Explorify - A Location Recommender.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing Required Packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import csv\n",
    "import requests\n",
    "import pprint\n",
    "import time\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.basemap import Basemap\n",
    "import os.path\n",
    "from sklearn.externals.joblib import Memory\n",
    "from hmmlearn import hmm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Declaring API Keys and Global Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Declaring API Keys Used\n",
    "places_api_web_service =  'PLACES_API_KEY'\n",
    "geocoding_api = 'GEOCODING_API_KEY'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Declaring Global Variables Used\n",
    "pause = 0.1\n",
    "max_api_requests = 150000 \n",
    "api_requests_count = 0\n",
    "pp = pprint.PrettyPrinter(indent=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cleaning Raw JSON Data of Google Location History and storing it into CSV File"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Specifying Input and Output Data Files\n",
    "input_jsonFile = 'Location History.json'\n",
    "output_csvFile = 'GoogleLocationHistory.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loading Data from JSON File\n",
    "data = json.load(open(input_jsonFile))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Writing Header Row in Output CSV File\n",
    "with open(output_csvFile, 'w') as myfile:\n",
    "    wr = csv.writer(myfile, quoting=csv.QUOTE_ALL)\n",
    "    heading = [\"timestamp\", \"latitude\", \"longitude\", \"accuracy\"]\n",
    "    wr.writerow(heading)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Writing Relevant Details from JSON File to Output CSV File Row-Wise\n",
    "with open(output_csvFile, 'a') as myfile:\n",
    "    wr = csv.writer(myfile, quoting=csv.QUOTE_ALL)\n",
    "    \n",
    "    for locs in data[\"locations\"]:\n",
    "        valList = [None] * 4\n",
    "        for key, value in locs.items():\n",
    "            if key == \"timestampMs\":\n",
    "                valList[0] = pd.to_datetime(value, unit='ms')\n",
    "            elif key == \"latitudeE7\":\n",
    "                valList[1] = (value*1.0)/(10**7)\n",
    "            elif key == \"longitudeE7\":\n",
    "                valList[2] = (value*1.0)/(10**7)\n",
    "            elif key == \"accuracy\":\n",
    "                valList[3] = value\n",
    "        wr.writerow(valList)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Intial Dataset after Performing Basic Cleaning Operations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv(output_csvFile, header=0)\n",
    "initial_size = df.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of records in dataset initially is: 229051\n",
      "\n",
      "Intial dataset after performing basic cleaning operations is as follows: \n"
     ]
    },
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>timestamp</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-03-20 17:43:43.627000</td>\n",
       "      <td>30.397125</td>\n",
       "      <td>78.074604</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-03-20 17:42:43.587000</td>\n",
       "      <td>30.397125</td>\n",
       "      <td>78.074604</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-03-20 17:41:43.564000</td>\n",
       "      <td>30.397125</td>\n",
       "      <td>78.074604</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-03-20 17:40:43.547000</td>\n",
       "      <td>30.397125</td>\n",
       "      <td>78.074604</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-03-20 17:39:42.418000</td>\n",
       "      <td>30.397154</td>\n",
       "      <td>78.074587</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2018-03-20 17:38:41.337000</td>\n",
       "      <td>30.397154</td>\n",
       "      <td>78.074587</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2018-03-20 17:37:21.868000</td>\n",
       "      <td>30.397154</td>\n",
       "      <td>78.074587</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2018-03-20 17:35:57.040000</td>\n",
       "      <td>30.397154</td>\n",
       "      <td>78.074587</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2018-03-20 17:34:25.569000</td>\n",
       "      <td>30.397141</td>\n",
       "      <td>78.074576</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2018-03-20 17:33:25.413000</td>\n",
       "      <td>30.397141</td>\n",
       "      <td>78.074576</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2018-03-20 17:32:05.999000</td>\n",
       "      <td>30.397141</td>\n",
       "      <td>78.074576</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2018-03-20 17:31:39.008000</td>\n",
       "      <td>30.397141</td>\n",
       "      <td>78.074576</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2018-03-20 17:30:35.318000</td>\n",
       "      <td>30.397141</td>\n",
       "      <td>78.074576</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2018-03-20 17:29:33.918000</td>\n",
       "      <td>30.397141</td>\n",
       "      <td>78.074576</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2018-03-20 17:28:10.998000</td>\n",
       "      <td>30.397141</td>\n",
       "      <td>78.074576</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     timestamp   latitude  longitude  accuracy\n",
       "0   2018-03-20 17:43:43.627000  30.397125  78.074604        23\n",
       "1   2018-03-20 17:42:43.587000  30.397125  78.074604        23\n",
       "2   2018-03-20 17:41:43.564000  30.397125  78.074604        23\n",
       "3   2018-03-20 17:40:43.547000  30.397125  78.074604        23\n",
       "4   2018-03-20 17:39:42.418000  30.397154  78.074587        24\n",
       "5   2018-03-20 17:38:41.337000  30.397154  78.074587        24\n",
       "6   2018-03-20 17:37:21.868000  30.397154  78.074587        24\n",
       "7   2018-03-20 17:35:57.040000  30.397154  78.074587        24\n",
       "8   2018-03-20 17:34:25.569000  30.397141  78.074576        22\n",
       "9   2018-03-20 17:33:25.413000  30.397141  78.074576        22\n",
       "10  2018-03-20 17:32:05.999000  30.397141  78.074576        22\n",
       "11  2018-03-20 17:31:39.008000  30.397141  78.074576        22\n",
       "12  2018-03-20 17:30:35.318000  30.397141  78.074576        22\n",
       "13  2018-03-20 17:29:33.918000  30.397141  78.074576        22\n",
       "14  2018-03-20 17:28:10.998000  30.397141  78.074576        22"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Total number of records in dataset initially is: \" + str(initial_size))\n",
    "\n",
    "print(\"\\nIntial dataset after performing basic cleaning operations is as follows: \")\n",
    "df.head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Description of the dataset is as follows: \n"
     ]
    },
    {
     "data": {
      "text/html": [
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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>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>229051.000000</td>\n",
       "      <td>229051.000000</td>\n",
       "      <td>229051.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>29.724839</td>\n",
       "      <td>77.776024</td>\n",
       "      <td>529.125169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.769143</td>\n",
       "      <td>1.202217</td>\n",
       "      <td>859.287945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>11.254696</td>\n",
       "      <td>69.603041</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>30.395682</td>\n",
       "      <td>78.074355</td>\n",
       "      <td>22.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>30.397131</td>\n",
       "      <td>78.074542</td>\n",
       "      <td>30.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>30.397217</td>\n",
       "      <td>78.074720</td>\n",
       "      <td>1466.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>30.459976</td>\n",
       "      <td>86.144839</td>\n",
       "      <td>22994.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            latitude      longitude       accuracy\n",
       "count  229051.000000  229051.000000  229051.000000\n",
       "mean       29.724839      77.776024     529.125169\n",
