Customer Churn Prediction Real Project

Machine Learning Project for Customer Retention, Churn Risk Scoring, and Business Decision-Making

Customer Churn Prediction Real Project is a client-ready machine learning portfolio project from DataScienceConsultingPro.com. It shows how customer data can be used to predict which customers are likely to leave, identify the main causes of churn, and help businesses take action before revenue is lost.

This project includes data cleaning, exploratory data analysis, machine learning model training, model evaluation, customer-level churn risk scoring, feature importance, and an interactive pivot-style dashboard for business users.

Best for: Telecom companies, SaaS businesses, subscription services, internet providers, e-commerce businesses, fintech companies, insurance firms, customer success teams, and retention managers.

Main business outcome: Identify high-risk customers early, reduce customer loss, and protect recurring revenue.

Request a Machine Learning Quote Today if you need a churn prediction model, customer risk dashboard, or predictive analytics solution for your business.

Project Snapshot

Item	Details
Project Name	Customer Churn Prediction Real Project
Project Category	Machine Learning Services
Business Focus	Customer retention and churn reduction
Project Type	Predictive analytics, classification modeling, customer risk scoring
Main Tool	Python
Notebook Format	Jupyter Notebook
Dashboard Type	Interactive pivot-style churn dashboard
Models Used	Logistic Regression, Random Forest, Gradient Boosting
Main Output	Customer churn probability and risk band
Risk Groups	High Risk, Medium Risk, Low Risk
Business Users	Managers, customer success teams, marketing teams, retention teams
Final Deliverables	Notebook, model, charts, churn scores, dashboard, client report

What This Project Does

This project predicts whether a customer is likely to churn.

Customer churn means a customer leaves the company, cancels a subscription, stops using a service, or switches to a competitor.

The project uses customer information such as contract type, tenure, payment method, monthly charges, total charges, internet service, technical support, and service usage to estimate each customer’s churn risk.

Instead of guessing which customers may leave, the business receives a structured churn prediction system that can answer:

Business Question	Project Answer
Which customers are likely to leave?	The model gives each customer a churn probability.
Which customers need urgent attention?	Customers are grouped into High, Medium, and Low Risk.
What causes churn?	Feature importance shows the main churn drivers.
Which groups have the highest churn?	The analysis compares churn by contract, tenure, payment method, and charges.
What should the business do next?	The report gives plain-language recommendations.
How can managers explore the results?	The pivot dashboard allows interactive business analysis.

Business Problem Solved

Many businesses lose customers without knowing who is at risk until the customer has already cancelled.

This creates serious business problems:

Problem	Business Impact
Customers leave without warning	Revenue drops unexpectedly
Retention teams contact the wrong customers	Time and money are wasted
Managers do not know why customers leave	Churn causes remain hidden
High-value customers are not prioritized	The business loses profitable accounts
Marketing spends more to replace lost customers	Customer acquisition costs increase
Customer experience issues remain unresolved	More customers may leave later

This project solves the problem by using machine learning to detect churn risk early.

The business can then contact high-risk customers, improve onboarding, review pricing, offer better support, and reduce avoidable customer loss.

Why This Project Is Strong Evidence of Machine Learning Skill

This is not just a basic analysis. It is a full client-style machine learning workflow.

It includes:

Evidence	Why It Builds Trust
Full Jupyter Notebook	Shows the complete Python workflow, code, comments, charts, and interpretation
Data Cleaning	Shows ability to prepare messy business data
Exploratory Analysis	Shows ability to find business patterns
Machine Learning Models	Shows ability to train and compare predictive models
Model Evaluation	Shows use of accuracy, precision, recall, F1 score, ROC AUC, confusion matrix, and ROC curve
Feature Importance	Shows ability to explain what drives churn
Customer Risk Scores	Shows practical business output
Pivot Dashboard	Shows dashboard and reporting capability
Plain-Language Interpretation	Shows ability to explain technical results to non-technical clients
Business Recommendations	Shows consulting value, not just coding ability

This makes the project suitable for a professional data science consulting portfolio.

