LTFS_Final_File.ipynb

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  "metadata": {
    "colab": {
      "name": "LTFS_Final_File.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": []
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
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      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.6.8"
    },
    "kernelspec": {
      "display_name": "Python 3 (Spyder)",
      "language": "python3",
      "name": "python3"
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    "accelerator": "GPU"
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  "cells": [
    {
      "metadata": {
        "colab_type": "text",
        "id": "db8EUqEtJMGj"
      },
      "cell_type": "markdown",
      "source": [
        "# LTFS Loan Prediction"
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "tNfgn2DFJarj"
      },
      "cell_type": "markdown",
      "source": [
        "# **Mounting GoogleDrive**"
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "L4qJbFuINJSw",
        "outputId": "c4ee8e5b-2711-4d20-d4b6-f60633ed05ea",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 125
        }
      },
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/gdrive')"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3Aietf%3Awg%3Aoauth%3A2.0%3Aoob&scope=email%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdocs.test%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive.photos.readonly%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fpeopleapi.readonly&response_type=code\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /gdrive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "KA8lhLaR-kjj",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import os\n",
        "os.chdir(\"E:\\\\ML Project\\\\Project2\\\\\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "JCRZKc_ENXXg",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import datetime as dt\n",
        "import seaborn\n",
        "from sklearn.metrics import classification_report\n",
        "from sklearn.ensemble.forest import RandomForestClassifier\n",
        "from imblearn.ensemble import BalancedRandomForestClassifier\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from xgboost import XGBClassifier\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import roc_auc_score\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.utils.multiclass import unique_labels\n",
        "seaborn.set(rc={'figure.figsize':(11.7,8.27)})"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "MwYspfBjJx3s"
      },
      "cell_type": "markdown",
      "source": [
        "# **Loading Train and Test Data**"
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "PTFzxQJCONpg",
        "outputId": "9ac3fd59-6716-4d62-a4f8-677f3907e58c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 244
        }
      },
      "cell_type": "code",
      "source": [
        "train=pd.read_csv(\"/gdrive/My Drive/Colab Notebooks/train.csv\")\n",
        "test=pd.read_csv('/gdrive/My Drive/Colab Notebooks/test_bqCt9Pv.csv')\n",
        "train.head()\n"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>UniqueID</th>\n",
              "      <th>disbursed_amount</th>\n",
              "      <th>asset_cost</th>\n",
              "      <th>ltv</th>\n",
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              "      <th>Employment.Type</th>\n",
              "      <th>...</th>\n",
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              "      <th>SEC.INSTAL.AMT</th>\n",
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              "      <th>DELINQUENT.ACCTS.IN.LAST.SIX.MONTHS</th>\n",
              "      <th>AVERAGE.ACCT.AGE</th>\n",
              "      <th>CREDIT.HISTORY.LENGTH</th>\n",
              "      <th>NO.OF_INQUIRIES</th>\n",
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              "      <td>67</td>\n",
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              "      <td>1</td>\n",
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              "      <td>1497</td>\n",
              "      <td>24-08-85</td>\n",
              "      <td>Self employed</td>\n",
              "      <td>...</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0yrs 0mon</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
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              "      <td>1501</td>\n",
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              "      <td>Self employed</td>\n",
              "      <td>...</td>\n",
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              "      <td>1</td>\n",
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              "      <td>67</td>\n",
              "      <td>22807</td>\n",
              "      <td>45</td>\n",
              "      <td>1495</td>\n",
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              "      <td>Self employed</td>\n",
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              "      <td>0yrs 0mon</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "<p>5 rows × 41 columns</p>\n",
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            "text/plain": [
              "   UniqueID  disbursed_amount  asset_cost    ltv  branch_id  supplier_id  \\\n",
              "0    420825             50578       58400  89.55         67        22807   \n",
              "1    537409             47145       65550  73.23         67        22807   \n",
              "2    417566             53278       61360  89.63         67        22807   \n",
              "3    624493             57513       66113  88.48         67        22807   \n",
              "4    539055             52378       60300  88.39         67        22807   \n",
              "\n",
              "   manufacturer_id  Current_pincode_ID Date.of.Birth Employment.Type  ...  \\\n",
              "0               45                1441      01-01-84        Salaried  ...   \n",
              "1               45                1502      31-07-85   Self employed  ...   \n",
              "2               45                1497      24-08-85   Self employed  ...   \n",
              "3               45                1501      30-12-93   Self employed  ...   \n",
              "4               45                1495      09-12-77   Self employed  ...   \n",
              "\n",
              "  SEC.SANCTIONED.AMOUNT  SEC.DISBURSED.AMOUNT  PRIMARY.INSTAL.AMT  \\\n",
              "0                     0                     0                   0   \n",
              "1                     0                     0                1991   \n",
              "2                     0                     0                   0   \n",
              "3                     0                     0                  31   \n",
              "4                     0                     0                   0   \n",
              "\n",
              "   SEC.INSTAL.AMT  NEW.ACCTS.IN.LAST.SIX.MONTHS  \\\n",
              "0               0                             0   \n",
              "1               0                             0   \n",
              "2               0                             0   \n",
              "3               0                             0   \n",
              "4               0                             0   \n",
              "\n",
              "   DELINQUENT.ACCTS.IN.LAST.SIX.MONTHS  AVERAGE.ACCT.AGE  \\\n",
              "0                                    0         0yrs 0mon   \n",
              "1                                    1        1yrs 11mon   \n",
              "2                                    0         0yrs 0mon   \n",
              "3                                    0         0yrs 8mon   \n",
              "4                                    0         0yrs 0mon   \n",
              "\n",
              "   CREDIT.HISTORY.LENGTH  NO.OF_INQUIRIES  loan_default  \n",
              "0              0yrs 0mon                0             0  \n",
              "1             1yrs 11mon                0             1  \n",
              "2              0yrs 0mon                0             0  \n",
              "3              1yrs 3mon                1             1  \n",
              "4              0yrs 0mon                1             1  \n",
              "\n",
              "[5 rows x 41 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "2BXW-g2BNhDE"
      },
      "cell_type": "markdown",
      "source": [
        "# **Combining Train and Test**\n"
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "JMqQsUWTNmku",
        "outputId": "8b3d2a3e-8e0b-448a-c117-c6a1f2b1dd10",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 244
        }
      },
      "cell_type": "code",
      "source": [
        "df=train.append(test,ignore_index=True)\n",
        "df.tail()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>UniqueID</th>\n",
              "      <th>disbursed_amount</th>\n",
              "      <th>asset_cost</th>\n",
              "      <th>ltv</th>\n",
              "      <th>branch_id</th>\n",
              "      <th>supplier_id</th>\n",
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              "      <th>Current_pincode_ID</th>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4yrs 10mon</td>\n",
              "      <td>9yrs 5mon</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
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              "      <td>1</td>\n",
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              "      <td>NaN</td>\n",
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            "text/plain": [
              "        UniqueID  disbursed_amount  asset_cost    ltv  branch_id  supplier_id  \\\n",
              "345541    755547             44875       72509  67.58         74        16646   \n",
              "345542    716994             53549       72509  78.61         74        16646   \n",
              "345543    709352             55243       72509  81.37         74        16646   \n",
              "345544    699795             55243       72509  81.37         74        16646   \n",
              "345545    755244             40274       72509  60.68         74        16646   \n",
              "\n",
              "        manufacturer_id  Current_pincode_ID Date.of.Birth Employment.Type  \\\n",
              "345541               86                2591      14-05-92   Self employed   \n",
              "345542               86                2593      24-04-84   Self employed   \n",
              "345543               86                2593      1/1/1986   Self employed   \n",
              "345544               86                2593      1/1/1973   Self employed   \n",
              "345545               86                2593      1/1/1974   Self employed   \n",
              "\n",
              "        ... SEC.SANCTIONED.AMOUNT  SEC.DISBURSED.AMOUNT  PRIMARY.INSTAL.AMT  \\\n",
              "345541  ...                     0                     0                   0   \n",
              "345542  ...                     0                     0                   0   \n",
              "345543  ...                     0                     0                   0   \n",
              "345544  ...                     0                     0                   0   \n",
              "345545  ...                     0                     0                   0   \n",
              "\n",
              "        SEC.INSTAL.AMT  NEW.ACCTS.IN.LAST.SIX.MONTHS  \\\n",
              "345541               0                             0   \n",
              "345542               0                             0   \n",
              "345543               0                             0   \n",
              "345544               0                             1   \n",
              "345545               0                             0   \n",
              "\n",
              "        DELINQUENT.ACCTS.IN.LAST.SIX.MONTHS  AVERAGE.ACCT.AGE  \\\n",
              "345541                                    0         0yrs 0mon   \n",
              "345542                                    0         0yrs 0mon   \n",
              "345543                                    0        4yrs 10mon   \n",
              "345544                                    1         0yrs 3mon   \n",
              "345545                                    0         0yrs 0mon   \n",
              "\n",
              "        CREDIT.HISTORY.LENGTH  NO.OF_INQUIRIES  loan_default  \n",
              "345541              0yrs 0mon                0           NaN  \n",
              "345542              0yrs 0mon                0           NaN  \n",
              "345543              9yrs 5mon                0           NaN  \n",
              "345544              0yrs 3mon                0           NaN  \n",
              "345545              0yrs 0mon                0           NaN  \n",
              "\n",
              "[5 rows x 41 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "nuEJjhHYKBO5"
      },
      "cell_type": "markdown",
      "source": [
        "# **Checking Missing values**"
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "hptJ7c3cKHU7",
        "outputId": "bde8072a-38d5-4ca2-b139-d7a347a6db02",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 774
        }
      },
      "cell_type": "code",
      "source": [
        "print(df.isnull().values.any())\n",
        "print(df.isnull().sum())\n"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "True\n",
            "UniqueID                                    0\n",
            "disbursed_amount                            0\n",
            "asset_cost                                  0\n",
            "ltv                                         0\n",
            "branch_id                                   0\n",
            "supplier_id                                 0\n",
            "manufacturer_id                             0\n",
            "Current_pincode_ID                          0\n",
            "Date.of.Birth                               0\n",
            "Employment.Type                         11104\n",
            "DisbursalDate                               0\n",
            "State_ID                                    0\n",
            "Employee_code_ID                            0\n",
            "MobileNo_Avl_Flag                           0\n",
            "Aadhar_flag                                 0\n",
            "PAN_flag                                    0\n",
            "VoterID_flag                                0\n",
            "Driving_flag                                0\n",
            "Passport_flag                               0\n",
            "PERFORM_CNS.SCORE                           0\n",
            "PERFORM_CNS.SCORE.DESCRIPTION               0\n",
            "PRI.NO.OF.ACCTS                             0\n",
            "PRI.ACTIVE.ACCTS                            0\n",
            "PRI.OVERDUE.ACCTS                           0\n",
            "PRI.CURRENT.BALANCE                         0\n",
            "PRI.SANCTIONED.AMOUNT                       0\n",
            "PRI.DISBURSED.AMOUNT                        0\n",
            "SEC.NO.OF.ACCTS                             0\n",
            "SEC.ACTIVE.ACCTS                            0\n",
            "SEC.OVERDUE.ACCTS                           0\n",
            "SEC.CURRENT.BALANCE                         0\n",
            "SEC.SANCTIONED.AMOUNT                       0\n",
            "SEC.DISBURSED.AMOUNT                        0\n",
            "PRIMARY.INSTAL.AMT                          0\n",
            "SEC.INSTAL.AMT                              0\n",
            "NEW.ACCTS.IN.LAST.SIX.MONTHS                0\n",
            "DELINQUENT.ACCTS.IN.LAST.SIX.MONTHS         0\n",
            "AVERAGE.ACCT.AGE                            0\n",
            "CREDIT.HISTORY.LENGTH                       0\n",
            "NO.OF_INQUIRIES                             0\n",
            "loan_default                           112392\n",
            "dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "Q6qZk3oIOBJW",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# imputing Employment.Type missing values with Self employed values\n",
        "df['Employment.Type'].fillna('Self employed',inplace=True)\n",
        "df['loan_default'].fillna(0,inplace=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "5gDCOrvgMV8u"
      },
      "cell_type": "markdown",
      "source": [
        "# **Feature Engineering**"
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "2O5f6XoXOTRZ",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def change_col_month(col):\n",
        "    year = int(col.split()[0].replace('yrs',''))\n",
        "    month = int(col.split()[1].replace('mon',''))\n",
        "    return year*12+month\n",
        "\n",
        "def calculate_age(dob):\n",
        "    if int(dob[-2:]) < 19:\n",
        "        year = int('20' + dob[-2:])\n",
        "    else:\n",
        "        year = int('19' + dob[-2:])    \n",
        "    return (pd.to_datetime('today').year - year)\n",
        "\n",
        "\n",
        "df['age'] = df['Date.of.Birth'].apply(calculate_age)\n",
        "del df['Date.of.Birth']\n",
        "\n",
        "df['AVERAGE.ACCT.AGE'] = df['AVERAGE.ACCT.AGE'].apply(change_col_month)\n",
        "df['CREDIT.HISTORY.LENGTH'] = df['CREDIT.HISTORY.LENGTH'].apply(change_col_month) \n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "DkhbXt7YP5cH"
      },
      "cell_type": "markdown",
      "source": [
        "# Creating BINS of PERFORM_CNS.SCORE.BINS and LTV"
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "gO-r4PEbOTf2",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# CNS SCORE & LTV\n",
        "df['PERFORM_CNS.SCORE.BINS']=pd.cut(df['PERFORM_CNS.SCORE'],5,labels=['Slim','Doubtful','Fair','Good','Great']).astype(np.object)\n",
        "df['ltv.BINS']=pd.cut(df['ltv'],3,labels=['Low','Medium','High']).astype(np.object)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "mA1rM9jNQKTp"
      },
      "cell_type": "markdown",
      "source": [
        "# Replacing Values of  PERFORM_CNS.SCORE.DESCRIPTION "
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "wSRh1EEmOt_p",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def splitting(v):\n",
        "    vv=v.split(\"-\")\n",
        "    if len(vv)!=1:\n",
        "        return vv[0]\n",
        "    else:\n",
        "        return 'Z'\n",
        "df['PERFORM_CNS.SCORE.DESCRIPTION.SPLITTING']=df['PERFORM_CNS.SCORE.DESCRIPTION'].apply(splitting).astype(np.object)\n",
        "df['PERFORM_CNS.SCORE.DESCRIPTION'].replace({'C-Very Low Risk':'Very Low Risk','A-Very Low Risk':'Very Low Risk','D-Very Low Risk':'Very Low Risk','B-Very Low Risk':'Very Low Risk',\n",
        "'M-Very High Risk':'Very High Risk','L-Very High Risk':'Very High Risk',\n",
        "                                               'F-Low Risk':'Low Risk','E-Low Risk':'Low Risk',\n",
        "                                               'H-Medium Risk':'Medium Risk','I-Medium Risk':'Medium Risk',\n",
        "                                               'J-High Risk':'High Risk','K-High Risk':'High Risk'},inplace=True)\n",
        "df['Not_Scored']=np.where(df['PERFORM_CNS.SCORE.DESCRIPTION'].str.contains('Not Scored'),1,0)\n",
        "df['Very_Low']=np.where(df['PERFORM_CNS.SCORE.DESCRIPTION'].str.contains('Very Low'),1,0)\n",
        "df['Very_High']=np.where(df['PERFORM_CNS.SCORE.DESCRIPTION'].str.contains('Very High'),1,0)\n",
        "df['No_History']=np.where(df['PERFORM_CNS.SCORE.DESCRIPTION'].str.contains('No Bureau'),1,0)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "-9kPSIseQ5yt"
      },
      "cell_type": "markdown",
      "source": [
        "# Generating New Features."
