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Rossmann Store Sales - Forecast sales using store, promotion, and competitor data

In this project you will work on creating a robust prediction model of Rossmann's daily sales using store, promotion, and competitor data

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What will you learn

  • Understanding of Sales Department
  • EDA and Pre-processing of Data
  • Visualization of Data
  • Comparison of Models
  • Ensemble models
  • Model Validation

What will you get

  • Access to recording of the complete project
  • Access to all material related to project like data files, solution files etc.


  • Language used: Python

Project Description

Rossmann operates over 3,000 drug stores in 7 European countries. Currently, Rossmann store managers are tasked with predicting their daily sales for up to six weeks in advance. Store sales are influenced by many factors, including promotions, competition, school and state holidays, seasonality, and locality. With thousands of individual managers predicting sales based on their unique circumstances, the accuracy of results can be quite varied.

In this hackerday you will work on forcasting 6 weeks of daily sales for 1,115 stores located across Germany. Reliable sales forecasts enable store managers to create effective staff schedules that increase productivity and motivation. By helping Rossmann create a robust prediction model, you will help store managers stay focused on what’s most important to them: their customers and their teams! 



Data Scientist / Business Consultant at GE

3 years of rich working experience in BIG Data, Business Intelligence & Analytics with CMMI Level 5 Organizations in BFSI, Manufacturing Sector. Excellent written and oral communications, strong analytical and problem solving capabilities. Constantly learning and experimenting emerging open source tools and technologie see more...