VitorMusachio / rossmann_sales_prediction

Machine learning time series case.

Geek Repo:Geek Repo

Github PK Tool:Github PK Tool

Rossmann Sales Prediction

The data set used and the business problem that motivated the development were taken from Kaggle.

Business

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.

The Data

You are provided with historical sales data for 1,115 Rossmann stores. The task is to forecast the "Sales" column for the test set. Note that some stores in the dataset were temporarily closed for refurbishment.

Files

train.csv - historical data including Sales test.csv - historical data excluding Sales sample_submission.csv - a sample submission file in the correct format store.csv - supplemental information about the stores

Data fields

Most of the fields are self-explanatory. The following are descriptions for those that aren't.

Feature Description
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

About

Machine learning time series case.


Languages

Language:Jupyter Notebook 100.0%