Analyzing the Egyptian Box Office

Project Overview:

The Egyptian movie industry is one of the oldest and most influential in the world, producing thousands of films in various genres and reflecting the rich and diverse culture of Egypt and its people. However, the industry faces many challenges, such as the rise of digital platforms, the competition of foreign films, and the demand of the audience. To address these challenges, this project aims to conduct an in-depth analysis of the Egyptian box office over the past decade. By leveraging Python, SQL, and Tableau, the project seeks to uncover valuable patterns and trends, providing stakeholders with actionable insights for strategic planning, marketing, and investment within the Egyptian movie industry.

About the Data:

The data for this project was scraped from elcinema.com. The dataset includes information on various aspects of movies, such as year, week, rank, title, rating, country, runtime, release date, genre, MPAA rating, director, writers, stars, weekly revenue, and total revenue. The data was structured into six normalized tables, with the relationships between tables defined.

• Tables:

1. movies_table: Contains general information about movies.
2. writers_table: Contains information about the writers of movies.
3. movie_writers_table: Serves as a bridge table connecting movies to their writers.
4. stars_table: Contains details about the stars of movies.
5. movie_stars_table: Serves as a bridge table connecting movies to their stars.
6. box_office_table: Contains data on weekly box office performance.
   

• ERD:

Methodology:

1. Data Collection: Utilized Python's Requests and Beautiful Soup libraries to scrape movie data from the online database, and organized the data into six normalized tables to avoid redundancy.
2. Data Cleaning: Pandas was used to clean the data, including error correction, handling null values, and removing duplicates.
3. Database Creation: SQL DDL and DML were employed to create and populate the six tables in a relational database, with established relationships between them.
4. Data Analysis using SQL: A variety of SQL queries were used to extract valuable insights from the data, such as trends in box office performance, popular genres, and successful directors and actors.
5. Data Analysis using Tableau: The cleaned data was loaded into Tableau for data visualization. New calculated fields were created and various visualizations were used to gain a deeper understanding of the data. In the end, a dynamic Tableau dashboard was created to summarize the project's findings and make it more accessible for stakeholders.
6. Presentation: Created a comprehensive presentation to better communicate the analysis findings and insights to stakeholders.

Findings:

For the detailed findings and insights, please refer to the Presentation.