Data Science - Support Vector Machine Work
Support Vector Machine Algorithm (SVM) :
Support Vector Machine or SVM is one of the most popular Supervised Learning algorithms, which is used for Classification as well as Regression problems. However, primarily, it is used for Classification problems in Machine Learning.
The goal of the SVM algorithm is to create the best line or decision boundary that can segregate n-dimensional space into classes so that we can easily put the new data point in the correct category in the future. This best decision boundary is called a hyperplane.
SVM chooses the extreme points/vectors that help in creating the hyperplane. These extreme cases are called as support vectors, and hence algorithm is termed as Support Vector Machine.
There are two different types of SVMs, each used for different things :
• Simple SVM : Typically used for linear regression and classification problems.
• Kernel SVM : Has more flexibility for non-linear data because you can add more features to fit a hyperplane instead of a two-dimensional space.
This assignment will study following Questions :
Problem Statement No 1 :
Classify the Size Categorie using SVM (forestfires.csv) - the burned area of the forest (Small, Large)
Problem Statement No 2 :
Prepare a classification model using SVM for salary data.