## The classification problem is always interesting to solve, SVM gives great predictions though😁 .

SVM (Support Vector Machine) is a comfortable algorithm to use to solve classification problems and regression too, mostly classification.

The objective of the support vector machine algorithm is to find a hyperplane in an N-dimensional space(N — the number of features) that distinctly classifies the data points.

**What is Support Vector Machine ?**

SVM model basically a representation of two classes using hyperplane in multidimensional space (N-dimentional space). SVM will generate hyperplane in iterative manner, so we can minimize the error to produce better classification model.

**Important Terminologies**

**Support Vector**

Support Vector is data points which are cloest to the hyperplane, Construction of hyperplane will depends on the Support Vector.

2. **Hyperplane**

Hyperplane is decision line or boundary line which separates different classes.

Equation of straight line : Y = mx + c

So mathematically what is hyperplane

*In geometry, a **hyperplane** is a subspace whose dimension is one less than that of its ambient space. If a space is 3-dimensional then its **hyperplanes** are the 2-dimensional planes, while if the space is 2-dimensional, its **hyperplanes** are the 1-dimensional lines.*