Prédiction_Loyer .ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# imports required libraries\n",
    "import pandas as pd  \n",
    "import numpy as np  \n",
    "import matplotlib.pyplot as plt  \n",
    "import seaborn as seabornInstance \n",
    "from sklearn.model_selection import train_test_split \n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn import metrics\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# imports the CSV dataset \n",
    "dataset = pd.read_csv('house_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(827, 3)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#explore the data by checking the number of rows and columns\n",
    "dataset.shape"
   ]
  },
  {
   "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": [
    "#Statistical details of the dataset\n",
    "dataset.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot data points on a 2-D graph \n",
    "dataset.plot(x='surface', y='price', style='o')  \n",
    "plt.title('Surface vs Price')  \n",
    "plt.xlabel('Surface')  \n",
    "plt.ylabel('Price')  \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ABONIA\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c1f67afa90>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Average Price is Between Nearly 0 and 5000 sq feet surface.\n",
    "\n",
    "plt.figure(figsize=(15,10))\n",
    "plt.tight_layout()\n",
    "seabornInstance.distplot(dataset['price'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset[:] = np.nan_to_num(dataset)\n",
    "X = dataset['surface'].values.reshape(-1,1)\n",
    "y = dataset['price'].values.reshape(-1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#training the algorithm\n",
    "\n",
    "regressor = LinearRegression()  \n",
    "regressor.fit(X_train, y_train) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-148.55078831]\n",
      "[[37.26014024]]\n"
     ]
    }
   ],
   "source": [
    "# linear regression model basically finds the best value for the intercept and slope, which results in a line that best fits the data\n",
    "#To retrieve the intercept:\n",
    "print(regressor.intercept_)\n",
    "#For retrieving the slope:\n",
    "print(regressor.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# To make predictions on the test data\n",
    "y_pred = regressor.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>Actual</th>\n",
       "      <th>Predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2212.0</td>\n",
       "      <td>2534.179309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2500.0</td>\n",
       "      <td>2720.480010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2050.0</td>\n",
       "      <td>2087.057626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5000.0</td>\n",
       "      <td>4918.828285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>930.0</td>\n",
       "      <td>522.131736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1740.0</td>\n",
       "      <td>1677.196084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>758.0</td>\n",
       "      <td>736.377542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>795.0</td>\n",
       "      <td>782.952718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1300.0</td>\n",
       "      <td>755.007613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2500.0</td>\n",
       "      <td>2832.260431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1400.0</td>\n",
       "      <td>1341.854821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2950.0</td>\n",
       "      <td>4210.885620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2300.0</td>\n",
       "      <td>1863.496785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>568.0</td>\n",
       "      <td>484.871596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2382.0</td>\n",
       "      <td>3875.544358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2249.0</td>\n",
       "      <td>1155.554120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>754.0</td>\n",
       "      <td>522.131736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1530.0</td>\n",
       "      <td>1155.554120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>885.0</td>\n",
       "      <td>1006.513559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1988.0</td>\n",
       "      <td>2571.439449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>3234.0</td>\n",
       "      <td>3689.243657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2008.0</td>\n",
       "      <td>3111.711483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1835.0</td>\n",
       "      <td>1975.277206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>3000.0</td>\n",
       "      <td>2981.300992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>3000.0</td>\n",
       "      <td>3204.861834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>790.0</td>\n",
       "      <td>745.692578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2941.0</td>\n",
       "      <td>3428.422675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>996.0</td>\n",
       "      <td>870.514047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1800.0</td>\n",
       "      <td>1714.456224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1400.0</td>\n",
       "      <td>1446.555815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>784.0</td>\n",
       "      <td>559.391876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>800.0</td>\n",
       "      <td>666.701080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>750.0</td>\n",
       "      <td>536.663191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>5950.0</td>\n",
       "      <td>8607.582169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>2800.0</td>\n",
       "      <td>4881.568144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>1787.0</td>\n",
       "      <td>2720.480010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>2100.0</td>\n",
       "      <td>1900.756925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>1350.0</td>\n",
       "      <td>1043.773699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>2000.0</td>\n",
       "      <td>2825.553606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>2100.0</td>\n",
       "      <td>2273.358327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>799.0</td>\n",
       "      <td>522.131736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>1795.0</td>\n",
       "      <td>2571.439449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>750.0</td>\n",
