├── .gitignore
└── SPARK_TASK_1_.ipynb


/.gitignore:
--------------------------------------------------------------------------------
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129 | .pyre/
130 | 


--------------------------------------------------------------------------------
/SPARK_TASK_1_.ipynb:
--------------------------------------------------------------------------------
   1 | {
   2 |   "nbformat": 4,
   3 |   "nbformat_minor": 0,
   4 |   "metadata": {
   5 |     "colab": {
   6 |       "name": "SPARK_TASK_1 .ipynb",
   7 |       "provenance": [],
   8 |       "collapsed_sections": [],
   9 |       "authorship_tag": "ABX9TyMqrE/S3DQSQkR1Eaz5PBwv",
  10 |       "include_colab_link": true
  11 |     },
  12 |     "kernelspec": {
  13 |       "name": "python3",
  14 |       "display_name": "Python 3"
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  16 |     "language_info": {
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  18 |     }
  19 |   },
  20 |   "cells": [
  21 |     {
  22 |       "cell_type": "markdown",
  23 |       "metadata": {
  24 |         "id": "view-in-github",
  25 |         "colab_type": "text"
  26 |       },
  27 |       "source": [
  28 |         "<a href=\"https://colab.research.google.com/github/bharadwaj-2003/SPARK-TASK-1/blob/main/SPARK_TASK_1_.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
  29 |       ]
  30 |     },
  31 |     {
  32 |       "cell_type": "code",
  33 |       "metadata": {
  34 |         "id": "88dV5rGW5Baa"
  35 |       },
  36 |       "source": [
  37 |         ""
  38 |       ],
  39 |       "execution_count": null,
  40 |       "outputs": []
  41 |     },
  42 |     {
  43 |       "cell_type": "markdown",
  44 |       "metadata": {
  45 |         "id": "Y5KNclE35Ftj"
  46 |       },
  47 |       "source": [
  48 |         "#THE SPARKS FOUNDATION\n",
  49 |         "Name : BHARADWAJ S\n",
  50 |         "\n",
  51 |         "#GRIPNOVEMBER21\n",
  52 |         "\n",
  53 |         "Task-1 : Prediction Using Supervised ML\n",
  54 |         "\n",
  55 |         "Problem: Predict the percentage of a student based on the number of study hours"
  56 |       ]
  57 |     },
  58 |     {
  59 |       "cell_type": "code",
  60 |       "metadata": {
  61 |         "id": "Gl25ERJo5QkO"
  62 |       },
  63 |       "source": [
  64 |         "#Importing the Libraries required for the problem\n",
  65 |         "import pandas as pd\n",
  66 |         "import matplotlib.pyplot as plt\n",
  67 |         "import seaborn as sns"
  68 |       ],
  69 |       "execution_count": 1,
  70 |       "outputs": []
  71 |     },
  72 |     {
  73 |       "cell_type": "code",
  74 |       "metadata": {
  75 |         "id": "cw7WpnNG5WJ-"
  76 |       },
  77 |       "source": [
  78 |         "#reading data\n",
  79 |         "data= pd.read_csv('https://raw.githubusercontent.com/AdiPersonalWorks/Random/master/student_scores%20-%20student_scores.csv')"
  80 |       ],
  81 |       "execution_count": 2,
  82 |       "outputs": []
  83 |     },
  84 |     {
  85 |       "cell_type": "code",
  86 |       "metadata": {
  87 |         "colab": {
  88 |           "base_uri": "https://localhost:8080/",
  89 |           "height": 817
  90 |         },
  91 |         "id": "wqLPjy705WMb",
  92 |         "outputId": "0be25dc4-43c4-41d7-dbd5-9793df338fea"
  93 |       },
  94 |       "source": [
  95 |         "data"
  96 |       ],
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  98 |       "outputs": [
  99 |         {
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 118 |               "  <thead>\n",
 119 |               "    <tr style=\"text-align: right;\">\n",
 120 |               "      <th></th>\n",
 121 |               "      <th>Hours</th>\n",
 122 |               "      <th>Scores</th>\n",
 123 |               "    </tr>\n",
 124 |               "  </thead>\n",
 125 |               "  <tbody>\n",
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 217 |               "      <th>18</th>\n",
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 248 |               "      <td>7.8</td>\n",
 249 |               "      <td>86</td>\n",
 250 |               "    </tr>\n",
 251 |               "  </tbody>\n",
 252 |               "</table>\n",
 253 |               "</div>"
 254 |             ],
 255 |             "text/plain": [
 256 |               "    Hours  Scores\n",
 257 |               "0     2.5      21\n",
 258 |               "1     5.1      47\n",
 259 |               "2     3.2      27\n",
 260 |               "3     8.5      75\n",
 261 |               "4     3.5      30\n",
 262 |               "5     1.5      20\n",
 263 |               "6     9.2      88\n",
 264 |               "7     5.5      60\n",
 265 |               "8     8.3      81\n",
