Many times, we can all agree that machine learning has been able to step in and readily improve the way that we are going to interact with all of the data we have, and with the internet. For example, when we talk about machine learning, we can see it works with the search engines that we use, spam filters that get rid of unwanted emails based on just snippets of the wording in each one, and even facial recognition software.
What Is a Predictive Analysis?
One topic that we need to explore a bit while we are here, before diving into how deep learning is able to help us out with it, is what predictive analytics is all about. To keep it simple, predictive analytics is going to be the use of techniques from machine learning, data, and statistical algorithms to help identify the likelihood of future outcomes based on data that is more historical. The goal is to go beyond what we know happened in the past to provide the best predictions and guess what will happen in the future.
Though the idea of predictive analytics is something that has been around for a long time, it is a technology that is starting to garner more attention than ever. Many companies, throughout a variety of industries, are turning to predictive analytics to increase their bottom line, and to add a competitive advantage to everyone who uses it. Some of the reasons why this predictive analysis is gaining so much popularity now include:
The data that is available can help with this. The growing volume, as well as the types of data, are a good place to start. And there is more interest from many companies in using data to help produce some great insights into the process.
The computers and systems that are needed to complete this predictive analysis are cheaper and faster than ever before.
The software to finish the predictive analysis is easier to use.
There is a lot of competition out there for companies to work again. Thanks to these tougher conditions in the economy, and with the competition, businesses need to find their own way to differentiate and become better than the competition. Predictive analysis can help them to do this.
With all of the interactive software that is easier than ever to use, predictive analytics has grown so much. It is no longer just the domain of those who study math and statistics. Business experts and even business analysts are able to use this kind of technology as well.
With this in mind, it is time to take a look at how the predictive analysis. Predictive models are going to work by using known results to help train or develop a model that can be used to predict values for different or new data. Modeling that comes with the predictive analysis can provide us with results, often in the form of a prediction that can represent a probability of the target variable. This variable is going to vary based on the results that you are looking to find and could include something like revenue.
Keep in mind here that this is going to be a bit different compared to the descriptive models that can help us understand what happened in the past, or some of the diagnostic models that we can use that help us understand some key relationships and determine why we say a certain situation happens in the past. In fact, there are entire books that will be devoted to the various techniques and methods that are more analytical than others. And there are even complete college curriculums that will dive into this subject as well, but we can take a look at some of the basics that come with this process and how we can use this for our needs as well.
First, there are two types of predictive models that we can take a look at first. These are going to include the classification models and regression models. To start with the classification models that work to predict the membership of a class. For example, you may work on a project to try and figure out whether an employee is likely to leave the company, whether a person is going to respond to solicitation from your business, or whether the person has good or bad credit with them before you loan out some money.
For this kind of model, we are going to stick with binary options, which means the results have to come in at 0 or 1. So, the model results will have these numbers, and 1 tells us that the event that you are targeting is likely to happen. This can be a great way to make sure that we see whether something is likely to happen or not.
Then we have the regression models. These are going to be responsible for predicting a number for us. A good example of this would be predicting how much revenue a customer is going to generate over the next year, or how many months we have before a piece of our equipment will start to fail on a machine so you can replace it.
There are a lot of different techniques that we are able to work with. The three most common types of techniques that fall into this category of predictive modeling will include regression, decision trees, and neural networks. Let’s take a look at some of these to see how they can all work together.
First on the list is a decision tree. These are an example of classification models that we can take a look at. This one is going to partition the data we want to work with and put it into subsets, based on categories of the variables that we use as input. A decision tree is going to look like a tree that has each branch representing one of the choices that we can make. when we set this up properly it is able to help us see how each choice we want to make compares to the alternatives. Each leaf out of this decision tree is going to represent a decision or a classification of the problem.
This model is helpful to work with because it looks at the data presented to it and then tries to find the one variable that is there that can split up the data. We want to make sure that the data is split up into logical groups that are the most different from each other.
The decision tree is going to be popular because it is easy to interpret and understand. They are also going to do well when it is time to handle missing values, and are useful when it comes to the preliminary selection of your data. So, if you are working with a set of data that is missing many values or you would like a quick and easy answer that you can interpret in no time, then a decision tree is a good one to work with.
