I would like some modifications to this code with explanation.

$($I use Logistic regression, and Dataset: Female Diabetes$).$

$-$ The accuracy for training the model on all features is approximately: $0\mathrm{.}8116883116883117.$ I tried to improve the model$$s performance by deleting outliers or feature selection, and the accuracy was either lower or was the same as the previous accuracy. I do not know if I was determining the most influential features correctly or incorrectly.

I want to improve the model in any way, with a diagram that shows...and an explanation of the steps in detail

$-$ Also, one of the things required in this lab is to provide interesting information that I found while performing the lab. What can I provide?

$-$Please be creative and show me your best.$$ This is also one of the things required in the laptop. How can I achieve it in this model?

$*$I have attached the requirements and part of the data$*$

$-------------------------------------------------$

$-$CODE:

import pandas as pd

# Assuming the dataset is in CSV format

df $=$ pd$.$read$\_$csv$(\text{'}/$path$/$to$/$your$/$female$\_$diabetes.csv$\text{'})$

print$($df$.$head$())$ # Display the first few rows

print$($df$.$info$())$ # Get info on data types and non$-$null counts

print$($df$.$describe$())$ # Summary statistics

print$($df$.$isnull$().$sum$())$

df$.$fillna$($df$.$mean$(),$ inplace$=$True$)$

from sklearn.preprocessing import StandardScaler

scaler $=$ StandardScaler$()$

features $=$ df$.$iloc$[$:$,$ :$-1]$ # All columns except the last one

scaled$\_$features $=$ scaler.fit$\_$transform$($features$)$

df$.$iloc$[$:$,$ :$-1]=$ scaled$\_$features

from sklearn.model$\_$selection import train$\_$test$\_$split

X $=$ df$.$iloc$[$:$,$ :$-1]$ # Features

y $=$ df$.$iloc$[$:$,-1]$ # Labels

X$\_$train, X$\_$test, y$\_$train, y$\_$test $=$ train$\_$test$\_$split$($X$,$ y$,$ test$\_$size$=0\mathrm{.}2,$ random$\_$state$=42)$

from sklearn.linear$\_$model import LogisticRegression

from sklearn.metrics import accuracy$\_$score, confusion$\_$matrix, classification$\_$report, roc$\_$auc$\_$score, roc$\_$curve, auc

model $=$ LogisticRegression$()$

model.fit$($X$\_$train, y$\_$train$)$

y$\_$pred $=$ model.predict$($X$\_$test$)$

y$\_$prob $=$ model.predict$\_$proba$($X$\_$test$)[$:$,1]$

print$("$Logistic Regression Accuracy:", accuracy$\_$score$($y$\_$test, y$\_$pred$))$

print$("$Confusion Matrix:

$",$ confusion$\_$matrix$($y$\_$test, y$\_$pred$))$

print$("$Classification Report:

$",$ classification$\_$report$($y$\_$test, y$\_$pred$))$

import matplotlib.pyplot as plt

import seaborn as sns

cm $=$ confusion$\_$matrix$($y$\_$test, y$\_$pred$)$

sns$.$heatmap$($cm$,$ annot$=$True, fmt$=\text{'}$d$\text{'},$ cmap$=$'Blues'$)$

plt$.$xlabel$(\text{'}$Predicted$\text{'})$

plt$.$ylabel$(\text{'}$True$\text{'})$

plt$.$title$(\text{'}$Confusion Matrix $-$ Logistic Regression'$)$

plt$.$show$()$

fpr$,$ tpr$,$ thresholds $=$ roc$\_$curve$($y$\_$test, y$\_$prob$)$

roc$\_$auc $=$ auc$($fpr$,$ tpr$)$

plt$.$figure$()$

plt$.$plot$($fpr$,$ tpr$,$ color$=$'darkorange', lw$=2,$ label$=$'ROC curve $($area $=\%0\mathrm{.}2$f$)\text{'}\%$ roc$\_$auc$)$

plt$.$plot$([0,1],[0,1],$ color$=$'navy', lw$=2,$ linestyle$=\text{'}--\text{'})$

plt$.$xlim$([0\mathrm{.}0,1\mathrm{.}0])$

plt$.$ylim$([0\mathrm{.}0,1\mathrm{.}05])$

plt$.$xlabel$(\text{'}$False Positive Rate'$)$

plt$.$ylabel$(\text{'}$True Positive Rate'$)$

plt$.$title$(\text{'}$Receiver Operating Characteristic $-$ Logistic Regression'$)$

plt$.$legend$($loc$=$"lower right"$)$

plt$.$show$()$

$----------------------------------------------------------------------------------------------$

Dataset $(0)$:

Female Diabetes dataset contains $768$ records. It has $8$ features and one label for each record as

described below:

$1-$ Number of times pregnant

$2-$ Plasma glucose concentration a $2$ hours in an oral glucose tolerance test

$3-$ Diastolic blood pressure $($mm Hg$)$

$4-$ Triceps skin fold thickness $($mm$)$

$5-2-$Hour serum insulin $($mu U$/$ml$)$

$6-$ Body mass index $($weight in kg$/($height in m$)^2)$

$7-$ Diabetes pedigree function

$8-$ Age $($years$)$

$9-$ Class variable $(0\{$has diabetes$\}$ or $1\{$has not diabetes$\})$