Movie revenue dataset Users can study the interplay between various factors, such as the relationship between star power, genre, and commercial performance. Top Box Office Data for US and International revenues across franchise, genre The table contains data on 722363 movies with 21 columns including id, title, genres, budget, revenue, and more. The dataset is divided into csv files and many columns are in json format. 6 million for the movie as of Sunday morning, more than four times the $19 million projected for the number two film, The King of Kings. Dataset: This Project aims at predicting revenue of movies using supervised learning approach. In the. It can be used to analyze trends in movie revenue over time and compare the performance of different release groups. Additionally, we perform different statistical analysis approaches on our dataset to find out how a movie's revenue is affected by different pre-released attributes such as budget, runtime, release month, content rating, genre etc. Jul 21, 2021 · This list of movie datasets include cast and crew member information, script, plot, screen time, reviews, and more, and can be used for Machine Learning purposes. The train data set has 3000 data points and the test data set is the remaining 4398 entries. We have developed a Command Line Interface (CLI) to allow users to input movie features and get revenue predictions. This tool provides an estimate of the inputted movie's revenue within specific ranges: Low Revenue: <= $10M; Medium-Low Revenue: $10M - $40M; Medium Revenue: $40M - $70M; Medium-High Revenue: $70M - $120M; High Revenue: $120M - $200M Movie revenue predictions with data from The Movie Database This project leverages a dataset of 3,000 movies with 22 variables from The Movie Database to predict movie revenues. Warner Bros. The Movie Database (TMDB) dataset includes ~27,000 movies released from 1930 to 2020, with variables descrbing genre, popularity, vote average & vote count and release date. Metadata on over 45,000 movies. df_movie_gross contains ~3,400 movies released between 2010 and 2018, and includes variables for movie studio, domestic gross revenues and foreign gross revenues. But can we predict what will be the revenue of a movie by using its genre or budget information? This is exactly what we’ll learn in this article, we will learn how to implement a machine learning algorithm that can predict a box office This repository contains a comprehensive analysis of the IMDb Top 1000 Movies dataset. - abdhye/moviesAnalysis Dec 6, 2020 · Specifically, there were 1,069 movies with an implausibly low budget and 1,451 movies with implausibly low revenue. 5 million titles and detail information of more than 200K movies. The TMDB dataset contains around 5000 movies and TV series. This table contains data on the box office collections of movies from 2010 to 2024, including worldwide, domestic, and foreign earnings. Our final dataset included 2510 movies. Features include both quantitative information such as the budget, length, and popularity of the movie as well as descriptive information such as the cast list, genre A comprehensive analysis of a movie dataset, exploring the relationships between budget, gross earnings, and audience ratings to uncover key factors influencing box office success. Features used for prediction include gender of cast members, movie genres, budget and production companies. This dataset can be used for analyzing trends in movie popularity, production companies, budgets, and revenues, as well as for building recommendation systems based on credits, keywords, and genres. We also removed all movies for which some of the data was not available (typically either the budget or the number of theaters on release was sometimes missing). is projecting $80. This dataset has 7398 entries for movies and 22 features for each of those movies. - F-odt/IMDB_Top1000_Movies_Analysis depends on multiple factors like cast, budget, review, rating, release year Oct 13, 2021 · The compiled dataset contains the summery data of 7. In the contemporary film industry, accurately predicting a movie’s earnings is paramount for maximizing profitability. With features like ratings, revenue, votes, and Metascore, the dataset provides a detailed understanding of what drives movie success. Apr 17, 2025 · A Minecraft Movie was always going to be the top earner at the box office this weekend, but it’s doing so in some style, dropping just 50% from its gargantuan opening. Dive into the world of cinema through data-driven exploration and visualization. data for the low revenue movies, we then dropped movies whose revenue was smaller than $1,000,000. Sep 30, 2024 · When a movie is produced then the director would certainly like to maximize his/her movie’s revenue. It is one of the biggest movie database on the web available. More May 19, 2024 · Abstract. 26 million ratings from over 270,000 users. There are a number of ways to deal with missing data in machine learning. The goal of this project is to help movie studios make informed decisions about the potential revenue of a movie before it is released. This project aims to develop a machine learning model for predicting movie earnings based on input features like the movie name, the MPAA rating of the movie, the genre of the movie, the year of release of the movie, the IMDb Rating, the votes by the watchers, the The model will be trained on a dataset containing information about thousands of movies, including their budget, release date, runtime, genre, and cast. Explore insights into movie ratings, box office performance, genre trends, director contributions, and more. jrmb ibfjboj ijkha zauai buhyfwd nnrsr xbatd kunyqfnf drl jif jzqslioj rlo gbhrxj twp ewghp