Daines Analytics is the developer of the CODA (Contract Opportunities Decision Analytics) suite of data solutions. We designed CODA to help procurement/contracting officers in large government organizations find small/medium businesses who can offer goods/services that are an excellent match to the needs of the contracting organizations. By leveraging predictive analytics, automated infrastructure, and process design, CODA seeks to maximize the synergies while minimizing the risks of the contracting opportunities between two or more organizations.
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Daines Analytics
Predictive Analytics | Automated Infrastructure | Process Design
Analytics Solutions for Building Tomorrow’s Digital Capabilities
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David Lowe
Chief Technology Officer, Daines AnalyticsAnalytics Journal
SUMMARY: The purpose of this project is to practice web scraping by extracting specific pieces of information from a website. The web scraping R code leverages the rvest package. INTRODUCTION: The O’Reilly Artificial Intelligence (AI) Conference covers the full range of topics in leveraging the AI technologies for developing software applications and creating innovative solutions.… Read More
Continue ReadingBinary Classification Deep Learning Model for Credit Card Default Using Keras
Template Credit: Adapted from a template made available by Dr. Jason Brownlee of Machine Learning Mastery. SUMMARY: The purpose of this project is to construct a predictive model using various machine learning algorithms and to document the end-to-end steps using a template. The Credit Card Default dataset is a binary classification situation where we are… Read More
Continue ReadingTime Series Model for Ozone Concentration at Arosa Using Python
Template Credit: Adapted from a template made available by Dr. Jason Brownlee of Machine Learning Mastery. SUMMARY: The purpose of this project is to construct a time series prediction model and document the end-to-end steps using a template. The Ozone Concentration at Arosa dataset is a time series situation where we are trying to forecast… Read More
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