International Research journal of Management Science and Technology

  ISSN 2250 - 1959 (online) ISSN 2348 - 9367 (Print) New DOI : 10.32804/IRJMST

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CLASSIFICATION OF ADARSH STATIONS IN SOUTH CENTRAL RAILWAYS VIJAYAWADA DIVISION USING NAÏVE BAYES CLASSIFICATION WITH THE HELP OF R

    1 Author(s):  DR. M.RANI REDDY

Vol -  10, Issue- 4 ,         Page(s) : 185 - 191  (2019 ) DOI : https://doi.org/10.32804/IRJMST

Abstract

Classification is a statistical method that can be used to classify any object with its attributes. This paper explores how we can use that method adopted to railway passengers’ demand forecasting with the help of a software tool R which can support both Arithmetic and Statistical methods. This tool simply accepts the input in proper format and forecasts the demand without the assistance of any other layouts which can be used in normal statistical methods to produce the result. R is a built in tool with graphical

Efron B. Mathematics. Bayes' theorem in the 21st century. Science 2013;340:1177-8. 10.1126/science.1236536
2) Medow MA, Lucey CR. A qualitative approach to Bayes' theorem. Evid Based Med 2011;16:163-7. 10.1136/ebm-2011-0007
3) López Puga J, Krzywinski M, Altman N. Points of significance: Bayes' theorem. Nat Methods 2015;12:277-8. 10.1038/nmeth.3335
4)  Zhang Z, Chen L, Ni H. Antipyretic therapy in critically ill patients with sepsis: an interaction with body temperature. PLoS One 2015;10:e0121919. 10.1371/journal.pone.0121919
5)  Cohen J, Vincent JL, Adhikari NK, et al. Sepsis: a roadmap for future research. Lancet Infect Dis 2015;15:581-614. 10.1016/S1473-3099(15)70112-X
6) Drewry AM, Hotchkiss RS1. Sepsis: Revising definitions of sepsis. Nat Rev Nephrol 2015;11:326-8. 10.1038/nrneph.2015.66 [
7) Murphy KP. Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning series). 1st ed. London: The MIT Press; 2012:1.
 8) Kononenko I. Machine learning for medical diagnosis: history, state of the art and perspective.
9)  Misc Functions of the Department of Statistics, Probability Theory Group (Formerly: E1071), TU Wien [R package e1071 version 1.6-7]. Comprehensive R Archive Network (CRAN); 2014. Available online:
 10) Hadorn DC, Draper D, Rogers WH, et al. Cross-validation performance of mortality prediction models. Stat Med 1992;11:475-89. 10.1002/sim.4780110409
11) Schumacher M, Holländer N, Sauerbrei W. Resampling and cross-validation techniques: a tool to reduce bias caused by model building? 

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