Data Preprocessing & Feature Engineering
Beginner • 12-15 minutes
Discover why “garbage in, garbage out” is the most important rule in machine learning. Watch how proper data preprocessing transforms messy real-world data into clean, model-ready features that dramatically improve learning performance.
Data Quality
Why clean data is essential for model success
Feature Scaling
Making all features comparable through normalization
Categorical Encoding
Converting text categories into meaningful numbers
Feature Engineering
Creating new features from existing data
What You'll Learn
Understand why data quality affects model performance
Learn different normalization and scaling techniques
Master categorical variable encoding methods
See the impact of preprocessing on learning
Real-World Applications
Data preprocessing is used everywhere in machine learning
Recommendation Systems
Netflix and Spotify preprocess user behavior data, normalizing viewing times and encoding genre preferences to recommend content you'll love.
Medical Diagnosis
Hospital systems normalize patient vital signs and encode symptoms to help AI detect diseases early and accurately.
Financial Trading
Trading algorithms preprocess market data, scaling prices and volumes to identify profitable patterns across different assets.
Key Takeaways
Data Quality Matters Most
Clean, well-prepared data is more important than complex algorithms. “Garbage in, garbage out” is the fundamental rule of ML.
Scale Features Consistently
Use Min-Max normalization or Z-score standardization to ensure all features contribute equally to model learning.
Engineer Meaningful Features
Combine existing features to create new ones that capture important relationships in your data.
The Problem with Raw Data
Real-world data is messy! Our student dataset has different scales, missing values, and mixed data types. Neural networks struggle with this inconsistency.
View Code Example →
# Raw student data
students = [
{"age": 22, "gpa": 3.8, "hours": 25, "income": "$45,000"},
{"age": 19, "gpa": 2.1, "hours": 8, "income": "$0"},
# Different scales, text values, inconsistent formats!
]Student Dataset Transformation6 samples
| Name | age Different scales | gpa Different scales | study hours Different scales | income Text | Passed |
|---|---|---|---|---|---|
| Alice | 22 | 3.8 | 25 | $45,000 | Yes |
| Bob | 19 | 2.1 | 8 | $0 | No |
| Carol | 24 | 3.9 | 30 | $65,000 | Yes |
| David | 20 | 2.8 | 15 | $25,000 | No |
| Eve | 23 | 3.5 | 22 | $55,000 | Yes |
| Frank | 18 | 1.9 | 5 | $0 | No |
⚠️ Problems Detected:
- • Different scales: Age (18-24) vs Study Hours (5-30) vs GPA (1.9-3.9)
- • Text values: Income column contains strings that neural networks can't process
- • Imbalanced influence: Larger numbers will dominate the model unfairly