       "std         1.769143       1.202217     859.287945\n",
       "min        11.254696      69.603041       3.000000\n",
       "25%        30.395682      78.074355      22.000000\n",
       "50%        30.397131      78.074542      30.000000\n",
       "75%        30.397217      78.074720    1466.000000\n",
       "max        30.459976      86.144839   22994.000000"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Description of the dataset\n",
    "print(\"Description of the dataset is as follows: \")\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data type of each field of the dataset is as follows: \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "timestamp     object\n",
       "latitude     float64\n",
       "longitude    float64\n",
       "accuracy       int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Data type of each field of the dataset\n",
    "print(\"Data type of each field of the dataset is as follows: \")\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mapping the Entire Dataset "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Specifing the colors used for different aspects of World Map\n",
    "land_color = '#f5f5f3'\n",
    "water_color = '#cdd2d4'\n",
    "coastline_color = '#f5f5f3'\n",
    "border_color = '#bbbbbb'\n",
    "meridian_color = '#f5f5f3'\n",
    "marker_fill_color = '#cc3300'\n",
    "marker_edge_color = 'None'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create the Plot\n",
    "fig = plt.figure(figsize=(20, 10))\n",
    "ax = fig.add_subplot(111, frame_on=False)\n",
    "ax.set_title('Google Location History', fontsize=25, color='#333333')\n",
    "\n",
    "\n",
    "# Draw the Basemap and its Features\n",
    "m = Basemap(projection='kav7', lon_0=0, resolution='c', area_thresh=10000)\n",
    "m.drawmapboundary(color=border_color, fill_color=water_color)\n",
    "m.drawcoastlines(color=coastline_color)\n",
    "m.drawcountries(color=border_color)\n",
    "m.fillcontinents(color=land_color, lake_color=water_color)\n",
    "m.drawparallels(np.arange(-90., 120., 30.), color=meridian_color)\n",
    "m.drawmeridians(np.arange(0., 420., 60.), color=meridian_color)\n",
    "\n",
    "\n",
    "# Project the Location History points then Scatter Plot them\n",
    "x, y = m(df['longitude'].values, df['latitude'].values)\n",
    "m.scatter(x, y, s=8, color=marker_fill_color, edgecolor=marker_edge_color, alpha=1, zorder=3)\n",
    "\n",
    "\n",
    "# Show the Map\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Removing Duplicate Records (Latitude - Longitude Pairs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.iloc[:, 1:3]\n",
    "df = df.drop_duplicates()\n",
    "rows = df.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of records in dataset after dropping duplicate records is: 42416\n",
      "\n",
      "Current dataset after dropping duplicate latitude - longitude pairs is as follows: \n"
     ]
    },
    {
     "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>latitude</th>\n",
       "      <th>longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30.397125</td>\n",
       "      <td>78.074604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>30.397154</td>\n",
       "      <td>78.074587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>30.397141</td>\n",
       "      <td>78.074576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>30.397143</td>\n",
       "      <td>78.074586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>30.397141</td>\n",
       "      <td>78.074569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>30.397138</td>\n",
       "      <td>78.074591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>30.397146</td>\n",
       "      <td>78.074570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30.397103</td>\n",
       "      <td>78.074592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>30.397097</td>\n",
       "      <td>78.074594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>30.397089</td>\n",
       "      <td>78.074566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>30.397123</td>\n",
       "      <td>78.074600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>30.397098</td>\n",
       "      <td>78.074600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>30.397150</td>\n",
       "      <td>78.074601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>30.397154</td>\n",
       "      <td>78.074594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>30.397157</td>\n",
       "      <td>78.074550</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     latitude  longitude\n",
       "0   30.397125  78.074604\n",
       "4   30.397154  78.074587\n",
       "8   30.397141  78.074576\n",
       "15  30.397143  78.074586\n",
       "21  30.397141  78.074569\n",
       "22  30.397138  78.074591\n",
       "24  30.397146  78.074570\n",
       "29  30.397103  78.074592\n",
       "34  30.397097  78.074594\n",
       "35  30.397089  78.074566\n",
       "37  30.397123  78.074600\n",
       "41  30.397098  78.074600\n",
       "46  30.397150  78.074601\n",
       "51  30.397154  78.074594\n",
       "52  30.397157  78.074550"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Total number of records in dataset after dropping duplicate records is: \" + str(rows))\n",
    "\n",
    "print(\"\\nCurrent dataset after dropping duplicate latitude - longitude pairs is as follows: \")\n",
    "df.head(15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bar Graph to depict the reduction of size of dataset after dropping duplicate records"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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B6nSjzQZukDQZeKqrMCJOLRaVmZl1lDqj0f4GXJbrDqzcWpI0SdI8STMqZcdJekjSHfm2Z2Xef0maJeleSbtXysflslmSjqyUbyXp5lx+UdfRl6S18uNZef6IGs/RzMwK6vHIJiK+DiBpvfx4Sc11nwOcAZzXrfy0iPhOtUDStsB+wBuBTYE/Stomz/4+8D5gDnCrpMkRcRfw7byuCyX9kHRZnTPz/aKIeJ2k/XK9j9aM2czMCqjzfzbbSbodmAnMlDRN0ht7Wi4irgMW1oxjb+DCiHg2Iv4OzAJ2yrdZETE7Ip4DLgT2VrpuznuAS/Ly5wLjK+s6N09fAuwmX2fHzKyt6nSjnQUcHhFbRsSWwBHAj5ejzUMk3Zm72Qbnss2AByt15uSyZuUbAY9HxPPdypdZV57/RK7/CpImSpoqaer8+fOX4ymZmVkrdZLNuhFxddeDiLgGWLeP7Z0JbE26ivRcXr7+WltExFn5Mjyjhg4d2s5QzMw6Wp1kM1vSMZJG5NvRpBFqvRYRj0bECxHxIunoaKc86yFg80rV4bmsWfkCYJCkAd3Kl1lXnr9Brm9mZm1S62+hgaHAr4BfAkNyWa9J2qTy8ENA10i1ycB+eSTZVsBI4BbgVmBkHnm2JmkQweSICOBqYJ+8/ATgN5V1TcjT+wB/yvXNzKxN6oxGWwQc2tsVS7oAGAMMkTQHOBYYI2l7IID7gc/kNmZKuhi4C3geODj/oBRJhwBXAKsDk/LfUgN8FbhQ0onA7cDZufxs4KeSZpEGKOzX29jNzGzF6jHZSJoC7BsRj+fHg0kjx3ZvtVxEfKxB8dkNyrrqfwP4RoPyy4HLG5TP5uVuuGr5M8C+rWIzM7P+VacbbUhXooGXjnQ2LheSmZl1mjrJ5kVJW3Q9kLQlqRvMzMysljrXRvtv4HpJ15L+FnoXYGLRqMzMrKPUGSDwB0k7AKNz0Rcj4rGyYZmZWSepc7kaAeOAHSLiMmAdSa84MW9mZtZMnXM2PwDeDnSNLltMujimmZlZLXXO2ewcETvki3ESEYv8Z2pmZtYbdY5slua/hw4ASUOBF4tGZWZmHaVOsjkd+DWwsaRvANcD3ywalZmZdZQ6o9HOlzQN2I009Hl8RNxdPDIzM+sYLZNN7j6bGRFvAO7pn5DMzKzTtOxGyxfDvLd6BQEzM7PeqjMabTDp76BvAZ7qKoyIvYpFZWZmHaVOsjmmeBRmZtbR6gwQuLY/AjEzs85VZ+izmZnZcnGyMTOz4pomG0lX5ftv9184ZmbWiVqds9lE0juAvSRdSPpB50si4raikZmZWcdolWy+RhqJNhw4tdu8AN5TKigzM+ssTZNNRFwCXCLpmIg4oR9jMjOzDlNn6PMJkvYC3pWLrsl/omZmZlZLnX/q/BZwGHBXvh0myVd9NjOz2upcQeD9wPYR8SKApHOB24GjSgZmZmado+7vbAZVpjcoEYiZmXWuOkc23wJul3Q1afjzu4Aji0ZlZmYdpc4AgQskXQO8LRd9NSIeKRqVmZl1lDpHNkTEXGBy4VjMzKxD+dpoZmZWnJONmZkV1zLZSFpd0j39FYyZmXWmlskmIl4A7pW0RT/FY2ZmHajOAIHBwExJtwBPdRVGxF7FojIzs45SJ9kcUzwKMzPraHV+Z3OtpC2BkRHxR0nrAKuXD83MzDpFnQtxHgRcAvwoF20GXFoyKDMz6yx1hj4fDLwTeBIgIu4DNu5pIUmTJM2TNKNStqGkKZLuy/eDc7kknS5plqQ7Je1QWWZCrn+fpAmV8h0lTc/LnC5JrdowM7P2qZNsno2I57oeSBpA+qfOnpwDjOtWdiRwVUSMBK7i5Wus7QGMzLeJwJm5rQ2BY4GdgZ2AYyvJ40zgoMpy43pow8zM2qROsrlW0lHA2pLeB/wC+G1PC0XEdcDCbsV7A+fm6XOB8ZXy8yK5CRgkaRNgd2BKRCyMiEXAFGBcnrd+RNwUEQGc121djdowM7M2qZNsjgTmA9OBzwCXA0f3sb1h+TprAI8Aw/L0ZsCDlXpzclmr8jkNylu18QqSJkqaKmnq/Pnz+/B0zMysjjqj0V7Mf5h2M6n77N58NLFcIiIkLfd6lqeNiDgLOAtg1KhRRWMxM1uV1RmN9n7gb8DpwBnALEl79LG9R3MXGPl+Xi5/CNi8Um94LmtVPrxBeas2zMysTep0o50C7BoRYyLi3cCuwGl9bG8y0DWibALwm0r5AXlU2mjgidwVdgUwVtLgPDBgLHBFnvekpNF5FNoB3dbVqA0zM2uTOlcQWBwRsyqPZwOLe1pI0gXAGGCIpDmkUWUnARdL+hTwAPCRXP1yYE9gFvA0cCBARCyUdAJwa653fER0DTr4PGnE29rA7/ONFm2YmVmbNE02kj6cJ6dKuhy4mHTOZl9e/vBvKiI+1mTWbg3qBun3PI3WMwmY1KB8KrBdg/IFjdowM7P2aXVk88HK9KPAu/P0fNLRhJmZWS1Nk01EHNifgZiZWefq8ZyNpK2ALwAjqvX9FwNmZlZXnQEClwJnk64a8GLZcMzMrBPVSTbPRMTpxSMxM7OOVSfZfFfSscCVwLNdhRFxW7GozMyso9RJNm8CPgG8h5e70SI/NjMz61GdZLMv8Nrq3wyYmZn1Rp3L1cwABpUOxMzMOledI5