This project develops machine learning and deep learning models to predict wine quality, based on physico-chemical characteristics. It uses two separate datasets for white and red wine, with models built separately for each type. TensorFlow, a powerful deep learning library, was used alongside other tools to analyse the data and build the models.
winequality-white.csv: Contains data and physico-chemical characteristics of white wines, as well as their quality.winequality-red.csv: Contains the data and physico-chemical characteristics of red wines, as well as their quality.
- Python
- TensorFlow
- Pandas
- Scikit-learn
- Matplotlib and Seaborn for visualisation
Wine_white_Prediction.ipynb: Notebook for white wine quality prediction.Wine_red_Prediction.ipynb: Notebook for red wine quality prediction.winequality-white.csv: Dataset for white wine.winequality-red.csv: Dataset for red wine.
Install the necessary dependencies by running the following command:
pip install numpy pandas scikit-learn matplotlib seaborn tensorflow jupyterOpen the Wine_white_Prediction.ipynb and Wine_red_Prediction.ipynb notebooks in Jupyter Notebook or JupyterLab to follow the complete process of modelling, training, evaluating and visualising prediction results for white and red wines.
The notebooks present a detailed analysis of the data, the construction and evaluation of the machine learning and deep learning models, and the interpretation of the results through various metrics and visualisations.
The project highlights the effectiveness of machine learning and deep learning models, using TensorFlow, in predicting the quality of white and red wines. This approach offers a valuable perspective on the application of artificial intelligence in the field of oenology and opens up avenues for future research aimed at improving the accuracy of predictions.