It was built a web application as a step forward in building a 3D recommendation system.

Access the Linear or RBF kernel SVM from OCaml using the R e1071 or svmpath packages

Python-based web application, Flask platform, utilizes a powerful Content-Based Filtering Algorithm to provide personalized recommendations excercises

💚 A heart disease classifier using 4 SVM kernels and decision trees, with PCA, ROC, pruning, grid search cv, confusion matrix, and more

PCA applied on images and Naive Bayes Classifier to classify them. Validation, cross validation and grid search with multi class SVM

GISETTE is a handwritten digit recognition problem. The problem is to separate the highly confusible digits ‘4’ and ‘9’. This dataset is one of five datasets of the NIPS 2003 feature selection challenge.

This code reads a dataset i.e, "Heart.csv". Preprocessing of dataset is done and we divide the dataset into training and testing datasets. Linear, rbf and Polynomial kernel SVC are applied and accuracy scores are calculated on the test data. Also, a graph is plotted to show change of accuracy with change in "C" value.

A simple spam classifier using SVM with a linear kernel.

Created a model from scratch (without using any libraries) to predict whether a person have a heart diseases using support vector machine. and then compare the model's accuracy with model created using Sklearn library.

In this repository, we will explore different classification models to predict whether a user will purchase a product based on age and estimated salary.

Support Vector Machines (SVMs in short) are supervised machine learning algorithms that are used for classification and regression purposes. In this kernel, I have build a Support Vector Machines classifier to classify a Pulsar star. I have used the Predicting a Pulsar Star dataset for this project.

Hyperparameter tuning using Support Vector Machine kernels

A recommendation engine

It is based on support vector machines ,first we used linear kernel for lesser featured dataset, Gaussian kernel for more complex dataset.

We will apply soft-margin SVM to handwritten digits from the processed US Postal Service Zip Code data set.

Second assignment of Artificial Intelligence course held by Professor Andrea Torsello of Ca' Foscari University of Venice, spam detectors with SVM classification using linear, polynomial of degree 2, RBF kernels and Naive Bayes and k-NN

Project for Machine Learning Data Mining course

Building a smodel using SVC