Data Science Capstone Project -- Identifying optimal Location to startup Tuition Classes or franchise of existing Classes in Mumbai.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a><img src = \"https://github.com/sahil-ansari-15/Data_Science_Capstone_Project_Coursera/blob/main/Title%20photo.jpg?raw=true\"> </a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Table of contents\n",
    "* [Introduction: Business Problem](#introduction)\n",
    "* [Data](#data)\n",
    "* [Methodology](#methodology)\n",
    "* [Analysis](#analysis)\n",
    "* [Results](#results)\n",
    "* [Discussion](#Discussion)\n",
    "* [Conclusion](#conclusion)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction: Business Problem <a name=\"introduction\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this project we will try to find an optimal location to startup Tuition Classes or franchise of existing Classes. This analysis will be targeted to stakeholders interested in opening a private Tuition Classes or management of existing Classes to expand thier bussiness & revenue by opening new branch/franchise in Mumbai, India.\n",
    "\n",
    "Since there are lots of Classes in Mumbai we will try to detect locations that are not already crowded with Classes and having good number of existing Schools & Colleges to ensure sufficient student admistions and less bussiness competition. We will also consider Commercial Property Rates of the locations.\n",
    "\n",
    "We will use our data science powers to generate a few most promissing Area based on these criteria so that best possible final location can be chosen by stakeholders."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data <a name=\"data\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on definition of our problem, factors that will influence our decission are:\n",
    "* Number of existing Schools & Colleges in the area.\n",
    "* Number of existing Tutorials & Classes in the area.\n",
    "* Commercial Property Rates of the area.\n",
    "\n",
    "Following data sources will be needed to extract/generate the required information:\n",
    "* List of all Areas in Mumbai along with their geographical coordinates (latitude & longitude) & location is web-scraped from **Wikipedia** website.\n",
    "* Commercial property rates of each area is collected from major Indian **online real estate platforms** (like MagicBricks & 99Acres).\n",
    "* Neighbourhood Schools, Colleges and Classes in each Area will be retrieved using **Foursquare API** (a location data provider)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Methodology <a name=\"methodology\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this project we will direct our efforts on detecting areas of Mumbai having high number of Schools & Colleges, low number of existing private tuition Classes and affordable property rates.\n",
    "\n",
    "Steps:\n",
    "* Make dataframe containing area name, location, latitude & longitude  data (all 67 Areas) web-scrapped from Wikipedia page (https://en.wikipedia.org/wiki/List_of_neighbourhoods_in_Mumbai#Other) and commercial property rate (per square feet) from major Indian online real estate platforms (like MagicBricks & 99Acres).\n",
    "* Plot all Areas on Map showing respective Property Rates using python folium library.\n",
    "* Collect JSON from **Foursquare API** containing data of all nearby Schools, Colleges & Classes in each of the Areas in radius of 2 kilometres.\n",
    "* Extract required data from JSON & put it in the dataframes. Then groupby dataframes using Area name such that we will get \n",
    ">(1) Total number of schools/colleges in each Area.\n",
    "(2) Total number of Classes in each Area.\n",
    "(3) Average property rates of each area.\n",
    "* Calculate **Business Opportunity Index** for each Area. A good Business Opportunity Index means there are more Schools/Colleges and less Classes in a Area. \n",
    "> Business Opportunity Index = (Total no. of Schools & Colleges) / [ (Total no. of Classes & Tutorials) + 1 ]\n",
    "* Cluster Areas using **k-Means clustering** algorithm. Use **Elbow method** for deciding number of clusters. Parameters  use for clustering each Area will be: \n",
    ">(1)Property rates.\n",
    ">>(2) Business Opportunity Index. \n",
    ">>>(a) Total number of Schools & Colleges.\n",
    "(b) Total number of Classes & Tutorials.\n",
    "* Select cluster of Areas having high number of Schools & college and less existing Classes and affordable property rates.\n",
    "* Now, for more short listing of Areas apply a limit on Property prices depending on the investing capacity of the stakeholder.\n",
    "* Plot the selected optimal areas of map using Folium library."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analysis <a name=\"analysis\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing the Dependencies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: geopy in c:\\users\\kakadiya\\anaconda3\\lib\\site-packages (2.0.0)\n",
      "Requirement already satisfied: geographiclib<2,>=1.49 in c:\\users\\kakadiya\\anaconda3\\lib\\site-packages (from geopy) (1.50)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ERROR: Invalid requirement: '##or'\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Libraries imported.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np # library to handle data in a vectorized manner\n",
    "\n",
    "import pandas as pd # library for data analsysis\n",
    "pd.set_option('display.max_columns', None)\n",
    "pd.set_option('display.max_rows', None)\n",
    "\n",
    "import json # library to handle JSON files\n",
    "\n",
    "# !conda install -c conda-forge geopy --yes # uncomment this line if you haven't completed the Foursquare API lab\n",
    "!pip install geopy \n",
    "### line added by me :)\n",
    "from geopy.geocoders import Nominatim # convert an address into latitude and longitude values\n",
    "\n",
    "import requests # library to handle requests\n",
    "from pandas.io.json import json_normalize # tranform JSON file into a pandas dataframe\n",
    "\n",
    "# Matplotlib and associated plotting modules\n",
    "import matplotlib.cm as cm\n",
    "import matplotlib.colors as colors\n",
    "\n",
    "# import k-means from clustering stage\n",
    "from sklearn.cluster import KMeans\n",
    "\n",
    "#!conda install -c conda-forge folium==0.5.0 --yes # uncomment this line if you haven't completed the Foursquare API lab\n",
    "! pip install folium==0.5.0       ##or\n",
    "import folium # map rendering library\n",
    "\n",
    "print('Libraries imported.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scraping the Neighborhoods(Areas) in Mumbai from the Wikipedia Page."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = \"https://en.wikipedia.org/wiki/List_of_neighbourhoods_in_Mumbai#Other\"\n",
    "dfs = pd.read_html(url)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(93, 4)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Area</th>\n",
       "      <th>Location</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Amboli</td>\n",
       "      <td>Andheri,Western Suburbs</td>\n",
       "      <td>19.129300</td>\n",
       "      <td>72.843400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Chakala, Andheri</td>\n",
       "      <td>Western Suburbs</td>\n",
       "      <td>19.111388</td>\n",
       "      <td>72.860833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>D.N. Nagar</td>\n",
       "      <td>Andheri,Western Suburbs</td>\n",
       "      <td>19.124085</td>\n",
       "      <td>72.831373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Four Bungalows</td>\n",
       "      <td>Andheri,Western Suburbs</td>\n",
       "      <td>19.124714</td>\n",
       "      <td>72.827210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Lokhandwala</td>\n",
       "      <td>Andheri,Western Suburbs</td>\n",
       "      <td>19.130815</td>\n",
       "      <td>72.829270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Marol</td>\n",
       "      <td>Andheri,Western Suburbs</td>\n",
       "      <td>19.119219</td>\n",
       "      <td>72.882743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Sahar</td>\n",
       "      <td>Andheri,Western Suburbs</td>\n",
       "      <td>19.098889</td>\n",
       "      <td>72.867222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Seven Bungalows</td>\n",
       "      <td>Andheri,Western Suburbs</td>\n",
       "      <td>19.129052</td>\n",
       "      <td>72.817018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Versova</td>\n",
       "      <td>Andheri,Western Suburbs</td>\n",
       "      <td>19.120000</td>\n",
       "      <td>72.820000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Mira Road</td>\n",
       "      <td>Mira-Bhayandar,Western Suburbs</td>\n",
       "      <td>19.284167</td>\n",
       "      <td>72.871111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Bhayandar</td>\n",
       "      <td>Mira-Bhayandar,Western Suburbs</td>\n",
       "      <td>19.290000</td>\n",
       "      <td>72.850000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Uttan</td>\n",
       "      <td>Mira-Bhayandar,Western Suburbs</td>\n",
       "      <td>19.280000</td>\n",
       "      <td>72.785000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Bandstand Promenade</td>\n",
       "      <td>Bandra,Western Suburbs</td>\n",
       "      <td>19.042718</td>\n",
       "      <td>72.819132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Kherwadi</td>\n",
       "      <td>Bandra,Western Suburbs</td>\n",
       "      <td>19.055300</td>\n",
       "      <td>72.831400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Pali Hill</td>\n",
       "      <td>Bandra,Western Suburbs</td>\n",
       "      <td>19.068000</td>\n",
       "      <td>72.826000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Area                        Location   Latitude  Longitude\n",
       "0                Amboli         Andheri,Western Suburbs  19.129300  72.843400\n",
       "1      Chakala, Andheri                 Western Suburbs  19.111388  72.860833\n",
       "2            D.N. Nagar         Andheri,Western Suburbs  19.124085  72.831373\n",
       "3        Four Bungalows         Andheri,Western Suburbs  19.124714  72.827210\n",
       "4           Lokhandwala         Andheri,Western Suburbs  19.130815  72.829270\n",
       "5                 Marol         Andheri,Western Suburbs  19.119219  72.882743\n",
       "6                 Sahar         Andheri,Western Suburbs  19.098889  72.867222\n",
       "7       Seven Bungalows         Andheri,Western Suburbs  19.129052  72.817018\n",
       "8               Versova         Andheri,Western Suburbs  19.120000  72.820000\n",
       "9             Mira Road  Mira-Bhayandar,Western Suburbs  19.284167  72.871111\n",
       "10            Bhayandar  Mira-Bhayandar,Western Suburbs  19.290000  72.850000\n",
       "11                Uttan  Mira-Bhayandar,Western Suburbs  19.280000  72.785000\n",
       "12  Bandstand Promenade          Bandra,Western Suburbs  19.042718  72.819132\n",
       "13             Kherwadi          Bandra,Western Suburbs  19.055300  72.831400\n",
       "14            Pali Hill          Bandra,Western Suburbs  19.068000  72.826000"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neighborhood_df = dfs[0]\n",
    "print(neighborhood_df.shape)\n",
    "neighborhood_df.head(15) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Downloading the Commercial Property Rate Data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(93, 3)\n"
     ]
    },
    {
     "data": {
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Area</th>\n",
       "      <th>Location</th>\n",
       "      <th>Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Amboli</td>\n",
       "      <td>Andheri,Western Suburbs</td>\n",
       "      <td>18000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Chakala, Andheri</td>\n",
       "      <td>Western Suburbs</td>\n",
       "      <td>14750.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>D.N. Nagar</td>\n",
       "      <td>Andheri,Western Suburbs</td>\n",
       "      <td>18000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Four Bungalows</td>\n",
       "      <td>Andheri,Western Suburbs</td>\n",
       "      <td>18000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Lokhandwala</td>\n",
       "      <td>Andheri,Western Suburbs</td>\n",
       "      <td>18000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Area                 Location     Rate\n",
       "0            Amboli  Andheri,Western Suburbs  18000.0\n",
       "1  Chakala, Andheri          Western Suburbs  14750.0\n",
       "2        D.N. Nagar  Andheri,Western Suburbs  18000.0\n",
       "3    Four Bungalows  Andheri,Western Suburbs  18000.0\n",
       "4       Lokhandwala  Andheri,Western Suburbs  18000.0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url_1 = 'https://drive.google.com/uc?export=download&id=1a2lBIVAgxwIPtavdJh7CLpObKaxXxuB9'\n",
    "property_rates_df = pd.read_csv(url_1)\n",
    "print(property_rates_df.shape)\n",
    "property_rates_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(67, 5)\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>Area</th>\n",
       "      <th>Location</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Amboli</td>\n",
       "      <td>Andheri,Western Suburbs</td>\n",
       "      <td>19.129300</td>\n",
       "      <td>72.843400</td>\n",
       "      <td>18000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Chakala, Andheri</td>\n",
       "      <td>Western Suburbs</td>\n",
       "      <td>19.111388</td>\n",
       "      <td>72.860833</td>\n",
       "      <td>14750.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>D.N. Nagar</td>\n",
       "      <td>Andheri,Western Suburbs</td>\n",
       "      <td>19.124085</td>\n",
       "      <td>72.831373</td>\n",
       "      <td>18000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Four Bungalows</td>\n",
       "      <td>Andheri,Western Suburbs</td>\n",
       "      <td>19.124714</td>\n",
       "      <td>72.827210</td>\n",
       "      <td>18000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Lokhandwala</td>\n",
       "      <td>Andheri,Western Suburbs</td>\n",
       "      <td>19.130815</td>\n",
       "      <td>72.829270</td>\n",
       "      <td>18000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Area                 Location   Latitude  Longitude     Rate\n",
       "0            Amboli  Andheri,Western Suburbs  19.129300  72.843400  18000.0\n",
       "1  Chakala, Andheri          Western Suburbs  19.111388  72.860833  14750.0\n",
       "2        D.N. Nagar  Andheri,Western Suburbs  19.124085  72.831373  18000.0\n",
       "3    Four Bungalows  Andheri,Western Suburbs  19.124714  72.827210  18000.0\n",
       "4       Lokhandwala  Andheri,Western Suburbs  19.130815  72.829270  18000.0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mumbai_df = pd.merge(neighborhood_df, property_rates_df, on=['Area', 'Location'])\n",
    "mumbai_df.dropna(inplace=True)\n",
    "print(mumbai_df.shape)\n",
    "mumbai_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Area</th>\n",
       "      <th>Location</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Rate</th>\n",
       "      <th>Suburb</th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Amboli</td>\n",
       "      <td>Andheri</td>\n",
       "      <td>19.129300</td>\n",
       "      <td>72.843400</td>\n",
       "      <td>18000.0</td>\n",
       "      <td>Western Suburbs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Chakala, Andheri</td>\n",
       "      <td>Western Suburbs</td>\n",
       "      <td>19.111388</td>\n",
       "      <td>72.860833</td>\n",
       "      <td>14750.0</td>\n",
       "      <td>Western Suburbs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>D.N. Nagar</td>\n",
       "      <td>Andheri</td>\n",
       "      <td>19.124085</td>\n",
       "      <td>72.831373</td>\n",
       "      <td>18000.0</td>\n",
       "      <td>Western Suburbs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Four Bungalows</td>\n",
       "      <td>Andheri</td>\n",
       "      <td>19.124714</td>\n",
       "      <td>72.827210</td>\n",
       "      <td>18000.0</td>\n",
       "      <td>Western Suburbs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Lokhandwala</td>\n",
       "      <td>Andheri</td>\n",
       "      <td>19.130815</td>\n",
       "      <td>72.829270</td>\n",
       "      <td>18000.0</td>\n",
       "      <td>Western Suburbs</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Area         Location   Latitude  Longitude     Rate  \\\n",
       "0            Amboli          Andheri  19.129300  72.843400  18000.0   \n",
       "1  Chakala, Andheri  Western Suburbs  19.111388  72.860833  14750.0   \n",
       "2        D.N. Nagar          Andheri  19.124085  72.831373  18000.0   \n",
       "3    Four Bungalows          Andheri  19.124714  72.827210  18000.0   \n",
       "4       Lokhandwala          Andheri  19.130815  72.829270  18000.0   \n",
       "\n",
       "            Suburb  \n",
       "0  Western Suburbs  \n",
       "1  Western Suburbs  \n",
       "2  Western Suburbs  \n",
       "3  Western Suburbs  \n",
       "4  Western Suburbs  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new = mumbai_df[\"Location\"].str.split(\",\", n = 1, expand = True) \n",
    "mumbai_df[\"Location\"] = new[0] \n",
    "mumbai_df[\"Suburb\"]= new[1] \n",
    "\n",
    "for i in range(mumbai_df.shape[0]):\n",
    "    if mumbai_df.iloc[i, 5] is None:\n",
    "        mumbai_df.iloc[i, 5] = mumbai_df.iloc[i, 1]\n",
    "\n",
    "mumbai_df.reset_index(drop=True, inplace=True)\n",
    "mumbai_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(67, 6)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mumbai_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting all Areas on Map showing respective Property Rates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The geograpical coordinate of Mumbai are 19.0549792, 72.8402203.\n"
     ]
    }
   ],
   "source": [
    "address = 'Bandra, Mumbai city, India'\n",
    "\n",
    "geolocator = Nominatim(user_agent=\"mum_explorer\")\n",
    "location = geolocator.geocode(address)\n",
    "latitude = location.latitude\n",
    "longitude = location.longitude\n",
    "print('The geograpical coordinate of Mumbai are {}, {}.'.format(latitude, longitude))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 8000., 17250., 26500., 35750., 45000.])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rate_range = np.linspace(mumbai_df.Rate.min(), mumbai_df.Rate.max(), 5)\n",
    "rate_range"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Color code**: \n",
    "\n",
    "|Property Rate|Color|\n",
    "|-------------|------|\n",
    "| High Property Rate | Black|\n",
    "| Medium High Property Rate | Blue|\n",
    "| Medium Low Property Rate | Red|\n",
    "| Low Property Rate | Orange|"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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            circle_marker_7ac9a3e6688f4a8196a2d44b577408ed.bindPopup(popup_eeaa25847efb45089f93b817ccfaedfd);

