Cassandra Ctrl Shift Intelligence.ipynb

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      "source": [
        "# This Python 3 environment comes with many helpful analytics libraries installed\n",
        "# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
        "# For example, here's several helpful packages to load\n",
        "import warnings\n",
        "warnings.simplefilter(action='ignore', category=Warning)\n",
        "import numpy as np # linear algebra\n",
        "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
        "\n",
        "# Input data files are available in the read-only \"../input/\" directory\n",
        "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
        "\n",
        "import os\n",
        "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
        "    for filename in filenames:\n",
        "        print(os.path.join(dirname, filename))\n",
        "\n",
        "# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n",
        "# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/kaggle/input/cassandra-udyam21/payment_history_data.csv\n",
            "/kaggle/input/cassandra-udyam21/user_data_training.csv\n",
            "/kaggle/input/cassandra-udyam21/Sample Submission.csv\n",
            "/kaggle/input/cassandra-udyam21/user_data_testing.csv\n"
          ],
          "name": "stdout"
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      "source": [
        "db = pd.read_csv('/kaggle/input/cassandra-udyam21/payment_history_data.csv')\n",
        "db"
      ],
      "execution_count": null,
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              "\n",
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        "db.sort_values(\"last_update\").groupby('id').get_group(2542835)[\"recent_payment_activity\"]"
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      "execution_count": null,
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              "2288    20/11/2015\n",
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              "Name: recent_payment_activity, dtype: object"
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              "           id  Late_2  Late_1  Late_3  days_late_Sum  normal_payment  p_code  \\\n",
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      "source": [
        "df = pd.read_csv('/kaggle/input/cassandra-udyam21/user_data_training.csv')\n",
        "df = df.sort_values('id')\n",
        "df"
      ],
      "execution_count": null,
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          "output_type": "execute_result",
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              "     label          id  Alpha    Beta  Gamma    Delta  Epsilon  Zeta  Eta  \\\n",
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              "695      0       83847   2615  1338.5      3   325000        2    15    5   \n",
              "675      1      115753   2615  1245.5      3    77000        2    15    5   \n",
              "178      0      127625   2615  1320.5      3   130000        2    15    5   \n",
              "469      1      136529   2615     NaN      2    90000        2    15    5   \n",
              "..     ...         ...    ...     ...    ...      ...      ...   ...  ...   \n",
              "396      1  1492728889   2092     NaN      2    94000        2     8   -1   \n",
              "583      0  1492824607   3138  1241.0      1    74000        2     6   -1   \n",
              "452      0  1492844641   3661  1268.0      3   162000        2    11    5   \n",
              "712      0  1492854287   2092     NaN      2   111000        2     8    5   \n",
              "279      0  1492861707   3661  1322.0      3    68000        2    11    5   \n",
              "\n",
              "     Theta  Iota   Kappa      Lambda   omikron  \n",
              "322     64     5  361026    1.000000  0.015625  \n",
              "695    110     5   60033  316.227766  0.027273  \n",
              "675    109     5  151300  244.948974  0.027523  \n",
              "178     85     5   60047  212.132034  0.035294  \n",
              "469     92     5   72014    1.000000  0.021739  \n",
              "..     ...   ...     ...         ...       ...  \n",
              "396    113     4   72000    1.000000  0.017699  \n",
              "583    105     3   60027  200.000000  0.009524  \n",
              "452    113     4  450081  141.421356  0.026549  \n",
              "712    110     4   60091  220.868739  0.018182  \n",
              "279     86     3   72015    1.000000  0.034884  \n",
              "\n",
              "[715 rows x 14 columns]"
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              "      <th>695</th>\n",
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              "      <td>1338.5</td>\n",
              "      <td>3</td>\n",
              "      <td>325000</td>\n",
              "      <td>2</td>\n",
              "      <td>15</td>\n",
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              "      <td>316.227766</td>\n",
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              "      <th>675</th>\n",
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              "      <td>15</td>\n",
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              "      <td>5</td>\n",
              "      <td>151300</td>\n",
              "      <td>244.948974</td>\n",
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              "      <td>136529</td>\n",
              "      <td>2615</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2</td>\n",
              "      <td>90000</td>\n",
              "      <td>2</td>\n",
              "      <td>15</td>\n",
              "      <td>5</td>\n",
              "      <td>92</td>\n",
              "      <td>5</td>\n",
              "      <td>72014</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.021739</td>\n",
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              "    <tr>\n",
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              "      <td>1492728889</td>\n",
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              "      <td>8</td>\n",
              "      <td>-1</td>\n",
              "      <td>113</td>\n",
              "      <td>4</td>\n",
              "      <td>72000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.017699</td>\n",
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              "    <tr>\n",
              "      <th>583</th>\n",
              "      <td>0</td>\n",
              "      <td>1492824607</td>\n",
              "      <td>3138</td>\n",
              "      <td>1241.0</td>\n",
              "      <td>1</td>\n",
              "      <td>74000</td>\n",
              "      <td>2</td>\n",
              "      <td>6</td>\n",
              "      <td>-1</td>\n",
              "      <td>105</td>\n",
              "      <td>3</td>\n",
              "      <td>60027</td>\n",
              "      <td>200.000000</td>\n",
              "      <td>0.009524</td>\n",
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              "    <tr>\n",
              "      <th>452</th>\n",
              "      <td>0</td>\n",
              "      <td>1492844641</td>\n",
              "      <td>3661</td>\n",
              "      <td>1268.0</td>\n",
              "      <td>3</td>\n",
              "      <td>162000</td>\n",
              "      <td>2</td>\n",
              "      <td>11</td>\n",
              "      <td>5</td>\n",
              "      <td>113</td>\n",
              "      <td>4</td>\n",
              "      <td>450081</td>\n",
              "      <td>141.421356</td>\n",
              "      <td>0.026549</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>712</th>\n",
              "      <td>0</td>\n",
              "      <td>1492854287</td>\n",
              "      <td>2092</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2</td>\n",
              "      <td>111000</td>\n",
              "      <td>2</td>\n",
              "      <td>8</td>\n",
              "      <td>5</td>\n",
              "      <td>110</td>\n",
              "      <td>4</td>\n",
              "      <td>60091</td>\n",
              "      <td>220.868739</td>\n",
              "      <td>0.018182</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>279</th>\n",
              "      <td>0</td>\n",
              "      <td>1492861707</td>\n",
              "      <td>3661</td>\n",
              "      <td>1322.0</td>\n",
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              "      <td>11</td>\n",
              "      <td>5</td>\n",
              "      <td>86</td>\n",
              "      <td>3</td>\n",
              "      <td>72015</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.034884</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>715 rows × 14 columns</p>\n",
              "</div>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 280
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "i0L5rmgziI9l",
        "outputId": "012ad662-22d2-4a93-a47c-f213134c6bb7"
      },
      "source": [
        "df_test = pd.read_csv('/kaggle/input/cassandra-udyam21/user_data_testing.csv')\n",
        "df_test = df_test.sort_values('id')\n",
        "df_test"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             id  Alpha    Beta  Gamma    Delta  Epsilon  Zeta  Eta  Theta  \\\n",
              "356           1    523  1130.0      2  1000000        2     4   -1    100   \n",
              "78        12615   1046  1349.0      1   286000        2     3   -1     80   \n",
              "71        40811   2615  1272.5      3    70000        2    15    5    113   \n",
              "380       65668   2615  1272.5      3   120000        1    15    5    110   \n",
              "41        72717   2615  1326.5      3    30000        2    15    5    110   \n",
              "..          ...    ...     ...    ...      ...      ...   ...  ...    ...   \n",
              "76   1492676578   2092  1266.5      1   191000        2     8    5     85   \n",
              "291  1492712936   2092  1257.5      3    35000        2     8    3    112   \n",
              "159  1492728518   2092  1241.0      1    86000        1     8    5    104   \n",
              "363  1492742245   2092  1266.5      1   104000        2     8   -1     92   \n",
              "151  1492771183   3138  1217.0      3    56000        2     6   -1    100   \n",
              "\n",
              "     Iota   Kappa      Lambda   omikron  \n",
              "356     5  650018    1.000000  0.020000  \n",
              "78      3  361003    1.000000  0.012500  \n",
              "71      5   60033  167.122709  0.026549  \n",
              "380     5   72000  187.082869  0.027273  \n",
              "41      5   60005    1.000000  0.027273  \n",
              "..    ...     ...         ...       ...  \n",
              "76      3   72000    1.000000  0.011765  \n",
              "291     4   72014    1.000000  0.026786  \n",
              "159     3   72000  187.082869  0.009615  \n",
              "363     5   60036    1.000000  0.010870  \n",
              "151     3   60091    1.000000  0.030000  \n",
              "\n",
              "[410 rows x 13 columns]"
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              "      <td>361003</td>\n",
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              "      <td>0.012500</td>\n",
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              "      <th>71</th>\n",
              "      <td>40811</td>\n",
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              "      <td>3</td>\n",
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              "      <td>60033</td>\n",
              "      <td>167.122709</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>380</th>\n",
              "      <td>65668</td>\n",
              "      <td>2615</td>\n",
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              "      <td>3</td>\n",
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              "      <td>72000</td>\n",
              "      <td>187.082869</td>\n",
              "      <td>0.027273</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>72717</td>\n",
              "      <td>2615</td>\n",
              "      <td>1326.5</td>\n",
              "      <td>3</td>\n",
              "      <td>30000</td>\n",
              "      <td>2</td>\n",
              "      <td>15</td>\n",
              "      <td>5</td>\n",
              "      <td>110</td>\n",
              "      <td>5</td>\n",
              "      <td>60005</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.027273</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
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              "      <td>...</td>\n",
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              "      <th>76</th>\n",
              "      <td>1492676578</td>\n",
              "      <td>2092</td>\n",
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              "      <td>1</td>\n",
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              "      <td>85</td>\n",
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              "      <td>1.000000</td>\n",
              "      <td>0.011765</td>\n",
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              "      <th>291</th>\n",
              "      <td>1492712936</td>\n",
              "      <td>2092</td>\n",
              "      <td>1257.5</td>\n",
              "      <td>3</td>\n",
              "      <td>35000</td>\n",
              "      <td>2</td>\n",
              "      <td>8</td>\n",
              "      <td>3</td>\n",
              "      <td>112</td>\n",
              "      <td>4</td>\n",
              "      <td>72014</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.026786</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>159</th>\n",
              "      <td>1492728518</td>\n",
              "      <td>2092</td>\n",
              "      <td>1241.0</td>\n",
              "      <td>1</td>\n",
              "      <td>86000</td>\n",
              "      <td>1</td>\n",
              "      <td>8</td>\n",
              "      <td>5</td>\n",
              "      <td>104</td>\n",
              "      <td>3</td>\n",
              "      <td>72000</td>\n",
              "      <td>187.082869</td>\n",
              "      <td>0.009615</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>363</th>\n",
              "      <td>1492742245</td>\n",
              "      <td>2092</td>\n",
              "      <td>1266.5</td>\n",
              "      <td>1</td>\n",
              "      <td>104000</td>\n",
              "      <td>2</td>\n",
              "      <td>8</td>\n",
              "      <td>-1</td>\n",
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              "      <td>5</td>\n",
              "      <td>60036</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.010870</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>151</th>\n",
              "      <td>1492771183</td>\n",
              "      <td>3138</td>\n",
              "      <td>1217.0</td>\n",
              "      <td>3</td>\n",
              "      <td>56000</td>\n",
              "      <td>2</td>\n",
              "      <td>6</td>\n",
              "      <td>-1</td>\n",
              "      <td>100</td>\n",
              "      <td>3</td>\n",
              "      <td>60091</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.030000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>410 rows × 13 columns</p>\n",
              "</div>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 281
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "6xNv5DLtiI9n",
        "outputId": "68f12605-53d6-4135-981b-b3cd46297968"
      },
      "source": [
        "db.groupby(\"p_code\").get_group(22)\n",
        "\n",
        "## hence 1487549729 is a outlier"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              id  Late_2  Late_1  Late_3  days_late_Sum  normal_payment  \\\n",
              "454   1490575605       0       0       0              0               1   \n",
              "4740      239296       0       0       0              0               1   \n",
              "4750  1487549729       1       0       0              1              35   \n",
              "\n",
              "      p_code   p_limit last_update  curr_remaining   max_bal  \\\n",
              "454       22       NaN  05/12/2015             0.0  219256.0   \n",
              "4740      22       NaN  20/10/2012             0.0  136068.0   \n",
              "4750      22  275000.0  14/10/2012             0.0   52286.0   \n",
              "\n",
              "     recent_payment_activity  \n",
              "454               09/07/2015  \n",
              "4740              31/03/2014  \n",
              "4750              19/04/2015  "
            ],
            "text/html": [
              "<div>\n",
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              "      <th>id</th>\n",
              "      <th>Late_2</th>\n",
              "      <th>Late_1</th>\n",
              "      <th>Late_3</th>\n",
              "      <th>days_late_Sum</th>\n",
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              "      <th>p_code</th>\n",
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              "      <td>22</td>\n",
              "      <td>275000.0</td>\n",
              "      <td>14/10/2012</td>\n",
              "      <td>0.0</td>\n",
              "      <td>52286.0</td>\n",
              "      <td>19/04/2015</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 282
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "NgSCC2wkiI9p",
        "outputId": "91032145-5860-4186-fc2f-ceaeef9a9270"
      },
      "source": [
        "flag  = 0\n",
        "main = db.groupby(\"id\")\n",
        "for grp in main.groups:\n",
        "    ele = main.get_group(grp)[\"last_update\"]\n",
        "    if len(ele) == ele.isnull().sum():\n",
        "        print(\"there is a problem\")\n",
        "        flag = 1\n",
        "if flag==0:\n",
        "    print(\"No problem go ahead\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "No problem go ahead\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "7AQIDN-JiI9u"
      },
      "source": [
        "db_edit = db.copy()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "yazH10hriI9x",
        "outputId": "13a9ab56-d970-476c-8208-305e6c6356b7"
      },
      "source": [
        "## as the dataframes seems to be sorted in inverse chronological order of last update and only the last 26 rows are missing using ffill is the best option\n",
