Binary Classification Support Vector Machine Model

Building a system in Python that can predict whether the person is Diabetic or Not based on the Recorded Data. For prediction Supervised Machine Learning Model is used and executed in Jupyter Notebook using Python. Dataset is Standardised before processing to obtain accurate results. Here, Support vector Machine (SVM) Model for Binary Classification problem. Model is trained for 70% of dataset and tested for 30% of dataset. Accuracy Score obtained for the test dataset was 78.78%.

This dataset is originally from the National Institute of Diabetes and Digestive and Kidney Diseases. The objective of the dataset is to diagnostically predict whether or not a patient has diabetes, based on certain diagnostic measurements included in the dataset. Several constraints were placed on the selection of these instances from a larger database. In particular, all patients here are females at least 21 years old of Pima Indian heritage. Get Dataset: Here

This model can be used to predict whether a women is Diabetic or Not by just entering the required input. i.e., Pregnancies, Glucose, Blood Pressure, Skin thickness, Insulin, BMI, DiabetesPedigreeFunction, and Age.

• Pregnancies : Number of times pregnant
• Glucose : Plasma glucose concentration a 2 hours in an oral glucose tolerance test.
• Blood Pressure : Diastolic blood pressure (mm Hg).
• Skin Thickness : Triceps skin fold thickness (mm).
• Insulin : 2-Hour serum insulin (mu U/ml).
• BMI : Body mass index (weight in kg/(height in m)^2).
• Diabetes Pedigree Function : Likelihood of diabetes based on family history.
• Age : Age in years.

• Clone the project
• Run pip install -r requirements.txt
• Add input data for which prediction is to be done.
• Run