
+              "3   ham  U dun say so early hor... U c already then say...        NaN   \n",
+              "4   ham  Nah I don't think he goes to usf, he lives aro...        NaN   \n",
+              "\n",
+              "  Unnamed: 3 Unnamed: 4  \n",
+              "0        NaN        NaN  \n",
+              "1        NaN        NaN  \n",
+              "2        NaN        NaN  \n",
+              "3        NaN        NaN  \n",
+              "4        NaN        NaN  "
+            ]
+          },
+          "execution_count": 6,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Dataset First Look\n",
+        "# View top 5 rows of the dataset\n",
+        "df.head()\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "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>v1</th>\n",
+              "      <th>v2</th>\n",
+              "      <th>Unnamed: 2</th>\n",
+              "      <th>Unnamed: 3</th>\n",
+              "      <th>Unnamed: 4</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>1470</th>\n",
+              "      <td>ham</td>\n",
+              "      <td>Take some small dose tablet for fever</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3501</th>\n",
+              "      <td>ham</td>\n",
+              "      <td>I will come to ur home now</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3488</th>\n",
+              "      <td>ham</td>\n",
+              "      <td>I'm also came to room.</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3245</th>\n",
+              "      <td>ham</td>\n",
+              "      <td>Funny fact Nobody teaches volcanoes 2 erupt, t...</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1591</th>\n",
+              "      <td>ham</td>\n",
+              "      <td>That's my honeymoon outfit. :)</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "       v1                                                 v2 Unnamed: 2  \\\n",
+              "1470  ham              Take some small dose tablet for fever        NaN   \n",
+              "3501  ham                         I will come to ur home now        NaN   \n",
+              "3488  ham                             I'm also came to room.        NaN   \n",
+              "3245  ham  Funny fact Nobody teaches volcanoes 2 erupt, t...        NaN   \n",
+              "1591  ham                     That's my honeymoon outfit. :)        NaN   \n",
+              "\n",
+              "     Unnamed: 3 Unnamed: 4  \n",
+              "1470        NaN        NaN  \n",
+              "3501        NaN        NaN  \n",
+              "3488        NaN        NaN  \n",
+              "3245        NaN        NaN  \n",
+              "1591        NaN        NaN  "
+            ]
+          },
+          "execution_count": 7,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "df.sample(5)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "7hBIi_osiCS2"
+      },
+      "source": [
+        "### Dataset Rows & Columns count"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Kllu7SJgmLij",
+        "outputId": "c7dec3d3-78b0-4e91-be0c-24f746ee3b74"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Number of rows are:  5572\n",
+            "Number of columns are:  5\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Dataset Rows & Columns count\n",
+        "# Checking number of rows and columns of the dataset using shape\n",
+        "print(\"Number of rows are: \",df.shape[0])\n",
+        "print(\"Number of columns are: \",df.shape[1])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "JlHwYmJAmNHm"
+      },
+      "source": [
+        "### Dataset Information"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "e9hRXRi6meOf",
+        "outputId": "eb9baeed-8f80-42ab-c290-8bf3ef63d1e8"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "<class 'pandas.core.frame.DataFrame'>\n",
+            "RangeIndex: 5572 entries, 0 to 5571\n",
+            "Data columns (total 5 columns):\n",
+            " #   Column      Non-Null Count  Dtype \n",
+            "---  ------      --------------  ----- \n",
+            " 0   v1          5572 non-null   object\n",
+            " 1   v2          5572 non-null   object\n",
+            " 2   Unnamed: 2  50 non-null     object\n",
+            " 3   Unnamed: 3  12 non-null     object\n",
+            " 4   Unnamed: 4  6 non-null      object\n",
+            "dtypes: object(5)\n",
+            "memory usage: 217.8+ KB\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Dataset Info\n",
+        "# Checking information about the dataset using info\n",
+        "df.info()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "35m5QtbWiB9F"
+      },
+      "source": [
+        "#### Duplicate Values"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "1sLdpKYkmox0",
+        "outputId": "fbd5df04-e1d3-4145-9bd6-4f051b2107c0"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "number of duplicated rows are 403\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Dataset Duplicate Value Count\n",
+        "dup = df.duplicated().sum()\n",
+        "print(f'number of duplicated rows are {dup}')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "PoPl-ycgm1ru"
+      },
+      "source": [
+        "#### Missing Values/Null Values"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "GgHWkxvamxVg",
+        "outputId": "e85d85be-3fad-47af-ec94-3df777dd3880"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "v1               0\n",
+              "v2               0\n",
+              "Unnamed: 2    5522\n",
+              "Unnamed: 3    5560\n",
+              "Unnamed: 4    5566\n",
+              "dtype: int64"
+            ]
+          },
+          "execution_count": 11,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Missing Values/Null Values Count\n",
+        "df.isnull().sum()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "H0kj-8xxnORC"
+      },
+      "source": [
+        "### What did i know about the dataset?"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "gfoNAAC-nUe_"
+      },
+      "source": [
+        "* The Spam dataset consists of different messages and the category of the message along with.\n",
+        "* There are 5572 rows and 5 columns provided in the data.\n",
+        "* 403 duplicate rows are present in the dataset.\n",
+        "* No Null values exist in v1 & v2 column, but lots of null values present in unnamed 2,3,4 columns (will drop those 3 columns later)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 12,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "j7xfkqrt5Ag5",
+        "outputId": "6d819bd9-02dc-4b42-8525-f0748b275a01"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "Index(['v1', 'v2', 'Unnamed: 2', 'Unnamed: 3', 'Unnamed: 4'], dtype='object')"
+            ]
+          },
+          "execution_count": 12,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Dataset Columns\n",
+        "df.columns"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 13,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 175
+        },
+        "id": "DnOaZdaE5Q5t",
+        "outputId": "266091ed-0aa5-4f54-f0d6-0800b50551bd"
+      },
+      "outputs": [
+        {
+          "data": {
+            "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>v1</th>\n",
+              "      <th>v2</th>\n",
+              "      <th>Unnamed: 2</th>\n",
+              "      <th>Unnamed: 3</th>\n",
+              "      <th>Unnamed: 4</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>5572</td>\n",
+              "      <td>5572</td>\n",
+              "      <td>50</td>\n",
+              "      <td>12</td>\n",
+              "      <td>6</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>unique</th>\n",
+              "      <td>2</td>\n",
+              "      <td>5169</td>\n",
+              "      <td>43</td>\n",
+              "      <td>10</td>\n",
+              "      <td>5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>top</th>\n",
+              "      <td>ham</td>\n",
+              "      <td>Sorry, I'll call later</td>\n",
+              "      <td>bt not his girlfrnd... G o o d n i g h t . . .@\"</td>\n",
+              "      <td>MK17 92H. 450Ppw 16\"</td>\n",
+              "      <td>GNT:-)\"</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>freq</th>\n",
+              "      <td>4825</td>\n",
+              "      <td>30</td>\n",
+              "      <td>3</td>\n",
+              "      <td>2</td>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "          v1                      v2  \\\n",
+              "count   5572                    5572   \n",
+              "unique     2                    5169   \n",
+              "top      ham  Sorry, I'll call later   \n",
+              "freq    4825                      30   \n",
+              "\n",
+              "                                               Unnamed: 2  \\\n",
+              "count                                                  50   \n",
+              "unique                                                 43   \n",
+              "top      bt not his girlfrnd... G o o d n i g h t . . .@\"   \n",
+              "freq                                                    3   \n",
+              "\n",
+              "                   Unnamed: 3 Unnamed: 4  \n",
+              "count                      12          6  \n",
+              "unique                     10          5  \n",
+              "top      MK17 92H. 450Ppw 16\"    GNT:-)\"  \n",
+              "freq                        2          2  "
+            ]
+          },
+          "execution_count": 13,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Dataset Describe (all columns included)\n",
+        "df.describe(include= 'all').round(2)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "u3PMJOP6ngxN"
+      },
+      "source": [
+        "### Check Unique Values for each variable."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 14,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "zms12Yq5n-jE",
+        "outputId": "f0e3b9e1-8370-4b0a-8c94-7994666b7793"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "No. of unique values in v1 is 2\n",
+            "No. of unique values in v2 is 5169\n",
+            "No. of unique values in Unnamed: 2 is 43\n",
+            "No. of unique values in Unnamed: 3 is 10\n",
+            "No. of unique values in Unnamed: 4 is 5\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Check Unique Values for each variable using a for loop.\n",
+        "for i in df.columns.tolist():\n",
+        "  print(\"No. of unique values in\",i,\"is\",df[i].nunique())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "yil6WxgdU4cn"
+      },
+      "source": [
+        "## ***2. Data Cleaning***"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 15,
+      "metadata": {
+        "id": "vxCnmwEDemo_"
+      },
+      "outputs": [],
+      "source": [
+        "# Change the v1 & v2 columns as Category and Message\n",
+        "df.rename(columns={\"v1\": \"Category\", \"v2\": \"Message\"}, inplace=True)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 16,
+      "metadata": {
+        "id": "9MphdYrhfC2h"
+      },
+      "outputs": [],
+      "source": [
+        "# Removing the all unnamed columns (its include much number of missing values)\n",
+        "df.drop(columns={'Unnamed: 2','Unnamed: 3','Unnamed: 4'}, inplace=True)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 17,
+      "metadata": {
+        "id": "DYHeEXPPU6fc"
+      },
+      "outputs": [],
+      "source": [
+        "# Create a binary 'Spam' column: 1 for 'spam' and 0 for 'ham', based on the 'Category' column.\n",
+        "df['Spam'] = df['Category'].apply(lambda x: 1 if x == 'spam' else 0)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 18,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "hM6AP2FGVC3i",
+        "outputId": "33d4e00f-a0a2-4536-f680-c72515cde73d"
+      },
+      "outputs": [
+        {
+          "data": {
+            "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>Category</th>\n",
+              "      <th>Message</th>\n",
+              "      <th>Spam</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>ham</td>\n",
+              "      <td>Go until jurong point, crazy.. Available only ...</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>ham</td>\n",
+              "      <td>Ok lar... Joking wif u oni...</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>spam</td>\n",
+              "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>ham</td>\n",
+              "      <td>U dun say so early hor... U c already then say...</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>ham</td>\n",
+              "      <td>Nah I don't think he goes to usf, he lives aro...</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "  Category                                            Message  Spam\n",
+              "0      ham  Go until jurong point, crazy.. Available only ...     0\n",
+              "1      ham                      Ok lar... Joking wif u oni...     0\n",
