Insurance Cost Prediction Web App

This project is a simple web application built with Streamlit that predicts medical insurance costs based on a person's attributes. It uses a Linear Regression model trained on a dataset of insurance beneficiaries to provide real-time cost estimations.

📋 Table of Contents

• Features
• Demo
• Project-Structure
• Getting Started
  • Prerequisites
  • Installation
• Usage
• Model Details
• Dataset

✨ Features

• Interactive UI: A user-friendly web interface built with Streamlit.
• Real-time Predictions: Get instant insurance cost estimates as you adjust the inputs.
• Dynamic BMI Calculation: Automatically calculates Body Mass Index (BMI) from height and weight.
• Predicts costs based on:
  • Age
  • Gender
  • BMI
  • Number of children
  • Smoking status
  • Geographical region

📸 Demo

Here is a quick look at the application's user interface:

(A screenshot of your running Streamlit application would be perfect here!)

📂 Project Structure

The project directory is organized as follows:

.
├── insurance.py        # The main Python script for the Streamlit app and ML model
├── insurance.csv       # The dataset used for training the model
└── README.md           # This file

🚀 Getting Started

Follow these instructions to get a copy of the project up and running on your local machine.

Prerequisites

Make sure you have Python 3.7+ installed on your system. You can download it from python.org.

Installation

1. Clone the repository (or download the files) If you are using Git, you can clone the repository. Otherwise, just ensure insurance.py and insurance.csv are in the same folder.

2. Install the required libraries Navigate to your project directory in the terminal and run the following command to install the necessary Python packages:

   pip install streamlit pandas scikit-learn numpy

Usage

1. Run the application Open your terminal, navigate to the project directory, and execute the following command:

   streamlit run insurance.py

2. Interact with the app Your web browser will automatically open a new tab at http://localhost:8501.

   • Use the sliders, radio buttons, and input fields to enter the required information.
   • The app will calculate the BMI automatically.
   • Click the "Predict Insurance Cost" button to see the estimated insurance charge.

🤖 Model Details

The prediction model is a Linear Regression algorithm from the scikit-learn library.

• Features (X): age, sex, bmi, children, smoker, region
• Target (Y): charges

Categorical features (sex, smoker, region) are encoded into numerical values before being fed into the model. The dataset is split into a training set (80%) and a testing set (20%) to train and evaluate the model's performance.

📊 Dataset

The project uses the insurance.csv dataset, which contains 1338 rows of data about insurance beneficiaries in the United States.

Columns:

• age: Age of the primary beneficiary.
• sex: Gender of the beneficiary (male, female).
• bmi: Body mass index.
• children: Number of children covered by health insurance.
• smoker: Whether the beneficiary is a smoker (yes, no).
• region: The beneficiary's residential area in the US (northeast, southeast, southwest, northwest).
• charges: Individual medical costs billed by health insurance.