# Integrating Machine Learning Techniques for Enhanced Safety and Crime Analysis in Maryland
## 3 Methodology
### Data Collection and Preprocessing  
The initial stage of our predictive model development involved an extensive data collection effort that integrated detailed crime types such as murder, rape, robbery, aggravated assault, burglary, larceny theft, and motor vehicle theft. To amplify the model's predictive power and address the complex socioeconomic dynamics, we included several additional variables:  
* **Socio-economic indicators:** Unemployment rates, household income, population below the poverty line, and education levels.
* **Demographic variables:** Migration patterns (domestic and international) and age distribution.
* **Geographical context:** Urban-rural classifications and each area's proximity to known crime hotspots. The integration of these variables was designed to capture the complex interrelationships among socioeconomic conditions, demographic trends, and crime rates, facilitating a comprehensive understanding of regional disparities.
* Handling missing data: Missing values in continuous variables (e.g., median household income) were addressed through mean imputation, while categorical variables (e.g., urban/rural classifications) were handled using mode imputation. Advanced techniques, such as k-nearest neighbors (KNN) imputation, were considered for variables with higher missing rates to maintain dataset integrity.
* Data type correction: Variables representing crime rates and socioeconomic indicators were converted to numeric formats, ensuring compatibility with machine learning algorithms. Errors in data entry, such as non-numeric characters appearing in numeric fields, were identified and corrected. A rigorous preprocessing pipeline ensured data quality and readiness for modeling:
* Normalization and scaling: To address the varying scales of features, Min-Max scaling was applied, thereby rescaling numerical variables to a uniform range between 0 and 1. This step ensured that no single variable disproportionately influenced model outcomes, particularly for high-magnitude variables like population and income.
* Outlier detection and treatment: Outliers were identified using the Z-score method, flagging values beyond three standard deviations from the mean. Domain knowledge was applied to determine appropriate actions, such as capping extreme values or removing anomalies that could distort crime trends. Advanced feature engineering techniques were employed to enhance the dataset's predictive capacity:
* Interaction terms: Relationships between key variables (e.g., unemployment rates and migration patterns) were modeled through interaction terms to reflect their combined influence on crime rates.
* Higher-order features: Polynomial features (up to degree 2) were created for significant predictors such as household income and poverty levels to model non-linear relationships.
* Feature selection: Recursive feature elimination (RFE) was utilized to identify impactful predictors, thereby improving model efficiency and interpretability. Importance scores from ensemble models (e.g., random forest and gradient boosting) guided the selection of features most relevant to crime prediction. This enhanced preprocessing framework, incorporating socioeconomic, demographic, and geographic dimensions, ensures that the dataset is clean, normalized, and tailored to robust predictive modeling. The combination of advanced feature engineering, normalization, and outlier handling aligns the methodology with high-impact journal standards while reflecting the complexities highlighted in the study's results.