This repository contains the implementation of Gaussian Naive Bayes from scratch in a Jupyter Notebook. Gaussian Naive Bayes is a simple and effective algorithm for classification tasks. It is based on Bayes' theorem with the assumption of independence between the features.
It has often been observed that there are a lot people visiting numerous online shopping websites to browse for their needs but the conversion rate into purchases is low. The main purpose of this project is to study the customer’s behavioral patterns for a getting a decent insight about the features that influence the sales.
Add a description, image, and links to the liklihood topic page so that developers can more easily learn about it.
To associate your repository with the liklihood topic, visit your repo's landing page and select "manage topics."