└── Heart_Disease_Prediction (1).ipynb


/Heart_Disease_Prediction (1).ipynb:
--------------------------------------------------------------------------------
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   7 |     },
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   9 |       "name": "python3",
  10 |       "display_name": "Python 3"
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  17 |     {
  18 |       "cell_type": "markdown",
  19 |       "metadata": {
  20 |         "id": "aTb-9TFFqprC"
  21 |       },
  22 |       "source": [
  23 |         "Importing the Dependencies"
  24 |       ]
  25 |     },
  26 |     {
  27 |       "cell_type": "code",
  28 |       "metadata": {
  29 |         "id": "3q9U3S_whh3-"
  30 |       },
  31 |       "source": [
  32 |         "import numpy as np\n",
  33 |         "import pandas as pd\n",
  34 |         "from sklearn.model_selection import train_test_split\n",
  35 |         "from sklearn.linear_model import LogisticRegression\n",
  36 |         "from sklearn.metrics import accuracy_score"
  37 |       ],
  38 |       "execution_count": null,
  39 |       "outputs": []
  40 |     },
  41 |     {
  42 |       "cell_type": "markdown",
  43 |       "metadata": {
  44 |         "id": "egMd5zeurTMR"
  45 |       },
  46 |       "source": [
  47 |         "Data Collection and Processing"
  48 |       ]
  49 |     },
  50 |     {
  51 |       "cell_type": "code",
  52 |       "metadata": {
  53 |         "id": "0q-3-LkQrREV"
  54 |       },
  55 |       "source": [
  56 |         "# loading the csv data to a Pandas DataFrame\n",
  57 |         "heart_data = pd.read_csv('/content/data.csv')"
  58 |       ],
  59 |       "execution_count": null,
  60 |       "outputs": []
  61 |     },
  62 |     {
  63 |       "cell_type": "code",
  64 |       "metadata": {
  65 |         "id": "M8dQxSTqriWD",
  66 |         "colab": {
  67 |           "base_uri": "https://localhost:8080/",
  68 |           "height": 206
  69 |         },
  70 |         "outputId": "0652f598-ce83-45d2-957d-fc07698432d7"
  71 |       },
  72 |       "source": [
  73 |         "# print first 5 rows of the dataset\n",
  74 |         "heart_data.head()"
  75 |       ],
  76 |       "execution_count": null,
  77 |       "outputs": [
  78 |         {
  79 |           "output_type": "execute_result",
  80 |           "data": {
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  82 |               "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
  83 |               "0   63    1   3       145   233    1        0      150      0      2.3      0   \n",
  84 |               "1   37    1   2       130   250    0        1      187      0      3.5      0   \n",
  85 |               "2   41    0   1       130   204    0        0      172      0      1.4      2   \n",
  86 |               "3   56    1   1       120   236    0        1      178      0      0.8      2   \n",
  87 |               "4   57    0   0       120   354    0        1      163      1      0.6      2   \n",
  88 |               "\n",
  89 |               "   ca  thal  target  \n",
  90 |               "0   0     1       1  \n",
  91 |               "1   0     2       1  \n",
  92 |               "2   0     2       1  \n",
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 366 |             ]
 367 |           },
 368 |           "metadata": {},
 369 |           "execution_count": 26
 370 |         }
 371 |       ]
 372 |     },
 373 |     {
 374 |       "cell_type": "code",
 375 |       "metadata": {
 376 |         "id": "Fx_aCZDgrqdR",
 377 |         "colab": {
 378 |           "base_uri": "https://localhost:8080/",
 379 |           "height": 206
 380 |         },
 381 |         "outputId": "628aa677-b738-401d-8831-ae29463eaf0b"
 382 |       },
 383 |       "source": [
 384 |         "# print last 5 rows of the dataset\n",
 385 |         "heart_data.tail()"
 386 |       ],
 387 |       "execution_count": null,
 388 |       "outputs": [
 389 |         {
 390 |           "output_type": "execute_result",
 391 |           "data": {
 392 |             "text/plain": [
 393 |               "     age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
 394 |               "298   57    0   0       140   241    0        1      123      1      0.2   \n",
 395 |               "299   45    1   3       110   264    0        1      132      0      1.2   \n",
 396 |               "300   68    1   0       144   193    1        1      141      0      3.4   \n",
 397 |               "301   57    1   0       130   131    0        1      115      1      1.2   \n",
 398 |               "302   57    0   1       130   236    0        0      174      0      0.0   \n",
 399 |               "\n",
 400 |               "     slope  ca  thal  target  \n",
 401 |               "298      1   0     3       0  \n",
 402 |               "299      1   0     3       0  \n",
 403 |               "300      1   2     3       0  \n",
 404 |               "301      1   1     3       0  \n",
 405 |               "302      1   1     2       0  "
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 490 |               "      <td>141</td>\n",
 491 |               "      <td>0</td>\n",
 492 |               "      <td>3.4</td>\n",
 493 |               "      <td>1</td>\n",
 494 |               "      <td>2</td>\n",
 495 |               "      <td>3</td>\n",
 496 |               "      <td>0</td>\n",
 497 |               "    </tr>\n",
 498 |               "    <tr>\n",
 499 |               "      <th>301</th>\n",
 500 |               "      <td>57</td>\n",
 501 |               "      <td>1</td>\n",
 502 |               "      <td>0</td>\n",
 503 |               "      <td>130</td>\n",
 504 |               "      <td>131</td>\n",
 505 |               "      <td>0</td>\n",
 506 |               "      <td>1</td>\n",
 507 |               "      <td>115</td>\n",
 508 |               "      <td>1</td>\n",
 509 |               "      <td>1.2</td>\n",
 510 |               "      <td>1</td>\n",
 511 |               "      <td>1</td>\n",
 512 |               "      <td>3</td>\n",
 513 |               "      <td>0</td>\n",
 514 |               "    </tr>\n",
 515 |               "    <tr>\n",
 516 |               "      <th>302</th>\n",
 517 |               "      <td>57</td>\n",
 518 |               "      <td>0</td>\n",
 519 |               "      <td>1</td>\n",
 520 |               "      <td>130</td>\n",
 521 |               "      <td>236</td>\n",
