Support vector machines (SVMs) are supervised machine learning models that analyze data used for classification and regression analysis. SVMs find a hyperplane that separates clusters of data points and maximizes the margin between the different classes. They can be used for applications like credit card approval predictions, patient risk assessments in hospitals, and categorizing text and web pages. SVMs work by finding the optimal separating hyperplane that maximizes the margin between different classes of data points in the training set.

1. SUPORT VECTOR MACHINE –SVM
Nilanjana Pradhan

2.  SVMs are linear classifiers that find a hyperplane to
separate two class of data, positive and negative.
 A support vector machine (SVM) is a
supervised machine learning model that uses
classification algorithms for two-group classification
problems.
 After giving an SVM model sets of labeled training
data for each category, they’re able to categorize
new text.

3. 3
ANOTHER APPLICATION
 A credit card company receives thousands of
applications for new cards. Each application
contains information about an applicant,
 age
 Marital status
 annual salary
 outstanding debts
 credit rating
 etc.
 Problem: to decide whether an application should
approved, or to classify applications into two
categories, approved and not approved.

4. AN EXAMPLE OF THE APPLICATION
 An emergency room in a hospital measures 17
variables (e.g., blood pressure, age, etc) of newly
admitted patients.
 A decision is needed: whether to put a new
patient in an intensive-care unit.
 Due to the high cost of ICU, those patients who
may survive less than a month are given higher
priority.
 Problem: to predict high-risk patients and
discriminate them from low-risk patients.

5. TEXT AND HYPERTEXT CATEGORIZATION
 Allows text and hypertext categorization.
 It Uses training data to classify documents into
different categories such as news articles,
e-mails, and web pages

6. EXAMPLES:
 Classification of news articles into “business” and
“Movies”
 Classification of web pages into personal home
pages and others
 For each document, calculate a score and compare
it with a predefined threshold value.
 When the score of a document surpasses threshold
value, then the document is classified into a definite
category.

7.  If it does not surpass threshold value then consider
it as a general document.
 Classify new instances by computing score for each
document and comparing it with the learned
threshold.

8.  Many marketing problems require accurately
predicting the outcome of a process or the future
state of a system.
 Support vector machine is used to predict
outcomes in emerging environments in marketing,
such as automated modeling, mass-produced
models, intelligent software agents, and data
mining.

9. HOW DOES SVM WORK?
 The basics of Support Vector Machines and how it
works are best understood with a simple example.
 Let’s imagine we have two tags: red and blue, and
our data has two features: x and y.

10.  We want a classifier that, given a pair
of (x,y) coordinates, outputs if it’s either red _or
_blue. We plot our already labeled training data on
a plane:

12.  A support vector machine takes these data points
and outputs the hyperplane (which in two
dimensions it’s simply a line) that best separates
the tags. This line is the decision boundary:
anything that falls to one side of it we will classify
as blue, and anything that falls to the other as red.

13. _IN 2D, THE BEST HYPERPLANE IS SIMPLY A
LINE_

14.  But, what exactly is the best hyperplane? For SVM,
it’s the one that maximizes the margins from both
tags. In other words: the hyperplane (remember it's
a line in this case) whose distance to the nearest
element of each tag is the largest.

16.  So, what is linear regression?
 Simply put, machines need to be supervised in
order to effectively learn new things.
 Linear regression is a machine
learning algorithm that enables this.
 The biggest ability of machines is that they can
learn about the problem and execute solutions
seamlessly. This greatly reduces and eliminates
human error.

17.  It is also used to find the relationship between
forecasting and variables. A task is performed
based on a dependable variable by analyzing the
impact of an independent variable on it. Those
proficient in programming software such as Python,
C can sci-kit learn the library to import the linear
regression model or create their own custom
algorithm before applying it to the machines.

18.  Forecasting
A top advantage of using a linear regression model in
machine learning is the ability to forecast trends and
make predictions that are feasible.
 Data scientists can use these predictions and make
further deductions based on machine learning. It is
quick, efficient, and accurate.
 This is predominantly since machines process large
volumes of data and there is minimum human
intervention. Once the algorithm is established, the
process of learning becomes simplified.

19.  Beneficial to small businesses
By altering one or two variables, machines can
understand the impact on sales.
 Since deploying linear regression is cost-effective, it is
greatly advantageous to small businesses since short-
and long-term forecasts can be made when it comes to
sales.
 This means that small businesses can plan their
resources well and create a growth trajectory for
themselves.
 They will also be to understand the market and its
preferences and learn about supply and demand.

20.  Preparing Strategies
Since machine learning enables prediction, one of
the biggest advantages of a linear regression model
in it is the ability to prepare a strategy for a given
situation, well in advance, and analyze various
outcomes.
 Meaningful information can be derived from the
regression model of forecasting thereby helping
companies plan strategically and make executive
decisions.

21. THANK YOU