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Level Up: Python Data Modeling and Model Evaluation Metrics

1h 2mAdvanced2023-02-13

Authors

Seth Berry

Associate Teaching Professor and MSBA Academic Co-Director at the University of Notre Dame

Course details

This course is integrated with GitHub Codespaces, an instant cloud developer environment that offers all the functionality of your favorite IDE without the need for any local machine setup. With GitHub Codespaces, you can get hands-on practice from any machine, at any time—all while using a tool that you’ll likely encounter in the workplace.

Each installment of the Level Up series offers at least 15 bite-sized opportunities to practice programming at various levels of difficulty, so you can challenge yourself and reinforce what you’ve learned. Check out the “Using GitHub Codespaces with this course” video to learn how to get a codespace up and running.

In this course, instructor Seth Berry presents 20 Python challenges, starting with a test of basic skills and moving on to more complex tests of your knowledge. Each video is self-contained, so you can pick and choose which challenges you want to try. Explore these practical exercises to work on your coding skills!

Skills covered

Data ModelingPythonProgramming LanguagesData ScienceOpen SourceSoftware DevelopmentOne-Off

Concepts

0. Introduction

01 - Python data modeling
02 - Using GitHub Codespaces with this course

1. Model Evaluation Metrics

03 - Calculating accuracy
04 - Calculating an F-score and MCC
05 - Evaluating ROC curves
06 - Calculating RMSE and MAE

2. Modeling

07 - Imputing missing values
08 - Balancing data
09 - Partitioning data
10 - Saving data for models
11 - Tuning your models
12 - Using linear regression
13 - Using logistic regression
14 - Using decision trees
15 - Using random forest
16 - Using XGBoost and SHAP plots
17 - Classification with deep neural networks
18 - Saving and deploying models