1 | Initial Setup 1.1 | Package Installation numpy and pandas for basic data-manipulation sklearn and associated modules to run our machine learning algorithms scipy associated modules (norm) to compute the cosime similarity later on seaborn to output graphs Miscellaneous packages for flow of the notebook ! pip install utils ! pip install scikit-learn import pandas as pd import numpy as np from numpy.linalg import norm import scipy as sc from sklearn....
Section 0 Preface for Imports, Data Handling, & Methodologies Section 0.1 Preface for Write-Up Interpretation & Acknowledgements Code was used from my own Github repository, found at www.github.com/sunnydigital/IDS_F21, including code derived from Stephen Spivak from Introduction to Data Science, Fall 2021. Most of the code falling under the aforementioned two categories surrounds the PCA and k-means analysis plots. Section 0.2 Imports & Installation of Packages, Libraries, Seaborn Settings, and Dataset(s) Below we set the random seed to the numeric portion of my ID: N12345678 import packages & libraries as well as set the settings for seaborn plots...
Reduce, reuse? In all honesty, whilst building RIEGSIL v2 for ML applications, I just happened to have leftover graphics processors, a CPU, and power supply With the GPUs having already been fitted with waterblocks, they were made very difficult to sell and as in the interest of reusability, I headed over to the local Micro Center (and a quick browse of the web) and bought a few parts to assemble a smaller form-factor PC than the original RIEGSIL v1....