Working with Predictive Data Models: A Comprehensive Guide 101

Amid the recent Covid pandemic, the news was filled with predictions. Headlines like “Study predicts that the cases in Mumbai will peak by the 2nd week of April”, or “The second wave could start in India in March” were common. The question arises about how are these predictions made. The answer is through Predictive Data Models.

In very simple terms, a Predictive Data Model, as the name suggests, uses statistical techniques to forecast or predict the likely outcome of a problem or forecast future events, based on both existing and historical data. Predictive data modeling can be used for predicting if a person is likely to default on a loan, or if a machine part is nearing the end of life and requires replacement. There are several other use cases as well, as we’ll see throughout this article. 

In this article, you will gain information about Predictive Data Models. You will also gain a holistic understanding of Predictive Modeling, types of predictive Data Models, popular algorithms, benefits and limitations of Predictive Data Models. Read along to find out in-depth information about Predictive Data Models.

Table of Contents

• What is Predictive Modeling?
• Types of Predictive Data Models
  • Predictive Data Models: Time Series Analysis
  • Predictive Data Models: Classification/Cluster Modeling
  • Predictive Data Models: Outlier Modeling
• Popular Algorithms for Predictive Data Models
• Benefits of using Predictive Data Models
• Limitations of Predictive Data Models
• Conclusion

What Is Predictive Modeling?

Wikipedia defines predictive modeling as “the use of scientifically proven mathematical statistics to predict event outcomes”. While the word ‘predict’ usually provides future connotations, predictive modeling can be generally used to predict any unknown event, irrespective of whether it has already occurred or yet to occur. Thus, predicting whether a transaction that is already committed is fraudulent or not also falls within predictive modeling. 

Within predictive modeling, we analyze both past and present data, and try to find patterns and trends to indicate the behavior of an unknown. Take the example of spam email classification. Several emails that have been marked as spam in the past are analyzed, their features are identified (like commonly used words, sentence structure, presence of cash figures, etc.), and based on them, a prediction is made on whether a new email is spam or not. This is just one type of a predictive data model. There are others as well, as we will see in the following section.

Types of Predictive Data Models

The different types of Predictive Data Models are as follows:

• Predictive Data Models: Time Series Analysis
• Predictive Data Models: Classification/Cluster Modeling
• Predictive Data Models: Outlier Modeling

1) Time Series Analysis

This predictive data model evaluates trends and patterns in time and uses them to make future predictions. The Covid analysis is an example of Time Series Analysis. The number of cases in the last couple of weeks, rate of growth of cases, the total population of the region under study, and other variables are considered, and a prediction is made on the number of cases to expect in the next couple of weeks.

Weather forecasting is another good example. Another example is predictive maintenance. If a tool’s or machine’s end-of-life can be predicted beforehand based on past time-series data, then it can be replaced in advance and production downtimes can be avoided.

2) Classification/Cluster Modeling

This predictive data model aims to group entities with similar attributes and predict the group in which a new entity would belong, or how an entity in one of the groups would behave over time. When data is labeled, it is known as Classification Modeling. With unlabeled data, it is called Cluster Modeling.

An example can be credit risk profiling of individuals (predicting the probability of loan defaults). Another can be, as discussed above, determining if an email is spam or not. Yet another example can be determining the purchasing tendency of individuals. For instance, if a model can predict the cluster of farmers that are likely to purchase tractors in the next few months, it can help in targeted marketing.

3) Outlier Modeling

In this predictive data model, you make predictions depending on the presence of outliers in your data. While it can also be considered as a subset of classification modeling (unbalanced classification modeling to be more precise), it deserves special importance when it comes to predictive modeling.

Take the example of predicting fraud. If a credit card transaction is recorded at a place where it was never recorded earlier, and that place is miles away from the usual locations where that particular customer transacts, it hints at a fraud. Similarly, if an email login is detected in another country altogether, then it hints at compromised credentials. Therefore, multi-factor authentication is triggered by Gmail or other service providers when it suspects that something is amiss.

Popular Algorithms for Predictive Data Models

For Supervised Classification, Random Forest and Gradient Boost algorithms of Predictive Data Modelsare are quite popular. These algorithms are ensemble methods of classification, relying on several decision trees. The difference between the two is that in Random Forest, the trees are unrelated, whereas, in Gradient Boost, the trees are related, each improving on the previous one. Note that apart from supervised classification, these algorithms can also be used for supervised regression.

For Unsupervised Clustering, the K-Means algorithm is popular for creating predictive data models. It is a simple algorithm that divides the data into K clusters depending on the distance between the data points. How the distance is defined depends on the problem, and choosing the right value of K requires some analysis.

For working with time-series data, Prophet (developed by Facebook) is a popular forecasting algorithm. It is often used for capacity planning. Built to be robust enough to handle missing data and outliers, it also allows users to use their domain knowledge to improve the forecasts by allowing tweaking and tuning.

These are just some of the algorithms used. There is a wide array of them for creating predictive data moels, and each has its own advantages and use cases. You can find a more detailed discussion on such algorithms here. 

Benefits of using Predictive Data Models

Some of the benefits of Predictive Data Models are as follows:

• Cost Reduction/savings: Several examples discussed above hint at cost reduction/ savings. Reducing production downtimes by replacing tools before their end of life can save a significant amount of money. Detecting and aborting fraudulent transactions can again save a lot of money and hassles.
• Better Planning: A government may better prepare for weather calamities like flooding based on the predictions provided by the meteorological department. Similarly, the government can prepare better for pandemics like Covid-19 based on the predicted case load. Businesses can plan production and inventory depending on demand forecasts. 
• Better Marketing: When predictive modeling provides demand forecasts from specific clusters, marketing campaigns can be tailored for those specific clusters. These clusters can be geographic, demographic, income-slab based, and so on.

Limitations of Predictive Data Models

Some of the challenges faced while creating Predictive Data Models are as follows:

• Data Shortage: Several predictive data models require training on large datasets for making accurate predictions, otherwise they may get overfitted. Massive datasets may not always be available or may take time to develop. Therefore, predictive data models need to be refined and updated constantly, and their performance should be evaluated in tandem.
• Data Labeling Errors: Predictive Data Models that rely on labeled data can provide erroneous results if the labels on the training data are incorrect. After all, a model is generally only as good as the data it is trained on. A strong system of checks needs to be in place to minimize such errors.
• Data Bias: Historically, racial minorities have been underrepresented in various jobs. So, if based on past data, a data model predicts the chances of a person being suitable for a role, then racial minorities will continue to be discriminated against. It is important to select the right features when training a model so that unrelated features like the race of a person do not influence the predictions. This is just an example. The point is that if the attributes contributing to historical biases are not removed, the model’s predictions will continue to reflect those biases. 
• Lack of Transparency: The Predictive Data Models (especially the ones built using Neural Nets) are essentially black boxes, and it is very difficult to determine what happens under the hood, and what the outputs of intermediate steps represent. This becomes a bottleneck if one wants to understand the working of the model and finetune it. The only way to control the model is to retrain it repeatedly by changing the cost function to better represent the expected output.

Conclusion

Predictive data modeling is still a young field and a lot needs to be done to improve it. That being said, the benefits of this modeling are very apparent now, and the widespread adoption of predictive data modeling is no longer a question of if, but when.

In this article, you have learned about Predictive Data Models. This article also provided information on Predictive Modeling, types of predictive Data Models, popular algorithms, benefits and limitations of Predictive Data Models.

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