Analyzing transactions of a retailer to predict promotional items.

Developed a Diabetes Risk Prediction system by evaluating five machine learning algorithms to identify the best-performing model. The top model is deployed as an interactive R Shiny app named DiaWellness.

ITM883 Capstone project

SVM app

App that aims to predict user keystrokes using SVM.

A machine learning project that predicts selling prices of used cars using a Cars24 dataset. Includes data cleaning, EDA, feature engineering, multiple regression models, performance comparison, and final prediction model selection. Ideal for learning supervised ML and real-world price prediction analysis.

SVM model predicting if passengers would survive the Titanic's maiden voyage

This is the repository for CKME136 Ryerson Big Data Certificate

An R implementation of the (multiple) Support Vector Machine Recursive Feature Elimination (mSVM-RFE) feature ranking algorithm

Variable selection for nonlinear support vector machines via elastic net penalty

Classification using SVM models. Trying to predict diabetes data taken from kaggle.com. There are three SVM models in 'R_SVM_with_Caret' file, using 'kernlab', 'pROC' & 'e1071' package via 'caret' package.

Shiny app que emplea SVM sobre datos de entrenamiento

Income Analysis Using SVM

Practice Code (R Codes)

An R Parallel implementation of the multiple Support Vector Machine Recursive Feature Elimination (mSVM-RFE) algorithm (feature selection)

NanostrIng MB cLassifiEr

A machine learning project applying Support Vector Machines (SVM) to predict passenger survival in the Titanic disaster, as part of Kaggle's renowned competition. This study explores various SVM techniques to analyze passenger data and develop an accurate predictive model for the classic machine learning challenge.

This scripts tries to predict the bioactivity of 131 compounds related to Aspartate Racemase enzyme with the aid of decision trees and SVM

This project demonstrates a collection of Data Science techniques using R. These include Data Analysis, Data Cleaning, Data Visualization, Support Vector Machines, Euclidean Distance, and K-Means Clustering.