The Atlanta University Consortium and Morgan-Stanley organized a data challenge in November-December 2023. The challenge involved identifying promising zip codes for business expansion based on the analysis of 1500 zip codes where the business was already performing well.
I led five amazing Spelman students from the Math Department: Mika Campell, Elon Davis, Nikira A. Walter, Jasmin J. Jean-Louis, and Naomi Logan in this competition. Our team Blue Barbies won the competition by ranking #1.
This repository includes our augmented dataset regarding zip codes. You can read the details in our presentation. We augmented the given dataset, consisting of 1500 US-based zip codes, in a way that we created a unique dataset of its kind that can be super useful for the general public. Here is a brief description of some of the files and folders:
Blue Barbies Presentation: this is our winning presentation. I highly suggest to go over this before looking at the codes and datasets. Pay special attention to interactive html files that can be accessed from the hyperlinks embedded on the images.
To get started, assuming you already have Python, do the following. The second one will download and extract all the datasets we use in this repo. You should see "datasets" folder in your current directory.
| pip install -r requirements.txt
| python download_datasets.py
data_sources.xls: this files details the data sources used to augment the initial 1500 zip codes. "final_data.csv" is the final version of the augmented data. For clarity, here are the columns we added to our data. All states are present and there is no missing data point.
| Column Name | Description |
|---|---|
zip |
Zip Code |
lat |
Latitude |
lng |
Longitude |
city |
City Name |
state_id |
State Abbreviation |
state_name |
Full State Name |
population |
Population Count |
density |
Population Density (per square km) |
county_name |
County Name |
target |
Target Zip Code(yes if 1) |
po_box |
PO Box Type (yes if 1) |
dist_highway |
Distance to Nearest Highway (in km) |
dist2_large_airport |
Distance to Nearest Large Airport (in km) |
dist2_medium_airport |
Distance to Nearest Medium-sized Airport (in km) |
dist_to_shore |
Distance to Nearest Shoreline (in km) |
number_of_business |
Number of Businesses |
adjusted_gross_income |
Adjusted Gross Income in the Area |
total_income_amount |
Total Income Amount in the Area |
number_of_returns |
Number of Tax Returns Filed |
data_augment.ipynb: Python codes used to augment the initial data. Start from the first one and move to the second one.These two files will reproduce the "final_data.csv".
machine_learning.ipynb: Python codes for our machine learning approach which includes implementation of One-Class-SVM and Isolation Forest in semi-supervised fashion.