Decision Tree

This project aims to predict a target variable using decision tree regression models applied to a movie dataset. The dataset includes features such as budget, marketing expenses, lead actor rating, trailer views, and other relevant variables. By training the decision tree models, we aim to understand how these features influence the target variable and to make accurate predictions.

Conclusion

The decision tree regression models demonstrated varying degrees of effectiveness in predicting the target variable. The performance metrics, including the Root Mean Squared Error (RMSE) and R² score, indicate that the models were able to capture significant patterns in the data. Visualizations such as confusion matrices and tree plots provided clear insights into the model’s decision-making process. Further tuning and feature selection could improve model accuracy and generalization. Overall, decision trees offer a transparent and interpretable approach to regression tasks, making them valuable tools for predictive modeling in this context.