            
        
    
            var circle_marker_b7161240152b4e70bb144890f573f292 = L.circleMarker(
                [19.042718,72.819132],
                {
  "bubblingMouseEvents": true,
  "color": "blue",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "blue",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_be4a8cb55980417385f4349da74ca795);
            
    
            var popup_463b826846144649a9e53e48989bf88e = L.popup({maxWidth: '300'});

            
                var html_cb4d8e010eeb4d3e9631c587841b774e = $('<div id="html_cb4d8e010eeb4d3e9631c587841b774e" style="width: 100.0%; height: 100.0%;">Bandstand Promenade, Rs.30000/sq.ft.</div>')[0];
                popup_463b826846144649a9e53e48989bf88e.setContent(html_cb4d8e010eeb4d3e9631c587841b774e);
            

            circle_marker_b7161240152b4e70bb144890f573f292.bindPopup(popup_463b826846144649a9e53e48989bf88e);

            
        
    
            var circle_marker_a905fa640c10448aae204b6fc17e586d = L.circleMarker(
                [19.0553,72.8314],
                {
  "bubblingMouseEvents": true,
  "color": "blue",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "blue",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_be4a8cb55980417385f4349da74ca795);
            
    
            var popup_a590ac869dc64c028b3a00eda389c9f7 = L.popup({maxWidth: '300'});

            
                var html_fb786ecdb3af47359d75a38b6fab6659 = $('<div id="html_fb786ecdb3af47359d75a38b6fab6659" style="width: 100.0%; height: 100.0%;">Kherwadi, Rs.27500/sq.ft.</div>')[0];
                popup_a590ac869dc64c028b3a00eda389c9f7.setContent(html_fb786ecdb3af47359d75a38b6fab6659);
            

            circle_marker_a905fa640c10448aae204b6fc17e586d.bindPopup(popup_a590ac869dc64c028b3a00eda389c9f7);

            
        
    
            var circle_marker_9460ac31f9fb47d2a495f14021760b01 = L.circleMarker(
                [19.067999999999998,72.82600000000001],
                {
  "bubblingMouseEvents": true,
  "color": "blue",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "blue",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_be4a8cb55980417385f4349da74ca795);
            
    
            var popup_9935bafa439844eebfd4a2a22c530341 = L.popup({maxWidth: '300'});

            
                var html_fd80d26fead841dd90554b2864e27b43 = $('<div id="html_fd80d26fead841dd90554b2864e27b43" style="width: 100.0%; height: 100.0%;">Pali Hill, Rs.30000/sq.ft.</div>')[0];
                popup_9935bafa439844eebfd4a2a22c530341.setContent(html_fd80d26fead841dd90554b2864e27b43);
            

            circle_marker_9460ac31f9fb47d2a495f14021760b01.bindPopup(popup_9935bafa439844eebfd4a2a22c530341);

            
        
    
            var circle_marker_dd91445147434d2e97f207cc6e3aca72 = L.circleMarker(
                [19.247039,72.84983000000001],
                {
  "bubblingMouseEvents": true,
  "color": "orange",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "orange",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_be4a8cb55980417385f4349da74ca795);
            
    
            var popup_44ae06c489d54ce9a76dfb75553a8dbd = L.popup({maxWidth: '300'});

            
                var html_91def5c469524386b660eacb0ad923a9 = $('<div id="html_91def5c469524386b660eacb0ad923a9" style="width: 100.0%; height: 100.0%;">I.C. Colony, Rs.10550/sq.ft.</div>')[0];
                popup_44ae06c489d54ce9a76dfb75553a8dbd.setContent(html_91def5c469524386b660eacb0ad923a9);
            

            circle_marker_dd91445147434d2e97f207cc6e3aca72.bindPopup(popup_44ae06c489d54ce9a76dfb75553a8dbd);

            
        
    
            var circle_marker_f5a3eddbbb75450680cd732def623c87 = L.circleMarker(
                [19.250057,72.782021],
                {
  "bubblingMouseEvents": true,
  "color": "orange",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "orange",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_be4a8cb55980417385f4349da74ca795);
            
    
            var popup_53ec2da2e8e44568902f849d8a9486ce = L.popup({maxWidth: '300'});

            
                var html_6b058feea29545c2a3ebd87af8597f91 = $('<div id="html_6b058feea29545c2a3ebd87af8597f91" style="width: 100.0%; height: 100.0%;">Gorai, Rs.10550/sq.ft.</div>')[0];
                popup_53ec2da2e8e44568902f849d8a9486ce.setContent(html_6b058feea29545c2a3ebd87af8597f91);
            

            circle_marker_f5a3eddbbb75450680cd732def623c87.bindPopup(popup_53ec2da2e8e44568902f849d8a9486ce);

            
        
    
            var circle_marker_a1e9d80511f3465e832c7d40485d83f1 = L.circleMarker(
                [19.250069,72.859347],
                {
  "bubblingMouseEvents": true,
  "color": "orange",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "orange",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_be4a8cb55980417385f4349da74ca795);
            
    
            var popup_57f48adbd4c84f1a834d30b9d2cb625f = L.popup({maxWidth: '300'});

            
                var html_6545cadeee8e46c587de2e185b5767c8 = $('<div id="html_6545cadeee8e46c587de2e185b5767c8" style="width: 100.0%; height: 100.0%;">Dahisa, Rs.9500/sq.ft.</div>')[0];
                popup_57f48adbd4c84f1a834d30b9d2cb625f.setContent(html_6545cadeee8e46c587de2e185b5767c8);
            

            circle_marker_a1e9d80511f3465e832c7d40485d83f1.bindPopup(popup_57f48adbd4c84f1a834d30b9d2cb625f);

            
        
    
            var circle_marker_4cb4bd10aee4488db5d122870717d63f = L.circleMarker(
                [19.148493,72.88175600000001],
                {
  "bubblingMouseEvents": true,
  "color": "orange",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "orange",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_be4a8cb55980417385f4349da74ca795);
            
    
            var popup_1d31d528c60e48afa7a90db5464a9109 = L.popup({maxWidth: '300'});

            
                var html_db39f7ab678d4573a4d55eaa49c4c6f5 = $('<div id="html_db39f7ab678d4573a4d55eaa49c4c6f5" style="width: 100.0%; height: 100.0%;">Aarey Milk Colony, Rs.12950/sq.ft.</div>')[0];
                popup_1d31d528c60e48afa7a90db5464a9109.setContent(html_db39f7ab678d4573a4d55eaa49c4c6f5);
            

            circle_marker_4cb4bd10aee4488db5d122870717d63f.bindPopup(popup_1d31d528c60e48afa7a90db5464a9109);

            
        
    
            var circle_marker_363bdd5aa70041ff817bea1391767520 = L.circleMarker(
                [19.167362,72.832252],
                {
  "bubblingMouseEvents": true,
  "color": "orange",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "orange",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_be4a8cb55980417385f4349da74ca795);
            
    
            var popup_b65aa229f74c4972aaef271fffabc581 = L.popup({maxWidth: '300'});

            
                var html_a67512d5b8384e49bab95211e81fff48 = $('<div id="html_a67512d5b8384e49bab95211e81fff48" style="width: 100.0%; height: 100.0%;">Bangur Nagar, Rs.13500/sq.ft.</div>')[0];
                popup_b65aa229f74c4972aaef271fffabc581.setContent(html_a67512d5b8384e49bab95211e81fff48);
            

            circle_marker_363bdd5aa70041ff817bea1391767520.bindPopup(popup_b65aa229f74c4972aaef271fffabc581);

            
        
    
            var circle_marker_767afffa29ca44168f3b2d840606f550 = L.circleMarker(
                [19.12,72.85],
                {
  "bubblingMouseEvents": true,
  "color": "orange",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "orange",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_be4a8cb55980417385f4349da74ca795);
            
    
            var popup_ee2c40e09ef546d1973f3ab15ccdb0c7 = L.popup({maxWidth: '300'});

            
                var html_3e310667374c41d8bc99fc1b5124c6f4 = $('<div id="html_3e310667374c41d8bc99fc1b5124c6f4" style="width: 100.0%; height: 100.0%;">Jogeshwari West, Rs.17000/sq.ft.</div>')[0];
                popup_ee2c40e09ef546d1973f3ab15ccdb0c7.setContent(html_3e310667374c41d8bc99fc1b5124c6f4);
            

            circle_marker_767afffa29ca44168f3b2d840606f550.bindPopup(popup_ee2c40e09ef546d1973f3ab15ccdb0c7);

            
        
    
            var circle_marker_b1ab09c60f874140af289be02cfccef9 = L.circleMarker(
                [19.1,72.83],
                {
  "bubblingMouseEvents": true,
  "color": "black",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "black",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_be4a8cb55980417385f4349da74ca795);
            
    
            var popup_f230b15016af49c58cb624e28f4c3bc9 = L.popup({maxWidth: '300'});

            
                var html_ea437a2e44854cf39ab7709a167a0686 = $('<div id="html_ea437a2e44854cf39ab7709a167a0686" style="width: 100.0%; height: 100.0%;">Juhu, Rs.37750/sq.ft.</div>')[0];
                popup_f230b15016af49c58cb624e28f4c3bc9.setContent(html_ea437a2e44854cf39ab7709a167a0686);
            

            circle_marker_b1ab09c60f874140af289be02cfccef9.bindPopup(popup_f230b15016af49c58cb624e28f4c3bc9);

            
        
    
            var circle_marker_332c3685aea34b5c805c78211d13a018 = L.circleMarker(
                [19.216182,72.830575],
                {
  "bubblingMouseEvents": true,
  "color": "orange",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "orange",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_be4a8cb55980417385f4349da74ca795);
            
    
            var popup_09927dfd158740c2bd310e5ddb2e9fc0 = L.popup({maxWidth: '300'});

            
                var html_79a7be29475b42e1ad910e3884f161dd = $('<div id="html_79a7be29475b42e1ad910e3884f161dd" style="width: 100.0%; height: 100.0%;">Charkop, Rs.15500/sq.ft.</div>')[0];
                popup_09927dfd158740c2bd310e5ddb2e9fc0.setContent(html_79a7be29475b42e1ad910e3884f161dd);
            

            circle_marker_332c3685aea34b5c805c78211d13a018.bindPopup(popup_09927dfd158740c2bd310e5ddb2e9fc0);

            
        
    
            var circle_marker_b042682f051f47ed90779e2e173aea84 = L.circleMarker(
                [19.204511,72.837639],
                {
  "bubblingMouseEvents": true,
  "color": "orange",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "orange",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_be4a8cb55980417385f4349da74ca795);
            
    
            var popup_3db473298e1d4edf983fac96810681bd = L.popup({maxWidth: '300'});

            
                var html_44581460345340edbea88682aa86f130 = $('<div id="html_44581460345340edbea88682aa86f130" style="width: 100.0%; height: 100.0%;">Poisar, Rs.15500/sq.ft.</div>')[0];
                popup_3db473298e1d4edf983fac96810681bd.setContent(html_44581460345340edbea88682aa86f130);
            

            circle_marker_b042682f051f47ed90779e2e173aea84.bindPopup(popup_3db473298e1d4edf983fac96810681bd);

            
        
    
            var circle_marker_f2a43217fda248d3812ab31ace147ce8 = L.circleMarker(
                [19.211319,72.842737],
                {
  "bubblingMouseEvents": true,
  "color": "orange",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "orange",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_be4a8cb55980417385f4349da74ca795);
            
    
            var popup_f64d8c659ce64ca8891d1ca3fab4b969 = L.popup({maxWidth: '300'});

