Projects

Comparison of a Logistic Regression, Support Vector Machine and Neural Network model for sentiment analysis using both Bag-of-words and Word Embeddings for feature extraction

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

Application of Artificial Neural Networks in Predicting Critical Rates for Vertical Wells in Oil Rim Reservoirs

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.