How to Improve Predictive Analytics with Machine Learning Techniques

The use of machine learning techniques has led to enhancements in the use of predictive analytics for businesses across a broad range of industries.

You may be wondering, “Is predictive analytics machine learning?” These two subjects are closely related but ultimately distinct. Predictive analytics refers to a broad field of study that focuses on quantifiable predictions about the future, while machine learning is a subfield within the broader field of artificial intelligence (AI) that involves training computers to recognize patterns. Predictive analytics is a common application for machine learning.

Within the world of business, the greater power and efficiency offered by machine learning models has created a number of lucrative use cases. For example, predictive analytics could be used to estimate how likely it is that someone will turn into a repeat customer. On e-commerce websites, predictive analytics helps websites make helpful suggestions for shoppers looking for their next purchase.

Machine learning offers a powerful tool for businesses that use predictive analytics to drive decision making. Because machine learning models can process vast quantities of data in a short span of time, they’re able to identify complex patterns in data more quickly than traditional data processing and analysis methods.

One of the major benefits to machine learning is that models can essentially teach themselves. That means machine learning models tend to become more accurate over time, which can improve forecast accuracy.

Machine learning and predictive modeling still require a human touch. Utilize the following strategies to get the most from AI for your business.

This process includes removing inconsistencies and accounting for missing data. You may also need to feature engineering for better model inputs.

Choose the appropriate algorithm for your problem. For example, a financial forecasting model may be better served by a regression model, while a categorical machine learning algorithm may do better with a decision tree.¹

Regardless of your model selection, it’s vital that your training models have sufficient and high-quality data.

Hyperparameter optimization refers to the process of tweaking certain variables to get an ideal outcome. For example, imagine you’re a digital marketer launching a machine-learning-powered pay-per-click advertisement campaign on Amazon. You’ve set an upper bound of $0.25 that you’re willing to bid on a specific keyword.

A machine learning algorithm could incorporate thousands of data points to determine that bidding $0.14 on this keyword may net you the best results on average.

Continuing with the example above, by using live data streams to refine predictions, the algorithm may determine that you’ll likely net a greater profit by raising your bid at certain times and lowering it at others. That’s just one benefit of using real-time analytics in a dynamic environment.

Another common example would be the use of dynamic pricing models used by airlines, concert and events sales and, increasingly, rental companies. By adjusting the price of a plane ticket in line with current demand, airlines can maximize profit by sensitizing ticket prices to customer demand.

Those working in machine learning in the predictive analytics space have several powerful tools and resources available.

• Python libraries: Python is a programming language commonly used for machine learning. You can access prebuilt coding and other resources through online libraries like Scikit-learn and TensorFlow
• Machine learning platforms: Take advantage of prebuilt, cloud-based predictive analytics tools like AWS SageMaker and Google AI. These machine learning programs offer scalable infrastructure at affordable costs
• Visualization tools for presenting predictive insights: Visualization tools like Power BI and Tableau let you present predictive insights to stakeholders in an elegant and intuitively graspable format

Predictive analytics and machine learning suffer from several large challenges including data privacy concerns, bias in machine learning models, and scalability issues. Fortunately, ongoing advances in the field have led to easily adoptable broad-based solutions.

One of the largest potential impacts of machine learning on businesses is the possibility of a costly data breach. One recent study put the average cost of a data breach for American businesses at just under $5 million.² Since machine learning algorithms often feed on sensitive consumer data, it’s vital that companies adopt cybersecurity best practices to mitigate the possibility of a costly hack.

Unfortunately, AI is no less bias prone than humans. Studies have shown that some machine learning models tend to engage in discriminatory behavior, such as showing higher paid job advertisements to men compared to women or predicting a higher rate of re-offense for African American criminals compared to white criminals.³

To ensure equitable treatment by machine learning, it’s important that organizations maintain vigorous human oversight to correct these errors as they occur.

Machine learning thrives on large datasets. To accommodate the extra computing power required, it’s vital that businesses use scalable solutions like cloud-based technology to get the most from their predictive analytics models.

Before you can implement a machine learning program, it’s important to sit down and define a clear business objective. Once you’ve identified your problem and how you intend to solve it using machine learning, it’s important to collect and prepare the relevant data. Removing extraneous data and outliers will prevent the common problem of underfitting your data, which can lead to suboptimal outcomes.

Next, you should build, test, and refine machine learning models. With machine learning, it’s unlikely that you’ll get it right on the first go-around. This technology requires constant iterations for maximum effect. Finally, monitor performance and update your predictive analytics models regularly.

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