Predictive modeling

identify and prevent illegal activity. Using predictive policing technology, law enforcement agencies forecast the people and places most likely to contribute to future crimes based on information gathered from local and national sources. Depending on the desired forecast, the agencies analyze the information using various forensic processes. Around the country, law enforcement agencies report substantial performance improvements resulting from predictive police technology implementation.…

Beginning in June of 2011, the Centers for Medicare and Medicaid Services (CMS) started streaming all fee-for-service (FFS) claims through predictive modeling protocol. The technology designed to profile medical providers, billing patterns and networks enables CMS to flag and investigate potential fraudulent claims activity. Utilizing advanced technology and traditional investigative strategies together to identify, or prevent, improper billing allowed CMS to recover a staggering $19.2 billion…

FCCI Insurance Company, originally a monoline workers’ compensation carrier, was established in Sarasota, Florida in 1959. FCCI now insures policyholders in multiple commercial lines in 20 different states and the District of Colombia. The company takes pride in its ability to mature agency relationships, provide incredible customer service, and maintain a local presence in each represented territory with field employees. Company executives emphasize the importance of employee investments and…

Introduction Business Intelligence (BI), a vital tool for building competitive advantage, aids organizations in learning and the creation of knowledge. Examples of predicative modeling employed by BI include regression and neural networks. As with any process, BI has negatives as well as positives. Some positives of BI include enhanced efficiency, a more focused customer value proposition, and the creation of value from an organization’s data, which otherwise would be a cost center. Examples of…

(trh+). At 25°C the strains likely kept the same order in their lag time (LT) value. However at 30°C, we got the (trh+) strain at first, then comes the mixture, (tlh+), (tdh/trh+) got the same LT and finally the (tdh+) strain. To develop a growth predictive model, various strains isolated from seafood must be compared for their growth kinetics under various…

Predictive modeling tools, which use data mining and probability, can fall short. Analytics managers have learned the hard way about the pitfalls of deploying and using predictive modeling without following best practices, such as ensuring data quality, measuring the right information in the right way and performing reality checks. For example…

variation in care, and total cost of care Current or near real-time reporting supports care management, identifying gaps in care and alerting physicians and patients for care planning from a population view With risk stratification and predictive modeling, you can classify patients by health risk to deliver cost-effective interventions and predict and prevent…

Predictive Analytics Questions Answered How can predictive models impact business decisions? Otherwise, one is forced to look at millions of rows every day and make decision. Human brain has non linear thinking to do this but, memory is limited. so we will end up doing the same thing over and over again. Predictive algorithms free human brain from its limitations and performs algorithms on computers do what human brains are not capable of- looking millions of records for each of at least three…

In “Predictive Policing and Reasonable Suspicion,” Andrew Guthrie Ferguson dissects two different kinds of predictive models used for anticipating when a crime will happen - the “near repeat theory” and “risk terrain modeling.” Near repeat theory asserts that if a crime occurs in one area, it is statistically more likely that the same crime will occur in the same…

```{r, echo=FALSE, warning=FALSE, message=FALSE} m9<-lm(nonViolent^2 ~ loglargeHH + I(notHSG^2) + logmedRent + I(tPar^2),data=cr4) plot(m9,1) ``` Looking at the $Y$ $vs$ $\hat{Y}$ plot, we see that the predictive nonviolent crime rates from our model does not fit the data very well. Those data points with low nonviolent crime rates cause the slope of the $Y=X$ line to decrease. They are good explanations for low adjusted-$R^2$ of $0.1741$. ```{r, echo=FALSE…