Feature Engineering

A toolkit / library / project for performing feature engineering: transforming raw data into informative features for machine learning models.

Overview

This project provides a set of feature engineering tools to preprocess, transform, and enrich datasets for machine learning.

Feature Extraction Techniques Implemented

• Principal Component Analysis (PCA) from scratch
• Integral Image feature extraction from scratch
• HAAR-like feature extraction from scratch

Tested on Machine Learning Models

• AdaBoost
• Decision Tree
• Gaussian Naive Bayes

Datasets

• Spambase Dataset
• Pollution Dataset

For implementation and results see: feature_engineering.ipynb