House Price Prediction - 4 Regression Models.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***Analysing California Housing Prices*** dataset that predicts the house prices based on various prediction models. This is a supervised multiple regression problem that aims to predict the prices of houses based on multiple features like housing_median_age, total_rooms, ocean_proximity etc. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from __future__ import division, print_function, unicode_literals\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import numpy.random as rnd\n",
    "import os\n",
    "%matplotlib inline\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-122.23</td>\n",
       "      <td>37.88</td>\n",
       "      <td>41.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>8.3252</td>\n",
       "      <td>452600.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-122.22</td>\n",
       "      <td>37.86</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7099.0</td>\n",
       "      <td>1106.0</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>1138.0</td>\n",
       "      <td>8.3014</td>\n",
       "      <td>358500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-122.24</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>352100.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1274.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>558.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>341300.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>3.8462</td>\n",
       "      <td>342200.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "0    -122.23     37.88                41.0        880.0           129.0   \n",
       "1    -122.22     37.86                21.0       7099.0          1106.0   \n",
       "2    -122.24     37.85                52.0       1467.0           190.0   \n",
       "3    -122.25     37.85                52.0       1274.0           235.0   \n",
       "4    -122.25     37.85                52.0       1627.0           280.0   \n",
       "\n",
       "   population  households  median_income  median_house_value ocean_proximity  \n",
       "0       322.0       126.0         8.3252            452600.0        NEAR BAY  \n",
       "1      2401.0      1138.0         8.3014            358500.0        NEAR BAY  \n",
       "2       496.0       177.0         7.2574            352100.0        NEAR BAY  \n",
       "3       558.0       219.0         5.6431            341300.0        NEAR BAY  \n",
       "4       565.0       259.0         3.8462            342200.0        NEAR BAY  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mydir = os.getcwd()\n",
    "housing = pd.read_csv(mydir + '/Dataset.csv')\n",
    "housing.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 20640 entries, 0 to 20639\n",
      "Data columns (total 10 columns):\n",
      "longitude             20640 non-null float64\n",
      "latitude              20640 non-null float64\n",
      "housing_median_age    20640 non-null float64\n",
      "total_rooms           20640 non-null float64\n",
      "total_bedrooms        20433 non-null float64\n",
      "population            20640 non-null float64\n",
      "households            20640 non-null float64\n",
      "median_income         20640 non-null float64\n",
      "median_house_value    20640 non-null float64\n",
      "ocean_proximity       20640 non-null object\n",
      "dtypes: float64(9), object(1)\n",
      "memory usage: 1.6+ MB\n"
     ]
    }
   ],
   "source": [
    "housing.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<1H OCEAN     9136\n",
       "INLAND        6551\n",
       "NEAR OCEAN    2658\n",
       "NEAR BAY      2290\n",
       "ISLAND           5\n",
       "Name: ocean_proximity, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Descriptive Statistics - Exploring Dataset - Categorical Variable first\n",
    "housing[\"ocean_proximity\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          longitude      latitude  housing_median_age   total_rooms  \\\n",
      "count  20640.000000  20640.000000        20640.000000  20640.000000   \n",
      "mean    -119.569704     35.631861           28.639486   2635.763081   \n",
      "std        2.003532      2.135952           12.585558   2181.615252   \n",
      "min     -124.350000     32.540000            1.000000      2.000000   \n",
      "25%     -121.800000     33.930000           18.000000   1447.750000   \n",
      "50%     -118.490000     34.260000           29.000000   2127.000000   \n",
      "75%     -118.010000     37.710000           37.000000   3148.000000   \n",
      "max     -114.310000     41.950000           52.000000  39320.000000   \n",
      "\n",
      "       total_bedrooms    population    households  median_income  \\\n",
      "count    20433.000000  20640.000000  20640.000000   20640.000000   \n",
      "mean       537.870553   1425.476744    499.539680       3.870671   \n",
      "std        421.385070   1132.462122    382.329753       1.899822   \n",
      "min          1.000000      3.000000      1.000000       0.499900   \n",
      "25%        296.000000    787.000000    280.000000       2.563400   \n",
      "50%        435.000000   1166.000000    409.000000       3.534800   \n",
      "75%        647.000000   1725.000000    605.000000       4.743250   \n",
      "max       6445.000000  35682.000000   6082.000000      15.000100   \n",
      "\n",
      "       median_house_value  \n",
      "count        20640.000000  \n",
      "mean        206855.816909  \n",
      "std         115395.615874  \n",
      "min          14999.000000  \n",
      "25%         119600.000000  \n",
      "50%         179700.000000  \n",
      "75%         264725.000000  \n",
      "max         500001.000000  \n"
     ]
    }
   ],
   "source": [
    "#Descriptive Statistics - Exploring Dataset - Continuous variables\n",
    "print(housing.describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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l1kzV1j/H219Vj3dq6nayXoPTt6zNrHyq9n2bz7AtD3iZRp134M3MzMzMzMwqwDXwZlYZ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J+GWQI8wcQ3WJfUyVhe/jHpk/FJ24y9GTr551+S1p/xp0ER8WRE/IeIWAz8X8Cn5dvbmVl9OdTh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\n9H3gi8BbJX0e2CdpI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AGQtJRiaNKZwDuBT0sae+fheuCyiDiD4r7Hk79nVnK4f6AzVN+/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\n6wH/Py+xqcsyUMJ/AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1db7a1eec88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting histograms for various features\n",
    "housing.hist(bins=50, figsize=(17,10))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20046</th>\n",
       "      <td>-119.01</td>\n",
       "      <td>36.06</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1505.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1392.0</td>\n",
       "      <td>359.0</td>\n",
       "      <td>1.6812</td>\n",
       "      <td>47700.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3024</th>\n",
       "      <td>-119.46</td>\n",
       "      <td>35.14</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2943.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1565.0</td>\n",
       "      <td>584.0</td>\n",
       "      <td>2.5313</td>\n",
       "      <td>45800.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15663</th>\n",
       "      <td>-122.44</td>\n",
       "      <td>37.80</td>\n",
       "      <td>52.0</td>\n",
       "      <td>3830.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1310.0</td>\n",
       "      <td>963.0</td>\n",
       "      <td>3.4801</td>\n",
