🚀 ML-Normalization

This repository contains implementations of various normalization techniques used in machine learning to preprocess data. Normalization ensures that the data scales properly, improving the performance of machine learning algorithms.

📂 Contents

• 📘 Notebooks:

  • 📊 feature-scaling-min-max-scaling.ipynb: Demonstrates Min-Max Scaling.
  • 📏 Mean-Max-Scaling.ipynb: Implements Mean-Max Scaling.
  • 📈 Max-Abs-Scaling.ipynb: Explains Max-Abs Scaling.
  • 📉 RobustScaling.ipynb: Covers Robust Scaling.

• 📜 License:

  • The repository is licensed under the MIT License.

🛠️ Techniques Covered

1. ⚖️ Min-Max Scaling

   • Scales the data to a fixed range, typically [0, 1].
   • 🔗 Example Notebook

2. 📏 Mean-Max Scaling

   • Normalizes data using the mean and maximum values.

3. 📈 Max-Abs Scaling

   • Scales each feature by its maximum absolute value.

4. 🛡️ Robust Scaling

   • Removes the median and scales data according to the interquartile range, making it robust to outliers.

📝 How to Use

1. Clone the repository:

   git clone https://github.com/Himel-Sarder/ML-Normalization.git

2. Navigate to the repository:

   cd ML-Normalization

3. Open the desired Jupyter Notebook to explore and run the examples:

   jupyter notebook

📦 Requirements

• 🐍 Python 3.x
• 📓 Jupyter Notebook
• Required libraries:
  • 🧮 NumPy
  • 📊 Pandas
  • 🤖 Scikit-learn
  • 🎨 Matplotlib (optional for visualization)

Install the required libraries using:

pip install numpy pandas scikit-learn matplotlib

🤝 Contributing

Contributions are welcome! 🎉 If you have suggestions for improvements or additional techniques, feel free to fork the repository and create a pull request.

📜 License

This project is licensed under the MIT License. See the LICENSE file for details.