machine_hacks_deloit_loandeafulter.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SQAXcgYhYN7d"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Igni1XaiMNuM",
        "outputId": "5f4ea127-9d0c-4290-8db7-18d7cbd67423"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['card1', 'card2', 'card3', 'card4', 'card5', 'card6']"
            ]
          },
          "metadata": {},
          "execution_count": 173
        }
      ],
      "source": [
        " [\"card{}\".format(i) for i in range(1,7)]\n",
        " "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "m7xPu7YZs93K"
      },
      "outputs": [],
      "source": [
        "k1= pd.read_csv('/content/drive/MyDrive/Colab Notebooks/machine hacks/Participants_Data_PLD/train.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "srkJoyhZjBio",
        "outputId": "0e136008-baf2-448f-a397-cff3bb07fa43"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ID                              0\n",
              "Loan Amount                     0\n",
              "Funded Amount                   0\n",
              "Funded Amount Investor          0\n",
              "Term                            0\n",
              "Batch Enrolled                  0\n",
              "Interest Rate                   0\n",
              "Grade                           0\n",
              "Sub Grade                       0\n",
              "Employment Duration             0\n",
              "Home Ownership                  0\n",
              "Verification Status             0\n",
              "Payment Plan                    0\n",
              "Loan Title                      0\n",
              "Debit to Income                 0\n",
              "Delinquency - two years         0\n",
              "Inquires - six months           0\n",
              "Open Account                    0\n",
              "Public Record                   0\n",
              "Revolving Balance               0\n",
              "Revolving Utilities             0\n",
              "Total Accounts                  0\n",
              "Initial List Status             0\n",
              "Total Received Interest         0\n",
              "Total Received Late Fee         0\n",
              "Recoveries                      0\n",
              "Collection Recovery Fee         0\n",
              "Collection 12 months Medical    0\n",
              "Application Type                0\n",
              "Last week Pay                   0\n",
              "Accounts Delinquent             0\n",
              "Total Collection Amount         0\n",
              "Total Current Balance           0\n",
              "Total Revolving Credit Limit    0\n",
              "Loan Status                     0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 175
        }
      ],
      "source": [
        "k1.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "U80FUPAZtK2R"
      },
      "outputs": [],
      "source": [
        "k= k1.copy()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 652
        },
        "id": "hr0lve7eX6hU",
        "outputId": "b18eac78-5c8a-4fd6-cb99-0e51184e0a32"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ID</th>\n",
              "      <th>Loan Amount</th>\n",
              "      <th>Funded Amount</th>\n",
              "      <th>Funded Amount Investor</th>\n",
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              "      <th>Employment Duration</th>\n",
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              "      <th>Payment Plan</th>\n",
              "      <th>Loan Title</th>\n",
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              "      <th>Inquires - six months</th>\n",
              "      <th>Open Account</th>\n",
              "      <th>Public Record</th>\n",
              "      <th>Revolving Balance</th>\n",
              "      <th>Revolving Utilities</th>\n",
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              "      <td>10000</td>\n",
              "      <td>32236</td>\n",
              "      <td>12329.36286</td>\n",
              "      <td>59</td>\n",
              "      <td>BAT2522922</td>\n",
              "      <td>11.135007</td>\n",
              "      <td>B</td>\n",
              "      <td>C4</td>\n",
              "      <td>MORTGAGE</td>\n",
              "      <td>176346.62670</td>\n",
              "      <td>Not Verified</td>\n",
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              "      <td>Debt Consolidation</td>\n",
              "      <td>16.284758</td>\n",
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              "      <td>13</td>\n",
              "      <td>0</td>\n",
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              "      <td>74.932551</td>\n",
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              "      <td>2.498291</td>\n",
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              "      <td>0</td>\n",
              "      <td>INDIVIDUAL</td>\n",
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              "      <td>0</td>\n",
              "      <td>31</td>\n",
              "      <td>311301</td>\n",
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              "    <tr>\n",
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              "      <td>1450153</td>\n",
              "      <td>3609</td>\n",
              "      <td>11940</td>\n",
              "      <td>12191.99692</td>\n",
              "      <td>59</td>\n",
              "      <td>BAT1586599</td>\n",
              "      <td>12.237563</td>\n",
              "      <td>C</td>\n",
              "      <td>D3</td>\n",
              "      <td>RENT</td>\n",
              "      <td>39833.92100</td>\n",
              "      <td>Source Verified</td>\n",
              "      <td>n</td>\n",
              "      <td>Debt consolidation</td>\n",
              "      <td>15.412409</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>12</td>\n",
              "      <td>0</td>\n",
              "      <td>812</td>\n",
              "      <td>78.297186</td>\n",
              "      <td>13</td>\n",
              "      <td>f</td>\n",
              "      <td>772.769385</td>\n",
              "      <td>0.036181</td>\n",
              "      <td>2.377215</td>\n",
              "      <td>0.974821</td>\n",
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              "      <td>INDIVIDUAL</td>\n",
              "      <td>109</td>\n",
              "      <td>0</td>\n",
              "      <td>53</td>\n",
              "      <td>182610</td>\n",
              "      <td>20885</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1969101</td>\n",
              "      <td>28276</td>\n",
              "      <td>9311</td>\n",
              "      <td>21603.22455</td>\n",
              "      <td>59</td>\n",
              "      <td>BAT2136391</td>\n",
              "      <td>12.545884</td>\n",
              "      <td>F</td>\n",
              "      <td>D4</td>\n",
              "      <td>MORTGAGE</td>\n",
              "      <td>91506.69105</td>\n",
              "      <td>Source Verified</td>\n",
              "      <td>n</td>\n",
              "      <td>Debt Consolidation</td>\n",
              "      <td>28.137619</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>14</td>\n",
              "      <td>0</td>\n",
              "      <td>1843</td>\n",
              "      <td>2.073040</td>\n",
              "      <td>20</td>\n",
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              "      <td>863.324396</td>\n",
              "      <td>18.778660</td>\n",
              "      <td>4.316277</td>\n",
              "      <td>1.020075</td>\n",
              "      <td>0</td>\n",
              "      <td>INDIVIDUAL</td>\n",
              "      <td>66</td>\n",
              "      <td>0</td>\n",
              "      <td>34</td>\n",
              "      <td>89801</td>\n",
              "      <td>26155</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>6651430</td>\n",
              "      <td>11170</td>\n",
              "      <td>6954</td>\n",
              "      <td>17877.15585</td>\n",
              "      <td>59</td>\n",
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              "      <td>16.731201</td>\n",
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              "      <td>C3</td>\n",
              "      <td>MORTGAGE</td>\n",
              "      <td>108286.57590</td>\n",
              "      <td>Source Verified</td>\n",
              "      <td>n</td>\n",
              "      <td>Debt consolidation</td>\n",
              "      <td>18.043730</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>13819</td>\n",
              "      <td>67.467951</td>\n",
              "      <td>12</td>\n",
              "      <td>w</td>\n",
              "      <td>288.173196</td>\n",
              "      <td>0.044131</td>\n",
              "      <td>0.107020</td>\n",
              "      <td>0.749971</td>\n",
              "      <td>0</td>\n",
              "      <td>INDIVIDUAL</td>\n",
              "      <td>39</td>\n",
              "      <td>0</td>\n",
              "      <td>40</td>\n",
              "      <td>9189</td>\n",
              "      <td>60214</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>14354669</td>\n",
              "      <td>16890</td>\n",
              "      <td>13226</td>\n",
              "      <td>13539.92667</td>\n",
              "      <td>59</td>\n",
              "      <td>BAT5341619</td>\n",
              "      <td>15.008300</td>\n",
              "      <td>C</td>\n",
              "      <td>D4</td>\n",
              "      <td>MORTGAGE</td>\n",
              "      <td>44234.82545</td>\n",
              "      <td>Source Verified</td>\n",
              "      <td>n</td>\n",
              "      <td>Credit card refinancing</td>\n",
              "      <td>17.209886</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>13</td>\n",
              "      <td>1</td>\n",
              "      <td>1544</td>\n",
              "      <td>85.250761</td>\n",
              "      <td>22</td>\n",
              "      <td>w</td>\n",
              "      <td>129.239553</td>\n",
              "      <td>19.306646</td>\n",
              "      <td>1294.818751</td>\n",
              "      <td>0.368953</td>\n",
              "      <td>0</td>\n",
              "      <td>INDIVIDUAL</td>\n",
              "      <td>18</td>\n",
              "      <td>0</td>\n",
              "      <td>430</td>\n",
              "      <td>126029</td>\n",
              "      <td>22579</td>\n",
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              "    <tr>\n",
              "      <th>67458</th>\n",
              "      <td>16164945</td>\n",
              "      <td>13601</td>\n",
              "      <td>6848</td>\n",
              "      <td>13175.28583</td>\n",
              "      <td>59</td>\n",
              "      <td>BAT3193689</td>\n",
              "      <td>9.408858</td>\n",
              "      <td>C</td>\n",
              "      <td>A4</td>\n",
              "      <td>MORTGAGE</td>\n",
              "      <td>83961.15003</td>\n",
              "      <td>Verified</td>\n",
              "      <td>n</td>\n",
              "      <td>Credit card refinancing</td>\n",
              "      <td>28.105127</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>13</td>\n",
              "      <td>0</td>\n",
              "      <td>4112</td>\n",
              "      <td>97.779389</td>\n",
              "      <td>19</td>\n",
              "      <td>w</td>\n",
              "      <td>1978.945960</td>\n",
              "      <td>0.023478</td>\n",
              "      <td>564.614852</td>\n",
              "      <td>0.865230</td>\n",
              "      <td>0</td>\n",
              "      <td>INDIVIDUAL</td>\n",
              "      <td>69</td>\n",
              "      <td>0</td>\n",
              "      <td>48</td>\n",
              "      <td>181775</td>\n",
              "      <td>34301</td>\n",
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              "    <tr>\n",
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              "      <td>35182714</td>\n",
              "      <td>8323</td>\n",
              "      <td>11046</td>\n",
              "      <td>15637.46301</td>\n",
              "      <td>59</td>\n",
              "      <td>BAT1780517</td>\n",
              "      <td>9.972104</td>\n",
              "      <td>C</td>\n",
              "      <td>B3</td>\n",
              "      <td>RENT</td>\n",
              "      <td>65491.12817</td>\n",
              "      <td>Source Verified</td>\n",
              "      <td>n</td>\n",
              "      <td>Credit card refinancing</td>\n",
              "      <td>17.694279</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>12</td>\n",
              "      <td>0</td>\n",
              "      <td>9737</td>\n",
              "      <td>15.690703</td>\n",
              "      <td>14</td>\n",
              "      <td>w</td>\n",
              "      <td>3100.803125</td>\n",
              "      <td>0.027095</td>\n",
              "      <td>2.015494</td>\n",
              "      <td>1.403368</td>\n",
              "      <td>0</td>\n",
              "      <td>INDIVIDUAL</td>\n",
              "      <td>14</td>\n",
              "      <td>0</td>\n",
              "      <td>37</td>\n",
              "      <td>22692</td>\n",
              "      <td>8714</td>\n",
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              "    <tr>\n",
              "      <th>67460</th>\n",
              "      <td>16435904</td>\n",
              "      <td>15897</td>\n",
              "      <td>32921</td>\n",
              "      <td>12329.45775</td>\n",
              "      <td>59</td>\n",
              "      <td>BAT1761981</td>\n",
              "      <td>19.650943</td>\n",
              "      <td>A</td>\n",
              "      <td>F3</td>\n",
              "      <td>MORTGAGE</td>\n",
              "      <td>34813.96985</td>\n",
              "      <td>Verified</td>\n",
              "      <td>n</td>\n",
              "      <td>Lending loan</td>\n",
              "      <td>10.295774</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "      <td>2195</td>\n",
              "      <td>1.500090</td>\n",
              "      <td>9</td>\n",
              "      <td>w</td>\n",
              "      <td>2691.995532</td>\n",
              "      <td>0.028212</td>\n",
              "      <td>5.673092</td>\n",
              "      <td>1.607093</td>\n",
              "      <td>0</td>\n",
              "      <td>INDIVIDUAL</td>\n",
              "      <td>137</td>\n",
              "      <td>0</td>\n",
              "      <td>17</td>\n",
              "      <td>176857</td>\n",
              "      <td>42330</td>\n",
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              "    <tr>\n",
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              "      <td>5300325</td>\n",
              "      <td>16567</td>\n",
              "      <td>4975</td>\n",
              "      <td>21353.68465</td>\n",
              "      <td>59</td>\n",
              "      <td>BAT2333412</td>\n",
              "      <td>13.169095</td>\n",
              "      <td>D</td>\n",
              "      <td>E3</td>\n",
              "      <td>OWN</td>\n",
              "      <td>96938.83564</td>\n",
              "      <td>Not Verified</td>\n",