Dataset Used

The project uses telecom customer churn data. Each row represents one customer.

The dataset includes customer profile, service usage, contract, billing, and churn information.

Data Category	Example Fields
Customer Profile	Gender, senior citizen status, partner, dependents
Customer Tenure	Number of months the customer has stayed with the company
Service Usage	Phone service, internet service, online security, backup, device protection, streaming
Support Services	Technical support, online security, device protection
Contract Details	Month-to-month, one-year, two-year contract
Billing Details	Paperless billing, payment method
Financial Details	Monthly charges, total charges
Target Variable	Churn: Yes or No

The target variable is Churn.

Churn Value	Meaning
Yes	The customer left the company
No	The customer stayed with the company

The model uses the other customer details to predict whether churn is likely.

Project Objectives

The main objective was to create a machine learning system that helps a business reduce customer churn.

The project objectives were to:

1. Load and inspect customer churn data.
2. Clean and prepare the dataset for analysis.
3. Convert incorrect data types into usable formats.
4. Create business-friendly features for interpretation.
5. Analyze churn by customer groups.
6. Train multiple machine learning models.
7. Compare model performance.
8. Select the best-performing churn prediction model.
9. Identify the strongest churn drivers.
10. Score every customer by churn probability.
11. Group customers into risk bands.
12. Build a pivot-style dashboard for business users.
13. Export client-ready outputs.
14. Provide plain-language recommendations.

Work Completed

1. Data Cleaning and Preparation

The first stage prepared the data for reliable analysis and modeling.

Cleaning Task	Why It Was Important
Checked dataset structure	Confirmed rows, columns, and data types
Checked missing values	Identified incomplete records
Converted TotalCharges to numeric	Made the column usable for modeling
Handled missing charge values	Prevented model errors
Converted churn to numeric format	Prepared the target variable
Separated categorical and numeric features	Enabled correct preprocessing
Removed customer ID from modeling	Prevented the model from using meaningless identifiers

Clean data is important because poor data quality leads to weak predictions and unreliable business decisions.

2. Exploratory Data Analysis

The project analyzed churn patterns before modeling.

This helped identify which customer groups were more likely to leave.

Analysis Area	Business Purpose
Overall churn distribution	Measures the size of the churn problem
Churn by contract type	Shows whether flexible contracts increase churn
Churn by tenure group	Shows whether newer customers leave more
Churn by payment method	Reveals billing-related churn patterns
Churn by monthly charges	Shows whether high-paying customers are at risk
Churn by service features	Shows whether support and service quality affect churn

Each analysis included charts and plain-language interpretation.

This makes the project useful for both technical teams and business managers.

3. Feature Engineering

Feature engineering created new variables that improved interpretation and reporting.

Feature Created	Purpose
Churn_Flag	Converts churn into 1 for churned and 0 for stayed
Tenure_Group	Groups customers by how long they have stayed
Monthly_Charge_Group	Groups customers by monthly payment level
Total_Charge_Group	Groups customers by total customer value
Risk_Band	Groups customers into High, Medium, and Low churn risk

These features make the output easier to explain to clients and managers.

4. Machine Learning Model Development

The project trained and compared multiple machine learning models.

Model	Why It Was Used
Logistic Regression	Provides a simple and interpretable baseline
Random Forest	Captures complex customer behavior patterns
Gradient Boosting	Performs well on structured business datasets

Using multiple models improves project quality because it avoids relying on one approach without comparison.

5. Model Evaluation

The models were evaluated using standard classification metrics.

Metric	Meaning
Accuracy	Overall correct predictions
Precision	How many predicted churners actually churned
Recall	How many actual churners the model correctly found
F1 Score	Balance between precision and recall
ROC AUC	Ability to separate churners from non-churners
Confusion Matrix	Shows correct and incorrect predictions
ROC Curve	Shows classification performance across thresholds

For churn prediction, recall is very important because the business does not want to miss too many customers who are likely to leave.

