└── logistic regression.ipynb


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


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