└── logistic_regression.ipynb


/logistic_regression.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |   "cells": [
  3 |     {
  4 |       "cell_type": "markdown",
  5 |       "metadata": {
  6 |         "id": "view-in-github",
  7 |         "colab_type": "text"
  8 |       },
  9 |       "source": [
 10 |         "<a href=\"https://colab.research.google.com/github/NandhanaRameshkumar/Logistic_regression/blob/main/logistic_regression.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
 11 |       ]
 12 |     },
 13 |     {
 14 |       "cell_type": "code",
 15 |       "execution_count": null,
 16 |       "id": "a30110cd",
 17 |       "metadata": {
 18 |         "id": "a30110cd"
 19 |       },
 20 |       "outputs": [],
 21 |       "source": [
 22 |         "import numpy as np\n",
 23 |         "import pandas as pd\n",
 24 |         "import matplotlib.pyplot as plt\n"
 25 |       ]
 26 |     },
 27 |     {
 28 |       "cell_type": "code",
 29 |       "execution_count": null,
 30 |       "id": "b0308af8",
 31 |       "metadata": {
 32 |         "id": "b0308af8"
 33 |       },
 34 |       "outputs": [],
 35 |       "source": [
 36 |         "df=pd.read_csv(\"D:\\\\Jeyashri\\\\IBM\\\\Datasets\\\\results.csv\")\n"
 37 |       ]
 38 |     },
 39 |     {
 40 |       "cell_type": "code",
 41 |       "execution_count": null,
 42 |       "id": "77e274a4",
 43 |       "metadata": {
 44 |         "id": "77e274a4",
 45 |         "outputId": "2fd6e4e9-a461-4104-d757-4e9dd34f3b79"
 46 |       },
 47 |       "outputs": [
 48 |         {
 49 |           "data": {
 50 |             "text/html": [
 51 |               "<div>\n",
 52 |               "<style scoped>\n",
 53 |               "    .dataframe tbody tr th:only-of-type {\n",
 54 |               "        vertical-align: middle;\n",
 55 |               "    }\n",
 56 |               "\n",
 57 |               "    .dataframe tbody tr th {\n",
 58 |               "        vertical-align: top;\n",
 59 |               "    }\n",
 60 |               "\n",
 61 |               "    .dataframe thead th {\n",
 62 |               "        text-align: right;\n",
 63 |               "    }\n",
 64 |               "</style>\n",
 65 |               "<table border=\"1\" class=\"dataframe\">\n",
 66 |               "  <thead>\n",
 67 |               "    <tr style=\"text-align: right;\">\n",
 68 |               "      <th></th>\n",
 69 |               "      <th>Hours</th>\n",
 70 |               "      <th>StudentId</th>\n",
 71 |               "      <th>Result</th>\n",
 72 |               "    </tr>\n",
 73 |               "  </thead>\n",
 74 |               "  <tbody>\n",
 75 |               "    <tr>\n",
 76 |               "      <th>0</th>\n",
 77 |               "      <td>1</td>\n",
 78 |               "      <td>10</td>\n",
 79 |               "      <td>0</td>\n",
 80 |               "    </tr>\n",
 81 |               "    <tr>\n",
 82 |               "      <th>1</th>\n",
 83 |               "      <td>1</td>\n",
 84 |               "      <td>15</td>\n",
 85 |               "      <td>0</td>\n",
 86 |               "    </tr>\n",
 87 |               "    <tr>\n",
 88 |               "      <th>2</th>\n",
 89 |               "      <td>1</td>\n",
 90 |               "      <td>21</td>\n",
 91 |               "      <td>0</td>\n",
 92 |               "    </tr>\n",
 93 |               "    <tr>\n",
 94 |               "      <th>3</th>\n",
 95 |               "      <td>1</td>\n",
 96 |               "      <td>16</td>\n",
 97 |               "      <td>0</td>\n",
 98 |               "    </tr>\n",
 99 |               "    <tr>\n",
100 |               "      <th>4</th>\n",
101 |               "      <td>2</td>\n",
102 |               "      <td>14</td>\n",
103 |               "      <td>0</td>\n",
104 |               "    </tr>\n",
105 |               "    <tr>\n",
106 |               "      <th>5</th>\n",
107 |               "      <td>2</td>\n",
108 |               "      <td>5</td>\n",
109 |               "      <td>1</td>\n",
110 |               "    </tr>\n",
111 |               "    <tr>\n",
112 |               "      <th>6</th>\n",
113 |               "      <td>2</td>\n",
114 |               "      <td>7</td>\n",
115 |               "      <td>0</td>\n",
116 |               "    </tr>\n",
117 |               "    <tr>\n",
118 |               "      <th>7</th>\n",
