Telecom customer churn dataset; Exploratory data analysis and building machine learning models
Hi everyone, this is a practical guide to advanced exploratory data analysis and machine learning. We will discuss how to explore the Telecom customer churn dataset and prepare it for business needs by exploring the data and answering a lot of questions that a business might need in order to improve their service or scale up. Additionally, we will be creating a simple machine-learning model to predict the churn of a customer.
For this tutorial, we will be using the Telecom customer churn dataset from Kaggle which you can download here: https://www.kaggle.com/datasets/blastchar/telco-customer-churn/data
First of all, let’s understand some important terms and concepts.
- Churn is the measure of how many customers stop using a product
- The dataset contains information about customers of a telecom company and whether they have churned or not
- Customers who have stayed longer with the company are less likely to churn
Our target
- To understand the dataset
- Clean the dataset
- To deeply explore the dataset and answer some questions that a business might need (using charts and Visualization)
