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diff --git a/blog/2020-07-26-business-analysis.org b/blog/2020-07-26-business-analysis.org deleted file mode 100644 index 4339eee..0000000 --- a/blog/2020-07-26-business-analysis.org +++ /dev/null @@ -1,378 +0,0 @@ -#+date: 2020-07-26 -#+title: Algorithmically Analyzing Local Businesses - -* Background Information - -This project aims to help investors learn more about a random city in order to -determine optimal locations for business investments. The data used in this -project was obtained using Foursquare's developer API. - -Fields include: - -- Venue Name -- Venue Category -- Venue Latitude -- Venue Longitude - -There are 232 records found using the center of Lincoln as the area of interest -with a radius of 10,000. - -* Import the Data - -The first step is the simplest: import the applicable libraries. We will be -using the libraries below for this project. - -#+BEGIN_SRC python -# Import the Python libraries we will be using -import pandas as pd -import requests -import folium -import math -import json -from pandas.io.json import json_normalize -from sklearn.cluster import KMeans -#+END_SRC - -To begin our analysis, we need to import the data for this project. The data we -are using in this project comes directly from the Foursquare API. The first step -is to get the latitude and longitude of the city being studied (Lincoln, NE) and -setting up the folium map. - -#+BEGIN_SRC python -# Define the latitude and longitude, then map the results -latitude = 40.806862 -longitude = -96.681679 -map_LNK = folium.Map(location=[latitude, longitude], zoom_start=12) - -map_LNK -#+END_SRC - -#+CAPTION: Blank Map -[[https://img.cleberg.net/blog/20200726-ibm-data-science/01_blank_map-min.png]] - -Now that we have defined our city and created the map, we need to go get the -business data. The Foursquare API will limit the results to 100 per API call, so -we use our first API call below to determine the total results that Foursquare -has found. Since the total results are 232, we perform the API fetching process -three times (100 + 100 + 32 = 232). - -#+BEGIN_SRC python -# Foursquare API credentials -CLIENT_ID = 'your-client-id' -CLIENT_SECRET = 'your-client-secret' -VERSION = '20180604' - -# Set up the URL to fetch the first 100 results -LIMIT = 100 -radius = 10000 -url = 'https://api.foursquare.com/v2/venues/explore?&client_id={}&client_secret={}&v={}&ll={},{}&radius={}&limit={}'.format( - CLIENT_ID, - CLIENT_SECRET, - VERSION, - latitude, - longitude, - radius, - LIMIT) - -# Fetch the first 100 results -results = requests.get(url).json() - -# Determine the total number of results needed to fetch -totalResults = results['response']['totalResults'] -totalResults - -# Set up the URL to fetch the second 100 results (101-200) -LIMIT = 100 -offset = 100 -radius = 10000 -url2 = 'https://api.foursquare.com/v2/venues/explore?&client_id={}&client_secret={}&v={}&ll={},{}&radius={}&limit={}&offset={}'.format( - CLIENT_ID, - CLIENT_SECRET, - VERSION, - latitude, - longitude, - radius, - LIMIT, - offset) - -# Fetch the second 100 results (101-200) -results2 = requests.get(url2).json() - -# Set up the URL to fetch the final results (201 - 232) -LIMIT = 100 -offset = 200 -radius = 10000 -url3 = 'https://api.foursquare.com/v2/venues/explore?&client_id={}&client_secret={}&v={}&ll={},{}&radius={}&limit={}&offset={}'.format( - CLIENT_ID, - CLIENT_SECRET, - VERSION, - latitude, - longitude, - radius, - LIMIT, - offset) - -# Fetch the final results (201 - 232) -results3 = requests.get(url3).json() -#+END_SRC - -* Clean the Data - -Now that we have our data in three separate dataframes, we need to combine them -into a single dataframe and make sure to reset the index so that we have a -unique ID for each business. The `get_category_type` function below will pull -the categories and name from each business's entry in the Foursquare data -automatically. Once all the data has been labeled and combined, the results are -stored in the =nearby_venues= dataframe. - -#+BEGIN_SRC python -# This function will extract the category of the venue from the API dictionary -def get_category_type(row): - try: - categories_list = row['categories'] - except: - categories_list = row['venue.categories'] - - if len(categories_list) == 0: - return None - else: - return categories_list[0]['name'] - -# Get the first 100 venues -venues = results['response']['groups'][0]['items'] -nearby_venues = json_normalize(venues) - -# filter columns -filtered_columns = ['venue.name', 'venue.categories', 'venue.location.lat', 'venue.location.lng'] -nearby_venues = nearby_venues.loc[:, filtered_columns] - -# filter the category for each row -nearby_venues['venue.categories'] = nearby_venues.apply(get_category_type, axis=1) - -# clean columns -nearby_venues.columns = [col.split(".")