📊 Customer Churn Prediction using XGBoost

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Welcome to the Customer Churn Prediction project!
This application predicts whether a customer will leave the bank or not based on key financial and personal features, using a powerful XGBoost machine learning model.

✅ Key Highlights:

• Built using XGBoost Classifier for high prediction accuracy.
• Streamlit app for interactive and user-friendly predictions.
• Integrated StandardScaler to maintain consistent feature scaling.
• Pickle files used for efficient model and scaler storage.
• Trained on real-world customer churn dataset (Churn_Modelling.csv).

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🚀 Features

• 📈 Predicts customer churn based on financial parameters like Credit Score, Balance, and Salary.
• 🖥️ Simple and elegant Streamlit interface to input customer details and get real-time predictions.
• 💾 Model and scaler loaded dynamically using pickle.
• 📊 Clean preprocessing ensuring high model performance and reliability.

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🛠️ Tech Stack

Technology	Purpose
Python	Core programming language
XGBoost	Model training and prediction
Streamlit	Web app deployment
Pickle	Saving and loading models/scalers
Pandas & NumPy	Data handling and preprocessing

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📂 Project Structure

├── Customer churn1.ipynb         # Jupyter Notebook for training the XGBoost model
├── scaler.pkl                   # Saved StandardScaler object for input feature scaling
├── churn_model.pkl              # Trained XGBoost model file
├── Churn_Modelling.csv          # Dataset used for training and testing
├── churn.py                     # Streamlit application for prediction (main app)
├── README.md                    # Project documentation file
├── requirements.txt             # Python dependencies

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📢 How to Run Locally

1. Clone the repository:

   git clone https://github.com/your-username/your-repo-name.git
   cd your-repo-name

2. Install dependencies:

   pip install -r requirements.txt

3. Run the Streamlit app:

   streamlit run app.py

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🎯 Future Improvements

• Add more advanced feature engineering (e.g., encode Geography and Gender smartly).
• Deploy the app using cloud services like AWS, GCP, or Streamlit Cloud.
• Improve UI/UX with better form design and loading animations.

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🙌 Acknowledgements

Thanks to the creators of the Churn Modelling Dataset for providing valuable data for machine learning projects.

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