Artificial Intelligence
WorkIQ — Remote Work Productivity Predictor
WorkIQ is a regression model trained on 50,000 remote worker records to predict productivity scores on a 60–100 scale. A Gradient Boosting pipeline with a ColumnTransformer handles both numeric features (work hours, tasks, sleep, mental health score) and categorical inputs (country, industry, work mode, burnout level). Mental health score is the dominant predictor at 51.8% importance, followed by tasks completed and weekly hours. Try it live below.
ML ModelsRegressionRemote WorkHR Analytics
2025
2 months
Open Source
Key Features
- Continuous Score Output: Predicts productivity on a 60–100 scale, rounded to 2 decimal places
- R² of 0.804: Gradient Boosting outperformed Random Forest and Linear Regression in grid search
- 13 Input Features: 9 numeric + 4 categorical (country, industry, work mode, burnout level)
- End-to-End Pipeline: ColumnTransformer with StandardScaler + OneHotEncoder baked into the model
- 50,000 Training Records: Covers 8 countries, 5 industries, and 3 work modes
- Dominant Signal: Mental health score contributes 51.8% of feature importance
- REST API: FastAPI endpoint at /predict/productivity with JSON request/response
- Live Demo: Interactive form on this page with sliders and dropdowns — no signup needed
📊
Try It Live
Enter your work profile — model predicts your productivity score (60–100)
32yrs
2159
40hrs
2060
70
30120
7hrs
49
3x
05
72/100
50100
8/11
511
$80k
$30k$200k
2024
20182025
📈
Fill in your profile and click Predict Productivity Score.
Technology Stack
Framework
scikit-learn + FastAPI
Platform
Web / Cloud
Language
Python 3.11
App Type
HR & Workforce Analytics
Tools & Services
PythonGradient BoostingColumnTransformerOneHotEncoderjoblibpandas
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