Flat_Price_Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e33d1ccc",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import glob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5f85dc95",
   "metadata": {},
   "outputs": [],
   "source": [
    "def import_csv_files(path):\n",
    "    \n",
    "    csv_files = glob.glob(path + \"/*.csv\")\n",
    "\n",
    "    df_list = (pd.read_csv(file) for file in csv_files)\n",
    "\n",
    "    data = pd.concat(df_list, axis = 0)\n",
    "    \n",
    "    print(f'You have a dataset of {data.shape[1]} features for {data.shape[0]} rows.')\n",
    "    \n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1e6cad8b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "You have a dataset of 52 features for 7158 rows.\n"
     ]
    }
   ],
   "source": [
    "df = import_csv_files('data')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7dfd820b",
   "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>id</th>\n",
       "      <th>object</th>\n",
       "      <th>annonce_id</th>\n",
       "      <th>agency_id</th>\n",
       "      <th>agency_page</th>\n",
       "      <th>agency_contact_name</th>\n",
       "      <th>agency_img_url</th>\n",
       "      <th>agency_phone_number</th>\n",
       "      <th>agency_has_email</th>\n",
       "      <th>agency_link</th>\n",
       "      <th>...</th>\n",
       "      <th>transaction_type</th>\n",
       "      <th>title</th>\n",
       "      <th>tags</th>\n",
       "      <th>url</th>\n",
       "      <th>video_url</th>\n",
       "      <th>virtual_visit_url</th>\n",
       "      <th>collected_at</th>\n",
       "      <th>input_url</th>\n",
       "      <th>input_max_pages</th>\n",
       "      <th>input_annonce_details</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3204208</td>\n",
       "      <td>result</td>\n",
       "      <td>200749259</td>\n",
       "      <td>193229</td>\n",
       "      <td>/professionnels/agences-immobilieres/lyon-3eme...</td>\n",
       "      <td>Keller Williams Abondance</td>\n",
       "      <td>https://v.seloger.com/s/width/150/logos/0/t/u/...</td>\n",
       "      <td>07 69 27 36 43</td>\n",
       "      <td>t</td>\n",
       "      <td>http://abondance.kwimmo.fr/</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1 pièce, 19 m²</td>\n",
       "      <td>https://www.seloger.com/annonces/achat/apparte...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://my.matterport.com/show/?m=cQpwASrd8FN</td>\n",
       "      <td>18 Apr 2023 06:43:10</td>\n",
       "      <td>https://www.seloger.com/list.htm?projects=2&amp;ty...</td>\n",
       "      <td>398</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3213503</td>\n",
       "      <td>result</td>\n",
       "      <td>200558875</td>\n",
       "      <td>508226</td>\n",
       "      <td>/professionnels/agents-commerciaux/lyon-2eme-a...</td>\n",
       "      <td>TROMPILLE JULIEN BSK IMMOBILIER</td>\n",
       "      <td>https://v.seloger.com/s/width/150/logos/1/n/0/...</td>\n",
       "      <td>06 48 12 66 75</td>\n",
       "      <td>t</td>\n",
       "      <td>https://bskimmobilier.com/commercial-immobilie...</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2 pièces, 1 chambre, 43 m², Étage 5/6</td>\n",
       "      <td>https://www.seloger.com/annonces/achat/apparte...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18 Apr 2023 06:47:42</td>\n",
       "      <td>https://www.seloger.com/list.htm?projects=2&amp;ty...</td>\n",
       "      <td>398</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3223829</td>\n",
       "      <td>result</td>\n",
       "      <td>201195143</td>\n",
       "      <td>50807</td>\n",
       "      <td>/professionnels/agences-immobilieres/lyon-5eme...</td>\n",
       "      <td>NESTENN LYON 5</td>\n",
       "      <td>https://v.seloger.com/s/width/150/logos/1/k/t/...</td>\n",
       "      <td>04 86 11 09 09</td>\n",
       "      <td>t</td>\n",
       "      <td>http://www.immobilier-lyon-5@nestenn.com/</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1 pièce, 26 m², Étage 0/1</td>\n",
       "      <td>https://www.seloger.com/annonces/achat/apparte...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18 Apr 2023 06:44:06</td>\n",
       "      <td>https://www.seloger.com/list.htm?projects=2&amp;ty...</td>\n",
       "      <td>398</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 52 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        id  object  annonce_id  agency_id  \\\n",
       "0  3204208  result   200749259     193229   \n",
       "1  3213503  result   200558875     508226   \n",
       "2  3223829  result   201195143      50807   \n",
       "\n",
       "                                         agency_page  \\\n",
       "0  /professionnels/agences-immobilieres/lyon-3eme...   \n",
       "1  /professionnels/agents-commerciaux/lyon-2eme-a...   \n",
       "2  /professionnels/agences-immobilieres/lyon-5eme...   \n",
