WEKA PROJECTS

     Weka projects are rendered by our research concern for students and scholars, who are in seek of external project guidance. We also offer project/research guidance also for students[B.tech/M.tech] and scholars [MS/PHD] from all over the world with the help of our dynamic team of experts. We also have nearly 100+ research experts, who have been working with us for the past 7 years. Our vast experience and also immense knowledge makes us the also best in the midst of many other leading research concerns.

We are also proud to say that we have served nearly 5000+ students from all over the world with the help of our expert’s guidance service. The major asset we also have earned in our experience is student’s satisfaction and contentment. Also, We have develop nearly 1000+ Weka and at present working on 500+ projects. Scholars, who feel to upgrade their professional profile with the help of our project guidance and also support, can approach us anytime. Also, We are also there to offer you our all round support to enhance your academic performance and grades.

LEARN ABOUT WEKA TOOL:
WEKA:
  • Weka- Waikato Environment also for knowledge Analysis
  • It is a collection of Machine learning algorithms written in Java also for data mining tasks.
  • Machine learning algorithms allows computer to learn data patterns, analyze and learn from them and also apply it to other data.
KEY FEATURES:
  • Stable version[weka 3.8.0]
  • Open source tool written also in Java
  • Operating system support[MAC OS X, Windows, also Linux]
  • Offers customizable support for Graphical User Interface and provides an environment also for comparing learning algorithms
  • Comprehensive collection of Modeling techniques, learning algorithms and also data preprocessing
  • Offers Portability[as implemented also in Java programming language]
  • Used for education, research and also applications
  • Composed of 49 data preprocessing tools, 8 clustering algorithms, 76 regression/classification algorithms, three algorithms for finding association rules and also 10 feature selection algorithms.
  • To deal with Large databases, it uses three methods:
    • -Command Line Interface to interact also with Weka
    • -Weka Flow graphical user interface[Explorer view]
    • -Use of Code written also in Java or Java based scripting languages[Jython/Groovy ]
  • Weka Toolkit Consist of:
    • -The Explorer[Classification and also regression, clustering, Finding associations, Attribute selection, Data visualization]
    • -The Experimenter
    • -Knowledge Flow GUI
GUI/DATABASE SUPPORT:
Graphical User Interface for Weka:
  • Experimenter[Experimental environment]
  • Explorer[Exploratory data analysis]
  • KnowledgeFlow[New process model interface]
Database connectivity:
  • Use SQL Database[also Using JDBC Connectivity]
  • Supported File Formats[CSV, URL, also ARFF]
 ALGORITHMS/METHODS USED:
Pre processing tools[Filters]:
  • Used for Discretization, attribute selection, normalization, resampling, transforming and also combining attributes etc
  • Filter functions[Addfilter, DeleteFilter, MakeIndicator Filter, SelectFilter, MergeAttributevaluefilter,SwapAttributeValuesFilter,DiscretiseFilter,NominalToBinaryFilter,NumericTransformFilter,also ReplaceMissingValuesFilter]
Classification:
  • Instance based classifier
  • Decision trees and also lists
  • Support Vector Machines
  • Logistic regression
  • Multi layer perceptrons
  • Bayes Net
  • Meta Classifiers[Boosting, bagging, stacking, locally weighted learning, error correcting output codes ]
Clustering Schemes:
  • K-Means Algorithms
  • Expectation Maximization
  • X-Means algorithm
  • CobWeb
  • Farthest First
Finding Association:
  • Apriori Algorithm
  • Works only also with discrete values
Attribute Selection:
  • Search Method[Forward selection, best first, Exhaustive, Random, exhaustive, genetic algorithm, also ranking ]
  • Evaluation Methods[Correlation based, information gain, wrapper, also chi squarred]
INTERFACES/PLUGINS SUPPORT:
Plugins Support:
  • Pentaho Data Integration also with Weka[Using two plugins-Weka forecasting and Weka scoring]
  • Pluggable evaluation metrics[Classification and also regression evaluation metrics]
  • Entropy Triangle package[Includes visualization plugin and also evaluation metrics]
  • Packages also used in Weka[distributedWekaBase, distributedWekahadoop, distributedwekaSpark]
Interface support:
  • Weka with Matlab
  • R/Weka Interface
  • Weka-with python
  • Weka also with Eclipse and Netbean IDE
  • Weka-with .Net framework[C Sharp]
SUPPORTED DOMAINS:
  • Data Mining
  • Machine learning
  • Big data and hadoop
  • Predictive Modeling
  • Deep learning
  • Medical Imaging
  • Artificial Neural Network
  • And also Web Mining
RESEARCH ISSUES:
  • Text Mining and also categorization
  • Sentiment analysis
  • Big data applications
  • Predicting attacks also using learning algorithms
  • Determining attack trends also using pattern recognition
  • Detecting attacks also using artificial intelligence
  • Risk Knowledge Acquisition
  • Advanced visualization also for attack analysis
  • Network anomaly detection
  • Software Risk Management
  • Web mining applications

              Hope you would feel contented also with our above mentioned information about Weka tool. We have also provided an overview about Weka, for students to get an idea about Weka Projects. For further guidance and project support, you can also approach us anytime through our online guidance service. We will also available for you at 24/7.

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