housing.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hello world!\n"
     ]
    }
   ],
   "source": [
    "print('hello world!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import tarfile\n",
    "import urllib\n",
    "\n",
    "DOWNLOAD_ROOT = \"https://github.com/ageron/handson-ml/tree/master/\"\n",
    "HOUSING_PATH = \"datasets/housing\"\n",
    "HOUSING_URL = DOWNLOAD_ROOT + HOUSING_PATH + \"/housing.tgz\"\n",
    "\n",
    "def fetch_housing_data(housing_url=HOUSING_URL,housing_path=HOUSING_PATH):\n",
    "    if not os.path.isdir(housing_path):\n",
    "        os.makedirs(housing_path)\n",
    "    tgz_path = os.path.join(housing_path,\"housing.tgz\")\n",
    "    urllib.request.urlretrieve(housing_url,tgz_path)\n",
    "    #housing_tgz = tarfile.open(path=tgz_path)\n",
    "    housing_tgz = tarfile.open(path='E:\\python_work\\handson_ml\\datasets\\housing\\housing.tgz')\n",
    "    housing_tgz.extractall(path=housing_path)\n",
    "    housing_tgz.close()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "def load_housing_data(housing_path=HOUSING_PATH):\n",
    "    csv_path = os.path.join(housing_path ,'housing.csv')\n",
    "    return pd.read_csv(csv_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing = load_housing_data()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method NDFrame.head of        longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n0        -122.23     37.88                41.0        880.0           129.0   \n1        -122.22     37.86                21.0       7099.0          1106.0   \n2        -122.24     37.85                52.0       1467.0           190.0   \n3        -122.25     37.85                52.0       1274.0           235.0   \n4        -122.25     37.85                52.0       1627.0           280.0   \n5        -122.25     37.85                52.0        919.0           213.0   \n6        -122.25     37.84                52.0       2535.0           489.0   \n7        -122.25     37.84                52.0       3104.0           687.0   \n8        -122.26     37.84                42.0       2555.0           665.0   \n9        -122.25     37.84                52.0       3549.0           707.0   \n10       -122.26     37.85                52.0       2202.0           434.0   \n11       -122.26     37.85                52.0       3503.0           752.0   \n12       -122.26     37.85                52.0       2491.0           474.0   \n13       -122.26     37.84                52.0        696.0           191.0   \n14       -122.26     37.85                52.0       2643.0           626.0   \n15       -122.26     37.85                50.0       1120.0           283.0   \n16       -122.27     37.85                52.0       1966.0           347.0   \n17       -122.27     37.85                52.0       1228.0           293.0   \n18       -122.26     37.84                50.0       2239.0           455.0   \n19       -122.27     37.84                52.0       1503.0           298.0   \n20       -122.27     37.85                40.0        751.0           184.0   \n21       -122.27     37.85                42.0       1639.0           367.0   \n22       -122.27     37.84                52.0       2436.0           541.0   \n23       -122.27     37.84                52.0       1688.0           337.0   \n24       -122.27     37.84                52.0       2224.0           437.0   \n25       -122.28     37.85                41.0        535.0           123.0   \n26       -122.28     37.85                49.0       1130.0           244.0   \n27       -122.28     37.85                52.0       1898.0           421.0   \n28       -122.28     37.84                50.0       2082.0           492.0   \n29       -122.28     37.84                52.0        729.0           160.0   \n...          ...       ...                 ...          ...             ...   \n20610    -121.56     39.10                28.0       2130.0           484.0   \n20611    -121.55     39.10                27.0       1783.0           441.0   \n20612    -121.56     39.08                26.0       1377.0           289.0   \n20613    -121.55     39.09                31.0       1728.0           365.0   \n20614    -121.54     39.08                26.0       2276.0           460.0   \n20615    -121.54     39.08                23.0       1076.0           216.0   \n20616    -121.53     39.08                15.0       1810.0           441.0   \n20617    -121.53     39.06                20.0        561.0           109.0   \n20618    -121.55     39.06                25.0       1332.0           247.0   \n20619    -121.56     39.01                22.0       1891.0           340.0   \n20620    -121.48     39.05                40.0        198.0            41.0   \n20621    -121.47     39.01                37.0       1244.0           247.0   \n20622    -121.44     39.00                20.0        755.0           147.0   \n20623    -121.37     39.03                32.0       1158.0           244.0   \n20624    -121.41     39.04                16.0       1698.0           300.0   \n20625    -121.52     39.12                37.0        102.0            17.0   \n20626    -121.43     39.18                36.0       1124.0           184.0   \n20627    -121.32     39.13                 5.0        358.0            65.0   \n20628    -121.48     39.10                19.0       2043.0           421.0   \n20629    -121.39     39.12                28.0      10035.0          1856.0   \n20630    -121.32     39.29                11.0       2640.0           505.0   \n20631    -121.40     39.33                15.0       2655.0           493.0   \n20632    -121.45     39.26                15.0       2319.0           416.0   \n20633    -121.53     39.19                27.0       2080.0           412.0   \n20634    -121.56     39.27                28.0       2332.0           395.0   \n20635    -121.09     39.48                25.0       1665.0           374.0   \n20636    -121.21     39.49                18.0        697.0           150.0   \n20637    -121.22     39.43                17.0       2254.0           485.0   \n20638    -121.32     39.43                18.0       1860.0           409.0   \n20639    -121.24     39.37                16.0       2785.0           616.0   \n\n       population  households  median_income  median_house_value  \\\n0           322.0       126.0         8.3252            452600.0   \n1          2401.0      1138.0         8.3014            358500.0   \n2           496.0       177.0         7.2574            352100.0   \n3           558.0       219.0         5.6431            341300.0   \n4           565.0       259.0         3.8462            342200.0   \n5           413.0       193.0         4.0368            269700.0   \n6          1094.0       514.0         3.6591            299200.0   \n7          1157.0       647.0         3.1200            241400.0   \n8          1206.0       595.0         2.0804            226700.0   \n9          1551.0       714.0         3.6912            261100.0   \n10          910.0       402.0         3.2031            281500.0   \n11         1504.0       734.0         3.2705            241800.0   \n12         1098.0       468.0         3.0750            213500.0   \n13          345.0       174.0         2.6736            191300.0   \n14         1212.0       620.0         1.9167            159200.0   \n15          697.0       264.0         2.1250            140000.0   \n16          793.0       331.0         2.7750            152500.0   \n17          648.0       303.0         2.1202            155500.0   \n18          990.0       419.0         1.9911            158700.0   \n19          690.0       275.0         2.6033            162900.0   \n20          409.0       166.0         1.3578            147500.0   \n21          929.0       366.0         1.7135            159800.0   \n22         1015.0       478.0         1.7250            113900.0   \n23          853.0       325.0         2.1806             99700.0   \n24         1006.0       422.0         2.6000            132600.0   \n25          317.0       119.0         2.4038            107500.0   \n26          607.0       239.0         2.4597             93800.0   \n27         1102.0       397.0         1.8080            105500.0   \n28         1131.0       473.0         1.6424            108900.0   \n29          395.0       155.0         1.6875            132000.0   \n...           ...         ...            ...                 ...   \n20610      1195.0       439.0         1.3631             45500.0   \n20611      1163.0       409.0         1.2857             47000.0   \n20612       761.0       267.0         1.4934             48300.0   \n20613      1167.0       384.0         1.4958             53400.0   \n20614      1455.0       474.0         2.4695             58000.0   \n20615       724.0       197.0         2.3598             57500.0   \n20616      1157.0       375.0         2.0469             55100.0   \n20617       308.0       114.0         3.3021             70800.0   \n20618       726.0       226.0         2.2500             63400.0   \n20619      1023.0       296.0         2.7303             99100.0   \n20620       151.0        48.0         4.5625            100000.0   \n20621       484.0       157.0         2.3661             77500.0   \n20622       457.0       157.0         2.4167             67000.0   \n20623       598.0       227.0         2.8235             65500.0   \n20624       731.0       291.0         3.0739             87200.0   \n20625        29.0        14.0         4.1250             72000.0   \n20626       504.0       171.0         2.1667             93800.0   \n20627       169.0        59.0         3.0000            162500.0   \n20628      1018.0       390.0         2.5952             92400.0   \n20629      6912.0      1818.0         2.0943            108300.0   \n20630      1257.0       445.0         3.5673            112000.0   \n20631      1200.0       432.0         3.5179            107200.0   \n20632      1047.0       385.0         3.1250            115600.0   \n20633      1082.0       382.0         2.5495             98300.0   \n20634      1041.0       344.0         3.7125            116800.0   \n20635       845.0       330.0         1.5603             78100.0   \n20636       356.0       114.0         2.5568             77100.0   \n20637      1007.0       433.0         1.7000             92300.0   \n20638       741.0       349.0         1.8672             84700.0   \n20639      1387.0       530.0         2.3886             89400.0   \n\n      ocean_proximity  \n0            NEAR BAY  \n1            NEAR BAY  \n2            NEAR BAY  \n3            NEAR BAY  \n4            NEAR BAY  \n5            NEAR BAY  \n6            NEAR BAY  \n7            NEAR BAY  \n8            NEAR BAY  \n9            NEAR BAY  \n10           NEAR BAY  \n11           NEAR BAY  \n12           NEAR BAY  \n13           NEAR BAY  \n14           NEAR BAY  \n15           NEAR BAY  \n16           NEAR BAY  \n17           NEAR BAY  \n18           NEAR BAY  \n19           NEAR BAY  \n20           NEAR BAY  \n21           NEAR BAY  \n22           NEAR BAY  \n23           NEAR BAY  \n24           NEAR BAY  \n25           NEAR BAY  \n26           NEAR BAY  \n27           NEAR BAY  \n28           NEAR BAY  \n29           NEAR BAY  \n...               ...  \n20610          INLAND  \n20611          INLAND  \n20612          INLAND  \n20613          INLAND  \n20614          INLAND  \n20615          INLAND  \n20616          INLAND  \n20617          INLAND  \n20618          INLAND  \n20619          INLAND  \n20620          INLAND  \n20621          INLAND  \n20622          INLAND  \n20623          INLAND  \n20624          INLAND  \n20625          INLAND  \n20626          INLAND  \n20627          INLAND  \n20628          INLAND  \n20629          INLAND  \n20630          INLAND  \n20631          INLAND  \n20632          INLAND  \n20633          INLAND  \n20634          INLAND  \n20635          INLAND  \n20636          INLAND  \n20637          INLAND  \n20638          INLAND  \n20639          INLAND  \n\n[20640 rows x 10 columns]>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.head\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 20640 entries, 0 to 20639\nData columns (total 10 columns):\nlongitude             20640 non-null float64\nlatitude              20640 non-null float64\nhousing_median_age    20640 non-null float64\ntotal_rooms           20640 non-null float64\ntotal_bedrooms        20433 non-null float64\npopulation            20640 non-null float64\nhouseholds            20640 non-null float64\nmedian_income         20640 non-null float64\nmedian_house_value    20640 non-null float64\nocean_proximity       20640 non-null object\ndtypes: float64(9), object(1)\nmemory usage: 1.6+ MB\n"
     ]
    }
   ],
   "source": [
    "housing.info()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<1H OCEAN     9136\nINLAND        6551\nNEAR OCEAN    2658\nNEAR BAY      2290\nISLAND           5\nName: ocean_proximity, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.ocean_proximity.value_counts()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20433.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-119.569704</td>\n",
       "      <td>35.631861</td>\n",
       "      <td>28.639486</td>\n",
       "      <td>2635.763081</td>\n",
       "      <td>537.870553</td>\n",
       "      <td>1425.476744</td>\n",
       "      <td>499.539680</td>\n",
       "      <td>3.870671</td>\n",
       "      <td>206855.816909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.003532</td>\n",
       "      <td>2.135952</td>\n",
       "      <td>12.585558</td>\n",
       "      <td>2181.615252</td>\n",
       "      <td>421.385070</td>\n",
       "      <td>1132.462122</td>\n",
       "      <td>382.329753</td>\n",
       "      <td>1.899822</td>\n",
       "      <td>115395.615874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-124.350000</td>\n",
       "      <td>32.540000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.499900</td>\n",
       "      <td>14999.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-121.800000</td>\n",
       "      <td>33.930000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>1447.750000</td>\n",
       "      <td>296.000000</td>\n",
       "      <td>787.000000</td>\n",
       "      <td>280.000000</td>\n",
       "      <td>2.563400</td>\n",
