Python analysis project focused on identifying which movie variables are most strongly related to gross revenue performance using correlation analysis and exploratory data analysis.
This project uses Python to explore movie data and understand which variables may influence or relate to gross revenue.
Explored movie gross revenue and compared it against available movie attributes.
Used correlation analysis to identify variables with stronger relationships to revenue.
Created visual analysis to better understand relationships and patterns in the dataset.
Movie performance can be influenced by many factors such as budget, votes, company, genre, release year, score, and runtime. Without analysis, it is difficult to determine which factors are most associated with gross revenue.
I used Python to analyze movie data, clean and prepare the dataset, calculate correlations, and visualize relationships between variables. The goal was to identify which features have the strongest relationship with gross revenue.
This project demonstrates how Python can be used to explore business performance drivers, identify meaningful relationships, and support data driven decision making through statistical analysis.
Key analytics skills demonstrated in this Python project.
Identified which movie variables had stronger relationships with gross revenue.
Used charts and heatmaps to communicate relationships and patterns clearly.
Converted movie data into useful insights about revenue related factors.
The complete Python notebook and source code are available on GitHub.
I can help build Python analysis workflows, SQL reports, Power BI dashboards, Excel dashboards, and automation solutions.