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "IsSnIbuLOuKl",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "df['ACTIVE.ACCTS']=df['PRI.ACTIVE.ACCTS']+df['SEC.ACTIVE.ACCTS']\n",
        "df['CURRENT.BALANCE']=df['PRI.CURRENT.BALANCE']+df['SEC.CURRENT.BALANCE']\n",
        "df['DISBURSED.AMOUNT']=df['PRI.DISBURSED.AMOUNT']+df['SEC.DISBURSED.AMOUNT']\n",
        "df['NO.OF.ACCTS']=df['SEC.NO.OF.ACCTS']+df['PRI.NO.OF.ACCTS']\n",
        "df['SANCTIONED.AMOUNT']=df['PRI.SANCTIONED.AMOUNT']+df['SEC.SANCTIONED.AMOUNT']\n",
        "df['INSTAL.AMT']=df['PRIMARY.INSTAL.AMT']+df['SEC.INSTAL.AMT']\n",
        "df['SANCTION_DISBURSED']=df['SANCTIONED.AMOUNT']-df['DISBURSED.AMOUNT']\n",
        "#df= pd.get_dummies(df,columns=['Employment.Type'],drop_first=True)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "sXpW3d-xGsKB"
      },
      "cell_type": "markdown",
      "source": [
        "# **Visualization**"
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "2P5JFTrWGy2t",
        "outputId": "b1778787-5d7f-4fee-a1dc-bb3437fc84ce",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 541
        }
      },
      "cell_type": "code",
      "source": [
        "df = df[df['ACTIVE.ACCTS']<28]\n",
        "seaborn.scatterplot(x=df['PERFORM_CNS.SCORE'],y=df['ACTIVE.ACCTS']) "
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8c661f9c88>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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K5weJt9Hj0U7jbak15nbsIzUWNxMhommLxEIYOzGJ7UPDGE1N2NtS98yLIpvRKpZXSiKV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k/3CxXAwrqksPbLyxLC8++91HFfDlWBvGA9nc+/RQYAYtu23fO0TzaOHDkByzr9bvDuFqbNvv0s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\nIAgN/wd43FF/SveV0wAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 842.4x595.44 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "0UT861BfGy9s",
        "outputId": "10191309-e313-451f-fda4-1b78ae249ae0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 541
        }
      },
      "cell_type": "code",
      "source": [
        "df = df[df['AVERAGE.ACCT.AGE']<225]\n",
        "seaborn.lineplot(x=df['AVERAGE.ACCT.AGE'],y=df['loan_default'])"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8c6292f048>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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LW4WpMrcrPaPneYmfJx6v1rZuT0AbzidphrGi+b777hs7iFKKopkQQghpghwR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DewYb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\nCIIgCILowv8PVlbqv6gUZqgAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 842.4x595.44 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "259EhDHpGzAz",
        "outputId": "08884e24-6337-48da-84b3-eb93bf7fdfa3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 541
        }
      },
      "cell_type": "code",
      "source": [
        "seaborn.lineplot(x=df['age'],y=df['PERFORM_CNS.SCORE'],hue=df['loan_default'])"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8c610b4e48>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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oBWgRERGRQqQSjoKlAC0iIiJSiFwXLEW1QqRRERERESlARiUcBUsBWkRERKQQGZVwFCoF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\n3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NY+dEMnKjuKyo4DAiLxJSsTeiYW1+nYvuLQjMViMVxvQ4OfJGSqhSA/gaB+Nz/u9rsI\nTr1J+fCzxDq2L10JHjsP5TxOz8rtG2B6nUWyBZ0Zq3xOuGa4E9NCtLyFXf+SA4QArqQiznvWym54\n2q98geWqYQW0ZUMRhIozAxkOfjjIq4cH2dyR4muf3kEy7pHLB0Qikr7ONBEREk4Po6aHQWmEG0HM\nVHlrbXYITr9F+dBPkO3biN77jYrhI13OgePhdm1b89Kp8KI4zb3IdDsqN4GaHjH+p9HEXNVDa4Uu\nZpDxNE7z9TkEZbFYLBVEE+B4aBXUbUBaSBdv/+cpv/7fCM68g7f9rqrbhQNHQDo4HTtr3rdMtVav\nQDuuSYBk5vs88JGxyjkYHfro6RGcXUtXxmWVVcfGuKQ9JXn9VI4nla553seyvtj1YcuGQWnNheEs\nP3r5NK8eHuTWna18+9HdJGIu0zmfZNxla1sENztIMHAUNTWE8OLIeBrhRVfV/+iffM2I585dpvK8\nhHh22tcunucj3AhOYydu756ZWOKS6Z8uF6CYQTZ1I9u2WvFssVg+MQghTcpfubDyxqvA6d6DbNuK\n//GvzHfsIrTWhANHkO3bVuWfLdJt6OyYaeubj3RQfsn8/zBALDFBqKaHQatlLfOqiWMhBA/vjDCa\nCXjto8Gaz9eyvlgBbdkwDE/k+dnr5zh6fpJP397L4w9sRQrBVLZMW1zR64whhk+gc5Nm4j6WXnJ5\nbjn8Yy/jv/9znO49RO/9vQqBrMs5kG7dxPN8hHRx0m24PTciW/tMhbpzJ05Dx3U9AGWxWCzVkIlG\nWCxILxMhBJGbHoFy3tiFLkJnhtG5Cdwa3DfmI1NtoEITAjP/eNIxj6sAQn8pAw4ToMLSA4RgfJ+l\nFBU/khs6HLqbPH708mmC0HpCbwRsC4dlQzCVLfHy+wO8eWSY23a18eBNXYRBSCEzRV8kRzoIEToC\nsXTNjhSzMbcqM4rOjJpe5Kkh1MgpnE37idzxZIUA1zPDH+shnucjpDQDUMnlI8EtFovleka4UUQs\nXeGvfLnIph6cvlsJTr6Ou+1OE4s+QzhwFGDZ9MGq5zpjZaezo5BqWfjcjBPHshZ2kwPgxRCJ5b/3\nPVcSao07ryFRCMFn9zbwd6+O8erhQR66ZfngF8v6YwW05apTKAW8fWyEX7x5gU3tSb5wzyaCzBhM\nD7Ol0SORTC/b2qBViM5PLhDKOjNqYlfnJ0RJF5Fuxd39IN7ez1RUfI14dtYc222xWCyW1SPSbajR\nM3onbhMAACAASURBVHWPI/f2fobw4of4h39J9J5Lvvth/xFk86ZVef3DJSs7lRnF6dq94DkttLE3\nXcHCTjb1rFgEch1BuazAWbjd7q4o23saePqV09y3rwvPtauWVxMroC1XlSBUHDk3wdOvnCEWdfjd\nT22HyX6cwiSdXW3EqgxizKLykwTHXiY4ewjUvJSmaNKY6vfuRcymdqXaEInGJdskrHi2WCyWq4OI\nJgFpBqzr2Mom4w14ux/A//jXhKNncdq2oPJTqMl+vH2fW8N5JiCSMMWZxc8h0H4J/CJUEdA6DNBT\nQzg771vxOJ7jUNAL0wjBVKGfPLCd//2/HeLF9/r57B2bVv0eLPXDCmjLVUNpzbnBDD966TTZgs93\nH91FqjBAkM/QvamLiLeEDVB2DP/oS4Tn3wfA6bsFp7XPiOVU26r9YHUpD44VzxaLxXI1ENJBplrQ\nuQmIJld+wSpwd95PcPpt/MPPIh/+I8LBtbVvzCLTbaaFo+IJY2Wn/UJVCzudmR0gXNl20XNlRRrh\nLHu3NrN7cxM/OXiGAzd3E/FWPwdkqQ9WQFuuGsMTeZ557SznhrN86f4++rxxipkMXd3tVcWzmh42\nwvnCYZAO7va7cHfdj1zCkL4WrHi2WCyWq49MNhNWqexeLsKN4O37HOW3f0h4/gPCgSOIVCsy3b62\n/aXbCPuPVD7huOhyAR2UEdHKVhQ1MZtAuHLvsitlRRrh3PGF4MkD2/iLf3iXX717kUfv7lvV+Vvq\nhxXQlqvCVLbEr9/t593jo9y9p407WnPks1la2ltJxhcKWTU5gH/0RcL+j40v86778Hbet+r+tcXo\nshXPFovFshEQkTh4USNA6/x97Gy+CXnyNfwPf4ku5XBraKNYCplqIyzn0aXcTOvJ7BOOeQyq9jir\nyX4TwpVsXvkYjqgIU5nPDX3N7N3azE9fO8vDt/YQi1gpdzWwHeiWK06hFPDGx0M8//YFtnam+MLO\nkGI+T7qpieZ50dzh+AWKB/+B4q/+mnDkFO4NDxF/9N8Q2f9IfcSz7Xm2WCyWDYNIt5kWiHrvV0i8\nmx5FFzOgVc3pg9WQM04cKrswkXA2xEsvoXzV1ACycekEwvk4NRhNPXlgO5m8z/NvX1h5Y8u6YG9b\nLFeUIFR8eGacp189Szru8nu3SPxSGS+Rpr0pjhCCcOw8/se/Qo2cgkgcb+9ncLfdtere5mpoFZoh\nD8e14tlisVg2EDLeiBL9dR8mBHDatuL07kNNXEA299b8Oq3CBa4ac1Z2mVFoXdg+odFVLey0ClFT\nQ7jb76npmFLKJa3wZtnR28jNO1r5+evn+PRtm0jErJy70tifuOWKobTm9MA0T714mlI54DsPJImI\nkMCN0dUSRwpJcPZdyu/+GCIJvP2P4G67A+HWbm2ktQYVGlcOFRrBLGYmpLUGx0PGUsimbiueLRaL\nZQMhHBeZaEQXsxBJ1H3/kTufhMCvWZzrUg4dlhGJS20XItFkkgerOXEIWd2BY3oYVIhsXnmAEGbS\nCGusQv9vf/Mmv3jrPE88uK2mfVvqhxXQlivG0FiOp185Q/9Yjq/dFqMjJciHETa3JvFcB//YK/gf\n/gLZsZ3o3b9XkyeoViGU88zermuEMeaPxMGLIb2oCURxXJAeQtquJYvFYtmoyFQrQXYchANupObg\nrFoQ0oUa+4XnijHSQWs9dx5CSESqFVXFiUNEU1X3pSZnBggbaxTQQuDMpBEup/W3dKW5Y3c7z715\njs/esYlUfP3CvyyVWAFtuSJMZkv88u2LHD49zoEdHnt7ouR8h67WOFFPUv7gOYITr+L07iNy55Pm\ni24ZtAqMg4aQyIYOZLxhRiS7df3CtVgsFssVJJJAtm2B3CSqlEMzI17d6Lqmw1bgFxHxRghKZkVz\n3rFlqg01NVjzrtTkgLkZWJReuBzV0gir8cSBbbxzbIRn3zjHVx/eUfP+LZePFdCWdadUDjl4