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The proposed project aims to build a movie recommender system using Python, leveraging the TMDB API to fetch movie metadata from 1980 until 2023. The TMDB (The Movie Database) is an online database that provides comprehensive information related to movies, TV shows, and other forms of visual media.

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movierecommender 🎥

The proposed project aims to build a movie recommender system using Python, leveraging the TMDB API to fetch movie metadata from 1980 until 2023. The TMDB (The Movie Database) is an online database that provides comprehensive information related to movies, TV shows, and other forms of visual media.

Table of contents

  1. Project Motivation
  2. How to run the project
  3. Credits
  4. License

Project Motivation

Movie recommendation systems have gained popularity in recent years due to the overwhelming number of movies available for viewers to choose from. The rise of streaming services such as Netflix, Hulu, and Amazon Prime has made it difficult for viewers to decide which movie to watch. Thus, to address this issue, we have decided to develop a project that utilizes TMDB's API, which provides readily available movie metadata, as a solution for movie selection dilemmas.

How to run the project

Prerequisites

  • Python 3.x installed in your local machine
  • Jupyter Notebook installed in your local machine

Installation

  1. Open a terminal window and navigate to the local directory where you want to clone the repository:
cd <repository-name>
  1. Clone this repository to your local machine using the following command:
git clone https://github.com/c3sk/movierecommender.git
  1. Launch Jupyter Notebook by running the following command:
jupyter notebook

Usage

  1. In the Jupyter Notebook interface, navigate to the project's directory and open the notebook file (movie_recommender.ipynb).
  2. Once the notebook is open, follow the instructions and run the code cells in sequence to reproduce the results.

Credits

This project was created by a team of three programmers:

  • Francesc Vilaró (@c3sk): Contributed mainly to the database creation and data fetching.
  • Berta Pfaff (@BertaPfaff): Contributed mainly to the exploratory data analysis and data visualization sections of the project.
  • Sergio Salvador (@Sersal10): Implemented the machine learning model.

We would like to thank our colleagues and mentors for their support and feedback throughout the project. Special thanks to Luciano Gabanelli (@LuchoGabba) for supervising the execution of this project.

License

This project is licensed under the GNU General Public License v3.0 - see the LICENSE file for details.

The GPL is a copyleft license that requires anyone who distributes this software or a derivative work to make the source code available under the same license. This ensures that the software remains free and open-source, and that any modifications or improvements made to the software also benefit the wider community.

By using, copying, or modifying this software, you agree to be bound by the terms of the GPL v3.0. If you do not agree to these terms, you must not use or distribute this software.

Please read the LICENSE file carefully before using or distributing this software. If you have any questions or concerns about the license, please contact us at francescvilaro@hotmail.com

About

The proposed project aims to build a movie recommender system using Python, leveraging the TMDB API to fetch movie metadata from 1980 until 2023. The TMDB (The Movie Database) is an online database that provides comprehensive information related to movies, TV shows, and other forms of visual media.

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