capstone final project.ipynb

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    "# Battle of Neighborhoods\n",
    "\n",
    "# Scarborough, Canada\n",
    "\n",
    "## Check Average Prices and School Ratings "
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**The Location:**\n",
    "Scarborough is a popular destination for new immigrants in Canada to reside. As a result, it is one of the most diverse and multicultural areas in the Greater Toronto Area, being home to various religious groups and places of worship.\n",
    "\n",
    "\n",
    "**Project Description:**\n",
    "The projects aim to create an  analysis of features for a neighborhood or a comparative analysis between neighborhoods with just sending out the names of the neighborhoods. The features include like population rate, median house price, school ratings, crime rates, weather conditions, recreational facilities. This would help people to get awareness of the places before moving to a new country, state, city or place for their work or to start a new life\n",
    "\n",
    "This Project would help users take a better decision on choosing the best neighborhood out of many neighborhoods to move into NewYork city based on the distribution of various facilities in and around that neighborhood. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Python packages and Dependencies"
   ]
  },
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   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json # library to handle JSON files\n",
    "import xml\n",
    "import pandas as pd #Python library data manipulation and analysis\n",
    "pd.set_option('display.max_columns', None)\n",
    "pd.set_option('display.max_rows', None)\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "from geopy.geocoders import Nominatim # convert an address into latitude and longitude values\n",
    "\n",
    "from pandas.io.json import json_normalize # tranform JSON file into a pandas dataframe\n",
    "\n",
    "# Matplotlib and associated plotting modules\n",
    "import matplotlib.cm as cm\n",
    "import matplotlib.colors as colors\n",
    "\n",
    "# import k-means from clustering stage\n",
    "from sklearn.cluster import KMeans\n",
    "\n",
    "import folium # map rendering library\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Import Data"
   ]
  },
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   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
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     "name": "stdout",
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     "text": [
      "(17, 5)\n"
     ]
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Postalcode</th>\n",
       "      <th>Borough</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>M1B</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Rouge, Malvern</td>\n",
       "      <td>43.811650</td>\n",
       "      <td>-79.195561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M1C</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Highland Creek, Rouge Hill, Port Union</td>\n",
       "      <td>43.785605</td>\n",
       "      <td>-79.158701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>M1E</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Guildwood, Morningside, West Hill</td>\n",
       "      <td>43.765690</td>\n",
       "      <td>-79.175299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M1G</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Woburn</td>\n",
       "      <td>43.768216</td>\n",
       "      <td>-79.217610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M1H</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Cedarbrae</td>\n",
       "      <td>43.769608</td>\n",
       "      <td>-79.239440</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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       "  Postalcode      Borough                            Neighborhood   Latitude  \\\n",
       "0        M1B  Scarborough                          Rouge, Malvern  43.811650   \n",
       "1        M1C  Scarborough  Highland Creek, Rouge Hill, Port Union  43.785605   \n",
       "2        M1E  Scarborough       Guildwood, Morningside, West Hill  43.765690   \n",
       "3        M1G  Scarborough                                  Woburn  43.768216   \n",
       "4        M1H  Scarborough                               Cedarbrae  43.769608   \n",
       "\n",
       "   Longitude  \n",
       "0 -79.195561  \n",
       "1 -79.158701  \n",
       "2 -79.175299  \n",
       "3 -79.217610  \n",
       "4 -79.239440  "
      ]
     },
     "execution_count": 101,
     "metadata": {},
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    }
   ],
   "source": [
    "df=pd.read_csv('data1.csv')\n",
    "print(df.shape)\n",
    "df.head(5)"
   ]
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   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
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     "name": "stdout",
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     "text": [
      "The geograpical coordinate of Seattle,Washington are 43.773077, -79.257774.\n"
     ]
    }
   ],
   "source": [
    "address = 'Scarborough,Toronto'\n",
    "\n",
    "geolocator = Nominatim()\n",
    "location = geolocator.geocode(address)\n",
    "latitude_x = location.latitude\n",
    "longitude_y = location.longitude\n",
    "print('The geograpical coordinate of Seattle,Washington are {}, {}.'.format(latitude_x, longitude_y))"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Map Scarborough Canada"
   ]
  },
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   "cell_type": "code",
   "execution_count": 173,
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  "lineJoin": "round",
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            var popup_dbd2ec9d3a6e4c08b5b5d8acedd21bc7 = L.popup({maxWidth: '300'});

            
                var html_3d0f12b8e1e6445e8b06ee9d0d2377da = $('<div id="html_3d0f12b8e1e6445e8b06ee9d0d2377da" style="width: 100.0%; height: 100.0%;">Dorset Park, Scarborough Town Centre, Wexford Heights</div>')[0];
                popup_dbd2ec9d3a6e4c08b5b5d8acedd21bc7.setContent(html_3d0f12b8e1e6445e8b06ee9d0d2377da);
            

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            var popup_de98be9964a84fdb8d955f5b056a565b = L.popup({maxWidth: '300'});

            
                var html_e0428d454244488ca4492ad42364f2fb = $('<div id="html_e0428d454244488ca4492ad42364f2fb" style="width: 100.0%; height: 100.0%;">Maryvale, Wexford</div>')[0];
                popup_de98be9964a84fdb8d955f5b056a565b.setContent(html_e0428d454244488ca4492ad42364f2fb);
            

            circle_marker_0ae563f20c104d0b9f340065690f4884.bindPopup(popup_de98be9964a84fdb8d955f5b056a565b);

            
        
    
            var circle_marker_ecd1d2de0c72487381aa7181f3d2d917 = L.circleMarker(
                [43.79394,-79.26797614],
                {
  "bubblingMouseEvents": true,
  "color": "blue",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "#3186cc",
  "fillOpacity": 0.7,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_61363faaf49546baa0ae038a2e7eea74);
            
    
            var popup_86be5a1977464c17824bd154b96f0e8b = L.popup({maxWidth: '300'});

            
                var html_879f7bd9b75d4b3fbc4ba7085edba272 = $('<div id="html_879f7bd9b75d4b3fbc4ba7085edba272" style="width: 100.0%; height: 100.0%;">Agincourt</div>')[0];
                popup_86be5a1977464c17824bd154b96f0e8b.setContent(html_879f7bd9b75d4b3fbc4ba7085edba272);
            

            circle_marker_ecd1d2de0c72487381aa7181f3d2d917.bindPopup(popup_86be5a1977464c17824bd154b96f0e8b);

            
        
    
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                [43.784725,-79.29924442],
                {
  "bubblingMouseEvents": true,
  "color": "blue",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "#3186cc",
  "fillOpacity": 0.7,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
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            var popup_e9d454f6fc524f3abf73ffa21252bbc6 = L.popup({maxWidth: '300'});

            
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                popup_e9d454f6fc524f3abf73ffa21252bbc6.setContent(html_259ca5db77844bc8b89996bc5b67fca5);
            

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  "fillColor": "#3186cc",
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  "fillRule": "evenodd",
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  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
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                var html_5864edb6e84e492d8c5149be3deb290b = $('<div id="html_5864edb6e84e492d8c5149be3deb290b" style="width: 100.0%; height: 100.0%;">Agincourt North, L&#39;Amoreaux East, Milliken, Steeles East</div>')[0];
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                {
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  "fillColor": "#3186cc",
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  "fillRule": "evenodd",
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  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
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            var popup_9ce95749a7ad4146ac359ff4f587ceb0 = L.popup({maxWidth: '300'});

            
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                popup_9ce95749a7ad4146ac359ff4f587ceb0.setContent(html_d219c24400f44e7bac43e337e3bc8804);
            

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                ).addTo(map_61363faaf49546baa0ae038a2e7eea74);
            