              "      <td>n</td>\n",
              "      <td>Debt consolidation</td>\n",
              "      <td>7.614624</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>14</td>\n",
              "      <td>0</td>\n",
              "      <td>1172</td>\n",
              "      <td>68.481882</td>\n",
              "      <td>15</td>\n",
              "      <td>f</td>\n",
              "      <td>3659.334202</td>\n",
              "      <td>0.074508</td>\n",
              "      <td>1.157454</td>\n",
              "      <td>0.207608</td>\n",
              "      <td>0</td>\n",
              "      <td>INDIVIDUAL</td>\n",
              "      <td>73</td>\n",
              "      <td>0</td>\n",
              "      <td>61</td>\n",
              "      <td>361339</td>\n",
              "      <td>39075</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>67462</th>\n",
              "      <td>65443173</td>\n",
              "      <td>15353</td>\n",
              "      <td>29875</td>\n",
              "      <td>14207.44860</td>\n",
              "      <td>59</td>\n",
              "      <td>BAT1930365</td>\n",
              "      <td>16.034631</td>\n",
              "      <td>B</td>\n",
              "      <td>D1</td>\n",
              "      <td>MORTGAGE</td>\n",
              "      <td>105123.15580</td>\n",
              "      <td>Verified</td>\n",
              "      <td>n</td>\n",
              "      <td>Debt consolidation</td>\n",
              "      <td>16.052112</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>30</td>\n",
              "      <td>0</td>\n",
              "      <td>8762</td>\n",
              "      <td>81.692328</td>\n",
              "      <td>16</td>\n",
              "      <td>f</td>\n",
              "      <td>1324.255922</td>\n",
              "      <td>0.000671</td>\n",
              "      <td>1.856480</td>\n",
              "      <td>0.366386</td>\n",
              "      <td>0</td>\n",
              "      <td>INDIVIDUAL</td>\n",
              "      <td>54</td>\n",
              "      <td>0</td>\n",
              "      <td>47</td>\n",
              "      <td>196960</td>\n",
              "      <td>66060</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>67463 rows × 34 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "             ID  ...  Total Revolving Credit Limit\n",
              "0      65087372  ...                          6619\n",
              "1       1450153  ...                         20885\n",
              "2       1969101  ...                         26155\n",
              "3       6651430  ...                         60214\n",
              "4      14354669  ...                         22579\n",
              "...         ...  ...                           ...\n",
              "67458  16164945  ...                         34301\n",
              "67459  35182714  ...                          8714\n",
              "67460  16435904  ...                         42330\n",
              "67461   5300325  ...                         39075\n",
              "67462  65443173  ...                         66060\n",
              "\n",
              "[67463 rows x 34 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 177
        }
      ],
      "source": [
        "k.iloc[:,:-1]\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "urv8bpK9Huws",
        "outputId": "b4b1a22f-05fc-4650-fc56-0bc1b6cceed6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0, (67463, 35))"
            ]
          },
          "metadata": {},
          "execution_count": 178
        }
      ],
      "source": [
        "#drop null values\n",
        "k.isnull().sum()[k.isnull().sum()== True].count(), k.shape\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NjJdR0ZFceMG",
        "outputId": "7477b478-5726-41ee-9738-9baf6a77072b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ID                              0\n",
              "Loan Amount                     0\n",
              "Funded Amount                   0\n",
              "Funded Amount Investor          0\n",
              "Term                            0\n",
              "Batch Enrolled                  0\n",
              "Interest Rate                   0\n",
              "Grade                           0\n",
              "Sub Grade                       0\n",
              "Employment Duration             0\n",
              "Home Ownership                  0\n",
              "Verification Status             0\n",
              "Payment Plan                    0\n",
              "Loan Title                      0\n",
              "Debit to Income                 0\n",
              "Delinquency - two years         0\n",
              "Inquires - six months           0\n",
              "Open Account                    0\n",
              "Public Record                   0\n",
              "Revolving Balance               0\n",
              "Revolving Utilities             0\n",
              "Total Accounts                  0\n",
              "Initial List Status             0\n",
              "Total Received Interest         0\n",
              "Total Received Late Fee         0\n",
              "Recoveries                      0\n",
              "Collection Recovery Fee         0\n",
              "Collection 12 months Medical    0\n",
              "Application Type                0\n",
              "Last week Pay                   0\n",
              "Accounts Delinquent             0\n",
              "Total Collection Amount         0\n",
              "Total Current Balance           0\n",
              "Total Revolving Credit Limit    0\n",
              "Loan Status                     0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 179
        }
      ],
      "source": [
        "k.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wH0ogfNODutE"
      },
      "outputs": [],
      "source": [
        "k.dropna(axis= 0, inplace= True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zHTjM39htL9w"
      },
      "outputs": [],
      "source": [
        "test= pd.read_csv('/content/drive/MyDrive/Colab Notebooks/machine hacks/Participants_Data_PLD/test.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K3F_EKWTcpA4",
        "outputId": "7f9c3a1b-9fe5-4208-e810-89153769ac9a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ID                                  0\n",
              "Loan Amount                         0\n",
              "Funded Amount                       0\n",
              "Funded Amount Investor              0\n",
              "Term                                0\n",
              "Batch Enrolled                      0\n",
              "Interest Rate                       0\n",
              "Grade                               0\n",
              "Sub Grade                           0\n",
              "Employment Duration                 0\n",
              "Home Ownership                      0\n",
              "Verification Status                 0\n",
              "Payment Plan                        0\n",
              "Loan Title                          0\n",
              "Debit to Income                     0\n",
              "Delinquency - two years             0\n",
              "Inquires - six months               0\n",
              "Open Account                        0\n",
              "Public Record                       0\n",
              "Revolving Balance                   0\n",
              "Revolving Utilities                 0\n",
              "Total Accounts                      0\n",
              "Initial List Status                 0\n",
              "Total Received Interest             0\n",
              "Total Received Late Fee             0\n",
              "Recoveries                          0\n",
              "Collection Recovery Fee             0\n",
              "Collection 12 months Medical        0\n",
              "Application Type                    0\n",
              "Last week Pay                       0\n",
              "Accounts Delinquent                 0\n",
              "Total Collection Amount             0\n",
              "Total Current Balance               0\n",
              "Total Revolving Credit Limit        0\n",
              "Loan Status                     28913\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 182
        }
      ],
      "source": [
        "test.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MWWgw66UtV1c",
        "outputId": "5c4ea4f4-b32a-4a8f-875e-506ee560362c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0    28885\n",
            "1       28\n",
            "Name: Accounts Delinquent, dtype: int64\n",
            "n    28913\n",
            "Name: Payment Plan, dtype: int64\n"
          ]
        }
      ],
      "source": [
        "#below cane be removed\n",
        "print(test['Accounts Delinquent'].value_counts())\n",
        "print(test['Payment Plan'].value_counts())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W_2Mt9sMGL5S",
        "outputId": "21c5e838-45b3-4dc7-ae69-628b21582675"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Batch Enrolled' 'Grade' 'Sub Grade' 'Employment Duration'\n",
            " 'Verification Status' 'Payment Plan' 'Loan Title' 'Initial List Status'\n",
            " 'Application Type']\n",
            "['Batch Enrolled' 'Grade' 'Sub Grade' 'Employment Duration'\n",
            " 'Verification Status' 'Payment Plan' 'Loan Title' 'Initial List Status'\n",
            " 'Application Type']\n"
          ]
        }
      ],
      "source": [
        "#below are categorical data hence we do seperate analysis\n",
        "print(k.select_dtypes(include=['object']).columns.values)\n",
        "print(test.select_dtypes(include=['object']).columns.values)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "bpl0RPmcGRtw",
        "outputId": "a942bd26-b615-4ce5-c0b5-24333f3a11e8"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>Batch Enrolled</th>\n",
              "      <th>Grade</th>\n",
              "      <th>Sub Grade</th>\n",
              "      <th>Employment Duration</th>\n",
              "      <th>Verification Status</th>\n",
              "      <th>Payment Plan</th>\n",
              "      <th>Loan Title</th>\n",
              "      <th>Initial List Status</th>\n",
              "      <th>Application Type</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>BAT2522922</td>\n",
              "      <td>B</td>\n",
              "      <td>C4</td>\n",
              "      <td>MORTGAGE</td>\n",
              "      <td>Not Verified</td>\n",
              "      <td>n</td>\n",
              "      <td>Debt Consolidation</td>\n",
              "      <td>w</td>\n",
              "      <td>INDIVIDUAL</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>BAT1586599</td>\n",
              "      <td>C</td>\n",
              "      <td>D3</td>\n",
              "      <td>RENT</td>\n",
              "      <td>Source Verified</td>\n",
              "      <td>n</td>\n",
              "      <td>Debt consolidation</td>\n",
              "      <td>f</td>\n",
              "      <td>INDIVIDUAL</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>BAT2136391</td>\n",
              "      <td>F</td>\n",
              "      <td>D4</td>\n",
              "      <td>MORTGAGE</td>\n",
              "      <td>Source Verified</td>\n",
              "      <td>n</td>\n",
              "      <td>Debt Consolidation</td>\n",
              "      <td>w</td>\n",
              "      <td>INDIVIDUAL</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Batch Enrolled Grade  ... Initial List Status Application Type\n",
              "0     BAT2522922     B  ...                   w       INDIVIDUAL\n",
              "1     BAT1586599     C  ...                   f       INDIVIDUAL\n",
              "2     BAT2136391     F  ...                   w       INDIVIDUAL\n",
              "\n",
              "[3 rows x 9 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 185
        }
      ],
      "source": [
        "k[['Batch Enrolled', 'Grade', 'Sub Grade', 'Employment Duration',\n",
        "       'Verification Status', 'Payment Plan', 'Loan Title',\n",
        "       'Initial List Status', 'Application Type']].head(3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fZGlqmPtGi3q",
        "outputId": "e9fbbf6b-ae22-46d7-f7fe-a104205eaf23"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "41 7 35 3 3 1 109 2 2\n"
          ]
        }
      ],
      "source": [
        "#below are number of value in each categorical columns\n",
        "print(len(k['Batch Enrolled'].value_counts()), len(k['Grade'].value_counts()), \n",
        "      len(k['Sub Grade'].value_counts()), len(k['Employment Duration'].value_counts()),\n",
        "      len(k['Verification Status'].value_counts()),len(k['Payment Plan'].value_counts()),\n",
        "      len(k['Loan Title'].value_counts()), len(k['Initial List Status'].value_counts()), \n",
        "      len(k['Application Type'].value_counts()))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ede3YpkStXx0"
      },
      "outputs": [],
      "source": [
        "# pd.read_csv('/content/submission.csv').tail(10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0C8F9NvrHR6B"
      },
      "outputs": [],
      "source": [
        "# savec= []\n",
        "# for column in k.columns:\n",
        "#   if len(set(k[column]))==1:\n",
        "#     k.drop(labels= column, axis= 1, inplace =True)\n",
        "#     savec.append(column)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mNqJ0CyTHr0A",
        "outputId": "dc31b67d-b82b-42b0-af0c-c11efc3732f8"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(67463, 35)"
            ]
          },
          "metadata": {},
          "execution_count": 189
        }
      ],
      "source": [
        "# k.shape, savec# use it to remove in test data as well\n",
        "k.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XOE_VfgAMd4Z"
      },
      "outputs": [],
      "source": [
        "catcolumns=k.select_dtypes(include= ['object']).columns.tolist()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "58rqvYJWdIwc",
        "outputId": "072bf6bb-a91d-4e5a-b3bc-54f0cf9c58cf"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Application Type\n"
          ]
        }
      ],
      "source": [
        "for i in catcolumns:\n",
        "  if (test[i].value_counts()/len(test) < 0.1).any() and len(test[i].value_counts())< 3:\n",
        "    print(i)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fCG0OzCLl9bP",
        "outputId": "134fc5df-e839-4d85-a70a-75c2752ec04d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 192
        }
      ],
      "source": [
        "len(k['Payment Plan'].value_counts()), len(test['Payment Plan'].value_counts())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Od9eKD0NcvdW"
      },
      "outputs": [],
      "source": [
        "def freqoncategory(df, acre):\n",
        "  drop_columns= []\n",
        "  print(df[catcolumns].describe().T)\n",
        "  for i in catcolumns:\n",
        "    # print(df[i].value_counts()/len(k)*100)\n",
        "    if (df[i].value_counts()/len(k) < 0.1).any() and len(df[i].value_counts())< 3:\n",
        "      drop_columns.append(i)\n",
        "    if len(set(df[i]))==1:\n",
        "      drop_columns.append(i)\n",
        "\n",
        "      \n",
        "  if acre:\n",
        "    df.drop(labels= drop_columns, axis=1, inplace= True)\n",