[-1] for col in nearby_venues.columns] - ---- - -# Get the second 100 venues -venues2 = results2['response']['groups'][0]['items'] -nearby_venues2 = json_normalize(venues2) # flatten JSON - -# filter columns -filtered_columns2 = ['venue.name', 'venue.categories', 'venue.location.lat', 'venue.location.lng'] -nearby_venues2 = nearby_venues2.loc[:, filtered_columns] - -# filter the category for each row -nearby_venues2['venue.categories'] = nearby_venues2.apply(get_category_type, axis=1) - -# clean columns -nearby_venues2.columns = [col.split(".")[-1] for col in nearby_venues.columns] -nearby_venues = nearby_venues.append(nearby_venues2) - ---- - -# Get the rest of the venues -venues3 = results3['response']['groups'][0]['items'] -nearby_venues3 = json_normalize(venues3) # flatten JSON - -# filter columns -filtered_columns3 = ['venue.name', 'venue.categories', 'venue.location.lat', 'venue.location.lng'] -nearby_venues3 = nearby_venues3.loc[:, filtered_columns] - -# filter the category for each row -nearby_venues3['venue.categories'] = nearby_venues3.apply(get_category_type, axis=1) - -# clean columns -nearby_venues3.columns = [col.split(".")[-1] for col in nearby_venues3.columns] - -nearby_venues = nearby_venues.append(nearby_venues3) -nearby_venues = nearby_venues.reset_index(drop=True) -nearby_venues -#+END_SRC - -#+CAPTION: Clean Data -[[https://img.cleberg.net/blog/20200726-ibm-data-science/02_clean_data-min.png]] - -* Visualize the Data - -We now have a complete, clean data set. The next step is to visualize this data -onto the map we created earlier. We will be using folium's =CircleMarker()= -function to do this. - -#+BEGIN_SRC python -# add markers to map -for lat, lng, name, categories in zip(nearby_venues['lat'], nearby_venues['lng'], nearby_venues['name'], nearby_venues['categories']): - label = '{} ({})'.format(name, categories) - label = folium.Popup(label, parse_html=True) - folium.CircleMarker( - [lat, lng], - radius=5, - popup=label, - color='blue', - fill=True, - fill_color='#3186cc', - fill_opacity=0.7, - ).add_to(map_LNK) - -map_LNK -#+END_SRC - - - -* Clustering: /k-means/ - -To cluster the data, we will be using the /k-means/ algorithm. This algorithm is -iterative and will automatically make sure that data points in each cluster are -as close as possible to each other, while being as far as possible away from -other clusters. - -However, we first have to figure out how many clusters to use (defined as the -variable /'k'/). To do so, we will use the next two functions to calculate the -sum of squares within clusters and then return the optimal number of clusters. - -#+BEGIN_SRC python -# This function will return the sum of squares found in the data -def calculate_wcss(data): - wcss = [] - for n in range(2, 21): - kmeans = KMeans(n_clusters=n) - kmeans.fit(X=data) - wcss.append(kmeans.inertia_) - - return wcss - -# Drop 'str' cols so we can use k-means clustering -cluster_df = nearby_venues.drop(columns=['name', 'categories']) - -# calculating the within clusters sum-of-squares for 19 cluster amounts -sum_of_squares = calculate_wcss(cluster_df) - -# This function will return the optimal number of clusters -def optimal_number_of_clusters(wcss): - x1, y1 = 2, wcss[0] - x2, y2 = 20, wcss[len(wcss)-1] - - distances = [] - for i in range(len(wcss)): - x0 = i+2 - y0 = wcss[i] - numerator = abs((y2-y1)*x0 - (x2-x1)*y0 + x2*y1 - y2*x1) - denominator = math.sqrt((y2 - y1)**2 + (x2 - x1)**2) - distances.append(numerator/denominator) - - return distances.index(max(distances)) + 2 - -# calculating the optimal number of clusters -n = optimal_number_of_clusters(sum_of_squares) -#+END_SRC - -Now that we have found that our optimal number of clusters is six, we need to -perform k-means clustering. When this clustering occurs, each business is -assigned a cluster number from 0 to 5 in the dataframe. - -#+BEGIN_SRC python -# set number of clusters equal to the optimal number -kclusters = n - -# run k-means clustering -kmeans = KMeans(n_clusters=kclusters, random_state=0).fit(cluster_df) - -# add clustering labels to dataframe -nearby_venues.insert(0, 'Cluster Labels', kmeans.labels_) -#+END_SRC - -Success! We now have a dataframe with clean business data, along with a cluster -number for each business. Now let's map the data using six different colors. - -#+BEGIN_SRC python -# create map with clusters -map_clusters = folium.Map(location=[latitude, longitude], zoom_start=12) -colors = ['#0F9D58', '#DB4437', '#4285F4', '#800080', '#ce12c0', '#171717'] - -# add markers