{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "name": "IBM Applied Data Science - Notebook.ipynb", "provenance": [], "collapsed_sections": [], "toc_visible": true, "authorship_tag": "ABX9TyPQunwd2sDgzTduLtePuv30", "include_colab_link": true }, "kernelspec": { "name": "python3", "display_name": "Python 3" } }, "cells": [ { "cell_type": "markdown", "metadata": { "id": "view-in-github", "colab_type": "text" }, "source": [ "\"Open" ] }, { "cell_type": "markdown", "metadata": { "id": "JcUC3UdSqZlT" }, "source": [ "#Notebook: Identifying Restaurant Business Opportunities\n", " \n", "\n", "*by Christoffer Haukvik, 2020*\n" ] }, { "cell_type": "markdown", "metadata": { "id": "lN0_WUnOA2V7" }, "source": [ "##Table of Contents\r\n", "\r\n", "1. Introduction\r\n", "2. Data: Collection and pre-processing\r\n", "3. Data Analysis for Methodology\r\n", "4. Conclusion" ] }, { "cell_type": "markdown", "metadata": { "id": "rPfEQ9GLH1n9" }, "source": [ "##1. Introduction \r\n", "\r\n", "This notebook is used to collect the data required for the task of identifying business opportunities, and is the basis for the results as provided in the final report." ] }, { "cell_type": "markdown", "metadata": { "id": "nAsjRgWpSrCn" }, "source": [ "##2. Data: Collection and pre-processing \r\n", "In this section, we will import the required libraries, define the project's input parameters, collect the required data, and perform any data pre-processing." ] }, { "cell_type": "markdown", "metadata": { "id": "wROGfDx-Sz4z" }, "source": [ "###Data collection: Code" ] }, { "cell_type": "markdown", "metadata": { "id": "CIuBu1SiS83w" }, "source": [ "####Defining Project Input Parameters\r\n", "\r\n", "We first need to define the project's input parameters for the city we are goint to analyze; we do this below." ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "58u8cLYyTOWs", "outputId": "3568c64a-1dab-4f07-82c7-3dd0a1327e94" }, "source": [ "# Defining input city\n", "CITY = 'Las Vegas'\n", "STATE = 'Nevada'\n", "COUNTRY = 'United States'\n", "radius = 15 # radius (km) from location center to include in scope\n", "\n", "INPUT_ADDRESS = ', '.join((CITY,STATE,COUNTRY))\n", "print('This project will investigate and analyse', INPUT_ADDRESS,\n", " 'and the surrounding area with a radius of', radius, 'km.')" ], "execution_count": null, "outputs": [ { "output_type": "stream", "text": [ "This project will investigate and analyse Las Vegas, Nevada, United States and the surrounding area with a radius of 15 km.\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "prLOOntCTS3P" }, "source": [ "#### Defining credentials for APIs, radius of project scope, and selected venue category" ] }, { "cell_type": "markdown", "metadata": { "id": "TvFC6DBYfDQV" }, "source": [ "Next, we must define the credentials to use with the Geonames and FourSquare APIs. \r\n", "\r\n", "Additionally, this is where we define the radius we will include in the scope, as well as which type of venue we are going to investigate.\r\n" ] }, { "cell_type": "code", "metadata": { "id": "dS3QCH-Oqm8o" }, "source": [ "# Required input parameters:\n", "gnames_user = 'xxx' # yor user name for geonames service\n", "\n", "# Foursquare Parameters\n", "CLIENT_ID = 'xxx' # your Foursquare ID\n", "CLIENT_SECRET = 'xxx' # your Foursquare Secret\n", "VERSION = '20180605' # Foursquare API version\n", "LIMIT = 50 # limit on how many entries Foursquare will return\n", "RADIUS = radius*1000 # radius in meters\n", "POPULARITY = 1 # venues will be sorted according to popularity\n", "\n", "# Defining FourSquare venue category\n", "SECTIONS = ['food', 'drinks', 'coffee', 'shops', 'arts', 'outdoors', 'sights'] # possible sections/categories\n", "SECTION = SECTIONS[0] # Selecting the Food section, which FourSquare treats as restaurants" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "Sv5FATgSEkCS" }, "source": [ "####Importing required libraries\r\n", "Below we import the required libraries for use in the project." ] }, { "cell_type": "code", "metadata": { "id": "gl4DvgTSqXHq", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "46f9c614-3c57-4324-aab4-8c64d1df0003" }, "source": [ "# Defining imports\n", "# importing necessary libraries\n", "import pandas as pd # library for analysis\n", "import numpy as np # library to handle data\n", "\n", "from geopy.geocoders import Nominatim # convert an address into latitude and longitude values\n", "import geopy\n", "# install the Geocoder\n", "!pip -q install geocoder\n", "import geocoder\n", "import folium # map rendering library\n", "\n", "import requests # library to handle requests\n", "import urllib.request # import the library we use to open URLs\n", "from pandas.io.json import json_normalize # library to flatten json objects\n", "\n", "# Matplotlib and associated plotting modules\n", "import matplotlib.cm as cm\n", "import matplotlib.colors as colors\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "\n", "# Math library for mathematical calculations\n", "import math\n", "\n", "# import k-means from clustering stage\n", "from sklearn.cluster import KMeans\n", "\n", "import warnings #used to suppress warnings\n", "\n", "print('Libraries imported.')" ], "execution_count": null, "outputs": [ { "output_type": "stream", "text": [ "\u001b[?25l\r\u001b[K |███▎ | 10kB 21.2MB/s eta 0:00:01\r\u001b[K |██████▋ | 20kB 25.6MB/s eta 0:00:01\r\u001b[K |██████████ | 30kB 15.8MB/s eta 0:00:01\r\u001b[K |█████████████▎ | 40kB 13.7MB/s eta 0:00:01\r\u001b[K |████████████████▋ | 51kB 8.6MB/s eta 0:00:01\r\u001b[K |████████████████████ | 61kB 9.1MB/s eta 0:00:01\r\u001b[K |███████████████████████▎ | 71kB 8.9MB/s eta 0:00:01\r\u001b[K |██████████████████████████▋ | 81kB 9.8MB/s eta 0:00:01\r\u001b[K |██████████████████████████████ | 92kB 9.2MB/s eta 0:00:01\r\u001b[K |████████████████████████████████| 102kB 5.2MB/s \n", "\u001b[?25hLibraries imported.\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "rVcNhqdhgM0L" }, "source": [ "Next we also define the defaults to use with this notebook." ] }, { "cell_type": "code", "metadata": { "id": "xnvtftA3xc3P" }, "source": [ "# Defining defaults\n", "sns.set_style('white')\n", "warnings.filterwarnings('ignore') # Suppressing warnings\n", "pd.set_option('display.max_columns', None)" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "XAZbSE0jUjsW" }, "source": [ "#### Retrieving location coordinates\n", "The first step of the analysis is to retrieve the geographical coordinates of the selected location. In order to do so, we will use Geocoder (https://geocoder.readthedocs.iofour)." ] }, { "cell_type": "code", "metadata": { "id": "0OpOn09-lc4v", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "a75831cc-b5d7-4f8c-8b40-ebcf1ebba47e" }, "source": [ "# Using Geocoder Arcgis source to look up city\n", "g = geocoder.arcgis(INPUT_ADDRESS)\n", "\n", "# Assigning coordinates\n", "latitude, longitude = g.latlng\n", "print('The geograpical coordinates of', CITY, 'are {}, {}.'.format(latitude, longitude))" ], "execution_count": null, "outputs": [ { "output_type": "stream", "text": [ "The geograpical coordinates of Las Vegas are 36.17193000000003, -115.14000999999996.\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "sv1vFc2YU_nG" }, "source": [ "#### Retrieving Zip/Postal code details of given area\n", "Next, we will use the GeoNames API to retrieve information of the area surrounding our location, such as the zip/postal codes, the latitudes and longitudes, as well as the surrounding location names." ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "EeMsagybrAPV", "outputId": "1871e275-d2a6-4194-a244-4dc10bd1cbe7" }, "source": [ "# Look up postal codes within given radius of city center\n", "maxRows = 500 # retrieve the maximum amount of postal codes\n", "\n", "# Define the url request\n", "url = 'http://api.geonames.org/findNearbyPostalCodesJSON?lat={}&lng={}&radius={}&maxRows={}&username={}'.format(\n", " latitude,\n", " longitude,\n", " radius,\n", " maxRows,\n", " gnames_user\n", ")\n", "postal_codes = requests.get(url).json() # Make the request\n", "print('There are', len(postal_codes['postalCodes']), 'postal codes in', CITY)" ], "execution_count": null, "outputs": [ { "output_type": "stream", "text": [ "There are 71 postal codes in Las Vegas\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "GAdY_ACbJ7Kl" }, "source": [ "Now, we will extract the necessary information from the received result." ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 238 }, "id": "3G1lYmhQn7_p", "outputId": "a1bb9a3d-9750-446d-9618-bf43d131c2b1" }, "source": [ "# Extracting the json results to list\n", "location_list = []\n", "location_list.append([(\n", " pc['postalCode'],\n", " pc['placeName'],\n", " pc['adminName1'],\n", " pc['adminCode1'],\n", " pc['countryCode'],\n", " pc['lat'],\n", " pc['lng']) for pc in postal_codes['postalCodes']])\n", "\n", "# Creating a dataframe from the list\n", "location_df = pd.DataFrame([entry for location_list in location_list for entry in location_list])\n", "location_df.columns = ['Postal_Code', 'Place_Name', 'State_Name', 'State_Code', 'Country_Code', 'PC_Latitude', 'PC_Longitude']\n", "location_df.sort_values(by='Postal_Code', inplace=True)\n", "print('The structure of the data we have of the surrounding area looks like thus:\\n')\n", "location_df.head(5)" ], "execution_count": null, "outputs": [ { "output_type": "stream", "text": [ "The structure of the data we have of the surrounding area looks like thus:\n", "\n" ], "name": "stdout" }, { "output_type": "execute_result", "data": { "text/html": [ "
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Postal_CodePlace_NameState_NameState_CodeCountry_CodePC_LatitudePC_Longitude
6589014HendersonNevadaNVUS36.056435-115.077968
3289030North Las VegasNevadaNVUS36.211457-115.124147
5389031North Las VegasNevadaNVUS36.258888-115.171801
3689032North Las VegasNevadaNVUS36.217968-115.170919
6089033North Las VegasNevadaNVUS36.284511-115.134488
\n", "
" ], "text/plain": [ " Postal_Code Place_Name State_Name State_Code Country_Code \\\n", "65 89014 Henderson Nevada NV US \n", "32 89030 North Las Vegas Nevada NV US \n", "53 89031 North Las Vegas Nevada NV US \n", "36 89032 North Las Vegas Nevada NV US \n", "60 89033 North Las Vegas Nevada NV US \n", "\n", " PC_Latitude PC_Longitude \n", "65 36.056435 -115.077968 \n", "32 36.211457 -115.124147 \n", "53 36.258888 -115.171801 \n", "36 36.217968 -115.170919 \n", "60 36.284511 -115.134488 " ] }, "metadata": { "tags": [] }, "execution_count": 9 } ] }, { "cell_type": "markdown", "metadata": { "id": "K7V-PTg4xI3o" }, "source": [ "Depending on the chosen location and geospatial data accuracy, there might be postal codes using the same geographical coordinates. To avoid such duplicates, we will group any duplicate geospatial points and merge the postal codes into one row, separated by a comma." ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "0hX0dGtoW91n", "outputId": "8c684f3d-4d0d-475f-fa04-02624ff7e6de" }, "source": [ "# grouping postal codes with the same geospatial coordinates\n", "pc_grouped = location_df.groupby(['PC_Latitude', 'PC_Longitude'])['Postal_Code'].transform(lambda x: ', '.join(x))\n", "\n", "# making new dataframe based on existing one for postal codes\n", "postal_codes_df = location_df.copy(deep=True)\n", "\n", "# overwriting the result back into the dataframe\n", "postal_codes_df['Postal_Code'] = pc_grouped\n", "\n", "# since any grouped rows will now be duplicates, we remove the dupes\n", "postal_codes_df.drop_duplicates(inplace=True)\n", "\n", "print('We are working with a number of', postal_codes_df.shape[0], 'separate geospatial locations.')" ], "execution_count": null, "outputs": [ { "output_type": "stream", "text": [ "We are working with a number of 46 separate geospatial locations.\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "F_QozwGga2Pt" }, "source": [ "To get an idea of the areas in our analysis, let us plot them into a map." ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 868 }, "id": "ZYG73A2ihWJb", "outputId": "749fb27a-8c60-4fef-9fc5-b97214e272e8" }, "source": [ "# Plotting the relevant areas on the map\n", "city_map = folium.Map(location=[latitude, longitude], \n", " tiles='OpenStreetMap',\n", " zoom_start=12)\n", "\n", "# Adding markers to map\n", "for lat, lng, label in zip(postal_codes_df['PC_Latitude'], postal_codes_df['PC_Longitude'], postal_codes_df['Postal_Code']):\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(city_map) \n", "\n", "# Display map of city with the associated labels\n", "city_map" ], "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/html": [ "
Make this Notebook Trusted to load map: File -> Trust Notebook
