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Prediction of Property Prices of Dublin Housing Market using Ensemble Learning

Mirg, Vani (2022) Prediction of Property Prices of Dublin Housing Market using Ensemble Learning. Masters thesis, Dublin, National College of Ireland.

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Dublin Housing Market is considered to be one of the most decisive factors contributing to the economy's growth. Over the recent years, the Property Regulatory Authority has noticed a difference in the market price and the selling price of the residential houses. This study tries to predict the prices of residential properties using the data from PSRA (Property Services Regulatory Authority) from 2010 through 2021 and determine the actual market value of any residential house. The research focuses on building a model using Ensemble Learning and traditional methods of Machine Learning and determining the best model for predicting property prices. The final results reveal the performances of the five models developed: Multiple Linear Regression, K Nearest Neighbour, Decision Tree Regressor, Random Forest Regressor and Gradient Boosting Regressor. The results suggest that the Gradient Boosting Regression Model accurately predicts the price and has an R-Square value of 75.22

Item Type: Thesis (Masters)
Subjects: Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science
H Social Sciences > HD Industries. Land use. Labor > Housing
Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
H Social Sciences > HD Industries. Land use. Labor > Specific Industries > Property Industry
Divisions: School of Computing > Master of Science in Data Analytics
Depositing User: Tamara Malone
Date Deposited: 23 Feb 2023 13:00
Last Modified: 02 Mar 2023 08:54

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