Open AccessA Road Mishaps Analysis using Decision Tree and Random Forest Algorithms
Author(s) -
S. Nandhini,
V. Hima Bindu,
Somnath Yadav,
Rajan Singh
Publication year - 2020
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.f4161.049620
Subject(s) - overfitting , decision tree , random forest , computer science , tree (set theory) , set (abstract data type) , machine learning , sample (material) , artificial intelligence , random tree , algorithm , data mining , mathematics , mathematical analysis , chemistry , chromatography , motion planning , artificial neural network , robot , programming language
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.