The project demonstrates the use of the PACE workflow (Plan → Analyze → Construct → Execute) to design a strategy for building a taxi fare estimation model for the New York City Taxi and Limousine Commission (TLC).

As a new data analyst at Automatidata, a data consulting firm, my role is to help the NYC TLC develop a regression model to estimate taxi fares before rides. TLC manages over 200,000 licensed vehicles, making approximately one million trips daily. By developing this model, TLC can:

Improve user experience through fare transparency.

Provide better tools for passengers and drivers.

Use data-driven insights to improve operations and planning.

This project showcases a full project-planning workflow, from initial task organization to stakeholder-ready deliverables.

Defined project scope, milestones, and deliverables.

Identified internal (Automatidata) and external (TLC) stakeholders.

Selected Python as the primary tool for analysis.

Created a high-level project proposal.

Inspected and cleaned TLC’s dataset.

Conducted exploratory data analysis (EDA) to understand patterns.

Identified relationships between key variables affecting fare estimation.

Proposed A/B testing to validate variable selection.

Built regression models to estimate fares.

Generated visualizations for key variables and pricing trends.

Prepared statistical summaries for stakeholders.

Tested model performance to ensure accuracy.

Created visuals and talking points for TLC leadership presentations.

Delivered final proposal and model recommendations.

PACE Strategy Executive Summaries .

Cleaned dataset and detailed EDA report.

Regression models and testing summaries.

Stakeholder-ready visuals and presentation materials.