119 |               "      <td>2</td>\n",
120 |               "      <td>2</td>\n",
121 |               "      <td>1</td>\n",
122 |               "    </tr>\n",
123 |               "    <tr>\n",
124 |               "      <th>8</th>\n",
125 |               "      <td>2</td>\n",
126 |               "      <td>17</td>\n",
127 |               "      <td>0</td>\n",
128 |               "    </tr>\n",
129 |               "    <tr>\n",
130 |               "      <th>9</th>\n",
131 |               "      <td>3</td>\n",
132 |               "      <td>18</td>\n",
133 |               "      <td>1</td>\n",
134 |               "    </tr>\n",
135 |               "  </tbody>\n",
136 |               "</table>\n",
137 |               "</div>"
138 |             ],
139 |             "text/plain": [
140 |               "   Hours  StudentId  Result\n",
141 |               "0      1         10       0\n",
142 |               "1      1         15       0\n",
143 |               "2      1         21       0\n",
144 |               "3      1         16       0\n",
145 |               "4      2         14       0\n",
146 |               "5      2          5       1\n",
147 |               "6      2          7       0\n",
148 |               "7      2          2       1\n",
149 |               "8      2         17       0\n",
150 |               "9      3         18       1"
151 |             ]
152 |           },
153 |           "execution_count": 3,
154 |           "metadata": {},
155 |           "output_type": "execute_result"
156 |         }
157 |       ],
158 |       "source": [
159 |         "df.head(10)"
160 |       ]
161 |     },
162 |     {
163 |       "cell_type": "code",
164 |       "execution_count": null,
165 |       "id": "620cb0e1",
166 |       "metadata": {
167 |         "id": "620cb0e1",
168 |         "outputId": "479da38f-2ca1-4927-8008-2932c8b37d0d"
169 |       },
170 |       "outputs": [
171 |         {
172 |           "data": {
173 |             "text/plain": [
174 |               "Text(0, 0.5, 'Pass')"
175 |             ]
176 |           },
177 |           "execution_count": 4,
178 |           "metadata": {},
179 |           "output_type": "execute_result"
180 |         },
181 |         {
182 |           "data": {
183 |             "image/png": 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\n",
184 |             "text/plain": [
185 |               "<Figure size 432x288 with 1 Axes>"
186 |             ]
187 |           },
188 |           "metadata": {
189 |             "needs_background": "light"
190 |           },
191 |           "output_type": "display_data"
192 |         }
193 |       ],
194 |       "source": [
195 |         "plt.scatter(df.Hours,df.Result,color='green')\n",
196 |         "plt.xlabel(\"Hours\")\n",
197 |         "plt.ylabel(\"Pass\")"
198 |       ]
199 |     },
200 |     {
201 |       "cell_type": "code",
202 |       "execution_count": null,
203 |       "id": "609e5016",
204 |       "metadata": {
205 |         "id": "609e5016"
206 |       },
207 |       "outputs": [],
208 |       "source": [
209 |         "df.dropna(inplace=True)"
210 |       ]
211 |     },
212 |     {
213 |       "cell_type": "code",
214 |       "execution_count": null,
215 |       "id": "4d8e2540",
216 |       "metadata": {
217 |         "id": "4d8e2540",
218 |         "outputId": "8e0bd9cb-1565-475d-86df-6cb8721f928c"
219 |       },
220 |       "outputs": [
221 |         {
222 |           "data": {
223 |             "text/html": [
224 |               "<div>\n",
225 |               "<style scoped>\n",
226 |               "    .dataframe tbody tr th:only-of-type {\n",
227 |               "        vertical-align: middle;\n",
228 |               "    }\n",
229 |               "\n",
230 |               "    .dataframe tbody tr th {\n",
231 |               "        vertical-align: top;\n",
232 |               "    }\n",
233 |               "\n",
234 |               "    .dataframe thead th {\n",
235 |               "        text-align: right;\n",
236 |               "    }\n",
237 |               "</style>\n",
238 |               "<table border=\"1\" class=\"dataframe\">\n",
239 |               "  <thead>\n",
240 |               "    <tr style=\"text-align: right;\">\n",
241 |               "      <th></th>\n",
242 |               "      <th>Hours</th>\n",
243 |               "      <th>Result</th>\n",
244 |               "    </tr>\n",
