This is one project is one I really enjoyed. It is based off of my “Introduction to Machine Learning” class. The goal was to be able to predict whether a review was good or bad using data from three domains: imdb.com, amazon.com, and yelp.com. This data was obtained from the paper by D Kotzias, M Denil, N De Freitas, P Smyth (2005) which was presented at the KDD ’15: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data . Here, I have used 2400 data samples of one-sentence reviews and their corresponding labels whether positive or negative (positive = 1, negative = 0) which will be split into training and testing sets. More details on this project can be found here
Critical coning rate determination as a measure of preventing coning in oil fields with underlying aquifers and overlying gas caps are a fundamental aspect of production planning in field development plans. This has always been carried out using existing correlations having large error margins. This study is aimed at developing a more accurate prediction model based on artificial neural network. The resulting paper was presented at the SPE Nigeria Annual International Conference and Exhibition, July 2017 and published in a top Petroleum Engineering Journal.