{"id":27877029,"url":"https://github.com/arif-miad/pcos-data-analysis-and-classification","last_synced_at":"2026-04-09T22:52:28.477Z","repository":{"id":280098381,"uuid":"940981318","full_name":"Arif-miad/PCOS-Data-Analysis-and-Classification","owner":"Arif-miad","description":null,"archived":false,"fork":false,"pushed_at":"2025-03-01T07:44:20.000Z","size":1649,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-05-05T02:56:04.853Z","etag":null,"topics":["classification","data-science","data-visualization","keras","machine-learning","python","sklearn"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"gpl-3.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Arif-miad.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","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":"2025-03-01T07:34:35.000Z","updated_at":"2025-03-03T01:50:48.000Z","dependencies_parsed_at":"2025-03-01T08:37:32.033Z","dependency_job_id":null,"html_url":"https://github.com/Arif-miad/PCOS-Data-Analysis-and-Classification","commit_stats":null,"previous_names":["arif-miad/pcos-data-analysis-and-classification"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/Arif-miad/PCOS-Data-Analysis-and-Classification","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Arif-miad%2FPCOS-Data-Analysis-and-Classification","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Arif-miad%2FPCOS-Data-Analysis-and-Classification/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Arif-miad%2FPCOS-Data-Analysis-and-Classification/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Arif-miad%2FPCOS-Data-Analysis-and-Classification/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Arif-miad","download_url":"https://codeload.github.com/Arif-miad/PCOS-Data-Analysis-and-Classification/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Arif-miad%2FPCOS-Data-Analysis-and-Classification/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":264777876,"owners_count":23662555,"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":["classification","data-science","data-visualization","keras","machine-learning","python","sklearn"],"created_at":"2025-05-05T02:55:28.885Z","updated_at":"2026-04-09T22:52:23.432Z","avatar_url":"https://github.com/Arif-miad.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# PCOS Data Analysis and Classification\n\n## Overview\nThis project focuses on analyzing and classifying Polycystic Ovary Syndrome (PCOS) using a dataset containing various health-related parameters. The primary goal is to perform data analysis, visualization, and classification using different machine learning models.\n\n## Dataset Description\nThe dataset consists of the following features:\n- **Age**: Age of the individual.\n- **BMI**: Body Mass Index.\n- **Menstrual Irregularity**: Binary indicator (1 for irregular, 0 for regular).\n- **Testosterone Level (ng/dL)**: Testosterone concentration in the blood.\n- **Antral Follicle Count**: Number of antral follicles observed.\n- **PCOS Diagnosis**: Target variable (1 for PCOS, 0 for non-PCOS).\n\n## Project Workflow\n1. **Data Preprocessing**:\n   - Handling missing values (if any).\n   - Standardizing numerical features.\n   - Splitting the dataset into training and test sets.\n2. **Data Visualization**:\n   - Distribution plots for each feature.\n   - Correlation heatmaps.\n   - Boxplots for outlier detection.\n   - Scatter plots and pair plots.\n3. **Machine Learning Models**:\n   - Logistic Regression\n   - K-Nearest Neighbors (KNN)\n   - Support Vector Machine (SVM)\n   - Naive Bayes\n   - Decision Tree\n   - Random Forest\n   - Gradient Boosting\n   - AdaBoost\n   - Extra Trees Classifier\n   - XGBoost\n4. **Model Evaluation**:\n   - Classification report (Precision, Recall, F1-score, Accuracy)\n   - AUC-ROC Curve for model comparison.\n   - Feature importance analysis (for tree-based models).\n\n## Implementation\nThe code follows these key steps:\n\n```python\n# Data Preprocessing\nscaler = StandardScaler()\nX_scaled = scaler.fit_transform(X)\nX_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state=42)\n\n# Model Training and Evaluation\nfor name, model in top_models.items():\n    model.fit(X_train, y_train)\n    y_pred = model.predict(X_test)\n    y_prob = model.predict_proba(X_test)[:, 1] if hasattr(model, \"predict_proba\") else None\n    print(f\"{name} Classification Report:\\n\", classification_report(y_test, y_pred))\n```\n\n## Results\n- The models were compared based on AUC scores, and the ROC curves were plotted.\n- The best-performing models were identified based on their classification metrics.\n\n## Conclusion\nThis project provides insights into PCOS diagnosis using machine learning. The results can help in understanding the most significant features affecting PCOS prediction.\n\n## Installation \u0026 Dependencies\nTo run this project, install the required libraries:\n```bash\npip install pandas numpy seaborn matplotlib scikit-learn xgboost\n```\n\n## Author\n[Arif Miah]\n\n## Connect with Me  \n- Kaggle: [Your Kaggle Profile](https://www.kaggle.com/miadul)\n- LinkedIn: [Your LinkedIn Profile](www.linkedin.com/in/arif-miah-8751bb217)  \n \n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Farif-miad%2Fpcos-data-analysis-and-classification","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Farif-miad%2Fpcos-data-analysis-and-classification","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Farif-miad%2Fpcos-data-analysis-and-classification/lists"}