🌸 IKIGAI – Mental Health & Productivity Correlator

An AI-powered Mental Health & Productivity Correlator inspired by the Japanese philosophy of Ikigai (生き甲斐). The system helps students understand the relationship between their daily habits, productivity, and well-being through explainable machine learning and behavioral analytics.

✨ Features

• 🧠 Behavioral habit tracking
• 📊 Stress & productivity analysis
• 🤖 Ensemble Machine Learning prediction
• ⚖️ Ethical rule-based overrides
• 🌸 Ikigai Balance Score (0–100)
• 💡 Personalized recommendations
• 🔐 User authentication system
• 📈 Explainable scoring methodology

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🏗️ Tech Stack

Frontend

• HTML5
• CSS3
• JavaScript

Backend

• Python
• Flask

Machine Learning

• Scikit-learn
• Random Forest
• Decision Tree
• Logistic Regression

Data Processing

• Pandas
• NumPy

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📂 Project Structure

IKIGAI/
│
├── backend/
│   ├── app.py
│   ├── train_model.py
│   ├── models/
│   ├── data/
│   └── requirements.txt
│
├── frontend/
│   ├── templates/
│   ├── static/
│   │   ├── css/
│   │   └── js/
│   └── assets/
│
└── README.md

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🚀 Getting Started

1️⃣ Clone the Repository

git clone https://github.com/your-username/IKIGAI-project.git
cd IKIGAI-project

2️⃣ Install Dependencies

pip install -r requirements.txt

3️⃣ Train the Models

Run once to generate training data and save machine learning models:

cd ikigai/backend
python train_model.py

4️⃣ Start the Flask Server

python app.py

The application will start at:

http://localhost:5000

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🧮 How It Works

Layer 1 – Behavioral Normalization

Daily habits are converted into standardized scores (0–100):

• Sleep Hours
• Study Hours
• Physical Activity
• Social Interaction
• Screen Time

Layer 2 – Stress & Productivity Analysis

Weighted behavioral scoring calculates:

• Stress Risk Score
• Productivity Score

Layer 3 – ML Prediction & Ethical Overrides

The system uses an ensemble of:

• Random Forest (60%)
• Decision Tree (25%)
• Logistic Regression (15%)

Rule-based safety checks always override machine learning predictions when necessary.

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🌸 Ikigai Framework

The final Ikigai Score is generated using four pillars:

Pillar	Meaning
Love	Physical Activity + Social Connection
Good At	Study Consistency
Need	Recovery & Sleep Balance
Value	Productivity Output

These pillars are combined to create a holistic balance score ranging from 0–100.

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⚠️ Disclaimer

This project is not a medical diagnostic tool.

It is intended solely for:

• Awareness
• Self-reflection
• Preventive well-being support

For professional mental health concerns, consult a licensed healthcare provider.

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👩‍💻 Author

Prachi Patil

B.Tech Information Technology Student | Open Source Contributor | GSSoC Contributor

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📜 License

This project is licensed under the MIT License.