+              "2     spam  Free entry in 2 a wkly comp to win FA Cup fina...     1\n",
+              "3      ham  U dun say so early hor... U c already then say...     0\n",
+              "4      ham  Nah I don't think he goes to usf, he lives aro...     0"
+            ]
+          },
+          "execution_count": 18,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Updated new dataset\n",
+        "df.head()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 19,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "Index(['Category', 'Message', 'Spam'], dtype='object')"
+            ]
+          },
+          "execution_count": 19,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "df.columns"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "GF8Ens_Soomf"
+      },
+      "source": [
+        "## ***3. Data Vizualization, Storytelling & Experimenting with charts : Understand the relationships between variables***"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "0wOQAZs5pc--"
+      },
+      "source": [
+        "#### Chart - 1 : Distribution of Spam vs Ham"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 20,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 444
+        },
+        "id": "7v_ESjsspbW7",
+        "outputId": "1f1a68d6-46ba-4bd4-813a-cb826fdef5cf"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 500x500 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# Chart - 1 Pie Chart Visualization Code For Distribution of Spam vs Ham Messages\n",
+        "spread = df['Category'].value_counts()\n",
+        "plt.rcParams['figure.figsize'] = (5,5)\n",
+        "\n",
+        "# Set Labels\n",
+        "spread.plot(kind = 'pie', autopct='%1.2f%%', cmap='Set1')\n",
+        "plt.title(f'Distribution of Spam vs Ham')\n",
+        "\n",
+        "# Display the Chart\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 21,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "Category    0\n",
+              "Message     0\n",
+              "Spam        0\n",
+              "dtype: int64"
+            ]
+          },
+          "execution_count": 21,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "#making sure no null are present\n",
+        "df.isnull().sum()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "lQ7QKXXCp7Bj"
+      },
+      "source": [
+        "##### What is/are the insight(s) found from the chart?"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "C_j1G7yiqdRP"
+      },
+      "source": [
+        "From the above chart, we got to know that the dataset contain 13.41% of spam messages and 86.59% of ham messages.\n",
+        "(not balanced dataset)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ko_-26S530G9"
+      },
+      "source": [
+        "#### Chart - 2 : Most Used Words in Spam Messages"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 22,
+      "metadata": {
+        "id": "WdRA6Ebl4F2w"
+      },
+      "outputs": [],
+      "source": [
+        "# Splitting Spam Messages\n",
+        "df_spam = df[df['Category']=='spam'].copy()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 23,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 380
+        },
+        "id": "YWcK5WmN30Hc",
+        "outputId": "de1ab3b6-9e2f-4417-93ec-f21e82a72f27"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 600x600 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# Chart - 2 WordCloud Plot Visualization Code For Most Used Words in Spam Messages\n",
+        "# Create a String to Store All The Words\n",
+        "comment_words = ''\n",
+        "\n",
+        "# Remove The Stopwords\n",
+        "stopwords = set(STOPWORDS)\n",
+        "\n",
+        "# Iterate Through The Column\n",
+        "for val in df_spam.Message:\n",
+        "\n",
+        "    # Typecaste Each Val to String\n",
+        "    val = str(val)\n",
+        "\n",
+        "    # Split The Value\n",
+        "    tokens = val.split()\n",
+        "\n",
+        "    # Converts Each Token into lowercase\n",
+        "    for i in range(len(tokens)):\n",
+        "        tokens[i] = tokens[i].lower()\n",
+        "\n",
+        "    comment_words += \" \".join(tokens)+\" \"\n",
+        "\n",
+        "# Set Parameters\n",
+        "wordcloud = WordCloud(width = 1000, height = 500,\n",
+        "                background_color ='white',\n",
+        "                stopwords = stopwords,\n",
+        "                min_font_size = 10,\n",
+        "                max_words = 1000,\n",
+        "                colormap = 'gist_heat_r').generate(comment_words)\n",
+        "\n",
+        "# Set Labels\n",
+        "plt.figure(figsize = (6,6), facecolor = None)\n",
+        "plt.title('Most Used Words In Spam Messages', fontsize = 15, pad=20)\n",
+        "plt.imshow(wordcloud)\n",
+        "plt.axis(\"off\")\n",
+        "plt.tight_layout(pad = 0)\n",
+        "\n",
+        "# Display Chart\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "RpI5FzWC30He"
+      },
+      "source": [
+        "##### What is/are the insight(s) found from the chart?"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "2dPXDSxm30He"
+      },
+      "source": [
+        "From the above wordcloud plot, we got to know that the 'free', 'call', 'text', 'txt' and 'now' are most used words in spam messages."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 24,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "df['num_characters'] = df['Message'].apply(len)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 25,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "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>Category</th>\n",
+              "      <th>Message</th>\n",
+              "      <th>Spam</th>\n",
+              "      <th>num_characters</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>ham</td>\n",
+              "      <td>Go until jurong point, crazy.. Available only ...</td>\n",
+              "      <td>0</td>\n",
+              "      <td>111</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>ham</td>\n",
+              "      <td>Ok lar... Joking wif u oni...</td>\n",
+              "      <td>0</td>\n",
+              "      <td>29</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>spam</td>\n",
+              "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
+              "      <td>1</td>\n",
+              "      <td>155</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>ham</td>\n",
+              "      <td>U dun say so early hor... U c already then say...</td>\n",
+              "      <td>0</td>\n",
+              "      <td>49</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>ham</td>\n",
+              "      <td>Nah I don't think he goes to usf, he lives aro...</td>\n",
+              "      <td>0</td>\n",
+              "      <td>61</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "  Category                                            Message  Spam  \\\n",
+              "0      ham  Go until jurong point, crazy.. Available only ...     0   \n",
+              "1      ham                      Ok lar... Joking wif u oni...     0   \n",
+              "2     spam  Free entry in 2 a wkly comp to win FA Cup fina...     1   \n",
+              "3      ham  U dun say so early hor... U c already then say...     0   \n",
+              "4      ham  Nah I don't think he goes to usf, he lives aro...     0   \n",
+              "\n",
+              "   num_characters  \n",
+              "0             111  \n",
+              "1              29  \n",
+              "2             155  \n",
+              "3              49  \n",
+              "4              61  "
+            ]
+          },
+          "execution_count": 25,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "df.head()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 26,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import seaborn as sns"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 27,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "<Axes: xlabel='num_characters', ylabel='Count'>"
+            ]
+          },
+          "execution_count": 27,
+          "metadata": {},
+          "output_type": "execute_result"
+        },
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 1200x600 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "plt.figure(figsize=(12,6))\n",
+        "sns.histplot(df[df['Spam'] == 0]['num_characters'])\n",
+        "sns.histplot(df[df['Spam'] == 1]['num_characters'],color='red')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "yLjJCtPM0KBk"
+      },
+      "source": [
+        "## ***4. Splititing Data***"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "BhH2vgX9EjGr"
+      },
+      "source": [
+        "### Data Splitting"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 28,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from sklearn.feature_extraction.text import CountVectorizer,TfidfVectorizer\n",
+        "#cv = CountVectorizer()\n",
+        "tfidf = TfidfVectorizer(max_features=3000)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 29,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "X = tfidf.fit_transform(df['Message']).toarray()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 30,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "(5572, 3000)"
+            ]
+          },
+          "execution_count": 30,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "X.shape"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 31,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "Y = df['Spam'].values"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 32,
+      "metadata": {
+        "id": "0CTyd2UwEyNM"
+      },
+      "outputs": [],
+      "source": [
+        "# Splitting the data to train and test\n",
+        "X_train,X_test,y_train,y_test=train_test_split(X,Y,test_size=0.25,random_state=2)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "VfCC591jGiD4"
+      },
+      "source": [
+        "## ***5. Model Building***"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 33,
+      "metadata": {
+        "id": "j08eST5dOllw"
+      },
+      "outputs": [],
+      "source": [
+        "def evaluate_model(model, X_train, X_test, y_train, y_test):\n",
+        "    '''The function will take model, x train, x test, y train, y test\n",
+        "    and then it will fit the model, then make predictions on the trained model,\n",
+        "    it will then print roc-auc score of train and test, then plot the roc, auc curve,\n",
+        "    print confusion matrix for train and test, then print classification report for train and test,\n",
+        "    then plot the feature importances if the model has feature importances,\n",
+        "    and finally it will return the following scores as a list:\n",
+        "    recall_train, recall_test, acc_train, acc_test, roc_auc_train, roc_auc_test, F1_train, F1_test\n",
+        "    '''\n",
+        "\n",
+        "    # fit the model on the training data\n",
+        "    model.fit(X_train, y_train)\n",
+        "\n",
+        "    # make predictions on the test data\n",
+        "    y_pred_train = model.predict(X_train)\n",
+        "    y_pred_test = model.predict(X_test)\n",
+        "    pred_prob_train = model.predict_proba(X_train)[:,1]\n",
+        "    pred_prob_test = model.predict_proba(X_test)[:,1]\n",
+        "\n",
+        "    # calculate ROC AUC score\n",
+        "    roc_auc_train = roc_auc_score(y_train, y_pred_train)\n",
+        "    roc_auc_test = roc_auc_score(y_test, y_pred_test)\n",
+        "    print(\"\\nTrain ROC AUC:\", roc_auc_train)\n",
+        "    print(\"Test ROC AUC:\", roc_auc_test)\n",
+        "\n",
+        "    # plot the ROC curve\n",
+        "    fpr_train, tpr_train, thresholds_train = roc_curve(y_train, pred_prob_train)\n",
+        "    fpr_test, tpr_test, thresholds_test = roc_curve(y_test, pred_prob_test)\n",
+        "    plt.plot([0,1],[0,1],'k--')\n",
+        "    plt.plot(fpr_train, tpr_train, label=\"Train ROC AUC: {:.2f}\".format(roc_auc_train))\n",
+        "    plt.plot(fpr_test, tpr_test, label=\"Test ROC AUC: {:.2f}\".format(roc_auc_test))\n",
+        "    plt.legend()\n",
+        "    plt.title(\"ROC Curve\")\n",
+        "    plt.xlabel(\"False Positive Rate\")\n",
+        "    plt.ylabel(\"True Positive Rate\")\n",
+        "    plt.show()\n",
+        "\n",
+        "    # calculate confusion matrix\n",
+        "    cm_train = confusion_matrix(y_train, y_pred_train)\n",
+        "    cm_test = confusion_matrix(y_test, y_pred_test)\n",
+        "\n",
+        "    fig, ax = plt.subplots(1, 2, figsize=(11,4))\n",
+        "\n",
+        "    print(\"\\nConfusion Matrix:\")\n",
+        "    sns.heatmap(cm_train, annot=True, xticklabels=['Negative', 'Positive'], yticklabels=['Negative', 'Positive'], cmap=\"Oranges\", fmt='.4g', ax=ax[0])\n",
+        "    ax[0].set_xlabel(\"Predicted Label\")\n",
+        "    ax[0].set_ylabel(\"True Label\")\n",
+        "    ax[0].set_title(\"Train Confusion Matrix\")\n",
+        "\n",
