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Customer Churn Prediction Model

This repository contains a machine learning model that predicts customer churn. It uses several machine learning classifiers like XGBoost, Random Forest, and K-Nearest Neighbors, along with techniques for hyperparameter tuning using Grid Search and Randomized Search.

Features

• Predicts customer churn using machine learning models.
• Provides explanations for churn predictions using OpenAI API.
• Includes hyperparameter tuning for optimizing model performance.
• Interactive web app built using Streamlit.
• Visualizations for customer metrics and model performance.

Installation

To install the project and its dependencies, follow these steps:

1. Clone the repository:

   git clone https://github.com/your-username/ChurnPredictionModel.git
   cd ChurnPredictionModel
   Create and activate a virtual environment (optional but recommended):

bash Copy code python3 -m venv .venv source .venv/bin/activate # For Linux/Mac Install the dependencies:

bash Copy code pip install -r requirements.txt Usage Running the Streamlit App You can run the interactive web app locally using Streamlit:

bash Copy code streamlit run main.py This will launch a local server, and you can open the app in your browser.

Running Predictions Programmatically You can also run the churn prediction models directly via the command line:

bash Copy code python predict_churn.py --customer_id 12345 Replace 12345 with the customer ID you want to predict churn for.

Model Details The project uses the following machine learning classifiers:

XGBoost Classifier Random Forest Classifier K-Nearest Neighbors The models are optimized using hyperparameter tuning (Grid Search and Randomized Search) to enhance performance.

License This project is licensed under the MIT License. See the LICENSE file for more details.

Contributing Contributions are welcome! Feel free to fork the repository and submit pull requests for any improvements.

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What to Update:

• Replace https://github.com/your-username/ChurnPredictionModel.git with your actual repository URL.
• You can modify the installation and usage instructions depending on your actual project structure and features.

Let me know if you need any additional sections!