• Bhushan Yogesh Bisane
• Prof.Swati R. Khokale
• Diksha Bharambe
• Divya Bhamare
• Harshada Pawar

Wine classification is a difficult task since taste because it is the least understood to the human senses, wine classification is challenging. Because sensory analysis is currently performed by human tasters, a subjective method, a good wine quality forecast can be very valuable in the certification phase. An automatic predictive system can be incorporated into a decision support system to improve performance speed and quality. A feature selection method can also aid in analysing the impact of analytical tests. If it is determined that certain input variables are very relevant in predicting wine quality, this information can be used to improve wine quality because some variables can be modified throughout the production process.

Machine Learning, SVM, Random ForestClassifier, K-means, Matplotlib, sk-learn, pandas, wine quality, data analysis.

"Review on Wine Quality Testing using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 5, page no.d606-d609, May-2022, Available :http://www.jetir.org/papers/JETIR2205496.pdf

"Review on Wine Quality Testing using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 5, page no. ppd606-d609, May-2022, Available at : http://www.jetir.org/papers/JETIR2205496.pdf