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

New York Yellow Taxi Demand prediction regression DSLS2023 Selection

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.

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.

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.

Jupyter Lite version of demand modelling tool.

Calculation of the optimal order volume for a given period, taking into account statistical data on demand for products and taking into account scenarios

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.

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