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Completedmachine-learning
Customer Churn Prediction System
End-to-end ML system to predict customer churn and identify business risks
April 2025 – May 2025Classification & Business Analytics
Tech Stack
PythonPandasScikit-learnEDAMatplotlib
What I Built
- 1Analyzed customer behavior data to identify churn patterns and business risks
- 2Preprocessed large datasets by handling missing values and categorical variables
- 3Built Logistic Regression and Random Forest models for churn prediction
- 4Optimized models using feature selection and hyperparameter tuning
- 5Improved churn prediction accuracy compared to baseline models
Key Metrics
10,000+
data Points
15
features Engineered
2
models Compared
Improved vs baseline
accuracy Improvement
Problem Context
This project addressed classification & business analytics challenges. The goal was to build a robust system that could handle real-world data and produce actionable insights for decision-making.
Architecture & Approach
┌─────────────────────────────────────────────────────────┐ │ Data Pipeline │ ├─────────────────────────────────────────────────────────┤ │ │ │ Raw Data ──▶ Preprocessing ──▶ Feature Engineering │ │ │ │ │ │ ▼ ▼ │ │ Data Cleaning Feature Selection │ │ │ │ │ │ └────────┬───────────┘ │ │ │ │ │ ▼ │ │ Model Training │ │ │ │ │ ▼ │ │ Evaluation & Tuning │ │ │ │ │ ▼ │ │ Final Predictions │ │ │ └─────────────────────────────────────────────────────────┘
Key Lessons
✓
Feature engineering has outsized impact on model performance. Domain knowledge matters more than model complexity.
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Cross-validation is essential for reliable evaluation. Single train-test splits can be misleading.
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Start simple (Linear/Logistic Regression) before complex models. Baselines provide crucial context.