{"id":18431657,"url":"https://github.com/ayushtiwari134/multiple_disorder_predictor","last_synced_at":"2026-04-18T13:33:21.246Z","repository":{"id":214234943,"uuid":"736026566","full_name":"ayushtiwari134/multiple_disorder_predictor","owner":"ayushtiwari134","description":"This application predicts the likelihood of obesity and diabetes in a person based on various inputs. 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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.\n\n## Overview\n\nThis 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.\n\n## Technology Stack\n\n- **Model Development:** Jupyter Notebook\n- **Machine Learning Algorithms:** Logistic Regression, Descision Tree Classifier, implemented using pipelines and column transformers\n- **Frontend:** Streamlit\n- **Deployment:** Streamlit Cloud Services\n\n## Getting Started\n\n### Clone the Repository\n\nTo run the application locally, clone this repository using the following command:\n\n`git clone https://github.com/ayushtiwari134/multiple_disorder_predictor`\n\n\n### Running the App\n\nAfter cloning the repository, navigate to the project directory and execute the following command to run the app:\n\n`streamlit run app.py`\n\n\nThis command will start the Streamlit web application locally, enabling access to the multiple disorder prediction interface.\n\n## Deployment\n\nThe application is deployed using Streamlit Cloud Services, offering a live environment to predict the likelihood of obesity and diabetes in individuals.\n\n## Features\n\n- **Input Parameters:** Users can input various health-related factors, such as BMI, blood sugar levels, age, etc.\n- **Prediction:** The application predicts whether a person is obesity and/or diabetic based on the provided inputs.\n- **Efficient Data Processing:** Utilizes pipelines and column transformers for efficient data handling and model predictions.\n\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fayushtiwari134%2Fmultiple_disorder_predictor","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fayushtiwari134%2Fmultiple_disorder_predictor","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fayushtiwari134%2Fmultiple_disorder_predictor/lists"}