Exercise solution to the Probability Theory course
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Updated
Jun 11, 2018 - TeX
Exercise solution to the Probability Theory course
Fast explication of Gaussian NB
Implementation of Bayes and naive Bayes for iris dataset
Estimate conditional probabilities, compare data distributions, and perform data transformations to analyze employee absences
Implementation of Naive Bayes & Bayes Theorem
Naive Bayes Classifier that utilizes Bayes theorem and normal distributions.
ML Topics include KNN. Naive Bayes and Support vectors both in Theory and Python Code. KNN Imputation technique is also explained in this branch.
This project aims to understand and build Naive Bayes classifier to predict the salary of a person.
This repository has been created to complete an assignment given by datainsightonline.com. This assignment is a part of Data Insight | Data Science Program 2021.
A geometric interpretation of Bayes Theorem showing how dependent probabilties relate to each other.
Quizzes & Assignment Solutions for Data Science Math Skills on Coursera. Also included a few resources on side that I found helpful.
In this mini-project, I engage in solving practice problems related to probabilities before transitioning to explore various statistical distributions.
about statistical techniques for Data Science
A full page Bayes' Theorem interactive visual
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.
Jupyter Notebook featuring hands-on exercises centered around Bayesian networks and Bayesian classifiers.
A Naive Bayes Text Classifier that classifies input text into one of two categories: either a BUSINESS article or a SPORT article
Interactive Tool for Interpreting positive COVID-19 antibody tests
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.
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