Housing.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import tarfile\n",
    "from six.moves import urllib\n",
    "DOWNLOAD_ROOT = \"https://raw.githubusercontent.com/ageron/handson-ml/master/\"\n",
    "HOUSING_PATH = \"datasets/housing\"\n",
    "HOUSING_URL = DOWNLOAD_ROOT + HOUSING_PATH + \"/housing.tgz\"\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(tgz_path)\n",
    "    housing_tgz.extractall(path=housing_path)\n",
    "    housing_tgz.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\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)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-122.23</td>\n",
       "      <td>37.88</td>\n",
       "      <td>41.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>8.3252</td>\n",
       "      <td>452600.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-122.22</td>\n",
       "      <td>37.86</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7099.0</td>\n",
       "      <td>1106.0</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>1138.0</td>\n",
       "      <td>8.3014</td>\n",
       "      <td>358500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-122.24</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>352100.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1274.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>558.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>341300.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>3.8462</td>\n",
       "      <td>342200.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "0    -122.23     37.88                41.0        880.0           129.0   \n",
       "1    -122.22     37.86                21.0       7099.0          1106.0   \n",
       "2    -122.24     37.85                52.0       1467.0           190.0   \n",
       "3    -122.25     37.85                52.0       1274.0           235.0   \n",
       "4    -122.25     37.85                52.0       1627.0           280.0   \n",
       "\n",
       "   population  households  median_income  median_house_value ocean_proximity  \n",
       "0       322.0       126.0         8.3252            452600.0        NEAR BAY  \n",
       "1      2401.0      1138.0         8.3014            358500.0        NEAR BAY  \n",
       "2       496.0       177.0         7.2574            352100.0        NEAR BAY  \n",
       "3       558.0       219.0         5.6431            341300.0        NEAR BAY  \n",
       "4       565.0       259.0         3.8462            342200.0        NEAR BAY  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fetch_housing_data()\n",
    "housing = load_housing_data()\n",
    "housing.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 20640 entries, 0 to 20639\n",
      "Data columns (total 10 columns):\n",
      "longitude             20640 non-null float64\n",
      "latitude              20640 non-null float64\n",
      "housing_median_age    20640 non-null float64\n",
      "total_rooms           20640 non-null float64\n",
      "total_bedrooms        20433 non-null float64\n",
      "population            20640 non-null float64\n",
      "households            20640 non-null float64\n",
      "median_income         20640 non-null float64\n",
      "median_house_value    20640 non-null float64\n",
      "ocean_proximity       20640 non-null object\n",
      "dtypes: float64(9), object(1)\n",
      "memory usage: 1.6+ MB\n"
     ]
    }
   ],
   "source": [
    "housing.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<1H OCEAN     9136\n",
       "INLAND        6551\n",
       "NEAR OCEAN    2658\n",
       "NEAR BAY      2290\n",
       "ISLAND           5\n",
       "Name: ocean_proximity, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing[\"ocean_proximity\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20433.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-119.569704</td>\n",
       "      <td>35.631861</td>\n",
       "      <td>28.639486</td>\n",
       "      <td>2635.763081</td>\n",
       "      <td>537.870553</td>\n",
       "      <td>1425.476744</td>\n",
       "      <td>499.539680</td>\n",
       "      <td>3.870671</td>\n",
       "      <td>206855.816909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.003532</td>\n",
       "      <td>2.135952</td>\n",
       "      <td>12.585558</td>\n",
       "      <td>2181.615252</td>\n",
       "      <td>421.385070</td>\n",
       "      <td>1132.462122</td>\n",
       "      <td>382.329753</td>\n",
       "      <td>1.899822</td>\n",
       "      <td>115395.615874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-124.350000</td>\n",
       "      <td>32.540000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.499900</td>\n",
       "      <td>14999.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-121.800000</td>\n",
       "      <td>33.930000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>1447.750000</td>\n",
       "      <td>296.000000</td>\n",
       "      <td>787.000000</td>\n",
       "      <td>280.000000</td>\n",
       "      <td>2.563400</td>\n",
       "      <td>119600.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-118.490000</td>\n",
       "      <td>34.260000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>2127.000000</td>\n",
       "      <td>435.000000</td>\n",
       "      <td>1166.000000</td>\n",
       "      <td>409.000000</td>\n",
       "      <td>3.534800</td>\n",
       "      <td>179700.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>-118.010000</td>\n",
       "      <td>37.710000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>3148.000000</td>\n",
       "      <td>647.000000</td>\n",
       "      <td>1725.000000</td>\n",
       "      <td>605.000000</td>\n",
       "      <td>4.743250</td>\n",
       "      <td>264725.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>-114.310000</td>\n",
       "      <td>41.950000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>39320.000000</td>\n",
       "      <td>6445.000000</td>\n",
       "      <td>35682.000000</td>\n",
       "      <td>6082.000000</td>\n",
       "      <td>15.000100</td>\n",
       "      <td>500001.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          longitude      latitude  housing_median_age   total_rooms  \\\n",
       "count  20640.000000  20640.000000        20640.000000  20640.000000   \n",
       "mean    -119.569704     35.631861           28.639486   2635.763081   \n",
       "std        2.003532      2.135952           12.585558   2181.615252   \n",
       "min     -124.350000     32.540000            1.000000      2.000000   \n",
       "25%     -121.800000     33.930000           18.000000   1447.750000   \n",
       "50%     -118.490000     34.260000           29.000000   2127.000000   \n",
       "75%     -118.010000     37.710000           37.000000   3148.000000   \n",
       "max     -114.310000     41.950000           52.000000  39320.000000   \n",
       "\n",
       "       total_bedrooms    population    households  median_income  \\\n",
       "count    20433.000000  20640.000000  20640.000000   20640.000000   \n",
       "mean       537.870553   1425.476744    499.539680       3.870671   \n",
       "std        421.385070   1132.462122    382.329753       1.899822   \n",
       "min          1.000000      3.000000      1.000000       0.499900   \n",
       "25%        296.000000    787.000000    280.000000       2.563400   \n",
       "50%        435.000000   1166.000000    409.000000       3.534800   \n",
       "75%        647.000000   1725.000000    605.000000       4.743250   \n",
       "max       6445.000000  35682.000000   6082.000000      15.000100   \n",
       "\n",
       "       median_house_value  \n",
       "count        20640.000000  \n",
       "mean        206855.816909  \n",
       "std         115395.615874  \n",
       "min          14999.000000  \n",
       "25%         119600.000000  \n",
       "50%         179700.000000  \n",
       "75%         264725.000000  \n",
       "max         500001.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "housing.hist(bins=50, figsize=(20,15))\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\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.iloc[train_indices],data.iloc[test_indices]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "suffled_indices = np.random.permutation(len(housing))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 9292,  7461,  3331, ..., 12837, 13005, 20607])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "suffled_indices\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16512 4128\n"
     ]
    }
   ],
   "source": [
    "train_set, test_set = split_train_test(housing, 0.2)\n",
    "print(len(train_set), len(test_set))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import hashlib\n",
    "\n",
    "def test_set_check(identifier, test_ratio, hash):\n",
    "    return hash(np.int64(identifier)).digest()[-1]<256*test_ratio\n",
    "def split_train_test_by_id(data,test_ratio, id_column, hash= hashlib.md5):\n",
    "    ids = data[id_column]\n",
    "    in_test_set = ids.apply(lambda id_: test_set_check(id_,test_ratio,hash))\n",
    "    return data.loc[~in_test_set], data.loc[in_test_set]\n",
    "\n",
    "housing_with_id = housing.reset_index()\n",
    "train_set, test_set = split_train_test_by_id(housing_with_id,0.2,\"index\")\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing_with_id[\"id\"] = housing[\"longitude\"]*1000 + housing[\"latitude\"]\n",
    "train_set, test_set = split_train_test_by_id(housing_with_id,0.2,\"id\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing_with_id\n",
    "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": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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xs0muTnJXVb0/yf4kj40xdiV5bNpPkhuT7Jq+9ib5fLISmpLcneSqJFcmuXs1NgEAAACw/awbjsYYL40xfm/a/j9Jnk1ySZKbkxycph1Mcsu0fXOSL4wVjyc5v6ouTnJ9kkNjjKNjjFeTHEpyw6a+GwAAAAA2zVy/46iqdib5uSRPJFkYY7yUrMSlJBdN0y5J8sKaw45MY6caBwAAAGAb2jHrxKo6L8l/SfJLY4w/q6pTTm3GxpuMn/w6e7NyiVsWFhayvLw86yn+NceOHdvQ8Ztp3+7jW30KrGPhHP87MTvrhXmcjvWyXX6+sfm203+/sP1ZL8zDemEe1gurZgpHVfXjWYlGvznG+K/T8MtVdfEY46XpUrRXpvEjSS5bc/ilSV6cxpdOGl8++bXGGAeSHEiSxcXFsbS0dPKUmS0vL2cjx2+mO/Y/utWnwDr27T6ee5+ZuaVylrNemMfpWC/P3760qc/H9rGd/vuF7c96YR7WC/OwXlg1y13VKsl9SZ4dY/zbNQ89kmT1zmh7kjy8Zvxj093Vrk7y2nQp25eTXFdVF0y/FPu6aQwAAACAbWiWf/78cJJ/kuSZqvrmNPbPk9yT5MGqujPJ95LcOj32pSQ3JTmc5PUkH0+SMcbRqvpUkieneZ8cYxzdlHcBAAAAwKZbNxyNMX43/e8nSpJrm/kjyV2neK77k9w/zwkCAAAAsDXmuqsaAAAAAGcP4QgAAACAlnAEAAAAQEs4AgAAAKAlHAEAAADQEo4AAAAAaAlHAAAAALSEIwAAAABawhEAAAAALeEIAAAAgJZwBAAAAEBLOAIAAACgJRwBAAAA0BKOAAAAAGgJRwAAAAC0hCMAAAAAWsIRAAAAAC3hCAAAAICWcAQAAABASzgCAAAAoCUcAQAAANASjgAAAABoCUcAAAAAtIQjAAAAAFrCEQAAAAAt4QgAAACAlnAEAAAAQEs4AgAAAKAlHAEAAADQEo4AAAAAaAlHAAAAALSEIwAAAABawhEAAAAALeEIAAAAgJZwBAAAAEBLOAIAAACgJRwBAAAA0BKOAAAAAGgJRwAAAAC0hCMAAAAAWsIRAAAAAC3hCAAAAICWcAQAAABASzgCAAAAoCUcAQAKdnKbAAAMwklEQVQAANASjgAAAABoCUcAAAAAtIQjAAAAAFrCEQAAAAAt4QgAAACAlnAEAAAAQEs4AgAAAKAlHAEAAADQEo4AAAAAaAlHAAAAALSEIwAAAABawhEAAAAALeEIAAAAgJZwBAAAAEBLOAIAAACgJRwBAAAA0BKOAAAAAGgJRwAAAAC0hCMAAAAAWsIRAAAAAC3hCP5/e/cbY2l5lgH8usPatBQVGixWlrhoCEpALW4QbWJAbAulgX5pUlLrojV8aWs1GLtNo03UmE2sf2NTQyqCkUAarCkBlG5Q0phYg0XLHxHZ1A0sINTQotgmuPH2wxzMZHmW3bMM53135vdLNjPnmXfOuWZz78zZa573PQAAAMCQ4ggAAACAIcURAAAAAEOKIwAAAACGFEcAAAAADCmOAAAAABhSHAEAAAAwpDgCAAAAYEhxBAAAAMCQ4ggAAACAIcURAAAAAEOKIwAAAACGFEcAAAAADCmOAAAAABhSHAEAAAAwpDgCAAAAYEhxBAAAAMCQ4ggAAACAIcURAAAAAEOKIwAAAACGFEcAAAAADCmOAAAAABhSHAEAAAAwpDgCAAAAYEhxBAAAAMCQ4ggAAACAIcURAAAAAEOKIwAAAACGtk0dAABY3o7dd0wdgUPs33P51BEAADacHUcAAAAADCmOAAAAABg6YnFUVddX1TNV9eC6tTdU1d6qenTx9pTFelXVH1TVvqq6v6rOX/c5uxbHP1pVu16dLwcAAACAjXI0O45uSHLpIWu7k9zd3WcluXtxO0kuS3LW4s81ST6VrBVNST6e5EeSXJDk4y+WTQAAAADM0xGLo+7+QpJnD1m+MsmNi/dvTPKudet/2mu+mOTkqnpTkrcn2dvdz3b315LszUvLKAAAAABmpLr7yAdV7Uhye3efu7j99e4+ed3Hv9bdp1TV7Un2dPffLtbvTvKRJBcleW13/8Zi/VeSfLO7PzF4rGuytlspp5122g/fcsstx/zFPf/88znppJOO+fM30gNPPDd1BI7gtNclT39z6hQcL8wLyzAvW8N5p3/7htzPnJ6/MH/mhWWYF5ZhXja3iy+++EvdvfNojt22wY9dg7V+mfWXLnZfl+S6JNm5c2dfdNFFxxzmnnvuySv5/I10tZdNnr1rzzuY335go/9JsFmZF5ZhXraG/e+9aEPuZ07PX5g/88IyzAvLMC+86FhfVe3pxSloWbx9ZrF+IMkZ647bnuTJl1kHAAAAYKaOtTi6LcmLr4y2K8nn1q3/9OLV1S5M8lx3P5XkriRvq6pTFhfFfttiDQAAAICZOuK++aq6OWvXKDq1qg5k7dXR9iT5TFW9P8ljSd69OPzOJO9Isi/JN5L8TJJ097NV9etJ7l0c92vdfegFtwEAAACYkSMWR9191WE+dMng2E7ygcPcz/VJrl8qHQAAAACTOdZT1QAAAADY5BRHAAAAAAwpjgAAAAAYUhwBAAAAMKQ4AgAAAGBIcQQAAADAkOIIAAAAgCHFEQAAAABDiiMAAAAAhhRHAAAAAAwpjgAAAAAYUhwBAAAAMKQ4AgAAAGBIcQQAAADAkOIIAAAAgCHFEQAAAABDiiMAAAAAhhRHAAAAAAwpjgAAAAAYUhwBAAAAMKQ4AgAAAGBIcQQAAADAkOIIAAAAgCHFEQAAAABDiiMAAAAAhhRHAAAAAAwpjgAAAAAYUhwBAAAAMKQ4AgAAAGBIcQQAAADAkOIIAAAAgCHFEQAAAABDiiMAAAAAhhRHAAAAAAwpjgAAAAAYUhwBAAAAMKQ4AgAAAGBIcQQAAADAkOIIAAAAgCHFEQAAAABDiiMAAAAAhhRHAAAAAAwpjgAAAAAYUhwBAAAAMKQ4AgAAAGBIcQQAAADAkOIIAAAAgCHFEQAAAABDiiMAAAAAhhRHAAAAAAwpjgAAAAAYUhwBAAAAMKQ4AgAAAGBIcQQAAADA0LapAwAAbAY7dt+xIfdz7XkHc/UG3RfJ/j2XTx0BAI5rdhwBAAAAMKQ4AgAAAGBIcQQAAADAkOIIAAAAgCHFEQAAAABDiiMAAAAAhhRHAAAAAAwpjgAAAAAYUhwBAAAAMKQ4AgAAAGBIcQQAAADAkOIIAAAAgCHFEQAAAABDiiMAAAAAhhRHAAAAAAwpjgAAAAAYUhwBAAAAMKQ4AgAAAGBIcQQAAADAkOIIAAAAgCHFEQAAAABDiiMAAAAAhhRHAAAAAAwpjgAAAAAYUhwBAAAAMLRt6gAAAPBq2bH7jqkjvKquPe9grj7Ovsb9ey6fOgIAS7DjCAAAAIAhxREAAAAAQ4ojAAAAAIYURwAAAAAMKY4AAAAAGFIcAQAAADCkOAIAAABgSHEEAAAAwNC2qQMAAABbx47dd0wdYcu69ryDufowf//791y+4jTA8cKOIwAAAACGFEcAAAAADDlVDQAAAGZm6tM6X+7Uxq1sK57WaccRAAAAAEN2HAEAAGxxU+9uAeZr5TuOqurSqnqkqvZV1e5VPz4AAAAAR2elxVFVnZDkk0kuS3JOkquq6pxVZgAAAADg6Kx6x9EFSfZ191e6+4UktyS5csUZAAAAADgKqy6OTk/y+LrbBxZrAAAAAMxMdffqHqzq3Une3t0/t7j9viQXdPeH1h1zTZJrFjfPTvLIK3jIU5P8xyv4fLYW88IyzAvLMC8sw7ywDPPCMswLyzAvm9t3d/d3HM2Bq35VtQNJzlh3e3uSJ9cf0N3XJbluIx6sqv6hu3duxH2x+ZkXlmFeWIZ5YRnmhWWYF5ZhXliGeeFFqz5V7d4kZ1XVmVX1miTvSXLbijMAAAAAcBRWuuOouw9W1QeT3JXkhCTXd/dDq8wAAAAAwNFZ9alq6e47k9y5oofbkFPe2DLMC8swLyzDvLAM88IyzAvLMC8sw7yQZMUXxwYAAADg+LHqaxwBAAAAcJzYtMVRVV1aVY9U1b6q2j11Huarqs6oqr+pqoer6qGq+vDUmZi3qjqhqv6xqm6fOgvzVlUnV9WtVfUvi+8xPzp1Juarqn5x8XPowaq6uapeO3Um5qWqrq+qZ6rqwXVrb6iqvVX16OLtKVNmZB4OMyu/tfh5dH9V/UVVnTxlRuZjNC/rPvZLVdVVdeoU2ZiHTVkcVdUJST6Z5LIk5yS5qqrOmTYVM3YwybXd/f1JLkzyAfPCEXw4ycNTh+C48PtJ/qq7vy/JD8bccBhVdXqSn0+ys7vPzdqLiLxn2lTM0A1JLj1kbXeSu7v7rCR3L27DDXnprOxNcm53/0CSf03y0VWHYrZuyEvnJVV1RpK3Jnls1YGYl01ZHCW5IMm+7v5Kd7+Q5JYkV06ciZnq7qe6+77F+/+Vtf/YnT5tKuaqqrYnuTzJp6fOwrxV1bcl+fEkf5wk3f1Cd3992lTM3LYkr6uqbUlOTPLkxHmYme7+QpJnD1m+MsmNi/dvTPKulYZilkaz0t2f7+6Di5tfTLJ95cGYpcN8b0mS303yy0lcGHmL26zF0elJHl93+0AUARyFqtqR5M1J/n7aJMzY72XtB+j/Th2E2fueJF9N8ieLUxs/XVWvnzoU89TdTyT5RNZ+q/tUkue6+/PTpuI4cVp3P5Ws/TIsyRsnzsPx4WeT/OXUIZivqroiyRPd/eWpszC9zVoc1WBNS8rLqqqTkvx5kl/o7v+cOg/zU1XvTPJMd39p6iwcF7YlOT/Jp7r7zUn+O04h4TAW16W5MsmZSb4ryeur6qemTQVsRlX1saxdquGmqbMwT1V1YpKPJfnVqbMwD5u1ODqQ5Ix1t7fHdm9eRlV9S9ZKo5u6+7NT52G23pLkiqran7VTYH+iqv5s2kjM2IEkB7r7xR2Mt2atSIKRn0zyb9391e7+nySfTfJjE2fi+PB0Vb0pSRZvn5k4DzNWVbuSvDPJe7vbL9Y5nO/N2i8yvrx43rs9yX1V9Z2TpmIym7U4ujfJWVV1ZlW9JmsXl7xt4kzMVFVV1q5B8nB3/87UeZiv7v5od2/v7h1Z+77y191tRwBD3f3vSR6vqrMXS5ck+ecJIzFvjyW5sKpOXPxcuiQups7RuS3JrsX7u5J8bsIszFhVXZrkI0mu6O5vTJ2H+eruB7r7jd29Y/G890CS8xfPbdiCNmVxtLjo2weT3JW1J12f6e6Hpk3FjL0lyfuytnvknxZ/3jF1KGBT+FCSm6rq/iQ/lOQ3J87DTC12pt2a5L4kD2TtOdp1k4Zidqrq5iR/l+TsqjpQVe9PsifJW6vq0ay9+tGeKTMyD4eZlT9M8q1J9i6e7/7RpCGZjcPMC/y/skMRAAAAgJFNueMIAAAAgFdOcQQAAADAkOIIAAAAgCHFEQAAAABDiiMAAAAAhhRHAAAAAAwpjgAAAAAYUhwBAAAAMPR/Wr3YWpHs8YQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "housing[\"median_income\"].hist(bins=10, figsize=(20,15))\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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)\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "strat_train_set, strat_test_set = train_test_split(housing, test_size =0.2,random_state=42,stratify =housing[\"income_cat\"] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>income_cat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>16512.000000</td>\n",
       "      <td>16512.000000</td>\n",
       "      <td>16512.000000</td>\n",
       "      <td>16512.000000</td>\n",
       "      <td>16354.000000</td>\n",
       "      <td>16512.000000</td>\n",
       "      <td>16512.000000</td>\n",
       "      <td>16512.000000</td>\n",
       "      <td>16512.000000</td>\n",
       "      <td>16512.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-119.575834</td>\n",
       "      <td>35.639577</td>\n",
       "      <td>28.653101</td>\n",
       "      <td>2622.728319</td>\n",
       "      <td>534.973890</td>\n",
       "      <td>1419.790819</td>\n",
       "      <td>497.060380</td>\n",
       "      <td>3.875589</td>\n",
       "      <td>206990.920724</td>\n",
       "      <td>3.006541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.001860</td>\n",
       "      <td>2.138058</td>\n",
       "      <td>12.574726</td>\n",
       "      <td>2138.458419</td>\n",
       "      <td>412.699041</td>\n",
       "      <td>1115.686241</td>\n",
       "      <td>375.720845</td>\n",
       "      <td>1.904950</td>\n",
       "      <td>115703.014830</td>\n",
       "      <td>1.054602</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>6.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.499900</td>\n",
       "      <td>14999.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-121.800000</td>\n",
       "      <td>33.940000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>1443.000000</td>\n",
       "      <td>295.000000</td>\n",
       "      <td>784.000000</td>\n",
       "      <td>279.000000</td>\n",
       "      <td>2.566775</td>\n",
       "      <td>119800.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-118.510000</td>\n",
       "      <td>34.260000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>2119.500000</td>\n",
       "      <td>433.000000</td>\n",
       "      <td>1164.000000</td>\n",
       "      <td>408.000000</td>\n",
       "      <td>3.540900</td>\n",
       "      <td>179500.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>-118.010000</td>\n",
       "      <td>37.720000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>3141.000000</td>\n",
       "      <td>644.000000</td>\n",
       "      <td>1719.250000</td>\n",
       "      <td>602.000000</td>\n",
       "      <td>4.744475</td>\n",
       "      <td>263900.000000</td>\n",
       "      <td>4.000000</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>6210.000000</td>\n",
       "      <td>35682.000000</td>\n",
       "      <td>5358.000000</td>\n",
       "      <td>15.000100</td>\n",
       "      <td>500001.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          longitude      latitude  housing_median_age   total_rooms  \\\n",
       "count  16512.000000  16512.000000        16512.000000  16512.000000   \n",
       "mean    -119.575834     35.639577           28.653101   2622.728319   \n",
       "std        2.001860      2.138058           12.574726   2138.458419   \n",
       "min     -124.350000     32.540000            1.000000      6.000000   \n",
       "25%     -121.800000     33.940000           18.000000   1443.000000   \n",
       "50%     -118.510000     34.260000           29.000000   2119.500000   \n",
       "75%     -118.010000     37.720000           37.000000   3141.000000   \n",
       "max     -114.310000     41.950000           52.000000  39320.000000   \n",
       "\n",
       "       total_bedrooms    population    households  median_income  \\\n",
       "count    16354.000000  16512.000000  16512.000000   16512.000000   \n",
       "mean       534.973890   1419.790819    497.060380       3.875589   \n",
       "std        412.699041   1115.686241    375.720845       1.904950   \n",
       "min          2.000000      3.000000      2.000000       0.499900   \n",
       "25%        295.000000    784.000000    279.000000       2.566775   \n",
       "50%        433.000000   1164.000000    408.000000       3.540900   \n",
       "75%        644.000000   1719.250000    602.000000       4.744475   \n",
       "max       6210.000000  35682.000000   5358.000000      15.000100   \n",
       "\n",
       "       median_house_value    income_cat  \n",
       "count        16512.000000  16512.000000  \n",
       "mean        206990.920724      3.006541  \n",
       "std         115703.014830      1.054602  \n",
       "min          14999.000000      1.000000  \n",
       "25%         119800.000000      2.000000  \n",
       "50%         179500.000000      3.000000  \n",
       "75%         263900.000000      4.000000  \n",
       "max         500001.000000      5.000000  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing[\"income_cat\"].value_counts() / len(housing)\n",
    "strat_train_set.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\berk_\\AppData\\Roaming\\Python\\Python37\\site-packages\\pandas\\core\\frame.py:3940: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  errors=errors)\n"
     ]
    }
   ],
   "source": [
    "for a in (strat_train_set, strat_test_set):\n",
