Machine Learning and Predictive Analytics approach a problem differently. Eventually, predictive analytics is likely to merge as one application of machine learning.
It’s similar to how the thirsty and the quenched come to the same glass of water. Machine learning is more adaptive, newer, and has larger degrees of freedom, so it can afford to be more flexible with its approach to a problem. Predictive analytics has been around longer and is more procedural in its use.
There is no problem predictive analytics can solve that machine learning cannot. But predictive analytics always has an intended audience, whereas machine learning does not.
Here are Common use cases for Machine Learning and Predictive Analytics.
Machine Learning
- Generative modelling
- Reinforcement leaning
- Image classification
- Language classifications
- Anomaly detection
- Real-time capabilities
Predictive Analytics
- Forecasting
- Predictive modelling
What is predictive analytics?
Both machine learning and predictive analytics are used to make predictions on a set of data about the future. Predictive analytics uses predictive modelling, which can include machine learning. Predictive analytics has a very specific purpose: to use historical data to predict the likelihood of a future outcome. At its most basic, analytics of any sort is simply applied mathematics—sometimes known as data science.
What is Machine Learning?
Machine learning is different from predictive analytics. Machine learning has less to do with reporting than it does to do with the modelling itself.
Machine learning is the top-shelf tool to conduct statistical analysis. Because of its learning feature, it can fine tune the parameters of its models just right to fit the data. This could take a lot of work if done by hand which would use advanced heteroskedastic methods and other tools by statistics to exclude various data points to fine-tune the parameters of their models.
Machine learning has used algorithms and compute resources to offer an abundance of computation that doesn’t have to spend a lot of time doing the fine-tooth combing through a model’s weights. In part, that is the good and the bad of the machine learning model. The model’s nodes define themselves, so a typical statistician doesn’t have to sift through them. But then, it’s also referred to as a black box because statisticians cannot sift through the nodes and determine what they mean.
Machine learning is a tool used by many companies, on many different kinds of applications. Companies like Microsoft, Amazon, Google, and many others offer machine learning as a service (MLaaS), where data can be submitted to the API and a model is returned. These companies also offer resources and even instruction on how to use machine learning in your application alongside their resources.
Some other uses for machine learning include:
- Building recommendation systems
- Flagging errors in transactions or data entry
- Personalizing shopper experience based on browsing history
- Uncovering patterns in market research
- Automating chatbots in applications to provide users with rapid customer service on web pages
Predictive analytics is a statistical process, while machine learning is a computational process that data analysts can employ as a tool for better predictive analytics.
Whether used separately or together, predictive analytics and machine learning both provide organizations with solutions to real-world problems while also helping to boost their bottom line. Business leaders are investing heavily in artificial intelligence and machine learning to improve processes, drive insights from their data, and make confident, data-backed decisions.