        "    return df, drop_columns\n",
        "  \n",
        "  else:\n",
        "    print(drop_columns)\n",
        "    return drop_columns\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jlPVIZ9PPtjG",
        "outputId": "924b782d-3751-4d5a-c618-7b5f736198b7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                     count unique                 top   freq\n",
            "Batch Enrolled       28913     41          BAT3873588   1623\n",
            "Grade                28913      7                   C   8104\n",
            "Sub Grade            28913     35                  B4   1902\n",
            "Employment Duration  28913      3            MORTGAGE  15530\n",
            "Verification Status  28913      3     Source Verified  13626\n",
            "Payment Plan         28913      1                   n  28913\n",
            "Loan Title           28913    109  Debt consolidation  12259\n",
            "Initial List Status  28913      2                   w  15848\n",
            "Application Type     28913      2          INDIVIDUAL  28883\n",
            "['Payment Plan', 'Application Type']\n",
            "['Payment Plan', 'Application Type']\n",
            "                     count unique                      top   freq\n",
            "Batch Enrolled       67463     41               BAT3873588   3626\n",
            "Grade                67463      7                        C  19085\n",
            "Sub Grade            67463     35                       B4   4462\n",
            "Employment Duration  67463      3                 MORTGAGE  36351\n",
            "Verification Status  67463      3          Source Verified  33036\n",
            "Payment Plan         67463      1                        n  67463\n",
            "Loan Title           67463    109  Credit card refinancing  30728\n",
            "Initial List Status  67463      2                        w  36299\n",
            "Application Type     67463      2               INDIVIDUAL  67340\n",
            "['Payment Plan', 'Application Type']\n",
            "['Payment Plan', 'Application Type']\n"
          ]
        }
      ],
      "source": [
        "print(freqoncategory(test, False))\n",
        "print(freqoncategory(k, False))\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "---czkCKHy5-",
        "outputId": "c7f1004f-95f0-4bdc-8cd9-961b7c5bfabd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                     count unique                      top   freq\n",
            "Batch Enrolled       67463     41               BAT3873588   3626\n",
            "Grade                67463      7                        C  19085\n",
            "Sub Grade            67463     35                       B4   4462\n",
            "Employment Duration  67463      3                 MORTGAGE  36351\n",
            "Verification Status  67463      3          Source Verified  33036\n",
            "Payment Plan         67463      1                        n  67463\n",
            "Loan Title           67463    109  Credit card refinancing  30728\n",
            "Initial List Status  67463      2                        w  36299\n",
            "Application Type     67463      2               INDIVIDUAL  67340\n"
          ]
        }
      ],
      "source": [
        "k, dropcolumns= freqoncategory(k, True)\n",
        "# freqoncategory(test, False)\n",
        "test.drop(labels= dropcolumns, axis= 1, inplace= True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s6pY1tMBQCEv",
        "outputId": "4cc596e7-ee98-4d44-cfdd-dead9220d43b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((28913, 33), (67463, 33))"
            ]
          },
          "metadata": {},
          "execution_count": 196
        }
      ],
      "source": [
        "test.shape, k.shape\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "w_eTVqbXHzAs"
      },
      "outputs": [],
      "source": [
        "# sum((newdf['Loan Title'].value_counts()/len(k)*100).values[3:])\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "Yk2Zx4aNrENd",
        "outputId": "bb8a3d56-6d83-422d-a5bf-366cbde094ef"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>Batch Enrolled</th>\n",
              "      <th>Grade</th>\n",
              "      <th>Sub Grade</th>\n",
              "      <th>Employment Duration</th>\n",
              "      <th>Verification Status</th>\n",
              "      <th>Loan Title</th>\n",
              "      <th>Initial List Status</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>67463</td>\n",
              "      <td>67463</td>\n",
              "      <td>67463</td>\n",
              "      <td>67463</td>\n",
              "      <td>67463</td>\n",
              "      <td>67463</td>\n",
              "      <td>67463</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>unique</th>\n",
              "      <td>41</td>\n",
              "      <td>7</td>\n",
              "      <td>35</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>109</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>top</th>\n",
              "      <td>BAT3873588</td>\n",
              "      <td>C</td>\n",
              "      <td>B4</td>\n",
              "      <td>MORTGAGE</td>\n",
              "      <td>Source Verified</td>\n",
              "      <td>Credit card refinancing</td>\n",
              "      <td>w</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>freq</th>\n",
              "      <td>3626</td>\n",
              "      <td>19085</td>\n",
              "      <td>4462</td>\n",
              "      <td>36351</td>\n",
              "      <td>33036</td>\n",
              "      <td>30728</td>\n",
              "      <td>36299</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Batch Enrolled  Grade  ...               Loan Title Initial List Status\n",
              "count           67463  67463  ...                    67463               67463\n",
              "unique             41      7  ...                      109                   2\n",
              "top        BAT3873588      C  ...  Credit card refinancing                   w\n",
              "freq             3626  19085  ...                    30728               36299\n",
              "\n",
              "[4 rows x 7 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 198
        }
      ],
      "source": [
        "k[['Batch Enrolled', 'Grade', 'Sub Grade', 'Employment Duration',\n",
        "       'Verification Status', 'Loan Title',\n",
        "       'Initial List Status']].describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dfmRWj9lQvUI",
        "outputId": "96fa16ba-ea77-4858-a573-ed1126a6c5bb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: catboost in /usr/local/lib/python3.7/dist-packages (1.0.3)\n",
            "Requirement already satisfied: plotly in /usr/local/lib/python3.7/dist-packages (from catboost) (4.4.1)\n",
            "Requirement already satisfied: numpy>=1.16.0 in /usr/local/lib/python3.7/dist-packages (from catboost) (1.19.5)\n",
            "Requirement already satisfied: pandas>=0.24.0 in /usr/local/lib/python3.7/dist-packages (from catboost) (1.1.5)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from catboost) (1.4.1)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from catboost) (3.2.2)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from catboost) (1.15.0)\n",
            "Requirement already satisfied: graphviz in /usr/local/lib/python3.7/dist-packages (from catboost) (0.10.1)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.24.0->catboost) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.24.0->catboost) (2018.9)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->catboost) (3.0.6)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->catboost) (1.3.2)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib->catboost) (0.11.0)\n",
            "Requirement already satisfied: retrying>=1.3.3 in /usr/local/lib/python3.7/dist-packages (from plotly->catboost) (1.3.3)\n"
          ]
        }
      ],
      "source": [
        "!pip install catboost"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZJ6r1HQvRmFv",
        "outputId": "439fd64d-07a8-4f31-d6c3-cc31581a8d0b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1.0.3\n"
          ]
        }
      ],
      "source": [
        "import catboost\n",
        "print(catboost.__version__)\n",
        "from sklearn.pipeline import make_pipeline\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "# catcolumns1= ['Batch Enrolled', 'Grade', 'Sub Grade', 'Employment Duration',\n",
        "#        'Verification Status', 'Loan Title',\n",
        "#        'Initial List Status']\n",
        "# catindex= [k.columns.get_loc(col) for col in catcolumns1]\n",
        "def mapping(df, check1mapping, check2fortest):\n",
        "  newcol= ['Grade', 'Employment Duration', 'Verification Status', 'Initial List Status']\n",
        "  if check1mapping:\n",
        "    df['Grade'] = df['Grade'].map({'A':7, 'B':6, 'C':5, 'D':4, 'E':3, 'F':2, 'G':1})\n",
        "    df['Employment Duration']= df['Employment Duration'].map({'MORTGAGE':1, 'OWN':3, 'RENT':2})\n",
        "    df['Verification Status']= df['Verification Status'].map({'Not Verified':1, 'Source Verified':2, 'Verified':3})\n",
        "    df['Initial List Status']= df['Initial List Status'].map({'f':2, 'w':1})\n",
        "    return df\n",
        "\n",
        "  if check2fortest:\n",
        "    for i in newcol:\n",
        "      if not set(k[newcol]).difference(set(test[newcol])):\n",
        "        print('pass')\n",
        "    return\n",
        "  \n",
        "\n",
        "\n",
        "\n",
        "# above creates same index for test hence not creating any function"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5VMdOh5CpWMk",
        "outputId": "d9624d02-5cc6-4449-a7d4-714989b4aee4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Batch Enrolled',\n",
              " 'Grade',\n",
              " 'Sub Grade',\n",
              " 'Employment Duration',\n",
              " 'Verification Status',\n",
              " 'Payment Plan',\n",
              " 'Loan Title',\n",
              " 'Initial List Status',\n",
              " 'Application Type']"
            ]
          },
          "metadata": {},
          "execution_count": 201
        }
      ],
      "source": [
        "catcolumns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "eUSIZGaSfiQt"
      },
      "outputs": [],
      "source": [
        "k2= k1.copy()\n",
        "k= mapping(k, True, False)\n",
        "# test[['Grade', 'Employment Duration', 'Verification Status', 'Initial List Status']]= mapping(test[['Grade', 'Employment Duration', 'Verification Status', 'Initial List Status']], True, False)\n",
        "# mapping(test[catcolumns1], False, True)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6TYHrq-PiwI4",
        "outputId": "d62ebe21-36ae-467d-c560-06012b80498d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(         ID  Loan Amount  ...  Total Revolving Credit Limit  Loan Status\n",
              " 0  65087372        10000  ...                          6619            0\n",
              " 1   1450153         3609  ...                         20885            0\n",
              " 2   1969101        28276  ...                         26155            0\n",
              " 3   6651430        11170  ...                         60214            0\n",
              " 4  14354669        16890  ...                         22579            0\n",
              " \n",
              " [5 rows x 33 columns], (67463, 33))"
            ]
          },
          "metadata": {},
          "execution_count": 203
        }
      ],
      "source": [
        "k.head(), k.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qmOlNSwOfsOj",
        "outputId": "340dbcf4-1875-49ca-a0a5-d1cab01698d2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "pass\n",
            "pass\n",
            "pass\n",
            "pass\n"
          ]
        }
      ],
      "source": [
        "#'Grade', 'Employment Duration', 'Verification Status', 'Initial List Status'\n",
        "mapping(test[['Grade', 'Employment Duration', 'Verification Status', 'Initial List Status']], False, True)\n",
        "test= mapping(test, True, False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UAAgU-T0jqrw",
        "outputId": "5e56a545-bbc4-4f3d-e7de-9a1f2401ad5b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ID                                  0\n",
              "Loan Amount                         0\n",
              "Funded Amount                       0\n",
              "Funded Amount Investor              0\n",
              "Term                                0\n",
              "Batch Enrolled                      0\n",
              "Interest Rate                       0\n",
              "Grade                               0\n",
              "Sub Grade                           0\n",
              "Employment Duration                 0\n",
              "Home Ownership                      0\n",
              "Verification Status                 0\n",
              "Loan Title                          0\n",
              "Debit to Income                     0\n",
              "Delinquency - two years             0\n",
              "Inquires - six months               0\n",
              "Open Account                        0\n",
              "Public Record                       0\n",
              "Revolving Balance                   0\n",
              "Revolving Utilities                 0\n",
              "Total Accounts                      0\n",
              "Initial List Status                 0\n",
              "Total Received Interest             0\n",
              "Total Received Late Fee             0\n",
              "Recoveries                          0\n",
              "Collection Recovery Fee             0\n",
              "Collection 12 months Medical        0\n",
              "Last week Pay                       0\n",
              "Accounts Delinquent                 0\n",
              "Total Collection Amount             0\n",
              "Total Current Balance               0\n",
              "Total Revolving Credit Limit        0\n",
              "Loan Status                     28913\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 205
        }
      ],
      "source": [
        "test.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "g8s7F3VAqEGq"
      },
      "outputs": [],
      "source": [
        "##below is only used for catboost\n",
        "e= LabelEncoder()\n",
        "test2=k[['Batch Enrolled',\t'Sub Grade', 'Loan Title']].apply(e.fit_transform)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "v9iD4_bSlQij"
      },
      "outputs": [],
      "source": [
        "\n",
        "#unseen batch id in test\n",
        "# unseen=test[['Batch Enrolled',\t'Sub Grade', 'Loan Title']].apply(e.transform)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "6qeIO8janFvX",
        "outputId": "df71ef0c-6222-4155-bffb-8663548dbaf6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>Batch Enrolled</th>\n",
              "      <th>Sub Grade</th>\n",
              "      <th>Loan Title</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>16</td>\n",
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              "      <th>1</th>\n",
              "      <td>4</td>\n",
              "      <td>17</td>\n",
              "      <td>49</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Batch Enrolled  Sub Grade  Loan Title\n",
