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

Verzeo Machine Learning With Python

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

The project applied machine learning to predict red wine quality using the UCI dataset. Key steps included data exploration, model selection (with a focus on a stacking classifier), and evaluation using metrics like F1 Score. Feature importance was also analyzed for insights.

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

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

Practice classification,evaluation and clustering with red-wine dataset.

This project aims to predict wine quality based on physicochemical properties. Using machine learning models including Random Forest and Gradient Boosting Classifier, this analysis identifies key factors affecting wine quality and builds a predictive model to classify wines.