Python-bloggers

In our last post, we explored mean-variance optimization (MVO) and finally reached the efficient frontier. In the process, we found that different return estimates yielded different frontiers both retrospectively and prospectively. We also introduced the concept of satsificing, originally developed by Herbert Simon. Simply put, satisficing is choosing the best ...

Jonas Kristoffer Lindeløv wrote a great visual explanation of how the most common statistical tests (t-test, ANOVA, ANCOVA, etc) are all linear models in the back-end. Jonas’ original blog uses R programming to visually show how the tests work, what the linear models look like, and how different approaches ...

When building a ML model, you must designate an evaluation metric, which tells the algorithm what you’re optimizing for. One commonly used evaluation metric is accuracy, that is, what percentage of your data your model makes the correct prediction for....

We have explained how we can reduce the dimensions by applying the following algorithms: PCA and t-SNE Autoencoders We will ... Read moreNon-Negative Matrix Factorization for Dimensionality Reduction

Description This dataset contains the votes From Country to To Country for Eurovision 2016. There are the Jury Votes and ... Read moreHierarchical Clustering of Countries based on Eurovision Votes

What was your journey to joining DataCamp? I studied applied computer science in Leuven, Belgium and to complete my undergraduate degree, I was required to complete an internship. I found out about DataCamp when they sponsored an app I worked on for ...

The logic Most images are an RGB array where we can easily apply K-Means Clustering. The Centers of each cluster ... Read moreGet the Dominant Colors of an Image with K-Means

We have provided an example of K-means clustering and now we will provide an example of Hierarchical Clustering. We will ... Read moreHierarchical Clustering in Python