Accepted for publication at

PMI Global Congress 2015 – North America
Orlando – Florida – USA – 2015

Related Podcasts

Abstract

This paper aims to discuss the use of the Artificial Neural Networks (ANN) to model aspects of the project budget where traditional algorithms and formulas are not available or not easy to apply. Neural networks use a process analogous to the human brain, where a training component takes place with existing data and subsequently, a trained neural network becomes an “expert” in the category of information it has been given to analyse. This “expert” can then be used to provide projections given new situations based on an adaptive learning (STERGIOU & CIGANOS, 1996).

The article also presents a fictitious example of the use of neural networks to determine the cost of project management activities based on the complexity, location, budget, duration and number of relevant stakeholders. The example is based on data from 500 projects and is used to predict the project management cost of a given project.

Artificial Neural Networks (Ann)

Some categories of problems and challenges faced in the project environment may depend on many subtle factors that a computer algorithm cannot be created to calculate the results (KRIESEL, 2005). Artificial Neural Networks (ANN) are a family of statistical learning models inspired by the way biological nervous systems, such as the brain, process information. They process records one at a time, and “learn” by comparing their classification of the record with the known actual classification of the record.

The errors from the initial classification of the first record are fed back into the network, and used to modify the networks algorithm the second time around, and so on for a large number of iterations in a learning process in order to predict reliable results from complicated or imprecise data (STERGIOU & CIGANOS, 1996) (Exhibit 01).

21418.pngExhibit 01 – Artificial Neural Networks Architecture (adapted from MCKIM, 1993 and STERGIOUS & CIGANOS, 1996)

Some typical applications of ANN are

  • handwriting recognition,
  • stock market prediction,
  • image compression,
  • risk management,
  • sales forecasting
  • industrial process control.

The mathematical process behind the calculation uses different neural network configurations to give the best fit to predictions. The most common network types are briefly described below.

Probabilistic Neural Networks (PNN) – Statistical algorithm where the operations are organized in multi-layered feedforward network with four layers (input, pattern, summation and output). It is fast to be trained but it has a slow execution and requires large memory. It is also not as general as the feedforward networks (CHEUNG & CANNONS, 2002).

Multi-Layer Feedforward Networks (MLF) – MLF neural networks, trained with a back-propagation learning algorithm (Exhibit 02). They are the most popular neural networks (SVOZIL, KVASNIČKA & POSPÍCHAL, 1997).

21409.pngExhibit 02 – Training data and generalization in a Multi-Layer Feedforward Network (SVOZIL, D , KVASNIČKA, V. & POSPÍCHAL, J. , 1997)

Generalized Regression Neural Networks (GRNN) – Closely related to PNN networks, it is a memory-based network that provides estimates of continuous variables. It is a one-pass learning algorithm with a highly parallel structure. The algorithmic form can be used for any regression problem in which an assumption of linearity is not justified (SPECHT, 2002).

Analogy Process and Data Set

One of the key factors of the Neural Networks is the data set used on the learning process. If the data set is not reliable, the results from the networks calculations will not be reliable. The use of Artificial Neural Networks can be considered one kind of analogy (BAILER-JONES & BAILER-JONES, 2002).

Analogy is a comparison between two or more elements, typically for the purpose of explanation or clarification (Exhibit 03). One of the most relevant uses of the analogy is to forecast future results based on similar results obtained in similar conditions (BARTHA, 2013). The challenge is to understand what a similar condition is. Projects in the past can be a reference for future projects if the underlining conditions where they were developed still exist in the project subject to the analysis.

21398.pngExhibit 03 – Simple analogy example “sock are to feet as gloves are to hands” (Adaptedfrom Spitzig, 2013)

One of the most relevant aspects of the analogy is related to the simple process of estimation based on similar events and facts. This process reduces the granularity of all calculations, where the final project costs can be determined by a set of fixed finite variables.

Data Set, Dependent and Independent Categories and Numeric Variables

The first step to develop an Artificial Neural Network is to prepare the basic data set that will be used as a reference for the “training process” of the neural network. It is important to highlight that usually the right dataset is expensive and time consuming to build (INGRASSIA & MORLINI, 2005). A dataset is composed by a set of variables filled with information that will be used as a reference. These references are called cases (Exhibit 04).

 

 

Variables

 

 

Independent variables

Dependent variable (output)

 

 

v1

v2

v3

 

 

 

 

vn

v’1

 

 

 

 

Cases

Case 1

 

 

 

 

 

 

Case 2

 

 

 

 

 

 

Case 3

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Case n

 

 

 

 

 

 

The most common variables types are

Dependent Category – dependent or output variable whose possible values are taken from a set of possible categories; for example Yes or No, or Red, Green or Blue.

Dependent Numeric – dependent or output variable whose possible values are numeric.

Independent Category – an independent variable whose possible values are taken from a set of possible categories; for example Yes or No, or Red, Green or Blue.

Independent Numeric – an independent variable whose possible values are numeric.

In the project environment, several variables can be used to calculate the project budget. Some common examples are

Complexity – Level of complexity of the project (Low, Medium, High). Usually it is an independent category.

Location – Location where the project works will happen. Associated to the complexity of the works and logistics. Most of the time it is an independent category.

Budget – Planned budget of the project. It is a numeric variable that can be independent or dependent (output).

Actual Cost – Actual Expenditure of the project. It is most of the time an independent numeric variable.

Cost Variance – The difference between the budget and the actual cost. It is a numeric variable that can be independent or dependent (output)

Baseline Duration – Duration of the project. Independent numeric variable.

Actual Duration – Actual duration of the project. Usually an independent numeric variable.

Duration Variance – The difference between the baseline duration and the actual duration.

Type of Contract – Independent category variable that defines the type of the contract used for the works in the project (ie: Fixed Firm Price, Cost Plus, Unit Price, etc).

Number of Relevant Stakeholder Groups – Independent numeric variable that reflect the number of relevant stakeholder groups in the project.

Some examples of input variables are presented at the Exhibit 05, 06 and 07.

Input Variables

Description

Unit

Range

PWA

Predominant Work Activity

Category

New Construction Asphalt or Concrete

WD

Work Duration

month

14–30

PW

Pavement Width

m

7–14

SW

Shoulder Width

m

0–2

GRF

Ground Rise Fall

nillan

2–7

ACG

Average Site Clear/Grub

m2/kin

12605–30297

EWV

Earthwork Volume

m3/kin

13134–31941

SURFCLASS

Surface Class

Category

Asphalt or Concrete

BASEMATE

Base Material

Category

Crushed Stone or Cement Stab.

USDPERKM

Unit Cost of New Construction Project

US Dollars (2000)

572.501.64-4.006.103.95

 

Description

Range

Ground floor

100–3668 m2

Area of the typical floor

0–2597 m2

No. of storeys

1–8

No. of columns

10–156

Type of foundation

1 – isolated

2 – isolated and combined

3 – raft or piles

No. of elevators

0–3

No. of rooms

2–38

Cost of structural skeleton

6,282469,680 USD

 

Project Characteristics

Unit

Type of information

Descriptors

Gross Floor Area (GFA)

m2

Quantitative

n.a

Principal structural material

No unit

Categorical

1 – steel

2 – concrete

Procurement route

No unit

Categorical

1 – traditional

2 – design and construct

Type of work

No unit

Categorical

1 – residential

2 – commercial

3 – office

Location

No unit

Categorical

1 – central business district

2 – metropolitan

3 – regional

Sector

No unit

Categorical

1 – private sector

2 – public sector

Estimating method

No unit

Categorical

1 – superficial method

2 – approximate quantities

Number of storey

No unit

Categorical

1 – one to two storey(s)

2 – three to seven storeys

3 – eight storeys and above

Estimated Sum

Cost/m2

Quantitative

n.a

Training Artificial Neural Networks

When the dataset is ready the network is ready to be trained. Two approaches can be used for the learning process: supervised or adaptive training.

In the supervised training, both inputs and outputs are provided and the network compares the results with the provided output. This allows the monitoring of how well an artificial neural network is converging on the ability to predict the right answer.

For the adaptive training, only the inputs are provided. Using self-organization mechanisms, the neural networks benefits from continuous learning in order to face new situations and environments. This kind of network is usually called self-organizing map (SOM) and was developed by Teuvo Kohonen (KOHONEN, 2014).