        "\n",
        "db_edit[\"last_update\"] = db[\"last_update\"].fillna(method=\"ffill\")\n",
        "db_edit[\"last_update\"] = pd.to_datetime(db_edit[\"last_update\"],format='%d/%m/%Y')\n",
        "db_edit[\"recent_payment_activity\"] = pd.to_datetime(db_edit[\"recent_payment_activity\"],format='%d/%m/%Y')\n",
        "db_edit"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              id  Late_2  Late_1  Late_3  days_late_Sum  normal_payment  \\\n",
              "0     1485873180       0       0       0              0               1   \n",
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              "8249     2542835       0       0       0              0               1   \n",
              "\n",
              "      p_code  p_limit last_update  curr_remaining   max_bal  \\\n",
              "0         10  16500.0  2016-12-04             0.0       NaN   \n",
              "1          5      NaN  2016-12-04        588720.0  491100.0   \n",
              "2          5      NaN  2016-12-04        840000.0  700500.0   \n",
              "3         10  37400.0  2016-12-03          8425.2    7520.0   \n",
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              "...      ...      ...         ...             ...       ...   \n",
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              "8249       5      NaN  1988-07-19        240000.0  200500.0   \n",
              "\n",
              "     recent_payment_activity  \n",
              "0                        NaT  \n",
              "1                        NaT  \n",
              "2                 2016-04-22  \n",
              "3                 2016-04-25  \n",
              "4                 2016-04-26  \n",
              "...                      ...  \n",
              "8245                     NaT  \n",
              "8246                     NaT  \n",
              "8247                     NaT  \n",
              "8248                     NaT  \n",
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              "[8250 rows x 12 columns]"
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              "      <th>Late_2</th>\n",
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              "      <td>20654.4</td>\n",
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              "      <td>NaT</td>\n",
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              "      <td>NaT</td>\n",
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              "</table>\n",
              "<p>8250 rows × 12 columns</p>\n",
              "</div>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 285
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "TLPaLBvMiI9z",
        "outputId": "9f1ce75e-456d-4340-fcd0-a438e8a57561"
      },
      "source": [
        "\"\"\"\n",
        "main = db_edit.groupby(\"id\")\n",
        "db_ediiit = db_edit.copy()\n",
        "for grp in main.groups:\n",
        "    ele = main.get_group(grp)\n",
        "    #print(ele[\"recent_payment_activity\"].index)\n",
        "    db_ediiit[\"recent_payment_activity\"].iloc[ele.index]=ele[\"recent_payment_activity\"].fillna(method=\"bfill\")\n",
        "db_ediiit\n",
        "\"\"\""
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'\\nmain = db_edit.groupby(\"id\")\\ndb_ediiit = db_edit.copy()\\nfor grp in main.groups:\\n    ele = main.get_group(grp)\\n    #print(ele[\"recent_payment_activity\"].index)\\n    db_ediiit[\"recent_payment_activity\"].iloc[ele.index]=ele[\"recent_payment_activity\"].fillna(method=\"bfill\")\\ndb_ediiit\\n'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 286
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "Oa2Q7JJfiI94",
        "outputId": "9cca8bf9-9cba-495c-a21b-e594b3ec4327"
      },
      "source": [
        "## only precedding value from futures are used to fill\n",
        "db_edit[\"recent_payment_activity\"] = db_edit.groupby(\"id\")[\"recent_payment_activity\"].ffill()\n",
        "db_edit"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              id  Late_2  Late_1  Late_3  days_late_Sum  normal_payment  \\\n",
              "0     1485873180       0       0       0              0               1   \n",
              "1     1488748059       0       0       0              0               1   \n",
              "2     1489508238       0       0       0              0               2   \n",
              "3        2320606       0       0       0              0               3   \n",
              "4        2007111       0       0       0              0               2   \n",
              "...          ...     ...     ...     ...            ...             ...   \n",
              "8245  1488869376       0       0       0              0               9   \n",
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              "8247     2172577       0       0       0              0               5   \n",
              "8248     3730406       0       0       0              0               1   \n",
              "8249     2542835       0       0       0              0               1   \n",
              "\n",
              "      p_code  p_limit last_update  curr_remaining   max_bal  \\\n",
              "0         10  16500.0  2016-12-04             0.0       NaN   \n",
              "1          5      NaN  2016-12-04        588720.0  491100.0   \n",
              "2          5      NaN  2016-12-04        840000.0  700500.0   \n",
              "3         10  37400.0  2016-12-03          8425.2    7520.0   \n",
              "4         10      NaN  2016-12-03         15147.6       NaN   \n",
              "...      ...      ...         ...             ...       ...   \n",
              "8245      15      NaN  1988-07-19             0.0       NaN   \n",
              "8246       2      NaN  1988-07-19             0.0       NaN   \n",
              "8247      13      NaN  1988-07-19         20654.4   33315.0   \n",
              "8248       2      NaN  1988-07-19             0.0       NaN   \n",
              "8249       5      NaN  1988-07-19        240000.0  200500.0   \n",
              "\n",
              "     recent_payment_activity  \n",
              "0                        NaT  \n",
              "1                        NaT  \n",
              "2                 2016-04-22  \n",
              "3                 2016-04-25  \n",
              "4                 2016-04-26  \n",
              "...                      ...  \n",
              "8245              2016-04-16  \n",
              "8246              2013-07-24  \n",
              "8247              2014-10-20  \n",
              "8248              2013-07-24  \n",
              "8249              2015-11-20  \n",
              "\n",
              "[8250 rows x 12 columns]"
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1489508238</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2016-12-04</td>\n",
              "      <td>840000.0</td>\n",
              "      <td>700500.0</td>\n",
              "      <td>2016-04-22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2320606</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>10</td>\n",
              "      <td>37400.0</td>\n",
              "      <td>2016-12-03</td>\n",
              "      <td>8425.2</td>\n",
              "      <td>7520.0</td>\n",
              "      <td>2016-04-25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2007111</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>10</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2016-12-03</td>\n",
              "      <td>15147.6</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2016-04-26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>8245</th>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>9</td>\n",
              "      <td>15</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1988-07-19</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2016-04-16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8246</th>\n",
              "      <td>3730406</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1988-07-19</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2013-07-24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8247</th>\n",
              "      <td>2172577</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>13</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1988-07-19</td>\n",
              "      <td>20654.4</td>\n",
              "      <td>33315.0</td>\n",
              "      <td>2014-10-20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8248</th>\n",
              "      <td>3730406</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1988-07-19</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2013-07-24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8249</th>\n",
              "      <td>2542835</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1988-07-19</td>\n",
              "      <td>240000.0</td>\n",
              "      <td>200500.0</td>\n",
              "      <td>2015-11-20</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8250 rows × 12 columns</p>\n",
              "</div>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 287
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "aOu_tNtHiI96"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "2i3MI4zgiI96"
      },
      "source": [
        "from datetime import timedelta"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "7pvJD697iI97",
        "outputId": "430739e6-4342-4a1f-f397-0e9902371bc7"
      },
      "source": [
        "db_edit[\"days\"] = db_edit[\"recent_payment_activity\"]-db_edit[\"last_update\"]\n",
        "db_edit[\"days\"] = db_edit[\"days\"].fillna(timedelta(0))\n",
        "db_edit"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              id  Late_2  Late_1  Late_3  days_late_Sum  normal_payment  \\\n",
              "0     1485873180       0       0       0              0               1   \n",
              "1     1488748059       0       0       0              0               1   \n",
              "2     1489508238       0       0       0              0               2   \n",
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              "...          ...     ...     ...     ...            ...             ...   \n",
              "8245  1488869376       0       0       0              0               9   \n",
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              "8248     3730406       0       0       0              0               1   \n",
              "8249     2542835       0       0       0              0               1   \n",
              "\n",
              "      p_code  p_limit last_update  curr_remaining   max_bal  \\\n",
              "0         10  16500.0  2016-12-04             0.0       NaN   \n",
              "1          5      NaN  2016-12-04        588720.0  491100.0   \n",
              "2          5      NaN  2016-12-04        840000.0  700500.0   \n",
              "3         10  37400.0  2016-12-03          8425.2    7520.0   \n",
              "4         10      NaN  2016-12-03         15147.6       NaN   \n",
              "...      ...      ...         ...             ...       ...   \n",
              "8245      15      NaN  1988-07-19             0.0       NaN   \n",
              "8246       2      NaN  1988-07-19             0.0       NaN   \n",
              "8247      13      NaN  1988-07-19         20654.4   33315.0   \n",
              "8248       2      NaN  1988-07-19             0.0       NaN   \n",
              "8249       5      NaN  1988-07-19        240000.0  200500.0   \n",
              "\n",
              "     recent_payment_activity       days  \n",
              "0                        NaT     0 days  \n",
              "1                        NaT     0 days  \n",
              "2                 2016-04-22  -226 days  \n",
              "3                 2016-04-25  -222 days  \n",
              "4                 2016-04-26  -221 days  \n",
              "...                      ...        ...  \n",
              "8245              2016-04-16 10133 days  \n",
              "8246              2013-07-24  9136 days  \n",
              "8247              2014-10-20  9589 days  \n",
              "8248              2013-07-24  9136 days  \n",
              "8249              2015-11-20  9985 days  \n",
              "\n",
              "[8250 rows x 13 columns]"
            ],
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              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>Late_2</th>\n",
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              "      <th>Late_3</th>\n",
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              "      <td>840000.0</td>\n",
              "      <td>700500.0</td>\n",
              "      <td>2016-04-22</td>\n",
              "      <td>-226 days</td>\n",
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              "      <td>2016-12-03</td>\n",
              "      <td>8425.2</td>\n",
              "      <td>7520.0</td>\n",
              "      <td>2016-04-25</td>\n",
              "      <td>-222 days</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>2016-12-03</td>\n",
              "      <td>15147.6</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2016-04-26</td>\n",
              "      <td>-221 days</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
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              "      <td>NaN</td>\n",
              "      <td>2016-04-16</td>\n",
              "      <td>10133 days</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2013-07-24</td>\n",
              "      <td>9136 days</td>\n",
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              "      <td>NaN</td>\n",
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              "</table>\n",
              "<p>8250 rows × 13 columns</p>\n",
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          },
          "metadata": {
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          "execution_count": 289
        }
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    },
    {
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      "metadata": {
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        "id": "JF_r7JoIiI99",
        "outputId": "45af0f12-ea6e-4649-db70-2db3c060fdd2"
      },
      "source": [
        "def datetime2day(x):\n",
        "    return x.days\n",
        "db_edit[\"days_val\"] = db_edit[\"days\"].apply(datetime2day)\n",
        "db_edit"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              id  Late_2  Late_1  Late_3  days_late_Sum  normal_payment  \\\n",
              "0     1485873180       0       0       0              0               1   \n",
              "1     1488748059       0       0       0              0               1   \n",
              "2     1489508238       0       0       0              0               2   \n",
              "3        2320606       0       0       0              0               3   \n",
              "4        2007111       0       0       0              0               2   \n",
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              "8247     2172577       0       0       0              0               5   \n",
              "8248     3730406       0       0       0              0               1   \n",
              "8249     2542835       0       0       0              0               1   \n",
              "\n",
              "      p_code  p_limit last_update  curr_remaining   max_bal  \\\n",
              "0         10  16500.0  2016-12-04             0.0       NaN   \n",
              "1          5      NaN  2016-12-04        588720.0  491100.0   \n",
              "2          5      NaN  2016-12-04        840000.0  700500.0   \n",
              "3         10  37400.0  2016-12-03          8425.2    7520.0   \n",
              "4         10      NaN  2016-12-03         15147.6       NaN   \n",
              "...      ...      ...         ...             ...       ...   \n",
              "8245      15      NaN  1988-07-19             0.0       NaN   \n",
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              "8247      13      NaN  1988-07-19         20654.4   33315.0   \n",
              "8248       2      NaN  1988-07-19             0.0       NaN   \n",
              "8249       5      NaN  1988-07-19        240000.0  200500.0   \n",
              "\n",
              "     recent_payment_activity       days  days_val  \n",
              "0                        NaT     0 days         0  \n",
              "1                        NaT     0 days         0  \n",
              "2                 2016-04-22  -226 days      -226  \n",
              "3                 2016-04-25  -222 days      -222  \n",
              "4                 2016-04-26  -221 days      -221  \n",
              "...                      ...        ...       ...  \n",
              "8245              2016-04-16 10133 days     10133  \n",
              "8246              2013-07-24  9136 days      9136  \n",
              "8247              2014-10-20  9589 days      9589  \n",
              "8248              2013-07-24  9136 days      9136  \n",
              "8249              2015-11-20  9985 days      9985  \n",
              "\n",
              "[8250 rows x 14 columns]"
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              "      <td>16500.0</td>\n",
              "      <td>2016-12-04</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaT</td>\n",
              "      <td>0 days</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1488748059</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2016-12-04</td>\n",
              "      <td>588720.0</td>\n",
              "      <td>491100.0</td>\n",
              "      <td>NaT</td>\n",
              "      <td>0 days</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1489508238</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2016-12-04</td>\n",
              "      <td>840000.0</td>\n",
              "      <td>700500.0</td>\n",
              "      <td>2016-04-22</td>\n",
              "      <td>-226 days</td>\n",
              "      <td>-226</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2320606</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>10</td>\n",
              "      <td>37400.0</td>\n",
              "      <td>2016-12-03</td>\n",
              "      <td>8425.2</td>\n",
              "      <td>7520.0</td>\n",
              "      <td>2016-04-25</td>\n",
              "      <td>-222 days</td>\n",
