Abstract

The Decision Trees are always simple to understand and interpret techniques, a single tree may not be enough for the model to learn the characteristics from. In this IMDb movie review prediction problem, evading all other simple mechanisms, the Random Forest, on the other hand, is a "Tree"-based algorithm that makes judgments by combining the attributes of numerous Decision Trees is used. The primary goal of this work is to evaluate the predictive performance of a random forest model with various parameters used for forecasting numerical user ratings of a movie based on pre-release data such as actors, directors, profit, social media reviews, and movie genres. Although a slight difference has been indicated by the results of the two variant models, one should also note that both these models show great similarities in terms of their prediction performance, making it hard to draw any general conclusions on which model yields the most accurate movie predictions.

Library of Congress Subject Headings

Motion pictures--Reviews--Forecasting; Natural language processing (Computer science); Machine learning

Publication Date

8-2025

Document Type

Thesis

Student Type

Graduate

Degree Name

Professional Studies (MS)

Department, Program, or Center

Graduate Programs & Research

Advisor

Sanjay Modak

Advisor/Committee Member

Ehsan Warriach

Campus

RIT Dubai

Plan Codes

PROFST-MS

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