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About Unnamed: 0 Sample Rock - Type Sio2 Tio2 Al2O3 Fe2O3 Mno Mgo Cao Na2O K2O P2O5 0 0 1 1 2 2 3 3 4 4
This document provides an overview of principal component analysis (PCA) and logistic regression, and describes a Python code tutorial for applying these techniques to analyze and classify rock samples. PCA is used to reduce the dimensionality of the rock sample data characterized by various oxide contents. Logistic regression is then used to efficiently classify the samples in the lower-dimensional feature space obtained from PCA. The tutorial walks through loading and preprocessing the sample data, performing PCA to identify principal components, transforming the data onto the top two principal components to capture most variance, and applying logistic regression to the transformed data for classification.
Detailed Information
| Author: | ['Nishant Tripathi'] |
|---|---|
| Publication Year: | 2019 |
| Pages: | 1 |
| Language: | English |
| Format: | |
| Price: | FREE |
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