DATA ANALYSIS • MACHINE LEARNING
Analyzing the patterns behind movie success
The goal was to analyze movie data, identify patterns associated with box office success, and build a machine learning model to classify movies as HIT or FLOP.
Timeline
24 hours — From raw data analysis to an interactive ML dashboard.
Background
Understanding what contributes to a movie's commercial success can provide useful insights for filmmakers and analysts. This project explores a dataset of 96 movies to investigate relationships between budget, revenue, ratings, cast power, runtime, genre, and release timing. After cleaning and analyzing the data, I identified key patterns and trained a Decision Tree model to classify movies as HIT or FLOP, achieving 80% test accuracy. The results were then presented through an interactive web dashboard.