Bank_Customer_Churn.ipynb

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      "provenance": [],
      "toc_visible": true
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hxtz52kvlwzT",
        "outputId": "ab7bc3c9-dd67-429c-ce04-01e1c84a07eb"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Import Libraries**"
      ],
      "metadata": {
        "id": "4xkPxyvJmbO8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from imblearn.over_sampling import SMOTE\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report"
      ],
      "metadata": {
        "id": "SxL9QMj3mRl8"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Load Data**"
      ],
      "metadata": {
        "id": "mrn2wVLWmd7k"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data_path = '/content/drive/MyDrive/Dataset Midterm/Customer-Churn-Records.csv'"
      ],
      "metadata": {
        "id": "Wkn_hlM0oDuw"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(data_path)\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 505
        },
        "id": "TfNV0GGTovhh",
        "outputId": "a29f66c6-f920-4aff-cde0-339730fe0e85"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      RowNumber  CustomerId    Surname  CreditScore Geography  Gender  Age  \\\n",
              "0             1    15634602   Hargrave          619    France  Female   42   \n",
              "1             2    15647311       Hill          608     Spain  Female   41   \n",
              "2             3    15619304       Onio          502    France  Female   42   \n",
              "3             4    15701354       Boni          699    France  Female   39   \n",
              "4             5    15737888   Mitchell          850     Spain  Female   43   \n",
              "...         ...         ...        ...          ...       ...     ...  ...   \n",
              "9995       9996    15606229   Obijiaku          771    France    Male   39   \n",
              "9996       9997    15569892  Johnstone          516    France    Male   35   \n",
              "9997       9998    15584532        Liu          709    France  Female   36   \n",
              "9998       9999    15682355  Sabbatini          772   Germany    Male   42   \n",
              "9999      10000    15628319     Walker          792    France  Female   28   \n",
              "\n",
              "      Tenure    Balance  NumOfProducts  HasCrCard  IsActiveMember  \\\n",
              "0          2       0.00              1          1               1   \n",
              "1          1   83807.86              1          0               1   \n",
              "2          8  159660.80              3          1               0   \n",
              "3          1       0.00              2          0               0   \n",
              "4          2  125510.82              1          1               1   \n",
              "...      ...        ...            ...        ...             ...   \n",
              "9995       5       0.00              2          1               0   \n",
              "9996      10   57369.61              1          1               1   \n",
              "9997       7       0.00              1          0               1   \n",
              "9998       3   75075.31              2          1               0   \n",
              "9999       4  130142.79              1          1               0   \n",
              "\n",
              "      EstimatedSalary  Exited  Complain  Satisfaction Score Card Type  \\\n",
              "0           101348.88       1         1                   2   DIAMOND   \n",
              "1           112542.58       0         1                   3   DIAMOND   \n",
              "2           113931.57       1         1                   3   DIAMOND   \n",
              "3            93826.63       0         0                   5      GOLD   \n",
              "4            79084.10       0         0                   5      GOLD   \n",
              "...               ...     ...       ...                 ...       ...   \n",
              "9995         96270.64       0         0                   1   DIAMOND   \n",
              "9996        101699.77       0         0                   5  PLATINUM   \n",
              "9997         42085.58       1         1                   3    SILVER   \n",
              "9998         92888.52       1         1                   2      GOLD   \n",
              "9999         38190.78       0         0                   3   DIAMOND   \n",
              "\n",
              "      Point Earned  \n",
              "0              464  \n",
              "1              456  \n",
              "2              377  \n",
              "3              350  \n",
              "4              425  \n",
              "...            ...  \n",
              "9995           300  \n",
              "9996           771  \n",
              "9997           564  \n",
              "9998           339  \n",
              "9999           911  \n",
              "\n",
              "[10000 rows x 18 columns]"
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-22f6e9e0-cdb2-4f50-8f97-8125943cd966 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-22f6e9e0-cdb2-4f50-8f97-8125943cd966');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8wSQxMSTo4G7",
        "outputId": "a623a9c5-93e9-41b2-e4cc-750ddf0e2404"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 10000 entries, 0 to 9999\n",
            "Data columns (total 18 columns):\n",
            " #   Column              Non-Null Count  Dtype  \n",
            "---  ------              --------------  -----  \n",
            " 0   RowNumber           10000 non-null  int64  \n",
            " 1   CustomerId          10000 non-null  int64  \n",
            " 2   Surname             10000 non-null  object \n",
            " 3   CreditScore         10000 non-null  int64  \n",
            " 4   Geography           10000 non-null  object \n",
            " 5   Gender              10000 non-null  object \n",
            " 6   Age                 10000 non-null  int64  \n",
            " 7   Tenure              10000 non-null  int64  \n",
            " 8   Balance             10000 non-null  float64\n",
            " 9   NumOfProducts       10000 non-null  int64  \n",
            " 10  HasCrCard           10000 non-null  int64  \n",
            " 11  IsActiveMember      10000 non-null  int64  \n",
            " 12  EstimatedSalary     10000 non-null  float64\n",
            " 13  Exited              10000 non-null  int64  \n",
            " 14  Complain            10000 non-null  int64  \n",
            " 15  Satisfaction Score  10000 non-null  int64  \n",
            " 16  Card Type           10000 non-null  object \n",
            " 17  Point Earned        10000 non-null  int64  \n",
            "dtypes: float64(2), int64(12), object(4)\n",
            "memory usage: 1.4+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for col in df.columns:\n",
        "  print('%s:%s'%(col, df[col].unique()))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_ez8yGsvpQ6A",
        "outputId": "24a0a326-02d5-4eb4-b4e3-991447c27b67"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "RowNumber:[    1     2     3 ...  9998  9999 10000]\n",
            "CustomerId:[15634602 15647311 15619304 ... 15584532 15682355 15628319]\n",
            "Surname:['Hargrave' 'Hill' 'Onio' ... 'Kashiwagi' 'Aldridge' 'Burbidge']\n",
            "CreditScore:[619 608 502 699 850 645 822 376 501 684 528 497 476 549 635 616 653 587\n",
            " 726 732 636 510 669 846 577 756 571 574 411 591 533 553 520 722 475 490\n",
            " 804 582 472 465 556 834 660 776 829 637 550 698 585 788 655 601 656 725\n",
            " 511 614 742 687 555 603 751 581 735 661 675 738 813 657 604 519 664 678\n",
            " 757 416 665 777 543 506 493 652 750 729 646 647 808 524 769 730 515 773\n",
            " 814 710 413 623 670 622 785 605 479 685 538 562 721 628 668 828 674 625\n",
            " 432 770 758 795 686 789 589 461 584 579 663 682 793 691 485 650 754 535\n",
            " 716 539 706 586 631 717 800 683 704 615 667 484 480 578 512 606 597 778\n",
            " 514 525 715 580 807 521 759 516 711 618 643 671 689 620 676 572 695 592\n",
            " 567 694 547 594 673 610 767 763 712 703 662 659 523 772 545 634 739 771\n",
            " 681 544 696 766 727 693 557 531 498 651 791 733 811 707 714 782 775 799\n",
            " 602 744 588 747 583 627 731 629 438 642 806 474 559 429 680 749 734 644\n",
            " 626 649 805 718 840 630 654 762 568 613 522 737 648 443 640 540 460 593\n",
            " 801 611 802 745 483 690 492 709 705 560 752 701 537 487 596 702 486 724\n",
            " 548 464 790 534 748 494 590 468 509 818 816 536 753 774 621 569 658 798\n",
            " 641 542 692 639 765 570 638 599 632 779 527 564 833 504 842 508 417 598\n",
            " 741 607 761 848 546 439 755 760 526 713 700 666 566 495 688 612 477 427\n",
            " 839 819 720 459 503 624 529 563 482 796 445 746 786 554 672 787 499 844\n",
            " 450 815 838 803 736 633 600 679 517 792 743 488 421 841 708 507 505 456\n",
            " 435 561 518 565 728 784 552 609 764 697 723 551 444 719 496 541 830 812\n",
            " 677 420 595 617 809 500 826 434 513 478 797 363 399 463 780 452 575 837\n",
            " 794 824 428 823 781 849 489 431 457 768 831 359 820 573 576 558 817 449\n",
            " 440 415 821 530 350 446 425 740 481 783 358 845 451 458 469 423 404 836\n",
            " 473 835 466 491 351 827 843 365 532 414 453 471 401 810 832 470 447 422\n",
            " 825 430 436 426 408 847 418 437 410 454 407 455 462 386 405 383 395 467\n",
            " 433 442 424 448 441 367 412 382 373 419]\n",
            "Geography:['France' 'Spain' 'Germany']\n",
            "Gender:['Female' 'Male']\n",
            "Age:[42 41 39 43 44 50 29 27 31 24 34 25 35 45 58 32 38 46 36 33 40 51 61 49\n",
            " 37 19 66 56 26 21 55 75 22 30 28 65 48 52 57 73 47 54 72 20 67 79 62 53\n",
            " 80 59 68 23 60 70 63 64 18 82 69 74 71 76 77 88 85 84 78 81 92 83]\n",
            "Tenure:[ 2  1  8  7  4  6  3 10  5  9  0]\n",
            "Balance:[     0.    83807.86 159660.8  ...  57369.61  75075.31 130142.79]\n",
            "NumOfProducts:[1 3 2 4]\n",
            "HasCrCard:[1 0]\n",
            "IsActiveMember:[1 0]\n",
            "EstimatedSalary:[101348.88 112542.58 113931.57 ...  42085.58  92888.52  38190.78]\n",
            "Exited:[1 0]\n",
            "Complain:[1 0]\n",
            "Satisfaction Score:[2 3 5 4 1]\n",
            "Card Type:['DIAMOND' 'GOLD' 'SILVER' 'PLATINUM']\n",
            "Point Earned:[ 464  456  377  350  425  484  206  282  251  342  264  249  119  549\n",
            "  318  308  163  544  732  477  568  336  887  665  225  659  236  448\n",
            "  499  343  298  628  611  922  276  877  471  994  944  419  545  828\n",
            "  789  567  884  296  779  730  960  268  829  982  657  221  406  954\n",
            "  823  769  481  903  882  379  469  683  770  985  725  259  979  988\n",
            "  833  527  220  983  582  281  798  917  255  511  498  651  852  807\n",
            "  470  752  559  375  411  722  231  488  965  289  449  520  234  458\n",
            "  915  299  838  786  509  328  347  534  709  749  541  736  529  594\n",
            "  462  429  370  510  638  718  437  485  410  666  595  578  831  497\n",
            "  374  814  247  912  883  402  764  608  652  800  697  427  620  701\n",
            "  953  635  250  662  699  855  388  612  801  561  643  818  775  872\n",
            "  435  415  430  929  681  810  739  280  991  482  423  260  642  570\n",
            "  790  727  909  799  861  512  478  615  321  726  516  639  244  292\n",
            "  245  848  974  958  629  310  621  924  359  811  583  959  901  729\n",
            "  756  873  865  690  535  580  930  943  248  763  619  815  566  774\n",
            "  695  902  532  895  333  866  671  670  768  565  483  926  932  899\n",
            "  467  717  716  650  693  759  946  465  600  306  747  424  913  751\n",
            "  219  587  278  526  632  301  806  563  669  517  673  809  460  777\n",
            "  837  737  486  252  275  361  352  337  597  381  668  495  378  731\n",
