🧠 MindEase

A transparent ML prototype for stress-aware language analysis.

🌐 Live Demo

💡 What It Is

MindEase is a Streamlit app that analyzes written language and estimates stress using three ML approaches built with NumPy. I built it to explore feature engineering, model implementation, and how ML can become a usable product instead of just a notebook.

Educational prototype only — the result is not a medical or psychological diagnosis.

🛠️ Tech Stack

• Python · NumPy
• Streamlit · Matplotlib · Pillow
• Hand-built text features with Python/Regex
• Linear Regression · Linear SVM · Decision Tree

⚙️ How It Works

User Text
   ↓
12 Text Features
   ↓
Regression + SVM + Decision Tree
   ↓
Ensemble Estimate
   ↓
Visual Feedback

The models are implemented from scratch rather than using ready-made ML estimators, making the learning and prediction pipeline easy to inspect.

🚀 Run Locally

git clone https://github.com/moizaiqbal40-ops/Mindease.git
cd Mindease
pip install -r requirements.txt
python train.py
streamlit run app.py

✨ What I Learned / Challenges

The biggest challenge was implementing the ML fundamentals myself and then connecting the models, feature engineering, evaluation, and UI into one working application.

📸 Screenshots