├── README.md
└── IPL_2024_Match.ipynb


/README.md:
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1 | # KMeans


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/IPL_2024_Match.ipynb:
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   1 | {
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   3 |   "nbformat_minor": 0,
   4 |   "metadata": {
   5 |     "colab": {
   6 |       "provenance": []
   7 |     },
   8 |     "kernelspec": {
   9 |       "name": "python3",
  10 |       "display_name": "Python 3"
  11 |     },
  12 |     "language_info": {
  13 |       "name": "python"
  14 |     }
  15 |   },
  16 |   "cells": [
  17 |     {
  18 |       "cell_type": "code",
  19 |       "execution_count": 7,
  20 |       "metadata": {
  21 |         "id": "DZRMQ4fQbkOv"
  22 |       },
  23 |       "outputs": [],
  24 |       "source": [
  25 |         "import pandas as pd\n",
  26 |         "import matplotlib.pyplot as plt\n",
  27 |         "import numpy as np\n",
  28 |         "import scipy.cluster.hierarchy as shc\n",
  29 |         "from sklearn.preprocessing import StandardScaler"
  30 |       ]
  31 |     },
  32 |     {
  33 |       "cell_type": "code",
  34 |       "source": [
  35 |         "data = pd.read_excel(\"/content/IPL MATCH.xlsx\")\n",
  36 |         "data"
  37 |       ],
  38 |       "metadata": {
  39 |         "colab": {
  40 |           "base_uri": "https://localhost:8080/",
  41 |           "height": 363
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  44 |         "outputId": "a62b0cbb-896a-4263-8822-606c127d3ab5"
  45 |       },
  46 |       "execution_count": 2,
  47 |       "outputs": [
  48 |         {
  49 |           "output_type": "execute_result",
  50 |           "data": {
  51 |             "text/plain": [
  52 |               "   Rank                        Teams  Match  W  L  Points    NRR\n",
  53 |               "0     1        Kolkata Knight Riders      3  3  0       6  2.518\n",
  54 |               "1     2             Rajasthan Royals      3  3  0       6  1.249\n",
  55 |               "2     3          Chennai Super Kings      4  2  2       4  0.517\n",
  56 |               "3     4         Lucknow Super Giants      3  2  1       4  0.483\n",
  57 |               "4     5          Sunrisers Hyderabad      4  2  2       4  0.409\n",
  58 |               "5     6                 Punjab Kings      4  2  2       4 -0.220\n",
  59 |               "6     7               Gujarat Titans      4  2  2       4 -0.580\n",
  60 |               "7     8  Royal Challengers Bengaluru      4  1  3       2 -0.876\n",
  61 |               "8     9               Delhi Capitals      4  1  3       2 -1.347\n",
  62 |               "9    10               Mumbai Indians      3  0  3       0 -1.423"
  63 |             ],
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  84 |               "      <th></th>\n",
  85 |               "      <th>Rank</th>\n",
  86 |               "      <th>Teams</th>\n",
  87 |               "      <th>Match</th>\n",
  88 |               "      <th>W</th>\n",
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  97 |               "      <td>1</td>\n",
  98 |               "      <td>Kolkata Knight Riders</td>\n",
  99 |               "      <td>3</td>\n",
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 108 |               "      <td>Rajasthan Royals</td>\n",
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 118 |               "      <td>Chennai Super Kings</td>\n",
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 127 |               "      <td>4</td>\n",
 128 |               "      <td>Lucknow Super Giants</td>\n",
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 148 |               "      <td>Punjab Kings</td>\n",
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 151 |               "      <td>2</td>\n",
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 178 |               "      <td>Delhi Capitals</td>\n",
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 459 |               "\n",
 460 |               "    </div>\n",
 461 |               "  </div>\n"
 462 |             ],
 463 |             "application/vnd.google.colaboratory.intrinsic+json": {
 464 |               "type": "dataframe",
 465 |               "variable_name": "data",
 466 |               "summary": "{\n  \"name\": \"data\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"Rank\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 1,\n        \"max\": 10,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          9,\n          2,\n          6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Teams\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"Delhi Capitals\",\n          \"Rajasthan Royals\",\n          \"Punjab Kings\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Match\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 3,\n        \"max\": 4,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          4,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"W\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 3,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"L\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 3,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Points\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 6,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          4,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"NRR\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.225035509689413,\n        \"min\": -1.423,\n        \"max\": 2.518,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          -1.347,\n          1.249\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
 467 |             }
 468 |           },
 469 |           "metadata": {},
 470 |           "execution_count": 2
 471 |         }
 472 |       ]
 473 |     },
 474 |     {
 475 |       "cell_type": "code",
 476 |       "source": [
 477 |         "data.shape"
 478 |       ],
 479 |       "metadata": {
 480 |         "colab": {
 481 |           "base_uri": "https://localhost:8080/"
 482 |         },
 483 |         "id": "QxRT8KnBdcy5",
 484 |         "outputId": "6d2b9feb-89ab-4085-f639-31d825228516"
 485 |       },
 486 |       "execution_count": 4,
 487 |       "outputs": [
 488 |         {
 489 |           "output_type": "execute_result",
 490 |           "data": {
 491 |             "text/plain": [
 492 |               "(10, 7)"
 493 |             ]
 494 |           },
 495 |           "metadata": {},
 496 |           "execution_count": 4
 497 |         }
 498 |       ]
 499 |     },
 500 |     {
 501 |       "cell_type": "code",
 502 |       "source": [
 503 |         "data.head()"
 504 |       ],
 505 |       "metadata": {
 506 |         "colab": {
 507 |           "base_uri": "https://localhost:8080/",
 508 |           "height": 206
 509 |         },
 510 |         "id": "KIRBzPfOfic6",
 511 |         "outputId": "1638e30e-41ed-4d97-d2f4-af5116d1ce04"
 512 |       },
 513 |       "execution_count": 13,
 514 |       "outputs": [
 515 |         {
 516 |           "output_type": "execute_result",
 517 |           "data": {
 518 |             "text/plain": [
 519 |               "   Rank                  Teams  Match  W  L  Points    NRR\n",
 520 |               "0     1  Kolkata Knight Riders      3  3  0       6  2.518\n",
 521 |               "1     2       Rajasthan Royals      3  3  0       6  1.249\n",
 522 |               "2     3    Chennai Super Kings      4  2  2       4  0.517\n",
 523 |               "3     4   Lucknow Super Giants      3  2  1       4  0.483\n",
