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