Pipeline 1: Collecting and preparing the dataset

This section will focus on handling and analyzing the Bank Customer Churn dataset. We will guide you through the steps of setting up your environment, manipulating data, and applying machine learning (ML) techniques. It is important to get the “feel” of a dataset with human analysis before using algorithms as tools. Human insights will always remain critical because of the flexibility of human creativity. As such, we will implement data collection and preparation in Python in three main steps:

1. Collecting and processing the dataset:
   • Setting up the Kaggle environment to authenticate and download datasets
   • Collecting and unzipping the Bank Customer Churn dataset
   • Simplifying the dataset by removing unnecessary columns
2. Exploratory data analysis:
   • Performing initial data inspections to understand the structure and type of data we have
   • Investigating...