Prédiction_Loyer Explore_Dataset.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Playing around Dataset\n",
    "Real Exercise_model training is done in Prédiction_Loyer  file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "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 mpl_toolkits\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"house_data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>price</th>\n",
       "      <th>surface</th>\n",
       "      <th>arrondissement</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1820</td>\n",
       "      <td>46.1</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1750</td>\n",
       "      <td>41.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1900</td>\n",
       "      <td>55.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1950</td>\n",
       "      <td>46.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1950</td>\n",
       "      <td>49.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   price  surface  arrondissement\n",
       "0   1820     46.1             1.0\n",
       "1   1750     41.0             1.0\n",
       "2   1900     55.0             1.0\n",
       "3   1950     46.0             1.0\n",
       "4   1950     49.0             1.0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>price</th>\n",
       "      <th>surface</th>\n",
       "      <th>arrondissement</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>827.000000</td>\n",
       "      <td>822.000000</td>\n",
       "      <td>822.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2007.342201</td>\n",
       "      <td>58.045925</td>\n",
       "      <td>4.142336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1890.894229</td>\n",
       "      <td>44.827522</td>\n",
       "      <td>3.202151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>390.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1050.000000</td>\n",
       "      <td>28.655000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1567.000000</td>\n",
       "      <td>46.100000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2346.000000</td>\n",
       "      <td>73.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>25000.000000</td>\n",
       "      <td>415.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              price     surface  arrondissement\n",
       "count    827.000000  822.000000      822.000000\n",
       "mean    2007.342201   58.045925        4.142336\n",
       "std     1890.894229   44.827522        3.202151\n",
       "min      390.000000   10.000000        1.000000\n",
       "25%     1050.000000   28.655000        2.000000\n",
       "50%     1567.000000   46.100000        3.000000\n",
