{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Machine Learning with Sklearn -- Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "house = pd.read_csv(\"../../../RepositoryData/data/hands-on-ml/housing.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
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       "        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>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": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "house.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "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",
      " #   Column              Non-Null Count  Dtype  \n",
      "---  ------              --------------  -----  \n",
      " 0   longitude           20640 non-null  float64\n",
      " 1   latitude            20640 non-null  float64\n",
      " 2   housing_median_age  20640 non-null  float64\n",
      " 3   total_rooms         20640 non-null  float64\n",
      " 4   total_bedrooms      20433 non-null  float64\n",
      " 5   population          20640 non-null  float64\n",
      " 6   households          20640 non-null  float64\n",
      " 7   median_income       20640 non-null  float64\n",
      " 8   median_house_value  20640 non-null  float64\n",
      " 9   ocean_proximity     20640 non-null  object \n",
      "dtypes: float64(9), object(1)\n",
      "memory usage: 1.6+ MB\n"
     ]
    }
   ],
   "source": [
    "house.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "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": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "house['ocean_proximity'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        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>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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20433.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-119.569704</td>\n",
       "      <td>35.631861</td>\n",
       "      <td>28.639486</td>\n",
       "      <td>2635.763081</td>\n",
       "      <td>537.870553</td>\n",
       "      <td>1425.476744</td>\n",
       "      <td>499.539680</td>\n",
       "      <td>3.870671</td>\n",
       "      <td>206855.816909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.003532</td>\n",
       "      <td>2.135952</td>\n",
       "      <td>12.585558</td>\n",
       "      <td>2181.615252</td>\n",
       "      <td>421.385070</td>\n",
       "      <td>1132.462122</td>\n",
       "      <td>382.329753</td>\n",
       "      <td>1.899822</td>\n",
       "      <td>115395.615874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-124.350000</td>\n",
       "      <td>32.540000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.499900</td>\n",
       "      <td>14999.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-121.800000</td>\n",
       "      <td>33.930000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>1447.750000</td>\n",
       "      <td>296.000000</td>\n",
       "      <td>787.000000</td>\n",
       "      <td>280.000000</td>\n",
       "      <td>2.563400</td>\n",
       "      <td>119600.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-118.490000</td>\n",
       "      <td>34.260000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>2127.000000</td>\n",
       "      <td>435.000000</td>\n",
       "      <td>1166.000000</td>\n",
       "      <td>409.000000</td>\n",
       "      <td>3.534800</td>\n",
       "      <td>179700.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>-118.010000</td>\n",
       "      <td>37.710000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>3148.000000</td>\n",
       "      <td>647.000000</td>\n",
       "      <td>1725.000000</td>\n",
       "      <td>605.000000</td>\n",
       "      <td>4.743250</td>\n",
       "      <td>264725.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>-114.310000</td>\n",
       "      <td>41.950000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>39320.000000</td>\n",
       "      <td>6445.000000</td>\n",
       "      <td>35682.000000</td>\n",
       "      <td>6082.000000</td>\n",
       "      <td>15.000100</td>\n",
       "      <td>500001.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          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  "
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "house.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "house.hist(bins=50, figsize=(20,15))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train-Test Split"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Simple random sampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "train, test = train_test_split(house, test_size = 0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16512"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4128"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stratified Random Sampling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The sampling should consider the income distributions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [],
   "source": [
    "house['income_cat'] = pd.cut(house['median_income'],\n",
    "                            bins=[0,1.5, 3.0, 4.5, 6.0,np.inf],\n",
    "                            labels=[1,2,3,4,5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 165,
     "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": [
    "house['income_cat'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import StratifiedShuffleSplit\n",
    "\n",
    "splitter = StratifiedShuffleSplit(n_splits=1, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [],
   "source": [
    "for train_index, test_index in splitter.split(house, house['income_cat']):\n",
    "    train_strat = house.loc[train_index]\n",
    "    test_strat = house.loc[test_index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    0.350594\n",
       "2    0.318859\n",
       "4    0.176296\n",
       "5    0.114402\n",
       "1    0.039850\n",
       "Name: income_cat, dtype: float64"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_strat['income_cat'].value_counts()/len(train_strat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    0.350533\n",