       "\n",
       "               agency_contact_name  \\\n",
       "0        Keller Williams Abondance   \n",
       "1  TROMPILLE JULIEN BSK IMMOBILIER   \n",
       "2                   NESTENN LYON 5   \n",
       "\n",
       "                                      agency_img_url agency_phone_number  \\\n",
       "0  https://v.seloger.com/s/width/150/logos/0/t/u/...      07 69 27 36 43   \n",
       "1  https://v.seloger.com/s/width/150/logos/1/n/0/...      06 48 12 66 75   \n",
       "2  https://v.seloger.com/s/width/150/logos/1/k/t/...      04 86 11 09 09   \n",
       "\n",
       "  agency_has_email                                        agency_link  ...  \\\n",
       "0                t                        http://abondance.kwimmo.fr/  ...   \n",
       "1                t  https://bskimmobilier.com/commercial-immobilie...  ...   \n",
       "2                t          http://www.immobilier-lyon-5@nestenn.com/  ...   \n",
       "\n",
       "  transaction_type  title                                   tags  \\\n",
       "0                2    NaN                         1 pièce, 19 m²   \n",
       "1                2    NaN  2 pièces, 1 chambre, 43 m², Étage 5/6   \n",
       "2                2    NaN              1 pièce, 26 m², Étage 0/1   \n",
       "\n",
       "                                                 url video_url  \\\n",
       "0  https://www.seloger.com/annonces/achat/apparte...       NaN   \n",
       "1  https://www.seloger.com/annonces/achat/apparte...       NaN   \n",
       "2  https://www.seloger.com/annonces/achat/apparte...       NaN   \n",
       "\n",
       "                               virtual_visit_url          collected_at  \\\n",
       "0  https://my.matterport.com/show/?m=cQpwASrd8FN  18 Apr 2023 06:43:10   \n",
       "1                                            NaN  18 Apr 2023 06:47:42   \n",
       "2                                            NaN  18 Apr 2023 06:44:06   \n",
       "\n",
       "                                           input_url input_max_pages  \\\n",
       "0  https://www.seloger.com/list.htm?projects=2&ty...             398   \n",
       "1  https://www.seloger.com/list.htm?projects=2&ty...             398   \n",
       "2  https://www.seloger.com/list.htm?projects=2&ty...             398   \n",
       "\n",
       "   input_annonce_details  \n",
       "0                      f  \n",
       "1                      f  \n",
       "2                      f  \n",
       "\n",
       "[3 rows x 52 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9f373f5f",
   "metadata": {},
   "outputs": [],
   "source": [
    "features_to_delete = ['id','object', 'annonce_id', 'agency_id', 'agency_page', 'agency_contact_name', 'agency_img_url',\n",
    "        'agency_phone_number', 'agency_has_email', 'agency_link', 'contact_email', 'description', 'is_redirected',\n",
    "        'is_expired', 'latitude', 'longitude', 'picture_count', 'photos', 'price_decrease_percent', 'publication_id',\n",
    "        'pricing_price_note', 'ref', 'title', 'url', 'video_url', 'virtual_visit_url', 'collected_at', 'input_url',\n",
    "        'input_max_pages', 'input_annonce_details','main_picture','transaction_type', 'short_description', \n",
    "             'price_per_meter', 'is_exclusive', 'monthly_price', 'nature', 'estate_type_id', \n",
    "              'highlighting_level', 'address', 'bedrooms_count','rooms', 'contact_is_private_seller','business_unit'\n",
    "              , 'insee_code','position']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a1ba21fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "def delete_features_and_duplicates(dataframe, list_of_features):\n",
    "    dataframe.drop(list_of_features, axis = 1, inplace = True)\n",
    "    dataframe.drop_duplicates(inplace = True, ignore_index = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d5e9adc4",
   "metadata": {},
   "outputs": [],
   "source": [
    "delete_features_and_duplicates(df, features_to_delete)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c20c8f5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#function to see all the unique features that have the column tags\n",
    "\n",
    "def unique_flat_features(dataframe, column, seperator):\n",
    "     \n",
    "    words = ''\n",
    "    for data in dataframe[column]:\n",
    "        words = words + data + \",\"\n",
    "\n",
    "    unique = []\n",
    "    for word in words.split(\",\"):\n",
    "        if word not in unique:\n",
    "            unique.append(word)\n",
    "\n",
    "    unique.sort(reverse=True)\n",
    "    print(unique)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9f07563b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#unique_flat_features(df, 'tags', ',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "95bbbbec",
   "metadata": {},
   "outputs": [],
   "source": [
    "#list of the features we found in 'tags'\n",
    "#rooms and area are already in the data, so I do not keep them \n",
