The nbaallelo$\_$log$.$csv file contains data on $126314$ NBA games from $1947$ to $2015.$ The dataset includes the features pts$,$ elo$\_$i$,$ win$\_$equiv, and game$\_$result. Using a sample of the csv file nbaallelo$\_$log$.$csv and scikit$-$learn's 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. Using scikit$-$learn's metrics module, calculate the various classification metrics for the model.

Build a logistic model with default parameters, fit to the input and output features X and y$.$

Use the model to predict the classification of instances in X$.$

Calculate the confusion matrix for the model.

Calculate the accuracy for the model.

Calculate the precision for the model.

Calculate the recall for the model.

Calculate kappa for the model.

import numpy as np

import pandas as pd

from sklearn import metrics

from sklearn.preprocessing import StandardScaler

from sklearn.linear$\_$model import LogisticRegression

# Input the random state

rand $=$ int$($input$())$

# Load sample set by a user$-$defined random state into a dataframe.

NBA $=$ pd$.$read$\_$csv$("$nbaallelo$\_$log$.$csv$").$sample$($n$=500,$ random$\_$state$=$rand$)$

# Create binary feature for game$\_$result with $0$ for L and $1$ for W

NBA$[\text{'}$win$\text{'}]=$ NBA$[\text{'}$game$\_$result'$].$replace$($to$\_$replace $=[\text{'}$L$\text{'},\text{'}$W$\text{'}],$ value $=[$int$(0),$ int$(1)])$

# Store relevant columns as variables

X $=$ NBA$[[\text{'}$elo$\_$i$\text{'}]]$

y $=$ NBA$[[\text{'}$win$\text{'}]]$

# Build logistic model with default parameters, fit to X and y

# Your code here

# Use the model to predict the classification of instances in X

logPredY $=$ # Your code here

# Calculate the confusion matrix for the model

confMatrix $=$ # Your code here

print$("$Confusion matrix:

$",$ confMatrix$)$

# Calculate the accuracy for the model

accuracy $=$ # Your code here

print$("$Accuracy:$",$ round$($accuracy$,3))$

# Calculate the precision for the model

precision $=$ # Your code here

print$("$Precision:$",$ round$($precision$,3))$

# Calculate the recall for the model

recall $=$ # Your code here

print$("$Recall:$",$ round$($recall$,3))$

# Calculate kappa for the model

kappa $=$ # Your code here

print$("$Kappa:$",$ round$($kappa$,3))$