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Customer-Churn Prediction
Developed a customer churn prediction system using data preprocessing, feature engineering, and machine learning models to identify at-risk customers and support retention strategies.
Approach
- Data collection: Gathered and consolidated customer data from multiple sources
- Data preprocessing: Cleaned, handled missing values, and prepared data for analysis.
- Feature engineering: Created meaningful features to capture customer behavior.
- Model development: Trained and tuned machine learning models for churn prediction.
- Model evaluation: Assessed performance using metrics to ensure accuracy and reliability.
Highlights
- Improved customer retention insights.
- Built ML model achieving ~87% accuracy.
- Improved prediction performance by ~15% .