## 2 Machine Learning Applications in Big Data
### Government and Public Policy  
In government and public policy, ML is being used to improve decision making, optimize resource allocation, and enhance public services. One of the primary applications is in predictive analytics, where ML models analyze data from various sources, including census data, economic indicators, and social media, to predict social, economic, and political trends. These insights help policymakers develop data-driven policies, allocate resources more effectively, and respond to emerging challenges [188; 189; 190].  
ML is also applied in public safety and law enforcement, where models analyze crime data, social media activity, and other sources to predict crime hotspots, identify suspects, and optimize patrol routes. These models enable law enforcement agencies to prevent crime, allocate resources more effectively, and improve public safety [191; 192; 193].  
In addition to predictive analytics and public safety, ML is used in public health to monitor and respond to outbreaks of diseases, such as COVID-19. By analyzing data from health records, social media, and other sources, ML models can predict the spread of diseases, identify at-risk populations, and recommend targeted interventions to control outbreaks [194; 195].  
ML is also applied in disaster response and emergency management, where models analyze data from satellite imagery, weather models, and social media to predict natural disasters, assess damage, and optimize response efforts. These models enable governments and humanitarian organizations to respond more quickly and effectively to disasters, saving lives and reducing the impact of disasters on communities [196; 197].  
Overall, the application of ML in government and public policy enhances the ability of governments to serve their citizens, improve public services, and respond to the challenges of the 21 st century [198; 199].  
These diverse applications across industries underscore the extensive impact of ML in big data environments. To provide a clearer overview of the key studies referenced throughout this section, Table 2 categorizes the literature according to specific application domains, offering a structured guide to the wealth of research discussed.  
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**Topic** & **References** & **Description** \\ \hline \multirow{2}{*}{Healthcare} & [20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34] & ML applications in predictive analytics, medical imaging, drug discovery, and healthcare data management. \\ \hline \multirow{2}{*}{Finance} & [35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49] & ML for risk management, fraud detection, algorithmic trading, and CRM. \\ \hline \multirow{2}{*}{Transportation \& Smart Cities} & [50; 51; 52; 53; 54; 55; 56; 57; 58; 59; 60; 61] & ML applications in autonomous vehicles, traffic management, and smart city infrastructure. \\ \hline \multirow{2}{*}{Retail \& E-commerce} & [62; 63; 64; 65; 66; 67; 68; 69; 70; 71; 72; 73] & ML for personalized recommendations, predictive analytics, dynamic pricing, and supply chain management. \\ \hline \multirow{2}{*}{Energy \& Utilities} & [74; 75; 76; 77; 78; 79; 80; 81; 82; 83; 84; 85] & ML applications in predictive maintenance, energy optimization, smart grids, and integration of renewable energy sources. \\ \cline{1-1}  & & & \\ \end{tabular}
\end{table}
Table 2: Structured summary of key studies across ML application domains.