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@article{170333,
        author = {C. Surekha and Uppala Sobhini and G sri Venkateswara lohith and Kalabandalapati Bhargavi and Garrepalli Shivani},
        title = {Road Accident Prediction Using Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {7},
        pages = {128-132},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=170333},
        abstract = {This project focuses on the critical public health issue of road traffic accidents in India. This project addresses the pressing public health challenge of road traffic accidents in India, which result in substantial economic losses and fatalities. It utilizes machine learning techniques to predict the severity of accidents based on a comprehensive dataset. The analysis focuses on key features such as accident severity, the number of victims, and the types of vehicles involved. The dataset is pre-processed to eliminate missing values and irrelevant features, ensuring that the models receive high-quality input.Three classification algorithms Random Forest, K-Nearest Neighbors (KNN), and Logistic Regression-are employed to assess the likelihood of accidents occurring. The models are evaluated for accuracy, providing valuable insights into the factors most closely linked to severe accidents. The primary objective is to create a predictive model capable of identifying high-risk scenarios, which would facilitate timely interventions to mitigate the frequency and impact of traffic accidents. The results emphasize critical factors that influence accidents and propose strategies for prevention, ultimately contributing to safer roadways.},
        keywords = {Traffic Accident Prediction, Machine Learning, Road Safety, Random Forest, KNN, Logistic Regression, Data Analysis.},
        month = {December},
        }