    "    a.drop([\"income_cat\"],axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = strat_train_set.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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5OUVC2rULY+iOwyZJrghcu2hKM6AyzX6YYktB1bcZxhn3uzEvLlW50aliCUE3zGiMS0Gn8w9ppljfG9GNMrYHIV//0R67vQTPlnzluXnmGx7DOKfuO1xrV88M3RxJVQjBZu9BCeoLCzV+tD1kb5jgOJKfuNrg5aUmFc8uEu4XDOdcJvRzmRLUo0S7OT6oqKSk5PF46h2D1oatfsxiwx0b6dMnBgf46osdfvaleaRl4TvWUaL3vGSw71jcaFfBgOdIXNsizRVaG2yOG0OtDYt1D2NgGGUYAUsNn41+hDGFE0lzhUGgjcGzbDzbQkpxNAkOTie2ldbsDkL++ev32OnGxAoCB/7oBwk/+fw8K80K6wcjtIEXF+szjedktOjJEtRUGX7qZodIK1whCbwH5aiXCedc9LWXLUEtS1ZLSp4MT7WIntaGOFfEWY4wFl+60aY64xOvtmzmqlUOwhwDbPXjI63/hwnC2bZktV1BaTgYJtzZD8m0ZqMf06o4ZMowiDOiVHGtXWGh5h01ze2NUuarHq2KhxTQj3OkMNR9p5jGJh8khSf9FQKOJDCSXJFrw1u3D3h3L+Ygh9DAfgp3ejl/8d4OP9zq8eHukFu7Q7Lx+2YJ8jUrDlmmSVXRQb3UDEgyxb1+xOEoY3eUko7fP+EyKqwPe+1lFVnLmQslJU+Op/bEMNlNpkrx7vYQ35Est+r8+FrEN24PMYAF1DzIkcRphmMJnl+o4VrylB5RmiukEDOTwb5jsdoKuH0w4nq7gusUJ4tumDFfddkaxBhtuLU7YKefUHFtXEcyFwj6cc5P3miz0YsIk4xuVFQB7QxTlClmR7cqDve60VFOIVUax5LEac7tvQHfXj9gljncHGjWRinGCNb3R+S5Lhzf1C67VXHohhm51hgJTd+mUXHJlWZ/lB77PFu9mNVWkV+YhHouI1FxXuhnVh4iyjLiXOGPT07TlCWrJSVPjqfSMUzvJh3Lpl1x+GAvpFmxee7KHHd7GUrlxKowKGmuiFNd5AgseUqPqFVxePt+D6UNlhTHksETjCjKVCe5BCkEcZax0VNYUnAQZ6zvDzkY5XzpegtLCA6jjGbg4LmF5PbtgxGdmo/rFPmDRGlWGj4b/alQlq1Jcs1yw2f9YESqNUk++zkoYLMfc2t3wEtXGihj2D2Ro3j7fo/rnQqB67Dago1ejC0lBujU3KPPY1uSfpxwe39UOINHCN2cF/o5mYcYxRlbvRhhwLKKpjjHlqeS/+c57JKSkkfjqXQM07tJbQyBZ9MIbJbrVVKlqLoWW70E3/UwKCSS93b7fL7b4lqnCnBkZLQ2dMOM6+0KQgrM+PuG7xxzDtOGLdeGzW5ENA5HebbEtSRV12EYK7Z7EatzVcIkp+HZxRwIzDHH4joWaa6JlCLXJ/sadKHVZAwqB/cc2zxMM9IkxbbGn2dqly2kOOr0Bqj6DkvAcivAlZJ73ehYP8b+MD2qsrpsyenDupVPSqFv9WI6NQcwxFnGm+shy40iLzFfL8plq57FO5t9jOFMh11SUnJ5nkrHcHL3OV9xuX8YIS3JF1bb9KKU330rQQiNQXCtHdAIXGqew72DiKtzwdF8hkk+IBiXZWJxlAw+GbLo1Fx2ejEbvRjXFtyYr7JxGLHdT7g5X6ixzlVdwkzx3s4AgWC+VjSGuZY8tuZhnLHZL3bX24MESWG4JzkHdzz607IlL6+22H6vO/NZRDnc78X80lyAbR+/hxmfgCbNcRPJiknoZjphrI2hU3WPqqwuG7qZOGspinLYwknpY++f5CHiXLE3iPnLD/ZJM00/yXlpscaVhsduP+GD3WFRVqs1VcdiseHjWnKmwy4pKbk8T6VjOGnUbNviZ19aYG+QIC3BL35uhf4oI1cCRU4z8PEcySvLDZSBq60Ab9y0NZ3wPavUcjpEooyhVbGZr/tIIViZC9gcq7nWA4c0yYlGitWWz9V25Vg+Y7LmOC2cwkrTpzo2dBu9mCU46q2wbclSM2Ch6vHiQpPdXsytnZh06jk0HKg4FrVxn4MwnKoO+vzVJt0wY5ScrhaaJT9+0ZLTSaLbEgIjik7qOM3Z7kVYlkQpTTNwECeSI1IKpIbv3OtScRyCis1GN+ZbHxyQKs1S3acXpSzUXcJE0Q5culHGSisgS1WZYygp+Qh4Kh0DHDdqWa7HMwAkRsDLV5r82z+xxpsfHHAQZriW5AvXW7i2hTIUid0pYz+d8D05dOZkiCTNFTuDhFagcR0L15J8bqWBUpr9MMNIwVzF5Wq7QmV8CpnsvKd3zEYUJwSAmu+wbIoQz3Qitu47fPl6m5pv0w1T5mshP9jskSSAhKorqfo2juPg2pIcQ8WxTzWGNXznzN6B6eTyRUtOP9wdstWPyZVGCEGrYjNKNGGak2SaesVmEOaEqcZ3Q1ZawbFcRWI0VdfGtgR7gwTftkhFEZ77cC8sTjpWnSw3aAxaQ5qpY+G/svGtpOTReWodAzyogrk/HgwTuEUoZm+U8urKHO2KR3eUEKbFPIFEaVaaAXC8kWyS8O1U3UL6YkrvZxKKmeQzpBDMVRwSpcnG8fvrnSrb/Zha4OBIyd3DkJ1+zFqnetQoNtl5SynwbQtbymO7c8uSp6pzpBSsdarsDhK+uNbmzp5N1bd5b2vAMM6o+0WYpeoI7h9ExGlOZSyNPb2rvmhl0cO6jbU2fLg75N2tAY4l2OjHJFmOZ0k6VY9MGW50Kmz2E661A5QBS3AqVxFYFoFr40iYq7hUXJtbOwPiVGPZknbNpTvKqXoWSabQRqAMrLT8S4sIlpSUnOapdgxwdlmjY0ueX6ij5mskqWKrHwPFiM9OzS0My9g4y7Hx3x0mBI51TO9ntRUghWAUZxxGGWlWxM2/uFrFc60isWwezGaGYue/vh8yiLKjAT8nDf5Fm8ccW7LSCqgHNhvdEc8vNKg4Nvthwu3tIdv9iEGUYkmL3//uBn/7c8ss1B9dV+g8J5Ipzda4gkoI2DgM0Rp8R2JZgt1BhmsXCW+ti2di25IkO55rcF2Ln35hnn/13h7dKMWxBF99bp5E54SJphE47A9jap5Lp+qx2PRpeEXIbf0gLOW4S0oek6feMZwnxzA5UeyNUgL3gcHfGyQkmWJ3UEhqK62p2BYVzz7W6JbkOUYUonNvrh8iBcUs6IrD3ihlzasc3WN6DbYUrI4T3GeNBL2oFtDkZ4M440ozwLMl8w2XP393hyhTDPKcbiQxYojv2ggp+PHrbdbaVSre7F//44RibAQIwf4oxpISYxRxqhhEik7NxRjDZi9GWoLFesC9g5BW4J4SCezUPH7588v00ozDMGW3m7AfGl5arGBbkoWaS8N3cOwi6dyP8iOHXvY2lJQ8Hk+9Yzhv9z3pjD4pcx1l6mhSW6Y0u4OYwCnKTS0pjlUHWUKALVlu+EcnhGkZi8mY0JNrWB4nuB+29ocZtEn55t2DkLpvM4xzHCm5vRcibahaDrky3D0YUXMFnbrLVi9ib5jy5bW5U87hYTIT5zkNx5IsNn3ybsj9VBfzGoRNI7CIcmh4NsutCo5j0wyKHEKmNHqqPW/W/RdqPldbOfcOQvaGKZkuek6kFPhTJ7jdQfLQQoGSkpKH88QdgxDCAl4H7htjfkUIcRP4HaANvAn8mjEmPe8aj8us3ffEAOW6CH8siyLJOxG382yLa22XD3aGeLZFnGs8W3FnP+Rap3JMeRVdNGEJOCZjcbI7+kmpgVZdm9V2haWGx2Gcsb47wLYlC15AP1YIFKky7A4j1vdGvHa9jTaae4chL0zpJz2s1+BhTkNKwfX5KrZVhM9Up6i62h+lSCH4wtUm0hLkGq62fLYGMRLBbj9hdS6n6tpn3r/uO7y81OC5KVmO+93oxAmuyBXtD9NSjruk5DH4OLSSfgN4Z+r7/xb4H4wxLwKHwN//GNaAlOIobDNtAOu+w0rTZ7MXM4wyMmVYaQVY447ow7hQPq24Nq2KV4yybPjH9H4mJ4JMGUZJMfJzlkGaXsNH/dlWWgGuY9Opery03OClxRqWBIxhFOcIAZmS9OKMP3tvl61eUsyfSB+0TZ+nCzX9zALHAooqoZPaRL5jsdwMWGkG+I6NJSXPLVR5ZbmBtOS4d6PoK+lFOYdhxsEo5d5BeNQzcpYulZQCz7HwxiKHk/AcPJidUXXtC+s3lZSUzOaJnhiEEKvALwP/DfCfCiEE8PPAvzd+yf8G/JfAP3mS6zjJyYT0dMfvUXOXLPSF4lRj+xZX6i5CCISYbdw/zvkAs8I5x+9f4z/4ac0/+eP32en3sS1Yqrq0ax4HYcZ8mI3zIYLdQUJ1XKl0Xj5m8sxyDbuDB9Lc83WP+risdrK2nUFCs+IyV/VIcoUxcG2ucqSxNIgz3t7oUXEsLEvSqrrsDBJudKoXlrl4WIJ+EoIrS1dLSi7Pkw4l/WPgHwH18fcdoGuMmWxT7wFXZ71RCPHrwK8DrK2tfaSLOmtI/XQ5qO8U+kWZ0vSjDKUNudGFZs8Zs5cvIyj3qJwXzpncX2vDQr3Cb/zCS/yz19f5s/d2UEhGqabqOVh20XR2pe4DzMyFRFkGhqMOcDQYbbjbC6k6dqGnZMwxxzK51rTT9WyLQZyhjMEbd037jsVSzUdagmGc0x1lRGNl14voUk14mDMuZblLSh6NJxZKEkL8CrBjjHlj+p9nvHSmTrIx5reMMa8ZY15bWFj4SNd2mdDPWqfKQsOnFTgs1H2uz1cvvfOcJXP9KFxUaloZQ5TlDJKcrWFGo+pT9x181yLOFfNVn+WmjzOWyJg0hWVjaY7FcQOfoCjfjTNFqjTDNOPD3REf7o8YJjkLNe9YqAeOO904U9zZH7HdT7jfjY6kzB1LMt/w2Bsm5ONZFIt1l71BwsEo5Vor4HqnwrVWQDfMTn2+yVrzXJ/bU1HKcpeUPBpP8sTw08CvCiF+CfCBBsUJoiWEsMenhlVg4wmu4Uwus9u0hOBKyz+2Mz7JWSGLj3LXelGpaWGKGctxqqj7RSVVL8qoeQ5Zrqj5Nq3AJVOGhbpHnCm2+zGDJCWMM4QlWaoF+H4xae7+QcjdwwF/8cEue72EhZpPuthAAhpYaQZHp6iJ093sRtw7jHBtwVqngi3FsUT2arvCziDBtQSWJbnS8EkyRZLkJLk5el5VzzqS6c6UJkxytgcxSa7oR4r5ukvg2Keea6Y0aa7wbOfcZ1VSUnKaJ+YYjDG/CfwmgBDi54B/aIz5u0KI/xv4OxSVSX8P+OdPag0P46zQz6zqnP1hSrU9+3GdZfxnXWejG3H1nP6FWWuZOJyLjsg0Amqexc6g2KVLJJ2Ki2sL8CRf+9wVKq7D3jBh6zDiXi9iqxfyx9/f4IP9ARbwb3zhKr/4+Wv4jsXvf+8+/+uf3+Kg6AHEBr76/Bz/8c+9wFLdZ6MXcaNdxbYL5+A7VqFdpDT1wDlSb50YZnQxZ3q1FWBZAs8uusZzITgMMyqORcW1iZKM3X7O1UbAZj9m/WDEDzf7CFloRl1p+FgCam37VPXURrcQLzwYpSy3AuyxVHhZulpS8nA+iT6G/wz4HSHEfw28BfzPn8AazuWiO/NJSGOjG+HZ8lSJ5cnr5Npw/7AQopt0PF92dOV5CdeJEzHKMEwUq60Kv/jqCr/7nXsMRoqrbY8fv9Hm+xsDBnGOYwnaFZf3t3r8H99a54ODBzOxb/3xOt+90+Mnnuvwf37rzpFTAMiBb9065O//jYxubDNKcjCwOlUF5FjFqFOtTVGiOnZiWa65P0jGiWxDbgzaFA2Ai81C0iLMFN0w5TBMcS151DwYpaqQ0dAQK02YKnaGCavtylFIK081t/eHBI7FWqfCVi9ifT9kdS5geZIvKSkpOZePxTEYY/4E+JPx1x8AP/lx3PdRucjO/GhCXK7Y7idFuMQ67kSmryOlYKsXHZXIamPOlWs4r6dgVghs2oloY6j5FrmCwJFcmwvwr9jcWKgijeT2/oiVpk/Nd9gPE16/vXXMKUBh/N+83cNoRZienpOdAt/b6PFTz/nUfBvPeaASC4VzXax77AySIyc2+f6kBtXkBAXFxLqKa7GZx1ypexiKEab7gxRLciRkaLQmzTSBa8bPVzKIMt6+12OjH1H1bF6+UmetU2UwVl99WEMnnswsAAAgAElEQVRhSUlJwVM98/lReVhy+mQfRJHYLEZvnpTcmFxnEGWkuTnatZ6s0T/JeT0FJ/shTiZaPUsyjBWdqoNtCxbqAUuNClXbZn9UdAc7toVAYCFJ1WyDqTTEuSawZ/+ZqPHAoPmaV5wOjGGU5qwfhNw9CNkZJCzWvaOeAseWpz7T5HlPD+vJtCHJC+HAxYZP4NpoYQBBxZMkSuE5klwpGr6NEIL5qssPNvsErqRT87CE4N3tAWmmcG3rzEqykpKS0zz1khiPynnJ6ZMhovNE8SbXycay3fb43x8m13DRfMKs9biORafmEqWFTlHNL6pycmPIlaEZFONOD6OcUZozX7FwgZPt564NVc/hC6s+Oz88IJv62VfWqtxcaFD3CmnxdCwtst2Px0KDxejQSf5BWqc1o9JMocezHqaf+412teg+d4pwVKdiGEQ5Smvc2KJdEVwZNxle61SpunYhbaINnu/QgEI/KcyIMsWN+VoZQiopuQSlYzgHKQU6N8RK4Up5lFw9abQfJoonpcCTFsut4EKKqZP3XCSfcF5SOjOajcOIg1GKtAQLVY/rnYClVsAgUgS2xK57/LtffZ5ISX7v7d2j2uGqBa9ca/LCUpM4V/z0Cy2+f6/LKIZmBeIc/urDfXpxzmItwHcl8zWPbpix1qmQqpztXlyMNx33Qzi2PAon9eOE/WFKp+pyrxsdy7fYtmS1XWGrFxcDhKTkq891yJRmc3wys6Vkda5ypPXkyuI0cmd/hGMXTqdTdXmuUzsal1pSUnIxSsdwDgejhL/6cI8wzqlVHL54rU2nWtT4d2pF3X2S62OieFobkkyRj8Msvm0dq9Z52DyD6Z+d9fqHJaWLGLxhoxuz2Y3ZGsRordEdzSsrDT6/3CoGGCnN5mFE4Nv8J7/wMp9bqfPdO3uEmWK1U+dKq8ZaK+DDgxFJmvO2gRAIQ9gMR9zZH5HlClbbPH+lRitw6EUpH+4MOIwzjAbbFiR5zpvrhyw3fCxLMl912errc+dHn/zsUPRU1H3nyPntDBLWHOsoDNWpuvSjjDhTCOBKwz969iUlJRendAxnMIwyfvubH/LW7S5ag8Dwwxs9fv6VZWwpccYGZ6HuHfU3TBq6frTd54PtIRXf5manyms3O7QqLnB2iexZJa8nX3+RpLTWhlu7A+7sDcmN4eZ8lVGsyDXsj9KiC9mxUNqwPUyQowTXtvgbL16hXamQC41rWeRKcfsgpBdm7IwShsfz03QT+Pb6QdHJ3PC5S4jR8J17PRDQqXrUhMW720MW6x6eayGArX6MgAenGynQuT5V9TX92R/oKM2uFFPGUPMdXrvZJs/10ZyHTGmkEaUkRknJJSgdwwy0Nry33eONDw5pVVxSZYizjDdud3lhqU674h9NX5v0N2hdiMrd3hvy9R/tcDBMEUg2DyMSpfn5l6/gjQ39yZPBw1RNpzmvlNaxZCGJIYq4fWr0eMIceI6FEYWU+ORUsz2eKz0ZMBTGmldW6mjgL27t0x0l3D+MAMNhPz6WY5iwP8y4sx/SrPV4ZalJw7fwLIljC640iqqi27tDlhr+A+MsFHGm2NtPEEJgjDk1k+EkZ4XLxFgaXRjQxtAPE3JddJlrBEYbxLiHoZTEKCm5GKVjmIEyhihX5AbCNKcb5RhTaPkMI8VcpTCu00YZIEpz3tnostlNqAc2ShVaQt/8YJ/lhs9c1aMZFI1leqwdtNwKjo0HhfO7dE+WwKZjmYlpoyql4Fq7ylzg0R0qhlGG41i0fJv5mstWPyZT+qjMdqUVoLXhIEzYH6TsDBK2exH3DkPW90b04pQ012hOI4Glho9vWyil2epndGouji3JNCitUMZQ9yw0hnSq9DWbnBZ04ezOY1bOpVVxuNeNUErTj1Pe2erz5+/tsdENcS3JWrvCz728xKvXWqc6r0tKSs6mdAwzsISg6bt4lmCzH2G0IFE5dc9hsx9ijyUcgGOVQtoYumGOsEAgkNKwN0xwbEE3ytgLU+7vj6gFLp5t0arYpErz/HytUBXNFEIWu9yHqYre2RuxMyhiO4sNj1RpfPlgN1wLHH7x88t84/1dbu2OEMKwNtZ5kgKqvo0bpmx2I66PTz+9MGex7nKvN+L23pB7hyGuY1M3DgOTUrMMI8WRg5jz4Qtrc3ztCyvsjzKUAcYT7/I8x3ckwzRnqeGz0Y14Y/0A15K0Aps4MzSrLoNY0akU6qpXmimtwD0z9zIx9mpcyXSvG6G0ZneU8K1be7y5fkiS5WgNoVLc2h3hWNu4dtF8F0U5rcCmMeMeJSUlDygdwwwKLZ8qr642+eYtRW40tnZ4aanBYi2gGdjcO4i4Oq5EmhiZq60KNd9GasNeP8K2JanKuTpXpTtM+MF2n3uHMT95o0295TCMFZiYG53qpVRFXUvi2pJr7eBITmLWbrhVcXlluclLV+p4tkWY5Xx/Y8DKnMJoqHqSXpgziDOkFNQ8i1GmEWOF1iTXGBTSkiw0CvmKOVcCiiSH5bkaP3a9zTBWaGUQVrG2wJa8cXufO3shB1HGStPH92ze2+iyO0zIlGa+XuEzy3W++vw8B2HhVL93r8dau1LIn49DPmflXjKlUUpzGGUYrRmlCiNhlGiwJFJrLCH4YHfIbj/BYLAsm5eX6/zNFxd4dXWuDCuVlJxB6RjOwHcsvnStzWorIAOiKMMIwbV2hRvzNUZJftSxm6nCCNV9h89dbeJakq1eRJzrsV6Q5Bu399kfxIySjO2+j+dY1Hy76FTWhm6Yca0doJVBYTgcpTR8Z2ZOQhmDASpu8euTiJmhp0mIq+I7CAPDoUJpzd29EXe7EWmmuNqu8MKVGi3f5c1uMcL05nwVz7MwRqNVcfrpJwq0YuVqjc+uLZDlgiSHq82AXqLIjWGpVajQfm+jy2YvwXEkLeHw4V6fD7cTYh5I6aYqROUZSab4zJUGr661cC2BEBzroD4r92KJQjq8EMorRqpaugjnGVE8UykFwzDBsW3qVZcX5h2Gcc5b64c0Ky4vLNTLk0NJyQxKx3AGjiVZGp8G9sOUYZTTClxW5gqD5dpFVc9mLzzazXZqLjc6Ndo1j1GcoQ3cPxjxe9/Z5P29IWmmsUSxq/1Kblio+6w93xnPiM7YHsTs9GKEBTXXoe7bVFybnbG20GTH7I5lIaI0PzoxTMtnT5xIlms2+zFSFCGvMM2QwO2DCEcKQg3dUcq//M59vnpznkQpBAaD4V+7OU+eGb6/2aXf1UykktbfHfKH7w55ba3GL/7YNTzP4stLdd7fGdKuOEgE/VFKnCnqns1IKbYPE6ITz7efgh9lbPQitNE4jsR3HdpVr8hPqKJK6eQ87ulE+0pz0heiWZuv0o1TdgcRG72YOAOtwZdg2aCVYf0wGqvKauKkVFotKTmL0jGcwWR+8aQp64WFKpaUGAOZMqd1f5Rmb5DgORYdS4ApNH7u7A14f7eP0uBYkOWwvhtyve1zc7HKYZjTC1PeuH3At9f36UU5aE214rBxGHK1FXBzoUYjcI/UWZcaPmmuj+UYrneqpOMd9SSRmyl9VHWUZIqtbkyuDRoYpjnNioMlACT9JMMRgr1hwnzVo1FxWJsPuLffY2d0/NnkwF+uD7HFOs6X1ogzRZIbvnO3T82THI4ykkwTpglhkjGcVc4EDCKwehE3WgGZhms1h3c2+7x0pcZGN0IZc2oe93TupeLZfHltjo1uVMhza41vCd64s0+SK3Z6KYJC1sNNU7LMsDeMuTpXwffsUmm1pOQMSsdwDr5jcaNTPdZkNfl6dtmoplN1+d79HlIUcxGULt7nWBKDwJIKBCw2qryy3CLJNN++e8g7G12++eEB4Tgs0/Rt+mFG4Dq8sFhjeS5gseZhCUmU5rQqLo2KQ5oViqO2ENzrRkeOKk5z7vcTXlisEXg2ea4ZxDm9uBCji7UBA5k21HyJ0YUWkUFgWQLXtri9F9KPTwvoTXjz7oh6ZZuXl+dYaQUsNz2klLy0VGe7l/CDrS5hpglsSGY4h8ADx7ZZqPtUPIlShmGek2lN05bYlkQCG72YZQOWJU91i1c8m+cWikl7giL0dH2+wSBMiBJDmKlxuA+wivDS5681WWtffuBSScmzQukYHsLJBrOjr0/o/kx2s64lma+5VFwbpTStqovvOOQ6w7FdhkpRcS1aNadoxLIEwzjlO/e7KAOBa9Ed5ewOcipuyAuLdd5cP6S5O8C3BPWKy4tLdX725SUkhRhflilSrU/pJUFxaglcG2UMFdcu8gmBy1vrB/TClGbFxncEB6PCci80fFaaAXGe0wgcAs+CaLZzSDQkWrM/SHAdyShT+LaFYwu+cL1Fw7fYHcbcceF7mzHTVxFA4Ah8WzJMcoIwR+WgjCZXD4T2Zs3jnvU78qTFtbkqniUYRhmxgvm6P5bIEFxv+bxydY4Xl4pu7ixTuBeciVFS8qxROoZLcDIJPJlUNkpyHEsyV3XZ7MfsDlIcK2Op4fPySouXl7u8tzUgzQ31wGW55aO1YGeU8HynSm4MKjcEtkUvSsl0Ea7ZG6Z4dsj+KCVwCmVUKYrTyfubff7mK8sEdmH0l+r+MUeldRHuMgZGSY4xhnbFwbUkL16pIwRsdEOEEVjSolN3ORhmRUOYVfRK3FyosnM45HY3nPk8PAvyTGMwpLlhGCe0qw4LrkcOaKOJU80gSU6916GI+weuxZ1uyHwroOY7LLc8bu+FtHyHylie/OQ87rNoVly+8vw8m72IrX4ynn3tMl8P+PKNFmGc8Dvf/IB/8cYdlpoVfvmLa3zl+YWyOqmk5ASlY7ggs8om4UGVjTKG7X5MzbOPBsTcPYy43q7wd167zv2DIZv9Yn7yQt1npeVT8ywOo5zXrrf5Z7UNdgaFQJygMJyOhK1BgtGaw5Ei0ZMeAs3vfGuD/WHMv/OV57naCtgbpafmH1yfr+JaklGaszdIkJbg9l5ImmvqnsNPPb/AZjdCWoLVVoW1Obh7GNEdJuwNM1660kApTS/d4Ic7x2NBdRuW5wLqvotlFUM+4yynHxnSTLE/TDiMcu53Q7Z7hpNnDgX4XmHwr7UC5nyHG51i0M9WL+HD/RGeYx/lTy6ys5dS8NKVBj92rc0XMCTKsN+POAwzvvXeHt9YHzx4LSPe3hjyn//q5/ip5xbLk0NJyRSlY7gAsyQr7h2GGG3wXYuq5xKnOZvdmMaig+/IowEx1ztVbs7XWN+vcWd/SD9W3FioYgtBkmuans1c1efvfvU6/9df3WEQpViBwbMl7YrHZi/BtSEdOwUBOOP8xQ82B/TjhOfdGkmmcWx5SnQvzzXbvRjPkbQ9D8+SrB+EXGtXSJWmn+QkqcKWksWax0rLR2nDj11tsn4Y4to2P37jCv/m5y16o4SN/RH7mcITNtfna9R8B2XAGOhFKT/ajsEUTmKUKpI0IZwRiRIUO/x23cMIi61+CrIHRtIMHF5eaRSlu6bojbgodd/h1dUWe8OYvWFKGnjcPezzl1NOAYpn+aOdiD/83n2+dLVNNXAe50+kpOSponQMF2DWiM57ByHKGFoVtxhUcyKmr7U5GhDjOYKbCzUQMEhyHFkkVreHMR/uDlkcxFQ8h//wbz7Hv3zrPsYoNBZKKXIMtjTsjBXsBMVJQhkYxRnfW+9TsW2uzlWPnMEkDxJninuHIfe7ETXfZr7mEbg2lizmJxyEGe2Kw74GpTQbvZhXrzbZGyTkxvDB7qiQ9jBwpekReB7PLc2x3Yu5MV9lruKx3Y/pxxmuI3jjTkKcFmM5t7oJcZFnnymlEdhwtVnl5mKd3V7M9rhsteE7rM1XkAhc12KU5IzijERraraN75//Jytl8awtKUiVwRLwL3bCmWtQQBLnpEZTfbQ/jZKSp5LSMVyAWSM6XVsWlUa6kL1YrHvHYvon5yc4lsR3bFxLchhl7PdCvn3nkBcW6mgDgQNxCl97dYW3bh+w1U8IM83nVuYYRAkHg5SNYRG4ShVUXPBch0TlvLc7olPzjq15csrxLEnVs4+vs+GR5JpumBZNeStFv0aWFeZzsxdzrzuiF6ZcbVeQQrDVT/BswVKzxkLNYxAplNHUA5tXlmt8d6PL4SBkfTdhcI7skQ/YAuZqNlrArZ0hUsB81Qaj2R1GrO8XQnyLdY+tfszvfaeHMkU+4mufW+bGfO3c35fvWDw3X0MAt3YHND0bC06Fs2zg5lKNql3+Nygpmab8H3EBpgXc0iQnzQ1rnaLRbXeQ0I8yqo7FWqeK71in5idMktaTHEDdt9kfJlxrV+jUPcJEsdVLEKIQpXvtZhtj4P2dAZkCYyost6p889Yu9w5jtIKaZ/HVmx2utqvMVxwOwoxMabyxXlKmNGmuqPsOC3WP3UHCMM5p+g6distWP8JoQ5RkbKlx5kJDpHL6Scj72wOGcQxS8NmlBnGmaQYOjhTMVXwsO2V/FPPdDw+4u3PA9zdS9s+ubD3Ct6FVlSy3q3QqPgdhyvrBiO9vZGhdiAm+ebfLF5ebBJ7F7f2QhZpHq+YTuBZ/8P1N/t5Xbj705GDbRYPi+v6IhUaFq62I9W5+7DU//UKLX/jsKq5rnSosKCl5likdwwWZNaLTtiQt3yZMciwh2Bkkp6SdTyatF+seCDDKMIhz7h6MGMQ5caaLzmEJ72wNaVcdmhWPwJEkqshl/MzLizi2oDfM6KcZWgju7Yf0Rw6fcexj99zoRmz3Ew5GKcutgCt1j6Ti0Ahs/vRHu0DhsNJM4bqStbkqCsV/9//+iDc+7JLqImT1/JUhvgUHYY7nSL6/OUBrxbfX9/ir93v0LuAMpqkGIKXFTjdmEKY0AodulJAkCmlZCAGDUcrd3ghLWOwNU2q+i5vkaGOouBbDPMfVpx3wqXu5Ntfmq/zsZ67QqTp8e/2QgzBipVXha69e5Xq7jpSSMMlPdZeXlUolzzKlY7gEJ0d0RlnG1iDh6lxw1Jk7LWY3K2m9M0hYbQU4jkXTt+lFGb0oJUpyRknK3jAFDEoZlpseQlpcnQuwpGRlrsoozql1bPZuJ+R5RtUvkt17/RiVabQlixCSLY+qo9b3Q1bnApbqPq+vH2BLQavi0gsTPtwrKqcOwpivv7vN6x920UDdF0Sp4f3NkNvzh/zM51YRQvD6B3t8/YebfNCbFbU/H0HRpKaUwXYFca7o7mfEmUEbqIybAJUunKYtilLYrV5M03fY6cU8v1jDFoL1g/ChhlzKotpqp5/wMy8v8YXrHfb6Ma2qx+pchcWGT54X3eSBaz10FkZJybNC6RgegcnpIc4VwhRyDTB7qtisOQtGFJPf7h9GvDBfpR+mVF2Lb6/3qXmCrV4xz+GH2yO+erOJLQV116KfKIZxyq2dITuDGFdK2oHLRqaxJPzprV1eu9EeG0yJJWF1rsIoyVlpBYX4ninWH2WKXpIzSjJ2RhE7I8G7O0NyA7aEJC8SBQbYCXNsSzBKFH/49n02Rmc9mfOpSnBtF0vAIDH4dpG/8W1BnAs8S9INEzRwOFQsNqtcn6uwM8q4fTCi6tr87VcW6Ub5TGE94NQpYlo244ouynSvNDxaFQ9tDNnY9k8a6s6bhQGne1lKSp5GSsfwiEgp8G0La9z1PN39PJHPOGvqmCUEVdemU3W4tdNnux+xN4rZ6kc0Ky6jNCfKcvaGKXv9iNW5gNdutOjGOX/x/i41zyFwbTKl+avbB1xf8GlXA3pRyg82+izUXHb68dGppTkuxbQRGAPDOGerH/H2vS79KCeKc6oVB8tojOFUeel7W33+/L0d7mwNHtkpAMxVIdcKI6Du2tR8myRXzNccNg5TekmKkIK6Z7HUqDBXLcah3uj4VGyXL95ssjxXZX+UnnK2ozRnf5jOPEVUPJuVVsBGN2Kh7rE/ykAIAsdmpRmwM0jO/B3CA2eQjfWpypBTydNO6Rgeg1lTxaYrkc77+TDKeHerz5+8u82tnRGW0ERxzn4vYpSBBUgJ7w1zfrgb80c/Ojy679VGyrW5OoejlK1eQpgpakGKbUlqnk2rYjOxa1mu2ezFhSKrFDQCm4NRwl4/JkxzOlWHZtVjEKfM1wLaQcj2lBRqRUK96vPD+wPW94eP/Kyu1m3mmwFKazBFCaslBa+sNKi6FlcaGonGtm18x0ZTSHyMYs1KK2Cx4XKzXWWrH43nUT8w5IKiCMCzJbZlkeaKewchNzpVbLvoAt8ZJASuRT1waAQOSaZZbQVFkno83W3W73CSI8p1cTJZbvozw4YlJU8TpWN4TCZhpbPCC7N+HiY5f/nBHm/dPmQUZTi2YBApDoYZw6kJaafqK8fc72uGUY80A8+FlZaPNoLv3jngSsPjSt1lqVVBK8PWIMbkBscpSmtHieK5VsB31vdZqHscDGICVxBliq1+TG6KrusMaDqwMufj2g6OVKTp5Z/P5xYsfvazy1yfb4LU7BxmXO/4vLMxYJTm5FpQ8Wwcqyh9TTLDXM0jTHIEmk5FEHgWH+yOOAwLKfOvfXYJIcSRIZ8fV13ZlixmSQ+LCixEEUo7OTrVtS0yZTDi/N/hdI7IsYqZD4dhRsW1HxpyKin5NFM6ho+Ak0J75/1ca8OHe0O2+xHdKCPRArTkYJASq9nNYLPojRUqmk4hmZ1mhmGc8433dvnhxiFL9Ro3lyocDnPm6i6OlDQrDtv9mG93Q751a5+dXkIvh0BAo1qUqzYDGylyDiIYZDCIMhqBoOk7NOoW/UuUIf3MjTpfvLlAw7cZJYVo3VzVJVXQCFwWmj5V1yFMczYOIySSlTmPu3uFfMi1OR8BvLs1wJhCj8lozR/8YItf++oNgvGgoknILs0Lp2C0oerZeONE/GorODOkd97vcDpHpI3BdSRJVsyw1urs8aslJZ92SsfwMZMpzcEgpeY5+I5F3I/YGQwZpkWiV/BAf+lhVMZDf9IsJlXgu/8/e28aY2d23nf+znn39+61V5FV3Hvf1JtakiVbljSOt7HlNbYzmMxM5EwcwJnMl0wAA5kZZJZMgHwIEHigABM4AxuJNzmOt8iK5JEtS2r1LqoXsrtJFmuvuvt99+XMh/dWdZGsYhXZrG6xdX9AgeSt+7733GLd85xn+z+glOL5N9tsxm1y4HQVzs6MEZ4tNrSJkslfXtjgjeY7wnaeAm9Q6B9N1SxyJFEe40WF1EXJMvjUg3OUL22x3t1in/EKO+jAD97T4Jl7pikZGi0/phclkCscU2ey4iKlRAiI45xU5UxWLIQmaA5CHEdH60c0/RhLStp+QpRmKARzDYe2l3BxvcdExUFRqNzWXYPNXkjHLybfTVWKbnQvKpL9Nwv57cf1OaKGY7ASZ0RxtqcE+IgRHxRGhuH9QAomyiaaVowDrTsWURzQv1GEdF9KQN2F5d3J4AheWhyw+zZv92Cl32KjH3J8ooxBznNX++xFP4Ugipitl2iUDKSCz/3gGfJU8MCxOlXboNmP+eaV3r7rMoCGC4kqEt/jZZN+nDFftgiGE+w6fsYDs2VeXevz1lYfW9OxDA2RZ7T8CEvTqboGXpTR8wO6fmFIdU3SC4oQ0aYX0ShZWIbGwItZ7/uUhsN3kmHD3m7PwDBu1JE6iBtyRFLy+EIDQ5ejqqQRH2hGhuE9xtAkU1WLta7P3JhL3TFoOBoV12Bxq083VHjpwffxAM8r/gMFRU5gsE8cKlSw1vYp2Qbnr3b3XxuQIwjjjHrJ4jMPznHvVJ3lTohQUHNMvv++KR44VuXSSouljk9rAFkGaBDmhbgfSrDRD7m42mW6YrEw7lKxdJqDmDTLcS2NOM2J0xyhCpXVNM3p+TE9PyXLEpQAy9SouBaTiaITpIRJiqkLGo5Jx0u5suXzynKH5bZPP0y4f7bK990zSZaz07sxOxzPCgeH/PbioBzSiBEfREaG4T1GSlHISCtYaUf4muD0VIULa11sXSfMYhY3PbYGisPkeiVgGqDn+87TAQrD4Yc+3ZsYnTEHPvfxMyxMlAjiHE3TiLKcM5Ml1rsBb6z3sXWNx+Yn+PjZKV682kYA317u8NpSm8gH1wDX1sizhI1+TC+KyYKEt+OMmarNZMUGAd0wZaZqUXEM1toBaz2f+bpNrWyy1Y/ZGkSF8cgyLEPnwZrDZNXBMiVBlFFzdS5u9nh9vYcuBLqUNL2El6+2+fSDs0RxUc1k3YFy0tsxKCNG3M2MDMP7gG1onJmuUHZ0vnpxE00TzNQcHjvZoDuI2fJCXlvpognBc283WQv2v1cMmAryA3LCYQh+tH9qWwd+7plTPLQwhoZAqQRFzguXW9Rdg26Q4loax+ouQoAfZYyXbRYaDmemy4UC6tqAVgytdmF92oMWriX50EKDjW5MnGYgBOMlkzjN8eMM29Q4OVmi7cVIQzJbsrE0wWY/4FitRNU1cQzBIMo5N1kmRdHsxyAEF9b75JnCsnUsXSvmTEc5YZhimcZOWGl00h8x4tYYGYb3CSkF01WHJxfGUBQlj8udgJ5IaZQcfuzRKh0/5tRkhd/7+mVWbpJ/8FIwgceOOWx6IcudG9PXHrDc2tt66MDPPD7Np+6fLjSfuhGuLfnqhRYqh7VuxGTZxI9T/ChBCMFyO8A1dWxLZxCmZFlK+zoXZy2Ab729xVTNxjF0gjhnrm4jFCy2YgRF+WySFWNELU2jPYhRwD0zVWZrNpahY+sa7bUuV9s+nSBhrGQSxhmWJmlnOYYuKFmS5U5ImhpkAkqWxmK7mDw3akYbMeLWODLDIISwga8C1vB1flcp9U+EEJ8C/jlFFGQA/G2l1JtHtY7vZqQUHBtzWeuGZFmOEvDAXIUr7YAsF5i6zv2zNX7sqXm+8K2rbO5hHErATB2ONcpMVV3umS7zH1/YZC8nY6/2NAt4dN4h8Dx+51uXsLWU9U6K0CVT5RKz42Xa/ZDX1rqMlW2CJCPPYKpmM1W1EErxwmKLF5fDPd/jRgDPX9ri8flxDEPHixJKlknNNfDDlLItEcLAlIJemFAIb44AACAASURBVFJrGARRRpjkWLrg3pky5692sXWNTOVs9iPiNMMxNQTQ9iM6XkSSFXMw5usWr17tUnJ86q7BXN1FHzawvZ/NaCMpjRF3E0fpMUTADyqlBkIIA/grIcSfAr8O/IRS6jUhxK8Avwb87SNcx3c1u3WXMgrxuOmKTTdISbIMXdP42NkpOn7Mn720Tu+6aJAHLHWgZGdYesTLl7p7GoX9iIBnr25fca3pMGnz6EIVS5MoAVGaM1U2EULy4fk6mpS8vd7jj19Zumn/xZXNECE6PHS8gVICS0IQZ0xXbTpBQn04DOjeusNY2UIoCNOMqy0fL0hY7wdUbBPb0Kg6CV0/5rlLTV5f7bDRC8lzSDIo2cUkuL9+s8WjCxWePDWFAk5NlMnT/H1rRttrLOzIexnx3cyRGQallOKdncYYfqnhV3X4eA1YOao13C1s6y5JBHGSU3EMDE1StjSiJINhL8F4WdK73jJQbO6vrAXcNBlxG8TAtxZ7TNowUy/x9IkajqET54UMhW1otCOfTf/m9/FTcEyNyYrJRj9gvR8yV3WouSZVx8CLU6ZLFkIKVK4wDA3SYtyormsIJclyhaFLTCm50vS41OzT93OkKO6fA0JkCJERRAlyKWe6WkYpmC5baMPO5feavRR232/vZcSIgzjUMF0hxD1CiP8shDg//PcjQohfO8R1mhDiJWAD+HOl1DeBvwP8iRBiCfivgP9zn2t/WQjxnBDiuc3NzcO+n7sWKcWOAmoviMkVjJdMEIJuFLPajQ/dFX2n8WPoBTEvLvV4bbXDRi+i5UV4Ucra9YmFPUgo3otraqQZ5GmhLpspRZIroiQnVYosV1xp+Wz1Q1a7IXN1m+mqw7nZCs1BRGsQEWcZUtfIsqIaS4pCOUQBgxiiHFJACHh9rcdic8CVdhFW2i11kWQ5eX7YVsLb553u6XfUW3NVhJVGjPhu5bBT1v818I8pPuMopV4B/uZBFymlMqXUY8Bx4GkhxEPAPwR+RCl1HPg3wL/Y59rPK6WeVEo9OTk5echl3t1sS0RPVW0ajoGha1RsjfNXuyRpihDvz2ZStinGkfZiWkHCVNWiH2XMVm3umznctOSVbsBGLyKMi7kTV9s+E2WTcddAk4KyqVMvmRyrFzIY0xWLkl1s5vfOVHl0vs5czWau7rAwZlO1TXQB8a7y2wxQOSSpIs0Vri64f67GmckyHT8hzxVhkrHY8rna8lls+YTJLU4augXyXJHnCkHRbAfsKccxYsR3G4cNJblKqWfFtb/Mh2jDKlBKdYQQfwH8MPDo0HMA+PfAnx32Pt8LuJbO6YkymVIIBYMgpuKYTNcdFrf6cKAgxZ1lpiQxDJ04j2mUTaarNiWzkMvuhTGGYTBuQPOAZXX9lI4XUbV1cgTfeGuL5bbPeNlikERcXO9QLukcK5dRwFjZxIhTLF1Dl4Jz01WmyhaLbQ8pFG9v9PHiGF3LsdLCKNgGaAJsQ8fWTYSuMVW1sA0dL0pJhmGcWw3r3E7iOEwyVjvBjmcSDyf/HVaOY8SI95PDGoYtIcQZhjI+QoifAVZvdoEQYhJIhkbBAT4N/DOgJoS4Ryl1AfgM8Nptr/4DynZDVZLlOKbOiYkyszWbkiHZ+NpV9u9dfvecrYDrSLw4x9A1DNMiz6Bq68w2HGxD40rTI8ly/r/X1nnuchPd5EB71epExDMxlzYVQVII6nWDkKUtn9fWPNIMEPDYfI3vv2+a+XGXsmUU41CrFieG87Rnlct42eHEWIU4zkkqORJFyTapWwbr/YBG2WK26vLQfI3lTshYyQIKnao0z3HMvQcr7cXtJI7zXHGl6RW9GdszMVyDY3WnMA4jozDiu5zDGoa/D3weuE8IsQxcAv7WAdfMAr8hhNAoQla/rZT6IyHE54DfE0LkQBv4b29v6R98NCEwdI2zkyXe3PQYLzv86JOz/NZzN7XJ74qxms2nHpilOUi4sD5AoAgzuH+2im0ZeGGGFxVS4X/8ygrdQ0pxd3L48utt8hwCbhQKdGQh7fHS1S62oVD5FJ96YAZDkyRKoQtBmuZs9SMemK1SdXSW2rUiZwFc2fRIcsV4zeKhY2PM1WwGUUYnSOgMYmxLZ70bstYLkUBpOFNBCoFQ7NkId7uJ4yTL2ehFVGx9R811qx9zeoLbNgqjctcR7yWHMgxKqbeBTwshSoBUSu2twnbtNa8AH9rj8S8AX7jVhX4v8o6IG9w/U+HkmMPj8zWee3uVC62jec1nl0KeXbqELcDSYX7c4WNnJzg7W6NhG2wMIvww5Y9eXt4xChr7jo64hv20nACCHGwBuYItL2XTi7nS9KiVLII4I0mHA3kGMa6pMV6yEEKy0gmYKJs8erxOwzV4c2OAZUiCRJGmhWifpgnKVrFJzwpY6YbMALqU1F2DpU6wp0ew32jWWy17zZViEEastj2myg6uaxz6WhiVu45477mpYRBC/I/7PA6AUmrPxPGIO8f1Im4dP2Km6nKhdUCN6LskVeBosNIJeHmpy7GGy1TZxtQ0PBL8qLAK2vArp/ACxk1oxYeXDt9NOLyo2fVZanu8vKTxkTPjuJZOL0xQqsghrHXD4rVKBg/MVjhWd2kHCZmC8bLF62t9DCkwDY37Jyu0/ZRG2RoK+OnMVm1m6w6mlCy2fTRRlNNuewjbHsHNRrPeDEOTTFUsOkHMIEo5v9TmxSsdvmSvU3ctfu6pee6dre08/2bewKjcdcT7wUEeQ2X4573AU8AfDv/94xRdzSPeA3aLuGkISiUHOFrDoFOcqJMsJ4xTMopei6mqRZ5nVOwisZDxjlGo6PCZh2fohzHfeqtFd9gM7erQPmSpggDGaw69IOP5K21OjJd4cK7GQBUn72KTFkRpDgI0KekEyXCsp0QTRSnsfN3BGU5a2+z3uLzpYegSpRR1x8TWNbw4ZbkdYBkSTQomytZOKalEHDi6dT+kFJyYKKG1BRfXunxnpcvxMZeqa9D1E37/hSX+wSddXNc40Bu4U17LiBG3wk0Ng1LqfwEQQnwReHw7hCSE+J+B3zny1Y24gVQp6q5JgyJBc2SvAyRJEftpuBbPnByn4poIVUx6+5mnTyJfuMorV3rkQMWBn378ON93bppU5ZycqhHFGc1+wDff2jy0YVDAUjvg1JSGrRu4lsRLMrI8J8sVlq7hWjpRkrMw7hJEGcnQE4DCSLW9BFMTOKZRTKSTRV+DyhWJKmZJ57liqx+hSUApskyx0vGZrjrXeAS3K7ttGxrzDZf1rodj6NTLJgAlS6cXxPSSBDvXD/QGbtdrGTHi3XDY5PMCXKMCHQMn7/hqRhyIrWuMlRzOzLs8d/VovAZBEUYyDMkDs1V+6SMnqbomuiYJ4hRNSp45M8HCmMtSe0Dbj/nEmQkM3cQyNeI0ox8U/QL9MOX4WIkVzzvwdbdphzA2iDkxXyKMipGlk8ONda0b4sUZ42WLlXZA1S4m4aVZjpSCjV7IVMXCNjSSNGe5EzBVsSiZOmu9EE0I1nohkxWLMMmI05xXNwegiulyJ8Zv7Mu4XdltQ5OMlW0MTdL3E0p2UTZrGxpVwziUN3C7XsuIEe+GwxqG/xd4VgjxBYpD3WeBf3tkqxqxL46l85EzY7yy2GTMhtbe2nXvihkLfuLxSR47O8fT8xO4jrGzMalcMVYy0EUxR+J4wyVTMFW2eGmpgxTFBmdqgiutAR0vwbVsHp6Gb68f3jistmM+85BFxdEReUaUpjiWQaagZEl0TaDUtkqtzUY/Io5S4lRxYqKEqUvyXOHFKSiKaiQBTT8lijO+vdwlyTKCOGOh4RLGKeu9kFdXunhhykzdwdE1lOC2K4GkFJyaqPCpB6f54vk1Wl5EzTH5qceP47oGea729Aaur5La7bUIBUoUuYeRcRhxVBy2Kul/GwrgfXz40H+jlHrx6JY1Yj+kFDw6P8YPPThLlEEQxXQGMVcHd66DdzWC//vrm/zkQPHRk1M7G5MXp2z2I6QQXGn5jJdNHENnpmqx0Y+Yq9k0vZgwyUhzhaVJNKBRLWSyTkQxAy85sBkOitPHV15d57m3m+RZhm3rPDE/zljVxjL1QhxvrIzUJIYuOV53CNMMTQh0WYRfchSmplGzdZbaPr0oxdIk8+MubS9itRMQpQrLSAtNKgFb/Yi2l2Cv9XAsjdmqM5xTXXgdt7oZ24bGD9wzzRPzDbw4pW6ZO1VJe3kD+1VJSSnwg5TFVmFcHVNntu7s5CNG5awj7iSHMgxCiAVgi11lpkKIBaXU4lEtbMT+lEydp05O0AlTTE1gmTrrHY8/fn6RK4c/lB/IH3x7C6m+xa999knqjklzEGPpkpKlU7aLOP/xulOcYJVC0yQK0KTANnWON1y8qBDDk6LIjYxXTKbChCjNCaOUlX2iYXUX1noRl7Yicopf1PNLA6YrGq6pU7clp6YbfOqhWSqmxmovAFV4K6lSO13Gddeg7RUzHrI0Z6LuIAEvyiibBg1XAjkXhuElP8lQWcLzGwOONVyW2z7Hag5XmhrH6g7HG+5OPuOwSCmolSxqw0a73VzvDSx1gj1zDp0g5ovfWaPjJxiaZGHcIc5yzk1ViIfPG5WzjrhTHPY3/I95pwLRAU4BbwAPHsWiRtwcKQUnpso8k0zw3OUW/TBlrlHmf/iRRwizmM9/6XUu9e7Ma/3++Q4npt/klz9x7zXxcFPXSDK1E2oRwGonwDY0TE0iBJyYKBGlsNb1SXKFRFK3NFSmkLIwFFN1uLjq4+2qb512oFa2aa6HxAwHehjQS6DXytjumvjq5YBvvN3i3pkqpq5Tdw3mx10eOl7jwdk6mhAsdQJMXXJ6sszLQYelVsBM1aLm6pi6Rp7DRjcgTDIsXWMQJTS9mPVeiBcnbA0S7pksMVMvkeU5G/2IxxcaexqH2z217+503yvnECUZLy+26fnxTuXUaidEl5KFhst6PxqVs464oxw2lPTw7n8LIR4H/u6RrGjEobANjccXxrh/ukqschypcanlsTWI+IWPneN3vnmZi607o6v0H19Y5mefPAFAGKeYhrYTH9/eBCcqFkvtAClzNCmYq7vUHZPjdZfVbsBba31eutpGSUGYFqEeP8544tQ4j51osN71WG36bHoJJdNEk4VOewwYOoR7vBUFvL0ZkuU5jx4fw4szLq736foJJ+olqiVzZ6PVNcmj8/Wiac4xaPkwU7FxLJ3ZioVraui6ZLUfcHGtV5TpRsUs6dV+RNmxSDKFbSgWmx5npyro+jsalHeiCW2/CiQvTlnphgySnHwQM142SXNFluWjctYRR8JtzWNQSr0ghHjqTi9mxK0hpaDkGJQoNiYpRaEqapr87NMn+cvXV7mwMmD9kLIV+9EcJJxfajNVLdENi1j4VMXixERp51RaMnWONRw0wY7hcEydc1MVzkxVaLgGG4OQi5s+0zUDpQRxnhFEOfeeqDFRdjg7nYFSbHkR/SBlqxsS+5AO5y3shQCyXOHFGZouCWNFU0S8eLXNM6cnrtloTV2yMF7iWN3hZK7Y6EcEcUYKzI25bPYjgjAlyBQls2iqk1qRxK6XDfwoJU5zkkyh65LjDRfb0A7dhHaQR7FXzmGqYrHaDSiZGnXbIExTllo+JUtjsmZhSjkqZx1xxzlsjmF3B7QEHgc++EMS7hK2N6aypXN2sowuBaZe5sOnJ3j+SovzV1t841KTlf7tTXToJPCvvnyRh+cafPjcJGenypi6xNTeOTFvz5NY64YEcbZzatZ1iQ0EiaJRsqn0E2xdI8lz7qlb2KbG/bNVqpbJVM1GCsFaL2CrH1Avmbx0pcVKJ0RPFHvl1w0BuhAEcUw/StCFYKpaBaVYavscb7hsefHORjtXd7CGJ/njmiRMM9Z7IeWSRc02WOn49IOUME3RpEAoRcUxybOcTphw71QFy9CQwPLw/nmuiNMMS99fnO+wHsX1fROZUggheGCuxndWumz2cyJyZmoOptRY6YXUXYOOn4zKWUfcMQ7rMVR2/T2lyDn83p1fzojbYXc4IZdq2ACWUXdMToy7DMIUx9D5Dy+t7rm5HkTDgH6keHG5zYYX8cn7pnn4eGNn49s+CZua3LMZTA27kccrJtlShpdmSF2QZVAyDObqDlIU09tKjsG5qSqTFRsvUjw0U6cbR1R1nW8tNvlP5zfxhu+hJOHB+QoTZZtLTZ8wThgrW6z1Qs6v9hGaRNclczUHQ5c7a9ouY93sR6RZznovYmG8ON3XHIsHjlW52gpoDkL8OOPhhRrjbrHZSilI0pyVbshaN6TpRfhhRpLnVGyD2bqzUxW1fWq/VVmLa/omcpBCULJ0nj41zsCPWe6FnJkqYxs6aZbT8ZOdIoBRVdKIO8FhDcOrSqlrOp2FED/LqPv5u4LrY9MNx+BKkPDm1oCVbkSc5yx1AhxTMAhuTcWoLKDi6GQqJwwzLq7luFoxAW1hzCUztANPwpoQuKbO/JjLx+6Z5IXLLXIEli44Pubw+a++xSDI0DTJh0+P88n7pmh7Ma6lU7E0otxCKMHnTk3w2aeOc2HNww987p0ZR+qStVZIP1zDKFvYtk7bT9GbA548UUdTcKXlcWa8jDTlzpyEpXaAqQumqjamLljtBMyPudQcnY1uwP2zVaCKbQrm6yVOj5dZ6gVsDSJsU2O9FyKE4ttLPSZLJilFee5i0+d4w2G27uxs0O8mD7A7vJQrhWZozNYcbEO/5l5KFA11I0bcCQ5rGP4xNxqBvR4b8T5wfWxaCMFM1cY2JLYpWdwc0I8yhJAcTge1+MWYKoOUBrFSxGFMOwBFxlonYjAMDZ2dLlO1jZuehHcnp89NVrB0yWzVRgl49u0mvSBjru6SZYoXrrRwNIlr63S8hGcvNzE1gaFJfvqJ43zk5AyPzKW8sthhy48xMkU+bKqbrlhoukamQvwkI81yXlhq89ZqkySTfPTeMY7Xaxi6xNCLE/3WIGKqarPUCvCilDHXZH7MoWwXzXRCwaYXY1shjZLJei8iTDKiLMeQRfVV2TEI4hTDkDRc85pwFbx7WYu9SlrjtAjXbRvkw9xr1Osw4rAcpK76w8CPAMeEEP9y17eq3MIEtxFHz+7NI88Vy50A19KZqcDcuMsLiy1ydTij0DCAHKIY0jQhF9AfXmoJ0DR4bbXHudUWpiF45FgDuPlJuGTqTFRMNvshrmUwiHN0CWGcYesSyxAIU9KPYK3vMyNcnr3UJM4yglRhS40vfmcNIQUgmKpZtMMEJRRBlDJeNsmFoGJqWFJD0xTfeLvJl19b58JW0R7+r7++xDMnq/zqp+9jaxAjUERpjqMLJismddeg64NjGgRJRtdPMYalt+SKnp8wWTUxhEAI2OxFmFohNGgMS191KW84ud8JWYvd4aW6a3B+uUuWKzQpeOhY7cB7jaS7R9wKB3kMK8BzwH8JPL/r8T7F7OYR30Vsbx65eEdqwbV07pmu8Mn7Z/jDZ99kKzj4Pu3t0tChMWhYYGRF1YFrgqFL/Fix3olYKnuUDclY2cI2daSQ+55eJQJL15goS5qDiBxRSFeojDRTxEkxBtPSdEAxiFLqJYO2n+DYOn6UE4QJW37CwpjLZMXEkJKKbaBLwRvrfZIso1EymaqafP3C6o5R0ClOMt+43KP2F6/y0fvnWe2EhHHKK1ebnBgvEyQZMzWLubrDlWZAL0w5PVlmrGSyMYhIMsWYa7AVpriWJMkV01WTMM2pWEU39tyuENJubleM73ryXNHxE06MuQhZ5GU6fkJ1OB97v2tG0t0jboWD1FVfBl4WQvymUmrkIdwlXBOXTnMmSjbfd26Sb1/Z4EL31jvf2lHxp01RNupFOeSw1vFoeRF/+OISFdvg/pkav/CRk3tuNplSGMNy0TxXnJos4YUp5ybL/Mn5NZZaHlJKnjo1xpnJMm0/olHWMYSkZutIAZnKWO5FvLjY4fW1HmOuyUTVRkOyMFFmpu6QKbCkpOYa/Pn5FWA4M6JocCYFLnUjSldbJElK0wt4/YpPTxXfE8C4BScmbE5N13Y8idVuyEzVZqJqUy/lREnOZ+6vsNGPUKpo9Juu3PwUfrtifNf/HPOh4N/2m/Oim+crRr0OI26Vg0JJv62U+jngRSHEDVlLpdQjR7ayEe+K60+oAP/1J07xny68fFv3syikq4Oo+HO8rNHyM7pBgJKw2Y/Z6EUoMn71Uw9QLZnXXL8dZ89ztRNnN/Vijf99o0QnjjGFpOqYxFnO81fanBqr8FZzQJIrVJJxdqrMWjdiqmwRphmXN/pcbfl89MwEJyeqO+qvV1s+XpJwdtLhxRW/mBmRv9O6b6uMlxe3eKt1Y2hNAVsRbC2HvLoaMggy7puro0u4f7aCFGKn69uxdE7bBl6cstWPaHoxbT850jDN7eQrRtLdI26Vg0JJ/2D4548d9UJG3HmuP6F+9OxxfvHJdX7rubUbnwtUTPad4ewYcHbSJkVSMiUKiR/nrPUyHEPHNIrN8rnFLt9e6fCRM5M3JKCnKhYrnQBEoZ+0HWe3bZ0Z+51fRV2XPHGiQd3RmWnYREmOoRWJ1pVOiGvrpKHiSisgHTaq/fxTC5yYKKNpkijNaHoJj5yY4o2tiFeWBztG4XhVUnIdXlk8WFQqyOHZi1sYmuC+uRrnVzo8cWIcW9eu2Vibgxhdip3QzlGGaW4nXzGS7h5xqxwUStqeOv8rSql/tPt7Qoh/BvyjG68a8d3M//4zT/Bjjy3zu9+4RKvfxzBdJuolJksuXS9iqeXxn9/q3nBdL4HVVsjJaZdz01WutiN6oUemAAlKCExNoAvYHEQkWY4l3zk1h0nGRj9CUJzKt2cm7IeUgrJtcFIvOntnKjYr3QA/yej7Md9Z6mJoGpNVA4ngy69u8PNPWawPIiqOQa1kkmU5f+PBKQyVsulFqFhRdy0ubR5eabCVwlff2OTtLY/5mkPPS/nw2XFOjpeBIkwTJClelO0kdkuWRpLlSCWu6Z24UxVBt5OvuFM5jhHfGxy2XPUz3GgEfniPx0bcBXz07DGePjlLL0pY7QT89cVlXl3zmHQl5ZLJhAmDGBLeKW7NgeUQVq/49BPB6Ykyll5mEGZ0/BTpShqOyYnxCmXz2l+r3clPxzRIs0KMbmEoJ3092883dYlrFU1crSBhYbxElGV88TtrdIOEE+Mus3UbTUrWeiEr/YAsK8T7AC6sd/namy1sy+JD43VaXsRaz9tXXmM/ugkk6z6bXR9NV2Qqx48SxssOJVNycb1H3daplx3iJGO1E2JKiRg2um13Jt/JiqDrvcHrDc9ehuhO5DhGfG9wUI7h7wG/ApwWQryy61sV4GtHubARR4uuS8Z0i//rT87z756/NrRkAoYEld/Y9ZADVzc9fvrJOYTSOT1d5qVLHaQmmK45fOzcJPPjpWtKNm81+bnf8w1d8vCxBlNlCz96m4qlYxk6eZZhaYKyodHNM7Isx7F0sjTHMTVKloGlSWxLw9IEjqNDcGu1FLGCzQC+8p0Wb611+KvXXe6bG2OtHzIIU2xd8KGTY9w3W0cphaYJHFMnTjLOL3c5Me7i6PqBFUG341lcX4p6FIZoxPcWB3kMvwX8KfB/AP/Trsf7SqnWka1qxHvCn75y6QajAIWiabIrWXsDAubqJSZLNlDj+05PsOUnzFRsxiv2NV2/cOvJz4OeP1F1+FsfPsEXX11nECWEccYn75tmplGiHCasdEMmyia6oXFivEzbT4