One of the biggest challenges of the training method is to decide on which network to use and the runtime process in the computer. Some networks can be trained in seconds but in some complex cases with several variables and cases, hours can be needed just for the training process.

The results of the training process are complex formulas that relate the input or independent variables with the outputs (dependable variables) like the graph presented in the Exhibit 2.

Most of the commercial software packages usually test the results of the training with some data points to evaluate the quality of the training. Around 10 to 20% of the sample is used for testing purposes (Exhibit 08).

training_results_palisade.pngExhibit 08 – Training results example to forecast the bloody pressure where some data is used for testing the network results (Palisade Neural Tools software example)

Prediction Results

After the training, the model is ready to predict future results. The most relevant information that should be a focus of investigation is the contribution of each individual variable to the predicted results (Exhibit 09) and the reliability of the model (Exhibit 10).

relative_variable_impacts.pngExhibit 09 – Example of Relative Variable impacts, demonstrating that the Salary variable is responsible for more than 50% of the impact in the dependent variable (Palisade Neural Tools software example)

histogram-probability_training.png Exhibit 10 – Example of histogram of Probability of Incorrect Categories showing a chance of 30% that 5% of the prediction can be wrong (Palisade Neural Tools software example)

It is important to highlight that one trained network that fails to get a reliable result in 30% of the cases is much more unreliable than another one that fails in only 1% of the cases.

Example of Cost Modeling using Artificial Neural Networks

In order to exemplify the process, a fictitious example was developed to predict the project management costs on historical data provided by 500 cases. The variables used are described in the Exhibit 11.

Name

Description

Variable Type

Project ID

ID Count of each project in the dataset

Location

Location where the project was developed (local or remote sites)

Independent Category

Complexity

Qualitative level of project complexity (Low, Medium and High)

Independent Category

Budget

Project Budget (between $500,000 and $2,000,000)

Independent Numeric

Duration

Project Duration (Between 12 and 36 months)

Independent Numeric

Relevant Stakeholder Groups

Number of relevant stakeholder groups for communication and monitoring (between 3 and 5)

Independent Category

PM Cost

Actual cost of the project management activities (planning, budgeting, controlling)

Dependent Numeric (Output)

The profiles of the cases used for the training are presented at the Exhibit 12, 13, 14, 15 and 16 and the full dataset is presented in the Appendix.

21235.pngExhibit 12 – Distribution of cases by Location

21225.pngExhibit 13 – Distribution of cases by Complexity

21214.pngExhibit 14 – Distribution of cases by Project Budget

 

21205.pngExhibit 15 – Distribution of cases by Project Duration

 

21194.pngExhibit 16 – Distribution of cases by Relevant Stakeholder Groups

The training and the tests were executed using the software Palisade Neural Tools. The test was executed in 20% of the sample and a GRNN Numeric Predictor. The summary of the training of the ANN is presented at the Exhibit 17.

21184.pngExhibit 17 – Palisade Neural Tools Summary Table

The relative impact of the five independent variables are described at the Exhibit 18, demonstrating that more than 50% of the impact in the Project Management cost is related to the project budget in this fictitious example.

 

21173.pngExhibit 18 – Relative Variable Impacts

The training and tests were used to predict the Project Management Cost of a fictitious project with the following variables

Name

Variable Type

Location

Local Project

Complexity

High Complexity

Budget

$810,756

Duration

18 months

Relevant Stakeholder Groups

5 Stakeholder groups

Relevant Stakeholder Groups

Independent Category

PM Cost

Dependent Numeric (Output)

After running the simulation, the Project Management cost predictions based on the patterns in the known data is $24,344.75, approximately 3% of the project budget.

Conclusions

The use of Artificial Neural Networks can be a helpful tool to determine aspects of the project budget like the cost of project management, the estimated bid value of a supplier or the insurance cost of equipment. The Neural Networks allows some precise decision making process without an algorithm or a formula based process.

With the recent development of software tools, the calculation process becomes very simple and straightforward. However, the biggest challenge to produce reliable results lies in the quality of the known information. The whole process is based on actual results, and most of the time the most expensive and laborious part of the process is related to getting enough reliable data to train and test the process.

References

AIBINU, A. A., DASSANAYAKE, D. & THIEN, V. C. (2011). Use of Artificial Intelligence to Predict the Accuracy of Pretender Building Cost Estimate. Amsterdam: Management and Innovation for a Sustainable Built Environment.

ARAFA, M. & ALQEDRA, M. (2011). Early Stage Cost Estimation of Buildings Construction Projects using Artificial Neural Networks. Faisalabad: Journal of Artificial Intelligence.

BAILER-JONES, D & BAILER-JONES, C. (2002). Modeling data: Analogies in neural networks, simulated annealing and genetic algorithms. New York: Model-Based Reasoning: Science, Technology, Values/Kluwer Academic/Plenum Publishers.

BARTHA, P (2013). Analogy and Analogical Reasoning. Palo Alto: Stanford Center for the Study of Language and Information.

CHEUNG, V. & CANNONS, K. (2002). An Introduction to Neural Networks. Winnipeg, University of Manitoba.

INGRASSIA, S & MORLINI, I (2005). Neural Network Modeling for Small Datasets In Technometrics: Vol 47, n 3. Alexandria: American Statistical Association and the American Society for Quality

KOHONEN, T. (2014). MATLAB Implementations and Applications of the Self-Organizing Map. Helsinki: Aalto University, School of Science.

KRIESEL, D. (2005). A Brief Introduction to Neural Networks. Downloaded on 07/01/2015 at http://www.dkriesel.com/_media/science/neuronalenetze-en-zeta2-2col-dkrieselcom.pdf

MCKIM, R. A. (1993). Neural Network Applications for Project Management. Newtown Square: Project Management Journal.

SODIKOV, J. (2005). Cost Estimation of Highway Projects in Developing Countries: Artificial Neural Network Approach. Tokyo: Journal of the Eastern Asia Society for Transportation Studies, Vol. 6.

SPECHT, D. F. (2002). A General Regression Neural Network. New York, IEEE Transactions on Neural Networks, Vol 2, Issue 6.

SPITZIG, S. (2013). Analogy in Literature: Definition & Examples in SAT Prep: Help and Review. Link accessed on 06/30/2015: http://study.com/academy/lesson/analogy-in-literature-definition-examples-quiz.html

STERGIOUS, C & CIGANOS, D. (1996). Neural Networks in Surprise Journal Vol 4, n 11. London, Imperial College London.

SVOZIL, D, KVASNIČKA, V. & POSPÍCHAL, J. (1997). Introduction to multi-layer feed-forward neural networks In Chemometrics and Intelligent Laboratory Systems, Vol 39. Amsterdam, Elsevier Journals.

Appendix – Example Dataset

LOCA-TION

COMPLE-XITY

BUDGET

DURA-TION

RELE-VANT STAKE-HOLDER GROUPS

PM COST

% PM COST

TAG USED

TEST

GOOD OR BAD

RESIDUAL

%

Remote

Medium

703,998.33

17

3

21,547.87

3.06%

train

 

 

 

 

Remote

High

902,327.29

17

5

33,934.58

3.76%

test

33,275.83

Good

658.75

1.94%

Local

Low

904,824.77

27

3

14,789.98

1.63%

train

 

 

 

 

Local

Low

640,833.02

17

3

15,128.69

2.36%

train

 

 

 

 

Local

Low

683,992.89

16

3

16,985.82

2.48%

test

17,240.60

Good

-254.78

-1.50%

Remote

High

1,467,802.93

20

5

49,416.03

3.37%

train

 

 

 

 

Remote

High

569,532.07

26

4

16,983.74

2.98%

train

 

 

 

 

Remote

Low

1,235,140.98

12

4

47,896.02

3.88%

train

 

 

 

 

Remote

Low

1,254,182.69

13

4

45,954.54

3.66%

test

43,510.55

Good

2,443.99

5.32%

Local

Low

634,127.64

16

3

15,747.50

2.48%

test

16,691.89

Good

-944.38

-6.00%

Remote

High

1,310,397.18

25

3

34,507.13

2.63%

train

 

 

 

 

Local

High

1,045,689.31

15

3

31,603.05

3.02%

test

29,729.94

Good

1,873.11

5.93%

Local

Medium

1,070,909.21

20

5

27,486.67

2.57%

train

 

