1 10 15 https://omeka.ibu.edu.ba/files/original/2be086ebd527b95bd4a2f3c3f47cd5d1.pdf 744ced70ce4f51e612f57151c316406c Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource IT Master's Theses Description An account of the resource IT Master's Thesis collection features master's theses authored by graduate students in the Department of Information Technology. Each thesis reflects a significant research effort, combining theoretical knowledge with practical application to address complex challenges in the IT domain. These works demonstrate students’ advanced understanding of information systems, software engineering, data science, cybersecurity, and emerging technologies. The theses serve as a testament to the students' capability to conduct independent research, propose innovative solutions, and contribute to the advancement of the IT field. Subject The topic of the resource IT Master's Thesis Language A language of the resource English Language Author Author IT Department Master’s Students Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Predictive Modeling for Diabetes: A Comprehensive Analysis Author Author Amina Kodžaga Abstract A summary of the resource. Diabetes is a growing global health issue, and early prediction is key to preventing its effects. This thesis develops predictive models for diabetes using various machine learning methods, including Logistic Regression, Decision Trees, K-Nearest Neighbors (KNN), Random Forest, Support Vector Machine (SVM), and XGBoost, using the Diabetes Health Indicators dataset, which covers clinical, lifestyle, and demographic factors. Feature selection identifies the most important diabetic predictors, and model performance is evaluated using macro average and weighted average metrics, accuracy, precision, recall, F1-score, and error metrics (MSE and RMSE) to provide a thorough evaluation of model performance across the classes. Both SVM and Random Forest performed best overall, with an accuracy of 0.86. They also performed exceptionally well in weighted average and macro average measures, with overall recall and F1-scores of 0.86. SVM has the highest precision performance at 0.88, with Random Forest achieving the next best score of 0.87. These models are very dependable for diabetes prediction tasks because of their remarkable balance while handling both classes. SVM and Random Forest offer more dependable performance on a range of metrics, as evidenced by the weaker outcomes of Decision Tree, KNN, XGBoost, and Logistic Regression. Keywords Keywords. Diabetes prediction, Machine learning techniques, Predictive modeling accuracy, Healthcare analytics, Classification performance Classification performance Diabetes prediction Healthcare analytics Machine learning techniques master's thesis Predictive modeling accuracy https://omeka.ibu.edu.ba/files/original/9d874f7a36c28321575471028120260c.pdf c190c7a95038ef00a84daaabc66c0d62 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource IT Master's Theses Description An account of the resource IT Master's Thesis collection features master's theses authored by graduate students in the Department of Information Technology. Each thesis reflects a significant research effort, combining theoretical knowledge with practical application to address complex challenges in the IT domain. These works demonstrate students’ advanced understanding of information systems, software engineering, data science, cybersecurity, and emerging technologies. The theses serve as a testament to the students' capability to conduct independent research, propose innovative solutions, and contribute to the advancement of the IT field. Subject The topic of the resource IT Master's Thesis Language A language of the resource English Language Author Author IT Department Master’s Students Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Machine Learning-Driven Prediction of Heart Strokes Author Author Selma Vreto Vatrić Abstract A summary of the resource. <div style="text-align:justify;">Heart strokes remain one of the leading health risks in the world today. Timely prediction can significantly improve patient outcomes and healthcare resource allocation. This study aims to harness machine learning techniques to develop efficient predictive models for the early detection of heart strokes. Research is based on a dataset created by combining different (five) datasets. The dataset encompasses patient demographics, clinical measurements, and historical medical records. The analysis focused on five machine learning models: Logistic Regression, Decision Tree, K-Nearest Neighbors, Random Forest, and Support Vector Machine. The goal was not only to test different algorithms, but also to understand how data preparation, feature selection, and model choice impact the final results. The models were trained and tested on both the original dataset and an extended version, where new features were added by combining existing ones. The results showed that models such as Logistic Regression, Decision Tree, and KNN performed better when applied to the original data. The Decision Tree model