Machine Learning bank churn prediction system using Random Forest, Python, Streamlit, and Scikit-learn.
🏦 Machine Learning model that predicts whether a bank customer is likely to churn, helping identify customers at risk of leaving. 📊 Data-driven customer churn prediction system built with Python and Machine Learning.
Kaggle-style machine learning engineering pipeline with feature engineering, cross-validation, hyperparameter tuning, model ensembling, and submission generation.
Bank customer churn analysis (10k accounts) using Power Query and Power BI. Identifies a 20.38% churn rate ($185.68M lost) driven by Germany (32.44%), ages 51–60 (56.21%), and 3+ product holders (82%–100%).
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