 522 |               "      <td>0</td>\n",
 523 |               "      <td>0</td>\n",
 524 |               "      <td>174</td>\n",
 525 |               "      <td>0</td>\n",
 526 |               "      <td>0.0</td>\n",
 527 |               "      <td>1</td>\n",
 528 |               "      <td>1</td>\n",
 529 |               "      <td>2</td>\n",
 530 |               "      <td>0</td>\n",
 531 |               "    </tr>\n",
 532 |               "  </tbody>\n",
 533 |               "</table>\n",
 534 |               "</div>\n",
 535 |               "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-01c2cdfb-884b-4fb3-8519-08cd19e6fc4f')\"\n",
 536 |               "              title=\"Convert this dataframe to an interactive table.\"\n",
 537 |               "              style=\"display:none;\">\n",
 538 |               "\n",
 539 |               "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
 540 |               "       width=\"24px\">\n",
 541 |               "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
 542 |               "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
 543 |               "  </svg>\n",
 544 |               "      </button>\n",
 545 |               "\n",
 546 |               "\n",
 547 |               "\n",
 548 |               "    <div id=\"df-10e0f377-d3d7-4043-b1fc-dcc92ac7b088\">\n",
 549 |               "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-10e0f377-d3d7-4043-b1fc-dcc92ac7b088')\"\n",
 550 |               "              title=\"Suggest charts.\"\n",
 551 |               "              style=\"display:none;\">\n",
 552 |               "\n",
 553 |               "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
 554 |               "     width=\"24px\">\n",
 555 |               "    <g>\n",
 556 |               "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
 557 |               "    </g>\n",
 558 |               "</svg>\n",
 559 |               "      </button>\n",
 560 |               "    </div>\n",
 561 |               "\n",
 562 |               "<style>\n",
 563 |               "  .colab-df-quickchart {\n",
 564 |               "    background-color: #E8F0FE;\n",
 565 |               "    border: none;\n",
 566 |               "    border-radius: 50%;\n",
 567 |               "    cursor: pointer;\n",
 568 |               "    display: none;\n",
 569 |               "    fill: #1967D2;\n",
 570 |               "    height: 32px;\n",
 571 |               "    padding: 0 0 0 0;\n",
 572 |               "    width: 32px;\n",
 573 |               "  }\n",
 574 |               "\n",
 575 |               "  .colab-df-quickchart:hover {\n",
 576 |               "    background-color: #E2EBFA;\n",
 577 |               "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
 578 |               "    fill: #174EA6;\n",
 579 |               "  }\n",
 580 |               "\n",
 581 |               "  [theme=dark] .colab-df-quickchart {\n",
 582 |               "    background-color: #3B4455;\n",
 583 |               "    fill: #D2E3FC;\n",
 584 |               "  }\n",
 585 |               "\n",
 586 |               "  [theme=dark] .colab-df-quickchart:hover {\n",
 587 |               "    background-color: #434B5C;\n",
 588 |               "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
 589 |               "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
 590 |               "    fill: #FFFFFF;\n",
 591 |               "  }\n",
 592 |               "</style>\n",
 593 |               "\n",
 594 |               "    <script>\n",
 595 |               "      async function quickchart(key) {\n",
 596 |               "        const containerElement = document.querySelector('#' + key);\n",
 597 |               "        const charts = await google.colab.kernel.invokeFunction(\n",
 598 |               "            'suggestCharts', [key], {});\n",
 599 |               "      }\n",
 600 |               "    </script>\n",
 601 |               "\n",
 602 |               "      <script>\n",
 603 |               "\n",
 604 |               "function displayQuickchartButton(domScope) {\n",
 605 |               "  let quickchartButtonEl =\n",
 606 |               "    domScope.querySelector('#df-10e0f377-d3d7-4043-b1fc-dcc92ac7b088 button.colab-df-quickchart');\n",
 607 |               "  quickchartButtonEl.style.display =\n",
 608 |               "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
 609 |               "}\n",
 610 |               "\n",
 611 |               "        displayQuickchartButton(document);\n",
 612 |               "      </script>\n",
 613 |               "      <style>\n",
 614 |               "    .colab-df-container {\n",
 615 |               "      display:flex;\n",
 616 |               "      flex-wrap:wrap;\n",
 617 |               "      gap: 12px;\n",
 618 |               "    }\n",
 619 |               "\n",
 620 |               "    .colab-df-convert {\n",
 621 |               "      background-color: #E8F0FE;\n",
 622 |               "      border: none;\n",
 623 |               "      border-radius: 50%;\n",
 624 |               "      cursor: pointer;\n",
 625 |               "      display: none;\n",
 626 |               "      fill: #1967D2;\n",
 627 |               "      height: 32px;\n",
 628 |               "      padding: 0 0 0 0;\n",
 629 |               "      width: 32px;\n",
 630 |               "    }\n",
 631 |               "\n",
 632 |               "    .colab-df-convert:hover {\n",
 633 |               "      background-color: #E2EBFA;\n",
 634 |               "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
 635 |               "      fill: #174EA6;\n",
 636 |               "    }\n",
 637 |               "\n",
 638 |               "    [theme=dark] .colab-df-convert {\n",
 639 |               "      background-color: #3B4455;\n",
 640 |               "      fill: #D2E3FC;\n",
 641 |               "    }\n",
 642 |               "\n",
 643 |               "    [theme=dark] .colab-df-convert:hover {\n",
 644 |               "      background-color: #434B5C;\n",
 645 |               "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
 646 |               "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
 647 |               "      fill: #FFFFFF;\n",
 648 |               "    }\n",
 649 |               "  </style>\n",
 650 |               "\n",
 651 |               "      <script>\n",
 652 |               "        const buttonEl =\n",
