python_for_analytics

1
{
2
 "cells": [
3
  {
4
   "cell_type": "markdown",
5
   "id": "ae58ad99",
6
   "metadata": {},
7
   "source": [
8
    "<h1 align=\"center\">3.5 Задачи классификации и кластеризации</h1>\n",
9
    "\n",
10
    "Задание:\n",
11
    "- Возьмите датасет с цветками iris’а (функция load_iris из библиотеки sklearn)\n",
12
    "- Оставьте два признака - sepal_length и sepal_width и целевую переменную - variety\n",
13
    "- Разделите данные на выборку для обучения и тестирования\n",
14
    "- Постройте модель LDA\n",
15
    "- Визуализируйте предсказания для тестовой выборки и центры классов\n",
16
    "- Отбросьте целевую переменную и оставьте только два признака - sepal_length и sepal_width\n",
17
    "- Подберите оптимальное число кластеров для алгоритма kmeans и визуализируйте полученную кластеризацию"
18
   ]
19
  },
20
  {
21
   "cell_type": "code",
22
   "execution_count": null,
23
   "id": "227bd386",
24
   "metadata": {},
25
   "outputs": [],
26
   "source": [
27
    "import pandas as pd\n",
28
    "import matplotlib.pyplot as plt\n",
29
    "\n",
30
    "from sklearn.datasets import load_iris\n",
31
    "from sklearn.model_selection import train_test_split\n",
32
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
33
    "from sklearn.metrics import accuracy_score\n",
34
    "from sklearn.cluster import KMeans"
35
   ]
36
  },
37
  {
38
   "cell_type": "code",
39
   "execution_count": 2,
40
   "id": "69c6c974",
41
   "metadata": {},
42
   "outputs": [],
43
   "source": [
44
    "iris = load_iris()"
45
   ]
46
  },
47
  {
48
   "cell_type": "code",
49
   "execution_count": 3,
50
   "id": "c82f8df0",
51
   "metadata": {},
52
   "outputs": [
53
    {
54
     "data": {
55
      "text/html": [
56
       "<div>\n",
57
       "<style scoped>\n",
58
       "    .dataframe tbody tr th:only-of-type {\n",
59
       "        vertical-align: middle;\n",
60
       "    }\n",
61
       "\n",
62
       "    .dataframe tbody tr th {\n",
63
       "        vertical-align: top;\n",
64
       "    }\n",
65
       "\n",
66
       "    .dataframe thead th {\n",
67
       "        text-align: right;\n",
68
       "    }\n",
69
       "</style>\n",
70
       "<table border=\"1\" class=\"dataframe\">\n",
71
       "  <thead>\n",
72
       "    <tr style=\"text-align: right;\">\n",
73
       "      <th></th>\n",
74
       "      <th>sepal length (cm)</th>\n",
75
       "      <th>sepal width (cm)</th>\n",
76
       "      <th>petal length (cm)</th>\n",
77
       "      <th>petal width (cm)</th>\n",
78
       "      <th>variety</th>\n",
79
       "    </tr>\n",
80
       "  </thead>\n",
81
       "  <tbody>\n",
82
       "    <tr>\n",
83
       "      <th>0</th>\n",
84
       "      <td>5.1</td>\n",
85
       "      <td>3.5</td>\n",
86
       "      <td>1.4</td>\n",
87
       "      <td>0.2</td>\n",
88
       "      <td>0</td>\n",
89
       "    </tr>\n",
90
       "    <tr>\n",
91
       "      <th>1</th>\n",
92
       "      <td>4.9</td>\n",
93
       "      <td>3.0</td>\n",
94
       "      <td>1.4</td>\n",
95
       "      <td>0.2</td>\n",
96
       "      <td>0</td>\n",
97
       "    </tr>\n",
98
       "    <tr>\n",
99
       "      <th>2</th>\n",
100
       "      <td>4.7</td>\n",
101
       "      <td>3.2</td>\n",
102
       "      <td>1.3</td>\n",
103
       "      <td>0.2</td>\n",
104
       "      <td>0</td>\n",
105
       "    </tr>\n",
106
       "    <tr>\n",
107
       "      <th>3</th>\n",
108
       "      <td>4.6</td>\n",
109
       "      <td>3.1</td>\n",
110
       "      <td>1.5</td>\n",
111
       "      <td>0.2</td>\n",
112
       "      <td>0</td>\n",
113
       "    </tr>\n",
114
       "    <tr>\n",
115
       "      <th>4</th>\n",
116
       "      <td>5.0</td>\n",
117
       "      <td>3.6</td>\n",
118
       "      <td>1.4</td>\n",
119
       "      <td>0.2</td>\n",
120
       "      <td>0</td>\n",
121
       "    </tr>\n",
122
       "    <tr>\n",
123
       "      <th>...</th>\n",
124
       "      <td>...</td>\n",
125
       "      <td>...</td>\n",
126
       "      <td>...</td>\n",
127
       "      <td>...</td>\n",
128
       "      <td>...</td>\n",
129
       "    </tr>\n",
130
       "    <tr>\n",
131
       "      <th>145</th>\n",
132
       "      <td>6.7</td>\n",
133
       "      <td>3.0</td>\n",
134
       "      <td>5.2</td>\n",
135
       "      <td>2.3</td>\n",
136
       "      <td>2</td>\n",
137
       "    </tr>\n",
138
       "    <tr>\n",
139
       "      <th>146</th>\n",
140
       "      <td>6.3</td>\n",
141
       "      <td>2.5</td>\n",
142
       "      <td>5.0</td>\n",
143
       "      <td>1.9</td>\n",
144
       "      <td>2</td>\n",
145
       "    </tr>\n",
146
       "    <tr>\n",
147
       "      <th>147</th>\n",
148
       "      <td>6.5</td>\n",
149
       "      <td>3.0</td>\n",
150
       "      <td>5.2</td>\n",
151
       "      <td>2.0</td>\n",
152
       "      <td>2</td>\n",
153
       "    </tr>\n",
154
       "    <tr>\n",
155
       "      <th>148</th>\n",
156
       "      <td>6.2</td>\n",
157
       "      <td>3.4</td>\n",
158
       "      <td>5.4</td>\n",
159
       "      <td>2.3</td>\n",
160
       "      <td>2</td>\n",
161
       "    </tr>\n",
162
       "    <tr>\n",
163
       "      <th>149</th>\n",
164
       "      <td>5.9</td>\n",
165
       "      <td>3.0</td>\n",
166
       "      <td>5.1</td>\n",
167
       "      <td>1.8</td>\n",
168
       "      <td>2</td>\n",
169
       "    </tr>\n",
170
       "  </tbody>\n",
171
       "</table>\n",
172
       "<p>150 rows × 5 columns</p>\n",
173
       "</div>"
174
      ],
175
      "text/plain": [
176
       "     sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
177
       "0                  5.1               3.5                1.4               0.2   \n",
178
       "1                  4.9               3.0                1.4               0.2   \n",
179
       "2                  4.7               3.2                1.3               0.2   \n",
180
       "3                  4.6               3.1                1.5               0.2   \n",
181
       "4                  5.0               3.6                1.4               0.2   \n",
182
       "..                 ...               ...                ...               ...   \n",
183
       "145                6.7               3.0                5.2               2.3   \n",
184
       "146                6.3               2.5                5.0               1.9   \n",
185
       "147                6.5               3.0                5.2               2.0   \n",
186
       "148                6.2               3.4                5.4               2.3   \n",
187
       "149                5.9               3.0                5.1               1.8   \n",
188
       "\n",
189
       "     variety  \n",
190
       "0          0  \n",
191
       "1          0  \n",
192
       "2          0  \n",
193
       "3          0  \n",
194
       "4          0  \n",
195
       "..       ...  \n",
196
       "145        2  \n",
