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Designed for strategic decision-makers, HR professionals, and data enthusiasts who seek **deep workforce insights** through interactive visualizations, statistical modeling, and machine learning techniques.\n\n## 🖼️ UI Preview\n\n![App Screenshot](assets/ui_screenshot.png)\n\n## 🎯 Analysis Output Screenshot\n\n![Prediction Result](assets/prediction_screenshot.png)\n\n## 🎯 AI \u0026 ML Output Screenshot\n\n![Prediction Result](assets/ml_screenshot.png)\n\n---\n\n## 🚀 Key Features\n\n### 📌 Executive Overview\n- Interactive **KPI Gauges** (Attrition Rate, Tenure, Income, etc.)\n- **3D Visual Analysis** with hover-driven storytelling\n- Dynamic filtering for real-time data slicing\n\n### 📊 Advanced Analytics\n- **Parallel Coordinates Plots** for multi-dimensional data views\n- **Sunburst Charts** and **Radar Plots** for categorical pattern discovery\n- **Waterfall Charts** to visualize factor-wise attrition breakdown\n\n### 🧠 Machine Learning Insights\n- **Principal Component Analysis (PCA)** for dimensionality reduction\n- **Clustering Algorithms** (K-Means, DBSCAN) for grouping employee types\n- **Predictive Models** (Logistic Regression, Decision Trees) for attrition forecasting\n- **Model Evaluation** with metrics like accuracy, ROC-AUC\n\n### 📈 Statistical Deep-Dive\n- **Correlation Matrices \u0026 Heatmaps**\n- **Cohort Analysis** to understand retention by join period\n- **Survival Analysis** to analyze employee tenure distributions\n\n### 🧾 Reporting \u0026 Exporting\n- Auto-generated **Strategic Recommendations**\n- Export **all visuals, dataframes, and model outputs** to CSV, PNG\n\n---\n\n## 📊 Technologies Used\n\n- **Frontend**: Streamlit\n- **Data**: Pandas, NumPy\n- **Visuals**: Plotly, Seaborn, Matplotlib, Altair\n- **ML \u0026 Stats**: Scikit-learn, Lifelines, SciPy, Statsmodels\n\n---\n\n## 🗂️ Project Structure\n\n```\nMoneyMind/\n│\n├── 📁 data/ # Sample and uploaded datasets\n├── 📁 models/ # Trained machine learning models\n├── 📁 pages/ # Streamlit multipage structure\n├── 📁 reports/ # Exported charts and reports\n├── 📜 app.py # Main Streamlit app\n├── 📜 utils.py # Helper functions\n├── 📜 requirements.txt # Project dependencies\n└── 📜 README.md # Project documentation\n```\n\n---\n\n## 🛠️ How to Run\n\n1. Clone the repository:\n   ```bash\n   git clone https://github.com/yourusername/AttritionPredict.git\n   cd AttritionPredict\n   ```\n\n2. Create a virtual environment (optional but recommended):\n   ```bash\n   python -m venv venv\n    .\\venv\\Scripts\\activate\n   ```\n\n3. Install dependencies:\n   ```bash\n   pip install -r requirements.txt\n   ```\n\n4. Run the app:\n   ```bash\n   streamlit run app.py\n   ```\n\n5. If you face issues with 0.0.0.0, use:\n   ```\n   streamlit run app.py --server.address=localhost --server.port=8501\n   ```\n---\n## 📌 Future Improvements\n\nReal-time API integration with HRMS\n\nAuth-enabled HR manager login\n\nTime-series forecasting of attrition trends\n\nFeedback-driven model fine-tuning\n\n---\n\n## 📘 License\n\nThis project is licensed under the MIT License – see the [LICENSE](/license.txt) file for details.\n\n---\n\n## 🙌 Credits\n\nMade with ❤️ by [Aditya Arora](https://www.linkedin.com/in/NeuralAditya)  \n\u0026copy; 2025 Aditya Arora. All rights reserved.\n\n---\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fneuraladitya%2Fattritionpredict","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fneuraladitya%2Fattritionpredict","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fneuraladitya%2Fattritionpredict/lists"}