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Metadata on ~5,000 movies from TMDb.
The Movie Database provides two datasets: tmdb_5000_movies.csv (Movie credits data): budget; genres; homepage; id; keywords; original_language; original_title; overview; popularity; production_companies; production_countries; release_date; revenue; runtime; spoken_languages; status; tagline; title; vote_average; vote_count; tmdb_5000_credits ...
This repository provides a content-based movie recommendation system based on the TMDB 5000 Movie Dataset from Kaggle. The system is built using Python and various data analysis and machine learning libraries. Additionally, it is deployed on Heroku, allowing for easy access to the recommendation system online.
Aug 19, 2022 · TMDB-5000 movies dataset, let's go through an EDA together. So, I bet everyone loves movies and we love the hype while waiting for a hot release (I'm projecting my current state!). Throughout this post, we'll be looking at some key insights on a dataset that has information on ~5000 movies.
TMDB-5000-Movie-Dataset-Analysis-and-Modeling. This project aims to predict the success of movies using machine learning algorithms. It involves data preprocessing, exploratory data analysis (EDA), feature selection, and model training using various classification algorithms.
AiresPucrs/tmdb-5000-movies · Datasets at Hugging Face. Dataset Viewer. Auto-converted to Parquet. API. View in Dataset Viewer. Split (1) train · 4.8k rows. We’re on a journey to advance and democratize artificial intelligence through open source and open science.
For the ML project, we use the TMDB 5000 Movie Dataset available on the Kaggle platform. This dataset was generated from the The Movie Database API. The principal question which arises from the description of the challenge is to predict which films will be highly rated, whether or not they are a commercial success.
