This repo is for displaying the final capstone project associated with Mars Petcare on Shopper Churn Prediction.
The python file "Mars.ipynb" that fully displays the project procedure from data cleaning, EDA, to Modeling and Prediction.
Based on the EDA graph presented in the python file, I label shoppers as "churn or not churn" and use ML models to predict the potential churn rate of 4 different brands of Mars Petcare food in 2023.
For more detail, please check the final report as reference.
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