Practical example – creating a recommendation engine

Let's build a recommendation engine that can recommend movies to a bunch of users. We will be using data put together by the GroupLens Research research group at the University of Minnesota.

 

Follow these steps:

1. First, we will import the relevant packages:

import pandas as pd 
import numpy as np

2. Now, let's import the user_id and item_id datasets:

df_reviews = pd.read_csv('reviews.csv')
df_movie_titles = pd.read_csv('movies.csv',index_col=False)

3. We merge the two DataFrames by the movie ID:

df = pd.merge(df_users, df_movie_titles, on='movieId')

The header of the df DataFrame, after running the preceding code, looks like the following:

The details of the columns are as follows:

• • userid: The unique ID of each of the users

  • movieid: The unique ID of each of the...