 266 |               "9     2.7      25\n",
 267 |               "10    7.7      85\n",
 268 |               "11    5.9      62\n",
 269 |               "12    4.5      41\n",
 270 |               "13    3.3      42\n",
 271 |               "14    1.1      17\n",
 272 |               "15    8.9      95\n",
 273 |               "16    2.5      30\n",
 274 |               "17    1.9      24\n",
 275 |               "18    6.1      67\n",
 276 |               "19    7.4      69\n",
 277 |               "20    2.7      30\n",
 278 |               "21    4.8      54\n",
 279 |               "22    3.8      35\n",
 280 |               "23    6.9      76\n",
 281 |               "24    7.8      86"
 282 |             ]
 283 |           },
 284 |           "metadata": {},
 285 |           "execution_count": 3
 286 |         }
 287 |       ]
 288 |     },
 289 |     {
 290 |       "cell_type": "code",
 291 |       "metadata": {
 292 |         "colab": {
 293 |           "base_uri": "https://localhost:8080/"
 294 |         },
 295 |         "id": "kEBfp80s5WPE",
 296 |         "outputId": "b4124c77-3ffb-4dee-99b5-9008fcda46d7"
 297 |       },
 298 |       "source": [
 299 |         "\n",
 300 |         "data.shape"
 301 |       ],
 302 |       "execution_count": 4,
 303 |       "outputs": [
 304 |         {
 305 |           "output_type": "execute_result",
 306 |           "data": {
 307 |             "text/plain": [
 308 |               "(25, 2)"
 309 |             ]
 310 |           },
 311 |           "metadata": {},
 312 |           "execution_count": 4
 313 |         }
 314 |       ]
 315 |     },
 316 |     {
 317 |       "cell_type": "code",
 318 |       "metadata": {
 319 |         "colab": {
 320 |           "base_uri": "https://localhost:8080/"
 321 |         },
 322 |         "id": "G7BajaN85WR2",
 323 |         "outputId": "20aec55f-139f-4910-952f-f49b7e49ddb6"
 324 |       },
 325 |       "source": [
 326 |         "\n",
 327 |         "data.info"
 328 |       ],
 329 |       "execution_count": 5,
 330 |       "outputs": [
 331 |         {
 332 |           "output_type": "execute_result",
 333 |           "data": {
 334 |             "text/plain": [
 335 |               "<bound method DataFrame.info of     Hours  Scores\n",
 336 |               "0     2.5      21\n",
 337 |               "1     5.1      47\n",
 338 |               "2     3.2      27\n",
 339 |               "3     8.5      75\n",
 340 |               "4     3.5      30\n",
 341 |               "5     1.5      20\n",
 342 |               "6     9.2      88\n",
 343 |               "7     5.5      60\n",
 344 |               "8     8.3      81\n",
 345 |               "9     2.7      25\n",
 346 |               "10    7.7      85\n",
 347 |               "11    5.9      62\n",
 348 |               "12    4.5      41\n",
 349 |               "13    3.3      42\n",
 350 |               "14    1.1      17\n",
 351 |               "15    8.9      95\n",
 352 |               "16    2.5      30\n",
 353 |               "17    1.9      24\n",
 354 |               "18    6.1      67\n",
 355 |               "19    7.4      69\n",
 356 |               "20    2.7      30\n",
 357 |               "21    4.8      54\n",
 358 |               "22    3.8      35\n",
 359 |               "23    6.9      76\n",
 360 |               "24    7.8      86>"
 361 |             ]
 362 |           },
 363 |           "metadata": {},
 364 |           "execution_count": 5
 365 |         }
 366 |       ]
 367 |     },
 368 |     {
 369 |       "cell_type": "code",
 370 |       "metadata": {
 371 |         "colab": {
 372 |           "base_uri": "https://localhost:8080/",
 373 |           "height": 356
 374 |         },
 375 |         "id": "KcWx4bXj5WUj",
 376 |         "outputId": "6dc14eb4-b3ee-4cfe-da32-106ab42403cc"
 377 |       },
 378 |       "source": [
 379 |         "data.head(10)"
 380 |       ],
 381 |       "execution_count": 6,
 382 |       "outputs": [
 383 |         {
 384 |           "output_type": "execute_result",
 385 |           "data": {
 386 |             "text/html": [
 387 |               "<div>\n",
 388 |               "<style scoped>\n",
 389 |               "    .dataframe tbody tr th:only-of-type {\n",
 390 |               "        vertical-align: middle;\n",
 391 |               "    }\n",
 392 |               "\n",
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 400 |               "</style>\n",
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 408 |               "  </thead>\n",
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 420 |               "    <tr>\n",
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 424 |               "    </tr>\n",
 425 |               "    <tr>\n",