    
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                var html_e38dfa243b3342fba0ddb5ba91768525 = $('<div id="html_e38dfa243b3342fba0ddb5ba91768525" style="width: 100.0%; height: 100.0%;">Upper Rouge</div>')[0];
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            circle_marker_bfec782125d04ec6adb9d94c1a1823b6.bindPopup(popup_354702af4e3145929bea4c2d00041c95);

            
        
</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 0x20a82854a90>"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create map of Toronto using latitude and longitude values\n",
    "map_Scarborough = folium.Map(location=[latitude_x, longitude_y], zoom_start=10)\n",
    "\n",
    "# add markers to map\n",
    "for lat, lng, nei in zip(df['Latitude'], df['Longitude'], df['Neighborhood']):\n",
    "    \n",
    "    label = '{}'.format(nei)\n",
    "    label = folium.Popup(label, parse_html=True)\n",
    "    folium.CircleMarker(\n",
    "        [lat, lng],\n",
    "        radius=5,\n",
    "        popup=label,\n",
    "        color='blue',\n",
    "        fill=True,\n",
    "        fill_color='#3186cc',\n",
    "        fill_opacity=0.7,\n",
    "        parse_html=False).add_to(map_Scarborough)  \n",
    "    \n",
    "map_Scarborough"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The geograpical coordinate of neighborhood_1 are 43.773077, -79.257774.\n"
     ]
    }
   ],
   "source": [
    "address = 'Scarborough,Toronto'\n",
    "\n",
    "geolocator = Nominatim()\n",
    "location = geolocator.geocode(address)\n",
    "latitude_n1 = location.latitude\n",
    "longitude_n1 = location.longitude\n",
    "print('The geograpical coordinate of neighborhood_1 are {}, {}.'.format(latitude_x, longitude_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# @hiddel_cell\n",
    "CLIENT_ID = '0MJA3NYYG3U2ZY1LTZN2OYEHS3Y3WVSON2GBSO3IL4EDYVIR' # your Foursquare ID\n",
    "CLIENT_SECRET = 'WGWSAF2TKVUQPE3PD0N3EOITFVBY5EYP1VCZI3BMUG0ROUS5' # your Foursquare Secret\n",
    "VERSION = '20180605' # Foursquare API version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "radius = 700 \n",
    "LIMIT = 100\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",
    "    latitude_n1, \n",
    "   longitude_n1, \n",
    "    radius, \n",
    "   LIMIT)\n",
    "results = requests.get(url).json()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['reasons.count', 'reasons.items', 'referralId', 'venue.categories',\n",
       "       'venue.events.count', 'venue.events.summary', 'venue.id',\n",
       "       'venue.location.address', 'venue.location.cc', 'venue.location.city',\n",
       "       'venue.location.country', 'venue.location.crossStreet',\n",
       "       'venue.location.distance', 'venue.location.formattedAddress',\n",
       "       'venue.location.labeledLatLngs', 'venue.location.lat',\n",
       "       'venue.location.lng', 'venue.location.neighborhood',\n",
       "       'venue.location.postalCode', 'venue.location.state', 'venue.name',\n",
       "       'venue.photos.count', 'venue.photos.groups', 'venue.venuePage.id'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "venues=results['response']['groups'][0]['items']\n",
    "nearby_venues = json_normalize(venues)\n",
    "nearby_venues.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_category_type(row):\n",
    "    try:\n",
    "        categories_list = row['categories']\n",
    "    except:\n",
    "        categories_list = row['venue.categories']\n",
    "        \n",
    "    if len(categories_list) == 0:\n",
    "        return None\n",
    "    else:\n",
    "        return categories_list[0]['name']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Nearby Venues"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "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>venue.name</th>\n",
       "      <th>venue.categories</th>\n",
       "      <th>venue.location.lat</th>\n",
       "      <th>venue.location.lng</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Disney store</td>\n",
       "      <td>[{'id': '4bf58dd8d48988d1f3941735', 'name': 'T...</td>\n",
       "      <td>43.775537</td>\n",
       "      <td>-79.256833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Canyon Creek Chophouse</td>\n",
       "      <td>[{'id': '4bf58dd8d48988d1cc941735', 'name': 'S...</td>\n",
       "      <td>43.776959</td>\n",
       "      <td>-79.261694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>St. Andrews Fish &amp; Chips</td>\n",
       "      <td>[{'id': '4edd64a0c7ddd24ca188df1a', 'name': 'F...</td>\n",
       "      <td>43.771865</td>\n",
       "      <td>-79.252645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DAVIDsTEA</td>\n",
       "      <td>[{'id': '4bf58dd8d48988d1dc931735', 'name': 'T...</td>\n",
       "      <td>43.776613</td>\n",
       "      <td>-79.258516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Tommy Hilfiger Company Store</td>\n",
       "      <td>[{'id': '4bf58dd8d48988d103951735', 'name': 'C...</td>\n",
       "      <td>43.776015</td>\n",
       "      <td>-79.257369</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     venue.name  \\\n",
       "0                  Disney store   \n",
       "1        Canyon Creek Chophouse   \n",
       "2      St. Andrews Fish & Chips   \n",
       "3                     DAVIDsTEA   \n",
       "4  Tommy Hilfiger Company Store   \n",
       "\n",
       "                                    venue.categories  venue.location.lat  \\\n",
       "0  [{'id': '4bf58dd8d48988d1f3941735', 'name': 'T...           43.775537   \n",
       "1  [{'id': '4bf58dd8d48988d1cc941735', 'name': 'S...           43.776959   \n",
       "2  [{'id': '4edd64a0c7ddd24ca188df1a', 'name': 'F...           43.771865   \n",
       "3  [{'id': '4bf58dd8d48988d1dc931735', 'name': 'T...           43.776613   \n",
       "4  [{'id': '4bf58dd8d48988d103951735', 'name': 'C...           43.776015   \n",
       "\n",
       "   venue.location.lng  \n",
       "0          -79.256833  \n",
       "1          -79.261694  \n",
       "2          -79.252645  \n",
       "3          -79.258516  \n",
       "4          -79.257369  "
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filtered_columns = ['venue.name', 'venue.categories', 'venue.location.lat', 'venue.location.lng']\n",
    "nearby_venues =nearby_venues.loc[:, filtered_columns]\n",
    "nearby_venues.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. Categories of Nearby Venues"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "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>name</th>\n",
       "      <th>categories</th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Disney store</td>\n",
       "      <td>Toy / Game Store</td>\n",
       "      <td>43.775537</td>\n",
       "      <td>-79.256833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Canyon Creek Chophouse</td>\n",
       "      <td>Steakhouse</td>\n",
       "      <td>43.776959</td>\n",
       "      <td>-79.261694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>St. Andrews Fish &amp; Chips</td>\n",
       "      <td>Fish &amp; Chips Shop</td>\n",
       "      <td>43.771865</td>\n",
       "      <td>-79.252645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DAVIDsTEA</td>\n",
       "      <td>Tea Room</td>\n",
       "      <td>43.776613</td>\n",
       "      <td>-79.258516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Tommy Hilfiger Company Store</td>\n",
       "      <td>Clothing Store</td>\n",
       "      <td>43.776015</td>\n",
       "      <td>-79.257369</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           name         categories        lat        lng\n",
       "0                  Disney store   Toy / Game Store  43.775537 -79.256833\n",
       "1        Canyon Creek Chophouse         Steakhouse  43.776959 -79.261694\n",
       "2      St. Andrews Fish & Chips  Fish & Chips Shop  43.771865 -79.252645\n",
       "3                     DAVIDsTEA           Tea Room  43.776613 -79.258516\n",
       "4  Tommy Hilfiger Company Store     Clothing Store  43.776015 -79.257369"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nearby_venues['venue.categories'] = nearby_venues.apply(get_category_type, axis=1)\n",
    "\n",
    "# clean columns\n",
    "nearby_venues.columns = [col.split(\".\")[-1] for col in nearby_venues.columns]\n",
    "\n",
    "nearby_venues.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Clothing Store         5\n",
       "Coffee Shop            4\n",
       "Wings Joint            2\n",
       "Restaurant             2\n",
       "Sporting Goods Shop    2\n",
       "Sandwich Place         2\n",
       "Gym                    2\n",
       "Tea Room               2\n",
       "Cosmetics Shop         2\n",
       "American Restaurant    2\n",
       "Name: categories, dtype: int64"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Top 10 Categories\n",
    "a=pd.Series(nearby_venues.categories)\n",
    "a.value_counts()[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getNearbyVenues(names, latitudes, longitudes, radius=700):\n",
