Machine learning projects
Time series forecasting: Analysis of the Global Energy Forecasting Competition “A wind power forecasting problem: predicting hourly power generation up to 48 hours ahead at 7 wind farms.” Prediction of energy based on locally weighted regression and autoregressive integrated moving average (ARIMA) models in R.
Music classification: Predict song genres from a model of their lyrics using the Million Song musiXmatch dataset. Utilized several machine learning classifiers such as random forest and logistic regression from the scikit-learn library in Python.
Social network analysis: Detection of fraudulent reviewers that skew product ratings in Amazon. Modified a graph algorithm for the “Fine Foods” dataset to compute user trust, item reliability and review honesty via iterative updates in Python.