Taxi Demand Prediction is a Flask web app that forecasts short-term taxi demand across New York City using zone-based clustering and historical trip data. It provides an interactive dashboard for selecting a zone and time, viewing demand predictions, trends, and nearby higher-demand areas.

Predictive modeling and machine learning analysis of NYC Yellow Taxi Trip Records to forecast fare amounts, payment types, and demand, optimizing urban mobility using Python, scikit-learn, and XGBoost.

End-to-end Snowflake + dbt project demonstrating modern ELT best practices using NYC Taxi data — includes environment-based dbt profiles, key-pair authentication, SQLFluff linting, CI/CD workflows, macros, staging/intermediate/core models, snapshots, and automated ingestion pipelines.

nyc-taxi-tips-weather-analysis

End-to-end GCP data engineering pipeline — NYC Uber trip data transformed into a BigQuery star schema using Mage, with Looker Studio analytics

🚕 CityPulse — AI-powered Streamlit dashboard predicting high-demand NYC taxi rides using ML models, 3M+ rows of real data, interactive filters, and geospatial visualizations

End-to-end data engineering pipeline for NYC taxi data using Airflow, PySpark, dbt, and MinIO.

This repository contains the NYC Taxi Data Engineering Pipeline project, which aims to build a comprehensive data engineering pipeline using NYC taxi data from the years 2022 and 2023. The pipeline involves extracting, transforming and loading (ETL) data into a Snowflake database, followed by creating a dashboard for visualisation.

End-to-End Data Pipeline for NYC Green Taxi trip data using Azure Data Factory, Data Lake, and Databricks following the Medallion architecture