tBwD2SbmXZczYe+mxmZrXUSTbHFo/CzMw6Wp3/s7m2PwIxM7POVecKAotJo88A1gTWAJ6KiPVLBmZmZp2jzpHNwK7p/N8xewOjSwZlZmadpc5otJdEcimwe6F4zMysA9XpRvtw5eFqwCjgmWIRmZlZx6kzGq36vzbPA/eTutLMzMxqqXPOxv9rY2Zmy6XV30J/rcVyEREnFIjHzMw6UKsjm6calK0LfArYCHCyMTOzWlr9LfQpXdOSBgKHAQcCFwKnNFvOzMysu5bnbCRtCBwO7A+cC+wQEYv6IzAzM+scrc7ZnAx8GDgLeFNELOm3qMzMrKO0+lHnEcCmwNHAw5KezLfFkp7sn/DMzKwTtDpn06urC5iZmTXjhGJmZsU52ZiZWXFONmZmVpyTjZmZFedkY2ZmxTnZmJlZcU42ZmZWnJONmZkV52RjZmbFOdmYmVlxTjZmZlZcW5KNpPslTZd0h6SpuWxDSVMk3ZfvB+dySTpd0ixJd0raobKeCbn+fZImVMp3zOuflZdV/z9LMzPr0s4jm10jYvuIGJUfHwlcFREjgavyY4A9gJH5NhE4E176r51jgZ2BnYBjuxJUrnNQZblx5Z+OmZk1szJ1o+1N+oM28v34Svl5kdwEDJK0CbA7MCUiFuY/dJsCjMvz1o+ImyIigPMq6zIzszZoV7IJ4EpJ0yRNzGXDImJunn4EGJanNwMerCw7J5e1Kp/ToNzMzNqk5d9CF/SvEfGQpI2BKZLuqc6MiJAUpYPIiW4iwBZbbFG6OTOzVVZbjmwi4qF8Pw/4Nemcy6O5C4x8Py9XfwjYvLL48FzWqnx4g/JGcZwVEaMiYtTQoUOX92mZmVkT/Z5sJK0raWDXNDAWmAFMBrpGlE0AfpOnJwMH5FFpo4EncnfbFcBYSYPzwICxwBV53pOSRudRaAdU1mVmZm3Qjm60YcCv82jkAcDPI+IPkm4FLpb0KeAB4CO5/uXAnsAs4GngQICIWCjpBODWXO/4iFiYpz8PnAOsDfw+38zMrE36PdlExGzgLQ3KFwC7NSgP4OAm65oETGpQPhXYbrmDNTOzFWJlGvpsZmYdql2j0cysH5025a/tDsFWYl963zbF2/CRjZmZFedkY2ZmxTnZmJlZcU42ZmZWnJONmZkV52RjZmbFOdmYmVlxTjZmZlack42ZmRXnZGNmZsU52ZiZWXFONmZmVpyTjZmZFedkY2ZmxTnZmJlZcU42ZmZWnJONmZkV52RjZmbFOdmYmVlxTjZmZlack42ZmRXnZGNmZsU52ZiZWXFONmZmVpyTjZmZFedkY2ZmxTnZmJlZcU42ZmZWnJONmZkV52RjZmbFOdmYmVlxTjZmZlack42ZmRXnZGNmZsU52ZiZWXEdm2wkjZN0r6RZko5sdzxmZquyjkw2klYHvg/sAWwLfEzStu2Nysxs1dWRyQbYCZgVEbMj4jngQmDvNsdkZrbKGtDuAArZDHiw8ngOsHP3SpImAhPzwyWS7u2H2FYFQ4DH2h3EyuDwdgdgzXgfrVjO/XTLOpU6NdnUEhFnAWe1O45OI2lqRIxqdxxmzXgf7X+d2o32ELB55fHwXGZmZm3QqcnmVmCkpK0krQnsB0xuc0xmZqusjuxGi4jnJR0CXAGsDkyKiJltDmtV4q5JW9l5H+1nioh2x2BmZh2uU7vRzMxsJeJkY2ZmxTnZdBBJS2rU+UnX1RQkHdVt3p9XUBvHSfpyD3XGr+irOkgaIenjK3KdVoakFyTdUbmNkDRK0unLsc77JQ3poc4nJZ3RQ50xkt7R1ziarHOQpM+vyHW+2jjZrGIi4tMRcVd+eFS3eSv0DdaD8aRLCa1IIwAnm1eHf0bE9pXb/RExNSIObXdgwBhgRb8XBgFONtZZ8jezayRdIukeSedLUp53Tf4GeRKwdv5WeX6etyTfryfpKkm3SZouqcdL/Uj6b0l/lXQ98PpK+UGSbpX0F0m/lLRO/ta4F3Bybn/rRvXy8vtKmpHLr8tlq0s6Ode/U9JncnMnAbvkdX5pxW1R6w95v70sTx8naVLeX2dLOrRS71JJ0yTNzFcB6Wm9B+Z98xbgnZXyD0q6WdLtkv4oaZikEcBngS/l/WiXRvXy8u+uHJndLmlgLv9KZd/8em7uJGDrXPfkFbTJXl0iwrcOuQFL8v0Y4AnSj1lXA24E/jXPuwYYVa3fYPkBwPp5eggwi5dHLi5p0O6OwHRgHWD9XP/Led5GlXonAl/I0+cA+1TmNas3HdgsTw/K9xOBo/P0WsBUYKv8vC9r9+vgW6199QXgjnz7dS576fUDjgP+nF/fIcACYI08b8N8vzYwo2vfAe4HhnRrZxPgH8BQYE3gBuCMPG9wZb/+NHBKpe0vV9bRrN5vgXfm6fXy+2YsaVi18nvvMuBdpKPuGe3e7u28deTvbAyAWyJiDoCkO0g7+/U1lxXwTUnvAl4kXWtuGPBIk/q7kD4wns7tVX9Au52kE0ndCOuRfvvUSLN6NwDnSLoY+FUuGwu8WdI++fEGwEjguZrPz9rvnxGxfQ91fhcRzwLPSppH2gfnAIdK+lCusznptV/QZB07A9dExHwASRcB2+R5w4GLJG1CSkR/b7KOZvVuAE7NPQO/iog5ksaS9s/bc531cnz/6OG5djx3o3WuZyvTL9C7H/DuT/omuGP+QHgUeE0f4zgHOCQi3gR8vcV6GtaLiM8CR5M+VKZJ2oiUDL8QL/f3bxURV/YxPlt5vWIfljQGeC/w9oh4C+lDva/75vdIRzlvAj7TYj0N60XESaQjnbWBGyS9gbRvfquyb74uIs7uY3wdxclm1bZU0hoNyjcA5kXEUkm70vNVXa8DxktaO/dbf7AybyAwN7ezf6V8cZ7Xsp6krSPi5oj4GjCflHSuAD7XFbukbSSt22Cd1nk2ABZFxNP5w310D/VvBt4taaO8v+zbbV1d10ycUCnvvh81rJf3zekR8W3SJbLeQNo3/0PSernOZpI2brDOVY6TzartLODOrgECFecDoyRNBw4A7mm1koi4DbgI+Avwe9Ibr8sxpDf8Dd3WcyHwlXxidesW9U7OgxRmkPrw/wL8BLgLuC2X/4h05HYn8EIeTOABAp3pD6QjnLtJJ91valU5IuaSzsHcSNq37q7MPg74haRpLPt3A78FPtQ1QKBFvS/mwSt3AkuB3+cj7J8DN+b3zyXAwIhYQDr6mbGqDhDw5WrMzKw4H9mYmVlxTjZmZlack42ZmRXnZGNmZsU52ZiZWXFONmZmVpyTjZmZFff/AfYmES8fwX/7AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "objects = ('Initial dataset', 'Final dataset')\n",
    "y_pos = np.arange(len(objects))\n",
    "\n",
    "performance = [initial_size, rows]\n",
    " \n",
    "plt.bar(y_pos, performance, align='center', alpha=0.5)\n",
    "plt.xticks(y_pos, objects)\n",
    "plt.ylabel('Number of records')\n",
    "plt.title('Comparision of initial and final size of dataset')\n",
    " \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Inserting New and Blank Fields in the Data Frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "df['latlng'] = df.latitude.astype(str).str.cat(df.longitude.astype(str), sep=',')\n",
    "df['name'] = ''\n",
    "df['type'] = ''\n",
    "df['delete_record'] = ''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Modified dataset after inserting new field and blank fields is as follows:\n"
     ]
    },
    {
     "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>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>latlng</th>\n",
       "      <th>name</th>\n",
       "      <th>type</th>\n",
       "      <th>delete_record</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30.397125</td>\n",
       "      <td>78.074604</td>\n",
       "      <td>30.397124800000004,78.07460440000001</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>30.397154</td>\n",
       "      <td>78.074587</td>\n",
       "      <td>30.397153999999997,78.07458670000001</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>30.397141</td>\n",
       "      <td>78.074576</td>\n",
       "      <td>30.3971412,78.07457579999999</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>30.397143</td>\n",
       "      <td>78.074586</td>\n",
       "      <td>30.3971434,78.0745858</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>30.397141</td>\n",
       "      <td>78.074569</td>\n",
       "      <td>30.3971407,78.0745689</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>30.397138</td>\n",
       "      <td>78.074591</td>\n",
       "      <td>30.397137899999997,78.07459109999999</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>30.397146</td>\n",
       "      <td>78.074570</td>\n",
       "      <td>30.3971456,78.0745703</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30.397103</td>\n",
       "      <td>78.074592</td>\n",
       "      <td>30.3971027,78.07459159999999</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>30.397097</td>\n",
       "      <td>78.074594</td>\n",
       "      <td>30.3970974,78.0745942</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>30.397089</td>\n",
       "      <td>78.074566</td>\n",
       "      <td>30.3970894,78.0745662</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>30.397123</td>\n",
       "      <td>78.074600</td>\n",
       "      <td>30.397122999999997,78.0746</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>30.397098</td>\n",
       "      <td>78.074600</td>\n",
       "      <td>30.397097600000002,78.0745996</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>30.397150</td>\n",
       "      <td>78.074601</td>\n",
       "      <td>30.397150399999997,78.0746012</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>30.397154</td>\n",
       "      <td>78.074594</td>\n",
       "      <td>30.3971537,78.0745936</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>30.397157</td>\n",
       "      <td>78.074550</td>\n",
       "      <td>30.3971568,78.0745501</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     latitude  longitude                                latlng name type  \\\n",
       "0   30.397125  78.074604  30.397124800000004,78.07460440000001             \n",
       "4   30.397154  78.074587  30.397153999999997,78.07458670000001             \n",
       "8   30.397141  78.074576          30.3971412,78.07457579999999             \n",
       "15  30.397143  78.074586                 30.3971434,78.0745858             \n",
       "21  30.397141  78.074569                 30.3971407,78.0745689             \n",
       "22  30.397138  78.074591  30.397137899999997,78.07459109999999             \n",
       "24  30.397146  78.074570                 30.3971456,78.0745703             \n",
       "29  30.397103  78.074592          30.3971027,78.07459159999999             \n",
       "34  30.397097  78.074594                 30.3970974,78.0745942             \n",
       "35  30.397089  78.074566                 30.3970894,78.0745662             \n",
       "37  30.397123  78.074600            30.397122999999997,78.0746             \n",
       "41  30.397098  78.074600         30.397097600000002,78.0745996             \n",
       "46  30.397150  78.074601         30.397150399999997,78.0746012             \n",
       "51  30.397154  78.074594                 30.3971537,78.0745936             \n",
       "52  30.397157  78.074550                 30.3971568,78.0745501             \n",
       "\n",
       "   delete_record  \n",
       "0                 \n",
       "4                 \n",
       "8                 \n",
