Brain-Stroke-Prediction-with-EDA

After applying Exploratory Data Analysis and Feature Engineering, the stroke prediction is done by using ML algorithms including Ensembling methods. 100% accuracy is reached in this notebook. The dataset is taken from https://www.kaggle.com/datasets/jillanisofttech/brain-stroke-dataset. Also, the notebook is available at my Kaggle account : https://www.kaggle.com/code/emreiekyurt/eda-prediction-100-on-brain-stroke-dataset/notebook

Attribute Information

1. gender: "Male", "Female" or "Other"
2. age: age of the patient
3. hypertension: 0 if the patient doesn't have hypertension, 1 if the patient has hypertension
4. heart disease: 0 if the patient doesn't have any heart diseases, 1 if the patient has a heart disease 5) ever-married: "No" or "Yes"
5. worktype: "children", "Govtjov", "Neverworked", "Private" or "Self-employed" 7) Residencetype: "Rural" or "Urban"
6. avgglucoselevel: average glucose level in blood
7. bmi: body mass index
8. smoking_status: "formerly smoked", "never smoked", "smokes" or "Unknown"*
9. stroke: 1 if the patient had a stroke or 0 if not

*Note: "Unknown" in smoking_status means that the information is unavailable for this patient

Real data sources: Data