This is a research project where we designed several algorithms for volume-weighted-average-price (VWAP) prediction. Our dataset consisted of millisecond-level limit-order books for multiple stocks. Random forest and logistic regression were used for VWAP direction classification (up or down), while PCA and random forest were used for feature selection. Least absolute shrinkage and selection operator (LASSO) regression was performed to predict real VWAP value. We also used a long short-term memory (LSTM) recurrent neural network to predict real VWAP value. See paper.pdf for more details.