Streamlit Web App

Author

Rafiq Islam

Published

August 30, 2024

About

Streamlit Web App

Welcome to MiCharge Predictor! This web app is a part of my data science project Insurance Cost Forecast by using Linear Regression, aimed at predicting the medical cost based on various personal and lifestyle factors. By leveraging advanced machine learning techniques, MiCharge Predictor provides an approximate estimates to help users understand potential medical expances.

Key Features:

  • User-Friendly Interface: Easily input your personal details and receive instant predictions.
  • Copmprehensive Data Analysis: Utilizes sophisticated algorithms to analyze factors such as age, BMI, smoking habits, and more.
  • Accessibility: Available both on web and mobile platforms.

How it works?

  1. Input Your Data: Enter details such as age, gender, BMI, number of children, smoking status, and region.
    • age: Minimum 18, maximum 100
    • gender: Male or Female
    • BMI: Minimum 15.0, maximum 60
    • number of children: Takes values from 0 to 5
    • smoking: Yes or No
    • region: Takes four string input: Northeast, Northwest, Southeast, Southwest
  2. Analyze: The algorithm process the given input
  3. Get Prediction: Based on the input, you get the output.

Usage

You can use the app directly from streamlit web using this link or just here..

Back to top

Citation

BibTeX citation:
@online{islam2024,
  author = {Islam, Rafiq},
  title = {Streamlit {Web} {App}},
  date = {2024-08-30},
  url = {https://mrislambd.github.io/portfolio/spd/webapp/},
  langid = {en}
}
For attribution, please cite this work as:
Islam, Rafiq. 2024. “Streamlit Web App.” August 30, 2024. https://mrislambd.github.io/portfolio/spd/webapp/.