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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What will you learn

  • Understanding the problem statement

  • Importing the dataset and parsing while importing it

  • Importing libraries and understanding its significance

  • Performing basic EDA and checking for null values

  • Filling the Null values using appropriate techniques

  • Plotting "lmplot" using seaborn for visualizing relation between target and dependent variables
    Using Correlation for visualizing the relationship between dependent variables

  • Visualizing correlation using "heatmap" plot from Seaborn

  • Creating new features from existing features(feature engineering)

  • Converting categorical into numerical vectors

  • Selecting the most important features

  • Setting up the train and test data for fitting into model

  • Defining evaluation metrics

  • Applying ensemble Random Forecast Regressor model

  • Applying boosting Gradient Boosting Regressor model

  • Applying Adaboost regressor along with decision tree

  • Selecting the best model and making the final predictions on test dataset

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

 
  3-Aug-2016
01h 27m
  4-Aug-2016
02h 34m