Machine Learning Tutorial 0/98 lessons ~6 min read Lesson 6
Data Cleaning
What is Data Cleaning?
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Focus
7 guided sections
Practice signal
Examples included
Career prep
Foundation builder
Introduction
What is Data Cleaning? Handle missing values, outliers, duplicates, and invalid entries before modeling. Machine learning systems learn patterns from data instead of hard-coded rules.
Understanding the topic
How Data Cleaning works:
- Handle missing values, outliers, duplicates, and invalid entries before modeling.
- Prepare or explore data as needed.
- Train or apply the model/technique.
- Evaluate results and iterate.
| Term | Description |
|---|---|
| Data Cleaning | Handle missing values, outliers, duplicates, and invalid entries before modeling |
| Training data | Examples used to learn patterns. |
| Features | Input variables (columns) fed to the model. |
| Target / label | What you predict (supervised learning). |
Step-by-step explanation
- Understand — Learn when and why to use Data Cleaning.
- Prepare data — Load, clean, and split datasets.
- Apply — Fit model or run algorithm in Python/sklearn.
- Evaluate — Measure accuracy, loss, or cluster quality.
Execution workflow
1Data Cleaning workflow
1 / 4Understand
Learn when and why to use Data Cleaning.
Best practices
- Split data into train/validation/test before tuning.
- Scale numeric features when algorithms are distance-based.
- Always evaluate on held-out data — not training accuracy alone.
Common mistakes
- Training on test data (data leakage).
- Ignoring class imbalance in classification metrics.
- Using accuracy alone on imbalanced datasets.
Summary
Data Cleaning is a core machine learning topic. Handle missing values, outliers, duplicates, and invalid entries before modeling
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