Business.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Best Business Place in Kanpur"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Introduction/Business Problem</h3>\n",
    "\n",
    "Find a suitable cluster of locations in the city Kanpur of Uttar Pradesh, India to open a business particularly dealing with food."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Data</h3>\n",
    "\n",
    "FourSquare API is used to get venues\n",
    "<br>\n",
    "Latitute and longitude of various places of Kanpur are manually collected"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import folium\n",
    "import requests\n",
    "import matplotlib.cm as cm\n",
    "import matplotlib.colors as colors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "CLIENT_ID = '3IUT4PW5215DP5MLXBOK1BO2DR2PSAZBVSLHQBMIZDPSQCSV' # your Foursquare ID\n",
    "CLIENT_SECRET = 'DMQF43K2IS42VOO3CXP5YLYZMU1FHSYPF231FV2VWPUTPYBF' # your Foursquare Secret\n",
    "VERSION = '20180605' # Foursquare API version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "locations = [\n",
    "    [\"Nawabganj\", \"26.492607\",\"80.301811\"],\n",
    "    [\"Barra\", \"26.427971\",\"80.296242\"],\n",
    "    [\"Yashoda Nagar\", \"26.4034\",\"80.3368\"],\n",
    "    [\"Arya Nagar\", \"26.4834\",\"80.3217\"],\n",
    "    [\"Ashok Nagar\", \"26.4713\",\"80.3172\"],\n",
    "    [\"Azad Nagar\", \"26.4543\",\"80.2786\"],\n",
    "    [\"Bhauti\", \"26.4476\",\"80.1934\"],\n",
    "    [\"Chowk Sarafa\", \"26.4697\",\"80.3496\"],\n",
    "    [\"Chunni Ganj\", \"26.4781\",\"80.3359\"],\n",
    "    [\"Civil Lines\", \"26.4750\",\"80.3533\"],\n",
    "    [\"Darshan Purwa\", \"26.4629\",\"80.3200\"],\n",
    "    [\"Defence Colony\", \"26.4276\",\"80.3979\"],\n",
    "    [\"Fazalganj\", \"26.4591\",\"80.3032\"],\n",
    "    [\"Ghumni Bazar\", \"26.4664\",\"80.3506\"],\n",
    "    [\"Govind Nagar\", \"26.442\",\"80.3094\"],\n",
    "    [\"Indira Nagar\", \"26.5090\",\"80.2642\"],\n",
    "    [\"Harjinder Nagar\", \"26.4235\",\"80.3916\"],\n",
    "    [\"Harsh Nagar\", \"26.4729\",\"80.3150\"],\n",
    "    [\"Jawahar Nagar\", \"26.4684\",\"80.3220\"],\n",
    "    [\"Kakadeo\", \"26.4802\",\"80.2907\"],\n",
    "    [\"Kalyanpur\", \"26.5108\",\"80.2469\"],\n",
    "    [\"Kaushal Puri\", \"26.4678\",\"80.3123\"],\n",
    "    [\"Kidwai Nagar\", \"26.4275\",\"80.3353\"],\n",
    "    [\"Koyla Nagar\", \"26.3966\",\"80.3610\"],\n",
    "    [\"Lajpat Nagar\", \"26.4688\",\"80.3067\"],\n",
    "    [\"Lakhanpur\", \"26.4945\",\"80.2796\"],\n",
    "    [\"Lal Bangla\", \"26.4268\",\"80.3867\"],\n",
    "    [\"Mall Road\", \"26.4732\",\"80.352\"],\n",
    "    [\"Nirala Nagar\", \"26.4426\",\"80.3117\"],\n",
    "    [\"Naubasta\", \"26.4018\",\"80.3145\"],\n",
    "    [\"Nayaganj\", \"26.4625\",\"80.3534\"],\n",
    "    [\"Pandu Nagar\", \"26.4733\",\"80.2976\"],\n",
    "    [\"Panki\", \"26.4705\",\"80.2407\"],\n",
    "    [\"Ratan Lal Nagar\", \"26.4434\",\"80.2865\"],\n",
    "    [\"Saket Nagar\", \"26.4329\",\"80.3126\"],\n",
    "    [\"Sarvodaya Nagar\", \"26.4797\",\"80.3060\"],\n",
    "    [\"Shastri Nagar\", \"26.4639\",\"80.3006\"],\n",
    "    [\"Shyam Nagar\", \"26.4077\",\"80.3683\"],\n",
    "    [\"Swaroop Nagar\", \"26.4878\",\"80.3206\"],\n",
    "    [\"Shanti Nagar\", \"26.4535\",\"80.3610\"],\n",
    "    [\"Sharda Nagar\", \"26.4861\",\"80.2857\"],\n",
    "    [\"Singhpur\", \"26.5232\",\"80.2586\"],\n",
    "    [\"Vikas Nagar\", \"26.4933\",\"80.2847\"],\n",
    "    [\"Vishnupuri\", \"26.4943\",\"80.3164\"],\n",
    "    [\"Tatya Tope Nagar\", \"26.4342\",\"80.2809\"],\n",
    "    [\"Tilak Nagar\", \"26.4896\",\"80.3225\"],\n",
    "]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\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>Neighborhood</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Nawabganj</td>\n",
       "      <td>26.492607</td>\n",
       "      <td>80.301811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Barra</td>\n",
       "      <td>26.427971</td>\n",
       "      <td>80.296242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Yashoda Nagar</td>\n",
       "      <td>26.4034</td>\n",
       "      <td>80.3368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Arya Nagar</td>\n",
       "      <td>26.4834</td>\n",
       "      <td>80.3217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Ashok Nagar</td>\n",
       "      <td>26.4713</td>\n",
       "      <td>80.3172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>Azad Nagar</td>\n",
       "      <td>26.4543</td>\n",
       "      <td>80.2786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>Bhauti</td>\n",
       "      <td>26.4476</td>\n",
       "      <td>80.1934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>Chowk Sarafa</td>\n",
       "      <td>26.4697</td>\n",
       "      <td>80.3496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>Chunni Ganj</td>\n",
       "      <td>26.4781</td>\n",
       "      <td>80.3359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>Civil Lines</td>\n",
       "      <td>26.4750</td>\n",
       "      <td>80.3533</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Neighborhood   Latitude  Longitude\n",
       "0      Nawabganj  26.492607  80.301811\n",
       "1          Barra  26.427971  80.296242\n",
       "2  Yashoda Nagar    26.4034    80.3368\n",
       "3     Arya Nagar    26.4834    80.3217\n",
       "4    Ashok Nagar    26.4713    80.3172\n",
       "5     Azad Nagar    26.4543    80.2786\n",
       "6         Bhauti    26.4476    80.1934\n",
       "7   Chowk Sarafa    26.4697    80.3496\n",
       "8    Chunni Ganj    26.4781    80.3359\n",
       "9    Civil Lines    26.4750    80.3533"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(locations, columns=[\"Neighborhood\", \"Latitude\", \"Longitude\"])\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getNearbyVenues(names, latitudes, longitudes, radius=500):\n",
    "    LIMIT=100\n",
    "    venues_list=[]\n",
    "    for name, lat, lng in zip(names, latitudes, longitudes):\n",
    "        print(name)\n",
    "        # create the API request URL\n",
    "        url = 'https://api.foursquare.com/v2/venues/explore?&client_id={}&client_secret={}&v={}&ll={},{}&radius={}&limit={}'.format(\n",
    "            CLIENT_ID, \n",
    "            CLIENT_SECRET, \n",
    "            VERSION, \n",
    "            lat, \n",
    "            lng, \n",
    "            radius, \n",
    "            LIMIT)\n",
    "            \n",
    "        # make the GET request\n",
    "        results = requests.get(url).json()[\"response\"]['groups'][0]['items']\n",
    "        \n",
    "        # return only relevant information for each nearby venue\n",
    "        venues_list.append([(\n",
    "            name, \n",
    "            lat, \n",
    "            lng, \n",
    "            v['venue']['name'], \n",
    "            v['venue']['location']['lat'], \n",
    "            v['venue']['location']['lng'],  \n",
    "            v['venue']['categories'][0]['name']) 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 = ['Neighborhood', \n",
    "                  'Neighborhood Latitude', \n",
    "                  'Neighborhood Longitude', \n",
    "                  'Venue', \n",
    "                  'Venue Latitude', \n",
    "                  'Venue Longitude', \n",
    "                  'Venue Category']\n",
    "    \n",
    "    return(nearby_venues)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Nawabganj\n",
      "Barra\n",
      "Yashoda Nagar\n",
      "Arya Nagar\n",
      "Ashok Nagar\n",
      "Azad Nagar\n",
      "Bhauti\n",
      "Chowk Sarafa\n",
      "Chunni Ganj\n",
      "Civil Lines\n",
      "Darshan Purwa\n",
      "Defence Colony\n",
      "Fazalganj\n",
      "Ghumni Bazar\n",
      "Govind Nagar\n",
      "Indira Nagar\n",
      "Harjinder Nagar\n",
      "Harsh Nagar\n",
      "Jawahar Nagar\n",
      "Kakadeo\n",
      "Kalyanpur\n",
      "Kaushal Puri\n",
      "Kidwai Nagar\n",
      "Koyla Nagar\n",
      "Lajpat Nagar\n",
      "Lakhanpur\n",
      "Lal Bangla\n",
      "Mall Road\n",
      "Nirala Nagar\n",
      "Naubasta\n",
      "Nayaganj\n",
      "Pandu Nagar\n",
      "Panki\n",
      "Ratan Lal Nagar\n",
      "Saket Nagar\n",
      "Sarvodaya Nagar\n",
      "Shastri Nagar\n",
      "Shyam Nagar\n",
      "Swaroop Nagar\n",
      "Shanti Nagar\n",
      "Sharda Nagar\n",
      "Singhpur\n",
      "Vikas Nagar\n",
      "Vishnupuri\n",
      "Tatya Tope Nagar\n",
      "Tilak Nagar\n"
     ]
    }
   ],
   "source": [
    "df_venues = getNearbyVenues(df['Neighborhood'], df['Latitude'], df['Longitude'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Neighborhood Latitude</th>\n",
       "      <th>Neighborhood Longitude</th>\n",
       "      <th>Venue</th>\n",
       "      <th>Venue Latitude</th>\n",
       "      <th>Venue Longitude</th>\n",
       "      <th>Venue Category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Barra</td>\n",
       "      <td>26.427971</td>\n",
       "      <td>80.296242</td>\n",
       "      <td>Axis Bank ATM</td>\n",
       "      <td>26.423720</td>\n",
       "      <td>80.295840</td>\n",
       "      <td>ATM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Yashoda Nagar</td>\n",
       "      <td>26.4034</td>\n",
       "      <td>80.3368</td>\n",
       "      <td>Axis Bank ATM</td>\n",
       "      <td>26.401577</td>\n",
       "      <td>80.341095</td>\n",
       "      <td>ATM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Arya Nagar</td>\n",
       "      <td>26.4834</td>\n",
       "      <td>80.3217</td>\n",
       "      <td>Upper Crust</td>\n",
       "      <td>26.482225</td>\n",
       "      <td>80.320857</td>\n",
       "      <td>Bakery</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Arya Nagar</td>\n",
       "      <td>26.4834</td>\n",
       "      <td>80.3217</td>\n",
       "      <td>Mithas</td>\n",
       "      <td>26.485217</td>\n",
       "      <td>80.321400</td>\n",
       "      <td>Indian Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Arya Nagar</td>\n",
       "      <td>26.4834</td>\n",
       "      <td>80.3217</td>\n",
       "      <td>Aromas, Arya Nagar</td>\n",
       "      <td>26.482953</td>\n",
       "      <td>80.318024</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>Arya Nagar</td>\n",
       "      <td>26.4834</td>\n",
       "      <td>80.3217</td>\n",
       "      <td>cafe coffee day</td>\n",
       "      <td>26.483661</td>\n",
       "      <td>80.320399</td>\n",
       "      <td>Coffee Shop</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>Arya Nagar</td>\n",
       "      <td>26.4834</td>\n",
       "      <td>80.3217</td>\n",
       "      <td>Makhan Singh (Biryani Spot)</td>\n",
       "      <td>26.481089</td>\n",
       "      <td>80.321161</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>Arya Nagar</td>\n",
       "      <td>26.4834</td>\n",
       "      <td>80.3217</td>\n",
       "      <td>KD Palace</td>\n",
       "      <td>26.480891</td>\n",
       "      <td>80.321732</td>\n",
       "      <td>Indian Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>Arya Nagar</td>\n",
       "      <td>26.4834</td>\n",
       "      <td>80.3217</td>\n",
       "      <td>Mr Brown Bakery</td>\n",
       "      <td>26.486043</td>\n",
       "      <td>80.323756</td>\n",
       "      <td>Bakery</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>Arya Nagar</td>\n",
       "      <td>26.4834</td>\n",
       "      <td>80.3217</td>\n",
       "      <td>Ganges Club</td>\n",
       "      <td>26.480379</td>\n",
       "      <td>80.323979</td>\n",
       "      <td>Club House</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Neighborhood Neighborhood Latitude Neighborhood Longitude  \\\n",
       "0          Barra             26.427971              80.296242   \n",
       "1  Yashoda Nagar               26.4034                80.3368   \n",
       "2     Arya Nagar               26.4834                80.3217   \n",
       "3     Arya Nagar               26.4834                80.3217   \n",
       "4     Arya Nagar               26.4834                80.3217   \n",
       "5     Arya Nagar               26.4834                80.3217   \n",
       "6     Arya Nagar               26.4834                80.3217   \n",
       "7     Arya Nagar               26.4834                80.3217   \n",
       "8     Arya Nagar               26.4834                80.3217   \n",
       "9     Arya Nagar               26.4834                80.3217   \n",
       "\n",
       "                         Venue  Venue Latitude  Venue Longitude  \\\n",
       "0                Axis Bank ATM       26.423720        80.295840   \n",
       "1                Axis Bank ATM       26.401577        80.341095   \n",
       "2                  Upper Crust       26.482225        80.320857   \n",
       "3                       Mithas       26.485217        80.321400   \n",
       "4           Aromas, Arya Nagar       26.482953        80.318024   \n",
       "5              cafe coffee day       26.483661        80.320399   \n",
       "6  Makhan Singh (Biryani Spot)       26.481089        80.321161   \n",
       "7                    KD Palace       26.480891        80.321732   \n",
       "8              Mr Brown Bakery       26.486043        80.323756   \n",
       "9                  Ganges Club       26.480379        80.323979   \n",
       "\n",
       "         Venue Category  \n",
       "0                   ATM  \n",
       "1                   ATM  \n",
       "2                Bakery  \n",
       "3     Indian Restaurant  \n",
       "4  Fast Food Restaurant  \n",
       "5           Coffee Shop  \n",
       "6  Fast Food Restaurant  \n",
       "7     Indian Restaurant  \n",
       "8                Bakery  \n",
       "9            Club House  "
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_venues.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>ATM</th>\n",
       "      <th>Afghan Restaurant</th>\n",
       "      <th>Asian Restaurant</th>\n",
       "      <th>Awadhi Restaurant</th>\n",
       "      <th>Bakery</th>\n",
       "      <th>Boat or Ferry</th>\n",
       "      <th>Bookstore</th>\n",
       "      <th>Bowling Alley</th>\n",
       "      <th>Breakfast Spot</th>\n",
       "      <th>...</th>\n",
       "      <th>Mexican Restaurant</th>\n",
       "      <th>Multiplex</th>\n",
       "      <th>Park</th>\n",
       "      <th>Pizza Place</th>\n",
       "      <th>Plaza</th>\n",
       "      <th>Restaurant</th>\n",
       "      <th>Shop &amp; Service</th>\n",
       "      <th>Shopping Mall</th>\n",
       "      <th>Spa</th>\n",
       "      <th>Tea Room</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Barra</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Yashoda Nagar</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Arya Nagar</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Arya Nagar</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Arya Nagar</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Neighborhood  ATM  Afghan Restaurant  Asian Restaurant  Awadhi Restaurant  \\\n",
       "0          Barra    1                  0                 0                  0   \n",
       "1  Yashoda Nagar    1                  0                 0                  0   \n",
