Multiple-Linear-Regression.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Germany Rent.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyPERZI3JjwL4rcpHRqLnRHt",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/varghese-ascalonic/Germany-Rent-Predictor/blob/main/Multiple-Linear-Regression.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HKoDa9CoTQYN"
      },
      "source": [
        "Fetch Data from kaggle.\n",
        "The dataset used can be found [here](https://www.kaggle.com/corrieaar/apartment-rental-offers-in-germany/code).\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": "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",
              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "99sGKxq1QOxx",
        "outputId": "f8101d9e-cd30-4677-c71c-4f6fe7e050f1"
      },
      "source": [
        "! pip install -q kaggle\n",
        "from google.colab import files\n",
        "files.upload()\n",
        "! mkdir ~/.kaggle\n",
        "! cp kaggle.json ~/.kaggle/\n",
        "! chmod 600 ~/.kaggle/kaggle.json\n",
        "! kaggle datasets list\n",
        "! kaggle datasets download -d \"corrieaar/apartment-rental-offers-in-germany/code\"\n",
        "! unzip \"apartment-rental-offers-in-germany.zip\"\n",
        "\n",
        "#the data is present is the CSV file immo_data.csv after the archive is extracted\n",
        "import pandas as pd\n",
        "df = pd.read_csv(\"immo_data.csv\")\n",
        "df.head()"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-142623b4-2fef-40e9-ae6c-5c6fcf121ece\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-142623b4-2fef-40e9-ae6c-5c6fcf121ece\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving kaggle.json to kaggle (1).json\n",
            "mkdir: cannot create directory ‘/root/.kaggle’: File exists\n",
            "Warning: Looks like you're using an outdated API Version, please consider updating (server 1.5.12 / client 1.5.4)\n",
            "ref                                                         title                                              size  lastUpdated          downloadCount  \n",
            "----------------------------------------------------------  ------------------------------------------------  -----  -------------------  -------------  \n",
            "gpreda/reddit-vaccine-myths                                 Reddit Vaccine Myths                              221KB  2021-03-26 07:27:32           1017  \n",
            "crowww/a-large-scale-fish-dataset                           A Large Scale Fish Dataset                          3GB  2021-02-17 16:10:44            795  \n",
            "dhruvildave/wikibooks-dataset                               Wikibooks Dataset                                   1GB  2021-02-18 10:08:27            707  \n",
            "imsparsh/musicnet-dataset                                   MusicNet Dataset                                   22GB  2021-02-18 14:12:19            306  \n",
            "fatiimaezzahra/famous-iconic-women                          Famous Iconic Women                               838MB  2021-02-28 14:56:00            238  \n",
            "nickuzmenkov/nih-chest-xrays-tfrecords                      NIH Chest X-rays TFRecords                         11GB  2021-03-09 04:49:23            148  \n",
            "mathurinache/the-lj-speech-dataset                          The LJ Speech Dataset                               3GB  2021-02-15 09:19:54             60  \n",
            "nickuzmenkov/ranzcr-clip-kfold-tfrecords                    RANZCR CLiP KFold TFRecords                         2GB  2021-02-21 13:29:51             41  \n",
            "alsgroup/end-als                                            End ALS Kaggle Challenge                           12GB  2021-03-16 22:31:35             83  \n",
            "simiotic/github-code-snippets                               GitHub Code Snippets                                7GB  2021-03-03 11:34:39             29  \n",
            "imsparsh/accentdb-core-extended                             AccentDB - Core & Extended                          6GB  2021-02-17 14:22:54             25  \n",
            "coloradokb/dandelionimages                                  DandelionImages                                     4GB  2021-02-19 20:03:47            106  \n",
            "mathurinache/twitter-edge-nodes                             Twitter Edge Nodes                                342MB  2021-03-08 06:43:04             47  \n",
            "landrykezebou/lvzhdr-tone-mapping-benchmark-dataset-tmonet  LVZ-HDR Tone Mapping Benchmark Dataset (TMO-Net)   24GB  2021-03-01 05:03:40             24  \n",