       "      <td>500001.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20484</th>\n",
       "      <td>-118.72</td>\n",
       "      <td>34.28</td>\n",
       "      <td>17.0</td>\n",
       "      <td>3051.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1705.0</td>\n",
       "      <td>495.0</td>\n",
       "      <td>5.7376</td>\n",
       "      <td>218600.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9814</th>\n",
       "      <td>-121.93</td>\n",
       "      <td>36.62</td>\n",
       "      <td>34.0</td>\n",
       "      <td>2351.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1063.0</td>\n",
       "      <td>428.0</td>\n",
       "      <td>3.7250</td>\n",
       "      <td>278000.0</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "20046    -119.01     36.06                25.0       1505.0             NaN   \n",
       "3024     -119.46     35.14                30.0       2943.0             NaN   \n",
       "15663    -122.44     37.80                52.0       3830.0             NaN   \n",
       "20484    -118.72     34.28                17.0       3051.0             NaN   \n",
       "9814     -121.93     36.62                34.0       2351.0             NaN   \n",
       "\n",
       "       population  households  median_income  median_house_value  \\\n",
       "20046      1392.0       359.0         1.6812             47700.0   \n",
       "3024       1565.0       584.0         2.5313             45800.0   \n",
       "15663      1310.0       963.0         3.4801            500001.0   \n",
       "20484      1705.0       495.0         5.7376            218600.0   \n",
       "9814       1063.0       428.0         3.7250            278000.0   \n",
       "\n",
       "      ocean_proximity  \n",
       "20046          INLAND  \n",
       "3024           INLAND  \n",
       "15663        NEAR BAY  \n",
       "20484       <1H OCEAN  \n",
       "9814       NEAR OCEAN  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Spliting the dataset into train(80%) and test(20%) using random sampling \n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "train_set, test_set = train_test_split(housing, test_size=0.2, random_state=42)\n",
    "test_set.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1db7b39c390>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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yS8CNs+y3Edg4w/g+YEXnMc3MbD74s4O65J6AmdWNPzvIzMw64iLQkyJ3gARF7gBJmrLm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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1db7a540048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Analysing the distribution of Housing dataset to check for skewness\n",
    "housing[\"median_income\"].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.0     7236\n",
       "2.0     6581\n",
       "4.0     3639\n",
       "5.0     1423\n",
       "1.0      822\n",
       "6.0      532\n",
       "7.0      189\n",
       "8.0      105\n",
       "9.0       50\n",
       "11.0      49\n",
       "10.0      14\n",
       "Name: income_cat, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing[\"income_cat\"] = np.ceil(housing[\"median_income\"] / 1.5)\n",
    "housing[\"income_cat\"].where(housing[\"income_cat\"] < 14, 5.0, inplace=True)\n",
    "housing[\"income_cat\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As the dataset is skewed to the right (positively skewed), we use stratified sampling to ensure the we have equal distribution of samples in both the test and train dataset. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import StratifiedShuffleSplit\n",
    "\n",
    "split = StratifiedShuffleSplit(n_splits=1, test_size=0.2, random_state=42)\n",
    "for train_index, test_index in split.split(housing, housing[\"income_cat\"]):\n",
    "    strat_train_set = housing.loc[train_index]\n",
    "    strat_test_set = housing.loc[test_index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Lets compare the error for the randomly sampled dataset and stratified sampled dataset with respect to the original dataset\n",
    "def income_cat_proportions(data):\n",
    "    return data[\"income_cat\"].value_counts() / len(data)\n",
    "\n",
    "train_set, test_set = train_test_split(housing, test_size=0.2, random_state=42)\n",
    "\n",
    "compare_props = pd.DataFrame({\n",
    "    \"Overall\": income_cat_proportions(housing),\n",
    "    \"Stratified\": income_cat_proportions(strat_test_set),\n",
    "    \"Random\": income_cat_proportions(test_set),\n",
    "}).sort_index()\n",
    "compare_props[\"Rand. %error\"] = 100 * compare_props[\"Random\"] / compare_props[\"Overall\"] - 100\n",
    "compare_props[\"Strat. %error\"] = 100 * compare_props[\"Stratified\"] / compare_props[\"Overall\"] - 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Overall</th>\n",
       "      <th>Random</th>\n",
       "      <th>Stratified</th>\n",
       "      <th>Rand. %error</th>\n",
       "      <th>Strat. %error</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>0.039826</td>\n",
       "      <td>0.040213</td>\n",
       "      <td>0.039729</td>\n",
       "      <td>0.973236</td>\n",
       "      <td>-0.243309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>0.318847</td>\n",
       "      <td>0.324370</td>\n",
       "      <td>0.318798</td>\n",
       "      <td>1.732260</td>\n",
       "      <td>-0.015195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>0.350581</td>\n",
       "      <td>0.358527</td>\n",
       "      <td>0.350533</td>\n",
       "      <td>2.266446</td>\n",
       "      <td>-0.013820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>0.176308</td>\n",
       "      <td>0.167393</td>\n",
       "      <td>0.176357</td>\n",
       "      <td>-5.056334</td>\n",
       "      <td>0.027480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.0</th>\n",
       "      <td>0.068944</td>\n",
       "      <td>0.064922</td>\n",
       "      <td>0.069041</td>\n",
       "      <td>-5.832748</td>\n",
       "      <td>0.140548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6.0</th>\n",
       "      <td>0.025775</td>\n",
       "      <td>0.024709</td>\n",
       "      <td>0.025678</td>\n",
       "      <td>-4.135338</td>\n",
       "      <td>-0.375940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7.0</th>\n",
       "      <td>0.009157</td>\n",
       "      <td>0.008721</td>\n",
       "      <td>0.009205</td>\n",
       "      <td>-4.761905</td>\n",
       "      <td>0.529101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8.0</th>\n",
       "      <td>0.005087</td>\n",
       "      <td>0.005572</td>\n",
       "      <td>0.005087</td>\n",
       "      <td>9.523810</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9.0</th>\n",
       "      <td>0.002422</td>\n",
       "      <td>0.002665</td>\n",
       "      <td>0.002422</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10.0</th>\n",
       "      <td>0.000678</td>\n",
       "      <td>0.000969</td>\n",
       "      <td>0.000727</td>\n",
       "      <td>42.857143</td>\n",
       "      <td>7.142857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11.0</th>\n",
       "      <td>0.002374</td>\n",
       "      <td>0.001938</td>\n",
       "      <td>0.002422</td>\n",
       "      <td>-18.367347</td>\n",
       "      <td>2.040816</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Overall    Random  Stratified  Rand. %error  Strat. %error\n",
       "1.0   0.039826  0.040213    0.039729      0.973236      -0.243309\n",
       "2.0   0.318847  0.324370    0.318798      1.732260      -0.015195\n",
       "3.0   0.350581  0.358527    0.350533      2.266446      -0.013820\n",
       "4.0   0.176308  0.167393    0.176357     -5.056334       0.027480\n",