 

 

 

Remote

High

1,069,089.15

25

4

31,359.95

2.93%

train

 

 

 

 

Remote

Low

600,174.43

14

4

21,491.96

3.58%

train

 

 

 

 

Remote

Low

1,274,790.04

17

4

39,018.57

3.06%

test

37,604.96

Good

1,413.61

3.62%

Remote

Low

1,333,972.58

13

5

50,212.10

3.76%

train

 

 

 

 

Remote

High

1,600,399.26

16

4

58,948.04

3.68%

train

 

 

 

 

Remote

High

1,208,443.26

32

3

28,297.71

2.34%

train

 

 

 

 

Local

Low

1,618,395.90

12

3

49,810.63

3.08%

test

43,252.94

Good

6,557.69

13.17%

Remote

Low

580,524.22

15

3

18,125.26

3.12%

test

18,178.96

Good

-53.70

-0.30%

Remote

Low

1,277,669.74

26

4

30,434.75

2.38%

train

 

 

 

 

Local

High

1,465,538.27

27

5

35,679.52

2.43%

test

36,732.46

Good

-1,052.93

-2.95%

Local

High

534,389.92

19

5

16,322.33

3.05%

test

16,106.84

Good

215.49

1.32%

Local

Low

1,110,809.34

19

4

26,152.74

2.35%

test

24,588.71

Good

1,564.03

5.98%

Remote

Low

938,755.52

14

4

33,616.39

3.58%

train

 

 

 

 

Remote

Medium

573,363.07

22

5

17,287.77

3.02%

train

 

 

 

 

Remote

High

1,030,776.33

24

3

27,716.43

2.69%

train

 

 

 

 

Remote

High

961,099.65

13

5

41,943.37

4.36%

train

 

 

 

 

Local

Medium

765,884.98

16

3

20,551.25

2.68%

train

 

 

 

 

Remote

High

1,074,273.06

15

3

37,838.28

3.52%

train

 

 

 

 

Local

Low

762,219.86

16

3

18,928.46

2.48%

train

 

 

 

 

Local

Low

964,410.00

20

3

19,931.14

2.07%

train

 

 

 

 

Remote

Low

911,404.26

24

4

23,595.24

2.59%

test

22,578.66

Good

1,016.59

4.31%

Remote

High

1,930,468.28

20

3

57,270.56

2.97%

train

 

 

 

 

Remote

High

981,611.00

23

5

31,895.24

3.25%

train

 

 

 

 

Local

Low

1,126,200.40

21

3

21,254.80

1.89%

test

22,151.95

Good

-897.15

-4.22%

Local

High

708,383.15

21

3

17,619.63

2.49%

train

 

 

 

 

Local

Low

852,403.45

17

4

22,680.62

2.66%

train

 

 

 

 

Remote

Low

816,178.39

16

3

24,349.32

2.98%

train

 

 

 

 

Remote

Low

1,151,686.39

22

5

31,270.03

2.72%

train

 

 

 

 

Remote

High

624,255.72

13

3

24,746.14

3.96%

train

 

 

 

 

Remote

High

531,076.00

25

3

14,516.08

2.73%

train

 

 

 

 

Local

Low

1,219,803.85

21

5

27,900.59

2.29%

train

 

 

 

 

Remote

Low

1,359,202.77

17

4

41,602.27

3.06%

train

 

 

 

 

Local

Low

693,228.75

15

3

18,178.00

2.62%

train

 

 

 

 

Local

High

801,510.16

28

3

16,755.38

2.09%

train

 

 

 

 

Local

High

511,096.39

26

5

13,196.77

2.58%

train

 

 

 

 

Local

Low

590,242.71

15

5

17,838.45

3.02%

train

 

 

 

 

Remote

Low

1,116,386.68

17

4

34,170.19

3.06%

train

 

 

 

 

Remote

Medium

1,123,846.83

23

4

30,897.64

2.75%

test

31,578.29

Good

-680.64

-2.20%

Remote

High

547,802.19

23

5

17,799.60

3.25%

train

 

 

 

 

Remote

High

966,086.13

15

4

37,892.04

3.92%

train

 

 

 

 

Local

Medium

1,273,716.73

22

3

25,667.32

2.02%

train

 

 

 

 

Local

High

778,993.21

20

4

22,331.14

2.87%

test

22,044.71

Good

286.43

1.28%

Local

Low

894,732.93

19

3

19,276.00

2.15%

train

 

 

 

 

Remote

Low

1,171,008.92

19

4

33,425.11

2.85%

train

 

 

 

 

Remote

Low

551,582.86

21

4

15,374.28

2.79%

train

 

 

 

 

Remote

High

546,599.66

29

4

15,574.95

2.85%

train

 

 

 

 

Remote

High

1,789,071.54

28

4

49,923.62

2.79%

train

 

 

 

 

Local

Low

1,323,310.37

28

3

19,723.63

1.49%

train

 

 

 

 

Remote

Low

845,707.18

20

3

21,706.48

2.57%

train

 

 

 

 

Local

Medium

782,095.09

21

5

20,235.16

2.59%

train

 

 

 

 

Local

Medium

512,318.31

18

4

14,098.24

2.75%

train

 

 

 

 

Remote

High

1,056,680.60

23

4

32,221.10

3.05%

train

 

 

 

 

Local

Low

1,399,151.60

12

4

47,260.23

3.38%

train

 

 

 

 

Remote

Low

1,629,835.05

19

4

46,521.78

2.85%

train

 

 

 

 

Local

Low

1,747,728.47

19

5

42,896.00

2.45%

train

 

 

 

 

Local

High

584,824.62

24

4

15,140.46

2.59%

train

 

 

 

 

Remote

High

1,522,611.48

36

4

38,460.04

2.53%

train

 

 

 

 

Remote

Low

1,234,685.15

19

4

35,242.68

2.85%

train

 

 

 

 

Local

Low

982,920.06

21

3

19,533.59

1.99%

train

 

 

 

 

Local

Medium

1,788,200.40

12

4

63,977.84

3.58%

train

 

 

 

 

Local

High

1,082,133.01

21

4

29,080.18

2.69%

test

28,236.53

Good

843.65

2.90%

Remote

High

1,035,386.38

25

4

30,371.33

2.93%

test

31,220.71

Good

-849.38

-2.80%

Remote

Medium

1,264,034.73

14

3

42,736.41

3.38%

train

 

 

 

 

Remote

High

1,367,409.84

19

4

45,868.20

3.35%

train

 

 

 

 

Remote

Low

1,002,553.31

13

5

37,737.13

3.76%

train

 

 

 

 

Local

Low

1,420,828.51

19

3

29,189.30

2.05%

train

 

 

 

 

Local

Low

1,709,337.52

15

4

48,241.30

2.82%

train

 

 

 

 

Local

High

609,335.11

28

4

14,566.01

2.39%

test

14,651.91

Good

-85.90

-0.59%

Remote

High

833,883.05

30

4

23,441.38

2.81%

train

 

 

 

 

Remote

Low

1,297,801.29

23

3

29,191.12

2.25%

train

 

 

 

 

Remote

Low

1,119,369.76

14

3

35,606.62

3.18%

train

 

 

 

 

Local

Low

925,628.02

19

4

22,718.48

2.45%

train

 

 

 

 

Local

High

667,414.59

24

3

15,276.38

2.29%

train

 

 

 

 

Remote

High

1,722,870.56

19

5

59,514.60

3.45%

train

 

 

 

 

Local

Low

951,195.05

23

5

21,395.00

2.25%

test

20,650.89

Good

744.11

3.48%

Local

Low

1,363,830.91

18

5

34,802.94

2.55%

train

 

 

 

 

Remote

Medium

1,151,990.74

24

4

30,975.75

2.69%

train

 

 

 

 

Local

High

1,125,818.31

30

5

26,018.91

2.31%

train

 

 

 

 

Remote

High

1,279,302.89

28

3

31,860.73

2.49%

train

 

 

 

 

Local

Medium

555,745.83

16

3

14,912.51

2.68%

test

17,164.08

Good

-2,251.57

-15.10%

Local

High

1,437,619.16

15

5

49,198.52

3.42%

train

 

 

 

 

Remote

Low

512,839.97

15

3

16,012.00

3.12%

train

 

 