achieved an accuracy of 87.8% and an F1 score of 0.881, while Logistic Regression and K-Nearest Neighbors each attained F1 scores of 0.850 and 0.849, respectively. On the other hand, Random Forest and SVM showed significant improvements with the extended dataset. Random Forest performed the best overall, with an F1 score of 0.920 and an accuracy of 91.6% with enhanced results. SVM also benefited from enhanced results, improving its F1 score from 0.892 to 0.879, which highlights how specific models can leverage additional features for improved generalization. This tool could help detect risks earlier, allowing for timely interventions and prevention, thereby reducing the burden of strokes on healthcare systems and improving patient care. Limitations include data quality and availability, as well as potential bias in healthcare records.</div> Keywords Keywords. heart stroke prediction, machine learning, Decision Tree Classifier (DTC), Random Forest Classifier (RFC), K-Nearest Neighbor (KNN), Logistic Regression (LR), Support Vector Machine (SVM), data, dataset data dataset Decision Tree Classifier (DTC) heart stroke prediction K-Nearest Neighbor (KNN) Logistic Regression (LR) machine learning master’s thesis Random Forest Classifier (RFC) Support Vector Machine (SVM) https://omeka.ibu.edu.ba/files/original/d696174f17483581e3f0bf5eb4829ddb.pdf 97cd18afae7fc35fc5001bd8a177156a Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource IT Master's Theses Description An account of the resource IT Master's Thesis collection features master's theses authored by graduate students in the Department of Information Technology. Each thesis reflects a significant research effort, combining theoretical knowledge with practical application to address complex challenges in the IT domain. These works demonstrate students’ advanced understanding of information systems, software engineering, data science, cybersecurity, and emerging technologies. The theses serve as a testament to the students' capability to conduct independent research, propose innovative solutions, and contribute to the advancement of the IT field. Subject The topic of the resource IT Master's Thesis Language A language of the resource English Language Author Author IT Department Master’s Students Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource ERP Project Failure Prediction Using Machine Learning Algorithms Author Author Ena Krvavac Abstract A summary of the resource. <div style="text-align:justify;">Enterprise Resource Planning (ERP) systems are of immense importance in simplifying business operations. However, most ERP projects fail owing to the complexity and scope of the projects. The present research attempted to determine the outcomes of ERP projects by employing machine learning methods and addressing factors which determine whether projects fail or succeed. This dissertation obtained data from different aspects of the projects that included successful and unsuccessful ERP deployments in terms of within which industry, project magnitude, the level of budget and time exceeding, background of team experience as well as technical challenges faced amongst others. The research includes machine learning methods such as logistic regression, decision trees, and random forests in order to assess the importance of the relevant predictors of any project. By training and testing these applications on a sample composed of both successful and non-successful ERP projects, the goal of the model is to seek for factors and patterns which could help in forecasting troubling tendencies. This research is aimed at devising a functional framework that can be used by project managers, enabling them to take action before issuing their project plans for ERP systems. Such a predictive model could significantly help in decreasing the rates of ERP failures and hence assist businesses in carrying out successful implementations and enhancing their returns on technology investment.</div> Keywords Keywords. ERP systems, machine learning algorithms, failure forecasting, success rate, project management ERP systems failure forecasting machine learning algorithms master’s thesis project management success rate https://omeka.ibu.edu.ba/files/original/28556f33ddae0dd5f66eae5b3f0e7736.pdf eefe08ca4abef6ae29451d58b69eec51 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource IT Master's Theses Description An account of the resource IT Master's Thesis collection features master's theses authored by graduate students in the Department of Information Technology. Each thesis reflects a significant research effort, combining theoretical knowledge with practical application to address complex challenges in the IT domain. These works demonstrate students’ advanced understanding of information systems, software engineering, data science, cybersecurity, and emerging technologies. The theses serve as a testament to the students' capability to conduct independent research, propose innovative solutions, and contribute to the advancement of the IT field. Subject The topic of the resource IT Master's Thesis Language