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 429 |               "    </tr>\n",
 430 |               "    <tr>\n",
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 432 |               "      <td>3.5</td>\n",
 433 |               "      <td>30</td>\n",
 434 |               "    </tr>\n",
 435 |               "    <tr>\n",
 436 |               "      <th>5</th>\n",
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 438 |               "      <td>20</td>\n",
 439 |               "    </tr>\n",
 440 |               "    <tr>\n",
 441 |               "      <th>6</th>\n",
 442 |               "      <td>9.2</td>\n",
 443 |               "      <td>88</td>\n",
 444 |               "    </tr>\n",
 445 |               "    <tr>\n",
 446 |               "      <th>7</th>\n",
 447 |               "      <td>5.5</td>\n",
 448 |               "      <td>60</td>\n",
 449 |               "    </tr>\n",
 450 |               "    <tr>\n",
 451 |               "      <th>8</th>\n",
 452 |               "      <td>8.3</td>\n",
 453 |               "      <td>81</td>\n",
 454 |               "    </tr>\n",
 455 |               "    <tr>\n",
 456 |               "      <th>9</th>\n",
 457 |               "      <td>2.7</td>\n",
 458 |               "      <td>25</td>\n",
 459 |               "    </tr>\n",
 460 |               "  </tbody>\n",
 461 |               "</table>\n",
 462 |               "</div>"
 463 |             ],
 464 |             "text/plain": [
 465 |               "   Hours  Scores\n",
 466 |               "0    2.5      21\n",
 467 |               "1    5.1      47\n",
 468 |               "2    3.2      27\n",
 469 |               "3    8.5      75\n",
 470 |               "4    3.5      30\n",
 471 |               "5    1.5      20\n",
 472 |               "6    9.2      88\n",
 473 |               "7    5.5      60\n",
 474 |               "8    8.3      81\n",
 475 |               "9    2.7      25"
 476 |             ]
 477 |           },
 478 |           "metadata": {},
 479 |           "execution_count": 6
 480 |         }
 481 |       ]
 482 |     },
 483 |     {
 484 |       "cell_type": "code",
 485 |       "metadata": {
 486 |         "colab": {
 487 |           "base_uri": "https://localhost:8080/",
 488 |           "height": 294
 489 |         },
 490 |         "id": "1-AaOezN5WYj",
 491 |         "outputId": "67b22a81-ca46-4092-b97f-a7d194df7f24"
 492 |       },
 493 |       "source": [
 494 |         "\n",
 495 |         "data.describe()"
 496 |       ],
 497 |       "execution_count": 7,
 498 |       "outputs": [
 499 |         {
 500 |           "output_type": "execute_result",
 501 |           "data": {
 502 |             "text/html": [
 503 |               "<div>\n",
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 518 |               "  <thead>\n",
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 520 |               "      <th></th>\n",
 521 |               "      <th>Hours</th>\n",
 522 |               "      <th>Scores</th>\n",
 523 |               "    </tr>\n",
 524 |               "  </thead>\n",
 525 |               "  <tbody>\n",
 526 |               "    <tr>\n",
 527 |               "      <th>count</th>\n",
 528 |               "      <td>25.000000</td>\n",
 529 |               "      <td>25.000000</td>\n",
 530 |               "    </tr>\n",
 531 |               "    <tr>\n",
 532 |               "      <th>mean</th>\n",
 533 |               "      <td>5.012000</td>\n",
 534 |               "      <td>51.480000</td>\n",
 535 |               "    </tr>\n",
 536 |               "    <tr>\n",
 537 |               "      <th>std</th>\n",
 538 |               "      <td>2.525094</td>\n",
 539 |               "      <td>25.286887</td>\n",
 540 |               "    </tr>\n",
 541 |               "    <tr>\n",
 542 |               "      <th>min</th>\n",
 543 |               "      <td>1.100000</td>\n",
 544 |               "      <td>17.000000</td>\n",
 545 |               "    </tr>\n",
 546 |               "    <tr>\n",
 547 |               "      <th>25%</th>\n",
 548 |               "      <td>2.700000</td>\n",
 549 |               "      <td>30.000000</td>\n",
 550 |               "    </tr>\n",
 551 |               "    <tr>\n",
 552 |               "      <th>50%</th>\n",
 553 |               "      <td>4.800000</td>\n",
 554 |               "      <td>47.000000</td>\n",
 555 |               "    </tr>\n",
 556 |               "    <tr>\n",
 557 |               "      <th>75%</th>\n",
 558 |               "      <td>7.400000</td>\n",
 559 |               "      <td>75.000000</td>\n",
 560 |               "    </tr>\n",
 561 |               "    <tr>\n",
 562 |               "      <th>max</th>\n",
 563 |               "      <td>9.200000</td>\n",
 564 |               "      <td>95.000000</td>\n",