gzhR8mVFybOUsji9ssDQ7fBb5tRnzg9WbO680Bz18ecHrKZrxq0/FzXrzcomIaTFZtDFn0NygKkT85XPvNjOLt9BpcX4oap0NDNObimAcbohEj9uKgHEMX6AK/ACCEmKKoWiwLIcqjQT13L2GY8ltfu7Lv92+2ZTqmwNAEFzc8ZmoWjmlw74yDQDDfcK+Ro4b9VUMzpQotoOs2rP2SpbsH0mi6xi98eIEwzWh7MWNlGwDX0pksmczWHCxdo2zqfPn1DbZ6Ia5p8PipOjXH4tx0na++scS3125fetYHLm6ENP0UTYd2YPCQH1IrmVxpeeiaJMuK3ow0LwJY+3Uq326vwfXelRSCLFfDRsBRaeqI2+Ow6qo/DvwLYA7YAE4ArzEa1HPX0ksSVH7zTVEDGiZs7Xpa3YLPPn6CV1eKuQZBojD1nG6QUrE0vCSlhH6Dcdid/EzSIsdws5PxXuW2iy3/mo2zE6TFSM9EkWY5aa5Y6wbEqcI0NBolEyEEn330GP0kQSqYbZQKvaR6QCcIcfQeV7YGtMIip1K01h1WOAQiIExSaoaBFyV4cWHIojjFVwpL0yg7GovNYlznfp3Kt9trcL13tf0aKlegMSpNHXFbHDb5/E+BZ4AvKaU+JIT4JEMvYsTdiSM15sbG4Or6vs+5d8JkYbpCnCQ0OyEPLzT4qScWCBJY7gaUbRNQtP2EPMt5K0qZ7obomuShYzXq7rW9DFIKyGH5kBPHdidL95tupgTM1GxWOgHL7WIs5sK4iy7FLtVRZ0djCKBsGwjp0wtSbFPn5GSVRpSg8hxNk3R6AWvB/jMgrieMwbGgbhusdyOu2APaXkScCSwdzkxVefxEA9PQULmi5cU4hlaMHx2+59vtNdjLu3roWI2On+BF709p6kiT6e7nsIYhUUo1hRBSCCGVUl8ZlquOuEspOQY/9Ogc37zc5Er32kSsAzxwzOHYWAXb0LBcm5PjdT52boKJaonFtodr6TRcneYg4UqzT9tLuX+2TMUxiJOUb761yTOnJ6g45jWbw506Ge/eOA1DcqzukGaF9PX2620bDk0IMhRTFYuNfkTPD3lxscts1cbRJR0vIVMpqZIYUqM6VUZtDFgdyodYFFIgvfhaT2JbZiNU4PUSyBMurEreWOlzYqrE8UYJP8n4xqUmT5xooEmBn2QstYOdBr9tb+nd9BrsVYpatY33ZXMeaTJ9MDisYegIIcrAV4HfFEJsMBLRu6uRUvD0+AsQCQAAIABJREFU6Uk+98mzfPGVVVp+hFAZY2WHqYrDTKOEY2qIQkGOhUaZubpLkuagBGfGSwySjDjNqJcsJko29ZLFm+t9lloeq92Q19cHfPTMOA8fb+xsDnfyZLx74zS0QrwuSovZzdsbU5LmLO8KW1VtnbYfEWcZmtToRxlvbw3w4pSqrZEYcHE1YHeQrebCbL2MEFA3JBdbA3peviMsqCgScd0BGFpAkuYYbcjSnLprESUpW4MQfMlyywcEC2Muhiau8ZbeTa/B9aWo70dp6kiT6YPDYQ3DTwAhhXDeLwE14H89qkWNeG8omTofPTPNI8fqrHcjMqGQSvLATIVOmLA5iFFKMVm2ODNVeA+ZUszWnaJZTQoaJZNHGg6vrfYJ45RXV7oIBBNlm7GSyStXO9Rck7OTlZ1BN3fyZLxNnOXEw9wFwFTVYr7hsrErbBWnGa+u9pgpmaQ5hFlG3TWKiqU4I0yyHV2o3fR8+KVnxqiXHK42A2qu5I9e7ey5xivdIgDVDXwGoWKunlN1DNpejGPpSCmZrlq0/Ji5ukOe5jc0w92tSeKRJtMHh0MZBqXU7lbR3ziitYx4j3lnHKdAkxqbg4jJikWK4PRkhXPT75zGd5qkKGYjLBhFd6+la5i65N7ZCs9fatELE8Zci4UJF9fU2Ygzen5Eyzepmgamqd2Rk3GeF8OCtj2NtW5I2dapugZxkpEp0KQgzjIEEsQ7FTu6qXH/TJmvv7XFG+s90lxRdw3COGV3PZZGETrKgKutiEapjK1Lekl+YHLay2GjF5DnKR87N8ls3aXmmthGiKHJwpAlGUmWszzMfdztoZeRJtMHh4Ma3PrsXbkoAKWUqh7Jqka8Z9iGxvG6w+VWzpmJMqahvdOZvCsEcH1CUUqBJTVmh3OedSl57HidZFgdZOkaXpTQ9WO+/naTixs+pi752NkJZmrOuzoZ745jB0mCDsSZYqrmFu/J1PGilI4f8/LVDuRgWZIzE6XCWCQZCslYycTSNMbLJmGcoduw7ic7r7OdfNaBRknDMgRTDZvnFw9Xs+QnsNJNuLQ1YBCljJUtpqo2q52AJFPEaV5Io5sapn73h15GmkwfHA7qY6jc7PsjPhio4WnaNPYOAdwsoXj96b9RsfiLNza42vLoBQlRmnO65DJRtkiynK+9ucWPPzyHad7eqXh3HPvCep/ff36JKMkwdMlnP3Sch+cbpMMKpu8s99AkeHFOL4zxwoxP3DPBpU2fsqXhmDp11yLNFYYUeJGgbiR0hrZh+0T02IkKs+NlxssWlzb7XFg+nNaSAOIcnn+7x1eml/jRDy3gmAaTZYuqa9D2Y7YGMWGaM1EutKN2/9zvxuqekSbTB4PD5hhGfIC5WQjgMAnF3af/mZrDTz5yjAubPZI857lLHeoli7YfM1mx6IUJQZZhcqNhOMxGuB3HDtOML7y4RNnUmajYZGnOH7y0xHTVouJY1F2DF7yI8bJJwxV4UcqVpsdmLyRTimN1B8fQuWe6yl9d2GC1E+BYGU+fbDCIIloDj8VmSDeEpU7E735ziY+eHcc2NHSDoulhDyoS+vnQKAwf84Hf/PoKbT/iJx8/xXjNZqsfUbZ0SpaOylUxYrRi7fzc7+bqnrs5TzKi4MgMgxDCpqhisoav87tKqX8iijKXfwr8LEX49teVUv9y/zuNOGpuFgLYr3/gZglFzZCU7WIEpqH3iJIcKWEQFqMyHe3GDe6wG+G2EdvqhiSpolw1yJWiUbPoRzEVW2dhzCVKMvJM0QsjyrpJx08wNEmtZBJlxewEQytmNH/6gRkGYYJlaiyMl1jvhLy62uI/vLBGrQyuaRImKX/x+haffWwaQw6n/lzHjANPnR7jucst1q5zKjzgD15u0vQUP/nUPFGS89SJMSYrFpv9iEGYUrMNjo8V4bBRdc/+3I2e1N3GUXoMEfCDSqmBEMIA/koI8afA/cA8cJ9SKh/KbIx4n9kvBHArCcXtD6xQRWhKaoKnTo7xjbebRGmGVhV8/NzkDWGkWylz3DZiXhwjBXS9mNmGQ88vmtUm3MKgtYOYtX7IyqKPlILJssUTJ8ewDZ2ZmmSx6VOxJd2giP0LKZmr2ZRsg7yiCBclOTBVdbAMjSw38Db7GIbJzz5zit/82lts7BqTWjbhqTOTfOzcJONVh3/79eUbknMJcH6lzeNbdWbrJZY6PmcmK0xXLCLX4ORYCV2Xt2WMv1e4mz2pu4kjMwxKKQUMhv80hl8K+HvALyql8uHzNo5qDSNujb1CAIdNKO6n8FmydH7g3CTVkkHDNvfMLdxKmWOeF5IP907V+O8+forf/tYSV5selqHxC08vUC6ZxHHG199qcnK8xFTVohcktAYhM8NQjS4FxxoOx+oOmhAowY5Mhxel6LrG06dr/OWbG2SZQtqCfhhTMnV0qRgr2fzNp0/y4pVNpBScbri4rk3TT3lz02O8ZHGiLrnUudarkECGoOsl3D9tkGaKfphg6hrHd2lMjap79mbUJ/HecaQ5BiGEBjwPnAX+lVLqm0KIM8DPCyE+C2wCv6qUurjHtb8M/DLAwsLCUS5zxAEclFDc6wP7jhwFB7r8h90Irzc+D8w1+LUfrtKJYuqWSXk4TjTIijLQsapNOdeZrFg4pkaK2pGJmKsXnsA22yW42+9xru7w809G/PtvLdIKYsqWwd/9gTN0/QxNU9wzW8M0DPpBzGTdZK0To0TRGzJRtjg7XWOz32awq4BJApZU5CpHaMXPZLpqX9OtDaPqnv0Y9Um8dxypYVBKZcBjQog68AUhxEMUOYdQKfWkEOKngP8H+Pge134e+DzAk08+eXh95BFHws0Sivt9YJXgmrkMN7v3QRvhzU6L5evmSzuahlKw2PSxDUmYFPITZ8craIbc11Dtfo+21Pjs4/N84p4J2oOIqm0ySHMubfTxkpyKbdIoJ7y21uHiep8wzxmzDUqTLhMVi7Gyw30zIYvdkNZQ3rtiwtmpOg8vjFF1TBquQdtPqNjGDXHzUXXPjYw8qfeO96QqSSnVEUL8BfA3gCXg94bf+gLwb96LNYw4Om73A7t7M9xLTXW7gU1KcUunRV2XnJsu8dKVDlGaIYEHj1Uwzb0nxu2HlILJqst42WGx5WNpkkbZpp4XvRo1txgGVCsbmGFKBiy3Ak6Olbl/tkLd1Xg0VlxtDSi7JnMNh4fmGtTLFrNVG12XeFGKF6c0B/ENcfPdhmqUcB15Uu8lR1mVNEkhvtcRQjjAp4F/BvwB8IMUnsL3AxeOag0j3htu5wO7XxJxv74JU5M7xkcKQZRmCNjT+GSqyAP80EOzO9pJyXBjvZ2Qw7ZRckx9p4rIj2LiWFF2TBCw3IsIU8VbmwNMQ3J6skbJsuikMdONMg8dq/PwfI0gyYmSDCUKSWwBbPYjLF1e4wlth+GEgiDN2OpHKLjm53GrhmIv43K3GZyRJ/XecJQewyzwG8M8gwR+Wyn1R0KIv6IQ4vuHFMnpv3OEaxjxHnErH9jdYSEpCuG71U7AifFiVvN+IaOZms2VpsdGb6iHVLGIsxxbXpvQ3vZgpBRUHLMwJmpvI3IYdntEtqExXbEomxpKKWqWztcvtxDkaICm5by63OOhYw0emKvR9kOiBJ481aBsm3hhwkqcEcUZmiaZGBoafRhy0zVJL4i43PJIspytfkyc5pSsostcl4IrWx6mLq8xFAdV5uxlbLd/1ndbhc+oT+LoOcqqpFeAD+3xeAf40aN63RHvH4f9wG6fwNMcNvvFxhQlOROVovs3zXMypfAHCaahIURxOjaHCqoLYw6moe0YmOurUm7Hg9ldant9wnyv+x0bc1ECzs5U+fKFDcSwGbDqGPTDlH6QYBs6lm5wasJCUTTZSSl5fKGBocsdQ9UU8U4YLk4zml7MfN2hE2WYGjS9hIqtsdoNmK3arHYDjjecorR2n5/B9e/temO70glQeTGbuueHLHZ8Fps6z5yavu2u9BEfHEadzyPeE3aHLDQhEMBqJxieUAVKKTb7EVNli5cW2/z1m03aXhFieWS+zs89uUBtmGS2zeLXVmpiJ89Azm0nb7dP00Gc0vRixssmjqHfVPoDQOYw2zCouQaSIrcRxxm9KCPOMxqusRPWWmi4+1Zo7TY6ea4YL5toejGNTWqSphcRpxk9P2ajYrPcC9GEwLVTJsoWubp5mGyv/ExrELHlxTz79ia/89xV4mE+52eeOM7f/9R9NwxZGvG9xcElIyNGvAvyXNH1Y97c6HOl6bHY8omzwjtIMkWcFd7BTM0hV4rLW33OL3Xwo5iyrRNmOd9Z7vClV9cIhqWmaVb0B2znG5I0Z7Hlc7Xls9jyCZOiRlRKcY0y7H7rW+uGaBL8JMPWJV6UoYlhmCV/pyBu9/0uNwf82Wur/Pl3tiDPaPsZSaYwDZ17pyt0/KwQznMt5uoOui73Xcu20Zkfczk5XsIxCpkMgPVuSMXSWGoHbA4S3lgf0HAM+lGKULDWLbrsbhYm2x0KA4jTjHaQ0PcDfue5q0RJjqUboBRfeHGFr7y2Qpoedn7ddyfb6ru7//9GHJ6RxzDiyAiTjMubA86v9NAlTFZtZqv2TnL1WMNBE+yEhQZBxuWmR9uP8eOMPCpCLEZu0I8TrrQ8To2X2fLinZDO9lS222162j5NG0KS5QrX1PHjFCEFeZrveRLv9CP+3bOLWLrGmGvg6BqteMDYZI1zkzUmKjYLEyWePDFG+boehf2SvbvDcFMVi5VugK1L/DhDKUndMZiattnohTimTprmRGlGmsN4ydzxmvYrw73eK5msWLQGIcGwlFcphWub9IKY1V5MnOfod+m5cdQd/e4ZGYYRR0KeK1Y6AVtejGNqOIZGL0wx9ZiGW2xk2xU+QTw84WsCU0qag4hLTY+tbgJyOBdBCqYqhfDdXN3ZidG/26an7dN0roqO6jAuDI7K1Z4lt3mueGOzR8eLmahYnF/ustJL6KeCC+sBmjR57MQ4x+pu8TPoDmh3Qxxbp16y6fgpCNCl3HPDCpNsZ9iQY2jcO1Om48eEsYESCsvQUCimajbTFZsozWl6MU0vvukmuDsUtj3/eryso0vI0wzLNomjGBQ0XIkp706jMOqOvjOMDMOIIyFTqkioDsMvUCiORnFGZGSsdILC3c9zSkahDxTECVKXVG2NziAhAWzANGBxa8DVtscj87VrZ0XkvKump92nadfQdnIMmWLPJjs/TukOYixdo+9HXN70kFIxV3doOAbrXR9NKs4vt/n1r7zBN9/coh0Wc3Bd4KMnDf6Lx8/wwGyDFaU4OV66pnR0e1NzTIN0ONtCCIltQD9OmCpb9KKUqm0ghkatKHWVB26C16rg2oRxykfPTvHXb27QD2M0AU+cGOMH7p3bkee42xh1R98ZRoZhxJGgCYGuSYSAmqPTHMREaY5jaDsn9LV+yAuLLb5xcQspFZkSPDhTGnYVG5SzHEMIciEQUqApBQrioeSFJbU70vS0+zR9bo+qJHgnPNELYt5u+cyPO3zr0hZeklK1dI7VHcJMEQwi/uD5q7yy2OTq4NrX8YEvXU740uXXeXqhwi9+5CSTFYuKbQB7b2qWofHo8Tqb/YgcG4lgpmpjmUX4bbkTXFPqetAmuB3KMjXJ2akKn/vEaZ5YKNMcZDQcyVNnppmuOrfwP/3dxag7+s4wMgwjjoTtsaHb4nRlS+fkuMmxhstWv5C9Xm57fOPNLVp+RNuLSDJ4danD2QmXvp8QFpEkHBPKRqF8utINyPPCC5mtO9jGuxsVunu9N9tMVzsBSin8JGO6atMLEj5+3yTrvRjbEHiJIk0zxlyTl5baLA/2vNUOzy72OTW+wb2TVe6dqyGl2HdTq9gGFdu4sTlNqFvaBPeKvT8wV6fmmERJhmVoHGvc3SGXUXf0nWFkGEYcGbahcW66wsmJonFtO6S0OYjw44TNXvHhbXkxKoOSaRDGEW83fQYJO3OVgxgqaVpU6yiYbxQ5ht1hk6NsevLilKV2gC5hcxAzU7URUjDmGnz28eM8f6X1/7f35tFxZfd95+e+/dVehR0EAW7N3tmtbkpqtWRZkmVbsSzJcpxxPGPHmUnixLOcSXzGiT2ZOT5ZTmZynFliO8exnMzJZDmyZc14LCuOJctaLXVb6k1sslduAAFiR+1Vb7/zxyuAAAmQAAgSoHg/5+B0o6pevXsLrPu797d8fyw0PYqOyaNjBc58tbGt952pdTm/1GS0kvaDvtWitlvlW7i57/1of+57qpL4ZhuFe63Se79QhkFxR1ntDb2e0aLL1EqbGIGUgiBM5SYSEoSmUesmN7TBmaxHXFysMlpyWWqn3eBulb+/FTtZHJIkra+wDIGpaRiaYLHlM5CzGC44jJWzfOLUKN84v0Qpl/aaFtvM9Fysd7iw2MYxl3nviQEytoGla4z0qpJvlWoL268438xN1Q1DvCjGMfRtiR3eS2y2UVDZStvne+tfg+KeIGMbvGuij0eGirzraBkpEprdAC+OcXS5SW+0lDfn2iy1A+Ik4Wq1k2oN7TBN3QvjTWsetiKW6Q2Giy5tP6Ta6XLuao3lls9s3aeStagUXL7/oSF0YRCEcKhy6/2WCYRSo+0HvDnX4uJik44fMbXSYabWZbbuEfTy8P0wpt0NaXnhpvUFN6vXiKKEThAhY7mhlqHthczVPWar3W19Duu5F2sE1p+YsraBqYsb6lQU11AnBsW+kHNNnn2gn9GyQxjDdy4ugwAvkhRbHeqbWIeO5zNdbdEJYnRNkHMMpmvdbYvK7SaVcdXv//Z8g995/jKXlztoQhCECR974hC1TkjBMSllLN41UeH8YpOf++DD/NtvnOfMfHfT9yzocHwoi2mY5B2LMIqZrXexDZ2MbaBpGkEYc2kxDVRcWm5zabFFxjI4XHY5fbRvW5XJKy2fM9M1oijGMHRODuUJYkk3TI3CSNEh55g7Sum8V3fdKltpZyjDoNg3MrbB8cE8n3hyjPc+0E/LjwmTiM88f4WXJxv4173+7GyXcr7K4UrIu4/2UcnZINm2qNxuFgdNE+Rsjc++MMVKJ6CSc7ANwXev1OjP23z4oeG164UucCyDdx7toz9r8dzFef7klWnmmul7ORocGbEZyGZZ9mIK2TSryrHS/hGxlESJZKnhEcYJU8sdyhmDyeUWy62ARXwWmx5hkvCDD49sSCldPVnEUuIYOl4Y88dnZ6m2A/w4JmMazNY9Pvp4ep2QkOtlQ213kbyXawRUttLOUIZBsa+YukbetShlLTQh6Pgh0w94ND2fc/MbTUM3ghcurDBd9RgoOriOyUjRZaHpc7jikrGMmy5Wu10cvDDGEBol1wFNEkYJQZQwvdymGYRr16++v6EJHhwpMt6X5YcfGyOMIuIEgkQytdRhvuGRa3QZL2fJ2CYFy+BQOYOpa8zVu6m/P5FIEpaaPldrPuWMiZRg6oLzCy3eezyiYFhr43t7vsEbc02khErWwjQEk4sdpJaOK9QT6t2A6WqHEwN5JKk0hmXoeH6EH8XIWKbVhFtwL++6VbbSzlCGQbGvrP/ChjINPp8YzHN5qMDUyiLNsPc60iylIIE4Tvjy2Su8eH6WR0cLnDg0gG3kgJsvVrtdHEq2hWvr1P0IESd0gwRJKp/hGNdWUk0Ta3IWQoKuazwyWtrg5oqOJlS9gIV6l4VWgABGii5HB3KEccLlpQ7twE8XYE2nGaQxlbjnC9eExvppJYlkZqXDhYU2RcdM1VK7AZPLLTRNkkiBY2vUOyEDBRs/jpmqdQiThLllD8sQXFnpUnBMllsBjx0qbumm2sywit4YEiG3pV67n9lAqpfD9lGGQbHvXP+FLTkm56arZE1BK5RIWAtId0J4bcFbu/aP32wyUZ7nH37iCR4aLd3yFLCbxSGXtfiZ9xzht752gUuLTXRd413H+vjEU4fJ2OaaEVovZyFJNY9WXVqrRsqydIYsl4Gcw8leIHg1cGz0qpiztkXGMmn5AV4QUclY1DoBBdfCNgTjlRwZyyBJJF4U0w1jEKz1sE4L3nQOVSwuLraptyUhkrGSS8tL6M9q5B0TR9P49tQKE5UMOcfCCyLOztR55mjfppXP1xvWME5AwpVqBySMllwsXWOl49FsB+QzFpWsQyTlpo2X9mOB3iqt+W4aroNgJG+FMgyKA8H6L2x/weFnnj3GYifkW2/NM9+IiUkzea6POwBMViN++ytv8Hc++hh9GfeWp4Dd1DycOlzhf/mxLK/MVMmaOoPF1FUVxukXfDM5i4Wmz7i5eTvRzdJ4pYC+vEXbj/GiGMsweHK8zPe7Jm/MNYgSiWsaPH6ouNbVLZaSlXZA1w9BJjimgUxgsGAzWnAwdcFiK6DkmoxXshiGhtUzIEIXSAmOmS4DjmXQDvybCuitGtYwTpipdUFKqt0QP4x5c67BdLXNl1+fp9oJKLkWH3l8hHce7aOStdfiEreKCd3thfNuBtTvleC9MgyKA0lf3uGn332Eh4ayvHa1ThglnF9oc26LTJ+5WocwiBkbde+Yzk8hZ/OuYwOp2yuRaOv0lMI4WfO/J70U19WGQ6tGKIoSgiTB0rRNx6gLgWsa5CwDoaVCfrGE0VKG4YJLkCQksWSh5TNT7WLqgpGSi2UK3pxv0fRCdE3wzNE+PvTQEJ0gZqSYtggdKbpkrTSLa9UVJGR6WgmiGEPX8IIIXRO3FNDTNIEmU1mTejckihPqXsjz5+d57sJKGq8QGm2vzWdemMKPIz5+anzt2pvFhO72wnk3A+rrJd5NoRElCdMrHY70ZQ+cNpUyDIoDiS4EBdfi2WODxLHGSicghi0Nw3Jbomsa8ja+y9vZqW7lilr1v7e9kGo3JAhTozBadDF1jVon4OxMnThJVVw38+Wvd9UkUbIhBqJpAi0RTDU66AJsU8PSNa7WOrx2tcFY2WWoUEp1pBJJxbUYzN/orrk+xvLeE/2cX2jRbflr49rOIqULAZK0xiKI8cOQeiemHabusYyl0Qmg0Qk5M1nn2aMew+UMQa9ewjZuDGCTbN3W9U6dHO5mQD2Wkm4Q0QljukFMrRtg6zoIGCtnDtTJQRkGxYHk2iIJTx0p8+p0jUQmjGeXmGrf+Pqjgy5CbN2w5laL/k52qutdUevfdzBv89JUFU2AZegMZkwWmj6jQnB2po5jaDiWcVNfvmPqjJXcTU8WYZwQRDFZx0DvzcEL0jailqEzXWvT7EREMuFKtchoKYd9nSvrevltKaDiWkTILU8yW30GoyWXq9Uuy02fThgjNEmSJCRxTGjoIBNMXaPgGsw3PXKOidAEg4W0al1DbIgJ7UfW091MYxUSltsBti7ohnEal4piTE0cuLRfZRgUBxbH1BnM20RxwnuO9zPf7PL2fJ2pt2obXpc34HBfjmP9+Ru+WEkiaQcRS03/pj7t3exUrzcmfTmLkZ7y6aoBavsR3TgmTuRaS9Kb+fK3MlBeGDNb6zLf8LE6aU+L5bZPGCXUuj4L9TbfeL1Ks1fI+6WzM/zY00f5wENDnBwqbJivpqUFetffZ6fujIxt8OThEvNNj4pr8OihEgutiDdnqzS6Pq5pcLQ/xxPjFSYqWUbKLo6hE/Q+3xsyw25TQn033M00VimgL2dR74S0/YiMZVCw9J4B3Lwp1H6hDIPiwJIkkoWmj2vpaJrBWwt1Li3ceFyYqLh87MkxMr2CrVXJho4fcaXWZnKpg2NpHO/P3yC+B7tzJ2xmTJaaPkJL+1lrmiCIYpJEYgttrQnQ6olhM1/+VgZqrOQyV/ewDI3xvgyztS6Tyx00DYJYcm5qme/MbHSxXawmfPHV6V7/ap2jfbk1OXHYO5eN6xg8cqhAsxtRyTvkHZOzAy4LDZ9K1uTUeIUnD5exe6m9miZwtM3dcftVa3C30lhXY0iZQuo+0gFN19aM80EqtlOGQXFgWb9ge37EdydXmKyFN7zu+HCGiUqWubrHYN7u6SC1+c7FJSaXOzS9gFLG4cRwjo8+Nopp6hsW/d24EzY3JgkDeZvlVkCj6681/Zlr+ZwYzHF+oUU72NqXv/qemkgbF+lCkMg0YL16L02TVDIml5Za9OdM/uTcLK/NbB53OTfv8/p0lbxj4kUxtp7uTgfyNlGSYPYC5bcyhEEQ0w4jDCGwTH3N7bP62WUtk4KduokmKhmemqhQsA2WO6kP3TT1Gxb4rTLD9qvW4E6q866/x6rhK7pp3UifoxMnNzaF2m+UYVAcWNYv2DGSeqO7Vs+gca22YaXppYtanDBd7bDS9rm01OTrby/S9mOEACkFr83EDOQtnj0+uGHR381OdStjkrUM3JLO5ZU2E5UMlpnuyL0w4V0TlS19+Uki12QtFhoempamwJazFpamrQW2lzsBlxdb1DohZVdPs4y2GKMEvjNZJREa1WbA8eE8IKh3fNp+kp5aTI2ya6Jp2tpnsj5ustD0+MobCyzUuzT9kMP9GTTSrJqMqzNeyjJUcml10z7ZhqYx0Z/DMXWGShtjGUlyrQjuZjGfu7FI3yluFcva0BRqYPOmUAcBZRgUB5b1C3YcSfIZe+259Rp7QjcJE4lhXNMMeuVKLV34DA1TFyy0fMYswUIjpJgx1xbeWEpkLEmQDGYsfBIMKdYqerdayNaPrRuGyETSn7fToi9SX7llbnRNCV2Q0W8MaK/GFeI4YbbuYWoC09CIEkkYxXSCiKytcW6mRRDH1LoRhi54a7FNHMU3XULnqz6Xcy1W2hHtKKI/57LU8nn2eB9RkmYVXQ1inhovr6WLXu2ltEokL16uEkQJUgiuVLt84dUZukF6z1LWYDBnc2KkxBNjJY4N5DkxeC2esVUsA7gncvl3ynYTGO4Fw6cMg+JAs36H9Zffd5Q/PDvPYueaWXA1eGqiTL0TcbQvx0LLpxslaAnkbJOml1ZJR3GCJXT6cyaurlNt+7w9W+ficpPLC01iTTAzv8Jio0M7gFOHS7z/kXGl+4O0AAAgAElEQVROjZUpuBYrnfCGL7xj6vRnLV67WuNKtYMXJwznMwwXbQRgamLtxLDeNbXeyADM1roIAaahYRmCMEnlxJdbHl86V8M1wQ8lWddkIO9QcQ1MXeO7s12EppFzwPNu+OhSH7YBQQz1TsD5uRatYkKSxKx0Ao735RCaIIyS1BBFCRcWmtS6IRqCdhBytd6lkrV4Y7bGmcll5tppJTrAohfx9nLEmek2r1yp864jfbz7WMCzvd4Sm8VMrta6CMAytC3jGzvtl3EQqojvZYHBzVCGQXHgWd1hnRws8w8+/jj/+xdfp9oNcAyNv/TOcT72xGEkYFs6oyWXVjfAsQyKSUK9q6+1rTw5kqOcs3ljvs7/++I0X3l9hqtbtOB8baXG73y3xmODGU4fLfHERJnDlQzljLP2he/4Eb/30mW+8eYiYZhQKbg8fiii4TlkLAND61J0THKuyVjPrVJr+EzV2ti6jmMZ2KbG1EoH19IJooSFusdC06MbRnz+5Wnm6iFeAhkdXEfnUMnhfScHSNAw0XhivMzDQzm+PbnM5HJISFohDqkrydAEGVsnicEwNNp+SF/B5u25FrauoWsa5axFGCVMVdu8eLlKjKSSTSUyrlY71FpdXp2uUeteMwrracWw2GjzypX0XqstQ1O12I2xjLafOr4ydrr0XB/f2Ena8EGqIr7dVNuDYuBWUYZBcU/xI0+OcfpImReu1BjKW4yWciRSrklTmKbGY2Nl/uYHdP7o1TnKWYs4kbzjaJljfXma3YhXr9R4/uIii7foywxwdqHDpcUO/98rV+nL2zx9uMJH33GInK3z+y9f4d9/a5JESixDQ9N1vnVhhQ89PEjDC7m61GSxEXBoMMNDgyVMXeNPX5/HtjUcTePBkQJxTzF12HBodAOaQcxCo8sXzs0y07jWPKcZQ9yOmY7bvHhJ5+kjfWQcg5OlPIahcXgwz8sXl3l1pkHdj9CBvKMx0Zfn5GCRjh9gWwZZ22AgZ9PyQgQijb8kksnlNksNj9lGF1PXaXQCYgkZS6flh3TCiGCLDkqS9FTiBTENL2K61uGBoQLxaqWvuBbLMPR0mdws0L+TXfdB26HfTj3EQTJwqyjDoLjnGCxl+VB21b8f39gbWRM8MFzkbxQyvLXYIOsYZCyTIIpp+S06UUQruLlvfj2xhKyh4Rg6r87UCGXC0skOX3ptnigOQEoW6rCw4pEzIfJbNHyYa3h4IegX4MRAnpxjkrFMmoFEJglXqm0eP5THNjWeOzdF1fdxDBvH0Vls3NhRrQN0PGCmQSVnYWiCrKPTn3N5cLjABx4YxNAkbyw2MYUgnzFZrIcEScxy00QiqWRtTF3nodEMh/syGLpGvRMwX/fIWjp9OZu5WhqEL7qprLkXGIyXXd7yu3S3Mg6hJGcbDBccWp2YIIyZb/oMFWxqnZCWF9DshpyeqKBpgqu1LogYQ9M2lRWBm++6D5oE+G5TbQ+agVtFGQbFPcl20hozGZOHRku9L2sCCEaLLjNLrVSKgO21szQMMHQdQ9NoRTGahCiAlXbAbHNdK1IJzQBmL6cOf420OY8I4exUk4Gizmgpy3zDoxPFdLoxX3ltiY3hgS63GtliCJ8/u0RZh5cuLDJYFJweH+An3vMA4+UC4/0FINVB8sKY6VqHrh9R70YUMwbNbsxo0cEy0oVIFwIhBIauMZi3WWn5eGEqb65bJpqhc6iSY2qpSxxAsMmYbEfjcH+WgmNgWhpTK22W2ql2U9MLuFrt0PZCwiRitJTF0fU1BVpLT9Nzhdx+gdvqDj0I4zVdqdutBbhdd85uUm0PmoFbRRkGxT3LdrI7rv+yBj1ZiSeuVPHDJebbN+/5awC2CeWMia5p2HpMJW8jrIT5WnfL/tSQZk6ti5PTrsfM1RtsEifewHa7L1djqLZhqi154eoC//L5BZ4chnceGeLZB4d5amKQrGUwUckC6WIqBYRRqvza9tOd7aFyhgSotgMKjkkiJSMll7FKhlrb543LLZYaProBRgRRsjErrGLDcN6m2g44N1NjsJAhb+uA4MyVGt++uMLkcpswjnG/a/H+k3385XceIe+aXFnpbFBaLWVMap1ww64bWDMcUrD234Jr8NrVBnHPKJwcTntamGzsf72dBX+v3Dk7zTi6mQtqP+MOyjAovudZ/2V1NJ0nDlf4Hz/q8p2Ly8zWWsy3PK4stvj629faiVrA+0+WeGSkyDfPr1DzQhIZ8+BwgR94ZBgvDLcQpr45tzIKt8src/DK3Dy//fw8fcDJQ/DooRKPHztE0dFY6kRUbIMHh8tkXAtX14kTiSCm0e6CLjg+kCVj6JiGYL7XD6La9ojjjYZulcGCgRdDvR1gCDgxUqDtx9S7ARcWm7w138DQJK5tIZOEVy7XODGwzPc9OHiD0mqtEzJWctcMQDuImFpu44Ux802PgmUQJFDJmlQ7IcMFG0na3vVPX59ntOgyUnKZ6MuuSYncasHfT3fOVi6oVdmQ/Yo7KMOguO/QNMFIKcOPnHJ6ekWCuh/y1TdnOXNpGaTGo0fKfPiREfK2yc8+G7Dc9XB0nULGptaNmK+36c8b1Fa2Ki/bf5aB52bguZkafHujvpQFPDPhYlgwPd/lrca150ZsODyYpT9nMl/vMLsYMBuyaUYSwBuLEUUr4tiASyfQubzYZt700QTUW0EaC3FtNJEGqNtBxEzd4+2FJiBuUFqVAuJEcmGhyXenqrSCkJmVDpom8GN4/FARKV00XTDX8Jmtd5ha8UiSBEtPq8UNIZjoy25rwd9vd871p1qAqZXOvsYd7phhEEI4wNcBu3efz0opf2Xd878O/JdSytydGoNCcTMMQ1sTsRuwdD755AQ//MghgDTdtFedbBdd+oru2nUF12Iob/OfvfsIn/rqeZY2V6Q40ATA1yc3H/isD7NXNpGwvQn1AF6e6XKyHCI0yVDOwTB1clkD09TwwhBT05CkYoI5S8c2NKKoV2Oi6QRhTNIrOLy01OLlySpnZupMLjepdyLGKxmyts5M1cPWNYaKDjP1DrV2iGNAInWu1rpU2yESQTlnbWvBv5sKq1ux/lS7kyD8neJOnhh84ENSypYQwgT+TAjxn6SUzwshTgOlO3hvhWLHGIZGwdi83/F6NE3g2gaffGqCMEz43W9eZOoeNA53greqEW9V65StOmN9LicGcjw4UmBqKXUHOabOhx4e5gMPDVLJOdTaAX6c0AoilpsBxYzBxaUWlxdbzDY8pExAgKFp1LshhqZRa/vEFZeCYzK50gGZECegCRBaKtRnaRor7SDts32LBX+/xPu24iAYqjtmGKSUEljNFDd7P1IIoQO/CvznwCfv1P0VijtNX9bmw4+MgoBf/5OLm8YPHiyBbVucnw8IYEtdo0MWzGyW7nOPUg0gnO/iBQlPjhf56feMYwqBpukMlzJ4kaTdDTENjbGCy4XlFkJIap2Aphfx9nyDth+QsXUMoaHr6S466+gEiSRn62QsgyfHSsw1fFZaPpeX2wghGC3YjFUySKC/J2p4qwV/v8T7Vrk+0LzfhuqOxhh6RuBF4ATwL6SUfy6E+O+Bz0kpZ8UBkplVKHaKpgkmBnK8Lx5icqXDH744x+rBQQPed7TIex8cZKTkUu+EfO21Wc4vtqi2IpoxCMDR4YHhHI+MFLi41OKly41N00HvRVoJXFz2kckSuqZhGQbHB/N0gohuGHNpsc3DI3kuV9tcWGzR9kPenm/y9nyL+XoXSxcUsxZZS8c0TFzToOSYnDpS5tkTg+QdkyBO0DUNISBIEgYLNscH8r0UWEnWMshWjG0t+HdDw2izTKOtAuT7aaiElDdP19uTmwhRAn4f+BXgnwAfkFJGQojWVjEGIcTPAT8HMD4+/vTk5OQdH6dCsRuSRFLvBLw5X2dyqUEcJzw+XuZIpUhCWu+QJJKmH7Dc8QnihDCJubrsESYxA7kslZzF1GKbc9PLXFhocGb+e8U8pEHGcgaGK1kGsg6HKi6DBZdKxubUWJGZeocvvjpPNwhZansEYUKCwEIQElNybU4M53lwuMjJwRwnhgprkhpwTTixHUTUOyFCEzvK5LlbaaGbGQBL19YFmlO3URjLPQs0CyFelFKe3ul1dyUrSUpZE0J8Ffgg6enhfO+0kBFCnJdSntjkmk8BnwI4ffr0nbdeCsUu0TRBOWfzrswATx/pv2GByTtmT346y3StS5IkVLshRdNlqe3z4FAex9J5ZqKPy8cqzDd9Xry8yL/8+pV9nNXekQBeCNW2TxhKNA2G8i7tMOKN+WbaClUXXKh1map28MMEx4CBnM1g0eH4QJ5TYyUeGCwwVs5gGtoG5dsgTphv+iRSIjTBQN4maxnbWlj3Uo7iZgZmq5TYkaKz74HmzbiTWUkDQNgzCi7wYeCfSimH172mtZlRUCjuRbZyRax/fH2jlqJj8vREeUMr0JNWkbzT5VDRRcaS3/rm9N2exq4ZtKAR3FirEZIahrKAhh8yUxVUsi1Giy6mJnBMjTCOqHc8qp2EGGiGsNj1uVrzcS2TZ81+hIBXpmsMFmyQMFRwyFrGDQvucisgW7n10raX9Qu3MjBbpcTC3W9nuh3u5IlhBPi/e3EGDfiMlPLzd/B+CsWB51a+Y8fUmejLEkvJ3/zQQxzpz/PLf/D6Po12+wigkNWxnQQ/kCx6G+sePGClGeFYOsNDDkf6c0wud1lqB5SzNrGEuhfdYFYbIXx3conBgsW7jw9gIDhX73BpqUnW0Dn9wAAZ08TNpb06drLj3qv6he0YmK0yjUxd2/dA82bcyaykM8A7bvEaVcOguO+4VZBz9flK1uaZB/p4tF/n3NJ2hTL2BwuIEdi6zaLnbVoMF4TQlxOUMha1TkTRMRku2mlDoKaPgSBjSJrRNaOSALNt+KMzc3z7whKGEExWfaIEdAGn3l7i408d5tkHBta0n7a7496rtNDtGJibZRpt1QN7P1GVzwrFAabiOoz2lzi3tLwv9zeB4SwUMjqL7ZhqJ3UNXU8MrLQjTC3a9HkAIaCStXl0tIAfJtiWzmDBpZQ1mFnuUG97TC7faFQk0OokLHeCG7SpvjnZYL75FnnX4KGR0o523HuVFrpdA7P+tCgkdIOIRjegYJk4jnGguropw6BQHGDyrsUnnjrEm7N1pup3V37DBH7gZIF8xmW5HYAeIESbege861bvGOgGIEywdehscsBxHai4NkGcsNj2GdIdYgkgGOvPk3EMvvDqDJ2lYINxyQoIb5J+stQK+fIb87z3+AB519rRwr4XaaE7MTCr7U5fuLzMN95aJOr19f7RU6Mc6T84DhRlGBSKA4ymCT788CihL/m/vvk2r87daRm+FAfIOfCRd0wwu9JltrVExtIpZV1k0kV2U2MgAVtAJMHQwDEgq2t0Whv39jrQnzU5PpzjsbECXT/BT4AEZmseQwWLo/0u/XmHN+fqnJlcYb7h0eimsuckIGLWRA7Xv69taLS8iHYYkXctwp6U+HYX+Z3UL2yVebRdA5MkksvLLZ47v0TeMbBNg5YX8oWzs/zsM0dxnIOxJB+MUSgUii1xTJ1PPH2Yd52o8MUzV/l/XrjM2cWNDptRGwIflvboniHgOBZ5S+NyInF1ja5MSBIwDRNdDzEl6eKugZHAYEFnuOhi6ha6Xme+HuOTxh8OVwzefXyIB0eKeCGYpo4hUzeMHwkG8g4lxwRNUHQthgsub801uLTcZKUV0gpiDB3yOhu0qQo2uI6JqQt0BFMrnRsyg/aqTuFWmUfbMTBhnNBo+XTDuBeLCAnjhFhKWlGEc0CW5IMxCoVCsSWrC9tIMctPPnOM95wc4rWZJZ5/a5acZfF9j47yziNDJFKy3OryylyVP3z+Il++uHsBJwE8e7TCUifm2eMVqu2A5U6NKI5xLQ3bsonjGEGCH0oKjslwJcdQ3qE/b3G4YuGaaTMeL0zQdY2BgsNYxWWuEeC1Y+rdkIlyBoRguenT8WMEgm4YI3SNR8fKnBgssthOmxddqXZp+gGlpketE6Hr4Jo2wwWXdx8foOXHWIaGoWvIXqbQYN5moVfjcDt1Creb2pokknYQsdDwqAYRiy0fIVJBxrafgBRktP1t57keZRgUigPMZrvUk0MFjg/m+fiTR5GCDTvhQsZior/Ah06M8o0Lc/zrL73Ky/M7v28EPDleZjDvMFLK8uwDA9imRieM6foxJCGzjS5ZS+dqNUTTBYaQTAxkiSLJQ6MVio7J+aUWGTthpJihL28y1wgoZ0wudUISCc1evwehga4LZCwoZyxKrkHBsaAXsJZIhrI2rSDEj2LOXW2w1ApBJByp5BgsOHTCmJVOkAZ/NYFr6oRRTMY2brtO4XZSW70wZrbWZbraxTIEh4sZHj9U5OxMnbYXUc7aPHuiH9NWhkGhUNyCrXapYyUXRM91sUWAs5ix+JFHD/PBEyNcqTX5D996nZcv1vA8OL9NRe0/ODPNX/u+EwRRTM4yeOxQiShJeHu2wR+dWaLph4Qh9BctxgtZ+nJp4RlCMlp2yNkGmpYuqgM5GyEEczUPp6BTdIzej5nKn+saw4U0YNv1IyRgGRq2oZNISRAlZFyTYq9eQdMNBBJDT6XRgyjh4lKLtpf2c1hq+pi6YKyc4fhgHkNP79HxApp+SNa8Jqu+HXab2rr6NxQCbFPD0jXaYcwTh8sc7c8ymHfIWQa6oe97Udt6lGFQKA4om+1SG12fyyttNHFrPSBNE2Rdk4fcCr/8o+9mupr63//03FX+1TcuUr0+knsdjXZAv2MyU/OYq6cVa44l+Oqbi3SD1EceBAkLjYCSo9Gfs2l5IaauEfgxV9shnTgmZxqMljNEccJs3aPaDVhsBRQcg5ofURTGWrFXIiWmoa+5gLphTBgnyEQyudzG1DVGSi6jJZe5ukcYpyepnG2w1AxoegEzK10MXYCUGEKj5cU8cbhEN4x4fbZJpd7F1DUeO1SklLm1zPrqZ7mb1NbVv6Fr6mi9hV9KKLsmYSzJ2SZ6r8jtINQvrKIMg0JxQLl+lxpEMcvtgIlKBsvcmWskYxucGMwTS8nJoYf4C0+O8O++eonPv3KVxU2yYHVgMGejmRpHiw6HSi7TKx1evLDAQq0DQLObxiJkAk0vZqnZRROSYwNFXrlSw+31fS4MmrS8kKxt8v0PDPDabIOjfRlaQUzeNkgSKGZMumG8UV3U1Anj9CRQ74ZoWtoHOYgTHhjMb6gJuLjUSuMLmkYsJQs1D8vU6fNCDF3w9nyT5Y7P4VKGUtbGCyLOztR55mjf2snhVkHq3aS2rv4NEykZyNvM1rqEscTI6Tw1XsY0tANT1LYeZRgUigPK9bvUJJH05Swsc3cSDuuzZo71l/jFjz3OJ5+Z4IWLC3z2uQu8Vk1fZwAnhjL8xDuPkrfS+83U23zqT1/lm1c2P2YsN0Js08cwNNozK4BgvJKllDXp+BELTZ8jpoFt6YyWXdzeHCTgBzFjPeO2fpHUNAExLDR8spaO1cswWmj4HOnLYps6GmItPbUvY3J+vkE3jJFCMFhwCKIECYRxTBRLOmGCG8U4lkE78PGiGFtAGCUsNH3iOEEKGC26GxRcN/sMd/o3TKRkqODQvwORv/1CGQaF4gBzfbXsdK27Z4JrGdvgsdEyD4+U+InTx/jO5AKvT9dxbZMnJyo8PFxioenz0uUl/t7vfpfGTVQ5qhH48x61uocnoS9voqcCypiaYMzIoOuCxaa/Nt602CtGaKkbabOF0gtjFps+LVPDNHSK7o1Lli4Euq5RzlhIKdA1EEhMTZAISBIo2CZRwprIXsk1iBPJfD2tC5lteJQyBi0vJkrSk9hT4+VNjcNO2e/eCrtBGQaF4oCzmTrrXgmurb63mbH44IOHeN+JEYC1hToXhPyrL792U6OwSgfo9Orvlv2Qpr+EoQ9iGDpRLDF1jbYfMZCzuVrvstIMSAQM5Cw6frRBZRZS185KO2Awb9EO0ljDXN3j5FAeU78WONY0wWDeZmqlzXhfqth6pE+nGyUMZq20S1xflqEk4c3ZJg3Px9BhIG/jWGlKbRhF/PnFOn1ZG8cycI20f/SxgdyeLOR3ownQXqIMg0JxD3End5+aJrCvy6X3wphutDspjtmmZGqpyeFKlqYf8PZcg0hKZuseQRSja2BpOgstj9fnmjw4lMOxjDU3TiwlEpjozzHf8IjjBC9KGCo6N9zLNDRGSy5jJZeZapellk83inl4OI9jGViGRkY3OHWoSCeKGS9lmG/5JLGkHUXM1T2WmgGOadDyIzK2wUjZ2ZHS6t1q+HM3UIZBobjHuJu7z5JtUXFN2GXD0Vo3ZKHe5fXZBoM5i4mBLEcHcrSDGFPX6AapW0zKhPOLbfpz1pobx+ll8hia4HA5Q6MbsNgMWGkHNLrRhowsXQgMTcPUBSeG84z5LqGUHOvLEUm54ZR1tD+HpWuszDd4eapGGMW8OVfD0nUytqDoOgRhTBQliG22CNvLhj8Hge0n8ioUivuOXNbiv/nhx7F3eX2tFdKNIqI44cJyiy+9Os+XX5/n8lIbL4hJpKTthXSCBF2Qpm8KwdVaWrU9XHQIY0nHj1hsBYyWHPI9CYy5ukeSpCv3apA3jCXdIEZoGuOVLIahrZ2yDlcyjFcyOKZOFCWcX2hhaNDohpyZbvC1Nxf4g5dm+LO3F7lS7RDGCdO1Ll54cz/a+nqTrG3cMLZ7EWUYFArFTTl9dJBf+eRD3OjAuTX1AKaWOwRxAkIDTeD5MR0/YrbRZbrWYanl0/EjhEjrGCxTA5HWAKwu6iNll+GiQ9YxgTQjK5Gp62aVzQzAKtp1Ae5unMYs4iTma2/Ms9z0aQUJjW7I2ekVlptdSllrW4v8tXoTbcux3Wsow6BQKG6KLgSnRiv88OP9O742Ai7PtZlaqLHS7BJEEbWuD6TCfCcG8jwwXAABLS8klpKya2Jo2obsJcfQMbS0liOKE4Io3jQj63oDsBW20Gj5EVcW29S6IQiwjNS4hLGk1glptYO0duIWi/z6ehPgwLTnvB2UYVAoFDdF0wRH+nM8OdHHkcLOr6/GcKUFFxd96l5ArR3y5kKDWsdP6zJ0wYPDBQRgaQJNu7ESWNMEpYzJ5HKH8wstJpc7lDLmroO8uqlxvD/D+cUm862IRgBtDzw/JJYJbT9kqtbh1StVGl2fJJFbnhrWu7HafkQYy21niyWJJIyTA+d2UsFnhUJxS3KuyTNH+/jsn5ts3sPt1kTApZWQ0YIEAZPVLp97eYanJsoYhsFI0cG1DcZK7g06RkmS7uInKhmEJpC93wvOzo1DkkiCIGapHVDt+Gtd4UJgwYNBF0zD4EuvL5AkCbrQ+Pg7Isb7clsGlXeaLbaqtrrU9JFw4ALW6sSgUChuSRQlXF7u0G7tziisp5w1sEyDOJbM1zqEUiKQ6IaGjkBet6YmicSLYuI4wTJ1TF1Lq6A3cfHcagfuhTFTKx0mV9rMrjR4beHGbKtEgheERHGCa5lIAd94e4l4NfPoJieH7bixOn7E+YUmL01WmW946Jo4cAFrdWJQKBS3JEgSoiShuvsWD2tESW/xDRPKGZO+jEkp49D0grUd92pNwKpURZQkzDU8RgTkHHNTP/71KaODeXuDFtH67KFixqLe3tzIeQHM1D3KOZt2IMg7Bk0vwEsSXKntqLbhejp+xEtTVZCSejdkqGCz2PQ5VHJJZHJb772XKMOgUChuiaVpOLqBrZP29NwleUBPwNIFjqkxUs4AGpcWW8QS+nMODS+k1gmJ44TZhsdoMU1R1YCrdY8Rme7O+/PXkmivlyhveyEvTVUZKThr6qV6L5C8qlZ7bDQP3Nisop2A3U2VYUuGgR+mNReG4LaCykkiuVrvognIuRbNIGalHVDOWvhbBNP3C+VKUigUt8QwNB4eK3ByPHtb73NixGW0nOHYQI53HuvjkaEisZQMFm2ePlIm5xicnamjC7AtHU1AtRuSSEnWMRkuOpRzFhJYbPpMrXTwwnhDymgiJdVuiNZ7j1U3jZCsZQ8lUnKokGM4d+NC3O+CaxtoYSoN4gUJTxwuYZnGbUmQrOpdWUYqBjiYt/HDmG4QIyUHSnpbnRgUCsW2GM67fOTUIb516a21gO12sYGffe8Yh/vzjFdcTD3VKFpt7lPMWGuLdpxIRE9p1TJ0gjCNGSSkLqJ6J8TuNfdZ37xofcpoECZYxjXtJT9KG/isak0FfoRjWfziRx7l17/0OpO1GAFMlHTKuSymIXnm5ACjpQyOoXH6SF96armNhfua2J9JtRMSRDF9OYdTY8Xbfu+9RhkGhUKxLQxD40efmODiQpt/89zMjozDL330BMcGypiGWCsE88OEkYJLtROSJBJNT109ei/rSDPTRXS27tENYwxNYyCf+uTXF5Ndv+hHSeqrH+yls66PR5imxnglQxgnmLrGsYEshysO//xLb+GFESPFLLomCGMY78twtC/PWDmzJyqr6yW4y66JFOaW8t77jZD3QHXe6dOn5QsvvLDfw1AoFKxmKNX54usz/PuvTHL1Fp3gfuqJCj/9/oeodiJGig4Zy8CPUvfJRF+WoLfrXw0alzImtU64aRAZYGql04slpCeGMJZrzYquD1rfTLtofbD68kKLL785T8sPsQyDH3p0iHcf678jfRPuptieEOJFKeXpHV+nDINCodgNSSL5k7em+e/+zZktJfaOF+C3//r3cbgvd9PF+vrF8maL53YF67azAK9/jedF1PyArGWQd60D5drZLbs1DAfvDKNQKO4JNE3w/cdH+ds/1OHXvnge77rnHx+2+QefeJIj/fm1HP9xU990sb5eMfZmCrLbLSbbjgrt+tdkMiaZjLmNmX/vowyDQqHYNY6p87c+cJKPPTnCcxfnef3KAkGgcfqBQU6NDXKonLmpAdgt91rjm3sNZRgUCsVtoWmCw5UChysFOP3A91TDmvsVZRgUCoLWMUQAAAccSURBVMWeonbz9z6qwE2hUCgUG1CGQaFQKBQbUIZBoVAoFBtQhkGhUCgUG1CGQaFQKBQbuCcqn4UQi8DkXbhVP7B0F+5zULjf5gv335zvt/mCmvN6JqSUAzt9s3vCMNwthBAv7KZ8/F7lfpsv3H9zvt/mC2rOe4FyJSkUCoViA8owKBQKhWIDyjBs5FP7PYC7zP02X7j/5ny/zRfUnG8bFWNQKBQKxQbUiUGhUCgUG7gvDYMQ4i8JIc4JIRIhxOl1j/+gEOJFIcSrvf9+aJNrPyeEOHt3R3z77HTOQoiMEOI/CiHe6F33v+7f6HfObv7GQoine4+fF0L8mhDinlKCu8mc+4QQXxFCtIQQv3HdNT/Vm/MZIcQfCyH67/7Id8cu52sJIT4lhHir92/7L979ke+e3cx53Wu2vXbdl4YBOAv8OPD16x5fAj4mpXwc+Fng361/Ugjx40Drroxw79nNnP+ZlPIh4B3Ae4UQf+GujHRv2M18fxP4OeCB3s9H7sI495Kt5uwB/zPwP6x/UAhhAP8c+KCU8hRwBvhv78I494odzbfH3wcWpJQngUeAr93REe49u5nzjteu+1J2W0r5OsD1G0Ip5cvrfj0HOEIIW0rpCyFywC+QLhyfuVtj3St2MecO8JXeawIhxEvA2F0a7m2z0/kCFaAgpXyud92/BX4M+E93ZcB7wE3m3Ab+TAhx4rpLRO8nK4RYBgrA+bsw1D1hF/MF+K+Ah3qvS7jHCuF2M+fdrF3364lhO/xF4GUp5Wqr838E/G9AZ/+GdMe5fs4ACCFKwMeAP92XUd051s/3EDC97rnp3mPfs0gpQ+DngVeBq6Q76H+9r4O6g/T+HQP8IyHES0KI3xNCDO3roO4OO167vmdPDEKILwHDmzz196WUf3CLax8F/inwQ73fnwROSCn/jhDiyB4Pdc/Yyzmve9wAPg38mpTy4l6NdS/Y4/luFk84cCl7tzPnTd7LJDUM7wAuAr8O/DLwj293nHvFXs6XdL0bA74ppfwFIcQvAP8M+JnbHOaessd/412tXd+zhkFK+eHdXCeEGAN+H/grUsoLvYffAzwthLhM+pkNCiG+KqX8wF6Mda/Y4zmv8ingbSnl/3m749tr9ni+02x0lY2R7qIPFLud8xY82XvPCwBCiM8Av7SH73/b7PF8l0l3zb/f+/33gL+2h++/J+zxnHe1dilX0jp6R83/CPyylPKbq49LKX9TSjkqpTwCvA9466AZhd2y1Zx7z/1joAj87f0Y253gJn/jWaAphHiml430V4Cd7kjvNWaAR4QQqyJrPwi8vo/juaPItGjrD4EP9B76AeC1fRvQXWDXa5eU8r77AT5JukP0gXngC73H/yegDbyy7mfwumuPAGf3ew53es6kO2ZJulCsPv7X93sed/JvDJwmzfq4APwGvQLQe+Vnqzn3nrsMrJBmpkwDj/Qe/1u9v/EZ0kWzb7/ncYfnO0Ga0XOGNGY2vt/zuNNzXvf8ttcuVfmsUCgUig0oV5JCoVAoNqAMg0KhUCg2oAyDQqFQKDagDINCoVAoNqAMg0KhUCg2oAyD4r5ACLHn4odCiI8LIX6p9/8/JoR4ZBfv8dX1KpkKxUFAGQaFYpdIKT8npVyVI/8xUq0hheKeRxkGxX2FSPlVIcTZXh+Cn+w9/oHe7v2zPZ3+/7Daj0EI8SO9x/6s16fh873H/6oQ4jeEEM8CHwd+VQjxihDi+PqTgBCivydJgBDCFUL8Tq//we8C7rqx/ZAQ4rl1Am+5u/vpKBQp37NaSQrFFvw4qUbQE0A/8B0hxKq2/TuAR0k1kr5J2oPiBeC3gPdLKS8JIT59/RtKKb8lhPgc8Hkp5WfhRlnkdfw80JFSnhJCnAJe6r2+n7Qq+8NSyrYQ4u+RSiX/w72YtEKxE5RhUNxvvA/4tJQyBuaFEF8D3gk0gG9LKacBhBCvkEoItICLUspLves/Taprv1veD/wagJTyjBDiTO/xZ0hdUd/sGRULeO427qNQ7BplGBT3Gzdr17m+D0VM+v3YbXvPiGuuWue65zbToRHAn0gpf2qX91Mo9gwVY1Dcb3wd+EkhhN5TFX0/8O2bvP4N4Ng6Lfuf3OJ1TSC/7vfLwNO9//+J6+7/XwAIIR4DTvUef57UdXWi91xGCHFyG/NRKPYcZRgU9xu/T6qs+V3gy8DflVLObfViKWUX+K+BPxZC/BmpomV9k5f+DvCLQoiXhRDHSRvA/LwQ4luksYxVfhPI9VxIf5eeUZJSLgJ/Ffh077nn6bWgVCjuNkpdVaG4BUKInJSy1ctS+hekjYv+j/0el0Jxp1AnBoXi1vyNXjD6HGnjot/a5/EoFHcUdWJQKBQKxQbUiUGhUCgUG1CGQaFQKBQbUIZBoVAoFBtQhkGhUCgUG1CGQaFQKBQbUIZBoVAoFBv4/wGN1gUEzJ50QwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train.plot(kind= \"scatter\", x=\"longitude\", y= \"latitude\",alpha =0.1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x18675b1ca20>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train.plot(kind = \"scatter\", x= \"longitude\", y=\"latitude\", alpha=0.4, s=train[\"population\"]/100,label =\"population\", c=\"median_house_value\", cmap=plt.get_cmap(\"jet\"),colorbar=True)\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "corr_matrix = train.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>longitude</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.924478</td>\n",
