00_preprocessing.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import math\n",
    "import random\n",
    "import seaborn\n",
    "import missingno as msno \n",
    "from scipy import stats\n",
    "from random import uniform\n",
    "from scipy.stats.stats import pearsonr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = pd.read_csv(\"datasets/cal-housing.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data visualization and anomalies detection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>919.0</td>\n",
       "      <td>213.0</td>\n",
       "      <td>413.0</td>\n",
       "      <td>193.0</td>\n",
       "      <td>4.0368</td>\n",
       "      <td>269700.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.84</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2535.0</td>\n",
       "      <td>489.0</td>\n",
       "      <td>1094.0</td>\n",
       "      <td>514.0</td>\n",
       "      <td>3.6591</td>\n",
       "      <td>299200.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.84</td>\n",
       "      <td>52.0</td>\n",
       "      <td>3104.0</td>\n",
       "      <td>687.0</td>\n",
       "      <td>1157.0</td>\n",
       "      <td>647.0</td>\n",
       "      <td>3.1200</td>\n",
       "      <td>241400.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-122.26</td>\n",
       "      <td>37.84</td>\n",
       "      <td>42.0</td>\n",
       "      <td>2555.0</td>\n",
       "      <td>665.0</td>\n",
       "      <td>1206.0</td>\n",
       "      <td>595.0</td>\n",
       "      <td>2.0804</td>\n",
       "      <td>226700.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.84</td>\n",
       "      <td>52.0</td>\n",
       "      <td>3549.0</td>\n",
       "      <td>707.0</td>\n",
       "      <td>1551.0</td>\n",
       "      <td>714.0</td>\n",
       "      <td>3.6912</td>\n",
       "      <td>261100.0</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",
       "5    -122.25     37.85                52.0        919.0           213.0   \n",
       "6    -122.25     37.84                52.0       2535.0           489.0   \n",
       "7    -122.25     37.84                52.0       3104.0           687.0   \n",
       "8    -122.26     37.84                42.0       2555.0           665.0   \n",
       "9    -122.25     37.84                52.0       3549.0           707.0   \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  \n",
       "5       413.0       193.0         4.0368            269700.0        NEAR BAY  \n",
       "6      1094.0       514.0         3.6591            299200.0        NEAR BAY  \n",
       "7      1157.0       647.0         3.1200            241400.0        NEAR BAY  \n",
       "8      1206.0       595.0         2.0804            226700.0        NEAR BAY  \n",
       "9      1551.0       714.0         3.6912            261100.0        NEAR BAY  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20640 10\n"
     ]
    }
   ],
   "source": [
    "row, col = dataset.shape\n",
    "print(row, col)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Longitude"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 7))\n",
    "plt.hist(dataset[\"longitude\"])\n",
    "plt.grid()\n",
    "plt.title(\"Longitude\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 7))\n",
    "plt.boxplot(dataset[\"longitude\"])\n",
    "plt.grid()\n",
    "plt.title(\"Longitude\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    20640.000000\n",
       "mean      -119.569704\n",
       "std          2.003532\n",
       "min       -124.350000\n",
       "25%       -121.800000\n",
       "50%       -118.490000\n",
       "75%       -118.010000\n",
       "max       -114.310000\n",
       "Name: longitude, dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[\"longitude\"].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Latitude"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 7))\n",
    "plt.hist(dataset[\"latitude\"])\n",
    "plt.grid()\n",
    "plt.title(\"Latitude\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 7))\n",
    "plt.boxplot(dataset[\"latitude\"])\n",
    "plt.grid()\n",
    "plt.title(\"Latitude\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    20640.000000\n",
       "mean        35.631861\n",
       "std          2.135952\n",
       "min         32.540000\n",
       "25%         33.930000\n",
       "50%         34.260000\n",
       "75%         37.710000\n",
       "max         41.950000\n",
       "Name: latitude, dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[\"latitude\"].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Housing median age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 7))\n",
    "plt.hist(dataset[\"housing_median_age\"])\n",
    "plt.grid()\n",
    "plt.title(\"Housing median age\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 7))\n",
    "plt.boxplot(dataset[\"housing_median_age\"])\n",
    "plt.grid()\n",
    "plt.title(\"Housing median age\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    20640.000000\n",
       "mean        28.639486\n",
       "std         12.585558\n",
       "min          1.000000\n",
       "25%         18.000000\n",
       "50%         29.000000\n",
       "75%         37.000000\n",
       "max         52.000000\n",
       "Name: housing_median_age, dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[\"housing_median_age\"].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Total rooms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 7))\n",
    "plt.hist(dataset[\"total_rooms\"])\n",
    "plt.grid()\n",
    "plt.title(\"Total rooms\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 7))\n",
    "plt.boxplot(dataset[\"total_rooms\"])\n",
    "plt.grid()\n",
    "plt.title(\"Total rooms\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    20640.000000\n",
       "mean      2635.763081\n",
       "std       2181.615252\n",
       "min          2.000000\n",
       "25%       1447.750000\n",
       "50%       2127.000000\n",
       "75%       3148.000000\n",
       "max      39320.000000\n",
       "Name: total_rooms, dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[\"total_rooms\"].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Total bedrooms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/numpy/lib/histograms.py:839: RuntimeWarning: invalid value encountered in greater_equal\n",
      "  keep = (tmp_a >= first_edge)\n",
      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/numpy/lib/histograms.py:840: RuntimeWarning: invalid value encountered in less_equal\n",
      "  keep &= (tmp_a <= last_edge)\n"
     ]
    },
    {
     "data": {
      "image/png": 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AAIBd7cufbHhPkv+W5AlVtaWqzkryfyf50STXVNUnq+rfJ0l335zksiS3JPlgknO6+7vTVbyXJ7k6ya1JLpvGJsmrkvzvVbU5s/f4XbSsrxAAAOAhbK+3d3b3i3ZT3mMw6+43JHnDbupXJblqN/XbMlvdEwAAgGW2qNs7AQAAWBuEPgAAgIEJfQAAAAMT+gAAAAYm9AEAAAxM6AMAABiY0AcAADAwoQ8AAGBgQh8AAMDAhD4AAICBCX0AAAADE/oAAAAGJvQBAAAMTOgDAAAYmNAHAAAwMKEPAABgYEIfAADAwIQ+AACAgQl9AAAAAxP6AAAABib0AQAADEzoAwAAGJjQBwAAMDChDwAAYGBCHwAAwMCEPgAAgIEJfQAAAAMT+gAAAAYm9AEAAAxM6AMAABiY0AcAADAwoQ8AAGBgQh8AAMDAhD4AAICBCX0AAAADE/oAAAAGJvQBAAAMTOgDAAAY2F5DX1VdXFV3VdVn5mqPqaprqurz0+dDp3pV1VuqanNVfbqqnjJ3zJnT+M9X1Zlz9adW1U3TMW+pqlruFwkAAPBQtS9X+t6V5NRdaucluba7j01y7bSdJM9Ocuz0cXaSdySzkJjk/CRPS3JCkvN3BsVpzK/PHbfrcwEAALBIew193f2RJNt2KZ+W5JLp8SVJnj9Xf3fPXJfkkKp6XJJnJbmmu7d1991Jrkly6rTvx7r7uu7uJO+eOxcAAABLtG6Rx63v7junx19Osn56fESSO+bGbZlq91ffspv6blXV2ZldQcz69euzsLCwyOk/cNYflJx7/I6VnsaasRp7uDvbt29fM3Nl3+nrmPR1TPo6Jn0dk76uPosNfd/X3V1VvRyT2YfnujDJhUmyYcOG3rhx44PxtPvlrZdekTfetOQv60PG7WdsXOkp7JOFhYWsxu83lkZfx6SvY9LXMenrmPR19Vns6p1fmW7NzPT5rqm+NclRc+OOnGr3Vz9yN3UAAACWwWJD35VJdq7AeWaSK+bqL5lW8Twxyb3TbaBXJzmlqg6dFnA5JcnV076vV9WJ06qdL5k7FwAAAEu01/sQq+o9STYmObyqtmS2CucFSS6rqrOSfDHJC6fhVyV5TpLNSb6Z5GVJ0t3bqup1SW6Yxr22u3cuDvMbma0QelCSP5s+AAAAWAZ7DX3d/aI97Dp5N2M7yTl7OM/FSS7eTf1jSZ60t3kAAACw/xZ7eycAAABrgNAHAAAwMKEPAABgYEIfAADAwIQ+AACAgQl9AAAAAxP6AAAABib0AQAADEzoAwAAGJjQBwAAMDChDwAAYGBCHwAAwMCEPgAAgIEJfQAAAAMT+gAAAAYm9AEAAAxM6AMAABiY0AcAADAwoQ8AAGBgQh8AAMDAhD4AAICBCX0AAAADE/oAAAAGJvQBAAAMTOgDAAAYmNAHAAAwMKEPAABgYEIfAADAwIQ+AACAgQl9AAAAAxP6AAAABib0AQAADEzoAwAAGJjQBwAAMDChDwAAYGBCHwAAwMCEPgAAgIEJfQAAAANbUuirqn9RVTdX1Weq6j1V9ciqOqaqrq+qzVX13qo6cBr7iGl787T/6LnzvHqqf66qnrW0lwQAAMBOiw59VXVEkn+eZEN3PynJAUlOT/J7Sd7U3Y9PcneSs6ZDzkpy91R/0zQuVXXcdNwTk5ya5O1VdcBi5wUAAMB9lnp757okB1XVuiQ/kuTOJM9Mcvm0/5Ikz58enzZtZ9p/clXVVN/U3d/u7i8k2ZzkhCXOCwAAgMxC26J099aq+v0kX0ryrSR/nuTGJPd0945p2JYkR0yPj0hyx3Tsjqq6N8lhU/26uVPPH/MDqursJGcnyfr167OwsLDY6T9g1h+UnHv8jr0PJElWZQ93Z/v27Wtmruw7fR2Tvo5JX8ekr2PS19Vn0aGvqg7N7CrdMUnuSfJHmd2e+YDp7guTXJgkGzZs6I0bNz6QT7cob730irzxpkV/WR9ybj9j40pPYZ8sLCxkNX6/sTT6OiZ9HZO+jklfx6Svq89Sbu/8n5N8obv/prv/Psn7kjw9ySHT7Z5JcmSSrdPjrUmOSpJp/6OTfG2+vptjAAAAWIKlhL4vJTmxqn5kem/eyUluSfLhJC+YxpyZ5Irp8ZXTdqb9H+runuqnT6t7HpPk2CQfXcK8AAAAmCzlPX3XV9XlST6eZEeST2R26+UHkmyqqtdPtYumQy5K8odVtTnJtsxW7Ex331xVl2UWGHckOae7v7vYeQEAAHCfJb35rLvPT3L+LuXbspvVN7v775L88h7O84Ykb1jKXAAAAPhhS/2TDQAAAKxiQh8AAMDAhD4AAICBCX0AAAADE/oAAAAGJvQBAAAMTOgDAAAYmNAHAAAwMKEPAABgYEIfAADAwIQ+AACAgQl9AAAAAxP6AAAABib0AQAADEzoAwAAGJjQBwAAMDChDwAAYGBCHwAAwMCEPgAAgIEJfQAAAAMT+gAAAAYm9AEAAAxM6AMAABiY0AcAADAwoQ8AAGBgQh8AAMDAhD4AAICBCX0AAAADE/oAAAAGJvQBAAAMTOgDAAAYmNAHAAAwMKEPAABgYEIfAADAwIQ+AACAgQl9AAAAAxP6AAAABib0AQAADEzoAwAAGNiSQl9VHVJVl1fVZ6vq1qr6+ap6TFVdU1Wfnz4fOo2tqnpLVW2uqk9X1VPmznPmNP7zVXXmUl8UAAAAM0u90vfmJB/s7p9J8rNJbk1yXpJru/vYJNdO20ny7CTHTh9nJ3lHklTVY5Kcn+RpSU5Icv7OoAgAAMDSLDr0VdWjkzwjyUVJ0t3f6e57kpyW5JJp2CVJnj89Pi3Ju3vmuiSHVNXjkjwryTXdva27705yTZJTFzsvAAAA7lPdvbgDq56c5MIkt2R2le/GJK9IsrW7D5nGVJK7u/uQqvrTJBd093+Z9l2b5FVJNiZ5ZHe/fqq/Jsm3uvv3d/OcZ2d2lTDr169/6qZNmxY19wfSXdvuzVe+tdKzWDuOP+LRKz2FfbJ9+/YcfPDBKz0Nlpm+jklfx6SvY9LXMenrg+ekk066sbs37G3cuiU8x7okT0nym919fVW9Offdypkk6e6uqsWlyt3o7gszC5rZsGFDb9y4cblOvWzeeukVeeNNS/myPrTcfsbGlZ7CPllYWMhq/H5jafR1TPo6Jn0dk76OSV9Xn6W8p29Lki3dff20fXlmIfAr022bmT7fNe3fmuSoueOPnGp7qgMAALBEiw593f3lJHdU1ROm0smZ3ep5ZZKdK3CemeSK6fGVSV4yreJ5YpJ7u/vOJFcnOaWqDp0WcDllqgEAALBES70P8TeTXFpVBya5LcnLMguSl1XVWUm+mOSF09irkjwnyeYk35zGpru3VdXrktwwjXttd29b4rwAAADIEkNfd38yye7eOHjybsZ2knP2cJ6Lk1y8lLkAAADww5b6d/oAAABYxYQ+AACAgQl9AAAAAxP6AAAABib0AQAADEzoAwAAGJjQBwAAMDChDwAAYGBCHwAAwMCEPgAAgIEJfQAAAAMT+gAAAAYm9AEAAAxM6AMAABiY0AcAADAwoQ8AAGBgQh8AAMDAhD4AAICBCX0AAAADE/oAAAAGJvQBAAAMTOgDAAAYmNAHAAAwMKEPAABgYEIfAADAwIQ+AACAgQl9AAAAAxP6AAAABib0AQAADEzoAwAAGJjQBwAAMDChDwAAYGBCHwAAwMCEPgAAgIEJfQAAAAMT+gAAAAYm9AEAAAxM6AMAABjYuqWeoKoOSPKxJFu7+3lVdUySTUkOS3Jjkhd393eq6hFJ3p3kqUm+luRXuvv26RyvTnJWku8m+efdffVS58XacPR5H1jpKeyTc4/fkZeugrnefsFzV3oKAACsMctxpe8VSW6d2/69JG/q7scnuTuzMJfp891T/U3TuFTVcUlOT/LEJKcmefsUJAEAAFiiJYW+qjoyyXOTvHPariTPTHL5NOSSJM+fHp82bWfaf/I0/rQkm7r72939hSSbk5ywlHkBAAAws9TbO/9dklcm+dFp+7Ak93T3jml7S5IjpsdHJLkjSbp7R1XdO40/Isl1c+ecP+YHVNXZSc5OkvXr12dhYWGJ019+6w+a3QrIWFZLX1fj9/xatn37dl/TAenrmPR1TPo6Jn1dfRYd+qrqeUnu6u4bq2rj8k1pz7r7wiQXJsmGDRt648YH5Wn3y1svvSJvvGnJb5VklTn3+B2roq+3n7FxpacwlIWFhazGnyMsjb6OSV/HpK9j0tfVZyn/F/v0JL9UVc9J8sgkP5bkzUkOqap109W+I5NsncZvTXJUki1VtS7JozNb0GVnfaf5YwAAAFiCRb+nr7tf3d1HdvfRmS3E8qHuPiPJh5O8YBp2ZpIrpsdXTtuZ9n+ou3uqn15Vj5hW/jw2yUcXOy8AAADu80Dcr/aqJJuq6vVJPpHkoql+UZI/rKrNSbZlFhTT3TdX1WVJbkmyI8k53f3dB2BeAAAADznLEvq6eyHJwvT4tuxm9c3u/rskv7yH49+Q5A3LMRcAAADusxx/pw8AAIBVSugDAAAYmNAHAAAwMKEPAABgYEIfAADAwIQ+AACAgQl9AAAAAxP6AAAABib0AQAADEzoAwAAGJjQBwAAMDChDwAAYGBCHwAAwMCEPgAAgIEJfQAAAAMT+gAAAAYm9AEAAAxM6AMAABiY0AcAADAwoQ8AAGBgQh8AAMDAhD4AAICBCX0AAAADE/oAAAAGJvQBAAAMTOgDAAAYmNAHAAAwMKEPAABgYEIfAADAwIQ+AACAgQl9AAAAAxP6AAAABib0AQAADEzoAwAAGJjQBwAAMDChDwAAYGBCHwAAwMCEPgAAgIEtOvRV1VFV9eGquqWqbq6qV0z1x1TVNVX1+enzoVO9quotVbW5qj5dVU+ZO9eZ0/jPV9WZS39ZAAAAJEu70rcjybndfVySE5OcU1XHJTkvybXdfWySa6ftJHl2kmOnj7OTvCOZhcQk5yd5WpITkpy/MygCAACwNIsOfd19Z3d/fHr8t0luTXJEktOSXDINuyTJ86fHpyV5d89cl+SQqnpckmcluaa7t3X33UmuSXLqYucFAADAfaq7l36SqqOTfCTJk5J8qbsPmeqV5O7uPqSq/jTJBd39X6Z91yZ5VZKNSR7Z3a+f6q9J8q3u/v3dPM/ZmV0lzPr165+6adOmJc99ud217d585VsrPQuW2/qDsir6evwRj17pKQxl+/btOfjgg1d6GiwzfR2Tvo5JX8ekrw+ek0466cbu3rC3ceuW+kRVdXCSP07yW9399VnOm+nurqqlp8r7zndhkguTZMOGDb1x48blOvWyeeulV+SNNy35y8oqc+7xO1ZFX28/Y+NKT2EoCwsLWY0/R1gafR2Tvo5JX8ekr6vPklbvrKqHZxb4Lu3u903lr0y3bWb6fNdU35rkqLnDj5xqe6oDAACwREtZvbOSXJTk1u7+g7ldVybZuQLnmUmumKu/ZFrF88Qk93b3nUmuTnJKVR06LeByylQDAABgiZZyv9rTk7w4yU1V9cmp9ttJLkhyWVWdleSLSV447bsqyXOSbE7yzSQvS5Lu3lZVr0tywzTutd29bQnzAgAAYLLo0DctyFJ72H3ybsZ3knP2cK6Lk1y82LkAAACwe0t6Tx8AAACrm9AHAAAwMKEPAABgYEIfAADAwIQ+AACAgQl9AAAAAxP6AAAABib0AQAADEzoAwAAGJjQBwAAMDChDwAAYGBCHwAAwMCEPgAAgIEJfQAAAAMT+gAAAAYm9AEAAAxM6AMAABiY0AcAADAwoQ8AAGBgQh8AAMDAhD4AAICBCX0AAAADE/oAAAAGJvQBAAAMTOgDAAAYmNAHAAAwsHUrPQFg3x193gdWegpryu0XPHelpwAAsOJc6QMAABiY0AcAADAwoQ8AAGBgQh8AAMDAhD4AAICBCX0AAAADE/oAAAAGJvQBAAAMTOgDAAAYmNAHAAAwMKEPAABgYOtWegIAD5Sjz/vA/e4/9/gdeelexjyU3H7Bc1d6CgDAA2DVXOmrqlOr6nNVtbmqzlvp+QAAAIxgVVzpq6oDkrwtyS8m2ZLkhqq6srtvWdmZATx07O3K6FrxYF3BdWUUgLViVYS+JCck2dzdtyVJVW1KcloSoQ+AVWnPEZnBAAAF/UlEQVSUkLxWPNRux/ZLBWA5VXev9BxSVS9Icmp3/9q0/eIkT+vul+8y7uwkZ0+bT0jyuQd1ovvm8CRfXelJsOz0dUz6OiZ9HZO+jklfx6SvD55/0N0/vrdBq+VK3z7p7guTXLjS87g/VfWx7t6w0vNgeenrmPR1TPo6Jn0dk76OSV9Xn9WykMvWJEfNbR851QAAAFiC1RL6bkhybFUdU1UHJjk9yZUrPCcAAIA1b1Xc3tndO6rq5UmuTnJAkou7++YVntZirerbT1k0fR2Tvo5JX8ekr2PS1zHp6yqzKhZyAQAA4IGxWm7vBAAA4AEg9AEAAAxM6FsmVXVqVX2uqjZX1XkrPR/uX1VdXFV3VdVn5mqPqaprqurz0+dDp3pV1Vum3n66qp4yd8yZ0/jPV9WZK/FauE9VHVVVH66qW6rq5qp6xVTX2zWsqh5ZVR+tqk9Nff3dqX5MVV0/9e+900JgqapHTNubp/1Hz53r1VP9c1X1rJV5RcyrqgOq6hNV9afTtr6ucVV1e1XdVFWfrKqPTTU/h9e4qjqkqi6vqs9W1a1V9fP6unYIfcugqg5I8rYkz05yXJIXVdVxKzsr9uJdSU7dpXZekmu7+9gk107byayvx04fZyd5RzL7D1iS85M8LckJSc7f+cOOFbMjybndfVySE5OcM/1b1Nu17dtJntndP5vkyUlOraoTk/xekjd19+OT3J3krGn8WUnunupvmsZl+l44PckTM/v3//bp5zcr6xVJbp3b1tcxnNTdT577W21+Dq99b07ywe7+mSQ/m9m/W31dI4S+5XFCks3dfVt3fyfJpiSnrfCcuB/d/ZEk23Ypn5bkkunxJUmeP1d/d89cl+SQqnpckmcluaa7t3X33UmuyQ8HSR5E3X1nd398evy3mf0H6Yjo7Zo29Wf7tPnw6aOTPDPJ5VN9177u7PflSU6uqprqm7r72939hSSbM/v5zQqpqiOTPDfJO6ftir6Oys/hNayqHp3kGUkuSpLu/k533xN9XTOEvuVxRJI75ra3TDXWlvXdfef0+MtJ1k+P99RffV/Fplu/fi7J9dHbNW+6BfCTSe7K7H8S/irJPd29Yxoy36Pv92/af2+Sw6Kvq9G/S/LKJN+btg+Lvo6gk/x5Vd1YVWdPNT+H17ZjkvxNkv803Y79zqp6VPR1zRD6YDd69rdM/D2TNaqqDk7yx0l+q7u/Pr9Pb9em7v5udz85yZGZXcX5mRWeEktUVc9Lcld337jSc2HZ/U/d/ZTMbvE7p6qeMb/Tz+E1aV2SpyR5R3f/XJJv5L5bOZPo62on9C2PrUmOmts+cqqxtnxluvUg0+e7pvqe+qvvq1BVPTyzwHdpd79vKuvtIKbbiT6c5Oczu11o3bRrvkff79+0/9FJvhZ9XW2enuSXqur2zN4W8czM3jOkr2tcd2+dPt+V5P2Z/aLGz+G1bUuSLd19/bR9eWYhUF/XCKFvedyQ5NhpxbEDM3tD+ZUrPCf235VJdq4idWaSK+bqL5lWojoxyb3TrQxXJzmlqg6d3oR8ylRjhUzv77koya3d/Qdzu/R2DauqH6+qQ6bHByX5xczer/nhJC+Yhu3a1539fkGSD02/gb4yyenTKpDHZLbAwEcfnFfBrrr71d19ZHcfndl/Nz/U3WdEX9e0qnpUVf3ozseZ/fz8TPwcXtO6+8tJ7qiqJ0ylk5PcEn1dM9btfQh70907qurlmX3THpDk4u6+eYWnxf2oqvck2Zjk8KraktlKUhckuayqzkryxSQvnIZfleQ5mS0O8M0kL0uS7t5WVa/LLPQnyWu7e9fFYXhwPT3Ji5PcNL3/K0l+O3q71j0uySXTiowPS3JZd/9pVd2SZFNVvT7JJzItMDB9/sOq2pzZgk2nJ0l331xVl2X2Pyo7kpzT3d99kF8Le/eq6Otatj7J+2e/g8u6JP9vd3+wqm6In8Nr3W8muXS6wHFbZr16WPR1TajZL8kAAAAYkds7AQAABib0AQAADEzoAwAAGJjQBwAAMDChDwAAYGBCHwAAwMCEPgAAgIH9/0NZpN0WkRmyAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 7))\n",
    "plt.hist(dataset[\"total_bedrooms\"])\n",
    "plt.grid()\n",
    "plt.title(\"Total bedrooms\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/matplotlib/cbook/__init__.py:1246: RuntimeWarning: invalid value encountered in less_equal\n",
      "  wiskhi = np.compress(x <= hival, x)\n",
      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/matplotlib/cbook/__init__.py:1253: RuntimeWarning: invalid value encountered in greater_equal\n",
      "  wisklo = np.compress(x >= loval, x)\n",
      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/matplotlib/cbook/__init__.py:1261: RuntimeWarning: invalid value encountered in less\n",
      "  np.compress(x < stats['whislo'], x),\n",
      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/matplotlib/cbook/__init__.py:1262: RuntimeWarning: invalid value encountered in greater\n",
      "  np.compress(x > stats['whishi'], x)\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA3wAAAGrCAYAAACbsJWcAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAFthJREFUeJzt3X+w5XV93/HXWzb+iLTID1kVTNZEaoq1TaY7Oq212RRFnNZiqjZa09COhD9aZ5qkaUrHCoZoR9NEmk5NJxulYWxHUVsjM2IZ1N4m4yQGtCYNJgaCWkBEASVdlRD03T/ul3i5vevueg+5u+99PGbu7Pl+v5/z/b7v8seZJ99zzlZ3BwAAgHkesdMDAAAA8PAQfAAAAEMJPgAAgKEEHwAAwFCCDwAAYCjBBwAAMJTgA2C8qnp0VXVVnXmQ479ZVT+8omu9oaresopzAcB2CT4AdkRVHdjw8/Wq+uqG7Vcc4rnnVdXNf1azAsCxatdODwDA8am7T3zwcVV9OsmF3f2BnZvo4VdVu7r7gZ2eA4Djhzt8AByVquoxVfXmqrqjqm6rqn9bVd9WVacmeU+S79pwR/DUqnp2VX2kqr5UVZ+tqsur6kj+x+bTquqjVXVvVf3XqjppwyzP2XDuj1XVszcce2pVfbiq/m9VvT/JyRuOfU9VPVBVP1pVtya5Ztn/4qr6xHK+D1TVWRue84yq+vXl2O9U1Qs2HHtHVf1CVV1XVV+uqrWqOr2qfnFZf2NVPWPD+tcsf39/VFW/V1XPOcL/DAAc4wQfAEern07yl5M8I8lfTbIvyU91991JfjDJLd194vJzd5I/SfKqJKcmeU6SFya58Aiu9yNJXpHkjCSPTPLzSVJVe5L8apJXJzklyb9O8qtVdXJVVZJ3Jfm15bo/l+QfbjrvCUmeleRpSc5fguxXkvyTJKcn+Z9J3ltVu6rq0Unet1zv8Un+RZJ3VdVTNpzvh5L8ZJLTsv5Ond9cznFq1oPyZ5e5/0qSf5zke5OclORvJ7ntCP4+ABhA8AFwtHpFkku7+67uvjPJ6/L/x9Sf6u7f6u7ru/tr3f2HSd6S5PuP4Hr/qbt/v7sPJLk0ycuX/Rck+W/d/YHu/np3X5PkE0nOTXJWkr+Y5Ke7+/7u/mCS/77FuS/p7q9091eTvCzJe7p7rbvvT/Jvsh53e7Meqp3kTd39J919bZLrsh55D3pXd//2cq73Jrm3u6/q7q8leWeS71vWPZDkMUnOTnJCd9/S3Z86gr8PAAYQfAAcdZY7Z09I8pkNuz+T9btvB3vO2VX1/qq6s6r+KMklWb8Ldrhu3XStb1/e1vmdSX54ecvkl6rqS1mPsyctP1/o7vs2PXejr3f3ZzdsP2njmiXUbl9+tycl+T/d3ZvOt/H3vnPD469usX3ict4bk1yc5PVJPl9V/6Wqdn+zvwAA5hF8ABx1luD5XNZj60HfkfUwStbvgm32y0k+luS7u/vPJ7ksSR3BZZ+86Vpf6e57sx6Cb+nux234eWx3X57kjiSnLW/F3Pjch/w6m7Y/u/H3qqoTsh50ty/HNj9/4+99RLr7yu7+60m+K8mjs36XFIDjiOAD4Gj19iSXLl/IcnrWP0P3n5djdyY5vapO3LD+z2X97Y0HqurpSX70CK/3j6rqLyznfG2Sq5b9VyZ5aVWdU1UnLF8mc05VPSHJHyT5ZJLXVNUjq+oHkpx3iOtcleQHq+pvVtW3Zf0u3N1Jbkjy60keUVU/tnym73lZf+voO4/wd3nwjuf3V9Wjsn7n76tJvn6k5wHg2Cb4ADhaXZL1z8rdmOTjST6c5QtJkvx2kquTfGZ5m+UpSX48yYVVdSDJm/ONYDtcb8t6ZN6e9TD650nS3bckeXHWv0Tmrqy/xfKfJXnEcify7yf5gST3JPmpfCNKt9Tdv5PklUl+KckXkpyT5PzufmB5a+jfSfKSrEfgm5L80DLDkXpM1r945q6s34k8MclrvoXzAHAMq4d+TAAAAIAp3OEDAAAYSvABAAAMJfgAAACGEnwAAABD7drpAb4Vp512Wu/Zs2enxwDgOPXlL385j33sY3d6DACOYx/96Efv6u7HH2rdMRl8e/bsyQ033LDTYwBwnFpbW8u+fft2egwAjmNV9ZnDWectnQAAAEMJPgAAgKEEHwAAwFCCDwAAYCjBBwAAMJTgAwAAGErwAQAADCX4AAAAhhJ8AAAAQwk+AACAoQQfAADAUIIPAABgKMEHAAAwlOADAAAYSvABAAAMJfgAAACGEnwAAABDCT4AAIChBB8AAMBQgg8AAGAowQcAADCU4AMAABhK8AEAAAwl+AAAAIYSfAAAAEMJPgAAgKEEHwAAwFCCDwAAYCjBBwAAMJTgAwAAGErwAQAADCX4AAAAhhJ8AAAAQwk+AACAoQQfAADAUIIPAABgKMEHAAAwlOADAAAYSvABAAAMJfgAAACGEnwAAABDCT4AAIChBB8AAMBQgg8AAGAowQcAADCU4AMAABhK8AEAAAy1kuCrqvOq6pNVdXNVXbzF8UdV1VXL8Y9U1Z5Nx7+jqg5U1U+uYh4AAABWEHxVdUKSNyd5QZKzk7y8qs7etOyVSb7Y3U9NcnmSN246/qYk79/uLAAAAHzDKu7wPTPJzd19S3ffn+QdSc7ftOb8JFcuj9+d5JyqqiSpqhcl+VSSG1cwCwAAAItdKzjHGUlu3bB9W5JnHWxNdz9QVfcmObWq7kvyL5M8L8k3fTtnVV2U5KIk2b17d9bW1lYwOgAcuQMHDngdAuCYsIrg247XJrm8uw8sN/wOqrv3J9mfJHv37u19+/Y97MMBwFbW1tbidQiAY8Eqgu/2JE/esH3msm+rNbdV1a4kJyW5O+t3Al9SVT+b5HFJvl5V93X3f1jBXAAAAMe1VQTf9UnOqqqnZD3sXpbkH2xac3WSC5L8RpKXJPlQd3eS5zy4oKpem+SA2AMAAFiNbQff8pm8VyW5NskJSa7o7hur6rIkN3T31UnemuRtVXVzknuyHoUAAAA8jFbyGb7uvibJNZv2XbLh8X1JXnqIc7x2FbMAAACwbiX/8DoAAABHH8EHAAAwlOADAAAYSvABAAAMJfgAAACGEnwAAABDCT4AAIChBB8AAMBQgg8AAGAowQcAADCU4AMAABhK8AEAAAwl+AAAAIYSfAAAAEMJPgAAgKEEHwAAwFCCDwAAYCjBBwAAMJTgAwAAGErwAQAADCX4AAAAhhJ8AAAAQwk+AACAoQQfAADAUIIPAABgKMEHAAAwlOADAAAYSvABAAAMJfgAAACGEnwAAABDCT4AAIChBB8AAMBQgg8AAGAowQcAADCU4AMAABhK8AEAAAwl+AAAAIYSfAAAAEMJPgAAgKEEHwAAwFCCDwAAYCjBBwAAMJTgAwAAGErwAQAADCX4AAAAhhJ8AAAAQwk+AACAoQQfAADAUIIPAABgKMEHAAAwlOADAAAYSvABAAAMJfgAAACGEnwAAABDCT4AAIChBB8AAMBQgg8AAGAowQcAADCU4AMAABhK8AEAAAy1kuCrqvOq6pNVdXNVXbzF8UdV1VXL8Y9U1Z5l//Oq6qNV9b+XP//WKuYBAABgBcFXVSckeXOSFyQ5O8nLq+rsTctemeSL3f3UJJcneeOy/64kL+zuZyS5IMnbtjsPAAAA61Zxh++ZSW7u7lu6+/4k70hy/qY15ye5cnn87iTnVFV19//q7s8u+29M8piqetQKZgIAADju7VrBOc5IcuuG7duSPOtga7r7gaq6N8mpWb/D96AXJ/lYd//xVhepqouSXJQku3fvztra2gpGB4Ajd+DAAa9DABwTVhF821ZVT8/62zzPPdia7t6fZH+S7N27t/ft2/dnMxwAbLK2thavQwAcC1bxls7bkzx5w/aZy74t11TVriQnJbl72T4zyXuS/Eh3/+EK5gEAACCrCb7rk5xVVU+pqkcmeVmSqzetuTrrX8qSJC9J8qHu7qp6XJL3Jbm4uz+8glkAAABYbDv4uvuBJK9Kcm2S30vyzu6+saouq6q/uyx7a5JTq+rmJD+R5MF/uuFVSZ6a5JKq+vjyc/p2ZwIAAGBFn+Hr7muSXLNp3yUbHt+X5KVbPO91SV63ihkAAAB4qJX8w+sAAAAcfQQfAADAUIIPAABgKMEHAAAwlOADAAAYSvABAAAMJfgAAACGEnwAAABDCT4AAIChBB8AAMBQgg8AAGAowQcAADCU4AMAABhK8AEAAAwl+AAAAIYSfAAAAEMJPgAAgKEEHwAAwFCCDwAAYCjBBwAAMJTgAwAAGErwAQAADCX4AAAAhhJ8AAAAQwk+AACAoQQfAADAUIIPAABgKMEHAAAwlOADAAAYSvABAAAMJfgAAACGEnwAAABDCT4AAIChBB8AAMBQgg8AAGAowQcAADCU4AMAABhK8AEAAAwl+AAAAIYSfAAAAEMJPgAAgKEEHwAAwFCCDwAAYCjBBwAAMJTgAwAAGErwAQAADCX4AAAAhhJ8AAAAQwk+AACAoQQfAADAUIIPAABgKMEHAAAwlOADAAAYSvABAAAMJfgAAACGEnwAAABDCT4AAIChBB8AAMBQgg8AAGAowQcAADDUSoKvqs6rqk9W1c1VdfEWxx9VVVctxz9SVXs2HPtXy/5PVtXzVzEPAAAAKwi+qjohyZuTvCDJ2UleXlVnb1r2yiRf7O6nJrk8yRuX556d5GVJnp7kvCS/uJwPAACAbVrFHb5nJrm5u2/p7vuTvCPJ+ZvWnJ/kyuXxu5OcU1W17H9Hd/9xd38qyc3L+QAAANimVQTfGUlu3bB927JvyzXd/UCSe5OcepjPBQAA4Fuwa6cHOFxVdVGSi5Jk9+7dWVtb29mBADhuHThwwOsQAMeEVQTf7UmevGH7zGXfVmtuq6pdSU5KcvdhPjdJ0t37k+xPkr179/a+fftWMDoAHLm1tbV4HQLgWLCKt3Ren+SsqnpKVT0y61/CcvWmNVcnuWB5/JIkH+ruXva/bPkWz6ckOSvJb61gJgAAgOPetu/wdfcDVfWqJNcmOSHJFd19Y1VdluSG7r46yVuTvK2qbk5yT9ajMMu6dyb5RJIHkvzT7v7admcCAABgRZ/h6+5rklyzad8lGx7fl+SlB3nu65O8fhVzAAAA8A0r+YfXAQAAOPoIPgAAgKEEHwAAwFCCDwAAYCjBBwAAMJTgAwAAGErwAQAADCX4AAAAhhJ8AAAAQwk+AACAoQQfAADAUIIPAABgKMEHAAAwlOADAAAYSvABAAAMJfgAAACGEnwAAABDCT4AAIChBB8AAMBQgg8AAGAowQcAADCU4AMAABhK8AEAAAwl+AAAAIYSfAAAAEMJPgAAgKEEHwAAwFCCDwAAYCjBBwAAMJTgAwAAGErwAQAADCX4AAAAhhJ8AAAAQwk+AACAoQQfAADAUIIPAABgKMEHAAAwlOADAAAYSvABAAAMJfgAAACGEnwAAABDCT4AAIChBB8AAMBQgg8AAGAowQcAADCU4AMAABhK8AEAAAwl+AAAAIYSfAAAAEMJPgAAgKEEHwAAwFCCDwAAYCjBBwAAMJTgAwAAGErwAQAADCX4AAAAhhJ8AAAAQwk+AACAoQQfAADAUIIPAABgKMEHAAAw1LaCr6pOqarrquqm5c+TD7LugmXNTVV1wbLv26vqfVX1+1V1Y1W9YTuzAAAA8FDbvcN3cZIPdvdZST64bD9EVZ2S5NIkz0ryzCSXbgjDn+vu70nyfUmeXVUv2OY8AAAALLYbfOcnuXJ5fGWSF22x5vlJruvue7r7i0muS3Jed3+lu/9HknT3/Uk+luTMbc4DAADAYtc2n7+7u+9YHn8uye4t1pyR5NYN27ct+/5UVT0uyQuT/MLBLlRVFyW5KEl2796dtbW1b31qANiGAwcOeB0C4JhwyOCrqg8kecIWh169caO7u6r6SAeoql1J3p7k33f3LQdb1937k+xPkr179/a+ffuO9FIAsBJra2vxOgTAseCQwdfdzz3Ysaq6s6qe2N13VNUTk3x+i2W3J9m3YfvMJGsbtvcnuam7/91hTQwAAMBh2e5n+K5OcsHy+IIk791izbVJzq2qk5cvazl32Zeqel2Sk5L82DbnAAAAYJPtBt8bkjyvqm5K8txlO1W1t6rekiTdfU+Sn0ly/fJzWXffU1VnZv1toWcn+VhVfbyqLtzmPAAAACy29aUt3X13knO22H9Dkgs3bF+R5IpNa25LUtu5PgAAAAe33Tt8AAAAHKUEHwAAwFCCDwAAYCjBBwAAMJTgAwAAGErwAQAADCX4AAAAhhJ8AAAAQwk+AACAoQQfAADAUIIPAABgKMEHAAAwlOADAAAYSvABAAAMJfgAAACGEnwAAABDCT4AAIChBB8AAMBQgg8AAGAowQcAADCU4AMAABhK8AEAAAwl+AAAAIYSfAAAAEMJPgAAgKEEHwAAwFCCDwAAYCjBBwAAMJTgAwAAGErwAQAADCX4AAAAhhJ8AAAAQwk+AACAoQQfAADAUIIPAABgKMEHAAAwlOADAAAYSvABAAAMJfgAAACGEnwAAABDCT4AAIChBB8AAMBQgg8AAGAowQcAADCU4AMAABhK8AEAAAwl+AAAAIYSfAAAAEMJPgAAgKEEHwAAwFCCDwAAYCjBBwAAMJTgAwAAGErwAQAADCX4AAAAhhJ8AAAAQwk+AACAoQQfAADAUIIPAABgKMEHAAAw1LaCr6pOqarrquqm5c+TD7LugmXNTVV1wRbHr66q393OLAAAADzUdu/wXZzkg919VpIPLtsPUVWnJLk0ybOSPDPJpRvDsKr+XpID25wDAACATbYbfOcnuXJ5fGWSF22x5vlJruvue7r7i0muS3JeklTViUl+IsnrtjkHAAAAm+za5vN3d/cdy+PPJdm9xZozkty6Yfu2ZV+S/EySn0/ylUNdqKouSnJRkuzevTtra2vf4sgAsD0HDhzwOgTAMeGQwVdVH0jyhC0OvXrjRnd3VfXhXriqvjfJd3f3j1fVnkOt7+79SfYnyd69e3vfvn2HeykAWKm1tbV4HQLgWHDI4Ovu5x7sWFXdWVVP7O47quqJST6/xbLbk+zbsH1mkrUkfy3J3qr69DLH6VW11t37AgAAwLZt9zN8Vyd58Fs3L0jy3i3WXJvk3Ko6efmylnOTXNvd/7G7n9Tde5L8jSR/IPYAAABWZ7vB94Ykz6uqm5I8d9lOVe2tqrckSXffk/XP6l2//Fy27AMAAOBhtK0vbenuu5Ocs8X+G5JcuGH7iiRXfJPzfDrJX9rOLAAAADzUdu/wAQAAcJQSfAAAAEMJPgAAgKEEHwAAwFCCDwAAYCjBBwAAMJTgAwAAGErwAQAADCX4AAAAhhJ8AAAAQwk+AACAoQQfAADAUIIPAABgKMEHAAAwlOADAAAYSvABAAAMJfgAAACGEnwAAABDCT4AAIChBB8AAMBQgg8AAGAowQcAADCU4AMAABhK8AEAAAwl+AAAAIYSfAAAAEMJPgAAgKEEHwAAwFCCDwAAYCjBBwAAMJTgAwAAGErwAQAADCX4AAAAhhJ8AAAAQwk+AACAoQQfAADAUIIPAABgKMEHAAAwlOADAAAYSvABAAAMJfgAAACGEnwAAABDCT4AAIChBB8AAMBQgg8AAGAowQcAADBUdfdOz3DEquoLST6z03MAcNw6LcldOz0EAMe17+zuxx9q0TEZfACwk6rqhu7eu9NzAMCheEsnAADAUIIPAABgKMEHAEdu/04PAACHw2f4AAAAhnKHDwAAYCjBBwAAMJTgA4DDVFVXVNXnq+p3d3oWADgcgg8ADt+vJDlvp4cAgMMl+ADgMHX3ryW5Z6fnAIDDJfgAAACGEnwAAABDCT4AAIChBB8AAMBQgg8ADlNVvT3JbyR5WlXdVlWv3OmZAOCbqe7e6RkAAAB4GLjDBwAAMJTgAwAAGErwAQAADCX4AAAAhhJ8AAAAQwk+AACAoQQfAADAUP8PQMw+BMyODmQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 7))\n",
    "plt.boxplot(dataset[\"total_bedrooms\"])\n",
    "plt.grid()\n",
    "plt.title(\"Total bedrooms\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Note</b>: we will discover later that this exception was thrown due to NaNs presence in <em>total_bedrooms</em>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to complete the analysis, let's visualize the boxplot of <em>total_bedrooms</em> without missing values, we will handle their presence later"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 7))\n",
    "plt.boxplot(dataset[-dataset.isnull().any(axis=1)][\"total_bedrooms\"])\n",
    "plt.grid()\n",
    "plt.title(\"Total bedrooms without missing values\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And we add <em>total_bedrooms</em> to the list of columns which show outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    20433.000000\n",
       "mean       537.870553\n",
       "std        421.385070\n",
       "min          1.000000\n",
       "25%        296.000000\n",
       "50%        435.000000\n",
       "75%        647.000000\n",
       "max       6445.000000\n",
       "Name: total_bedrooms, dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[\"total_bedrooms\"].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Population"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 7))\n",
    "plt.hist(dataset[\"population\"])\n",
    "plt.grid()\n",
    "plt.title(\"Population\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 7))\n",
    "plt.boxplot(dataset[\"population\"])\n",
    "plt.grid()\n",
    "plt.title(\"Population\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    20640.000000\n",
       "mean      1425.476744\n",
       "std       1132.462122\n",
       "min          3.000000\n",
       "25%        787.000000\n",
       "50%       1166.000000\n",
       "75%       1725.000000\n",
       "max      35682.000000\n",
       "Name: population, dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[\"population\"].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Households"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 7))\n",
    "plt.hist(dataset[\"households\"])\n",
    "plt.grid()\n",
    "plt.title(\"Households\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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G2AoBgLnIyB0ADLAVAgBzkXAHAANshQDAXCTcAcAAWyEAMBcJdwAwwFYIAMxFFlQBgAG2QgBgLjJyBwAA0ANG7gBggK0QAJiLqrU27BqmNTIy0rZs2TLsMgA4xaxatSqXXXZZbr755sPTMg+df/vb3x52eQCcQqrqjtbayEz6GrkDgAFbt27N7t27f27k7gc/+MGwSwOAaXnmDgAGLFq0KO9+97ufsIn5u9/97ixatGjYpQHAtIzcAcCAffv25ROf+ER+/dd/PRMTE9m8eXM+8YlPZN++fcMuDQCmJdwBwICVK1fmsssue8JWCG9729ty8803D7s0AJiWcAcAA9avX3/E1TI3bNgw7NIAYFrCHQAMsIk5AHORBVUAAAB6wMgdAAywiTkAc5FNzAFggE3MAZgtbGIOAMfBJuYAzEWeuQOAATYxB2AuMnIHAANsYg7AXCTcAcAAm5gDMBcJdwAwwCbmAMxFwh0ADLj88svz13/917nkkkuyd+/eLF68OL/7u79rGwQAZjXhDgAGbNq0KX/+53+er3zlK08YuXvlK18p4AEwa1ktEwAGbNiwIRs3bnzCapkbN240LROAWU24A4AB27Zty/bt27Nq1aqsXr06q1atyvbt27Nt27ZhlwYA05rxtMyqmp9kS5IdrbXXVdULknw2ybOT3JHkd1pr+6pqcZJPJ3lpkgeTvKW19oPuPX4/yZokE0l+r7X21RP5ZQDgRHjuc5+bdevW5YYbbjg8LfNtb3tbnvvc5w67NACY1pMZuXtPkqn/ZXlNko+21l6YZFcmQ1u6111d+0e7fqmqlUnemuRXk7wmyae6wAgAs05r7ajnADDbzCjcVdW5Sf5pkv+jO68kFyW5qetyfZLLuuNLu/N011d3/S9N8tnW2t7W2t1J7kryshPxJQDgRPrhD3+YN77xjbnkkkvyqle9Kpdcckne+MY35oc//OGwSwOAac10Wua/SfK+JL/QnT87ycOttQPd+fYky7vj5UnuS5LW2oGq+mnXf3mSb0x5z6n3HFZVVya5MkmWLl2a8fHxmX4XADghzjrrrNx44435wz/8w7zgBS/I3XffnQ9/+MM566yz/C4BMGsdM9xV1euS/Li1dkdVjT7dBbXWrk1ybZKMjIy00dGn/SMB4AlOO+20PPjgg1m3bl3279+fhQsXZsGCBXn2s58dv0sAzFYzmZb5G0neUFU/yOQCKhcl+ViSM6rqUDg8N8mO7nhHkuclSXf9WZlcWOVw+xHuAYBZY8eOHdmzZ0/OOuusJJMjeXv27MmOHX62AJi9jhnuWmu/31o7t7X2/EwuiPK11trbkmxO8qau2xVJvtAd39Kdp7v+tTb5FPotSd5aVYu7lTYvSPI3J+ybAMAJdNFFF+Xss8/OvHnzcvbZZ+eiiy4adkkAcFQz3grhCNYl+WxVfTjJf0qysWvfmOTfV9VdSR7KZCBMa+07VfW5JFuTHEjyrtbaxHF8PgA8LVpr+frXv55rrrkmK1euzNatW7Nu3TorZgIwq9Vs/qEaGRlpW7ZsGXYZAJxi5s2bl9WrV2fnzp3Ztm1bVqxYkWXLluW2227LwYMHh10eAKeQqrqjtTYyk77HM3IHAL21efPm/PEf//Hhkbv3ve99wy4JAI5KuAOAAStXrswznvGMvPe9701rLVWVl770pXn88ceHXRoATGtGm5gDwKlkbGwsd955Zz7ykY/kK1/5Sj7ykY/kzjvvzNjY2LBLA4BpGbkDgAGbN2/O6173unzgAx/I3r17s3jx4rzuda/L5s2bh10aAExLuAOAAVu3bs1jjz2Wr3zlK5mYmMj8+fPzzne+M/fcc8+wSwOAaZmWCQADFi1alLVr12ZsbCwLFizI2NhY1q5dm0WLFg27NACYlq0QAGDAoY3LlyxZknvuuSfnn39+HnvssTzwwAO2QgDgpHoyWyEYuQOAAcuXL8/+/fuTJFWVJNm/f3+WL18+zLIA4KiEOwA4gkMzWwZfAWC2Eu4AYMCOHTuycOHCJP8wcrdw4cLs2LFjmGUBwFEJdwAwYNGiRXn1q1+dJUuWJEmWLFmSV7/61RZUAWBWsxUCAAzYu3dvbrzxxlxzzTVZuXJltm7dmnXr1uXAgQPDLg0ApiXcAcCAxYsX501velOuu+66bNu2LStWrMhb3vKW3HTTTcMuDQCmJdwBwIB9+/blr/7qr3Ldddc9YRPzffv2Dbs0AJiWcAcAA1auXJlnPOMZWb16dVprqaq89KUvPfwMHgDMRhZUAYABy5cvz5YtW3LVVVfli1/8Yq666qps2bLFPncAzGo1m/ftGRkZaVu2bBl2GQCcYk477bS86U1vyp133nn4mbsXv/jFuemmm7Jnz55hlwfAKaSq7mitjcyor3AHAE9UVXnsscdy+umnZ3x8PKOjo9m9e3eWLFliM3MATqonE+48cwcAAxYvXpwrr7zy50buFi9ePOzSAGBawh0ADPjN3/zN3HDDDTnzzDPTWssPf/jDfOc738nFF1887NIAYFoWVAGAAVu3bs3pp5+eRx99NK21PProozn99NOzdevWYZcGANMS7gBgwPbt2/Oe97wnL3rRizJv3ry86EUvynve855s37592KUBwLRMywSAI/izP/uzfOYznzm8iflv/dZvDbskADgq4Q4ABixYsCA/+9nP8s53vjP33ntvzjvvvPzsZz/LggV+NgGYvfxKAcCAiYmJ7N69O48//ngOHjyYxx9/PLt37x52WQBwVJ65A4ABixYtyitf+co8/PDDSZKHH344r3zlK7No0aIhVwYA0zNyBwAD9u7dm29+85u55pprsnLlymzdujXr1q3LgQMHhl0aAExLuAOAAYsXL87IyEg+8IEPZO/evVm8eHFe/vKXZ8uWLcMuDQCmJdwBwAAjdwDMRcIdAAwwcgfAXCTcAcAAI3cAzEXCHQAMWLx4cc4///y8973vTWstVZULLrgg99xzz7BLA4Bp2QoBAAbs3bs33/3ud3PVVVfli1/8Yq666qp897vfzd69e4ddGgBMq1prw65hWiMjI83zDQCcbPPmzcvy5cuzY8eOwyN3h84PHjw47PIAOIVU1R2ttZGZ9DVyBwADWmvZvn17zjjjjCTJGWecke3bt2c2/4coAAh3AHAE8+bNy65du5Iku3btyrx5fjIBmN38UgHAERw8eDBveMMb8vnPfz5veMMbTMcEYNazWiYAHMHZZ5+dL37xi7nllltSVTn77LPzwAMPDLssAJiWkTsAOIIHHnggZ5xxRubNm5czzjhDsANg1hPuAGAajzzySA4ePJhHHnlk2KUAwDEJdwBwBIemYiaTUzSrasgVAcDRCXcAcASvf/3r8/DDDydJHn744bz+9a8fckUAcHTCHQAMOPfcc/P1r389y5YtS1Vl2bJl+frXv55zzz132KUBwLSEOwAYcNlll+WRRx7Jfffdl9Za7rvvvjzyyCO57LLLhl0aAExLuAOAATfffHMWLlyYiYmJJMnExEQWLlyYm2++eciVAcD0hDsAGLB9+/bs378/55xzTubNm5dzzjkn+/fvz/bt24ddGgBMS7gDgCNYvHhxTjvttLTWctppp2Xx4sXDLgkAjkq4A4Aj2Lt3b9auXZsvf/nLWbt2bfbu3TvskgDgqBYcq0NVnZbkL5Ms7vrf1Fq7uqpekOSzSZ6d5I4kv9Na21dVi5N8OslLkzyY5C2ttR907/X7SdYkmUjye621r574rwQAx2/RokX5xCc+kXvuuSfnn39+Fi1aJOABMKvNZORub5KLWmu/luTFSV5TVa9Ick2Sj7bWXphkVyZDW7rXXV37R7t+qaqVSd6a5FeTvCbJp6pq/on8MgBwouzbty979uxJVWXPnj3Zt2/fsEsCgKM6Zrhrkx7tThd2fy3JRUlu6tqvT3JofehLu/N011dXVXXtn22t7W2t3Z3kriQvOyHfAgBOoHPPPTfz5s3L/fffn4MHD+b+++/PvHnz7HMHwKx2zGmZSdKNsN2R5IVJ/iTJf07ycGvtQNdle5Ll3fHyJPclSWvtQFX9NJNTN5cn+caUt516z9TPujLJlUmydOnSjI+PP7lvBADH6Zxzzsn27dtTVWmtpaoyMTGRc845x+8SALPWjMJda20iyYur6owkn0/yK09XQa21a5NcmyQjIyNtdHT06fooADiiO++8M0uWLMlznvOcw8/c/eQnP8mdd94Zv0sAzFZParXM1trDSTYn+cdJzqiqQ+Hw3CQ7uuMdSZ6XJN31Z2VyYZXD7Ue4BwBmjQMHDmRsbCw7d+5May07d+7M2NhYDhw4cOybAWBIjhnuquo53YhdquoZSV6VZFsmQ96bum5XJPlCd3xLd57u+tdaa61rf2tVLe5W2rwgyd+cqC8CACfSl770pcOrY+7duzdf+tKXhlwRABzdTKZlLktyfffc3bwkn2utfamqtib5bFV9OMl/SrKx678xyb+vqruSPJTJFTLTWvtOVX0uydYkB5K8q5vuCQCz0mmnnZY9e/YcfgWA2awmB9Vmp5GRkbZly5ZhlwHAKWZykedk/vz5mZiYOPyaJLP5dxOA/qmqO1prIzPp+6SeuQOAU8Xpp59+ONBNTEzk9NNPH3JFAHB0wh0AHMHu3bsPj+BVVXbv3j3kigDg6IQ7AJjGoSmYpmICMBcIdwAAAD0g3AEAAPSAcAcA01i4cOETXgFgNhPuAGAa+/fvf8IrAMxmwh0AAEAPCHcAAAA9INwBAAD0gHAHAADQA8IdAABADwh3AAAAPSDcAQAA9IBwBwDTsIk5AHOJcAcA07CJOQBziXAHAADQA8IdAABADwh3AAAAPSDcAQAA9IBwBwDTsFomAHOJcAcA07BaJgBziXAHAADQA8IdAABADwh3AAAAPSDcAQAA9IBwBwAA0APCHQAAQA8IdwAAAD0g3AEAAPSAcAcAANADwh0AAEAPCHcAAAA9INwBAAD0gHAHAADQA8IdAABADwh3AAAAPSDcAQAA9IBwBwAA0APCHQAAQA8IdwAAAD0g3AEAAPSAcAcAANADwh0AAEAPCHcAAAA9INwBAAD0gHAHAADQA8IdAABADxwz3FXV86pqc1VtrarvVNV7uvazqurWqvpe93pm115V9fGququq/raqXjLlva7o+n+vqq54+r4WAADAqWUmI3cHkvzL1trKJK9I8q6qWpnk/Ulua61dkOS27jxJLklyQfd3ZZI/TSbDYJKrk7w8ycuSXH0oEAIAAHB8jhnuWms7W2v/b3f8syTbkixPcmmS67tu1ye5rDu+NMmn26RvJDmjqpYleXWSW1trD7XWdiW5NclrTui3AQAAOEUteDKdq+r5SX49yTeTLG2t7ewu3Z9kaXe8PMl9U27b3rVN1z74GVdmcsQvS5cuzfj4+JMpEQCeVn6XAJitZhzuquqZSf6vJP9ta+2Rqjp8rbXWqqqdiIJaa9cmuTZJRkZG2ujo6Il4WwA4IfwuATBbzWi1zKpamMlgd0Nr7T90zT/qplume/1x174jyfOm3H5u1zZdOwAAAMdpJqtlVpKNSba11v7XKZduSXJoxcsrknxhSvvbu1UzX5Hkp930za8mubiqzuwWUrm4awMAAOA4zWRa5m8k+Z0kf1dVd3ZtH0jyR0k+V1VrktyT5M3dtS8neW2Su5LsTvKOJGmtPVRVH0ryra7fB1trD52QbwEAAHCKq9ZOyKNyT4uRkZG2ZcuWYZcBwClm6nPlg2bz7yYA/VNVd7TWRmbSd0bP3AEAADC7CXcAAAA9INwBAAD0gHAHAADQA8IdAABADwh3AAAAPSDcAQAA9IBwBwAA0APCHQAAQA8IdwAAAD0g3AEAAPSAcAcAANADwh0AAEAPCHcAAAA9INwBAAD0gHAHAADQA8IdAABADwh3AAAAPSDcAQAA9IBwBwAA0APCHQAAQA8IdwAAAD0g3AEAAPSAcAcAANADwh0AAEAPCHcAAAA9INwBAAD0gHAHAADQA8IdAABADwh3AAAAPSDcAQAA9IBwBwAA0APCHQAAQA8IdwAAAD0g3AEAAPSAcAcAANADwh0AAEAPCHcAAAA9INwBAAD0gHAHAADQA8IdAABADwh3AAAAPSDcAQAA9IBwBwAA0APCHQAAQA8iKKjGAAAF2UlEQVQsGHYBAHA0VTXsEp7gZNfTWjupnwfA3CXcATCrDSPcHC3ACVsAzFamZQIAAPTAMcNdVV1XVT+uqm9PaTurqm6tqu91r2d27VVVH6+qu6rqb6vqJVPuuaLr/72quuLp+ToAcPymG50zagfAbDaTkbt/l+Q1A23vT3Jba+2CJLd150lySZILur8rk/xpMhkGk1yd5OVJXpbk6kOBEABmo9ba4TA39RgAZqtjhrvW2l8meWig+dIk13fH1ye5bEr7p9ukbyQ5o6qWJXl1kltbaw+11nYluTU/HxgBAAB4ip7qgipLW2s7u+P7kyztjpcnuW9Kv+1d23TtP6eqrszkqF+WLl2a8fHxp1giAJwYfosAmAuOe7XM1lqrqhM2V6W1dm2Sa5NkZGSkjY6Onqi3BoCnxG8RAHPBU10t80fddMt0rz/u2ncked6Ufud2bdO1AwAAcAI81XB3S5JDK15ekeQLU9rf3q2a+YokP+2mb341ycVVdWa3kMrFXRsAAAAnwDGnZVbVpiSjSc6uqu2ZXPXyj5J8rqrWJLknyZu77l9O8tokdyXZneQdSdJae6iqPpTkW12/D7bWBhdpAQAA4Cmq2by088jISNuyZcuwywDgFFZVtkEAYGiq6o7W2shM+j7VaZkAAADMIsIdAABADwh3AAAAPSDcAQAA9IBwBwAA0APCHQAAQA8IdwAAAD0g3AEAAPSAcAcAANADwh0AAEAPCHcAAAA9INwBAAD0gHAHAADQA8IdAABADwh3AAAAPSDcAQAA9MCCYRcAwOx31llnZdeuXcMuY2iqatglDMWZZ56Zhx56aNhlADBDwh0Ax7Rr16601oZdxlCMj49ndHR02GUMxakaagHmKtMyAQAAekC4AwAA6AHhDgAAoAeEOwAAgB4Q7gAAAHpAuAMAAOgB4Q4AAKAHhDsAAIAeEO4AAAB6YMGwCwBg9mtX/2Lyr5817DKGYjRJxodbw7C0q39x2CUA8CQIdwAcU/3BI2mtDbuMoRgfH8/o6OiwyxiKqkr718OuAoCZMi0TAACgB4Q7AACAHhDuAAAAekC4AwAA6AHhDgAAoAeEOwAAgB6wFQIAM1JVwy6Bk+zMM88cdgkAPAnCHQDHdKrucZd0e72dwt8fgLnDtEwAAIAeEO4AAAB6QLgDAADoAeEOAACgB4Q7AACAHhDuAAAAekC4AwAA6AHhDgAAoAeEOwAAgB4Q7gAAAHrgpIe7qnpNVf1/VXVXVb3/ZH8+AABAH53UcFdV85P8SZJLkqxMcnlVrTyZNQAAAPTRyR65e1mSu1pr32+t7Uvy2SSXnuQaAAAAeudkh7vlSe6bcr69awMAAOA4LBh2AYOq6sokVybJ0qVLMz4+PtyCABiqsbGxYZeQqhraZ2/evHlonw3A3HKyw92OJM+bcn5u13ZYa+3aJNcmycjISBsdHT1pxQEw+7TWhvr54+Pj8VsEwFxwsqdlfivJBVX1gqpalOStSW45yTUAAAD0zkkduWutHaiqdyf5apL5Sa5rrX3nZNYAAADQRyf9mbvW2peTfPlkfy4AAECfnfRNzAEAADjxhDsAAIAeEO4AAAB6QLgDAADoAeEOAACgB4Q7AACAHhDuAAAAekC4AwAA6AHhDgAAoAeEOwAAgB4Q7gAAAHpAuAMAAOiBaq0Nu4ZpVdVPktwz7DoAOKWdneSBYRcBwCnr/Nbac2bScVaHOwAYtqra0lobGXYdAHAspmUCAAD0gHAHAADQA8IdABzdtcMuAABmwjN3AAAAPWDkDgAAoAeEOwAAgB4Q7gDgCKrquqr6cVV9e9i1AMBMCHcAcGT/Lslrhl0EAMyUcAcAR9Ba+8skDw27DgCYKeEOAACgB4Q7AACAHhDuAAAAekC4AwAA6AHhDgCOoKo2Jfl/kvxyVW2vqjXDrgkAjqZaa8OuAQAAgONk5A4AAKAHhDsAAIAeEO4AAAB6QLgDAADoAeEOAACgB4Q7AACAHhDuAAAAeuD/Bwlyz3CTA/4JAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 7))\n",
    "plt.boxplot(dataset[\"households\"])\n",
    "plt.grid()\n",
    "plt.title(\"Households\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    20640.000000\n",
       "mean       499.539680\n",
       "std        382.329753\n",
       "min          1.000000\n",
       "25%        280.000000\n",
       "50%        409.000000\n",
       "75%        605.000000\n",
       "max       6082.000000\n",
       "Name: households, dtype: float64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[\"households\"].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Median income"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 7))\n",
    "plt.hist(dataset[\"median_income\"])\n",
    "plt.grid()\n",
    "plt.title(\"Median income\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 7))\n",
    "plt.boxplot(dataset[\"median_income\"])\n",
    "plt.grid()\n",
    "plt.title(\"Median income\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    20640.000000\n",
       "mean         3.870671\n",
       "std          1.899822\n",
       "min          0.499900\n",
       "25%          2.563400\n",
       "50%          3.534800\n",
       "75%          4.743250\n",
       "max         15.000100\n",
       "Name: median_income, dtype: float64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[\"median_income\"].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Median house value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 7))\n",
    "plt.hist(dataset[\"median_house_value\"])\n",
    "plt.grid()\n",
    "plt.title(\"Median house value\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 7))\n",
    "plt.boxplot(dataset[\"median_house_value\"])\n",
    "plt.grid()\n",
    "plt.title(\"Median house value\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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",
       "Name: median_house_value, dtype: float64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[\"median_house_value\"].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ocean proximity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 7))\n",
    "plt.title(\"Ocean proximity\")\n",
    "dataset[\"ocean_proximity\"].value_counts().plot(kind=\"bar\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['NEAR BAY', '<1H OCEAN', 'INLAND', 'NEAR OCEAN', 'ISLAND'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[\"ocean_proximity\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": true
   },
   "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": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[\"ocean_proximity\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At the end of this section we noticed some outliers in <em>total_rooms</em>, <em>population</em>, <em>households</em>, <em>median_income</em> and <em>median_house_value</em>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preprocessing and Data Wrangling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Duplicate removal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20640"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20640"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = dataset.drop_duplicates()\n",
    "len(dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fortunately, in this dataset there are no duplicates"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Handling categorical values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this dataset *ocean proximity* has categorical (nominal) values, and they are not usable for our next regression, since it takes in input values belonging to $ℝ^d$.\n",
    "We don't know the impact that this column / feature can have on the final accuracy, but not taking into account its values just by deleting them might be an error.\n",
