WEKA is a popular open-source machine learning software suite written in Java that contains tools for data pre-processing, classification, regression, clustering, visualization, and feature selection. It has a graphical user interface that allows quick setup and operation, and works with data represented as flat files where each data point is described by a fixed number of attributes. WEKA's algorithms can be directly applied to datasets or called from Java code, and it is portable across platforms since it is implemented in Java.

1. Installation Guidelines

2. https://www.cs.waikato.ac.nz/ml/weka/

15. WEKA (Waikato Environment for Knowledge Analysis) is a popular suite of
machine learning software written in Java, developed at the University of Waikato,
New Zealand.
WEKA is an open-source application that is freely available under the GNU general
public license agreement. Originally written in C, the WEKA application has been
completely rewritten in Java and is compatible with almost every computing platform.
It is user friendly with a graphical interface that allows for quick set up and operation.
WEKA operates on the predication that the user data is available as a flat file or
relation. This means that each data object is described by a fixed number of attributes
that usually are of a specific type, normal alpha-numeric or numeric values.
Introduction

16. ADVANTAGES OF WEKA:
 The advantage of a package like WEKA is that a whole range of data preparation, feature selection and
data mining algorithms are integrated. This means that only one data format is needed and trying out and
comparing different approaches becomes easy. The package also comes with a GUI, which should make
it easier to use.
 Portability, since it is fully implemented in the Java programming language and thus runs on almost any
modern computing platform.
 A comprehensive collection of data preprocessing and modeling techniques.
 Ease of use due to its graphical user interfaces.
 WEKA supports several standard data mining tasks, more specifically, data preprocessing, clustering,
classification, regression, visualization, and feature selection.
 All WEKA's techniques are predicated on the assumption that the data is available as a single flat file or
relation, where each data point is described by a fixed number of attributes (normally, numeric or
nominal attributes, but some other attribute types are also supported).

17. • Weka is a collection of machine learning algorithms for solving real-
world data mining problems. It is written in Java and runs on almost any
platform. The algorithms can either be applied directly to a dataset or
called from your own Java code.
Features:
1.Machine learning
2.Data mining
3.Preprocessing
4.Classification
5.Regression
6.Clustering
Features:
7. Association rules
8. Attribute selection
9. Experiments
10. Workflow
11. Visualization
Features: