IMDb Movie Analysis Report

In my role as a Business Intelligence and Data Analyst, I developed a comprehensive IMDb Movie Analysis Report. This project involved extracting, transforming, and analyzing data from IMDb to provide insights into movie trends, ratings, and performance. The report aimed to assist filmmakers, producers, and movie enthusiasts in making data-driven decisions.

Key Responsibilities:

Data Extraction and Transformation:

Extracted movie data from IMDb using web scraping techniques and IMDb datasets.

Cleaned and transformed the data to ensure accuracy and consistency using Python and SQL.

Data Modeling:

Designed and implemented a data model to structure the IMDb data for efficient analysis.

Created relationships between various tables, including movies, directors, actors, genres, and ratings.

Dashboard Development:

Utilized Power BI to create interactive dashboards and reports. Key visualizations included:

Movie Ratings Distribution: Developed a histogram to display the distribution of movie ratings, highlighting the most common rating ranges.

Top-Rated Movies: Created a table to list the top-rated movies based on IMDb ratings, including details such as title, director, and release year.

Genre Analysis: Used a bar chart to visualize the average rating and number of movies for each genre, helping to identify popular genres and their performance.

Yearly Trends: Utilized line charts to illustrate trends in the number of movies released and average ratings over the years, enabling the identification of industry trends.

Director and Actor Performance: Implemented a scatter plot to analyze the performance of directors and actors based on the average rating of their movies.

Data Analysis and Reporting:

Conducted detailed data analysis to identify key trends, patterns, and anomalies in the IMDb data.

Generated insights and recommendations to help stakeholders understand movie performance and audience preferences.