Feature Engineering Laboratory Notes
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.
Subject at a glance
1
Units
11
Topics
1
Credits
1
Infographics
Unit-wise Syllabus
1 units — open any unit for detailed topic-wise notes, diagrams and important exam questions
- 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
- 1Explore
Histograms, boxplots, correlation
- 2Clean
Missing and invalid values
- 3Transform
Scale, log-transform, bin
- 4Encode
One-hot or label
- 5Reduce
Select features or apply PCA
Study guide
In this Python lab you handle missing values and invalid entries, apply Min-Max normalisation, perform exploratory data analysis (histograms, boxplots, correlation matrix), bin numerical data, create polynomial and interaction features, apply logarithmic transformation and one-hot encoding, preprocess text (tokenisation, stemming, lemmatisation, Bag-of-Words, TF-IDF), augment images (resize, normalise, rotate, translate), decompose a time series and run PCA with visualisation. Print the data before and after each step so you can see what the transformation did.
Why Feature Engineering Laboratory Matters
These are the exact preprocessing steps used in real machine learning projects across tabular, text, image and time-series data, and they make your later AI and ML projects far stronger.
How to Prepare for Feature Engineering Laboratory
- Use pandas and scikit-learn and read their documentation for each function.
- Visualise before and after every transformation.
- Keep a notebook with explanations in your own words.
Feature Engineering Laboratory: Frequently Asked Questions
What is covered in the Feature Engineering lab?
Missing values, normalisation, EDA, binning, polynomial features, encoding, text preprocessing, image augmentation, time-series decomposition and PCA, all in Python.