197
       "146        2  \n",
198
       "147        2  \n",
199
       "148        2  \n",
200
       "149        2  \n",
201
       "\n",
202
       "[150 rows x 5 columns]"
203
      ]
204
     },
205
     "execution_count": 3,
206
     "metadata": {},
207
     "output_type": "execute_result"
208
    }
209
   ],
210
   "source": [
211
    "data = pd.DataFrame(iris.data, columns = iris.feature_names)\n",
212
    "data_class = data['variety'] = iris.target\n",
213
    "data"
214
   ]
215
  },
216
  {
217
   "cell_type": "code",
218
   "execution_count": 4,
219
   "id": "7bc24e97",
220
   "metadata": {},
221
   "outputs": [
222
    {
223
     "data": {
224
      "text/html": [
225
       "<div>\n",
226
       "<style scoped>\n",
227
       "    .dataframe tbody tr th:only-of-type {\n",
228
       "        vertical-align: middle;\n",
229
       "    }\n",
230
       "\n",
231
       "    .dataframe tbody tr th {\n",
232
       "        vertical-align: top;\n",
233
       "    }\n",
234
       "\n",
235
       "    .dataframe thead th {\n",
236
       "        text-align: right;\n",
237
       "    }\n",
238
       "</style>\n",
239
       "<table border=\"1\" class=\"dataframe\">\n",
240
       "  <thead>\n",
241
       "    <tr style=\"text-align: right;\">\n",
242
       "      <th></th>\n",
243
       "      <th>sepal length (cm)</th>\n",
244
       "      <th>sepal width (cm)</th>\n",
245
       "      <th>variety</th>\n",
246
       "    </tr>\n",
247
       "  </thead>\n",
248
       "  <tbody>\n",
249
       "    <tr>\n",
250
       "      <th>0</th>\n",
251
       "      <td>5.1</td>\n",
252
       "      <td>3.5</td>\n",
253
       "      <td>0</td>\n",
254
       "    </tr>\n",
255
       "    <tr>\n",
256
       "      <th>1</th>\n",
257
       "      <td>4.9</td>\n",
258
       "      <td>3.0</td>\n",
259
       "      <td>0</td>\n",
260
       "    </tr>\n",
261
       "    <tr>\n",
262
       "      <th>2</th>\n",
263
       "      <td>4.7</td>\n",
264
       "      <td>3.2</td>\n",
265
       "      <td>0</td>\n",
266
       "    </tr>\n",
267
       "    <tr>\n",
268
       "      <th>3</th>\n",
269
       "      <td>4.6</td>\n",
270
       "      <td>3.1</td>\n",
271
       "      <td>0</td>\n",
272
       "    </tr>\n",
273
       "    <tr>\n",
274
       "      <th>4</th>\n",
275
       "      <td>5.0</td>\n",
276
       "      <td>3.6</td>\n",
277
       "      <td>0</td>\n",
278
       "    </tr>\n",
279
       "    <tr>\n",
280
       "      <th>...</th>\n",
281
       "      <td>...</td>\n",
282
       "      <td>...</td>\n",
283
       "      <td>...</td>\n",
284
       "    </tr>\n",
285
       "    <tr>\n",
286
       "      <th>145</th>\n",
287
       "      <td>6.7</td>\n",
288
       "      <td>3.0</td>\n",
289
       "      <td>2</td>\n",
290
       "    </tr>\n",
291
       "    <tr>\n",
292
       "      <th>146</th>\n",
293
       "      <td>6.3</td>\n",
294
       "      <td>2.5</td>\n",
295
       "      <td>2</td>\n",
296
       "    </tr>\n",
297
       "    <tr>\n",
298
       "      <th>147</th>\n",
299
       "      <td>6.5</td>\n",
300
       "      <td>3.0</td>\n",
301
       "      <td>2</td>\n",
302
       "    </tr>\n",
303
       "    <tr>\n",
304
       "      <th>148</th>\n",
305
       "      <td>6.2</td>\n",
306
       "      <td>3.4</td>\n",
307
       "      <td>2</td>\n",
308
       "    </tr>\n",
309
       "    <tr>\n",
310
       "      <th>149</th>\n",
311
       "      <td>5.9</td>\n",
312
       "      <td>3.0</td>\n",
313
       "      <td>2</td>\n",
314
       "    </tr>\n",
315
       "  </tbody>\n",
316
       "</table>\n",
317
       "<p>150 rows × 3 columns</p>\n",
318
       "</div>"
319
      ],
320
      "text/plain": [
321
       "     sepal length (cm)  sepal width (cm)  variety\n",
322
       "0                  5.1               3.5        0\n",
323
       "1                  4.9               3.0        0\n",
324
       "2                  4.7               3.2        0\n",
325
       "3                  4.6               3.1        0\n",
326
       "4                  5.0               3.6        0\n",
327
       "..                 ...               ...      ...\n",
328
       "145                6.7               3.0        2\n",
329
       "146                6.3               2.5        2\n",
330
       "147                6.5               3.0        2\n",
331
       "148                6.2               3.4        2\n",
332
       "149                5.9               3.0        2\n",
333
       "\n",
334
       "[150 rows x 3 columns]"
335
      ]
336
     },
337
     "execution_count": 4,
338
     "metadata": {},
339
     "output_type": "execute_result"
340
    }
341
   ],
342
   "source": [
343
    "data = data[['sepal length (cm)' , 'sepal width (cm)', 'variety']]\n",
344
    "data"
345
   ]
346
  },
347
  {
348
   "cell_type": "code",
349
   "execution_count": 5,
350
   "id": "26b40370",
351
   "metadata": {},
352
   "outputs": [
353
    {
354
     "data": {
355
      "text/plain": [
356
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
357
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
358
       "       0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
359
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
360
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
361
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
362
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])"
363
      ]
364
     },
365
     "execution_count": 5,
366
     "metadata": {},
367
     "output_type": "execute_result"
368
    }
369
   ],
370
   "source": [
371
    "data_class"
372
   ]
373
  },
374
  {
375
   "cell_type": "code",
376
   "execution_count": 6,
377
   "id": "53647fd8",
378
   "metadata": {},
379
   "outputs": [],
380
   "source": [
381
    "X_train, X_test, y_train, y_test = train_test_split(data, data_class, test_size=0.25, random_state=80)"
382
   ]
383
  },
384
  {
385
   "cell_type": "code",
386
   "execution_count": 7,
387
   "id": "3e0cda17",
388
   "metadata": {
389
    "collapsed": true
390
   },
391
   "outputs": [
392
    {
393
     "data": {
394
      "text/html": [
395
       "<div>\n",
396
       "<style scoped>\n",
397
       "    .dataframe tbody tr th:only-of-type {\n",
398
       "        vertical-align: middle;\n",
399
       "    }\n",
400
       "\n",
401
       "    .dataframe tbody tr th {\n",
402
       "        vertical-align: top;\n",
403
       "    }\n",
404
       "\n",
405
       "    .dataframe thead th {\n",
406
       "        text-align: right;\n",
407
       "    }\n",
408
       "</style>\n",
409
       "<table border=\"1\" class=\"dataframe\">\n",
410
       "  <thead>\n",
411
       "    <tr style=\"text-align: right;\">\n",
412
       "      <th></th>\n",
413
       "      <th>0</th>\n",
414
       "      <th>1</th>\n",
415
       "    </tr>\n",
416
       "  </thead>\n",
417
       "  <tbody>\n",
418
       "    <tr>\n",
419
       "      <th>0</th>\n",
420