    "    \n",
    "    venues_list=[]\n",
    "    for name, lat, lng in zip(names, latitudes, longitudes):\n",
    "        print(name)\n",
    "            \n",
    "        # create the API request URL\n",
    "        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",
    "        venue_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 venue_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": 208,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rouge, Malvern\n",
      "Highland Creek, Rouge Hill, Port Union\n",
      "Guildwood, Morningside, West Hill\n",
      "Woburn\n",
      "Cedarbrae\n",
      "Scarborough Village\n",
      "East Birchmount Park, Ionview, Kennedy Park\n",
      "Clairlea, Golden Mile, Oakridge\n",
      "Cliffcrest, Cliffside, Scarborough Village West\n",
      "Birch Cliff, Cliffside West\n",
      "Dorset Park, Scarborough Town Centre, Wexford Heights\n",
      "Maryvale, Wexford\n",
      "Agincourt\n",
      "Clarks Corners, Sullivan, Tam O'Shanter\n",
      "Agincourt North, L'Amoreaux East, Milliken, Steeles East\n",
      "L'Amoreaux West, Steeles West\n",
      "Upper Rouge\n"
     ]
    }
   ],
   "source": [
    "# Nearby Venues\n",
    "Scarborough_venues = getNearbyVenues(names=df['Neighborhood'],\n",
    "                                   latitudes=df['Latitude'],\n",
    "                                   longitudes=df['Longitude']\n",
    "                                  )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 76 uniques categories.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "    <tr>\n",
       "      <th>Neighborhood</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Agincourt</th>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Agincourt North, L'Amoreaux East, Milliken, Steeles East</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Birch Cliff, Cliffside West</th>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cedarbrae</th>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Clairlea, Golden Mile, Oakridge</th>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    Neighborhood Latitude  \\\n",
       "Neighborhood                                                                \n",
       "Agincourt                                                              27   \n",
       "Agincourt North, L'Amoreaux East, Milliken, Ste...                      3   \n",
       "Birch Cliff, Cliffside West                                             9   \n",
       "Cedarbrae                                                               8   \n",
       "Clairlea, Golden Mile, Oakridge                                        15   \n",
       "\n",
       "                                                    Neighborhood Longitude  \\\n",
       "Neighborhood                                                                 \n",
       "Agincourt                                                               27   \n",
       "Agincourt North, L'Amoreaux East, Milliken, Ste...                       3   \n",
       "Birch Cliff, Cliffside West                                              9   \n",
       "Cedarbrae                                                                8   \n",
       "Clairlea, Golden Mile, Oakridge                                         15   \n",
       "\n",
       "                                                    Venue  Venue Latitude  \\\n",
       "Neighborhood                                                                \n",
       "Agincourt                                              27              27   \n",
       "Agincourt North, L'Amoreaux East, Milliken, Ste...      3               3   \n",
       "Birch Cliff, Cliffside West                             9               9   \n",
       "Cedarbrae                                               8               8   \n",
       "Clairlea, Golden Mile, Oakridge                        15              15   \n",
       "\n",
       "                                                    Venue Longitude  \\\n",
       "Neighborhood                                                          \n",
       "Agincourt                                                        27   \n",
       "Agincourt North, L'Amoreaux East, Milliken, Ste...                3   \n",
       "Birch Cliff, Cliffside West                                       9   \n",
       "Cedarbrae                                                         8   \n",
       "Clairlea, Golden Mile, Oakridge                                  15   \n",
       "\n",
       "                                                    Venue Category  \n",
       "Neighborhood                                                        \n",
       "Agincourt                                                       27  \n",
       "Agincourt North, L'Amoreaux East, Milliken, Ste...               3  \n",
       "Birch Cliff, Cliffside West                                      9  \n",
       "Cedarbrae                                                        8  \n",
       "Clairlea, Golden Mile, Oakridge                                 15  "
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('There are {} uniques categories.'.format(len(Scarborough_venues['Venue Category'].unique())))\n",
    "Scarborough_venues.groupby('Neighborhood').count().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### One Hot Encoding of Features"
   ]
  },
  {
   "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>African Restaurant</th>\n",
       "      <th>Athletics &amp; Sports</th>\n",
       "      <th>Auto Garage</th>\n",
       "      <th>Bakery</th>\n",
       "      <th>Bank</th>\n",
       "      <th>Bar</th>\n",
       "      <th>Bistro</th>\n",
       "      <th>Breakfast Spot</th>\n",
       "      <th>Brewery</th>\n",
       "      <th>Bubble Tea Shop</th>\n",
       "      <th>Burger Joint</th>\n",
       "      <th>Bus Line</th>\n",
       "      <th>Bus Station</th>\n",
       "      <th>Bus Stop</th>\n",
       "      <th>Café</th>\n",
       "      <th>Cantonese Restaurant</th>\n",
       "      <th>Caribbean Restaurant</th>\n",
       "      <th>Chinese Restaurant</th>\n",
       "      <th>Coffee Shop</th>\n",
       "      <th>College Stadium</th>\n",
       "      <th>Construction &amp; Landscaping</th>\n",
       "      <th>Convenience Store</th>\n",
       "      <th>Deli / Bodega</th>\n",
       "      <th>Department Store</th>\n",
       "      <th>Diner</th>\n",
       "      <th>Discount Store</th>\n",
       "      <th>Fast Food Restaurant</th>\n",
       "      <th>Filipino Restaurant</th>\n",
       "      <th>Fish &amp; Chips Shop</th>\n",
       "      <th>Flower Shop</th>\n",
       "      <th>Fried Chicken Joint</th>\n",
       "      <th>Furniture / Home Store</th>\n",
       "      <th>General Entertainment</th>\n",
       "      <th>Golf Course</th>\n",
       "      <th>Greek Restaurant</th>\n",
       "      <th>Grocery Store</th>\n",
       "      <th>Gym</th>\n",
       "      <th>Gym / Fitness Center</th>\n",
       "      <th>Gym Pool</th>\n",
       "      <th>Hakka Restaurant</th>\n",
       "      <th>Hobby Shop</th>\n",
       "      <th>Hong Kong Restaurant</th>\n",
       "      <th>Hookah Bar</th>\n",
       "      <th>Ice Cream Shop</th>\n",
       "      <th>Indian Restaurant</th>\n",
       "      <th>Intersection</th>\n",
       "      <th>Japanese Restaurant</th>\n",
       "      <th>Liquor Store</th>\n",
       "      <th>Malay Restaurant</th>\n",
       "      <th>Market</th>\n",
       "      <th>Mediterranean Restaurant</th>\n",
       "      <th>Metro Station</th>\n",
       "      <th>Mexican Restaurant</th>\n",
       "      <th>Moving Target</th>\n",
       "      <th>Other Great Outdoors</th>\n",
       "      <th>Paintball Field</th>\n",
       "      <th>Park</th>\n",
       "      <th>Pet Store</th>\n",
       "      <th>Pharmacy</th>\n",
       "      <th>Pizza Place</th>\n",
       "      <th>Pool</th>\n",
       "      <th>Rental Service</th>\n",
       "      <th>Restaurant</th>\n",
       "      <th>Sandwich Place</th>\n",
       "      <th>Shanghai Restaurant</th>\n",
       "      <th>Shopping Mall</th>\n",
       "      <th>Skating Rink</th>\n",
       "      <th>Supermarket</th>\n",
       "      <th>Sushi Restaurant</th>\n",
       "      <th>Taiwanese Restaurant</th>\n",
       "      <th>Thai Restaurant</th>\n",
       "      <th>Train Station</th>\n",
       "      <th>Video Game Store</th>\n",
       "      <th>Vietnamese Restaurant</th>\n",
       "      <th>Wine Shop</th>\n",
       "      <th>Zoo Exhibit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Rouge, Malvern</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",
       "      <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",
       "      <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>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>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",
       "      <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",
       "      <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",
