Project Overview
Our Crime Forecasting Project aims to leverage advanced machine learning techniques to predict crime incidents and enhance public safety. This project involves analyzing historical crime data to identify patterns and trends, enabling law enforcement agencies to allocate resources more effectively.
Technical Details
- Data Collection: We gathered extensive crime data from various sources, including police reports and public databases.
- Data Preprocessing: The data was cleaned and preprocessed to handle missing values, outliers, and inconsistencies.
- Feature Engineering: Key features such as time, location, and type of crime were extracted to improve model accuracy.
- Model Development: We used advanced algorithms like Random Forest, XGBoost, and Neural Networks to build predictive models.
- Evaluation: The models were evaluated using metrics like accuracy, precision, recall, and F1-score to ensure reliability.
- Visualization: Graphs and charts were created to visualize crime trends and model predictions.
Achievements
- High Accuracy: Our models achieved an accuracy of over 85%, significantly improving crime prediction capabilities.
- Resource Optimization: The project helped in optimizing the allocation of law enforcement resources, leading to a more efficient response to crime incidents.
- Community Impact: By predicting crime hotspots, we contributed to enhancing community safety and reducing crime rates.
Crime Forecasting Project: Enhancing Public Safety with AI