I Built a Full-Stack NYC Neighborhood Intelligence App
📰 Medium · Machine Learning
Learn how to build a full-stack NYC neighborhood intelligence app using machine learning and SHAP explainability in five phases
Action Steps
- Collect and preprocess 311 complaint data using Python and relevant libraries
- Build a machine learning model to analyze the data and predict neighborhood trends
- Implement SHAP explainability to provide insights into the model's decisions
- Design and develop a live dashboard to visualize the results using a framework like Flask or Django
- Deploy the app to a cloud platform like AWS or Google Cloud to make it accessible to users
Who Needs to Know This
Data scientists and software engineers can benefit from this article as it provides a comprehensive guide on building a full-stack ML-powered dashboard
Key Insight
💡 Using SHAP explainability can provide valuable insights into a machine learning model's decisions and improve the overall effectiveness of the app
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🗽️ Built a full-stack NYC neighborhood intelligence app with ML and SHAP explainability! 📊
Key Takeaways
Learn how to build a full-stack NYC neighborhood intelligence app using machine learning and SHAP explainability in five phases
Full Article
From 1.35 million raw 311 complaints to a live ML-powered dashboard with SHAP explainability — in five phases Continue reading on Medium »
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