Sökning: "XGBoost modell"

Visar resultat 1 - 5 av 22 uppsatser innehållade orden XGBoost modell.

1. 1. Optimizing Flight Ranking:A Machine Learning Approach : Applying Machine Learning to Upgrade Flight Sorting and User Experience

   M1-uppsats, KTH/Hälsoinformatik och logistik

   Författare :Habib Jabeli; [2024]
   Nyckelord :Machine Learning; Flight Comparison; Flygresor.se; Neural Networks; Flight Ranking; Random Forest; XGBoost;

   Sammanfattning : Flygresor.se, a leading flight comparison platform, uses machine learning to rankflights based on their likelihood of being clicked. The main goal of this project was toimprove this flight sorting to obtain a better user experience. The platform's existingmodel is based on a neural network approach and a limited set of features. LÄS MER

3. 2. Monthly heatwave prediction in Sweden based on Machine Learning techniques with remote sensing data

   Master-uppsats, KTH/Hållbar utveckling, miljövetenskap och teknik

   Författare :Zhuoran Li; [2023]
   Nyckelord :Heatwave Prediction; Sweden; Machine Learning; Remote Sensing Data; Värmebölja förutsägelse; Sverige; Maskininlärning; Fjärravkänningsdata;

   Sammanfattning : Heatwave events as a kind of extreme climate event, have plagued the human race for the past few years. It severely influences people’s life quality, sometimes even leads to some serious diseases. In order to alleviate the possible damages heatwave events can do, some targeted actions are necessary and forecasting heatwaves is one of them. LÄS MER

4. 3. Segmentation and Valuation in  Stockholm Housing Market : Spatial Continuous and Discontinuous Submarkets Evaluating by Hedonic Price Model and XGBoost Model

   Master-uppsats, KTH/Fastighetsekonomi och finans

   Författare :Xianglin Sun; [2023]
   Nyckelord : housing market segmentation ; spatial continuity ; hedonic price model ; XGBoost model ; segmentering av bostadsmarknaden ; rumslig kontinuitet ; hedonisk prismodell ; XGBoost modell ;

   Sammanfattning : The housing market segmentation could provide a reference for more targeted policymaking and investment strategies. Although there have been many studies, there are no consistent submarkets delineating methods because of a lack of theoretical support and subjective evaluation. In this paper, two market segmentation methods are introduced. LÄS MER

5. 4. Predicting Short-term Absences of a Railway Crew using Historical Data

   Master-uppsats, KTH/Matematisk statistik

   Författare :Agnes Björnfot; Sandra Fjelkestam; [2023]
   Nyckelord :statistics; machine learning; absence prediction; random forest; XGBoost; quantile regression; statistik; maskininlärning; frånvaroprognoser; random forest; XGBoost; kvantilregression;

   Sammanfattning : Transportation via train is considered the most environmentally friendly way of traveling and is widely seen as the future of transportation. Canceled and delayed trains worsen customer satisfaction; thus, punctual trains are crucial for railway companies. LÄS MER

7. 5. The Predictive Power of Implied Volatility in Option Pricing

   Kandidat-uppsats, KTH/Matematisk statistik

   Författare :Lovisa Berglund; [2023]
   Nyckelord :Option Pricing; Black-Scholes; Finance; Implied Volatility; Applied Mathematics; Machine Learning; Optionsprissättning; Black-Scholes; Finans; Implicit Volatilitet; Tillämpad Matematik; Maskininlärning;

   Sammanfattning : During the last few years, financial derivatives have been growing in trading volume. There seem to be a high demand and supply of derivatives on the market and one common derivative is the option contract. The option contract is frequently the subject of studies and many different pricing models have been created for options. LÄS MER