AI-Productivity-Tracker

KO | EN

gitlite — search

#python #react #ai #android #machine-learning #linux #java #nodejs #awesome #cli #kubernetes #vue #openai #open-source

AI-Productivity-Tracker

(★ 35)

Analyze and predict daily productivity using SQL, machine learning, and psychology. This project combines behavioral data, circadian rhythm analysis, and ElasticNet regression to model focus, stress, and performance, transforming work patterns into actionable insights.

AI-Productivity-Tracker 최신버젼 다운로드

최종 버전 다운로드 (.zip)

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# AI Productivity Tracker (SQL + ML + Psychology)

Predict and analyze daily productivity using **behavioral data, SQL feature engineering, and machine learning**, integrating psychological principles such as **circadian rhythm**, **stress-performance dynamics**, and **habit efficiency**.

This project demonstrates how modern data science can quantify human productivity in knowledge work, blending **psychology, data engineering, and predictive analytics**.

---

## Overview

This project models daily productivity based on personal and contextual factors such as sleep, stress, meetings, breaks, and focus patterns.  
It uses:

- **SQL (SQLite)** for feature engineering and psychological metric derivation  
- **Python (pandas, scikit-learn)** for data processing, training, and visualization  
- **ElasticNet Regression** for interpretable prediction  
- **Behavioral Science Insights** to ensure meaningful features

---

## Project Structure

```
ai-productivity-tracker/
├─ data/
│  ├─ events_train.csv
│  └─ events_candidates.csv
├─ src/
│  ├─ create_db.py
│  ├─ queries.sql
│  ├─ train_regression.py
│  ├─ score_new_days.py
│  └─ utils.py
├─ outputs/
│  ├─ metrics.json
│  ├─ feature_importance.csv
│  ├─ predictions_train.csv
│  └─ charts/
│     ├─ actual_vs_predicted.png
│     ├─ residuals_hist.png
│     └─ feature_importance.png
└─ README.md
```

---

## Data Description

| Column | Description |
|--------|--------------|
| `sleep_hours` | Hours of sleep the previous night |
| `chronotype` | Morning or evening preference |
| `focus_start_hour` | Hour when deep work begins |
| `deep_work_minutes` | Minutes of uninterrupted work |
| `meetings_minutes` | Total meeting duration |
| `late_meetings_minutes` | Evening meetings (negative for energy) |
| `breaks_count` | Number of breaks during the day |
| `avg_break_minutes` | Average break duration |
| `context_switches` | Task changes / app switches |
| `notifications` | Distractions from notifications |
| `steps`, `hydration_glasses`, `caffeine_mg` | Physical activity and health proxies |
| `stress_level`, `mood` | Psychological self-assessments |
| `productivity_score` | Target variable (0–100 scale) |

---

## Psychology-Informed Feature Engineering

Feature creation in `queries.sql` integrates **behavioral science theories**:

| Feature | Formula | Psychological Meaning |
|----------|----------|------------------------|
| `sleep_deficit` | \|sleep_hours − 8\| | Cognitive fatigue impact |
| `circadian_alignment` | match between chronotype & work start | Energy–focus match quality |
| `yerkes_arousal` | stress × (1 − stress−3 /2) | Optimal stress improves focus (Yerkes–Dodson Law) |
| `break_quality` | breaks × avg_break_minutes | Balance between rest and continuity |
| `meeting_load` | meetings + 1.5×late_meetings | Collaboration vs overload |
| `context_penalty` | notifications + context_switches | Distraction index |
| `health_score` | steps/10k + caffeine balance | Physical energy proxy |

These features translate psychology into quantifiable variables for regression modeling.

---

## Model Pipeline

1. **SQL Feature Engineering:** Derived features created using SQLite views (`features_train`, `features_candidates`).  
2. **Data Standardization:** Scaling numeric and one-hot encoding categorical (`chronotype`).  
3. **ElasticNet Regression:** Combines L1 and L2 regularization for interpretability and generalization.  
4. **Evaluation:** Metrics include R² and MAE with full residual diagnostics.  
5. **Visualization:** Insights and diagnostics via Matplotlib charts.

