{"id":26334901,"url":"https://github.com/bdeekshith066/diabetes-prediction-ml-model","last_synced_at":"2025-10-14T06:16:15.593Z","repository":{"id":219963904,"uuid":"750394032","full_name":"bdeekshith066/Diabetes-Prediction-ML-model","owner":"bdeekshith066","description":"Diabetes prediction ML model with 78.664% accuracy trained on a dataset with 768 entries and 9 health metrics, facilitating early identification of diabetes risk.","archived":false,"fork":false,"pushed_at":"2024-01-30T15:46:47.000Z","size":964,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-03-16T00:33:14.432Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/bdeekshith066.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2024-01-30T15:06:35.000Z","updated_at":"2024-03-02T02:53:02.000Z","dependencies_parsed_at":null,"dependency_job_id":"be90c1be-03d5-4632-9888-89e0e4e2c808","html_url":"https://github.com/bdeekshith066/Diabetes-Prediction-ML-model","commit_stats":null,"previous_names":["bdeekshith066/diabetes-prediction-ml-model"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/bdeekshith066/Diabetes-Prediction-ML-model","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/bdeekshith066%2FDiabetes-Prediction-ML-model","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/bdeekshith066%2FDiabetes-Prediction-ML-model/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/bdeekshith066%2FDiabetes-Prediction-ML-model/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/bdeekshith066%2FDiabetes-Prediction-ML-model/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/bdeekshith066","download_url":"https://codeload.github.com/bdeekshith066/Diabetes-Prediction-ML-model/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/bdeekshith066%2FDiabetes-Prediction-ML-model/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":279018122,"owners_count":26086280,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","status":"online","status_checked_at":"2025-10-14T02:00:06.444Z","response_time":60,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2025-03-16T00:30:31.598Z","updated_at":"2025-10-14T06:16:15.588Z","avatar_url":"https://github.com/bdeekshith066.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Diabetes Prediction Machine Learning Model\n\nThis repository hosts a machine learning model for predicting the likelihood of diabetes in individuals based on key health metrics. The model, trained on the provided dataset named **dataset.cnv**, demonstrates an accuracy of 78.664% in predicting diabetes.\n\n## Overview\n\nDiabetes is a prevalent health concern, and early prediction can play a pivotal role in effective management and prevention. This machine learning model leverages a dataset with 768 entries and 9 columns, including features such as pregnancies, glucose levels, blood pressure, skin thickness, insulin levels, BMI, diabetes pedigree function, age, and the outcome (0 for non-diabetic, 1 for diabetic).\n\n## Features\n\n- **Machine Learning Algorithm**: The model employs a state-of-the-art machine learning algorithm fine-tuned for diabetes prediction.\n\n- **Accuracy**: Achieving an accuracy of 78.664% on the test dataset, this model provides reliable predictions.\n\n- **Input Features**: The model considers a comprehensive set of health metrics, ensuring a robust analysis for accurate diabetes predictions.\n\n## Data\n\n### Columns\n\n1. **Pregnancies**: Number of times pregnant\n2. **Glucose**: Plasma glucose concentration a 2 hours in an oral glucose tolerance test\n3. **BloodPressure**: Diastolic blood pressure (mm Hg)\n4. **SkinThickness**: Triceps skin fold thickness (mm)\n5. **Insulin**: 2-Hour serum insulin (mu U/ml)\n6. **BMI**: Body mass index (weight in kg/(height in m)^2)\n7. **DiabetesPedigreeFunction**: Diabetes pedigree function\n8. **Age**: Age in years\n9. **Outcome**: Class variable (0 if non-diabetic, 1 if diabetic)\n\n### Usage\n\n1. **Download the Dataset**: Access the dataset which is uploaded above.\n\n2. **File Format**: The dataset is provided in the \".cnv\" format, facilitating seamless integration for training and evaluation.\n\n3. **Data Exploration**: Perform an exploratory data analysis to understand feature distributions before utilizing the dataset for model training.\n\n### Dataset Structure\n\n- **dataset.cnv**: The main dataset file with 768 rows and 9 columns.\n\n### How to Use\n\n1. **Training**: Utilize the provided Jupyter notebook (*diabetes_prediction_model.ipynb*) or script to train the model on your dataset.\n\n2. **Prediction**: Leverage the trained model for diabetes prediction by providing relevant input features.\n\n### How to Contribute\n\nContributions are welcome! Whether you're enhancing the model, adding features, or improving documentation, follow the standard GitHub workflow. Fork the repository, create a branch, make changes, and submit a pull request.\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbdeekshith066%2Fdiabetes-prediction-ml-model","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fbdeekshith066%2Fdiabetes-prediction-ml-model","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbdeekshith066%2Fdiabetes-prediction-ml-model/lists"}