Support-Vector-Machines-in-Python

In this lesson we will 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.

Below are the key objectives of this project:

1. Importing data into, and manipulating a pandas dataframe.
2. Identifying and dealing with missing data.
3. Formatting the data for a support vector machine, including One-Hot Encoding
4. Optimizing parameters for the radial basis function and classification
5. Building, evaluating, drawing and interpreting a support vector machine

Below is the summary of the tasks performed in this project:

• Task 1: Import the modules that will do all the work
• Task 2: Import the data
• Task 3: Missing Data Part 1: Identifying Missing Data
• Task 4: Missing Data Part 2: Dealing With Missing Data
• Task 5: Format Data Part 1: Split the Data into Dependent and Independent Variables
• Task 6: Format the Data Part 2: One-Hot Encoding
• Task 7: Format the Data Part 3: Centering and Scaling
• Task 8: Build A Preliminary Support Vector Machine
• Task 9: Optimize Parameters with Cross Validation
• Task 10: Building, Evaluating, Drawing, and Interpreting the Final Support Vector Machine