       "2     Arya Nagar    0                  0                 0                  0   \n",
       "3     Arya Nagar    0                  0                 0                  0   \n",
       "4     Arya Nagar    0                  0                 0                  0   \n",
       "\n",
       "   Bakery  Boat or Ferry  Bookstore  Bowling Alley  Breakfast Spot  ...  \\\n",
       "0       0              0          0              0               0  ...   \n",
       "1       0              0          0              0               0  ...   \n",
       "2       1              0          0              0               0  ...   \n",
       "3       0              0          0              0               0  ...   \n",
       "4       0              0          0              0               0  ...   \n",
       "\n",
       "   Mexican Restaurant  Multiplex  Park  Pizza Place  Plaza  Restaurant  \\\n",
       "0                   0          0     0            0      0           0   \n",
       "1                   0          0     0            0      0           0   \n",
       "2                   0          0     0            0      0           0   \n",
       "3                   0          0     0            0      0           0   \n",
       "4                   0          0     0            0      0           0   \n",
       "\n",
       "   Shop & Service  Shopping Mall  Spa  Tea Room  \n",
       "0               0              0    0         0  \n",
       "1               0              0    0         0  \n",
       "2               0              0    0         0  \n",
       "3               0              0    0         0  \n",
       "4               0              0    0         0  \n",
       "\n",
       "[5 rows x 38 columns]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# one hot encoding\n",
    "df_onehot = pd.get_dummies(df_venues[['Venue Category']], prefix=\"\", prefix_sep=\"\")\n",
    "\n",
    "# add neighborhood column back to dataframe\n",
    "df_onehot['Neighborhood'] = df_venues['Neighborhood'] \n",
    "\n",
    "# move neighborhood column to the first column\n",
    "fixed_columns = [df_onehot.columns[-1]] + list(df_onehot.columns[:-1])\n",
    "df_onehot = df_onehot[fixed_columns]\n",
    "\n",
    "df_onehot.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "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>Neighborhood</th>\n",
       "      <th>ATM</th>\n",
       "      <th>Afghan Restaurant</th>\n",
       "      <th>Asian Restaurant</th>\n",
       "      <th>Awadhi Restaurant</th>\n",
       "      <th>Bakery</th>\n",
       "      <th>Boat or Ferry</th>\n",
       "      <th>Bookstore</th>\n",
       "      <th>Bowling Alley</th>\n",
       "      <th>Breakfast Spot</th>\n",
       "      <th>...</th>\n",
       "      <th>Mexican Restaurant</th>\n",
       "      <th>Multiplex</th>\n",
       "      <th>Park</th>\n",
       "      <th>Pizza Place</th>\n",
       "      <th>Plaza</th>\n",
       "      <th>Restaurant</th>\n",
       "      <th>Shop &amp; Service</th>\n",
       "      <th>Shopping Mall</th>\n",
       "      <th>Spa</th>\n",
       "      <th>Tea Room</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Arya Nagar</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Ashok Nagar</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Azad Nagar</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Barra</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Chowk Sarafa</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>Civil Lines</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>Darshan Purwa</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>Defence Colony</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>Fazalganj</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>Ghumni Bazar</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>Govind Nagar</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>Harjinder Nagar</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>Harsh Nagar</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>Indira Nagar</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>Jawahar Nagar</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>Kakadeo</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>Kalyanpur</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>Kaushal Puri</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>Kidwai Nagar</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>Lajpat Nagar</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>Mall Road</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>Naubasta</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>Nayaganj</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.2</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>Nirala Nagar</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>Pandu Nagar</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>Panki</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>Ratan Lal Nagar</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>Shanti Nagar</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>Sharda Nagar</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>Shastri Nagar</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30</td>\n",
       "      <td>Singhpur</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31</td>\n",
       "      <td>Swaroop Nagar</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32</td>\n",
       "      <td>Tatya Tope Nagar</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33</td>\n",
       "      <td>Tilak Nagar</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34</td>\n",
       "      <td>Vikas Nagar</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35</td>\n",
       "      <td>Vishnupuri</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36</td>\n",
       "      <td>Yashoda Nagar</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>37 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Neighborhood  ATM  Afghan Restaurant  Asian Restaurant  \\\n",
       "0         Arya Nagar  0.0                0.0               0.0   \n",
       "1        Ashok Nagar  0.0                0.0               0.0   \n",
       "2         Azad Nagar  0.0                0.0               0.0   \n",
       "3              Barra  1.0                0.0               0.0   \n",
       "4       Chowk Sarafa  0.0                0.0               0.0   \n",
       "5        Civil Lines  0.0                0.0               0.0   \n",
       "6      Darshan Purwa  0.0                0.0               0.0   \n",
       "7     Defence Colony  0.0                0.0               0.0   \n",
       "8          Fazalganj  0.0                0.0               0.0   \n",
       "9       Ghumni Bazar  0.0                0.0               0.0   \n",
       "10      Govind Nagar  0.0                0.0               0.0   \n",
       "11   Harjinder Nagar  0.0                0.0               0.0   \n",
       "12       Harsh Nagar  0.0                0.0               0.0   \n",
       "13      Indira Nagar  0.0                0.0               0.0   \n",
       "14     Jawahar Nagar  0.0                0.0               0.0   \n",
       "15           Kakadeo  0.5                0.0               0.0   \n",
       "16         Kalyanpur  0.0                0.0               0.0   \n",
       "17      Kaushal Puri  0.0                0.0               0.0   \n",
       "18      Kidwai Nagar  0.0                0.0               0.0   \n",
       "19      Lajpat Nagar  0.0                0.0               0.0   \n",
       "20         Mall Road  0.0                0.0               0.0   \n",
       "21          Naubasta  0.0                0.0               0.0   \n",
       "22          Nayaganj  0.0                0.0               0.0   \n",
       "23      Nirala Nagar  0.0                0.0               0.0   \n",
       "24       Pandu Nagar  0.0                0.0               0.0   \n",
       "25             Panki  0.0                0.0               0.0   \n",
       "26   Ratan Lal Nagar  0.0                0.0               0.0   \n",
       "27      Shanti Nagar  0.0                0.0               0.0   \n",
       "28      Sharda Nagar  0.0                1.0               0.0   \n",
       "29     Shastri Nagar  0.0                0.0               0.0   \n",
       "30          Singhpur  0.0                0.0               0.0   \n",
       "31     Swaroop Nagar  0.0                0.0               0.0   \n",
       "32  Tatya Tope Nagar  0.0                0.0               0.0   \n",
       "33       Tilak Nagar  0.0                0.0               0.0   \n",
       "34       Vikas Nagar  0.0                0.0               1.0   \n",
       "35        Vishnupuri  0.0                0.0               0.0   \n",
       "36     Yashoda Nagar  1.0                0.0               0.0   \n",
       "\n",
       "    Awadhi Restaurant  Bakery  Boat or Ferry  Bookstore  Bowling Alley  \\\n",
       "0            0.000000    0.20            0.0        0.0       0.000000   \n",
       "1            0.000000    0.00            0.0        0.0       0.000000   \n",
       "2            0.000000    0.00            0.0        0.0       0.000000   \n",
       "3            0.000000    0.00            0.0        0.0       0.000000   \n",
       "4            0.000000    0.00            0.0        0.0       0.000000   \n",
       "5            0.000000    0.00            0.0        0.0       0.000000   \n",
       "6            0.000000    0.00            0.0        0.0       0.000000   \n",
       "7            0.000000    0.20            0.0        0.2       0.000000   \n",
       "8            0.000000    0.00            0.0        0.0       0.000000   \n",
       "9            0.000000    0.00            0.0        0.0       0.000000   \n",
       "10           0.000000    0.50            0.0        0.0       0.000000   \n",
       "11           0.000000    0.00            0.0        0.0       0.000000   \n",
       "12           0.000000    0.00            0.0        0.0       0.000000   \n",
       "13           0.000000    0.00            0.0        0.0       0.000000   \n",
       "14           0.000000    0.00            0.0        0.0       0.000000   \n",
       "15           0.000000    0.00            0.0        0.0       0.000000   \n",
       "16           0.000000    0.00            0.0        0.0       0.000000   \n",
       "17           0.000000    0.00            0.0        0.0       0.000000   \n",
       "18           0.000000    0.00            0.0        0.0       0.000000   \n",
       "19           0.000000    0.00            0.0        0.0       0.000000   \n",
       "20           0.000000    0.00            0.0        0.0       0.111111   \n",
       "21           0.000000    0.00            0.0        0.0       0.000000   \n",
       "22           0.000000    0.00            0.2        0.0       0.000000   \n",
       "23           0.000000    1.00            0.0        0.0       0.000000   \n",
       "24           0.333333    0.00            0.0        0.0       0.000000   \n",
       "25           0.000000    0.00            0.0        0.0       0.000000   \n",
       "26           0.000000    0.00            0.0        0.0       0.000000   \n",
       "27           0.000000    0.00            0.0        0.0       0.000000   \n",
       "28           0.000000    0.00            0.0        0.0       0.000000   \n",
       "29           0.000000    0.00            0.0        0.0       0.000000   \n",
       "30           0.000000    1.00            0.0        0.0       0.000000   \n",
       "31           0.000000    0.20            0.0        0.0       0.000000   \n",
       "32           0.000000    0.00            0.0        0.0       0.000000   \n",
       "33           0.000000    0.25            0.0        0.0       0.000000   \n",
       "34           0.000000    0.00            0.0        0.0       0.000000   \n",
       "35           0.000000    0.00            0.0        0.0       0.000000   \n",
       "36           0.000000    0.00            0.0        0.0       0.000000   \n",
       "\n",
       "    Breakfast Spot  ...  Mexican Restaurant  Multiplex      Park  Pizza Place  \\\n",
       "0              0.0  ...                 0.0   0.000000  0.000000     0.000000   \n",
       "1              0.0  ...                 0.0   0.000000  0.250000     0.000000   \n",
       "2              0.0  ...                 0.0   0.000000  0.000000     0.000000   \n",
       "3              0.0  ...                 0.0   0.000000  0.000000     0.000000   \n",
       "4              0.0  ...                 0.0   0.166667  0.000000     0.000000   \n",
       "5              0.0  ...                 0.0   0.100000  0.000000     0.100000   \n",
       "6              0.0  ...                 0.0   0.000000  0.000000     0.000000   \n",
       "7              0.0  ...                 0.2   0.000000  0.000000     0.000000   \n",
       "8              0.0  ...                 0.0   0.000000  0.000000     0.000000   \n",
       "9              0.0  ...                 0.0   0.000000  0.000000     0.000000   \n",
       "10             0.0  ...                 0.0   0.000000  0.000000     0.000000   \n",
       "11             0.0  ...                 0.0   0.000000  0.000000     0.000000   \n",
       "12             0.0  ...                 0.0   0.000000  0.250000     0.000000   \n",
       "13             0.0  ...                 0.0   0.000000  0.000000     0.000000   \n",
       "14             0.0  ...                 0.0   0.000000  0.000000     0.000000   \n",
       "15             0.0  ...                 0.0   0.000000  0.000000     0.000000   \n",
       "16             0.0  ...                 0.0   0.000000  0.000000     0.000000   \n",
       "17             0.0  ...                 0.0   0.000000  0.000000     0.000000   \n",
       "18             0.0  ...                 0.0   0.000000  0.000000     0.000000   \n",
       "19             0.0  ...                 0.0   0.000000  0.000000     0.000000   \n",
       "20             0.0  ...                 0.0   0.111111  0.000000     0.111111   \n",
       "21             0.0  ...                 0.0   0.000000  0.000000     0.000000   \n",
       "22             0.2  ...                 0.0   0.000000  0.000000     0.000000   \n",
       "23             0.0  ...                 0.0   0.000000  0.000000     0.000000   \n",
       "24             0.0  ...                 0.0   0.000000  0.333333     0.000000   \n",
       "25             0.0  ...                 0.0   0.000000  0.000000     0.000000   \n",
       "26             0.0  ...                 0.0   0.000000  0.000000     0.000000   \n",
       "27             0.0  ...                 0.0   0.000000  0.000000     0.000000   \n",
       "28             0.0  ...                 0.0   0.000000  0.000000     0.000000   \n",
       "29             0.0  ...                 0.0   0.500000  0.000000     0.000000   \n",
       "30             0.0  ...                 0.0   0.000000  0.000000     0.000000   \n",
       "31             0.0  ...                 0.0   0.000000  0.000000     0.000000   \n",
       "32             1.0  ...                 0.0   0.000000  0.000000     0.000000   \n",
