Scribd
Upload
14 views1 page

Capstone

The document outlines a customer churn prediction analysis aimed at identifying patterns in customer data and building a machine learning model for predicting churn. It includes methodologies for data collection, processing, model training, and evaluation, applicable to various industries. The expected outcomes are an accurate churn prediction model, insights into customer departure reasons, and strategies for improving customer retention.

Uploaded by

vishalpalv43004
Copyright
© © All Rights Reserved
We take content rights seriously. If you suspect this is your content, claim it here.
Available Formats
Download as PDF, TXT or read online on Scribd
14 views1 page

Capstone

The document outlines a customer churn prediction analysis aimed at identifying patterns in customer data and building a machine learning model for predicting churn. It includes methodologies for data collection, processing, model training, and evaluation, applicable to various industries. The expected outcomes are an accurate churn prediction model, insights into customer departure reasons, and strategies for improving customer retention.

Uploaded by

vishalpalv43004
Copyright
© © All Rights Reserved
We take content rights seriously. If you suspect this is your content, claim it here.
Available Formats
Download as PDF, TXT or read online on Scribd
You are on page 1/ 1

Customer Churn Prediction Analysis

1. Introduction
Customer churn refers to customers leaving a service. Predicting churn helps businesses
retain customers by identifying why they leave and taking proactive actions.

2. Objective
 Analyze customer data to find churn patterns.
 Build a machine learning model to predict churn.
 Provide insights for customer retention strategies.

3. Scope
Applicable to industries like telecom, banking, e-commerce, and subscription services
(Netflix, Spotify, etc.).

4. Methodology
Data Collection – Customer demographics, usage, payments, and churn status.
Data Processing – Handle missing values and perform EDA.
Model Training – Use Logistic Regression, Decision Trees, and XGBoost.
Evaluation – Check accuracy using Precision, Recall, and AUC Score.
Insights & Recommendations – Identify high-risk customers and suggest retention
strategies.

5. Expected Outcome
 A model that accurately predicts churn.
 Key insights into why customers leave.
 Business strategies to reduce churn and improve customer loyalty.

You might also like