Lab 1: Getting Started with Data Mining in Python β NumPy Foundations
Kickstarting my data mining journey with Python: setting up the environment, exploring NumPy arrays, and understanding the foundations of multidimensional data.
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Computer Science student at University of Victoria
A portfolio-style space where I share projects, experiments, and lessons across Data Mining, Kaggle, Math, SQL, Theory, and Generative AI.
Kickstarting my data mining journey with Python: setting up the environment, exploring NumPy arrays, and understanding the foundations of multidimensional data.
Read more βLearning how to measure model performance: from true/false positives to confusion matrices, error types, and ROC curves β with hands-on examples in Python.
Read more βExperimenting with perceptrons, vectorization, and multi-layer neural networks β from toy datasets to digit recognition and universal approximation.
Read more βExploring parameter estimation through coin flips: learning how MLE and MAP differ, and why Bayesian priors make estimates more robust.
Read more βFrom probability distributions to SMOTE: exploring sampling techniques and applying them to balance datasets for better machine learning performance.
Read more βApplying logistic regression to classify newsgroup posts: exploring odds ratios, feature importance, and TF-IDF text representations.
Read more βExploring unsupervised learning through hierarchical clustering, dendrograms, and a music genre classification dataset.
Read more βReducing complexity with PCA and uncovering hidden signals with ICA β applying dimensionality reduction techniques to the Iris dataset and beyond.
Read more βIn this blog post, I analyze the UCI Heart Disease dataset to predict whether a patient has heart disease using two machine learning models: Logistic Regression and Random Forest. This project builds on my...
Read more βLogistic Regression Model: Interpretation & Explanation
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