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This study presents a predictive model to forecast high school students' college plans using artificial intelligence. The model, known as AIRPCP (Artificial Intelligence for Educational Planning of College Pursuits), achieves promising performance in predicting students' intentions to pursue college education. Key steps in the model development process, such as data preprocessing, feature engineering, and hyperparameter tuning, are discussed. Advanced techniques using scikit-learn's GridSearchCV for hyperparameter optimization are highlighted. The study emphasizes the importance of these techniques in enhancing the model's predictive accuracy and relevance.