https://github.com/arnabsaha7/customer-churn_prediction---analysis

Predict customer churn using machine learning. This project employs a RandomForestClassifier to analyze customer data and determine the likelihood of churn. Explore the Jupyter Notebook for insights into the data and model, and contribute to the project's development.
https://github.com/arnabsaha7/customer-churn_prediction---analysis

customer-churn-prediction data-analysis machine-learning

Last synced: 7 months ago
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Predict customer churn using machine learning. This project employs a RandomForestClassifier to analyze customer data and determine the likelihood of churn. Explore the Jupyter Notebook for insights into the data and model, and contribute to the project's development.

• Host: GitHub
• URL: https://github.com/arnabsaha7/customer-churn_prediction---analysis
• Owner: arnabsaha7
• License: mit
• Created: 2024-01-08T17:43:24.000Z (almost 2 years ago)
• Default Branch: main
• Last Pushed: 2024-01-08T17:46:19.000Z (almost 2 years ago)
• Last Synced: 2025-01-12T21:43:56.679Z (9 months ago)
• Topics: customer-churn-prediction, data-analysis, machine-learning
• Language: Jupyter Notebook
• Homepage:
• Size: 2.8 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# Customer Churn Prediction

## Overview

This repository contains code and resources for predicting customer churn in a business context. The project utilizes a RandomForestClassifier to predict whether a customer is likely to churn based on various features.

## Features

- Python

- scikit-learn

- pandas

- seaborn

- matplotlib

## Getting Started

1. **Clone the repository:**

   ```bash

   git clone https://github.com/arnabsaha7/customer-churn-prediction.git

   cd customer-churn-prediction

   ```

2. **Install dependencies:**

   ```bash

   pip install -r requirements.txt

   ```

3. **Run the Jupyter Notebook:**

   ```bash

   jupyter notebook Customer_Churn_Prediction.ipynb

   ```

   Follow the instructions and explore the notebook to understand the EDA, model training, and evaluation steps.

## Project Structure

- `Customer_Churn_Prediction.ipynb`: Jupyter Notebook containing the main code for data analysis, model training, and evaluation.

- `requirements.txt`: List of Python packages required for the project.

- `data/`: Directory to store the dataset (if not included in the repository).

- `images/`: Directory to store images or plots generated during the analysis.

## Results

The trained model achieved excellent accuracy; however, further investigation is needed to ensure generalization and address potential overfitting.

## Contributing

1. Fork the repository.

2. Create a new branch: `git checkout -b feature/your-feature`.

3. Make your changes and commit them: `git commit -m 'Add your feature'`.

4. Push to the branch: `git push origin feature/your-feature`.

5. Open a pull request.

## License

This project is licensed under the [MIT License](LICENSE).

Feel free to customize this template according to your project's specific details and requirements. Include additional sections if needed, such as a description of the dataset, model architecture, or any specific instructions for users.