       "      <td>-0.105848</td>\n",
       "      <td>0.048871</td>\n",
       "      <td>0.076598</td>\n",
       "      <td>0.108030</td>\n",
       "      <td>0.063070</td>\n",
       "      <td>-0.019583</td>\n",
       "      <td>-0.047432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>latitude</th>\n",
       "      <td>-0.924478</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.005766</td>\n",
       "      <td>-0.039184</td>\n",
       "      <td>-0.072419</td>\n",
       "      <td>-0.115222</td>\n",
       "      <td>-0.077647</td>\n",
       "      <td>-0.075205</td>\n",
       "      <td>-0.142724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>housing_median_age</th>\n",
       "      <td>-0.105848</td>\n",
       "      <td>0.005766</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.364509</td>\n",
       "      <td>-0.325047</td>\n",
       "      <td>-0.298710</td>\n",
       "      <td>-0.306428</td>\n",
       "      <td>-0.111360</td>\n",
       "      <td>0.114110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_rooms</th>\n",
       "      <td>0.048871</td>\n",
       "      <td>-0.039184</td>\n",
       "      <td>-0.364509</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.929379</td>\n",
       "      <td>0.855109</td>\n",
       "      <td>0.918392</td>\n",
       "      <td>0.200087</td>\n",
       "      <td>0.135097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_bedrooms</th>\n",
       "      <td>0.076598</td>\n",
       "      <td>-0.072419</td>\n",
       "      <td>-0.325047</td>\n",
       "      <td>0.929379</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.876320</td>\n",
       "      <td>0.980170</td>\n",
       "      <td>-0.009740</td>\n",
       "      <td>0.047689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>population</th>\n",
       "      <td>0.108030</td>\n",
       "      <td>-0.115222</td>\n",
       "      <td>-0.298710</td>\n",
       "      <td>0.855109</td>\n",
       "      <td>0.876320</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.904637</td>\n",
       "      <td>0.002380</td>\n",
       "      <td>-0.026920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>households</th>\n",
       "      <td>0.063070</td>\n",
       "      <td>-0.077647</td>\n",
       "      <td>-0.306428</td>\n",
       "      <td>0.918392</td>\n",
       "      <td>0.980170</td>\n",
       "      <td>0.904637</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.010781</td>\n",
       "      <td>0.064506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median_income</th>\n",
       "      <td>-0.019583</td>\n",
       "      <td>-0.075205</td>\n",
       "      <td>-0.111360</td>\n",
       "      <td>0.200087</td>\n",
       "      <td>-0.009740</td>\n",
       "      <td>0.002380</td>\n",
       "      <td>0.010781</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.687160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median_house_value</th>\n",
       "      <td>-0.047432</td>\n",
       "      <td>-0.142724</td>\n",
       "      <td>0.114110</td>\n",
       "      <td>0.135097</td>\n",
       "      <td>0.047689</td>\n",
       "      <td>-0.026920</td>\n",
       "      <td>0.064506</td>\n",
       "      <td>0.687160</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    longitude  latitude  housing_median_age  total_rooms  \\\n",
       "longitude            1.000000 -0.924478           -0.105848     0.048871   \n",
       "latitude            -0.924478  1.000000            0.005766    -0.039184   \n",
       "housing_median_age  -0.105848  0.005766            1.000000    -0.364509   \n",
       "total_rooms          0.048871 -0.039184           -0.364509     1.000000   \n",
       "total_bedrooms       0.076598 -0.072419           -0.325047     0.929379   \n",
       "population           0.108030 -0.115222           -0.298710     0.855109   \n",
       "households           0.063070 -0.077647           -0.306428     0.918392   \n",
       "median_income       -0.019583 -0.075205           -0.111360     0.200087   \n",
       "median_house_value  -0.047432 -0.142724            0.114110     0.135097   \n",
       "\n",
       "                    total_bedrooms  population  households  median_income  \\\n",
       "longitude                 0.076598    0.108030    0.063070      -0.019583   \n",
       "latitude                 -0.072419   -0.115222   -0.077647      -0.075205   \n",
       "housing_median_age       -0.325047   -0.298710   -0.306428      -0.111360   \n",
       "total_rooms               0.929379    0.855109    0.918392       0.200087   \n",
       "total_bedrooms            1.000000    0.876320    0.980170      -0.009740   \n",
       "population                0.876320    1.000000    0.904637       0.002380   \n",
       "households                0.980170    0.904637    1.000000       0.010781   \n",
       "median_income            -0.009740    0.002380    0.010781       1.000000   \n",
       "median_house_value        0.047689   -0.026920    0.064506       0.687160   \n",
       "\n",
       "                    median_house_value  \n",
       "longitude                    -0.047432  \n",
       "latitude                     -0.142724  \n",
       "housing_median_age            0.114110  \n",
       "total_rooms                   0.135097  \n",
       "total_bedrooms                0.047689  \n",
       "population                   -0.026920  \n",
       "households                    0.064506  \n",
       "median_income                 0.687160  \n",
       "median_house_value            1.000000  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "median_house_value    1.000000\n",
       "median_income         0.687160\n",
       "total_rooms           0.135097\n",
       "housing_median_age    0.114110\n",
       "households            0.064506\n",
       "total_bedrooms        0.047689\n",
       "population           -0.026920\n",
       "longitude            -0.047432\n",
       "latitude             -0.142724\n",
       "Name: median_house_value, dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_matrix[\"median_house_value\"].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x000001867864DC50>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001867865AA58>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001867633BFD0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001867630F588>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x00000186758C1B00>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000018675BBA0B8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000018675BB0630>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000018675C3EBA8>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x0000018675C3EBE0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000018675C05668>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000018675679BE0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000018675659198>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x0000018675752710>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001867575FC88>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000018675BC6240>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x00000186756F47B8>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas\n",
    "from pandas.plotting import scatter_matrix\n",
    "attributes = [\"median_house_value\", \"median_income\", \"total_rooms\", \"housing_median_age\"]\n",
    "scatter_matrix(train[attributes], figsize=(12,8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17606</th>\n",
       "      <td>-121.89</td>\n",
       "      <td>37.29</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1568.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>710.0</td>\n",
       "      <td>339.0</td>\n",
       "      <td>2.7042</td>\n",
       "      <td>286600.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18632</th>\n",
       "      <td>-121.93</td>\n",
       "      <td>37.05</td>\n",
       "      <td>14.0</td>\n",
       "      <td>679.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>306.0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>6.4214</td>\n",
       "      <td>340600.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14650</th>\n",
       "      <td>-117.20</td>\n",
       "      <td>32.77</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1952.0</td>\n",
       "      <td>471.0</td>\n",
       "      <td>936.0</td>\n",
       "      <td>462.0</td>\n",
       "      <td>2.8621</td>\n",
       "      <td>196900.0</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3230</th>\n",
       "      <td>-119.61</td>\n",
       "      <td>36.31</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1847.0</td>\n",
       "      <td>371.0</td>\n",
       "      <td>1460.0</td>\n",
       "      <td>353.0</td>\n",
       "      <td>1.8839</td>\n",
       "      <td>46300.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3555</th>\n",
       "      <td>-118.59</td>\n",
       "      <td>34.23</td>\n",
       "      <td>17.0</td>\n",
       "      <td>6592.0</td>\n",
       "      <td>1525.0</td>\n",
       "      <td>4459.0</td>\n",
       "      <td>1463.0</td>\n",
       "      <td>3.0347</td>\n",
       "      <td>254500.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19480</th>\n",
       "      <td>-120.97</td>\n",
       "      <td>37.66</td>\n",
       "      <td>24.0</td>\n",
       "      <td>2930.0</td>\n",
       "      <td>588.0</td>\n",
       "      <td>1448.0</td>\n",
       "      <td>570.0</td>\n",
       "      <td>3.5395</td>\n",
       "      <td>127900.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8879</th>\n",
       "      <td>-118.50</td>\n",
       "      <td>34.04</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2233.0</td>\n",
       "      <td>317.0</td>\n",
       "      <td>769.0</td>\n",
       "      <td>277.0</td>\n",
       "      <td>8.3839</td>\n",
       "      <td>500001.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13685</th>\n",
       "      <td>-117.24</td>\n",
       "      <td>34.15</td>\n",
       "      <td>26.0</td>\n",
       "      <td>2041.0</td>\n",
       "      <td>293.0</td>\n",
       "      <td>936.0</td>\n",
       "      <td>375.0</td>\n",
       "      <td>6.0000</td>\n",
       "      <td>140200.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4937</th>\n",
       "      <td>-118.26</td>\n",
       "      <td>33.99</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1865.0</td>\n",
       "      <td>465.0</td>\n",
       "      <td>1916.0</td>\n",
       "      <td>438.0</td>\n",
       "      <td>1.8242</td>\n",
       "      <td>95000.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4861</th>\n",
       "      <td>-118.28</td>\n",
       "      <td>34.02</td>\n",
       "      <td>29.0</td>\n",
       "      <td>515.0</td>\n",
       "      <td>229.0</td>\n",
       "      <td>2690.0</td>\n",
       "      <td>217.0</td>\n",
       "      <td>0.4999</td>\n",
       "      <td>500001.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16365</th>\n",
       "      <td>-121.31</td>\n",
       "      <td>38.02</td>\n",
       "      <td>24.0</td>\n",
       "      <td>4157.0</td>\n",
       "      <td>951.0</td>\n",
       "      <td>2734.0</td>\n",
       "      <td>879.0</td>\n",
       "      <td>2.7981</td>\n",
       "      <td>92100.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19684</th>\n",
       "      <td>-121.62</td>\n",
       "      <td>39.14</td>\n",
       "      <td>41.0</td>\n",
       "      <td>2183.0</td>\n",
       "      <td>559.0</td>\n",
       "      <td>1202.0</td>\n",
       "      <td>506.0</td>\n",
       "      <td>1.6902</td>\n",
       "      <td>61500.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19234</th>\n",
       "      <td>-122.69</td>\n",
       "      <td>38.51</td>\n",
       "      <td>18.0</td>\n",
       "      <td>3364.0</td>\n",
       "      <td>501.0</td>\n",
       "      <td>1442.0</td>\n",
       "      <td>506.0</td>\n",
       "      <td>6.6854</td>\n",
       "      <td>313000.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13956</th>\n",
       "      <td>-117.06</td>\n",
       "      <td>34.17</td>\n",
       "      <td>21.0</td>\n",
       "      <td>2520.0</td>\n",
       "      <td>582.0</td>\n",
       "      <td>416.0</td>\n",
       "      <td>151.0</td>\n",
       "      <td>2.7120</td>\n",
       "      <td>89000.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2390</th>\n",
       "      <td>-119.46</td>\n",
       "      <td>36.91</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2980.0</td>\n",
       "      <td>495.0</td>\n",
       "      <td>1184.0</td>\n",
       "      <td>429.0</td>\n",
       "      <td>3.9141</td>\n",
       "      <td>123900.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11176</th>\n",
       "      <td>-117.96</td>\n",
       "      <td>33.83</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2838.0</td>\n",
       "      <td>649.0</td>\n",
       "      <td>1758.0</td>\n",
       "      <td>593.0</td>\n",
       "      <td>3.3831</td>\n",
       "      <td>197400.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15614</th>\n",
       "      <td>-122.41</td>\n",
       "      <td>37.81</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1178.0</td>\n",
       "      <td>545.0</td>\n",
       "      <td>592.0</td>\n",
       "      <td>441.0</td>\n",
       "      <td>3.6728</td>\n",
       "      <td>500001.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2953</th>\n",
       "      <td>-119.02</td>\n",
       "      <td>35.35</td>\n",
       "      <td>42.0</td>\n",
       "      <td>1239.0</td>\n",
       "      <td>251.0</td>\n",
       "      <td>776.0</td>\n",
       "      <td>272.0</td>\n",
       "      <td>1.9830</td>\n",
       "      <td>63300.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13209</th>\n",
       "      <td>-117.72</td>\n",
       "      <td>34.05</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1841.0</td>\n",
       "      <td>409.0</td>\n",
       "      <td>1243.0</td>\n",
       "      <td>394.0</td>\n",
       "      <td>4.0614</td>\n",
       "      <td>107000.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6569</th>\n",
       "      <td>-118.15</td>\n",
       "      <td>34.20</td>\n",
       "      <td>46.0</td>\n",
       "      <td>1505.0</td>\n",
       "      <td>261.0</td>\n",
       "      <td>857.0</td>\n",
       "      <td>269.0</td>\n",
       "      <td>4.5000</td>\n",
       "      <td>184200.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5825</th>\n",
       "      <td>-118.30</td>\n",
       "      <td>34.19</td>\n",
       "      <td>14.0</td>\n",
       "      <td>3615.0</td>\n",
       "      <td>913.0</td>\n",
       "      <td>1924.0</td>\n",
       "      <td>852.0</td>\n",
       "      <td>3.5083</td>\n",
       "      <td>280900.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18086</th>\n",
       "      <td>-122.05</td>\n",
       "      <td>37.31</td>\n",
       "      <td>25.0</td>\n",
       "      <td>4111.0</td>\n",
       "      <td>538.0</td>\n",
       "      <td>1585.0</td>\n",
       "      <td>568.0</td>\n",
       "      <td>9.2298</td>\n",
       "      <td>500001.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16718</th>\n",
       "      <td>-120.66</td>\n",
       "      <td>35.49</td>\n",
       "      <td>17.0</td>\n",
       "      <td>4422.0</td>\n",
       "      <td>945.0</td>\n",
       "      <td>2307.0</td>\n",
       "      <td>885.0</td>\n",
       "      <td>2.8285</td>\n",
       "      <td>171300.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13600</th>\n",
       "      <td>-117.25</td>\n",
       "      <td>34.16</td>\n",
       "      <td>37.0</td>\n",
       "      <td>1709.0</td>\n",
       "      <td>278.0</td>\n",
       "      <td>744.0</td>\n",
       "      <td>274.0</td>\n",
       "      <td>3.7188</td>\n",
       "      <td>116600.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13989</th>\n",
       "      <td>-117.19</td>\n",
       "      <td>34.94</td>\n",
       "      <td>31.0</td>\n",
       "      <td>2034.0</td>\n",
       "      <td>444.0</td>\n",
       "      <td>1097.0</td>\n",
       "      <td>367.0</td>\n",
       "      <td>2.1522</td>\n",
       "      <td>60800.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15168</th>\n",
       "      <td>-117.06</td>\n",
       "      <td>33.02</td>\n",
       "      <td>24.0</td>\n",
       "      <td>830.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>279.0</td>\n",
       "      <td>196.0</td>\n",
       "      <td>1.9176</td>\n",
       "      <td>121100.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6747</th>\n",
       "      <td>-118.07</td>\n",
       "      <td>34.11</td>\n",
       "      <td>41.0</td>\n",
       "      <td>2869.0</td>\n",
       "      <td>563.0</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>533.0</td>\n",
       "      <td>5.0736</td>\n",
       "      <td>270700.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7398</th>\n",
       "      <td>-118.24</td>\n",
       "      <td>33.96</td>\n",
       "      <td>44.0</td>\n",
       "      <td>1338.0</td>\n",
       "      <td>366.0</td>\n",
       "      <td>1765.0</td>\n",
       "      <td>388.0</td>\n",
       "      <td>1.7778</td>\n",
       "      <td>109900.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5562</th>\n",
       "      <td>-118.28</td>\n",
       "      <td>33.91</td>\n",
       "      <td>41.0</td>\n",
       "      <td>620.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>642.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>2.6546</td>\n",
       "      <td>159600.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16121</th>\n",
       "      <td>-122.46</td>\n",
       "      <td>37.79</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2059.0</td>\n",
       "      <td>416.0</td>\n",
       "      <td>999.0</td>\n",
       "      <td>402.0</td>\n",
       "      <td>3.7419</td>\n",
       "      <td>500001.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12380</th>\n",
       "      <td>-116.47</td>\n",
       "      <td>33.77</td>\n",
       "      <td>26.0</td>\n",
       "      <td>4300.0</td>\n",
       "      <td>767.0</td>\n",
       "      <td>1557.0</td>\n",
       "      <td>669.0</td>\n",
       "      <td>4.4107</td>\n",
       "      <td>122500.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5618</th>\n",
       "      <td>-118.23</td>\n",
       "      <td>33.78</td>\n",
       "      <td>20.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2.5588</td>\n",
       "      <td>350000.0</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10060</th>\n",
       "      <td>-121.06</td>\n",
       "      <td>39.25</td>\n",
       "      <td>17.0</td>\n",
       "      <td>3127.0</td>\n",
       "      <td>539.0</td>\n",
       "      <td>1390.0</td>\n",
       "      <td>520.0</td>\n",
       "      <td>3.9537</td>\n",
       "      <td>172800.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18067</th>\n",
       "      <td>-122.03</td>\n",
       "      <td>37.29</td>\n",
       "      <td>22.0</td>\n",
       "      <td>3118.0</td>\n",
       "      <td>438.0</td>\n",
       "      <td>1147.0</td>\n",
       "      <td>425.0</td>\n",
       "      <td>10.3653</td>\n",
       "      <td>500001.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4471</th>\n",
       "      <td>-118.17</td>\n",
       "      <td>34.09</td>\n",
       "      <td>33.0</td>\n",
       "      <td>2907.0</td>\n",
       "      <td>797.0</td>\n",
       "      <td>3212.0</td>\n",
       "      <td>793.0</td>\n",
       "      <td>2.2348</td>\n",
       "      <td>146600.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19786</th>\n",
       "      <td>-122.86</td>\n",
       "      <td>40.56</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1350.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>423.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>1.7393</td>\n",
       "      <td>81300.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9969</th>\n",
       "      <td>-122.48</td>\n",
       "      <td>38.51</td>\n",
       "      <td>49.0</td>\n",
       "      <td>1977.0</td>\n",
       "      <td>393.0</td>\n",
       "      <td>741.0</td>\n",
       "      <td>339.0</td>\n",
       "      <td>3.1312</td>\n",
       "      <td>247600.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14621</th>\n",
       "      <td>-117.17</td>\n",
       "      <td>32.78</td>\n",
       "      <td>17.0</td>\n",
       "      <td>3845.0</td>\n",
       "      <td>1051.0</td>\n",
       "      <td>3102.0</td>\n",
       "      <td>944.0</td>\n",
       "      <td>2.3658</td>\n",
       "      <td>164100.0</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>579</th>\n",
       "      <td>-122.07</td>\n",
       "      <td>37.71</td>\n",
       "      <td>40.0</td>\n",
       "      <td>1808.0</td>\n",
       "      <td>302.0</td>\n",
       "      <td>746.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>5.3015</td>\n",
       "      <td>254900.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11682</th>\n",
       "      <td>-118.01</td>\n",
       "      <td>33.87</td>\n",
       "      <td>25.0</td>\n",
       "      <td>6348.0</td>\n",
       "      <td>1615.0</td>\n",
       "      <td>4188.0</td>\n",
       "      <td>1497.0</td>\n",
       "      <td>3.1390</td>\n",