       "2    0.318798\n",
       "4    0.176357\n",
       "5    0.114583\n",
       "1    0.039729\n",
       "Name: income_cat, dtype: float64"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_strat['income_cat'].value_counts()/len(test_strat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [],
   "source": [
    "for set_ in (train_strat, test_strat):\n",
    "    set_.drop(\"income_cat\", axis = 1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploring Training Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fb45b976470>"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_strat.plot(kind=\"scatter\", x=\"longitude\", y=\"latitude\", alpha=0.4,\n",
    "                s=train_strat[\"population\"]/100, label=\"population\",figsize=(10,7),\n",
    "                c=\"median_house_value\", cmap=plt.get_cmap(\"jet\"),colorbar=True,)\n",
    "plt.legend()\n",
    "\n",
    "## size: population\n",
    "## color: median house value"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preparing Data for ML"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Wrap things in functions\n",
    "    - Allow you to reproduce the same transformations easily on any dataset\n",
    "    - Build a self-defined library of transformation functions\n",
    "    - Allow you to apply the same transformation to new data in live system\n",
    "    - Allow you to experiment with different transformations easily"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\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>18632</th>\n",
       "      <td>-121.93</td>\n",
       "      <td>37.05</td>\n",
       "      <td>14.0</td>\n",
       "      <td>679.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>306.0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>6.4214</td>\n",
       "      <td>&lt;1H OCEAN</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>...</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>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",
       "18632    -121.93     37.05                14.0        679.0           108.0   \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",
       "...          ...       ...                 ...          ...             ...   \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",
       "18632       306.0       113.0         6.4214       <1H OCEAN  \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",
       "...           ...         ...            ...             ...  \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": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing = train_strat.drop(\"median_house_value\", axis=1)\n",
    "housing_labels = train_strat[\"median_house_value\"].copy()\n",
    "housing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### NA values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SimpleImputer(add_indicator=False, copy=True, fill_value=None,\n",
       "              missing_values=nan, strategy='median', verbose=0)"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.impute import SimpleImputer\n",
    "\n",
    "imputer = SimpleImputer(strategy=\"median\")\n",
    "\n",
    "housing_num = housing.drop(\"ocean_proximity\", axis=1)\n",
    "imputer.fit(housing_num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-118.51  ,   34.26  ,   29.    , 2119.5   ,  433.    , 1164.    ,\n",
       "        408.    ,    3.5409])"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imputer.statistics_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "longitude             -118.5100\n",
       "latitude                34.2600\n",
       "housing_median_age      29.0000\n",
       "total_rooms           2119.5000\n",
       "total_bedrooms         433.0000\n",
       "population            1164.0000\n",
       "households             408.0000\n",
       "median_income            3.5409\n",
       "dtype: float64"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_num.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\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>0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-121.93</td>\n",
       "      <td>37.05</td>\n",
       "      <td>14.0</td>\n",
       "      <td>679.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>306.0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>6.4214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</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",
       "    </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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16507</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16508</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16509</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16510</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16511</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>16512 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "0        -121.89     37.29                38.0       1568.0           351.0   \n",
       "1        -121.93     37.05                14.0        679.0           108.0   \n",
       "2        -117.20     32.77                31.0       1952.0           471.0   \n",
       "3        -119.61     36.31                25.0       1847.0           371.0   \n",
       "4        -118.59     34.23                17.0       6592.0          1525.0   \n",
       "...          ...       ...                 ...          ...             ...   \n",
       "16507    -118.13     34.20                46.0       1271.0           236.0   \n",
       "16508    -117.56     33.88                40.0       1196.0           294.0   \n",
       "16509    -116.40     34.09                 9.0       4855.0           872.0   \n",
       "16510    -118.01     33.82                31.0       1960.0           380.0   \n",
       "16511    -122.45     37.77                52.0       3095.0           682.0   \n",
       "\n",
       "       population  households  median_income  \n",