A language of the resource English Language Author Author IT Department Master’s Students Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Real Time AI Music Generation System Based on Weather Conditions Author Author Nedim Karavdić Abstract A summary of the resource. The Generative Artificial Intelligence is gaining popularity every day and uses contextual data to personalize and enhance user experiences. The Thesis explores music generation that is conditioned on weather and it influences musical compositions by connecting MIDI music data with corresponding weather attributes, for example sunny and cloudy weather. In this thesis, three generative models are compared for the task of weather based music generation; Conditional Variational Autoencoder (cVAE), a Conditional Generative Adversarial Network (cGAN) and Long Short-Term Memory (LSTM) network. Mentioned models are implemented and trained on combination of large MIDI corpus with historical weather information. Performance of models in this paper are evaluated using metrics that capture musical diversity and quality, such as pitch range, unique pitches and pitch variance, and for fidelity to real data is measured by mean squared error and KL divergence. Results of the paper showed that the cVAE produced the most diverse music for music that is context sensitive. Building upon these findings, the thesis presents the architecture and functionality of a real time full stack application. This system acquires weather data, processes it through the cVAE and generates musical compositions. These compositions are then delivered and rendered through a web interface. This research shows the potential of combining environmental data with AI generated music and gives a framework for applications such as adaptive game soundtracks, mood-based music therapy, or dynamic background music systems that respond to the user's environment. Thesis also points out limitations of the models and gives future research directions in terms of hybrid architectures, richer environmental data representations, and user perception studies. Keywords Keywords. AI Music Generation, cVAE, cGAN, LSTM, MIDI AI Music Generation cGAN cVAE LSTM master’s thesis MIDI https://omeka.ibu.edu.ba/files/original/0019c00e368c84a90212a2e8d5aa8bf0.pdf 1bf003b378422d6682af5bbb401cae6b Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource IT Master's Theses Description An account of the resource IT Master's Thesis collection features master's theses authored by graduate students in the Department of Information Technology. Each thesis reflects a significant research effort, combining theoretical knowledge with practical application to address complex challenges in the IT domain. These works demonstrate students’ advanced understanding of information systems, software engineering, data science, cybersecurity, and emerging technologies. The theses serve as a testament to the students' capability to conduct independent research, propose innovative solutions, and contribute to the advancement of the IT field. Subject The topic of the resource IT Master's Thesis Language A language of the resource English Language Author Author IT Department Master’s Students Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Leveraging of Machine Learning for Early Cancer Risk Identification and Predictive Flagging Author Author Belmin Šestić Abstract A summary of the resource. Early detection of cancer remains a vital component in reducing mortality and enhancing treatment outcomes. Traditional diagnostic approaches, such as biopsies, imaging scans, and clinical assessments, often identify cancer at a stage where the disease has already advanced. This delay in detection arises because early-stage cancers typically exhibit minimal or no symptoms, increasing the risk of late diagnoses and reduced chances of recovery. This proposed study investigates the potential of machine learning methodologies in facilitating early cancer risk assessment by analyzing complex medical datasets. The primary objective is to assess whether machine learning models can reliably identify patients at heightened risk before the disease becomes clinically evident. Through this approach, the study aims to contribute to the development of predictive systems that can trigger early interventions and encourage proactive health monitoring. The research seeks to answer the core question: “Can machine learning models effectively assess the risk of early-stage cancer using molecular-level data, such as gene expression profiles, prior to the onset of clinical symptoms?” Sub-questions to be explored include the accuracy of early-stage cancer detection using machine learning, the types of data that most influence prediction performance, and the feasibility of using such models to prompt timely medical evaluations in the absence of traditional diagnostic markers. The findings are expected to support advancements in personalized medicine by laying the groundwork for tools that assist in identifying high-risk individuals, potentially transforming the current approach to cancer screening