# How I created a movie recommendation system? PRACTICAL

So have you ever thought of how Netflix works?

Amazon product recommendations, Facebook friends, Linkedin connections

Hell lot of applications are there that can be used with this recommendation system.

# Let's Decode this; (Reverse Order)

We need 50 movies related to a movie

so finally we need a list of 50 films like this =[“ Harry Potter”,” StartDust”,” Hobbit”,” Avengers”,…,” Mickey Mouse”]

Now we can say **Harry** **Potter **is related to **Hobbit** but very different from **Mickey Mouse**

So let's convert this into numbers(now random will be better later on😛)

Lets assume list of numbers say =[**1**,0.8,**0.9**,0.4,….,0.01]

where 0.9 is **Hobbit** and 1 is **Harry Potter**

So we can have a sorted list and we can directly say which movie is close to Harry Potter right?

Cool, Now Let’s hover on how to get these Numbers

This is known as **similarity_score **check

## Text A: [“Apple Ball Apple”]

## Text B: [“Ball Ball Apple”]

How much they’re similar?

Apple:2 Ball:1 **P1(2,1)**

Ball:2 Apple:1 **P2(1,2)**

Lets plot this

Now Let’s calculate euclidean distance sqrt((1–2)²+(2–1)²) = 1.414

This can be done using predefined function **cosine_similarity **function in **sklearn**

Let’s dive into coding now!

we will download a dataset *movie_dataset.csv*

https://drive.google.com/file/d/1CoosLdeiHjFDPmFgiBTGDJTClaSNZrNC/view?usp=sharing

Now let's read this csv using pandas and run the below code

This finally gives result as movies to recommend are:

Thank you Code Heroku