🏥 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.

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📂 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)

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⚙️ Tech Stack

• Python 3.12+
• Flask
• Pandas
• NumPy
• Scikit-learn
• HTML/CSS (for the frontend)

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🚀 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

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📦 Installation

Clone the repository:

git clone https://github.com/pythonicshariful/insurance-charge-predictor.git
cd insurance-charge-predictor

Create and activate a virtual environment:

python -m venv venv
source venv/bin/activate   # On Linux/Mac
venv\Scripts\activate      # On Windows

Install dependencies:

pip install -r requirements.txt

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▶️ Usage

Run the Flask app:

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 🎉

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📸 Example UI

🎥 Tutorial Video

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📊 Model Performance

• Algorithm used: RandomForestRegressor (or whichever you used)
• Evaluation metrics: RMSE, R² Score

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🏷️ Tags

#machine-learning #flask #insurance #regression #python #scikit-learn #ml-app

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📜 License

This project is licensed under the MIT License.

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👨‍💻 Author: Shariful Islam
🚀 Happy Coding!