and prevention. Keywords Keywords. Machine Learning, Cancer Risk Assessment, Early Detection, Medical Data, Predictive Modeling Cancer Risk Assessment Early Detection machine learning master’s thesis Medical Data Predictive Modeling https://omeka.ibu.edu.ba/files/original/8d66acc444227b11b0604c66cff36946.pdf 6841d67bcbb7734abb69327017b5db0a Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource IT Master's Theses Description An account of the resource IT Master's Thesis collection features master's theses authored by graduate students in the Department of Information Technology. Each thesis reflects a significant research effort, combining theoretical knowledge with practical application to address complex challenges in the IT domain. These works demonstrate students’ advanced understanding of information systems, software engineering, data science, cybersecurity, and emerging technologies. The theses serve as a testament to the students' capability to conduct independent research, propose innovative solutions, and contribute to the advancement of the IT field. Subject The topic of the resource IT Master's Thesis Language A language of the resource English Language Author Author IT Department Master’s Students Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Comparison of Performanse and Security Aspects of Database Access via Stored Procedures and APIs Author Author Ramiz Šuvalija Abstract A summary of the resource. Modern applications typically get the information in one of two modalities, namely API as an intermediary layer or stored procedures in the same database. The aim of this study is to contrast these methods, mainly performance-wise, and then securitywise, as well as suitability for maintenance as well as scalability. The project will implement identical stored procedures in the PostgreSQL database, and a API backend in Python. Execution time for a query, resource consumption as well as susceptibility to security flaws will be evaluated. The plan is to perform 10 runs for each comparison so as to ensure the obtained results are as accurate as well as dependable as possible. And one of the aims is to devise practical recommendations as to when to apply a stored procedure, and when the API method, where a boundary (equilibrium) has to be drawn between the logic of intermixing in the same data as well as the logic in the app layer. Today with applications being used in distributed environments on a widespread basis, awareness of them is most important in ensuring smooth and effective development of information systems, particularly in those fields where a lot of information has to be processed, such as e-business, banks etc. Keywords Keywords. Stored Procedures, Api, Comparison, Database Access, Postgresql Api Comparison Database Access master’s thesis Postgresql Stored Procedures https://omeka.ibu.edu.ba/files/original/727d86ddfca78fa793fcb6c54b3b92f3.pdf 97116e206fdc2d31dff2cfc1ae5a3693 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource IT Master's Theses Description An account of the resource IT Master's Thesis collection features master's theses authored by graduate students in the Department of Information Technology. Each thesis reflects a significant research effort, combining theoretical knowledge with practical application to address complex challenges in the IT domain. These works demonstrate students’ advanced understanding of information systems, software engineering, data science, cybersecurity, and emerging technologies. The theses serve as a testament to the students' capability to conduct independent research, propose innovative solutions, and contribute to the advancement of the IT field. Subject The topic of the resource IT Master's Thesis Language A language of the resource English Language Author Author IT Department Master’s Students Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Tweet Categorization and Sentiment Analysis of Tweets Author Author Vedad Fejzagić Abstract A summary of the resource. In today’s era, using internet platforms to convey information to others, whether family, friends, or strangers has become the norm. One of the leading social platforms in that regard is “Twitter” (now “X”). The effectiveness of communication on such platforms can be analyzed through the process of sentiment analysis. Sentiment analysis is considered a classification problem that determines whether an input is positive or negative. The research aimed to show to what extent certain machine learning models outperform others for the given subset of data, depending on the choice of preprocessing steps within the sentiment analysis domain. This can be divided into two goals. The first goal was to present results on how one pipeline of preprocessing steps affects each machine learning model compared to the other preprocessing pipeline. The second goal was to present results on the viability of using several machine learning models for sentiment analysis of tweets by comparing the accuracies of each. For that purpose, a single subset was taken from the data and duplicated two times. Each subset