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Data

38 learners

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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