Exploratory Data Analysis and Insights on Movie Rentals and Popularity
This project was developed as part of my Data Science diploma program. It analyzes movie rental data from a video club, integrating information stored in both PostgreSQL and MongoDB databases. The goal is to identify popular actors, rental patterns, and uncover underutilized high-rated films by combining structured rental records with IMDb ratings and reviews.
- Databases: PostgreSQL, MongoDB
- Languages: Python (Pandas, PyMongo, SQLAlchemy)
- Environment: Jupyter Notebooks
- Other: Data visualization with Matplotlib and Seaborn
- Which actors are most popular among renters?
- What movies have the highest rental turnover but low availability?
- Are there highly rated movies that are underutilized?
- How do user reviews and ratings correlate with rental trends?
- Clone the repository
- Configure access to PostgreSQL and MongoDB databases (credentials, connection strings)
- Open the notebooks and run the cells step-by-step to reproduce the analysis
The databases used in this project are public and intended for educational purposes. The credentials provided are shared by the course instructor and used here solely to facilitate learning. Please do not use these credentials for any other purposes.
For questions or feedback, contact me at:
📧 [email protected]
💼 LinkedIn
Thanks for checking out my work! ❤️