Cleaning & Preparing Data for Machine Learning — The Complete Pipeline | Master AI & ML, E8

Data scientists spend 80% of their time cleaning data — not building models. In this episode we walk through the complete data preparation pipeline: missing values, duplicates, outliers, encoding, normalization, train/test splitting, and data leakage — with a practical checklist you can use on any ML project.

In this episode:
→ What messy real-world data actually looks like (live demo)
→ The three types of missingness — MCAR, MAR, MNAR — and how to handle each
→ Duplicates, noise, and outlier handling — when to remove and when to keep
→ Encoding categorical variables — ordinal, one-hot, and target encoding
→ Normalization vs. standardization — when each applies
→ Train / validation / test splits — why three sets, not two
→ Data leakage — the silent killer of ML projects
→ An 8-step data preparation checklist

This is Episode 8 of Master AI & Machine Learning — Module 2: Data Essentials.

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📋 FULL COURSE PLAYLIST
⬅ Ep 07 — Data Types Explained
➡ Ep 09 — Bias in Data
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