              "0              16         13          42\n",
              "1               4         17          49"
            ]
          },
          "metadata": {},
          "execution_count": 208
        }
      ],
      "source": [
        "test2.head(2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DLMDtY5fjxDq"
      },
      "outputs": [],
      "source": [
        "test3=pd.concat([test2, k[['Grade', 'Employment Duration', 'Verification Status', 'Initial List Status']]], axis= 1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "AyYNGmWxiott",
        "outputId": "a7a57a6e-af59-48a3-fe85-0bf652126c92"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "    .dataframe thead th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Batch Enrolled</th>\n",
              "      <th>Sub Grade</th>\n",
              "      <th>Loan Title</th>\n",
              "      <th>Grade</th>\n",
              "      <th>Employment Duration</th>\n",
              "      <th>Verification Status</th>\n",
              "      <th>Initial List Status</th>\n",
              "    </tr>\n",
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              "  <tbody>\n",
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              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
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              "      <th>1</th>\n",
              "      <td>4</td>\n",
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              "      <td>49</td>\n",
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              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
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              "      <th>2</th>\n",
              "      <td>11</td>\n",
              "      <td>18</td>\n",
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              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Batch Enrolled  Sub Grade  ...  Verification Status  Initial List Status\n",
              "0              16         13  ...                    1                    1\n",
              "1               4         17  ...                    2                    2\n",
              "2              11         18  ...                    2                    1\n",
              "\n",
              "[3 rows x 7 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 210
        }
      ],
      "source": [
        "test3.head(3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 408,
          "referenced_widgets": [
            "f23dc3a363064cbc9af9d74974be2ee9"
          ]
        },
        "id": "Ts5rbpW5UjlF",
        "outputId": "7fc6129c-efda-480d-ca97-cf948e11b13d"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f23dc3a363064cbc9af9d74974be2ee9",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "MetricVisualizer(layout=Layout(align_self='stretch', height='500px'))"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "You should provide test set for use best model. use_best_model parameter has been switched to false value.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Learning rate set to 0.062222\n",
            "0:\ttotal: 17.3ms\tremaining: 17.2s\n",
            "200:\ttotal: 3.04s\tremaining: 12.1s\n",
            "400:\ttotal: 6.12s\tremaining: 9.15s\n",
            "600:\ttotal: 9.29s\tremaining: 6.17s\n",
            "800:\ttotal: 12.5s\tremaining: 3.11s\n",
            "999:\ttotal: 15.8s\tremaining: 0us\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>Feature Id</th>\n",
              "      <th>Importances</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Sub Grade</td>\n",
              "      <td>23.643254</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Batch Enrolled</td>\n",
              "      <td>23.266811</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Loan Title</td>\n",
              "      <td>16.839973</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Grade</td>\n",
              "      <td>13.805366</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Employment Duration</td>\n",
              "      <td>9.303363</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Verification Status</td>\n",
              "      <td>8.311662</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Initial List Status</td>\n",
              "      <td>4.829569</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            Feature Id  Importances\n",
              "0            Sub Grade    23.643254\n",
              "1       Batch Enrolled    23.266811\n",
              "2           Loan Title    16.839973\n",
              "3                Grade    13.805366\n",
              "4  Employment Duration     9.303363\n",
              "5  Verification Status     8.311662\n",
              "6  Initial List Status     4.829569"
            ]
          },
          "metadata": {},
          "execution_count": 211
        }
      ],
      "source": [
        "params= {'loss_function': 'Logloss', 'eval_metric': 'AUC', 'verbose': 200}\n",
        "cbc9= catboost.CatBoostClassifier(**params)\n",
        "cbc9.fit(test3,k.iloc[:,-1], use_best_model=True, \n",
        "          plot=True)\n",
        "cbc9.get_feature_importance(prettified=True)\n",
        "#considering same Feature importance for test data as well we keep test data as Unseen"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OcC-I9r5qf0Z",
        "outputId": "52325566-32c2-43b0-924e-ba7204b93cd2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0.236433\n",
              "1    0.232668\n",
              "2    0.168400\n",
              "3    0.138054\n",
              "4    0.093034\n",
              "5    0.083117\n",
              "6    0.048296\n",
              "Name: Importances, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 212
        }
      ],
      "source": [
        "cbc9.get_feature_importance(prettified=True)['Importances']/100"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GxpYjoxnWZ0J"
      },
      "outputs": [],
      "source": [
        "#removing subgrade and initial list status from test and train to reduce the effort of labelling on subgrade\n",
        "k.drop(labels= ['Verification Status', 'Initial List Status'], axis= 1, inplace= True)\n",
        "test.drop(labels= ['Verification Status', 'Initial List Status'], axis= 1, inplace= True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qQxHV9ikuD50",
        "outputId": "935371cd-ed08-4cd0-bb4c-e33ccf881b93"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((67463, 31), (28913, 31))"
            ]
          },
          "metadata": {},
          "execution_count": 214
        }
      ],
      "source": [
        "k.shape, test.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MFgPAgAUz5GM",
        "outputId": "b03496fc-4eed-48e0-f7d2-36ed743233d9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(67463, 28913)"
            ]
          },
          "metadata": {},
          "execution_count": 215
        }
      ],
      "source": [
        "#Dropping ID and concatenating it later after prediction\n",
        "len(k['ID'].unique()), len(test['ID'].unique())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fbNHGOZquEG5",
        "outputId": "8feccaef-81c6-4bd2-9f7d-e9f8e1bcc88b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: category_encoders in /usr/local/lib/python3.7/dist-packages (2.3.0)\n",
            "Requirement already satisfied: patsy>=0.5.1 in /usr/local/lib/python3.7/dist-packages (from category_encoders) (0.5.2)\n",
            "Requirement already satisfied: pandas>=0.21.1 in /usr/local/lib/python3.7/dist-packages (from category_encoders) (1.1.5)\n",
            "Requirement already satisfied: scipy>=1.0.0 in /usr/local/lib/python3.7/dist-packages (from category_encoders) (1.4.1)\n",
            "Requirement already satisfied: numpy>=1.14.0 in /usr/local/lib/python3.7/dist-packages (from category_encoders) (1.19.5)\n",
            "Requirement already satisfied: statsmodels>=0.9.0 in /usr/local/lib/python3.7/dist-packages (from category_encoders) (0.10.2)\n",
            "Requirement already satisfied: scikit-learn>=0.20.0 in /usr/local/lib/python3.7/dist-packages (from category_encoders) (1.0.1)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.21.1->category_encoders) (2018.9)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.21.1->category_encoders) (2.8.2)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from patsy>=0.5.1->category_encoders) (1.15.0)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.20.0->category_encoders) (3.0.0)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.20.0->category_encoders) (1.1.0)\n"
          ]
        }
      ],
      "source": [
        "#we use target labelling \n",
        "!pip install category_encoders\n",
        "import category_encoders as ce"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CwOQLpHauEKd"
      },
      "outputs": [],
      "source": [
        "encoder = ce.cat_boost.CatBoostEncoder()\n",
        "\n",
        "encoder.fit(k[['Batch Enrolled', 'Loan Title', 'Sub Grade']], k.iloc[:,-1])\n",
        "k[['Batch Enrolled', 'Loan Title', 'Sub Grade']] = encoder.transform(k[['Batch Enrolled', 'Loan Title', 'Sub Grade']])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xiPuujrG0zPp"
      },
      "outputs": [],
      "source": [
        "test[['Batch Enrolled', 'Loan Title', 'Sub Grade']] = encoder.transform(test[['Batch Enrolled', 'Loan Title', 'Sub Grade']])\n",
        "# permutations = np.random.permutation(len(X_train))\n",
        "# predictors = X_train.iloc[permutations]\n",
        "# target = y_train.iloc[permutations]\n",
        "# cat_encoder = CatBoostEncoder(cols=categorical_columns)\n",
        "# X_train = cat_encoder.fit_transform(predictors, target)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RGeyQVhx1c9D"
      },
      "outputs": [],
      "source": [
        "traindata= k.iloc[:, :-1].copy()\n",
        "testdata= test.iloc[:, :-1].copy()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153
        },
        "id": "XqBMMRUlfP9J",
        "outputId": "c818eeac-569e-427f-9eeb-dbc1ad8f2bfa"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>ID</th>\n",
              "      <th>Loan Amount</th>\n",
              "      <th>Funded Amount</th>\n",
              "      <th>Funded Amount Investor</th>\n",
              "      <th>Term</th>\n",
              "      <th>Batch Enrolled</th>\n",
              "      <th>Interest Rate</th>\n",
              "      <th>Grade</th>\n",
              "      <th>Sub Grade</th>\n",
              "      <th>Employment Duration</th>\n",
              "      <th>Home Ownership</th>\n",
              "      <th>Loan Title</th>\n",
              "      <th>Debit to Income</th>\n",
              "      <th>Delinquency - two years</th>\n",
              "      <th>Inquires - six months</th>\n",
              "      <th>Open Account</th>\n",
              "      <th>Public Record</th>\n",
              "      <th>Revolving Balance</th>\n",
              "      <th>Revolving Utilities</th>\n",
              "      <th>Total Accounts</th>\n",
              "      <th>Total Received Interest</th>\n",
              "      <th>Total Received Late Fee</th>\n",
              "      <th>Recoveries</th>\n",
              "      <th>Collection Recovery Fee</th>\n",
              "      <th>Collection 12 months Medical</th>\n",
              "      <th>Last week Pay</th>\n",
              "      <th>Accounts Delinquent</th>\n",
              "      <th>Total Collection Amount</th>\n",
              "      <th>Total Current Balance</th>\n",
              "      <th>Total Revolving Credit Limit</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>56492997</td>\n",
              "      <td>17120</td>\n",
              "      <td>10365</td>\n",
              "      <td>16025.08269</td>\n",
              "      <td>59</td>\n",
              "      <td>0.094372</td>\n",
              "      <td>12.163926</td>\n",
              "      <td>7</td>\n",
              "      <td>0.093523</td>\n",
              "      <td>2</td>\n",
              "      <td>76468.8219</td>\n",
              "      <td>0.095431</td>\n",
              "      <td>16.749219</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>12</td>\n",
              "      <td>1</td>\n",
              "      <td>3576</td>\n",
              "      <td>67.278287</td>\n",
              "      <td>5</td>\n",
              "      <td>4469.449851</td>\n",
              "      <td>0.088031</td>\n",
              "      <td>8.425776</td>\n",
              "      <td>0.731797</td>\n",
              "      <td>0</td>\n",
              "      <td>135</td>\n",
              "      <td>0</td>\n",
              "      <td>24</td>\n",
              "      <td>475442</td>\n",
              "      <td>4364</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         ID  Loan Amount  ...  Total Current Balance  Total Revolving Credit Limit\n",
              "0  56492997        17120  ...                 475442                          4364\n",
              "\n",
              "[1 rows x 30 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 220
        }
      ],
      "source": [
        "testdata.head(1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "loGqkXQdsTZD",
        "outputId": "5a044354-83ac-4f3f-beac-27807ecb5c41"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((67463, 31), (28913, 31))"
            ]
          },
          "metadata": {},
          "execution_count": 221
        }
      ],
      "source": [
        "k.shape, test.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5JtITx560Oa3"
      },
      "outputs": [],
      "source": [
        "correlation= k.corr()['Loan Status'].sort_values(ascending= False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4Y1ndd8vWN2n",
        "outputId": "5fc5debc-e697-474d-f3dc-8be53b2c9f00"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Loan Status                     1.000000\n",
              "Loan Title                      0.043312\n",
              "Batch Enrolled                  0.024451\n",
              "Sub Grade                       0.022193\n",
              "Employment Duration             0.015426\n",
              "Public Record                   0.010590\n",
              "Delinquency - two years         0.009990\n",
              "Total Current Balance           0.009828\n",
              "Total Received Late Fee         0.009365\n",
              "Total Collection Amount         0.007892\n",
              "Last week Pay                   0.006117\n",
              "Revolving Utilities             0.004120\n",
              "Home Ownership                  0.003716\n",
              "Interest Rate                   0.002900\n",
              "Total Received Interest         0.001680\n",
              "Total Revolving Credit Limit    0.001454\n",
              "Funded Amount                   0.001364\n",
              "Inquires - six months           0.000578\n",
              "ID                              0.000472\n",
              "Total Accounts                  0.000222\n",
              "Funded Amount Investor         -0.000091\n",
              "Recoveries                     -0.000652\n",
              "Collection 12 months Medical   -0.000686\n",
              "Revolving Balance              -0.001073\n",
              "Debit to Income                -0.003057\n",
              "Term                           -0.003410\n",
              "Collection Recovery Fee        -0.003828\n",
