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Predicting Employee Productivity Using Tree Models

For this project, we’ll step into the role of data scientists to determine the best working conditions for maximizing productivity in a garment factory using decision trees and random forests in Python.

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4.8 (359 reviews)
366,013 learners enrolled in this project
  • Intermediate
  • Jupyter project
  • 9 steps
  • Python
Lisa Reiber

“The projects helped me apply what I learned and understand how to tackle all the little things that come along in real life.”



Lisa Reiber

Data Analyst

Predicting Employee Productivity Using Tree Models

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Project overview

In this project, you’ll assume the role of a data scientist working with a garment factory dataset to predict employee productivity. Using Python and the scikit-learn library, you’ll clean the data, build a decision tree model, evaluate its performance, and explain the results to a non-technical audience.

This project allows you to showcase your skills in data preparation, machine learning modeling with decision trees and random forests, and communicating data-driven insights. You’ll develop a robust model to identify the key factors driving productivity, providing actionable recommendations to improve factory operations.

Objective: Leverage decision tree and random forest algorithms to predict garment worker productivity and deliver insights to guide factory enhancements.

Key skill required

To complete this project, it's recommended to build these foundational skills in Python

  • Preprocessing data for machine learning in scikit-learn
  • Building and visualizing decision trees in scikit-learn
  • Evaluating machine learning model performance using metrics
  • Optimizing models by tuning hyperparameters

Projects steps

Step 1: Introduction

Step 2: Dataset Exploration

Step 3: Dataset Cleaning (I)

Step 4: Dataset Cleaning (II)

Step 5: Building the Tree

Step 6: Visualizing and Evaluating the Tree

Step 7: Explaining the Tree

Step 8: Using Random Forest

Step 9: Next Steps

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