https://www.analyticsvidhya.com/blog/2022/01/churn-analysis-of-a-telecom-company/
EDA is a phenomenon under data analysis used for gaining a better understanding of data aspects like:
– main features of data
– variables and relationships that hold between them
– identifying which variables are important for our problem
In this lesson, we will be working on the telecom churn analysis and here we will be doing a complete EDA process to determine if the customer from that particular telecom industry will leave that telecom service or not meanwhile we will draw some insights from data visualization and analysis so that we could get the factors which will affect the output i.e. churn of the customer.
This article was published as a part of the Data Science Blogathon.
In this article, we will be working on the telecom churn analysis and here we will be doing a complete EDA process to determine if the customer from that particular telecom industry will leave that telecom service or not meanwhile we will draw some insights from data visualization and analysis so that we could get the factors which will affect the output i.e. churn of the customer.
The dataset is the sample dataset if we know the difference between the sample and the population dataset then we may know that sample is drawn randomly from the population and this sample dataset has the customers who have left the telecom company.
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.ticker as mtick
import matplotlib.pyplot as plt
%matplotlib inline
telecom = pd.read_csv('WA_Fn-UseC_-Telco-Customer-Churn.csv')
Now while using the head function we can see that beginning records.
telecom.head()
From the shape attribute, we can see the shape of the data i.e number of records and number of columns in the dataset like (1200,13) so that particular dataset will have 1200 rows and 13 columns.
telecom.shape