Wine-Quality-Prediction

This repository contains a machine learning model built using linear regression to predict the quality of wine. The dataset used for training and evaluation consists of various features such as acidity, alcohol content, and pH, which are commonly associated with wine quality.Here's a detailed overview:

Feature Description

It will contain the following features to detect the Quality of the Wine :-

• Fixed Acidity: Main acids in wine (e.g., tartaric acid).
• Volatile Acidity: Acetic acid amount; high levels cause vinegar taste.
• Citric Acid: Adds freshness and flavor to wine.
• Residual Sugar: Remaining sugar post-fermentation.
• Chlorides: Salt content in wine.
• Free Sulfur Dioxide: SO2 that prevents oxidation and microbial growth.
• Total Sulfur Dioxide: Total amount of SO2.
• Density: Wine's density, correlates with sugar and alcohol.
• pH: Measures acidity; affects taste and preservation.
• Sulphates: Aids in wine preservation.
• Alcohol: Alcohol content.
• Wine Type: Classification as red or white wine.

Use Case

Winery and wine producing businesses find this approach useful because it helps them determine how much better their products are likely to outperform others across similar categories. How it works is that one needs to enter things like taste description, price etc. in order for this software to calculate chances that people will like wines based on these examples without having tasted them first.

Benefits

• Quality Control: Helps in maintaining consistent wine quality by identifying key factors affecting it.
• Cost Efficiency: Reduces the need for extensive sensory testing by providing early predictions.
• Production Optimization: Enables producers to adjust their processes to improve quality based on predicted outcomes.
• Market Competitiveness: Enhances product quality, leading to better market positioning and customer satisfaction.
• Data-Driven Decisions: Facilitates informed decision-making using quantitative data on wine characteristics.

File Structure

• Wine_Quality_Prediction.ipynb: Python script containing the code for data preprocessing, model training, and prediction.
• WineQT.csv : Dataset of Fixed Acidity, Volatile Acidity, Citric Acid, Residual Sugar, Chlorides, Free Sulfur Dioxide, Density, pH, Sulphates, Alcohol, Wine Type of this Wine Quality Prediction project.
• README.md: Markdown file describing the project and usage instructions.

Author

Soumodip Das