Support Vector Classification
Support Vector Classification(SVC) is a supervised learning algorithm used for classification. It is widely popular for its performance and efficiency in Machine Learning and is a class of Support Vector Machines. Support Vector Classifiers are linear as well as non-probabilistic. SVM also supports the kernel method called kernel SVM which allows us to tackle non-linearity.
For a deeper understanding of SVC, use the following resources:
In this practice session, we will learn to code the SVM Classifier. We will perform the following steps to build a simple classifier using the popular Iris dataset. You can find the dataset here.
Step 1. Data Preprocessing
- Importing the libraries.
- Importing dataset (Dataset Link https://archive.ics.uci.edu/ml/datasets/iris).
- Dealing with the categorical variable.
- Classifying dependent and independent variables.
- Splitting the data into a training set and test set.
- Feature scaling.
Step 2. SVM Classification
- Create a Support Vector classifier.
- Feed the training data to the classifier.
- Predicting the species for the test set.
- Using the confusion matrix to find accuracy.
Let’s go to the practice session to code the Support Vector Classification algorithm in Python.