            "promptcloud/careerbuilder-job-listing-2020                  Careerbuilder Job Listing 2020                     42MB  2021-03-05 06:59:52             72  \n",
            "stuartjames/lights                                          LightS: Light Specularity Dataset                  18GB  2021-02-18 14:32:26             23  \n",
            "shivamb/netflix-shows                                       Netflix Movies and TV Shows                         1MB  2021-01-18 16:20:26         120999  \n",
            "arashnic/hr-analytics-job-change-of-data-scientists         HR Analytics: Job Change of Data Scientists       295KB  2020-12-07 00:25:10          16805  \n",
            "jsphyg/weather-dataset-rattle-package                       Rain in Australia                                   4MB  2020-12-11 10:26:12          41134  \n",
            "michau96/restaurant-business-rankings-2020                  Restaurant Business Rankings 2020                  16KB  2021-01-30 14:20:45          10386  \n",
            "apartment-rental-offers-in-germany.zip: Skipping, found more recently modified local copy (use --force to force download)\n",
            "Archive:  apartment-rental-offers-in-germany.zip\n",
            "replace immo_data.csv? [y]es, [n]o, [A]ll, [N]one, [r]ename: y\n",
            "  inflating: immo_data.csv           \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "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>regio1</th>\n",
              "      <th>serviceCharge</th>\n",
              "      <th>heatingType</th>\n",
              "      <th>telekomTvOffer</th>\n",
              "      <th>telekomHybridUploadSpeed</th>\n",
              "      <th>newlyConst</th>\n",
              "      <th>balcony</th>\n",
              "      <th>picturecount</th>\n",
              "      <th>pricetrend</th>\n",
              "      <th>telekomUploadSpeed</th>\n",
              "      <th>totalRent</th>\n",
              "      <th>yearConstructed</th>\n",
              "      <th>scoutId</th>\n",
              "      <th>noParkSpaces</th>\n",
              "      <th>firingTypes</th>\n",
              "      <th>hasKitchen</th>\n",
              "      <th>geo_bln</th>\n",
              "      <th>cellar</th>\n",
              "      <th>yearConstructedRange</th>\n",
              "      <th>baseRent</th>\n",
              "      <th>houseNumber</th>\n",
              "      <th>livingSpace</th>\n",
              "      <th>geo_krs</th>\n",
              "      <th>condition</th>\n",
              "      <th>interiorQual</th>\n",
              "      <th>petsAllowed</th>\n",
              "      <th>street</th>\n",
              "      <th>streetPlain</th>\n",
              "      <th>lift</th>\n",
              "      <th>baseRentRange</th>\n",
              "      <th>typeOfFlat</th>\n",
              "      <th>geo_plz</th>\n",
              "      <th>noRooms</th>\n",
              "      <th>thermalChar</th>\n",
              "      <th>floor</th>\n",
              "      <th>numberOfFloors</th>\n",
              "      <th>noRoomsRange</th>\n",
              "      <th>garden</th>\n",
              "      <th>livingSpaceRange</th>\n",
              "      <th>regio2</th>\n",
              "      <th>regio3</th>\n",
              "      <th>description</th>\n",
              "      <th>facilities</th>\n",
              "      <th>heatingCosts</th>\n",
              "      <th>energyEfficiencyClass</th>\n",
              "      <th>lastRefurbish</th>\n",
              "      <th>electricityBasePrice</th>\n",
              "      <th>electricityKwhPrice</th>\n",
              "      <th>date</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Nordrhein_Westfalen</td>\n",
              "      <td>245.00</td>\n",
              "      <td>central_heating</td>\n",
              "      <td>ONE_YEAR_FREE</td>\n",
              "      <td>NaN</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>6</td>\n",
              "      <td>4.62</td>\n",
              "      <td>10.0</td>\n",
              "      <td>840.0</td>\n",
              "      <td>1965.0</td>\n",
              "      <td>96107057</td>\n",
              "      <td>1.0</td>\n",
              "      <td>oil</td>\n",
              "      <td>False</td>\n",
              "      <td>Nordrhein_Westfalen</td>\n",
              "      <td>True</td>\n",
              "      <td>2.0</td>\n",
              "      <td>595.0</td>\n",
              "      <td>244</td>\n",
              "      <td>86.00</td>\n",
              "      <td>Dortmund</td>\n",
              "      <td>well_kept</td>\n",
