R codes for common Machine Learning Algorithms

High performance implementation of the Naive Bayes algorithm in R

Code to classify commits into maintenance activities

Designing and applying unsupervised learning on the Radar signals to perform clustering using K-means and Expectation maximization for Gausian mixture models to study ionosphere structure. Both the algorithms have been implemented without the use of any built-in packages. The Dataset can be found here: https://archive.ics.uci.edu/ml/datasets/ion…

Machine learning project for predicting movie ratings in Movielens data set. Naive with regularization method used. Created as a capstone project for Data Science HarvardX course.

A collection of problems solved with machine learning algorithms, using R.

A Survey on ML Techniques for Airbnb Price Prediction

Six machine learning models for predicting bladder cancer progression using Caret package

Data Science Projects in R

One Data Set with multiple Algorithms

SASTuit a free tool for sentiment analysis on Twitter, analyze the technological trends in this social media for the classification of tweets in Spanish as positive, negative and neutral using artificial intelligence. Developer by Anibal Armando Herrera Contreras

This script demonstrate the Naive Bayes concepts

This project was conducted in my 3rd semester of Btech Data Science. Took a survey on home remedies from all kinds of people. We first cleaned our data and then predicted if one would prefer using home remedies or not using Naïve Bayes theorem in Excel as well as Python. Also tested a few hypotheses we made at the start of the project using z-te…

Customer Churn is a burning problem for Telecom companies. In this project, we simulate one such case of customer churn where we work on a data of postpaid customers with a contract. The data has information about the customer usage behavior, contract details and the payment details. The data also indicates which were the customers who canceled …

KNN, Naive Bayes and Trees - Wine UCI Dataset

Implementation of Naive Bayes algorithm for categorical data

Fit four different neural networks: (a) Two distinct single hidden layer neural networks. (b) Two distinct neural networks with two hidden layers. Compare the accuracy of these four Neural networks among them. Also compare it to other classification methods.

Imbalanced classification with loan clients dataset.

This project focuses on predicting the attrition rate of employees by using different ML algorithms. The dataset is a fictional data taken from Kaggle