Machine Learning — BYU CS 270

Four machine learning labs completed for BYU's CS 270 (Machine Learning), implemented in Jupyter notebooks using scikit-learn. Each lab applies a different fundamental machine learning algorithm to one or more real datasets, covering data preprocessing, model training, model evaluation, and analysis of results. Labs have been updated, cleaned, and reorganized to highlight the underlying concepts and techniques learned in this course.

Labs

Lab	Folder	Description
Clustering	clustering/	K-means and agglomerative clustering, evaluated with silhouette scores.
Decision Trees	decision_trees/	Decision tree classification and regression, cross-validation. Including a from-scratch implementation of the C4.5 algorithm, compared against scikit-learn's built-in implementations.
K-Nearest Neighbors	knn/	KNN classification and regression with feature scaling.
Neural Networks	neural_networks/	Backpropagation-based neural network classification and regression using scikit-learn's MLP models, with hyperparameter tuning via grid and randomized search.

Each folder contains the notebook for that lab along with the datasets it uses.

Setup

Clone the repo and install dependencies:

git clone https://github.com/samanthaljohn/machine-learning.git
cd machine-learning
pip install -r requirements.txt

Then launch Jupyter and open any notebook:

jupyter notebook

Datasets

Most datasets are sourced from the UCI Machine Learning Repository, provided as .arff or .csv files alongside the relevant notebook. No external downloads are required — everything needed to run a notebook is included in its lab folder.