This project aims to explore a movie dataset using Python and Pandas, focusing on discovering key insights and comparing different movies based on their revenue, popularity, and release dates.
As part of Week 5 of the Digital Futures program, this project introduced me to Exploratory Data Analysis (EDA) using Python. I applied newly learned Python skills and Pandas to clean and analyse a dataset of movie data to uncover two major findings and perform a comparison of specific movie franchises.
Data Cleaning: The dataset was cleaned by handling null values and ensuring the data was in a usable format. Exploration & Analysis: The data was explored using various Pandas tools and visualized using a correlation map to discover relationships between features. Tools Used: This project was completed using Jupyter Notebooks, leveraging Python libraries such as Pandas for data manipulation.
Animation Genre and Revenue: I found that the animation genre had the highest average revenue, likely due to larger budgets that allowed for better production quality. This was reflected by a strong correlation (73.1%) between high budgets and revenue. I also observed that animation films were the second most popular genre with a correlation of 64.5% to popularity.
Release Date and Revenue: Contrary to expectations, the release date did not appear to significantly impact film revenue. This could be due to advances in film technologies and inflation masking the impact of release year on appeal.
Ice Age Film Comparison: When analyzing the Ice Age franchise, I found that although the budget for each movie increased, the popularity of the films actually decreased over time.
This project helped me practice the complete EDA pipeline: data cleaning, handling missing values, exploring correlations, and visually presenting findings in a clear and understandable way. I also had the opportunity to present the findings to my line manager, gaining practical experience in presenting data insights professionally. Requirements
Python 3.x Jupyter Notebook Pandas and other relevant Python libraries