🚀 Feature Engineering Toolkit

Welcome to the Feature Engineering repository! This project provides practical code, examples, and utilities to accelerate your machine learning workflow by transforming raw data into meaningful features. Whether you’re a beginner or a seasoned data scientist, you’ll find reusable scripts and helpful guides here to supercharge your feature engineering process. ✨

📂 Directory Structure

.
├── data/                # Example datasets for practice
├── notebooks/           # Jupyter Notebooks for interactive demos
├── src/                 # Core Python scripts and utilities
├── requirements.txt     # Python dependencies
├── feature_engineering.py # Main feature engineering functions
├── README.md            # This readme file!

🧩 Main Files

• feature_engineering.py: Core Python module with reusable functions for feature extraction, transformation, and selection.
• notebooks/: Interactive demos showing practical feature engineering steps.
• data/: Toy datasets to experiment with feature engineering techniques.
• requirements.txt: List of Python packages needed to run the code.

🛠️ Features

• 🏗️ Feature Creation: Build new features from raw data.
• 🔄 Feature Transformation: Scale, encode, and preprocess features.
• 🕵️ Feature Selection: Identify the most relevant features for your models.
• 📊 Visualization: Tools to visualize feature distributions and relationships.

🚦 Getting Started

1. Clone the repository

   git clone https://github.com/mdzaheerjk/Feature-Engineering.git

2. Install dependencies

   pip install -r requirements.txt

3. Try out the notebooks
   Open the notebooks in notebooks/ to explore feature engineering techniques interactively!

🤝 Contributing

Pull requests, ideas, and suggestions are welcome!
Feel free to open issues or contribute improvements.

📚 References

• Feature Engineering for Machine Learning
• Scikit-learn Documentation

---

Made with ❤️ by mdzaheerjk