PARIS HOUSING PRICE PREDICTION USING LINEAR REGRESSION AND RANDOM FOREST

RIANA, YOGA DINDA (2023) PARIS HOUSING PRICE PREDICTION USING LINEAR REGRESSION AND RANDOM FOREST. Other thesis, Universitas Katholik Soegijapranata Semarang.

	Text 17.K1.0053-YOGA DINDA RIANA-COVER_a.pdf Download (713kB)
	Text 17.K1.0053-YOGA DINDA RIANA-BAB I_a.pdf Download (128kB)
	Text 17.K1.0053-YOGA DINDA RIANA-BAB II_a.pdf Restricted to Registered users only Download (132kB)
	Text 17.K1.0053-YOGA DINDA RIANA-BAB III_a.pdf Download (242kB)
	Text 17.K1.0053-YOGA DINDA RIANA-BAB IV_a.pdf Download (926kB)
	Text 17.K1.0053-YOGA DINDA RIANA-BAB V_a.pdf Download (1MB)
	Text 17.K1.0053-YOGA DINDA RIANA-BAB VI_a.pdf Download (125kB)
	Text 17.K1.0053-YOGA DINDA RIANA-DAPUS_a.pdf Download (376kB)
	Text 17.K1.0053-YOGA DINDA RIANA-LAMP_a.pdf Download (273kB)

Abstract

This project has the purpose to get the result of the accuracy of linear regression and random forest for a test which one of those two has a better result for regression using data “Paris housing price”. That data has been processed and already sampling data, then using linear regression and random forest to get a prediction, and then comparing those two methods to get a prediction result, which one of those two has a better prediction. The results of research Comparing the Linear Regression and Random Forest Algorithms in the regression of the Paris housing dataset. It can be concluded that linear regression is accurate works in this study and is also it suitable for this dataset, but for the random forest, the answer is no because random forest output has too many errors.

Item Type:	Thesis (Other)
Subjects:	000 Computer Science, Information and General Works > 004 Data processing & computer science
Divisions:	Faculty of Computer Science > Department of Informatics Engineering
Depositing User:	mr AM. Pudja Adjie Sudoso
Date Deposited:	04 Apr 2023 06:01
Last Modified:	04 Apr 2023 06:01
URI:	http://repository.unika.ac.id/id/eprint/31397

Actions (login required)

View Item