https://github.com/sidjey-y/wine-quality-machine-learning

A machine learning project to predict wine quality based on physicochemical properties. It includes comparisons of model performance with and without imputation techniques using various algorithms.
https://github.com/sidjey-y/wine-quality-machine-learning

gaussian-naive-bayes jupyter k-nearest-neighbours logistic-regression machine-learning

Last synced: 3 months ago
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A machine learning project to predict wine quality based on physicochemical properties. It includes comparisons of model performance with and without imputation techniques using various algorithms.

• Host: GitHub
• URL: https://github.com/sidjey-y/wine-quality-machine-learning
• Owner: sidjey-y
• Created: 2024-08-27T16:45:50.000Z (4 months ago)
• Default Branch: main
• Last Pushed: 2024-08-27T16:53:09.000Z (4 months ago)
• Last Synced: 2024-09-28T23:21:21.364Z (3 months ago)
• Topics: gaussian-naive-bayes, jupyter, k-nearest-neighbours, logistic-regression, machine-learning
• Language: Jupyter Notebook
• Homepage:
• Size: 321 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Wine Quality Prediction Model Using Machine Learning

This repository contains a machine learning project designed to predict the quality of wine based on physicochemical properties. The project evaluates various machine learning models and compares their performance, both with and without imputation techniques.

## Project Overview

The goal of this project is to develop and assess a machine learning model for predicting wine quality. The dataset used includes 1599 entries with 11 numeric attributes related to the physicochemical properties of wine. The dataset can be used for both classification and regression tasks.

## Features

- **Exploratory Data Analysis**: Includes data distribution, correlation plots, and correlograms.

- **Machine Learning Algorithms**:

  - **Logistic Regression**: A supervised learning algorithm for classification problems.

  - **Gaussian Naive Bayes**: A probabilistic classification algorithm based on Bayes' theorem with strong independence assumptions.

  - **K-Nearest Neighbors (KNN)**: A supervised learning algorithm used for both classification and regression tasks.

## Goals

- Develop machine learning models to predict wine quality.

- Compare the performance of different models with and without imputation techniques.