" ], "text/plain": [ "" ] }, "metadata": { "tags": [] }, "execution_count": 11 } ] }, { "cell_type": "markdown", "metadata": { "id": "W6UF79mTMRp4" }, "source": [ "The above map shows the overall scope of the area we are going to investigate in this analysis. Each point refers to a postal code within the given scope. \n", "The next step is to gather data about the relevant restaurants in that area." ] }, { "cell_type": "markdown", "metadata": { "id": "NHyyKqggENr6" }, "source": [ "#### Retrieve the relevant venues in the area according to popularity\n", "An important feature of this analysis is determining the popularity of the various venues in the radius of the given city/location. In order to do this, we are going to use FourSquare's API and ask them to return the results by order of popularity. \n", "\n", "However, since FourSquare only returns 50 results per call, we need to create a function to create a number of calls to access the different result pages. This is done in the below function, which fetches as many venues as possible in the call. This is done by including the \"offset\" parameter in the call." ] }, { "cell_type": "code", "metadata": { "id": "6ASTbIxmFugL" }, "source": [ "# Collecting venue ranks across given location\n", "\n", "def getVenueRanking(latitude, longitude, radius, venue_category, max_results):\n", " venues_list = []\n", " pages = math.ceil(max_results / LIMIT) # calculating the max possible number of FourSquare results pages\n", "\n", " for page in range(0,pages): # looping through each of the results pages (0-indexed)\n", "\n", " # determining rank offset (0-indexed) based on which results page is retrieved\n", " rank_offset = page * LIMIT\n", " \n", " # create the API request URL using the Foursquare format and pre-defined paramenters\n", " # we select the popularity feature, meaning the results will be ranked in order of most popular\n", " url = 'https://api.foursquare.com/v2/venues/explore?&client_id={}&client_secret={}&v={}&ll={},{}&radius={}&limit={}§ion={}&popularity={}&offset={}'.format(\n", " CLIENT_ID, \n", " CLIENT_SECRET, \n", " VERSION, \n", " latitude, \n", " longitude, \n", " radius, \n", " LIMIT,\n", " venue_category,\n", " POPULARITY,\n", " rank_offset)\n", " \n", " # making the GET request\n", " response = requests.get(url).json()\n", " total_results = response['response']['totalResults'] # retrieving the total number of results\n", "\n", " # fetching the necessary info from each request\n", " results = response['response']['groups'][0]['items']\n", "\n", " # return only relevant information for each nearby venue\n", " # since results are ranked, we use enumerate to assign (1-indexed) rank per postal code area\n", " venues_list.append([(\n", " rank,\n", " v['venue']['id'],\n", " v['venue']['name'],\n", " v['venue']['location']['postalCode'],\n", " v['venue']['location']['lat'], \n", " v['venue']['location']['lng'], \n", " v['venue']['categories'][0]['name']) for rank, v in enumerate(results, rank_offset)])\n", " \n", " # if there are less total results than what we have received\n", " if (total_results < (LIMIT + page*LIMIT)): \n", " break # no more futher API calls\n", " \n", " # Creating a dataframe to store the relevant details of the ranked venues\n", " ranked_venues = pd.DataFrame([item for venue_list in venues_list for item in venue_list])\n", " ranked_venues.columns = ['Venue_Rank', \n", " 'Venue_ID',\n", " 'Venue_Name',\n", " 'Venue_Postal_Code', \n", " 'Venue_Latitude', \n", " 'Venue_Longitude', \n", " 'Venue_Category']\n", " \n", " return(ranked_venues)" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "P5GEpEEyVD4G" }, "source": [ "Having defined the above function, we will call it with the pre-defined project parameters." ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 238 }, "id": "xrHGSg30LiiX", "outputId": "f6b07f14-0b5b-4280-e45c-b61a16456452" }, "source": [ "# Making the call to the function to retrieve the dataframe of ranked venues\n", "max_results = 500\n", "ranked_venues = getVenueRanking(latitude, longitude, RADIUS, SECTION, max_results)\n", "print('Retrieved a total of', ranked_venues.shape[0], 'venues, ranked according to popularity.\\n')\n", "ranked_venues.head(5)" ], "execution_count": null, "outputs": [ { "output_type": "stream", "text": [ "Retrieved a total of 245 venues, ranked according to popularity.\n", "\n" ], "name": "stdout" }, { "output_type": "execute_result", "data": { "text/html": [ "
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Venue_RankVenue_IDVenue_NameVenue_Postal_CodeVenue_LatitudeVenue_LongitudeVenue_Category
005a32fb62c530935f37812611Eureka!8910136.168976-115.139580American Restaurant
1151cde2b08bbd23404bdc1798Pizza Rock8910136.171707-115.142343Pizza Place
22552ae36a498e9b3b1e232a6eVegeNation8910136.167398-115.139421Vegetarian / Vegan Restaurant
33539a4129498e2eba5804ba4aCarson Kitchen8910136.167884-115.140664Gastropub
44503cec78e4b0f39ae12141dbeat.8910136.166927-115.139055Breakfast Spot
\n", "
" ], "text/plain": [ " Venue_Rank Venue_ID Venue_Name Venue_Postal_Code \\\n", "0 0 5a32fb62c530935f37812611 Eureka! 89101 \n", "1 1 51cde2b08bbd23404bdc1798 Pizza Rock 89101 \n", "2 2 552ae36a498e9b3b1e232a6e VegeNation 89101 \n", "3 3 539a4129498e2eba5804ba4a Carson Kitchen 89101 \n", "4 4 503cec78e4b0f39ae12141db eat. 89101 \n", "\n", " Venue_Latitude Venue_Longitude Venue_Category \n", "0 36.168976 -115.139580 American Restaurant \n", "1 36.171707 -115.142343 Pizza Place \n", "2 36.167398 -115.139421 Vegetarian / Vegan Restaurant \n", "3 36.167884 -115.140664 Gastropub \n", "4 36.166927 -115.139055 Breakfast Spot " ] }, "metadata": { "tags": [] }, "execution_count": 13 } ] }, { "cell_type": "markdown", "metadata": { "id": "KNRQY7RPm_Av" }, "source": [ "#### Retrieve venue price ranges\n", "\n", "Due to the constraints of the free account with FourSquare, we need to make separate calls to retrieve information about the price ranges of the various venues.\n", "\n", "We will do this by making four separate calls to FourSquare: One for each price range (price range 1, 2, 3, and 4)." ] }, { "cell_type": "code", "metadata": { "id": "ptBwLxnCmTxi" }, "source": [ "# Get data about which venues are in which price ranges\n", "def getVenuePriceRange(latitude, longitude, radius, venue_category, max_results):\n", " venues_list = []\n", " pages = math.ceil(max_results / LIMIT) # calculating the max possible number of FourSquare results pages\n", "\n", " for price in range(1,5): # for each price range (FourSquare ranges from 1-4)\n", " for page in range(0,pages): # looping through each of the results pages (0-indexed)\n", "\n", " # determining rank offset (0-indexed) based on which results page is retrieved\n", " rank_offset = page * LIMIT\n", "\n", " # create the API request URL using the Foursquare format and pre-defined paramenters\n", " # we select the popularity feature, meaning the results will be ranked in order of most popular\n", " url = 'https://api.foursquare.com/v2/venues/explore?&client_id={}&client_secret={}&v={}&ll={},{}&radius={}&limit={}§ion={}&popularity={}&offset={}&price={}'.format(\n", " CLIENT_ID, \n", " CLIENT_SECRET, \n", " VERSION, \n", " latitude, \n", " longitude, \n", " radius, \n", " LIMIT,\n", " venue_category,\n", " POPULARITY,\n", " rank_offset, # this page parameter is for the given page of the result set as defined in the loop\n", " price) # the price category of the given venue\n", " \n", " # determining rank offset (0-indexed) based on which results page is retrieved\n", " rank_offset = page * LIMIT\n", "\n", " # making the GET request\n", " response = requests.get(url).json()\n", " total_results = response['response']['totalResults'] # retrieving the total number of results\n", "\n", " # fetching the necessary info from each request\n", " results = response['response']['groups'][0]['items']\n", "\n", " # we only want the id of the restaurant and the associated price rante\n", " venues_list.append([(\n", " v['venue']['id'],\n", " price) for v in results])\n", " \n", " # if there are less total results than what we have received\n", " if (total_results < (LIMIT + page*LIMIT)): \n", " break # no more futher API calls\n", "\n", " # Creating a dataframe to store the relevant details of the ranked venues\n", " price_venues = pd.DataFrame([item for venue_list in venues_list for item in venue_list])\n", " price_venues.columns = ['Venue_ID',\n", " 'Venue_Price_Category']\n", " \n", " return(price_venues)" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "5jwdogL0mSUl", "colab": { "base_uri": "https://localhost:8080/", "height": 238 }, "outputId": "7ef47f8d-00e2-4ada-fd1e-73f97fa2502b" }, "source": [ "# Making the call to the function to retrieve the dataframe of \n", "price_range_max_results = max_results # for completeness, we will return the defined number of venues\n", "price_range_venues = getVenuePriceRange(latitude, longitude, RADIUS, SECTION, price_range_max_results)\n", "print('Retrieved a total of venues with the given price ranges:', price_range_venues.shape[0], '\\n')\n", "price_range_venues.head(5)" ], "execution_count": null, "outputs": [ { "output_type": "stream", "text": [ "Retrieved a total of venues with the given price ranges: 244 \n", "\n" ], "name": "stdout" }, { "output_type": "execute_result", "data": { "text/html": [ "
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Venue_IDVenue_Price_Category
0503cec78e4b0f39ae12141db1
14fa59a6fe4b0bbcd4a17c02b1
2571c2f22498e4066dfa5b10b1
35822215bb4f96244a79b09f51
44c68bd85897b1b8d83a2ad171
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" ], "text/plain": [ " Venue_ID Venue_Price_Category\n", "0 503cec78e4b0f39ae12141db 1\n", "1 4fa59a6fe4b0bbcd4a17c02b 1\n", "2 571c2f22498e4066dfa5b10b 1\n", "3 5822215bb4f96244a79b09f5 1\n", "4 4c68bd85897b1b8d83a2ad17 1" ] }, "metadata": { "tags": [] }, "execution_count": 15 } ] }, { "cell_type": "markdown", "metadata": { "id": "1OIncjgqXUDu" }, "source": [ "As can be seen from above, we now know the price category of each venue. To get some insight into what we have just retrieved, we can list the number of venues within the various price categories." ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 204 }, "id": "2iTWdnndPtN7", "outputId": "a64131f6-a170-4bf4-d101-513b54d834f0" }, "source": [ "price_range_venues.groupby('Venue_Price_Category')['Venue_Price_Category'].agg(['count'])" ], "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
count
Venue_Price_Category
173
2106
342
423
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" ], "text/plain": [ " count\n", "Venue_Price_Category \n", "1 73\n", "2 106\n", "3 42\n", "4 23" ] }, "metadata": { "tags": [] }, "execution_count": 16 } ] }, { "cell_type": "markdown", "metadata": { "id": "4M6H23HWYHSk" }, "source": [ "Now we will join the price category to the dataframe we already have of the ranked venues." ] }, { "cell_type": "code", "metadata": { "id": "J2wz4N3wqsui" }, "source": [ "# Performing inner join to only include the venues with ranking in this analysis:\n", "venue_df = pd.merge(ranked_venues,price_range_venues,on='Venue_ID', how='inner')" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "w6a1oXTVYjc_" }, "source": [ "Finally, we will join the venue dataframe with the postal code dataframe to gather all the information we need for our analysis in an analysis dataframe." ] }, { "cell_type": "code", "metadata": { "id": "vKqxT49tqmNR", "colab": { "base_uri": "https://localhost:8080/", "height": 566 }, "outputId": "e7bce736-5bfd-4548-a44e-1a477763e7db" }, "source": [ "# Joining the venue dataframe to the postal code dataframe\n", "analysis_df = pd.merge(venue_df, location_df, left_on='Venue_Postal_Code', right_on='Postal_Code')\n", "print('Number of venues for analysis:', analysis_df.shape[0], '\\n')\n", "analysis_df.head(10)" ], "execution_count": null, "outputs": [ { "output_type": "stream", "text": [ "Number of venues for analysis: 239 \n", "\n" ], "name": "stdout" }, { "output_type": "execute_result", "data": { "text/html": [ "
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Venue_RankVenue_IDVenue_NameVenue_Postal_CodeVenue_LatitudeVenue_LongitudeVenue_CategoryVenue_Price_CategoryPostal_CodePlace_NameState_NameState_CodeCountry_CodePC_LatitudePC_Longitude
005a32fb62c530935f37812611Eureka!8910136.168976-115.139580American Restaurant289101Las VegasNevadaNVUS36.17193-115.14001
1151cde2b08bbd23404bdc1798Pizza Rock8910136.171707-115.142343Pizza Place289101Las VegasNevadaNVUS36.17193-115.14001
22552ae36a498e9b3b1e232a6eVegeNation8910136.167398-115.139421Vegetarian / Vegan Restaurant289101Las VegasNevadaNVUS36.17193-115.14001
33539a4129498e2eba5804ba4aCarson Kitchen8910136.167884-115.140664Gastropub389101Las VegasNevadaNVUS36.17193-115.14001
44503cec78e4b0f39ae12141dbeat.8910136.166927-115.139055Breakfast Spot189101Las VegasNevadaNVUS36.17193-115.14001
55510a01d5e4b07b50d4c6a4aeJoe Vicari's Andiamo Steakhouse8910136.169942-115.142740Steakhouse489101Las VegasNevadaNVUS36.17193-115.14001
