Use python machine learning.A group of data scientists want to analyze some data. They already cleaned up the data, with the result being a Dataframe called X_train. The Dataframe X_train has 1058 rows and 13 columns. It has the below columns (picture attached on how it looks like):'LotFrontage','LotArea','BsmtFinSF1', 'BsmtFinSF2', 'BsmtUnfSF','TotalBsmtSF','1stFlrSF', '2ndFlrSF', 'LowQualFinSF', 'GrLivArea','TotRmsAbvGrd','GarageArea','OpenPorchSF' Answer the following questions:1. Transform X_train using PCA. Assign the output to a variable X_train_pca.2. What is wrong with above approach? Scale the data, then repeat the above. 0123414521453145414551459LotFrontage LotArea BsmtFin SF1 BsmtFinSF2 BsmtUnfSF TotalBsmtSF 1stFlrSF 2nd FlrSF LowQualFin SF706854097848621665565.080.068.060.084.0845062.075.096001125095501426035.0367590.0 1721762.07500791799371058 rows x 13 columns...5470410083000000ooo。0000290150284434540490011408119531368561262920756114554711401221953125685612629209611145107211401221953125608667561053000694000000000 0000GrLivArea TotRmsAbvGrd Garage Area O548460608642171012621786171721981072114012211647125686679566768365250400460276

Here's the code to perform PCA on the DataFrame X_train: from sklearn.decomposition import PCA pca =…

Answered: A group of data scientists want to…

Database System Concepts

7th Edition

ISBN:9780078022159

Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Chapter1: Introduction

Section: Chapter Questions

Problem 1PE

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Question

Use python machine learning.

A group of data scientists want to analyze some data. They already cleaned up the data, with the result being a Dataframe called X_train. The Dataframe X_train has 1058 rows and 13 columns. It has the below columns (picture attached on how it looks like):

'LotFrontage','LotArea','BsmtFinSF1', 'BsmtFinSF2', 'BsmtUnfSF','TotalBsmtSF','1stFlrSF', '2ndFlrSF', 'LowQualFinSF', 'GrLivArea','TotRmsAbvGrd','GarageArea','OpenPorchSF'

Answer the following questions:

1. Transform X_train using PCA. Assign the output to a variable X_train_pca.

2. What is wrong with above approach? Scale the data, then repeat the above.

0
1
2
3
4
1452
1453
1454
1455
1459
LotFrontage LotArea BsmtFin SF1 BsmtFinSF2 BsmtUnfSF TotalBsmtSF 1stFlrSF 2nd FlrSF LowQualFin SF
706
854
0
978
486
216
655
65.0
80.0
68.0
60.0
84.0
8450
62.0
75.0
9600
11250
9550
14260
35.0
3675
90.0 17217
62.0
7500
7917
9937
1058 rows x 13 columns
...
547
0
410
0
830
0
0
0
0
0
ooo。
0
0
0
0
290
150
284
434
540
490
0
1140
811
953
136
856
1262
920
756
1145
547
1140
1221
953
1256
856
1262
920
961
1145
1072
1140
1221
953
1256
0
866
756
1053
0
0
0
694
0
0
0
0
0
0
000 00
0
0
GrLivArea TotRmsAbvGrd Garage Area O
548
460
608
642
1710
1262
1786
1717
2198
1072
1140
1221
1647
1256
8
6
6
7
9
5
6
6
7
6
836
525
0
400
460
276

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