       "      <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",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Rouge, Malvern</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",
       "      <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",
       "      <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",
       "      <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",
       "      <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",
       "      <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",
       "      <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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Rouge, Malvern</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",
       "      <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",
       "      <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",
       "      <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",
       "      <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",
       "      <td>0</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>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",
       "      <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",
       "      <th>3</th>\n",
       "      <td>Rouge, Malvern</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",
       "      <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",
       "      <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>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",
       "      <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",
       "      <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",
       "      <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",
       "      <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",
       "      <th>4</th>\n",
       "      <td>Highland Creek, Rouge Hill, Port Union</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",
       "      <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",
       "      <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",
       "      <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",
       "      <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",
       "      <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>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",
       "      <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",
       "</div>"
      ],
      "text/plain": [
       "                             Neighborhood  African Restaurant  \\\n",
       "0                          Rouge, Malvern                   0   \n",
       "1                          Rouge, Malvern                   0   \n",
       "2                          Rouge, Malvern                   0   \n",
       "3                          Rouge, Malvern                   0   \n",
       "4  Highland Creek, Rouge Hill, Port Union                   0   \n",
       "\n",
       "   Athletics & Sports  Auto Garage  Bakery  Bank  Bar  Bistro  Breakfast Spot  \\\n",
       "0                   0            0       0     0    0       0               0   \n",
       "1                   0            0       0     0    0       0               0   \n",
       "2                   0            0       0     0    0       0               0   \n",
       "3                   0            0       0     0    0       0               0   \n",
       "4                   0            0       0     0    0       0               0   \n",
       "\n",
       "   Brewery  Bubble Tea Shop  Burger Joint  Bus Line  Bus Station  Bus Stop  \\\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",
       "   Café  Cantonese Restaurant  Caribbean Restaurant  Chinese Restaurant  \\\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",
       "   Coffee Shop  College Stadium  Construction & Landscaping  \\\n",
       "0            0                0                           0   \n",
       "1            0                0                           0   \n",
       "2            0                0                           0   \n",
       "3            0                0                           1   \n",
       "4            0                0                           0   \n",
       "\n",
       "   Convenience Store  Deli / Bodega  Department Store  Diner  Discount Store  \\\n",
       "0                  0              0                 0      0               0   \n",
       "1                  0              0                 0      0               0   \n",
       "2                  0              0                 0      0               0   \n",
       "3                  0              0                 0      0               0   \n",
       "4                  0              0                 0      0               0   \n",
       "\n",
       "   Fast Food Restaurant  Filipino Restaurant  Fish & Chips Shop  Flower Shop  \\\n",
       "0                     1                    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",
       "   Fried Chicken Joint  Furniture / Home Store  General Entertainment  \\\n",
       "0                    0                       0                      0   \n",
       "1                    0                       0                      0   \n",
       "2                    0                       0                      0   \n",
       "3                    0                       0                      0   \n",
       "4                    0                       0                      0   \n",
       "\n",
       "   Golf Course  Greek Restaurant  Grocery Store  Gym  Gym / Fitness Center  \\\n",
       "0            0                 0              0    0                     0   \n",
       "1            0                 0              0    0                     0   \n",
       "2            0                 0              0    0                     0   \n",
       "3            0                 0              0    0                     0   \n",
       "4            0                 0              0    0                     0   \n",
       "\n",
       "   Gym Pool  Hakka Restaurant  Hobby Shop  Hong Kong Restaurant  Hookah Bar  \\\n",
       "0         0                 0           0                     0           0   \n",
       "1         0                 0           0                     0           0   \n",
       "2         0                 0           0                     0           0   \n",
       "3         0                 0           0                     0           0   \n",
       "4         0                 0           0                     0           0   \n",
       "\n",
       "   Ice Cream Shop  Indian Restaurant  Intersection  Japanese Restaurant  \\\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",
       "   Liquor Store  Malay Restaurant  Market  Mediterranean Restaurant  \\\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",
       "   Metro Station  Mexican Restaurant  Moving Target  Other Great Outdoors  \\\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              1                     0   \n",
       "\n",
       "   Paintball Field  Park  Pet Store  Pharmacy  Pizza Place  Pool  \\\n",
       "0                0     0          0         0            0     0   \n",
       "1                0     0          0         0            0     0   \n",
       "2                1     0          0         0            0     0   \n",
       "3                0     0          0         0            0     0   \n",
       "4                0     0          0         0            0     0   \n",
       "\n",
       "   Rental Service  Restaurant  Sandwich Place  Shanghai Restaurant  \\\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",
       "   Shopping Mall  Skating Rink  Supermarket  Sushi Restaurant  \\\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",
       "   Taiwanese Restaurant  Thai Restaurant  Train Station  Video Game Store  \\\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",
       "   Vietnamese Restaurant  Wine Shop  Zoo Exhibit  \n",
       "0                      0          0            0  \n",
       "1                      0          0            1  \n",
       "2                      0          0            0  \n",
       "3                      0          0            0  \n",
       "4                      0          0            0  "
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# one hot encoding\n",
    "Scarborough_onehot = pd.get_dummies(Scarborough_venues[['Venue Category']], prefix=\"\", prefix_sep=\"\")\n",
    "\n",
    "# add neighborhood column back to dataframe\n",
    "Scarborough_onehot['Neighborhood'] = Scarborough_venues['Neighborhood'] \n",
    "\n",
    "# move neighborhood column to the first column\n",
    "fixed_columns = [Scarborough_onehot.columns[-1]] + list(Scarborough_onehot.columns[:-1])\n",
    "Scarborough_onehot = Scarborough_onehot[fixed_columns]\n",
    "Scarborough_grouped = Scarborough_onehot.groupby('Neighborhood').mean().reset_index()\n",
