Classification models for Pump it Up project

In this submodule, we’ll build a number of classification models for the pumpitup project. In particular, we will explore:

• using sklearn transformers and pipelines to streamline workflow,

• logistic regression with regularization,

• random forests and boosted trees and other ensembles.

You’ll be working in your newly created pumpitup project folder.

Start by opening the model_exploration.ipynb notebook in Jupyter Lab.

Here is are screencasts to help guide you through the notebook:

• SCREENCAST: Intro and data preprocessing (5:46)

• SCREENCAST: Logistric regression review and overview of regularization (10:49)

• SCREENCAST: Preprocessing with column transformers (8:10)

• SCREENCAST: Logistic regression model and solvers (3:11)

• SCREENCAST: Creating a preprocessing and model estimation pipeline (3:17)

• SCREENCAST: Data partitioning and modeling fitting (17:39)

• SCREENCAST: Cross validation and Predictions (7:41)

• SCREENCAST: Automation and Model persistence (14:03)

• SCREENCAST: Random forests (6:10)

OPTIONAL ADVANCED MATERIAL

If you want to learn a bit about one more popular machine learning technique, gradient boosting machines, you can check out the following short intro in the gradient_boosting.ipynb notebook - just take a stroll through to learn about one of the newer classification techniques available in sklearn.

And we’re done with Module 2

Next we’ll be using Python to do a bunch of analytics work that we’d usually do in Excel.