A Machine Learning Based Approach for Wine Quality Prediction

IEEE BASE PAPER ABSTRACT:

Wine is a popular drink across the globe and the gender. Older the Wine, better is the taste but, expensive. The Wine quality is measured based on the important parameters, such as free Sulphur dioxide, Volatile acidity, Citric Acid and Residual sugar. The traditional way of Wine quality assessment was time consuming. This paper gives an automatic prediction of Wine quality, as good or bad, using machine learning approaches which are Neural Networks, Logistic Regression and Support Vector Machine are implemented on standard datasets of Portuguese “Vinho Verde” Wine. The results are compared with standard values. The support vector Machine has achieved superior than other techniques with error of 0.003. The quality rate for SVM is 7.99. The work is useful in Wine industry for quality testing and assurance for customers.

PROJECT OUTPUT VIDEO:

ALGORITHM / MODEL USED:

Random Forest Classifier.

OUR PROPOSED ABSTRACT:

Since taste is the sense that has received the least amount of research, classifying wines can be challenging. Since sensory analysis is now conducted by human tasters, which is obviously a subjective approach, a good wine quality prediction can be very helpful in the certification step. Wine quality certification is crucial to the wine industry. Both wine industry professionals and consumers care about a wine’s quality.

The conventional (professional) method of evaluating wine quality takes time. Machine learning models are crucial tools today for replacing human labour. There are a number of features that can be used to forecast the wine quality in this scenario, but not all of the attributes will be useful for a more accurate prediction. We utilized the Random Forest Classifier technique to create a classification model.

We used the Vinho Verde dataset from Kaggle for this project. The proposed method in this research uses a Random Forest Classifier to identify the wine based on its components, which helps us predict the quality of wine. We also designed a user interface using the Flask web framework to make the task much more user-friendly.

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS:

System : Pentium i3 Processor.
Hard Disk : 500 GB.
Monitor : 15’’ LED
Input Devices : Keyboard, Mouse
Ram : 4 GB

SOFTWARE REQUIREMENTS:

Operating System : Windows 10 / 11.
Coding Language : Python 3.8.
Web Framework : Flask.
Frontend : HTML, CSS, JavaScript.

REFERENCE:

Basvaraj. S. Anami, Kavita Mainalli, Shanta Kallur, Vijeeta Patil, “A Machine Learning Based Approach for Wine Quality Prediction”, 2022 2nd Asian Conference on Innovation in Technology (ASIANCON), IEEE Conference, 2022.

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A Machine Learning Based Approach for Wine Quality Prediction