Machine Learning Tutorial 0/98 lessons ~6 min read Lesson 12
Exploratory Data Analysis
What is Exploratory Data Analysis?
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Focus
7 guided sections
Practice signal
Examples included
Career prep
Foundation builder
Introduction
What is Exploratory Data Analysis? EDA summarizes distributions, correlations, and anomalies before modeling. Machine learning systems learn patterns from data instead of hard-coded rules.
Understanding the topic
How Exploratory Data Analysis works:
- EDA summarizes distributions, correlations, and anomalies before modeling.
- Prepare or explore data as needed.
- Train or apply the model/technique.
- Evaluate results and iterate.
| Term | Description |
|---|---|
| Exploratory Data Analysis | EDA summarizes distributions, correlations, and anomalies 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 Exploratory Data Analysis.
- 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
1Exploratory Data Analysis workflow
1 / 4Understand
Learn when and why to use Exploratory Data Analysis.
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
Exploratory Data Analysis is a core machine learning topic. EDA summarizes distributions, correlations, and anomalies before modeling
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