 565 |               "    </tr>\n",
 566 |               "  </tbody>\n",
 567 |               "</table>\n",
 568 |               "</div>"
 569 |             ],
 570 |             "text/plain": [
 571 |               "           Hours     Scores\n",
 572 |               "count  25.000000  25.000000\n",
 573 |               "mean    5.012000  51.480000\n",
 574 |               "std     2.525094  25.286887\n",
 575 |               "min     1.100000  17.000000\n",
 576 |               "25%     2.700000  30.000000\n",
 577 |               "50%     4.800000  47.000000\n",
 578 |               "75%     7.400000  75.000000\n",
 579 |               "max     9.200000  95.000000"
 580 |             ]
 581 |           },
 582 |           "metadata": {},
 583 |           "execution_count": 7
 584 |         }
 585 |       ]
 586 |     },
 587 |     {
 588 |       "cell_type": "code",
 589 |       "metadata": {
 590 |         "colab": {
 591 |           "base_uri": "https://localhost:8080/",
 592 |           "height": 300
 593 |         },
 594 |         "id": "ImNGT6Xz5WaJ",
 595 |         "outputId": "d01cd333-3bf6-4baf-d49e-def08358214e"
 596 |       },
 597 |       "source": [
 598 |         "\n",
 599 |         "#Visualizing the data\n",
 600 |         "sns.set_style('darkgrid')\n",
 601 |         "sns.scatterplot(y=data['Scores'],x=data['Hours'])\n",
 602 |         "plt.title('Hours Studied vs Percentage Score',size=20)\n",
 603 |         "plt.xlabel('Hours Studied')\n",
 604 |         "plt.ylabel('Percentage Score')\n",
 605 |         "plt.show()"
 606 |       ],
 607 |       "execution_count": 8,
 608 |       "outputs": [
 609 |         {
 610 |           "output_type": "display_data",
 611 |           "data": {
 612 |             "image/png": 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\n",
 613 |             "text/plain": [
 614 |               "<Figure size 432x288 with 1 Axes>"
 615 |             ]
 616 |           },
 617 |           "metadata": {}
 618 |         }
 619 |       ]
 620 |     },
 621 |     {
 622 |       "cell_type": "code",
 623 |       "metadata": {
 624 |         "id": "RjalYNm25Wcn"
 625 |       },
 626 |       "source": [
 627 |         "\n",
 628 |         "#From the above graph, we can see a positive linear relation between the hours studied and the percentage obtained(score).\n",
 629 |         "\n",
 630 |         "#Training the Model\n",
 631 |         "\n",
 632 |         "#1. Preparing the Data"
 633 |       ],
 634 |       "execution_count": null,
 635 |       "outputs": []
 636 |     },
 637 |     {
 638 |       "cell_type": "code",
 639 |       "metadata": {
 640 |         "id": "nOmd_Sim5WfZ"
 641 |       },
 642 |       "source": [
 643 |         "X =data.iloc[:, :-1].values  \n",
 644 |         "y =data.iloc[:, 1].values"
 645 |       ],
 646 |       "execution_count": 9,
 647 |       "outputs": []
 648 |     },
 649 |     {
 650 |       "cell_type": "code",
 651 |       "metadata": {
 652 |         "id": "x6nbtoJN5Wh-"
 653 |       },
 654 |       "source": [
 655 |         "#the next step is to split this data into training and test sets.\n",
 656 |         "from sklearn.model_selection import train_test_split\n",
 657 |         "X_train, X_test, y_train, y_test =train_test_split(X, y,test_size=0.2, random_state=0)"
 658 |       ],
 659 |       "execution_count": 10,
 660 |       "outputs": []
 661 |     },
 662 |     {
 663 |       "cell_type": "code",
 664 |       "metadata": {
 665 |         "colab": {
 666 |           "base_uri": "https://localhost:8080/"
 667 |         },
 668 |         "id": "-ahPNhAi504N",
 669 |         "outputId": "2f7e78f8-e676-4668-f8d7-70c9371bab60"
 670 |       },
 671 |       "source": [
 672 |         "from sklearn.linear_model import LinearRegression  \n",
 673 |         "regressor = LinearRegression()  \n",
 674 |         "regressor.fit(X_train, y_train) \n",
 675 |         "print(\"Training complete.\")"
 676 |       ],
 677 |       "execution_count": 11,
 678 |       "outputs": [
 679 |         {
 680 |           "output_type": "stream",
 681 |           "name": "stdout",
 682 |           "text": [
 683 |             "Training complete.\n"
 684 |           ]
 685 |         }
 686 |       ]
 687 |     },
 688 |     {
 689 |       "cell_type": "code",
 690 |       "metadata": {
 691 |         "colab": {
 692 |           "base_uri": "https://localhost:8080/",
 693 |           "height": 265
 694 |         },
 695 |         "id": "YNn4gAiS51DG",
 696 |         "outputId": "c06db9d2-9ac2-4369-e1cc-eff04edfc135"
 697 |       },
 698 |       "source": [
 699 |         "\n",
 700 |         "# Plotting the regression line\n",
 701 |         "line = regressor.coef_*X+regressor.intercept_\n",
 702 |         "\n",