eJDf\nHLrI7naHz9yYIO87tDREScdcyu/8iPDce7jb78a7+bFll9d06KPLBXAcZHMPMtm0oti2WCwWy7WB\nEAIn2QzJZuTMzIoqZtH5SXRhGg1GSHvRuvdJLyAsIxv6ULlxdCG7QECLdBt64GN0GCCqeD4vRk32\nm7bBVZyv50gK5QCc5X2eN7WnuOvGDn751gU+f9dmGhK2NfFKYdezLeuKUprDp8d45uAZmhOCr96R\nohS4JGMuzQmH0mvfJzz3Ht6Nn8a7+QtLJwUGJVRhGnSIbO3D7b4RJ91mxbPFYrFcpwjpIKJJnMZO\n3O4bcHr24LRvRcRT6HIeXZhGFafRYbDyzlaB9osQTRqbOi++MOmWGScOrdG58ZX3NTNAWGv7xiyu\nK1FLmUEv4okHt1EOQn722tlVHcNyeVj1YVlXLo5m+effnCQMQ75xXwPScRFIOlIC/9W/RY1fwLv1\ni3jb7qz6eu0X0UEJEUmYL85Yan2rDhaLxWLZkAg3Yoa/4w3I5k0z1ekMYWESqF/lVQcl3BYTky28\nKGqROZ1MzVjZZUaRDR3L7yszCipANq8coDIf15EoVdu23a1J7t/XxQvvXOSRu/poTtc+eG9ZO1dE\niUxMTPDHf/zHPProozz++OP8yZ/8CePj5s7t0KFDfOlLX+LRRx/lD//wDxkbu+StuNxzlo3PdK7E\nj14+zeB4gSduitGSjhKGmq5EgP/y36Am+4nc/btVxbOpLkwh3Ahu506czp3IeIMVzxaLxWJBCIGI\nxHEaOnBSzWYmpg7ooIzwYnOR4sLxKrqQRbrVbFst0nsRarIfoKYI7/ksl0ZYjccf3IZSmp8etFXo\nK8UVUSNCCP7oj/6IZ599lqeffprNmzfzl3/5lyil+Lf/9t/yZ3/2Zzz77LPceeed/OVf/iXAss9Z\nNj5lP+Q3h/p559god2122LspTaEU0h0roV79G3Rhiuj938Lt3bvgdVprVGHaVJy7duN0bEdEk3Yw\n0GKxWCxVibT2gPLRusaS7TJov4ho6Lx0zXG8Cktm4UYR8YaqVnaLUZMD4HiIVOuqzsNxBKu57HU0\nxXnw5m5+895FxqaKqzqWZW1cEQHd1NTEPfdcMhC/9dZb6e/v5/Dhw0SjUe6801Qgv/71r/Pzn/8c\nYNnnLBsbpTUfnh7nZ6+fo6tB8sj+NPlSSKecgNf+K1oFxA78AU77Qt9KrRW6MIVMtyHb+uoSnGKx\nWCyW6xsZiSEaO9Gl3GXtR4fGbUMmGuYeE9IB6RnL1HmIVFuNFeiBVQ8QgrGyWy2P378VgKdfPbPq\n11pWzxVfD1dK8Y//+I985jOfYWBggJ6eS31BLS0tKKWYnJxc9jnLxmZoPM8/v3gKpRRfuzVCqCVN\npYt4b/8Dwo0Qe+gPK5aztArRhWlkU/eavmwsFovF8slFpttAyssaKNTlPLKxo+L6IyMxCCsHCVVm\n1PhFL7U/rVBTg6tu3wBTgV4tLQ0xHr6ll1c+GGB4sv6R6JaFXPEhwj//8z8nkUjwrW99i1/84hfr\nfrzW1urG5huJ9vb01T6FupHJl/nZz47QP5rjW3cn6eluQo6cJPrhD3AbO2h79F/izLu7B3PXr4pZ\nvL49eA3tV+nM68f19Pu0GOzv9PrC/j6vPzo6m/ETu/EHT+OkVp9iqFWIiqWIb9m8ILoboOy0E0wO\n48Qv7Tfb0cPUqTdojAU4icaq+/QnhiiEPumerSSaV3dOWmtGMmWSMZNtUJKKeENsxc/udx7fx0vv\n9/PcWxf4N9+4fVXH3EhcC3+jV1RA/8Vf/AVnz57lr/7qr5BS0t3dTX9//9zz4+PjSClpampa9rnV\nMDaWRalVdOJfYdrb04yMZK72adQFP1D84s1zvPxeP3f0RdjR5jI5Mkb3Rz9BpNvx7v8O0yUX5g17\n6NCHch7RthWnFINr/GdxPf0+LQb7O72+sL/P64/Z36nWLmFJQn7cDAKuAlXMIBu7yY1VDiOqfEg4\nkUUWL1WmQ8cUgiYunMdprx7wEpw7BUAh0kppYvVDjoV8iaDsI6XALxTxRbSmz+6nb+/luTfP89nb\neuhuTa76uFebq/E3KqVYdcH1iq2T/6f/9J84fPgw3/ve94hEjN3M/v37KRaLvPXWWwB8//vf57HH\nHlvxOcvGQ2vN0XMTPPPaOToaPL5wg6QQOHQOvARBieidX6noadZBCe0XcTp2LHkHb7FYLBZLLQgh\ncJp7ICgt21qxmNnhQ5msXqATrsdiK45ZJ47lBgnVZD84LiLdVvO5zMdbhRf0fL5w7xYirsOPXj69\npuNaauOKVKCPHz/OX//1X7N161a+/vWvA7Bp0ya+973v8R/+w3/g3//7f0+pVKK3t5f/+B//IwBS\nyiWf+6SiVQgqND6YG4yRyQL/9KsTBEHI790VJZAxmnOnkEMf4+39TIVXpvaLaB3idu60w4IWi8Vi\nqQsikkAkW9CFKYjWWFEs55ANbUunCjqRSiu7WINx6FhWQA8gG7vWPNPjOQ6Fsg/LhxFW0JCI8Lk7\nN/HTg2f54n1ZNnVs/FbWa5ErIqB37drF0aNHqz53++238/TTT6/6uU8iaqIfHZRxOzdW3n2hFPDD\nl05xYSTHk7cmaUoJtPJJnHwe0dSNu+uBBdvrch6kxG3fifCs4bvFYrFY6ods7CTIT5qCk1xefWqt\nQClkcmmbOeG4aCFBqzkxLIQwg4RLOHHMDhC6fbeu+X24rkSV1taC+ujdfbzwzgWeevk0f/KVm9Z8\nDpalsVYH1wgqP4XKjkE5Z2JGNwhBqHjxvX5e/2iYW7amuKUzoKwjdPS/COUC0dufWPAFpktZcFyc\n9u1WPFssFoul7gg3gmzqrs3WrlxApFpXXNmVkXiFE4dIty1ZgdbZMQjKa3LgmMV1RM1phItJxT0e\nuauPd46NcHbQ9vyvB1ZAXwPooEw4fh4RTYIQqPzU1T4lwPQ9n7gwyU9ePUNbQ5Qv7grJhzE6/Avo\n/g/xbngI2dg1t/1cQErH9g3ZhmKxWCyW6wOZbEF4EXRQXnIbrTWo0FjgrUQVAS1TbejCdNVjqMkB\ns81lCejLk2ifv3MzyZjLD186dVn7sVTHCugNjtYaNTmAwESK4iVmvCcvP3HpchnPFPn+Cyco+SG/\ne0cSpaAhBu6RnyMaOnFveHBuW1XIIJNNyLatCHnF3RMtFovF8glCSIls7kX7y/gh+0VEvLE2xw4v\njtaVFWiYqTYvQk0OgHQR6bVbszpSUBGDuAoSMZfH7unj/ZNjnLi4MQpv1xNWQG9wVH4SlZtEzAxD\nCOmACi87celyKZYDnnrxNOeGsjx2ezvtbg4ZS9J4/kUo5Yje8cScUNblPCKaQLZsQkj7kbNYLBbL\n+iOiKWS8wczdVCMsIxtqc8iQVVZNZyvX1Zw41GQ/srFzxR7sZY8pRIX7x2r57B2bSCc8nrJV6Lpj\n1cwGRgcl1PgFRGyhj6NwvKp3vFcKPwh56f0BXv1wkP1bm7ijNUuJKF1BP+r8e7i7DyCbTIqkVgFa\nhTitm2y6oMVisViuGEII00IRBhWrttovQjRpWiNrwfEqnTiSLYCoGCTUWqEm15ZAuOCQa0gjXEws\n4vLb927hozMTHD03cdn7s1zCKpoNitYKNX4RIZzKlgcvhs5PLdvbtV4EoeLw6XGefuUMzakoX9wf\npVgOaG/w0B88YwJTbnhobntdyiFbNiNcOzBosVgsliuL8GKIxvaKVVsdlHDmzeisiOOiYYG/tHA8\nRLKpYpBQ5yYgKM0VktaKFEZAr8EKegGfuq2XplSEH754alX+2JblsQJ6g6Ky46hiBhGtjP8UQqCF\nRBWmr+w5KTM0+P3nZ/qeH+xB5sZJNzQRO/VrdDFL5I4vz3lp6lLW9D3bkBSLxWKxXCVkqh2kRM8M\nAeqgbPqea60+A0JIhBsDtagPOtVW0cKhJk2K8uVWoIUQuI68bNEb8Ry+eP9Wjl2Y4qMztgpdL6yA\nrjO6nF+636rWffhF9MTFub7naggvhsqMXLG7SaU1Zwan+cdfHmdsqsjvPryNDjWCiCZo8QcIz76L\nu+t+nOZe8x5CHy0EsqkHIS5/GcpisVgslrUgHNfY2pVNFVr7RURj16qvTSJaxYkj3YbOji1oETED\nhA6iYe0DhLN4jiRUl3+dP3BzD60NUX74kq1C1wsroOuMyk8T9B8hnBpak1OGVopw7LxJPlpm+EA4\nHgRluEyxXtM5aU3/SI7vP3+C8yM5nnigj52pDIHv09USIzj0E0SqDe/GT81tTzmP09pnztNisVgs\nlquITDQhInHTyuF4yHh69Tvx4hUVaJluAxWg59nLqskBZENnXRynXFei6yCgPVfy+APbONU/zXsn\nr94M1fWEFdDrgHAjqKkhwuFTaL+0qteqzIhxragh3lpID5Vb/+WY4ck8/+M3JzhxcYpH7uzhluYs\npekpWjvbkEd/hS5MEbn9iTmxrEtZRLoNGVvDF5TFYrFYLHVGCInT3IsOysjGjjUNtQsvil7kKydS\nM1Z2M20cs9azl9u+MYvrSMLL8bKbx/37u+hoivOUrULXBSug1wWJjDdA4BMMHiPMjdf0YdXlPHpq\nELGE8FS5iYX7icTMY4vuiOvJeKbIj18+w/snx7l/XzsPdBco5rK0tLeSyl0kOP0W7s77cFo3m/cQ\nlBCuh2zsXLdzslgsFotltYhoEtm+DZloWtvrqzhxzFnZzThx6PwE+MW6CWjPlVCn2AfXkXzpwa2c\nG8ryzrGR+uz0E4wV0OuIiMQRkQRq9Dxq7Bw69JfcVqvQtG64sap3xuHwKYrP/Z8ER1+8tH8hQWtU\nIbsu5z+dK/Gz185y8MMhbtnezCNbShTyRRqaW2iKCcrv/BiRbMHb+xnzHrQCv4TTstmGpVgsFotl\nw+Ekm9buzex4lbXgSMKErMxUoC8lEF6eA8csrhR1qj8b7t3bRXdrgqdeOo2qQ2vIJxkroNcZIR1k\nohFdzBAMHkMVq2fSq6lhtF9CeJV2bzooUz70NAD+iYPGv3J2/14Mnan/nWS+GPD8Oxd44Z2L7OxJ\n8eUbfAqlgFRjA62NMYKPnkfnJxe2bhSziMbO2n01LRaLxWK5RhDSAemhVXjpMSGQ6UtOHGqiH4RE\nNHTU5ZjyMtMIq+3viQe3cXE0xxtHhuq3408gVkBfIUQ0hXAiqKGThBP9C/4AdTGLzgwjYtVdN/wj\nv0bnJvBuehT8IsHJ1y/t141AOb9AVF8upXLIS+/189OD5+htifON/YpyAIlUivamBOrsOwSn3sDd\ncQ9O2xbzHsoFRCSOvIzYUovFYrFYNjIyEqtw4hDptrkWDjU1gGjomLNzvezjyctPI1zMnXs62NSe\n4kcvnyFUdeoP+QRiBXSdKQch4RLuG8LxIN6IyowSDp1AlwvoMCAYOw9evGrrhproJzh+EGfr7Xg7\n78Pp2l1RhUY6qPxkXc7fD0Je/3iIp14+RXPK49u3QoBLJJ6gsyVOeOwlyu8+jezcibf3s4BpPyH0\ncVo326hui8VisVy/RBKVThypNijl0OU8amKgbu0bAE6dK9BgAlq+fGAbQ+N5Dh62Vei1YtVOnZnO\nlRgaL6CWGBoUQpgBQw3h4DFKg6cQKjSV5EVoFVJ698eIWJLIvs8D4O552FShT71xaUMvjsqMoi/z\nTjIIFYdOjPLff3WCiCv4zu0C4UVwIlG6WuKEHzyL/9ELOJtvJnrvN+bOWZeyiOZeY0xvsVgsFst1\niojEF6wgg6lAA4TDJ8EvIJvrM0AIRuwKcflphIu5bVcbW7rS/PiV0wShrUKvBSug14HpfJmJzPIt\nFcKLQiyNKhWWTEMKjr+KnhrEu+W352ztnOZeZOcu/OMH5yzyhHRAKXRp7cOESmuOnBvn+8+fIAwV\n373DJRFPgHTpbo4SvvND07ax8z6TNjgzhKFLOWQ8jUy1rPnYFovFYrFcCwjHq2ipmHXiCM9/YP7d\nWD8BLYTAcSSqzmVoIQRPHtjO6FSRlz8YqOu+PylYAb0ORCOSsakSuUJ52e2EkDjxVNU0JJUdwz/y\na5yeG3F7blzwnLfnYfALBKffvLQvN4LOrN0c/eJIlr9/7jiZXJlv3e7S1JgkQNLT5KLe/D7hhcN4\n+z6Ht/+RuVYTHQZoNLJ5k00btFgsFsv1j+MhFpWDRaIJhCQcOgFC1t3G1XMki4redeGm7S3s6G3g\n6VfO4AfrcIDrHCug1wGBIB51GJworOlDqbWi/O6PwXHxbv5CxfNOyyZk507846+igxmR7kbRxelL\n/14FuUKZv/nZEYYnC/zerS7d7Q34IfSkQb/296iR00RufwJv94NzQllrjS5njXiu0n5isVgsFsv1\nhnBctHQXJA0L6SBSraAVoqG95gTe5axt5+O5csm20MtBCMFXDmxnIlPiN4f6677/6x0roNcJxxFI\nAUMTS/dDL0V45l3U6Fki+x8x/dJV8G54GMr5uSq0EAItJCo/vapjBaHiqZdOcnogw2/t8djZ20jJ\nV/QkAnjt/0NNDxO55+u4W25b8DpdzCCTrTjJtRnSWywWi8VyLVLNiWO2jaPW9g2tQnSNQWiuW/8W\njllu3NrCnr4mnjl4lpJvq9CrwQrodSQacSiUAiama7eYU4Vpyh8+h2zbirPl9iW3c1o3Izu2L6hC\nCy9uosBXIdjfPz7Ibw4NsKtdcufOJgolRXcki3jtv6JLOaIPfge3+4a57bXWqMI0MtmEbK7fpLHF\nYrFYLNcEkXillV2qFaD262JQgmiiYj/V8FzJEuZedeHLB7YzlSvzq3curt9BrkOsgF5nEjGXseni\niv3Qs/jv/RTCkMhtj6/YV+zd8DCUcgRn3gbM0hKhD+V8TccaHZvkn54/RsSFL93eTL4U0ilGkW/8\nHQCxh/4Ap7VvbnutFbowjUy3IVs2rT3NyWKxWCyWaxUvjtaLKtAzwSm1WthpFZjQsRoq0I6ov5Xd\nfHZvbmL/thZ++tpZCqWVz8disAJ6nREC4lGXwYkC5RX6oYOLHxEOHMG78VPImbvZ+RTKAZl5Qtxp\n24Js34Z/7JW5XirhuKjs+IrnVcpmeOq59xjOKL50ayMumvbSOdy3v4+IJok+/C+RDZcGIbQK0cVp\nZFMXsqm7qme1xWKxWCzXO7LK3I/Tu4/ofb+PbO5d8fVmlVggE00LeqmXPN46hKks5ssHtpMt+Dz/\n9oX1PdB1hFVBdSaski0/1w+9jD+0Lhcov/dTRGMX7s77Fu1TMTJZ4GL/GEMDI4xninNtGt6eh6GU\nnatC48VQ+Un0MstCQW6SN954j4NnQ27ri7GzwyM1/jHRwz9ENnYQe+gPkYlLvc06DKCYRbb24TR2\nWscNi8VisXxycbwKPSukg9O1u7brY1hGRhMIL4aoobLsSMF6Z5Rt72ng1p1t/Pz1c+SLtQ03ftKx\nArqOnOyf4n/9H+f4aLBSvEYjDsVywPgS/dDlw7+Aco7o7V9a0BqRLwWcG8ownS2S9EISiThjY9OM\nTBox7rRtRbZtIZipQpvKsEYVM1WPE2ZGGT5xlB+879OUkDy2P0Xk3Js0nHwW2b6N6IPfNctKM+ig\nDOU8smMbTtJ6PVssFovlE47jomFV80bz0UEZEk3gejV1ZjiOQF4BufblA9vIlwKee/P8uh/resAK\n6DqyqS3FppYIP/qgzKFzhYrnEzGX8ekS2UX90OHIacKz7+DuvG+ufypUiqGJHBeGsziOIOGUUKkO\nVFMvKS9kKltiaDyP0gpvz6fQxQzBmXcAEG4MnRlZcAytFeHEAIWhc3z/PZ/pouYrtzeQPPMijRde\nmlt+Em700mv8Ijos43TuWNINxGKxWCyWTxJCSIQbq6l/uerrtUZGkwjpGjG+gsmzFBLnCqi1vs40\nd97QznNvnidbsFXolbACuo5EIw5/8KlOtrRIfvhOhjdPLxTRQkA85jA0nqc8YxejA5/yu08jks1G\nCGtNtlDmzFCGbD4gGXdxhQLhMBamCJ0YKtVOyimRL/r0j+bRLX3I1j6CYy+jw8D4MpcL6LI5vlYh\navwCwdQQL54KOdxf5sCuGLtGXyA9+BbO1juI3PVV88c8gy7l0Vrjdu5cUJG2WCwWi+WTjohWOnHU\nglYB2vFgplglvXhNQtx1r8zQ/hMPbqNUDvnZ62evyPGuZayAriPh6FkaXv4/+Nfxn/CvW1/BP/oy\npz94H5kbmfsDcaTAkYLBCVM9nn73F+jcOJHbHicUDoMTeS6O5ok4knjMQQgIizmePib5v374Ef/l\np0cYCZLgeMQ9RdkPuTiaR+w6YKrQZ981JyOdmV5on3DkDCo/xZkph2c+yLK5SfBFfk18+DBi5wNE\nbv3igqFAXcqC6+J27kB4savxo7RYLBaLZeNSo/CtwC8hk02XeqWrWOJVw3GuzOxRb3uKe/Z18vzb\nF5jKrT6Y7ZOEu/ImllqRDe2E3Tejx8+zQ42wS59ETABvGwcaFWtEJVqIxVsoek1MjieIHXkRp+9W\nssleRoeyCAHpxKVfy9BYjv9+KGAkM8VN21s4cXGKv376KJ+/tYN72iaJxdKUfEW/bqezeRPBsZdw\nt94GXhyVHUMXpkEF5FWEf3pjGKHK/KvGV4iOnSPc/VnS+w4seA+qMI2Mp5CtfQsq0haLxWKxWAzC\ni6LQqzbH0DrEiaUv7ScSQ6lwxf14jmQdraAX8MQD23jjo2F+evAs3/jcrit01GsPq5DqiIgk8G/4\nHJnRYaLJFKFf5sV3LzA1OsLdHQV2JjPI/ATRyfPEZu9coynGeh4kN1YgHnOMXQ2gtOb1k3l+8WGO\neNTl9z+/i529jWTyZZ5+5Qw/e3uQox1RntyXpaExjR8oxtrvpPXYU4RnD+Fuu9PsX2tCJ8ZP3x5l\naDzD/9LxaxKZEXK7HqNt7z1z5661NjZ1yRZkcy9ivUd+LRaLxWK5RhFVnDhWwljWSVN1nrcfanDu\ncF3BlaoHd7YkuH9/F7969yKP3dNHczq68os+gVgBvY44XoSH79rG04fa+OuzRe7bEefR21MIn25H\nhwAAGiZJREFUQJSmkbkxVLKFIh7JxKX+pkwx5Kl3MpwYLrO7O87jD+8mGfMASCcifONzu3j3+CjP\nvnGe772k+K39gpu3JAnbtlG+2IU+8hLOltsQ0RRaaw6fmeLtoyP8z82/pCHIMLHri3TuuXVuCWlO\nPKfbkU1d1uPZYrFYLJblcIyDxqpEdFBGxlMLQ8gcryYru4gj8dcxTGUxX3pgKwc/HOQnB8/w7Udu\nWHH7TyJWKa0zUggevzXNPdvjHDxZ4CfvZVGAjjUStm4n3d5NLHLpj+noQIn//MI4Z0bLfHF/7P9v\n796Dqyrv/Y9/nrX2Pff7BRCICkbsERSLchGNnKIYkY5nCoefOm3tz55jx9p2bEvRIt46Ymd6mUqn\n08tpp6O/9jcdKkq8oFMUlZ8CVqnNiUethnsIkAC5J/vy/P4ISUEQsknI2nv7fv2VvVey8t3zZO18\n8uRZ30eL500eDM8DjDG6ZFKJ/uPGKSotCOvP27r0f7ccUW/cqGf8TJmeI2r/oL8vdEtbjza8uVNf\nz3lOBU6XDkxapNyqi+Q/ekOCtYljwjMbpAAAcDrGcSXHf9oOGseysT4pnH/8k65f1ui0G6o4jiNn\nFLdgKM4Pa87FlXpl214dPHJiVzEQoEeFY4yu+0y2Zp8f0Zvbu/XUW+0nbKjSF7Oq+1u7/s/mI8oN\nufrPWUFdetH4/u25j7LWKh7/50VWkBPUrdddqH+9KFfvN/dp9YYWNfRVKpZdJn34mg4eatcrWz7Q\n/3KeVbY/odYL/03BsirlhANHzzewNXfp0fDMBikAAAyFEwgl1YnDyMgJRo5/zpj+m/VPE8T9PkdZ\n4RN3QDybaq8YL2OM1m3aPqrfN10QoEeJMUbzLszS1RdkaduuHq15s21w18J9R6L65cZWbW3s1szz\nwrp9ZkjFRXmywezBr4/GEmrvjKo3mlBbR5+isf4g7ThGV1xSpf+YFVFO0NEftrTp5dhU+Xrb1P72\nes1tWyfj+tRx8RJFs8tUkh+WMeZoeG6Xk1d2dNkG4RkAgCELRobcicPGo5I/0N9m9mNMICzFT913\n2ec6ioRGd9VtYW5IV02r1Ka/71Pzoa5R/d7pgDXQI8zIKHGS7byl/hB91QVZ8rnSi//dqWj8iCaP\niemZtw8pEnB068x8nVvik9PXpWh2+eCNBV09MVlJVWPyFAn51NbZp32tXeru7FM46Mrv86l4zFh9\nNbJTLzUGVfd+qS7ML1LlkXodUJ7s1C+oz8nSmPywfK7zz/CcXyonl/AMAECyjD+sRCI2tHXQ0V6Z\nvNKTHwtEZDtbk74pcTRcf/l4vbJtr55+rVH/+4YpXpeTUgjQIywvO6Bom19tXTGFgq58J+ndOPv8\nLPldo2ff6dB7+/p0QXlAC6flKivoyPR2Kp5dKvkCstaqvSuqrJBf40qzFfD3r1vOzw4qNyug9s4+\nNR/qVltHn0LBLIXC+Zp3XocmlRfqubcu12fs/yjrX2pU6M9SXsSvrHCA8AwAwAgYageNfgk5oeyT\nHnF8ASVsav4uzssOqubSsVq/eaeuv2KCKovZWG0AAXqE+VxXZQUR5SR8aj7crWjUKhT0nXCNzaiK\nKDfkyvX7dH6x0x9k41HJ9SkRLlAsnlBnd0xlBWGVFkQG29sNcIxR3tEg3dEd1b7WLh12CpQTP6Jx\n+UGVXXWB2nvOV17YUTxuVZwXPiY8l8nJLSM8AwBwpobYQcPahKxxpE/amMznH3oO98B1M87RS2/v\n0VOvNeo/F13kdTkpgzXQZ0lWOKDxZTnKyw6qsyc2uGb5WNWVQU2vyu4PstbKRHsUz61UT9Sqpzeu\niRU5Ki/KOiE8H8sYo5xIQOeNyVPVuGLZnEr1tLfJWKvCLJ96+hIqK8ySY9TfbSO/nPAMAMAwGdcn\n67in7aChaK+cSP4nd7k62hLP2lHsU5eEnEhA/zp9nLb+z37tbG73upyUQYA+i1zHUXFeWOeU9ofk\nzu6YPuk6M9EeJYK5aosF5LqOzh+br9ysoTcvN8YoO+zXhKpxKq8slz/Rq47uqApzggoHnP7wnFch\nN4/wDADASBhKJw6biMqEcz/xuDFO/82FSbTEG23zPztO4aBPT73W6HUpKYMAPQpCAZ/GlmSrJC+k\n7r6Yevs+dpHYhBKJmA45hSrMCaqqMlfBY3pDJ8NxHGVXnKPywpDGl0ZUkBPob1V3NDwDAIAREgif\nvpWd7d+p+FSMPzzkjh5eyAr5de1nx+ntDw6qsanN63JSAgF6lDjGKD8npHNKcxTwu+rsig22sYt1\ndajDX6RzKgs0piRb7jC30Ta+oJyCSgUS3TI97XIKKgnPAACMNH9Y1n5y8LWxPplA+Lg9HU4qEEmq\np7QX5k0fp+ywX0+++pHXpaQEAvQoC/hdVRZlqbw4or5YQm1HOmTdoCZUTVBBzifcYHAGnKxCOeE8\nOQVj5eZ+QuscAABwxpyT9HU+lo31yGQXnvY8xh9I2TXQA8JBn66bcY7qP2rVP3Yf8bocz41KgF61\napVqamo0efJkvf/++4PPNzY2avHixZo/f74WL16s7du3D+lYujPGKCcc0DnFEVXkuxo36XxFRniH\nIWOMnOIJcnOLR/S8AADgKNd/yv7NRpITPH3rN+MLyCi1A7Qk1VwyVrlZAWahNUoB+pprrtETTzyh\nMWPGHPf8fffdp6VLl2r9+vVaunSpVqxYMaRj6c7ahBI97XJtn4qrJskf+eSbC4aDmwUBADiLTtFB\nwybiso5P8g2hIYDrV4q2gj5OMODq+svH690dh/TujkNel+OpUQnQ06dPV0VFxXHPtbS0qKGhQbW1\ntZKk2tpaNTQ0qLW19ZTH0p3t65J6OuTkFMtXPln+nCKvSwIAAGfAGCPjC538BsBoz9H2dadPxsZx\nJccvm8KdOAZcNa1SBTlBPfnqRym/7ORs8mwjlaamJpWVlcl1+7tNuK6r0tJSNTU1yVr7iccKC0+/\nluhYRUUn3/nnbOlz2hTzdcsNHX/HbSLWp0Rvl9yCIgWKxsgJhAePlZTkjGqNOLsYz8zDmGYWxjPz\neDmmvbZYia52OcHwcc/Hu6IKVlTKjQyttt5YkRLRHjkn2XAl0WfkhLMUTJGf3X//3GT9fM072t3a\no0suGPn7rNLhGs34nQhbWjqUSIzeX0jxw12yHd0ywf6/OG0iLtvbKeMPyC0YK+PLlo7EJPU3Iy8p\nydGBAzQmzxSMZ+ZhTDML45l5vB7TeEdC9nCbTOifWcNaK9vbI192XKZzaLXFO61se7tM8MQNI2y0\nR6bHkWtS42d3alWhivNC+u26eo0tnD6iS0a9GE/HMUlPuHrWhaOiokLNzc2Kx/v/XRGPx7V//35V\nVFSc8li66L942mX7uuQUjJFbNkkmNLqz4QAA4Owy/qDsx28AjPXKCWb3L80Y8nlCabGEQ5J8rqMb\nZk3Q9n3t2vaPg16X4wnPAnRRUZGqq6tVV1cnSaqrq1N1dbUKCwtPeSwd2Fiv1N0mk1UoX+UFcnOK\nZIbZ2xkAAKQe4wZO6MRhY31SVn5y5/H5dcqWHilm5kXlKisIa+2rjUp8CtdCj0qqe+ihh3TllVdq\n3759+tKXvqTrr79ekrRy5Uo9/vjjmj9/vh5//HHdf//9g19zqmMpzXFkwrlyKybJLRgj4/q9rggA\nAJwtru+EBnT97etOvfvgiec5dUu8VOM6jhbOnqhd+zv01/cOeF3OqDM2w2+hHO010NYmJJkhrwfy\neu0WRhbjmXkY08zCeGaeVBjT6J53+3s5O65sPCrZhHwVk5M/z+56mUBExhw/v2mjPTKhLLmF40aq\n5BGRSFit+K8tstbqwdtmyHGG/ycAa6A/pYxx6L8MAMCniBOMSPFo/4Nor0wkueUbg+cJhFN+S+9j\nOY7RotkT1dTSpc3vNntdzqgiQAMAAAxHIDzYC9oqIedMmwakWYCWpEsml2hcabaeeq1R8cSJHUQy\nFQEaAABgGIw