       "33             0.0  ...                 0.0   0.000000  0.000000     0.000000   \n",
       "34             0.0  ...                 0.0   0.000000  0.000000     0.000000   \n",
       "35             0.0  ...                 0.0   0.000000  0.000000     0.000000   \n",
       "36             0.0  ...                 0.0   0.000000  0.000000     0.000000   \n",
       "\n",
       "    Plaza  Restaurant  Shop & Service  Shopping Mall  Spa  Tea Room  \n",
       "0     0.0        0.00             0.0       0.000000  0.0      0.00  \n",
       "1     0.0        0.00             0.0       0.000000  0.0      0.25  \n",
       "2     0.0        0.00             0.0       0.000000  0.0      0.00  \n",
       "3     0.0        0.00             0.0       0.000000  0.0      0.00  \n",
       "4     0.0        0.00             0.0       0.166667  0.0      0.00  \n",
       "5     0.0        0.00             0.0       0.100000  0.0      0.00  \n",
       "6     0.0        0.00             0.0       0.000000  0.0      0.00  \n",
       "7     0.0        0.20             0.0       0.000000  0.2      0.00  \n",
       "8     0.0        0.00             0.0       0.000000  0.0      0.00  \n",
       "9     0.0        0.00             0.0       0.000000  0.0      0.00  \n",
       "10    0.5        0.00             0.0       0.000000  0.0      0.00  \n",
       "11    0.0        0.00             0.0       0.000000  0.5      0.00  \n",
       "12    0.0        0.00             0.0       0.000000  0.0      0.25  \n",
       "13    0.0        0.00             0.0       0.000000  0.0      0.00  \n",
       "14    0.0        0.00             0.0       0.000000  0.0      0.00  \n",
       "15    0.0        0.50             0.0       0.000000  0.0      0.00  \n",
       "16    0.0        0.00             0.0       0.000000  0.0      0.00  \n",
       "17    0.0        0.25             0.0       0.000000  0.0      0.00  \n",
       "18    0.0        0.00             0.0       0.000000  0.0      0.00  \n",
       "19    0.0        0.00             0.0       0.000000  0.0      0.00  \n",
       "20    0.0        0.00             0.0       0.111111  0.0      0.00  \n",
       "21    0.0        0.00             0.0       0.000000  0.0      0.00  \n",
       "22    0.0        0.00             0.0       0.000000  0.0      0.00  \n",
       "23    0.0        0.00             0.0       0.000000  0.0      0.00  \n",
       "24    0.0        0.00             0.0       0.000000  0.0      0.00  \n",
       "25    0.0        0.00             0.0       0.000000  0.0      0.00  \n",
       "26    0.0        0.00             0.0       0.000000  0.0      0.00  \n",
       "27    0.0        0.00             0.0       0.000000  0.0      0.00  \n",
       "28    0.0        0.00             0.0       0.000000  0.0      0.00  \n",
       "29    0.0        0.00             0.0       0.000000  0.0      0.00  \n",
       "30    0.0        0.00             0.0       0.000000  0.0      0.00  \n",
       "31    0.0        0.00             0.0       0.000000  0.0      0.00  \n",
       "32    0.0        0.00             0.0       0.000000  0.0      0.00  \n",
       "33    0.0        0.00             0.0       0.000000  0.0      0.00  \n",
       "34    0.0        0.00             0.0       0.000000  0.0      0.00  \n",
       "35    0.0        0.00             0.5       0.000000  0.0      0.00  \n",
       "36    0.0        0.00             0.0       0.000000  0.0      0.00  \n",
       "\n",
       "[37 rows x 38 columns]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_grouped = df_onehot.groupby('Neighborhood').mean().reset_index()\n",
    "df_grouped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "def return_most_common_venues(row, num_top_venues):\n",
    "    row_categories = row.iloc[1:]\n",
    "    row_categories_sorted = row_categories.sort_values(ascending=False)\n",
    "    \n",
    "    return row_categories_sorted.index.values[0:num_top_venues]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "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>Neighborhood</th>\n",
       "      <th>1st Most Common Venue</th>\n",
       "      <th>2nd Most Common Venue</th>\n",
       "      <th>3rd Most Common Venue</th>\n",
       "      <th>4th Most Common Venue</th>\n",
       "      <th>5th Most Common Venue</th>\n",
       "      <th>6th Most Common Venue</th>\n",
       "      <th>7th Most Common Venue</th>\n",
       "      <th>8th Most Common Venue</th>\n",
       "      <th>9th Most Common Venue</th>\n",
       "      <th>10th Most Common Venue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Arya Nagar</td>\n",
       "      <td>Indian Restaurant</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>Bakery</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Club House</td>\n",
       "      <td>Tea Room</td>\n",
       "      <td>Bus Station</td>\n",
       "      <td>Clothing Store</td>\n",
       "      <td>Camera Store</td>\n",
       "      <td>Café</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Ashok Nagar</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>Tea Room</td>\n",
       "      <td>Park</td>\n",
       "      <td>Breakfast Spot</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Club House</td>\n",
       "      <td>Clothing Store</td>\n",
       "      <td>Camera Store</td>\n",
       "      <td>Café</td>\n",
       "      <td>Bus Station</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Azad Nagar</td>\n",
       "      <td>Diner</td>\n",
       "      <td>Dessert Shop</td>\n",
       "      <td>Convenience Store</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Club House</td>\n",
       "      <td>Clothing Store</td>\n",
       "      <td>Camera Store</td>\n",
       "      <td>Café</td>\n",
       "      <td>Bus Station</td>\n",
       "      <td>Breakfast Spot</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Barra</td>\n",
       "      <td>ATM</td>\n",
       "      <td>Dessert Shop</td>\n",
       "      <td>Convenience Store</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Club House</td>\n",
       "      <td>Clothing Store</td>\n",
       "      <td>Camera Store</td>\n",
       "      <td>Café</td>\n",
       "      <td>Bus Station</td>\n",
       "      <td>Breakfast Spot</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Chowk Sarafa</td>\n",
       "      <td>Dessert Shop</td>\n",
       "      <td>Shopping Mall</td>\n",
       "      <td>Clothing Store</td>\n",
       "      <td>Multiplex</td>\n",
       "      <td>Cosmetics Shop</td>\n",
       "      <td>Bus Station</td>\n",
       "      <td>Convenience Store</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Club House</td>\n",
       "      <td>Camera Store</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Neighborhood 1st Most Common Venue 2nd Most Common Venue  \\\n",
       "0    Arya Nagar     Indian Restaurant  Fast Food Restaurant   \n",
       "1   Ashok Nagar                 Hotel              Tea Room   \n",
       "2    Azad Nagar                 Diner          Dessert Shop   \n",
       "3         Barra                   ATM          Dessert Shop   \n",
       "4  Chowk Sarafa          Dessert Shop         Shopping Mall   \n",
       "\n",
       "  3rd Most Common Venue 4th Most Common Venue 5th Most Common Venue  \\\n",
       "0                Bakery           Coffee Shop            Club House   \n",
       "1                  Park        Breakfast Spot           Coffee Shop   \n",
       "2     Convenience Store           Coffee Shop            Club House   \n",
       "3     Convenience Store           Coffee Shop            Club House   \n",
       "4        Clothing Store             Multiplex        Cosmetics Shop   \n",
       "\n",
       "  6th Most Common Venue 7th Most Common Venue 8th Most Common Venue  \\\n",
       "0              Tea Room           Bus Station        Clothing Store   \n",
       "1            Club House        Clothing Store          Camera Store   \n",
       "2        Clothing Store          Camera Store                  Café   \n",
       "3        Clothing Store          Camera Store                  Café   \n",
       "4           Bus Station     Convenience Store           Coffee Shop   \n",
       "\n",
       "  9th Most Common Venue 10th Most Common Venue  \n",
       "0          Camera Store                   Café  \n",
       "1                  Café            Bus Station  \n",
       "2           Bus Station         Breakfast Spot  \n",
       "3           Bus Station         Breakfast Spot  \n",
       "4            Club House           Camera Store  "
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_top_venues = 10\n",
    "\n",
    "indicators = ['st', 'nd', 'rd']\n",
    "\n",
    "# create columns according to number of top venues\n",
    "columns = ['Neighborhood']\n",
    "for ind in np.arange(num_top_venues):\n",
    "    try:\n",
    "        columns.append('{}{} Most Common Venue'.format(ind+1, indicators[ind]))\n",
    "    except:\n",
    "        columns.append('{}th Most Common Venue'.format(ind+1))\n",
    "\n",
    "# create a new dataframe\n",
    "neighborhoods_venues_sorted = pd.DataFrame(columns=columns)\n",
    "neighborhoods_venues_sorted['Neighborhood'] = df_grouped['Neighborhood']\n",
    "\n",
    "for ind in np.arange(df_grouped.shape[0]):\n",
    "    neighborhoods_venues_sorted.iloc[ind, 1:] = return_most_common_venues(df_grouped.iloc[ind, :], num_top_venues)\n",
    "\n",
    "neighborhoods_venues_sorted.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'matplotlib.pyplot' from '/usr/local/lib/python3.7/dist-packages/matplotlib/pyplot.py'>"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "import matplotlib.pyplot as plt  \n",
    "cost =[] \n",
    "for i in range(1, 11): \n",
    "    KM = KMeans(n_clusters = i, max_iter = 500) \n",
    "    KM.fit(df_grouped.drop('Neighborhood', 1)) \n",
    "      \n",
    "    cost.append(KM.inertia_)      \n",
    "  \n",
    "# plot the cost against K values \n",
    "plt.plot(range(1, 11), cost, color ='g', linewidth ='3') \n",
    "plt.xlabel(\"Value of K\") \n",
    "plt.ylabel(\"Sqaured Error (Cost)\") \n",
    "plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 3, 1, 4, 1, 1, 1, 1, 5, 2], dtype=int32)"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# set number of clusters\n",
    "kclusters = 6\n",
    "\n",
    "df_grouped_clustering = df_grouped.drop('Neighborhood', 1)\n",
    "\n",
    "# run k-means clustering\n",
    "kmeans = KMeans(n_clusters=kclusters, random_state=0).fit(df_grouped_clustering)\n",
    "\n",
    "# check cluster labels generated for each row in the dataframe\n",
    "kmeans.labels_[0:10]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "neighborhoods_venues_sorted.drop(\"Cluster Labels\", 1, inplace=True)\n",
    "neighborhoods_venues_sorted.insert(0, 'Cluster Labels', kmeans.labels_)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "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>Neighborhood</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Cluster Labels</th>\n",
       "      <th>1st Most Common Venue</th>\n",
       "      <th>2nd Most Common Venue</th>\n",
       "      <th>3rd Most Common Venue</th>\n",
       "      <th>4th Most Common Venue</th>\n",
       "      <th>5th Most Common Venue</th>\n",
       "      <th>6th Most Common Venue</th>\n",
       "      <th>7th Most Common Venue</th>\n",
       "      <th>8th Most Common Venue</th>\n",
       "      <th>9th Most Common Venue</th>\n",
       "      <th>10th Most Common Venue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Barra</td>\n",
       "      <td>26.427971</td>\n",
       "      <td>80.296242</td>\n",
       "      <td>4.0</td>\n",
       "      <td>ATM</td>\n",
       "      <td>Dessert Shop</td>\n",
       "      <td>Convenience Store</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Club House</td>\n",
       "      <td>Clothing Store</td>\n",
       "      <td>Camera Store</td>\n",
       "      <td>Café</td>\n",
       "      <td>Bus Station</td>\n",
       "      <td>Breakfast Spot</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Yashoda Nagar</td>\n",
       "      <td>26.4034</td>\n",
       "      <td>80.3368</td>\n",
       "      <td>4.0</td>\n",
       "      <td>ATM</td>\n",
       "      <td>Dessert Shop</td>\n",
       "      <td>Convenience Store</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Club House</td>\n",
       "      <td>Clothing Store</td>\n",
       "      <td>Camera Store</td>\n",
       "      <td>Café</td>\n",
       "      <td>Bus Station</td>\n",
       "      <td>Breakfast Spot</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Arya Nagar</td>\n",
       "      <td>26.4834</td>\n",
       "      <td>80.3217</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Indian Restaurant</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>Bakery</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Club House</td>\n",
       "      <td>Tea Room</td>\n",
       "      <td>Bus Station</td>\n",
       "      <td>Clothing Store</td>\n",
       "      <td>Camera Store</td>\n",
       "      <td>Café</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Ashok Nagar</td>\n",
       "      <td>26.4713</td>\n",
       "      <td>80.3172</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>Tea Room</td>\n",
       "      <td>Park</td>\n",
       "      <td>Breakfast Spot</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Club House</td>\n",
       "      <td>Clothing Store</td>\n",
       "      <td>Camera Store</td>\n",
       "      <td>Café</td>\n",
       "      <td>Bus Station</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>Azad Nagar</td>\n",
       "      <td>26.4543</td>\n",
       "      <td>80.2786</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Diner</td>\n",
       "      <td>Dessert Shop</td>\n",
       "      <td>Convenience Store</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Club House</td>\n",
       "      <td>Clothing Store</td>\n",
       "      <td>Camera Store</td>\n",
       "      <td>Café</td>\n",
       "      <td>Bus Station</td>\n",
       "      <td>Breakfast Spot</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>Chowk Sarafa</td>\n",
       "      <td>26.4697</td>\n",
       "      <td>80.3496</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Dessert Shop</td>\n",
       "      <td>Shopping Mall</td>\n",
       "      <td>Clothing Store</td>\n",
       "      <td>Multiplex</td>\n",
       "      <td>Cosmetics Shop</td>\n",
       "      <td>Bus Station</td>\n",
       "      <td>Convenience Store</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Club House</td>\n",
       "      <td>Camera Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>Civil Lines</td>\n",
       "      <td>26.4750</td>\n",
       "      <td>80.3533</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>Diner</td>\n",
       "      <td>Multiplex</td>\n",
       "      <td>Dessert Shop</td>\n",
       "      <td>Clothing Store</td>\n",
       "      <td>Café</td>\n",
       "      <td>Cosmetics Shop</td>\n",
       "      <td>Pizza Place</td>\n",
       "      <td>Shopping Mall</td>\n",
       "      <td>Bakery</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>Darshan Purwa</td>\n",
       "      <td>26.4629</td>\n",
