Overview

SmartPark SD is a machine learning-powered web application that uses real-world city datasets to recommend the best parking meters in San Diego. It prioritizes affordability, availability, and legal status—guiding drivers to the smartest spot.

How It Works

Component Function
Dataset San Diego city parking meter data
ML Model Regression algorithm for meter value prediction
UI Interactive interface that visualizes optimal choices

My Unique Contributions

  • Engineered the machine learning model to predict optimal parking decisions
  • Processed and cleaned real-world civic datasets
  • Developed a frontend dashboard to visualize ML results
  • Connected model output to user experience via Flask backend

What I Learned

  • ML pipelines from dataset wrangling to evaluation
  • Responsible civic engineering using open data ethically
  • UI for data storytelling, making results useful to real people

Microcredential Earned

CSP: Data Analysis with Python Certificate
Awarded for building a trained regression model, data preprocessing, and Flask integration.
Issued by: Open Coding Society

NFT Achievement

NFT: Smart City Tech (CSP Category)
This token certifies my unique contribution to civic technology through a machine learning interface designed to improve urban navigation.
Status: Pending Blockchain Storage
Verified by: Open Coding Society NFT Registry

Tech Stack

  • Python, Pandas, Scikit-learn
  • Frontend: HTML, JavaScript
  • Backend: Flask

Picture

Timeline

  • Started: January 2025
  • Completed: February 2025

Reflection

“Turning open data into real-world guidance was a breakthrough. I saw how machine learning can empower everyday decisions and make cities more accessible.”

This project aligns with the HyFlex Learning Model—showcasing both certified skills (via microcredentials) and creative engineering (via NFTs). The result: real-world, verifiable achievements that prepare me for modern careers in civic tech.