Recommender Systems with Machine Learning - Dataset Usage

The video tutorial covers the development of a Spotify songs recommender system using content-based filtering. It introduces the necessary Python libraries, such as pandas, numpy, and matplotlib, and demonstrates how to load and explore a dataset of songs. The tutorial also discusses identifying missing values in the dataset and plans to develop a function for this purpose in the next video.

What is the primary purpose of the content-based filtering discussed in the video?

• To filter out unwanted songs from a playlist

• To recommend songs based on user preferences

• To create a new music streaming service

• To analyze the lyrics of songs

Which Python library is NOT mentioned as necessary for developing the recommender system?

• pandas

• numpy

• matplotlib

• scikit-learn

How many songs are included in the dataset used in the video?

• 12000

• 10000

• 9643

• 5000

What information is NOT part of the song details in the dataset?

• Title

• Artist

• Year

• Genre

What is the next step planned for handling missing values in the dataset?

• Ignoring the missing values

• Filling missing values with zeros

• Creating a new data frame to identify missing values

• Deleting all rows with missing values