The 'diabetes.csv' dataset is related to diabetes prediction. Each row represents a different individual, and the columns represent various attributes and characteristics:
- Some samples of data is as shown:-
- Pregnancies: The number of pregnancies the individual has had.
- Glucose: The plasma glucose concentration after a 2-hour oral glucose tolerance test.
- Blood Pressure: The diastolic blood pressure (mm Hg).
- Skin Thickness: The skinfold thickness of the triceps (mm).
- Insulin: 2-Hour serum insulin (mu U/ml).
- BMI (Body Mass Index): A measure of body fat based on height and weight.
- Diabetes Pedigree Function: A function that represents the diabetes mellitus history in relatives and the genetic relationship of those relatives to the individual.
- Age: The age of the individual.
- Outcome: This is the target variable. It indicates whether the individual has diabetes (1) or does not have diabetes (0).
Using this data a machine learning model is built to predict diabetes based on the provided attributes. SVM is uesd to train the data and by using streamlit a interactive web application is created.
