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{
  "cells": [
    {
      "cell_type": "markdown",
      "id": "0701738d",
      "metadata": {
        "id": "0701738d"
      },
      "source": [
        "# Notebook 2: Analysis & Visualization"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "id": "3486528e",
      "metadata": {
        "id": "3486528e"
      },
      "outputs": [],
      "source": [
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "id": "e116aab2",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 244
        },
        "id": "e116aab2",
        "outputId": "3e5d057c-6dd9-4b1f-8e77-699d095b44f9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Property_ID  Arrondissement Property_Type  Size_sqm  Rooms  Floor  \\\n",
              "0      P10000               4     Apartment       117      4      7   \n",
              "1      P10001               8        Studio        89      3      3   \n",
              "2      P10002               4     Apartment       164      5      5   \n",
              "3      P10003               2     Apartment        35      1      5   \n",
              "4      P10004               7        Studio        73      2      2   \n",
              "\n",
              "   Year_Built         Condition  Distance_to_Center_km   Price_EUR  \n",
              "0        1870         Renovated                   2.76  2270802.89  \n",
              "1        1953              Good                  10.77  1637076.12  \n",
              "2        1979  Needs Renovation                   3.14  3220782.59  \n",
              "3        1938               New                   4.72   407781.74  \n",
              "4        1957               New                   7.96   624879.12  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f7df10ee-eaa6-4034-a5fb-b1c80691be6b\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Property_ID</th>\n",
              "      <th>Arrondissement</th>\n",
              "      <th>Property_Type</th>\n",
              "      <th>Size_sqm</th>\n",
              "      <th>Rooms</th>\n",
              "      <th>Floor</th>\n",
              "      <th>Year_Built</th>\n",
              "      <th>Condition</th>\n",
              "      <th>Distance_to_Center_km</th>\n",
              "      <th>Price_EUR</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>P10000</td>\n",
              "      <td>4</td>\n",
              "      <td>Apartment</td>\n",
              "      <td>117</td>\n",
              "      <td>4</td>\n",
              "      <td>7</td>\n",
              "      <td>1870</td>\n",
              "      <td>Renovated</td>\n",
              "      <td>2.76</td>\n",
              "      <td>2270802.89</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>P10001</td>\n",
              "      <td>8</td>\n",
              "      <td>Studio</td>\n",
              "      <td>89</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>1953</td>\n",
              "      <td>Good</td>\n",
              "      <td>10.77</td>\n",
              "      <td>1637076.12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>P10002</td>\n",
              "      <td>4</td>\n",
              "      <td>Apartment</td>\n",
              "      <td>164</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>1979</td>\n",
              "      <td>Needs Renovation</td>\n",
              "      <td>3.14</td>\n",
              "      <td>3220782.59</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>P10003</td>\n",
              "      <td>2</td>\n",
              "      <td>Apartment</td>\n",
              "      <td>35</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>1938</td>\n",
              "      <td>New</td>\n",
              "      <td>4.72</td>\n",
              "      <td>407781.74</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>P10004</td>\n",
              "      <td>7</td>\n",
              "      <td>Studio</td>\n",
              "      <td>73</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1957</td>\n",
              "      <td>New</td>\n",
              "      <td>7.96</td>\n",
              "      <td>624879.12</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f7df10ee-eaa6-4034-a5fb-b1c80691be6b')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-f7df10ee-eaa6-4034-a5fb-b1c80691be6b button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-f7df10ee-eaa6-4034-a5fb-b1c80691be6b');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 1200,\n  \"fields\": [\n    {\n      \"column\": \"Property_ID\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1200,\n        \"samples\": [\n          \"P11178\",\n          \"P10865\",\n          \"P10101\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Arrondissement\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5,\n        \"min\": 1,\n        \"max\": 20,\n        \"num_unique_values\": 20,\n        \"samples\": [\n          4,\n          6,\n          19\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Property_Type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Studio\",\n          \"Penthouse\",\n          \"Apartment\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Size_sqm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 54,\n        \"min\": 15,\n        \"max\": 199,\n        \"num_unique_values\": 185,\n        \"samples\": [\n          66,\n          172,\n          46\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Rooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 1,\n        \"max\": 5,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          3,\n          2,\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Floor\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 0,\n        \"max\": 9,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          4,\n          3,\n          6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Year_Built\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 49,\n        \"min\": 1850,\n        \"max\": 2023,\n        \"num_unique_values\": 174,\n        \"samples\": [\n          1956,\n          1859,\n          1864\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Condition\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Good\",\n          \"New\",\n          \"Renovated\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Distance_to_Center_km\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4.179961254539273,\n        \"min\": 0.51,\n        \"max\": 14.99,\n        \"num_unique_values\": 805,\n        \"samples\": [\n          11.19,\n          8.59,\n          7.57\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Price_EUR\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 873789.6431763031,\n        \"min\": 120525.79,\n        \"max\": 3950831.82,\n        \"num_unique_values\": 1200,\n        \"samples\": [\n          1440641.44,\n          2451590.76,\n          1512200.9\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "import pandas as pd\n",
        "\n",
        "# Load processed data from Notebook 1\n",
        "df = pd.read_csv(\"/content/paris_housing_prices_dataset 2 (1).csv\")\n",
        "\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "id": "46ae912f",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "46ae912f",
        "outputId": "32b7f39c-37f5-4d23-ead7-02d54351b3d0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Average price per sqm: 14043.241108249147\n",
            "Median price per sqm: 13997.493494318182\n"
          ]
        }
      ],
      "source": [
        "df['price_per_sqm'] = df['Price_EUR'] / df['Size_sqm']\n",
        "\n",
        "# Basic stats\n",
        "print(\"Average price per sqm:\", df['price_per_sqm'].mean())\n",
        "print(\"Median price per sqm:\", df['price_per_sqm'].median())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "id": "22bca94c",
      "metadata": {
        "id": "22bca94c"
      },
      "outputs": [],
      "source": [
        "\n",
        "# Group analysis if arrondissement exists\n",
        "if 'arrondissement' in df.columns:\n",
        "    print(df.groupby('arrondissement')['price_per_sqm'].mean())\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "id": "2080b14e",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "2080b14e",
        "outputId": "d238f385-47f9-467a-eaaa-8516dbf09708"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "\n",
        "# Histogram\n",
        "plt.figure()\n",
        "plt.hist(df['price_per_sqm'])\n",
        "plt.title(\"Price per sqm distribution\")\n",
        "plt.xlabel(\"Price per sqm\")\n",
        "plt.ylabel(\"Frequency\")\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "id": "effa6c00",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "effa6c00",
        "outputId": "dbbefb4c-a092-47fa-afd0-a321f88c0b14"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "\n",
        "# Scatter plot\n",
        "price_col = [c for c in df.columns if 'price' in c.lower()][0]\n",
        "surface_col = [c for c in df.columns if 'surface' in c.lower() or 'sqm' in c.lower()][0]\n",
        "\n",
        "plt.figure()\n",
        "plt.scatter(df[surface_col], df[price_col])\n",
        "plt.xlabel(\"Surface\")\n",
        "plt.ylabel(\"Price\")\n",
        "plt.title(\"Price vs Surface\")\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "6166964a",
      "metadata": {
        "id": "6166964a"
      },
      "source": [
        "\n",
        "## Key Insights\n",
        "- Price per sqm is the key metric for comparison\n",
        "- Larger homes increase total price but not always efficiency\n",
        "- Location strongly impacts value\n"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "language_info": {
      "name": "python"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}