 524 |               "4     5    Sunrisers Hyderabad      4  2  2       4  0.409"
 525 |             ],
 526 |             "text/html": [
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 542 |               "</style>\n",
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 544 |               "  <thead>\n",
 545 |               "    <tr style=\"text-align: right;\">\n",
 546 |               "      <th></th>\n",
 547 |               "      <th>Rank</th>\n",
 548 |               "      <th>Teams</th>\n",
 549 |               "      <th>Match</th>\n",
 550 |               "      <th>W</th>\n",
 551 |               "      <th>L</th>\n",
 552 |               "      <th>Points</th>\n",
 553 |               "      <th>NRR</th>\n",
 554 |               "    </tr>\n",
 555 |               "  </thead>\n",
 556 |               "  <tbody>\n",
 557 |               "    <tr>\n",
 558 |               "      <th>0</th>\n",
 559 |               "      <td>1</td>\n",
 560 |               "      <td>Kolkata Knight Riders</td>\n",
 561 |               "      <td>3</td>\n",
 562 |               "      <td>3</td>\n",
 563 |               "      <td>0</td>\n",
 564 |               "      <td>6</td>\n",
 565 |               "      <td>2.518</td>\n",
 566 |               "    </tr>\n",
 567 |               "    <tr>\n",
 568 |               "      <th>1</th>\n",
 569 |               "      <td>2</td>\n",
 570 |               "      <td>Rajasthan Royals</td>\n",
 571 |               "      <td>3</td>\n",
 572 |               "      <td>3</td>\n",
 573 |               "      <td>0</td>\n",
 574 |               "      <td>6</td>\n",
 575 |               "      <td>1.249</td>\n",
 576 |               "    </tr>\n",
 577 |               "    <tr>\n",
 578 |               "      <th>2</th>\n",
 579 |               "      <td>3</td>\n",
 580 |               "      <td>Chennai Super Kings</td>\n",
 581 |               "      <td>4</td>\n",
 582 |               "      <td>2</td>\n",
 583 |               "      <td>2</td>\n",
 584 |               "      <td>4</td>\n",
 585 |               "      <td>0.517</td>\n",
 586 |               "    </tr>\n",
 587 |               "    <tr>\n",
 588 |               "      <th>3</th>\n",
 589 |               "      <td>4</td>\n",
 590 |               "      <td>Lucknow Super Giants</td>\n",
 591 |               "      <td>3</td>\n",
 592 |               "      <td>2</td>\n",
 593 |               "      <td>1</td>\n",
 594 |               "      <td>4</td>\n",
 595 |               "      <td>0.483</td>\n",
 596 |               "    </tr>\n",
 597 |               "    <tr>\n",
 598 |               "      <th>4</th>\n",
 599 |               "      <td>5</td>\n",
 600 |               "      <td>Sunrisers Hyderabad</td>\n",
 601 |               "      <td>4</td>\n",
 602 |               "      <td>2</td>\n",
 603 |               "      <td>2</td>\n",
 604 |               "      <td>4</td>\n",
 605 |               "      <td>0.409</td>\n",
 606 |               "    </tr>\n",
 607 |               "  </tbody>\n",
 608 |               "</table>\n",
 609 |               "</div>\n",
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 619 |               "  </svg>\n",
 620 |               "    </button>\n",
 621 |               "\n",
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 623 |               "    .colab-df-container {\n",
 624 |               "      display:flex;\n",
 625 |               "      gap: 12px;\n",
 626 |               "    }\n",
 627 |               "\n",
 628 |               "    .colab-df-convert {\n",
 629 |               "      background-color: #E8F0FE;\n",
 630 |               "      border: none;\n",
 631 |               "      border-radius: 50%;\n",
 632 |               "      cursor: pointer;\n",
 633 |               "      display: none;\n",
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 635 |               "      height: 32px;\n",
 636 |               "      padding: 0 0 0 0;\n",
 637 |               "      width: 32px;\n",
 638 |               "    }\n",
 639 |               "\n",
 640 |               "    .colab-df-convert:hover {\n",
 641 |               "      background-color: #E2EBFA;\n",
 642 |               "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
 643 |               "      fill: #174EA6;\n",
 644 |               "    }\n",
 645 |               "\n",
 646 |               "    .colab-df-buttons div {\n",
 647 |               "      margin-bottom: 4px;\n",
 648 |               "    }\n",
 649 |               "\n",
 650 |               "    [theme=dark] .colab-df-convert {\n",
 651 |               "      background-color: #3B4455;\n",
 652 |               "      fill: #D2E3FC;\n",
 653 |               "    }\n",
 654 |               "\n",
 655 |               "    [theme=dark] .colab-df-convert:hover {\n",
 656 |               "      background-color: #434B5C;\n",
 657 |               "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
 658 |               "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
 659 |               "      fill: #FFFFFF;\n",
 660 |               "    }\n",
 661 |               "  </style>\n",
 662 |               "\n",
 663 |               "    <script>\n",
 664 |               "      const buttonEl =\n",
 665 |               "        document.querySelector('#df-13860b47-7a8f-4fde-b39c-d30e8aa19cdd button.colab-df-convert');\n",
 666 |               "      buttonEl.style.display =\n",
 667 |               "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
 668 |               "\n",
 669 |               "      async function convertToInteractive(key) {\n",
 670 |               "        const element = document.querySelector('#df-13860b47-7a8f-4fde-b39c-d30e8aa19cdd');\n",
 671 |               "        const dataTable =\n",
 672 |               "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
 673 |               "                                                    [key], {});\n",
 674 |               "        if (!dataTable) return;\n",
 675 |               "\n",
 676 |               "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
 677 |               "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
 678 |               "          + ' to learn more about interactive tables.';\n",
 679 |               "        element.innerHTML = '';\n",
 680 |               "        dataTable['output_type'] = 'display_data';\n",
 681 |               "        await google.colab.output.renderOutput(dataTable, element);\n",
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 699 |               "    </g>\n",
 700 |               "</svg>\n",
 701 |               "  </button>\n",
 702 |               "\n",
 703 |               "<style>\n",
 704 |               "  .colab-df-quickchart {\n",
 705 |               "      --bg-color: #E8F0FE;\n",
 706 |               "      --fill-color: #1967D2;\n",
 707 |               "      --hover-bg-color: #E2EBFA;\n",
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 710 |               "      --disabled-bg-color: #DDD;\n",
 711 |               "  }\n",
 712 |               "\n",
 713 |               "  [theme=dark] .colab-df-quickchart {\n",
 714 |               "      --bg-color: #3B4455;\n",
 715 |               "      --fill-color: #D2E3FC;\n",
 716 |               "      --hover-bg-color: #434B5C;\n",
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 718 |               "      --disabled-bg-color: #3B4455;\n",
 719 |               "      --disabled-fill-color: #666;\n",
