Overview

Worker productivity is a critical metric in various industries, influencing operational efficiency and profitability. This project explores how machine learning models, specifically decision trees and random forests, can predict worker productivity based on workplace factors. Using data on hours worked, tasks completed, and other performance indicators, the project demonstrates the power of tree-based models in understanding and forecasting productivity.

You can find the Jupyter Notebook of this project here.

Objective

The primary goal of this project is to predict worker productivity using tree-based models. By analyzing workplace data, we aim to:

1. Identify key factors influencing productivity.
2. Build predictive models with high accuracy.
3. Evaluate the effectiveness of tree models in solving real-world business problems.

Data Preparation

The dataset used in this project includes records on:

• Hours Worked: Total hours logged by employees.
• Tasks Completed: Number of tasks accomplished within a specific time frame.
• Breaks Taken: Frequency and duration of breaks.
• External Factors: Variables like workplace conditions or external stressors.

Preprocessing Steps:

1. Cleaning: Missing and anomalous values were identified and handled.
2. Feature Engineering: Derived features like task completion rate and productivity ratio.
3. Encoding: Categorical variables were converted into numerical format for model compatibility.
4. Normalization: Continuous variables were scaled to enhance model performance.

Model Building

Tree-based models were chosen for their interpretability and ability to capture non-linear relationships.

1. Decision Tree

• Implementation: A decision tree was built to identify splits in data that best predict productivity.
• Hyperparameters Tuned:
  • Maximum tree depth.
  • Minimum samples per split.

2. Random Forest

• Implementation: An ensemble of decision trees was trained to reduce overfitting and improve accuracy.
• Key Features:
  • Bootstrapping to train multiple trees on subsets of data.
  • Aggregating results through majority voting for classification tasks or averaging for regression.

Evaluation Metrics

• Mean Absolute Error (MAE): Measures prediction accuracy.
• R-squared (R²): Evaluates model fit.

Key Findings

1. Top Predictors of Productivity:
   • Hours worked had the highest predictive power.
   • Task complexity and environmental factors also played significant roles.
2. Model Performance:
   • The random forest model outperformed the decision tree, achieving an R² of 0.85 and a lower MAE.
3. Insights:
   • Productivity decreases with excessively long working hours, indicating diminishing returns.
   • Breaks positively influenced productivity when kept within optimal durations.

Conclusion

Tree-based models, especially random forests, provide a robust framework for predicting worker productivity. The findings emphasize the need for balanced work hours and optimal break durations to maintain high productivity levels. This approach can be extended to various industries to enhance workforce management strategies.