            
                var html_e2f360c0af534f80b9ac98eeabaead4d = $('<div id="html_e2f360c0af534f80b9ac98eeabaead4d" style="width: 100.0%; height: 100.0%;">Mahavir Nagar, Rs.15500/sq.ft.</div>')[0];
                popup_f64d8c659ce64ca8891d1ca3fab4b969.setContent(html_e2f360c0af534f80b9ac98eeabaead4d);
            

            circle_marker_f2a43217fda248d3812ab31ace147ce8.bindPopup(popup_f64d8c659ce64ca8891d1ca3fab4b969);

            
        
    
            var circle_marker_3247b5486340446d97f11976a31d75c1 = L.circleMarker(
                [19.210206,72.87298],
                {
  "bubblingMouseEvents": true,
  "color": "orange",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "orange",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_be4a8cb55980417385f4349da74ca795);
            
    
            var popup_57e8f52ac50040aa97167ddfe8b76574 = L.popup({maxWidth: '300'});

            
                var html_9ec0dea1b5574eba94f228e92ad88fab = $('<div id="html_9ec0dea1b5574eba94f228e92ad88fab" style="width: 100.0%; height: 100.0%;">Thakur village, Rs.11500/sq.ft.</div>')[0];
                popup_57e8f52ac50040aa97167ddfe8b76574.setContent(html_9ec0dea1b5574eba94f228e92ad88fab);
            

            circle_marker_3247b5486340446d97f11976a31d75c1.bindPopup(popup_57e8f52ac50040aa97167ddfe8b76574);

            
        
    
            var circle_marker_26e43da5659b41d5a1e2d17a2fe8befb = L.circleMarker(
                [19.062742,72.829396],
                {
  "bubblingMouseEvents": true,
  "color": "red",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "red",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_be4a8cb55980417385f4349da74ca795);
            
    
            var popup_e7edcb6b723a43db86fe9bb0b613276f = L.popup({maxWidth: '300'});

            
                var html_594ebe64432049aaa9e4866d52ba71d2 = $('<div id="html_594ebe64432049aaa9e4866d52ba71d2" style="width: 100.0%; height: 100.0%;">Pali Naka, Rs.21500/sq.ft.</div>')[0];
                popup_e7edcb6b723a43db86fe9bb0b613276f.setContent(html_594ebe64432049aaa9e4866d52ba71d2);
            

            circle_marker_26e43da5659b41d5a1e2d17a2fe8befb.bindPopup(popup_e7edcb6b723a43db86fe9bb0b613276f);

            
        
    
            var circle_marker_f5c3335e1f304dc88831e2f58da80655 = L.circleMarker(
                [19.068598,72.84004200000001],
                {
  "bubblingMouseEvents": true,
  "color": "red",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "red",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_be4a8cb55980417385f4349da74ca795);
            
    
            var popup_2268c26b84ee487fb9e48306e82623c0 = L.popup({maxWidth: '300'});

            
                var html_d317de92f4f1484f8e0fae16b98d368b = $('<div id="html_d317de92f4f1484f8e0fae16b98d368b" style="width: 100.0%; height: 100.0%;">Khar Danda, Rs.21500/sq.ft.</div>')[0];
                popup_2268c26b84ee487fb9e48306e82623c0.setContent(html_d317de92f4f1484f8e0fae16b98d368b);
            

            circle_marker_f5c3335e1f304dc88831e2f58da80655.bindPopup(popup_2268c26b84ee487fb9e48306e82623c0);

            
        
    
            var circle_marker_7ad3fbf0ae734edf81cd937c0ce9b5f4 = L.circleMarker(
                [19.176382,72.864891],
                {
  "bubblingMouseEvents": true,
  "color": "orange",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "orange",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_be4a8cb55980417385f4349da74ca795);
            
    
            var popup_3626ff0f9b774be2a108dfaae943ca08 = L.popup({maxWidth: '300'});

            
                var html_176397bc4c5e471e9fb54d700bcf025a = $('<div id="html_176397bc4c5e471e9fb54d700bcf025a" style="width: 100.0%; height: 100.0%;">Dindoshi, Rs.11050/sq.ft.</div>')[0];
                popup_3626ff0f9b774be2a108dfaae943ca08.setContent(html_176397bc4c5e471e9fb54d700bcf025a);
            

            circle_marker_7ad3fbf0ae734edf81cd937c0ce9b5f4.bindPopup(popup_3626ff0f9b774be2a108dfaae943ca08);

            
        
    
            var circle_marker_4562ef96da3a4c8085f947701b788f27 = L.circleMarker(
                [19.175,72.842],
                {
  "bubblingMouseEvents": true,
  "color": "orange",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "orange",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_be4a8cb55980417385f4349da74ca795);
            
    
            var popup_fdec0e8d5f344ce1898698f2da0429b7 = L.popup({maxWidth: '300'});

            
                var html_3ba4f2a7090c4ee39c84fe63c96f1f8d = $('<div id="html_3ba4f2a7090c4ee39c84fe63c96f1f8d" style="width: 100.0%; height: 100.0%;">Sunder Nagar, Rs.13600/sq.ft.</div>')[0];
                popup_fdec0e8d5f344ce1898698f2da0429b7.setContent(html_3ba4f2a7090c4ee39c84fe63c96f1f8d);
            

            circle_marker_4562ef96da3a4c8085f947701b788f27.bindPopup(popup_fdec0e8d5f344ce1898698f2da0429b7);

            
        
    
            var circle_marker_4f19507c3cec4be59fafc18c63bbe256 = L.circleMarker(
                [19.081667000000003,72.841389],
                {
  "bubblingMouseEvents": true,
  "color": "red",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "red",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_be4a8cb55980417385f4349da74ca795);
            
    
            var popup_fce16ea4c05d4c72bc4c94cb6e96b653 = L.popup({maxWidth: '300'});

            
                var html_65307b9ba32c495cbe739195f0d1541a = $('<div id="html_65307b9ba32c495cbe739195f0d1541a" style="width: 100.0%; height: 100.0%;">Kalina, Rs.20000/sq.ft.</div>')[0];
                popup_fce16ea4c05d4c72bc4c94cb6e96b653.setContent(html_65307b9ba32c495cbe739195f0d1541a);
            

            circle_marker_4f19507c3cec4be59fafc18c63bbe256.bindPopup(popup_fce16ea4c05d4c72bc4c94cb6e96b653);

            
        
    
            var circle_marker_848f6855a2054dc78ada17cff6932398 = L.circleMarker(
                [19.108056,72.83805600000001],
                {
  "bubblingMouseEvents": true,
  "color": "red",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "red",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_be4a8cb55980417385f4349da74ca795);
            
    
            var popup_00f1dbaabd674048969f4cbe99e2257b = L.popup({maxWidth: '300'});

            
                var html_66c8439967aa425eb7dc05e5e6fc695b = $('<div id="html_66c8439967aa425eb7dc05e5e6fc695b" style="width: 100.0%; height: 100.0%;">Irla, Rs.20750/sq.ft.</div>')[0];
                popup_00f1dbaabd674048969f4cbe99e2257b.setContent(html_66c8439967aa425eb7dc05e5e6fc695b);
            

            circle_marker_848f6855a2054dc78ada17cff6932398.bindPopup(popup_00f1dbaabd674048969f4cbe99e2257b);

            
        
    
            var circle_marker_b4dc9dde74de4e73b7cdb8028dc0f0e9 = L.circleMarker(
                [19.1,72.83],
                {
  "bubblingMouseEvents": true,
  "color": "orange",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "orange",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_be4a8cb55980417385f4349da74ca795);
            
    
            var popup_fb8265dd522849a8a2cbbae5ac3936d7 = L.popup({maxWidth: '300'});

            
                var html_ec4f9207fd1b41d88ff6b153a4543693 = $('<div id="html_ec4f9207fd1b41d88ff6b153a4543693" style="width: 100.0%; height: 100.0%;">Vile Parle, Rs.17000/sq.ft.</div>')[0];
                popup_fb8265dd522849a8a2cbbae5ac3936d7.setContent(html_ec4f9207fd1b41d88ff6b153a4543693);
            

            circle_marker_b4dc9dde74de4e73b7cdb8028dc0f0e9.bindPopup(popup_fb8265dd522849a8a2cbbae5ac3936d7);

            
        
    
            var circle_marker_b49c59014cff441487d6b26d2f0dfd05 = L.circleMarker(
                [19.14,72.93],
                {
  "bubblingMouseEvents": true,
  "color": "orange",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "orange",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_be4a8cb55980417385f4349da74ca795);
            
    
            var popup_97a37708406d4bec8f1e6fce9bd22577 = L.popup({maxWidth: '300'});

            
                var html_3112f7cb5fa34576bcf3e930894e04e6 = $('<div id="html_3112f7cb5fa34576bcf3e930894e04e6" style="width: 100.0%; height: 100.0%;">Bhandup, Rs.15000/sq.ft.</div>')[0];
                popup_97a37708406d4bec8f1e6fce9bd22577.setContent(html_3112f7cb5fa34576bcf3e930894e04e6);
            

            circle_marker_b49c59014cff441487d6b26d2f0dfd05.bindPopup(popup_97a37708406d4bec8f1e6fce9bd22577);

            
        
    
            var circle_marker_f969562214c041f1b50bde50c092d4f7 = L.circleMarker(
                [19.102076999999998,72.912835],
                {
  "bubblingMouseEvents": true,
  "color": "orange",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "orange",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_be4a8cb55980417385f4349da74ca795);
            
    
            var popup_762bf5d638d347b8a91af84af527dbd2 = L.popup({maxWidth: '300'});

            
                var html_02ce69f1d5da4285b0ed52433a931b05 = $('<div id="html_02ce69f1d5da4285b0ed52433a931b05" style="width: 100.0%; height: 100.0%;">Amrut Nagar, Rs.16150/sq.ft.</div>')[0];
                popup_762bf5d638d347b8a91af84af527dbd2.setContent(html_02ce69f1d5da4285b0ed52433a931b05);
            

            circle_marker_f969562214c041f1b50bde50c092d4f7.bindPopup(popup_762bf5d638d347b8a91af84af527dbd2);

            
        
    
            var circle_marker_63ba12ef4b9447239b314d30e26c52bc = L.circleMarker(
                [19.090999999999998,72.90100000000001],
                {
  "bubblingMouseEvents": true,
  "color": "orange",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "orange",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_be4a8cb55980417385f4349da74ca795);
            
    
            var popup_6023c4e566e14d16a1fbacae1910f354 = L.popup({maxWidth: '300'});

            
                var html_bb0961726cea45de9a81f8273824b070 = $('<div id="html_bb0961726cea45de9a81f8273824b070" style="width: 100.0%; height: 100.0%;">Asalfa, Rs.16150/sq.ft.</div>')[0];
                popup_6023c4e566e14d16a1fbacae1910f354.setContent(html_bb0961726cea45de9a81f8273824b070);
            

            circle_marker_63ba12ef4b9447239b314d30e26c52bc.bindPopup(popup_6023c4e566e14d16a1fbacae1910f354);

            
        
    
            var circle_marker_82cd50a46c4d4b81ac4c1f152403c624 = L.circleMarker(
                [19.08,72.91],
                {
  "bubblingMouseEvents": true,
  "color": "orange",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "orange",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_be4a8cb55980417385f4349da74ca795);
            
    
            var popup_1fe1b95a1cb2437f9abcfedfaf20c813 = L.popup({maxWidth: '300'});

            
                var html_c7d78b376c004773b0b19d83ebc1f924 = $('<div id="html_c7d78b376c004773b0b19d83ebc1f924" style="width: 100.0%; height: 100.0%;">Pant Nagar, Rs.11500/sq.ft.</div>')[0];
                popup_1fe1b95a1cb2437f9abcfedfaf20c813.setContent(html_c7d78b376c004773b0b19d83ebc1f924);
            

            circle_marker_82cd50a46c4d4b81ac4c1f152403c624.bindPopup(popup_1fe1b95a1cb2437f9abcfedfaf20c813);

            
        
    
            var circle_marker_91ae1d8d3beb471ebe952f5281331987 = L.circleMarker(
                [19.13,72.94],
                {
  "bubblingMouseEvents": true,
  "color": "orange",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "orange",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_be4a8cb55980417385f4349da74ca795);
            
    
            var popup_1adb6b7c0a3143869d0d991165f498f4 = L.popup({maxWidth: '300'});

            
                var html_7f0798bd9a40408eba8e2c31b3515abb = $('<div id="html_7f0798bd9a40408eba8e2c31b3515abb" style="width: 100.0%; height: 100.0%;">Kanjurmarg, Rs.10550/sq.ft.</div>')[0];
                popup_1adb6b7c0a3143869d0d991165f498f4.setContent(html_7f0798bd9a40408eba8e2c31b3515abb);
            

            circle_marker_91ae1d8d3beb471ebe952f5281331987.bindPopup(popup_1adb6b7c0a3143869d0d991165f498f4);

            
        
    
            var circle_marker_6d23cc62463f4d2a8d0c6e6160207611 = L.circleMarker(
                [15.451685999999999,74.971977],
                {
  "bubblingMouseEvents": true,
  "color": "orange",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "orange",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_be4a8cb55980417385f4349da74ca795);
            
    
            var popup_d4bc3f96bef14758a7170cc271f010e6 = L.popup({maxWidth: '300'});

            
                var html_ff95c5b38d1a48d7b3cd92e0183495ae = $('<div id="html_ff95c5b38d1a48d7b3cd92e0183495ae" style="width: 100.0%; height: 100.0%;">Nehru Nagar, Rs.10000/sq.ft.</div>')[0];
                popup_d4bc3f96bef14758a7170cc271f010e6.setContent(html_ff95c5b38d1a48d7b3cd92e0183495ae);
            

            circle_marker_6d23cc62463f4d2a8d0c6e6160207611.bindPopup(popup_d4bc3f96bef14758a7170cc271f010e6);

            
        
    
            var circle_marker_dc7e87613a8c4475bdd33b0bc9b0c925 = L.circleMarker(
                [19.157,72.941],
                {
  "bubblingMouseEvents": true,
  "color": "orange",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "orange",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_be4a8cb55980417385f4349da74ca795);
            
    
            var popup_e4f7b57ac2624a49aa7814ab46047f2b = L.popup({maxWidth: '300'});

