A hands-on Streamlit application for quick evaluation, implementation, and analysis of feature engineering and preprocessing strategies to improve machine learning model performance.
🔗 Live Application: https://feature-evaluation-lab.streamlit.app/
Feature_Engineering_SandBox is designed as a practical experimentation workspace where data preprocessing, feature extraction, and evaluation decisions can be tested quickly and observed directly through model behavior and metrics.
The focus is not on building a single optimized model, but on enabling:
- Rapid validation of preprocessing and feature choices
- Direct comparison of feature pipelines
- Clear understanding of how features affect model performance
- Faster iteration during data preparation and experimentation
- Evaluating preprocessing strategies before full model training
- Understanding feature relevance and redundancy
- Testing feature extraction ideas in a controlled environment
- Identifying stable versus sensitive features
- Making data-driven decisions for better downstream models
- Interactive preprocessing configuration (imputation, encoding, scaling)
- Feature extraction and transformation analysis
- Feature correlation and distribution inspection
- Model evaluation under identical feature pipelines
- Feature ablation and sensitivity checks
- Clear visual feedback for faster iteration
Screenshots illustrating the workflow and evaluation views.
Feature_Engineering_SandBox/
│
├── app.py # Streamlit entry point
├── requirements.txt
├── README.md
├── .gitignore
│
├── src/
│ ├── data/ # Data loading & validation
│ ├── preprocessing/ # Cleaning & transformations
│ ├── features/ # Feature extraction & analysis
│ ├── models/ # Model training & checks
│ ├── evaluation/ # Ablation & sensitivity analysis
│ └── visualization/ # Plot generation
│
├── scripts/ # Debug & experiments
└── docs/
└── screenshots/ # UI screenshots
- Python
- Streamlit
- Pandas, NumPy
- Scikit-learn
- Matplotlib / Plotly
git clone https://github.com/BhattAyush17/Feature_Engineering_SandBox.git cd Feature_Engineering_SandBox pip install -r requirements.txt streamlit run app.py
- Fast feedback over long training cycles
- Explicit feature and preprocessing control
- Minimal abstraction to keep behavior visible
- Reusable logic for real ML pipelines
- Designed for iteration, not one-off results
- Extending support beyond Random Forest, Decision Tree, and Logistic Regression
- Enhanced feature importance comparison across supported models
- Exportable summaries for feature and preprocessing evaluations
- More configurable feature extraction pipelines for tabular datasets
Feature_Engineering_SandBox helps developers gain clear insights into feature behavior, evaluate preprocessing and extraction strategies, and make precise, data-driven decisions to improve feature-driven model performance.