{"id":14825,"date":"2019-06-22T23:12:45","date_gmt":"2019-06-23T03:12:45","guid":{"rendered":"http:\/\/bangla.salearningschool.com\/recent-posts\/machine-learning-apply-pca-on-datasets-and-related\/"},"modified":"2020-02-08T09:28:00","modified_gmt":"2020-02-08T14:28:00","slug":"machine-learning-apply-pca-on-datasets-and-related","status":"publish","type":"post","link":"http:\/\/bangla.sitestree.com\/?p=14825","title":{"rendered":"Machine Learning: Apply PCA on Datasets and Related"},"content":{"rendered":"

FactorAnalyzer
\nhttps:\/\/github.com\/EducationalTestingService\/factor_analyzer<\/a>
\n—
\nsruti-jain\/Marketing-Analysis-for-Hotel-Chain-website
\nhttps:\/\/github.com\/sruti-jain\/Marketing-Analysis-for-Hotel-Chain-website<\/a>
\n—<\/p>\n

Understanding PCA (Principal Component Analysis) with Python
\nhttps:\/\/towardsdatascience.com\/dive-into-pca-principal-component-analysis-with-python-43ded13ead21<\/a><\/p>\n

The code for the most part will work though it used an earlier version of Python
\nYou will need module: StandardScaler
\nOtherwise you might find the code below to be useful:
\n[# ref: https:\/\/python-for-multivariate-analysis.readthedocs.io\/a_little_book_of_python_for_multivariate_analysis.html<\/a>]<\/p>\n

import sklearn
\nfrom sklearn import preprocessing<\/p>\n

standardisedX = sklearn.preprocessing.scale(cancer.data)
\nstandardisedX = pd.DataFrame(standardisedX) #, index=cancer.data.index, columns=cancer.data.columns)
\nstandardisedX.apply(np.mean)<\/p>\n

X_scaled = standardisedX<\/p>\n

from sklearn import decomposition
\npca = decomposition.PCA(n_components=3).fit(standardisedX)
\npca = decomposition.PCA(n_components=3).fit_transform(standardisedX)
\nX_pca = pca<\/p>\n

ex_variance=np.var(X_pca,axis=0)
\nex_variance_ratio = ex_variance\/np.sum(ex_variance)
\nex_variance_ratio<\/p>\n

Xax=X_pca[:,0]
\nYax=X_pca[:,1]
\nlabels=cancer.target
\ncdict={0:’red’,1:’green’}
\nlabl={0:’Malignant’,1:’Benign’}
\nmarker={0:’*’,1:’o’}
\nalpha={0:.3, 1:.5}
\nfig,ax=plt.subplots(figsize=(7,5))
\nfig.patch.set_facecolor(‘white’)
\nfor l in np.unique(labels):
\nix=np.where(labels==l)
\nax.scatter(Xax[ix],Yax[ix],c=cdict[l],s=40,
\nlabel=labl[l],marker=marker[l],alpha=alpha[l])<\/p>\n

# for loop ends
\nplt.xlabel("First Principal Component",fontsize=14)
\nplt.ylabel("Second Principal Component",fontsize=14)
\nplt.legend()
\nplt.show()<\/p>\n

Sayed Ahmed<\/p>\n

Linkedin: https:\/\/ca.linkedin.com\/in\/sayedjustetc<\/a><\/p>\n

Blog: http:\/\/sitestree.com<\/a>, http:\/\/bangla.salearningschool.com<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

FactorAnalyzer https:\/\/github.com\/EducationalTestingService\/factor_analyzer — sruti-jain\/Marketing-Analysis-for-Hotel-Chain-website https:\/\/github.com\/sruti-jain\/Marketing-Analysis-for-Hotel-Chain-website — Understanding PCA (Principal Component Analysis) with Python https:\/\/towardsdatascience.com\/dive-into-pca-principal-component-analysis-with-python-43ded13ead21 The code for the most part will work though it used an earlier version of Python You will need module: StandardScaler Otherwise you might find the code below to be useful: [# ref: https:\/\/python-for-multivariate-analysis.readthedocs.io\/a_little_book_of_python_for_multivariate_analysis.html] import sklearn from sklearn import preprocessing standardisedX … <\/p>\n

Continue reading<\/a><\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[1910,182],"tags":[],"class_list":["post-14825","post","type-post","status-publish","format-standard","hentry","category-ai-ml-ds-rl-dl-nn-nlp-data-mining-optimization","category---blog","item-wrap"],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"jetpack-related-posts":[{"id":14860,"url":"http:\/\/bangla.sitestree.com\/?p=14860","url_meta":{"origin":14825,"position":0},"title":"PCA: Understand the affecting Factors for your portfolio, or credit score, or food pattern (causing diseases)","author":"Sayed","date":"July 5, 2019","format":false,"excerpt":"Overview for Principal Components Analysis https:\/\/support.minitab.com\/en-us\/minitab\/18\/help-and-how-to\/modeling-statistics\/multivariate\/how-to\/principal-components\/before-you-start\/overview\/ \" The goal of principal components analysis is to explain the maximum amount of variance with the fewest number of principal components.\" Interpret the key results for Principal Components Analysis https:\/\/support.minitab.com\/en-us\/minitab\/18\/help-and-how-to\/modeling-statistics\/multivariate\/how-to\/principal-components\/interpret-the-results\/key-results\/ Interpret all statistics and graphs for Principal Components Analysis https:\/\/support.minitab.com\/en-us\/minitab\/18\/help-and-how-to\/modeling-statistics\/multivariate\/how-to\/principal-components\/interpret-the-results\/all-statistics-and-graphs\/ Methods and formulas\u2026","rel":"","context":"In "AI ML DS RL DL NN NLP Data Mining Optimization"","block_context":{"text":"AI ML DS RL DL NN NLP Data Mining Optimization","link":"http:\/\/bangla.sitestree.com\/?cat=1910"},"img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":76257,"url":"http:\/\/bangla.sitestree.com\/?p=76257","url_meta":{"origin":14825,"position":1},"title":"Can you answer these questions on Data Science Project Development","author":"Sayed","date":"August 24, 2024","format":false,"excerpt":"Can you answer these questions on Data Science Project Development Questions to answer 1. 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