              "      <td>-222</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2007111</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>10</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2016-12-03</td>\n",
              "      <td>15147.6</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2016-04-26</td>\n",
              "      <td>-221 days</td>\n",
              "      <td>-221</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8245</th>\n",
              "      <td>1488869376</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>9</td>\n",
              "      <td>15</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1988-07-19</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2016-04-16</td>\n",
              "      <td>10133 days</td>\n",
              "      <td>10133</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8246</th>\n",
              "      <td>3730406</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1988-07-19</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2013-07-24</td>\n",
              "      <td>9136 days</td>\n",
              "      <td>9136</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8247</th>\n",
              "      <td>2172577</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>13</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1988-07-19</td>\n",
              "      <td>20654.4</td>\n",
              "      <td>33315.0</td>\n",
              "      <td>2014-10-20</td>\n",
              "      <td>9589 days</td>\n",
              "      <td>9589</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8248</th>\n",
              "      <td>3730406</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1988-07-19</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2013-07-24</td>\n",
              "      <td>9136 days</td>\n",
              "      <td>9136</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8249</th>\n",
              "      <td>2542835</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1988-07-19</td>\n",
              "      <td>240000.0</td>\n",
              "      <td>200500.0</td>\n",
              "      <td>2015-11-20</td>\n",
              "      <td>9985 days</td>\n",
              "      <td>9985</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8250 rows × 14 columns</p>\n",
              "</div>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 290
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mbAM67aaiI9_"
      },
      "source": [
        "## Analysing variables in user data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "QIvXluqhiI-A",
        "outputId": "b5eb5abd-3655-4986-ad81-11f760fda7ad"
      },
      "source": [
        "df.isnull().sum()\n",
        "## got to fill only Beta"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "label       0\n",
              "id          0\n",
              "Alpha       0\n",
              "Beta       94\n",
              "Gamma       0\n",
              "Delta       0\n",
              "Epsilon     0\n",
              "Zeta        0\n",
              "Eta         0\n",
              "Theta       0\n",
              "Iota        0\n",
              "Kappa       0\n",
              "Lambda      0\n",
              "omikron     0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 291
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "Is-ak6ooiI-C"
      },
      "source": [
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "XcP36OLliI-C",
        "outputId": "fa25963b-17db-494a-e1a7-10d2e4d31328"
      },
      "source": [
        "t1= df.groupby(\"label\")[\"Beta\"].get_group(1).values\n",
        "t2 = df.groupby(\"label\")[\"Beta\"].get_group(0).values\n",
        "plt.scatter(t1,np.ones_like(t1),color='green')\n",
        "plt.scatter(t2,0.5*np.ones_like(t2),color='red')\n",
        "#t2.max()\n",
        "plt.show()\n",
        "\n",
        "## Average to fill nan seems to be a good option"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "JUfRIE5oiI-E"
      },
      "source": [
        "df_edit = df.copy()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "numI97pWiI-E",
        "outputId": "fc0f9d69-4465-45f2-b09b-ef35081aed9b"
      },
      "source": [
        "df_test[\"Beta\"] = df_test[\"Beta\"].fillna(df[\"Beta\"].mean())\n",
        "df_edit[\"Beta\"] = df[\"Beta\"].fillna(df[\"Beta\"].mean())\n",
        "df_edit"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     label          id  Alpha         Beta  Gamma    Delta  Epsilon  Zeta  \\\n",
              "322      0        1114    523  1391.000000      1  1200000        2     5   \n",
              "695      0       83847   2615  1338.500000      3   325000        2    15   \n",
              "675      1      115753   2615  1245.500000      3    77000        2    15   \n",
              "178      0      127625   2615  1320.500000      3   130000        2    15   \n",
              "469      1      136529   2615  1284.454106      2    90000        2    15   \n",
              "..     ...         ...    ...          ...    ...      ...      ...   ...   \n",
              "396      1  1492728889   2092  1284.454106      2    94000        2     8   \n",
              "583      0  1492824607   3138  1241.000000      1    74000        2     6   \n",
              "452      0  1492844641   3661  1268.000000      3   162000        2    11   \n",
              "712      0  1492854287   2092  1284.454106      2   111000        2     8   \n",
              "279      0  1492861707   3661  1322.000000      3    68000        2    11   \n",
              "\n",
              "     Eta  Theta  Iota   Kappa      Lambda   omikron  \n",
              "322   -1     64     5  361026    1.000000  0.015625  \n",
              "695    5    110     5   60033  316.227766  0.027273  \n",
              "675    5    109     5  151300  244.948974  0.027523  \n",
              "178    5     85     5   60047  212.132034  0.035294  \n",
              "469    5     92     5   72014    1.000000  0.021739  \n",
              "..   ...    ...   ...     ...         ...       ...  \n",
              "396   -1    113     4   72000    1.000000  0.017699  \n",
              "583   -1    105     3   60027  200.000000  0.009524  \n",
              "452    5    113     4  450081  141.421356  0.026549  \n",
              "712    5    110     4   60091  220.868739  0.018182  \n",
              "279    5     86     3   72015    1.000000  0.034884  \n",
              "\n",
              "[715 rows x 14 columns]"
            ],
            "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>label</th>\n",
              "      <th>id</th>\n",
              "      <th>Alpha</th>\n",
              "      <th>Beta</th>\n",
              "      <th>Gamma</th>\n",
              "      <th>Delta</th>\n",
              "      <th>Epsilon</th>\n",
              "      <th>Zeta</th>\n",
              "      <th>Eta</th>\n",
              "      <th>Theta</th>\n",
              "      <th>Iota</th>\n",
              "      <th>Kappa</th>\n",
              "      <th>Lambda</th>\n",
              "      <th>omikron</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>322</th>\n",
              "      <td>0</td>\n",
              "      <td>1114</td>\n",
              "      <td>523</td>\n",
              "      <td>1391.000000</td>\n",
              "      <td>1</td>\n",
              "      <td>1200000</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>-1</td>\n",
              "      <td>64</td>\n",
              "      <td>5</td>\n",
              "      <td>361026</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.015625</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>695</th>\n",
              "      <td>0</td>\n",
              "      <td>83847</td>\n",
              "      <td>2615</td>\n",
              "      <td>1338.500000</td>\n",
              "      <td>3</td>\n",
              "      <td>325000</td>\n",
              "      <td>2</td>\n",
              "      <td>15</td>\n",
              "      <td>5</td>\n",
              "      <td>110</td>\n",
              "      <td>5</td>\n",
              "      <td>60033</td>\n",
              "      <td>316.227766</td>\n",
              "      <td>0.027273</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>675</th>\n",
              "      <td>1</td>\n",
              "      <td>115753</td>\n",
              "      <td>2615</td>\n",
              "      <td>1245.500000</td>\n",
              "      <td>3</td>\n",
              "      <td>77000</td>\n",
              "      <td>2</td>\n",
              "      <td>15</td>\n",
              "      <td>5</td>\n",
              "      <td>109</td>\n",
              "      <td>5</td>\n",
              "      <td>151300</td>\n",
              "      <td>244.948974</td>\n",
              "      <td>0.027523</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>178</th>\n",
              "      <td>0</td>\n",
              "      <td>127625</td>\n",
              "      <td>2615</td>\n",
              "      <td>1320.500000</td>\n",
              "      <td>3</td>\n",
              "      <td>130000</td>\n",
              "      <td>2</td>\n",
              "      <td>15</td>\n",
              "      <td>5</td>\n",
              "      <td>85</td>\n",
              "      <td>5</td>\n",
              "      <td>60047</td>\n",
              "      <td>212.132034</td>\n",
              "      <td>0.035294</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>469</th>\n",
              "      <td>1</td>\n",
              "      <td>136529</td>\n",
              "      <td>2615</td>\n",
              "      <td>1284.454106</td>\n",
              "      <td>2</td>\n",
              "      <td>90000</td>\n",
              "      <td>2</td>\n",
              "      <td>15</td>\n",
              "      <td>5</td>\n",
              "      <td>92</td>\n",
              "      <td>5</td>\n",
              "      <td>72014</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.021739</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>396</th>\n",
              "      <td>1</td>\n",
              "      <td>1492728889</td>\n",
              "      <td>2092</td>\n",
              "      <td>1284.454106</td>\n",
              "      <td>2</td>\n",
              "      <td>94000</td>\n",
              "      <td>2</td>\n",
              "      <td>8</td>\n",
              "      <td>-1</td>\n",
              "      <td>113</td>\n",
              "      <td>4</td>\n",
              "      <td>72000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.017699</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>583</th>\n",
              "      <td>0</td>\n",
              "      <td>1492824607</td>\n",
              "      <td>3138</td>\n",
              "      <td>1241.000000</td>\n",
              "      <td>1</td>\n",
              "      <td>74000</td>\n",
              "      <td>2</td>\n",
              "      <td>6</td>\n",
              "      <td>-1</td>\n",
              "      <td>105</td>\n",
              "      <td>3</td>\n",
              "      <td>60027</td>\n",
              "      <td>200.000000</td>\n",
              "      <td>0.009524</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>452</th>\n",
              "      <td>0</td>\n",
              "      <td>1492844641</td>\n",
              "      <td>3661</td>\n",
              "      <td>1268.000000</td>\n",
              "      <td>3</td>\n",
              "      <td>162000</td>\n",
              "      <td>2</td>\n",
              "      <td>11</td>\n",
              "      <td>5</td>\n",
              "      <td>113</td>\n",
              "      <td>4</td>\n",
              "      <td>450081</td>\n",
              "      <td>141.421356</td>\n",
              "      <td>0.026549</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>712</th>\n",
              "      <td>0</td>\n",
              "      <td>1492854287</td>\n",
              "      <td>2092</td>\n",
              "      <td>1284.454106</td>\n",
              "      <td>2</td>\n",
              "      <td>111000</td>\n",
              "      <td>2</td>\n",
              "      <td>8</td>\n",
              "      <td>5</td>\n",
              "      <td>110</td>\n",
              "      <td>4</td>\n",
              "      <td>60091</td>\n",
              "      <td>220.868739</td>\n",
              "      <td>0.018182</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>279</th>\n",
              "      <td>0</td>\n",
              "      <td>1492861707</td>\n",
              "      <td>3661</td>\n",
              "      <td>1322.000000</td>\n",
              "      <td>3</td>\n",
              "      <td>68000</td>\n",
              "      <td>2</td>\n",
              "      <td>11</td>\n",
              "      <td>5</td>\n",
              "      <td>86</td>\n",
              "      <td>3</td>\n",
              "      <td>72015</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.034884</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>715 rows × 14 columns</p>\n",
              "</div>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 295
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "QLnqg718iI-F"
      },
      "source": [
        "db_edit[\"p_val\"] = db[\"p_limit\"].fillna(-1000) + 500*(db[\"p_code\"]==10)\n",
        "#db_edit[\"p_val\"].iloc[4750] *= -1\n",
        "\n",
        "## The outlier found earlier"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "CU9450x5iI-G"
      },
      "source": [
        "## B fill because we are storing maximum balance in history so previous records are filled\n",
        "## and zero is placed in the first max_bal as assuming we open empty accounts\n",
        "db_edit[\"max_bal\"] = db_edit.groupby(\"id\")[\"max_bal\"].fillna(method=\"bfill\")\n",
        "db_edit[\"max_bal\"] = db_edit[\"max_bal\"].fillna(0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "OiM43QdniI-H",
        "outputId": "9ac0bf1b-8c2e-451a-c42e-8f5d8658d4cb"
      },
      "source": [
        "db_edit"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              id  Late_2  Late_1  Late_3  days_late_Sum  normal_payment  \\\n",
              "0     1485873180       0       0       0              0               1   \n",
              "1     1488748059       0       0       0              0               1   \n",
              "2     1489508238       0       0       0              0               2   \n",
              "3        2320606       0       0       0              0               3   \n",
              "4        2007111       0       0       0              0               2   \n",
              "...          ...     ...     ...     ...            ...             ...   \n",
              "8245  1488869376       0       0       0              0               9   \n",
              "8246     3730406       0       0       0              0               1   \n",
              "8247     2172577       0       0       0              0               5   \n",
              "8248     3730406       0       0       0              0               1   \n",
              "8249     2542835       0       0       0              0               1   \n",
              "\n",
              "      p_code  p_limit last_update  curr_remaining   max_bal  \\\n",
              "0         10  16500.0  2016-12-04             0.0   35500.0   \n",
              "1          5      NaN  2016-12-04        588720.0  491100.0   \n",
              "2          5      NaN  2016-12-04        840000.0  700500.0   \n",
              "3         10  37400.0  2016-12-03          8425.2    7520.0   \n",
              "4         10      NaN  2016-12-03         15147.6   12699.0   \n",
              "...      ...      ...         ...             ...       ...   \n",
              "8245      15      NaN  1988-07-19             0.0       0.0   \n",
              "8246       2      NaN  1988-07-19             0.0       0.0   \n",
              "8247      13      NaN  1988-07-19         20654.4   33315.0   \n",
              "8248       2      NaN  1988-07-19             0.0       0.0   \n",
              "8249       5      NaN  1988-07-19        240000.0  200500.0   \n",
              "\n",
              "     recent_payment_activity       days  days_val    p_val  \n",
              "0                        NaT     0 days         0  17000.0  \n",
              "1                        NaT     0 days         0  -1000.0  \n",
              "2                 2016-04-22  -226 days      -226  -1000.0  \n",
              "3                 2016-04-25  -222 days      -222  37900.0  \n",
              "4                 2016-04-26  -221 days      -221   -500.0  \n",
              "...                      ...        ...       ...      ...  \n",
              "8245              2016-04-16 10133 days     10133  -1000.0  \n",
              "8246              2013-07-24  9136 days      9136  -1000.0  \n",
              "8247              2014-10-20  9589 days      9589  -1000.0  \n",
              "8248              2013-07-24  9136 days      9136  -1000.0  \n",
              "8249              2015-11-20  9985 days      9985  -1000.0  \n",
              "\n",
              "[8250 rows x 15 columns]"
            ],
            "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>Late_2</th>\n",
              "      <th>Late_1</th>\n",
              "      <th>Late_3</th>\n",
              "      <th>days_late_Sum</th>\n",
              "      <th>normal_payment</th>\n",
              "      <th>p_code</th>\n",
              "      <th>p_limit</th>\n",
              "      <th>last_update</th>\n",
              "      <th>curr_remaining</th>\n",
              "      <th>max_bal</th>\n",
              "      <th>recent_payment_activity</th>\n",
              "      <th>days</th>\n",
              "      <th>days_val</th>\n",
              "      <th>p_val</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1485873180</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "      <td>16500.0</td>\n",
              "      <td>2016-12-04</td>\n",
              "      <td>0.0</td>\n",
              "      <td>35500.0</td>\n",
              "      <td>NaT</td>\n",
              "      <td>0 days</td>\n",
              "      <td>0</td>\n",
              "      <td>17000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1488748059</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2016-12-04</td>\n",
              "      <td>588720.0</td>\n",
              "      <td>491100.0</td>\n",
              "      <td>NaT</td>\n",
              "      <td>0 days</td>\n",
              "      <td>0</td>\n",
              "      <td>-1000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1489508238</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2016-12-04</td>\n",
