uilt an end-to-end customer churn prediction system using a 20,000-row telecom-style dataset (ten...uilt an end-to-end customer churn prediction system using a 20,000-row telecom-style dataset (ten...
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Johan Cornelis's avatar
Johan Cornelis Adi Saputra
• 8h
uilt an end-to-end customer churn prediction system using a 20,000-row telecom-style dataset (tenure, monthly charges, contract type, payment method, internet service, support calls, etc.).
The workflow covered data cleaning, exploratory data analysis, stratified train-test splitting, feature encoding (one-hot & binary), and model comparison between Logistic Regression and Random Forest. Model performance was evaluated using precision, recall, F1-score, and ROC-AUC, with SHAP used to interpret which features drive churn risk.
The final model was deployed as an interactive Streamlit dashboard, allowing users to input customer data and get a live churn risk prediction along with the key factors behind it.
This project reflects my focus on building practical, interpretable ML solutions — from raw data to a usable, deployed tool.
Completed work
StreamlitData AnalysisData ModelingData VisualizationMatplotlibscikit-learn
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