What is Predictive Modelling?

Predictive Modelling 

 

Predictive modelling is the process of analyzing current outcomes and known information to predict future outcomes. In predictive analytics, predictive modelling algorithms are used to procure possible future outcomes. 

 

With data science at its peak, predictive modelling has emerged as a helpful data mining technique that has enabled organizations and corporations to extract predictive outcomes based on whatever data is known currently. 

 

In the process of predictive modelling, data is recorded, a statistical model or an algorithm is applied, and future outcomes are predicted. This way, various models and algorithms are validated as successful predictive analytics is achieved. 

 

With the help of artificial intelligence technology and machine learning algorithms, predictive modelling has become a part of data mining over time. Not only has it helped to achieve an insight into future possibilities, but it has also influenced decision-making processes in a more accurate and enhanced way. Thanks to predictive modelling, the role of technology in business and that of AI in business has increased significantly.  

 

For any organization that desires to get a predicted outcome for its current step forward, predictive modelling is exactly the machine learning tool that would work the best. That said, let us find out more about predictive modelling. 

 

Even when this concept has been in practice for more than half a century, it has just recently gained the significance that it deserved from the very first day on. While the early years went into investigating the efficacy of this data science technique, recent times have led industries and organizations to implement this successfully. 

 

Furthermore, this has led to the collaboration of historical data and future assumptions that have together made decision-making easier and have enhanced the way institutions function.  

 

 

Predictive Analytics Techniques

 

While predictive modelling is defined as a predictive analytics tool to extract future outcomes with the help of past data, it can also be considered as a mathematical procedure used to calculate future possibilities. 

 

Also known as predictive analytics, predictive modelling is of different types of predictive models. Let us know more about the same. 

 

1. Classification Model

 

Among all the predictive modelling techniques in machine learning, the classification model is one of the widely used techniques. In classification predictive modelling, an input is classified into a specific category where it is treated as a label and its class is predicted. 

 

In predictive modelling, a general data point is inserted in the software that classifies the input and predicts the class of the output. 

 

(Also check: Machine Learning methods)

 

 

2. Forecast Model

 

One of the most popular and accurate predictive models, the forecast model is used to forecast/predict metric values based on past data. With the help of historical data, the forecast model computes data points consisting of numerical values and allots them values based on historical data. 

 

Much like what data analytics does, predictive analytics algorithms also take into account the essence of past data that helps the predictive modelling tools to forecast a future outcome. 

 

 

3. Outliers Model

 

The outliers model revolves around detecting the ‘outliers’ in a dataset or anomalies. Simply put, the outliers model is one of the types of predictive models that helps to detect anomalies in a data set and even predict related information of a particular data set. 

 

Especially in the field of finance, the outliers model helps predictive modelling to detect whether a transaction is a fraud or safe. 

 

“For example, when identifying fraudulent transactions, the model can assess not only amount, but also location, time, purchase history and the nature of a purchase.”Algorithms in Predictive Analytics 

 

 

Predictive Modelling Tools 

 

1. Oracle DataScience

 

Using AI, Oracle DataScience is a leading predictive analytics tool that incorporates modern machine learning algorithms to conduct predictive modelling on a variety of data sets. 

 

Some of the leading features of this tool are that it provides a high-quality platform to build modern algorithms, gives better results, and delivers trusted outcomes. With built-in machine learning algorithms, Oracle DataScience offers other services like enhanced business analytics, comprehensive decision-making, and increased accuracy of outcomes. 

 

Apart from predictive analytics in business, this platform also provides other artificial intelligence technology tools like storage, networking, cloud computing, etc.  

 

2. H2O

 

A leading platform for data analytics, H2O is a predictive modelling platform that supports a variety of sectors like healthcare, finance, marketing, and telecommunications. 

 

By implementing predictive modelling algorithms for its world-renowned clients, H2O aims to democratize artificial intelligence so that it can be used efficiently by people from around the world. 