664ea5ca369adf05b8d0006a20Le Thai8910136.168839-115.139921Thai Restaurant289101Las VegasNevadaNVUS36.17193-115.14001
774fa59a6fe4b0bbcd4a17c02bPop Up Pizza8910136.171171-115.147438Pizza Place189101Las VegasNevadaNVUS36.17193-115.14001
88571c2f22498e4066dfa5b10bThe Goodwich8910136.159999-115.147533Sandwich Place189101Las VegasNevadaNVUS36.17193-115.14001
99510ae7a6e4b005681ba9169dPark on Fremont8910136.169220-115.140416Gastropub289101Las VegasNevadaNVUS36.17193-115.14001
\n", "
" ], "text/plain": [ " Venue_Rank Venue_ID Venue_Name \\\n", "0 0 5a32fb62c530935f37812611 Eureka! \n", "1 1 51cde2b08bbd23404bdc1798 Pizza Rock \n", "2 2 552ae36a498e9b3b1e232a6e VegeNation \n", "3 3 539a4129498e2eba5804ba4a Carson Kitchen \n", "4 4 503cec78e4b0f39ae12141db eat. \n", "5 5 510a01d5e4b07b50d4c6a4ae Joe Vicari's Andiamo Steakhouse \n", "6 6 4ea5ca369adf05b8d0006a20 Le Thai \n", "7 7 4fa59a6fe4b0bbcd4a17c02b Pop Up Pizza \n", "8 8 571c2f22498e4066dfa5b10b The Goodwich \n", "9 9 510ae7a6e4b005681ba9169d Park on Fremont \n", "\n", " Venue_Postal_Code Venue_Latitude Venue_Longitude \\\n", "0 89101 36.168976 -115.139580 \n", "1 89101 36.171707 -115.142343 \n", "2 89101 36.167398 -115.139421 \n", "3 89101 36.167884 -115.140664 \n", "4 89101 36.166927 -115.139055 \n", "5 89101 36.169942 -115.142740 \n", "6 89101 36.168839 -115.139921 \n", "7 89101 36.171171 -115.147438 \n", "8 89101 36.159999 -115.147533 \n", "9 89101 36.169220 -115.140416 \n", "\n", " Venue_Category Venue_Price_Category Postal_Code Place_Name \\\n", "0 American Restaurant 2 89101 Las Vegas \n", "1 Pizza Place 2 89101 Las Vegas \n", "2 Vegetarian / Vegan Restaurant 2 89101 Las Vegas \n", "3 Gastropub 3 89101 Las Vegas \n", "4 Breakfast Spot 1 89101 Las Vegas \n", "5 Steakhouse 4 89101 Las Vegas \n", "6 Thai Restaurant 2 89101 Las Vegas \n", "7 Pizza Place 1 89101 Las Vegas \n", "8 Sandwich Place 1 89101 Las Vegas \n", "9 Gastropub 2 89101 Las Vegas \n", "\n", " State_Name State_Code Country_Code PC_Latitude PC_Longitude \n", "0 Nevada NV US 36.17193 -115.14001 \n", "1 Nevada NV US 36.17193 -115.14001 \n", "2 Nevada NV US 36.17193 -115.14001 \n", "3 Nevada NV US 36.17193 -115.14001 \n", "4 Nevada NV US 36.17193 -115.14001 \n", "5 Nevada NV US 36.17193 -115.14001 \n", "6 Nevada NV US 36.17193 -115.14001 \n", "7 Nevada NV US 36.17193 -115.14001 \n", "8 Nevada NV US 36.17193 -115.14001 \n", "9 Nevada NV US 36.17193 -115.14001 " ] }, "metadata": { "tags": [] }, "execution_count": 18 } ] }, { "cell_type": "markdown", "metadata": { "id": "8LjdBoROAFAH" }, "source": [ "####Data pre-processing" ] }, { "cell_type": "markdown", "metadata": { "id": "JI6mJsBfquAo" }, "source": [ "Before proceeding, let us clean up the analysis dataframe to improve the workflow later on:\n", "1. Rank the venues from 1 to last (not from 0 to last)\n", "2. Change the data type of the Postal Codes, Venue Categories, and Price Categories into a type Categorical" ] }, { "cell_type": "code", "metadata": { "id": "qDEcj4Tgq_gn", "colab": { "base_uri": "https://localhost:8080/", "height": 292 }, "outputId": "dfdc3ed2-8219-4212-a05b-f9ad78cd32ad" }, "source": [ "# Ranking venues with 1-indexation for later on\n", "analysis_df['Venue_Rank'] = list(range(1,analysis_df.shape[0]+1))\n", "\n", "# Changing data types of Postal Codes, Price Categories, into categorial data types\n", "analysis_df['Venue_Postal_Code'] = analysis_df['Venue_Postal_Code'].astype('category')\n", "analysis_df['Venue_Category'] = analysis_df['Venue_Category'].astype('category')\n", "analysis_df['Venue_Price_Category'] = analysis_df['Venue_Price_Category'].astype('category')\n", "\n", "analysis_df.head(5)" ], "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/html": [ "
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Venue_RankVenue_IDVenue_NameVenue_Postal_CodeVenue_LatitudeVenue_LongitudeVenue_CategoryVenue_Price_CategoryPostal_CodePlace_NameState_NameState_CodeCountry_CodePC_LatitudePC_Longitude
015a32fb62c530935f37812611Eureka!8910136.168976-115.139580American Restaurant289101Las VegasNevadaNVUS36.17193-115.14001
1251cde2b08bbd23404bdc1798Pizza Rock8910136.171707-115.142343Pizza Place289101Las VegasNevadaNVUS36.17193-115.14001
23552ae36a498e9b3b1e232a6eVegeNation8910136.167398-115.139421Vegetarian / Vegan Restaurant289101Las VegasNevadaNVUS36.17193-115.14001
34539a4129498e2eba5804ba4aCarson Kitchen8910136.167884-115.140664Gastropub389101Las VegasNevadaNVUS36.17193-115.14001
45503cec78e4b0f39ae12141dbeat.8910136.166927-115.139055Breakfast Spot189101Las VegasNevadaNVUS36.17193-115.14001
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" ], "text/plain": [ " Venue_Rank Venue_ID Venue_Name Venue_Postal_Code \\\n", "0 1 5a32fb62c530935f37812611 Eureka! 89101 \n", "1 2 51cde2b08bbd23404bdc1798 Pizza Rock 89101 \n", "2 3 552ae36a498e9b3b1e232a6e VegeNation 89101 \n", "3 4 539a4129498e2eba5804ba4a Carson Kitchen 89101 \n", "4 5 503cec78e4b0f39ae12141db eat. 89101 \n", "\n", " Venue_Latitude Venue_Longitude Venue_Category \\\n", "0 36.168976 -115.139580 American Restaurant \n", "1 36.171707 -115.142343 Pizza Place \n", "2 36.167398 -115.139421 Vegetarian / Vegan Restaurant \n", "3 36.167884 -115.140664 Gastropub \n", "4 36.166927 -115.139055 Breakfast Spot \n", "\n", " Venue_Price_Category Postal_Code Place_Name State_Name State_Code \\\n", "0 2 89101 Las Vegas Nevada NV \n", "1 2 89101 Las Vegas Nevada NV \n", "2 2 89101 Las Vegas Nevada NV \n", "3 3 89101 Las Vegas Nevada NV \n", "4 1 89101 Las Vegas Nevada NV \n", "\n", " Country_Code PC_Latitude PC_Longitude \n", "0 US 36.17193 -115.14001 \n", "1 US 36.17193 -115.14001 \n", "2 US 36.17193 -115.14001 \n", "3 US 36.17193 -115.14001 \n", "4 US 36.17193 -115.14001 " ] }, "metadata": { "tags": [] }, "execution_count": 19 } ] }, { "cell_type": "markdown", "metadata": { "id": "xdRUTH7H1N-e" }, "source": [ "To get a feel for the locations we are analyzing, let's plot them into a map according to the price category of each venue." ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 868 }, "id": "KLAVZ0f52x2G", "outputId": "9535c6d2-24f5-415e-a70c-790cbb971303" }, "source": [ "import branca.colormap\n", "\n", "# create map\n", "map_price_range = folium.Map(location=[latitude, longitude], \n", " tiles='OpenStreetMap',\n", " zoom_start=12)\n", "\n", "# set color scheme for the clusters\n", "price_cat = 4 # 4 price categories\n", "x = np.arange(price_cat)\n", "ys = [i + x + (i*x)**2 for i in range(price_cat)]\n", "colors_array = cm.bwr(np.linspace(0, 1, len(ys)))\n", "rainbow = [colors.rgb2hex(i) for i in colors_array]\n", "\n", "# add markers to the map\n", "markers_colors = []\n", "for lat, lon, v_name, p_cat in zip(analysis_df['Venue_Latitude'], analysis_df['Venue_Longitude'], analysis_df['Venue_Name'], analysis_df['Venue_Price_Category']):\n", " label = folium.Popup(str(v_name) + ' Price Category ' + str(p_cat), parse_html=True)\n", " folium.CircleMarker(\n", " [lat, lon],\n", " radius=4,\n", " popup=label,\n", " color=rainbow[p_cat-1],\n", " fill=True,\n", " fill_color=rainbow[p_cat-1],\n", " fill_opacity=0.9).add_to(map_price_range)\n", " \n", "map_price_range" ], "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/html": [ "
Make this Notebook Trusted to load map: File -> Trust Notebook
" ], "text/plain": [ "" ] }, "metadata": { "tags": [] }, "execution_count": 20 } ] }, { "cell_type": "markdown", "metadata": { "id": "DQNCaUCQ2S_e" }, "source": [ "With the above, we now have the data we need to carry out the analysis." ] }, { "cell_type": "markdown", "metadata": { "id": "wUvNubJLtjvN" }, "source": [ "##3. Data Analysis for Methodology " ] }, { "cell_type": "markdown", "metadata": { "id": "2sK9MZkgbgXL" }, "source": [ "In this section, we define the code for use in three different areas:\n", "1. Exploratory data analysis\n", "2. Statistically creating a metric for identifying potential business opportunities\n", "3. Applying machine learning using k-Means clustering to see if we can categorize the features that make a business model more successful.\n", "\n", "Given the above, we should be able to provide insight as to what might be predictors of success." ] }, { "cell_type": "markdown", "metadata": { "id": "CSJxWxXQbyAT" }, "source": [ "###Exploratory data analysis" ] }, { "cell_type": "markdown", "metadata": { "id": "oeR2tqpoZtRR" }, "source": [ "First, let's investigate some basic statistics on the dataset." ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "yhvL-WBhcGQ5", "outputId": "e69da866-39d4-4d0a-9c6e-c6fcb64e0c7e" }, "source": [ "# Calculating the count of the various venues\r\n", "vid_count = analysis_df['Venue_ID'].unique().shape[0]\r\n", "pc_count = analysis_df['Postal_Code'].unique().shape[0]\r\n", "cat_count = analysis_df['Venue_Category'].unique().shape[0]\r\n", "\r\n", "print('The analysis contains the following:')\r\n", "print(' - Number of venues:', vid_count)\r\n", "print(' - Number of Postal Areas:', pc_count)\r\n", "print(' - Number of venue categories:', cat_count)" ], "execution_count": null, "outputs": [ { "output_type": "stream", "text": [ "The analysis contains the following:\n", " - Number of venues: 239\n", " - Number of Postal Areas: 31\n", " - Number of venue categories: 51\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "oUVsBCG5qGU-" }, "source": [ "Next, let's assess the various features of our data, paying particular interest in what relates to the higher ranked venues in our dataset." ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 465 }, "id": "BusBqaORZtRa", "outputId": "e2f47bfb-a67c-421e-a3ed-d70f310e88b3" }, "source": [ "# Investigating which postal codes have the most venues \r\n", "top_postal_count = analysis_df.groupby('Venue_Postal_Code')['Venue_ID'].count().sort_values(ascending=False)[0:10].reset_index()\r\n", "top_postal_count.columns = ['Postal Code', '# Venues']\r\n", "top_postal_count['Postal Code'].cat.remove_unused_categories(inplace=True)\r\n", "top_postal_count.sort_values(by='# Venues', ascending=False, inplace=True)\r\n", "\r\n", "# Plot the common venue categories\r\n", "fig, ax = plt.subplots(figsize=(10, 6))\r\n", "sns.barplot(x='# Venues', y='Postal Code',\r\n", " data=top_postal_count,\r\n", " order=top_postal_count['Postal Code'].values)\r\n", "fig.suptitle('Most Common Postal Codes Overall', fontsize=14)\r\n", "fig.show()\r\n", "\r\n", "print('The most common postal code overall is',\r\n", " top_postal_count['Postal Code'][0] + '.\\n')" ], "execution_count": null, "outputs": [ { "output_type": "stream", "text": [ "The most common postal code overall is 89109.\n", "\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "image/png": 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\n", 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" ] }, "metadata": { "tags": [] } } ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 465 }, "id": "4ZKz2ZO0mfDB", "outputId": "5df8c0c8-afca-4700-ed02-2cd5e1503250" }, "source": [ "# Investigating which postal codes have the highest popularity\r\n", "top_postal_rank = analysis_df.groupby('Venue_Postal_Code')['Venue_Rank'].mean().sort_values(ascending=True)[0:10].reset_index()\r\n", "top_postal_rank.columns = ['Postal Code', 'Average Rank']\r\n", "top_postal_rank['Postal Code'].cat.remove_unused_categories(inplace=True)\r\n", "top_postal_rank.sort_values(by='Average Rank', ascending=True, inplace=True)\r\n", "\r\n", "# Plotting the results\r\n", "# Plot the common venue categories\r\n", "fig, ax = plt.subplots(figsize=(10, 6))\r\n", "sns.barplot(x='Average Rank', y='Postal Code',\r\n", " data=top_postal_rank,\r\n", " order=top_postal_rank['Postal Code'].values)\r\n", "fig.suptitle('Best Ranked Postal Codes Overall (lower=better)', fontsize=14)\r\n", "fig.show()\r\n", "\r\n", "print('Best top ranked postal code overall is',\r\n", " top_postal_rank['Postal Code'][0] + '.\\n')" ], "execution_count": null, "outputs": [ { "output_type": "stream", "text": [ "Best top ranked postal code overall is 89101.\n", "\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "image/png": 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\n", 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" ] }, "metadata": { "tags": [] } } ] }, { "cell_type": "markdown", "metadata": { "id": "8iIDcBcTZtRb" }, "source": [ "Next, we will explore the same for the various venue categories." ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 465 }, "id": "Or4ggjdqZtRc", "outputId": "4b645bd9-94fa-481a-aada-62202cbb8baf" }, "source": [ "# Get the 10 most common venue categories\r\n", "top_cat_count = analysis_df.groupby('Venue_Category')['Venue_ID'].count().sort_values(ascending=False)[0:10].reset_index()\r\n", "top_cat_count.columns = ['Venue Category', '# Venues']\r\n", "top_cat_count['Venue Category'].cat.remove_unused_categories(inplace=True)\r\n", "\r\n", "top_cat_rating = analysis_df.groupby('Venue_Category')['Venue_Rank'].mean().sort_values(ascending=True)[0:10].reset_index()\r\n", "top_cat_rating.columns = ['Venue Category', 'Average Rank']\r\n", "top_cat_rating['Venue Category'].cat.remove_unused_categories(inplace=True)\r\n", "\r\n", "top_cat_count.sort_values(by='# Venues', ascending=False, inplace=True)\r\n", "\r\n", "# Plot the common venue categories\r\n", "fig, ax = plt.subplots(figsize=(10, 6))\r\n", "sns.barplot(x='# Venues', y='Venue Category',\r\n", " data=top_cat_count,\r\n", " order=top_cat_count['Venue Category'].values)\r\n", "fig.suptitle('Most Common Venue Categories Overall', fontsize=14)\r\n", "fig.show()\r\n", "\r\n", "print('The most common venue category overall is',\r\n", " top_cat_count['Venue Category'][0] + '.\\n')" ], "execution_count": null, "outputs": [ { "output_type": "stream", "text": [ "The most common venue category overall is Breakfast Spot.\n", "\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "image/png": 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\n", 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" ] }, "metadata": { "tags": [] } } ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 465 }, "id": "KEAApMu7d6CN", "outputId": "1309ac5a-f25c-4239-ea40-1771ee37df1f" }, "source": [ "# Getting the 10 most highly rated venue categories overall\n", "top_cat_rating = analysis_df.groupby('Venue_Category')['Venue_Rank'].mean().sort_values(ascending=True)[0:10].reset_index()\n", "top_cat_rating.columns = ['Venue Category', 'Average Rank']\n", "top_cat_rating['Venue Category'].cat.remove_unused_categories(inplace=True)\n", "top_cat_rating.sort_values(by='Average Rank', inplace=True, ascending=True)\n", "\n", "# Plot the top ranked venue categories\n", "fig, ax = plt.subplots(figsize=(10, 6))\n", "sns.barplot(x='Average Rank', y='Venue Category',\n", " data=top_cat_rating,\n", " order=top_cat_rating['Venue Category'].values)\n", "fig.suptitle('Best Ranked Venue Categories Overall (lower=better)', fontsize=14)\n", "fig.show()\n", "\n", "print('The best ranked venue category overall is',\n", " top_cat_rating['Venue Category'][0] + '.', '\\n')" ], "execution_count": null, "outputs": [ { "output_type": "stream", "text": [ "The best ranked venue category overall is Gastropub. \n", "\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "image/png": 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\n", 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" ] }, "metadata": { "tags": [] } } ] }, { "cell_type": "markdown", "metadata": { "id": "8NvB6vfFZtRc" }, "source": [ "Finally, we will explore the distribution of price categories across the selection of venues, as well as how the ranking is in these venues." ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 788 }, "id": "r40-UH7wZtRc", "outputId": "a2b4a8d5-7f76-4ba0-926d-4b8c13626510" }, "source": [ "# Evaluating the distribution of price categories and plotting results\r\n", "\r\n", "# Creating plot for # Venues per Price Category\r\n", "sns.catplot(x='Venue_Price_Category', kind='count', \r\n", " data=analysis_df)\r\n", "plt.title('Venues per Price Category', size=16)\r\n", "plt.xlabel('Price Category', size=12)\r\n", "plt.ylabel('# venues', size=12)\r\n", "plt.show()\r\n", "\r\n", "# Creating plot for Distribution of venue rankings per price category\r\n", "sns.catplot(x='Venue_Price_Category', y='Venue_Rank', data=analysis_df)\r\n", "plt.title('Distribution of Venue Rank per Price Category', size=16)\r\n", "plt.xlabel('Price Category', size=12)\r\n", "plt.ylabel('Venue Rank', size=12)\r\n", "plt.show()" ], "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "image/png": 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\n", 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\n", 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" ] }, "metadata": { "tags": [] } } ] }, { "cell_type": "markdown", "metadata": { "id": "I96ep2j6ZtRc" }, "source": [ "Given the above, we can see there are most venues in price category 2 (the second lowest price category). As for distribution of rankings, there is limited correlation between the categories and ratings for the venues with highest ratings (between 0 and 50), except for it being represented by price categories 1, 2, and 3 - not 4." ] }, { "cell_type": "markdown", "metadata": { "id": "CKKT7ukFq5_f" }, "source": [ "###Metric for determining business opportunities\n", "In order to provide recommendations for business opportunities, we will create a metric that answers the following:\n", "1. Which areas are most attractive for new businesses?\n", "2. Which venue categories are recommended in each area?\n", "3. In the most attractive area, what are the recommended venue categories and associated price ranges of the given venue?\n", "\n", "We will calculate this as follows:\n", "- Select the 50 most popular venues\n", "- Recommend the most attractive areas according on the mean venue rating for each area\n", "- Recommend venue category based on which venue category is most popular in that area\n", "- Drill down into the most popular postal area, and recommend business opportunities in that area based upon the existing popular venues\n", "\n", "Given the above, one will have a good understanding both on the various popular areas, as well as more specifics on the most popular area in Las Vegas.\n" ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 204 }, "id": "TKbEF0UF7QYl", "outputId": "9e707df5-0a19-45e4-e992-676d44289e02" }, "source": [ "# Fetch top 50 venues\n", "top50 = analysis_df.iloc[0:50]\n", "top50['Venue_Postal_Code'].cat.remove_unused_categories(inplace=True)\n", "\n", "# Group according to posta codel\n", "top50_df = top50.groupby('Venue_Postal_Code').agg({'Venue_Rank': 'mean', 'Venue_ID' : 'count'}).reset_index().sort_values(by='Venue_Rank')\n", "top50_df.columns = ['Postal Code', 'Average Rank', '# Venues']\n", "top50_df" ], "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/html": [ "
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Postal CodeAverage Rank# Venues
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48910945.510
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" ], "text/plain": [ " Postal Code Average Rank # Venues\n", "0 89101 7.0 13\n", "1 89102 22.5 18\n", "3 89106 32.5 2\n", "2 89104 37.0 7\n", "4 89109 45.5 10" ] }, "metadata": { "tags": [] }, "execution_count": 27 } ] }, { "cell_type": "code", "metadata": { "id": "hxdvUf2K3Q1E" }, "source": [ "# Calculate the most popular venue categories per postal code\n", "\n", "# Count the number of categories\n", "top_cat = top50.groupby(['Postal_Code','Venue_Category']).agg({'Venue_Category': 'count'})\n", "top_cat.columns = ['Category Count']\n", "top_cat.reset_index()\n", "\n", "# Sort according to most popular categories, then drop the rest\n", "top_cat = top_cat.sort_values(by=['Postal_Code', 'Category Count'], ascending=False).reset_index()\n", "top_cat.columns = ['Postal Code', 'Venue Category', 'Category Count']\n", "top_cat.drop_duplicates(subset=['Postal Code'], keep='first', inplace=True)" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 204 }, "id": "CnoPy0Yt_Ph_", "outputId": "552d556a-3510-4d54-def7-e072bc3dfe31" }, "source": [ "top50_summary = pd.merge(top50_df, top_cat, on=['Postal Code'])\n", "top50_summary['Venue Category'].cat.remove_unused_categories(inplace=True)\n", "top50_summary" ], "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/html": [ "
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Postal CodeAverage Rank# VenuesVenue CategoryCategory Count
0891017.013Pizza Place3
18910222.518Breakfast Spot3
28910632.52American Restaurant1
38910437.07Thai Restaurant2
48910945.510American Restaurant2
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" ], "text/plain": [ " Postal Code Average Rank # Venues Venue Category Category Count\n", "0 89101 7.0 13 Pizza Place 3\n", "1 89102 22.5 18 Breakfast Spot 3\n", "2 89106 32.5 2 American Restaurant 1\n", "3 89104 37.0 7 Thai Restaurant 2\n", "4 89109 45.5 10 American Restaurant 2" ] }, "metadata": { "tags": [] }, "execution_count": 29 } ] }, { "cell_type": "markdown", "metadata": { "id": "jRmOHtRqHkc2" }, "source": [ "The above is useful: We now know which are the most popular areas, and which venue types in those areas have the highest prevalence. \n", "If one is looking to open a food venue in Las Vegas, one should seriously consider the above postal areas and venue categories." ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 431 }, "id": "nIAGWTdH8hjH", "outputId": "b2f1a238-7646-4502-e5e7-d44d23ba9388" }, "source": [ "# Plot the above in a graph\n", "# Plot the top ranked venue categories\n", "fig, ax = plt.subplots(figsize=(12, 6))\n", "ax = sns.barplot(x='Postal Code', y='Average Rank',\n", " data=top50_summary,\n", " order=top50_summary['Postal Code'].values)\n", "\n", "# Annotating graph with values\n", "for p in ax.patches:\n", " ax.annotate(top50_summary.loc[top50_summary['Average Rank']==p.get_height()]['Venue Category'].values[0],\n", " (p.get_x() + p.get_width() / 2., p.get_height()), \n", " ha = 'center', va = 'top', \n", " xytext = (0, 12),\n", " rotation = 0,\n", " textcoords = 'offset points',\n", " size=12\n", " )\n", "\n", "fig.suptitle('Most Popular Categories in Areas (lower=better)', fontsize=14)\n", "fig.show()" ], "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "image/png": 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" ] }, "metadata": { "tags": [] } } ] }, { "cell_type": "markdown", "metadata": { "id": "rRpguQCkaroS" }, "source": [ "Finally, let us drill down to the most attractive area in this analysis; Postal Code 89101 - which strongly represents the most popular venues." ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 292 }, "id": "QBHlBSUAbSI1", "outputId": "75a25d34-be14-4b4b-81ca-0012729369d7" }, "source": [ "top_area = analysis_df.loc[analysis_df['Postal_Code']==top50_summary.iloc[0,0]]\n", "top_area.head(5)" ], "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/html": [ "
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Venue_RankVenue_IDVenue_NameVenue_Postal_CodeVenue_LatitudeVenue_LongitudeVenue_CategoryVenue_Price_CategoryPostal_CodePlace_NameState_NameState_CodeCountry_CodePC_LatitudePC_Longitude
015a32fb62c530935f37812611Eureka!8910136.168976-115.139580American Restaurant289101Las VegasNevadaNVUS36.17193-115.14001
1251cde2b08bbd23404bdc1798Pizza Rock8910136.171707-115.142343Pizza Place289101Las VegasNevadaNVUS36.17193-115.14001
23552ae36a498e9b3b1e232a6eVegeNation8910136.167398-115.139421Vegetarian / Vegan Restaurant289101Las VegasNevadaNVUS36.17193-115.14001
34539a4129498e2eba5804ba4aCarson Kitchen8910136.167884-115.140664Gastropub389101Las VegasNevadaNVUS36.17193-115.14001
45503cec78e4b0f39ae12141dbeat.8910136.166927-115.139055Breakfast Spot189101Las VegasNevadaNVUS36.17193-115.14001
\n", "
" ], "text/plain": [ " Venue_Rank Venue_ID Venue_Name Venue_Postal_Code \\\n", "0 1 5a32fb62c530935f37812611 Eureka! 89101 \n", "1 2 51cde2b08bbd23404bdc1798 Pizza Rock 89101 \n", "2 3 552ae36a498e9b3b1e232a6e VegeNation 89101 \n", "3 4 539a4129498e2eba5804ba4a Carson Kitchen 89101 \n", "4 5 503cec78e4b0f39ae12141db eat. 89101 \n", "\n", " Venue_Latitude Venue_Longitude Venue_Category \\\n", "0 36.168976 -115.139580 American Restaurant \n", "1 36.171707 -115.142343 Pizza Place \n", "2 36.167398 -115.139421 Vegetarian / Vegan Restaurant \n", "3 36.167884 -115.140664 Gastropub \n", "4 36.166927 -115.139055 Breakfast Spot \n", "\n", " Venue_Price_Category Postal_Code Place_Name State_Name State_Code \\\n", "0 2 89101 Las Vegas Nevada NV \n", "1 2 89101 Las Vegas Nevada NV \n", "2 2 89101 Las Vegas Nevada NV \n", "3 3 89101 Las Vegas Nevada NV \n", "4 1 89101 Las Vegas Nevada NV \n", "\n", " Country_Code PC_Latitude PC_Longitude \n", "0 US 36.17193 -115.14001 \n", "1 US 36.17193 -115.14001 \n", "2 US 36.17193 -115.14001 \n", "3 US 36.17193 -115.14001 \n", "4 US 36.17193 -115.14001 " ] }, "metadata": { "tags": [] }, "execution_count": 31 } ] }, { "cell_type": "markdown", "metadata": { "id": "3YdwyC5RcXns" }, "source": [ "To summarize our findings in this area, we will determine the features of the venue category as above, but also include the price ranges.\n", "\n", "Based on these features, a new venue with similar features might be able to emulate the popularity of the other venues." ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 331 }, "id": "42sakHPndDnT", "outputId": "1f0cf631-01c0-43e8-f34d-a2e033ad5442" }, "source": [ "# Change type of Price category to calculate its average\n", "top_area['Venue_Price_Category'] = top_area['Venue_Price_Category'].astype('int32')\n", "\n", "# Calculate the features of each venue category\n", "top_summary = top_area.groupby('Venue_Category').agg({'Venue_Category': 'count', \n", " 'Venue_Rank': 'mean', \n", " 'Venue_Price_Category': 'mean'}).dropna()\n", "\n", "# Formatting dataframe for presentation\n", "colnames = ['Venues in category', 'Mean Rank', 'Mean price range']\n", "top_summary.columns = colnames\n", "top_summary = top_summary.sort_values(by='Mean Rank', ascending=True).reset_index()\n", "top_summary.columns = ['Venue Category'] + colnames\n", "\n", "print('Characteristics of most popular venues in postal area', top50_summary.iloc[0,0] + '.\\n')\n", "top_summary" ], "execution_count": null, "outputs": [ { "output_type": "stream", "text": [ "Characteristics of most popular venues in postal area 89101.\n", "\n" ], "name": "stdout" }, { "output_type": "execute_result", "data": { "text/html": [ "
