Skip to content

saugad88/movie_recommender_system

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

3 Commits
 
 
 
 

Repository files navigation

Movie-Recommender-System

In an age where entertainment options are more abundant than ever, choosing the perfect movie to watch can be an impossible task. We've all been there: scrolling endlessly through Netflix and Hulu just to settle on a mediocre movie because you're just too overwhelmed by the options. In the 80s, there were only about a hundred movies released per year. Now, there's over 800 movies made per year in the US alone! Luckily, we've used our knowledge about data science and machine learning to help you make the perfect choice.

The purpose of this project is not just to create a movie recommender, it is mainly to walk you through the data science lifecycle. There are steps that most data scientists follow in a project, which we will The first is data collection, which consists of looking for data that could be interesting to analyze based on a central inquiry. Next comes cleaning and processing the data so that it is in a usable form for analysis. The processed data is then visualized for better understanding of trends and spread. Once the data is analyzed and visualized, it is modelled through ML. Finally, the analyzed data and the results of the model drive new in sights on the central question and policy decisions can be made with greater certainty.

We hope you enjoy this journey into the world of data science and find our results intriguing!

About

A Tutorial on Data Science through Movies applies the full data-science pipeline—data collection, cleaning, visualization, and machine learning—to explore what makes a movie successful. Using APIs like OMDB and TMDB, it analyzes factors such as budget, genre, and rating to predict hits and recommend films.

Topics

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

 
 
 

Contributors