{"id":22967829,"url":"https://github.com/ibensusan/medical-data-visualizer","last_synced_at":"2025-04-02T05:19:10.115Z","repository":{"id":258259304,"uuid":"862922510","full_name":"iBensusan/Medical-Data-Visualizer","owner":"iBensusan","description":"Medical Data Visualizer Project from FreeCodeCamp using Python","archived":false,"fork":false,"pushed_at":"2024-09-28T09:20:51.000Z","size":3,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-02-07T19:49:32.290Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Python","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/iBensusan.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":"2024-09-25T12:18:38.000Z","updated_at":"2024-10-08T08:17:04.000Z","dependencies_parsed_at":"2024-10-18T07:02:32.136Z","dependency_job_id":null,"html_url":"https://github.com/iBensusan/Medical-Data-Visualizer","commit_stats":null,"previous_names":["ibensusan/medical-data-visualizer"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/iBensusan%2FMedical-Data-Visualizer","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/iBensusan%2FMedical-Data-Visualizer/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/iBensusan%2FMedical-Data-Visualizer/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/iBensusan%2FMedical-Data-Visualizer/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/iBensusan","download_url":"https://codeload.github.com/iBensusan/Medical-Data-Visualizer/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":246758660,"owners_count":20828971,"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":[],"created_at":"2024-12-14T21:14:53.652Z","updated_at":"2025-04-02T05:19:10.084Z","avatar_url":"https://github.com/iBensusan.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Project: Medical Data Analysis and Visualization\n\nThis project demonstrates how to preprocess, analyze, and visualize medical examination data, focusing on BMI calculation, normalization of health indicators, and correlation analysis.\n\n## Objectives:\n\n1. **Data Preprocessing**:\n    - Load and clean the dataset using pandas.\n    - Calculate the Body Mass Index (BMI) and create a new column `overweight` to indicate individuals with a BMI greater than 25.\n    - Normalize the `cholesterol` and `gluc` columns by adjusting the values to either `0` (normal) or `1` (above normal).\n\n2. **Categorical Analysis**:\n    - Transform the dataset into a long format using `pd.melt()` to facilitate categorical plotting.\n    - Create a categorical plot to compare the distribution of health indicators (cholesterol, glucose, smoking, alcohol consumption, activity, and overweight) for individuals with and without cardiovascular disease.\n\n3. **Outlier Removal**:\n    - Filter the dataset to remove inconsistent and extreme values:\n        - Ensure systolic blood pressure (`ap_hi`) is greater than or equal to diastolic blood pressure (`ap_lo`).\n        - Remove outliers based on height and weight using the 2.5th and 97.5th percentiles.\n\n4. **Correlation Analysis**:\n    - Calculate a correlation matrix for numerical variables in the cleaned dataset.\n    - Visualize the correlations using a heatmap, masking the upper triangle to improve readability.\n\n## Tools and Libraries:\n\n- **Pandas**: For data loading, manipulation, and cleaning.\n- **NumPy**: For numerical operations and matrix calculations.\n- **Matplotlib \u0026 Seaborn**: For data visualization, including categorical plots and heatmaps.\n\n## Outcomes:\n\n- A cleaned and preprocessed dataset ready for analysis.\n- Visualization of the distribution of health indicators based on cardiovascular disease presence.\n- Identification of key correlations between medical variables using a heatmap.\n- Understanding of how cholesterol, glucose levels, smoking, alcohol consumption, activity, and BMI relate to cardiovascular disease.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fibensusan%2Fmedical-data-visualizer","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fibensusan%2Fmedical-data-visualizer","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fibensusan%2Fmedical-data-visualizer/lists"}