Inspiration
Anticipating customer churn optimizes profitability through targeted retention strategies. Applying AI in this context broadens innovative possibilities, providing practical exposure to classical ML solutions and data intricacies in the wealth management sector.
What it does
Our model predicts whether or not a customer will churn given various data information.
How we built it
Sklearn, numpy, pandas, google colab
Challenges we ran into
Data processing (imputing missing values, dealing with categorical data) Feature/Model selection (how many features to use, how to pick features)
Accomplishments that we're proud of
Figuring ways around the limited data descriptions and getting a good model
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