       "      <td>80.3200</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Electronics Store</td>\n",
       "      <td>Tea Room</td>\n",
       "      <td>Bus Station</td>\n",
       "      <td>Convenience Store</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Club House</td>\n",
       "      <td>Clothing Store</td>\n",
       "      <td>Camera Store</td>\n",
       "      <td>Café</td>\n",
       "      <td>Breakfast Spot</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>Defence Colony</td>\n",
       "      <td>26.4276</td>\n",
       "      <td>80.3979</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Spa</td>\n",
       "      <td>Restaurant</td>\n",
       "      <td>Bakery</td>\n",
       "      <td>Bookstore</td>\n",
       "      <td>Mexican Restaurant</td>\n",
       "      <td>Tea Room</td>\n",
       "      <td>Bus Station</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Club House</td>\n",
       "      <td>Clothing Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>Fazalganj</td>\n",
       "      <td>26.4591</td>\n",
       "      <td>80.3032</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Indian Restaurant</td>\n",
       "      <td>Tea Room</td>\n",
       "      <td>Bus Station</td>\n",
       "      <td>Convenience Store</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Club House</td>\n",
       "      <td>Clothing Store</td>\n",
       "      <td>Camera Store</td>\n",
       "      <td>Café</td>\n",
       "      <td>Breakfast Spot</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>Ghumni Bazar</td>\n",
       "      <td>26.4664</td>\n",
       "      <td>80.3506</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Market</td>\n",
       "      <td>Tea Room</td>\n",
       "      <td>Convenience Store</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Club House</td>\n",
       "      <td>Clothing Store</td>\n",
       "      <td>Camera Store</td>\n",
       "      <td>Café</td>\n",
       "      <td>Bus Station</td>\n",
       "      <td>Breakfast Spot</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>Govind Nagar</td>\n",
       "      <td>26.442</td>\n",
       "      <td>80.3094</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Plaza</td>\n",
       "      <td>Bakery</td>\n",
       "      <td>Tea Room</td>\n",
       "      <td>Bus Station</td>\n",
       "      <td>Convenience Store</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Club House</td>\n",
       "      <td>Clothing Store</td>\n",
       "      <td>Camera Store</td>\n",
       "      <td>Café</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>Indira Nagar</td>\n",
       "      <td>26.5090</td>\n",
       "      <td>80.2642</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>Tea Room</td>\n",
       "      <td>Bus Station</td>\n",
       "      <td>Convenience Store</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Club House</td>\n",
       "      <td>Clothing Store</td>\n",
       "      <td>Camera Store</td>\n",
       "      <td>Café</td>\n",
       "      <td>Breakfast Spot</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>Harjinder Nagar</td>\n",
       "      <td>26.4235</td>\n",
       "      <td>80.3916</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Spa</td>\n",
       "      <td>Café</td>\n",
       "      <td>Bus Station</td>\n",
       "      <td>Cosmetics Shop</td>\n",
       "      <td>Convenience Store</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Club House</td>\n",
       "      <td>Clothing Store</td>\n",
       "      <td>Camera Store</td>\n",
       "      <td>Tea Room</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>Harsh Nagar</td>\n",
       "      <td>26.4729</td>\n",
       "      <td>80.3150</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>Tea Room</td>\n",
       "      <td>Park</td>\n",
       "      <td>Breakfast Spot</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Club House</td>\n",
       "      <td>Clothing Store</td>\n",
       "      <td>Camera Store</td>\n",
       "      <td>Café</td>\n",
       "      <td>Bus Station</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>Jawahar Nagar</td>\n",
       "      <td>26.4684</td>\n",
       "      <td>80.3220</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>Furniture / Home Store</td>\n",
       "      <td>Tea Room</td>\n",
       "      <td>Bus Station</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Club House</td>\n",
       "      <td>Clothing Store</td>\n",
       "      <td>Camera Store</td>\n",
       "      <td>Café</td>\n",
       "      <td>Breakfast Spot</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>Kakadeo</td>\n",
       "      <td>26.4802</td>\n",
       "      <td>80.2907</td>\n",
       "      <td>4.0</td>\n",
       "      <td>ATM</td>\n",
       "      <td>Restaurant</td>\n",
       "      <td>Breakfast Spot</td>\n",
       "      <td>Convenience Store</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Club House</td>\n",
       "      <td>Clothing Store</td>\n",
       "      <td>Camera Store</td>\n",
       "      <td>Café</td>\n",
       "      <td>Bus Station</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>Kalyanpur</td>\n",
       "      <td>26.5108</td>\n",
       "      <td>80.2469</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Food Court</td>\n",
       "      <td>Tea Room</td>\n",
       "      <td>Bus Station</td>\n",
       "      <td>Convenience Store</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Club House</td>\n",
       "      <td>Clothing Store</td>\n",
       "      <td>Camera Store</td>\n",
       "      <td>Café</td>\n",
       "      <td>Breakfast Spot</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>Kaushal Puri</td>\n",
       "      <td>26.4678</td>\n",
       "      <td>80.3123</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>Restaurant</td>\n",
       "      <td>Indian Restaurant</td>\n",
       "      <td>Tea Room</td>\n",
       "      <td>Bus Station</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Club House</td>\n",
       "      <td>Clothing Store</td>\n",
       "      <td>Camera Store</td>\n",
       "      <td>Café</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>Kidwai Nagar</td>\n",
       "      <td>26.4275</td>\n",
       "      <td>80.3353</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Market</td>\n",
       "      <td>Tea Room</td>\n",
       "      <td>Convenience Store</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Club House</td>\n",
       "      <td>Clothing Store</td>\n",
       "      <td>Camera Store</td>\n",
       "      <td>Café</td>\n",
       "      <td>Bus Station</td>\n",
       "      <td>Breakfast Spot</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>Lajpat Nagar</td>\n",
       "      <td>26.4688</td>\n",
       "      <td>80.3067</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Convenience Store</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>Indian Restaurant</td>\n",
       "      <td>Bus Station</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Club House</td>\n",
       "      <td>Clothing Store</td>\n",
       "      <td>Camera Store</td>\n",
       "      <td>Café</td>\n",
       "      <td>Tea Room</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>Mall Road</td>\n",
       "      <td>26.4732</td>\n",
       "      <td>80.352</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Cosmetics Shop</td>\n",
       "      <td>Shopping Mall</td>\n",
       "      <td>Dessert Shop</td>\n",
       "      <td>Clothing Store</td>\n",
       "      <td>Pizza Place</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>Multiplex</td>\n",
       "      <td>Café</td>\n",
       "      <td>Bowling Alley</td>\n",
       "      <td>Bus Station</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>Nirala Nagar</td>\n",
       "      <td>26.4426</td>\n",
       "      <td>80.3117</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Bakery</td>\n",
       "      <td>Tea Room</td>\n",
       "      <td>Bus Station</td>\n",
       "      <td>Convenience Store</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Club House</td>\n",
       "      <td>Clothing Store</td>\n",
       "      <td>Camera Store</td>\n",
       "      <td>Café</td>\n",
       "      <td>Breakfast Spot</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>Naubasta</td>\n",
       "      <td>26.4018</td>\n",
       "      <td>80.3145</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Dessert Shop</td>\n",
       "      <td>Tea Room</td>\n",
       "      <td>Convenience Store</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Club House</td>\n",
       "      <td>Clothing Store</td>\n",
       "      <td>Camera Store</td>\n",
       "      <td>Café</td>\n",
       "      <td>Bus Station</td>\n",
       "      <td>Breakfast Spot</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30</td>\n",
       "      <td>Nayaganj</td>\n",
       "      <td>26.4625</td>\n",
       "      <td>80.3534</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Jewelry Store</td>\n",
       "      <td>Breakfast Spot</td>\n",
       "      <td>Boat or Ferry</td>\n",
       "      <td>Bus Station</td>\n",
       "      <td>Convenience Store</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Club House</td>\n",
       "      <td>Clothing Store</td>\n",
       "      <td>Camera Store</td>\n",
       "      <td>Café</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31</td>\n",
       "      <td>Pandu Nagar</td>\n",
       "      <td>26.4733</td>\n",
       "      <td>80.2976</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Awadhi Restaurant</td>\n",
       "      <td>Park</td>\n",
       "      <td>Indian Restaurant</td>\n",
       "      <td>Tea Room</td>\n",
       "      <td>Bus Station</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Club House</td>\n",
       "      <td>Clothing Store</td>\n",
       "      <td>Camera Store</td>\n",
       "      <td>Café</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32</td>\n",
       "      <td>Panki</td>\n",
       "      <td>26.4705</td>\n",
       "      <td>80.2407</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Bus Station</td>\n",
       "      <td>Tea Room</td>\n",
       "      <td>Dessert Shop</td>\n",
       "      <td>Convenience Store</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Club House</td>\n",
       "      <td>Clothing Store</td>\n",
       "      <td>Camera Store</td>\n",
       "      <td>Café</td>\n",
       "      <td>Breakfast Spot</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33</td>\n",
       "      <td>Ratan Lal Nagar</td>\n",
       "      <td>26.4434</td>\n",
       "      <td>80.2865</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Indian Restaurant</td>\n",
       "      <td>Tea Room</td>\n",
       "      <td>Bus Station</td>\n",
       "      <td>Convenience Store</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Club House</td>\n",
       "      <td>Clothing Store</td>\n",
       "      <td>Camera Store</td>\n",
       "      <td>Café</td>\n",
       "      <td>Breakfast Spot</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36</td>\n",
       "      <td>Shastri Nagar</td>\n",
       "      <td>26.4639</td>\n",
       "      <td>80.3006</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Indian Restaurant</td>\n",
       "      <td>Multiplex</td>\n",
       "      <td>Tea Room</td>\n",
       "      <td>Breakfast Spot</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Club House</td>\n",
       "      <td>Clothing Store</td>\n",
       "      <td>Camera Store</td>\n",
       "      <td>Café</td>\n",
       "      <td>Bus Station</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38</td>\n",
       "      <td>Swaroop Nagar</td>\n",
       "      <td>26.4878</td>\n",
       "      <td>80.3206</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Bakery</td>\n",
       "      <td>Furniture / Home Store</td>\n",
       "      <td>Camera Store</td>\n",
       "      <td>Indian Restaurant</td>\n",
       "      <td>Tea Room</td>\n",
       "      <td>Bus Station</td>\n",
       "      <td>Club House</td>\n",
       "      <td>Clothing Store</td>\n",
       "      <td>Café</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39</td>\n",
       "      <td>Shanti Nagar</td>\n",
       "      <td>26.4535</td>\n",
       "      <td>80.3610</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>Indian Restaurant</td>\n",
       "      <td>Tea Room</td>\n",
       "      <td>Bus Station</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Club House</td>\n",
       "      <td>Clothing Store</td>\n",
       "      <td>Camera Store</td>\n",
       "      <td>Café</td>\n",
       "      <td>Breakfast Spot</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40</td>\n",
       "      <td>Sharda Nagar</td>\n",
       "      <td>26.4861</td>\n",
       "      <td>80.2857</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Afghan Restaurant</td>\n",
       "      <td>Tea Room</td>\n",
       "      <td>Bus Station</td>\n",
       "      <td>Convenience Store</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Club House</td>\n",
       "      <td>Clothing Store</td>\n",
       "      <td>Camera Store</td>\n",
       "      <td>Café</td>\n",
       "      <td>Breakfast Spot</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>41</td>\n",
       "      <td>Singhpur</td>\n",
       "      <td>26.5232</td>\n",
       "      <td>80.2586</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Bakery</td>\n",
       "      <td>Tea Room</td>\n",
       "      <td>Bus Station</td>\n",
       "      <td>Convenience Store</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Club House</td>\n",
       "      <td>Clothing Store</td>\n",
       "      <td>Camera Store</td>\n",
       "      <td>Café</td>\n",
       "      <td>Breakfast Spot</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>42</td>\n",
       "      <td>Vikas Nagar</td>\n",
       "      <td>26.4933</td>\n",
       "      <td>80.2847</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Asian Restaurant</td>\n",
       "      <td>Tea Room</td>\n",
       "      <td>Bus Station</td>\n",
       "      <td>Convenience Store</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Club House</td>\n",
       "      <td>Clothing Store</td>\n",
       "      <td>Camera Store</td>\n",
       "      <td>Café</td>\n",
       "      <td>Breakfast Spot</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>43</td>\n",
       "      <td>Vishnupuri</td>\n",
       "      <td>26.4943</td>\n",
       "      <td>80.3164</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Shop &amp; Service</td>\n",
       "      <td>Furniture / Home Store</td>\n",
       "      <td>Tea Room</td>\n",
       "      <td>Breakfast Spot</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Club House</td>\n",
       "      <td>Clothing Store</td>\n",
       "      <td>Camera Store</td>\n",
       "      <td>Café</td>\n",
       "      <td>Bus Station</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>44</td>\n",
       "      <td>Tatya Tope Nagar</td>\n",
       "      <td>26.4342</td>\n",
       "      <td>80.2809</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Breakfast Spot</td>\n",
       "      <td>Dessert Shop</td>\n",
