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Analysis and Performance Comparison of Various Machine Learning Based Algorithms

EasyChair Preprint no. 7678

4 pagesDate: March 29, 2022


In this paper, we analyze and compare the performance of machine learning based algorithms like K-Nearest Neighbour, Random Forest, Logistic Regression and Decision Tree. These analyses use data from the dataset which contains the data of accidents severity which is collected from various sources and made into a single dataset. We explored the utilization of single and various algorithms for expectation and utilized four different machine learning approaches with both exactness and execution time execution utilized for the examinations. The outcomes showed most reliable outcomes and that the Random Forest approach was more exact over all blends of input data from the dataset.

Keyphrases: Accident Prediction, Classification Techniques, machine learning

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Vippathi Mani Mounica and Lanke Lahari and Sambhanu Satya Shivani and M.Siva Ganga Prasad},
  title = {Analysis and Performance Comparison of Various Machine Learning  Based Algorithms},
  howpublished = {EasyChair Preprint no. 7678},

  year = {EasyChair, 2022}}
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