Predict Employee Turnover with scikit-learn
Apply decision trees and random forests with scikit-learn to classification problems
Interpret decision trees and random forest models using feature importances
Tune model hyperparamters to improve classification accuracy
Create interactive, GUI components in Jupyter notebooks using widgets
Showcase this hands-on experience in an interview
Welcome to this project-based course on Predicting Employee Turnover with Decision Trees and Random Forests using scikit-learn. In this project, you will use Python and scikit-learn to grow decision trees and random forests, and apply them to an important business problem. Additionally, you will learn to interpret decision trees and random forest models using feature importance plots. Leverage Jupyter widgets to build interactive controls, you can change the parameters of the models on the fly with graphical controls, and see the results in real time! This course runs on Coursera's hands-on project platform called Rhyme. On Rhyme, you do projects in a hands-on manner in your browser. You will get instant access to pre-configured cloud desktops containing all of the software and data you need for the project. Everything is already set up directly in your internet browser so you can just focus on learning. For this project, you’ll get instant access to a cloud desktop with Python, Jupyter, and scikit-learn pre-installed.
Some experience in Python programming and machine learning theory is recommended.
Skills you will develop
Learn step-by-step
In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:
Introduction and Importing Libraries
Exploratory Data Analysis
Encode Categorical Features
Visualize Class Imbalance
Create Training and Test Sets
Build a Decision Tree Classifier with Interactive Controls
Build a Decision Tree Classifier with Interactive Controls (Continued)
Build a Random Forest Classifier with Interactive Controls
Feature Importance and Evaluation Metrics
How Guided Projects work
Your workspace is a cloud desktop right in your browser, no download required
In a split-screen video, your instructor guides you step-by-step
Reviews
TOP REVIEWS FROM PREDICT EMPLOYEE TURNOVER WITH SCIKIT-LEARN
Good practical overview of decision tree and random forest model with Python. The interface for code typing is a bit difficult to navigate with some lag time; hence -1 star in the review.
I am glad to have taken this course. I came across some unknown features of Pandas (profile), sklearn library. New python libraries like yellowbrick.
I was looking for Elaborated explanation of the project and implement it to clear the concept. This course did explain it all.
Doing hands on project on Rhyme was very helpful as we could listen to the instructions and learn and type it ourselves.
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