Question 2 Dataset seeds.csv contains size related measurements of 210 seeds of three varieties. Seven types of measurements are taken. This dataset is to be used as a training dataset for a classification analysis to predict a variety of a seed based on the taken measurements. Read in the data and check the structure. Convert the outcome variable to factor, replacing "out- come_var" with the name of the outcome variable in the code below. seeds Area Perimeter Compactn Kerneller Kernel Wic Asymmeti KernelGrc Variety 15.26 14.84 0.871 5.763 3.312 2.221 5.22 1 14.88 14.57 0.8811 5.554 3.333 1.018 4.956 1 14.29 14.09 0.905 5.291 3.337 2.699 4.825 1 13.84 13.94 0.8955 5.324 3.379 2.259 4.805 1 16.14 14.99 0.9034 5.658 3.562 1.355 5.175 1 14.38 14.21 0.8951 5.386 3.312 2.462 4.956 1 14.69 14.49 0.8799 5.563 3.259 3.586 5.219 1 14.11 14.1 0.8911 5.42 3.302 2.7 5 1 16.63 15.46 0.8747 6.053 3.465 2.04 5.877 1 16.44 15.25 0.888 5.884 3.505 1.969 5.533 1 15.26 14.85 0.8696 5.714 3.242 4.543 5.314 1 14.03 14.16 0.8796 5.438 3.201 1.717 5.001 1 13.89 14.02 0.888 5.439 3.199 3.986 4.738 1 13.78 14.06 0.8759 5.479 3.156 3.136 4.872 1 13.74 14.05 0.8744 5.482 3.114 2.932 4.825 1 14.59 14.28 0.8993 5.351 3.333 4.185 4.781 1 13.99 13.83 0.9183 5.119 3.383 5.234 4.781 1 15.69 14.75 0.9058 5.527 3.514 1.599 5.046 1 14.7 14.21 0.9153 5.205 3.466 1.767 4.649 1 12.72 13.57 0.8686 5.226 3.049 4.102 4.914 1 14.16 14.4 0.8584 5.658 3.129 3.072 5.176 1 14.11 14.26 0.8722 5.52 3.168 2.688 5.219 1 15.88 14.9 0.8988 5.618 3.507 0.7651 5.091 1 12.08 13.23 0.8664 5.099 2.936 1.415 4.961 1 15.01 14.76 0.8657 5.789 3.245 1.791 5.001 1 16.19 15.16 0.8849 5.833 3.421 0.903 5.307 1 13.02 13.76 0.8641 5.395 3.026 3.373 4.825 1 12.74 13.67 0.8564 5.395 2.956 2.504 4.869 1 14.11 14.18 0.882 5.541 3.221 2.754 5.038 1 13.45 14.02 0.8604 5.516 3.065 3.531 5.097 1 13.16 13.82 0.8662 5.454 2.975 0.8551 5.056 1 15.49 14.94 0.8724 5.757 3.371 3.412 5.228 1 14.09 14.41 0.8529 5.717 3.186 3.92 5.299 1 13.94 14.17 0.8728 5.585 3.15 2.124 5.012 1 15.05 14.68 0.8779 5.712 3.328 2.129 5.36 1 16.12 15 0.9 5.709 3.485 2.27 5.443 1 16.2 15.27 0.8734 5.826 3.464 2.823 5.527 1 17.08 15.38 0.9079 5.832 3.683 2.956 5.484 1