 653 |               "          document.querySelector('#df-01c2cdfb-884b-4fb3-8519-08cd19e6fc4f button.colab-df-convert');\n",
 654 |               "        buttonEl.style.display =\n",
 655 |               "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
 656 |               "\n",
 657 |               "        async function convertToInteractive(key) {\n",
 658 |               "          const element = document.querySelector('#df-01c2cdfb-884b-4fb3-8519-08cd19e6fc4f');\n",
 659 |               "          const dataTable =\n",
 660 |               "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
 661 |               "                                                     [key], {});\n",
 662 |               "          if (!dataTable) return;\n",
 663 |               "\n",
 664 |               "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
 665 |               "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
 666 |               "            + ' to learn more about interactive tables.';\n",
 667 |               "          element.innerHTML = '';\n",
 668 |               "          dataTable['output_type'] = 'display_data';\n",
 669 |               "          await google.colab.output.renderOutput(dataTable, element);\n",
 670 |               "          const docLink = document.createElement('div');\n",
 671 |               "          docLink.innerHTML = docLinkHtml;\n",
 672 |               "          element.appendChild(docLink);\n",
 673 |               "        }\n",
 674 |               "      </script>\n",
 675 |               "    </div>\n",
 676 |               "  </div>\n"
 677 |             ]
 678 |           },
 679 |           "metadata": {},
 680 |           "execution_count": 27
 681 |         }
 682 |       ]
 683 |     },
 684 |     {
 685 |       "cell_type": "code",
 686 |       "metadata": {
 687 |         "id": "8nX1tIzbrz0u",
 688 |         "colab": {
 689 |           "base_uri": "https://localhost:8080/"
 690 |         },
 691 |         "outputId": "a489a6bb-c67b-49a3-e579-2112481230be"
 692 |       },
 693 |       "source": [
 694 |         "# number of rows and columns in the dataset\n",
 695 |         "heart_data.shape"
 696 |       ],
 697 |       "execution_count": null,
 698 |       "outputs": [
 699 |         {
 700 |           "output_type": "execute_result",
 701 |           "data": {
 702 |             "text/plain": [
 703 |               "(303, 14)"
 704 |             ]
 705 |           },
 706 |           "metadata": {},
 707 |           "execution_count": 28
 708 |         }
 709 |       ]
 710 |     },
 711 |     {
 712 |       "cell_type": "code",
 713 |       "metadata": {
 714 |         "id": "7_xTcw1Sr6aJ",
 715 |         "colab": {
 716 |           "base_uri": "https://localhost:8080/"
 717 |         },
 718 |         "outputId": "bf8a473c-7315-4f79-a33c-a314f2f86e34"
 719 |       },
 720 |       "source": [
 721 |         "# getting some info about the data\n",
 722 |         "heart_data.info()"
 723 |       ],
 724 |       "execution_count": null,
 725 |       "outputs": [
 726 |         {
 727 |           "output_type": "stream",
 728 |           "name": "stdout",
 729 |           "text": [
 730 |             "<class 'pandas.core.frame.DataFrame'>\n",
 731 |             "RangeIndex: 303 entries, 0 to 302\n",
 732 |             "Data columns (total 14 columns):\n",
 733 |             " #   Column    Non-Null Count  Dtype  \n",
 734 |             "---  ------    --------------  -----  \n",
 735 |             " 0   age       303 non-null    int64  \n",
 736 |             " 1   sex       303 non-null    int64  \n",
 737 |             " 2   cp        303 non-null    int64  \n",
 738 |             " 3   trestbps  303 non-null    int64  \n",
 739 |             " 4   chol      303 non-null    int64  \n",
 740 |             " 5   fbs       303 non-null    int64  \n",
 741 |             " 6   restecg   303 non-null    int64  \n",
 742 |             " 7   thalach   303 non-null    int64  \n",
 743 |             " 8   exang     303 non-null    int64  \n",
 744 |             " 9   oldpeak   303 non-null    float64\n",
 745 |             " 10  slope     303 non-null    int64  \n",
 746 |             " 11  ca        303 non-null    int64  \n",
 747 |             " 12  thal      303 non-null    int64  \n",
 748 |             " 13  target    303 non-null    int64  \n",
 749 |             "dtypes: float64(1), int64(13)\n",
 750 |             "memory usage: 33.3 KB\n"
 751 |           ]
 752 |         }
 753 |       ]
 754 |     },
 755 |     {
 756 |       "cell_type": "code",
 757 |       "metadata": {
 758 |         "id": "GjHtW31rsGlb",
 759 |         "colab": {
 760 |           "base_uri": "https://localhost:8080/"
 761 |         },
 762 |         "outputId": "5eac0aea-4005-4dfb-cf01-8094d11c46cd"
 763 |       },
 764 |       "source": [
 765 |         "# checking for missing values\n",
 766 |         "heart_data.isnull().sum()"
 767 |       ],
 768 |       "execution_count": null,
 769 |       "outputs": [
 770 |         {
 771 |           "output_type": "execute_result",
 772 |           "data": {
 773 |             "text/plain": [
 774 |               "age         0\n",
 775 |               "sex         0\n",
 776 |               "cp          0\n",
 777 |               "trestbps    0\n",
 778 |               "chol        0\n",
 779 |               "fbs         0\n",
 780 |               "restecg     0\n",
 781 |               "thalach     0\n",
 782 |               "exang       0\n",
 783 |               "oldpeak     0\n",
 784 |               "slope       0\n",
 785 |               "ca          0\n",
 786 |               "thal        0\n",
 787 |               "target      0\n",
 788 |               "dtype: int64"
 789 |             ]
 790 |           },
 791 |           "metadata": {},
 792 |           "execution_count": 30
 793 |         }
 794 |       ]
 795 |     },
 796 |     {
 797 |       "cell_type": "code",
 798 |       "metadata": {
 799 |         "id": "OHmcP7DJsSEP",
 800 |         "colab": {
 801 |           "base_uri": "https://localhost:8080/",
 802 |           "height": 300
 803 |         },
 804 |         "outputId": "dcb3f424-1e0b-45a2-9a40-98e5097ec628"
 805 |       },
 806 |       "source": [
 807 |         "# statistical measures about the data\n",
 808 |         "heart_data.describe()"
 809 |       ],
 810 |       "execution_count": null,
 811 |       "outputs": [
 812 |         {
 813 |           "output_type": "execute_result",
 814 |           "data": {
