Define Customer Churn Problem and Objectives

Welcome aboard!

Today is your first day as a Junior Data Scientist on the Telecom Customer Intelligence Project at AutoVision Analytics.

The company is facing customer churn, leading to revenue loss and higher acquisition costs. To address this, it plans to build a Machine Learning-based Customer Churn Prediction System that identifies customers likely to leave.

Your first task is to understand the churn problem, define the project objective, and determine what the model should predict.

Topic : Classification Models

1) Logistic Regression

2) Decision Tree Classification

3) Pruning Techniques

4) Support Vector Machine (SVM)

5) Naive Bayes Algorithm

Before building any Machine Learning model, the management team wants every member of the Data Science team to understand the business problem thoroughly.

Customer Churn Prediction is one of the most important applications of Machine Learning in the telecom industry. By identifying customers who are likely to leave, the company can take preventive actions such as personalized offers, better customer support, or loyalty programs to retain them.

Your task is to understand what Customer Churn means, why it occurs, and why predicting it is valuable for the business.

What is Customer Churn?

Customer Churn refers to the situation where a customer stops using a company's products or services.

Common reasons for churn include:

• Better offers from competitors
• High service charges
• Poor customer service
• Network or service issues
• Customer dissatisfaction

Predicting churn allows businesses to identify at-risk customers and take proactive measures to improve customer retention and revenue.

Open Google Colab

Since this is a new Machine Learning project, create a new Google Colab notebook.

Download the Dataset

Click to download dataset : Customer_Churn_dataset.csv

Import the Required Library

 Load the Dataset

Verify that the Dataset has Loaded Successfully

After understanding the problem, your manager asks you to define the project's business objective before any data analysis or model building begins.

A clearly defined objective helps the Data Science team align technical solutions with business goals and ensures that the project delivers measurable value.

Your task is to identify:

• Business Problem
• Business Objective
• Expected Outcome

Prepare a Project Objective Report.

Before building any Machine Learning model, a Data Scientist must identify the target variable, i.e., the value that the model is expected to predict.

The management team wants to predict whether a customer is likely to leave the telecom service or continue using it. Therefore, your responsibility is to explore the dataset and identify the target variable for this project.

For this project:

Target Variable = Churn

Possible values:

• Yes → Customer is likely to leave the company.
• No → Customer will continue using the company's services.

Since the target variable contains categories rather than continuous numerical values, this is a Classification Problem.

Check Dataset Information

Observe:

• Number of rows and columns
• Data types of features
• Missing values (if any)

Display All Column Names

 Identify the Target Variable and view its classes

Count Customers in Each Class

This helps understand how many customers have churned and how many have not churned.

The Senior Data Scientist introduces the Machine Learning algorithms that will be used in the upcoming phases of the project.

Your task today is not to implement these algorithms, but to understand when and why they are used for classification problems like Customer Churn Prediction.

Logistic Regression

Logistic Regression is a supervised Machine Learning algorithm used for binary classification problems, where the output belongs to one of two categories such as Yes/No or Churn/No Churn.

Decision Tree Classification

Decision Tree Classification creates a tree-like structure of decision rules to classify observations into different categories. It is simple to interpret and widely used in business applications.

Pruning Techniques

Pruning is the process of removing unnecessary branches from a Decision Tree to make it simpler and prevent overfitting, thereby improving performance on unseen data.

Support Vector Machine (SVM)

Support Vector Machine is a classification algorithm that identifies the best decision boundary (hyperplane) to separate different classes.

It performs well for both simple and complex classification problems.

Naive Bayes Algorithm

Naive Bayes is a probabilistic classification algorithm based on Bayes' Theorem. It assumes that features are independent and is commonly used in spam detection, sentiment analysis, and customer classification problems.

Prepare a Classification Model Summary.

Great job!

You have successfully completed Lab 7: Define Customer Churn Problem and Objectives.

In this lab, you identified the customer churn problem, defined the business objective and target variable, understood why churn prediction is a classification problem, and gained an introduction to Logistic Regression, Decision Trees, Pruning, SVM, and Naive Bayes.

You are now ready to proceed to the next stage of the Telecom Customer Intelligence Project.

Topic : Classification Models

1) Logistic Regression

2) Model Evaluation Metrics (Accuracy, Precision, Recall, F1-score, Confusion Matrix, ROC-AUC)