https://github.com/pythonicshariful/insurance-charge-predictor
This project predicts medical insurance charges based on personal details such as age, gender, BMI, number of children, smoking habits, and region. It uses a Machine Learning model trained on the insurance.csv dataset and provides a Flask web app interface for user input
https://github.com/pythonicshariful/insurance-charge-predictor
flask insura machine-learning mlapp python regression scikit-learn
Last synced: 30 days ago
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This project predicts medical insurance charges based on personal details such as age, gender, BMI, number of children, smoking habits, and region. It uses a Machine Learning model trained on the insurance.csv dataset and provides a Flask web app interface for user input
- Host: GitHub
- URL: https://github.com/pythonicshariful/insurance-charge-predictor
- Owner: pythonicshariful
- Created: 2025-09-28T12:38:06.000Z (8 months ago)
- Default Branch: main
- Last Pushed: 2025-09-30T08:34:04.000Z (8 months ago)
- Last Synced: 2025-10-05T20:53:37.106Z (8 months ago)
- Topics: flask, insura, machine-learning, mlapp, python, regression, scikit-learn
- Language: Jupyter Notebook
- Homepage:
- Size: 206 MB
- Stars: 3
- Watchers: 0
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 🏥 Insurance Charges Prediction App
This project predicts **medical insurance charges** based on personal details such as age, gender, BMI, number of children, smoking habits, and region.
It uses a **Machine Learning model** trained on the `insurance.csv` dataset and provides a **Flask web app** interface for user input.
---
## 📂 Dataset
The dataset (`insurance.csv`) contains the following columns:
- `age` → Age of the individual
- `sex` → Gender (`male` / `female`)
- `bmi` → Body Mass Index
- `children` → Number of children
- `smoker` → Smoking status (`yes` / `no`)
- `region` → Residential region (`northeast`, `northwest`, `southeast`, `southwest`)
- `charges` → Medical insurance cost (Target variable)
---
## ⚙️ Tech Stack
- Python 3.12+
- Flask
- Pandas
- NumPy
- Scikit-learn
- HTML/CSS (for the frontend)
---
## 🚀 Features
✅ Train a regression model on `insurance.csv`
✅ Flask app takes user input (Age, BMI, Children, Smoker, Region, etc.)
✅ Predicts expected **insurance charges**
✅ Simple web UI
---
## 📦 Installation
Clone the repository:
```bash
git clone https://github.com/pythonicshariful/insurance-charge-predictor.git
cd insurance-charge-predictor
```
Create and activate a virtual environment:
```bash
python -m venv venv
source venv/bin/activate # On Linux/Mac
venv\Scripts\activate # On Windows
```
Install dependencies:
```bash
pip install -r requirements.txt
```
---
## ▶️ Usage
Run the Flask app:
```bash
python app.py
```
Open your browser and go to:
```
http://127.0.0.1:5000
```
Enter details like Age, BMI, Smoker, etc. → Get predicted charges 🎉
---
## 📸 Example UI
---
## 🎥 Tutorial Video
[](https://youtu.be/eqWBtlbKYj0)
---
## 📊 Model Performance
- Algorithm used: `RandomForestRegressor` (or whichever you used)
- Evaluation metrics: RMSE, R² Score
---
## 🏷️ Tags
`#machine-learning` `#flask` `#insurance` `#regression` `#python` `#scikit-learn` `#ml-app`
---
## 📜 License
This project is licensed under the MIT License.
---
👨💻 **Author:** [Shariful Islam](https://github.com/pythonicshariful)
🚀 Happy Coding!