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Unsupervised Machine Learning analysis to find patterns in Cryptocurrencies market valuations.
K-means clustering of texts (survey answers) using word-embeddings, finding optimal elbow-point, and averaging multiple-word expressions.
Using k-Means algorithm and a Principal Component Analysis (PCA) to cluster cryptocurrencies.
The objective of this project is to categorise the countries using some socio-economic and health factors that determine the overall development of the country and then accordingly suggest the NGO the country which is in dire need of help.
We created a report that includes what cryptocurrencies are on the trading market and how they could be grouped to create a classification system for this new investment.The data Martha provided us was not ideal, so we processed to fit the machine learning models. Since there is no known output for what Martha is looking for, we decided to use unsupervised learning. To group the cryptocurrencies, Martha and us decided on a clustering algorithm. We used data visualizations to share our findings with the board.
Unsupervised Machine Learning and Cryptocurrencies
Used unsupervised machine Learning predictive algorithm to analyze the investment prospects and tendencies of cryptocurrencies.
Machine learning with elbow curves and K-Means model
An analytic report outlining how different cryptocurrencies on the trading market can be grouped to create a new classification system for an investment portfolio and the investment's bank customers.
Unsupervised Learning
using machine learning to create a class system for tradable cryptocurrencies
The purpose of this project is to analyze and cluster cryptocurrencies based on their price change percentage over different time periods.
Unsupervised Machine Learning
Employ unsupervised machine learning to predict outcomes with K-Means and PCA
This project involved clustering participants of the Iowa Gambling Task based on their decision model parameters
clustering of night time satellite images and depicting them by use of different colors
Application of unsupervised learning to create a classification system for cryptocurrencies.
Use unsupervised machine learning techniques to analyze cryptocurrency data
Unsupervised machine learning models used to group the cryptocurrencies to help prepare for a new investment.
Use unsupervised machine learning techniques to analyze cryptocurrency data.
Used machine learning techiques to cluster and visualize cryptocurrency data.
Unsupervised Machine Learning Technique - KMeans Clustering to classify cryptocurrency data using Principle Component Analysis (PCA)to to reduce the number of dimensions of the scaled data.
This project is about creating a report that includes what cryptocurrencies are on the trading market and how they could be grouped to create a classification system for a new investment.
Unsupervised machine learning was used to establish a classification system for actively trading cryptocurrencies for potential investment prospects.
Python, unsupervised machine learning
Unsupervised machine learning analysis finding patterns in cryptocurrencies' market valuations.
Using unsupervised machine learning algorithms to classify entries in a database of cryptocurrencies.
Supervised Learning Recap
Generated analysis of cryptocurrencies is available on the trading market and how they can be grouped using classification. To do this I used unsupervised learning and Amazon SageMaker by clustering cryptocurrencies and creating plots to present results.
Analysis of cryptocurrency data using unsupervised machine learning.