       "15                \n",
       "21                \n",
       "22                \n",
       "24                \n",
       "29                \n",
       "34                \n",
       "35                \n",
       "37                \n",
       "41                \n",
       "46                \n",
       "51                \n",
       "52                "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Modified dataset after inserting new field and blank fields is as follows:\")\n",
    "df.head(15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### List of all possible type of Locations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "allowed_types = ['accounting', 'airport', 'amusement_park', 'aquarium', 'art_gallery', 'atm', 'bakery', 'bank', 'bar', 'beauty_salon', 'bicycle_store',\n",
    "                 'book_store', 'bowling_alley', 'bus_station', 'cafe', 'campground', 'car_dealer', 'car_rental', 'car_repair', 'car_wash', 'casino',\n",
    "                 'cemetery', 'church', 'city_hall', 'clothing_store', 'convenience_store', 'courthouse', 'dentist', 'department_store', 'doctor',\n",
    "                 'electrician', 'electronics_store', 'embassy', 'fire_station', 'florist', 'funeral_home', 'furniture_store', 'gas_station', 'gym', \n",
    "                 'hair_care', 'hardware_store', 'hindu_temple', 'home_goods_store', 'hospital', 'insurance_agency', 'jewelry_store', 'laundry', \n",
    "                 'lawyer', 'library', 'liquor_store', 'local_government_office', 'locksmith', 'lodging', 'meal_delivery', 'meal_takeaway', 'mosque',\n",
    "                 'movie_rental', 'movie_theater', 'moving_company', 'museum', 'night_club', 'painter', 'park', 'parking', 'pet_store', 'pharmacy',\n",
    "                 'physiotherapist', 'plumber', 'police', 'post_office', 'real_estate_agency', 'restaurant', 'roofing_contractor', 'rv_park', 'school',\n",
    "                 'shoe_store', 'shopping_mall', 'spa', 'stadium', 'storage', 'store', 'subway_station', 'supermarket', 'synagogue', 'taxi_stand',\n",
    "                 'train_station', 'transit_station', 'travel_agency', 'veterinary_care', 'zoo']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reverse Geocoding Latitudes and Longitudes inorder to find their corresponding Location Type and other Information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Specifying the path of Cache File and value of local variable 'radius'\n",
    "cache_file = \"latlong_details.csv\"\n",
    "radius = 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a Cache File and write Header Row if Cache File does not exist \n",
    "if not os.path.exists(cache_file):\n",
    "    with open(cache_file, 'w') as myfile:\n",
    "        wr = csv.writer(myfile, quoting=csv.QUOTE_ALL)\n",
    "        heading = [\"latlng\", \"name\", \"type\", \"delete_record\"]\n",
    "        wr.writerow(heading)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load data from Cache File\n",
    "cache_df = pd.read_csv(cache_file, header=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "with open(cache_file, 'a') as myfile:\n",
    "    wr = csv.writer(myfile, quoting=csv.QUOTE_ALL)\n",
    "    \n",
    "    for x in range (0, rows):\n",
    "        latlng = df.iloc[x, 2]\n",
    "\n",
    "        global api_requests_count\n",
    "        result = {}\n",
    "        \n",
    "        # Check if required 'latlng' details are present in Cache File\n",
    "        found = False\n",
    "        \n",
    "        for y in range(0, cache_df.shape[0]):\n",
    "            if cache_df.iloc[y, 0] == latlng:\n",
    "                df.iloc[x, 3] = cache_df.iloc[y, 1]\n",
    "                df.iloc[x, 4] = cache_df.iloc[y, 2]\n",
    "                df.iloc[x, 5] = cache_df.iloc[y, 3]\n",
    "                found = True\n",
    "                break\n",
    "    \n",
    "        # If required 'latlng' details are not present in Cache File then make a reqiured API call and store the result obtained in Cache File\n",
    "        if (api_requests_count < max_api_requests) and (found == False):  \n",
    "            time.sleep(pause)\n",
    "            api_requests_count += 1\n",
    "\n",
    "            reverse_geocode_url = \"https://maps.googleapis.com/maps/api/place/nearbysearch/json?location=\" + latlng + \"&radius=\" + str(radius) + \"&key=\" + places_api_web_service\n",
    "            \n",
    "            data = requests.get(reverse_geocode_url).json()\n",
    "        \n",
    "            if len(data['results']) > 0:\n",
    "                result = data['results']\n",
    "                name = ''\n",
    "                types = []\n",
    "            \n",
    "                for i in range (0, len(result)):\n",
    "                    name = name + str(result[i]['name']) + \" \"\n",
    "                    types = types + result[i]['types']\n",
    "                \n",
    "                \n",
    "                # Determine if the type of Location is present in the list of allowed types. If the type of location is not present in allowed types \n",
    "                # then set 'delete_record' field to True\n",
    "                flag = False\n",
    "                \n",
    "                for item in allowed_types:\n",
    "                    if item in types:\n",
    "                        types = item\n",
    "                        flag = True\n",
    "                        break\n",
    "                \n",
    "                df.iloc[x, 3] = name\n",
    "                df.iloc[x, 4] = str(types)\n",
    "                df.iloc[x, 5] = (not flag)\n",
    "                \n",
    "                valList = [latlng, name, types, not flag]\n",
    "                wr.writerow(valList)\n",
    "        elif api_requests_count > max_api_requests:\n",
    "            break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Head of dataset after reverse geocoding is as follows: \n"
     ]
    },
    {
     "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>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>latlng</th>\n",
       "      <th>name</th>\n",
       "      <th>type</th>\n",
       "      <th>delete_record</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30.397125</td>\n",
       "      <td>78.074604</td>\n",
       "      <td>30.397124800000004,78.07460440000001</td>\n",
       "      <td>Dehradun Raman Hostel Block 1 Bhagwant Pur</td>\n",
       "      <td>[u'locality', u'political', u'point_of_interes...</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>30.397154</td>\n",
       "      <td>78.074587</td>\n",
       "      <td>30.397153999999997,78.07458670000001</td>\n",
       "      <td>Dehradun Raman Hostel Block 1 Bhagwant Pur</td>\n",
       "      <td>[u'locality', u'political', u'point_of_interes...</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>30.397141</td>\n",
       "      <td>78.074576</td>\n",
       "      <td>30.3971412,78.07457579999999</td>\n",
       "      <td>Dehradun Raman Hostel Block 1 Bhagwant Pur</td>\n",
       "      <td>[u'locality', u'political', u'point_of_interes...</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>30.397143</td>\n",
       "      <td>78.074586</td>\n",
       "      <td>30.3971434,78.0745858</td>\n",
       "      <td>Dehradun Raman Hostel Block 1 Bhagwant Pur</td>\n",
       "      <td>[u'locality', u'political', u'point_of_interes...</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>30.397141</td>\n",
       "      <td>78.074569</td>\n",
       "      <td>30.3971407,78.0745689</td>\n",
       "      <td>Dehradun Raman Hostel Block 1 Bhagwant Pur</td>\n",
       "      <td>[u'locality', u'political', u'point_of_interes...</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>30.397138</td>\n",
       "      <td>78.074591</td>\n",
       "      <td>30.397137899999997,78.07459109999999</td>\n",
       "      <td>Dehradun Raman Hostel Block 1 Bhagwant Pur</td>\n",
       "      <td>[u'locality', u'political', u'point_of_interes...</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>30.397146</td>\n",
       "      <td>78.074570</td>\n",
       "      <td>30.3971456,78.0745703</td>\n",
       "      <td>Dehradun Raman Hostel Block 1 Bhagwant Pur</td>\n",
