A category-guessing model, trained with bayes theorem

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.

Implementation of Naive Bayes & Bayes Theorem

In this mini-project, I engage in solving practice problems related to probabilities before transitioning to explore various statistical distributions.

The Coffee Bean Sales Dataset offers a multifaceted exploration of the thriving coffee industry, providing a comprehensive view of sales, customer profiles, and coffee product details. This rich dataset is a gateway to understanding consumer behavior, optimizing product offerings, and improving business strategies in the world of coffee.

Vrinda Store wants to create an annual sales report for 2022. So that, Vrinda can understand their customers and grow more sales in 2023

A geometric interpretation of Bayes Theorem showing how dependent probabilties relate to each other.

School activities on application of Bayesian Statistics in Python.

A Naive Bayes Text Classifier that classifies input text into one of two categories: either a BUSINESS article or a SPORT article

ML Topics include KNN. Naive Bayes and Support vectors both in Theory and Python Code. KNN Imputation technique is also explained in this branch.

Project involved the analysis of a covid-19 dataset, applying bayes theorem to estimate probabilities and using KNN ML algorithm to train a model and make predictions based on the data

Web Games for Teaching Rational Decision Making

Naive Bayes Classifier that utilizes Bayes theorem and normal distributions.

This project aims to understand and build Naive Bayes classifier to predict the salary of a person.

Fast explication of Gaussian NB

A full page Bayes' Theorem interactive visual

Exercise solution to the Probability Theory course

Jupyter Notebook featuring hands-on exercises centered around Bayesian networks and Bayesian classifiers.

Implementation of Bayes and naive Bayes for iris dataset