kunalk3 / customer_churn_prediction_automl

Repo consists the customer behaviors in order to select the business areas of improvements, recommendation and revenue increments factor. As per market demand, we need to analyse out customers, so that we can optimize company revenue as per customer segmentations.

Customer Churn Predictive Analytics with Modeling

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🧊 Overview:

• Understanding why customers do—or don’t—purchase more goods or services is essential to success for any company. Gaining this insight begins with analyzing how many clients are taking their business elsewhere. Then, you can dig in to find commonalities among leavers, identify opportunities to improve and avoid losing additional customers.

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💡 Demo

demo_streamlit_deployment.mp4

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🔧 Installation

• Create virtual environment python -m venv VIRTUAL_ENV_NAME and activate it .\VIRTUAL_ENV_NAME\Scripts\activate.
• Install necessary library for this project from the file requirements.txt or manually install by pip.

  pip install -r requirements.txt
  

  To create project library requirements, use below command,

  pip freeze > requirements.txt
  

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🔖 Directory Structure

    .                                       # Root directory (Streamlit_webApp)
    ├── Churn.csv                           # Source customer data
    ├── app.py                              # Application file
    ├── Soy.ogg                             # External dependency I (music)
    ├── Churn2.jpg                          # External dependency II (image)
    └── requirements.txt                    # Project requirements library with versions
    

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