https://github.com/ayushtiwari134/doctor_fees_predictor
This app is designed to predict the consultation fees of a doctor based on various inputs using a Random Forest Regressor model.
https://github.com/ayushtiwari134/doctor_fees_predictor
machine-learning pipelines python regression streamlit support-vector-machine
Last synced: about 2 months ago
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This app is designed to predict the consultation fees of a doctor based on various inputs using a Random Forest Regressor model.
- Host: GitHub
- URL: https://github.com/ayushtiwari134/doctor_fees_predictor
- Owner: ayushtiwari134
- Created: 2023-12-18T17:03:02.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2023-12-18T18:52:44.000Z (over 1 year ago)
- Last Synced: 2025-02-16T09:22:13.147Z (4 months ago)
- Topics: machine-learning, pipelines, python, regression, streamlit, support-vector-machine
- Language: Python
- Homepage: https://doctor-fees-predictor.onrender.com/
- Size: 440 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Doctor Consultation Fee Prediction Web App
Welcome to the Doctor Consultation Fee Prediction web application! This app is designed to predict the consultation fees of a doctor based on various inputs using a Random Forest Regressor model.
## Overview
This project utilizes a machine learning model created in a Jupyter Notebook environment. The algorithm employed for prediction is the Random Forest Regressor, which is trained on relevant features to estimate the consultation fees.
## Technology Stack
- **Model Development:** Jupyter Notebook
- **Machine Learning Algorithm:** Random Forest Regressor
- **Frontend:** Streamlit
- **Deployment:** Render
## Getting Started
### Clone the Repository
To run the application locally, clone this repository using the following command:
`git clone https://github.com/your-username/doctor-consultation-fee-prediction.git`
### Running the App
After cloning the repository, navigate to the project directory and execute the following command to run the app:
`streamlit run app.py`
This command will start the Streamlit web application locally, allowing you to access the doctor consultation fee prediction interface.
## Deployment
The application is deployed using Render, providing a live environment to predict consultation fees. Visit the deployed application at: [Doctor_Fee_Predictor](https://doctor-fees-predictor.onrender.com/).
## Features
- **Input Parameters:** Users can input various factors such as doctor's experience, qualifications, location, etc.
- **Prediction:** The model predicts the consultation fees based on the provided inputs.
- **User-Friendly Interface:** Streamlit offers an intuitive and interactive frontend for easy usability.