Practical Supervised Machine Learning Projects in Python

This page presents my machine learning work, with a focus on practical model comparison, structured experimentation and clear technical documentation.

My main machine learning project compares 60 supervised learning models across regression and binary classification tasks. The aim was not only to identify strong-performing models, but also to understand how different model families behave in practice, and how performance, speed, complexity, prediction behaviour and usability affect model selection.

Featured Project: Supervised Machine Learning Model Comparison

This project is a practical comparison of supervised machine learning models using Python. It includes two main parts:

  • Regression model comparison
  • Binary classification model comparison

Across the project, I tested and compared multiple machine learning algorithms using a consistent workflow. The project includes data preparation, preprocessing, model training, evaluation, hyperparameter tuning, visual analysis and practical model selection.

Regression: Predicting Diamond Prices

The regression part focuses on predicting diamond prices using structured data. I compared a wide range of regression models to understand how different algorithms handle the same prediction task.

The project includes baseline models, linear models, regularised models, tree-based models, ensemble models, boosting models and other supervised learning approaches.

The aim was to compare not only accuracy, but also practical factors such as prediction behaviour, runtime, model size and suitability for real-world use.

Binary Classification: Predicting a Diamond Clarity Group

The binary classification part focuses on predicting whether a diamond belongs to a higher clarity group. This allowed me to explore classification-specific evaluation methods and compare how different models perform on a supervised classification problem.

This part includes classification metrics, probability-based evaluation, model comparison tables, visual analysis and practical discussion of model strengths and weaknesses.

What the Project Includes

The project demonstrates a complete supervised machine learning workflow, including:

  • Data loading and preparation
  • Feature preprocessing
  • Train/test splitting
  • Model training
  • Cross-validation
  • Hyperparameter tuning
  • Final test evaluation
  • Runtime comparison
  • Model size comparison
  • Visual analysis
  • Practical model selection
  • GitHub documentation

Why I Built This Project

I built this project to go beyond simple model training and develop a deeper understanding of how machine learning models compare in practice.

Instead of testing only one or two algorithms, I wanted to create a broader comparison that shows the differences between model families, evaluation metrics, tuning results, prediction behaviour and practical usefulness.

This helped me understand that the “best” model is not always just the one with the highest score. A practical model also needs to be understandable, reliable, efficient and suitable for the task.

Key Skills Demonstrated

This project demonstrates skills in:

  • Python programming
  • pandas
  • NumPy
  • scikit-learn
  • supervised machine learning
  • regression
  • binary classification
  • model evaluation
  • cross-validation
  • hyperparameter tuning
  • data preprocessing
  • result comparison
  • data visualisation
  • technical documentation
  • GitHub project presentation

What I Learned

Through this project, I developed a stronger understanding of the full machine learning workflow. I learned how different models behave, how evaluation metrics affect interpretation, and why practical model selection requires more than simply choosing the highest-performing result.

The project also helped me improve my Python structure, documentation, visualisation and ability to present technical results clearly.

Project Links

GitHub repository: https://github.com/Mils3x3

Regression section: https://github.com/Mils3x3/supervised-ml-model-comparison/tree/main/regression

Binary classification section: https://github.com/Mils3x3/supervised-ml-model-comparison/tree/main/binary-classification