The mining analyst wants to use historical sales data and store properties to create a prediction model that can be used to predict store sales for some day in the future.
| Column name | Logical name | Description |
|---|---|---|
| STORE_ID | store | ID of store (part 1 of key) |
| DAY | day | Day (part 2 of key) |
| MONTH | month | Month (part 3 of key) |
| YEAR | year | Year (part 4 of key) |
| STORE_TYPE | store type | Type of the store |
| DATE | date | Date (yyyy-mm-dd) |
| QUARTER | quarter | Calendar quarter (1-4) |
| DAY_OF_WEEK | day of week | 1=Sunday, 2=Monday, 3=Tuesday, 4=Wednesday, 5=Thursday, 6=Friday, 7=Saturday |
| DAY_TYPE | type of day | Working day versus Saturday or Sunday |
| SALES | total sales | Total sales (for store on day) |
| SALES_TRX | number of sales transactions | Number of sales transactions as an approximation (upper limit) of the number of customers |
| SALES_PROFIT | total profit | Total profit (difference of sales amount and product price) |
| SALES_WK | total week sales | Total sales in week including date |
| SALES_AVG_MTH | average sales per day in month | Average sales of stores per day in the month including date |
| SALES_WK_PCT | sales as percentage within a week | Percentage of sales on date with respect to week |
| SALES_PERF | sales performance class | Classification of day for store as poor, mediocre, good, or outstanding |
| SALES_FURNITURE | furniture sales | Total sales in the furniture department |
| SALES_SPORTSWEAR | sportswear sales | Total sales in the sportswear department |
| SALES_ELECTRONICS | electronic sales | Total sales in the electronics department |
| SALES_OTHER | other sales | Total sales in all departments except furniture, sportswear, and electronics |
| SALES_7_DAYS | past 7 day sales | Sales in past 7 shopping days excluding day |
This scenario is used throughout the next sections to illustrate how to prepare the input data for the mining algorithms.