Anomaly Detection Pipeline with Isolation Forest model and Kedro framework

A Python wrapper for GNU parallel that naturally embeds shell code into Python scripts or Jupyter notebooks (no delimiter hell). Features parameter substitution, environment substitution, and cross-product generation to eliminate shell loops. Perfect for integrating Unix programs into Python environments for bioinformatics and data science.

Contains Multipage Streamlit applications showing all steps of machine learning pipeline with additional recommendations at the end.

Refactor a typical data science notebook into a well-structured Kedro project, explore its core features and workflow and software engineering best practices.

End-to-End data preprocessing and exploratory data analysis (EDA) techniques. It covers data cleaning, handling missing values, feature engineering, encoding categorical variables, scaling, and visualizations to understand patterns and insights in datasets