This project investigated several learning algorithms to predict first year grades and awards based on input parameters of selected courses which have been cited in the literature as key performance indicators of Computer Science (CS) undergrad performances. The accuracy of predicting first year GPA, was determined by discrete and continuous classifications. Both multiclass and binary classifications were performed for the discrete predications. Naive Bayes, neural network, support vector machine and bagging returned the highest accuracy among discrete classifiers and support vector machine among continuous classifiers. Several learning algorithms were used in the experiments including aggregated methods. Also, the accuracy of the selection test grades in predicting the final class of awards for a bachelor’s degree was investigated for a smaller fourth dataset.

Social media comments have in the past had an instantaneous effect on stock markets. This paper investigates the sentiments expressed on the social media platform Twitter and their predictive impact on the Jamaica Stock Exchange. A hybrid predictive model of sentiment analysis and machine learning algorithms including decision trees, neural networks and support vector machines are used to predict the Jamaica Stock Exchange. The architecture created, SentAMaL, investigated the impact of sentiments on medical marijuana legalization on relevant stock indices. Due to the unstructured nature of tweets, a customized preprocessing routine was developed prior to determining sentiment and to perform the prediction. Experimental results show 87% accuracy in the movement prediction and 0.99 correlation coefficient for price prediction.