{"id":28276304,"url":"https://github.com/susmnty/client-iq","last_synced_at":"2025-06-16T16:30:40.405Z","repository":{"id":292551168,"uuid":"981244631","full_name":"susmnty/Client-IQ","owner":"susmnty","description":"The project focuses on predicting customer status using machine learning. It classifies customers as active, inactive, or at risk. Python and ML libraries are used for data analysis and modeling. The goal is to help businesses reduce churn. It supports better decision-making through predictive insights.","archived":false,"fork":false,"pushed_at":"2025-05-10T17:05:44.000Z","size":68,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-05-21T05:13:11.663Z","etag":null,"topics":["business-analytics","churn-prediction","customer-segmentation","data-preprocessing","feature-engineering","machine-learning-models","model-evaluation"],"latest_commit_sha":null,"homepage":"","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/susmnty.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,"zenodo":null}},"created_at":"2025-05-10T17:01:40.000Z","updated_at":"2025-05-10T17:12:28.000Z","dependencies_parsed_at":"2025-05-10T18:29:00.052Z","dependency_job_id":null,"html_url":"https://github.com/susmnty/Client-IQ","commit_stats":null,"previous_names":["susmnty/customer-status-prediction"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/susmnty/Client-IQ","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/susmnty%2FClient-IQ","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/susmnty%2FClient-IQ/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/susmnty%2FClient-IQ/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/susmnty%2FClient-IQ/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/susmnty","download_url":"https://codeload.github.com/susmnty/Client-IQ/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/susmnty%2FClient-IQ/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":260196971,"owners_count":22973058,"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":["business-analytics","churn-prediction","customer-segmentation","data-preprocessing","feature-engineering","machine-learning-models","model-evaluation"],"created_at":"2025-05-21T05:10:20.612Z","updated_at":"2025-06-16T16:30:40.387Z","avatar_url":"https://github.com/susmnty.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Customer Account Status Prediction: ML Model Comparison \u0026 Deployment\n\nThis project leverages a comprehensive machine learning pipeline to predict the current status of bank customer accounts. Using a dataset enriched with demographic, behavioral, and transactional features, multiple classification algorithms are compared to identify the most accurate and robust model. The project includes full preprocessing, outlier handling, feature encoding, model training, evaluation, and deployment with Streamlit.\n\n## 📊 Features\n- Cleans and preprocesses real-world financial customer data\n- Compares 12+ models including Random Forest, XGBoost, Neural Networks, and Ensemble methods\n- Visual comparison of model performance\n- Saves the best-performing model using `joblib`\n- Deploys with a user-friendly Streamlit web interface for live predictions\n\n## 🚀 How to Run\n\n### 1. Clone this repo\n```bash\ngit clone https://github.com/yourusername/ML-Customer-Status-Prediction.git\ncd ML-Customer-Status-Prediction\n```\n\n### 2. Install dependencies\n```bash\npip install -r requirements.txt\n```\n\n### 3. Run Streamlit app\n```bash\nstreamlit run app.py\n```\n\n## 📁 Files\n- `Data_TarImp_CleanOutlier.csv`: Cleaned dataset\n- `model_training.py`: Script for data processing, model training \u0026 comparison\n- `best_model.pkl`: Saved trained model\n- `app.py`: Streamlit app\n- `requirements.txt`: All dependencies\n\n---\n\n📄 requirements.txt\n\npandas\nnumpy\nmatplotlib\nseaborn\nscikit-learn\nxgboost\njoblib\nstreamlit\n\n---\n# Road map \n![Image](https://github.com/user-attachments/assets/a05b6abd-b2f9-4d64-bf6f-7938af1cac05)\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsusmnty%2Fclient-iq","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsusmnty%2Fclient-iq","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsusmnty%2Fclient-iq/lists"}