       "      <td>Convenience Store</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Club House</td>\n",
       "      <td>Clothing Store</td>\n",
       "      <td>Camera Store</td>\n",
       "      <td>Café</td>\n",
       "      <td>Bus Station</td>\n",
       "      <td>Tea Room</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>45</td>\n",
       "      <td>Tilak Nagar</td>\n",
       "      <td>26.4896</td>\n",
       "      <td>80.3225</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Bakery</td>\n",
       "      <td>Furniture / Home Store</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>Indian Restaurant</td>\n",
       "      <td>Tea Room</td>\n",
       "      <td>Café</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Club House</td>\n",
       "      <td>Clothing Store</td>\n",
       "      <td>Camera Store</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Neighborhood   Latitude  Longitude  Cluster Labels  \\\n",
       "1              Barra  26.427971  80.296242             4.0   \n",
       "2      Yashoda Nagar    26.4034    80.3368             4.0   \n",
       "3         Arya Nagar    26.4834    80.3217             1.0   \n",
       "4        Ashok Nagar    26.4713    80.3172             3.0   \n",
       "5         Azad Nagar    26.4543    80.2786             1.0   \n",
       "7       Chowk Sarafa    26.4697    80.3496             1.0   \n",
       "9        Civil Lines    26.4750    80.3533             1.0   \n",
       "10     Darshan Purwa    26.4629    80.3200             1.0   \n",
       "11    Defence Colony    26.4276    80.3979             1.0   \n",
       "12         Fazalganj    26.4591    80.3032             5.0   \n",
       "13      Ghumni Bazar    26.4664    80.3506             2.0   \n",
       "14      Govind Nagar     26.442    80.3094             1.0   \n",
       "15      Indira Nagar    26.5090    80.2642             3.0   \n",
       "16   Harjinder Nagar    26.4235    80.3916             1.0   \n",
       "17       Harsh Nagar    26.4729    80.3150             3.0   \n",
       "18     Jawahar Nagar    26.4684    80.3220             1.0   \n",
       "19           Kakadeo    26.4802    80.2907             4.0   \n",
       "20         Kalyanpur    26.5108    80.2469             1.0   \n",
       "21      Kaushal Puri    26.4678    80.3123             3.0   \n",
       "22      Kidwai Nagar    26.4275    80.3353             2.0   \n",
       "24      Lajpat Nagar    26.4688    80.3067             1.0   \n",
       "27         Mall Road    26.4732     80.352             1.0   \n",
       "28      Nirala Nagar    26.4426    80.3117             1.0   \n",
       "29          Naubasta    26.4018    80.3145             1.0   \n",
       "30          Nayaganj    26.4625    80.3534             1.0   \n",
       "31       Pandu Nagar    26.4733    80.2976             1.0   \n",
       "32             Panki    26.4705    80.2407             1.0   \n",
       "33   Ratan Lal Nagar    26.4434    80.2865             5.0   \n",
       "36     Shastri Nagar    26.4639    80.3006             5.0   \n",
       "38     Swaroop Nagar    26.4878    80.3206             1.0   \n",
       "39      Shanti Nagar    26.4535    80.3610             5.0   \n",
       "40      Sharda Nagar    26.4861    80.2857             1.0   \n",
       "41          Singhpur    26.5232    80.2586             1.0   \n",
       "42       Vikas Nagar    26.4933    80.2847             0.0   \n",
       "43        Vishnupuri    26.4943    80.3164             1.0   \n",
       "44  Tatya Tope Nagar    26.4342    80.2809             1.0   \n",
       "45       Tilak Nagar    26.4896    80.3225             1.0   \n",
       "\n",
       "   1st Most Common Venue   2nd Most Common Venue   3rd Most Common Venue  \\\n",
       "1                    ATM            Dessert Shop       Convenience Store   \n",
       "2                    ATM            Dessert Shop       Convenience Store   \n",
       "3      Indian Restaurant    Fast Food Restaurant                  Bakery   \n",
       "4                  Hotel                Tea Room                    Park   \n",
       "5                  Diner            Dessert Shop       Convenience Store   \n",
       "7           Dessert Shop           Shopping Mall          Clothing Store   \n",
       "9   Fast Food Restaurant                   Diner               Multiplex   \n",
       "10     Electronics Store                Tea Room             Bus Station   \n",
       "11                   Spa              Restaurant                  Bakery   \n",
       "12     Indian Restaurant                Tea Room             Bus Station   \n",
       "13                Market                Tea Room       Convenience Store   \n",
       "14                 Plaza                  Bakery                Tea Room   \n",
       "15                 Hotel                Tea Room             Bus Station   \n",
       "16                   Spa                    Café             Bus Station   \n",
       "17                 Hotel                Tea Room                    Park   \n",
       "18  Fast Food Restaurant  Furniture / Home Store                Tea Room   \n",
       "19                   ATM              Restaurant          Breakfast Spot   \n",
       "20            Food Court                Tea Room             Bus Station   \n",
       "21                 Hotel              Restaurant       Indian Restaurant   \n",
       "22                Market                Tea Room       Convenience Store   \n",
       "24     Convenience Store    Fast Food Restaurant       Indian Restaurant   \n",
       "27        Cosmetics Shop           Shopping Mall            Dessert Shop   \n",
       "28                Bakery                Tea Room             Bus Station   \n",
       "29          Dessert Shop                Tea Room       Convenience Store   \n",
       "30         Jewelry Store          Breakfast Spot           Boat or Ferry   \n",
       "31     Awadhi Restaurant                    Park       Indian Restaurant   \n",
       "32           Bus Station                Tea Room            Dessert Shop   \n",
       "33     Indian Restaurant                Tea Room             Bus Station   \n",
       "36     Indian Restaurant               Multiplex                Tea Room   \n",
       "38           Coffee Shop                  Bakery  Furniture / Home Store   \n",
       "39  Fast Food Restaurant       Indian Restaurant                Tea Room   \n",
       "40     Afghan Restaurant                Tea Room             Bus Station   \n",
       "41                Bakery                Tea Room             Bus Station   \n",
       "42      Asian Restaurant                Tea Room             Bus Station   \n",
       "43        Shop & Service  Furniture / Home Store                Tea Room   \n",
       "44        Breakfast Spot            Dessert Shop       Convenience Store   \n",
       "45                Bakery  Furniture / Home Store                   Hotel   \n",
       "\n",
       "   4th Most Common Venue 5th Most Common Venue 6th Most Common Venue  \\\n",
       "1            Coffee Shop            Club House        Clothing Store   \n",
       "2            Coffee Shop            Club House        Clothing Store   \n",
       "3            Coffee Shop            Club House              Tea Room   \n",
       "4         Breakfast Spot           Coffee Shop            Club House   \n",
       "5            Coffee Shop            Club House        Clothing Store   \n",
       "7              Multiplex        Cosmetics Shop           Bus Station   \n",
       "9           Dessert Shop        Clothing Store                  Café   \n",
       "10     Convenience Store           Coffee Shop            Club House   \n",
       "11             Bookstore    Mexican Restaurant              Tea Room   \n",
       "12     Convenience Store           Coffee Shop            Club House   \n",
       "13           Coffee Shop            Club House        Clothing Store   \n",
       "14           Bus Station     Convenience Store           Coffee Shop   \n",
       "15     Convenience Store           Coffee Shop            Club House   \n",
       "16        Cosmetics Shop     Convenience Store           Coffee Shop   \n",
       "17        Breakfast Spot           Coffee Shop            Club House   \n",
       "18           Bus Station           Coffee Shop            Club House   \n",
       "19     Convenience Store           Coffee Shop            Club House   \n",
       "20     Convenience Store           Coffee Shop            Club House   \n",
       "21              Tea Room           Bus Station           Coffee Shop   \n",
       "22           Coffee Shop            Club House        Clothing Store   \n",
       "24           Bus Station           Coffee Shop            Club House   \n",
       "27        Clothing Store           Pizza Place  Fast Food Restaurant   \n",
       "28     Convenience Store           Coffee Shop            Club House   \n",
       "29           Coffee Shop            Club House        Clothing Store   \n",
       "30           Bus Station     Convenience Store           Coffee Shop   \n",
       "31              Tea Room           Bus Station           Coffee Shop   \n",
       "32     Convenience Store           Coffee Shop            Club House   \n",
       "33     Convenience Store           Coffee Shop            Club House   \n",
       "36        Breakfast Spot           Coffee Shop            Club House   \n",
       "38          Camera Store     Indian Restaurant              Tea Room   \n",
       "39           Bus Station           Coffee Shop            Club House   \n",
       "40     Convenience Store           Coffee Shop            Club House   \n",
       "41     Convenience Store           Coffee Shop            Club House   \n",
       "42     Convenience Store           Coffee Shop            Club House   \n",
       "43        Breakfast Spot           Coffee Shop            Club House   \n",
       "44           Coffee Shop            Club House        Clothing Store   \n",
       "45     Indian Restaurant              Tea Room                  Café   \n",
       "\n",
       "   7th Most Common Venue 8th Most Common Venue 9th Most Common Venue  \\\n",
       "1           Camera Store                  Café           Bus Station   \n",
       "2           Camera Store                  Café           Bus Station   \n",
       "3            Bus Station        Clothing Store          Camera Store   \n",
       "4         Clothing Store          Camera Store                  Café   \n",
       "5           Camera Store                  Café           Bus Station   \n",
       "7      Convenience Store           Coffee Shop            Club House   \n",
       "9         Cosmetics Shop           Pizza Place         Shopping Mall   \n",
       "10        Clothing Store          Camera Store                  Café   \n",
       "11           Bus Station           Coffee Shop            Club House   \n",
       "12        Clothing Store          Camera Store                  Café   \n",
       "13          Camera Store                  Café           Bus Station   \n",
       "14            Club House        Clothing Store          Camera Store   \n",
       "15        Clothing Store          Camera Store                  Café   \n",
       "16            Club House        Clothing Store          Camera Store   \n",
       "17        Clothing Store          Camera Store                  Café   \n",
       "18        Clothing Store          Camera Store                  Café   \n",
       "19        Clothing Store          Camera Store                  Café   \n",
       "20        Clothing Store          Camera Store                  Café   \n",
       "21            Club House        Clothing Store          Camera Store   \n",
       "22          Camera Store                  Café           Bus Station   \n",
       "24        Clothing Store          Camera Store                  Café   \n",
       "27             Multiplex                  Café         Bowling Alley   \n",
       "28        Clothing Store          Camera Store                  Café   \n",
       "29          Camera Store                  Café           Bus Station   \n",
       "30            Club House        Clothing Store          Camera Store   \n",
       "31            Club House        Clothing Store          Camera Store   \n",
       "32        Clothing Store          Camera Store                  Café   \n",
       "33        Clothing Store          Camera Store                  Café   \n",
       "36        Clothing Store          Camera Store                  Café   \n",
       "38           Bus Station            Club House        Clothing Store   \n",
       "39        Clothing Store          Camera Store                  Café   \n",
       "40        Clothing Store          Camera Store                  Café   \n",
       "41        Clothing Store          Camera Store                  Café   \n",
       "42        Clothing Store          Camera Store                  Café   \n",
       "43        Clothing Store          Camera Store                  Café   \n",
       "44          Camera Store                  Café           Bus Station   \n",
       "45           Coffee Shop            Club House        Clothing Store   \n",
       "\n",
       "   10th Most Common Venue  \n",
       "1          Breakfast Spot  \n",
       "2          Breakfast Spot  \n",
       "3                    Café  \n",
       "4             Bus Station  \n",
       "5          Breakfast Spot  \n",
       "7            Camera Store  \n",
       "9                  Bakery  \n",
       "10         Breakfast Spot  \n",
       "11         Clothing Store  \n",
       "12         Breakfast Spot  \n",
       "13         Breakfast Spot  \n",
       "14                   Café  \n",
       "15         Breakfast Spot  \n",
       "16               Tea Room  \n",
       "17            Bus Station  \n",
       "18         Breakfast Spot  \n",
       "19            Bus Station  \n",
       "20         Breakfast Spot  \n",
       "21                   Café  \n",
       "22         Breakfast Spot  \n",
       "24               Tea Room  \n",
       "27            Bus Station  \n",
       "28         Breakfast Spot  \n",
       "29         Breakfast Spot  \n",
       "30                   Café  \n",
       "31                   Café  \n",
       "32         Breakfast Spot  \n",
       "33         Breakfast Spot  \n",
       "36            Bus Station  \n",
       "38                   Café  \n",
       "39         Breakfast Spot  \n",
       "40         Breakfast Spot  \n",
       "41         Breakfast Spot  \n",
       "42         Breakfast Spot  \n",
       "43            Bus Station  \n",
       "44               Tea Room  \n",
       "45           Camera Store  "
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_merged = df.join(neighborhoods_venues_sorted.set_index('Neighborhood'), on='Neighborhood')\n",
    "df_merged.dropna(inplace=True)\n",
    "df_merged"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
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   },
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        circle_marker_af3946d0fea54067be72cd6689507646.bindPopup(popup_983f8f2208f24e6caa225c8b59c9d1ea)
        ;