            "  230  919  641  457  849  720  317  269  391  373  237  645  624  224\n",
            "  723  973  463  253  241  537  332  390  240  978  366  356  907  687\n",
            "  703  579  793  291  951  952  616  706  552  351  906  886  431  362\n",
            "  309  513  302  858  543  939  797  743  767  246  480  708  827  468\n",
            "  523  560  530  367  393  860  576  396  874  680  319  781  918  340\n",
            "  625  923  859  794  702  816  404  977  426  368  261  614  765  961\n",
            "  345  330  863  371  937  956  496  927  550  420  760  500  850  678\n",
            "  547  238  314  257  540  331  813  279  685  898  466  757  453  694\n",
            "  304  633  989  936  689  267  613  585  452  358  771  344  405  408\n",
            "  503  881  758  363  969  403  980  283  354  938  753  742  844  776\n",
            "  812  647  839  847  934  656  808  525  746  258  817  418  380  955\n",
            "  274  455  451  603  660  577  824  445  589  908  395  878  778  229\n",
            "  834  265  819  992  444  325  691  921  335  661  562  888  300  869\n",
            "  910  515  592  475  307  766  564  677  288  518  653  606  792  326\n",
            "  522  748  602  459  588  494  256  664  821  287  623  963  705  519\n",
            "  773  854  254  935  914  539  277  735  369  740  461  329  970  925\n",
            "  446  514  711  804  987  348  686  341  692  417  506  242  323  719\n",
            "  596  372  599  558  796  421  738  780  454  365  654  933  573  721\n",
            "  434  610  450  802  631  805  476  897  273  433  487  233  803  975\n",
            "  397  948  684  715  400  263  667  997  581  795  413  788  885  297\n",
            "  553  528  835  479  443  676  893  409  324  822  447  867  473  830\n",
            "  542  383  508  376  875  644  704  928  504  312  784  672  846  945\n",
            "  728  981  609  360  555  707  876  783  436  663  714  505  439  894\n",
            "  320  223  962  836  507  655  696  266  845  950  322  841  649  636\n",
            "  490  474  546  295  842  357  791  840  679  892  900  990  942  416\n",
            "  826  949  744  243  688  398  916  572 1000  574  591  674  353  870\n",
            "  940  993  843  998  472  905  710  533  785  334  598  327  232  586\n",
            "  226  305  984  634  593  389  538  995  618  724  584  889  712  303\n",
            "  733  920  761  285  964  311  911  501  851  972  440  346  239  750\n",
            "  493  284  741  601  338  658  871  931  999  271  502  700  536  626\n",
            "  957  428  966  968  313  227  637  856  432  698  896  986  412  385\n",
            "  976  590  524  407  825  904  575  734  548  489  868  879  675  648\n",
            "  853  640  832  622  384  627  441  521  967  607  339  880  364  682\n",
            "  556  401  762  864  862  947  604  293  890  442  891  394  569  316\n",
            "  222  782  438  630  387  551  315  386  755  557  349  605  754  772\n",
            "  617  262  492  399  272  382  554  971  531  857  286  491  290  941\n",
            "  270  294  787  422  235  355  646  571  392  820  414  745  713  228\n",
            "  996]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **EDA**"
      ],
      "metadata": {
        "id": "ffuJpal6sZAG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df2 = df.copy()"
      ],
      "metadata": {
        "id": "KO7637vUBidS"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df2['Exited'] = df['Exited'].replace({0:\"No\", 1:\"Yes\"})"
      ],
      "metadata": {
        "id": "Lb2gd9uKBn_b"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "exit_count = df2['Exited'].value_counts()\n",
        "exit_count"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x0ey3R12sW31",
        "outputId": "8ba80a18-01a9-43a2-b604-aabae020e97a"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "No     7962\n",
              "Yes    2038\n",
              "Name: Exited, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.pie(exit_count, labels=exit_count.index, colors=['#4477CE','#F7D060'], explode=[0,0.1], autopct = '%1.2f%%', startangle=90)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        },
        "id": "OzLcXWMXvNJp",
        "outputId": "9718d1f0-80da-4417-fbf7-53a4ce9aefdb"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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ePgKFw5MxriAFL//f2ahvdmLb3r535Va7B29+VIH5907BeWcMwqTCNLz8yHRs3NGKkp1HPyNg7qxhuODMbAzNiUfh8GTMv3cKbA4vdld0AgDau9yo3G/DvT8di7EjUzA8LwGP/up0uNw+7K3sBgCUV3fj6ouHYkReIm69aiTKqvteML1eBXf9cQP+9uA0GAw6enlReuBr/kB0FSGho+/awFm+pQOrd3SLLoMinM3R96YkOdECANhW2gGvT8EFUwcF7lMwNAlDBsWiZMeJTaX0eP14bdE+JMaZMHZkCgAgNcmCkfkJePeTSjh7vPD5FLz6QTkyUqIwsTAVADC2IAWrN7XA51Ow/KtGjBmZDAD4y5u78IPJWTi9KC1oX3e48Heth+KqFl3GgDOKLiDcNXf04vmPG0SXQRFOUVT85qlNmDYhA0XD+16AW9p7YDbJSEqwHHbfjJTo444rfLq2HjfdvwYutw9ZaTH4z4szkZYcBQCQJAlLX5qJa+9ZifSz3oEsS0hPicKSF2Yg+eC17rtlLO760wYUzl6EvOw4vPiH6aiss2HBJ1VY/fYs3PnHr/DFhiZMKkzDCw+dhcR48wA8K6Gmwtv4DszDfwdJkkQXM2DYUjgGRVHxxL/q4OrlQAKJdffjG7Gnsgtvzz83KI937pQslLx/GVa9NQvF0wfjx/NWo7WzL0hUVcU9j29EenIUvnj9EqxbcCkuOy8XV921As1tfWMKifFmvPXEudj336ux/LVLMPq0JNzxx6/w2D2TsfDTatQ0OLBz8ZWIiTLgsZd3BKXmcKC6quHv+lJ0GQOKoXAMSze1Y28dN7ojse5+fCM+XVuPz1+9GDmZsYHbs9Ki4fEq6LYdvjtva2cPMlOjj/mYsdEmnJabgKnjMvDiw9NhNEh466MKAMDqTc34dG0D3p5/Ls6amImJo1Px7IPTEG0xYMEnlUd8vLcXVyAx3ozZ5+di7dctmH1+LkwmGVfOyMe6r/V1LK2v+QOo/mO3xLSMoXAUXXYvXvuMR2qSOKqq4u7HN+Ljlfvx2csXI39w/GH/P3F0KkxGGas2Hfo53VdrRX2zE1PHp5/UtRQV6PX0nQXicvf9LcuHd5HIsgT1CI3mtk43Hnt5B/5y/9S+x1JUeH19d/T6FPj1NmXPZ4O/Y6XoKgYMQ+Eo/vGfRjjdPDCHxLn7sY1YuLQKbz1+DuJijWhpd6Gl3YUed99CqsR4M26eMwL3P70ZazY3Y+vedvzsoS8xdVw6po7LCDzO+Cs+xJKVdQAAZ48XD/1tC0p2tqKuyYGte9tx+x++RFOrE1fOyAcATB2XjuQEM277/ZfYWd6JijorHvjLZtQ2OnDxD3K+V+dvntqEX91UhMEHWzFnjs/Ae0urUFbdjdcX7cO0CZkD/EyFnq9tGVRFn7MROdB8BFsqbNwOm4R7+d/lAIDi2z47/PZHpuPGy0cAAJ68bwpkScJ1965Cr0fBRWdl49nfnnnY/ffV2mCz972AGWQJ5bVWLLi3Ch3dbqQkWTC5KA1fvD4LhQcHsNOSo7Dk7zPw8PNbccnPPofXp2D0aUn49zMXYFxBymGPvfyrRlTV2/D6n34QuO0Xc0dj694OnHPjUkwek4bf/nx8cJ+YcOCzwd+xGsb0YtGVBJ2kqip3dfsWj1fB/3umjKeoRZgRg6Pxwp2jjnkff+d6eOtfDVFFFPZMybCMehKSrK/31uw++o53Vx1gIBDR8Xm74O9aJ7qKoGMofEt9mxv/WsNDc4joxPhb/wtV1dfYI0PhW/72UT28PCOBiE6Q6mmD0rVBdBlBxVA4aPnWTmyvdogug4g0xte6FOqR5upqFEMBQE+vHy9/2ii6DCLSILW3BUr3ZtFlBA1DAcCH69vQ7YicQzSIKLh8rf+BXiZyRnwoOHp8+GDt0bcZJiI6HtXdAMWuj0OFIj4UPljXCgdXLhNRP/k714guISgiOhSsTh8+/PLE9p0nIjoWxbYdqlf757dHdCgsXH0APR79zBogIoFUP/xd60VX0W8RGwodNi8+2chWAhEFj79T+yucIzYU3l3Vgl6vPmYLEFF4UHuboTgrRJfRLxEZCq3dHvx3U4foMohIh/wda0WX0C8RGQr//KKF21kQ0YDwWzdr+mS2iAuF5o5eLN/KVgIRDRClF/7uEtFVnLKIC4UlG9qgt9MBiSi8aHnAOaJCwe1R8PnXnaLLICKdU13VUHrqRZdxSiIqFFZu7+TqZSIKCa2uWYioUFiyoV10CUQUIRTrVtElnJKICYVdNQ5UN2t3RgARaYvqadNkF1LEhMLHG7h6mYhCS4uthYgIhQ6bF1/usYoug4gijN/GUAhLS0va4eNiNSIKMbVnP1SPttZFGUUXMNB8fhVLN3GAmYhCRzJnQE6YADlhPGBKEl3OSdF9KHy5uxuddh61SUQDyQApdjgMCeP7wiBqkOiCTpnuQ+HTzWwlENEAMMRCjh8DQ8IEyPFjIRljRVcUFLoOhU67FzuqHKLLICKdkCxZkBMmwJAwHlLsCEiSQXRJQafrUFizswsKx5eJ6FRJBsixIyF/0y1kyRRd0YDTdSis2tElugQi0hpDHOSEsQe7hcZAMsSIriikdBsKLZ29KN3vEl0GEWmAZMn+VrfQcEhSRMzWPyLdhsLqnd2iSyCicCUZIccWBKaNypZ00RWFDd2Gwpe7u0WXQEThxBgPQ/z4vhCIL4JkiBZdUVjSZSi0WT3Y18iuI6JIJ0UNgZwwHoaECZBihkZ0t9CJ0mUorN9thcpZR0SRRzJCjht9aHzAnCq6Is3RZyjs6RZdAhGFijERhoRxfeMDcUWQDJaQXdrrU2Ay6qv1obtQsDl92FnLBWtEeiZF5x5qDUQPhSRJIbmuX1Gxt86JjWVWbCy1oSAnBvOuyQvJtUNFd6HwdYUNiiK6CiIKKsnc1y2UeHB8wJQcsks73X5sLrdhY6kVm/fZYHMdOtLX7vJBVdWQhVIo6C4UdlSzlUCkC8akQxvMxRdCks0hu3RDuxsbS20oKbViV60D/qO80exy+FDZ1IMRg/WzwE13obCToUCkURKk6LxDQRCTH7Ir+/0qdtc6sLGsr0XQ0N57wp+7qdzGUAhXnXbvSX0ziUgw2Qw5rujgtNHxkEJ49oDd5cPmfTZsKLViyz477D3+43/SEWwut+GGC7KCXJ04ugoFthKINMCUcqg1EDcakmwK2aXrWt0oKbViY6kVe/Y7gzL+WFrvhNPtR2yUPnZM1VUocDyBKBxJkGKG9m0wlzAecnRuyK7s86vYWePAxlIrSsqsaOrwBP0aigKUN7gwaXh80B9bBF2Fws4ahgJRWJCjIMcf7BaKHw/JlBCyS1udPmwqs2JjmQ1f77PB1Tvw0xHL6p0MhXDT5fBif6tbdBlEEUsyp0GOHw85cQLk2FGQ5NC9vNS09KCkzIoNpTaU7XeG/ByVMh3tyKybUNjFVgJRiEmQYg6eS5w4AXLU4JBd2etTsKP6YLdQuQ0tncHvFjoZZfVOodcPJt2EAgeZiUJAjj50LnHCWEjG0HWZdDm8KCnrWzuwpcKOHk/4rFLtcvhwoMuDzOTQraUYKLoJBQ4yEw0MyZxx6NyBuJGQpNC9bFQ1uQJrB/Y1uML6eN2yeidDIVw43X7UcTyBKEhkSLEjDk0bjRoUsit7fAq2V9n7VhOXWdHa7Q3ZtfurdL8T544L3fYbA0UXoVB7oIdbZRP1hyEGcvzYviCIHwfJGBuyS3favdhY2rfB3LYqO9xh1C10Msrq9THYrItQqDvAVgLRyZIsWYcOoIkdAUkK3eKrikZXXxCU2VDR6NLFm7rKJhf8fhUGg7Y3x2MoEEUMA+S4Ed86lzh0WzP0ehVsrbCjpMyKkjIb2m3a6RY6Ub1eFdUt2t8cTx+hwPEEoiMzxEJOGHfwbOKxkAyhe8Fqt3oCg8Tbq+zo9eqgOXAcZfVOhkI4YEuB6BDJkv2tbqHhITuXWFVV7GtwYcPBQeLKpp6QXDec7G/V/oacmg8Fp9uvy6Yo0QmTjJBjR36rWygjZJfu8fixtaJvttCmcis67b6QXTscNXcyFIRj1xFFJGM85PhxB2cLjYFkiA7ZpVu7PdhQakVJqRXbqx3w+vTfLXSimgWvrA4G7YcCu44oQkhROYe6hWKGhaxbSFFUlNW7Dp5LbEVNC3/njqals1fzx3PqIBQir9+SIoRkhBw36tAB9ea0kF3a1evHln12bDh4LnG3I7K7hU6Ux6eiw+ZFWqJ2VzZrPhS4MyrpijHh4Eri8ZDjiiAZokJ26ebO3sDagV3VDnj97BY6FU0dHoaCSBxTIK2TonIhJx5sDUQPDVnXg19RUbrfGVhNzN+l4Gju7MW4YXGiyzhlmg4FVVU584i0RzJBjis8dC6xOSVkl3a6/dhcbsPGMis2l9tgc53aucR0dFqfgaTpULC5/EE5Y5VowBmTDm0wFz8akmwJ2aUb2w92C5VasbvOCR+7hQaU1mcgaToUuh1sJVC4kiBF5wWCQIrOC123kF/F7jpHYKfR+jZtv3PVmqYObT/fGg8FzoigMCKbD3YLHRwfMCWF7NJ2lw+b99mwsbTvXGJ7D7uFRGFLQaBuJ0OBBDOlfGu2UCEk2RSyS+9vdQfOJd5b54CfXalhwer0weNTYDaGZh1JsGk6FLrYUqCQkyDFDD00PhCdG7Ir+/wqdtU4AtNGtd5NoWcutx/mOIZCyLH7iEJJji+EpfCvkEyJIbumzelDSXnf2MDX++xwutktpAWuXgVJGp2Vqu1QcHKgmUJHMoXmqMXaAz3YWNq35XRpvZMz7DRIy+Gt7VBgS4F0wOtTsKPagZKyvvUDLRofqKS+7iOtYigQCdDt8PaFQKkVWyvtcPWyOaAnTg1/P7UdCpx9RBpS3dzTt+V0mRXl9S4oXEOmW+w+EsTBudgUxjw+Bdur7IFFZK3dHAOLFK5e7b42aToUvD7tNtFInzrth3cLuT38GY1EHFMQxM/2N4WBikYXNpZaUVJmw75GF1T+WEY8dh8Jwv3eSYRer4JtlfZAEHCnXvouLU8c0GwoqKrK+dsUMu1WDzaW2VBSasW2Kjt6vXxDQkfHMQUBuP0vDSRVVbGvwYWNB8cHKpt47CudOC13IWo2FLT8pFN4cnsUbK2wYUOpDZvKrei0c8oznZoQ7ZI+IDQbCtDwk07hp+6AG1f93054fHy3Qf0nazgVNBsK2n3KKRwxDCiYZA2/QGlzb1doO4mJSN9CdcreQNBsKLCpQEThyqDdV1bthoKWm2dEpG9mk2ZfWrUbCpIkwWJiMhBR+LEwFMSIj9bsODkR6ZjZqN03rNoOhRiD6BKIiL6HLQVB2FIgonDEMQVB2FIgonAUY9HuS6t2KwdbCkQUnlLiTaJLOGWaDoWEWLYUiCj8pCYwFIRgS4GIwlEqWwpiJHBMgYjCjMkoISFWu29YNR0KbCkQUbjRcisB0HoosKVARGEmRcPjCYDGQyEpji0FIgovWh5kBjQeCtkpFm6MR0Rhhd1HAplNMtITzaLLICIKSE3Qdg+GpkMBAHLSLaJLICIKYEtBsJw0hgIRhY9Ujfde6CAUokSXQEQUkKvx3gvth4LGvwFEpB8JMQaksaUgFruPiChcDM2KFl1Cv2k+FDKSzJo+5YiI9IOhEAZkWUJ2KlsLRCTeaYMYCmFhSDoHm4lIvKEMhfDAwWYiEk2WgfxM7b9B1UUojBwcI7oEIopwg1MtsGj4bOZvaP8rAFCUHyu6BCKKcMN00HUE6CQUkuNMHGwmIqH0MPMI0EkoAEBRHlsLRCSOHmYeAToKhTHsQiIigQpy9DG2qZtQYEuBiETJz4xCssZ3R/2GbkIhNyMK8dE8npOIQm/i8HjRJQSNbkJBkiQUsrVARAJMPI2hEJbYhUREoWaQgXHD4kSXETS6CoUx+fr5xhCRNhTkxCI2Sj9d17oKhYKcGJgM3DGViEJn4nB9vRnVVSiYTTJG57ILiYhCR0/jCYDOQgEAzhydILoEIooQFpOE0Toby9RdKEwbnSi6BCKKEEX5cTAb9fUyqq+vBkBOehSP6CSikJiko/UJ39BdKADAmWwtEFEITB7BUNAEdiER0UAbnGbBadn62O/o23QZCmPyY5EUZxRdBhHp2Dljk0SXMCB0GQqyLOHsoiTRZRCRjjEUNOYHOv2GEZF4g9MsGK7DriNAx6EwflgcEmPZhUREwafXVgKg41AwyBLOKuSAMxEF34UTkkWXMGB0GwoAcOFE/X7jiEiMEYOjkZepj6M3j0TXoTB+WDyGpHMhGxEFz4UTUkSXMKB0HQoAcOnUNNElEJFOyDJwvo67joAICIUZk1JgNnI7bSLqv9OHxyNFJ2cxH43uQyE+xqjrmQJEFDozJ6eKLmHA6T4UAODSM9mFRET9k5FkwtljkkSXMeAiIhSK8uKQnxklugwi0rDLp6XDIOu/KzoiQgEAfsgBZyI6RVFmGbPO0H/XERBBoTBjUgqiTBHz5RJREBWfnoK46MjYISFiXiVjoww4b3yS6DKISGMkCZgzPV10GSETMaEAcM0CEZ28MwoSkJMWOWOSERUKBUNiMX5YnOgyiEhDroygVgIQYaEAADdelCW6BCLSiKFZUZg0IkF0GSEVcaEwflg8xg1la4GIjm/O9AzRJYRcxIUCwNYCER1fUqxR11tkH01EhsKE0+Ixlq0FIjqGa8/LhDkCp7FHxsTbI7jxwizMe7VSdBkD4st//BBuW/P3bs+ZeDVGFT8AV1c9KlY9g+6GbVD8XqQOPQsFM+bBEnvsxTlueysqVz+Ljuqv4Pe5EZ00BEWzHkbCoEIofi+q1r2A9qr16LE2wGiJQ0reVIw49y5Y4vsG6hSfB3s/+z+0VayBJTYVBcUPIDV/auDxa0vegtvWglEz7g/uE0J0kjKSTLhsWmTOVozYUJg4PB5j82Oxq9YpupSgO+MnC6Aq/sC/He1V2Pb+L5Axagb8nh5s+9cvEZcxAqdf9xIAoGrdP7Bj0d2YcuNbkKQjvzPyum34esEtSM6djAlXPwdzTDJcXfthjIoHACg+N+wtZRh21m2IyxgJn9uG8hV/xvYP78bUn7wDAGjY8SHsLaWY8uM30VG9Hrs/+S3OueMLSJKEnu5GNO34CGf8ZMEAPztEx3fTRYNgNkZeKwGI0O6jb/z4okGiSxgQ5phkWOLSAn/aK9ciOikHyUNOR3fjdvRYm1A06xHEpY9AXPoIFP3wEdia96KzbvNRH7N245uISshE0Q8fQWL2GEQnDUbq0GmISR4CADBa4jFp7j+QOboYsan5SBw8DgUz7oe9pTTQanF11CBt+LmISz8NOZOugdfVBW9PNwCgbNljGH7eXTBa2K1HYuVnRmHGJH0fpHMsER0Kk4bHoygvVnQZA0rxe9Gy97/IHnc5JEmC4vdAggTZYA7cRzZYIEkyuhu2HfVx2ivXID6rEDsXz8Oa5y7ExjeuQ+P2D495bV+vA4AEo6WvNRGXMQLdDdvh97rRUbMB5rg0mKKT0LznU8hGCzJGXhCUr5moP26ZmQ05Aja+O5qIDgVA/zOR2vatgs9tR/aYywAAidnjIJuiUbH6Wfi9PfB7erBv1V+hqn54HO1HfZye7kY0bvsAMclDMPGavyNn4o9QvuIpNO365Ij39/t6Ubn6WWQVXhx495899nLEZ4zAhtd+hNoNr2Hc5fPhc9tQ/eWLKLhoHirX/h3rX7oMW9//H7jtrcF/MoiOoygvFmcVJoouQ6iIHVP4xukjEjBxeBy2VTpElzIgGncuRuqwswKDveaYZIy7Yj7Klj2O+i0LIUkyMgtnIj5zFHCU8QQAUFUFCVmFGH7unQCAhMxRcLZXoXH7B8geO/uw+yp+L3Yt6RssHlX8QOB22WA67N8AsGfpHzDk9LmwHyhHW8VqnHnL+6gteRPlXzyJ8XP+HJTngOhE3XZJtugShIv4lgIA3HHZEBgN+msu9lib0Fm3Cdnj5xx2e+rQaZh++8c4584vcM5dKzHm0j+i19GG6KTBR30sS1waYtOGHXZbbOpQuG0th93WFwj/C7e1GROvfeGYYwSddZvh7KjGkEnXomv/10gbNh0GczQyRxWje/+WU/iKiU7dmaMSMCafY1oMBQC5GVG63N+kadfHMMekIO20s4/4/+aYZJii4tFZtwkeZyfSh5971MdKHDwBrs7aw25zdtYhKuHQYP03geDq2o9Jc1+EOTrpqI/n9/WifPkTGDXzQUiyAaqqQFF8AABV8UFV/Uf9XKJgkyXgpxezlQAwFAJ+fGEW0hL0cyC3qipo3vUxBo25FLJ8eC9h084lsDbuhKurHs17lmLX4vuRO+UGxKbmB+6zZeHtqN+yMPDv3Ck3wNq0GzUbXoOraz9a9v4XjTs+RM6kawD0BcLOxfNga9mLMbP/BFXxo9fRjl5HOxS/93v11Xz1ClKHnY2EzFEAgKTB49G2byXsrftQv/V9JA6eEPwnhegoLpyYgqFZ0aLLCAsRP6bwjWiLAbf/cDD+9F6t6FKCorO2BG5bC7LHXf69/3N21qFy7fPw9lgRnZiN/Gm3InfKDYfdp6erAZ6D00UBIHFQEcbN+TMq1zyPmvWvICoxGwUX3IdBRbMAAL2ONrRXrgEAlLwx97DHmnTdy0jJnRz4t6OtEgfKluPMmw+FTsaoi9BVvwVfv3MbYlPzMGb2n/r9HBCdiNgoA1sJ3yKpqqqKLiKc/OaVCmyv0uegMxF9311XDMHsMyNz9fKRsPvoO+64LEeXg85E9H1j82Nx6dTIOHv5RDEUviMvMzqijt4jilQmo4R7rsqFJPFN4LcxFI7gRp0NOhPR991wfhaGpEfOMZsniqFwBNEWA372w6PP2ScibRuaFYVrz8sUXUZYYigcxfnjkzF1VGQdw0cUCWQJ+PVVuRw7PAqGwjH8+qpcJMZy1i6Rnlx+VjpGDdH3Rpj9wVA4hpR4E351xRDRZRBRkGQmmXHLTH1umR8sDIXj+MHYJFw4MfLOaSXSo1/NGYJos0F0GWGNoXAC7rx8CNITORuJSMuuOCsdUwo4Tng8DIUTEBtlwANz8yHz2SLSpBGDo/GzWdzK4kTwZe4EjR0ahx9foO8DeYj0KNos47fX5cMUoWcunyw+Syfh+guyMHYo91sn0pJfzRmCnDQuUjtRDIWTYJAlPDA3D/ExHKgi0oKLJ6fgwokposvQFIbCSUpPNGPe1XngdilE4W14djTuvJxTyk8WQ+EUnDk6EbcUc64zUbiKjzbgoR8PhdnEl7iTxWfsFF13fhYu4voForAjScD91+ZhUIpFdCmaxFDoh3uuykVhHpfLE4WT68/PwtRRiaLL0CyGQj+YjTIevnEoMpPMokshIgDnjE3CT2Zw6nh/MBT6KTnOhEdvHoZoM59KIpHGDo3D/dfm8dCcfuIrWRAMzYruW/HMn0UiIfIyovDIjUNh5gK1fuMzGCTTChNx68VcRk8UaqkJJjz209MQH8Nt7oOBoRBE15ybiZmnc6EMUajEWGQ8dstpyOC4XtAwFILsV3OGYNLweNFlEOmeySDh4RuHYdigaNGl6ApDIchMRhmP3DQM47hHEtGAkSTgvqtzMZFvwIKOoTAAoswy/njzMBRxDQPRgLj14mxcMIFdtQOBoTBAoi0GPPbT0zA6N0Z0KUS6Mmd6Oq49N1N0GbrFUBhAMRYDHv/pcIwYzD5PomC46ux0/M/sHNFl6Jqkqqoqugi9s7t8+M0rlahq7hFdCpFm3XBBJm4u5rTvgcZQCBGb04f7XqlATYtbdClEmnPLzEG4/nxuXxEKDIUQ6nJ4cd/LldjfymAgOlG/uHQwrjw7Q3QZEYOhEGKddi9+8wqDgeh4JAm464ohuHRqmuhSIgpDQQC7y4c//LMGu2ocokshCkuyDNx3VS5mnJ4qupSIw1AQxONT8NS/67B6R7foUojCikEGHpibj3PH8RArERgKAqmqilc/a8K/1rSKLoUoLFhMEn47Nx9nFSWJLiViMRTCwCcb2/H8x/VQFNGVEImTnmjCwzcOw8gcLvgUiaEQJkrKrPjju7Vwe5gMFHlG58bg4RuHISXeJLqUiMdQCCP7Glz43ZtV6HL4RJdCFDIzJqXg7iuH8ICcMMFQCDMtnb148M1qTlkl3ZMl4LZLsnH1OdzHKJwwFMKQ3eXDE+/XYVO5TXQpRAMixiLjwevzcUZBouhS6DsYCmFKVVW8t/oA3lrezAFo0pXsVAse/ckw5GZEiS6FjoChEOa2V9nx2Hu1HGcgXZg0PB6/uz6f5ymHMYaCBnTYvPjTe7VcAU2aJcvAjy/IwvUXZMEgS6LLoWNgKGiEX1Hx7soWLFjZwu4k0pSsZDP+d24eivJ4RK0WMBQ0ZnetA48vrEVrt1d0KUTHdd74JNw9JxexUQbRpdAJYihokKPHh798WI91u7pFl0J0RLFRBvxy9mBuaKdBDAUNW761Ey8tbYTVyUFoCh+TRsTjvh/lIj3RLLoUOgUMBY2zOX146dNGLNvSKboUinBRZhk/m5WN2Wemiy6F+oGhoBPbKu14dnE9Gtt7RZdCEWjCsDjcc1UuslMtokuhfuJmI/108803Q5IkPPHEE4fdvnjxYkhS6KbeTRwej5d/NQo3XJAJo4FT/ig00hNN+N31+XjqZyMYCDrBUAiCqKgozJ8/H11dXULrMJtk3FycjRfvKkBRXqzQWkjfTEYJN1yQidfvLeRhODrDUAiCiy66CFlZWXj88cePep9FixahqKgIFosF+fn5ePrppwesnrzMaPz15yPwqzlDOBWQgu6swkS8ds9o3FycjSgzX0L0ht/RIDAYDHjsscfw3HPPoaGh4Xv/v2XLFlxzzTWYO3cudu3ahYcffhi///3v8eabbw5YTZIk4dKpaXj93tGYdUYqDPxOUz8NSbfgsZ+ehkduGoZB7CrSLQ4099PNN9+M7u5uLF68GNOmTUNhYSFee+01LF68GHPmzIGqqrjhhhvQ1taGZcuWBT5v3rx5WLp0Kfbs2ROSOhva3HhreTPW7OoGv+N0MmIsMm64IAtXnp3B8aoIwPePQTR//ny89dZbKC0tPez20tJSTJ8+/bDbpk+fjoqKCvj9/pDUlpMehQevH4oX7izAGQUJIbkmaZvRIGHWGal4/d5CXHMuJzBECoZCEJ1zzjmYOXMmHnjgAdGlHNXw7Bj86ZbT8Nefj8DYfA5G0/eZjBIum5aGt35TiHuuzEVqAo/IjCTcvzbInnjiCUyYMAEFBQWB20aPHo3169cfdr/169dj5MiRMBjEDASPyY/DX34+EpvLbXj98yZUNvUIqYPCR5RJxg+npuLqczIZBBGMoRBkY8eOxQ033IC//e1vgdvuvfdeTJkyBY8++iiuvfZabNiwAc8//zxeeOEFgZX2mVKQgMkj47F2VzcWrGhB7QEeAxppos0yLpuWhh/9IANJcQyDSMeB5n769kDzN2pra1FQUACPx4Nvnt5FixbhoYceQkVFBQYNGoQ777wT9913n6Cqj25bpR2Lv2rDxlIrFP5k6FpslAFXnJWOK89ORwIPvaGDGAp0RM2dvfh4Qzs+29wBhzs0g+EUGplJZsyamorLp6VzHQt9D0OBjqnH48cXW7uw5Ks21LWya0mrjAYJZ45KwKypaTh9eDxknn5GR8FQoBO2tcKGj75qw6YyG7uWNCI71YJLpqRi5ukpSI7neAEdH0OBTlpzRy+Wb+3Eiu1daOrgrqzhxmSUcHZREmadkYrxw+JCujEjaR9DgfqlvN6JFdu7sHpHF7ocPOxHpGFZUZg5ORUXTUxBQiwHjunUMBQoKPyKih1VdqzbbcX6Pd0MiBCQJKAgJwbTi5Jw9phE5KRFiS6JdIChQEGnKCr21Dmxbnc3vtzdjTarV3RJumEySBg7LA7TRiVi+phEHnlJQcdQoAFX1+rGjio7tlc5sKPaDpuLU1xPRmqCCWcUJGDqqARMGh6PaAunkdLAYShQSKmqipoWN7ZX2bGj2oGd1Q6ug/iOwWkWFObGYnRuLIryYjFsULTokiiCMBRIKEVRUdnUgx3VfS2J8gYXrM7IGY+IMssYlROD0XmxgSBI5CAxCcRQoLDTZfei5oAbtS09qGlxo/ZAD+oOuNHjUUSX1i9xUQZkp1mQm27B6Ny+EBg6KBoGLiSjMMJQIE1QVRUHujyoPeBGTUsPag+4Ud/mRqfdh26HF/4wyYu4KAMGp1mQnWo57O/BqRa2AEgTGAqkeaqqwur0ocvhQ6fdi067D112Lzrt3sBtXXYfejx+eH1q3x+/Aq9PPebKbItJQmyUAXHRRsRFGRAX3fcn9uDH8d/6OC3RjJxUC9cHkOYxFCii+f0qlIO/AoFfBBWQZYknjVFEYigQEVEAj+MkIqIAhgIREQUwFIiIKIChQEREAQwFIiIKYCgQEVEAQ4GIiAIYCkREFMBQICKiAIYCEREFMBSIiCiAoUBERAEMBSIiCmAoEBFRAEOBiIgCGApERBTAUCAiogCGAhERBTAUiIgogKFAREQBDAUiIgpgKBARUQBDgYiIAhgKREQUwFAgIqIAhgIREQUwFIiIKIChQEREAQwFIiIKYCgQEVEAQ4GIiAIYCkREFMBQICKiAIYCEREFMBSIiCiAoUBERAEMBSIiCmAoEBFRAEOBiIgCGApERBTAUCAiogCGAhERBTAUiIgogKFAREQBDAUiIgr4/xMVf+b9St1/AAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Function for EDA**"
      ],
      "metadata": {
        "id": "ukCo8A1A1g5G"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def qcut_func(df, col, new_col, q, labels):\n",
        "  '''\n",
        "  This function is used for cutting the values of a column into different groups.\n",
        "\n",
        "  Paramater:\n",
        "  df: data frame\n",
        "  col: column that needs to be cut\n",
        "  new_col: new column that has cut values\n",
        "  q: number of quantiles\n",
        "  labels: the labels for each group\n",
        "\n",
        "  This function will return a data frame.\n",
        "  '''\n",
        "\n",
        "  df[new_col] = pd.qcut(df[col], q=q, labels=labels)\n",
        "\n",
        "  return df"
      ],
      "metadata": {
        "id": "hKGjwtnY4t0v"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def count_exit_df(df, groupby_col, exited_col):\n",
        "  '''\n",
        "  This function is used for creating a new data frame that counts number of 'Exited' users grouped by a specific column.\n",
        "\n",
        "  Parameters:\n",
        "  df: data frame\n",
        "  groupby_col: a group-by column\n",
        "  exited_col: a calculated column - an 'Exited' column\n",
        "\n",
        "  This function will return a new data frame.\n",
        "  '''\n",
        "\n",
        "  new_df = df.groupby(groupby_col)[exited_col].value_counts().unstack().reset_index()\n",
        "  new_df.fillna(0, inplace=True)\n",
        "  new_df['Total'] = new_df['Yes'] + new_df['No']\n",
        "  new_df['Exit_Rate'] = np.where(new_df['Total']==0, 0, (new_df['Yes']/new_df['Total'])*100)\n",
        "\n",
        "  return new_df"
      ],
      "metadata": {
        "id": "sxOmjiVsDbxj"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def combination(df, combined_col, col_1, col_2):\n",
        "  '''\n",
        "  This function is used for combining 2 columns.\n",
        "\n",
        "  Parameter:\n",
        "  df: data frame\n",
        "  col_1: 1st column\n",
        "  col_2: 2nd column\n",
        "\n",
        "  This function will return a data frame.\n",
        "  '''\n",
        "  df[combined_col] = df[col_1].astype('str')+ \" - \" + df[col_2].astype('str')\n",
        "\n",
        "  return df"
      ],
      "metadata": {
        "id": "pRlB8PimXepM"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def calculate_mean(df, groupby_col, calculated_col):\n",
        "  '''\n",
        "  This function is used for calculating mean grouped by one column.\n",
        "\n",
        "  Parameters:\n",
        "  df: data frame\n",
        "  groupby_col: a grouped column\n",
        "  calculated_col: a column needs calculating\n",
        "\n",
        "  This function will return a new df based on an output series.\n",
        "  '''\n",
        "\n",
        "  new_df = df.groupby[groupby_col](calculated_col).mean().round(2).reset_index\n",
        "\n",
        "  return new_df"
      ],
      "metadata": {
        "id": "ASnxAludr0i-"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def barplot(data, x, y, title):\n",
        "  '''\n",
        "  This function is used for visualizing a bar chart.\n",
        "\n",
        "  Parameters:\n",
        "  data: data frame or array\n",
        "  x: x variables\n",
        "  y: y variables\n",
        "  title: the title of a bar chart\n",
        "\n",
        "  This function will draw a bar chart.\n",
        "  '''\n",
        "\n",
        "  if data[x].nunique() <= 3:\n",
        "    palette = ['#F24A72', '#FFD93D', '#6BCB77'] #If the number of columns is less or equal to 3, the palette contains 3 specific colors.\n",
        "  else:\n",
        "    palette = sns.color_palette('husl', data[x].nunique())\n",
        "\n",
        "  plt.figure(figsize=(10,8))\n",
        "  sns.barplot(data=data, x=x, y=y, palette=palette)\n",
        "\n",
        "  if data[x].nunique() > 7:\n",
        "    plt.xticks(rotation=90)\n",
        "  else:\n",
        "    plt.xticks(rotation=0)\n",
        "\n",
        "  plt.xlabel(x)\n",
        "  plt.ylabel(y)\n",
        "  plt.title(title)\n",
        "  plt.show()"
      ],
      "metadata": {
        "id": "RmWQT6cWJJ9b"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Exploration**"
      ],
      "metadata": {
        "id": "jUG5k-4BvnwG"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Age\n",
        "> Whether younger users tend to leave a bank than older users.\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "Kk8Lxn_aRgAm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "labels_age = ['18-32', '32-37', '37-44', '44-92']"
      ],
      "metadata": {
        "id": "Kr78nhxvgGTd"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df2 = qcut_func(df2, 'Age', 'Age_Group', q=4, labels=labels_age)"
      ],
      "metadata": {
        "id": "VNJS4I7-3jnM"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Age_group = count_exit_df(df2, 'Age_Group', 'Exited')\n",
        "Age_group"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 174
        },
        "id": "3HhZAUbrOkqf",
        "outputId": "b806235d-9b96-4555-8992-90b29040d665"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Exited Age_Group    No   Yes  Total  Exit_Rate\n",
              "0          18-32  2577   213   2790   7.634409\n",
              "1          32-37  2048   249   2297  10.840226\n",
              "2          37-44  2041   532   2573  20.676253\n",
              "3          44-92  1296  1044   2340  44.615385"
            ],
            "text/html": [
              "\n",
              "\n",
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              "    <div class=\"colab-df-container\">\n",
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              "      <th>Age_Group</th>\n",
              "      <th>No</th>\n",
              "      <th>Yes</th>\n",
              "      <th>Total</th>\n",
              "      <th>Exit_Rate</th>\n",
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              "      <td>10.840226</td>\n",
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              "      <td>2573</td>\n",
              "      <td>20.676253</td>\n",
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              "      <th>3</th>\n",
              "      <td>44-92</td>\n",
              "      <td>1296</td>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a61732a0-2586-4bb1-930c-2c77780ae5e5')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "    <div id=\"df-a1fd0bde-cbf5-4ec0-854b-633dfd83a8d2\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-a1fd0bde-cbf5-4ec0-854b-633dfd83a8d2')\"\n",
              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-a1fd0bde-cbf5-4ec0-854b-633dfd83a8d2 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-a61732a0-2586-4bb1-930c-2c77780ae5e5 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-a61732a0-2586-4bb1-930c-2c77780ae5e5');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x = 'Age_Group'\n",
        "y = 'Exit_Rate'\n",
        "title = 'Exit Rate by Different Age Groups'\n",
        "age_barplot = barplot(Age_group, x, y, title)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 718
        },
        "id": "PY3cumJcTdQS",
        "outputId": "9192b41a-99a6-42a5-8629-4fec608ef0e1"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The exit rate seems different from the hypothesis mentioned above."
