└── 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 | {
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50 | " \n",
51 | " | \n",
52 | " Hours | \n",
53 | " StudentId | \n",
54 | " Result | \n",
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123 | " Hours StudentId Result\n",
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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": {
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164 | "text/plain": [
165 | ""
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 | "\n",
200 | "\n",
213 | "
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215 | " \n",
216 | " | \n",
217 | " Hours | \n",
218 | " Result | \n",
219 | "
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220 | " \n",
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223 | " 0 | \n",
224 | " 1 | \n",
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231 | "
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232 | " \n",
233 | " 2 | \n",
234 | " 1 | \n",
235 | " 0 | \n",
236 | "
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237 | " \n",
238 | " 3 | \n",
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240 | " 0 | \n",
241 | "
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242 | " \n",
243 | " 4 | \n",
244 | " 2 | \n",
245 | " 0 | \n",
246 | "
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247 | " \n",
248 | "
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249 | "
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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": [
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280 | "\n",
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299 | "
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300 | " \n",
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303 | " 0 | \n",
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305 | " 0 | \n",
306 | "
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307 | " \n",
308 | " 1 | \n",
309 | " 1 | \n",
310 | " 0 | \n",
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312 | " \n",
313 | " 2 | \n",
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324 | "1 1 0\n",
325 | "2 1 0"
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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 | "\n",
348 | "\n",
361 | "
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363 | " \n",
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365 | " Hours | \n",
366 | " Result | \n",
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392 | "
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394 | " 3 | \n",
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396 | " 0 | \n",
397 | " 1 | \n",
398 | " 0 | \n",
399 | "
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456 | " \n",
457 | "
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458 | "
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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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