Movie Data Analysis

Exploratory data analysis of movie datasets covering box office performance, ratings, genre trends, and inflation-adjusted ROI. Built as a personal project to practice data cleaning, merging datasets, and visualization with Python.

What's Covered

• Data cleaning and null handling across multiple datasets
• Merging movie metadata with ratings and financial data
• Inflation adjustment of historical box office revenue using CPI data
• ROI calculation (budget vs. revenue)
• Interactive visualizations with Plotly
• Static plots with Matplotlib

Datasets

File	Description
movies.csv	Core movie metadata (title, genre, year, budget, revenue)
US CPI.csv	US Consumer Price Index for inflation adjustment

Project Structure

movie-data-analysis/
├── Movie_Graph/
│   ├── movies.ipynb              # Main analysis notebook (Plotly)
│   └── movieGraph_matplot.py     # Matplotlib version of key charts
├── movies.html                   # Exported interactive Plotly chart
├── movies.csv
├── US CPI.csv

Requirements

pandas
numpy
matplotlib
plotly
openpyxl

Install with:

pip install pandas numpy matplotlib plotly openpyxl

Usage

Open the notebook in Jupyter:

jupyter notebook Movie_Graph/movies.ipynb

Or run the matplotlib script directly:

python Movie_Graph/movieGraph_matplot.py

---

Hidekel Irizarry | Mechanical Engineering @ UCF