Interview Prep
Career
GenAI
Prompt Engg
ChatGPT
LLM
Langchain
RAG
AI Agents
Machine Learning
Deep Learning
GenAI Tools
LLMOps
Python
NLP
SQL
AIML Projects

Reading list

Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

Churn Analysis of a Telecom Company

Aman Preet
Aman Preet Last Updated : 15 Mar, 2022
7 min read

This article was published as a part of the Data Science Blogathon.

Overview

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.

 

Churn analysis
Image Source: Kaggle

Dataset Info

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 the required libraries

import numpy as np 
import pandas as pd 
import seaborn as sns 
import matplotlib.ticker as mtick  
import matplotlib.pyplot as plt
%matplotlib inline

Load the data file

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()

Output:

 

Output 1
Output 2 | Churn analysis

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

Output:

(7043, 21)

Now let’s see the columns in our dataset.

telecom.columns.values

Output:

array(['customerID', 'gender', 'SeniorCitizen', 'Partner', 'Dependents',
       'tenure', 'PhoneService', 'MultipleLines', 'InternetService',
       'OnlineSecurity', 'OnlineBackup', 'DeviceProtection',
       'TechSupport', 'StreamingTV', 'StreamingMovies', 'Contract',
       'PaperlessBilling', 'PaymentMethod', 'MonthlyCharges',
       'TotalCharges', 'Churn'], dtype=object)

Here we will be checking what type of data our dataset holds.

telecom.dtypes

Output:

 

Output 3 | Churn analysis

Now let’s see the statistics part of our data i.e. mean, standard deviation, and so on.

telecom.describe()

Output:

 

Output 4 | Churn analysis

Inference:

  • Senior citizen is actually categorical hence the 25%-50%-75% distribution is not proper
  • We can also conclude that 75% of people have tenure.
  • Average Monthly charges are USD 64.76 whereas 25% of customers pay more than USD 89.85 per month
telecom['Churn'].value_counts().plot(kind='barh', figsize=(8, 6))
plt.xlabel("Count", labelpad=14)
plt.ylabel("Target Variable", labelpad=14)
plt.title("Count of TARGET Variable per category", y=1.02)

Output:

 

Target Variable | Churn analysis
100*telecom['Churn'].value_counts()/len(telecom['Churn'])

Output:

No     73.463013
Yes    26.536987
Name: Churn, dtype: float64

Let’s see the value count of our target variable

telecom['Churn'].value_counts()

Output:

No     5174
Yes    1869
Name: Churn, dtype: int64

Inference: From the above analysis we can conclude that.

  • In the above output, we can see that our dataset is not balanced at all i.e. Yes is 27 around and No is 73 around
  • So we analyze the data with other features while taking the target values separately to get some insights.

This code will return us valid and valuable information about the dataset also we can see that the verbose mode is on so it will give all the hidden information also.

telecom.info(verbose = True)

Output:

 

Churn analysis
missing = pd.DataFrame((telecom.isnull().sum())*100/telecom.shape[0]).reset_index()
plt.figure(figsize=(16,5))
ax = sns.pointplot('index',0,data=missing)
plt.xticks(rotation =90,fontsize =7)
plt.title("Percentage of Missing values")
plt.ylabel("PERCENTAGE")
plt.show()

Output:

 

Percentage of missing value

Missing Data – Initial Intuition

Here, we don’t have any missing data.

General Thumb Rules:

  • When we see a lot of outliers in the dataset then don’t really use mean because if the dataset has lots of outliers then we would be in the situation where the regression analysis will have drastic changes.
  • And if we get features that have a high number of missing values then it’s better to drop them.
  • As there’s no thumb rule on what criteria do we delete the columns with a high number of missing values, still one can delete the columns, if you have more than 30-40% of missing values.

Data Cleaning

1. Here we will be copying the telecom data to preprocess it further.

telco_data = telecom.copy()

2. Total Charges should be numeric amounts. So it’s better to convert them to numeral types.

telco_data.TotalCharges = pd.to_numeric(telco_data.TotalCharges, errors='coerce')
telco_data.isnull().sum()

Output:

 

Data Cleaning

3. Here we can see that there are a lot of missing values in the Total charges column.

telco_data.loc[telco_data ['TotalCharges'].isnull() == True]

Output:

 

Output 6 | Churn analysis
Churn analysis

4. Missing Value Treatment

Since the % of these records compared to a total dataset is very low ie 0.15%, it is safe to ignore them from further processing.

Removing missing values

telco_data.dropna(how = 'any', inplace = True)
#telco_data.fillna(0)

5. Now we will be dividing the persons e.g. for tenure < 12 months: assign a tenure group of 1-12, for tenure between 1 to 2 Yrs, tenure group of 13-24; so on.

