www.allitebooks.com

Chapter 3
If you are facing trouble extracting features of these types, check the pandas
documentation at http://pandas.pydata.org/pandas-docs/stable/ for help.
Alternatively, you can try an online forum such as Stack Overflow for assistance.
More extreme examples could use player data to estimate the strength of each
team's sides to predict who won. These types of complex features are used every
day by gamblers and sports betting agencies to try to turn a profit by predicting the
outcome of sports matches.
Summary
In this chapter, we extended our use of scikit-learn's classifiers to perform
classification and introduced the pandas library to manage our data. We analyzed
real-world data on basketball results from the NBA, saw some of the problems that
even well-curated data introduces, and created new features for our analysis.
We saw the effect that good features have on performance and used an ensemble
algorithm, Random forests, to further improve the accuracy.
In the next chapter, we will extend the affinity analysis that we performed in the first
chapter to create a program to find similar books. We will see how to use algorithms
for ranking and also use approximation to improve the scalability of data mining.
[ 59 ]