duplicate had different preprocessing steps applied to it. Afterward, both subsets were fed to several machine-learning models in order to gauge their performance. Finally, this paper presented results on the aforementioned processes for which it was found that the Naïve Bayes machine learning model had the best accuracy, while the choice of preprocessing steps proved to be almost negligible in improving the overall model accuracy. Keywords Keywords. Sentiment Analysis, Machine Learning, Preprocessing, Natural Language Processing, Twitter machine learning master's thesis natural language processing preprocessing sentiment analysis twitter https://omeka.ibu.edu.ba/files/original/898d3390481715985e1899366de2077e.pdf c83f8b36a7dcda7b6bb84552da258b06 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource IT Master's Theses Description An account of the resource IT Master's Thesis collection features master's theses authored by graduate students in the Department of Information Technology. Each thesis reflects a significant research effort, combining theoretical knowledge with practical application to address complex challenges in the IT domain. These works demonstrate students’ advanced understanding of information systems, software engineering, data science, cybersecurity, and emerging technologies. The theses serve as a testament to the students' capability to conduct independent research, propose innovative solutions, and contribute to the advancement of the IT field. Subject The topic of the resource IT Master's Thesis Language A language of the resource English Language Author Author IT Department Master’s Students Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Optimization of Email Marketing Campaigns Leveraging Machine Learning Techniques Author Author Nejla Zukorlić Abstract A summary of the resource. Email marketing is widely recognized as an effective digital marketing channel, offering a considerable return on investment (ROI). One key challenge is determining the optimal day and time to send emails to maximize customer response rates. This thesis explores the application of machine learning (ML) algorithms to predict the best send times for email marketing campaigns, focusing on improving response rates. The research utilizes historical email marketing data, including customer demographics, response behavior, and email send dates. Based on this data, various machine learning models, including decision trees and random forests, as well as ensemble methods at the end, will be used to predict the optimal day for sending emails. The study will also examine how factors like customer age and tenure influence response rates at different times. The question is if the machine learning-based predictions of the optimal send day and time will significantly improve response rates compared to traditional methods. Also, incorporating demographic factors, such as age and tenure, hopefully will improve the accuracy of these predictions. The expected outcome is that MLbased optimization will outperform traditional scheduling methods, providing a more effective and data-driven strategy for email campaign timing. Keywords Keywords. Email Marketing, Machine Learning, Send-Time Optimization, Customer Engagement, Response Rates, Data-Driven Marketing Customer Engagement Data-Driven Marketing Email Marketing machine learning master's thesis Response Rates Send-Time Optimization https://omeka.ibu.edu.ba/files/original/1e77e2888b1af34ce1b04e8c6c8b1578.pdf 3102207937ea6ff2b0c16d894cdecc49 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource IT Master's Theses Description An account of the resource IT Master's Thesis collection features master's theses authored by graduate students in the Department of Information Technology. Each thesis reflects a significant research effort, combining theoretical knowledge with practical application to address complex challenges in the IT domain. These works demonstrate students’ advanced understanding of information systems, software engineering, data science, cybersecurity, and emerging technologies. The theses serve as a testament to the students' capability to conduct independent research, propose innovative solutions, and contribute to the advancement of the IT field. Subject The topic of the resource IT Master's Thesis Language A language of the resource English Language Author Author IT Department Master’s Students Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Comparative Analysis of Machine Learning Algorithms for Real Estate Price Prediction: Bosnia and Herzegovina vs. USA Author Author Adnan Krndžija Abstract A summary of the resource. Real estate markets are impacted by a variety of variables, including changes in the population, urban development projects, and changes in economic policy. This thesis sets out to investigate the effectiveness of machine learning algorithms in predicting real estate prices, paying close attention to the particular circumstances of Bosnia and Herzegovina as well as the United States. While the US real estate market has a long history and is well-known for its