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…
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…
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…
We’ve all been there; you’ve worked night and day to finally get an accurate model for your dataset. You’ve finally got an output from your model – but it’s scaled. What do you do? How do you reverse standardization? In…
As data scientists, you’ll often face datasets too large to understand. With the recent advances in machine learning, we can visualize nearly any variable, parse massive amounts of text into understandable pieces and find hidden trends within our datasets. After…
When it comes to classification problems, your population data is critical. While investigating our target class, we often notice disproportionate sampling. In these instances, what do we do? The Stratified sampling technique means that your sample data will have the…