Skip to main content

A toolkit for customer retention analysis and prediction.

Project description

Setup and Configuration

For installing the package, run the following:

pip install customer_retention_toolkit

Follow these steps to set up and run the package:

  1. Create a new virtual environment:
    python -m venv venv
    
  2. Install the required packages using the provided requirements.txt:
    pip install -r requirements.txt
    

3.For demo purposes, execute the code cells in example.ipynb.

When you get to the API section:

  1. Start the API with:
    python run.py
    

For web usage of the API:

  • Visit: http://127.0.0.1:5000
  • For detailed API documentation, go to: http://127.0.0.1:5000/docs (Press Enter).
  • Click on get_info, then try it out. Input any ID from 1 to 3 and hit execute.
  • The result will appear in the Response body.

For comprehensive documentation on the project, including step-by-step guides and detailed explanations, visit our Documentation.


In the competitive world of subscription services, customer retention is key to sustained business success. High churn rates can significantly impact revenue and growth. This project aims to tackle churn by predicting which customers may leave using advanced analytics and machine learning, based on their interaction history and engagement patterns.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

customer_retention_toolkit-0.1.1.tar.gz (11.8 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

customer_retention_toolkit-0.1.1-py3-none-any.whl (13.5 kB view details)

Uploaded Python 3

File details

Details for the file customer_retention_toolkit-0.1.1.tar.gz.

File metadata

File hashes

Hashes for customer_retention_toolkit-0.1.1.tar.gz
Algorithm Hash digest
SHA256 53d6394666b2687ee49ea69b1d2630db52b42343346dd6d3a513f40ed0fabf79
MD5 fb110fc3eeb54dbf64e62b4d8a5057dc
BLAKE2b-256 cca07e4a01354ed03041e1017a4c929db83c763ec1216b65198e97a0323b4507

See more details on using hashes here.

File details

Details for the file customer_retention_toolkit-0.1.1-py3-none-any.whl.

File metadata

File hashes

Hashes for customer_retention_toolkit-0.1.1-py3-none-any.whl
Algorithm Hash digest
SHA256 2f0deb7f18b51c17997bec22fb2bd7cf1f0ad3cb42714de0f67a3d41aaa905e9
MD5 a2f44b501946e9f6a6361fb9da2346c0
BLAKE2b-256 0a94eab99ddde672ef81add613033dfe4fc6d14c8c16ce4c06778bb553a376cf

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page