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Venue CategoryVenues in categoryMean RankMean price range
0American Restaurant11.02.000000
1Vegetarian / Vegan Restaurant13.02.000000
2Gastropub27.02.500000
3Pizza Place37.01.333333
4Thai Restaurant17.02.000000
5Breakfast Spot29.01.500000
6Sandwich Place19.01.000000
7Steakhouse29.03.500000
\n", "
" ], "text/plain": [ " Venue Category Venues in category Mean Rank \\\n", "0 American Restaurant 1 1.0 \n", "1 Vegetarian / Vegan Restaurant 1 3.0 \n", "2 Gastropub 2 7.0 \n", "3 Pizza Place 3 7.0 \n", "4 Thai Restaurant 1 7.0 \n", "5 Breakfast Spot 2 9.0 \n", "6 Sandwich Place 1 9.0 \n", "7 Steakhouse 2 9.0 \n", "\n", " Mean price range \n", "0 2.000000 \n", "1 2.000000 \n", "2 2.500000 \n", "3 1.333333 \n", "4 2.000000 \n", "5 1.500000 \n", "6 1.000000 \n", "7 3.500000 " ] }, "metadata": { "tags": [] }, "execution_count": 32 } ] }, { "cell_type": "markdown", "metadata": { "id": "4OIy1QM_tQ8B" }, "source": [ "The above gives us insight into the characteristics of the most popular venue categories in the most popular postal area. \n", "\n", "If one is to establish a new venture in this area, it would likely be beneficial to consider using the venue categories in the above list, while ensuring the price range is within the similar range." ] }, { "cell_type": "markdown", "metadata": { "id": "WNaZEBmbqml9" }, "source": [ "###Machine Learning to assign Venues to Clusters with k-Means" ] }, { "cell_type": "markdown", "metadata": { "id": "XJRli9p_qmmA" }, "source": [ "In this section, we will use the k-Means machine learning algorithm to see if we can group the various venues we have in our dataset into clusters based on their similar features.\n", "\n", "Since many of the features we are using in the dataset are categorical variables (also called factor variables), we are going to create dummies with these features through one-hot encoding.\n", "\n", "Specifically, we are going to do the below steps:\n", "1. Transform the ratings of the venues into 5 groups, with ratings from \"Very high\" to \"Very low\" \n", "2. Include all features into a dataframe, one-hot enocde the data\n", "3. We will create the cluster according to the postal codes, since these appear to be highly relevant in determining the rank of a given venue\n", "4. Evaluate the clusters for the high-rated venues, and investigate which features seem to be linked to high (and low) ratings" ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "SgnsVE4cqmmB", "outputId": "1ea6affc-52e9-4aac-aaf9-e9f9d904bca0" }, "source": [ "# Grouping the venue ratings into categories\n", "pop_groups = 5 # making 5 groups\n", "pop_labels = ['Very high', 'High', 'Medium', 'Low', 'Very low']\n", "cluster_df = analysis_df.copy(deep=True)\n", "cluster_df['Venue_Popularity'] = pd.cut(cluster_df['Venue_Rank'],\n", " bins = pop_groups,\n", " labels = pop_labels,\n", " precision = 0,\n", " ordered = False)\n", "cluster_df['Venue_Popularity'].value_counts()" ], "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "Very low 48\n", "Low 48\n", "High 48\n", "Very high 48\n", "Medium 47\n", "Name: Venue_Popularity, dtype: int64" ] }, "metadata": { "tags": [] }, "execution_count": 33 } ] }, { "cell_type": "markdown", "metadata": { "id": "SVWOiEYu2hIF" }, "source": [ "#### Converting features using one-hot encoding\n", "Having done the above, we will apply one-hot enconding using dummies." ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 142 }, "id": "P2UEnbLAqmmC", "outputId": "b211809c-8693-402f-ea8c-c82e3ad66f0e" }, "source": [ "# Converting Venue Popularity to one hot encoding using dummies\n", "pop_onehot = pd.get_dummies(cluster_df[['Venue_Popularity']], prefix='', prefix_sep='')\n", "pop_onehot = pd.concat([cluster_df['Venue_Postal_Code'], pop_onehot], axis=1)\n", "\n", "# Grouping the postal codes according to the mean number of each popularity category\n", "pop_grouped = pop_onehot.groupby('Venue_Postal_Code').mean().reset_index()\n", "\n", "# Renaming the columns for legibility:\n", "colnames = pop_grouped.columns.values\n", "colnames = np.append(colnames[0], 'Popularity ' + np.array(colnames[1:]))\n", "pop_grouped.columns = colnames\n", "\n", "pop_grouped.head(3)" ], "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/html": [ "
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Venue_Postal_CodePopularity Very highPopularity HighPopularity MediumPopularity LowPopularity Very low
0890140.00.00.00.01.0
1890300.00.01.00.00.0
2890310.00.00.00.01.0
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" ], "text/plain": [ " Venue_Postal_Code Popularity Very high Popularity High Popularity Medium \\\n", "0 89014 0.0 0.0 0.0 \n", "1 89030 0.0 0.0 1.0 \n", "2 89031 0.0 0.0 0.0 \n", "\n", " Popularity Low Popularity Very low \n", "0 0.0 1.0 \n", "1 0.0 0.0 \n", "2 0.0 1.0 " ] }, "metadata": { "tags": [] }, "execution_count": 34 } ] }, { "cell_type": "markdown", "metadata": { "id": "Mo-1VTJV2rK3" }, "source": [ "Next, we will also convert the venues' price categories to dummies using the same procedure as above." ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 142 }, "id": "Xfstt500qmmC", "outputId": "5f6b82e2-3610-4edb-e0e8-712dee95f576" }, "source": [ "# Convert Venue Price Category to one hot encoding using dummies\n", "pricecat_onehot = pd.get_dummies(cluster_df[['Venue_Price_Category']], prefix='', prefix_sep='')\n", "\n", "# add the postal code into the one hot dataframe\n", "pricecat_onehot = pd.concat([cluster_df['Venue_Postal_Code'], pricecat_onehot], axis=1)\n", "\n", "# grouping the postal areas according to the mean number of each price category\n", "pricecat_grouped = pricecat_onehot.groupby('Venue_Postal_Code').mean().reset_index()\n", "\n", "# Renaming price category dummy names for legibility\n", "colnames = pricecat_grouped.columns.values\n", "colnames = np.append(colnames[0], 'Price Category ' + np.array(colnames[1:]))\n", "pricecat_grouped.columns = colnames\n", "\n", "pricecat_grouped.head(3)" ], "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/html": [ "
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Venue_Postal_CodePrice Category 1Price Category 2Price Category 3Price Category 4
0890140.50.3750.1250.0
1890300.60.4000.0000.0
2890310.01.0000.0000.0
\n", "
" ], "text/plain": [ " Venue_Postal_Code Price Category 1 Price Category 2 Price Category 3 \\\n", "0 89014 0.5 0.375 0.125 \n", "1 89030 0.6 0.400 0.000 \n", "2 89031 0.0 1.000 0.000 \n", "\n", " Price Category 4 \n", "0 0.0 \n", "1 0.0 \n", "2 0.0 " ] }, "metadata": { "tags": [] }, "execution_count": 35 } ] }, { "cell_type": "markdown", "metadata": { "id": "JH3X1aPt26Bl" }, "source": [ "As the final step of the one-hot encoding, we will do this for each venue category as well." ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 213 }, "id": "pKabnGrTqmmD", "outputId": "c2f0c205-fcfe-4b58-da35-11804d4a8fdd" }, "source": [ "# Convert Venue Category to one hot encoding using dummies\n", "ven_cat_onehot = pd.get_dummies(cluster_df[['Venue_Category']], prefix='', prefix_sep='')\n", "\n", "# add the postal code into the one hot dataframe\n", "ven_cat_onehot = pd.concat([cluster_df['Venue_Postal_Code'], ven_cat_onehot], axis=1)\n", "\n", "# grouping the postal areas according to the mean number of each category\n", "ven_cat_grouped = ven_cat_onehot.groupby('Venue_Postal_Code').mean().reset_index()\n", "\n", "# Renaming category dummy names for readability\n", "colnames = ven_cat_grouped.columns.values\n", "colnames = np.append(colnames[0], 'Category: ' + np.array(colnames[1:]))\n", "ven_cat_grouped.columns = colnames\n", "\n", "ven_cat_grouped.head(3)" ], "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/html": [ "
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Venue_Postal_CodeCategory: American RestaurantCategory: Andhra RestaurantCategory: Arepa RestaurantCategory: Asian RestaurantCategory: BBQ JointCategory: Bagel ShopCategory: BakeryCategory: Brazilian RestaurantCategory: Breakfast SpotCategory: BuffetCategory: Burger JointCategory: CaféCategory: Cajun / Creole RestaurantCategory: Caribbean RestaurantCategory: Deli / BodegaCategory: DinerCategory: Dumpling RestaurantCategory: Eastern European RestaurantCategory: Fast Food RestaurantCategory: French RestaurantCategory: Fried Chicken JointCategory: GastropubCategory: German RestaurantCategory: Greek RestaurantCategory: Hawaiian RestaurantCategory: Indian RestaurantCategory: Irish PubCategory: Italian RestaurantCategory: Japanese RestaurantCategory: Korean RestaurantCategory: Latin American RestaurantCategory: Mexican RestaurantCategory: Middle Eastern RestaurantCategory: New American RestaurantCategory: Noodle HouseCategory: Pizza PlaceCategory: RestaurantCategory: Sandwich PlaceCategory: Seafood RestaurantCategory: Snack PlaceCategory: Southern / Soul Food RestaurantCategory: Spanish RestaurantCategory: SteakhouseCategory: Sushi RestaurantCategory: Taco PlaceCategory: Tapas RestaurantCategory: Thai RestaurantCategory: Theme RestaurantCategory: Vegetarian / Vegan RestaurantCategory: Vietnamese RestaurantCategory: Wings Joint
0890140.00.1250.00.00.00.00.1250.00.00.00.00.00.00.00.1250.00.00.00.00.00.1250.00.00.00.1250.00.00.00.00.1250.00.00.00.00.00.00.1250.1250.00.00.00.00.00.00.00.00.00.00.00.00.0
1890300.00.0000.00.00.00.00.0000.00.00.00.00.00.00.00.0000.00.00.00.00.00.2000.00.00.00.0000.00.00.00.00.0000.00.60.00.00.00.20.0000.0000.00.00.00.00.00.00.00.00.00.00.00.00.0
2890310.00.0000.00.00.00.00.0000.00.00.00.00.00.00.00.0000.00.00.00.00.00.0000.00.00.00.0000.00.00.00.00.0000.00.00.00.00.00.00.0000.0000.00.00.00.00.00.00.00.01.00.00.00.00.0
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" ], "text/plain": [ " Venue_Postal_Code Category: American Restaurant \\\n", "0 89014 0.0 \n", "1 89030 0.0 \n", "2 89031 0.0 \n", "\n", " Category: Andhra Restaurant Category: Arepa Restaurant \\\n", "0 0.125 0.0 \n", "1 0.000 0.0 \n", "2 0.000 0.0 \n", "\n", " Category: Asian Restaurant Category: BBQ Joint Category: Bagel Shop \\\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", "\n", " Category: Bakery Category: Brazilian Restaurant Category: Breakfast Spot \\\n", "0 0.125 0.0 0.0 \n", "1 0.000 0.0 0.0 \n", "2 0.000 0.0 0.0 \n", "\n", " Category: Buffet Category: Burger Joint Category: Café \\\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", "\n", " Category: Cajun / Creole Restaurant Category: Caribbean Restaurant \\\n", "0 0.0 0.0 \n", "1 0.0 0.0 \n", "2 0.0 0.0 \n", "\n", " Category: Deli / Bodega Category: Diner Category: Dumpling Restaurant \\\n", "0 0.125 0.0 0.0 \n", "1 0.000 0.0 0.0 \n", "2 0.000 0.0 0.0 \n", "\n", " Category: Eastern European Restaurant Category: Fast Food Restaurant \\\n", "0 0.0 0.0 \n", "1 0.0 0.0 \n", "2 0.0 0.0 \n", "\n", " Category: French Restaurant Category: Fried Chicken Joint \\\n", "0 0.0 0.125 \n", "1 0.0 0.200 \n", "2 0.0 0.000 \n", "\n", " Category: Gastropub Category: German Restaurant \\\n", "0 0.0 0.0 \n", "1 0.0 0.0 \n", "2 0.0 0.0 \n", "\n", " Category: Greek Restaurant Category: Hawaiian Restaurant \\\n", "0 0.0 0.125 \n", "1 0.0 0.000 \n", "2 0.0 0.000 \n", "\n", " Category: Indian Restaurant Category: Irish Pub \\\n", "0 0.0 0.0 \n", "1 0.0 0.0 \n", "2 0.0 0.0 \n", "\n", " Category: Italian Restaurant Category: Japanese Restaurant \\\n", "0 0.0 0.0 \n", "1 0.0 0.0 \n", "2 0.0 0.0 \n", "\n", " Category: Korean Restaurant Category: Latin American Restaurant \\\n", "0 0.125 0.0 \n", "1 0.000 0.0 \n", "2 0.000 0.0 \n", "\n", " Category: Mexican Restaurant Category: Middle Eastern Restaurant \\\n", "0 0.0 0.0 \n", "1 0.6 0.0 \n", "2 0.0 0.0 \n", "\n", " Category: New American Restaurant Category: Noodle House \\\n", "0 0.0 0.0 \n", "1 0.0 0.0 \n", "2 0.0 0.0 \n", "\n", " Category: Pizza Place Category: Restaurant Category: Sandwich Place \\\n", "0 0.0 0.125 0.125 \n", "1 0.2 0.000 0.000 \n", "2 0.0 0.000 0.000 \n", "\n", " Category: Seafood Restaurant Category: Snack Place \\\n", "0 0.0 0.0 \n", "1 0.0 0.0 \n", "2 0.0 0.0 \n", "\n", " Category: Southern / Soul Food Restaurant Category: Spanish Restaurant \\\n", "0 0.0 0.0 \n", "1 0.0 0.0 \n", "2 0.0 0.0 \n", "\n", " Category: Steakhouse Category: Sushi Restaurant Category: Taco Place \\\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", "\n", " Category: Tapas Restaurant Category: Thai Restaurant \\\n", "0 0.0 0.0 \n", "1 0.0 0.0 \n", "2 0.0 1.0 \n", "\n", " Category: Theme Restaurant Category: Vegetarian / Vegan Restaurant \\\n", "0 0.0 0.0 \n", "1 0.0 0.0 \n", "2 0.0 0.0 \n", "\n", " Category: Vietnamese Restaurant Category: Wings Joint \n", "0 0.0 0.0 \n", "1 0.0 0.0 \n", "2 0.0 0.0 " ] }, "metadata": { "tags": [] }, "execution_count": 36 } ] }, { "cell_type": "markdown", "metadata": { "id": "Fh5qcIQV3fkj" }, "source": [ "Having converted the features above using one-hot encoding, we will merge them together into one dataframe which combines all the dummies (features)." ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 430 }, "id": "fkq6S2j1qmmD", "outputId": "e680b5f5-5f45-4365-8bec-f480b0edcf3a" }, "source": [ "# Joining the dummies dataframes together\n", "areas_grouped = pd.merge(pop_grouped, pricecat_grouped, on='Venue_Postal_Code')\n", "areas_grouped = pd.merge(areas_grouped, ven_cat_grouped, on='Venue_Postal_Code')\n", "areas_grouped.head(10)" ], "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/html": [ "
