Abhi0323/End-to-End-Employee-Churn-Prediction-with-Azure-Databricks
Developed an end-to-end machine learning solution for predicting employee churn using Azure Databricks, leveraging Spark for data processing, MLflow for managing the ML workflow, and deploying the model using Databricks model serving.
This project helps HR and business leaders predict which employees are likely to leave the company. By analyzing your existing employee data, it identifies patterns that lead to churn, providing a list of at-risk individuals. This allows organizations to proactively intervene and retain valuable talent.
No commits in the last 6 months.
Use this if your organization faces significant costs and disruptions from employee turnover and you need a data-driven way to identify and prevent it.
Not ideal if you don't have historical employee data or the resources to act on churn predictions.
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Jupyter Notebook
License
MIT
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Last pushed
May 29, 2024
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