This repository is dedicated to the exploration and application of feature engineering techniques in machine learning. Feature engineering is the process of using domain knowledge to extract features from raw data that make machine learning algorithms work more effectively.

• Introduction
• What is Feature Engineering?
• Techniques
  • Imputation
  • Handling Outliers
  • Binning
  • Encoding Categorical Variables
  • Feature Scaling
  • Feature Selection
• Practical Examples
• Resources
• Contributing

Feature engineering is a crucial step in the machine learning pipeline. It involves creating new features from existing ones to increase the predictive power of the learning algorithm.

Imputation is the process of replacing missing data with substituted values. Techniques include mean/mode imputation, using regression models, or more complex algorithms like KNN or MICE.

Outliers can significantly affect the performance of machine learning models. Techniques to handle outliers include trimming, capping, or using robust scaling methods.

Binning is used to group a set of numerical values into a smaller number of bins to have a better understanding of the data distribution.

Categorical variables can be transformed into numerical values through various encoding techniques such as one-hot encoding, label encoding, or binary encoding.

Feature scaling methods, such as normalization and standardization, are used to standardize the range of independent variables or features of data.

Feature selection techniques are used to select important features based on various statistical tests to reduce overfitting and improve model performance.

Here, we provide Jupyter notebooks demonstrating the application of feature engineering techniques on various datasets.

Contributions are welcome! For major changes, please open an issue first to discuss what you would like to change.

This README is a brief overview of the feature engineering process. For more detailed explanations and practical examples, please refer to the individual files and folders in this repository.