Life_insurance_price_prediction

End to end project on life insurance price prediction

ML Model Deployment with Flask

This project demonstrates how to deploy a machine learning model using Flask to create a simple web application for making predictions.

The project is organized as follows:

app.py: The main Flask application that handles web requests and model predictions.
model.py: Contains functions for training and making predictions using a machine learning model.
requirements.txt: Contains all the required libraries.
templates/: Directory for HTML templates used in the web application.
- index.html: The main HTML template for the web app's user interface.

Follow these steps to set up and run the project:

Clone the repository:

git clone <repository-url>
cd ml-model-deployment-flask

Create a virtual environment (optional but recommended):
```
virtualenv venv
source venv/bin/activate
```
Install dependencies:
```
pip install -r requirements.txt
```
Run the Flask application:
```
python app.py
```
The web app will be accessible at http://localhost:8080.