       "5.0   0.068944  0.064922    0.069041     -5.832748       0.140548\n",
       "6.0   0.025775  0.024709    0.025678     -4.135338      -0.375940\n",
       "7.0   0.009157  0.008721    0.009205     -4.761905       0.529101\n",
       "8.0   0.005087  0.005572    0.005087      9.523810       0.000000\n",
       "9.0   0.002422  0.002665    0.002422     10.000000       0.000000\n",
       "10.0  0.000678  0.000969    0.000727     42.857143       7.142857\n",
       "11.0  0.002374  0.001938    0.002422    -18.367347       2.040816"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compare_props"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# We see that the strat error is lesser than rond error, that is the distribution of overall dataset matches closely to that of strat dataset over rand dataset.\n",
    "# Now lets drop the income_cat as this column is not correlate to the price of the house\n",
    "for set in (strat_train_set, strat_test_set):\n",
    "    set.drop(\"income_cat\", axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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JYCWEEDeJhIQEWeh9g0hISBjqLnRplrcCPUoCKyGEuElcfCyMEP0lbwV6lgRWQgghxDDm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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1db78a1d1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#lets plot to see the role of latitude, longitude and population on the price of the house taken together\n",
    "\n",
    "housing.plot(kind=\"scatter\", x=\"longitude\", y=\"latitude\", s=housing['population']/100, label=\"population\",\n",
    "    c=\"median_house_value\", cmap=plt.get_cmap(\"jet\"), colorbar=True, alpha=0.4, figsize=(10,7),)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "median_house_value    1.000000\n",
       "median_income         0.688075\n",
       "income_cat            0.668189\n",
       "total_rooms           0.134153\n",
       "housing_median_age    0.105623\n",
       "households            0.065843\n",
       "total_bedrooms        0.049686\n",
       "population           -0.024650\n",
       "longitude            -0.045967\n",
       "latitude             -0.144160\n",
       "Name: median_house_value, dtype: float64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's see the correlation indices of all the features to determine which ones to include and which to exclude in our analysis\n",
    "\n",
    "corr_matrix = housing.corr()\n",
    "corr_matrix[\"median_house_value\"].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17606</th>\n",
       "      <td>-121.89</td>\n",
       "      <td>37.29</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1568.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>710.0</td>\n",
       "      <td>339.0</td>\n",
       "      <td>2.7042</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15698</th>\n",
       "      <td>-122.46</td>\n",
       "      <td>37.79</td>\n",
       "      <td>52.0</td>\n",
       "      <td>899.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>304.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>14.2959</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14650</th>\n",
       "      <td>-117.20</td>\n",
       "      <td>32.77</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1952.0</td>\n",
       "      <td>471.0</td>\n",
       "      <td>936.0</td>\n",
       "      <td>462.0</td>\n",
       "      <td>2.8621</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3230</th>\n",
       "      <td>-119.61</td>\n",
       "      <td>36.31</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1847.0</td>\n",
       "      <td>371.0</td>\n",
       "      <td>1460.0</td>\n",
       "      <td>353.0</td>\n",
       "      <td>1.8839</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3555</th>\n",
       "      <td>-118.59</td>\n",
       "      <td>34.23</td>\n",
       "      <td>17.0</td>\n",
       "      <td>6592.0</td>\n",
       "      <td>1525.0</td>\n",
       "      <td>4459.0</td>\n",
       "      <td>1463.0</td>\n",
       "      <td>3.0347</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19480</th>\n",
       "      <td>-120.97</td>\n",
       "      <td>37.66</td>\n",
       "      <td>24.0</td>\n",
       "      <td>2930.0</td>\n",
       "      <td>588.0</td>\n",
       "      <td>1448.0</td>\n",
       "      <td>570.0</td>\n",
       "      <td>3.5395</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9026</th>\n",
       "      <td>-118.86</td>\n",
       "      <td>34.07</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1409.0</td>\n",
       "      <td>244.0</td>\n",
       "      <td>970.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>8.0144</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13685</th>\n",
       "      <td>-117.24</td>\n",
       "      <td>34.15</td>\n",
       "      <td>26.0</td>\n",
       "      <td>2041.0</td>\n",
       "      <td>293.0</td>\n",
       "      <td>936.0</td>\n",
       "      <td>375.0</td>\n",
       "      <td>6.0000</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4937</th>\n",
       "      <td>-118.26</td>\n",
       "      <td>33.99</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1865.0</td>\n",
       "      <td>465.0</td>\n",
       "      <td>1916.0</td>\n",
       "      <td>438.0</td>\n",
       "      <td>1.8242</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4861</th>\n",
       "      <td>-118.28</td>\n",
       "      <td>34.02</td>\n",
       "      <td>29.0</td>\n",
       "      <td>515.0</td>\n",
       "      <td>229.0</td>\n",
       "      <td>2690.0</td>\n",
       "      <td>217.0</td>\n",
       "      <td>0.4999</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16365</th>\n",
       "      <td>-121.31</td>\n",
       "      <td>38.02</td>\n",
       "      <td>24.0</td>\n",
       "      <td>4157.0</td>\n",
       "      <td>951.0</td>\n",
       "      <td>2734.0</td>\n",
       "      <td>879.0</td>\n",
       "      <td>2.7981</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19684</th>\n",
       "      <td>-121.62</td>\n",
       "      <td>39.14</td>\n",
       "      <td>41.0</td>\n",
       "      <td>2183.0</td>\n",
       "      <td>559.0</td>\n",
       "      <td>1202.0</td>\n",
       "      <td>506.0</td>\n",
       "      <td>1.6902</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20330</th>\n",
       "      <td>-119.03</td>\n",
       "      <td>34.25</td>\n",
       "      <td>25.0</td>\n",
       "      <td>3344.0</td>\n",
       "      <td>502.0</td>\n",
       "      <td>1483.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>6.1960</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13956</th>\n",
       "      <td>-117.06</td>\n",
       "      <td>34.17</td>\n",
       "      <td>21.0</td>\n",
       "      <td>2520.0</td>\n",
       "      <td>582.0</td>\n",
       "      <td>416.0</td>\n",
       "      <td>151.0</td>\n",
       "      <td>2.7120</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2390</th>\n",
       "      <td>-119.46</td>\n",
       "      <td>36.91</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2980.0</td>\n",
       "      <td>495.0</td>\n",
       "      <td>1184.0</td>\n",
       "      <td>429.0</td>\n",
       "      <td>3.9141</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11176</th>\n",
       "      <td>-117.96</td>\n",
       "      <td>33.83</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2838.0</td>\n",
       "      <td>649.0</td>\n",
       "      <td>1758.0</td>\n",
       "      <td>593.0</td>\n",
       "      <td>3.3831</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15614</th>\n",
