CrimePredict

Predicting future crime hotspots using advanced machine learning models.

  • Master Project by Jyotshna Nallabothula

CrimePredict is an innovative project aimed at leveraging historical crime data to forecast the likelihood of future criminal activities in specific areas. By utilizing cutting-edge machine learning techniques like Graph Neural Networks (GNNs) and Long Short-Term Memory (LSTM) networks, this project aspires to assist law enforcement and urban planners in identifying high-risk areas and proactively curbing criminal activities.

Project Overview

Using historical crime data, CrimePredict builds a model that analyzes patterns, trends, and spatial-temporal correlations to predict the probability of crimes in a given location. This project highlights the application of artificial intelligence to solve real-world societal challenges and demonstrates the potential of data-driven approaches in promoting public safety.

Objectives

  1. Data Analysis: Analyze historical crime data to identify trends and patterns.
  2. Model Development: Explore and implement advanced machine learning techniques such as GNNs and LSTMs to predict future crime hotspots.
  3. Impactful Insights: Provide actionable insights to assist in crime prevention and resource allocation.
  4. Hands-On Experience: Equip Jyotshna with expertise in applying machine learning to real-world problems.