I Built a Full-Stack NYC Neighborhood Intelligence App

📰 Medium · Data Science

Learn how to build a full-stack NYC neighborhood intelligence app using NYC Open Data, Databricks SQL, XGBoost, and Streamlit to analyze 311 complaints and provide insights on neighborhood patterns

intermediate Published 17 Apr 2026

Action Steps

Collect and preprocess 311 complaint data from NYC Open Data
Build a data ingestion pipeline using Databricks SQL
Train an XGBoost model to predict complaint patterns
Deploy the model using Streamlit and create a live ML-powered dashboard
Implement SHAP explainability to provide insights on feature importance

Who Needs to Know This

Data scientists and software engineers can benefit from this tutorial to build a comprehensive data analysis and visualization platform for urban planning and development

Key Insight

💡 By leveraging open data and machine learning, you can create a comprehensive platform to analyze and predict neighborhood complaint patterns