Data Preprocessing Techniques: A Guide to Data Cleaning, Transformation, and Feature Engineering

Goal: To provide a comprehensive understanding of data preprocessing techniques, including cleaning, transformation, and feature engineering.

Introduction to data preprocessing

Introduction to data preprocessing

Stages of Data Preprocessing

Data cleaning and error handling: Cleaning data corrects errors and inconsistencies; removing duplicates, correcting common mistakes, standardizing formats, handling invalid values, and validating data ensures data quality for further analysis.

Data cleaning addresses errors and inconsistencies: removing duplicates, correcting common errors, standardizing formats, handling invalid values, and validating data. This ensures data quality for subsequent analysis.

Feature Engineering and Feature Creation

Feature engineering creates new features from existing data to improve models: feature combinations, interactions, polynomial features, binning, categorical encoding, date extraction (day of the week, month), and aggregation. Proper feature engineering significantly improves model quality.

Handling problematic data

Frequently asked questions