Result: A Standardized Threshold Approach for Outlier Detection Using Python Algorithms

This study aims to standardise threshold techniques and dataset preprocessing steps to improve outlier detection using Python-based algorithms. The objective of the study is to get results that are more precise in the algorithm for most types of datasets. The methodology involved using samples datasets and testing the results when using the normal thresholds of the python outliers detection algorithm, and comparing that results with the results have been done by using the generalize threshold which is mean-median. The results obtained from the supervised results showed that when standardising the threshold using the formula (Mean-Med) produced more precise and more generalised outcomes. The study also applied algorithms that use quartiles (Q1, Median, Q3) and found that adjusting the first quartile to 15% instead of the standard 25% helped to better isolate outliers in the lower range. Similarly, modifying the third quartile threshold to 80% instead of 75% provided more effective detection of upper-range outliers. More precise and more generalize results were obtained when using the formula in the python algorithms use threshold or normal thresholds, which are 0.1 to 2.5 as datasets threshold, compare to use the formula (Mean-Med). The study used some sample datasets for analysis and indicated the potential for applying the method to many other unsupervised datasets in future research.