              "Loan Amount                    -0.004473\n",
              "Open Account                   -0.007073\n",
              "Grade                          -0.011657\n",
              "Accounts Delinquent                  NaN\n",
              "Name: Loan Status, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 223
        }
      ],
      "source": [
        "correlation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qfVpd8Zw6iP1"
      },
      "outputs": [],
      "source": [
        "corrmatrix= k.corr().abs()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "h4lSxOeE62J4"
      },
      "outputs": [],
      "source": [
        "uppert= corrmatrix.where(np.triu(np.ones(corrmatrix.shape), k=1).astype(np.bool))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FzkzirDX7uTo"
      },
      "outputs": [],
      "source": [
        "#selecting the upper shape here 1 stands for True:-https://www.geeksforgeeks.org/numpy-triu-python/\n",
        "# np.triu(np.ones(corrmatrix.shape), k=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p6eIJU3c7vR_",
        "outputId": "dea1835d-af6e-4765-bcfa-ea46e25a748e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['ID', 'Loan Amount', 'Funded Amount', 'Funded Amount Investor', 'Term',\n",
              "       'Batch Enrolled', 'Interest Rate', 'Grade', 'Sub Grade',\n",
              "       'Employment Duration', 'Home Ownership', 'Loan Title',\n",
              "       'Debit to Income', 'Delinquency - two years', 'Inquires - six months',\n",
              "       'Open Account', 'Public Record', 'Revolving Balance',\n",
              "       'Revolving Utilities', 'Total Accounts', 'Total Received Interest',\n",
              "       'Total Received Late Fee', 'Recoveries', 'Collection Recovery Fee',\n",
              "       'Collection 12 months Medical', 'Last week Pay', 'Accounts Delinquent',\n",
              "       'Total Collection Amount', 'Total Current Balance',\n",
              "       'Total Revolving Credit Limit', 'Loan Status'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 227
        }
      ],
      "source": [
        "uppert.columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "id": "1HgihXy8_0zk",
        "outputId": "f51094ff-2a14-4eed-b6ef-daf23c48d575"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Figure size 1152x720 with 0 Axes>"
            ]
          },
          "metadata": {},
          "execution_count": 228
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1152x720 with 0 Axes>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "plt.figure(figsize=(16, 10))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "FiGBFlin8AvD",
        "outputId": "e744f1aa-63c9-4ed0-b6db-739813a174e0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f2fe378e490>"
            ]
          },
          "metadata": {},
          "execution_count": 229
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "sns.heatmap(k.corr())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b24QY7yFAYtR",
        "outputId": "348c1a66-20ee-4f0c-aa47-7e6af5a5024b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "([], (67463, 31))"
            ]
          },
          "metadata": {},
          "execution_count": 230
        }
      ],
      "source": [
        "uppertcolumn= [column for column in uppert.columns if any(uppert[column]> 0.6)]\n",
        "# k.drop(columns= uppertcolumn, inplace= True)\n",
        "uppertcolumn, k.shape\n",
        "# no columns to drop"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bP-GJx4_KwSD"
      },
      "outputs": [],
      "source": [
        "def thres(data, t):\n",
        "  dropempty= []\n",
        "  y= data['Loan Status']\n",
        "  data= data.drop(labels=['Loan Status'], axis= 1)\n",
        "  corrmatrix1= data.corr()\n",
        "  iters=len(corrmatrix1.columns)-1\n",
        "  for i in range(iters):\n",
        "    for j in range(i):\n",
        "      items= corrmatrix1.iloc[j:j+1, i:i+1]\n",
        "      column= items.columns\n",
        "      row= items.index\n",
        "      value= abs(items.values)\n",
        "      if value > t:\n",
        "        dropempty.append(column.values[0])\n",
        "\n",
        "  set(dropempty)\n",
        "  data= data.drop(labels= dropempty, axis= 1)\n",
        "  print(dropempty)\n",
        "  data['Loan Status']= y\n",
        "\n",
        "  return data\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NiBxBLFrSjo5",
        "outputId": "d2337830-db27-4dfb-b0b7-6abdeecd1587"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[]\n"
          ]
        }
      ],
      "source": [
        "new_data= thres(k, 0.6)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hKgCG_g9Sky_",
        "outputId": "c34c6d30-3db4-488c-a860-ba21083ab1b2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((67463, 31), (67463, 31))"
            ]
          },
          "metadata": {},
          "execution_count": 233
        }
      ],
      "source": [
        "# k.corr().iloc[2:3, 0:1]\n",
        "new_data.shape, k.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HS3Pu3-DUGn5"
      },
      "outputs": [],
      "source": [
        "#variance threshold , outliers, distribution \n",
        "numcolumns1=k1.select_dtypes(exclude= ['object']).columns.tolist()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ol6Rt_UO79Lz",
        "outputId": "e5f123b2-9659-448f-9dc5-78c92beb81e3"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "26"
            ]
          },
          "metadata": {},
          "execution_count": 235
        }
      ],
      "source": [
        "len(numcolumns1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lPfSV6G1WbK1"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "from sklearn.feature_selection import VarianceThreshold\n",
        "  \n",
        "  "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aDIi6LxtXOp0",
        "outputId": "25e119f7-1cf5-4a99-8727-c95dbfd530cc"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['ID',\n",
              " 'Loan Amount',\n",
              " 'Funded Amount',\n",
              " 'Funded Amount Investor',\n",
              " 'Term',\n",
              " 'Interest Rate',\n",
              " 'Home Ownership',\n",
              " 'Debit to Income',\n",
              " 'Delinquency - two years',\n",
              " 'Inquires - six months',\n",
              " 'Open Account',\n",
              " 'Public Record',\n",
              " 'Revolving Balance',\n",
              " 'Revolving Utilities',\n",
              " 'Total Accounts',\n",
              " 'Total Received Interest',\n",
              " 'Total Received Late Fee',\n",
              " 'Recoveries',\n",
              " 'Collection Recovery Fee',\n",
              " 'Collection 12 months Medical',\n",
              " 'Last week Pay',\n",
              " 'Accounts Delinquent',\n",
              " 'Total Collection Amount',\n",
              " 'Total Current Balance',\n",
              " 'Total Revolving Credit Limit',\n",
              " 'Loan Status']"
            ]
          },
          "metadata": {},
          "execution_count": 237
        }
      ],
      "source": [
        "numcolumns1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xZ7bA9Cy2fJ3",
        "outputId": "80c0cbad-09e0-43b0-bf47-14b836e4294b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[6.5087372e+07, 1.0000000e+04, 3.2236000e+04, ..., 3.1000000e+01,\n",
              "        3.1130100e+05, 6.6190000e+03],\n",
              "       [1.4501530e+06, 3.6090000e+03, 1.1940000e+04, ..., 5.3000000e+01,\n",
              "        1.8261000e+05, 2.0885000e+04],\n",
              "       [1.9691010e+06, 2.8276000e+04, 9.3110000e+03, ..., 3.4000000e+01,\n",
              "        8.9801000e+04, 2.6155000e+04],\n",
              "       ...,\n",
              "       [1.6435904e+07, 1.5897000e+04, 3.2921000e+04, ..., 1.7000000e+01,\n",
              "        1.7685700e+05, 4.2330000e+04],\n",
              "       [5.3003250e+06, 1.6567000e+04, 4.9750000e+03, ..., 6.1000000e+01,\n",
              "        3.6133900e+05, 3.9075000e+04],\n",
              "       [6.5443173e+07, 1.5353000e+04, 2.9875000e+04, ..., 4.7000000e+01,\n",
              "        1.9696000e+05, 6.6060000e+04]])"
            ]
          },
          "metadata": {},
          "execution_count": 238
        }
      ],
      "source": [
        "selector1= VarianceThreshold(threshold=0.5)\n",
        "selector1.fit_transform(k[['ID',\n",
        " 'Loan Amount',\n",
        " 'Funded Amount',\n",
        " 'Funded Amount Investor',\n",
        " 'Term',\n",
        " 'Interest Rate',\n",
        " 'Home Ownership',\n",
        " 'Debit to Income',\n",
        " 'Delinquency - two years',\n",
        " 'Inquires - six months',\n",
        " 'Open Account',\n",
        " 'Public Record',\n",
        " 'Revolving Balance',\n",
        " 'Revolving Utilities',\n",
        " 'Total Accounts',\n",
        " 'Total Received Interest',\n",
        " 'Total Received Late Fee',\n",
        " 'Recoveries',\n",
        " 'Collection Recovery Fee',\n",
        " 'Collection 12 months Medical',\n",
        " 'Last week Pay',\n",
        " 'Accounts Delinquent',\n",
        " 'Total Collection Amount',\n",
        " 'Total Current Balance',\n",
        " 'Total Revolving Credit Limit']])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Auitok_YYRSr",
        "outputId": "6988a2e3-98b3-49c3-eb2d-3b71cc47f756"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
              "       False,  True, False,  True,  True,  True,  True,  True,  True,\n",
              "        True, False,  True, False,  True,  True,  True])"
            ]
          },
          "metadata": {},
          "execution_count": 239
        }
      ],
      "source": [
        "#'Delinquency - two years', 'Collection 12 months Medical', 'Public Record', 'Delinquency - two years', 'Inquires - six months are having less variance\n",
        "mask=selector1.get_support()\n",
        "mask"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tSgI0y6pd3gV"
      },
      "outputs": [],
      "source": [
        "dummy=pd.DataFrame([list(mask), numcolumns1[:-1]]).T\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LifkrMsrnYjw"
      },
      "outputs": [],
      "source": [
        "newlist= ['Loan Amount',\n",
        " 'Funded Amount',\n",
        " 'Funded Amount Investor',\n",
        " 'Term',\n",
        " 'Interest Rate',\n",
        " 'Home Ownership',\n",
        " 'Debit to Income',\n",
        " 'Delinquency - two years',\n",
        " 'Inquires - six months',\n",
        " 'Open Account',\n",
        " 'Public Record',\n",
        " 'Revolving Balance',\n",
        " 'Revolving Utilities',\n",
        " 'Total Accounts',\n",
        " 'Total Received Interest',\n",
        " 'Total Received Late Fee',\n",
        " 'Recoveries',\n",
        " 'Collection Recovery Fee',\n",
        " 'Last week Pay',\n",
        " 'Total Collection Amount',\n",
        " 'Total Current Balance',\n",
        " 'Total Revolving Credit Limit']\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LZgHekXgpcY4",
        "outputId": "497b32c0-349f-47b3-86ed-9851e1396422"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Inquires - six months                                          [0, 3, 2, 1, 4, 5]\n",
              "Delinquency - two years                               [1, 0, 3, 6, 2, 7, 5, 4, 8]\n",
              "Public Record                                                     [0, 1, 2, 4, 3]\n",
              "Open Account                    [13, 12, 14, 7, 16, 11, 6, 17, 10, 8, 9, 26, 3...\n",
              "Term                                                                 [59, 36, 58]\n",
              "Total Accounts                  [7, 13, 20, 12, 22, 37, 33, 17, 30, 46, 21, 19...\n",
              "Collection 12 months Medical                                               [0, 1]\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 242
        }
      ],
      "source": [
        "#below no can vary as these details can vary in test data cannot be considered as categorical, we will see which has less impact\n",
        "k[['Inquires - six months', 'Delinquency - two years', 'Public Record', 'Open Account','Term','Total Accounts', 'Collection 12 months Medical']].apply(lambda col: col.unique())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AZO7IYq_g3zB",
        "outputId": "904f99e5-9179-49f5-89cf-7e406b3828fc"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ID                              [56492997, 22540813, 9862181, 10097822, 477718...\n",
              "Loan Amount                     [17120, 7133, 25291, 30781, 8878, 12147, 16214...\n",
              "Funded Amount                   [10365, 11650, 25825, 9664, 9419, 10289, 16482...\n",
              "Funded Amount Investor          [16025.082690000001, 12615.7956, 11621.28083, ...\n",
              "Term                                                             [59, 58, 36, 60]\n",
              "Batch Enrolled                  [0.09437223647848855, 0.08656294653194595, 0.1...\n",
              "Interest Rate                   [12.16392574, 6.564295831, 14.72990011, 10.523...\n",
              "Grade                                                       [7, 6, 5, 3, 4, 2, 1]\n",
              "Sub Grade                       [0.09352269590572682, 0.08932867622422627, 0.0...\n",
              "Employment Duration                                                     [2, 1, 3]\n",
              "Home Ownership                  [76468.8219, 38079.013439999995, 51275.93268, ...\n",
              "Loan Title                      [0.09543061029314066, 0.09398589312924037, 0.0...\n",
              "Debit to Income                 [16.74921941, 18.15797499, 15.190010800000001,...\n",
              "Delinquency - two years                            [1, 0, 6, 2, 7, 3, 5, 8, 4, 9]\n",
              "Inquires - six months                                          [0, 1, 3, 2, 4, 5]\n",
              "Open Account                    [12, 11, 22, 6, 13, 17, 9, 14, 26, 34, 8, 31, ...\n",
              "Public Record                                                     [1, 0, 3, 4, 2]\n",
              "Revolving Balance               [3576, 1932, 598, 5222, 553, 7420, 10840, 6539...\n",
              "Revolving Utilities             [67.27828675, 71.31315685, 50.88306534, 82.449...\n",
              "Total Accounts                  [5, 21, 23, 28, 9, 15, 20, 48, 12, 26, 29, 22,...\n",
              "Total Received Interest         [4469.449850999999, 993.9075295, 729.1133792, ...\n",
              "Total Received Late Fee         [0.088031249, 0.041236964, 0.021745213, 0.0923...\n",
              "Recoveries                      [8.425776424, 6.157007776, 5.705076639, 2.4696...\n",
              "Collection Recovery Fee         [0.731796903, 0.992918036, 0.281579857, 0.9591...\n",
              "Collection 12 months Medical                                               [0, 1]\n",
              "Last week Pay                   [135, 56, 3, 21, 104, 72, 96, 133, 68, 22, 140...\n",
              "Accounts Delinquent                                                        [0, 1]\n",
              "Total Collection Amount         [24, 1, 26, 32, 33, 21, 34, 46, 308, 25, 13, 5...\n",
              "Total Current Balance           [475442, 72412, 284825, 40842, 90825, 443777, ...\n",
              "Total Revolving Credit Limit    [4364, 2573, 19676, 7226, 26145, 1260, 22035, ...\n",