 720 |               "  }\n",
 721 |               "\n",
 722 |               "  .colab-df-quickchart {\n",
 723 |               "    background-color: var(--bg-color);\n",
 724 |               "    border: none;\n",
 725 |               "    border-radius: 50%;\n",
 726 |               "    cursor: pointer;\n",
 727 |               "    display: none;\n",
 728 |               "    fill: var(--fill-color);\n",
 729 |               "    height: 32px;\n",
 730 |               "    padding: 0;\n",
 731 |               "    width: 32px;\n",
 732 |               "  }\n",
 733 |               "\n",
 734 |               "  .colab-df-quickchart:hover {\n",
 735 |               "    background-color: var(--hover-bg-color);\n",
 736 |               "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
 737 |               "    fill: var(--button-hover-fill-color);\n",
 738 |               "  }\n",
 739 |               "\n",
 740 |               "  .colab-df-quickchart-complete:disabled,\n",
 741 |               "  .colab-df-quickchart-complete:disabled:hover {\n",
 742 |               "    background-color: var(--disabled-bg-color);\n",
 743 |               "    fill: var(--disabled-fill-color);\n",
 744 |               "    box-shadow: none;\n",
 745 |               "  }\n",
 746 |               "\n",
 747 |               "  .colab-df-spinner {\n",
 748 |               "    border: 2px solid var(--fill-color);\n",
 749 |               "    border-color: transparent;\n",
 750 |               "    border-bottom-color: var(--fill-color);\n",
 751 |               "    animation:\n",
 752 |               "      spin 1s steps(1) infinite;\n",
 753 |               "  }\n",
 754 |               "\n",
 755 |               "  @keyframes spin {\n",
 756 |               "    0% {\n",
 757 |               "      border-color: transparent;\n",
 758 |               "      border-bottom-color: var(--fill-color);\n",
 759 |               "      border-left-color: var(--fill-color);\n",
 760 |               "    }\n",
 761 |               "    20% {\n",
 762 |               "      border-color: transparent;\n",
 763 |               "      border-left-color: var(--fill-color);\n",
 764 |               "      border-top-color: var(--fill-color);\n",
 765 |               "    }\n",
 766 |               "    30% {\n",
 767 |               "      border-color: transparent;\n",
 768 |               "      border-left-color: var(--fill-color);\n",
 769 |               "      border-top-color: var(--fill-color);\n",
 770 |               "      border-right-color: var(--fill-color);\n",
 771 |               "    }\n",
 772 |               "    40% {\n",
 773 |               "      border-color: transparent;\n",
 774 |               "      border-right-color: var(--fill-color);\n",
 775 |               "      border-top-color: var(--fill-color);\n",
 776 |               "    }\n",
 777 |               "    60% {\n",
 778 |               "      border-color: transparent;\n",
 779 |               "      border-right-color: var(--fill-color);\n",
 780 |               "    }\n",
 781 |               "    80% {\n",
 782 |               "      border-color: transparent;\n",
 783 |               "      border-right-color: var(--fill-color);\n",
 784 |               "      border-bottom-color: var(--fill-color);\n",
 785 |               "    }\n",
 786 |               "    90% {\n",
 787 |               "      border-color: transparent;\n",
 788 |               "      border-bottom-color: var(--fill-color);\n",
 789 |               "    }\n",
 790 |               "  }\n",
 791 |               "</style>\n",
 792 |               "\n",
 793 |               "  <script>\n",
 794 |               "    async function quickchart(key) {\n",
 795 |               "      const quickchartButtonEl =\n",
 796 |               "        document.querySelector('#' + key + ' button');\n",
 797 |               "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
 798 |               "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
 799 |               "      try {\n",
 800 |               "        const charts = await google.colab.kernel.invokeFunction(\n",
 801 |               "            'suggestCharts', [key], {});\n",
 802 |               "      } catch (error) {\n",
 803 |               "        console.error('Error during call to suggestCharts:', error);\n",
 804 |               "      }\n",
 805 |               "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
 806 |               "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
 807 |               "    }\n",
 808 |               "    (() => {\n",
 809 |               "      let quickchartButtonEl =\n",
 810 |               "        document.querySelector('#df-66783a6b-7526-4136-933f-f191fdfd9bd0 button');\n",
 811 |               "      quickchartButtonEl.style.display =\n",
 812 |               "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
 813 |               "    })();\n",
 814 |               "  </script>\n",
 815 |               "</div>\n",
 816 |               "\n",
 817 |               "    </div>\n",
 818 |               "  </div>\n"
 819 |             ],
 820 |             "application/vnd.google.colaboratory.intrinsic+json": {
 821 |               "type": "dataframe",
 822 |               "variable_name": "data",
 823 |               "summary": "{\n  \"name\": \"data\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"Rank\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 1,\n        \"max\": 10,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          9,\n          2,\n          6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Teams\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"Delhi Capitals\",\n          \"Rajasthan Royals\",\n          \"Punjab Kings\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Match\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 3,\n        \"max\": 4,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          4,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"W\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 3,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"L\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 3,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Points\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 6,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          4,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"NRR\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.225035509689413,\n        \"min\": -1.423,\n        \"max\": 2.518,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          -1.347,\n          1.249\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
 824 |             }
 825 |           },
 826 |           "metadata": {},
 827 |           "execution_count": 13
 828 |         }
 829 |       ]
 830 |     },
 831 |     {
 832 |       "cell_type": "code",
 833 |       "source": [
 834 |         "X=data.drop(['Teams'],axis=1)"
 835 |       ],
 836 |       "metadata": {
 837 |         "id": "JSVn7i9Fg8GM"
 838 |       },
 839 |       "execution_count": 18,
 840 |       "outputs": []
 841 |     },
 842 |     {
 843 |       "cell_type": "code",
 844 |       "source": [
 845 |         "X"
 846 |       ],
 847 |       "metadata": {
 848 |         "colab": {
 849 |           "base_uri": "https://localhost:8080/",
 850 |           "height": 363
 851 |         },
 852 |         "id": "SqVXiq5ViG-a",
 853 |         "outputId": "915e3e2b-b05d-47f3-b557-d2300a1e9e83"
 854 |       },
 855 |       "execution_count": 19,
