Data-Driven Approaches to Micromobility Demand Modeling: Paper developing and comparing demand modeling approaches in both urban and suburban areas.

Flexible framework for identifying third-party data demand in public sector job postings, combining automated data scraping, weakly supervised labeling, structured feature engineering, and NLP, Integrated into a PyPI package (data_demand_mapper) for scoring and lead generation.

A SAS analysis project on US housing demand

This repository has the dataset and code underlying the findings described in the following paper: Alsaleh, N., & Farooq, B. (2022). The Impact of COVID-19 Pandemic on Ridesourcing Services Differed Between Small Towns and Large Cities. arXiv preprint arXiv:2201.10961.

Performing Profit Simulation using Breakpoint Method and Profit Optimization using Optimise Function in R given the uncertain demands with unknown data for a hypothetical company named GWS.

New York Yellow Taxi Demand prediction regression DSLS2023 Selection

An R econometrics project on demand curves for Steam video games

Optimized demand forecasting using time series modeling with Prophet and NeuralProphet. Includes autoregressive memory, holiday effects, time-aware cross-validation, and hyperparameter tuning. Delivers interpretable, multi-horizon predictions for short-term accuracy and long-term grid planning purposes.

Jupyter Lite version of demand modelling tool.

Calculation of the optimal order volume for 8 weeks, taking into account forecast of product demand and scenarios of probabilities

Machine Learning Demand Model

ChainSim is a C++ library, server and frontend (TS) for simulating and analyzing supply chain inventory management systems. It provides a flexible framework for testing different purchasing policies and analyzing their effectiveness under various conditions.

A demand-aware framework, that welcomes constants over predictions, and intentions over impressions. Introducing Expression of Demand and less-predicted producer-consumer scenarios of the new ad-biz.

Quantitative Insights in Carsharing: Demand, User Preferences, and Profitability.