Feature Engineering Laboratory
Subject Overview
The practical companion to Feature Engineering, implementing missing-value handling, feature scaling and binning, categorical encoding, text preprocessing (tokenization, stemming, lemmatization, TF-IDF), image augmentation, time-series decomposition and PCA in Python. A 1-credit elective lab paper.
Unit-wise Syllabus
1 units — click WhatsApp below to get the full notes for each
Unit 1: Feature engineering in Python
Handling missing values and invalid entries, Min-Max normalization, exploratory data analysis (histograms, boxplots, correlation matrix), binning numerical data, polynomial/interaction features, logarithmic transformation, one-hot encoding, text preprocessing (tokenization, stemming, lemmatization, Bag-of-Words, TF-IDF), image augmentation (resizing, normalization, rotation, translation), time-series decomposition, Principal Component Analysis and visualization