https://github.com/manulthanura/medical_insurance_premium_prediction

Predict medical insurance cost with machine learning. The objective of this case study is to predict the health insurance cost incurred by Individuals based on their age, gender, Body Mass Index (BMI), number of children, smoking habits, and geo-location.
https://github.com/manulthanura/medical_insurance_premium_prediction

artificial-neural-networks machine-learning medical-insurance-costs predi

Last synced: 10 days ago
JSON representation

Predict medical insurance cost with machine learning. The objective of this case study is to predict the health insurance cost incurred by Individuals based on their age, gender, Body Mass Index (BMI), number of children, smoking habits, and geo-location.

• Host: GitHub
• URL: https://github.com/manulthanura/medical_insurance_premium_prediction
• Owner: manulthanura
• License: mit
• Created: 2024-05-19T06:41:35.000Z (6 months ago)
• Default Branch: main
• Last Pushed: 2024-05-19T13:10:55.000Z (6 months ago)
• Last Synced: 2024-05-20T07:35:29.547Z (6 months ago)
• Topics: artificial-neural-networks, machine-learning, medical-insurance-costs, predi
• Language: Jupyter Notebook
• Homepage:
• Size: 2.07 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

        
# Medical_Insurance_Premium_Prediction

Predict medical insurance cost with machine learning. The objective of this case study is to predict the health insurance cost incurred by Individuals based on their age, gender, Body Mass Index (BMI), number of children, smoking habits, and geo-location.

Dataset: [insurance](./insurance.csv)

Or [Medical Cost Personal Datasets](https://www.kaggle.com/datasets/mirichoi0218/insurance)

## Dataset Content

- 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: Smoking

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

- charges: Individual medical costs billed by health insurance

## Machine learning

This model predicts medical insurance costs with machine learning and artificial neural networks (ANN) to get more accuracy.

Source files

- [Google Colab](./Medical_Insurance_Premium_Prediction_with_Machine_Learning.ipynb)

- [Python](./medical_insurance_premium_prediction_with_machine_learning.py)