 703 |         "# Plotting for the test data\n",
 704 |         "plt.scatter(X, y)\n",
 705 |         "plt.plot(X, line);\n",
 706 |         "plt.show()"
 707 |       ],
 708 |       "execution_count": 12,
 709 |       "outputs": [
 710 |         {
 711 |           "output_type": "display_data",
 712 |           "data": {
 713 |             "image/png": 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\n",
 714 |             "text/plain": [
 715 |               "<Figure size 432x288 with 1 Axes>"
 716 |             ]
 717 |           },
 718 |           "metadata": {}
 719 |         }
 720 |       ]
 721 |     },
 722 |     {
 723 |       "cell_type": "code",
 724 |       "metadata": {
 725 |         "colab": {
 726 |           "base_uri": "https://localhost:8080/",
 727 |           "height": 302
 728 |         },
 729 |         "id": "z1CXd5Hl51Fx",
 730 |         "outputId": "f122cd18-6248-4cfa-80a0-3419a82152b8"
 731 |       },
 732 |       "source": [
 733 |         "data.plot.bar(x=\"Hours\",y=\"Scores\")"
 734 |       ],
 735 |       "execution_count": 13,
 736 |       "outputs": [
 737 |         {
 738 |           "output_type": "execute_result",
 739 |           "data": {
 740 |             "text/plain": [
 741 |               "<matplotlib.axes._subplots.AxesSubplot at 0x7f1067895f90>"
 742 |             ]
 743 |           },
 744 |           "metadata": {},
 745 |           "execution_count": 13
 746 |         },
 747 |         {
 748 |           "output_type": "display_data",
 749 |           "data": {
 750 |             "image/png": 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\n",
 751 |             "text/plain": [
 752 |               "<Figure size 432x288 with 1 Axes>"
 753 |             ]
 754 |           },
 755 |           "metadata": {}
 756 |         }
 757 |       ]
 758 |     },
 759 |     {
 760 |       "cell_type": "code",
 761 |       "metadata": {
 762 |         "colab": {
 763 |           "base_uri": "https://localhost:8080/",
 764 |           "height": 302
 765 |         },
 766 |         "id": "5FvwAFfZ51Ia",
 767 |         "outputId": "6b497b2d-77af-43e9-a187-b5514cb7d9c0"
 768 |       },
 769 |       "source": [
 770 |         "\n",
 771 |         "#sorting the data\n",
 772 |         "data.sort_values([\"Hours\"], axis=0, ascending=[True],inplace=True)\n",
 773 |         "\n",
 774 |         "#plotting the data\n",
 775 |         "data.plot.bar(x=\"Hours\",y=\"Scores\")"
 776 |       ],
 777 |       "execution_count": 14,
 778 |       "outputs": [
 779 |         {
 780 |           "output_type": "execute_result",
 781 |           "data": {
 782 |             "text/plain": [
 783 |               "<matplotlib.axes._subplots.AxesSubplot at 0x7f106783f810>"
 784 |             ]
 785 |           },
 786 |           "metadata": {},
 787 |           "execution_count": 14
 788 |         },
 789 |         {
 790 |           "output_type": "display_data",
 791 |           "data": {
 792 |             "image/png": 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\n",
 793 |             "text/plain": [
 794 |               "<Figure size 432x288 with 1 Axes>"
 795 |             ]
 796 |           },
 797 |           "metadata": {}
 798 |         }
 799 |       ]
 800 |     },
 801 |     {
 802 |       "cell_type": "code",
 803 |       "metadata": {
 804 |         "id": "5pXdqEP251LW"
 805 |       },
 806 |       "source": [
 807 |         "x = data.iloc[:,:-1].values\n",
 808 |         "y = data.iloc[:,1].values"
 809 |       ],
 810 |       "execution_count": 15,
 811 |       "outputs": []
 812 |     },
 813 |     {
 814 |       "cell_type": "code",
 815 |       "metadata": {
 816 |         "colab": {
 817 |           "base_uri": "https://localhost:8080/"
 818 |         },
 819 |         "id": "4UR7d3ob51N_",
 820 |         "outputId": "6858c395-5c8f-458e-c24e-e5b3519ff6c1"
 821 |       },
 822 |       "source": [
 823 |         "print(x)"
 824 |       ],
 825 |       "execution_count": 16,
 826 |       "outputs": [
 827 |         {
 828 |           "output_type": "stream",
 829 |           "name": "stdout",
 830 |           "text": [
 831 |             "[[1.1]\n",
 832 |             " [1.5]\n",
 833 |             " [1.9]\n",
 834 |             " [2.5]\n",
 835 |             " [2.5]\n",
 836 |             " [2.7]\n",
 837 |             " [2.7]\n",
 838 |             " [3.2]\n",
 839 |             " [3.3]\n",
 840 |             " [3.5]\n",
 841 |             " [3.8]\n",
 842 |             " [4.5]\n",
 843 |             " [4.8]\n",
 844 |             " [5.1]\n",
 845 |             " [5.5]\n",
 846 |             " [5.9]\n",
 847 |             " [6.1]\n",
 848 |             " [6.9]\n",
 849 |             " [7.4]\n",
 850 |             " [7.7]\n",
 851 |             " [7.8]\n",
