Feature Engineering:

Welcome to the Feature Engineering repository! This project contains hands-on implementation of essential techniques used in preprocessing and transforming data for machine learning models. The focus is on building a strong foundation in practical feature engineering skills.

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Topics Covered

🔹 Handling Missing Data

• Drop missing values
• Mean/Median/Mode Imputation
• Random Sample Imputation
• Capturing NaNs with Indicators
• End of Distribution Imputation
• Arbitrary Value Imputation

🔹 Encoding Categorical Variables

• One Hot Encoding
• Ordinal Encoding
• Count/Frequency Encoding
• Target Mean Encoding

🔹 Variable Transformation

• Logarithmic Transformation
• Box-Cox Transformation

🔹 Outlier Handling

• IQR Method
• Z-score Method

🔹 Discretization (Binning)

• Equal Width Binning
• Equal Frequency Binning

🔹 Feature Scaling

• Min-Max Scaling
• Standardization (Z-score Normalization)
• Robust Scaling

🔹 Feature Extraction

• Date/Time Feature Extraction
• Text Feature Extraction

🔹 Feature Selection

• Filter Methods (Correlation)
• Wrapper Methods
• Embedded Methods

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🚀 Usage

Each topic is implemented and explained step-by-step in Jupyter notebooks. You can clone the repository and run the notebooks for practice and learning:

git clone https://github.com/Abdullah-Niaz/Feature-Engineering.git
cd Feature-Engineering

Open the notebooks in JupyterLab or Google Colab to explore the techniques interactively.

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👨‍💻 Author

Abdullah Niaz

Connect with me on GitHub