<![CDATA[This study explores the use of the C4.5 decision tree algorithm on the Play Tennis dataset through two approaches: manual calculations and a Python-based program. As an improved version of the ID3 algorithm, C4.5 is capable of managing both categorical and numerical inputs, dealing with missing data, and utilizing entropy and information gain to determine the most important features. The dataset contains 14 entries with attributes such as Outlook, Temperature, Humidity, Windy, and the target variable PlayTennis. Entropy and information gain were calculated manually to construct the decision tree in a step-by-step manner. The resulting tree was then compared with one generated using Python tools like Pandas, NumPy, and Scikit-learn. Both trees were identical, confirming the accuracy of the method. A comparison with previous research highlights the flexibility and clarity of decision tree algorithms, making them suitable for various fields such as healthcare, finance, privacy-conscious machine learning, and materials science. These findings support the real-world usefulness of such algorithms. Overall, the study finds that C4.5 is highly effective for small classification problems and shows promise for use in larger, more complex datasets. Additionally, this research supports deeper learning of how decision tree algorithms work, making it a helpful reference for both educational and applied data science contexts.]]>
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