 852 |             " [8.3]\n",
 853 |             " [8.5]\n",
 854 |             " [8.9]\n",
 855 |             " [9.2]]\n"
 856 |           ]
 857 |         }
 858 |       ]
 859 |     },
 860 |     {
 861 |       "cell_type": "code",
 862 |       "metadata": {
 863 |         "id": "IBi8Pv-A6FTs"
 864 |       },
 865 |       "source": [
 866 |         "\n",
 867 |         "#Now , we are dividing the data for training and testing the model\n",
 868 |         "#importing the train_test_split\n",
 869 |         "\n",
 870 |         "from sklearn.model_selection import train_test_split\n",
 871 |         "\n",
 872 |         "# splitting the data into X_train, X_test, y_train, y_test\n",
 873 |         "\n",
 874 |         "X_train, X_test, y_train, y_test = train_test_split(x,y, test_size=0.2,random_state=0)"
 875 |       ],
 876 |       "execution_count": 17,
 877 |       "outputs": []
 878 |     },
 879 |     {
 880 |       "cell_type": "code",
 881 |       "metadata": {
 882 |         "colab": {
 883 |           "base_uri": "https://localhost:8080/"
 884 |         },
 885 |         "id": "gxf_gHRR6FX6",
 886 |         "outputId": "5fb82b3f-b186-46bd-c117-71f0e6a6eff5"
 887 |       },
 888 |       "source": [
 889 |         "print(X_train.shape)"
 890 |       ],
 891 |       "execution_count": 18,
 892 |       "outputs": [
 893 |         {
 894 |           "output_type": "stream",
 895 |           "name": "stdout",
 896 |           "text": [
 897 |             "(20, 1)\n"
 898 |           ]
 899 |         }
 900 |       ]
 901 |     },
 902 |     {
 903 |       "cell_type": "code",
 904 |       "metadata": {
 905 |         "colab": {
 906 |           "base_uri": "https://localhost:8080/"
 907 |         },
 908 |         "id": "7Wx3imgI6KST",
 909 |         "outputId": "5a9cdfdf-8186-4658-fec5-75e2d3d0a3d0"
 910 |       },
 911 |       "source": [
 912 |         "print(X_test.shape)"
 913 |       ],
 914 |       "execution_count": 19,
 915 |       "outputs": [
 916 |         {
 917 |           "output_type": "stream",
 918 |           "name": "stdout",
 919 |           "text": [
 920 |             "(5, 1)\n"
 921 |           ]
 922 |         }
 923 |       ]
 924 |     },
 925 |     {
 926 |       "cell_type": "code",
 927 |       "metadata": {
 928 |         "colab": {
 929 |           "base_uri": "https://localhost:8080/"
 930 |         },
 931 |         "id": "ZVEJ_C406KVg",
 932 |         "outputId": "22c6158f-8401-4e2b-c335-82e5fa79149c"
 933 |       },
 934 |       "source": [
 935 |         "print(y_train.shape)\n"
 936 |       ],
 937 |       "execution_count": 20,
 938 |       "outputs": [
 939 |         {
 940 |           "output_type": "stream",
 941 |           "name": "stdout",
 942 |           "text": [
 943 |             "(20,)\n"
 944 |           ]
 945 |         }
 946 |       ]
 947 |     },
 948 |     {
 949 |       "cell_type": "code",
 950 |       "metadata": {
 951 |         "colab": {
 952 |           "base_uri": "https://localhost:8080/"
 953 |         },
 954 |         "id": "ehp8x_Fv6KY5",
 955 |         "outputId": "2ef1525c-d88d-4217-9ec4-fc6ca772be26"
 956 |       },
 957 |       "source": [
 958 |         "print(y_test.shape)"
 959 |       ],
 960 |       "execution_count": 21,
 961 |       "outputs": [
 962 |         {
 963 |           "output_type": "stream",
 964 |           "name": "stdout",
 965 |           "text": [
 966 |             "(5,)\n"
 967 |           ]
 968 |         }
 969 |       ]
 970 |     },
 971 |     {
 972 |       "cell_type": "code",
 973 |       "metadata": {
 974 |         "colab": {
 975 |           "base_uri": "https://localhost:8080/"
 976 |         },
 977 |         "id": "MB0M8cKb6Kbf",
 978 |         "outputId": "ec5a6976-2214-42f7-d6c1-79572f26c634"
 979 |       },
 980 |       "source": [
 981 |         "#Predicting the % score\n",
 982 |         "print(X_test)\n",
 983 |         "y_pred = regressor.predict(X_test)\n"
 984 |       ],
 985 |       "execution_count": 22,
 986 |       "outputs": [
 987 |         {
 988 |           "output_type": "stream",
 989 |           "name": "stdout",
 990 |           "text": [
 991 |             "[[2.7]\n",
 992 |             " [1.9]\n",
 993 |             " [7.7]\n",
 994 |             " [6.1]\n",
 995 |             " [4.5]]\n"
 996 |           ]
 997 |         }
 998 |       ]
 999 |     },
1000 |     {
1001 |       "cell_type": "code",
1002 |       "metadata": {
1003 |         "colab": {
1004 |           "base_uri": "https://localhost:8080/",
1005 |           "height": 202
1006 |         },
1007 |         "id": "CtvWYggA51Qm",
1008 |         "outputId": "76f97010-77ad-4f31-a32a-4582867917c3"
1009 |       },
1010 |       "source": [
1011 |         "#Comparing the result with acutal data\n",