245 |               "  </thead>\n",
246 |               "  <tbody>\n",
247 |               "    <tr>\n",
248 |               "      <th>0</th>\n",
249 |               "      <td>1</td>\n",
250 |               "      <td>0</td>\n",
251 |               "    </tr>\n",
252 |               "    <tr>\n",
253 |               "      <th>1</th>\n",
254 |               "      <td>1</td>\n",
255 |               "      <td>0</td>\n",
256 |               "    </tr>\n",
257 |               "    <tr>\n",
258 |               "      <th>2</th>\n",
259 |               "      <td>1</td>\n",
260 |               "      <td>0</td>\n",
261 |               "    </tr>\n",
262 |               "    <tr>\n",
263 |               "      <th>3</th>\n",
264 |               "      <td>1</td>\n",
265 |               "      <td>0</td>\n",
266 |               "    </tr>\n",
267 |               "    <tr>\n",
268 |               "      <th>4</th>\n",
269 |               "      <td>2</td>\n",
270 |               "      <td>0</td>\n",
271 |               "    </tr>\n",
272 |               "  </tbody>\n",
273 |               "</table>\n",
274 |               "</div>"
275 |             ],
276 |             "text/plain": [
277 |               "   Hours  Result\n",
278 |               "0      1       0\n",
279 |               "1      1       0\n",
280 |               "2      1       0\n",
281 |               "3      1       0\n",
282 |               "4      2       0"
283 |             ]
284 |           },
285 |           "execution_count": 7,
286 |           "metadata": {},
287 |           "output_type": "execute_result"
288 |         }
289 |       ],
290 |       "source": [
291 |         "x=df.drop(\"StudentId\",axis=1)\n",
292 |         "x.head()"
293 |       ]
294 |     },
295 |     {
296 |       "cell_type": "code",
297 |       "execution_count": null,
298 |       "id": "4d26e8c5",
299 |       "metadata": {
300 |         "id": "4d26e8c5",
301 |         "outputId": "879b8db7-3019-471d-921a-1ff9601a5fcc"
302 |       },
303 |       "outputs": [
304 |         {
305 |           "data": {
306 |             "text/html": [
307 |               "<div>\n",
308 |               "<style scoped>\n",
309 |               "    .dataframe tbody tr th:only-of-type {\n",
310 |               "        vertical-align: middle;\n",
311 |               "    }\n",
312 |               "\n",
313 |               "    .dataframe tbody tr th {\n",
314 |               "        vertical-align: top;\n",
315 |               "    }\n",
316 |               "\n",
317 |               "    .dataframe thead th {\n",
318 |               "        text-align: right;\n",
319 |               "    }\n",
320 |               "</style>\n",
321 |               "<table border=\"1\" class=\"dataframe\">\n",
322 |               "  <thead>\n",
323 |               "    <tr style=\"text-align: right;\">\n",
324 |               "      <th></th>\n",
325 |               "      <th>0</th>\n",
326 |               "      <th>1</th>\n",
327 |               "    </tr>\n",
328 |               "  </thead>\n",
329 |               "  <tbody>\n",
330 |               "    <tr>\n",
331 |               "      <th>0</th>\n",
332 |               "      <td>1</td>\n",
333 |               "      <td>0</td>\n",
334 |               "    </tr>\n",
335 |               "    <tr>\n",
336 |               "      <th>1</th>\n",
337 |               "      <td>1</td>\n",
338 |               "      <td>0</td>\n",
339 |               "    </tr>\n",
340 |               "    <tr>\n",
341 |               "      <th>2</th>\n",
342 |               "      <td>1</td>\n",
343 |               "      <td>0</td>\n",
344 |               "    </tr>\n",
345 |               "  </tbody>\n",
346 |               "</table>\n",
347 |               "</div>"
348 |             ],
349 |             "text/plain": [
350 |               "   0  1\n",
351 |               "0  1  0\n",
352 |               "1  1  0\n",
353 |               "2  1  0"
354 |             ]
355 |           },
356 |           "execution_count": 8,
357 |           "metadata": {},
358 |           "output_type": "execute_result"
359 |         }
360 |       ],
361 |       "source": [
362 |         "result=pd.get_dummies(df[\"Result\"])\n",
363 |         "result.head(3)"
364 |       ]
365 |     },
366 |     {
367 |       "cell_type": "code",
368 |       "execution_count": null,
