Predictive Pricing with NYC Taxi Data – AI Fare Estimator
Problem
A ride-hailing aggregator operating in NYC needed to optimize pricing for thousands of daily trips. Their legacy model failed to account for zone, time, and surge, leading to lost revenue and customer complaints.
Challenge
Deliver a scalable, accurate fare prediction engine that adapts to city-scale data and changing ride patterns, with automated retraining and business-ready dashboards.
Solution
Megicode built a robust data pipeline: ingesting and cleaning NYC taxi trip data, extracting features (distance, duration, time of day), and training regression models for fare estimation. We implemented model monitoring and retraining scripts for continuous improvement.
Impact
Fare prediction error dropped by 12%. The new estimator enabled better pricing, improved customer satisfaction, and increased gross margin by 7%. The model’s insights were presented at a national urban mobility conference.
Implementation
The fare estimator was built and deployed in 10 weeks, with weekly model performance reviews and automated retraining. The dashboard provided actionable insights for pricing and business decisions.
Process
Data Acquisition & Cleaning (NYC TLC dataset)
Exploratory Data Analysis & Feature Extraction
Model Selection & Training (scikit-learn)
Performance Benchmarking & Error Analysis
Dashboard Prototyping (Streamlit)
Automated Model Retraining (Airflow)
Deployment & Monitoring
Tools Used
Pythonpandas
seabornscikit-learnJupyter
AirflowGitStreamlit
seabornscikit-learnJupyterAirflowGitStreamlit Lessons Learned
Automated retraining is key for dynamic pricing models.
Feature selection has a major impact on prediction accuracy.
Business stakeholder feedback shapes dashboard usability.
Next Steps
Planned roadmap includes adding surge pricing prediction and expanding to new cities with transfer learning.
Key Metrics
Prediction Error-12%
Gross Margin+7%
Customer Satisfaction+18%
Model RetrainingAutomated (weekly)
Tech Stack
Pythonpandasseabornscikit-learnJupyterAirflow
seabornscikit-learnJupyterAirflow Screenshots
Megicode delivered exactly what we needed—fast, reliable, and with full transparency. Our pricing is now a competitive advantage. — Data Lead, Ride-Hailing Co.