Customer Churn Prediction Modeling

This project focuses on predicting customer churn using machine learning models.
It applies data preprocessing, feature engineering, and classification techniques to help businesses identify at-risk customers and take proactive actions.

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📊 Project Overview

• Goal: Predict customer churn and understand the key drivers of churn.
• Dataset: Customer records with demographic, account, and usage features.
• Approach:
  • Data cleaning & preprocessing
  • Exploratory Data Analysis (EDA)
  • Feature engineering
  • Model training (Logistic Regression, Random Forest, Gradient Boosting, etc.)
  • Model evaluation (Accuracy, Precision, Recall, F1-score, ROC-AUC)

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⚙️ Tools & Technologies

• Python (pandas, NumPy, scikit-learn, matplotlib, seaborn)
• Jupyter Notebook
• Git & GitHub for version control

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📁 Repository Structure