1012 |         "df= pd.DataFrame({'ACTUAL' : y_test, 'PREDICTION' : y_pred})\n",
1013 |         "df"
1014 |       ],
1015 |       "execution_count": 23,
1016 |       "outputs": [
1017 |         {
1018 |           "output_type": "execute_result",
1019 |           "data": {
1020 |             "text/html": [
1021 |               "<div>\n",
1022 |               "<style scoped>\n",
1023 |               "    .dataframe tbody tr th:only-of-type {\n",
1024 |               "        vertical-align: middle;\n",
1025 |               "    }\n",
1026 |               "\n",
1027 |               "    .dataframe tbody tr th {\n",
1028 |               "        vertical-align: top;\n",
1029 |               "    }\n",
1030 |               "\n",
1031 |               "    .dataframe thead th {\n",
1032 |               "        text-align: right;\n",
1033 |               "    }\n",
1034 |               "</style>\n",
1035 |               "<table border=\"1\" class=\"dataframe\">\n",
1036 |               "  <thead>\n",
1037 |               "    <tr style=\"text-align: right;\">\n",
1038 |               "      <th></th>\n",
1039 |               "      <th>ACTUAL</th>\n",
1040 |               "      <th>PREDICTION</th>\n",
1041 |               "    </tr>\n",
1042 |               "  </thead>\n",
1043 |               "  <tbody>\n",
1044 |               "    <tr>\n",
1045 |               "      <th>0</th>\n",
1046 |               "      <td>30</td>\n",
1047 |               "      <td>28.776933</td>\n",
1048 |               "    </tr>\n",
1049 |               "    <tr>\n",
1050 |               "      <th>1</th>\n",
1051 |               "      <td>24</td>\n",
1052 |               "      <td>20.848407</td>\n",
1053 |               "    </tr>\n",
1054 |               "    <tr>\n",
1055 |               "      <th>2</th>\n",
1056 |               "      <td>85</td>\n",
1057 |               "      <td>78.330215</td>\n",
1058 |               "    </tr>\n",
1059 |               "    <tr>\n",
1060 |               "      <th>3</th>\n",
1061 |               "      <td>67</td>\n",
1062 |               "      <td>62.473165</td>\n",
1063 |               "    </tr>\n",
1064 |               "    <tr>\n",
1065 |               "      <th>4</th>\n",
1066 |               "      <td>41</td>\n",
1067 |               "      <td>46.616114</td>\n",
1068 |               "    </tr>\n",
1069 |               "  </tbody>\n",
1070 |               "</table>\n",
1071 |               "</div>"
1072 |             ],
1073 |             "text/plain": [
1074 |               "   ACTUAL  PREDICTION\n",
1075 |               "0      30   28.776933\n",
1076 |               "1      24   20.848407\n",
1077 |               "2      85   78.330215\n",
1078 |               "3      67   62.473165\n",
1079 |               "4      41   46.616114"
1080 |             ]
1081 |           },
1082 |           "metadata": {},
1083 |           "execution_count": 23
1084 |         }
1085 |       ]
1086 |     },
1087 |     {
1088 |       "cell_type": "code",
1089 |       "metadata": {
1090 |         "colab": {
1091 |           "base_uri": "https://localhost:8080/"
1092 |         },
1093 |         "id": "xOVpPyP-6agJ",
1094 |         "outputId": "f8c47533-ebb8-4049-ce9e-244d9293eb5a"
1095 |       },
1096 |       "source": [
1097 |         "\n",
1098 |         "#Custom input(9.25 hours) and the prediction of percentage\n",
1099 |         "hours = [9.25]\n",
1100 |         "own_pred = regressor.predict([hours])\n",
1101 |         "print(\"No of Hours = {}\".format(hours))\n",
1102 |         "print(\"Predicted Score = {}\".format(own_pred[0]))"
1103 |       ],
1104 |       "execution_count": 24,
1105 |       "outputs": [
1106 |         {
1107 |           "output_type": "stream",
1108 |           "name": "stdout",
1109 |           "text": [
1110 |             "No of Hours = [9.25]\n",
1111 |             "Predicted Score = 93.69173248737539\n"
1112 |           ]
1113 |         }
1114 |       ]
1115 |     },
1116 |     {
1117 |       "cell_type": "code",
1118 |       "metadata": {
1119 |         "colab": {
1120 |           "base_uri": "https://localhost:8080/"
1121 |         },
1122 |         "id": "TDBgWdNA6aji",
1123 |         "outputId": "8f671470-3050-4f85-a3d8-587d512d9d9e"
1124 |       },
1125 |       "source": [
1126 |         "#Evaluating the Model(Accuracy)\n",
1127 |         "from sklearn import metrics  \n",
1128 |         "print('Mean Absolute Error:', \n",
1129 |         "      metrics.mean_absolute_error(y_test, y_pred))"
1130 |       ],
1131 |       "execution_count": 25,
1132 |       "outputs": [
1133 |         {
1134 |           "output_type": "stream",
1135 |           "name": "stdout",
1136 |           "text": [
1137 |             "Mean Absolute Error: 4.237478958953777\n"
1138 |           ]
1139 |         }