       "      <td>185700.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>-122.21</td>\n",
       "      <td>37.78</td>\n",
       "      <td>43.0</td>\n",
       "      <td>1702.0</td>\n",
       "      <td>460.0</td>\n",
       "      <td>1227.0</td>\n",
       "      <td>407.0</td>\n",
       "      <td>1.7188</td>\n",
       "      <td>126800.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12130</th>\n",
       "      <td>-117.23</td>\n",
       "      <td>33.94</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2405.0</td>\n",
       "      <td>537.0</td>\n",
       "      <td>1594.0</td>\n",
       "      <td>517.0</td>\n",
       "      <td>3.0789</td>\n",
       "      <td>114200.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16441</th>\n",
       "      <td>-121.29</td>\n",
       "      <td>38.14</td>\n",
       "      <td>34.0</td>\n",
       "      <td>2770.0</td>\n",
       "      <td>544.0</td>\n",
       "      <td>1409.0</td>\n",
       "      <td>535.0</td>\n",
       "      <td>3.2338</td>\n",
       "      <td>101800.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11016</th>\n",
       "      <td>-117.82</td>\n",
       "      <td>33.76</td>\n",
       "      <td>33.0</td>\n",
       "      <td>2774.0</td>\n",
       "      <td>428.0</td>\n",
       "      <td>1229.0</td>\n",
       "      <td>407.0</td>\n",
       "      <td>6.2944</td>\n",
       "      <td>265600.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19934</th>\n",
       "      <td>-119.34</td>\n",
       "      <td>36.31</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1635.0</td>\n",
       "      <td>422.0</td>\n",
       "      <td>870.0</td>\n",
       "      <td>399.0</td>\n",
       "      <td>2.7000</td>\n",
       "      <td>88900.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1364</th>\n",
       "      <td>-122.14</td>\n",
       "      <td>38.03</td>\n",
       "      <td>42.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2.5795</td>\n",
       "      <td>225000.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1236</th>\n",
       "      <td>-120.37</td>\n",
       "      <td>38.23</td>\n",
       "      <td>13.0</td>\n",
       "      <td>4401.0</td>\n",
       "      <td>829.0</td>\n",
       "      <td>924.0</td>\n",
       "      <td>383.0</td>\n",
       "      <td>2.6942</td>\n",
       "      <td>123500.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5364</th>\n",
       "      <td>-118.42</td>\n",
       "      <td>34.04</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1358.0</td>\n",
       "      <td>272.0</td>\n",
       "      <td>574.0</td>\n",
       "      <td>267.0</td>\n",
       "      <td>5.6454</td>\n",
       "      <td>500001.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11703</th>\n",
       "      <td>-117.97</td>\n",
       "      <td>33.88</td>\n",
       "      <td>16.0</td>\n",
       "      <td>2003.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>1172.0</td>\n",
       "      <td>318.0</td>\n",
       "      <td>6.0394</td>\n",
       "      <td>321600.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10356</th>\n",
       "      <td>-117.67</td>\n",
       "      <td>33.60</td>\n",
       "      <td>25.0</td>\n",
       "      <td>3164.0</td>\n",
       "      <td>449.0</td>\n",
       "      <td>1517.0</td>\n",
       "      <td>453.0</td>\n",
       "      <td>6.7921</td>\n",
       "      <td>266000.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15270</th>\n",
       "      <td>-117.29</td>\n",
       "      <td>33.08</td>\n",
       "      <td>18.0</td>\n",
       "      <td>3225.0</td>\n",
       "      <td>515.0</td>\n",
       "      <td>1463.0</td>\n",
       "      <td>476.0</td>\n",
       "      <td>5.7787</td>\n",
       "      <td>346700.0</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3754</th>\n",
       "      <td>-118.37</td>\n",
       "      <td>34.18</td>\n",
       "      <td>36.0</td>\n",
       "      <td>1608.0</td>\n",
       "      <td>373.0</td>\n",
       "      <td>1217.0</td>\n",
       "      <td>374.0</td>\n",
       "      <td>2.9728</td>\n",
       "      <td>190200.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12166</th>\n",
       "      <td>-117.14</td>\n",
       "      <td>33.81</td>\n",
       "      <td>13.0</td>\n",
       "      <td>4496.0</td>\n",
       "      <td>756.0</td>\n",
       "      <td>2044.0</td>\n",
       "      <td>695.0</td>\n",
       "      <td>3.2778</td>\n",
       "      <td>148800.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6003</th>\n",
       "      <td>-117.77</td>\n",
       "      <td>34.08</td>\n",
       "      <td>27.0</td>\n",
       "      <td>5929.0</td>\n",
       "      <td>932.0</td>\n",
       "      <td>2817.0</td>\n",
       "      <td>828.0</td>\n",
       "      <td>6.0434</td>\n",
       "      <td>214800.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7364</th>\n",
       "      <td>-118.20</td>\n",
       "      <td>33.97</td>\n",
       "      <td>43.0</td>\n",
       "      <td>825.0</td>\n",
       "      <td>212.0</td>\n",
       "      <td>820.0</td>\n",
       "      <td>184.0</td>\n",
       "      <td>1.8897</td>\n",
       "      <td>174300.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6563</th>\n",
       "      <td>-118.13</td>\n",
       "      <td>34.20</td>\n",
       "      <td>46.0</td>\n",
       "      <td>1271.0</td>\n",
       "      <td>236.0</td>\n",
       "      <td>573.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>4.9312</td>\n",
       "      <td>240200.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12053</th>\n",
       "      <td>-117.56</td>\n",
       "      <td>33.88</td>\n",
       "      <td>40.0</td>\n",
       "      <td>1196.0</td>\n",
       "      <td>294.0</td>\n",
       "      <td>1052.0</td>\n",
       "      <td>258.0</td>\n",
       "      <td>2.0682</td>\n",
       "      <td>113000.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13908</th>\n",
       "      <td>-116.40</td>\n",
       "      <td>34.09</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4855.0</td>\n",
       "      <td>872.0</td>\n",
       "      <td>2098.0</td>\n",
       "      <td>765.0</td>\n",
       "      <td>3.2723</td>\n",
       "      <td>97800.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11159</th>\n",
       "      <td>-118.01</td>\n",
       "      <td>33.82</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1960.0</td>\n",
       "      <td>380.0</td>\n",
       "      <td>1356.0</td>\n",
       "      <td>356.0</td>\n",
       "      <td>4.0625</td>\n",
       "      <td>225900.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15775</th>\n",
       "      <td>-122.45</td>\n",
       "      <td>37.77</td>\n",
       "      <td>52.0</td>\n",
       "      <td>3095.0</td>\n",
       "      <td>682.0</td>\n",
       "      <td>1269.0</td>\n",
       "      <td>639.0</td>\n",
       "      <td>3.5750</td>\n",
       "      <td>500001.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>16512 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "17606    -121.89     37.29                38.0       1568.0           351.0   \n",
       "18632    -121.93     37.05                14.0        679.0           108.0   \n",
       "14650    -117.20     32.77                31.0       1952.0           471.0   \n",
       "3230     -119.61     36.31                25.0       1847.0           371.0   \n",
       "3555     -118.59     34.23                17.0       6592.0          1525.0   \n",
       "19480    -120.97     37.66                24.0       2930.0           588.0   \n",
       "8879     -118.50     34.04                52.0       2233.0           317.0   \n",
       "13685    -117.24     34.15                26.0       2041.0           293.0   \n",
       "4937     -118.26     33.99                47.0       1865.0           465.0   \n",
       "4861     -118.28     34.02                29.0        515.0           229.0   \n",
       "16365    -121.31     38.02                24.0       4157.0           951.0   \n",
       "19684    -121.62     39.14                41.0       2183.0           559.0   \n",
       "19234    -122.69     38.51                18.0       3364.0           501.0   \n",
       "13956    -117.06     34.17                21.0       2520.0           582.0   \n",
       "2390     -119.46     36.91                12.0       2980.0           495.0   \n",
       "11176    -117.96     33.83                30.0       2838.0           649.0   \n",
       "15614    -122.41     37.81                25.0       1178.0           545.0   \n",
       "2953     -119.02     35.35                42.0       1239.0           251.0   \n",
       "13209    -117.72     34.05                 8.0       1841.0           409.0   \n",
       "6569     -118.15     34.20                46.0       1505.0           261.0   \n",
       "5825     -118.30     34.19                14.0       3615.0           913.0   \n",
       "18086    -122.05     37.31                25.0       4111.0           538.0   \n",
       "16718    -120.66     35.49                17.0       4422.0           945.0   \n",
       "13600    -117.25     34.16                37.0       1709.0           278.0   \n",
       "13989    -117.19     34.94                31.0       2034.0           444.0   \n",
       "15168    -117.06     33.02                24.0        830.0           190.0   \n",
       "6747     -118.07     34.11                41.0       2869.0           563.0   \n",
       "7398     -118.24     33.96                44.0       1338.0           366.0   \n",
       "5562     -118.28     33.91                41.0        620.0           133.0   \n",
       "16121    -122.46     37.79                52.0       2059.0           416.0   \n",
       "...          ...       ...                 ...          ...             ...   \n",
       "12380    -116.47     33.77                26.0       4300.0           767.0   \n",
       "5618     -118.23     33.78                20.0         59.0            24.0   \n",
       "10060    -121.06     39.25                17.0       3127.0           539.0   \n",
       "18067    -122.03     37.29                22.0       3118.0           438.0   \n",
       "4471     -118.17     34.09                33.0       2907.0           797.0   \n",
       "19786    -122.86     40.56                12.0       1350.0           300.0   \n",
       "9969     -122.48     38.51                49.0       1977.0           393.0   \n",
       "14621    -117.17     32.78                17.0       3845.0          1051.0   \n",
       "579      -122.07     37.71                40.0       1808.0           302.0   \n",
       "11682    -118.01     33.87                25.0       6348.0          1615.0   \n",
       "245      -122.21     37.78                43.0       1702.0           460.0   \n",
       "12130    -117.23     33.94                 8.0       2405.0           537.0   \n",
       "16441    -121.29     38.14                34.0       2770.0           544.0   \n",
       "11016    -117.82     33.76                33.0       2774.0           428.0   \n",
       "19934    -119.34     36.31                14.0       1635.0           422.0   \n",
       "1364     -122.14     38.03                42.0        118.0            34.0   \n",
       "1236     -120.37     38.23                13.0       4401.0           829.0   \n",
       "5364     -118.42     34.04                52.0       1358.0           272.0   \n",
       "11703    -117.97     33.88                16.0       2003.0           300.0   \n",
       "10356    -117.67     33.60                25.0       3164.0           449.0   \n",
       "15270    -117.29     33.08                18.0       3225.0           515.0   \n",
       "3754     -118.37     34.18                36.0       1608.0           373.0   \n",
       "12166    -117.14     33.81                13.0       4496.0           756.0   \n",
       "6003     -117.77     34.08                27.0       5929.0           932.0   \n",
       "7364     -118.20     33.97                43.0        825.0           212.0   \n",
       "6563     -118.13     34.20                46.0       1271.0           236.0   \n",
       "12053    -117.56     33.88                40.0       1196.0           294.0   \n",
       "13908    -116.40     34.09                 9.0       4855.0           872.0   \n",
       "11159    -118.01     33.82                31.0       1960.0           380.0   \n",
       "15775    -122.45     37.77                52.0       3095.0           682.0   \n",
       "\n",
       "       population  households  median_income  median_house_value  \\\n",
       "17606       710.0       339.0         2.7042            286600.0   \n",
       "18632       306.0       113.0         6.4214            340600.0   \n",
       "14650       936.0       462.0         2.8621            196900.0   \n",
       "3230       1460.0       353.0         1.8839             46300.0   \n",
       "3555       4459.0      1463.0         3.0347            254500.0   \n",
       "19480      1448.0       570.0         3.5395            127900.0   \n",
       "8879        769.0       277.0         8.3839            500001.0   \n",
       "13685       936.0       375.0         6.0000            140200.0   \n",
       "4937       1916.0       438.0         1.8242             95000.0   \n",
       "4861       2690.0       217.0         0.4999            500001.0   \n",
       "16365      2734.0       879.0         2.7981             92100.0   \n",
       "19684      1202.0       506.0         1.6902             61500.0   \n",
       "19234      1442.0       506.0         6.6854            313000.0   \n",
       "13956       416.0       151.0         2.7120             89000.0   \n",
       "2390       1184.0       429.0         3.9141            123900.0   \n",
       "11176      1758.0       593.0         3.3831            197400.0   \n",
       "15614       592.0       441.0         3.6728            500001.0   \n",
       "2953        776.0       272.0         1.9830             63300.0   \n",
       "13209      1243.0       394.0         4.0614            107000.0   \n",
       "6569        857.0       269.0         4.5000            184200.0   \n",
       "5825       1924.0       852.0         3.5083            280900.0   \n",
       "18086      1585.0       568.0         9.2298            500001.0   \n",
       "16718      2307.0       885.0         2.8285            171300.0   \n",
       "13600       744.0       274.0         3.7188            116600.0   \n",
       "13989      1097.0       367.0         2.1522             60800.0   \n",
       "15168       279.0       196.0         1.9176            121100.0   \n",
       "6747       1627.0       533.0         5.0736            270700.0   \n",
       "7398       1765.0       388.0         1.7778            109900.0   \n",
       "5562        642.0       162.0         2.6546            159600.0   \n",
       "16121       999.0       402.0         3.7419            500001.0   \n",
       "...           ...         ...            ...                 ...   \n",
       "12380      1557.0       669.0         4.4107            122500.0   \n",
       "5618         69.0        23.0         2.5588            350000.0   \n",
       "10060      1390.0       520.0         3.9537            172800.0   \n",
       "18067      1147.0       425.0        10.3653            500001.0   \n",
       "4471       3212.0       793.0         2.2348            146600.0   \n",
       "19786       423.0       172.0         1.7393             81300.0   \n",
       "9969        741.0       339.0         3.1312            247600.0   \n",
       "14621      3102.0       944.0         2.3658            164100.0   \n",
       "579         746.0       270.0         5.3015            254900.0   \n",
       "11682      4188.0      1497.0         3.1390            185700.0   \n",
       "245        1227.0       407.0         1.7188            126800.0   \n",
       "12130      1594.0       517.0         3.0789            114200.0   \n",
       "16441      1409.0       535.0         3.2338            101800.0   \n",
       "11016      1229.0       407.0         6.2944            265600.0   \n",
       "19934       870.0       399.0         2.7000             88900.0   \n",
       "1364         54.0        30.0         2.5795            225000.0   \n",
       "1236        924.0       383.0         2.6942            123500.0   \n",
       "5364        574.0       267.0         5.6454            500001.0   \n",
       "11703      1172.0       318.0         6.0394            321600.0   \n",
       "10356      1517.0       453.0         6.7921            266000.0   \n",
       "15270      1463.0       476.0         5.7787            346700.0   \n",
       "3754       1217.0       374.0         2.9728            190200.0   \n",
       "12166      2044.0       695.0         3.2778            148800.0   \n",
       "6003       2817.0       828.0         6.0434            214800.0   \n",
       "7364        820.0       184.0         1.8897            174300.0   \n",
       "6563        573.0       210.0         4.9312            240200.0   \n",
       "12053      1052.0       258.0         2.0682            113000.0   \n",
       "13908      2098.0       765.0         3.2723             97800.0   \n",
       "11159      1356.0       356.0         4.0625            225900.0   \n",
       "15775      1269.0       639.0         3.5750            500001.0   \n",
       "\n",
       "      ocean_proximity  \n",
       "17606       <1H OCEAN  \n",
       "18632       <1H OCEAN  \n",
       "14650      NEAR OCEAN  \n",
       "3230           INLAND  \n",
       "3555        <1H OCEAN  \n",
       "19480          INLAND  \n",
       "8879        <1H OCEAN  \n",
       "13685          INLAND  \n",
       "4937        <1H OCEAN  \n",
       "4861        <1H OCEAN  \n",
       "16365          INLAND  \n",
       "19684          INLAND  \n",
       "19234       <1H OCEAN  \n",
       "13956          INLAND  \n",
       "2390           INLAND  \n",
       "11176       <1H OCEAN  \n",
       "15614        NEAR BAY  \n",
       "2953           INLAND  \n",
       "13209          INLAND  \n",
       "6569           INLAND  \n",
       "5825        <1H OCEAN  \n",
       "18086       <1H OCEAN  \n",
       "16718       <1H OCEAN  \n",
       "13600          INLAND  \n",
       "13989          INLAND  \n",
       "15168       <1H OCEAN  \n",
       "6747        <1H OCEAN  \n",
       "7398        <1H OCEAN  \n",
       "5562        <1H OCEAN  \n",
       "16121        NEAR BAY  \n",
       "...               ...  \n",
       "12380          INLAND  \n",
       "5618       NEAR OCEAN  \n",
       "10060          INLAND  \n",
       "18067       <1H OCEAN  \n",
       "4471        <1H OCEAN  \n",
       "19786          INLAND  \n",
       "9969           INLAND  \n",
       "14621      NEAR OCEAN  \n",
       "579          NEAR BAY  \n",
       "11682       <1H OCEAN  \n",
       "245          NEAR BAY  \n",
       "12130          INLAND  \n",
       "16441          INLAND  \n",
       "11016       <1H OCEAN  \n",
       "19934          INLAND  \n",
       "1364         NEAR BAY  \n",
       "1236           INLAND  \n",
       "5364        <1H OCEAN  \n",
       "11703       <1H OCEAN  \n",
       "10356       <1H OCEAN  \n",
       "15270      NEAR OCEAN  \n",
       "3754        <1H OCEAN  \n",
       "12166          INLAND  \n",
       "6003           INLAND  \n",
       "7364        <1H OCEAN  \n",
       "6563           INLAND  \n",
       "12053          INLAND  \n",
       "13908          INLAND  \n",
       "11159       <1H OCEAN  \n",
       "15775        NEAR BAY  \n",
       "\n",
       "[16512 rows x 10 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train.plot(kind=\"scatter\", x=\"median_income\", y=\"median_house_value\", alpha=0.1)\n",
    "train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "train[\"rooms_per_household\"] = train[\"total_rooms\"]/train[\"households\"]\n",
    "train[\"bedrooms_per_household\"] = train[\"total_bedrooms\"] / train[\"households\"]\n",
    "train[\"population_per_household\"] = train[\"population\"] / train[\"households\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "corr_matrix = train.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'strat_train_Set' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-103-bef947f398c8>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mcorr_matrix\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"median_house_value\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msort_values\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mascending\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mstrat_train_Set\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'strat_train_Set' is not defined"
     ]
    }
   ],
   "source": [
    "corr_matrix[\"median_house_value\"].sort_values(ascending = False)\n",
    "strat_train_Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 16512 entries, 17606 to 15775\n",
      "Data columns (total 10 columns):\n",
      "longitude             16512 non-null float64\n",
      "latitude              16512 non-null float64\n",
      "housing_median_age    16512 non-null float64\n",
      "total_rooms           16512 non-null float64\n",
      "total_bedrooms        16354 non-null float64\n",
      "population            16512 non-null float64\n",
      "households            16512 non-null float64\n",
      "median_income         16512 non-null float64\n",
      "median_house_value    16512 non-null float64\n",
      "ocean_proximity       16512 non-null object\n",
      "dtypes: float64(9), object(1)\n",
      "memory usage: 1.4+ MB\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 16512 entries, 17606 to 15775\n",
      "Data columns (total 9 columns):\n",
      "longitude             16512 non-null float64\n",
      "latitude              16512 non-null float64\n",
      "housing_median_age    16512 non-null float64\n",
      "total_rooms           16512 non-null float64\n",
      "total_bedrooms        16354 non-null float64\n",
      "population            16512 non-null float64\n",
      "households            16512 non-null float64\n",
      "median_income         16512 non-null float64\n",
      "ocean_proximity       16512 non-null object\n",
      "dtypes: float64(8), object(1)\n",
      "memory usage: 1.3+ MB\n"
     ]
    },
    {
     "ename": "AttributeError",
     "evalue": "'Series' object has no attribute 'info'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-107-cbad68ba2c88>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mstrat_train_set\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minfo\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mtrain\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minfo\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m \u001b[0mtrain_labels\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minfo\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python37\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36m__getattr__\u001b[1;34m(self, name)\u001b[0m\n\u001b[0;32m   5065\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_info_axis\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_can_hold_identifiers_and_holds_name\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5066\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 5067\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   5068\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5069\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__setattr__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'Series' object has no attribute 'info'"
     ]
