🏦 Credit Card Churn Prediction Model

📋 Overview This repository contains a machine learning model designed to predict customer churn in a credit card company. By identifying key factors that lead to customer attrition, this model can help the company take proactive measures to retain valuable customers.

⚙️ Features Language: Python Libraries: Pandas, NumPy, Scikit-learn, XGBoost, Matplotlib, Seaborn Modeling Techniques: Logistic Regression, Random Forest, XGBoost Evaluation Metrics: Accuracy Precision & Recall F1-Score ROC-AUC 📊 Key Insights Top Churn Indicators: High credit utilization, late payments, and low engagement. Model Performance: Achieved an ROC-AUC score of 0.85, demonstrating strong predictive capabilities. Business Impact: Insights from the model can inform targeted retention strategies to reduce churn rates. 🚀 Getting Started Follow these steps to explore and run the model on your local machine:

Clone the Repository: bash Copy code git clone https://github.com//credit-card-churn-prediction.git Install Required Dependencies: bash Copy code pip install -r requirements.txt Run the Jupyter Notebook: bash Copy code jupyter notebook churn_prediction.ipynb 📈 Next Steps Explore advanced models like Gradient Boosting or Neural Networks for improved accuracy. Deploy the model using Flask or Django for real-time prediction. 📝 License This project is licensed under the MIT License. See the LICENSE file for more details. This project is licensed under the MIT License.