{"id":24154890,"url":"https://github.com/chandadiya2004/diabetes-prediction","last_synced_at":"2026-05-01T12:31:48.724Z","repository":{"id":269955961,"uuid":"908944145","full_name":"chandadiya2004/diabetes-prediction","owner":"chandadiya2004","description":"The Diabetes Prediction project utilizes machine learning techniques to determine the probability of an individual having diabetes based on various health metrics like age, BMI, and blood pressure. 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The dataset includes features like age, BMI, blood pressure, and other health-related factors. The Support Vector Machine (SVM) algorithm is utilized to build an efficient classification model.\n\n# Features:\n1. Data Preprocessing: Missing values are handled using SimpleImputer.\n                       Categorical variables are encoded using OneHotEncoder.\n2. Modeling: The SVM classifier is implemented for accurate diabetes prediction.\n3. Pipeline: A streamlined Pipeline integrates preprocessing and model training, ensuring scalability and simplicity.\n4. Evaluation: The model's performance is assessed through metrics such as accuracy and classification reports.\n5. Deployment: The project incorporates Streamlit for user-friendly deployment, enabling users to input health details and instantly receive predictions.\n\n# Technologies Used:\nPython for development\n\nscikit-learn for machine learning tasks\n\nStreamlit for building an interactive web application\n\nPickle for saving and reusing the trained model\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fchandadiya2004%2Fdiabetes-prediction","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fchandadiya2004%2Fdiabetes-prediction","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fchandadiya2004%2Fdiabetes-prediction/lists"}