Duration: Apr 2025 - Jun 2025.

Description: This project aims to compare traditional, machine learning, and deep learning models for short-term Bitcoin price forecasting. 

Technologies: Python, Machine Learning, Deep Learning.

Duration: Sep 2024 - Dec 2024.

Description: Developed and analyzed a data warehouse for credit card transactions, implementing ETL, designing schemas, building OLAP cubes, and performing multidimensional data analysis. Conducted data visualization for decision-support reporting and applied machine learning to extract valuable insights. 

Technologies: Microsoft SQL Server, Visual Studio, SSIS, SSAS, MDX, Excel Pivot, Power BI, Looker Studio, Python, Machine Learning. 

Duration: Nov 2024 - Jan 2025.

Description: This project aims to build an e-commerce web application to sell plants, bringing green and fresh space to the house as well as the living environment of customers.

Technologies: Laravel, PHP, HTML, CSS, Javascript, jQuery, Boostrap, MySQL, XAMPP, PhpMyAdmin.

Duration: Apr 2024 - Jun 2024.

Description: This is my very first project with team members at university. The project aims to build a strong connection platform in the UIT community, which UIT users and stakeholders can fully and quickly grasp media posts about events and competitions,... takes place in UIT. In addition, this platform also allows users to post and exchange information on a variety of topics such as learning info, jobs, scholarships, scientific researchs,... The noble goal of this project is to bring the best experiences to UIT users. 

Technologies: Java, PL/SQL, Oracle, NetBeans.

Duration: Sep 2024 - Dec 2024.

Description: This project falls under the field of Computer Vision and Image Processing, utilizing the MobileNetV2 model for the tomato leaf disease classification task. Through this, it demonstrates the superiority of MobileNetV2, which is highly suitable for low-end devices and mobile devices due to its lightweight architecture, while still achieving high accuracy comparable to other modern CNN-based models.

Technologies: Python, Tensorflow, Machine Learning, Deep Learning, Computer Vision.

Duration: Sep 2024 - Dec 2024.

Description: This project aims to explore and experiment with attack techniques based on gradient leakage and implement defense techniques to mitigate and prevent such attacks. The goal is to enhance the security of machine learning, protecting it from attacks that could compromise sensitive and private information in the training dataset. 

Technologies: Python, PyTorch, Machine Learning, Deep Learning, Computer Vision.