Prediction of Solid Garbage Waste Generation in Smart Cities using Naive Bayes Algorithm
Rashmi G1, S Sathish Kumar2

1Rashmi G, Department of Computer Science and Engineering, RNSIT Research Centre, Bengaluru (Karnataka), India.

2S Sathish Kumar, Department of Information Science and Engineering, RNSIT, Bengaluru (Karnataka), India.

Manuscript received on 03 December 2019 | Revised Manuscript received on 11 December 2019 | Manuscript Published on 31 December 2019 | PP: 53-56 | Volume-9 Issue-2S December 2019 | Retrieval Number: B10311292S19/2019©BEIESP | DOI: 10.35940/ijitee.B1031.1292S19

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Abstract: Smart cities which are becoming overcrowded today are making human beings life miserable and prone to more challenges on daily basis. Overcrowded is leading to vast generation of wastes contributing to air pollution and in turn is affecting health causing various diseases. Even though various measures are taken to recycle wastes, the rate at which it is being produced is becoming higher and higher. This paper deals with prediction of waste generation using Naïve Bayes machine learning algorithm(Classifier) based on the statistics of previous waste datasets. The datasets used for the future prediction are obtained from reliable sources. The implementation of the algorithm is done in Pyspark using Anaconda Jupyter. The performance of the classifier on the datasets is analyzed with confusion matrix and accuracy metric is used to rate the efficiency of the classifier. The accuracy obtained indicates that algorithm can be effectively used for real time prediction and it gives more accurate results for huge input datasets based on independence assumption.

Keywords: Machine Learning, Big Data, Naïve Bayes Classifier, PySpark, Solid Garbage Waste.
Scope of the Article: Smart Cities