    "Scarborough_onehot.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----Agincourt----\n",
      "                venue  freq\n",
      "0       Shopping Mall  0.11\n",
      "1  Chinese Restaurant  0.11\n",
      "2         Supermarket  0.07\n",
      "3    Sushi Restaurant  0.04\n",
      "4         Pizza Place  0.04\n",
      "\n",
      "\n",
      "----Agincourt North, L'Amoreaux East, Milliken, Steeles East----\n",
      "             venue  freq\n",
      "0         Pharmacy  0.33\n",
      "1      Coffee Shop  0.33\n",
      "2   Sandwich Place  0.33\n",
      "3        Pet Store  0.00\n",
      "4  Paintball Field  0.00\n",
      "\n",
      "\n",
      "----Birch Cliff, Cliffside West----\n",
      "                   venue  freq\n",
      "0                   Park  0.33\n",
      "1               Gym Pool  0.11\n",
      "2  General Entertainment  0.11\n",
      "3        College Stadium  0.11\n",
      "4                   Café  0.11\n",
      "\n",
      "\n",
      "----Cedarbrae----\n",
      "                venue  freq\n",
      "0    Hakka Restaurant  0.12\n",
      "1              Bakery  0.12\n",
      "2                Bank  0.12\n",
      "3  Athletics & Sports  0.12\n",
      "4   Indian Restaurant  0.12\n",
      "\n",
      "\n",
      "----Clairlea, Golden Mile, Oakridge----\n",
      "               venue  freq\n",
      "0           Bus Line  0.13\n",
      "1              Diner  0.13\n",
      "2        Coffee Shop  0.13\n",
      "3  Convenience Store  0.07\n",
      "4       Intersection  0.07\n",
      "\n",
      "\n",
      "----Clarks Corners, Sullivan, Tam O'Shanter----\n",
      "                   venue  freq\n",
      "0   Fast Food Restaurant  0.10\n",
      "1  Vietnamese Restaurant  0.06\n",
      "2            Pizza Place  0.06\n",
      "3            Coffee Shop  0.06\n",
      "4     Chinese Restaurant  0.06\n",
      "\n",
      "\n",
      "----Cliffcrest, Cliffside, Scarborough Village West----\n",
      "            venue  freq\n",
      "0  Ice Cream Shop  0.14\n",
      "1    Burger Joint  0.07\n",
      "2        Pharmacy  0.07\n",
      "3     Pizza Place  0.07\n",
      "4     Coffee Shop  0.07\n",
      "\n",
      "\n",
      "----Dorset Park, Scarborough Town Centre, Wexford Heights----\n",
      "            venue  freq\n",
      "0            Park  0.29\n",
      "1       Wine Shop  0.14\n",
      "2          Bakery  0.14\n",
      "3     Coffee Shop  0.14\n",
      "4  Rental Service  0.14\n",
      "\n",
      "\n",
      "----East Birchmount Park, Ionview, Kennedy Park----\n",
      "                venue  freq\n",
      "0    Department Store  0.17\n",
      "1          Hobby Shop  0.17\n",
      "2         Coffee Shop  0.17\n",
      "3       Train Station  0.17\n",
      "4  Chinese Restaurant  0.17\n",
      "\n",
      "\n",
      "----Guildwood, Morningside, West Hill----\n",
      "                  venue  freq\n",
      "0    Athletics & Sports  0.25\n",
      "1  Gym / Fitness Center  0.25\n",
      "2                   Gym  0.25\n",
      "3                  Park  0.25\n",
      "4      Malay Restaurant  0.00\n",
      "\n",
      "\n",
      "----Highland Creek, Rouge Hill, Port Union----\n",
      "                venue  freq\n",
      "0                 Bar  0.50\n",
      "1       Moving Target  0.25\n",
      "2   Fish & Chips Shop  0.25\n",
      "3  African Restaurant  0.00\n",
      "4              Market  0.00\n",
      "\n",
      "\n",
      "----L'Amoreaux West, Steeles West----\n",
      "                  venue  freq\n",
      "0  Fast Food Restaurant  0.13\n",
      "1    Chinese Restaurant  0.13\n",
      "2        Sandwich Place  0.07\n",
      "3           Auto Garage  0.07\n",
      "4              Pharmacy  0.07\n",
      "\n",
      "\n",
      "----Maryvale, Wexford----\n",
      "                venue  freq\n",
      "0  African Restaurant  0.25\n",
      "1          Hookah Bar  0.25\n",
      "2         Pizza Place  0.25\n",
      "3   Convenience Store  0.25\n",
      "4      Ice Cream Shop  0.00\n",
      "\n",
      "\n",
      "----Rouge, Malvern----\n",
      "                        venue  freq\n",
      "0                 Zoo Exhibit  0.25\n",
      "1             Paintball Field  0.25\n",
      "2        Fast Food Restaurant  0.25\n",
      "3  Construction & Landscaping  0.25\n",
      "4                      Market  0.00\n",
      "\n",
      "\n",
      "----Scarborough Village----\n",
      "                  venue  freq\n",
      "0        Ice Cream Shop  0.25\n",
      "1        Sandwich Place  0.25\n",
      "2         Train Station  0.12\n",
      "3     Indian Restaurant  0.12\n",
      "4  Fast Food Restaurant  0.12\n",
      "\n",
      "\n",
      "----Woburn----\n",
      "                  venue  freq\n",
      "0                  Park  0.25\n",
      "1           Coffee Shop  0.25\n",
      "2    Chinese Restaurant  0.25\n",
      "3  Fast Food Restaurant  0.25\n",
      "4             Pet Store  0.00\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "num_top_venues = 5\n",
    "for hood in Scarborough_grouped['Neighborhood']:\n",
    "    print(\"----\"+hood+\"----\")\n",
    "    temp =Scarborough_grouped[Scarborough_grouped['Neighborhood'] == hood].T.reset_index()\n",
    "    temp.columns = ['venue','freq']\n",
    "    temp = temp.iloc[1:]\n",
    "    temp['freq'] = temp['freq'].astype(float)\n",
    "    temp = temp.round({'freq': 2})\n",
    "    print(temp.sort_values('freq', ascending=False).reset_index(drop=True).head(num_top_venues))\n",
    "    print('\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "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": "markdown",
   "metadata": {},
   "source": [
    "### Most Common venues near neighborhood"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "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",
       "      <th>0</th>\n",
       "      <td>Agincourt</td>\n",
       "      <td>Shopping Mall</td>\n",
       "      <td>Chinese Restaurant</td>\n",
       "      <td>Supermarket</td>\n",
       "      <td>Pool</td>\n",
       "      <td>Breakfast Spot</td>\n",
       "      <td>Malay Restaurant</td>\n",
       "      <td>Mediterranean Restaurant</td>\n",
       "      <td>Hong Kong Restaurant</td>\n",
       "      <td>Department Store</td>\n",
       "      <td>Bubble Tea Shop</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Agincourt North, L'Amoreaux East, Milliken, St...</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Pharmacy</td>\n",
       "      <td>Sandwich Place</td>\n",
       "      <td>Zoo Exhibit</td>\n",
       "      <td>Construction &amp; Landscaping</td>\n",
       "      <td>Convenience Store</td>\n",
       "      <td>Deli / Bodega</td>\n",
       "      <td>Department Store</td>\n",
       "      <td>Diner</td>\n",
       "      <td>Discount Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Birch Cliff, Cliffside West</td>\n",
       "      <td>Park</td>\n",
       "      <td>Gym</td>\n",
       "      <td>Gym Pool</td>\n",
       "      <td>General Entertainment</td>\n",
       "      <td>Café</td>\n",
       "      <td>Skating Rink</td>\n",
       "      <td>College Stadium</td>\n",
       "      <td>Discount Store</td>\n",
       "      <td>Convenience Store</td>\n",
       "      <td>Deli / Bodega</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Cedarbrae</td>\n",
       "      <td>Flower Shop</td>\n",
       "      <td>Athletics &amp; Sports</td>\n",
       "      <td>Bakery</td>\n",
       "      <td>Thai Restaurant</td>\n",
       "      <td>Bank</td>\n",
       "      <td>Hakka Restaurant</td>\n",
       "      <td>Caribbean Restaurant</td>\n",
       "      <td>Indian Restaurant</td>\n",
       "      <td>Filipino Restaurant</td>\n",
       "      <td>Deli / Bodega</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Clairlea, Golden Mile, Oakridge</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Bus Line</td>\n",
       "      <td>Diner</td>\n",
       "      <td>General Entertainment</td>\n",
       "      <td>Ice Cream Shop</td>\n",
       "      <td>Intersection</td>\n",
       "      <td>Metro Station</td>\n",
       "      <td>Convenience Store</td>\n",
       "      <td>Park</td>\n",
       "      <td>Bus Station</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        Neighborhood 1st Most Common Venue  \\\n",
       "0                                          Agincourt         Shopping Mall   \n",
       "1  Agincourt North, L'Amoreaux East, Milliken, St...           Coffee Shop   \n",
       "2                        Birch Cliff, Cliffside West                  Park   \n",
       "3                                          Cedarbrae           Flower Shop   \n",
       "4                    Clairlea, Golden Mile, Oakridge           Coffee Shop   \n",
       "\n",
       "  2nd Most Common Venue 3rd Most Common Venue  4th Most Common Venue  \\\n",
       "0    Chinese Restaurant           Supermarket                   Pool   \n",
       "1              Pharmacy        Sandwich Place            Zoo Exhibit   \n",
       "2                   Gym              Gym Pool  General Entertainment   \n",