        
    
    
            var circle_marker_26b3745674de44c1a5293c4f4e89cadd = L.circleMarker(
                [26.4034, 80.3368],
                {"bubblingMouseEvents": true, "color": "#b2f396", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#b2f396", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_9f88d3abdb1943ef8e0332ff2e6413f1);
        
    
        var popup_22e5ba5c53604829babe0774b075f4ac = L.popup({"maxWidth": "100%"});

        
            var html_742a8dfcda6640f2bb7e404e585e00e4 = $(`<div id="html_742a8dfcda6640f2bb7e404e585e00e4" style="width: 100.0%; height: 100.0%;">Yashoda Nagar Cluster 4.0</div>`)[0];
            popup_22e5ba5c53604829babe0774b075f4ac.setContent(html_742a8dfcda6640f2bb7e404e585e00e4);
        

        circle_marker_26b3745674de44c1a5293c4f4e89cadd.bindPopup(popup_22e5ba5c53604829babe0774b075f4ac)
        ;

        
    
    
            var circle_marker_a8f5d83882de47879109db88f82f0de4 = L.circleMarker(
                [26.4834, 80.3217],
                {"bubblingMouseEvents": true, "color": "#8000ff", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#8000ff", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_9f88d3abdb1943ef8e0332ff2e6413f1);
        
    
        var popup_5e6abcb186c746dd87e7b42967bffe45 = L.popup({"maxWidth": "100%"});

        
            var html_e1e9e43db9c94d3387ba7ef508e3cda0 = $(`<div id="html_e1e9e43db9c94d3387ba7ef508e3cda0" style="width: 100.0%; height: 100.0%;">Arya Nagar Cluster 1.0</div>`)[0];
            popup_5e6abcb186c746dd87e7b42967bffe45.setContent(html_e1e9e43db9c94d3387ba7ef508e3cda0);
        

        circle_marker_a8f5d83882de47879109db88f82f0de4.bindPopup(popup_5e6abcb186c746dd87e7b42967bffe45)
        ;

        
    
    
            var circle_marker_a76a45d3c62c4a579320c4a65c47a472 = L.circleMarker(
                [26.4713, 80.3172],
                {"bubblingMouseEvents": true, "color": "#4df3ce", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#4df3ce", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_9f88d3abdb1943ef8e0332ff2e6413f1);
        
    
        var popup_e3b921245d34421bb94b0a7cb4d01d46 = L.popup({"maxWidth": "100%"});

        
            var html_f0e64f2b50df4189bbb9a3c45c6561d7 = $(`<div id="html_f0e64f2b50df4189bbb9a3c45c6561d7" style="width: 100.0%; height: 100.0%;">Ashok Nagar Cluster 3.0</div>`)[0];
            popup_e3b921245d34421bb94b0a7cb4d01d46.setContent(html_f0e64f2b50df4189bbb9a3c45c6561d7);
        

        circle_marker_a76a45d3c62c4a579320c4a65c47a472.bindPopup(popup_e3b921245d34421bb94b0a7cb4d01d46)
        ;

        
    
    
            var circle_marker_89bb47046a054b82b5256b14460b26c5 = L.circleMarker(
                [26.4543, 80.2786],
                {"bubblingMouseEvents": true, "color": "#8000ff", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#8000ff", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_9f88d3abdb1943ef8e0332ff2e6413f1);
        
    
        var popup_74081f61791e4ef28a729018775320d9 = L.popup({"maxWidth": "100%"});

        
            var html_691e6a6670364082bc2ff68a0497cf15 = $(`<div id="html_691e6a6670364082bc2ff68a0497cf15" style="width: 100.0%; height: 100.0%;">Azad Nagar Cluster 1.0</div>`)[0];
            popup_74081f61791e4ef28a729018775320d9.setContent(html_691e6a6670364082bc2ff68a0497cf15);
        

        circle_marker_89bb47046a054b82b5256b14460b26c5.bindPopup(popup_74081f61791e4ef28a729018775320d9)
        ;

        
    
    
            var circle_marker_68f9424bfdb34bb9bac5602f42b835c8 = L.circleMarker(
                [26.4697, 80.3496],
                {"bubblingMouseEvents": true, "color": "#8000ff", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#8000ff", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_9f88d3abdb1943ef8e0332ff2e6413f1);
        
    
        var popup_6c7d9cfaa8e8422e9ade5b986220754a = L.popup({"maxWidth": "100%"});

        
            var html_d249b41558e64575818eb75c315f3026 = $(`<div id="html_d249b41558e64575818eb75c315f3026" style="width: 100.0%; height: 100.0%;">Chowk Sarafa Cluster 1.0</div>`)[0];
            popup_6c7d9cfaa8e8422e9ade5b986220754a.setContent(html_d249b41558e64575818eb75c315f3026);
        

        circle_marker_68f9424bfdb34bb9bac5602f42b835c8.bindPopup(popup_6c7d9cfaa8e8422e9ade5b986220754a)
        ;

        
    
    
            var circle_marker_848c6321523a4cd2a9b483ff76953e05 = L.circleMarker(
                [26.475, 80.3533],
                {"bubblingMouseEvents": true, "color": "#8000ff", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#8000ff", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_9f88d3abdb1943ef8e0332ff2e6413f1);
        
    
        var popup_59f1eba5a69846acba17e5f366a6b771 = L.popup({"maxWidth": "100%"});

        
            var html_3723583610844f42a35d40c9489beaf1 = $(`<div id="html_3723583610844f42a35d40c9489beaf1" style="width: 100.0%; height: 100.0%;">Civil Lines Cluster 1.0</div>`)[0];
            popup_59f1eba5a69846acba17e5f366a6b771.setContent(html_3723583610844f42a35d40c9489beaf1);
        

        circle_marker_848c6321523a4cd2a9b483ff76953e05.bindPopup(popup_59f1eba5a69846acba17e5f366a6b771)
        ;

        
    
    
            var circle_marker_bca2adef566e4a2fac424cfc5ff4f657 = L.circleMarker(
                [26.4629, 80.32],
                {"bubblingMouseEvents": true, "color": "#8000ff", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#8000ff", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_9f88d3abdb1943ef8e0332ff2e6413f1);
        
    
        var popup_89b0d299323946b18cbc48e689477169 = L.popup({"maxWidth": "100%"});

        
            var html_bd05b3fa91b94abeb39a7e7e88e583ea = $(`<div id="html_bd05b3fa91b94abeb39a7e7e88e583ea" style="width: 100.0%; height: 100.0%;">Darshan Purwa Cluster 1.0</div>`)[0];
            popup_89b0d299323946b18cbc48e689477169.setContent(html_bd05b3fa91b94abeb39a7e7e88e583ea);
        

        circle_marker_bca2adef566e4a2fac424cfc5ff4f657.bindPopup(popup_89b0d299323946b18cbc48e689477169)
        ;

        
    
    
            var circle_marker_5377ddece06e42a39e6732d8e25916f3 = L.circleMarker(
                [26.4276, 80.3979],
                {"bubblingMouseEvents": true, "color": "#8000ff", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#8000ff", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_9f88d3abdb1943ef8e0332ff2e6413f1);
        
    
        var popup_c52c87715ad1485589d4c7d6f79e20fe = L.popup({"maxWidth": "100%"});

        
            var html_7b810fc5f7374e3692b18612be936b30 = $(`<div id="html_7b810fc5f7374e3692b18612be936b30" style="width: 100.0%; height: 100.0%;">Defence Colony Cluster 1.0</div>`)[0];
            popup_c52c87715ad1485589d4c7d6f79e20fe.setContent(html_7b810fc5f7374e3692b18612be936b30);
        

        circle_marker_5377ddece06e42a39e6732d8e25916f3.bindPopup(popup_c52c87715ad1485589d4c7d6f79e20fe)
        ;

        
    
    
            var circle_marker_7479e8d8d26f46ff94618e56d0997313 = L.circleMarker(
                [26.4591, 80.3032],
                {"bubblingMouseEvents": true, "color": "#ff964f", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff964f", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_9f88d3abdb1943ef8e0332ff2e6413f1);
        
    
        var popup_8c8f3cf42d484de7843b17fc64c03033 = L.popup({"maxWidth": "100%"});

        
            var html_c0497ce32d2c460394835e7d3e1b3462 = $(`<div id="html_c0497ce32d2c460394835e7d3e1b3462" style="width: 100.0%; height: 100.0%;">Fazalganj Cluster 5.0</div>`)[0];
            popup_8c8f3cf42d484de7843b17fc64c03033.setContent(html_c0497ce32d2c460394835e7d3e1b3462);
        

        circle_marker_7479e8d8d26f46ff94618e56d0997313.bindPopup(popup_8c8f3cf42d484de7843b17fc64c03033)
        ;

        
    
    
            var circle_marker_d6ae172c1b234e9b9280555660ac7f39 = L.circleMarker(
                [26.4664, 80.3506],
                {"bubblingMouseEvents": true, "color": "#1996f3", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#1996f3", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_9f88d3abdb1943ef8e0332ff2e6413f1);
        
    
        var popup_d84a85d6ff88477093e95a65709abe7b = L.popup({"maxWidth": "100%"});

        
            var html_84f285be47c4433083ec22faa2180fff = $(`<div id="html_84f285be47c4433083ec22faa2180fff" style="width: 100.0%; height: 100.0%;">Ghumni Bazar Cluster 2.0</div>`)[0];
            popup_d84a85d6ff88477093e95a65709abe7b.setContent(html_84f285be47c4433083ec22faa2180fff);
        

        circle_marker_d6ae172c1b234e9b9280555660ac7f39.bindPopup(popup_d84a85d6ff88477093e95a65709abe7b)
        ;

        
    
    
            var circle_marker_5b2cc8ccfa444e26a6230c21783971ae = L.circleMarker(
                [26.442, 80.3094],
                {"bubblingMouseEvents": true, "color": "#8000ff", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#8000ff", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_9f88d3abdb1943ef8e0332ff2e6413f1);
        
    
        var popup_005eac110c56472a88718cc713b2acad = L.popup({"maxWidth": "100%"});

        
            var html_600ad0a3f97f499d9e0c3f0d5da34c8f = $(`<div id="html_600ad0a3f97f499d9e0c3f0d5da34c8f" style="width: 100.0%; height: 100.0%;">Govind Nagar Cluster 1.0</div>`)[0];
            popup_005eac110c56472a88718cc713b2acad.setContent(html_600ad0a3f97f499d9e0c3f0d5da34c8f);
        

        circle_marker_5b2cc8ccfa444e26a6230c21783971ae.bindPopup(popup_005eac110c56472a88718cc713b2acad)
        ;

