GitHub - Advaitiyer/advanced-data-mining: Topics: Network Analysis with NetworkX, Regularization and Resampling Methods, Non-cryptographic Hashing Algorithms, Machine Learning, and Natural Language Processing

HW1: Evaluating bounds of Euclidean and Manhattan distances for n d-dimensional arrays.
HW2: Text clustering using k-means with eigenvalue decomposition.
HW3: Regularized Linear models (both Lasso and Ridge implementations), as well as Spectral Clustering implementation with Networkx.
HW4: Implementation of Flajolet Martin algorithm for counting highest trailing zeros; Bloom Filter for spam detection; MapReduce for Locality-Sensitive Hashing (LSH); Jaccard Similarity between k-shingles.
Project: Network analysis of Amazon's co-purchased products (Stanford Network Analysis Library).

Name		Name	Last commit message	Last commit date
Latest commit History 44 Commits
HW1		HW1
HW2		HW2
HW3		HW3
HW4		HW4
Project		Project
HW1.png		HW1.png
HW2.png		HW2.png
HW3.png		HW3.png
HW4.png		HW4.png
Project.png		Project.png
README.md		README.md
average-rating.png		average-rating.png
centrality-high.png		centrality-high.png
clustering-coefficient.png		clustering-coefficient.png
clustering-high.png		clustering-high.png
degree-centrality.png		degree-centrality.png
degree-laplacian.png		degree-laplacian.png
eigenvecs-vals.png		eigenvecs-vals.png
false-positive.png		false-positive.png
lasso-features.png		lasso-features.png
less-reg.png		less-reg.png
linear-regression.png		linear-regression.png
more-reg.png		more-reg.png
proj-lasso-reg.png		proj-lasso-reg.png
proj-linear-reg.png		proj-linear-reg.png
proj-ridge-reg.png		proj-ridge-reg.png
ridge-regression.png		ridge-regression.png
spectral-clustering.png		spectral-clustering.png
summary-statistics.png		summary-statistics.png
trailing-zeros.png		trailing-zeros.png

Advaitiyer/advanced-data-mining