              "Loan Status                                                                 [nan]\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 243
        }
      ],
      "source": [
        "test.apply(lambda col: col.unique())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "p6uYozMUaKiz"
      },
      "outputs": [],
      "source": [
        "#dropping 'Accounts Delinquent' and 'Collection 12 months Medical' as 0 in both cases overpowers 90% of the time, doesnt provide imp info\n",
        "#dropping ID because its doesnt provide any value to ML model\n",
        "k.drop(labels= ['ID', 'Accounts Delinquent', 'Collection 12 months Medical'], axis= 1, inplace= True)\n",
        "test.drop(labels= ['ID', 'Accounts Delinquent', 'Collection 12 months Medical'], axis= 1, inplace= True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TbTNFzsUb3A9"
      },
      "outputs": [],
      "source": [
        "#Funded Amount Investor ls, \n",
        "import seaborn as sns\n",
        "import scipy.stats as stat\n",
        "import pylab \n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Vo0ou8f4f9iu"
      },
      "outputs": [],
      "source": [
        "def plotgraph(df, feat):\n",
        "  df10= df.copy()\n",
        "  \n",
        "  fig, axes = plt.subplots(6,4,  figsize=(28, 22))\n",
        "\n",
        "  for i, ax in enumerate(axes.flatten()):\n",
        "     sns.distplot(df10[feat[i]], ax= ax)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kDVKXUZNhal0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "37b652e7-b3f2-4bfa-879d-50c891aba86c"
      },
      "outputs": [
        {
          "output_type": "error",
          "ename": "IndexError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-247-db2691680609>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# most medians are shifted towards left , and left skewed a\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mplotgraph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mnewlist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m<ipython-input-246-6d12b7d04da9>\u001b[0m in \u001b[0;36mplotgraph\u001b[0;34m(df, feat)\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m   \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mflatten\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m      \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdistplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf10\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mfeat\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mIndexError\u001b[0m: list index out of range"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 2016x1584 with 24 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# most medians are shifted towards left , and left skewed a\n",
        "plotgraph(k,newlist)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "09vE1Zdqwyim"
      },
      "outputs": [],
      "source": [
        "# above graph is following normal distribution in most cases hence standard scaling is the best scaling for the above\n",
        "###lets find out outlier as it doesnt work well with outliers, we remove them first"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kB7S1_dvZBg5",
        "outputId": "2168d92d-ec4b-49c2-8bef-e05bfa1b40f4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "4.932245152384351e-05\n",
            "7.77369093698583e-05\n",
            "1.1871520356331458e-05\n",
            "7.015568350950141e-06\n",
            "5.137716117500097e-06\n",
            "5.1730349155359946e-05\n"
          ]
        }
      ],
      "source": [
        "\n",
        "print(k['Term'].std()/len(k))\n",
        "print(k['Total Received Late Fee'].std()/len(k))\n",
        "print(k['Delinquency - two years'].std()/len(k))\n",
        "print(k['Inquires - six months'].std()/len(k))\n",
        "print(k['Public Record'].std()/len(k))\n",
        "print(k['Collection Recovery Fee'].std()/len(k))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "M2-Qll9J8qMq"
      },
      "outputs": [],
      "source": [
        "k4= k1.copy()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "14nudDY5mg8c"
      },
      "outputs": [],
      "source": [
        "# i will only remove the extreme if model accuracy is low will chnage to 1.5 and create new dataset for outliers\n",
        "def normdistoutliersremoval(df, feat1):\n",
        "  li= []\n",
        "  df1= df.copy()\n",
        "  for i in feat1:\n",
        "    \n",
        "    upper_boundary=df1[i].median() + 1.5* df1[i].std()\n",
        "    lower_boundary=df1[i].median() -  1.5* df1[i].std()\n",
        "    li.append([lower_boundary, upper_boundary, i, df1[i].median(), df1[i].mean()])\n",
        "    df1.loc[df1[i]>=upper_boundary, i]= upper_boundary\n",
        "    \n",
        "\n",
        "  return li, df1\n",
        "\n",
        "# df['Height'] = np.where(df['Height'] >= upper_limit,\n",
        "#         upper_limit,\n",
        "#         np.where(df['Height'] <= lower_limit,\n",
        "#         lower_limit,\n",
        "#         df['Height']))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9uj0NLvwPJEu"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "s5uyaC1mmcR2"
      },
      "outputs": [],
      "source": [
        "standardscaler= ['Funded Amount Investor',\n",
        "'Interest Rate',\n",
        "'Home Ownership',\n",
        " 'Open Account',\n",
        " 'Revolving Balance',\n",
        " 'Revolving Utilities',\n",
        " 'Total Accounts',\n",
        " 'Total Received Interest',\n",
        " 'Total Current Balance',\n",
        " 'Total Revolving Credit Limit']\n",
        "result= []\n",
        "resulttest=[]\n",
        "result, newk5=  normdistoutliersremoval(k, standardscaler)\n",
        "resulttest, newtest5=  normdistoutliersremoval(test, standardscaler)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "n0m-bn4H5HSp"
      },
      "outputs": [],
      "source": [
        "# plotgraph(newk5,newlist)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BKGo2jlg_cLe"
      },
      "outputs": [],
      "source": [
        "def quantiles(df, feat1):\n",
        "  li= []\n",
        "  df1= df.copy()\n",
        "  for i in feat1:\n",
        "    l= df1[i].quantile(0.25)\n",
        "    u= df1[i].quantile(0.75)\n",
        "    IQR= u-l\n",
        "    lower_boundary=l - 1.5*IQR\n",
        "    upper_boundary=u + 1.5*IQR\n",
        "    \n",
        "    li.append([lower_boundary, upper_boundary, i, df1[i].median(), df1[i].mean()])\n",
        "    df1.loc[df1[i]>=upper_boundary, i]= upper_boundary\n",
        "  return li, df1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "v7Zv_5TWrebM"
      },
      "outputs": [],
      "source": [
        "#outliers columns\n",
        "minmax= ['Loan Amount',\n",
        " 'Funded Amount',\n",
        " 'Term',\n",
        " 'Debit to Income',\n",
        " 'Delinquency - two years',\n",
        " 'Inquires - six months',\n",
        " 'Public Record',\n",
        " 'Total Received Late Fee',\n",
        " 'Recoveries',\n",
        " 'Collection Recovery Fee',\n",
        " 'Last week Pay',\n",
        " 'Total Collection Amount']\n",
        "\n",
        "result1=[]\n",
        "result1test=[]\n",
        "result1, newk8= quantiles(newk5, minmax)\n",
        "result1test, newtest8= quantiles(newtest5, minmax)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "bbbb"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 165
        },
        "id": "9PlgQW14KFpf",
        "outputId": "00f6bc7f-a83c-4bb6-9e60-c0592b8f7b2a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-256-13f9ed0898e3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mbbbb\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m: name 'bbbb' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "rNYRia2CypkK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# plotgraph(newk8, standardscaler)"
      ],
      "metadata": {
        "id": "DOkjQD7IKX_h"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RWo_aBMzPPV5"
      },
      "outputs": [],
      "source": [
        "def concatextremes(df, feat2):\n",
        "  df1= df.copy()\n",
        "  llo= pd.DataFrame()\n",
        "  # ll= pd.DataFrame()\n",
        "  for i in feat2:\n",
        "    \n",
        "    # dfn= df1[df1[i[2]]< i[1]]\n",
        "    # df= k.loc[k[i[2]]<= i[0] and k[i[2]]>= i[1]]\n",
        "    # print(df)\n",
        "    if i[1]!= 0:\n",
        "      # print(i)\n",
        "      # print(llo.shape)\n",
        "      # print(ll.shape)\n",
        "      dfo= df1.loc[df1[i[2]]> i[1]]\n",
        "      # dfno= df1.loc[df1[i[2]]<= i[1]]\n",
        "      llo= pd.concat([llo, dfo], axis= 0)\n",
        "      # ll= pd.concat([ll, dfno], axis= 0)\n",
        "    \n",
        "  # ll.drop_duplicates(inplace= True)\n",
        "  llo.drop_duplicates(inplace= True)\n",
        "  return llo"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nE7bRyPVQKTH"
      },
      "outputs": [],
      "source": [
        "\n",
        "outlier= concatextremes(k, result+result1)\n",
        "testoutlier= concatextremes(test, resulttest+result1test)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "bbbb"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 165
        },
        "id": "L1zDlkboXxHD",
        "outputId": "fb2128c5-fe91-42b3-965f-a33c3c2a7ad6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-260-13f9ed0898e3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mbbbb\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m: name 'bbbb' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def concatextremes1(df, feat2):\n",
        "  df1= df.copy()\n",
        "  llo= pd.DataFrame()\n",
        "  # ll= pd.DataFrame()\n",
        "  for i in feat2:\n",
        "    \n",
        "    # dfn= df1[df1[i[2]]< i[1]]\n",
        "    # df= k.loc[k[i[2]]<= i[0] and k[i[2]]>= i[1]]\n",
        "    # print(df)\n",
        "    if i[1]!= 0:\n",
        "      # print(i)\n",
        "      # print(llo.shape)\n",
        "      # print(ll.shape)\n",
        "      dfo= df1.loc[df1[i[2]]< i[1]]\n",
        "      # dfno= df1.loc[df1[i[2]]<= i[1]]\n",
        "      llo= pd.concat([llo, dfo], axis= 0)\n",
        "      # ll= pd.concat([ll, dfno], axis= 0)\n",
        "    \n",
        "  # ll.drop_duplicates(inplace= True)\n",
        "  llo.drop_duplicates(inplace= True)\n",
        "  return llo"
      ],
      "metadata": {
        "id": "Q5bnYHSCdR7H"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "nooutlier= concatextremes1(newk8, result+result1)\n",
        "notestoutlier= concatextremes1(newtest8, resulttest+result1test)"
      ],
      "metadata": {
        "id": "RnvizijAXyYh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "nooutlier.shape, outlier.shape\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K4OMDqG5XyeS",
        "outputId": "8ebacf9d-df18-4884-a4d1-7349e0685e57"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((67463, 28), (47632, 28))"
            ]
          },
          "metadata": {},
          "execution_count": 265
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CLisydkFlyim"
      },
      "outputs": [],
      "source": [
        "# plotgraph(outlier23467, minmax)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "K8UI9lU14Jed"
      },
      "outputs": [],
      "source": [
        "# no_outlier1=k.drop(index=list(outlier.index))\n",
        "# no_outlier1.drop_duplicates(inplace= True)\n",
        "# no_outlier1.drop_duplicates(inplace= True)\n",
        "# _, test5=  normdistoutliersremoval(test, standardscaler)\n",
        "# _, test6= quantiles(test5, minmax)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "c0mO10UsTRY3"
      },
      "outputs": [],
      "source": [
        "# plotgraph(no_outlier1,newlist)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sbGRbUyErpzh"
      },
      "outputs": [],
      "source": [
        "# plotgraph(newk8,newlist)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RSBoQHFZsj9X",
        "outputId": "a938650f-9c61-4e05-98d6-33a768527b25"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0    61222\n",
              " 1     6241\n",
              " Name: Loan Status, dtype: int64, 0    43167\n",
              " 1     4465\n",
              " Name: Loan Status, dtype: int64)"
            ]
          },
          "metadata": {},
          "execution_count": 267
        }
      ],
      "source": [
        "nooutlier.iloc[:, -1].value_counts(), outlier.iloc[:, -1].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yA0buRT_Ct7V"
      },
      "outputs": [],
      "source": [
        "###i will be creating 2 types of model\n",
        "# newk8.shape, test6.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QQ0pXb4hjlDO"
      },
      "outputs": [],
      "source": [
        "# newk8.iloc[:,-1].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "oJLK4AT8CuGT"
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import RobustScaler\n",
        "scaler31= RobustScaler()\n",
        "outlier[minmax+standardscaler]= scaler31.fit_transform(outlier[minmax+standardscaler])\n",
        "testoutlier[minmax+standardscaler]= scaler31.transform(testoutlier[minmax+standardscaler])\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "mm= MinMaxScaler()\n",
        "nooutlier[minmax+standardscaler]= mm.fit_transform(nooutlier[minmax+standardscaler])\n",
        "notestoutlier[minmax+standardscaler]= mm.transform(notestoutlier[minmax+standardscaler])\n",
        "\n"
      ],
      "metadata": {
        "id": "Xj9ui8p3I_KS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# plotgraph(outlier, newlist)"
      ],
      "metadata": {
        "id": "xZlprz87JhNf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "aaaaa"
      ],
      "metadata": {
        "id": "sYcdizmnJPgn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2FR-N6YEXiDs"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.ensemble import AdaBoostClassifier, GradientBoostingClassifier\n",
        "from sklearn.pipeline import Pipeline \n",
        "from sklearn.svm import SVC\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import f1_score, accuracy_score\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics  import log_loss\n",
        "from sklearn.feature_selection import RFE,RFECV"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dxRvwKtJXfAY",
        "outputId": "54acf002-de8a-468d-8976-b15300a646c1"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RFE(estimator=GradientBoostingClassifier())"
            ]
          },
          "metadata": {},
          "execution_count": 271
        }
      ],
      "source": [
        "#finding out important feature\n",
        "rv1=RFE(estimator= GradientBoostingClassifier())\n",
        "rv1.fit(nooutlier.iloc[:, :-1], nooutlier.iloc[:, -1])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Eg2WzPBsX-Wq"
      },
      "outputs": [],
      "source": [
        "ranking_column= pd.DataFrame([nooutlier.columns, rv1.support_, rv1.ranking_]).T"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ranking_column.sort_values(by= [2])"
      ],
      "metadata": {
        "id": "J1vcZwdmhEat"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0i7DDCn6cmxD"
      },
      "outputs": [],
      "source": [
        "important_columns= ranking_column[ranking_column[2]< 8][0].values.tolist()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#['Loan Amount',\n",
        " 'Funded Amount',\n",
        " 'Funded Amount Investor',\n",
        " 'Term',\n",
        " 'Batch Enrolled',\n",
        " 'Sub Grade',\n",