       "3    Athletics & Sports                Bakery        Thai Restaurant   \n",
       "4              Bus Line                 Diner  General Entertainment   \n",
       "\n",
       "        5th Most Common Venue 6th Most Common Venue     7th Most Common Venue  \\\n",
       "0              Breakfast Spot      Malay Restaurant  Mediterranean Restaurant   \n",
       "1  Construction & Landscaping     Convenience Store             Deli / Bodega   \n",
       "2                        Café          Skating Rink           College Stadium   \n",
       "3                        Bank      Hakka Restaurant      Caribbean Restaurant   \n",
       "4              Ice Cream Shop          Intersection             Metro Station   \n",
       "\n",
       "  8th Most Common Venue 9th Most Common Venue 10th Most Common Venue  \n",
       "0  Hong Kong Restaurant      Department Store        Bubble Tea Shop  \n",
       "1      Department Store                 Diner         Discount Store  \n",
       "2        Discount Store     Convenience Store          Deli / Bodega  \n",
       "3     Indian Restaurant   Filipino Restaurant          Deli / Bodega  \n",
       "4     Convenience Store                  Park            Bus Station  "
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "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'] = Scarborough_grouped['Neighborhood']\n",
    "\n",
    "for ind in np.arange(Scarborough_grouped.shape[0]):\n",
    "    neighborhoods_venues_sorted.iloc[ind, 1:] = return_most_common_venues(Scarborough_grouped.iloc[ind, :], num_top_venues)\n",
    "\n",
    "neighborhoods_venues_sorted.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### k-means Clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 2, 0, 0, 0, 0, 2, 0, 2, 1, 0, 0, 0, 0, 0])"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# set number of clusters\n",
    "# Using k-means to cluster the neighborhood into 3 clusters.\n",
    "\n",
    "Scarborough_grouped_clustering = Scarborough_grouped.drop('Neighborhood', 1)\n",
    "#Scarborough_grouped_clustering = Scarborough_grouped\n",
    "\n",
    "# run k-means clustering\n",
    "kmeans = KMeans(n_clusters=3, random_state=0).fit(Scarborough_grouped_clustering)\n",
    "\n",
    "# check cluster labels generated for each row in the dataframe\n",
    "kmeans.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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>Postalcode</th>\n",
       "      <th>Borough</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",
       "      <th>0</th>\n",
       "      <td>M1B</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Rouge, Malvern</td>\n",
       "      <td>43.811650</td>\n",
       "      <td>-79.195561</td>\n",
       "      <td>0</td>\n",
       "      <td>Zoo Exhibit</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>Construction &amp; Landscaping</td>\n",
       "      <td>Paintball Field</td>\n",
       "      <td>General Entertainment</td>\n",
       "      <td>Furniture / Home Store</td>\n",
       "      <td>Fried Chicken Joint</td>\n",
       "      <td>Flower Shop</td>\n",
       "      <td>Fish &amp; Chips Shop</td>\n",
       "      <td>Gym</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M1C</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Highland Creek, Rouge Hill, Port Union</td>\n",
       "      <td>43.785605</td>\n",
       "      <td>-79.158701</td>\n",
       "      <td>0</td>\n",
       "      <td>Bar</td>\n",
       "      <td>Fish &amp; Chips Shop</td>\n",
       "      <td>Moving Target</td>\n",
       "      <td>Filipino Restaurant</td>\n",
       "      <td>Convenience Store</td>\n",
       "      <td>Deli / Bodega</td>\n",
       "      <td>Department Store</td>\n",
       "      <td>Diner</td>\n",
       "      <td>Discount Store</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>M1E</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Guildwood, Morningside, West Hill</td>\n",
       "      <td>43.765690</td>\n",
       "      <td>-79.175299</td>\n",
       "      <td>2</td>\n",
       "      <td>Gym / Fitness Center</td>\n",
       "      <td>Gym</td>\n",
       "      <td>Park</td>\n",
       "      <td>Athletics &amp; Sports</td>\n",
       "      <td>Auto Garage</td>\n",
       "      <td>College Stadium</td>\n",
       "      <td>Convenience Store</td>\n",
       "      <td>Deli / Bodega</td>\n",
       "      <td>Department Store</td>\n",
       "      <td>Diner</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M1G</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Woburn</td>\n",
       "      <td>43.768216</td>\n",
       "      <td>-79.217610</td>\n",
       "      <td>0</td>\n",
       "      <td>Park</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>Chinese Restaurant</td>\n",
       "      <td>General Entertainment</td>\n",
       "      <td>Furniture / Home Store</td>\n",
       "      <td>Fried Chicken Joint</td>\n",
       "      <td>Flower Shop</td>\n",
       "      <td>Fish &amp; Chips Shop</td>\n",
       "      <td>Golf Course</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M1H</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Cedarbrae</td>\n",
       "      <td>43.769608</td>\n",
       "      <td>-79.239440</td>\n",
       "      <td>0</td>\n",
       "      <td>Flower Shop</td>\n",
       "      <td>Athletics &amp; Sports</td>\n",
       "      <td>Bakery</td>\n",
       "      <td>Thai Restaurant</td>\n",
       "      <td>Bank</td>\n",
       "      <td>Hakka Restaurant</td>\n",
       "      <td>Caribbean Restaurant</td>\n",
       "      <td>Indian Restaurant</td>\n",
       "      <td>Filipino Restaurant</td>\n",
       "      <td>Deli / Bodega</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Postalcode      Borough                            Neighborhood   Latitude  \\\n",
       "0        M1B  Scarborough                          Rouge, Malvern  43.811650   \n",
       "1        M1C  Scarborough  Highland Creek, Rouge Hill, Port Union  43.785605   \n",
       "2        M1E  Scarborough       Guildwood, Morningside, West Hill  43.765690   \n",
       "3        M1G  Scarborough                                  Woburn  43.768216   \n",
       "4        M1H  Scarborough                               Cedarbrae  43.769608   \n",
       "\n",
       "   Longitude  Cluster Labels 1st Most Common Venue 2nd Most Common Venue  \\\n",
       "0 -79.195561               0           Zoo Exhibit  Fast Food Restaurant   \n",
       "1 -79.158701               0                   Bar     Fish & Chips Shop   \n",
       "2 -79.175299               2  Gym / Fitness Center                   Gym   \n",
       "3 -79.217610               0                  Park           Coffee Shop   \n",
       "4 -79.239440               0           Flower Shop    Athletics & Sports   \n",
       "\n",
       "        3rd Most Common Venue 4th Most Common Venue  5th Most Common Venue  \\\n",
       "0  Construction & Landscaping       Paintball Field  General Entertainment   \n",
       "1               Moving Target   Filipino Restaurant      Convenience Store   \n",
       "2                        Park    Athletics & Sports            Auto Garage   \n",
       "3        Fast Food Restaurant    Chinese Restaurant  General Entertainment   \n",
       "4                      Bakery       Thai Restaurant                   Bank   \n",
       "\n",
       "    6th Most Common Venue 7th Most Common Venue 8th Most Common Venue  \\\n",
       "0  Furniture / Home Store   Fried Chicken Joint           Flower Shop   \n",
       "1           Deli / Bodega      Department Store                 Diner   \n",
       "2         College Stadium     Convenience Store         Deli / Bodega   \n",
       "3  Furniture / Home Store   Fried Chicken Joint           Flower Shop   \n",
       "4        Hakka Restaurant  Caribbean Restaurant     Indian Restaurant   \n",
       "\n",
       "  9th Most Common Venue 10th Most Common Venue  \n",
       "0     Fish & Chips Shop                    Gym  \n",
       "1        Discount Store   Fast Food Restaurant  \n",
       "2      Department Store                  Diner  \n",
       "3     Fish & Chips Shop            Golf Course  \n",
       "4   Filipino Restaurant          Deli / Bodega  "
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Scarborough_merged =df.iloc[:16,:]\n",
    "\n",
    "# add clustering labels\n",
    "Scarborough_merged['Cluster Labels'] = kmeans.labels_\n",
    "\n",
    "# merge toronto_grouped with toronto_data to add latitude/longitude for each neighborhood\n",
    "Scarborough_merged = Scarborough_merged.join(neighborhoods_venues_sorted.set_index('Neighborhood'), on='Neighborhood')\n",
    "\n",
    "Scarborough_merged.head()# check the last columns!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Map of Clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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            circle_marker_877299a2caaa4865819ae6412607a8b1.bindPopup(popup_911b772b02844eeeb153a40b51695552);

