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 "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.
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.
Interview task submission
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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
Grupo Bimbo inventory demand Kaggle competition
An exploration of demand analysis and prediction
Neste projeto, รฉ realizada a previsรฃo de demanda dos produtos da indรบstria Adventure Works (AW). ๐ตโโ๏ธ
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.
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
๐LBF presentation on demand forecasting models
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Introduction to AWS Forecast, and how to implement forecast prediction on AWS Forecast
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