Comprehensive Guide to Feature Engineering with Python Libraries

📖 Overview

This collection provides comprehensive tutorials and implementations for automated feature engineering using powerful Python libraries. Transform raw data into meaningful features that boost your machine learning models' performance.

🎯 What You'll Learn

• Featuretools: Deep feature synthesis and automated feature generation
• Feature Engine: Advanced categorical encoding and rare label handling
• MLJAR: Automated feature extraction and selection
• Dask: Parallel processing for large-scale feature engineering

📂 File Structure

1. Featuretools Tutorials

FileFocus AreaDifficultyTimefeaturetools_basic_usage.pyIntroduction to EntitySets and DFS⭐⭐☆☆☆2-3 hoursfeaturetools_deep_feature_synthesis.pyMulti-table feature generation⭐⭐⭐☆☆3-4 hoursfeaturetools_time_series.pyTemporal feature engineering⭐⭐⭐⭐☆4-5 hours

2. Feature Engine Tutorials

FileFocus AreaDifficultyTimefeature_engine_rare_label_encoder.pyBasic rare label handling⭐⭐☆☆☆1-2 hoursfeature_engine_rare_label_encoder_space.pySpace exploration data example⭐⭐⭐☆☆2-3 hours

3. MLJAR & Parallel Processing

FileFocus AreaDifficultyTimemljar_feature_extraction.pyAutomated feature selection⭐⭐⭐☆☆2-3 hoursdask_featuretools_parallel.pyLarge-scale parallel processing⭐⭐⭐⭐☆3-4 hours

🚀 Quick Start Guide

Prerequisites