Unique Approach of Movie Rating Prediction Using Support Vector Machine Algorithm and K-Nearest Neighbors Algorithm

Abstract

The aim is to predict movie rating using Support Vector Machine Algorithm and K-Nearest Neighbors Algorithm. An aggregate of 392 examples were gathered from film datasets and these datasets were taken fromkaggle dataset. Two experimental calculations were performed, one with K-Nearest Neighbors algorithm and another with Novel Support Vector Machine algorithm. Sample size of N=5 is taken for both algorithms. The computation processes were executed and verified for exactness. SPSS was used for predicting significance value of the dataset considering G-Power value as 80%. Novel Support Vector Machine calculation was applied and it had accomplished mean accuracy of 91.8% when compared with mean accuracy of 53% of K-Nearest Neighbors algorithm. The outcomes were obtained with a significance value of 0.03 (p<0.05). A unique approach model is affirmed to have higher exactness than K-Nearest Neighbors calculation.