    }
   ],
   "source": [
    "train = strat_train_set.drop(\"median_house_value\", axis =1)\n",
    "train_labels = strat_train_set[\"median_house_value\"].copy()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17606</th>\n",
       "      <td>-121.89</td>\n",
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       "      <td>1568.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>710.0</td>\n",
       "      <td>339.0</td>\n",
       "      <td>2.7042</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
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       "      <th>18632</th>\n",
       "      <td>-121.93</td>\n",
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       "      <td>679.0</td>\n",
       "      <td>108.0</td>\n",
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       "      <td>113.0</td>\n",
       "      <td>6.4214</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
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       "    <tr>\n",
       "      <th>14650</th>\n",
       "      <td>-117.20</td>\n",
       "      <td>32.77</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1952.0</td>\n",
       "      <td>471.0</td>\n",
       "      <td>936.0</td>\n",
       "      <td>462.0</td>\n",
       "      <td>2.8621</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3230</th>\n",
       "      <td>-119.61</td>\n",
       "      <td>36.31</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1847.0</td>\n",
       "      <td>371.0</td>\n",
       "      <td>1460.0</td>\n",
       "      <td>353.0</td>\n",
       "      <td>1.8839</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3555</th>\n",
       "      <td>-118.59</td>\n",
       "      <td>34.23</td>\n",
       "      <td>17.0</td>\n",
       "      <td>6592.0</td>\n",
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       "      <td>4459.0</td>\n",
       "      <td>1463.0</td>\n",
       "      <td>3.0347</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
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       "    <tr>\n",
       "      <th>19480</th>\n",
       "      <td>-120.97</td>\n",
       "      <td>37.66</td>\n",
       "      <td>24.0</td>\n",
       "      <td>2930.0</td>\n",
       "      <td>588.0</td>\n",
       "      <td>1448.0</td>\n",
       "      <td>570.0</td>\n",
       "      <td>3.5395</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8879</th>\n",
       "      <td>-118.50</td>\n",
       "      <td>34.04</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2233.0</td>\n",
       "      <td>317.0</td>\n",
       "      <td>769.0</td>\n",
       "      <td>277.0</td>\n",
       "      <td>8.3839</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13685</th>\n",
       "      <td>-117.24</td>\n",
       "      <td>34.15</td>\n",
       "      <td>26.0</td>\n",
       "      <td>2041.0</td>\n",
       "      <td>293.0</td>\n",
       "      <td>936.0</td>\n",
       "      <td>375.0</td>\n",
       "      <td>6.0000</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4937</th>\n",
       "      <td>-118.26</td>\n",
       "      <td>33.99</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1865.0</td>\n",
       "      <td>465.0</td>\n",
       "      <td>1916.0</td>\n",
       "      <td>438.0</td>\n",
       "      <td>1.8242</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4861</th>\n",
       "      <td>-118.28</td>\n",
       "      <td>34.02</td>\n",
       "      <td>29.0</td>\n",
       "      <td>515.0</td>\n",
       "      <td>229.0</td>\n",
       "      <td>2690.0</td>\n",
       "      <td>217.0</td>\n",
       "      <td>0.4999</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16365</th>\n",
       "      <td>-121.31</td>\n",
       "      <td>38.02</td>\n",
       "      <td>24.0</td>\n",
       "      <td>4157.0</td>\n",
       "      <td>951.0</td>\n",
       "      <td>2734.0</td>\n",
       "      <td>879.0</td>\n",
       "      <td>2.7981</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19684</th>\n",
       "      <td>-121.62</td>\n",
       "      <td>39.14</td>\n",
       "      <td>41.0</td>\n",
       "      <td>2183.0</td>\n",
       "      <td>559.0</td>\n",
       "      <td>1202.0</td>\n",
       "      <td>506.0</td>\n",
       "      <td>1.6902</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19234</th>\n",
       "      <td>-122.69</td>\n",
       "      <td>38.51</td>\n",
       "      <td>18.0</td>\n",
       "      <td>3364.0</td>\n",
       "      <td>501.0</td>\n",
       "      <td>1442.0</td>\n",
       "      <td>506.0</td>\n",
       "      <td>6.6854</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13956</th>\n",
       "      <td>-117.06</td>\n",
       "      <td>34.17</td>\n",
       "      <td>21.0</td>\n",
       "      <td>2520.0</td>\n",
       "      <td>582.0</td>\n",
       "      <td>416.0</td>\n",
       "      <td>151.0</td>\n",
       "      <td>2.7120</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2390</th>\n",
       "      <td>-119.46</td>\n",
       "      <td>36.91</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2980.0</td>\n",
       "      <td>495.0</td>\n",
       "      <td>1184.0</td>\n",
       "      <td>429.0</td>\n",
       "      <td>3.9141</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11176</th>\n",
       "      <td>-117.96</td>\n",
       "      <td>33.83</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2838.0</td>\n",
       "      <td>649.0</td>\n",
       "      <td>1758.0</td>\n",
       "      <td>593.0</td>\n",
       "      <td>3.3831</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15614</th>\n",
       "      <td>-122.41</td>\n",
       "      <td>37.81</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1178.0</td>\n",
       "      <td>545.0</td>\n",
       "      <td>592.0</td>\n",
       "      <td>441.0</td>\n",
       "      <td>3.6728</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2953</th>\n",
       "      <td>-119.02</td>\n",
       "      <td>35.35</td>\n",
       "      <td>42.0</td>\n",
       "      <td>1239.0</td>\n",
       "      <td>251.0</td>\n",
       "      <td>776.0</td>\n",
       "      <td>272.0</td>\n",
       "      <td>1.9830</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13209</th>\n",
       "      <td>-117.72</td>\n",
       "      <td>34.05</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1841.0</td>\n",
       "      <td>409.0</td>\n",
       "      <td>1243.0</td>\n",
       "      <td>394.0</td>\n",
       "      <td>4.0614</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6569</th>\n",
       "      <td>-118.15</td>\n",
       "      <td>34.20</td>\n",
       "      <td>46.0</td>\n",
       "      <td>1505.0</td>\n",
       "      <td>261.0</td>\n",
       "      <td>857.0</td>\n",
       "      <td>269.0</td>\n",
       "      <td>4.5000</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5825</th>\n",
       "      <td>-118.30</td>\n",
       "      <td>34.19</td>\n",
       "      <td>14.0</td>\n",
       "      <td>3615.0</td>\n",
       "      <td>913.0</td>\n",
       "      <td>1924.0</td>\n",
       "      <td>852.0</td>\n",
       "      <td>3.5083</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18086</th>\n",
       "      <td>-122.05</td>\n",
       "      <td>37.31</td>\n",
       "      <td>25.0</td>\n",
       "      <td>4111.0</td>\n",
       "      <td>538.0</td>\n",
       "      <td>1585.0</td>\n",
       "      <td>568.0</td>\n",
       "      <td>9.2298</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16718</th>\n",
       "      <td>-120.66</td>\n",
       "      <td>35.49</td>\n",
       "      <td>17.0</td>\n",
       "      <td>4422.0</td>\n",
       "      <td>945.0</td>\n",
       "      <td>2307.0</td>\n",
       "      <td>885.0</td>\n",
       "      <td>2.8285</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13600</th>\n",
       "      <td>-117.25</td>\n",
       "      <td>34.16</td>\n",
       "      <td>37.0</td>\n",
       "      <td>1709.0</td>\n",
       "      <td>278.0</td>\n",
       "      <td>744.0</td>\n",
       "      <td>274.0</td>\n",
       "      <td>3.7188</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13989</th>\n",
       "      <td>-117.19</td>\n",
       "      <td>34.94</td>\n",
       "      <td>31.0</td>\n",
       "      <td>2034.0</td>\n",
       "      <td>444.0</td>\n",
       "      <td>1097.0</td>\n",
       "      <td>367.0</td>\n",
       "      <td>2.1522</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15168</th>\n",
       "      <td>-117.06</td>\n",
       "      <td>33.02</td>\n",
       "      <td>24.0</td>\n",
       "      <td>830.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>279.0</td>\n",
       "      <td>196.0</td>\n",
       "      <td>1.9176</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6747</th>\n",
       "      <td>-118.07</td>\n",
       "      <td>34.11</td>\n",
       "      <td>41.0</td>\n",
       "      <td>2869.0</td>\n",
       "      <td>563.0</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>533.0</td>\n",
       "      <td>5.0736</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7398</th>\n",
       "      <td>-118.24</td>\n",
       "      <td>33.96</td>\n",
       "      <td>44.0</td>\n",
       "      <td>1338.0</td>\n",
       "      <td>366.0</td>\n",
       "      <td>1765.0</td>\n",
       "      <td>388.0</td>\n",
       "      <td>1.7778</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5562</th>\n",
       "      <td>-118.28</td>\n",
       "      <td>33.91</td>\n",
       "      <td>41.0</td>\n",
       "      <td>620.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>642.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>2.6546</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16121</th>\n",
       "      <td>-122.46</td>\n",
       "      <td>37.79</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2059.0</td>\n",
       "      <td>416.0</td>\n",
       "      <td>999.0</td>\n",
       "      <td>402.0</td>\n",
       "      <td>3.7419</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12380</th>\n",
       "      <td>-116.47</td>\n",
       "      <td>33.77</td>\n",
       "      <td>26.0</td>\n",
       "      <td>4300.0</td>\n",
       "      <td>767.0</td>\n",
       "      <td>1557.0</td>\n",
       "      <td>669.0</td>\n",
       "      <td>4.4107</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5618</th>\n",
       "      <td>-118.23</td>\n",
       "      <td>33.78</td>\n",
       "      <td>20.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2.5588</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10060</th>\n",
       "      <td>-121.06</td>\n",
       "      <td>39.25</td>\n",
       "      <td>17.0</td>\n",
       "      <td>3127.0</td>\n",
       "      <td>539.0</td>\n",
       "      <td>1390.0</td>\n",
       "      <td>520.0</td>\n",
       "      <td>3.9537</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18067</th>\n",
       "      <td>-122.03</td>\n",
       "      <td>37.29</td>\n",
       "      <td>22.0</td>\n",
       "      <td>3118.0</td>\n",
       "      <td>438.0</td>\n",
       "      <td>1147.0</td>\n",
       "      <td>425.0</td>\n",
       "      <td>10.3653</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4471</th>\n",
       "      <td>-118.17</td>\n",
       "      <td>34.09</td>\n",
       "      <td>33.0</td>\n",
       "      <td>2907.0</td>\n",
       "      <td>797.0</td>\n",
       "      <td>3212.0</td>\n",
       "      <td>793.0</td>\n",
       "      <td>2.2348</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19786</th>\n",
       "      <td>-122.86</td>\n",
       "      <td>40.56</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1350.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>423.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>1.7393</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9969</th>\n",
       "      <td>-122.48</td>\n",
       "      <td>38.51</td>\n",
       "      <td>49.0</td>\n",
       "      <td>1977.0</td>\n",
       "      <td>393.0</td>\n",
       "      <td>741.0</td>\n",
       "      <td>339.0</td>\n",
       "      <td>3.1312</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14621</th>\n",
       "      <td>-117.17</td>\n",
       "      <td>32.78</td>\n",
       "      <td>17.0</td>\n",
       "      <td>3845.0</td>\n",
       "      <td>1051.0</td>\n",
       "      <td>3102.0</td>\n",
       "      <td>944.0</td>\n",
       "      <td>2.3658</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>579</th>\n",
       "      <td>-122.07</td>\n",
       "      <td>37.71</td>\n",
       "      <td>40.0</td>\n",
       "      <td>1808.0</td>\n",
       "      <td>302.0</td>\n",
       "      <td>746.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>5.3015</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11682</th>\n",
       "      <td>-118.01</td>\n",
       "      <td>33.87</td>\n",
       "      <td>25.0</td>\n",
       "      <td>6348.0</td>\n",
       "      <td>1615.0</td>\n",
       "      <td>4188.0</td>\n",
       "      <td>1497.0</td>\n",
       "      <td>3.1390</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>-122.21</td>\n",
       "      <td>37.78</td>\n",
       "      <td>43.0</td>\n",
       "      <td>1702.0</td>\n",
       "      <td>460.0</td>\n",
       "      <td>1227.0</td>\n",
       "      <td>407.0</td>\n",
       "      <td>1.7188</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12130</th>\n",
       "      <td>-117.23</td>\n",
       "      <td>33.94</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2405.0</td>\n",
       "      <td>537.0</td>\n",
       "      <td>1594.0</td>\n",
       "      <td>517.0</td>\n",
       "      <td>3.0789</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16441</th>\n",
       "      <td>-121.29</td>\n",
       "      <td>38.14</td>\n",
       "      <td>34.0</td>\n",
       "      <td>2770.0</td>\n",
       "      <td>544.0</td>\n",
       "      <td>1409.0</td>\n",
       "      <td>535.0</td>\n",
       "      <td>3.2338</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11016</th>\n",
       "      <td>-117.82</td>\n",
       "      <td>33.76</td>\n",
       "      <td>33.0</td>\n",
       "      <td>2774.0</td>\n",
       "      <td>428.0</td>\n",
       "      <td>1229.0</td>\n",
       "      <td>407.0</td>\n",
       "      <td>6.2944</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19934</th>\n",
       "      <td>-119.34</td>\n",
       "      <td>36.31</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1635.0</td>\n",
       "      <td>422.0</td>\n",
       "      <td>870.0</td>\n",
       "      <td>399.0</td>\n",
       "      <td>2.7000</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1364</th>\n",
       "      <td>-122.14</td>\n",
       "      <td>38.03</td>\n",
       "      <td>42.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2.5795</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1236</th>\n",
       "      <td>-120.37</td>\n",
       "      <td>38.23</td>\n",
       "      <td>13.0</td>\n",
       "      <td>4401.0</td>\n",
       "      <td>829.0</td>\n",
       "      <td>924.0</td>\n",
       "      <td>383.0</td>\n",
       "      <td>2.6942</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5364</th>\n",
       "      <td>-118.42</td>\n",
       "      <td>34.04</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1358.0</td>\n",
       "      <td>272.0</td>\n",
       "      <td>574.0</td>\n",
       "      <td>267.0</td>\n",
       "      <td>5.6454</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11703</th>\n",
       "      <td>-117.97</td>\n",
       "      <td>33.88</td>\n",
       "      <td>16.0</td>\n",
       "      <td>2003.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>1172.0</td>\n",
       "      <td>318.0</td>\n",
       "      <td>6.0394</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10356</th>\n",
       "      <td>-117.67</td>\n",
       "      <td>33.60</td>\n",
       "      <td>25.0</td>\n",
       "      <td>3164.0</td>\n",
       "      <td>449.0</td>\n",
       "      <td>1517.0</td>\n",
       "      <td>453.0</td>\n",
       "      <td>6.7921</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15270</th>\n",
       "      <td>-117.29</td>\n",
       "      <td>33.08</td>\n",
       "      <td>18.0</td>\n",
       "      <td>3225.0</td>\n",
       "      <td>515.0</td>\n",
       "      <td>1463.0</td>\n",
       "      <td>476.0</td>\n",
       "      <td>5.7787</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3754</th>\n",
       "      <td>-118.37</td>\n",
       "      <td>34.18</td>\n",
       "      <td>36.0</td>\n",
       "      <td>1608.0</td>\n",
       "      <td>373.0</td>\n",
       "      <td>1217.0</td>\n",
       "      <td>374.0</td>\n",
       "      <td>2.9728</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12166</th>\n",
       "      <td>-117.14</td>\n",
       "      <td>33.81</td>\n",
       "      <td>13.0</td>\n",
       "      <td>4496.0</td>\n",
       "      <td>756.0</td>\n",
       "      <td>2044.0</td>\n",
       "      <td>695.0</td>\n",
       "      <td>3.2778</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6003</th>\n",
       "      <td>-117.77</td>\n",
       "      <td>34.08</td>\n",
       "      <td>27.0</td>\n",
       "      <td>5929.0</td>\n",
       "      <td>932.0</td>\n",
       "      <td>2817.0</td>\n",
       "      <td>828.0</td>\n",
       "      <td>6.0434</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7364</th>\n",
       "      <td>-118.20</td>\n",
       "      <td>33.97</td>\n",
       "      <td>43.0</td>\n",
       "      <td>825.0</td>\n",
       "      <td>212.0</td>\n",
       "      <td>820.0</td>\n",
       "      <td>184.0</td>\n",
       "      <td>1.8897</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6563</th>\n",
       "      <td>-118.13</td>\n",
       "      <td>34.20</td>\n",
       "      <td>46.0</td>\n",
       "      <td>1271.0</td>\n",
       "      <td>236.0</td>\n",
       "      <td>573.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>4.9312</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12053</th>\n",
       "      <td>-117.56</td>\n",
       "      <td>33.88</td>\n",
       "      <td>40.0</td>\n",
       "      <td>1196.0</td>\n",
       "      <td>294.0</td>\n",
       "      <td>1052.0</td>\n",
       "      <td>258.0</td>\n",
       "      <td>2.0682</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13908</th>\n",
       "      <td>-116.40</td>\n",
       "      <td>34.09</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4855.0</td>\n",
       "      <td>872.0</td>\n",
       "      <td>2098.0</td>\n",
       "      <td>765.0</td>\n",
       "      <td>3.2723</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11159</th>\n",
       "      <td>-118.01</td>\n",
       "      <td>33.82</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1960.0</td>\n",
       "      <td>380.0</td>\n",
       "      <td>1356.0</td>\n",
       "      <td>356.0</td>\n",
       "      <td>4.0625</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15775</th>\n",
       "      <td>-122.45</td>\n",
       "      <td>37.77</td>\n",
       "      <td>52.0</td>\n",
       "      <td>3095.0</td>\n",
       "      <td>682.0</td>\n",
       "      <td>1269.0</td>\n",
       "      <td>639.0</td>\n",
       "      <td>3.5750</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>16354 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "17606    -121.89     37.29                38.0       1568.0           351.0   \n",
       "18632    -121.93     37.05                14.0        679.0           108.0   \n",
       "14650    -117.20     32.77                31.0       1952.0           471.0   \n",
       "3230     -119.61     36.31                25.0       1847.0           371.0   \n",
       "3555     -118.59     34.23                17.0       6592.0          1525.0   \n",
       "19480    -120.97     37.66                24.0       2930.0           588.0   \n",
       "8879     -118.50     34.04                52.0       2233.0           317.0   \n",
       "13685    -117.24     34.15                26.0       2041.0           293.0   \n",
       "4937     -118.26     33.99                47.0       1865.0           465.0   \n",
       "4861     -118.28     34.02                29.0        515.0           229.0   \n",
       "16365    -121.31     38.02                24.0       4157.0           951.0   \n",
       "19684    -121.62     39.14                41.0       2183.0           559.0   \n",
       "19234    -122.69     38.51                18.0       3364.0           501.0   \n",
       "13956    -117.06     34.17                21.0       2520.0           582.0   \n",
       "2390     -119.46     36.91                12.0       2980.0           495.0   \n",
       "11176    -117.96     33.83                30.0       2838.0           649.0   \n",
       "15614    -122.41     37.81                25.0       1178.0           545.0   \n",
       "2953     -119.02     35.35                42.0       1239.0           251.0   \n",
       "13209    -117.72     34.05                 8.0       1841.0           409.0   \n",
       "6569     -118.15     34.20                46.0       1505.0           261.0   \n",
       "5825     -118.30     34.19                14.0       3615.0           913.0   \n",
       "18086    -122.05     37.31                25.0       4111.0           538.0   \n",
       "16718    -120.66     35.49                17.0       4422.0           945.0   \n",
       "13600    -117.25     34.16                37.0       1709.0           278.0   \n",
       "13989    -117.19     34.94                31.0       2034.0           444.0   \n",
       "15168    -117.06     33.02                24.0        830.0           190.0   \n",
       "6747     -118.07     34.11                41.0       2869.0           563.0   \n",
       "7398     -118.24     33.96                44.0       1338.0           366.0   \n",
       "5562     -118.28     33.91                41.0        620.0           133.0   \n",
       "16121    -122.46     37.79                52.0       2059.0           416.0   \n",
       "...          ...       ...                 ...          ...             ...   \n",
       "12380    -116.47     33.77                26.0       4300.0           767.0   \n",
       "5618     -118.23     33.78                20.0         59.0            24.0   \n",
       "10060    -121.06     39.25                17.0       3127.0           539.0   \n",
       "18067    -122.03     37.29                22.0       3118.0           438.0   \n",
       "4471     -118.17     34.09                33.0       2907.0           797.0   \n",
       "19786    -122.86     40.56                12.0       1350.0           300.0   \n",
       "9969     -122.48     38.51                49.0       1977.0           393.0   \n",
       "14621    -117.17     32.78                17.0       3845.0          1051.0   \n",
       "579      -122.07     37.71                40.0       1808.0           302.0   \n",
       "11682    -118.01     33.87                25.0       6348.0          1615.0   \n",
       "245      -122.21     37.78                43.0       1702.0           460.0   \n",
       "12130    -117.23     33.94                 8.0       2405.0           537.0   \n",
       "16441    -121.29     38.14                34.0       2770.0           544.0   \n",
       "11016    -117.82     33.76                33.0       2774.0           428.0   \n",
       "19934    -119.34     36.31                14.0       1635.0           422.0   \n",
