Constructed adjacency lists describing the music influence network topology of musicians, establishing relationships between influencers and followers through weighted directed paths. Computed the music influence value for each musician using DFS recursion
Combined graph networks with mathematical modeling to calculate musician similarity using cosine similarity and music feature influence using Pearson correlation coefficient
New York City Taxi Fare Analysis & Music Preference Analysis
Peter the Great St.Petersburg Polytechnic University Big Data & Machine Learning Project
Employed SVM, XGBoost, deep neural networks, and more for data analysis
Long-term wind prediction in wind farm areas based on machine learning
Graduation Project at BUAA
Implemented wind speed long-term forecasting using a deep neural network based on LSTM