Analyzing Crime Trends, Budgets, and Urban Safety in Los Angeles (2010–2023)

Digging Into Crime in LA is a data-driven initiative aimed at uncovering patterns and insights in Los Angeles crime data from 2010 to 2023. This project leverages Python, Pandas, and machine learning techniques to analyze crime incidents, assess the allocation of funding from the Department of Homeland Security, and evaluate the effectiveness of crime prevention efforts in the city.

• Analyze crime trends across time, geography, and category
• Investigate the relationship between high public safety spending and crime rates
• Identify high-risk zones and crime-prone premises
• Assess the effectiveness of funding and security initiatives
• Present actionable insights for law enforcement, urban planners, and the general public

• Python 3.x: Core language for analysis
• Pandas: Data manipulation and preprocessing
• Matplotlib / Seaborn / Plotly: Data visualization
• Scikit-learn: Machine learning modeling (e.g., classification, clustering, regression)
• GeoPandas / Folium: Spatial data analysis and mapping
• Jupyter Notebooks: Interactive analysis and reporting