Predictive Modeling
Predictive Modeling

 What is Predictive Modeling?

Predictive modeling is a mathematical procedure that analyzes patterns in a set of input data to anticipate future events/outcomes. It is an essential part of predictive analytics, a sort of data analytics that makes use of both recent and old data to predict activity, behavior, and trends. Predictive modeling applications include determining the quality of a sales lead, the likelihood of spam, or the likelihood that a user would click a link or make a purchase. To troubleshoot and enhance performance, it is important to understand the mechanics of predictive modeling. Although the term “predictive modeling” suggests a focus on future projections, it can also predict results (Example, a transaction is fraudulent). Additionally, predictive modeling can help with what-if analysis or forecasting future requirements.

Types of Predictive Modeling:

Predictive models can be categorized in a variety of ways, and for the best outcomes, it is often possible to mix different model types.

  • Classification Model:

    It classifies data for straightforward and direct query response, making it the simplest model. For example, is this a fraudulent transaction? is a use case that comes to mind.

  • Clustering Model:

    This approach groups data based on shared qualities. It functions by classifying objects or people based on common traits or behaviors, then developing more extensive plans for each group’s strategy. As an example, consider the process of assessing a loan applicant’s credit risk based on prior behavior of others in comparable or the same circumstances.

  • Forecast Model:

    Based on learning from past data, this approach is particularly well-liked and applied to anything having a numerical value. For example, the algorithm uses previous data to determine how much lettuce a restaurant should purchase the next week or how many calls a customer service worker should be able to manage daily or weekly.

  • Outliers Model:

    By examining unusual or outlying data items, this model operates. For instance, a bank may use an outlier model to spot fraud. By determining if a transaction is out of the ordinary for the customer. Or whether a cost falls under a certain category that is typical or not. For example, A $1,000 credit card charge for a washer and dryer at the cardholder’s favorite large box retailer would not raise any red flags. But a $1,000 purchase of designer apparel somewhere the consumer has never charged anything else may be a clue of a compromised account.

  • Time Series Model:

    This model assesses a time-based series of data items. For example, it is possible to forecast how many patients the hospital will admit during the coming week, month, or year by looking at the number of stroke patients admitted to the hospital in the previous four months. As a result, a single statistic monitored and compared  across time has greater significance than a average.

Common Predictive Algorithm

One of two methods—deep learning or machine learning—is used by predictive algorithms. Both fall under the umbrella of artificial intelligence (AI). Structured data, like spreadsheets or machine data, is a component of machine learning (ML). Deep learning (DL) is concerned with unstructured data, such as voice, video, text, social media postings, and images—basically, anything that humans use to communicate that is not a number or metric read.

  • Random Forest:

This technique can categorize enormous volumes of data using both classification and regression . It is derived from a mixture of decision trees, none of which are connected.

  • Generalized Linear Model (GLM) for two values:

The “best fit” is determined by this procedure, which reduces the number of variables. To get the “best fit” result, it may figure out tipping points and alter data collection and other effects, including categorical predictors, to get around shortcomings in other models, like a conventional linear regression.

  • Gradient Boosted Model:

The decision trees used in this technique are mixed as well, but unlike Random Forest, the trees are related. It expands one tree at a time, allowing d defects in the previous tree are fixed in the following tree.

  • K-Means:

K-Means, a well-liked and quick algorithm, clusters data points based on similarities, making it a common choice for the clustering model. When there are a million or more clients who all share a similar preference for lined red wool coats, it can quickly deliver things like individualized retail offers to each individual inside the large group.

  • Prophet:

This technique is employed in time-series or forecast models for capacity planning, including resource allocation, inventory needs, and sales quotas. It is quite adaptable and may readily take into account a variety of helpful assumptions and heuristics.

Benefits of Predictive Modeling

Predictive analytics, in short, save time, money, and effort when predicting business outcomes. The mathematical computation can take into account variables. This includes market conditions, competitive intelligence, regulatory changes, and environmental issues to produce more comprehensive views at comparatively low prices. Demand forecasting, manpower planning, churn analysis, external factors, competitive analysis, fleet and IT hardware maintenance, and financial risks are some specialized forecasting techniques that are advantageous to firms.

Challenges of Predictive Modeling

Since not all of the data this technology uncovers is helpful, it’s critical to keep predictive analytics focused on generating useful business insights. Some information that has been mined is just useful for fulfilling one’s curiosity and has little to no business ramifications. Additionally, using more data for predictive modeling has its benefits, but only to a certain extent. An excessive amount of data can bias the calculation and produce a meaningless or incorrect result. For instance, as the weather gets colder, more coats are sold.  But just up to a point. When the temperature is -20 degrees Fahrenheit outdoors, people do not purchase more coats than when it is -5 degrees below freezing. After a certain point, cold is cold enough to prompt coat purchases. And further freezing temperatures no longer significantly alter that pattern.


  • Error data labeling:

generative adversarial networks or reinforcement learning can help to overcome problems (GANs).

  • Shortage of massive data sets needed to train machine learning:

One-shot learning, in which a machine learns from a few examples rather than a large body of data, is a potential solution.

  • Human’s inability to explain what and why it did what it did:

In contrast to humans, machines cannot “think” or “learn”.  The reasoning in their calculations can also be extremely difficult for humans to decipher, let alone follow. All of this makes it challenging for both machines and people to describe how they work. Human safety is the primary reason for the need for model openness, but there are other factors as well. Attention methods and local-interpretable-model-agnostic explanations (LIME) are promising possible solutions.

  • Generalizability of learning, rather lack thereof:

Machines struggle to apply what they have learnt, in contrast to humans. In other words, they struggle to adapt what they’ve learnt to a brand-new situation. Everything it has discovered is solely applicable to one use case. Transfer learning could be a solution to make machine learning-based predictive modeling reusable, or applicable in several use cases.

  • Bias in data and algorithm:

Non-representation can skew results and result in the treatment of sizable human populations unfairly. Baked-in prejudices are also challenging to uncover and eliminate later. In other words, prejudices frequently reinforce themselves. There is no certain solution at this time because it is a moving target.

The Future of Predictive Modelling:

The advantages for enterprises and society will increase as approaches, methods, tools, and technology advance.
However, after the technology has matured and the problems ironed out, businesses won’t be able to afford to employ these technologies. Simply put, a late adopter cannot overcome the near-term benefits and continue to compete.

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Predictive Modeling

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