You're a data scientist at CityTech Solutions, tasked with developing a predictive algorithm for the city's social services department. The system will help allocate limited resources (housing assistance, job training, healthcare vouchers) to residents who apply for help. You must make critical decisions about data collection, processing methods, and algorithmic approaches.

Project Requirements:

• Predict which applicants are most likely to benefit from different types of assistance
• Process 10,000+ applications monthly with limited caseworker time
• Ensure fair distribution of resources across diverse communities
• Balance accuracy with ethical considerations
• Meet legal compliance requirements while optimizing outcomes

Available Data Sources:

• Application forms (income, family size, employment history, housing status)
• Public records (education level, criminal history, previous service usage)
• Social media data (posts, network connections, activity patterns)
• Credit scores and financial histories
• Geographic data (neighborhood demographics, crime rates, school quality)
• Health records (with consent, for health-related services)

Algorithm Options:

• Decision trees: Transparent rules, easy to audit, but may oversimplify
• Neural networks: High accuracy, but "black box" decision-making
• Ensemble methods: Balanced accuracy and interpretability
• Rule-based systems: Fully transparent, but limited adaptability