capacity to bounce back from downturns in the economy, the tale of the BiH real estate industry is very different. In contrast to the United States, which has seen centuries of economic expansion, financial crises, and legislative changes, Bosnia and Herzegovina's market development is a result of a combination of past influences and present difficulties. Beyond simple quantitative comparisons, our research takes a holistic method to uncover the predictive capability of machine learning models. We explore the complexities of random forests and decision trees, making use of their ability to reveal intricate patterns in real estate databases. This research also includes time series modeling to recognize and comprehend the evolving patterns that characterize real estate dynamics throughout time. The analysis of SARIMAX, ARIMA, and Holt-Winters time-series models shows ARIMA's consistent accuracy, while SARIMAX and Holt-Winters excel in stability and trend capture, respectively. In machine learning, Decision Trees offer interpretability, while Random Forests show reduced error rates and enhanced accuracy. In the US dataset, SARIMAX has a Mean Absolute Percentage Error (MAPE) of 3.35% and ARIMA achieves 1.66%, while Holt-Winters shows 3.54%. Decision Trees have a MAPE of 2.97%, and Random Forests achieve 2.10%. In the BiH dataset, SARIMAX has a MAPE of 5.08%, ARIMA achieves 1.22%, while Holt-Winters shows 2.17%. Decision Trees have a MAPE of 0.83%, and Random Forests achieve 0.82%. Keywords Keywords. Real Estate, Prediction, Bosnia and Herzegovina, United States Bosnia and Herzegovina master's thesis prediction Real Estate United States https://omeka.ibu.edu.ba/files/original/85f02200d8b6a059ec3861f417da6e72.pdf 1f82ffbbe02c17154c3399fe61ce48b1 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource IT Master's Theses Description An account of the resource IT Master's Thesis collection features master's theses authored by graduate students in the Department of Information Technology. Each thesis reflects a significant research effort, combining theoretical knowledge with practical application to address complex challenges in the IT domain. These works demonstrate students’ advanced understanding of information systems, software engineering, data science, cybersecurity, and emerging technologies. The theses serve as a testament to the students' capability to conduct independent research, propose innovative solutions, and contribute to the advancement of the IT field. Subject The topic of the resource IT Master's Thesis Language A language of the resource English Language Author Author IT Department Master’s Students Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Sentiment Analysis And Price Prediction For Accommodation Reviews in Bosnia And Herzegovina: A Comparative Study of NLTK and Hugging Face NLP Techniques Author Author Amila Čaušević Abstract A summary of the resource. The growing field of natural language processing (NLP) has huge potential in the advancement of consumer feedback and its application in determining pricing strategy in the hospitality industry. In this thesis, sentiment analysis and price predictions of accommodation reviews in Bosnia and Herzegovina are analyzed through a comparative study of two of the most commonly used approaches in NLP: NLTK - representing traditional methods, and Hugging Face - representing modern techniques. Initially, a long process of text preprocessing is performed that includes tokenization, lemmatization, stopword removal, and filtering of positive and negative reviews. Quantitative analysis such as word frequency distributions, measures of lexical diversity, and word co-occurrence tests reveal patterns within language use as well as the relationship between review attributes and sentiment. Different frameworks for sentiment analysis are then compared. The Hugging Face sentiment pipeline and more modern and recent transformer architectures like BERT, RoBERTa, and XLNet are compared with more traditional techniques (e.g., NLTK/VADER). Metrics for evaluation such as accuracy, precision, recall, and F1-score are used to assess the performance of the sentiment models. In order to develop predictive price models based on regression techniques like Linear Regression, Random Forest, and Gradient Boosting, the thesis additionally integrates sentiment scores with quantitative metadata, such as review ratings, location ratings, and accommodation categories. The results show that Random Forest regression is the most effective method for identifying subtle, non-linear sentiment-price correlations, even though transformer-based sentiment analysis can show promise in identifying subtle signals within guest reviews. Last but not least, this work offers helpful recommendations to help hoteliers in Bosnia and Herzegovina to create focused pricing strategies while also enhancing the general guest experience. Keywords Keywords. NLP, Sentiment Analysis, Accommodation Reviews, Price Prediction, Bosnia and Herzegovina Accommodation Reviews Bosnia and Herzegovina NLP Price Prediction sentiment analysis