       "      <td>[u'locality', u'political', u'point_of_interes...</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30.397103</td>\n",
       "      <td>78.074592</td>\n",
       "      <td>30.3971027,78.07459159999999</td>\n",
       "      <td>Dehradun Raman Hostel Block 1 Bhagwant Pur</td>\n",
       "      <td>[u'locality', u'political', u'point_of_interes...</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>30.397097</td>\n",
       "      <td>78.074594</td>\n",
       "      <td>30.3970974,78.0745942</td>\n",
       "      <td>Dehradun Raman Hostel Block 1 Bhagwant Pur</td>\n",
       "      <td>[u'locality', u'political', u'point_of_interes...</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>30.397089</td>\n",
       "      <td>78.074566</td>\n",
       "      <td>30.3970894,78.0745662</td>\n",
       "      <td>Dehradun Raman Hostel Block 1 Bhagwant Pur</td>\n",
       "      <td>[u'locality', u'political', u'point_of_interes...</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>30.397123</td>\n",
       "      <td>78.074600</td>\n",
       "      <td>30.397122999999997,78.0746</td>\n",
       "      <td>Dehradun Raman Hostel Block 1 Bhagwant Pur</td>\n",
       "      <td>[u'locality', u'political', u'point_of_interes...</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>30.397098</td>\n",
       "      <td>78.074600</td>\n",
       "      <td>30.397097600000002,78.0745996</td>\n",
       "      <td>Dehradun Raman Hostel Block 1 Bhagwant Pur</td>\n",
       "      <td>[u'locality', u'political', u'point_of_interes...</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>30.397150</td>\n",
       "      <td>78.074601</td>\n",
       "      <td>30.397150399999997,78.0746012</td>\n",
       "      <td>Dehradun Raman Hostel Block 1 Bhagwant Pur</td>\n",
       "      <td>[u'locality', u'political', u'point_of_interes...</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>30.397154</td>\n",
       "      <td>78.074594</td>\n",
       "      <td>30.3971537,78.0745936</td>\n",
       "      <td>Dehradun Raman Hostel Block 1 Bhagwant Pur</td>\n",
       "      <td>[u'locality', u'political', u'point_of_interes...</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>30.397157</td>\n",
       "      <td>78.074550</td>\n",
       "      <td>30.3971568,78.0745501</td>\n",
       "      <td>Dehradun Bhagwant Pur</td>\n",
       "      <td>[u'locality', u'political', u'sublocality_leve...</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>30.397145</td>\n",
       "      <td>78.074495</td>\n",
       "      <td>30.3971447,78.0744948</td>\n",
       "      <td>Dehradun Bhagwant Pur</td>\n",
       "      <td>[u'locality', u'political', u'sublocality_leve...</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>30.397141</td>\n",
       "      <td>78.074570</td>\n",
       "      <td>30.3971406,78.07457</td>\n",
       "      <td>Dehradun Raman Hostel Block 1 Bhagwant Pur</td>\n",
       "      <td>[u'locality', u'political', u'point_of_interes...</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>30.397294</td>\n",
       "      <td>78.074268</td>\n",
       "      <td>30.3972939,78.0742675</td>\n",
       "      <td>Dehradun Bhagwant Pur</td>\n",
       "      <td>[u'locality', u'political', u'sublocality_leve...</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>30.397146</td>\n",
       "      <td>78.074507</td>\n",
       "      <td>30.397145899999998,78.0745074</td>\n",
       "      <td>Dehradun Bhagwant Pur</td>\n",
       "      <td>[u'locality', u'political', u'sublocality_leve...</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>30.397145</td>\n",
       "      <td>78.074495</td>\n",
       "      <td>30.397145399999996,78.0744954</td>\n",
       "      <td>Dehradun Bhagwant Pur</td>\n",
       "      <td>[u'locality', u'political', u'sublocality_leve...</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>30.398543</td>\n",
       "      <td>78.075712</td>\n",
       "      <td>30.398543,78.0757122</td>\n",
       "      <td>Dehradun Bhagwant Pur</td>\n",
       "      <td>[u'locality', u'political', u'sublocality_leve...</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>30.397140</td>\n",
       "      <td>78.074430</td>\n",
       "      <td>30.3971403,78.0744297</td>\n",
       "      <td>Dehradun Bhagwant Pur</td>\n",
       "      <td>[u'locality', u'political', u'sublocality_leve...</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>30.397162</td>\n",
       "      <td>78.074461</td>\n",
       "      <td>30.397162400000003,78.0744612</td>\n",
       "      <td>DIT Hostel Block 1 Dehradun Bhagwant Pur</td>\n",
       "      <td>lodging</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>30.397116</td>\n",
       "      <td>78.074557</td>\n",
       "      <td>30.397115600000003,78.0745566</td>\n",
       "      <td>Dehradun Raman Hostel Block 1 Bhagwant Pur</td>\n",
       "      <td>[u'locality', u'political', u'point_of_interes...</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>30.397143</td>\n",
       "      <td>78.074437</td>\n",
       "      <td>30.3971435,78.074437</td>\n",
       "      <td>Dehradun Bhagwant Pur</td>\n",
       "      <td>[u'locality', u'political', u'sublocality_leve...</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     latitude  longitude                                latlng  \\\n",
       "0   30.397125  78.074604  30.397124800000004,78.07460440000001   \n",
       "4   30.397154  78.074587  30.397153999999997,78.07458670000001   \n",
       "8   30.397141  78.074576          30.3971412,78.07457579999999   \n",
       "15  30.397143  78.074586                 30.3971434,78.0745858   \n",
       "21  30.397141  78.074569                 30.3971407,78.0745689   \n",
       "22  30.397138  78.074591  30.397137899999997,78.07459109999999   \n",
       "24  30.397146  78.074570                 30.3971456,78.0745703   \n",
       "29  30.397103  78.074592          30.3971027,78.07459159999999   \n",
       "34  30.397097  78.074594                 30.3970974,78.0745942   \n",
       "35  30.397089  78.074566                 30.3970894,78.0745662   \n",
       "37  30.397123  78.074600            30.397122999999997,78.0746   \n",
       "41  30.397098  78.074600         30.397097600000002,78.0745996   \n",
       "46  30.397150  78.074601         30.397150399999997,78.0746012   \n",
       "51  30.397154  78.074594                 30.3971537,78.0745936   \n",
       "52  30.397157  78.074550                 30.3971568,78.0745501   \n",
       "53  30.397145  78.074495                 30.3971447,78.0744948   \n",
       "54  30.397141  78.074570                   30.3971406,78.07457   \n",
       "55  30.397294  78.074268                 30.3972939,78.0742675   \n",
       "56  30.397146  78.074507         30.397145899999998,78.0745074   \n",
       "57  30.397145  78.074495         30.397145399999996,78.0744954   \n",
       "62  30.398543  78.075712                  30.398543,78.0757122   \n",
       "85  30.397140  78.074430                 30.3971403,78.0744297   \n",
       "91  30.397162  78.074461         30.397162400000003,78.0744612   \n",
       "95  30.397116  78.074557         30.397115600000003,78.0745566   \n",
       "96  30.397143  78.074437                  30.3971435,78.074437   \n",
       "\n",
       "                                           name  \\\n",
       "0   Dehradun Raman Hostel Block 1 Bhagwant Pur    \n",
       "4   Dehradun Raman Hostel Block 1 Bhagwant Pur    \n",
       "8   Dehradun Raman Hostel Block 1 Bhagwant Pur    \n",
       "15  Dehradun Raman Hostel Block 1 Bhagwant Pur    \n",
       "21  Dehradun Raman Hostel Block 1 Bhagwant Pur    \n",
       "22  Dehradun Raman Hostel Block 1 Bhagwant Pur    \n",
       "24  Dehradun Raman Hostel Block 1 Bhagwant Pur    \n",
       "29  Dehradun Raman Hostel Block 1 Bhagwant Pur    \n",
       "34  Dehradun Raman Hostel Block 1 Bhagwant Pur    \n",
       "35  Dehradun Raman Hostel Block 1 Bhagwant Pur    \n",
       "37  Dehradun Raman Hostel Block 1 Bhagwant Pur    \n",
       "41  Dehradun Raman Hostel Block 1 Bhagwant Pur    \n",
       "46  Dehradun Raman Hostel Block 1 Bhagwant Pur    \n",
       "51  Dehradun Raman Hostel Block 1 Bhagwant Pur    \n",
       "52                       Dehradun Bhagwant Pur    \n",
