[READ-ONLY MIRROR] LaTeX files for the paper "Real-time Demand Forecasting for an Urban Delivery Platform"

The project is analyzing Ice Games sales throughout their lifetime in order to plan the next marketing campaign, as well as spotting and profiling potentially top-selling games.

A demand forecasting model for an E-Commerce retailer, built using KPIs from Google Analytics & implemented in RStudio. Models: time-series, ARIMA, Regression (multivariate & dynamic). Open-source & contributions welcome.

The "Sales Demand Forecasting Regression Model" project aims to develop a predictive model that forecasts future sales demand based on historical data and relevant influencing factors. The project follows a structured approach, encompassing data collection, preprocessing, model selection, training, evaluation, and deployment.

Discussed various methodologies that may help while implementing Time Series

C++ Simulation Discrete Event Management Library

A Linear Regression model using Python designed to predict demands for a bike rental company based on weather conditions.

This Jupyter Notebook implements Bayesian modeling techniques to fit a posterior distribution and forecast demand for an e-commerce company.

In tune with conventional big data and data science practitioners’ line of thought, currently causal analysis was the only approach considered for our demand forecasting effort which was applicable across the product portfolio. Experience dictates that not all data are same. Each group of data has different data patterns based on how they were s…

The goal of the project is to utilize Recurrent Neural network model (biLSTM) to forecast demand of bike rentals using seasonal and exogenous features

Evaluating tree-based approaches for timeseries

The final project for Stanford Continuing Studies course BUS 150 W "Financial Modeling and Business Decisions" by Iddo Hadar, Summer 2017. This online course provides students with the methods and the mindset to make complex financial and economic decisions, by framing them as analytical models and using Microsoft Excel spreadsheets to solve them.

I used 28 relevant attributes to price hotel rooms using casual inference analysis between price and demand. PCA and K-Means Clustering were used to compare prices only among rooms with similar enough features. The analysis only found which room type is more price sensitive, but also the main drivers of demand and prices, respectively. For more …

Electricity Demand Forecasting: I used numerous ML models and statistical TS forecasting to perform a long-term demand prediction.

This repository is for stats 551 final project

R package for the Station Demand Forecasting Tool

Interview task submission

imod 이력자료 데이터 분석/수요예측모형

Implemented an end-to-end product demand forecasting solution for a company, utilizing historical sales data using Python, SQL and Deployed the forecasting model on the Azure cloud platform using MLFlow, enabling real-time demand predictions for inventory planning.

In today's dynamic marketplace, accurately forecasting product demand is essential for optimizing inventory management, production planning, and ensuring customer satisfaction. This project capitalizes on the potential of machine learning to tackle this critical business challenge.