Real Estates Price Estimator.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## To predict the Price of the Houses aka.Real Estates Price Estimator "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing = pd.read_csv(\"data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "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>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "      <th>MEDV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00632</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.31</td>\n",
       "      <td>0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>6.575</td>\n",
       "      <td>65.2</td>\n",
       "      <td>4.0900</td>\n",
       "      <td>1</td>\n",
       "      <td>296</td>\n",
       "      <td>15.3</td>\n",
       "      <td>396.90</td>\n",
       "      <td>4.98</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.02731</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>6.421</td>\n",
       "      <td>78.9</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2</td>\n",
       "      <td>242</td>\n",
       "      <td>17.8</td>\n",
       "      <td>396.90</td>\n",
       "      <td>9.14</td>\n",
       "      <td>21.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.02729</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>7.185</td>\n",
       "      <td>61.1</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2</td>\n",
       "      <td>242</td>\n",
       "      <td>17.8</td>\n",
       "      <td>392.83</td>\n",
       "      <td>4.03</td>\n",
       "      <td>34.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.03237</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>6.998</td>\n",
       "      <td>45.8</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3</td>\n",
       "      <td>222</td>\n",
       "      <td>18.7</td>\n",
       "      <td>394.63</td>\n",
       "      <td>2.94</td>\n",
       "      <td>33.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.06905</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>7.147</td>\n",
       "      <td>54.2</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3</td>\n",
       "      <td>222</td>\n",
       "      <td>18.7</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.33</td>\n",
       "      <td>36.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD  TAX  PTRATIO  \\\n",
       "0  0.00632  18.0   2.31     0  0.538  6.575  65.2  4.0900    1  296     15.3   \n",
       "1  0.02731   0.0   7.07     0  0.469  6.421  78.9  4.9671    2  242     17.8   \n",
       "2  0.02729   0.0   7.07     0  0.469  7.185  61.1  4.9671    2  242     17.8   \n",
       "3  0.03237   0.0   2.18     0  0.458  6.998  45.8  6.0622    3  222     18.7   \n",
       "4  0.06905   0.0   2.18     0  0.458  7.147  54.2  6.0622    3  222     18.7   \n",
       "\n",
       "        B  LSTAT  MEDV  \n",
       "0  396.90   4.98  24.0  \n",
       "1  396.90   9.14  21.6  \n",
       "2  392.83   4.03  34.7  \n",
       "3  394.63   2.94  33.4  \n",
       "4  396.90   5.33  36.2  "
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 506 entries, 0 to 505\n",
      "Data columns (total 14 columns):\n",
      " #   Column   Non-Null Count  Dtype  \n",
      "---  ------   --------------  -----  \n",
      " 0   CRIM     506 non-null    float64\n",
      " 1   ZN       506 non-null    float64\n",
      " 2   INDUS    506 non-null    float64\n",
      " 3   CHAS     506 non-null    int64  \n",
      " 4   NOX      506 non-null    float64\n",
      " 5   RM       501 non-null    float64\n",
      " 6   AGE      506 non-null    float64\n",
      " 7   DIS      506 non-null    float64\n",
      " 8   RAD      506 non-null    int64  \n",
      " 9   TAX      506 non-null    int64  \n",
      " 10  PTRATIO  506 non-null    float64\n",
      " 11  B        506 non-null    float64\n",
      " 12  LSTAT    506 non-null    float64\n",
      " 13  MEDV     506 non-null    float64\n",
      "dtypes: float64(11), int64(3)\n",
      "memory usage: 55.5 KB\n"
     ]
    }
   ],
   "source": [
    "housing.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    471\n",
       "1     35\n",
       "Name: CHAS, dtype: int64"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing['CHAS'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rows in train set: 404\n",
      "Rows in test set: 102\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "train_set, test_set  = train_test_split(housing, test_size=0.2, random_state=42)\n",
    "print(f\"Rows in train set: {len(train_set)}\\nRows in test set: {len(test_set)}\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import StratifiedShuffleSplit\n",
    "split = StratifiedShuffleSplit(n_splits=1, test_size=0.2, random_state=42)\n",
    "for train_index, test_index in split.split(housing, housing['CHAS']):\n",
    "    strat_train_set = housing.loc[train_index]\n",
    "    strat_test_set = housing.loc[test_index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing = strat_train_set.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MEDV       1.000000\n",