 

 

Remote

Low

1,108,388.88

18

3

29,392.83

2.65%

train

 

 

 

 

Local

Low

1,491,757.71

14

4

44,468.59

2.98%

train

 

 

 

 

Local

High

573,367.88

25

4

14,525.32

2.53%

train

 

 

 

 

Local

High

577,732.27

28

5

14,388.28

2.49%

test

13,498.17

Good

890.11

6.19%

Remote

Low

1,340,923.44

30

3

25,626.54

1.91%

train

 

 

 

 

Local

Medium

1,218,034.19

30

3

19,623.88

1.61%

train

 

 

 

 

Remote

Medium

982,929.62

15

4

35,603.90

3.62%

train

 

 

 

 

Remote

Low

918,511.12

15

3

28,677.96

3.12%

train

 

 

 

 

Remote

High

799,134.56

34

3

19,022.54

2.38%

train

 

 

 

 

Local

Medium

1,699,228.84

14

4

54,051.66

3.18%

train

 

 

 

 

Local

Medium

557,737.83

20

4

14,315.27

2.57%

train

 

 

 

 

Local

Medium

1,308,696.78

25

4

27,918.86

2.13%

train

 

 

 

 

Local

Low

823,502.63

21

3

16,365.48

1.99%

train

 

 

 

 

Local

Low

1,277,239.09

22

5

28,292.78

2.22%

train

 

 

 

 

Remote

High

951,405.82

17

3

31,974.70

3.36%

train

 

 

 

 

Remote

Low

615,510.45

19

5

18,800.06

3.05%

train

 

 

 

 

Local

Low

852,551.98

24

3

15,251.21

1.79%

test

16,513.71

Good

-1,262.50

-8.28%

Local

Low

514,229.05

22

5

11,905.18

2.32%

train

 

 

 

 

Local

Medium

831,541.04

19

4

22,072.31

2.65%

train

 

 

 

 

Local

Medium

1,035,118.41

21

4

24,711.40

2.39%

train

 

 

 

 

Remote

High

813,527.00

16

4

30,778.44

3.78%

test

31,776.14

Good

-997.71

-3.24%

Local

Low

534,936.99

27

5

10,883.66

2.03%

test

11,947.44

Good

-1,063.79

-9.77%

Remote

High

839,992.75

27

3

22,130.18

2.63%

test

21,463.60

Good

666.58

3.01%

Local

High

968,941.49

20

4

27,776.32

2.87%

train

 

 

 

 

Local

High

1,455,430.69

23

3

32,736.64

2.25%

train

 

 

 

 

Remote

Low

553,402.62

20

4

15,864.21

2.87%

train

 

 

 

 

Remote

Low

1,550,217.54

15

3

46,851.02

3.02%

train

 

 

 

 

Remote

Medium

1,571,769.84

20

3

41,913.86

2.67%

train

 

 

 

 

Local

Low

958,266.50

21

4

21,918.44

2.29%

train

 

 

 

 

Remote

High

1,203,129.39

12

5

53,873.46

4.48%

train

 

 

 

 

Local

Medium

512,774.70

19

4

13,611.02

2.65%

train

 

 

 

 

Remote

Low

1,572,775.22

24

3

34,426.30

2.19%

train

 

 

 

 

Local

High

928,720.44

21

3

23,100.08

2.49%

test

22,313.20

Good

786.88

3.41%

Local

Low

1,286,047.40

13

5

41,977.91

3.26%

test

43,382.91

Good

-1,405.01

-3.35%

Local

Medium

897,200.07

21

3

19,624.47

2.19%

train

 

 

 

 

Local

Medium

506,773.64

28

3

9,073.66

1.79%

test

10,009.00

Good

-935.33

-10.31%

Remote

Medium

1,561,191.51

16

5

54,381.50

3.48%

test

53,120.34

Good

1,261.17

2.32%

Local

High

903,316.02

20

4

25,895.06

2.87%

train

 

 

 

 

Remote

Medium

580,211.77

20

3

16,052.53

2.77%

test

17,675.50

Good

-1,622.97

-10.11%

Remote

Low

595,520.47

15

5

20,975.55

3.52%

train

 

 

 

 

Local

Low

1,001,793.43

19

4

23,586.08

2.35%

test

21,799.23

Good

1,786.85

7.58%

Local

Low

655,421.89

16

5

18,898.00

2.88%

train

 

 

 

 

Local

High

897,256.60

20

4

25,721.36

2.87%

train

 

 

 

 

Remote

Low

604,357.31

19

3

16,041.98

2.65%

train

 

 

 

 

Remote

High

868,980.86

18

4

30,864.91

3.55%

train

 

 

 

 

Local

Medium

1,054,258.00

16

3

27,235.00

2.58%

train

 

 

 

 

Local

Low

504,023.79

19

3

10,858.62

2.15%

train

 

 

 

 

Remote

Medium

984,726.14

26

3

23,456.68

2.38%

train

 

 

 

 

Remote

Low

914,671.35

20

3

23,476.56

2.57%

test

23,342.33

Good

134.24

0.57%

Local

Low

816,984.05

33

3

11,520.30

1.41%

train

 

 

 

 

Local

Medium

1,102,518.04

15

5

34,423.06

3.12%

test

32,270.39

Good

2,152.67

6.25%

Local

Medium

1,568,418.96

18

3

36,886.89

2.35%

train

 

 

 

 

Local

Low

866,386.50

27

4

16,760.84

1.93%

train

 

 

 

 

Remote

Low

945,814.91

19

3

25,105.58

2.65%

train

 

 

 

 

Remote

Medium

1,352,496.54

25

4

35,615.74

2.63%

train

 

 

 

 

Remote

Low

1,007,543.31

21

3

24,053.10

2.39%

train

 

 

 

 

Local

Medium

1,585,230.00

17

4

43,764.78

2.76%

train

 

 

 

 

Remote

High

599,627.37

28

3

15,533.20

2.59%

test

15,007.94

Good

525.27

3.38%

Local

Medium

1,063,937.52

33

3

16,066.53

1.51%

test

19,098.46

Good

-3,031.93

-18.87%

Remote

Low

1,316,509.72

17

3

36,345.99

2.76%

train

 

 

 

 

Local

Low

819,992.37

36

5

14,152.46

1.73%

train

 

 

 

 

Remote

Medium

1,059,271.62

15

3

34,132.09

3.22%

test

34,598.62

Good

-466.54

-1.37%

Remote

High

661,598.27

36

3

15,388.29

2.33%

train

 

 

 

 

Local

Low

556,860.84

22

3

10,664.73

1.92%

train

 

 

 

 

Remote

High

1,629,259.58

20

4

53,222.48

3.27%

train

 

 

 

 

Local

Medium

560,885.36

27

5

12,533.36

2.23%

train

 

 

 

 

Remote

Low

1,128,949.92

36

3

19,484.84

1.73%

train

 

 

 

 

Remote

Low

1,140,022.19

16

3

32,870.64

2.88%

train

 

 

 

 

Local

Medium

1,277,998.06

23

5

30,023.69

2.35%

test

29,794.51

Good

229.18

0.76%

Local

Low

1,370,381.07

13

4

43,360.26

3.16%

train

 

 

 

 

Remote

Medium

622,821.80

20

3

17,231.40

2.77%

train

 

 

 

 

Local

Low

606,852.57

26

5

12,634.98

2.08%

train

 

 

 

 

Remote

Low

951,616.00

12

5

38,804.79

4.08%

train

 

 

 

 

Remote

Medium

617,490.46

35

3

12,673.26

2.05%

train

 

 

 

 

Local

Low

704,413.02

34

3

9,723.66

1.38%

test

9,729.60

Good

-5.94

-0.06%

Remote

Low

580,202.08

32

3

11,265.59

1.94%

train

 

 

 

 

Local

Low

1,283,482.92

30

5

23,245.30

1.81%

train

 

 

 

 

Remote

Low

1,615,066.28

23

4

41,172.49

2.55%

test

38,529.89

Good

2,642.60

6.42%

Remote

Medium

1,221,684.39

25

3

28,505.97

2.33%

train

 

 

 

 

Local

Low

1,554,072.32

21

4

33,992.25

2.19%

test

32,400.12

Good

1,592.13

4.68%

Local

Medium

1,147,660.40

21

3

23,955.13

2.09%

test

25,670.99

Good

-1,715.86

-7.16%

Remote

Low

1,226,103.02

27

3

24,945.90

2.03%

train

 

