Machine Learning Code Implementations in Python

kernalized t-Distributed Stochastic Neighbor Embedding (t-SNE)

Easy to use x-DTT MATLAB package for DTT and Integer DTT transform kernel generation

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.

This project focuses on classifying pulsar stars using the Support Vector Machine (SVM) algorithm, a powerful method in the realm of supervised learning. The goal is to automate the identification process of pulsar stars from candidates collected during surveys, based on predictive modeling.

Hyperparameter tuning using Support Vector Machine kernels

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

A SVM classifier created to classify data on the IRIS dataset. A linear SVM as well as a Radial Basis SVM was created and also a polynomial kernel was created which classifies the data correctly to their original class.

Linear Regression with L2 Regularization, Online, Average, and Polynomial Kernel Perceptron for Optical Character Recognition, Decision Tree Ensemble, Random Forest, AdaBoost

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.

This repository contains codes for running naive bayes and k-NN classification algorithms on large dataset in python

SVMs are used for Classification as well as Regression problems. However, it is primarily used for Classification problems. #%% md # The Technique (Support Vector Machine) Support Vector Machine or SVM is one of the most popular Supervised Learning algorithms, which is used for Classification as well as Regression problems. However, primarily, i…

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

Classification of tweets as positive or negative sentiments using different SVM kernels

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.