This project is about the prediction of red wine quality using different machine learning algorithms

Linear discriminant Analysis(LDA) for Wine Dataset of Machine Learning

Performed different tasks such as data preprocessing, cleaning, classification, and feature extraction/reduction on wine dataset.

Building classification models to predict quality of wines. (Accuracy = 71.33%)

PCA(Principle Component Analysis) For Wine dataset in ML

Wine Dataset with Gaussian Classifier

NTHU EE6550: Machine Learning

This repository contains my machine learning models implementation code using streamlit in the Python programming language.

Webscraping of Signorvino.com, an Italian wine e-commerce website. The task is performed with Selenium library in Python

LDA(Linear discriminant Analysis) for Wine Dataset in machine learning

Introducing Flask Program for wine Dataset

MSDS 410 Data Modeling for Supervised Learning (R)

Store the exercises carried out in the discipline "Computing Inspired by Nature" of the PPGCC of UNESP.

This repository contains machine learning programs in the Python programming language.

Applying Clustering algorithm on famous WIne Dataset from Kaggle.

A web app to show how easy it is to analyze datasets with a large number of attributes using Chernoff faces concept.

Implementation of Hybrid fuzzy-rough Rule induction and feature selection paper 2009 by Richard Jensen

statistical modelling of the wine data-set available at https://www.kaggle.com/zynicide/wine-reviews

This repo contains machine learning projects about some popular datasets. In each project, exploratory data analysis is made before building the model.

This project implements two algorithms, K-Nearest Neighbors (KNN) and Large Margin Nearest Neighbor (LMNN) using the Neighbourhood Component Analysis (NCA) approach.