Predictive Modeling applies statistics to forecast outcomes based on prior data. Usually, most often what one wishes to predict is going to be in the near future, but sometimes predictive modeling can be used to any kind of an uncertain event, no matter how long ago it happened. In this article we will explain how Predictive Modeling works and give some examples in which it is very useful. The main idea behind Predictive Modeling is that the past is predictive of the future.
There are a lot of predictive modeling initiatives which are implemented by governments and large corporations. There are a lot of reasons why these initiatives are adopted: avoiding future errors, preventing potential disasters, improving stock market performance, improving infrastructure etc. The biggest challenge for companies adopting predictive modeling is that it can be difficult to get the same information for a large number of data sources. One way of overcoming this problem is to implement algorithms, that when fed with enough information from various sources, can create a comprehensive predictive model.
Most companies have access to the necessary data to implement predictive modeling. For small or medium sized organizations it is really hard to get such data, which is necessary to implement predictive analytics, but this can be achieved, and even if you don’t have this much data, using these predictive modeling techniques can help you make judgements about your future sales and/or revenue growth.
The challenges of implementing Predictive Modeling are: measuring and controlling the deviation of the distribution of results, ensuring the quality of the metrics used to build the models, maintaining the consistency of the model’s output, checking the robustness of the predictor and of the metrics used to build the model. In fact any challenge to the validity of the metrics used to construct the predictive analytics poses a challenge to the validity of the whole system, since it would imply that you might have made wrong assumptions or mistakes in the construction of your models. While the accuracy of the metrics used to construct a predictive model would be a positive feature, its inaccuracy could lead to false conclusions and an inability to build a sound predictive model. This is why it is very important to have a trained partner and that the data you use to implement predictive analytics is well collected, reliable and has been thoroughly vetted.
However the most interesting piece of functionality that one may extract from predictive modeling is the ability to extract predictive analytics from unstructured data points. Such points may come from online surveys, IP browsing, health records, customer interviews, web browsing history, and so on and so forth. The data points thus extracted can then be used to train a neural network, in order to build a predictive model that can, say, predict a customer’s next shopping spree if he or she visits a particular website, as indicated by his past shopping behavior. This is essentially a very sophisticated form of artificial intelligence, as it takes the hard work out of trying to classify and isolate the different attributes of a data point and instead focuses on generalizing them, and then using the best combination.
Of course the power of Predictive Modeling comes from the way in which it is able to fine tune the models that it applies to. This can be done through the use of mathematical algorithms. These algorithms are typically very well written and so can be designed in such a way as to take advantage of the strengths and weaknesses of the data that they’re feeding. However, there can exist many possible situations where the application of such algorithms can cause the results to deviate from the desired outcomes that they’re after.
Luckily these days most leading software development companies have seen the potential in predictive modeling and are implementing tools that allow for very finely tuned algorithms. In fact many of the most cutting edge analytical software programs now contain both forward and backward predictions. What this means is that while the algorithms may not specifically take into account the precise situations that the data sets are going to be tested in, they are designed in such a way as to ensure that the final prediction of the result it gives is accurate. So instead of having to spend weeks or months analyzing large amounts of data, the end results can come from a single algorithm, which is much more reliable.
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