https://github.com/pb2204/medical-insurance-cost-prediction

This Medical Insurance Cost Prediction AI Model Will Predict The Cost Of Any Medical Insurance With Around 84.26 % Accuracy...
https://github.com/pb2204/medical-insurance-cost-prediction

ai artificial-intelligence hacktoberfest hacktoberfest-accepted jupyter-notebook machine-learning python streamlit streamlit-webapp

Last synced: about 1 month ago
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This Medical Insurance Cost Prediction AI Model Will Predict The Cost Of Any Medical Insurance With Around 84.26 % Accuracy...

• Host: GitHub
• URL: https://github.com/pb2204/medical-insurance-cost-prediction
• Owner: PB2204
• License: apache-2.0
• Created: 2023-06-09T19:10:11.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2024-06-08T01:38:33.000Z (7 months ago)
• Last Synced: 2024-06-08T02:39:35.959Z (7 months ago)
• Topics: ai, artificial-intelligence, hacktoberfest, hacktoberfest-accepted, jupyter-notebook, machine-learning, python, streamlit, streamlit-webapp
• Language: Jupyter Notebook
• Homepage: https://pb2204-medical-insurance-cost-prediction-main-e9x4iq.streamlit.app/
• Size: 2.24 MB
• Stars: 19
• Watchers: 1
• Forks: 0
• Open Issues: 2
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Medical-Insurance-Cost-Prediction

This Medical Insurance Cost Prediction AI Model Will Predict The Cost Of Any Medical Insurance With Around 84.26 % Accuracy...

![Type](https://img.shields.io/badge/Machine-Learning-red.svg)

![IDE](https://img.shields.io/badge/IDE-JupyterNotebook-orange.svg)

![Type](https://img.shields.io/badge/Type-Supervised-yellow.svg)

![Status](https://img.shields.io/badge/Status-Completed-cherryred.svg)

Link to the web app : 



    

 Work Flow Of This Project 


```mermaid

flowchart TD

A[Step 0 : Collect Data] --> B[Step 1 : Import Libraries/Modules In The Workspace]

B[Step 1 : Import Libraries/Modules In The Workspace] --> C[Step 2 : Import The Collected Data Into The Workspace]

C[Step 2 : Import The Collected Data Into The Workspace] --> D[Step 3 : Data Preprocessing]

D[Step 3 : Data Preprocessing] --> E[Step 4 : Training A ML Model Using Random Forest Regression Algorithm]

E[Step 4 : Training A ML Model Using Random Forest Regression Algorithm] --> F[Step 5 : Deploy The ML model As A Web App With Stremlit]

```