# UBC ML Kaggle Competition

GOAL:

Predict a mysterious dataset using a given set of samples

TECHNOLOGY:

Python, Scikit, Jupyter, Google Colab

COMPANY:

UBC

DESCRIPTION:

Learning from an advanced machine learning course, I was able to implement my own algorithms at low levels. We analyzed decision trees, naive bayes, knn, ensemble methods, random forests, kmeans, DBSCAN, hierarchical, regression, gradient descent, RBF, perceptron, SVM, stochastic gradient, various boosting methods, MLE, MAP, PCA, matrix factorization, dimension reduction, compression, convolutional neural networks. I finished 2nd in the final class Kaggle competition, with my neural network. Archived Github upon request.