There is a good number of people who consider themselves to be “risk-takers.” They thrive on the thrill of the unknown and get enjoyment in not knowing what comes next. There are times and places in which unpredictability is exciting.
With rampant volatility, it makes sense that many would prefer not to take any chances with their money. Even if fluctuations are not in relation to finances, they still would rather have a sense of predictability. Unfortunately, life is nothing if not unpredictable.
To combat this there is an abundance of methods for prediction, but a more notable one is ‘predictive modeling.’ It has the closest correlation to ‘predictive analytics’ and is therefore among the more prominent techniques of its kind.
What does predictive modeling mean?
The main objective behind ‘predictive modeling’ is to predict outcomes. It is the process of utilizing certain results to construct, process, and validate a model. This model is something that can be used as a means to forecast any future outcomes. They can help predict anything from sports results to TV ratings to even technological advances and corporate earnings.
Predictive modeling is frequently compared and contrasted with more causal modeling/analysis. Concerning the former, one may find it satisfactory to use indicators of - or surrogates for - the outcome of interest. When it comes to the latter, one will aim to determine concrete cause-and-effect relationships. This divergence leads to a rise in prospering literature in the fields of research methods and statistics. Furthermore, it boosts the prominence of the statistical statement, “Correlation does not imply causation.”
Predictive modeling is a common tool in ‘predictive analytics’, which is a data mining technique. It generally attempts to answer the question, "What might possibly happen in the future?" Moreover, predictive analytics describes the use of statistics and modeling in order to determine future performance. They draw these results from both current and historical data.
Predictive analytics look at patterns existing within data to figure out if those patterns may potentially emerge again. This would allow businesses and investors to alter where exactly they use their resources. Thus, it will permit them to take advantage of any future events.
Understanding its purpose and importance
In recent years, we have seen a noticeable - and inevitable - migration to digital products. This effectively creates an array of data that is easily accessible for businesses of all sorts. Companies typically utilize big data as a way to essentially revamp the dynamics of the customer-to-business relationship. The prominent sources of real-time data include social media platforms, internet browsing history, cell phone data, and cloud computing systems.
By closely examining historical events, it’s possible that a business could predict and anticipate what may happen in the future. With this information, they are able to plan accordingly. However, this data is usually without structure and is too complex for us humans to analyze. What’s more, it’s too intricate to examine in a short period of time. Because of the complexity that stems from large amounts of data, companies are now employing predictive analytics tools. With them, they can forecast an event’s likely outcome that may occur in the near future.
The various types of predictive models
Predictive models exist in two camps: parametric and non-parametric. Despite these terms appearing to be needlessly intricate technical lingo, there is a notable difference. That being parametric models develop more assumptions. On top of that, they are assumptions that are more specific and are about the characteristics of the population creating the model.
Some of the distinct types of predictive models are:
- Ordinary Least Squares
- Generalized Linear Models (GLM)
- Random Forests
- Logistic Regression
- Neural Networks (we will discuss this one later)
- Decision Trees
- Multivariate Adaptive Regression Splines (MARS)
Each one of these possesses a specific use and answers a particular question. Alternatively, they utilize a certain type of dataset. Regardless of the methodological and mathematical differences among the models, the common goal of each is the same. They all aim to predict either future or unknown outcomes, drawing primarily from data about outcomes from the past.
Four types of analytics
Out of the four types of data analytics, predictive modeling is the closest in relation to the predictive analytics category. The four types of data analytics are the following.
1 - Descriptive Analytics
The directive of ‘descriptive analytics’ is to describe the data. It is the interpretation of historical data to properly understand any changes that occur in a business. Descriptive analytics illustrates the use of a wide variety of historic data in order to draw comparisons.
2 - Diagnostic Analytics
‘Diagnostic analytics’ is basically the “why” that exists behind descriptive analytics. It is a specific type of advanced analytics that examines data or content to figure out the “why.” Moreover, it is generally characterized by an array of techniques. These include drill-down, data discovery, data mining, and correlations.
3 - Predictive Analytics
‘Predictive analytics’ utilize various techniques as a way to predict what might happen next. It cannot necessarily predict the future, but it can look at existing data and determine an outcome from that. Data analysts are able to build predictive models as soon as they have enough data to construct outcomes.
4 - Prescriptive Analytics
‘Prescriptive Analytics’ takes that final step and proposes a recommendation deriving from a predicted outcome. Once a predictive model is in place, it is able to then recommend actions drawing from historical data. Not only that, but it also draws from external data sources and machine learning algorithms.
Misconceptions about analytics
Arguably the most common misconception of predictive analytics is that it and ‘machine learning’ are one and the same. At the center, predictive analytics consists of several statistical techniques, which includes machine learning, predictive modeling, and data mining. Moreover, it uses historical and current statistics to estimate - or predict - future outcomes. Predictive analytics effectively assist in the understanding of potential occurrences by way of analyzing the events of the past.
How it works
To explain how predictive modeling works, we have to detail how exactly predictive analytics works, as they are close in relation. This way, we can organically transition into the construction of predictive models.
Predictive analytics collects, compiles, and processes historical data in huge amounts. It employs the use of powerful computers to evaluate past events. Following this, it will then provide a complete assessment of what will occur in the future.
Predictive analytics utilizes predictors or familiar features to create predictive models that will be tools in acquiring an output. A predictive model has the ability to learn how diverse points of data link up with each other. Two of the more commonly used predictive modeling techniques are, if you remember, regression and neural networks.
A neural network is a series of algorithms. One that aspires to identify underlying relationships within a set of data through a certain process. This process, generally speaking, mirrors the way in which the human brain operates. Neural networks have the capability of adapting to constantly changing input. So, the network produces the best possible outcome without ever needing to reconstruct the output criteria.