Predicting Government Spending on Professional Services PowerPoint Presentation

Predicting Government Spending on Professional Services PowerPoint Presentation

2018-11-18 9K 9 0 0


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

Embed code:

Download this presentation

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


Problem Description

In the business of federal contracting, companies are very dependent on the budget planning and spending trends across federal



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



such, it is crucial for service providers to be able to anticipate federal spending



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.


proper planning and foresight, firms’ success and growth are left to


Primary question: How

much are agencies expected to spend on professional services in the future?


Data Source



data source for this project

was the

open source USA Spending data feed ( 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.


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.


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


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





















The extracted 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.


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.



About DocSlides
DocSlides allows users to easily upload and share presentations, PDF documents, and images.Share your documents with the world , watch,share and upload any time you want. How can you benefit from using DocSlides? DocSlides consists documents from individuals and organizations on topics ranging from technology and business to travel, health, and education. Find and search for what interests you, and learn from people and more. You can also download DocSlides to read or reference later.