GradVis AI – Gradient Descent & ML Visualizer

GradVis AI is a hands-on educational Python project that teaches machine learning foundations through manual implementations of optimization algorithms.

What it includes

• Linear Regression using gradient descent and MSE
• Logistic Regression using gradient descent and cross-entropy
• Polynomial Regression (optional non-linear extension)
• Optional L1/L2 regularization for optimization intuition
• Interactive Streamlit dashboard with:
  • algorithm switching
  • synthetic or CSV-based datasets
  • hyperparameter controls
  • loss curves, parameter trajectories, and fitted boundaries/lines
  • calculus and statistics explanations

Why this is portfolio-ready

This project emphasizes:

• Calculus: explicit partial derivatives + update rules
• Statistics: loss, variance/bias intuition, and model diagnostics
• Optimization: convergence behavior with learning-rate and regularization controls

Quickstart

python -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
streamlit run app.py

Data format for CSV upload

• Include at least two numeric columns
• All numeric columns except the last are treated as features (X)
• The final numeric column is treated as target (y)

Project structure

app.py
src/gradvis_ai/models/
src/gradvis_ai/utils/
tests/

Notes

• No scikit-learn or high-level ML model libraries are used for training algorithms.
• NumPy is used for vectorized math and gradient calculations.