Garbage In, Garbage Out
5. Data Cleaning & Quality
Handling missing values, outliers, and duplicates.
Raw (Dirty) Dataset
| ID | Name | Age | City | Issues |
|---|---|---|---|---|
| 1 | Pradeep | 33 | Delhi | |
| 2 | Rahul | NULL | Mumbai | MISSING |
| 1 | Pradeep | 33 | Delhi | DUPLICATE |
| 4 | Anita | 250 | London | OUTLIER |
Deduplication
Removes redundant rows
Imputation
Fills missing values
Outlier Handling
Caps extreme values
Why this matters
In the real world, data is messy, incomplete, and buried in multiple locations. Data Engineering is the "plumbing" that builds reliable roads for your AI to travel on.
The Architecture
Every pixel of an AI model comes from original training data. If your engineering is flawed, your model will eventually collapse due to "Technical Debt" or "Data Drift".