 

 

 

Remote

Low

514,184.61

22

3

12,418.34

2.42%

train

 

 

 

 

Remote

Medium

1,559,320.98

22

3

39,219.29

2.52%

train

 

 

 

 

Remote

Medium

904,655.73

18

3

26,704.10

2.95%

train

 

 

 

 

Remote

Low

1,304,661.29

22

4

34,118.87

2.62%

test

32,420.45

Good

1,698.42

4.98%

Remote

Medium

573,409.51

16

4

19,973.76

3.48%

train

 

 

 

 

Remote

High

545,633.58

21

3

16,299.72

2.99%

train

 

 

 

 

Remote

High

503,090.27

33

4

13,634.25

2.71%

train

 

 

 

 

Local

Medium

525,195.05

30

5

11,087.45

2.11%

train

 

 

 

 

Local

Low

894,012.12

17

5

24,681.75

2.76%

train

 

 

 

 

Local

Medium

833,563.20

22

3

17,631.12

2.12%

train

 

 

 

 

Local

Low

535,711.70

22

4

11,866.83

2.22%

train

 

 

 

 

Local

Medium

1,325,009.13

24

4

29,002.98

2.19%

train

 

 

 

 

Remote

Medium

590,318.95

22

5

17,799.01

3.02%

train

 

 

 

 

Local

High

1,770,395.16

22

3

40,987.33

2.32%

train

 

 

 

 

Local

High

1,405,512.56

16

3

40,525.61

2.88%

train

 

 

 

 

Local

Medium

1,286,163.78

17

3

31,649.72

2.46%

train

 

 

 

 

Local

Low

1,103,463.05

15

3

27,831.79

2.52%

train

 

 

 

 

Remote

Medium

885,202.32

20

4

27,146.20

3.07%

test

25,689.17

Good

1,457.04

5.37%

Local

Low

1,220,977.54

27

4

22,399.66

1.83%

train

 

 

 

 

Remote

High

679,641.98

31

3

16,822.97

2.48%

train

 

 

 

 

Local

Medium

1,158,479.42

20

5

29,734.31

2.57%

train

 

 

 

 

Remote

High

1,297,008.10

16

3

43,882.11

3.38%

train

 

 

 

 

Local

Medium

595,980.69

31

3

9,984.28

1.68%

train

 

 

 

 

Remote

Low

812,827.47

19

3

21,575.58

2.65%

train

 

 

 

 

Remote

Low

800,720.74

20

4

22,953.99

2.87%

train

 

 

 

 

Local

Low

1,360,528.32

31

4

22,792.51

1.68%

test

22,768.65

Good

23.86

0.10%

Remote

Medium

622,078.94

25

3

15,137.25

2.43%

train

 

 

 

 

Local

Medium

1,048,802.19

22

4

24,281.36

2.32%

test

25,044.15

Good

-762.79

-3.14%

Local

High

964,150.49

20

4

27,638.98

2.87%

train

 

 

 

 

Remote

Low

1,270,776.17

21

4

34,149.59

2.69%

train

 

 

 

 

Local

Medium

1,236,912.47

26

5

26,990.06

2.18%

test

27,084.03

Good

-93.96

-0.35%

Local

Medium

828,706.86

16

4

24,723.09

2.98%

train

 

 

 

 

Remote

High

946,925.63

15

3

34,299.75

3.62%

train

 

 

 

 

Local

Medium

826,666.64

20

5

22,044.44

2.67%

train

 

 

 

 

Local

Medium

744,008.05

22

4

17,968.92

2.42%

train

 

 

 

 

Local

Medium

1,335,476.56

18

3

31,408.43

2.35%

train

 

 

 

 

Remote

Low

540,059.74

12

3

19,862.20

3.68%

train

 

 

 

 

Remote

Medium

1,937,816.91

19

3

53,374.96

2.75%

train

 

 

 

 

Remote

Medium

769,785.60

17

3

23,561.48

3.06%

train

 

 

 

 

Local

Medium

1,094,632.16

20

4

27,000.93

2.47%

train

 

 

 

 

Remote

High

1,280,061.70

22

3

36,035.68

2.82%

train

 

 

 

 

Remote

High

896,347.09

36

4

23,537.41

2.63%

train

 

 

 

 

Local

Low

704,793.42

16

3

17,502.37

2.48%

train

 

 

 

 

Remote

High

849,940.50

21

4

27,940.11

3.29%

train

 

 

 

 

Local

Low

1,325,031.76

28

4

23,724.38

1.79%

train

 

 

 

 

Remote

Medium

1,493,825.11

21

5

44,625.06

2.99%

train

 

 

 

 

Remote

Low

640,849.31

33

3

12,240.87

1.91%

test

11,703.26

Good

537.61

4.39%

Remote

Medium

536,908.21

19

3

15,325.43

2.85%

train

 

 

 

 

Remote

Low

1,167,617.40

16

4

37,169.15

3.18%

train

 

 

 

 

Local

High

1,192,348.18

26

3

24,825.30

2.08%

train

 

 

 

 

Remote

Low

531,703.85

15

3

16,600.98

3.12%

test

17,783.95

Good

-1,182.98

-7.13%

Remote

Medium

1,510,277.92

19

3

41,598.88

2.75%

train

 

 

 

 

Local

Medium

1,438,409.49

35

3

20,891.19

1.45%

test

19,508.51

Good

1,382.68

6.62%

Remote

Medium

866,217.66

17

5

29,977.92

3.46%

train

 

 

 

 

Local

High

1,830,390.71

28

4

37,189.23

2.03%

train

 

 

 

 

Remote

Medium

993,322.40

12

4

41,498.80

4.18%

train

 

 

 

 

Local

Low

948,143.98

15

3

24,862.44

2.62%

train

 

 

 

 

Local

Medium

1,379,684.09

21

4

32,937.22

2.39%

train

 

 

 

 

Remote

Low

1,120,685.21

14

5

40,131.20

3.58%

test

38,958.02

Good

1,173.18

2.92%

Local

Medium

1,163,330.12

19

4

29,715.94

2.55%

train

 

 

 

 

Local

Medium

1,028,805.86

21

3

21,474.28

2.09%

train

 

 

 

 

Local

Medium

698,116.99

19

3

16,436.37

2.35%

train

 

 

 

 

Local

Medium

520,721.77

21

3

11,389.76

2.19%

train

 

 

 

 

Remote

Medium

1,761,126.50

20

5

54,007.88

3.07%

train

 

 

 

 

Remote

High

1,657,808.50

31

4

44,350.83

2.68%

test

46,338.40

Good

-1,987.57

-4.48%

Local

Medium

1,458,640.17

25

4

31,117.66

2.13%

train

 

 

 

 

Remote

High

1,143,918.74

19

3

34,939.69

3.05%

train

 

 

 

 

Remote

Low

539,042.04

24

5

14,494.24

2.69%

test

16,519.13

Good

-2,024.88

-13.97%

Local

Medium

809,443.86

15

5

26,082.08

3.22%

train

 

 

 

 

Local

High

1,767,884.50

25

3

37,714.87

2.13%

train

 

 

 

 

Local

High

771,280.77

21

3

19,184.08

2.49%

train

 

 

 

 

Remote

Low

625,360.95

26

5

16,147.14

2.58%

train

 

 

 

 

Remote

Low

599,119.11

15

5

21,102.31

3.52%

train

 

 

 

 

Local

High

854,967.79

22

3

20,648.77

2.42%

train

 

 

 

 

Local

Low

614,910.44

18

4

15,691.60

2.55%

train

 

 

 

 

Local

High

1,010,812.89

16

3

29,145.11

2.88%

train

 

 

 

 

Local

Low

1,605,359.49

15

3

40,490.73

2.52%

train

 

 

 

 

Local

Low

909,185.58

25

5

19,395.96

2.13%

train

 

 

 

 

Remote

Medium

559,258.89

15

3

18,579.82

3.32%

train

 

 

 

 

Remote

High

575,367.10

20

4

19,370.69

3.37%

test

18,927.68

Good

443.01

2.29%

Remote

Medium

565,256.88

30

5

14,759.49

2.61%

train

 

 

 

 

