Hands On Feature Engineering in ML | Industry Relevant AI ML Course

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Whether you're a student, working professional, developer, or complete beginner, this detailed module will build a strong foundation for the rest of the course.

This is the practical implementation video of my previous lecture on Feature Engineering. If you haven’t watched the theory video yet, watch that first—then this coding session will make perfect sense.

In this video, we implement feature engineering step-by-step in Python using NumPy, Pandas, Matplotlib/Seaborn, and Scikit-learn on the same sample dataset (Age, Income, Area, Bedrooms, Distance, City, Segmentation → Target: House Price).

What you will learn (with code):

Visualize distributions to detect skewness (Income vs Age example)

Right-skew handling using Log / Square Root / Cube Root and selecting the best transformation by plotting

Left-skew handling using the flip trick (max − x), then applying transformations to make it closer to normal

Polynomial features (Distance → Distance²) using:

Scikit-learn PolynomialFeatures, and

a simple manual approach (x**2)

Binning / Bucketing (Age groups: Young, Adult, Mid-age, Senior) and how to use these buckets later with encoding

Categorical encoding

Label Encoding (quick encoding for categories)

One-Hot Encoding using pd.get_dummies and Scikit-learn OneHotEncoder + concatenation

Feature scaling

Standardization (StandardScaler) and why values usually fall near -3 to +3

Min-Max Scaling to bring features into [0, 1]

Feature interaction (Area × Bedrooms) to create stronger predictive signals

Feature selection using correlation with the target (when to remove low-signal features, especially in high-dimensional datasets)

By the end, you’ll know how to clean data the right way and build leakage-free ML pipelines that generalize well.

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Видео Hands On Feature Engineering in ML | Industry Relevant AI ML Course канала Sonu Yadav AIML [IIT-DELHI]