The project aims to create educational software that visualizes machine learning algorithms to enhance understanding through visual learning. Since humans retain information better through visuals than text, the tool will help users grasp key concepts by displaying algorithmic processes visually. It will support visualization of various algorithms, including Linear and Logistic Regression, Neural Networks, SVMs (linear and non-linear), K-means, Naive Bayes, Decision Trees, and PCA.

Getting Started

Creating an environment (recommended)

Using Anaconda

conda create -n ENV_NAME python=3.7

Where ENV_NAME is the name of the environment. After creating the environment, activate it using:

conda activate ENV_NAME

Using venv

python3 -m venv ENV_NAME

Where ENV_NAME is the name of the environment. After creating the environment, activate it using:

source ENV_NAME/bin/activate

Installing requirements

Install all the requirements from requirements.txt using:

pip install -r requirements.txt

Running the program

Open Visualizer.ipynb and run all cells.