Datamining on Stroke Dataset
This dataset contains information about 5110 people and their health in regards to their stroke history. There are 11 features (including the id) in the dataset which we will use to create models to predict whether or not someone is prone to stroke. These features include:
Gender
Age
Hypertension (known as high blood pressure, or a blood pressure above 140/90)
Heart Disease
Marital History (if the subject is or was ever married)
Type of Work (Government, Private, Self-Employed, Never Worked, or Children)
Residency (Rural or Urban)
Average Glucose Level
BMI
Smoking History (Smokes, Formally Smoked, Never Smoked or Unknown)
We wanted to be able to accurately predict whether someone is prone to having a stroke in the future based on these features. If we were able to accurately predict signs of a stroke, it could become extremely valuable to the medical field. Modeling this data could help hospitals and doctors detect those prone to stroke and begin treating or warning their patients of possible strokes in the future. However, we must create a model first, and that requires us to preprocess our data.