To address these questions, throughout the research, traditional machine learning models including Naive Bayes, Linear Support Vector Classification (SVC) and Logistic Regression were implemented. Among these approaches, Linear SVC achieved the best scores across all evaluation metrics. In the deep learning category, Long Short-Term Memory (LSTM) networks were applied. This approach provided exceptional performance which was overall better than traditional models. RNNs scored similarly as Linear SVC, while outperforming other traditional algorithms.

A hybrid approach including the BERT model was another method that was explored, which combined specific architecture with deep learning-based contextual understanding. The results demonstrated high classification results, which supports the hypothesis that hybrid models can increase performance of sentiment prediction. Furthermore, fine-tuning of different models improved their performance, which highlights the importance of optimizing pretrained models for specific types of analysis.

Overall, the findings confirm that deep learning models usually outperform traditional variants of machine learning methods while hybrid models can offer additional potential and perspective for enhancing sentiment classification in news articles. The study provides deep and valuable insights into effectiveness of different sentiment analysis and natural language processing (NLP) techniques, while at the same time discussing new possibilities and improvements in the field.]]>
Therefore, improvement of credit card fraud detection systems is the main priority for all banks, systems that are providing credit card-based payments and all the participants in the digital payments market. This also comes for the purpose of the large percentage of the population that is using their credit cards daily, from everyday payments to international transactions that are of great value.

The goal was to train multiple models to define if referenced transactions should be treated as fraud, and the results were measured by standard machine learning parameters. The model that had best results is Ensembled model using Decision Tree, Logistic Regression and K-Nearest Neighbor models with overall accuracy of 99.91% with Feature Selection algorithm applied. Ensemble method combines multiple models and creates the model with the best metrics possible. Along with this model, we have trained Logistic Regression model, K-Nearest Neighbors, Support Vectors Machines and Neural Networks, with accuracies respectively 88.37%, 85.48%, 00.73% and 98.11% with features selected.

This research also covers the part of data preprocessing, as this step is crucial when building a model for credit card fraud detection systems. These systems must be fast and precise in order to be usable, as they are dealing with large sets of imbalanced data.

At the end of the study, individuals will have better insight in credit card transactions, will also be familiar with the different methods for detecting credit card frauds and will have insight in which model suits the needs of this case the most.

]]> Over recent decades, machine learning technology has increasingly been used to predict sports performance. The sports industry generates extensive statistical data on players, teams, and seasons. Traditional prediction methods have shown limited accuracy. With data mining, sports organizations have recognized the outdated analysis in their data and are now utilizing it effectively.

The goal of this thesis is to explore accurate sports result predictions. Identifying significant features and analysing their impact on match outcomes is essential. Key variables include team statistics, player statistics, and historical data. These factors help managers and club directors forecast match winners and determine strategies. Machine learning techniques like KNN, Random Forest, logistic regression, and SVM are often applied to predict match results.

These predictions help coaches and managers assess player performance, evaluate skills, anticipate injuries, and strategize for upcoming games. Additionally, accurate predictions have significantly fuelled the sports betting industry, which is expanding rapidly thanks to the convenience of mobile and tablet devices.

This research proposes an AI-based framework to predict football match outcomes. Itexamines the effectiveness of system learning algorithms and reviews data mining techniques for predicting sports performance, highlighting their strengths and weaknesses. Despite previous research attempts, achieving high precision in game result predictions remains challenging.

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]]> Neuromarketing combines neuroscience and marketing to analyze consumer behavior through tools like electroencephalography (EEG), which captures subconscious and emotional responses. This thesis applies machine learning (ML) techniques to EEG data for predicting purchase decisions, addressing the limitations of traditional marketing methods. Using the NeuMa dataset, which includes EEG and eye-tracking data, key features such as frontal alpha asymmetry (FAA), power spectral density (PSD), and alpha-beta power ratios were extracted to build predictive models. Four ML algorithms—Support Vector Machines (SVM), Random Forest (RF), Artificial Neural Networks (ANN), and Convolutional Neural Networks (CNN)—were evaluated based on accuracy, ROC AUC, and execution time. SVM emerged as the best-performing model, achieving 94.3% accuracy. 99% ROC AUC, with efficient processing time, making it suitable for neuromarketing research. The results confirm the critical role of EEG features from the frontal region, particularly FAA and alpha-beta power ratios, in predicting consumer preferences. These metrics reflect emotional and subconscious responses, emphasizing their importance in purchase decisions. This study demonstrates the value of integrating EEG with ML for consumer analysis, offering a scalable, unbiased, and data-driven approach to marketing research. By combining neuroscience with modern methods, this research provides a foundation for improving consumer preference analysis. It highlights the potential of EEG-based metrics and ML models to enhance marketing strategies, moving beyond traditional self-report methods toward more objective and accurate insights.

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