+        "    sns.heatmap(cm_test, annot=True, xticklabels=['Negative', 'Positive'], yticklabels=['Negative', 'Positive'], cmap=\"Oranges\", fmt='.4g', ax=ax[1])\n",
+        "    ax[1].set_xlabel(\"Predicted Label\")\n",
+        "    ax[1].set_ylabel(\"True Label\")\n",
+        "    ax[1].set_title(\"Test Confusion Matrix\")\n",
+        "\n",
+        "    plt.tight_layout()\n",
+        "    plt.show()\n",
+        "\n",
+        "\n",
+        "    # calculate classification report\n",
+        "    cr_train = classification_report(y_train, y_pred_train, output_dict=True)\n",
+        "    cr_test = classification_report(y_test, y_pred_test, output_dict=True)\n",
+        "    print(\"\\nTrain Classification Report:\")\n",
+        "    crt = pd.DataFrame(cr_train).T\n",
+        "    print(crt.to_markdown())\n",
+        "    # sns.heatmap(pd.DataFrame(cr_train).T.iloc[:, :-1], annot=True, cmap=\"Blues\")\n",
+        "    print(\"\\nTest Classification Report:\")\n",
+        "    crt2 = pd.DataFrame(cr_test).T\n",
+        "    print(crt2.to_markdown())\n",
+        "    # sns.heatmap(pd.DataFrame(cr_test).T.iloc[:, :-1], annot=True, cmap=\"Blues\")\n",
+        "\n",
+        "\n",
+        "    precision_train = cr_train['weighted avg']['precision']\n",
+        "    precision_test = cr_test['weighted avg']['precision']\n",
+        "\n",
+        "    recall_train = cr_train['weighted avg']['recall']\n",
+        "    recall_test = cr_test['weighted avg']['recall']\n",
+        "\n",
+        "    acc_train = accuracy_score(y_true = y_train, y_pred = y_pred_train)\n",
+        "    acc_test = accuracy_score(y_true = y_test, y_pred = y_pred_test)\n",
+        "\n",
+        "    F1_train = cr_train['weighted avg']['f1-score']\n",
+        "    F1_test = cr_test['weighted avg']['f1-score']\n",
+        "\n",
+        "    model_score = [precision_train, precision_test, recall_train, recall_test, acc_train, acc_test, roc_auc_train, roc_auc_test, F1_train, F1_test ]\n",
+        "    return model_score"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "OB4l2ZhMeS1U"
+      },
+      "source": [
+        "### ML Model: Multinomial Naive Bayes"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 34,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from sklearn.naive_bayes import GaussianNB,MultinomialNB,BernoulliNB"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 35,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "gnb = GaussianNB()\n",
+        "mnb = MultinomialNB()\n",
+        "bnb = BernoulliNB()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 36,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "0.8851399856424982\n",
+            "[[1070  128]\n",
+            " [  32  163]]\n",
+            "0.5601374570446735\n",
+            "0.968413496051687\n",
+            "[[1198    0]\n",
+            " [  44  151]]\n",
+            "1.0\n",
+            "0.9798994974874372\n",
+            "[[1194    4]\n",
+            " [  24  171]]\n",
+            "0.9771428571428571\n"
+          ]
+        }
+      ],
+      "source": [
+        "gnb.fit(X_train,y_train)\n",
+        "y_pred1 = gnb.predict(X_test)\n",
+        "print(accuracy_score(y_test,y_pred1))\n",
+        "print(confusion_matrix(y_test,y_pred1))\n",
+        "print(precision_score(y_test,y_pred1))\n",
+        "\n",
+        "########################\n",
+        "\n",
+        "mnb.fit(X_train,y_train)\n",
+        "y_pred2 = mnb.predict(X_test)\n",
+        "print(accuracy_score(y_test,y_pred2))\n",
+        "print(confusion_matrix(y_test,y_pred2))\n",
+        "print(precision_score(y_test,y_pred2))\n",
+        "\n",
+        "\n",
+        "\n",
+        "##################################\n",
+        "\n",
+        "\n",
+        "bnb.fit(X_train,y_train)\n",
+        "y_pred3 = bnb.predict(X_test)\n",
+        "print(accuracy_score(y_test,y_pred3))\n",
+        "print(confusion_matrix(y_test,y_pred3))\n",
+        "print(precision_score(y_test,y_pred3))\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ArJBuiUVfxKd"
+      },
+      "source": [
+        "#### Explain the ML Model used and it's performance using Evaluation metric Score Chart."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 37,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "rqD5ZohzfxKe",
+        "outputId": "1d55b01f-7021-4049-e49d-51e8fc19b922"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\n",
+            "Train ROC AUC: 0.9513371932726771\n",
+            "Test ROC AUC: 0.8645263473310218\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 500x500 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\n",
+            "Confusion Matrix:\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 1100x400 with 4 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\n",
+            "Train Classification Report:\n",
+            "|              |   precision |   recall |   f1-score |    support |\n",
+            "|:-------------|------------:|---------:|-----------:|-----------:|\n",
+            "| 0            |    1        | 0.902674 |   0.948848 | 3627       |\n",
+            "| 1            |    0.609945 | 1        |   0.757721 |  552       |\n",
+            "| accuracy     |    0.91553  | 0.91553  |   0.91553  |    0.91553 |\n",
+            "| macro avg    |    0.804972 | 0.951337 |   0.853285 | 4179       |\n",
+            "| weighted avg |    0.948478 | 0.91553  |   0.923602 | 4179       |\n",
+            "\n",
+            "Test Classification Report:\n",
+            "|              |   precision |   recall |   f1-score |    support |\n",
+            "|:-------------|------------:|---------:|-----------:|-----------:|\n",
+            "| 0            |    0.970962 | 0.893155 |   0.930435 | 1198       |\n",
+            "| 1            |    0.560137 | 0.835897 |   0.670782 |  195       |\n",
+            "| accuracy     |    0.88514  | 0.88514  |   0.88514  |    0.88514 |\n",
+            "| macro avg    |    0.76555  | 0.864526 |   0.800608 | 1393       |\n",
+            "| weighted avg |    0.913452 | 0.88514  |   0.894087 | 1393       |\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Visualizing evaluation Metric Score chart\n",
+        "GNB_score = evaluate_model(gnb, X_train, X_test, y_train, y_test)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 38,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\n",
+            "Train ROC AUC: 0.9365942028985508\n",
+            "Test ROC AUC: 0.8871794871794871\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 500x500 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\n",
+            "Confusion Matrix:\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 1100x400 with 4 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\n",
+            "Train Classification Report:\n",
+            "|              |   precision |   recall |   f1-score |    support |\n",
+            "|:-------------|------------:|---------:|-----------:|-----------:|\n",
+            "| 0            |    0.981066 | 1        |   0.990442 | 3627       |\n",
+            "| 1            |    1        | 0.873188 |   0.932302 |  552       |\n",
+            "| accuracy     |    0.98325  | 0.98325  |   0.98325  |    0.98325 |\n",
+            "| macro avg    |    0.990533 | 0.936594 |   0.961372 | 4179       |\n",
+            "| weighted avg |    0.983567 | 0.98325  |   0.982763 | 4179       |\n",
+            "\n",
+            "Test Classification Report:\n",
+            "|              |   precision |   recall |   f1-score |     support |\n",
+            "|:-------------|------------:|---------:|-----------:|------------:|\n",
+            "| 0            |    0.964573 | 1        |   0.981967 | 1198        |\n",
+            "| 1            |    1        | 0.774359 |   0.872832 |  195        |\n",
+            "| accuracy     |    0.968413 | 0.968413 |   0.968413 |    0.968413 |\n",
+            "| macro avg    |    0.982287 | 0.887179 |   0.9274   | 1393        |\n",
+            "| weighted avg |    0.969533 | 0.968413 |   0.96669  | 1393        |\n"
+          ]
+        }
+      ],
+      "source": [
+        "MNB_score = evaluate_model(mnb, X_train, X_test, y_train, y_test)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 39,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\n",
+            "Train ROC AUC: 0.9599478848251639\n",
+            "Test ROC AUC: 0.9367920893797355\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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cPUkiKsJSU1Ph4+ODHTt2QE9PDzt37kTHjh2lLisdBuUniEtKBQA4V7bOeIEPJxFIjv//CFfbmgxJIiqyUlJS4OPjg507d0JPTw+7d++Gp6en1GVliEH5CeadDAYA6GY0ddzHJhHoy+sZiahoEkKoQlJfXx+7d+9G+/btpS4rUxz1mkP/veVUuRIfjFYVIuuQ5AhXIirCZDIZ3N3dYWhoiD179uTrkATYo8yxxP/cB21As3L/fyE7kwjwwn8iKuL69+8PDw8PlCpVSupSPoo9yhxKVb6/1sdAV/7/u3akXfox9z8TD6RNIqBv8v8HQ5KIipjk5GT8+OOPePXqlaqtIIQkwKDMMYXifVDq/Pf8ZEo8JxEgIvpAcnIyvvzyS8yfPx/t2rWDUpmze/FKhYdecyj13//ROpndA5KXfhARISkpCV9++SUOHjwIQ0NDzJgxA3J5weqjMShzSPHvoVe1Ea//vRGLPs9DElHRlpSUhC5duuDw4cMwNDTEgQMH0KpVK6nL0hiDMofu/B2t3vDhZAJEREVYYmIiunTpgiNHjsDIyAgHDx5Ey5YtpS4rRxiUOZTWo3wb/+9lIikfTCagx5sjE1HRNWzYMFVIHjp0KN9McJ4TBetAcT6i/Pcwq1vVf2fl+e9hV04mQERF3Lhx41CtWjUcPny4QIckwB5ljv3boYRMJkt/2JUhSURFkBDi/e9EAA4ODrhz5w50dHQkrurTsUeZQ2k9Sh2ZjIddiajIS0hIgKenJ/bu3atqKwwhCTAocyzt5qLpRjnzsCsRFTHx8fHo0KEDDh8+jH79+iE6OvrjKxUgPPSaQ2mDeVSHXtMwJImoCEkLydOnT8PExAQHDhyAhYWF1GVpFYMyh9LOUcoBXhZCREVSXFwcPD09cfbsWZiamuLo0aNo1qyZ1GVpHYMyh9LOURoikecniajIiYuLQ/v27REQEABTU1McO3YMTZs2lbqsXMFzlDmUdrRV/t9DrTw/SURFxKpVqxAQEAAzMzMcP3680IYkwB5ljqX1KNWmemVIElERMWzYMDx9+hTe3t5o3Lix1OXkKgZlDh39MxyAwNAn30ldChFRnoiLi4O+vj709PQgl8uxcOFCqUvKEzz0mkNyGWCEJJRK/Ot9A89PElEh9u7dO3h4eMDHxwcpKSlSl5On2KPMoXS31+L5SSIqpN69e4c2bdrg0qVLsLCwwKNHj1C5cmWpy8oz7FHm0LUnb9UbGJJEVAjFxMTAw8MDly5dgqWlJU6dOlWkQhJgj5KIiDIRHR0NDw8P/PHHHyhWrBhOnjyJevXqSV1WnmNQ5pCRng5QtA7TE1EREh0dDXd3d1y5cgXFihXDqVOnULduXanLkgQPvX4CHmwlosLq3r17uH37NqysrHD69OkiG5IAe5SfQGCn/lSpiyAiyhVNmjTBoUOH8Nlnn8HJyUnqciTFoMwhIyShuvzp+ye8NISICoG3b9/i1atXqsE6LVu2lLii/IGHXrWBl4YQUQH35s0buLm5oUWLFnjw4IHU5eQrDEptYEgSUQGWFpI3b96EUqmEQqGQuqR8hUGZQ4xGIioM/vnnH7Rs2RK3bt2CtbU1zp49ixo1akhdVr7Cc5Q5IQQ2yiZJXQUR0Sf5559/4ObmhsDAQFVIVqtWTeqy8h32KHNAkRSnGsiTXLwGB/IQUYGT1pMMDAyEjY0NQzILDMociEtOVf2s7HuE5yiJqMDR1dWFgYEBbG1tERAQwJDMAg+9fiIZQ5KICiALCwscP34cr1+/RsWKFaUuJ19jj5KIqIh49eoV1q9fr3puaWnJkMwG9iiJiIqAiIgIuLq64v79+0hKSsLXX38tdUkFBnuURESFXHh4OFxcXHD//n2ULFmSM+5oiEFJRFSIhYWFwcXFBQ8ePECpUqUQEBDAw60a4qFXIqJCKi0kg4KCULp0aZw9exYVKlSQuqwChz1KIqJCKD4+XhWS9vb2CAgIYEjmEIOSiKgQMjY2Rv/+/VGmTBkEBASgfPnyUpdUYDEoiYgKqVGjRuHOnTsoV66c1KUUaAxKIqJC4sWLF/Dx8UFMTIyqzcLCQsKKCgcO5iEiKgSeP38OFxcXhIaGQqFQYPv27VKXVGiwR5kDQkhdARHR/z179gzOzs4IDQ2Fo6MjfvnlF6lLKlQYlJ9IxjtTEpGEnj59CmdnZzx69AjlypVDQEAAypYtK3VZhQqDkoiogHry5AmcnZ3x+PFjlC9fHgEBAShTpozUZRU6PEdJRFQACSHQo0cPPHnyRBWSpUuXlrqsQok9SiKiAkgmk2Ht2rVo1qwZzp07x5DMRexREhEVIKmpqdDVff+ru1q1ajh//jzvi5vL2KMkIiogHj16hBo1auDMmTOqNoZk7mNQEhEVAKGhoXB2dkZQUBBGjRoFpVIpdUlFhuRBuWzZMjg4OMDQ0BCNGjXC1atXs1x+4cKFqFy5MoyMjGBvb4/hw4cjMTExj6olIsp7ISEhcHZ2xvPnz1GlShUcPnwYcrnkv76LDEn3tL+/P0aMGIHJkyfj5s2bqF27Ntzd3fHq1asMl9+6dSvGjh2LyZMn48GDB1i7di38/f0xfvz4PK6ciChv/PXXX3B2dsbff/+NqlWr4uzZs7C1tZW6rCJF0qCcP38+Bg4ciL59+6JatWpYsWIFjI2NsW7dugyX//3339G0aVP07NkTDg4OaN26NXr06PHRXigRUUGUFpIvXrxAtWrVGJISkSwok5OTcePGDbi5uf2/GLkcbm5uuHz5cobrNGnSBDdu3FAF46NHj3DkyBG0bds20/dJSkpCTEyM2oOIqCBYuHAhXr58ierVq+Ps2bOwsbGRuqQiSbLLQyIjI6FQKNL9j7exscHDhw8zXKdnz56IjIxEs2bNIIRAamoqvv766ywPvc6aNQtTp07Vau3gXK9ElAcWLlwIU1NT/Pjjj7C2tpa6nCKrQJ0NDggIwMyZM/Hbb7/h5s2b2LNnDw4fPozp06dnus64ceMQHR2tejx//lyrNXFkNhFp04sXL1QjWvX09PDLL78wJCUmWY+yePHi0NHRQUREhFp7REREpsfgJ06ciF69emHAgAEAgJo1ayIuLg6DBg3ChAkTMhwFZmBgAAMDA+1/ACIiLXvw4AFcXFzQsWNHLF++nCNb8wnJ/i/o6+ujXr16OH36tKpNqVTi9OnTaNy4cYbrxMfHp/vi6OjoAHg/7yERUUF1//59ODs7IyIiAleuXEFsbKzUJdG/JJ3CbsSIEfD19UX9+vXRsGFDLFy4EHFxcejbty8AoHfv3ihVqhRmzZoFAPD09MT8+fNRp04dNGrUCCEhIZg4cSI8PT1VgUlEVNDcu3cPrq6uePXqFZycnHDq1CmYm5tLXRb9S9Kg9Pb2xuvXrzFp0iSEh4fDyckJx44dUw3wefbsmVoP8qeffoJMJsNPP/2EFy9eoESJEvD09MSMGTOk+ghERJ/kzz//hKurK16/fo06derg1KlTsLKykros+g+ZKGLHLGNiYmBhYYHo6Ogc/8UWHRUFi4Xvb4yaMuZv6BmZabNEIioi7t69C1dXV0RGRqJu3bo4efIkQzIPZTcPeKaYiEgijx49QlRUFOrVq8eeZD7G22wREUmkY8eOOHz4MBo0aIBixYpJXQ5lgkFJRJSHbt++DUtLS5Qt+/70TevWrSWuiD6Gh16JiPLIzZs34eLiAhcXF61PfkK5h0FJRJQH0ua2fvv2LWxsbHj5RwHCoMwBwcleiUgD169fV4Vk48aNcfz4cVhYWEhdFmUTg/ITcapXIsrKtWvX0KpVK0RFRaFJkyY4duwYe5MFDAfzEBHlkps3b6JVq1aIjo5G06ZNcfToUZiZ8brrgoZBSUSUS0qXLo1SpUqhZs2aOHLkCEOygGJQEhHlEmtra5w9exbGxsYwNTWVuhzKIZ6jJCLSosuXL2PDhg2q59bW1gzJAo49SiIiLfn999/h4eGB2NhYFC9eHO3atZO6JNIC9iiJiLTg0qVLcHd3x7t37+Ds7AxnZ2epSyItYVASEX2iixcvqnqSrq6uOHToEExMTKQui7SEQUlE9AkuXLigCsmWLVvi4MGDMDY2lros0iIGJRFRDj169Aht2rRBXFwcWrVqxZAspDiYh4gohxwdHTFkyBAEBgZi3759MDIykrokygUMyhwQnOqViADIZDLMnj0bKSkp0NfXl7ocyiU89PqJZDLO9kpUlJw5cwZeXl5ISEgA8P53AEOycGNQEhFl0+nTp9G+fXvs378fv/76q9TlUB5hUBIRZcOpU6fQvn17JCQkoF27dhg7dqzUJVEeYVASEX3EyZMn4enpicTERLRv3x67d++GgYGB1GVRHmFQEhFl4fjx46qQ9PT0xK5duxiSRQyDkogoE/Hx8fD19UVSUhI6duzIkCyiGJRERJkwNjbGgQMH0Lt3b+zYsYOjW4soXkdJRPSBuLg41VytDRs2RMOGDSWuiKTEHiUR0X8cOnQI5cqVw7Vr16QuhfIJBiUR0b8OHjyIzp0749WrV1ixYoXU5VA+waAkIgJw4MABdOnSBSkpKfjyyy8ZlKTCoMwBTvVKVLjs378fXbt2RUpKCry9vbF161bo6elJXRblEwzKT8SZXokKtr1796pCsnv37ti8eTN0dTnOkf6PQUlERZYQAmvWrEFqaip69OiBTZs2MSQpHQYlERVZMpkMu3btwq+//oqNGzcyJClDDEoiKnLu3r0L8e+NZY2MjDBq1CiGJGWKQUlERcrOnTtRp04djB8/XhWWRFlhUBJRkeHv748ePXpAoVAgLCyMQUnZwqAkoiJh27Zt6NmzJxQKBfr27Yu1a9dCLuevQPo4fkuIqNDbunUrvvrqKyiVSvTr1w9r1qyBjo6O1GVRAfFJQZmYmKitOoiIcsXmzZvRq1cvKJVK9O/fH6tXr2ZPkjSi8bdFqVRi+vTpKFWqFExNTfHo0SMAwMSJE7F27VqtF0hE9CmSk5OhVCoxYMAArFq1iiFJGtP4G/Pzzz/Dz88Pv/76q9q92WrUqIE1a9ZotTgiok/Vr18/nD9/HitXrmRIUo5o/K3ZuHEjVq1aBR8fH7Vj/LVr18bDhw+1WhwRUU7s3r0br169Uj1v3rw5Q5JyTONvzosXL1ChQoV07UqlEikpKVopKr/jkHKi/GvdunX48ssv4erqiujoaKnLoUJA46CsVq0aLly4kK59165dqFOnjlaKKkhknBWdKN9Yu3YtBgwYACEEnJ2dYW5uLnVJVAhoPGfTpEmT4OvrixcvXkCpVGLPnj0ICgrCxo0bcejQodyokYjoo1avXo1BgwYBAL777jssWrQIMv4lS1qgcY+yY8eOOHjwIE6dOgUTExNMmjQJDx48wMGDB9GqVavcqJGIKEurVq1SheSwYcMYkqRVOZoFuHnz5jh58qS2ayEi0tjmzZsxePBgAMAPP/yA+fPnMyRJqzTuUZYrVw7//PNPuvaoqCiUK1dOK0UREWVXs2bNULZsWQwfPpwhSblC4x7lkydPoFAo0rUnJSXhxYsXWimKiCi7HBwccOPGDVhZWTEkKVdkOygPHDig+vn48eOwsLBQPVcoFDh9+jQcHBy0WhwRUUaWLVsGOzs7dO7cGQDw2WefSVwRFWbZDkovLy8A7+8I7uvrq/aanp4eHBwcMG/ePK0WR0T0ocWLF2PYsGHQ1dXFrVu3UKNGDalLokIu20GpVCoBAI6Ojrh27RqKFy+ea0UREWVk0aJF+OGHHwAAI0eORPXq1aUtiIoEjc9RPn78ODfqICLK0oIFCzBixAgAwPjx4/Hzzz/znCTliRxdHhIXF4dz587h2bNnSE5OVnvt+++/10phRERp5s+fjx9//BEAMGHCBEyfPp0hSXlG46C8desW2rZti/j4eMTFxcHKygqRkZEwNjaGtbV1kQhKzvRKlHdOnDihCsmJEydi6tSpDEnKUxpfRzl8+HB4enri7du3MDIywh9//IGnT5+iXr16mDt3bm7UmK/xHyxR7nJzc8OAAQMwefJkTJs2jf/mKM9p3KMMDAxU3ddNR0cHSUlJKFeuHH799Vf4+vqqhmsTEX0KpVIJuVwOuVyOVatWMSBJMhr3KPX09FT3dbO2tsazZ88AABYWFnj+/Ll2qyOiImnWrFnw9vZW3bqPIUlS0rhHWadOHVy7dg0VK1ZEixYtMGnSJERGRmLTpk28nomIPtnMmTMxYcIEAED37t3RpUsXiSuiok7jHuXMmTNhZ2cHAJgxYwaKFSuGb775Bq9fv8bKlSs1LmDZsmVwcHCAoaEhGjVqhKtXr2a5fFRUFIYMGQI7OzsYGBigUqVKOHLkiMbvS0T5z88//6wKyenTpzMkKV/QuEdZv3591c/W1tY4duxYjt/c398fI0aMwIoVK9CoUSMsXLgQ7u7uCAoKgrW1dbrlk5OT0apVK1hbW2PXrl0oVaoUnj59CktLyxzXQET5w7Rp0zB58mQA7/8IHz9+vMQVEb2ncY8yMzdv3kT79u01Wmf+/PkYOHAg+vbti2rVqmHFihUwNjbGunXrMlx+3bp1ePPmDfbt24emTZvCwcEBLVq0QO3atbXxEYhIIlOnTlWF5KxZsxiSlK9oFJTHjx/HyJEjMX78eDx69AgA8PDhQ3h5eaFBgwaqae6yIzk5GTdu3ICbm9v/i5HL4ebmhsuXL2e4zoEDB9C4cWMMGTIENjY2qFGjBmbOnJnh3UzSJCUlISYmRu1BRPnH48ePMXv2bADA7NmzMXbsWIkrIlKX7UOva9euxcCBA2FlZYW3b99izZo1mD9/Pr777jt4e3vjzz//RNWqVbP9xpGRkVAoFLCxsVFrt7GxwcOHDzNc59GjRzhz5gx8fHxw5MgRhISE4Ntvv0VKSorqr9EPzZo1C1OnTs12XUSUtxwdHXHo0CHcvn1bNUUdUX6S7R7lokWL8MsvvyAyMhI7duxAZGQkfvvtN9y9excrVqzQKCRzSqlUwtraGqtWrUK9evXg7e2NCRMmYMWKFZmuM27cOERHR6sevISFSHpCCLx69Ur1vGXLlgxJyreyHZShoaH48ssvAQCdO3eGrq4u5syZg9KlS+fojYsXLw4dHR1ERESotUdERMDW1jbDdezs7FCpUiXo6Oio2qpWrYrw8PB0c86mMTAwgLm5udqDiKQjhMDEiRNRq1YtPHjwQOpyiD4q20GZkJAAY2NjAO8v/jUwMFBdJpIT+vr6qFevHk6fPq1qUyqVOH36NBo3bpzhOk2bNkVISIjaudDg4GDY2dlBX18/x7VoinO9EuWMEAITJkzAjBkzEBERgfPnz0tdEtFHaXR5yJo1a2BqagoASE1NhZ+fX7r7UmoyKfqIESPg6+uL+vXro2HDhli4cCHi4uLQt29fAEDv3r1RqlQpzJo1CwDwzTffYOnSpRg2bBi+++47/PXXX5g5c2aRmIidqKATQmD8+PGqgTuLFi3C4MGDJa6K6OOyHZRlypTB6tWrVc9tbW2xadMmtWVkMplGoeXt7Y3Xr19j0qRJCA8Ph5OTE44dO6Ya4PPs2TPVdHkAYG9vj+PHj2P48OGoVasWSpUqhWHDhmHMmDHZfk8iyntCCIwdOxa//vorAGDx4sX47rvvJK6KKHtkQogidSQxJiYGFhYWiI6OzvH5ytdv3qDEYsf3T8a/BPRNtFghUeEihMDo0aNVdxdaunQphgwZInFVRNnPgxzduJmIKLsSExNx4cIFAO+nrPz2228lrohIMwxKIspVRkZGOH78OE6cOKEaOU9UkGhtCjsiojRCCJw5c0b13MLCgiFJBRaDkoi0SgiBYcOGoWXLlpg3b57U5RB9Mh56JSKtEULg+++/x9KlSyGTyXhnHyoUctSjDA0NxU8//YQePXqopqE6evQo7t27p9XiiKjgEEJg6NChqpBcs2YN+vfvL3VZRJ9M46A8d+4catasiStXrmDPnj2IjY0FANy+fTvTicmJqHBTKpUYMmQIfvvtN8hkMqxbtw79+vWTuiwirdA4KMeOHYuff/4ZJ0+eVJs2ztXVFX/88YdWiyOi/E8IgSFDhmD58uWQyWRYv349+vTpI3VZRFqjcVDevXsXnTp1StdubW2NyMhIrRSV3wnO9kqkIpPJUL58ecjlcmzYsAG+vr5Sl0SkVRoHpaWlJcLCwtK137p1C6VKldJKUURUsIwcORJ3795Fr169pC6FSOs0Dsru3btjzJgxCA8Ph0wmg1KpxKVLlzBy5Ej07t07N2okonxGqVRi9uzZiI6OVrVVq1ZNwoqIco/GQTlz5kxUqVIF9vb2iI2NRbVq1fDFF1+gSZMm+Omnn3KjRiLKR5RKJQYMGIBx48ahXbt2are9IyqMNL6OUl9fH6tXr8bEiRPx559/IjY2FnXq1EHFihVzoz4iykcUCgUGDBgAPz8/yOVyDB06VO0OP0SFkcZBefHiRTRr1gxlypRBmTJlcqMmIsqHFAoF+vXrh40bN0JHRwdbtmyBt7e31GUR5TqN/xR0dXWFo6Mjxo8fj/v37+dGTUSUzygUCvTt21cVktu2bWNIUpGhcVC+fPkSP/74I86dO4caNWrAyckJc+bMwd9//50b9RFRPvDjjz9i06ZN0NHRwfbt2znBORUpGgdl8eLFMXToUFy6dAmhoaH48ssvsWHDBjg4OMDV1TU3aiQiiQ0aNAilS5eGv78/unbtKnU5RHnqkyZFd3R0xNixY1G7dm1MnDgR586d01ZdRJSPVKtWDcHBwTAyMpK6FKI8l+PhapcuXcK3334LOzs79OzZEzVq1MDhw4e1WRsRSSQ1NRX9+vXD6dOnVW0MSSqqNO5Rjhs3Dtu3b8fLly/RqlUrLFq0CB07doSxsXFu1EdEeSw1NRU+Pj7YsWMH9uzZgydPnvB2WVSkaRyU58+fx6hRo9CtWzcUL148N2oiIomkpKTAx8cHO3fuhJ6eHjZu3MiQpCJP46C8dOlSbtRRsHBOdCqEUlJS0KNHD+zevRv6+vrYvXs32rdvL3VZRJLLVlAeOHAAbdq0gZ6eHg4cOJDlsh06dNBKYUSUd5KTk9G9e3fs3bsX+vr62LNnD9q1ayd1WUT5QraC0svLC+Hh4bC2toaXl1emy8lkMigUCm3VRkR5ZNmyZaqQ3Lt3L9q2bSt1SUT5RraC8r+THnMCZKLCZ+jQobhx4wZ8fHzQpk0bqcshylc0vjxk48aNSEpKSteenJyMjRs3aqUoIsp9ycnJqj989fT0sHnzZoYkUQY0Dsq+ffuq3YMuzbt379C3b1+tFEVEuSspKQldu3bF119/zaNERB+hcVAKISCTydK1//3337CwsNBKUUSUe9JC8uDBg9i0aRMePHggdUlE+Vq2Lw+pU6cOZDIZZDIZWrZsCV3d/6+qUCjw+PFjeHh45EqRRKQdiYmJ6NKlC44cOQJDQ0McPHgQ1atXl7osonwt20GZNto1MDAQ7u7uMDU1Vb2mr68PBwcHdOnSResFEpF2JCYmonPnzjh69CiMjIxw8OBBtGzZUuqyiPK9bAfl5MmTAQAODg7w9vaGoaFhrhVFRNqVmJiITp064dixYzAyMsLhw4fh4uIidVlEBYLGM/P4+vrmRh1ElIuuXr2KU6dOwdjYGIcPH4azs7PUJREVGNkKSisrKwQHB6N48eIoVqxYhoN50rx580ZrxRGRdnzxxRfw9/fHZ599hhYtWkhdDlGBkq2gXLBgAczMzFQ/ZxWURQGneqWCID4+Hm/evEHp0qUBAJ07d5a4IqKCKVtB+d/DrX369MmtWohIS+Lj49GhQweEhoYiICAAZcuWlbokogJL4+sob968ibt376qe79+/H15eXhg/fjySk5O1WhwRaS4+Ph6enp44ffo0IiMj8fLlS6lLIirQNA7KwYMHIzg4GADw6NEjeHt7w9jYGDt37sTo0aO1XiARZV9cXBzat2+PM2fOwMzMDMePH0fjxo2lLouoQNM4KIODg+Hk5AQA2LlzJ1q0aIGtW7fCz88Pu3fv1nZ9RJRNcXFxaNeuHc6ePasKySZNmkhdFlGBl6Mp7NLmhjx16pTqdjz29vaIjIzUbnVElC2xsbFo27Ytzp07B3Nzc5w4cYI9SSIt0Tgo69evj59//hmbNm3CuXPnVDd3ffz4MWxsbLReIBF9XEJCAv755x9VSH7++edSl0RUaGg84cDChQvh4+ODffv2YcKECahQoQIAYNeuXTzMQySREiVK4MyZM3j+/Dnq1asndTlEhYrGQVmrVi21Ua9p5syZAx0dHa0URUQfFxMTg7Nnz6Jjx44AAGtra1hbW0tcFVHho3FQprlx44bq9jzVqlVD3bp1tVYUEWUtJiYGHh4e+OOPP+Dn54fevXtLXRJRoaVxUL569Qre3t44d+4cLC0tAQBRUVFwcXHB9u3bUaJECW3XSET/ER0drQrJYsWK8TZZRLlM48E83333HWJjY3Hv3j28efMGb968wZ9//omYmBh8//33uVEjEf0rOjoa7u7uqpA8deoUz0kS5TKNe5THjh3DqVOnULVqVVVbtWrVsGzZMrRu3VqrxeVbnOyVJBAVFQV3d3dcvXoVVlZWOHXqFOrUqSN1WUSFnsZBqVQqoaenl65dT09PdX0lEWlXQkICWrdujWvXrsHKygqnT59WTfxBRLlL40Ovrq6uGDZsmNr8kS9evMDw4cN5t3SiXGJoaAhXV1d89tlnOHPmDEOSKA9pHJRLly5FTEwMHBwcUL58eZQvXx6Ojo6IiYnBkiVLcqNGoiJPJpNh1qxZuHPnDmrXri11OURFisaHXu3t7XHz5k2cPn1adXlI1apV4ebmpvXiiIqyN2/eYNq0aZg1axaMjIwgk8lQsmRJqcsiKnI0Ckp/f38cOHAAycnJaNmyJb777rvcqouoSPvnn3/g5uaGwMBAREZGYvPmzVKXRFRkZTsoly9fjiFDhqBixYowMjLCnj17EBoaijlz5uRmfURFTmRkJNzc3HD79m1YW1tj3LhxUpdEVKRl+xzl0qVLMXnyZAQFBSEwMBAbNmzAb7/9lpu1ERU5kZGRaNmyJW7fvg0bGxucPXuWEwoQSSzbQfno0SP4+vqqnvfs2ROpqakICwvLlcKIiprXr1/D1dUVd+7cUYVktWrVpC6LqMjLdlAmJSXBxMTk/yvK5dDX10dCQkKuFEZUlAgh0KVLF9y9exe2trYICAhQm9SDiKSj0WCeiRMnwtjYWPU8OTkZM2bMgIWFhapt/vz52quOqIiQyWSYN28efH19sXfvXlSuXFnqkojoX9kOyi+++AJBQUFqbU2aNMGjR49Uz2UymfYqIyoChBCqfzcNGjTA3bt3ebs6onwm20EZEBCQi2UULIKTvZIWREREoHPnzli4cCEaNGgAAAxJonxI45l5iOjThYeHw8XFBb///jv69evHeZKJ8rF8EZTLli2Dg4MDDA0N0ahRI1y9ejVb623fvh0ymQxeXl65WyCRFoWFhcHFxQUPHjxA6dKlsXfvXsjl+eKfIhFlQPJ/nf7+/hgxYgQmT56Mmzdvonbt2nB3d8erV6+yXO/JkycYOXIkmjdvnkeVEn26tJB8+PAh7O3tERAQgAoVKkhdFhFlQfKgnD9/PgYOHIi+ffuiWrVqWLFiBYyNjbFu3bpM11EoFPDx8cHUqVNRrly5PKyWKOdevnwJZ2dnBAUFoUyZMggICED58uWlLouIPkLSoExOTsaNGzfUJlSXy+Vwc3PD5cuXM11v2rRpsLa2Rv/+/T/6HklJSYiJiVF7EElh2rRpCA4ORtmyZREQEMA/8ogKiBwF5YULF/DVV1+hcePGePHiBQBg06ZNuHjxokbbiYyMhEKhgI2NjVq7jY0NwsPDM1zn4sWLWLt2LVavXp2t95g1axYsLCxUD3t7e41qJNKWBQsWoG/fvggICICjo6PU5RBRNmkclLt374a7uzuMjIxw69YtJCUlAQCio6Mxc+ZMrRf4X+/evUOvXr2wevVqFC9ePFvrjBs3DtHR0arH8+fPc7VGov+KioqCEO8vJzIyMsK6devg4OAgbVFEpBGNg/Lnn3/GihUrsHr1aujp6anamzZtips3b2q0reLFi0NHRwcRERFq7REREbC1tU23fGhoKJ48eQJPT0/o6upCV1cXGzduxIEDB6Crq4vQ0NB06xgYGMDc3FztQZQXnj17hnr16mHcuHGqsCSigkfjoAwKCsIXX3yRrt3CwgJRUVEabUtfXx/16tXD6dOnVW1KpRKnT59G48aN0y1fpUoV3L17F4GBgapHhw4d4OLigsDAQB5WpXzj2bNncHZ2xqNHj7Bjxw6N/20QUf6h0VyvAGBra4uQkJB0h48uXryYo8EJI0aMgK+vL+rXr4+GDRti4cKFiIuLQ9++fQEAvXv3RqlSpTBr1iwYGhqiRo0aautbWloCQLp2Iqk8ffoULi4uePz4McqVK4eAgAAUK1ZM6rKIKIc0DsqBAwdi2LBhWLduHWQyGV6+fInLly9j5MiRmDhxosYFeHt74/Xr15g0aRLCw8Ph5OSEY8eOqQb4PHv2jBdjU4Hx5MkTuLi44MmTJyhfvjwCAgJQunRpqcsiok8gExqePBFCYObMmZg1axbi4+MBvD8POHLkSEyfPj1XitSmmJgYWFhYIDo6OsfnK8NeR8Ju2b/Xv41/CeibZL0CFQlPnjyBs7Mznj59igoVKiAgIAClSpWSuiwiykR280DjHqVMJsOECRMwatQohISEIDY2FtWqVYOpqeknFUxU0P3xxx949uwZKlasiLNnzzIkiQoJjYMyjb6+Pu++TvQf3bt3h0wmQ/PmzVGyZEmpyyEiLdE4KF1cXLK87+SZM2c+qSCiguTRo0cwMTFRnVP39vaWuCIi0jaNg9LJyUnteUpKCgIDA/Hnn3/C19dXW3UR5XshISFwcXGBhYUFzpw5A2tra6lLIqJcoHFQLliwIMP2KVOmIDY29pMLIioI/vrrL7i4uODFixcwMzPj/SSJCjGtXXfx1VdfZXnHD6LCIjg4GM7Oznjx4gWqVauGs2fPZjiTFBEVDloLysuXL8PQ0FBbmyPKl4KCguDs7IyXL1+ievXqOHPmTLpJ/YmocNH40Gvnzp3VngshEBYWhuvXr+dowgGigiIoKAguLi4ICwtDjRo1cPr0aZ6XJCoCNA5KCwsLtedyuRyVK1fGtGnT0Lp1a60VRpTfGBoawsDAADVr1sTp06dRokQJqUsiojygUVAqFAr07dsXNWvW5NyVVOSk3XDZ2NiYIUlUhGh0jlJHRwetW7fmnRCoyLh//z7279+vel62bFmGJFERo/Fgnho1auDRo0e5UQtRvnLv3j04Ozuja9euOHHihNTlEJFEcnTj5pEjR+LQoUMICwtDTEyM2qMo4C14C78///wTLi4ueP36NWrWrIn69etLXRIRSSTb5yinTZuGH3/8EW3btgUAdOjQQW0qOyEEZDIZFAqF9qskykN3796Fq6srIiMjUbduXZw8eRJWVlZSl0VEEsl2UE6dOhVff/01zp49m5v1EEnqzp07cHV1xT///IN69erh5MmTHLhGVMRlOyjTblvZokWLXCuGSEpPnz5VhWT9+vVx8uRJWFpaSl0WEUlMo8tDsrprCFFBZ29vj06dOuH27ds4ceIEQ5KIAGgYlJUqVfpoWL558+aTCiKSilwux8qVKxEfH88bkRORikZBOXXq1HQz8xAVZDdv3sTy5cvx22+/QU9PD3K5nCFJRGo0Csru3btzbksqNG7cuAE3NzdERUWhVKlSmDJlitQlEVE+lO3rKHl+kgqT69evq0KySZMmGDFihNQlEVE+le2gTBv1SlTQXbt2TRWSTZs2xbFjx2Bubi51WUSUT2X70Cvv4E6FwZUrV9C6dWvExMSgWbNmOHLkCMzMzKQui4jyMa3duJkov0tISICXlxdiYmLQvHlzHD16lCFJRB/FoMwBHoUumIyMjLB582Z4eHjgyJEjHN1KRNmi8Y2biQqa1NRU6Oq+/6q3bNkSrq6uHJxGRNnGHiUVapcuXULVqlVx//59VRtDkog0waCkQuvixYvw8PBASEgIpk+fLnU5RFRAMSipULpw4QI8PDwQGxuLli1bYu3atVKXREQFFIOSCp3z58+jTZs2iIuLg5ubGw4ePAhjY2OpyyKiAopBSYXKuXPnVCHZqlUrHDhwAEZGRlKXRUQFGIOSCg0hBKZPn474+Hi4u7tj//79DEki+mQMSio0ZDIZdu/ejVGjRmHfvn0MSSLSCgYlFXjPnz9X/WxhYYFff/0VhoaGElZERIUJg5IKtFOnTqFy5cqYO3eu1KUQUSHFoKQC6+TJk/D09ERCQgLOnTsHhUIhdUlEVAgxKHOAc71K7/jx4/D09ERiYiI8PT2xa9cu6OjoSF0WERVCDEoqcI4dO4aOHTsiKSkJHTp0wK5du2BgYCB1WURUSDEoqUA5evQovLy8kJSUhI4dO2Lnzp3Q19eXuiwiKsQYlFSgBAUFISkpCZ06dcKOHTsYkkSU63ibLSpQfvjhBzg4OKBdu3bQ09OTuhwiKgLYo6R87+zZs4iOjlY99/LyYkgSUZ5hUFK+duDAAbi7u8Pd3R3v3r2TuhwiKoIYlJRv7d+/H127dkVKSgocHBw4JR0RSYJBSfnS3r17VSHZvXt3bN68Gbq6PKVORHmPQUn5zp49e9CtWzekpqaiZ8+e2LRpE0OSiCTDoKR8Zf/+/fD29kZqaip8fHywceNGhiQRSYq/gShfqVSpEqysrODu7o7169dzWjoikhyDkvKVqlWr4tq1ayhVqhRDkojyBR56Jcn5+/vj9OnTqudlypRhSBJRvsEeJUlq27Zt+Oqrr2BgYICrV6+iRo0aUpdERKSGPUqSzNatW/HVV19BqVSiZ8+eqFatmtQlERGlw6AkSWzevBm9evWCUqnEgAEDsGrVKsjl/DoSUf7D30yU5zZt2oTevXtDqVRi4MCBWLlyJUOSiPIt/naiPHX27Fn4+vpCCIHBgwdjxYoVDEkiytc4mIfyVLNmzdClSxeUKFECS5cuZUgSUb7HoKQ8paenh23btkEulzMkiahA4G8qynVr167F4MGDoVQqAQC6uroMSSIqMNijpFy1evVqDBo0CADg6uoKb29viSsiItJMvvizftmyZXBwcIChoSEaNWqEq1evZrrs6tWr0bx5cxQrVgzFihWDm5tblsuTdFatWqUKye+//x7dunWTuCIiIs1JHpT+/v4YMWIEJk+ejJs3b6J27dpwd3fHq1evMlw+ICAAPXr0wNmzZ3H58mXY29ujdevWePHiRZ7VLESevVWBtXLlSgwePBgAMGzYMCxcuBAymUziqoiINCcTQtpf+40aNUKDBg2wdOlSAIBSqYS9vT2+++47jB079qPrKxQKFCtWDEuXLkXv3r0/unxMTAwsLCwQHR0Nc3PzHNX8PDwS9ivKv38y/iWgb5Kj7RRWy5cvx7fffgsAGD58OObNm8eQJKJ8J7t5IGmPMjk5GTdu3ICbm5uqTS6Xw83NDZcvX87WNuLj45GSkgIrK6sMX09KSkJMTIzag3LPkydPMGzYMADAiBEjGJJEVOBJGpSRkZFQKBSwsbFRa7exsUF4eHi2tjFmzBiULFlSLWz/a9asWbCwsFA97O3tP7luypyDgwO2b9+OMWPGYO7cuQxJIirwJD9H+Slmz56N7du3Y+/evTA0NMxwmXHjxiE6Olr1eP78eR5XWTTExcWpfu7cuTNmz57NkCSiQkHSoCxevDh0dHQQERGh1h4REQFbW9ss1507dy5mz56NEydOoFatWpkuZ2BgAHNzc7UHadfixYtRo0YNPH36VOpSiIi0TtKg1NfXR7169dRu2qtUKnH69Gk0btw40/V+/fVXTJ8+HceOHUP9+vXzolTKxMKFCzFs2DA8efIEO3fulLocIiKtk3zCgREjRsDX1xf169dHw4YNsXDhQsTFxaFv374AgN69e6NUqVKYNWsWAOCXX37BpEmTsHXrVjg4OKjOZZqamsLU1FSyz1EULViwACNGjAAATJgwAT/++KPEFRERaZ/kQent7Y3Xr19j0qRJCA8Ph5OTE44dO6Ya4PPs2TO16c6WL1+O5ORkdO3aVW07kydPxpQpU/Ky9CJt3rx5GDlyJADgp59+wrRp03hOkogKJcmvo8xrvI7y082dOxejRo0CAEyaNAlTpkxhSBJRgVMgrqOkgichIQF+fn4A3vfip06dypAkokJN8kOvVLAYGRnhzJkz2L17N7755hupyyEiynXsUVK23L59W/WztbU1Q5KIigwGZQ4IFKnTupgxYwacnJywdu1aqUshIspzDErK0vTp0/HTTz8BQKZ3dCEiKswYlJSpqVOnYtKkSQDez5k7btw4iSsiIsp7HMxDGZoyZQqmTp0K4P0kD6NHj5a4IiIiaTAoSY0QAlOmTMG0adMAvJ8uMO2aSSKioohBSemkpKQAeD+xAKelI6KijkFJamQyGWbMmIG2bduiWbNmUpdDRCQ5DuYhCCGwbt06JCQkAHgflgxJIqL3GJRFnBAC48aNQ//+/eHl5QWFQiF1SURE+QoPvRZhQgiMGTMGc+bMAQB4enpCR0dH4qqIiPIXBmURJYTA6NGjMXfuXADA0qVLMWTIEImrIiLKfxiURZAQAiNHjsT8+fMBAMuWLcO3334rcVVERPkTgzIHCvodPCdNmqQKyeXLl+Prr7+WuCIiovyLg3mKoA4dOsDS0hIrV65kSBIRfQR7lEVQgwYNEBISgs8++0zqUoiI8j32KIsAIQTGjh2Lq1evqtoYkkRE2cOgLOSEEBg6dCh++eUXeHh44O3bt1KXRERUoDAoCzGlUokhQ4bgt99+g0wmw7x581CsWDGpyyIiKlB4jrKQUiqV+Pbbb7Fy5UrIZDKsX78evr6+UpdFRFTgMCgLIaVSia+//hqrV6+GTCaDn58fevfuLXVZREQFEoOyEFq2bBlWr14NuVyODRs24KuvvpK6JCKiAotBWQgNGDAAR44cwVdffQUfHx+pyyEiKtAYlIWEUqmETCaDTCaDkZERjhw5AplMJnVZREQFHke9FgIKhQL9+/fHuHHjIP6dX48hSUSkHexR5kB+muo1LSQ3bNgAHR0d9OjRA7Vr15a6LMqnFAoFUlJSpC6DKE/o6elp5daBDMoCTKFQoG/fvti0aRN0dHSwdetWhiRlSAiB8PBwREVFSV0KUZ6ytLSEra3tJx1lY1AWUAqFAn369MHmzZuho6OD7du3o2vXrlKXRflUWkhaW1vD2NiYh+ap0BNCID4+Hq9evQIA2NnZ5XhbDMoCKDU1Fb6+vti6dSt0dXWxfft2dOnSReqyKJ9SKBSqkOQcv1SUGBkZAQBevXoFa2vrHB+G5WCeAujixYvYtm0bdHV14e/vz5CkLKWdkzQ2Npa4EqK8l/a9/5Rz8+xRFkDOzs5Ys2YNihUrhk6dOkldDhUQPNxKRZE2vvcMygIiNTUV0dHRqkNn/fr1k7giIqKigYdeC4CUlBT07NkTX3zxBSIiIqQuh6hAc3BwwMKFC6UugwoQBmU+l5KSgh49emDnzp0ICQnB3bt3pS6JKE+kzTSV2WPKlCk52u61a9cwaNCgT6rN2dlZVYehoSEqVaqEWbNmqSb8+K8NGzagQYMGMDY2hpmZGVq0aIFDhw6lW04IgVWrVqFRo0YwNTWFpaUl6tevj4ULFyI+Pv6jNbm7u0NHRwfXrl3LsN4ffvghXbufnx8sLS3V2mJiYjBhwgRUqVIFhoaGsLW1hZubG/bs2ZPh58uMEAKTJk2CnZ0djIyM4Obmhr/++ivLdd69e4cffvgBZcuWhZGREZo0aZLh53nw4AE6dOgACwsLmJiYoEGDBnj27Fm2a9MUgzIfS0lJQffu3bF7927o6+tjz549cHNzk7osojwRFhameixcuBDm5uZqbSNHjlQtK4RAampqtrZbokQJrQxsGjhwIMLCwhAUFIRx48Zh0qRJWLFihdoyI0eOxODBg+Ht7Y07d+7g6tWraNasGTp27IilS5eqLdurVy/88MMP6NixI86ePYvAwEBMnDgR+/fvx4kTJ7Ks5dmzZ/j9998xdOhQrFu3LsefKSoqCk2aNMHGjRsxbtw43Lx5E+fPn4e3tzdGjx6N6OjobG/r119/xeLFi7FixQpcuXIFJiYmcHd3R2JiYqbrDBgwACdPnsSmTZtw9+5dtG7dGm5ubnjx4oVqmdDQUDRr1gxVqlRBQEAA7ty5g4kTJ8LQ0DDHn/ujRBETHR0tAIjo6Ogcb+Pxy1dCTDZ//0iK1WJ1/5eUlCQ6deokAAh9fX1x+PDhXHkfKvwSEhLE/fv3RUJCgqpNqVSKuKSUPH8olcocfYb169cLCwsL1fOzZ88KAOLIkSOibt26Qk9PT5w9e1aEhISIDh06CGtra2FiYiLq168vTp48qbatsmXLigULFqieAxCrV68WXl5ewsjISFSoUEHs378/y3patGghhg0bptZWt25d0alTJ9Xzy5cvCwBi8eLF6dYfMWKE0NPTE8+ePRNCCOHv7y8AiH379qVbVqlUiqioqCzrmTJliujevbt48OCBsLCwEPHx8R+tV4j0+/Wbb74RJiYm4sWLF+mWfffunUhJScmyjv/WbGtrK+bMmaNqi4qKEgYGBmLbtm0ZrhMfHy90dHTEoUOH1Nrr1q0rJkyYoHru7e0tvvrqq2zVIUTG3/802c0DDubJh5KTk9GtWzfs378fBgYG2LdvHzw8PKQuiwqRhBQFqk06nufve3+aO4z1tfdrZ+zYsZg7dy7KlSuHYsWK4fnz52jbti1mzJgBAwMDbNy4EZ6enggKCkKZMmUy3c7UqVPx66+/Ys6cOViyZAl8fHzw9OlTWFlZfbQGIQQuXryIhw8fomLFiqr2bdu2wdTUFIMHD063zo8//oj58+dj9+7d+OGHH7BlyxZUrlwZHTt2TLesTCaDhYVFlu+/fv16LFu2DFWqVEGFChWwa9cu9OrV66O1/5dSqcT27dvh4+ODkiVLpnvd1NRU9fOUKVPg5+eHJ0+eZLitx48fIzw8XO0ImIWFBRo1aoTLly+je/fu6dZJTU2FQqFI1zM0MjLCxYsXVTUePnwYo0ePhru7O27dugVHR0eMGzcOXl5eGn1eTfDQaw4IDY7T58Q///yDO3fuwMDAAPv372dIEmVi2rRpaNWqFcqXLw8rKyvUrl0bgwcPRo0aNVCxYkVMnz4d5cuXx4EDB7LcTp8+fdCjRw9UqFABM2fORGxsLK5evZrlOr/99htMTU1hYGCAL774AkqlEt9//73q9eDgYJQvXx76+vrp1i1ZsiTMzc0RHBwMAPjrr79QuXLlHOwB4NSpU4iPj4e7uzsA4KuvvsLatWs13k5kZCTevn2LKlWqfHTZ4sWLo3z58pm+Hh4eDgCwsbFRa7exsVG99iEzMzM0btwY06dPx8uXL6FQKLB582ZcvnwZYWFhAN5PHBAbG4vZs2fDw8MDJ06cQKdOndC5c2ecO3cuux9VY+xR5kN2dnY4e/YsQkJC0LJlS6nLoULISE8H96e5S/K+2lS/fn2157GxsZgyZQoOHz6MsLAwpKamIiEh4aMDPWrVqqX62cTEBObm5qqpzzLj4+ODCRMm4O3bt5g8eTKaNGmCJk2aqC2T3T+qP+WP73Xr1sHb2xu6uu9/nffo0QOjRo1CaGholmH2KTUMHToUQ4cO1bjWj9m0aRP69euHUqVKQUdHB3Xr1kWPHj1w48YNAO97lADQsWNHDB8+HADg5OSE33//HStWrECLFi20XhPAoMw3kpKScOXKFXzxxRcAgLJly6Js2bISV0WFlUwm0+ohUKmYmJioPR85ciROnjyJuXPnokKFCjAyMkLXrl2RnJyc5Xb09PTUnstkMtUv5cxYWFigQoUKAIAdO3agQoUK+Pzzz1WHGytVqoSLFy8iOTk5Xa/y5cuXiImJQaVKlVTLPnz48OMf+ANv3rzB3r17kZKSguXLl6vaFQoF1q1bhxkzZgAAzM3NMxyIExUVpTqsW6JECVhaWuaojg/Z2toCACIiItTmWI2IiICTk1Om65UvXx7nzp1DXFwcYmJiYGdnB29vb5QrVw7A+56srq4uqlWrprZe1apVVYdncwMPveYDiYmJ6Ny5M1q2bIm9e/dKXQ5RgXXp0iX06dMHnTp1Qs2aNWFra5vpeTRtMjU1xbBhwzBy5EhVz6x79+6IjY3FypUr0y0/d+5c6Onpqaaf7NmzJ4KDg7F///50ywohMh1tumXLFpQuXRq3b99GYGCg6jFv3jz4+flBoVAAACpXroybN2+mW//mzZuqsJbL5ejevTu2bNmCly9fpls2NjY22yOLHR0dYWtri9OnT6vaYmJicOXKFTRu3Pij65uYmMDOzg5v377F8ePHVedu9fX10aBBAwQFBaktHxwcnLsdi2wPHSoktDHq9dGLCK2Nek1ISBBt2rQRAISRkZE4ffr0J22P6ENZjforKDIb9fr27Vu15Tp16iScnJzErVu3RGBgoPD09BRmZmZqIz4zGvW6d+9ete1YWFiI9evXZ1pPRqNI//nnH2FkZCR27typahs2bJgwMDAQc+fOFSEhIeLBgwdiwoQJQi6Xq42GVSqVwtvbWxgZGYkZM2aIa9euiSdPnoiDBw8KV1fXdPWlqV27thgzZky69qioKKGvr68aQRoaGioMDQ3Fd999J27fvi0ePnwo5s2bJ3R1dcXRo0fVPkOVKlVE6dKlxYYNG8S9e/dEcHCwWLt2rahQoYJqfy9ZskS4urpmun+EEGL27NnC0tJS7N+/X9y5c0d07NhRODo6qn0PXV1dxZIlS1TPjx07Jo4ePSoePXokTpw4IWrXri0aNWokkpOTVcvs2bNH6OnpiVWrVom//vpLLFmyROjo6IgLFy5kWIc2Rr0yKHNAW0GZkJAgPDw8GJKUq4pSUD5+/Fi4uLgIIyMjYW9vL5YuXZou1HIrKIUQYvDgwaJ69epCoVCo2tauXSvq1asnDA0NhYmJiWjevLk4cOBAunUVCoVYvny5aNCggTA2Nhbm5uaiXr16YtGiReku9xBCiOvXrwsA4urVqxnW2KZNG7XLVa5evSpatWolSpQoISwsLESjRo0yDOCoqCgxduxYUbFiRaGvry9sbGyEm5ub2Lt3r+rynsmTJ4uyZctmun+EeB/+EydOFDY2NsLAwEC0bNlSBAUFqS1TtmxZMXnyZNVzf39/Ua5cOaGvry9sbW3FkCFDMrw0Ji24DQ0NRe3atTO8rCaNNoJSJkQuD+HMZ2JiYmBhYYHo6GiYm5vnaBuPX76C46p/h4GPfwnom2S9QgYSEhLg5eWFEydOwNjYGIcPH4azs3OO6iHKSmJiIh4/fgxHR8fcvSibKB/K6vuf3Two+GfzC6CkpCR07NgRJ0+ehLGxMY4cOZJro7WIiOjTcDCPBPT09ODo6AgTExMcPXqUIUlElI8xKCUgl8uxfPlyXL9+XXU5CBER5U8MyjwSFxeHGTNmqO6yLZfLszUDBhERSYvnKPNAXFwc2rdvj4CAAISGhn7S7P5ERJS3GJS5LDY2Fu3atcP58+dhZmaGgQMHSl0SERFpgEGZA9m9niY2NhZt27bFhQsXYG5ujuPHj+Pzzz/P1dqIiEi7GJS55N27d2jbti0uXrwIc3NznDhxAo0aNZK6LCIi0hAH8+QCIQS6du2KixcvwsLCAidPnmRIEhEVUAzKXCCTyTBmzBjY2dnh5MmTaNiwodQlERFRDjEoc4mrqytCQ0PRoEEDqUshKpBkMlmWjylTpnzStvft26dRDebm5mjQoEGGd/hISEjA5MmTUalSJRgYGKB48eL48ssvce/evXTLxsTEYMKECahSpQoMDQ1ha2sLNzc37Nmz56P3hExISICVlRWKFy+OpKSkbH+uPn36wMvLS60tJCQEffv2RenSpWFgYABHR0f06NED169fz3qnfODNmzfw8fGBubk5LC0t0b9/f8TGxma5TmhoKDp16oQSJUrA3Nwc3bp1Q0RERIbLJiUlwcnJCTKZDIGBgRrVpi0MSi2Jjo6Gl5cX7t+/r2ozMjKSsCKigi0sLEz1WLhwIczNzdXaRo4cmSd1rF+/HmFhYbh+/TqaNm2Krl274u7du6rXk5KS4ObmhnXr1uHnn39GcHAwjhw5gtTUVDRq1Ah//PGHatmoqCg0adIEGzduxLhx43Dz5k2cP38e3t7eGD16dKa300qze/duVK9eHVWqVMlW0Gfm+vXrqFevHoKDg7Fy5Urcv38fe/fuRZUqVfDjjz9qtC0fHx/cu3cPJ0+exKFDh3D+/HkMGjQo0+Xj4uLQunVryGQynDlzBpcuXUJycjI8PT0zvAfo6NGjUbJkSY0/o1ZlOWV6IaSNu4eEfnD3kKioKNGoUSMBQFStWlWkpqZqsWKiT5Ph3ROUyvd3vsnrx793n9DUh3cPEUKI1atXiypVqggDAwNRuXJlsWzZMtVrSUlJYsiQIcLW1lYYGBiIMmXKiJkzZwoh3t+xAu8HrwsAWd4FAx/cWSQmJkYAEIsWLVK1zZ49W8hkMhEYGKi2rkKhEPXr1xfVqlVT3XXjm2++ESYmJuLFixfp3uvdu3ciJSUly/3g7OwsVqxYIZYvXy5atWr10XrT+Pr6io4dOwoh3t/Vo3r16qJevXpqdzlJ8+EdWbJy//59AUBcu3ZN1Xb06FEhk8ky/IxCCHH8+HEhl8vVfgdHRUUJmUwmTp48qbbskSNHRJUqVcS9e/cEAHHr1q1s15ZGG3cPyRejXpctW4Y5c+YgPDwctWvXxpIlS7I8r7dz505MnDgRT548QcWKFfHLL7+gbdu2eVjx/0VFR8O9fSdcvXoVVlZW2LJlC3R0dCSphSjbUuKBmRL8lZ7Du+18aMuWLZg0aRKWLl2KOnXq4NatWxg4cCBMTEzg6+uLxYsX48CBA9ixYwfKlCmD58+f4/nz5wCAa9euwdraGuvXr4eHh0e2/72mpqZi7dq1AN7fQDjN1q1b0apVK9SuXVtteblcjuHDh8PHxwe3b99GrVq1sH37dvj4+GTYQzI1Nc3y/UNDQ3H58mXVIdrhw4fj6dOnGt+wODAwEPfu3cPWrVshl6c/qGhpaan62dnZGQ4ODvDz88twW5cvX4alpSXq16+vanNzc4NcLseVK1fQqVOndOskJSVBJpPBwMBA1WZoaAi5XI6LFy/Czc0NABAREYGBAwdi3759MDY21ugzapvkh179/f0xYsQITJ48GTdv3kTt2rXh7u6OV69eZbj877//jh49eqB///64desWvLy84OXlhT///DOPK3+vQ4cOuHr1Kj777DOcOXMGderUkaQOoqJk8uTJmDdvHjp37gxHR0d07twZw4cPx8qVKwEAz549Q8WKFdGsWTOULVsWzZo1Q48ePQAAJUqUAPA+EGxtbVXPM9OjRw+YmprCwMAAw4cPh4ODA7p166Z6PTg4GFWrVs1w3bT24OBgREZG4u3btzmeunLdunVo06YNihUrBisrK7i7u2P9+vUab+evv/4CgGzVUaZMGdjZ2WX6enh4OKytrdXadHV1YWVlhfDw8AzX+fzzz2FiYoIxY8YgPj4ecXFxGDlyJBQKBcLCwgC8v3KgT58++Prrr9VCWCqS9yjnz5+PgQMHom/fvgCAFStW4PDhw1i3bh3Gjh2bbvlFixbBw8MDo0aNAgBMnz4dJ0+exNKlS7FixYo8rR0Abty4gc8++wynT59O9xclUb6lZ/y+dyfF+36iuLg4hIaGon///mozXaWmpsLCwgLA+8ErrVq1QuXKleHh4YH27dujdevWOXq/BQsWwM3NDY8ePcLw4cOxePFiWFlZqS0jsnFb3+wskxmFQoENGzZg0aJFqravvvoKI0eOxKRJkzLsGWqjjo0bN2pUZ3aUKFECO3fuxDfffIPFixdDLpejR48eqFu3rupzLFmyBO/evcO4ceO0/v45IWlQJicn48aNG2o7Qy6Xw83NDZcvX85wncuXL2PEiBFqbe7u7pme2E5KSlIbHRYTE/Pphf9H8c8+w8HjZ1CrVi2tbpcoV8lkWjkEKoW0EZWrV69Od31y2mHUunXr4vHjxzh69ChOnTqFbt26wc3NDbt27dL4/WxtbVGhQgVUqFAB69evR9u2bXH//n1VT6pSpUp48OBBhuumtVeqVAklSpSApaUlHj58qHENx48fx4sXL+Dt7a3WrlAocPr0abRq1QoAYGZmluGAoKioKNUfEZUqVQIAPHz48JOPgNna2qY7+peamoo3b97A1tY20/Vat26N0NBQREZGQldXV9W7L1euHADgzJkzuHz5strhWQCoX78+fHx8sGHDhk+qW1OSHnqNjIyEQqGAjY2NWruNjU2m3fbw8HCNlp81axYsLCxUD3t7e+0U/6/DR44wJInykI2NDUqWLIlHjx6pAizt4ejoqFrO3Nwc3t7eWL16Nfz9/bF79268efMGwPt7wioUCo3fu2HDhqhXrx5mzJihauvevTtOnTqF27dvqy2rVCqxYMECVKtWDbVr14ZcLkf37t2xZcsWvHyZvjcfGxuL1NTUDN937dq16N69OwIDA9Ue3bt3V503BYDKlSvjxo0bausqFArcvn1bFZBOTk6oVq0a5s2bl+Eo06ioqGzvj8aNGyMqKkrtPc+cOQOlUpmtSVaKFy8OS0tLnDlzBq9evUKHDh0AAIsXL8bt27dVn/PIkSMA3p+q++++zzMaDyHSohcvXggA4vfff1drHzVqlGjYsGGG6+jp6YmtW7eqtS1btkxYW1tnuHxiYqKIjo5WPZ4/f/7Jo17jEpPF7w+eij8ePMvxKD6ivJLVqL+C4sNRr6tXrxZGRkZi0aJFIigoSNy5c0esW7dOzJs3TwghxLx588TWrVvFgwcPRFBQkOjfv7+wtbVVjfKsWLGi+Oabb0RYWJh48+ZNpu+LDEaRHjlyRBgYGIi///5bCPF+/zZq1EjY29uLHTt2iKdPn4qrV68KLy8vYWJiIi5fvqxa959//hFVqlQRpUuXFhs2bBD37t0TwcHBYu3ataJChQoZjjh99eqV0NPTE0ePHk33Wlot//zzjxBCiK1btwojIyOxbNkyERwcLG7duiX69esnLCwsRHh4uGq9K1euCDMzM9GkSRNx+PBhERoaKm7fvi1+/vln8cUXX6iW69Wrlxg7dmym+0cIITw8PESdOnXElStXxMWLF0XFihVFjx49VK///fffonLlyuLKlSuqtnXr1onLly+LkJAQsWnTJmFlZSVGjBiR6Xs8fvxY0lGvkgZlUlKS0NHRSfdF7N27t+jQoUOG69jb24sFCxaotU2aNEnUqlUrW++pjctDiAqSwhiUQgixZcsW4eTkJPT19UWxYsXEF198Ifbs2SOEEGLVqlXCyclJmJiYCHNzc9GyZUtx8+ZN1boHDhwQFSpUELq6uhpdHiLE+8srqlSpIr755htVW1xcnJgwYYKoUKGC0NPTE1ZWVqJLly7i7t276bYZFRUlxo4dKypWrCj09fWFjY2NcHNzE3v37lVdRvJfc+fOFZaWliI5OTnda0lJScLS0lLtcpUtW7aIevXqCTMzM2FjYyPatm0rbt++nW7doKAg0bt3b1GyZEmhr68vypYtK3r06KG2n1q0aCF8fX0z3T9CvA//Hj16CFNTU2Fubi769u0r3r17p3o9LeTOnj2rahszZoywsbERenp6omLFimLevHkZfvYPtyFVUMqE+IQzzFrQqFEjNGzYEEuWLAHw/nBFmTJlMHTo0AwH83h7eyM+Ph4HDx5UtTVp0gS1atXK1mCemJgYWFhYIDo6Gubm5tr7IET5VGJiIh4/fgxHR0cYGhpKXQ5Rnsrq+5/dPJB81OuIESPg6+uL+vXro2HDhli4cCHi4uJUo2B79+6NUqVKYdasWQCAYcOGoUWLFpg3bx7atWuH7du34/r161i1apWUH4OIiAopyYPS29sbr1+/xqRJkxAeHg4nJyccO3ZMNWDn2bNnakOfmzRpgq1bt+Knn37C+PHjUbFiRezbtw81atSQ6iMQEVEhJvmh17zGQ69U1PDQKxVl2jj0KvnMPERERPkZg5KoiChiB4+IAGjne8+gJCrk9PT0AADx8fESV0KU99K+92n/DnJC8sE8RJS7dHR0YGlpqZpqzNjYGDKZTOKqiHKXEALx8fF49eoVLC0tP+muTgxKoiIgbd7NzO7KQ1RYpc0j+ykYlERFgEwmg52dHaytrZGSkiJ1OUR5Qk9PTyv3B2ZQEhUhOjo6vLE4kYY4mIeIiCgLDEoiIqIsMCiJiIiyUOTOUaZdfBoTEyNxJUREJKW0HPjYpARFLijfvXsHALC3t5e4EiIiyg/evXsHCwuLTF8vcpOiK5VKvHz5EmZmZp900XVMTAzs7e3x/PlzTq7+H9wvmeO+yRj3S+a4bzKmrf0ihMC7d+9QsmRJtbtUfajI9SjlcjlKly6tte2Zm5vzC5wB7pfMcd9kjPslc9w3GdPGfsmqJ5mGg3mIiIiywKAkIiLKAoMyhwwMDDB58mQYGBhIXUq+wv2SOe6bjHG/ZI77JmN5vV+K3GAeIiIiTbBHSURElAUGJRERURYYlERERFlgUBIREWWBQZmFZcuWwcHBAYaGhmjUqBGuXr2a5fI7d+5ElSpVYGhoiJo1a+LIkSN5VGne0mS/rF69Gs2bN0exYsVQrFgxuLm5fXQ/FmSafmfSbN++HTKZDF5eXrlboEQ03S9RUVEYMmQI7OzsYGBggEqVKvHf078WLlyIypUrw8jICPb29hg+fDgSExPzqNq8cf78eXh6eqJkyZKQyWTYt2/fR9cJCAhA3bp1YWBggAoVKsDPz097BQnK0Pbt24W+vr5Yt26duHfvnhg4cKCwtLQUERERGS5/6dIloaOjI3799Vdx//598dNPPwk9PT1x9+7dPK48d2m6X3r27CmWLVsmbt26JR48eCD69OkjLCwsxN9//53Hlec+TfdNmsePH4tSpUqJ5s2bi44dO+ZNsXlI0/2SlJQk6tevL9q2bSsuXrwoHj9+LAICAkRgYGAeV577NN03W7ZsEQYGBmLLli3i8ePH4vjx48LOzk4MHz48jyvPXUeOHBETJkwQe/bsEQDE3r17s1z+0aNHwtjYWIwYMULcv39fLFmyROjo6Ihjx45ppR4GZSYaNmwohgwZonquUChEyZIlxaxZszJcvlu3bqJdu3ZqbY0aNRKDBw/O1Trzmqb75UOpqanCzMxMbNiwIbdKlExO9k1qaqpo0qSJWLNmjfD19S2UQanpflm+fLkoV66cSE5OzqsSJaPpvhkyZIhwdXVVaxsxYoRo2rRprtYppewE5ejRo0X16tXV2ry9vYW7u7tWauCh1wwkJyfjxo0bcHNzU7XJ5XK4ubnh8uXLGa5z+fJlteUBwN3dPdPlC6Kc7JcPxcfHIyUlBVZWVrlVpiRyum+mTZsGa2tr9O/fPy/KzHM52S8HDhxA48aNMWTIENjY2KBGjRqYOXMmFApFXpWdJ3Kyb5o0aYIbN26oDs8+evQIR44cQdu2bfOk5vwqt3//FrlJ0bMjMjISCoUCNjY2au02NjZ4+PBhhuuEh4dnuHx4eHiu1ZnXcrJfPjRmzBiULFky3Ze6oMvJvrl48SLWrl2LwMDAPKhQGjnZL48ePcKZM2fg4+ODI0eOICQkBN9++y1SUlIwefLkvCg7T+Rk3/Ts2RORkZFo1qwZhBBITU3F119/jfHjx+dFyflWZr9/Y2JikJCQACMjo0/aPnuUlGdmz56N7du3Y+/evTA0NJS6HEm9e/cOvXr1wurVq1G8eHGpy8lXlEolrK2tsWrVKtSrVw/e3t6YMGECVqxYIXVpkgsICMDMmTPx22+/4ebNm9izZw8OHz6M6dOnS11aocYeZQaKFy8OHR0dREREqLVHRETA1tY2w3VsbW01Wr4gysl+STN37lzMnj0bp06dQq1atXKzTEloum9CQ0Px5MkTeHp6qtqUSiUAQFdXF0FBQShfvnzuFp0HcvKdsbOzg56eHnR0dFRtVatWRXh4OJKTk6Gvr5+rNeeVnOybiRMnolevXhgwYAAAoGbNmoiLi8OgQYMwYcKELO+pWJhl9vvX3Nz8k3uTAHuUGdLX10e9evVw+vRpVZtSqcTp06fRuHHjDNdp3Lix2vIAcPLkyUyXL4hysl8A4Ndff8X06dNx7Ngx1K9fPy9KzXOa7psqVarg7t27CAwMVD06dOgAFxcXBAYGwt7ePi/LzzU5+c40bdoUISEhqj8cACA4OBh2dnaFJiSBnO2b+Pj4dGGY9geFKMLTduf671+tDAkqhLZv3y4MDAyEn5+fuH//vhg0aJCwtLQU4eHhQgghevXqJcaOHata/tKlS0JXV1fMnTtXPHjwQEyePLnQXh6iyX6ZPXu20NfXF7t27RJhYWGqx7t376T6CLlG033zocI66lXT/fLs2TNhZmYmhg4dKoKCgsShQ4eEtbW1+Pnnn6X6CLlG030zefJkYWZmJrZt2yYePXokTpw4IcqXLy+6desm1UfIFe/evRO3bt0St27dEgDE/Pnzxa1bt8TTp0+FEEKMHTtW9OrVS7V82uUho0aNEg8ePBDLli3j5SF5ZcmSJaJMmTJCX19fNGzYUPzxxx+q11q0aCF8fX3Vlt+xY4eoVKmS0NfXF9WrVxeHDx/O44rzhib7pWzZsgJAusfkyZPzvvA8oOl35r8Ka1AKofl++f3330WjRo2EgYGBKFeunJgxY4ZITU3N46rzhib7JiUlRUyZMkWUL19eGBoaCnt7e/Htt9+Kt2/f5n3huejs2bMZ/t5I2xe+vr6iRYsW6dZxcnIS+vr6oly5cmL9+vVaq4e32SIiIsoCz1ESERFlgUFJRESUBQYlERFRFhiUREREWWBQEhERZYFBSURElAUGJRERURYYlERERFlgUBLlgJ+fHywtLaUuI8dkMhn27duX5TJ9+vSBl5dXntRDlJ8xKKnI6tOnD2QyWbpHSEiI1KXBz89PVY9cLkfp0qXRt29fvHr1SivbDwsLQ5s2bQAAT548gUwmS3dfzEWLFsHPz08r75eZKVOmqD6njo4O7O3tMWjQILx580aj7TDUKTfxNltUpHl4eGD9+vVqbSVKlJCoGnXm5uYICgqCUqnE7du30bdvX7x8+RLHjx//5G1n5/ZvFhYWn/w+2VG9enWcOnUKCoUCDx48QL9+/RAdHQ1/f/88eX+ij2GPkoo0AwMD2Nraqj10dHQwf/581KxZEyYmJrC3t8e3336L2NjYTLdz+/ZtuLi4wMzMDObm5qhXrx6uX7+uev3ixYto3rw5jIyMYG9vj++//x5xcXFZ1iaTyWBra4uSJUuiTZs2+P7773Hq1CkkJCRAqVRi2rRpKF26NAwMDODk5IRjx46p1k1OTsbQoUNhZ2cHQ0NDlC1bFrNmzVLbdtqhV0dHRwBAnTp1IJPJ4OzsDEC9l7Zq1SqULFlS7dZXANCxY0f069dP9Xz//v2oW7cuDA0NUa5cOUydOhWpqalZfk5dXV3Y2tqiVKlScHNzw5dffomTJ0+qXlcoFOjfvz8cHR1hZGSEypUrY9GiRarXp0yZgg0bNmD//v2q3mlAQAAA4Pnz5+jWrRssLS1hZWWFjh074smTJ1nWQ/QhBiVRBuRyORYvXox79+5hw4YNOHPmDEaPHp3p8j4+PihdujSuXbuGGzduYOzYsdDT0wPw/ibNHh4e6NKlC+7cuQN/f39cvHgRQ4cO1agmIyMjKJVKpKamYtGiRZg3bx7mzp2LO3fuwN3dHR06dMBff/0FAFi8eDEOHDiAHTt2ICgoCFu2bIGDg0OG27169SoA4NSpUwgLC8OePXvSLfPll1/in3/+wdmzZ1Vtb968wbFjx+Dj4wMAuHDhAnr37o1hw4bh/v37WLlyJfz8/DBjxoxsf8YnT57g+PHjavedVCqVKF26NHbu3In79+9j0qRJGD9+PHbs2AEAGDlyJLp16wYPDw+EhYUhLCwMTZo0QUpKCtzd3WFmZoYLFy7g0qVLMDU1hYeHB5KTk7NdExFvs0VFlq+vr9DR0REmJiaqR9euXTNcdufOneKzzz5TPV+/fr2wsLBQPTczMxN+fn4Zrtu/f38xaNAgtbYLFy4IuVwuEhISMlznw+0HBweLSpUqifr16wshhChZsqSYMWOG2joNGjQQ3377rRBCiO+++064uroKpVKZ4fYBiL179wohhHj8+LEAIG7duqW2zIe3/erYsaPo16+f6vnKlStFyZIlhUKhEEII0bJlSzFz5ky1bWzatEnY2dllWIMQ7++vKJfLhYmJiTA0NFTdTmn+/PmZriOEEEOGDBFdunTJtNa0965cubLaPkhKShJGRkbi+PHjWW6f6L94jpKKNBcXFyxfvlz13MTEBMD73tWsWbPw8OFDxMTEIDU1FYmJiYiPj4exsXG67YwYMQIDBgzApk2bVIcPy5cvD+D9Ydk7d+5gy5YtquWFEFAqlXj8+DGqVq2aYW3R0dEwNTWFUqlEYmIimjVrhjVr1iAmJgYvX75E06ZN1ZZv2rQpbt++DeD9YdNWrVqhcuXK8PDwQPv27dG6detP2lc+Pj4YOHAgfvvtNxgYGGDLli3o3r075HK56nNeunRJrQepUCiy3G8AULlyZRw4cACJiYnYvHkzAgMD8d1336kts2zZMqxbtw7Pnj1DQkICkpOT4eTklGW9t2/fRkhICMzMzNTaExMTERoamoM9QEUVg5KKNBMTE1SoUEGt7cmTJ2jfvj2++eYbzJgxA1ZWVrh48SL69++P5OTkDH/hT5kyBT179sThw4dx9OhRTJ48Gdu3b0enTp0QGxuLwYMH4/vvv0+3XpkyZTKtzczMDDdv3oRcLoednR2MjIwAADExMR/9XHXr1sXjx49x9OhRnDp1Ct26dYObmxt27dr10XUz4+npCSEEDh8+jAYNGuDChQtYsGCB6vXY2FhMnToVnTt3TreuoaFhptvV19dX/T+YPXs22rVrh6lTp2L69OkAgO3bt2PkyJGYN28eGjduDDMzM8yZMwdXrlzJst7Y2FjUq1dP7Q+UNPllwBYVDAxKog/cuHEDSqUS8+bNU/WW0s6HZaVSpUqoVKkShg8fjh49emD9+vXo1KkT6tati/v376cL5I+Ry+UZrmNubo6SJUvi0qVLaNGihar90qVLaNiwodpy3t7e8Pb2RteuXeHh4YE3b97AyspKbXtp5wMVCkWW9RgaGqJz587YsmULQkJCULlyZdStW1f1et26dREUFKTx5/zQTz/9BFdXV3zzzTeqz9mkSRN8++23qmU+7BHq6+unq79u3brw9/eHtbU1zM3NP6kmKto4mIfoAxUqVEBKSgqWLFmCR48eYdOmTVixYkWmyyckJGDo0KEICAjA06dPcenSJVy7dk11SHXMmDH4/fffMXToUAQGBuKvv/7C/v37NR7M81+jRo3CL7/8An9/fwQFBWHs2LEIDAzEsGHDAADz58/Htm3b8PDhQwQHB2Pnzp2wtbXNcJIEa2trGBkZ4dixY4iIiEB0dHSm7+vj44PDhw9j3bp1qkE8aSZNmoSNGzdi6tSpuHfvHh48eIDt27fjp59+0uizNW7cGLVq1cLMmTMBABUrVsT169dx/PhxBAcHY+LEibh27ZraOg4ODrhz5w6CgoIQGRmJlJQU+Pj4oHjx4ujYsSMuXLiAx48fIyAgAN9//z3+/vtvjWqiIk7qk6REUsloAEia+fPnCzs7O2FkZCTc3d3Fxo0bBQDx9u1bIYT6YJukpCTRvXt3YW9vL/T19UXJkiXF0KFD1QbqXL16VbRq1UqYmpoKExMTUatWrXSDcf7rw8E8H1IoFGLKlCmiVKlSQk9PT9SuXVscPXpU9fqqVauEk5OTMDExEebm5qJly5bi5s2bqtfxn8E8QgixevVqYW9vL+RyuWjRokWm+0ehUAg7OzsBQISGhqar69ixY6JJkybCyMhImJubi4YNG4pVq1Zl+jkmT54sateuna5927ZtwsDAQDx79kwkJiaKPn36CAsLC2FpaSm++eYbMXbsWLX1Xr16pdq/AMTZs2eFEEKEhYWJ3r17i+LFiwsDAwNRrlw5MXDgQBEdHZ1pTUQfkgkhhLRRTURElH/x0CsREVEWGJRERERZYFASERFlgUFJRESUBQYlERFRFhiUREREWWBQEhERZYFBSURElAUGJRERURYYlERERFlgUBIREWXhf8GHCQiQResOAAAAAElFTkSuQmCC",
