We present a machine learning predictor for academic results datasets (PARD), for missing academic results based on chi-squared expected calculation, positional clustering, progressive approximation of relative residuals, and positional averages of the data in a sampled population. Academic results datasets are data originating from academic institutions’ results repositories. It is a technique designed specifically for predicting missing academic results. Since the whole essence of data mining is to elicit useful information and gain knowledge-driven insights into datasets, PARD positions data explorer at this advantageous perspective. PARD promises to solve missing academic results dataset problems more quickly over and above what currently obtains in literatures. The predictor was implemented using Python, and the results obtained show that it is admissible in a minimum of up to 93.6 average percent accurate predictions of the sampled cases. The results demonstrate that PARD shows a tendency toward greater precision in providing the better solution to the problems of predictions of missing academic results datasets in universities.