└── 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 | "
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" 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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" 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 | "
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\n", 458 | "
" 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 | --------------------------------------------------------------------------------