📊 Customer Churn Prediction

📌 Overview

Customer churn is a major challenge in the subscription-based industry. This project aims to predict whether a customer will churn using historical data and machine learning techniques. By identifying at-risk customers, businesses can take proactive measures to improve retention.

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✅ Key Features

• Data Preprocessing: Handles missing values, encodes categorical variables, and scales features.
• Exploratory Data Analysis (EDA): Visualizations to uncover trends and churn patterns.
• Model Training: Logistic Regression, Random Forest, and XGBoost for churn prediction.
• Evaluation Metrics: Accuracy, Precision, Recall, F1-score, ROC-AUC.
• Deployment Ready: Model saved for API or app integration.

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🚀 Getting Started

1️⃣ Clone the Repository

git clone https://github.com/rahatmoktadir03/customer-churn-prediction.git
cd customer-churn-prediction

2️⃣ Install Dependencies

pip install -r requirements.txt

3️⃣ Run Jupyter Notebook

jupyter notebook notebooks/churn_prediction.ipynb

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📌 Future Enhancements

• Deploy as a Flask/FastAPI web service
• Add hyperparameter tuning with GridSearchCV
• Build an interactive dashboard (Streamlit/Dash)