MULTIPLE REGRESSION ANALYSIS SYSTEM IN MACHINE LEARNING AND ESTIMATING EFFECTS OF DATA TRANSFORMATION&NORMALIZATION

Abstract

Machine learning area is a recent topic in data analysis and a researcher or worker of the area is called “Data Scientist” which nowadays has been a highly preferred job title in computing. In this study, we have two aims that the first is to implement a multiple regression analysis system which is developed in Ubuntu operating system on the Anaconda platform using Python3 in order to construct models of each attribute to make their estimations for future decisions taking less risk in advance of past experiences hided in cumulated data and the second aim is to find out effects of data transformation and min-max normalization in the data preparation before building models. After the system implementation, we test the system to determine the best estimation model of each attribute of the vehicles sold in the five European countries between 1970 and 1999. We have constructed six versions of the original dataset and these versions are used to construct regression models for further estimations. Finally, we compute the regression criterion value of R-Squared for each constructed-model and we compare the models according to these values. Computational results are very promising that the system can be used efficiently and the effects of the data transformation and min-max normalization are significant for some attributes.