Nirpy Research

Snippets and data from the blog of Nirpy Research.

Data

Thsi folder contains the original datasets used in some of the posts.

milk.csv

This dataset is used in the following posts:

• Classification of NIR spectra by Principal Components Analysis

milk-powder.csv

Used in the following posts:

• Classification of NIR Spectra by Linear Discriminant Analysis in Python
• PLS Discriminant Analysis for binary classification in Python

peach_spectra+brixvalues.csv

This dataset is used in the following posts:

• Principal Component Regression in Python
• Partial Least Square Regression in Python
• A Variable Selection Method for PLS in Python
• Two Scatter Correction Techniques for NIR Spectroscopy in Python
• Exporting NIR Regression Models Built in Python
• Principal Component Regression in Python Revisited
• Principal Component Selection with a Greedy Algorithm
• Principal Component Selection with Simulated Annealing

plums.csv

Used in the following posts:

• Detecting outliers using the Mahalanobis distance with PCA in Python

Snippets

Whatever piece of code that can be of general use or will not make it in the last versions of the posts will be (in time) posted here.

• bootstrap.py - Data splitter implementing the Bootstrap cross-validation method. This is not currently available in scikit-learn. This class is consistent with scikit-learn usage in a limited case. For more info on how to use this class, read our tutorial K-fold and Montecarlo cross-validation vs Bootstrap: a primer.