{"id":20388161,"url":"https://github.com/sanskaryo/customer_offer_predict","last_synced_at":"2025-04-12T10:31:53.986Z","repository":{"id":262404176,"uuid":"887087636","full_name":"sanskaryo/Customer_offer_predict","owner":"sanskaryo","description":"Webpage url","archived":false,"fork":false,"pushed_at":"2024-11-12T08:49:54.000Z","size":94,"stargazers_count":4,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-03-26T05:22:56.952Z","etag":null,"topics":["customer","offer-prediction","random-forest","sklearn","streamlit"],"latest_commit_sha":null,"homepage":"https://ecommerce-offer-predict.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/sanskaryo.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":"2024-11-12T06:25:53.000Z","updated_at":"2025-01-12T18:30:58.000Z","dependencies_parsed_at":"2024-11-12T09:32:49.024Z","dependency_job_id":null,"html_url":"https://github.com/sanskaryo/Customer_offer_predict","commit_stats":null,"previous_names":["sanskaryo/customer_offer_predict"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sanskaryo%2FCustomer_offer_predict","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sanskaryo%2FCustomer_offer_predict/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sanskaryo%2FCustomer_offer_predict/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sanskaryo%2FCustomer_offer_predict/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/sanskaryo","download_url":"https://codeload.github.com/sanskaryo/Customer_offer_predict/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":248553109,"owners_count":21123380,"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","offer-prediction","random-forest","sklearn","streamlit"],"created_at":"2024-11-15T03:07:38.692Z","updated_at":"2025-04-12T10:31:53.578Z","avatar_url":"https://github.com/sanskaryo.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"\n# Customer Offer Prediction 🎁\n\nWelcome to the **Customer Offer Prediction** repository! This project is a machine learning-powered application built with **Streamlit** to predict whether an e-commerce customer is eligible for an offer based on their behavioral metrics. The app uses a **Random Forest classifier** to determine offer eligibility, providing valuable insights for marketing and customer engagement.\n\n## 📌 Project Overview\n\nIn e-commerce, personalizing offers based on customer behavior can significantly increase customer retention and satisfaction. This project predicts whether a customer should receive an offer based on specific parameters, such as:\n\n- Time spent on the website\n- Time spent on the app\n- Yearly amount spent\n- Length of membership\n\nBy analyzing these factors, the app aims to help businesses make data-driven decisions to boost customer loyalty and engagement.\n\n## 🎯 Key Features\n\n- **Real-time Predictions**: Input customer metrics to get an immediate prediction of offer eligibility.\n- **Streamlit Interface**: An intuitive and interactive web-based interface.\n- **Customizable Parameters**: Adjust sliders to simulate different customer profiles.\n- **Styled Results Display**: Visually distinct \"Offer\" and \"No Offer\" indicators for clear insights.\n\n## 🛠️ Technology Stack\n\n- **Python**: Core programming language.\n- **Streamlit**: Frontend for building a responsive web application.\n- **Scikit-Learn**: Random Forest classifier for predictive modeling.\n- **Pickle**: Model serialization for easy deployment.\n\n  ## Page View - ![image](https://github.com/user-attachments/assets/a0c6cf76-ba1f-4f00-90dc-798a193fb825)\n\n\n## 🚀 Getting Started\n\n### Prerequisites\n\nEnsure you have **Python 3.7+** installed. Then, install the necessary packages:\n\n```bash\npip install streamlit scikit-learn pandas\n```\n\n### Installation\n\n1. **Clone the Repository**:\n    ```bash\n    git clone https://github.com/sanskaryo/Customer_offer_predict.git\n    ```\n   \n2. **Navigate to the Project Directory**:\n    ```bash\n    cd Customer_offer_predict\n    ```\n\n3. **Run the App**:\n    ```bash\n    streamlit run app.py\n    ```\n\n4. **Access the Application**: Go to `http://localhost:8501` in your browser.\n\n### Input Parameters\n\nThe app requires the following inputs:\n- **Time on Website**: Time spent on the website (0-40 minutes).\n- **Yearly Amount Spent**: Total amount spent annually by the customer (0-800 USD).\n- **Time on App**: Time spent on the app (0-20 minutes).\n- **Length of Membership**: Duration of membership (0-8 years).\n\n### Example Usage\n\nAfter inputting the parameters, click **\"Predict Eligibility\"** to see if the customer qualifies for an offer. Results are displayed with color-coded visuals:\n- **Green**: Eligible for offer\n- **Red**: Not eligible for offer\n\n## 📂 Project Structure\n\n```\n.\n├── app.py               # Main Streamlit application\n├── model.pkl            # Serialized Random Forest model\n├── README.md            # Project documentation\n└── requirements.txt     # List of dependencies\n```\n\n## 📈 Model Information\n\nThe model was trained using a Random Forest classifier with historical customer data to identify patterns in spending and activity. This model can be further fine-tuned by adding more relevant features or experimenting with different algorithms for better accuracy.\n\n## ✨ Future Enhancements\n\nSome potential enhancements include:\n- **Improving Model Accuracy**: Experiment with additional features.\n- **Enhanced UI**: Add charts and interactive components.\n- **Backend Integration**: Store customer data in a database for real-time analytics.\n\n## 🤝 Contributing\n\nFeel free to fork this repository and create a pull request if you'd like to contribute. Any ideas for improving this project are welcome!\n\n## 📄 License\n\nThis project is licensed under the MIT License.\n\n## 📝 Acknowledgments\n\nThanks to the open-source community and **Streamlit** for making this project possible.\n\n---\n\nEnjoy the app, and happy predicting! 🎉\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsanskaryo%2Fcustomer_offer_predict","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsanskaryo%2Fcustomer_offer_predict","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsanskaryo%2Fcustomer_offer_predict/lists"}