       "      <td>-122.41</td>\n",
       "      <td>37.81</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1178.0</td>\n",
       "      <td>545.0</td>\n",
       "      <td>592.0</td>\n",
       "      <td>441.0</td>\n",
       "      <td>3.6728</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2953</th>\n",
       "      <td>-119.02</td>\n",
       "      <td>35.35</td>\n",
       "      <td>42.0</td>\n",
       "      <td>1239.0</td>\n",
       "      <td>251.0</td>\n",
       "      <td>776.0</td>\n",
       "      <td>272.0</td>\n",
       "      <td>1.9830</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13209</th>\n",
       "      <td>-117.72</td>\n",
       "      <td>34.05</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1841.0</td>\n",
       "      <td>409.0</td>\n",
       "      <td>1243.0</td>\n",
       "      <td>394.0</td>\n",
       "      <td>4.0614</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6569</th>\n",
       "      <td>-118.15</td>\n",
       "      <td>34.20</td>\n",
       "      <td>46.0</td>\n",
       "      <td>1505.0</td>\n",
       "      <td>261.0</td>\n",
       "      <td>857.0</td>\n",
       "      <td>269.0</td>\n",
       "      <td>4.5000</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5825</th>\n",
       "      <td>-118.30</td>\n",
       "      <td>34.19</td>\n",
       "      <td>14.0</td>\n",
       "      <td>3615.0</td>\n",
       "      <td>913.0</td>\n",
       "      <td>1924.0</td>\n",
       "      <td>852.0</td>\n",
       "      <td>3.5083</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18116</th>\n",
       "      <td>-122.02</td>\n",
       "      <td>37.30</td>\n",
       "      <td>32.0</td>\n",
       "      <td>2134.0</td>\n",
       "      <td>328.0</td>\n",
       "      <td>903.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>6.3590</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16718</th>\n",
       "      <td>-120.66</td>\n",
       "      <td>35.49</td>\n",
       "      <td>17.0</td>\n",
       "      <td>4422.0</td>\n",
       "      <td>945.0</td>\n",
       "      <td>2307.0</td>\n",
       "      <td>885.0</td>\n",
       "      <td>2.8285</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13600</th>\n",
       "      <td>-117.25</td>\n",
       "      <td>34.16</td>\n",
       "      <td>37.0</td>\n",
       "      <td>1709.0</td>\n",
       "      <td>278.0</td>\n",
       "      <td>744.0</td>\n",
       "      <td>274.0</td>\n",
       "      <td>3.7188</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13989</th>\n",
       "      <td>-117.19</td>\n",
       "      <td>34.94</td>\n",
       "      <td>31.0</td>\n",
       "      <td>2034.0</td>\n",
       "      <td>444.0</td>\n",
       "      <td>1097.0</td>\n",
       "      <td>367.0</td>\n",
       "      <td>2.1522</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15168</th>\n",
       "      <td>-117.06</td>\n",
       "      <td>33.02</td>\n",
       "      <td>24.0</td>\n",
       "      <td>830.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>279.0</td>\n",
       "      <td>196.0</td>\n",
       "      <td>1.9176</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6747</th>\n",
       "      <td>-118.07</td>\n",
       "      <td>34.11</td>\n",
       "      <td>41.0</td>\n",
       "      <td>2869.0</td>\n",
       "      <td>563.0</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>533.0</td>\n",
       "      <td>5.0736</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7398</th>\n",
       "      <td>-118.24</td>\n",
       "      <td>33.96</td>\n",
       "      <td>44.0</td>\n",
       "      <td>1338.0</td>\n",
       "      <td>366.0</td>\n",
       "      <td>1765.0</td>\n",
       "      <td>388.0</td>\n",
       "      <td>1.7778</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5562</th>\n",
       "      <td>-118.28</td>\n",
       "      <td>33.91</td>\n",
       "      <td>41.0</td>\n",
       "      <td>620.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>642.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>2.6546</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16121</th>\n",
       "      <td>-122.46</td>\n",
       "      <td>37.79</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2059.0</td>\n",
       "      <td>416.0</td>\n",
       "      <td>999.0</td>\n",
       "      <td>402.0</td>\n",
       "      <td>3.7419</td>\n",
       "      <td>NEAR BAY</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12380</th>\n",
       "      <td>-116.47</td>\n",
       "      <td>33.77</td>\n",
       "      <td>26.0</td>\n",
       "      <td>4300.0</td>\n",
       "      <td>767.0</td>\n",
       "      <td>1557.0</td>\n",
       "      <td>669.0</td>\n",
       "      <td>4.4107</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5618</th>\n",
       "      <td>-118.23</td>\n",
       "      <td>33.78</td>\n",
       "      <td>20.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2.5588</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10060</th>\n",
       "      <td>-121.06</td>\n",
       "      <td>39.25</td>\n",
       "      <td>17.0</td>\n",
       "      <td>3127.0</td>\n",
       "      <td>539.0</td>\n",
       "      <td>1390.0</td>\n",
       "      <td>520.0</td>\n",
       "      <td>3.9537</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>494</th>\n",
       "      <td>-122.24</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>3726.0</td>\n",
       "      <td>474.0</td>\n",
       "      <td>1366.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>9.3959</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4471</th>\n",
       "      <td>-118.17</td>\n",
       "      <td>34.09</td>\n",
       "      <td>33.0</td>\n",
       "      <td>2907.0</td>\n",
       "      <td>797.0</td>\n",
       "      <td>3212.0</td>\n",
       "      <td>793.0</td>\n",
       "      <td>2.2348</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19786</th>\n",
       "      <td>-122.86</td>\n",
       "      <td>40.56</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1350.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>423.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>1.7393</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9969</th>\n",
       "      <td>-122.48</td>\n",
       "      <td>38.51</td>\n",
       "      <td>49.0</td>\n",
       "      <td>1977.0</td>\n",
       "      <td>393.0</td>\n",
       "      <td>741.0</td>\n",
       "      <td>339.0</td>\n",
       "      <td>3.1312</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14621</th>\n",
       "      <td>-117.17</td>\n",
       "      <td>32.78</td>\n",
       "      <td>17.0</td>\n",
       "      <td>3845.0</td>\n",
       "      <td>1051.0</td>\n",
       "      <td>3102.0</td>\n",
       "      <td>944.0</td>\n",
       "      <td>2.3658</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>579</th>\n",
       "      <td>-122.07</td>\n",
       "      <td>37.71</td>\n",
       "      <td>40.0</td>\n",
       "      <td>1808.0</td>\n",
       "      <td>302.0</td>\n",
       "      <td>746.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>5.3015</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11682</th>\n",
       "      <td>-118.01</td>\n",
       "      <td>33.87</td>\n",
       "      <td>25.0</td>\n",
       "      <td>6348.0</td>\n",
       "      <td>1615.0</td>\n",
       "      <td>4188.0</td>\n",
       "      <td>1497.0</td>\n",
       "      <td>3.1390</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>-122.21</td>\n",
       "      <td>37.78</td>\n",
       "      <td>43.0</td>\n",
       "      <td>1702.0</td>\n",
