Data Science Commons^™

Predicting Heart Disease Using Machine Learning Models, Zeynep Cetin

Williams Honors College, Honors Research Projects

Heart disease remains the leading cause of death in the United States, particularly among the elderly population. The growing availability of large-scale health data and the advancement of machine learning tools present an opportunity to create more accurate and individualized predictive models. This study utilizes a subset of the 2020 Behavioral Risk Factor Surveillance System (BRFSS) dataset, focusing on individuals aged 70 and above, to explore predictive modeling using logistic regression, random forests, and XGBoost. The models were evaluated using key performance metrics, including sensitivity, specificity, accuracy, and the area under the ROC curve (AUC). The findings suggest that while …

Automated Classification Of Cardiac Anomalies Through The Analysis Of Electrocardiogram Signals Using Convolutional Neural Networks., Yhosely Isset Villegas Baldiviezo

ICT

Electrocardiography (ECG) is a widely used non-invasive method for monitoring cardiac activity and detecting heart abnormalities. However, manual interpretation can be time-consuming and prone to human error, particularly for non-specialists. This study investigates the use of deep learning techniques, specifically convolutional neural networks (CNNs), to automate ECG signal classification using a large-scale dataset. A classical machine learning model is also included as a baseline for comparison. The aim is to evaluate the potential of deep learning as a clinical decision-support tool that can assist healthcare professionals in improving diagnostic accuracy and supporting early detection of cardiac conditions.

Neural Networks Activation Functions And Hybrid Activations Functions Accuracy And Loss Comparison On Small Dataset Against Large Datasets For Classification Problems., Antonio Felipe Cora Martins

ICT

Even on the era of Big Data, small datasets are the reality of many companies and sectors. Many datasets in rare disease diagnosis, custom manufacturing, military sciences, bioengineering, and disaster events are commonly limited in size, making machine learning predictive modelling difficult. Being the Activation Function choice crucial for Neural Networks learning, it raises the question of their effectiveness in such scenarios. This study compares five standard (single) activation functions (Sigmoid, Tanh, ReLU, Leaky ReLU, ELU) and two hybrid variants (one a mix of ReLU plus Tanh and a Learnable Activation Function with a trainable weight (alpha) that balances ReLU …

European Air Pollution And The Proposed Timelines Of Implementing The World Health Organization 2021 Air Quality Guidelines Ca3., Lukia Hartin

ICT

This research examines Ireland’s air pollution trends and evaluates whether current reductions in PM2.5, PM10, and NO₂ are sufficient to meet the WHO 2021 Air Quality Guidelines by 2040. Using four years of EPA-validated pollutant data (2020–2023), alongside Building Energy Rating (BER) and national transport datasets, the study applies the CRISP-DM methodology to guide analysis, preprocessing, modelling, and evaluation. Extensive data cleaning and alignment were required due to inconsistent station coverage, varying formats, and missing values. Forecasting models—including Random Forest, Gradient Boosting, SVR, and linear regression—were assessed using MSE, R², and trend significance to project pollutant levels across different Irish …

Analysis Of The Patterns And Features Importance In Predicting Demand For Sustainable Urban Mobility. A Comparative Analysis Of Machine Learning Models Using The Dublin Bikes Dataset., Wojciech Sochanski

ICT

This study explores the application of machine learning models to predict demand in the Dublin bike-sharing scheme. A comprehensive literature review examines prior work on sustainable urban mobility, the evolution of bike-sharing schemes, the application of machine learning in predictive analytics and relevant case studies. Secondary data were gathered from publicly available sources, pre-processed and merged into a single, unified dataset. Exploratory data analysis was conducted before preparing the data for modelling to assess the data quality, with a focus on missing values.

Four predictive machine learning models – Linear Regression (LR), Decision Tree (CART), Random Forest (RF) and Gradient …

Optimizing Lstm Neural Network For Multimodal Multivariate Footfall Prediction., Aws Al Adhami

ICT

Accurate forecasting of pedestrian activity is important for smart city planning, retail analytics, and public service management. This study presents a forecasting framework using Long Short-Term Memory (LSTM) neural networks that integrates multiple data sources, including weather, calendar variables, and engineered temporal features. The model development follows a structured pipeline from baseline univariate models to a fully optimized multivariate model using feature engineering and hyperparameter tuning techniques such as Hyperband and Bayesian optimization. The final model achieved a Mean Absolute Error of 24.05 and explained 97.10% of the variance in unseen data, effectively capturing both regular patterns and short-term fluctuations …

Comparative Evaluation Of Ai-Generated Synthetic Data And Real-World Data Performance In Predictive Analytics, Corey Louise Hughes

