Category : pca

I am trying to run PCA (Principal component analysis) on GPU. I am using skcuda.linalg.PCA for that purpose, but it’s not working. From their tutorial (https://scikit-cuda.readthedocs.io/en/latest/generated/skcuda.linalg.PCA.html): import pycuda.autoinit import pycuda.gpuarray as gpuarray import numpy as np import skcuda.linalg as linalg from skcuda.linalg import PCA as cuPCA pca = cuPCA(n_components=4) # map the data to 4 ..

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I wanted to draw 2d biplot using my data set (credit card churn data set). But my diagram includes my target variable also as a feature. How can I remove it? Dataset sample showing the headers I have attached the code I’ve used import pandas as pd import numpy as np from sklearn.preprocessing import StandardScaler ..

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I have used a data set related to credit card churn and derived PCA information as below. But I hardly have an idea to interpret them. Furthermore, how can I identify factors that contribute most to the customer’s decision of termination of credit card usage? Source: Python..

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I’m getting the below error Traceback (most recent call last): File "G:EduMy academicsMSc in CSrd semResearchPython filesimpot.py", line 84, in <module> plt.show(‘Principal Component’) File "C:UsersacerAppDataRoamingPythonPython39site-packagesmatplotlibpyplot.py", line 378, in show return _backend_mod.show(*args, **kwargs) TypeError: show() takes 1 positional argument but 2 were given The code I used is as below loading_scores = pd.Series(pca.components_[0]) sorted_loading_scores = loading_scores.abs().sort_values(ascending ..

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This is code that I found to help me understand how PCA works in detail; i am hitting a wall trying to understand the pca.components_.T loadings = pd.DataFrame( data=pca.components_.T * np.sqrt(pca.explained_variance_), columns=[f’PC{i}’ for i in range(1, len(X_train.columns) + 1)], index=X_train.columns ) loadings.head() Source: Python..

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