        
    
    
            var circle_marker_4c4a209eaea84c8aa10bb975549aec39 = L.circleMarker(
                [26.509, 80.2642],
                {"bubblingMouseEvents": true, "color": "#4df3ce", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#4df3ce", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_9f88d3abdb1943ef8e0332ff2e6413f1);
        
    
        var popup_0d108df81261455cb751f9288d4c4c1d = L.popup({"maxWidth": "100%"});

        
            var html_2dcd53914b1c46d9bd4e66e3019631ff = $(`<div id="html_2dcd53914b1c46d9bd4e66e3019631ff" style="width: 100.0%; height: 100.0%;">Indira Nagar Cluster 3.0</div>`)[0];
            popup_0d108df81261455cb751f9288d4c4c1d.setContent(html_2dcd53914b1c46d9bd4e66e3019631ff);
        

        circle_marker_4c4a209eaea84c8aa10bb975549aec39.bindPopup(popup_0d108df81261455cb751f9288d4c4c1d)
        ;

        
    
    
            var circle_marker_80e8c41822a04ac4ae4695eedc41fd5a = L.circleMarker(
                [26.4235, 80.3916],
                {"bubblingMouseEvents": true, "color": "#8000ff", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#8000ff", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_9f88d3abdb1943ef8e0332ff2e6413f1);
        
    
        var popup_04268da0e80f4bc9a96ef9605c30d82a = L.popup({"maxWidth": "100%"});

        
            var html_031fcb02e43341aca7e70d43069c9e36 = $(`<div id="html_031fcb02e43341aca7e70d43069c9e36" style="width: 100.0%; height: 100.0%;">Harjinder Nagar Cluster 1.0</div>`)[0];
            popup_04268da0e80f4bc9a96ef9605c30d82a.setContent(html_031fcb02e43341aca7e70d43069c9e36);
        

        circle_marker_80e8c41822a04ac4ae4695eedc41fd5a.bindPopup(popup_04268da0e80f4bc9a96ef9605c30d82a)
        ;

        
    
    
            var circle_marker_27681e3eefca4a2d9bd3635959f6b836 = L.circleMarker(
                [26.4729, 80.315],
                {"bubblingMouseEvents": true, "color": "#4df3ce", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#4df3ce", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_9f88d3abdb1943ef8e0332ff2e6413f1);
        
    
        var popup_b1b7e0bd87d7444eb34d6eb4c6d3da1a = L.popup({"maxWidth": "100%"});

        
            var html_373e83581be647928fbef0c618d859b3 = $(`<div id="html_373e83581be647928fbef0c618d859b3" style="width: 100.0%; height: 100.0%;">Harsh Nagar Cluster 3.0</div>`)[0];
            popup_b1b7e0bd87d7444eb34d6eb4c6d3da1a.setContent(html_373e83581be647928fbef0c618d859b3);
        

        circle_marker_27681e3eefca4a2d9bd3635959f6b836.bindPopup(popup_b1b7e0bd87d7444eb34d6eb4c6d3da1a)
        ;

        
    
    
            var circle_marker_852817d117b34e48babbe8266cb5c91a = L.circleMarker(
                [26.4684, 80.322],
                {"bubblingMouseEvents": true, "color": "#8000ff", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#8000ff", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_9f88d3abdb1943ef8e0332ff2e6413f1);
        
    
        var popup_f659f905a45e48a3ab4b1d456d0b9498 = L.popup({"maxWidth": "100%"});

        
            var html_bf2ff5947a9e440db8d3f3991d65422c = $(`<div id="html_bf2ff5947a9e440db8d3f3991d65422c" style="width: 100.0%; height: 100.0%;">Jawahar Nagar Cluster 1.0</div>`)[0];
            popup_f659f905a45e48a3ab4b1d456d0b9498.setContent(html_bf2ff5947a9e440db8d3f3991d65422c);
        

        circle_marker_852817d117b34e48babbe8266cb5c91a.bindPopup(popup_f659f905a45e48a3ab4b1d456d0b9498)
        ;

        
    
    
            var circle_marker_77d6c39b4abe43569453cc02e732fc31 = L.circleMarker(
                [26.4802, 80.2907],
                {"bubblingMouseEvents": true, "color": "#b2f396", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#b2f396", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_9f88d3abdb1943ef8e0332ff2e6413f1);
        
    
        var popup_509f1741b83f42ed83e6ee2fc99c0e7b = L.popup({"maxWidth": "100%"});

        
            var html_937fdae7aa0941e69109cd4b89532d26 = $(`<div id="html_937fdae7aa0941e69109cd4b89532d26" style="width: 100.0%; height: 100.0%;">Kakadeo Cluster 4.0</div>`)[0];
            popup_509f1741b83f42ed83e6ee2fc99c0e7b.setContent(html_937fdae7aa0941e69109cd4b89532d26);
        

        circle_marker_77d6c39b4abe43569453cc02e732fc31.bindPopup(popup_509f1741b83f42ed83e6ee2fc99c0e7b)
        ;

        
    
    
            var circle_marker_339643d9990f47e0ad50d448c39b4042 = L.circleMarker(
                [26.5108, 80.2469],
                {"bubblingMouseEvents": true, "color": "#8000ff", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#8000ff", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_9f88d3abdb1943ef8e0332ff2e6413f1);
        
    
        var popup_a6dab92cd4984600adb3d05be17ab7bc = L.popup({"maxWidth": "100%"});

        
            var html_d264ab55dbd84592af556a4b4f556ea5 = $(`<div id="html_d264ab55dbd84592af556a4b4f556ea5" style="width: 100.0%; height: 100.0%;">Kalyanpur Cluster 1.0</div>`)[0];
            popup_a6dab92cd4984600adb3d05be17ab7bc.setContent(html_d264ab55dbd84592af556a4b4f556ea5);
        

        circle_marker_339643d9990f47e0ad50d448c39b4042.bindPopup(popup_a6dab92cd4984600adb3d05be17ab7bc)
        ;

        
    
    
            var circle_marker_bb7d464f460c4993a4173f61acc3494e = L.circleMarker(
                [26.4678, 80.3123],
                {"bubblingMouseEvents": true, "color": "#4df3ce", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#4df3ce", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_9f88d3abdb1943ef8e0332ff2e6413f1);
        
    
        var popup_8fe50f42c9ff41acb2b0eb162a16e87c = L.popup({"maxWidth": "100%"});

        
            var html_0fc1b170307f445aa53a5a3f27d7f5e1 = $(`<div id="html_0fc1b170307f445aa53a5a3f27d7f5e1" style="width: 100.0%; height: 100.0%;">Kaushal Puri Cluster 3.0</div>`)[0];
            popup_8fe50f42c9ff41acb2b0eb162a16e87c.setContent(html_0fc1b170307f445aa53a5a3f27d7f5e1);
        

        circle_marker_bb7d464f460c4993a4173f61acc3494e.bindPopup(popup_8fe50f42c9ff41acb2b0eb162a16e87c)
        ;

        
    
    
            var circle_marker_676fb73bab334814b837e62ba7221b6c = L.circleMarker(
                [26.4275, 80.3353],
                {"bubblingMouseEvents": true, "color": "#1996f3", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#1996f3", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_9f88d3abdb1943ef8e0332ff2e6413f1);
        
    
        var popup_3a3702afc4124696b9a620e1e57a11b8 = L.popup({"maxWidth": "100%"});

        
            var html_d6bd1da88b16460fb153d31354e45268 = $(`<div id="html_d6bd1da88b16460fb153d31354e45268" style="width: 100.0%; height: 100.0%;">Kidwai Nagar Cluster 2.0</div>`)[0];
            popup_3a3702afc4124696b9a620e1e57a11b8.setContent(html_d6bd1da88b16460fb153d31354e45268);
        

        circle_marker_676fb73bab334814b837e62ba7221b6c.bindPopup(popup_3a3702afc4124696b9a620e1e57a11b8)
        ;

        
    
    
            var circle_marker_239b800277244a8bb01c4f26e7b990a8 = L.circleMarker(
                [26.4688, 80.3067],
                {"bubblingMouseEvents": true, "color": "#8000ff", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#8000ff", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_9f88d3abdb1943ef8e0332ff2e6413f1);
        
    
        var popup_a6f9308cd2d54f0b993964c23df78893 = L.popup({"maxWidth": "100%"});

        
            var html_ec876326dd244f4780151b55e7adc400 = $(`<div id="html_ec876326dd244f4780151b55e7adc400" style="width: 100.0%; height: 100.0%;">Lajpat Nagar Cluster 1.0</div>`)[0];
            popup_a6f9308cd2d54f0b993964c23df78893.setContent(html_ec876326dd244f4780151b55e7adc400);
        

        circle_marker_239b800277244a8bb01c4f26e7b990a8.bindPopup(popup_a6f9308cd2d54f0b993964c23df78893)
        ;

        
    
    
            var circle_marker_3f980e3a349c470ab6af1926617ff908 = L.circleMarker(
                [26.4732, 80.352],
                {"bubblingMouseEvents": true, "color": "#8000ff", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#8000ff", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_9f88d3abdb1943ef8e0332ff2e6413f1);
        
    
        var popup_fcdae5acd6804e4e99b9912a148572f9 = L.popup({"maxWidth": "100%"});

        
            var html_e4cae3b8a525465982fd2248d0588e28 = $(`<div id="html_e4cae3b8a525465982fd2248d0588e28" style="width: 100.0%; height: 100.0%;">Mall Road Cluster 1.0</div>`)[0];
            popup_fcdae5acd6804e4e99b9912a148572f9.setContent(html_e4cae3b8a525465982fd2248d0588e28);
        

        circle_marker_3f980e3a349c470ab6af1926617ff908.bindPopup(popup_fcdae5acd6804e4e99b9912a148572f9)
        ;

        
    
    
            var circle_marker_d63c1fce972845bd916d34187e8a3dfc = L.circleMarker(
                [26.4426, 80.3117],
                {"bubblingMouseEvents": true, "color": "#8000ff", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#8000ff", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_9f88d3abdb1943ef8e0332ff2e6413f1);
        
    
        var popup_063b2265aa7c4329ae11d4fb557c4dd1 = L.popup({"maxWidth": "100%"});

        
            var html_404c383c48154ba8aaf5d9e27f3df86b = $(`<div id="html_404c383c48154ba8aaf5d9e27f3df86b" style="width: 100.0%; height: 100.0%;">Nirala Nagar Cluster 1.0</div>`)[0];
            popup_063b2265aa7c4329ae11d4fb557c4dd1.setContent(html_404c383c48154ba8aaf5d9e27f3df86b);
        

        circle_marker_d63c1fce972845bd916d34187e8a3dfc.bindPopup(popup_063b2265aa7c4329ae11d4fb557c4dd1)
        ;

        
    
    
            var circle_marker_42ca45d92f8a45abaf84c09b2696e338 = L.circleMarker(
                [26.4018, 80.3145],
                {"bubblingMouseEvents": true, "color": "#8000ff", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#8000ff", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_9f88d3abdb1943ef8e0332ff2e6413f1);
        
    
        var popup_a5ac9c02b7af449c88ccd785dc310636 = L.popup({"maxWidth": "100%"});

        
            var html_5c8ddded65514558aa1c34bb65b1d5c3 = $(`<div id="html_5c8ddded65514558aa1c34bb65b1d5c3" style="width: 100.0%; height: 100.0%;">Naubasta Cluster 1.0</div>`)[0];
            popup_a5ac9c02b7af449c88ccd785dc310636.setContent(html_5c8ddded65514558aa1c34bb65b1d5c3);
        

        circle_marker_42ca45d92f8a45abaf84c09b2696e338.bindPopup(popup_a5ac9c02b7af449c88ccd785dc310636)
        ;

        
    
    
            var circle_marker_5339951f568141f3b786a30a991f1a9a = L.circleMarker(
                [26.4625, 80.3534],
                {"bubblingMouseEvents": true, "color": "#8000ff", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#8000ff", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_9f88d3abdb1943ef8e0332ff2e6413f1);
        
    
        var popup_544820f415cb4037a65693db71b8ca50 = L.popup({"maxWidth": "100%"});

        
            var html_eb5e31451da44aec95a550b0a07076fe = $(`<div id="html_eb5e31451da44aec95a550b0a07076fe" style="width: 100.0%; height: 100.0%;">Nayaganj Cluster 1.0</div>`)[0];
            popup_544820f415cb4037a65693db71b8ca50.setContent(html_eb5e31451da44aec95a550b0a07076fe);
        

        circle_marker_5339951f568141f3b786a30a991f1a9a.bindPopup(popup_544820f415cb4037a65693db71b8ca50)
        ;

