The Movie Database for all language movies
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Updated
Jun 16, 2023 - Jupyter Notebook
The Movie Database for all language movies
🎥 🍿 Streaming Public domain movies
A dataset of films, directors, actresses and actors
A small CLI app to scrap high-quality movie snapshots from various websites.
Recommends top 10 movies for you
The Movie Recommender uses the Streamlit framework for the frontend and cosine similarity for the recommendation system.
API for showing the crawled wiki movies content and list of movies
Website code for sublearning.com
Movie Recommender System developed using Streamlit, Python, and The Movie Database (TMDb) API. It recommends movies based on similarity scores between movies.
Created a python function to automate scraping of top movies from IMDB for any given genre using BeautifulSoup
Search for the movies of your choice and make your custom movie list.
Film Release Date Analysis - Collaborative Database, Machine Learning, Tableau and Presentation.
Welcome to the 2000s Movie Database, the dataset contains 2100 films released between 2000 and 2009. Data points include title, genre, year, language and country of production, content rating, duration, aspect ratio, director, cast, budget, box office, number of reviews (by critics and users) and IMDB score.
The aim of the project is to analyze the TMDB Prediction Dataset.
Content-Based Recommender System recommends movies similar to the movie user likes and analyses the sentiments on the reviews given by the user for that movie.
Uncovering insights from more than 5000 movies using diverse visualizations, revealing key factors influencing film success
The goal of this project was to identify revenue trends in movies data and provide insights.
The main Goal of this movies Recommendation System provides movies suggestions to the users through a Content Based Filtering that is based on browsing history. It is an implementation of Machine Learning Algorithm.
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