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Predictive Modeling: Types, Benefits, and Algorithms

Analyzing data to make predictions of future outcomes is known as predictive modeling. It’s one of the most successful ways for a company to see its future and make informed decisions. It is commonly used since, although it isn’t foolproof, it has high accuracy rates.

How Does Predictive Modeling Work?

Data mining and machine learning are both used in predictive modeling to predict and forecast likely future outcomes using historical and existing data. Using historical and current data, it builds a model based on what it learns and forecasts likely outcomes based on those scenarios.

From TV ratings and what customers will buy next to credit risks and corporate profits, predictive modeling can be used on just about anything.

In order to incorporate changes in underlying data into the predictive model, it must be validated or revised regularly. That is, it isn’t a one-shot prediction. By making assumptions about the past and the present, predictive models are able to make predictions.

Upon receiving new data, it is important to recalculate the impacts on the likely future outcomes as well. Software companies could, for instance, model historical sales data using marketing expenditures across multiple regions in order to build a picture of future revenue based on the effect of marketing.

Calculations are often completed in real-time for most predictive models. Because of this, banks and retailers can, for example, forecast the risk attached to an online mortgage or credit card application and decide whether to accept the request or decline it almost immediately.

As technological advances have made people more capable of computing, including computational power, some predictive models are more complex than credit card applications, which require more time to compute than a credit card application.


Top 5 Types of Predictive Models

For all applications, we don’t have to develop prescriptive models from scratch. A variety of statistical models and algorithms can be used with predictive analytics tools for a range of scenarios. Time has perfected the techniques of predictive modeling. These models can be used to perform more complex calculations as data, more muscular computing, artificial intelligence, and machine learning are added.

Five of the best predictive analytics models are:

Classification model:  With this model, data is categorized for a direct and simple response to queries. A possible use case would be the question, “Are these transactions fraudulent?”

Model using attributes of data: This model uses attributes to nest data. When things or people that exhibit similar characteristics are grouped together, the computer can plan more effective strategies on a larger scale. Using past performance as a method of assessing credit risk is an example of this.

Forecast model: This model utilizes historical data from the past to predict any numerical value. A computer system uses historical data to answer questions such as how much lettuce a restaurant should order next week or how many customer services call a customer support agent should be able to handle on a daily or weekly basis.

Outlier’s model:  The model analyzes data points that are abnormal or out of the ordinary. As an example, if a bank is analyzing an outlier model to identify fraud, it asks whether a given transaction differs from what the customer’s normal purchasing habits are or whether a particular expense belongs to a particular category.

An account breach would not signal a $1,000 charge for washing machines and dryers in the cardholder’s favorite big box store, but a $1000 charge for designer clothing, in a store where the customer does not charge anything else could reflect a breach.

Model of time series: Data points are evaluated according to the time in this model. Using the number of stroke patients admitted to the hospital in the last four months, for example, the hospital can evaluate the number of patients it might plan to admit in the coming weeks or months. A simple average is, therefore, less useful than comparing one metric over time.

Typical Predictive Algorithms

Machine learning or deep learning are two methods used for predictive algorithms. AI and machine learning are both subsets of artificial intelligence (AI). Structured data is used in machine learning (ML), such as spreadsheets and machine data. In deep learning (DL),

unstructured data is processed, including videos, audio files, text, social media posts, and photographs – basically, the kind of data humans communicate with and which cannot be reduced to numbers or metrics.

Among the most common predictive algorithms are:

Real-time random forest: is derived from several decision trees, none of which are related, and can accurately classify a wide array of data in real-time.

Generalized Linear Model (GLM):

Using this technique, the variables are narrowed down to determine the “best fit.” It can determine tipping points and influence factors such as data capture and predictors such as categorical variables, thus overcoming the disadvantages of other approaches, such as a regular linear regression.

With this algorithm, although each tree is a different type of tree, they are connected, unlike with Random Forest. A single tree is built at a time, thus allowing the previous tree to be corrected by the next tree. On search engine results pages, the metric is often used in rankings.

The K-Means algorithm:

Known for its speed and popularity, K-Means groups data points based on similarities, making it a popular clustering algorithm. Individuals within a huge group can quickly receive personalized retail offers in the form of tailored offers, such as lined red wool coats loved by a million customers.



Algorithms such as this are used in time-series and forecast models for capacity planning, such as inventory allocation, sales quotas, and resource allocation. A wide range of useful assumptions can be accommodated, including heuristics.

Data analytics and predictive modeling

Predictive modeling is also called predictive analytics. For commercial applications of predictive modeling, the term “predictive analytics” is preferred over the term “predictive modeling” in academic settings.

It is imperative to have unfettered access to sufficient volumes of accurate, clean, and relevant data in order to use predictive analytics effectively. The most complex predictive models are always neural networks; that is, the models by which computers are trained to predict outcomes.

Decision trees and k-means clustering are two examples of such models. Using machine learning, patterns can be identified within exceptionally large datasets and correlations can be found.

