Part 1: Exploratory Data Analysis. Understanding customer churn is vital to the success of a company and a churn analysis is the first step to understanding the customer.
In todays commercial world competition is high and every customer is valuable. Understanding the customer is of utmost importance, including being able to understand the behavior patterns of that customer. Customer Churn is the rate at which a commercial (very prevalent in SaaS platforms) customer leaves the commercial business and takes their money elsewhere. Understanding customer churn is vital to the success of a company and a churn analysis is the first step to understanding the customer.
I decided to perform a churn analysis from a Kaggle data set which gives the customer information data of a telecommunications company (Telcom) trying to better understand their customer churn likelihood. While we will eventually build a classification model to predict likelihood of customer churn, we must first take a deep dive into the Exploratory Data Analysis (EDA) process to get a better understanding of our data. Github Repository with code and notebooks can be found here.
As mentioned above, the data is sourced from Kaggle. In our dataset, we have 7043 rows (each representing a unique customer) with 21 columns: 19 features, 1 target feature (Churn). The data is composed of both numerical and categorical features, so we will need to address each of the datatypes respectively.
Data science is omnipresent to advanced statistical and machine learning methods. For whatever length of time that there is data to analyse, the need to investigate is obvious.
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DISCLAIMER: absolutely subjective point of view, for the official definition check out vocabularies or Wikipedia. And come on, you wouldn’t read an entire article just to get the definition.
Suppose you are looking to book a flight ticket for a trip of yours. Now, you will not go directly to a specific site and book the first ticket that you see.