This project aims to build accurate and scalable demand forecasting models for e-commerce and retail businesses

A complete exploratory data analysis (EDA) and forecasting project focused on retail sales data. The project identifies key sales patterns, seasonal trends, and builds predictive models to forecast future demand at the item-store level.

A data analytics project using Python, Excel, and machine learning to forecast retail demand and optimize inventory levels. Includes scalable ETL pipelines, advanced forecasting models, and interactive dashboards, with weekly updates to showcase progress and commitment.

Demand-Forecasting-in-Retail is a machine-learning project that predicts daily retail sales using historical data, feature engineering (holidays, weekends, seasonality), and models like XGBoost to generate accurate 30-day demand forecasts for better inventory and planning decisions.

It is challenging to build useful forecasts for sparse demand products. If the forecast is lower than the actual demand, it can lead to poor assortment and replenishment decisions, and customers will not be able to get the products they want when they need them. If the forecast is higher than the actual demand, the unsold products will occupy inventory shelves, and if the products are perishable, they will have to be liquidated at low costs to prevent spoilage. The overall objective of the model is to use the retail data which provides us with historic sales across various countries and products for a firm. We use this information given, and make use of FM’ s to predict the sparse demand with missing transactions. The above step then enhances the overall demand forecast achieved with LSTM analysis. As part of the this project we answered the following questions: How well does matrix factorization perform at predicting intermittent demand How does matrix factorization approach improve the overall time-series forecasting

This project aims to forecast weekly sales for retail stores using historical sales and economic data. By applying advanced time series forecasting models, we enable better inventory management, demand planning, and revenue optimization for retail chains. The project includes both traditional statistical models and deep learning techniques.

Project on Forecasting Demand of Products at a Retail Outlet Based on Historical Data

This project uses LSTM networks for accurate sales forecasting based on Walmart’s historical sales data and external features. By incorporating time-series data, it helps improve demand forecasting and inventory management in retail. Built with Python and PyTorch.

How To Apply Time Embeddings To A Classification and Quantity Forecast In Tensorflow Embedding time along with categorical and continuous features offsets the errors caused by intermittent time series. This event often occurs in manufacturing or retail businesses that distribute through multiple overlapping distributors or stores and regions with a large parts list. The solution in tensorflow is demonstrated for an international specialty fastener manufacturer with over 2000 part items and 200 distributors in 5 regions. The manufacturer needed pricing support at the quote level by part, distributor and region to project whether or not a quote would become an order and the expected quantity to be sold during a successful order so that it could align with their current demand planning methods. The first session half discusses applications and solutions while the session second half explains the feature development and the non-linear tensorflow model in depth. The annotated open source code in a jupyter notebook is provided for reference.

Here I have developed a forecasting system with the help of ARIMA models and facebook's prophet library. This for forecasting system is used for predicting the demand for products at a retail outlet based on historical data.

Final MSc thesis project on machine learning-based retail demand forecasting with promotional data, using XGBoost and Power BI.

Explore my data science internship project focused on demand forecasting for a retail store. Discover predictive analytics techniques to optimize inventory management and enhance business decision-making.

**Retail Demand Forecasting** using a **Prophet + LSTM Hybrid Model**. This project delivers multi-step predictions by combining seasonality modeling with deep learning residuals. It includes comprehensive feature engineering, strict time-series cross-validation, and outputs derived **Inventory Optimization** policies.

Over the years, I’ve used Microsoft Azure—from basics like Data Factory & Storage, to advanced tools like Synapse & Cognitive Services—to build projects in healthcare (EHR analysis), retail (customer segmentation), and supply chain (demand forecasting). This repo shares workflows, insights, and improvement ideas!

This project uses LSTM neural networks to forecast monthly product demand for an online retailer using the "Online Retail II" dataset. It optimizes inventory management, reduces stockouts, and explores generative AI for personalized marketing and product design.

The global IoT node and gateway market is projected to exhibit a significant growth, at a CAGR of around 5.8%, during the forecast period. A significant rise in smart city projects has been noticed in the countries, such as the US, China, India, and Germany. These projects are aimed to improve standards of living, local development, and harnessing technology to create smart outcomes for citizens. For instance, IoT-EPI is a European Initiative started in 2017. This initiative addresses the new EU-funded H2020 programs regarding the development of IoT platform. There are seven research and innovation projects at the core of IoT-EPI: BIG IoT, Inter-IoT, symbIoTe, AGILE, VICINITY, TagItSmart!, and bIoTope. These projects with total funding of $61.3 million, and in partnership with 120 established companies and organizations, aim at the development of innovative platform technologies and foster technology adoption via community and business building. Request a free sample of our report on Global IoT Node and Gateway Market: https://www.omrglobal.com/request-sample/iot-node-and-gateway-market Emerging economies such as China and India are also competing in implementing smart city projects across their province. For instance, in China, the development of innovative smart city projects involves some key factors, such as digital integration, new and sustainable mobility solutions, distributed energy generation, personalized healthcare, and automated waste management. Furthermore, the Chinese smartphone producers and telecommunication vendors have been joined for the smart city program. For instance, Huawei Technologies Co., Ltd. has been joined by Chinese technology leaders, such as Alibaba Group Holding Ltd. and Ping an Insurance (Group) Company of China, Ltd. and Tencent Holdings, Ltd. for smart city projects. It has been shown that more than 500 cities in China have already been working to the new smart city initiative “PATH”. There is significant importance of location-based services coupled with the IoT solutions to locate and track the objects within the city and maintaining the safety of citizens. Therefore, the rising focus on smart city projects is anticipated to drive the demand for IoT node and gateway. A full report of Global IoT Node and Gateway Market is available at: https://www.omrglobal.com/industry-reports/iot-node-and-gateway-market The Asian Development Bank (ADB) is assisting smart city projects in India, totaling more than $2 billion for both national and state levels. ADB’s niche areas for its Smart City program in India include water, wastewater, solid waste, drainage, smart water technologies, and innovative management approaches. The United Smart Cities program was jointly initiated by the United Nations Economic Commission for Europe (UNECE) and other industrial partners. The major areas include ICT, waste management, urban mobility, sustainable housing, and clean energy. The government initiatives are significantly contributing to the development of smart cities across countries, which in turn, are offering significant opportunity for IoT technology, and in turn growth to the IoT node and gateway market Global IoT Node and Gateway Market-Segmentation By Hardware Processors Sensors Memory Devices Connected Integrated Circuits Others (Logic Devices) By End-User Consumer Electronics Healthcare BFSI Automotive & Transportation Others (Retail and Oil & Gas) By Region North America United States Canada Europe Germany United Kingdom France Spain Italy Rest of Europe Asia-Pacific China Japan India Rest of Asia-Pacific Rest of the World Middle East & Africa Latin America Company Profiles AAEON Technology Inc. ADLINK Technology Inc. Advantech Co., Ltd. Cisco Systems Inc. Dell Technologies, Inc. EasyReach Solutions Pvt. Ltd. Embitel Technologies (I) Pvt. Ltd. Estimote, Inc. Eurotech S.p.A. Hewlett Packard Enterprise Co. For more customized data, request for report customization @ https://www.omrglobal.com/report-customization/iot-node-and-gateway-market About us: Orion Market Research (OMR) is a market research and consulting company known for its crisp and concise reports. The company is equipped with an experienced team of analysts and consultants. OMR offers quality syndicated research reports, customized research reports, consulting and other research-based services. For More Information, Visit https://www.omrglobal.com/ Media Contact: Company Name: Orion Market Research Contact Person: Mr. Anurag Tiwari Email: info@omrglobal.com Contact no: +1 646-755-7667, +91 780-304-0404

End-to-end retail sales analysis project using Excel, SQL, Python, and Power BI to uncover trends, top products, and demand patterns

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Forecasting System - Project Demand of Products at a Retail Outlet Based on Historical Data

RIO-125: Forecasting System - Project Demand of Products at a Retail Outlet Based on Historical Data

RIO-125-Forecasting-System---Project-Demand-of-Products-at-a-Retail-Outlet-Based-on-Historical-Data

An end-to-end demand forecasting and analytics project using Python, SQL, and PostgreSQL. Built with synthetic retail data to analyze category-level demand, forecast trends, and deliver insights via APIs and dashboards.

Business Intelligence and machine learning project analysing Walmart weekly sales data to forecast demand and generate strategic retail insights.

A Python-based multi-agent system for retail inventory management. This project leverages intelligent agents to automate demand forecasting,inventory replenishment and pricing optimization to streamline operations in a retail setting.

This project uses ML & data analysis for retail store optimization and demand forecasting. It features sales anomaly detection, demand prediction, customer segmentation, and integrates external economic factors to boost customer experience and inventory management.

📈 Retail sales analytics project implementing RFM customer segmentation 👥 and statistical time-series forecasting models (Exponential Smoothing, SARIMA) 🧠 for demand prediction and business insights 📊.

The retail industry faces challenges such as managing inventory, accurately forecasting demand, and targeting the right customer segments. This project provides a comprehensive solution to these issues by using data engineering techniques to clean, process, and analyze retail data for sales forecasting and customer segmentation.

A project to forecast product demand in a retail setting using historical sales data and ML models. Inspired by real-world challenges.

End-to-end MLOps project for retail demand forecasting and inventory intelligence, featuring time-series modeling, GCP-based pipelines, monitoring, and a manager-facing chat and dashboard interface.