Customer Churn Prediction with Machine Learning

Welcome to the Customer Churn Prediction repository!
This project uses machine learning to predict customer churn based on user behavior and demographics. It includes data preprocessing, model training, evaluation metrics, and a deployed web app for real-time predictions. Great for exploring real-world applications of ML in business analytics.

---

🚀 Features

• Real-world business use-case
• End-to-end ML pipeline (data preprocessing → model building → evaluation → web deployment)
• Clean, modular code & notebooks
• Web-based prediction interface (Flask/Streamlit)
• Suitable for portfolios and interviews

---

🧰 Tech Stack

• Languages: Python, Jupyter Notebook, HTML, CSS, JavaScript
• Libraries & Frameworks: Pandas, NumPy, scikit-learn, XGBoost, imbalanced-learn (SMOTE), Matplotlib, Seaborn, Flask/Streamlit
• Deployment: Streamlit/Flask web app

---

🔍 Project Workflow

1. Data Preprocessing

   • Handling missing values
   • Encoding categorical variables
   • Feature scaling (StandardScaler)
   • Addressing class imbalance (SMOTE)

2. Exploratory Data Analysis (EDA)

   • Churn vs. Non-Churn distribution
   • Correlation heatmap
   • Feature importance & visual insights

3. Model Building

   • Logistic Regression
   • Decision Tree
   • Random Forest
   • XGBoost

4. Model Evaluation

   • Confusion Matrix
   • Accuracy, Precision, Recall, F1-Score
   • ROC Curve and AUC

5. Web Application (Flask/Streamlit)

   • Clean UI for inputting customer data
   • Real-time churn prediction
   • Suitable for deployment or demos

---

🖥️ Usage

1. Upload your dataset or use the provided sample.
2. Run the notebook to preprocess data and train models.
3. Evaluate models using provided metrics and plots.
4. Enter customer data in the web app to get instant churn prediction.

---

📊 Results

• Achieved high accuracy and robust performance on test data.
• Visualizations provide business insights (feature importance, churn drivers).
• The web app allows non-technical users to interactively predict churn.

---

📢 Contributions

We welcome contributions!
Feel free to fork this repo, add new features, improve code quality, or build new models.

1. Fork the repository
2. Create a new branch (git checkout -b feature-xyz)
3. Commit your changes
4. Open a pull request

Let’s improve this together 🙌

---

📄 License

This project is licensed under the MIT License.

---

🙋‍♀️ Contact

For questions or collaborations: PratikshaNR

Suitable for portfolios and interviews

📢 Contributions Feel free to fork this repo, add new features, improve code quality, or build new models. Pull requests are welcome! Let’s improve this together 🙌