{"id":25129488,"url":"https://github.com/gurpreet0022/unveiling-pcos","last_synced_at":"2026-04-12T02:34:36.782Z","repository":{"id":275771740,"uuid":"927137176","full_name":"Gurpreet0022/Unveiling-PCOS","owner":"Gurpreet0022","description":"Data Driven approach to get insights about PCOS","archived":false,"fork":false,"pushed_at":"2025-02-04T13:36:58.000Z","size":756,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-04-02T21:43:55.998Z","etag":null,"topics":["analysis","eda","insights","matplotlib","numpy","pandas","python3","scipy-stats","seaborn","visualisation"],"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/Gurpreet0022.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":"2025-02-04T13:19:29.000Z","updated_at":"2025-02-04T13:37:02.000Z","dependencies_parsed_at":"2025-02-04T14:34:11.067Z","dependency_job_id":"97f24b8c-5bd4-476d-88f2-72b7660a0e31","html_url":"https://github.com/Gurpreet0022/Unveiling-PCOS","commit_stats":null,"previous_names":["gurpreet0022/unveiling-pcos"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/Gurpreet0022/Unveiling-PCOS","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Gurpreet0022%2FUnveiling-PCOS","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Gurpreet0022%2FUnveiling-PCOS/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Gurpreet0022%2FUnveiling-PCOS/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Gurpreet0022%2FUnveiling-PCOS/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Gurpreet0022","download_url":"https://codeload.github.com/Gurpreet0022/Unveiling-PCOS/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Gurpreet0022%2FUnveiling-PCOS/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":31702580,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-04-11T21:17:31.016Z","status":"online","status_checked_at":"2026-04-12T02:00:06.763Z","response_time":58,"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":["analysis","eda","insights","matplotlib","numpy","pandas","python3","scipy-stats","seaborn","visualisation"],"created_at":"2025-02-08T12:17:46.626Z","updated_at":"2026-04-12T02:34:36.762Z","avatar_url":"https://github.com/Gurpreet0022.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# PCOS Data Analysis - Exploratory Data Analysis (EDA)\n\n## 📌 Project Overview\nPolycystic Ovary Syndrome (PCOS) is a prevalent hormonal disorder affecting women worldwide. This project focuses on analyzing PCOS data to uncover key insights related to symptoms, lifestyle factors, and potential associations.\n\n## 📊 Objectives\n- Explore the prevalence of PCOS in different demographics.\n- Identify common symptoms and their associations.\n- Examine the impact of BMI, lifestyle, and mental health on PCOS.\n- Use statistical methods to find meaningful correlations.\n\n## 📝 About PCOS\nPCOS is a hormonal disorder causing enlarged ovaries with small cysts. Symptoms include irregular periods, excessive androgen levels, weight gain, and insulin resistance. Understanding PCOS is crucial for early diagnosis and lifestyle management.\n\n## 📂 Dataset Overview\n- **Type:** Categorical-heavy dataset (mostly Yes/No values)\n- **Key Features:**\n  - **Symptoms:** Menstrual irregularity, hormonal imbalance, hyperandrogenism, hirsutism, etc.\n  - **Lifestyle Factors:** Diet, exercise, and sleep habits\n  - **Health Metrics:** BMI, mental health status, family history\n\n## 🛠️ Tools \u0026 Technologies Used\n- Python (Pandas, NumPy, Seaborn, Matplotlib)\n- Jupyter Notebook for analysis\n- Feature Engineering \u0026 Normalization\n- Cramér’s V for correlation analysis    \n\n## 🔬 Methodology\n### 1️⃣ Data Preprocessing\n- Label encoding categorical values\n- Feature engineering (Sleep Score, Diet Score, Exercise Score, Healthy Lifestyle Score)\n- Normalization of numerical values\n\n### 2️⃣ Exploratory Data Analysis (EDA)\n- **PCOS Prevalence:** 22% of women in the dataset have PCOS\n- **Common Symptoms:** Menstrual Irregularity, Hormonal Imbalance, and Hirsutism\n- **BMI \u0026 PCOS:** Higher BMI observed in PCOS cases, but no direct age correlation\n- **Lifestyle Impact:** Women without PCOS tend to have healthier habits\n- **Childhood Trauma:** Possible association with PCOS cases\n- **Cramer's V Analysis:** Strong correlation with hormonal imbalance, hyperandrogenism, and mental health\n\n## 📌 Key Insights\n✅ PCOS is most common in the **20-25 age group** and **unmarried women**\n✅ Lifestyle factors such as **diet, exercise, and sleep quality** may influence PCOS risk\n✅ **Mental health and childhood trauma** may be potential risk factors\n✅ **Statistical correlations** confirm strong links between PCOS and hormonal disorders\n\n## 🚧 Challenges Faced\n- High categorical dominance made predictive modeling difficult\n- Complex feature engineering required to quantify lifestyle factors\n- Needed effective visualizations to communicate insights better\n\n## 📍 Conclusion\nThis analysis provides valuable insights into PCOS prevalence and related factors. Due to the categorical nature of the data, the project was concluded at the **EDA stage** rather than proceeding to predictive modeling.\n\n## 📎 Next Steps\n- Extend analysis with additional datasets to improve generalizability\n- Explore time-series data for tracking PCOS symptoms over time\n- Investigate possible interventions based on lifestyle factors\n\n💡 **Open for feedback and collaboration! Let’s discuss more about PCOS and data-driven insights in healthcare.**\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgurpreet0022%2Funveiling-pcos","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fgurpreet0022%2Funveiling-pcos","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgurpreet0022%2Funveiling-pcos/lists"}