- Explain how predictive analytics can assist with decision-making.
- Differentiate between the types of data that are used.
- Apply the correct functions to Python code to produce optimal results.
- Explain why data needs to be preprocessed before using predictive models.
- Distinguish between the different predictive models available.
Skill Level Advanced
- [Isil] Have you ever wondered how real estate websites come up with housing price estimates or how companies estimate their number of sales for the next product launch or how insurance companies predict healthcare costs? Well, this is done through predictive analytics. It helps programmers efficiently extract information from data to make an informed guess, which then leads to calculated decisions and impactful actions, adding value for businesses. In this course, I'm going to show you how to use prebuilt Python libraries to make and evaluate predictive models for decision-making. You will learn how to prepare your data and know which models to use when and apply these concepts in your own work. I'm Isil Berkun, and I have been engineering data for about 15 years. Data tells us a story, where it came from and where it's going next. I invite you to join me in my LinkedIn Learning course about applying predictive analytics with the Python programming language.