Movie Studio Analytics is an end-to-end Data Analytics project that combines Python, Pandas, MySQL, SQL, and Matplotlib to analyze movie performance and generate business insights.
The project analyzes movie budgets, revenues, ratings, popularity, genres, and studios to identify industry trends.
Movie studios invest millions of dollars in films every year.
This project answers questions like:
- Which movies earned the highest revenue?
- Which studios perform the best?
- Which genres are most successful?
- What is the relationship between budget and revenue?
- Which movies received the highest ratings?
- How has movie production changed over time?
- Python
- Pandas
- NumPy
- Matplotlib
- MySQL
- SQL
- Jupyter Notebook
- Git & GitHub
Datasets Used
- movie_studio_data.csv
- tmdb_5000_credits.csv
Total Movies Analyzed:
- 600+
Additional Credits Dataset:
- 4,803 Movies
Database Name
movie_studio_db
Tables
movie_studio
movie_credits
✔ Highest Revenue Movies
✔ Highest Budget Movies
✔ Highest Rated Movies
✔ Most Popular Movies
✔ Revenue Distribution
✔ Rating Distribution
✔ Top Movie Studios
✔ Top Genres
✔ Budget vs Revenue Analysis
✔ Movies Released Per Year
- High-budget movies generally generate higher revenue.
- Action and Adventure dominate the industry.
- A small number of studios generate a large share of revenue.
- Higher popularity often leads to higher earnings.
- Revenue distribution is highly skewed.
- Movie production has increased significantly over the years.
movie-studio-analytics-sql-python
│
├── CHARTS
│
├── DATA SET
│
├── PYTHON
│
├── SQL
│
├── requirements.txt
│
├── LICENSE
│
└── README.md
- Power BI Dashboard
- Interactive Dashboard
- Predict Movie Revenue using Machine Learning
- Genre Recommendation System
- Movie Success Prediction
Divanshu Singh
Data Analyst | SQL | Python | MySQL | Pandas | Data Visualization
GitHub
https://github.com/singhdivanshu455-star
movie-studio-analytics-sql-python
│
├── 📁 SQL
│ └── 01_database.sql
│
├── 📁 PYTHON
│ ├── movie_studio_analysis.ipynb
│ ├── movie_studio_analysis.py
│ └── movie_studio_data.csv
│
├── 📁 CHARTS
│ ├── movie_studio_banner.png
│ ├── budget_vs_revenue.png
│ ├── movie_rating_distribution.png
│ ├── movies_released_per_year.png
│ ├── revenue_distribution.png
│ ├── top_genres.png
│ ├── top_popular_movies.png
│ ├── top_rated_movies.png
│ ├── top_studios.png
│ ├── top10_highest_budget_movies.png
│ └── top10_highest_revenue_movies.png
│
├── requirements.txt
├── LICENSE
└── README.md
- 🎬 Identified the highest revenue-generating movies.
- 💰 Compared movie budgets with total revenue.
- ⭐ Analyzed rating distribution across all movies.
- 🎭 Discovered the most popular movie genres.
- 🏢 Compared performance of major movie studios.
- 📈 Visualized yearly movie release trends.
- 🔥 Identified the most popular and highest-rated movies.
- 📊 Built business-focused visualizations using Python and Matplotlib.
- Budget vs Revenue
- Revenue Distribution
- Rating Distribution
- Movies Released Per Year
- Top Genres
- Top Studios
- Top Rated Movies
- Top Popular Movies
- Top 10 Highest Budget Movies
- Top 10 Highest Revenue Movies
- SQL
- MySQL
- Python
- Pandas
- NumPy
- Matplotlib
- Jupyter Notebook
- Interactive Power BI Dashboard
- Tableau Dashboard
- Machine Learning Movie Revenue Prediction
- Streamlit Web Application
- Movie Recommendation System
- Real-time Movie Analytics Dashboard
Divanshu Singh
Aspiring Data Analyst passionate about SQL, Python, Data Visualization, and Business Analytics.
https://github.com/singhdivanshu455-star
https://www.linkedin.com/in/divanshu-singh-7663262b5/
⭐ If you found this project useful, don't forget to Star this repository.