House Value Prediction - Califonia Dataset..ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Author: Abhishek Sharma\n",
    "#### Program: Prediction of House Value using California State dataset. \n",
    "#### Last Modified: Feb 25, 2018"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import division, print_function, unicode_literals\n",
    "\n",
    "import os\n",
    "import numpy as np\n",
    "\n",
    "np.random.seed(42)\n",
    "\n",
    "%matplotlib inline\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.rcParams['axes.labelsize'] = 14\n",
    "plt.rcParams['xtick.labelsize'] = 12\n",
    "plt.rcParams['ytick.labelsize'] = 12\n",
    "\n",
    "# Function to save the figures. \n",
    "PROJECT_ROOT_DIR = \".\"\n",
    "PROGRAM = \"california_state_data\"\n",
    "IMAGES_PATH = os.path.join(PROJECT_ROOT_DIR, \"images\", PROGRAM)\n",
    "\n",
    "def save_fig (fig_id, tight_layout=True, fig_extension='png',resolution=300):\n",
    "    path = os.path.join(IMAGES_PATH, fig_id + \".\" + fig_extension)\n",
    "    print(\"Saving figure\", fig_id)\n",
    "    if tight_layout:\n",
    "        plt.tight_layout()\n",
    "    plt.savefig(path,format=fig_extension, dpi=resolution)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import tarfile\n",
    "from six.moves import urllib\n",
    "\n",
    "DOWNLOAD_ROOT = \"https://raw.githubusercontent.com/ageron/handson-ml/master/\"\n",
    "HOUSING_PATH = os.path.join(\"datasets\", \"housing\")\n",
    "HOUSING_URL = DOWNLOAD_ROOT + \"datasets/housing/housing.tgz\"\n",
    "\n",
    "\n",
    "def fetch_housing_data(housing_url=HOUSING_URL, housing_path=HOUSING_PATH):\n",
    "    if not os.path.isdir(housing_path):\n",
    "        os.makedirs(housing_path)\n",
    "    tgz_path = os.path.join(housing_path,\"housing.tgz\")\n",
    "    urllib.request.urlretrieve(housing_url,tgz_path)\n",
    "    housing_tgz = tarfile.open(tgz_path)\n",
    "    housing_tgz.extractall(path=housing_path)\n",
    "    housing_tgz.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fetch_housing_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "def load_housing_data(housing_path=HOUSING_PATH):\n",
    "    csv_path = os.path.join(housing_path, \"housing.csv\")\n",
    "    return pd.read_csv(csv_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>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": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing = load_housing_data()\n",
    "housing.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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": 13,
   "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": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing[\"ocean_proximity\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure attribute_histogram_plots\n"
     ]
    },
    {
     "data": {
      "image/png": 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SJEnSwEryOOA9wLuq6rYkS4GdfYfdBxwFLO153h+jjY+X+yhVtQXYArB27doa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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112dec438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline \n",
    "import matplotlib.pyplot as plt\n",
    "housing.hist(bins=50, figsize=(20,15))\n",
    "save_fig(\"attribute_histogram_plots\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}