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Sometimes confused with linear regression by novices - due to sharing the term regression - logistic regression is far different from linear regression. While linear regression predicts values such as 2, 2.45, 6.77 or continuous values, making it a regression algorithm, logistic regression predicts values such as 0...
Cássia Sampaio
Byte
Working with Pandas DataFrames, you'll eventually face the situation where you need to find the maximum value for all of its columns or rows. Let's assume you already have a properly formatted DataFrame: import pandas as pd df_data = { "column1": [24, 9, 20, 24], "column2": [17,...
Dimitrije Stamenic
Assume you have a CSV (Comma Separated Values) file containing a dataset and would like to load it into memory for data manipulation with Python and Pandas. The process of importing a CSV is pretty straightforward. Although Python has a built-in csv module for reading CSV files, the chance is...
Missing values are common and occur either due to human error, instrument error, processing from another team, or otherwise just a lack of data for a certain observation. In this Byte, we'll take a look at how to fill NaNs in a DataFrame, if you choose to handle NaNs by...
David Landup
K-Means clustering is one of the most widely used unsupervised machine learning algorithms that form clusters of data based on the similarity between data instances. In this guide, we will first take a look at a simple example to understand how the K-Means algorithm works before implementing it using Scikit-Learn....
Pandas DataFrame columns give context into the values of the rows/entries we're working with. Sometimes, we need to remove them, when saving data for proprietary libraries that don't support columns, and sometimes we just want to export them in a different format. In any case - saving the columns...
Scikit-Learn offers several datasets to play around with - most of them being toy datasets to learn from and test things out. Some beginners find the comfort of a tabular Pandas DataFrame format more intuitive than NumPy arrays. Thankfully, you can import a dataset as a Bunch object containing a...
When string-based columns have quotes - we'll oftentimes want to get rid of them, in large part because 'string is technically a different string to string, which more often than not isn't a distinction we want to make. Whether you'll be performing NLP and tokenizing words (in which case, you'll...
When working with datasets from external sources - column names can get wild. Different naming conventions, cases (snake_case, CamelCase, etc.), as well as names are common. A common headache is caused by really long column names, that you might have to call on many times in the lifecycle of...
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