BIT 5534 – Applied Business Intelligence and analytics. Lars Gustavson, Tapan Puntikura, Kevin Marinak. Problem Description. In the business of federal contracting, companies are very dependent on the budget planning and spending trends across federal . ID: 730476
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Presentations text content in Predicting Government Spending on Professional Services
Slide1
Predicting Government Spending on Professional Services
BIT 5534 – Applied Business Intelligence and analytics
Lars Gustavson, Tapan Puntikura, Kevin Marinak
Slide2
Problem Description
In the business of federal contracting, companies are very dependent on the budget planning and spending trends across federal
agencies
This
is particularly true for consulting firms that provide professional program management services as agency spending on these services tends to fluctuate more veritably than other
products
As
such, it is crucial for service providers to be able to anticipate federal spending
trends
Doing
so enables firms to efficiently allocate resources for marketing and business development. It also enables them to appropriately invest in staff and capability development for the services that the government demands.
Without
proper planning and foresight, firms’ success and growth are left to
chance
Primary question: How
much are agencies expected to spend on professional services in the future?
Slide3
Data Source
P
rimary
data source for this project
was the
open source USA Spending data feed (www.usaspending.gov/data). This site provides historical data on federal contracts, loans, grants, and direct payments.The dataset had 225 attributes separated into nine categories. Data was collected for all contract awards since government fiscal year 2013 (Oct. 1 2012) to date. This resulted in a data set with 54,852 records.
Slide4
Data Exploration & Preparation
Data Exploration – Studied the
225 potential variables and
selected the
response variable “
dollarsobligated”Data Preparation - The dataset was prepared by removing unnecessary (redundant and irrelevant) attributes. Basic linear fit models, scatterplots, and correlation were analyzed to assess significance of independent variables in relation to the target variables. The number of attributes was reduced down to 65 from the original 225.
Data Transformation – Data transformation efforts required
removal of additional redundant variables, replacing missing values, and data coding. Some of the numerical attributes were more useful after they were discretized by applying grouping ranges and coding the variables.
Slide5
Model development
Linear Model – JMP’s model fit tool was used to develop an appropriate linear regression model for predicting the target variable. This included trial and error of attribute selection using stepwise regression, as well as optimizing the number of input variables based on model complexity and accuracy.
R-Square = 0.22
Neural Network - A Neural Network model was developed as an attempt to improve on the overall fit achieved by the Linear Model. Various versions of the model were tested for accuracy with the best fit ultimately being tested against a validation set of data that was not included in the training set. The validation of the model against new inputs tested the model’s accuracy against known target values.
R-Square = 0.39
Slide6
Findings & Results
Linear Model – The mean
of value for a typical contract was $ 449,441.
However, the low R-Square value and high root mean squared error (RMSE) indicate that the model is not very effective at predicting the value of a typical contract
.
Neural Network - An improvement over the Linear Model, but it’s overall fit was still on the low side. The large amount of variance within the dataset made the modelling technique somewhat unreliable. Key Input Variables - Six attributes were identified with the most significance in determining the amount of dollars obligated
.
Response Variable
Input Variable
P Value
Dollarsobligated
Vendorname
0
Dollarsobligated
Fundingrequestingofficeid
2.4623E-31
Dollarsobligated
Currentcompletiondate
1.5975E-27
Dollarsobligated
Performancebasedservicecontract
1.8508E-06
Dollarsobligated
Typeofcontractpricing
0.0007
Dollarsobligated
Statecode
0.00395
Slide7
Conclusion
The extracted USAspending.gov dataset turned out to be less useful than the group originally
expected. Many
of the input variables were duplicative in nature and/or statistically insignificant.
All
of the models that were developed had low R2 values, indicating a poor overall fit and inferior predictive ability. The group concluded that the dataset did not contain attributes that were sufficient for developing a model with the purpose of determining how money would be allocated for future contract awards.
