This repository contains a comprehensive analysis of a movie database. The project involves extracting data from a SQL Server database, performing various data analyses, and generating visualizations to answer specific business questions about the movie industry.
- Data Extraction: Scripts to connect to SQL Server and retrieve data from the movie database.
- Data Analysis: Python scripts and Jupyter notebooks that perform data analysis on the retrieved data. Analysis includes:
- Identifying the highest-grossing Oscar-winning films.
- Determining the top 10 countries by film profit.
- Listing films for each country.
- Visualizations: Plots and charts generated to visualize the analysis results and insights derived from the data.
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Python: Ensure Python is installed on your machine.
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Libraries: This project uses the following Python libraries:
pandasnumpypyodbcmatplotlibseaborn
You can install the required libraries using
pip. -
SQL Server: Access to a SQL Server database containing the movie data.
Database Connection:
- Ensure you have the correct connection details for your SQL Server database.
- Update the connection settings in the data retrieval scripts.
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Data Retrieval:
- Use the provided scripts to connect to SQL Server and fetch data. Ensure the SQL Server library is installed and configured correctly.
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Data Analysis:
- Analyze the data using the provided Python scripts or Jupyter notebooks. The analysis includes:
- Highest-grossing Oscar-winning films
- Top 10 countries by film profit
- Films listed by country
- Analyze the data using the provided Python scripts or Jupyter notebooks. The analysis includes:
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Visualizations:
- Generate visualizations using Matplotlib and Seaborn. Visualizations include:
- Bar charts of the top 10 countries by total film profit
- Other relevant charts based on analysis results
- Generate visualizations using Matplotlib and Seaborn. Visualizations include:
For any questions or feedback, please reach out to [[email protected]] or create an issue in the repository.