Movie Data Analysis

This repository contains a data analysis project focused on exploring a dataset of movies. The analysis aims to gain insights into various aspects of the film industry, including revenue, popularity, genres, directors, and more. The project utilizes Python and popular data analysis libraries such as Pandas, Matplotlib, Seaborn, and WordCloud.

Dataset

The analysis is based on a dataset sourced from The Movie Database (TMDb). The dataset includes information about movies, including their titles, release dates, budgets, revenues, genres, directors, and more. The dataset provides a rich source of data for conducting exploratory data analysis and answering research questions related to the film industry.

Research Questions

The analysis explores the following research questions:

1. Which director has the highest average revenue per film?
2. Is there a correlation between the number of films an actor has been in and the average revenue of those films?
3. What is the average revenue difference between movies that have a homepage and those that do not?
4. Are movies with a single genre or multiple genres more popular?
5. What is the trend of the 'Popularity' score over the years?
6. Which month has seen the maximum releases of high-grossing movies?
7. Has the ratio of budget to revenue changed over time?
8. Is there a keyword or set of keywords that are particularly prevalent in high revenue movies?

Usage

To run the analysis, follow these steps:

1. Clone the repository:

   git clone https://github.com/MahmoudHassan/movie-data-analysis.git

2. Install the required dependencies:

   pip install pandas matplotlib seaborn wordcloud plotly

3. Run the Jupyter Notebook or Python script to execute the analysis:

   jupyter notebook movie_data_analysis.ipynb

Conclusion

The analysis provides valuable insights into various aspects of the film industry, including directors' impact on revenue, the correlation between actors and revenue, the influence of homepage presence, genre popularity, trends over time, and the impact of budget-revenue ratio and keywords on movie success. However, it's important to note that the analysis is based on the available dataset and has limitations. The conclusions should be interpreted in the context of the dataset and may not be universally applicable.

Further analysis, considering additional factors and datasets, could provide deeper insights into the film industry and its dynamics.