Regression is a supervised machine learning method for understanding the relationship between independent variables or features and a dependent variable or outcome. One of the algorithms used for regression is the “Polynomial Regression”.
Here, we use the Wine_Quality.csv dataset for regression purposes and you can download the dataset from the link on top of this page. This dataset has 11 features:
“fixed acidity”, “volatile acidity”, “citric acid”, “residual sugar”, “chlorides”, “free sulfur dioxide”, “total sulfur dioxide”, “density”, “pH”, “sulphates” and “alcohol”
And the prediction variable is: “Wine Quality”.
In the above form, you enter the values of 11 features and the model returns the Wine Quality in a pop-up form. Notice that the values and ranges of each feature are as below:
The sample code used to train a “Polynomial Regression” model, is provided in the link on top of this page. Each time you run this form and predictions happen, the values will store in the database, and with the link “result” on top of this page, you will see the previous results of the model prediction. Your recent run will be added to the end of this list.