Support code for Chapter 8: Recommendations.

The code refers to the second edition of the book and this code has been significantly refactored when compared to the first one.

Note that since the partition of the data into training and testing is random, everytime you run the code, the results will be different.

load_ml100k.py

Load data & partition into test/train

norm.py

Normalize the data

corrneighbours.py

Neighbour models based on ncrroaltoin

regression.py

Regression models

stacked.py

Stacked predictions

averaged.py

Averaging of predictions (mentioned in book, but code is not shown there).

Check the folder apriori/

apriori/histogram.py

Print a histogram of how many times each product was bought

apriori/apriori.py

Implementation of Apriori algorithm and association rule building

apriori/apriori_example.py

Example of Apriori algorithm in retail dataset