This project is to identify the factors that contribute to the success of a movie, specifically whether the production company, the movie's budget, or other variables play a significant role in determining a movie's success. By performing a data analysis using Python, this project aims to provide valuable insights that can help movie production companies make more informed decisions about which movies to produce, which companies to partner with, and how to allocate resources to maximize the chances of success.
The dataset used for this analysis includes information such as:
- Movie name
- Rating
- Genre
- Year
- Released Date (Country)
- Score and Votes
- Director, Writer and Star
- Budget, Gross, and Runtime
- Country
- Company
- Python (pandas, numpy, matplotlib, seaborn, scipy)
- Jupyter Notebook
- Budget vs. Gross: Examining the correlation between production budget and gross.
- Genre Trends: Identifying which genres perform better at the box office.
- Ideal Runtime: Identifying the ideal runtime for the movie.
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While exceptions exist, a well-funded movie typically performs better at the box office.
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The best time of year to release a movie is March and July based on the overall gross earnings in this period.
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The factors contributing to the success of a movie are votes and budget as they have the highest correlation. It was also determined that the Company, Movie name, Genre had no correlation.
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The ideal runtime seems to be between 145 to 170 minutes.
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The Action genre had the highest level of success across all movies, whereas the Animations genre had the highest average success rate.
- Clone the repository:
git clone https://github.com/TGSK07/Movie_Success_Analysis.git cd Movie_Success_Analysis - Install dependencies:
pip install -r requirements.txt
- Run the Jupyter Notebook:
jupyter notebook
- Expanding the dataset to include more movies and streaming performance.
- Implementing machine learning models to predict a movie's success.
Sonu Kumar