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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.
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.
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 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.