       "      <td>460.0</td>\n",
       "      <td>1227.0</td>\n",
       "      <td>407.0</td>\n",
       "      <td>1.7188</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12130</th>\n",
       "      <td>-117.23</td>\n",
       "      <td>33.94</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2405.0</td>\n",
       "      <td>537.0</td>\n",
       "      <td>1594.0</td>\n",
       "      <td>517.0</td>\n",
       "      <td>3.0789</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16441</th>\n",
       "      <td>-121.29</td>\n",
       "      <td>38.14</td>\n",
       "      <td>34.0</td>\n",
       "      <td>2770.0</td>\n",
       "      <td>544.0</td>\n",
       "      <td>1409.0</td>\n",
       "      <td>535.0</td>\n",
       "      <td>3.2338</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17108</th>\n",
       "      <td>-122.22</td>\n",
       "      <td>37.44</td>\n",
       "      <td>32.0</td>\n",
       "      <td>4281.0</td>\n",
       "      <td>501.0</td>\n",
       "      <td>1318.0</td>\n",
       "      <td>484.0</td>\n",
       "      <td>15.0001</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19934</th>\n",
       "      <td>-119.34</td>\n",
       "      <td>36.31</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1635.0</td>\n",
       "      <td>422.0</td>\n",
       "      <td>870.0</td>\n",
       "      <td>399.0</td>\n",
       "      <td>2.7000</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1364</th>\n",
       "      <td>-122.14</td>\n",
       "      <td>38.03</td>\n",
       "      <td>42.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2.5795</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1236</th>\n",
       "      <td>-120.37</td>\n",
       "      <td>38.23</td>\n",
       "      <td>13.0</td>\n",
       "      <td>4401.0</td>\n",
       "      <td>829.0</td>\n",
       "      <td>924.0</td>\n",
       "      <td>383.0</td>\n",
       "      <td>2.6942</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5364</th>\n",
       "      <td>-118.42</td>\n",
       "      <td>34.04</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1358.0</td>\n",
       "      <td>272.0</td>\n",
       "      <td>574.0</td>\n",
       "      <td>267.0</td>\n",
       "      <td>5.6454</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15500</th>\n",
       "      <td>-117.26</td>\n",
       "      <td>33.09</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2398.0</td>\n",
       "      <td>407.0</td>\n",
       "      <td>349.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>7.0423</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8754</th>\n",
       "      <td>-118.36</td>\n",
       "      <td>33.82</td>\n",
       "      <td>28.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>6.1359</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15270</th>\n",
       "      <td>-117.29</td>\n",
       "      <td>33.08</td>\n",
       "      <td>18.0</td>\n",
       "      <td>3225.0</td>\n",
       "      <td>515.0</td>\n",
       "      <td>1463.0</td>\n",
       "      <td>476.0</td>\n",
       "      <td>5.7787</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3754</th>\n",
       "      <td>-118.37</td>\n",
       "      <td>34.18</td>\n",
       "      <td>36.0</td>\n",
       "      <td>1608.0</td>\n",
       "      <td>373.0</td>\n",
       "      <td>1217.0</td>\n",
       "      <td>374.0</td>\n",
       "      <td>2.9728</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12166</th>\n",
       "      <td>-117.14</td>\n",
       "      <td>33.81</td>\n",
       "      <td>13.0</td>\n",
       "      <td>4496.0</td>\n",
       "      <td>756.0</td>\n",
       "      <td>2044.0</td>\n",
       "      <td>695.0</td>\n",
       "      <td>3.2778</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12430</th>\n",
       "      <td>-116.26</td>\n",
       "      <td>33.65</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7437.0</td>\n",
       "      <td>1222.0</td>\n",
       "      <td>574.0</td>\n",
       "      <td>302.0</td>\n",
       "      <td>10.2948</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7364</th>\n",
       "      <td>-118.20</td>\n",
       "      <td>33.97</td>\n",
       "      <td>43.0</td>\n",
       "      <td>825.0</td>\n",
       "      <td>212.0</td>\n",
       "      <td>820.0</td>\n",
       "      <td>184.0</td>\n",
       "      <td>1.8897</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6563</th>\n",
       "      <td>-118.13</td>\n",
       "      <td>34.20</td>\n",
       "      <td>46.0</td>\n",
       "      <td>1271.0</td>\n",
       "      <td>236.0</td>\n",
       "      <td>573.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>4.9312</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12053</th>\n",
       "      <td>-117.56</td>\n",
       "      <td>33.88</td>\n",
       "      <td>40.0</td>\n",
       "      <td>1196.0</td>\n",
       "      <td>294.0</td>\n",
       "      <td>1052.0</td>\n",
       "      <td>258.0</td>\n",
       "      <td>2.0682</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13908</th>\n",
       "      <td>-116.40</td>\n",
       "      <td>34.09</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4855.0</td>\n",
       "      <td>872.0</td>\n",
       "      <td>2098.0</td>\n",
       "      <td>765.0</td>\n",
       "      <td>3.2723</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11159</th>\n",
       "      <td>-118.01</td>\n",
       "      <td>33.82</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1960.0</td>\n",
       "      <td>380.0</td>\n",
       "      <td>1356.0</td>\n",
       "      <td>356.0</td>\n",
       "      <td>4.0625</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15775</th>\n",
       "      <td>-122.45</td>\n",
       "      <td>37.77</td>\n",
       "      <td>52.0</td>\n",
       "      <td>3095.0</td>\n",
       "      <td>682.0</td>\n",
       "      <td>1269.0</td>\n",
       "      <td>639.0</td>\n",
       "      <td>3.5750</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>16512 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "17606    -121.89     37.29                38.0       1568.0           351.0   \n",
       "15698    -122.46     37.79                52.0        899.0            96.0   \n",
       "14650    -117.20     32.77                31.0       1952.0           471.0   \n",
       "3230     -119.61     36.31                25.0       1847.0           371.0   \n",
       "3555     -118.59     34.23                17.0       6592.0          1525.0   \n",
       "19480    -120.97     37.66                24.0       2930.0           588.0   \n",
       "9026     -118.86     34.07                16.0       1409.0           244.0   \n",
       "13685    -117.24     34.15                26.0       2041.0           293.0   \n",
       "4937     -118.26     33.99                47.0       1865.0           465.0   \n",
       "4861     -118.28     34.02                29.0        515.0           229.0   \n",
       "16365    -121.31     38.02                24.0       4157.0           951.0   \n",
       "19684    -121.62     39.14                41.0       2183.0           559.0   \n",
       "20330    -119.03     34.25                25.0       3344.0           502.0   \n",
       "13956    -117.06     34.17                21.0       2520.0           582.0   \n",
       "2390     -119.46     36.91                12.0       2980.0           495.0   \n",
       "11176    -117.96     33.83                30.0       2838.0           649.0   \n",
       "15614    -122.41     37.81                25.0       1178.0           545.0   \n",
