{"id":23386992,"url":"https://github.com/ayaanjawaid/brain_stroke_prediction","last_synced_at":"2026-05-07T06:36:28.520Z","repository":{"id":178590070,"uuid":"626315851","full_name":"Ayaanjawaid/Brain_Stroke_Prediction","owner":"Ayaanjawaid","description":"project aims to predict the likelihood of a stroke based on various health parameters using machine learning models. The dataset is preprocessed, analyzed, and multiple models are trained to achieve the best prediction accuracy.","archived":false,"fork":false,"pushed_at":"2024-10-28T22:41:28.000Z","size":4743,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-02-14T08:37:08.939Z","etag":null,"topics":["decision-trees","exploratory-data-analysis","matplotlib","numpy","pandas","python","regression","xgboost"],"latest_commit_sha":null,"homepage":"","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/Ayaanjawaid.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":"2023-04-11T08:15:22.000Z","updated_at":"2024-10-28T22:41:32.000Z","dependencies_parsed_at":null,"dependency_job_id":"f8c76ccd-bb7c-4f18-9bee-6d5f2dda8f42","html_url":"https://github.com/Ayaanjawaid/Brain_Stroke_Prediction","commit_stats":null,"previous_names":["ayaanjawaid/brain_stroke_prediction"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Ayaanjawaid%2FBrain_Stroke_Prediction","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Ayaanjawaid%2FBrain_Stroke_Prediction/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Ayaanjawaid%2FBrain_Stroke_Prediction/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Ayaanjawaid%2FBrain_Stroke_Prediction/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Ayaanjawaid","download_url":"https://codeload.github.com/Ayaanjawaid/Brain_Stroke_Prediction/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":247842754,"owners_count":21005342,"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":["decision-trees","exploratory-data-analysis","matplotlib","numpy","pandas","python","regression","xgboost"],"created_at":"2024-12-22T01:14:10.301Z","updated_at":"2026-05-07T06:36:23.479Z","avatar_url":"https://github.com/Ayaanjawaid.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"\n## Project Overview\nThis project aims to predict the likelihood of a stroke based on various health parameters using machine learning models. The dataset is preprocessed, analyzed, and multiple models are trained to achieve the best prediction accuracy.\n\n## Libraries Used\nPandas: For data manipulation and analysis.\nNumPy: For numerical operations.\nMatplotlib and Seaborn: For data visualization.\nScikit-learn: For implementing machine learning models.\nXGBoost: For the implementation of the XGBoost model.\n\n## Data Preprocessing\nData Loading: The dataset is loaded using Pandas.\nData Cleaning: Missing values are handled, and unnecessary columns are removed.\nFeature Engineering: New features are created to enhance model performance.\nEncoding: Categorical variables are encoded using one-hot encoding.\n\n## Exploratory Data Analysis (EDA)\nVisualization: Various plots (histograms, bar plots, correlation heatmaps) are used to understand the distribution and relationships of the data.\nStatistical Analysis: Summary statistics are computed to gain insights into the dataset.\n\n## Model Training\nMultiple machine learning models are trained to predict strokes. The models used include:\nLogistic Regression\nDecision Tree Classifier\nRandom Forest Classifier\nSupport Vector Machine (SVM)\nK-Nearest Neighbors (KNN)\nXGBoost\n\n## Model Evaluation\nConfusion Matrix: Used to evaluate the performance of the classification models.\nAccuracy, Precision, Recall, and F1-Score: Computed for each model to compare their performance.\nROC Curve and AUC Score: Analyzed to understand the models' ability to distinguish between classes.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fayaanjawaid%2Fbrain_stroke_prediction","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fayaanjawaid%2Fbrain_stroke_prediction","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fayaanjawaid%2Fbrain_stroke_prediction/lists"}