This project employs real-time data analysis from Twitter, Reddit, and TMDB to predict the success of TV shows. By leveraging cutting-edge technologies and advanced methodologies, it identifies key factors influencing a show's popularity and visualizes actionable insights.
Utilizing a robust tech-stack including Python, Flask, MongoDB, and Dash, the project aggregates, preprocesses, and analyzes real-time data streams from Twitter, Reddit, and TMDB. Leveraging machine learning techniques, it predicts TV show success based on a variety of factors including genre, cast, social media buzz, and user sentiment.
- Python: Core programming language
- Flask: Micro web framework for backend development
- MongoDB: Non-relational document database for data storage
- Dash: Interactive web-based data visualization library
- Twitter Streaming API: Real-time stream of tweets for sentiment analysis
- Reddit API: Data retrieval from various TV show-related subreddits
- TMDB API: Community-built TV and movie database for show details
- Machine Learning: Supervised learning models for prediction
- Natural Language Processing (NLP): Text analysis for sentiment detection
- Data Visualization: Matplotlib, Plotly for graphical representation
- Install dependencies:
pip install -r requirements.txt - Launch scheduler for Reddit and TMDB APIs:
python3 app.py - Run Twitter stream:
python3 twitterstream.py
An interactive dashboard updates in real-time, showcasing predictive analytics results. Users can filter data based on their queries, gaining deeper insights into TV show success dynamics.
Through advanced data visualization techniques, insights are presented via interactive graphs and plots, offering actionable intelligence for stakeholders in the TV industry.
- Genre, cast, and social media buzz heavily influence TV show success.
- Positive sentiment on Twitter correlates with higher show ratings.
- Subreddit analysis reveals popular phrases indicative of show trends.
- Predictive analytics models accurately forecast show success metrics.
- Incorporate additional data sources for more comprehensive analysis.
- Implement advanced machine learning algorithms for improved predictions.
- Enhance dashboard interactivity with user-friendly features.
- Expand analysis to include international TV markets for global insights.
Contributions are welcome! If you'd like to contribute, feel free to fork the repository, make your changes, and submit a pull request.