              "      <td>normal</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Sch&amp;uuml;ruferstra&amp;szlig;e</td>\n",
              "      <td>Schüruferstraße</td>\n",
              "      <td>False</td>\n",
              "      <td>4</td>\n",
              "      <td>ground_floor</td>\n",
              "      <td>44269</td>\n",
              "      <td>4.0</td>\n",
              "      <td>181.4</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4</td>\n",
              "      <td>True</td>\n",
              "      <td>4</td>\n",
              "      <td>Dortmund</td>\n",
              "      <td>Schüren</td>\n",
              "      <td>Die ebenerdig zu erreichende Erdgeschosswohnun...</td>\n",
              "      <td>Die Wohnung ist mit Laminat ausgelegt. Das Bad...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>May19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Rheinland_Pfalz</td>\n",
              "      <td>134.00</td>\n",
              "      <td>self_contained_central_heating</td>\n",
              "      <td>ONE_YEAR_FREE</td>\n",
              "      <td>NaN</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>8</td>\n",
              "      <td>3.47</td>\n",
              "      <td>10.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1871.0</td>\n",
              "      <td>111378734</td>\n",
              "      <td>2.0</td>\n",
              "      <td>gas</td>\n",
              "      <td>False</td>\n",
              "      <td>Rheinland_Pfalz</td>\n",
              "      <td>False</td>\n",
              "      <td>1.0</td>\n",
              "      <td>800.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>89.00</td>\n",
              "      <td>Rhein_Pfalz_Kreis</td>\n",
              "      <td>refurbished</td>\n",
              "      <td>normal</td>\n",
              "      <td>no</td>\n",
              "      <td>no_information</td>\n",
              "      <td>NaN</td>\n",
              "      <td>False</td>\n",
              "      <td>5</td>\n",
              "      <td>ground_floor</td>\n",
              "      <td>67459</td>\n",
              "      <td>3.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "      <td>False</td>\n",
              "      <td>4</td>\n",
              "      <td>Rhein_Pfalz_Kreis</td>\n",
              "      <td>Böhl_Iggelheim</td>\n",
              "      <td>Alles neu macht der Mai – so kann es auch für ...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2019.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>May19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Sachsen</td>\n",
              "      <td>255.00</td>\n",
              "      <td>floor_heating</td>\n",
              "      <td>ONE_YEAR_FREE</td>\n",
              "      <td>10.0</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>8</td>\n",
              "      <td>2.72</td>\n",
              "      <td>2.4</td>\n",
              "      <td>1300.0</td>\n",
              "      <td>2019.0</td>\n",
              "      <td>113147523</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>False</td>\n",
              "      <td>Sachsen</td>\n",
              "      <td>True</td>\n",
              "      <td>9.0</td>\n",
              "      <td>965.0</td>\n",
              "      <td>4</td>\n",
              "      <td>83.80</td>\n",
              "      <td>Dresden</td>\n",
              "      <td>first_time_use</td>\n",
              "      <td>sophisticated</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Turnerweg</td>\n",
              "      <td>Turnerweg</td>\n",
              "      <td>True</td>\n",
              "      <td>6</td>\n",
              "      <td>apartment</td>\n",
              "      <td>1097</td>\n",
              "      <td>3.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3</td>\n",
              "      <td>False</td>\n",
              "      <td>4</td>\n",
              "      <td>Dresden</td>\n",
              "      <td>Äußere_Neustadt_Antonstadt</td>\n",
              "      <td>Der Neubau entsteht im Herzen der Dresdner Neu...</td>\n",
              "      <td>* 9 m² Balkon\\n* Bad mit bodengleicher Dusche,...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Oct19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Sachsen</td>\n",
              "      <td>58.15</td>\n",
              "      <td>district_heating</td>\n",
              "      <td>ONE_YEAR_FREE</td>\n",
              "      <td>NaN</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>9</td>\n",
              "      <td>1.53</td>\n",
              "      <td>40.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1964.0</td>\n",
              "      <td>108890903</td>\n",
