https://github.com/shaadclt/movie-correlation-analysis
This repository provides a movie correlation analysis using Python. The analysis aims to explore the relationships and correlations between different movie attributes, such as ratings, genres, and revenue.
https://github.com/shaadclt/movie-correlation-analysis
seaborn
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This repository provides a movie correlation analysis using Python. The analysis aims to explore the relationships and correlations between different movie attributes, such as ratings, genres, and revenue.
- Host: GitHub
- URL: https://github.com/shaadclt/movie-correlation-analysis
- Owner: shaadclt
- Created: 2022-09-29T05:36:20.000Z (about 3 years ago)
- Default Branch: main
- Last Pushed: 2023-07-06T17:38:38.000Z (over 2 years ago)
- Last Synced: 2025-04-09T17:02:28.935Z (6 months ago)
- Topics: seaborn
- Language: Jupyter Notebook
- Homepage:
- Size: 650 KB
- Stars: 4
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Movie Correlation Analysis using Python
This repository provides a movie correlation analysis using Python. The analysis aims to explore the relationships and correlations between different movie attributes, such as ratings, genres, and revenue.
## Dataset
The dataset used in this project consists of movie data, including information such as movie title, genre, ratings, and revenue. The dataset includes the following columns:
- Name
- Rating
- Genre
- Year
- Released
- Score
- Votes
- Director
- Writer
- Star
- Country
- Budget
- Gross
- Company
- Runtime
The dataset is available in the file `movies.csv`.
## Analysis
In this repository, we have implemented movie correlation analysis using Python and popular data analysis libraries, such as Pandas, NumPy, and Matplotlib. The code for performing the correlation analysis can be found in the `Movie Correlation Project using Python.ipynb` Jupyter Notebook.
The analysis includes the following steps:
1. Loading and preprocessing the movie dataset.
2. Exploratory data analysis to gain insights into the dataset.
3. Computing correlation coefficients between different movie attributes.
4. Visualizing the correlation matrix using heatmaps and scatter plots.
## Getting Started
To get started with the movie correlation analysis, follow these steps:
1. Clone this repository to your local machine.
2. Install the required dependencies.
3. Ensure you have Jupyter Notebook installed. If not, install it using `pip install jupyter`.
4. Open the `Movie Correlation Project using Python.ipynb` Notebook using Jupyter Notebook.
5. Follow the instructions in the Notebook to load and analyze the movie dataset.
6. Modify the code or add additional analysis as needed.
## Results
After performing the movie correlation analysis, you will gain insights into the relationships between different movie attributes. The Notebook provides visualizations and correlation coefficients to help you understand the impact of factors like ratings and genres on movie revenue.
## Contributions
Contributions to this repository are welcome. If you have any suggestions, bug fixes, or enhancements, please submit a pull request. We appreciate your contributions!
## License
This project is licensed under the MIT License. See the `LICENSE` file for more details.
## Acknowledgments
We would like to acknowledge the creators of the movie dataset used in this project for providing valuable data for analysis.
## Contact
For any questions or inquiries, please contact [Mohamed Shaad] at [shaadclt@gmail.com].