    "A better solution is to convert these values to a numeric domain:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### One-hot encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>&lt;1H OCEAN</th>\n",
       "      <th>INLAND</th>\n",
       "      <th>ISLAND</th>\n",
       "      <th>NEAR BAY</th>\n",
       "      <th>NEAR OCEAN</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>919.0</td>\n",
       "      <td>213.0</td>\n",
       "      <td>413.0</td>\n",
       "      <td>193.0</td>\n",
       "      <td>4.0368</td>\n",
       "      <td>269700.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.84</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2535.0</td>\n",
       "      <td>489.0</td>\n",
       "      <td>1094.0</td>\n",
       "      <td>514.0</td>\n",
       "      <td>3.6591</td>\n",
       "      <td>299200.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.84</td>\n",
       "      <td>52.0</td>\n",
       "      <td>3104.0</td>\n",
       "      <td>687.0</td>\n",
       "      <td>1157.0</td>\n",
       "      <td>647.0</td>\n",
       "      <td>3.1200</td>\n",
       "      <td>241400.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-122.26</td>\n",
       "      <td>37.84</td>\n",
       "      <td>42.0</td>\n",
       "      <td>2555.0</td>\n",
       "      <td>665.0</td>\n",
       "      <td>1206.0</td>\n",
       "      <td>595.0</td>\n",
       "      <td>2.0804</td>\n",
       "      <td>226700.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.84</td>\n",
       "      <td>52.0</td>\n",
       "      <td>3549.0</td>\n",
       "      <td>707.0</td>\n",
       "      <td>1551.0</td>\n",
       "      <td>714.0</td>\n",
       "      <td>3.6912</td>\n",
       "      <td>261100.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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",
       "5    -122.25     37.85                52.0        919.0           213.0   \n",
       "6    -122.25     37.84                52.0       2535.0           489.0   \n",
       "7    -122.25     37.84                52.0       3104.0           687.0   \n",
       "8    -122.26     37.84                42.0       2555.0           665.0   \n",
       "9    -122.25     37.84                52.0       3549.0           707.0   \n",
       "\n",
       "   population  households  median_income  median_house_value  <1H OCEAN  \\\n",
       "0       322.0       126.0         8.3252            452600.0          0   \n",
       "1      2401.0      1138.0         8.3014            358500.0          0   \n",
       "2       496.0       177.0         7.2574            352100.0          0   \n",
       "3       558.0       219.0         5.6431            341300.0          0   \n",
       "4       565.0       259.0         3.8462            342200.0          0   \n",
       "5       413.0       193.0         4.0368            269700.0          0   \n",
       "6      1094.0       514.0         3.6591            299200.0          0   \n",
       "7      1157.0       647.0         3.1200            241400.0          0   \n",
       "8      1206.0       595.0         2.0804            226700.0          0   \n",
       "9      1551.0       714.0         3.6912            261100.0          0   \n",
       "\n",
       "   INLAND  ISLAND  NEAR BAY  NEAR OCEAN  \n",
       "0       0       0         1           0  \n",
       "1       0       0         1           0  \n",
       "2       0       0         1           0  \n",
       "3       0       0         1           0  \n",
       "4       0       0         1           0  \n",
       "5       0       0         1           0  \n",
       "6       0       0         1           0  \n",
       "7       0       0         1           0  \n",
       "8       0       0         1           0  \n",
       "9       0       0         1           0  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dummies = pd.get_dummies(dataset[\"ocean_proximity\"])\n",
    "dataset[\"<1H OCEAN\"] = dummies[\"<1H OCEAN\"] \n",
    "dataset[\"INLAND\"] = dummies[\"INLAND\"]\n",
    "dataset[\"ISLAND\"] = dummies[\"ISLAND\"]\n",
    "dataset[\"NEAR BAY\"] = dummies[\"NEAR BAY\"]\n",
    "dataset[\"NEAR OCEAN\"] = dummies[\"NEAR OCEAN\"]\n",
    "dataset = dataset.drop(\"ocean_proximity\", axis=1)\n",
    "dataset.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corrMatrix = dataset.corr()\n",
    "plt.figure(figsize=(15,7))\n",
    "seaborn.heatmap(corrMatrix, annot=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Outliers removal"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Outliers are values which show huge variance with respect to the rest of the distribution, this means that\n",
    "1. they could be due to an error\n",
    "2. they could be due to a legitimate property but, they could mislead the regressor in catching wrong dependencies between features\n",
    "\n",
    "in both cases leaving them wouldn't be an acceptable solution, usually data scientist choose to cut them out or to replace them with consistent values.\n",
    "I think that it will be better to cut them out, since we can't distinguish errors from correct values that have this peculiarity and, by replacing them we could mislead the regressor anyway."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As introduced in the first section, there are some outliers whose presence may impact on the final behavior of our regressor, so it's better to remove them, in particular, they are contained in <em>total_rooms</em>, <em>total_bedrooms</em>, <em>population</em>, <em>households</em>, <em>median_income</em> and <em>median_house_value</em> so we will investigate these columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will first identify them, then evaluate their fraction over the entire dataset, and then remove them."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to identify outliers, we can compute the interquartile distance, set it equal to iqr, and set\n",
    "- max_threshold 3rd quartile + 1.5 * iqr\n",
    "- min_threshold 1st quartile - 1.5 * iqr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 687,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6.235 %\n"
     ]
    }
   ],
   "source": [
    "iqr = dataset[\"total_rooms\"].quantile(0.75) - dataset[\"total_rooms\"].quantile(0.25)\n",
    "max_threshold_tot_rooms = dataset[\"total_rooms\"].quantile(0.75) + 1.5*iqr\n",
    "min_threshold_tot_rooms = dataset[\"total_rooms\"].quantile(0.75) - 1.5*iqr\n",
    "print(round(len(dataset[(dataset[\"total_rooms\"] > max_threshold_tot_rooms)]) / len(dataset)*100, 3), \"%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 688,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6.158 %\n"
     ]
    }
   ],
   "source": [
    "iqr = dataset[\"total_bedrooms\"].quantile(0.75) - dataset[\"total_bedrooms\"].quantile(0.25)\n",
    "max_threshold_tot_bedrooms = dataset[\"total_bedrooms\"].quantile(0.75) + 1.5*iqr\n",
    "min_threshold_tot_bedrooms = dataset[\"total_bedrooms\"].quantile(0.25) - 1.5*iqr\n",
    "print(round(len(dataset[(dataset[\"total_bedrooms\"] > max_threshold_tot_bedrooms)]) / len(dataset) * 100, 3), \"%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 689,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.795 %\n"
     ]
    }
   ],
   "source": [
    "iqr = dataset[\"population\"].quantile(0.75) - dataset[\"population\"].quantile(0.25)\n",
    "max_threshold_population = dataset[\"population\"].quantile(0.75) + 1.5*iqr\n",
    "min_threshold_population = dataset[\"population\"].quantile(0.25) - 1.5*iqr\n",
    "print(round(len(dataset[(dataset[\"population\"] > max_threshold_population)]) / len(dataset) * 100, 3), \"%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 690,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.911 %\n"
     ]
    }
   ],
   "source": [
    "iqr = dataset[\"households\"].quantile(0.75) - dataset[\"households\"].quantile(0.25)\n",
    "max_threshold_households = dataset[\"households\"].quantile(0.75) + 1.5*iqr\n",
    "min_threshold_households = dataset[\"households\"].quantile(0.25) - 1.5*iqr\n",
    "print(round(len(dataset[(dataset[\"households\"] > max_threshold_households)]) / len(dataset) * 100, 3), \"%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 691,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.299 %\n"
     ]
    }
   ],
   "source": [
    "iqr = dataset[\"median_income\"].quantile(0.75) - dataset[\"median_income\"].quantile(0.25)\n",
    "max_threshold_median_income = dataset[\"median_income\"].quantile(0.75) + 1.5*iqr\n",
    "min_threshold_median_income = dataset[\"median_income\"].quantile(0.25) - 1.5*iqr\n",
    "print(round(len(dataset[(dataset[\"median_income\"] > max_threshold_median_income)]) / len(dataset) * 100, 3), \"%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 692,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.189 %\n"
     ]
    }
   ],
   "source": [
    "iqr = dataset[\"median_house_value\"].quantile(0.75) - dataset[\"median_house_value\"].quantile(0.25)\n",
    "max_threshold_median_house_value = dataset[\"median_house_value\"].quantile(0.75) + 1.5*iqr\n",
    "min_threshold_median_house_value = dataset[\"median_house_value\"].quantile(0.25) - 1.5*iqr\n",
    "print(round(len(dataset[(dataset[\"median_house_value\"] > max_threshold_median_house_value)]) / len(dataset) * 100, 3), \"%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From this we see that the proportion of outliers is not the same for every column, this means that not all of the outliers involved are belonging to the same row"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Note</b>: now there is an issue, because we contemporary want to:\n",
    "1. remove outliers without collateral effects due to the presence of missing values (i.e. NaNs affect the threshold result)\n",
    "2. replace missing values without being affected by outliers\n",
    "\n",
    "for this reason, the order of our pipeline is crucial: we first apply outliers removal and then missing values replacement.\n",
    "So, in order to satisfy the first point we replace NaNs with a special value (-1), which is impossible for <em>total_bedrooms</em>, we apply outliers removal, and we replace -1s with NaNs again so that we will handle their replacement later in a proper way satisfying the second one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 693,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 693,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.isnull().values.any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 694,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    }
   ],
   "source": [
    "for index, i in dataset[[\"total_bedrooms\"]].iterrows():\n",
    "    if math.isnan(i[0]):\n",
    "        dataset[\"total_bedrooms\"][index] = -1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We notice that we temporary replaced all missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 695,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 695,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.isnull().values.any()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We discover that part of outliers removal involves also NaNs, this means that there are some rows which present at the same time missing value and outliers (we tested with all features involved in outliers and they all have this issue)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 696,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <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>&lt;1H OCEAN</th>\n",
       "      <th>INLAND</th>\n",
       "      <th>ISLAND</th>\n",
       "      <th>NEAR BAY</th>\n",
       "      <th>NEAR OCEAN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4046</th>\n",
       "      <td>-118.49</td>\n",
       "      <td>34.13</td>\n",
       "      <td>24.0</td>\n",
       "      <td>4394.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1443.0</td>\n",
       "      <td>528.0</td>\n",
       "      <td>11.2979</td>\n",
       "      <td>500001.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6590</th>\n",
       "      <td>-118.18</td>\n",
       "      <td>34.19</td>\n",
       "      <td>48.0</td>\n",
       "      <td>1371.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>528.0</td>\n",
       "      <td>155.0</td>\n",
       "      <td>15.0001</td>\n",
       "      <td>500001.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10761</th>\n",
       "      <td>-117.87</td>\n",
       "      <td>33.62</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1266.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>375.0</td>\n",
       "      <td>183.0</td>\n",
       "      <td>9.8020</td>\n",
       "      <td>500001.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11512</th>\n",
       "      <td>-118.10</td>\n",
       "      <td>33.74</td>\n",
       "      <td>32.0</td>\n",
       "      <td>2035.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>934.0</td>\n",
       "      <td>512.0</td>\n",
       "      <td>4.2287</td>\n",
       "      <td>500001.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>-1.0</td>\n",
       "      <td>1310.0</td>\n",
       "      <td>963.0</td>\n",
       "      <td>3.4801</td>\n",
       "      <td>500001.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16880</th>\n",
       "      <td>-122.39</td>\n",
       "      <td>37.59</td>\n",
       "      <td>32.0</td>\n",
       "      <td>4497.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1846.0</td>\n",
       "      <td>715.0</td>\n",
       "      <td>6.1323</td>\n",
       "      <td>500001.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16885</th>\n",
       "      <td>-122.40</td>\n",
       "      <td>37.58</td>\n",
       "      <td>26.0</td>\n",
       "      <td>3281.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1145.0</td>\n",
       "      <td>480.0</td>\n",
       "      <td>6.3580</td>\n",
       "      <td>500001.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "4046     -118.49     34.13                24.0       4394.0            -1.0   \n",
       "6590     -118.18     34.19                48.0       1371.0            -1.0   \n",
       "10761    -117.87     33.62                 8.0       1266.0            -1.0   \n",
       "11512    -118.10     33.74                32.0       2035.0            -1.0   \n",
       "15663    -122.44     37.80                52.0       3830.0            -1.0   \n",
       "16880    -122.39     37.59                32.0       4497.0            -1.0   \n",
       "16885    -122.40     37.58                26.0       3281.0            -1.0   \n",
       "\n",
       "       population  households  median_income  median_house_value  <1H OCEAN  \\\n",
       "4046       1443.0       528.0        11.2979            500001.0          1   \n",
       "6590        528.0       155.0        15.0001            500001.0          1   \n",
       "10761       375.0       183.0         9.8020            500001.0          1   \n",
       "11512       934.0       512.0         4.2287            500001.0          0   \n",
       "15663      1310.0       963.0         3.4801            500001.0          0   \n",
       "16880      1846.0       715.0         6.1323            500001.0          0   \n",
       "16885      1145.0       480.0         6.3580            500001.0          0   \n",
       "\n",
       "       INLAND  ISLAND  NEAR BAY  NEAR OCEAN  \n",
       "4046        0       0         0           0  \n",
       "6590        0       0         0           0  \n",
       "10761       0       0         0           0  \n",
       "11512       0       0         0           1  \n",
       "15663       0       0         1           0  \n",
       "16880       0       0         0           1  \n",
       "16885       0       0         0           1  "
      ]
     },
     "execution_count": 696,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[(dataset[\"median_house_value\"] > max_threshold_median_house_value) & (dataset[\"total_bedrooms\"] == -1)].head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "it can be possible also that multiple feature have outliers at the same time and missing value,  \n",
    "in this example we can see outliers both for <em>median_house_value</em> and <em>median_income</em> and a missing value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 697,
   "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>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
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       "      <th>4046</th>\n",
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      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "4046     -118.49     34.13                24.0       4394.0            -1.0   \n",
       "6590     -118.18     34.19                48.0       1371.0            -1.0   \n",
       "10761    -117.87     33.62                 8.0       1266.0            -1.0   \n",
       "\n",
       "       population  households  median_income  median_house_value  <1H OCEAN  \\\n",
       "4046       1443.0       528.0        11.2979            500001.0          1   \n",
       "6590        528.0       155.0        15.0001            500001.0          1   \n",
       "10761       375.0       183.0         9.8020            500001.0          1   \n",
       "\n",
       "       INLAND  ISLAND  NEAR BAY  NEAR OCEAN  \n",
       "4046        0       0         0           0  \n",
       "6590        0       0         0           0  \n",
       "10761       0       0         0           0  "
      ]
     },
     "execution_count": 697,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[(dataset[\"median_house_value\"] > max_threshold_median_house_value) & (dataset[\"median_income\"] > max_threshold_median_income) & (dataset[\"total_bedrooms\"] == -1)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We apply safe outliers removal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 698,
   "metadata": {},
   "outputs": [],
   "source": [
    "def filter_outliers(dataset, key, min_threshold, max_threshold):\n",
    "    return dataset[(dataset[key] >= min_threshold) & (dataset[key] <= max_threshold)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 699,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = filter_outliers(dataset, \"total_rooms\", min_threshold_tot_rooms, max_threshold_tot_rooms)\n",
    "dataset = filter_outliers(dataset, \"total_bedrooms\", min_threshold_tot_bedrooms, max_threshold_tot_bedrooms)\n",
    "dataset = filter_outliers(dataset, \"population\", min_threshold_population, max_threshold_population)\n",
    "dataset = filter_outliers(dataset, \"households\", min_threshold_households, max_threshold_households)\n",
    "dataset = filter_outliers(dataset, \"median_income\", min_threshold_median_income, max_threshold_median_income)\n",