/JGsT/cs4jdMfhM+EPyxr0ytAO8bo83OqtP9Qt/7f3/d5Xc6oIUADAAAMgxm4ATDa\nKyece8Ia5qFyfIERrWu0XHxekSZW5OrpTdsVi386ZqEJ0AAAAMPh+iUr2XjsjNc/D5wnHe+iMsbo\n83MmqqWtR6/+ba/X5YwKAjQAAMAwGNcn67iSrMyZLt+QBlvipWODtCkTC3X+2DzVvb5D0Vh6bAgz\nHARoAACAYXICIZlAeFj7PxjjyPhCgzckphNzdC30ofZevfx25s9CE6ABAACGKxCRySoY9mlMMP06\ncQy4YHyBqscX6JnXt6u3L7NnoQnQAAAAw+TkFMtJcvvuk/KH03IGesDn51SprSuqDW/t9rqUs4oA\nDQAAMEzG9cs4vmGfx/GHZE/YHDx9nDc2T5+pKtJzm3equzd9/xA4HQI0AABAqkjTThzHWjRnojq6\no3rxzV1el3LWEKABAABShetXGjbhOM7EilxNO79Y67fsUmdP1OtyzgoCNAAAQIowjiPjD8im8Tpo\nSVo0p0rdvTGt35KZs9AEaAAAgBRi/OnbiWPAuNJsXXZBqV58c5fau/q8LmfEEaABAABSSSC9O3EM\nuHH2RPVF43pu806vSxlxBGgAAIAUYvwhWZvwuoxhqyzO0uUXlmvDX3frSEev1+WMKAI0AABACjEZ\n0IljwMLZExSLWz3z+g6vSxlRBGgAAIBUkgGdOAaUFUQ0+1/K9fK2PWpt6/G6nBFDgAYAAEghxvVJ\nrk82kRnbYdfOnCBrpboMmoUmQAMAAKQYJ8239D5WcV5YV06t1Kt/26sDh7u9LmdEEKABAABSTSD9\nW9kdq/aKCXIco3WbtntdyoggQAMAAKQYEwhlzBIOSSrICerqaWO0qb5J+1q7vC5n2AjQAAAAKca4\nfslkSi+OfgsuHy+/z9HTrzV6XcqwEaABAABSjeuXyZBOHANyswKad+k4bW5o1p4DHV6XMywEaAAA\ngFTj+mWNMmJDlWNdO+McBQOu1qb5LDQBGgAAIMUYY2T8ISmD1kFLUnbYr89dNk5/fe+Adja3e13O\nGSNAAwAApCCTYZ04BnzusnHKCvm09tX0nYUmQAMAAKSiQEQ2EfW6ihEXCfk1/7PnaNs/DurDvUe8\nLueMEKABAABSkOMLSDazOnEMmDd9rLLD/rSdhSZAAwAApCKfP9M62Q0KBXxacPl4/Xdjq97fddjr\ncpJGgAYAAEhFrl9WkmyG9bM76upLxigvK6AnX/lINs1eIwEaAAAgBRnjyPgCks2sThwDgn5X118x\nXu/tOqx3dxzyupykEKABAABSlPGHZTOwE8eAuVPHqDA3qCdfTa9ZaAI0AABAqgpEMrKV3QC/z1Ht\nzAn6cE+b/v5Ri9flDBkBGgAAIEUZf0DKsN0IP272ZypUkh/Sk682ps0sNAEaAAAgRRnXL5nMjms+\n19HCWRO1Y1+73qjf53U5Q5LZIwIAAJDOfAHJ5/O6irPu8illqqrMVXNrp9elDEnmjwgAAECaMo4r\nuUGvyzjrXMfR8lsuVVlprg4caPe6nNNiBhoAACCFOYGQ1yWMCieNdo0hQAMAAKSyQMTrCvAxBGgA\nAIAUZvyhjL+RMN2k/Gg0NjZq8eLFmj9/vhYvXqzt27d7XRIAAMCoMf6QDLPQKSXlA/R9992npUuX\nav369Vq6dKlWrFjhdUkAAACjxviDcrIKvC4Dx0jpAN3S0qKGhgbV1tZKkmpra9XQ0KDW1laPKwMA\nAMCnVUoH6KamJpWVlcl1XUmS67oqLS1VU1OTx5UBAADg0yrj+0AXFWV7XcJplZTkeF0CRhDjmXkY\n08zCeGYexjSzpMN4pnSArqioUHNzs+LxuFzXVTwe1/79+1VRUTHkc7S0dCiRSN191UtKctKiYTiG\nhvHMPIxpZmE8Mw9jmlm8GE/HMUlPuKb0Eo6ioiJVV1errq5OklRXV6fq6moVFhZ6XBkAAAA+rVJ6\nBlqSVq5cqWXLlunnP/+5cnNztWrVKq9LAgAAwKdYygfoc889V3/605+8LgMAAACQlOJLOAAAAIBU\nQ4AGAAAAkkCABgAAAJJAgAYAAACSQIAGAAAAkkCABgAAAJJAgAYAAACSQIAGAAAAkkCABgAAAJJA\ngAYAAACSkPJbeQ+X4xivSzitdKgRQ8d4Zh7GNLMwnpmHMc0soz2eZ/L9jLXWnoVaAAAAgIzEEg4A\nAAAgCQRoAAAAIAkEaAAAACAJBGgAAAAgCQRoAAAAIAkEaAAAACAJBGgAAAAgCQRoAAAAIAkEaAAA\nACAJGb+Vd6o4dOiQvvOd72jnzp0KBAIaP368HnjgARUWFmrbtm1asWKFent7NWbMGP3whz9UUVGR\n1yXjNO644w7t3r1bjuMoEono+9//vqqrq9XY2Khly5bp8OHDys/P16pVqzRhwgSvy8UQPfbYY/rZ\nz36mdevWadKkSVyfaaympkaBQEDBYFCSdPfdd2vOnDmMaZrq7e3VD37wA73++usKBoOaOnWqHnzw\nQd5z09Tu3bv1ta99bfBxe3u7Ojo6tGXLlvQYU4tRcejQIfvGG28MPn7kkUfs9773PRuPx+28efPs\n1q1brbXWrl692i5btsyrMpGEtra2wY9ffPFFu2jRImuttbfccotdu3attdbatWvX2ltuucWT+pC8\n+vp6e9ttt9mrr77avvfee1yfaW5gHI/FmKavBx980D788MM2kUhYa609cOCAtZb33Ezx0EMP2fvv\nv99amx5jyhKOUZKfn68ZM2YMPp46dar27t2r+vp6BYNBTZ8+XZK0ZMkSPf/8816ViSTk5OQMftzR\n0SFjjFpaWtTQ0KDa2lpJUm1trRoaGtTa2upVmRiivr4+PfDAA1q5cuXgc1yfmYcxTU+dnZ1au3at\n7rrrLhljJEnFxcW852aIvr4+rVu3TjfddFPajClLODyQSCT0hz/8QTU1NWpqalJlZeXgscLCQiUS\nicF/WyC13XPPPdq0aZOstfr1r3+tpqYmlZWVyXVdSZLruiotLVVTU5MKCws9rhan8tOf/lQLFy7U\n2LFjB5/j+kx/d999t6y1uvTSS/Wtb32LMU1Tu3btUn5+vh577DFt3rxZWVlZuuuuuxQKhXjPzQAb\nNmxQWVmZpkyZovr6+rQYU2agPfDggw8qEono5ptv9roUDNPDDz+sl19+Wd/85jf16KOPel0OztDb\nb7+t+vp6LV261OtSMIKeeOIJPf3001qzZo2stXrggQe8LglnKB6Pa9euXbrwwgv15z//WXfffbfu\nvPNOdXV1eV0aRsCaNWt00003eV1GUgjQo2zVqlXasWOHfvKTn8hxHFVUVGjv3r2Dx1tbW+U4DjMh\naWbRokXavHmzysvL1dzcrHg8Lqn/TX///v2qqKjwuEKcytatW/Xhhx/qmmuuUU1Njfbt26fbbrtN\nO3bs4PpMYwPXXSAQ0NKlS/XWW2/xnpumKioq5PP5Bv+tf/HFF6ugoEChUIj33DTX3NysrVu36oYb\nbpDUP9bpMKYE6FH0ox/9SPX19Vq9erUCgYAk6aKLLlJPT4/efPNNSdIf//hHXXvttV6WiSHo7OxU\nU1PT4OMNGzYoLy9PRUVFqq6uVl1dnSSprq5O1dXVKfVvJ5zo9ttv12uvvaYNGzZow4YNKi8v129+\n8xt95Stf4fpMU11dXWpvb5ckWWv17LPPqrq6mvfcNFVYWKgZM2Zo06ZNkqTGxka1tLRowoQJvOem\nuSeffFJz585VQUGBJKXN71FjrbVeF/Fp8MEHH6i2tlYTJkxQKBSSJI0dO1arV6/WW2+9pfvuu++4\nlkrFxcUeV4xTOXjwoO644w51d3fLcRzl5eXpu9/9rqZMmaIPP/xQy5YtU1tbm3Jzc7Vq1SpVVVV5\nXTKSUFNTo1/84heaNGkS12ea2rVrl+68807F43ElEgmde+65uvfee1VaWsqYpqldu3Zp+fLlOnz4\nsHw+n77xjW9o7ty5vOemufnz5+uee+7RlVdeOfhcOowpARoAAABIAks4AAAAgCQQoAEAAIAkEKAB\nAACAJBCgAQAAgCQQoAEAAIAkEKABAACAJBCgAQAAgCQQoAEAAIAkEKABIEP88pe/1Lx58zRt2jQt\nWLBAL774oiQpHo/rkUce0YwZM1RTU6PHH39ckydPViwWkyS1t7dr+fLlmj17tubMmaMf//jHisfj\nXr4UAEhpPq8LAACMjHHjxumJJ55QSUmJnn/+eX3729/WCy+8oL/85S965ZVX9NRTTykcDuuuu+46\n7uuWLVumoqIivfDCC+ru7tZXv/pVVVRUaMmSJR69EgBIbcxAA0CGuO6661RWVibHcbRgwQKNHz9e\n77zzjp577jndeuutKi8vV15enm6//fbBrzl48KA2btyo5cuXKxKJqKioSF/84hf1zDPPePhKACC1\nMQMNABli7dq1+u1vf6s9e/ZIkrq6unTo0CHt379fFRUVg59XXl4++PHevXsVi8U0e/bswecSicRx\nnw8AOB4BGgAywJ49e3Tvvffqd7/7naZNmybXdXXjjTdKkkpKSrRv377Bzz324/LycgUCAb3xxhvy\n+fiVAABDwRIOAMgA3d3dMsaosLBQkrRmzRp98MEHkvqXdvz+979Xc3Oz2tra9Ktf/Wrw60pLSzVr\n1iw98sgj6ujoUCKR0M6dO7VlyxZPXgcApAMCNABkgPPOO09f/vKXtWTJEs2cOVPvv/++LrnkEknS\nF77wBc2aNUsLFy7UokWLNHfuXPl8PrmuK0l69NFHFY1GtWDBAl122WX6+te/rgMHDnj5cgAgpRlr\nrfW6CADA6Nm4caNWrlypl156yetSACAtMQMNABmup6dHGzduVCwWU3Nzs1avXq158+Z5XRYApC1m\noAEgw3V3d+vmm2/WRx99pFAopKuuukr33HOPsrOzvS4NANISARoAAABIAks4AAAAgCQQoAEAAIAk\nEKABAACAJBCgAQAAgCQQoAEAAIAkEKABAACAJPx/0E1NBTLtebUAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 842.4x595.44 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "Is7QFn0xGzDo",
        "outputId": "69ef5b2d-12fc-4ace-808c-eeaeeaa2f2c0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 541
        }
      },
      "cell_type": "code",
      "source": [
        "#boxplot\n",
        "seaborn.boxplot(x=df[\"disbursed_amount\"])\n",
        "seaborn.boxplot(x=df[\"asset_cost\"])"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8c62925f60>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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piFMAANIQpwAApCFOAQBIQ5wCAJCGOAUAIA1xCgBAGuIUAIA0Gmq9gN01efJxPZ4bMWJE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\n5fv8aAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 842.4x595.44 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "w-bTJjF7GzJG",
        "outputId": "6cb744f6-48a4-48d0-c3a2-59930e9641fe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 743
        }
      },
      "cell_type": "code",
      "source": [
        "#correlation\n",
        "corr=round(df.corr(),2)\n",
        "seaborn.heatmap(corr,annot=False)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8c61004668>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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RoEEDDB48GE8//fQ9t09ERERUrjAZYzJGD7ZLly5h6tSp2LBhA+rWrQuLxYLj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DA1kNcikoKKCmpgbp6ekYMGAA1NXViXt4Xb58Wep9WVkZjh49ipMnT2Lu3LksjJY5XMx9gJug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\n1xqTRFRyuE2RiMiGNG/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TDK77+fmhb9++spOxsmXLyrqnBGv3Utu+fTvCw8OhVqula127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\nRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 842.4x595.44 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "1LjmuDQNRIkY"