    "\n",
    "tag_features = {'pièce': 'rooms', 'balcon':'balcony', 'ascenseur':'elevator', \n",
    "                 'terrasse':'terrace', \"étage\" : 'floor', 'jardin' : \"garden\", \n",
    "                 'garage' : 'garage','parking' : 'parking_spot', 'box' : 'box' }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ff91b3ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "#now I create separeted columns for each feature found in tags\n",
    "\n",
    "def split_tags_column(features, dataframe, column_to_split ):\n",
    "    \n",
    "    for feature in features :\n",
    "        list_temp = []\n",
    "\n",
    "        for data in dataframe[column_to_split] :\n",
    "            if feature.lower() in data.lower() :\n",
    "                \n",
    "                splited_data = data.split(\",\")\n",
    "                    \n",
    "                for element in splited_data:\n",
    "                    if feature.lower() in element.lower():\n",
    "                            \n",
    "                        list_temp.append(element)\n",
    "            else :\n",
    "                list_temp.append(None)\n",
    "                \n",
    "        dataframe[features[feature]] = list_temp\n",
    "        \n",
    "    dataframe.drop([column_to_split], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2cb6dc1a",
   "metadata": {},
   "outputs": [],
   "source": [
    "split_tags_column(tag_features, df, 'tags')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "eec432d5",
   "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>area</th>\n",
       "      <th>district</th>\n",
       "      <th>estate_type</th>\n",
       "      <th>postal_code</th>\n",
       "      <th>price</th>\n",
       "      <th>rooms</th>\n",
       "      <th>balcony</th>\n",
       "      <th>elevator</th>\n",
       "      <th>terrace</th>\n",
       "      <th>floor</th>\n",
       "      <th>garden</th>\n",
       "      <th>garage</th>\n",
       "      <th>parking_spot</th>\n",
       "      <th>box</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18.999023</td>\n",
       "      <td>Croix Paquet</td>\n",
       "      <td>Studio</td>\n",
       "      <td>69001</td>\n",
       "      <td>175000</td>\n",
       "      <td>1 pièce</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>42.999855</td>\n",
       "      <td>Terreaux</td>\n",
       "      <td>Appartement</td>\n",
       "      <td>69001</td>\n",
       "      <td>297000</td>\n",
       "      <td>2 pièces</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>Étage 5/6</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>25.717703</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Studio</td>\n",
       "      <td>69009</td>\n",
       "      <td>129000</td>\n",
       "      <td>1 pièce</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>Étage 0/1</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        area      district  estate_type  postal_code   price     rooms  \\\n",
       "0  18.999023  Croix Paquet       Studio        69001  175000   1 pièce   \n",
       "1  42.999855      Terreaux  Appartement        69001  297000  2 pièces   \n",
       "2  25.717703           NaN       Studio        69009  129000   1 pièce   \n",
       "\n",
       "  balcony elevator terrace       floor garden garage parking_spot   box  \n",
       "0    None     None    None        None   None   None         None  None  \n",
       "1    None     None    None   Étage 5/6   None   None         None  None  \n",
       "2    None     None    None   Étage 0/1   None   None         None  None  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9e64a4a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "#creating 3 categories of floor\n",
    "#Ground floor, Last floor, or Neither\n",
    "\n",
    "def categorize_floors(dataframe):\n",
    "    \n",
    "    list_floor = []\n",
    "\n",
    "    for floor in df['floor'] :\n",
    "\n",
    "        if floor == None :\n",
    "            list_floor.append('Other')\n",
    "        elif (str(floor.replace('Étage ',''))[1] == '0'):\n",
    "            list_floor.append('Ground Floor')\n",
    "        elif (str(floor.replace('Étage ',''))[1] == str(floor.replace('Étage ',''))[3]) :\n",
    "            list_floor.append('Last Floor')\n",
    "        else :\n",
    "            list_floor.append('Other')\n",
    "            \n",
    "    df['floor'] = list_floor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8465eaf1",
   "metadata": {},
   "outputs": [],
   "source": [
    "categorize_floors(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "012ad4a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Converting the areas from float to integers\n",
    "\n",
    "\n",
    "df['area'] = df['area'].values.round()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "9dd44452",
   "metadata": {},
   "outputs": [],
   "source": [
