Here you’ll find everything about correlation and covariance, whether it’s Python tutorials that introduce Pearson correlation, articles that show how to implement it in more sophisticated use cases, or conceptual articles that explain different types of correlation.
Correlation is a statistical measure that indicates the degree to which two variables are related. It can take values between -1 and 1, where a value of -1 indicates a strong negative correlation (i.e., as one variable increases, the other decreases), a value of 0 indicates no correlation, and a value of 1 indicates a strong positive correlation (i.e., as one variable increases, the other also increases). Correlation is commonly used in statistics and machine learning to understand the relationship between different variables in a dataset. For example, it can be used to identify features that are strongly related to the target variable, or to identify features that are highly correlated with each other and may be redundant. A high degree of correlation between two variables can be useful for making predictions, because it indicates that there is a strong relationship between the variables that the model can use.
Cryptocurrencies like Bitcoin or Ethereum are built on public blockchains, meaning anyone can see the transactions and trades happening on these networks. This transparency makes on-chain data an excellent resource for data science and machine learning. By examining transaction activity and the holdings of Bitcoin addresses, analysts can better understand … Read more
Content-based recommender systems are a popular type of machine learning algorithm that recommends relevant articles based on what a user has previously consumed or liked. This approach aims to identify items with certain keywords, understand what the customer likes, and then identify other items that are similar to items the … Read more
This article introduces affinity propagation – an unsupervised clustering technique that stands out from other clustering approaches by its capacity to resolve the number of clusters in a dataset. This tutorial shows how the affinity propagation model works by applying it to the cryptocurrency market. If you have followed recent … Read more
Cluster analysis is an unsupervised machine learning technique that groups similar objects into clusters and separates them from different ones. In unsupervised learning, the model recognizes patterns and associations from the data without requiring a target variable. This technique is beneficial for distinguishing objects whose similarities and differences are unknown. … Read more
The rapid rise in the price of Bitcoin and other cryptocurrencies has led to increased interest in analyzing and visualizing price trends in the crypto space. Line charts that use color as an overlay are handy for this purpose. The color of the line plot can illustrate changes in a … Read more