Then we need to move on to the regression. We are going to take a look at logistic and linear regression. The regression is going to be one of the most popular models to work with. The regression analysis is going to estimate the relationship that is present among the variables. It is also intended for continuous data that can be assumed to follow a normal distribution, it finds any of the big patterns that are found in sets of data, and it is often used to determine some of the specific factors that answer our business questions, such as the price that can influence the movement of an asset.
As we work through the regression analysis, we want to make sure that we can predict a number, which is called the response, or in this case the Y variable. With some linear regressions, we are going to have one independent variable that can be used to help explain, or else predict, the outcome of Y. And then the multiple regression is going to work with two or more independent variables to help us predict what the outcome will be.
Then we can move on to the logistic regression. With this one, we are going to see that it is the unknown variable of a discrete variable that is predicted based on the known value of some of the variables. The response variable is going to be more categorical, which means that it can assume only a limited number of values compared to the others.
And finally, we have the binary logistic regression. This one is going to be a response variable that has only two values that go with it. All of the results that happen will come out as either 0 or 1. If we see 0, this means that the expected result is not going to happen. And if it shows up as a 1, then this means that our expected result is going to happen.
And then we can end with the neural networks as we talked about before. These are going to be a more sophisticated technique that we are able to work with that has the ability to model really complex relationships. These are popular for a lot of reasons, but one of the biggest reasons is that neural networks are so flexible and powerful.
The power that we are able to see with the neural network is going to come with the ability that these have to handle nonlinear relationships in data, which is going to become more and more common as we work to collect some more data. Many times, a data scientist will choose to work with the neural network to help confirm the findings that come with the other techniques that you used, including decision trees and regression.
The neural networks are going to be based on a lot of features, including pattern recognition and some of the processes of artificial intelligence that can model our parameters in a graphical manner. These are going to work well when no mathematical formula is known that relates to both the inputs and the outputs that we are doing; when prediction is going to be more important than working with the explanation, or when there is a ton of training data that we can work with.
Another option that we have to look at is artificial neural networks. These were originally developed by researchers who were trying to mimic what we can find in the brain of a human on a machine. And when they were successful, we got a lot of the modern pieces of technology that we like to use today.
Predictive analysis is going to do a lot of great things for your business. It can ensure that you will be able to handle some of the more complex problems that your business faces, and it will make it easier to know what is likely to happen in the future. Basing your big business decisions on data, and the insights found with predictive analysis can make it easier to beat out the competition and get ahead.
Looking into Deep Learning
Now, going apart from some of the categories that we manually code, a machine is able to use some of the neural networks to help it learn the features of the objects it is working with. Looking back at the example above, we can use the neural network to help the machine learn some of the features that happen with peaches and mangoes, and the machine can do it all on its own.
Remember from before, these networks are going to work in a manner that is similar to what we see with the brain, and they are going to comprise artificial neurons to help us model the data. There is also going to be a type of synaptic response as well with the network that uses different values as the inputs. The values that come with the featured output can then be used as the new input to the other artificial neurons. Another deep learning article TensorFlow Library for Deep Learning
These neural networks are going to be the very core of what we see with machines that can learn on their own. And we can expect, after the right kind of training and testing, that they will segregate peaches from mangoes by learning the traits from the perceived images.
This is a great place for deep learning to come into play because it will help us to define the number of layers that we need to use inside our neural network. To make this easier, this is basically the term for the depth of a given network. For the network to be considered exhaustive, we need to add in as many layers as possible, so we need to make it as deep as we can.
Even when you have taken the time to create a neural network, and the features are all set up, many of them aren’t going to be put into real practice. The main reason that this happens is that there isn’t enough data to make the training work. If you have not been able to collect a lot of training data to use to train the model, then the model will never know how it should behave, and there is no way to ensure accuracy.
The most important pitfall that comes with this one though, is that the machine, or the system, may not be able to generate enough computing power to get the work done. This is again a place where big data can come in and help. This big data is able to help deep learning and networks in a natural manner and can offer some computational power with the help of business intelligence. If you are able to add in enough power behind what you are doing here, and you use big data in the proper manner, you will find that the issues with computational power will not be as big of an issue.