Predict Employee Turnover with scikit-learn

Now, we you will apply decision trees and random forests using scikit-learn and Python to build an employee churn prediction application with interactive controls. We will accomplish this with the help of following tasks in the project:

1. Introduction and Import Libraries
2. Exploratory Data Analysis
3. Encode Categorical Features
4. Visualize Class Imbalance
5. Create Training and Test Sets
6. Build a Decision Tree Classifier with Interactive Controls
7. Build a Random Forest Classifier with Interactive Controls
8. Feature Importance Plots and Evaluation Metrics

Project Structure

The hands on project on Predict Employee Churn with Decision Trees and Random Forests is divided into the following tasks:

Task 1: Introduction and Import Libraries

Introduction to the data set and the problem overview. See a demo of the final product you will build by the end of this project. Introduction to the Rhyme interface. Import essential modules and helper functions from NumPy, Matplotlib, and scikit-learn.

Task 2: Exploratory Data Analysis

Load the employee dataset using pandas Explore the data visually by graphing various features against the target with Matplotlib.

Task 3: Encode Categorical Features

The dataset contains two categorical variables: Department and Salary. Create dummy encoded variables for both categorical variables.

Task 4: Visualize Class Imbalance

Use Yellowbrick's Class Balance visualizer to create a frequency plot of both classes. The presence or absence of a class balance problem will inform your sampling strategory while creating training and validation sets.

Task 5: Create Training and Validation Sets

Split the data into a 80/20 training/validation split. Use a stratified sampling strategy

Tasks 6: Build a Decision Tree Classifier with Interactive Controls

Use the interact function to automatically create UI controls for function arguments. Build and train a decision tree classifier with scikit-learn. Calculate the training and validation accuracies. Display the fitted decision tree graphically.

Task 7: Build a Random Forest Classifier with Interactive Controls

Use the interact function again to automatically create UI controls for function arguments. To overcome the variance problem associated with decision trees, build and train a random forests classifier with scikit-learn. Calculate the training and validation accuracies. Display a fitted tree graphically.

Task 8: Feature Importance Plots and Evaluation Metrics

Many model forms describe the underlying impact of features relative to each other. Decision Tree models and Random Forest in scikit-learn, feature_importances_ attribute when fitted. Utilize this attribute to rank and plot the features.