       "53                       Dehradun Bhagwant Pur    \n",
       "54  Dehradun Raman Hostel Block 1 Bhagwant Pur    \n",
       "55                       Dehradun Bhagwant Pur    \n",
       "56                       Dehradun Bhagwant Pur    \n",
       "57                       Dehradun Bhagwant Pur    \n",
       "62                       Dehradun Bhagwant Pur    \n",
       "85                       Dehradun Bhagwant Pur    \n",
       "91    DIT Hostel Block 1 Dehradun Bhagwant Pur    \n",
       "95  Dehradun Raman Hostel Block 1 Bhagwant Pur    \n",
       "96                       Dehradun Bhagwant Pur    \n",
       "\n",
       "                                                 type delete_record  \n",
       "0   [u'locality', u'political', u'point_of_interes...          True  \n",
       "4   [u'locality', u'political', u'point_of_interes...          True  \n",
       "8   [u'locality', u'political', u'point_of_interes...          True  \n",
       "15  [u'locality', u'political', u'point_of_interes...          True  \n",
       "21  [u'locality', u'political', u'point_of_interes...          True  \n",
       "22  [u'locality', u'political', u'point_of_interes...          True  \n",
       "24  [u'locality', u'political', u'point_of_interes...          True  \n",
       "29  [u'locality', u'political', u'point_of_interes...          True  \n",
       "34  [u'locality', u'political', u'point_of_interes...          True  \n",
       "35  [u'locality', u'political', u'point_of_interes...          True  \n",
       "37  [u'locality', u'political', u'point_of_interes...          True  \n",
       "41  [u'locality', u'political', u'point_of_interes...          True  \n",
       "46  [u'locality', u'political', u'point_of_interes...          True  \n",
       "51  [u'locality', u'political', u'point_of_interes...          True  \n",
       "52  [u'locality', u'political', u'sublocality_leve...          True  \n",
       "53  [u'locality', u'political', u'sublocality_leve...          True  \n",
       "54  [u'locality', u'political', u'point_of_interes...          True  \n",
       "55  [u'locality', u'political', u'sublocality_leve...          True  \n",
       "56  [u'locality', u'political', u'sublocality_leve...          True  \n",
       "57  [u'locality', u'political', u'sublocality_leve...          True  \n",
       "62  [u'locality', u'political', u'sublocality_leve...          True  \n",
       "85  [u'locality', u'political', u'sublocality_leve...          True  \n",
       "91                                            lodging         False  \n",
       "95  [u'locality', u'political', u'point_of_interes...          True  \n",
       "96  [u'locality', u'political', u'sublocality_leve...          True  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Head of dataset after reverse geocoding is as follows: \")\n",
    "df.head(25)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Filtering Required Records only"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of records in dataset after filtering required records is: 2361\n",
      "Head of dataset after filtering required records is as follows: \n"
     ]
    },
    {
     "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>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>latlng</th>\n",
       "      <th>name</th>\n",
       "      <th>type</th>\n",
       "      <th>delete_record</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>30.397162</td>\n",
       "      <td>78.074461</td>\n",
       "      <td>30.397162400000003,78.0744612</td>\n",
       "      <td>DIT Hostel Block 1 Dehradun Bhagwant Pur</td>\n",
       "      <td>lodging</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>30.401179</td>\n",
       "      <td>78.077862</td>\n",
       "      <td>30.4011786,78.0778622</td>\n",
       "      <td>Ishwar Nagar Road Bihari Dhabha Dehradun</td>\n",
       "      <td>restaurant</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>30.397167</td>\n",
       "      <td>78.074477</td>\n",
       "      <td>30.3971675,78.0744773</td>\n",
       "      <td>DIT Hostel Block 1 Dehradun Bhagwant Pur</td>\n",
       "      <td>lodging</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>30.399631</td>\n",
       "      <td>78.076726</td>\n",
       "      <td>30.3996305,78.07672600000001</td>\n",
       "      <td>Dehradun Vedanta Bhagwant Pur</td>\n",
       "      <td>library</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>388</th>\n",
       "      <td>30.397164</td>\n",
       "      <td>78.074466</td>\n",
       "      <td>30.397163800000005,78.0744659</td>\n",
       "      <td>DIT Hostel Block 1 Dehradun Bhagwant Pur</td>\n",
       "      <td>lodging</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500</th>\n",
       "      <td>30.399020</td>\n",
       "      <td>78.074767</td>\n",
       "      <td>30.3990197,78.07476679999999</td>\n",
       "      <td>Dehradun Vishveswaraya Bhagwant Pur</td>\n",
       "      <td>school</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>507</th>\n",
       "      <td>30.399036</td>\n",
       "      <td>78.074756</td>\n",
       "      <td>30.3990364,78.0747556</td>\n",
       "      <td>Dehradun Vishveswaraya Bhagwant Pur</td>\n",
       "      <td>school</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1364</th>\n",
       "      <td>30.397161</td>\n",
       "      <td>78.074493</td>\n",
       "      <td>30.3971607,78.0744929</td>\n",
       "      <td>DIT Hostel Block 1 Dehradun Bhagwant Pur</td>\n",
       "      <td>lodging</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1365</th>\n",
       "      <td>30.397168</td>\n",
       "      <td>78.074467</td>\n",
       "      <td>30.3971684,78.0744669</td>\n",
       "      <td>DIT Hostel Block 1 Dehradun Bhagwant Pur</td>\n",
       "      <td>lodging</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1371</th>\n",
       "      <td>30.397178</td>\n",
       "      <td>78.074483</td>\n",
       "      <td>30.397178399999998,78.0744825</td>\n",
       "      <td>DIT Hostel Block 1 Dehradun Bhagwant Pur</td>\n",
       "      <td>lodging</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1413</th>\n",
       "      <td>30.399608</td>\n",
       "      <td>78.076748</td>\n",
       "      <td>30.3996076,78.076748</td>\n",
       "      <td>Dehradun Vedanta Bhagwant Pur</td>\n",
       "      <td>library</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1497</th>\n",
       "      <td>30.398282</td>\n",
       "      <td>78.074803</td>\n",
       "      <td>30.3982817,78.0748033</td>\n",
       "      <td>Dehradun Team Audacious Workshop BAJA Bhagwant...</td>\n",
       "      <td>school</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1510</th>\n",
       "      <td>30.397173</td>\n",
       "      <td>78.074435</td>\n",
       "      <td>30.3971734,78.07443470000001</td>\n",
       "      <td>DIT Hostel Block 1 Dehradun Bhagwant Pur</td>\n",
       "      <td>lodging</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1533</th>\n",
       "      <td>30.397209</td>\n",
       "      <td>78.074410</td>\n",
       "      <td>30.3972093,78.0744105</td>\n",
       "      <td>DIT Hostel Block 1 Dehradun Bhagwant Pur</td>\n",
       "      <td>lodging</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1932</th>\n",
       "      <td>30.397163</td>\n",
       "      <td>78.074459</td>\n",
       "      <td>30.397162599999998,78.07445859999999</td>\n",
       "      <td>DIT Hostel Block 1 Dehradun Bhagwant Pur</td>\n",
       "      <td>lodging</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2031</th>\n",
       "      <td>30.397188</td>\n",
       "      <td>78.074482</td>\n",
       "      <td>30.3971876,78.0744816</td>\n",
       "      <td>DIT Hostel Block 1 Dehradun Bhagwant Pur</td>\n",
       "      <td>lodging</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2066</th>\n",
       "      <td>30.397168</td>\n",
       "      <td>78.074441</td>\n",
       "      <td>30.397167600000003,78.07444100000001</td>\n",
       "      <td>DIT Hostel Block 1 Dehradun Bhagwant Pur</td>\n",
       "      <td>lodging</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2439</th>\n",
       "      <td>30.397155</td>\n",
