{"id":23792213,"url":"https://github.com/juliusmarkwei/customer-churn-eda-balancing-and-ml","last_synced_at":"2025-08-01T08:14:05.344Z","repository":{"id":177154750,"uuid":"659995016","full_name":"juliusmarkwei/Customer-Churn-EDA-Balancing-and-ML","owner":"juliusmarkwei","description":"Bank customer churn prediction using multiple ml models","archived":false,"fork":false,"pushed_at":"2023-09-22T11:49:27.000Z","size":151457,"stargazers_count":4,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2023-09-23T14:44:09.625Z","etag":null,"topics":["algorithm","application","data","data-science","machine-learning","python","streamlit"],"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/juliusmarkwei.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}},"created_at":"2023-06-29T02:42:07.000Z","updated_at":"2023-09-23T13:07:19.000Z","dependencies_parsed_at":null,"dependency_job_id":"b7e1edfe-4c85-4736-8f1e-cd31dd5f1420","html_url":"https://github.com/juliusmarkwei/Customer-Churn-EDA-Balancing-and-ML","commit_stats":null,"previous_names":["juliusmarkwei/customer-churn-eda-balancing-and-ml"],"tags_count":0,"template":null,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/juliusmarkwei%2FCustomer-Churn-EDA-Balancing-and-ML","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/juliusmarkwei%2FCustomer-Churn-EDA-Balancing-and-ML/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/juliusmarkwei%2FCustomer-Churn-EDA-Balancing-and-ML/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/juliusmarkwei%2FCustomer-Churn-EDA-Balancing-and-ML/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/juliusmarkwei","download_url":"https://codeload.github.com/juliusmarkwei/Customer-Churn-EDA-Balancing-and-ML/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":232113799,"owners_count":18474498,"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":["algorithm","application","data","data-science","machine-learning","python","streamlit"],"created_at":"2025-01-01T18:28:38.751Z","updated_at":"2025-01-01T18:28:39.152Z","avatar_url":"https://github.com/juliusmarkwei.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Customer Churn Prediction App\n\n## Overview\n\nThis project is a machine learning classifier for predicting whether a bank customer is likely to churn (leave) or not. It includes a Streamlit web application that allows users to interact with the predictive model and visualize the results.\n\n## Table of Contents\n\n- [Demo](#demo)\n- [Getting Started](#getting-started)\n  - [Prerequisites](#prerequisites)\n  - [Installation](#installation)\n- [Streamlit App](#streamlit-app)\n- [Model Training](#model-training)\n- [Contributing](#contributing)\n- [License](#license)\n\n## Demo\n\nClick [here](https://customerchurnpredict.streamlit.app/) to view the prediction app in your web browser.\n\nHere are some pictures of what the app looks like:\n\n\u003cfont size=4 color=\"#ffffff\"\u003e1. Prediction Page\u003c/font\u003e\n\n\u003chr\u003e\u003cbr\u003e\n\u003cimg src=\"./assets/images/prediction image.png\"\u003e\n\u003cbr\u003e\n\n\u003cbr\u003e\u003cbr\u003e\n\u003cfont size=4 color=\"#ffffff\"\u003e2. Visualization Page\u003c/font\u003e\n\u003chr\u003e\u003cbr\u003e\n\u003cimg src=\"./assets/images/viz image.png\"\u003e\n\n## Getting Started\n\n### Prerequisites\n\nList the prerequisites that users need to have installed or set up before using the project.\n\n```bash\npython\u003e=3.8\nrequirements.txt\n```\n\n### Installation\n\nTo use my application, follow this steps below to successfully install and run the program.\n\n```bash\n# Clone the repository\ngit clone https://github.com/juliusmarkwei/Customer-Churn-EDA-Balancing-and-ML.git\n\n# Change directory\ncd Customer-Churn-EDA-Balancing-and-ML/\n\n# Install dependencies\npip install -r requirements.txt\n```\n\n## Streamlit App\n\nCarefully type the command below in your teminal of the \"Customer-Churn-EDA-Balancing-and-ML/\" directory to run the app.\n```bash\n# Run the Streamlit app\nstreamlit run app.py\n```\n\n\n## Model Training\n\nOur machine learning model was trained using a dataset containing [describe your dataset]. The training process involved the following steps:\n\n- **Data Preprocessing:** We performed data cleaning, handled missing values, and encoded categorical features as part of data preparation.\n\n- **Model Selection:** We selected the Random Forest model for the prediciton app after evaluation as the base model due to its suitability for our problem.\n\n- **Model Evaluation:** The model's performance was evaluated using metrics accuracy and F1-score. Cross-validation was used to assess its generalization ability.\n\n- **Hyperparameter Tuning:** We fine-tuned the model's hyperparameters to optimize performance.\n\nFor detailed information on the model training process, please refer to the [training notebook](https://github.com/juliusmarkwei/Customer-Churn-EDA-Balancing-and-ML/notebooks/main.ipynb).\n\n\n## Contributing\n\nWe welcome contributions to improve this project! Whether it's bug reports, feature suggestions, or code contributions, we appreciate your help.\n\n- **Reporting Issues:** If you encounter a problem or have a suggestion, [open an issue](https://github.com/juliusmarkwei/Customer-Churn-EDA-Balancing-and-ML/issues) with details.\n\n- **Making Pull Requests:** Feel free to submit pull requests for fixes or enhancements. Follow common coding standards and provide clear descriptions for your changes.\n\nThank you for your contributions!\n\n---\n## License\n\nThis project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjuliusmarkwei%2Fcustomer-churn-eda-balancing-and-ml","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fjuliusmarkwei%2Fcustomer-churn-eda-balancing-and-ml","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjuliusmarkwei%2Fcustomer-churn-eda-balancing-and-ml/lists"}