Open AccessData Analysis and Data Classification in Machine Learning using Linear Regression and Principal Component Analysis
Author(s) -
B S Lokasree
Publication year - 2021
Publication title -
türk bilgisayar ve matematik eğitimi dergisi
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.218
H-Index - 3
ISSN - 1309-4653
DOI - 10.17762/turcomat.v12i2.1092
Subject(s) - principal component analysis , lagrange multiplier , principal component regression , computer science , component analysis , component (thermodynamics) , linear regression , regression analysis , artificial intelligence , proper linear model , regression , pattern recognition (psychology) , machine learning , mathematics , polynomial regression , statistics , mathematical optimization , physics , thermodynamics
In this paper step-by-step procedure to implement linear regression and principal component analysis by considering two examples for each model is explained, to predict the continuous values of target variables. Basically linear regression methods are widely used in prediction, forecasting and error reduction. And principle component analysis is applied for facial recognition, computer vision etc. In Principal component analysis, it is explained how to select a point with respect to variance. And also Lagrange multiplier is used to maximize the principle component function, so that optimized solution is obtained