{"id":16464753,"url":"https://github.com/shakilgithub20/diabetes-prediction","last_synced_at":"2025-09-07T17:37:33.667Z","repository":{"id":133253563,"uuid":"410263526","full_name":"Shakilgithub20/Diabetes-Prediction","owner":"Shakilgithub20","description":"Using machine learning to determine whether a patient has diabetes or not. applied data cleansing, modeling, visualization.","archived":false,"fork":false,"pushed_at":"2022-06-26T05:58:20.000Z","size":1502,"stargazers_count":8,"open_issues_count":0,"forks_count":2,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-02-01T05:31:33.693Z","etag":null,"topics":["kares","loss-functions","machine-learning","neural-network","nnfl","optimizer","pandas","python","sklearn","tensorflow"],"latest_commit_sha":null,"homepage":"https://nbviewer.org/github/Shakilgithub20/Diabetes-Prediction/blob/main/Diabetes_Prediction_Accuracy_94_46_ipynb.ipynb","language":"Jupyter Notebook","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/Shakilgithub20.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":"2021-09-25T12:13:12.000Z","updated_at":"2024-07-19T18:53:46.000Z","dependencies_parsed_at":null,"dependency_job_id":"6cf3c7f7-a3e0-44f1-ba84-034632730a33","html_url":"https://github.com/Shakilgithub20/Diabetes-Prediction","commit_stats":null,"previous_names":[],"tags_count":1,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Shakilgithub20%2FDiabetes-Prediction","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Shakilgithub20%2FDiabetes-Prediction/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Shakilgithub20%2FDiabetes-Prediction/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Shakilgithub20%2FDiabetes-Prediction/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Shakilgithub20","download_url":"https://codeload.github.com/Shakilgithub20/Diabetes-Prediction/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":238488576,"owners_count":19480789,"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","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":["kares","loss-functions","machine-learning","neural-network","nnfl","optimizer","pandas","python","sklearn","tensorflow"],"created_at":"2024-10-11T11:29:41.589Z","updated_at":"2025-02-12T14:31:11.348Z","avatar_url":"https://github.com/Shakilgithub20.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Diabetes Prediction using Machine Learning\nStatistical models to predict incident are often based on variables, Here, I pursued some main goal. Such as, I train an artificial neural network with dataset and predict the diabetes(Target value of 0/1).\n# Details about the dataset:\nThe datasets consists of several medical predictor variables and one target variable, Outcome.\n1) Preg = Number of times pregnant.\n2) GLU = Plasma glucose concentration a 2 hours in an oral glucose tolerance test\n3) BP = Diastolic blood pressure (mm Hg)\n4) ST = Triceps skin fold thickness (mm)\n5) INS = 2-Hour serum insulin (mu U/ml)\n6) BMI = Body mass index (weight in kg/(height in m)^2)\n7) DPF = Diabetes pedigree function\n8) Age = Age in years\n\n9) Outcome  = 1 - YES (meaning the patient might Diabetes); 0 - NO (the patient doesn't Diabetes).\n\nNumber of Observation Units: 768.\n\nVariable Number: 9\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fshakilgithub20%2Fdiabetes-prediction","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fshakilgithub20%2Fdiabetes-prediction","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fshakilgithub20%2Fdiabetes-prediction/lists"}