From fdd80eadcc2f147d0198d94b7b908764778184a2 Mon Sep 17 00:00:00 2001 From: Christian Cleberg Date: Mon, 29 Apr 2024 14:18:55 -0500 Subject: format line wrapping and fix escaped characters --- content/blog/2020-07-26-business-analysis.md | 132 +++++++++++++-------------- 1 file changed, 63 insertions(+), 69 deletions(-) (limited to 'content/blog/2020-07-26-business-analysis.md') diff --git a/content/blog/2020-07-26-business-analysis.md b/content/blog/2020-07-26-business-analysis.md index 7f96ac1..4105d04 100644 --- a/content/blog/2020-07-26-business-analysis.md +++ b/content/blog/2020-07-26-business-analysis.md @@ -7,24 +7,24 @@ draft = false # 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. +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 +- 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. +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. +The first step is the simplest: import the applicable libraries. We will be +using the libraries below for this project. ``` python # Import the Python libraries we will be using @@ -37,10 +37,10 @@ from pandas.io.json import json_normalize from sklearn.cluster import KMeans ``` -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. +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. ``` python # Define the latitude and longitude, then map the results @@ -54,11 +54,11 @@ map_LNK ![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). +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). ``` python # Foursquare API credentials @@ -122,13 +122,12 @@ results3 = requests.get(url3).json() # 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~categorytype~` 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. +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~categorytype~` 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. ``` python # This function will extract the category of the venue from the API dictionary @@ -200,9 +199,9 @@ Data](https://img.cleberg.net/blog/20200726-ibm-data-science/02_clean_data-min.p # 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. +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. ``` python # add markers to map @@ -227,15 +226,14 @@ map](https://img.cleberg.net/blog/20200726-ibm-data-science/03_data_map-min.png) # 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. +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. +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. ``` python # This function will return the sum of squares found in the data @@ -273,9 +271,9 @@ def optimal_number_of_clusters(wcss): n = optimal_number_of_clusters(sum_of_squares) ``` -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. +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. ``` python # set number of clusters equal to the optimal number @@ -288,9 +286,8 @@ kmeans = KMeans(n_clusters=kclusters, random_state=0).fit(cluster_df) nearby_venues.insert(0, 'Cluster Labels', kmeans.labels_) ``` -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. +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. ``` python # create map with clusters @@ -318,12 +315,11 @@ 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. +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. ``` python # Show how many venues are in each cluster @@ -337,9 +333,9 @@ for x in range(0,6): ![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. +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. ``` python # Calculate how many venues there are in each category @@ -371,19 +367,17 @@ with pd.option_context('display.max_rows', None, 'display.max_columns', None): # 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. +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. -- cgit v1.2.3-70-g09d2