    "dataset = filter_outliers(dataset, \"median_house_value\", min_threshold_median_house_value, max_threshold_median_house_value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 700,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16715"
      ]
     },
     "execution_count": 700,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And we restore the initial situation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 701,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    }
   ],
   "source": [
    "for index, i in dataset[[\"total_bedrooms\"]].iterrows():\n",
    "    if i[0] == -1:\n",
    "        dataset[\"total_bedrooms\"][index] = math.nan"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Missing values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, as seen previously, we know that another anomaly in this dataset is missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 702,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 702,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.isnull().values.any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 703,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "longitude:  False\n",
      "latitude:  False\n",
      "housing_median_age:  False\n",
      "total_rooms:  False\n",
      "total_bedrooms:  True\n",
      "population:  False\n",
      "households:  False\n",
      "median_income:  False\n",
      "median_house_value:  False\n"
     ]
    }
   ],
   "source": [
    "col = ['longitude', 'latitude', 'housing_median_age', 'total_rooms', 'total_bedrooms', 'population', 'households', 'median_income', 'median_house_value']\n",
    "\n",
    "for i in col:\n",
    "    print(i + \": \", dataset[i].isnull().values.any())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 704,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>-122.14</td>\n",
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       "      <td>1705.0</td>\n",
       "      <td>600.0</td>\n",
       "      <td>4.7120</td>\n",
       "      <td>158600.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1456</th>\n",
       "      <td>-121.98</td>\n",
       "      <td>37.96</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2987.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1420.0</td>\n",
       "      <td>540.0</td>\n",
       "      <td>3.6500</td>\n",
       "      <td>204100.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1493</th>\n",
       "      <td>-122.01</td>\n",
       "      <td>37.94</td>\n",
       "      <td>23.0</td>\n",
       "      <td>3741.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1339.0</td>\n",
       "      <td>499.0</td>\n",
       "      <td>6.7061</td>\n",
       "      <td>322300.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1606</th>\n",
       "      <td>-122.08</td>\n",
       "      <td>37.88</td>\n",
       "      <td>26.0</td>\n",
       "      <td>2947.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>825.0</td>\n",
       "      <td>626.0</td>\n",
       "      <td>2.9330</td>\n",
       "      <td>85000.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2028</th>\n",
       "      <td>-119.75</td>\n",
       "      <td>36.71</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1481.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1543.0</td>\n",
       "      <td>372.0</td>\n",
       "      <td>1.4577</td>\n",
       "      <td>49800.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2301</th>\n",
       "      <td>-119.78</td>\n",
       "      <td>36.82</td>\n",
       "      <td>25.0</td>\n",
       "      <td>5016.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2133.0</td>\n",
       "      <td>928.0</td>\n",
       "      <td>3.6250</td>\n",
       "      <td>89500.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2323</th>\n",
       "      <td>-119.73</td>\n",
       "      <td>36.83</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3602.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1959.0</td>\n",
       "      <td>580.0</td>\n",
       "      <td>5.3478</td>\n",
       "      <td>138800.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2334</th>\n",
       "      <td>-119.69</td>\n",
       "      <td>36.83</td>\n",
       "      <td>32.0</td>\n",
       "      <td>1098.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>726.0</td>\n",
       "      <td>224.0</td>\n",
       "      <td>1.4913</td>\n",
       "      <td>54600.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2351</th>\n",
       "      <td>-119.68</td>\n",
       "      <td>36.79</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1551.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1010.0</td>\n",
       "      <td>292.0</td>\n",
       "      <td>3.5417</td>\n",
       "      <td>71300.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2412</th>\n",
       "      <td>-119.45</td>\n",
       "      <td>36.61</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1302.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>693.0</td>\n",
       "      <td>243.0</td>\n",
       "      <td>3.7917</td>\n",
       "      <td>90500.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2420</th>\n",
       "      <td>-119.44</td>\n",
       "      <td>36.58</td>\n",
       "      <td>37.0</td>\n",
       "      <td>1054.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>879.0</td>\n",
       "      <td>257.0</td>\n",
       "      <td>2.5234</td>\n",
       "      <td>63500.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2578</th>\n",
       "      <td>-124.06</td>\n",
       "      <td>40.86</td>\n",
       "      <td>34.0</td>\n",
       "      <td>4183.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1891.0</td>\n",
       "      <td>669.0</td>\n",
       "      <td>3.2216</td>\n",
       "      <td>98100.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2608</th>\n",
       "      <td>-124.00</td>\n",
       "      <td>40.92</td>\n",
       "      <td>29.0</td>\n",
       "      <td>1429.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>672.0</td>\n",
       "      <td>266.0</td>\n",
       "      <td>2.9485</td>\n",
       "      <td>98800.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3328</th>\n",
       "      <td>-122.72</td>\n",
       "      <td>38.88</td>\n",
       "      <td>29.0</td>\n",
       "      <td>2781.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>890.0</td>\n",
       "      <td>310.0</td>\n",
       "      <td>1.9906</td>\n",
       "      <td>96600.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3354</th>\n",
       "      <td>-120.67</td>\n",
       "      <td>40.50</td>\n",
       "      <td>15.0</td>\n",
       "      <td>5343.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2503.0</td>\n",
       "      <td>902.0</td>\n",
       "      <td>3.5962</td>\n",
       "      <td>85900.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3376</th>\n",
       "      <td>-118.28</td>\n",
       "      <td>34.25</td>\n",
       "      <td>29.0</td>\n",
       "      <td>2559.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1886.0</td>\n",
       "      <td>769.0</td>\n",
       "      <td>2.6036</td>\n",
       "      <td>162100.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3482</th>\n",
       "      <td>-118.45</td>\n",
       "      <td>34.30</td>\n",
       "      <td>27.0</td>\n",
       "      <td>2676.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2661.0</td>\n",
       "      <td>623.0</td>\n",
       "      <td>4.3047</td>\n",
       "      <td>152100.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3721</th>\n",
       "      <td>-118.43</td>\n",
       "      <td>34.20</td>\n",
       "      <td>28.0</td>\n",
       "      <td>3386.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2240.0</td>\n",
       "      <td>737.0</td>\n",
       "      <td>3.0221</td>\n",
       "      <td>290100.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3921</th>\n",
       "      <td>-118.56</td>\n",
       "      <td>34.20</td>\n",
       "      <td>35.0</td>\n",
       "      <td>2273.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1431.0</td>\n",
       "      <td>403.0</td>\n",
       "      <td>4.0789</td>\n",
       "      <td>196700.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3958</th>\n",
       "      <td>-118.59</td>\n",
       "      <td>34.20</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1789.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2300.0</td>\n",
       "      <td>677.0</td>\n",
       "      <td>2.7540</td>\n",
       "      <td>179800.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4043</th>\n",
       "      <td>-118.50</td>\n",
       "      <td>34.17</td>\n",
       "      <td>37.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>369.0</td>\n",
       "      <td>155.0</td>\n",
       "      <td>4.1429</td>\n",
       "      <td>303600.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4186</th>\n",
       "      <td>-118.23</td>\n",
       "      <td>34.13</td>\n",
       "      <td>48.0</td>\n",
       "      <td>1308.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>835.0</td>\n",
       "      <td>294.0</td>\n",
       "      <td>4.2891</td>\n",
       "      <td>214800.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4279</th>\n",
       "      <td>-118.31</td>\n",
       "      <td>34.09</td>\n",
       "      <td>37.0</td>\n",
       "      <td>773.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>835.0</td>\n",
       "      <td>312.0</td>\n",
       "      <td>1.8576</td>\n",
       "      <td>193800.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4309</th>\n",
       "      <td>-118.32</td>\n",
       "      <td>34.09</td>\n",
       "      <td>44.0</td>\n",
       "      <td>2666.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2297.0</td>\n",
       "      <td>726.0</td>\n",
       "      <td>1.6760</td>\n",
       "      <td>208800.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17923</th>\n",
       "      <td>-121.97</td>\n",
       "      <td>37.35</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1955.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>999.0</td>\n",
       "      <td>386.0</td>\n",
       "      <td>4.6328</td>\n",
       "      <td>287100.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17928</th>\n",
       "      <td>-121.97</td>\n",
       "      <td>37.34</td>\n",
       "      <td>33.0</td>\n",
       "      <td>3162.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1553.0</td>\n",
       "      <td>686.0</td>\n",
       "      <td>3.6682</td>\n",
       "      <td>266100.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17973</th>\n",
       "      <td>-121.95</td>\n",
       "      <td>37.31</td>\n",
       "      <td>27.0</td>\n",
       "      <td>4140.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2135.0</td>\n",
       "      <td>893.0</td>\n",
       "      <td>3.6292</td>\n",
       "      <td>264600.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18246</th>\n",
       "      <td>-122.08</td>\n",
       "      <td>37.39</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2292.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1050.0</td>\n",
       "      <td>584.0</td>\n",
       "      <td>4.8036</td>\n",
       "      <td>340000.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18261</th>\n",
       "      <td>-122.08</td>\n",
       "      <td>37.37</td>\n",
       "      <td>29.0</td>\n",
       "      <td>1229.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>707.0</td>\n",
       "      <td>194.0</td>\n",
       "      <td>7.1108</td>\n",
       "      <td>465000.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18466</th>\n",
       "      <td>-121.75</td>\n",
       "      <td>37.11</td>\n",
       "      <td>18.0</td>\n",
       "      <td>3167.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1414.0</td>\n",
       "      <td>482.0</td>\n",
       "      <td>6.8773</td>\n",
       "      <td>467700.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18786</th>\n",
       "      <td>-122.42</td>\n",
       "      <td>40.44</td>\n",
       "      <td>16.0</td>\n",
       "      <td>994.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>495.0</td>\n",
       "      <td>181.0</td>\n",
       "      <td>2.1875</td>\n",
       "      <td>76400.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18873</th>\n",
       "      <td>-122.22</td>\n",
       "      <td>38.08</td>\n",
       "      <td>37.0</td>\n",
       "      <td>2811.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1574.0</td>\n",
       "      <td>516.0</td>\n",
       "      <td>3.1053</td>\n",
       "      <td>96700.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18914</th>\n",
       "      <td>-122.22</td>\n",
       "      <td>38.15</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5129.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2824.0</td>\n",
       "      <td>738.0</td>\n",
       "      <td>5.5138</td>\n",
       "      <td>171100.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19060</th>\n",
       "      <td>-122.41</td>\n",
       "      <td>38.16</td>\n",
       "      <td>37.0</td>\n",
       "      <td>1549.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>863.0</td>\n",
       "      <td>275.0</td>\n",
       "      <td>2.7457</td>\n",
       "      <td>254700.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19071</th>\n",
       "      <td>-122.53</td>\n",
       "      <td>38.32</td>\n",
       "      <td>22.0</td>\n",
       "      <td>3577.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1371.0</td>\n",
       "      <td>501.0</td>\n",
       "      <td>5.7950</td>\n",
       "      <td>332300.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19122</th>\n",
       "      <td>-122.65</td>\n",
       "      <td>38.25</td>\n",
       "      <td>23.0</td>\n",
       "      <td>4030.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1852.0</td>\n",
       "      <td>778.0</td>\n",
       "      <td>3.4020</td>\n",
       "      <td>193300.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19150</th>\n",
       "      <td>-122.70</td>\n",
       "      <td>38.35</td>\n",
       "      <td>14.0</td>\n",
       "      <td>2313.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>954.0</td>\n",
       "      <td>397.0</td>\n",
       "      <td>3.7813</td>\n",
       "      <td>146500.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19332</th>\n",
       "      <td>-122.91</td>\n",
       "      <td>38.46</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2021.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>912.0</td>\n",
       "      <td>329.0</td>\n",
       "      <td>4.5000</td>\n",
       "      <td>251900.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19402</th>\n",
       "      <td>-120.93</td>\n",
       "      <td>37.73</td>\n",
       "      <td>14.0</td>\n",
       "      <td>2799.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2294.0</td>\n",
       "      <td>596.0</td>\n",
       "      <td>2.6343</td>\n",
       "      <td>81500.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19485</th>\n",
       "      <td>-120.98</td>\n",
       "      <td>37.66</td>\n",
       "      <td>10.0</td>\n",
       "      <td>934.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>401.0</td>\n",
       "      <td>255.0</td>\n",
       "      <td>0.9336</td>\n",
       "      <td>127100.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19559</th>\n",
       "      <td>-120.98</td>\n",
       "      <td>37.60</td>\n",
       "      <td>36.0</td>\n",
       "      <td>1437.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1073.0</td>\n",
       "      <td>320.0</td>\n",
       "      <td>2.1779</td>\n",
       "      <td>58400.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19638</th>\n",
       "      <td>-120.97</td>\n",
       "      <td>37.43</td>\n",
       "      <td>27.0</td>\n",
       "      <td>1380.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>810.0</td>\n",
       "      <td>262.0</td>\n",
       "      <td>2.1875</td>\n",
       "      <td>137500.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19766</th>\n",
       "      <td>-122.10</td>\n",
       "      <td>40.03</td>\n",
       "      <td>25.0</td>\n",
       "      <td>2516.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1266.0</td>\n",
       "      <td>494.0</td>\n",
       "      <td>1.7566</td>\n",
       "      <td>58400.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19818</th>\n",
       "      <td>-119.30</td>\n",
       "      <td>36.57</td>\n",
       "      <td>32.0</td>\n",
       "      <td>728.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>461.0</td>\n",
       "      <td>149.0</td>\n",
       "      <td>3.0156</td>\n",
       "      <td>109100.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19833</th>\n",
       "      <td>-119.38</td>\n",
       "      <td>36.53</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1281.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1423.0</td>\n",
       "      <td>293.0</td>\n",
       "      <td>1.9602</td>\n",
       "      <td>51400.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19890</th>\n",
       "      <td>-119.15</td>\n",
       "      <td>36.29</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1435.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>657.0</td>\n",
       "      <td>254.0</td>\n",
       "      <td>2.4281</td>\n",
       "      <td>72500.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19932</th>\n",
       "      <td>-119.33</td>\n",
       "      <td>36.30</td>\n",
       "      <td>11.0</td>\n",
       "      <td>3045.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1563.0</td>\n",
       "      <td>516.0</td>\n",
       "      <td>5.4337</td>\n",
       "      <td>133800.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19959</th>\n",
       "      <td>-119.32</td>\n",
       "      <td>36.25</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1231.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>609.0</td>\n",
       "      <td>206.0</td>\n",
       "      <td>2.8365</td>\n",
       "      <td>90000.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20125</th>\n",
       "      <td>-118.80</td>\n",
       "      <td>34.41</td>\n",
       "      <td>45.0</td>\n",
       "      <td>1610.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1148.0</td>\n",
       "      <td>347.0</td>\n",
       "      <td>2.7000</td>\n",
       "      <td>120400.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20268</th>\n",
       "      <td>-119.18</td>\n",
       "      <td>34.19</td>\n",
       "      <td>19.0</td>\n",
       "      <td>2393.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1938.0</td>\n",
       "      <td>762.0</td>\n",
