How to use svm regression model to train the part of the data and test rest of the data
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Hi, I have below data, and I want to build svm based regression model using part of the data, and test the built model on rest of the data. Kindly some one help how can I do this. Many thanks in advance,
A=(each column is a variable, six variables A,B,C,D,E,F
and each row is a data point of corresponding variable)
0.815 0.276 0.162 0.417 0.644 0.963
0.906 0.680 0.794 0.050 0.379 0.547
0.127 0.655 0.311 0.903 0.812 0.521
0.913 0.163 0.529 0.945 0.533 0.232
0.632 0.119 0.166 0.491 0.351 0.489
0.098 0.498 0.602 0.489 0.939 0.624
0.278 0.960 0.263 0.338 0.876 0.679
0.547 0.340 0.654 0.900 0.550 0.396
0.958 0.585 0.689 0.369 0.622 0.367
0.965 0.224 0.748 0.111 0.587 0.988
0.158 0.751 0.451 0.780 0.208 0.038
0.971 0.255 0.084 0.390 0.301 0.885
0.957 0.506 0.229 0.242 0.471 0.913
0.485 0.699 0.913 0.404 0.230 0.796
0.800 0.891 0.152 0.096 0.844 0.099
0.142 0.959 0.826 0.132 0.195 0.262
0.422 0.547 0.538 0.942 0.226 0.335
0.916 0.139 0.996 0.956 0.171 0.680
0.792 0.149 0.078 0.575 0.228 0.137
0.959 0.258 0.443 0.060 0.436 0.721
0.656 0.841 0.107 0.235 0.311 0.107
0.036 0.254 0.962 0.353 0.923 0.654
0.849 0.814 0.005 0.821 0.430 0.494
0.934 0.244 0.775 0.015 0.185 0.779
0.679 0.929 0.817 0.043 0.905 0.715
0.758 0.350 0.869 0.169 0.980 0.904
0.743 0.197 0.084 0.649 0.439 0.891
0.392 0.251 0.400 0.732 0.111 0.334
0.655 0.616 0.260 0.648 0.258 0.699
0.171 0.473 0.800 0.451 0.409 0.198
0.706 0.352 0.431 0.547 0.595 0.031
0.032 0.831 0.911 0.296 0.262 0.744
0.277 0.585 0.182 0.745 0.603 0.500
0.046 0.550 0.264 0.189 0.711 0.480
0.097 0.917 0.146 0.687 0.222 0.905
0.823 0.286 0.136 0.184 0.117 0.610
0.695 0.757 0.869 0.368 0.297 0.618
0.317 0.754 0.580 0.626 0.319 0.859
0.950 0.380 0.550 0.780 0.424 0.805
0.034 0.568 0.145 0.081 0.508 0.577
0.439 0.076 0.853 0.929 0.086 0.183
0.382 0.054 0.622 0.776 0.262 0.240
0.766 0.531 0.351 0.487 0.801 0.887
0.795 0.779 0.513 0.436 0.029 0.029
0.187 0.934 0.402 0.447 0.929 0.490
0.490 0.130 0.076 0.306 0.730 0.168
0.446 0.569 0.240 0.509 0.489 0.979
0.646 0.469 0.123 0.511 0.579 0.713
0.709 0.012 0.184 0.818 0.237 0.500
0.755 0.337 0.240 0.795 0.459 0.471
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