Q.5 Given a dataset, you wish to explore the variations that are present in the features. However, suppose that the dataset consists of 1000 features. It is not easy to represent all of these features in a single plot to gain an idea of the trends contained within. What will you do in this scenario? For simplicity of implementation, consider the mtcars dataset present in the R environment. mpg cyl disp hp drat wt qsec vs am gear carb Mazda RX4 21.0 160.0 110 3.90 2.620 16.46 1 Mazda RX4 Wag 21.0 6. 160.0 110 3.90 2.875 17.02 1 4 4 Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 Hornet 4 Drive 21.4 6. 258.0 110 3.08 3.215 19.44 1 1 Hornet Sportabout 18.7 Data Flaie 360.0 175 3.15 3.440 17.02 3 Valiant 18.1 225.0 2.76 3.460 20.22 1 1 Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 3 4 Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 4 2 Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 4 eata Merc 280 19.2 pata 6. 167.6 123 3.92 3.440 18.30 1 4 4 Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 4 4 Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 3 3 OP Merc 450SL 17.3 3 Data 8 275.8 180 3.07 3.730 17.60 3

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Author:James Kurose, Keith Ross
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Q.5 Given a dataset, you wish to explore the
variations that are present in the features.
However, suppose that the dataset consists of
1000 features. It is not easy to represent all of
these features in a single plot to gain an idea
of the trends contained within. What will you
do in this scenario? For simplicity of
implementation, consider the mtcars dataset
present in the R environment.
Đata
mpg
cyl
disp
hp
drat
wt
qsec
am
carb
vs
gear
Mazda RX4 21.0
6
160.0
110
3.90
2.620
16.46
4
Mazda RX4 Wag 21.0
6
160.0
110
3.90
2.875
17.02
1
4
4
Datsun 710 22.8
4
108.0
93
3.85
2.320
18.61
1
4
1
Hornet 4 Drive 21.4
258.0
110
3.08
3.215
19.44
1
3
1
Hornet Sportabout 18.7
3Data
Flair
8
360.0
175
3.15
3.440
17.02
2
Valiant 18.1
6
225.0
105
2.76
3.460
20.22
1
3
1
Duster 360 14.3
8
360.0
245
3.21
3.570
15.84
3
4
Merc 240D 24.4
4
146.7
62
3.69
3.190
20.00
1
4
Merc 230 22.8
4
140.8
95
3.92
3.150
22.90
1
4
2
pata
6
Pata
Merc 280 19.2
167.6
123
3.92
3.440
18.30
1
4
4
Merc 280C 17.8
6
167.6
123
3.92
3.440
18.90
1
4
Merc 450SE 16.4
8
275.8
180
3.07
4.070
17.40
3
3 Date
Flair
Merc 450SL 17.3
8
275.8
180
3.07
3.730
17.60
3
Transcribed Image Text:Q.5 Given a dataset, you wish to explore the variations that are present in the features. However, suppose that the dataset consists of 1000 features. It is not easy to represent all of these features in a single plot to gain an idea of the trends contained within. What will you do in this scenario? For simplicity of implementation, consider the mtcars dataset present in the R environment. Đata mpg cyl disp hp drat wt qsec am carb vs gear Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 4 Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 1 4 4 Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 4 1 Hornet 4 Drive 21.4 258.0 110 3.08 3.215 19.44 1 3 1 Hornet Sportabout 18.7 3Data Flair 8 360.0 175 3.15 3.440 17.02 2 Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 3 1 Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 3 4 Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 4 Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 4 2 pata 6 Pata Merc 280 19.2 167.6 123 3.92 3.440 18.30 1 4 4 Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 4 Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 3 3 Date Flair Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 3
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