Worked Projects

Tripper - the tour guide of the world 🌍

👯‍♂️Connecting global travellers with local guides | 🌍one-stop solution to explore different parts of the world | 🤖AI personalized travel plan | 👋 social interactions

🔧 技术栈 Tech Stack

Front End: React, JavaScript, HTML, CSS | Back End: FastAPI, Python, PostgreSQL, Docker | Webpage Design: Figma | Other Technologies: REST APIs, OAuth 2.0

Project Overview Video

For detailed information, please view the provided Materials

📄Detailed report for Tripper (PDF)

For detailed Wireframe diagrams, please view the provided Materials

📄Figma_Wireframe for Tripper (PDF)

🔑Key Functions


🌐Third-Party APIs

Bitcoin Price Prediction

This study aims to create an accurate sequence prediction model to forecast Bitcoin price trends. The publicly available dataset is from Kaggle, covering Bitcoin price data from May 5, 2013, to October 23, 2022.

Data Processing and Feature Engineering

Data Processing: Cleaned data, handled missing values and anomalies, converted timestamps to datetime format.

Feature Engineering: Calculated VWAP, selected monthly data to reduce noise, performed stationarity tests, and STL decomposition.

Model Development

LightGBM: Improved training efficiency and prediction accuracy using histogram optimization and leaf-wise growth strategy.

XGBoost: Achieved high accuracy predictions by combining gradient boosting and regularization.

Transformer: Processed complex sequences efficiently through self-attention mechanisms and parallel processing.

Prophet: Modeled seasonality and trends in time series data.

LSTM: Captured long-term dependencies in time series data using long short-term memory networks.

SVM: Implemented classification and regression predictions through kernel functions and support vectors.

Model Comparison

Deep Learning Models: Performed best, effectively capturing long-term dependencies.

Seasonal Models: Handled seasonality and trends well.

Traditional Machine Learning Models: Struggled with long-term dependencies.

🔧 Tech Stack

Data Processing: Python, Pandas

Feature Engineering: VWAP, STL Decomposition

Machine Learning Models: ARIMA, LightGBM, XGBoost, SVM

Deep Learning Models: Transformer, LSTM

Time Series Models: Prophet

Libraries: scikit-learn, statsmodels, lightgbm, xgboost, pytorch, fbprophet

Evaluation Metrics: MSE, RMSE, MAE

Comprehensive Detailed Design& Research for a 22-story Podium Single Tower 🏢 Structure

🔧 Tech Stack

Structural Design: SAP2000, ETABS | Architectural Design: AutoCAD, Revit | Analysis: MATLAB, Python, Excel | Rendering: Twinmotion, Lumion | Documentation: Microsoft Office, LaTeX

Project Overview Video

🖼️ Rendering Pictures

Technical Drawings

📄View Technical Drawings (PDF)

📊 Detailed calculation report

📑View Detailed Calculation Report (PDF)

Final Design Report

📑View Final Design Report (PDF)

🏢Sustainable Design and Analysis of a Nine-Story Steel Frame Office Building

🔧 Tech Stack

Structural Design: SAP2000, Autodesk Revit | Architectural Design: AutoCAD | Analysis: Python, MATLAB, Excel | Rendering: Twinmotion | Documentation: Microsoft Office, LaTeX

Steel Structure Detailed Design

📄View Steel Structure Detailed Design (PDF)

Analysis of Hexagonal and Triangular Lattice Structures in MATLAB

Still a lot.......I will list when I got time