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

Predicting Loan State with SVM method, preprocessing and feature selction

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

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

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

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

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.

In this project, I predict whether a patient has a low or high chance of having a heart attack using classification

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.

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

Movie Recommender

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.

Loan approval system using svm

Building a smodel using SVC

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.

Predicting Diabetes Mellitus Using Machine Learning Techniques 🩺🤖

I have implemented support vector machine classifier on the same dataset but using different kernels and have compared their accuracies with each other

Hyperparameter tuning using Support Vector Machine kernels

A recommendation engine