      },
      "cell_type": "markdown",
      "source": [
        "# Dropping irrelevant columns"
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "A9dTa3G6O3EJ",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "##\n",
        "df.drop(['DisbursalDate','UniqueID','MobileNo_Avl_Flag','Current_pincode_ID','Driving_flag','Employee_code_ID','NO.OF_INQUIRIES','State_ID','VoterID_flag','branch_id','manufacturer_id','supplier_id'],axis=1,inplace=True)\n",
        "#df.drop([''],axis=1,inplace=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "i_aAMXPVO3Qf",
        "outputId": "4f011738-d896-43f9-b5a9-e4441dae8c74",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 862
        }
      },
      "cell_type": "code",
      "source": [
        "dftrain=df[df['loan_default'].isnull()!=True]\n",
        "dftest=df[df['loan_default'].isnull()==True]\n",
        "dftrain.head()\n",
        "df.info()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 345507 entries, 0 to 345545\n",
            "Data columns (total 43 columns):\n",
            "disbursed_amount                           345507 non-null int64\n",
            "asset_cost                                 345507 non-null int64\n",
            "ltv                                        345507 non-null float64\n",
            "Employment.Type                            345507 non-null object\n",
            "Aadhar_flag                                345507 non-null int64\n",
            "PAN_flag                                   345507 non-null int64\n",
            "Passport_flag                              345507 non-null int64\n",
            "PERFORM_CNS.SCORE                          345507 non-null int64\n",
            "PERFORM_CNS.SCORE.DESCRIPTION              345507 non-null object\n",
            "PRI.NO.OF.ACCTS                            345507 non-null int64\n",
            "PRI.ACTIVE.ACCTS                           345507 non-null int64\n",
            "PRI.OVERDUE.ACCTS                          345507 non-null int64\n",
            "PRI.CURRENT.BALANCE                        345507 non-null int64\n",
            "PRI.SANCTIONED.AMOUNT                      345507 non-null int64\n",
            "PRI.DISBURSED.AMOUNT                       345507 non-null int64\n",
            "SEC.NO.OF.ACCTS                            345507 non-null int64\n",
            "SEC.ACTIVE.ACCTS                           345507 non-null int64\n",
            "SEC.OVERDUE.ACCTS                          345507 non-null int64\n",
            "SEC.CURRENT.BALANCE                        345507 non-null int64\n",
            "SEC.SANCTIONED.AMOUNT                      345507 non-null int64\n",
            "SEC.DISBURSED.AMOUNT                       345507 non-null int64\n",
            "PRIMARY.INSTAL.AMT                         345507 non-null int64\n",
            "SEC.INSTAL.AMT                             345507 non-null int64\n",
            "NEW.ACCTS.IN.LAST.SIX.MONTHS               345507 non-null int64\n",
            "DELINQUENT.ACCTS.IN.LAST.SIX.MONTHS        345507 non-null int64\n",
            "AVERAGE.ACCT.AGE                           345507 non-null int64\n",
            "CREDIT.HISTORY.LENGTH                      345507 non-null int64\n",
            "loan_default                               345507 non-null float64\n",
            "age                                        345507 non-null int64\n",
            "PERFORM_CNS.SCORE.BINS                     345507 non-null object\n",
            "ltv.BINS                                   345507 non-null object\n",
            "PERFORM_CNS.SCORE.DESCRIPTION.SPLITTING    345507 non-null object\n",
            "Not_Scored                                 345507 non-null int64\n",
            "Very_Low                                   345507 non-null int64\n",
            "Very_High                                  345507 non-null int64\n",
            "No_History                                 345507 non-null int64\n",
            "ACTIVE.ACCTS                               345507 non-null int64\n",
            "CURRENT.BALANCE                            345507 non-null int64\n",
            "DISBURSED.AMOUNT                           345507 non-null int64\n",
            "NO.OF.ACCTS                                345507 non-null int64\n",
            "SANCTIONED.AMOUNT                          345507 non-null int64\n",
            "INSTAL.AMT                                 345507 non-null int64\n",
            "SANCTION_DISBURSED                         345507 non-null int64\n",
            "dtypes: float64(2), int64(36), object(5)\n",
            "memory usage: 116.0+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "C-C2D9rkRZ3c"
      },
      "cell_type": "markdown",
      "source": [
        "# Train Test Split"
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "BPIBltAiBldN",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "X,y=dftrain.drop('loan_default',axis=1),dftrain['loan_default']\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import accuracy_score,f1_score\n",
        "X_train,X_val,y_train,y_val = train_test_split(X,y,test_size=0.3,random_state = 10)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "MFbrwL1GBlgC",
        "outputId": "fad1b330-4a43-4e6d-fa07-e7abdae255ef",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "#categorical features\n",
        "categorical_features_indices = np.where(X_train.dtypes =='object')[0]\n",
        "categorical_features_indices\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 3,  8, 28, 29, 30], dtype=int64)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "0HZJBNQxCQly"
      },
      "cell_type": "markdown",
      "source": [
        "# Installing CatBoost\n",
        "\n",
        "1. CatBoost is a machine learning algorithm that uses gradient boosting on decision trees.\n",
        "2. CatBoost converts categorical values into numbers using various statistics on combinations of categorical features and combinations of categorical and numerical features. \n",
        "3.  It reduces the need for extensive hyper-parameter tuning and lower the chances of overfitting.\n",
        "4. Traing time and prediction time is much faster than Random forest and XGBoost."
