Comprehensive Machine Learning Portfolio
Course: Machine Learning (ISM 6251)
Timeline: August 2025 - November 2025
Project Type: Coursework
Python Flask HTML/CSS JavaScript
Project Description
This is my collection of machine learning projects from my ISM 6251 course, where I got hands-on experience with various ML algorithms and techniques. I worked with Python and popular libraries like scikit-learn, pandas, and matplotlib to build predictive models. The projects cover different types of machine learning classification algorithms like decision trees and random forests, regression models for prediction, clustering techniques for grouping data, and model evaluation methods to measure accuracy. I learned how to preprocess data, select appropriate features, train models, and validate their performance using techniques like cross-validation. Each project includes Jupyter notebooks with code, visualizations, and my analysis of the results. This portfolio demonstrates my ability to apply machine learning concepts to real datasets and interpret what the models are telling us.
Project Resources
Includes documentation, ERDs, data dictionaries, and other project materials.
Course Information
Machine Learning
ISM 6251
This was an intensive, hands-on course in machine learning that really challenged me. We covered a wide range of ML algorithms like, decision trees, random forests, regression models, neural networks, support vector machines, k-nearest neighbors, and clustering techniques. The course emphasized practical implementation using Python with libraries like scikit-learn, PyTorch, and TensorFlow. Every week we had coding assignments where we applied these algorithms to real datasets, learning how to preprocess data, engineer features, train models, and evaluate their performance. We worked in groups on larger projects that required us to tackle real-world prediction problems from start to finish. The course taught me not just how the algorithms work mathematically, but how to choose the right approach for different problems and interpret what the models are telling us. It was demanding but incredibly rewarding, I came out of it confident in my ability to apply machine learning to business problems.
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