      ],
      "metadata": {
        "id": "7egODwyAR3Dl"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Geography"
      ],
      "metadata": {
        "id": "6IHYk_vjvSnA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "geography = count_exit_df(df2, 'Geography', 'Exited')\n",
        "geography"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "4_cS9fImvSZI",
        "outputId": "38cbca9c-50e5-4270-8ce6-5e214425a890"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Exited Geography    No  Yes  Total  Exit_Rate\n",
              "0         France  4203  811   5014  16.174711\n",
              "1        Germany  1695  814   2509  32.443204\n",
              "2          Spain  2064  413   2477  16.673395"
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-64ce14f0-ec1a-47e4-a749-dec32f2d04db\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>Exited</th>\n",
              "      <th>Geography</th>\n",
              "      <th>No</th>\n",
              "      <th>Yes</th>\n",
              "      <th>Total</th>\n",
              "      <th>Exit_Rate</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>France</td>\n",
              "      <td>4203</td>\n",
              "      <td>811</td>\n",
              "      <td>5014</td>\n",
              "      <td>16.174711</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Germany</td>\n",
              "      <td>1695</td>\n",
              "      <td>814</td>\n",
              "      <td>2509</td>\n",
              "      <td>32.443204</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Spain</td>\n",
              "      <td>2064</td>\n",
              "      <td>413</td>\n",
              "      <td>2477</td>\n",
              "      <td>16.673395</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-64ce14f0-ec1a-47e4-a749-dec32f2d04db')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <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",
              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-6367a955-3bdb-431a-a905-09509be87536\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-6367a955-3bdb-431a-a905-09509be87536')\"\n",
              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <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",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-6367a955-3bdb-431a-a905-09509be87536 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-64ce14f0-ec1a-47e4-a749-dec32f2d04db button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-64ce14f0-ec1a-47e4-a749-dec32f2d04db');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x = 'Geography'\n",
        "y = 'Exit_Rate'\n",
        "title = 'Exit Rate by Geography'\n",
        "geo_barplot = barplot(geography, x, y, title)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 718
        },
        "id": "ZydzUGjqvjIw",
        "outputId": "b74fb7de-ec7a-4285-f0f3-8659f38f13d1"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Tenure\n",
        "\n",
        "\n",
        "> If a customer has engaged with a bank for a long time, he/she is not likely to leave a bank.\n",
        "\n"
      ],
      "metadata": {
        "id": "WR6Kk5RPR_lx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "labels_tenure = ['0-2', '2-4', '4-6', '6-8','8-10']"
      ],
      "metadata": {
        "id": "0AjMxbNcXesy"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df2 = qcut_func(df2,'Tenure','Tenure_Group', 5, labels_tenure)"
      ],
      "metadata": {
        "id": "0g31o_enLOHF"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tenure_group = count_exit_df(df2, 'Tenure_Group', 'Exited')\n",
        "tenure_group"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "-t8Ew7IbasT9",
        "outputId": "112d9b3e-ffff-4676-b268-4c9486420346"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Exited Tenure_Group    No  Yes  Total  Exit_Rate\n",
              "0               0-2  1968  528   2496  21.153846\n",
              "1               2-4  1582  416   1998  20.820821\n",
              "2               4-6  1574  405   1979  20.464881\n",
              "3               6-8  1679  374   2053  18.217243\n",
              "4              8-10  1159  315   1474  21.370421"
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-0d5ab14a-0f91-49b3-891b-de0d9e1b23f8\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>Exited</th>\n",
              "      <th>Tenure_Group</th>\n",
              "      <th>No</th>\n",
              "      <th>Yes</th>\n",
              "      <th>Total</th>\n",
              "      <th>Exit_Rate</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0-2</td>\n",
              "      <td>1968</td>\n",
              "      <td>528</td>\n",
              "      <td>2496</td>\n",
              "      <td>21.153846</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2-4</td>\n",
              "      <td>1582</td>\n",
              "      <td>416</td>\n",
              "      <td>1998</td>\n",
              "      <td>20.820821</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4-6</td>\n",
              "      <td>1574</td>\n",
              "      <td>405</td>\n",
              "      <td>1979</td>\n",
              "      <td>20.464881</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>6-8</td>\n",
              "      <td>1679</td>\n",
              "      <td>374</td>\n",
              "      <td>2053</td>\n",
              "      <td>18.217243</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>8-10</td>\n",
              "      <td>1159</td>\n",
              "      <td>315</td>\n",
              "      <td>1474</td>\n",
              "      <td>21.370421</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0d5ab14a-0f91-49b3-891b-de0d9e1b23f8')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <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",
              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-8557e82d-5709-4fd7-b096-5ef1c0941279\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-8557e82d-5709-4fd7-b096-5ef1c0941279')\"\n",
              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <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",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-8557e82d-5709-4fd7-b096-5ef1c0941279 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-0d5ab14a-0f91-49b3-891b-de0d9e1b23f8 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-0d5ab14a-0f91-49b3-891b-de0d9e1b23f8');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x = 'Tenure_Group'\n",
        "y = 'Exit_Rate'\n",
        "title = 'Exit Rate by Different Tenure Groups'\n",
        "tenure_barplot = barplot(tenure_group, x, y, title)"
      ],
      "metadata": {
        "id": "53P2O4xvbYtX",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 718
        },
        "outputId": "cb6772c7-51b7-40d0-f3c9-c3c9a0c63668"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "There are no differences between long-term users and short-term users in exit rates.\n",
        "\n",
        "However, the number of exited customers who had stayed for 6-8 years seems noticeable"
      ],
      "metadata": {
        "id": "fNh--719dR7Y"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Number of Products**\n",
        "\n",
        "> If a customer use many products at a bank, he/she tends to stay longer.\n",
        "\n"
      ],
      "metadata": {
        "id": "1E3Hb0CkdkeP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "num_of_products = count_exit_df(df2, 'NumOfProducts', 'Exited')\n",
        "num_of_products"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 174
        },
        "id": "8m6ov--IfB7D",
        "outputId": "cac8918c-df80-44ae-bd34-7151c9b0b474"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Exited  NumOfProducts      No     Yes   Total   Exit_Rate\n",
              "0                   1  3675.0  1409.0  5084.0   27.714398\n",
              "1                   2  4241.0   349.0  4590.0    7.603486\n",
              "2                   3    46.0   220.0   266.0   82.706767\n",
              "3                   4     0.0    60.0    60.0  100.000000"
            ],
            "text/html": [
              "\n",
              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>Exited</th>\n",
              "      <th>NumOfProducts</th>\n",
              "      <th>No</th>\n",
              "      <th>Yes</th>\n",
              "      <th>Total</th>\n",
              "      <th>Exit_Rate</th>\n",
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              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>3675.0</td>\n",
              "      <td>1409.0</td>\n",
              "      <td>5084.0</td>\n",
              "      <td>27.714398</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>4241.0</td>\n",
              "      <td>349.0</td>\n",
              "      <td>4590.0</td>\n",
              "      <td>7.603486</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>46.0</td>\n",
              "      <td>220.0</td>\n",
              "      <td>266.0</td>\n",
              "      <td>82.706767</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>0.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>60.0</td>\n",
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              "  </tbody>\n",
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              "              style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <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",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-90d2ee68-7425-4ebd-b08e-6e54aa9bd911 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-81aae4c8-dd35-41bb-9625-ad68090d1e6a button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-81aae4c8-dd35-41bb-9625-ad68090d1e6a');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x = 'NumOfProducts'\n",
        "y = 'Exit_Rate'\n",
        "title = 'Exit Rate by Number of Products'\n",
        "numofprd_barplot = barplot(num_of_products, x, y, title)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 718
        },
        "id": "Fnz168djf-Iy",
        "outputId": "cc3559e6-f802-435d-85dc-ba8f40dd9401"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The data is opposite to the hypothesis."
      ],
      "metadata": {
        "id": "9BLWIbusk3iQ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Card Type and Point Earned\n",
        "\n",
        "\n",
        "> A customer having a low tier member card at a bank is more likely to leave.\n",
        "\n",
        "\n",
        "> The lower the point is earned, the higher the posibility that a customer leave a bank.\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "GJ9J7eC6lQV7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "col_1 = df2['Card Type'].unique()\n",
        "col_1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3bThXiY-lyR5",
        "outputId": "ffab4c78-db6c-42d8-8c54-2c6cb55a38bb"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['DIAMOND', 'GOLD', 'SILVER', 'PLATINUM'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "max_point = df2['Point Earned'].max()\n",
        "print(max_point)\n",
        "min_point = df2['Point Earned'].min()\n",
        "print(min_point)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r2vPo3HOmKSM",
        "outputId": "49053de1-36f9-49c3-9a63-f57746f6fbb6"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1000\n",
            "119\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "order = ['SILVER', 'GOLD', 'PLATINUM', 'DIAMOND']\n",
        "df2['Card Type'] = pd.Categorical(df2['Card Type'], categories=order, ordered=True)"
      ],
      "metadata": {
        "id": "04216rQoskYF"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "card_type = count_exit_df(df2, 'Card Type', 'Exited')\n",
        "card_type"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 174
        },
        "id": "EQNH1vmunTND",
        "outputId": "10846222-ecc1-48dd-de47-df44bd2cc4db"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Exited Card Type    No  Yes  Total  Exit_Rate\n",
              "0         SILVER  1994  502   2496  20.112179\n",
              "1           GOLD  2020  482   2502  19.264588\n",
              "2       PLATINUM  1987  508   2495  20.360721\n",
              "3        DIAMOND  1961  546   2507  21.779019"
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-c506e8d5-6324-485d-a98f-72d7c1b197eb\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>Exited</th>\n",
              "      <th>Card Type</th>\n",
              "      <th>No</th>\n",
              "      <th>Yes</th>\n",
              "      <th>Total</th>\n",
              "      <th>Exit_Rate</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>SILVER</td>\n",
              "      <td>1994</td>\n",
              "      <td>502</td>\n",
              "      <td>2496</td>\n",
              "      <td>20.112179</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>GOLD</td>\n",
              "      <td>2020</td>\n",
              "      <td>482</td>\n",
              "      <td>2502</td>\n",
              "      <td>19.264588</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>PLATINUM</td>\n",
              "      <td>1987</td>\n",
              "      <td>508</td>\n",
              "      <td>2495</td>\n",
              "      <td>20.360721</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>DIAMOND</td>\n",
              "      <td>1961</td>\n",
              "      <td>546</td>\n",
              "      <td>2507</td>\n",
              "      <td>21.779019</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "\n",
              "\n",
              "    <div id=\"df-caed602c-ebcc-4720-98af-91b062455970\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-caed602c-ebcc-4720-98af-91b062455970')\"\n",
              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <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",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-caed602c-ebcc-4720-98af-91b062455970 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-c506e8d5-6324-485d-a98f-72d7c1b197eb button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-c506e8d5-6324-485d-a98f-72d7c1b197eb');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x = 'Card Type'\n",
        "y = 'Exit_Rate'\n",
        "title = 'Exit Rate by Card Type'\n",
        "card_barplot = barplot(card_type, x, y, title)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 718
        },
        "id": "MMzx2OSbqmbM",
        "outputId": "2d5ca365-8b50-4561-804d-61c3460eef5d"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "labels_point = ['119-410', '410-605', '605-801', '801-1000']"
      ],
      "metadata": {
        "id": "juLA4ltpPXN-"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df2 = qcut_func(df2, 'Point Earned', 'Point_Earned_Group', 4, labels_point)"
      ],
      "metadata": {
        "id": "j4UbRBewP4qP"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "point = count_exit_df(df2, 'Point_Earned_Group', 'Exited')\n",
        "point"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 174
        },
        "id": "r-4BIoLBRw2g",
        "outputId": "03eba92a-fd98-41c2-b22e-75d347e9556d"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Exited Point_Earned_Group    No  Yes  Total  Exit_Rate\n",
              "0                 119-410  1974  527   2501  21.071571\n",
              "1                 410-605  2029  481   2510  19.163347\n",
              "2                 605-801  1969  538   2507  21.459912\n",
              "3                801-1000  1990  492   2482  19.822724"
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-abcc4670-7b5b-4d69-91b1-6f6ab845269f\">\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>Exited</th>\n",
              "      <th>Point_Earned_Group</th>\n",
              "      <th>No</th>\n",
              "      <th>Yes</th>\n",
              "      <th>Total</th>\n",
              "      <th>Exit_Rate</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>119-410</td>\n",
              "      <td>1974</td>\n",
              "      <td>527</td>\n",
              "      <td>2501</td>\n",
              "      <td>21.071571</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>410-605</td>\n",
              "      <td>2029</td>\n",
              "      <td>481</td>\n",
              "      <td>2510</td>\n",
              "      <td>19.163347</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>605-801</td>\n",
              "      <td>1969</td>\n",
              "      <td>538</td>\n",
              "      <td>2507</td>\n",
              "      <td>21.459912</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>801-1000</td>\n",
              "      <td>1990</td>\n",
              "      <td>492</td>\n",
              "      <td>2482</td>\n",
              "      <td>19.822724</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "     width=\"24px\">\n",
              "    <g>\n",
              "        <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",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-6566453a-15e8-4df4-a1c2-71d2b5f33f1c button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-abcc4670-7b5b-4d69-91b1-6f6ab845269f button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-abcc4670-7b5b-4d69-91b1-6f6ab845269f');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x = 'Point_Earned_Group'\n",
        "y = 'Exit_Rate'\n",
        "title = 'Exit Rate by Point Earned'\n",
        "point_barplot = barplot(point, x, y, title)"
      ],
      "metadata": {
        "id": "xVL9nVsorTvb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 718
        },
        "outputId": "1f18675f-6630-4df7-e3ad-295dc96cdf62"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Combine 2 features Card Type and Point Earned"
      ],
      "metadata": {
        "id": "reHPlnRTrjC4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df2 = combination(df2, 'Point_CardType', 'Point_Earned_Group', 'Card Type')"
      ],
      "metadata": {
        "id": "4xcd98y7rnVU"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "card_point = count_exit_df(df2, 'Point_CardType', 'Exited')\n",
        "card_point"
      ],
      "metadata": {
        "id": "8LerYJxpsrvf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 551
        },
        "outputId": "a986766c-6270-4e96-b7ff-11f78a393b35"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Exited       Point_CardType   No  Yes  Total  Exit_Rate\n",
              "0         119-410 - DIAMOND  486  149    635  23.464567\n",
              "1            119-410 - GOLD  492  124    616  20.129870\n",
              "2        119-410 - PLATINUM  512  118    630  18.730159\n",
              "3          119-410 - SILVER  484  136    620  21.935484\n",
              "4         410-605 - DIAMOND  498  129    627  20.574163\n",
              "5            410-605 - GOLD  529  112    641  17.472699\n",
              "6        410-605 - PLATINUM  478  132    610  21.639344\n",
              "7          410-605 - SILVER  524  108    632  17.088608\n",
              "8         605-801 - DIAMOND  483  142    625  22.720000\n",
              "9            605-801 - GOLD  505  135    640  21.093750\n",
              "10       605-801 - PLATINUM  486  126    612  20.588235\n",
              "11         605-801 - SILVER  495  135    630  21.428571\n",
              "12       801-1000 - DIAMOND  494  126    620  20.322581\n",
              "13          801-1000 - GOLD  494  111    605  18.347107\n",
              "14      801-1000 - PLATINUM  511  132    643  20.528771\n",
              "15        801-1000 - SILVER  491  123    614  20.032573"
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-21df6ae8-ef0c-44f1-bb49-e7d453480c70\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>Exited</th>\n",
              "      <th>Point_CardType</th>\n",
              "      <th>No</th>\n",
              "      <th>Yes</th>\n",
              "      <th>Total</th>\n",
              "      <th>Exit_Rate</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>119-410 - DIAMOND</td>\n",
              "      <td>486</td>\n",
              "      <td>149</td>\n",
              "      <td>635</td>\n",
              "      <td>23.464567</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>119-410 - GOLD</td>\n",
              "      <td>492</td>\n",
              "      <td>124</td>\n",
              "      <td>616</td>\n",
              "      <td>20.129870</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>119-410 - PLATINUM</td>\n",
              "      <td>512</td>\n",
              "      <td>118</td>\n",
              "      <td>630</td>\n",
              "      <td>18.730159</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>119-410 - SILVER</td>\n",
              "      <td>484</td>\n",
              "      <td>136</td>\n",
              "      <td>620</td>\n",
              "      <td>21.935484</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>410-605 - DIAMOND</td>\n",
              "      <td>498</td>\n",
              "      <td>129</td>\n",
              "      <td>627</td>\n",
              "      <td>20.574163</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>410-605 - GOLD</td>\n",
              "      <td>529</td>\n",
              "      <td>112</td>\n",
              "      <td>641</td>\n",
              "      <td>17.472699</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>410-605 - PLATINUM</td>\n",
              "      <td>478</td>\n",
              "      <td>132</td>\n",
              "      <td>610</td>\n",
              "      <td>21.639344</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>410-605 - SILVER</td>\n",
              "      <td>524</td>\n",
              "      <td>108</td>\n",
              "      <td>632</td>\n",
              "      <td>17.088608</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>605-801 - DIAMOND</td>\n",
              "      <td>483</td>\n",
              "      <td>142</td>\n",
              "      <td>625</td>\n",
              "      <td>22.720000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>605-801 - GOLD</td>\n",
              "      <td>505</td>\n",
              "      <td>135</td>\n",
              "      <td>640</td>\n",
              "      <td>21.093750</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>605-801 - PLATINUM</td>\n",
              "      <td>486</td>\n",
              "      <td>126</td>\n",
              "      <td>612</td>\n",
              "      <td>20.588235</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>605-801 - SILVER</td>\n",
              "      <td>495</td>\n",
              "      <td>135</td>\n",
              "      <td>630</td>\n",
              "      <td>21.428571</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>801-1000 - DIAMOND</td>\n",
              "      <td>494</td>\n",
              "      <td>126</td>\n",
              "      <td>620</td>\n",
              "      <td>20.322581</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>801-1000 - GOLD</td>\n",
              "      <td>494</td>\n",
              "      <td>111</td>\n",
              "      <td>605</td>\n",
              "      <td>18.347107</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>801-1000 - PLATINUM</td>\n",
              "      <td>511</td>\n",
              "      <td>132</td>\n",
              "      <td>643</td>\n",
              "      <td>20.528771</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>801-1000 - SILVER</td>\n",
              "      <td>491</td>\n",
              "      <td>123</td>\n",
              "      <td>614</td>\n",
              "      <td>20.032573</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-21df6ae8-ef0c-44f1-bb49-e7d453480c70')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <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",
              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-5f7ae6bc-f9ea-4185-a6bd-7956ce1cf71f\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-5f7ae6bc-f9ea-4185-a6bd-7956ce1cf71f')\"\n",
              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <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",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-5f7ae6bc-f9ea-4185-a6bd-7956ce1cf71f button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-21df6ae8-ef0c-44f1-bb49-e7d453480c70 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-21df6ae8-ef0c-44f1-bb49-e7d453480c70');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x = 'Point_CardType'\n",
        "y = 'Exit_Rate'\n",
        "title = 'Exit Rate by Point_CardType'\n",
        "point_card_barplot = barplot(card_point, x, y, title)"
      ],
      "metadata": {
        "id": "sR_32o5rtFMo",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 854
        },
        "outputId": "b20f089a-010f-4c4b-9c39-8609e780cb9d"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The gap between different groups is not huge."
