According to Gartner, every company told that they have Artificial Intelligence capabilities integrated into their business, or they are planning to hold it soon. And AI is directly linked to other ideas such as predictive Modeling, protective data, forecasting, and training models for machine learning.

Although these all sound like a buzzword and they come along the definition of what the future in technology may look like. Here, we will talk, which is a part of predictive analysis, which includes the statistics process about predictive Modelisses, decision optimization, data warehousing, and more.

What is predictive Modeling, and why is it so important? How can we move from theory and adapt it in real-life scenarios?

Let us understand through this blog –

What is Predictive Modeling?

Predictive Modeling is a tool that is used in predictive analysis. It refers to the process of using mathematical and computational methods to develop predictive models. The predictive models examine current and historical datasets for the patterns and calculate the chances of an outcome.

The predictive modeling process starts with collecting data, formulating a statistical model, making predictions, and as new data becomes available the model is revised.

The model is selected based on testing using the detection theory to guess the probability of an outcome. The models can use one or more classifiers in determining the likelihood of the set of data belonging to other sets. The models available on the modeling portfolio of predictive analytics software allows us to derive information about the data and to develop new predictive models.

What are the types of Predictive Modeling?

The predictive model falls under two sections, and those are parametric and non-parametric. Although these terms seem like technical jargon, the significant difference is that parametric models make specific assumptions about the population characteristics used in creating the model.

Some of the different types of Predictive Modeling are –

  • Ordinary Least Squares
  • Generalized Linear Model
  • Logistic Regression
  • Random Forests
  • Decision Trees
  • Neural Networks

Each of these types has its use and answers a specific question or uses a particular type of dataset. Despite methodological and mathematical differences among the model types, the overall goal of each is similar, i.e., to predict future or unknown outcomes based on data about past results.

What does Predictive Modeling do?

According to a 2014 TDWI report, it was found that organizations want to use predictive analysis to predict trends, understand customers, improve business performance, drive strategic decision making, and predict behavior.

Here are some common uses of predictive analysis –

  • Marketing – Predictive analytics can help you understand your customers. Mostly, organizations use predictive analytics to determine customer responses or purchases.

    Predictive models help businesses attract, retain, and grow the most profitable customers and maximize their marketing spending.

  • Fraud Detection and Security – Predictive analytics can also help in stopping losses which occur due to fraudulent activity, By combining multiple detection methods – business rules, anomaly detection, predictive analytics, link, and analytics, you can get accuracy and better predictive performance.

    With the advent of technology, cybersecurity is a growing concern. High-performance behavioral analytics examines all actions on a network in real-time so that abnormalities can be spotted.

  • Operations – Predictive analytics plays a crucial role in operations for many organizations, allowing them to function smoothly and efficiently. The companies use predictive models to forecast inventory and manage factory resources. Others use them for special needs; airlines use predictive analytics to decide how many tickets to sell at each price for a flight.

    The hotels also use predictive analytics to decide the number of guests they can expect on any given night so that they can adjust prices.

    Predictive analytics are also used in human resources, asset maintenance, and life sciences.

What are the benefits of Predictive Modeling?

Predictive Modeling reduces the cost required for companies to forecast business outcomes, environmental factors, competitive intelligence, and market conditions. Here are a few ways in which Predictive Modeling can provide value –

  • Reduce Cost – The processes in business combine technology and people to turn input into an output. The process requires investment from the business, the staff to execute it, managers to monitor it, and executives to resource it.

    If the same process can be handled by a predictive model at a desired level of accuracy, it allows businesses to repurpose the staff and resources.

  • Increase in Response Time – There is no doubt that a security analyst may be an expert at interpreting incident logs, but a predictive model can faster the response time.

  • Maximize Scale and Leverage – A business may have an incredible profit process, but it can be too costly to execute at a scale.

For example, a retailer knew that a customer buying X items and Y would also tend to buy Z. If the retailer cannot get that information to the customer early enough, or if the recommendation relies on judgment, then the value of that process degrades severely.

By comparison, recommendations retain their value when they can be given to customers at the right time.

Limitations of Predictive Modeling

Despite its high-value benefits, predictive Modeling does have its limitations. Unless conditions are met, predictive Modeling may not give their full value. If these conditions are not fulfilled properly, predictive models may not provide any value over legacy methods.

Hence, it is essential to consider the limitations so that the maximum amount of value can be withdrawn from predictive Modeling. Here are some of the challenges –

  • Data Labeling – The data must be labeled and categorized appropriately in machine learning. The process can be imprecise, full of errors, and a generally colossal undertaking. However, it is an essential component of constructing a model, but if classification is not completed, any predictive model will suffer from poor performance.

  • Generalizability of Learning – Generalizability refers to the ability of the model to generalize from one use to another. Unlike humans, models struggle with Generalizability, which is also known as external validity. When a model is constructed for an A case, it should not be used in B case.

    Although there are methods to minimize this limitation, it remains a concern for predictive learning.

  • Bias in Data and Algorithms – Although it is more of an ethical or philosophical issue than a technical one, some researchers argue that researchers and professionals creating Predictive models must be careful in choosing which data to use and which to exclude.

The historical biases can be ingrained at the lowest level of data. Still, care must be taken while attempting to address these biases or their consequences can harm the future of predictive models.

Examples of Predictive Modeling

Let us understand how predictive Modeling is used in different sectors –

  • Retail – Retail is the largest sector who use predictive analytics; retail is always looking to improve its sales position and make better relations with customers. One of the best examples would be Amazon’s recommendations. Whenever you make a purchase, it puts you in a list of similar items that other buyers purchase.

  • Health – The best example will be Google Flu Trends (GFT). The model helps monitor millions of users’ health tracking behaviors online and compare it to a historic baseline level of influenza.

  • Internet of Things – It has been estimated that 1% of the data generated today is being analyzed, and that will only increase as more IoT devices come online, such as smart cars.

For example, Cisco and Rockwell Automation helped a Japanese automation equipment maker reduce downtime of its manufacturing robots go near zero by applying predictive analytics to operational data.

Predictive Modeling Tools –

  • Apache Hadoop – It is recognized in the technology industry for its distinctive yellow elephant logo. It is also referred to as Hadoop, a collection of open-source software utilities designed to help network computers.

    Hadoop functions as a storage and processing utility. The processing utility is a MapReduce model. It can also refer to several additional software packages in the Apache Hadoop ecosystem, such as Apache Phoenix, Apache Pig, Apache HBase, etc.

  • Python – Python is high-level programming that is made for general programming. While R was built explicitly for statistics, Python exceeds R when it comes to data mining, imaging, and data flow capabilities.

    It is more versatile than R and is also used with other programs. Python is easier to learn than R and is used for automation in tasks.

Conclusion

intelligence. As computing power increases, data collection rises exponentially, new technologies replace the old ones, and companies will bear the brunt of load when it comes to creating models.

The future of Predictive Modeling would be –

  • Technological Advancements – Due to recent advancements in computing power and data quantities, predictive technologies have impacted the regular newsworthy breakthrough.

    Predictive algorithms are becoming sophisticated in many fields, notably computer vision, complex games, and natural language.

  • Changes in Work – With more intelligent computers, predictive modeling professionals will change to adapt the available predictive technology. The people who work in predictive Modeling will not become obsolete, but their roles will shift in a new way.

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