       "      <td>0</td>\n",
421
       "      <td>0</td>\n",
422
       "    </tr>\n",
423
       "    <tr>\n",
424
       "      <th>1</th>\n",
425
       "      <td>0</td>\n",
426
       "      <td>0</td>\n",
427
       "    </tr>\n",
428
       "    <tr>\n",
429
       "      <th>2</th>\n",
430
       "      <td>0</td>\n",
431
       "      <td>0</td>\n",
432
       "    </tr>\n",
433
       "    <tr>\n",
434
       "      <th>3</th>\n",
435
       "      <td>1</td>\n",
436
       "      <td>2</td>\n",
437
       "    </tr>\n",
438
       "    <tr>\n",
439
       "      <th>4</th>\n",
440
       "      <td>0</td>\n",
441
       "      <td>0</td>\n",
442
       "    </tr>\n",
443
       "    <tr>\n",
444
       "      <th>5</th>\n",
445
       "      <td>1</td>\n",
446
       "      <td>1</td>\n",
447
       "    </tr>\n",
448
       "    <tr>\n",
449
       "      <th>6</th>\n",
450
       "      <td>1</td>\n",
451
       "      <td>1</td>\n",
452
       "    </tr>\n",
453
       "    <tr>\n",
454
       "      <th>7</th>\n",
455
       "      <td>0</td>\n",
456
       "      <td>0</td>\n",
457
       "    </tr>\n",
458
       "    <tr>\n",
459
       "      <th>8</th>\n",
460
       "      <td>0</td>\n",
461
       "      <td>0</td>\n",
462
       "    </tr>\n",
463
       "    <tr>\n",
464
       "      <th>9</th>\n",
465
       "      <td>0</td>\n",
466
       "      <td>0</td>\n",
467
       "    </tr>\n",
468
       "    <tr>\n",
469
       "      <th>10</th>\n",
470
       "      <td>2</td>\n",
471
       "      <td>2</td>\n",
472
       "    </tr>\n",
473
       "    <tr>\n",
474
       "      <th>11</th>\n",
475
       "      <td>1</td>\n",
476
       "      <td>1</td>\n",
477
       "    </tr>\n",
478
       "    <tr>\n",
479
       "      <th>12</th>\n",
480
       "      <td>1</td>\n",
481
       "      <td>1</td>\n",
482
       "    </tr>\n",
483
       "    <tr>\n",
484
       "      <th>13</th>\n",
485
       "      <td>0</td>\n",
486
       "      <td>0</td>\n",
487
       "    </tr>\n",
488
       "    <tr>\n",
489
       "      <th>14</th>\n",
490
       "      <td>2</td>\n",
491
       "      <td>2</td>\n",
492
       "    </tr>\n",
493
       "    <tr>\n",
494
       "      <th>15</th>\n",
495
       "      <td>1</td>\n",
496
       "      <td>1</td>\n",
497
       "    </tr>\n",
498
       "    <tr>\n",
499
       "      <th>16</th>\n",
500
       "      <td>0</td>\n",
501
       "      <td>0</td>\n",
502
       "    </tr>\n",
503
       "    <tr>\n",
504
       "      <th>17</th>\n",
505
       "      <td>1</td>\n",
506
       "      <td>1</td>\n",
507
       "    </tr>\n",
508
       "    <tr>\n",
509
       "      <th>18</th>\n",
510
       "      <td>0</td>\n",
511
       "      <td>0</td>\n",
512
       "    </tr>\n",
513
       "    <tr>\n",
514
       "      <th>19</th>\n",
515
       "      <td>0</td>\n",
516
       "      <td>0</td>\n",
517
       "    </tr>\n",
518
       "    <tr>\n",
519
       "      <th>20</th>\n",
520
       "      <td>0</td>\n",
521
       "      <td>0</td>\n",
522
       "    </tr>\n",
523
       "    <tr>\n",
524
       "      <th>21</th>\n",
525
       "      <td>2</td>\n",
526
       "      <td>2</td>\n",
527
       "    </tr>\n",
528
       "    <tr>\n",
529
       "      <th>22</th>\n",
530
       "      <td>2</td>\n",
531
       "      <td>2</td>\n",
532
       "    </tr>\n",
533
       "    <tr>\n",
534
       "      <th>23</th>\n",
535
       "      <td>1</td>\n",
536
       "      <td>1</td>\n",
537
       "    </tr>\n",
538
       "    <tr>\n",
539
       "      <th>24</th>\n",
540
       "      <td>1</td>\n",
541
       "      <td>2</td>\n",
542
       "    </tr>\n",
543
       "    <tr>\n",
544
       "      <th>25</th>\n",
545
       "      <td>0</td>\n",
546
       "      <td>0</td>\n",
547
       "    </tr>\n",
548
       "    <tr>\n",
549
       "      <th>26</th>\n",
550
       "      <td>2</td>\n",
551
       "      <td>1</td>\n",
552
       "    </tr>\n",
553
       "    <tr>\n",
554
       "      <th>27</th>\n",
555
       "      <td>1</td>\n",
556
       "      <td>1</td>\n",
557
       "    </tr>\n",
558
       "    <tr>\n",
559
       "      <th>28</th>\n",
560
       "      <td>1</td>\n",
561
       "      <td>1</td>\n",
562
       "    </tr>\n",
563
       "    <tr>\n",
564
       "      <th>29</th>\n",
565
       "      <td>2</td>\n",
566
       "      <td>2</td>\n",
567
       "    </tr>\n",
568
       "    <tr>\n",
569
       "      <th>30</th>\n",
570
       "      <td>0</td>\n",
571
       "      <td>0</td>\n",
572
       "    </tr>\n",
573
       "    <tr>\n",
574
       "      <th>31</th>\n",
575
       "      <td>1</td>\n",
576
       "      <td>1</td>\n",
577
       "    </tr>\n",
578
       "    <tr>\n",
579
       "      <th>32</th>\n",
580
       "      <td>0</td>\n",
581
       "      <td>0</td>\n",
582
       "    </tr>\n",
583
       "    <tr>\n",
584
       "      <th>33</th>\n",
585
       "      <td>1</td>\n",
586
       "      <td>1</td>\n",
587
       "    </tr>\n",
588
       "    <tr>\n",
589
       "      <th>34</th>\n",
590
       "      <td>2</td>\n",
591
       "      <td>1</td>\n",
592
       "    </tr>\n",
593
       "    <tr>\n",
594
       "      <th>35</th>\n",
595
       "      <td>1</td>\n",
596
       "      <td>1</td>\n",
597
       "    </tr>\n",
598
       "    <tr>\n",
599
       "      <th>36</th>\n",
600
       "      <td>0</td>\n",
601
       "      <td>0</td>\n",
602
       "    </tr>\n",
603
       "    <tr>\n",
604
       "      <th>37</th>\n",
605
       "      <td>1</td>\n",
606
       "      <td>2</td>\n",
607
       "    </tr>\n",
608
       "  </tbody>\n",
609
       "</table>\n",
610
       "</div>"
611
      ],
612
      "text/plain": [
613
       "    0  1\n",
614
       "0   0  0\n",
615
       "1   0  0\n",
616
       "2   0  0\n",
617
       "3   1  2\n",
618
       "4   0  0\n",
619
       "5   1  1\n",
620
       "6   1  1\n",
621
       "7   0  0\n",
622
       "8   0  0\n",
623
       "9   0  0\n",
624
       "10  2  2\n",
625
       "11  1  1\n",
626
       "12  1  1\n",
627
       "13  0  0\n",
628
       "14  2  2\n",
629
       "15  1  1\n",
630
       "16  0  0\n",
631
       "17  1  1\n",
632
       "18  0  0\n",
633
       "19  0  0\n",
634
       "20  0  0\n",
635
       "21  2  2\n",
636
       "22  2  2\n",
637
       "23  1  1\n",
638
       "24  1  2\n",
639
       "25  0  0\n",
640
       "26  2  1\n",
641
       "27  1  1\n",
642
       "28  1  1\n",
643
       "29  2  2\n",
644
       "30  0  0\n",
645
       "31  1  1\n",
646
       "32  0  0\n",
647
       "33  1  1\n",
648
       "34  2  1\n",
649
       "35  1  1\n",
650
       "36  0  0\n",
651
       "37  1  2"
652
      ]
653
     },
654
     "execution_count": 7,
655
     "metadata": {},
656
     "output_type": "execute_result"
657
    }
658
   ],
659
   "source": [
660
    "lda = LinearDiscriminantAnalysis()\n",
661
    "lda.fit(X_train, y_train)\n",
662
    "y_pred = lda.predict(X_test)\n",
663