       "      <td>119600.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-118.490000</td>\n",
       "      <td>34.260000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>2127.000000</td>\n",
       "      <td>435.000000</td>\n",
       "      <td>1166.000000</td>\n",
       "      <td>409.000000</td>\n",
       "      <td>3.534800</td>\n",
       "      <td>179700.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>-118.010000</td>\n",
       "      <td>37.710000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>3148.000000</td>\n",
       "      <td>647.000000</td>\n",
       "      <td>1725.000000</td>\n",
       "      <td>605.000000</td>\n",
       "      <td>4.743250</td>\n",
       "      <td>264725.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>-114.310000</td>\n",
       "      <td>41.950000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>39320.000000</td>\n",
       "      <td>6445.000000</td>\n",
       "      <td>35682.000000</td>\n",
       "      <td>6082.000000</td>\n",
       "      <td>15.000100</td>\n",
       "      <td>500001.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20433.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-119.569704</td>\n",
       "      <td>35.631861</td>\n",
       "      <td>28.639486</td>\n",
       "      <td>2635.763081</td>\n",
       "      <td>537.870553</td>\n",
       "      <td>1425.476744</td>\n",
       "      <td>499.539680</td>\n",
       "      <td>3.870671</td>\n",
       "      <td>206855.816909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.003532</td>\n",
       "      <td>2.135952</td>\n",
       "      <td>12.585558</td>\n",
       "      <td>2181.615252</td>\n",
       "      <td>421.385070</td>\n",
       "      <td>1132.462122</td>\n",
       "      <td>382.329753</td>\n",
       "      <td>1.899822</td>\n",
       "      <td>115395.615874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-124.350000</td>\n",
       "      <td>32.540000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.499900</td>\n",
       "      <td>14999.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-121.800000</td>\n",
       "      <td>33.930000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>1447.750000</td>\n",
       "      <td>296.000000</td>\n",
       "      <td>787.000000</td>\n",
       "      <td>280.000000</td>\n",
       "      <td>2.563400</td>\n",
       "      <td>119600.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-118.490000</td>\n",
       "      <td>34.260000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>2127.000000</td>\n",
       "      <td>435.000000</td>\n",
       "      <td>1166.000000</td>\n",
       "      <td>409.000000</td>\n",
       "      <td>3.534800</td>\n",
       "      <td>179700.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>-118.010000</td>\n",
       "      <td>37.710000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>3148.000000</td>\n",
       "      <td>647.000000</td>\n",
       "      <td>1725.000000</td>\n",
       "      <td>605.000000</td>\n",
       "      <td>4.743250</td>\n",
       "      <td>264725.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>-114.310000</td>\n",
       "      <td>41.950000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>39320.000000</td>\n",
       "      <td>6445.000000</td>\n",
       "      <td>35682.000000</td>\n",
       "      <td>6082.000000</td>\n",
       "      <td>15.000100</td>\n",
       "      <td>500001.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.describe()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method NDFrame.describe of        longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n0        -122.23     37.88                41.0        880.0           129.0   \n1        -122.22     37.86                21.0       7099.0          1106.0   \n2        -122.24     37.85                52.0       1467.0           190.0   \n3        -122.25     37.85                52.0       1274.0           235.0   \n4        -122.25     37.85                52.0       1627.0           280.0   \n5        -122.25     37.85                52.0        919.0           213.0   \n6        -122.25     37.84                52.0       2535.0           489.0   \n7        -122.25     37.84                52.0       3104.0           687.0   \n8        -122.26     37.84                42.0       2555.0           665.0   \n9        -122.25     37.84                52.0       3549.0           707.0   \n10       -122.26     37.85                52.0       2202.0           434.0   \n11       -122.26     37.85                52.0       3503.0           752.0   \n12       -122.26     37.85                52.0       2491.0           474.0   \n13       -122.26     37.84                52.0        696.0           191.0   \n14       -122.26     37.85                52.0       2643.0           626.0   \n15       -122.26     37.85                50.0       1120.0           283.0   \n16       -122.27     37.85                52.0       1966.0           347.0   \n17       -122.27     37.85                52.0       1228.0           293.0   \n18       -122.26     37.84                50.0       2239.0           455.0   \n19       -122.27     37.84                52.0       1503.0           298.0   \n20       -122.27     37.85                40.0        751.0           184.0   \n21       -122.27     37.85                42.0       1639.0           367.0   \n22       -122.27     37.84                52.0       2436.0           541.0   \n23       -122.27     37.84                52.0       1688.0           337.0   \n24       -122.27     37.84                52.0       2224.0           437.0   \n25       -122.28     37.85                41.0        535.0           123.0   \n26       -122.28     37.85                49.0       1130.0           244.0   \n27       -122.28     37.85                52.0       1898.0           421.0   \n28       -122.28     37.84                50.0       2082.0           492.0   \n29       -122.28     37.84                52.0        729.0           160.0   \n...          ...       ...                 ...          ...             ...   \n20610    -121.56     39.10                28.0       2130.0           484.0   \n20611    -121.55     39.10                27.0       1783.0           441.0   \n20612    -121.56     39.08                26.0       1377.0           289.0   \n20613    -121.55     39.09                31.0       1728.0           365.0   \n20614    -121.54     39.08                26.0       2276.0           460.0   \n20615    -121.54     39.08                23.0       1076.0           216.0   \n20616    -121.53     39.08                15.0       1810.0           441.0   \n20617    -121.53     39.06                20.0        561.0           109.0   \n20618    -121.55     39.06                25.0       1332.0           247.0   \n20619    -121.56     39.01                22.0       1891.0           340.0   \n20620    -121.48     39.05                40.0        198.0            41.0   \n20621    -121.47     39.01                37.0       1244.0           247.0   \n20622    -121.44     39.00                20.0        755.0           147.0   \n20623    -121.37     39.03                32.0       1158.0           244.0   \n20624    -121.41     39.04                16.0       1698.0           300.0   \n20625    -121.52     39.12                37.0        102.0            17.0   \n20626    -121.43     39.18                36.0       1124.0           184.0   \n20627    -121.32     39.13                 5.0        358.0            65.0   \n20628    -121.48     39.10                19.0       2043.0           421.0   \n20629    -121.39     39.12                28.0      10035.0          1856.0   \n20630    -121.32     39.29                11.0       2640.0           505.0   \n20631    -121.40     39.33                15.0       2655.0           493.0   \n20632    -121.45     39.26                15.0       2319.0           416.0   \n20633    -121.53     39.19                27.0       2080.0           412.0   \n20634    -121.56     39.27                28.0       2332.0           395.0   \n20635    -121.09     39.48                25.0       1665.0           374.0   \n20636    -121.21     39.49                18.0        697.0           150.0   \n20637    -121.22     39.43                17.0       2254.0           485.0   \n20638    -121.32     39.43                18.0       1860.0           409.0   \n20639    -121.24     39.37                16.0       2785.0           616.0   \n\n       population  households  median_income  median_house_value  \\\n0           322.0       126.0         8.3252            452600.0   \n1          2401.0      1138.0         8.3014            358500.0   \n2           496.0       177.0         7.2574            352100.0   \n3           558.0       219.0         5.6431            341300.0   \n4           565.0       259.0         3.8462            342200.0   \n5           413.0       193.0         4.0368            269700.0   \n6          1094.0       514.0         3.6591            299200.0   \n7          1157.0       647.0         3.1200            241400.0   \n8          1206.0       595.0         2.0804            226700.0   \n9          1551.0       714.0         3.6912            261100.0   \n10          910.0       402.0         3.2031            281500.0   \n11         1504.0       734.0         3.2705            241800.0   \n12         1098.0       468.0         3.0750            213500.0   \n13          345.0       174.0         2.6736            191300.0   \n14         1212.0       620.0         1.9167            159200.0   \n15          697.0       264.0         2.1250            140000.0   \n16          793.0       331.0         2.7750            152500.0   \n17          648.0       303.0         2.1202            155500.0   \n18          990.0       419.0         1.9911            158700.0   \n19          690.0       275.0         2.6033            162900.0   \n20          409.0       166.0         1.3578            147500.0   \n21          929.0       366.0         1.7135            159800.0   \n22         1015.0       478.0         1.7250            113900.0   \n23          853.0       325.0         2.1806             99700.0   \n24         1006.0       422.0         2.6000            132600.0   \n25          317.0       119.0         2.4038            107500.0   \n26          607.0       239.0         2.4597             93800.0   \n27         1102.0       397.0         1.8080            105500.0   \n28         1131.0       473.0         1.6424            108900.0   \n29          395.0       155.0         1.6875            132000.0   \n...           ...         ...            ...                 ...   \n20610      1195.0       439.0         1.3631             45500.0   \n20611      1163.0       409.0         1.2857             47000.0   \n20612       761.0       267.0         1.4934             48300.0   \n20613      1167.0       384.0         1.4958             53400.0   \n20614      1455.0       474.0         2.4695             58000.0   \n20615       724.0       197.0         2.3598             57500.0   \n20616      1157.0       375.0         2.0469             55100.0   \n20617       308.0       114.0         3.3021             70800.0   \n20618       726.0       226.0         2.2500             63400.0   \n20619      1023.0       296.0         2.7303             99100.0   \n20620       151.0        48.0         4.5625            100000.0   \n20621       484.0       157.0         2.3661             77500.0   \n20622       457.0       157.0         2.4167             67000.0   \n20623       598.0       227.0         2.8235             65500.0   \n20624       731.0       291.0         3.0739             87200.0   \n20625        29.0        14.0         4.1250             72000.0   \n20626       504.0       171.0         2.1667             93800.0   \n20627       169.0        59.0         3.0000            162500.0   \n20628      1018.0       390.0         2.5952             92400.0   \n20629      6912.0      1818.0         2.0943            108300.0   \n20630      1257.0       445.0         3.5673            112000.0   \n20631      1200.0       432.0         3.5179            107200.0   \n20632      1047.0       385.0         3.1250            115600.0   \n20633      1082.0       382.0         2.5495             98300.0   \n20634      1041.0       344.0         3.7125            116800.0   \n20635       845.0       330.0         1.5603             78100.0   \n20636       356.0       114.0         2.5568             77100.0   \n20637      1007.0       433.0         1.7000             92300.0   \n20638       741.0       349.0         1.8672             84700.0   \n20639      1387.0       530.0         2.3886             89400.0   \n\n      ocean_proximity  \n0            NEAR BAY  \n1            NEAR BAY  \n2            NEAR BAY  \n3            NEAR BAY  \n4            NEAR BAY  \n5            NEAR BAY  \n6            NEAR BAY  \n7            NEAR BAY  \n8            NEAR BAY  \n9            NEAR BAY  \n10           NEAR BAY  \n11           NEAR BAY  \n12           NEAR BAY  \n13           NEAR BAY  \n14           NEAR BAY  \n15           NEAR BAY  \n16           NEAR BAY  \n17           NEAR BAY  \n18           NEAR BAY  \n19           NEAR BAY  \n20           NEAR BAY  \n21           NEAR BAY  \n22           NEAR BAY  \n23           NEAR BAY  \n24           NEAR BAY  \n25           NEAR BAY  \n26           NEAR BAY  \n27           NEAR BAY  \n28           NEAR BAY  \n29           NEAR BAY  \n...               ...  \n20610          INLAND  \n20611          INLAND  \n20612          INLAND  \n20613          INLAND  \n20614          INLAND  \n20615          INLAND  \n20616          INLAND  \n20617          INLAND  \n20618          INLAND  \n20619          INLAND  \n20620          INLAND  \n20621          INLAND  \n20622          INLAND  \n20623          INLAND  \n20624          INLAND  \n20625          INLAND  \n20626          INLAND  \n20627          INLAND  \n20628          INLAND  \n20629          INLAND  \n20630          INLAND  \n20631          INLAND  \n20632          INLAND  \n20633          INLAND  \n20634          INLAND  \n20635          INLAND  \n20636          INLAND  \n20637          INLAND  \n20638          INLAND  \n20639          INLAND  \n\n[20640 rows x 10 columns]>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.describe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20433.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-119.569704</td>\n",
