Python-bloggers

Do you want to deal with large number of dimensions/features in data? Techniques like PCA, t-SNE and autoencoders can help you untangle these high level dimensions.

Where did we go wrong? Not another model! As we saw in the last post, one iteration of the yield curve – the spread between 10-year and 3-month Treasuries – doesn’t generate a great model of recession probabilities. Part of this is that recessio...

All models are wrong Build the model In the last post, we discussed the yield curve, why investors focus on it, and looked at one measure of the curve – the spread between 10-year and 3-month Treasury yields. In this post, we build a model that...

Introduction What is a yield curve? (Skip if you already know!) Show me the data! (Start here if don’t want the background) Investing pundits like to quote the yield curve as a nearly infallible indicator for the next recession. But what do the ...

In my neverending quest to track The Popularity of Data Science Software, it’s time to update the section on Scholarly Articles. The rapid growth of R could not go on forever and, as you’ll see below, its use actually declined … Continue reading →

Random forest is a type of machine learning algorithm in which the algorithm makes multiple decision trees that may use different features and subsample to making as many trees as you specify. The trees then vote to determine the class of an example. This approach helps to deal with the ...

How to use Python code inside R

Out of all the Python scientific libraries and packages available, which ones are not only popular but the most useful in getting the job done? We are going to look in three categories: Data Manipulation, Data Visualization and Data Modelling.

Do you wish to make your exploratory data analysis more intuitive, interactive and informative? Let's talk about Plotly.py and how you can create dynamic, professional-looking plots and interactive dashboards.

Since, R and Python remain the most popular languages, it seems reasonable to debate which one is better. We'll evaluate the two languages in four categories: Data Visualization, Modelling Libraries, Ease of learning and Community support.