NxtMovie is an innovative system designed to analyze and visualize movie-related data using decision trees and graph-based approaches. Utilizing datasets scraped from Metacritic and employing libraries such as scikit-learn and NetworkX, the project uncovers patterns in movie ratings, genre trends, and relationships between various cinematic factors. By leveraging graph theory and decision tree analysis, ProjectNxtMovie provides insights into movie performance and audience preferences, helping to identify emerging trends in the industry.
The project follows a structured approach, beginning with data collection and preprocessing. Movie metadata, including ratings, reviews, and release details, is scraped and cleaned to ensure consistency. Feature extraction is performed to identify key variables influencing ratings, while graph construction using NetworkX explores relationships between movies, actors, and directors. Decision trees implemented via scikit-learn analyze factors impacting movie ratings, providing interpretable insights. Evaluation metrics focus on classification accuracy, effectiveness of graph-based visualizations, and user engagement with the system. The results demonstrate that network analysis reveals significant connections among industry professionals, while decision trees offer clear explanations for rating variations without requiring complex predictive modeling.
NxtMovie effectively processes large datasets, enabling real-time visualization and analysis with minimal latency. The integration of decision trees and graph-based techniques highlights significant relationships within the movie industry, offering data-driven discoveries related to audience preferences and critic reviews. The system sets the stage for further research in AI-driven entertainment analytics and has the potential to enhance recommendation systems and industry forecasting. Future enhancements may include incorporating additional sources of user-generated content and refining the models for even greater predictive power. Please refer to the following link to see the application in action: https://youtu.be/BfWAH9884qQ
