Model Building
In the vast and ever-evolving field of recommender systems, there are numerous approaches you can explore to enhance user experiences and drive personalized recommendations. Some of the popular methods include collaborative filtering, content-based filtering, and hybrid models that combine the strengths of both.
For the purpose of this project, we will delve into the fascinating realm of collaborative filtering, utilizing the powerful capabilities of Python's scikit-surprise library. By leveraging collaborative filtering, we can effectively match users with similar tastes and preferences, consequently providing them with tailored suggestions that align with their unique interests and preferences.
Installation and Importing Libraries
First, install the necessary packages:
pip install numpy pandas scikit-surprise
Now, let's import the libraries:
from surprise import Reader, Dataset, SVD
from surprise.model_selection import cross_validate
import pandas...