DATA SET: https://archive.ics.uci.edu/ml/datasets/Heart+Disease

I have built this Support Vector Machine for classification using scikit-learn and the Radial Basis Function (RBF) Kernel. The training data set contains continuous and categorical data from the UCI Machine Learning Repository to predict whether or not a patient has heart disease.

Concepts covered:

programming in Python and the concepts behind Support Vector Machines, the Radial Basis Function, Regularization, Cross Validation and Confusion Matrices.

Steps Involved:

Import the modules that will do all the work

Import the data

Identifying Missing Data

Dealing With Missing Data

Split the Data into Dependent and Independent Variables

One-Hot Encoding

Centering and Scaling

Build A Preliminary Support Vector Machine

Optimize Parameters with Cross Validation

Building, Evaluating, Drawing, and Interpreting the Final Support Vector Machine

DATASET OVERVIEW:

age,

sex,

cp, chest pain

restbp, resting blood pressure (in mm Hg)

chol, serum cholesterol in mg/dl

fbs, fasting blood sugar

restecg, resting electrocardiographic results

thalach, maximum heart rate achieved

exang, exercise induced angina

oldpeak, ST depression induced by exercise relative to rest

slope, the slope of the peak exercise ST segment.

ca, number of major vessels (0-3) colored by fluoroscopy

thal, this is short of thalium heart scan.

hd, diagnosis of heart disease, the predicted attribute