              "      <td>840000.0</td>\n",
              "      <td>700500.0</td>\n",
              "      <td>2016-04-22</td>\n",
              "      <td>-226 days</td>\n",
              "      <td>-226</td>\n",
              "      <td>-1000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2320606</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>10</td>\n",
              "      <td>37400.0</td>\n",
              "      <td>2016-12-03</td>\n",
              "      <td>8425.2</td>\n",
              "      <td>7520.0</td>\n",
              "      <td>2016-04-25</td>\n",
              "      <td>-222 days</td>\n",
              "      <td>-222</td>\n",
              "      <td>37900.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2007111</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>10</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2016-12-03</td>\n",
              "      <td>15147.6</td>\n",
              "      <td>12699.0</td>\n",
              "      <td>2016-04-26</td>\n",
              "      <td>-221 days</td>\n",
              "      <td>-221</td>\n",
              "      <td>-500.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8245</th>\n",
              "      <td>1488869376</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>9</td>\n",
              "      <td>15</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1988-07-19</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2016-04-16</td>\n",
              "      <td>10133 days</td>\n",
              "      <td>10133</td>\n",
              "      <td>-1000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8246</th>\n",
              "      <td>3730406</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1988-07-19</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2013-07-24</td>\n",
              "      <td>9136 days</td>\n",
              "      <td>9136</td>\n",
              "      <td>-1000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8247</th>\n",
              "      <td>2172577</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>13</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1988-07-19</td>\n",
              "      <td>20654.4</td>\n",
              "      <td>33315.0</td>\n",
              "      <td>2014-10-20</td>\n",
              "      <td>9589 days</td>\n",
              "      <td>9589</td>\n",
              "      <td>-1000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8248</th>\n",
              "      <td>3730406</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1988-07-19</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2013-07-24</td>\n",
              "      <td>9136 days</td>\n",
              "      <td>9136</td>\n",
              "      <td>-1000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8249</th>\n",
              "      <td>2542835</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1988-07-19</td>\n",
              "      <td>240000.0</td>\n",
              "      <td>200500.0</td>\n",
              "      <td>2015-11-20</td>\n",
              "      <td>9985 days</td>\n",
              "      <td>9985</td>\n",
              "      <td>-1000.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8250 rows × 15 columns</p>\n",
              "</div>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 298
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "LiHfR242iI-J",
        "outputId": "6554ba37-b3e0-4f38-99e6-ea125428f583"
      },
      "source": [
        "db_edit.groupby(\"id\").get_group(83847)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         id  Late_2  Late_1  Late_3  days_late_Sum  normal_payment  p_code  \\\n",
              "3118  83847       0       0       0              0              19       2   \n",
              "3436  83847       0       0       0              0              16      10   \n",
              "4804  83847       0       0       0              0              34      10   \n",
              "5058  83847       0       0       0              0              36      10   \n",
              "5748  83847       0       0       0              0               2      10   \n",
              "5773  83847       0       0       0              0              35      10   \n",
              "6314  83847       0       0       0              0              36       2   \n",
              "6372  83847       1       0       0             17               6      10   \n",
              "6373  83847       0       0       0              0              33      10   \n",
              "6702  83847       0       0       0              0              33       1   \n",
              "6752  83847       0       0       0              0              23      10   \n",
              "7177  83847       0       0       0              0               1      10   \n",
              "7276  83847       0       0       0              0               0      10   \n",
              "7296  83847       0       0       0              0               5      10   \n",
              "7353  83847       0       0       0              0               0      10   \n",
              "7488  83847       0       0       0              0               1      10   \n",
              "7577  83847       0       0       0              0               1      10   \n",
              "7645  83847       0       0       0              0              33      10   \n",
              "7693  83847       0       0       0              0               7      10   \n",
              "7913  83847       0       0       0              0               1       1   \n",
              "7992  83847       0       0       0              0               1      10   \n",
              "8130  83847       0       0       0              0               0       1   \n",
              "\n",
              "       p_limit last_update  curr_remaining    max_bal recent_payment_activity  \\\n",
              "3118       NaN  2014-05-04         72666.0    79462.0              2015-09-17   \n",
              "3436       NaN  2014-01-19          1417.2    22889.0              2015-08-08   \n",
              "4804  410300.0  2012-09-20         11510.4    12214.0              2015-09-20   \n",
              "5058       NaN  2012-04-30             0.0    91631.0              2015-02-07   \n",
              "5748       NaN  2010-06-21             0.0     1052.0              2015-02-07   \n",
              "5773  275000.0  2010-05-16             0.0   250500.0              2014-08-19   \n",
              "6314       NaN  2008-10-26       1768387.2  2057500.0              2015-09-17   \n",
              "6372       NaN  2008-09-16          6264.0   400500.0              2015-09-17   \n",
              "6373       NaN  2008-09-16         41757.6   400500.0              2015-09-16   \n",
              "6702       NaN  2008-03-20             0.0   400500.0              2011-02-25   \n",
              "6752       NaN  2008-02-15             0.0      609.0              2011-02-25   \n",
              "7177   68200.0  2007-05-05             0.0      609.0              2011-02-25   \n",
              "7276       NaN  2007-02-15             0.0      609.0              2011-02-25   \n",
              "7296       NaN  2007-01-29             0.0      609.0              2011-02-25   \n",
              "7353       NaN  2006-12-06             0.0      609.0              2011-02-25   \n",
              "7488       NaN  2006-07-19             0.0     2671.0              2011-02-25   \n",
              "7577       NaN  2006-04-06             0.0     2671.0              2006-10-27   \n",
              "7645       NaN  2006-01-21             0.0     5226.0              2006-10-27   \n",
              "7693       NaN  2005-12-17             0.0     5226.0              2006-02-13   \n",
              "7913       NaN  2004-12-29             0.0   270500.0              2006-01-16   \n",
              "7992   25300.0  2004-04-12             0.0    15258.0              2008-10-02   \n",
              "8130       NaN  2001-09-11             0.0    86500.0              2008-10-02   \n",
              "\n",
              "          days  days_val     p_val  \n",
              "3118  501 days       501   -1000.0  \n",
              "3436  566 days       566    -500.0  \n",
              "4804 1095 days      1095  410800.0  \n",
              "5058 1013 days      1013    -500.0  \n",
              "5748 1692 days      1692    -500.0  \n",
              "5773 1556 days      1556  275500.0  \n",
              "6314 2517 days      2517   -1000.0  \n",
              "6372 2557 days      2557    -500.0  \n",
              "6373 2556 days      2556    -500.0  \n",
              "6702 1072 days      1072   -1000.0  \n",
              "6752 1106 days      1106    -500.0  \n",
              "7177 1392 days      1392   68700.0  \n",
              "7276 1471 days      1471    -500.0  \n",
              "7296 1488 days      1488    -500.0  \n",
              "7353 1542 days      1542    -500.0  \n",
              "7488 1682 days      1682    -500.0  \n",
              "7577  204 days       204    -500.0  \n",
              "7645  279 days       279    -500.0  \n",
              "7693   58 days        58    -500.0  \n",
              "7913  383 days       383   -1000.0  \n",
              "7992 1634 days      1634   25800.0  \n",
              "8130 2578 days      2578   -1000.0  "
            ],
            "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>Late_2</th>\n",
              "      <th>Late_1</th>\n",
              "      <th>Late_3</th>\n",
              "      <th>days_late_Sum</th>\n",
              "      <th>normal_payment</th>\n",
              "      <th>p_code</th>\n",
              "      <th>p_limit</th>\n",
              "      <th>last_update</th>\n",
              "      <th>curr_remaining</th>\n",
              "      <th>max_bal</th>\n",
              "      <th>recent_payment_activity</th>\n",
              "      <th>days</th>\n",
              "      <th>days_val</th>\n",
              "      <th>p_val</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>3118</th>\n",
              "      <td>83847</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>19</td>\n",
              "      <td>2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2014-05-04</td>\n",
              "      <td>72666.0</td>\n",
              "      <td>79462.0</td>\n",
              "      <td>2015-09-17</td>\n",
              "      <td>501 days</td>\n",
              "      <td>501</td>\n",
              "      <td>-1000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3436</th>\n",
              "      <td>83847</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>16</td>\n",
              "      <td>10</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2014-01-19</td>\n",
              "      <td>1417.2</td>\n",
              "      <td>22889.0</td>\n",
              "      <td>2015-08-08</td>\n",
              "      <td>566 days</td>\n",
              "      <td>566</td>\n",
              "      <td>-500.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4804</th>\n",
              "      <td>83847</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>34</td>\n",
              "      <td>10</td>\n",
              "      <td>410300.0</td>\n",
              "      <td>2012-09-20</td>\n",
              "      <td>11510.4</td>\n",
              "      <td>12214.0</td>\n",
              "      <td>2015-09-20</td>\n",
              "      <td>1095 days</td>\n",
              "      <td>1095</td>\n",
              "      <td>410800.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5058</th>\n",
              "      <td>83847</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>36</td>\n",
              "      <td>10</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2012-04-30</td>\n",
              "      <td>0.0</td>\n",
              "      <td>91631.0</td>\n",
              "      <td>2015-02-07</td>\n",
              "      <td>1013 days</td>\n",
              "      <td>1013</td>\n",
              "      <td>-500.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5748</th>\n",
              "      <td>83847</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>10</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2010-06-21</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1052.0</td>\n",
              "      <td>2015-02-07</td>\n",
              "      <td>1692 days</td>\n",
              "      <td>1692</td>\n",
              "      <td>-500.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5773</th>\n",
              "      <td>83847</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>35</td>\n",
              "      <td>10</td>\n",
              "      <td>275000.0</td>\n",
              "      <td>2010-05-16</td>\n",
              "      <td>0.0</td>\n",
              "      <td>250500.0</td>\n",
              "      <td>2014-08-19</td>\n",
              "      <td>1556 days</td>\n",
              "      <td>1556</td>\n",
              "      <td>275500.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6314</th>\n",
              "      <td>83847</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>36</td>\n",
              "      <td>2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2008-10-26</td>\n",
              "      <td>1768387.2</td>\n",
              "      <td>2057500.0</td>\n",
              "      <td>2015-09-17</td>\n",
              "      <td>2517 days</td>\n",
              "      <td>2517</td>\n",
              "      <td>-1000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6372</th>\n",
              "      <td>83847</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>17</td>\n",
              "      <td>6</td>\n",
              "      <td>10</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2008-09-16</td>\n",
              "      <td>6264.0</td>\n",
              "      <td>400500.0</td>\n",
              "      <td>2015-09-17</td>\n",
              "      <td>2557 days</td>\n",
              "      <td>2557</td>\n",
              "      <td>-500.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6373</th>\n",
              "      <td>83847</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>33</td>\n",
              "      <td>10</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2008-09-16</td>\n",
              "      <td>41757.6</td>\n",
              "      <td>400500.0</td>\n",
              "      <td>2015-09-16</td>\n",
              "      <td>2556 days</td>\n",
              "      <td>2556</td>\n",
              "      <td>-500.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6702</th>\n",
              "      <td>83847</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>33</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2008-03-20</td>\n",
              "      <td>0.0</td>\n",
              "      <td>400500.0</td>\n",
              "      <td>2011-02-25</td>\n",
              "      <td>1072 days</td>\n",
              "      <td>1072</td>\n",
              "      <td>-1000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6752</th>\n",
              "      <td>83847</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>23</td>\n",
              "      <td>10</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2008-02-15</td>\n",
              "      <td>0.0</td>\n",
              "      <td>609.0</td>\n",
              "      <td>2011-02-25</td>\n",
              "      <td>1106 days</td>\n",
              "      <td>1106</td>\n",
              "      <td>-500.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7177</th>\n",
              "      <td>83847</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "      <td>68200.0</td>\n",
              "      <td>2007-05-05</td>\n",
              "      <td>0.0</td>\n",
              "      <td>609.0</td>\n",
              "      <td>2011-02-25</td>\n",
              "      <td>1392 days</td>\n",
              "      <td>1392</td>\n",
              "      <td>68700.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7276</th>\n",
              "      <td>83847</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>10</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2007-02-15</td>\n",
              "      <td>0.0</td>\n",
              "      <td>609.0</td>\n",
              "      <td>2011-02-25</td>\n",
              "      <td>1471 days</td>\n",
              "      <td>1471</td>\n",
              "      <td>-500.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7296</th>\n",
              "      <td>83847</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>10</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2007-01-29</td>\n",
              "      <td>0.0</td>\n",
              "      <td>609.0</td>\n",
              "      <td>2011-02-25</td>\n",
              "      <td>1488 days</td>\n",
              "      <td>1488</td>\n",
              "      <td>-500.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7353</th>\n",
              "      <td>83847</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>10</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2006-12-06</td>\n",
              "      <td>0.0</td>\n",
              "      <td>609.0</td>\n",
              "      <td>2011-02-25</td>\n",
              "      <td>1542 days</td>\n",
              "      <td>1542</td>\n",
              "      <td>-500.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7488</th>\n",
              "      <td>83847</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2006-07-19</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2671.0</td>\n",
              "      <td>2011-02-25</td>\n",
              "      <td>1682 days</td>\n",
              "      <td>1682</td>\n",
              "      <td>-500.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7577</th>\n",
              "      <td>83847</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2006-04-06</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2671.0</td>\n",
              "      <td>2006-10-27</td>\n",
              "      <td>204 days</td>\n",
              "      <td>204</td>\n",
              "      <td>-500.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7645</th>\n",
              "      <td>83847</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>33</td>\n",
              "      <td>10</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2006-01-21</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5226.0</td>\n",
              "      <td>2006-10-27</td>\n",
              "      <td>279 days</td>\n",
              "      <td>279</td>\n",