Local

Medium

868,794.53

20

3

19,692.68

2.27%

test

19,750.18

Good

-57.50

-0.29%

Local

Medium

513,426.44

24

3

10,211.48

1.99%

train

 

 

 

 

Local

Medium

565,225.63

33

3

9,100.70

1.61%

train

 

 

 

 

Remote

Low

1,750,698.16

16

5

57,481.26

3.28%

train

 

 

 

 

Remote

Low

777,901.92

18

3

21,406.71

2.75%

train

 

 

 

 

Local

Low

1,485,078.05

32

4

24,380.03

1.64%

test

25,014.55

Good

-634.52

-2.60%

Local

High

785,613.65

34

3

14,772.62

1.88%

train

 

 

 

 

Local

Low

706,311.75

19

3

15,216.68

2.15%

train

 

 

 

 

Remote

Low

739,540.41

21

5

21,352.76

2.89%

train

 

 

 

 

Remote

High

1,342,549.88

20

3

39,828.98

2.97%

train

 

 

 

 

Local

High

1,201,962.84

21

3

28,694.48

2.39%

train

 

 

 

 

Remote

High

735,242.88

17

3

24,709.93

3.36%

test

24,688.38

Good

21.55

0.09%

Remote

High

1,712,608.43

16

3

57,943.25

3.38%

train

 

 

 

 

Local

High

1,050,306.81

16

4

33,434.77

3.18%

train

 

 

 

 

Remote

Medium

538,418.45

28

4

13,947.60

2.59%

train

 

 

 

 

Local

Medium

606,669.12

32

5

12,386.16

2.04%

train

 

 

 

 

Local

Low

888,601.69

20

3

18,364.43

2.07%

train

 

 

 

 

Local

High

602,631.37

25

3

13,458.77

2.23%

train

 

 

 

 

Local

Medium

528,769.94

28

5

11,582.58

2.19%

train

 

 

 

 

Remote

Medium

733,381.20

24

5

21,186.57

2.89%

test

19,471.21

Good

1,715.35

8.10%

Remote

Low

615,606.68

31

5

14,622.31

2.38%

train

 

 

 

 

Local

Low

1,038,350.37

30

3

14,652.28

1.41%

train

 

 

 

 

Remote

Low

1,008,605.83

15

3

30,482.31

3.02%

train

 

 

 

 

Remote

Medium

1,537,920.89

23

4

42,281.68

2.75%

train

 

 

 

 

Remote

Medium

1,246,255.59

14

4

45,874.07

3.68%

train

 

 

 

 

Remote

Medium

563,905.89

21

4

16,845.57

2.99%

train

 

 

 

 

Local

High

1,033,174.25

18

4

30,497.77

2.95%

test

28,677.64

Good

1,820.13

5.97%

Remote

Low

658,752.67

20

3

16,907.99

2.57%

train

 

 

 

 

Remote

Medium

829,602.26

20

3

22,952.33

2.77%

train

 

 

 

 

Local

Medium

1,771,365.49

16

5

52,845.74

2.98%

train

 

 

 

 

Remote

Medium

511,380.29

22

4

14,907.51

2.92%

test

17,273.01

Good

-2,365.50

-15.87%

Local

Medium

1,543,534.66

20

3

33,443.25

2.17%

train

 

 

 

 

Remote

Medium

629,687.82

27

3

14,700.49

2.33%

train

 

 

 

 

Remote

Medium

895,421.00

25

4

24,474.84

2.73%

train

 

 

 

 

Remote

Low

556,260.00

14

4

19,919.41

3.58%

test

20,022.91

Good

-103.50

-0.52%

Local

Medium

1,817,837.94

27

3

31,531.63

1.73%

train

 

 

 

 

Local

Medium

1,622,698.23

20

4

40,026.56

2.47%

test

38,438.01

Good

1,588.54

3.97%

Remote

Medium

846,257.51

27

3

19,756.46

2.33%

train

 

 

 

 

Local

Medium

1,611,292.85

16

3

41,625.07

2.58%

train

 

 

 

 

Remote

Low

1,262,421.26

18

5

38,527.23

3.05%

test

37,941.67

Good

585.56

1.52%

Remote

Medium

612,061.28

36

3

12,399.91

2.03%

train

 

 

 

 

Remote

High

1,129,246.13

12

3

46,048.15

4.08%

train

 

 

 

 

Local

Low

537,807.95

25

4

10,935.43

2.03%

train

 

 

 

 

Local

Low

955,684.84

26

3

16,075.11

1.68%

train

 

 

 

 

Remote

High

1,134,051.12

25

4

33,265.50

2.93%

train

 

 

 

 

Remote

Medium

600,240.51

19

4

18,933.90

3.15%

train

 

 

 

 

Remote

Low

995,130.04

27

3

21,241.73

2.13%

train

 

 

 

 

Remote

Low

1,141,834.26

19

3

29,166.85

2.55%

train

 

 

 

 

Remote

Medium

950,139.68

16

3

30,246.11

3.18%

train

 

 

 

 

Remote

High

1,236,433.40

12

3

50,419.01

4.08%

train

 

 

 

 

Local

Low

1,149,263.50

28

5

21,726.55

1.89%

test

22,898.96

Good

-1,172.40

-5.40%

Local

High

697,640.20

21

3

17,352.42

2.49%

train

 

 

 

 

Remote

Medium

584,742.74

26

3

13,928.87

2.38%

train

 

 

 

 

Local

High

1,439,365.41

15

3

43,500.82

3.02%

train

 

 

 

 

Local

High

644,115.39

35

5

14,507.93

2.25%

train

 

 

 

 

Remote

Low

875,107.31

19

3

23,228.73

2.65%

train

 

 

 

 

Local

Low

1,471,608.90

30

5

26,652.47

1.81%

train

 

 

 

 

Local

Medium

630,681.97

21

5

16,317.64

2.59%

train

 

 

 

 

Local

Low

1,134,830.22

15

3

28,622.94

2.52%

test

27,401.45

Good

1,221.49

4.27%

Local

Low

1,515,009.77

24

3

25,586.83

1.69%

train

 

 

 

 

Local

Medium

503,379.10

24

4

11,521.79

2.29%

train

 

 

 

 

Local

Medium

1,289,329.63

13

5

44,663.70

3.46%

train

 

 

 

 

Remote

Low

540,092.44

20

3

13,862.37

2.57%

train

 

 

 

 

Remote

Medium

1,506,018.23

27

3

33,653.00

2.23%

test

36,646.12

Good

-2,993.12

-8.89%

Local

Low

1,223,357.49

20

3

24,059.36

1.97%

train

 

 

 

 

Remote

Low

1,177,260.33

22

3

27,255.36

2.32%

train

 

 

 

 

Remote

Low

567,631.63

20

3

14,569.21

2.57%

train

 

 

 

 

Remote

Low

1,059,977.22

30

3

20,257.34

1.91%

train

 

 

 

 

Remote

High

1,426,212.38

33

3

32,946.95

2.31%

train

 

 

 

 

Remote

Low

1,078,018.55

34

5

23,505.03

2.18%

test

20,705.97

Good

2,799.07

11.91%

Local

Medium

1,642,148.81

23

4

36,936.45

2.25%

train

 

 

 

 

Remote

Medium

568,875.01

27

3

13,280.77

2.33%

train

 

 

 

 

Local

Medium

1,323,716.42

12

5

48,683.35

3.68%

test

45,392.41

Good

3,290.94

6.76%

Remote

Low

1,242,074.92

22

3

28,755.92

2.32%

train

 

 

 

 

Remote

Low

533,466.84

19

5

16,294.14

3.05%

train

 

 

 

 

Remote

High

1,341,511.76

20

5

45,164.23

3.37%

train

 

 

 

 

Remote

Medium

1,190,106.86

17

3

35,236.50

2.96%

train

 

 

 

 

Remote

Medium

1,639,194.71

12

4

66,842.72

4.08%

train

 

 

 

 

Local

High

557,365.65

17

4

17,617.13

3.16%

train

 

 

 

 

Remote

Low

840,319.07

15

4

28,757.59

3.42%

test

29,966.92

Good

-1,209.33

-4.21%

Local

Low

575,092.91

30

3

8,690.29

1.51%

train

 

 

 

 

Local

Medium

865,197.22

19

3

20,370.08

2.35%

train

 

