Support Vector Machine (SVM) Implementation in Machine Learning

In this video, we are going to see the implementation of the SVM Machine Learning algorithm using Python. The SVM is a supervised machine learning technique that can be used for classification as well as regression.

Classification: When the output variable is a category, such as "red" or "blue," or "disease" and "no disease,". 
A classification model attempts to draw some conclusions from observed values. Given one or more inputs a classification model will try to predict the value of one or more outcomes.

Regression: A regression problem is when the output variable is a real or continuous value, such as “salary” or “weight”. Many different models can be used the most basic of which being linear regression. It tries to fit data by using the best hyper-plane that passes through the points.

Implementing SVM in Python - 
We will start with the standard libraries and then create a sample dataset, having linearly separable data, then divides the classes from each other by simply finding a line in case of two dimensions to achieve SVM is to divide the datasets into classes to find a maximum marginal hyperplane then we will use Scikit-Learn’s support vector classifier to train an SVM model on this data.

SVM in Machine Learning: https://www.geeksforgeeks.org/support-vector-machine-algorithm/
Classifying data using SVM in Python: https://www.geeksforgeeks.org/classifying-data-using-support-vector-machinessvms-in-python/