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Deep Learning Project on Store Item Demand Forecasting

In this project, we are going to predict item-level sales data using different forecasting techniques.
What are the prerequisites for this project?
  • Anaconda should be installed
  • Python 3.6 should be installed

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.



What is Hackerday?

Stay updated in technology trends by working on projects

Live online coding sessions led by industry experts

Build 2-4 projects a month each lasting 6 hours designed to teach you advanced concepts

Code in groups and connect with your community