Movie Data Analysis 🎬 This project is all about diving into a dataset of movies to see how different factors like budget, ratings, and more relate to a movie's success. We use Python libraries like Pandas, NumPy, Matplotlib, and Seaborn for the analysis.
#What's Inside Dataset: Info on movies like title, rating, genre, budget, gross revenue, and more. Analysis: We explore correlations, check out data distributions, spot outliers, and see how different features relate. Visuals: Plenty of plots and charts to make the data easier to digest.
#Getting Started Clone the repo:
#bash Copy code git clone https://github.com/yourusername/moviedata-analysis.git Install the necessary Python packages:
#bash Copy code pip install -r requirements.txt Run the analysis by opening the Jupyter notebook or running the Python script:
bash Copy code jupyter notebook MovieDataAnalysis.ipynb or
bash Copy code python MovieDataAnalysis.py Key Takeaways Budget vs. Gross: There's a strong link between how much is spent on a movie and how much it earns. Ratings and Genres: Certain ratings and genres tend to have higher gross revenue. Top Movies: We also look at which companies are behind the big earners. Contributing Got ideas or found something off? Feel free to open an issue or submit a pull request.