The global Covid19 pandemic has, on an economic scale, shaken markets from all levels from micro to macro. But, did it shake the Nasdaq stock index and, if so, which factors of daily US Covid19 case numbers were predominant predictors?

I took a look at the daily US Covid19 case…


Many machine learning algorithms work better when features are on a relatively similar scale and close to normally distributed. MinMaxScaler, RobustScaler, StandardScaler, and Normalizer are scikit-learn methods to preprocess data for machine learning. Which method you need, if any, depends on your model type and your feature values.

These scalers…


The Null Hypothesis is one in which human beings' innate curiosity has never so much appreciated as compared to finding reason in ideas. Relaxing the null hypothesis makes for great ideas and greater curiosity.

Which information and which perspectives we take into account, when we try to decide whether a…


There are two major reasons behind non-stationarity of a time series:

Trend: Varying mean over time

Seasonality: Certain variations at specific time-frames


Working on data with many features and training models may take way too long when iterating through all the combinations for stepwise selection.

sklearn (nor Python)seem to have a forward selection algorithm/library(yet). However, it(sklearn) does provide recursive feature elimination, which is a greedy feature elimination algorithm similar to sequential backward…


For me, the thing about data science that makes it so exciting to the modern world is its unparalleled ubiquity — I see data science everywhere, even in the lifespan of the butterfly.

Data science allows me to dive deep, here and now, with computer science and mathematics into the…

DataScience Deep Dive

Deep Diving with Data Science

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