 

 

 

Local

Low

1,283,649.31

21

4

28,077.28

2.19%

train

 

 

 

 

Local

High

1,127,308.76

21

4

30,294.19

2.69%

train

 

 

 

 

Remote

Low

863,172.40

15

5

30,402.85

3.52%

train

 

 

 

 

Local

Medium

822,039.76

24

4

18,815.58

2.29%

train

 

 

 

 

Remote

Low

562,812.48

15

4

19,260.69

3.42%

train

 

 

 

 

Local

Low

502,502.19

24

4

10,496.71

2.09%

test

11,330.66

Good

-833.95

-7.94%

Remote

Low

518,239.43

34

5

11,817.89

2.28%

train

 

 

 

 

Local

High

1,282,007.44

15

4

42,591.14

3.32%

train

 

 

 

 

Remote

Medium

774,354.55

35

3

15,892.71

2.05%

train

 

 

 

 

Remote

Low

589,499.15

21

4

16,431.12

2.79%

test

16,242.83

Good

188.28

1.15%

Remote

Medium

1,682,541.00

24

3

40,194.04

2.39%

train

 

 

 

 

Local

Medium

838,064.15

24

3

16,668.16

1.99%

test

18,249.56

Good

-1,581.39

-9.49%

Remote

Medium

1,197,097.75

15

3

38,573.15

3.22%

train

 

 

 

 

Local

Low

673,022.37

15

3

17,648.14

2.62%

train

 

 

 

 

Local

Medium

989,563.79

14

4

32,467.12

3.28%

train

 

 

 

 

Local

Low

1,314,990.27

26

3

20,803.82

1.58%

test

21,387.48

Good

-583.66

-2.81%

Local

High

1,768,637.41

33

5

39,088.67

2.21%

train

 

 

 

 

Remote

Medium

902,133.76

34

3

18,767.92

2.08%

train

 

 

 

 

Remote

Medium

768,791.62

21

4

22,966.12

2.99%

train

 

 

 

 

Local

Medium

834,143.17

21

3

18,245.23

2.19%

train

 

 

 

 

Local

Low

1,721,279.85

20

3

33,851.84

1.97%

train

 

 

 

 

Remote

Medium

649,359.38

28

4

16,821.50

2.59%

test

14,796.02

Good

2,025.48

12.04%

Remote

Medium

1,292,141.59

30

5

32,447.11

2.51%

train

 

 

 

 

Local

Medium

1,162,828.90

31

3

18,317.68

1.58%

test

19,475.00

Good

-1,157.31

-6.32%

Remote

Low

522,425.32

24

3

11,957.74

2.29%

test

13,711.25

Good

-1,753.51

-14.66%

Remote

Low

1,259,321.65

18

5

38,432.63

3.05%

train

 

 

 

 

Local

High

753,129.64

34

3

14,161.79

1.88%

test

14,469.25

Good

-307.46

-2.17%

Local

High

1,591,469.31

13

4

58,313.07

3.66%

train

 

 

 

 

Local

High

1,815,026.04

15

5

62,114.22

3.42%

train

 

 

 

 

Remote

Medium

539,535.55

16

4

18,793.82

3.48%

test

19,527.43

Good

-733.61

-3.90%

Remote

Medium

1,012,917.15

17

3

29,990.29

2.96%

train

 

 

 

 

Local

Low

1,191,074.21

24

3

20,115.92

1.69%

train

 

 

 

 

Remote

Medium

1,242,927.66

25

3

29,001.65

2.33%

train

 

 

 

 

Local

Low

866,382.17

26

3

14,572.99

1.68%

train

 

 

 

 

Remote

High

1,809,778.83

14

3

66,617.10

3.68%

train

 

 

 

 

Local

Low

1,200,895.52

19

4

28,273.72

2.35%

train

 

 

 

 

Remote

High

664,897.90

21

3

19,862.51

2.99%

train

 

 

 

 

Remote

Low

838,060.77

19

3

22,245.37

2.65%

test

22,111.30

Good

134.06

0.60%

Local

High

708,604.12

15

3

22,124.20

3.12%

train

 

 

 

 

Remote

High

721,495.49

21

5

24,439.23

3.39%

test

22,337.15

Good

2,102.08

8.60%

Local

Medium

1,455,977.09

15

3

39,634.93

2.72%

train

 

 

 

 

Local

Low

1,111,810.02

27

3

17,061.48

1.53%

test

17,956.39

Good

-894.91

-5.25%

Remote

Low

1,587,492.55

16

5

52,122.67

3.28%

train

 

 

 

 

Local

Low

879,426.14

20

3

18,174.81

2.07%

train

 

 

 

 

Remote

Low

814,569.24

24

3

18,644.58

2.29%

train

 

 

 

 

Remote

Low

550,677.71

29

3

11,285.73

2.05%

train

 

 

 

 

Remote

Low

654,244.47

28

3

13,676.82

2.09%

train

 

 

 

 

Local

Medium

1,142,844.15

19

5

30,335.49

2.65%

train

 

 

 

 

Local

Medium

873,476.34

21

3

19,105.56

2.19%

train

 

 

 

 

Remote

Medium

554,435.96

13

3

20,315.10

3.66%

train

 

 

 

 

Local

Medium

825,566.09

15

4

25,776.01

3.12%

train

 

 

 

 

Remote

Low

633,649.59

26

3

13,826.56

2.18%

train

 

 

 

 

Remote

High

587,307.33

20

4

19,772.68

3.37%

train

 

 

 

 

Remote

Low

641,383.96

35

3

11,880.87

1.85%

train

 

 

 

 

Remote

Medium

1,481,728.02

17

3

43,870.77

2.96%

test

42,949.19

Good

921.58

2.10%

Local

Medium

1,007,413.35

34

5

18,943.32

1.88%

train

 

 

 

 

Local

Medium

643,538.40

26

5

14,685.88

2.28%

train

 

 

 

 

Remote

Low

1,827,161.32

16

3

52,683.15

2.88%

train

 

 

 

 

Remote

Low

964,830.02

15

4

33,018.63

3.42%

train

 

 

 

 

Local

Low

632,906.70

22

3

12,121.12

1.92%

train

 

 

 

 

Local

Low

526,027.21

34

3

7,261.24

1.38%

train

 

 

 

 

Remote

Medium

731,439.19

20

3

20,236.48

2.77%

train

 

 

 

 

Local

Low

886,535.29

20

3

18,321.73

2.07%

train

 

 

 

 

Remote

Medium

1,055,346.94

20

4

31,308.63

2.97%

test

31,889.28

Good

-580.66

-1.85%

Local

Low

1,248,941.20

33

3

16,362.39

1.31%

train

 

 

 

 

Local

High

618,518.54

21

3

15,384.42

2.49%

train

 

 

 

 

Local

Low

530,522.61

15

3

13,911.48

2.62%

train

 

 

 

 

Local

Low

1,009,146.22

22

4

21,344.97

2.12%

train

 

 

 

 

Local

High

1,021,674.63

27

3

20,786.66

2.03%

train

 

 

 

 

Remote

Low

1,121,376.46

22

5

30,447.07

2.72%

train

 

 

 

 

Local

Low

638,464.57

31

4

11,334.46

1.78%

train

 

 

 

 

Remote

Medium

1,256,833.06

34

3

24,890.22

1.98%

train

 

 

 

 

Remote

High

546,298.17

30

4

15,357.05

2.81%

train

 

 

 

 

Local

Medium

987,396.42

15

3

27,866.52

2.82%

test

26,778.88

Good

1,087.64

3.90%

Remote

Medium

1,046,307.06

21

5

31,256.35

2.99%

train

 

 

 

 

Local

High

694,023.76

29

4

16,305.57

2.35%

train

 

 

 

 

Local

Medium

1,234,868.04

27

5

26,359.10

2.13%

train

 

 

 

 

Local

Medium

632,587.31

24

3

12,581.46

1.99%

train

 

 

 

 

Remote

High

1,366,884.27

21

4

43,566.72

3.19%

train

 

 

 

 

Remote

Medium

869,516.65

12

3

33,717.92

3.88%

test

34,888.20

Good

-1,170.28

-3.47%

Remote

High

924,774.23

20

4

31,134.07

3.37%

train

 

 

 

 