       "      <td>1.6953</td>\n",
       "      <td>167400.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20372</th>\n",
       "      <td>-118.88</td>\n",
       "      <td>34.17</td>\n",
       "      <td>15.0</td>\n",
       "      <td>4260.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1701.0</td>\n",
       "      <td>669.0</td>\n",
       "      <td>5.1033</td>\n",
       "      <td>410700.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20460</th>\n",
       "      <td>-118.75</td>\n",
       "      <td>34.29</td>\n",
       "      <td>17.0</td>\n",
       "      <td>5512.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2734.0</td>\n",
       "      <td>814.0</td>\n",
       "      <td>6.6073</td>\n",
       "      <td>258100.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>163 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "290      -122.16     37.77                47.0       1256.0             NaN   \n",
       "341      -122.17     37.75                38.0        992.0             NaN   \n",
       "563      -122.24     37.75                45.0        891.0             NaN   \n",
       "696      -122.10     37.69                41.0        746.0             NaN   \n",
       "738      -122.14     37.67                37.0       3342.0             NaN   \n",
       "1097     -121.77     39.66                20.0       3759.0             NaN   \n",
       "1456     -121.98     37.96                22.0       2987.0             NaN   \n",
       "1493     -122.01     37.94                23.0       3741.0             NaN   \n",
       "1606     -122.08     37.88                26.0       2947.0             NaN   \n",
       "2028     -119.75     36.71                38.0       1481.0             NaN   \n",
       "2301     -119.78     36.82                25.0       5016.0             NaN   \n",
       "2323     -119.73     36.83                 8.0       3602.0             NaN   \n",
       "2334     -119.69     36.83                32.0       1098.0             NaN   \n",
       "2351     -119.68     36.79                16.0       1551.0             NaN   \n",
       "2412     -119.45     36.61                24.0       1302.0             NaN   \n",
       "2420     -119.44     36.58                37.0       1054.0             NaN   \n",
       "2578     -124.06     40.86                34.0       4183.0             NaN   \n",
       "2608     -124.00     40.92                29.0       1429.0             NaN   \n",
       "3024     -119.46     35.14                30.0       2943.0             NaN   \n",
       "3328     -122.72     38.88                29.0       2781.0             NaN   \n",
       "3354     -120.67     40.50                15.0       5343.0             NaN   \n",
       "3376     -118.28     34.25                29.0       2559.0             NaN   \n",
       "3482     -118.45     34.30                27.0       2676.0             NaN   \n",
       "3721     -118.43     34.20                28.0       3386.0             NaN   \n",
       "3921     -118.56     34.20                35.0       2273.0             NaN   \n",
       "3958     -118.59     34.20                21.0       1789.0             NaN   \n",
       "4043     -118.50     34.17                37.0        880.0             NaN   \n",
       "4186     -118.23     34.13                48.0       1308.0             NaN   \n",
       "4279     -118.31     34.09                37.0        773.0             NaN   \n",
       "4309     -118.32     34.09                44.0       2666.0             NaN   \n",
       "...          ...       ...                 ...          ...             ...   \n",
       "17923    -121.97     37.35                30.0       1955.0             NaN   \n",
       "17928    -121.97     37.34                33.0       3162.0             NaN   \n",
       "17973    -121.95     37.31                27.0       4140.0             NaN   \n",
       "18246    -122.08     37.39                 4.0       2292.0             NaN   \n",
       "18261    -122.08     37.37                29.0       1229.0             NaN   \n",
       "18466    -121.75     37.11                18.0       3167.0             NaN   \n",
       "18786    -122.42     40.44                16.0        994.0             NaN   \n",
       "18873    -122.22     38.08                37.0       2811.0             NaN   \n",
       "18914    -122.22     38.15                 7.0       5129.0             NaN   \n",
       "19060    -122.41     38.16                37.0       1549.0             NaN   \n",
       "19071    -122.53     38.32                22.0       3577.0             NaN   \n",
       "19122    -122.65     38.25                23.0       4030.0             NaN   \n",
       "19150    -122.70     38.35                14.0       2313.0             NaN   \n",
       "19332    -122.91     38.46                18.0       2021.0             NaN   \n",
       "19402    -120.93     37.73                14.0       2799.0             NaN   \n",
       "19485    -120.98     37.66                10.0        934.0             NaN   \n",
       "19559    -120.98     37.60                36.0       1437.0             NaN   \n",
       "19638    -120.97     37.43                27.0       1380.0             NaN   \n",
       "19766    -122.10     40.03                25.0       2516.0             NaN   \n",
       "19818    -119.30     36.57                32.0        728.0             NaN   \n",
       "19833    -119.38     36.53                38.0       1281.0             NaN   \n",
       "19890    -119.15     36.29                18.0       1435.0             NaN   \n",
       "19932    -119.33     36.30                11.0       3045.0             NaN   \n",
       "19959    -119.32     36.25                21.0       1231.0             NaN   \n",
       "20046    -119.01     36.06                25.0       1505.0             NaN   \n",
       "20125    -118.80     34.41                45.0       1610.0             NaN   \n",
       "20268    -119.18     34.19                19.0       2393.0             NaN   \n",
       "20372    -118.88     34.17                15.0       4260.0             NaN   \n",
       "20460    -118.75     34.29                17.0       5512.0             NaN   \n",
       "20484    -118.72     34.28                17.0       3051.0             NaN   \n",
       "\n",
       "       population  households  median_income  median_house_value  <1H OCEAN  \\\n",
       "290         570.0       218.0         4.3750            161900.0          0   \n",
       "341         732.0       259.0         1.6196             85100.0          0   \n",
       "563         384.0       146.0         4.9489            247100.0          0   \n",
       "696         387.0       161.0         3.9063            178400.0          0   \n",
       "738        1635.0       557.0         4.7933            186900.0          0   \n",
       "1097       1705.0       600.0         4.7120            158600.0          0   \n",
       "1456       1420.0       540.0         3.6500            204100.0          0   \n",
       "1493       1339.0       499.0         6.7061            322300.0          0   \n",
       "1606        825.0       626.0         2.9330             85000.0          0   \n",
       "2028       1543.0       372.0         1.4577             49800.0          0   \n",
       "2301       2133.0       928.0         3.6250             89500.0          0   \n",
       "2323       1959.0       580.0         5.3478            138800.0          0   \n",
       "2334        726.0       224.0         1.4913             54600.0          0   \n",
       "2351       1010.0       292.0         3.5417             71300.0          0   \n",
       "2412        693.0       243.0         3.7917             90500.0          0   \n",
       "2420        879.0       257.0         2.5234             63500.0          0   \n",
       "2578       1891.0       669.0         3.2216             98100.0          0   \n",
       "2608        672.0       266.0         2.9485             98800.0          1   \n",
       "3024       1565.0       584.0         2.5313             45800.0          0   \n",
       "3328        890.0       310.0         1.9906             96600.0          0   \n",
       "3354       2503.0       902.0         3.5962             85900.0          0   \n",
       "3376       1886.0       769.0         2.6036            162100.0          1   \n",
       "3482       2661.0       623.0         4.3047            152100.0          1   \n",
       "3721       2240.0       737.0         3.0221            290100.0          1   \n",
       "3921       1431.0       403.0         4.0789            196700.0          1   \n",
       "3958       2300.0       677.0         2.7540            179800.0          1   \n",
       "4043        369.0       155.0         4.1429            303600.0          1   \n",
       "4186        835.0       294.0         4.2891            214800.0          1   \n",
       "4279        835.0       312.0         1.8576            193800.0          1   \n",
       "4309       2297.0       726.0         1.6760            208800.0          1   \n",
       "...           ...         ...            ...                 ...        ...   \n",
       "17923       999.0       386.0         4.6328            287100.0          1   \n",
       "17928      1553.0       686.0         3.6682            266100.0          1   \n",
       "17973      2135.0       893.0         3.6292            264600.0          1   \n",
       "18246      1050.0       584.0         4.8036            340000.0          0   \n",
       "18261       707.0       194.0         7.1108            465000.0          0   \n",
       "18466      1414.0       482.0         6.8773            467700.0          1   \n",
       "18786       495.0       181.0         2.1875             76400.0          0   \n",
       "18873      1574.0       516.0         3.1053             96700.0          0   \n",
       "18914      2824.0       738.0         5.5138            171100.0          0   \n",
       "19060       863.0       275.0         2.7457            254700.0          0   \n",
       "19071      1371.0       501.0         5.7950            332300.0          1   \n",
       "19122      1852.0       778.0         3.4020            193300.0          1   \n",
       "19150       954.0       397.0         3.7813            146500.0          1   \n",
       "19332       912.0       329.0         4.5000            251900.0          1   \n",
       "19402      2294.0       596.0         2.6343             81500.0          0   \n",
       "19485       401.0       255.0         0.9336            127100.0          0   \n",
       "19559      1073.0       320.0         2.1779             58400.0          0   \n",
       "19638       810.0       262.0         2.1875            137500.0          0   \n",
       "19766      1266.0       494.0         1.7566             58400.0          0   \n",
       "19818       461.0       149.0         3.0156            109100.0          0   \n",
       "19833      1423.0       293.0         1.9602             51400.0          0   \n",
       "19890       657.0       254.0         2.4281             72500.0          0   \n",
       "19932      1563.0       516.0         5.4337            133800.0          0   \n",
       "19959       609.0       206.0         2.8365             90000.0          0   \n",
       "20046      1392.0       359.0         1.6812             47700.0          0   \n",
       "20125      1148.0       347.0         2.7000            120400.0          1   \n",
       "20268      1938.0       762.0         1.6953            167400.0          0   \n",
       "20372      1701.0       669.0         5.1033            410700.0          1   \n",
       "20460      2734.0       814.0         6.6073            258100.0          1   \n",
       "20484      1705.0       495.0         5.7376            218600.0          1   \n",
       "\n",
       "       INLAND  ISLAND  NEAR BAY  NEAR OCEAN  \n",
       "290         0       0         1           0  \n",
       "341         0       0         1           0  \n",
       "563         0       0         1           0  \n",
       "696         0       0         1           0  \n",
       "738         0       0         1           0  \n",
       "1097        1       0         0           0  \n",
       "1456        1       0         0           0  \n",
       "1493        0       0         1           0  \n",
       "1606        0       0         1           0  \n",
       "2028        1       0         0           0  \n",
       "2301        1       0         0           0  \n",
       "2323        1       0         0           0  \n",
       "2334        1       0         0           0  \n",
       "2351        1       0         0           0  \n",
       "2412        1       0         0           0  \n",
       "2420        1       0         0           0  \n",
       "2578        0       0         0           1  \n",
       "2608        0       0         0           0  \n",
       "3024        1       0         0           0  \n",
       "3328        1       0         0           0  \n",
       "3354        1       0         0           0  \n",
       "3376        0       0         0           0  \n",
       "3482        0       0         0           0  \n",
       "3721        0       0         0           0  \n",
       "3921        0       0         0           0  \n",
       "3958        0       0         0           0  \n",
       "4043        0       0         0           0  \n",
       "4186        0       0         0           0  \n",
       "4279        0       0         0           0  \n",
       "4309        0       0         0           0  \n",
       "...       ...     ...       ...         ...  \n",
       "17923       0       0         0           0  \n",
       "17928       0       0         0           0  \n",
       "17973       0       0         0           0  \n",
       "18246       0       0         1           0  \n",
       "18261       0       0         1           0  \n",
       "18466       0       0         0           0  \n",
       "18786       1       0         0           0  \n",
       "18873       0       0         1           0  \n",
       "18914       0       0         1           0  \n",
       "19060       0       0         1           0  \n",
       "19071       0       0         0           0  \n",
       "19122       0       0         0           0  \n",
       "19150       0       0         0           0  \n",
       "19332       0       0         0           0  \n",
       "19402       1       0         0           0  \n",
       "19485       1       0         0           0  \n",
       "19559       1       0         0           0  \n",
       "19638       1       0         0           0  \n",
       "19766       1       0         0           0  \n",
       "19818       1       0         0           0  \n",
       "19833       1       0         0           0  \n",
       "19890       1       0         0           0  \n",
       "19932       1       0         0           0  \n",
       "19959       1       0         0           0  \n",
       "20046       1       0         0           0  \n",
       "20125       0       0         0           0  \n",
       "20268       0       0         0           1  \n",
       "20372       0       0         0           0  \n",
       "20460       0       0         0           0  \n",
       "20484       0       0         0           0  \n",
       "\n",
       "[163 rows x 14 columns]"
      ]
     },
     "execution_count": 704,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[dataset.isnull().any(axis=1)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After this first analysis we have highlighted the presence of missing values only in the *total_bedrooms* column.  \n",
    "The two main ways of handling missing values are: cutting them out or replacing them with other values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to choose the best criterion, let's evaluate the real proportion of missing values with respect to the entire dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is the number of rows which contain a missing value at *total_bedrooms* column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 705,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "163"
      ]
     },
     "execution_count": 705,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(dataset.isnull().any(axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 706,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total rows: 16715\n"
     ]
    }
   ],
   "source": [
    "print(\"total rows:\", len(dataset))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 707,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9752 %\n"
     ]
    }
   ],
   "source": [
    "print(round(sum(dataset.isnull().any(axis=1))/len(dataset) * 100, 4), \"%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "is the percentage of rows that contain missing values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Given that the proportion of missing values is not big, cutting them out could be a possible solution but, first, let's see if these missing values have a pattern or if they are missing randomly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 708,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1398a5128>"
      ]
     },
     "execution_count": 708,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "msno.matrix(dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Possible ways to replace missing values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In general, *total_bedrooms* must be a subset of *total_rooms*; on average, the proportion between the number of total bedrooms and total rooms is 0.21, and its standard deviation is approximately around 0.06, a possible solution in order to keep 1% of data, could be to randomly extract a value between 0.16 and 0.26 and multiply it with the number of *total_rooms* (at i-th row), in this way we won't recreate the exact value but we will have a chance of guessing a coherent value for *total_bedrooms*, because we assume there is a positive correlation between these values and *total_rooms*.\n",
    "For this project, we will try both approaches, and see if there is any benefit in final accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 709,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8685028476672898\n"
     ]
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 7))\n",
    "plt.title(\"Proportion of total bedrooms over total rooms\")\n",
    "plt.hist(dataset[-dataset.isnull().any(axis=1)][\"total_bedrooms\"]/dataset[-dataset.isnull().any(axis=1)][\"total_rooms\"])\n",
    "plt.grid()\n",
    "plt.show()\n",