 856 |       "outputs": [
 857 |         {
 858 |           "output_type": "execute_result",
 859 |           "data": {
 860 |             "text/plain": [
 861 |               "   Rank  Match  W  L  Points    NRR\n",
 862 |               "0     1      3  3  0       6  2.518\n",
 863 |               "1     2      3  3  0       6  1.249\n",
 864 |               "2     3      4  2  2       4  0.517\n",
 865 |               "3     4      3  2  1       4  0.483\n",
 866 |               "4     5      4  2  2       4  0.409\n",
 867 |               "5     6      4  2  2       4 -0.220\n",
 868 |               "6     7      4  2  2       4 -0.580\n",
 869 |               "7     8      4  1  3       2 -0.876\n",
 870 |               "8     9      4  1  3       2 -1.347\n",
 871 |               "9    10      3  0  3       0 -1.423"
 872 |             ],
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 973 |               "      <td>-0.876</td>\n",
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 976 |               "      <th>8</th>\n",
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1000 |               "            title=\"Convert this dataframe to an interactive table.\"\n",
1001 |               "            style=\"display:none;\">\n",
1002 |               "\n",
1003 |               "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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1005 |               "  </svg>\n",
1006 |               "    </button>\n",
1007 |               "\n",
1008 |               "  <style>\n",
1009 |               "    .colab-df-container {\n",
1010 |               "      display:flex;\n",
1011 |               "      gap: 12px;\n",
1012 |               "    }\n",
1013 |               "\n",
1014 |               "    .colab-df-convert {\n",
1015 |               "      background-color: #E8F0FE;\n",
1016 |               "      border: none;\n",
1017 |               "      border-radius: 50%;\n",
1018 |               "      cursor: pointer;\n",
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1020 |               "      fill: #1967D2;\n",
1021 |               "      height: 32px;\n",
1022 |               "      padding: 0 0 0 0;\n",
1023 |               "      width: 32px;\n",
1024 |               "    }\n",
1025 |               "\n",
1026 |               "    .colab-df-convert:hover {\n",
1027 |               "      background-color: #E2EBFA;\n",
1028 |               "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
1029 |               "      fill: #174EA6;\n",
1030 |               "    }\n",
1031 |               "\n",
1032 |               "    .colab-df-buttons div {\n",
1033 |               "      margin-bottom: 4px;\n",
1034 |               "    }\n",
1035 |               "\n",
1036 |               "    [theme=dark] .colab-df-convert {\n",
1037 |               "      background-color: #3B4455;\n",
1038 |               "      fill: #D2E3FC;\n",
1039 |               "    }\n",
1040 |               "\n",
1041 |               "    [theme=dark] .colab-df-convert:hover {\n",
1042 |               "      background-color: #434B5C;\n",
1043 |               "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
1044 |               "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
1045 |               "      fill: #FFFFFF;\n",
1046 |               "    }\n",
1047 |               "  </style>\n",
1048 |               "\n",
1049 |               "    <script>\n",
1050 |               "      const buttonEl =\n",
1051 |               "        document.querySelector('#df-14cd94d8-641e-4316-9fcf-57fb0a107dbc button.colab-df-convert');\n",
1052 |               "      buttonEl.style.display =\n",
1053 |               "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
1054 |               "\n",
1055 |               "      async function convertToInteractive(key) {\n",
1056 |               "        const element = document.querySelector('#df-14cd94d8-641e-4316-9fcf-57fb0a107dbc');\n",
1057 |               "        const dataTable =\n",
1058 |               "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
1059 |               "                                                    [key], {});\n",
1060 |               "        if (!dataTable) return;\n",
1061 |               "\n",
1062 |               "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
1063 |               "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
1064 |               "          + ' to learn more about interactive tables.';\n",
1065 |               "        element.innerHTML = '';\n",
1066 |               "        dataTable['output_type'] = 'display_data';\n",
1067 |               "        await google.colab.output.renderOutput(dataTable, element);\n",
1068 |               "        const docLink = document.createElement('div');\n",
1069 |               "        docLink.innerHTML = docLinkHtml;\n",
1070 |               "        element.appendChild(docLink);\n",
1071 |               "      }\n",
1072 |               "    </script>\n",
1073 |               "  </div>\n",
1074 |               "\n",
1075 |               "\n",
1076 |               "<div id=\"df-2ad41acb-77ff-4c47-ab39-26df3d990af9\">\n",
1077 |               "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-2ad41acb-77ff-4c47-ab39-26df3d990af9')\"\n",
1078 |               "            title=\"Suggest charts\"\n",
1079 |               "            style=\"display:none;\">\n",
1080 |               "\n",
1081 |               "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
1082 |               "     width=\"24px\">\n",
1083 |               "    <g>\n",
1084 |               "        <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",
1085 |               "    </g>\n",
1086 |               "</svg>\n",
1087 |               "  </button>\n",
1088 |               "\n",
1089 |               "<style>\n",
1090 |               "  .colab-df-quickchart {\n",
1091 |               "      --bg-color: #E8F0FE;\n",
1092 |               "      --fill-color: #1967D2;\n",
1093 |               "      --hover-bg-color: #E2EBFA;\n",
1094 |               "      --hover-fill-color: #174EA6;\n",
1095 |               "      --disabled-fill-color: #AAA;\n",
1096 |               "      --disabled-bg-color: #DDD;\n",
1097 |               "  }\n",
1098 |               "\n",
1099 |               "  [theme=dark] .colab-df-quickchart {\n",
1100 |               "      --bg-color: #3B4455;\n",
1101 |               "      --fill-color: #D2E3FC;\n",
1102 |               "      --hover-bg-color: #434B5C;\n",
1103 |               "      --hover-fill-color: #FFFFFF;\n",
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1105 |               "      --disabled-fill-color: #666;\n",
1106 |               "  }\n",
1107 |               "\n",
1108 |               "  .colab-df-quickchart {\n",
1109 |               "    background-color: var(--bg-color);\n",
1110 |               "    border: none;\n",
1111 |               "    border-radius: 50%;\n",
1112 |               "    cursor: pointer;\n",
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1115 |               "    height: 32px;\n",
1116 |               "    padding: 0;\n",
1117 |               "    width: 32px;\n",
1118 |               "  }\n",
1119 |               "\n",
1120 |               "  .colab-df-quickchart:hover {\n",
1121 |               "    background-color: var(--hover-bg-color);\n",
1122 |               "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
1123 |               "    fill: var(--button-hover-fill-color);\n",