       "      <td>78.074457</td>\n",
       "      <td>30.397154999999998,78.0744572</td>\n",
       "      <td>DIT Hostel Block 1 Dehradun Bhagwant Pur</td>\n",
       "      <td>lodging</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2536</th>\n",
       "      <td>30.397203</td>\n",
       "      <td>78.074469</td>\n",
       "      <td>30.397203499999996,78.07446920000001</td>\n",
       "      <td>DIT Hostel Block 1 Dehradun Bhagwant Pur</td>\n",
       "      <td>lodging</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3118</th>\n",
       "      <td>30.397154</td>\n",
       "      <td>78.074443</td>\n",
       "      <td>30.397154200000003,78.0744428</td>\n",
       "      <td>Dehradun DIT Hostel Block 1 Bhagwant Pur</td>\n",
       "      <td>lodging</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3221</th>\n",
       "      <td>30.397181</td>\n",
       "      <td>78.074474</td>\n",
       "      <td>30.397181300000003,78.0744739</td>\n",
       "      <td>DIT Hostel Block 1 Dehradun Bhagwant Pur</td>\n",
       "      <td>lodging</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3295</th>\n",
       "      <td>30.362672</td>\n",
       "      <td>78.067594</td>\n",
       "      <td>30.3626723,78.0675937</td>\n",
       "      <td>Doon Vihar Lane Number 1 A-One Hair Cutting Sa...</td>\n",
       "      <td>hair_care</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3298</th>\n",
       "      <td>30.362906</td>\n",
       "      <td>78.067159</td>\n",
       "      <td>30.3629057,78.06715859999998</td>\n",
       "      <td>Doon Vihar Lane Number 1 EuroKids Dehradun</td>\n",
       "      <td>school</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3396</th>\n",
       "      <td>30.363491</td>\n",
       "      <td>78.069261</td>\n",
       "      <td>30.363491200000002,78.06926130000001</td>\n",
       "      <td>Dehradun Cafe Coffee Day - Jakhan Jakhan</td>\n",
       "      <td>cafe</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3522</th>\n",
       "      <td>30.397168</td>\n",
       "      <td>78.074413</td>\n",
       "      <td>30.3971684,78.0744128</td>\n",
       "      <td>DIT Hostel Block 1 Dehradun Bhagwant Pur</td>\n",
       "      <td>lodging</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       latitude  longitude                                latlng  \\\n",
       "91    30.397162  78.074461         30.397162400000003,78.0744612   \n",
       "104   30.401179  78.077862                 30.4011786,78.0778622   \n",
       "155   30.397167  78.074477                 30.3971675,78.0744773   \n",
       "198   30.399631  78.076726          30.3996305,78.07672600000001   \n",
       "388   30.397164  78.074466         30.397163800000005,78.0744659   \n",
       "500   30.399020  78.074767          30.3990197,78.07476679999999   \n",
       "507   30.399036  78.074756                 30.3990364,78.0747556   \n",
       "1364  30.397161  78.074493                 30.3971607,78.0744929   \n",
       "1365  30.397168  78.074467                 30.3971684,78.0744669   \n",
       "1371  30.397178  78.074483         30.397178399999998,78.0744825   \n",
       "1413  30.399608  78.076748                  30.3996076,78.076748   \n",
       "1497  30.398282  78.074803                 30.3982817,78.0748033   \n",
       "1510  30.397173  78.074435          30.3971734,78.07443470000001   \n",
       "1533  30.397209  78.074410                 30.3972093,78.0744105   \n",
       "1932  30.397163  78.074459  30.397162599999998,78.07445859999999   \n",
       "2031  30.397188  78.074482                 30.3971876,78.0744816   \n",
       "2066  30.397168  78.074441  30.397167600000003,78.07444100000001   \n",
       "2439  30.397155  78.074457         30.397154999999998,78.0744572   \n",
       "2536  30.397203  78.074469  30.397203499999996,78.07446920000001   \n",
       "3118  30.397154  78.074443         30.397154200000003,78.0744428   \n",
       "3221  30.397181  78.074474         30.397181300000003,78.0744739   \n",
       "3295  30.362672  78.067594                 30.3626723,78.0675937   \n",
       "3298  30.362906  78.067159          30.3629057,78.06715859999998   \n",
       "3396  30.363491  78.069261  30.363491200000002,78.06926130000001   \n",
       "3522  30.397168  78.074413                 30.3971684,78.0744128   \n",
       "\n",
       "                                                   name        type  \\\n",
       "91            DIT Hostel Block 1 Dehradun Bhagwant Pur      lodging   \n",
       "104           Ishwar Nagar Road Bihari Dhabha Dehradun   restaurant   \n",
       "155           DIT Hostel Block 1 Dehradun Bhagwant Pur      lodging   \n",
       "198                      Dehradun Vedanta Bhagwant Pur      library   \n",
       "388           DIT Hostel Block 1 Dehradun Bhagwant Pur      lodging   \n",
       "500                Dehradun Vishveswaraya Bhagwant Pur       school   \n",
       "507                Dehradun Vishveswaraya Bhagwant Pur       school   \n",
       "1364          DIT Hostel Block 1 Dehradun Bhagwant Pur      lodging   \n",
       "1365          DIT Hostel Block 1 Dehradun Bhagwant Pur      lodging   \n",
       "1371          DIT Hostel Block 1 Dehradun Bhagwant Pur      lodging   \n",
       "1413                     Dehradun Vedanta Bhagwant Pur      library   \n",
       "1497  Dehradun Team Audacious Workshop BAJA Bhagwant...      school   \n",
       "1510          DIT Hostel Block 1 Dehradun Bhagwant Pur      lodging   \n",
       "1533          DIT Hostel Block 1 Dehradun Bhagwant Pur      lodging   \n",
       "1932          DIT Hostel Block 1 Dehradun Bhagwant Pur      lodging   \n",
       "2031          DIT Hostel Block 1 Dehradun Bhagwant Pur      lodging   \n",
       "2066          DIT Hostel Block 1 Dehradun Bhagwant Pur      lodging   \n",
       "2439          DIT Hostel Block 1 Dehradun Bhagwant Pur      lodging   \n",
       "2536          DIT Hostel Block 1 Dehradun Bhagwant Pur      lodging   \n",
       "3118          Dehradun DIT Hostel Block 1 Bhagwant Pur      lodging   \n",
       "3221          DIT Hostel Block 1 Dehradun Bhagwant Pur      lodging   \n",
       "3295  Doon Vihar Lane Number 1 A-One Hair Cutting Sa...   hair_care   \n",
       "3298        Doon Vihar Lane Number 1 EuroKids Dehradun       school   \n",
       "3396          Dehradun Cafe Coffee Day - Jakhan Jakhan         cafe   \n",
       "3522          DIT Hostel Block 1 Dehradun Bhagwant Pur      lodging   \n",
       "\n",
       "     delete_record  \n",
       "91           False  \n",
       "104          False  \n",
       "155          False  \n",
       "198          False  \n",
       "388          False  \n",
       "500          False  \n",
       "507          False  \n",
       "1364         False  \n",
       "1365         False  \n",
       "1371         False  \n",
       "1413         False  \n",
       "1497         False  \n",
       "1510         False  \n",
       "1533         False  \n",
       "1932         False  \n",
       "2031         False  \n",
       "2066         False  \n",
       "2439         False  \n",
       "2536         False  \n",
       "3118         False  \n",
       "3221         False  \n",
       "3295         False  \n",
       "3298         False  \n",
       "3396         False  \n",
       "3522         False  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df = df[df['delete_record']==False]\n",
    "\n",
    "print(\"Total number of records in dataset after filtering required records is: \" + str(new_df.shape[0]))\n",
    "\n",
    "print(\"Head of dataset after filtering required records is as follows: \")\n",
    "new_df.head(25)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fetching current location of the user and radius within which recommendations are to be made"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "current_loc = input(\"Enter your current location as address: \")\n",
    "radius = input(\"Enter the radius (in meters): \")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using Hidden Markov Model (HMM) to predict the type of most likely activity to be performed by the user"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of unique type of activities present in the filtered dataset is: 53\n"
     ]
