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.

Project Structure

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.

Getting Started

Follow these steps to set up and run the project:

1. Clone the repository:

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

2. Create a virtual environment (optional but recommended):

   virtualenv venv
   source venv/bin/activate

3. Install dependencies:

   pip install -r requirements.txt

4. Run the Flask application:

   python app.py

   The web app will be accessible at http://localhost:8080.

Usage

1. Open your web browser and navigate to http://localhost:8080.

2. Enter a values in the input field and click the "Predict" button.