to the map -for lat, lng, name, categories, cluster in zip(nearby_venues['lat'], nearby_venues['lng'], nearby_venues['name'], nearby_venues['categories'], nearby_venues['Cluster Labels']): - label = '[{}] {} ({})'.format(cluster, name, categories) - label = folium.Popup(label, parse_html=True) - folium.CircleMarker( - [lat, lng], - radius=5, - popup=label, - color=colors[int(cluster)], - fill=True, - fill_color=colors[int(cluster)], - fill_opacity=0.7).add_to(map_clusters) - -map_clusters -#+END_SRC - -#+CAPTION: Clustered Map -[[https://img.cleberg.net/blog/20200726-ibm-data-science/04_clusters-min.png]] - -* Investigate Clusters - -Now that we have figured out our clusters, let's do a little more analysis to -provide more insight into the clusters. With the information below, we can see -which clusters are more popular for businesses and which are less popular. The -results below show us that clusters 0 through 3 are popular, while clusters 4 -and 5 are not very popular at all. - -#+BEGIN_SRC python -# Show how many venues are in each cluster -color_names = ['Dark Green', 'Red', 'Blue', 'Purple', 'Pink', 'Black'] -for x in range(0,6): - print("Color of Cluster", x, ":", color_names[x]) - print("Venues found in Cluster", x, ":", nearby_venues.loc[nearby_venues['Cluster Labels'] == x, nearby_venues.columns[:]].shape[0]) - print("---") -#+END_SRC - -#+CAPTION: Venues per Cluster -[[https://img.cleberg.net/blog/20200726-ibm-data-science/05_venues_per_cluster-min.png]] - -Our last piece of analysis is to summarize the categories of businesses within -each cluster. With these results, we can clearly see that restaurants, coffee -shops, and grocery stores are the most popular. - -#+BEGIN_SRC python -# Calculate how many venues there are in each category -# Sort from largest to smallest -temp_df = nearby_venues.drop(columns=['name', 'lat', 'lng']) - -cluster0_grouped = temp_df.loc[temp_df['Cluster Labels'] == 0].groupby(['categories']).count().sort_values(by='Cluster Labels', ascending=False) -cluster1_grouped = temp_df.loc[temp_df['Cluster Labels'] == 1].groupby(['categories']).count().sort_values(by='Cluster Labels', ascending=False) -cluster2_grouped = temp_df.loc[temp_df['Cluster Labels'] == 2].groupby(['categories']).count().sort_values(by='Cluster Labels', ascending=False) -cluster3_grouped = temp_df.loc[temp_df['Cluster Labels'] == 3].groupby(['categories']).count().sort_values(by='Cluster Labels', ascending=False) -cluster4_grouped = temp_df.loc[temp_df['Cluster Labels'] == 4].groupby(['categories']).count().sort_values(by='Cluster Labels', ascending=False) -cluster5_grouped = temp_df.loc[temp_df['Cluster Labels'] == 5].groupby(['categories']).count().sort_values(by='Cluster Labels', ascending=False) - -# show how many venues there are in each cluster (> 1) -with pd.option_context('display.max_rows', None, 'display.max_columns', None): - print("\n\n", "Cluster 0:", "\n", cluster0_grouped.loc[cluster0_grouped['Cluster Labels'] > 1]) - print("\n\n", "Cluster 1:", "\n", cluster1_grouped.loc[cluster1_grouped['Cluster Labels'] > 1]) - print("\n\n", "Cluster 2:", "\n", cluster2_grouped.loc[cluster2_grouped['Cluster Labels'] > 1]) - print("\n\n", "Cluster 3:", "\n", cluster3_grouped.loc[cluster3_grouped['Cluster Labels'] > 1]) - print("\n\n", "Cluster 4:", "\n", cluster4_grouped.loc[cluster4_grouped['Cluster Labels'] > 1]) - print("\n\n", "Cluster 5:", "\n", cluster5_grouped.loc[cluster5_grouped['Cluster Labels'] > 1]) -#+END_SRC - -#+CAPTION: Venues per Cluster, pt. 1 -[[https://img.cleberg.net/blog/20200726-ibm-data-science/06_categories_per_cluster_pt1-min.png]] - -#+CAPTION: Venues per Cluster, pt. 2 -[[https://img.cleberg.net/blog/20200726-ibm-data-science/07_categories_per_cluster_pt2-min.png]] - -* Discussion - -In this project, we gathered location data for Lincoln, Nebraska, USA and -clustered the data using the k-means algorithm in order to identify the unique -clusters of businesses in Lincoln. Through these actions, we found that there -are six unique business clusters in Lincoln and that two of the clusters are -likely unsuitable for investors. The remaining four clusters have a variety of -businesses, but are largely dominated by restaurants and grocery stores. - -Using this project, investors can now make more informed decisions when deciding -the location and category of business in which to invest. - -Further studies may involve other attributes for business locations, such as -population density, average wealth across the city, or crime rates. In addition, -further studies may include additional location data and businesses by utilizing -multiple sources, such as Google Maps and OpenStreetMap. |