How Build A Movie Recommendation System Using Python | Python Tutorial For Beginners | Simplilearn

In this video, we are going to cover how to build a movie recommendation system using Python. This video will help you to understand what is a movie recommendation system, How a movie recommendation system works, the Filtering Strategies of the recommendation system - Filtering based on content, and Collaboration filtering after which will do a hands-on lab demo to build a movie recommendation system using python.

✅ How To Build A Movie Recommendation System
03:00 What is a movie recommendation system
03:30 How the movie recommendation system works
04:24 Filtering strategies of movie recommendation system.
09:00 Hands-on lab demo

➡️ What is Movie Recommendation System?
A movie recommendation system, also known as a movie recommender system, uses machine learning (ML) to predict or filter users' film preferences based on their prior decisions and actions.

➡️ How does the movie recommendation system work?
Every recommender system primarily consists of two components: users and items. Users receive movie predictions from the system, and the actual movies are the products.
Filtering and predicting only the movies that a matching user is most likely to wish to see is the main objective of movie recommendation systems. The user information from the system's database is used by the ML algorithms for these recommendation systems. Based on information from the past, this data is used to forecast the user in question's behavior in the future.

➡️ Filtering Strategies of recommendation system
- Filtering based on content
A method of filtering movies in movie recommendation systems that makes advantage of the items' data (movies). This information, which is taken from just one user, is quite important in this case. This technique uses an ML algorithm to suggest movies that are comparable to the user's past choices. Therefore, the information about the prior movie choices and likes of just one person is used to generate similarity in content-based filtering.

- Collaboration Filtering
As the name implies, this filtering technique is based on the interactions between the relevant person and other users. For the best outcomes, the system contrasts and compares these behaviors. It combines the film choices and usage patterns of several people.

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[bikkishaw]

which filtering technique you have used? is it content- based or collaboratobe-based

[chamodnilupul]

How can you use movies in line of 47 instead of movies_df ?

[arychouhan]

I am getting name error in input 47 . It is showing name error that name movies not defined.

[SajjadZarei-toee]

Thank you so much, please share full code 🙏🏻

[raghulraj]

Hey, Thank you so much for the content. It's really helpful in brushing up the basics. However, I wanted to point out that you're vectorizing the text before applying stemming. I feel it should be the other way around. Please take a look!

[shekharkumar]

getting error as : 'NoneType' object is not iterable
at code:
movies["genres"]= x:[i.replace(" ", "")for i in x])

[manojyadav-ejkz]

How to measure it's performance.. Kindly make that video also

[DewangShaw]

Which filtering technique you have used

[sanjanashinde]

shall we use this as a final year project(BE COMP)???

[tejasmore]

hi sir i understood your code now i have to deploy this project on we using django can you guide me on this

[hardishah]

What code should i write if in the end i want to display whole info of the movie like cast, rating(which was in the original table). Like it gives whole row along with title also

[Nothing-iuuy]

We can do it by api too isn't it?

[comparethis-pg]

The best Machine Learning language is Python 🐍

[daisygracedkhar]

what is 'obj ' that is used in function

[msl_lingam_]

Sir I have some project how Start how to do I cont understand can I send you project questions

[daveherring]

excellent video. i can follow but when i try to vectorize my variables i crash my google colab, any suggestions?

[alifallahi]

hey thanks for that great course
after that code: len(cv.get_features_names())
I got an Error:
'CountVectorizer' object has no attribute 'get_feature_names'
what should i do?

[saisandeep]

movies_df['genres'] =
movies_df['keywords'] =
movies_df.head()
ValueError: malformed node or string: ['Action'] this is the error

in last recommendation is giving same for all the movies
recommend('Avatar')
Pirates of the Caribbean: At World's End
Spectre
The Dark Knight Rises
John Carter
Spider-Man 3
M
recommend('Iron M

recommend('Iron Man')
Pirates of the Caribbean: At World's End
Spectre
The Dark Knight Rises
John Carter
Spider-Man 3