Building a Basic Recommender System in Python

In this article, we will learn how to build a simple recommender system in Python using the MovieLens dataset. Image courtesy of https://www.dasca.org/

A recommender system is a type of information filtering system that helps users find items that they might be interested in. Recommender systems are commonly used in e-commerce, social media, and other applications to help users find products or content that they might like based on their preferences or past behavior. In this article, we will learn how to build a simple recommender system in Python using the MovieLens dataset.

The MovieLens dataset is a popular dataset for building and testing recommender systems. It contains ratings for over 10,000 movies from around 600 users. We will use this dataset to build a simple collaborative filtering recommender system that recommends movies to users based on their past ratings.

Step 1: Loading the Data

The first step in building a recommender system is to load the data. We will use the pandas library to load the data from the MovieLens dataset. Here’s the code to do that:

import pandas as pd

# Load the ratings dataset
ratings_data = pd.read_csv('ratings.csv')

# Load the movies dataset
movies_data = pd.read_csv('movies.csv')

The ratings dataset contains information about user ratings for each movie. It has four columns: userId, movieId, rating, and timestamp. The movies dataset contains information…