1124 |               "  }\n",
1125 |               "\n",
1126 |               "  .colab-df-quickchart-complete:disabled,\n",
1127 |               "  .colab-df-quickchart-complete:disabled:hover {\n",
1128 |               "    background-color: var(--disabled-bg-color);\n",
1129 |               "    fill: var(--disabled-fill-color);\n",
1130 |               "    box-shadow: none;\n",
1131 |               "  }\n",
1132 |               "\n",
1133 |               "  .colab-df-spinner {\n",
1134 |               "    border: 2px solid var(--fill-color);\n",
1135 |               "    border-color: transparent;\n",
1136 |               "    border-bottom-color: var(--fill-color);\n",
1137 |               "    animation:\n",
1138 |               "      spin 1s steps(1) infinite;\n",
1139 |               "  }\n",
1140 |               "\n",
1141 |               "  @keyframes spin {\n",
1142 |               "    0% {\n",
1143 |               "      border-color: transparent;\n",
1144 |               "      border-bottom-color: var(--fill-color);\n",
1145 |               "      border-left-color: var(--fill-color);\n",
1146 |               "    }\n",
1147 |               "    20% {\n",
1148 |               "      border-color: transparent;\n",
1149 |               "      border-left-color: var(--fill-color);\n",
1150 |               "      border-top-color: var(--fill-color);\n",
1151 |               "    }\n",
1152 |               "    30% {\n",
1153 |               "      border-color: transparent;\n",
1154 |               "      border-left-color: var(--fill-color);\n",
1155 |               "      border-top-color: var(--fill-color);\n",
1156 |               "      border-right-color: var(--fill-color);\n",
1157 |               "    }\n",
1158 |               "    40% {\n",
1159 |               "      border-color: transparent;\n",
1160 |               "      border-right-color: var(--fill-color);\n",
1161 |               "      border-top-color: var(--fill-color);\n",
1162 |               "    }\n",
1163 |               "    60% {\n",
1164 |               "      border-color: transparent;\n",
1165 |               "      border-right-color: var(--fill-color);\n",
1166 |               "    }\n",
1167 |               "    80% {\n",
1168 |               "      border-color: transparent;\n",
1169 |               "      border-right-color: var(--fill-color);\n",
1170 |               "      border-bottom-color: var(--fill-color);\n",
1171 |               "    }\n",
1172 |               "    90% {\n",
1173 |               "      border-color: transparent;\n",
1174 |               "      border-bottom-color: var(--fill-color);\n",
1175 |               "    }\n",
1176 |               "  }\n",
1177 |               "</style>\n",
1178 |               "\n",
1179 |               "  <script>\n",
1180 |               "    async function quickchart(key) {\n",
1181 |               "      const quickchartButtonEl =\n",
1182 |               "        document.querySelector('#' + key + ' button');\n",
1183 |               "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
1184 |               "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
1185 |               "      try {\n",
1186 |               "        const charts = await google.colab.kernel.invokeFunction(\n",
1187 |               "            'suggestCharts', [key], {});\n",
1188 |               "      } catch (error) {\n",
1189 |               "        console.error('Error during call to suggestCharts:', error);\n",
1190 |               "      }\n",
1191 |               "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
1192 |               "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
1193 |               "    }\n",
1194 |               "    (() => {\n",
1195 |               "      let quickchartButtonEl =\n",
1196 |               "        document.querySelector('#df-2ad41acb-77ff-4c47-ab39-26df3d990af9 button');\n",
1197 |               "      quickchartButtonEl.style.display =\n",
1198 |               "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
1199 |               "    })();\n",
1200 |               "  </script>\n",
1201 |               "</div>\n",
1202 |               "\n",
1203 |               "  <div id=\"id_ab7787ed-68c2-4ebd-a0db-3ccc8c1af384\">\n",
1204 |               "    <style>\n",
1205 |               "      .colab-df-generate {\n",
1206 |               "        background-color: #E8F0FE;\n",
1207 |               "        border: none;\n",
1208 |               "        border-radius: 50%;\n",
1209 |               "        cursor: pointer;\n",
1210 |               "        display: none;\n",
1211 |               "        fill: #1967D2;\n",
1212 |               "        height: 32px;\n",
1213 |               "        padding: 0 0 0 0;\n",
1214 |               "        width: 32px;\n",
1215 |               "      }\n",
1216 |               "\n",
1217 |               "      .colab-df-generate:hover {\n",
1218 |               "        background-color: #E2EBFA;\n",
1219 |               "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
1220 |               "        fill: #174EA6;\n",
1221 |               "      }\n",
1222 |               "\n",
1223 |               "      [theme=dark] .colab-df-generate {\n",
1224 |               "        background-color: #3B4455;\n",
1225 |               "        fill: #D2E3FC;\n",
1226 |               "      }\n",
1227 |               "\n",
1228 |               "      [theme=dark] .colab-df-generate:hover {\n",
1229 |               "        background-color: #434B5C;\n",
1230 |               "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
1231 |               "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
1232 |               "        fill: #FFFFFF;\n",
1233 |               "      }\n",
1234 |               "    </style>\n",
1235 |               "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('X')\"\n",
1236 |               "            title=\"Generate code using this dataframe.\"\n",
1237 |               "            style=\"display:none;\">\n",
1238 |               "\n",
1239 |               "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
1240 |               "       width=\"24px\">\n",
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1242 |               "  </svg>\n",
1243 |               "    </button>\n",
1244 |               "    <script>\n",
1245 |               "      (() => {\n",
1246 |               "      const buttonEl =\n",
1247 |               "        document.querySelector('#id_ab7787ed-68c2-4ebd-a0db-3ccc8c1af384 button.colab-df-generate');\n",
1248 |               "      buttonEl.style.display =\n",
1249 |               "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
1250 |               "\n",
1251 |               "      buttonEl.onclick = () => {\n",
1252 |               "        google.colab.notebook.generateWithVariable('X');\n",
1253 |               "      }\n",
1254 |               "      })();\n",
1255 |               "    </script>\n",
1256 |               "  </div>\n",
1257 |               "\n",
1258 |               "    </div>\n",
1259 |               "  </div>\n"
1260 |             ],
1261 |             "application/vnd.google.colaboratory.intrinsic+json": {
1262 |               "type": "dataframe",
1263 |               "variable_name": "X",