            
                var html_5c8a6891154e4174a9dc8b4ae2564fb7 = $('<div id="html_5c8a6891154e4174a9dc8b4ae2564fb7" style="width: 100.0%; height: 100.0%;">Nahur, Rs.11600/sq.ft.</div>')[0];
                popup_e4f7b57ac2624a49aa7814ab46047f2b.setContent(html_5c8a6891154e4174a9dc8b4ae2564fb7);
            

            circle_marker_dc7e87613a8c4475bdd33b0bc9b0c925.bindPopup(popup_e4f7b57ac2624a49aa7814ab46047f2b);

            
        
    
            var circle_marker_00efaab228654068b794974d67f28e16 = L.circleMarker(
                [19.11,72.9],
                {
  "bubblingMouseEvents": true,
  "color": "red",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "red",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_be4a8cb55980417385f4349da74ca795);
            
    
            var popup_6b8ed95a011c46f2b2a94361e760fc33 = L.popup({maxWidth: '300'});

            
                var html_09cf0f06065043118c87889e6c94225d = $('<div id="html_09cf0f06065043118c87889e6c94225d" style="width: 100.0%; height: 100.0%;">Chandivali, Rs.19500/sq.ft.</div>')[0];
                popup_6b8ed95a011c46f2b2a94361e760fc33.setContent(html_09cf0f06065043118c87889e6c94225d);
            

            circle_marker_00efaab228654068b794974d67f28e16.bindPopup(popup_6b8ed95a011c46f2b2a94361e760fc33);

            
        
    
            var circle_marker_0b4a62239b7441069efd2bb07b6ca2ad = L.circleMarker(
                [19.118986,72.911767],
                {
  "bubblingMouseEvents": true,
  "color": "red",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "red",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_be4a8cb55980417385f4349da74ca795);
            
    
            var popup_838428d0a70e4348a88612e8fe3c2fd3 = L.popup({maxWidth: '300'});

            
                var html_9aaa3b4e63dc4693bb98edbe7f9179e9 = $('<div id="html_9aaa3b4e63dc4693bb98edbe7f9179e9" style="width: 100.0%; height: 100.0%;">Hiranandani Gardens, Rs.19500/sq.ft.</div>')[0];
                popup_838428d0a70e4348a88612e8fe3c2fd3.setContent(html_9aaa3b4e63dc4693bb98edbe7f9179e9);
            

            circle_marker_0b4a62239b7441069efd2bb07b6ca2ad.bindPopup(popup_838428d0a70e4348a88612e8fe3c2fd3);

            
        
    
            var circle_marker_886892708cd845f08005f256bf393f9e = L.circleMarker(
                [19.11,72.94],
                {
  "bubblingMouseEvents": true,
  "color": "orange",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "orange",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_be4a8cb55980417385f4349da74ca795);
            
    
            var popup_01e10bb0b6ed4760843426a3e11dbf89 = L.popup({maxWidth: '300'});

            
                var html_28a86f56664c427cb1e918655c89f460 = $('<div id="html_28a86f56664c427cb1e918655c89f460" style="width: 100.0%; height: 100.0%;">Vikhroli, Rs.12000/sq.ft.</div>')[0];
                popup_01e10bb0b6ed4760843426a3e11dbf89.setContent(html_28a86f56664c427cb1e918655c89f460);
            

            circle_marker_886892708cd845f08005f256bf393f9e.bindPopup(popup_01e10bb0b6ed4760843426a3e11dbf89);

            
        
    
            var circle_marker_1cb218ae1b264c89ab000247b8d7641e = L.circleMarker(
                [19.051,72.89399999999999],
                {
  "bubblingMouseEvents": true,
  "color": "red",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "red",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_be4a8cb55980417385f4349da74ca795);
            
    
            var popup_8f7279cf30db4952b338bda48d8d44ba = L.popup({maxWidth: '300'});

            
                var html_1f1b7ae78d39457d8f812e780ad4b00b = $('<div id="html_1f1b7ae78d39457d8f812e780ad4b00b" style="width: 100.0%; height: 100.0%;">Chembur, Rs.19250/sq.ft.</div>')[0];
                popup_8f7279cf30db4952b338bda48d8d44ba.setContent(html_1f1b7ae78d39457d8f812e780ad4b00b);
            

            circle_marker_1cb218ae1b264c89ab000247b8d7641e.bindPopup(popup_8f7279cf30db4952b338bda48d8d44ba);

            
        
    
            var circle_marker_d46012490d6342bdbbfd1efe25f90e2a = L.circleMarker(
                [19.05,72.93],
                {
  "bubblingMouseEvents": true,
  "color": "orange",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "orange",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_be4a8cb55980417385f4349da74ca795);
            
    
            var popup_67e39b1a02e145058e4ab4370ae1d2f0 = L.popup({maxWidth: '300'});

            
                var html_103d9eb37b3340e1a838545a71ac449b = $('<div id="html_103d9eb37b3340e1a838545a71ac449b" style="width: 100.0%; height: 100.0%;">Mankhurd, Rs.10500/sq.ft.</div>')[0];
                popup_67e39b1a02e145058e4ab4370ae1d2f0.setContent(html_103d9eb37b3340e1a838545a71ac449b);
            

            circle_marker_d46012490d6342bdbbfd1efe25f90e2a.bindPopup(popup_67e39b1a02e145058e4ab4370ae1d2f0);

            
        
    
            var circle_marker_6831b85783ba4ae6b2ce1ccb6cc229e8 = L.circleMarker(
                [18.9681,72.8095],
                {
  "bubblingMouseEvents": true,
  "color": "black",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "black",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_be4a8cb55980417385f4349da74ca795);
            
    
            var popup_9313c4be16bd4029b58b198fce64c4b7 = L.popup({maxWidth: '300'});

            
                var html_f19f77c07a5845b0a942ec4209d6a4e1 = $('<div id="html_f19f77c07a5845b0a942ec4209d6a4e1" style="width: 100.0%; height: 100.0%;">Altamount Road, Rs.45000/sq.ft.</div>')[0];
                popup_9313c4be16bd4029b58b198fce64c4b7.setContent(html_f19f77c07a5845b0a942ec4209d6a4e1);
            

            circle_marker_6831b85783ba4ae6b2ce1ccb6cc229e8.bindPopup(popup_9313c4be16bd4029b58b198fce64c4b7);

            
        
    
            var circle_marker_1445f1d9d5f84b50b8eba81b879a2a25 = L.circleMarker(
                [18.967,72.805],
                {
  "bubblingMouseEvents": true,
  "color": "black",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "black",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_be4a8cb55980417385f4349da74ca795);
            
    
            var popup_b06535e2c25c44d399ce4dd1518069b3 = L.popup({maxWidth: '300'});

            
                var html_da7e25eea78543519da72a6cf04951c6 = $('<div id="html_da7e25eea78543519da72a6cf04951c6" style="width: 100.0%; height: 100.0%;">Breach Candy, Rs.43500/sq.ft.</div>')[0];
                popup_b06535e2c25c44d399ce4dd1518069b3.setContent(html_da7e25eea78543519da72a6cf04951c6);
            

            circle_marker_1445f1d9d5f84b50b8eba81b879a2a25.bindPopup(popup_b06535e2c25c44d399ce4dd1518069b3);

            
        
    
            var circle_marker_e494f4d71bd043f09d111852518503e1 = L.circleMarker(
                [18.93,72.82],
                {
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                ).addTo(map_be4a8cb55980417385f4349da74ca795);
            
    
            var popup_4d08847b239b4656872b24ff15570a35 = L.popup({maxWidth: '300'});

            
                var html_bc2a922dfbb8465d96980d60577e52e2 = $('<div id="html_bc2a922dfbb8465d96980d60577e52e2" style="width: 100.0%; height: 100.0%;">Churchgate, Rs.28500/sq.ft.</div>')[0];
                popup_4d08847b239b4656872b24ff15570a35.setContent(html_bc2a922dfbb8465d96980d60577e52e2);
            

            circle_marker_e494f4d71bd043f09d111852518503e1.bindPopup(popup_4d08847b239b4656872b24ff15570a35);

            
        
    
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}
                ).addTo(map_be4a8cb55980417385f4349da74ca795);
            
    
            var popup_71d375210ea0450eb756f6ab3b22a67d = L.popup({maxWidth: '300'});

            
                var html_0fa78236f0fc4127a89e5e8bacdd1a76 = $('<div id="html_0fa78236f0fc4127a89e5e8bacdd1a76" style="width: 100.0%; height: 100.0%;">Cuffe Parade, Rs.27500/sq.ft.</div>')[0];
                popup_71d375210ea0450eb756f6ab3b22a67d.setContent(html_0fa78236f0fc4127a89e5e8bacdd1a76);
            

            circle_marker_4f21f78ff72d4d2f808cb20b09008fa8.bindPopup(popup_71d375210ea0450eb756f6ab3b22a67d);

            
        
    
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                ).addTo(map_be4a8cb55980417385f4349da74ca795);
            
    
            var popup_60ca4c98c13a481eb9bdb6a59fed3f92 = L.popup({maxWidth: '300'});

            
                var html_e73c3a19217c47f3878742fa5c0776ef = $('<div id="html_e73c3a19217c47f3878742fa5c0776ef" style="width: 100.0%; height: 100.0%;">Kemps Corner, Rs.40000/sq.ft.</div>')[0];
                popup_60ca4c98c13a481eb9bdb6a59fed3f92.setContent(html_e73c3a19217c47f3878742fa5c0776ef);
            

            circle_marker_20e7ee657bd24393a4bde79fb123663d.bindPopup(popup_60ca4c98c13a481eb9bdb6a59fed3f92);

            
        
    
            var circle_marker_d591a53b334d4803b4fc55c56230aa9e = L.circleMarker(
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}
                ).addTo(map_be4a8cb55980417385f4349da74ca795);
            
    
            var popup_680125ba6a1f46579026126bfa112a97 = L.popup({maxWidth: '300'});

            
                var html_e726f46bc4ec4490b5988462920ca9c8 = $('<div id="html_e726f46bc4ec4490b5988462920ca9c8" style="width: 100.0%; height: 100.0%;">Lower Parel, Rs.28750/sq.ft.</div>')[0];
                popup_680125ba6a1f46579026126bfa112a97.setContent(html_e726f46bc4ec4490b5988462920ca9c8);
            

            circle_marker_d591a53b334d4803b4fc55c56230aa9e.bindPopup(popup_680125ba6a1f46579026126bfa112a97);

            
        
    
            var circle_marker_cd2c05da378b43c58a2dc455a145b3e5 = L.circleMarker(
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}
                ).addTo(map_be4a8cb55980417385f4349da74ca795);
            
    
            var popup_0ac4316c4aa24f7593a5cba466e6db88 = L.popup({maxWidth: '300'});

            
                var html_51da96d6c40b460480d544a9e2d66669 = $('<div id="html_51da96d6c40b460480d544a9e2d66669" style="width: 100.0%; height: 100.0%;">Mahalaxmi, Rs.27500/sq.ft.</div>')[0];
                popup_0ac4316c4aa24f7593a5cba466e6db88.setContent(html_51da96d6c40b460480d544a9e2d66669);
            

            circle_marker_cd2c05da378b43c58a2dc455a145b3e5.bindPopup(popup_0ac4316c4aa24f7593a5cba466e6db88);

            
        
    
            var circle_marker_c5ac2474e10e4417985dec0ba250435b = L.circleMarker(
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  "fill": true,
  "fillColor": "red",
  "fillOpacity": 0.5,
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}
                ).addTo(map_be4a8cb55980417385f4349da74ca795);
            
    
            var popup_b9ac1eed75f94d18ad9a769ff5a5e54d = L.popup({maxWidth: '300'});

            
                var html_e15b25dc5be7419bb8501c606680ed3f = $('<div id="html_e15b25dc5be7419bb8501c606680ed3f" style="width: 100.0%; height: 100.0%;">Mahim, Rs.18500/sq.ft.</div>')[0];
                popup_b9ac1eed75f94d18ad9a769ff5a5e54d.setContent(html_e15b25dc5be7419bb8501c606680ed3f);
            

            circle_marker_c5ac2474e10e4417985dec0ba250435b.bindPopup(popup_b9ac1eed75f94d18ad9a769ff5a5e54d);

            
        
    
            var circle_marker_da0a84ed59f2438baac7d7b84b0311e5 = L.circleMarker(
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}
                ).addTo(map_be4a8cb55980417385f4349da74ca795);
            
    
            var popup_a247b88b1c634a3087343e2bed968ab5 = L.popup({maxWidth: '300'});

            
                var html_30472c3500b348078e1179410cd0f71e = $('<div id="html_30472c3500b348078e1179410cd0f71e" style="width: 100.0%; height: 100.0%;">Marine Drive, Rs.40000/sq.ft.</div>')[0];
                popup_a247b88b1c634a3087343e2bed968ab5.setContent(html_30472c3500b348078e1179410cd0f71e);
            

            circle_marker_da0a84ed59f2438baac7d7b84b0311e5.bindPopup(popup_a247b88b1c634a3087343e2bed968ab5);

            
        
    
            var circle_marker_c97de877efcb4705b35e38e31dcd2f1f = L.circleMarker(
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  "color": "blue",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "blue",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
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  "opacity": 1.0,
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}
                ).addTo(map_be4a8cb55980417385f4349da74ca795);
            
    
            var popup_f10e3331d8534efe82c80aaa0cc359d1 = L.popup({maxWidth: '300'});

            
                var html_ac37b8bf6fb9462c967f12251c1aaeb0 = $('<div id="html_ac37b8bf6fb9462c967f12251c1aaeb0" style="width: 100.0%; height: 100.0%;">Marine Lines, Rs.29000/sq.ft.</div>')[0];
                popup_f10e3331d8534efe82c80aaa0cc359d1.setContent(html_ac37b8bf6fb9462c967f12251c1aaeb0);
            

            circle_marker_c97de877efcb4705b35e38e31dcd2f1f.bindPopup(popup_f10e3331d8534efe82c80aaa0cc359d1);

            
        
    
            var circle_marker_0a9ceadc848d44acb930ea1ff4f9ba6f = L.circleMarker(
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  "bubblingMouseEvents": true,
  "color": "red",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "red",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_be4a8cb55980417385f4349da74ca795);
            
    
            var popup_e5f97aee6e2845cf8d2b4544a202bdc3 = L.popup({maxWidth: '300'});

