HAR Activity Classifier — Streamlit App

A Streamlit web app for the Human Activity Recognition (HAR) ML pipeline built with custom sklearn transformers + SVM + Optuna hyperparameter tuning.

📁 Project Structure

├── app.py              # Streamlit application
├── requirements.txt    # Python dependencies
└── README.md

🚀 Run Locally

# 1. Install dependencies
pip install -r requirements.txt

# 2. Launch the app
streamlit run app.py

The app opens at http://localhost:8501.

☁️ Deploy on Streamlit Community Cloud (free)

1. Push to GitHub

   git init
   git add app.py requirements.txt README.md
   git commit -m "Initial commit"
   # Create a new repo on github.com, then:
   git remote add origin https://github.com/<your-username>/<repo-name>.git
   git push -u origin main

2. Go to share.streamlit.io and sign in with GitHub.

3. Click New app → select your repo → set Main file path to app.py → Deploy.

4. Your app will be live at https://<your-app>.streamlit.app in ~2 minutes.

☁️ Deploy on Hugging Face Spaces (free)

1. Create a new Space at huggingface.co/spaces.
2. Choose Streamlit as the SDK.
3. Upload app.py and requirements.txt.
4. The Space builds and serves your app automatically.

📊 App Features

Feature	Details
File upload	Train + Test CSV via sidebar
EDA	Class distribution, correlation matrix, missing/duplicate stats
Preprocessing	Configurable variance / correlation / PCA thresholds
Hyperparameter tuning	Manual sliders or Optuna (configurable trials)
Results	CV accuracy, test accuracy, classification report, confusion matrix
Dimensionality report	Feature count at each pipeline stage

📝 Expected CSV Format

Both CSVs must contain:

• An Activity column (target label)
• Optionally a subject column (dropped automatically)
• All remaining columns treated as features