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This intelligent movie recommend system works on an advance machine learning model which learns the taste of a perticular user by collecting relevant data of his/her recently watched movies.

Home Page: https://my-movie-guide.herokuapp.com/

License: Apache License 2.0

Procfile 0.07% Python 3.11% Shell 0.23% Jupyter Notebook 96.59%
cosine-distance cosine-similarity flask-application heroku movierecommendation

movie-guide's Introduction

Movie Guide

Confused? Can't figure out which movie to watch next? Here's the solution! Just click on the link of my "Movie Guide" website and enter the movie name that you have watched recently and BOOM! my advance Machine Learning enabled website will suggest you the movie name that you should watch next according to you taste based on which movie you have recently watched.

Website link : https://my-movie-guide.herokuapp.com/

Note: Open the web apllication preferably on Google Chrome.

Documentation

This intelligent movie recommend system works on an advance machine learning model which learns the taste of a perticular user by collecting relevant data of his/her recently watched movies.

For example, if a user recently watched a movie named "Toy Story" and that person enters the name of that movie on my website, then the machine learning model behind the sences captures the name of that perticular movie and fetch the name of that movie so that it can firstly look for any available sequal of that movie.

Then the ML model also looks for the other movies with similar cast, crew, release date-month-year and most importantly the genre matching to the movie the user has recently watched.

Tech Used

cosine_similarity and cosine_distance is used to make this advance recommendation system.

Formula : 1 - cosine_similarity = cosine_distance

For more info about cosine_similarity : https://en.wikipedia.org/wiki/Cosine_similarity

For better understanding : https://youtu.be/ieMjGVYw9ag

Demo

Here you can access the web-application.

Screen.Recording.2022-09-02.at.2.00.35.AM.mov

Screenshots

Screenshot 2022-09-02 at 2 03 27 AM

Lessons Learned

Learned how to use cosine_similarity to get a better recommendation based on the taste of every particular user.

Also leanred about ML model implementation using Streamlit framework wich is very convinient for those who doesn't know much about web-development.

๐Ÿš€ About Me

I am a AI and Machine Learning Enthusiast & growing Android Developer (kotlin). Both the fields, Machine Learning and Android Development, fascinates me a lot. And I also have worked on Azure Cloud Computing platform to deploy machine learning models.

๐Ÿ”— Links

linkedin twitter


License

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