Machine learning algorithms were selected based on their ability to handle regression tasks effectively and their distinct advantages in analyzing complex datasets such as the movie dataset. Below is an explanation of why each algorithm was chosen:
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### Model Performance on Original Data
## Model Performance on Original Data
### 1. Random Forest Regressor (RF)
-**R²:** 0.6997
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-**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:
## 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.
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### Model Performance After Adding Fake Data
## Model Performance After Adding Fake Data
### 1. Random Forest Regressor (RF)
-**R²:** 0.5800
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### Conclusion:
## Conclusion:
The performance of all models declined significantly after adding fake data.
-**R² values** were notably lower, indicating a drop in the models' ability to explain the variability in the dataset.
