Performance Analysis for Prediction of Trending Footwear Classification using DensNet compared with Logistic Regression model

The purpose of this work is to investigate the effectiveness of DensNet and Logistic Regression in terms of accurately predicting the classification of footwear trends.In this study, there are two groups: the Novel DensNet model and the Logistic Regression model. Each group is being iterated for a sample size of ten, which is being generated with the ClinCalc software. The alpha value is set at 0.05, and the confidence interval is set at 95%. We took into consideration the footwear prediction dataset that was available on Kaggle.com and had a size of 15854. The statistical analysis reveals a significant difference between the DensNet algorithm and the Logistic Regression model, with a value of p=0.001 (independent sample t-test p<0.05). The DensNet algorithm exhibits a significantly higher accuracy rate of 94.56% when compared to the Logistic Regression model, which records an accuracy rate of 90.62%.In comparison to the Logistic Regression model, the DensNet algorithm has a higher level of accuracy when it comes to predicting the Novel Footwear Trending situation.