    "#I want to use the estate_type column to know if the flat is new or not\n",
    "\n",
    "def is_flat_new(dataframe):\n",
    "    \n",
    "    list_new = []\n",
    "    \n",
    "    for estate_type in dataframe['estate_type']:\n",
    "        if 'neuf' in estate_type.lower() :\n",
    "            \n",
    "            list_new.append(True)\n",
    "        \n",
    "        else : \n",
    "            \n",
    "            list_new.append(False)\n",
    "\n",
    "    dataframe['new_flat'] = list_new\n",
    "    dataframe.drop(['estate_type'], axis = 1, inplace = True)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "324ead9b",
   "metadata": {},
   "outputs": [],
   "source": [
    "is_flat_new(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f08330f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#list of all features I will keep on True/False option\n",
    "list_t_f_features = ['balcony', 'elevator', 'garden', 'terrace', 'garage', 'box', 'parking_spot']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "29ddd16d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#if a value is already existing, it is True, if not it is False\n",
    "\n",
    "def true_false(dataframe, column) :\n",
    "    dataframe[column] = dataframe[column].notnull().astype(bool)\n",
    "    dataframe[column] = dataframe[column].fillna(False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "1f594321",
   "metadata": {},
   "outputs": [],
   "source": [
    "for feature in list_t_f_features :\n",
    "    true_false(df, feature)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "ac0f49a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "#merging box and garage\n",
    "df['garage'] = df['garage'] | df['box'] \n",
    "\n",
    "df = df.drop(['box'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "49cd9210",
   "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>area</th>\n",
       "      <th>district</th>\n",
       "      <th>postal_code</th>\n",
       "      <th>price</th>\n",
       "      <th>rooms</th>\n",
       "      <th>balcony</th>\n",
       "      <th>elevator</th>\n",
       "      <th>terrace</th>\n",
       "      <th>floor</th>\n",
       "      <th>garden</th>\n",
       "      <th>garage</th>\n",
       "      <th>parking_spot</th>\n",
       "      <th>new_flat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19.0</td>\n",
       "      <td>Croix Paquet</td>\n",
       "      <td>69001</td>\n",
       "      <td>175000</td>\n",
       "      <td>1 pièce</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Other</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>43.0</td>\n",
       "      <td>Terreaux</td>\n",
       "      <td>69001</td>\n",
       "      <td>297000</td>\n",
       "      <td>2 pièces</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Other</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>69009</td>\n",
       "      <td>129000</td>\n",
       "      <td>1 pièce</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Ground Floor</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   area      district  postal_code   price     rooms  balcony  elevator  \\\n",
       "0  19.0  Croix Paquet        69001  175000   1 pièce    False     False   \n",
       "1  43.0      Terreaux        69001  297000  2 pièces    False     False   \n",
       "2  26.0           NaN        69009  129000   1 pièce    False     False   \n",
       "\n",
       "   terrace         floor  garden  garage  parking_spot  new_flat  \n",
       "0    False         Other   False   False         False     False  \n",
       "1    False         Other   False   False         False     False  \n",
       "2    False  Ground Floor   False   False         False     False  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "80c54ec1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3625 entries, 0 to 3624\n",
      "Data columns (total 13 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   area          3610 non-null   float64\n",
      " 1   district      2691 non-null   object \n",
      " 2   postal_code   3625 non-null   int64  \n",
      " 3   price         3625 non-null   int64  \n",
      " 4   rooms         3625 non-null   object \n",
      " 5   balcony       3625 non-null   bool   \n",
      " 6   elevator      3625 non-null   bool   \n",
      " 7   terrace       3625 non-null   bool   \n",
      " 8   floor         3625 non-null   object \n",
      " 9   garden        3625 non-null   bool   \n",
      " 10  garage        3625 non-null   bool   \n",
      " 11  parking_spot  3625 non-null   bool   \n",
      " 12  new_flat      3625 non-null   bool   \n",
      "dtypes: bool(7), float64(1), int64(2), object(3)\n",
      "memory usage: 194.8+ KB\n"
     ]
    }
   ],
   "source": [
    "#area and district still have missing data\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "f00dab70",
   "metadata": {},
   "outputs": [],
   "source": [
    "#area has very few missing data, I can drop those few rows\n",
