Introduction to SVM:
In machine learning, support vector machines (SVMs, also support vector networks) are supervised learning models with associated learning algorithms that analyze data used to classify and regression analysis.
Support Vector Machine (SVM) — it is a discriminatory classifier formally defined by a dividing hyperplane. In other words, given the labeled training data (supervised learning), the algorithm produces an optimal hyperplane that classifies new examples.
What is a reference vector machine?
The SVM is a representation of examples in the form of points in space, displayed in such a way that the examples of the individual categories are separated by a clear space, as wide as possible.
In addition to performing linear classification, SVMs can efficiently perform nonlinear classification by implicitly mapping their input to feature spaces.
What does SVM do?
Given a set of training examples, each of which is marked as belonging to one or the other of the two categories, the SVM learning algorithm builds a model that assigns new examples to a particular category, turning it into a nonprobabilistic binary linear classifier.
Have some basic knowledge from this Prerequisites : Numpy , matplotlib , scikitlearn
Let`s take a quick look at support vector classification. First we need to create a dataset:

Output:
What they do support vector machines, — it is not only to draw a line here between the two classes, but also to consider the area around a line of some given width. Here`s an example of how this might look:

Importing datasets
This is the intuition of support vector machines that optimize a linear discriminant model representing the perpendicular distance between datasets. Now, let`s train the classifier using our training data. Before training, we need to import the cancer datasets as a csv file, where we will train two of them all.

[[122.8 1001.] [132.9 1326.] [130. 1203.] ..., [108.3 858.1] [140.1 1265.] [47.92 181.]] array ([0., 0. , 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0 ., 1., 1., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 1., 1., 1., 1. , 1., 0., 0., 1., 0., 0., 1., 1., 1., 1., 0., 1., ...., 1.])
Support Vector Fitting
Now let`s fit the SVM machine classifier to these points. While the mathematical details of the likelihood model are interesting, we`ll cover them elsewhere. Instead, we will simply view scikitlearn`s algorithm as a black box that does the above task.
# import vector classifier support
from
sklearn.svm
import SVC
# & quot; Support Vector Classifier & quot;
clf
=
SVC (kernel
=
` linear`
)
# trying on x samples and classes
clf.fit (x, y)
After fitting, the model can be used to predict new values:

array ([0.]) array ([1.])
Let`s look at the graph as it shows.
This is obtained by analyzing the received data and preprocessing methods to create optimal hyperplanes using the matplotlib function.
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