This project is work in progress!
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Wine has been classified as a major promoter of health due to its anti-inflamatory and antioxidant properties. These properties have been linked to health benefits such as longevity, cardiovascular health and prevention of chronic diseases.
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Wine is very popular and has been consumed for centuries. Statistics show that in 2020 alone, over 234 million hectoliters of wine was consumed globally. This demonstrates how the wine industry has played a very important role in the history of mankind. Wine quality is a very big deal, no wonder there so many wine tasting events and we have so many wine critics. These critics try to define what good wine is and what metrics should be used to determine the quality of wine.
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However, there hasn't been any consensus amongst wine critics on what "quality wine" is and what metrics should be used to decide. Standardizing this process has become a very important to the wine industry and the comsumers as well. Nobody wants to spend money on bad wine and the manufacturers would be happy if there was a way to measure the quality of wine before mass production.
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Wine is made form grapes and most and experts can identify quality wine based on smell, flavor, acidity, alcohol content and so many other characteristics. There is a lot of data from experts who have over the years determined the quality of wine based on its physio-chemical properties. With the availability of such data, data scientists can create machine learning models to predict wine quality.
In this project, the goal is to obtain wine quality data from Kaggle, clean the data, do some descriptive analysis, create a predictive model and make sense of the data as well as make inferences.