       "0           710.0       339.0         2.7042  \n",
       "1           306.0       113.0         6.4214  \n",
       "2           936.0       462.0         2.8621  \n",
       "3          1460.0       353.0         1.8839  \n",
       "4          4459.0      1463.0         3.0347  \n",
       "...           ...         ...            ...  \n",
       "16507       573.0       210.0         4.9312  \n",
       "16508      1052.0       258.0         2.0682  \n",
       "16509      2098.0       765.0         3.2723  \n",
       "16510      1356.0       356.0         4.0625  \n",
       "16511      1269.0       639.0         3.5750  \n",
       "\n",
       "[16512 rows x 8 columns]"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = imputer.transform(housing_num)\n",
    "housing_tr = pd.DataFrame(X, columns=housing_num.columns)\n",
    "housing_tr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Type Conversion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Categorical to Ordinal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import OrdinalEncoder\n",
    "ordinal_encoder = OrdinalEncoder(categories='auto')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17606</th>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18632</th>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14650</th>\n",
       "      <td>NEAR OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3230</th>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3555</th>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19480</th>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8879</th>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13685</th>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4937</th>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4861</th>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      ocean_proximity\n",
       "17606       <1H OCEAN\n",
       "18632       <1H OCEAN\n",
       "14650      NEAR OCEAN\n",
       "3230           INLAND\n",
       "3555        <1H OCEAN\n",
       "19480          INLAND\n",
       "8879        <1H OCEAN\n",
       "13685          INLAND\n",
       "4937        <1H OCEAN\n",
       "4861        <1H OCEAN"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ocean_proximity_cat = housing[[\"ocean_proximity\"]]\n",
    "ocean_proximity_cat.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.],\n",
       "       [0.],\n",
       "       [4.],\n",
       "       [1.],\n",
       "       [0.],\n",
       "       [1.],\n",
       "       [0.],\n",
       "       [1.],\n",
       "       [0.],\n",
       "       [0.]])"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ocean_proximity_ordinal = ordinal_encoder.fit_transform(ocean_proximity_cat)\n",
    "ocean_proximity_ordinal[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array(['<1H OCEAN', 'INLAND', 'ISLAND', 'NEAR BAY', 'NEAR OCEAN'],\n",
       "       dtype=object)]"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ordinal_encoder.categories_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    7276\n",
       "1.0    5263\n",
       "4.0    2124\n",
       "3.0    1847\n",
       "2.0       2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(ocean_proximity_oridinal).value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- One-hot encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder\n",
    "onehot_encoder = OneHotEncoder()\n",
    "ocean_proximity_onehot = onehot_encoder.fit_transform(ocean_proximity_cat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<16512x5 sparse matrix of type '<class 'numpy.float64'>'\n",
       "\twith 16512 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ocean_proximity_onehot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 0., 0., 0., 0.],\n",
       "       [1., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 1.],\n",
       "       ...,\n",
       "       [0., 1., 0., 0., 0.],\n",
       "       [1., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 1., 0.]])"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ocean_proximity_onehot.toarray()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler, MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [],
   "source": [
    "standard_scaler = StandardScaler()\n",
    "minmax_scaler = MinMaxScaler()\n",
    "\n",
    "housing_num_minmax = minmax_scaler.fit_transform(housing_num)\n",
    "housing_num_standard = standard_scaler.fit_transform(housing_num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.24501992, 0.50478215, 0.7254902 , ..., 0.01981558, 0.06292009,\n",
       "        0.15201859],\n",
       "       [0.24103586, 0.47927736, 0.25490196, ..., 0.00849239, 0.02072442,\n",
       "        0.40837368],\n",
       "       [0.71215139, 0.02444208, 0.58823529, ..., 0.02614984, 0.08588499,\n",
       "        0.1629081 ],\n",
       "       ...,\n",
       "       [0.79183267, 0.16471838, 0.15686275, ..., 0.05871801, 0.14245706,\n",
       "        0.19119736],\n",
       "       [0.6314741 , 0.1360255 , 0.58823529, ..., 0.03792147, 0.0660941 ,\n",
       "        0.24569316],\n",
       "       [0.18924303, 0.55579171, 1.        , ..., 0.03548306, 0.11893204,\n",
       "        0.21207294]])"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_num_minmax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.15604281,  0.77194962,  0.74333089, ..., -0.63621141,\n",
       "        -0.42069842, -0.61493744],\n",
       "       [-1.17602483,  0.6596948 , -1.1653172 , ..., -0.99833135,\n",
       "        -1.02222705,  1.33645936],\n",
       "       [ 1.18684903, -1.34218285,  0.18664186, ..., -0.43363936,\n",
       "        -0.0933178 , -0.5320456 ],\n",
       "       ...,\n",
       "       [ 1.58648943, -0.72478134, -1.56295222, ...,  0.60790363,\n",
       "         0.71315642, -0.3167053 ],\n",
       "       [ 0.78221312, -0.85106801,  0.18664186, ..., -0.05717804,\n",
       "        -0.37545069,  0.09812139],\n",
       "       [-1.43579109,  0.99645926,  1.85670895, ..., -0.13515931,\n",