 815 |             "text/plain": [
 816 |               "              age         sex          cp    trestbps        chol         fbs  \\\n",
 817 |               "count  303.000000  303.000000  303.000000  303.000000  303.000000  303.000000   \n",
 818 |               "mean    54.366337    0.683168    0.966997  131.623762  246.264026    0.148515   \n",
 819 |               "std      9.082101    0.466011    1.032052   17.538143   51.830751    0.356198   \n",
 820 |               "min     29.000000    0.000000    0.000000   94.000000  126.000000    0.000000   \n",
 821 |               "25%     47.500000    0.000000    0.000000  120.000000  211.000000    0.000000   \n",
 822 |               "50%     55.000000    1.000000    1.000000  130.000000  240.000000    0.000000   \n",
 823 |               "75%     61.000000    1.000000    2.000000  140.000000  274.500000    0.000000   \n",
 824 |               "max     77.000000    1.000000    3.000000  200.000000  564.000000    1.000000   \n",
 825 |               "\n",
 826 |               "          restecg     thalach       exang     oldpeak       slope          ca  \\\n",
 827 |               "count  303.000000  303.000000  303.000000  303.000000  303.000000  303.000000   \n",
 828 |               "mean     0.528053  149.646865    0.326733    1.039604    1.399340    0.729373   \n",
 829 |               "std      0.525860   22.905161    0.469794    1.161075    0.616226    1.022606   \n",
 830 |               "min      0.000000   71.000000    0.000000    0.000000    0.000000    0.000000   \n",
 831 |               "25%      0.000000  133.500000    0.000000    0.000000    1.000000    0.000000   \n",
 832 |               "50%      1.000000  153.000000    0.000000    0.800000    1.000000    0.000000   \n",
 833 |               "75%      1.000000  166.000000    1.000000    1.600000    2.000000    1.000000   \n",
 834 |               "max      2.000000  202.000000    1.000000    6.200000    2.000000    4.000000   \n",
 835 |               "\n",
 836 |               "             thal      target  \n",
 837 |               "count  303.000000  303.000000  \n",
 838 |               "mean     2.313531    0.544554  \n",
 839 |               "std      0.612277    0.498835  \n",
 840 |               "min      0.000000    0.000000  \n",
 841 |               "25%      2.000000    0.000000  \n",
 842 |               "50%      2.000000    1.000000  \n",
 843 |               "75%      3.000000    1.000000  \n",
 844 |               "max      3.000000    1.000000  "
 845 |             ],
 846 |             "text/html": [
 847 |               "\n",
 848 |               "\n",
 849 |               "  <div id=\"df-c6bcd052-ef17-4a1f-8d63-849c7ec9c794\">\n",
 850 |               "    <div class=\"colab-df-container\">\n",
 851 |               "      <div>\n",
 852 |               "<style scoped>\n",
 853 |               "    .dataframe tbody tr th:only-of-type {\n",
 854 |               "        vertical-align: middle;\n",
 855 |               "    }\n",
 856 |               "\n",
 857 |               "    .dataframe tbody tr th {\n",
 858 |               "        vertical-align: top;\n",
 859 |               "    }\n",
 860 |               "\n",
 861 |               "    .dataframe thead th {\n",
 862 |               "        text-align: right;\n",
 863 |               "    }\n",
 864 |               "</style>\n",
 865 |               "<table border=\"1\" class=\"dataframe\">\n",
 866 |               "  <thead>\n",
 867 |               "    <tr style=\"text-align: right;\">\n",
 868 |               "      <th></th>\n",
 869 |               "      <th>age</th>\n",
 870 |               "      <th>sex</th>\n",
 871 |               "      <th>cp</th>\n",
 872 |               "      <th>trestbps</th>\n",
 873 |               "      <th>chol</th>\n",
 874 |               "      <th>fbs</th>\n",
 875 |               "      <th>restecg</th>\n",
 876 |               "      <th>thalach</th>\n",
 877 |               "      <th>exang</th>\n",
 878 |               "      <th>oldpeak</th>\n",
 879 |               "      <th>slope</th>\n",
 880 |               "      <th>ca</th>\n",
 881 |               "      <th>thal</th>\n",
 882 |               "      <th>target</th>\n",
 883 |               "    </tr>\n",
 884 |               "  </thead>\n",
 885 |               "  <tbody>\n",
 886 |               "    <tr>\n",
 887 |               "      <th>count</th>\n",
 888 |               "      <td>303.000000</td>\n",
 889 |               "      <td>303.000000</td>\n",
 890 |               "      <td>303.000000</td>\n",
 891 |               "      <td>303.000000</td>\n",
 892 |               "      <td>303.000000</td>\n",
 893 |               "      <td>303.000000</td>\n",
 894 |               "      <td>303.000000</td>\n",
 895 |               "      <td>303.000000</td>\n",
 896 |               "      <td>303.000000</td>\n",
 897 |               "      <td>303.000000</td>\n",
 898 |               "      <td>303.000000</td>\n",
 899 |               "      <td>303.000000</td>\n",
 900 |               "      <td>303.000000</td>\n",
 901 |               "      <td>303.000000</td>\n",
 902 |               "    </tr>\n",
 903 |               "    <tr>\n",
 904 |               "      <th>mean</th>\n",
 905 |               "      <td>54.366337</td>\n",
 906 |               "      <td>0.683168</td>\n",
 907 |               "      <td>0.966997</td>\n",
 908 |               "      <td>131.623762</td>\n",
 909 |               "      <td>246.264026</td>\n",
 910 |               "      <td>0.148515</td>\n",
 911 |               "      <td>0.528053</td>\n",
 912 |               "      <td>149.646865</td>\n",
 913 |               "      <td>0.326733</td>\n",
 914 |               "      <td>1.039604</td>\n",
 915 |               "      <td>1.399340</td>\n",
 916 |               "      <td>0.729373</td>\n",
 917 |               "      <td>2.313531</td>\n",
 918 |               "      <td>0.544554</td>\n",
 919 |               "    </tr>\n",
 920 |               "    <tr>\n",
 921 |               "      <th>std</th>\n",
 922 |               "      <td>9.082101</td>\n",
 923 |               "      <td>0.466011</td>\n",
 924 |               "      <td>1.032052</td>\n",
 925 |               "      <td>17.538143</td>\n",
 926 |               "      <td>51.830751</td>\n",
 927 |               "      <td>0.356198</td>\n",
 928 |               "      <td>0.525860</td>\n",
 929 |               "      <td>22.905161</td>\n",
