{"id":15159751,"url":"https://github.com/wasay8/classification-customer-churn","last_synced_at":"2026-02-07T08:02:56.207Z","repository":{"id":255259330,"uuid":"849033701","full_name":"wasay8/Classification-Customer-Churn","owner":"wasay8","description":"A web application hosted on Streamlit to predict customer churn using a deep learning model.","archived":false,"fork":false,"pushed_at":"2024-08-28T21:37:42.000Z","size":363,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-04-07T17:45:03.148Z","etag":null,"topics":["data-preprocessing","feedforward-neural-network","streamlit"],"latest_commit_sha":null,"homepage":"https://ann-classification-customer-churn-a9cutfyvfxgt8gxebeeuvq.streamlit.app/","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/wasay8.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2024-08-28T21:19:31.000Z","updated_at":"2024-08-29T07:46:31.000Z","dependencies_parsed_at":"2024-08-28T22:42:21.272Z","dependency_job_id":"1bbc0b69-09d5-40b8-8d20-7901623de4eb","html_url":"https://github.com/wasay8/Classification-Customer-Churn","commit_stats":null,"previous_names":["wasay8/ann-classification-customer-churn"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/wasay8/Classification-Customer-Churn","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/wasay8%2FClassification-Customer-Churn","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/wasay8%2FClassification-Customer-Churn/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/wasay8%2FClassification-Customer-Churn/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/wasay8%2FClassification-Customer-Churn/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/wasay8","download_url":"https://codeload.github.com/wasay8/Classification-Customer-Churn/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/wasay8%2FClassification-Customer-Churn/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":268431637,"owners_count":24249413,"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","status":"online","status_checked_at":"2025-08-02T02:00:12.353Z","response_time":74,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"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":["data-preprocessing","feedforward-neural-network","streamlit"],"created_at":"2024-09-26T22:00:22.722Z","updated_at":"2026-02-07T08:02:56.165Z","avatar_url":"https://github.com/wasay8.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Classification-Customer-Churn\n\nThis project focuses on predicting customer churn using machine learning. The dataset and code included help train a model to identify customers who are likely to leave a service.\n\n## Project Structure\n\n- **Churn_Modelling.csv**: This is the dataset used for training the model. It contains customer data including demographics, account information, and whether the customer has churned.\n- **app.py**: The main application file. This file contains the code to run the model, including preprocessing steps and predictions.\n- **model.h5**: This is the trained machine learning model in HDF5 format, which is used to predict churn.\n- **Label_Encoder_Gender.pkl**: A pickle file containing the label encoder for the 'Gender' feature.\n- **onehot_encoder_geo.pkl**: A pickle file containing the one-hot encoder for the 'Geography' feature.\n- **scaler.pkl**: A pickle file containing the scaler used for feature scaling.\n- **building_model.ipynb**: Jupyter notebook that contains the code for building and training the churn prediction model.\n- **prediction.ipynb**: Jupyter notebook for loading the trained model and making predictions on new data.\n- **requirements.txt**: Contains the list of dependencies and libraries required to run the project.\n\n## Setup Instructions\n\n1. Clone the repository:\n   ```bash\n   git clone https://github.com/your-username/churn-prediction.git\n   ```\n2. Navigate to the project directory:\n   ```bash\n   cd churn-prediction\n   ```\n3. Install the required dependencies:\n   ```bash\n   pip install -r requirements.txt\n   ```\n4. Run the application locally:\n   ```bash\n   streamlit run app.py\n   ```\n\n## Web Application\n\nYou can also access the web version of this application hosted on Streamlit by clicking the following link:\n\n[Customer Churn Prediction Web App](https://ann-classification-customer-churn-a9cutfyvfxgt8gxebeeuvq.streamlit.app/)\n\n## Dataset\n\nThe dataset (`Churn_Modelling.csv`) includes the following key features:\n\n- **CustomerID**: Unique identifier for each customer.\n- **Geography**: The country from which the customer belongs.\n- **Gender**: The gender of the customer.\n- **Age**: The age of the customer.\n- **Tenure**: The number of years the customer has been with the company.\n- **Balance**: The account balance of the customer.\n- **Exited**: Indicates whether the customer has churned (1) or not (0).\n\n## Model\n\nThe model is a deep learning neural network saved in `model.h5`. It uses multiple features such as geography, gender, age, balance, and tenure to predict whether a customer will churn.\n\n## Notebooks\n\n- **building_model.ipynb**: This notebook contains the code for building, training, and evaluating the churn prediction model using the dataset.\n- **prediction.ipynb**: This notebook loads the pre-trained model and demonstrates how to make predictions on new customer data.\n\n## Usage\n\nAfter running `app.py`, the model will load, preprocess the data, and predict churn for new customer data.\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fwasay8%2Fclassification-customer-churn","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fwasay8%2Fclassification-customer-churn","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fwasay8%2Fclassification-customer-churn/lists"}