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- 2024
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Mark
Stock Price Predictions for FAANG Companies Using Machine Learning Models
(
- Bach. Degree
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Mark
Leveraging LlaMA 2 for sentiment analysis
(
- Bach. Degree
- 2023
-
Mark
Maskininlärning & Random Forest: Överträffar traditionella kreditmodeller
(
- Bach. Degree
-
Mark
The use of Machine Learning to predict adverse birth outcomes: Empirical real world evidence from a human cohort study in Adama, Ethiopia
(
- Master (Two yrs)
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Mark
Theoretical & Practical Investigation of Algorithmic Trading
(
- Master (Two yrs)
-
Mark
Out of the Books and Into the Woods
(
- Master (One yr)
-
Mark
Nowcasting U.S. inflation using mixed frequency real-time data
(
- Master (Two yrs)
-
Mark
Estimation of surface soil moisture from Sentinel-1 Synthetic Aperture Radar imagery using machine learning method
(
- Master (Two yrs)
-
Mark
A Comparative Analysis of Decision Tree Models in Identifying Landslide Susceptibility and Type Classification
(
- Bach. Degree
-
Mark
Predicting the Movement of the S&P 500 Index using Machine Learning
(
- Master (One yr)
-
Mark
Wind power forecasting using random forests
(
- Master (Two yrs)
- 2022
-
Mark
Klassificering av kreditkortskunder
(
- Bach. Degree
-
Mark
Analysis of the water quality dynamics of Lake Vomb; Interactions between water quality profile and cyanobacterial bloom in a eutrophic lake in Sweden
(
- Master (Two yrs)
-
Mark
Inflation forecasting with Random Forest
(
- Bach. Degree
-
Mark
Corporate default prediction: a comparison between Merton model and random forest in an environment of data scarcity
(
- Master (One yr)
-
Mark
Comparison of Machine Learning Algorithms in Predicting the Age Distribution Parameters of H&M Product Customers
(
- Master (One yr)
- 2021
-
Mark
Prediction of quote acceptance in a B2B environment using Random Forests and Gradient Boosting Machines
(
- Master (Two yrs)
-
Mark
Topographic controls of drought impact on Swedish primary forests
(
- Master (Two yrs)
- 2020
-
Mark
Predicting Corporate Takeover Outcomes Using Machine Learning
(
- Bach. Degree
-
Mark
Improving High-Risk Consumer Credit Scoring with Financial Transaction Data
(
- Master (Two yrs)
-
Mark
Machine Learning for the Prevention of Injuries in the Construction Industry
(
- Master (Two yrs)
-
Mark
Machine learning methods on Swedish geological data
(
- Master (Two yrs)
- 2019
-
Mark
Predicting Bank Insolvency with Random Forest Classification
(
- Master (One yr)
-
Mark
Utilizing Machine Learning for Trading Algorithms Exploiting the Time Series Momentum Anomaly
(
- Master (Two yrs)
-
Mark
Will You Stay or Will You Go? Churn Prediction for an App-Delivered International Calling Service
(
- Master (Two yrs)
-
Mark
Crime Prediction in Swedish Municipalities with machine learning algorithms
(
- Bach. Degree
-
Mark
High-risk Consumer Credit Scoring using Machine Learning Classification
(
- Master (Two yrs)
-
Mark
RGB and Multispectral UAV image classification of agricultural fields using a machine learning algorithm
(
- Master (Two yrs)
-
Mark
Detecting bark beetle damage with Sentinel-2 multi-temporal data in Sweden
(
- Master (Two yrs)
-
Mark
Assessment of machine learning methods for the classification and weight estimation of meals
(
- Master (Two yrs)
- 2018
-
Mark
Improving the OpenStreetMap Data Set using Deep Learning
(
- Prof. qual. >4 yrs
-
Mark
Assessing edge pixel classification and growing stock volume estimation in forest stands using a machine learning algorithm and Sentinel-2 data
(
- Master (Two yrs)
-
Mark
How 140 Characters can be related to the Stock Market Movements: Sentiment Analysis of Twitter
(
- Master (One yr)
-
Mark
Evaluation of Features for Prediction of Hospitalization in Patients with End Stage Renal Disease
(
- Master (Two yrs)
- 2017
-
Mark
Joint use of Sentinel-1 and Sentinel-2 for land cover classification : a machine learning approach
2017) In Lund University GEM thesis series NGEM01 20162(
Dept of Physical Geography and Ecosystem Science- Master (Two yrs)
-
Mark
Comparison of multi-temporal and multispectral Sentinel-2 and Unmanned Aerial Vehicle imagery for crop type mapping
2017) In Lund Un iversity GEM thesis series NGEM01 20162(
Dept of Physical Geography and Ecosystem Science- Master (Two yrs)
-
Mark
Machine learning and its applications within insurance hit rates and credit risk modelling
(
- Master (Two yrs)
- 2016
-
Mark
The use of Machine Learning Algorithms for Adaptive Question Selection in Questionnaire-based Mental Health Data Collection Apps
(
- Master (Two yrs)
- 2013
-
Mark
Image based Wheel Detection using Random Forest Classification
2013) In Master's Theses in Mathematical Sciences 2013:E7 FMA820 20131(
Mathematics (Faculty of Engineering)- Master (Two yrs)
- 2010
-
Mark
Drivers of global wildfires : statistical analyses
2010) In Lunds universitets Naturgeografiska institution - Seminarieuppsatser(
Dept of Physical Geography and Ecosystem Science- Master (Two yrs)