 930 |               "      <td>0.469794</td>\n",
 931 |               "      <td>1.161075</td>\n",
 932 |               "      <td>0.616226</td>\n",
 933 |               "      <td>1.022606</td>\n",
 934 |               "      <td>0.612277</td>\n",
 935 |               "      <td>0.498835</td>\n",
 936 |               "    </tr>\n",
 937 |               "    <tr>\n",
 938 |               "      <th>min</th>\n",
 939 |               "      <td>29.000000</td>\n",
 940 |               "      <td>0.000000</td>\n",
 941 |               "      <td>0.000000</td>\n",
 942 |               "      <td>94.000000</td>\n",
 943 |               "      <td>126.000000</td>\n",
 944 |               "      <td>0.000000</td>\n",
 945 |               "      <td>0.000000</td>\n",
 946 |               "      <td>71.000000</td>\n",
 947 |               "      <td>0.000000</td>\n",
 948 |               "      <td>0.000000</td>\n",
 949 |               "      <td>0.000000</td>\n",
 950 |               "      <td>0.000000</td>\n",
 951 |               "      <td>0.000000</td>\n",
 952 |               "      <td>0.000000</td>\n",
 953 |               "    </tr>\n",
 954 |               "    <tr>\n",
 955 |               "      <th>25%</th>\n",
 956 |               "      <td>47.500000</td>\n",
 957 |               "      <td>0.000000</td>\n",
 958 |               "      <td>0.000000</td>\n",
 959 |               "      <td>120.000000</td>\n",
 960 |               "      <td>211.000000</td>\n",
 961 |               "      <td>0.000000</td>\n",
 962 |               "      <td>0.000000</td>\n",
 963 |               "      <td>133.500000</td>\n",
 964 |               "      <td>0.000000</td>\n",
 965 |               "      <td>0.000000</td>\n",
 966 |               "      <td>1.000000</td>\n",
 967 |               "      <td>0.000000</td>\n",
 968 |               "      <td>2.000000</td>\n",
 969 |               "      <td>0.000000</td>\n",
 970 |               "    </tr>\n",
 971 |               "    <tr>\n",
 972 |               "      <th>50%</th>\n",
 973 |               "      <td>55.000000</td>\n",
 974 |               "      <td>1.000000</td>\n",
 975 |               "      <td>1.000000</td>\n",
 976 |               "      <td>130.000000</td>\n",
 977 |               "      <td>240.000000</td>\n",
 978 |               "      <td>0.000000</td>\n",
 979 |               "      <td>1.000000</td>\n",
 980 |               "      <td>153.000000</td>\n",
 981 |               "      <td>0.000000</td>\n",
 982 |               "      <td>0.800000</td>\n",
 983 |               "      <td>1.000000</td>\n",
 984 |               "      <td>0.000000</td>\n",
 985 |               "      <td>2.000000</td>\n",
 986 |               "      <td>1.000000</td>\n",
 987 |               "    </tr>\n",
 988 |               "    <tr>\n",
 989 |               "      <th>75%</th>\n",
 990 |               "      <td>61.000000</td>\n",
 991 |               "      <td>1.000000</td>\n",
 992 |               "      <td>2.000000</td>\n",
 993 |               "      <td>140.000000</td>\n",
 994 |               "      <td>274.500000</td>\n",
 995 |               "      <td>0.000000</td>\n",
 996 |               "      <td>1.000000</td>\n",
 997 |               "      <td>166.000000</td>\n",
 998 |               "      <td>1.000000</td>\n",
 999 |               "      <td>1.600000</td>\n",
1000 |               "      <td>2.000000</td>\n",
1001 |               "      <td>1.000000</td>\n",
1002 |               "      <td>3.000000</td>\n",
1003 |               "      <td>1.000000</td>\n",
1004 |               "    </tr>\n",
1005 |               "    <tr>\n",
1006 |               "      <th>max</th>\n",
1007 |               "      <td>77.000000</td>\n",
1008 |               "      <td>1.000000</td>\n",
1009 |               "      <td>3.000000</td>\n",
1010 |               "      <td>200.000000</td>\n",
1011 |               "      <td>564.000000</td>\n",
1012 |               "      <td>1.000000</td>\n",
1013 |               "      <td>2.000000</td>\n",
1014 |               "      <td>202.000000</td>\n",
1015 |               "      <td>1.000000</td>\n",
1016 |               "      <td>6.200000</td>\n",
1017 |               "      <td>2.000000</td>\n",
1018 |               "      <td>4.000000</td>\n",
1019 |               "      <td>3.000000</td>\n",
1020 |               "      <td>1.000000</td>\n",
1021 |               "    </tr>\n",
1022 |               "  </tbody>\n",
1023 |               "</table>\n",
1024 |               "</div>\n",
1025 |               "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c6bcd052-ef17-4a1f-8d63-849c7ec9c794')\"\n",
1026 |               "              title=\"Convert this dataframe to an interactive table.\"\n",
1027 |               "              style=\"display:none;\">\n",
1028 |               "\n",
1029 |               "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
1030 |               "       width=\"24px\">\n",
1031 |               "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
1032 |               "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
1033 |               "  </svg>\n",
1034 |               "      </button>\n",
1035 |               "\n",
1036 |               "\n",
1037 |               "\n",
1038 |               "    <div id=\"df-4de37bb1-b249-4016-b099-616366be6ddc\">\n",
1039 |               "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-4de37bb1-b249-4016-b099-616366be6ddc')\"\n",
1040 |               "              title=\"Suggest charts.\"\n",
1041 |               "              style=\"display:none;\">\n",
1042 |               "\n",
1043 |               "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
1044 |               "     width=\"24px\">\n",
1045 |               "    <g>\n",
1046 |               "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
1047 |               "    </g>\n",
1048 |               "</svg>\n",
1049 |               "      </button>\n",
1050 |               "    </div>\n",
1051 |               "\n",
1052 |               "<style>\n",
1053 |               "  .colab-df-quickchart {\n",
1054 |               "    background-color: #E8F0FE;\n",
1055 |               "    border: none;\n",
1056 |               "    border-radius: 50%;\n",
1057 |               "    cursor: pointer;\n",
1058 |               "    display: none;\n",
1059 |               "    fill: #1967D2;\n",
1060 |               "    height: 32px;\n",
1061 |               "    padding: 0 0 0 0;\n",