1264 |               "summary": "{\n  \"name\": \"X\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"Rank\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 1,\n        \"max\": 10,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          9,\n          2,\n          6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Match\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 3,\n        \"max\": 4,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          4,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"W\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 3,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"L\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 3,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Points\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 6,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          4,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"NRR\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.225035509689413,\n        \"min\": -1.423,\n        \"max\": 2.518,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          -1.347,\n          1.249\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
1265 |             }
1266 |           },
1267 |           "metadata": {},
1268 |           "execution_count": 19
1269 |         }
1270 |       ]
1271 |     },
1272 |     {
1273 |       "cell_type": "code",
1274 |       "source": [
1275 |         "plt.figure(figsize=(10, 9))\n",
1276 |         "plt.title(\"IPL 2024 Dendograms\")\n",
1277 |         "plt.xlabel(\"Teams\")\n",
1278 |         "plt.ylabel('Distance')\n",
1279 |         "dend=shc.dendrogram(shc.linkage(X,method='centroid'))"
1280 |       ],
1281 |       "metadata": {
1282 |         "colab": {
1283 |           "base_uri": "https://localhost:8080/",
1284 |           "height": 799
1285 |         },
1286 |         "id": "fyk7TUpwdlno",
1287 |         "outputId": "a9dcd5ee-1119-4b2a-bf2a-3bcb110b9056"
1288 |       },
1289 |       "execution_count": 25,
1290 |       "outputs": [
1291 |         {
1292 |           "output_type": "display_data",
1293 |           "data": {
1294 |             "text/plain": [
1295 |               "<Figure size 1000x900 with 1 Axes>"
1296 |             ],
1297 |             "image/png": 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rYuLEidG3b9+45ZZb4vDDD6/eZsGCBVFWVhYRsdbPoxk1alR1BNWXc845JyL+/UGtHTp0iG233TauuuqqOProo2u97379+sXTTz8d559/fkycODEWLFgQnTp1ikGDBlU/z7q66KKLomXLlnHttdfGI488EoMHD44HHnggDjjggBrbbbrpptWf9/OrX/0qVqxYEQMHDox77rmnxratW7eOhx9+OE455ZT4xS9+Ea1bt44jjzwydtttt/jGN75RI+o+TceOHePee++NH/zgB3H22WdH+/bt4zvf+U585Stf+dTzoeqitLQ0TjrppHjggQfizjvvjKqqqujTp09cc801tS6EAdAQFWWFdBYoACTmqquuitNOOy3efvvtGlewA2D9EUEAsIGUl5fXOJdqxYoVMWjQoKisrIzXXnstx8kA0uJwOADYQA455JDYfPPNY/vtt49FixbFLbfcEq+88kpMmjQp79EAkiKCAGAD2W+//eLGG2+MSZMmRWVlZfTr1y9uv/326qvnAbBhOBwOAABIis8JAgAAkiKCAACApDToc4KqqqrinXfeiTZt2nyuD5kDAAAapyzLYsmSJdG1a9fqD6r+NA06gt55553o3r173mMAAAAFoqysLLp16/aZ2zToCFrzieFlZWXRtm3bnKcBAADysnjx4ujevXt1I3yWBh1Baw6Ba9u2rQgCAAA+12kyLowAAAAkRQQBAABJEUEAAEBSRBAAAJAUEQQAACRFBAEAAEkRQQAAQFJEEAAAkBQRBAAAJEUEAQAASRFBAABAUkQQAACQFBEEAAAkRQQBAABJEUEAAEBSRBAAAJAUEQQAACRFBAEAAEkRQQAAQFJEEAAAkBQRBAAAJEUEAQAASRFBAABAUkQQAACQFBEEAAAkRQQBAABJEUEAAEBSRBAAAJCUZnkPQMOWZVmUr6rMewyApJU0bxpFRUV5jwHQYIgg6izLsjj02qfimXmf5D0KQNJ27NE+7jhhVyEE8Dk5HI46K19VKYAACsDT8z6xVx5gHdgTRL14+uyhUdqiad5jACRl+crK2PGiaXmPAdDgiCDqRWmLplHawv9OAAAUPofDAQAASRFBAABAUkQQAACQFBEEAAAkRQQBAABJEUEAAEBSRBAAAJAUEQQAACRFBAEAAEkRQQAAQFJEEAAAkBQRBAAAJEUEAQAASRFBAABAUkQQAACQFBEEAAAkRQQBAABJEUEAAEBSRBAAAJAUEQQAACRFBAEAAEkRQQAAQFJEEAAAkBQRBAAAJEUEAQAASRFBAABAUkQQAACQFBEEAAAkRQQBAABJEUEAAEBSRBAAAJAUEQQAACRFBAEAAEkRQQAAQFJEEAAAkBQRBAAAJEUEAQAASRFBAABAUkQQAACQFBEEAAAkRQQBAABJEUEAAEBSRBAAAJAUEQQAACRFBAEAAEkRQQAAQFJEEAAAkBQRBAAAJEUEAQAASRFBAABAUkQQAACQFBEEAAAkRQQBAABJEUEAAEBSRBAAAJAUEQQAACRFBAEAAEkRQQAAQFJEEAAAkJTcI2j+/Pnxne98Jzp27BglJSWx7bbbxtNPP533WAAAQCPVLM8X/+STT+LLX/5y7L333nHffffFJptsEnPmzIn27dvnORYAANCI5RpBl112WXTv3j1uvvnm6rVevXrlOBEAANDY5Xo43JQpU2LHHXeMkSNHRqdOnWLQoEFxww03fOr2FRUVsXjx4ho3AACAdZFrBL355psxYcKE6Nu3b9x///1x4oknxpgxY+K3v/3tWre/5JJLol27dtW37t27b+CJAQCAhi7XCKqqqooddtghLr744hg0aFAcd9xxceyxx8a111671u3Hjh0bixYtqr6VlZVt4IkBAICGLtcI6tKlS/Tr16/G2jbbbBP/+te/1rp9cXFxtG3btsYNAABgXeQaQV/+8pfj1VdfrbH22muvRY8ePXKaCAAAaOxyjaDTTjstZsyYERdffHG8/vrrceutt8b1118fo0ePznMsAACgEcs1gnbaaae466674rbbbosBAwbEhRdeGFdddVUcccQReY4FAAA0Yrl+TlBExNe+9rX42te+lvcYAABAInLdEwQAALChiSAAACApIggAAEiKCAIAAJIiggAAgKSIIAAAICkiCAAASIoIAgAAkiKCAACApIggAAAgKSIIAABIiggCAACSIoIAAICkiCAAACApIggAAEiKCAIAAJIiggAAgKSIIAAAICkiCAAASIoIAgAAkiKCAACApIggAAAgKSIIAABIiggCAACSIoIAAICkiCAAACApIggAAEiKCAIAAJIiggAAgKSIIAAAICkiCAAASIoIAgAAkiKCAACApIggAAAgKSIIAABIiggCAACSIoIAAICkiCAAACApIggAAEiKCAIAAJIiggAAgKSIIAAAICkiCAAASIoIAgAAkiKCAACApIggAAAgKSIIAABIiggCAACSIoIAAICkiCAAACApIggAAEiKCAIAAJIiggAAgKSIIAAAICkiCAAASIoIAgAAkiKCAACApIggAAAgKSIIAABIiggCAACSIoIAAICkiCAAACApIggAAEiKCAIAAJIiggAAgKSIIAAAICkiCAAASIoIAgAAkiKCAACApIggAAAgKSIIAABIiggCAACSIoIAAICkiCAAACApIggAAEiKCAIAAJIiggAAgKSIIAAAICkiCAAASIoIAgAAkiKCAACApIggAAAgKSIIAABIiggCAACSIoIAAICkiCAAACApIggAAEiKCAIAAJKSawSdd955UVRUVOO29dZb5zkSAADQyDXLe4D+/fvHtGnTqu83a5b7SAAAQCOWe3E0a9YsOnfunPcYAABAInI/J2jOnDnRtWvX6N27dxxxxBHxr3/961O3raioiMWLF9e4AQAArItcI2jw4MExceLEmDp1akyYMCHeeuut2GOPPWLJkiVr3f6SSy6Jdu3aVd+6d+++gScGAAAaulwjaNiwYTFy5MgYOHBg7LfffvHXv/41Fi5cGH/4wx/Wuv3YsWNj0aJF1beysrINPDEAANDQ5X5O0H/aaKONYsstt4zXX399rY8XFxdHcXHxBp4KAABoTHI/J+g/LV26NN54443o0qVL3qMAAACNVK4RdMYZZ8Sjjz4ac+fOjSeffDIOPvjgaNq0aXz729/OcywAAKARy/VwuLfffju+/e1vx4IFC2KTTTaJ3XffPWbMmBGbbLJJnmMB5CbLsihfVZn3GDQQy1euXuvX8FlKmjeNoqKivMeAXOUaQbfffnueLw9QULIsi0OvfSqemfdJ3qPQAO140UN5j0ADsWOP9nHHCbsKIZJWUOcEAaSsfFWlAALWu6fnfWKPM8krqKvDAfBvT589NEpbNM17DKARWb6yMna8aFreY0BBEEEABai0RdMobeGPaABYHxwOBwAAJEUEAQAASRFBAABAUkQQAACQFBEEAAAkRQQBAABJEUEAAEBSRBAAAJAUEQQAACRFBAEAAEkRQQAAQFJEEAAAkBQRBAAAJEUEAQAASRFBAABAUkQQAACQFBEEAAAkRQQBAABJEUEAAEBSRBAAAJAUEQQAACRFBAEAAEkRQQAAQFJEEAAAkBQRBAAAJEUEAQAASRFBAABAUkQQAACQFBEEAAAkRQQBAABJEUEAAEBSRBAAAJAUEQQAACRFBAEAAEkRQQAAQFJEEAAAkBQRBAAAJEUEAQAASRFBAABAUkQQAACQFBEEAAAkRQQBAABJEUEAAEBSRBAAAJAUEQQAACRFBAEAAEkRQQAAQFJEEAAAkBQRBAAAJEUEAQAASRFBAABAUkQQAACQFBEEAAAkRQQBAABJEUEAAEBSRBAAAJAUEQQAACRFBAEAAEkRQQAAQFJEEAAAkBQRBAAAJEUEAQAASRFBAABAUkQQAACQFBEEAAAkRQQBAABJEUEAAEBSRBAAAJAUEQQAACRFBAEAAEkRQQAAQFJEEAAAkBQRBAAAJEUEAQAASRFBAABAUkQQAACQFBEEAAAkRQQBAABJEUEAAEBSRBAAAJAUEQQAACRFBAEAAEkRQQAAQFJEEAAAkBQRBAAAJKVgIujSSy+NoqKiOPXUU/MeBQAAaMQKIoJmzZoV1113XQwcODDvUQAAgEYu9whaunRpHHHEEXHDDTdE+/bt8x4HAABo5HKPoNGjR8cBBxwQQ4cO/Z/bVlRUxOLFi2vcAAAA1kWzPF/89ttvj2effTZmzZr1uba/5JJL4vzzz1/PUwEAAI1ZbnuCysrK4vvf/35MmjQpWrZs+bm+Z+zYsbFo0aLqW1lZ2XqeEgAAaGxy2xP0zDPPxAcffBA77LBD9VplZWU89thjcfXVV0dFRUU0bdq0xvcUFxdHcXHxhh4VAABoRHKLoK985Svxwgsv1Fg7+uijY+utt44zzzyzVgABAADUh9wiqE2bNjFgwIAaa61atYqOHTvWWgcAAKgvuV8dDgAAYEPK9epw/2369Ol5jwAAADRy9gQBAABJEUEAAEBSRBAAAJAUEQQAACRFBAEAAEkRQQAAQFJEEAAAkBQRBAAAJEUEAQAASRFBAABAUkQQAACQFBEEAAAkRQQBAABJEUEAAEBSRBAAAJCULxxBK1asqI85AAAANog6RVBVVVVceOGFsdlmm0Xr1q3jzTffjIiIcePGxW9+85t6HRAAAKA+1SmCLrroopg4cWJcfvnl0aJFi+r1AQMGxI033lhvwwEAANS3OkXQ7373u7j++uvjiCOOiKZNm1avb7fddvHKK6/U23AAAAD1rU4RNH/+/OjTp0+t9aqqqli1atUXHgoAAGB9qVME9evXLx5//PFa63/84x9j0KBBX3goAACA9aVZXb7pnHPOiVGjRsX8+fOjqqoq7rzzznj11Vfjd7/7Xdx77731PSMAAEC9qdOeoK9//etxzz33xLRp06JVq1ZxzjnnxMsvvxz33HNPfPWrX63vGQEAAOpNnfYERUTsscce8eCDD9bnLAAAAOtdnfYEzZo1K2bOnFlrfebMmfH0009/4aEAAADWlzpF0OjRo6OsrKzW+vz582P06NFfeCgAAID1pU4R9NJLL8UOO+xQa33QoEHx0ksvfeGhAAAA1pc6RVBxcXG8//77tdbffffdaNaszqcZAQAArHd1iqB99903xo4dG4sWLapeW7hwYfz4xz92dTgAAKCg1Wm3zU9/+tPYc889o0ePHtUfjvrcc8/FpptuGr///e/rdUAAAID6VKcI2myzzeIf//hHTJo0KZ5//vkoKSmJo48+Or797W9H8+bN63tGAACAelPnE3hatWoVxx13XH3OAgAAsN7VOYLmzJkTjzzySHzwwQdRVVVV47FzzjnnCw8GAACwPtQpgm644YY48cQTY+ONN47OnTtHUVFR9WNFRUUiCAAAKFh1iqCLLrooxo8fH2eeeWZ9zwMAALBe1ekS2Z988kmMHDmyvmcBAABY7+oUQSNHjowHHnigvmcBAABY7+p0OFyfPn1i3LhxMWPGjNh2221rXRZ7zJgx9TIcAABAfatTBF1//fXRunXrePTRR+PRRx+t8VhRUZEIAgAACladIuitt96q7zkAAAA2iDqdEwQAANBQ1fnDUt9+++2YMmVK/Otf/4qVK1fWeOxnP/vZFx4MAABgfahTBD300EMxYsSI6N27d7zyyisxYMCAmDt3bmRZFjvssEN9zwgAAFBv6nQ43NixY+OMM86IF154IVq2bBl/+tOfoqysLIYMGeLzgwAAgIJWpwh6+eWX48gjj4yIiGbNmkV5eXm0bt06LrjggrjsssvqdUAAAID6VKcIatWqVfV5QF26dIk33nij+rGPPvqofiYDAABYD+p0TtAuu+wSf/vb32KbbbaJ4cOHxw9+8IN44YUX4s4774xddtmlvmcEAACoN3WKoJ/97GexdOnSiIg4//zzY+nSpTF58uTo27evK8MBAAAFrU4R1Lt37+qvW7VqFddee229DQQAALA+1emcoN69e8eCBQtqrS9cuLBGIAEAABSaOkXQ3Llzo7KystZ6RUVFzJ8//wsPBQAAsL6s0+FwU6ZMqf76/vvvj3bt2lXfr6ysjIceeih69uxZb8MBAADUt3WKoIMOOigiIoqKimLUqFE1HmvevHn07NkzrrzyynobDgAAoL6tUwRVVVVFRESvXr1i1qxZsfHGG6+XoQAAANaXOl0d7q233qq1tnDhwthoo42+6DwAAADrVZ0ujHDZZZfF5MmTq++PHDkyOnToEJtttlk8//zz9TYcAABAfatTBF177bXRvXv3iIh48MEHY9q0aTF16tQYNmxY/PCHP6zXAQEAAOpTnQ6He++996oj6N57743DDjss9t133+jZs2cMHjy4XgcEAACoT3XaE9S+ffsoKyuLiIipU6fG0KFDIyIiy7K1fn4QAABAoajTnqBDDjkkDj/88Ojbt28sWLAghg0bFhERs2fPjj59+tTrgAAAAPWpThH085//PHr27BllZWVx+eWXR+vWrSMi4t13342TTjqpXgcEAACoT3WKoObNm8cZZ5xRa/200077wgMBAACsT587gqZMmRLDhg2L5s2bx5QpUz5z2xEjRnzhwQAAANaHzx1BBx10ULz33nvRqVOnOOiggz51u6KiIhdHAAAACtbnjqCqqqq1fg0AANCQrPM5QVVVVTFx4sS48847Y+7cuVFUVBS9e/eOb3zjG/F///d/UVRUtD7mBAAAqBfr9DlBWZbFiBEj4nvf+17Mnz8/tt122+jfv3/MnTs3jjrqqDj44IPX15wAAAD1Yp32BE2cODEee+yxeOihh2Lvvfeu8djDDz8cBx10UPzud7+LI488sl6HBAAAqC/rtCfotttuix//+Me1AigiYp999omzzjorJk2aVG/DAQAA1Ld1iqB//OMfsf/++3/q48OGDYvnn3/+Cw8FAACwvqxTBH388cex6aabfurjm266aXzyySdfeCgAAID1ZZ0iqLKyMpo1+/TTiJo2bRqrV6/+wkMBAACsL+t0YYQsy+Koo46K4uLitT5eUVFRL0MBAACsL+sUQaNGjfqf27gyHAAAUMjWKYJuvvnm9TUHAADABrFO5wQBAAA0dCIIAABIiggCAACSIoIAAICkiCAAACApIggAAEiKCAIAAJIiggAAgKSIIAAAICkiCAAASIoIAgAAkiKCAACApIggAAAgKblG0IQJE2LgwIHRtm3baNu2bey6665x33335TkSAADQyOUaQd26dYtLL700nnnmmXj66adjn332ia9//evxz3/+M8+xAACARqxZni9+4IEH1rg/fvz4mDBhQsyYMSP69++f01QAAEBjlmsE/afKysq44447YtmyZbHrrruudZuKioqoqKiovr948eINNR4AANBI5H5hhBdeeCFat24dxcXFccIJJ8Rdd90V/fr1W+u2l1xySbRr16761r179w08LQAA0NDlHkFbbbVVPPfcczFz5sw48cQTY9SoUfHSSy+tdduxY8fGokWLqm9lZWUbeFoAAKChy/1wuBYtWkSfPn0iIuJLX/pSzJo1K37xi1/EddddV2vb4uLiKC4u3tAjAgAAjUjue4L+W1VVVY3zfgAAAOpTrnuCxo4dG8OGDYvNN988lixZErfeemtMnz497r///jzHAgAAGrFcI+iDDz6II488Mt59991o165dDBw4MO6///746le/mudYAABAI5ZrBP3mN7/J8+UBAIAEFdw5QQAAAOuTCAIAAJKS+yWyG60si1i1PO8p1q+Vlf/x9fKIaJrbKBtE89KIoqK8pwAA4AsSQetDlkXctF9E2cy8J1m/suKIuPnfX1/RJ6KokV/avPsuEd+dKoQAABo4EbQ+rFre+AMoIkqLKmJuy8PzHmPDKZvx7/+2LVrlPQkAAF+ACFrfzng9okVp3lPwRaxcHvHTPnlPAQBAPRFB61uLUnsOAACggLg6HAAAkBQRBAAAJEUEAQAASRFBAABAUkQQAACQFBEEAAAkRQQBAABJEUEAAEBSRBAAAJAUEQQAACRFBAEAAEkRQQAAQFJEEAAAkBQRBAAAJEUEAQAASRFBAABAUprlPQAA0PBlWRZZeXneY/AZqlZW/v+vl5dH1eqmOU7D51FUUhJFRUV5j9EoiSAA4AvJsizmHX5ElM+enfcofIYVTVtEHHhxRETM+fLu0bJyZc4T8b+U7LBD9Jh0ixBaD0QQAPCFZOXlAqgBaFm5Mu778xl5j8E6KH/22cjKy6OotDTvURodEQQA1Ju+T/wtmpSU5D0GNGhV5eUx58u75z1GoyaCAIB606SkJJr4rTVQ4FwdDgAASIoIAgAAkiKCAACApIggAAAgKSIIAABIiggCAACSIoIAAICkiCAAACApIggAAEiKCAIAAJIiggAAgKSIIAAAICkiCAAASIoIAgAAkiKCAACApIggAAAgKSIIAABIiggCAACSIoIAAICkiCAAACApIggAAEiKCAIAAJIiggAAgKSIIAAAICkiCAAASIoIAgAAkiKCAACApIggAAAgKSIIAABIiggCAACSIoIAAICkiCAAACApIggAAEiKCAIAAJIiggAAgKSIIAAAICkiCAAASIoIAgAAkiKCAACApIggAAAgKSIIAABIiggCAACSIoIAAICkiCAAACApIggAAEiKCAIAAJIiggAAgKSIIAAAICkiCAAASIoIAgAAkiKCAACApIggAAAgKSIIAABIiggCAACSIoIAAICkiCAAACApIggAAEiKCAIAAJIiggAAgKSIIAAAICm5RtAll1wSO+20U7Rp0yY6deoUBx10ULz66qt5jgQAADRyuUbQo48+GqNHj44ZM2bEgw8+GKtWrYp99903li1bludYAABAI9YszxefOnVqjfsTJ06MTp06xTPPPBN77rlnTlMBAACNWa4R9N8WLVoUEREdOnRY6+MVFRVRUVFRfX/x4sUbZC4AAKDxKJgLI1RVVcWpp54aX/7yl2PAgAFr3eaSSy6Jdu3aVd+6d+++gacEAAAauoKJoNGjR8eLL74Yt99++6duM3bs2Fi0aFH1raysbANOCAAANAYFcTjcySefHPfee2889thj0a1bt0/drri4OIqLizfgZAAAQGOTawRlWRannHJK3HXXXTF9+vTo1atXnuMAAAAJyDWCRo8eHbfeemvcfffd0aZNm3jvvfciIqJdu3ZRUlKS52gAAEAjles5QRMmTIhFixbFXnvtFV26dKm+TZ48Oc+xAACARiz3w+EAAAA2pIK5OhwAAMCGIIIAAICkiCAAACApIggAAEiKCAIAAJIiggAAgKSIIAAAICkiCAAASIoIAgAAkiKCAACApIggAAAgKSIIAABIiggCAACSIoIAAICkiCAAACApIggAAEiKCAIAAJLSLO8BAAAgD1mWRVZenvcYtVT9x0xVBThfUUlJFBUV5T3GFyKCAABITpZlMe/wI6J89uy8R/lMc768e94j1FKyww7RY9ItDTqEHA4HAEBysvLygg+gQlX+7LMFuQdtXdgTBADrSaEealPfCv3QnfrWGA4Foqa+T/wtmpSU5D1GwasqLy/IPVN1IYIAYD1oKIfa1LfG8g+kz9IYDgWipiYlJdGktDTvMdiAHA4HAOuBQ20ar8ZwKBCkzp4gAFjPHGrTODSmQ4EgdSIIANYzh9oAFBaHwwEAAEkRQQAAQFJEEAAAkBQRBAAAJMWFEYDkZFkW5asL7/K2y1dV/sfX5RFFTXOcZu1KmvmQSAAaPhEEJCXLsjjyviPjuQ+fy3uUWrKq5hFxYURE7PWHIVHUZFW+A63FoE6D4rf7/1YIAdCgiSAgKeWrywsygCIiipqsijbbnJX3GJ9p9gezo3x1eZQ2d7lnABouEQQka/ph06OkmQ+w/DzKV5fHXn/YK+8xAKBeiCAgWSXNSuzRAIAEuTocAACQFBEEAAAkRQQBAABJEUEAAEBSRBAAAJAUEQQAACRFBAEAAEkRQQAAQFJEEAAAkBQRBAAAJEUEAQAASRFBAABAUkQQAACQFBEEAAAkRQQBAABJEUEAAEBSRBAAAJAUEQQAACRFBAEAAEkRQQAAQFJEEAAAkBQRBAAAJEUEAQAASRFBAABAUkQQAACQFBEEAAAkRQQBAABJEUEAAEBSRBAAAJAUEQQAACRFBAEAAEkRQQAAQFJEEAAAkBQRBAAAJEUEAQAASRFBAABAUkQQAACQFBEEAAAkRQQBAABJEUEAAEBSRBAAAJAUEQQAACRFBAEAAEkRQQAAQFJEEAAAkJRmeQ8A1bIsYtXyvKeobeXytX9dSJqXRhQV5T0FAECDIIIoDFkWcdN+EWUz857ks/20T94TrF33XSK+O1UIAQB8Dg6HozCsWl74AVTIymYU5l40AIACZE8QheeM1yNalOY9RcOwcnnh7p0CAChQIojC06I0okWrvKcAICdZlkVWXp73GLVU/cdMVQU4X1FJSRQ5LBo+FxEEABSMLMti3uFHRPns2XmP8pnmfHn3vEeopWSHHaLHpFuEEHwOzgkCAApGVl5e8AFUqMqffbYg96BBIbInCAAoSH2f+Fs0KSnJe4yCV1VeXpB7pqCQiSAAoCA1KSmJJqUulAPUv1wPh3vsscfiwAMPjK5du0ZRUVH8+c9/znMcAAAgAblG0LJly2K77baLX//613mOAQAAJCTXw+GGDRsWw4YNy3MEAAAgMQ3qnKCKioqoqKiovr948eIcpwEAABqiBnWJ7EsuuSTatWtXfevevXveIwEAAA1Mg4qgsWPHxqJFi6pvZWVleY8EAAA0MA3qcLji4uIoLi7OewwAAKABa1B7ggAAAL6oXPcELV26NF5//fXq+2+99VY899xz0aFDh9h8881znAwAAGisco2gp59+Ovbee+/q+6effnpERIwaNSomTpyY01QAAEBjlmsE7bXXXpFlWZ4jAAAAiXFOEAAAkBQRBAAAJEUEAQAASRFBAABAUkQQAACQFBEEAAAkRQQBAABJEUEAAEBSRBAAAJAUEQQAACRFBAEAAEkRQQAAQFJEEAAAkBQRBAAAJEUEAQAASRFBAABAUkQQAACQFBEEAAAkRQQBAABJEUEAAEBSRBAAAJAUEQQAACRFBAEAAEkRQQAAQFJEEAAAkBQRBAAAJEUEAQAASRFBAABAUkQQAACQFBEEAAAkRQQBAABJEUEAAEBSRBAAAJAUEQQAACRFBAEAAEkRQQAAQFJEEAAAkBQRBAAAJEUEAQAASRFBAABAUkQQAACQFBEEAAAkRQQBAABJEUEAAEBSRBAAAJAUEQQAACRFBAEAAEkRQQAAQFJEEAAAkBQRBAAAJEUEAQAASRFBAABAUkQQAACQFBEEAAAkRQQBAABJEUEAAEBSRBAAAJAUEQQAACRFBAEAAEkRQQAAQFJEEAAAkBQRBAAAJEUEAQAASRFBAABAUkQQAACQFBEEAAAkRQQBAABJEUEAAEBSRBAAAJAUEQQAACRFBAEAAEkRQQAAQFJEEAAAkBQRBAAAJEUEAQAASRFBAABAUkQQAACQFBEEAAAkRQQBAABJEUEAAEBSRBAAAJAUEQQAACRFBAEAAEkRQQAAQFJEEAAAkBQRBAAAJEUEAQAASRFBAABAUgoign79619Hz549o2XLljF48OD4+9//nvdIAABAI5V7BE2ePDlOP/30OPfcc+PZZ5+N7bbbLvbbb7/44IMP8h4NAABohHKPoJ/97Gdx7LHHxtFHHx39+vWLa6+9NkpLS+Omm27KezQAAKARapbni69cuTKeeeaZGDt2bPVakyZNYujQofHUU0/V2r6ioiIqKiqq7y9atCgiIhYvXrz+h10XK5dFVGT//nrx4ogWlfnO0xD4mdWNn9s6W75qeVSW//vntHjx4ljdfHXOEzUMfm7rrmr58lha+f9/Zk1W+5l9Hn5u687PrG783NZdof/M1jRBlmX/c9ui7PNstZ688847sdlmm8WTTz4Zu+66a/X6j370o3j00Udj5syZNbY/77zz4vzzz9/QYwIAAA1EWVlZdOvW7TO3yXVP0LoaO3ZsnH766dX3q6qq4uOPP46OHTtGUVFRjpMBAAB5yrIslixZEl27dv2f2+YaQRtvvHE0bdo03n///Rrr77//fnTu3LnW9sXFxVFcXFxjbaONNlqfIwIAAA1Eu3btPtd2uV4YoUWLFvGlL30pHnrooeq1qqqqeOihh2ocHgcAAFBfcj8c7vTTT49Ro0bFjjvuGDvvvHNcddVVsWzZsjj66KPzHg0AAGiEco+gb37zm/Hhhx/GOeecE++9915sv/32MXXq1Nh0003zHg0AAGiEcr06HAAAwIaW+4elAgAAbEgiCAAASIoIAgAAkiKCAACApIigevbMM8/E/vvvH23bto02bdrEvvvuG88991zeYxW0o446KoqKij71Nn/+/LxHLEhz5syJb33rW9GtW7coLS2NrbfeOi644IJYvnx53qMVrIqKijjzzDOja9euUVJSEoMHD44HH3ww77EK2tKlS+Pcc8+N/fffPzp06BBFRUUxceLEvMdqcMaPHx9FRUUxYMCAvEcpWNOnT//UvwdmzJiR93gF7dlnn40RI0ZEhw4dorS0NAYMGBC//OUv8x6rYM2aNStOPvnk6N+/f7Rq1So233zzOOyww+K1117Le7SC9c9//jNGjhwZvXv3jtLS0th4441jzz33jHvuuSfv0eos90tkNybPPvts7L777tG9e/c499xzo6qqKq655poYMmRI/P3vf4+tttoq7xEL0vHHHx9Dhw6tsZZlWZxwwgnRs2fP2GyzzXKarHCVlZXFzjvvHO3atYuTTz45OnToEE899VSce+658cwzz8Tdd9+d94gF6aijjoo//vGPceqpp0bfvn1j4sSJMXz48HjkkUdi9913z3u8gvTRRx/FBRdcEJtvvnlst912MX369LxHanDefvvtuPjii6NVq1Z5j9IgjBkzJnbaaacaa3369MlpmsL3wAMPxIEHHhiDBg2KcePGRevWreONN96It99+O+/RCtZll10WTzzxRIwcOTIGDhwY7733Xlx99dWxww47xIwZM/yyYi3mzZsXS5YsiVGjRkXXrl1j+fLl8ac//SlGjBgR1113XRx33HF5j7juMurN8OHDs/bt22cfffRR9do777yTtW7dOjvkkENynKzhefzxx7OIyMaPH5/3KAVp/PjxWURkL774Yo31I488MouI7OOPP85pssI1c+bMLCKyK664onqtvLw822KLLbJdd901x8kK24oVK7J33303y7IsmzVrVhYR2c0335zvUA3MN7/5zWyfffbJhgwZkvXv3z/vcQrWI488kkVEdscdd+Q9SoOxaNGibNNNN80OPvjgrLKyMu9xGownnngiq6ioqLH22muvZcXFxdkRRxyR01QNz+rVq7Ptttsu22qrrfIepU4cDlePHn/88Rg6dGh07Nixeq1Lly4xZMiQuPfee2Pp0qU5Ttew3HrrrVFUVBSHH3543qMUpMWLF0dE1PpQ4S5dukSTJk2iRYsWeYxV0P74xz9G06ZNa/y2qmXLlnHMMcfEU089FWVlZTlOV7iKi4ujc+fOeY/RYD322GPxxz/+Ma666qq8R2lQlixZEqtXr857jIJ36623xvvvvx/jx4+PJk2axLJly6KqqirvsQrebrvtVuvvyb59+0b//v3j5Zdfzmmqhqdp06bRvXv3WLhwYd6j1IkIqkcVFRVRUlJSa720tDRWrlwZL774Yg5TNTyrVq2KP/zhD7HbbrtFz5498x6nIO21114REXHMMcfEc889F2VlZTF58uSYMGFCjBkzxmE3azF79uzYcssto23btjXWd95554gI5+5R7yorK+OUU06J733ve7HtttvmPU6DcfTRR0fbtm2jZcuWsffee8fTTz+d90gFa9q0adG2bduYP39+bLXVVtG6deto27ZtnHjiibFixYq8x2tQsiyL999/PzbeeOO8Ryloy5Yti48++ijeeOON+PnPfx733XdffOUrX8l7rDpxTlA92mqrrWLGjBlRWVkZTZs2jYiIlStXxsyZMyMinOD/Od1///2xYMGCOOKII/IepWDtv//+ceGFF8bFF18cU6ZMqV7/yU9+EhdddFGOkxWud999N7p06VJrfc3aO++8s6FHopG79tprY968eTFt2rS8R2kQWrRoEd/4xjdi+PDhsfHGG8dLL70UP/3pT2OPPfaIJ598MgYNGpT3iAVnzpw5sXr16vj6178exxxzTFxyySUxffr0+NWvfhULFy6M2267Le8RG4xJkybF/Pnz44ILLsh7lIL2gx/8IK677rqIiGjSpEkccsghcfXVV+c8Vd2IoHp00kknxYknnhjHHHNM/OhHP4qqqqq46KKL4t13342IiPLy8pwnbBhuvfXWaN68eRx22GF5j1LQevbsGXvuuWd84xvfiI4dO8Zf/vKXuPjii6Nz585x8skn5z1ewSkvL4/i4uJa6y1btqx+HOrLggUL4pxzzolx48bFJptskvc4DcJuu+0Wu+22W/X9ESNGxKGHHhoDBw6MsWPHxtSpU3OcrjAtXbo0li9fHieccEL11eAOOeSQWLlyZVx33XVxwQUXRN++fXOesvC98sorMXr06Nh1111j1KhReY9T0E499dQ49NBD45133ok//OEPUVlZGStXrsx7rDpxOFw9OuGEE+LHP/5x3HrrrdG/f//Ydttt44033ogf/ehHERHRunXrnCcsfEuXLo2777479ttvvxrnVlHT7bffHscdd1zceOONceyxx8YhhxwSv/nNb2LUqFFx5plnxoIFC/IeseCUlJRERUVFrfU1h4ys7VBWqKuzzz47OnToEKecckreozRoffr0ia9//evxyCOPRGVlZd7jFJw1f259+9vfrrG+5nzap556aoPP1NC89957ccABB0S7du2qzx3l02299dYxdOjQOPLII6vPdz/wwAMjy7K8R1tnIqiejR8/Pt5///14/PHH4x//+EfMmjWr+iTFLbfcMufpCt+f//znWL58uUPh/odrrrkmBg0aFN26dauxPmLEiFi+fHnMnj07p8kKV5cuXar3yv6nNWtdu3bd0CPRSM2ZMyeuv/76GDNmTLzzzjsxd+7cmDt3bqxYsSJWrVoVc+fOjY8//jjvMRuM7t27x8qVK2PZsmV5j1Jw1vy59d8XyenUqVNERHzyyScbfKaGZNGiRTFs2LBYuHBhTJ061d8DdXDooYfGrFmzGuRnLImg9aB9+/ax++67V58IO23atOjWrVtsvfXWOU9W+CZNmhStW7eOESNG5D1KQXv//ffX+lvRVatWRUS4qtJabL/99vHaa69VX1lvjTXn7G2//fY5TEVjNH/+/KiqqooxY8ZEr169qm8zZ86M1157LXr16uW8g3Xw5ptvRsuWLR1NsRZf+tKXIqL2OcdrznF0KOanW7FiRRx44IHx2muvxb333hv9+vXLe6QGac2h5IsWLcp5knUngtazyZMnx6xZs+LUU0+NJk38uD/Lhx9+GNOmTYuDDz44SktL8x6noG255ZYxe/bsWr95ue2226JJkyYxcODAnCYrXIceemhUVlbG9ddfX71WUVERN998cwwePDi6d++e43Q0JgMGDIi77rqr1q1///6x+eabx1133RXHHHNM3mMWnA8//LDW2vPPPx9TpkyJfffd19+ha7Hm3Nnf/OY3NdZvvPHGaNasWfWVRKmpsrIyvvnNb8ZTTz0Vd9xxR+y66655j1TwPvjgg1prq1atit/97ndRUlLSICPShRHq0WOPPRYXXHBB7LvvvtGxY8eYMWNG3HzzzbH//vvH97///bzHK3iTJ0+O1atXOxTuc/jhD38Y9913X+yxxx5x8sknR8eOHePee++N++67L773ve/Zpb8WgwcPjpEjR8bYsWPjgw8+iD59+sRvf/vbmDt3bq1/QFDT1VdfHQsXLqz+7fI999xT/Wn0p5xySrRr1y7P8QrOxhtvHAcddFCt9TWfFbS2x4j45je/GSUlJbHbbrtFp06d4qWXXorrr78+SktL49JLL817vII0aNCg+O53vxs33XRTrF69OoYMGRLTp0+PO+64I8aOHevvgk/xgx/8IKZMmRIHHnhgfPzxx3HLLbfUePw73/lOTpMVruOPPz4WL14ce+65Z2y22Wbx3nvvxaRJk+KVV16JK6+8smHuqc3701obk9dffz3bd999s4033jgrLi7Ott566+ySSy6p9anErN0uu+ySderUKVu9enXeozQIM2fOzIYNG5Z17tw5a968ebbllltm48ePz1atWpX3aAWrvLw8O+OMM7LOnTtnxcXF2U477ZRNnTo177EKXo8ePbKIWOvtrbfeynu8BmPIkCFZ//798x6jYP3iF7/Idt5556xDhw5Zs2bNsi5dumTf+c53sjlz5uQ9WkFbuXJldt5552U9evTImjdvnvXp0yf7+c9/nvdYBW3IkCGf+meafxqv3W233ZYNHTo023TTTbNmzZpl7du3z4YOHZrdfffdeY9WZ0VZ1gAv5wAAAFBHDrAFAACSIoIAAICkiCAAACApIggAAEiKCAIAAJIiggAAgKSIIAAAICkiCAAASIoIAgAAkiKCAACApIggADaooqKiz7ydd955eY8IQCPXLO8BAEjLu+++W/315MmT45xzzolXX321eq1169Z5jAVAQuwJAmCD6ty5c/WtXbt2UVRUVGPt9ttvj2222SZatmwZW2+9dVxzzTU1vv/MM8+MLbfcMkpLS6N3794xbty4WLVqVfXj5513Xmy//fZx0003xeabbx6tW7eOk046KSorK+Pyyy+Pzp07R6dOnWL8+PHV35NlWZx33nmx+eabR3FxcXTt2jXGjBmzwX4mAGxY9gQBUDAmTZoU55xzTlx99dUxaNCgmD17dhx77LHRqlWrGDVqVEREtGnTJiZOnBhdu3aNF154IY499tho06ZN/OhHP6p+njfeeCPuu+++mDp1arzxxhtx6KGHxptvvhlbbrllPProo/Hkk0/Gd7/73Rg6dGgMHjw4/vSnP8XPf/7zuP3226N///7x3nvvxfPPP5/XjwGA9UwEAVAwzj333LjyyivjkEMOiYiIXr16xUsvvRTXXXdddQSdffbZ1dv37NkzzjjjjLj99ttrRFBVVVXcdNNN0aZNm+jXr1/svffe8eqrr8Zf//rXaNKkSWy11VZx2WWXxSOPPBKDBw+Of/3rX9G5c+cYOnRoNG/ePDbffPPYeeedN+ybB2CDEUEAFIRly5bFG2+8Ecccc0wce+yx1eurV6+Odu3aVd+fPHly/PKXv4w33ngjli5dGqtXr462bdvWeK6ePXtGmzZtqu9vuumm0bRp02jSpEmNtQ8++CAiIkaOHBlXXXVV9O7dO/bff/8YPnx4HHjggdGsmb8mARoj5wQBUBCWLl0aERE33HBDPPfcc9W3F198MWbMmBEREU899VQcccQRMXz48Lj33ntj9uzZ8ZOf/CRWrlxZ47maN29e435RUdFa16qqqiIionv37vHqq6/GNddcEyUlJXHSSSfFnnvuWeNcIwAaD7/iAqAgbLrpptG1a9d4880344gjjljrNk8++WT06NEjfvKTn1SvzZs3r15ev6SkJA488MA48MADY/To0bH11lvHCy+8EDvssEO9PD8AhUMEAVAwzj///BgzZky0a9cu9t9//6ioqIinn346Pvnkkzj99NOjb9++8a9//Stuv/322GmnneIvf/lL3HXXXV/4dSdOnBiVlZUxePDgKC0tjVtuuSVKSkqiR48e9fCuACg0DocDoGB873vfixtvvDFuvvnm2HbbbWPIkCExceLE6NWrV0REjBgxIk477bQ4+eSTY/vtt48nn3wyxo0b94Vfd6ONNoobbrghvvzlL8fAgQNj2rRpcc8990THjh2/8HMDUHiKsizL8h4CAABgQ7EnCAAASIoIAgAAkiKCAACApIggAAAgKSIIAABIiggCAACSIoIAAICkiCAAACApIggAAEiKCAIAAJIiggAAgKT8P1Y/NuLF2dmpAAAAAElFTkSuQmCC\n"
1298 |           },
1299 |           "metadata": {}
1300 |         }
1301 |       ]
1302 |     },
1303 |     {
1304 |       "cell_type": "code",
1305 |       "source": [
1306 |         "from sklearn.cluster import AgglomerativeClustering\n",
1307 |         "cluster = AgglomerativeClustering(n_clusters=6, linkage='average')\n",
1308 |         "clt1=cluster.fit_predict(X)"
1309 |       ],
1310 |       "metadata": {
1311 |         "id": "Jz7qSMaZlMc9"
1312 |       },
1313 |       "execution_count": 30,
1314 |       "outputs": []
1315 |     },
1316 |     {
1317 |       "cell_type": "code",
1318 |       "source": [
1319 |         "clt1"
1320 |       ],
1321 |       "metadata": {
1322 |         "colab": {
1323 |           "base_uri": "https://localhost:8080/"
1324 |         },
1325 |         "id": "P2Q0D3zCldNo",
1326 |         "outputId": "4efdaed4-0645-4e1f-ed9e-2c880ab99041"
1327 |       },
1328 |       "execution_count": 31,
1329 |       "outputs": [
1330 |         {
1331 |           "output_type": "execute_result",
1332 |           "data": {
1333 |             "text/plain": [
1334 |               "array([2, 2, 5, 4, 0, 0, 0, 1, 1, 3])"
1335 |             ]
1336 |           },
1337 |           "metadata": {},
1338 |           "execution_count": 31
1339 |         }
1340 |       ]
1341 |     },
1342 |     {
1343 |       "cell_type": "code",
1344 |       "source": [
1345 |         "plt.figure(figsize=(5, 4))\n",
1346 |         "plt.scatter(X.iloc[:,0], X.iloc[:,3], c=cluster.labels_)"
1347 |       ],
1348 |       "metadata": {
1349 |         "id": "8en_MPEPlz7z",
1350 |         "outputId": "543dc0fd-96c9-404c-97a3-e0a3b32bad56",
1351 |         "colab": {
1352 |           "base_uri": "https://localhost:8080/",
1353 |           "height": 385
1354 |         }
1355 |       },
1356 |       "execution_count": 34,
1357 |       "outputs": [
1358 |         {
1359 |           "output_type": "execute_result",
1360 |           "data": {
1361 |             "text/plain": [
1362 |               "<matplotlib.collections.PathCollection at 0x7e367e7f6260>"
1363 |             ]
1364 |           },
1365 |           "metadata": {},
1366 |           "execution_count": 34
1367 |         },
1368 |         {
1369 |           "output_type": "display_data",
1370 |           "data": {
1371 |             "text/plain": [
1372 |               "<Figure size 500x400 with 1 Axes>"
1373 |             ],
1374 |             "image/png": 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\n"
1375 |           },
1376 |           "metadata": {}
1377 |         }
1378 |       ]
1379 |     }
1380 |   ]
1381 | }


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