            
                var html_898223b5f3a347a6b6628dede3e72fef = $('<div id="html_898223b5f3a347a6b6628dede3e72fef" style="width: 100.0%; height: 100.0%;">Mumbai Central, Rs.22500/sq.ft.</div>')[0];
                popup_e5f97aee6e2845cf8d2b4544a202bdc3.setContent(html_898223b5f3a347a6b6628dede3e72fef);
            

            circle_marker_0a9ceadc848d44acb930ea1ff4f9ba6f.bindPopup(popup_e5f97aee6e2845cf8d2b4544a202bdc3);

            
        
    
            var circle_marker_25031d20a7334fb0b556554b02e71e9c = L.circleMarker(
                [18.926,72.82300000000001],
                {
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  "color": "blue",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "blue",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_be4a8cb55980417385f4349da74ca795);
            
    
            var popup_6bc97c74d3ca4287b713a77e93d15bc6 = L.popup({maxWidth: '300'});

            
                var html_b9acee9eab0b4f0a94a6a1163d6ed95a = $('<div id="html_b9acee9eab0b4f0a94a6a1163d6ed95a" style="width: 100.0%; height: 100.0%;">Nariman Point, Rs.33500/sq.ft.</div>')[0];
                popup_6bc97c74d3ca4287b713a77e93d15bc6.setContent(html_b9acee9eab0b4f0a94a6a1163d6ed95a);
            

            circle_marker_25031d20a7334fb0b556554b02e71e9c.bindPopup(popup_6bc97c74d3ca4287b713a77e93d15bc6);

            
        
    
            var circle_marker_6200f053766d4924a4e44d9638dd8899 = L.circleMarker(
                [19.0166,72.8295],
                {
  "bubblingMouseEvents": true,
  "color": "blue",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "blue",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_be4a8cb55980417385f4349da74ca795);
            
    
            var popup_9c260a00b0074b6d8de812fc4ac9dfc6 = L.popup({maxWidth: '300'});

            
                var html_ef7249b76855420290d36abdefdc5917 = $('<div id="html_ef7249b76855420290d36abdefdc5917" style="width: 100.0%; height: 100.0%;">Prabhadevi, Rs.30750/sq.ft.</div>')[0];
                popup_9c260a00b0074b6d8de812fc4ac9dfc6.setContent(html_ef7249b76855420290d36abdefdc5917);
            

            circle_marker_6200f053766d4924a4e44d9638dd8899.bindPopup(popup_9c260a00b0074b6d8de812fc4ac9dfc6);

            
        
    
            var circle_marker_006ac6b782964c6ebcbfd535a7e37c65 = L.circleMarker(
                [19.04,72.86],
                {
  "bubblingMouseEvents": true,
  "color": "red",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "red",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_be4a8cb55980417385f4349da74ca795);
            
    
            var popup_a65b6b146a714806bca57ea5da97b09f = L.popup({maxWidth: '300'});

            
                var html_91f60fcffdd5406da860e89c6439646e = $('<div id="html_91f60fcffdd5406da860e89c6439646e" style="width: 100.0%; height: 100.0%;">Sion, Rs.18500/sq.ft.</div>')[0];
                popup_a65b6b146a714806bca57ea5da97b09f.setContent(html_91f60fcffdd5406da860e89c6439646e);
            

            circle_marker_006ac6b782964c6ebcbfd535a7e37c65.bindPopup(popup_a65b6b146a714806bca57ea5da97b09f);

            
        
    
            var circle_marker_cebeb850127a4cf9abe3e66b4d0a2c49 = L.circleMarker(
                [18.947596,72.795957],
                {
  "bubblingMouseEvents": true,
  "color": "black",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "black",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_be4a8cb55980417385f4349da74ca795);
            
    
            var popup_9e904ba097ae42608819566d10349f01 = L.popup({maxWidth: '300'});

            
                var html_da35c6c0d62c41c7abf8f53c5d0b487c = $('<div id="html_da35c6c0d62c41c7abf8f53c5d0b487c" style="width: 100.0%; height: 100.0%;">Walkeshwar, Rs.43000/sq.ft.</div>')[0];
                popup_9e904ba097ae42608819566d10349f01.setContent(html_da35c6c0d62c41c7abf8f53c5d0b487c);
            

            circle_marker_cebeb850127a4cf9abe3e66b4d0a2c49.bindPopup(popup_9e904ba097ae42608819566d10349f01);

            
        
    
            var circle_marker_2418017a4ee141b1be89dd31ded94de0 = L.circleMarker(
                [19.0,72.815],
                {
  "bubblingMouseEvents": true,
  "color": "blue",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "blue",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_be4a8cb55980417385f4349da74ca795);
            
    
            var popup_6fd91337a7e2462d96249690caf0766c = L.popup({maxWidth: '300'});

            
                var html_c60f806743d44fdf9d4aa3edd8ff5206 = $('<div id="html_c60f806743d44fdf9d4aa3edd8ff5206" style="width: 100.0%; height: 100.0%;">Worli, Rs.27500/sq.ft.</div>')[0];
                popup_6fd91337a7e2462d96249690caf0766c.setContent(html_c60f806743d44fdf9d4aa3edd8ff5206);
            

            circle_marker_2418017a4ee141b1be89dd31ded94de0.bindPopup(popup_6fd91337a7e2462d96249690caf0766c);

            
        
    
            var circle_marker_4ed05d3a95174869833dd200c1c1a901 = L.circleMarker(
                [18.977129,72.829131],
                {
  "bubblingMouseEvents": true,
  "color": "orange",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "orange",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_be4a8cb55980417385f4349da74ca795);
            
    
            var popup_39452e65afb6472a95522f1703b63d53 = L.popup({maxWidth: '300'});

            
                var html_1a6b7f8c0fbf4a74ae8a87a53894208d = $('<div id="html_1a6b7f8c0fbf4a74ae8a87a53894208d" style="width: 100.0%; height: 100.0%;">Dagdi Chawl, Rs.17000/sq.ft.</div>')[0];
                popup_39452e65afb6472a95522f1703b63d53.setContent(html_1a6b7f8c0fbf4a74ae8a87a53894208d);
            

            circle_marker_4ed05d3a95174869833dd200c1c1a901.bindPopup(popup_39452e65afb6472a95522f1703b63d53);

            
        
    
            var circle_marker_0095b3dd1955455db36affe6a5a5134d = L.circleMarker(
                [18.9012,72.8101],
                {
  "bubblingMouseEvents": true,
  "color": "blue",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "blue",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_be4a8cb55980417385f4349da74ca795);
            
    
            var popup_642a55812e904e23a3ef7ad2e319597d = L.popup({maxWidth: '300'});

            
                var html_d934a405441c4a078449bb7a5be5b4cd = $('<div id="html_d934a405441c4a078449bb7a5be5b4cd" style="width: 100.0%; height: 100.0%;">Navy Nagar, Rs.27500/sq.ft.</div>')[0];
                popup_642a55812e904e23a3ef7ad2e319597d.setContent(html_d934a405441c4a078449bb7a5be5b4cd);
            

            circle_marker_0095b3dd1955455db36affe6a5a5134d.bindPopup(popup_642a55812e904e23a3ef7ad2e319597d);

            
        
    
            var circle_marker_94d85f3433b6494d8b855cd6f41fd54a = L.circleMarker(
                [19.020841,19.020841],
                {
  "bubblingMouseEvents": true,
  "color": "red",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "red",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_be4a8cb55980417385f4349da74ca795);
            
    
            var popup_a07324d846cb41c992a3199d4b6c2566 = L.popup({maxWidth: '300'});

            
                var html_f0e306081b224698a1ce227c7175184e = $('<div id="html_f0e306081b224698a1ce227c7175184e" style="width: 100.0%; height: 100.0%;">Hindu colony, Rs.22500/sq.ft.</div>')[0];
                popup_a07324d846cb41c992a3199d4b6c2566.setContent(html_f0e306081b224698a1ce227c7175184e);
            

            circle_marker_94d85f3433b6494d8b855cd6f41fd54a.bindPopup(popup_a07324d846cb41c992a3199d4b6c2566);

            
        
    
            var circle_marker_1f730a1fd6de46dbb997ed508968e161 = L.circleMarker(
                [18.95,72.84],
                {
  "bubblingMouseEvents": true,
  "color": "blue",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "blue",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_be4a8cb55980417385f4349da74ca795);
            
    
            var popup_6a67cd2d10114ea295b953dfcb0fd5c8 = L.popup({maxWidth: '300'});

            
                var html_5970e8da85b24326b626c0fd72057ed0 = $('<div id="html_5970e8da85b24326b626c0fd72057ed0" style="width: 100.0%; height: 100.0%;">Ballard Estate, Rs.29000/sq.ft.</div>')[0];
                popup_6a67cd2d10114ea295b953dfcb0fd5c8.setContent(html_5970e8da85b24326b626c0fd72057ed0);
            

            circle_marker_1f730a1fd6de46dbb997ed508968e161.bindPopup(popup_6a67cd2d10114ea295b953dfcb0fd5c8);

            
        
    
            var circle_marker_03617efae5454ffe8566ef1f564fe35e = L.circleMarker(
                [19.017979999999998,72.844763],
                {
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  "dashOffset": null,
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  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_be4a8cb55980417385f4349da74ca795);
            
    
            var popup_5cdbe28e27974aa38023f4a166abf542 = L.popup({maxWidth: '300'});

            
                var html_db111377cacd461abfc2539a278dd7ca = $('<div id="html_db111377cacd461abfc2539a278dd7ca" style="width: 100.0%; height: 100.0%;">Matunga, Rs.22500/sq.ft.</div>')[0];
                popup_5cdbe28e27974aa38023f4a166abf542.setContent(html_db111377cacd461abfc2539a278dd7ca);
            

            circle_marker_03617efae5454ffe8566ef1f564fe35e.bindPopup(popup_5cdbe28e27974aa38023f4a166abf542);

            
        
    
            var circle_marker_be4510b0bfe1480c857b358e84f2acd1 = L.circleMarker(
                [18.99,72.84],
                {
  "bubblingMouseEvents": true,
  "color": "red",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "red",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_be4a8cb55980417385f4349da74ca795);
            
    
            var popup_cfca7a75bced422eac3ff9c00a922d0b = L.popup({maxWidth: '300'});

            
                var html_70d9952b20a544a2bf89638f7edcb740 = $('<div id="html_70d9952b20a544a2bf89638f7edcb740" style="width: 100.0%; height: 100.0%;">Parel, Rs.18000/sq.ft.</div>')[0];
                popup_cfca7a75bced422eac3ff9c00a922d0b.setContent(html_70d9952b20a544a2bf89638f7edcb740);
            

            circle_marker_be4510b0bfe1480c857b358e84f2acd1.bindPopup(popup_cfca7a75bced422eac3ff9c00a922d0b);

            
        
    
            var circle_marker_5c9c4e7035c64b758ffa83d6d7db0cc7 = L.circleMarker(
                [18.96245,72.809703],
                {
  "bubblingMouseEvents": true,
  "color": "blue",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "blue",
  "fillOpacity": 0.5,
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  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_be4a8cb55980417385f4349da74ca795);
            
    
            var popup_a322f264a565420b9940c44338f6805b = L.popup({maxWidth: '300'});

            
                var html_4cdce5b1492042958bcf134486499ba7 = $('<div id="html_4cdce5b1492042958bcf134486499ba7" style="width: 100.0%; height: 100.0%;">Gowalia Tank, Rs.31000/sq.ft.</div>')[0];
                popup_a322f264a565420b9940c44338f6805b.setContent(html_4cdce5b1492042958bcf134486499ba7);
            

            circle_marker_5c9c4e7035c64b758ffa83d6d7db0cc7.bindPopup(popup_a322f264a565420b9940c44338f6805b);

            
        
