Impact of the high-frequency public transport on the performance of the Machine Learning model for predicting the rental price in Dublin

Kulkarni, Neha (2024) Impact of the high-frequency public transport on the performance of the Machine Learning model for predicting the rental price in Dublin. Masters thesis, Dublin, National College of Ireland.

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Abstract

In years the rising population, in cities has made it increasingly important to find affordable rental houses that match their true value. To address this concern our research employs a machine learning technology to predict rental house prices in the Dublin housing market. By acknowledging that multiple factors influence housing prices we adopt an approach that combines algorithms to enhance model accuracy and prediction. This study incorporates machine learning algorithms such, as Decision Tree, Gradient Boosting and K-Nearest Neighbour(KNN). This study predicts the prices of residential rental properties using the data from RTB (Residential Tenancies Board) which is open source data. The dataset has various features like location, property features etc. Additional features have also been added to the dataset by looking into the connectivity of the rental property with the high-frequency public transportation of Dublin. The high-frequency public transportation in Dublin is the luas and the darts. The luas have two different lines one is the green line luas, and another is the red line luas. We will also add more features like a number of luas and dart stops and also identified and then classify the postcodes of Dublin into North and South of Dublin. This study aims to provide accurate rental price prediction and also explain the connection between the various factors. After applying the regression machine learning algorithm, we also predicted the rental house price from the year 2023 to 2026 by applying the ARIMA Model Grid Search and Forecasting.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Hafeez, Taimur UNSPECIFIED
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science D History General and Old World > DA Great Britain > Ireland > Dublin H Social Sciences > HD Industries. Land use. Labor > Housing Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning H Social Sciences > HE Transportation and Communications > Urban Transportation
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Ciara O'Brien
Date Deposited:	15 May 2025 15:51
Last Modified:	15 May 2025 15:51
URI:	https://norma.ncirl.ie/id/eprint/7555

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