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Copyright (c) 2019 - 2020
Level: Intermediate
Recommended Use: Regression Models
Domain: Business
Daily Demand Forecasting Orders Data Set
Predict total number of demand of orders
This intermediate level data set has 60 rows and 13 columns. The dataset was collected during 60 days, this is a real database of a brazilian logistics company. The dataset has twelve predictive attributes and a target that is the total of orders for daily treatment.
This data set is recommended for learning and practicing your skills in exploratory data analysis, data visualization, and regression modelling techniques. It also allows you to practice with large number of features. Feel free to explore the data set with multiple supervised and unsupervised learning techniques. The Following data dictionary gives more details on this data set:
Data Dictionary
Column Position | Atrribute Name | Definition | Data Type | Example | % Null Ratios |
---|---|---|---|---|---|
1 | Week of the month | Week of the month (1: first, 2: second, 3: third, 4: fourth, 5:fifth) | Quantitative | 1, 2, 3 | 0 |
2 | Day of the week | Day of the week (2: Monday, 3: Tuesday, 4: Wednesday, 5:Thursday, 6:Friday) | Quantitative | 2, 3, 4 | 0 |
3 | Non-urgent order | Non-urgent order | Quantitative | 171.297, 220.343, 127.805 | 0 |
4 | Urgent order | Urgent order | Quantitative | 127.667, 141.406, 114.813 | 0 |
5 | Order type A | Order type A | Quantitative | 41.542, 46.241, 39.025 | 0 |
6 | Order type B | Order type B | Quantitative | 113.294, 120.865, 110.74 | 0 |
7 | Order type C | Order type C | Quantitative | 162.284, 196.296, 94.47 | 0 |
8 | Fiscal sector orders | Fiscal sector orders | Quantitative | 18.156, 1.653, 1.617 | 0 |
9 | Orders from the traffic controller sector | Orders from the traffic controller sector | Quantitative | 49971, 34878, 33366 | |
10 | Banking orders (1) | Banking orders (1) | Quantitative | 33703, 32905, 21103 | 0 |
11 | Banking orders (2) | Banking orders (2) | Quantitative | 69054, 117137, 84558 | 0 |
12 | Banking orders (3) | Banking orders (3) | Quantitative | 18423, 29188, 16683 | 0 |
13 | Target (Total orders) | Target (Total orders) | Quantitative | 317.12, 363.402, 244.235 | 0 |
Acknowledgement
This data set has been sourced from the Machine Learning Repository of University of California, Irvine Daily Demand Forecasting Orders Data Set (UC Irvine). The UCI page mentions the following publication as the original source of the data set:
Ferreira, R. P., Martiniano, A., Ferreira, A., Ferreira, A., & Sassi, R. J. (2016). Study on daily demand forecasting orders using artificial neural network. IEEE Latin America Transactions, 14(3), 1519-1525