Some rudimentary work using SVM classifier Here we are having a hands on exploration on SVM using PY libs and understanding few key points on the same.
We built this Support Vector Machine for classification using scikit-learn and the Radial Basis Function (RBF) Kernel. Our training data set contains continuous and categorical data from the UCI Machine Learning Repository to predict whether or not a patient has heart disease.
Steps:
- Importing the Data From a File
- Identifying Missing Data
- Dealing with Missing Data
- Split data into Dependent and Independent Variables
- One-Hot-Encoding
- Centering and Scaling the Data
- Building a Preliminary Support Vector Machine
- Opimizing Parameters with Cross Validation (Cross Validation For Finding the Best Values for Gamma and Regularization)
- Building, Evaluating, Drawing and Interpreting the Final Support Vector Machine
N.B. We need to install the following dependencies:
- python=3.6
- pandas
- numpy
- matplotlib
- scikit-learn
Results:
Predicted Data vs Actual Data:
Graphical representation of percentage of explained variance vs degree of components:
