SOLD26 {regclass}

Predicting future sales

Description

Predicting future sales based on sales data in first quarter after release

Usage

data("SOLD26")

Format

A data frame with 2768 observations on the following 16 variables.

SoldWeek26

a numeric vector, the number of items sold 26 weeks after release and the quantity to predict

StoresSelling1

a numeric vector, the number of stores selling the item 1 week after release

StoresSelling3

a numeric vector

StoresSelling5

a numeric vector

StoresSelling7

a numeric vector

StoresSelling9

a numeric vector

StoresSelling11

a numeric vector

StoresSelling13

a numeric vector

StoresSelling26

a numeric vector, the planned number of stores selling the item 26 weeks after release

Sold1

a numeric vector, the number of items sold 1 week after release

Sold3

a numeric vector

Sold5

a numeric vector

Sold7

a numeric vector

Sold9

a numeric vector

Sold11

a numeric vector

Sold13

a numeric vector, the number of items sold 13 weeks after release

Details

Inspired by the dunnhumby hackathon hosted at https://www.kaggle.com/c/hack-reduce-dunnhumby-hackathon. The goal is to predict the number of items sold 26 weeks after released based on the characteristics of its sales during the first 13 weeks after release (along with information about how many stores are planning to sell the product 26 weeks after release).

Source

Adapted from https://www.kaggle.com/c/hack-reduce-dunnhumby-hackathon