       "      <td>35.631861</td>\n",
       "      <td>28.639486</td>\n",
       "      <td>2635.763081</td>\n",
       "      <td>537.870553</td>\n",
       "      <td>1425.476744</td>\n",
       "      <td>499.539680</td>\n",
       "      <td>3.870671</td>\n",
       "      <td>206855.816909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.003532</td>\n",
       "      <td>2.135952</td>\n",
       "      <td>12.585558</td>\n",
       "      <td>2181.615252</td>\n",
       "      <td>421.385070</td>\n",
       "      <td>1132.462122</td>\n",
       "      <td>382.329753</td>\n",
       "      <td>1.899822</td>\n",
       "      <td>115395.615874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-124.350000</td>\n",
       "      <td>32.540000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.499900</td>\n",
       "      <td>14999.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-121.800000</td>\n",
       "      <td>33.930000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>1447.750000</td>\n",
       "      <td>296.000000</td>\n",
       "      <td>787.000000</td>\n",
       "      <td>280.000000</td>\n",
       "      <td>2.563400</td>\n",
       "      <td>119600.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-118.490000</td>\n",
       "      <td>34.260000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>2127.000000</td>\n",
       "      <td>435.000000</td>\n",
       "      <td>1166.000000</td>\n",
       "      <td>409.000000</td>\n",
       "      <td>3.534800</td>\n",
       "      <td>179700.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>-118.010000</td>\n",
       "      <td>37.710000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>3148.000000</td>\n",
       "      <td>647.000000</td>\n",
       "      <td>1725.000000</td>\n",
       "      <td>605.000000</td>\n",
       "      <td>4.743250</td>\n",
       "      <td>264725.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>-114.310000</td>\n",
       "      <td>41.950000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>39320.000000</td>\n",
       "      <td>6445.000000</td>\n",
       "      <td>35682.000000</td>\n",
       "      <td>6082.000000</td>\n",
       "      <td>15.000100</td>\n",
       "      <td>500001.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20433.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-119.569704</td>\n",
       "      <td>35.631861</td>\n",
       "      <td>28.639486</td>\n",
       "      <td>2635.763081</td>\n",
       "      <td>537.870553</td>\n",
       "      <td>1425.476744</td>\n",
       "      <td>499.539680</td>\n",
       "      <td>3.870671</td>\n",
       "      <td>206855.816909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.003532</td>\n",
       "      <td>2.135952</td>\n",
       "      <td>12.585558</td>\n",
       "      <td>2181.615252</td>\n",
       "      <td>421.385070</td>\n",
       "      <td>1132.462122</td>\n",
       "      <td>382.329753</td>\n",
       "      <td>1.899822</td>\n",
       "      <td>115395.615874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-124.350000</td>\n",
       "      <td>32.540000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.499900</td>\n",
       "      <td>14999.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-121.800000</td>\n",
       "      <td>33.930000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>1447.750000</td>\n",
       "      <td>296.000000</td>\n",
       "      <td>787.000000</td>\n",
       "      <td>280.000000</td>\n",
       "      <td>2.563400</td>\n",
       "      <td>119600.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-118.490000</td>\n",
       "      <td>34.260000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>2127.000000</td>\n",
       "      <td>435.000000</td>\n",
       "      <td>1166.000000</td>\n",
       "      <td>409.000000</td>\n",
       "      <td>3.534800</td>\n",
       "      <td>179700.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>-118.010000</td>\n",
       "      <td>37.710000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>3148.000000</td>\n",
       "      <td>647.000000</td>\n",
       "      <td>1725.000000</td>\n",
       "      <td>605.000000</td>\n",
       "      <td>4.743250</td>\n",
       "      <td>264725.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>-114.310000</td>\n",
       "      <td>41.950000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>39320.000000</td>\n",
       "      <td>6445.000000</td>\n",
       "      <td>35682.000000</td>\n",
       "      <td>6082.000000</td>\n",
       "      <td>15.000100</td>\n",
       "      <td>500001.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20433.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-119.569704</td>\n",
       "      <td>35.631861</td>\n",
       "      <td>28.639486</td>\n",
       "      <td>2635.763081</td>\n",
       "      <td>537.870553</td>\n",
       "      <td>1425.476744</td>\n",
       "      <td>499.539680</td>\n",
       "      <td>3.870671</td>\n",
       "      <td>206855.816909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.003532</td>\n",
       "      <td>2.135952</td>\n",
       "      <td>12.585558</td>\n",
       "      <td>2181.615252</td>\n",
       "      <td>421.385070</td>\n",
       "      <td>1132.462122</td>\n",
       "      <td>382.329753</td>\n",
       "      <td>1.899822</td>\n",
       "      <td>115395.615874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-124.350000</td>\n",
       "      <td>32.540000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.499900</td>\n",
       "      <td>14999.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-121.800000</td>\n",
       "      <td>33.930000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>1447.750000</td>\n",
       "      <td>296.000000</td>\n",
       "      <td>787.000000</td>\n",
       "      <td>280.000000</td>\n",
       "      <td>2.563400</td>\n",
       "      <td>119600.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-118.490000</td>\n",
       "      <td>34.260000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>2127.000000</td>\n",
       "      <td>435.000000</td>\n",
       "      <td>1166.000000</td>\n",
       "      <td>409.000000</td>\n",
       "      <td>3.534800</td>\n",
       "      <td>179700.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>-118.010000</td>\n",
       "      <td>37.710000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>3148.000000</td>\n",
       "      <td>647.000000</td>\n",
       "      <td>1725.000000</td>\n",
       "      <td>605.000000</td>\n",
       "      <td>4.743250</td>\n",
       "      <td>264725.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>-114.310000</td>\n",
       "      <td>41.950000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>39320.000000</td>\n",
       "      <td>6445.000000</td>\n",
       "      <td>35682.000000</td>\n",
       "      <td>6082.000000</td>\n",
       "      <td>15.000100</td>\n",
       "      <td>500001.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20433.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-119.569704</td>\n",
       "      <td>35.631861</td>\n",
       "      <td>28.639486</td>\n",
       "      <td>2635.763081</td>\n",
       "      <td>537.870553</td>\n",
       "      <td>1425.476744</td>\n",
       "      <td>499.539680</td>\n",
       "      <td>3.870671</td>\n",
       "      <td>206855.816909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.003532</td>\n",
       "      <td>2.135952</td>\n",
       "      <td>12.585558</td>\n",
       "      <td>2181.615252</td>\n",
       "      <td>421.385070</td>\n",
       "      <td>1132.462122</td>\n",
       "      <td>382.329753</td>\n",
       "      <td>1.899822</td>\n",
       "      <td>115395.615874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-124.350000</td>\n",
       "      <td>32.540000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.499900</td>\n",
       "      <td>14999.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-121.800000</td>\n",
       "      <td>33.930000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>1447.750000</td>\n",
       "      <td>296.000000</td>\n",
       "      <td>787.000000</td>\n",
       "      <td>280.000000</td>\n",
       "      <td>2.563400</td>\n",
       "      <td>119600.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-118.490000</td>\n",
       "      <td>34.260000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>2127.000000</td>\n",
       "      <td>435.000000</td>\n",
       "      <td>1166.000000</td>\n",
       "      <td>409.000000</td>\n",
       "      <td>3.534800</td>\n",
       "      <td>179700.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>-118.010000</td>\n",
       "      <td>37.710000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>3148.000000</td>\n",
       "      <td>647.000000</td>\n",
       "      <td>1725.000000</td>\n",
       "      <td>605.000000</td>\n",
       "      <td>4.743250</td>\n",
       "      <td>264725.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>-114.310000</td>\n",
       "      <td>41.950000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>39320.000000</td>\n",
       "      <td>6445.000000</td>\n",
       "      <td>35682.000000</td>\n",
       "      <td>6082.000000</td>\n",
       "      <td>15.000100</td>\n",
       "      <td>500001.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.describe()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method NDFrame.head of        longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n0        -122.23     37.88                41.0        880.0           129.0   \n1        -122.22     37.86                21.0       7099.0          1106.0   \n2        -122.24     37.85                52.0       1467.0           190.0   \n3        -122.25     37.85                52.0       1274.0           235.0   \n4        -122.25     37.85                52.0       1627.0           280.0   \n5        -122.25     37.85                52.0        919.0           213.0   \n6        -122.25     37.84                52.0       2535.0           489.0   \n7        -122.25     37.84                52.0       3104.0           687.0   \n8        -122.26     37.84                42.0       2555.0           665.0   \n9        -122.25     37.84                52.0       3549.0           707.0   \n10       -122.26     37.85                52.0       2202.0           434.0   \n11       -122.26     37.85                52.0       3503.0           752.0   \n12       -122.26     37.85                52.0       2491.0           474.0   \n13       -122.26     37.84                52.0        696.0           191.0   \n14       -122.26     37.85                52.0       2643.0           626.0   \n15       -122.26     37.85                50.0       1120.0           283.0   \n16       -122.27     37.85                52.0       1966.0           347.0   \n17       -122.27     37.85                52.0       1228.0           293.0   \n18       -122.26     37.84                50.0       2239.0           455.0   \n19       -122.27     37.84                52.0       1503.0           298.0   \n20       -122.27     37.85                40.0        751.0           184.0   \n21       -122.27     37.85                42.0       1639.0           367.0   \n22       -122.27     37.84                52.0       2436.0           541.0   \n23       -122.27     37.84                52.0       1688.0           337.0   \n24       -122.27     37.84                52.0       2224.0           437.0   \n25       -122.28     37.85                41.0        535.0           123.0   \n26       -122.28     37.85                49.0       1130.0           244.0   \n27       -122.28     37.85                52.0       1898.0           421.0   \n28       -122.28     37.84                50.0       2082.0           492.0   \n29       -122.28     37.84                52.0        729.0           160.0   \n...          ...       ...                 ...          ...             ...   \n20610    -121.56     39.10                28.0       2130.0           484.0   \n20611    -121.55     39.10                27.0       1783.0           441.0   \n20612    -121.56     39.08                26.0       1377.0           289.0   \n20613    -121.55     39.09                31.0       1728.0           365.0   \n20614    -121.54     39.08                26.0       2276.0           460.0   \n20615    -121.54     39.08                23.0       1076.0           216.0   \n20616    -121.53     39.08                15.0       1810.0           441.0   \n20617    -121.53     39.06                20.0        561.0           109.0   \n20618    -121.55     39.06                25.0       1332.0           247.0   \n20619    -121.56     39.01                22.0       1891.0           340.0   \n20620    -121.48     39.05                40.0        198.0            41.0   \n20621    -121.47     39.01                37.0       1244.0           247.0   \n20622    -121.44     39.00                20.0        755.0           147.0   \n20623    -121.37     39.03                32.0       1158.0           244.0   \n20624    -121.41     39.04                16.0       1698.0           300.0   \n20625    -121.52     39.12                37.0        102.0            17.0   \n20626    -121.43     39.18                36.0       1124.0           184.0   \n20627    -121.32     39.13                 5.0        358.0            65.0   \n20628    -121.48     39.10                19.0       2043.0           421.0   \n20629    -121.39     39.12                28.0      10035.0          1856.0   \n20630    -121.32     39.29                11.0       2640.0           505.0   \n20631    -121.40     39.33                15.0       2655.0           493.0   \n20632    -121.45     39.26                15.0       2319.0           416.0   \n20633    -121.53     39.19                27.0       2080.0           412.0   \n20634    -121.56     39.27                28.0       2332.0           395.0   \n20635    -121.09     39.48                25.0       1665.0           374.0   \n20636    -121.21     39.49                18.0        697.0           150.0   \n20637    -121.22     39.43                17.0       2254.0           485.0   \n20638    -121.32     39.43                18.0       1860.0           409.0   \n20639    -121.24     39.37                16.0       2785.0           616.0   \n\n       population  households  median_income  median_house_value  \\\n0           322.0       126.0         8.3252            452600.0   \n1          2401.0      1138.0         8.3014            358500.0   \n2           496.0       177.0         7.2574            352100.0   \n3           558.0       219.0         5.6431            341300.0   \n4           565.0       259.0         3.8462            342200.0   \n5           413.0       193.0         4.0368            269700.0   \n6          1094.0       514.0         3.6591            299200.0   \n7          1157.0       647.0         3.1200            241400.0   \n8          1206.0       595.0         2.0804            226700.0   \n9          1551.0       714.0         3.6912            261100.0   \n10          910.0       402.0         3.2031            281500.0   \n11         1504.0       734.0         3.2705            241800.0   \n12         1098.0       468.0         3.0750            213500.0   \n13          345.0       174.0         2.6736            191300.0   \n14         1212.0       620.0         1.9167            159200.0   \n15          697.0       264.0         2.1250            140000.0   \n16          793.0       331.0         2.7750            152500.0   \n17          648.0       303.0         2.1202            155500.0   \n18          990.0       419.0         1.9911            158700.0   \n19          690.0       275.0         2.6033            162900.0   \n20          409.0       166.0         1.3578            147500.0   \n21          929.0       366.0         1.7135            159800.0   \n22         1015.0       478.0         1.7250            113900.0   \n23          853.0       325.0         2.1806             99700.0   \n24         1006.0       422.0         2.6000            132600.0   \n25          317.0       119.0         2.4038            107500.0   \n26          607.0       239.0         2.4597             93800.0   \n27         1102.0       397.0         1.8080            105500.0   \n28         1131.0       473.0         1.6424            108900.0   \n29          395.0       155.0         1.6875            132000.0   \n...           ...         ...            ...                 ...   \n20610      1195.0       439.0         1.3631             45500.0   \n20611      1163.0       409.0         1.2857             47000.0   \n20612       761.0       267.0         1.4934             48300.0   \n20613      1167.0       384.0         1.4958             53400.0   \n20614      1455.0       474.0         2.4695             58000.0   \n20615       724.0       197.0         2.3598             57500.0   \n20616      1157.0       375.0         2.0469             55100.0   \n20617       308.0       114.0         3.3021             70800.0   \n20618       726.0       226.0         2.2500             63400.0   \n20619      1023.0       296.0         2.7303             99100.0   \n20620       151.0        48.0         4.5625            100000.0   \n20621       484.0       157.0         2.3661             77500.0   \n20622       457.0       157.0         2.4167             67000.0   \n20623       598.0       227.0         2.8235             65500.0   \n20624       731.0       291.0         3.0739             87200.0   \n20625        29.0        14.0         4.1250             72000.0   \n20626       504.0       171.0         2.1667             93800.0   \n20627       169.0        59.0         3.0000            162500.0   \n20628      1018.0       390.0         2.5952             92400.0   \n20629      6912.0      1818.0         2.0943            108300.0   \n20630      1257.0       445.0         3.5673            112000.0   \n20631      1200.0       432.0         3.5179            107200.0   \n20632      1047.0       385.0         3.1250            115600.0   \n20633      1082.0       382.0         2.5495             98300.0   \n20634      1041.0       344.0         3.7125            116800.0   \n20635       845.0       330.0         1.5603             78100.0   \n20636       356.0       114.0         2.5568             77100.0   \n20637      1007.0       433.0         1.7000             92300.0   \n20638       741.0       349.0         1.8672             84700.0   \n20639      1387.0       530.0         2.3886             89400.0   \n\n      ocean_proximity  \n0            NEAR BAY  \n1            NEAR BAY  \n2            NEAR BAY  \n3            NEAR BAY  \n4            NEAR BAY  \n5            NEAR BAY  \n6            NEAR BAY  \n7            NEAR BAY  \n8            NEAR BAY  \n9            NEAR BAY  \n10           NEAR BAY  \n11           NEAR BAY  \n12           NEAR BAY  \n13           NEAR BAY  \n14           NEAR BAY  \n15           NEAR BAY  \n16           NEAR BAY  \n17           NEAR BAY  \n18           NEAR BAY  \n19           NEAR BAY  \n20           NEAR BAY  \n21           NEAR BAY  \n22           NEAR BAY  \n23           NEAR BAY  \n24           NEAR BAY  \n25           NEAR BAY  \n26           NEAR BAY  \n27           NEAR BAY  \n28           NEAR BAY  \n29           NEAR BAY  \n...               ...  \n20610          INLAND  \n20611          INLAND  \n20612          INLAND  \n20613          INLAND  \n20614          INLAND  \n20615          INLAND  \n20616          INLAND  \n20617          INLAND  \n20618          INLAND  \n20619          INLAND  \n20620          INLAND  \n20621          INLAND  \n20622          INLAND  \n20623          INLAND  \n20624          INLAND  \n20625          INLAND  \n20626          INLAND  \n20627          INLAND  \n20628          INLAND  \n20629          INLAND  \n20630          INLAND  \n20631          INLAND  \n20632          INLAND  \n20633          INLAND  \n20634          INLAND  \n20635          INLAND  \n20636          INLAND  \n20637          INLAND  \n20638          INLAND  \n20639          INLAND  \n\n[20640 rows x 10 columns]>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.head\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 20640 entries, 0 to 20639\nData columns (total 10 columns):\nlongitude             20640 non-null float64\nlatitude              20640 non-null float64\nhousing_median_age    20640 non-null float64\ntotal_rooms           20640 non-null float64\ntotal_bedrooms        20433 non-null float64\npopulation            20640 non-null float64\nhouseholds            20640 non-null float64\nmedian_income         20640 non-null float64\nmedian_house_value    20640 non-null float64\nocean_proximity       20640 non-null object\ndtypes: float64(9), object(1)\nmemory usage: 1.6+ MB\n"
     ]
    }
   ],
   "source": [
    "housing.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20433.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-119.569704</td>\n",
       "      <td>35.631861</td>\n",
       "      <td>28.639486</td>\n",
       "      <td>2635.763081</td>\n",
       "      <td>537.870553</td>\n",
       "      <td>1425.476744</td>\n",
       "      <td>499.539680</td>\n",
       "      <td>3.870671</td>\n",
       "      <td>206855.816909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.003532</td>\n",
       "      <td>2.135952</td>\n",
       "      <td>12.585558</td>\n",
       "      <td>2181.615252</td>\n",
       "      <td>421.385070</td>\n",
       "      <td>1132.462122</td>\n",
       "      <td>382.329753</td>\n",
       "      <td>1.899822</td>\n",
       "      <td>115395.615874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-124.350000</td>\n",
       "      <td>32.540000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.499900</td>\n",
       "      <td>14999.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-121.800000</td>\n",
       "      <td>33.930000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>1447.750000</td>\n",
       "      <td>296.000000</td>\n",
       "      <td>787.000000</td>\n",
       "      <td>280.000000</td>\n",
       "      <td>2.563400</td>\n",
       "      <td>119600.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-118.490000</td>\n",
       "      <td>34.260000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>2127.000000</td>\n",
       "      <td>435.000000</td>\n",
       "      <td>1166.000000</td>\n",
       "      <td>409.000000</td>\n",
       "      <td>3.534800</td>\n",
       "      <td>179700.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>-118.010000</td>\n",
       "      <td>37.710000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>3148.000000</td>\n",
       "      <td>647.000000</td>\n",
       "      <td>1725.000000</td>\n",
       "      <td>605.000000</td>\n",
       "      <td>4.743250</td>\n",
       "      <td>264725.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>-114.310000</td>\n",
       "      <td>41.950000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>39320.000000</td>\n",
       "      <td>6445.000000</td>\n",
       "      <td>35682.000000</td>\n",
       "      <td>6082.000000</td>\n",
       "      <td>15.000100</td>\n",
       "      <td>500001.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20433.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-119.569704</td>\n",
       "      <td>35.631861</td>\n",
       "      <td>28.639486</td>\n",
       "      <td>2635.763081</td>\n",
       "      <td>537.870553</td>\n",
       "      <td>1425.476744</td>\n",
       "      <td>499.539680</td>\n",
       "      <td>3.870671</td>\n",
       "      <td>206855.816909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.003532</td>\n",
       "      <td>2.135952</td>\n",
       "      <td>12.585558</td>\n",
       "      <td>2181.615252</td>\n",
       "      <td>421.385070</td>\n",
       "      <td>1132.462122</td>\n",
       "      <td>382.329753</td>\n",
       "      <td>1.899822</td>\n",
       "      <td>115395.615874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-124.350000</td>\n",
       "      <td>32.540000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.499900</td>\n",
       "      <td>14999.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-121.800000</td>\n",
       "      <td>33.930000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>1447.750000</td>\n",
       "      <td>296.000000</td>\n",
       "      <td>787.000000</td>\n",
       "      <td>280.000000</td>\n",
       "      <td>2.563400</td>\n",
       "      <td>119600.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-118.490000</td>\n",
       "      <td>34.260000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>2127.000000</td>\n",
       "      <td>435.000000</td>\n",
       "      <td>1166.000000</td>\n",
       "      <td>409.000000</td>\n",
       "      <td>3.534800</td>\n",
       "      <td>179700.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>-118.010000</td>\n",
       "      <td>37.710000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>3148.000000</td>\n",
       "      <td>647.000000</td>\n",
       "      <td>1725.000000</td>\n",
       "      <td>605.000000</td>\n",
       "      <td>4.743250</td>\n",
       "      <td>264725.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>-114.310000</td>\n",
       "      <td>41.950000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>39320.000000</td>\n",
       "      <td>6445.000000</td>\n",
       "      <td>35682.000000</td>\n",
       "      <td>6082.000000</td>\n",
       "      <td>15.000100</td>\n",
       "      <td>500001.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 20640 entries, 0 to 20639\nData columns (total 10 columns):\nlongitude             20640 non-null float64\nlatitude              20640 non-null float64\nhousing_median_age    20640 non-null float64\ntotal_rooms           20640 non-null float64\ntotal_bedrooms        20433 non-null float64\npopulation            20640 non-null float64\nhouseholds            20640 non-null float64\nmedian_income         20640 non-null float64\nmedian_house_value    20640 non-null float64\nocean_proximity       20640 non-null object\ndtypes: float64(9), object(1)\nmemory usage: 1.6+ MB\n"
     ]
    }
   ],
   "source": [
    "housing.info()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
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MIkmSJEkaeUme3e5AIskK4MXAZ4HrgUvaZpcA17Xl64H1SY5LciqdAbRva13fHklyTpuV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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9471278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "% matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "housing.hist(bins=50,figsize=(20,15))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "def split_train_test(data,test_ratio):\n",
    "    shuffled_indices = np.random.permutation(len(data))\n",
    "    test_set_size = int(len(data)*test_ratio)\n",
    "    test_indices = shuffled_indices[:test_set_size]\n",
    "    train_indices = shuffled_indices[test_set_size:]\n",
    "    return data.ix[train_indices], data.ix[test_indices]\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16512 train +  4128 test\n"
     ]
    }
   ],
   "source": [
    "train_set, test_set = split_train_test(housing,0.2)\n",
    "print(len(train_set),\"train + \",len(test_set),\"test\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import hashlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "train_set,test_set = train_test_split(housing ,test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xc937cf8>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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sOHXW351/5O7rz+rx9c7hMwdJUsNykCQ1LAdJUsNykCQ1LAdJUsNykCQ1LAdJUsNykCQ1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\nhQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9a4e550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "housing['median_income'].hist(bins=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing['income_cat'] = np.ceil(housing['median_income']/1.5)\n",
    "housing['income_cat'].where(housing['income_cat']<5,5.0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import StratifiedShuffleSplit\n",
    "split = StratifiedShuffleSplit(n_splits=1,test_size=0.2,random_state=42)\n",
    "for train_index,test_index in split.split(housing,housing['income_cat']):\n",
