Machine Learning Project to Forecast Rossmann Store Sales

Machine Learning Project to Forecast Rossmann Store Sales

In this machine learning 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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Hiren Ahir

Microsoft Azure SQL Sever Developer, BI Developer

I'm a Graduate student and came into the job market and found a university degree wasn't sufficient to get a good paying job. I aimed at hottest technology in the market Big Data but the word BigData... Read More


Lead Consultant, ITC Infotech

The project orientation is very much unique and it helps to understand the real time scenarios most of the industries are dealing with. And there is no limit, one can go through as many projects... Read More

What will you learn

Understanding the problem statement
Data exploration
Data Visualization
Handling missing values
Handling Outliers
Exploring exceptional cases
Converting categorical to numeric forms
Creating heatmaps
Feature selection & its importance
Implementation using linear regression
Implementation using stochastic gradient descent
Implementation using random forest
Implementation using decision trees
Understanding feature importance

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 machine learning project, you will work on forecasting 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! 

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Curriculum For This Mini Project

Understanding the Rossmann Store Sales Problem
Data Loading
Data Exploration
Fixing Missing Values
Sales vs Competition Distance
Dealing with Outliers
Exploring Exceptional Cases
Converting Categorical to Numerical Variables
Heatmap showing Correlations
Understanding the Open Feature
Modelling - Linear Regression
Modelling - Stochastic Gradient Descent
Modelling - Random Forest
Modelling - Decision Tree
Feature Importance and Conclusion
Modular Code walk-through