              "      <td>-500.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7693</th>\n",
              "      <td>83847</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>10</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2005-12-17</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5226.0</td>\n",
              "      <td>2006-02-13</td>\n",
              "      <td>58 days</td>\n",
              "      <td>58</td>\n",
              "      <td>-500.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7913</th>\n",
              "      <td>83847</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2004-12-29</td>\n",
              "      <td>0.0</td>\n",
              "      <td>270500.0</td>\n",
              "      <td>2006-01-16</td>\n",
              "      <td>383 days</td>\n",
              "      <td>383</td>\n",
              "      <td>-1000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7992</th>\n",
              "      <td>83847</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "      <td>25300.0</td>\n",
              "      <td>2004-04-12</td>\n",
              "      <td>0.0</td>\n",
              "      <td>15258.0</td>\n",
              "      <td>2008-10-02</td>\n",
              "      <td>1634 days</td>\n",
              "      <td>1634</td>\n",
              "      <td>25800.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8130</th>\n",
              "      <td>83847</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2001-09-11</td>\n",
              "      <td>0.0</td>\n",
              "      <td>86500.0</td>\n",
              "      <td>2008-10-02</td>\n",
              "      <td>2578 days</td>\n",
              "      <td>2578</td>\n",
              "      <td>-1000.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 299
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "Q2rVa88-iI-Q"
      },
      "source": [
        "df_edit[\"late1\"] = 0; df_test[\"late1\"] = 0; \n",
        "df_edit[\"late2\"] = 0; df_test[\"late2\"] = 0;\n",
        "df_edit[\"late3\"] = 0; df_test[\"late3\"] = 0;\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "TMtOeJKgiI-T"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "lhHoDMcPiI-V"
      },
      "source": [
        "## mean vs max vs sum\n",
        "lt1 = db_edit.groupby(\"id\")[\"Late_1\"]\n",
        "lt2 = db_edit.groupby(\"id\")[\"Late_2\"]\n",
        "lt3 = db_edit.groupby(\"id\")[\"Late_3\"]\n",
        "ltd = db_edit.groupby(\"id\")[\"days_late_Sum\"]\n",
        "for i,ids in enumerate(df_edit[\"id\"]):\n",
        "    df_edit[\"late1\"].iloc[i] = (lt1.get_group(ids)*ltd.get_group(ids)).mean()\n",
        "    df_edit[\"late2\"].iloc[i] = (lt2.get_group(ids)*ltd.get_group(ids)).mean()\n",
        "    df_edit[\"late3\"].iloc[i] = (lt3.get_group(ids)*ltd.get_group(ids)).mean()\n",
        "    \n",
        "for i,ids in enumerate(df_test[\"id\"]):\n",
        "    df_test[\"late1\"].iloc[i] = (lt1.get_group(ids)*ltd.get_group(ids)).mean()\n",
        "    df_test[\"late2\"].iloc[i] = (lt2.get_group(ids)*ltd.get_group(ids)).mean()\n",
        "    df_test[\"late3\"].iloc[i] = (lt3.get_group(ids)*ltd.get_group(ids)).mean()\n",
        "    \n",
        "    \n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "RL3yORluiI-W",
        "outputId": "fe9717d1-221d-4c70-87b6-a98cfd2e1884"
      },
      "source": [
        "db_edit.groupby(\"id\").get_group(83847)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         id  Late_2  Late_1  Late_3  days_late_Sum  normal_payment  p_code  \\\n",
              "3118  83847       0       0       0              0              19       2   \n",
              "3436  83847       0       0       0              0              16      10   \n",
              "4804  83847       0       0       0              0              34      10   \n",
              "5058  83847       0       0       0              0              36      10   \n",
              "5748  83847       0       0       0              0               2      10   \n",
              "5773  83847       0       0       0              0              35      10   \n",
              "6314  83847       0       0       0              0              36       2   \n",
              "6372  83847       1       0       0             17               6      10   \n",
              "6373  83847       0       0       0              0              33      10   \n",
              "6702  83847       0       0       0              0              33       1   \n",
              "6752  83847       0       0       0              0              23      10   \n",
              "7177  83847       0       0       0              0               1      10   \n",
              "7276  83847       0       0       0              0               0      10   \n",
              "7296  83847       0       0       0              0               5      10   \n",
              "7353  83847       0       0       0              0               0      10   \n",
              "7488  83847       0       0       0              0               1      10   \n",
              "7577  83847       0       0       0              0               1      10   \n",
              "7645  83847       0       0       0              0              33      10   \n",
              "7693  83847       0       0       0              0               7      10   \n",
              "7913  83847       0       0       0              0               1       1   \n",
              "7992  83847       0       0       0              0               1      10   \n",
              "8130  83847       0       0       0              0               0       1   \n",
              "\n",
              "       p_limit last_update  curr_remaining    max_bal recent_payment_activity  \\\n",
              "3118       NaN  2014-05-04         72666.0    79462.0              2015-09-17   \n",
              "3436       NaN  2014-01-19          1417.2    22889.0              2015-08-08   \n",
              "4804  410300.0  2012-09-20         11510.4    12214.0              2015-09-20   \n",
              "5058       NaN  2012-04-30             0.0    91631.0              2015-02-07   \n",
              "5748       NaN  2010-06-21             0.0     1052.0              2015-02-07   \n",
              "5773  275000.0  2010-05-16             0.0   250500.0              2014-08-19   \n",
              "6314       NaN  2008-10-26       1768387.2  2057500.0              2015-09-17   \n",
              "6372       NaN  2008-09-16          6264.0   400500.0              2015-09-17   \n",
              "6373       NaN  2008-09-16         41757.6   400500.0              2015-09-16   \n",
              "6702       NaN  2008-03-20             0.0   400500.0              2011-02-25   \n",
              "6752       NaN  2008-02-15             0.0      609.0              2011-02-25   \n",
              "7177   68200.0  2007-05-05             0.0      609.0              2011-02-25   \n",
              "7276       NaN  2007-02-15             0.0      609.0              2011-02-25   \n",
              "7296       NaN  2007-01-29             0.0      609.0              2011-02-25   \n",
              "7353       NaN  2006-12-06             0.0      609.0              2011-02-25   \n",
              "7488       NaN  2006-07-19             0.0     2671.0              2011-02-25   \n",
              "7577       NaN  2006-04-06             0.0     2671.0              2006-10-27   \n",
              "7645       NaN  2006-01-21             0.0     5226.0              2006-10-27   \n",
              "7693       NaN  2005-12-17             0.0     5226.0              2006-02-13   \n",
              "7913       NaN  2004-12-29             0.0   270500.0              2006-01-16   \n",
              "7992   25300.0  2004-04-12             0.0    15258.0              2008-10-02   \n",
              "8130       NaN  2001-09-11             0.0    86500.0              2008-10-02   \n",
              "\n",
              "          days  days_val     p_val  \n",
              "3118  501 days       501   -1000.0  \n",
              "3436  566 days       566    -500.0  \n",
              "4804 1095 days      1095  410800.0  \n",
              "5058 1013 days      1013    -500.0  \n",
              "5748 1692 days      1692    -500.0  \n",
              "5773 1556 days      1556  275500.0  \n",
              "6314 2517 days      2517   -1000.0  \n",
              "6372 2557 days      2557    -500.0  \n",
              "6373 2556 days      2556    -500.0  \n",
              "6702 1072 days      1072   -1000.0  \n",
              "6752 1106 days      1106    -500.0  \n",
              "7177 1392 days      1392   68700.0  \n",
              "7276 1471 days      1471    -500.0  \n",
              "7296 1488 days      1488    -500.0  \n",
              "7353 1542 days      1542    -500.0  \n",
              "7488 1682 days      1682    -500.0  \n",
              "7577  204 days       204    -500.0  \n",
              "7645  279 days       279    -500.0  \n",
              "7693   58 days        58    -500.0  \n",
              "7913  383 days       383   -1000.0  \n",
              "7992 1634 days      1634   25800.0  \n",
              "8130 2578 days      2578   -1000.0  "
            ],
            "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>Late_2</th>\n",
              "      <th>Late_1</th>\n",
              "      <th>Late_3</th>\n",
              "      <th>days_late_Sum</th>\n",
              "      <th>normal_payment</th>\n",
              "      <th>p_code</th>\n",
              "      <th>p_limit</th>\n",
              "      <th>last_update</th>\n",
              "      <th>curr_remaining</th>\n",
              "      <th>max_bal</th>\n",
              "      <th>recent_payment_activity</th>\n",
              "      <th>days</th>\n",
              "      <th>days_val</th>\n",
              "      <th>p_val</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>3118</th>\n",
              "      <td>83847</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>19</td>\n",
              "      <td>2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2014-05-04</td>\n",
              "      <td>72666.0</td>\n",
              "      <td>79462.0</td>\n",
              "      <td>2015-09-17</td>\n",
              "      <td>501 days</td>\n",
              "      <td>501</td>\n",
              "      <td>-1000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3436</th>\n",
              "      <td>83847</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>16</td>\n",
              "      <td>10</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2014-01-19</td>\n",
              "      <td>1417.2</td>\n",
              "      <td>22889.0</td>\n",
              "      <td>2015-08-08</td>\n",
              "      <td>566 days</td>\n",
              "      <td>566</td>\n",
              "      <td>-500.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4804</th>\n",
              "      <td>83847</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>34</td>\n",
              "      <td>10</td>\n",
              "      <td>410300.0</td>\n",
              "      <td>2012-09-20</td>\n",
              "      <td>11510.4</td>\n",
              "      <td>12214.0</td>\n",
              "      <td>2015-09-20</td>\n",
              "      <td>1095 days</td>\n",
              "      <td>1095</td>\n",
              "      <td>410800.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5058</th>\n",
              "      <td>83847</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>36</td>\n",
              "      <td>10</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2012-04-30</td>\n",
              "      <td>0.0</td>\n",
              "      <td>91631.0</td>\n",
              "      <td>2015-02-07</td>\n",
              "      <td>1013 days</td>\n",
              "      <td>1013</td>\n",
              "      <td>-500.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5748</th>\n",
              "      <td>83847</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>10</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2010-06-21</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1052.0</td>\n",
              "      <td>2015-02-07</td>\n",
              "      <td>1692 days</td>\n",
              "      <td>1692</td>\n",
              "      <td>-500.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5773</th>\n",
              "      <td>83847</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>35</td>\n",
              "      <td>10</td>\n",
              "      <td>275000.0</td>\n",
              "      <td>2010-05-16</td>\n",
              "      <td>0.0</td>\n",
              "      <td>250500.0</td>\n",
              "      <td>2014-08-19</td>\n",
              "      <td>1556 days</td>\n",
              "      <td>1556</td>\n",
              "      <td>275500.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6314</th>\n",
              "      <td>83847</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>36</td>\n",
              "      <td>2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2008-10-26</td>\n",
              "      <td>1768387.2</td>\n",
              "      <td>2057500.0</td>\n",
              "      <td>2015-09-17</td>\n",
              "      <td>2517 days</td>\n",
              "      <td>2517</td>\n",
              "      <td>-1000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6372</th>\n",
              "      <td>83847</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>17</td>\n",
              "      <td>6</td>\n",
              "      <td>10</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2008-09-16</td>\n",
              "      <td>6264.0</td>\n",
              "      <td>400500.0</td>\n",
              "      <td>2015-09-17</td>\n",
              "      <td>2557 days</td>\n",
              "      <td>2557</td>\n",
              "      <td>-500.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6373</th>\n",
              "      <td>83847</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>33</td>\n",
              "      <td>10</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2008-09-16</td>\n",
              "      <td>41757.6</td>\n",
              "      <td>400500.0</td>\n",
              "      <td>2015-09-16</td>\n",
              "      <td>2556 days</td>\n",
              "      <td>2556</td>\n",
              "      <td>-500.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6702</th>\n",
              "      <td>83847</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>33</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2008-03-20</td>\n",
              "      <td>0.0</td>\n",
              "      <td>400500.0</td>\n",
              "      <td>2011-02-25</td>\n",
              "      <td>1072 days</td>\n",
              "      <td>1072</td>\n",
              "      <td>-1000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6752</th>\n",
              "      <td>83847</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>23</td>\n",
              "      <td>10</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2008-02-15</td>\n",
              "      <td>0.0</td>\n",
              "      <td>609.0</td>\n",
              "      <td>2011-02-25</td>\n",
              "      <td>1106 days</td>\n",
              "      <td>1106</td>\n",
              "      <td>-500.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7177</th>\n",
              "      <td>83847</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "      <td>68200.0</td>\n",
              "      <td>2007-05-05</td>\n",
              "      <td>0.0</td>\n",
              "      <td>609.0</td>\n",
              "      <td>2011-02-25</td>\n",
              "      <td>1392 days</td>\n",
              "      <td>1392</td>\n",
              "      <td>68700.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7276</th>\n",
              "      <td>83847</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>10</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2007-02-15</td>\n",
              "      <td>0.0</td>\n",
              "      <td>609.0</td>\n",
              "      <td>2011-02-25</td>\n",
              "      <td>1471 days</td>\n",
              "      <td>1471</td>\n",
              "      <td>-500.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7296</th>\n",
              "      <td>83847</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>10</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2007-01-29</td>\n",
              "      <td>0.0</td>\n",
              "      <td>609.0</td>\n",
              "      <td>2011-02-25</td>\n",
              "      <td>1488 days</td>\n",
              "      <td>1488</td>\n",
              "      <td>-500.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7353</th>\n",
              "      <td>83847</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>10</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2006-12-06</td>\n",
              "      <td>0.0</td>\n",
              "      <td>609.0</td>\n",
              "      <td>2011-02-25</td>\n",
              "      <td>1542 days</td>\n",
              "      <td>1542</td>\n",
              "      <td>-500.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7488</th>\n",
              "      <td>83847</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2006-07-19</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2671.0</td>\n",
              "      <td>2011-02-25</td>\n",
              "      <td>1682 days</td>\n",
              "      <td>1682</td>\n",
              "      <td>-500.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7577</th>\n",
              "      <td>83847</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2006-04-06</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2671.0</td>\n",
              "      <td>2006-10-27</td>\n",
              "      <td>204 days</td>\n",
              "      <td>204</td>\n",
              "      <td>-500.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7645</th>\n",
              "      <td>83847</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>33</td>\n",
              "      <td>10</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2006-01-21</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5226.0</td>\n",
              "      <td>2006-10-27</td>\n",
              "      <td>279 days</td>\n",
              "      <td>279</td>\n",
              "      <td>-500.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7693</th>\n",
              "      <td>83847</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>10</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2005-12-17</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5226.0</td>\n",