      ],
      "metadata": {
        "id": "vHObMhpHuSaI"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Satisfaction and Complaint\n",
        "\n",
        "\n",
        "> The higher the satisfaction score is, the longer a customer stay.\n",
        "\n",
        "\n",
        "> A customer who complain a bank. Is he/she unhappy with a bank service? Or does he/she want to make feedbacks to help a bank improve its service?\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "iva47AqEug5v"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "satisfaction = count_exit_df(df2, 'Satisfaction Score', 'Exited')\n",
        "satisfaction"
      ],
      "metadata": {
        "id": "4lKxtsA1v5Qi",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "f35e88a9-8820-470e-8a74-e83ed81519da"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Exited  Satisfaction Score    No  Yes  Total  Exit_Rate\n",
              "0                        1  1545  387   1932  20.031056\n",
              "1                        2  1575  439   2014  21.797418\n",
              "2                        3  1641  401   2042  19.637610\n",
              "3                        4  1594  414   2008  20.617530\n",
              "4                        5  1607  397   2004  19.810379"
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-aea1941c-997b-41e7-87ea-b8d3528c67a0\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>Exited</th>\n",
              "      <th>Satisfaction Score</th>\n",
              "      <th>No</th>\n",
              "      <th>Yes</th>\n",
              "      <th>Total</th>\n",
              "      <th>Exit_Rate</th>\n",
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              "  </thead>\n",
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              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>1545</td>\n",
              "      <td>387</td>\n",
              "      <td>1932</td>\n",
              "      <td>20.031056</td>\n",
              "    </tr>\n",
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              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>1575</td>\n",
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              "      <td>2014</td>\n",
              "      <td>21.797418</td>\n",
              "    </tr>\n",
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              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>1641</td>\n",
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              "      <td>2042</td>\n",
              "      <td>19.637610</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>1594</td>\n",
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              "      <td>2008</td>\n",
              "      <td>20.617530</td>\n",
              "    </tr>\n",
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              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>1607</td>\n",
              "      <td>397</td>\n",
              "      <td>2004</td>\n",
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              "  </tbody>\n",
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              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  [theme=dark] .colab-df-quickchart:hover {\n",
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              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-aea1941c-997b-41e7-87ea-b8d3528c67a0 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-aea1941c-997b-41e7-87ea-b8d3528c67a0');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x = 'Satisfaction Score'\n",
        "y = 'Exit_Rate'\n",
        "title = 'Exit Rate by Satisfaction Score'\n",
        "satisfaction_barplot = barplot(satisfaction, x, y, title)"
      ],
      "metadata": {
        "id": "khiW0X9BxP_L",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 718
        },
        "outputId": "00b6678f-9d48-4602-8f52-ecf690427533"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df2['Complain'] = df2['Complain'].replace({0:\"No\", 1:\"Yes\"})"
      ],
      "metadata": {
        "id": "C-VqELQ4yYXa"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "complain = count_exit_df(df2, 'Complain', 'Exited')\n",
        "complain"
      ],
      "metadata": {
        "id": "oWWSN9rhyh1J",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "outputId": "0b6e9414-3b91-4c7c-849b-fa7e22a30cf3"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Exited Complain    No   Yes  Total  Exit_Rate\n",
              "0            No  7952     4   7956   0.050277\n",
              "1           Yes    10  2034   2044  99.510763"
            ],
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
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              "          document.querySelector('#df-98a1bf90-804b-4593-b236-ac5e76788169 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-98a1bf90-804b-4593-b236-ac5e76788169');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "This output answers the hypothesis above that most customers making a complaint about a bank are not satisfied with this. Thus, they leave the bank."
      ],
      "metadata": {
        "id": "_JA7MKtO15Fy"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Monetary Factors"
      ],
      "metadata": {
        "id": "FCulIgqh7NRn"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Credit Card"
      ],
      "metadata": {
        "id": "T9amKyyD9351"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df2['HasCrCard'] = df2['HasCrCard'].replace({0:\"No\", 1:\"Yes\"})"
      ],
      "metadata": {
        "id": "pMH6v8TL7cKP"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cr_card = count_exit_df(df2, 'HasCrCard', 'Exited')\n",
        "cr_card"
      ],
      "metadata": {
        "id": "vyi5JNMB9t3C",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "outputId": "f3f36562-b70c-44e2-dc22-6b8f36146b24"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Exited HasCrCard    No   Yes  Total  Exit_Rate\n",
              "0             No  2332   613   2945  20.814941\n",
              "1            Yes  5630  1425   7055  20.198441"
            ],
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              "\n",
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              "\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Balance\n",
        "\n",
        "\n",
        "> The lower the balance is, the more likely customers leave a bank.\n",
        "\n"
      ],
      "metadata": {
        "id": "qwBK9jXy95Tr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "min_balance = df2['Balance'].min()\n",
        "print(min_balance)\n",
        "max_balance = int(df2['Balance'].max())\n",
        "print(max_balance)"
      ],
      "metadata": {
        "id": "DVqM4_rV9-fB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9c9c1316-b053-46e5-e30f-1022fc39935c"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.0\n",
            "250898\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "labels_balance = ['0-73K','73K-110K','110K-134K', '134K-251K']\n",
        "df2['Balance_group'] = pd.qcut(df2['Balance'], q=5, labels=labels_balance, duplicates='drop')"
      ],
      "metadata": {
        "id": "Qx7zqsDs40zw"
      },
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "balance_group = count_exit_df(df2, 'Balance_group', 'Exited')\n",
        "balance_group"
      ],
      "metadata": {
        "id": "0ADeDAeHBBS6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 174
        },
        "outputId": "545859cd-bbb8-4d7b-f1c9-47a597868738"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Exited Balance_group    No  Yes  Total  Exit_Rate\n",
              "0              0-73K  3410  590   4000      14.75\n",
              "1           73K-110K  1553  447   2000      22.35\n",
              "2          110K-134K  1461  539   2000      26.95\n",
              "3          134K-251K  1538  462   2000      23.10"
            ],
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-72bc3d37-9957-4624-b53d-ff91a0cca84f button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-72bc3d37-9957-4624-b53d-ff91a0cca84f');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x = 'Balance_group'\n",
        "y = 'Exit_Rate'\n",
        "title = 'Exit Rate by Balance'\n",
        "balance_barplot = barplot(balance_group, x, y, title)"
      ],
      "metadata": {
        "id": "u1Fjr6D7DzVR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 718
        },
        "outputId": "0e244e01-58dd-4d42-c69f-b95424c9e7c5"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 1 Axes>"
            ],
            "image/png": 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s8ydzKCkpiRkzZkTdunUjl8tV9jgAAEAlybIsFi1aFE2aNIkqVdb+mtE2H0ozZsyIpk2bVvYYAADAFuKjjz6K3Xbbba3rbPOhVLdu3Yj46oNRr169Sp4GAACoLAsXLoymTZvmG2FttvlQWn24Xb169YQSAACwXm/JcTIHAACAhFACAABICCUAAICEUAIAAEgIJQAAgIRQAgAASAglAACAhFACAABICCUAAICEUAIAAEgIJQAAgIRQAgAASAglAACAhFACAABICCUAAICEUAIAAEgIJQAAgIRQAgAASAglAACAhFACAABICCUAAICEUAIAAEgIJQAAgIRQAgAASAglAACAhFACAABIVKvsAQBga3Rk8W2VPQLbiX+ecV5ljwDbJa8oAQAAJIQSAABAQigBAAAkhBIAAEBCKAEAACSEEgAAQEIoAQAAJIQSAABAQigBAAAkhBIAAEBCKAEAACSEEgAAQEIoAQAAJIQSAABAQigBAAAkhBIAAEBCKAEAACSEEgAAQEIoAQAAJIQSAABAQigBAAAkhBIAAEBCKAEAACSEEgAAQEIoAQAAJIQSAABAQigBAAAkhBIAAEBCKAEAACSEEgAAQEIoAQAAJIQSAABAQigBAAAkhBIAAEBCKAEAACSEEgAAQEIoAQAAJIQSAABAolJDafjw4fGd73wn6tatGzvvvHMce+yxMWXKlFLrdOnSJXK5XKnLeeedV0kTAwAA24NKDaXnn38++vbtG+PHj4/Ro0fHihUr4sgjj4wlS5aUWu+cc86JTz/9NH+57rrrKmliAABge1CtMh981KhRpa6PHDkydt5555gwYUJ06tQpv3yHHXaIoqKi9drmsmXLYtmyZfnrCxcu3DjDAgAA240t6j1KCxYsiIiIBg0alFp+7733xk477RT77rtvDBw4ML744os1bmP48OFRWFiYvzRt2nSTzgwAAGx7KvUVpa8rKSmJiy++OA499NDYd99988tPPfXU2H333aNJkybx5ptvxuWXXx5TpkyJRx99tNztDBw4MAYMGJC/vnDhQrEEAABUyBYTSn379o233nor/vWvf5Vafu655+b/vd9++0Xjxo3je9/7XkybNi1atGhRZjsFBQVRUFCwyecFAAC2XVvEoXf9+vWLv//97zF27NjYbbfd1rruQQcdFBERU6dO3RyjAQAA26FKfUUpy7K48MIL47HHHovnnnsumjdvvs77TJw4MSIiGjduvImnAwAAtleVGkp9+/aN++67L/76179G3bp1Y+bMmRERUVhYGLVq1Ypp06bFfffdF0cddVQ0bNgw3nzzzejfv3906tQp2rZtW5mjAwAA27BKDaVbb701Ir76o7JfV1xcHH369IkaNWrEM888EzfccEMsWbIkmjZtGj179oxBgwZVwrQAAMD2otIPvVubpk2bxvPPP7+ZpgEAAPjKFnEyBwAAgC2JUAIAAEgIJQAAgIRQAgAASAglAACAhFACAABICCUAAICEUAIAAEgIJQAAgIRQAgAASAglAACAhFACAABICCUAAICEUAIAAEgIJQAAgIRQAgAASAglAACAhFACAABICCUAAICEUAIAAEgIJQAAgIRQAgAASAglAACAhFACAABICCUAAICEUAIAAEgIJQAAgIRQAgAASAglAACAhFACAABICCUAAICEUAIAAEgIJQAAgIRQAgAASFSr7AEAANg6/eX2mZU9AtuJH59btNkf0ytKAAAACaEEAACQEEoAAAAJoQQAAJAQSgAAAAmhBAAAkBBKAAAACaEEAACQEEoAAAAJoQQAAJAQSgAAAAmhBAAAkBBKAAAACaEEAACQEEoAAACJapU9ALD1+c0tXSt7BLYT/78Lxlb2CABsp7yiBAAAkBBKAAAACaEEAACQEEoAAAAJoQQAAJAQSgAAAAmhBAAAkBBKAAAACaEEAACQEEoAAAAJoQQAAJAQSgAAAAmhBAAAkBBKAAAACaEEAACQEEoAAAAJoQQAAJAQSgAAAAmhBAAAkBBKAAAACaEEAACQEEoAAAAJoQQAAJAQSgAAAAmhBAAAkBBKAAAACaEEAACQEEoAAAAJoQQAAJAQSgAAAAmhBAAAkBBKAAAACaEEAACQEEoAAAAJoQQAAJAQSgAAAAmhBAAAkBBKAAAACaEEAACQEEoAAAAJoQQAAJAQSgAAAAmhBAAAkBBKAAAACaEEAACQEEoAAACJSg2l4cOHx3e+852oW7du7LzzznHsscfGlClTSq3z5ZdfRt++faNhw4ZRp06d6NmzZ8yaNauSJgYAALYHlRpKzz//fPTt2zfGjx8fo0ePjhUrVsSRRx4ZS5Ysya/Tv3//eOKJJ+Lhhx+O559/PmbMmBHHH398JU4NAABs66pV5oOPGjWq1PWRI0fGzjvvHBMmTIhOnTrFggUL4q677or77rsvvvvd70ZERHFxceyzzz4xfvz4OPjggytjbAAAYBu3Rb1HacGCBRER0aBBg4iImDBhQqxYsSKOOOKI/DqtW7eOZs2axcsvv1zuNpYtWxYLFy4sdQEAAKiILSaUSkpK4uKLL45DDz009t1334iImDlzZtSoUSPq169fat1ddtklZs6cWe52hg8fHoWFhflL06ZNN/XoAADANmaLCaW+ffvGW2+9FQ888MA32s7AgQNjwYIF+ctHH320kSYEAAC2F5X6HqXV+vXrF3//+9/jhRdeiN122y2/vKioKJYvXx7z588v9arSrFmzoqioqNxtFRQUREFBwaYeGQAA2IZV6itKWZZFv3794rHHHotnn302mjdvXur2Dh06RPXq1WPMmDH5ZVOmTIkPP/wwOnbsuLnHBQAAthOV+opS375947777ou//vWvUbdu3fz7jgoLC6NWrVpRWFgYZ511VgwYMCAaNGgQ9erViwsvvDA6duzojHcAAMAmU6mhdOutt0ZERJcuXUotLy4ujj59+kRExO9+97uoUqVK9OzZM5YtWxbdunWLW265ZTNPCgAAbE8qNZSyLFvnOjVr1owRI0bEiBEjNsNEAAAAW9BZ7wAAALYUQgkAACAhlAAAABJCCQAAICGUAAAAEkIJAAAgIZQAAAASQgkAACAhlAAAABJCCQAAICGUAAAAEkIJAAAgIZQAAAASQgkAACAhlAAAABJCCQAAICGUAAAAEkIJAAAgIZQAAAASQgkAACAhlAAAABJCCQAAICGUAAAAEkIJAAAgIZQAAAASQgkAACAhlAAAABJCCQAAICGUAAAAEkIJAAAgIZQAAAASQgkAACAhlAAAABJCCQAAICGUAAAAEkIJAAAgIZQAAAASQgkAACAhlAAAABJCCQAAICGUAAAAEkIJAAAgIZQAAAASQgkAACAhlAAAABJCCQAAICGUAAAAEkIJAAAgIZQAAAASQgkAACAhlAAAABJCCQAAICGUAAAAEkIJAAAgIZQAAAASQgkAACAhlAAAABJCCQAAICGUAAAAEkIJAAAgIZQAAAASQgkAACAhlAAAABJCCQAAICGUAAAAEkIJAAAgIZQAAAASQgkAACAhlAAAABJCCQAAICGUAAAAEkIJAAAgIZQAAAASQgkAACAhlAAAABJCCQAAICGUAAAAEkIJAAAgIZQAAAASQgkAACAhlAAAABJCCQAAICGUAAAAEkIJAAAgIZQAAAASQgkAACAhlAAAABJCCQAAICGUAAAAEkIJAAAgIZQAAAASQgkAACDxjUJp2rRpMWjQoDjllFPis88+i4iIp556Kt5+++2NMhwAAEBl2OBQev7552O//faLf//73/Hoo4/G4sWLIyLijTfeiMGDB2+0AQEAADa3DQ6ln/3sZ/HLX/4yRo8eHTVq1Mgv/+53vxvjx4/fKMMBAABUhg0OpUmTJsVxxx1XZvnOO+8cc+bM+UZDAQAAVKYNDqX69evHp59+Wmb566+/Hrvuuus3GgoAAKAybXAonXzyyXH55ZfHzJkzI5fLRUlJSbz44otxySWXxOmnn74xZwQAANisNjiUrrnmmmjdunU0bdo0Fi9eHG3atIlOnTrFIYccEoMGDVqvbbzwwgtx9NFHR5MmTSKXy8Xjjz9e6vY+ffpELpcrdenevfuGjgwAALBeqm3oHWvUqBF33HFHXHnllTFp0qRYvHhxfPvb345WrVqt9zaWLFkS7dq1izPPPDOOP/74ctfp3r17FBcX568XFBRs6MgAAADrZYNDadiwYXHJJZdE06ZNo2nTpvnlS5cujeuvvz6uvPLKdW6jR48e0aNHj7WuU1BQEEVFRRs6JgAAQIVt8KF3Q4cOzf/tpK/74osvYujQod9oqK977rnnYuedd4699947zj///Pj888/Xuv6yZcti4cKFpS4AAAAVscGhlGVZ5HK5MsvfeOONaNCgwTcaarXu3bvH3XffHWPGjIlf/epX8fzzz0ePHj1i1apVa7zP8OHDo7CwMH/5+qtdAAAA66PCh97tuOOO+RMr7LXXXqViadWqVbF48eI477zzNspwJ598cv7f++23X7Rt2zZatGgRzz33XHzve98r9z4DBw6MAQMG5K8vXLhQLAEAABVS4VC64YYbIsuyOPPMM2Po0KFRWFiYv61GjRqxxx57RMeOHTfqkKvtueeesdNOO8XUqVPXGEoFBQVO+AAAAHwjFQ6l3r17R0RE8+bN45BDDonq1atv9KHW5OOPP47PP/88GjduvNkeEwAA2P5s8FnvOnfunP/3l19+GcuXLy91e7169da5jcWLF8fUqVPz16dPnx4TJ06MBg0aRIMGDWLo0KHRs2fPKCoqimnTpsVll10WLVu2jG7dum3o2AAAAOu0waH0xRdfxGWXXRYPPfRQuWeiW9sJF1b7z3/+E127ds1fX/3eot69e8ett94ab775ZvzpT3+K+fPnR5MmTeLII4+Mq666qlIPrft02O8r7bHZvjS+sl9ljwAAsN3a4FC69NJLY+zYsXHrrbfGaaedFiNGjIhPPvkk/vCHP8S11167Xtvo0qVLZFm2xtuffvrpDR0PAABgg21wKD3xxBNx9913R5cuXeKMM86Iww8/PFq2bBm777573HvvvdGrV6+NOScAAMBms8F/R2nu3Lmx5557RsRX70eaO3duREQcdthh8cILL2yc6QAAACrBBofSnnvuGdOnT4+IiNatW8dDDz0UEV+90lS/fv2NMhwAAEBl2OBQOuOMM+KNN96IiIif/exnMWLEiKhZs2b0798/Lr300o02IAAAwOa2we9R6t+/f/7fRxxxRLz77rsxYcKEaNmyZbRt23ajDAcAAFAZNjiUUrvvvnvsvvvuERHxl7/8JX784x9vrE0DAABsVht06N3KlSvjrbfeiv/+97+llv/1r3+Ndu3aOeMdAACwVatwKL311lvRsmXLaNeuXeyzzz5x/PHHx6xZs6Jz585x5plnRo8ePWLatGmbYlYAAIDNosKH3l1++eXRsmXL+P3vfx/3339/3H///TF58uQ466yzYtSoUVGrVq1NMScAAMBmU+FQevXVV+Of//xntG/fPg4//PC4//774+c//3mcdtppm2I+AACAza7Ch97NmTMnmjRpEhERhYWFUbt27Tj44IM3+mAAAACVpcKvKOVyuVi0aFHUrFkzsiyLXC4XS5cujYULF5Zar169ehttSAAAgM2pwqGUZVnstddepa5/+9vfLnU9l8vFqlWrNs6EAAAAm1mFQ2ns2LGbYg4AAIAtRoVDqXPnzhVa/9prr43zzjsv6tevX9GHAgAAqBQb9AdnK+Kaa66JuXPnbuqHAQAA2Gg2eShlWbapHwIAAGCj2uShBAAAsLURSgAAAAmhBAAAkBBKAAAAiU0eSocffnjUqlVrUz8MAADARrPBoVS1atX47LPPyiz//PPPo2rVqvnrTz75ZDRu3HhDHwYAAGCz2+BQWtNpv5ctWxY1atTY4IEAAAAqW7WK3uGmm26KiIhcLhd33nln1KlTJ3/bqlWr4oUXXojWrVtvvAkBAAA2swqH0u9+97uI+OoVpdtuu63UYXY1atSIPfbYI2677baNNyEAAMBmVuFQmj59ekREdO3aNR599NHYcccdN/pQAAAAlanCobTa2LFjN+YcAAAAW4wKhdKAAQPiqquuitq1a8eAAQPWuu5vf/vbbzQYAABAZalQKL3++uuxYsWK/L/XJJfLfbOpAAAAKlGFQunrh9s59A4AANhWbfDfUZo9e/Yab5s0adKGbhYAAKDSbXAo7bfffvGPf/yjzPJf//rXceCBB36joQAAACrTBofSgAEDomfPnnH++efH0qVL45NPPonvfe97cd1118V99923MWcEAADYrDY4lC677LJ4+eWXY9y4cdG2bdto27ZtFBQUxJtvvhnHHXfcxpwRAABgs9rgUIqIaNmyZey7777xwQcfxMKFC+Okk06KoqKijTUbAABApdjgUHrxxRejbdu28d5778Wbb74Zt956a1x44YVx0kknxbx58zbmjAAAAJvVBofSd7/73TjppJNi/Pjxsc8++8TZZ58dr7/+enz44Yex3377bcwZAQAANqsK/R2lr/vnP/8ZnTt3LrWsRYsW8eKLL8bVV1/9jQcDAACoLBV+Remoo46KBQsW5CPp2muvjfnz5+dvnzdvXtx///0bbUAAAIDNrcKh9PTTT8eyZcvy16+55pqYO3du/vrKlStjypQpG2c6AACASlDhUMqybK3XAQAAtnbf6PTgAAAA26IKh1Iul4tcLldmGQAAwLaiwme9y7Is+vTpEwUFBRER8eWXX8Z5550XtWvXjogo9f4lAACArVGFQ6l3796lrv/kJz8ps87pp5++4RMBAABUsgqHUnFx8aaYAwAAYIvhZA4AAAAJoQQAAJAQSgAAAAmhBAAAkBBKAAAACaEEAACQEEoAAAAJoQQAAJAQSgAAAAmhBAAAkBBKAAAACaEEAACQEEoAAAAJoQQAAJAQSgAAAAmhBAAAkBBKAAAACaEEAACQEEoAAAAJoQQAAJAQSgAAAAmhBAAAkBBKAAAACaEEAACQEEoAAAAJoQQAAJAQSgAAAAmhBAAAkBBKAAAACaEEAACQEEoAAAAJoQQAAJAQSgAAAAmhBAAAkBBKAAAACaEEAACQEEoAAAAJoQQAAJAQSgAAAAmhBAAAkBBKAAAACaEEAACQEEoAAAAJoQQAAJAQSgAAAAmhBAAAkBBKAAAACaEEAACQEEoAAAAJoQQAAJAQSgAAAIlKDaUXXnghjj766GjSpEnkcrl4/PHHS92eZVlceeWV0bhx46hVq1YcccQR8d5771XOsAAAwHajUkNpyZIl0a5duxgxYkS5t1933XVx0003xW233Rb//ve/o3bt2tGtW7f48ssvN/OkAADA9qRaZT54jx49okePHuXelmVZ3HDDDTFo0KD40Y9+FBERd999d+yyyy7x+OOPx8knn7w5RwUAALYjW+x7lKZPnx4zZ86MI444Ir+ssLAwDjrooHj55ZfXeL9ly5bFwoULS10AAAAqYosNpZkzZ0ZExC677FJq+S677JK/rTzDhw+PwsLC/KVp06abdE4AAGDbs8WG0oYaOHBgLFiwIH/56KOPKnskAABgK7PFhlJRUVFERMyaNavU8lmzZuVvK09BQUHUq1ev1AUAAKAitthQat68eRQVFcWYMWPyyxYuXBj//ve/o2PHjpU4GQAAsK2r1LPeLV68OKZOnZq/Pn369Jg4cWI0aNAgmjVrFhdffHH88pe/jFatWkXz5s3jiiuuiCZNmsSxxx5beUMDAADbvEoNpf/85z/RtWvX/PUBAwZERETv3r1j5MiRcdlll8WSJUvi3HPPjfnz58dhhx0Wo0aNipo1a1bWyAAAwHagUkOpS5cukWXZGm/P5XIxbNiwGDZs2GacCgAA2N5tse9RAgAAqCxCCQAAICGUAAAAEkIJAAAgIZQAAAASQgkAACAhlAAAABJCCQAAICGUAAAAEkIJAAAgIZQAAAASQgkAACAhlAAAABJCCQAAICGUAAAAEkIJAAAgIZQAAAASQgkAACAhlAAAABJCCQAAICGUAAAAEkIJAAAgIZQAAAASQgkAACAhlAAAABJCCQAAICGUAAAAEkIJAAAgIZQAAAASQgkAACAhlAAAABJCCQAAICGUAAAAEkIJAAAgIZQAAAASQgkAACAhlAAAABJCCQAAICGUAAAAEkIJAAAgIZQAAAASQgkAACAhlAAAABJCCQAAICGUAAAAEkIJAAAgIZQAAAASQgkAACAhlAAAABJCCQAAICGUAAAAEkIJAAAgIZQAAAASQgkAACAhlAAAABJCCQAAICGUAAAAEkIJAAAgIZQAAAASQgkAACAhlAAAABJCCQAAICGUAAAAEkIJAAAgIZQAAAASQgkAACAhlAAAABJCCQAAICGUAAAAEkIJAAAgIZQAAAASQgkAACAhlAAAABJCCQAAICGUAAAAEkIJAAAgIZQAAAASQgkAACAhlAAAABJCCQAAICGUAAAAEkIJAAAgIZQAAAASQgkAACAhlAAAABJCCQAAICGUAAAAEkIJAAAgIZQAAAASQgkAACAhlAAAABJCCQAAICGUAAAAEkIJAAAgIZQAAAASQgkAACAhlAAAABJCCQAAILHFh9KQIUMil8uVurRu3bqyxwIAALZh1Sp7gPXxrW99K5555pn89WrVtoqxAQCArdRWURzVqlWLoqKiyh4DAADYTmzxh95FRLz33nvRpEmT2HPPPaNXr17x4YcfrnHdZcuWxcKFC0tdAAAAKmKLD6WDDjooRo4cGaNGjYpbb701pk+fHocffngsWrSo3PWHDx8ehYWF+UvTpk0388QAAMDWbosPpR49esQJJ5wQbdu2jW7dusWTTz4Z8+fPj4ceeqjc9QcOHBgLFizIXz766KPNPDEAALC12yreo/R19evXj7322iumTp1a7u0FBQVRUFCwmacCAAC2JVv8K0qpxYsXx7Rp06Jx48aVPQoAALCN2uJD6ZJLLonnn38+Pvjgg3jppZfiuOOOi6pVq8Ypp5xS2aMBAADbqC3+0LuPP/44TjnllPj888+jUaNGcdhhh8X48eOjUaNGlT0aAACwjdriQ+mBBx6o7BEAAIDtzBZ/6B0AAMDmJpQAAAASQgkAACAhlAAAABJCCQAAICGUAAAAEkIJAAAgIZQAAAASQgkAACAhlAAAABJCCQAAICGUAAAAEkIJAAAgIZQAAAASQgkAACAhlAAAABJCCQAAICGUAAAAEkIJAAAgIZQAAAASQgkAACAhlAAAABJCCQAAICGUAAAAEkIJAAAgIZQAAAASQgkAACAhlAAAABJCCQAAICGUAAAAEkIJAAAgIZQAAAASQgkAACAhlAAAABJCCQAAICGUAAAAEkIJAAAgIZQAAAASQgkAACAhlAAAABJCCQAAICGUAAAAEkIJAAAgIZQAAAASQgkAACAhlAAAABJCCQAAICGUAAAAEkIJAAAgIZQAAAASQgkAACAhlAAAABJCCQAAICGUAAAAEkIJAAAgIZQAAAASQgkAACAhlAAAABJCCQAAICGUAAAAEkIJAAAgIZQAAAASQgkAACAhlAAAABJCCQAAICGUAAAAEkIJAAAgIZQAAAASQgkAACAhlAAAABJCCQAAICGUAAAAEkIJAAAgIZQAAAASQgkAACAhlAAAABJCCQAAICGUAAAAEkIJAAAgIZQAAAASQgkAACAhlAAAABJCCQAAICGUAAAAEkIJAAAgIZQAAAASQgkAACAhlAAAABJCCQAAICGUAAAAEkIJAAAgIZQAAAASQgkAACAhlAAAABJCCQAAICGUAAAAEkIJAAAgIZQAAAASW0UojRgxIvbYY4+oWbNmHHTQQfHKK69U9kgAAMA2bIsPpQcffDAGDBgQgwcPjtdeey3atWsX3bp1i88++6yyRwMAALZRW3wo/fa3v41zzjknzjjjjGjTpk3cdtttscMOO8Qf//jHyh4NAADYRlWr7AHWZvny5TFhwoQYOHBgflmVKlXiiCOOiJdffrnc+yxbtiyWLVuWv75gwYKIiFi4cOFGmWnRl0s3ynZgXWpvpK/ZTeHLpSsrewS2Exvre/emsHKpnwdsHlvyfvDF0kWVPQLbiYULd9hI2/lqf8qybJ3rbtGhNGfOnFi1alXssssupZbvsssu8e6775Z7n+HDh8fQoUPLLG/atOkmmRE2meGXVfYEUOkGXVJY2SNApSvsO6CyR4DKd/HG3dyiRYuisHDtP2O26FDaEAMHDowBA/7fN5SSkpKYO3duNGzYMHK5XCVOtv1auHBhNG3aND766KOoV69eZY8DlcJ+wPbOPgD2gy1BlmWxaNGiaNKkyTrX3aJDaaeddoqqVavGrFmzSi2fNWtWFBUVlXufgoKCKCgoKLWsfv36m2pEKqBevXq+KbDdsx+wvbMPgP2gsq3rlaTVtuiTOdSoUSM6dOgQY8aMyS8rKSmJMWPGRMeOHStxMgAAYFu2Rb+iFBExYMCA6N27dxxwwAFx4IEHxg033BBLliyJM844o7JHAwAAtlFbfCiddNJJMXv27Ljyyitj5syZ0b59+xg1alSZEzyw5SooKIjBgweXOSQStif2A7Z39gGwH2xtctn6nBsPAABgO7JFv0cJAACgMgglAACAhFACAABICCUAAICEUGKdRowYEXvssUfUrFkzDjrooHjllVfWuO5zzz0XuVyu3Murr74aERFTpkyJrl27xi677BI1a9aMPffcMwYNGhQrVqzIb2fIkCHRvn37UtseN25c1K9fPy6++OJwDhI2xB577FHu12bfvn0jIuKnP/1ptGjRImrVqhWNGjWKH/3oR/Huu+/m7//BBx9ELpeLiRMn5pctWrQounbtGm3atImPP/64zGM++uijceSRR0bDhg3L3He122+/Pbp06RL16tWLXC4X8+fPL7PO3Llzo1evXlGvXr2oX79+nHXWWbF48eL87av3va/fd8aMGbHffvtFp06dYsGCBRX/gLFNeOGFF+Loo4+OJk2aRC6Xi8cff7zU7evzNfrll19G3759o2HDhlGnTp3o2bNnqT8Gv6n2jXXtk1/3+eefx2677VZmPxg5cmSZPzw/efLkaNq0aZxwwgmxfPnycrfHtmVd+8GQIUOidevWUbt27dhxxx3jiCOOiH//+9/lbmvZsmXRvn37Ml+3Ff0+PHfu3Ljwwgtj7733jlq1akWzZs3ioosuKrNeeT+3Hnjggfztn376aZx66qmx1157RZUqVeLiiy8uM7PfqzaMUGKtHnzwwRgwYEAMHjw4XnvttWjXrl1069YtPvvss3LXP+SQQ+LTTz8tdTn77LOjefPmccABB0RERPXq1eP000+Pf/7znzFlypS44YYb4o477ojBgwevcY5//OMf0a1btxgwYEDccMMNkcvlNsnzZdv26quvlvraHD16dEREnHDCCRER0aFDhyguLo7JkyfH008/HVmWxZFHHhmrVq0qd3uzZ8+Orl27xpIlS2LcuHGx2267lVlnyZIlcdhhh8WvfvWrNc71xRdfRPfu3ePnP//5Gtfp1atXvP322zF69Oj4+9//Hi+88EKce+65a1x/2rRpcdhhh8Xuu+8eTz/99Hr/FXK2PUuWLIl27drFiBEj1nj7ur5G+/fvH0888UQ8/PDD8fzzz8eMGTPi+OOPX+P6G2vfqMg+edZZZ0Xbtm3XuK3VXn311Tj88MOje/fu8eCDD0aNGjXWeR+2fuvaD/baa6/4/e9/H5MmTYp//etfsccee8SRRx4Zs2fPLrPuZZddFk2aNFnnY67r+/CMGTNixowZ8etf/zreeuutGDlyZIwaNSrOOuusMtsqLi4u9fPr2GOPzd+2bNmyaNSoUQwaNCjatWu3zrki/F613jJYiwMPPDDr27dv/vqqVauyJk2aZMOHD1+v+y9fvjxr1KhRNmzYsLWu179//+ywww7LXx88eHDWrl27LMuy7N57781q1KiR3XzzzRV/ArAW//d//5e1aNEiKykpKff2N954I4uIbOrUqVmWZdn06dOziMhef/317MMPP8z23nvv7Lvf/W62aNGidT7W1++7JmPHjs0iIps3b16p5e+8804WEdmrr76aX/bUU09luVwu++STT8rc94033siKioqyU089NVuxYsU6Z2P7ERHZY489Vu5ta/oanT9/fla9evXs4Ycfzi+bPHlyFhHZyy+/XOa+m2LfWC3dJ1e75ZZbss6dO2djxowpsw8VFxdnhYWFWZZl2ZgxY7I6depkl1122Tofi23X2vaD1RYsWJBFRPbMM8+UWv7kk09mrVu3zt5+++0yX7cb4/vwQw89lNWoUaPUfdZn3tU6d+6c/d///V+Z5X6v2jBeUWKNli9fHhMmTIgjjjgiv6xKlSpxxBFHxMsvv7xe2/jb3/4Wn3/+eZxxxhlrXGfq1KkxatSo6Ny5c5nbRowYEWeccUb88Y9/jH79+lX8ScAaLF++PO65554488wzy/2ftCVLlkRxcXE0b948mjZtWuq2KVOmxKGHHhpt2rSJJ598MurUqbNJZ3355Zejfv36+VdlIyKOOOKIqFKlSplDQ1566aXo3Llz9OzZM+65556oVm2L/7vibOEmTJgQK1asKPWzoHXr1tGsWbMyPws25b6xpn3ynXfeiWHDhsXdd98dVaqs+deaxx57LH7wgx/EoEGD1voqFixfvjxuv/32KCwsLPUKzaxZs+Kcc86JP//5z7HDDjus8f7f5PvwggULol69emXu07dv39hpp53iwAMPjD/+8Y8bfKic36sqRiixRnPmzIlVq1bFLrvsUmr5LrvsEjNnzlyvbdx1113RrVu3cg+7OOSQQ6JmzZrRqlWrOPzww2PYsGGlbp88eXL069cvbr311ujVq9eGPxEox+OPPx7z58+PPn36lFp+yy23RJ06daJOnTrx1FNPxejRo8scmnP66adHy5Yt4+GHH94sf1195syZsfPOO5daVq1atWjQoEGZffG4446Lo48+On7/+987lIKNYubMmVGjRo0y7/Mp72fBptg31rZPLlu2LE455ZS4/vrro1mzZmvcxuLFi+OEE06ISy+9NC6//PKNMhfbnr///e9Rp06dqFmzZvzud7+L0aNHx0477RQREVmWRZ8+feK8884r9Z9W5dnQ78Nz5syJq666qsxh1cOGDYuHHnooRo8eHT179owLLrggbr755go/P79XVZxQYoOdd955+R9e5f2v4ccffxxPP/10ucfaRnz1/qfXXnst7rvvvvjHP/4Rv/71r0vdvttuu8X+++8f119/fXz66aeb5Dmw/brrrruiR48eZY4z79WrV7z++uvx/PPPx1577RUnnnhifPnll6XWOeaYY2LcuHHx6KOP5pfde++9pfaHcePGbZbnkfrRj34Ujz32WKU9Ptu3TbFvrG2fHDhwYOyzzz7xk5/8ZK3bqFWrVnz/+9+PO+64IyZPnlzxJ8Z2oWvXrjFx4sR46aWXonv37nHiiSfm35N98803x6JFi2LgwIHr3E5534evueaaUvvBhx9+WOo+CxcujB/84AfRpk2bGDJkSKnbrrjiijj00EPj29/+dlx++eVx2WWXxfXXX1/h5+f3qooTSqzRTjvtFFWrVi11ZqOIr156LioqimHDhsXEiRPzl1RxcXE0bNgwjjnmmHK337Rp02jTpk2ccsopce2118aQIUNKvUG3bt268cwzz0Tt2rWja9eudmo2mv/973/xzDPPxNlnn13mtsLCwmjVqlV06tQp/vKXv8S7774bjz32WKl1fvGLX8SVV14Zp556ajz00EMR8dUviF/fH9b1P44VUVRUVOYEKitXroy5c+dGUVFRqeV/+MMf4uSTT44ePXrECy+8sNFmYPtVVFQUy5cvL3M2xtU/C75uU+wba9snn3322Xj44YejWrVqUa1atfje974XEV/9/Pr6CYKqVq0ajz/+eOy///7RtWtXsUS5ateuHS1btoyDDz447rrrrqhWrVrcddddEfHV19rLL78cBQUFUa1atWjZsmVERBxwwAHRu3fvUtsp7/vweeedV2o/+Pp/0i1atCi6d+8edevWjcceeyyqV6++1jkPOuig+Pjjj2PZsmUVen5+r6o4B6+zRjVq1IgOHTrEmDFj8mdXKSkpiTFjxkS/fv1i5513LnM40GpZlkVxcXGcfvrp69zhV293xYoVUVJSElWrVs0v33HHHeOZZ56JI488Mrp06RJjx45drzPNwNoUFxfHzjvvHD/4wQ/Wul6WZZFlWbk/jK644oqoUqVK9OrVK7Isi5NOOinq1q27Sebt2LFjzJ8/PyZMmBAdOnSIiK9+aJeUlMRBBx1Uat1cLhe33357VKlSJY466qj4xz/+Ue77/2B9dejQIapXrx5jxoyJnj17RsRX70X68MMPo2PHjmXW35T7RrpPPvLII7F06dL87a+++mqceeaZMW7cuGjRokWp+xYUFMSjjz4aP/7xj6Nr167x7LPPRps2bTbKXGybSkpK8l9rN910U/zyl7/M3zZjxozo1q1bPPjgg+v9fbhBgwZlHmPhwoXRrVu3KCgoiL/97W