       "75%     2346.000000   73.000000        4.000000\n",
       "max    25000.000000  415.000000       10.000000"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function seaborn.utils.despine(fig=None, ax=None, top=True, right=True, left=False, bottom=False, offset=None, trim=False)>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['surface'].value_counts().plot()\n",
    "plt.title('Surface')\n",
    "plt.xlabel('Surface')\n",
    "plt.ylabel('Count')\n",
    "sns.despine\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ySZKkxgxkkiRJjRnIJEmSGjOQSZIkNbak9QA0vVVnv2mQfpe99LgfW3bxZ984SC2AFS/6i8H61uLwxq98apB+/2K/Fw/Sr9o5+4K7B+v7pc/ebrC+1cY9Z319kH63/Y1fmHUbt5BJkiQ1ZiCTJElqzEAmSZLUmIFMkiSpMQOZJElSYxMJZElOTnJHkitGlm2b5Lwk1/Y/t5nEWCRJkuabSW0hOwV43hrL3g6cX1VPAs7vb0uSJC06EwlkVXURcM8ai18InNpfPxU4eBJjkSRJmm9anhj2sVV1G0BV3ZZkh+numOQw4DCAXXfddULD05DOP+f1g/W9/ws+PFjf89UbLnzrIP1+aN/3D9Kv2nrL+SsH6ffY/ZcP0q+0GCyIg/qr6sSqWlFVK5YuXdp6OJIkSWPVMpDdnmRHgP7nHQ3HIkmS1EzLQHYOcGh//VDg7xqORZIkqZlJnfbiDOBfgD2S3JLk1cAxwC8nuRb45f62JEnSojORg/qr6pBpVu0/ifqSJEnz2YI4qF+SJGlDZiCTJElqzEAmSZLUmIFMkiSpsZZn6l9w7vrUSYP0u/2LXz1Iv5Lm5ojz/36Qfj+4//MH6VfSwucWMkmSpMYMZJIkSY0ZyCRJkhozkEmSJDVmIJMkSWrMQCZJktSYgUySJKkxA5kkSVJjBjJJkqTGDGSSJEmNGcgkSZIaM5BJkiQ1ZiCTJElqzEAmSZLUmIFMkiSpMQOZJElSYwYySZKkxgxkkiRJjRnIJEmSGjOQSZIkNWYgkyRJasxAJkmS1JiBTJIkqTEDmSRJUmMGMkmSpMYMZJIkSY0ZyCRJkhozkEmSJDVmIJMkSWrMQCZJktSYgUySJKkxA5kkSVJjS1oPIMkq4D7gIeDBqlrRdkSSJEmT1TyQ9Z5dVXe1HoQkSVIL7rKUJElqbD4EsgK+lOSSJIe1HowkSdKkzYddlvtU1XeS7ACcl+RbVXXR6B36oHYYwK677tpijJIkSYNpvoWsqr7T/7wD+Cyw9xT3ObGqVlTViqVLl056iJIkSYNqGsiSbJ5ky9XXgecCV7QckyRJ0qS13mX5WOCzSVaP5fSq+oe2Q5IkSZqspoGsqm4AntpyDJIkSa01P4ZMkiRpsTOQSZIkNWYgkyRJasxAJkmS1JiBTJIkqTEDmSRJUmMGMkmSpMYMZJIkSY0ZyCRJkhozkEmSJDVmIJMkSWrMQCZJktSYgUySJKkxA5kkSVJjBjJJkqTGDGSSJEmNGcgkSZIaM5BJkiQ1ZiCTJElqzEAmSZLUmIFMkiSpMQOZJElSYwYySZKkxgxkkiRJjRnIJEmSGjOQSZIkNWYgkyRJasxAJkmS1JiBTJIkqTEDmSRJUmMGMkmSpMYMZJIkSY0ZyCRJkhozkEmSJDVmIJMkSWrMQCZJktSYgUySJKmx5oEsyfOSXJ3kuiRvbz0eSZKkSWsayJJsDPwl8HxgT+CQJHu2HJMkSdKktd5CtjdwXVXdUFU/BM4EXth4TJIkSRPVOpDtDNw8cvuWfpkkSdKikapqVzx5CXBAVb2mv/1yYO+qev0a9zsMOKy/uQdw9RzKbQ/ctR7Dtd7iqbchz8161rNeu3ob8tysN73HV9XSdd1pyRw6HqdbgF1Gbv8k8J0171RVJwInrk+hJBdX1Yr16cN6i6Pehjw361nPeu3qbchzs976a73L8t+AJyV5QpJHAS8Dzmk8JkmSpIlquoWsqh5McjjwRWBj4OSqurLlmCRJkiat9S5Lqupc4NwJlFqvXZ7WW1T1NuS5Wc961mtXb0Oem/XWU9OD+iVJktT+GDJJkqRFb8EHsiQnJ7kjyRUjy5Yn+XqSlUkuTrL3NG0PTXJtfzl0Peo9Ncm/JPlmks8necw0bWf1NVFJdklyQZKrklyZ5I398m2TnNeP+7wk24xjfmup95L+9sNJpv2EybjmN7L+LUkqyfYDz+/oJLf2z5eVSQ4ccn5JzhqptSrJyoHrDfJ6SPITSf41yWV9vff0y5+Q5Bt9P2f1H9iZqv1R/dyuTnLAetQ7qV92eZJPJdliyHoj6z+c5PtraT/jemuZ2ylJvj3yfFk+Tftx/e72T3JpX+trSZ64vnNbo93GSf49yRf624M8V9ZSb5DHs2+zKt3fgJVJLu6XDfJevZZ6g7yX9W227l9f30r3HvOLQ80vyR4jc1iZ5N4kRwz8eB7ZvxauSHJG/xoZ9Pn5Y6pqQV+AZwJPB64YWfYl4Pn99QOBC6doty1wQ/9zm/76NnOs92/As/rrrwL+aIp2GwPXA7sBjwIuA/ZcR60dgaf317cErqH7iqn3A2/vl78deN845reWek+hO//bhcCKadqObX797V3oPuxxI7D9wPM7GnjLOtqOdX4j9/kA8O6Bny+DvB6AAFv01zcBvgH8AnA28LJ++UeB107Rds9+TpsCT+jnuvEc6z1m5D7H0b82hqrX314BnAZ8f5q2s6q3lrmdArx4HeMc5+/uGuAp/fLXAaeM47Ecafsm4HTgC/3tQZ4ra6k3yOPZt1vFGu9VDPRevZZ6RzPAe1nf7lTgNf31RwFbDzm/Ncb7XeDxQ9WjOyH9t4FHjzwvXzH083PNy4LfQlZVFwH3rLkYWL2VaiumOLcZcABwXlXdU1X/AZwHPG+O9fYALuqvnwf8+hRNZ/01UVV1W1Vd2l+/D7iK7onzQroXB/3Pg6doPuv5TVevqq6qqnWdjHec8wM4Hngr3e9yKmOb3zrmtdq450eSAC8Fzhi43iCvh+qs3kK0SX8pYD/gU/3y6Z6fLwTOrKofVNW3gev6Oc+6XlXdC//7eD6aqZ8zY6uX7jt4/5zu+TmdWdVby2M5E+P83c3kuTLrxxIgyU8CBwEf72+HgZ4rU9WbhTn9bZjGIO/V62nW7y3p9vo8EzgJoKp+WFXfYzLz2x+4vqpuHLjeEuDRSZYAmwG3MeDzcyoLPpBN4wjgz5PcDBwLHDXFfcb5tU1XAC/or7+ER57sdiz1kiwDnkb3n+xjq+o26P4IAzsMXG8mxlYvyQuAW6vqsknU6xcdnm6X18nTbAYf4vH8P8DtVXXtwPUGez30u4RWAnfQvfFdD3yvqh5cRz9jqVdV3+iX/zXdf9E/BXx44HqHA+esfg1OY9b1ppsb8Cf9c/P4JJuOo9Za6r0GODfJLcDLgWPGVQ/4IF2Ifbi/vR0DPlemqLfaII8nXZj9UpJL0n27DAz7Xj1VPRjmvWw34E7gr9PtAv54ks2ZwN8iuvOTrv6ndZB6VXUr3XvjTXRB7D+BSxj2+fljNtRA9lrgyKraBTiSPtWvIVMsm+tHTl8F/H6SS+h2Ff1wnPXSHRPzaeCI1VsDZtJsIdYDHgTeCbx7EvX6+X0E2B1YTvdi/MDA9VY7hKm3jo273mCvh6p6qKqW033Lxt50u7dn0s9Y6iX56X75K4Gd6LYK/saA9Z5J90/XVKFvvepNM7ej6ELmz9HtgnnbOGqtpd6RwIFV9ZPAX9PtAl7vekl+Bbijqi6ZQz/jqgcDPp7APlX1dOD5dH8PnjmDNuOuN9R72RK6Q3U+UlVPA+6n22U4E+vzXvYouo0dn5xhrTnV64PrC+l2Oe4EbE73uM6kn7FliQ01kB0KfKa//kmm3nw4o69tmomq+lZVPbeqfpbuD+z146qXZBO6P66fqKrVc7o9yY79+h3p/sMdst5MjKve7nQvisuSrOr7uTTJ4waqR1Xd3v9xehj4GGN8vkz3ePabxX8NOGuapuOsN/jrod99cSHdcUhb9/NbWz/jqve8kWUP0T2eUx0yMK56zwaeCFzXPz83S3LdOOuNzq3fDV1V9QO6gDTk7+75wFNHtsydBfzSmOrtA7ygf8zOpNsV9EGGe678WL0kfzvk41lV3+l/3gF8tu97sPfqqeoN+F52C3DLyHPjU3QBbbD59Z4PXFpVt/e3h6r3HODbVXVnVf0P3fvlLzGB97JHqDkceDbfLsAyHnmQ/VXAvv31/YFLpmizLd1BfNv0l28D286x3g79z42AvwFeNUWbJXQHFz6BHx1Iudc66qTv74NrLP9zHnlg4/vHMb/p6o2sv5DpD+of2/zWuM8qpj+ofyzzA3YcuX4k3fEAg86PLkj841rajvP5MsjrAVgKbN1ffzTwVeBX6ELf6IGwr5ui7V488kDYG1j3QfZT1ftV4Ikj8z8WOHbAer+yxn2mO6h/VvXW8ljuODK3DwLHDPy7uwt4cr/81cCnx/FYrtF+X350kP0gz5W11Bvq8dwc2HLk+j/TvcaHeq+ert4g72V9u68Ce/TXj+7nNsj8RtqeCbxy5PZQj+fPA1fSHTsWuuPFXj+J5+cj+ppLo/l0odsidRvwP3RJ9dXAM+j2/15GdwzNz/b3XQF8fKTtq+gOwLtu9Jc+h3pvpPt00jV0x1ysPuHuTsC5I20P7O9zPfDOGdR6Bt2mz8uBlf3lQLpjL84Hru1/bjuO+a2l3ov6uf4AuB344pDzW+M+q+gD2YDzOw34Zr/8HH70pj3Y/Og+7fV7a9x/qOfLIK8H4GeAf+/rXUH/aVG6403+te/nk8Cm/fIXAO8daf/Ofm5X038KdLb16P4J+qf+93cF8An6T10OUW+K+3x/5Pqc663lsfzKyNz+lh99MnKo392L+nqX0f0Dtts4Hss1au/LjwLSIM+VtdQb6vHcrX/MLqP7w/7OfvlQ79XT1Rvkvaxvsxy4uO/7c3RhZ5D59W02A+4GthpZNmS99wDf6p8bp9EFrMGfn6MXz9QvSZLU2IZ6DJkkSdKCYSCTJElqzEAmSZLUmIFMkiSpMQOZJElSYwYySYtWkj36r4K5L8kbWo9H0uK1ZN13kaQN1luBC6v7OhhJasYtZJIWnZGvQ3k83Yk1JakpA5mkBSfJ25Lc2u9qvDrJ/klOSfLHI/fZN8ktI7dX9e0uB+5P8hW676Y8Icn3kzw5yUH9Lsx7k9yc5Og16j4jyT8n+V6//hX98k2THJvkpiS3J/lokkdP5MGQtEEwkElaUJLsARwO/FxVbQkcQPcVWzNxCHAQ3fc47kf3/XyHV9UWVXUNcD/w28DW/f1em+Tgvu6uwN8DH6b7LsjldF9PBfA+4Mn9sicCO9N9tZMkzYjHkElaaB6i+565PZPcWVWrAJLMpO2Hqurm6VZW1YUjNy9PcgbwLLrv7vst4MtVdUa//m7g7nSFfwf4maq6px/LnwKnA0fNYl6SFjEDmaQFpaquS3IEcDSwV5IvAm+aYfNpwxhAkp8HjgF+GngUXfD7ZL96F7ovEF7TUrovQr5kJBQG2HiGY5Ikd1lKWniq6vSqegbdQflFt8vwfrpgtNrjpmq6jq5PB84BdqmqrYCP0oUr6MLc7lO0uQv4b2Cvqtq6v2xVVVvMeEKSFj0DmaQFpT932H5JNgUeoAtDD9Edz3Vgkm2TPA44Yg7dbwncU1UPJNkb+M2RdZ8AnpPkpUmWJNkuyfKqehj4GHB8kh36Me6c5ID1mKakRcZAJmmh2ZRut+JdwHeBHYB3AKcBl9Ed4P8l4Kw59P064L1J7qM7KP/s1Suq6ibgQODNwD10AfCp/eq3AdcBX09yL/BlYI851Je0SKVqXVvwJUmSNCS3kEmSJDVmIJMkSWrMQCZJktSYgUySJKkxA5kkSVJjBjJJkqTGDGSSJEmNGcgkSZIaM5BJkiQ19v8BsYpeFldILmgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "# Plotting a bar graph of the number of stores in each city, for the first ten cities listed\n",
    "# in the column 'City'\n",
    "surface_count  = data['surface'].value_counts()\n",
    "surface_count = surface_count[:20,]\n",
    "plt.figure(figsize=(10,5))\n",
    "sns.barplot(surface_count.index, surface_count.values, alpha=0.8)\n",
    "plt.title('count of surface')\n",
    "plt.ylabel('Count', fontsize=12)\n",
    "plt.xlabel('surface', fontsize=12)\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Arrondissement vs Price')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(data.price,data.arrondissement)\n",
    "plt.xlabel(\"Price\")\n",
    "plt.ylabel('Arrondissement')\n",
    "plt.title(\"Arrondissement vs Price\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<function seaborn.utils.despine(fig=None, ax=None, top=True, right=True, left=False, bottom=False, offset=None, trim=False)>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.scatter(data.surface,data.price)\n",
    "plt.title(\"Surface and Price \")\n",
    "plt.xlabel(\"Surface\")\n",
    "plt.ylabel(\"Price\")\n",
    "plt.show()\n",
    "sns.despine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "train1 = data.drop(['surface', 'price'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>arrondissement</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   arrondissement\n",
       "0             1.0\n",
       "1             1.0\n",
       "2             1.0\n",
       "3             1.0\n",
       "4             1.0"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Which is the pricey location by arrondissement?')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(data.arrondissement,data.price)\n",
    "plt.title(\"Which is the pricey location by arrondissement?\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(data['surface'], data['price'], 'ro', markersize=4)\n",
    "plt.show()\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}