Is the TV Show going to be popular?

This project explores the use of Principal Component Regression (PCR) to predict the popularity of TV shows, effectively addressing multicollinearity issues often encountered in traditional regression methods. Utilizing data from NBC shows, including Gross Ratings Points, Projected Engagement, and audience survey responses, the project employs PCR, a blend of Principal Component Analysis and multiple linear regression, to ensure a balanced analysis. This approach is validated through various plots, including Residuals vs Fitted and Normal Q-Q. Key findings reveal PCR's efficacy in managing multicollinearity and high-dimensional data, with a specific focus on the impact of audience engagement elements like humor and predictability. A comparative analysis with LASSO regression underscores PCR's robustness. The conclusion highlights PCR's significant potential in predictive modeling, especially in deriving insights about TV show popularity from audience engagement metrics.