1062 |               "    width: 32px;\n",
1063 |               "  }\n",
1064 |               "\n",
1065 |               "  .colab-df-quickchart:hover {\n",
1066 |               "    background-color: #E2EBFA;\n",
1067 |               "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
1068 |               "    fill: #174EA6;\n",
1069 |               "  }\n",
1070 |               "\n",
1071 |               "  [theme=dark] .colab-df-quickchart {\n",
1072 |               "    background-color: #3B4455;\n",
1073 |               "    fill: #D2E3FC;\n",
1074 |               "  }\n",
1075 |               "\n",
1076 |               "  [theme=dark] .colab-df-quickchart:hover {\n",
1077 |               "    background-color: #434B5C;\n",
1078 |               "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
1079 |               "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
1080 |               "    fill: #FFFFFF;\n",
1081 |               "  }\n",
1082 |               "</style>\n",
1083 |               "\n",
1084 |               "    <script>\n",
1085 |               "      async function quickchart(key) {\n",
1086 |               "        const containerElement = document.querySelector('#' + key);\n",
1087 |               "        const charts = await google.colab.kernel.invokeFunction(\n",
1088 |               "            'suggestCharts', [key], {});\n",
1089 |               "      }\n",
1090 |               "    </script>\n",
1091 |               "\n",
1092 |               "      <script>\n",
1093 |               "\n",
1094 |               "function displayQuickchartButton(domScope) {\n",
1095 |               "  let quickchartButtonEl =\n",
1096 |               "    domScope.querySelector('#df-4de37bb1-b249-4016-b099-616366be6ddc button.colab-df-quickchart');\n",
1097 |               "  quickchartButtonEl.style.display =\n",
1098 |               "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
1099 |               "}\n",
1100 |               "\n",
1101 |               "        displayQuickchartButton(document);\n",
1102 |               "      </script>\n",
1103 |               "      <style>\n",
1104 |               "    .colab-df-container {\n",
1105 |               "      display:flex;\n",
1106 |               "      flex-wrap:wrap;\n",
1107 |               "      gap: 12px;\n",
1108 |               "    }\n",
1109 |               "\n",
1110 |               "    .colab-df-convert {\n",
1111 |               "      background-color: #E8F0FE;\n",
1112 |               "      border: none;\n",
1113 |               "      border-radius: 50%;\n",
1114 |               "      cursor: pointer;\n",
1115 |               "      display: none;\n",
1116 |               "      fill: #1967D2;\n",
1117 |               "      height: 32px;\n",
1118 |               "      padding: 0 0 0 0;\n",
1119 |               "      width: 32px;\n",
1120 |               "    }\n",
1121 |               "\n",
1122 |               "    .colab-df-convert:hover {\n",
1123 |               "      background-color: #E2EBFA;\n",
1124 |               "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
1125 |               "      fill: #174EA6;\n",
1126 |               "    }\n",
1127 |               "\n",
1128 |               "    [theme=dark] .colab-df-convert {\n",
1129 |               "      background-color: #3B4455;\n",
1130 |               "      fill: #D2E3FC;\n",
1131 |               "    }\n",
1132 |               "\n",
1133 |               "    [theme=dark] .colab-df-convert:hover {\n",
1134 |               "      background-color: #434B5C;\n",
1135 |               "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
1136 |               "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
1137 |               "      fill: #FFFFFF;\n",
1138 |               "    }\n",
1139 |               "  </style>\n",
1140 |               "\n",
1141 |               "      <script>\n",
1142 |               "        const buttonEl =\n",
1143 |               "          document.querySelector('#df-c6bcd052-ef17-4a1f-8d63-849c7ec9c794 button.colab-df-convert');\n",
1144 |               "        buttonEl.style.display =\n",
1145 |               "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
1146 |               "\n",
1147 |               "        async function convertToInteractive(key) {\n",
1148 |               "          const element = document.querySelector('#df-c6bcd052-ef17-4a1f-8d63-849c7ec9c794');\n",
1149 |               "          const dataTable =\n",
1150 |               "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
1151 |               "                                                     [key], {});\n",
1152 |               "          if (!dataTable) return;\n",
1153 |               "\n",
1154 |               "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
1155 |               "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
1156 |               "            + ' to learn more about interactive tables.';\n",
1157 |               "          element.innerHTML = '';\n",
1158 |               "          dataTable['output_type'] = 'display_data';\n",
1159 |               "          await google.colab.output.renderOutput(dataTable, element);\n",
1160 |               "          const docLink = document.createElement('div');\n",
1161 |               "          docLink.innerHTML = docLinkHtml;\n",
1162 |               "          element.appendChild(docLink);\n",
1163 |               "        }\n",
1164 |               "      </script>\n",
1165 |               "    </div>\n",
1166 |               "  </div>\n"
1167 |             ]
1168 |           },
1169 |           "metadata": {},
1170 |           "execution_count": 31
1171 |         }
1172 |       ]
1173 |     },
1174 |     {
1175 |       "cell_type": "code",
1176 |       "metadata": {
1177 |         "id": "4InaOSIUsfWP",
1178 |         "colab": {
1179 |           "base_uri": "https://localhost:8080/"
1180 |         },
1181 |         "outputId": "2226e15f-0b21-4665-ff2c-8dcd9d919a65"
1182 |       },
1183 |       "source": [
1184 |         "# checking the distribution of Target Variable\n",
1185 |         "heart_data['target'].value_counts()"
1186 |       ],
1187 |       "execution_count": null,
1188 |       "outputs": [
1189 |         {
1190 |           "output_type": "execute_result",
1191 |           "data": {
1192 |             "text/plain": [
1193 |               "1    165\n",
1194 |               "0    138\n",
1195 |               "Name: target, dtype: int64"
