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 a cryptocurrency network’s health and … 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 user has previously consumed or … Read more
Affinity propagation is a powerful unsupervised clustering technique that can identify hidden patterns in large datasets. In the cryptocurrency world, where new coins are constantly emerging and prices can be highly volatile, affinity propagation can help investors simplify the chaos. By analyzing historical price data, affinity propagation groups coins into clusters based on their past … Read more
Embark on a journey into the world of unsupervised machine learning with this beginner-friendly Python tutorial focusing on K-Means clustering, a powerful technique used to group similar data points into distinct clusters. This invaluable tool helps us make sense of complex datasets, finding hidden patterns and associations without the need for a predetermined target variable. … Read more
Are you intrigued by the fascinating world of cryptocurrency and looking to visually decipher its price trends? Welcome aboard! In this comprehensive tutorial, we will explore creating color-coded line charts using Python and Matplotlib, a powerful tool for effective analysis of changes along a third dimension. The past few years have witnessed a meteoric rise … Read more
Correlation analysis is a powerful tool in financial market analysis, helping investors to better understand the interdependence of different assets. But what happens when an unprecedented global pandemic like COVID-19 shakes up the market? In this tutorial, we will show you how to create a correlation matrix in Python that will help you visualize the … Read more
