22 Jul Predictive Analytics Deep Dive – the Shape of Things to Come
K, get ready for it, we’re going to get down and dirty with predictive analytics and when I say dirty, I mean the mathematics of the different forms of predictive models dirty. Geekfest? Yes, but close your eyes and extrapolate how predictive analytics can be applied to your situation. By understanding how it works you will also understand the limits of what it can and cannot do. In this episode of the IoT Business Show, I deep dive with Anil Gandhi and emerge with a better understanding of predictive analytics and how it really relates to real-time and descriptive analytics.
In this episode of the IoT Business Show, I deep dive with Anil Gandhi and emerge with a better understanding of predictive analytics and how it really relates to real-time and descriptive analytics.
Anil is a data scientist and President of Qualicent Analytics. He has 25 years of experience in semiconductor and electronics systems where he’s used advanced analytics and Industrial IoT signals to make technical, financial and productivity breakthroughs.
It’s all in the math. Understand the math, even at a high level, and you’ll understand the way analytics works. Understand how analytics works and you’ll know the capabilities and limitations of its application to your scenario. The trick: think of the model as a shape and predictive analytics as an exercise in comparing shapes. Shape matching to situations/shapes from the past help you predict the future and the more accurate the shape, the more accurate the prediction.
Here’s What We’ll Cover in this Episode
- Applying predictive analytics through different stages of the product life cycle.
- Supervised learning vs. unsupervised learning in predictive analytics.
- The curse of dimensionality in predictive analytics modeling.
- Multivariate relationships and the mindboggling combinations they produce.
- Model differences between predictive analytics and descriptive analytics.
- Model differences between predictive analytics and real-time analytics.
Mentioned in this Episode and Other Useful Links
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