    "print(pearsonr(dataset[-dataset.isnull().any(axis=1)][\"total_bedrooms\"], dataset[-dataset.isnull().any(axis=1)][\"total_rooms\"])[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 710,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.21315669268051768, 0.05027428526401439)"
      ]
     },
     "execution_count": 710,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(dataset[-dataset.isnull().any(axis=1)][\"total_bedrooms\"]/dataset[-dataset.isnull().any(axis=1)][\"total_rooms\"]), np.std(dataset[-dataset.isnull().any(axis=1)][\"total_bedrooms\"]/dataset[-dataset.isnull().any(axis=1)][\"total_rooms\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another interesting approach could be considering *households* as a possible estimator of *total_bedrooms* but in this case there is a quite high standard deviation, so it wouldn't be the best method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 711,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1.0906315531829605, 0.4492873286018552)"
      ]
     },
     "execution_count": 711,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(dataset[\"total_bedrooms\"]/dataset[\"households\"]), np.std(dataset[\"total_bedrooms\"]/dataset[\"households\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another helpful point of view is considering *population* as estimator because, on average, it is the double of *total_bedrooms* values but also in this case standard deviation is not sufficiently low."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 712,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.3981553504360808, 0.2350093579505213)"
      ]
     },
     "execution_count": 712,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(dataset[\"total_bedrooms\"]/dataset[\"population\"]), np.std(dataset[\"total_bedrooms\"]/dataset[\"population\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Replacing missing values (stochastic regression imputation)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since standard deviation in the first approach is around 0.05 and mean is 0.21 we can define a range (0.16, 0.26) from which we can uniformly extract a number, with the aim of recreating a coherent value which can replace the missing one.\n",
    "In order to keep consistency, we have to round the value to an integer value, since we are dealing with bedrooms and they belong to a discrete set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 713,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "285.0\n",
      "165.0\n",
      "151.0\n",
      "136.0\n",
      "568.0\n",
      "679.0\n",
      "620.0\n",
      "759.0\n",
      "525.0\n",
      "245.0\n",
      "821.0\n",
      "638.0\n",
      "261.0\n",
      "292.0\n",
      "282.0\n",
      "201.0\n",
      "679.0\n",
      "274.0\n",
      "701.0\n",
      "697.0\n",
      "1265.0\n",
      "586.0\n",
      "630.0\n",
      "758.0\n",
      "497.0\n",
      "349.0\n",
      "218.0\n",
      "324.0\n",
      "186.0\n",
      "592.0\n",
      "226.0\n",
      "410.0\n",
      "281.0\n",
      "386.0\n",
      "263.0\n",
      "444.0\n",
      "128.0\n",
      "342.0\n",
      "447.0\n",
      "293.0\n",
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      "912.0\n",
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      "983.0\n",
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      "448.0\n",
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      "410.0\n",
      "419.0\n",
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      "1200.0\n",
      "535.0\n",
      "308.0\n",
      "834.0\n",
      "578.0\n",
      "469.0\n",
      "812.0\n",
      "986.0\n",
      "393.0\n",
      "271.0\n",
      "689.0\n",
      "231.0\n",
      "488.0\n",
      "1023.0\n",
      "380.0\n",
      "737.0\n",
      "704.0\n",
      "433.0\n",
      "520.0\n",
      "507.0\n",
      "204.0\n",
      "250.0\n",
      "229.0\n",
      "434.0\n",
      "188.0\n",
      "288.0\n",
      "250.0\n",
      "497.0\n",
      "286.0\n",
      "373.0\n",
      "330.0\n",
      "479.0\n",
      "1082.0\n",
      "1179.0\n",
      "499.0\n"
     ]
    }
   ],
   "source": [
    "for index, i in dataset[dataset.isnull().any(axis=1)].iterrows():\n",
    "    print(round(i[\"total_rooms\"] * uniform(0.16, 0.26))) # possible values for total_bedrooms"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this way we probably won't have the original exact values but we are sure that they will be consistent, which is the most important thing when giving more examples to the model; in general, this is the same valid approach for data augmentation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's build two datasets: one with NaNs removed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 714,
   "metadata": {},
   "outputs": [],
   "source": [
    "reduced_dataset = dataset[-dataset.isnull().any(axis=1)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and one with its values replaced as explained above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 715,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    }
   ],
   "source": [
    "for index, i in dataset[[\"total_rooms\", \"total_bedrooms\"]].iterrows():\n",
    "    if math.isnan(i[1]):\n",
    "        dataset[\"total_bedrooms\"][index] = round(dataset[\"total_rooms\"][index] * uniform(0.16, 0.26))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "let's check again the presence of missing values..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 716,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 716,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.isnull().values.any()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and the size of the dataset is the same as before"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 717,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16715"
      ]
     },
     "execution_count": 717,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "we can see that the previous cases of NaNs have been replaced by consistent values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 718,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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",
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       "      <th>333</th>\n",
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       "      <td>2248.0</td>\n",
       "      <td>422.0</td>\n",
       "      <td>870.0</td>\n",
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       "      <th>619</th>\n",
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       "      <td>37.73</td>\n",
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       "      <th>790</th>\n",
       "      <td>-122.08</td>\n",
       "      <td>37.64</td>\n",
       "      <td>36.0</td>\n",
       "      <td>1340.0</td>\n",
       "      <td>245.0</td>\n",
       "      <td>789.0</td>\n",
       "      <td>248.0</td>\n",
       "      <td>3.8000</td>\n",
       "      <td>172000.0</td>\n",
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       "      <td>3314.0</td>\n",
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       "      <td>-121.70</td>\n",
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       "      <td>19.0</td>\n",
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       "      <td>405.0</td>\n",
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       "      <th>1572</th>\n",
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       "      <td>458.0</td>\n",
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       "      <th>1738</th>\n",
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       "      <td>-120.01</td>\n",
       "      <td>38.89</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1669.0</td>\n",
       "      <td>422.0</td>\n",
       "      <td>589.0</td>\n",
       "      <td>281.0</td>\n",
       "      <td>3.0089</td>\n",
       "      <td>100800.0</td>\n",
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       "      <td>1</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "333     -122.17     37.74                43.0        818.0           193.0   \n",
       "387     -122.29     37.89                52.0       2248.0           422.0   \n",
       "619     -122.17     37.73                46.0       2163.0           470.0   \n",
       "645     -122.13     37.72                26.0       2862.0           394.0   \n",
       "790     -122.08     37.64                36.0       1340.0           245.0   \n",
       "836     -122.04     37.60                17.0       3314.0           638.0   \n",
       "1273    -121.70     37.93                19.0       2005.0           405.0   \n",
       "1572    -122.00     37.82                20.0       2206.0           458.0   \n",
       "1738    -122.33     37.98                32.0       1967.0           348.0   \n",
       "1776    -122.34     37.94                42.0       2206.0           451.0   \n",
       "1899    -120.01     38.89                24.0       1669.0           422.0   \n",
       "\n",
       "      population  households  median_income  median_house_value  <1H OCEAN  \\\n",
       "333        494.0       179.0         2.4776            101600.0          0   \n",
       "387        870.0       377.0         3.4732            246200.0          0   \n",
       "619        925.0       435.0         3.2500            177500.0          0   \n",
       "645       1030.0       397.0         7.9120            367300.0          0   \n",
       "790        789.0       248.0         3.8000            172000.0          0   \n",
       "836       1873.0       602.0         4.3875            238500.0          0   \n",
       "1273       972.0       403.0         2.2216            156700.0          0   \n",
       "1572       926.0       432.0         4.6042            256400.0          1   \n",
       "1738      1144.0       364.0         4.4135            150100.0          0   \n",
       "1776       989.0       444.0         3.1250            143900.0          0   \n",
       "1899       589.0       281.0         3.0089            100800.0          0   \n",
       "\n",
       "      INLAND  ISLAND  NEAR BAY  NEAR OCEAN  \n",
       "333        0       0         1           0  \n",
       "387        0       0         1           0  \n",
       "619        0       0         1           0  \n",
       "645        0       0         1           0  \n",
       "790        0       0         1           0  \n",
       "836        0       0         1           0  \n",
       "1273       1       0         0           0  \n",
       "1572       0       0         0           0  \n",
       "1738       0       0         1           0  \n",
       "1776       0       0         1           0  \n",
       "1899       1       0         0           0  "
      ]
     },
     "execution_count": 718,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nans_indexes = [290, 341, 538, 563, 696, 738, 1097, 1350, 1456, 1493, 1606]\n",
    "\n",
    "dataset.iloc[nans_indexes]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Normalization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As final step of preprocessing we perform normalization, basically, there are two main categories: min-max and z-score; we will try both to see which one is better for our data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Min-Max"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " <img src=\"images/minmax.png\" width=\"200\"/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 719,
   "metadata": {},
   "outputs": [],
   "source": [
    "def simple_split(dataset):\n",
    "    y = dataset[\"median_house_value\"]\n",
    "    x = dataset.drop(\"median_house_value\", axis = 1)\n",
    "    return x, y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 720,
   "metadata": {},
   "outputs": [
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       "      <td>1.000000</td>\n",
       "      <td>0.132653</td>\n",
       "      <td>0.124413</td>\n",
       "      <td>0.171070</td>\n",
       "      <td>0.189415</td>\n",
       "      <td>0.684719</td>\n",
       "      <td>0.698417</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.212982</td>\n",
       "      <td>0.564293</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.201923</td>\n",
       "      <td>0.159624</td>\n",
       "      <td>0.173325</td>\n",
       "      <td>0.226555</td>\n",
       "      <td>0.445496</td>\n",
       "      <td>0.700343</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.212982</td>\n",
       "      <td>0.564293</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.062991</td>\n",
       "      <td>0.107199</td>\n",
       "      <td>0.124356</td>\n",
       "      <td>0.165274</td>\n",
       "      <td>0.470871</td>\n",
       "      <td>0.545164</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.212982</td>\n",
       "      <td>0.563231</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.380102</td>\n",
       "      <td>0.323161</td>\n",
       "      <td>0.343750</td>\n",
       "      <td>0.463324</td>\n",
       "      <td>0.420587</td>\n",
       "      <td>0.608306</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.212982</td>\n",
       "      <td>0.563231</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.491758</td>\n",
       "      <td>0.478091</td>\n",
       "      <td>0.364046</td>\n",
       "      <td>0.586815</td>\n",
       "      <td>0.348816</td>\n",
       "      <td>0.484590</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.211968</td>\n",
       "      <td>0.563231</td>\n",
       "      <td>0.803922</td>\n",
       "      <td>0.384027</td>\n",
       "      <td>0.460876</td>\n",
       "      <td>0.379832</td>\n",
       "      <td>0.538533</td>\n",
       "      <td>0.210414</td>\n",
       "      <td>0.453126</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.212982</td>\n",
       "      <td>0.563231</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.579082</td>\n",
       "      <td>0.493740</td>\n",
       "      <td>0.490979</td>\n",
       "      <td>0.649025</td>\n",
       "      <td>0.424861</td>\n",
       "      <td>0.526756</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.211968</td>\n",
       "      <td>0.564293</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.314757</td>\n",
       "      <td>0.280125</td>\n",
       "      <td>0.284472</td>\n",
       "      <td>0.359331</td>\n",
       "      <td>0.359880</td>\n",
       "      <td>0.570420</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.211968</td>\n",
       "      <td>0.564293</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.570055</td>\n",
       "      <td>0.528951</td>\n",
       "      <td>0.475838</td>\n",
       "      <td>0.667595</td>\n",
       "      <td>0.368853</td>\n",
       "      <td>0.485446</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "2    0.213996  0.564293            1.000000     0.170526        0.089202   \n",
       "3    0.212982  0.564293            1.000000     0.132653        0.124413   \n",
       "4    0.212982  0.564293            1.000000     0.201923        0.159624   \n",
       "5    0.212982  0.564293            1.000000     0.062991        0.107199   \n",
       "6    0.212982  0.563231            1.000000     0.380102        0.323161   \n",
       "7    0.212982  0.563231            1.000000     0.491758        0.478091   \n",
       "8    0.211968  0.563231            0.803922     0.384027        0.460876   \n",
       "9    0.212982  0.563231            1.000000     0.579082        0.493740   \n",
       "10   0.211968  0.564293            1.000000     0.314757        0.280125   \n",
       "11   0.211968  0.564293            1.000000     0.570055        0.528951   \n",
       "\n",
       "    population  households  median_income  median_house_value  <1H OCEAN  \\\n",
       "2     0.151095    0.150418       0.899633            0.721533        0.0   \n",
       "3     0.171070    0.189415       0.684719            0.698417        0.0   \n",
       "4     0.173325    0.226555       0.445496            0.700343        0.0   \n",
       "5     0.124356    0.165274       0.470871            0.545164        0.0   \n",
       "6     0.343750    0.463324       0.420587            0.608306        0.0   \n",
       "7     0.364046    0.586815       0.348816            0.484590        0.0   \n",
       "8     0.379832    0.538533       0.210414            0.453126        0.0   \n",
       "9     0.490979    0.649025       0.424861            0.526756        0.0   \n",
       "10    0.284472    0.359331       0.359880            0.570420        0.0   \n",
       "11    0.475838    0.667595       0.368853            0.485446        0.0   \n",
       "\n",
       "    INLAND  ISLAND  NEAR BAY  NEAR OCEAN  \n",
       "2      0.0     0.0       1.0         0.0  \n",
       "3      0.0     0.0       1.0         0.0  \n",
       "4      0.0     0.0       1.0         0.0  \n",
       "5      0.0     0.0       1.0         0.0  \n",
       "6      0.0     0.0       1.0         0.0  \n",
       "7      0.0     0.0       1.0         0.0  \n",
       "8      0.0     0.0       1.0         0.0  \n",
       "9      0.0     0.0       1.0         0.0  \n",
       "10     0.0     0.0       1.0         0.0  \n",
       "11     0.0     0.0       1.0         0.0  "
      ]
     },
     "execution_count": 720,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "min_max_norm_dataset = dataset\n",
    "#X, y = simple_split(min_max_norm_dataset)\n",
    "#X = ((X - X.min()) / (X.max() - X.min()))\n",
    "#X[\"median_house_value\"] = y\n",
    "#min_max_norm_dataset = X\n",
    "min_max_norm_dataset = (min_max_norm_dataset - min_max_norm_dataset.min()) / (min_max_norm_dataset.max() - min_max_norm_dataset.min())\n",
    "min_max_norm_dataset.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we apply the same normalization for the reduced dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 721,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>&lt;1H OCEAN</th>\n",
       "      <th>INLAND</th>\n",
       "      <th>ISLAND</th>\n",
       "      <th>NEAR BAY</th>\n",
       "      <th>NEAR OCEAN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.213996</td>\n",
       "      <td>0.564293</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.170526</td>\n",
       "      <td>0.103920</td>\n",
       "      <td>0.151095</td>\n",
       "      <td>0.150418</td>\n",
       "      <td>0.899633</td>\n",
       "      <td>0.721533</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.212982</td>\n",
       "      <td>0.564293</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.132653</td>\n",
       "      <td>0.144941</td>\n",
       "      <td>0.171070</td>\n",
       "      <td>0.189415</td>\n",
       "      <td>0.684719</td>\n",
       "      <td>0.698417</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.212982</td>\n",
       "      <td>0.564293</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.201923</td>\n",
