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Data

Foundations of Feature Engineering

Master the basics of feature engineering by learning to clean, handle missing data, scale, and normalize datasets. Prepare raw data for transformation and analysis, setting a solid foundation for advanced data engineering tasks.

Pandas

Python

Scikit-learn

See path

4 lessons

18 practices

1 hour

Data Cleaning and Preprocessing

Lessons and practices

Lesson 1: Introduction to Feature Engineering

Loading The Titanic Dataset

Exploring Dataset Structure

Peek at Your Data Preview

Customize Data Preview Settings

Understanding Numbers Through Statistics

Lesson 2: Identifying and Handling Missing Data

Detecting Missing Data Like a Pro

Missing Ages Need Fixing

Switching to Mean Imputation for Missing Ages

Mode Imputation for Missing Ports

Missing Data Handling for Passenger Decks

Lesson 3: Detecting and Addressing Outliers

Calculating Quartiles for Outlier Detection

Adjusting Outlier Detection Sensitivity

Outliers in Need of Detection

Capping Outliers Effectively

Lesson 4: Exploring Data Scaling Techniques

Scale Your First Dataset

Moving to Standard Scaling

Data Scaling Gone Wrong

Reverting Scaled Data in Practice

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