According to the most recent statistics, India is a developing wine market in Asia\'s New World countries. Although the Indian wine business is in its infancy in terms of area, predictions, and marketing of wines, eighty percent of consumption is concentrated in cities. In today’s world, it is very crucial to find out the quality of the wine we are using as it can affect our health, badly.  This model aims to predict the quality of different types of wines, based on parameters like percentage of acidity, sulphates, chlorides, sugar, pH used. This model demonstrates, how statistically it can be used to identify the components that mainly control the wine before it is produced in the country. This will also assist winemakers regulate quality, which will benefit both, the country\'s economy, and people\'s health.
The analysis of our applied machine learning model shows the accuracy achieved of 95.10% which is highest using Random Forest Classifier model as till now.
IV. FUTURE SCOPE
In this project, Quality of wine is predicted accurately using Random Forest Classifier. In future, this prediction system can be enhanced for a better result with better accuracy by using bigger datasets or using different ML algorithms. This model has doors opened for further improvement. During manufacturing of wines, organization can provide more minute details about the product so that more accurate result can be achieved.
Words cannot express my gratitude to my professors for his invaluable patience and feedback. I would be remiss in not mentioning my family, especially my parents. Their belief in me has kept my spirits and motivation high during this process.