       "      <td>0.185962</td>\n",
       "      <td>0.173325</td>\n",
       "      <td>0.226555</td>\n",
       "      <td>0.445496</td>\n",
       "      <td>0.700343</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.212982</td>\n",
       "      <td>0.564293</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.062991</td>\n",
       "      <td>0.124886</td>\n",
       "      <td>0.124356</td>\n",
       "      <td>0.165274</td>\n",
       "      <td>0.470871</td>\n",
       "      <td>0.545164</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.212982</td>\n",
       "      <td>0.563231</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.380102</td>\n",
       "      <td>0.376481</td>\n",
       "      <td>0.343750</td>\n",
       "      <td>0.463324</td>\n",
       "      <td>0.420587</td>\n",
       "      <td>0.608306</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.212982</td>\n",
       "      <td>0.563231</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.491758</td>\n",
       "      <td>0.556974</td>\n",
       "      <td>0.364046</td>\n",
       "      <td>0.586815</td>\n",
       "      <td>0.348816</td>\n",
       "      <td>0.484590</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.211968</td>\n",
       "      <td>0.563231</td>\n",
       "      <td>0.803922</td>\n",
       "      <td>0.384027</td>\n",
       "      <td>0.536919</td>\n",
       "      <td>0.379832</td>\n",
       "      <td>0.538533</td>\n",
       "      <td>0.210414</td>\n",
       "      <td>0.453126</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.212982</td>\n",
       "      <td>0.563231</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.579082</td>\n",
       "      <td>0.575205</td>\n",
       "      <td>0.490979</td>\n",
       "      <td>0.649025</td>\n",
       "      <td>0.424861</td>\n",
       "      <td>0.526756</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.211968</td>\n",
       "      <td>0.564293</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.314757</td>\n",
       "      <td>0.326345</td>\n",
       "      <td>0.284472</td>\n",
       "      <td>0.359331</td>\n",
       "      <td>0.359880</td>\n",
       "      <td>0.570420</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.211968</td>\n",
       "      <td>0.564293</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.570055</td>\n",
       "      <td>0.616226</td>\n",
       "      <td>0.475838</td>\n",
       "      <td>0.667595</td>\n",
       "      <td>0.368853</td>\n",
       "      <td>0.485446</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "2    0.213996  0.564293            1.000000     0.170526        0.103920   \n",
       "3    0.212982  0.564293            1.000000     0.132653        0.144941   \n",
       "4    0.212982  0.564293            1.000000     0.201923        0.185962   \n",
       "5    0.212982  0.564293            1.000000     0.062991        0.124886   \n",
       "6    0.212982  0.563231            1.000000     0.380102        0.376481   \n",
       "7    0.212982  0.563231            1.000000     0.491758        0.556974   \n",
       "8    0.211968  0.563231            0.803922     0.384027        0.536919   \n",
       "9    0.212982  0.563231            1.000000     0.579082        0.575205   \n",
       "10   0.211968  0.564293            1.000000     0.314757        0.326345   \n",
       "11   0.211968  0.564293            1.000000     0.570055        0.616226   \n",
       "\n",
       "    population  households  median_income  median_house_value  <1H OCEAN  \\\n",
       "2     0.151095    0.150418       0.899633            0.721533        0.0   \n",
       "3     0.171070    0.189415       0.684719            0.698417        0.0   \n",
       "4     0.173325    0.226555       0.445496            0.700343        0.0   \n",
       "5     0.124356    0.165274       0.470871            0.545164        0.0   \n",
       "6     0.343750    0.463324       0.420587            0.608306        0.0   \n",
       "7     0.364046    0.586815       0.348816            0.484590        0.0   \n",
       "8     0.379832    0.538533       0.210414            0.453126        0.0   \n",
       "9     0.490979    0.649025       0.424861            0.526756        0.0   \n",
       "10    0.284472    0.359331       0.359880            0.570420        0.0   \n",
       "11    0.475838    0.667595       0.368853            0.485446        0.0   \n",
       "\n",
       "    INLAND  ISLAND  NEAR BAY  NEAR OCEAN  \n",
       "2      0.0     0.0       1.0         0.0  \n",
       "3      0.0     0.0       1.0         0.0  \n",
       "4      0.0     0.0       1.0         0.0  \n",
       "5      0.0     0.0       1.0         0.0  \n",
       "6      0.0     0.0       1.0         0.0  \n",
       "7      0.0     0.0       1.0         0.0  \n",
       "8      0.0     0.0       1.0         0.0  \n",
       "9      0.0     0.0       1.0         0.0  \n",
       "10     0.0     0.0       1.0         0.0  \n",
       "11     0.0     0.0       1.0         0.0  "
      ]
     },
     "execution_count": 721,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "min_max_norm_reduced_dataset = reduced_dataset\n",
    "#X, y = simple_split(min_max_norm_reduced_dataset)\n",
    "#X = ((X - X.min()) / (X.max() - X.min()))\n",
    "#X[\"median_house_value\"] = y\n",
    "#min_max_norm_reduced_dataset = X\n",
    "min_max_norm_reduced_dataset = (min_max_norm_reduced_dataset - min_max_norm_reduced_dataset.min()) / (min_max_norm_reduced_dataset.max() - min_max_norm_reduced_dataset.min())\n",
    "min_max_norm_reduced_dataset.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Z-score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " <img src=\"images/zscore.png\" width=\"200\"/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 722,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>&lt;1H OCEAN</th>\n",
       "      <th>INLAND</th>\n",
       "      <th>ISLAND</th>\n",
       "      <th>NEAR BAY</th>\n",
       "      <th>NEAR OCEAN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.319182</td>\n",
       "      <td>0.999125</td>\n",
       "      <td>1.869305</td>\n",
       "      <td>-0.752144</td>\n",
       "      <td>-1.305628</td>\n",
       "      <td>-1.305431</td>\n",
       "      <td>-1.324942</td>\n",
       "      <td>2.547547</td>\n",
       "      <td>1.743141</td>\n",
       "      <td>-0.882688</td>\n",
       "      <td>-0.696125</td>\n",
       "      <td>-0.017298</td>\n",
       "      <td>2.875876</td>\n",
       "      <td>-0.382255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.324179</td>\n",
       "      <td>0.999125</td>\n",
       "      <td>1.869305</td>\n",
       "      <td>-0.938804</td>\n",
       "      <td>-1.092339</td>\n",
       "      <td>-1.197896</td>\n",
       "      <td>-1.109645</td>\n",
       "      <td>1.419003</td>\n",
       "      <td>1.627918</td>\n",
       "      <td>-0.882688</td>\n",
       "      <td>-0.696125</td>\n",
       "      <td>-0.017298</td>\n",
       "      <td>2.875876</td>\n",
       "      <td>-0.382255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.324179</td>\n",
       "      <td>0.999125</td>\n",
       "      <td>1.869305</td>\n",
       "      <td>-0.597401</td>\n",
       "      <td>-0.879051</td>\n",
       "      <td>-1.185755</td>\n",
       "      <td>-0.904601</td>\n",
       "      <td>0.162805</td>\n",
       "      <td>1.637520</td>\n",
       "      <td>-0.882688</td>\n",
       "      <td>-0.696125</td>\n",
       "      <td>-0.017298</td>\n",
       "      <td>2.875876</td>\n",
       "      <td>-0.382255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-1.324179</td>\n",
       "      <td>0.999125</td>\n",
       "      <td>1.869305</td>\n",
       "      <td>-1.282142</td>\n",
       "      <td>-1.196614</td>\n",
       "      <td>-1.449390</td>\n",
       "      <td>-1.242924</td>\n",
       "      <td>0.296052</td>\n",
       "      <td>0.864035</td>\n",
       "      <td>-0.882688</td>\n",
       "      <td>-0.696125</td>\n",
       "      <td>-0.017298</td>\n",
       "      <td>2.875876</td>\n",
       "      <td>-0.382255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-1.324179</td>\n",
       "      <td>0.994511</td>\n",
       "      <td>1.869305</td>\n",
       "      <td>0.280771</td>\n",
       "      <td>0.111556</td>\n",
       "      <td>-0.268235</td>\n",
       "      <td>0.402558</td>\n",
       "      <td>0.032005</td>\n",
       "      <td>1.178764</td>\n",
       "      <td>-0.882688</td>\n",
       "      <td>-0.696125</td>\n",
       "      <td>-0.017298</td>\n",
       "      <td>2.875876</td>\n",
       "      <td>-0.382255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-1.324179</td>\n",
       "      <td>0.994511</td>\n",
       "      <td>1.869305</td>\n",
       "      <td>0.831078</td>\n",
       "      <td>1.050025</td>\n",
       "      <td>-0.158966</td>\n",
       "      <td>1.084330</td>\n",
       "      <td>-0.344875</td>\n",
       "      <td>0.562110</td>\n",
       "      <td>-0.882688</td>\n",
       "      <td>-0.696125</td>\n",
       "      <td>-0.017298</td>\n",
       "      <td>2.875876</td>\n",
       "      <td>-0.382255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-1.329175</td>\n",
       "      <td>0.994511</td>\n",
       "      <td>1.044328</td>\n",
       "      <td>0.300114</td>\n",
       "      <td>0.945751</td>\n",
       "      <td>-0.073978</td>\n",
       "      <td>0.817773</td>\n",
       "      <td>-1.071651</td>\n",
       "      <td>0.405279</td>\n",
       "      <td>-0.882688</td>\n",
       "      <td>-0.696125</td>\n",
       "      <td>-0.017298</td>\n",
       "      <td>2.875876</td>\n",
       "      <td>-0.382255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-1.324179</td>\n",
       "      <td>0.994511</td>\n",
       "      <td>1.869305</td>\n",
       "      <td>1.261459</td>\n",
       "      <td>1.144820</td>\n",
       "      <td>0.524404</td>\n",
       "      <td>1.427780</td>\n",
       "      <td>0.054446</td>\n",
       "      <td>0.772284</td>\n",
       "      <td>-0.882688</td>\n",
       "      <td>-0.696125</td>\n",
       "      <td>-0.017298</td>\n",
       "      <td>2.875876</td>\n",
       "      <td>-0.382255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>-1.329175</td>\n",
       "      <td>0.999125</td>\n",
       "      <td>1.869305</td>\n",
       "      <td>-0.041290</td>\n",
       "      <td>-0.149130</td>\n",
       "      <td>-0.587373</td>\n",
       "      <td>-0.171567</td>\n",
       "      <td>-0.286780</td>\n",
       "      <td>0.989927</td>\n",
       "      <td>-0.882688</td>\n",
       "      <td>-0.696125</td>\n",
       "      <td>-0.017298</td>\n",
       "      <td>2.875876</td>\n",
       "      <td>-0.382255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>-1.329175</td>\n",
       "      <td>0.999125</td>\n",
       "      <td>1.869305</td>\n",
       "      <td>1.216971</td>\n",
       "      <td>1.358108</td>\n",
       "      <td>0.442886</td>\n",
       "      <td>1.530302</td>\n",
       "      <td>-0.239662</td>\n",
       "      <td>0.566377</td>\n",
       "      <td>-0.882688</td>\n",
       "      <td>-0.696125</td>\n",
       "      <td>-0.017298</td>\n",
       "      <td>2.875876</td>\n",
       "      <td>-0.382255</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "2   -1.319182  0.999125            1.869305    -0.752144       -1.305628   \n",
       "3   -1.324179  0.999125            1.869305    -0.938804       -1.092339   \n",
       "4   -1.324179  0.999125            1.869305    -0.597401       -0.879051   \n",
       "5   -1.324179  0.999125            1.869305    -1.282142       -1.196614   \n",
       "6   -1.324179  0.994511            1.869305     0.280771        0.111556   \n",
       "7   -1.324179  0.994511            1.869305     0.831078        1.050025   \n",
       "8   -1.329175  0.994511            1.044328     0.300114        0.945751   \n",
       "9   -1.324179  0.994511            1.869305     1.261459        1.144820   \n",
       "10  -1.329175  0.999125            1.869305    -0.041290       -0.149130   \n",
       "11  -1.329175  0.999125            1.869305     1.216971        1.358108   \n",
       "\n",
       "    population  households  median_income  median_house_value  <1H OCEAN  \\\n",
       "2    -1.305431   -1.324942       2.547547            1.743141  -0.882688   \n",
       "3    -1.197896   -1.109645       1.419003            1.627918  -0.882688   \n",
       "4    -1.185755   -0.904601       0.162805            1.637520  -0.882688   \n",
       "5    -1.449390   -1.242924       0.296052            0.864035  -0.882688   \n",
       "6    -0.268235    0.402558       0.032005            1.178764  -0.882688   \n",
       "7    -0.158966    1.084330      -0.344875            0.562110  -0.882688   \n",
       "8    -0.073978    0.817773      -1.071651            0.405279  -0.882688   \n",
       "9     0.524404    1.427780       0.054446            0.772284  -0.882688   \n",
       "10   -0.587373   -0.171567      -0.286780            0.989927  -0.882688   \n",
       "11    0.442886    1.530302      -0.239662            0.566377  -0.882688   \n",
       "\n",
       "      INLAND    ISLAND  NEAR BAY  NEAR OCEAN  \n",
       "2  -0.696125 -0.017298  2.875876   -0.382255  \n",
       "3  -0.696125 -0.017298  2.875876   -0.382255  \n",
       "4  -0.696125 -0.017298  2.875876   -0.382255  \n",
       "5  -0.696125 -0.017298  2.875876   -0.382255  \n",
       "6  -0.696125 -0.017298  2.875876   -0.382255  \n",
       "7  -0.696125 -0.017298  2.875876   -0.382255  \n",
       "8  -0.696125 -0.017298  2.875876   -0.382255  \n",
       "9  -0.696125 -0.017298  2.875876   -0.382255  \n",
       "10 -0.696125 -0.017298  2.875876   -0.382255  \n",
       "11 -0.696125 -0.017298  2.875876   -0.382255  "
      ]
     },
     "execution_count": 722,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zscore_norm_dataset = dataset\n",
    "#X, y = simple_split(zscore_norm_dataset)\n",
    "#X = (X - np.mean(X)) / np.std(X)\n",
    "#X[\"median_house_value\"] = y\n",
    "#zscore_norm_dataset = X\n",
    "zscore_norm_dataset = (zscore_norm_dataset - np.mean(zscore_norm_dataset)) / np.std(zscore_norm_dataset)\n",
    "zscore_norm_dataset.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and, again, let's apply the same to the reduced dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 723,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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",
       "      <th>&lt;1H OCEAN</th>\n",
       "      <th>INLAND</th>\n",
       "      <th>ISLAND</th>\n",
       "      <th>NEAR BAY</th>\n",
       "      <th>NEAR OCEAN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.318009</td>\n",
       "      <td>0.997875</td>\n",
       "      <td>1.868229</td>\n",
       "      <td>-0.752970</td>\n",
       "      <td>-1.308807</td>\n",
       "      <td>-1.305524</td>\n",
       "      <td>-1.325364</td>\n",
       "      <td>2.547422</td>\n",
       "      <td>1.743782</td>\n",
       "      <td>-0.881863</td>\n",
       "      <td>-0.696814</td>\n",
       "      <td>-0.017383</td>\n",
       "      <td>2.872646</td>\n",
       "      <td>-0.381921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.323005</td>\n",
       "      <td>0.997875</td>\n",
       "      <td>1.868229</td>\n",
       "      <td>-0.939910</td>\n",
       "      <td>-1.094874</td>\n",
       "      <td>-1.197902</td>\n",
       "      <td>-1.109937</td>\n",
       "      <td>1.418899</td>\n",
       "      <td>1.628533</td>\n",
       "      <td>-0.881863</td>\n",
       "      <td>-0.696814</td>\n",
       "      <td>-0.017383</td>\n",
       "      <td>2.872646</td>\n",
       "      <td>-0.381921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.323005</td>\n",
       "      <td>0.997875</td>\n",
       "      <td>1.868229</td>\n",
       "      <td>-0.597994</td>\n",
       "      <td>-0.880940</td>\n",
       "      <td>-1.185751</td>\n",
       "      <td>-0.904768</td>\n",
       "      <td>0.162724</td>\n",
       "      <td>1.638137</td>\n",
       "      <td>-0.881863</td>\n",
       "      <td>-0.696814</td>\n",
       "      <td>-0.017383</td>\n",
       "      <td>2.872646</td>\n",
       "      <td>-0.381921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-1.323005</td>\n",
       "      <td>0.997875</td>\n",
       "      <td>1.868229</td>\n",
       "      <td>-1.283763</td>\n",
       "      <td>-1.199463</td>\n",
       "      <td>-1.449598</td>\n",
       "      <td>-1.243297</td>\n",
       "      <td>0.295968</td>\n",
       "      <td>0.864470</td>\n",
       "      <td>-0.881863</td>\n",
       "      <td>-0.696814</td>\n",
       "      <td>-0.017383</td>\n",
       "      <td>2.872646</td>\n",
       "      <td>-0.381921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-1.323005</td>\n",
       "      <td>0.993261</td>\n",
       "      <td>1.868229</td>\n",
       "      <td>0.281496</td>\n",
       "      <td>0.112662</td>\n",
       "      <td>-0.267492</td>\n",
       "      <td>0.403182</td>\n",
       "      <td>0.031926</td>\n",
       "      <td>1.179273</td>\n",
       "      <td>-0.881863</td>\n",
       "      <td>-0.696814</td>\n",
       "      <td>-0.017383</td>\n",
       "      <td>2.872646</td>\n",
       "      <td>-0.381921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-1.323005</td>\n",
       "      <td>0.993261</td>\n",
       "      <td>1.868229</td>\n",
       "      <td>0.832629</td>\n",
       "      <td>1.053969</td>\n",
       "      <td>-0.158134</td>\n",
       "      <td>1.085368</td>\n",
       "      <td>-0.344948</td>\n",
       "      <td>0.562473</td>\n",
       "      <td>-0.881863</td>\n",
       "      <td>-0.696814</td>\n",
       "      <td>-0.017383</td>\n",
       "      <td>2.872646</td>\n",
       "      <td>-0.381921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-1.328001</td>\n",
       "      <td>0.993261</td>\n",
       "      <td>1.043756</td>\n",
       "      <td>0.300868</td>\n",
       "      <td>0.949380</td>\n",
       "      <td>-0.073078</td>\n",
       "      <td>0.818649</td>\n",
       "      <td>-1.071710</td>\n",
       "      <td>0.405606</td>\n",
       "      <td>-0.881863</td>\n",
       "      <td>-0.696814</td>\n",
       "      <td>-0.017383</td>\n",
       "      <td>2.872646</td>\n",
       "      <td>-0.381921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-1.323005</td>\n",
       "      <td>0.993261</td>\n",
       "      <td>1.868229</td>\n",
       "      <td>1.263657</td>\n",
       "      <td>1.149051</td>\n",
       "      <td>0.525787</td>\n",
       "      <td>1.429026</td>\n",
       "      <td>0.054366</td>\n",
       "      <td>0.772697</td>\n",
       "      <td>-0.881863</td>\n",
       "      <td>-0.696814</td>\n",