       "1364     -122.14     38.03                42.0        118.0            34.0   \n",
       "1236     -120.37     38.23                13.0       4401.0           829.0   \n",
       "5364     -118.42     34.04                52.0       1358.0           272.0   \n",
       "11703    -117.97     33.88                16.0       2003.0           300.0   \n",
       "10356    -117.67     33.60                25.0       3164.0           449.0   \n",
       "15270    -117.29     33.08                18.0       3225.0           515.0   \n",
       "3754     -118.37     34.18                36.0       1608.0           373.0   \n",
       "12166    -117.14     33.81                13.0       4496.0           756.0   \n",
       "6003     -117.77     34.08                27.0       5929.0           932.0   \n",
       "7364     -118.20     33.97                43.0        825.0           212.0   \n",
       "6563     -118.13     34.20                46.0       1271.0           236.0   \n",
       "12053    -117.56     33.88                40.0       1196.0           294.0   \n",
       "13908    -116.40     34.09                 9.0       4855.0           872.0   \n",
       "11159    -118.01     33.82                31.0       1960.0           380.0   \n",
       "15775    -122.45     37.77                52.0       3095.0           682.0   \n",
       "\n",
       "       population  households  median_income ocean_proximity  \n",
       "17606       710.0       339.0         2.7042       <1H OCEAN  \n",
       "18632       306.0       113.0         6.4214       <1H OCEAN  \n",
       "14650       936.0       462.0         2.8621      NEAR OCEAN  \n",
       "3230       1460.0       353.0         1.8839          INLAND  \n",
       "3555       4459.0      1463.0         3.0347       <1H OCEAN  \n",
       "19480      1448.0       570.0         3.5395          INLAND  \n",
       "8879        769.0       277.0         8.3839       <1H OCEAN  \n",
       "13685       936.0       375.0         6.0000          INLAND  \n",
       "4937       1916.0       438.0         1.8242       <1H OCEAN  \n",
       "4861       2690.0       217.0         0.4999       <1H OCEAN  \n",
       "16365      2734.0       879.0         2.7981          INLAND  \n",
       "19684      1202.0       506.0         1.6902          INLAND  \n",
       "19234      1442.0       506.0         6.6854       <1H OCEAN  \n",
       "13956       416.0       151.0         2.7120          INLAND  \n",
       "2390       1184.0       429.0         3.9141          INLAND  \n",
       "11176      1758.0       593.0         3.3831       <1H OCEAN  \n",
       "15614       592.0       441.0         3.6728        NEAR BAY  \n",
       "2953        776.0       272.0         1.9830          INLAND  \n",
       "13209      1243.0       394.0         4.0614          INLAND  \n",
       "6569        857.0       269.0         4.5000          INLAND  \n",
       "5825       1924.0       852.0         3.5083       <1H OCEAN  \n",
       "18086      1585.0       568.0         9.2298       <1H OCEAN  \n",
       "16718      2307.0       885.0         2.8285       <1H OCEAN  \n",
       "13600       744.0       274.0         3.7188          INLAND  \n",
       "13989      1097.0       367.0         2.1522          INLAND  \n",
       "15168       279.0       196.0         1.9176       <1H OCEAN  \n",
       "6747       1627.0       533.0         5.0736       <1H OCEAN  \n",
       "7398       1765.0       388.0         1.7778       <1H OCEAN  \n",
       "5562        642.0       162.0         2.6546       <1H OCEAN  \n",
       "16121       999.0       402.0         3.7419        NEAR BAY  \n",
       "...           ...         ...            ...             ...  \n",
       "12380      1557.0       669.0         4.4107          INLAND  \n",
       "5618         69.0        23.0         2.5588      NEAR OCEAN  \n",
       "10060      1390.0       520.0         3.9537          INLAND  \n",
       "18067      1147.0       425.0        10.3653       <1H OCEAN  \n",
       "4471       3212.0       793.0         2.2348       <1H OCEAN  \n",
       "19786       423.0       172.0         1.7393          INLAND  \n",
       "9969        741.0       339.0         3.1312          INLAND  \n",
       "14621      3102.0       944.0         2.3658      NEAR OCEAN  \n",
       "579         746.0       270.0         5.3015        NEAR BAY  \n",
       "11682      4188.0      1497.0         3.1390       <1H OCEAN  \n",
       "245        1227.0       407.0         1.7188        NEAR BAY  \n",
       "12130      1594.0       517.0         3.0789          INLAND  \n",
       "16441      1409.0       535.0         3.2338          INLAND  \n",
       "11016      1229.0       407.0         6.2944       <1H OCEAN  \n",
       "19934       870.0       399.0         2.7000          INLAND  \n",
       "1364         54.0        30.0         2.5795        NEAR BAY  \n",
       "1236        924.0       383.0         2.6942          INLAND  \n",
       "5364        574.0       267.0         5.6454       <1H OCEAN  \n",
       "11703      1172.0       318.0         6.0394       <1H OCEAN  \n",
       "10356      1517.0       453.0         6.7921       <1H OCEAN  \n",
       "15270      1463.0       476.0         5.7787      NEAR OCEAN  \n",
       "3754       1217.0       374.0         2.9728       <1H OCEAN  \n",
       "12166      2044.0       695.0         3.2778          INLAND  \n",
       "6003       2817.0       828.0         6.0434          INLAND  \n",
       "7364        820.0       184.0         1.8897       <1H OCEAN  \n",
       "6563        573.0       210.0         4.9312          INLAND  \n",
       "12053      1052.0       258.0         2.0682          INLAND  \n",
       "13908      2098.0       765.0         3.2723          INLAND  \n",
       "11159      1356.0       356.0         4.0625       <1H OCEAN  \n",
       "15775      1269.0       639.0         3.5750        NEAR BAY  \n",
       "\n",
       "[16354 rows x 9 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.dropna(subset=[\"total_bedrooms\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SimpleImputer(copy=True, fill_value=None, missing_values=nan,\n",
       "       strategy='median', verbose=0)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.impute import SimpleImputer\n",
    "imputer = SimpleImputer(strategy = \"median\")\n",
    "train_int = train.drop(\"ocean_proximity\", axis=1)\n",
    "imputer.fit(train_int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17606</th>\n",
       "      <td>-121.89</td>\n",
       "      <td>37.29</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1568.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>710.0</td>\n",
       "      <td>339.0</td>\n",
       "      <td>2.7042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18632</th>\n",
       "      <td>-121.93</td>\n",
       "      <td>37.05</td>\n",
       "      <td>14.0</td>\n",
       "      <td>679.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>306.0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>6.4214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14650</th>\n",
       "      <td>-117.20</td>\n",
       "      <td>32.77</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1952.0</td>\n",
       "      <td>471.0</td>\n",
       "      <td>936.0</td>\n",
       "      <td>462.0</td>\n",
       "      <td>2.8621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3230</th>\n",
       "      <td>-119.61</td>\n",
       "      <td>36.31</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1847.0</td>\n",
       "      <td>371.0</td>\n",
       "      <td>1460.0</td>\n",
       "      <td>353.0</td>\n",
       "      <td>1.8839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3555</th>\n",
       "      <td>-118.59</td>\n",
       "      <td>34.23</td>\n",
       "      <td>17.0</td>\n",
       "      <td>6592.0</td>\n",
       "      <td>1525.0</td>\n",
       "      <td>4459.0</td>\n",
       "      <td>1463.0</td>\n",
       "      <td>3.0347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19480</th>\n",
       "      <td>-120.97</td>\n",
       "      <td>37.66</td>\n",
       "      <td>24.0</td>\n",
       "      <td>2930.0</td>\n",
       "      <td>588.0</td>\n",
       "      <td>1448.0</td>\n",
       "      <td>570.0</td>\n",
       "      <td>3.5395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8879</th>\n",
       "      <td>-118.50</td>\n",
       "      <td>34.04</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2233.0</td>\n",
       "      <td>317.0</td>\n",
       "      <td>769.0</td>\n",
       "      <td>277.0</td>\n",
       "      <td>8.3839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13685</th>\n",
       "      <td>-117.24</td>\n",
       "      <td>34.15</td>\n",
       "      <td>26.0</td>\n",
       "      <td>2041.0</td>\n",
       "      <td>293.0</td>\n",
       "      <td>936.0</td>\n",
       "      <td>375.0</td>\n",
       "      <td>6.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4937</th>\n",
       "      <td>-118.26</td>\n",
       "      <td>33.99</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1865.0</td>\n",
       "      <td>465.0</td>\n",
       "      <td>1916.0</td>\n",
       "      <td>438.0</td>\n",
       "      <td>1.8242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4861</th>\n",
       "      <td>-118.28</td>\n",
       "      <td>34.02</td>\n",
       "      <td>29.0</td>\n",
       "      <td>515.0</td>\n",
       "      <td>229.0</td>\n",
       "      <td>2690.0</td>\n",
       "      <td>217.0</td>\n",
       "      <td>0.4999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16365</th>\n",
       "      <td>-121.31</td>\n",
       "      <td>38.02</td>\n",
       "      <td>24.0</td>\n",
       "      <td>4157.0</td>\n",
       "      <td>951.0</td>\n",
       "      <td>2734.0</td>\n",
       "      <td>879.0</td>\n",
       "      <td>2.7981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19684</th>\n",
       "      <td>-121.62</td>\n",
       "      <td>39.14</td>\n",
       "      <td>41.0</td>\n",
       "      <td>2183.0</td>\n",
       "      <td>559.0</td>\n",
       "      <td>1202.0</td>\n",
       "      <td>506.0</td>\n",
       "      <td>1.6902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19234</th>\n",
       "      <td>-122.69</td>\n",
       "      <td>38.51</td>\n",
       "      <td>18.0</td>\n",
       "      <td>3364.0</td>\n",
       "      <td>501.0</td>\n",
       "      <td>1442.0</td>\n",
       "      <td>506.0</td>\n",
       "      <td>6.6854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13956</th>\n",
       "      <td>-117.06</td>\n",
       "      <td>34.17</td>\n",
       "      <td>21.0</td>\n",
       "      <td>2520.0</td>\n",
       "      <td>582.0</td>\n",
       "      <td>416.0</td>\n",
       "      <td>151.0</td>\n",
       "      <td>2.7120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2390</th>\n",
       "      <td>-119.46</td>\n",
       "      <td>36.91</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2980.0</td>\n",
       "      <td>495.0</td>\n",
       "      <td>1184.0</td>\n",
       "      <td>429.0</td>\n",
       "      <td>3.9141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11176</th>\n",
       "      <td>-117.96</td>\n",
       "      <td>33.83</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2838.0</td>\n",
       "      <td>649.0</td>\n",
       "      <td>1758.0</td>\n",
       "      <td>593.0</td>\n",
       "      <td>3.3831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15614</th>\n",
       "      <td>-122.41</td>\n",
       "      <td>37.81</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1178.0</td>\n",
       "      <td>545.0</td>\n",
       "      <td>592.0</td>\n",
       "      <td>441.0</td>\n",
       "      <td>3.6728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2953</th>\n",
       "      <td>-119.02</td>\n",
       "      <td>35.35</td>\n",
       "      <td>42.0</td>\n",
       "      <td>1239.0</td>\n",
       "      <td>251.0</td>\n",
       "      <td>776.0</td>\n",
       "      <td>272.0</td>\n",
       "      <td>1.9830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13209</th>\n",
       "      <td>-117.72</td>\n",
       "      <td>34.05</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1841.0</td>\n",
       "      <td>409.0</td>\n",
       "      <td>1243.0</td>\n",
       "      <td>394.0</td>\n",
       "      <td>4.0614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6569</th>\n",
       "      <td>-118.15</td>\n",
       "      <td>34.20</td>\n",
       "      <td>46.0</td>\n",
       "      <td>1505.0</td>\n",
       "      <td>261.0</td>\n",
       "      <td>857.0</td>\n",
       "      <td>269.0</td>\n",
       "      <td>4.5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5825</th>\n",
       "      <td>-118.30</td>\n",
       "      <td>34.19</td>\n",
       "      <td>14.0</td>\n",
       "      <td>3615.0</td>\n",
       "      <td>913.0</td>\n",
       "      <td>1924.0</td>\n",
       "      <td>852.0</td>\n",
       "      <td>3.5083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18086</th>\n",
       "      <td>-122.05</td>\n",
       "      <td>37.31</td>\n",
       "      <td>25.0</td>\n",
       "      <td>4111.0</td>\n",
       "      <td>538.0</td>\n",
       "      <td>1585.0</td>\n",
       "      <td>568.0</td>\n",
       "      <td>9.2298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16718</th>\n",
       "      <td>-120.66</td>\n",
       "      <td>35.49</td>\n",
       "      <td>17.0</td>\n",
       "      <td>4422.0</td>\n",
       "      <td>945.0</td>\n",
       "      <td>2307.0</td>\n",
       "      <td>885.0</td>\n",
       "      <td>2.8285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13600</th>\n",
       "      <td>-117.25</td>\n",
       "      <td>34.16</td>\n",
       "      <td>37.0</td>\n",
       "      <td>1709.0</td>\n",
       "      <td>278.0</td>\n",
       "      <td>744.0</td>\n",
       "      <td>274.0</td>\n",
       "      <td>3.7188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13989</th>\n",
       "      <td>-117.19</td>\n",
       "      <td>34.94</td>\n",
       "      <td>31.0</td>\n",
       "      <td>2034.0</td>\n",
       "      <td>444.0</td>\n",
       "      <td>1097.0</td>\n",
       "      <td>367.0</td>\n",
       "      <td>2.1522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15168</th>\n",
       "      <td>-117.06</td>\n",
       "      <td>33.02</td>\n",
       "      <td>24.0</td>\n",
       "      <td>830.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>279.0</td>\n",
       "      <td>196.0</td>\n",
       "      <td>1.9176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6747</th>\n",
       "      <td>-118.07</td>\n",
       "      <td>34.11</td>\n",
       "      <td>41.0</td>\n",
       "      <td>2869.0</td>\n",
       "      <td>563.0</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>533.0</td>\n",
       "      <td>5.0736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7398</th>\n",
       "      <td>-118.24</td>\n",
       "      <td>33.96</td>\n",
       "      <td>44.0</td>\n",
       "      <td>1338.0</td>\n",
       "      <td>366.0</td>\n",
       "      <td>1765.0</td>\n",
       "      <td>388.0</td>\n",
       "      <td>1.7778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5562</th>\n",
       "      <td>-118.28</td>\n",
       "      <td>33.91</td>\n",
       "      <td>41.0</td>\n",
       "      <td>620.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>642.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>2.6546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16121</th>\n",
       "      <td>-122.46</td>\n",
       "      <td>37.79</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2059.0</td>\n",
       "      <td>416.0</td>\n",
       "      <td>999.0</td>\n",
       "      <td>402.0</td>\n",
       "      <td>3.7419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12380</th>\n",
       "      <td>-116.47</td>\n",
       "      <td>33.77</td>\n",
       "      <td>26.0</td>\n",
       "      <td>4300.0</td>\n",
       "      <td>767.0</td>\n",
       "      <td>1557.0</td>\n",
       "      <td>669.0</td>\n",
       "      <td>4.4107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5618</th>\n",
       "      <td>-118.23</td>\n",
       "      <td>33.78</td>\n",
       "      <td>20.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2.5588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10060</th>\n",
       "      <td>-121.06</td>\n",
       "      <td>39.25</td>\n",
       "      <td>17.0</td>\n",
       "      <td>3127.0</td>\n",
       "      <td>539.0</td>\n",
       "      <td>1390.0</td>\n",
       "      <td>520.0</td>\n",
       "      <td>3.9537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18067</th>\n",
       "      <td>-122.03</td>\n",
       "      <td>37.29</td>\n",
       "      <td>22.0</td>\n",
       "      <td>3118.0</td>\n",
       "      <td>438.0</td>\n",
       "      <td>1147.0</td>\n",
       "      <td>425.0</td>\n",
       "      <td>10.3653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4471</th>\n",
       "      <td>-118.17</td>\n",
       "      <td>34.09</td>\n",
       "      <td>33.0</td>\n",
       "      <td>2907.0</td>\n",
       "      <td>797.0</td>\n",
       "      <td>3212.0</td>\n",
       "      <td>793.0</td>\n",
       "      <td>2.2348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19786</th>\n",
       "      <td>-122.86</td>\n",
       "      <td>40.56</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1350.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>423.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>1.7393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9969</th>\n",
       "      <td>-122.48</td>\n",
       "      <td>38.51</td>\n",
       "      <td>49.0</td>\n",
       "      <td>1977.0</td>\n",
       "      <td>393.0</td>\n",
       "      <td>741.0</td>\n",
       "      <td>339.0</td>\n",
       "      <td>3.1312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14621</th>\n",
       "      <td>-117.17</td>\n",
       "      <td>32.78</td>\n",
       "      <td>17.0</td>\n",
       "      <td>3845.0</td>\n",
       "      <td>1051.0</td>\n",
       "      <td>3102.0</td>\n",
       "      <td>944.0</td>\n",
       "      <td>2.3658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>579</th>\n",
       "      <td>-122.07</td>\n",
       "      <td>37.71</td>\n",
       "      <td>40.0</td>\n",
       "      <td>1808.0</td>\n",
       "      <td>302.0</td>\n",
       "      <td>746.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>5.3015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11682</th>\n",
       "      <td>-118.01</td>\n",
       "      <td>33.87</td>\n",
       "      <td>25.0</td>\n",
       "      <td>6348.0</td>\n",
       "      <td>1615.0</td>\n",
       "      <td>4188.0</td>\n",
       "      <td>1497.0</td>\n",
       "      <td>3.1390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>-122.21</td>\n",
       "      <td>37.78</td>\n",
       "      <td>43.0</td>\n",
       "      <td>1702.0</td>\n",
       "      <td>460.0</td>\n",
       "      <td>1227.0</td>\n",
       "      <td>407.0</td>\n",
       "      <td>1.7188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12130</th>\n",
       "      <td>-117.23</td>\n",
       "      <td>33.94</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2405.0</td>\n",
       "      <td>537.0</td>\n",
       "      <td>1594.0</td>\n",
       "      <td>517.0</td>\n",
       "      <td>3.0789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16441</th>\n",
       "      <td>-121.29</td>\n",
       "      <td>38.14</td>\n",
       "      <td>34.0</td>\n",
       "      <td>2770.0</td>\n",
       "      <td>544.0</td>\n",
       "      <td>1409.0</td>\n",
       "      <td>535.0</td>\n",
       "      <td>3.2338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11016</th>\n",
       "      <td>-117.82</td>\n",
       "      <td>33.76</td>\n",
       "      <td>33.0</td>\n",
       "      <td>2774.0</td>\n",
       "      <td>428.0</td>\n",
       "      <td>1229.0</td>\n",
       "      <td>407.0</td>\n",
       "      <td>6.2944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19934</th>\n",
       "      <td>-119.34</td>\n",
       "      <td>36.31</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1635.0</td>\n",
       "      <td>422.0</td>\n",
       "      <td>870.0</td>\n",
       "      <td>399.0</td>\n",
       "      <td>2.7000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1364</th>\n",
       "      <td>-122.14</td>\n",
       "      <td>38.03</td>\n",
       "      <td>42.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2.5795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1236</th>\n",
       "      <td>-120.37</td>\n",
       "      <td>38.23</td>\n",
       "      <td>13.0</td>\n",
       "      <td>4401.0</td>\n",
       "      <td>829.0</td>\n",
       "      <td>924.0</td>\n",
       "      <td>383.0</td>\n",
       "      <td>2.6942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5364</th>\n",
       "      <td>-118.42</td>\n",
       "      <td>34.04</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1358.0</td>\n",
       "      <td>272.0</td>\n",
       "      <td>574.0</td>\n",
       "      <td>267.0</td>\n",
       "      <td>5.6454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11703</th>\n",
       "      <td>-117.97</td>\n",
       "      <td>33.88</td>\n",
       "      <td>16.0</td>\n",
       "      <td>2003.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>1172.0</td>\n",
       "      <td>318.0</td>\n",
       "      <td>6.0394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10356</th>\n",
       "      <td>-117.67</td>\n",
       "      <td>33.60</td>\n",
       "      <td>25.0</td>\n",
       "      <td>3164.0</td>\n",
       "      <td>449.0</td>\n",
       "      <td>1517.0</td>\n",
       "      <td>453.0</td>\n",
       "      <td>6.7921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15270</th>\n",
       "      <td>-117.29</td>\n",
       "      <td>33.08</td>\n",
       "      <td>18.0</td>\n",
       "      <td>3225.0</td>\n",
       "      <td>515.0</td>\n",
       "      <td>1463.0</td>\n",
       "      <td>476.0</td>\n",
       "      <td>5.7787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3754</th>\n",
       "      <td>-118.37</td>\n",
       "      <td>34.18</td>\n",
       "      <td>36.0</td>\n",
       "      <td>1608.0</td>\n",
       "      <td>373.0</td>\n",
       "      <td>1217.0</td>\n",
       "      <td>374.0</td>\n",
       "      <td>2.9728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12166</th>\n",
       "      <td>-117.14</td>\n",
       "      <td>33.81</td>\n",
       "      <td>13.0</td>\n",
       "      <td>4496.0</td>\n",
       "      <td>756.0</td>\n",
       "      <td>2044.0</td>\n",
       "      <td>695.0</td>\n",
       "      <td>3.2778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6003</th>\n",
       "      <td>-117.77</td>\n",
       "      <td>34.08</td>\n",
       "      <td>27.0</td>\n",
       "      <td>5929.0</td>\n",
       "      <td>932.0</td>\n",
       "      <td>2817.0</td>\n",
       "      <td>828.0</td>\n",
       "      <td>6.0434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7364</th>\n",
       "      <td>-118.20</td>\n",
       "      <td>33.97</td>\n",
       "      <td>43.0</td>\n",
       "      <td>825.0</td>\n",
       "      <td>212.0</td>\n",
       "      <td>820.0</td>\n",
