Abstract: Over the last few decades, social media platforms have gained a lot of popularity. People of all ages, gender, and areas of life have their presence on at least one of the social platforms. The data that is generated on these platforms has been and is being used for better recommendations, marketing activities, forecasting, and predictions. Considering predictions, the movie industry worldwide produces a large number of movies per year. The success of these movies depends on various factors like budget, director, actor, etc. However, it has become a trend to predict the rating of the movie based on the data collected from social media related to the movie. This will help a number of businesses relying on the movie industry in making promotional and marketing decisions. In this report, the aim is to collect movie data from IMDB and its social media data from YouTube and Wikipedia and compare the performance of two machine learning algorithms – Random Forest and XGBoost – best known for their high accuracy with small datasets, but large feature set. The collection of data is done from multiple sources or APIs.

Zahabiya Mhowwala, A. Razia Sulthana and Sujala D. Shetty, “Movie Rating Prediction using Ensemble Learning Algorithms” International Journal of Advanced Computer Science and Applications(IJACSA), 11(8), 2020. http://dx.doi.org/10.14569/IJACSA.2020.0110849

@article{Mhowwala2020,
title = {Movie Rating Prediction using Ensemble Learning Algorithms},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2020.0110849},
url = {http://dx.doi.org/10.14569/IJACSA.2020.0110849},
year = {2020},
publisher = {The Science and Information Organization},
volume = {11},
number = {8},
author = {Zahabiya Mhowwala and A. Razia Sulthana and Sujala D. Shetty}
}