Python Labs

3 lab collections make one learning progression: object-oriented Python systems, exploratory data analysis and classical machine learning. They are practice, the ground where I consolidated the fundamentals behind the shipped systems.

Each collection keeps its own repository and its own arc. The arcs run from procedural scripts to class design, and from raw data to neural networks.

Links

• Object-oriented systems: python-oop-mini-systems on GitHub
• Exploratory data analysis: python-eda-mini-projects on GitHub
• Machine learning: machine learning course projects on GitHub

Object-oriented Python systems

A series of milestone builds moves from procedural games to multi-class applications. Tic-Tac-Toe practises function decomposition. Blackjack composes Card, Deck, Hand and Chips classes. A credit card validator implements the Luhn algorithm with rule-based card classification.

The later systems exercise inheritance and polymorphism. A bank account manager routes transfers through Account subclasses. An inventory system links Inventory and Product classes for create, update and search workflows. A library system tracks Book, Journal and DVD loans across Member records.

Exploratory data analysis

2 end-to-end pipelines practise the cleaning-to-insight arc. The 911 calls analysis extracts time features from emergency call records and maps call volumes by reason, day and month. The finance analysis pulls multi-stock price data, computes moving averages and returns, and reads co-movement from correlation matrices.

Machine learning

12 sections cover the classical toolkit in scikit-learn, ending with neural networks in TensorFlow and Keras. Regression and classification come first: linear and logistic regression, K-nearest neighbours, decision trees, random forests and support vector machines. Ensemble methods add gradient boosting and XGBoost.

Unsupervised methods follow: K-means clustering, hierarchical clustering and principal component analysis. Naive Bayes text classification, recommender systems, cross-validation and an introduction to PySpark complete the set.

Stack

Python 3 · pandas · NumPy · matplotlib · seaborn · Plotly · scikit-learn · XGBoost · TensorFlow · Keras · Jupyter · Git/GitHub