Crime Forecasting to Enhance Safety Measures in São Paulo Using Machine Learning

Challenge Background

Public safety is a topic of great importance, and predicting crimes can be a valuable tool for resource allocation and strategic planning for law enforcement agencies. In this project, we aim to develop a machine learning model to predict the number of crimes by crime category, month/year and region inside of São Paulo State. Additionally, we will create an interactive web app to visualize crime predictions on a map, allowing users to explore the predictions for future dates.

The Problem

Predicting future crimes can provide valuable insights for decision-making and the planning of public safety policies. With a reliable prediction model and an interactive web app, we can empower users to visualize crime predictions by region in an intuitive format, facilitating the understanding and utilization of this information to enhance public safety.

Goal of the Project

• Develop a machine learning model capable of predicting the number of crimes by region and crime category for future periods, using a historical dataset containing information about crimes.
• Create an interactive web app that allows users to visualize crime predictions on a map, with the ability to filter by region, crime category, and specific dates.
• Facilitate the understanding and utilization of future crime predictions, making the information accessible and useful to the general public.

Project Timeline

What you'll learn

Data analysis. Machine learning modeling. Web app development. Data visualization. Project management. Problem solving. Analytical thinking.