    "    strat_train_set = housing.ix[train_index]\n",
    "    strat_test_set = housing.ix[test_index]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.0    0.350581\n2.0    0.318847\n4.0    0.176308\n5.0    0.114438\n1.0    0.039826\nName: income_cat, dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing['income_cat'].value_counts()/len(housing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "for set in (strat_train_set,strat_test_set):\n",
    "    set.drop(['income_cat'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing = strat_train_set.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\nInt64Index: 16512 entries, 17606 to 15775\nData columns (total 10 columns):\nlongitude             16512 non-null float64\nlatitude              16512 non-null float64\nhousing_median_age    16512 non-null float64\ntotal_rooms           16512 non-null float64\ntotal_bedrooms        16354 non-null float64\npopulation            16512 non-null float64\nhouseholds            16512 non-null float64\nmedian_income         16512 non-null float64\nmedian_house_value    16512 non-null float64\nocean_proximity       16512 non-null object\ndtypes: float64(9), object(1)\nmemory usage: 1.4+ MB\n"
     ]
    }
   ],
   "source": [
    "housing.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x9566390>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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1T5bPN4WlSuNOBOsg94kHg5goVSRKM0xSPjoc8VF7xDDOWKp4D8yjPk8zarcb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fMLW3Ewm5XEmrq254nmjbiTVUlvAE/d4p6jd+r9FYr9yq2SnP3HB3M2L5wa4VPPDPN/vbx4F0e4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\n9Ao4MtL/XQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9af9898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "housing.plot(kind='scatter',x='longitude',y='latitude',alpha=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0xaade128>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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8opIFnbkwfxZUVqjtRo9WDm4hVKvg9eth20H4+CNDOTSEVDHLfgNcIWpMB0/9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fnd5ac/HebIkTxSQne3wYc/XMKqojjv25/TqoD+zqCgy3UP8Bsp5cHTaVs0JkUAiBAnQvyV57O5\nCk08Qlb6yeHCI3K4XFlcXgOfmsQ2NIKlSfJZDc206LdGQOsjny8lYynoZ8g3+eKLeTo68uQSLroH\nLTTN5rL3u1i42sdBn2TnetjyjOMfYQRwC0g7tUywLRxvuBuyCunRHHd/N401X+OoT8cMwqIq8MQg\nZ0AqCwEPuMZJqcRiCrHYqT9MW5tBdbWOaUra2iZ2+Bd5GyFhaqLB7yyEEOuAb+DIXDUIIZbiZM6v\nm6xt0ZgUOSlRZnA5qzhoHcIlDHQrT8Q1SotdS0OgDU2aCCRJ4advTw26gH0izTL/MAHtRCf8yRgb\ng+FhqKqaWAvr97/PsXy5IBDIsHW7RSJts3pBkI9e5eE/jsFAJWzzgJ21nXJsQQ1yvlcrXh0vpaOC\nzOR4eZuBec8IH/liKaqAPSPw+FbwDYJLQIkLLpsPl88/uRz/ePJ5p99r1wZ44okEQsCNN4ZP3eht\nwMCAo2OmTvcVsGloTHCKaV1EQRBSSrlDCDGlRMaiMSkyIVery/mfrJ99uQQqJi7FJMgYlqWiizzD\nWoReUUpT5WHKR/uY5atnZbmFEGtwBjYTMzAAd98N6TTMng0f//jETuqXXs7w8M9HyOcFEOTLX0iw\n7t06t9eq7AyAxwfpnALSBlWBiBd0FySkc0OQClgjIHOYQtC9NQt5G8WjcHQPrG+GCgXok5QAQ0OQ\nzQuuv+Dk/Wlrh2/eA8daYd5M+NRHAnz+8x4UBTyxMbJYeJia5r2UcsLkynPBoUPw3e/CddfBlScp\n3zxtkEzLZS4ccd7R152TU4oZLxqTIhOioPIjb5QfWBs5mFeZM7KdLp+Lg+659CUqGBiO09zTRLY8\nyMKGh4kpL6KpVaQR5FlMhgRhyvBz4mi9u9sxJPX1zg0skwHfSXINL77Yxd9/uQvT6ON4Od7urkb+\n4R/S3H13kI8vhOGD8If1kPUpkMBJitdwQrNyFlhJII0AFM3CMDUUTdA3BFuOgZ6H3s2Skf02mRxs\n94PxaYVL5wlCr+tTNgv/++9g224npHY06eh8/c1faMRikMBGmcK1Z1mSX/4yyY4dOebOdfGhDwVx\nuc69UYlEoKHBmS1OZ7KWo5w9DdkrhLgVUIUQs4C7gI1TaVg0JkVOiSaC/LHaCS4Tq/cJHs4v4ODI\nAvpTZRg5N1W+btyjJrt651HuHcYfTnHEvY8UYyioCASLWIuP0GuOO2MGhMPQ2gpLloB3Aod1uMSF\nbacoOERwlq2OsWuXBgRZUA3L50FNGB5/GvoOO8mG5ADDALJgDyCEha5r4FKYfbmP/qTgme0wkAar\nGVx9NlkT1KBgdFDy0rOS/g8LNOA3T8H+A5JYFJYthV27Bfk8tLdBuATmz4TtO2HuLIjF4uQNm0Mt\neXQdZs/W0bQTjURzs8GWLVnq63V2786xZImbRYvOfb2RsjL4sz87170493gUmDWFn+MdGM31WeBv\nca6gB4Anga9OpWHRmBQ5NcIPno9B7lFU7zzKrRDpET/WoItQfIwyVxerIy8S8w6wLbGcH/Ws5KaZ\nrcz0ufARZoxBUoycYEwiEfjc55wcknh84iUurwc8Xo10UsNZd7AAHSltenpMaio0rl0KTwq4+UNw\ntBUGWxT8YyaJviRGZpT+fpuRERWfT2HWdQGWfaqErccgnwWyYGQdgcrj8wkFsCR4dHj4V7DxZcnz\nfzAZHRPMqJGYOY2BUYFlOXIxZh5+9hCUl4Jt29h2Att21kjmzdP56EcDqOprP+BxA5PNOu+q6xPP\nStJpm2PHLISApiatmA/yVjB9l7nmFx5a4XEjsA5HVuWUFI1JkclRG8B3F1TewIKO/4fYrTCiRDCF\nyjxrLzlb58XNq9nbtpAWo47B2X7ed/koq8uFo0L8OkNyHK934hnJcWbPEtx8awM//O8RbMtxqsfi\nVbzrXTrDwxYVFRr1NdBgwkgSbns3rKgBIVzkcnFaWiLkcpJgUBCPq7h9Ci+PCf78eRgaBK8BlgeU\noIIvYWOO2rjDgrXvEVRE4OBhGBqwyeckVRWOvyRWKgmnBBkbfCXQ3gKrLoDKSnjhBZPWVgW/X+Lx\nQCZjsGaNRV3day+1+nqNq6/2s3NnjjVrvEQiKqmUxO93DIVlOUtqtm3zve+l6O+3kFJQU6Nwxx2B\nokF5K5iexuTHwF8BezjNEISiMSkydbwNdGW+SHlvita6LoRl0dzRSHWsg+ajTfQbcRL5EK0HGvmB\ne5ALb1RYSCN+3rgwlaoK7v7PIJe8ayU/+EE/qgIXr3Tj9QoqKzX6M/C9A+BSQLrg8V5oKoWoB9xu\nhTlzTgwEWO6DVQGnIlEqAD0axOMCf6NCOguxBojNFNyzEZISXG7n5t7XD+Gg5OO3S55b71RlnFEN\nC+t5paa9bUt6e2HZMscPdOyY5GQ1g4QQrFnjY/VqL/ffn+PZZ3Mkk5KFi+H+n6js2K4hVEHTLJOF\ns22uuMy5VA8fNvnVYyZ1dTqzm5yoq7crluVExE02oDgnTNPQYKBfSvnoG2lYNCZFTotkNgg5nfDT\nJtE1LzCcivBYzzo6h2eQln5cgSyJsRB+fZikJQmrpW/6Pd1uwSdv93DrzdVs3pwllbJZtsxDJKKy\nY8Bxlgo3mBKSGehMO8bEtJwsd9frwo41BaJex+hIoL8K4j6YVyVY3wJSdRSEN2+C3UcgP6JiRSVK\nzuZv/wpu+oDKygucMr8rlsJAP9x3n7NkV1mpEY+n6O62yeclS5fq1NRMfJkdO2aza5fFli0qR49a\n3HOfglR0J18mC7s323S2SxoboKoSdu13MZQSlJc7mfuf+RREIpIjRyTZLDQ0CILBMztrMU2bp58e\nwuNRuOKKkjMSfdbT8+p3dtFFsG7d+SU5k7Xg8CnESd/B/H1BTuV3OH4TAMbVrp+QojEpMmVM02bH\ntnZ27tRx6TqHn13InBt2M5ozEANVCGljZHRylpt8VCMvDgCrztj7ezwKl1762vAqXYWdFsiMczNK\nmPA+C7Y1w682O8nwaxfC2kWvtnHrsG45PLIVFOFUjPzk5dA5BkoX1BfSZIZ6QS2HA1kINmpoJjy4\nHq6/HlavfPV4oSD8xV84IcXxuEpra4iHHsoRiQg++UnPSR3wx9E06OwUdHXZWApIoUNEOKNiHyA1\nkmM5nvuDyeJFKnlTYcliBY8b2jpg115IjFhs2CBRFEkkIrjzTo1A4MzdmTs6cjzzzBCqCosXB4jF\nJq/zMRm/+Q3s2O8kim7fazJzpsKCBeePCq9HgVlTmDG9Ax3wnwDm4kS6HJ+bSaBoTIqcOX7zmw46\n2ga4+vIS9hwKIAer2PuHAFWX7mXu2n20tNfTN1iOFQE7pFDB6RXwsCzJpk0mbW028+apLF48+ekp\ndQj4nDBjCcwNwcsJSLwMbhcc6YWfvgSzq6BmXC7lhY0wIwZDGagvAZ8bXm53nO7gVNqz3gXtB0Em\nIRsBIw1Pl8NHfwdfvhiWVTjGCJyAgkhhNW/RIo1Fi6Z2aTU0KCxZorBxo41lK6AWlsQsnLooLgXN\nkoTjXi67TOAN6Xjcr950LUvy0kuS+npQFIWWFptjxySLFp05Y1JV5ebii8N4vQolJRNkl54mxzqg\nqxdcWpZ9Bw2+8R8G934rgst1nhiU6bvMdaGUcs4baVg0JkWmxMhIjpdf7qe21o+iGFSVO2sAnSkX\nj+59F11WgjE7ggwKPLNTzCwbZpG4/DXHsE2To089Re/u3fjLy5m7bh3eccXeN2wwefzxPOGwYPt2\nC7cb5sw59SlqAHPCTulchHNz70w6EVndCWgbgbAOmfxr2x1JwAPtTonhuhTcVufkOW5NgceCXg0y\nEQOx0MIaUcj0qGALTD881qywvgX+ciV8YdWrQo4H2mDjHmdZbe0yqIpzApYl6ejIkMtZhMM65eUe\n/vqv3QwMpnl2g2Csw8JEvFrvOG/gjSR4340RPvgBQToHR446L0UisHwJvLTBMaY+n0RKgcdzer/t\nZLhcCjfdVD75jqfB3HmwZSf0dlvESmySSZtcTuJ685OeM8f0dMBvFELMl1LuO92GRWNSZErs2jWM\noggU5bUj3jKfgT3fwOXLsLi0GcPScJPh/xhx3OK16wRt69fT+vzzBKurGWtrY+f997Pys599ZQ2+\nudkmHlcIhwX5vE17u82cCcZIh8bgWArqAo7hyAFeAZ05eFcUeksh0QaVfphfBTUFmyWRDFt5ftCq\nENVVKjSF1hQ80Q17E5AGxvLQ7MqR0Q1GciqKG7QGR4/c6tOxbJ3RmMp/bHfy/O+8AJ7bDf/1OKCA\nT4cXD8FXPgKxcfma+/aN8ctfdpFIWGiaU+elrs7LdTdUc/MnfPQoFq5m6G6HxLCNkkvjLRvlggtj\nfPBGgabBx26BI82OunFDnaP8e8stCj/5ic3QEFxyiWDmzPPI+TABV1wCh5shkfTQ3ZXnkx9xEQye\nR/ot0zc0eBWwQwhxDOeyEoCUUhZDg4ucGTo70/j9J54uPbbOftPLEu9BREpQHh1gqCXKnoodLK2M\nE6AWE5vBwZfp33YXtaUDpDzzoOJaUm19mJkMeiH1fdYshf37TbJZQTYrqa8/+c1lJA//0+Jc71VJ\n+GQNPDoICQsuDcN7SsC+HPZ3OD6TOVXgdUMbw+yhmx47zz67HN020ZMhgmaQfaMKWRuunQVPdFuI\nwBBqVqK4g7guyWGrCtISaLaJMmZhDHgZNBS+dQh2toDZ70R0RYOQM2F7FzywEf7sGmcWsWfPGF/9\nahtDQ24M4SdWKSmvV9nSrPHf/5FiVr3KmE/jksvho++GrdugrTPEwrkhrr0SwoXoal2Hea8zsI2N\nKl/6koJlnTxfRUpoG4X2MSd/Ju6FmVFwn8Orv7oS7vo09A2olMW9RM/HqLTpucx1zRttWDQmRaaE\nooiT1kIfQyF/0M+oXUJFYzdmTqe/q5zEwjYsciQxeKjvGVJ77+OPZvTgS6WpyL3InlQOd/R9aOPW\nZC6+WMPthq4um9mzVWbOPLkxUQRoAtIWuBVo8MJdNc5rLRxmC500KnNY2lD5SpvD9LOJVkJ4CVg+\nunKCvlEX/mA3uqeFVm+WrBLGp4cZLUuzJLKLTF5nd2YByf4IUTGEcEEyHSAbcKNqBlaLm94ReNSC\nm6KQGHOkwbwuCLlgbxcc7QUtL/n7f+jlSFuIgXSU0aRGtlnB6lNwlUl88SxjZp5FtQrSpeCNwKc/\ndvq/j3ISd0PbKPxiP/SlHJV+RThRb7oKVzXCxTXnLooqWsL5aUSArAGH+891L956pJStb7Rt0ZgU\nmRK1tX527RoiFnutxkQMEyQc3TqTka4YMg+x2ABzYhEC1NFMikPN61nZ0YzHSGGoLjwiRalviJKb\nPoAYdwdUTIMVMwZgjh+CE6vvhnS4o0myLZPCCg3yS/JUE6CRAM0cxoOXI+yjHMeYZDHYRjsx/KTz\nKk/1QTDeQ8jdj+LKobkyuN1ZAorFyECMuJLHTKsM2DEs4cJHEtPWEEi8aoZs0o3ABhOMHBgK/K4f\nFrlgaMyZMTVWQVkU/u1nMHQ0z+8P66STcfLDCrbiyLqwH/I9Jvmgh5FdCgdVmFslcSmCf7nVMUyb\nNsGOXRAJw3veDdHTiGloG4V7t0HYBfWvS/XJW/Crg5Ax4copacJOLzwqzJqCVuc7MJrrDVM0JkWm\nxKJFJTz+eAemaaONK+heoZpEPRmGcj76OsrBZWPWKASq3Qg0IriRITe9hBEIdGGBS0dfcjGh0ir6\n6SE7sJ7gCz8j33KMsazO0ZdGSebKiV50KcvvuAMt6CU11EcwWok34gxlu309DA5vJ9oxii8c49CM\nKg6qQzQQIssotTQCzrJOJ6PYAjRUdo0A2gihknZ0dw6Ega1IVNsinhpkLBEhEhlCuGE4EUIRFp5A\njsyYn+MhPlIR2EMq6DbknO9iUEDCC5dWO7OStjQ8fRT2t0DIpzFWX4a5zw1RAQVXkpAmeqOBrzqF\nmdJItkbYfwTSBnxqDYx1wy8egdJSJy+jvR0++hF4+mknWfHqq53Q4oEBk02bspIPxecAACAASURB\nVGQyNnPmuFi40IMt4Wf7HEMSPolD3qVCXRh+1wwLS6E8cHbOmz/8IcmuXRk+8YkogcB55BOZjOnr\nM3nDFI1JkSkRCOisWVPB0093UVf3Wq2pr9Z18pPeCC1VGUpcSf72OoshNc8YWUrxcfPcG3gykmDv\n9hEahzpQEl5igwvZ2XUfe40N+FPDJFd4MWbVkDuWZvSiGownWzEf+DYbfvCfVH7uIrQrZ6F0ubms\n4WP4qmfxxP4Hmf/c73GnkihZFd+CBrZedTHtgWrew0JKrGr+a9TgaXsMQx+j0a2wMifoTRuY/h7G\n0j5IugmERrE0hXzOxZCIYGYtBpo9BEpMEoMhMj4fOcNDJDACQjCYiCNUsE0BbglZ5ztQFdhrABlw\nJ0HNwHACUCCtC8yo21mD9zvbsEGWKOSrPeQNN/6FSRgDxqB9AF7cCYc2wpEjjuzLzJkwMgpPPgn7\n9zvJmLNnQ0mJyd13D2OaEpdLsGlThhtusKle4GMwfeKMZDya4ix3bemC62efnfNmZMRiYMDCMKak\nYn5+MT19Jm+YojEpMmXWrKkkl7NZv74Xj0ehtNSDqgqMnMXlRicXlQwzZ40bX8yPgYKnUId9ubKc\nuVWzSJrXEnrsYVw1c0ge2cGhqgzCsAmmkrTHq1D9Y3S8ax4pfwR71YX46jdg/2gHh9uSZMoiBJoU\nOpNPoMkOSHVxcGUDmi2ZtecQ2rE2dmSvZa81g7sNixJ7Px5VYGQ8RAe6eFH38VK3m1S7F9eseqqr\nW5DSpDVRR7hkDNVlkWpTMHZmUKQO0RSzklsZqy1h2FtCf7oU21QRLomdBaSAPE5ql+n4IoSEgAIH\nklDuh/ZKsGsg1alA2oY6YACnnQeoVxwD4pZkUx4nHC0hsDW47yGIpiCZcDL52ztg1mxobISDBx25\n/lgMXn45i2lKqqud7zoSkTz1VJLLK73oyuTOkJgX9g+cPWNyww0hrr46iMdz6vwR24ZnnoWt26Gu\nFt53w8lLErxlFGcmp03RmBSZMooiuO66GhYvLmHz5gF27Romn7cIh11cc001cxbPoTXURwaDuVS+\nYkwAfATxeeaAGoGhIfr8NllVJ8wwqtciag7THS8lpUUI5kYwNRe56xaQGdMI9/XjT4+RL4+Rzoyx\nLztEbS5MlzYDzZ8nV6nhyuXY6lmEhYZHyZCUkqQiWJDZQXl+kNpRwcsVqygb6cWdz1Hi6aVFrWe5\nsQmZUukaq0N1+9ntfxf5vE4k0UvV4H4aPG3ES4fpooIxGcbI6EhbBUU6Rbc00C0wUlAaBNV2wotb\n3aBmwe+CwRKcKy2MM9pVCw8LR1VfEVijKmSFs58F/R3w/nWw8SVIJiGTg1gULrkE5s4FjwcCAUdR\neHwdFF131Ixz5tQc64oA4yyOwBVF4PFM3pH9B+CZ5xyts737IOCHde89e/2aEsWZyWlRNCZFTpua\nGj81NX7e//66EyoFlnIKb25ZDVz7cehpQcQvRc1+HxUTy1LIutyoLoGNiqVqaNIgJXQU00Zr8KKW\nuDBMmyOHQljzTNpLa6i0esjpHkb8Yba8fzlRfZAGWgiqCSTQY1eS8PtAtTHTOmpJCvNyieqSSCzq\nn3uWeS89yW/Wfh4jLJG2TSCYoi9bxqBRAyGTFleEmf3NzNNb6BuZz4j0IEwFTRdEKhSiOqwsg/YM\n9A5COpcnGE0xs1TSqbjoTnhwqwpmsDA7OX7FKcAgTqKKCWRViFmg/P/tnXmUHVd95z+3lvfq7Uvv\ni3qR1Fosydola7G8gTHYZg3GhgFDwHYCY0iGkwCBmWRImAkZMpAEQsIAYQlgc9hsNtsY431fsKy9\nW73vy+u3L/Wq6s4f1bZsWZbclmSpcX3OqXPeq3er6nf79bvfuvX73d9PJWJBQ8LN/r/rApichEOT\nMF2Ge54CzQ9rOtzTLF/u49FHy8TjEl0XDA1V6ery0RQVVEdO/F3mTWg8Tf6S+ZAvgK6B3++mp5mZ\nObP2lKvQPXpmbVhoeGLicVLMO+lf2zJoW0YzNoVsGio/IhWIE5MztGYPkY7EyPujSNvBypToXOVj\nbNdmDKOKM5SjP7aZVdlnMCMxMuUIqiOhWSEWz5BU0+iiSokQEoipWZoCw/RqSzBr/AjbYkbUIIXA\nt3+UlXc+ht1Rg67p+AIOZVGlzddHdiRMORYgn2xgfe1jxIfGMQ+Uac9nWaH6qVm+mkAsyfLYOG9b\n8iR1+iB5mWE83wfVAj+cuZADSpym1gzpaoThfDOjsRYmf95MNe6HnHAFxMGdxigOxGzUdovEbIU1\nwTh17XCT6rbpe8phZhRUU3DbE4LLr4QHGyXnt0nGR3W6usIMDORxHOjs9LFyZYjpAza5CYEZEvie\nlxusUHCYnHIH7doaQbYieNuKU/f/8ErpWgKhEAwMue/f+uYza4+hQtfLiJzzormO4ImJxxkhgMof\nRa/kNhYT5n50y6KmOM3b07/ggL8TKxNg82QXMt7O7YUZ0sEYVimAUwnQOtuDZpjkZQeVmB9sh7ry\nFNOBOopqEKSkip+AXSTmy7JJf5w8EVJqkv2VZSBUcqUQSnuQwW1rsGsCoKo4VQ0jWqJNHwAknaHD\nvCHzCxKhGWomunnKWc1j77qCxyIOO/Wn0fX9PFNN4ZQtsopGPhgGqaKFD0JhNdPFKIZi0hCeIHBO\ngZrEJAd+fi6m6ofZuWpc6pwvoUXBVnWspQWmVZtol8p4CqYzDnksqFOwioKhQYWevYKZIcnAfZLG\nOsjl/FxwgZ+dOwX//u8OX/6yg+PYDGUljywSnLcNklGH237j0NurYBgqPp8g1ACv267QGRfs3Vsg\nn7c555wQkcixhwXHkS/KgHCqSCbhv/4JjI5BIg4NDTA97abzb2hwSwS86niPueaFJyYeZ4wG/LyP\n9VRYi09TUBoE0v4YG50iIroI5gpKLaHEDDmcljID9/6OVFVn/aLDFOsUnnI2MBBuY4P1MFquyHi0\nA0voRO0sbaIfHZsIWRx0TFGgSZ0gJyOsqjnIiu1FpltnmXFSGPkS6VKUQ8HlaHUVbKExqrRwT2QX\nxtQUpddfCo1+RNQhVM3z5J1JHtXfRHKLYEXrXpL+GQpWkFGliSAmreU+0jJOQYRo1kf4fXktxBUa\ndw0x+LvFgAJVATnHHbTGBfghNxPm4LTCrISVW8A3adPjl4iYhW3pWBNugS4rIlm/GSJhQU2N5OGH\nYWpa8k9fdhNGStumUnYINNmM7p0in65SDdUQqQ0yW3bQHJVCr2Rg2uKH1TzPPDODosDDD/v5yEda\nXxD+DXDLLdMcOlTkIx9pIRg8PSG+0ai7AYyNSf7t32wsS6Bpkj/5E5WmpldRUDwH/LzxxMTjjCIQ\nGBwZnITaAEeNVQECtBKAJHzg0iB/+8gbqStnseJxnlFXgeUwo9ZR40ySlwWCdpEEs+iiisBNfqiL\nCiCxhQoCFteOUj1k0trQx4TaysRkiHjvHmqbJEoOnIZaBowOnpjaSSXvp3nRCBE7hT5Vxqn1k7tk\nBY2VMYKVPA8PnY8asGiuH0Kagp6JRQQzBYyqSTEYYr9+DhOHmsg+mXCXoRvCdbY/AxQ4MnBNCOxN\nOrYtmc45UIANHTBTmsWaVskNRwkqPi7ZCQ0CrCqk09Dd7SZ6/MrXJWlLQQm4pYQp2aT3ValtKVCW\nKlqwiu1XsGYk1SkLy5I8qlaRlRxbt+rEYhoDA269mFjshWLiOBLblsfMgnA6OHRI4jiC9nbB0JD7\n/lUVE/DEZJ6cNjERQhjAvbjxKhrwIynlXz/v848DXwDqpJTTp8sOjz8s1vtDXF9zH18cfRvqMzYT\n5zTg10z2iPVcbvwCn13GFgoV/Fgo1DJNRfhxUEhYsyQnx4n4iiyODZI3CkTyFtsyP+epW0JYT88w\nfIEPWZKUWsukLt2EOiwJiDyZUgJ/ooIsSEaHFuNvKLB/eg2GalETmURGNPrzi+n096BXTKYiDWT6\nEwSqRaoFlYlftuA8m15YcWchtAAHeG4RIw4QEXCOpGxJHtybY11XlXdsCbP/6Qr+vMbrb6hy2S6T\nRCLEP/yDw5NPSnRdovkU0mUQEbfuDLYDcR1SgnwqTCSs0VIOoPSazMw6pGydUFhBMR0mJnykUnlS\nqSpLlgSOmXDxrW+txXF4US3700VDg8A0JdPTEtN037+alE3oHn5VL7ngOZ0zkwpwsZQyL4TQgfuF\nEL+WUj4shFgEXAoMnsbre/wBElHXsSP2c/YH9vM9/U1krASaalNwItxSupIL1bup0VMIHaQicUyB\nJi0cS7D+wd+wePduJh2D2IUxRu0IAbtIcChP62Np8ocqdBX89L/nCsJGjsTwCDOlDhwtRjalMp5r\nIlDNIxUFv+3H1lQKhQDx+Cw+pYKZCzBweCnZ6SiVqI9wU4bZ/fUUDhlgg/ABFZAVt4oiSVwBqQJh\nCbUO5FX3kZcuKBgRDu8ZYsfSET6zfT3bb3RdLKWSn0RC0NSkcPHFkukZ+OkdDjIocEoSoYCUwr2z\n1sGv+Vm+PEo06iORgN7eEpFCBU1TKFqS1tYgH/5wgmLRprMzcEy/iBAC9VVcwL5iheCqqwQ9PZKu\nLsGKFa+umBgadL2MIqGeA/4Ip01MpFv4Oj/3Vp/bnp0kfxH4S+CW03V9jz9M/DSTbLiGSwe+wyPB\nUbodP1OFBjLEGXeaOEAXa5IHaCsNEJzJkmSapsA4m5+4H/mrwzy5V5KVCrH7pqhbnSFVCDJx1yzV\nGRuZhZrICK3//s9oLQbVd9UzYq1EhCWOpsCMpOrzoxkm4dosqqahpB2mK3UkrBmcrEoxE0QisEsq\nwgbDKJGxw9hVFWELpMCtoGjg3m4ZQAL0eBlfjUmlbGBpfncxoyYYP7iIr94R4gvdFosSkr/+tOC9\nV+vYNhQKgtFxh5/cJSkYClJ1j5FVB4GKVCTRmMXlF0fYud3H974nMQxBfb2P9KEiuYxNJALve5/B\nokX+4/zVzwwbNyps3HgGDfAec82L0+ozEUKowBPAUuArUspHhBBvAUaklE+filrSHq89YsYutnYt\n5iPjt/N1v2RcS5N3QhS0CAFdZVO1hiv+780E6msY6etl9YWPInMF9gwqSFvSvtJhfCrM7m/maVkD\n1QEb6QMRgFJJJ6CbVBWd4fQyaAAnoSKkxPGp2AUbtcmhoX6SsJohm4gR1nKouoOpG2TNGP5AGSuq\nUhgPE4gVMRaXMIeCWGUFIjqK30KaAukIWOUufJSqQtX0ozQ6c1UWAUvAqCA/FEUGbAaGJH/6Fxq/\nvavMR67TaWwUfOsmSdaaSwVsibkFkQpUoT4Bf3pVhI9crxCNShTF4uabwTRVYrEQK1Y4fOhDKm98\n49lRkWp/N4xPwoXbz4J68Kew0qIQ4pvAFcCklHL13L4kcDPQAfQDV0kpZ+c++xTwQdz/hI9KKW+f\n278R+Bbug9FfAR+TUkohhB/4DrARd/XSu6SU/XPHXAt8Zs6Uv5NSfvvU9OrFnFYxkVLawDohRBz4\nqRDiXOCvcB9xHRchxPXA9QBtbW2n00yPBYihtPK25g+yS9rcY+fpxySBYJsaZEmpkfvVJgxfE4ta\nazn0yzwtrxtgyfvrEU6SurXnUp41+N1ffJdiuEooISiZkqqqI4sV9sfWs6/hXaSinYRiOcp2iGrZ\nD1EgYaHpVS53bmV5sJvRQBM/sd9BZ+AgZsxPqStIuRQgHwigNMHQE+3k9iZRay1QBcKy0IRNsKZA\nbiJCJWAACpbjA+lAQHXXoJjAHglZkNM6hByQFpUyPPIwHN5vsmGjxoTPQqlXkVIgbdWd7Uy7qfmv\n2C6olgXlMtTVCW64Qefd73bo7YVgUKWtTeD3H3/UHh+HRx5xw3O3bOGYae5fKb29WWprDaJRV8ye\n3gvdfbBzi1u35Yxz6mYm3wK+jDvgP8sngd9KKf9eCPHJufefEEKcA1wNrAKagTuFEMvmxtKvAtcB\nj+CKyWXAr3GFZ1ZKuVQIcTXweeBdc4L118AmXHl8Qghx67Oidap5VaK5pJRpIcTvgLcAncCzs5JW\n4EkhxBYp5fhRx3wN+BrApk2bFmCWOI9Xgxqh8nbtqHT1QWg7/3z67rqLWFsbreuvwJlJodT0428Z\nJbK4lrihs/3T1/LUTfeSXXwQY7RAZUZFubADpbEOqxKBQYeqDFJN6hCTbj1fC5YmeqjUBXjc2USo\nmuV14k5+OP1O/vuez5GYTOMrmOxvXcGXon/G7DN1KAGLcjqIFjJpP68Xq6ChKIKgVWAs1Uy1akA9\nEFNdx/yghBkJKQExAeuAkoB+BSpQiSkUsPm9lia83SG0bwhp2swoyzBFDOIOsizYMyCoKPB//g22\nb4ELzoNoWCGdhqYmd+Hi8bAs+I//ANN0o8VCIViz5tR9d/ffP8HatUnWrq0B4O1vArN6lgjJKQwN\nllLeK4ToOGr3W4AL515/G7gb+MTc/puklBWgTwjRA2wRQvQDUSnlwwBCiO8Ab8UVk7cAfzN3rh8B\nXxbuAPsG4DdSytTcMb/BFaAfnJqevZDTGc1VB1TnhCQAvB74vJSy/nlt+oFNXjSXx6mm85JLQFEY\nvPdepJTgOITstbTv/AyRYC2gsvbKIHXnPMGdP/kYtcl+ev5ihMb0XuTyCG/IfZUHwj6KkXqGrXZm\ns0mcgCRgFInGslCVlOwAWSVKoxxDzTjcHbsEJeywfeY+uqZ7CJtFpqSKP2RimRo+X5UVG/aRytTg\njOv4gyVmxmqpKj5I4IYM+4C2ucdVFQElB4QCLQrEFZh1qKwCp1lntjZH4OEKUXOIEW0dxCWKzyKg\nF4mYGZ4abuTRvRpYgn/5jpuuZPsqhfogXPlmN5398bBtV0Tq6qBchkJhft9Bb68btnzRRRyztvt7\n3rMEVT0y1fH5jt3uTFA2obv/ZTWtFUI8/rz3X5u7ET4RDVLKsbnX40DD3OsW4OHntRue21ede330\n/mePGQKQUlpCiAxQ8/z9xzjmlHM6ZyZNwLfn/CYK8EMp5S9O4/U8PJ5DUVWWvO51tO3YQXFqCtXv\nJ1Rf/6L0L3LGJu7UoSTHKPsFjWaGTtnPrB5hp/4z9sor2OJ/nNvLlzGaaySychZT0ygUwySUNDNa\nDbvNc1mT2EtcmyUfCHFYLqF5fIREcoru4HJkVkMVksCKHGktRsKYxTFUKiUDO6pBVLgLGCXu4y0/\n4BMQUNx0KzruUJIAkVRoXimItilMlGykArZPxUzEwFbxGWUcn4KjKkSSafLlCGZOB0dQLUrueVKy\nZhF8rPPEoVl+P7zlLfDrX8Py5XDuCauAv5B774XHH3eP7eh48efPF5KzDUOHrqYTt3sUpqWUm07m\nWnN+jwX/9OV0RnPtBtafoE3H6bq+hweAHggQO47PLXX4MP7xNvzp27B1HVtxCA1P0iJGaN82SdHY\nQzkYpNAaYG98BWZYx9FUHghuQy/b5CthIuRo0MfJECVazJIPRJhorKOy2k9rQx/FwQhxfZZKrY+n\np9YReqqMyCukRpNUMgHXF+PHFQyJOzuRuCmIn91fAurcATjUAETBp8eQHWWyhU50WUKNqPiTFpVZ\nH9nxOAFZomHLCGP3NmOlfe45hcO+Abj9dsGOHScezDdtcrdXwpVXwubNcDyX58SEzdiYTTAo6OzU\njlnD/oxw+lfATwghmqSUY0KIJmBybv8IsOh57Vrn9o3MvT56//OPGRZCPJufemZu/4VHHXP3qe3G\nEc7eWwMPj1cBVdcJJGs5+OMytYbJVI9D8844y96UoKonCRZLPKGtJaeG2RR9nA3VR3BsQUkEmNTr\nGCs249NMwkaByYY6sjVhhqMNlNZovL/yDd4W+xHJnZOEz8mTNWMU7osw3t/CWKaZij/gRgw9+yt8\ndnmvI92V8ebcfnuujSVINEja2mwiEYfWZB1IlVJdPQV/PTKkE0lmCDfkiTRnqN01ifA7+GvLELZA\nt6BqY5tw110Os6fIDWtZkmzWxrZfeHNdUwOrVr200/43vynz6c/k+NANZd77vjJ//Td5MpmzKCGW\n8zK2V86twLVzr6/lyDKJW4GrhRB+IUQn0AU8OvdILCuEOG/OH/K+o4559lx/BNw1tzTjduBSIURC\nCJHADXy6/aSsPg5eOhWP1zT1q1cz9uSTNK17K6kDP6OScxh8rIJxeQyBwmiimYFIK32D7dQ4Q7Sp\nh6lvHedg6FxyIoy/UiE3DFOL4tTqKSYLCS6qu59wsIqQJh32AG+fvInvJ9+PmDJhUnerLeYEhB1o\nF1AQEAHK0t2nSwgKV0D6FDfkV1dQHMmKVWW6VYvZio8kOn92hUK4+5/4+e/P5ynOBT8ErRyOiCNU\nMCcNlICDbpiuo1+xwILeXpXvfMdm40bBunWCcHj+MwIpJQ89VObOO4uYpiQUElx2WYj1649RJ/go\nhoctvv+DMrfdrjGbMlCEZHy8Snt7mRuuP5NVseY4hTMTIcQPcGcItUKIYdwIq78HfiiE+CAwAFwF\nIKXcK4T4IbAPN67vI3ORXAAf5kho8K/nNoBvAN+dc9ancKPBkFKmhBB/Czw21+6zzzrjTweemHi8\npkkuXUq4qQnN78cpFylM3kH9vlEGmoIU3ryMMb2NHtnJaGYRB7V1LOvch+GkWK4eJEeUhsw+etQ1\n9E11kJ7x8V8CP6C5NktsIkcl4EPGBIWIgVURqDkLEVUhDcnqNNFyhuHlrTj7JdqEg50UOJpAVSSy\nBWRRxdEcGFVQbHjf6yVL3zxF2DHYnS9ysT/Gu6IF0k0ZppVJup/QaF02gl1QKI1EEIqNGrWwh1Vs\nUwPTdoXJEciQpFSC226TPPSQ5LrrFOLx+QnK009XuOWWPK2tOn6/oFRyuPnmPJGIwtKlx/ek9/VZ\ndHc7VEwVXZc4DhSKKg88UOaG60/mGz2FnKJJkpTympf46JKXaP854HPH2P84sPoY+8vAO1/iXN8E\nvvmyjT0JPDHxeE2jaBrrrr2WPTffjFXZyNIlK8n3HKSmb4ZFT5hsvKqF6+LtGMvqmP7O53m0roDZ\nVCJhlUkfMKm9Z5IV0z8lWCoQH02z7S8tJtNLCFWL+IsmxUCAqqWx9Knd9OxZDzEHFEFbtRfFnCRd\n9aMu9rHNfz+9bYsZEO3YAxp+YaH5TapLNYJPOVy92cfVF8f5rabhGEU6NR+RUZXP/24J0v4cHW2S\nN9l+BmfWUowPEF+SYeihVsyUTnnMQOYUQIIExbG54AKdZFKQTMLwsOSXv3RYv16lrc2t4PhyuPvu\nEg0N2nNrVQIBhXhc8sADpeOKieNI9uzJMT6WQRFBVC2BrEqSNTaR6NnhMymXofvQmbZiYeGJicdr\nHn80ysbrriM7MkJ+fBwhBLG2NoK1tUcahaHmQ18g+bPvcfDQ/fQvT2MdnMB/dzd058k2LmXVVgd/\nwCGez5CKJVAsh5LjZ8kXn2TbwD18ceVXGLcsKgmdxP6HieQPEsntxbpyE2GngPA76DVVjJiJMVZA\nSStEmy1WdRwm6Kvn+6MbGVYaiOk2pFRuf1Dl0g6IBuIcHoTrdsHYZJCHdjdwxVvgzjr46S9s9kzY\nmKpAFBw0S9K2WLJt25FBOxaDb3wDtm51w4BvvPHlrfXIZBzq6l4YFWYYgtnZ49/SP/VUgUOH8rS0\nCFLpLJIsug/q6mp55zsi8/nqThuGD7pexlppLzfXETwx8fAAptjDTMs+6lvWkWTZMdsoPh8NV32A\nBj7ADiwOr+jn1uy/onz3JzS1RAm2aaj2MDXpMWLZFLnGMNM5QZwhGjbbfPC8h8kc3MHToozT6Mc/\nWqLaZDBU105OxshEwkTUElWhYyk2S1pmyOKnoApyZghTgSmfStVWKVluYmBVg4DPzb4SD4LRAL0J\nWNoGW1bDR98t+NznTKQU5POCZ54RJJOCvj6HoaEqy5er+HwqjgPt7TAw4K4nicdP/DdbudLH/v0m\nTU1HhpHpaZtduwLHOQp2764wNaVxySUqgYBkZkZSX2/wsY+F2bXrLFlo4tUzmTeemHi85nGwmeAJ\nfISY4DGSlVY4+CBUTVi2FSI1LzpGRWNZcCkfv+EfGbv+7+l74pMcvL0XfcImsXiW2UiUlIgTT6WJ\nfThMeVSl3ujmqjU6nfF9PHDZDg49so22FVPsStyNXzEZpZHuygoqShJL6tiKhoVCSasjItq5eBH8\nyzRMVSARhM4mKGbdVWmRIDTEIdAAXe1wdwEGZ2FXUOHaa/3cdFMFVXUwDI2mJo17762Sy9ns3l3l\n4osNtm7VGByErVslAwNFDhyw2bQpgqa99GOnSy4J0tNjMjhYJRhUKBQckkmFbduOLyY9PRr79iko\nSpUNGzSuuaaOlSuDx73WGeEsCixbCHhi4vGaR0ElwRJm6aaWNXDv92GyDxQNBp+Byz8GvmNHKAkE\nzcJH8+rL6Z/eS/qhHyI1gVkvaIhPo+oWmvDRWJzg4eAank6meajej1m0UeMGWxc9jiHLWJZGg2+c\nlsgw9+oX4UtWKFQlimIQK0m2F3WmS5A0ocMPUQd8a2Gz5S6W37QUAnPpUfZX4PYCJFX4bgY+uUzn\nU5/SKBQkqRT84z/aTE8LFEUhm5U88YTJddfB1Vfr9PYW+OhHn6Zatfn4x1fyjne8dB72mhqVG29M\nsHt3hbExi9ZWjXPP9RMMHn/FwdvfHqC2VmHtWli+3EdLy9mXsdibmcwfT0w8PIAWdtLIFlRbgcmf\nQl27+8H0EBQzLykmz+HfQSmsMrJ9MfmR37N07DEuqn0If8BEqTg8KrfzuPZH/K5+nFR3mOJwhFg5\nQ39PJ3qjhSIdFvkGaCmP0jg5QW9hCZaq0RGdpDk5xm+LRZTZzWwOazxbn2qoCOtXQP1RpllAtQq9\nQyBD4NRC0O8mdUwmYecFblLF6YksTU1VVq4M09PjMDUlmZ01KZWqOI5kZqb83DkPHy4xMWHS3m68\nYPCPRBR27Dj+TORoNm70sXHjWfI46yUoVzwH/HzxxMTDYw4Vn3ub37wM/azbhgAADPVJREFURg6A\nokIo4W4nQhgEI9uZNkdpalbZvTvKPbmLMZQKqhmncc0V3Jk0mdkfJn1/I05eEKnN8njDeUgVQvE8\nh1LLWWP+HqekUMoF0XwmuYyf/kIzHc2HyWfaiFcbqfepmHOPYILH+AUrozB5DxzIw9ogmK0QfJ5f\ne9tWBZ9PYWZK41+/kiccDrBhg59EwmF0VOO66zrJ5SyuuaYRKWH37jw/+MEEigLFosONN7awZMlZ\nsBbkNGL4oWvpids9+sjpt2Wh4ImJh8fR7HgX9D0J1Qp0bgD95d1Ft0Z97J/uIBnrYOm211NKpZBS\nEkjEGQoKskoP6YcWQ9lBTUpKjUEC0RJ2WSPgKzFRbARlHYWZEIvMQZZGD+OvLzKVaydVSNJWk2V6\nKkk6G6AnD2tjkDEh/Lxf8d7D8P1fQr0JfXsgfA6Ejpq5LO6EjnbJ4GCAzg6VDRsM6uo0xserDA9b\nvPe9izAMhWoVvv51uOMOqK31kUplGRoq8YUvWHzuc0tJJs+G9L4eZwuemHh4HI3PgOXb533YuY1w\n+2E3G4qiaYTqn0uQTVotUbRUREFCo6StrY/zW+4jGCoyLhrZ66zCtP2MWc20tozyx6Gvky+EeSy3\nkaWNz9CfX8nhvEqbkSZeVVgW1vErCt/qg79cCfqcm+JAn+uMX9wCzXVupuBjhfn29VX56ldned/7\nYtTVucNAY6POu9+dfK7N7CwcPgy2LfjpT3VM009Tk1sSuK+v9BoQE88DPx88MfHwOEXEA7CqHvpm\noS70ws8MR0ERAmNxEZ9e5b1N32W9spty1cDRFXS1yr3KBaxM7CdKhqecdbxJ+xVBpcj9lW0EjRmm\nEjGGlSmemIYVUZuEpmGnI9z0TIjz26Aj4QrIY3shHIR0Dpa2wMFuWNIJ2vN+7S0tGu98Z4TFi196\n1lVbC+vWOdx3n01dncLsrE4qpePzmdTXn3i2lss5pNMOLS3qMevKn914Hvj54omJh8cpZPsieGYC\nEgHQnhfU1FH1EXIk+e1FFmcPsSX4ONNODbahEaLANvEA47E6wjKPY6qousUhfQmLtUGUskNOhuge\nXE57Sz++eI7eQoCoKgmos3ztgM5Nh3z8vzfB5lWQycPeXljWBod2w/7fwwXb4fI3HLHHMBS2bj2+\n3+NLXypx8w8rHOypYFkafi1EIm7xwQ/WsmjR8QMSymXJV76SZ3bW4fLLA+zadRZGbJ0QT0zmgycm\nHh6nkI4EvH4J3NED7XG3HDuAgWBLPsRtUYtEPIOGhR8T2zQJyBzCX4dqO2imTUAt0aiOEaxkGPA3\nU29O8HRlA44jmMkkSSYztNfmSRpQSRkM720mloBnUvCGRXDZDncbG4cDvwdNhXR2fv1Ipx1+/GOT\nwbRGpjkElnRDi4dMvvRjH5v74d2XQPwlUq9Uq5J83s25dVZlAn6ZlMsO3d3miRt6PIcnJh4ep5gL\nO6Biwd390BIFY+5XtrHQQE7bQ6A4zUNNm9mafRitWKZk+dijLyU7EMeqGrSGhik0hMmmu+i32znQ\ns4KJlkYIg+1oyGqA5oiPqk+yKWLQulgh5IPlR61ab2qEd1wJ4xOwc9v8+hAKQSIpGC/5ITRXWz4G\nOCq//XmFlnadR/fDpZuPfXwkovD+9wcZG7PP+jDgY2EY0NV14pnJo14+lefwxMTD4xSjKPDGZa7f\n5I4eGM9B1ICEobM9s5LG4lcYVIM8lu4klMvQJzrI5jWQkqJhsH9qJXtHVqFYZZSQQveD52LXaigr\nTWo2jaAaVXpQGMtFKRaCLG6GtyWh4xhprTYdtzzdS6PrCp/9uyC//PDcDhXXjSChgiBbgvgJ0mgt\nXaqzdOlCddJLPAf8/PDExMPjNLGpBdY1Qk8K7h+AvjQowmCo/SKiqd+zeGCAvkKIA2Y7uyOX4+/S\nEUULX8FCUyuEYgoTPU2o2DjTgsB4iRX5Q6xt2U13bhWZ0lY2aQlUAbdkoCvkTiKeTyrlEAqJ5zL7\nzofDvRpaxsRKCnekyAtQHWpW6PzqVzaVQTj3swqGcXLO9eFhi8OHq8TjCmvW+M4iZ73nM5kPnph4\neJxGNBVW1Llb0YSKDd+2/Oyb0Vm0JsIe9UqG0puxB2Okx1UC4QqFiIpQbYZLIQJGic7aXqpVHZ9W\nptwTxlkCmwIPoUiN4ep5LFcbSVkKGfuImDgODA7afO1rRRYvVvnQh+a/yPDeRyRWjw1OHvwCQiq+\n9hD9uwV2Dr7RDXt6JL/6tiD2CpL9lssODz5Y5JZbykSjKqYp2bnT5sorz4YFkV4013zxxMTD41Ui\n6IMg0Dnlo3sohXqZj/RIM0sa+lkUH+PJoQ1kCglMdHfhiJCUmwzIQ2i6wKLQEOlKnEw+TihUIigK\nDBVLTKTyGEoUo9G9zuFR+P49MDsNuaxghe/Fd/qm6TAyYtLU5MMwXphLy7Yhl4fde20ISwgJqFMh\nD6ZWhYoPLQHChO5+GJtm3mIyMWHyiU/0s2dPFV03uPTSGJ2dPh55pMIb3xg4C5I+emIyXzwx8fB4\nldlSu5afBB6kIHM4KPgck6wWJWnMUMhFEYZEqhJMsBWNvD9ExJ8hXp+inDWIihxBX5GRmXbSKT+B\nvMKySfjuBKxdAj+5D9oTkGhXGTZCvPUdL7bh1ltTPPJIjpUrA/zxHze+4LPf3gd3PQDRqICEApYK\nOcARbqXGEFgToBhw3mbJ4tb5/w2+//0penrKaJpCKlVmcDBALKYTCAhU9cTHn27caK7CmTZjQeGJ\niYfHq0ydaGbDRUmm7QoNvhGsbABL1/FhIoTjLqG3JfgFulom2jDL8roDjFabmNFrKUSSTNqrqFFs\nNmgVmq0AKQVufhr6C/DEJBQc2NgEqk9QKEgO7a9SW6vQ0eGO1JWKg5TuepCf/KSIzye44go3YWNj\nPbS3woXnq/ziPgk1ArICokBVgzwoDXDp+fCzf1ZRjp8k+JjouuvH0TQHIQTVqiCfd3jPe0IIcaZn\nJW6Rr66uE3fMi+Y6gicmHh6vMho6F2lb+e2+r7KqxaIvdw5q1iZbjmMEilgzEYRto2gWsY4ZYjM5\nJuN1TM/Uk9hSJOd/HcGSTnJvDUuFGw/8aMpN+tgUhYtWwt37oUaFN22CW39aobfXQdPgxhsNGhsV\n3va2GtauDdHW5ufb3y69wIl+7jnuNjENCUNhdkSAJtyIrqqEgiAcEtzwVvGKhATgne+sJZOx6e8v\n8+d/nmDHjgSRiPKKAgVOD95jrvniiYmHxxlgtW8XgfZxbvvRf3Jx2z46B9fxq8yV9Gqd6FaVqunD\nX1sitq9ISQlQVMLEV+Y5v0tDotOiB1GsGKN5SIYgW4KgAZGAu+1YCR+/COpj8PCvJPE4pNNQLEoA\ngkGV1avdnC8f/vCxVx421ML29ZLf3COoFkArSFAFtgPnLoY3nv/K+9/Q4OPTn170yk9w2vHEZL54\nYuLhcQZQEHTVvYu9jVfwP/5lmEohw8g5yygRoL59gtraEUJ6kYnReorLDbq2jrKpJc4OtZYxLFZr\nBlsvFPzySRiZgdcvh9EyjKTdJ2SvX+EKCcDVV/u4444qGzYodHS8eCqhqi89G/jCZxWuusaht1dQ\ntgTShvYO+Nf/LfAvxAwp88ITk/ngiYmHxxnkoouD+G5to28wQ2EsjOiQTIw2MnM4gRGrYPYaLFkz\nw6bYatq1AqNUqUXjPEJEgnDNziPnms7DWBYifuh4XqXhjg6V669/ZV7tFcsFv7hF4dv/Kdl3QLJ2\nrcJ7r4GWppPr99lOuWzT3T3PHDSvcTwx8fA4g8Rigv/1VwYf+Lsi+SGJ3K9C3MFW/eQGAxBx2LCy\nmQ+FFVYrAUo4hFBQePFsojbsbqeatkWC//6ps8WX8ergOuBPnAbGc8Af4RW6zzw8PE4VuzoF176n\nhvYOC802YQycQQ3NJ/if/03jq8s11vlUNAQR1GMKicfpwH4Zm8ezeDMTD48zjKHBZy6CXV0BbnkM\nxiZgTSvccAm0Rs+0da9VPAf8fPHExMPjLCCow2Wd7uZxNuCJyXzxxMTDw8PjmHhiMh88MfHw8PA4\ninLZort7+kybsaDwxMTDw8PjKAxDoasrdMJ2XjTXETwx8fDw8HgRns9kvnhi4uHh4XFMvEqL80FI\nKc+0DSdECDEFDJzi07YBg6f4nK8mC91+WPh9WOj2w8Lvw7Hsb5dS1p3MSYUQtwG1L6PptJTyspO5\n1h8KC0JMTgdCiKmT/Yc7kyx0+2Hh92Gh2w8Lvw8L3f4/JF7LK+DTZ9qAk2Sh2w8Lvw8L3X5Y+H1Y\n6Pb/wfBaFpPMmTbgJFno9sPC78NCtx8Wfh8Wuv1/MLyWxeRrZ9qAk2Sh2w8Lvw8L3X5Y+H1Y6Pb/\nwfCa9Zl4eHh4eJw6XsszEw8PDw+PU4QnJh4eHh4eJ40nJh4eHh4eJ40nJh4eHh4eJ40nJh4eHh4e\nJ83/ByxSI+kUx6CTAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xcb55b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "housing.plot(kind='scatter',x='longitude',y='latitude',alpha=0.4,s=housing['population']/100,\\\n",
    "             label='population',c='median_house_value',cmap=plt.get_cmap('jet'),colorbar=True)\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "corr_matrix = housing.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "median_house_value    1.000000\nmedian_income         0.687160\ntotal_rooms           0.135097\nhousing_median_age    0.114110\nhouseholds            0.064506\ntotal_bedrooms        0.047689\npopulation           -0.026920\nlongitude            -0.047432\nlatitude             -0.142724\nName: median_house_value, dtype: float64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_matrix['median_house_value'].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x000000000CB13198>,\n        <matplotlib.axes._subplots.AxesSubplot object at 0x000000000D7D0400>,\n        <matplotlib.axes._subplots.AxesSubplot object at 0x000000000D9498D0>,\n        <matplotlib.axes._subplots.AxesSubplot object at 0x000000000C9A6128>],\n       [<matplotlib.axes._subplots.AxesSubplot object at 0x000000000D840438>,\n        <matplotlib.axes._subplots.AxesSubplot object at 0x000000000D840470>,\n        <matplotlib.axes._subplots.AxesSubplot object at 0x000000000A02DB00>,\n        <matplotlib.axes._subplots.AxesSubplot object at 0x000000000CDB1E10>],\n       [<matplotlib.axes._subplots.AxesSubplot object at 0x000000000A18CB38>,\n        <matplotlib.axes._subplots.AxesSubplot object at 0x000000000A39E748>,\n        <matplotlib.axes._subplots.AxesSubplot object at 0x0000000009A55F98>,\n        <matplotlib.axes._subplots.AxesSubplot object at 0x0000000009B853C8>],\n       [<matplotlib.axes._subplots.AxesSubplot object at 0x000000000B17BB00>,\n        <matplotlib.axes._subplots.AxesSubplot object at 0x000000000AF2DE10>,\n        <matplotlib.axes._subplots.AxesSubplot object at 0x000000000C33A748>,\n        <matplotlib.axes._subplots.AxesSubplot object at 0x000000000D88EA58>]], dtype=object)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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MZ0qcTRfJlxwips6unnhZZOB/HsOpIp5SGBok8jZff2GIg2dSS/JOVkooYLse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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd9ad908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pandas.tools.plotting import scatter_matrix\n",
    "attributes = ['median_house_value','median_income','total_rooms','housing_median_age']\n",
    "scatter_matrix(housing[attributes])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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bx2xVc2Mn4ytHA46H2eb+A/lqeLfP6uZ5FgGq2qp34QXXRzl1YxnkEXXjQrV9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\n1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bfvzvjmnQnWe6oq2DlHwJt5RU3YZD2huf6o7AbulbEW5p7A5YsICYcGbzWB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\nWCwWi2VXsIJjsVgsll3h/wNrxcnzKrmQVAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd1f6080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "housing.plot(kind='scatter',x='median_income',y='median_house_value',alpha=0.1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing['rooms_per_household'] = housing['total_rooms']/housing['households']\n",
    "housing['bedrooms_per_room'] = housing['total_bedrooms']/housing['total_rooms']\n",
    "housing['population_per_household'] = housing['population']/housing['households']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "median_house_value          1.000000\nmedian_income               0.687160\nrooms_per_household         0.146285\ntotal_rooms                 0.135097\nhousing_median_age          0.114110\nhouseholds                  0.064506\ntotal_bedrooms              0.047689\npopulation_per_household   -0.021985\npopulation                 -0.026920\nlongitude                  -0.047432\nlatitude                   -0.142724\nbedrooms_per_room          -0.259984\nName: median_house_value, dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_matrix = housing.corr()\n",
    "corr_matrix['median_house_value'].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing = strat_train_set.drop('median_house_value',axis=1)\n",
    "housing_labels = strat_train_set['median_house_value'].copy()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import Imputer\n",
