Customer Churn

1. Workflow description

With this project you'll be able to read data on customers from an Excel file and predict whether the customer will churn or not. 

This is Telco data so each line will have a customer ID and multiple attributes such as: Gender, Senior, if they have different services, how they're paying, etc - see the Input Data Churn.csv file. 

This data will run through AI Fabric model and will output an Excel file ChurnResults.csv with the following output: 
customerID	Churn	Confidence
3524-EDGVD	No	0.90107606      --> this means for this customer we can say with a confidence level of 90% that will not churn.
4535-WHSVT	Yes	0.771159276     --> this means that for this customer we can say with a confidence level of 77% that will churn.


2. ML Package
The package is already zipped and ready to be upload, but I would encourage you to have a look at it and get familiar with it, especially main.py and requirements.txt files. 

You need to select JSON as input type, the model is expecting a json with the details about customers and will return a json with customer ID, Prediction and Confidence. 

Once you uploaded the ML Package, you then need to deploy ML Skill on your tenant and call this ML Skill from the ML Skill activity in the provided workflow. Don't forget to connect your Robot to Orchestrator to be able to see the ML models in Studio.