 

With the increasing use of machine learning tools and algorithms in almost every sector, the platform has built-in capabilities to formulate models and customize tools as per the requirements of the need.  

 

3. Q Research

 

An advanced data analysis tool, Q Research is a popular platform for predictive modelling as it enables one to obtain future outcomes or possibilities with the help of the patterns established by past data records. 

 

Apart from predictive modelling, the platform also offers big data analytics services that enhance the way organizations work and operate on a day-to-day basis. 

 

Simply put, Q Research is a comprehensive guide to market research for anyone who is willing to implement AI-driven techniques for business analytics.

 

(Also read - Techniques of Business Analytics)   

 

 

Applications of Predictive Modelling 

 

As we have already learned about predictive modelling in the previous segments, we will now learn about the various fields that involve this data analysis technique for better decision-making processes and operating activities. Let us now discover the applications of predictive modelling in the real world. 

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Applications of predictive modelling

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1. Sales 

 

Sales are one of the most essential aspects of a business that keeps it running. Based on the kind of sales a company has achieved in the past, predictive analysis techniques and tools can very well establish the future for the company in terms of sales and profits. 

 

Furthermore, it can also detect the anomalies wherein the sales department is lagging which leads to an enhanced performance of the company in the determined areas or demographic circles.  

 

 

2. Marketing

 

Another application of predictive analytics is marketing. As marketing is the act of promoting a particular service or a commodity to a group of target customers, it involves predicting the reaction of customers and forecasting the customer requirements based on the data collected by customer feedback. 

 

Herein, the historical data is accumulated and analyzed accordingly to give way to future outcomes and predict the kind of upcoming services a customer shall desire. 

 

(Also read: Effective Marketing techniques)

 

 

3. Social Media

 

Social media is the hub of unstructured, heterogeneous, and vast data. 

 

A platform where millions of people interact and use the internet on a day-to-day basis, social media requires predictive modelling for forecasting customer feedback and determining the kind of response a product or a response on the platform will get. 

 

That said, the importance of social media and it is one of the widely used applications of predictive modelling that helps various platforms to detect customer activity and compute future outcomes accordingly. 

 

(Recommended reading: Social media marketing)

 

 

4. Risk Assessment

 

A major application of predictive modelling is risk assessment. Risk assessment is often practiced in financial institutions and fraud detection cases where one might want to assess the kind of risk that s/he is subject to. 

 

Based on the data analysis of the past records, predictive analytics tools can help an individual, company, or organization to conduct a risk assessment and determine the depth of risk or profit that the future beholds. 

 

What’s more, risk assessment along with predictive modelling can also indicate the kind of risks an action can bring to the company. Yet, predictive modelling is one of the most efficient methods of risk assessment in an organization. 

 

(Learn more about risk management)

 

 

5. Quality Enhancement

 

The last of all applications of predictive analytics is quality enhancement. It is somehow related to the use of predictive modelling in the field of marketing. 

 

Based on what kind of responses or feedback a product or a service gets from the customer over time. Quality enhancement involves considering past feedback, improvizations, and recommendations that may lead to the enhancement of the quality of an institution or a company. 

 

In addition, quality enhancement also involves fitting improvised versions of the same service/product to assess the kind of response it will get. Perhaps predictive modelling can be very helpful in the field of quality enhancement. 

 

In the End 

 

In the end, predictive modelling is a useful data analytics technique that has helped the field of artificial intelligence to advance and enhance the way the world operates. Not only does it help to forecast future outcomes, but it also determines the way future decisions will impact current situations by using various machine learning tools and techniques. 

 

With the help of various types of predictive models like the classification model, the clustering model, or the outliers model, this branch of data analytics is quite useful for industrial purposes. To sum up, predictive modelling, also termed predictive analytics, is here to stay for a long time and will only improve the way artificial intelligence impacts people’s lives.