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. # import the necessary libraries

# load nbaallelo$\_$log$.$csv into a dataframe

df $=$ # code to load csv file

# Converts the feature "game$\_$result" to a binary feature and adds as new column "wins"

wins $=$ df$.$game$\_$result $=="$W$"$

bool$\_$val $=$ np$.$multiply$($wins$,1)$

wins $=$ pd$.$DataFrame$($bool$\_$val, columns $=["$game$\_$result"$])$

wins$\_$new $=$ wins.rename$($columns $=\{"$game$\_$result": "wins"$\})$

df$\_$final $=$ pd$.$concat$([$df$,$ wins$\_$new$],$ axis$=1)$

# split the data df$\_$final into training and test sets with a test size of $0\mathrm{.}3$ and random$\_$state $=0$

train, test $=$ # code to split df$\_$final into training and test sets

# build the logistic model using the LogisticRegression function with wins as the target variable and elo$\_$i as the predictor.

# use the test set to predict the wins from the elo$\_$i score

predictions $=$ # code to predict wins

# generate confusion matrix

conf $=$ # code to generate confusion matrix

print$("$confusion matrix is

$",$ conf$)$

# calculate the sensitivity

sens $=$ # code to calculate the sensitivity

print$($f$\text{'}$Sensitivity is $\{$sens:$\mathrm{.}6$f$\}\text{'})$

# calculate the specificity

spec $=$ # code to calculate the specificity

print $($f$\text{'}$Specificity is $\{$spec:$\mathrm{.}6$f$\}\text{'})$