/Customer_Churn-Predection

Welcome to ChurnPredictX, your advanced solution for predicting customer churn. This machine learning project utilizes powerful algorithms such as Random Forest, PCA (Principal Component Analysis), and Decision Tree to forecast the likelihood of a customer churning. To enhance user experience, the model is seamlessly integrated into the frontend us

Primary LanguageJupyter Notebook

ChurnPredictX - Customer Churn Prediction 🔄🔮

Welcome to ChurnPredictX, your advanced solution for predicting customer churn. This machine learning project utilizes powerful algorithms such as Random Forest, PCA (Principal Component Analysis), and Decision Tree to forecast the likelihood of a customer churning. To enhance user experience, the model is seamlessly integrated into the frontend using Flask, HTML, and CSS3.

Table of Contents

  1. Introduction
  2. Key Features
  3. Technologies Used
  4. Project Structure
  5. Integration
  6. Demo
  7. Contributing
  8. License

Introduction 🌐📈

ChurnPredictX is a machine learning model developed to predict customer churn, allowing businesses to proactively address potential issues and retain valuable customers. The incorporation of advanced algorithms, including Random Forest, PCA, and Decision Tree, ensures accurate predictions and actionable insights.

Key Features 🚀🔍

  • Advanced Algorithms: Utilizes Random Forest, PCA, and Decision Tree for accurate churn predictions.
  • Flask Integration: Seamlessly integrated into the frontend using Flask for a cohesive user experience.
  • Responsive Design: The frontend, built with HTML and CSS3, ensures a visually appealing and user-friendly interface.
  • Predictive Analytics: Empowers businesses to identify potential churners and take proactive measures.

Technologies Used 💻🔧

  • Backend: Flask, Python
  • Machine Learning Libraries: Numpy, Scikit-learn
  • Frontend: HTML, CSS3, JavaScript
  • Algorithms: Random Forest, PCA, Decision Tree
  • Classification Algorithm: Used for predicting customer churn

Project Structure 📁🚀

churnpredictx/
|-- app/
|   |-- __init__.py
|   |-- routes.py
|   |-- static/
|   |   |-- css/
|   |   |   |-- styles.css
|   |   |-- images/
|   |   |   |-- churn_icon.png
|   |-- templates/
|   |   |-- index.html
|   |   |-- result.html
|-- model/
|   |-- churn_model.pkl
|-- README.md
|-- requirements.txt
|-- run.py

Integration 🔄💻

  1. Clone the Repository: git clone https://github.com/yourusername/churnpredictx.git
  2. Install Dependencies: Navigate to the project directory and run pip install -r requirements.txt.
  3. Run the Application: Execute python run.py to start the Flask application.
  4. Access the Website: Open your browser and go to http://localhost:5000 to experience ChurnPredictX.

Demo 📺🚀

ChurnPredictX Demo

Click on the image above to watch the ChurnPredictX demo on YouTube.

Contributing 🤝🚀

We welcome contributions from the community. Fork the repository, make your changes, and submit a pull request.

License 📝⚖️

This project is licensed under the MIT License.

Feel free to reach out if you have any questions or feedback. Predict customer churn like never before with ChurnPredictX! 🔮🚀