The consideration of wine quality before consumption or use is not a new decision scheme across ages, fields, and people. Gone were the days when quality of wine solely depended on taste or other physical checks. In this age of data science and machine learning, we can make decisions on the best wine quality with reference to different features/variables. This work was done with in predicting the dependent variable while using existing models to analyze the independent variables. This work utilizes the R programming language for this prediction, while comparing different machine learning models like Linear regression, Neural network, Naive Bayes Classification, Linear Discriminant Analysis (LDA), Classification and Regression Trees (CART), k-Nearest Neighbors (kNN), Support Vector Machines (SVM) with a linear kernel, and Random Forest (RF). The provided data was divided into the testing and training portions with parts for validation. It was achieved that Random Forest has a better model for this prediction when cross cross-validated in 10-folds. The accuracy was then used to select the optimal model. Hence, alcohol is the feature variable that contributes more to wine quality while volatile acidity and chloride contribute the least to the quality of wine. This would assist breweries in determining the right additions and subtraction when wine quality is in question

The present study aims to comprehend the thermodynamics of the Al-Ni-Cr superalloy system utilizing the latest Thermocalc 2022b databases. Thermocalc is a software that has a large database which has become vast over the years. The thermodynamic behavior and stability of the system were examined under varying conditions, including temperature and composition. The findings of this study provide crucial insight into the phase behavior and stability of the Al-Ni-Cr superalloy system, which can inform the optimization of its properties and performance for various industrial applications, compared to previous studies and research. The results of this study contribute to a deeper understanding of the thermodynamics of superalloy systems and can be of great benefit to the materials science and engineering communities. The databases used for the binary systems were NIDEMO v2.0 (Nickel Demo database v2.0, including Ni, Cr, and Al - a subset of TCNI) and TCBIN V1.1 (TC Binary Solutions Database, Version 1.0), while for the ternary systems, NIDEMO V2.0 and PURE 5SGTE V5.1 (Pure Elements - Unary Database, Scientific Group Thermodata Europe) were used. The results here demonstrate the great benefits of studying the thermodynamics of this alloy through available database systems and comparing the results with experimental studies.