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 Rahim Rasool committed Jan 24, 2019 1 ``````Data Science Dojo
`````` Rebecca Merrett committed Feb 10, 2020 2 ``````Copyright (c) 2019 - 2020 `````` Rahim Rasool committed Jan 24, 2019 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 `````` --- **Level:** Intermediate
**Recommended Use:** Regression Models
**Domain:** Business
## Daily Demand Forecasting Orders Data Set ### Predict total number of demand of orders --- ![](294036-P6YS7U-202.jpg) --- 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)](https://archive.ics.uci.edu/ml/datasets/Daily+Demand+Forecasting+Orders). 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* ``````