Create, Evaluate, and Score a Churn Prediction Model
Create, Evaluate, and Score a Churn Prediction Model
Are you interested in learning how to create, evaluate, and score a churn prediction model in Microsoft Fabric? In this webinar, we'll present an end-to-end example of a Synapse Data Science workflow in Microsoft Fabric. The scenario builds a model to predict whether or not bank customers churn. The churn rate, or the rate of attrition, involves the rate at which bank customers end their business with the bank.
This tutorial covers these steps:
Install custom libraries
Load the data
Understand and process the data through exploratory data analysis, and show the use of the Fabric Data Wrangler feature
Use scikit-learn and LightGBM to train machine learning models, and track experiments with the MLflow and Fabric Autologging features
Evaluate and save the final machine learning model
Show the model performance with Power BI visualizations
#HackTogether #MicrosoftFabric
This tutorial covers these steps:
Install custom libraries
Load the data
Understand and process the data through exploratory data analysis, and show the use of the Fabric Data Wrangler feature
Use scikit-learn and LightGBM to train machine learning models, and track experiments with the MLflow and Fabric Autologging features
Evaluate and save the final machine learning model
Show the model performance with Power BI visualizations
#HackTogether #MicrosoftFabric
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