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

This Repository Contains different Machine Learning Projects on various dataset. From Exploratory Data Analysis - Visualization to Prediction and Classification..

Data Science Projects done at Data Trained Education during PG Data Science & ML Course

Hello friends, I am making a Machine Learning repo. where I will upload several datasets and its solution with explanation. Starting from the basic and moving up in difficulty level.

A visualization of the dataset winequality-red.csv and exploratory data analysis[ EDA ] in R language

Exploratory Data Analysis (EDA) is the numerical and graphical examination of data characteristics and relationships before formal, rigorous statistical analyses are applied.

This repository stored the output of IBM SPSS's multiple linear regression and factor analysis of red wine quality dataset. The dataset used is from Kaggle (https://www.kaggle.com/uciml/red-wine-quality-cortez-et-al-2009). The software used in this repository is IBM SPSS 26

These datasets can be viewed as classification or regression tasks. The classes are ordered and not balanced (e.g. there are much more normal wines than excellent or poor ones).

I implemented the Random Forest Algorithm, from the scratch, to predict the quality of red wine. The algorithm proved to be quite accurate.

This project is about the prediction of red wine quality using different machine learning algorithms with MLOps and CICD pipeline.

Repository for diffrent solutions of Kaggle classification competitions

This project endeavors to predict wine quality through machine learning, unraveling the intricate factors influencing wine characteristics.

Wine Quality Classification using KNN, SVM, and Random Forest