    "\n",
664
    "result = pd.DataFrame([y_test, y_pred]).T\n",
665
    "result"
666
   ]
667
  },
668
  {
669
   "cell_type": "code",
670
   "execution_count": 9,
671
   "id": "f0377999",
672
   "metadata": {},
673
   "outputs": [
674
    {
675
     "data": {
676
      "text/plain": [
677
       "0.868421052631579"
678
      ]
679
     },
680
     "execution_count": 9,
681
     "metadata": {},
682
     "output_type": "execute_result"
683
    }
684
   ],
685
   "source": [
686
    "accuracy_score(y_test, y_pred)"
687
   ]
688
  },
689
  {
690
   "cell_type": "code",
691
   "execution_count": 27,
692
   "id": "d5ae009f",
693
   "metadata": {},
694
   "outputs": [
695
    {
696
     "data": {
697
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAkAAAAGwCAYAAABB4NqyAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAA9hAAAPYQGoP6dpAABboklEQVR4nO3deVxU5f4H8M+ZGXZhFBNEQURFFBT3BVxzwdzTUvvVtSy1LEvTtquZV+/Ni1aWeq9hlmlmLjfRsHLNBDPR1EDNBVFxQ3CHYVFgZp7fH8joCMM6zBlmPu/Xa1465zxnzvccj8yH55zzHEkIIUBERERkRxRyF0BERERkaQxAREREZHcYgIiIiMjuMAARERGR3WEAIiIiIrvDAERERER2hwGIiIiI7I5K7gKskV6vx9WrV+Hu7g5JkuQuh4iIiMpBCIGsrCw0aNAACkXpfTwMQCW4evUq/Pz85C6DiIiIKuHy5cvw9fUttQ0DUAnc3d0BFO5ADw8PmashIiKi8tBoNPDz8zN8j5eGAagERae9PDw8GICIiIhqmPJcvsKLoImIiMjuMAARERGR3WEAIiIiIrvDAERERER2hwGIiIiI7A4DEBEREdkdBiAiIiKyOwxAREREZHcYgIiIiMjucCRoIpKNXq9H7Ib92LJ0Oy6cuAwnVyf0GhWGEVMGwaeJt9zlEZENs5oeoMjISEiShDfffLPUdnFxcejQoQOcnZ3RpEkTLFu2rFib6OhoBAcHw8nJCcHBwdi8eXM1VU1ElaXT6TDvmc8Q+dxinDpwBjmZubiddgcxn2/HxNC38Ne+U3KXSEQ2zCoC0KFDh7B8+XKEhoaW2i4lJQWDBg1Cjx49kJCQgJkzZ2LKlCmIjo42tImPj8eYMWMwduxYHD16FGPHjsXo0aNx8ODB6t4MIqqAmP9sx97oAwAAvV4Ypuu1euTfy8fsER8h726eXOURkY2TPQBlZ2fjueeew5dffok6deqU2nbZsmVo1KgRFi1ahJYtW2LChAl46aWX8MknnxjaLFq0CP3798eMGTPQokULzJgxA3379sWiRYuqeUuIqLyEEIhe/DMgTMzXC2Tdysbe7w9YtjAishuyB6DJkydj8ODB6NevX5lt4+PjERERYTRtwIABOHz4MAoKCkpts3//fpOfm5eXB41GY/QiouqTeVOD6xdvlNpGqVLiZHyShSoiInsjawBav349jhw5gsjIyHK1T09Ph7e38YWR3t7e0Gq1uHnzZqlt0tPTTX5uZGQk1Gq14eXn51fBLSGiilAoy/GjRyoMQURE1UG2AHT58mVMnToV3333HZydncu9nCRJRu+FEMWml9Tm0WkPmzFjBjIzMw2vy5cvl7seIqo49zq10CTUH5LC9P9LXYEO7fuVfl0gEVFlyRaAjhw5guvXr6NDhw5QqVRQqVSIi4vDkiVLoFKpoNPpii1Tv379Yj05169fh0qlQt26dUtt82iv0MOcnJzg4eFh9CKi6iNJEsa8OxxCX/JFQAqVAg2aeqPLkPYWroyI7IVsAahv3744fvw4EhMTDa+OHTviueeeQ2JiIpTK4l3fYWFh2LVrl9G0nTt3omPHjnBwcCi1TXh4ePVtDBFVWJ9ne+C5WU8BAJSqwh9FkiQBEuBZvw7mbX2/xJ8DRETmINtAiO7u7mjVqpXRNDc3N9StW9cwfcaMGUhNTcXq1asBAJMmTcJ///tfTJ8+HRMnTkR8fDxWrFiBdevWGT5j6tSp6NmzJxYsWIDhw4cjJiYGv/zyC/bt22e5jSOichn3z2fQ7cnO+OmLXTh/7CJc3Z3R46kw9H2uO1xquchdHhHZMKseCTotLQ2XLl0yvA8ICMDWrVsxbdo0LF26FA0aNMCSJUvw1FNPGdqEh4dj/fr1mDVrFj744AM0bdoUGzZsQJcuXeTYBCIqQ2D7Jpj2xStyl0FEdkYSRVcRk4FGo4FarUZmZiavByIiIqohKvL9Lfs4QERERESWxgBEREREdocBiIiIiOwOAxARERHZHQYgohom/14+rl28Ac2tLLlLISKqsaz6NngiekBzKwvfzv0e21f+ins5eQCA1j1bYuzsUWjXp7XM1RER1SzsASKqATS3svBG2ExsidphCD8AcGLfabzX/1+I+99+GasjIqp5GICIaoBv/rEB6SnXodfpjabr9QICAp+M/xx3s+/KVB0RUc3DAERk5fLu5mHHqthi4cdAAPdy87BnPXuBiIjKiwGIyMrdunoHebl5pbZRqZS4dOqKhSoiIqr5GICIrJxLLecy2+iFgKs7Hx5KRFReDEBEVq6Od2207BoISSGZbKPX6tHjKT7wl4iovBiAiGqAsf8YDVPPLVYoFQgb1hEBrf0tXBURUc3FAERUA3Qa0Bbvrnwdji6OkCQJKgcllKrC/75dBrXHjDVTZK6QiKhmkYSpXyvtmEajgVqtRmZmJjw8POQuh8ggJzMHu7/bh8tJqXB1d0HPUWFo2qax3GUREVmFinx/cyRoohrETe2GYa8NkLsMIqIaj6fAiIiIyO4wABEREZHdYQAiIiIiu8MARERERHaHAYiIiIjsDu8CIyKz0ev1OBl/BnfSM+DpUwctuwZCoeDvWURkfRiAiMgs9sccwufTVuLahRuGaT5NvDB58UvoMriDjJURERXHX82IqMp+/+EP/GPkR7h28YbR9PSU6/hg2ALE/3hYpsqIiErGAEREVaLT6fDfKV8XvnlkXPnCceYFPn9zJfR6vaVLIyIyiQGIiKrk+N5TuHnlVrHwU0SIwp6gk/uTLFsYEVEpGICIqEpuXb1TrnY3U29XcyVEROXHAEREVVKnfm2ztiMisgQGICKqkja9guHpU6fUNvX86qJV9xYWqoiIqGwMQERUJUqVEpMWvlBqm0kLX4BSqbRQRUREZeM4QERUZY8/0w1CCCx76xvcSc8wTPf0qYPXFr2Ink+HyVccEVEJZO0BioqKQmhoKDw8PODh4YGwsDBs27bNZPtx48ZBkqRir5CQEEObVatWldjm3r17ltgkIrvV5/+6Y92lZYjcPgvvrJyM+TtmYe3FKPQaxfBDRNZH1h4gX19fzJ8/H82aNQMAfPPNNxg+fDgSEhKMQk2RxYsXY/78+Yb3Wq0Wbdq0wahRo4zaeXh4ICnJ+JZbZ2fnatgCInqYUqVEx4g2cpdBRFQmWQPQ0KFDjd7PmzcPUVFROHDgQIkBSK1WQ61WG97/8MMPuHPnDl588UWjdpIkoX79+tVTNBEREdV4VnMRtE6nw/r165GTk4OwsPJ1ma9YsQL9+vWDv7+/0fTs7Gz4+/vD19cXQ4YMQUJCQqmfk5eXB41GY/QiIiIi2yV7ADp+/Dhq1aoFJycnTJo0CZs3b0ZwcHCZy6WlpWHbtm2YMGGC0fQWLVpg1apV2LJlC9atWwdnZ2d069YNycnJJj8rMjLS0LukVqvh5+dX5e0iIiIi6yUJIUwMYG8Z+fn5uHTpEjIyMhAdHY2vvvoKcXFxZYagyMhILFy4EFevXoWjo6PJdnq9Hu3bt0fPnj2xZMmSEtvk5eUhLy/P8F6j0cDPzw+ZmZnw8PCo3IYRERGRRWk0GqjV6nJ9f8t+G7yjo6PhIuiOHTvi0KFDWLx4Mb744guTywgh8PXXX2Ps2LGlhh8AUCgU6NSpU6k9QE5OTnBycqrcBhAREVGNI3sAepQQwqg3piRxcXE4e/Ysxo8fX67PS0xMROvWrc1VItkwnU6Hw9sTcTL+DBRKBToOaIvgsOaQJEnu0oiIyIxkDUAzZ87EwIED4efnh6ysLKxfvx6xsbHYvn07AGDGjBlITU3F6tWrjZZbsWIFunTpglatWhX7zLlz56Jr164IDAyERqPBkiVLkJiYiKVLl1pkm6jmOpuYgjkjPsa1izegdFACQmDNvzYisEMT/POHd/FYw7pyl0hERGYiawC6du0axo4di7S0NKjVaoSGhmL79u3o378/gMILnS9dumS0TGZmJqKjo7F48eISPzMjIwMvv/wy0tPToVar0a5dO+zduxedO3eu9u2hmuvGlVt4u88c3M0qHDBTV6AzzDt/9ALe6TsXXyR+Akfn0k+5EhFRzSD7RdDWqCIXUZFt+Orva/D9wh+h1+lNtnl31evo/3wvC1ZFREQVUZHvb9lvgyeyBru/+63U8CMpJOxZv8+CFRERUXViACICkKu5W+p8oRfIzsi1UDVERFTdGICIADRs7gNJYfpOL6VKgUYtGliwIiIiqk4MQEQAhr06AEJv+nI4nVaPQRP7WbAiIiKqTgxARAD6je2Jdn1bm+wFGvbaAASHBVm4KiIiqi4MQEQAVA4qfPjTDDw7YyTc67gZptfzq4vJi1/C6/8pe9BNIiKqOXgbfAl4G7x9K8gvQNr561AoFWjQ1BsKBX9PICKqCWrUs8CIrI2DowMatWgodxlERFSN+KstERER2R0GICIiIrI7DEBERERkdxiAiIiIyO4wABEREZHd4V1gZPV0Oh3+2JqAX9ftQ+YNDXwCvPDE+L5o2SVQ7tKIiMokhB7I3wtxdwugvwUoG0JyGQU4tIUkmX4ED1UvjgNUAo4DZD1yMnMwc9C/cTL+DBRKBfQ6PZQqBXRaPZ4Y3wfTvniF4/QQkdUS+myIO68ABYcAKAHoHvzp/CQkdSQkSSlvkTakIt/f/OYgq7bghf/i9B9nAQB6nR5A4XO5AGD7il/xv49iZKuNiKgsIvN9oODI/Xc64z/vxQA5n8tRFoEBiKzYlTNXEb/lsCH4lOT7hT+iIL/AglUREZWP0F4B8rYDMPUzTEDkrIQQ9yxZFt3HAERW6/DOo2WeH9fcysK5xAuWKYiIqCLyfwdQxlUmIhsoOG6RcsgYAxBZLW2+FuW5PlCbr63+YoiIKkoUACjHDzHBXmw5MACR1Qrq1Ax6fem/PakcVfAP8bNQRUREFeDQGmX2AEEFOLSwRDX0CAYgslqtureAf7AvFMqSD1OFUoH+Y3vCvU4tC1dGRFQODqGAKhiFd32VRAk4D4ak8LRkVXQfAxBZLUmSMGvDdLipXYuFIEkhoXGIH17++HmZqiMiKp0kSZBqfwYo1CgeghSAMgCSx/tylEZgACIr1zjED8uPfoKnpw1BbS8PqByU8GnijQmRz2HRvn+hVm03uUskIjJJUgVAqrsFcBsPKB4DoAIUvpBqTYdU93+QFLXlLtFucSDEEnAgRCIiopqHAyESERERlYIBiIiIiOwOAxARERHZHQYgIiIisjsMQERERGR3GICIiIjI7qjkLoCoOhTkF2DX6r34adkOpJ5Nh5vaFf3+1hPDJj+BxxrY16irR+NOYPPirTgWdwKQJHSICMXIqUPQskug3KUREclG1h6gqKgohIaGwsPDAx4eHggLC8O2bdtMto+NjS0cWfOR1+nTp43aRUdHIzg4GE5OTggODsbmzZure1PIiuTdzcOMJ+bhs5eX4WziBeRq7uLG5VvY8FEMXg59Cyl/XZK7RIvZ8FEM3n58Dg78dBhZd3KQdTsbv208gCnhM/Hz8l1yl0dEJBtZA5Cvry/mz5+Pw4cP4/Dhw+jTpw+GDx+OEydOlLpcUlIS0tLSDK/AwAe/ycbHx2PMmDEYO3Ysjh49irFjx2L06NE4ePBgdW8OWYlv536P43tPAgDEQw9T1ev0yMnMxZyRH0Ov18tVnsWc2J+Er/6+BgCg0z7YXp1WDwhg8atf4sKJy3KVR0QkK6sbCdrT0xMff/wxxo8fX2xebGwsHn/8cdy5cwe1a9cucfkxY8ZAo9EY9SQ98cQTqFOnDtatW1euGjgSdM2VdzcPo30mIldzt9R283fMQof+bSxUlTzmPfsZftt4wCj8PEypUmDQxP6YsnSChSsjIqoeNXIkaJ1Oh/Xr1yMnJwdhYWGltm3Xrh18fHzQt29f7Nmzx2hefHw8IiIijKYNGDAA+/fvN/l5eXl50Gg0Ri+qmVKT08sMP0qVAifjz1ioIvn89dtpk+EHKOwJOv7bSQtWRERkPWQPQMePH0etWrXg5OSESZMmYfPmzQgODi6xrY+PD5YvX47o6Ghs2rQJQUFB6Nu3L/bu3Wtok56eDm9vb6PlvL29kZ6ebrKGyMhIqNVqw8vPz888G0cW9+hT40siBKBUPfpkZtujUJW9L+xhPxARlUT2u8CCgoKQmJiIjIwMREdH44UXXkBcXFyJISgoKAhBQUGG92FhYbh8+TI++eQT9OzZ0zBdkiSj5YQQxaY9bMaMGZg+fbrhvUajYQiqofyCGsDTpzZup2WYbKPX6dG+X2vLFSWTLgPbY+tXv5jsBVIoFeg8sJ2FqyIisg6y9wA5OjqiWbNm6NixIyIjI9GmTRssXry43Mt37doVycnJhvf169cv1ttz/fr1Yr1CD3NycjLciVb0oppJqVJi1FvDSpmvQHBYc7TobPu3gA9/Y2DhX0rI/pIkQeWgxJBJEcVnEhHZAdkD0KOEEMjLyyt3+4SEBPj4+Bjeh4WFYdcu49t7d+7cifDwcLPVSNZt5JuDMWhiXwCFgQcAJEVhCmjQzAezN74tW22W5N/SF++vmwalSml0alChkODg7IA5m9+Fl99jMlZIRCQfWU+BzZw5EwMHDoSfnx+ysrKwfv16xMbGYvv27QAKT02lpqZi9erVAIBFixahcePGCAkJQX5+PtasWYPo6GhER0cbPnPq1Kno2bMnFixYgOHDhyMmJga//PIL9u3bJ8s2kuUpFAq8uewV9B/bCz9/+QuunLkKd093PP5MN/QaFQZHZ0e5S7SYHk91xTdn/oOfl+/CsbiTkBQS2vcLxaCJ/VDXp47c5RERyUbWAHTt2jWMHTsWaWlpUKvVCA0Nxfbt29G/f38AQFpaGi5dejBoXX5+Pt5++22kpqbCxcUFISEh+PnnnzFo0CBDm/DwcKxfvx6zZs3CBx98gKZNm2LDhg3o0qWLxbeP5CNJElp1b4lW3VvKXYrsvP3r4aV5z8pdBhGRVbG6cYCsAccBIiIiqnlq5DhARNXlyhVgz57CP4mIiAAGILJxK1YA/v5Anz6Ff65YIXdFRERkDRiAyGZduQK8/DJQ9NgvvR545RX2BBEREQMQ2bDk5Afhp4hOB5w9K089RERkPRiAyGYFBgKKR45wpRJo1qx61nfnWgauX7oBbYG2elZARERmI/ujMIiqi68vsHx54Wkvna4w/HzxReF0c/pt00GsnReNswkpAAD3Om4YMikCz77/FJxdncy7MiIiMgveBl8C3gZvW65cKTzt1ayZ+cPPpkU/I2r6KkgKCUL/4L+SQqlAUKem+Hj3P+DkwhBERGQJvA2e6CG+vkDv3uYPP9cv3cCyt78BAKPwAxQ+cPX0H2execk2866UiIjMggGIqJK2rfgVklTCk0bvE3qBHz/fYcGKiIiovBiAiCrp0ulUlHUG+frlm8jPK7BQRUREVF4MQESV5OLmDIXCdA8QACgdlFA5KC1UERERlRcDEFEl9Xi6K3Ravcn5SpUCPZ7qAsWj9+ITEZHs+JOZqJI6DmiDwPZNoFAV/28kKSRIkoQx7zxp+cKIiKhMDEBElaRUKvHvbTMR1LFwZEWl6sHpLld3F/wz5j00axcgZ4lERGQCB0IkqoLa9dRY/PuHOLE/CQd+PIz8ewVo2rYxeo0O5yCIRERWjAGIqIokSUKrbi3QqlsLuUshIqJy4ikwIiIisjsMQERERGR3GICIiIjI7jAAERERkd1hACIiIiK7w7vAyKadO3oBV8+mw622G0J7toTKgYc8WS+hzwDy/wSgAxxaQ1LWl7skIpvFbwOySUmHz2HRK1/gbEKKYZr6MXe88M9nMHRShIyVERUnxD0Izb+Bu9EAih6eq4BwioCkngtJUUfO8ohsEgMQ2ZyziSmY3ms2tPlao+mZN7Ow5LUvcS/7Hka9PUym6oiMCaGDuDMJyD8A4OFny+mBvF0Qt88Bnv+DpHCTq0Qim8RrgMjmrPj7d9Dma6HXlfyg0pUfrEPWnWwLV0VkQl4skL8fxuGniA7Qnr3fM0RE5sQARDblVtodHN511GT4AQBtvg57v4+3YFVEpom7GwEoy2jzP8sUQ2RHGIDIptxJzwBE6W0UKgVupt62SD1EZdKlAdCV0kAAumuWqobIbjAAkU2p7eVRZhu9Vg/P+rWrvxii8lB6o8wfxYrHLFIKkT1hACKb8ljDumj7eCsolKYPbaVKgV6jwy1YFZFpkstIlHz9j6EFJNdRliqHyG4wAJHNGR/5LBRKBSSFVOL8v30wCh513S1cFZEJTn0Bh/Yo+cexElA2AlxGW7oqIpvHAEQ2p0XnQHy0azYaNjMeRM7VwwUvfzQWz74/UqbKiIqTJBWkOl8BzkNQ7EeyYzdInmshKWrJUhuRLZOEEGVcMlp9oqKiEBUVhQsXLgAAQkJCMHv2bAwcOLDE9ps2bUJUVBQSExORl5eHkJAQzJkzBwMGDDC0WbVqFV588cViy969exfOzs7lqkuj0UCtViMzMxMeHmVfU0LWSQiBE/uTkHbuGtxqu6JD/1A4uTjJXRaRSUJ3Dcg/hMKRoNtCUvnLXRJRjVKR729ZB0L09fXF/Pnz0axZMwDAN998g+HDhyMhIQEhISHF2u/duxf9+/fHv//9b9SuXRsrV67E0KFDcfDgQbRr187QzsPDA0lJSUbLljf8kO2QJAmturVAq24t5C6FqFwkpTfgMkTuMojsgqw9QCXx9PTExx9/jPHjx5erfUhICMaMGYPZs2cDKOwBevPNN5GRkVHpGtgDREREVPNU5Pvbaq4B0ul0WL9+PXJychAWFlauZfR6PbKysuDp6Wk0PTs7G/7+/vD19cWQIUOQkJBQ6ufk5eVBo9EYvYiIiMh2yR6Ajh8/jlq1asHJyQmTJk3C5s2bERwcXK5lFy5ciJycHIwe/eAOiRYtWmDVqlXYsmUL1q1bB2dnZ3Tr1g3JyckmPycyMhJqtdrw8vPzq/J2ERERkfWS/RRYfn4+Ll26hIyMDERHR+Orr75CXFxcmSFo3bp1mDBhAmJiYtCvXz+T7fR6Pdq3b4+ePXtiyZIlJbbJy8tDXl6e4b1Go4Gfnx9PgREREdUgNeYiaABwdHQ0XATdsWNHHDp0CIsXL8YXX3xhcpkNGzZg/Pjx+P7770sNPwCgUCjQqVOnUnuAnJyc4OTEu4OIiIjshewB6FFCCKPemEetW7cOL730EtatW4fBgweX6/MSExPRunVrc5ZpEUIInIw/g8M7EqHX6dGya3N0GtgWSmXpD060lPx7+Vg993sc/+0kHBwcEDGuFyJeeFzussgOCCFw4MplHEi9DCGAzg19Ee7XCAqp5MEvbZnQXgLubYcQGkjKAMB5ICSFq9xl0SOELh24txVCfxuSsiHgPAiSQi13WXZN1lNgM2fOxMCBA+Hn54esrCysX78e8+fPx/bt29G/f3/MmDEDqampWL16NYDC8PP8889j8eLFGDnywWB2Li4uUKsLD6S5c+eia9euCAwMhEajwZIlS/Dtt9/i999/R+fOnctVlzXcBXYz9Rb+MeJjnDl8DkqVApAk6Ap08Pavhzmb3kGzdgGy1FVk29e/4rOXl0HojQ8fF3cXLNk/D41DeB0VVY+LGRl4+acfkHz7FlSSApAArV6PJrXrYPnQJ9GkjmfZH2IDhMiHyJwF3PsBhZdzKgBoAckFkseHkFyGylsgAQCE0EFkfQTkfnN/ihKAFoADJPd3Ibk9L2N1tqci39+VCkC7d+/G7t27cf36dej1xs+w+frrr8v9OePHj8fu3buRlpYGtVqN0NBQvPfee+jfvz8AYNy4cbhw4QJiY2MBAL1790ZcXFyxz3nhhRewatUqAMC0adOwadMmpKenQ61Wo127dpgzZ0657ywD5A9A+ffy8Urbt3H1/DXotcb7V6FUwMXdGV8e+xT1fOtavDYASPj1ON7t90+T8x2dHRB982s4u3LsJTIvTV4enlizCjdyc6B75EeXUpJQx8UFO54bhzouLjJVaDn6jHeBe1tQ8nPEJEh1voTk1NPSZdEj9FkLgRzTl3RI6gWQXEZYsCLbVq0BaO7cufjnP/+Jjh07wsfHB9IjXc6bN2+ueMVWRu4AtGt1HD4a91+T8xVKBZ6ePhQTF/zNglU9MLH1dFw4cbnUNqPeHoaXPxproYrIXnydcATzfouFqR9aEiS8E94dkzqWr7e3phLaSxA3+wMm94QCULWC4rGNliyLHiH0GRDXuwEoMN1I4QOp3h5Ikuw3ZduEar0IetmyZVi1ahXGjuWXW3WJ3fA7JIVU7PRSEb1Oj93f/SZbACor/ACF28AAROa25cxpk1/5ACAgEJN0yuYDEO5tByDBdADSA9pjELo0SEofCxZGRvL2oNTwAwD6NKDgOODYxiIl0QMVjpz5+fkIDw+vjlrovqw7OSbDT5G7WXctVE3l5OXmy10C2SDNvXtltskq5SYKWyFEFsr141ufXe21UCn0WSgMqmUQ/HeSQ4UD0IQJE7B27drqqIXua9SiQeGFzyZICgkNA+ubnF/dVA5l34X2WEP7uBCVLKuppyeUpdzppZQkNPG0/WNPUgag8ELa0qgApXw/JwiAqjFM99I9RNmouiuhEpTrFNj06dMNf9fr9Vi+fDl++eUXhIaGwsHBwajtp59+at4K7dCgl/tjx6pYk/OFXmDoqwMsV9AjugzugN9/+KPUNn+b/bSFqiF78mzrNtidct7kfJ0QeLaVHZxKcH4CyPonIHJNNFACzoMhKdwtWhY9wrEboKgP6K+h5CCkBBw7Q1Lxrlk5lCsAPfosrbZt2wIA/vrrL7MXREBw1+YYPvkJxCzdXmyepJDQpncI+j/fS4bKCr399av4c/cx3M0q+XREiy7N0GNkVwtXRfagt38AhjVvgS1nThebJwGIaBqIiKbNLF+YhUkKV8DjQ4jMt1C45Q/fCaYEFJ6Q3N+SqToqIklKQB0JcWciCv+NHvl3klwhecyWqTqS/VEY1kjuu8CAwoHeYpZux/efbMH1SzcBALXquGHYqwPw3AdPw9HJoYxPqF4ZNzWYNSQSSX+cNUxTqpR4/JlueGfVZCgUvKOBqodOr8dXCYfxdcIR3Mgt7AGp6+KKcW3b45UOnaCyo2NP5P0Gkb0EKDh6f4oKcB4KyX0aJJ7+shoiPwEi+zMg/8D9KQrAqT8k9+mQVPKO6WZrqvU2+JdeegmLFy+Gu7tx12pOTg7eeOONCo0DZK2sIQAV0ev1uHruGnRaHRo09YaDo7zB51HZGdk4vi8Jzq6OaNM7hMGHLEar1+NSZgYAoJG6tl0Fn0cJXXrhBbdKH0iKWnKXQyYI3Q1AfwdQekFS1Ja7HJtUrQFIqVQiLS0NXl5eRtNv3ryJ+vXrQ6st68I862dNAYiIiIjKp1rGAdJoNBBCQAiBrKwsODs/GOVXp9Nh69atxUIRERERkTUqdwCqXbs2JEmCJElo3rx5sfmSJGHu3LlmLY6IiIioOpQ7AO3ZswdCCPTp0wfR0dHwfGisDUdHR/j7+6NBgwbVUiQRERGROZU7APXqVXjbdUpKCho1alTsGWBERERENUW5AtCxY8eM3h8/ftxk29DQ0KpVRERERFTNyhWA2rZtC0mSIIQos+dHp9OZpTCyTblZd7F7zV78sS0BBflaBHVsisEv94NXo3pyl0ZERHakXAEoJSXF8PeEhAS8/fbbeOeddxAWFgYAiI+Px8KFC/HRRx9VT5VkE84dvYD3Iv6FzJsaSCgM1Am7j2P9gh/w1levIuKF3nKXSEREdqLC4wB17twZc+bMwaBBg4ymb926FR988AGOHDli1gLlwHGAzO9u9l2Mbfo6sm5nQ6/TF5svSRIW7fsXgsOCZKiOiIhsQUW+vys8dOrx48cREFB86O6AgACcPHmyoh9HdmL3d/uQeVNTYvgBAIVSwsZPf7JwVUREZK8qHIBatmyJDz/8EPfuPXgQZl5eHj788EO0bNnSrMWR7Ti0PQESTF8/ptPq8cfWPy1YERER2bNy3wZfZNmyZRg6dCj8/PzQpk0bAMDRo0chSRJ++om/wVPJCvK1KOtsq1bLC+iJiMgyKhyAOnfujJSUFKxZswanT5+GEAJjxozBs88+Czc3t+qokWxAi07NcGTnUdOnwBQSmrdvYuGqiIjIXlU4AAGAq6srXn75ZXPXQjZs0MS+WPvvaJPz9XqBEVMHW7AiIiKyZ+UKQFu2bMHAgQPh4OCALVu2lNp22LBhZimMbMtjDevi3VWvY/7z/4FCIUGnLewJUigV0Ov0GDSxL3qPCZe5SiIishflug1eoVAgPT0dXl5eUChMXzctSZJNDITI2+CrT9Khs9j42Y848NOf0BVoEdi+CUZMGYReo8P5eBUiIqqSinx/V3gcIHvAAERERFTzVOs4QLm5uZUujIiIiMgaVPgi6Nq1a6Njx47o3bs3evXqhe7du/PuLyIiIqpRKtwDFBcXh2HDhuHPP//EqFGjUKdOHXTt2hV///vfsW3btuqokYiIiMisqnQNkE6nw6FDh7Bs2TJ899130Ov1vAiaiIiIZFGR7+9KjQN0+vRpxMbGIi4uDrGxsSgoKMDQoUPRq1evShVMREREZEkVDkD169dHQUEB+vTpg969e2PmzJlo3bp1ddRGREREVC0qFYBOnTqFS5cu4dKlS7hy5QoCAgJQq1at6qjPJggh8PsPf+CH/27D2YQUODg6oPuIzhgxdTAatWgod3kWlfLXJWxa9DPifzwMbYEWLTo1w5NvDEKXwe3NOg5QQX4Bdq3ei5+W7UDq2XS4qV3R7289MWzyE3isgafZ1mNJWr0eMUmnsOZYIs7fuQ03B0cMC2qB59u0QwN3856qvZiRgVVH/8TW5DO4py1AUN16eL5NWwwKDILCCsZr+jPtKlYmHsHvly9BCCDM1w/j2rZH54a+JpdJ1WjwzdE/8eOZ08gtKEBTz7p4PrQthjZvAWUp45tR4c8w5O2ByF0NFBwH4AA494Pk+gIkh0C5yyOqlEpdA5SRkYG9e/ciLi4OcXFxOHHiBEJDQ/H4449j/vz55f6cqKgoREVF4cKFCwCAkJAQzJ49GwMHDjS5TFxcHKZPn44TJ06gQYMGePfddzFp0iSjNtHR0fjggw9w7tw5NG3aFPPmzcOIESPKXZc5rwESQmDRpC+w9cvdhlGPAUCpUkBSKPDPH95FpyfaVWkdNcW+zQfx4ZhPAaDYSNAj3xyMSQtfMEsIyrubh/cHR+Jo7AlICglCLwzrclO7YmHsXAS0alTl9VhSgU6HV36KQezFFCgkCfr7/22VkgQXBwesGTEKod71zbKug1cuY1zMJmj1Oujur6donUObt8CnEQNlDQzfHkvEP2J3QylJhvqK/v5+j94Y365DsWWOpqfhuc3fI0+rLbZN/QKaYumgoXBQKi26