Comparison and Analysis of Linear Regression & Artificial Neural Network

摘要

Recently, Artificial intelligence is gaining traction after learning of data on computers after focusing on learning data on computers through big data analysis. In this thesis, companies were expected to use accounting data to generate continuous investments in industries in the future industries using accounting data. In other words, after studying survey data on the status of school companies in terms of school establishment type, school class, income statement, region, and industry, we studied which algorithm is more efficient. The learning process of this paper is comparing and analyzing the results of accuracy through the algorithm of linear regression and deep learning neural network through supervised learning based on correct answer label. In addition, we investigated the problem of Neural Net with respect to the number of hidden layers. In conclusion, we present the conclusion as to which algorithm is more efficient if the numeric data is a single attribute value.