        " 'Home Ownership',\n",
        " 'Loan Title',\n",
        " 'Debit to Income',\n",
        " 'Open Account',\n",
        " 'Revolving Balance',\n",
        " 'Revolving Utilities',\n",
        " 'Total Accounts',\n",
        " 'Total Received Late Fee',\n",
        " 'Recoveries',\n",
        " 'Collection Recovery Fee',\n",
        " 'Last week Pay',\n",
        " 'Total Collection Amount',\n",
        " 'Total Current Balance']\n",
        "important_columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f7brn87_gxgx",
        "outputId": "5af75e82-08ed-4ed9-c6c6-2aafb5daeb3c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Loan Amount',\n",
              " 'Funded Amount',\n",
              " 'Funded Amount Investor',\n",
              " 'Term',\n",
              " 'Batch Enrolled',\n",
              " 'Sub Grade',\n",
              " 'Home Ownership',\n",
              " 'Loan Title',\n",
              " 'Debit to Income',\n",
              " 'Open Account',\n",
              " 'Revolving Balance',\n",
              " 'Revolving Utilities',\n",
              " 'Total Accounts',\n",
              " 'Total Received Late Fee',\n",
              " 'Recoveries',\n",
              " 'Collection Recovery Fee',\n",
              " 'Last week Pay',\n",
              " 'Total Collection Amount',\n",
              " 'Total Current Balance']"
            ]
          },
          "metadata": {},
          "execution_count": 275
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7fwzAcZ6fxrx"
      },
      "outputs": [],
      "source": [
        "dataset= nooutlier[important_columns]\n",
        "dataseto= outlier[important_columns]\n",
        "datasettestn= notestoutlier[important_columns]\n",
        "datasettesto= testoutlier[important_columns]\n",
        "X_train, X_test, y_train, y_test= train_test_split(dataset, nooutlier.iloc[:, -1], test_size=0.20)\n",
        "X_train1, X_test1, y_train1, y_test1= train_test_split(dataseto, outlier.iloc[:, -1], test_size=0.20)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UB4zYN_hX9Bm",
        "outputId": "291b2e48-f297-4a49-fe6b-401184f9813a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((28913, 19), (19204, 19))"
            ]
          },
          "metadata": {},
          "execution_count": 296
        }
      ],
      "source": [
        "datasettestn.shape, datasettesto.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-0nroHQv2cY2"
      },
      "outputs": [],
      "source": [
        "# we see an imbalanced data we will apply both over and under smapling create a ratio of 1:5 or some no \n",
        "y_train.value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IQFSIWYU2vmX",
        "outputId": "731d2fa6-45b3-460b-abcb-2cd64907b1fc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: imbalanced-learn in /usr/local/lib/python3.7/dist-packages (0.8.1)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from imbalanced-learn) (1.1.0)\n",
            "Requirement already satisfied: scikit-learn>=0.24 in /usr/local/lib/python3.7/dist-packages (from imbalanced-learn) (1.0.1)\n",
            "Requirement already satisfied: scipy>=0.19.1 in /usr/local/lib/python3.7/dist-packages (from imbalanced-learn) (1.4.1)\n",
            "Requirement already satisfied: numpy>=1.13.3 in /usr/local/lib/python3.7/dist-packages (from imbalanced-learn) (1.19.5)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.24->imbalanced-learn) (3.0.0)\n"
          ]
        }
      ],
      "source": [
        "\n",
        "!pip install imbalanced-learn"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "u562AJT72kX2"
      },
      "outputs": [],
      "source": [
        "from imblearn.over_sampling import SVMSMOTE\n",
        "\n",
        "oversample= SVMSMOTE(sampling_strategy=0.3)\n",
        "smotex, smotey= oversample.fit_resample(X_train, y_train)\n",
        "from imblearn.under_sampling import RandomUnderSampler\n",
        "under = RandomUnderSampler(sampling_strategy=0.5)\n",
        "dummy2, dummy3= under.fit_resample(smotex, smotey)\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dummy3.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lwp3fVEtqSy1",
        "outputId": "f4a3f162-9d96-485b-d22a-6300d58240c8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    29390\n",
              "1    14695\n",
              "Name: Loan Status, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 282
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vJCcVwX8B8sO"
      },
      "outputs": [],
      "source": [
        "oversample1= SVMSMOTE(sampling_strategy=0.3)\n",
        "smotex1, smotey1= oversample1.fit_resample(X_train1, y_train1)\n",
        "\n",
        "under1 = RandomUnderSampler(sampling_strategy=0.5)\n",
        "dummy21, dummy31= under1.fit_resample(smotex1, smotey1)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dummy31.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A1oe1lPnq2zv",
        "outputId": "81d16ee8-629f-44c0-e990-b3beb3334bd5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    13322\n",
              "1     6661\n",
              "Name: Loan Status, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 284
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "xxxx"
      ],
      "metadata": {
        "id": "a-IK4RfAcjM3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "P3swQYjQkiq_"
      },
      "outputs": [],
      "source": [
        "smotey.value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Uw3q732P4uVH",
        "outputId": "800bc39a-9fcd-4ded-e77a-3c364b5842d0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.003164556962025317\n",
            "[[12231     5]\n",
            " [ 1255     2]]\n",
            "3.2252917998647166\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(None, None, None)"
            ]
          },
          "metadata": {},
          "execution_count": 285
        }
      ],
      "source": [
        "rfc_balanced = RandomForestClassifier(random_state = 42, class_weight = \"balanced\")\n",
        "# usampling5= RandomUnderSampler(sampling_strategy= 0.84)\n",
        "# overx6, overy6= usampling5.fit_resample(overx5, overy5)\n",
        "rfc_balanced.fit(dummy2, dummy3)\n",
        "# overy6.value_counts()\n",
        "y_pred15 = rfc_balanced.predict(X_test)\n",
        "print(f1_score(y_test, y_pred15)), print(confusion_matrix(y_test, y_pred15)), print(log_loss(y_test, y_pred15))\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4vSxnUen4_LH"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3kHxt7ykie9z",
        "outputId": "221c43f7-aec1-43b5-84cf-7582c8e1f013"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.02761627906976744\n",
            "[[12136   100]\n",
            " [ 1238    19]]\n",
            "3.424958332182133\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(None, None, None)"
            ]
          },
          "metadata": {},
          "execution_count": 286
        }
      ],
      "source": [
        "import lightgbm as lgb\n",
        "\n",
        "lgbc_balanced = lgb.LGBMClassifier(random_state = 42, n_jobs = -1, class_weight = \"balanced\")\n",
        "\n",
        "lgbc_balanced.fit(dummy2, dummy3)\n",
        "\n",
        "y_pred16 = lgbc_balanced.predict(X_test)\n",
        "print(f1_score(y_test, y_pred16)), print(confusion_matrix(y_test, y_pred16)), print(log_loss(y_test, y_pred16))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "t8UYdUijjLPs"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HGIeMJjJXiJZ"
      },
      "outputs": [],
      "source": [
        "# pipe = Pipeline([(\"classifier\", LogisticRegression())])\n",
        "# # Create dictionary with  learning algorithms and their hyperparameters\n",
        "# hyperparameter_comb = [{\"classifier\":[SVC()],\n",
        "#                            \"classifier__C\":[0.01],\n",
        "#                            \"classifier__kernel\":['rbf','linear']}]\n",
        "\n",
        "# # create a gridsearch of the pipeline, the fit the best model\n",
        "# gridsearch = GridSearchCV(pipe, hyperparameter_comb, cv=5, verbose=0) # Fit grid search\n",
        "# best_model = gridsearch.fit(overx5, overy5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F4KZDRnsA17C",
        "outputId": "7bbadb0b-4f70-44dd-d884-90f11d09c7b5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.04951690821256038\n",
            "[[11878   358]\n",
            " [ 1216    41]]\n",
            "4.0290758394329975\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(None, None, None)"
            ]
          },
          "metadata": {},
          "execution_count": 287
        }
      ],
      "source": [
        "# ab15=GradientBoostingClassifier(n_estimators= 1000,learning_rate=1.7)\n",
        "# ab15=GradientBoostingClassifier(learning_rate=0.8, min_samples_split=800,min_samples_leaf=100,max_depth=12,max_features='sqrt',subsample=0.9,random_state=10)\n",
        "ab15=GradientBoostingClassifier(learning_rate=0.5, n_estimators=1000,max_depth=15, min_samples_split=1200,min_samples_leaf=60, subsample=0.85, random_state=10)\n",
        "ab15.fit(dummy2, dummy3)\n",
        "y_pred14 = ab15.predict(X_test)\n",
        "\n",
        "print(f1_score(y_test, y_pred14)), print(confusion_matrix(y_test, y_pred14)), print(log_loss(y_test, y_pred14))"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ab16=GradientBoostingClassifier(learning_rate=0.5, n_estimators=1000,max_depth=15, min_samples_split=1200,min_samples_leaf=60, subsample=0.85, random_state=10)\n",
        "ab16.fit(dummy21, dummy31)\n",
        "y_pred16 = ab16.predict(X_test)\n",
        "\n",
        "print(f1_score(y_test, y_pred16)), print(confusion_matrix(y_test, y_pred16)), print(log_loss(y_test, y_pred16))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QHm-ijRMuwsV",
        "outputId": "af7d18a4-2b7a-48a2-cb14-5e3ee07518cb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.166908037653874\n",
            "[[8430 3806]\n",
            " [ 796  461]]\n",
            "11.780218797687562\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(None, None, None)"
            ]
          },
          "metadata": {},
          "execution_count": 289
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "acraqiyle2RW",
        "outputId": "5fe82d86-e7fd-4cc4-e9a5-605ce2087dc8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.09644194756554307\n",
            "[[11460   776]\n",
            " [ 1154   103]]\n"
          ]
        }
      ],
      "source": [
        "from xgboost.sklearn import XGBClassifier\n",
        "xgb9 = XGBClassifier(\n",
        " learning_rate =0.8,\n",
        " n_estimators=3000,\n",
        " max_depth=5,\n",
        " min_child_weight=1,\n",
        " gamma=0.7,\n",
        " subsample=0.8,\n",
        " colsample_bytree=0.8,\n",
        " objective= 'binary:logistic',\n",
        " nthread=4,\n",
        " scale_pos_weight=0.7,\n",
        " seed=27)\n",
        "xgb9.fit(dummy2, dummy3)\n",
        "y_pred9 = xgb9.predict(X_test)\n",
        "print(f1_score(y_test, y_pred9))\n",
        "print(confusion_matrix(y_test, y_pred9))\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sdnadnas = xgb9.predict_proba(X_test)\n",
        "log_loss(y_test, sdnadnas[:, 1])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K8V6UEQvi0cW",
        "outputId": "03a11534-55aa-481c-ae05-9545fa8dda18"
      },
      "execution_count": 464,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.5796948306061235"
            ]
          },
          "metadata": {},
          "execution_count": 464
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "testouput = xgb9.predict(datasettestn)"
      ],
      "metadata": {
        "id": "jbBvOUHt1wbX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "nooutliertestfile= datasettestn.copy()\n",
        "nooutliertestfile['target']= testouput\n",
        "nooutliertestfile.to_csv('nooutliertestfilexg9.csv')"
      ],
      "metadata": {
        "id": "7c8Bkum03dNE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "testouput12 = xgb9.predict_proba(datasettestn)\n",
        "nooutliertestfile1= datasettestn.copy()\n",
        "nooutliertestfile1['target']= testouput12[:, 1]\n",
        "nooutliertestfile1.to_csv('nooutliertestfilexg91.csv')"
      ],
      "metadata": {
        "id": "rFASBXzpfG8w"
      },
      "execution_count": 465,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "testouput1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4BBhLcbVf_Rd",
        "outputId": "75193ddf-999e-4b6a-bedb-ded1a2f645c9"
      },
      "execution_count": 458,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[9.71754849e-01, 2.82451436e-02],\n",
              "       [9.99978840e-01, 2.11801816e-05],\n",
              "       [9.55628455e-01, 4.43715341e-02],\n",
              "       ...,\n",
              "       [9.94754374e-01, 5.24559757e-03],\n",
              "       [8.00912321e-01, 1.99087664e-01],\n",
              "       [9.99985695e-01, 1.42766385e-05]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 458
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "\n",
        "oversample22= SVMSMOTE(sampling_strategy=0.4)\n",
        "smotex22, smotey22= oversample22.fit_resample(X_train, y_train)\n",
        "\n",
        "under22 = RandomUnderSampler(sampling_strategy=0.5)\n",
        "dummy222, dummy322= under22.fit_resample(smotex22, smotey22)"
      ],
      "metadata": {
        "id": "pkT0XOtT5isH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from xgboost.sklearn import XGBClassifier\n",
        "xgb912 = XGBClassifier(\n",
        " learning_rate =0.8,\n",
        " n_estimators=4000,\n",
        " max_depth=15,\n",
        " min_child_weight=1,\n",
        " gamma=0.7,\n",
        " subsample=0.8,\n",
        " colsample_bytree=0.8,\n",
        " objective= 'binary:logistic',\n",
        " nthread=4,\n",
        " scale_pos_weight=0.5,\n",
        " seed=27)\n",
        "xgb912.fit(dummy222, dummy322)\n",
        "y_pred912= xgb912.predict(X_test)\n",
        "print(f1_score(y_test, y_pred912))\n",
        "print(confusion_matrix(y_test, y_pred912))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xrDimXkByGA2",
        "outputId": "b01b00ae-86f5-417b-b207-d713ae002d43"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.0802139037433155\n",
            "[[11698   538]\n",
            " [ 1182    75]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "cNrILmlN5Ory"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "14695/29390"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lNOKn7fGyQgU",
        "outputId": "2c780957-d0c9-4ece-9eb3-ee34bf2cad69"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.5"
            ]
          },
          "metadata": {},
          "execution_count": 294
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred292 = xgb912.predict_proba(X_test)\n",
        "log_loss(y_test, y_pred292)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4DuPbpJlGyP9",
        "outputId": "2896d77d-9648-4fbd-9a03-dab6a3f9bf47"
      },
      "execution_count": 459,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.523165717463569"
            ]
          },
          "metadata": {},
          "execution_count": 459
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "log_loss(y_test,y_pred292[:, 1])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hhagPPBmihdQ",
        "outputId": "d377b49c-6428-488b-fe15-9e508c001ab8"
      },
      "execution_count": 463,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.523165717463569"
            ]