HTWFEAJC8y/g7hoASgBF13kqAUiQav8HknNf+Qokeki1jgMEFN4KP2zYMLz//vuYOXMmRo8ejT///BMff/xxhT7H19cX8+fPx+HDh3H48GH06dMHw4cPx4kTJ0psn5KSgkGDBqFHjx5ISEjAzJkzMWXKFERHP3jGVHx8PMaMGYOxY8fi6NGjGDt2LEaPHo2DBw9WZlOr7Jdv92Lrl7sBwOhBoDqtHroCHeY+/Qmy7mTLUpsl3U6/g3n/twg6nd4QfoAH+2TTop+xb/MfZlnXt3O/x/G9JwHAEH6K1pWTmYs5Iz+GXl/yQ1mt1RdHDiHuYgoAGMIPAOiEQG5BAV756QdozbBNdwsK8MrPMSh4KPw8vM6fzpzGd8ePVnk9lXXqxnXMiS38/6R7ZD8AwLzfYnE0Pc1omXydDhN/+gH3Hgo/wINt2p1yDisSjlRz5TXYva33ww/wIPwU/V0HkfEmhP62DIURVU2FA9DmzZsxdepUtGnTBl5eXnj11VeRk5ODzz77DMeOHavQZw0dOhSDBg1C8+bN0bx5c8ybNw+1atXCgQMHSmy/bNkyNGrUCIsWLULLli0xYcIEvPTSS/jkk08MbRYtWoT+/ftjxowZaNGiBWbMmIG+ffti0aJFFd1Us4he9BMkRcm9GkII5N8twK5v4ixcleVt++pX6LQ6wER/o0KpwObFP1d5PXl38/Djsp3Q60tekV6nx9Wz6UjYfbzK67IUrV6Pb44mmNp10AuBazk52J1yrsrr2nLmNDR5eUYh61FfJx6BXAPIrz6WWOopOKUkYfWxRKNpO88l42ZursltEgBWJf4JXQ0LxZYiclfB9FeFAJAP5Jp+0DGRtapwAHrllVeQmpqKiRMnIjExEdeuXcPGjRvx+uuvIyQkpNKF6HQ6rF+/Hjk5OQgLCyuxTXx8PCIiIoymDRgwAIcPH0ZBQUGpbfbv329y3Xl5edBoNEYvc9BpdTiXeMGoF6IYCTh5IMks67NmJw+cKXU/6HV6nDpwpsrrSU1OR67mbqltlCoFTsZXfV2WkpaVhVt3Sx+BXaVQ4M+0q1VeV2J6GpSlBAwB4FJmJjR5eVVeV2Ucuppq1IvzKJ0Q+CP1itG0hPQ0qMo4ZXc9NwfXcmy/J7aihBD3r/kpPRyKgj8tUxCRGVX4Iujr16+btYDjx48jLCwM9+7dQ61atbB582YEBweX2DY9PR3e3t5G07y9vaHVanHz5k34+PiYbJOenm6yhsjISMydO7fqG/MoCZAkoLRfliVJglJl+9ceKJWKMveFQln160rK8xlCoEbtc4WJHkQjAlBKZth/klR4HVYZPTxyXQNUWjgr8mjYKc8yJS1HRRQoKwBVckQVIlnJ/j8+KCgIiYmJOHDgAF599VW88MILOHnypMn2j14kW9QV//D0ktqUdnHtjBkzkJmZaXhdvny5MptSjFKpRJverUr9Utbr9GjfL9Qs67Nm7fuHmjyFAwAKlQId+rep8nr8ghrA06d2qW0K93nNGbqhQS13NFKrUdrXuFbo0b2Rf5XX1b2Rf6nXEikkCa29vFHL0bHK66qMxxsHlBpolJKE3o0DjKZ1b9S41G2SADSpXQf1XPlIn0dJkgQ4hqPwgmdTBCSnbpYqichsZA9Ajo6OaNasGTp27IjIyEi0adMGixcvLrFt/fr1i/XkXL9+HSqVCnXr1i21zaO9Qg9zcnKCh4eH0ctcRr8zzOji54cplArU8Vaj95hws63PWvV/vhfca9eCQlnyl5dep8fTbw2t8nqUKiVGvTWslPkKBIc1R4vONefWXUmS8EqHziYDpFKSEFT3MYT5+lV5Xf0CmsLX3cNkyNALgVc6dKryeirrudZtoVQoSgyDEgr31djQtkbTuzfyR7M6nia3SQB4pWNnsw7DYEskt/Ewvvj5YQpAqgM4V/3/LpGlyR6AHiWEQJ6J6wvCwsKwa9cuo2k7d+5Ex44d4eDgUGqb8HB5QkanJ9rhlU+eB1DYywHg/qkxCe513BC5fRacXJxkqc2S3DxcEbn9fbi4uxhdFK5QKSBJEqYsnYjQniWf+qyokW8OxqCJhbflKu/v86J1Nmjmg9kb3zbLeizpmZDWeKlt4e3dRV/k0v0Y0NDdA18NG2GWL3AHpRKrnnwKdV1c768DRut8s0s4BgUGVXk9leWnViNq8DA4KpVGF0MrJAkqhQJLBw5FkzrG4zwpJAkrho2ETy13AMW36eX2HfF0y8pfv2jrJKcwSO4foHDPPdwTJAGSOyTPFZAU7D2jmqdKzwKrqpkzZ2LgwIHw8/NDVlYW1q9fj/nz52P79u2GO7lSU1OxevVqAIW3wbdq1QqvvPIKJk6ciPj4eEyaNAnr1q3DU089BQDYv38/evbsiXnz5mH48OGIiYnBrFmzsG/fPnTp0qVcdVXHs8AunLiMn5btxJkj5+Hk4ojw4Z3Q//leqFXbvn5waG5nYcfKWBz46TAK8rRo2SUQQyb1h1+QeQeEFELgxO+n8fOXv+DKmatw93TH4890Q69RYXB0luf0jTkkpqdh3V9HkXzrNtydHDE4MAhDm7eAy/1fAMwlOz8fP5w+iR3nkpFbUICW9bzwbKtQBNfzMut6KutadjbW/XUM+y9fgoBAmG8jPNOqdakDQuYWFODHpFP4OfkMsvPz0bxuXfxf6zZoY6bxk2yd0J6DyF0PFBwFJCdITn0Al5GQFGq5SyMyqMj3t6wBaPz48di9ezfS0tKgVqsRGhqK9957D/379wcAjBs3DhcuXEBsbKxhmbi4OEybNs0wEOJ7771XbCDEjRs3YtasWTh//rxhIMSRI0eWuy4+DJWIiKjmMXsAqkh42LRpU7nbWisGICKiQleuAMnJQGAg4Gv6SSNEVsHsT4NXq9nFSURkb1asAF5+GdDrAYUCWL4cGD9e7qqIzEPWU2DWij1ARGTvrlwB/P0Lw08RpRK4cIE9QWS9qv1ZYEREZNuSk43DDwDodMDZs/LUQ2RulRq+c+PGjfjf//6HS5cuIT8/32jen39ySHQiopouMLDwtNejPUDNmslXE5E5VbgHaMmSJXjxxRfh5eWFhIQEdO7cGXXr1sX58+cxcODA6qiRiMhsbt/NRapGgzytVu5SzEboMyC0VyCE+Z7R5utbeM2P8v7QP0olsGyZDg19rkLobsj2QFwic6nwNUAtWrTAP/7xD/zf//0f3N3dcfToUTRp0gSzZ8/G7du38d///re6arUYXgNEZHv2XbqIJQfjcTgtFQDg6uCA0SGtMbVzGNTOzjJXVzki/xBE9n+A/AP3pzgDriMhub0BSVnXLOsovAssH019voVvnS8AkVE4QxUEye1VSC6DzLIeInOo1nGAXF1dcerUKfj7+8PLywu7du1CmzZtkJycjK5du+LWrVtVKt4aMAAR2ZaYpFOYvmMrJEmC/qEfeUpJgn/t2oge9WyNC0Hi3i6IjDfuv3v4Yh0loKwPyfN7SMrHqr4ekQ9x+0Wg4Mgj65EACEi13oJU65Uqr4fIHKr1Iuj69esbQo6/vz8OHCj8zSMlJYVdokRkdTR5eZixeycEYBR+AEAnBC5mZGDJH/HyFFdJQtyDyPw7Cp9k9uizBnWALh0i61PzrCx3HVBwuIT1FO5Lkf0phPaiedZFZEEVDkB9+vTBjz/+CKBwJOdp06ahf//+GDNmDEaMGGH2AomIqmJL0qlSr/fRCYENJ47XrGuC7u0ARBZg8hG5OuDeFgh9dpVXJXLXlNFCAXH3f1VeD5GlVfgusOXLl0N//7aASZMmwdPTE/v27cPQoUOLPZKCiEhu5+/chlKhgPbRe7ofkltQgJu5uWhYQ055C+05FP74Li205QO6NEARWPn1CAHoLsF00AIAHaA9V+l1EMmlwgFIoVBAoXjQcTR69GiMHj3arEUREZmLq4NjuU7Pu5r5gbLVSZJcIYqdkiqBwrWK65Eg4AzgbimtlIBkXw91JttQqXGA7ty5gxUrVuDUqVOQJAktW7bEiy++CE9PT3PXR0RUJU80C8Tnhw+anK+QJHTwaYA6Li4WrKqKnCOA7NKu8ZEAVQtIyoZmWNdA4F4MAJ2JBjpIzgOqvh4iC6vwNUBxcXEICAjAkiVLcOfOHdy+fRtLlixBQEAA4uLiqqNGIqJKa+Xljd7+jaGQpBLnCyHwRucwC1dVNZKqCeA0EKZ/hAtItaaYZ11u4wEoUXjX16OUgKo54NTHLOsisqQK3wbfqlUrhIeHIyoqCsr7I2TpdDq89tpr+P333/HXX39VS6GWxNvgiWxLdn4+pm7/CXsupEApSZAkCTq9Hk4qFSL7RmB4UEu5S6wwIe5BZLwD5O3Ag4CiA+AAyWM2JFfzXZog8uILb7kXGjw4caAFVK0h1VkGSVnPbOsiqopqHQfIxcUFiYmJCAoKMpqelJSEtm3b4u7d0s4V1wwMQES26cT1a9h2Nhm5Bflo6lkXw5q3gLuTk9xlVYkoOANxbxsgsiAp/QGXYZAUavOvR+QB97ZBFJwAJEdITr0Bh46QTPSsEcmhIt/fFb4GqH379jh16lSxAHTq1Cm0bdu2oh9HRGQxIV7eCPHylrsMs5IcmkNyaF7965GcAJcnIbk8We3rIrKECgegKVOmYOrUqTh79iy6du0KADhw4ACWLl2K+fPn49ixY4a2oaGh5quUiIiIyEwqfArs4VvgS/xASYIQovAcu87UXQPWjafAiIiIap5qPQWWkpJS6cKIiIiIrEGFA5C/v3911EFERERkMRUeBwgAvv32W3Tr1g0NGjTAxYuFD8FbtGgRYmJizFocERERUXWocACKiorC9OnTMWjQIGRkZBiu86lduzYWLVpk7vqIqAbRC4HDV1Ox7ewZHElLLfb09ZpICIGEtKvYdvYMDl29YhPbRCQnoT0LcW8HRN6+wuEVZFLhU2D/+c9/8OWXX+LJJ5/E/PnzDdM7duyIt99+26zFEVHNsevcWfxr7x5cydIYpvl5qPGPXn3QJ6CJjJVV3m8XL2B27G5czMwwTGtQyx0ze/TGoMDqv/WcyJYI7VmIzPeBgoQHEyV3wG0S4DbB4mNKVbgHKCUlBe3atSs23cnJCTk5OWYpiohqlp3nkjHp5xikPhR+AOCKJhMTf9yM3edr3tPCf7t4AS9u2YRLD4UfALianYXXt/2In86clqcwohpIaC9C3BoDFBx7ZEYWRPbHENkLLV5ThQNQQEAAEhMTi03ftm0bgoODzVETEdUgOr0ec+J+BQA8enKo6P3cvb/WqFNHQgjMjfsVQohi21RkbtweFNTQoT6ILE1k/wcQuTD5UN2cLyF0aRatqcKnwN555x1MnjwZ9+7dgxACf/zxB9atW4fIyEh89dVX1VEjEVmxP1KvID072+R8AeCKRoMjaano1MDXcoVVwbHr13A+406pbW7dzcX+y5fQq3GAhaoiqpmEPhe4txUmww8AQALu/gDUetVCVVUiAL344ovQarV49913kZubi2effRYNGzbE4sWL8cwzz1RHjURkxa6V89T3tVJCkrW5Xs5a03NqzjYRyUZkANCW0UgBob8GS14FVOEABAATJ07ExIkTcfPmTej1enh5eZm7LiKqIeq5uZavnatbNVdi2pUrQHIyEBgI+JajE6qeW/lqlXObiGoMSQ1AidJ7gAQkhWWzRIWvAbp79y5yc3MBAI899hju3r2LRYsWYefOnWYvjoisX5eGfvAqIwj41HJHxwYNLVSRsRUrAH9/oE+fwj9XrCh7mTbe9dFIrS71t9E6zi7o3ogDwxKVRVK4AU4DUBiCTNEDzsMsVRKASgSg4cOHY/Xq1QCAjIwMdO7cGQsXLsTw4cMRFRVl9gKJyLqpFAq837N3qW3e79EbyjKeI1gdrlwBXn4Z0OsL3+v1wCuvFE4vjSRJ+KDH44V/N9FmZo9ecFSW9gOdiIpI7q8DkiNMxg7XsZBUlr1GsMI/kf7880/06NEDALBx40bUr18fFy9exOrVq7FkyRKzF0hE1m9o8xZYNGAQ6rkanw7zcnXDfwcOkW3MnOTkB+GniE4HnD1b9rJ9mzRF1OBh8HKrZTTd08UFn/R/Ak+1DDFjpUS2TVI1g+S5FlAFPjLHGXCbDMl9psVrqvA1QLm5uXB3dwcA7Ny5EyNHjoRCoUDXrl0Nj8Uor8jISGzatAmnT5+Gi4sLwsPDsWDBAgQFBZlcZty4cfjmm2+KTQ8ODsaJEycAAKtWrcKLL75YrM3du3fh7OxcoRqJqHyGBbXEoMAgxF++hOu5OfByc0OYbyOoZOj5KRIYCCgUxiFIqQSaNSvf8hFNA9E3oCkOpF5GenY2HnNxRbhfIziw54eowiSHEKDuFkB7HNCeAyRXwLEbJEWtsheuBhUOQM2aNcMPP/yAESNGYMeOHZg2bRoA4Pr162U+ev5RcXFxmDx5Mjp16gStVov3338fEREROHnyJNxMXIS4ePFioxGotVot2rRpg1GjRhm18/DwQFJSktE0hh+i6qVSKNDDv7HcZRj4+gLLlxee9tLpCsPPF1+U70LoIkqFAt38eK0PkTlIkgQ4hBa+ZFbhADR79mw8++yzmDZtGvr27YuwsDAAhb1BJY0QXZrt27cbvV+5ciW8vLxw5MgR9OzZs8Rl1Go11Gq14f0PP/yAO3fuFOvxkSQJ9evXr1A9RGR7xo8HBgwoPO3VrFnFwg8R2a4KB6Cnn34a3bt3R1paGtq0aWOY3rdvX4wYMaJKxWRmZgIAPD09y73MihUr0K9fP/j7G/+Glp2dDX9/f+h0OrRt2xb/+te/TAa0vLw85OU9eCCbRqMpsR0R1Uy+vgw+RGRMEsI6xqcXQmD48OG4c+cOfvvtt3Itk5aWBj8/P6xduxajR482TD9w4ADOnj2L1q1bQ6PRYPHixdi6dSuOHj2KwMBHL8AC5syZg7lz5xabnpmZWeHTekRERCQPjUYDtVpdru9vqwlAkydPxs8//4x9+/bBt5y/qkVGRmLhwoW4evUqHB0dTbbT6/Vo3749evbsWeKdaiX1APn5+TEAERER1SAVCUCVGgna3N544w1s