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\n", "4 0.000000 0.000000 0.000000 \n", "5 0.153846 0.076923 0.076923 \n", "6 0.166667 0.000000 0.000000 \n", "7 0.000000 0.083333 0.000000 \n", "8 0.142857 0.000000 0.142857 \n", "9 0.500000 0.000000 0.500000 \n", "\n", " Category: Andhra Restaurant Category: Arepa Restaurant \\\n", "0 0.125000 0.000000 \n", "1 0.000000 0.000000 \n", "2 0.000000 0.000000 \n", "3 0.000000 0.000000 \n", "4 0.000000 0.000000 \n", "5 0.000000 0.000000 \n", "6 0.000000 0.000000 \n", "7 0.083333 0.000000 \n", "8 0.000000 0.142857 \n", "9 0.000000 0.000000 \n", "\n", " Category: Asian Restaurant Category: BBQ Joint Category: Bagel Shop \\\n", "0 0.000000 0.0 0.0 \n", "1 0.000000 0.0 0.0 \n", "2 0.000000 0.0 0.0 \n", "3 0.250000 0.0 0.0 \n", "4 0.000000 0.0 0.0 \n", "5 0.000000 0.0 0.0 \n", "6 0.000000 0.0 0.0 \n", "7 0.000000 0.0 0.0 \n", "8 0.142857 0.0 0.0 \n", "9 0.000000 0.0 0.0 \n", "\n", " Category: Bakery Category: Brazilian Restaurant Category: Breakfast Spot \\\n", "0 0.125 0.0 0.000000 \n", "1 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0.00 \n", "8 0.000000 0.00 \n", "9 0.000000 0.00 " ] }, "metadata": { "tags": [] }, "execution_count": 37 } ] }, { "cell_type": "markdown", "metadata": { "id": "I2bf6KYUqmmD" }, "source": [ "####Clustering venues \r\n", "Now we have the data we need to carry out the k-Means clustering. We are going to perform this using 5 clusters." ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "hVh-Iuz1qmmE", "outputId": "4526759b-2c2d-4f9b-d97a-d60582be024c" }, "source": [ "# defining number of clusters\n", "klusters = 5\n", "\n", "# keep only the relevant features; i.e. dropping the postal code\n", "areas_cluster = areas_grouped.drop('Venue_Postal_Code', 1)\n", "\n", "# run K-means clustering\n", "kmeans = KMeans(n_clusters=klusters, random_state=0).fit(areas_cluster)\n", "\n", "# list the labels; i.e. the cluster categories generated by the algorithm\n", "kmeans.labels_" ], "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "array([0, 2, 0, 1, 0, 3, 3, 1, 3, 3, 2, 0, 2, 2, 2, 0, 0, 2, 0, 1, 4, 0,\n", " 4, 0, 1, 4, 1, 1, 0, 1, 2], dtype=int32)" ] }, "metadata": { "tags": [] }, "execution_count": 38 } ] }, { "cell_type": "markdown", "metadata": { "id": "ap7pN6Zy4Oz9" }, "source": [ "Having performed the clustering, we will merge them into the dataframe with the features. In the below dataframe, these clusters are now listed in the \"Cluster\" column." ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 430 }, "id": "dN7xp4tkqmmE", "outputId": "0292f7db-ad6c-4e2f-eec1-1eaa45b3216f" }, "source": [ "# add clusters back into dataframe\n", "areas_grouped.insert(1, 'Cluster', kmeans.labels_)\n", "areas_grouped.head(10)" ], "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/html": [ "
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Restaurant Category: Sandwich Place \\\n", "0 0.000000 0.125 0.125000 \n", "1 0.200000 0.000 0.000000 \n", "2 0.000000 0.000 0.000000 \n", "3 0.000000 0.000 0.000000 \n", "4 0.500000 0.000 0.000000 \n", "5 0.230769 0.000 0.076923 \n", "6 0.055556 0.000 0.055556 \n", "7 0.000000 0.000 0.000000 \n", "8 0.000000 0.000 0.000000 \n", "9 0.000000 0.000 0.000000 \n", "\n", " Category: Seafood Restaurant Category: Snack Place \\\n", "0 0.000000 0.0 \n", "1 0.000000 0.0 \n", "2 0.000000 0.0 \n", "3 0.000000 0.0 \n", "4 0.000000 0.0 \n", "5 0.000000 0.0 \n", "6 0.055556 0.0 \n", "7 0.083333 0.0 \n", "8 0.000000 0.0 \n", "9 0.000000 0.0 \n", "\n", " Category: Southern / Soul Food Restaurant Category: Spanish 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", "5 0.0 0.0 \n", "6 0.0 0.0 \n", "7 0.0 0.0 \n", "8 0.0 0.0 \n", "9 0.5 0.0 \n", "\n", " Category: Steakhouse Category: Sushi Restaurant Category: Taco Place \\\n", "0 0.000000 0.000000 0.000000 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"2 0.000000 0.00 \n", "3 0.000000 0.25 \n", "4 0.000000 0.00 \n", "5 0.000000 0.00 \n", "6 0.055556 0.00 \n", "7 0.000000 0.00 \n", "8 0.000000 0.00 \n", "9 0.000000 0.00 " ] }, "metadata": { "tags": [] }, "execution_count": 39 } ] }, { "cell_type": "markdown", "metadata": { "id": "WoLQC8ZT4np5" }, "source": [ "To get some more sense out of the characteristics of each individual cluster, let's sort the dataframe to investigate whether the patterns seem to make sense." ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "JjjLet-iqmmE", "outputId": "916b327c-43d0-4fd6-a3d7-9e06ea9fff58" }, "source": [ "areas_grouped.sort_values(by='Cluster')" ], "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/html": [ "
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" ], "text/plain": [ " Venue_Postal_Code Cluster Popularity Very high Popularity High \\\n", "0 89014 0 0.000000 0.000000 \n", "28 89147 0 0.000000 0.000000 \n", "23 89129 0 0.000000 0.000000 \n", "21 89123 0 0.000000 0.000000 \n", "18 89120 0 0.000000 0.000000 \n", "16 89118 0 0.000000 0.000000 \n", "11 89108 0 0.000000 0.000000 \n", "15 89117 0 0.000000 0.000000 \n", "2 89031 0 0.000000 0.000000 \n", "4 89084 0 0.000000 0.000000 \n", "19 89121 1 0.000000 0.000000 \n", "27 89146 1 0.000000 0.000000 \n", "26 89145 1 0.000000 0.000000 \n", "24 89130 1 0.000000 0.000000 \n", "3 89032 1 0.000000 0.000000 \n", "29 89158 1 0.000000 0.000000 \n", "7 89103 1 0.000000 0.000000 \n", "1 89030 2 0.000000 0.000000 \n", "30 89169 2 0.000000 0.000000 \n", "14 89115 2 0.000000 0.000000 \n", "13 89110 2 0.000000 0.000000 \n", "12 89109 2 0.111111 0.666667 \n", "10 89107 2 0.000000 0.000000 \n", "17 89119 2 0.000000 0.000000 \n", "5 89101 3 1.000000 0.000000 \n", "6 89102 3 1.000000 0.000000 \n", "9 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\n", "1 0.000000 0.000000 \n", "30 0.000000 0.000000 \n", "14 0.000000 0.000000 \n", "13 0.000000 0.000000 \n", "12 0.000000 0.013889 \n", "10 0.000000 0.000000 \n", "17 0.000000 0.000000 \n", "5 0.000000 0.000000 \n", "6 0.055556 0.000000 \n", "9 0.000000 0.000000 \n", "8 0.000000 0.000000 \n", "20 0.000000 0.000000 \n", "22 0.000000 0.000000 \n", "25 0.000000 0.000000 " ] }, "metadata": { "tags": [] }, "execution_count": 40 } ] }, { "cell_type": "markdown", "metadata": { "id": "9rPsJd084xPf" }, "source": [ "The above clusters seem to make a good job of categorizing the various venue types. In order to make this more clear, we will group them together and calculate the mean of each feature." ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 275 }, "id": "9aR3ksPnqmmE", "outputId": "ac972909-03aa-4af4-9842-f81f9b777252" }, "source": [ "# Grouping on the given clusters to view their relevant details\n", "mean_clusters = areas_grouped.groupby('Cluster').mean().reset_index()\n", "mean_clusters['Cluster'] = mean_clusters['Cluster'].astype('category')\n", "mean_clusters" ], "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/html": [ "
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\n", "
" ], "text/plain": [ " Cluster Popularity Very high Popularity High Popularity Medium \\\n", "0 0 0.000000 0.000000 0.000000 \n", "1 1 0.000000 0.000000 0.000000 \n", "2 2 0.015873 0.095238 0.888889 \n", "3 3 1.000000 0.000000 0.000000 \n", "4 4 0.000000 0.000000 0.000000 \n", "\n", " Popularity Low Popularity Very low Price Category 1 Price Category 2 \\\n", "0 0.000000 1.000000 0.283333 0.693056 \n", "1 0.982143 0.017857 0.325000 0.452381 \n", "2 0.000000 0.000000 0.320635 0.494841 \n", "3 0.000000 0.000000 0.364621 0.375305 \n", "4 0.000000 1.000000 0.833333 0.000000 \n", "\n", " Price Category 3 Price Category 4 Category: American Restaurant \\\n", "0 0.012500 0.011111 0.000000 \n", "1 0.182143 0.040476 0.065476 \n", "2 0.131746 0.052778 0.017460 \n", "3 0.240842 0.019231 0.179945 \n", "4 0.166667 0.000000 0.000000 \n", "\n", " Category: Andhra Restaurant Category: Arepa Restaurant \\\n", "0 0.029167 0.000000 \n", "1 0.011905 0.000000 \n", "2 0.000000 0.000000 \n", "3 0.000000 0.035714 \n", "4 0.000000 0.000000 \n", "\n", " Category: Asian Restaurant Category: BBQ Joint Category: Bagel Shop \\\n", "0 0.000000 0.000000 0.000000 \n", "1 0.035714 0.035714 0.017857 \n", "2 0.003968 0.005952 0.000000 \n", "3 0.035714 0.000000 0.000000 \n", "4 0.000000 0.000000 0.000000 \n", "\n", " Category: Bakery Category: Brazilian Restaurant Category: Breakfast Spot \\\n", "0 0.012500 0.000000 0.200000 \n", "1 0.000000 0.000000 0.047619 \n", "2 0.037698 0.045238 0.005952 \n", "3 0.000000 0.000000 0.080128 \n", "4 0.083333 0.000000 0.750000 \n", "\n", " Category: Buffet Category: Burger Joint Category: Café \\\n", "0 0.000000 0.033333 0.038889 \n", "1 0.000000 0.023810 0.070238 \n", "2 0.001984 0.015476 0.075397 \n", "3 0.000000 0.000000 0.000000 \n", "4 0.000000 0.000000 0.000000 \n", "\n", " Category: Cajun / Creole Restaurant Category: Caribbean Restaurant \\\n", "0 0.000000 0.000000 \n", "1 0.000000 0.000000 \n", "2 0.000000 0.009524 \n", "3 0.013889 0.000000 \n", "4 0.000000 0.000000 \n", "\n", " Category: Deli / Bodega Category: Diner Category: Dumpling Restaurant \\\n", "0 0.045833 0.000000 0.000000 \n", "1 0.000000 0.000000 0.028571 \n", "2 0.000000 0.001984 0.000000 \n", "3 0.000000 0.000000 0.000000 \n", "4 0.000000 0.000000 0.000000 \n", "\n", " Category: Eastern European Restaurant Category: Fast Food Restaurant \\\n", "0 0.000000 0.050000 \n", "1 0.011905 0.035714 \n", "2 0.000000 0.140079 \n", "3 0.000000 0.063492 \n", "4 0.000000 0.000000 \n", "\n", " Category: French Restaurant Category: Fried Chicken Joint \\\n", "0 0.000000 0.029167 \n", "1 0.000000 0.011905 \n", "2 0.015873 0.030556 \n", "3 0.000000 0.000000 \n", "4 0.083333 0.000000 \n", "\n", " Category: Gastropub Category: German Restaurant \\\n", "0 0.000000 0.016667 \n", "1 0.000000 0.000000 \n", "2 0.000000 0.000000 \n", "3 0.038462 0.000000 \n", "4 0.000000 0.000000 \n", "\n", " Category: Greek Restaurant Category: Hawaiian Restaurant \\\n", "0 0.016667 0.0125 \n", "1 0.000000 0.0000 \n", "2 0.000000 0.0000 \n", "3 0.000000 0.0000 \n", "4 0.000000 0.0000 \n", "\n", " Category: Indian Restaurant Category: Irish Pub \\\n", "0 0.000000 0.000000 \n", "1 0.000000 0.011905 \n", "2 0.009524 0.000000 \n", "3 0.000000 0.000000 \n", "4 0.000000 0.000000 \n", "\n", " Category: Italian Restaurant Category: Japanese Restaurant \\\n", "0 0.011111 0.033333 \n", "1 0.077381 0.023810 \n", "2 0.032937 0.013492 \n", "3 0.013889 0.013889 \n", "4 0.000000 0.000000 \n", "\n", " Category: Korean Restaurant Category: Latin American Restaurant \\\n", "0 0.012500 0.000000 \n", "1 0.011905 0.000000 \n", "2 0.035714 0.001984 \n", "3 0.013889 0.000000 \n", "4 0.000000 0.000000 \n", "\n", " Category: Mexican Restaurant Category: Middle Eastern Restaurant \\\n", "0 0.027778 0.033333 \n", "1 0.111905 0.000000 \n", "2 0.139286 0.000000 \n", "3 0.000000 0.000000 \n", "4 0.000000 0.000000 \n", "\n", " Category: New American Restaurant Category: Noodle House \\\n", "0 0.000000 0.000000 \n", "1 0.000000 0.035714 \n", "2 0.013492 0.000000 \n", "3 0.013889 0.000000 \n", "4 0.083333 0.000000 \n", "\n", " Category: Pizza Place Category: Restaurant Category: Sandwich Place \\\n", "0 0.072222 0.0125 0.023611 \n", "1 0.035714 0.0000 0.000000 \n", "2 0.034524 0.0000 0.073413 \n", "3 0.071581 0.0000 0.033120 \n", "4 0.000000 0.0000 0.000000 \n", "\n", " Category: Seafood Restaurant Category: Snack Place \\\n", "0 0.000000 0.000000 \n", "1 0.069048 0.000000 \n", "2 0.088889 0.003968 \n", "3 0.013889 0.000000 \n", "4 0.000000 0.000000 \n", "\n", " Category: Southern / Soul Food Restaurant Category: Spanish Restaurant \\\n", "0 0.000000 0.000000 \n", "1 0.000000 0.000000 \n", "2 0.001984 0.001984 \n", "3 0.125000 0.000000 \n", "4 0.000000 0.000000 \n", "\n", " Category: Steakhouse Category: Sushi Restaurant Category: Taco Place \\\n", "0 0.027778 0.111111 0.000000 \n", "1 0.053571 0.083333 0.000000 \n", "2 0.061111 0.015476 0.051587 \n", "3 0.038462 0.041667 0.035714 \n", "4 0.000000 0.000000 0.000000 \n", "\n", " Category: Tapas Restaurant Category: Thai Restaurant \\\n", "0 0.000000 0.150000 \n", "1 0.011905 0.011905 \n", "2 0.000000 0.000000 \n", "3 0.000000 0.104548 \n", "4 0.000000 0.000000 \n", "\n", " Category: Theme Restaurant Category: Vegetarian / Vegan Restaurant \\\n", "0 0.000000 0.000000 \n", "1 0.000000 0.017857 \n", "2 0.001984 0.009524 \n", "3 0.000000 0.019231 \n", "4 0.000000 0.000000 \n", "\n", " Category: Vietnamese Restaurant Category: Wings Joint \n", "0 0.000000 0.000000 \n", "1 0.011905 0.035714 \n", "2 0.000000 0.001984 \n", "3 0.013889 0.000000 \n", "4 0.000000 0.000000 " ] }, "metadata": { "tags": [] }, "execution_count": 41 } ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 359 }, "id": "63GVtYcelVRV", "outputId": "5a0d207d-6adc-4e0a-d9c8-97e44c290912" }, "source": [ "# Creating subset of Price category features\r\n", "\r\n", "# Converting category columns to rows\r\n", "pcat_df = mean_clusters.iloc[:,6:10].stack().reset_index()\r\n", "\r\n", "# Renaming columns\r\n", "pcat_df.columns = ['Cluster', 'Price Category', 'Mean value']\r\n", "\r\n", "# Removing uncessary label text\r\n", "pcat_df['Price Category'] = pcat_df['Price Category'].str.replace('Price Category ','')\r\n", "pcat_df.head(10)" ], "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/html": [ "
