Skip to content

This is a customer churn prediction repo which helps to predict whether a customer will leave your service or not based on the attribute provided. This Machine Learning project is built using Flask and deployed on Heroku.

Notifications You must be signed in to change notification settings

imakash3011/CustomerChurnPrediction

Repository files navigation


Web App Link

https://mycustomerchurn.herokuapp.com/

About Dataset

Context

Predict behavior to retain customers. You can analyze all relevant customer data and develop focused customer retention programs.

Content

Each row represents a customer, each column contains customer’s attributes described on the column Metadata.

The data set includes information about:

  • Customers who left within the last month – the column is called Churn
  • Services that each customer has signed up for – phone, multiple lines, internet, online security, online backup, device protection, tech support, and streaming TV and movies
  • Customer account information – how long they’ve been a customer, contract, payment method, paperless billing, monthly charges, and total charges
  • Demographic info about customers – gender, age range, and if they have partners and dependents

About Web App

  • Web app is build using Flask and deployed on Heroku.

  • Fill all the input boxes and click on submit button to know whether the customer will churn on not.

Details Description of Dataset

  • Churn Value: 1 = the customer left the company this quarter. 0 = the customer remained with the company. Directly related to Churn Label.

  • CustomerID: A unique ID that identifies each customer.

  • Gender: The customer’s gender: Male, Female

  • Senior Citizen: Indicates if the customer is 65 or older: Yes, No

  • Dependents: Indicates if the customer lives with any dependents: Yes, No. Dependents could be children, parents, grandparents, etc.

  • Tenure in Months: Indicates the total amount of months that the customer has been with the company by the end of the quarter specified above.

  • Partner: Indicates if the customer is married: Yes, No

  • Phone Service: Indicates if the customer subscribes to home phone service with the company: Yes, No

  • Multiple Lines: Indicates if the customer subscribes to multiple telephone lines with the company: Yes, No

  • Internet Service: Indicates if the customer subscribes to Internet service with the company: No, DSL, Fiber Optic, Cable.

  • Online Security: Indicates if the customer subscribes to an additional online security service provided by the company: Yes, No

  • Online Backup: Indicates if the customer subscribes to an additional online backup service provided by the company: Yes, No

  • Contract: Indicates the customer’s current contract type: Month-to-Month, One Year, Two Year.

  • Paperless Billing: Indicates if the customer has chosen paperless billing: Yes, No

  • Payment Method: Indicates how the customer pays their bill: Bank Withdrawal, Credit Card, Mailed Check

  • Monthly Charge: Indicates the customer’s current total monthly charge for all their services from the company.

  • Total Charges: Indicates the customer’s total charges, calculated to the end of the quarter specified above.

  • Streaming TV: Indicates if the customer uses their Internet service to stream television programing from a third party provider: Yes, No. The company does not charge an additional fee for this service.

  • Streaming Movies: Indicates if the customer uses their Internet service to stream movies from a third party provider: Yes, No. The company does not charge an additional fee for this service.


Thank you

About

This is a customer churn prediction repo which helps to predict whether a customer will leave your service or not based on the attribute provided. This Machine Learning project is built using Flask and deployed on Heroku.

Topics

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages