Background: The increase in the prevalence of Autism Spectrum Disorder (ASD) and the complications related to effectively diagnosing the disorder has led to an urgent need for the development of easy implementing and effective screening methods. Problem: Diagnosing ASD is often a lengthy process that requires extremely careful assessment by clinical experts for an accurate diagnosis. This results in lengthy waiting times and high cost for an ASD diagnosis. Although ASD can be a life-long disorder, early treatments and education can greatly improve a person__ampersandsignrsquo;s symptoms and ability to function. Hence, fast and effective screening methods that are easily accessible is imperative to help health professionals and inform individuals whether they require a formal clinical diagnosis for ASD. Purpose: Thus, this study focused on analysing a dataset from an ASD screening tool, to meticulously process the raw data and derive meaningful insights from it. This study made use of a dataset that contained information about adolescent from age 12 to 16. The steps involved from the pre-processing of the data to analysing the data to deriving meaningful insights from the data are discussed in detail. Findings from the data are critically discussed and suggestions for improvement are also provided. Conclusion: In a nutshell, this study aims to serve as a complete guide to effectively manage, from raw data to derive meaningful insights, regarding ASD screening.