https://github.com/gustavohenriqueschmitz/insurance-costs-prediction-ai

An Linear Regression Model that predicts the insurance costs.
https://github.com/gustavohenriqueschmitz/insurance-costs-prediction-ai

linear-regression machine-learning pandas python3 tensorflow

Last synced: about 2 months ago
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An Linear Regression Model that predicts the insurance costs.

• Host: GitHub
• URL: https://github.com/gustavohenriqueschmitz/insurance-costs-prediction-ai
• Owner: GustavoHenriqueSchmitz
• Created: 2023-12-18T15:01:02.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2024-10-27T00:32:53.000Z (3 months ago)
• Last Synced: 2024-10-27T01:38:24.368Z (3 months ago)
• Topics: linear-regression, machine-learning, pandas, python3, tensorflow
• Language: PureBasic
• Homepage:
• Size: 184 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Insurance Costs Prediction AI

## About the model:

An Linear Regression Model that predicts the insurance costs basing on the following informations:

 - **Age:** Age of primary beneficiary.  

 - **Sex:** Insurance contractor gender, female, male.  

 - **BMI:** Body mass index, providing an understanding of body, weights that are relatively high or low relative to height,

 objective index of body weight (kg / m ^ 2) using the ratio of height to weight, ideally 18.5 to 24.9.  

 - **Children:** Number of children covered by health insurance / Number of dependents.  

 - **Smoker:** Yes/No  

 - **Region:** The beneficiary's residential area in the US, northeast, southeast, southwest, northwest.

## Used technologies

- Python

- TensorFlow

## How to test it?

**1 -** First clone the project:

```

git clone https://github.com/GustavoHenriqueSchmitz/Insurance-Costs-Prediction-AI.git

```

**2 -** Install the necessary dependencies:

```

pip install -r requirements.txt

```

After installing the required dependencies, to create the model run:

```

python train.py

```

To use the model run:

```

python main.py

```

## Author

**Gustavo Henrique Schmitz**

**Linkedin:** https://www.linkedin.com/in/gustavo-henrique-schmitz  

**Portfolio:** https://gustavohenriqueschmitz.com  

**Email:** [email protected]