              "      <td>NaN</td>\n",
              "      <td>district_heating</td>\n",
              "      <td>False</td>\n",
              "      <td>Sachsen</td>\n",
              "      <td>False</td>\n",
              "      <td>2.0</td>\n",
              "      <td>343.0</td>\n",
              "      <td>35</td>\n",
              "      <td>58.15</td>\n",
              "      <td>Mittelsachsen_Kreis</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Gl&amp;uuml;ck-Auf-Stra&amp;szlig;e</td>\n",
              "      <td>Glück-Auf-Straße</td>\n",
              "      <td>False</td>\n",
              "      <td>2</td>\n",
              "      <td>other</td>\n",
              "      <td>9599</td>\n",
              "      <td>3.0</td>\n",
              "      <td>86.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "      <td>False</td>\n",
              "      <td>2</td>\n",
              "      <td>Mittelsachsen_Kreis</td>\n",
              "      <td>Freiberg</td>\n",
              "      <td>Abseits von Lärm und Abgasen in Ihre neue Wohn...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>87.23</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>May19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Bremen</td>\n",
              "      <td>138.00</td>\n",
              "      <td>self_contained_central_heating</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>19</td>\n",
              "      <td>2.46</td>\n",
              "      <td>NaN</td>\n",
              "      <td>903.0</td>\n",
              "      <td>1950.0</td>\n",
              "      <td>114751222</td>\n",
              "      <td>NaN</td>\n",
              "      <td>gas</td>\n",
              "      <td>False</td>\n",
              "      <td>Bremen</td>\n",
              "      <td>False</td>\n",
              "      <td>1.0</td>\n",
              "      <td>765.0</td>\n",
              "      <td>10</td>\n",
              "      <td>84.97</td>\n",
              "      <td>Bremen</td>\n",
              "      <td>refurbished</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Hermann-Henrich-Meier-Allee</td>\n",
              "      <td>Hermann-Henrich-Meier-Allee</td>\n",
              "      <td>False</td>\n",
              "      <td>5</td>\n",
              "      <td>apartment</td>\n",
              "      <td>28213</td>\n",
              "      <td>3.0</td>\n",
              "      <td>188.9</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "      <td>False</td>\n",
              "      <td>4</td>\n",
              "      <td>Bremen</td>\n",
              "      <td>Neu_Schwachhausen</td>\n",
              "      <td>Es handelt sich hier um ein saniertes Mehrfami...</td>\n",
              "      <td>Diese Wohnung wurde neu saniert und ist wie fo...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Feb20</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                regio1  serviceCharge  ... electricityKwhPrice   date\n",
              "0  Nordrhein_Westfalen         245.00  ...                 NaN  May19\n",
              "1      Rheinland_Pfalz         134.00  ...                 NaN  May19\n",
              "2              Sachsen         255.00  ...                 NaN  Oct19\n",
              "3              Sachsen          58.15  ...                 NaN  May19\n",
              "4               Bremen         138.00  ...                 NaN  Feb20\n",
              "\n",
              "[5 rows x 49 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UIbMoZQ9fMeA"
      },
      "source": [
        "Living Space v/s Total Rent"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "n4XHxiTvUHzR",
        "outputId": "e07a83a4-a3a4-4acd-b48d-a3481541cf0c"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "df = df.dropna()\n",
        "\n",
        "y = df[[\"totalRent\"]]\n",
        "x = df[[\"livingSpace\"]]\n",
        "\n",
        "plt.scatter(x, y)\n",
        "plt.xlabel(\"Living Space (in sqmm)\")\n",
        "plt.ylabel(\"Total Rent (in euroes)\")\n",
        "plt.show()"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1fh1eRM5iXpX"
      },
      "source": [
        "Linear Regression with living space as independant variable"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 330
        },
        "id": "5OycLChMfXDf",
        "outputId": "fdb0d3f2-df0a-47d5-ad72-be1b10feaaf6"
      },
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import r2_score\n",
        "\n",
        "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3)\n",
        "reg = LinearRegression().fit(x_train, y_train)\n",
        "\n",
        "plt.scatter(x, y,  color='blue')\n",
