Keywords—classification, supervised learning, Random Forest, K-Nearest Neighbor(K-NN), Naïve Bayes, Decision tree, Support Vector machine

• Used Naïve Bayes, K-Nearest Neighbor, Support Vector Machine, Random Forest, and Decision tree algorithms to classify the Red Wine Quality dataset and provided a comparative discussion on the most efficient result.

• Project Jupyter is a project with goals to develop open-source software, open standards, and services for interactive computing across multiple programming languages. It was spun off from IPython in 2014 by Fernando Pérez and Brian Granger.
• JupyterLab is the latest web-based interactive development environment for notebooks, code, and data. Its flexible interface allows users to configure and arrange workflows in data science, scientific computing, computational journalism, and machine learning. A modular design invites extensions to expand and enrich functionality.

• Project Jupyter’s tools are available for installation via the Python Package Index, the leading repository of software created for the Python programming language. This page uses instructions with pip, the recommended installation tool for Python. If you require environment management as opposed to just installation, look into conda, mamba, and pipenv.

Install JupyterLab with pip:

pip install jupyterlab

• Note: If you install JupyterLab with conda or mamba, we recommend using the conda-forge channel.

Once installed, launch JupyterLab with:

jupyter-lab

Install the classic Jupyter Notebook with:

pip install notebook

To run the notebook:

jupyter notebook