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "04K2102NBwBQ",
        "outputId": "e8112862-1f6b-4ab9-9be9-773cc6cef849",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 158
        }
      },
      "cell_type": "code",
      "source": [
        "pip install catboost"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: catboost in /usr/local/lib/python3.6/dist-packages (0.14.2)\n",
            "Requirement already satisfied: graphviz in /usr/local/lib/python3.6/dist-packages (from catboost) (0.10.1)\n",
            "Requirement already satisfied: enum34 in /usr/local/lib/python3.6/dist-packages (from catboost) (1.1.6)\n",
            "Requirement already satisfied: numpy>=1.11.1 in /usr/local/lib/python3.6/dist-packages (from catboost) (1.16.3)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from catboost) (1.12.0)\n",
            "Requirement already satisfied: pandas>=0.19.1 in /usr/local/lib/python3.6/dist-packages (from catboost) (0.24.2)\n",
            "Requirement already satisfied: pytz>=2011k in /usr/local/lib/python3.6/dist-packages (from pandas>=0.19.1->catboost) (2018.9)\n",
            "Requirement already satisfied: python-dateutil>=2.5.0 in /usr/local/lib/python3.6/dist-packages (from pandas>=0.19.1->catboost) (2.5.3)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "nbxbVdrtBwES",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from catboost import CatBoostClassifier"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "3FZujvDUClRS",
        "outputId": "be353ffa-233a-47f3-d17d-b7cfc43a57e1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 457
        }
      },
      "cell_type": "code",
      "source": [
        "m=CatBoostClassifier(n_estimators=1000,random_state=10,eval_metric='AUC',max_depth=6,od_wait=50\n",
        "                     ,l2_leaf_reg=5,cat_features=categorical_features_indices,random_strength=100,\n",
        "                     use_best_model=True)\n",
        "m.fit(X_train,y_train,eval_set=[(X_train,y_train),(X_val, y_val)], early_stopping_rounds=100,verbose=100)\n",
        "p2=m.predict(X_val)\n",
        "p2\n",
        "print(classification_report(y_val,p2))\n",
        "roc_auc_score(y_val,p2)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0:\ttest: 0.5470096\ttest1: 0.5487652\tbest: 0.5487652 (0)\ttotal: 829ms\tremaining: 13m 48s\n",
            "100:\ttest: 0.5989997\ttest1: 0.5992747\tbest: 0.5994223 (99)\ttotal: 37.8s\tremaining: 5m 36s\n",
            "200:\ttest: 0.6054404\ttest1: 0.6048789\tbest: 0.6048789 (200)\ttotal: 1m 15s\tremaining: 5m 1s\n",
            "300:\ttest: 0.6095741\ttest1: 0.6091897\tbest: 0.6091897 (300)\ttotal: 1m 54s\tremaining: 4m 25s\n",
            "400:\ttest: 0.6131958\ttest1: 0.6124345\tbest: 0.6124491 (399)\ttotal: 2m 32s\tremaining: 3m 47s\n",
            "500:\ttest: 0.6236602\ttest1: 0.6225174\tbest: 0.6225174 (500)\ttotal: 3m 11s\tremaining: 3m 10s\n",
            "600:\ttest: 0.6388788\ttest1: 0.6358761\tbest: 0.6358761 (600)\ttotal: 3m 54s\tremaining: 2m 35s\n",
            "700:\ttest: 0.6455169\ttest1: 0.6396749\tbest: 0.6396749 (700)\ttotal: 4m 39s\tremaining: 1m 59s\n",
            "800:\ttest: 0.6496511\ttest1: 0.6413600\tbest: 0.6413604 (799)\ttotal: 5m 23s\tremaining: 1m 20s\n",
            "900:\ttest: 0.6528074\ttest1: 0.6423532\tbest: 0.6423553 (897)\ttotal: 6m 8s\tremaining: 40.5s\n",
            "999:\ttest: 0.6556833\ttest1: 0.6432218\tbest: 0.6432218 (999)\ttotal: 6m 52s\tremaining: 0us\n",
            "\n",
            "bestTest = 0.6432217776\n",
            "bestIteration = 999\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       0.85      1.00      0.92     88533\n",
            "         1.0       0.60      0.00      0.00     15120\n",
            "\n",
            "   micro avg       0.85      0.85      0.85    103653\n",
            "   macro avg       0.73      0.50      0.46    103653\n",
            "weighted avg       0.82      0.85      0.79    103653\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.5000879111259563"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "OeBQ9ZVkJv_w"
      },
      "cell_type": "markdown",
      "source": [
        "# 5 fold cross validation"
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "outputId": "6e8750f2-9d54-42db-aa45-10e7ab4cd907",
        "id": "yu391WOw--WB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1457
        }
      },
      "cell_type": "code",
      "source": [
        "err=[]\n",
        "y_pred_total=[]\n",
        "from sklearn.model_selection import KFold,StratifiedKFold\n",
        "fold=StratifiedKFold(n_splits=5,shuffle=True,random_state=100)\n",
        "i=1\n",
        "for train_index, test_index in fold.split(X,y):\n",
        "    X_train, X_test = X.iloc[train_index], X.iloc[test_index]\n",
        "    y_train, y_test = y[train_index], y[test_index]\n",
        "    m=CatBoostClassifier(n_estimators=1000,random_state=100,eval_metric='AUC',max_depth=6,od_wait=50\n",
        "                     ,l2_leaf_reg=10,cat_features=categorical_features_indices,random_strength=100,\n",
        "                     use_best_model=True)\n",
        "    m.fit(X_train,y_train,eval_set=[(X_train,y_train),(X_test, y_test)], early_stopping_rounds=100,verbose=100)\n",
        "    preds=m.predict_proba(X_test)[:,-1]\n",
        "    print(\"err: \",roc_auc_score(y_test,preds))\n",
        "    err.append(roc_auc_score(y_test,preds))\n",
        "    p = m.predict_proba(dftest)[:,-1]\n",
        "    y_pred_total.append(p)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0:\ttest: 0.5158823\ttest1: 0.5175297\tbest: 0.5175297 (0)\ttotal: 464ms\tremaining: 7m 43s\n",
            "100:\ttest: 0.6160149\ttest1: 0.6136910\tbest: 0.6137496 (97)\ttotal: 55.6s\tremaining: 8m 14s\n",
            "200:\ttest: 0.6193491\ttest1: 0.6165620\tbest: 0.6166609 (188)\ttotal: 1m 50s\tremaining: 7m 19s\n",
            "300:\ttest: 0.6220742\ttest1: 0.6195408\tbest: 0.6195452 (298)\ttotal: 2m 46s\tremaining: 6m 26s\n",
            "400:\ttest: 0.6244438\ttest1: 0.6219612\tbest: 0.6219852 (390)\ttotal: 3m 41s\tremaining: 5m 30s\n",
            "500:\ttest: 0.6283745\ttest1: 0.6264057\tbest: 0.6264057 (500)\ttotal: 4m 36s\tremaining: 4m 35s\n",
            "600:\ttest: 0.6392991\ttest1: 0.6363489\tbest: 0.6363489 (600)\ttotal: 5m 43s\tremaining: 3m 47s\n",
            "700:\ttest: 0.6452806\ttest1: 0.6399801\tbest: 0.6399845 (699)\ttotal: 6m 49s\tremaining: 2m 54s\n",
            "800:\ttest: 0.6495489\ttest1: 0.6419429\tbest: 0.6419429 (800)\ttotal: 7m 55s\tremaining: 1m 58s\n",
            "900:\ttest: 0.6527972\ttest1: 0.6429595\tbest: 0.6429595 (900)\ttotal: 9m 1s\tremaining: 59.5s\n",
            "999:\ttest: 0.6556392\ttest1: 0.6435544\tbest: 0.6435544 (999)\ttotal: 10m 6s\tremaining: 0us\n",
            "\n",
            "bestTest = 0.6435543532\n",
            "bestIteration = 999\n",
            "\n",
            "err:  0.6435543532185389\n",
            "0:\ttest: 0.5361612\ttest1: 0.5377341\tbest: 0.5377341 (0)\ttotal: 660ms\tremaining: 10m 59s\n",
            "100:\ttest: 0.6165203\ttest1: 0.6157568\tbest: 0.6157942 (98)\ttotal: 57.3s\tremaining: 8m 29s\n",
            "200:\ttest: 0.6197728\ttest1: 0.6194463\tbest: 0.6194821 (194)\ttotal: 1m 53s\tremaining: 7m 30s\n",
            "300:\ttest: 0.6227392\ttest1: 0.6216682\tbest: 0.6216682 (300)\ttotal: 2m 48s\tremaining: 6m 30s\n",
            "400:\ttest: 0.6243145\ttest1: 0.6226805\tbest: 0.6226805 (400)\ttotal: 3m 43s\tremaining: 5m 34s\n",
            "500:\ttest: 0.6290433\ttest1: 0.6264671\tbest: 0.6264671 (500)\ttotal: 4m 38s\tremaining: 4m 37s\n",
            "600:\ttest: 0.6397908\ttest1: 0.6346995\tbest: 0.6346995 (600)\ttotal: 5m 44s\tremaining: 3m 48s\n",
            "700:\ttest: 0.6457711\ttest1: 0.6381142\tbest: 0.6381142 (700)\ttotal: 6m 50s\tremaining: 2m 54s\n",
            "800:\ttest: 0.6497232\ttest1: 0.6397290\tbest: 0.6397290 (800)\ttotal: 7m 54s\tremaining: 1m 57s\n",
            "900:\ttest: 0.6529119\ttest1: 0.6409102\tbest: 0.6409102 (900)\ttotal: 8m 55s\tremaining: 58.8s\n",
            "999:\ttest: 0.6554951\ttest1: 0.6416375\tbest: 0.6416375 (999)\ttotal: 9m 58s\tremaining: 0us\n",
            "\n",
            "bestTest = 0.6416375482\n",
            "bestIteration = 999\n",
            "\n",
            "err:  0.641637548220294\n",
            "0:\ttest: 0.5371262\ttest1: 0.5325880\tbest: 0.5325880 (0)\ttotal: 676ms\tremaining: 11m 15s\n",
            "100:\ttest: 0.6139405\ttest1: 0.6072281\tbest: 0.6072281 (100)\ttotal: 54.4s\tremaining: 8m 4s\n",
            "200:\ttest: 0.6207413\ttest1: 0.6133231\tbest: 0.6133231 (200)\ttotal: 1m 46s\tremaining: 7m 4s\n",
            "300:\ttest: 0.6239712\ttest1: 0.6159793\tbest: 0.6159793 (300)\ttotal: 2m 41s\tremaining: 6m 15s\n",
            "400:\ttest: 0.6257529\ttest1: 0.6175335\tbest: 0.6175335 (400)\ttotal: 3m 34s\tremaining: 5m 19s\n",
            "500:\ttest: 0.6301036\ttest1: 0.6210910\tbest: 0.6210910 (500)\ttotal: 4m 31s\tremaining: 4m 30s\n",
            "600:\ttest: 0.6410109\ttest1: 0.6302992\tbest: 0.6302992 (600)\ttotal: 5m 36s\tremaining: 3m 43s\n",
            "700:\ttest: 0.6472666\ttest1: 0.6337944\tbest: 0.6337944 (700)\ttotal: 6m 41s\tremaining: 2m 51s\n",
            "800:\ttest: 0.6513428\ttest1: 0.6353358\tbest: 0.6353358 (800)\ttotal: 7m 46s\tremaining: 1m 55s\n",
            "900:\ttest: 0.6542793\ttest1: 0.6362445\tbest: 0.6362445 (900)\ttotal: 8m 49s\tremaining: 58.2s\n",
            "999:\ttest: 0.6572918\ttest1: 0.6368216\tbest: 0.6368216 (999)\ttotal: 9m 52s\tremaining: 0us\n",
            "\n",
            "bestTest = 0.6368216304\n",
            "bestIteration = 999\n",
            "\n",
            "err:  0.6368216304187869\n",
            "0:\ttest: 0.5224479\ttest1: 0.5253676\tbest: 0.5253676 (0)\ttotal: 640ms\tremaining: 10m 38s\n",
            "100:\ttest: 0.6162398\ttest1: 0.6154215\tbest: 0.6154215 (100)\ttotal: 56.1s\tremaining: 8m 19s\n",
            "200:\ttest: 0.6209086\ttest1: 0.6195292\tbest: 0.6195382 (199)\ttotal: 1m 51s\tremaining: 7m 24s\n",
