This repository contains a Jupyter Notebook that demonstrates fundamental Machine Learning techniques applied to an insurance dataset. The notebook walks through essential steps in data analysis, preprocessing, model training, and evaluation, providing insights into predictive analytics.
- Data exploration and preprocessing
- Application of key ML models
- Model evaluation and performance comparison
- Insights into predictive insurance analytics
To run this notebook, you will need:
- Python 3.x
- Jupyter Notebook
- Standard ML libraries such as Pandas, Scikit-learn, and Matplotlib
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Clone the repository:
git clone <repository-url>
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Navigate to the project directory:
cd <repository-folder>
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Launch Jupyter Notebook:
jupyter notebook
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Open and run
test_descartes_underwriting.ipynb.
or
Open in Google Collab
This project is available under the MIT License.
[Florian Calatayud]