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A machine learning project that forecast sales in all the stores across several cities six weeks ahead of time.

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Pharmaceutical_Sales_prediction

A machine learning project that forecast sales in all the stores across several cities six weeks ahead of time.

Data and Features

We can use the following questions as a guide during our analysis. It is important to come

up with more questions to explore. This is part of our expectation for an excellent analysis.

● Check for seasonality in both training and test sets - are the seasons similar between
these two groups?● Check & compare sales behavior before, during and after holidays
● Find out any seasonal (Christmas, Easter etc) purchase behaviours,
● What can you say about the correlation between sales and number of customers?
● How does promo affect sales? Are the promos attracting more customers? How does
it affect already existing customers?
● Could the promos be deployed in more effective ways? Which stores should promos
be deployed in?
● Trends of customer behavior during store open and closing times
● Which stores are opened on all weekdays? How does that affect their sales on
weekends?
● Check how the assortment type affects sales
● How does the distance to the next competitor affect sales? What if the store and its
competitors all happen to be in city centres, does the distance matter in that case?
● How does the opening or reopening of new competitors affect stores? Check for
stores with NA as competitor distance but later on has values for competitor distance

Data fields

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

Id

- an Id that represents a (Store, Date) duple within the test setStore - 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. Read more
about assortment here

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

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A machine learning project that forecast sales in all the stores across several cities six weeks ahead of time.

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