cyberdata-mlai

This repository includes a full example of the data processing cycle that includes:

• Reading, cleaning, and pre-processing raw data,
• Exploratory Data Analysis (EDA) with statistical and graphical techniques,
• Feature engineering for numeric and categorical features,
• Model training and evaluation.

The contents of the repo are:

• Code: Jupyter notebook named cyberdata_mlai.ipynb,
• Data: included in directory data,
• PackagesI requirements.txt. Can be installed using pip install -r requirements.txt.

You can follow the instructions in vs-code-ml if you want to setup a development environment using VS Code and Jupyter. The Jupyter notebook can be used in Google Colab, but this has not been tested.