Credit Card Transaction Analysis Using Machine Learning

Objective:

Analyze customer behavior data to identify patterns that lead to churn and build predictive models to retain customers.

Skills & Tools Used:

Python, Pandas, Seaborn, Scikit-learn

Logistic Regression, Decision Trees

Data cleaning, feature engineering, and EDA

Model evaluation (confusion matrix, ROC-AUC)

Outcome:

A dashboard and report that help the company identify at-risk customers and suggest retention strategies.