Guide: Machine Learning (ML) vs. Predictive Analytics (PA)

Both predictive analytics and machine learning have become powerful tools in the field of data science. Both can help organizations unlock hidden insights in large, complex data sets and predict future outcomes. Along with providing future predictions, both techniques improve efficiency, strengthen decision making, and gain insights into market trends and opportunities.

However, as the big data space continues to grow — it’s estimated the market will reach $105 billion by 2027 — the importance of both machine learning and predictive analytics can’t be underestimated. While both help organizations gain insights from business data, they differ in how they help you do this.

Below, we’ll explore more of the differences between machine learning and predictive analytics, what each involves, and where the approaches intersect.

What is Machine Learning?

Machine learning is part of the data science family tree. It uses data and algorithms to imitate the way humans learn so it can easily recognize patterns, make predictions, and improve performance. With machine learning, you can more easily identify business opportunities and solve problems faster.

There are three types of machine learning:

• Supervised learning which trains a model with labeled data
• Unsupervised learning discovers patterns in unlabeled data
• Reinforcement learning focuses on improving decision-making through reward-based systems

How Does Machine Learning Work?

Hundreds of new machine learning algorithms are published every day; however, a machine learning algorithm’s learning system contains the same general components to learn them. These include:

• Decision process: The machine learning process starts when an algorithm makes an estimate based on a pattern it finds in labeled or unlabeled data.
• Error function: From here, machine learning evaluates the estimate or prediction it generated. The error function will then compare the model with a known example to determine how accurate the model is.
• Model optimization process: Should the error function determine that the model can be improved to better fit the data points, the model is then adjusted. This helps to rescue any discrepancies.
• Repeat until accurate: The technology will then repeat the error function and model optimization process until a predetermined level of accuracy is met.

The resulting machine learning algorithm can then be used for numerous purposes, including predicting future outcomes, detecting trends, and more.

Who Uses Machine Learning?

Machine learning has found uses across industries — from healthcare and finance to retail and technology. Data scientists, researchers, and engineers typically rely on the approach to unlock insights from datasets. These insights are then used to predict traffic patterns, product recommendations, speech and image recognition, catching security vulnerabilities, as a virtual assistant, and more.

What is Predictive Analytics?

Predictive analytics is a broad term for using data to predict and model the future. It uses statistical techniques, data mining, machine learning, and artificial intelligence to enable companies to anticipate trends and behaviors. It then uses tools to create models based on large historical data sets, which can be used to drive decision making.

How Does Predictive Analytics Work?

Predictive analytics operates through the development and application of predictive models, which rely on advanced statistical techniques, machine learning algorithms, and historical data to forecast future outcomes. Here’s a closer look at the process, its components, and the methodologies involved:

The Predictive Analytics Workflow

1. Define the Problem – The predictive process begins by clearly identifying the problem to solve or the outcome to predict. For instance, businesses often use predictive analytics to identify potential customer churn, forecast product demand, or detect fraud.
2. Data Collection and Organization – Data is the foundation of predictive analytics. Organizations gather structured and unstructured data from multiple sources, such as sales transactions, customer interactions, or IoT devices, and store it in centralized repositories, like data warehouses, for analysis.
3. Data Preprocessing – Raw data is cleaned and prepared to ensure accuracy. This involves addressing missing data points, removing outliers, and standardizing the format. Preprocessing is critical to building a model capable of delivering reliable results.
4. Develop Predictive Models – Data scientists use a range of tools and techniques to create predictive models tailored to the problem. Whether it’s regression analysis, neural networks, or decision trees, the right type of model is selected based on the data and the desired outcome. Machine learning algorithms are often employed to “train” these models, enabling them to detect patterns in historical data.
5. Validation and Deployment – The models are tested for accuracy and refined as needed. Once validated, the models are deployed within business systems to generate predictions in real time or for specific timeframes.
6. Continuous Optimization – Predictive models improve over time, thanks to machine learning. By ingesting new data and feedback, their accuracy and adaptability to changing scenarios grow, ensuring they stay relevant and effective.

Who Uses Predictive Analytics?

Similar to machine learning, predictive analytics is used across a variety of industries in numerous business processes. It’s due to the technology’s versatility that 95% of organizations are using predictive analytics in one way or another.

Conclusion

Machine learning and predictive analytics are both valuable tools in the world of data analysis — each with their own unique strengths. Machine learning is a versatile learner, capable of adapting to different tasks and learning from diverse data. Predictive analytics uses past data to forecast specific outcomes, making it particularly useful for making informed decisions about the future. While machine learning is broader and more adaptable, predictive analytics is specialized in foreseeing particular results. Regardless, both approaches can help you sharpen decision making, increase efficiency, and drive business goals forward.