Text Classification of News Headlines using Naive Bayes Classification

What is Naive Bayes?

Naive Bayes classifier works on the principle of condition probability as given by the Bayes theorem. The Bayes theorem gives us the conditional probability of an event A given that an event B has occurred. In the Bayes theorem, the probability of A occurring given that B has occurred, is the probability of B occurring given that A has occurred times the probability of A over the probability of B.

Naive Bayes and Machine Learning

It is important to understand where the Naive Bayes fits in the hierarchy of Machine Learning. So under machine learning there is Supervised Learning and Unsupervised Learning. Under the supervised learning there is the Classification and Regression. And under Classification we have the Naive Bayes.

Text Classification of News Headlines into News Groups

Getting Started

Running the Jupyter notebook

1. Sign up for Watson Studio

1. Create a new Project

Note: By creating a project in Watson Studio a free tier Object Storage service will be created in your IBM Cloud account. Take note of your service names as you will need to select them in the following steps.

On Watson Studio's Welcome Page select New Project.
Choose the Data Science option and click Create Project.
Name your project, select the Cloud Object Storage service instance and click Create

1. Import notebook to Watson Studio

Create a New Notebook.
Import the notebook found in this repository
Give a name to the notebook and select a Python 3.5 runtime environment, then click Create.

6. Follow the steps in the notebook

The steps in the notebook should allow you to understand how to download the dataset, create a model that uses Naive Bayes Classification and then visualize it using a Confusion Matrix and Heat map.

Finally you should be able to test the model and check it's accuracy.

References

Naive Bayes Classifier | Naive Bayes Algorithm | Naive Bayes Classifier With Example | Simplilearn

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