Forecasting housing prices in Turkey by machine learning methods

Kayakuş, Mehmet; Terzioğlu, Mustafa; Yetiz, Filiz

doi:10.36253/aestim-12320

Cited by 9 publications

(1 citation statement)

References 23 publications

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“…The process to reach these weights is called "training the network.". Accordingly, for a network to be trainable, the weight values must be dynamically changeable within a certain rule [22,23].…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Estimation Of Turkey's Carbon Dioxide Emission with Machine Learning

ÇOŞGUN

2024

IJCESEN

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Carbon dioxide emissions are an important factor in the increase of greenhouse gases in the atmosphere and climate change. Controlling and reducing carbon dioxide emissions plays an important role in combating global warming and climate change. Various national and international efforts are being carried out to reduce greenhouse gas emissions and switch to sustainable energy sources. For this reason, estimating carbon dioxide emissions in the coming years is important for determining the measures to be taken. In this study, Turkey's carbon dioxide emissions are successfully estimated using two different machine learning models. The success of the study was evaluated using three different statistical measures: R2, MSE and MAE. The R2 of decision trees was 89.4%, MSE was 0.013 and MAE was 0.011; the R2 of artificial neural networks was 92.7%, MSE was 0.009 and MAE was 0.006. When we compare the two models, it is seen that ANN is more successful than decision trees and predicts with less error.

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Section: Artificial Neural Networkmentioning

confidence: 99%