https://github.com/abu14/churn-prediction

This project assesses the likelihood of customer churn using various performance metrics. It employs multiple algorithms, including Logistic Regression, Random Forest, and XGBoost Classifier, to achieve Accuracy of 80% & ROC-AUC score of 79%
https://github.com/abu14/churn-prediction

churn-prediction flask random-forest

Last synced: 2 months ago
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This project assesses the likelihood of customer churn using various performance metrics. It employs multiple algorithms, including Logistic Regression, Random Forest, and XGBoost Classifier, to achieve Accuracy of 80% & ROC-AUC score of 79%

• Host: GitHub
• URL: https://github.com/abu14/churn-prediction
• Owner: abu14
• License: mit
• Created: 2025-01-20T20:06:20.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2025-04-08T15:53:20.000Z (about 1 year ago)
• Last Synced: 2025-04-08T16:39:17.843Z (about 1 year ago)
• Topics: churn-prediction, flask, random-forest
• Language: Jupyter Notebook
• Homepage:
• Size: 8.21 MB
• Stars: 0
• Watchers: 1
• Forks: 1
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# Hello there 👋

This is a project I did trying to solve the age old Churn problem, specific to Telecom. 

### What is Churn?

> In the telecom industry, "**churn**" refers to the rate at which customers stop using a service provider's plans and switch to another company or simply discontinue their service,

essentially representing the percentage of customers who leave within a specific period, signifying a loss of subscribers and impacting the company's revenue. 

I've been part of the CVM team tasked with re-engaging churning customers. Customer inactivity and churn was a constant problem we worked on. Throughout my time we've had various successes recovering these churning customers with tailor made offers. The churn prediction model being the main enabler of these initiatives.

# Project Workflow

```

Churn Predictiton 

├── Data Collection    

├── Data Preprocessing

├── Model Training           

├── Model Evaluation         

├── Hyperparameter Tuning   

└── Deployment         

```

For these project I used these tools.





















  #### Demo

  ![Image](https://github.com/user-attachments/assets/7bd32ce3-a9ec-4cfa-bbf2-f3ef6050285d)

  

Connect with me:



  

    

  

  

    

  

  

    

  

  

    

  

  

    

  



 

Project Link: [Github Repo](https://github.com/abu14/Telecom-Churn-Prediction)