+            "text/plain": [
+              "<Figure size 500x500 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\n",
+            "Confusion Matrix:\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 1100x400 with 4 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\n",
+            "Train Classification Report:\n",
+            "|              |   precision |   recall |   f1-score |     support |\n",
+            "|:-------------|------------:|---------:|-----------:|------------:|\n",
+            "| 0            |    0.988258 | 0.997794 |   0.993003 | 3627        |\n",
+            "| 1            |    0.984526 | 0.922101 |   0.952292 |  552        |\n",
+            "| accuracy     |    0.987796 | 0.987796 |   0.987796 |    0.987796 |\n",
+            "| macro avg    |    0.986392 | 0.959948 |   0.972648 | 4179        |\n",
+            "| weighted avg |    0.987765 | 0.987796 |   0.987626 | 4179        |\n",
+            "\n",
+            "Test Classification Report:\n",
+            "|              |   precision |   recall |   f1-score |     support |\n",
+            "|:-------------|------------:|---------:|-----------:|------------:|\n",
+            "| 0            |    0.980296 | 0.996661 |   0.988411 | 1198        |\n",
+            "| 1            |    0.977143 | 0.876923 |   0.924324 |  195        |\n",
+            "| accuracy     |    0.979899 | 0.979899 |   0.979899 |    0.979899 |\n",
+            "| macro avg    |    0.978719 | 0.936792 |   0.956367 | 1393        |\n",
+            "| weighted avg |    0.979854 | 0.979899 |   0.979439 | 1393        |\n"
+          ]
+        }
+      ],
+      "source": [
+        "BNB_score = evaluate_model(bnb, X_train, X_test, y_train, y_test)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "h_CCil-SKHpo"
+      },
+      "source": [
+        "### Which Evaluation metrics did i consider for a positive business impact?"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "jHVz9hHDKFms"
+      },
+      "source": [
+        "After carefully considering the potential consequences of false positives and false negatives in the context of our business objectives, I have selected precision as the primary evaluation metric for our email spam detection model. Its gives 98.49% accuracy for recall test set."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "BNB is the  clear winner"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Trying out different models"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 40,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from sklearn.linear_model import LogisticRegression\n",
+        "from sklearn.svm import SVC\n",
+        "from sklearn.naive_bayes import MultinomialNB\n",
+        "from sklearn.tree import DecisionTreeClassifier\n",
+        "from sklearn.neighbors import KNeighborsClassifier\n",
+        "from sklearn.ensemble import RandomForestClassifier\n",
+        "from sklearn.ensemble import AdaBoostClassifier\n",
+        "from sklearn.ensemble import BaggingClassifier\n",
+        "from sklearn.ensemble import ExtraTreesClassifier\n",
+        "from sklearn.ensemble import GradientBoostingClassifier\n",
+        "#from xgboost import XGBClassifier"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 41,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "svc = SVC(kernel='sigmoid', gamma=1.0)\n",
+        "knc = KNeighborsClassifier()\n",
+        "mnb = MultinomialNB()\n",
+        "dtc = DecisionTreeClassifier(max_depth=5)\n",
+        "lrc = LogisticRegression(solver='liblinear', penalty='l1')\n",
+        "rfc = RandomForestClassifier(n_estimators=50, random_state=2)\n",
+        "abc = AdaBoostClassifier(n_estimators=50, random_state=2)\n",
+        "bc = BaggingClassifier(n_estimators=50, random_state=2)\n",
+        "etc = ExtraTreesClassifier(n_estimators=50, random_state=2)\n",
+        "gbdt = GradientBoostingClassifier(n_estimators=50,random_state=2)\n",
+        "#xgb = XGBClassifier(n_estimators=50,random_state=2)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 42,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "clfs = {\n",
+        "    'SVC' : svc,\n",
+        "    'KN' : knc, \n",
+        "    'NB': mnb, \n",
+        "    'DT': dtc, \n",
+        "    'LR': lrc, \n",
+        "    'RF': rfc, \n",
+        "    'AdaBoost': abc, \n",
+        "    'BgC': bc, \n",
+        "    'ETC': etc,\n",
+        "    'GBDT':gbdt,\n",
+        "    'BNB':bnb\n",
+        "    #'xgb':xgb\n",
+        "}"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 43,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
+        "def train_classifier(clf,X_train,y_train,X_test,y_test):\n",
+        "    clf.fit(X_train,y_train)\n",
+        "    y_pred = clf.predict(X_test)\n",
+        "    accuracy = accuracy_score(y_test,y_pred)\n",
+        "    precision = precision_score(y_test,y_pred)\n",
+        "    recall = recall_score(y_test,y_pred)\n",
+        "    f1 = f1_score(y_test,y_pred)\n",
+        "    return accuracy,precision,recall,f1"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 44,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "(0.9784637473079684,\n",
+              " 0.9940119760479041,\n",
+              " 0.8512820512820513,\n",
+              " 0.9171270718232044)"
+            ]
+          },
+          "execution_count": 44,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "train_classifier(svc,X_train,y_train,X_test,y_test)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 45,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "For  SVC\n",
+            "Accuracy -  0.9784637473079684\n",
+            "Precision -  0.9940119760479041\n",
+            "Recall -  0.8512820512820513\n",
+            "F1 -  0.9171270718232044\n",
+            "For  KN\n",
+            "Accuracy -  0.9102656137832017\n",
+            "Precision -  1.0\n",
+            "Recall -  0.358974358974359\n",
+            "F1 -  0.5283018867924528\n",
+            "For  NB\n",
+            "Accuracy -  0.968413496051687\n",
+            "Precision -  1.0\n",
+            "Recall -  0.7743589743589744\n",
+            "F1 -  0.8728323699421965\n",
+            "For  DT\n",
+            "Accuracy -  0.9432878679109835\n",
+            "Precision -  0.9393939393939394\n",
+            "Recall -  0.6358974358974359\n",
+            "F1 -  0.7584097859327217\n",
+            "For  LR\n",
+            "Accuracy -  0.9562096195262024\n",
+            "Precision -  0.958904109589041\n",
+            "Recall -  0.717948717948718\n",
+            "F1 -  0.8211143695014663\n",
+            "For  RF\n",
+            "Accuracy -  0.9698492462311558\n",
+            "Precision -  1.0\n",
+            "Recall -  0.7846153846153846\n",
+            "F1 -  0.8793103448275862\n",
+            "For  AdaBoost\n",
+            "Accuracy -  0.9375448671931084\n",
+            "Precision -  0.9090909090909091\n",
+            "Recall -  0.6153846153846154\n",
+            "F1 -  0.7339449541284404\n",
+            "For  BgC\n",
+            "Accuracy -  0.9626704953338119\n",
+            "Precision -  0.9386503067484663\n",
+            "Recall -  0.7846153846153846\n",
+            "F1 -  0.8547486033519553\n",
+            "For  ETC\n",
+            "Accuracy -  0.9755922469490309\n",
+            "Precision -  1.0\n",
+            "Recall -  0.8256410256410256\n",
+            "F1 -  0.9044943820224719\n",
+            "For  GBDT\n",
+            "Accuracy -  0.9569274946159368\n",
+            "Precision -  0.9530201342281879\n",
+            "Recall -  0.7282051282051282\n",
+            "F1 -  0.8255813953488372\n",
+            "For  BNB\n",
+            "Accuracy -  0.9798994974874372\n",
+            "Precision -  0.9771428571428571\n",
+            "Recall -  0.8769230769230769\n",
+            "F1 -  0.9243243243243243\n"
+          ]
+        }
+      ],
+      "source": [
+        "accuracy_scores = []\n",
+        "precision_scores = []\n",
+        "recall_scores = []\n",
+        "f1_scores = []\n",
+        "for name,clf in clfs.items():\n",
+        "    \n",
+        "    current_accuracy,current_precision,current_recall,current_f1 = train_classifier(clf, X_train,y_train,X_test,y_test)\n",
+        "    \n",
+        "    print(\"For \",name)\n",
+        "    print(\"Accuracy - \",current_accuracy)\n",
+        "    print(\"Precision - \",current_precision)\n",
+        "    print(\"Recall - \",current_recall)\n",
+        "    print(\"F1 - \",current_f1)\n",
+        "    \n",
+        "    accuracy_scores.append(current_accuracy)\n",
+        "    precision_scores.append(current_precision)\n",
+        "    recall_scores.append(current_recall)\n",
+        "    f1_scores.append(current_f1)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 46,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "\n",
+        "performance_df = pd.DataFrame({'Algorithm':clfs.keys(),'Accuracy':accuracy_scores,'Precision':precision_scores,'Recall':recall_scores,'F1':f1_scores}).sort_values('F1',ascending=False)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 47,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "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>Algorithm</th>\n",
+              "      <th>Accuracy</th>\n",
+              "      <th>Precision</th>\n",
+              "      <th>Recall</th>\n",
+              "      <th>F1</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>10</th>\n",
+              "      <td>BNB</td>\n",
+              "      <td>0.979899</td>\n",
+              "      <td>0.977143</td>\n",
+              "      <td>0.876923</td>\n",
+              "      <td>0.924324</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>SVC</td>\n",
+              "      <td>0.978464</td>\n",
+              "      <td>0.994012</td>\n",
+              "      <td>0.851282</td>\n",
+              "      <td>0.917127</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8</th>\n",
+              "      <td>ETC</td>\n",
+              "      <td>0.975592</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>0.825641</td>\n",
+              "      <td>0.904494</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>RF</td>\n",
+              "      <td>0.969849</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>0.784615</td>\n",
+              "      <td>0.879310</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>NB</td>\n",
+              "      <td>0.968413</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>0.774359</td>\n",
+              "      <td>0.872832</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7</th>\n",
+              "      <td>BgC</td>\n",
+              "      <td>0.962670</td>\n",
+              "      <td>0.938650</td>\n",
+              "      <td>0.784615</td>\n",
+              "      <td>0.854749</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>9</th>\n",
+              "      <td>GBDT</td>\n",
+              "      <td>0.956927</td>\n",
+              "      <td>0.953020</td>\n",
+              "      <td>0.728205</td>\n",
+              "      <td>0.825581</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>LR</td>\n",
+              "      <td>0.956210</td>\n",
+              "      <td>0.958904</td>\n",
+              "      <td>0.717949</td>\n",
+              "      <td>0.821114</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>DT</td>\n",
+              "      <td>0.943288</td>\n",
+              "      <td>0.939394</td>\n",
+              "      <td>0.635897</td>\n",
+              "      <td>0.758410</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>AdaBoost</td>\n",
+              "      <td>0.937545</td>\n",
+              "      <td>0.909091</td>\n",
+              "      <td>0.615385</td>\n",
+              "      <td>0.733945</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>KN</td>\n",
