Netflix Recommendation System with Python

The use of a Recommendation system is to provide users with recommendations based on their search preferences. In this article, I will introduce you to a machine learning project on the Netflix recommendation system with Python.

Netflix Recommendation System

Netflix is ​​a company that manages a large collection of TV shows and movies, streaming it anytime via online. This business is profitable because users make a monthly payment to access the platform. However, customers can cancel their subscriptions at any time.

Also, Read – 100+ Machine Learning Projects Solved and Explained.

Therefore, the company must keep the users hooked on the platform and not lose their interest. This is where recommendation systems start to play an important role, providing valuable suggestions to users is essential.

Netflix’s recommendation system helps them increase their popularity among service providers as they help increase number of items sold, offer a diverse selection of items, increase user satisfaction, as well as user loyalty to the company, and they are very helpful in getting a better understanding of what the user wants. Then it’s easier to get the user to make better decisions from a wide variety of movie products.

The idea behind the Netflix recommendation system is to recommend the most popular movies to users. They could be the most-watched, or also the ones with the highest ratings. The popularity of recommendations can be built based on usage data and article content.

Netflix Recommendation System with Python

In this section, I will take you through a Machine Learning project on Netflix Recommendation System with Python. I will start by importing all the python libraries and the dataset:

Now let’s have a look at the spread of the dataset and the ratings of the people on Netflix movies:

We see that the score tends to be relatively positive (> 3). This may be because unhappy customers tend to just leave instead of making an effort to rate. We can keep this in mind that low-rating movies mean they’re generally really bad.

Data Preparation

The Movie ID column is a mess. We’re going to improve this by first creating a NumPy array with the correct length, then adding the entire array as a column in the main dataframe:

The dataset is super huge. Therefore, we need to reduce the data volume by improving data quality. We can use these two approaches:

1. Delete the movie with too many reviews (they are relatively unpopular)
2. Remove the customer who gives too less notice (he is relatively less active)

Netflix Recommendation Algorithm

Now let’s build the Netflix recommendation system:

For movie (What the #$*! Do We Know!?)
- Top 10 movies recommended based on Pearsons'R correlation - 
 PearsonR                                      Name  count      mean
 1.000000                What the #$*! Do We Know!?  14910  3.189805
 0.505500                                 Inu-Yasha   1883  4.554434
 0.452807  Captain Pantoja and the Special Services   1801  3.417546
 0.442354                 Without a Trace: Season 1   2124  3.980226
 0.384179                      Yu-Gi-Oh!: The Movie   3173  3.331547
 0.383959                                  Scorched   2430  2.894239
 0.381173   All Creatures Great and Small: Series 1   2327  3.938118
 0.381112           As Time Goes By: Series 1 and 2   2249  4.164073
 0.373018                          Cowboys & Angels   2368  3.589527
 0.371981                            Biggie & Tupac   1866  3.019293

I hope you liked this article on a machine learning project on Netflix recommendation system with Python. Feel free to ask your valuable questions in the comments section below.