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]]

# load the necessary libraries

# load the dataset df =

# subset the data containing pts, elo_i, and win_equivalent x = # code to subset the data

# subset the data containing the labels y = # code to subset the data

# splits the data into 75 % training and 25 % test sets. Set random_state = 0

# standardizes the data scaler = StandardScaler() scaler.fit(x_train) x_train = scaler.transform(x_train) x_test = scaler.transform(x_test)

# initializes and run the decision tree cart = # to initialize the decision tree with the training data y_pred = # code to run the decision tree on the test data

# give the confusion matrix conf = # code to calculate the confusion matrix

print(conf)