Unsupervised Machine Learning analysis to find patterns in Cryptocurrencies market valuations.

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 u…

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 project involved using KMeans clustering to segment customers based on behavioral patterns and preferences providing customer segments for targeted marketing strategies.

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.

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.

Used machine learning techiques to cluster and visualize cryptocurrency data.

Unsupervised Machine Learning

Unsupervised machine learning models used to group the cryptocurrencies to help prepare for a new investment.

Machine learning with elbow curves and K-Means model

Use unsupervised machine learning techniques to analyze cryptocurrency data

Using unsupervised machine learning algorithms to classify entries in a database of cryptocurrencies.

Used unsupervised machine Learning predictive algorithm to analyze the investment prospects and tendencies of cryptocurrencies.

Application of unsupervised learning to create a classification system for 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 was used to establish a classification system for actively trading cryptocurrencies for potential investment prospects.

This project analyzes customer behavior in online retail using cohort analysis, **Recency, Frequency, Monetary (RFM)** metrics, and K-means clustering to segment customers. It identifies key groups like Best, At-Risk, and Average Customers, offering strategies to enhance engagement and drive loyalty.