List of Listings

Chapter 2. Extracting structure from data: clustering and transforming your data

Listing 2.1. Interrogating the Iris dataset in scikit-learn (interactive shell)

Listing 2.2. Interrogating the Iris dataset in scikit-learn (interactive shell, continued)

Listing 2.3. K-means pseudo-code: expectation maximization

Listing 2.4. The k-means algorithm in action

Listing 2.5. Visualizing the output of k-means

Listing 2.6. Clustering the Iris dataset using a Gaussian mixture model

Listing 2.7. Principal component analysis of the Iris dataset

Chapter 3. Recommending relevant content

Listing 3.1. Calculating the similarity of users based on their ratings of movies

Listing 3.2. Calculating the similarity between two users

Listing 3.3. User-based CF recommendation

Listing 3.4. recommend method

Listing 3.5. Predicting the ratings of a user

Listing 3.6. SimilarityMatrix class

Listing 3.7. RatingCountMatrix class

Listing 3.8. SVD for recommendation

Listing 3.9. Factorizing a matrix using SVD for recommendation

Listing 3.10. Output from model-based recommendation

Listing 3.11. Films rated by user 10

Listing 3.12. Investigating user recommendations in model-based CF

Listing 3.13. Analyzing the ratings of users recommended Star Wars: TPM

Listing 3.14. Calculating the root-mean-square error for a recommender

Chapter 4. Classification: placing things where they belong

Listing 4.1. Importing the libraries and dataset

Listing 4.2. Masking categorical features