-**Explanation:** The Random Forest model demonstrates strong performance, with a high R² and low MSE. This indicates that the model was effective in predicting movie ratings using the provided features. Random Forest works well in capturing non-linear relationships between features, making it suitable for this type of regression task.
### 2. Gradient Boosting Regressor (GBR)
-**R²:** 0.6534
-**MSE:** 0.0120
-**Explanation:** Gradient Boosting performed slightly worse than Random Forest, with a lower R² and a slightly higher MSE. Although it is still a strong model, there is some room for improvement compared to Random Forest. Gradient Boosting works by combining multiple weak learners to create a stronger model, but in this case, it was less effective than Random Forest for the given dataset.
### 3. Support Vector Regressor (SVR)
-**R²:** 0.7150
-**MSE:** 0.0099
-**Explanation:** The Support Vector Regressor (SVM) outperformed both Random Forest and Gradient Boosting in terms of R² and MSE. It achieved the highest R² and lowest MSE, demonstrating the best predictive accuracy. SVM is known for its ability to generalize well, particularly in high-dimensional feature spaces, and it performed optimally for predicting movie ratings in this dataset.
### Conclusion:
-**Support Vector Regressor (SVR)** yielded the best results, achieving the highest R² and lowest MSE, which indicates its ability to generalize well to the dataset.
-**Random Forest** showed strong performance, following closely behind SVR, while **Gradient Boosting** had slightly lower performance than the other two models in terms of both R² and MSE.