369 |       "id": "d2c0f4a4",
370 |       "metadata": {
371 |         "id": "d2c0f4a4",
372 |         "outputId": "59a36a48-188d-497f-fe61-1baa9a20addc"
373 |       },
374 |       "outputs": [
375 |         {
376 |           "data": {
377 |             "text/html": [
378 |               "<div>\n",
379 |               "<style scoped>\n",
380 |               "    .dataframe tbody tr th:only-of-type {\n",
381 |               "        vertical-align: middle;\n",
382 |               "    }\n",
383 |               "\n",
384 |               "    .dataframe tbody tr th {\n",
385 |               "        vertical-align: top;\n",
386 |               "    }\n",
387 |               "\n",
388 |               "    .dataframe thead th {\n",
389 |               "        text-align: right;\n",
390 |               "    }\n",
391 |               "</style>\n",
392 |               "<table border=\"1\" class=\"dataframe\">\n",
393 |               "  <thead>\n",
394 |               "    <tr style=\"text-align: right;\">\n",
395 |               "      <th></th>\n",
396 |               "      <th>Hours</th>\n",
397 |               "      <th>Result</th>\n",
398 |               "      <th>0</th>\n",
399 |               "      <th>1</th>\n",
400 |               "    </tr>\n",
401 |               "  </thead>\n",
402 |               "  <tbody>\n",
403 |               "    <tr>\n",
404 |               "      <th>0</th>\n",
405 |               "      <td>1</td>\n",
406 |               "      <td>0</td>\n",
407 |               "      <td>1</td>\n",
408 |               "      <td>0</td>\n",
409 |               "    </tr>\n",
410 |               "    <tr>\n",
411 |               "      <th>1</th>\n",
412 |               "      <td>1</td>\n",
413 |               "      <td>0</td>\n",
414 |               "      <td>1</td>\n",
415 |               "      <td>0</td>\n",
416 |               "    </tr>\n",
417 |               "    <tr>\n",
418 |               "      <th>2</th>\n",
419 |               "      <td>1</td>\n",
420 |               "      <td>0</td>\n",
421 |               "      <td>1</td>\n",
422 |               "      <td>0</td>\n",
423 |               "    </tr>\n",
424 |               "    <tr>\n",
425 |               "      <th>3</th>\n",
426 |               "      <td>1</td>\n",
427 |               "      <td>0</td>\n",
428 |               "      <td>1</td>\n",
429 |               "      <td>0</td>\n",
430 |               "    </tr>\n",
431 |               "    <tr>\n",
432 |               "      <th>4</th>\n",
433 |               "      <td>2</td>\n",
434 |               "      <td>0</td>\n",
435 |               "      <td>1</td>\n",
436 |               "      <td>0</td>\n",
437 |               "    </tr>\n",
438 |               "    <tr>\n",
439 |               "      <th>5</th>\n",
440 |               "      <td>2</td>\n",
441 |               "      <td>1</td>\n",
442 |               "      <td>0</td>\n",
443 |               "      <td>1</td>\n",
444 |               "    </tr>\n",
445 |               "    <tr>\n",
446 |               "      <th>6</th>\n",
447 |               "      <td>2</td>\n",
448 |               "      <td>0</td>\n",
449 |               "      <td>1</td>\n",
450 |               "      <td>0</td>\n",
451 |               "    </tr>\n",
452 |               "    <tr>\n",
453 |               "      <th>7</th>\n",
454 |               "      <td>2</td>\n",
455 |               "      <td>1</td>\n",
456 |               "      <td>0</td>\n",
457 |               "      <td>1</td>\n",
458 |               "    </tr>\n",
459 |               "    <tr>\n",
460 |               "      <th>8</th>\n",
461 |               "      <td>2</td>\n",
462 |               "      <td>0</td>\n",
463 |               "      <td>1</td>\n",
464 |               "      <td>0</td>\n",
465 |               "    </tr>\n",
466 |               "    <tr>\n",
467 |               "      <th>9</th>\n",
468 |               "      <td>3</td>\n",
469 |               "      <td>1</td>\n",
470 |               "      <td>0</td>\n",
471 |               "      <td>1</td>\n",
472 |               "    </tr>\n",
473 |               "    <tr>\n",
474 |               "      <th>10</th>\n",
475 |               "      <td>3</td>\n",
476 |               "      <td>1</td>\n",
477 |               "      <td>0</td>\n",
478 |               "      <td>1</td>\n",
479 |               "    </tr>\n",
