Feature Selection using sklearn

In this post, we will understand how to perform Feature Selection using sklearn.

  • Dropping features which have low variance
    • Dropping features with zero variance
    • Dropping features with variance below the threshold variance
  • Univariate feature selection
  • Model based feature selection
  • Feature Selection using pipeline

Feature Engineering for Machine Learning

In this post, let us explore:

  • What is the difference between Feature Selection, Feature Extraction, Feature Engineering and Feature Learning
  • Process of Feature Engineering 
  • And examples of Feature Engineering

Feature Selection: Filter method, Wrapper method and Embedded method

In this post, let us explore:
  • What is feature selection?
  • Why we need to perform feature selection?
  • Methods

Naïve Bayes classification model for Natural Language Processing problem using Python

In this post, let us understand how to fit a classification model using Naïve Bayes (read about Naïve Bayes in this post) to a natural language processing (NLP) problem.

Download free ebook 'Machine Learning Techniques with examples'


You can download my ebook (186 pages) for free from this link. No emails asked, no sign-ins, nothing. Just free.

Happy learning. I wish you all the best.

Hierarchical and K-means cluster analysis with examples using sklearn

In this post, we will explore:

  • What is cluster analysis?
  • Hierarchical cluster analysis
  • K-means cluster analysis
  • Applications