A Road Mishaps Analysis using Decision Tree and Random Forest Algorithms
S. Nandhini1, V. Hima Bindu2, Sanjit Yadav3, Rajan Singh4
1S. Nandhini*, Assistant Professor, Department of ME CSE, SRM Institute of Science and Technology, Ramapuram, Chennai, Tamil Nadu, India.
2V. Hima Bindu, Student, Department of CSE, SRM Institute of Science and Technology, Ramapuram, Chennai, Tamil Nadu, India.
3Sanjit Yadav, Student, Department of CSE, SRM Institute of Technology, Ramapuram, Chennai, Tamil Nadu, India.
4Rajan Singh, Student, Department of CSE, SRM Institute of Science and Technology, Ramapuram, Chennai, Tamil Nadu, India.
Manuscript received on March 15, 2020. | Revised Manuscript received on April 02, 2020. | Manuscript published on April 10, 2020. | PP: 2067-2069 | Volume-9 Issue-6, April 2020. | Retrieval Number: F4161049620/2020©BEIESP | DOI: 10.35940/ijitee.F4161.049620
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: AI (ML) is the investigation of calculations and factual models that PC frameworks use to play out a particular activity without utilizing guidelines and depending on designs. It is communicated as subset of man-made brainpower. In this, the sample data is split into test set and the training set. Major drawback for the deaths in world is recorded by the road accidents. Most of the deaths are occurred in the middle-income countries. These studies result in finding the major factors for road accidents using decision tree and random forests. Decision tree is a choice help device that is a like a tree model which contains just control explanations. Random forest corrects the decision tree for overfitting to their training set. In this, the decision tree and the random forest algorithms are used to find the severity and the factors for the road-accidents using driver’s personal information. Results conclude that the possibilities for the road accidents using the machine learning algorithms.
Keywords: Data Pre-processing, Feature Extraction, Feature Selection, Random forest, Decision tree, Test set, Training set.
Scope of the Article: Approximation And Randomized Algorithms