Wine Quality Prediction using SVM and K-Nearest Neighbor

Not all of the features in our datasets are useful. It is highly likely that least important features will lower your model's accuracy. So we better find out which ones are important and which ones are not. That's what I did in this project. The accuracy of the model before and after applying correlation analysis it considerably changed.

Secondly, for classification tasks, Support Vector Machines and K-NN models are often used.

In Wine-Quality-EDA notebook you will find different important exploratry data analysis techniques for getting better understanding of the features.

Check out Wine-Quality-svm-knn notebook for the code and accuracy comparison.

I'll work on this now and then to make a better understanding of applying interesting data science techniques, please check back later and hope you find this work useful.