              "      <td>2006-02-13</td>\n",
              "      <td>58 days</td>\n",
              "      <td>58</td>\n",
              "      <td>-500.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7913</th>\n",
              "      <td>83847</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2004-12-29</td>\n",
              "      <td>0.0</td>\n",
              "      <td>270500.0</td>\n",
              "      <td>2006-01-16</td>\n",
              "      <td>383 days</td>\n",
              "      <td>383</td>\n",
              "      <td>-1000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7992</th>\n",
              "      <td>83847</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "      <td>25300.0</td>\n",
              "      <td>2004-04-12</td>\n",
              "      <td>0.0</td>\n",
              "      <td>15258.0</td>\n",
              "      <td>2008-10-02</td>\n",
              "      <td>1634 days</td>\n",
              "      <td>1634</td>\n",
              "      <td>25800.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8130</th>\n",
              "      <td>83847</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2001-09-11</td>\n",
              "      <td>0.0</td>\n",
              "      <td>86500.0</td>\n",
              "      <td>2008-10-02</td>\n",
              "      <td>2578 days</td>\n",
              "      <td>2578</td>\n",
              "      <td>-1000.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 302
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "wO1BIXHtiI-X"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "YdD60sTviI-X",
        "outputId": "55dfa4d7-3667-4722-ca5c-c14db24ebd20"
      },
      "source": [
        "df_edit.groupby(\"label\").get_group(1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     label          id  Alpha         Beta  Gamma   Delta  Epsilon  Zeta  Eta  \\\n",
              "675      1      115753   2615  1245.500000      3   77000        2    15    5   \n",
              "469      1      136529   2615  1284.454106      2   90000        2    15    5   \n",
              "479      1      186614   2615  1361.000000      3   30000        1    15    5   \n",
              "410      1      448169   2615  1284.454106      2   47000        2    15    5   \n",
              "650      1      660752   2615  1230.500000      3   35000        2    15    5   \n",
              "..     ...         ...    ...          ...    ...     ...      ...   ...  ...   \n",
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      "execution_count": null,
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              "      <td>1.000000</td>\n",
              "      <td>0.013514</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>583</th>\n",
              "      <td>0</td>\n",
              "      <td>1492824607</td>\n",
              "      <td>3138</td>\n",
              "      <td>1241.000000</td>\n",
              "      <td>1</td>\n",
              "      <td>74000</td>\n",
              "      <td>2</td>\n",
              "      <td>6</td>\n",
              "      <td>-1</td>\n",
              "      <td>105</td>\n",
              "      <td>3</td>\n",
              "      <td>60027</td>\n",
              "      <td>200.000000</td>\n",
              "      <td>0.009524</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>452</th>\n",
              "      <td>0</td>\n",
              "      <td>1492844641</td>\n",
              "      <td>3661</td>\n",
              "      <td>1268.000000</td>\n",
              "      <td>3</td>\n",
              "      <td>162000</td>\n",
              "      <td>2</td>\n",
              "      <td>11</td>\n",
              "      <td>5</td>\n",
              "      <td>113</td>\n",
              "      <td>4</td>\n",
              "      <td>450081</td>\n",
              "      <td>141.421356</td>\n",
              "      <td>0.026549</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>712</th>\n",
              "      <td>0</td>\n",
              "      <td>1492854287</td>\n",
              "      <td>2092</td>\n",
              "      <td>1284.454106</td>\n",
              "      <td>2</td>\n",
              "      <td>111000</td>\n",
              "      <td>2</td>\n",
              "      <td>8</td>\n",
              "      <td>5</td>\n",
              "      <td>110</td>\n",
              "      <td>4</td>\n",
              "      <td>60091</td>\n",
              "      <td>220.868739</td>\n",
              "      <td>0.018182</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>9.333333</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>279</th>\n",
              "      <td>0</td>\n",
              "      <td>1492861707</td>\n",
              "      <td>3661</td>\n",
              "      <td>1322.000000</td>\n",
              "      <td>3</td>\n",
              "      <td>68000</td>\n",
              "      <td>2</td>\n",
              "      <td>11</td>\n",
              "      <td>5</td>\n",
              "      <td>86</td>\n",
              "      <td>3</td>\n",
              "      <td>72015</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.034884</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>600 rows × 17 columns</p>\n",
              "</div>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 304
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "oVQPB4SCiI-Z",
        "outputId": "7f530d49-06db-444c-a42c-04674e12acdd"
      },
      "source": [
        "val = []\n",
        "for ids in df.groupby(\"label\").get_group(1).id:\n",
        "    try:\n",
        "        ele = db.groupby(\"id\").get_group(ids)[\"p_code\"].value_counts()\n",
        "        #print(ele.index[0])\n",
        "        val.append(ele.index[0])\n",
        "    except:\n",
        "        print(\"No 10 Key\")\n",
        "        \n",
        "val = np.array(val)\n",
        "print(sum(val==10)/len(val))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.6434782608695652\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "XqcqYtp5iI-b"
      },
      "source": [
        "df_edit[\"p_code\"] = 0; df_test[\"p_code\"] = 0; \n",
        "df_edit[\"p_val\"] = 0; df_test[\"p_val\"] = 0;\n",
        "df_edit[\"p_val_std\"] = 0; df_test[\"p_val_std\"] = 0;\n",
        "df_edit[\"no_tran\"] = 0; df_test[\"no_tran\"] = 0;"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "IXVTut20iI-c"
      },
      "source": [
        "## mean vs max vs sum\n",
        "pv = db_edit.groupby(\"id\")[\"p_val\"]\n",
        "pc = db_edit.groupby(\"id\")[\"p_code\"]\n",
        "pl = db_edit.groupby(\"id\")[\"p_limit\"]\n",
        "nt = db_edit.groupby(\"id\")\n",
        "for i,ids in enumerate(df_edit[\"id\"]):\n",
        "    df_edit[\"p_code\"].iloc[i] = pc.get_group(ids).value_counts().index[0]\n",
        "    df_edit[\"p_val\"].iloc[i] = pv.get_group(ids).mean()\n",
        "    df_edit[\"p_val_std\"].iloc[i] = pv.get_group(ids).std()\n",
        "    df_edit[\"no_tran\"].iloc[i] = len(nt.get_group(ids))\n",
        "        \n",
        "for i,ids in enumerate(df_test[\"id\"]):\n",
        "    df_test[\"p_code\"].iloc[i] = pc.get_group(ids).value_counts().index[0]\n",
        "    df_test[\"p_val\"].iloc[i] = pv.get_group(ids).mean()\n",
        "    df_test[\"p_val_std\"].iloc[i] = pv.get_group(ids).std()\n",
        "    df_test[\"no_tran\"].iloc[i] = len(nt.get_group(ids))\n",
        "    \n",
        "    \n",
        "df_test[\"p_val_std\"] = df_test[\"p_val_std\"].fillna(0)\n",
        "df_edit[\"p_val_std\"] = df_edit[\"p_val_std\"].fillna(0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "cAFp6dZOiI-c"
      },
      "source": [
        "## Bfill for"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "chcvKaY9iI-d"
      },
      "source": [
        "df_edit[\"no_pay\"] = 0; df_test[\"no_pay\"] = 0; \n",
        "df_edit[\"no_pay_std\"] = 0; df_test[\"no_pay_std\"] = 0;\n",
        "df_edit[\"max_bal\"] = 0; df_test[\"max_bal\"] = 0;\n",
        "df_edit[\"max_bal_std\"] = 0; df_test[\"max_bal_std\"] = 0;\n",
        "df_edit[\"rem_bal\"] = 0; df_test[\"rem_bal\"] = 0;\n",
        "df_edit[\"rem_bal_std\"] = 0; df_test[\"rem_bal_std\"] = 0;"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "4_JI1XDPiI-d"
      },
      "source": [
        "## mean vs max vs sum\n",
        "nop = db_edit.groupby(\"id\")[\"normal_payment\"]\n",
        "mb = db_edit.groupby(\"id\")[\"max_bal\"]\n",
        "rb = db_edit.groupby(\"id\")[\"curr_remaining\"]\n",
        "for i,ids in enumerate(df_edit[\"id\"]):\n",
        "    df_edit[\"no_pay\"].iloc[i] = nop.get_group(ids).max()\n",
        "    df_edit[\"no_pay_std\"].iloc[i] = nop.get_group(ids).std()\n",
        "    df_edit[\"max_bal\"].iloc[i] = mb.get_group(ids).mean()\n",
        "    df_edit[\"max_bal_std\"].iloc[i] = mb.get_group(ids).std()\n",
        "    df_edit[\"rem_bal\"].iloc[i] = rb.get_group(ids).mean()\n",
        "    df_edit[\"rem_bal_std\"].iloc[i] = rb.get_group(ids).std()\n",
        "    \n",
        "for i,ids in enumerate(df_test[\"id\"]):\n",
        "    df_test[\"no_pay\"].iloc[i] = nop.get_group(ids).max()\n",
        "    df_test[\"no_pay_std\"].iloc[i] = nop.get_group(ids).std()\n",
        "    df_test[\"max_bal\"].iloc[i] = mb.get_group(ids).mean()\n",
        "    df_test[\"max_bal_std\"].iloc[i] = mb.get_group(ids).std()\n",
        "    df_test[\"rem_bal\"].iloc[i] = rb.get_group(ids).mean()\n",
        "    df_test[\"rem_bal_std\"].iloc[i] = rb.get_group(ids).std()\n",
        "    \n",
        "    \n",
        "df_test[\"no_pay_std\"] = df_test[\"no_pay_std\"].fillna(0)\n",
        "df_edit[\"no_pay_std\"] = df_edit[\"no_pay_std\"].fillna(0)\n",
        "df_test[\"max_bal_std\"] = df_test[\"max_bal_std\"].fillna(0)\n",
        "df_edit[\"max_bal_std\"] = df_edit[\"max_bal_std\"].fillna(0)\n",
        "df_test[\"rem_bal_std\"] = df_test[\"rem_bal_std\"].fillna(0)\n",
        "df_edit[\"rem_bal_std\"] = df_edit[\"rem_bal_std\"].fillna(0)\n",
        "\n",
        "\n",
        "df_edit[\"rem_bal\"] = df_edit[\"rem_bal\"].apply(lambda x: min(2500000, x))         ## binning\n",
        "df_edit[\"rem_bal_std\"] = df_edit[\"rem_bal_std\"].apply(lambda x: min(3050000, x)) ## binning\n",
        "df_edit[\"max_bal\"] = df_edit[\"max_bal\"].apply(lambda x: min(2.5*(10**6), x))         ## binning\n",
        "df_edit[\"max_bal_std\"] = df_edit[\"max_bal_std\"].apply(lambda x: min(3.5*(10**6), x)) ## binning\n",
        "\n",
        "df_test[\"rem_bal\"] = df_test[\"rem_bal\"].apply(lambda x: min(2500000, x))         ## binning\n",
        "df_test[\"rem_bal_std\"] = df_test[\"rem_bal_std\"].apply(lambda x: min(3050000, x)) ## binning\n",
        "df_test[\"max_bal\"] = df_test[\"max_bal\"].apply(lambda x: min(2.5*(10**6), x))         ## binning\n",
        "df_test[\"max_bal_std\"] = df_test[\"max_bal_std\"].apply(lambda x: min(3.5*(10**6), x)) ## binning"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "imaAvQqkiI-e"
      },
      "source": [
        "df_edit[\"dates\"] = 0; df_test[\"dates\"] = 0; \n",
        "df_edit[\"dates_std\"] = 0; df_test[\"dates_std\"] = 0;\n",
        "df_edit[\"last_up\"] = 0; df_test[\"last_up\"] = 0;\n",
        "df_edit[\"past\"] = 0; df_test[\"past\"]=0;\n",
        "df_edit[\"amb\"] = 0; df_test[\"amb\"]=0;"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "ycqWc3ADiI-f"
      },
      "source": [
        "dv = db_edit.groupby(\"id\")[\"days_val\"]\n",
        "lu = db_edit.groupby(\"id\")[\"last_update\"]\n",
        "for i,ids in enumerate(df_edit[\"id\"]):\n",
        "    df_edit[\"dates\"].iloc[i] = dv.get_group(ids).mean()\n",
        "    df_edit[\"dates_std\"].iloc[i] = dv.get_group(ids).std()\n",
        "    df_edit[\"last_up\"].iloc[i] = lu.get_group(ids).iloc[0]\n",
        "    df_edit[\"past\"].iloc[i] = (lu.get_group(ids).iloc[0] - lu.get_group(ids).iloc[-1]).days\n",
        "    df_edit[\"amb\"].iloc[i] = int((dv.get_group(ids)<0).any())\n",
        "    \n",
        "for i,ids in enumerate(df_test[\"id\"]):\n",
        "    df_test[\"dates\"].iloc[i] = dv.get_group(ids).mean()\n",
        "    df_test[\"dates_std\"].iloc[i] = dv.get_group(ids).std()\n",
        "    df_test[\"last_up\"].iloc[i] = lu.get_group(ids).iloc[0]\n",
        "    df_test[\"past\"].iloc[i] = (lu.get_group(ids).iloc[0] - lu.get_group(ids).iloc[-1]).days\n",
        "    df_test[\"amb\"].iloc[i] = int((dv.get_group(ids)<0).any())\n",
        "    \n",
        "df_test[\"dates_std\"] = df_test[\"dates_std\"].fillna(0)\n",
        "df_edit[\"dates_std\"] = df_edit[\"dates_std\"].fillna(0)\n",
        "\n",
        "\n",
        "recent_most_updated = df_edit[\"last_up\"].min()\n",
        "df_test[\"last_up\"] = df_test[\"last_up\"].apply(lambda x : (x-recent_most_updated).days)\n",
        "df_edit[\"last_up\"] = df_edit[\"last_up\"].apply(lambda x : (x-recent_most_updated).days)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "XVaaYbaniI-g",
        "outputId": "324674bd-ef08-42c3-a0b9-5fdc881a853e"
      },
      "source": [
        "df_edit"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     label          id  Alpha         Beta  Gamma    Delta  Epsilon  Zeta  \\\n",
              "322      0        1114    523  1391.000000      1  1200000        2     5   \n",
              "695      0       83847   2615  1338.500000      3   325000        2    15   \n",
              "675      1      115753   2615  1245.500000      3    77000        2    15   \n",
              "178      0      127625   2615  1320.500000      3   130000        2    15   \n",
              "469      1      136529   2615  1284.454106      2    90000        2    15   \n",
              "..     ...         ...    ...          ...    ...      ...      ...   ...   \n",
              "396      1  1492728889   2092  1284.454106      2    94000        2     8   \n",
              "583      0  1492824607   3138  1241.000000      1    74000        2     6   \n",
              "452      0  1492844641   3661  1268.000000      3   162000        2    11   \n",
              "712      0  1492854287   2092  1284.454106      2   111000        2     8   \n",
              "279      0  1492861707   3661  1322.000000      3    68000        2    11   \n",
              "\n",
              "     Eta  Theta  ...  no_pay_std        max_bal    max_bal_std        rem_bal  \\\n",
              "322   -1     64  ...   14.817943  196649.285714  276735.785384    2277.771429   \n",
              "695    5    110  ...   15.059837  186720.227273  442211.982889   86454.654545   \n",
              "675    5    109  ...    7.141428   91298.000000   68639.620604   40112.400000   \n",
              "178    5     85  ...    7.778175   34626.000000   20060.619382    8752.800000   \n",
              "469    5     92  ...   10.816140   60931.800000  105032.841397   49843.440000   \n",
              "..   ...    ...  ...         ...            ...            ...            ...   \n",
              "396   -1    113  ...    3.214550   22841.000000   19227.395846   18115.200000   \n",
              "583   -1    105  ...    8.539126   49595.750000   60696.516519   14652.300000   \n",
              "452    5    113  ...   11.360961   50328.625000   24775.431551   13651.350000   \n",
              "712    5    110  ...   14.730920  189248.666667  269777.221797  179909.200000   \n",
              "279    5     86  ...    9.073772   16780.333333    3734.477250     830.800000   \n",
              "\n",
              "       rem_bal_std        dates    dates_std  last_up  past  amb  \n",
              "322    6023.771407  3690.428571  1765.737887        0  2924    0  \n",
              "695  376066.996872  1315.545455   783.557682     2876  4618    0  \n",
              "675   75851.355441  1774.500000  1029.476404     3148  2816    0  \n",
              "178   11869.211586   665.500000   453.255447     3062   668    0  \n",
              "469  140756.750315   929.000000  1242.147513     3344  4024    1  \n",
              "..             ...          ...          ...      ...   ...  ...  \n",
              "396   23163.582345    61.333333   106.232450     3791   356    0  \n",
              "583    9007.151312   256.000000   280.795299     3638   719    0  \n",
              "452   30201.805753   533.500000   356.177724     3437   984    0  \n",
              "712  301856.158793   855.000000   462.003247     3212   798    0  \n",
              "279    1438.987811   409.333333   208.269857     3199   409    0  \n",
              "\n",
              "[715 rows x 32 columns]"
            ],
            "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>label</th>\n",
              "      <th>id</th>\n",
              "      <th>Alpha</th>\n",
              "      <th>Beta</th>\n",
              "      <th>Gamma</th>\n",
              "      <th>Delta</th>\n",
              "      <th>Epsilon</th>\n",
              "      <th>Zeta</th>\n",
              "      <th>Eta</th>\n",
              "      <th>Theta</th>\n",
              "      <th>...</th>\n",
              "      <th>no_pay_std</th>\n",
              "      <th>max_bal</th>\n",
              "      <th>max_bal_std</th>\n",
              "      <th>rem_bal</th>\n",
              "      <th>rem_bal_std</th>\n",
              "      <th>dates</th>\n",
              "      <th>dates_std</th>\n",
              "      <th>last_up</th>\n",
              "      <th>past</th>\n",
              "      <th>amb</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>322</th>\n",
              "      <td>0</td>\n",
              "      <td>1114</td>\n",
              "      <td>523</td>\n",
              "      <td>1391.000000</td>\n",
              "      <td>1</td>\n",
              "      <td>1200000</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>-1</td>\n",
              "      <td>64</td>\n",
              "      <td>...</td>\n",
              "      <td>14.817943</td>\n",
              "      <td>196649.285714</td>\n",
              "      <td>276735.785384</td>\n",
              "      <td>2277.771429</td>\n",
              "      <td>6023.771407</td>\n",
              "      <td>3690.428571</td>\n",
              "      <td>1765.737887</td>\n",
              "      <td>0</td>\n",
              "      <td>2924</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>695</th>\n",
              "      <td>0</td>\n",
              "      <td>83847</td>\n",
              "      <td>2615</td>\n",
              "      <td>1338.500000</td>\n",
              "      <td>3</td>\n",
              "      <td>325000</td>\n",
              "      <td>2</td>\n",
