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.

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

Interview task submission

imod 이력자료 데이터 분석/수요예측모형

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

Time Series Forecasting Best Practices & Examples

Grupo Bimbo inventory demand Kaggle competition

An exploration of demand analysis and prediction

Neste projeto, é realizada a previsão de demanda dos produtos da indústria Adventure Works (AW). 🚵‍♀️

We forecast demand of products for hierarchical time series data.

Labelling and Forecasting various pharmaceuticals

Six Sigma Green Belt-Enhanced inventory planning process by implementing ML ARIMA model

This repository is for stats 551 final project

How to make forecast with python ? I develop a software that allows to : - Make commercial forecasts from a history - Compare several forecasting methods - Display the results (forecasts and comparison)

EDA, feature engineering, polynomial featurization, comparison of several machine learning algorithms and cross-validation.

Demand pattern recognition using k-means algorithm in Python

In this project, the Seoul Bike Share Demand dataset was used to understand bike share use trends, apply machine learning techniques to predict the number of bikes rented at any given hour and provide reasonable explanations from the best predicting model to understand factors affecting bike share demands.

Machine Learning CAISO electric demand forecast using https://github.com/romilan24/load-weather-dataset

Forecasted product sales using time series models such as Holt-Winters, SARIMA and causal methods, e.g. Regression. Evaluated performance of models using forecasting metrics such as, MAE, RMSE, MAPE and concluded that Linear Regression model produced the best MAPE in comparison to other models