Feature Engineering & Data Preprocessing:

Preparing raw data for machine learning models.
Techniques: Handling missing data, scaling, encoding categorical variables.
Applications: Improving model accuracy, making data suitable for ML algorithms.

Sub Tracks:
Data Cleaning & Handling Missing Values
Feature Scaling & Transformation
Feature Selection & Dimensionality Reduction
Feature Engineering & Encoding