A Comparative Study of Multiple Linear Regression and K Nearest Neighbours using Machine Learning

In recent times, Machine Learning methods are widely used to handle large and complex data to generate interesting patterns and trends. Supervised Learning methods are generally used to classify different types of real life datasets. In this paper, the two methods Multiple Linear Regression and K Nearest Neighbours have been used to classify the quality of wine and compare the accuracy. As a result, it is found that K Nearest Neighbours gives the good accuracy. The calculated Mean Squared Error (MSE) and calculated Root Mean Squared Error (RMSE) give the model perfection. Result shows that the value of MSE and RMSE applying K Nearest Neighbours (KNN) is higher than Multiple Linear Regression (MLR). The classification performance of the methods is compared with their accuracy. Based on these methods, the highest accuracy of KNN with K = 5 is 0.9444. Meanwhile, for the Multiple Linear Regression, the accuracy reached to 0.6657. Also, MSE and RMSE are calculated as 0.0555 and 0.2357 for KNN with k=5. Multiple Linear Regression has MSE (0.1692) and RMSE (0.4113). The experimental result shows that KNN can be used as alternative method for predicting the new instances. From UCI Machine Learning Repository, the wine dataset is taken which are tested in this research paper.