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Ryan Nolan

Ryan is a Data Scientist at a fintech company, where he focuses on fraud prevention in underwriting and risk. Before that, he worked as a Data Analyst at a tax software company. He holds a degree in Electrical Engineering from UCF.

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scikit-learn

Multicollinearity

May 21, 2025 Ryan Nolan No comments yet

dividing the total number of bases a player records by their total number of at-batsmaybe replace this with something else? CORRELATION MATRIX VIF Instead of using raw height, you might normalize or categorize height into bins, which could reduce the numerical interdependence.Calculate Condition Index (CI) How to address MulticollinearityDrop a Feature (At Bats) look at […]

Python Pandas

Pandas Sample

May 21, 2025 Ryan Nolan No comments yet

We are going to be looking at Pandas Sample(). The sample() method returns a specified number of random rows. it also returns one row if a number is not specified https://youtu.be/REhRhRUcluI Example 1 – if else state location To start with, we are going to be importing various libraries. pandas as pd random string  numpy […]

Time Series

Simple Exponential Smoothing

May 21, 2025 Ryan Nolan No comments yet
Python Pandas

Pandas MultiIndex

May 21, 2025 Ryan Nolan No comments yet

Working with structured data in Python often calls for more than just a flat table. When your dataset has multiple levels of information—like years and quarters, countries and cities, or products and categories—Pandas MultiIndex can be a powerful tool. It allows you to represent hierarchical relationships within your data, enabling advanced analysis, cleaner code, and […]

Python Pandas

Pandas Replace

May 21, 2025 Ryan Nolan No comments yet

In this Python Pandas tutorial we’re going to be taking a look at replace() which allows you to match a full value within your data frame and then replace it with something that you want.  This article will cover seven different examples of Pandas replace increasing the complexity along the way. https://youtu.be/uMuyRonKMk4 We are going […]

Python Pandas

Pandas Where

May 19, 2025 Ryan Nolan No comments yet

https://www.youtube.com/watch?v=Y7HMkDuR_DA&feature=youtu.be The where() function in Pandas is used to replace values in a DataFrame or Series where a condition is not met.  It is used to check a data frame for one or more conditions and return the result. To start with we import pandas and numpy To start we’re going to import two essential […]

Python Pandas

Pandas Mask

May 18, 2025 Ryan Nolan No comments yet

The mask() method is used to replce values where certian conditions are met. The mask() method in Pandas is used to replace values where certain conditions are met. https://youtu.be/rsh_9lZ2ToM Prep the Data To start we’re going to import pandas as pd. and also import numpy as np import pandas as pd import numpy as np […]

Python Pandas

Pandas Interpolation

May 12, 2025 Ryan Nolan No comments yet

To start we’re going to create a simple dataframe in python: https://youtu.be/BJHwPeRvyPE?si=lvsDqXBjb0mcC4ae import pandas as pd import numpy as np data = { ‘day’: pd.date_range(start=’2025-04-19′, periods=7), ‘temperature’: [np.nan, 30, np.nan, np.nan, 45, 40, np.nan] } df = pd.DataFrame(data) df2 = df.copy() df3 = df.copy() df4 = df.copy() Example 1 To start we’re going to create […]

Python Pandas

Pandas diff

May 6, 2025 Ryan Nolan No comments yet

By utilizing diff in Python Pandas we can find the difference between different rows and columns. In this article we will go over 9 different examples of utilizing it in different capacities. If you would like to watch a YouTube video based around the written tutorial, it is embedded below. We also have other Pandas […]

Python Pandas

Pandas Percentage Change

May 3, 2025 Ryan Nolan No comments yet

PCT_change() works nearly identical to .diff() within Python Pandas. The only difference is that we will get a decimal change instead of subtracting the two values. While the Pandas documentation calls this “Percentage Change” it really is the decimal representation of it and we need to multiply our value by 100 to get a true […]

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