Classification is a popular data mining technique that assigns items to target categories or classes. It builds models called classifiers to predict the class of records with unknown class labels. Some common applications of classification include fraud detection, target marketing, and medical diagnosis. Classification involves a learning step where a model is constructed by analyzing a training set with class labels, and a classification step where the model predicts labels for new data. Supervised learning uses labeled data to train machine learning algorithms to produce correct outcomes for new examples.

1. Classification
• Classification is a most familiar and most popular data mining
technique.
• Classification is a form of data analysis that extracts models
describing important data classes.
• Classification is a data mining function that assigns items in a
collection to target categories or classes.
• The goal of classification is to build a concise model that can be use
to predict the class of records whose class label is not know.
• models, called classifiers, predict categorical (discrete, unordered)
class labels.
• Such analysis can help provide us with a better understanding of the
data at large.
• applications, including fraud detection, target marketing,
performance prediction, manufacturing, and medical diagnosis.

2. Classification
•A bank loans officer needs analysis of her data to learn which loan
applicants are “safe” and which are “risky” for the bank.
•A marketing manager at AllElectronics needs data analysis to help
guess whether a customer with a given profile will buy a new
computer.
•A medical researcher wants to analyze breast cancer data to predict
which one of three specific treatments a patient should receive.
•the data analysis task is classification, where a model or classifier is
constructed to predict class (categorical) labels, such as “safe” or
“risky” for the loan application data; “yes” or “no” for the marketing
data; or “treatment A,” “treatment B,” or “treatment C” for the medical
data.

3. Classification
• Suppose that the marketing manager wants to predict how
much a given customer will spend during a sale at AllElectronics.
•This data analysis task is an example of numeric prediction,
where the model constructed predicts a continuous-valued
function, or ordered value, as opposed to a class label.
•This model is a predictor.
•Regression analysis is a statistical methodology that is most
often used for numeric prediction;
•hence the two terms tend to be used synonymously, although
other methods for numeric prediction exist.
• Classification and numeric prediction are the two major types of
prediction problems.

4. Classification
Data classification is a two-step process.
1.learning step (where a classification model is constructed)
2. classification step (where the model is used to predict class
labels for given data).
learning step (or training phase), where a classification algorithm
builds the classifier by analyzing or “learning from” a training set
made up of database tuples and their associated class labels.

5. Classification
In the model build (training) process, a
classification algorithm finds relationships
between the values of the predictors and the
values of the target. Different classification
algorithms use different techniques for finding
relationships. These relationships are summarized
in a model, which can then be applied to a
different data set in which the class assignments
are unknown.
Classification models are tested by comparing the
predicted values to known target values in a set of
test data. The historical data for a classification
project is typically divided into two data sets: one
for building the model; the other for testing the
model.
The class label attribute is discrete-valued and
unordered.
•The individual tuples making up the training set are
referred to as training tuples and are randomly
sampled from the database under analysis.
• In the context of classification, data tuples can be
referred to as samples, examples, instances, data
points, or objects.

6. Classification

7. Supervised Learning
supervised learning is when we teach or train the machine using data
that is well labeled.
Which means some data is already labeled with the correct answer.
After that, the machine is provided with a new set of examples(data) so
that the supervised learning algorithm analyses the training data(set of
training examples) and produces a correct outcome
For instance, suppose you are given a basket filled with different kinds
of fruits. Now the first step is to train the machine with all different
fruits one by one from labeled data.

8. Supervised Learning
If the shape of the object is rounded and has a depression at the top, is
red in color, then it will be labeled as –Apple.
If the shape of the object is a long curving cylinder having Green-
Yellow color, then it will be labeled as –Banana.
Now suppose after training the data, you have given a new separate
fruit, say Banana from the basket, and asked to identify it.