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:

[[10317 5522] [ 5021 10719]]