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https://github.com/kasraskari/medical-insurance
Medical Insurance Payout
https://github.com/kasraskari/medical-insurance
jupyter-notebook machine-learning medical medical-insurance python
Last synced: 2 days ago
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Medical Insurance Payout
- Host: GitHub
- URL: https://github.com/kasraskari/medical-insurance
- Owner: KasrAskari
- Created: 2024-05-28T18:25:01.000Z (7 months ago)
- Default Branch: main
- Last Pushed: 2024-05-28T18:47:33.000Z (7 months ago)
- Last Synced: 2024-11-09T17:12:15.155Z (about 2 months ago)
- Topics: jupyter-notebook, machine-learning, medical, medical-insurance, python
- Language: Jupyter Notebook
- Homepage:
- Size: 306 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Medical Insurance Cost Prediction
## Overview
This project focuses on predicting individual medical insurance costs using demographic and health-related features. By leveraging machine learning models, the repository provides insights into how factors such as age, BMI, and smoking status affect insurance premiums.
---
## Features
- **Data Preprocessing**: Handling missing values, encoding categorical data, and feature scaling.
- **Exploratory Data Analysis (EDA)**: Visualizing trends and correlations between features and insurance costs.
- **Model Training**: Implementing machine learning algorithms to predict insurance charges.
- **Performance Metrics**: Evaluating the accuracy and reliability of models using metrics like Mean Absolute Error (MAE).
---
## Project Structure
```
Medical-Insurance/
├── data/ # Dataset for training and testing
├── notebooks/ # Jupyter notebooks for EDA and model development
├── scripts/ # Python scripts for preprocessing and model training
├── visualizations/ # Charts and graphs for insights
├── models/ # Trained machine learning models
├── README.md # Project documentation
└── LICENSE # License information
```
---
## Technologies Used
- **Python**: Core programming language.
- **Pandas**: For data manipulation and preprocessing.
- **Matplotlib/Seaborn**: Visualizing relationships between features.
- **Scikit-learn**: Building and evaluating machine learning models.
- **NumPy**: Efficient numerical computations.
---
## Dataset
The dataset includes the following features:
- **Age**: Age of the individual.
- **Sex**: Gender (male/female).
- **BMI**: Body mass index.
- **Children**: Number of dependents.
- **Smoker**: Whether the individual is a smoker.
- **Region**: Geographical region.
- **Charges**: Medical insurance costs (target variable).
The dataset can be sourced from platforms such as [Kaggle](https://www.kaggle.com/datasets/harshsingh2209/medical-insurance-payout).
---
## Resources
For further reading and reference:
1. [Medical Cost Personal Dataset - Kaggle](https://www.kaggle.com/mirichoi0218/insurance)
2. [Scikit-learn Documentation](https://scikit-learn.org/stable/)
3. [Exploratory Data Analysis Guide](https://towardsdatascience.com/exploratory-data-analysis-8fc1cb20fd15)