Forecasting Uber demand in NYC neighborhoods

Uber trip demand forecasting using XGBoost, Random Forest, GBRT & Ensemble on 4.1M NYC rides — Python · Pandas · Statsmodels · Scikit-learn · Chart.js

End-to-end analysis and forecasting of Uber trip demand using NYC FOIL data. Includes exploratory data analysis, time-series feature engineering, ML & Prophet-based forecasting, and an interactive Dash dashboard.

This project analyzes and forecasts Uber pickup demand in New York City (April 2014). Using machine learning and deep learning (MLP neural networks), the goal is to predict hourly Uber demand to help optimize driver allocation and pricing strategies.

No description available

This project provides an advanced machine learning solution for forecasting Uber trip demand in New York City. Leveraging the Uber TLC FOIL Response dataset which covers over 4.5 million pickups from April to September 2014, the repository meticulously documents the entire data science lifecycle, from hourly data resampling to building and evaluate

No description available

NUS ISS Data Science Masters Group Project 3 - Specialized Predictive Modelling & Forecasting - UBER_NYC_ride_forecasting

NYC Uber demand forecasting comparing classical time-series models with LSTM. Uses FiveThirtyEight's Uber pickups dataset.

Advanced data analytics and machine learning project analyzing Uber trip demand using time-series forecasting, ensemble models, and real-world NYC TLC data.

Uber Trips Demand Analysis & Prediction using NYC TLC FOIL Uber-Jan-Feb-2015 data. Built an end-to-end daily demand forecasting pipeline: data cleaning, feature engineering (lags, rolling means, day-of-week), time-series split, Random Forest + Gradient Boosting ensemble, evaluation with MAPE/RMSE/R²

End-to-end data analytics and machine learning project analyzing NYC Uber trip demand (Jan–Feb 2015). Includes exploratory data analysis, feature engineering, Random Forest–based demand forecasting, business insights, and an interactive Power BI dashboard for operational decision-making.