480 |               "    <tr>\n",
481 |               "      <th>11</th>\n",
482 |               "      <td>3</td>\n",
483 |               "      <td>1</td>\n",
484 |               "      <td>0</td>\n",
485 |               "      <td>1</td>\n",
486 |               "    </tr>\n",
487 |               "  </tbody>\n",
488 |               "</table>\n",
489 |               "</div>"
490 |             ],
491 |             "text/plain": [
492 |               "    Hours  Result  0  1\n",
493 |               "0       1       0  1  0\n",
494 |               "1       1       0  1  0\n",
495 |               "2       1       0  1  0\n",
496 |               "3       1       0  1  0\n",
497 |               "4       2       0  1  0\n",
498 |               "5       2       1  0  1\n",
499 |               "6       2       0  1  0\n",
500 |               "7       2       1  0  1\n",
501 |               "8       2       0  1  0\n",
502 |               "9       3       1  0  1\n",
503 |               "10      3       1  0  1\n",
504 |               "11      3       1  0  1"
505 |             ]
506 |           },
507 |           "execution_count": 9,
508 |           "metadata": {},
509 |           "output_type": "execute_result"
510 |         }
511 |       ],
512 |       "source": [
513 |         "x=pd.concat([x,result],axis=1)\n",
514 |         "x.head(15)"
515 |       ]
516 |     },
517 |     {
518 |       "cell_type": "code",
519 |       "execution_count": null,
520 |       "id": "0de58cc4",
521 |       "metadata": {
522 |         "id": "0de58cc4"
523 |       },
524 |       "outputs": [],
525 |       "source": [
526 |         "x=df.drop(\"Result\",axis=1)\n"
527 |       ]
528 |     },
529 |     {
530 |       "cell_type": "code",
531 |       "execution_count": null,
532 |       "id": "0aff5e01",
533 |       "metadata": {
534 |         "id": "0aff5e01"
535 |       },
536 |       "outputs": [],
537 |       "source": [
538 |         "from sklearn.model_selection import train_test_split\n"
539 |       ]
540 |     },
541 |     {
542 |       "cell_type": "code",
543 |       "execution_count": null,
544 |       "id": "fcf945a2",
545 |       "metadata": {
546 |         "id": "fcf945a2"
547 |       },
548 |       "outputs": [],
549 |       "source": [
550 |         "X1 = df.drop ('Hours', axis = 1)\n",
551 |         "y = df ['Hours']\n",
552 |         "X1_train, X1_test, y_train, y_test = train_test_split (X1, y, test_size = 2, random_state = 5)\n"
553 |       ]
554 |     },
555 |     {
556 |       "cell_type": "code",
557 |       "execution_count": null,
558 |       "id": "f3974c3e",
559 |       "metadata": {
560 |         "id": "f3974c3e"
561 |       },
562 |       "outputs": [],
563 |       "source": [
564 |         "from sklearn.linear_model import LogisticRegression"
565 |       ]
566 |     },
567 |     {
568 |       "cell_type": "code",
569 |       "execution_count": null,
570 |       "id": "50dba9ef",
571 |       "metadata": {
572 |         "id": "50dba9ef",
573 |         "outputId": "5ba1fecb-3da1-4696-b720-917dfd6759dd"
574 |       },
575 |       "outputs": [
576 |         {
577 |           "data": {
578 |             "text/plain": [
579 |               "LogisticRegression()"
580 |             ]
581 |           },
582 |           "execution_count": 18,
583 |           "metadata": {},
584 |           "output_type": "execute_result"
585 |         }
586 |       ],
587 |       "source": [
588 |         "model = LogisticRegression()\n",
589 |         "model.fit(X1_train, y_train)"
590 |       ]
591 |     },
592 |     {
593 |       "cell_type": "code",
594 |       "execution_count": null,
595 |       "id": "c3ae326c",
596 |       "metadata": {
597 |         "id": "c3ae326c",
598 |         "outputId": "e0ee0837-f832-4dd3-d143-87ce9d5b0700"
599 |       },
600 |       "outputs": [
601 |         {
602 |           "data": {
603 |             "text/plain": [
604 |               "array([3, 3], dtype=int64)"
605 |             ]
606 |           },
607 |           "execution_count": 19,
608 |           "metadata": {},
609 |           "output_type": "execute_result"
610 |         }
611 |       ],
612 |       "source": [
613 |         "predic=model.predict(X1_test)\n",
614 |         "predic"
615 |       ]
616 |     },
617 |     {
618 |       "cell_type": "code",
619 |       "execution_count": null,
620 |       "id": "7d46e6e8",
621 |       "metadata": {
