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Predicting the customer churn with machine learning methods : case: private insurance customer data
(2019)
Customer churn prediction is a field that uses machine learning to predict whether a customer is going to leave the company or not. The goal of this thesis is to study the churn prediction field and apply the knowledge in ...
Predicting short-term traffic speed : a model assessment using spatiotemporal variables
The focus of the thesis is to examine different machine learning models’ ability to predict short-term traffic speed. An autoregressive model, ARIMA model, Linear Regression, K-Nearest Neighbor and Extreme Gradient Boosted ...
Deep reinforcement learning in portfolio management : policy gradient method for S&P-500 stock selection
(2019)
Tämän maisterintutkielman tavoitteena on tutkia syvän vahvistusoppimisen (deep reinforcement learning, DRL) soveltuvuutta salkunhoitoon S&P500-indeksin osakkeista koostuvan osakeportfolion riskikorjatun tuoton parantamiseksi. ...
Insurance claim risk scoring with machine learning algorithms: A case study on developing a predictive system for assessing corporate customers' claim risk
(2018)
Predictive learning algorithms offer tools to automate and improve insurance risk management. The aim of this thesis is to study classification algorithms in risk scoring applications and to evaluate them in the creation ...
Neural network based binary and multi-class trading strategies using probability thresholds for trading actions on S&P 500 index
(2020)
There have been many attempts to predict stock market returns using regression algorithms. However, from the viewpoint of an investor, the stock market can be interpreted as a classification problem with the decision to ...
Konkurssin ennustaminen koneoppimisen avulla pienissä suomalaisissa palvelualan yrityksissä
(2021)
Tämän kandidaatintutkielman tavoitteena on muodostaa koneoppimiseen perustuva konkurssinennustamismalli. Tutkimus käsittelee konkurssiyrityksen tunnistamista maksuhäiriöriskiltä suojautumisen työkaluna. Aihetta tutkitaan ...
Segmentation of investor customers using machine learning in banking
(2021)
The purpose of this study is to analyze customer data from a local retail bank using machine learning. The goal is to detect attributes that investment customers have. Furthermore, this study compares performances of ...
Real estate insurance claims prediction with machine learning algorithms
(2022)
Nowadays insurance companies are increasingly implementing machine learning algorithms in their business routine. An ability to determine beforehand an emergence of claims could offer a tool to increase a profitability of ...
Machine learning in predictive maintenance : classification approach for remaining useful life prediction
(2020)
This thesis focuses on predicting remaining useful life (RUL) with classification approach. The methodology is demonstrated with NASA’s turbofan engine degradation dataset. Three classification systems with different ...
Network demand forecasting with data science
(2022)
During 2020-2022 the aviation market has experienced the biggest demand disruption in its history. The global pandemic and traveling restrictions have seriously disrupted the seasonal and steadily growing aviation travel ...