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.

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Natural Language Processing made simple: Word Cloud, Sentiment Analysis and Topic Modelling

In this chapter, let us understand
  • What is NLP?
  • Concepts
  • How to get word cloud?
  • How to perform sentiment analysis?
  • How to build Topic modelling?
  • Summary

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