Building Predictive Models with Machine Learning: 7 steps with FAQs

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Frequently asked questions about how to build a predictive model

What data do you need for a predictive model?

A data model for machine learning predictions is only as good as your data. For starters, your modeling dataset must accurately portray the reality of how your business operates. Second, model building data needs to know the outcome of each case, or row, in the historical data. When these conditions are in place, you can develop models to learn which combinations of preconditions lead to each outcome. 

What’s the first step in creating a predictive AI model?  

Using data science for predictions doesn’t start with data. Our team’s approach is to start by interviewing the business experts and stakeholders about how they operate, the problems they need to solve, and what they think is causing them. Our data scientists also generate hypotheses by combining our expertise in machine learning and customer experience. We’ve written a Data Science 101 series on using data science for predictions, take a look.  

How do you choose a predictive model? 

To help ease companies into a deeper understanding of what AI can provide, our data science team tries to avoid black box solutions for the first deployment of a model. They are highly complex and don’t allow you to see how the model determined predictions. Instead, we prefer to start with algorithms that provide explanations for their predictions. That way we can better validate the reasoning to ensure the model derived answers in sensible ways. 

Once the model is consistently performing in line with identified metrics, and the data pipeline is stable and understood, you have the option of exploring powerful black box solutions such as neural networks. 

How can you reduce churn with predictive modeling? 

What does the workflow look like? For us, the process starts with our data scientists working with client subject matter experts to understand the business problem, and then carefully deciding which data to use. Once we select (and obtain!) the data, it’s cleaned, preprocessed, and transformed so it is ready for machine learning. From there, our team explores different training techniques to build and fine-tune the models. After testing, the models that deliver the “best” predictions are selected. But— there are many ways to evaluate the value of a predictive model. Finally, we deploy models into production to start generating real-world insights. 

How do you plan a predictive AI project? 

We define the business problem we are trying to solve with AI. The modeling target is what we want to predict. We plan for a solution that is consistent with the way managers think about the industry and the value the company provides. This is how we arrive at an approach that delivers AI benefits for business. 

How do you select the right AI model?  

To create a final AI model, we evaluate up to a dozen algorithms. We may combine algorithms into ensembles, and apply different algorithms to subsets of the data. By the end of the AI development process, we may create hundreds of candidate models. 

What are unstructured data sources? 

Think about what you can learn about customer behavior from mining call center notes, survey responses, in-store feedback, and social media. You can apply unstructured data analysis to any source of text that your organization captures. Even maintenance logs, corrective action reports, work orders, support tickets, and other operational databases can be rich sources of unstructured data.