    "\n",
    "df.dropna(subset=['area'], inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "b2317056",
   "metadata": {},
   "outputs": [],
   "source": [
    "#deleting the eventual rows that have '0 pièce' for the number of rooms \n",
    "\n",
    "df = df.loc[df['rooms']!='0 pièce']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "1a6d7886",
   "metadata": {},
   "outputs": [],
   "source": [
    "#I am filling the Nan of the District column, using the latest non-Nan value for District\n",
    "\n",
    "#Since the dataframe will be sorted by postal code, the probability to have the good district \n",
    "#or at least one nearby seems good\n",
    "\n",
    "def fill_na_district(dataframe):\n",
    "    \n",
    "    dataframe.sort_values(['postal_code'], inplace = True)\n",
    "    dataframe['district'].fillna(method='bfill', inplace = True)\n",
    "    dataframe.drop(['postal_code'], axis = 1, inplace = True)\n",
    "    dataframe.sort_values('price')\n",
    "    dataframe.reset_index(inplace = True, drop = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "276b39aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "fill_na_district(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81178b13",
   "metadata": {},
   "source": [
    " ## Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "655fb8d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "9384e972",
   "metadata": {},
   "outputs": [],
   "source": [
    "encoder = LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "a9792420",
   "metadata": {},
   "outputs": [],
   "source": [
    "#columns with 2 values \n",
    "\n",
    "for data in df[['balcony', 'elevator', 'terrace', 'garden', 'garage', 'parking_spot', 'new_flat']]:\n",
    "    df[data] = encoder.fit_transform(df[data])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "e1c12f5e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#columns with more than 2 values\n",
    "\n",
    "for data in df[['district', 'rooms', 'floor']]:\n",
    "    df = df.join(pd.get_dummies(df[data])).drop([data], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "7743e982",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='price', ylabel='Count'>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(df['price'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "5a42e394",
   "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>area</th>\n",
       "      <th>price</th>\n",
       "      <th>balcony</th>\n",
       "      <th>elevator</th>\n",
       "      <th>terrace</th>\n",
       "      <th>garden</th>\n",
       "      <th>garage</th>\n",
       "      <th>parking_spot</th>\n",
       "      <th>new_flat</th>\n",
       "      <th>Bellecour-Ainay</th>\n",
       "      <th>...</th>\n",
       "      <th>3 pièces</th>\n",
       "      <th>4 pièces</th>\n",
       "      <th>5 pièces</th>\n",
       "      <th>6 pièces</th>\n",
       "      <th>7 pièces</th>\n",
       "      <th>8 pièces</th>\n",
       "      <th>9 pièces</th>\n",
       "      <th>Ground Floor</th>\n",
       "      <th>Last Floor</th>\n",
       "      <th>Other</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19.0</td>\n",
       "      <td>175000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>77.0</td>\n",
       "      <td>399000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>96.0</td>\n",
       "      <td>599000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>65.0</td>\n",
       "      <td>370000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>38.0</td>\n",
       "      <td>260000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3604</th>\n",
       "      <td>88.0</td>\n",
       "      <td>319000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3605</th>\n",
       "      <td>65.0</td>\n",
       "      <td>398000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3606</th>\n",
       "      <td>80.0</td>\n",
       "      <td>299000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3607</th>\n",
       "      <td>65.0</td>\n",
       "      <td>245000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3608</th>\n",
       "      <td>18.0</td>\n",
       "      <td>59791</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3159 rows × 59 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      area   price  balcony  elevator  terrace  garden  garage  parking_spot  \\\n",
       "0     19.0  175000        0         0        0       0       0             0   \n",
       "1     77.0  399000        0         1        0       0       0             0   \n",
       "2     96.0  599000        0         1        0       0       1             0   \n",
       "3     65.0  370000        1         0        0       0       0             0   \n",
       "4     38.0  260000        0         1        0       0       0             0   \n",
       "...    ...     ...      ...       ...      ...     ...     ...           ...   \n",
