Customer Churn Prediction Project

Overview

This project predicts customer churn using a logistic regression model on the Telco Customer Churn dataset. The workflow includes data preprocessing, EDA, feature engineering, model training, and evaluation.

Results Summary

Metric	Value
Accuracy	0.805
Precision	0.655
Recall	0.559
F1 Score	0.603
AUC-ROC	0.842

• The model achieves strong performance, especially in AUC-ROC.
• Top features driving churn are visualized in the script.

Visualizations

• Confusion Matrix: Shows model prediction breakdown for churn vs. no churn.
• ROC Curve: Illustrates model discrimination ability.
• Top Features Bar Chart: Highlights the most influential features for churn.

How to Run

1. Install dependencies from requirements.txt.
2. Run churn_model.py to train the model and view results.

Project Structure

├── README.md
├── churn_model.py
├── requirements.txt
├── .gitignore
├── data/
│   └── WA_Fn-UseC_-Telco-Customer-Churn.csv