What is Predictive Data Mining?

What is Data Mining?

Data mining is the process of analyzing data to uncover hidden knowledge; this is also known as knowledge discovery. The data is analyzed to find patterns and other valuable information that can benefit our business. Data mining is performed by analyzing large amounts of usually raw data and applying different techniques to extract patterns and information that can help businesses make decisions, mitigate risks, optimize their processes, better allocate their funds, and many more.

Regression Analysis

Regression analysis is one of the methods we use in predictive data mining. With the help of statistics, it established the relationship between several variables. The two main types are logistic and simple/multiple linear regression. Logistic regression is used when we try to predict whether an example will fall into one of the two classes.

Logistic regression examples:

• Spam Detection: Predicting if the email is spam,
• Medicine: Predicting if a given mass of tissue is benign or malignant,
• Marketing: Predicting if a user will buy a product,
• Banking: Predicting if a customer will pay off the loan.

Linear regression predicts some numerical value based on one or, in the case of multiple linear regression, several other values.

Linear regression examples:

• SAT -> GPA: Predicting GPA scored based on SAT scores,
• IQ -> performance: Predicting performance based on IQ scores,
• Rainfall -> Soil erosion: Predicting erosion based on rainfall.

Multiple linear regression is a data mining method that uses several explanatory variables to predict the outcome of a response variable. For example, we may not only want to consider the house’s size when predicting the house’s price but also the location and the last time that the house was renovated.

Decision Trees

A decision tree is a type of supervised (meaning it uses labeled data to train models) machine learning that is suitable for prediction and classification tasks. Data is continuously split according to a certain attribute. We need to find this splitting attribute to apply a decision tree algorithm. This is the first step of the algorithm. The splitting attribute needs to be such that it is a good predictor and can separate different examples. A decision tree is represented by a tree graph with nodes representing the different attributes for each example. The final result can also be nicely visually represented in a hierarchical structure where each tree root is the splitting attribute or the example attribute, which is a good predictor.

Rule induction

Rule induction is a data mining method where we extract formal rules based on observations. The simplest forms of those rules are written in the if-then form. For example, if the apple is red, then it is ripe. These rules can be concatenated and followed step by step to reach the conclusion. 

Neural networks

Artificial neural networks are a group of algorithms that can recognize patterns and perform tasks such as classification and prediction. The algorithms are based on mimicking the way the human brain processes information. Neural networks are well suited for working with numeric attributes. They can express complex clusters in the attribute space, but their downside is that the found models are typically hard to interpret.

Clustering

Cluster analysis or clustering is grouping a set of objects into groups or clusters so that those objects within one group are more similar to each other than to the others. This can be useful for market segmentation, understanding our target audience better so we can offer specific products to specific people depending on their segment. It has many further uses, such as in medical imaging, crime analysis, and biology. K-means is an iterative, unsupervised algorithm. It will iterate until each data point belongs to only one group. It is unsupervised because we do not know what the correct answer is in advance. We do not know how many clusters the data is supposed to have or where each point is supposed to belong. Due to its simplicity, it is one of the most popular algorithms for clustering.

Outlier detection

Outlier detection is used to find some examples that stand out or to detect some knowledge that hasn’t been discovered before. Some examples may stand out because the data was collected improperly because of human error. In this case, it may not be interesting to us other than to remove it, so it doesn’t affect the further analysis. But it can also lead to unexpected discoveries of knowledge that can be useful for our business.

Benefits of Predictive Data Mining

Improve decision-making process

Business stakeholders and managers have to make numerous decisions throughout the year. The decision-making process is impacted by the constantly changing environment, the new trends in the market, changes in client preferences, and many other external factors. Using computer power to process vast amounts of data can help us navigate the competitive environment and help improve decision-making.

Risk reduction

Risk management is a process that includes identifying potential risks, quantifying those risks to understand the level of impact they can have on the business, and developing a strategy to mitigate them. Each organization, regardless of its size, needs to analyze and prepare for potential risks. Predictive analytics can be used to help factor in unexpected risks and to make risk identification simpler. The data predictive analytics uses can help us manage the risks better and develop a better risk mitigation strategy.

Marketing campaigns optimization

Marketing analytics is a field that is constantly evolving together with the technologies that it has at its disposal. As customers today have more choices than ever, especially because the store’s proximity and location do not limit them, marketing has become more important than ever. Businesses today compete on a global scale, and the only way to stay relevant is to use the power of data and analytics to market your products. The data that can be analyzed is vast, from a simple grouping of customers to create targeted ads to analyzing user-level interactions and tracking parts of the process that can be improved to increase/attract the customer’s attention.

Implementation/optimization of systems

We can use data mining on real data coming from production facilities to understand the performance and look for possible pain points in our production. This data can help us when it comes to optimizing the systems that are currently in place. With data mining, we can identify the weak points of the systems together with potential risks and unexpected failure, as well as gaps in our production processes that could be filled with some changes in the process. It can also help us when it comes to the implementation of new systems. We can understand our needs, problems, and daily patterns. Using this knowledge, we can customize the system we want to implement to suit our needs in the best way possible.

Summary

In today’s competitive environment, every business needs to unleash the power of data mining. If your business has data, you need to start using it. If your business doesn’t have data, you need to start collecting the data as soon as possible. Data mining holds the power to take your business to the next level. Predictive data mining can be challenging, but your business is guaranteed to benefit from it.