Nishudhanaa

The rising crime rates in Austin, especially homicides and thefts, inspired me to explore how data and forecasting could help law enforcement and policymakers take proactive steps to ensure public safety. I wanted to turn raw crime data into actionable insights.

I used time series forecasting techniques (Smoothing, ARIMA, Regression, and Prophet) to predict crime incidents in Austin using historical data for top crimes like homicide, vehicle burglary, family disturbance,

Handling seasonal trends, choosing the best model for each crime type, and ensuring the forecasts were accurate with minimal error

Handling seasonal trends, choosing the best model for each crime type, and ensuring the forecasts were accurate with minimal error

I learned how to apply multiple forecasting models, analyze crime seasonality, and interpret model accuracy effectively for real-world policy impact

Built With

• r
• rstudio