Presumably if you are reading this, you are here to learn about the importance of an R predictive model which is highly demanded in most data-related works. Many-a-times problems arise in data analysis works which requires understanding of predictive modeling in R.
But before we get to the solution of the problem, we must first understand why this arises? To understand the root of the problem, some idea about Big Data is required.
In today’s industry the amount of data being generated everyday is of huge amounts, and someone needs to analyze it all. Who does this mammoth task? Well, while there are humans behind making such important decisions the major bulk of the task is done by machines. To be precise, there is specialized data analysis software available to take care of most of the calculation component.
Currently predictive analysis is the trending topic in the corporate environment which is selling like hot cake. After all, the bottom line is what is of value to all businesses and everyone wants to jump into the bandwagon of predictive data analytics.
Then what is the problem? Predictive data analytics might have a huge demand in the market, but the main challenge with it is that it requires a huge amount of mathematical calculations based on the enormous amount of data available. This is not only a memory intensive process but is also a cumbersome task for any human regardless of his/her skill and intellect. The problem deepens when one is calculating this data on computational grounds, when calculating complex formulas becomes even more difficult.
Mostly, two types of problems are seen when doing data analysis:
Now that we know what sort of problems prevails in this area, we can get to the solutions which can help to solve these problems. What do we recommend as a solution? Look to Hadoop ecosystems along with parallel calculation power. Presently, this is the trending open source solution for problems associated with big data.
The concept behind Hadoop is based on parallel computation in a bunch with Hadoop distributed file systems. So in order to run the ML algorithm over a Hadoop group and it also requires the knowledge of map-reducing programming. This could be especially difficult if programming is not one of your strong suites. So, in such circumstances, Hadoop fails to offer us with feasible solutions.
Such situations call for alternative options like R and MySQL to handle data-related tasks with ease.
But before we get to the solution of the problem, we must first understand why this arises? To understand the root of the problem, some idea about Big Data is required.
In today’s industry the amount of data being generated everyday is of huge amounts, and someone needs to analyze it all. Who does this mammoth task? Well, while there are humans behind making such important decisions the major bulk of the task is done by machines. To be precise, there is specialized data analysis software available to take care of most of the calculation component.
Currently predictive analysis is the trending topic in the corporate environment which is selling like hot cake. After all, the bottom line is what is of value to all businesses and everyone wants to jump into the bandwagon of predictive data analytics.
Then what is the problem? Predictive data analytics might have a huge demand in the market, but the main challenge with it is that it requires a huge amount of mathematical calculations based on the enormous amount of data available. This is not only a memory intensive process but is also a cumbersome task for any human regardless of his/her skill and intellect. The problem deepens when one is calculating this data on computational grounds, when calculating complex formulas becomes even more difficult.
Mostly, two types of problems are seen when doing data analysis:
- How can the predictive analysis model be optimized for big data when there is a stark limitation in resources?
- How to process large amount of data with systemic memory limitations?
Now that we know what sort of problems prevails in this area, we can get to the solutions which can help to solve these problems. What do we recommend as a solution? Look to Hadoop ecosystems along with parallel calculation power. Presently, this is the trending open source solution for problems associated with big data.
The concept behind Hadoop is based on parallel computation in a bunch with Hadoop distributed file systems. So in order to run the ML algorithm over a Hadoop group and it also requires the knowledge of map-reducing programming. This could be especially difficult if programming is not one of your strong suites. So, in such circumstances, Hadoop fails to offer us with feasible solutions.
Such situations call for alternative options like R and MySQL to handle data-related tasks with ease.
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