https://github.com/computingvictor/insurance-company-benchmark-practice

1st Practice for the subject of Machine Learning
https://github.com/computingvictor/insurance-company-benchmark-practice

cunef data-science eda insurance-company jupyter-notebook machine-learning python

Last synced: about 2 months ago
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1st Practice for the subject of Machine Learning

• Host: GitHub
• URL: https://github.com/computingvictor/insurance-company-benchmark-practice
• Owner: ComputingVictor
• Created: 2022-10-31T11:22:29.000Z (about 2 years ago)
• Default Branch: main
• Last Pushed: 2022-12-30T22:33:53.000Z (almost 2 years ago)
• Last Synced: 2023-05-30T19:30:01.336Z (over 1 year ago)
• Topics: cunef, data-science, eda, insurance-company, jupyter-notebook, machine-learning, python
• Language: Jupyter Notebook
• Homepage:
• Size: 1.27 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Insurance Company Benchmark (COIL 2000) Practice

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## About the project

The "Insurance Company Benchmark" database contains a series of records from different clients. Each client record contains sociodemographic attributes and values ​​that indicate the number of policies contracted on different goods. 

The business objective is, through the point of view of an insurer, to predict, using the data we have, whether a client will sign a mobile home insurance policy or not.

**For this practice, at the moment, we will have to perform only an exploratory data analysis of the dataset**.

Find the dataset [here](https://archive-beta.ics.uci.edu/ml/datasets/insurance+company+benchmark+coil+2000#Descriptive)

## Built with 

- Python 3.9.12

- Jupyter Notebook

## Content of the repository

- `data`: 

    - `Raw`: Documents downloaded from the source of the dataset.

    - `Processed data`: Data dictionay processed.

- `docs`: Complementary files of the notebook with functions ( Py files).

- `notebooks`: Notebooks of the project

## Roadmap

- [x] EDA

- [ ] Data Modelation

- [ ] Data Validation

- [ ] Deployment & Optimization

## Contact

Víctor Viloria Vázquez - 

Project Link: 

Linkedin - 

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