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ClusterPrice CategoryMean value
0010.283333
1020.693056
2030.012500
3040.011111
4110.325000
5120.452381
6130.182143
7140.040476
8210.320635
9220.494841
\n", "
" ], "text/plain": [ " Cluster Price Category Mean value\n", "0 0 1 0.283333\n", "1 0 2 0.693056\n", "2 0 3 0.012500\n", "3 0 4 0.011111\n", "4 1 1 0.325000\n", "5 1 2 0.452381\n", "6 1 3 0.182143\n", "7 1 4 0.040476\n", "8 2 1 0.320635\n", "9 2 2 0.494841" ] }, "metadata": { "tags": [] }, "execution_count": 42 } ] }, { "cell_type": "markdown", "metadata": { "id": "Paf61qqSjsNL" }, "source": [ "Let's break down the popularity per cluster visually." ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 359 }, "id": "IiI5XHEMbDbX", "outputId": "4ff28f2f-da15-49fc-c848-f608a72c2ba9" }, "source": [ "# Creating subset of Popularity features\r\n", "\r\n", "# Converting popularity columns to rows\r\n", "pop_df = mean_clusters.iloc[:,1:6].stack().reset_index()\r\n", "\r\n", "# Renaming columns\r\n", "pop_df.columns = ['Cluster', 'Popularity', 'Mean value']\r\n", "\r\n", "# Removing uncessary label text\r\n", "pop_df['Popularity'] = pop_df['Popularity'].str.replace('Popularity ','')\r\n", "pop_df.head(10)" ], "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/html": [ "
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ClusterPopularityMean value
00Very high0.000000
10High0.000000
20Medium0.000000
30Low0.000000
40Very low1.000000
51Very high0.000000
61High0.000000
71Medium0.000000
81Low0.982143
91Very low0.017857
\n", "
" ], "text/plain": [ " Cluster Popularity Mean value\n", "0 0 Very high 0.000000\n", "1 0 High 0.000000\n", "2 0 Medium 0.000000\n", "3 0 Low 0.000000\n", "4 0 Very low 1.000000\n", "5 1 Very high 0.000000\n", "6 1 High 0.000000\n", "7 1 Medium 0.000000\n", "8 1 Low 0.982143\n", "9 1 Very low 0.017857" ] }, "metadata": { "tags": [] }, "execution_count": 43 } ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 433 }, "id": "vBQ-eoFWjX9k", "outputId": "dd09a86f-8676-4ad6-be10-53bd6d5c73c6" }, "source": [ "# Create facet grids of results\r\n", "g1 = sns.FacetGrid(pop_df, col='Cluster')\r\n", "g1.map(sns.barplot, 'Popularity', 'Mean value')\r\n", "plt.show()\r\n", "\r\n", "g2 = sns.FacetGrid(pcat_df, col='Cluster')\r\n", "g2.map(sns.barplot, 'Price Category', 'Mean value')\r\n", "\r\n", "plt.show()" ], "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "image/png": 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\n", 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" ] }, "metadata": { "tags": [] } } ] }, { "cell_type": "markdown", "metadata": { "id": "unFed7RDqmmF" }, "source": [ "The above gives us very useful feedback. We can see the following:\n", "- Which clusters have the highest average popularity\n", "- Which clusters have the lowest average popularity\n", "- How the price categories are distributed according to the clusters\n", "\n", "Since these clusters perform an average of all the postal codes, there will be individual variations across the various postal areas.\n", "\n", "If one is going to establish a new business in one of the individual postal codes, it makes sense to look at what the features are per postal area. We do this below by subsetting on the most popular clusters." ] }, { "cell_type": "markdown", "metadata": { "id": "Ns4WCy9dppBf" }, "source": [ "Finally we will do the following:\r\n", "1. Create a dataframe with the two clusters with highest popularity\r\n", "2. Show each postal code associated with the cluster\r\n", "3. Break down the three most popular venue categories in the cluster\r\n", "4. Break down the two most popular price categories in the cluster" ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 275 }, "id": "NyuAeCvjqQCI", "outputId": "9da3a126-9055-4cb5-bba7-cfffb9583f9f" }, "source": [ "# Subsetting data frame into the poplar clusters and recommended clusters\r\n", "pop_subset = areas_grouped.loc[(areas_grouped['Cluster'] == 2) | (areas_grouped['Cluster'] == 3)].sort_values(by=['Cluster'])\r\n", "rec_subset = areas_grouped.loc[areas_grouped['Cluster']==3].sort_values(by=['Venue_Postal_Code'])\r\n", "\r\n", "# Investigating the subset of all the popular venues\r\n", "pop_subset.head(5)" ], "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/html": [ "
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Venue_Postal_CodeClusterPopularity Very highPopularity HighPopularity MediumPopularity LowPopularity Very lowPrice Category 1Price Category 2Price Category 3Price Category 4Category: American RestaurantCategory: Andhra RestaurantCategory: Arepa RestaurantCategory: Asian RestaurantCategory: BBQ JointCategory: Bagel ShopCategory: BakeryCategory: Brazilian RestaurantCategory: Breakfast SpotCategory: BuffetCategory: Burger JointCategory: CaféCategory: Cajun / Creole RestaurantCategory: Caribbean RestaurantCategory: Deli / BodegaCategory: DinerCategory: Dumpling RestaurantCategory: Eastern European RestaurantCategory: Fast Food RestaurantCategory: French RestaurantCategory: Fried Chicken JointCategory: GastropubCategory: German RestaurantCategory: Greek RestaurantCategory: Hawaiian RestaurantCategory: Indian RestaurantCategory: Irish PubCategory: Italian RestaurantCategory: Japanese RestaurantCategory: Korean RestaurantCategory: Latin American RestaurantCategory: Mexican RestaurantCategory: Middle Eastern RestaurantCategory: New American RestaurantCategory: Noodle HouseCategory: Pizza PlaceCategory: RestaurantCategory: Sandwich PlaceCategory: Seafood RestaurantCategory: Snack PlaceCategory: Southern / Soul Food RestaurantCategory: Spanish RestaurantCategory: SteakhouseCategory: Sushi RestaurantCategory: Taco PlaceCategory: Tapas RestaurantCategory: Thai RestaurantCategory: Theme RestaurantCategory: Vegetarian / Vegan RestaurantCategory: Vietnamese RestaurantCategory: Wings Joint
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" ], "text/plain": [ " Venue_Postal_Code Cluster Popularity Very high Popularity High \\\n", "1 89030 2 0.000000 0.000000 \n", "10 89107 2 0.000000 0.000000 \n", "12 89109 2 0.111111 0.666667 \n", "13 89110 2 0.000000 0.000000 \n", "14 89115 2 0.000000 0.000000 \n", "\n", " Popularity Medium Popularity Low Popularity Very low Price Category 1 \\\n", "1 1.000000 0.0 0.0 0.600000 \n", "10 1.000000 0.0 0.0 0.000000 \n", "12 0.222222 0.0 0.0 0.194444 \n", "13 1.000000 0.0 0.0 1.000000 \n", "14 1.000000 0.0 0.0 0.000000 \n", "\n", " Price Category 2 Price Category 3 Price Category 4 \\\n", "1 0.400000 0.000000 0.000000 \n", "10 1.000000 0.000000 0.000000 \n", "12 0.347222 0.222222 0.236111 \n", "13 0.000000 0.000000 0.000000 \n", "14 1.000000 0.000000 0.000000 \n", "\n", " Category: American Restaurant Category: Andhra Restaurant \\\n", "1 0.000000 0.0 \n", "10 0.000000 0.0 \n", "12 0.055556 0.0 \n", "13 0.000000 0.0 \n", "14 0.000000 0.0 \n", "\n", " Category: Arepa Restaurant Category: Asian Restaurant \\\n", "1 0.0 0.000000 \n", "10 0.0 0.000000 \n", "12 0.0 0.027778 \n", "13 0.0 0.000000 \n", "14 0.0 0.000000 \n", "\n", " Category: BBQ Joint Category: Bagel Shop Category: Bakery \\\n", "1 0.000000 0.0 0.000000 \n", "10 0.000000 0.0 0.000000 \n", "12 0.041667 0.0 0.013889 \n", "13 0.000000 0.0 0.000000 \n", "14 0.000000 0.0 0.000000 \n", "\n", " Category: Brazilian Restaurant Category: Breakfast Spot \\\n", "1 0.0 0.000000 \n", "10 0.0 0.000000 \n", "12 0.0 0.041667 \n", "13 0.0 0.000000 \n", "14 0.0 0.000000 \n", "\n", " Category: Buffet Category: Burger Joint Category: Café \\\n", "1 0.000000 0.000000 0.000000 \n", "10 0.000000 0.000000 0.000000 \n", "12 0.013889 0.041667 0.027778 \n", "13 0.000000 0.000000 0.000000 \n", "14 0.000000 0.000000 0.500000 \n", "\n", " Category: Cajun / Creole Restaurant Category: Caribbean Restaurant \\\n", "1 0.0 0.0 \n", "10 0.0 0.0 \n", "12 0.0 0.0 \n", "13 0.0 0.0 \n", "14 0.0 0.0 \n", "\n", " Category: Deli / Bodega Category: Diner Category: Dumpling Restaurant \\\n", "1 0.0 0.000000 0.0 \n", "10 0.0 0.000000 0.0 \n", "12 0.0 0.013889 0.0 \n", "13 0.0 0.000000 0.0 \n", "14 0.0 0.000000 0.0 \n", "\n", " Category: Eastern European Restaurant Category: Fast Food Restaurant \\\n", "1 0.0 0.000000 \n", "10 0.0 0.000000 \n", "12 0.0 0.013889 \n", "13 0.0 0.333333 \n", "14 0.0 0.500000 \n", "\n", " Category: French Restaurant Category: Fried Chicken Joint \\\n", "1 0.000000 0.200000 \n", "10 0.000000 0.000000 \n", "12 0.111111 0.013889 \n", "13 0.000000 0.000000 \n", "14 0.000000 0.000000 \n", "\n", " Category: Gastropub Category: German Restaurant \\\n", "1 0.0 0.0 \n", "10 0.0 0.0 \n", "12 0.0 0.0 \n", "13 0.0 0.0 \n", "14 0.0 0.0 \n", "\n", " Category: Greek Restaurant Category: Hawaiian Restaurant \\\n", "1 0.0 0.0 \n", "10 0.0 0.0 \n", "12 0.0 0.0 \n", "13 0.0 0.0 \n", "14 0.0 0.0 \n", "\n", " Category: Indian Restaurant Category: Irish Pub \\\n", "1 0.0 0.0 \n", "10 0.0 0.0 \n", "12 0.0 0.0 \n", "13 0.0 0.0 \n", "14 0.0 0.0 \n", "\n", " Category: Italian Restaurant Category: Japanese Restaurant \\\n", "1 0.000000 0.000000 \n", "10 0.000000 0.000000 \n", "12 0.097222 0.027778 \n", "13 0.000000 0.000000 \n", "14 0.000000 0.000000 \n", "\n", " Category: Korean Restaurant Category: Latin American Restaurant \\\n", "1 0.0 0.000000 \n", "10 0.0 0.000000 \n", "12 0.0 0.013889 \n", "13 0.0 0.000000 \n", "14 0.0 0.000000 \n", "\n", " Category: Mexican Restaurant Category: Middle Eastern Restaurant \\\n", "1 0.600000 0.0 \n", "10 0.000000 0.0 \n", "12 0.041667 0.0 \n", "13 0.333333 0.0 \n", "14 0.000000 0.0 \n", "\n", " Category: New American Restaurant Category: Noodle House \\\n", "1 0.000000 0.0 \n", "10 0.000000 0.0 \n", "12 0.027778 0.0 \n", "13 0.000000 0.0 \n", "14 0.000000 0.0 \n", "\n", " Category: Pizza Place Category: Restaurant Category: Sandwich Place \\\n", "1 0.200000 0.0 0.000000 \n", "10 0.000000 0.0 0.500000 \n", "12 0.041667 0.0 0.013889 \n", "13 0.000000 0.0 0.000000 \n", "14 0.000000 0.0 0.000000 \n", "\n", " Category: Seafood Restaurant Category: Snack Place \\\n", "1 0.000000 0.000000 \n", "10 0.500000 0.000000 \n", "12 0.055556 0.027778 \n", "13 0.000000 0.000000 \n", "14 0.000000 0.000000 \n", "\n", " Category: Southern / Soul Food Restaurant Category: Spanish Restaurant \\\n", "1 0.000000 0.000000 \n", "10 0.000000 0.000000 \n", "12 0.013889 0.013889 \n", "13 0.000000 0.000000 \n", "14 0.000000 0.000000 \n", "\n", " Category: Steakhouse Category: Sushi Restaurant Category: Taco Place \\\n", "1 0.000000 0.000000 0.000000 \n", "10 0.000000 0.000000 0.000000 \n", "12 0.111111 0.041667 0.027778 \n", "13 0.000000 0.000000 0.333333 \n", "14 0.000000 0.000000 0.000000 \n", "\n", " Category: Tapas Restaurant Category: Thai Restaurant \\\n", "1 0.0 0.0 \n", "10 0.0 0.0 \n", "12 0.0 0.0 \n", "13 0.0 0.0 \n", "14 0.0 0.0 \n", "\n", " Category: Theme Restaurant Category: Vegetarian / Vegan Restaurant \\\n", "1 0.000000 0.0 \n", "10 0.000000 0.0 \n", "12 0.013889 0.0 \n", "13 0.000000 0.0 \n", "14 0.000000 0.0 \n", "\n", " Category: Vietnamese Restaurant Category: Wings Joint \n", "1 0.0 0.000000 \n", "10 0.0 0.000000 \n", "12 0.0 0.013889 \n", "13 0.0 0.000000 \n", "14 0.0 0.000000 " ] }, "metadata": { "tags": [] }, "execution_count": 45 } ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 275 }, "id": "ME7pfRIpriwP", "outputId": "8d095e87-abc7-4759-aeee-d62734b0bd24" }, "source": [ "# Subset on Postal Code and Category\r\n", "pop_df = pop_subset[pop_subset.columns[11:]].copy(deep=True)\r\n", "pop_df.insert(0,'Postal Code', pop_subset['Venue_Postal_Code'])\r\n", "pop_df.head(5)" ], "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/html": [ "
