Wine Quality Analysis

Introduction

This project analyzes wine quality based on physicochemical properties using machine learning. The pipeline includes data preprocessing, model training, and evaluation using RandomForestRegressor.

Prerequisites

• Docker installed on your machine.

Setup & Usage

Cloning the Repository:

git clone https://github.com/Tani303/Wine_Quality_Analysis.git

Change to the project directory

cd Wine_Quality_Analysis

Running the Application:

• Build Docker Image

docker build -t wine_quality_analysis_app .

• Run the Docker Image

docker run wine_quality_analysis_app

• In orderr to see the result plot and text file use the command below

docker run --rm -v "${PWD}/results:/wine_quality_analysis/results" wine_quality_analysis_app

Dataset Information

• Name: Wine Quality Dataset

• Source: UCI Machine Learning Repository

• File Used: winequality-red.csv

• Location: data/

• Description:
  This dataset contains physicochemical attributes (such as acidity, alcohol content, pH, etc.) of different red wine samples, along with a quality score (0-10) given by wine tasters. The goal of the analysis is to predict the quality score based on these features.

• Features:

• Fixed Acidity

• Volatile Acidity

• Citric Acid

• Residual Sugar

• Chlorides

• Free Sulfur Dioxide

• Total Sulfur Dioxide

• Density

• pH

• Sulphates

• Alcohol

• Quality (Target Variable)

What Happens When You Run the Code?

• The script loads the dataset from the data/ folder.
• Preprocesses the data and splits it into training and testing sets.
• A RandomForestRegressor model is trained to predict wine quality.
• The model's performance is evaluated using Mean Squared Error (MSE) and R² Score.
• Results are displayed in the console and in the results folder depending on the chosen command.