Wine Quality Prediction Model

This project is a machine learning-based web application that predicts whether the wine is of good or bad quality based on 11 physicochemical features (without including 'quality'). The model is trained using a RandomForestClassifier and deployed using Streamlit.

Features

• Predicts the quality of red wine as either good or bad.
• Takes 11 input features and provides a prediction.
• The machine learning model is built using the RandomForestClassifier from scikit-learn.
• The web interface is created using Streamlit.

Dataset

The dataset used for this project is the Wine Quality Dataset, which contains the following features:

1. Fixed Acidity
2. Volatile Acidity
3. Citric Acid
4. Residual Sugar
5. Chlorides
6. Free Sulfur Dioxide
7. Total Sulfur Dioxide
8. Density
9. pH
10. Sulphates
11. Alcohol
12. Quality (Target variable)

The target variable (quality) is categorized into:

• Good Quality: Wine with a quality score of 7 or above.
• Bad Quality: Wine with a quality score of less than 7.

The model will use the other 11 features (excluding 'quality') to predict if the wine is good or bad.

How to Use

Prerequisites

Ensure you have Python installed. You can download it from here.

Step 1: Clone the Repository

git clone https://github.com/FahithKRM/ML_Wine-Quality-Prediction.git

cd wine-quality-prediction

Step 2: Create a Virtual Environment

On Windows :

python -m venv env

.\env\Scripts\activate

On macOS/Linux :

python3 -m venv env

source env/bin/activate

Step 3: Install Dependencies

pip install -r requirements.txt

Step 4: Run the Web Application

streamlit run app.py

Step 5: Input Features

On the web interface, you'll need to input the 11 physicochemical features (excluding 'quality') as a comma-separated string. For example:

7.4,0.7,0,1.9,0.076,11,34,0.9978,3.51,0.56,9.4

These values correspond to the following features:

1. Fixed Acidity
2. Volatile Acidity
3. Citric Acid
4. Residual Sugar
5. Chlorides
6. Free Sulfur Dioxide
7. Total Sulfur Dioxide
8. Density
9. pH
10. Sulphates
11. Alcohol

Once you input the features, the model will predict if the wine is of Good Quality or Bad Quality.

Step 6: View Prediction

The model will output either:

• Good Quality Wine if the wine's predicted quality is 7 or more.
• Bad Quality Wine otherwise.

Examples :

Input :

11.2,0.28,0.56,1.9,0.075,17.0,60.0,0.998,3.16,0.58,9.8

Output :

Input :

7.5,0.52,0.16,1.9,0.085,12.0,35.0,0.9968,3.38,0.62,9.5

Output :

Project Structure :

License

This project is licensed under the MIT License - see the LICENSE file for details.