$7\mathrm{.}6$ LAB: Performing logistic regression using LogisticRegression$()$

The nbaallelo$\_$log 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 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.

Hot encode the game$\_$result variable as a numeric variable with $0$ for $L$ and $1$ for $W$

Use the LogisticRegression function to construct a logistic regression model with game$\_$result as the target and elo$\_$i as

the predictor.

Predict the probability of a win from an elo$\_$i score of $1310.$

Predict whether a team with an elo$\_$i score of $1310$ will win.

Note: Use ravel $()$ from numpy to flatten the second argument of LogisticRegression. fit $()$ into a $1-$D array.

Ex: If a elo$\_$i score of $1410$ is used instead of $1310,$ the output is:

A team with the given elo$\_$i score has predicted probability:

$0\mathrm{.}593$ losing

$0\mathrm{.}407$ winning

and the overall prediction is $0$

# Import the necessary Libraries

import pandas as pd

from sklearn.linear$\_$model import LogisticRegression

import numpy as $np$

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

NBA $=$ pd$.$read$\_$csv$("$nbaallelo$\_$log$.$csv$")$

# Hot encode the game$\_$result variable as a numeric variable with $0$ for $L$ and $1$ for $W$

# Your code here

# Store relevant columns as variables

$x=$ NBA$[[\text{'}$elo$\_$i$\text{'}]].$values.reshape $(-1,1)$

'game$\_$result'$]].$values.reshape $(-1,1).$astype$($int$)$

# Initialize and fit the logistic model using the LogisticRegression function

# Your code here

# Predict the probability that an elo$\_$i score of $1310$ is a win $/$ loss

outcomeProb $=$# Your code here

# Predict whether an elo$\_$i score of $1310$ is a win $(1)$ or loss $(0)$

outcomePred $=$# Your code here

print$("$A team with the given elo$\_$i score has predicted probability:

$",$ end$="")$

print$(\text{'}\%\mathrm{.}3$f$\text{'}\%$ outcomeProb$[0][1]+"$ winning"$)$

print$("$and the overall prediction is$",$

outcomePred $[0])$