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Artificial Intelligence & Machine Learning · Module 3 of 10

Data preparation

Prepare data so models can learn well.

📖 Key ideas

  1. Collect relevant, representative data.
  2. Clean: handle missing values, duplicates and outliers.
  3. Encode categories (one-hot, label encoding).
  4. Scale features (standardisation, normalisation).
  5. Split into train, validation and test sets.
💡 Real-life example: Data preparation is washing and cutting vegetables: good prep makes a good dish.
FeatureInput variable
One-hot encodingCategory to binary columns
ScalingBringing features to similar ranges
Train/test splitSeparating data for evaluation