            
        
    
            var circle_marker_13214556dde64c59b1888c23f32c2d2c = L.circleMarker(
                [43.76960807,-79.23944],
                {
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  "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_260066ca41e14688af3a9af3f11d2d93);
            
    
            var popup_3a6ee246b55c4f9e928c5d6784103a7f = L.popup({maxWidth: '300'});

            
                var html_d5337174fd90442caef8454325d2c9cc = $('<div id="html_d5337174fd90442caef8454325d2c9cc" style="width: 100.0%; height: 100.0%;">Cedarbrae Cluster 0</div>')[0];
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            circle_marker_13214556dde64c59b1888c23f32c2d2c.bindPopup(popup_3a6ee246b55c4f9e928c5d6784103a7f);

            
        
    
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                {
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  "dashOffset": null,
  "fill": true,
  "fillColor": "#ff0000",
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  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_260066ca41e14688af3a9af3f11d2d93);
            
    
            var popup_163b7f91bdf54331bcf903e1db5ba939 = L.popup({maxWidth: '300'});

            
                var html_e4c73333eacb44628fb2af35b8a96428 = $('<div id="html_e4c73333eacb44628fb2af35b8a96428" style="width: 100.0%; height: 100.0%;">Scarborough Village Cluster 0</div>')[0];
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            circle_marker_e39396f06c924f6f95f3535f154533eb.bindPopup(popup_163b7f91bdf54331bcf903e1db5ba939);

            
        
    
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                {
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  "fillColor": "#ff0000",
  "fillOpacity": 0.7,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
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}
                ).addTo(map_260066ca41e14688af3a9af3f11d2d93);
            
    
            var popup_357b933e9754419c9b81743b2df267ac = L.popup({maxWidth: '300'});

            
                var html_c644c8ba3abc49d1835ed65f6a88c69c = $('<div id="html_c644c8ba3abc49d1835ed65f6a88c69c" style="width: 100.0%; height: 100.0%;">East Birchmount Park, Ionview, Kennedy Park Cluster 0</div>')[0];
                popup_357b933e9754419c9b81743b2df267ac.setContent(html_c644c8ba3abc49d1835ed65f6a88c69c);
            

            circle_marker_1032b3c5f9114c0f9a44b73a345423c8.bindPopup(popup_357b933e9754419c9b81743b2df267ac);

            
        
    
            var circle_marker_409cbd720b8b48579ec0274b73574163 = L.circleMarker(
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                {
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  "dashOffset": null,
  "fill": true,
  "fillColor": "#2adddd",
  "fillOpacity": 0.7,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_260066ca41e14688af3a9af3f11d2d93);
            
    
            var popup_b10d86a0a77545d4b3d5c1e6459e69b3 = L.popup({maxWidth: '300'});

            
                var html_b19aecae5e4746a6bdb5a011ca7bc16b = $('<div id="html_b19aecae5e4746a6bdb5a011ca7bc16b" style="width: 100.0%; height: 100.0%;">Clairlea, Golden Mile, Oakridge Cluster 2</div>')[0];
                popup_b10d86a0a77545d4b3d5c1e6459e69b3.setContent(html_b19aecae5e4746a6bdb5a011ca7bc16b);
            

            circle_marker_409cbd720b8b48579ec0274b73574163.bindPopup(popup_b10d86a0a77545d4b3d5c1e6459e69b3);

            
        
    
            var circle_marker_4e0b986b6c9d47ba9449144c2cc0450c = L.circleMarker(
                [43.723575,-79.23497618],
                {
  "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_260066ca41e14688af3a9af3f11d2d93);
            
    
            var popup_8587d656903f41a69a4c2d512b1ae3ed = L.popup({maxWidth: '300'});

            
                var html_133a5173d26941e781a37f353361cdda = $('<div id="html_133a5173d26941e781a37f353361cdda" style="width: 100.0%; height: 100.0%;">Cliffcrest, Cliffside, Scarborough Village West Cluster 0</div>')[0];
                popup_8587d656903f41a69a4c2d512b1ae3ed.setContent(html_133a5173d26941e781a37f353361cdda);
            

            circle_marker_4e0b986b6c9d47ba9449144c2cc0450c.bindPopup(popup_8587d656903f41a69a4c2d512b1ae3ed);

            
        
    
            var circle_marker_8ed7fa9f7712455f94a857d4a24159fb = L.circleMarker(
                [43.696690000000004,-79.26006896],
                {
  "bubblingMouseEvents": true,
  "color": "#2adddd",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "#2adddd",
  "fillOpacity": 0.7,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_260066ca41e14688af3a9af3f11d2d93);
            
    
            var popup_f0c881ada86d416aaf2f440b6c3c323d = L.popup({maxWidth: '300'});

            
                var html_4f520e8fa4a14657ad9b0de36274d755 = $('<div id="html_4f520e8fa4a14657ad9b0de36274d755" style="width: 100.0%; height: 100.0%;">Birch Cliff, Cliffside West Cluster 2</div>')[0];
                popup_f0c881ada86d416aaf2f440b6c3c323d.setContent(html_4f520e8fa4a14657ad9b0de36274d755);
            

            circle_marker_8ed7fa9f7712455f94a857d4a24159fb.bindPopup(popup_f0c881ada86d416aaf2f440b6c3c323d);

            
        
    
            var circle_marker_454cd2c0c122453cbaa88fab1dc2c969 = L.circleMarker(
                [43.759975,-79.26897403],
                {
  "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_260066ca41e14688af3a9af3f11d2d93);
            
    
            var popup_c921db219a2d4c94934852a81ab89d76 = L.popup({maxWidth: '300'});

            
                var html_1affc0989f864eb4ad3de1b88d9f6258 = $('<div id="html_1affc0989f864eb4ad3de1b88d9f6258" style="width: 100.0%; height: 100.0%;">Dorset Park, Scarborough Town Centre, Wexford Heights Cluster 1</div>')[0];
                popup_c921db219a2d4c94934852a81ab89d76.setContent(html_1affc0989f864eb4ad3de1b88d9f6258);
            

            circle_marker_454cd2c0c122453cbaa88fab1dc2c969.bindPopup(popup_c921db219a2d4c94934852a81ab89d76);

            
        
    
            var circle_marker_d72316f0ba824feba4d06663abf24b6c = L.circleMarker(
                [43.75080267,-79.30056],
                {
  "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_260066ca41e14688af3a9af3f11d2d93);
            
    
            var popup_919be88735504fc3819b8ce5e53f70c0 = L.popup({maxWidth: '300'});