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Postal CodeCategory: American RestaurantCategory: Andhra RestaurantCategory: Arepa RestaurantCategory: Asian RestaurantCategory: BBQ JointCategory: Bagel ShopCategory: BakeryCategory: Brazilian RestaurantCategory: Breakfast SpotCategory: BuffetCategory: Burger JointCategory: CaféCategory: Cajun / Creole RestaurantCategory: Caribbean RestaurantCategory: Deli / BodegaCategory: DinerCategory: Dumpling RestaurantCategory: Eastern European RestaurantCategory: Fast Food RestaurantCategory: French RestaurantCategory: Fried Chicken JointCategory: GastropubCategory: German RestaurantCategory: Greek RestaurantCategory: Hawaiian RestaurantCategory: Indian RestaurantCategory: Irish PubCategory: Italian RestaurantCategory: Japanese RestaurantCategory: Korean RestaurantCategory: Latin American RestaurantCategory: Mexican RestaurantCategory: Middle Eastern RestaurantCategory: New American RestaurantCategory: Noodle HouseCategory: Pizza PlaceCategory: RestaurantCategory: Sandwich PlaceCategory: Seafood RestaurantCategory: Snack PlaceCategory: Southern / Soul Food RestaurantCategory: Spanish RestaurantCategory: SteakhouseCategory: Sushi RestaurantCategory: Taco PlaceCategory: Tapas RestaurantCategory: Thai RestaurantCategory: Theme RestaurantCategory: Vegetarian / Vegan RestaurantCategory: Vietnamese RestaurantCategory: Wings Joint
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"13 0.000000 0.000000 0.000000 \n", "14 0.000000 0.000000 0.500000 \n", "\n", " Category: Cajun / Creole Restaurant Category: Caribbean Restaurant \\\n", "1 0.0 0.0 \n", "10 0.0 0.0 \n", "12 0.0 0.0 \n", "13 0.0 0.0 \n", "14 0.0 0.0 \n", "\n", " Category: Deli / Bodega Category: Diner Category: Dumpling Restaurant \\\n", "1 0.0 0.000000 0.0 \n", "10 0.0 0.000000 0.0 \n", "12 0.0 0.013889 0.0 \n", "13 0.0 0.000000 0.0 \n", "14 0.0 0.000000 0.0 \n", "\n", " Category: Eastern European Restaurant Category: Fast Food Restaurant \\\n", "1 0.0 0.000000 \n", "10 0.0 0.000000 \n", "12 0.0 0.013889 \n", "13 0.0 0.333333 \n", "14 0.0 0.500000 \n", "\n", " Category: French Restaurant Category: Fried Chicken Joint \\\n", "1 0.000000 0.200000 \n", "10 0.000000 0.000000 \n", "12 0.111111 0.013889 \n", "13 0.000000 0.000000 \n", "14 0.000000 0.000000 \n", "\n", " Category: Gastropub Category: German Restaurant \\\n", "1 0.0 0.0 \n", "10 0.0 0.0 \n", "12 0.0 0.0 \n", "13 0.0 0.0 \n", "14 0.0 0.0 \n", "\n", " Category: Greek Restaurant Category: Hawaiian Restaurant \\\n", "1 0.0 0.0 \n", "10 0.0 0.0 \n", "12 0.0 0.0 \n", "13 0.0 0.0 \n", "14 0.0 0.0 \n", "\n", " Category: Indian Restaurant Category: Irish Pub \\\n", "1 0.0 0.0 \n", "10 0.0 0.0 \n", "12 0.0 0.0 \n", "13 0.0 0.0 \n", "14 0.0 0.0 \n", "\n", " Category: Italian Restaurant Category: Japanese Restaurant \\\n", "1 0.000000 0.000000 \n", "10 0.000000 0.000000 \n", "12 0.097222 0.027778 \n", "13 0.000000 0.000000 \n", "14 0.000000 0.000000 \n", "\n", " Category: Korean Restaurant Category: Latin American Restaurant \\\n", "1 0.0 0.000000 \n", "10 0.0 0.000000 \n", "12 0.0 0.013889 \n", "13 0.0 0.000000 \n", "14 0.0 0.000000 \n", "\n", " Category: Mexican Restaurant Category: Middle Eastern Restaurant \\\n", "1 0.600000 0.0 \n", "10 0.000000 0.0 \n", "12 0.041667 0.0 \n", "13 0.333333 0.0 \n", "14 0.000000 0.0 \n", "\n", " Category: New American Restaurant Category: Noodle House \\\n", "1 0.000000 0.0 \n", "10 0.000000 0.0 \n", "12 0.027778 0.0 \n", "13 0.000000 0.0 \n", "14 0.000000 0.0 \n", "\n", " Category: Pizza Place Category: Restaurant Category: Sandwich Place \\\n", "1 0.200000 0.0 0.000000 \n", "10 0.000000 0.0 0.500000 \n", "12 0.041667 0.0 0.013889 \n", "13 0.000000 0.0 0.000000 \n", "14 0.000000 0.0 0.000000 \n", "\n", " Category: Seafood Restaurant Category: Snack Place \\\n", "1 0.000000 0.000000 \n", "10 0.500000 0.000000 \n", "12 0.055556 0.027778 \n", "13 0.000000 0.000000 \n", "14 0.000000 0.000000 \n", "\n", " Category: Southern / Soul Food Restaurant Category: Spanish Restaurant \\\n", "1 0.000000 0.000000 \n", "10 0.000000 0.000000 \n", "12 0.013889 0.013889 \n", "13 0.000000 0.000000 \n", "14 0.000000 0.000000 \n", "\n", " Category: Steakhouse Category: Sushi Restaurant Category: Taco Place \\\n", "1 0.000000 0.000000 0.000000 \n", "10 0.000000 0.000000 0.000000 \n", "12 0.111111 0.041667 0.027778 \n", "13 0.000000 0.000000 0.333333 \n", "14 0.000000 0.000000 0.000000 \n", "\n", " Category: Tapas Restaurant Category: Thai Restaurant \\\n", "1 0.0 0.0 \n", "10 0.0 0.0 \n", "12 0.0 0.0 \n", "13 0.0 0.0 \n", "14 0.0 0.0 \n", "\n", " Category: Theme Restaurant Category: Vegetarian / Vegan Restaurant \\\n", "1 0.000000 0.0 \n", "10 0.000000 0.0 \n", "12 0.013889 0.0 \n", "13 0.000000 0.0 \n", "14 0.000000 0.0 \n", "\n", " Category: Vietnamese Restaurant Category: Wings Joint \n", "1 0.0 0.000000 \n", "10 0.0 0.000000 \n", "12 0.0 0.013889 \n", "13 0.0 0.000000 \n", "14 0.0 0.000000 " ] }, "metadata": { "tags": [] }, "execution_count": 46 } ] }, { "cell_type": "markdown", "metadata": { "id": "fnzkfQh2idgF" }, "source": [ "Now we will make a function that takes a row as input and returns that row with the x-most popular venue categories." ] }, { "cell_type": "code", "metadata": { "id": "Bs02U25DKyUc" }, "source": [ "def stack_most_common_venues(row, no_top_venues):\r\n", " \"\"\"Function to retrieve and stack the most common venue categories\"\"\"\r\n", " top_venues_stacked = row.iloc[1:].sort_values(ascending=False).reset_index().iloc[0:no_top_venues,:]\r\n", " top_venues_stacked.insert(0,'Postal Code',row.iloc[0])\r\n", " top_venues_stacked.columns.values[1:] = ['Venue Category', 'Category Prevalence %']\r\n", " top_venues_stacked['Rank in Postal Area'] = np.arange(1,no_top_venues+1)\r\n", " top_venues_stacked['Venue Category'] = top_venues_stacked['Venue Category'].str.replace('Category: ','')\r\n", " # converting to percentage\r\n", " top_venues_stacked['Category Prevalence %'] = top_venues_stacked['Category Prevalence %'] * 100\r\n", " \r\n", " return top_venues_stacked" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "mjqUjrCxis19" }, "source": [ "Having defined the above, we will make calls to it and create a new dataframe that lists the most popular postal code areas, along with the most popular venue categories, as well as their rank and prevalence within that area." ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 204 }, "id": "CZoR5Q8jQRrz", "outputId": "9de3708d-c99f-486e-96b5-6822155b34ce" }, "source": [ "# Creating a dataframe to Details of the 3 most common venue categories per postal code\r\n", "no_top_venues = 3\r\n", "\r\n", "col_names = ['Postal Code', 'Venue Category', 'Category Prevalence %', 'Rank in Postal Area']\r\n", "pop_venues = pd.DataFrame(columns=col_names)\r\n", "for ind in np.arange(pop_df.shape[0]):\r\n", " pop_venues = pd.concat([pop_venues, stack_most_common_venues(pop_df.iloc[ind,:],no_top_venues)])\r\n", "\r\n", "# resetting the index\r\n", "pop_venues.reset_index(drop=True, inplace=True)\r\n", "\r\n", "# dropping rows with 0% prevalence\r\n", "pop_venues.drop(pop_venues.loc[pop_venues['Category Prevalence %'] == 0].index, \r\n", " inplace=True)\r\n", "\r\n", "pop_venues.head(5)" ], "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { 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Postal CodeVenue CategoryCategory Prevalence %Rank in Postal Area
089030Mexican Restaurant601
189030Fried Chicken Joint202
289030Pizza Place203
389107Sandwich Place501
489107Seafood Restaurant502
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" ], "text/plain": [ " Postal Code Venue Category Category Prevalence % Rank in Postal Area\n", "0 89030 Mexican Restaurant 60 1\n", "1 89030 Fried Chicken Joint 20 2\n", "2 89030 Pizza Place 20 3\n", "3 89107 Sandwich Place 50 1\n", "4 89107 Seafood Restaurant 50 2" ] }, "metadata": { "tags": [] }, "execution_count": 48 } ] }, { "cell_type": "markdown", "metadata": { "id": "qonwnhZYi6Fi" }, "source": [ "In order to represent the above visually, we will create a function that will make a facet grid for us with the relenvant details." ] }, { "cell_type": "code", "metadata": { "id": "lUfGzoiMxSW-" }, "source": [ "def plot_venues(venue_df, cols, title):\n", " \"\"\"Fuction to plot the most hot venues per postal code\"\"\"\n", " sns.set_theme(style=\"ticks\")\n", " no_graphs = len(venue_df['Postal Code'].unique())\n", " rows = math.ceil(no_graphs/cols)\n", "\n", " # Initialize the graph\n", " fig, ax = plt.subplots(nrows=rows, ncols=cols, figsize=(20,4*rows))\n", " fig.suptitle(title, fontsize=15)\n", " fig.subplots_adjust(hspace=0.7, wspace=0.3)\n", "\n", " # Create a for loop to create a separate graph per postal area\n", " for count, pc in enumerate(venue_df['Postal Code'].unique(),1):\n", " # calculate the row- and column axis\n", " row = math.ceil(count/cols)-1\n", " col = (count-1) - row*cols\n", " # Add plot\n", " g = sns.barplot(data=venue_df.loc[venue_df['Postal Code']==pc], \n", " x='Venue Category',\n", " y='Category Prevalence %', ax = ax[row,col])\n", " g.set_xticklabels(g.get_xticklabels(), rotation=20)\n", " g.set_xlabel('')\n", " ax[row,col].set_title('Postal Code '+ pc)\n", " #fig.tight_layout()\n", " fig.show() " ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "namXG1tCjB9j" }, "source": [ "Having defined the above function, we will first make graphs of all the popular areas to investigate their characteristics. In the step afterwards, we will do the same but only for the venues we are going to recommend for this analysis." ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 850 }, "id": "WcxwNcPKxY0q", "outputId": "18f71aca-8eaf-44a5-b005-2bc740c1bdca" }, "source": [ "plot_venues(pop_venues, 4, 'Popular Venues per Postal Area')" ], "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "image/png": 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\n", 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" ] }, "metadata": { "tags": [] } } ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 390 }, "id": "o5beBd7E7P1L", "outputId": "5857fbc3-0d57-49bd-a04c-5d2dd0b864c9" }, "source": [ "# Now let's only look at the recommended venues (most popular)\r\n", "\r\n", "# First subset the popular venues from the most popular cluster\r\n", "rec_venues = pop_venues.loc[pop_venues['Postal Code'].isin(rec_subset['Venue_Postal_Code'])]\r\n", "\r\n", "# list venues\r\n", "rec_venues" ], "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/html": [ "
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Postal CodeVenue CategoryCategory Prevalence %Rank in Postal Area
2189101Pizza Place23.07691
2289101Gastropub15.38462
2389101Steakhouse15.38463
2489102Breakfast Spot16.66671
2589102Sushi Restaurant16.66672
2689102Fast Food Restaurant11.11113
2789104Thai Restaurant28.57141
2889104American Restaurant14.28572
2989104Arepa Restaurant14.28573
3089106American Restaurant501
3189106Southern / Soul Food Restaurant502
\n", "
" ], "text/plain": [ " Postal Code Venue Category Category Prevalence % \\\n", "21 89101 Pizza Place 23.0769 \n", "22 89101 Gastropub 15.3846 \n", "23 89101 Steakhouse 15.3846 \n", "24 89102 Breakfast Spot 16.6667 \n", "25 89102 Sushi Restaurant 16.6667 \n", "26 89102 Fast Food Restaurant 11.1111 \n", "27 89104 Thai Restaurant 28.5714 \n", "28 89104 American Restaurant 14.2857 \n", "29 89104 Arepa Restaurant 14.2857 \n", "30 89106 American Restaurant 50 \n", "31 89106 Southern / Soul Food Restaurant 50 \n", "\n", " Rank in Postal Area \n", "21 1 \n", "22 2 \n", "23 3 \n", "24 1 \n", "25 2 \n", "26 3 \n", "27 1 \n", "28 2 \n", "29 3 \n", "30 1 \n", "31 2 " ] }, "metadata": { "tags": [] }, "execution_count": 51 } ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 603 }, "id": "kw5Zv3pZ0wC3", "outputId": "2d072a87-b45a-4316-92d9-d5929f5adc8c" }, "source": [ "# Now let's plot the top recommended venues\n", "plot_venues(rec_venues, \n", " 3, 'Recommended Venue Categories per Postal Area')" ], "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "image/png": 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\n", 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" ] }, "metadata": { "tags": [] } } ] }, { "cell_type": "markdown", "metadata": { "id": "yZlf_-Qbw3Xa" }, "source": [ "##4. Conclusion \r\n", "\r\n", "Given the above data collection, pre-processing, and analysis, we now have a good basis for identifying what might be good business opportunities for food in the Las Vegas area." ] } ] }