       "      <td>782.952718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>3480.0</td>\n",
       "      <td>4024.584919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>3040.0</td>\n",
       "      <td>2198.838047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>2400.0</td>\n",
       "      <td>2012.537346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>1900.0</td>\n",
       "      <td>1900.756925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>1488.0</td>\n",
       "      <td>2221.566733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>2280.0</td>\n",
       "      <td>1900.756925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>1195.0</td>\n",
       "      <td>1490.895382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>1250.0</td>\n",
       "      <td>1230.074401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>2595.0</td>\n",
       "      <td>3130.341553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>780.0</td>\n",
       "      <td>791.895151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>850.0</td>\n",
       "      <td>1155.554120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>3500.0</td>\n",
       "      <td>4061.845059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>1166.0</td>\n",
       "      <td>857.472998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>775.0</td>\n",
       "      <td>596.652017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>1159.0</td>\n",
       "      <td>1453.635242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>2324.0</td>\n",
       "      <td>3465.682815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>747.0</td>\n",
       "      <td>522.131736</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>166 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Actual    Predicted\n",
       "0    2212.0  2534.179309\n",
       "1    2500.0  2720.480010\n",
       "2    2050.0  2087.057626\n",
       "3    5000.0  4918.828285\n",
       "4     930.0   522.131736\n",
       "5    1740.0  1677.196084\n",
       "6     758.0   736.377542\n",
       "7     795.0   782.952718\n",
       "8    1300.0   755.007613\n",
       "9    2500.0  2832.260431\n",
       "10   1400.0  1341.854821\n",
       "11   2950.0  4210.885620\n",
       "12   2300.0  1863.496785\n",
       "13    568.0   484.871596\n",
       "14   2382.0  3875.544358\n",
       "15   2249.0  1155.554120\n",
       "16    754.0   522.131736\n",
       "17   1530.0  1155.554120\n",
       "18    885.0  1006.513559\n",
       "19   1988.0  2571.439449\n",
       "20   3234.0  3689.243657\n",
       "21   2008.0  3111.711483\n",
       "22   1835.0  1975.277206\n",
       "23   3000.0  2981.300992\n",
       "24   3000.0  3204.861834\n",
       "25    790.0   745.692578\n",
       "26   2941.0  3428.422675\n",
       "27    996.0   870.514047\n",
       "28   1800.0  1714.456224\n",
       "29   1400.0  1446.555815\n",
       "..      ...          ...\n",
       "136   784.0   559.391876\n",
       "137   800.0   666.701080\n",
       "138   750.0   536.663191\n",
       "139  5950.0  8607.582169\n",
       "140  2800.0  4881.568144\n",
       "141  1787.0  2720.480010\n",
       "142  2100.0  1900.756925\n",
       "143  1350.0  1043.773699\n",
       "144  2000.0  2825.553606\n",
       "145  2100.0  2273.358327\n",
       "146   799.0   522.131736\n",
       "147  1795.0  2571.439449\n",
       "148   750.0   782.952718\n",
       "149  3480.0  4024.584919\n",
       "150  3040.0  2198.838047\n",
       "151  2400.0  2012.537346\n",
       "152  1900.0  1900.756925\n",
       "153  1488.0  2221.566733\n",
       "154  2280.0  1900.756925\n",
       "155  1195.0  1490.895382\n",
       "156  1250.0  1230.074401\n",
       "157  2595.0  3130.341553\n",
       "158   780.0   791.895151\n",
       "159   850.0  1155.554120\n",
       "160  3500.0  4061.845059\n",
       "161  1166.0   857.472998\n",
       "162   775.0   596.652017\n",
       "163  1159.0  1453.635242\n",
       "164  2324.0  3465.682815\n",
       "165   747.0   522.131736\n",
       "\n",
       "[166 rows x 2 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Now compare the actual output values for X_test with the predicted values\n",
    "df = pd.DataFrame({'Actual': y_test.flatten(), 'Predicted': y_pred.flatten()})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#visualize comparison result as a bar graph\n",
    "#As the number of records is huge, for representation purpose I’m taking just 20 records\n",
    "df1 = df.head(20)\n",
    "df1.plot(kind='bar',figsize=(16,10))\n",
    "plt.grid(which='major', linestyle='-', linewidth='0.5', color='green')\n",
    "plt.grid(which='minor', linestyle=':', linewidth='0.5', color='black')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot our straight line with the test data \n",
    "\n",
    "plt.scatter(X_test, y_test,  color='gray')\n",
    "plt.plot(X_test, y_pred, color='red', linewidth=2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluate the performance of the algorithm\n",
    "For regression algorithms, three evaluation metrics are commonly used:\n",
    "\n",
    "1. Mean Absolute Error (MAE) is the mean of the absolute value of the errors. It is calculated as:\n",
    "Mean Absolute Error:\n",
    "\n",
    "\\begin{align}\n",
    "\\text{MAE} = \\frac{1}{n}\\sum_{j=1}^n{(Y_j-Y_j)}\n",
    "\\end{align}<br>\n",
    "2. Mean Squared Error (MSE) is the mean of the squared errors and is calculated as:\n",
    "Mean Squared Error:\n",
    "\n",
    "\\begin{align}\n",
    "\\text{MSE} = \\frac{1}{n}\\sum_{j}^n{(Y_j-Y_j)^2}\n",
    "\\end{align}<br>\n",
    "\n",
    "3. Root Mean Squared Error (RMSE) is the square root of the mean of the squared errors:\n",
    "Root Mean Squared Error:\n",
    "\n",
    "\\begin{align}\n",
    "\\text{RMSE} = \\sqrt{\\frac{1}{n}\\sum_{j=1}^n{(Y_j-Y_j)^2}}\n",
    "\\end{align}<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 487.91769129808887\n",
      "Mean Squared Error: 1597358.8534322046\n",
      "Root Mean Squared Error: 1263.866628023782\n"
     ]
    }
   ],
   "source": [
    "# evaluate the performance of the algorithm\n",
    "print('Mean Absolute Error:', metrics.mean_absolute_error(y_test, y_pred))  \n",
    "print('Mean Squared Error:', metrics.mean_squared_error(y_test, y_pred))  \n",
    "print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(y_test, y_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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
}