            
                var html_b5da88487cf94c5c86fb5e3f77a1202b = $('<div id="html_b5da88487cf94c5c86fb5e3f77a1202b" style="width: 100.0%; height: 100.0%;">Maryvale, Wexford Cluster 0</div>')[0];
                popup_919be88735504fc3819b8ce5e53f70c0.setContent(html_b5da88487cf94c5c86fb5e3f77a1202b);
            

            circle_marker_d72316f0ba824feba4d06663abf24b6c.bindPopup(popup_919be88735504fc3819b8ce5e53f70c0);

            
        
    
            var circle_marker_9c0f4d68663e4cadba49c2691a82c01d = L.circleMarker(
                [43.79394,-79.26797614],
                {
  "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_260066ca41e14688af3a9af3f11d2d93);
            
    
            var popup_fc4690a756b645e9809355ca7209e6a9 = L.popup({maxWidth: '300'});

            
                var html_0292cb6731864a889543ed96093ec011 = $('<div id="html_0292cb6731864a889543ed96093ec011" style="width: 100.0%; height: 100.0%;">Agincourt Cluster 0</div>')[0];
                popup_fc4690a756b645e9809355ca7209e6a9.setContent(html_0292cb6731864a889543ed96093ec011);
            

            circle_marker_9c0f4d68663e4cadba49c2691a82c01d.bindPopup(popup_fc4690a756b645e9809355ca7209e6a9);

            
        
    
            var circle_marker_ee3ff02da8ac4fea871e55c17c371733 = L.circleMarker(
                [43.784725,-79.29924442],
                {
  "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_260066ca41e14688af3a9af3f11d2d93);
            
    
            var popup_cc682f2f51744996bf7005ee4b1c2e15 = L.popup({maxWidth: '300'});

            
                var html_5c0381a38da84c69b09ec71bf8e153f1 = $('<div id="html_5c0381a38da84c69b09ec71bf8e153f1" style="width: 100.0%; height: 100.0%;">Clarks Corners, Sullivan, Tam O&#39;Shanter Cluster 0</div>')[0];
                popup_cc682f2f51744996bf7005ee4b1c2e15.setContent(html_5c0381a38da84c69b09ec71bf8e153f1);
            

            circle_marker_ee3ff02da8ac4fea871e55c17c371733.bindPopup(popup_cc682f2f51744996bf7005ee4b1c2e15);

            
        
    
            var circle_marker_6b40eb73d53d4901bb3fb59edd3df174 = L.circleMarker(
                [43.817595000000004,-79.2801466],
                {
  "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_260066ca41e14688af3a9af3f11d2d93);
            
    
            var popup_2eb03e826b394c12a6d845482c3b8f14 = L.popup({maxWidth: '300'});

            
                var html_3dcf0156260d4faca9116731cadcbbe2 = $('<div id="html_3dcf0156260d4faca9116731cadcbbe2" style="width: 100.0%; height: 100.0%;">Agincourt North, L&#39;Amoreaux East, Milliken, Steeles East Cluster 0</div>')[0];
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            circle_marker_6b40eb73d53d4901bb3fb59edd3df174.bindPopup(popup_2eb03e826b394c12a6d845482c3b8f14);

            
        
    
            var circle_marker_732516e31b3f491fa2a1883553fa2417 = L.circleMarker(
                [43.800698,-79.32074],
                {
  "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_260066ca41e14688af3a9af3f11d2d93);
            
    
            var popup_507fb3590194419aa28e153032926776 = L.popup({maxWidth: '300'});

            
                var html_182b8adbb2d746daa0fc43d1580972fb = $('<div id="html_182b8adbb2d746daa0fc43d1580972fb" style="width: 100.0%; height: 100.0%;">L&#39;Amoreaux West, Steeles West Cluster 0</div>')[0];
                popup_507fb3590194419aa28e153032926776.setContent(html_182b8adbb2d746daa0fc43d1580972fb);
            

            circle_marker_732516e31b3f491fa2a1883553fa2417.bindPopup(popup_507fb3590194419aa28e153032926776);

            
        
</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 0x20a817b2e10>"
      ]
     },
     "execution_count": 232,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create map\n",
    "map_clusters = folium.Map(location=[latitude_x, longitude_y], zoom_start=11)\n",
    "\n",
    "# set color scheme for the clusters\n",
    "x = np.arange(kclusters)\n",
    "colors_array = cm.rainbow(np.linspace(0, 1, kclusters))\n",
    "rainbow = [colors.rgb2hex(i) for i in colors_array]\n",
    "print(rainbow)\n",
    "# add markers to the map\n",
    "markers_colors = []\n",
    "for lat, lon, nei , cluster in zip(Scarborough_merged['Latitude'], \n",
    "                                   Scarborough_merged['Longitude'], \n",
    "                                   Scarborough_merged['Neighborhood'], \n",
    "                                   Scarborough_merged['Cluster Labels']):\n",
    "    label = folium.Popup(str(nei) + ' Cluster ' + str(cluster), parse_html=True)\n",
    "    folium.CircleMarker(\n",
    "        [lat, lon],\n",
    "        radius=5,\n",
    "        popup=label,\n",
    "        color=rainbow[cluster-1],\n",
    "        fill=True,\n",
    "        fill_color=rainbow[cluster-1],\n",
    "        fill_opacity=0.7).add_to(map_clusters)\n",
    "       \n",
    "map_clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1=Scarborough_merged.loc[Scarborough_merged['Cluster Labels'] == 0,Scarborough_merged.columns[[2] + list(range(5, Scarborough_merged.shape[1]))]]\n",
    "\n",
    "df2=Scarborough_merged.loc[Scarborough_merged['Cluster Labels'] == 1,Scarborough_merged.columns[[2] + list(range(5, Scarborough_merged.shape[1]))]]\n",
    "df3=Scarborough_merged.loc[Scarborough_merged['Cluster Labels'] == 2,Scarborough_merged.columns[[2] + list(range(5, Scarborough_merged.shape[1]))]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Average Housing Prices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [],
   "source": [
    "Scarborough_Avg_HousingPrice=pd.DataFrame({\"Neighborhood\":df[\"Neighborhood\"],\n",
    "                                      \"Average_Housing_Price\":[335000.0,286600.0,175000.0,225900.0,219400.0,\n",
    "                                                               573900.0,225000.0,370500.0,370500.0,433500.0,279200.0,\n",
    "                                                               279200.0,225000.0,370500.,255400.0,433500.0,433500.0]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20a824a4128>"
      ]
     },
     "execution_count": 246,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Scarborough_Avg_HousingPrice.set_index('Neighborhood',inplace=True,drop=True)\n",
    "Scarborough_Avg_HousingPrice.plot(kind='bar',figsize=(8,6),alpha=0.75)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### School Rating by Clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [],
   "source": [
    "clusters=pd.DataFrame({\"Cluster1\":df1[\"Neighborhood\"],\n",
    "                      \"Cluster2\":df2[\"Neighborhood\"],\n",
    "                      \"Cluster4\":df3[\"Neighborhood\"]})\n",
    "clusters = clusters.replace(np.nan, '', regex=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_Scarborough=Scarborough_merged.set_index(\"Neighborhood\",drop=True)\n",
    "#Source:https://www.greatschools.org\n",
    "Scarborough_school_ratings=pd.DataFrame({\"Neighborhood\":df[\"Neighborhood\"],\n",
    "                                      \"Top School Rating\":[7,9,5,8,10,10,7,10,1,2,1,2,7,2,3,2,6,]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Scarborough_school_ratings.set_index('Neighborhood',inplace=True,drop=True)\n",
    "\n",
    "Scarborough_school_ratings.plot(kind='bar',figsize=(10,7),color='green',alpha=0.75);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Conclusion:\n",
    "In this project, through a k-means cluster algorithm we separate the neighborhood into 03 clusters, which have similar neighborhoods around them. Using the charts above decision loving to a particular neighborhood based on average house prices and school rating can be made"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}