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## Project Structure
```bash
|-- README.md
|-- app.py
|-- train_model.py
|-- predict_model.py
|-- model/
| |-- insurance_claim_prediction_model.joblib
|-- dataset/
| |-- insurance2.csv
|-- requirements.txt
```
# Insurance Claim Prediction App
This project implements a Streamlit web application for predicting whether an individual is likely to make an insurance
claim based on various input parameters. The prediction model is trained using a decision tree classifier.
## Project Structure
- **`app.py`**: Streamlit web application code for user interface.
- **`train_model.py`**: Python script for training the machine learning model and saving it.
- **`predict_model.py`**: Python script for loading the trained model and making predictions.
- **`model/insurance_claim_prediction_model.joblib`**: Saved trained model using joblib.
- **`dataset/insurance2.csv`**: Dataset used for training and testing the model.
- **`requirements.txt`**: List of Python dependencies required to run the application.
## Getting Started
### Prerequisites
Ensure you have Python installed. You can install it from [python.org](https://www.python.org).
### Installation
```bash
pip install -r requirements.txt
```
### Running the App
To run the Streamlit app, execute the following command:
```bash
streamlit run app.py
```
This will start a local server and open your default web browser to the app.
### Usage
User Input Parameters: Adjust the sliders and dropdowns in the sidebar to input different values for age, sex, BMI,
children, smoker status, region, and medical charges.
Predict Button: Click on the "Predict" button in the sidebar to see whether the individual is likely to make an insurance
claim.
Analysis Dashboard: View average medical charges for claims made and not made based on demo data.
### Examples
#### Example 1: Predicting Insurance Claim Likelihood
Suppose a 40-year-old male with a BMI of 25.3, 2 children, non-smoker from the southeast region, and medical charges of $2900.0 wants to predict the likelihood of making an insurance claim. After inputting these details and clicking "Predict," the app predicts whether this individual is likely to make an insurance claim.
### Dependencies
```bash
Python 3.x
pandas
joblib
scikit-learn
streamlit
```
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