This document is a combined professional portfolio demonstrating Python programming, data analysis, and machine learning skills. It is intended for academic or professional assessment, likely as part of a coursework submission in occupational safety and health (OSH) or data science. The portfolio is divided into three parts. Part 1 covers Python practice and improvement, including importing and…
This document is a combined professional portfolio demonstrating Python programming, data analysis, and machine learning skills. It is intended for academic or professional assessment, likely as part of a coursework submission in occupational safety and health (OSH) or data science.
The portfolio is divided into three parts. Part 1 covers Python practice and improvement, including importing and exploring a public safety dataset using Pandas, working with variables and conditional statements, and using loops and functions to assess risk levels at a construction site. It also includes debugging evidence and personal reflections in both Malay and English.
Part 2 focuses on data analysis evidence. It walks through data inspection and preparation steps such as checking rows and columns, examining data types, identifying missing values and duplicates, and performing data cleaning. Numerical analysis covers calculating mean, standard deviation, and correlation between variables, with interpretations provided.
Part 2 also includes data visualisation using bar charts and histograms, with code examples and explanations. The document references the California Housing Prices dataset and a public safety report dataset, applying the techniques to OSH-related scenarios such as workplace accidents and hazard categories.
Part 3, which is not fully shown in the provided text, is described as covering machine learning understanding and reflection. The overall portfolio combines practical coding exercises, analytical methods, and personal learning outcomes, structured as a professional submission.