ICT

There are growing restraints when it comes to Real World Data (RWD), these include topics such as privacy regulations, ethical concerns, and the cost of collecting the data, and they have drove an interest in AI-generated synthetic data as a potential alternative in predictive analytics. This project examines the possibilities of synthetic data and if it can act as a reliable substitute for RWD in predictive modelling. This project uses Wasserstein Generative Adversarial Networks with Gradient Penalty (WGAN-GP) to generate synthetic reproductive health data and evaluates its predictive performance against RWD using linear regression and key metrics, including Mean Absolute …

Customer Service Support. Utilizing Machine Learning To Classify, Prioritize And Summarize Issues., Hoai Nhan Nguyen

ICT

This capstone project investigates the application of machine learning and natural language processing (NLP) to enhance customer support operations through automated ticket classification, prioritization, and summarization. Using the multilingual Customer Support Emails dataset from Kaggle, the project follows the CRISP-DM methodology, performing extensive data cleaning, preprocessing, feature engineering, and class balancing. Five machine learning models—Decision Tree, KNN, LinearSVC, Naive Bayes, and Random Forest—were evaluated using hyperparameter tuning, cross-validation, confusion matrix analysis, and learning curves. LinearSVC demonstrated the strongest performance for both queue and priority classification, achieving accuracies of 89.8% and 81.2% respectively, with consistent generalization across folds. For summarization, extractive …

Comparison Of Statistical, Machine Learning, And Deep Learning Models For Time-Series Forecasting Using Weather Data From Dublin, Ireland., Erick Eduardo Rios Treviño

ICT

This study compares three time-series forecasting paradigms—statistical, machine learning, and deep learning—using ten-years of historical weather data from Dublin. The objective is to evaluate the performance of Prophet, XGBoost, and LSTM when forecasting daily solar radiation under multiple preprocessing strategies. An extensive data analysis was conducted, including descriptive statistics, inferential testing, stationarity assessment, outlier detection, feature selection, and exploratory visualisation. These steps revealed strong annual seasonality, nonlinear feature relationships, and variability across meteorological variables, informing the modelling framework and feature-engineering decisions. Four experiments were conducted using cleaned data, differenced data, log-transformed data, and cross-validation. Results show that XGBoost achieved the …

Enhancing Insider Threat Detection Through A Hybrid Approach Using Different Artificial Intelligence Techniques., Jose Roberto Da Silva Dure

ICT

Insider threats pose significant challenges in cybersecurity due to their origin from individuals with legitimate access. Traditional defenses often fail to detect malicious behavior embedded within normal activities. This study proposes a hybrid artificial intelligence framework that integrates unsupervised anomaly detection, supervised and ensemble learning, and deep learning to enhance insider threat detection. Using the CERT 4.1 dataset, features encompassing temporal, behavioral, network, and psychometric aspects were engineered. Anomaly detection models informed supervised and ensemble classifiers, while a multi-input deep learning architecture captured sequential and contextual patterns. Performance evaluation using ROC-AUC, precision, recall, F1-score, and cost-sensitive analysis demonstrates that the …

Time Series Forecasting In Financial Markets: Benchmarking The Temporal Fusion Transformer Against N-Beats., Fergus Fleury

ICT

This study compares the performance of two deep learning architectures, the Temporal Fusion Transformer (TFT) and N-BEATS, for 10-day stock price forecasting. Both models were implemented using the Darts Python library, which ensured consistent preprocessing, training, and evaluation. The dataset, sourced from Yahoo Finance, included daily equity prices, technical indicators, a market sentiment index, and earnings announcements.

TFT was applied as a multivariate model incorporating past, future, and static covariates, while N-BEATS was trained as separate univariate models with past covariates only. A rolling forecast cross-validation approach was used for evaluation. Results show that TFT consistently outperformed N-BEATS, particularly under …

Brain Tumor Classification Using Deep Learning- Poster, Rayen Bentemessek

ICT

The project presents deep learning solutions to classify brain tumors through MRI images. Two Convolutional Neural Network (CNN) models were developed, a custom CNN designed from scratch and a pretrained ResNet50 that was transfer learned and fine-tuned.

Both models were implemented following CRISP-DM methodology from data understanding to deployment, and they were evaluated using different metrics such as accuracy, precision, recall and F1-score.

Key Highlights: •The custom CNN model achieved higher accuracy but failed to locate tumors. •ResNet50 provided a good performance while balancing explainability through Grad-CAM. •Model was deployed through Gradio to demonstrate a real-world use of the solution.

Predicting Repeat Purchases In E-Commerce Using Interpretable Machine Learning, Zahid Bhatti

ICT

This project investigates the prediction of repeat purchase behaviour in e-commerce using machine learning, with a focus on balancing predictive accuracy and interpretability. Large volumes of transactional and behavioural data are analysed to identify customer-level features that drive loyalty and repeat purchases. Various supervised learning models, including Random Forests and Logistic Regression, are evaluated for predictive performance, while SHAP (SHapley Additive Explanations) is employed to provide both global and local interpretability. The study aims to generate actionable insights for customer relationship management and marketing strategy, demonstrating how advanced predictive models can support informed business decisions without sacrificing transparency.