       "RM         0.680857\n",
       "B          0.361761\n",
       "ZN         0.339741\n",
       "DIS        0.240451\n",
       "CHAS       0.205066\n",
       "AGE       -0.364596\n",
       "RAD       -0.374693\n",
       "CRIM      -0.393715\n",
       "NOX       -0.422873\n",
       "TAX       -0.456657\n",
       "INDUS     -0.473516\n",
       "PTRATIO   -0.493534\n",
       "LSTAT     -0.740494\n",
       "Name: MEDV, dtype: float64"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_matrix = housing.corr()\n",
    "corr_matrix['MEDV'].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='RM', ylabel='MEDV'>"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "housing.plot(kind=\"scatter\", x=\"RM\", y=\"MEDV\", alpha=0.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing[\"TAXRM\"] = housing['TAX']/housing['RM']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "scrolled": true
   },
   "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>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "      <th>MEDV</th>\n",
       "      <th>TAXRM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>254</th>\n",
       "      <td>0.04819</td>\n",
       "      <td>80.0</td>\n",
       "      <td>3.64</td>\n",
       "      <td>0</td>\n",
       "      <td>0.392</td>\n",
       "      <td>6.108</td>\n",
       "      <td>32.0</td>\n",
       "      <td>9.2203</td>\n",
       "      <td>1</td>\n",
       "      <td>315</td>\n",
       "      <td>16.4</td>\n",
       "      <td>392.89</td>\n",
       "      <td>6.57</td>\n",
       "      <td>21.9</td>\n",
       "      <td>51.571709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>348</th>\n",
       "      <td>0.01501</td>\n",
       "      <td>80.0</td>\n",
       "      <td>2.01</td>\n",
       "      <td>0</td>\n",
       "      <td>0.435</td>\n",
       "      <td>6.635</td>\n",
       "      <td>29.7</td>\n",
       "      <td>8.3440</td>\n",
       "      <td>4</td>\n",
       "      <td>280</td>\n",
       "      <td>17.0</td>\n",
       "      <td>390.94</td>\n",
       "      <td>5.99</td>\n",
       "      <td>24.5</td>\n",
       "      <td>42.200452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>476</th>\n",
       "      <td>4.87141</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.10</td>\n",
       "      <td>0</td>\n",
       "      <td>0.614</td>\n",
       "      <td>6.484</td>\n",
       "      <td>93.6</td>\n",
       "      <td>2.3053</td>\n",
       "      <td>24</td>\n",
       "      <td>666</td>\n",
       "      <td>20.2</td>\n",
       "      <td>396.21</td>\n",
       "      <td>18.68</td>\n",
       "      <td>16.7</td>\n",
       "      <td>102.714374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>321</th>\n",
       "      <td>0.18159</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.38</td>\n",
       "      <td>0</td>\n",
       "      <td>0.493</td>\n",
       "      <td>6.376</td>\n",
       "      <td>54.3</td>\n",
       "      <td>4.5404</td>\n",
       "      <td>5</td>\n",
       "      <td>287</td>\n",
       "      <td>19.6</td>\n",
       "      <td>396.90</td>\n",
       "      <td>6.87</td>\n",
       "      <td>23.1</td>\n",
       "      <td>45.012547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>326</th>\n",
       "      <td>0.30347</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.38</td>\n",
       "      <td>0</td>\n",
       "      <td>0.493</td>\n",
       "      <td>6.312</td>\n",
       "      <td>28.9</td>\n",
       "      <td>5.4159</td>\n",
       "      <td>5</td>\n",
       "      <td>287</td>\n",
       "      <td>19.6</td>\n",
       "      <td>396.90</td>\n",
       "      <td>6.15</td>\n",
       "      <td>23.0</td>\n",
       "      <td>45.468948</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD  TAX  \\\n",
       "254  0.04819  80.0   3.64     0  0.392  6.108  32.0  9.2203    1  315   \n",
       "348  0.01501  80.0   2.01     0  0.435  6.635  29.7  8.3440    4  280   \n",
       "476  4.87141   0.0  18.10     0  0.614  6.484  93.6  2.3053   24  666   \n",
       "321  0.18159   0.0   7.38     0  0.493  6.376  54.3  4.5404    5  287   \n",
       "326  0.30347   0.0   7.38     0  0.493  6.312  28.9  5.4159    5  287   \n",
       "\n",
       "     PTRATIO       B  LSTAT  MEDV       TAXRM  \n",
       "254     16.4  392.89   6.57  21.9   51.571709  \n",
       "348     17.0  390.94   5.99  24.5   42.200452  \n",
       "476     20.2  396.21  18.68  16.7  102.714374  \n",
       "321     19.6  396.90   6.87  23.1   45.012547  \n",
       "326     19.6  396.90   6.15  23.0   45.468948  "
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MEDV       1.000000\n",
       "RM         0.680857\n",
       "B          0.361761\n",
       "ZN         0.339741\n",
       "DIS        0.240451\n",
       "CHAS       0.205066\n",
       "AGE       -0.364596\n",
