Project 02

MSc Artificial Intelligence

A growing collection of academic notebook projects completed as part of my MSc in AI — covering fraud detection, regression, data analytics, and explainable AI. Early work, documented honestly.

MSc AI academic projects

Where I Am

My MSc in Artificial Intelligence is currently underway. The projects on this page are the early exercises and assignments from the programme — Jupyter notebooks built to learn the fundamentals: how to handle real datasets, apply standard ML algorithms, and critically evaluate results.

These are not production systems. They are the foundation. The jump from textbook notebook to the production pipeline in Project 1 was deliberate — I wanted to show both ends of the spectrum: what the learning looks like, and where it leads.

5 Notebook projects
3 Domains covered
2 Languages compared

Notebook Projects

Credit Card Fraud Detection

Binary classification on a 10k-row dataset with ~1.5% fraud rate — realistic class imbalance typical of real banking data. Applied EDA to assess feature correlations, then trained a Random Forest classifier. Evaluated with precision, recall, and confusion matrix.

Random Forest Class Imbalance EDA Scikit-learn
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Digital Payment Fraud Detection

A deliberate model comparison exercise: Logistic Regression vs Random Forest on the same payment dataset. The dataset turned out to be poorly representative (hence the "bad dataset" label) — a useful lesson in how data quality affects model trustworthiness.

Logistic Regression Random Forest Model Comparison Pandas
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Housing Price Regression

My first end-to-end ML pipeline — data loading, preprocessing, train/test split, model training, and evaluation. A regression problem on real estate data. Simple by current standards, but where the habit of structuring ML work properly began.

Regression Preprocessing Scikit-learn NumPy
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Covid-19 Global Data Analytics

Data cleaning and visualisation exercise on a global Covid-19 dataset. Handled missing continent labels caused by non-country location entries, then produced comparative continent charts using Matplotlib with logarithmic scaling to handle order-of-magnitude differences.

Pandas Matplotlib Data Cleaning Data Visualisation
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Diabetes — Explainable AI

Classification on the Kaggle diabetes dataset (768 rows, 9 features) with a focus on explainability rather than pure accuracy. Demonstrates how to interrogate model decisions and understand which features drive predictions — a skill directly relevant to high-stakes domains like healthcare and security.

Explainable AI Classification Kaggle Scikit-learn
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Write-ups & Analysis

A1 Name Sorting Algorithm — Code Exploration
Implemented a name-sorting algorithm in both Python and JavaScript as an introduction to language syntax, data structures, and algorithmic thinking. The write-up documents the implementation approach and observations on how each language handles the same problem.
A2 Python vs JavaScript — Language Comparison
A structured comparison of Python and JavaScript across syntax, typing, use cases, and ecosystem. Covers where each language excels — Python's dominance in data and ML workflows vs JavaScript's role in web and asynchronous programming — and what using both teaches about thinking in different paradigms.
A3 Artificial Intelligence in 10 Years
An essay written at the start of the MSc exploring where AI is heading over the next decade — covering advances in large language models, AI in defence and security, the role of explainability in high-stakes systems, and the ethical and societal implications of increasingly autonomous systems. A snapshot of my thinking at the beginning of the programme.

Studies & Industry — Hand in Hand

The goal was never to finish the MSc in isolation. Each module I complete, each concept I study, I want to be putting into practice in a real engineering environment. The network anomaly detector in Project 1 is proof of that — it was built during the programme, not after it.

Landing a role in ML or AI engineering will accelerate the MSc, not compete with it. Production problems surface edge cases and constraints that academic datasets never do. That gap between textbook and real-world is exactly where the most important learning happens — and I intend to be working in it.