A Novel Method for Automatic Payment of Car Parking Charges with Random Forest and Comparison over Naïve-Bayes Classification

The focus of the article is to develop a new RandomForest technique and compare it to the Naïve Bayes Algorithm to produce an automatic reduction in amount for car parking rental and reduce excess of waiting time. No. of two groups were chosen for the investigation, with sample sizes of 10 for each group. Random Forest is in group 1, and NaiveBayes is in group 2. The new RandomForest method and the naive Bayes algorithm are introduced for thorough experimentation. The two sample sizes were tested using the 452 rows of data in the Smart Parking System dataset, which included title time and p-class label variables. In comparison with the NaiveBayes algorithm, which has an accuracy rate of 70.66%, the new RandomForest approach has a slightly better accuracy of 74.10%. 0.001 (p<0.05) and indicates that there is no statistical significance between these groups. Statistically significant disparities between these groups are obviously present. While the comparison the two algorithms, the Novel RandomForest classification is much accurate than Naïve-Bayes Algorithm.