       "RAD       -0.374693\n",
       "CRIM      -0.393715\n",
       "NOX       -0.422873\n",
       "TAX       -0.456657\n",
       "INDUS     -0.473516\n",
       "PTRATIO   -0.493534\n",
       "TAXRM     -0.528626\n",
       "LSTAT     -0.740494\n",
       "Name: MEDV, dtype: float64"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_matrix = housing.corr()\n",
    "corr_matrix['MEDV'].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='TAXRM', ylabel='MEDV'>"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "housing.plot(kind=\"scatter\", x=\"TAXRM\", y=\"MEDV\", alpha=0.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing = strat_train_set.drop(\"MEDV\", axis=1)\n",
    "housing_labels = strat_train_set[\"MEDV\"].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "median = housing[\"RM\"].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "254    6.108\n",
       "348    6.635\n",
       "476    6.484\n",
       "321    6.376\n",
       "326    6.312\n",
       "       ...  \n",
       "155    6.152\n",
       "423    6.103\n",
       "98     7.820\n",
       "455    6.525\n",
       "216    5.888\n",
       "Name: RM, Length: 404, dtype: float64"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing[\"RM\"].fillna(median)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(404, 13)"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SimpleImputer(strategy='median')"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.impute import SimpleImputer\n",
    "imputer = SimpleImputer(strategy=\"median\")\n",
    "imputer.fit(housing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2.86735e-01, 0.00000e+00, 9.90000e+00, 0.00000e+00, 5.38000e-01,\n",
       "       6.20900e+00, 7.82000e+01, 3.12220e+00, 5.00000e+00, 3.37000e+02,\n",
       "       1.90000e+01, 3.90955e+02, 1.15700e+01])"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imputer.statistics_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = imputer.transform(housing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing_tr = pd.DataFrame(X, columns=housing.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "my_pipeline = Pipeline([\n",
    "    ('imputer', SimpleImputer(strategy=\"median\")),\n",
    "    ('std_scaler', StandardScaler()),\n",
    "])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing_num_tr = my_pipeline.fit_transform(housing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(404, 13)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_num_tr.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor()"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "model = RandomForestRegressor()\n",
    "model.fit(housing_num_tr, housing_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "some_data = housing.iloc[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "some_labels = housing_labels.iloc[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "prepared_data = my_pipeline.transform(some_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([22.237, 25.65 , 16.325, 23.441, 23.732])"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(prepared_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[21.9, 24.5, 16.7, 23.1, 23.0]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(some_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "housing_predictions = model.predict(housing_num_tr)\n",
    "mse = mean_squared_error(housing_labels, housing_predictions)\n",
    "rmse = np.sqrt(mse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.1920492973143855"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Output.joblib']"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from joblib import dump, load\n",
    "dump(model, 'Output.joblib') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test = strat_test_set.drop(\"MEDV\", axis=1)\n",
    "Y_test = strat_test_set[\"MEDV\"].copy()\n",
    "X_test_prepared = my_pipeline.transform(X_test)\n",
    "final_predictions = model.predict(X_test_prepared)\n",
    "final_mse = mean_squared_error(Y_test, final_predictions)\n",
    "final_rmse = np.sqrt(final_mse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.983492930882767"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.43942006,  3.12628155, -1.12165014, -0.27288841, -1.42262747,\n",
       "       -0.23979304, -1.31238772,  2.61111401, -1.0016859 , -0.5778192 ,\n",
       "       -0.97491834,  0.41164221, -0.86091034])"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prepared_data[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([24.764])"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from joblib import dump, load\n",
    "model = load('Output.joblib') \n",
    "features = np.array([[-5.43942006, 4.12628155, -1.6165014, -0.67288841, -1.42262747,\n",
    "       -11.44443979304, -49.31238772,  7.61111401, -26.0016879 , -0.5778192 ,\n",
    "       -0.97491834,  0.41164221, -66.86091034]])\n",
    "model.predict(features)"
   ]
  },
  {
   "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}