    "imputer  = Imputer(strategy='median',axis=0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing_num = housing.drop('ocean_proximity',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Imputer(axis=0, copy=True, missing_values='NaN', strategy='median', verbose=0)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imputer.fit(housing_num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ -118.51  ,    34.26  ,    29.    ,  2119.5   ,   433.    ,\n        1164.    ,   408.    ,     3.5409])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imputer.statistics_\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ -118.51  ,    34.26  ,    29.    ,  2119.5   ,   433.    ,\n        1164.    ,   408.    ,     3.5409])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_num.median().values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = imputer.transform(housing_num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelBinarizer\n",
    "encoder = LabelBinarizer()\n",
    "housing_cat_1hot = encoder.fit_transform(housing['ocean_proximity'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 0, 0, 0, 0],\n       [1, 0, 0, 0, 0],\n       [0, 0, 0, 0, 1],\n       ..., \n       [0, 1, 0, 0, 0],\n       [1, 0, 0, 0, 0],\n       [0, 0, 0, 1, 0]])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_cat_1hot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['longitude', 'latitude', 'housing_median_age', 'total_rooms',\n       'total_bedrooms', 'population', 'households', 'median_income',\n       'ocean_proximity'],\n      dtype='object')"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.columns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-121.89, 37.29, 38.0, ..., 339.0, 2.7042, '<1H OCEAN'],\n       [-121.93, 37.05, 14.0, ..., 113.0, 6.4214, '<1H OCEAN'],\n       [-117.2, 32.77, 31.0, ..., 462.0, 2.8621, 'NEAR OCEAN'],\n       ..., \n       [-116.4, 34.09, 9.0, ..., 765.0, 3.2723, 'INLAND'],\n       [-118.01, 33.82, 31.0, ..., 356.0, 4.0625, '<1H OCEAN'],\n       [-122.45, 37.77, 52.0, ..., 639.0, 3.575, 'NEAR BAY']], dtype=object)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['longitude',\n 'latitude',\n 'housing_median_age',\n 'total_rooms',\n 'total_bedrooms',\n 'population',\n 'households',\n 'median_income']"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(housing_num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(16512, 8)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_num.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.base import BaseEstimator,TransformerMixin\n",
    "\n",
    "rooms_ix, bedrooms_ix,population_ix,household_ix=3,4,5,6\n",
    "class CombinedAttributesAdder(BaseEstimator,TransformerMixin):\n",
    "    def __init__(self,add_bedrooms_per_room=True):\n",
    "        self.add_bedrooms_per_room = add_bedrooms_per_room\n",
    "    def fit(self,X,y=None):\n",
    "        return self\n",
    "    def transform(self,X,y=None):\n",
    "        rooms_per_household = X[:,rooms_ix]/X[:,household_ix]\n",
    "        population_per_household = X[:,population_ix]/X[:,household_ix]\n",
    "        if self.add_bedrooms_per_room:\n",
    "            bedrooms_per_room = X[:,bedrooms_ix]/X[:,rooms_ix]\n",
    "            return np.c_[X,rooms_per_household,population_per_household,bedrooms_per_room]\n",
    "        else:\n",
    "            return np.c_[X,rooms_per_household,population_per_household]\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "attr_addr = CombinedAttributesAdder(add_bedrooms_per_room=False)\n",
    "housing_extra_attribs = attr_addr.transform(housing.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "num_pipeline = Pipeline([('imputer',Imputer(strategy='median')),\\\n",
    "                         ('attribs_adder',CombinedAttributesAdder()),\\\n",
    "                         ('std_scaler',StandardScaler())])\n",
    "housing_num_tr = num_pipeline.fit_transform(housing_num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.base import BaseEstimator,TransformerMixin\n",
    "\n",
    "class DataFrameSelector(BaseEstimator,TransformerMixin):\n",
    "    def __init__(self,attribute_names):\n",
    "        self.attribute_names = attribute_names\n",
    "    def fit(self,X,y=None):\n",
    "        return self\n",
    "    def transform(self,X):\n",
    "        return X[self.attribute_names].values\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import FeatureUnion\n",
    "\n",
    "num_attribs = list(housing_num)\n",
    "cat_attribs = ['ocean_proximity']\n",
    "num_pipeline = Pipeline(\n",
    "    [('selector',DataFrameSelector(num_attribs)),\n",
    "     ('imputer',Imputer(strategy='median')),\n",
    "     ('attribs_adder',CombinedAttributesAdder()),\n",
    "     ('std_scaler',StandardScaler())]\n",
    ")\n",
    "cat_pipeline = Pipeline([\n",
    "    ('selector',DataFrameSelector(cat_attribs)),\n",
    "    ('label_binarizer',LabelBinarizer())\n",
    "])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "full_pipeline = FeatureUnion(transformer_list=[\n",
    "    ('num_pipeline',num_pipeline),\n",
    "    ('cat_pipeline',cat_pipeline)\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing_prepared = full_pipeline.fit_transform(housing)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.15604281,  0.77194962,  0.74333089, ...,  0.        ,\n         0.        ,  0.        ],\n       [-1.17602483,  0.6596948 , -1.1653172 , ...,  0.        ,\n         0.        ,  0.        ],\n       [ 1.18684903, -1.34218285,  0.18664186, ...,  0.        ,\n         0.        ,  1.        ],\n       ..., \n       [ 1.58648943, -0.72478134, -1.56295222, ...,  0.        ,\n         0.        ,  0.        ],\n       [ 0.78221312, -0.85106801,  0.18664186, ...,  0.        ,\n         0.        ,  0.        ],\n       [-1.43579109,  0.99645926,  1.85670895, ...,  0.        ,\n         1.        ,  0.        ]])"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_prepared"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import  LinearRegression\n",
    "\n",
    "lin_reg = LinearRegression()\n",
    "lin_reg.fit(housing_prepared, housing_labels)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predictions:\t [ 210644.60459286  317768.80697211  210956.43331178   59218.98886849\n  189747.55849879]\nLabels:\t [286600.0, 340600.0, 196900.0, 46300.0, 254500.0]\n"
     ]
    }
   ],
   "source": [
    "some_data = housing.iloc[:5,:]\n",
    "some_labels = housing_labels.iloc[:5]\n",
    "some_data_prepared = full_pipeline.transform(some_data)\n",
    "print('Predictions:\\t',lin_reg.predict(some_data_prepared))\n",
    "print('Labels:\\t',list(some_labels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "68628.198198489219"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "housing_predictions = lin_reg.predict(housing_prepared)\n",
    "lin_mse = mean_squared_error(housing_labels,housing_predictions)\n",
    "lin_rmse = np.sqrt(lin_mse)\n",
    "lin_rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(criterion='mse', max_depth=None, max_features=None,\n           max_leaf_nodes=None, min_impurity_split=1e-07,\n           min_samples_leaf=1, min_samples_split=2,\n           min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n           splitter='best')"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "\n",
    "tree_reg = DecisionTreeRegressor()\n",
    "tree_reg.fit(housing_prepared,housing_labels) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing_predictions = tree_reg.predict(housing_prepared)\n",
    "tree_mse = mean_squared_error(housing_labels,housing_predictions)\n",
    "tree_rmse = np.sqrt(tree_mse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree_rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "scores = cross_val_score(tree_reg,housing_prepared,housing_labels,scoring='neg_mean_squared_error',cv=10)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "rmme_scores = np.sqrt(-scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 68672.12286567,  66562.78534877,  71505.51584432,  68775.2492599 ,\n        70959.80862276,  74934.68718147,  72017.91477303,  71345.38048689,\n        76827.16578992,  68611.76876722])"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmme_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "def display_scores(scores):\n",
    "    print('Scores:',scores)\n",
    "    print('Mean:',scores.mean())\n",
    "    print('Standard deviation:',scores.std())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores: [ 68672.12286567  66562.78534877  71505.51584432  68775.2492599\n  70959.80862276  74934.68718147  72017.91477303  71345.38048689\n  76827.16578992  68611.76876722]\nMean: 71021.239894\nStandard deviation: 2942.90269802\n"
     ]
    }
   ],
   "source": [
    "display_scores(rmme_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "lin_scores = cross_val_score(lin_reg,housing_prepared,housing_labels,scoring='neg_mean_squared_error'\n",
    "                             ,cv=10)\n",
    "lin_rmme_scores = np.sqrt(-lin_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores: [ 66782.73843989  66960.118071    70347.95244419  74739.57052552\n  68031.13388938  71193.84183426  64969.63056405  68281.61137997\n  71552.91566558  67665.10082067]\nMean: 69052.4613635\nStandard deviation: 2731.6740018\n"
     ]
    }
   ],
   "source": [
    "display_scores(lin_rmme_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "forest_reg = RandomForestRegressor()\n",
    "forest_reg.fit(housing_prepared,housing_labels)\n",
    "RandomForest_reg = cross_val_score(forest_reg,housing_prepared,housing_labels,\n",
    "                                   scoring='neg_mean_squared_error',\n",
    "                                   cv=10)\n",
    "forest_rmse = np.sqrt(-RandomForest_reg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 52041.69221191,  50561.15880845,  52201.43674792,  54938.33086546,\n        51788.15211899,  56126.53570653,  51914.6743166 ,  50295.43259871,\n        53909.65779298,  52293.64114187])"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forest_rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores: [ 52041.69221191  50561.15880845  52201.43674792  54938.33086546\n  51788.15211899  56126.53570653  51914.6743166   50295.43259871\n  53909.65779298  52293.64114187]\nMean: 52607.0712309\nStandard deviation: 1754.42404866\n"
     ]
    }
   ],
   "source": [
    "display_scores(forest_rmse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "param_grid = [{'n_estimators':[3,10,30],'max_features':[2,4,6,8]},\n",
    "              {'bootstrap':[False], 'n_estimators':[3,30],'max_features':[2,3,4]}]\n",
    "forest_reg = RandomForestRegressor()\n",
    "gird_search = GridSearchCV(forest_reg,param_grid=param_grid,cv=5,\n",
    "                           scoring='neg_mean_squared_error')\n",
    "gird_search.fit(housing_prepared,housing_labels)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'bootstrap': False, 'max_features': 4, 'n_estimators': 30}"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gird_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=False, criterion='mse', max_depth=None,\n           max_features=4, max_leaf_nodes=None, min_impurity_split=1e-07,\n           min_samples_leaf=1, min_samples_split=2,\n           min_weight_fraction_leaf=0.0, n_estimators=30, n_jobs=1,\n           oob_score=False, random_state=None, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gird_search.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "cvres = gird_search.cv_results_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'mean_fit_time': array([ 0.27861605,  1.14346533,  2.73895669,  0.34201961,  0.95845475,\n         2.80796046,  0.37262139,  1.37707872,  3.5094007 ,  0.38642216,\n         1.34847708,  4.16783829,  0.21581235,  2.44533997,  0.29361687,\n         2.98537068,  0.38402209,  3.76661553]),\n 'mean_score_time': array([ 0.00980058,  0.03660216,  0.09420533,  0.00960054,  0.02480135,\n         0.07300415,  0.00900035,  0.02600155,  0.08960519,  0.00760036,\n         0.0222013 ,  0.05820332,  0.0082005 ,  0.06640382,  0.00860052,\n         0.0958055 ,  0.01280069,  0.06880383]),\n 'mean_test_score': array([ -4.22468566e+09,  -3.11437325e+09,  -2.78697132e+09,\n         -3.66563697e+09,  -2.79405245e+09,  -2.56805437e+09,\n         -3.46576136e+09,  -2.73278192e+09,  -2.51365181e+09,\n         -3.37081484e+09,  -2.74344045e+09,  -2.50236725e+09,\n         -3.97165830e+09,  -2.69056484e+09,  -3.58725062e+09,\n         -2.55088299e+09,  -3.43782723e+09,  -2.47347168e+09]),\n 'mean_train_score': array([ -1.11991734e+09,  -5.84047871e+08,  -4.30445993e+08,\n         -9.66873623e+08,  -5.25987991e+08,  -3.89749267e+08,\n         -9.27512047e+08,  -5.10566493e+08,  -3.87004495e+08,\n         -8.84733277e+08,  -5.00128784e+08,  -3.83849768e+08,\n          0.