box-office-data-analysis

Machine Learning regression and classification framework predicting movie box office revenue, ROI, and commercial success using pre-release metadata and feature analytics.

This repository hosts "From Script to Score: Sentiment Analysis of Film Content", a project exploring how sentiment in movie reviews varies by genre, trigger warnings, ratings, and box office performance. Using data from TMDB, IMDB, Letterboxd, and doesthedogdie.com, and tools like VADER, it uncovers patterns in audience reception and sensitivity.

Machine learning pipeline for predicting movie box office success. Includes web scraping, data pipelines, feature engineering, ML models, MLflow tracking, and a web interface for predictions.

Interactive landing page showcasing top-grossing action movie subgenres with dynamic charts and insightful trends.

This repository contains Python code and datasets for analyzing box office data. Explore trends, patterns, and factors influencing movie performance.

Predictive analysis of Japanese media IP to optimise global market expansion.

Using Hybrid models to predict movie box office revenue using historical data and features like cast, genre, release date.

🎬 Machine learning project to analyze and predict box office performance using enhanced movie data and exploratory analysis.

🎬🦠 Data Analysis Repository: This repository offers a deep dive into the world of data analysis, covering diverse topics from the enchanting realm of box office movies to the critical study of the COVID-19 pandemic. Uncover the secrets behind blockbuster success or navigate through pandemic data to understand its impact.