        
    
    
            var circle_marker_5bfb6b4dc5474b3b8030c244a54b8d18 = L.circleMarker(
                [26.4733, 80.2976],
                {"bubblingMouseEvents": true, "color": "#8000ff", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#8000ff", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_9f88d3abdb1943ef8e0332ff2e6413f1);
        
    
        var popup_0833ad8edd054951942cb2314d996bc9 = L.popup({"maxWidth": "100%"});

        
            var html_425af57e878746e5b02f2289b579f2a2 = $(`<div id="html_425af57e878746e5b02f2289b579f2a2" style="width: 100.0%; height: 100.0%;">Pandu Nagar Cluster 1.0</div>`)[0];
            popup_0833ad8edd054951942cb2314d996bc9.setContent(html_425af57e878746e5b02f2289b579f2a2);
        

        circle_marker_5bfb6b4dc5474b3b8030c244a54b8d18.bindPopup(popup_0833ad8edd054951942cb2314d996bc9)
        ;

        
    
    
            var circle_marker_fa09db0afeb34f559ce46064bf1e3135 = L.circleMarker(
                [26.4705, 80.2407],
                {"bubblingMouseEvents": true, "color": "#8000ff", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#8000ff", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_9f88d3abdb1943ef8e0332ff2e6413f1);
        
    
        var popup_2a2475fcc37d450ca09c99b37c86f2f7 = L.popup({"maxWidth": "100%"});

        
            var html_169a12e43d8e4e878f9f29d6d0697d27 = $(`<div id="html_169a12e43d8e4e878f9f29d6d0697d27" style="width: 100.0%; height: 100.0%;">Panki Cluster 1.0</div>`)[0];
            popup_2a2475fcc37d450ca09c99b37c86f2f7.setContent(html_169a12e43d8e4e878f9f29d6d0697d27);
        

        circle_marker_fa09db0afeb34f559ce46064bf1e3135.bindPopup(popup_2a2475fcc37d450ca09c99b37c86f2f7)
        ;

        
    
    
            var circle_marker_3b4b8de37ae448fcae16c551b67c9eda = L.circleMarker(
                [26.4434, 80.2865],
                {"bubblingMouseEvents": true, "color": "#ff964f", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff964f", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_9f88d3abdb1943ef8e0332ff2e6413f1);
        
    
        var popup_91c63d2f40ed4efc846ab6d9f3e8c949 = L.popup({"maxWidth": "100%"});

        
            var html_0e3932e924eb4d15b3c90371d1a88b22 = $(`<div id="html_0e3932e924eb4d15b3c90371d1a88b22" style="width: 100.0%; height: 100.0%;">Ratan Lal Nagar Cluster 5.0</div>`)[0];
            popup_91c63d2f40ed4efc846ab6d9f3e8c949.setContent(html_0e3932e924eb4d15b3c90371d1a88b22);
        

        circle_marker_3b4b8de37ae448fcae16c551b67c9eda.bindPopup(popup_91c63d2f40ed4efc846ab6d9f3e8c949)
        ;

        
    
    
            var circle_marker_5f658e9d941241f8ae0a155d43ba3771 = L.circleMarker(
                [26.4639, 80.3006],
                {"bubblingMouseEvents": true, "color": "#ff964f", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff964f", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_9f88d3abdb1943ef8e0332ff2e6413f1);
        
    
        var popup_f55cba892a664803affa364e59e9f1f0 = L.popup({"maxWidth": "100%"});

        
            var html_083236d5c1674aa1bba569ef903610fd = $(`<div id="html_083236d5c1674aa1bba569ef903610fd" style="width: 100.0%; height: 100.0%;">Shastri Nagar Cluster 5.0</div>`)[0];
            popup_f55cba892a664803affa364e59e9f1f0.setContent(html_083236d5c1674aa1bba569ef903610fd);
        

        circle_marker_5f658e9d941241f8ae0a155d43ba3771.bindPopup(popup_f55cba892a664803affa364e59e9f1f0)
        ;

        
    
    
            var circle_marker_aff209b354fe486fa1cb5634da09faf5 = L.circleMarker(
                [26.4878, 80.3206],
                {"bubblingMouseEvents": true, "color": "#8000ff", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#8000ff", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_9f88d3abdb1943ef8e0332ff2e6413f1);
        
    
        var popup_1d027b0f4db7474b9beb3a3baf4732cd = L.popup({"maxWidth": "100%"});

        
            var html_e42271dcbb2644748f48399229e34ab8 = $(`<div id="html_e42271dcbb2644748f48399229e34ab8" style="width: 100.0%; height: 100.0%;">Swaroop Nagar Cluster 1.0</div>`)[0];
            popup_1d027b0f4db7474b9beb3a3baf4732cd.setContent(html_e42271dcbb2644748f48399229e34ab8);
        

        circle_marker_aff209b354fe486fa1cb5634da09faf5.bindPopup(popup_1d027b0f4db7474b9beb3a3baf4732cd)
        ;

        
    
    
            var circle_marker_148a77da3e284222aa0bce9762384161 = L.circleMarker(
                [26.4535, 80.361],
                {"bubblingMouseEvents": true, "color": "#ff964f", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff964f", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_9f88d3abdb1943ef8e0332ff2e6413f1);
        
    
        var popup_a9da022cced74273a05bb4b7e087eefa = L.popup({"maxWidth": "100%"});

        
            var html_5eabf71db6b14052821384a0748d47ed = $(`<div id="html_5eabf71db6b14052821384a0748d47ed" style="width: 100.0%; height: 100.0%;">Shanti Nagar Cluster 5.0</div>`)[0];
            popup_a9da022cced74273a05bb4b7e087eefa.setContent(html_5eabf71db6b14052821384a0748d47ed);
        

        circle_marker_148a77da3e284222aa0bce9762384161.bindPopup(popup_a9da022cced74273a05bb4b7e087eefa)
        ;

        
    
    
            var circle_marker_30c7a0f7460142c9877fb73a79c20d1c = L.circleMarker(
                [26.4861, 80.2857],
                {"bubblingMouseEvents": true, "color": "#8000ff", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#8000ff", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_9f88d3abdb1943ef8e0332ff2e6413f1);
        
    
        var popup_a6088e6d4aa34cfaadc6545c00fcab00 = L.popup({"maxWidth": "100%"});

        
            var html_d1629f72aab84661a373dbea95d069a7 = $(`<div id="html_d1629f72aab84661a373dbea95d069a7" style="width: 100.0%; height: 100.0%;">Sharda Nagar Cluster 1.0</div>`)[0];
            popup_a6088e6d4aa34cfaadc6545c00fcab00.setContent(html_d1629f72aab84661a373dbea95d069a7);
        

        circle_marker_30c7a0f7460142c9877fb73a79c20d1c.bindPopup(popup_a6088e6d4aa34cfaadc6545c00fcab00)
        ;

        
    
    
            var circle_marker_92276a7f8865486db27d156f406a15ce = L.circleMarker(
                [26.5232, 80.2586],
                {"bubblingMouseEvents": true, "color": "#8000ff", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#8000ff", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_9f88d3abdb1943ef8e0332ff2e6413f1);
        
    
        var popup_32da003c7b5f494c938b750f481dfd59 = L.popup({"maxWidth": "100%"});

        
            var html_4176b477121d4176b332d945ee4a5877 = $(`<div id="html_4176b477121d4176b332d945ee4a5877" style="width: 100.0%; height: 100.0%;">Singhpur Cluster 1.0</div>`)[0];
            popup_32da003c7b5f494c938b750f481dfd59.setContent(html_4176b477121d4176b332d945ee4a5877);
        

        circle_marker_92276a7f8865486db27d156f406a15ce.bindPopup(popup_32da003c7b5f494c938b750f481dfd59)
        ;

        
    
    
            var circle_marker_e021e094af9c40e49a0092c2bdac76ba = L.circleMarker(
                [26.4933, 80.2847],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_9f88d3abdb1943ef8e0332ff2e6413f1);
        
    
        var popup_8cdd21f3a1f94df799cafe54ef257a81 = L.popup({"maxWidth": "100%"});

        
            var html_6e3420d1e15243e4a74ea0bfa5c531a0 = $(`<div id="html_6e3420d1e15243e4a74ea0bfa5c531a0" style="width: 100.0%; height: 100.0%;">Vikas Nagar Cluster 0.0</div>`)[0];
            popup_8cdd21f3a1f94df799cafe54ef257a81.setContent(html_6e3420d1e15243e4a74ea0bfa5c531a0);
        

        circle_marker_e021e094af9c40e49a0092c2bdac76ba.bindPopup(popup_8cdd21f3a1f94df799cafe54ef257a81)
        ;

        
    
    
            var circle_marker_eca38bf0cd524113841683d8eb5fc343 = L.circleMarker(
                [26.4943, 80.3164],
                {"bubblingMouseEvents": true, "color": "#8000ff", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#8000ff", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_9f88d3abdb1943ef8e0332ff2e6413f1);
        
    
        var popup_9cbd0a450c804636bbbbdbd6468a0d28 = L.popup({"maxWidth": "100%"});

        
            var html_4fe31f57400e4982bb65df090758e07a = $(`<div id="html_4fe31f57400e4982bb65df090758e07a" style="width: 100.0%; height: 100.0%;">Vishnupuri Cluster 1.0</div>`)[0];
            popup_9cbd0a450c804636bbbbdbd6468a0d28.setContent(html_4fe31f57400e4982bb65df090758e07a);
        

        circle_marker_eca38bf0cd524113841683d8eb5fc343.bindPopup(popup_9cbd0a450c804636bbbbdbd6468a0d28)
        ;

        
    
    
            var circle_marker_f92cf60c1ff748488ff76e7665460a96 = L.circleMarker(
                [26.4342, 80.2809],
                {"bubblingMouseEvents": true, "color": "#8000ff", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#8000ff", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_9f88d3abdb1943ef8e0332ff2e6413f1);
        
    
        var popup_bf380e189a7040f58bba5574aa379ce0 = L.popup({"maxWidth": "100%"});

        
            var html_685a7e0b6a9d43d8966f36eed86927e9 = $(`<div id="html_685a7e0b6a9d43d8966f36eed86927e9" style="width: 100.0%; height: 100.0%;">Tatya Tope Nagar Cluster 1.0</div>`)[0];
            popup_bf380e189a7040f58bba5574aa379ce0.setContent(html_685a7e0b6a9d43d8966f36eed86927e9);
        

        circle_marker_f92cf60c1ff748488ff76e7665460a96.bindPopup(popup_bf380e189a7040f58bba5574aa379ce0)
        ;

        
    
    
            var circle_marker_e23fe765d7064e37bb494859759c012c = L.circleMarker(
                [26.4896, 80.3225],
                {"bubblingMouseEvents": true, "color": "#8000ff", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#8000ff", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_9f88d3abdb1943ef8e0332ff2e6413f1);
        
    
        var popup_835d500af9204a4ea59fecc9cf397f68 = L.popup({"maxWidth": "100%"});

        
            var html_d5444bf546924c03ab4791a60ef8df6e = $(`<div id="html_d5444bf546924c03ab4791a60ef8df6e" style="width: 100.0%; height: 100.0%;">Tilak Nagar Cluster 1.0</div>`)[0];
            popup_835d500af9204a4ea59fecc9cf397f68.setContent(html_d5444bf546924c03ab4791a60ef8df6e);
        

        circle_marker_e23fe765d7064e37bb494859759c012c.bindPopup(popup_835d500af9204a4ea59fecc9cf397f68)
        ;

        
    
</script>\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
      ],
      "text/plain": [
       "<folium.folium.Map at 0x7ffa7a757cc0>"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "map_clusters = folium.Map(location=[26.4499, 80.3319], zoom_start=12)\n",
    "\n",
    "# set color scheme for the clusters\n",
    "x = np.arange(kclusters)\n",
    "ys = [i + x + (i*x)**2 for i in range(kclusters)]\n",
    "colors_array = cm.rainbow(np.linspace(0, 1, len(ys)))\n",
    "rainbow = [colors.rgb2hex(i) for i in colors_array]\n",
    "\n",
    "# add markers to the map\n",
    "markers_colors = []\n",
    "for lat, lon, poi, cluster in zip(df_merged['Latitude'], df_merged['Longitude'], df_merged['Neighborhood'], df_merged['Cluster Labels']):\n",
    "    label = folium.Popup(str(poi) + ' Cluster ' + str(cluster), parse_html=True)\n",
    "    folium.CircleMarker(\n",
    "        [lat, lon],\n",
    "        radius=5,\n",
    "        popup=label,\n",
    "        color=rainbow[int(cluster-1)],\n",
    "        fill=True,\n",
    "        fill_color=rainbow[int(cluster-1)],\n",
    "        fill_opacity=0.7).add_to(map_clusters)\n",
    "       \n",
    "map_clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Arya Nagar', 'Azad Nagar', 'Chowk Sarafa', 'Civil Lines',\n",
       "       'Darshan Purwa', 'Defence Colony', 'Govind Nagar',\n",
       "       'Harjinder Nagar', 'Jawahar Nagar', 'Kalyanpur', 'Lajpat Nagar',\n",
       "       'Mall Road', 'Nirala Nagar', 'Naubasta', 'Nayaganj', 'Pandu Nagar',\n",
       "       'Panki', 'Swaroop Nagar', 'Sharda Nagar', 'Singhpur', 'Vishnupuri',\n",
       "       'Tatya Tope Nagar', 'Tilak Nagar'], dtype=object)"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_merged[df_merged[\"Cluster Labels\"] == 1.0][\"Neighborhood\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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