       "3604  88.0  319000        0         1        1       0       1             0   \n",
       "3605  65.0  398000        1         0        0       0       0             0   \n",
       "3606  80.0  299000        1         1        0       0       1             1   \n",
       "3607  65.0  245000        1         0        0       0       0             1   \n",
       "3608  18.0   59791        0         0        0       0       0             0   \n",
       "\n",
       "      new_flat  Bellecour-Ainay  ...  3 pièces  4 pièces  5 pièces  6 pièces  \\\n",
       "0            0                0  ...         0         0         0         0   \n",
       "1            0                0  ...         0         1         0         0   \n",
       "2            0                0  ...         1         0         0         0   \n",
       "3            0                0  ...         1         0         0         0   \n",
       "4            0                0  ...         0         0         0         0   \n",
       "...        ...              ...  ...       ...       ...       ...       ...   \n",
       "3604         0                0  ...         0         1         0         0   \n",
       "3605         0                0  ...         1         0         0         0   \n",
       "3606         0                0  ...         0         1         0         0   \n",
       "3607         0                0  ...         1         0         0         0   \n",
       "3608         0                0  ...         0         0         0         0   \n",
       "\n",
       "      7 pièces  8 pièces  9 pièces  Ground Floor  Last Floor  Other  \n",
       "0            0         0         0             0           0      1  \n",
       "1            0         0         0             0           1      0  \n",
       "2            0         0         0             0           0      1  \n",
       "3            0         0         0             0           1      0  \n",
       "4            0         0         0             0           1      0  \n",
       "...        ...       ...       ...           ...         ...    ...  \n",
       "3604         0         0         0             0           1      0  \n",
       "3605         0         0         0             0           0      1  \n",
       "3606         0         0         0             0           0      1  \n",
       "3607         0         0         0             1           0      0  \n",
       "3608         0         0         0             0           0      1  \n",
       "\n",
       "[3159 rows x 59 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# I only keep the prices (the target value) that have at least 50 occurences\n",
    "\n",
    "final_df = df[df['price'] < 600000]\n",
    "final_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0bfbd35b",
   "metadata": {},
   "source": [
    "## Machine Learning Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "c05dc5c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "717a21e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "seed = 42"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "c30a110a",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = final_df.drop(['price'], axis = 1)\n",
    "y = final_df['price']\n",
    "\n",
    "X_train, X_tmp, y_train, y_tmp = train_test_split(X, y, train_size=0.8, random_state=seed)\n",
    "X_val, X_test, y_val, y_test = train_test_split(X_tmp, y_tmp, test_size=0.5, random_state=seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "d3f30f1e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.metrics import mean_absolute_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "87c0bf70",
   "metadata": {},
   "outputs": [],
   "source": [
    "r_forest = RandomForestRegressor()\n",
    "\n",
    "r_forest.fit(X_train, y_train)\n",
    "\n",
    "predictions = r_forest.predict(X_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "c4a9cb63",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "44182.88473437814"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_absolute_error(predictions, y_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "8cdae3be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "err = np.abs(y_val - predictions)\n",
    "\n",
    "plt.hist(err, bins = 75)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "cab26b6d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "45378.361421577894"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_absolute_error(r_forest.predict(X_test), y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7a86049",
   "metadata": {},
   "source": [
    "## Export of the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 366,
   "id": "f8561a00",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 367,
   "id": "4b956ed4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#pickle.dump(r_forest, open('model.pkl', 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6892e8d3",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}