DhanushN2005/Movie_BoxOffice_Revenue-Prediction-using-Hybrid-Models
A model that predicts movie box office revenue using historical data and features like cast, genre, release date.
Implements a hybrid ensemble approach comparing Linear Regression, Random Forest, and Gradient Boosting models with feature engineering for temporal effects and categorical encoding. The pipeline includes comprehensive EDA, cross-validation evaluation using RMSE/MAE metrics, and model comparison visualizations to identify optimal revenue prediction performance across different ML architectures.
Stars
13
Forks
—
Language
HTML
License
—
Category
Last pushed
Jan 09, 2026
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/DhanushN2005/Movie_BoxOffice_Revenue-Prediction-using-Hybrid-Models"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
MengtingWan/oscar
predicting oscars using machine learning methods
LJSthu/Movie-Analysis
使用机器学习算法的电影推荐系统以及票房预测系统
Vikranth3140/Movie-Revenue-Prediction
Movie Revenue Prediction System predicts the revenue of a movie with 14 parameters: name,...
devinw03/movie-genre-nlp
🎬 Classify movie genres from plot summaries using various models, including Transformers, with...
Zer0-Bug/IMDB_Prediction
End-to-End ML Pipeline for IMDB Rating Prediction w/ Advanced Feature Engineering.