This is the last series of my 3 posts analyzing US zip codes by types of restaurants. In part 1 I described the work in general and presented the results, which found no way to segment locations by restaurant categories. In part 2 I have shown how the data was collected and cleaned.
In this part, I’ll walk you throw the clustering and regression techniques that were used during the project.
The full code is available in this repo.
Clustering
Using cluster analysis our goal is to find a number of unique groups that our data can…
In part 1 of this post I have describes the research I did, trying to segment US zip codes by different restaurant categories.
The second and the third parts will be more technical going throw the how to of the project.
Downloading the data:
- For US zip code data you can simply download the file from OpenDataSoft, and since we are working with Pandas the best format will be CSV. (https://public.opendatasoft.com/explore/dataset/us-zip-code-latitude-and-longitude/export/)
- I downloaded the house values information from Zillow research section, simply download the preferred data (Under Geography select zipcode), After downloading the file, you will have a 60 MB’s…
As most of us found ourselves at home during the covid-19 crisis I decided to use the time and learn a new subject I always wanted and didn’t found the time for it.
I have joined a course over Coursera that teaches both python and some machine learning. As the final part of this course, our task was to choose a project using location data and research an area based on that data.
I’m passionate about food, So why not try to combine both, the locations of restaurants and this project, trying to find some patterns.
The project explores different…
