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Showing result 1 - 5 of 12 essays matching the above criteria.
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1. Enhancing House Rental Price Prediction Models for the Swedish Market : Exploring External features, Prediction intervals and Uncertainty Management in Predicting House Rental Prices
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Exakt förutsägelse av hyrespriserna för hus är ett avgörande problem i verkligheten fastighetsdomän, vilket underlättar informerat beslutsfattande för både hyresgäster och hyresvärdar. Denna studie presenterar en omfattande utforskning av olika maskininlärningstekniker som tillämpas på en mångsidig datauppsättning av husfunktioner, med det övergripande målet att avslöja den mest effektiva algoritmen för förutsäga hyrespriser. READ MORE
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2. Modelling Airbnb Prices in the Maltese Islands
University essay from Lunds universitet/Nationalekonomiska institutionen; Lunds universitet/Statistiska institutionenAbstract : The digital platform Airbnb has gained popularity in a number of countries particu- larly in the Maltese islands. Striking a balance in setting a price that is competitive and also renders a good profit can be a challenge. READ MORE
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3. Prediction of Stock Returns Using Accounting Data with a Machine Learning Approach
University essay from Göteborgs universitet/Graduate SchoolAbstract : The relationship between accounting data and stock price prediction has been a hot topic for over half a century. Researchers have been trying to identify the relationship and investigate how it may be useful when trying to improve prediction accuracy. READ MORE
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4. Credit risk modelling and prediction: Logistic regression versus machine learning boosting algorithms
University essay from Uppsala universitet/Statistiska institutionenAbstract : The use of machine learning methods in credit risk modelling has been proven to yield good results in terms of increasing the accuracy of the risk score as- signed to customers. In this thesis, the aim is to examine the performance of the machine learning boosting algorithms XGBoost and CatBoost, with logis- tic regression as a benchmark model, in terms of assessing credit risk. READ MORE
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5. Prediction of Short-term Default Probability of Credit Card Invoices Using Behavioural Data
University essay from KTH/Matematisk statistikAbstract : Probability of Default (PD) is a standard metric to model and monitor credit risk, a major risk facing financial institutions. Traditional PD models are used to forecast risk levels in the long-term, while short-term PD predictions are rarer, but they can support management decisions on an operational level. READ MORE
