Stock market forecasting, cryptocurrency forecasting or a recommendation engine for films which can use the viewers comments, film critics, IMDB ratings and the watched films by the user to suggest similar films. This recommendation engine for films can also be applied for books or songs. In both forecasting and recommendation engine, deep learning techniques will be used. In addition to that, some classification algorithms such as XGBoost, SVM and Random Forest will be applied to the models as well. In forecasting models, sentiment analysis of comments or newspaper articles can be useful. In recommendation engine, K-means clustering can be used to create the clusters of similar movies. The performance evaluation of ML algorithms will be observed during modelling process.To sum up, large data sets will be analyzed and a set of different machine learning algorithms will be applied to make a precise model for the problem.
Faculty Department of Project Supervisor:
Faculty of Engineering and Natural Sciences
Number of Students:
Related Areas of Project:
Computer Science and Engineering