The aim of this project is to apply a prediction model to sales in Rossmann stores according provided data. Rossmann is one of the largest drug store chains in Europe established in 1972.
train.csv- training data for sales
holdout.csv- test set
store.csv - information about each store
Id - an Id that represents a (Store, Date) duple within the test set
Store - a unique Id for each store
Sales - the turnover for any given day (this is what you are predicting)
Customers - the number of customers on a given day
Open - an indicator for whether the store was open: 0 = closed, 1 = open
StateHoliday - indicates a state holiday. Normally all stores, with few exceptions, are closed on state holidays. Note that all schools are closed on public holidays and weekends. a = public holiday, b = Easter holiday, c = Christmas, 0 = None
SchoolHoliday - indicates if the (Store, Date) was affected by the closure of public schools
StoreType - differentiates between 4 different store models: a, b, c, d
Assortment - describes an assortment level: a = basic, b = extra, c = extended
CompetitionDistance - distance in meters to the nearest competitor store
CompetitionOpenSince[Month/Year] - gives the approximate year and month of the time the nearest competitor was opened
Promo - indicates whether a store is running a promo on that day
Promo2 - Promo2 is a continuing and consecutive promotion for some stores: 0 = store is not participating, 1 = store is participating
Promo2Since[Year/Week] - describes the year and calendar week when the store started participating in Promo2
PromoInterval - describes the consecutive intervals Promo2 is started, naming the months the promotion is started anew. E.g. "Feb,May,Aug,Nov" means each round starts in February, May, August, November of any given year for that store
Python 3.7 or later and the following packages:
pandas
numpy
scikit-learn
matplotlib
seaborn
xgboost
holidays
Clone the repository to your local machine by running:
git clone https://github.com/wrijupan/Rossmann-DSR-minicomp
Create a python environment (3.7 or later) and install the requirements from file
requirements.txt.
Run the notebook
rossmann_sales_predict.ipynb
that is found in the results directory using this environment.