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

Wine Quality Prediction using machine learning with python .i did this project in AINN(Artificial Intelligence and Neural Network) course .in this project i used red and white wine databases and machine learning libraries available in python

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

Used Multiple Linear Regression which will predict the quality of wine using 11 features.

Wine Predicts

Wine Quality Prediction Model (ML) ExtraTreesClassifier 88% Accuracy

Train a neural network to predict the quality of red-wine using chemical properties such as pH, density, alcohol percentage etc.

Implementation of various ML algorithms and their application to real world problems

K means clustering implementation on the wine quality dataset

ANN model which predicts wine quality

ML algorithms for Regression, Classification, Clustering and Dimensional Reduction applied in a Wine Quality Dataset.

Previsão de Qualidade de Vinho com Redes Neurais Este repositório contém uma aplicação Flask que utiliza uma rede neural para prever a qualidade do vinho com base em suas características.

Using data to help us choice high quality wine

A web app featuring five classification projects: Spam Mail Prediction, Titanic Survival Prediction, Wine Quality Prediction, Loan Status Prediction, and Credit Card Fraud Detection, all built with Streamlit.

Wine Quality Prediction

Predicting quality of white wine using chemical attributes

A wine quality prediction machine learning model 🍷📈 uses data to assess and forecast the quality of wines, aiding wine enthusiasts and producers in making informed choices. 🤖👍🍇

This repository includes ml model of 1.House price prediction using linear regression model | 2.Wine Quality prediction using linear regression model | 3. IRIS Flowers Classification using logistic regression model

A continuous Machine Learning integration workflow using Github Actions. This project is based upon team collaboration for aesthetic version control for ML pipelines and deployement.