https://github.com/ayushtiwari134/multiple_disorder_predictor
This application predicts the likelihood of obesity and diabetes in a person based on various inputs. It utilizes machine learning models, pipelines, and column transformers to efficiently handle data and provide predictions.
https://github.com/ayushtiwari134/multiple_disorder_predictor
column-transformer decision-tree logistic-regression machine-learning pipeline python streamlit
Last synced: 2 months ago
JSON representation
This application predicts the likelihood of obesity and diabetes in a person based on various inputs. It utilizes machine learning models, pipelines, and column transformers to efficiently handle data and provide predictions.
- Host: GitHub
- URL: https://github.com/ayushtiwari134/multiple_disorder_predictor
- Owner: ayushtiwari134
- Created: 2023-12-26T19:15:50.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2023-12-26T19:33:32.000Z (over 1 year ago)
- Last Synced: 2025-04-14T01:17:13.929Z (2 months ago)
- Topics: column-transformer, decision-tree, logistic-regression, machine-learning, pipeline, python, streamlit
- Language: Python
- Homepage: https://multipledisorderpredictor.streamlit.app/
- Size: 5.86 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Multiple Disorder Prediction App
Welcome to the Multiple Disorder Prediction app! This application predicts the likelihood of obesity and diabetes in a person based on various inputs. It utilizes machine learning models, pipelines, and column transformers to efficiently handle data and provide predictions.
## Overview
This project incorporates machine learning models trained to predict the probability of obesity and diabetes in individuals. The models are developed in a Jupyter Notebook environment using pipelines, column transformers, and exported as pickle files for easy deployment.
## Technology Stack
- **Model Development:** Jupyter Notebook
- **Machine Learning Algorithms:** Logistic Regression, Descision Tree Classifier, implemented using pipelines and column transformers
- **Frontend:** Streamlit
- **Deployment:** Streamlit Cloud Services
## Getting Started
### Clone the Repository
To run the application locally, clone this repository using the following command:
`git clone https://github.com/ayushtiwari134/multiple_disorder_predictor`
### 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, enabling access to the multiple disorder prediction interface.
## Deployment
The application is deployed using Streamlit Cloud Services, offering a live environment to predict the likelihood of obesity and diabetes in individuals.
## Features
- **Input Parameters:** Users can input various health-related factors, such as BMI, blood sugar levels, age, etc.
- **Prediction:** The application predicts whether a person is obesity and/or diabetic based on the provided inputs.
- **Efficient Data Processing:** Utilizes pipelines and column transformers for efficient data handling and model predictions.