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Housing Price Prediction and Classification Based on Crime Occurrence using Machine Learning Algorithms: Ireland

Adedokun, Salam Adekunle (2020) Housing Price Prediction and Classification Based on Crime Occurrence using Machine Learning Algorithms: Ireland. Masters thesis, Dublin, National College of Ireland.

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Housing is a basic need for humans and the acquisition of a house is an action that should be taken carefully because of the financial implications. This project aims to examine the comparative performance of both predictive and classification models and how the crime occurrence, distance to both the nearest primary school and bus-stop can improve model performance. The crime occurrence variables include attempts to murder, assaults, burglary, theft, fraud, drugs, weapon offences, damage to property and social code offence. This model would help real estate investors and prospective house buyers to predict and classify housing prices thereby reducing loss for real estate developers and giving the negotiating power to prospective house buyers. Comparative analysis performed on the data mining techniques; generalised linear model, ridge, lasso, support vector machine and random forest was evaluated based on their Mean Absolute Error and Root Mean Square Error while the comparative analysis of classification data mining techniques: random forest, C5.0, k-nearest neighbours, support vector machine and multinomial logistics regression was evaluated based on accuracy. The output of the algorithms was visualised with tableau to give a clear insight on how the models performed for both the prospective house buyers and real estate investors.
Area - Machine Learning: is the capability of a computer to learn by finding consistent or hidden patterns in a data to enable it accurately classify or make predictions, and this is used in this project for house price prediction and classification.

Item Type: Thesis (Masters)
Subjects: Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science
Q Science > QA Mathematics > Computer software
T Technology > T Technology (General) > Information Technology > Computer software
Divisions: School of Computing > Master of Science in Data Analytics
Depositing User: Dan English
Date Deposited: 10 Jun 2020 14:32
Last Modified: 10 Jun 2020 14:32

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