Prerequisites: Familiarity with Python programming, basic machine learning concepts, and core data science libraries (Pandas, NumPy, Scikit-learn).

Level: Intermediate

What You'll Learn

• Data Preparation

  Implement strategies for handling missing data and outliers effectively.

• Feature Transformation

  Apply various scaling and transformation techniques to numerical features.

• Categorical Feature Encoding

  Compare and apply different methods for encoding categorical variables.

• Feature Creation

  Generate new features from existing data using interaction terms and polynomial features.

• Feature Selection

  Utilize filter, wrapper, and embedded methods for selecting relevant features.

• Practical Implementation

  Apply feature engineering techniques using Pandas and Scikit-learn in practical scenarios.