Integrating Different Machine Learning Techniques for Assessment and Forecasting of Data

  • P. Vidyullatha
  • D. Rajeswara Rao
  • Y. Prasanth
  • Ravindra Changala
  • Lakshmi Narayana
Conference paper


Machine learning techniques are useful for solving different problems in many applications. Different machine learning techniques are available for assessment and forecasting of data. For illustration, this paper is focused alight on four machine learning techniques that are Weka, Tanagra, R software and MATLAB for showing different views to analyze and forecast the data. Weka is the most effective machine learning technique for regression and classification problems. Tanagra, the data mining tool, which is a supervised learning technique and also suitable for statistical analysis, classification and clustering problems. R software is a flexible programming accent for statistical computing and graphical settings. Finally, MATLAB is exclusive for technical computing representing the data in 2D, 3D and it is very effective tool for predictive analysis.


Machine learning Meteorological parameters nntool Tanagra R software WEKA 


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Copyright information

© Springer India 2016

Authors and Affiliations

  • P. Vidyullatha
    • 1
  • D. Rajeswara Rao
    • 1
  • Y. Prasanth
    • 1
  • Ravindra Changala
    • 1
  • Lakshmi Narayana
    • 1
  1. 1.Department of CSEKL UniversityGunturIndia

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