       "2953     -119.02     35.35                42.0       1239.0           251.0   \n",
       "13209    -117.72     34.05                 8.0       1841.0           409.0   \n",
       "6569     -118.15     34.20                46.0       1505.0           261.0   \n",
       "5825     -118.30     34.19                14.0       3615.0           913.0   \n",
       "18116    -122.02     37.30                32.0       2134.0           328.0   \n",
       "16718    -120.66     35.49                17.0       4422.0           945.0   \n",
       "13600    -117.25     34.16                37.0       1709.0           278.0   \n",
       "13989    -117.19     34.94                31.0       2034.0           444.0   \n",
       "15168    -117.06     33.02                24.0        830.0           190.0   \n",
       "6747     -118.07     34.11                41.0       2869.0           563.0   \n",
       "7398     -118.24     33.96                44.0       1338.0           366.0   \n",
       "5562     -118.28     33.91                41.0        620.0           133.0   \n",
       "16121    -122.46     37.79                52.0       2059.0           416.0   \n",
       "...          ...       ...                 ...          ...             ...   \n",
       "12380    -116.47     33.77                26.0       4300.0           767.0   \n",
       "5618     -118.23     33.78                20.0         59.0            24.0   \n",
       "10060    -121.06     39.25                17.0       3127.0           539.0   \n",
       "494      -122.24     37.85                52.0       3726.0           474.0   \n",
       "4471     -118.17     34.09                33.0       2907.0           797.0   \n",
       "19786    -122.86     40.56                12.0       1350.0           300.0   \n",
       "9969     -122.48     38.51                49.0       1977.0           393.0   \n",
       "14621    -117.17     32.78                17.0       3845.0          1051.0   \n",
       "579      -122.07     37.71                40.0       1808.0           302.0   \n",
       "11682    -118.01     33.87                25.0       6348.0          1615.0   \n",
       "245      -122.21     37.78                43.0       1702.0           460.0   \n",
       "12130    -117.23     33.94                 8.0       2405.0           537.0   \n",
       "16441    -121.29     38.14                34.0       2770.0           544.0   \n",
       "17108    -122.22     37.44                32.0       4281.0           501.0   \n",
       "19934    -119.34     36.31                14.0       1635.0           422.0   \n",
       "1364     -122.14     38.03                42.0        118.0            34.0   \n",
       "1236     -120.37     38.23                13.0       4401.0           829.0   \n",
       "5364     -118.42     34.04                52.0       1358.0           272.0   \n",
       "15500    -117.26     33.09                22.0       2398.0           407.0   \n",
       "8754     -118.36     33.82                28.0         67.0            15.0   \n",
       "15270    -117.29     33.08                18.0       3225.0           515.0   \n",
       "3754     -118.37     34.18                36.0       1608.0           373.0   \n",
       "12166    -117.14     33.81                13.0       4496.0           756.0   \n",
       "12430    -116.26     33.65                 3.0       7437.0          1222.0   \n",
       "7364     -118.20     33.97                43.0        825.0           212.0   \n",
       "6563     -118.13     34.20                46.0       1271.0           236.0   \n",
       "12053    -117.56     33.88                40.0       1196.0           294.0   \n",
       "13908    -116.40     34.09                 9.0       4855.0           872.0   \n",
       "11159    -118.01     33.82                31.0       1960.0           380.0   \n",
       "15775    -122.45     37.77                52.0       3095.0           682.0   \n",
       "\n",
       "       population  households  median_income ocean_proximity  \n",
       "17606       710.0       339.0         2.7042       <1H OCEAN  \n",
       "15698       304.0       110.0        14.2959        NEAR BAY  \n",
       "14650       936.0       462.0         2.8621      NEAR OCEAN  \n",
       "3230       1460.0       353.0         1.8839          INLAND  \n",
       "3555       4459.0      1463.0         3.0347       <1H OCEAN  \n",
       "19480      1448.0       570.0         3.5395          INLAND  \n",
       "9026        970.0       172.0         8.0144      NEAR OCEAN  \n",
       "13685       936.0       375.0         6.0000          INLAND  \n",
       "4937       1916.0       438.0         1.8242       <1H OCEAN  \n",
       "4861       2690.0       217.0         0.4999       <1H OCEAN  \n",
       "16365      2734.0       879.0         2.7981          INLAND  \n",
       "19684      1202.0       506.0         1.6902          INLAND  \n",
       "20330      1483.0       496.0         6.1960       <1H OCEAN  \n",
       "13956       416.0       151.0         2.7120          INLAND  \n",
       "2390       1184.0       429.0         3.9141          INLAND  \n",
       "11176      1758.0       593.0         3.3831       <1H OCEAN  \n",
       "15614       592.0       441.0         3.6728        NEAR BAY  \n",
       "2953        776.0       272.0         1.9830          INLAND  \n",
       "13209      1243.0       394.0         4.0614          INLAND  \n",
       "6569        857.0       269.0         4.5000          INLAND  \n",
       "5825       1924.0       852.0         3.5083       <1H OCEAN  \n",
       "18116       903.0       322.0         6.3590       <1H OCEAN  \n",
       "16718      2307.0       885.0         2.8285       <1H OCEAN  \n",
       "13600       744.0       274.0         3.7188          INLAND  \n",
       "13989      1097.0       367.0         2.1522          INLAND  \n",
       "15168       279.0       196.0         1.9176       <1H OCEAN  \n",
       "6747       1627.0       533.0         5.0736       <1H OCEAN  \n",
       "7398       1765.0       388.0         1.7778       <1H OCEAN  \n",
       "5562        642.0       162.0         2.6546       <1H OCEAN  \n",
       "16121       999.0       402.0         3.7419        NEAR BAY  \n",
       "...           ...         ...            ...             ...  \n",
       "12380      1557.0       669.0         4.4107          INLAND  \n",
       "5618         69.0        23.0         2.5588      NEAR OCEAN  \n",
       "10060      1390.0       520.0         3.9537          INLAND  \n",
       "494        1366.0       496.0         9.3959        NEAR BAY  \n",
       "4471       3212.0       793.0         2.2348       <1H OCEAN  \n",
       "19786       423.0       172.0         1.7393          INLAND  \n",
       "9969        741.0       339.0         3.1312          INLAND  \n",
       "14621      3102.0       944.0         2.3658      NEAR OCEAN  \n",
       "579         746.0       270.0         5.3015        NEAR BAY  \n",
       "11682      4188.0      1497.0         3.1390       <1H OCEAN  \n",
       "245        1227.0       407.0         1.7188        NEAR BAY  \n",
       "12130      1594.0       517.0         3.0789          INLAND  \n",
       "16441      1409.0       535.0         3.2338          INLAND  \n",
       "17108      1318.0       484.0        15.0001      NEAR OCEAN  \n",
       "19934       870.0       399.0         2.7000          INLAND  \n",
       "1364         54.0        30.0         2.5795        NEAR BAY  \n",
       "1236        924.0       383.0         2.6942          INLAND  \n",
       "5364        574.0       267.0         5.6454       <1H OCEAN  \n",