622 |         "id": "7d46e6e8",
623 |         "outputId": "dcdffb9d-b64a-4033-f485-deaf2693ffa1"
624 |       },
625 |       "outputs": [
626 |         {
627 |           "name": "stdout",
628 |           "output_type": "stream",
629 |           "text": [
630 |             "[[0 2]\n",
631 |             " [0 0]]\n"
632 |           ]
633 |         }
634 |       ],
635 |       "source": [
636 |         "from sklearn.metrics import confusion_matrix, accuracy_score\n",
637 |         "cm=confusion_matrix(y_test,predic)\n",
638 |         "print (cm)"
639 |       ]
640 |     },
641 |     {
642 |       "cell_type": "code",
643 |       "execution_count": null,
644 |       "id": "c56fa5c4",
645 |       "metadata": {
646 |         "id": "c56fa5c4",
647 |         "outputId": "5550214a-52e8-4021-a16e-93b15cf2d9ee"
648 |       },
649 |       "outputs": [
650 |         {
651 |           "name": "stdout",
652 |           "output_type": "stream",
653 |           "text": [
654 |             "0.0\n"
655 |           ]
656 |         }
657 |       ],
658 |       "source": [
659 |         "acc=accuracy_score(y_test,predic)\n",
660 |         "print(acc)"
661 |       ]
662 |     },
663 |     {
664 |       "cell_type": "code",
665 |       "execution_count": null,
666 |       "id": "00203d18",
667 |       "metadata": {
668 |         "id": "00203d18",
669 |         "outputId": "169ca0ee-41a6-4381-de30-4468cc0b1244"
670 |       },
671 |       "outputs": [
672 |         {
673 |           "name": "stderr",
674 |           "output_type": "stream",
675 |           "text": [
676 |             "C:\\Users\\Lenovo\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
677 |             "  _warn_prf(average, modifier, msg_start, len(result))\n",
678 |             "C:\\Users\\Lenovo\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1318: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
679 |             "  _warn_prf(average, modifier, msg_start, len(result))\n",
680 |             "C:\\Users\\Lenovo\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
681 |             "  _warn_prf(average, modifier, msg_start, len(result))\n",
682 |             "C:\\Users\\Lenovo\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1318: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
683 |             "  _warn_prf(average, modifier, msg_start, len(result))\n",
684 |             "C:\\Users\\Lenovo\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
685 |             "  _warn_prf(average, modifier, msg_start, len(result))\n",
686 |             "C:\\Users\\Lenovo\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1318: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
687 |             "  _warn_prf(average, modifier, msg_start, len(result))\n"
688 |           ]
689 |         },
690 |         {
691 |           "data": {
692 |             "text/plain": [
693 |               "'              precision    recall  f1-score   support\\n\\n           2       0.00      0.00      0.00       2.0\\n           3       0.00      0.00      0.00       0.0\\n\\n    accuracy                           0.00       2.0\\n   macro avg       0.00      0.00      0.00       2.0\\nweighted avg       0.00      0.00      0.00       2.0\\n'"
694 |             ]
695 |           },
696 |           "execution_count": 22,
697 |           "metadata": {},
698 |           "output_type": "execute_result"
699 |         }
700 |       ],
701 |       "source": [
702 |         "from sklearn.metrics import classification_report\n",
703 |         "classification_report(y_test,predic)"
704 |       ]
705 |     }
706 |   ],
707 |   "metadata": {
708 |     "kernelspec": {
709 |       "display_name": "Python 3 (ipykernel)",
710 |       "language": "python",
711 |       "name": "python3"
712 |     },
713 |     "language_info": {
714 |       "codemirror_mode": {
715 |         "name": "ipython",
716 |         "version": 3
717 |       },
718 |       "file_extension": ".py",
719 |       "mimetype": "text/x-python",
720 |       "name": "python",
721 |       "nbconvert_exporter": "python",
722 |       "pygments_lexer": "ipython3",
723 |       "version": "3.9.12"
724 |     },
725 |     "colab": {
726 |       "provenance": [],
727 |       "include_colab_link": true
728 |     }
729 |   },
730 |   "nbformat": 4,
731 |   "nbformat_minor": 5
732 | }


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