              "      <td>15</td>\n",
              "      <td>5</td>\n",
              "      <td>110</td>\n",
              "      <td>...</td>\n",
              "      <td>15.059837</td>\n",
              "      <td>186720.227273</td>\n",
              "      <td>442211.982889</td>\n",
              "      <td>86454.654545</td>\n",
              "      <td>376066.996872</td>\n",
              "      <td>1315.545455</td>\n",
              "      <td>783.557682</td>\n",
              "      <td>2876</td>\n",
              "      <td>4618</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>675</th>\n",
              "      <td>1</td>\n",
              "      <td>115753</td>\n",
              "      <td>2615</td>\n",
              "      <td>1245.500000</td>\n",
              "      <td>3</td>\n",
              "      <td>77000</td>\n",
              "      <td>2</td>\n",
              "      <td>15</td>\n",
              "      <td>5</td>\n",
              "      <td>109</td>\n",
              "      <td>...</td>\n",
              "      <td>7.141428</td>\n",
              "      <td>91298.000000</td>\n",
              "      <td>68639.620604</td>\n",
              "      <td>40112.400000</td>\n",
              "      <td>75851.355441</td>\n",
              "      <td>1774.500000</td>\n",
              "      <td>1029.476404</td>\n",
              "      <td>3148</td>\n",
              "      <td>2816</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>178</th>\n",
              "      <td>0</td>\n",
              "      <td>127625</td>\n",
              "      <td>2615</td>\n",
              "      <td>1320.500000</td>\n",
              "      <td>3</td>\n",
              "      <td>130000</td>\n",
              "      <td>2</td>\n",
              "      <td>15</td>\n",
              "      <td>5</td>\n",
              "      <td>85</td>\n",
              "      <td>...</td>\n",
              "      <td>7.778175</td>\n",
              "      <td>34626.000000</td>\n",
              "      <td>20060.619382</td>\n",
              "      <td>8752.800000</td>\n",
              "      <td>11869.211586</td>\n",
              "      <td>665.500000</td>\n",
              "      <td>453.255447</td>\n",
              "      <td>3062</td>\n",
              "      <td>668</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>469</th>\n",
              "      <td>1</td>\n",
              "      <td>136529</td>\n",
              "      <td>2615</td>\n",
              "      <td>1284.454106</td>\n",
              "      <td>2</td>\n",
              "      <td>90000</td>\n",
              "      <td>2</td>\n",
              "      <td>15</td>\n",
              "      <td>5</td>\n",
              "      <td>92</td>\n",
              "      <td>...</td>\n",
              "      <td>10.816140</td>\n",
              "      <td>60931.800000</td>\n",
              "      <td>105032.841397</td>\n",
              "      <td>49843.440000</td>\n",
              "      <td>140756.750315</td>\n",
              "      <td>929.000000</td>\n",
              "      <td>1242.147513</td>\n",
              "      <td>3344</td>\n",
              "      <td>4024</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>396</th>\n",
              "      <td>1</td>\n",
              "      <td>1492728889</td>\n",
              "      <td>2092</td>\n",
              "      <td>1284.454106</td>\n",
              "      <td>2</td>\n",
              "      <td>94000</td>\n",
              "      <td>2</td>\n",
              "      <td>8</td>\n",
              "      <td>-1</td>\n",
              "      <td>113</td>\n",
              "      <td>...</td>\n",
              "      <td>3.214550</td>\n",
              "      <td>22841.000000</td>\n",
              "      <td>19227.395846</td>\n",
              "      <td>18115.200000</td>\n",
              "      <td>23163.582345</td>\n",
              "      <td>61.333333</td>\n",
              "      <td>106.232450</td>\n",
              "      <td>3791</td>\n",
              "      <td>356</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>583</th>\n",
              "      <td>0</td>\n",
              "      <td>1492824607</td>\n",
              "      <td>3138</td>\n",
              "      <td>1241.000000</td>\n",
              "      <td>1</td>\n",
              "      <td>74000</td>\n",
              "      <td>2</td>\n",
              "      <td>6</td>\n",
              "      <td>-1</td>\n",
              "      <td>105</td>\n",
              "      <td>...</td>\n",
              "      <td>8.539126</td>\n",
              "      <td>49595.750000</td>\n",
              "      <td>60696.516519</td>\n",
              "      <td>14652.300000</td>\n",
              "      <td>9007.151312</td>\n",
              "      <td>256.000000</td>\n",
              "      <td>280.795299</td>\n",
              "      <td>3638</td>\n",
              "      <td>719</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>452</th>\n",
              "      <td>0</td>\n",
              "      <td>1492844641</td>\n",
              "      <td>3661</td>\n",
              "      <td>1268.000000</td>\n",
              "      <td>3</td>\n",
              "      <td>162000</td>\n",
              "      <td>2</td>\n",
              "      <td>11</td>\n",
              "      <td>5</td>\n",
              "      <td>113</td>\n",
              "      <td>...</td>\n",
              "      <td>11.360961</td>\n",
              "      <td>50328.625000</td>\n",
              "      <td>24775.431551</td>\n",
              "      <td>13651.350000</td>\n",
              "      <td>30201.805753</td>\n",
              "      <td>533.500000</td>\n",
              "      <td>356.177724</td>\n",
              "      <td>3437</td>\n",
              "      <td>984</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>712</th>\n",
              "      <td>0</td>\n",
              "      <td>1492854287</td>\n",
              "      <td>2092</td>\n",
              "      <td>1284.454106</td>\n",
              "      <td>2</td>\n",
              "      <td>111000</td>\n",
              "      <td>2</td>\n",
              "      <td>8</td>\n",
              "      <td>5</td>\n",
              "      <td>110</td>\n",
              "      <td>...</td>\n",
              "      <td>14.730920</td>\n",
              "      <td>189248.666667</td>\n",
              "      <td>269777.221797</td>\n",
              "      <td>179909.200000</td>\n",
              "      <td>301856.158793</td>\n",
              "      <td>855.000000</td>\n",
              "      <td>462.003247</td>\n",
              "      <td>3212</td>\n",
              "      <td>798</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>279</th>\n",
              "      <td>0</td>\n",
              "      <td>1492861707</td>\n",
              "      <td>3661</td>\n",
              "      <td>1322.000000</td>\n",
              "      <td>3</td>\n",
              "      <td>68000</td>\n",
              "      <td>2</td>\n",
              "      <td>11</td>\n",
              "      <td>5</td>\n",
              "      <td>86</td>\n",
              "      <td>...</td>\n",
              "      <td>9.073772</td>\n",
              "      <td>16780.333333</td>\n",
              "      <td>3734.477250</td>\n",
              "      <td>830.800000</td>\n",
              "      <td>1438.987811</td>\n",
              "      <td>409.333333</td>\n",
              "      <td>208.269857</td>\n",
              "      <td>3199</td>\n",
              "      <td>409</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>715 rows × 32 columns</p>\n",
              "</div>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 313
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "uQBcXUFNiI-h",
        "outputId": "c4bfe68c-9518-42ed-aa99-2d49b6d63aee"
      },
      "source": [
        "df_edit[\"amb\"].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    597\n",
              "1    118\n",
              "Name: amb, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 314
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "f-q1BgDJiI-h"
      },
      "source": [
        "df_edit[\"Zeta\"] = df_edit[\"Zeta\"].apply(lambda x : min((x-11)*(8-x)*(x-15),100))\n",
        "df_test[\"Zeta\"] = df_test[\"Zeta\"].apply(lambda x : min((x-11)*(8-x)*(x-15),100))\n",
        "df_edit[\"Eta\"] = df_edit[\"Eta\"].apply(lambda x : (x-5)*(x-3)*(x+1)*(x-9))\n",
        "df_test[\"Eta\"] = df_test[\"Eta\"].apply(lambda x : (x-5)*(x-3)*(x+1)*(x-9))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "nh_TWzTOiI-i"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "kUG5bHEwiI-j",
        "outputId": "7bdc1138-9b26-4dce-95bc-46536935fd6f"
      },
      "source": [
        "for col in df_edit.columns[2:]:\n",
        "    t1= df_edit.groupby(\"label\")[col].get_group(1).values\n",
        "    t2 = df_edit.groupby(\"label\")[col].get_group(0).values\n",
        "    plt.scatter(t1,np.ones_like(t1),color='green')\n",
        "    plt.scatter(t2,0.5*np.ones_like(t2),color='red')\n",
        "    #t2.max()\n",
        "    plt.title(col)\n",
        "    plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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du2HNmqHPVjOMjS1+HmvWdNYXLM16O+ec2cfPtqzeWqRRWo7tc66D69MvYAx4BDifF06KXjijzff0fP5p4L5+813wSdGqzsmDTZuqks77ZZeN/iTisF7J0i/jtNPmX/7OnS/8GUPV6tWd97POqjrjjLm/e9ZZndf0epl5kmfmetu584Xh3u9Ofz6eE6JzLcsTolpJhrB9spiTogBJrgR+h84VL7dX1e4kt3RnvC/Ju4CrgGeBrwE7q+r/zDfPBZ8UlSTNe1J0oEBfCga6JC3cYq9ykSSdAAx0SWqEgS5JjTDQJakRIzspmmQKGPQ34euBry5hOcutpf601Bdoqz/2ZeVaTH82VdWsv8wcWaAvRJLJuc7qnoha6k9LfYG2+mNfVq6l6o+HXCSpEQa6JDXiRAn0PaMuYMha6k9LfYG2+mNfVq4l6c8JcQxdktTfibKHLknqw0CXpEasqEAf4GHUpyX50+70v0iyeQRlDmSAvvzbJAe7T3n6aJJNo6hzUP3609PuTUkqyYq9xGyQviT52e76OZDkT5a7xoUYYFvbmOTeJJ/pbm9XjqLOfpLcnuQrSf56julJ8l+7/fxckh9e7hoXYoD+XNPtx4NJPpXkBxe90Lnuq7vcLwZ7GPUu4N3dz9uBPx113Yvoy48Da7qfd67Uvgzan267M4FPAPfRecbsyGs/znWzBfgM8N3d4bNHXfci+7OHzi2tAbYCj4667jn68lrgh4G/nmP6lcCfAQFeA/zFqGteZH/+Uc829vph9Gcl7aFfAhyqqkeq6mngTmDbjDbbeOHxdh8ELkuSZaxxUH37UlX3VtU3u4P30XkS1Eo1yLoB+I/AfwL+33IWt0CD9OUtwK1V9SRAVX1lmWtciEH6U8DLup/XAV9cxvoGVlWfoPM8hblsA95bHfcBL0/yPctT3cL1609VfWp6G2NIGbCSAn2Qh1E/36aqngWOAWctS3ULM+iDtaddR2fPY6Xq25/uf3/Pq6qPLGdhx2GQdXMBcEGSTya5L8kVy1bdwg3Sn5uBNyc5AtwN3LA8pQ3dQv9enUiGkgFDeICkFiPJm4EJ4HWjruV4JVkF/BZw7YhLGZYxOoddLqWz1/SJJK+sqqdGWdQiXA38cVX9lyQ/CrwvyUVV9Z1RFyZI8uN0Av0fL3ZeK2kPve/DqHvbJBmj89/HJ5aluoUZpC8kuRy4CbiqXnjI9krUrz9nAhcBH0/yKJ3jm/tW6InRQdbNEWBfVT1TVV8AHqYT8CvRIP25DrgLoKo+DZxO5+ZQJ5qB/l6dSJK8CngPsK2qFp1lKynQ7we2JDk/yal0Tnrum9FmH/AL3c//AvhYdc8orDB9+5Lk1cAf0AnzlXyMFvr0p6qOVdX6qtpcVZvpHA+8qqpW4jMGB9nOPkRn75wk6+kcgnlkGWtciEH6cxi4DCDJD9AJ9KllrXI49gE/373a5TXAsar60qiLOl5JNgL/Hfi5qnp4KDMd9ZngGWd9r6SzN/Q3wE3dcbfQCQfobIgfAA4Bfwm8YtQ1L6Ivfw58Gfhs97Vv1DUvpj8z2n6cFXqVy4DrJnQOIR0EHgS2j7rmRfZnK/BJOlfAfBb4Z6OueY5+3AF8CXiGzv+SrgPeCry1Z73c2u3ngyt5GxuwP+8BnuzJgMnFLtOf/ktSI1bSIRdJ0iIY6JLUCANdkhphoEtSIwx0SVoG/W7WNUv7Bd8gzqtcJGkZJHkt8A0696O5qE/bLXR+DPZPq+rJJGfXAL9XcQ9dkpZBzXKzriTfm+R/Jtmf5H8n+f7upOO6QZyBLkmjswe4oaouBt4O3NYdf1w3iPPmXJI0AknW0rkn+gd67gJ+Wvf9uG4QZ6BL0misAp6qqh+aZdoROg+8eAb4QpLpG8Td32+GkqRlVlV/Syesfwaef8Te9GPoPsRx3CDOQJekZZDkDuDTwPclOZLkOuAa4LokDwAHeOFpU/cATyQ5CNwL/EoNcHtdL1uUpEa4hy5JjTDQJakRBrokNcJAl6RGGOiS1AgDXZIaYaBLUiP+PywHxtIg3etrAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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NURdMpl/Q+8/4X5IcSrJnwPaRvifXcqCvVp+i910LPw78FfDBcU6e5ALgH4Dfq6qvjnPuxSxR10R6VlXfqKqfoHef3O1JnjOOeZcyRF1D3ZR9lJK8GDhZVYdWeq7lGLKusferz09V1RXAtcCrkrxgJSdby4G+5M2rJ6Gqvnr6V+aqOgCcl2TDOOZOch690Hx3Vb1/wJCJ9GypuibZs27Ox4B7gR3zNn2rX0nWA08HHp10XTWOm7Kf6SpgZ5KHgH3Azyb5+3ljJtGvJeuaUL9Oz328+/Mk8AFg+7whI31PruVA3w+8vDtLfCXweFV9cdJFJfnB08cNk2yn1+MVD4FuzrcD91fVWxcYNvaeDVPXJHqWZCrJRd3z7wWuAf5j3rD9wK91z18K3FPdmaxJ1pVx3JR9nqq6uao2VdUWeic876mql80bNvZ+DVPXJPrVzft9SS48/Rz4eWD+1XEjfU8ueZPoSUlyJ72rHzYkOQa8gd4JIqrqNuAAvTPEs8DXgFeskrpeCvxWklPA/wC7V/qHunMV8KvA57rjrwB/DDyzr7ZJ9GyYuibRs0uAdyZZR+8/kLuq6kP5zpufvx14V5JZeifCd69wTcPWNZ6bsg9hFfRrmLom1a+LgQ90+yrrgfdU1T8n+U1YmfekH/2XpEas5UMukqQ+BrokNcJAl6RGGOiS1AgDXZIaYaBLUiMMdElqxP8DPTqwRxB4QzIAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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v27THJjLdeHTZZ5L8AvBuYEd99+bcs2rYPj8LeCnwmSRfoneucWHGL4x2+TovAQtV9e2q+iLwEL3Az6ou+3w9cBtAVd0DPJPeL7FqVad/72fjQg760JtT9x+/pf/5rwCfrv7VhhnV5YbcrwD+ml7MZ/28KgzZ56p6oqo2VNXmqtpM77rBjqqa5fsXdvm7/Ql6R+ck2UDvFMzxCc44al32+SvANQBJXkIv6MsTnXKyFoA399/t8mrgiar66nk947SvBA+5Sryd3pHJw8C7+8v20vsHDb0v+D8Cx4B/B1487ZknsM//CnwNONz/WJj2zOPe5xXbfoYZf5dLx69z6J1qOgp8Htg17ZknsM9bgbvpvQPmMPCL0575PPf3VuCrwLfpfcd1PfB24O0DX+N9/T+Pz4/i77U/+i9JjbiQT7lIks6CQZekRhh0SWqEQZekRhh0SWqEQZekRhh0SWrE/wOyMky8aWQcuwAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "c46XIAsdiI-k"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "cIdeZ2QjiI-k",
        "outputId": "2a9753a1-49eb-40ee-e109-0945853352b0"
      },
      "source": [
        "df_edit.groupby(\"label\").get_group(1)[\"Iota\"].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5    59\n",
              "3    31\n",
              "4    25\n",
              "Name: Iota, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 319
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "gQfAUVAqiI-l"
      },
      "source": [
        "df_final = df_edit.drop([\"Beta\",\"Epsilon\",\"p_val_std\",\"no_pay\",\"no_pay_std\",\"max_bal_std\",\"rem_bal\",\"rem_bal_std\"],axis=1)\n",
        "df_test_final = df_test.drop([\"Beta\",\"Epsilon\",\"p_val_std\",\"no_pay\",\"no_pay_std\",\"max_bal_std\",\"rem_bal\",\"rem_bal_std\"],axis=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "0DARCKuWiI-m",
        "outputId": "8fe8fecd-7e89-4ca3-b832-cae45b40294e"
      },
      "source": [
        "df_final"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     label          id  Alpha  Gamma    Delta  Zeta  Eta  Theta  Iota   Kappa  \\\n",
              "322      0        1114    523      1  1200000   100    0     64     5  361026   \n",
              "695      0       83847   2615      3   325000     0    0    110     5   60033   \n",
              "675      1      115753   2615      3    77000     0    0    109     5  151300   \n",
              "178      0      127625   2615      3   130000     0    0     85     5   60047   \n",
              "469      1      136529   2615      2    90000     0    0     92     5   72014   \n",
              "..     ...         ...    ...    ...      ...   ...  ...    ...   ...     ...   \n",
              "396      1  1492728889   2092      2    94000     0    0    113     4   72000   \n",
              "583      0  1492824607   3138      1    74000    90    0    105     3   60027   \n",
              "452      0  1492844641   3661      3   162000     0    0    113     4  450081   \n",
              "712      0  1492854287   2092      2   111000     0    0    110     4   60091   \n",
              "279      0  1492861707   3661      3    68000     0    0     86     3   72015   \n",
              "\n",
              "     ...     late3  p_code         p_val  no_tran        max_bal        dates  \\\n",
              "322  ...       0.0      10  46428.571429        7  196649.285714  3690.428571   \n",
              "695  ...       0.0      10  34968.181818       22  186720.227273  1315.545455   \n",
              "675  ...   77389.0      10  14750.000000        4   91298.000000  1774.500000   \n",
              "178  ...       0.0      10  67650.000000        2   34626.000000   665.500000   \n",
              "469  ...  106647.3      10   7170.000000       10   60931.800000   929.000000   \n",
              "..   ...       ...     ...           ...      ...            ...          ...   \n",
              "396  ...       0.0      10  22766.666667        3   22841.000000    61.333333   \n",
              "583  ...       0.0      10  14350.000000        4   49595.750000   256.000000   \n",
              "452  ...       0.0      10  65725.000000        8   50328.625000   533.500000   \n",
              "712  ...       0.0      10  10666.666667        3  189248.666667   855.000000   \n",
              "279  ...       0.0      10   4800.000000        3   16780.333333   409.333333   \n",
              "\n",
              "       dates_std  last_up  past  amb  \n",
              "322  1765.737887        0  2924    0  \n",
              "695   783.557682     2876  4618    0  \n",
              "675  1029.476404     3148  2816    0  \n",
              "178   453.255447     3062   668    0  \n",
              "469  1242.147513     3344  4024    1  \n",
              "..           ...      ...   ...  ...  \n",
              "396   106.232450     3791   356    0  \n",
              "583   280.795299     3638   719    0  \n",
              "452   356.177724     3437   984    0  \n",
              "712   462.003247     3212   798    0  \n",
              "279   208.269857     3199   409    0  \n",
              "\n",
              "[715 rows x 24 columns]"
            ],
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>label</th>\n",
              "      <th>id</th>\n",
              "      <th>Alpha</th>\n",
              "      <th>Gamma</th>\n",
              "      <th>Delta</th>\n",
              "      <th>Zeta</th>\n",
              "      <th>Eta</th>\n",
              "      <th>Theta</th>\n",
              "      <th>Iota</th>\n",
              "      <th>Kappa</th>\n",
              "      <th>...</th>\n",
              "      <th>late3</th>\n",
              "      <th>p_code</th>\n",
              "      <th>p_val</th>\n",
              "      <th>no_tran</th>\n",
              "      <th>max_bal</th>\n",
              "      <th>dates</th>\n",
              "      <th>dates_std</th>\n",
              "      <th>last_up</th>\n",
              "      <th>past</th>\n",
              "      <th>amb</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>322</th>\n",
              "      <td>0</td>\n",
              "      <td>1114</td>\n",
              "      <td>523</td>\n",
              "      <td>1</td>\n",
              "      <td>1200000</td>\n",
              "      <td>100</td>\n",
              "      <td>0</td>\n",
              "      <td>64</td>\n",
              "      <td>5</td>\n",
              "      <td>361026</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>10</td>\n",
              "      <td>46428.571429</td>\n",
              "      <td>7</td>\n",
              "      <td>196649.285714</td>\n",
              "      <td>3690.428571</td>\n",
              "      <td>1765.737887</td>\n",
              "      <td>0</td>\n",
              "      <td>2924</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>695</th>\n",
              "      <td>0</td>\n",
              "      <td>83847</td>\n",