       "      <td>-0.017383</td>\n",
       "      <td>2.872646</td>\n",
       "      <td>-0.381921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>-1.328001</td>\n",
       "      <td>0.997875</td>\n",
       "      <td>1.868229</td>\n",
       "      <td>-0.041048</td>\n",
       "      <td>-0.148812</td>\n",
       "      <td>-0.586886</td>\n",
       "      <td>-0.171290</td>\n",
       "      <td>-0.286854</td>\n",
       "      <td>0.990391</td>\n",
       "      <td>-0.881863</td>\n",
       "      <td>-0.696814</td>\n",
       "      <td>-0.017383</td>\n",
       "      <td>2.872646</td>\n",
       "      <td>-0.381921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>-1.328001</td>\n",
       "      <td>0.997875</td>\n",
       "      <td>1.868229</td>\n",
       "      <td>1.219101</td>\n",
       "      <td>1.362984</td>\n",
       "      <td>0.444202</td>\n",
       "      <td>1.531610</td>\n",
       "      <td>-0.239736</td>\n",
       "      <td>0.566742</td>\n",
       "      <td>-0.881863</td>\n",
       "      <td>-0.696814</td>\n",
       "      <td>-0.017383</td>\n",
       "      <td>2.872646</td>\n",
       "      <td>-0.381921</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "2   -1.318009  0.997875            1.868229    -0.752970       -1.308807   \n",
       "3   -1.323005  0.997875            1.868229    -0.939910       -1.094874   \n",
       "4   -1.323005  0.997875            1.868229    -0.597994       -0.880940   \n",
       "5   -1.323005  0.997875            1.868229    -1.283763       -1.199463   \n",
       "6   -1.323005  0.993261            1.868229     0.281496        0.112662   \n",
       "7   -1.323005  0.993261            1.868229     0.832629        1.053969   \n",
       "8   -1.328001  0.993261            1.043756     0.300868        0.949380   \n",
       "9   -1.323005  0.993261            1.868229     1.263657        1.149051   \n",
       "10  -1.328001  0.997875            1.868229    -0.041048       -0.148812   \n",
       "11  -1.328001  0.997875            1.868229     1.219101        1.362984   \n",
       "\n",
       "    population  households  median_income  median_house_value  <1H OCEAN  \\\n",
       "2    -1.305524   -1.325364       2.547422            1.743782  -0.881863   \n",
       "3    -1.197902   -1.109937       1.418899            1.628533  -0.881863   \n",
       "4    -1.185751   -0.904768       0.162724            1.638137  -0.881863   \n",
       "5    -1.449598   -1.243297       0.295968            0.864470  -0.881863   \n",
       "6    -0.267492    0.403182       0.031926            1.179273  -0.881863   \n",
       "7    -0.158134    1.085368      -0.344948            0.562473  -0.881863   \n",
       "8    -0.073078    0.818649      -1.071710            0.405606  -0.881863   \n",
       "9     0.525787    1.429026       0.054366            0.772697  -0.881863   \n",
       "10   -0.586886   -0.171290      -0.286854            0.990391  -0.881863   \n",
       "11    0.444202    1.531610      -0.239736            0.566742  -0.881863   \n",
       "\n",
       "      INLAND    ISLAND  NEAR BAY  NEAR OCEAN  \n",
       "2  -0.696814 -0.017383  2.872646   -0.381921  \n",
       "3  -0.696814 -0.017383  2.872646   -0.381921  \n",
       "4  -0.696814 -0.017383  2.872646   -0.381921  \n",
       "5  -0.696814 -0.017383  2.872646   -0.381921  \n",
       "6  -0.696814 -0.017383  2.872646   -0.381921  \n",
       "7  -0.696814 -0.017383  2.872646   -0.381921  \n",
       "8  -0.696814 -0.017383  2.872646   -0.381921  \n",
       "9  -0.696814 -0.017383  2.872646   -0.381921  \n",
       "10 -0.696814 -0.017383  2.872646   -0.381921  \n",
       "11 -0.696814 -0.017383  2.872646   -0.381921  "
      ]
     },
     "execution_count": 723,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zscore_norm_reduced_dataset = reduced_dataset\n",
    "#X, y = simple_split(zscore_norm_reduced_dataset)\n",
    "#X = (X - np.mean(X)) / np.std(X)\n",
    "#X[\"median_house_value\"] = y\n",
    "#zscore_norm_reduced_dataset = X\n",
    "zscore_norm_reduced_dataset = (zscore_norm_reduced_dataset - np.mean(zscore_norm_reduced_dataset)) / np.std(zscore_norm_reduced_dataset)\n",
    "zscore_norm_reduced_dataset.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We compute mean of standard deviations to see the maximum value that loss will reach"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 724,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_stds(dataset):\n",
    "    std_dev = []\n",
    "    for index, i in dataset.iteritems():\n",
    "        std_dev.append(np.std(i))\n",
    "\n",
    "    return np.mean(std_dev)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 725,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9999999999999686"
      ]
     },
     "execution_count": 725,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compute_stds(zscore_norm_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 726,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9999999999999972"
      ]
     },
     "execution_count": 726,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compute_stds(zscore_norm_reduced_dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's add a constant feature of 1's in order to add the intercept"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 727,
   "metadata": {},
   "outputs": [],
   "source": [
    "min_max_norm_dataset[\"intercept\"] = 1\n",
    "min_max_norm_reduced_dataset[\"intercept\"] = 1\n",
    "zscore_norm_dataset[\"intercept\"] = 1\n",
    "zscore_norm_reduced_dataset[\"intercept\"] = 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Writing preprocessed datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 728,
   "metadata": {},
   "outputs": [],
   "source": [
    "min_max_norm_dataset.to_csv(\"datasets/min_max_norm_dataset\")\n",
    "min_max_norm_reduced_dataset.to_csv(\"datasets/min_max_norm_reduced_dataset\")\n",
    "\n",
    "zscore_norm_dataset.to_csv(\"datasets/zscore_norm_dataset\")\n",
    "zscore_norm_reduced_dataset.to_csv(\"datasets/zscore_norm_reduced_dataset\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 729,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\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",
       "      <th>&lt;1H OCEAN</th>\n",
       "      <th>INLAND</th>\n",
       "      <th>ISLAND</th>\n",
       "      <th>NEAR BAY</th>\n",
       "      <th>NEAR OCEAN</th>\n",
       "      <th>intercept</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.213996</td>\n",
       "      <td>0.564293</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.170526</td>\n",
       "      <td>0.089202</td>\n",
       "      <td>0.151095</td>\n",
       "      <td>0.150418</td>\n",
       "      <td>0.899633</td>\n",
       "      <td>0.721533</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.212982</td>\n",
       "      <td>0.564293</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.132653</td>\n",
       "      <td>0.124413</td>\n",
       "      <td>0.171070</td>\n",
       "      <td>0.189415</td>\n",
       "      <td>0.684719</td>\n",
       "      <td>0.698417</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.212982</td>\n",
       "      <td>0.564293</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.201923</td>\n",
       "      <td>0.159624</td>\n",
       "      <td>0.173325</td>\n",
       "      <td>0.226555</td>\n",
       "      <td>0.445496</td>\n",
       "      <td>0.700343</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.212982</td>\n",
       "      <td>0.564293</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.062991</td>\n",
       "      <td>0.107199</td>\n",
       "      <td>0.124356</td>\n",
       "      <td>0.165274</td>\n",
       "      <td>0.470871</td>\n",
       "      <td>0.545164</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.212982</td>\n",
       "      <td>0.563231</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.380102</td>\n",
       "      <td>0.323161</td>\n",
       "      <td>0.343750</td>\n",
       "      <td>0.463324</td>\n",
       "      <td>0.420587</td>\n",
       "      <td>0.608306</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.212982</td>\n",
       "      <td>0.563231</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.491758</td>\n",
       "      <td>0.478091</td>\n",
       "      <td>0.364046</td>\n",
       "      <td>0.586815</td>\n",
       "      <td>0.348816</td>\n",
       "      <td>0.484590</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.211968</td>\n",
       "      <td>0.563231</td>\n",
       "      <td>0.803922</td>\n",
       "      <td>0.384027</td>\n",
       "      <td>0.460876</td>\n",
       "      <td>0.379832</td>\n",
       "      <td>0.538533</td>\n",
       "      <td>0.210414</td>\n",
       "      <td>0.453126</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.212982</td>\n",
       "      <td>0.563231</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.579082</td>\n",
       "      <td>0.493740</td>\n",
       "      <td>0.490979</td>\n",
       "      <td>0.649025</td>\n",
       "      <td>0.424861</td>\n",
       "      <td>0.526756</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.211968</td>\n",
       "      <td>0.564293</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.314757</td>\n",
       "      <td>0.280125</td>\n",
       "      <td>0.284472</td>\n",
       "      <td>0.359331</td>\n",
       "      <td>0.359880</td>\n",
       "      <td>0.570420</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.211968</td>\n",
       "      <td>0.564293</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.570055</td>\n",
       "      <td>0.528951</td>\n",
       "      <td>0.475838</td>\n",
       "      <td>0.667595</td>\n",
       "      <td>0.368853</td>\n",
       "      <td>0.485446</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "2    0.213996  0.564293            1.000000     0.170526        0.089202   \n",
       "3    0.212982  0.564293            1.000000     0.132653        0.124413   \n",
       "4    0.212982  0.564293            1.000000     0.201923        0.159624   \n",
       "5    0.212982  0.564293            1.000000     0.062991        0.107199   \n",
       "6    0.212982  0.563231            1.000000     0.380102        0.323161   \n",
       "7    0.212982  0.563231            1.000000     0.491758        0.478091   \n",
       "8    0.211968  0.563231            0.803922     0.384027        0.460876   \n",
       "9    0.212982  0.563231            1.000000     0.579082        0.493740   \n",
       "10   0.211968  0.564293            1.000000     0.314757        0.280125   \n",
       "11   0.211968  0.564293            1.000000     0.570055        0.528951   \n",
       "\n",
       "    population  households  median_income  median_house_value  <1H OCEAN  \\\n",
       "2     0.151095    0.150418       0.899633            0.721533        0.0   \n",
       "3     0.171070    0.189415       0.684719            0.698417        0.0   \n",
       "4     0.173325    0.226555       0.445496            0.700343        0.0   \n",
       "5     0.124356    0.165274       0.470871            0.545164        0.0   \n",
       "6     0.343750    0.463324       0.420587            0.608306        0.0   \n",
       "7     0.364046    0.586815       0.348816            0.484590        0.0   \n",
       "8     0.379832    0.538533       0.210414            0.453126        0.0   \n",
       "9     0.490979    0.649025       0.424861            0.526756        0.0   \n",
       "10    0.284472    0.359331       0.359880            0.570420        0.0   \n",
       "11    0.475838    0.667595       0.368853            0.485446        0.0   \n",
       "\n",
       "    INLAND  ISLAND  NEAR BAY  NEAR OCEAN  intercept  \n",
       "2      0.0     0.0       1.0         0.0          1  \n",
       "3      0.0     0.0       1.0         0.0          1  \n",
       "4      0.0     0.0       1.0         0.0          1  \n",
       "5      0.0     0.0       1.0         0.0          1  \n",
       "6      0.0     0.0       1.0         0.0          1  \n",
       "7      0.0     0.0       1.0         0.0          1  \n",
       "8      0.0     0.0       1.0         0.0          1  \n",
       "9      0.0     0.0       1.0         0.0          1  \n",
       "10     0.0     0.0       1.0         0.0          1  \n",
       "11     0.0     0.0       1.0         0.0          1  "
      ]
     },
     "execution_count": 729,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "min_max_norm_dataset.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 730,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
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       "      <th>INLAND</th>\n",
       "      <th>ISLAND</th>\n",
       "      <th>NEAR BAY</th>\n",
       "      <th>NEAR OCEAN</th>\n",
       "      <th>intercept</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.213996</td>\n",
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       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.212982</td>\n",
       "      <td>0.564293</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.132653</td>\n",
       "      <td>0.144941</td>\n",
       "      <td>0.171070</td>\n",
       "      <td>0.189415</td>\n",
       "      <td>0.684719</td>\n",
       "      <td>0.698417</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.212982</td>\n",
       "      <td>0.564293</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.201923</td>\n",
       "      <td>0.185962</td>\n",
       "      <td>0.173325</td>\n",
       "      <td>0.226555</td>\n",
       "      <td>0.445496</td>\n",
       "      <td>0.700343</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.212982</td>\n",
       "      <td>0.564293</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.062991</td>\n",
       "      <td>0.124886</td>\n",
       "      <td>0.124356</td>\n",
       "      <td>0.165274</td>\n",
       "      <td>0.470871</td>\n",
       "      <td>0.545164</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.212982</td>\n",
       "      <td>0.563231</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.380102</td>\n",
       "      <td>0.376481</td>\n",
       "      <td>0.343750</td>\n",
       "      <td>0.463324</td>\n",
       "      <td>0.420587</td>\n",
       "      <td>0.608306</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.212982</td>\n",
       "      <td>0.563231</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.491758</td>\n",
       "      <td>0.556974</td>\n",
       "      <td>0.364046</td>\n",
       "      <td>0.586815</td>\n",
       "      <td>0.348816</td>\n",
       "      <td>0.484590</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.211968</td>\n",
       "      <td>0.563231</td>\n",
       "      <td>0.803922</td>\n",
       "      <td>0.384027</td>\n",
       "      <td>0.536919</td>\n",
       "      <td>0.379832</td>\n",
       "      <td>0.538533</td>\n",
       "      <td>0.210414</td>\n",
       "      <td>0.453126</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.212982</td>\n",
       "      <td>0.563231</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.579082</td>\n",
       "      <td>0.575205</td>\n",
       "      <td>0.490979</td>\n",
       "      <td>0.649025</td>\n",
       "      <td>0.424861</td>\n",
       "      <td>0.526756</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.211968</td>\n",
       "      <td>0.564293</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.314757</td>\n",
       "      <td>0.326345</td>\n",
       "      <td>0.284472</td>\n",
       "      <td>0.359331</td>\n",
       "      <td>0.359880</td>\n",
       "      <td>0.570420</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.211968</td>\n",
       "      <td>0.564293</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.570055</td>\n",
       "      <td>0.616226</td>\n",
       "      <td>0.475838</td>\n",
       "      <td>0.667595</td>\n",
       "      <td>0.368853</td>\n",
       "      <td>0.485446</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "2    0.213996  0.564293            1.000000     0.170526        0.103920   \n",
       "3    0.212982  0.564293            1.000000     0.132653        0.144941   \n",
       "4    0.212982  0.564293            1.000000     0.201923        0.185962   \n",
       "5    0.212982  0.564293            1.000000     0.062991        0.124886   \n",
       "6    0.212982  0.563231            1.000000     0.380102        0.376481   \n",
       "7    0.212982  0.563231            1.000000     0.491758        0.556974   \n",
       "8    0.211968  0.563231            0.803922     0.384027        0.536919   \n",
       "9    0.212982  0.563231            1.000000     0.579082        0.575205   \n",
       "10   0.211968  0.564293            1.000000     0.314757        0.326345   \n",
       "11   0.211968  0.564293            1.000000     0.570055        0.616226   \n",
       "\n",
       "    population  households  median_income  median_house_value  <1H OCEAN  \\\n",
       "2     0.151095    0.150418       0.899633            0.721533        0.0   \n",
       "3     0.171070    0.189415       0.684719            0.698417        0.0   \n",
       "4     0.173325    0.226555       0.445496            0.700343        0.0   \n",
       "5     0.124356    0.165274       0.470871            0.545164        0.0   \n",
       "6     0.343750    0.463324       0.420587            0.608306        0.0   \n",
       "7     0.364046    0.586815       0.348816            0.484590        0.0   \n",
       "8     0.379832    0.538533       0.210414            0.453126        0.0   \n",
       "9     0.490979    0.649025       0.424861            0.526756        0.0   \n",
       "10    0.284472    0.359331       0.359880            0.570420        0.0   \n",
       "11    0.475838    0.667595       0.368853            0.485446        0.0   \n",
       "\n",
       "    INLAND  ISLAND  NEAR BAY  NEAR OCEAN  intercept  \n",
       "2      0.0     0.0       1.0         0.0          1  \n",
       "3      0.0     0.0       1.0         0.0          1  \n",
       "4      0.0     0.0       1.0         0.0          1  \n",
       "5      0.0     0.0       1.0         0.0          1  \n",
       "6      0.0     0.0       1.0         0.0          1  \n",
       "7      0.0     0.0       1.0         0.0          1  \n",
       "8      0.0     0.0       1.0         0.0          1  \n",
       "9      0.0     0.0       1.0         0.0          1  \n",
       "10     0.0     0.0       1.0         0.0          1  \n",
       "11     0.0     0.0       1.0         0.0          1  "
      ]
     },
     "execution_count": 730,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "min_max_norm_reduced_dataset.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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