</script> onload=\"this.contentDocument.open();this.contentDocument.write(atob(this.getAttribute('data-html')));this.contentDocument.close();\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
      ],
      "text/plain": [
       "<folium.folium.Map at 0x19c6620e4f0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "map_Areas = folium.Map(location=[latitude, longitude], zoom_start=10)\n",
    "\n",
    "# add markers to map\n",
    "for lat, lng, area, rate in zip(mumbai_df['Latitude'], mumbai_df['Longitude'], mumbai_df['Area'], mumbai_df['Rate']):\n",
    "    col = 'orange' if rate<=rate_range[1] else 'red' if rate<=rate_range[2] else \"blue\" if rate<=rate_range[3] else \"black\"\n",
    "    label = '{}, Rs.{}/sq.ft.'.format(area , int(rate))\n",
    "    label = folium.Popup(label, parse_html=True)\n",
    "    folium.CircleMarker(\n",
    "        [lat, lng],\n",
    "        radius=5,\n",
    "        popup=label,\n",
    "        color= col ,\n",
    "        fill=True,\n",
    "        fill_color= col ,\n",
    "        fill_opacity=0.5,\n",
    "        parse_html=False).add_to(map_Areas)  \n",
    "    \n",
    "map_Areas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using *FourSquare API* to collect JSON data of all nearby Schools, Colleges & Classes in each of the Areas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "CLIENT_ID = 'YZ5ZMVZNRQQ3A0K4FFXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX' # your Foursquare ID\n",
    "CLIENT_SECRET = 'YXQXO3UPLP0TKPA3I1XXXXXXXXXXXXXXXXXXXXXXXXXXXXXX' # your Foursquare Secret\n",
    "VERSION = '20180605' # Foursquare API version\n",
    "LIMIT = 200   # Foursquare API limit value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# function that extracts the category of the venue\n",
    "def get_category_type(row):\n",
    "    try:\n",
    "        categories_list = row['categories']\n",
    "    except:\n",
    "        categories_list = row['venue.categories']\n",
    "        \n",
    "    if len(categories_list) == 0:\n",
    "        return None\n",
    "    else:\n",
    "        return categories_list[0]['name']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getNearbyVenues(names, latitudes, longitudes, query_list, radius=2000):\n",
    "    \n",
    "    venues_list=[]\n",
    "    for name, lat, lng in zip(names, latitudes, longitudes):\n",
    "        print(name)\n",
    "            \n",
    "        # create the API request URL\n",
    "        for query in query_list:\n",
    "            search_query = query\n",
    "            url = 'https://api.foursquare.com/v2/venues/search?client_id={}&client_secret={}&ll={},{}&v={}&query={}&radius={}&limit={}'.format(\n",
    "                CLIENT_ID, CLIENT_SECRET, lat, lng, VERSION, search_query, radius, LIMIT)\n",
    "            \n",
    "            # make the GET request\n",
    "            results = requests.get(url).json()['response']['venues']\n",
    "        \n",
    "            # return only relevant information for each nearby venue\n",
    "            venues_list.append([(\n",
    "                name, \n",
    "                lat, \n",
    "                lng, \n",
    "                v['name'], \n",
    "                v['location']['lat'], \n",
    "                v['location']['lng']) for v in results])\n",
    "            \n",
    "    nearby_venues = pd.DataFrame([item for venue_list in venues_list for item in venue_list])\n",
    "    nearby_venues.columns = ['Area', \n",
    "                  'Area Latitude', \n",
    "                  'Area Longitude', \n",
    "                  'Venue', \n",
    "                  'Venue Latitude', \n",
    "                  'Venue Longitude']\n",
    "    \n",
    "    return(nearby_venues)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Amboli\n",
      "Chakala, Andheri\n",
      "D.N. Nagar\n",
      "Four Bungalows\n",
      "Lokhandwala\n",
      "Marol\n",
      "Sahar\n",
      "Seven Bungalows\n",
      "Versova\n",
      "Mira Road\n",
      "Bhayandar\n",
      "Bandstand Promenade\n",
      "Kherwadi\n",
      "Pali Hill\n",
      "I.C. Colony\n",
      "Gorai\n",
      "Dahisa\n",
      "Aarey Milk Colony\n",
      "Bangur Nagar\n",
      "Jogeshwari West\n",
      "Juhu\n",
      "Charkop\n",
      "Poisar\n",
      "Mahavir Nagar\n",
      "Thakur village\n",
      "Pali Naka\n",
      "Khar Danda\n",
      "Dindoshi\n",
      "Sunder Nagar\n",
      "Kalina\n",
      "Irla\n",
      "Vile Parle\n",
      "Bhandup\n",
      "Amrut Nagar\n",
      "Asalfa\n",
      "Pant Nagar\n",
      "Kanjurmarg\n",
      "Nehru Nagar\n",
      "Nahur\n",
      "Chandivali\n",
      "Hiranandani Gardens\n",
      "Vikhroli\n",
      "Chembur\n",
      "Mankhurd\n",
      "Altamount Road\n",
      "Breach Candy\n",
      "Churchgate\n",
      "Cuffe Parade\n",
      "Kemps Corner\n",
      "Lower Parel\n",
      "Mahalaxmi\n",
      "Mahim\n",
      "Marine Drive\n",
      "Marine Lines\n",
      "Mumbai Central\n",
      "Nariman Point\n",
      "Prabhadevi\n",
      "Sion\n",
      "Walkeshwar\n",
      "Worli\n",
      "Dagdi Chawl\n",
      "Navy Nagar\n",
      "Hindu colony\n",
      "Ballard Estate\n",
      "Matunga\n",
      "Parel\n",
      "Gowalia Tank\n"
     ]
    }
   ],
   "source": [
    "Schools_Colleges_df = getNearbyVenues(names=mumbai_df['Area'],\n",
    "                                   latitudes=mumbai_df['Latitude'],\n",
    "                                   longitudes=mumbai_df['Longitude'],\n",
    "                                   query_list=['high school', 'college']\n",
    "                                  )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4027, 6)\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>Area</th>\n",
       "      <th>Area Latitude</th>\n",
       "      <th>Area Longitude</th>\n",
       "      <th>Venue</th>\n",
       "      <th>Venue Latitude</th>\n",
       "      <th>Venue Longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Amboli</td>\n",
       "      <td>19.1293</td>\n",
       "      <td>72.8434</td>\n",
       "      <td>St. Blaise High School</td>\n",
       "      <td>19.127114</td>\n",
       "      <td>72.846202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Amboli</td>\n",
       "      <td>19.1293</td>\n",
       "      <td>72.8434</td>\n",
       "      <td>Faroukh High School</td>\n",
       "      <td>19.131832</td>\n",
       "      <td>72.847237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Amboli</td>\n",
       "      <td>19.1293</td>\n",
       "      <td>72.8434</td>\n",
       "      <td>Bhavan's A. H. Wadia High School</td>\n",
       "      <td>19.126847</td>\n",
       "      <td>72.834842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Amboli</td>\n",
       "      <td>19.1293</td>\n",
       "      <td>72.8434</td>\n",
       "      <td>Swss high school</td>\n",
       "      <td>19.117710</td>\n",
       "      <td>72.841095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Amboli</td>\n",
       "      <td>19.1293</td>\n",
       "      <td>72.8434</td>\n",
       "      <td>Infant Jesus Church &amp; High School</td>\n",
       "      <td>19.131871</td>\n",
       "      <td>72.855746</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Area  Area Latitude  Area Longitude                              Venue  \\\n",
       "0  Amboli        19.1293         72.8434             St. Blaise High School   \n",
       "1  Amboli        19.1293         72.8434                Faroukh High School   \n",
       "2  Amboli        19.1293         72.8434   Bhavan's A. H. Wadia High School   \n",
       "3  Amboli        19.1293         72.8434                   Swss high school   \n",
       "4  Amboli        19.1293         72.8434  Infant Jesus Church & High School   \n",
       "\n",
       "   Venue Latitude  Venue Longitude  \n",
       "0       19.127114        72.846202  \n",
       "1       19.131832        72.847237  \n",
       "2       19.126847        72.834842  \n",
       "3       19.117710        72.841095  \n",
       "4       19.131871        72.855746  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(Schools_Colleges_df.shape)\n",
    "Schools_Colleges_df.head() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Data Cleaning of Schools_Colleges_df**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(Schools_Colleges_df.shape[0]):\n",
    "    name = Schools_Colleges_df.loc[i, 'Venue']  \n",
    "    drop_condition = ((\"school\" not in name) and (\"School\" not in name) and (\"college\" not in name) and (\"College\" not in name))\n",
    "    if drop_condition:\n",
    "        Schools_Colleges_df.drop(i, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3485, 6)\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>Area</th>\n",
       "      <th>Area Latitude</th>\n",
       "      <th>Area Longitude</th>\n",
       "      <th>Venue</th>\n",
       "      <th>Venue Latitude</th>\n",
       "      <th>Venue Longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Amboli</td>\n",
       "      <td>19.1293</td>\n",
       "      <td>72.8434</td>\n",
       "      <td>St. Blaise High School</td>\n",
       "      <td>19.127114</td>\n",
       "      <td>72.846202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Amboli</td>\n",
       "      <td>19.1293</td>\n",
       "      <td>72.8434</td>\n",
       "      <td>Faroukh High School</td>\n",
       "      <td>19.131832</td>\n",
       "      <td>72.847237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Amboli</td>\n",
       "      <td>19.1293</td>\n",
       "      <td>72.8434</td>\n",
       "      <td>Bhavan's A. H. Wadia High School</td>\n",
       "      <td>19.126847</td>\n",
       "      <td>72.834842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Amboli</td>\n",
       "      <td>19.1293</td>\n",
       "      <td>72.8434</td>\n",
       "      <td>Swss high school</td>\n",
       "      <td>19.117710</td>\n",
       "      <td>72.841095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Amboli</td>\n",
       "      <td>19.1293</td>\n",
       "      <td>72.8434</td>\n",
       "      <td>Infant Jesus Church &amp; High School</td>\n",
       "      <td>19.131871</td>\n",
       "      <td>72.855746</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Area  Area Latitude  Area Longitude                              Venue  \\\n",
       "0  Amboli        19.1293         72.8434             St. Blaise High School   \n",
       "1  Amboli        19.1293         72.8434                Faroukh High School   \n",
       "2  Amboli        19.1293         72.8434   Bhavan's A. H. Wadia High School   \n",
       "3  Amboli        19.1293         72.8434                   Swss high school   \n",
       "4  Amboli        19.1293         72.8434  Infant Jesus Church & High School   \n",
       "\n",
       "   Venue Latitude  Venue Longitude  \n",
       "0       19.127114        72.846202  \n",
       "1       19.131832        72.847237  \n",
       "2       19.126847        72.834842  \n",
       "3       19.117710        72.841095  \n",
       "4       19.131871        72.855746  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Schools_Colleges_df.reset_index(inplace = True, drop = True)\n",
    "print(Schools_Colleges_df.shape)\n",
    "Schools_Colleges_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Dropping unwanted venues**(like driving school, medical & engineering college, etc) from \"Schools_Colleges_df\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Driving',\n",
       " 'driving',\n",
       " 'Law',\n",
       " 'law',\n",
       " 'Engineering',\n",
       " 'engineering',\n",
       " 'Architecture',\n",
       " 'architecture',\n",
       " 'Business',\n",
       " 'business']"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "upperlist = ['Driving', 'Law', 'Engineering', 'Architecture', 'Business', 'Medical', 'Management', 'Defense','Preschool',\n",
    "             'Pre-Schools', 'Homeopathic', 'Soccer', 'Motor', 'Training', 'Technology', 'Pharmacy', 'Optometry', \n",
    "             'Bus Stop', 'Blind School', 'Hostel', 'Dental', 'Style', 'Hospital', 'Acting', 'Soccer', 'Pastry', 'Swimming'] \n",
    "lowerlist = [x.lower() for x in upperlist]\n",
    "list = []\n",
    "for upper, lower in zip(upperlist, lowerlist):\n",
    "    list.append(upper)\n",
    "    list.append(lower)\n",
    "list[0:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "for venue_type in list:\n",
    "    for i in range(Schools_Colleges_df.shape[0]):\n",
    "        name = Schools_Colleges_df.loc[i, 'Venue'] \n",
    "        if venue_type in name:\n",
    "            Schools_Colleges_df.drop(i, inplace = True)    \n",
    "    Schools_Colleges_df.reset_index(inplace = True, drop = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2886, 6)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Area</th>\n",
       "      <th>Area Latitude</th>\n",
       "      <th>Area Longitude</th>\n",
       "      <th>Venue</th>\n",
       "      <th>Venue Latitude</th>\n",
       "      <th>Venue Longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Amboli</td>\n",
       "      <td>19.1293</td>\n",
       "      <td>72.8434</td>\n",
       "      <td>St. Blaise High School</td>\n",
       "      <td>19.127114</td>\n",
       "      <td>72.846202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Amboli</td>\n",
       "      <td>19.1293</td>\n",
       "      <td>72.8434</td>\n",
       "      <td>Faroukh High School</td>\n",
       "      <td>19.131832</td>\n",
       "      <td>72.847237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Amboli</td>\n",
       "      <td>19.1293</td>\n",
       "      <td>72.8434</td>\n",
       "      <td>Bhavan's A. H. Wadia High School</td>\n",
       "      <td>19.126847</td>\n",
       "      <td>72.834842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Amboli</td>\n",
       "      <td>19.1293</td>\n",
       "      <td>72.8434</td>\n",
       "      <td>Swss high school</td>\n",
       "      <td>19.117710</td>\n",
       "      <td>72.841095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Amboli</td>\n",
       "      <td>19.1293</td>\n",
       "      <td>72.8434</td>\n",
       "      <td>Infant Jesus Church &amp; High School</td>\n",
       "      <td>19.131871</td>\n",
       "      <td>72.855746</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Area  Area Latitude  Area Longitude                              Venue  \\\n",
       "0  Amboli        19.1293         72.8434             St. Blaise High School   \n",
       "1  Amboli        19.1293         72.8434                Faroukh High School   \n",
       "2  Amboli        19.1293         72.8434   Bhavan's A. H. Wadia High School   \n",
       "3  Amboli        19.1293         72.8434                   Swss high school   \n",
       "4  Amboli        19.1293         72.8434  Infant Jesus Church & High School   \n",
       "\n",
       "   Venue Latitude  Venue Longitude  \n",
       "0       19.127114        72.846202  \n",
       "1       19.131832        72.847237  \n",
       "2       19.126847        72.834842  \n",
       "3       19.117710        72.841095  \n",
       "4       19.131871        72.855746  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(Schools_Colleges_df.shape)\n",
    "Schools_Colleges_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Amboli\n",
      "Chakala, Andheri\n",
      "D.N. Nagar\n",
      "Four Bungalows\n",
      "Lokhandwala\n",
      "Marol\n",
      "Sahar\n",
      "Seven Bungalows\n",
      "Versova\n",
      "Mira Road\n",
      "Bhayandar\n",
      "Bandstand Promenade\n",
      "Kherwadi\n",
      "Pali Hill\n",
      "I.C. Colony\n",
      "Gorai\n",
      "Dahisa\n",
      "Aarey Milk Colony\n",
      "Bangur Nagar\n",
      "Jogeshwari West\n",
      "Juhu\n",
      "Charkop\n",
      "Poisar\n",
      "Mahavir Nagar\n",
      "Thakur village\n",
      "Pali Naka\n",
      "Khar Danda\n",
      "Dindoshi\n",
      "Sunder Nagar\n",
      "Kalina\n",
      "Irla\n",
      "Vile Parle\n",
      "Bhandup\n",
      "Amrut Nagar\n",
      "Asalfa\n",
      "Pant Nagar\n",
      "Kanjurmarg\n",
      "Nehru Nagar\n",
      "Nahur\n",
      "Chandivali\n",
      "Hiranandani Gardens\n",
      "Vikhroli\n",
      "Chembur\n",
      "Mankhurd\n",
      "Altamount Road\n",
      "Breach Candy\n",
      "Churchgate\n",
      "Cuffe Parade\n",
      "Kemps Corner\n",
      "Lower Parel\n",
      "Mahalaxmi\n",
      "Mahim\n",
      "Marine Drive\n",
      "Marine Lines\n",
      "Mumbai Central\n",
      "Nariman Point\n",
      "Prabhadevi\n",
      "Sion\n",
      "Walkeshwar\n",
      "Worli\n",
      "Dagdi Chawl\n",
      "Navy Nagar\n",
      "Hindu colony\n",
      "Ballard Estate\n",
      "Matunga\n",
      "Parel\n",
      "Gowalia Tank\n"
     ]
    }
   ],
   "source": [
    "Tutorials_Classes_df = getNearbyVenues(names=mumbai_df['Area'],\n",
    "                                   latitudes=mumbai_df['Latitude'],\n",
    "                                   longitudes=mumbai_df['Longitude'],\n",
    "                                   query_list=['tutorials', 'classes']\n",
    "                                  )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(695, 6)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Area</th>\n",
       "      <th>Area Latitude</th>\n",
       "      <th>Area Longitude</th>\n",
       "      <th>Venue</th>\n",
       "      <th>Venue Latitude</th>\n",
       "      <th>Venue Longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Amboli</td>\n",
       "      <td>19.1293</td>\n",
       "      <td>72.8434</td>\n",
       "      <td>Harsh Tutorials</td>\n",
       "      <td>19.123740</td>\n",
       "      <td>72.843190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Amboli</td>\n",
       "      <td>19.1293</td>\n",
       "      <td>72.8434</td>\n",
       "      <td>Standard Tutorials</td>\n",
       "      <td>19.119875</td>\n",
       "      <td>72.845735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Amboli</td>\n",
       "      <td>19.1293</td>\n",
       "      <td>72.8434</td>\n",
       "      <td>Mahesh Tutorials</td>\n",
       "      <td>19.119847</td>\n",
       "      <td>72.845947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Amboli</td>\n",
       "      <td>19.1293</td>\n",
       "      <td>72.8434</td>\n",
       "      <td>Mahesh tutorials commerce</td>\n",
       "      <td>19.118845</td>\n",
       "      <td>72.845268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Amboli</td>\n",
       "      <td>19.1293</td>\n",
       "      <td>72.8434</td>\n",
       "      <td>Prof. Manish Nadkarni's Tutorials</td>\n",
       "      <td>19.116002</td>\n",
       "      <td>72.831820</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Area  Area Latitude  Area Longitude                              Venue  \\\n",
       "0  Amboli        19.1293         72.8434                    Harsh Tutorials   \n",
       "1  Amboli        19.1293         72.8434                 Standard Tutorials   \n",
       "2  Amboli        19.1293         72.8434                   Mahesh Tutorials   \n",
       "3  Amboli        19.1293         72.8434          Mahesh tutorials commerce   \n",
       "4  Amboli        19.1293         72.8434  Prof. Manish Nadkarni's Tutorials   \n",
       "\n",
       "   Venue Latitude  Venue Longitude  \n",
       "0       19.123740        72.843190  \n",
       "1       19.119875        72.845735  \n",
       "2       19.119847        72.845947  \n",
       "3       19.118845        72.845268  \n",
       "4       19.116002        72.831820  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(Tutorials_Classes_df.shape)\n",
    "Tutorials_Classes_df.head() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(67, 64, 62)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(mumbai_df.Area.unique()), len(Schools_Colleges_df.Area.unique()), len(Tutorials_Classes_df.Area.unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**No. of Schools_Colleges in each Area**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Area</th>\n",
       "      <th>Venue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Aarey Milk Colony</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Altamount Road</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Amboli</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Amrut Nagar</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Asalfa</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Area  Venue\n",
       "0  Aarey Milk Colony     10\n",
       "1     Altamount Road     63\n",
       "2             Amboli     59\n",
       "3        Amrut Nagar     38\n",
       "4             Asalfa     44"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Schools_Colleges_group = Schools_Colleges_df[['Area','Venue']].groupby(\"Area\").count()\n",
    "Schools_Colleges_group.reset_index(inplace = True, drop = False)\n",
    "Schools_Colleges_group.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**No. of Tutorials_Classes in each Area**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Area</th>\n",
       "      <th>Venue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Altamount Road</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Amboli</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Amrut Nagar</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Asalfa</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ballard Estate</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Area  Venue\n",
       "0  Altamount Road      9\n",
       "1          Amboli     21\n",
       "2     Amrut Nagar      8\n",
       "3          Asalfa     18\n",
       "4  Ballard Estate      4"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Tutorials_Classes_group = Tutorials_Classes_df[['Area','Venue']].groupby(\"Area\").count()\n",
    "Tutorials_Classes_group.reset_index(inplace = True, drop = False)\n",
    "Tutorials_Classes_group.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Area</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Amboli</td>\n",
       "      <td>19.129300</td>\n",
       "      <td>72.843400</td>\n",
       "      <td>18000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Chakala, Andheri</td>\n",
       "      <td>19.111388</td>\n",
       "      <td>72.860833</td>\n",
       "      <td>14750.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>D.N. Nagar</td>\n",
       "      <td>19.124085</td>\n",
       "      <td>72.831373</td>\n",
       "      <td>18000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Four Bungalows</td>\n",
       "      <td>19.124714</td>\n",
       "      <td>72.827210</td>\n",
       "      <td>18000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Lokhandwala</td>\n",
       "      <td>19.130815</td>\n",
       "      <td>72.829270</td>\n",
       "      <td>18000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Area   Latitude  Longitude     Rate\n",
       "0            Amboli  19.129300  72.843400  18000.0\n",
       "1  Chakala, Andheri  19.111388  72.860833  14750.0\n",
       "2        D.N. Nagar  19.124085  72.831373  18000.0\n",
       "3    Four Bungalows  19.124714  72.827210  18000.0\n",
       "4       Lokhandwala  19.130815  72.829270  18000.0"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mumbai_df[['Area','Latitude','Longitude','Rate']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "merge_temp = pd.merge(mumbai_df[['Area','Latitude','Longitude','Rate']], Schools_Colleges_group, on = \"Area\", how = \"outer\")\n",
    "merge_temp.rename(columns = {'Venue' : 'Schools_Colleges'}, inplace = True)\n",
    "Area_df = merge_temp.merge(Tutorials_Classes_group, on = \"Area\", how = \"outer\")\n",
    "Area_df.rename(columns = {'Venue' : 'Tutorials_Classes'}, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(67, 6)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Area</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Rate</th>\n",
       "      <th>Schools_Colleges</th>\n",
       "      <th>Tutorials_Classes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Amboli</td>\n",
       "      <td>19.129300</td>\n",
       "      <td>72.843400</td>\n",
       "      <td>18000.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Chakala, Andheri</td>\n",
       "      <td>19.111388</td>\n",
       "      <td>72.860833</td>\n",
       "      <td>14750.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>D.N. Nagar</td>\n",
       "      <td>19.124085</td>\n",
       "      <td>72.831373</td>\n",
       "      <td>18000.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Four Bungalows</td>\n",
       "      <td>19.124714</td>\n",
       "      <td>72.827210</td>\n",
       "      <td>18000.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Lokhandwala</td>\n",
       "      <td>19.130815</td>\n",
       "      <td>72.829270</td>\n",
       "      <td>18000.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Area   Latitude  Longitude     Rate  Schools_Colleges  \\\n",
       "0            Amboli  19.129300  72.843400  18000.0              59.0   \n",
       "1  Chakala, Andheri  19.111388  72.860833  14750.0              49.0   \n",
       "2        D.N. Nagar  19.124085  72.831373  18000.0              58.0   \n",
       "3    Four Bungalows  19.124714  72.827210  18000.0              58.0   \n",
       "4       Lokhandwala  19.130815  72.829270  18000.0              59.0   \n",
       "\n",
       "   Tutorials_Classes  \n",
       "0               21.0  \n",
       "1               27.0  \n",
       "2               22.0  \n",
       "3               22.0  \n",
       "4               14.0  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(Area_df.shape)\n",
    "Area_df = Area_df.fillna(0)\n",
    "Area_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Calculate **Business Opportunity Index** for each Area. A good Business Opportunity Index means there are more Schools/Colleges and less Classes in a Area. \n",
    "> Business Opportunity Index = (Total no. of Schools & Colleges) / [ (Total no. of Classes & Tutorials) + 1 ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Area</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Rate</th>\n",
       "      <th>Schools_Colleges</th>\n",
       "      <th>Tutorials_Classes</th>\n",
       "      <th>Startup Opportunity Index</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Parel</td>\n",
       "      <td>18.99</td>\n",
       "      <td>72.840</td>\n",
       "      <td>18000.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>13.333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Kanjurmarg</td>\n",
       "      <td>19.13</td>\n",
       "      <td>72.940</td>\n",
       "      <td>10550.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Worli</td>\n",
       "      <td>19.00</td>\n",
       "      <td>72.815</td>\n",
       "      <td>27500.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12.500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Churchgate</td>\n",
       "      <td>18.93</td>\n",
       "      <td>72.820</td>\n",
       "      <td>28500.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>12.200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ballard Estate</td>\n",
       "      <td>18.95</td>\n",
       "      <td>72.840</td>\n",
       "      <td>29000.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>12.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Area  Latitude  Longitude     Rate  Schools_Colleges  \\\n",
       "0           Parel     18.99     72.840  18000.0              40.0   \n",
       "1      Kanjurmarg     19.13     72.940  10550.0              13.0   \n",
       "2           Worli     19.00     72.815  27500.0              25.0   \n",
       "3      Churchgate     18.93     72.820  28500.0              61.0   \n",
       "4  Ballard Estate     18.95     72.840  29000.0              60.0   \n",
       "\n",
       "   Tutorials_Classes  Startup Opportunity Index  \n",
       "0                2.0                     13.333  \n",
       "1                0.0                     13.000  \n",
       "2                1.0                     12.500  \n",
       "3                4.0                     12.200  \n",
       "4                4.0                     12.000  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Area_df[\"Startup Opportunity Index\"] = Area_df[\"Schools_Colleges\"]/(Area_df[\"Tutorials_Classes\"] + 1)\n",
    "Area_df = Area_df.round({\"Startup Opportunity Index\":3}) \n",
    "Area_df.sort_values(by = 'Startup Opportunity Index', ascending=False, inplace = True) \n",
    "Area_df.reset_index(inplace = True, drop = True)\n",
    "Area_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# K-Means Clustering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pre-processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rate</th>\n",
       "      <th>Startup Opportunity Index</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18000.0</td>\n",
       "      <td>13.333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10550.0</td>\n",
       "      <td>13.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>27500.0</td>\n",
       "      <td>12.500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>28500.0</td>\n",
       "      <td>12.200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>29000.0</td>\n",
       "      <td>12.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Rate  Startup Opportunity Index\n",
       "0  18000.0                     13.333\n",
       "1  10550.0                     13.000\n",
       "2  27500.0                     12.500\n",
       "3  28500.0                     12.200\n",
       "4  29000.0                     12.000"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clustering_data = Area_df[['Rate', 'Startup Opportunity Index']]\n",
    "clustering_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's normalize the dataset. But why do we need normalization in the first place? Normalization is a statistical method that helps mathematical-based algorithms interpret features with different magnitudes and distributions equally. We use **StandardScaler()** to normalize our dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.30779519,  2.43127857],\n",
       "       [-1.12219207,  2.33122442],\n",
       "       [ 0.73069747,  2.18099297],\n",
       "       [ 0.84001249,  2.09085409],\n",
       "       [ 0.89467   ,  2.03076151]])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "clustering_data = StandardScaler().fit_transform(clustering_data)\n",
    "clustering_data[0:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will be choosing optimum number of clusters by using **Elbow method**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "#Area_cluster = mum_bai_grouped.drop('Neighborhood', 1)\n",
    "distortions = []\n",
    "K = range(1,15)\n",
    "for k in K:\n",
    "    kmeanModel = KMeans(init = \"k-means++\", n_clusters=k)\n",
    "    kmeanModel.fit(clustering_data)\n",
    "    distortions.append(kmeanModel.inertia_)\n",
    "plt.figure(figsize=(10,5))\n",
    "plt.plot(K, distortions, 'bx-')\n",
    "plt.xlabel('k')\n",
    "plt.ylabel('Distortion')\n",
    "plt.title('The Elbow Method showing the optimal k')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Elbow Method suggests 2 as the optimum number of clusters, as the distortion in the data drops suddenly at 2 clusters and very slowly decreases from there. But considering insights from the exploratory data analysis, we will proceed with grouping the data into 3 clusters, as it will be easier to gather insights that way."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Modeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 2, 2, 2, 1, 2, 1, 1, 1, 1, 0, 1, 0,\n",
       "       1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0,\n",
       "       0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,\n",
       "       0])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kclusters = 3\n",
    "kmeanModel = KMeans(init=\"k-means++\", n_clusters=kclusters, random_state=0)\n",
    "kmeanModel.fit(clustering_data)\n",
    "cluster_labels = kmeanModel.labels_      \n",
    "cluster_labels                   # check cluster labels generated for each row in the datapoint"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cluster insights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Area</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Rate</th>\n",
       "      <th>Schools_Colleges</th>\n",
       "      <th>Tutorials_Classes</th>\n",
       "      <th>Startup Opportunity Index</th>\n",
       "      <th>Cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Parel</td>\n",
       "      <td>18.99</td>\n",
       "      <td>72.840</td>\n",
       "      <td>18000.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>13.333</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Kanjurmarg</td>\n",
       "      <td>19.13</td>\n",
       "      <td>72.940</td>\n",
       "      <td>10550.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Worli</td>\n",
       "      <td>19.00</td>\n",
       "      <td>72.815</td>\n",
       "      <td>27500.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12.500</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Churchgate</td>\n",
       "      <td>18.93</td>\n",
       "      <td>72.820</td>\n",
       "      <td>28500.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>12.200</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ballard Estate</td>\n",
       "      <td>18.95</td>\n",
       "      <td>72.840</td>\n",
       "      <td>29000.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>12.000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Area  Latitude  Longitude     Rate  Schools_Colleges  \\\n",
       "0           Parel     18.99     72.840  18000.0              40.0   \n",
       "1      Kanjurmarg     19.13     72.940  10550.0              13.0   \n",
       "2           Worli     19.00     72.815  27500.0              25.0   \n",
       "3      Churchgate     18.93     72.820  28500.0              61.0   \n",
       "4  Ballard Estate     18.95     72.840  29000.0              60.0   \n",
       "\n",
       "   Tutorials_Classes  Startup Opportunity Index  Cluster  \n",
       "0                2.0                     13.333        2  \n",
       "1                0.0                     13.000        2  \n",
       "2                1.0                     12.500        2  \n",
       "3                4.0                     12.200        2  \n",
       "4                4.0                     12.000        2  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Area_df['Cluster'] = cluster_labels\n",
    "Area_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
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}
                ).addTo(map_a13cc97bfbb74b7b82961ed135f67c5d);
            