1140 |       ]
1141 |     },
1142 |     {
1143 |       "cell_type": "code",
1144 |       "metadata": {
1145 |         "colab": {
1146 |           "base_uri": "https://localhost:8080/"
1147 |         },
1148 |         "id": "GvFLQgy76amW",
1149 |         "outputId": "5f185206-ce13-4026-defb-51936d891f61"
1150 |       },
1151 |       "source": [
1152 |         "# importing LinearRegression\n",
1153 |         "from sklearn.linear_model import LinearRegression\n",
1154 |         "\n",
1155 |         "#creating an object for LinearRegression\n",
1156 |         "model = LinearRegression()\n",
1157 |         "\n",
1158 |         "# fitting the model\n",
1159 |         "model.fit(X_train, y_train)"
1160 |       ],
1161 |       "execution_count": 26,
1162 |       "outputs": [
1163 |         {
1164 |           "output_type": "execute_result",
1165 |           "data": {
1166 |             "text/plain": [
1167 |               "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
1168 |             ]
1169 |           },
1170 |           "metadata": {},
1171 |           "execution_count": 26
1172 |         }
1173 |       ]
1174 |     },
1175 |     {
1176 |       "cell_type": "code",
1177 |       "metadata": {
1178 |         "colab": {
1179 |           "base_uri": "https://localhost:8080/",
1180 |           "height": 265
1181 |         },
1182 |         "id": "ZyM0-n6j6apS",
1183 |         "outputId": "75c8b0d9-77f2-4e82-dc9f-f85ff25e12ba"
1184 |       },
1185 |       "source": [
1186 |         "#Fitting The Regression Line\n",
1187 |         "# plotting the regression line\n",
1188 |         "line = model.coef_ * x + model.intercept_\n",
1189 |         "\n",
1190 |         "#plotting for test data\n",
1191 |         "plt.scatter(x,y,c=\"g\")\n",
1192 |         "plt.plot(x,line,c=\"r\")\n",
1193 |         "plt.show()"
1194 |       ],
1195 |       "execution_count": 27,
1196 |       "outputs": [
1197 |         {
1198 |           "output_type": "display_data",
1199 |           "data": {
1200 |             "image/png": 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\n",
1201 |             "text/plain": [
1202 |               "<Figure size 432x288 with 1 Axes>"
1203 |             ]
1204 |           },
1205 |           "metadata": {}
1206 |         }
1207 |       ]
1208 |     },
1209 |     {
1210 |       "cell_type": "code",
1211 |       "metadata": {
1212 |         "colab": {
1213 |           "base_uri": "https://localhost:8080/"
1214 |         },
1215 |         "id": "FtxSRQ316asF",
1216 |         "outputId": "979e1428-1195-4d07-b6b3-c35e6f69dad2"
1217 |       },
1218 |       "source": [
1219 |         "#Making Predictions\n",
1220 |         "# testing the model\n",
1221 |         "y_pred = model.predict(X_test)\n",
1222 |         "\n",
1223 |         "#checking accuracy of our model\n",
1224 |         "data = pd.DataFrame({\"Actual\" : y_test,\"Predicted\":y_pred})\n",
1225 |         "print(data)"
1226 |       ],
1227 |       "execution_count": 28,
1228 |       "outputs": [
1229 |         {
1230 |           "output_type": "stream",
1231 |           "name": "stdout",
1232 |           "text": [
1233 |             "   Actual  Predicted\n",
1234 |             "0      30  28.617714\n",
1235 |             "1      24  20.888033\n",
1236 |             "2      85  76.928222\n",
1237 |             "3      67  61.468859\n",
1238 |             "4      41  46.009497\n"
1239 |           ]
1240 |         }
1241 |       ]
1242 |     },
1243 |     {
1244 |       "cell_type": "code",
1245 |       "metadata": {
1246 |         "colab": {
1247 |           "base_uri": "https://localhost:8080/"
1248 |         },
1249 |         "id": "Umd2Wep96oqJ",
1250 |         "outputId": "05d3bee2-5e64-488b-8301-9307bff3f73d"
1251 |       },
1252 |       "source": [
1253 |         "#Evaluating the model\n",
1254 |         "from sklearn import metrics as mts\n",
1255 |         "\n",
1256 |         "#mean abolute error\n",
1257 |         "mean_abs_error = mts.mean_absolute_error(y_test,y_pred)\n",
1258 |         "\n",
1259 |         "print(\"Mean Absolute Error : \",mean_abs_error)"
1260 |       ],
1261 |       "execution_count": 29,
1262 |       "outputs": [
1263 |         {
1264 |           "output_type": "stream",
1265 |           "name": "stdout",
1266 |           "text": [
1267 |             "Mean Absolute Error :  4.621333622532767\n"
1268 |           ]
1269 |         }
1270 |       ]
1271 |     },
1272 |     {
1273 |       "cell_type": "code",
1274 |       "metadata": {
1275 |         "id": "Af7BR4H46xjK"
1276 |       },
1277 |       "source": [
1278 |         ""
1279 |       ],
1280 |       "execution_count": null,
1281 |       "outputs": []
1282 |     }
1283 |   ]
1284 | }


--------------------------------------------------------------------------------