Given "Telco Customer Churn" dataset

Each row represents a customer, each column contains customer's attributes.

The data includes $7043$ records, one record for each customer

The data includes $21$ columns representing the features.

The "Churn" column is our target.

The data set includes information about:

Customers who left within the last month $-$ the column is called Churn $($our target$)$

Services that each customer has signed up for $-$ phone, multiple lines, internet, online security, online backup, device protection, tech support, and streaming TV and movies

Customer account information $-$ how long they've been a customer, contract, payment method, paperless billing, monthly charges, and total charges

Demographic info about customers $-$ gender, age range, and if they have partners and dependents

Write a Python program to demonstrate the following:

Perform data visualization

Identify the independent features and the label feature

Split the dataset into training and test sets

Perform all the required variables transformation $($Example: convert categorical variables into a numerical representation$)$

Create the code of two of the classifiers $($KNN$,$ NB$)$

Train the classifiers

Apply both classifiers to the test dataset

Calculate the accuracy of each classifier

Perform $5-$fold cross$-$validation for each classifier

Compare the mean cross$-$validation scores of the two classifiers

Present the model with the higher score as the better$-$performing model