import pandas as pd

import numpy as np

import matplotlib.pyplot as plt

import seaborn as sns

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

from sklearn.pipeline import Pipeline

from sklearn.impute import SimpleImputer

from sklearn.preprocessing import StandardScaler, OneHotEncoder

from sklearn.compose import ColumnTransformer

from sklearn.preprocessing import StandardScaler

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

from sklearn.linear$\_$model import LogisticRegression

from sklearn.tree import DecisionTreeClassifier

from sklearn.ensemble import RandomForestClassifier

from sklearn.svm import SVC

from sklearn.neighbors import KNeighborsClassifier

# Load dataset

df $=$ pd$.$read$\_$csv$(\text{'}$heart$\_$disease$\_$uci.csv$\text{'})$

# Check the first few rows of the dataset

df$.$head$()$

# # Get basic information and summary statistics

df$.$info$()$

df$.$describe$()$

# # Check Data Types and Missing Values

# Data types and missing values

df$.$info$()$

# Checking for missing values

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

# # Distribution of Age

plt$.$figure$($figsize$=(10,6))$

sns$.$histplot$($df$[\text{'}$age$\text{'}],$ bins$=20,$ kde$=$True, color$=$'blue'$)$

plt$.$title$(\text{'}$Age Distribution'$)$

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

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

plt$.$show$()$

# # Heart Disease by Sex

plt$.$figure$($figsize$=(8,5))$

sns$.$countplot$($x$=$'sex', hue$=$'num', data$=$df$)$

plt$.$title$(\text{'}$Heart Disease by Gender'$)$

plt$.$xlabel$(\text{'}$Sex $(0=$ Female, $1=$ Male$)\text{'})$

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

plt$.$legend$($title$=$'Heart Disease', labels$=[\text{'}$No$\text{'},$ 'Yes'$])$

plt$.$show$()$

# # Chest Pain Type $($cp$)$ Distribution

plt$.$figure$($figsize$=(8,5))$

sns$.$countplot$($x$=\text{'}$cp$\text{'},$ data$=$df$)$

plt$.$title$(\text{'}$Chest Pain Type Distribution'$)$

plt$.$xlabel$(\text{'}$Chest Pain Type $(0-3)\text{'})$

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

plt$.$show$()$

# # Correlation Heatmap

import pandas as pd

import numpy as np

import seaborn as sns

import matplotlib.pyplot as plt

# Load dataset

df $=$ pd$.$read$\_$csv$(\text{'}$heart$\_$disease$\_$uci.csv$\text{'})$

# Convert categorical columns to numeric

df$[\text{'}$sex$\text{'}]=$ df$[\text{'}$sex$\text{'}].$map$(\{\text{'}$Male$\text{'}$: $1,$ 'Female': $0\})$

df$[\text{'}$cp$\text{'}]=$ pd$.$Categorical$($df$[\text{'}$cp$\text{'}]).$codes

df$[\text{'}$thal$\text{'}]=$ pd$.$Categorical$($df$[\text{'}$thal$\text{'}]).$codes

df$[\text{'}$fbs$\text{'}]=$ df$[\text{'}$fbs$\text{'}].$map$(\{$True: $1,$ False: $0\})$

df$[\text{'}$dataset$\text{'}]=$ pd$.$Categorical$($df$[\text{'}$dataset$\text{'}]).$codes # Convert 'dataset' column to numeric if needed

# Convert other categorical columns

df$[\text{'}$restecg$\text{'}]=$ pd$.$Categorical$($df$[\text{'}$restecg$\text{'}]).$codes

df$[\text{'}$slope$\text{'}]=$ pd$.$Categorical$($df$[\text{'}$slope$\text{'}]).$codes

# Drop non$-$numeric columns if any remain

df $=$ df$.$select$\_$dtypes$($include$=[$np$.$number$])$

# Create the correlation matrix

plt$.$figure$($figsize$=(12,8))$

corr$\_$matrix $=$ df$.$corr$()$

sns$.$heatmap$($corr$\_$matrix, annot$=$True, cmap$=$'coolwarm', linewidths$=0\mathrm{.}5)$

plt$.$title$(\text{'}$Correlation Heatmap'$)$

plt$.$show$()$

# # Resting Blood Pressure $($trestbps$)$ Distribution

plt$.$figure$($figsize$=(10,6))$

sns$.$histplot$($df$[\text{'}$trestbps$\text{'}],$ bins$=20,$ kde$=$True, color$=$'green'$)$

plt$.$title$(\text{'}$Resting Blood Pressure Distribution'$)$

plt$.$xlabel$(\text{'}$Resting Blood Pressure $($mm Hg$)\text{'})$

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

plt$.$show$()$

# # Cholesterol Levels Distribution $($chol$)$

plt$.$figure$($figsize$=(10,6))$

sns$.$histplot$($df$[\text{'}$chol$\text{'}],$ bins$=20,$ kde$=$True, color$=$'red'$)$

plt$.$title$(\text{'}$Cholesterol Levels Distribution'$)$

plt$.$xlabel$(\text{'}$Cholesterol $($mg$/$dl$)\text{'})$

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

plt$.$show$()$

# # Oldpeak Distribution by Heart Disease

plt$.$figure$($figsize$=(8,6))$

sns$.$boxplot$($x$=$'num', y$=$'oldpeak', data$=$df$)$

plt$.$title$(\text{'}$Oldpeak Distribution by Heart Disease'$)$

plt$.$xlabel$(\text{'}$Heart Disease $(0=$ No$,1=$ Yes$)\text{'})$

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

plt$.$show$()$

# # Thalach $($Maximum Heart Rate Achieved$)$ vs$.$ Age

plt$.$figure$($figsize$=(10,6))$

sns$.$scatterplot$($x$=$'age', y$=$'thalch', hue$=$'num', data$=$df$,$ palette$=$'coolwarm'$)$

plt$.$title$(\text{'}$Age vs$.$ Maximum Heart Rate Achieved $($Thalach$)\text{'})$

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

plt$.$ylabel$(\text{'}$Maximum Heart Rate Achieved'$)$

plt$.$legend$($title$=$'Heart Disease', labels$=[\text{'}$No$\text{'},$ 'Yes'$])$

plt$.$show$()$

# # Number of Major Vessels $($ca$)$ vs$.$ Heart Disease

plt$.$figure$($figsize$=(8,6))$

sns$.$countplot$($x$=\text{'}$ca$\text{'},$ hue$=$'num', data$=$df$,$ palette$=$'Set$2\text{'})$

plt$.$title$(\text{'}$Number of Major Vessels $($ca$)$ vs$.$ Heart Disease'$)$

plt$.$xlabel$(\text{'}$Number of Major Vessels'$)$

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

plt$.$legend$($title$=$'Heart Disease', labels$=[\text{'}$No$\text{'},$ 'Yes'$])$

plt$.$show$()$

# # Data Preprocessing

# # Identify Features and Target Variable

# Features and target variable

X $=$ df$.$drop$(\text{'}$num$\text{'},$ axis$=1)$

y $=$ df$[\text{'}$num$\text{'}]$

Kindly explain this code $?$