Machine Learning Tutorial 0/98 lessons ~6 min read Lesson 14
Advanced EDA
What is Advanced EDA?
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Focus
7 guided sections
Practice signal
Examples included
Career prep
Foundation builder
Introduction
What is Advanced EDA? Pair plots, correlation heatmaps, target analysis, and leakage checks. Machine learning systems learn patterns from data instead of hard-coded rules.
Understanding the topic
How Advanced EDA works:
- Pair plots, correlation heatmaps, target analysis, and leakage checks.
- Prepare or explore data as needed.
- Train or apply the model/technique.
- Evaluate results and iterate.
| Term | Description |
|---|---|
| Advanced EDA | Pair plots, correlation heatmaps, target analysis, and leakage checks |
| 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 Advanced EDA.
- 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
1Advanced EDA workflow
1 / 4Understand
Learn when and why to use Advanced EDA.
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
Advanced EDA is a core machine learning topic. Pair plots, correlation heatmaps, target analysis, and leakage checks
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