Predict Employee Turnover with scikit-learn

Welcome to the project-based course on Predicting Employee Turnover with Decision Trees and Random Forests using scikit-learn. In this course, you will apply Python and scikit-learn to grow decision trees and random forests, and utilize them to address an important business problem. You will also gain insights into interpreting decision trees and random forest models using feature importance plots. Additionally, you will learn to tune model hyperparameters to enhance classification accuracy and create interactive GUI components in Jupyter notebooks using widgets.

• Apply decision trees and random forests with scikit-learn to classification problems
• Interpret decision trees and random forest models using feature importances
• Tune model hyperparameters to improve classification accuracy
• Create interactive, GUI components in Jupyter notebooks using widgets

This project runs on Coursera's hands-on project platform called Rhyme, allowing you to work on projects in a hands-on manner in your browser. You will have instant access to pre-configured cloud desktops containing all the necessary software and data for the project. Everything is already set up directly in your internet browser, enabling you to focus solely on learning and applying your skills.