The world of Feature Engineering 101

Greetings, my name is Brijesh Soni, and I have created a comprehensive note on Machine Learning, specifically focusing on the topic of feature engineering.

The purpose of this note is to provide assistance to individuals who wish to expand their knowledge in this field.

The note is a series of 14-topics, which are listed below:

Standardization and Normalization

Column TransFormer

Power Transformer

Function TransFormer

Encoding

Pipeline

Handling Mixed Date & Time Variables

Deal with Missing Data

MICE or Multivariate Imputation with Chain-Equation

Outliers

Feature Construction & Splitting

COD or Curse of Dimensionality

Binarization

Principal Component Analysis or PCA

I hope that this note will prove to be a valuable resource for anyone seeking to enhance their understanding of feature engineering in Machine Learning.

👋👋Stay tuned and Happy learning!!👋👋

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