    
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}
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}
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                var html_7604ea784b694db788c67344454b5632 = $('<div id="html_7604ea784b694db788c67344454b5632" style="width: 100.0%; height: 100.0%;">Gorai, Cluster 0</div>')[0];
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                var html_6eaac925d9c2432a90023335a10763ba = $('<div id="html_6eaac925d9c2432a90023335a10763ba" style="width: 100.0%; height: 100.0%;">Hindu colony, Cluster 0</div>')[0];
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</script> onload=\"this.contentDocument.open();this.contentDocument.write(atob(this.getAttribute('data-html')));this.contentDocument.close();\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
      ],
      "text/plain": [
       "<folium.folium.Map at 0x19c67b3f190>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Clusters_map = folium.Map(location=[latitude, longitude], zoom_start=11)\n",
    "\n",
    "# add Selected_Areas to map\n",
    "for lat, lng, area, cluster in zip(Area_df['Latitude'], Area_df['Longitude'], Area_df['Area'],\n",
    "                                           Area_df['Cluster']):\n",
    "    col = 'orange' if cluster==0 else 'red' if cluster==1 else \"blue\"\n",
    "    label = '{}, Cluster {}'.format(area ,cluster)\n",
    "    label = folium.Popup(label, parse_html=True)\n",
    "    folium.CircleMarker(\n",
    "        [lat, lng],\n",
    "        radius=5,\n",
    "        popup=label,\n",
    "        color= col ,\n",
    "        fill=True,\n",
    "        fill_color= col ,\n",
    "        fill_opacity=0.7,\n",
    "        parse_html=False).add_to(Clusters_map)      \n",
    "Clusters_map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>No. of Areas</th>\n",
       "      <th>Average Property Rate</th>\n",
       "      <th>Average No. of Schools &amp; Colleges</th>\n",
       "      <th>Average No. of Tutorials &amp; Classes</th>\n",
       "      <th>Startup Opportunity Index</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cluster</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>40</td>\n",
       "      <td>15616.250000</td>\n",
       "      <td>42.625000</td>\n",
       "      <td>13.575000</td>\n",
       "      <td>3.267425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15</td>\n",
       "      <td>33983.333333</td>\n",
       "      <td>44.333333</td>\n",
       "      <td>7.600000</td>\n",
       "      <td>6.093867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12</td>\n",
       "      <td>21687.500000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>3.166667</td>\n",
       "      <td>10.754750</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         No. of Areas  Average Property Rate  \\\n",
       "Cluster                                        \n",
       "0                  40           15616.250000   \n",
       "1                  15           33983.333333   \n",
       "2                  12           21687.500000   \n",
       "\n",
       "         Average No. of Schools & Colleges  \\\n",
       "Cluster                                      \n",
       "0                                42.625000   \n",
       "1                                44.333333   \n",
       "2                                43.000000   \n",
       "\n",
       "         Average No. of Tutorials & Classes  Startup Opportunity Index  \n",
       "Cluster                                                                 \n",
       "0                                 13.575000                   3.267425  \n",
       "1                                  7.600000                   6.093867  \n",
       "2                                  3.166667                  10.754750  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "insight_columns = ['Rate','Schools_Colleges','Tutorials_Classes','Startup Opportunity Index','Cluster']\n",
    "Cluster_insights = Area_df[insight_columns].groupby(\"Cluster\").mean()\n",
    "Cluster_insights.insert(0, 'No. of Areas', Area_df[['Area','Cluster']].groupby(\"Cluster\").count())\n",
    "Cluster_insights.columns = ['No. of Areas','Average Property Rate','Average No. of Schools & Colleges','Average No. of Tutorials & Classes',\n",
    "                            'Startup Opportunity Index']\n",
    "Cluster_insights"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " **Cluster insights**:\n",
    " \n",
    " All Clusters have similar 'Average No. of Schools & Colleges'\n",
    " * Cluster 0: Low 'Average Property Rate', More 'Average No. of Tutorials & Classes', Low 'Startup Opportunity Index'.\n",
    " * Cluster 1: High 'Average Property Rate', Moderate 'Average No. of Tutorials & Classes', Medium 'Startup Opportunity Index'.\n",
    " * Cluster 2: Medium 'Average Property Rate', Less 'Average No. of Tutorials & Classes', High 'Startup Opportunity Index'.\n",
    "\n",
    "|Cluster| Property Rate|Startup Opportunity Index|No. of Bussiness Competitors|\n",
    "|-----|--------|------|---------|\n",
    "|0   | Cheap | Low|High|\n",
    "|1   | Expensive   | Medium|Medium|\n",
    "|2   | Mid-Range  | High|Low|\n",
    "\n",
    "**Selecting \"Cluster 2\"** as it has less business competitors & moderate/affordable property rates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Area</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Rate</th>\n",
       "      <th>Schools_Colleges</th>\n",
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       "      <th>Cluster</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Parel</td>\n",
       "      <td>18.990000</td>\n",
       "      <td>72.840000</td>\n",
       "      <td>18000.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>13.333</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Kanjurmarg</td>\n",
       "      <td>19.130000</td>\n",
       "      <td>72.940000</td>\n",
       "      <td>10550.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.000</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Worli</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>72.815000</td>\n",
       "      <td>27500.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12.500</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Churchgate</td>\n",
       "      <td>18.930000</td>\n",
       "      <td>72.820000</td>\n",
       "      <td>28500.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>12.200</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ballard Estate</td>\n",
       "      <td>18.950000</td>\n",
       "      <td>72.840000</td>\n",
       "      <td>29000.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>12.000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Nariman Point</td>\n",
       "      <td>18.926000</td>\n",
       "      <td>72.823000</td>\n",
       "      <td>33500.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>11.200</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Bangur Nagar</td>\n",
       "      <td>19.167362</td>\n",
       "      <td>72.832252</td>\n",
       "      <td>13500.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>10.000</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Aarey Milk Colony</td>\n",
       "      <td>19.148493</td>\n",
       "      <td>72.881756</td>\n",
       "      <td>12950.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Marine Lines</td>\n",
       "      <td>18.944700</td>\n",
       "      <td>72.824400</td>\n",
       "      <td>29000.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>9.500</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Dagdi Chawl</td>\n",
       "      <td>18.977129</td>\n",
       "      <td>72.829131</td>\n",
       "      <td>17000.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.800</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Chembur</td>\n",
       "      <td>19.051000</td>\n",
       "      <td>72.894000</td>\n",
       "      <td>19250.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.667</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Pali Naka</td>\n",
       "      <td>19.062742</td>\n",
       "      <td>72.829396</td>\n",
       "      <td>21500.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.857</td>\n",
       "      <td>2</td>\n",
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      "text/plain": [
       "                 Area   Latitude  Longitude     Rate  Schools_Colleges  \\\n",
       "0               Parel  18.990000  72.840000  18000.0              40.0   \n",
       "1          Kanjurmarg  19.130000  72.940000  10550.0              13.0   \n",
       "2               Worli  19.000000  72.815000  27500.0              25.0   \n",
       "3          Churchgate  18.930000  72.820000  28500.0              61.0   \n",
       "4      Ballard Estate  18.950000  72.840000  29000.0              60.0   \n",
       "5       Nariman Point  18.926000  72.823000  33500.0              56.0   \n",
       "6        Bangur Nagar  19.167362  72.832252  13500.0              50.0   \n",
       "7   Aarey Milk Colony  19.148493  72.881756  12950.0              10.0   \n",
       "10       Marine Lines  18.944700  72.824400  29000.0              76.0   \n",
       "11        Dagdi Chawl  18.977129  72.829131  17000.0              44.0   \n",
       "12            Chembur  19.051000  72.894000  19250.0              26.0   \n",
       "14          Pali Naka  19.062742  72.829396  21500.0              55.0   \n",
       "\n",
       "    Tutorials_Classes  Startup Opportunity Index  Cluster  \n",
       "0                 2.0                     13.333        2  \n",
       "1                 0.0                     13.000        2  \n",
       "2                 1.0                     12.500        2  \n",
       "3                 4.0                     12.200        2  \n",
       "4                 4.0                     12.000        2  \n",
       "5                 4.0                     11.200        2  \n",
       "6                 4.0                     10.000        2  \n",
       "7                 0.0                     10.000        2  \n",
       "10                7.0                      9.500        2  \n",
       "11                4.0                      8.800        2  \n",
       "12                2.0                      8.667        2  \n",
       "14                6.0                      7.857        2  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
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   ],
   "source": [
    "Cluster_2 = Area_df[Area_df['Cluster'] == 2]\n",
    "Cluster_2"
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, for more short listing of Areas apply a limit on Property prices depending on the investing capacity of the stakeholder."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Area</th>\n",
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       "      <th>0</th>\n",
       "      <td>Kanjurmarg</td>\n",
       "      <td>Eastern Suburbs</td>\n",
       "      <td>19.130000</td>\n",
       "      <td>72.940000</td>\n",
       "      <td>10550.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.0</td>\n",
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       "      <th>1</th>\n",
       "      <td>Bangur Nagar</td>\n",
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       "                Area                  Location   Latitude  Longitude     Rate  \\\n",
       "0         Kanjurmarg           Eastern Suburbs  19.130000  72.940000  10550.0   \n",
       "1       Bangur Nagar  Goregaon,Western Suburbs  19.167362  72.832252  13500.0   \n",
       "2  Aarey Milk Colony  Goregaon,Western Suburbs  19.148493  72.881756  12950.0   \n",
       "\n",
       "   Schools_Colleges  Tutorials_Classes  Startup Opportunity Index  Cluster  \n",
       "0              13.0                0.0                       13.0        2  \n",
       "1              50.0                4.0                       10.0        2  \n",
       "2              10.0                0.0                       10.0        2  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "condition_PropertyRateLimit = Cluster_2['Rate'] <= 15000\n",
    "Selected_Areas = Cluster_2 [condition_PropertyRateLimit]\n",
    "Selected_Areas.reset_index(inplace = True, drop = True)\n",
    "Selected_Areas = Selected_Areas.merge(neighborhood_df, on = [\"Area\",\"Latitude\",\"Longitude\"], how = \"inner\")\n",
    "cols = Selected_Areas.columns.tolist()\n",
    "cols = cols[0:1] + cols[-1:] + cols[1:-1]\n",
    "Selected_Areas = Selected_Areas[cols]\n",
    "Selected_Areas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "scrolled": true
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   "outputs": [
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  "color": "blue",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "blue",
  "fillOpacity": 0.7,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
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                ).addTo(map_7f0b6526e66b4de48aa2ee138260c286);
            
