Wine Quality Prediction using Linear Regression

Published on 05 Jul 2021. Written by Amitesh kumar

Wine predictor is used for predicting the quality and taste of wine on a scale of 0-10. It requires a set of inputs, which is based on many other parameters such as acidity, concentration, etc. The project involves the concept of machine learning, which thoroughly studies the pattern and data and predicts the results.

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The dataset involves is very important for the application as the output is based on the inputs. The application will be very useful to the commercial producers of good wine and the common people also.

What are the inputs required for the application?

The application uses several input variables that are necessary for the prediction of the best wine. The first crucial parameter is the acidity of the wine; the second is the residual sugar concentration. However, both these are the most important for the inputs, but other inputs have to be kept in mind. The application will also consider the sulfates, pH value, density, total sulfur dioxide values in the wine to predict the best results.

Project Implementation

Initially, one will need to know the coding in Python language. As the algorithm will be written in the python language, and for that, one will need to download some python libraries. The libraries will help in writing the program. Libraries that are helpful are Pandas, matplotlib, numpy, and scikit-learn which can be downloaded using pip. The libraries contain the basic and required coding for the study of data and patterns.

Create variables for the various inputs, and once the dataset is created, it is time to build the Linear Regression Model. The linear regression model is one of the essential parts of machine learning. It can simply be implemented by using built-in functions. After the dataset is fed, there is a need to figure out the errors which are achieved by the RMSE (root mean square error) application.

After the coefficients of all the variables are determined, the project step will be moved to the training and testing set, which includes checking the results thoroughly on the basis of its inputs.

Conclusion:

The result by the application is represented on a scale of 1-10, where 10 is the best and 1 being the worst. Initially, Machine learning was limited to sources that included high calculations and result tracking. Nowadays, it has become a part of developing many applications. One should be aware of Python language and data handling in order to build this project efficiently.

Once made properly, it will be very useful in the wine industry as a parameter of quality. This project is simple and effective, and you will learn many things related to wine also. First, test the application by putting rigorous inputs of the coefficients and checking the output by tallying it from the results predicted by another device.

Kit required to develop Wine Quality Prediction using Linear Regression:

• NA

Technologies you will learn by working on Wine Quality Prediction using Linear Regression:

• Machine Learning

Any Questions?