Netflix Recommendation System

Deployment Site @ https://davidsonity-netflix-recommendation-app-gopbme.streamlit.app/

View Notebook @ https://github.com/Davidsonity/Netflix-Recommendation-System/blob/main/NoteBook/Netflix_Recommendation_Engine.ipynb

INTRODUCTION

The content-based recommender system is highly based on the similarity calculation among items. The similarity or closeness of items is measured based on the similarity in the content or features of those items. The important features used in this project are:

• DIRECTOR
• CAST
• COUNTRY
• GENRES
• TYPE

Main Objectives

The main objective of this project is to create a recommendation engine to recommend similar movies to users.

About Dataset

Netflix is one of the most popular media and video streaming platforms. They have over 8000 movies or tv shows available on their platform, as of mid-2021, they have over 200M Subscribers globally. This tabular dataset consists of listings of all the movies and tv shows available on Netflix, along with details such as - cast, directors, ratings, release year, duration, etc.

Data Source: https://www.kaggle.com/datasets/shivamb/netflix-shows

Steps taken to build the recommender systems

• Data Collection
• Data Wrangling.
• Data Cleaning
• Build the Recommender System
• Build the app using Streamlit

Website

https://davidsonity-netflix-recommendation-app-gopbme.streamlit.app/