{"id":20056777,"url":"https://github.com/arnabsaha7/customer-churn_prediction---analysis","last_synced_at":"2025-03-02T09:27:28.696Z","repository":{"id":216158474,"uuid":"740612036","full_name":"arnabsaha7/Customer-Churn_Prediction---Analysis","owner":"arnabsaha7","description":"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.","archived":false,"fork":false,"pushed_at":"2024-01-08T17:46:19.000Z","size":2939,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-01-12T21:43:56.679Z","etag":null,"topics":["customer-churn-prediction","data-analysis","machine-learning"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/arnabsaha7.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null}},"created_at":"2024-01-08T17:43:24.000Z","updated_at":"2024-11-18T15:26:49.000Z","dependencies_parsed_at":"2024-01-08T19:23:15.383Z","dependency_job_id":null,"html_url":"https://github.com/arnabsaha7/Customer-Churn_Prediction---Analysis","commit_stats":null,"previous_names":["arnabsaha7/customer-churn_prediction---analysis"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/arnabsaha7%2FCustomer-Churn_Prediction---Analysis","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/arnabsaha7%2FCustomer-Churn_Prediction---Analysis/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/arnabsaha7%2FCustomer-Churn_Prediction---Analysis/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/arnabsaha7%2FCustomer-Churn_Prediction---Analysis/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/arnabsaha7","download_url":"https://codeload.github.com/arnabsaha7/Customer-Churn_Prediction---Analysis/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":241484551,"owners_count":19970309,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["customer-churn-prediction","data-analysis","machine-learning"],"created_at":"2024-11-13T12:56:19.054Z","updated_at":"2025-03-02T09:27:28.659Z","avatar_url":"https://github.com/arnabsaha7.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Customer Churn Prediction\n\n## Overview\n\nThis 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.\n\n## Features\n\n- Python\n- scikit-learn\n- pandas\n- seaborn\n- matplotlib\n\n## Getting Started\n\n1. **Clone the repository:**\n\n   ```bash\n   git clone https://github.com/arnabsaha7/customer-churn-prediction.git\n   cd customer-churn-prediction\n   ```\n\n2. **Install dependencies:**\n\n   ```bash\n   pip install -r requirements.txt\n   ```\n\n3. **Run the Jupyter Notebook:**\n\n   ```bash\n   jupyter notebook Customer_Churn_Prediction.ipynb\n   ```\n\n   Follow the instructions and explore the notebook to understand the EDA, model training, and evaluation steps.\n\n## Project Structure\n\n- `Customer_Churn_Prediction.ipynb`: Jupyter Notebook containing the main code for data analysis, model training, and evaluation.\n- `requirements.txt`: List of Python packages required for the project.\n- `data/`: Directory to store the dataset (if not included in the repository).\n- `images/`: Directory to store images or plots generated during the analysis.\n\n## Results\n\nThe trained model achieved excellent accuracy; however, further investigation is needed to ensure generalization and address potential overfitting.\n\n## Contributing\n\n1. Fork the repository.\n2. Create a new branch: `git checkout -b feature/your-feature`.\n3. Make your changes and commit them: `git commit -m 'Add your feature'`.\n4. Push to the branch: `git push origin feature/your-feature`.\n5. Open a pull request.\n\n## License\n\nThis project is licensed under the [MIT License](LICENSE).\n\n\nFeel 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.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Farnabsaha7%2Fcustomer-churn_prediction---analysis","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Farnabsaha7%2Fcustomer-churn_prediction---analysis","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Farnabsaha7%2Fcustomer-churn_prediction---analysis/lists"}