Welcome to the Data Science and Data Mining collection! This collection showcases the projects and assignments of students enrolled in the Data Science and Data Mining courses at the University of Central Florida. The collection covers topics such as data preprocessing, data visualization, statistical data analysis, data mining algorithms, machine learning, big data analytics and more. The students apply their skills and knowledge to various real-world datasets from different domains such as health, education, psychology, sports, social media and more. The collection aims to highlight the diversity and creativity of data science and data mining applications.
For more information, please contact Dr. Rui Xie.

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Submissions from 2024

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Advancing Cancer Classifcation through Machine Learning Analysis of RNA-Seq Gene Expression Data, Emil Agbemade, Amina Issoufou Anaroua, and Dimitri Bamba

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Predicting Road Accident Injury Severity for Drivers in Automobile Crashes in United States Using Machine Learning Models and AI, Emil Agbemade and Benedict Kongyir

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Combating Cyberbullying on Social Media: A Machine Learning Approach with Text Analysis on Twitter, Amir Alipour Yengejeh

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Optimizing AI with Advanced Data Structuring: A Comparative Analysis of K-means and GMM Clustering Techniques, Amir Alipour Yengejeh

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XGBoost Hyperberd Model Using Steam Platform, Yuh-Haur Chen

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Cyberbullying Detection on Twitter Data Using Machine Learning Classifiers, Pradip Dhakal

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Machine Learning Approaches for Cyberbullying Detection, Roland Fiagbe

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Predicting Superconducting Critical Temperature Using Regression Analysis, Roland Fiagbe

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Diagnostic in Neuroimaging: A Comparative Study of Deep Learning and Traditional Approaches, Amina Issoufou Anaroua

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Bootstrap Regression for Investigating Macroeconomics Factors Affecting USA Home Prices, Benedict Kongyir and Emil Agbemade

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Predicting Telecommunication Customer Attrition Using the Hopfeld Neural Network Model., Benedict Kongyir, Emil Agbemade, and Kelvin Njuki

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Modeling Health Insurance Premium Using Bayesian Hierarchical Models, Bennedict Kongyir and Emil Agbemade

Submissions from 2023

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Developing a Data-Driven Statistical Model for Accurately Predicting the Superconducting Critical Temperature of Materials using Multiple Regression and Gradient-Boosted Methods, Emil Agbemade

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Predicting Heart Disease using Tree-based Model, Emil Agbemade

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Silent Agony: Automated Detection of Ethnic and Religious Cyberbullying Using Machine Learning, Emil Agbemade

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Variable Selection and Regression Analysis, Emil Agbemade

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Analyzing the Impact of Health, Economic, and Demographic Factors on Life Expectancy: A Comparative Study of Developed and Developing Countries, Mahyar Alinejad

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A Linear Regression Model to Predict the Critical Temperature of a Superconductor, Amir Alipour Yengejeh

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Analysis of Credit Approval by Decision Tree, Amir Alipour Yengejeh

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A Recommender System for Movie Ratings with Matrix Factorization Algorithm, Amir Alipour Yengejeh

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Genome-Wide Association Study of The Maize Crop by The Lasso Regression Analysis, Amir Alipour Yengejeh

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Machine Learning-based Approaches for Predicting the Critical Temperature of Superconductor, Pradip Dhakal

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Variable Selection Using Lasso and Elastic Net Regression on High Dimensional Genetic Architecture Data of Maize Flowering Time, Pradip Dhakal

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Classification of Adult Income Using Decision Tree, Roland Fiagbe

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Linear Regression with Regularization on the Genetic Architecture of Maize Flowering Time, Roland Fiagbe

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Movie Recommender System Using Matrix Factorization, Roland Fiagbe