Data-Driven Information Technology Systems: Evaluating Machine Learning Algorithms on the Iris Dataset
DOI:
https://doi.org/10.29304/jqcsm.2026.18.22780Keywords:
Naive Bayes, Machine learning, Classification, Decision Trees, Iris dataset and SVMAbstract
Machine learning is the core of the present-day Information Technology (IT) systems. It allows processing the data intelligently and allowing the decision making to be automated. The present study uses four classification algorithms, namely Naive Bayes, Decision Trees, Support Vector Machines (SVM) and a Deep Learning model that is built upon an Artificial Neural Network (ANN), as a benchmark to run on the Iris dataset. The experiment replicates a classic IT setting where data has been preprocessed, divided into training and testing data, and it has been utilized to create predictive models. The experimental findings prove the fact that both Decision Trees and SVM have 100 percent accuracy, which proves their suitability as the means of working with structured data. Naive Bayes had an accuracy of 98% and it is a fast and computationally efficient problem solver that is applicable in real time IT applications. ANN model has an 97% accuracy which shows that it is capable of representing complex patterns, but this is affected by the fact that the dataset is relatively small. These results demonstrate why proper choice of machine learning methods is important in developing intelligent IT systems, including decision support system, data analytics platform, and automated classification applications. All in all, this paper provides a viable basis to implementing machine learning algorithms into the modern information systems and improving efficiency, scalability, and decision-making processes.
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Copyright (c) 2026 Maha Khalil Ibrahim
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