       "15500       349.0       169.0         7.0423      NEAR OCEAN  \n",
       "8754         49.0        11.0         6.1359       <1H OCEAN  \n",
       "15270      1463.0       476.0         5.7787      NEAR OCEAN  \n",
       "3754       1217.0       374.0         2.9728       <1H OCEAN  \n",
       "12166      2044.0       695.0         3.2778          INLAND  \n",
       "12430       574.0       302.0        10.2948          INLAND  \n",
       "7364        820.0       184.0         1.8897       <1H OCEAN  \n",
       "6563        573.0       210.0         4.9312          INLAND  \n",
       "12053      1052.0       258.0         2.0682          INLAND  \n",
       "13908      2098.0       765.0         3.2723          INLAND  \n",
       "11159      1356.0       356.0         4.0625       <1H OCEAN  \n",
       "15775      1269.0       639.0         3.5750        NEAR BAY  \n",
       "\n",
       "[16512 rows x 9 columns]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing = strat_train_set.drop(\"median_house_value\", axis=1)\n",
    "housing_labels = strat_train_set[\"median_house_value\"].copy()\n",
    "\n",
    "# As the attribute total_bedrooms has some missing value lets substitute blank spaces with median\n",
    "median = housing[\"total_bedrooms\"].median()\n",
    "housing[\"total_bedrooms\"].fillna(median, inplace=True)\n",
    "housing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>-122.02</td>\n",
       "      <td>37.30</td>\n",
       "      <td>32.0</td>\n",
       "      <td>2134.0</td>\n",
       "      <td>328.0</td>\n",
       "      <td>903.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>6.3590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>-120.66</td>\n",
       "      <td>35.49</td>\n",
       "      <td>17.0</td>\n",
       "      <td>4422.0</td>\n",
       "      <td>945.0</td>\n",
       "      <td>2307.0</td>\n",
       "      <td>885.0</td>\n",
       "      <td>2.8285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>-117.25</td>\n",
       "      <td>34.16</td>\n",
       "      <td>37.0</td>\n",
       "      <td>1709.0</td>\n",
       "      <td>278.0</td>\n",
       "      <td>744.0</td>\n",
       "      <td>274.0</td>\n",
       "      <td>3.7188</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "21    -122.02     37.30                32.0       2134.0           328.0   \n",
       "22    -120.66     35.49                17.0       4422.0           945.0   \n",
       "23    -117.25     34.16                37.0       1709.0           278.0   \n",
       "\n",
       "    population  households  median_income  \n",
       "21       903.0       322.0         6.3590  \n",
       "22      2307.0       885.0         2.8285  \n",
       "23       744.0       274.0         3.7188  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import Imputer\n",
    "\n",
    "imputer = Imputer(strategy='median')\n",
    "housing_num = housing.drop(\"ocean_proximity\", axis=1)\n",
    "imputer.fit(housing_num)\n",
    "X = imputer.transform(housing_num)\n",
    "housing_tr = pd.DataFrame(X, columns=housing_num.columns)\n",
    "housing_tr.iloc[21:24]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 3, 4, ..., 1, 0, 3], dtype=int64)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Converting text to numbers\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "encoder = LabelEncoder()\n",
    "housing_cat = housing[\"ocean_proximity\"]\n",
    "housing_cat_encoded = encoder.fit_transform(housing_cat)\n",
    "housing_cat_encoded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<16512x5 sparse matrix of type '<class 'numpy.float64'>'\n",
       "\twith 16512 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sparse matrix for categorical variables with multiple categories \n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "\n",
    "encoder = OneHotEncoder()\n",
    "housing_cat_1hot = encoder.fit_transform(housing_cat_encoded.reshape(-1,1))\n",
    "housing_cat_1hot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Feature Scaling\n",
    "from sklearn.base import BaseEstimator, TransformerMixin\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "rooms_ix, bedrooms_ix, population_ix, household_ix = 3, 4, 5, 6\n",
    "\n",
    "class CombinedAttributesAdder(BaseEstimator, TransformerMixin):\n",
    "    def __init__(self, add_bedrooms_per_room = True): # no *args or **kargs\n",
    "        self.add_bedrooms_per_room = add_bedrooms_per_room\n",
    "    def fit(self, X, y=None):\n",
    "        return self  # nothing else to do\n",
    "    def transform(self, X, y=None):\n",
    "        rooms_per_household = X[:, rooms_ix] / X[:, household_ix]\n",
    "        population_per_household = X[:, population_ix] / X[:, household_ix]\n",
    "        if self.add_bedrooms_per_room:\n",
    "            bedrooms_per_room = X[:, bedrooms_ix] / X[:, rooms_ix]\n",
    "            return np.c_[X, rooms_per_household, population_per_household, bedrooms_per_room]\n",
    "        else:\n",
    "            return np.c_[X, rooms_per_household, population_per_household]\n",
    "\n",
    "attr_adder = CombinedAttributesAdder(add_bedrooms_per_room=False)\n",
    "housing_extra_attribs = attr_adder.transform(housing.values)\n",
    "\n",
    "housing_extra_attribs = pd.DataFrame(housing_extra_attribs, columns=list(housing.columns)+[\"rooms_per_household\", \"population_per_household\"])\n",
    "housing_extra_attribs.head()\n",
    "\n",
    "num_pipeline = Pipeline([\n",
    "        ('imputer', Imputer(strategy=\"median\")),\n",
    "        ('attribs_adder', CombinedAttributesAdder()),\n",
    "        ('std_scaler', StandardScaler()),\n",
    "    ])\n",
    "housing_prepared = num_pipeline.fit_transform(housing_num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.15788621,  0.77388697,  0.74440696, ...,  0.        ,\n",
       "         0.        ,  0.        ],\n",
       "       [-1.44259512,  1.0077626 ,  1.85708974, ...,  0.        ,\n",
       "         1.        ,  0.        ],\n",
       "       [ 1.18471864, -1.3403487 ,  0.18806557, ...,  0.        ,\n",
       "         0.        ,  1.        ],\n",
       "       ..., \n",
       "       [ 1.58431009, -0.72291704, -1.56043594, ...,  0.        ,\n",
       "         0.        ,  0.        ],\n",
       "       [ 0.7801323 , -0.84920988,  0.18806557, ...,  0.        ,\n",
       "         0.        ,  0.        ],\n",
       "       [-1.43760023,  0.99840757,  1.85708974, ...,  0.        ,\n",
       "         1.        ,  0.        ]])"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.pipeline import FeatureUnion\n",
    "from sklearn.preprocessing import LabelBinarizer\n",
    "\n",
    "encoder = LabelBinarizer()\n",
    "encoder.fit_transform(housing_cat)\n",
    "\n",
    "class DataFrameSelector(BaseEstimator, TransformerMixin):\n",
    "    def __init__(self, attribute_names):\n",
    "        self.attribute_names = attribute_names\n",
    "    def fit(self, X, y=None):\n",
    "        return self\n",
    "    def transform(self, X):\n",
    "        return X[self.attribute_names].values\n",
    "\n",
    "num_attribs = list(housing_num)\n",
    "cat_attribs = [\"ocean_proximity\"]\n",
    "\n",
    "num_pipeline = Pipeline([\n",
    "        ('selector', DataFrameSelector(num_attribs)),\n",
    "        ('imputer', Imputer(strategy=\"median\")),\n",
    "        ('attribs_adder', CombinedAttributesAdder()),\n",
    "        ('std_scaler', StandardScaler()),\n",
    "    ])\n",
    "\n",
    "cat_pipeline = Pipeline([\n",
    "        ('selector', DataFrameSelector(cat_attribs)),\n",
    "        ('label_binarizer', LabelBinarizer()),\n",
    "    ])\n",
    "\n",
