Road Accidents Prediction Flask in Python Projects

Road Accidents Prediction Flask in Python Projects
Abstract
The Road Accidents Prediction Flask Project is a Python-based system designed to predict the likelihood of road accidents using historical accident data, traffic patterns, weather conditions, and road characteristics. The system employs machine learning algorithms such as Random Forest, Logistic Regression, Gradient Boosting, and Neural Networks to identify risk factors and forecast accident-prone scenarios. Users can interact with the system through a Flask web application, where they input relevant data or select specific locations and conditions, and receive predictive insights regarding accident probability. Python libraries like Pandas, NumPy, Scikit-learn, and Matplotlib are used for data preprocessing, feature engineering, model training, and visualization. This project aims to assist traffic authorities, urban planners, and drivers in improving road safety and reducing accident rates.
Existing System
Traditional road accident analysis relies on manual reporting, statistical studies, or historical accident records analyzed through basic regression models. These approaches often fail to capture complex interactions among multiple factors like weather, traffic volume, road type, and time of day. Existing predictive systems are limited in scalability and usually provide only static risk assessments without real-time or interactive prediction capabilities. Manual accident monitoring is slow and prone to error, making it difficult to implement proactive preventive measures or allocate resources efficiently to high-risk zones.

Proposed System
The proposed system introduces an automated machine learning-based road accident prediction framework integrated into a Flask web application for interactive usage. Accident datasets are preprocessed to handle missing values, encode categorical variables, and normalize numerical features. Feature engineering identifies influential factors such as weather conditions, vehicle density, road type, and historical accident frequency. The processed data is then used to train models like Random Forest or Gradient Boosting, which can predict accident probability for a given scenario. Users can access the Flask interface to input parameters and visualize prediction results along with risk scores. This system improves prediction accuracy, enables real-time risk assessment, and supports traffic management and accident prevention strategies through data-driven decision-making.

Customer Reviews (0)

Share Your Experience

Rating

Your Comment

Submit Review Cancel

No reviews yet. Be the first!