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.
- Python
- scikit-learn
- pandas
- seaborn
- matplotlib
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Clone the repository:
git clone https://github.com/arnabsaha7/customer-churn-prediction.git cd customer-churn-prediction -
Install dependencies:
pip install -r requirements.txt
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Run the Jupyter Notebook:
jupyter notebook Customer_Churn_Prediction.ipynb
Follow the instructions and explore the notebook to understand the EDA, model training, and evaluation steps.
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.
The trained model achieved excellent accuracy; however, further investigation is needed to ensure generalization and address potential overfitting.
- Fork the repository.
- Create a new branch:
git checkout -b feature/your-feature. - Make your changes and commit them:
git commit -m 'Add your feature'. - Push to the branch:
git push origin feature/your-feature. - Open a pull request.
This project is licensed under the MIT 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.