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DownloadNote - The PPT/PDF document "Predicting Government Spending on Profes..." is the property of its rightful owner. Permission is granted to download and print the materials on this web site for personal, non-commercial use only, and to display it on your personal computer provided you do not modify the materials and that you retain all copyright notices contained in the materials. By downloading content from our website, you accept the terms of this agreement.
Presentations text content in Predicting Government Spending on Professional Services
Predicting Government Spending on Professional Services
BIT 5534 – Applied Business Intelligence and analytics
Lars Gustavson, Tapan Puntikura, Kevin Marinak
Slide2Problem Description
In the business of federal contracting, companies are very dependent on the budget planning and spending trends across federal
agencies
This
is particularly true for consulting firms that provide professional program management services as agency spending on these services tends to fluctuate more veritably than other
products
As
such, it is crucial for service providers to be able to anticipate federal spending
trends
Doing
so enables firms to efficiently allocate resources for marketing and business development. It also enables them to appropriately invest in staff and capability development for the services that the government demands.
Without
proper planning and foresight, firms’ success and growth are left to
chance
Primary question: How
much are agencies expected to spend on professional services in the future?
Slide3Data Source
P
rimary
data source for this project
was the
open source USA Spending data feed (www.usaspending.gov/data). This site provides historical data on federal contracts, loans, grants, and direct payments.The dataset had 225 attributes separated into nine categories. Data was collected for all contract awards since government fiscal year 2013 (Oct. 1 2012) to date. This resulted in a data set with 54,852 records.
Slide4Data Exploration & Preparation
Data Exploration – Studied the
225 potential variables and
selected the
response variable “
dollarsobligated”Data Preparation - The dataset was prepared by removing unnecessary (redundant and irrelevant) attributes. Basic linear fit models, scatterplots, and correlation were analyzed to assess significance of independent variables in relation to the target variables. The number of attributes was reduced down to 65 from the original 225.
Data Transformation – Data transformation efforts required
removal of additional redundant variables, replacing missing values, and data coding. Some of the numerical attributes were more useful after they were discretized by applying grouping ranges and coding the variables.
Slide5Model development
Linear Model – JMP’s model fit tool was used to develop an appropriate linear regression model for predicting the target variable. This included trial and error of attribute selection using stepwise regression, as well as optimizing the number of input variables based on model complexity and accuracy.
R-Square = 0.22
Neural Network - A Neural Network model was developed as an attempt to improve on the overall fit achieved by the Linear Model. Various versions of the model were tested for accuracy with the best fit ultimately being tested against a validation set of data that was not included in the training set. The validation of the model against new inputs tested the model’s accuracy against known target values.
R-Square = 0.39
Slide6Findings & Results
Linear Model – The mean
of value for a typical contract was $ 449,441.
However, the low R-Square value and high root mean squared error (RMSE) indicate that the model is not very effective at predicting the value of a typical contract
.
Neural Network - An improvement over the Linear Model, but it’s overall fit was still on the low side. The large amount of variance within the dataset made the modelling technique somewhat unreliable. Key Input Variables - Six attributes were identified with the most significance in determining the amount of dollars obligated
.
Response Variable
Input Variable
P Value
Dollarsobligated
Vendorname
0
Dollarsobligated
Fundingrequestingofficeid
2.4623E-31
Dollarsobligated
Currentcompletiondate
1.5975E-27
Dollarsobligated
Performancebasedservicecontract
1.8508E-06
Dollarsobligated
Typeofcontractpricing
0.0007
Dollarsobligated
Statecode
0.00395
Slide7Conclusion
The extracted USAspending.gov dataset turned out to be less useful than the group originally
expected. Many
of the input variables were duplicative in nature and/or statistically insignificant.
All
of the models that were developed had low R2 values, indicating a poor overall fit and inferior predictive ability. The group concluded that the dataset did not contain attributes that were sufficient for developing a model with the purpose of determining how money would be allocated for future contract awards.
Slide8Slide9