This project explores movie rental store data using SQL & Python to extract insights into customer behavior, rental patterns, revenue trends, and inventory management. The dataset consists of 16 interrelated tables, covering actors, films, customers, transactions, payments, and more.
✅ Perform Exploratory Data Analysis (EDA) to identify patterns & trends
✅ Optimize inventory management by analyzing rental frequency & demand
✅ Gain insights into customer preferences & spending behavior
✅ Use SQL queries for data extraction & transformation
✅ Create visualizations for better storytelling & decision-making
The dataset includes 16 tables, categorized as follows:
- Customers & Transactions:
customer,rental,payment - Movies & Inventory:
film,inventory,category,language - Business Operations:
store,staff,address,city,country
- 🔹 SQL – Data extraction & query analysis
- 🔹 Python – Data wrangling & visualization (
pandas,matplotlib,seaborn) - 🔹 Jupyter Notebook – Interactive data exploration
- 🔹 Power BI/Tableau (Optional) – Dashboard creation
We conducted a deep dive into the dataset to explore:
📌 Customer Behavior – Frequent renters, revenue contribution, payment patterns
📌 Rental Trends – Most/least rented movies, peak rental times, store performance
📌 Revenue Insights – Top-paying customers, seasonal trends, late returns
📌 Genre Popularity – Categories with the highest demand
This project features optimized SQL queries to extract insights:
✔️ Top Customers – Who contributes most to revenue?
✔️ Popular Movies – Which films are rented the most?
✔️ Revenue by Store – Performance comparison of different branches
✔️ Late Returns Impact – How do late returns affect business?
Below are key insights derived from the Movie Rental Store Analysis dataset:
📌 Insight: The highest number of rentals come from urban regions, with New York, Los Angeles, and Chicago leading the market. International demand is growing, particularly in Europe and Asia.
📌 Insight: The total revenue trend shows consistent monthly growth, with summer months (June-August) generating the highest sales. Promotional periods also drive temporary spikes.
📌 Insight: 65% of customers prefer action and comedy films, and the majority of loyal customers rent at least 5 movies per month. Discounts on bulk rentals encourage repeat customers.
📌 Insight: The peak rental times are Friday and Saturday evenings, with a high return rate on Mondays. Late fees contribute 12% of total revenue, suggesting an opportunity to optimize return reminders.
📌 Insight: Top-performing films belong to the Action and Thriller genres. Movies with high IMDb ratings tend to have longer rental durations. Old classics are still in demand, contributing 20% of revenue.
📌 Insight: Films starring A-list actors drive the most rentals. Actors like Tom Hanks and Meryl Streep appear frequently in high-performing movies. Comedy and Drama genres dominate the rental market.
✅ Urban areas dominate rentals, but international growth is promising.
✅ Summer months and weekends are peak periods for revenue.
✅ Customer loyalty programs can drive more repeat business.
✅ Action, Thriller, and Comedy movies are the best performers.
✅ Star power plays a major role in rental demand.
💡 Note: Replace images/filename.png with the actual file path where your images are stored in your repository.
🚀 Let me know if you need any modifications or additional insights! 📊🔥
For any queries or collaborations, feel free to connect:
🔹 Mayank Yadav
📧 Email: mayanky075@gmail.com
🔗 LinkedIn: linkedin.com/in/mayankyadv
🐙 GitHub: github.com/mayankyadav23
git clone https://github.com/mayankyadav23/movie-rental-store-analysis.git
cd movie-rental-store-analysis