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...

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

Extracted 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

Jun 11, 2022
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