A collection of machine learning models I’ve implemented from scratch and with libraries — including Linear Regression, Logistic Regression, and K-Nearest Neighbors (KNN), XGBoost, SVM — for learning, analysis, and visualization.
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Update .gitignore to include additional directories and file types
c529587View on GitHubchore: clean up empty code change sections in the changes log
7dc6e86View on GitHubIn this project, I built and trained a Convolutional Neural Network (CNN) to classify handwritten digits using the MNIST dataset.
ff3a8bbView on GitHubImplement news crawler with Scrapy, including item processing and initial data storage
f02c1f2View on GitHubAdd initial implementation of news crawler with Scrapy framework
c0b541eView on GitHubRefactor code structure for improved readability and maintainability
aa8f1d0View on GitHub225e3b8View on GitHubIn this notebook, I compare Principal Component Analysis (PCA) and t-SNE as dimensionality reduction techniques for visualizing high-dimensional data.
0892695View on GitHubworked on an unsupervised learning project focused on customer segmentation using Hierarchical Clustering on the Mall Customers dataset.
c0785dbView on GitHub