2bIFe/fuLXf4EULg66+/xtixY0sNPwCgUCjQqVMnJCcnlzjfyckJTk5OFa6biIiIaiZZA5AQAm+88QY2b96M2NhYBASU/6GCcXFxOHv2LMaPH1+u9SQmJqJ169ZVKZeIqkGeVottZ5ORfPsmXB0cENEkEIF161bLus7fuY1tZ5ORW5CPZnXqYmBgIJxVDtWyLiKybrIGoMmTJ2Pt2rWIiYmBu7s70tPTARTe6eXi4gIAmDFjBlJTUw2jTxdZsWIFunTpglatWhX73Llz56Jr164IDAyERqPBkiVLkJiYiKVLl1b/RhFRuf2ach7Td26FJi8PKoUCQggsjP8d/Zs0xacRg+BWRu9ued0tKMA7u7Zj69kzUEoSJEmCVq/HnLjd+Kj/ExjQtPi1gURk2+QboQxAVFQUMjMz0bt3b/j4+BheGzZsMLRJS0vDpUuXjJbLzMxEdHS0yd6fjIwMvPzyy2jZsiUiIiKQmpqKvXv3onPnztW6PURUfglpV/HKTz8g6/71d1q9Hrr7lyTuTjmPN7b9ZLZ1Td+5DdvPFZ4C1wkB7f2REbPz8zF564/4I7WMZ2MQkc2xmougrUlFLqIiosp5KWYTfrt0wRB6SrJ5zHNo41218bxO37yBQWtXm5yvkCR0beiLNSNHm2xDRDVDRb6/Ze0BIiL7lJOfj7iLKaWGH6WkwNbkJJPzy2trcuFpL1P0QmD/lcvIuHe3yusiopqDAYiILC63oABldT1LUuEpqqrKys8rHH6/DDn5BVVeFxHVHAxARGRxtZ2dUauMC5z1QqBx7dpVXlfj2rWhe/SR8I9wUanw2CNPsici28YAREQW56BUYkxI61JPTSkkCSNahFR5XU8GBZf69HalJOGpliFwUlnFsGhEZCEMQEQki8mdusBPrS4WgorezerR2yy9MmpnZ8zt3ff+ZxuvSylJ8Knljqldwqu8HiKqWRiAiEgWtZ1dsHHU/2FMq1A4PdRD07zuY/h80DA836bkhxdXxpiQ1lg+ZDiC69UzTHNUKvFUyxBsGvMc6vL0F5Hd4W3wJeBt8ESWlZOfj9QsDdwcHNHA3b1cFy1XVmqWBjn5+Wjg7lHmdUhEVLPUuGeBEZF9c3N0RPO6j1lkXQ3d+UsNEfEUGBEREdkhBiAiIiKyOwxAREREZHcYgIiIiMjuMAARERGR3eFdYEQ27kZuDv534i8cvnoFgIRufo3wdHAIaju7yF0aEZFsGICIbNivKecxeesWFOj10N8f8mvvxRQsPrgfXw0dgS6+fjJXSEQkD54CI7JRKRl38OrPMcjX6QzhBwAEgLtaLV7asgnXsrPlK5CISEYMQEQ26tujCdALgZKGetcLgTydDuv+OmbxuoiIrAEDEJGN+iXlHHSlPOlGLwR2p5yzYEVERNaDAYjIRhXo9GW2ydfpLFAJEZH1YQAislHtfXygLOWhokpJQgefBhasiIjIejAAEdmo50PblXoKTCcExoa2tVxBRERWhAGIyEZ18fXDlM5hAGDUE1T091k9eqNlPS9ZaiMikhvHASKyYW92DUeb+vWxMuEIDl1NhSRJCPdthPHtOiDMr5Hc5RERyYYBiMjGPd64CR5v3ETuMoiIrApPgREREZHdYQAiIiIiu8MARERERHaHAYiIiIjsDgMQERER2R0GICIiIrI7DEBERERkd2QNQJGRkejUqRPc3d3h5eWFJ598EklJSaUuExsbC0mSir1Onz5t1C46OhrBwcFwcnJCcHAwNm/eXJ2bQkRERDWIrAEoLi4OkydPxoEDB7Br1y5otVpEREQgJyenzGWTkpKQlpZmeAUGBhrmxcfHY8yYMRg7diyOHj2KsWPHYvTo0Th48GB1bg4RERHVEJIQpTwt0cJu3LgBLy8vxMXFoWfPniW2iY2NxeOPP447d+6gdu3aJbYZM2YMNBoNtm3bZpj2xBNPoE6dOli3bl2ZdWg0GqjVamRmZsLDw6NS20JERESWVZHvb6u6BigzMxMA4OnpWWbbdu3awcfHB3379sWePXuM5sXHxyMiIsJo2oABA7B///4SPysvLw8ajcboRURERLbLagKQEALTp09H9+7d0apVK5PtfHx8sHz5ckRHR2PTpk0ICgpC3759sXfvXkOb9PR0eHt7Gy3n7e2N9PT0Ej8zMjISarXa8PLz8zPPRhEREZFVspqHob7++us4duwY9u3bV2q7oKAgBAUFGd6HhYXh8uXL+OSTT4xOm0mSZLScEKLYtCIzZszA9OnTDe81Gg1DEBERkQ2zih6gN954A1u2bMGePXvg6+tb4eW7du2K5ORkw/v69esX6+25fv16sV6hIk5OTvDw8DB6ERERke2SNQAJIfD6669j06ZN+PXXXxEQEFCpz0lISICPj4/hfVhYGHbt2mXUZufOnQgPD69SvURERGQbZD0FNnnyZKxduxYxMTFwd3c39Nqo1Wq4uLgAKDw9lZqaitWrVwMAFi1ahMaNGyMkJAT5+flYs2YNoqOjER0dbfjcqVOnomfPnliwYAGGDx+OmJgY/PLLL2WeXiMiIiL7IGsAioqKAgD07t3baPrKlSsxbtw4AEBaWhouXbpkmJefn4+3334bqampcHFxQUhICH7++WcMGjTI0CY8PBzr16/HrFmz8MEHH6Bp06bYsGEDunTpUu3bRERERNbPqsYBshYcB4iIiKjmqbHjABERERFZAgMQERER2R0GICIiIrI7DEBERERkd6xmJGgiqj56IXAtOxuSBHi71TI5KjoRkb1gACKyYTq9HisT/8TXiUeQnp0NAGikVmNi+054tlUogxAR2S0GICIbpRcC03duw49nThtNv5yZiQ/2/ILTN2/gn737MgQRkV3iNUBENmrX+bPFwg8AFA389d3xoziYesWyRRERWQkGICIbteZYIhSl9O4oJQnfHT9qwYqIiKwHAxCRjUq+fQv6UgZ61wmBpFs3LVgREZH1YAAislFuDo5ltqnlWHYbIiJbxABEZKOGNA8q9RSYBGBIYJDlCiIisiIMQEQ26m+t28Ld0QnKEkKQUpJQz9UNTwe3kqEyIiL5MQAR2ah6bm5YO3IU6rm5AQBUCgVUisL/8g091Fj71Gh4ODnJWSIRkWw4DhCRDWtZzwt7x03EL+fP4fDVVEgSEObbCL38G0Op4O8/RGS/GICIbJxKocATzQLxRLNAuUshIrIa/BWQiIiI7A4DEBEREdkdBiAiIiKyOwxAREREZHcYgIiIiMju8C4wIjtwPScbR9PTIUlAe58G8HRxlbskIiJZMQAR2TBN3j18sGc3fk5OMjwYVaVQYGSLYMzu1QeuDg4yV0hEJA8GICIbdU9bgGc3/Q9JN28aPRVeq9dj46kTSMm4gzUjRsFBqZSxSiIiefAaICIbtenUSZy8cQO6h8JPEb0QOHQ1FbvOn5WhMiIi+TEAEdmoDSeOw/Sz4AGFJGHDieMWq4eIyJowABHZqPTsbBTv+3lALwSuZmVZrB4iImvCAERko7zc3MrsAapfq5bF6iEisiYMQEQ2alRwq1Ln64XA02W0ISKyVQxARDbq6eBWaFrHE0qpeD+QUpIQ6uWNgc2ay1AZEZH8GICIbJSrgwPWPTUGvRsHGJ0KU0gSBjZrjtUjRsGRt8ATkZ2SNQBFRkaiU6dOcHd3h5eXF5588kkkJSWVusymTZvQv39/1KtXDx4eHggLC8OOHTuM2qxatQqSJBV73bt3rzo3h8jq1HV1xZdDRyD2hQn4NGIQPhswCL+Nm4glA4fAw8lJ7vKIiGQjawCKi4vD5MmTceDAAezatQtarRYRERHIyckxuczevXvRv39/bN26FUeOHMHjjz+OoUOHIiEhwaidh4cH0tLSjF7Ozs7VvUlEVslPrcaTLVpieFBL+Li7y10OEZHsJCFKGCVNJjdu3ICXlxfi4uLQs2fPci8XEhKCMWPGYPbs2QAKe4DefPNNZGRkVKoOjUYDtVqNzMxMeHh4VOoziIiIyLIq8v1tVdcAZWZmAgA8PT3LvYxer0dWVlaxZbKzs+Hv7w9fX18MGTKkWA/Rw/Ly8qDRaIxeREREZLusJgAJITB9+nR0794drVqV/9bchQsXIicnB6NHjzZMa9GiBVatWoUtW7Zg3bp1cHZ2Rrdu3ZCcnFziZ0RGRkKtVhtefn5+Vd4eIiIisl5Wcwps8uTJ+Pnnn7Fv3z74+vqWa5l169ZhwoQJiImJQb9+/Uy20+v1aN++PXr27IklS5YUm5+Xl4e8vDzDe41GAz8/P54CIyIiqkEqcgrMKp4G/8Ybb2DLli3Yu3dvucPPhg0bMH78eHz//felhh8AUCgU6NSpk8keICcnJzjxjhgiIiK7IespMCEEXn/9dWzatAm//vorAgICyrXcunXrMG7cOKxduxaDBw8u13oSExPh4+NT1ZKJiIjIBsjaAzR58mSsXbsWMTExcHd3R3p6OgBArVbDxcUFADBjxgykpqZi9erVAArDz/PPP4/Fixeja9euhmVcXFygVqsBAHPnzkXXrl0RGBgIjUaDJUuWIDExEUuXLpVhK4mIiMjayNoDFBUVhczMTPTu3Rs+Pj6G14YNGwxt0tLScOnSJcP7L774AlqtFpMnTzZaZurUqYY2GRkZePnll9GyZUtEREQgNTUVe/fuRefOnS26fURERGSdrOYiaGvCcYCIiIhqnho7DhARERGRJTAAERERkd1hACIiIiK7wwBEREREdocBiIiIiOwOAxARERHZHQYgIiIisjsMQERERGR3GICIiIjI7jAAERERkd1hACIiIiK7wwBEREREdocBiIiIiOwOAxARERHZHQYgIiIisjsMQERERGR3GICIiIjI7jAAERERkd1hACIiIiK7wwBEREREdocBiIiIiOwOAxARERHZHQYgIiIisjsMQERERGR3GICIiIjI7jAAERERkd1hACIiIiK7wwBEREREdocBiIiIiOwOAxARERHZHZXcBRBVh3ydDptPncCa40dxMTMD7o5OGNEiGGND28K7Vi25yyMiIpnJ2gMUGRmJTp06wd3dHV5eXnjyySeRlJRU5nJxcXHo0KEDnJ2d0aRJEyxbtqxYm+joaAQHB8PJyQnBwcHYvHlzdWwCWaF72gKM+yEaM37dhZM3riM7Px9p2VlYduQPDFz7DZJu3ZS7RCIikpmsASguLg6TJ0/GgQMHsGvXLmi1WkRERCAnJ8fkMikpKRg0aBB69OiBhIQEzJw5E1OmTEF0dLShTXx8PMaMGYOxY8fi6NGjGDt2LEaPHo2DBw9aYrNIZosPxuOPq1cAAOKh6XohkJWXh0k/xUAvRMkLExGRXZCEsJ5vghs3bsDLywtxcXHo2bNniW3ee+89bNmyBadOnTJMmzRpEo4ePYr4+HgAwJgxY6DRaLBt2zZDmyeeeAJ16tTBunXryqxDo9FArVYjMzMTHh4eVdwqsqR72gJ0/moZsvPzS233zZNPoUejxpYpioiILKIi399WdRF0ZmYmAMDT09Nkm/j4eERERBhNGzBgAA4fPoyCgoJS2+zfv7/Ez8zLy4NGozF6Uc2UkpFRZvhRShIS0tIsVBEREVkjqwlAQghMnz4d3bt3R6tWrUy2S09Ph7e3t9E0b29vaLVa3Lx5s9Q26enpJX5mZGQk1Gq14eXn51fFrSG5KCWpfO0UVnPoExGRDKzmW+D111/HsWPHynWKSnrkS67oLN7D00tq8+i0IjNmzEBmZqbhdfny5YqWT1aiSR1P1HN1K7WNTgh092tkoYqIiMgaWUUAeuONN7Blyxbs2bMHvr6+pbatX79+sZ6c69evQ6VSoW7duqW2ebRXqIiTkxM8PDyMXlQzqRQKTGzf0eR8pSShff0GaFPfx4JVERGRtZE1AAkh8Prrr2PTpk349ddfERAQUOYyYWFh2LVrl9G0nTt3omPHjnBwcCi1TXh4uPmKJ6v1UrsOeCakNYAHp8SKDvTGtevg88FDZaqMiIishawDIU6ePBlr165FTEwM3N3dDb02arUaLi4uAApPT6WmpmL16tUACu/4+u9//4vp06dj4sSJiI+Px4oVK4xOnU2dOhU9e/bEggULMHz4cMTExOCXX37Bvn37LL+RZHEKScK8Pv0xomUw1v91HCl3bkPt7IJhzVtgUGBzOKk4/icRkb2T9TZ4U9fkrFy5EuPGjQMAjBs3DhcuXEBsbKxhflxcHKZNm4YTJ06gQYMGeO+99zBp0iSjz9i4cSNmzZqF8+fPo2nTppg3bx5GjhxZrrp4GzwREVHNU5Hvb6saB8haMAARERHVPDV2HCAiIiIiS2AAIiIiIrvDAERERER2hwGIiIiI7A4DEBEREdkdBiAiIiKyOwxAREREZHcYgIiIiMjuMAARERGR3eFDkUpQNDi2RqORuRIiIiIqr6Lv7fI85IIBqARZWVkAAD8/P5krISIioorKysqCWq0utQ2fBVYCvV6Pq1evwt3d3eQDW62dRqOBn58fLl++bPfPM+O+KMT9UIj74QHui0LcD4VsYT8IIZCVlYUGDRpAoSj9Kh/2AJVAoVDA19dX7jLMwsPDo8YeyObGfVGI+6EQ98MD3BeFuB8K1fT9UFbPTxFeBE1ERER2hwGIiIiI7A4DkI1ycnLCP/7xDzg5Ocldiuy4LwpxPxTifniA+6IQ90Mhe9sPvAiaiIiI7A57gIiIiMjuMAARERGR3WEAIiIiIrvDAERERER2hwHIBkRGRkKSJLz55psm28TGxkKSpGKv06dPW67QajBnzpxi21S/fv1Sl4mLi0OHDh3g7OyMJk2aYNmyZRaqtvpUdD/Y6vEAAKmpqfjb3/6GunXrwtXVFW3btsWRI0dKXcYWjwmg4vvCFo+Lxo0bl7hNkydPNrmMLR4PFd0PtngsPIojQddwhw4dwvLlyxEaGlqu9klJSUYjfNarV6+6SrOYkJAQ/PLLL4b3SqXSZNuUlBQMGjQIEydOxJo1a/D777/jtddeQ7169fDUU09ZotxqU5H9UMTWjoc7d+6gW7duePzxx7Ft2zZ4eXnh3LlzqF27tsllbPWYqMy+KGJLx8WhQ4eg0+kM7//66y/0798fo0aNKrG9rR4PFd0PRWzpWHgUA1ANlp2djeeeew5ffvklPvzww3It4+XlVa4fgDWJSqUqs9enyLJly9CoUSMsWrQIANCyZUscPnwYn3zySY3+4QZUbD8UsbXjYcGCBfDz88PKlSsN0xo3blzqMrZ6TFRmXxSxpePi0S/s+fPno2nTpujVq1eJ7W31eKjofihiS8fCo3gKrAabPHkyBg8ejH79+pV7mXbt2sHHxwd9+/bFnj17qrE6y0lOTkaDBg0QEBCAZ555BufPnzfZNj4+HhEREUbTBgwYgMOHD6OgoKC6S61WFdkPRWzteNiyZQs6duyIUaNGwcvLC+3atcOXX35Z6jK2ekxUZl8UsbXjokh+fj7WrFmDl156yeSDrm31eHhYefZDEVs9FgAGoBpr/fr1OHLkCCIjI8vV3sfHB8uXL0d0dDQ2bdqEoKAg9O3bF3v37q3mSqtXly5dsHr1auzYsQNffvkl0tPTER4ejlu3bpXYPj09Hd7e3kbTvL29odVqcfPmTUuUXC0quh9s9Xg4f/48oqKiEBgYiB07dmDSpEmYMmUKVq9ebXIZWz0mKrMvbPW4KPLDDz8gIyMD48aNM9nGVo+Hh5VnP9j6sQAAEFTjXLp0SXh5eYnExETDtF69eompU6dW6HOGDBkihg4daubq5JWdnS28vb3FwoULS5wfGBgo/v3vfxtN27dvnwAg0tLSLFGiRZS1H0piC8eDg4ODCAsLM5r2xhtviK5du5pcxlaPicrsi5LYwnFRJCIiQgwZMqTUNrZ6PDysPPuhJLZ0LAghBHuAaqAjR47g+vXr6NChA1QqFVQqFeLi4rBkyRKoVCqjC91K07VrVyQnJ1dztZbl5uaG1q1bm9yu+vXrIz093Wja9evXoVKpULduXUuUaBFl7YeS2MLx4OPjg+DgYKNpLVu2xKVLl0wuY6vHRGX2RUls4bgAgIsXL+KXX37BhAkTSm1nq8dDkfLuh5LYyrFQhAGoBurbty+OHz+OxMREw6tjx4547rnnkJiYWK67fwAgISEBPj4+1VytZeXl5eHUqVMmtyssLAy7du0ymrZz50507NgRDg4OlijRIsraDyWxheOhW7duSEpKMpp25swZ+Pv7m1zGVo+JyuyLktjCcQEAK1euhJeXFwYPHlxqO1s9HoqUdz+UxFaOBQO5u6DIPB49Bfb3v/9djB071vD+s88+E5s3bxZnzpwRf/31l/j73/8uAIjo6GgZqjWft956S8TGxorz58+LAwcOiCFDhgh3d3dx4cIFIUTx/XD+/Hnh6uoqpk2bJk6ePClWrFghHBwcxMaNG+XaBLOo6H6w1ePhjz/+ECqVSsybN08kJyeL7777Tri6uoo1a9YY2tjLMVGZfWGrx4VOpxONGjUS7733XrF59nI8CFGx/WCrx8LDGIBsxKMB6IUXXhC9evUyvF+wYIFo2rSpcHZ2FnXq1BHdu3cXP//8s+ULNbMxY8YIHx8f4eDgIBo0aCBGjhwpTpw4YZj/6H4QQojY2FjRrl074ejoKBo3biyioqIsXLX5VXQ/2OrxIIQQP/74o2jVqpVwcnISLVq0EMuXLzeaby/HhBAV3xe2elzs2LFDABBJSUnF5tnT8VCR/WCrx8LDJCGEkLMHioiIiMjSeA0QERER2R0GICIiIrI7DEBERERkdxiAiIiIyO4wABEREZHdYQAiIiIiu8MARERERHaHAYiIiIjsDgMQEdmUcePG4cknnzQ5f9WqVahdu7bF6ilL48aNsWjRIrnLILI7DEBERBZgbcGLyN4xABEREZHdYQAiIrPZuHEjWrduDRcXF9StWxf9+vVDTk6OYf7KlSvRsmVLODs7o0WLFvj8888N8y5cuABJkrB+/XqEh4fD2dkZISEhiI2NNbTR6XQYP348AgIC4OLigqCgICxevLjKdf/444/o0KEDnJ2d0aRJE8ydOxdardYwX5IkfPXVVxgxYgRcXV0RGBiILVu2GH3Gli1bEBgYCBcXFzz++OP45ptvIEkSMjIyEBsbixdffBGZmZmQJAmSJGHOnDmGZXNzc/HSSy/B3d0djRo1wvLly6u8TURUBrmfxkpEtuHq1atCpVKJTz/9VKSkpIhjx46JpUuXiqysLCGEEMuXLxc+Pj4iOjpanD9/XkRHRwtPT0+xatUqIYQQKSkpAoDw9fUVGzduFCdPnhQTJkwQ7u7u4ubNm0IIIfLz88Xs2bPFH3/8Ic6fPy/WrFkjXF1dxYYNGwx1vPDCC2L48OEm61y5cqVQq9WG99u3bxceHh5i1apV4ty5c2Lnzp2icePGYs6cOYY2RXWtXbtWJCcniylTpohatWqJW7duGWp3cHAQb7/9tjh9+rRYt26daNiwoQAg7ty5I/Ly8sSiRYuEh4eHSEtLE2lpaYb94u/vLzw9PcXSpUtFcnKyiIyMFAqFQpw6dcos/y5EVDIGICIyiyNHjggA4sKFCyXO9/PzE2vXrjWa9q9//UuEhYUJIR4EoPnz5xvmFxQUCF9fX7FgwQKT633ttdfEU089ZXhf0QDUo0cP8e9//9uozbfffit8fHwM7wGIWbNmGd5nZ2cLSZLEtm3bhBBCvPfee6JVq1ZGn/H+++8bAlBJ6y3i7+8v/va3vxne6/V64eXlJaKiokxuAxFVnUrGzicisiFt2rRB37590bp1awwYMAARERF4+umnUadOHdy4cQOXL1/G+PHjMXHiRMMyWq0WarXa6HPCwsIMf1epVOjYsSNOnTplmLZs2TJ89dVXuHjxIu7evYv8/Hy0bdu20nUfOXIEhw4dwrx58wzTdDod7t27h9zcXLi6ugIAQkNDDfPd3Nzg7u6O69evAwCSkpLQqVMno8/t3LlzuWt4+LMlSUL9+vUNn01E1YMBiIjMQqlUYteuXdi/fz927tyJ//znP3j//fdx8OBBQ4j48ssv0aVLl2LLlUWSJADA//73P0ybNg0LFy5EWFgY3N3d8fHHH+PgwYOVrluv12Pu3LkYOXJksXnOzs6Gvzs4OBSrSa/XAwCEEIYaiwghyl1DaZ9NRNWDAYiIzEaSJHTr1g3dunXD7Nmz4e/vj82bN2P69Olo2LAhzp8/j+eee67Uzzhw4AB69uwJoLCH6MiRI3j99dcBAL/99hvCw8Px2muvGdqfO3euSjW3b98eSUlJaNasWaU/o0WLFti6davRtMOHDxu9d3R0hE6nq/Q6iMi8GICIyCwOHjyI3bt3IyIiAl5eXjh48CBu3LiBli1bAgDmzJmDKVOmwMPDAwMHDkReXh4OHz6MO3fuYPr06YbPWbp0KQIDA9GyZUt89tlnuHPnDl566SUAQLNmzbB69Wrs2LEDAQEB+Pbbb3Ho0CEEBARUuu7Zs2djyJAh8PPzw6hRo6BQKHDs2DEcP34cH374Ybk+45VXXsGnn36K9957D+PHj0diYiJWrVoF4EHvVePGjZGdnY3du3ejTZs2cHV1NfSMEZHl8TZ4IjILDw8P7N27F4MGDULz5s0xa9YsLFy4EAMHDgQATJgwAV999RVWrVqF1q1bo1evXli1alWx8DJ//nwsWLAAbdq0wW+//YaYmBg89thjAIBJkyZh5MiRGDNmDLp06YJbt24Z9QZVxoABA/DTTz9h165d6NSpE7p27YpPP/0U/v7+5f6MgIAAbNy4EZs2bUJoaCiioqLw/vvvAwCcnJwAAOHh4Zg0aRLGjBmDevXq4aOPPqpS3URUNZKoyIlqIqJqcuHCBQQEBCAhIaFKFzVbi3nz5mHZsmW4fPmy3KUQUQl4CoyIyAw+//xzdOrUCXXr1sXvv/+Ojz/+2HDtEhFZHwYgIiIzSE5Oxocffojbt2+jUaNGeOuttzBjxgy5yyIiE3gKjIiIiOwOL4ImIiIiu8MARERERHaHAYiIiIjsDgMQERER2R0GICIiIrI7DEBERERkdxiAiIiIyO4wABEREZHd+X8O6y+GJJD91gAAAABJRU5ErkJggg==\n",
698
      "text/plain": [
699
       "<Figure size 640x480 with 1 Axes>"
700
      ]
701
     },
702
     "metadata": {},
703
     "output_type": "display_data"
704
    }
705
   ],
706
   "source": [
707
    "plt.scatter(X_test['sepal length (cm)'], X_test['sepal width (cm)'], c=y_pred)\n",
708
    "plt.scatter(lda.means_[:, 0], lda.means_[:, 1], c='blue', marker='.')\n",
709
    "plt.xlabel('sepal length')\n",
710
    "plt.ylabel('sepal width')\n",
711
    "plt.show()"
712
   ]
713
  },
714
  {
715
   "cell_type": "code",
716
   "execution_count": 19,
717
   "id": "ddb40ab9",
718
   "metadata": {},
719
   "outputs": [
720
    {
721
     "name": "stderr",
722
     "output_type": "stream",
723
     "text": [
724
      "C:\\Users\\shali\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1036: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
725
      "  warnings.warn(\n"
726
     ]
727
    },
728
    {
729
     "data": {
730
      "image/png": "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\n",
731
      "text/plain": [
732
       "<Figure size 640x480 with 1 Axes>"
733
      ]
734
     },
735
     "metadata": {},
736
     "output_type": "display_data"
737
    }
738
   ],
739
   "source": [
740
    "inertia = []\n",
741
    "ks = range(1, 10)\n",
742
    "\n",
743
    "for i in ks:\n",
744
    "    k_means = KMeans(n_clusters=i)\n",
745
    "    k_means.fit_predict(data)\n",
746
    "    inertia.append(k_means.inertia_)\n",
747
    "    \n",
748
    "plt.plot(ks, inertia)\n",
749
    "plt.plot(ks, inertia ,'ro')\n",
750
    "plt.text(3, 45, 'оптимальное число кластеров')\n",
751
    "plt.show()"
752
   ]
753
  },
754
  {
755
   "cell_type": "code",
756
   "execution_count": 20,
757
   "id": "4caa5986",
758
   "metadata": {},
759
   "outputs": [
760
    {
761
     "data": {
762
      "image/png": "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\n",
763
      "text/plain": [
764
       "<Figure size 640x480 with 1 Axes>"
765
      ]
766
     },
767
     "metadata": {},
768
     "output_type": "display_data"
769
    }
770
   ],
771
   "source": [
772
    "k_means = KMeans(n_clusters=3)\n",
773
    "clusters = k_means.fit_predict(data)\n",
774
    "plt.scatter(data['sepal length (cm)'], data['sepal width (cm)'], c=clusters)\n",
775
    "plt.show()"
776
   ]
777
  },
778
  {
779
   "cell_type": "markdown",
780
   "id": "559a3c3b",
781
   "metadata": {},
782
   "source": [
783
    "![](Scikit_learn_logo.png)"
784
   ]
785
  }
786
 ],
787
 "metadata": {
788
  "kernelspec": {
789
   "display_name": "Python 3 (ipykernel)",
790
   "language": "python",
791
   "name": "python3"
792
  },
793
  "language_info": {
794
   "codemirror_mode": {
795
    "name": "ipython",
796
    "version": 3
797
   },
798
   "file_extension": ".py",
799
   "mimetype": "text/x-python",
800
   "name": "python",
801
   "nbconvert_exporter": "python",
802
   "pygments_lexer": "ipython3",
803
   "version": "3.9.13"
804
  }
805
 },
806
 "nbformat": 4,
807
 "nbformat_minor": 5
808
}
809