Analyzing transactions of a retailer to predict promotional items.
-
Updated
Jan 10, 2017 - R
Analyzing transactions of a retailer to predict promotional items.
ITM883 Capstone project
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.
SVM app
This is the repository for CKME136 Ryerson Big Data Certificate
App that aims to predict user keystrokes using SVM.
An R implementation of the (multiple) Support Vector Machine Recursive Feature Elimination (mSVM-RFE) feature ranking algorithm
SVM model predicting if passengers would survive the Titanic's maiden voyage
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
Practice Code (R Codes)
An R Parallel implementation of the multiple Support Vector Machine Recursive Feature Elimination (mSVM-RFE) algorithm (feature selection)
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
NanostrIng MB cLassifiEr
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.
Add a description, image, and links to the svm-model topic page so that developers can more easily learn about it.
To associate your repository with the svm-model topic, visit your repo's landing page and select "manage topics."