Computational Analysis of Genre Effects on Movie Ratings Using MLP Algorithms

Abstract

Predictive analytics are what the entertainment industry depends on so much in prognosticating movie ratings, which is why they inform filmmakers, distributors and stream platforms strategic moves. Traditional prediction models most times only succeed in missing out on the intricate dynamics that are genre-specific to movie ratings thereby leading to inaccuracies and suboptimal decision making. The paper proposes this research presents a comprehensive mechanism that combines extensive metadata with Multi-Layer Perceptron (MLP) models to increase the accuracy of predictions across multiple cinematic genres. Therefore, we had a goal of establishing fine patterns due to MLP regressors based on specific genres as well as addressing limitations linked to traditional approaches. To conduct this study; we used principal component analysis and one-hot encoding for 950 films followed by genre-specific modeling alongside statistical tests such as ANOVA, t-tests, Gradient Boosting Classifiers among others for model validation. They found that adventure movies were more predictable than other genres (MSE = 0.023) such as action (MSE = 2.816). It’s clear then that accurate modelling requires an examination by gender and broad data sources integration. The research emphasizes the potential of improved machine learning methods to change predictive modeling in the area of art. Further work will seek to develop more accurate feature selection, deal with data imbalance and incorporate real-time audience engagement measures into the optimization process for better predictions that would help film makers make better strategic decisions.