DUE $20$ MAY PLEASE GIVE CORRECT CODE WITH DETAILED EXPLANATION I WANT TO UNDERSTAND ALSO NOT JUST SEE A CODE

The goal of this homework assignment is to explore the KMeans algorithm using the given dataset airlines.csv$.$ Throughout this assignment, you will perform various tasks including data description, data preprocessing, exploratory data analysis, and determining the optimal number of clusters using KMeans.

Tasks

$1.$ Data Description

The provided raw data is in the airlines.csv file.

The description of the raw data is as follows:

id: Unique ID

balance: Number of miles eligible for award travel

qual$\_$mile: Number of miles counted as qualifying for Topflight status.

cc$1\_$miles: Number of miles earned with freq. flyer credit card in the past $12$ months:

cc$2\_$miles: Number of miles earned with Rewards credit card in the past $12$ months:

cc$3\_$miles: Number of miles earned with Small Business credit card in the past $12$ months:

$1$: under $5,000$

$2$: $5,000-10,000$

$3$: $10,001-25,000$

$4$: $25,001-50,000$

$5$: over $50,000$

bonus$\_$miles: Number of miles earned from non$-$flight bonus transactions in the past $12$ months.

bonus$\_$trans: Number of non$-$flight bonus transactions in the past $12$ months.

flight$\_$miles$\_12$mo: Number of flight miles in the past $12$ months.

flight$\_$trans$\_12$: Number of flight transactions in the past $12$ months.

days$\_$since$\_$enrolled: Number of days since enrolled in flier program.

award: whether that person had an award flight $($free flight$)$ or not.

$2.$ Check for Missing Values

Perform data preprocessing to check for any missing values in the dataset.

$3.$ Analyze Features

Create histograms to understand the distribution of different features in the dataset.

$4.$ Calculate Percentage of Customers with$/$without Award

Find the percentage of customers who do not have an award flight and those who do have an award flight.

$5.$ Correlation Analysis

$-$ Find which feature is correlated with the balance feature.

$-$ Draw a correlation heatmap to visualize the correlations among different features.

$6.$ Plotting

Plot the relationship between frequent flying bonuses and non$-$flight bonus transactions.

$7.$ Determining Optimal Number of Clusters

$-$ Apply MinMaxScaler to normalize the data.

$-$ Use the Elbow Method and Silhouette Score to find the ideal number of clusters for KMeans algorithm.

this is a short part from the information in the airline.csv file:

id$,$balance,qual$\_$miles,cc$1\_$miles,cc$2\_$miles,cc$3\_$miles,bonus$\_$miles,bonus$\_$trans,flight$\_$miles$\_12$mo$,$flight$\_$trans$\_12,$days$\_$since$\_$enroll,award

$1,28143,0,1,1,1,174,1,0,0,7000,0$

$2,19244,0,1,1,1,215,2,0,0,6968,0$

$3,41354,0,1,1,1,4123,4,0,0,7034,0$

$4,14776,0,1,1,1,500,1,0,0,6952,0$

$5,97752,0,4,1,1,43300,26,2077,4,6935,1$

$6,16420,0,1,1,1,0,0,0,0,6942,0$

$7,84914,0,3,1,1,27482,25,0,0,6994,0$

$8,20856,0,1,1,1,5250,4,250,1,6938,1$

$9,443003,0,3,2,1,1753,43,3850,12,6948,1$

$10,104860,0,3,1,1,28426,28,1150,3,6931,1$

the file has $4000$ lines in total with similar information, I attached a photo also