Churn Prediction Model

Note: This is a prototype and not the actual code used in production.

Overview

This project focuses on building a Churn Prediction Model using machine learning techniques. The goal is to predict customer churn based on historical data, identifying key features that influence retention and providing actionable insights to reduce churn rates.

Features

• Data Preprocessing: Handling missing values, feature engineering, and data scaling.
• Model Training: Utilizing classification models such as Logistic Regression, Random Forest, XGBoost, and Neural Networks.
• Model Evaluation: Performance metrics including Accuracy, Precision, Recall, F1-Score, and ROC-AUC.
• Hyperparameter Tuning: Grid Search and Random Search for model optimization.
• Interpretability: Feature importance analysis using SHAP values.

Installation

Prerequisites

Ensure you have the following installed:

• Python 3.8+
• Jupyter Notebook (if running locally)
• Required dependencies from requirements.txt

Setup

1. Clone the repository:

   git clone https://github.com/your-repo/churn-prediction.git
   cd churn-prediction
   

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Feel free to reach out to me if you want to see the details seunghyk@tepper.cmu.edu