$1.$ Load data from "Live$\_20210128.$csv$"$ file. Remove unwanted features if required.

$2.$ Select the optimum k value using Silhouette Coefficient and plot the optimum k values.

$3.$ Create clusters using Kmeans and Kmeans$++$ algorithms with optimal k value found in the previous problem.

Report performances using appropriate evaluation metrics. Compare the results.

$4.$ Repeat clustering using Kmeans for $50$ times and report the average performance.

Again compare the results that you have obtained in Q$3$ using Kmeans$++$ and explain the difference $($if any$).$

I have the dataset loaded into the same folder. I can't upload the entire dataset from here. I$\text{'}$m happy to draw conclusions if you can provide the code to make it work. This is my original code for the first question, happy for you to change it:

# $1.$ Load data from "Live$\_20210128.$csv$"$ file. Remove unwanted features if required.

import pandas as pd

# Load the data from the CSV file

data $=$ pd$.$read$\_$csv$("$Live$\_20210128.$csv$")$

# Display the first few rows of the data

print$("$Original Data:"$)$

print$($data$.$head$())$

# Remove unwanted features if required

# Fill NaN values with a specific value $($e$.$g$.,0)$

data $=$ data.fillna$(0)$

# Display the first few rows of the modified data

print$("$Modified Data:"$)$

print$($data$.$head$())$

# For example, if certain columns are not relevant for clustering, you can drop them.

# Assuming "unwanted$\_$feature$1"$ and "unwanted$\_$feature$2"$ are unwanted features, you can drop them like this:

# data $=$ data.drop$(["$unwanted$\_$feature$1",$ "unwanted$\_$feature$2"],$ axis$=1)$