Python Tutorial : Introduction and basetable structure
Key Takeaways
Introduces the basics of predictive analytics using Python and scikit-learn
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Hi! Welcome to the first video of the Foundations of Predictive Analytics course. My name is Nele, I'm a data scientist at Python Predictions. I will introduce you to the fascinating world of predictive analytics, and help you to construct your first predictive models.
First, let's get a better understanding of what predictive analytics entails. Consider the example of a non-profit organization. This organization has a donor base with people that have donated in the past. Assume that the organization wants to send a letter to their donors, to ask to donate for a specific project. One option would be to send the letter to all the candidate donors. However, this would be really expensive. Predictive analytics allows to determine the donors that are most likely to donate. This is exactly what the organization needs: instead of writing a letter to all donors, they can send it to a smaller group of donors, that is most likely to donate.
In general, predictive analytics is the process that aims to predict an event, using historical data. This data is gathered in the analytical basetable.
An analytical basetable is typically stored in a `pandas` dataframe. There are three important concepts in the analytical basetable: the population, the candidate predictors and the target.
The population is the group of people or objects you want to make a prediction for. In the fundraising example, it consists of the donors that are in scope for receiving a letter. The basetable has one row for each object in the population. You can check the size of your population using the `len` method in python.
The candidate predictors describe the objects in the population. It is information that can be used to predict the event. For instance, variables li
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