Project Title

Wine Quality Prediction Using Machine Learning with Python

Domain

Python | Artificial Intelligence (AI) | Machine Learning (ML) | Data Science | Predictive Analytics

Project Level

Beginner to Intermediate

Project Objective

Wine quality assessment is an important process in the beverage industry. Traditionally, wine quality is evaluated by expert tasters, which can be time-consuming and subjective. The objective of this project is to build an intelligent Machine Learning model capable of analyzing the physicochemical properties of wine and predicting its quality score.

Students will learn the complete Machine Learning workflow, including data collection, preprocessing, exploratory data analysis, feature engineering, model training, testing, evaluation, and prediction. This project provides practical exposure to Artificial Intelligence and Predictive Analytics concepts used in food and beverage quality control systems.

Session Access Link

Project Training Session

Session Link: Click here to Watch your uploaded session

Learning Outcomes

After successful completion of this project, students will be able to:

• Understand the fundamentals of Machine Learning.

• Learn how quality prediction systems work.

• Understand classification and regression concepts.

• Perform data cleaning and preprocessing.

• Conduct exploratory data analysis (EDA).

• Work with real-world datasets.

• Handle numerical features effectively.

• Train and evaluate machine learning models.

• Measure model performance using different metrics.

• Build a real-world quality prediction system.

• Improve analytical and problem-solving skills.

Programming Language & Technologies Used

Programming Language

• Python 3.x

Machine Learning Libraries

• Scikit-learn

• NumPy

• Pandas

Data Visualization Libraries

• Matplotlib

• Seaborn

Development Platforms

• Google Colab

• Jupyter Notebook

• VS Code (Optional)

Data Storage

• CSV Files

• Google Drive

Project Workflow

Phase 1: Dataset Collection

Students will collect a Wine Quality Dataset containing:

• Fixed Acidity

• Volatile Acidity

• Citric Acid

• Residual Sugar

• Chlorides

• Free Sulfur Dioxide

• Total Sulfur Dioxide

• Density

• pH

• Sulphates

• Alcohol

• Quality Score

Dataset Format:

• CSV File

Phase 2: Data Preprocessing

Students will perform:

• Removal of duplicate records

• Handling missing values

• Data cleaning

• Feature selection

• Outlier detection and treatment

• Data normalization/scaling

Purpose:

To improve data quality and model performance.

Phase 3: Exploratory Data Analysis (EDA)

Students will analyze the dataset using:

• Summary statistics

• Correlation matrix

• Histograms

• Box plots

• Heatmaps

• Feature distribution analysis

Benefits:

• Understand relationships between features.

• Identify important variables affecting wine quality.

• Detect trends and anomalies in data.

Phase 4: Feature Engineering

Students will prepare data for machine learning using:

• Feature Scaling

• Standardization

• Data Transformation

• Correlation-Based Feature Selection

Benefits:

• Improves model efficiency.

• Enhances prediction accuracy.

• Reduces unnecessary features.

Phase 5: Model Building

Students will train Machine Learning models such as:

Logistic Regression

• Simple and efficient classification model.

• Useful when quality categories are grouped.

Decision Tree Classifier

• Easy to understand and interpret.

• Identifies key factors affecting wine quality.

Random Forest Classifier

• Ensemble learning method.

• Provides better prediction accuracy.

Support Vector Machine (SVM)

• Effective for classification problems.

• Performs well on structured datasets.

K-Nearest Neighbors (KNN)

• Simple and intuitive algorithm.

• Useful for comparison and benchmarking.

Phase 6: Model Evaluation

Students will evaluate model performance using:

• Accuracy Score

• Confusion Matrix

• Precision

• Recall

• F1-Score

• Cross-Validation Score

Expected Accuracy:

80% – 95% (depending on dataset quality and model selection)

Phase 7: Prediction System

Students will create a system where users can:

• Enter wine characteristics.

• Click Predict.

• Receive output:

  • High Quality Wine

  • Medium Quality Wine

  • Low Quality Wine

or

• Predicted Wine Quality Score

Final Project Deliverables

Each student must submit:

Source Code

• Python Files (.py)

• Jupyter Notebook (.ipynb)

Dataset

• CSV Dataset Files

Documentation

• Project Report (PDF)

Screenshots

• Dataset Import

• Data Cleaning

• Exploratory Data Analysis

• Model Training

• Accuracy Results

• Prediction Results

Presentation

• PPT (Minimum 10 Slides)

Additional Files

• Trained Model (.pkl file)

• Output Results

Google Drive Submission Process

Step 1:

Create a Google Drive Folder.

Folder Name Format:

WineQualityPrediction_StudentName

Step 2:

Upload all project files.

Step 3:

Share the folder with:

support@corporatewebsolutions.in

Permission:

Viewer Access

Step 4:

Copy the shared Google Drive link.

Step 5:

Submit the link through the Google Form.

Status Report Submission Form

Google Form Link: https://forms.gle/RNLyVNgnsbeuffP27

Important Instructions

• All assignments must be completed and submitted on time.

• Students must submit original work only.

• Copying projects from online sources is strictly prohibited.

• Every student must maintain a project progress report.

• All project files must be uploaded to Google Drive before final submission.

• Ensure the shared folder is accessible before submitting the link.

• Late submissions may lead to reduced marks.

• Students should attend all project guidance sessions.