Efficient emergency response is critical for public safety, yet response times in New York City can vary widely due to multiple influencing factors. This project focuses on predicting emergency response times by analyzing NYC Dispatch data, which contains historical records of emergency responses. Additional features, including the type of incident, location, and time of day, are incorporated to identify key patterns affecting response efficiency.
The methodology involves preprocessing the Dispatch data to ensure quality and consistency, followed by exploratory data analysis (EDA) to uncover trends and disparities in response times. Predictive models, such as linear regression, decision trees, and ensemble models will be used to estimate response times across various scenarios. Feature engineering and model optimization are employed to enhance prediction accuracy, while visualization are used to communicate key findings effectively.
The project’s outcomes are intended to assist emergency services in making data-driven decisions, improving operational efficiency, and minimizing delays in critical situations. By leveraging predictive modeling and NYC Dispatch data, this study aims to provide actionable insights for emergency response planning and contribute to enhancing public safety across the city.