Using both python and R separately, to conduct. PCA (principal component analysis).The task needs to be done inbothPython (Jupiter notebook)andR-studioseparately. The submission will be...

Using both python and R separately, to conduct. PCA (principal component analysis).











The task needs to be done in both Python (Jupiter notebook) and R-studio separately. The submission will be python file and R file. Only coding, no MS-WORD file is needed, but please provide kind comment-out explanation in your coding. 1) import the attached/given data file. (200 rows and one head row) 2) display the data or data head in Jupiter notebook and R-studio 3) calculate and show the covariance matrix of the imported data. 4) conduct PCA (principal component analysis) with the given data. 5) Display each (all 10) eingen vector and eigen value separately. 6) Calculate the explanatory power when we use first big 3 principal components. 7) Calculate the explanatory power when we use first big 5 principal components. MSFT DateA1A2A3A4A5A6A7A8A9A10 1/25/22291.519989294.98999285.170013288.48999285.81189269.743347273.166656260.799988269.956665269.956665 1/26/22307.98999308.5293.029999296.709991293.955566271.670013292.286682271.570007290.143341290.143341 1/27/22302.660004307.299988297.929993299.839996297.056549289.893341296.626678284.593323288.123322288.123322 1/28/22300.230011308.480011294.450012308.26001305.398407290.709991295.493347281.42334293.296661293.296661 1/31/22308.950012312.380005306.369995310.980011308.09314292.92334295.480011277.533325279.763336279.763336 2/1/22310.410004310.630005305.130005308.76001305.893738283.033325285.216675275.053345279.429993279.429993 2/2/22309.630005315.119995308.880005313.459991310.55011285.433319288.713318268.190002268.193329268.193329 2/3/22309.48999311.230011299.959991301.25298.45343265.176666283.329987260.723328274.799988274.799988 2/4/22300.209991308.799988299.970001305.940002303.099915279.82666286.853333277.33667286.323334286.323334 2/7/22306.170013307.839996299.899994300.950012298.15625283.816681284.816681270.119995279.433319279.433319 2/8/22301.25305.559998299.950012304.559998301.732697280.066681281.266663264.589996265.116669265.116669 2/9/22309.869995311.929993307.390015311.209991308.320984260.203339266.899994252.013336255.456665255.456665 2/10/22304.040009309.119995300.700012302.380005299.572937258.42334268.523346252.190002267.296661267.296661 2/11/22303.190002304.290009294.220001295.040009292.301086269.666656280.666656267.420013280.07666280.07666 2/14/22293.769989296.76001291.350006295292.261505276.996674291.666656275.23999290.533325290.533325 2/15/22300.01001300.799988297.019989300.470001297.680695291.496674302.616669289.130005301.796661301.796661 2/16/22298.369995300.869995293.679993299.5297.333221304.993347314.283325302.363342307.053345307.053345 2/17/22296.359985296.799988290290.730011288.626678310332.619995307.25331.32666331.32666 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