Predictive modeling benefits

As a result, predictive analytics help businesses forecast business outcomes in a more precise, time-saving manner. To render more accurate views at relatively low cost, environmental factors, competitive intelligence, regulatory changes, and market conditions can be considered as variables in the mathematical calculation.

Businesses can benefit from forecasting services such as demand forecasting, headcount planning, churn analysis, external factors, competitive analysis, fleet and IT equipment maintenance, and financial risks analysis.

Predictive modeling challenges

Because not everything this technology uncovers is beneficial, it’s important to focus predictive analytics on producing useful business insights. Mining information may only be valuable for satisfying your curiosity but may have little or no business implications. It’s impossible for most businesses to afford to get sidetracked.

Furthermore, predictive modeling benefits from using more data only to some extent. The calculation can be skewered if too much data is provided, leading to erroneous or meaningless results. A decrease in temperature, for example, causes coat sales to increase. There is a limit to this, however. It does not matter how cold it is outside, whether it’s -20 degrees Fahrenheit or -5 degrees below freezing; people are not likely to buy more coats when it’s -20 degrees outside.

When temperatures get cold enough, coat purchases are no longer significantly affected by frosty temperatures. Maintaining security and privacy will also be difficult due to the massive volumes of data involved in predictive modeling. The limitations of machine learning pose further challenges.

Limitations of Predictive Models

McKinsey describes three common limitations and their “best fixes”:

Inaccurate data labeling: These can be overcome with reinforcement learning or generative adversarial networks (GANs).

Shortage of massive data sets needed to train machine learning: A common solution is to train the machine with a few demonstrations, rather than using large amounts of data.

The inability of the machine to explain its actions:  Humans do not “think” or “learn” like machines. They can also compute so complexly that human beings cannot even grasp their logic, as it is so hard to understand. As a result, humans, and machines find explanations of their work difficult.

In spite of this, model transparency has many benefits, including human safety. Possible fixes include local agnostic model-interpretable explanations (LIME) and attention-centered techniques.

Machines are not likely to be able to generalize their learning: Unlike humans, they do not carry what they have learned forward. They find it difficult to apply what they’ve learned to a new situation. Its learning is only relevant for one particular use case.

Because of this, we need not worry about the rise of artificial intelligence any time soon. Transfer learning might be a way to make predictive modeling involving machine learning reusable – that is, useful in more than one use case.

Data and algorithms that are biased: Large groups of humans can be disadvantaged through non-representation. Additionally, it is difficult to find and remove baked-in biases later. Consequently, bias tends to self-perpetuate. We don’t have a clear fix for this, and it’s a moving target.

A Future for Predictive Modeling

There is much more to come when it comes to predictive modeling, as well as predictive analytics and machine learning, which is both emerging technologies. Technology, methods, tools, and techniques are improving, which will have a positive impact on businesses and society.

Nevertheless, these aren’t technologies that businesses can adopt later when the technology becomes mature and all the kinks have been ironed out. It’s simply impossible for a late adopter to compete with the early adopters and remain competitive in the short term.

You can grow business benefits later as technology advances by understanding and deploying technology now.

Predictive Modeling in Platforms

In most organizations, reaping the benefits of predictive analytics is easiest when the ERP system is made up of the technology and contains pre-trained machine learning. Statistical modeling features such as planning, forecasting, and budgeting can be used to create multiple scenarios that respond to changing market conditions rapidly.

The supply planner, or supply capacity function, may also be able to predict and mitigate potential delivery delays, purchase orders, and other impacts. The dashboard can also include alternative suppliers, allowing companies to pivot to meet their needs. These advanced technologies can be useful in a number of areas, including financial modeling, planning, and budgeting, without overwhelming your team.

Applications of predictive modeling

Although predictive modeling is usually associated with meteorology, it is applicable to many business fields as well. Predictive modeling is most often used in online advertising and marketing. Using historical data from web surfers, modelers run it through algorithms to predict which products and links users are more likely to click on.

Based on predictive modeling, Bayesian spam filters predict the likelihood that a particular message is spam. Fraud detection employs predictive modeling to locate data outliers that indicate fraud.

Customers’ messages are targeted by predictive modeling based on their likelihood of purchase in customer relationship management (CRM). Other applications include change management, disaster recovery (DR), engineering, physical and digital security management, and city planning.

Methods of modeling

Via sampling can allow models to be developed faster and deployed more quickly by analyzing representative portions of the available information. The data scientist must then select the most appropriate model once the sample data is gathered. Predictive models based on linear regression are among the simplest. A linear model plots two correlated variables on an x-axis and a y-axis, where one is independent and the other is dependent.

Data points are fitted with a best-fitting line based on the model. Using the data, scientists can make predictions about the dependent variable’s future occurrence. You should ask questions before you deploy a predictive modeling tool. It is important to clarify the following: Who will be running the software, how many tools will be interacting with your predictive analytics, and what the budget will be.


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