Data Mining Course Assignments - Fall 2019

Incorporated unsupervised machine learning, PCA algorithm, and K-Means clustering to analyze and classify a database of cryptocurrencies.

Employing unsupervised learning techniques to cluster Italian wines grown by three different cultivars

NETFLIX MOVIES AND TV SHOWS CLUSTERING is a project that aims to cluster the available movies and TV shows on Netflix based on their attributes such as genre, release year, and country of production.

Code to accompany paper: "Features underlying speech versus music as categories of auditory experience"

Use unsupervised machine learning, PCA algorithm, and K-Means clustering to analyze and classify a database of cryptocurrencies.

Analyzing Cryptocurrency data utilizing unsupervised ML

Stardew Valley: Missed Connections is the thesis project of Meghan Andrews for her Masters of Professional Studies in Information and Data Visualization from Maryland Institute College of Art , completed December 2020

Using Unsupervised Machine Learning to examine the outcome of Cryptocurrencies data and how to analyze it.

Exploration of various ML models and techniques for cognitive computing tasks. The primary focus is analysing hidden representations and the effectiveness in classifying data

In Agglomerative we start with all points as individual clusters and then keep on combining clusters until required number of clusters are not formed using linkages like single, complete, average, ward or centroid.

Data science techniques for pattern recognition, data mining, k-means clustering, and hierarchical clustering, and KDE.

Agglomerative hierarchical clustering on 39.7K female fragrances 🤖

Dendrograms for the best Rafal ever

For an UK based non-store online retail for which we need to cluster it's customers in to different groups so that we can run targeted campaign for each group

Python implementation of WhatsUp for co-occurring event resolution in social media data

To identify different segments in the existing customer, based on their spending patterns as well as past interaction with the bank, using clustering algorithms, and provide recommendations to the bank on how to better market to and service these customers.