Feature Creation & Selection

Summary

Engineering new, more predictive features from raw data (e.g. 'days since last purchase' from a date column), and then selecting which features actually help versus add noise.

How to Learn This

1Create 3-5 new features from a raw dataset based on domain intuition.
2Learn a simple feature selection method (e.g. correlation with the target, feature importance).
3Practice explaining why a specific engineered feature should help the model.

Encoding Categorical Variables

Dimensionality Reduction (PCA Overview)

More topics in Exploratory Data Analysis & Feature Engineering

EDA Process & Techniques Handling Missing Data Outlier Detection & Treatment Feature Scaling (Normalization / Standardization)Encoding Categorical Variables Dimensionality Reduction (PCA Overview)Handling Imbalanced Data Correlation Analysis

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