5.10 LAB: Evaluating logistic regression using LogisticRegression() Using the csv file nbaallelo_log.csv and the LogisticRegression function, construct a logistic regression model to classify whether a team will win or lose a game based on the team's elo_i score and evaluate the model. Read in the file nbaaello_log.csv. The target feature will be converted from string to a binary feature by the provided code. Split the data into 70 percent training set and 30 percent testing set. Set random_state = 0. Use the LogisticRegression function to construct a logistic regression model with wins as the target and elo_i as the predictor. Use the test set to predict the wins from the elo_i score. Construct and print the confusion matrix. Calculate and print the sensitivity. Calculate and print the specificity. Note: Use ravel() from numpy to flatten the second argument of LogisticRegression.fit() into a 1-D array. Ex: If the feature pts is used as the predictor, rather than elo_i, the output is: confusion matrix is [[12220 6730] [ 6530 12415]] Sensitivity is 0.644855 Specificity is 0.655318