Application of supervised learning and deep learning methods to predict car resale price • Positioned among the top 10 out of 86 teams in the Kaggle competition by employing machine learning models including random forest, gradient boosting, and neural networks to forecast the price of used cars based on Python. • Built a recommender system that allows users to select a car based on browsing habits and personal preferences. • Investigated the correlation between the cost of a new car and its rate of price depreciation using linear regression techniques.

Note-taking web app with back&front-end - Establish accessible and responsive websites with modern JavaScript framework React.JS, HTML, and CSS. - Develop object-oriented code in NodeJS in accordance with industry standards. - Design and implement interfaces with creating, editing, and deleting functionalities. - Create the user experience that fetches, parses, and formats data, and relays it into React components.

Implementing an iOS App using Xcode • Implement an iOS app with map features that allow users to search for up to 10 locations within a selected radius. • Design UI / UX of the mobile app to translate conceptual design into the user interface on iPhone. • Integrate APIs such as MapKit and UIKit to provide functions such as showing geographical locations and adding/deleting input fields. • Incorporate Firebase cloud-hosted database for storing data, allowing user registration and login.