This project involves comprehensive data analysis and application development using a dataset of approximately 160K TV shows. The analyses aim to uncover trends in TV show popularity, predict show success, develop a recommendation system, and more.
- Explore trends in TV show popularity.
- Predict the success of TV shows based on features like vote count, average, and popularity.
- Build a recommendation system based on user's favorite genres or languages.
- Investigate TV show production trends across countries and networks.
- Analyze overviews of TV shows for sentiment and themes.
- TV Shows Dataset: A collection of data about 160K TV shows, including details like air dates, genres, languages, production companies, and voting data.
- Language Analysis of TV Show Overviews: Using NLP techniques to identify prevalent themes and sentiments in TV show descriptions.
- Success Prediction Model: A machine learning model predicting a TV show's success based on popularity, vote count, and average rating.
- Recommendation System: A system suggesting TV shows based on user preferences in genres and languages.
- Production Trends Analysis: Analyzing production trends to identify the most active countries and networks in TV show production.
- Python for data analysis and machine learning.
- Libraries: pandas, sklearn, NLTK/spaCy (for NLP).
- Jupyter Notebook for interactive development and analysis.
- Diverse themes and sentiments in TV show overviews.
- High potential for predicting show success using popularity metrics.
- Effective genre-based recommendations, with scope for enhanced personalization.
- Dominance of specific countries and networks in TV show production.