Remote

High

1,729,408.45

21

3

49,933.24

2.89%

train

 

 

 

 

Local

Low

1,633,982.66

35

4

25,365.64

1.55%

train

 

 

 

 

Local

Medium

814,029.10

15

4

25,415.80

3.12%

train

 

 

 

 

Local

High

994,502.93

21

3

24,736.29

2.49%

test

24,108.59

Good

627.70

2.54%

Remote

Low

573,443.71

23

3

13,471.77

2.35%

train

 

 

 

 

Local

Medium

586,644.29

30

5

12,384.71

2.11%

test

12,332.61

Good

52.10

0.42%

Local

Low

1,194,191.56

20

3

23,485.77

1.97%

train

 

 

 

 

Local

Low

640,851.46

29

4

11,852.07

1.85%

train

 

 

 

 

Local

High

653,026.72

15

3

20,388.95

3.12%

train

 

 

 

 

Remote

Low

1,123,457.64

30

3

21,470.52

1.91%

train

 

 

 

 

Remote

Medium

807,182.36

23

3

20,577.30

2.55%

train

 

 

 

 

Remote

Low

1,367,692.12

17

4

41,862.11

3.06%

train

 

 

 

 

Local

Low

573,685.93

27

4

11,098.34

1.93%

train

 

 

 

 

Local

Medium

567,217.09

20

3

12,856.92

2.27%

train

 

 

 

 

Remote

Medium

1,188,181.44

21

4

34,306.38

2.89%

train

 

 

 

 

Local

Medium

552,218.25

12

3

18,652.71

3.38%

train

 

 

 

 

Local

Medium

575,508.83

19

5

15,851.73

2.75%

train

 

 

 

 

Local

Low

1,022,032.17

16

3

24,358.43

2.38%

test

24,273.38

Good

85.06

0.35%

Remote

High

1,245,112.01

17

3

40,600.42

3.26%

train

 

 

 

 

Local

Medium

764,762.97

35

3

11,872.03

1.55%

test

12,294.49

Good

-422.46

-3.56%

Remote

High

534,546.78

16

4

20,223.69

3.78%

train

 

 

 

 

Local

Medium

934,959.92

19

5

25,752.40

2.75%

train

 

 

 

 

Local

High

1,680,626.26

17

5

53,120.97

3.16%

train

 

 

 

 

Local

Medium

1,072,671.58

24

4

23,479.59

2.19%

train

 

 

 

 

Remote

High

1,556,942.09

17

3

50,768.52

3.26%

test

52,653.72

Good

-1,885.20

-3.71%

Local

Low

1,368,257.92

16

4

36,714.92

2.68%

train

 

 

 

 

Remote

Low

551,086.24

20

3

14,144.55

2.57%

train

 

 

 

 

Local

Medium

860,268.78

25

5

20,072.94

2.33%

train

 

 

 

 

Local

High

584,003.61

16

4

19,174.79

3.28%

train

 

 

 

 

Remote

Low

625,248.12

18

3

17,205.90

2.75%

train

 

 

 

 

Local

Low

1,689,416.73

30

4

28,907.80

1.71%

test

26,945.85

Good

1,961.94

6.79%

Remote

Medium

1,069,349.15

15

5

38,734.20

3.62%

train

 

 

 

 

Remote

Low

627,751.93

15

4

21,483.07

3.42%

test

20,197.79

Good

1,285.28

5.98%

Local

High

1,268,675.38

20

4

35,100.02

2.77%

train

 

 

 

 

Local

High

1,547,340.00

27

5

37,671.04

2.43%

train

 

 

 

 

Local

Medium

1,294,799.07

20

4

31,938.38

2.47%

train

 

 

 

 

Remote

High

1,185,245.00

32

4

31,310.22

2.64%

test

28,602.72

Good

2,707.50

8.65%

Local

Low

524,430.06

33

3

7,394.99

1.41%

train

 

 

 

 

Local

Medium

663,249.69

28

5

14,528.33

2.19%

train

 

 

 

 

Local

Medium

1,653,133.09

15

4

49,961.36

3.02%

train

 

 

 

 

Local

Medium

528,820.61

20

4

13,573.06

2.57%

train

 

 

 

 

Local

Medium

519,719.13

30

3

8,892.97

1.71%

train

 

 

 

 

Remote

Medium

743,037.32

33

5

18,650.99

2.51%

train

 

 

 

 

Remote

Medium

856,258.79

24

3

21,311.33

2.49%

test

21,521.34

Good

-210.01

-0.99%

Local

High

620,702.06

31

4

14,122.64

2.28%

train

 

 

 

 

Remote

Medium

1,284,054.66

24

4

34,526.80

2.69%

test

33,189.13

Good

1,337.68

3.87%

Remote

Low

1,221,724.86

15

5

41,810.14

3.42%

train

 

 

 

 

Local

Low

1,713,664.43

16

4

45,983.33

2.68%

train

 

 

 

 

Remote

Medium

1,277,241.72

36

3

24,598.73

1.93%

test

25,616.68

Good

-1,017.95

-4.14%

Remote

Medium

1,376,535.12

15

3

44,355.02

3.22%

train

 

 

 

 

Local

Medium

1,201,960.23

30

3

19,364.91

1.61%

train

 

 

 

 

Local

Medium

1,048,096.64

16

4

30,220.12

2.88%

train

 

 

 

 

Local

Medium

781,372.88

16

3

20,966.84

2.68%

train

 

 

 

 

Local

Low

889,253.80

16

4

24,750.90

2.78%

test

22,245.27

Good

2,505.62

10.12%

Remote

Low

635,992.33

32

5

14,892.82

2.34%

test

13,943.56

Good

949.26

6.37%

Local

Low

510,489.36

31

4

9,062.56

1.78%

train

 

 

 

 

Local

Medium

880,766.98

15

3

24,857.20

2.82%

train

 

 

 

 

Remote

Medium

843,268.82

20

3

23,330.44

2.77%

test

23,204.29

Good

126.15

0.54%

Local

High

1,296,821.96

23

5

34,356.38

2.65%

train

 

 

 

 

Local

Medium

1,484,991.68

14

5

48,721.87

3.28%

train

 

 

 

 

Local

Medium

1,273,825.30

24

3

24,061.14

1.89%

train

 

 

 

 

Local

Low

1,066,325.50

20

3

20,971.07

1.97%

train

 

 

 

 

Remote

Low

941,441.39

16

5

31,852.10

3.38%

train

 

 

 

 

Local

Low

546,951.49

23

3

10,114.64

1.85%

train

 

 

 

 

Remote

Low

661,069.81

20

4

18,950.67

2.87%

train

 

 

 

 

Local

Medium

568,661.89

20

4

14,595.66

2.57%

train

 

 

 

 

Remote

Low

1,240,392.01

16

3

35,764.64

2.88%

train

 

 

 

 

Remote

Medium

1,474,071.34

34

4

33,614.61

2.28%

train

 

 

 

 

Remote

High

504,448.03

31

3

12,486.44

2.48%

train

 

 

 

 

Remote

Low

869,608.71

23

4

23,038.33

2.65%

train

 

 

 

 

Remote

Medium

1,484,723.62

20

4

44,046.80

2.97%

train

 

 

 

 

Remote

Low

568,098.78

22

3

13,720.45

2.42%

train

 

 

 

 

Remote

High

809,584.87

21

5

27,423.08

3.39%

test

28,005.36

Good

-582.28

-2.12%

Remote

Medium

1,283,825.59

19

5

40,496.81

3.15%

train

 

 

 

 

Remote

Low

568,442.98

34

3

10,688.96

1.88%

test

11,691.38

Good

-1,002.42

-9.38%

Local

Medium

543,071.16

17

5

16,079.17

2.96%

train

 

 

 

 

Remote

Medium

1,206,922.14

20

3

32,184.59

2.67%

train

 

 

 

 

Local

Low

1,565,873.75

23

5

33,654.94

2.15%

train

 

 

 

 

Remote

Low

686,957.91

19

3

18,234.51

2.65%

train

 

 

 

 

Tags

Budget, Cost, Neural Networks,

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Analytical Hierarchy Process, Earned Value and other Project Management Themes – Second Edition

Presenting a compendium of Ricardo Vargas’s work, that brings fourteen articles from 1999 to 2015 that will help you understand better the project management context.

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