<![CDATA[This study examines the application of Python-based Google Colab in implementing tree theory through the Random Forest Classifier algorithm for income classification in data science, artificial intelligence, and machine learning professions. The research adopts an experimental quantitative approach using secondary data sourced from a global employment dataset. The methodological process includes data preprocessing, feature selection, class balancing, model training, and performance evaluation within the Google Colab environment. The results demonstrate that Random Forest effectively represents tree theory through ensemble decision structures capable of handling complex and heterogeneous data. Model evaluation indicates a satisfactory level of accuracy, confirming the classifier’s ability to generalize patterns across different income categories. Feature importance analysis reveals that job title, experience level, and company location play a significant role in determining income classification. These findings highlight the relevance of Random Forest as both a predictive and interpretative model, while emphasizing Google Colab’s effectiveness as a computational platform for machine learning experimentation. Overall, the study contributes to the practical understanding of tree-based algorithms and their application in analyzing labor market dynamics within the digital economy. ]]>
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