          },
          "metadata": {},
          "execution_count": 463
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "oversample1= SVMSMOTE(sampling_strategy=0.3)\n",
        "smotex1, smotey1= oversample1.fit_resample(X_train1, y_train1)\n",
        "\n",
        "under1 = RandomUnderSampler(sampling_strategy=0.6)\n",
        "dummy21, dummy31= under1.fit_resample(smotex1, smotey1)"
      ],
      "metadata": {
        "id": "ZszpTq9M6GWG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dummy31.value_counts(), 7045/11741"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_RlAoAax7Vj8",
        "outputId": "61c96d3a-2073-4db9-e17f-8cf42ed44957"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0    11741\n",
              " 1     7045\n",
              " Name: Loan Status, dtype: int64, 0.6000340686483264)"
            ]
          },
          "metadata": {},
          "execution_count": 313
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from xgboost.sklearn import XGBClassifier\n",
        "xgb91 = XGBClassifier(\n",
        " learning_rate =0.37,\n",
        " n_estimators=3000,\n",
        " max_depth=9,\n",
        " min_child_weight=1,\n",
        " gamma=0.7,\n",
        " subsample=0.8,\n",
        " colsample_bytree=0.8,\n",
        " objective= 'binary:logistic',\n",
        " nthread=4,\n",
        " scale_pos_weight=0.6,\n",
        " seed=27)\n",
        "xgb91.fit(dummy21, dummy31)\n",
        "y_pred91 = xgb91.predict(X_test)\n",
        "print(f1_score(y_test, y_pred91))\n",
        "print(confusion_matrix(y_test, y_pred91))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ouF1u3f3xXbx",
        "outputId": "b1aca1eb-f337-430a-d040-99a6be7836e6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.11211340206185566\n",
            "[[10563  1673]\n",
            " [ 1083   174]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "testpreout= xgb91.predict(datasettesto)"
      ],
      "metadata": {
        "id": "HxmuZCvH9-du"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "testdataoutlier= datasettesto.copy()\n",
        "testdataoutlier['target']=testpreout\n"
      ],
      "metadata": {
        "id": "eEz7GzJS-x2Z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "testdataoutlier.to_csv('testdataoutlierxg91.csv')"
      ],
      "metadata": {
        "id": "JsMLJTQuA8lb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "testpreout1= xgb91.predict_proba(datasettesto)\n",
        "testdataoutlier1= datasettesto.copy()\n",
        "testdataoutlier1['target']=testpreout1[:, 1]\n",
        "testdataoutlier1.to_csv('testdataoutlierxg911.csv')"
      ],
      "metadata": {
        "id": "uWS48hovj7mr"
      },
      "execution_count": 466,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "testdataoutlier['target'].value_counts(), nooutliertestfile['target'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7FcCl8Ni_JoM",
        "outputId": "f95fd35a-1069-45c5-d191-e03b849e5efc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0    17517\n",
              " 1     1687\n",
              " Name: target, dtype: int64, 0    23176\n",
              " 1     5737\n",
              " Name: target, dtype: int64)"
            ]
          },
          "metadata": {},
          "execution_count": 324
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from xgboost.sklearn import XGBClassifier\n",
        "xgb912222 = XGBClassifier(\n",
        " learning_rate =0.33,\n",
        " n_estimators=3000,\n",
        " max_depth=13,\n",
        " min_child_weight=1,\n",
        " gamma=0.7,\n",
        " subsample=0.8,\n",
        " colsample_bytree=0.8,\n",
        " objective= 'binary:logistic',\n",
        " nthread=4,\n",
        " scale_pos_weight=0.6,\n",
        " seed=27)\n",
        "xgb912222.fit(dummy21, dummy31)\n",
        "y_pred912222= xgb912222.predict(X_test)\n",
        "print(f1_score(y_test, y_pred912222))\n",
        "print(confusion_matrix(y_test, y_pred912222))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HLnfURcB8lXo",
        "outputId": "fcd940fb-3a5b-4db4-949f-b9a27c55ae19"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.11428571428571428\n",
            "[[10886  1350]\n",
            " [ 1099   158]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lfPn7lmTH85R",
        "outputId": "fa0548bd-593f-454c-d09f-aae09f4cb4d5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0    17517\n",
              " 1     1687\n",
              " Name: target, dtype: int64, 0    23176\n",
              " 1     5737\n",
              " Name: target, dtype: int64)"
            ]
          },
          "metadata": {},
          "execution_count": 328
        }
      ],
      "source": [
        "testdataoutlier['target'].value_counts(), nooutliertestfile['target'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "copytestdataoutlier= testdataoutlier.copy()\n",
        "copynooutliertestfile= nooutliertestfile.copy()"
      ],
      "metadata": {
        "id": "-4ydNIQzAO6d"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2n6Ww31IxY2P",
        "outputId": "a2a29efa-eb01-4390-bd59-9d75454947b1"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0    17517\n",
              " 1     1687\n",
              " Name: target, dtype: int64, 0    23176\n",
              " 1     5737\n",
              " Name: target, dtype: int64)"
            ]
          },
          "metadata": {},
          "execution_count": 400
        }
      ],
      "source": [
        "# pd.read_csv('/content/testdataoutlierxg91', index_col= 'Unnamed: 0')\n",
        "copytestdataoutlier['target'].value_counts(), copynooutliertestfile['target'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "copytestdataoutlier['target'].value_counts(), copynooutliertestfile['target'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QmdxvkhM_9ia",
        "outputId": "80cba220-27b5-48a7-f3b4-d630ca08d561"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0    17517\n",
              " 1     1687\n",
              " Name: target, dtype: int64, 0    23176\n",
              " 1     5737\n",
              " Name: target, dtype: int64)"
            ]
          },
          "metadata": {},
          "execution_count": 401
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "submission= pd.read_csv('/content/drive/MyDrive/Colab Notebooks/machine hacks/Participants_Data_PLD/submission.csv')"
      ],
      "metadata": {
        "id": "UN5NTqthCEHT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "submission.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "96aWgAkJCsKk",
        "outputId": "2af083e1-5c10-4777-9396-1d3325234d78"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(28913, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 403
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for i in copytestdataoutlier.index.tolist():\n",
        "  if i in copynooutliertestfile.index.tolist():\n",
        "    copynooutliertestfile.drop(index= i, inplace= True)"
      ],
      "metadata": {
        "id": "ioHyGPrgC-0R"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "copynooutliertestfile.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4HlKM7IJLYj4",
        "outputId": "4619c078-db84-4d63-a57e-fd2e7d8e6c3a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(9709, 20)"
            ]
          },
          "metadata": {},
          "execution_count": 405
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dummy676= pd.concat([copytestdataoutlier, copynooutliertestfile], axis= 0)"
      ],
      "metadata": {
        "id": "w5J0csBxEbNt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "submission.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "NoEw0dJ8GiBa",
        "outputId": "48eea48c-5cf6-4ef0-ff5d-7657d99c863c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>Loan Status</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Loan Status\n",
              "0            0\n",
              "1            0\n",
              "2            0\n",
              "3            0\n",
              "4            0"
            ]
          },
          "metadata": {},
          "execution_count": 407
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "newdata= dummy676.join(submission)"
      ],
      "metadata": {
        "id": "kwII5o8GGoC3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "k['Loan Status'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nzaU_IGoHI2h",
        "outputId": "e5d4b900-29a2-4c74-abf5-648cff1b05b8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    61222\n",
              "1     6241\n",
              "Name: Loan Status, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 409
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.concat([submission, dummy676], axis= 1)['target'].value_counts(), 5737/23176*100"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j78NOaPfHpcY",
        "outputId": "227ce741-bb9d-4a14-9d5b-39f54bfdf75f"
      },
      "execution_count": 415,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0    26156\n",
              " 1     2757\n",
              " Name: target, dtype: int64, 24.754055919917157)"
            ]
          },
          "metadata": {},
          "execution_count": 415
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(newdata['target'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Kw2jpA4uH4IT",
        "outputId": "655a78e9-c620-4c6d-d7f8-b8771e8903f4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "28913"
            ]
          },
          "metadata": {},
          "execution_count": 414
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#ratio is same  :- 6241/61222*100, 2757/26156*100"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D64QXj0eK8jM",
        "outputId": "1d8308c1-8601-433c-8f75-8112798480a9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10.194047891280912, 10.540602538614467)"
            ]
          },
          "metadata": {},
          "execution_count": 411
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "newdata['target']"
      ],
      "metadata": {
        "id": "H6qTcJYLUcPD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "newdata1 = newdata.sort_index(axis=0)"
      ],
      "metadata": {
        "id": "InrMh-2aL_C6"
      },
      "execution_count": 419,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "newdata1.drop(['Loan Status'], axis=1, inplace= True)"
      ],
      "metadata": {
        "id": "mKmBDqZbTXGl"
      },
      "execution_count": 429,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "newdata1['Loan Status']= newdata1['target']"
      ],
      "metadata": {
        "id": "AXej_vB0THYU"
      },
      "execution_count": 437,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "newdata1['Loan Status'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wNi2c52KTOnB",
        "outputId": "ab66e3bc-eb10-4170-d9a8-bcf2bc7dc02d"
      },
      "execution_count": 439,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    26156\n",
              "1     2757\n",
              "Name: Loan Status, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 439
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "2757/26156*100"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kEfixNvIWEP9",
        "outputId": "11a251da-2730-4e61-9e2b-95f47aee344c"
      },
      "execution_count": 440,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "10.540602538614467"
            ]
          },
          "metadata": {},
          "execution_count": 440
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "newdata1[['Loan Status']].to_csv('my_submission_file.csv', index=False)"
      ],
      "metadata": {
        "id": "URot8W51Wk7F"
      },
      "execution_count": 445,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "copytestdataoutlier1= testdataoutlier1.copy()\n",
        "copynooutliertestfile1= nooutliertestfile1.copy()\n",
        "copytestdataoutlier1['target'].value_counts(), copynooutliertestfile1['target'].value_counts()\n",
        "for i in copytestdataoutlier1.index.tolist():\n",
        "  if i in copynooutliertestfile1.index.tolist():\n",
        "    copynooutliertestfile1.drop(index= i, inplace= True)\n",
        "\n",
        "dummy6761= pd.concat([copytestdataoutlier1, copynooutliertestfile1], axis= 0)\n",
        "newdata12= dummy6761.join(submission)\n",
        "newdata12 = newdata12.sort_index(axis=0)\n",
        "newdata12.drop(['Loan Status'], axis=1, inplace= True)\n",
        "newdata12['Loan Status']= newdata12['target']\n",
        "newdata12['Loan Status'].value_counts()\n",
        "newdata12[['Loan Status']].to_csv('my_submission_file.csv', index=False)\n"
      ],
      "metadata": {
        "id": "aWWl00CSdgbq"
      },
      "execution_count": 472,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "newdata12[['Loan Status']]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "OUw5g0sLmR8Y",
        "outputId": "d2d0db50-f307-4dfc-ab05-b754b6125357"
      },
      "execution_count": 475,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Loan Status</th>\n",
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              "      <td>0.000021</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.126230</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.002418</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.008852</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
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              "      <td>0.010765</td>\n",
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              "      <td>0.070256</td>\n",
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              "    <tr>\n",
              "      <th>28910</th>\n",
              "      <td>0.000174</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28911</th>\n",
              "      <td>0.322557</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28912</th>\n",
              "      <td>0.097259</td>\n",
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              "</table>\n",
              "<p>28913 rows × 1 columns</p>\n",
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              "       Loan Status\n",
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              "28911     0.322557\n",
              "28912     0.097259\n",
              "\n",
              "[28913 rows x 1 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 475
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "submission"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "9qLQFBv-mkpz",
        "outputId": "543e0ffd-a464-4af8-dd05-ccc316cf46a4"
      },
      "execution_count": 476,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    .dataframe tbody tr th {\n",
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              "<p>28913 rows × 1 columns</p>\n",
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              "       Loan Status\n",
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              "4                0\n",
              "...            ...\n",
              "28908            0\n",
              "28909            0\n",
              "28910            0\n",
              "28911            0\n",
              "28912            0\n",
              "\n",
              "[28913 rows x 1 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 476
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "_6CydmkXp-1R"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [],
      "name": "Untitled19.ipynb",
      "provenance": []
    },
    "kernelspec": {
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