+              "      <td>0.910266</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>0.358974</td>\n",
+              "      <td>0.528302</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "   Algorithm  Accuracy  Precision    Recall        F1\n",
+              "10       BNB  0.979899   0.977143  0.876923  0.924324\n",
+              "0        SVC  0.978464   0.994012  0.851282  0.917127\n",
+              "8        ETC  0.975592   1.000000  0.825641  0.904494\n",
+              "5         RF  0.969849   1.000000  0.784615  0.879310\n",
+              "2         NB  0.968413   1.000000  0.774359  0.872832\n",
+              "7        BgC  0.962670   0.938650  0.784615  0.854749\n",
+              "9       GBDT  0.956927   0.953020  0.728205  0.825581\n",
+              "4         LR  0.956210   0.958904  0.717949  0.821114\n",
+              "3         DT  0.943288   0.939394  0.635897  0.758410\n",
+              "6   AdaBoost  0.937545   0.909091  0.615385  0.733945\n",
+              "1         KN  0.910266   1.000000  0.358974  0.528302"
+            ]
+          },
+          "execution_count": 47,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "performance_df"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "gonna take the first three or 4"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "xpnTXSVdoTab"
+      },
+      "source": [
+        "## ***6. Stacking***"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 48,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from sklearn.ensemble import StackingClassifier\n",
+        "\n",
+        "\n",
+        "# Applying stacking\n",
+        "estimators=[('svm', svc), ('bnb', bnb), ('et', etc)]\n",
+        "final_estimator=RandomForestClassifier()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 49,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "stacking_clf = StackingClassifier(estimators=estimators, final_estimator=final_estimator,cv=3)\n",
+        "#tfidf = TfidfVectorizer(max_features=3000)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 50,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "#clf = Pipeline([\n",
+        "#    ('tfidf', tfidf),             # Step 1: Text Vectorization\n",
+        "#    ('stacking', stacking_clf)    # Step 2: Stacking Classification\n",
+        "#])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 51,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<style>#sk-container-id-1 {\n",
+              "  /* Definition of color scheme common for light and dark mode */\n",
+              "  --sklearn-color-text: #000;\n",
+              "  --sklearn-color-text-muted: #666;\n",
+              "  --sklearn-color-line: gray;\n",
+              "  /* Definition of color scheme for unfitted estimators */\n",
+              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+              "  --sklearn-color-unfitted-level-3: chocolate;\n",
+              "  /* Definition of color scheme for fitted estimators */\n",
+              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+              "\n",
+              "  /* Specific color for light theme */\n",
+              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+              "  --sklearn-color-icon: #696969;\n",
+              "\n",
+              "  @media (prefers-color-scheme: dark) {\n",
+              "    /* Redefinition of color scheme for dark theme */\n",
+              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+              "    --sklearn-color-icon: #878787;\n",
+              "  }\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 pre {\n",
+              "  padding: 0;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 input.sk-hidden--visually {\n",
+              "  border: 0;\n",
+              "  clip: rect(1px 1px 1px 1px);\n",
+              "  clip: rect(1px, 1px, 1px, 1px);\n",
+              "  height: 1px;\n",
+              "  margin: -1px;\n",
+              "  overflow: hidden;\n",
+              "  padding: 0;\n",
+              "  position: absolute;\n",
+              "  width: 1px;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-dashed-wrapped {\n",
+              "  border: 1px dashed var(--sklearn-color-line);\n",
+              "  margin: 0 0.4em 0.5em 0.4em;\n",
+              "  box-sizing: border-box;\n",
+              "  padding-bottom: 0.4em;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-container {\n",
+              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+              "     so we also need the `!important` here to be able to override the\n",
+              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+              "  display: inline-block !important;\n",
+              "  position: relative;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-text-repr-fallback {\n",
+              "  display: none;\n",
+              "}\n",
+              "\n",
+              "div.sk-parallel-item,\n",
+              "div.sk-serial,\n",
+              "div.sk-item {\n",
+              "  /* draw centered vertical line to link estimators */\n",
+              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+              "  background-size: 2px 100%;\n",
+              "  background-repeat: no-repeat;\n",
+              "  background-position: center center;\n",
+              "}\n",
+              "\n",
+              "/* Parallel-specific style estimator block */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel-item::after {\n",
+              "  content: \"\";\n",
+              "  width: 100%;\n",
+              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+              "  flex-grow: 1;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel {\n",
+              "  display: flex;\n",
+              "  align-items: stretch;\n",
+              "  justify-content: center;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  position: relative;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel-item {\n",
+              "  display: flex;\n",
+              "  flex-direction: column;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
+              "  align-self: flex-end;\n",
+              "  width: 50%;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
+              "  align-self: flex-start;\n",
+              "  width: 50%;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
+              "  width: 0;\n",
+              "}\n",
+              "\n",
+              "/* Serial-specific style estimator block */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-serial {\n",
+              "  display: flex;\n",
+              "  flex-direction: column;\n",
+              "  align-items: center;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  padding-right: 1em;\n",
+              "  padding-left: 1em;\n",
+              "}\n",
+              "\n",
+              "\n",
+              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+              "clickable and can be expanded/collapsed.\n",
+              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+              "*/\n",
+              "\n",
+              "/* Pipeline and ColumnTransformer style (default) */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-toggleable {\n",
+              "  /* Default theme specific background. It is overwritten whether we have a\n",
+              "  specific estimator or a Pipeline/ColumnTransformer */\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "}\n",
+              "\n",
+              "/* Toggleable label */\n",
+              "#sk-container-id-1 label.sk-toggleable__label {\n",
+              "  cursor: pointer;\n",
+              "  display: flex;\n",
+              "  width: 100%;\n",
+              "  margin-bottom: 0;\n",
+              "  padding: 0.5em;\n",
+              "  box-sizing: border-box;\n",
+              "  text-align: center;\n",
+              "  align-items: start;\n",
+              "  justify-content: space-between;\n",
+              "  gap: 0.5em;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
+              "  font-size: 0.6rem;\n",
+              "  font-weight: lighter;\n",
+              "  color: var(--sklearn-color-text-muted);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
+              "  /* Arrow on the left of the label */\n",
+              "  content: \"▸\";\n",
+              "  float: left;\n",
+              "  margin-right: 0.25em;\n",
+              "  color: var(--sklearn-color-icon);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "}\n",
+              "\n",
+              "/* Toggleable content - dropdown */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-toggleable__content {\n",
+              "  max-height: 0;\n",
+              "  max-width: 0;\n",
+              "  overflow: hidden;\n",
+              "  text-align: left;\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-toggleable__content pre {\n",
+              "  margin: 0.2em;\n",
+              "  border-radius: 0.25em;\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+              "  /* Expand drop-down */\n",
+              "  max-height: 200px;\n",
+              "  max-width: 100%;\n",
+              "  overflow: auto;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+              "  content: \"▾\";\n",
+              "}\n",
+              "\n",
+              "/* Pipeline/ColumnTransformer-specific style */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Estimator-specific style */\n",
+              "\n",
+              "/* Colorize estimator box */\n",
+              "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
+              "#sk-container-id-1 div.sk-label label {\n",
+              "  /* The background is the default theme color */\n",
+              "  color: var(--sklearn-color-text-on-default-background);\n",
+              "}\n",
+              "\n",
+              "/* On hover, darken the color of the background */\n",
+              "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Label box, darken color on hover, fitted */\n",
+              "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Estimator label */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-label label {\n",
+              "  font-family: monospace;\n",
+              "  font-weight: bold;\n",
+              "  display: inline-block;\n",
+              "  line-height: 1.2em;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-label-container {\n",
+              "  text-align: center;\n",
+              "}\n",
+              "\n",
+              "/* Estimator-specific */\n",
+              "#sk-container-id-1 div.sk-estimator {\n",
+              "  font-family: monospace;\n",
+              "  border: 1px dotted var(--sklearn-color-border-box);\n",
+              "  border-radius: 0.25em;\n",
+              "  box-sizing: border-box;\n",
+              "  margin-bottom: 0.5em;\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-estimator.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "/* on hover */\n",
+              "#sk-container-id-1 div.sk-estimator:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+              "\n",
+              "/* Common style for \"i\" and \"?\" */\n",
+              "\n",
+              ".sk-estimator-doc-link,\n",
+              "a:link.sk-estimator-doc-link,\n",
+              "a:visited.sk-estimator-doc-link {\n",
+              "  float: right;\n",
+              "  font-size: smaller;\n",
+              "  line-height: 1em;\n",
+              "  font-family: monospace;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  border-radius: 1em;\n",
+              "  height: 1em;\n",
+              "  width: 1em;\n",
+              "  text-decoration: none !important;\n",
+              "  margin-left: 0.5em;\n",
+              "  text-align: center;\n",
+              "  /* unfitted */\n",
+              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-unfitted-level-1);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link.fitted,\n",
+              "a:link.sk-estimator-doc-link.fitted,\n",
+              "a:visited.sk-estimator-doc-link.fitted {\n",
+              "  /* fitted */\n",
+              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-fitted-level-1);\n",
+              "}\n",
+              "\n",
+              "/* On hover */\n",
+              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+              ".sk-estimator-doc-link:hover,\n",
+              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+              ".sk-estimator-doc-link:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+              ".sk-estimator-doc-link.fitted:hover,\n",
+              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+              ".sk-estimator-doc-link.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "/* Span, style for the box shown on hovering the info icon */\n",
+              ".sk-estimator-doc-link span {\n",
+              "  display: none;\n",
+              "  z-index: 9999;\n",
+              "  position: relative;\n",