The Actuarial Applications Of Machine Learning And Big Data In The Life Assurance Industry: Managing Customer Retention And Customer Outcomes By The Application Of Data Science., Brian Cunningham

ICT

Lapses are an issue in the insurance industry in general. They affect a company’s profitability, cash flows and solvency. High levels of lapses can cause reputational damage that could provoke a cycle of even more lapses. It is therefore incumbent on a company to do its utmost to retain the business it has written for the term it was written for.

If a company could predict which of its policies were about to lapse, it could proactively attempt to prevent them by contacting the policyholder and engaging in a discussion to ascertain the likelihood of their choosing to leave. In …

Assemble The Ensemble: A Multi Model Approach For Customer Churn Prediction In The Gambling Industry., Paul Corcoran

ICT

Churn rates are remarkably high in the gambling industry, an extremely competitive landscape coupled with a severe lack of brand loyalty among its customer base makes churn prediction one of the main problems an operator will face. This paper explores the range of possible modelling solutions with a key emphasis on ensemble learning to improve on existing methods. During this exploration, a host of modelling techniques are formulated with a focus on scalability facilitated by Apache Spark distributed computing language. Thirteen variations of models, including single classifiers and ensemble families are evaluated as to their suitability in solving the problem. …

A Comparative Analysis Of Machine Learning And Neural Network Performance In House Price Prediction: Dublin Vs. Other Irish Regions, Diarmuid Carroll

ICT

This research presents a comparative analysis of machine learning and neural network performance in predicting house prices across Ireland’s regional housing markets. It addresses important methodological challenges and offers new empirical insights into how market complexity influences algorithm accuracy and performance. Drawing on 627,294 residential transactions from the Irish Property Price Register (2012–2024), the study applies a dual validation strategy, temporal and stratified sampling, across four regional classifications: Dublin, Other Cities, the Commuter Belt, and Rural areas.

The study makes three main contributions to data analytics theory and practice. First, it identifies and resolves temporal confounding effects in algorithm evaluation. …

Predictive Analytics For Customer Churns In Financial Services., Thant Thiha

ICT

This project presents a customer churn prediction analysis in the telecommunications sector, achieving an ROC-AUC of approximately 0.86 using statistically validated features and interpretable AI models. Key churn drivers identified include the number of products held, customer age, and geographic location. Ensemble models, such as Random Forest and Gradient Boosting, provided the highest predictive performance. Ethical AI principles were applied to ensure fairness, transparency, privacy, and accountability. Business insights derived from the analysis inform targeted retention strategies, prioritising multi-product users, specific age groups, and geographic segments. Deployment recommendations include the tuned Random Forest model with ongoing monitoring, governance, and future …

Predicting Customer Churn And Enhancing Retention Strategies Through Machine Learning., Swan Saung Lwin

ICT

Customer Churn is a critical challenge faced by businesses across industries, especially in the digital market. Many companies struggle to predict customer churn accurately and have difficulties in carrying out effective retention strategies. Key challenges include ineffective traditional methods, lack of insights into impact of different services, generalized retention strategies, and the need to have cost-effective retention strategies. This project aims to predict customer churn using machine learning and identify the impact of key services offered by a telecommunication company.

Customer Response To Marketing Campaigns., Alexandru Enoiu

ICT

This project applies machine learning to predict customer responses to marketing campaigns, aiming to enhance customer engagement and optimise resource allocation. Using a dataset containing demographic, lifestyle, and purchase behaviour data, a Random Forest classifier was developed to identify potential responders to marketing initiatives. The project follows a structured methodology including data exploration, preprocessing, model building, and evaluation. By accurately predicting customer behaviour, businesses can improve campaign targeting, design personalised marketing strategies, and allocate resources more efficiently. The results provide actionable insights to support customer segmentation and strategic decision-making, promoting business growth and customer satisfaction.

Neural Networks Activation Functions And Hybrid Activations Functions Accuracy And Loss Comparison On Small Dataset Against Large Datasets For Classification Problems, Antonio Felipe Cora Martins

ICT

Even on the era of Big Data, small datasets are the reality of many companies and sectors. Many datasets in rare disease diagnosis, custom manufacturing, military sciences, bioengineering, and disaster events are commonly limited in size, making machine learning predictive modelling difficult. Being the Activation Function choice crucial for Neural Networks learning, it raises the question of their effectiveness in such scenarios. This study compares five standard (single) activation functions (Sigmoid, Tanh, ReLU, Leaky ReLU, ELU) and two hybrid variants (one a mix of ReLU plus Tanh and a Learnable Activation Function with a trainable weight (alpha) that balances ReLU …