Supervisor
Dr. Muhammad Iqbal
Programme
BSc (Hons) in Computing in IT
Subject
Computer Science
Abstract
This project investigated the use of machine learning techniques to predict short-term Bitcoin price direction using historical market data obtained from Yahoo Finance. Following the CRISP-DM methodology, the dataset was analysed, prepared, and transformed through feature engineering techniques including moving averages, volatility indicators, return measures and price position metrics. Multiple classification algorithms were evaluated, including Bayesian Classification, K-Nearest Neighbour, Decision Tree, Random Forest, Logistic Regression, Support Vector Machine and XGBoost. Several optimisation strategies were also tested, including feature selection, hyperparameter tuning, feature scaling and class weight balancing. Results showed that predicting short-term Bitcoin price movements remains challenging, with most models achieving accuracy values close to 50%. XGBoost produced the most balanced predictions and achieved the best overall performance. The findings suggest that historical price data alone may not provide sufficient predictive power for reliable Bitcoin forecasting and that future improvements may require additional market, sentiment or macroeconomic features.
Date of Award
2026
Full Publication Date
2026
Access Rights
open access
Document Type
Undergraduate Project
Resource Type
bachelor thesis
Recommended Citation
Azevedo de Castro, C.
(2026) Bitcoin Prediction System Usind Machine Learning Techniques CCT College Dublin.
DOI: https://doi.org/10.63227/652.299.141