High performance implementation of the Naive Bayes algorithm in R
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Updated
Mar 20, 2024 - R
High performance implementation of the Naive Bayes algorithm in R
R codes for common Machine Learning Algorithms
A collection of problems solved with machine learning algorithms, using R.
This app is intended to dynamically integrate machine learning techniques to explore multivariate data sets.
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…
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
Predict whether or not an employee will use Car as a mode of transport from given employee information about their mode of transport as well as their personal and professional details like age, salary, work exp. Also, which variables are a significant predictor behind this decision?
This project focuses on predicting the attrition rate of employees by using different ML algorithms. The dataset is a fictional data taken from Kaggle
Fake News analysis and prediction in R Script. Naive Bayes, Random Forest, SVM, NNET, ROC, Confusion Matrix, Accuracy, F1 score.
AI-Models
Six machine learning models for predicting bladder cancer progression using Caret package
A research for customer retention prediction using different algorithm in R
R package for machine learning classification model evaluation.
Code to classify commits into maintenance activities
This script demonstrate the Naive Bayes concepts
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.
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