1196 |             ]
1197 |           },
1198 |           "metadata": {},
1199 |           "execution_count": 32
1200 |         }
1201 |       ]
1202 |     },
1203 |     {
1204 |       "cell_type": "markdown",
1205 |       "metadata": {
1206 |         "id": "aSOBu4qDtJy5"
1207 |       },
1208 |       "source": [
1209 |         "1 --> Defective Heart\n",
1210 |         "\n",
1211 |         "0 --> Healthy Heart"
1212 |       ]
1213 |     },
1214 |     {
1215 |       "cell_type": "markdown",
1216 |       "metadata": {
1217 |         "id": "tW8i4igjtPRC"
1218 |       },
1219 |       "source": [
1220 |         "Splitting the Features and Target"
1221 |       ]
1222 |     },
1223 |     {
1224 |       "cell_type": "code",
1225 |       "metadata": {
1226 |         "id": "Q6yfbswrs7m3"
1227 |       },
1228 |       "source": [
1229 |         "X = heart_data.drop(columns='target', axis=1)\n",
1230 |         "Y = heart_data['target']"
1231 |       ],
1232 |       "execution_count": null,
1233 |       "outputs": []
1234 |     },
1235 |     {
1236 |       "cell_type": "code",
1237 |       "metadata": {
1238 |         "id": "XJoCp4ZKtpZy",
1239 |         "colab": {
1240 |           "base_uri": "https://localhost:8080/"
1241 |         },
1242 |         "outputId": "10cf1a29-e527-486e-a31a-cbef50501491"
1243 |       },
1244 |       "source": [
1245 |         "print(X)"
1246 |       ],
1247 |       "execution_count": null,
1248 |       "outputs": [
1249 |         {
1250 |           "output_type": "stream",
1251 |           "name": "stdout",
1252 |           "text": [
1253 |             "     age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
1254 |             "0     63    1   3       145   233    1        0      150      0      2.3   \n",
1255 |             "1     37    1   2       130   250    0        1      187      0      3.5   \n",
1256 |             "2     41    0   1       130   204    0        0      172      0      1.4   \n",
1257 |             "3     56    1   1       120   236    0        1      178      0      0.8   \n",
1258 |             "4     57    0   0       120   354    0        1      163      1      0.6   \n",
1259 |             "..   ...  ...  ..       ...   ...  ...      ...      ...    ...      ...   \n",
1260 |             "298   57    0   0       140   241    0        1      123      1      0.2   \n",
1261 |             "299   45    1   3       110   264    0        1      132      0      1.2   \n",
1262 |             "300   68    1   0       144   193    1        1      141      0      3.4   \n",
1263 |             "301   57    1   0       130   131    0        1      115      1      1.2   \n",
1264 |             "302   57    0   1       130   236    0        0      174      0      0.0   \n",
1265 |             "\n",
1266 |             "     slope  ca  thal  \n",
1267 |             "0        0   0     1  \n",
1268 |             "1        0   0     2  \n",
1269 |             "2        2   0     2  \n",
1270 |             "3        2   0     2  \n",
1271 |             "4        2   0     2  \n",
1272 |             "..     ...  ..   ...  \n",
1273 |             "298      1   0     3  \n",
1274 |             "299      1   0     3  \n",
1275 |             "300      1   2     3  \n",
1276 |             "301      1   1     3  \n",
1277 |             "302      1   1     2  \n",
1278 |             "\n",
1279 |             "[303 rows x 13 columns]\n"
1280 |           ]
1281 |         }
1282 |       ]
1283 |     },
1284 |     {
1285 |       "cell_type": "code",
1286 |       "metadata": {
1287 |         "id": "nukuj-YItq1w",
1288 |         "colab": {
1289 |           "base_uri": "https://localhost:8080/"
1290 |         },
1291 |         "outputId": "2445f02b-a639-4485-cb0b-a78a3622c6cf"
1292 |       },
1293 |       "source": [
1294 |         "print(Y)"
1295 |       ],
1296 |       "execution_count": null,
1297 |       "outputs": [
1298 |         {
1299 |           "output_type": "stream",
1300 |           "name": "stdout",
1301 |           "text": [
1302 |             "0      1\n",
1303 |             "1      1\n",
1304 |             "2      1\n",
1305 |             "3      1\n",
1306 |             "4      1\n",
1307 |             "      ..\n",
1308 |             "298    0\n",
1309 |             "299    0\n",
1310 |             "300    0\n",
1311 |             "301    0\n",
1312 |             "302    0\n",
1313 |             "Name: target, Length: 303, dtype: int64\n"
1314 |           ]
1315 |         }
1316 |       ]
1317 |     },
1318 |     {
1319 |       "cell_type": "markdown",
1320 |       "metadata": {
1321 |         "id": "_EcjSE3Et18n"
1322 |       },
1323 |       "source": [
1324 |         "Splitting the Data into Training data & Test Data"
1325 |       ]
1326 |     },
1327 |     {
1328 |       "cell_type": "code",
1329 |       "metadata": {
1330 |         "id": "a-UUfRUxtuga"
1331 |       },
1332 |       "source": [
1333 |         "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, stratify=Y, random_state=2)"
1334 |       ],
1335 |       "execution_count": null,
1336 |       "outputs": []
1337 |     },
1338 |     {
1339 |       "cell_type": "code",
1340 |       "metadata": {
1341 |         "id": "x7PrjC6zuf6X",
1342 |         "colab": {
1343 |           "base_uri": "https://localhost:8080/"
1344 |         },
1345 |         "outputId": "513e921e-42ce-4f86-9100-8220174b5751"
1346 |       },
1347 |       "source": [
1348 |         "print(X.shape, X_train.shape, X_test.shape)"
1349 |       ],
1350 |       "execution_count": null,
1351 |       "outputs": [
1352 |         {
1353 |           "output_type": "stream",
1354 |           "name": "stdout",
1355 |           "text": [
1356 |             "(303, 13) (242, 13) (61, 13)\n"
1357 |           ]
1358 |         }
1359 |       ]
1360 |     },
1361 |     {
1362 |       "cell_type": "markdown",
1363 |       "metadata": {
1364 |         "id": "beSkZmpVuvn9"
1365 |       },
1366 |       "source": [
1367 |         "Model Training"
1368 |       ]
1369 |     },
1370 |     {
1371 |       "cell_type": "markdown",
1372 |       "metadata": {
1373 |         "id": "gi2NOWZjuxzw"
1374 |       },
1375 |       "source": [
1376 |         "Logistic Regression"
1377 |       ]
1378 |     },
1379 |     {
1380 |       "cell_type": "code",
1381 |       "metadata": {
1382 |         "id": "4-Md74FYuqNL"
1383 |       },
1384 |       "source": [
1385 |         "model = LogisticRegression()"
1386 |       ],
1387 |       "execution_count": null,
