Deep Learning Project on Store Item Demand Forecasting

Deep Learning Project on Store Item Demand Forecasting

In this project, we are going to predict item-level sales data using different forecasting techniques.
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Swati Patra linkedin profile url

Systems Advisor , IBM

I have 11 years of experience and work with IBM. My domain is Travel, Hospitality and Banking - both sectors process lots of data. The way the projects were set up and the mentors' explanation was... Read More

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Mohamed Yusef Ahmed linkedin profile url

Software Developer at Taske

Recently I became interested in Hadoop as I think its a great platform for storing and analyzing large structured and unstructured data sets. The experts did a great job not only explaining the... Read More

What will you learn

Time Series Forecasting
How to deal with Seasonality
More than one series prediction
Alternate Model Identification
Application of Boosting Models

Project Description

This Hackerday is as a way to explore different time series techniques on a relatively simple and clean dataset. You are given 5 years of store-item sales data and asked to predict 3 months of sales for 50 different items at 10 different stores. What's the best way to deal with seasonality? Should stores be modeled separately, or can you pool them together? Does deep learning work better than ARIMA? Can either beat xgboost? This is a great competition to explore different models and improve your skills in forecasting.

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Curriculum For This Mini Project