       "         0.3777909 , -0.15779865]])"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_num_standard"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Transformation Pipelines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "num_pipeline = Pipeline([\n",
    "    ('imputer', SimpleImputer(strategy=\"median\")),\n",
    "    ('standardizer', StandardScaler()),\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing_num_tr = num_pipeline.fit_transform(housing_num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.15604281,  0.77194962,  0.74333089, ..., -0.63621141,\n",
       "        -0.42069842, -0.61493744],\n",
       "       [-1.17602483,  0.6596948 , -1.1653172 , ..., -0.99833135,\n",
       "        -1.02222705,  1.33645936],\n",
       "       [ 1.18684903, -1.34218285,  0.18664186, ..., -0.43363936,\n",
       "        -0.0933178 , -0.5320456 ],\n",
       "       ...,\n",
       "       [ 1.58648943, -0.72478134, -1.56295222, ...,  0.60790363,\n",
       "         0.71315642, -0.3167053 ],\n",
       "       [ 0.78221312, -0.85106801,  0.18664186, ..., -0.05717804,\n",
       "        -0.37545069,  0.09812139],\n",
       "       [-1.43579109,  0.99645926,  1.85670895, ..., -0.13515931,\n",
       "         0.3777909 , -0.15779865]])"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_num_tr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Handle all columns all at once"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.compose import ColumnTransformer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_columns = list(housing_num)\n",
    "cat_columns = ['ocean_proximity']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [],
   "source": [
    "full_pipeline = ColumnTransformer([\n",
    "    ('num', num_pipeline, num_columns),\n",
    "    ('cat', OneHotEncoder(), cat_columns)\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing_prepared = full_pipeline.fit_transform(housing)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{important}\n",
    "The output of the `full_pipeline` includes more columns than the original data frame `housing`. This is due to the one-hot encoding  transformation.\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Select and Train a Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Linear Regresssion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "69055.57008288472"
      ]
     },
     "execution_count": 200,
     "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)\n",
    "\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "housing_predictions = lin_reg.predict(housing_prepared)\n",
    "\n",
    "lin_mse = mean_squared_error(housing_labels, housing_predictions)\n",
    "lin_rmes = np.sqrt(lin_mse)\n",
    "lin_rmes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "tree_reg = DecisionTreeRegressor()\n",
    "tree_reg.fit(housing_prepared, housing_labels)\n",
    "\n",
    "tree_mse = mean_squared_error(housing_labels, tree_reg.predict(housing_prepared))\n",
    "tree_rmes = np.sqrt(tree_mse)\n",
    "tree_rmes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Cross Validation to check over/under-fitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- CV for Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([67139.82432539, 67382.97266737, 73570.81763384, 68716.64962785,\n",
       "       67808.75573625, 75295.66005943, 66336.18100097, 69901.7711193 ,\n",
       "       69017.19332426, 69541.31134573])"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree_scores = cross_val_score(tree_reg, housing_prepared, housing_labels,\n",
    "                             scoring=\"neg_mean_squared_error\", cv=10)\n",
    "tree_rmse_cv = np.sqrt(-tree_scores)\n",
    "tree_rmse_cv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [],
   "source": [
    "def display_cv_scores(scores):\n",
    "    print('Scores:', scores)\n",
    "    print('Mean:', scores.mean())\n",
    "    print('Standard deviation:', scores.std())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores: [67139.82432539 67382.97266737 73570.81763384 68716.64962785\n",
      " 67808.75573625 75295.66005943 66336.18100097 69901.7711193\n",
      " 69017.19332426 69541.31134573]\n",
      "Mean: 69471.1136840389\n",
      "Standard deviation: 2721.856830394127\n"
     ]
    }
   ],
   "source": [
    "display_cv_scores(tree_rmse_cv)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- CV for Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores: [67452.77701485 67389.76532656 68361.84864912 74651.7801139\n",
      " 68314.56738182 71640.79787192 65361.14176205 68571.62738037\n",
      " 72475.91110732 68098.06828865]\n",
      "Mean: 69231.82848965636\n",
      "Standard deviation: 2656.3702353976896\n"
     ]
    }
   ],
   "source": [
    "linreg_scores = cross_val_score(lin_reg, housing_prepared, housing_labels,\n",
    "                                scoring=\"neg_mean_squared_error\", cv=10)\n",
    "linreg_rmse_cv = np.sqrt(-linreg_scores)\n",
    "display_cv_scores(linreg_rmse_cv)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- CV for Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores: [47632.49179689 46373.11835198 49272.19573687 50147.15388662\n",
      " 49036.42363423 53397.31601372 48902.61322735 50348.23351741\n",
      " 51248.22704525 49552.02150292]\n",
      "Mean: 49590.979471321756\n",
      "Standard deviation: 1821.8742073775495\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "forest_reg = RandomForestRegressor()\n",
    "forest_scores = cross_val_score(forest_reg, housing_prepared, housing_labels,\n",
    "                               scoring=\"neg_mean_squared_error\", cv=10)\n",
    "forest_rmse_cv=np.sqrt(-forest_scores)\n",
    "display_cv_scores(forest_rmse_cv)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{important}\n",
    "- Try different ML algorithms first, before spending too much time tweaking the hyperparameters of one single algorithm.\n",
    "\n",
    "- The first goal is to shortlist a few (2 to 5) promising models.\n",
    "```"
   ]
  }
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