GitHub / krishcy25 / DimensionalityReduction-PrincipalComponentAnalysis-Using-Python
This repository contains code for PCA (Principal Component Analysis with fixed number of components) that reduces the number of dimensions, PCA with Scree plot (finding number of optimal components that explains maximum variance) in Fraud data set from Kaggle competition that contains more than 500 variables.
JSON API: http://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/krishcy25%2FDimensionalityReduction-PrincipalComponentAnalysis-Using-Python
PURL: pkg:github/krishcy25/DimensionalityReduction-PrincipalComponentAnalysis-Using-Python
Stars: 0
Forks: 0
Open issues: 0
License: None
Language: Jupyter Notebook
Size: 86.9 KB
Dependencies parsed at: Pending
Created at: almost 5 years ago
Updated at: 10 months ago
Pushed at: almost 5 years ago
Last synced at: 10 months ago
Topics: dimensionality-reduction, kaggle-competition, kaggle-fraud-detection, pca-analysis, principal-component-analysis, screeplot