            "300:\ttest: 0.6233250\ttest1: 0.6215714\tbest: 0.6215714 (300)\ttotal: 2m 49s\tremaining: 6m 32s\n",
            "400:\ttest: 0.6258919\ttest1: 0.6237804\tbest: 0.6237804 (400)\ttotal: 3m 43s\tremaining: 5m 33s\n",
            "500:\ttest: 0.6296742\ttest1: 0.6272666\tbest: 0.6272666 (500)\ttotal: 4m 39s\tremaining: 4m 38s\n",
            "600:\ttest: 0.6405447\ttest1: 0.6347297\tbest: 0.6347297 (600)\ttotal: 5m 44s\tremaining: 3m 48s\n",
            "700:\ttest: 0.6468867\ttest1: 0.6372258\tbest: 0.6372258 (700)\ttotal: 6m 50s\tremaining: 2m 54s\n",
            "800:\ttest: 0.6511452\ttest1: 0.6385798\tbest: 0.6385908 (799)\ttotal: 7m 55s\tremaining: 1m 58s\n",
            "900:\ttest: 0.6545830\ttest1: 0.6394161\tbest: 0.6394161 (900)\ttotal: 8m 59s\tremaining: 59.3s\n",
            "999:\ttest: 0.6574215\ttest1: 0.6401071\tbest: 0.6401071 (999)\ttotal: 10m 2s\tremaining: 0us\n",
            "\n",
            "bestTest = 0.640107075\n",
            "bestIteration = 999\n",
            "\n",
            "err:  0.6401070750257616\n",
            "0:\ttest: 0.5240750\ttest1: 0.5238154\tbest: 0.5238154 (0)\ttotal: 614ms\tremaining: 10m 13s\n",
            "100:\ttest: 0.6145434\ttest1: 0.6168687\tbest: 0.6171484 (98)\ttotal: 55.8s\tremaining: 8m 16s\n",
            "200:\ttest: 0.6195465\ttest1: 0.6220430\tbest: 0.6220679 (195)\ttotal: 1m 50s\tremaining: 7m 17s\n",
            "300:\ttest: 0.6207952\ttest1: 0.6234019\tbest: 0.6234654 (293)\ttotal: 2m 42s\tremaining: 6m 18s\n",
            "400:\ttest: 0.6228415\ttest1: 0.6250606\tbest: 0.6250659 (398)\ttotal: 3m 36s\tremaining: 5m 22s\n",
            "500:\ttest: 0.6277126\ttest1: 0.6294289\tbest: 0.6294289 (500)\ttotal: 4m 31s\tremaining: 4m 30s\n",
            "600:\ttest: 0.6389673\ttest1: 0.6376718\tbest: 0.6376718 (600)\ttotal: 5m 35s\tremaining: 3m 42s\n",
            "700:\ttest: 0.6452758\ttest1: 0.6410083\tbest: 0.6410083 (700)\ttotal: 6m 40s\tremaining: 2m 50s\n",
            "800:\ttest: 0.6495017\ttest1: 0.6425510\tbest: 0.6425510 (800)\ttotal: 7m 45s\tremaining: 1m 55s\n",
            "900:\ttest: 0.6526752\ttest1: 0.6434021\tbest: 0.6434021 (900)\ttotal: 8m 48s\tremaining: 58.1s\n",
            "999:\ttest: 0.6554287\ttest1: 0.6441615\tbest: 0.6441732 (998)\ttotal: 9m 51s\tremaining: 0us\n",
            "\n",
            "bestTest = 0.6441732289\n",
            "bestIteration = 998\n",
            "\n",
            "Shrink model to first 999 iterations.\n",
            "err:  0.6441732288778772\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "ewTWw5Gv7YVJ",
        "outputId": "6d958a9b-7ee0-48ee-efc7-84fed068133d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 533
        }
      },
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import roc_curve, auc\n",
        "fpr, tpr, thresholds = roc_curve(y_test,preds)\n",
        "roc_auc = auc(fpr,tpr)\n",
        "\n",
        "plt.title('Receiver Operating Characteristic')\n",
        "plt.plot(fpr, tpr, 'b',label='AUC = %0.3f'% roc_auc)\n",
        "plt.legend(loc='lower right')\n",
        "plt.plot([0,1],[0,1],'r--')\n",
        "plt.xlim([-0.1,1.0])\n",
        "plt.ylim([-0.1,1.01])\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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mET3juGxwK10AWAtcBQVkvL6Ekh3bCWjXHqOiAou/v9mxvIrKs4iIiNSoSqeb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\n",
            "text/plain": [
              "<Figure size 842.4x595.44 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "90BH6E2vLAxN"
      },
      "cell_type": "markdown",
      "source": [
        "#**Random Forest**\n",
        "------"
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "gTLPI8JuK1Je",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "X,y=dftrain.drop('loan_default',axis=1),dftrain['loan_default']\n",
        "X = pd.get_dummies(data=X,columns=['Employment.Type','PERFORM_CNS.SCORE.DESCRIPTION','PERFORM_CNS.SCORE.DESCRIPTION.SPLITTING','ltv.BINS','PERFORM_CNS.SCORE.BINS'],drop_first=True)\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import accuracy_score,f1_score\n",
        "X_train,X_val,y_train,y_val = train_test_split(X,y,test_size=0.3,random_state = 10)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "WBVajw8wK1u2",
        "outputId": "42262f44-ac6e-4308-dc10-17d69e176c68",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 158
        }
      },
      "cell_type": "code",
      "source": [
        "from imblearn.over_sampling import SMOTE\n",
        "print(\"Before OverSampling, counts of label '1': {}\".format(sum(y_train==1)))\n",
        "print(\"Before OverSampling, counts of label '0': {} \\n\".format(sum(y_train==0)))\n",
        "\n",
        "sm = SMOTE(random_state=10)\n",
        "X_train_res, y_train_res = sm.fit_sample(X_train, y_train.ravel())\n",
        "\n",
        "print('After OverSampling, the shape of train_X: {}'.format(X_train_res.shape))\n",
        "print('After OverSampling, the shape of train_y: {} \\n'.format(y_train_res.shape))\n",
        "\n",
        "print(\"After OverSampling, counts of label '1': {}\".format(sum(y_train_res==1)))\n",
        "print(\"After OverSampling, counts of label '0': {}\".format(sum(y_train_res==0)))"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Before OverSampling, counts of label '1': 35486\n",
            "Before OverSampling, counts of label '0': 206368 \n",
            "\n",
            "After OverSampling, the shape of train_X: (412736, 68)\n",
            "After OverSampling, the shape of train_y: (412736,) \n",
            "\n",
            "After OverSampling, counts of label '1': 206368\n",
            "After OverSampling, counts of label '0': 206368\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "7Lmu9cnDK1yw",
        "outputId": "0fd84ab1-6179-4f02-f7c4-f9607353d1b6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        }
      },
      "cell_type": "code",
      "source": [
        "Rf = BalancedRandomForestClassifier(max_depth=4,verbose=True,random_state=10)\n",
        "Rf.fit(X_train_res, y_train_res.ravel())"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            "[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed:  1.0min finished\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "BalancedRandomForestClassifier(bootstrap=True, class_weight=None,\n",
              "                criterion='gini', max_depth=4, max_features='auto',\n",
              "                max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
              "                min_samples_leaf=2, min_samples_split=2,\n",
              "                min_weight_fraction_leaf=0.0, n_estimators=100, n_jobs=1,\n",
              "                oob_score=False, random_state=10, replacement=False,\n",
              "                sampling_strategy='auto', verbose=True, warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "jht1Pb-tK12E",
        "outputId": "5d9734f9-8c5f-4dbd-f047-9f9ece7612a5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "cell_type": "code",
      "source": [
        "y_pred = Rf.predict(X_val)"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            "[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed:    0.5s finished\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "yTZhnC7HK15C",
        "outputId": "7040ec10-a5a5-4e6b-9541-d70f2fcbd803",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 193
        }
      },
      "cell_type": "code",
      "source": [
        "print(classification_report(y_val,y_pred))\n",
        "print('Random Forest AUC ROC Score: {}'.format(roc_auc_score(y_val,y_pred)))"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       0.87      0.71      0.78     88533\n",
            "         1.0       0.18      0.37      0.24     15120\n",
            "\n",
            "   micro avg       0.66      0.66      0.66    103653\n",
            "   macro avg       0.52      0.54      0.51    103653\n",
            "weighted avg       0.77      0.66      0.70    103653\n",
            "\n",
            "Random Forest AUC ROC Score: 0.5409414528238864\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "AKZC_IsMLrmE"
      },
      "cell_type": "markdown",
      "source": [
        "#**XGBClassifier**\n",
        "-------"
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "s8oKZLkQK1_Q",
        "outputId": "7fb842dc-4c14-4bd2-b55f-ef6d57e2c68c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        }
      },
      "cell_type": "code",
      "source": [
        "xgb1 = XGBClassifier()\n",
        "xgb1.fit(X_train, y_train)"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
              "       colsample_bytree=1, gamma=0, learning_rate=0.1, max_delta_step=0,\n",
              "       max_depth=3, min_child_weight=1, missing=None, n_estimators=100,\n",
              "       n_jobs=1, nthread=None, objective='binary:logistic', random_state=0,\n",
              "       reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,\n",
              "       silent=True, subsample=1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "metadata": {
        "id": "s65IT9Q6-klD",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "y_pred = xgb1.predict(X_val)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "sXwmmxfBg64t",
        "outputId": "44710e55-1498-45e4-c65d-7991ee1fc1b4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "print(\"ROC AUC Score: {}\".format(roc_auc_score(y_val,y_pred)))"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "ROC AUC Score: 0.5\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "vJZDjs4yu1Er"
      },
      "cell_type": "markdown",
      "source": [
        "# Best ROC Score"
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "mie9HVmhufn8",
        "outputId": "ec7dd316-54b8-4c0d-c7ca-90c641aa439b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "print(\"err: \",roc_auc_score(y_test,preds))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "err:  0.6441732288778772\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "lCRF9i88Fo60"
      },
      "cell_type": "markdown",
      "source": [
        "# End"
      ]
    }
  ]
}