In data science, there are many different ways to slice the pie. While many refer to independent and dependent variables differently, they usually mean the same thing. Your predictor variables are your independent variables, and with these, you’ll (hopefully) be…
Normal Distribution Vs. Uniform Distribution Python Code Using Pandas import numpy as np import pandas as pd # generate an array of normal values normal_distribution = np.random.normal(size=100000) # generate an array of uniform values uniform_distribution = np.random.uniform(size=100000) # create a…
In some machine learning problems, it’s not uncommon to have thousands of variables. While this is nice, keeping that many variables in your dataset will lead to many problems down the road. But don’t worry, we’ll teach you how to…
Data science and data mining are hot topics in the industry. Companies can’t seem to hire enough people to crunch their numbers and do their analytics. Harvard Business School even calls data science the sexiest job of the 21st century!…
While you’re probably much more familiar with the Student’s t-test (independent t-test), there is another t-test that doesn’t get nearly enough credit. The Welch’s t-test is a test that you need in your toolbox if you want to succeed as…
Even though clustering is a cornerstone of data science and data mining, many falsely assume that clustering does not come without its challenges. In this 9-minute read, we will go over the challenges of clustering, what clustering is, the different…
Normality has been shown to help provide more stable machine learning models and improve the accuracy of these models in the long term. The problem is everyone is using their power transformer wrong. This 2-minute guide will show you how…
In natural language processing, understanding the meaning (semantics) of a corpus (text) is essential. But how can computers derive meaning from text if computers can’t read? How can they find sentences with similar similarity metrics if they, again, can’t read?…
Natural Language Processing is a cornerstone of data science. With so many different tools out there, sometimes it’s easy to get a bit confused. One thing many data scientists (including me) mess up on is the differences between the CountVectorizer…
As its names suggest, “outliers” refers to a group or singular piece of information that doesn’t seem to fit in with the other information. Our job as data scientists is to identify and deal with these potential outliers. In this…