        "plt.plot(x_train, reg.coef_[0][0]*x_train + reg.intercept_[0], '-r')\n",
        "plt.xlabel(\"Living Space (in sqmm)\")\n",
        "plt.ylabel(\"Total Rent (in euroes)\")\n",
        "\n",
        "predicted_y = reg.predict(x_test)\n",
        "\n",
        "print(\"Mean absolute error: %.2f\" % np.mean(np.absolute(predicted_y - y_test)))\n",
        "print(\"Residual sum of squares (MSE): %.2f\" % np.mean((predicted_y - y_test) ** 2))\n",
        "print(\"R2-score: %.2f\" % r2_score(predicted_y , y_test) )"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean absolute error: 331.92\n",
            "Residual sum of squares (MSE): 232023.50\n",
            "R2-score: -0.73\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sXSN95vfib9h"
      },
      "source": [
        "**Result : NOT ACCEPTABLE**\n",
        "\n",
        "The Living space and the rent doesn't have an acceptable linear relationship"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8CBXKC0Y0Hcw"
      },
      "source": [
        "Now let's try incorporating another variable - the region. For example the rent in Bavaria is normally higher than the rest of Germany. We did not take that into account before. \n",
        "\n",
        "To convert categorical variable geo_bln into a numerical variable with some meaning, sort the regions based on the average rent in each region. The index of the resultant array will provide a suitable number for each category corresponding to the region."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 319
        },
        "id": "mvIp_gSBjdyP",
        "outputId": "cbca25af-98ac-4fc9-eea1-758a562d30d7"
      },
      "source": [
        "states = df[\"geo_bln\"].unique()\n",
        "\n",
        "# sort the regions based on the average rent\n",
        "region_mean = df.groupby(\"geo_bln\").mean()[\"totalRent\"]\n",
        "region_mean = region_mean.sort_values().keys().to_list()\n",
        "\n",
        "print(region_mean)\n",
        "\n",
        "# add a new column region_id which is proportional to the average rent in that region\n",
        "for index, row in df.iterrows():\n",
        "  df.loc[index,'region_id'] = region_mean.index(row[\"geo_bln\"])\n",
        "\n",
        "y = df[[\"totalRent\"]]\n",
        "region = df[[\"region_id\"]]\n",
        "space = df[[\"livingSpace\"]]\n",
        "\n",
        "plt.scatter(region, y)"
      ],
      "execution_count": 143,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['Brandenburg', 'Sachsen', 'Sachsen_Anhalt', 'Nordrhein_Westfalen', 'Mecklenburg_Vorpommern', 'Schleswig_Holstein', 'Hamburg', 'Hessen', 'Rheinland_Pfalz', 'Berlin', 'Bayern']\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f5ae19516d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 143
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m_ZD12vj1BXg"
      },
      "source": [
        "Now try multiple linear regression with region id and living space as independant variables."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9zmr6LEdy36x",
        "outputId": "805fc4fa-f8ba-4391-8082-bfcfa3bc4de8"
      },
      "source": [
        "x = np.asanyarray(df[['region_id','livingSpace']])\n",
        "y = np.asanyarray(df[['totalRent']])\n",
        "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3)\n",
        "\n",
        "reg = LinearRegression()\n",
        "reg.fit (x, y)\n",
        "# The coefficients\n",
        "print ('Coefficients: ', reg.coef_)\n",
        "\n",
        "predicted_y = reg.predict(x_test)\n",
        "\n",
        "print(\"Mean absolute error: %.2f\" % np.mean(np.absolute(predicted_y - y_test)))\n",
        "print(\"Residual sum of squares (MSE): %.2f\" % np.mean((predicted_y - y_test) ** 2))\n",
        "print(\"R2-score: %.2f\" % r2_score(predicted_y , y_test))"
      ],
      "execution_count": 139,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Coefficients:  [[113.17232364   6.98574762]]\n",
            "Mean absolute error: 122.66\n",
            "Residual sum of squares (MSE): 21809.82\n",
            "R2-score: 0.90\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "votIfbBS1K1D"
      },
      "source": [
        "A quite acceptable R2 score! \n",
        "\n",
        "NOTE : Another approach would be using the average rent in the region directly instead of the region id. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6siEcXdA2Qu-",
        "outputId": "ebc06242-a66d-4f8b-a8be-6f42030e92bd"
      },
      "source": [
        "reg.predict([[1, 58]])"
      ],
      "execution_count": 145,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[386.90433266]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 145
        }
      ]
    }
  ]
}