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NYC TLC yellow taxi trip and revenue analysis

I built an end-to-end transport analytics project that moves from raw trip data to a dashboard showing trips, revenue, vendors and payment behaviour.

I used public NYC TLC data, built a cloud pipeline with Mage AI and BigQuery, transformed raw trip records and created a reporting layer for operational analysis.

Operations

Dataset type: Public data. This label is shown clearly so the project is honest about whether the evidence is public, mock, synthetic or portfolio data.I separate source type from method because the reliability and limitations depend on where the data came from.

NYC TLC yellow taxi trip and revenue analysis main dashboard preview

Why this matters

Build summary

This project shows the full journey from raw operational records to a reporting-ready dataset and dashboard.

I used public NYC TLC data, built a cloud pipeline with Mage AI and BigQuery, transformed raw trip records and created a reporting layer for operational analysis.

Key numbers

Public
NYC TLC data
BigQuery
warehouse
Mage AI
pipeline

What I wanted the report to help with

How I approached it

  • Make the main performance question easier to scan.
  • Separate headline totals from the detail that explains them.
  • Show enough context for a non-technical user to trust the next action.
  • Used public NYC TLC taxi trip data.
  • Built a cloud-style pipeline using Mage AI and BigQuery.
  • Prepared summary tables for trip volume, revenue, vendor and payment analysis.

Technical inspection notes

  • Used public NYC TLC trip data as a pipeline-style project rather than only a dashboard exercise.
  • Structured the flow from raw trips to warehouse tables to a reporting layer.
  • Used BigQuery-style thinking to separate storage, transformation and analysis concerns.
  • Focused the report on trips, revenue, vendors and payment behaviour so the output stayed operational.

Dashboard snapshots

These snapshots show how the analysis was turned into decision-ready visuals.

Click any screenshot to inspect the dashboard evidence more closely.

NYC TLC yellow taxi trip and revenue analysis snapshot
Taxi dashboard: Shows trip and revenue trends.
NYC TLC yellow taxi trip and revenue analysis snapshot
Trip view: Helps compare demand patterns.
NYC TLC yellow taxi trip and revenue analysis snapshot
Report walkthrough: Shows dashboard pages together.

What the analysis showed

  • The dashboard focused on trip volume and revenue.
  • Payment and vendor behaviour were separated into comparable views.
  • The build demonstrates pipeline thinking, not only dashboarding.

What I would improve next

  • Add scheduled refresh.
  • Add data-quality checks at ingestion.
  • Add partitioning and cost-control notes for BigQuery.