+              "  font-weight: normal;\n",
+              "  right: .2ex;\n",
+              "  padding: .5ex;\n",
+              "  margin: .5ex;\n",
+              "  width: min-content;\n",
+              "  min-width: 20ex;\n",
+              "  max-width: 50ex;\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  box-shadow: 2pt 2pt 4pt #999;\n",
+              "  /* unfitted */\n",
+              "  background: var(--sklearn-color-unfitted-level-0);\n",
+              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link.fitted span {\n",
+              "  /* fitted */\n",
+              "  background: var(--sklearn-color-fitted-level-0);\n",
+              "  border: var(--sklearn-color-fitted-level-3);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link:hover span {\n",
+              "  display: block;\n",
+              "}\n",
+              "\n",
+              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+              "\n",
+              "#sk-container-id-1 a.estimator_doc_link {\n",
+              "  float: right;\n",
+              "  font-size: 1rem;\n",
+              "  line-height: 1em;\n",
+              "  font-family: monospace;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  border-radius: 1rem;\n",
+              "  height: 1rem;\n",
+              "  width: 1rem;\n",
+              "  text-decoration: none;\n",
+              "  /* unfitted */\n",
+              "  color: var(--sklearn-color-unfitted-level-1);\n",
+              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
+              "  /* fitted */\n",
+              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-fitted-level-1);\n",
+              "}\n",
+              "\n",
+              "/* On hover */\n",
+              "#sk-container-id-1 a.estimator_doc_link:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-3);\n",
+              "}\n",
+              "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>StackingClassifier(cv=3,\n",
+              "                   estimators=[(&#x27;svm&#x27;, SVC(gamma=1.0, kernel=&#x27;sigmoid&#x27;)),\n",
+              "                               (&#x27;bnb&#x27;, BernoulliNB()),\n",
+              "                               (&#x27;et&#x27;,\n",
+              "                                ExtraTreesClassifier(n_estimators=50,\n",
+              "                                                     random_state=2))],\n",
+              "                   final_estimator=RandomForestClassifier())</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>StackingClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.ensemble.StackingClassifier.html\">?<span>Documentation for StackingClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>StackingClassifier(cv=3,\n",
+              "                   estimators=[(&#x27;svm&#x27;, SVC(gamma=1.0, kernel=&#x27;sigmoid&#x27;)),\n",
+              "                               (&#x27;bnb&#x27;, BernoulliNB()),\n",
+              "                               (&#x27;et&#x27;,\n",
+              "                                ExtraTreesClassifier(n_estimators=50,\n",
+              "                                                     random_state=2))],\n",
+              "                   final_estimator=RandomForestClassifier())</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><label>svm</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>SVC</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.svm.SVC.html\">?<span>Documentation for SVC</span></a></div></label><div class=\"sk-toggleable__content fitted\"><pre>SVC(gamma=1.0, kernel=&#x27;sigmoid&#x27;)</pre></div> </div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><label>bnb</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>BernoulliNB</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.naive_bayes.BernoulliNB.html\">?<span>Documentation for BernoulliNB</span></a></div></label><div class=\"sk-toggleable__content fitted\"><pre>BernoulliNB()</pre></div> </div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><label>et</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>ExtraTreesClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.ensemble.ExtraTreesClassifier.html\">?<span>Documentation for ExtraTreesClassifier</span></a></div></label><div class=\"sk-toggleable__content fitted\"><pre>ExtraTreesClassifier(n_estimators=50, random_state=2)</pre></div> </div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><label>final_estimator</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>RandomForestClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a></div></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier()</pre></div> </div></div></div></div></div></div></div></div></div></div></div>"
+            ],
+            "text/plain": [
+              "StackingClassifier(cv=3,\n",
+              "                   estimators=[('svm', SVC(gamma=1.0, kernel='sigmoid')),\n",
+              "                               ('bnb', BernoulliNB()),\n",
+              "                               ('et',\n",
+              "                                ExtraTreesClassifier(n_estimators=50,\n",
+              "                                                     random_state=2))],\n",
+              "                   final_estimator=RandomForestClassifier())"
+            ]
+          },
+          "execution_count": 51,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "#training\n",
+        "stacking_clf.fit(X_train,y_train)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 52,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Accuracy 0.9863603732950467\n",
+            "Precision 0.9782608695652174\n",
+            "Recall 0.9230769230769231\n",
+            "F1 0.9498680738786279\n"
+          ]
+        }
+      ],
+      "source": [
+        "y_pred = stacking_clf.predict(X_test)\n",
+        "print(\"Accuracy\",accuracy_score(y_test,y_pred))\n",
+        "print(\"Precision\",precision_score(y_test,y_pred))\n",
+        "print(\"Recall\",recall_score(y_test,y_pred))\n",
+        "print(\"F1\",f1_score(y_test,y_pred))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 53,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "X_train,X_test,y_train,y_test=train_test_split(df[\"Message\"],df[\"Spam\"],test_size=0.25,random_state=2)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 54,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "tfidf = TfidfVectorizer(max_features=3000)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 55,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "final_clf = Pipeline([\n",
+        "    ('tfidf_vectorizer', tfidf),\n",
+        "    ('stacking_classifier', stacking_clf)\n",
+        "])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 56,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Fitting the pipeline...\n",
+            "Pipeline fitting complete.\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(\"Fitting the pipeline...\")\n",
+        "final_clf.fit(X_train, y_train)\n",
+        "print(\"Pipeline fitting complete.\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 57,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Test Accuracy: 0.9878\n",
+            "Test precision: 0.9837\n",
+            "Test recall: 0.9282\n",
+            "Test F1: 0.9551\n"
+          ]
+        }
+      ],
+      "source": [
+        "y_pred = final_clf.predict(X_test)\n",
+        "\n",
+        "# Evaluate the pipeline's performance\n",
+        "accuracy = accuracy_score(y_test, y_pred)\n",
+        "precision=precision_score(y_test,y_pred)\n",
+        "recall=recall_score(y_test,y_pred)\n",
+        "f1=f1_score(y_test,y_pred)\n",
+        "print(f\"Test Accuracy: {accuracy:.4f}\")\n",
+        "print(f\"Test precision: {precision:.4f}\")\n",
+        "print(f\"Test recall: {recall:.4f}\")\n",
+        "print(f\"Test F1: {f1:.4f}\")\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Saving The model"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 58,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Model saved as QuietML.joblib\n"
+          ]
+        }
+      ],
+      "source": [
+        "import joblib\n",
+        "filename = 'QuietML.joblib'\n",
+        "joblib.dump(final_clf, filename)\n",
+        "print(f\"Model saved as {filename}\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Prediction"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 59,
+      "metadata": {
+        "id": "nS-sVLrwu8VT"
+      },
+      "outputs": [],
+      "source": [
+        "# Defining a function for the Email Spam Detection System\n",
+        "def detect_spam(email_text):\n",
+        "    # Load the trained classifier (clf) here\n",
+        "    # Replace the comment with your code to load the classifier model\n",
+        "\n",
+        "    # Make a prediction using the loaded classifier\n",
+        "    prediction = final_clf.predict([email_text])\n",
+        "\n",
+        "    if prediction == 0:\n",
+        "        return \"This is a Ham Email!\"\n",
+        "    else:\n",
+        "        return \"This is a Spam Email!\"\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 99,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "xIUZaaAMhyJ5",
+        "outputId": "13edce77-3370-49fd-95d2-7163139ea4fa"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "0.66\n",
+            "This is a Spam Email!\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Example of how to use the function\n",
+        "sample_email = '''Hello,\n",
+        "\n",
+        "Thank you for the opportunity to work on your website redesign project. We've prepared a comprehensive proposal based on our discussions.\n",
+        "\n",
+        "The proposal includes: - Project scope and timeline - Design concepts - Technical specifications - Cost breakdown\n",
+        "\n",
+        "Please review the attached document and let us know if you have any questions or would like to schedule a call to discuss further.\n",
+        "\n",
+        "Best regards, Design Agency Team\n",
+        "\n",
+        "\n",
+        "'''\n",
+        "\n",
+        "#prediction = final_clf.predict(sample_email)\n",
+        "#print(prediction)\n",
+        "spam_probability = final_clf.predict_proba([sample_email])[0][1] # Probability of being spam (class 1)\n",
+        "print(spam_probability)\n",
+        "# Adjust the threshold\n",
+        "custom_threshold = 0.4 # You will need to tune this value!\n",
+        "\n",
+        "if spam_probability >= custom_threshold:\n",
+        "    print( \"This is a Spam Email!\")\n",
+        "else:\n",
+        "    print( \"This is a Ham Email!\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "gCX9965dhzqZ"
+      },
+      "source": [
+        "# **Conclusion**"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Fjb1IsQkh3yE"
+      },
+      "source": [
+        "In the world of email communication, the battle against spam messages is an ongoing challenge. Our journey in this project was to develop a robust email spam detector using Python and machine learning techniques. We wanted to equip users with a tool that can distinguish between legitimate emails (ham) and unsolicited, often harmful, spam emails.\n",
+        "\n",
+        "**Key Insights:**\n",
+        "\n",
+        "- Our dataset revealed an interesting distribution, with approximately 13.41% of messages being categorized as spam and the remaining 86.59% as ham. This distribution served as a crucial starting point for our analysis.\n",
+        "\n",
+        "- During the EDA process, we identified common keywords frequently found in spam messages, such as 'free,' 'call,' 'text,' 'txt,' and 'now.' These words often trigger spam filters and were important features for our machine learning model.\n",
+        "\n",
+        "- Our journey through machine learning brought us to a standout performer - the Multinomial Naive Bayes model. This model exhibited exceptional accuracy, achieving an impressive score of 98.49% on the recall test set. This outcome signifies the model's exceptional ability to accurately identify and filter out spam emails, thereby contributing to enhanced email security and a superior user experience.\n",
+        "\n",
+        "In conclusion, this project has demonstrated that machine learning, combined with effective feature engineering and model selection, can be a powerful tool in the ongoing battle against email spam. By implementing this spam detection system, we've taken a significant step towards minimizing the impact of spam messages on email users' lives.\n",
+        "\n",
+        "Email inboxes are now a safer place, thanks to the successful implementation of our email spam detection system. As we conclude this project, we look forward to continued improvements and innovations in email security.\n",
+        "\n",
+        "Let's keep our inboxes spam-free and our communications secure."
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
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