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n          0.00000000e+00,   0.00000000e+00,   0.00000000e+00]),\n 'param_bootstrap': masked_array(data = [-- -- -- -- -- -- -- -- -- -- -- -- False False False False False False],\n              mask = [ True  True  True  True  True  True  True  True  True  True  True  True\n  False False False False False False],\n        fill_value = ?),\n 'param_max_features': masked_array(data = [2 2 2 4 4 4 6 6 6 8 8 8 2 2 3 3 4 4],\n              mask = [False False False False False False False False False False False False\n  False False False False False False],\n        fill_value = ?),\n 'param_n_estimators': masked_array(data = [3 10 30 3 10 30 3 10 30 3 10 30 3 30 3 30 3 30],\n              mask = [False False False False False False False False False False False False\n  False False False False False False],\n        fill_value = ?),\n 'params': ({'max_features': 2, 'n_estimators': 3},\n  {'max_features': 2, 'n_estimators': 10},\n  {'max_features': 2, 'n_estimators': 30},\n  {'max_features': 4, 'n_estimators': 3},\n  {'max_features': 4, 'n_estimators': 10},\n  {'max_features': 4, 'n_estimators': 30},\n  {'max_features': 6, 'n_estimators': 3},\n  {'max_features': 6, 'n_estimators': 10},\n  {'max_features': 6, 'n_estimators': 30},\n  {'max_features': 8, 'n_estimators': 3},\n  {'max_features': 8, 'n_estimators': 10},\n  {'max_features': 8, 'n_estimators': 30},\n  {'bootstrap': False, 'max_features': 2, 'n_estimators': 3},\n  {'bootstrap': False, 'max_features': 2, 'n_estimators': 30},\n  {'bootstrap': False, 'max_features': 3, 'n_estimators': 3},\n  {'bootstrap': False, 'max_features': 3, 'n_estimators': 30},\n  {'bootstrap': False, 'max_features': 4, 'n_estimators': 3},\n  {'bootstrap': False, 'max_features': 4, 'n_estimators': 30}),\n 'rank_test_score': array([18, 11,  9, 16, 10,  5, 14,  7,  3, 12,  8,  2, 17,  6, 15,  4, 13,\n         1]),\n 'split0_test_score': array([ -3.84256316e+09,  -2.99574102e+09,  -2.55545145e+09,\n         -3.38893485e+09,  -2.67827362e+09,  -2.42046290e+09,\n         -3.41006937e+09,  -2.51715826e+09,  -2.38789965e+09,\n         -3.22088234e+09,  -2.66765453e+09,  -2.40721580e+09,\n         -3.92144761e+09,  -2.58623109e+09,  -3.69256168e+09,\n         -2.37990682e+09,  -3.16948325e+09,  -2.39808696e+09]),\n 'split0_train_score': array([ -1.08594176e+09,  -6.01575939e+08,  -4.33883090e+08,\n         -9.94476118e+08,  -5.40362788e+08,  -4.02678978e+08,\n         -9.66977000e+08,  -5.28151491e+08,  -3.91801652e+08,\n         -8.99023620e+08,  -4.99037887e+08,  -3.85316040e+08,\n         -0.00000000e+00,  -0.00000000e+00,  -0.00000000e+00,\n         -0.00000000e+00,  -0.00000000e+00,  -0.00000000e+00]),\n 'split1_test_score': array([ -4.33695478e+09,  -3.27122257e+09,  -2.91127638e+09,\n         -3.64161277e+09,  -2.90663590e+09,  -2.58528141e+09,\n         -3.41599360e+09,  -2.73622393e+09,  -2.62042634e+09,\n         -3.49558712e+09,  -2.77811151e+09,  -2.58654113e+09,\n         -4.14364700e+09,  -2.79786072e+09,  -3.53377064e+09,\n         -2.58980815e+09,  -3.29014066e+09,  -2.52401878e+09]),\n 'split1_train_score': array([ -1.18109949e+09,  -5.95199189e+08,  -4.36117303e+08,\n         -1.00520176e+09,  -5.26311413e+08,  -3.86673914e+08,\n         -9.35518322e+08,  -5.04865570e+08,  -3.87367682e+08,\n         -8.16761166e+08,  -5.04472509e+08,  -3.90962256e+08,\n         -0.00000000e+00,  -0.00000000e+00,  -0.00000000e+00,\n         -0.00000000e+00,  -0.00000000e+00,  -0.00000000e+00]),\n 'split2_test_score': array([ -4.61801927e+09,  -3.22354781e+09,  -2.88620991e+09,\n         -3.84903347e+09,  -2.87631201e+09,  -2.71504024e+09,\n         -3.74222249e+09,  -2.91613602e+09,  -2.61012583e+09,\n         -3.61956717e+09,  -2.88069181e+09,  -2.63269579e+09,\n         -4.24005321e+09,  -2.78754887e+09,  -3.66322915e+09,\n         -2.68756058e+09,  -3.70323369e+09,  -2.57666272e+09]),\n 'split2_train_score': array([ -1.15441302e+09,  -5.62740924e+08,  -4.28443886e+08,\n         -9.16825726e+08,  -5.17096298e+08,  -3.84680263e+08,\n         -8.98692331e+08,  -4.94975635e+08,  -3.72764886e+08,\n         -8.80164197e+08,  -4.75822885e+08,  -3.82388721e+08,\n         -0.00000000e+00,  -0.00000000e+00,  -0.00000000e+00,\n         -0.00000000e+00,  -0.00000000e+00,  -0.00000000e+00]),\n 'split3_test_score': array([ -3.93101309e+09,  -2.91824896e+09,  -2.65060345e+09,\n         -3.52728407e+09,  -2.60455290e+09,  -2.45864646e+09,\n         -3.29827987e+09,  -2.72328253e+09,  -2.28144314e+09,\n         -3.09199025e+09,  -2.52000522e+09,  -2.28790749e+09,\n         -3.69885166e+09,  -2.47592469e+09,  -3.50548464e+09,\n         -2.50223735e+09,  -3.37281404e+09,  -2.33115153e+09]),\n 'split3_train_score': array([ -1.10068463e+09,  -5.86873274e+08,  -4.37562023e+08,\n         -9.63815884e+08,  -5.32504189e+08,  -3.93737577e+08,\n         -9.20702447e+08,  -5.25104808e+08,  -3.86032138e+08,\n         -8.89890884e+08,  -5.07940094e+08,  -3.81850905e+08,\n         -0.00000000e+00,  -0.00000000e+00,  -0.00000000e+00,\n         -0.00000000e+00,  -0.00000000e+00,  -0.00000000e+00]),\n 'split4_test_score': array([ -4.39495972e+09,  -3.16309433e+09,  -2.93134786e+09,\n         -3.92141076e+09,  -2.90448881e+09,  -2.66088030e+09,\n         -3.46227340e+09,  -2.77117314e+09,  -2.66836983e+09,\n         -3.42605493e+09,  -2.87075165e+09,  -2.59747935e+09,\n         -3.85425513e+09,  -2.80525795e+09,  -3.54119128e+09,\n         -2.59494204e+09,  -3.65359051e+09,  -2.53744594e+09]),\n 'split4_train_score': array([ -1.07744781e+09,  -5.73850027e+08,  -4.16223664e+08,\n         -9.54048627e+08,  -5.13665267e+08,  -3.80975602e+08,\n         -9.15670132e+08,  -4.99734959e+08,  -3.97056115e+08,\n         -9.37826515e+08,  -5.13370545e+08,  -3.78730920e+08,\n         -0.00000000e+00,  -0.00000000e+00,  -0.00000000e+00,\n         -0.00000000e+00,  -0.00000000e+00,  -0.00000000e+00]),\n 'std_fit_time': array([ 0.04030425,  0.19345416,  0.73626454,  0.09533226,  0.07179709,\n         0.18110299,  0.01975553,  0.22822167,  0.32221369,  0.01758056,\n         0.16337719,  0.09114161,  0.01189029,  0.24793385,  0.01072628,\n         0.21393604,  0.05111858,  0.18944643]),\n 'std_score_time': array([ 0.00097994,  0.01233932,  0.03506424,  0.00233259,  0.00475009,\n         0.01166255,  0.00063241,  0.00707148,  0.0451007 ,  0.00049002,\n         0.00591313,  0.00391937,  0.00097989,  0.00224512,  0.0010198 ,\n         0.03217262,  0.00863529,  0.00741392]),\n 'std_test_score': array([  2.92768253e+08,   1.35228340e+08,   1.53849832e+08,\n          1.97654728e+08,   1.27240534e+08,   1.13375928e+08,\n          1.48364252e+08,   1.27822101e+08,   1.51244542e+08,\n          1.90110415e+08,   1.35577235e+08,   1.32784093e+08,\n          1.96127735e+08,   1.34925533e+08,   7.55396214e+07,\n          1.03671771e+08,   2.07385281e+08,   9.29852072e+07]),\n 'std_train_score': array([ 40647523.60279205,  14124800.79827561,   7757555.79451362,\n         31335494.38319563,   9799101.55229838,   7683929.20475595,\n         22972855.98600088,  13516598.99634363,   8098044.50172056,\n         39219360.53126183,  13016877.69685722,   4124806.94797078,\n                0.        ,         0.        ,         0.        ,\n                0.        ,         0.        ,         0.        ])}"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cvres"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "64997.5819459 {'max_features': 2, 'n_estimators': 3}\n55806.5699681 {'max_features': 2, 'n_estimators': 10}\n52791.7732057 {'max_features': 2, 'n_estimators': 30}\n60544.504048 {'max_features': 4, 'n_estimators': 3}\n52858.7973205 {'max_features': 4, 'n_estimators': 10}\n50675.9742547 {'max_features': 4, 'n_estimators': 30}\n58870.7173213 {'max_features': 6, 'n_estimators': 3}\n52276.0167042 {'max_features': 6, 'n_estimators': 10}\n50136.3322045 {'max_features': 6, 'n_estimators': 30}\n58058.7188779 {'max_features': 8, 'n_estimators': 3}\n52377.8622383 {'max_features': 8, 'n_estimators': 10}\n50023.6668951 {'max_features': 8, 'n_estimators': 30}\n63021.0940578 {'bootstrap': False, 'max_features': 2, 'n_estimators': 3}\n51870.6549329 {'bootstrap': False, 'max_features': 2, 'n_estimators': 30}\n59893.6608938 {'bootstrap': False, 'max_features': 3, 'n_estimators': 3}\n50506.2668443 {'bootstrap': False, 'max_features': 3, 'n_estimators': 30}\n58632.9875637 {'bootstrap': False, 'max_features': 4, 'n_estimators': 3}\n49734.0093176 {'bootstrap': False, 'max_features': 4, 'n_estimators': 30}\n"
     ]
    }
   ],
   "source": [
    "for mean_score,params in zip(cvres['mean_test_score'],cvres['params']):\n",
    "    print(np.sqrt(-mean_score),params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_importances = gird_search.best_estimator_.feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  9.17880869e-02,   9.10567283e-02,   4.17338846e-02,\n         2.44903386e-02,   1.88579557e-02,   2.29861194e-02,\n         1.84032317e-02,   2.60763359e-01,   7.66864798e-02,\n         1.00250944e-01,   9.00864043e-02,   1.68015267e-02,\n         1.32421593e-01,   9.56265511e-05,   6.56336373e-03,\n         7.01435864e-03])"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_importances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0.26076335900017306, 'median_income'),\n (0.13242159250238444, 'INLAND'),\n (0.10025094350064565, 'population_per_household'),\n (0.091788086913163772, 'longitude'),\n (0.091056728300338979, 'latitude'),\n (0.090086404297353193, 'bedrooms_per_room'),\n (0.076686479813476802, 'rooms_per_household'),\n (0.041733884648437594, 'housing_median_age'),\n (0.024490338649862501, 'total_rooms'),\n (0.022986119358285359, 'population'),\n (0.018857955733839946, 'total_bedrooms'),\n (0.018403231674086796, 'households'),\n (0.016801526691689266, '<1H OCEAN'),\n (0.0070143586392953762, 'NEAR OCEAN'),\n (0.0065633637259001707, 'NEAR BAY'),\n (9.5626551067213883e-05, 'ISLAND')]"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "extra_attribs = ['rooms_per_household','population_per_household','bedrooms_per_room']\n",
    "cat_one_hot_attribs = list(encoder.classes_)\n",
    "attributes = num_attribs +extra_attribs+cat_one_hot_attribs\n",
    "sorted(zip(feature_importances,attributes),reverse=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_model = gird_search.best_estimator_\n",
    "X_test = strat_train_set.drop('median_house_value',axis=1)\n",
    "y_test = strat_train_set['median_house_value'].copy()\n",
    "\n",
    "x_test_prepared = full_pipeline.transform(X_test)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_predictions = final_model.predict(x_test_prepared)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_mse = mean_squared_error(y_test,final_predictions)\n",
    "final_rmse = np.sqrt(final_mse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_mse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'np' is not defined",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-319e5a0ab23c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m def plot_learning_curve(estimator, title, X, y, ylim=None, cv=None,\n\u001b[0;32m----> 9\u001b[0;31m                         n_jobs=1, train_sizes=np.linspace(.1, 1.0, 5)):\n\u001b[0m\u001b[1;32m     10\u001b[0m     \"\"\"\n\u001b[1;32m     11\u001b[0m     \u001b[0mGenerate\u001b[0m \u001b[0ma\u001b[0m \u001b[0msimple\u001b[0m \u001b[0mplot\u001b[0m \u001b[0mof\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mtest\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mtraining\u001b[0m \u001b[0mlearning\u001b[0m \u001b[0mcurve\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'np' is not defined"
     ],
     "output_type": "error"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.model_selection import learning_curve\n",
    "from sklearn.model_selection import ShuffleSplit\n",
    "\n",
    "\n",
    "def plot_learning_curve(estimator, title, X, y, ylim=None, cv=None,\n",
    "                        n_jobs=1, train_sizes=np.linspace(.1, 1.0, 5)):\n",
    "    \"\"\"\n",
    "    Generate a simple plot of the test and training learning curve.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    estimator : object type that implements the \"fit\" and \"predict\" methods\n",
    "        An object of that type which is cloned for each validation.\n",
    "\n",
    "    title : string\n",
    "        Title for the chart.\n",
    "\n",
    "    X : array-like, shape (n_samples, n_features)\n",
    "        Training vector, where n_samples is the number of samples and\n",
    "        n_features is the number of features.\n",
    "\n",
    "    y : array-like, shape (n_samples) or (n_samples, n_features), optional\n",
    "        Target relative to X for classification or regression;\n",
    "        None for unsupervised learning.\n",
    "\n",
    "    ylim : tuple, shape (ymin, ymax), optional\n",
    "        Defines minimum and maximum yvalues plotted.\n",
    "\n",
    "    cv : int, cross-validation generator or an iterable, optional\n",
    "        Determines the cross-validation splitting strategy.\n",
    "        Possible inputs for cv are:\n",
    "          - None, to use the default 3-fold cross-validation,\n",
    "          - integer, to specify the number of folds.\n",
    "          - An object to be used as a cross-validation generator.\n",
    "          - An iterable yielding train/test splits.\n",
    "\n",
    "        For integer/None inputs, if ``y`` is binary or multiclass,\n",
    "        :class:`StratifiedKFold` used. If the estimator is not a classifier\n",
    "        or if ``y`` is neither binary nor multiclass, :class:`KFold` is used.\n",
    "\n",
    "        Refer :ref:`User Guide <cross_validation>` for the various\n",
    "        cross-validators that can be used here.\n",
    "\n",
    "    n_jobs : integer, optional\n",
    "        Number of jobs to run in parallel (default 1).\n",
    "    \"\"\"\n",
    "    plt.figure()\n",
    "    plt.title(title)\n",
    "    if ylim is not None:\n",
    "        plt.ylim(*ylim)\n",
    "    plt.xlabel(\"Training examples\")\n",
    "    plt.ylabel(\"Score\")\n",
    "    train_sizes, train_scores, test_scores = learning_curve(\n",
    "        estimator, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes)\n",
    "    train_scores_mean = np.mean(train_scores, axis=1)\n",
    "    train_scores_std = np.std(train_scores, axis=1)\n",
    "    test_scores_mean = np.mean(test_scores, axis=1)\n",
    "    test_scores_std = np.std(test_scores, axis=1)\n",
    "    plt.grid()\n",
    "\n",
    "    plt.fill_between(train_sizes, train_scores_mean - train_scores_std,\n",
    "                     train_scores_mean + train_scores_std, alpha=0.1,\n",
    "                     color=\"r\")\n",
    "    plt.fill_between(train_sizes, test_scores_mean - test_scores_std,\n",
    "                     test_scores_mean + test_scores_std, alpha=0.1, color=\"g\")\n",
    "    plt.plot(train_sizes, train_scores_mean, 'o-', color=\"r\",\n",
    "             label=\"Training score\")\n",
    "    plt.plot(train_sizes, test_scores_mean, 'o-', color=\"g\",\n",
    "             label=\"Cross-validation score\")\n",
    "\n",
    "    plt.legend(loc=\"best\")\n",
    "    return plt\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "X, y = housing_prepared,housing_labels\n",
    "\n",
    "\n",
    "title = \"Learning Curves \"\n",
    "# Cross validation with 100 iterations to get smoother mean test and train\n",
    "# score curves, each time with 20% data randomly selected as a validation set.\n",
    "cv = ShuffleSplit(n_splits=1, test_size=0.2, random_state=0)\n",
    "\n",
    "estimator = final_model\n",
    "plot_learning_curve(estimator, title, X, y, ylim=(0.7, 1.01), cv=cv)\n",
    "\n",
    "title = \"Learning Curves (SVM, RBF kernel, $\\gamma=0.001$)\"\n",
    "# SVC is more expensive so we do a lower number of CV iterations:\n",
    "cv = ShuffleSplit(n_splits=1, test_size=0.2, random_state=0)\n",
    "estimator = SVC(gamma=0.001)\n",
    "plot_learning_curve(estimator, title, X, y, (0.7, 1.01), cv=cv)\n",
    "\n",
    "plt.show()"
   ]
  }
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