    
            var popup_813ca4cb3e95496bb371f1e361534c7e = L.popup({maxWidth: '300'});

            
                var html_ecf23b37ee11429d8831f616f0324996 = $('<div id="html_ecf23b37ee11429d8831f616f0324996" style="width: 100.0%; height: 100.0%;">Area: Aarey Milk Colony, Location: Goregaon,Western Suburbs, Property Rate: Rs.12950/sq.ft.</div>')[0];
                popup_813ca4cb3e95496bb371f1e361534c7e.setContent(html_ecf23b37ee11429d8831f616f0324996);
            

            circle_marker_c0aa3cc1fafc4dd9acc1149db3ea13f7.bindPopup(popup_813ca4cb3e95496bb371f1e361534c7e);

            
        
</script> onload=\"this.contentDocument.open();this.contentDocument.write(atob(this.getAttribute('data-html')));this.contentDocument.close();\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
      ],
      "text/plain": [
       "<folium.folium.Map at 0x19c67ccb2b0>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Selected_Areas_map = folium.Map(location=[19.088493, 72.881756], zoom_start=11)\n",
    "\n",
    "# add Selected_Areas to map\n",
    "for lat, lng, area, rate, location in zip(Selected_Areas['Latitude'], Selected_Areas['Longitude'], Selected_Areas['Area'],\n",
    "                                           Selected_Areas['Rate'], Selected_Areas['Location']):\n",
    "    label = 'Area: {}, Location: {}, Property Rate: Rs.{}/sq.ft.'.format(area ,location, int(rate))\n",
    "    label = folium.Popup(label, parse_html=True)\n",
    "    folium.CircleMarker(\n",
    "        [lat, lng],\n",
    "        radius=5,\n",
    "        popup=label,\n",
    "        color= 'blue' ,\n",
    "        fill=True,\n",
    "        fill_color= 'blue' ,\n",
    "        fill_opacity=0.7,\n",
    "        parse_html=False).add_to(Selected_Areas_map)  \n",
    "    \n",
    "Selected_Areas_map"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Results <a name=\"results\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Optimal Locations to startup Tuition Classes or franchise of existing Classes in Mumbai having high number of Schools & college and less existing Classes and affordable property rates are\n",
    "* Kanjurmarg\n",
    "* Bangur Nagar\n",
    "* Aarey Milk Colony\t"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Discussion<a name=\"Discussion\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our analysis shows that:\n",
    "* Property rates are increasing as we move from North to South region of Mumbai. 'Cluster 2' is in central region of Mumbai, therefore having mid-range Properties Rates. \n",
    "* All 3 Clusters are having similar number of Schols & Colleges. But 'Cluster 2' is having lowest number of existing Classes & Tutorials, resulting in less Bussiness competition, therefore having good Bussiness Opportunity.\n",
    "\n",
    "Suggestions for Future research:\n",
    "* Population Density of each Areas can be web-scrapped from Wikipedia and can be a parameter/feature in clustering locations. \n",
    "* Areas with high Population Density should be given preferences."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion <a name=\"conclusion\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this project, we have gone through the process of identifying the business problem , specifying the data required, extracting and preparing the data, performing machine learning by clustering the data into 3 clusters(no. of cluster decided by Elbow Method) based on their similarities, and lastly providing recommendations to the stakeholders interested in opening a private Tuition Classes or management of existing Classes to expand thier bussiness & revenue by opening new branch/franchise in Mumbai. To answer the business question that was raised in the introduction session , the answer proposed by this project is: The Areas in cluster 2 are the most optimal locations to open a new Classes and with a properties rates limit depending on investment capacity of stakeholders(Rupees 15,000/- per square feet), top 3 optimal Areas are (1) Kanjurmarg. (2) Bangur Nagar. (3) Aarey Milk Colony."
   ]
  }
 ],
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