Construct a decision tree to classify whether an NBA team won or lost a game based on the team's points in the game, elo score entering

the game, and win equivalence.

Read nbaallelo$\_$log$.$csv into a data frame.

Subset the data containing pts$,$ elo$\_$i$,$ and win$\_$equivalent.

Subset the data containing the labels, which are in the feature game$\_$result.

Standardize the data.

Split the data into $75\%$ train and $25\%$ test sets.

Use DecisionTreeClassifier$()$ to initialize a classification tree using the train data.

Use the classification tree to predict the results for the test data.

Construct and print the confusion matrix. Ex: If only pts and elo$\_$i are used, the output is:

$\left[\begin{array}{c}10317,5522\\ 5021,10719\end{array}\right]$

Here is my current code:

import pandas as pd

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

from sklearn.preprocessing import StandardScaler

from sklearn.tree import DecisionTreeClassifier

from sklearn.metrics import confusion.matrix

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

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

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

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

scaler $=$ StandardScaler$()$

scaler.fit$($x$\_$train$)$

x$\_$train $=$ scaler.transfrom$($x$\_$train$)$

x$\_$test $=$ scaler.transfrom$($x$\_$test$)$

cart $=$ DecisionTreeClassifier$()$

cart.fit$($x$\_$train, y$\_$train$)$

y$\_$pred $=$ cart.predict$($x$\_$test$)$

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

print $=($conf$)$