hamedonline/ml-workflow
A hands-on case study for demonstrating the stages involved in a machine learning project, from EDA to production.
This project offers a hands-on guide for building a machine learning solution to predict insurance claim severity. It takes raw tabular data, like that used by Allstate, and demonstrates how to process it and develop a model to predict the 'loss' value for a claim. This is useful for data scientists or machine learning engineers who need to understand the practical steps in an ML workflow.
No commits in the last 6 months.
Use this if you are a data scientist or machine learning engineer looking for a step-by-step guide on how to take a tabular dataset from initial exploration to a deployable machine learning model.
Not ideal if you are looking for advanced techniques to achieve the absolute best predictive accuracy or if you are not comfortable with Python and Jupyter notebooks.
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Language
Jupyter Notebook
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Last pushed
Jul 25, 2023
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