However, precision also matters because retention teams should not waste too much time contacting low-risk customers.

6. Churn Risk Scoring

After selecting the best model, the project scored every customer.

Each customer received a predicted churn probability.

Example:

Customer Output	Meaning
Churn Probability = 82%	The customer is very likely to leave
Churn Probability = 55%	The customer has moderate churn risk
Churn Probability = 18%	The customer is less likely to leave

The project then grouped customers into risk bands.

Risk Band	Business Meaning	Recommended Action
High Risk	Customer is likely to leave	Contact urgently
Medium Risk	Customer may leave	Monitor and target with support
Low Risk	Customer is less likely to leave	Maintain service quality

This is one of the most valuable outputs because it gives the business a direct retention action list.

Interactive Pivot Dashboard

The project includes an interactive pivot-style churn dashboard.

The dashboard allows business users to explore churn patterns without writing code.

Users can compare churn risk by:

• Risk band
• Contract type
• Payment method
• Internet service
• Tenure group
• Monthly charge group
• Actual churn status
• Technical support
• Online security
• Customer value group

Example Dashboard Views

Dashboard View	Business Use
Risk Band by Contract Type	Shows which contract groups contain high-risk customers
Average Churn Risk by Payment Method	Shows whether billing method is linked to churn
Actual Churn by Tenure Group	Shows whether newer customers leave more
Monthly Charges by Risk Band	Shows whether high-paying customers are at risk
Churn Risk by Internet Service	Shows service-related churn patterns
Technical Support by Risk Band	Shows whether support reduces churn risk
High-Value Customer Risk	Helps prioritize valuable customers

The dashboard turns machine learning results into a practical decision-making tool.

Key Business Insights

This project can help a business uncover insights such as:

1. Customers on flexible contracts may have higher churn risk.
2. Newer customers may need stronger onboarding and early support.
3. Some payment methods may be associated with higher churn.
4. High monthly charges may increase churn if customers do not see enough value.
5. Customers without technical support may be more likely to leave.
6. Customer service and support features can influence retention.
7. Not all customers need the same retention strategy.
8. High-risk and high-value customers should be prioritized first.

These insights help the business move from general customer support to targeted retention action.

Business Recommendations

Based on the churn analysis and model outputs, the business should take the following actions.

Recommendation	Why It Matters
Contact high-risk customers first	Helps prevent customer loss before cancellation
Improve onboarding for new customers	Reduces early-stage churn
Encourage longer contracts	Reduces churn among flexible-contract customers
Review high monthly charges	Helps protect high-value customers
Improve technical support	Reduces frustration and service dissatisfaction
Monitor risky payment groups	Helps detect billing-related churn
Use churn scores monthly	Turns the model into a continuous retention system
Track retention outcomes	Measures whether interventions reduce churn

Project Deliverables

This project produces professional, client-ready deliverables.

Deliverable	Description
Jupyter Notebook	Full Python notebook with code, comments, charts, results, and interpretation
Cleaned Dataset	Prepared dataset ready for analysis and modeling
Exploratory Charts	Visual analysis of churn by customer group
Machine Learning Models	Trained churn prediction models
Model Comparison Table	Performance comparison across models
Confusion Matrix	Prediction performance breakdown
ROC Curve	Model classification performance
Feature Importance Table	Main churn drivers
Customer Risk Scores	Customer-level churn probabilities
High-Risk Customer List	Priority list for retention action
Pivot Dashboard	Interactive dashboard for business exploration
Client Report	Plain-language findings and recommendations
Exported CSV Files	Reusable business outputs
Saved ML Model	Model file for future scoring or deployment

Example Files Included

File Name	Purpose
customer_churn_prediction_real_project.ipynb	Main Jupyter Notebook
customer_churn_risk_scores_all_customers.csv	Customer-level churn risk scores
customer_churn_model_comparison_metrics.csv	Model performance table
customer_churn_feature_importance.csv	Churn driver table
customer_churn_threshold_tuning_results.csv	Threshold analysis for retention planning
customer_churn_prediction_model.joblib	Saved machine learning model
customer_churn_live_pivot_dashboard.html	Interactive pivot dashboard
customer_churn_prediction_client_report.html	Client-friendly HTML report

What the Client Gets

A client who orders this type of project from DataScienceConsultingPro.com receives more than a model.

