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  1. Predict Churn
    1. Customer base churn
    2. Steps
    3. Data
    4. Video
    5. Jupyter Lab file

Predict Churn

How to predict churn, using deep learning.

Customer base churn

Churn rate, when applied to a customer base, refers to the proportion of contractual customers or subscribers who leave a supplier during a given time period.

It is a possible indicator of customer dissatisfaction, cheaper and/or better offers from the competition, more successful sales and/or marketing by the competition, or reasons having to do with the customer life cycle.

Steps

  1. Build a simple Tensorflow model to predict Churn
  2. Training the model and make predictions on test data with Pandas
  3. Save your model to disc and reload it to a Jupyter Notebook for reuse

Data

Use the data Churn.csv

Video

Jupyter Lab file

Get the Jupyter Lab file her - TensorflowDemo.ipynb

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