    }
   ],
   "source": [
    "print(\"Total number of unique type of activities present in the filtered dataset is: \" + str(new_df.type.nunique()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Differnt types of activites which are present in dataset are: \n",
      "['lodging' 'restaurant' 'library' 'school' 'hair_care' 'cafe'\n",
      " 'home_goods_store' 'store' 'furniture_store' 'doctor' 'insurance_agency'\n",
      " 'clothing_store' 'bar' 'bank' 'museum' 'car_repair' 'shoe_store'\n",
      " 'dentist' 'cemetery' 'gym' 'electronics_store' 'park' 'book_store'\n",
      " 'shopping_mall' 'bus_station' 'real_estate_agency' 'bakery' 'atm'\n",
      " 'beauty_salon' 'jewelry_store' 'pharmacy' 'hindu_temple' 'accounting'\n",
      " 'gas_station' 'hospital' 'movie_theater' 'local_government_office'\n",
      " 'mosque' 'transit_station' 'department_store' 'veterinary_care' 'police'\n",
      " 'bicycle_store' 'spa' 'parking' 'travel_agency' 'lawyer' 'hardware_store'\n",
      " 'liquor_store' 'courthouse' 'fire_station' 'convenience_store'\n",
      " 'car_dealer']\n"
     ]
    }
   ],
   "source": [
    "print(\"Differnt types of activites which are present in dataset are: \")\n",
    "print(new_df.type.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Count of each type of activity in dataset is: \n",
      "lodging                    1376\n",
      "school                      147\n",
      "library                     137\n",
      "store                        97\n",
      "clothing_store               91\n",
      "cafe                         60\n",
      "restaurant                   50\n",
      "parking                      42\n",
      "doctor                       31\n",
      "beauty_salon                 26\n",
      "atm                          24\n",
      "accounting                   21\n",
      "home_goods_store             19\n",
      "bank                         19\n",
      "real_estate_agency           18\n",
      "shoe_store                   18\n",
      "car_repair                   16\n",
      "jewelry_store                13\n",
      "hindu_temple                 13\n",
      "electronics_store            13\n",
      "shopping_mall                12\n",
      "museum                        9\n",
      "park                          9\n",
      "furniture_store               8\n",
      "bakery                        7\n",
      "gym                           7\n",
      "hair_care                     7\n",
      "pharmacy                      7\n",
      "insurance_agency              5\n",
      "lawyer                        5\n",
      "bus_station                   5\n",
      "gas_station                   5\n",
      "travel_agency                 4\n",
      "book_store                    4\n",
      "hospital                      4\n",
      "bar                           4\n",
      "local_government_office       3\n",
      "transit_station               3\n",
      "dentist                       3\n",
      "car_dealer                    3\n",
      "department_store              2\n",
      "spa                           2\n",
      "liquor_store                  2\n",
      "movie_theater                 1\n",
      "bicycle_store                 1\n",
      "fire_station                  1\n",
      "police                        1\n",
      "hardware_store                1\n",
      "convenience_store             1\n",
      "courthouse                    1\n",
      "mosque                        1\n",
      "veterinary_care               1\n",
      "cemetery                      1\n",
      "Name: type, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(\"Count of each type of activity in dataset is: \")\n",
    "print(new_df.type.value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f6892651160>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "type_count = new_df.type.value_counts()\n",
    "type_count.plot.barh(figsize=(15,15))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "typ = \"restaurant\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Recommending various Point of Interests within a certain radius based on type of next activity to be performed by the user"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recommended places in your vicinity are: \n",
      "\n",
      "Domino's Pizza\n",
      "Rating: 3.8\n",
      "Address: Rajpur Road, Jakhan, Dehradun\n",
      "\n",
      "\n",
      "Subway\n",
      "Rating: 3.9\n",
      "Address: LG-29, Pacific Mall, Rajpur Rd, Jakhan, Dehradun\n",
      "\n",
      "\n",
      "Dunkin' Donuts\n",
      "Rating: 4.2\n",
      "Address: UG 03, Pacific Mall, Rajpur Rd, Dehradun\n",
      "\n",
      "\n",
      "KFC\n",
      "Rating: 4\n",
      "Address: 65-A, Pacific Mall, Rajpur Road, Jakhan, Dehradun\n",
      "\n",
      "\n",
      "Taste Of America\n",
      "Rating: 3.8\n",
      "Address: Pacific Mall,, Rajpur Road, Dehradun\n",
      "\n",
      "\n",
      "Amritsari Express\n",
      "Rating: 4.3\n",
      "Address: Rajpur Road, Jakhan, Dehradun\n",
      "\n",
      "\n",
      "Pizza Hut\n",
      "Rating: 3.8\n",
      "Address: S 13, Pacific Mall, Jakhan, Dehradun\n",
      "\n",
      "\n",
      "Punjab Grill\n",
      "Rating: N.A.\n",
      "Address: Malsi, Dehradun\n",
      "\n",
      "\n",
      "Cafe Coffee Day - Jakhan\n",
      "Rating: 4.2\n",
      "Address: 152, Inside The Axiom Building, Rajpur Road, Jakhan, Near Scholars Home School, Dehradun\n",
      "\n",
      "\n",
      "Royal Restaurant\n",
      "Rating: N.A.\n",
      "Address: Rajpur Road, Doon Vihar, Jakhan, Dehradun\n",
      "\n",
      "\n",
      "BMG- All Day Dining\n",
      "Rating: 4\n",
      "Address: Rajpur Road, Jakhan, Dehradun\n",
      "\n",
      "\n",
      "Nautika\n",
      "Rating: 4\n",
      "Address: Rajpur Road, Jakhan, Dehradun\n",
      "\n",
      "\n",
      "Burger King\n",
      "Rating: 4.1\n",
      "Address: 156 A, Rajpur Road, Kishanpur, Dehradun\n",
      "\n",
      "\n",
      "Lazeez Kathi Rolls\n",
      "Rating: 3.5\n",
      "Address: Lazeez Kathi Rolls, 89 Dilaram, Rajpur Road, Uttranchal, Jakhan, Dehradun\n",
      "\n",
      "\n",
      "Andaaz E Lucknow\n",
      "Rating: 4.3\n",
      "Address: Rajpur Road, Jakhan, Dehradun\n",
      "\n",
      "\n",
      "Chef Chinese\n",
      "Rating: 2.7\n",
      "Address: Rajpur Road, Jakhan, Dehradun\n",
      "\n",
      "\n",
      "KC Sup Bar\n",
      "Rating: 1\n",
      "Address: Jakhan, Dehradun\n",
      "\n",
      "\n",
      "Café Coffee Day\n",
      "Rating: 4\n",
      "Address: Rajpur Road, Doon Vihar, Rajpur Road, Dehradun\n",
      "\n",
      "\n",
      "Bayleaf\n",
      "Rating: N.A.\n",
      "Address: 11-, A,Â Dehradun Gpo, Malsi, Rajpur Road\n",
      "\n",
      "\n",
      "Qb Lounge\n",
      "Rating: 4.6\n",
      "Address: Jakhan, Dehradun\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "current_loc = current_loc.replace(\" \", \"+\")\n",
    "\n",
    "geocode_url = \"https://maps.googleapis.com/maps/api/geocode/json?address=\" + current_loc + \"&key=\" + geocoding_api\n",
    "\n",
    "response = requests.get(geocode_url)\n",
    "data = json.loads(response.text)\n",
    "\n",
    "current_lat = data['results'][0]['geometry']['location']['lat']\n",
    "current_long = data['results'][0]['geometry']['location']['lng']\n",
    "\n",
    "nearby_url = \"https://maps.googleapis.com/maps/api/place/nearbysearch/json?location=\"\n",
    "\n",
    "response = requests.get(nearby_url + str(current_lat) + \",\" + str(current_long) + \"&radius=\" + str(radius) + \"&type=\" + typ + \"&key=\" + places_api_web_service)\n",
    "\n",
    "data = json.loads(response.text)\n",
    "\n",
    "print (\"Recommended places in your vicinity are: \"+\"\\n\")\n",
    "\n",
    "for i in range (0, len(data['results'])):\n",
    "    print(data['results'][i]['name'])\n",
    "    \n",
    "    if 'rating' in data['results'][i]:\n",
    "        print (\"Rating: \" + str(data['results'][i]['rating']))\n",
    "    else:\n",
    "        print (\"Rating: N.A.\")\n",
    "    print (\"Address: \" + (data['results'][i]['vicinity']))\n",
    "    print (\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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