Predictive Modeling in Business Analytics

Predictive modeling is a technique used by businesses and organizations on available results for creating, processing, and validating a model for future use in business forecasting. This tool is an integral part of predictive analytics, a technique in data mining to understand possible future outcomes.

Predictive modeling is widely used across multiple sectors to mitigate risks and possible losses. Companies use predictive modeling extensively for forecasting events, consumer behavior, and risks related to finances, economy, and market.

Why Applied Predictive Modelling Is Important In Business Analytics

Predictive modeling includes the analysis of historical events. Therefore, it plays an integral role in business analytics through which companies are given the ability to forecast events, the behavior of customers, and possible risks.

With the advent of technology, digital products such as mobile phones and computers have become a basic necessity. This has resulted in the overwhelming amounts of real-time data retrieved from social media, browsing histories, cloud computing platforms, etc. This data is available for businesses to use. This vast amount of data falls under the category of big data. Predictive modeling plays a vital part in analyzing Big Data that is further utilized by companies for improving their operations and relationships with the consumer base.

Predictive modeling tools can manage vast proportions of unstructured and complex data that is difficult to analyze manually. Predictive modeling is used instead to analyze data over a short period with the help of computer software programs. These programs are used to process large datasets from historical data to assess and identify data patterns that help in forecasting. Hence, businesses can use predictive models to predict consumer behavior or market trends.

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How Predictive Modelling Works?

Predictive modeling is not fixed. It is revised and validated regularly for updating and making changes to the data. Predictive models primarily work based on the assumptions of previous events and current events. If newly acquired data shows significant changes at present, its impact on the future is also recalculated accordingly. Predictive models are designed to work fast and handle massive datasets to perform calculations in a fraction of time. However, complex predictive models like in computational biology and quantum outputs take longer to process.

Five Kinds Of Predictive Analytics Models

Predictive models need not be created from the very beginning for every application. These tools are used for many critical models and algorithms for the application in numerous use cases. Technological advancements have also led to advancements in analytics, via which the use of these models has expanded exponentially. The five important predictive analytics models are as follows:-:

• Classification model: This is the simplest model, designed to categorize data for direct and simple query responses.
• Clustering model: This model is designed to stack data together via common attributes. It groups things or people with common attributes or behaviors and makes further plans and strategies for each group.
• Forecast model: This is the most popular amongst predictive models. It is designed to work on numerical value and learn from historical data.
• Outliers model: This model analyzes anomalous or outlying data points.
• Time series model: This model is designed to evaluate a series of data points based on time.

Common Predictive Algorithms in Predictive Modelling 

Predictive algorithms use historical data to predict future events that help build mathematical models for capturing important trends. Predictive algorithms depend on either machine learning or deep learning, which are subtypes of artificial intelligence (AI). Some of the most important and commonly used predictive algorithms are:-

• Random Forests: This algorithm has been taken from a cluster of decision trees that aren’t related and can use regression and classification to classify large datasets.
• Generalized Linear Model (GLM) for Two Values: This algorithm reduces the list of variables to look for what fits the best. It is designed to calculate tipping points and alter data capture and other influences, like categorical predictors, for determining the outcome that works best. This algorithm helps overcome drawbacks in various other models, like the regular linear regression.
• Gradient Boosted Model: This algorithm uses combined decision trees. However, these trees are related, unlike Random Forest. It builds trees one at a time, thus helping the next tree to rectify flaws in the previous tree. This algorithm is often used in rankings, like on search engine outputs.
• K-Means: This algorithm is quite popular because it is fast. It is designed to group data points based on their similarities and is commonly used for the clustering model. It can render things quickly.
• Prophet: This algorithm is widely used in forecast models and time-series as it is designed for capacity planning, like inventory, resource allocations, sales quotas, and the like. It is preferred due to its high flexibility and because it can easily accommodate heuristics and an array of good assumptions.

Limitations of Predictive Modelling

Despite being widely used for business analytics, predictive modeling is no stranger to limitations and challenges. Down below, we have listed some of the challenges and their solutions:-

• Errors in labeling data: This can be easily rectified through reinforcement learning or generative adversarial networks (GANs).
• Scarcity of massive data sets required to train machine learning: This can be easily overcome with “one-shot learning”.
• The inability of a machine to explain the purpose behind its actions: Machines cannot function as humans. Some of their computations can be exceptionally complex for humans to find and make sense of. This can be easily overcome with the help of model transparency necessary for human safety, potential fixes, attention techniques, and local-interpretable-model-agnostic explanations (LIME).
• Ability or lack of generalizing learning: Machines cannot carry forward what they have learned and have trouble applying their knowledge to new circumstances because they apply to one particular use case. This is where machine learning comes in. Predictive modeling largely depends on machine learning to be reusable and for application in multiple use cases.
• Bias in algorithms and data: This is the only limitation that still doesn’t have a solution. No representation can alter outcomes resulting in the mistreatment of large groups of people.

Conclusion

Needless to say, predictive analytics tools are widely used by data analysts for reducing time and costs and increasing efficiency. It has dramatically helped organizations forecast business outcomes by considering variables like competitive intelligence, environmental factors, market conditions, and regulation changes.

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