AI-Powered Personal Study Tracker

An intelligent productivity analytics app built with Python, SQLite, Machine Learning, and Streamlit.
It empowers learners to track, visualize, and improve their study habits while using AI to estimate their expected productivity.

---

Overview

The AI-Powered Personal Study Tracker transforms daily study logs into actionable insights.
It leverages a Random Forest Regressor to model productivity based on mood, distractions, study duration, caffeine intake, and more.

You can:

• Log study sessions (with mood, focus level, and caffeine)
• Analyze study performance trends and patterns
• Estimate expected productivity before studying
• View aggregated insights and KPIs

---

Project Structure

ai-study-tracker/
├── app/
│   └── streamlit_app.py         # Main Streamlit dashboard
├── src/
│   ├── generate_sample.py       # Generate synthetic study sessions
│   ├── ingest.py                # Load CSV into SQLite
│   ├── train_model.py           # Train RandomForestRegressor
│   └── utils.py                 # Helper functions
├── sql/
│   └── schema.sql               # Database schema
├── data/
│   ├── sessions.csv             # Study session data
│   └── study.db                 # SQLite database
├── models/
│   └── pipeline.joblib          # Saved ML model
└── README.md

---

Dashboard Preview

Productivity Overview

Session Log and Estimator

---

Quickstart

# Clone the repo
git clone https://github.com/yourusername/ai-study-tracker.git
cd ai-study-tracker

# Set up a virtual environment
python -m venv .venv
# Windows
.venv\Scripts\activate
# macOS/Linux
source .venv/bin/activate

# Install dependencies
pip install -r requirements.txt

# Generate sample data
python src/generate_sample.py --days 28 --sessions_per_day 2 --out data/sessions.csv

# Import to SQLite
python src/ingest.py --csv data/sessions.csv --db data/study.db --schema sql/schema.sql

# Train the ML model
python src/train_model.py --db data/study.db --out models/pipeline.joblib

# Launch the dashboard
streamlit run app/streamlit_app.py

---

Tech Stack

Layer	Technology
Frontend	Streamlit + Plotly
Backend	SQLite
ML Model	RandomForestRegressor (scikit-learn)
Data Handling	pandas, SQLAlchemy
Language	Python 3.10+

---

Key Features

• Log daily study sessions with mood, distractions, caffeine, and techniques
• Predict productivity with ML model
• Visualize time trends and subject-wise averages
• Local SQLite database (portable and private)
• Add new sessions interactively via the Streamlit sidebar
• Estimate productivity in real-time

---

Data Schema

Column	Description
date	Session date (YYYY-MM-DD)
start_time / end_time	HH:MM 24h format
duration_min	Computed from start–end times
subject	Math, Physics, CS, Biology, History
technique	Pomodoro, Active Recall, etc.
distractions	Number of interruptions
mood	1-5 scale
caffeine_mg	Caffeine intake (mg)
productivity	Self-rated (1-5)

---

Machine Learning

The app uses RandomForestRegressor from scikit-learn trained on historical study logs.

Features used for prediction:

• Duration (minutes)
• Subject
• Technique
• Distractions
• Mood
• Caffeine (mg)

Target:

• Productivity (1–5 scale)

The model is modular, you can upgrade it to XGBoost or Neural Networks.

---

Example Insights

• Productivity tends to increase with mood and decrease with distractions
• “Active Recall” and “Spaced Repetition” correlate with higher productivity
• Optimal caffeine range: 100–200 mg

---

UI Highlights

• Clean, interactive Streamlit interface
• Real-time productivity estimation
• KPI tiles for total minutes, average mood, and average productivity
• Line chart: Study Minutes Over Time
• Bar chart: Average Productivity by Subject