A Comparative Analysis of Classification Algorithms for Predictive Data Analytics
Keywords:
Predictive Data Analytics, Classification Algorithms, Decision Trees, Naïve Bayes, Support Vector Machines, Logistic Regression, Artificial Neural Networks, Random Forests, Machine Learning, Data MiningAbstract
Predictive data analytics has become an essential component of modern decision-making, enabling organizations to identify patterns, forecast outcomes, and develop data-driven strategies. Classification algorithms play a significant role in predictive analytics by categorizing data into predefined classes based on historical observations and identified relationships. This study presents a comparative analysis of major classification algorithms, including Decision Trees, Naïve Bayes, K-Nearest Neighbors, Logistic Regression, Support Vector Machines, Artificial Neural Networks, and Random Forests. The analysis examines their underlying principles, predictive capabilities, computational requirements, interpretability, scalability, and suitability for different datasets. Particular attention is given to classification accuracy, precision, recall, F-measure, computational efficiency, and model generalization. Existing research published between 1990 and 2010 provides the theoretical foundation for evaluating the strengths and limitations of these techniques. The discussion highlights that no single classification algorithm consistently performs best across all analytical situations because performance depends on dataset characteristics, feature selection, sample size, class distribution, and preprocessing techniques. The study emphasizes the importance of selecting classification methods according to analytical objectives and application requirements. It concludes that systematic algorithm comparison, appropriate validation, and hybrid classification approaches can improve the reliability and effectiveness of predictive data analytics
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