Document Type
Event
Start Date
28-4-2022 5:00 PM
Description
Many country wide internal roads and national highways have dim lights or no street lights all over the world. We usually observe some turns on roads more prone to accidents than other places. In our paper we used Logistic Regression and Decision Trees that together build an Ensemble learning for predicting these accident zones. For validating the results, five evaluation metrics such as Accuracy, Precision, f-measures, Re-call and Area under curve are used. State of art model for US accident dataset gives F1 score of 57%. We are implementing using ensemble learning wherein logistic regression gives F1-score of 53%.
Included in
GR-232 - Accident Prediction using Big Data Analysis using Ensemble Learning
Many country wide internal roads and national highways have dim lights or no street lights all over the world. We usually observe some turns on roads more prone to accidents than other places. In our paper we used Logistic Regression and Decision Trees that together build an Ensemble learning for predicting these accident zones. For validating the results, five evaluation metrics such as Accuracy, Precision, f-measures, Re-call and Area under curve are used. State of art model for US accident dataset gives F1 score of 57%. We are implementing using ensemble learning wherein logistic regression gives F1-score of 53%.