---

## How to Run

```bash
# Create virtual environment
python -m venv .venv
source .venv/bin/activate  # (Windows: .venv\Scripts\activate)

# Install dependencies
pip install -r requirements.txt

# Load data into SQLite
python src/create_db.py --train data/events_train.csv --candidates data/events_candidates.csv --db productivity.db

# Train and evaluate the model
python src/train_regression.py --db productivity.db --sql src/queries.sql --outdir outputs

# Score new days (unlabeled)
python src/score_new_days.py --db productivity.db --sql src/queries.sql --model outputs/model.joblib --outdir outputs
```

Outputs include metrics, predictions, and charts under `outputs/`.

---

## Results and Visualizations

### ** Actual vs Predicted Productivity**
<img width="1050" height="900" alt="actual_vs_predicted" src="https://github.com/user-attachments/assets/f945a765-53d2-4461-8dde-e0827ff57291" />

**Interpretation:**  
- Points close to the diagonal (y=x) show accurate predictions.  
- Strong correlation indicates the model captures real productivity behavior.  
- Minor deviation near extreme productivity values is expected due to behavioral noise.

**Insight:**  
The model explains individual productivity patterns effectively, balancing accuracy and interpretability.

---

### ** Feature Importance (Standardized Coefficients)**
<img width="1350" height="750" alt="feature_importance" src="https://github.com/user-attachments/assets/6a5741f6-3074-40c4-a4d7-79bad5f1a718" />

| Feature | Direction | Meaning |
|----------|------------|----------|
| `yerkes_arousal` | ↑ | Moderate stress enhances focus (Yerkes–Dodson Law) |
| `deep_work_minutes` | ↑ | More deep work → higher productivity |
| `circadian_alignment` | ↑ | Starting work at optimal time improves flow |
| `meeting_load` | ↓ | Too many meetings reduce focus time |
| `context_penalty` | ↓ | Distractions lower overall efficiency |
| `sleep_deficit` | ↓ | Sleep deprivation strongly lowers productivity |

**Insight:**  
Productivity is a **multi-factor balance**, biological rhythm, mental stress, workload, and interruptions all interact.  
The model provides **interpretable coefficients**, not just predictions.

---

### ** Residuals Distribution (Actual − Predicted)**
<img width="1050" height="750" alt="residuals_hist" src="https://github.com/user-attachments/assets/01e682a9-fd91-4833-bc94-0f9365e3a40c" />

**Interpretation:**  
- The histogram is bell-shaped, centered near zero.  
- Indicates **no systematic bias**, model neither overpredicts nor underpredicts.  
- Small tails = few outliers (e.g., burnout or exceptional days).

**Insight:**  
Errors are random and symmetric → model generalizes well.  
Residual shape suggests stable performance and reliable psychological feature design.

---

## Key Behavioral Insights

1. **Moderate stress (arousal) improves output**, validating the **Yerkes–Dodson law**.  
2. **Circadian alignment** (working in sync with your biological clock) strongly correlates with productivity.  
3. **Sleep deficit and meeting overload** are the most consistent negative predictors.  
4. **Physical health markers** (steps, hydration) show secondary but positive effects.  
5. **Balance > quantity**, overworking past cognitive limits lowers productivity quality.

---

## Metrics Summary

| Metric | Description | Example Value |
|--------|--------------|----------------|
| `R²` | Proportion of explained variance | ~0.83 |
| `MAE` | Mean absolute error (0–100 scale) | ~3.5 |

The model explains most of the variance in daily productivity with **minimal error**, a strong result for behavioral prediction.

---

## Technologies Used

- **SQL (SQLite)**  feature computation and preprocessing  
- **Python (pandas, scikit-learn, matplotlib)**  analysis, modeling, visualization  
- **ElasticNet Regression**  interpretable linear model with regularization  
- **Joblib**  efficient model serialization

AI-Productivity-Tracker

AI-Productivity-Tracker 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

AI-Productivity-Tracker

AI-Productivity-Tracker 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

Download README