Logistic Regression Classifier
Let’s explore together the full Data Scientist routine using Python, Jupyter and Sci-Kit Learn.
Our goal will be to create a predictor model that tries to guess if a person makes over 50K/year based on Census Data.
In order to achieve that, we will start by learning how to explore the raw dataset, understanding variables (and bias) and getting insights from the data, then we will use Logistic Regression to create a classifier. We will examine how to train, validate/ calibrate, and test our model, using standard metrics such as accuracy, precision, confusion matrix and ROC.
Last but not least, after completing the session, you will get a challenge exercise to test your understanding on your own pace, in which you can apply a second method (Decision Tree) to the same problem.
You can test it using Binder: https://mybinder.org/v2/gh/rodsenra/mltutorial/master