🤖 Wine Class Prediction

A project to predict wine class using different models.

📋 Table of Contents

• Setup
• Running the Scripts
• Project Structure
• Troubleshooting
• Next Steps

⚙️ Setup

First, ensure you have Python 3.11+ installed on your system. Install the required dependencies:

# Create a virtual environment (recommended)
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

requirements.txt:

pandas
scikit-learn

🚀 Running the Scripts

Train the Model

Train the models by running:

python train.py

This script performs the following operations:

• Loads the data from data/wine_data.csv
• Preprocesses the features and target variables
• Trains a logistic regression model and a support vector machine model on the data
• Saves the trained model as model.pkl and the scaler as scaler.pkl under the model folder

📁 Project Structure

ml-project/
├── data/
│   └── wine_data.csv
├── train.py
├── requirements.txt
├── model/
│   └── model.pkl (generated after training)
│   └── scaler.pkl (generated after training)
└── docs/
    ├── README.md
    └── LICENSE

❓ Troubleshooting

• Missing dependencies: Ensure all packages are installed via pip install -r requirements.txt
• File not found errors: Check that your data file exists in the data/ directory
• Version conflicts: Verify your Python version is 3.11+ and package versions match requirements

🔮 Next Steps

• Experiment with different ML algorithms
• Implement hyperparameter tuning to optimize model performance
• Add data visualization to better understand your dataset