9Rs2bNdc41ceLE2HHHHTfo8Fa/V1WMUGKtBgwYEL17944DDjggDjzwwLjhhhtiyZIlaz05Q8RXv8RNnz693P+xv/fee6N69eqx3377RUFBQfznP/+JgQMHxkknnVRuVNWvXz9Gjx4d3bp1iy5dusRzzz1np2aDlZSURHFxcfTu3bvUm2Xff//9ePDBB+PII4+MRo0axccffxzXXntt1KpVK4466qhyt/WLX/wiqlatGr169YqSkpI45ZRTyqwzd+7c+PDDD2PGjBkR8dUvmBFf/S/96v+NnzlzZsycOTOmTp0aERGTJk2KunXrRrNmzaJBgwaxzz77RPfu3eOcc86J2267LVasWBH9+vWLk08+udx9IZfLxW233RZVq1bN/5Du0qXLN/q4sfVavHhx/msrImL69OkxceLEaNCgQTRr1mydX6OFhYVx1llnxYABA6JBgwZRr169uPDCC6Njx45x8MEHl/uYG2PfWJ99Mo2hOXPmRETEPvvsU+Y9VRFfxdIjjzwSJ5xwQj6WvvWtb1Xkw8lWam37QcOGDePqq6+OY445Jho3bhxz5syJESNGxCeffJL/8xHpe+BWv+WgRYsW5b4Pe32+Dy9cuDCOPPLI+OKLL+Kee+6JhQsXxsKFCyMiolGjRlG1atV44oknYtasWXHwwQdHzZo1Y/To0XHNNdfEJZdcUmpbq4/sWbx4ccyePTsmTpwYNWrUKPc/A/xeVQGVdr49tho333xz1qxZs6xGjRrZgQcemI0fP36d9znllFOyQw45pNzbHnjggWz//ffP6tSpk9WuXTtr06ZNds0112RLly7Nr/P101iutmDBgqxjx45Zy5Yts48//vgbPSe2X08//XQWEdmUKVNKLf/kk0+yHj16ZDvvvHNWvXr1bLfddstOPfXU7N13382vs6bTGP/qV7/Kqlatmt17771lHq+4uDiLiDKXwYMH59cZPHhwuesUFxfn1/n888+zU045JatTp05Wr1697Iwzzih16uXyTi1eUlKS9e3bN9thhx2yZ599dsM+YGz1Vn9tpJfevXtnWbZ+X6NLly7NLrjggmzHHXfMdthhh+y4447LPv300/ztm2LfWJ99ck3PdU2nB19t+fLl2bHHHps1atQomzRp0np9HNm6rW0/WLp0aXbcccdlTZo0yWrUqJE1btw4O+aYY7JXXnlljdsr72u+ot+H1zRTRGTTp0/PsuyrPwXRvn37/O9M7dq1y2677bZs1apVpbZV3jZ23333/O1+r9owuSzzp3gBAAC+zskcAAAAEkIJAAAgIZQAAAASQgkAACAhlAAAABJCCQAAICGUAAAAEkIJAAAgIZQA2OSGDBkS7du3r+wxAGC9CSUA1qpPnz6Ry+Xyl4YNG0b37t3jzTffrOzRAGCTEUoArFP37t3j008/jU8//TTGjBkT1apVix/+8IeVPdYWJcuyWLlyZWWPAcBGIpQAWKeCgoIoKiqKoqKiaN++ffzsZz+Ljz76KGbPnh0REZdffnnstddescMOO8See+4ZV1xxRaxYsWKN23v11Vfj+9//fuy0005RWFgYnTt3jtdee63UOrlcLu6888447rjjYocddohWrVrF3/72t1LrvP322/HDH/4w6tWrF3Xr1o3DDz88pk2blr/9zjvvjH322Sdq1qwZrVu3jltuuWW9n/NLL70U7du3j5o1a8YBBxwQjz/+eORyuZg4cWJERDz33HORy+Xiqaeeig4dOkRBQUH861//imXLlsVFF10UO++8c9SsWTMOO+ywePXVV/PbHTlyZNSvX7/UY63e9mqrD1X8wx/+EE2bNo0ddtghTjzxxFiwYMF6zw/ANyOUAKiQxYsXxz333BMtW7aMhg0bRkRE3bp1Y+TIkfHOO+/EjTfeGHfccUf87ne/W+M2Fi1aFL17945//etfMX78+GjVqlUcddRRsWjRolLrDR06NE488cR4880346ijjopevXrF3LlzIyLik08+iU6dOkVBQUE8++yzMWHChDjzzDPzr+rce++9ceWVV8bVV18dkydPjmuuuSauuOKK+NOf/rTO57hw4cI4+uijY7/99ovXXnstrrrqqrj88svLXfdnP/tZXHvttTF58uRo27ZtXHbZZfHII4/En/70p3jttdeiZcuW0a1bt/zc62vq1Knx0EMPxRNPPBGjRo2K119/PS644IIKbQOAbyADgLXo3bt3VrVq1ax27dpZ7dq1s4jIGjdunE2YMGGN97n++uuzDh065K8PHjw4a9eu3RrXX7VqVVa3bt3siSeeyC+LiGzQoEH564sXL84iInvqqaeyLMuygQMHZs2bN8+WL19e7jZbtGiR3XfffaWWXXXVVVnHjh3X+nyzLMtuvfXWrGHDhtnSpUvzy+64444sIrLXX389y7IsGzt2bBYR2eOPP15qxurVq2f33ntvftny5cuzJk2aZNddd12WZVlWXFycFRYWlnq8xx57LPv6j+TBgwdnVatWzT7++OP8sqeeeiqrUqVK9umnn65zfgC+uWqVGWkAbB26du0at956a0REzJs3L2655Zbo0aNHvPLKK7H77rvHgw8+GDfddFNMmzYtFi9eHCtXrox69eqtcXuzZs2KQYMGxXPPPRefffZZrFq1Kr744ov48MMPS63Xtm3b/L9r164d9erVi88++ywiIiZOnBiHH354VK9evcz2lyxZEtOmTYuzzjorzjnnnPzylStXRmFh4Tqf75QpU6Jt27ZRs2bN/LIDDzyw3HUPOOCA/L+nTZsWK1asiEMPPTS/rHr16nHggQfG5MmT1/m4X9esWbPYdddd89c7duwYJSUlMWXKlCgqKqrQtgCoOKEEwDrVrl07WrZsmb9+5513RmFhYdxxxx3xgx/8IHr16hVDhw6Nbt26RWFhYTzwwAPxm9/8Zo3b6927d3z++edx4403xu677x4FBQXRsWPHWL58ean10gjK5XJRUlISERG1atVa4/YXL14cERF33HFHHHTQQaVuq1q16vo96fVUu3btCq1fpUqVyLKs1LK1vZ8LgMrhPUoAVFgul4sqVarE0qVL46WXXordd989fvGLX8QBBxwQrVq1iv/9739rvf+LL74YF110URx11FHxrW99KwoKCmLOnDkVmqFt27Yxbty4ciNjl112iSZNmsT7778fLVu2LHVp3rz5Ore99957x6RJk2LZsmX5ZV8/IcOatGjRImrUqBEvvvhiftmKFSvi1VdfjTZt2kRERKNGjWLRokWxZMmS/DqrTxDxdR9++GHMmDEjf338+PFRpUqV2Hvvvdc5BwDfnFACYJ2WLVsWM2fOjJkzZ8bkyZPjwgsvjMWLF8fRRx8drVq1ig8//DAeeOCBmDZtWtx0003x2GOPrXV7rVq1ij//+c8xefLk+Pe//x29evVa6ytE5enXr18sXLgwTj755PjPf/4T7733Xvz5z3+OKVOmRMRXJ4IYPnx43HTTTfHf//43Jk2aFMXFxfHb3/52nds+9dRTo6SkJM4999yYPHlyPP300/HrX/86IqLU2elStWvXjvPPPz8uvfTSGDVqVLzzzjtxzjnnxBdffBFnnXVWREQcdNBBscMOO8TPf/7zmDZtWtx3330xcuTIMtuqWbNm9O7dO954440YN25cXHTRRXHiiSc67A5gMxFKAKzTqFGjonHjxtG4ceM46KCD4tVXX42HH344unTpEsccc0z0798/+vXrF+3bt4+XXnoprrjiirVu76677op58+bF/vvvH6eddlr+dNoV0bBhw3j22Wdj8eLF0blz5+jQoUPccccd+cP1zj777LjzzjujuLg49ttvv+jcuXOMHDlyvV5RqlevXjzxxBMxceLEaN++ffziF7+IK6+8MiKi1PuWynPttddGz54947TTTov9998/pk6dGk8//XTsuOOOERHRoEGDuOeee+LJJ5+M/fbbL+6///4YMmRIme20bNkyjj/++DjqqKPiyCOPjLZt21bo9OYAfDO5LD1QGgAo4957740zzjgjFixYUOFXvypqyJAh8fjjj5d7SB4Am4eTOQBAOe6+++7Yc889Y9ddd4033ngjLr/88jjxxBM3eSQBsGVw6B0A251rrrkm6tSpU+6lR48eERExc+bM+MlPfhL77LNP9O/fP0444YS4/fbbK3lyADYXh94BsN2ZO3duzJ07t9zbatWqVervFwGwfRJKAAAACYfeAQAAJIQSAABAQigBAAAkhBIAAEBCKAEAACSEEgAAQEIoAQAAJP4/5eHiF3FqB8kAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df2['CreditScore'].max()"
      ],
      "metadata": {
        "id": "9O-940mIIvke",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "02108767-4c14-4298-c65b-20bea79b0b92"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "850"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df2['CreditScore'].min()"
      ],
      "metadata": {
        "id": "nHGQhhAVI3mN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0f17e045-79c1-4687-db08-55e68f7cf5dc"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "350"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "labels_credit = ['350-608', '608-695', '695-850']"
      ],
      "metadata": {
        "id": "yk9IHz_yduy3"
      },
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df2 = qcut_func(df2, 'CreditScore', 'Credit', 3, labels_credit)"
      ],
      "metadata": {
        "id": "7KrPnMZkJDxZ"
      },
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "credit = count_exit_df(df2, 'Credit', 'Exited')\n",
        "credit"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "hk7ErhUanK8D",
        "outputId": "f89e0c1c-f1d3-4cb7-c9d8-bd77d64f5f3b"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Exited   Credit    No  Yes  Total  Exit_Rate\n",
              "0       350-608  2639  724   3363  21.528397\n",
              "1       608-695  2675  659   3334  19.766047\n",
              "2       695-850  2648  655   3303  19.830457"
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-bce1f2aa-05ec-4d4a-a5f3-2fc2e1bdb037\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>Exited</th>\n",
              "      <th>Credit</th>\n",
              "      <th>No</th>\n",
              "      <th>Yes</th>\n",
              "      <th>Total</th>\n",
              "      <th>Exit_Rate</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>350-608</td>\n",
              "      <td>2639</td>\n",
              "      <td>724</td>\n",
              "      <td>3363</td>\n",
              "      <td>21.528397</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>608-695</td>\n",
              "      <td>2675</td>\n",
              "      <td>659</td>\n",
              "      <td>3334</td>\n",
              "      <td>19.766047</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>695-850</td>\n",
              "      <td>2648</td>\n",
              "      <td>655</td>\n",
              "      <td>3303</td>\n",
              "      <td>19.830457</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bce1f2aa-05ec-4d4a-a5f3-2fc2e1bdb037')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <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",
              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-26bfd20f-6392-4285-8cf3-9d0b4ce0a508\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-26bfd20f-6392-4285-8cf3-9d0b4ce0a508')\"\n",
              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <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",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-26bfd20f-6392-4285-8cf3-9d0b4ce0a508 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-bce1f2aa-05ec-4d4a-a5f3-2fc2e1bdb037 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-bce1f2aa-05ec-4d4a-a5f3-2fc2e1bdb037');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x = 'Credit'\n",
        "y = 'Exit_Rate'\n",
        "title = 'Exit Rate by Credit Scores'\n",
        "credit_barplot = barplot(credit, x, y, title)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 718
        },
        "id": "lfEnpwexnSwM",
        "outputId": "78dd77d9-1313-47c0-9d51-c72fa51c3cf2"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Data Preprocessing**"
      ],
      "metadata": {
        "id": "bHq5vHQ7bGDf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def replace_cardtype(df, col):\n",
        "  '''\n",
        "  This function is used for replacing a sorted column.\n",
        "\n",
        "  Parameters:\n",
        "  df: data frame\n",
        "  col: a column that needs replacing values\n",
        "  '''\n",
        "\n",
        "  df[col] = df[col].replace({'SILVER':0, 'GOLD':1, 'PLATINUM':2, 'DIAMOND':3})\n",
        "\n",
        "  return df"
      ],
      "metadata": {
        "id": "iaOPtEG_bJHH"
      },
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def encode(df, col):\n",
        "  '''\n",
        "  This function is used for transforming categorical variables to numeric values.\n",
        "\n",
        "  Parameters:\n",
        "  df: data frame\n",
        "  col: a column that needs encoding.\n",
        "  '''\n",
        "\n",
        "  df = pd.get_dummies(df, col)\n",
        "\n",
        "  return df\n"
      ],
      "metadata": {
        "id": "vInY6v4DwJiC"
      },
      "execution_count": 68,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def scale_data(df, list_of_cols):\n",
        "  '''\n",
        "  This function is used for scaling data.\n",
        "\n",
        "  Parameters:\n",
        "  df: data frame\n",
        "  list_of_cols: a list of columns that need scaling\n",
        "  '''\n",
        "  scaler = StandardScaler()\n",
        "  df[list_of_cols] = scaler.fit_transform(df[list_of_cols])\n",
        "\n",
        "  return df"
      ],
      "metadata": {
        "id": "Ftww7Rdxdbnk"
      },
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def imbalance_process(df, label):\n",
        "  X = df.drop(label, axis=1)\n",
        "  y = df[label]\n",
        "  smote = SMOTE(random_state=42)\n",
        "  X_resample, y_resample = smote.fit_resample(X, y)\n",
        "\n",
        "  return X_resample, y_resample"
      ],
      "metadata": {
        "id": "QY4tZ9jHyshF"
      },
      "execution_count": 64,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Master Data for Training**"
      ],
      "metadata": {
        "id": "AmQ-vGMiJVj2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Create a new data frame that includes relative features mentioned on the hypothesis above for modeling\n",
        "\n",
        "df_model = df.loc[:,['Age','Geography', 'Tenure', 'NumOfProducts', 'Card Type', 'Point Earned', 'Complain', 'Balance', 'Exited']]\n",
        "df_model"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "QLfvZEM-JqS8",
        "outputId": "3aa91102-027d-48db-aaeb-853d90e0437b"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Age Geography  Tenure  NumOfProducts Card Type  Point Earned  Complain  \\\n",
              "0      42    France       2              1   DIAMOND           464         1   \n",
              "1      41     Spain       1              1   DIAMOND           456         1   \n",
              "2      42    France       8              3   DIAMOND           377         1   \n",
              "3      39    France       1              2      GOLD           350         0   \n",
              "4      43     Spain       2              1      GOLD           425         0   \n",
              "...   ...       ...     ...            ...       ...           ...       ...   \n",
              "9995   39    France       5              2   DIAMOND           300         0   \n",
              "9996   35    France      10              1  PLATINUM           771         0   \n",
              "9997   36    France       7              1    SILVER           564         1   \n",
