CaseStudy4 1KhateMarienLeobo
CaseStudy4-2KhateMarienManaloLeobo
1. Many predictive analytic models are based on neural network technologies. What is the role of neural networks in predictive analytics? How can neural networks help predict the likelihood of future events. In answering these questions, specifically reference Blue Cross Blue Shield of Tennessee.
Traditionally analysts in retail, manufacturing and many other industries use a variety of statistical methods to solve a range of problems in forecasting, data classification and pattern recognition. Some of these methods include regression analysis, logistic regression, survival and reliability analysis and Auto-Regressive Integrated Moving Average (ARIMA) modeling. However, because each of these methods uses different software algorithms with different data assumptions, forecasters must learn to use an assortment of tools to solve problems and produce best solutions (answers). Neural networks can replace all of these methods and produce forecasts as accurate as or better than those available from other statistical methods. Advantages of neural networks are improved accuracy over traditional statistical methods, a unified approach to a wide variety of predictive analytics problems and they requires fewer statistical assumptions and can manage complex predictive analytics tasks in a more automated way, which saves time for analysts and programmers. With these it could help to for see the patterns of failures in heart, kidney or diabetics and find the solutions.
2. What is the Richmond police began to add demographic data to its predictive analytics system to further attempt to determine the type of person (by demographic) who would in all likelihood commit a crime. Is predicting the type of person who would commit a crime by demographic data (ethnicity, gender, income level and so on) good or bad?
It can be possible for both good and bad. As in good side it will narrow down to the result with possible heads that might cause the crime.
1. Many predictive analytic models are based on neural network technologies. What is the role of neural networks in predictive analytics? How can neural networks help predict the likelihood of future events. In answering these questions, specifically reference Blue Cross Blue Shield of Tennessee.
Traditionally analysts in retail, manufacturing and many other industries use a variety of statistical methods to solve a range of problems in forecasting, data classification and pattern recognition. Some of these methods include regression analysis, logistic regression, survival and reliability analysis and Auto-Regressive Integrated Moving Average (ARIMA) modeling. However, because each of these methods uses different software algorithms with different data assumptions, forecasters must learn to use an assortment of tools to solve problems and produce best solutions (answers). Neural networks can replace all of these methods and produce forecasts as accurate as or better than those available from other statistical methods. Advantages of neural networks are improved accuracy over traditional statistical methods, a unified approach to a wide variety of predictive analytics problems and they requires fewer statistical assumptions and can manage complex predictive analytics tasks in a more automated way, which saves time for analysts and programmers. With these it could help to for see the patterns of failures in heart, kidney or diabetics and find the solutions.
2. What is the Richmond police began to add demographic data to its predictive analytics system to further attempt to determine the type of person (by demographic) who would in all likelihood commit a crime. Is predicting the type of person who would commit a crime by demographic data (ethnicity, gender, income level and so on) good or bad?
It can be possible for both good and bad. As in good side it will narrow down to the result with possible heads that might cause the crime.