<p>This study investigates the associations between hate crimes and victim characteristics using data from Montgomery County, Maryland. The aim is to identify the significant relationships and trends in bias incidents, particularly focusing on different victim groups and locations. Data from Montgomery County, Maryland, were analyzed using chi-squared tests and p-values to identify the significant relationships between hate crimes and victim characteristics. Visualization techniques were employed to reveal trends and patterns in the data. The analysis revealed a rise in bias incidents, particularly in schools and colleges, with a peak in February 2023. The incidents were notably against individuals with anti-Black, anti-Jewish, anti-Asian, and anti-homosexual biases. Chi-squared values indicated an increasing association of victims with bias codes for individuals, while there was a decreasing trend for schools and colleges since 2023. The findings highlight the need for targeted interventions and increased awareness to address the rising trend of hate crimes. Continuous monitoring is essential to understand and mitigate these incidents effectively. These evidence-based findings suggest the necessity for targeted interventions and increased awareness campaigns. Additionally, continuous monitoring of hate crimes is crucial. Generative AI can assist novices in Python code generation for such analyses, but user verification remains crucial to ensure accuracy and reliability.</p>