    "preparation_pipeline = FeatureUnion(transformer_list=[\n",
    "        (\"num_pipeline\", num_pipeline),\n",
    "        (\"cat_pipeline\", cat_pipeline),\n",
    "    ])\n",
    "housing_prepared = preparation_pipeline.fit_transform(housing)\n",
    "housing_prepared"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***Prediction Model 1: Linear Regression***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "lin_reg = LinearRegression()\n",
    "lin_reg.fit(housing_prepared, housing_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predictions:\t [ 209300.18149413  661078.1135578   210913.91718561   57466.84542129\n",
      "  186707.41879655]\n",
      "Labels:\t\t [286600.0, 500001.0, 196900.0, 46300.0, 254500.0]\n"
     ]
    }
   ],
   "source": [
    "# let's try the full pipeline on a few training instances\n",
    "some_data = housing.iloc[:5]\n",
    "some_labels = housing_labels.iloc[:5]\n",
    "some_data_prepared = preparation_pipeline.transform(some_data)\n",
    "\n",
    "print(\"Predictions:\\t\", lin_reg.predict(some_data_prepared))\n",
    "print(\"Labels:\\t\\t\", list(some_labels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Root Mean Square Error: 68321.7051304\n",
      "Mean Absolute Error: 49237.4978192\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "\n",
    "housing_predictions = lin_reg.predict(housing_prepared)\n",
    "lin_mse = mean_squared_error(housing_labels, housing_predictions)\n",
    "lin_rmse = np.sqrt(lin_mse)\n",
    "print(\"Root Mean Square Error:\",lin_rmse)\n",
    "lin_mae = mean_absolute_error(housing_labels, housing_predictions)\n",
    "print(\"Mean Absolute Error:\",lin_mae)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***Prediction Model 2: Decision Tree Regressor***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Root mean square error scores: [ 68262.53439997  64690.12021593  66978.28593533  69160.84997068\n",
      "  70667.37565178  75698.78386113  68755.30742608  71687.35547241\n",
      "  74580.58358348  72214.54215159]\n",
      "Mean: 70269.5738668\n",
      "Standard deviation: 3217.54155766\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "\n",
    "tree_reg = DecisionTreeRegressor()\n",
    "tree_reg.fit(housing_prepared, housing_labels)\n",
    "housing_predictions = tree_reg.predict(housing_prepared)\n",
    "\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "tree_scores = cross_val_score(tree_reg, housing_prepared, housing_labels,\n",
    "                              scoring=\"neg_mean_squared_error\", cv=10)\n",
    "tree_rmse_scores = np.sqrt(-tree_scores)\n",
    "\n",
    "def display_scores(scores):\n",
    "    print(\"Root mean square error scores:\", scores)\n",
    "    print(\"Mean:\", scores.mean())\n",
    "    print(\"Standard deviation:\", scores.std())\n",
    "\n",
    "display_scores(tree_rmse_scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***Prediction Model 3: Random Forest Regressor***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Root mean square error scores: [ 51262.6406342   49959.00192482  51671.17354803  54803.13653085\n",
      "  53396.84353043  57685.10579285  50200.94879319  51511.5901573\n",
      "  55115.14124714  53485.49837652]\n",
      "Mean: 52909.1080535\n",
      "Standard deviation: 2325.24919639\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "forest_reg = RandomForestRegressor()\n",
    "forest_reg.fit(housing_prepared, housing_labels)\n",
    "housing_predictions = forest_reg.predict(housing_prepared)\n",
    "forest_mse = mean_squared_error(housing_labels, housing_predictions)\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "forest_scores = cross_val_score(forest_reg, housing_prepared, housing_labels,\n",
    "                                scoring=\"neg_mean_squared_error\", cv=10)\n",
    "forest_rmse_scores = np.sqrt(-forest_scores)\n",
    "display_scores(forest_rmse_scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***Prediction Model 4: Support Vector Regressor***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Root mean square error: 110914.791356\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVR\n",
    "\n",
    "svm_reg = SVR(kernel=\"linear\")\n",
    "svm_reg.fit(housing_prepared, housing_labels)\n",
    "housing_predictions = svm_reg.predict(housing_prepared)\n",
    "svm_mse = mean_squared_error(housing_labels, housing_predictions)\n",
    "svm_rmse = np.sqrt(svm_mse)\n",
    "print(\"Root mean square error:\",svm_rmse)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***Fine Tuning the Hyperparameters for Random Forest Regressor***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=5, error_score='raise',\n",
       "          estimator=RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "           max_features='auto', max_leaf_nodes=None,\n",
       "           min_impurity_split=1e-07, min_samples_leaf=1,\n",
       "           min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "           n_estimators=10, n_jobs=1, oob_score=False, random_state=None,\n",
       "           verbose=0, warm_start=False),\n",
       "          fit_params={}, iid=True, n_iter=10, n_jobs=1,\n",
       "          param_distributions={'max_features': <scipy.stats._distn_infrastructure.rv_frozen object at 0x000001DB7A0D4BA8>, 'n_estimators': <scipy.stats._distn_infrastructure.rv_frozen object at 0x000001DB7A3DCEB8>},\n",
       "          pre_dispatch='2*n_jobs', random_state=None, refit=True,\n",
       "          return_train_score=True, scoring='neg_mean_squared_error',\n",
       "          verbose=0)"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from scipy.stats import randint\n",
    "\n",
    "param_distribs = {\n",
    "        'n_estimators': randint(low=1, high=200),\n",
    "        'max_features': randint(low=1, high=8),\n",
    "    }\n",
    "\n",
    "forest_reg = RandomForestRegressor()\n",
    "rnd_search = RandomizedSearchCV(forest_reg, param_distributions=param_distribs,\n",
    "                                n_iter=10, cv=5, scoring='neg_mean_squared_error')\n",
    "rnd_search.fit(housing_prepared, housing_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "49331.1763582 {'n_estimators': 132, 'max_features': 6}\n",
      "49261.2835608 {'n_estimators': 196, 'max_features': 7}\n",
      "49902.9652842 {'n_estimators': 137, 'max_features': 4}\n",
      "49756.1181686 {'n_estimators': 112, 'max_features': 4}\n",
      "49707.8806797 {'n_estimators': 180, 'max_features': 4}\n",
      "54701.2221917 {'n_estimators': 166, 'max_features': 1}\n",
      "49756.2448425 {'n_estimators': 160, 'max_features': 4}\n",
      "49522.7770958 {'n_estimators': 121, 'max_features': 5}\n",
      "64099.8225022 {'n_estimators': 2, 'max_features': 5}\n",
      "51720.8819357 {'n_estimators': 153, 'max_features': 2}\n"
     ]
    }
   ],
   "source": [
    "cvres = rnd_search.cv_results_\n",
    "for mean_score, params in zip(cvres[\"mean_test_score\"], cvres[\"params\"]):\n",
    "    print(np.sqrt(-mean_score), params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Let’s display importance scores next to corresponding attribute names\n",
    "extra_attribs = [\"rooms_per_household\", \"population_per_household\", \"bedrooms_per_room\"]\n",
    "cat_one_hot_attribs = list(encoder.classes_)\n",
    "attributes = num_attribs + extra_attribs + cat_one_hot_attribs\n",
    "sorted(zip(feature_importances, attributes), reverse=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Apparently only one ocean_proximity category is really useful, so you could try dropping the others. Also the best RMSE was obtained using Randomized approach with RMSE value of 49261.2835608 "
   ]
  }
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