A innovative integrated system to detect online sales customer allegiance and improve mining performance using apriori based method comparing with reduction

The purpose of this Innovated apriori based mining effort is to strengthen the fidelity of customer identification. Comparisons are made between reduction algorithms and innovative apriori-based mining algorithms for categorising robust integrated detection. Fifty thousand records from the kaggle dataset were used in the research. The G power calculator yields a sample size of 10 individuals per group. There are twenty samples in all. Power before the test is 80%, and the confidence interval is 95%. The Innovated Apriori algorithm has a 95.06 percent accuracy rate in predicting online sales client loyalty, while the Reduction algorithm fares just slightly worse at 89.05 percent. The level of significance is 0.003 (p 0.05, two-tailed). The proposed approach, the Innovated Apriori Based Method, outperforms the reduction algorithm in terms of accuracy on several datasets and has received greater praise from reviewers.