       "      <td>184.0</td>\n",
       "      <td>1.8897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6563</th>\n",
       "      <td>-118.13</td>\n",
       "      <td>34.20</td>\n",
       "      <td>46.0</td>\n",
       "      <td>1271.0</td>\n",
       "      <td>236.0</td>\n",
       "      <td>573.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>4.9312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12053</th>\n",
       "      <td>-117.56</td>\n",
       "      <td>33.88</td>\n",
       "      <td>40.0</td>\n",
       "      <td>1196.0</td>\n",
       "      <td>294.0</td>\n",
       "      <td>1052.0</td>\n",
       "      <td>258.0</td>\n",
       "      <td>2.0682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13908</th>\n",
       "      <td>-116.40</td>\n",
       "      <td>34.09</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4855.0</td>\n",
       "      <td>872.0</td>\n",
       "      <td>2098.0</td>\n",
       "      <td>765.0</td>\n",
       "      <td>3.2723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11159</th>\n",
       "      <td>-118.01</td>\n",
       "      <td>33.82</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1960.0</td>\n",
       "      <td>380.0</td>\n",
       "      <td>1356.0</td>\n",
       "      <td>356.0</td>\n",
       "      <td>4.0625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15775</th>\n",
       "      <td>-122.45</td>\n",
       "      <td>37.77</td>\n",
       "      <td>52.0</td>\n",
       "      <td>3095.0</td>\n",
       "      <td>682.0</td>\n",
       "      <td>1269.0</td>\n",
       "      <td>639.0</td>\n",
       "      <td>3.5750</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>16512 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "17606    -121.89     37.29                38.0       1568.0           351.0   \n",
       "18632    -121.93     37.05                14.0        679.0           108.0   \n",
       "14650    -117.20     32.77                31.0       1952.0           471.0   \n",
       "3230     -119.61     36.31                25.0       1847.0           371.0   \n",
       "3555     -118.59     34.23                17.0       6592.0          1525.0   \n",
       "19480    -120.97     37.66                24.0       2930.0           588.0   \n",
       "8879     -118.50     34.04                52.0       2233.0           317.0   \n",
       "13685    -117.24     34.15                26.0       2041.0           293.0   \n",
       "4937     -118.26     33.99                47.0       1865.0           465.0   \n",
       "4861     -118.28     34.02                29.0        515.0           229.0   \n",
       "16365    -121.31     38.02                24.0       4157.0           951.0   \n",
       "19684    -121.62     39.14                41.0       2183.0           559.0   \n",
       "19234    -122.69     38.51                18.0       3364.0           501.0   \n",
       "13956    -117.06     34.17                21.0       2520.0           582.0   \n",
       "2390     -119.46     36.91                12.0       2980.0           495.0   \n",
       "11176    -117.96     33.83                30.0       2838.0           649.0   \n",
       "15614    -122.41     37.81                25.0       1178.0           545.0   \n",
       "2953     -119.02     35.35                42.0       1239.0           251.0   \n",
       "13209    -117.72     34.05                 8.0       1841.0           409.0   \n",
       "6569     -118.15     34.20                46.0       1505.0           261.0   \n",
       "5825     -118.30     34.19                14.0       3615.0           913.0   \n",
       "18086    -122.05     37.31                25.0       4111.0           538.0   \n",
       "16718    -120.66     35.49                17.0       4422.0           945.0   \n",
       "13600    -117.25     34.16                37.0       1709.0           278.0   \n",
       "13989    -117.19     34.94                31.0       2034.0           444.0   \n",
       "15168    -117.06     33.02                24.0        830.0           190.0   \n",
       "6747     -118.07     34.11                41.0       2869.0           563.0   \n",
       "7398     -118.24     33.96                44.0       1338.0           366.0   \n",
       "5562     -118.28     33.91                41.0        620.0           133.0   \n",
       "16121    -122.46     37.79                52.0       2059.0           416.0   \n",
       "...          ...       ...                 ...          ...             ...   \n",
       "12380    -116.47     33.77                26.0       4300.0           767.0   \n",
       "5618     -118.23     33.78                20.0         59.0            24.0   \n",
       "10060    -121.06     39.25                17.0       3127.0           539.0   \n",
       "18067    -122.03     37.29                22.0       3118.0           438.0   \n",
       "4471     -118.17     34.09                33.0       2907.0           797.0   \n",
       "19786    -122.86     40.56                12.0       1350.0           300.0   \n",
       "9969     -122.48     38.51                49.0       1977.0           393.0   \n",
       "14621    -117.17     32.78                17.0       3845.0          1051.0   \n",
       "579      -122.07     37.71                40.0       1808.0           302.0   \n",
       "11682    -118.01     33.87                25.0       6348.0          1615.0   \n",
       "245      -122.21     37.78                43.0       1702.0           460.0   \n",
       "12130    -117.23     33.94                 8.0       2405.0           537.0   \n",
       "16441    -121.29     38.14                34.0       2770.0           544.0   \n",
       "11016    -117.82     33.76                33.0       2774.0           428.0   \n",
       "19934    -119.34     36.31                14.0       1635.0           422.0   \n",
       "1364     -122.14     38.03                42.0        118.0            34.0   \n",
       "1236     -120.37     38.23                13.0       4401.0           829.0   \n",
       "5364     -118.42     34.04                52.0       1358.0           272.0   \n",
       "11703    -117.97     33.88                16.0       2003.0           300.0   \n",
       "10356    -117.67     33.60                25.0       3164.0           449.0   \n",
       "15270    -117.29     33.08                18.0       3225.0           515.0   \n",
       "3754     -118.37     34.18                36.0       1608.0           373.0   \n",
       "12166    -117.14     33.81                13.0       4496.0           756.0   \n",
       "6003     -117.77     34.08                27.0       5929.0           932.0   \n",
       "7364     -118.20     33.97                43.0        825.0           212.0   \n",
       "6563     -118.13     34.20                46.0       1271.0           236.0   \n",
       "12053    -117.56     33.88                40.0       1196.0           294.0   \n",
       "13908    -116.40     34.09                 9.0       4855.0           872.0   \n",
       "11159    -118.01     33.82                31.0       1960.0           380.0   \n",
       "15775    -122.45     37.77                52.0       3095.0           682.0   \n",
       "\n",
       "       population  households  median_income  \n",
       "17606       710.0       339.0         2.7042  \n",
       "18632       306.0       113.0         6.4214  \n",
       "14650       936.0       462.0         2.8621  \n",
       "3230       1460.0       353.0         1.8839  \n",
       "3555       4459.0      1463.0         3.0347  \n",
       "19480      1448.0       570.0         3.5395  \n",
       "8879        769.0       277.0         8.3839  \n",
       "13685       936.0       375.0         6.0000  \n",
       "4937       1916.0       438.0         1.8242  \n",
       "4861       2690.0       217.0         0.4999  \n",
       "16365      2734.0       879.0         2.7981  \n",
       "19684      1202.0       506.0         1.6902  \n",
       "19234      1442.0       506.0         6.6854  \n",
       "13956       416.0       151.0         2.7120  \n",
       "2390       1184.0       429.0         3.9141  \n",
       "11176      1758.0       593.0         3.3831  \n",
       "15614       592.0       441.0         3.6728  \n",
       "2953        776.0       272.0         1.9830  \n",
       "13209      1243.0       394.0         4.0614  \n",
       "6569        857.0       269.0         4.5000  \n",
       "5825       1924.0       852.0         3.5083  \n",
       "18086      1585.0       568.0         9.2298  \n",
       "16718      2307.0       885.0         2.8285  \n",
       "13600       744.0       274.0         3.7188  \n",
       "13989      1097.0       367.0         2.1522  \n",
       "15168       279.0       196.0         1.9176  \n",
       "6747       1627.0       533.0         5.0736  \n",
       "7398       1765.0       388.0         1.7778  \n",
       "5562        642.0       162.0         2.6546  \n",
       "16121       999.0       402.0         3.7419  \n",
       "...           ...         ...            ...  \n",
       "12380      1557.0       669.0         4.4107  \n",
       "5618         69.0        23.0         2.5588  \n",
       "10060      1390.0       520.0         3.9537  \n",
       "18067      1147.0       425.0        10.3653  \n",
       "4471       3212.0       793.0         2.2348  \n",
       "19786       423.0       172.0         1.7393  \n",
       "9969        741.0       339.0         3.1312  \n",
       "14621      3102.0       944.0         2.3658  \n",
       "579         746.0       270.0         5.3015  \n",
       "11682      4188.0      1497.0         3.1390  \n",
       "245        1227.0       407.0         1.7188  \n",
       "12130      1594.0       517.0         3.0789  \n",
       "16441      1409.0       535.0         3.2338  \n",
       "11016      1229.0       407.0         6.2944  \n",
       "19934       870.0       399.0         2.7000  \n",
       "1364         54.0        30.0         2.5795  \n",
       "1236        924.0       383.0         2.6942  \n",
       "5364        574.0       267.0         5.6454  \n",
       "11703      1172.0       318.0         6.0394  \n",
       "10356      1517.0       453.0         6.7921  \n",
       "15270      1463.0       476.0         5.7787  \n",
       "3754       1217.0       374.0         2.9728  \n",
       "12166      2044.0       695.0         3.2778  \n",
       "6003       2817.0       828.0         6.0434  \n",
       "7364        820.0       184.0         1.8897  \n",
       "6563        573.0       210.0         4.9312  \n",
       "12053      1052.0       258.0         2.0682  \n",
       "13908      2098.0       765.0         3.2723  \n",
       "11159      1356.0       356.0         4.0625  \n",
       "15775      1269.0       639.0         3.5750  \n",
       "\n",
       "[16512 rows x 8 columns]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imputer.statistics_\n",
    "train_int.median().values\n",
    "train_int\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = imputer.transform(train_int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_tr = pd.DataFrame(X,columns = train_int.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 4, ..., 1, 0, 3])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "encoder = LabelEncoder()\n",
    "train_cat = train[\"ocean_proximity\"]\n",
    "train_cat_encoded = encoder.fit_transform(train_cat)\n",
    "train_cat_encoded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['<1H OCEAN' 'INLAND' 'ISLAND' 'NEAR BAY' 'NEAR OCEAN']\n"
     ]
    }
   ],
   "source": [
    "print(encoder.classes_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:371: FutureWarning: The handling of integer data will change in version 0.22. Currently, the categories are determined based on the range [0, max(values)], while in the future they will be determined based on the unique values.\n",
      "If you want the future behaviour and silence this warning, you can specify \"categories='auto'\".\n",
      "In case you used a LabelEncoder before this OneHotEncoder to convert the categories to integers, then you can now use the OneHotEncoder directly.\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<16512x5 sparse matrix of type '<class 'numpy.float64'>'\n",
       "\twith 16512 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder\n",
    "encoder = OneHotEncoder()\n",
    "train_cat_1hot = encoder.fit_transform(train_cat_encoded.reshape(-1,1))\n",
    "train_cat_1hot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<16512x5 sparse matrix of type '<class 'numpy.float64'>'\n",
       "\twith 16512 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelBinarizer\n",
    "encoder = LabelBinarizer()\n",
    "train_cat_1hot = encoder.fit_transform(train_cat)\n",
    "train_cat_1hot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.base import BaseEstimator, TransformerMixin\n",
    "rooms_ix, bedrooms_ix, population_ix, household_ix =3,4,5,6\n",
    "\n",
    "class CombinedAttributesAdder(BaseEstimator,TransformerMixin):\n",
    "    def __init__(self,add_bedrooms_per_room=True):\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,\n",
    "        bedrooms_per_room]\n",
    "        else:\n",
    "            return np.c_[X, rooms_per_household, population_per_household]\n",
    "attr_adder = CombinedAttributesAdder(add_bedrooms_per_room=False)\n",
    "housing_extra_attribs = attr_adder.transform(housing.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "num_pipeline = Pipeline([('imputer', SimpleImputer(strategy =\"median\")),('attribs_adder', CombinedAttributesAdder()), ('std_scaler',StandardScaler())])\n",
    "train.new = num_pipeline.fit_transform(train_int)\n",
    "class LabelBinarizerPipelineFriendly(LabelBinarizer):\n",
    "    def fit(self, X, y=None):\n",
    "        \"\"\"this would allow us to fit the model based on the X input.\"\"\"\n",
    "        super(LabelBinarizerPipelineFriendly, self).fit(X)\n",
    "    def transform(self, X, y=None):\n",
    "        return super(LabelBinarizerPipelineFriendly, self).transform(X)\n",
    "\n",
    "    def fit_transform(self, X, y=None):\n",
    "        return super(LabelBinarizerPipelineFriendly, self).fit(X).transform(X)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "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",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.pipeline import FeatureUnion\n",
    "num_attribs = list(train_int)\n",
    "cat_attribs = [\"ocean_proximity\"]\n",
    "\n",
    "num_pipeline = Pipeline([('selector', DataFrameSelector(num_attribs)), ('imputer', SimpleImputer(strategy=\"median\")), ('attribs_adder', CombinedAttributesAdder()), ('std_scaler',StandardScaler())])\n",
    "cat_pipeline = Pipeline([('selector',DataFrameSelector(cat_attribs)),('label_binarizer', LabelBinarizerPipelineFriendly())])\n",
    "full_pipeline = FeatureUnion(transformer_list=[(\"num_pipeline\",num_pipeline),(\"cat_pipeline\", cat_pipeline)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'DataFrame' object has no attribute 'column'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-100-94f5d0c2c037>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mtrain_labels\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtrain\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python37\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36m__getattr__\u001b[1;34m(self, name)\u001b[0m\n\u001b[0;32m   5065\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_info_axis\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_can_hold_identifiers_and_holds_name\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5066\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 5067\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   5068\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5069\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__setattr__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'DataFrame' object has no attribute 'column'"
     ]
    }
   ],
   "source": [
    "train_labels = train.columns()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_prepared = full_pipeline.fit_transform(train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "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": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_prepared"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n",
       "         normalize=False)"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "lin_reg = LinearRegression()\n",
    "lin_reg.fit(train_prepared, train_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "some_data = train. iloc[:5]\n",
    "some_labels = train_labels.iloc[:5]\n",
    "some_data_prep = full_pipeline.transform(some_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predictions:\t  [210644.60459286 317768.80697211 210956.43331178  59218.98886849\n",
      " 189747.55849879]\n"
     ]
    }
   ],
   "source": [
    "print(\"Predictions:\\t \", lin_reg.predict(some_data_prep))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Labels:\t\t [286600.0, 340600.0, 196900.0, 46300.0, 254500.0]\n"
     ]
    }
   ],
   "source": [
    "print(\"Labels:\\t\\t\",list(some_labels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "68628.19819848923"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "train_prediction = lin_reg.predict(train_prepared)\n",
    "lin_mse = mean_squared_error(train_labels, train_prediction)\n",
    "lin_rmse = np.sqrt(lin_mse)\n",
    "lin_rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(criterion='mse', max_depth=None, max_features=None,\n",
       "           max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
       "           min_impurity_split=None, min_samples_leaf=1,\n",
       "           min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "           presort=False, random_state=None, splitter='best')"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "\n",
    "tree_reg = DecisionTreeRegressor()\n",
    "tree_reg.fit(train_prepared, train_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_prediction = tree_reg.predict(train_prepared)\n",
    "tree_mse = mean_squared_error(train_labels,train_prediction)\n",
    "tree_rmse = np.sqrt(tree_mse)\n",
    "tree_rmse "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "scores = cross_val_score(tree_reg, train_prepared, train_labels, scoring = \"neg_mean_squared_error\",cv=10)\n",
    "rmse_scores = np.sqrt(-scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores: [69204.48091163 66337.10736172 71018.48091457 68971.19240747\n",
      " 71410.87407278 73119.54685943 71455.54687245 70428.19720738\n",
      " 77073.41865098 70035.01732624]\n",
      "Mean: 70905.3862584657\n",
      "Standard Deviation 2683.406868032978\n"
     ]
    }
   ],
   "source": [
    "def display_scores(scores):\n",
    "    print(\"Scores:\", scores)\n",
    "    print(\"Mean:\", scores.mean())\n",
    "    print(\"Standard Deviation\", scores.std())\n",
    "    \n",
    "display_scores(rmse_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "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]\n",
      "Mean: 69052.46136345083\n",
      "Standard Deviation 2731.6740017983425\n"
     ]
    }
   ],
   "source": [
    "score_lin_reg = cross_val_score(lin_reg,train_prepared, train_labels, scoring = \"neg_mean_squared_error\", cv=10)\n",
    "linreg_rmse_scores = np.sqrt(-score_lin_reg)\n",
    "display_scores(linreg_rmse_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores: [52352.60755588 50118.09169425 51651.76126871 54526.51058986\n",
      " 51856.40086496 56288.91276418 52267.88229961 50200.98977248\n",
      " 55082.88571313 52231.77732339]\n",
      "Mean: 52657.78198464529\n",
      "Standard Deviation 1926.7891094894899\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "forest_reg = RandomForestRegressor()\n",
    "forest_reg.fit(train_prepared,train_labels)\n",
    "score_forest= cross_val_score(forest_reg,train_prepared, train_labels, scoring = \"neg_mean_squared_error\", cv=10)\n",
    "forest_rmse_scores = np.sqrt(-score_forest)\n",
    "display_scores(forest_rmse_scores)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise-deprecating',\n",
       "       estimator=RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "           max_features='auto', max_leaf_nodes=None,\n",
       "           min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "           min_samples_leaf=1, min_samples_split=2,\n",
       "           min_weight_fraction_leaf=0.0, n_estimators='warn', n_jobs=None,\n",
       "           oob_score=False, random_state=None, verbose=0, warm_start=False),\n",
       "       fit_params=None, iid='warn', n_jobs=None,\n",
       "       param_grid=[{'n_estimators': [3, 10, 30], 'max_features': [2, 4, 6, 8]}, {'bootstrap': [False], 'n_estimators': [3, 10], 'max_features': [2, 3, 4]}],\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring='neg_mean_squared_error', verbose=0)"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "param_grid = [\n",
    "{'n_estimators': [3, 10, 30], 'max_features': [2, 4, 6, 8]},\n",
    "{'bootstrap': [False], 'n_estimators': [3, 10], 'max_features': [2, 3, 4]},\n",
    "]\n",
    "forest_reg = RandomForestRegressor()\n",
    "grid_search = GridSearchCV(forest_reg, param_grid, cv=5,\n",
    "scoring='neg_mean_squared_error')\n",
    "grid_search.fit(train_prepared, train_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_features': 8, 'n_estimators': 30}"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([6.80565923e-02, 6.38903947e-02, 4.33382355e-02, 1.52619792e-02,\n",
       "       1.52326083e-02, 1.48534952e-02, 1.46570677e-02, 3.71347413e-01,\n",
       "       5.63928343e-02, 1.10135157e-01, 4.93660326e-02, 7.60552598e-03,\n",
       "       1.65248326e-01, 2.87050272e-05, 1.81049822e-03, 2.77513487e-03])"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_importances = grid_search.best_estimator_.feature_importances_\n",
    "feature_importances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0.37134741303875923, 'median_income'),\n",
       " (0.16524832568435802, 'INLAND'),\n",
       " (0.1101351573134371, 'pop_per_hhold'),\n",
       " (0.06805659226301118, 'longitude'),\n",
       " (0.06389039472735933, 'latitude'),\n",
       " (0.056392834262856927, 'rooms_per_hhold'),\n",
       " (0.049366032635802075, 'bedrooms_per_room'),\n",
       " (0.04333823550813736, 'housing_median_age'),\n",
       " (0.015261979159267311, 'total_rooms'),\n",
       " (0.015232608323395961, 'total_bedrooms'),\n",
       " (0.014853495245830175, 'population'),\n",
       " (0.014657067745449682, 'households'),\n",
       " (0.007605525977799035, '<1H OCEAN'),\n",
       " (0.0027751348676388525, 'NEAR OCEAN'),\n",
       " (0.0018104982196782099, 'NEAR BAY'),\n",
       " (2.870502721943185e-05, 'ISLAND')]"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "extra_attribs = [\"rooms_per_hhold\", \"pop_per_hhold\", \"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": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:652: Warning: The least populated class in y has only 1 members, which is too few. The minimum number of members in any class cannot be less than n_splits=10.\n",
      "  % (min_groups, self.n_splits)), Warning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn import svm\n",
    "svm_reg = svm.SVC(kernel=\"linear\")\n",
    "svm_reg_ms = cross_val_score(svm_reg,train_prepared,train_labels,scoring = \"neg_mean_squared_error\",  cv = 10)\n",
    "svm_rmse = np.sqrt(-svm_reg_ms)\n",
    "display.scores(svm_rmse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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