Data Preprocessing & Normalization

Beginner

Master data preprocessing techniques essential for ML pipelines. Learn Z-score standardization, robust scaling, min-max normalization, and encoding methods with mathematical rigor and hands-on practice.

60 min Lab: notebook 5 objectives 4 evidence types
Open Interactive Lab
60
Minutes
5
Objectives
4
Evidence Types
4
Success Criteria

Case Narrative

Learning Objectives

1
Understand why preprocessing is critical for ML model performance
2
Apply Z-score, robust, and min-max normalization correctly
3
Choose appropriate normalization for different data distributions
4
Implement encoding strategies for categorical and sequential data
5
Build complete preprocessing pipelines with inverse transforms

Required Evidence

Normalization Comparison Not collected yet
Distribution Analysis Not collected yet
Pipeline Construction Not collected yet
Encoding Evaluation Not collected yet

Case Details

Difficulty
Beginner
Duration
60 min
Lab Type
notebook
Slug
data-preprocessing

Prerequisites

No prerequisites - open to all

Success Criteria

Distributions Analyzed 2
Inverse Transform Verified Required
Normalizations Applied 3
Pipeline Constructed Required

Tags