They receive a complete churn analytics solution.

Client Need	Project Output
“I want to know who may leave.”	Churn probability for each customer
“I need a priority list.”	High-risk customer list
“I want to know why customers leave.”	Feature importance and churn analysis
“I need something managers can understand.”	Plain-language report
“I want a dashboard.”	Pivot-style churn dashboard
“I want reusable outputs.”	CSV files, model file, and notebook
“I want business recommendations.”	Retention action plan

Business Value

This project helps the client:

• Reduce customer churn
• Protect recurring revenue
• Identify high-risk customers early
• Improve retention campaign targeting
• Reduce wasted marketing effort
• Understand key churn drivers
• Improve onboarding
• Review pricing and service value
• Strengthen technical support
• Prioritize high-value customers
• Make data-driven retention decisions

The main value is simple:

The business can act before customers leave.

Who Needs This Project?

This project is suitable for any business with repeat customers, subscriptions, customer accounts, or recurring revenue.

Business Type	Churn Prediction Use Case
Telecom companies	Predict customer cancellation
SaaS companies	Identify users likely to cancel subscriptions
Internet service providers	Reduce customer switching
E-commerce businesses	Predict inactive or lost customers
Banks and fintech companies	Predict account closure or customer drop-off
Insurance companies	Predict policy cancellation
Subscription businesses	Protect recurring revenue
Utility companies	Identify customers likely to leave or default
Marketing teams	Target retention campaigns
Customer success teams	Prioritize customer outreach

Related Services

This project connects naturally with other DataScienceConsultingPro.com services:

• Machine Learning Services
• Predictive Analytics Services
• Data Analysis Services
• Data Cleaning Services
• Dashboard Development Services
• Business Intelligence Services
• Customer Analytics Services
• Power BI Dashboard Services
• Python Data Analysis Services

Use internal links on your website by linking those service names to their relevant pages.

Portfolio Catalogue Description

Customer Churn Prediction Real Project is a machine learning portfolio project that demonstrates how DataScienceConsultingPro.com builds predictive analytics solutions for customer retention. The project includes data cleaning, exploratory data analysis, machine learning model training, model evaluation, feature importance, customer risk scoring, and an interactive pivot-style dashboard.

The final output helps businesses identify high-risk customers, understand churn drivers, prioritize retention campaigns, and reduce customer loss before cancellation happens.

Customer Churn Prediction Using Machine Learning

Predict which customers are likely to leave using customer data, machine learning models, churn risk scoring, and an interactive pivot dashboard.

Item	Detail
Category	Machine Learning Services
Best For	Telecom, SaaS, e-commerce, fintech, insurance, subscription businesses
Main Output	Customer churn risk score
Tools	Python, Jupyter Notebook, Scikit-learn, Plotly, Pivot Dashboard
Deliverables	Notebook, model, risk scores, dashboard, report
Business Outcome	Identify high-risk customers early and reduce churn

Request a Machine Learning Quote Today

Need a Customer Churn Prediction Model for Your Business?

DataScienceConsultingPro.com can help you build a customer churn prediction model that identifies high-risk customers, explains churn drivers, and supports better retention decisions.

Whether you have telecom data, SaaS subscription data, e-commerce customer data, banking records, insurance data, or service usage data, we can help you turn your customer data into a practical churn reduction system.

Our churn prediction deliverables can include:

• Cleaned customer dataset
• Machine learning model
• Churn risk scores
• High-risk customer list
• Feature importance report
• Interactive dashboard
• Business recommendations
• Client-ready report
• Reusable Python notebook

Request a Machine Learning Quote Today and get a client-ready churn prediction solution with model results, customer risk scores, dashboard views, and plain-language business recommendations.