How to train data in SVM?

I have 366x265 of dataset. How can I train this type of data?

or I should normalize the data first?

here is my data looks like

53
72
132
160
183
132
133
160
209
210
42
209
22
42
220
42
233
234
233
278
207
208
247
248
278
279
208
247
13
12
220
258
267
266
271
299
207
207
207
34
34
35
204
204
204
284
285
285
238
238
238
202
205
208
203
205
199
285
328
197
198
284
285
297
328
296
298
328
198
199
304
306
307
307
290
291
290
252
253
299
27
253
287
288
39
40
266
267
288
332
265
267
287
331
332
24
262
249
297
295
49
50
295
49
295
286
265
267
303
303
293
294
303
313
314
20
246
294
314
19
20
74
74
75
174
273
275
342
172
171
172
48
47
48
166
163
166
167
312
68
286
288
312
68
287
288
312
347
347
347
304
304
304
47
305
157
305
305
277
316
348
316
348
316
348
19
20
19
153
154
298
297
298
264
265
318
335
351
318
335
351
35
103
312
313
318
335
339
340
351
34
35
312
338
340
245
86
322
323
322
84
146
269
270
269
351
334
268
269
314
334
333
334
293
293
268
280
281
293
268
281
268
344
345
345
103
103
103
105
104
105
257
258
258
319
318
319
277
38
266
232
266
266
266
265
266
267
302
257
267
221
222
257
258
267
269
207
208
221
255
256
269
207
256
269
309
311
12
308
311
12
209
311
231
232
284
295
314
295
245
295
246
247
248
322
248
300
302
333
188
333
188
296
333
16
296
16
296
16
20
20
287
298
286
277
292
188
189
267
277
291
242
255
277
292
242
242
255
327
255
280
255
280
280
280
233
234
259
279
281
233
235
254
259
279
281
312
174
234
235
254
255
259
276
278
312
313
111
174
111
250
273
274
109
110
251
273
110
181
330
253
293
253
291
253
264
263
264
258
282
258
258
252
227
251
252
19
239
240
267
18
19
240
258

Answer 1

[Kautuk Raj]

In general, it is a good practice to normalize your data before training a machine learning model. Normalizing the data ensures that all features (columns) have a similar scale so that no feature dominates the others. This can lead to better performance of the machine learning model.

To normalize your data in MATLAB, you can use the normalize function. More details about this function can be found on the documentation page.