Acidity calibre check in wine using machine learning

The wine quality will help the winemakers to make decisions during production such as adjusting fermentation techniques or blending ratios to improve the quality. This in turn helps them to make profit while the wine seekers buy a top quality product. The acidity calibre check project aims to develop a machine learning model to predict the acid quantity in wines based on various physicochemical features. The project involves data preprocessing, exploratory data analysis, feature selection, model training, and evaluation. To address the potential case of class imbalance in the dataset, SMOTE (Synthetic Minority Oversampling Technique) is applied to generate synthetic samples for the minority class, ensuring a balanced distribution of data. Machine learning algorithms, including linear regression, decision trees, random forests, and gradient boosting machines, are employed to build predictive models. The best-performing model achieves a high level of accuracy for the aid in the wine industry by providing objective quality assessments. The insights gained from this project can help winemakers in improving the overall quality of their products and making informed decisions during the production process.