Machine learning is a rapidly growing field that has gained a lot of attention in recent years.

Of course, the simplest strategy is to use your intuition. Sometimes it's obvious that some columns will not be used in any form in the final model (columns such as "ID", "FirstName", "LastName" etc). If you know that a particular column will not be used, feel free to drop it upfront. In our data, none of the columns stand out as such, so I'm not removing any in this step. Having missing values is not acceptable in machine learning, so people apply different strategies to clean up missing data (e.g., imputation).

This is a not-particularly-systematic attempt to curate a handful of my favorite resources for learning statistics and machine learning. This isn't meant to be comprehensive, and in fact is still missing the vast majority of my favorite explainers. Rather, it's just a smattering of resources I've found myself turning to multiple times and thus would like to have in one place. Finally, I've added a section with links to a few miscellaneous websites that often produce great content. Of the above, the second section is both the most incomplete and the one that I am most excited about.

R language is a free statistical computing environment; hence there are multiple ways/packages to achieve a particular statistical/quantitative output. I am going to discuss here a concise list of R packages that one can use for the modeling of financial risks and/or portfolio optimization with utmost efficiency and effectiveness. The intended audience for this article is financial market analysts interested in using R, and also for quantitatively inclined folks with a background in finance, statistics, and mathematics. Given the rise in the frequency of crises (the frequency of occurrence of financial market crises has certainly increased during the last 18 years or so; since the 1999 bubble burst), the modeling and measurement of financial market risk have gained tremendously in importance and the focus of portfolio allocation has shifted from the average side of the (mean, SD) coin to the SD side. Hence, it has become necessary to devise and employ methods and techniques that are better able to cope with the empirically observed extreme fluctuations in the financial markets.

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