              "9998   42   Germany       3              2      GOLD           339         1   \n",
              "9999   28    France       4              1   DIAMOND           911         0   \n",
              "\n",
              "        Balance  Exited  \n",
              "0          0.00       1  \n",
              "1      83807.86       0  \n",
              "2     159660.80       1  \n",
              "3          0.00       0  \n",
              "4     125510.82       0  \n",
              "...         ...     ...  \n",
              "9995       0.00       0  \n",
              "9996   57369.61       0  \n",
              "9997       0.00       1  \n",
              "9998   75075.31       1  \n",
              "9999  130142.79       0  \n",
              "\n",
              "[10000 rows x 9 columns]"
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-6ec3d0c0-2f86-48a6-814f-530a61b26cd2\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Age</th>\n",
              "      <th>Geography</th>\n",
              "      <th>Tenure</th>\n",
              "      <th>NumOfProducts</th>\n",
              "      <th>Card Type</th>\n",
              "      <th>Point Earned</th>\n",
              "      <th>Complain</th>\n",
              "      <th>Balance</th>\n",
              "      <th>Exited</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>42</td>\n",
              "      <td>France</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>DIAMOND</td>\n",
              "      <td>464</td>\n",
              "      <td>1</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>41</td>\n",
              "      <td>Spain</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>DIAMOND</td>\n",
              "      <td>456</td>\n",
              "      <td>1</td>\n",
              "      <td>83807.86</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>42</td>\n",
              "      <td>France</td>\n",
              "      <td>8</td>\n",
              "      <td>3</td>\n",
              "      <td>DIAMOND</td>\n",
              "      <td>377</td>\n",
              "      <td>1</td>\n",
              "      <td>159660.80</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>39</td>\n",
              "      <td>France</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>GOLD</td>\n",
              "      <td>350</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>43</td>\n",
              "      <td>Spain</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>GOLD</td>\n",
              "      <td>425</td>\n",
              "      <td>0</td>\n",
              "      <td>125510.82</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9995</th>\n",
              "      <td>39</td>\n",
              "      <td>France</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>DIAMOND</td>\n",
              "      <td>300</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9996</th>\n",
              "      <td>35</td>\n",
              "      <td>France</td>\n",
              "      <td>10</td>\n",
              "      <td>1</td>\n",
              "      <td>PLATINUM</td>\n",
              "      <td>771</td>\n",
              "      <td>0</td>\n",
              "      <td>57369.61</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9997</th>\n",
              "      <td>36</td>\n",
              "      <td>France</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "      <td>SILVER</td>\n",
              "      <td>564</td>\n",
              "      <td>1</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9998</th>\n",
              "      <td>42</td>\n",
              "      <td>Germany</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>GOLD</td>\n",
              "      <td>339</td>\n",
              "      <td>1</td>\n",
              "      <td>75075.31</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9999</th>\n",
              "      <td>28</td>\n",
              "      <td>France</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>DIAMOND</td>\n",
              "      <td>911</td>\n",
              "      <td>0</td>\n",
              "      <td>130142.79</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>10000 rows × 9 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6ec3d0c0-2f86-48a6-814f-530a61b26cd2')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <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",
              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-b79dc517-9a10-4911-a2ed-3edbac99a900\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-b79dc517-9a10-4911-a2ed-3edbac99a900')\"\n",
              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <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",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-b79dc517-9a10-4911-a2ed-3edbac99a900 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-6ec3d0c0-2f86-48a6-814f-530a61b26cd2 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-6ec3d0c0-2f86-48a6-814f-530a61b26cd2');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_model = replace_cardtype(df_model, 'Card Type')"
      ],
      "metadata": {
        "id": "qNOwrn-BIhIr"
      },
      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_model = encode(df_model, 'Geography')"
      ],
      "metadata": {
        "id": "ykDYICjFyahc"
      },
      "execution_count": 70,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cols_to_scale = ['Age', 'Tenure', 'NumOfProducts', 'Complain', 'Point Earned', 'Balance']"
      ],
      "metadata": {
        "id": "QDsgj2gUIlyl"
      },
      "execution_count": 71,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_model = scale_data(df_model, cols_to_scale)"
      ],
      "metadata": {
        "id": "R_uiap2UJNZ_"
      },
      "execution_count": 72,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_resample, y_resample = imbalance_process(df_model, 'Exited')"
      ],
      "metadata": {
        "id": "Vk_lFSVFyvaB"
      },
      "execution_count": 73,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Prepare Data for Training**"
      ],
      "metadata": {
        "id": "LJG4D6RZIWEi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def split_data(df, label):\n",
        "  '''\n",
        "  This function is used for splitting data into train and test.\n",
        "\n",
        "  Parameters:\n",
        "  df: data frame that used for building model\n",
        "  label: a predicted column\n",
        "\n",
        "  This function will return X_train, X_test, y_train, y_test\n",
        "  '''\n",
        "\n",
        "  X = df.drop(label, axis=1)\n",
        "  y = df[label]\n",
        "\n",
        "  X_train, X_test, y_train, y_test = train_test_split(X_resample, y_resample, test_size=0.2, random_state=43)\n",
        "\n",
        "  return X_train, X_test, y_train, y_test"
      ],
      "metadata": {
        "id": "QJordRrbHFC4"
      },
      "execution_count": 74,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = split_data(df_model, 'Exited')"
      ],
      "metadata": {
        "id": "Lmodkb_LJgKz"
      },
      "execution_count": 75,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Build Model**"
      ],
      "metadata": {
        "id": "N_zVx_Q-L1wT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def build_model(df, model_type):\n",
        "  '''\n",
        "  This function is used for building model.\n",
        "\n",
        "  Parameters:\n",
        "  df: data frame that used for modeling\n",
        "  model_type: name of the model\n",
        "\n",
        "  This function will return a model.\n",
        "  '''\n",
        "\n",
        "  model_type == 'LogReg'\n",
        "  model = LogisticRegression(random_state=43)\n",
        "  model.fit(X_train, y_train)\n",
        "\n",
        "  return model"
      ],
      "metadata": {
        "id": "Y7VzQDuQL3Kb"
      },
      "execution_count": 77,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_logreg = build_model(df_model, 'LogReg')"
      ],
      "metadata": {
        "id": "BkEtRqOXNQ2Z"
      },
      "execution_count": 78,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Evaluate Model**"
      ],
      "metadata": {
        "id": "oNu844ahN8ig"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def evaluate(df, model):\n",
        "  '''\n",
        "  This function is used for evaluation a built model.\n",
        "\n",
        "  Parameters:\n",
        "  df: data frame that is used for modeling\n",
        "  model: name of a model\n",
        "\n",
        "  This function will return a model score, an accuracy score, a classification report and a heatmap showing confusion matrix.\n",
        "  '''\n",
        "\n",
        "  y_predict = model.predict(X_test)\n",
        "  print(model.score(X_train, y_train))\n",
        "  print(accuracy_score(y_test, y_predict))\n",
        "  cf_matrix = confusion_matrix(y_test, y_predict)\n",
        "  sns.heatmap(cf_matrix, annot=True)\n",
        "  print(classification_report(y_test, y_predict))"
      ],
      "metadata": {
        "id": "zF2n469hOP2G"
      },
      "execution_count": 79,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "evaluate(df_model, model_logreg)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 626
        },
        "id": "inMoP7LiQz8n",
        "outputId": "a95cad12-9fd8-4ed3-cd89-a307338d05f9"
      },
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.9985085171520528\n",
            "0.9984301412872841\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00      1603\n",
            "           1       1.00      1.00      1.00      1582\n",
            "\n",
            "    accuracy                           1.00      3185\n",
            "   macro avg       1.00      1.00      1.00      3185\n",
            "weighted avg       1.00      1.00      1.00      3185\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def feature_importance_lg(model, X_train, model_type):\n",
        "      '''\n",
        "      This function is used for plotting feature importances of Logistic Regression Model.\n",
        "\n",
        "      Parameters:\n",
        "      model: name of a model\n",
        "      X_train: feature columns\n",
        "      model_type: a type of model that is used in a buiding-model step above.\n",
        "\n",
        "      This function will return a chart that shows feature importances.\n",
        "      '''\n",
        "\n",
        "      features = X_train.columns\n",
        "      importances = np.abs(model.coef_[0]) #hệ số coef cho\n",
        "      indices = np.argsort(importances)\n",
        "\n",
        "      #Draw a chart\n",
        "      plt.figure(figsize=(8, len(indices) * 0.3))\n",
        "      plt.title('Feature Importances')\n",
        "      plt.barh(range(len(indices)), importances[indices], color='#FFB84C', align='center')\n",
        "      plt.yticks(range(len(indices)), [features[i] for i in indices])\n",
        "      plt.xlabel('Absolute Coefficient')\n",
        "      plt.show()"
      ],
      "metadata": {
        "id": "UGxm72sDRQwq"
      },
      "execution_count": 81,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "feature_importance_lg(model_logreg, X_train, 'LogReg')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 333
        },
        "id": "9J8f-jc8RUDo",
        "outputId": "ea6bf73a-25f1-4717-9319-445325102523"
      },
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x300 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}