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Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# ~~ Medical Examination Research ~~\n## Description\nThe rows in the dataset represent patients and the columns represent information like body measurements, results from various blood tests, and lifestyle choices.\n\n| Feature | Variable Type | Variable      | Value Type |\n|:-------:|:------------:|:-------------:|:----------:|\n| Age | Objective Feature | age | int (days) |\n| Height | Objective Feature | height | int (cm) |\n| Weight | Objective Feature | weight | float (kg) |\n| Gender | Objective Feature | gender | categorical code |\n| Systolic blood pressure | Examination Feature | ap_hi | int |\n| Diastolic blood pressure | Examination Feature | ap_lo | int |\n| Cholesterol | Examination Feature | cholesterol | 1: normal, 2: above normal, 3: well above normal |\n| Glucose | Examination Feature | gluc | 1: normal, 2: above normal, 3: well above normal |\n| Smoking | Subjective Feature | smoke | binary |\n| Alcohol intake | Subjective Feature | alco | binary |\n| Physical activity | Subjective Feature | active | binary |\n| Presence or absence of cardiovascular disease | Target Variable | cardio | binary |\n\n## Tasks\n#### 1) Preprocess\n* \"cholesterol\" and \"gluc\" variables' values turned into appropriate values.\n* \"overweight\" variable was added.\n* Incorrect and outlier data was eliminated.\n\n#### 2) EDA \u0026 Visualization\n* Data was converted to long format and a chart was created that shows the value counts of the categorical features. Chart was splitted by \"cardio\" variable.\n* A correlation matrix of the dataset was created and was used for creating a heatmap.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fserhatderya%2Fmedical_examination_research","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fserhatderya%2Fmedical_examination_research","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fserhatderya%2Fmedical_examination_research/lists"}