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Usefulness of Vaccine-Adverse Event-Reporting System (VAERS) data and protocols required for statistical analyses were pinpointed with a set of recommendations for the application of machine learning modeling or exploratory analyses on VAERS data with a case study of COVID-19 vaccines (Pfizer-BioNTech, Moderna, Janssen). A total of 262,454 duplicate reports (29%) from 905,976 reports were identified, which were merged into a total of 643,522 distinct reports. A customized online survey was also conducted providing 211 reports. A total of 20 highest reported adverse events were first identified. Differences in results after applying various machine learning algorithms (association rule mining, self-organizing maps, hierarchical clustering, bipartite graphs) on VAERS data were noticed. Moderna reports showed <i>injection-site</i>-related AEs of higher frequencies by 15.2%, consistent with the online survey (12% higher reporting rate for <i>pain in the muscle</i> for Moderna compared to Pfizer-BioNTech). AEs {<i>headache</i>, <i>pyrexia</i>, <i>fatigue</i>, <i>chills</i>, <i>pain</i>, <i>dizziness</i>} constituted >50% of the total reports. <i>Chest pain</i> in male children reports was 295% higher than in female children reports. <i>Penicillin</i> and <i>sulfa</i> were of the highest frequencies (22%, and 19%, respectively). Analysis of uncleaned VAERS data demonstrated major differences from the above (7% variations). Spelling/grammatical mistakes in allergies were discovered (e.g., ~14% reports with incorrect spellings for <i>penicillin</i>).