import pandas as pd

from sklearn.preprocessing import StandardScaler

from sklearn.model$\_$selection import train$\_$test$\_$split

from sklearn.tree import DecisionTreeClassifier

from sklearn.metrics import confusion$\_$matrix

df $=$ pd$.$read$\_$csv$(\text{'}$nbaallelo$\_$log$.$csv$\text{'})$

X $=$ df$[[\text{'}$pts$\text{'},$ 'elo$\_$i$\text{'},$ 'win$\_$equivalent'$]]$

y $=$ df$[\text{'}$game$\_$result'$]$

X$\_$standardized $=$ StandardScaler$().$fit$\_$transform$($X$)$

X$\_$train, X$\_$test, y$\_$train, y$\_$test $=$ train$\_$test$\_$split$($X$,$ y$,$ test$\_$size$=0\mathrm{.}25)$

dt$\_$classifier $=$ DecisionTreeClassifier$()$

dt$\_$classifier.fit$($X$\_$train, y$\_$train$)$

y$\_$pred $=$ dt$\_$classifier.predict$($X$\_$test$)$

conf$\_$matrix $=$ confusion$\_$matrix$($y$\_$test, y$\_$pred$)$

print$(\text{'}$Confusion Matrix:$\text{'})$

print$($conf$\_$matrix$)$