1388 |       "outputs": []
1389 |     },
1390 |     {
1391 |       "cell_type": "code",
1392 |       "metadata": {
1393 |         "id": "kCdHYxGUu7XD",
1394 |         "colab": {
1395 |           "base_uri": "https://localhost:8080/",
1396 |           "height": 213
1397 |         },
1398 |         "outputId": "89c4c41a-5dc5-4474-9a9a-459a93ba3263"
1399 |       },
1400 |       "source": [
1401 |         "# training the LogisticRegression model with Training data\n",
1402 |         "model.fit(X_train, Y_train)"
1403 |       ],
1404 |       "execution_count": null,
1405 |       "outputs": [
1406 |         {
1407 |           "output_type": "stream",
1408 |           "name": "stderr",
1409 |           "text": [
1410 |             "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
1411 |             "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
1412 |             "\n",
1413 |             "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
1414 |             "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
1415 |             "Please also refer to the documentation for alternative solver options:\n",
1416 |             "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
1417 |             "  n_iter_i = _check_optimize_result(\n"
1418 |           ]
1419 |         },
1420 |         {
1421 |           "output_type": "execute_result",
1422 |           "data": {
1423 |             "text/plain": [
1424 |               "LogisticRegression()"
1425 |             ],
1426 |             "text/html": [
1427 |               "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
1428 |             ]
1429 |           },
1430 |           "metadata": {},
1431 |           "execution_count": 39
1432 |         }
1433 |       ]
1434 |     },
1435 |     {
1436 |       "cell_type": "markdown",
1437 |       "metadata": {
1438 |         "id": "ZYIw8Gi9vXfU"
1439 |       },
1440 |       "source": [
1441 |         "Model Evaluation"
1442 |       ]
1443 |     },
1444 |     {
1445 |       "cell_type": "markdown",
1446 |       "metadata": {
1447 |         "id": "wmxAekfZvZa9"
1448 |       },
1449 |       "source": [
1450 |         "Accuracy Score"
1451 |       ]
1452 |     },
1453 |     {
1454 |       "cell_type": "code",
1455 |       "metadata": {
1456 |         "id": "g19JaUTMvPKy"
1457 |       },
1458 |       "source": [
1459 |         "# accuracy on training data\n",
1460 |         "X_train_prediction = model.predict(X_train)\n",
1461 |         "training_data_accuracy = accuracy_score(X_train_prediction, Y_train)"
1462 |       ],
1463 |       "execution_count": null,
1464 |       "outputs": []
1465 |     },
1466 |     {
1467 |       "cell_type": "code",
1468 |       "metadata": {
1469 |         "id": "uQBZvBh8v7R_",
1470 |         "colab": {
1471 |           "base_uri": "https://localhost:8080/"
1472 |         },
1473 |         "outputId": "09b4704b-7767-4baf-9eba-4c786d4065bf"
1474 |       },
1475 |       "source": [
1476 |         "print('Accuracy on Training data : ', training_data_accuracy)"
1477 |       ],
1478 |       "execution_count": null,
1479 |       "outputs": [
1480 |         {
1481 |           "output_type": "stream",
1482 |           "name": "stdout",
1483 |           "text": [
1484 |             "Accuracy on Training data :  0.8512396694214877\n"
1485 |           ]
1486 |         }
1487 |       ]
1488 |     },
1489 |     {
1490 |       "cell_type": "code",
1491 |       "metadata": {
1492 |         "id": "mDONDJdlwBIO"
1493 |       },
1494 |       "source": [
1495 |         "# accuracy on test data\n",
1496 |         "X_test_prediction = model.predict(X_test)\n",
1497 |         "test_data_accuracy = accuracy_score(X_test_prediction, Y_test)"
1498 |       ],
1499 |       "execution_count": null,
1500 |       "outputs": []
1501 |     },
1502 |     {
1503 |       "cell_type": "code",
1504 |       "metadata": {
1505 |         "id": "_MBS-OqdwYpf",
1506 |         "colab": {
1507 |           "base_uri": "https://localhost:8080/"
1508 |         },
1509 |         "outputId": "3ba127e8-e117-4496-a73a-90993a586577"
1510 |       },
1511 |       "source": [
1512 |         "print('Accuracy on Test data : ', test_data_accuracy)"
1513 |       ],
1514 |       "execution_count": null,
1515 |       "outputs": [
1516 |         {
1517 |           "output_type": "stream",
1518 |           "name": "stdout",
1519 |           "text": [
1520 |             "Accuracy on Test data :  0.819672131147541\n"
1521 |           ]
1522 |         }
1523 |       ]
1524 |     },
1525 |     {
1526 |       "cell_type": "markdown",
1527 |       "metadata": {
1528 |         "id": "jIruVh3Qwq0e"
1529 |       },
1530 |       "source": [
1531 |         "Building a Predictive System"
1532 |       ]
1533 |     },
1534 |     {
1535 |       "cell_type": "code",
1536 |       "metadata": {
1537 |         "id": "9ercruC9wb4C",
1538 |         "colab": {
1539 |           "base_uri": "https://localhost:8080/"
1540 |         },
1541 |         "outputId": "75ca6857-fd9a-43e7-96c9-d7f5d8e1819b"
1542 |       },
1543 |       "source": [
1544 |         "input_data = (44,1,1,120,263,0,1,173,0,0,2,0,3)\n",
1545 |         "\n",
1546 |         "# change the input data to a numpy array\n",
1547 |         "input_data_as_numpy_array= np.asarray(input_data)\n",
1548 |         "\n",
1549 |         "# reshape the numpy array as we are predicting for only on instance\n",
1550 |         "input_data_reshaped = input_data_as_numpy_array.reshape(1,-1)\n",
1551 |         "\n",
1552 |         "prediction = model.predict(input_data_reshaped)\n",
1553 |         "print(prediction)\n",
1554 |         "\n",
1555 |         "if (prediction[0]== 0):\n",
1556 |         "  print('The Person does not have a Heart Disease')\n",
1557 |         "else:\n",
1558 |         "  print('The Person has Heart Disease')"
1559 |       ],
1560 |       "execution_count": null,
1561 |       "outputs": [
1562 |         {
1563 |           "output_type": "stream",
1564 |           "name": "stdout",
1565 |           "text": [
1566 |             "[1]\n",
1567 |             "The Person has Heart Disease\n"
1568 |           ]
1569 |         },
1570 |         {
1571 |           "output_type": "stream",
1572 |           "name": "stderr",
1573 |           "text": [
1574 |             "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
1575 |             "  warnings.warn(\n"
1576 |           ]
1577 |         }
1578 |       ]
1579 |     }
1580 |   ]
1581 | }


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