Get the max tenure

print(telco_data['tenure'].max())

Output:

72

Here we will try to group the tenures

telco_data['tenure_group'].value_counts()

Output:

1 - 12     2175
61 - 72    1407
13 - 24    1024
49 - 60     832
25 - 36     832
37 - 48     762
Name: tenure_group, dtype: int64

6. Remove columns not required for processing

Drop column customerID and tenure

telco_data.drop(columns= ['customerID','tenure'], axis=1, inplace=True)
telco_data.head()

Output:

 

Missing value | Churn analysis
Missing value treatment

Data Exploration

Univariate Analysis

for i, predictor in enumerate(telco_data.drop(columns=['Churn', 'TotalCharges', 'MonthlyCharges'])):
    plt.figure(i)
    sns.countplot(data=telco_data, x=predictor, hue='Churn')

Output:

Data Exploration
Output | Churn analysis
output for partner | Churn analysis
output for dependents
Phoneservice | Churn analysis
Multiple lines | Churn analysis
Paperless Billing | Churn analysis
Churn analysis
Tenure Group

 

2. Here as we know we can’t have character values for our ML model so hence we should convert it into binary numerical values i.e. Yes=1; No = 0

telco_data['Churn'] = np.where(telco_data.Churn == 'Yes',1,0)
telco_data.head()

Output:

 

Churn analysis
Output | Churn analysis

3. Now we have to convert the categorical data into dummy variables with the getting dummies function.

telco_data_dummies = pd.get_dummies(telco_data)
telco_data_dummies.head()

Output:

Churn analysis

9. Here we will be making a regression plot between charges.

sns.lmplot(data=telco_data_dummies, x='MonthlyCharges', y='TotalCharges', fit_reg=False)

Output:

 

Monthly Charges | Churn analysis

Inference: Here from the above graph it is clear that as the monthly charges are increasing we can experience the total charges also increase which shows the positive correlation too.

10. Measure the churn by charges

Mth = sns.kdeplot(telco_data_dummies.MonthlyCharges[(telco_data_dummies["Churn"] == 0) ],
                color="Red", shade = True)
Mth = sns.kdeplot(telco_data_dummies.MonthlyCharges[(telco_data_dummies["Churn"] == 1) ],
                ax =Mth, color="Blue", shade= True)
Mth.legend(["No Churn","Churn"],loc='upper right')
Mth.set_ylabel('Density')
Mth.set_xlabel('Monthly Charges')
Mth.set_title('Monthly charges by churn')

Output:

 

Output | Churn analysis

Insight: Here it is evident that when the churn is high then the charges are high.

Tot = sns.kdeplot(telco_data_dummies.TotalCharges[(telco_data_dummies["Churn"] == 0) ],
                color="Red", shade = True)
Tot = sns.kdeplot(telco_data_dummies.TotalCharges[(telco_data_dummies["Churn"] == 1) ],
                ax =Tot, color="Blue", shade= True)
Tot.legend(["No Churn","Churn"],loc='upper right')
Tot.set_ylabel('Density')
Tot.set_xlabel('Total Charges')
Tot.set_title('Total charges by churn')

Output:

 

Total charges by churn

Inference: Here we get the surprising insight that as we can see more churn is there with lower charges.

Tenure, Monthly Charges & Total Charges then the picture is a bit clear:- Higher Monthly Charge at lower tenure results into lower Total Charge. Hence, all these 3 factors viz Higher Monthly Charge, Lower tenure, and Lower Total Charge are linked to High Churn.

We can also analyze the heat map of our complete dataset.

plt.figure(figsize=(12,12))
sns.heatmap(telco_data_dummies.corr(), cmap="Paired")

Output:

 

Overall Output

 

Conclusion

These are some of the quick insights on churn analysis from this exercise:

  1. Electronic check mediums are the highest churners
  2. Contract Type – Monthly customers are more likely to churn because of no contract terms, as they are free-to-go customers.
  3. No Online security, No Tech Support category are high churners
  4. Non-senior Citizens are high churners

Note: There could be many more such insights, so take this as an assignment and try to get more insights.

Here’s the repo link to this article. Hope you liked my article on churn analysis. If you have any opinions or questions, then comment below.

About Me

Greeting to everyone, I’m currently working in TCS and previously, I worked as a Data Science Analyst in Zorba Consulting India. Along with full-time work, I’ve got an immense interest in the same field, i.e. Data Science, along with its other subsets of Artificial Intelligence such as Computer Vision, Machine Learning, and Deep learning; feel free to collaborate with me on any project on the domains mentioned above (LinkedIn).

Here you can access my other articles, which are published on Analytics Vidhya as a part of the Blogathon (link).

The media shown in this article is not owned by Analytics Vidhya and are used at the Author’s discretion. 

Aman Preet
Aman Preet

Free Courses

Generative AI 4.7

Generative AI - A Way of Life

Explore Generative AI for beginners: create text and images, use top AI tools, learn practical skills, and ethics.
Large Language Models 4.5

Getting Started with Large Language Models

Master Large Language Models (LLMs) with this course, offering clear guidance in NLP and model training made simple.
Prompt Engineering 4.6

Building LLM Applications using Prompt Engineering

This free course guides you on building LLM apps, mastering prompt engineering, and developing chatbots with enterprise data.
LlamaIndex 4.8

Improving Real World RAG Systems: Key Challenges & Practical Solutions

Explore practical solutions, advanced retrieval strategies, and agentic RAG systems to improve context, relevance, and accuracy in AI-driven applications.
RAG systems using LlamaIndex 4.7

Microsoft Excel: Formulas & Functions

Master MS Excel for data analysis with key formulas, functions, and LookUp tools in this comprehensive course.

Recommended Articles

Responses From Readers

Clear

vivek
vivek

Why total charges is taken as "Object" variable instead of numeric or float. In this dataset we have 11 missing values in Total_Charges, but it got ignored in your analysis

123

Write for us

Write, captivate, and earn accolades and rewards for your work
  • Reach a Global Audience
  • Get Expert Feedback
  • Build Your Brand & Audience
  • Cash In on Your Knowledge
  • Join a Thriving Community
  • Level Up Your Data Science Game
imag

We use cookies essential for this site to function well. Please click to help us improve its usefulness with additional cookies. Learn about our use of cookies in our Privacy Policy & Cookies Policy.

Show details