              "      <td>2615</td>\n",
              "      <td>3</td>\n",
              "      <td>325000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>110</td>\n",
              "      <td>5</td>\n",
              "      <td>60033</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>10</td>\n",
              "      <td>34968.181818</td>\n",
              "      <td>22</td>\n",
              "      <td>186720.227273</td>\n",
              "      <td>1315.545455</td>\n",
              "      <td>783.557682</td>\n",
              "      <td>2876</td>\n",
              "      <td>4618</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>675</th>\n",
              "      <td>1</td>\n",
              "      <td>115753</td>\n",
              "      <td>2615</td>\n",
              "      <td>3</td>\n",
              "      <td>77000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>109</td>\n",
              "      <td>5</td>\n",
              "      <td>151300</td>\n",
              "      <td>...</td>\n",
              "      <td>77389.0</td>\n",
              "      <td>10</td>\n",
              "      <td>14750.000000</td>\n",
              "      <td>4</td>\n",
              "      <td>91298.000000</td>\n",
              "      <td>1774.500000</td>\n",
              "      <td>1029.476404</td>\n",
              "      <td>3148</td>\n",
              "      <td>2816</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>178</th>\n",
              "      <td>0</td>\n",
              "      <td>127625</td>\n",
              "      <td>2615</td>\n",
              "      <td>3</td>\n",
              "      <td>130000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>85</td>\n",
              "      <td>5</td>\n",
              "      <td>60047</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>10</td>\n",
              "      <td>67650.000000</td>\n",
              "      <td>2</td>\n",
              "      <td>34626.000000</td>\n",
              "      <td>665.500000</td>\n",
              "      <td>453.255447</td>\n",
              "      <td>3062</td>\n",
              "      <td>668</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>469</th>\n",
              "      <td>1</td>\n",
              "      <td>136529</td>\n",
              "      <td>2615</td>\n",
              "      <td>2</td>\n",
              "      <td>90000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>92</td>\n",
              "      <td>5</td>\n",
              "      <td>72014</td>\n",
              "      <td>...</td>\n",
              "      <td>106647.3</td>\n",
              "      <td>10</td>\n",
              "      <td>7170.000000</td>\n",
              "      <td>10</td>\n",
              "      <td>60931.800000</td>\n",
              "      <td>929.000000</td>\n",
              "      <td>1242.147513</td>\n",
              "      <td>3344</td>\n",
              "      <td>4024</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>396</th>\n",
              "      <td>1</td>\n",
              "      <td>1492728889</td>\n",
              "      <td>2092</td>\n",
              "      <td>2</td>\n",
              "      <td>94000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>113</td>\n",
              "      <td>4</td>\n",
              "      <td>72000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>10</td>\n",
              "      <td>22766.666667</td>\n",
              "      <td>3</td>\n",
              "      <td>22841.000000</td>\n",
              "      <td>61.333333</td>\n",
              "      <td>106.232450</td>\n",
              "      <td>3791</td>\n",
              "      <td>356</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>583</th>\n",
              "      <td>0</td>\n",
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              "      <td>1</td>\n",
              "      <td>74000</td>\n",
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              "      <td>105</td>\n",
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              "      <td>60027</td>\n",
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              "      <td>10</td>\n",
              "      <td>14350.000000</td>\n",
              "      <td>4</td>\n",
              "      <td>49595.750000</td>\n",
              "      <td>256.000000</td>\n",
              "      <td>280.795299</td>\n",
              "      <td>3638</td>\n",
              "      <td>719</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>452</th>\n",
              "      <td>0</td>\n",
              "      <td>1492844641</td>\n",
              "      <td>3661</td>\n",
              "      <td>3</td>\n",
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              "      <td>0</td>\n",
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              "      <td>450081</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>10</td>\n",
              "      <td>65725.000000</td>\n",
              "      <td>8</td>\n",
              "      <td>50328.625000</td>\n",
              "      <td>533.500000</td>\n",
              "      <td>356.177724</td>\n",
              "      <td>3437</td>\n",
              "      <td>984</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>712</th>\n",
              "      <td>0</td>\n",
              "      <td>1492854287</td>\n",
              "      <td>2092</td>\n",
              "      <td>2</td>\n",
              "      <td>111000</td>\n",
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              "      <td>0</td>\n",
              "      <td>110</td>\n",
              "      <td>4</td>\n",
              "      <td>60091</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>10</td>\n",
              "      <td>10666.666667</td>\n",
              "      <td>3</td>\n",
              "      <td>189248.666667</td>\n",
              "      <td>855.000000</td>\n",
              "      <td>462.003247</td>\n",
              "      <td>3212</td>\n",
              "      <td>798</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>279</th>\n",
              "      <td>0</td>\n",
              "      <td>1492861707</td>\n",
              "      <td>3661</td>\n",
              "      <td>3</td>\n",
              "      <td>68000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>72015</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>10</td>\n",
              "      <td>4800.000000</td>\n",
              "      <td>3</td>\n",
              "      <td>16780.333333</td>\n",
              "      <td>409.333333</td>\n",
              "      <td>208.269857</td>\n",
              "      <td>3199</td>\n",
              "      <td>409</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>715 rows × 24 columns</p>\n",
              "</div>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 676
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "w86w4e20iI-n",
        "outputId": "be5950e3-bb21-4d93-ca44-f189bb489d57"
      },
      "source": [
        "df_test_final"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             id  Alpha  Gamma    Delta  Zeta  Eta  Theta  Iota   Kappa  \\\n",
              "356           1    523      2  1000000   100    0    100     5  650018   \n",
              "78        12615   1046      1   286000   100    0     80     3  361003   \n",
              "71        40811   2615      3    70000     0    0    113     5   60033   \n",
              "380       65668   2615      3   120000     0    0    110     5   72000   \n",
              "41        72717   2615      3    30000     0    0    110     5   60005   \n",
              "..          ...    ...    ...      ...   ...  ...    ...   ...     ...   \n",
              "76   1492676578   2092      1   191000     0    0     85     3   72000   \n",
              "291  1492712936   2092      3    35000     0    0    112     4   72014   \n",
              "159  1492728518   2092      1    86000     0    0    104     3   72000   \n",
              "363  1492742245   2092      1   104000     0    0     92     5   60036   \n",
              "151  1492771183   3138      3    56000    90    0    100     3   60091   \n",
              "\n",
              "         Lambda  ...         late3  p_code         p_val  no_tran  \\\n",
              "356    1.000000  ...  61343.666667      10  86311.111111       18   \n",
              "78     1.000000  ...      0.000000       6  39345.454545       11   \n",
              "71   167.122709  ...      0.000000       5  -1000.000000        2   \n",
              "380  187.082869  ...      0.000000      10  47300.000000        4   \n",
              "41     1.000000  ...      0.000000      17  -1000.000000       10   \n",
              "..          ...  ...           ...     ...           ...      ...   \n",
              "76     1.000000  ...      0.000000      10  39928.571429        7   \n",
              "291    1.000000  ...      0.000000      10  28000.000000        1   \n",
              "159  187.082869  ...      0.000000      10  49050.000000        8   \n",
              "363    1.000000  ...      0.000000      10  52800.000000        2   \n",
              "151    1.000000  ...      0.000000      10  23466.666667        3   \n",
              "\n",
              "           max_bal        dates    dates_std  last_up  past  amb  \n",
              "356  222571.222222  1985.833333  2098.254099     2991  7192    1  \n",
              "78   238270.000000  1210.272727  1226.618367     3090  4096    0  \n",
              "71    78500.000000   525.000000    29.698485     2849   801    0  \n",
              "380   12787.750000   365.000000   185.983870     3169   444    0  \n",
              "41   671850.000000   706.500000   389.869565     3286  2694    0  \n",
              "..             ...          ...          ...      ...   ...  ...  \n",
              "76    82958.857143   894.000000   883.043978     3262  2374    0  \n",
              "291   27528.000000   614.000000     0.000000     3035     0    0  \n",
              "159  108150.375000   432.125000   507.918845     3485  2150    0  \n",
              "363   18773.500000   233.500000   224.152850     3542   316    0  \n",
              "151  105173.666667   504.666667   408.725254     3527   748    0  \n",
              "\n",
              "[410 rows x 23 columns]"
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>Alpha</th>\n",
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              "      <td>10</td>\n",
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              "      <td>222571.222222</td>\n",
              "      <td>1985.833333</td>\n",
              "      <td>2098.254099</td>\n",
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              "      <th>71</th>\n",
              "      <td>40811</td>\n",
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              "      <td>113</td>\n",
              "      <td>5</td>\n",
              "      <td>60033</td>\n",
              "      <td>167.122709</td>\n",
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              "      <td>5</td>\n",
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              "      <td>2</td>\n",
              "      <td>78500.000000</td>\n",
              "      <td>525.000000</td>\n",
              "      <td>29.698485</td>\n",
              "      <td>2849</td>\n",
              "      <td>801</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>380</th>\n",
              "      <td>65668</td>\n",
              "      <td>2615</td>\n",
              "      <td>3</td>\n",
              "      <td>120000</td>\n",
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              "      <td>0</td>\n",
              "      <td>110</td>\n",
              "      <td>5</td>\n",
              "      <td>72000</td>\n",
              "      <td>187.082869</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>10</td>\n",
              "      <td>47300.000000</td>\n",
              "      <td>4</td>\n",
              "      <td>12787.750000</td>\n",
              "      <td>365.000000</td>\n",
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              "      <td>3169</td>\n",
              "      <td>444</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>72717</td>\n",
              "      <td>2615</td>\n",
              "      <td>3</td>\n",
              "      <td>30000</td>\n",
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              "      <td>5</td>\n",
              "      <td>60005</td>\n",
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              "      <td>389.869565</td>\n",
              "      <td>3286</td>\n",
              "      <td>2694</td>\n",
              "      <td>0</td>\n",
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              "      <th>76</th>\n",
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              "      <td>1</td>\n",
              "      <td>191000</td>\n",
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              "      <td>0</td>\n",
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              "      <td>72000</td>\n",
              "      <td>1.000000</td>\n",
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              "      <td>82958.857143</td>\n",
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              "      <td>3262</td>\n",
              "      <td>2374</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>291</th>\n",
              "      <td>1492712936</td>\n",
              "      <td>2092</td>\n",
              "      <td>3</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>112</td>\n",
              "      <td>4</td>\n",
              "      <td>72014</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>10</td>\n",
              "      <td>28000.000000</td>\n",
              "      <td>1</td>\n",
              "      <td>27528.000000</td>\n",
              "      <td>614.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3035</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>159</th>\n",
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              "      <td>1</td>\n",
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              "      <td>0</td>\n",
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              "      <td>49050.000000</td>\n",
              "      <td>8</td>\n",
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              "      <td>1</td>\n",
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              "      <td>0</td>\n",
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              "      <td>60036</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>10</td>\n",
              "      <td>52800.000000</td>\n",
              "      <td>2</td>\n",
              "      <td>18773.500000</td>\n",
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              "      <td>316</td>\n",
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              "    <tr>\n",
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              "      <td>3138</td>\n",
              "      <td>3</td>\n",
              "      <td>56000</td>\n",
              "      <td>90</td>\n",
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              "      <td>10</td>\n",
              "      <td>23466.666667</td>\n",
              "      <td>3</td>\n",
              "      <td>105173.666667</td>\n",
              "      <td>504.666667</td>\n",
              "      <td>408.725254</td>\n",
              "      <td>3527</td>\n",
              "      <td>748</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>410 rows × 23 columns</p>\n",
              "</div>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 677
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "rWqyMEK4iI-o"
      },
      "source": [
        "for col in df_final.columns[2:-1]:\n",
        "    df_test_final[col] = (df_test_final[col]-df_final[col].mean())/(df_final[col].max()-df_final[col].min())\n",
        "    df_final[col] = (df_final[col]-df_final[col].mean())/(df_final[col].max() - df_final[col].min())"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "RRgphOQHiI-o"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "UMHu7GTEiI-p"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "w-dV53yaiI-p"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "uWrIR2ldiI-q"
      },
      "source": [
        "Y = np.array(df_final[\"label\"])\n",
        "X = np.array(df_final.drop([\"label\",\"id\"], axis=1))\n",
        "X_test = np.array(df_test_final.drop([\"id\"],axis=1))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "aO4oqJ33iI-q"
      },
      "source": [
        "X_edit = np.append(X[:,:-1],(X[:,:-1]**2),axis=1)\n",
        "X_test_edit = np.append(X_test[:,:-1],(X_test[:,:-1]**2),axis=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "VwHANrsIiI-q",
        "outputId": "7f69c745-2451-49f6-d36b-d7ecab4bcd45"
      },
      "source": [
        "X_edit.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(715, 30)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 687
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "2RzLsNdpiI-r"
      },
      "source": [
        "from sklearn.decomposition import PCA\n",
        "pca = PCA(n_components=29)\n",
        "X_edit = pca.fit_transform(X_edit)\n",
        "X_test_edit = pca.transform(X_test_edit)\n",
        "X_edit = np.append(X_edit,X[:,-1:],axis=1)\n",
        "X_test_edit = np.append(X_test_edit,X_test[:,-1:],axis=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "skmTr52wiI-s"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "Ul0-HBIuiI-s"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "VGPRpif-iI-t"
      },
      "source": [
        "from imblearn.over_sampling import SMOTE\n",
        "from imblearn.under_sampling import RandomUnderSampler\n",
        "from imblearn.pipeline import Pipeline\n",
        "\n",
        "over = SMOTE(sampling_strategy = 0.25, k_neighbors=10)\n",
        "under = RandomUnderSampler(sampling_strategy=0.3)\n",
        "steps = [('o', over), ('u', under)]\n",
        "pipeline = Pipeline(steps=steps)\n",
        "train_x, train_y = over.fit_resample(X_edit, Y)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "y8n3dZVpiI-t",
        "outputId": "a7ebf833-21a2-4d32-be67-3663572f3e8e"
      },
      "source": [
        "train_x.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(750, 30)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 692
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "jRIewZyQiI-u"
      },
      "source": [
        "X_inliers = X_edit[Y==0]\n",
        "X_outliers = X_edit[Y==1]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "wq4z9o3FiI-u"
      },
      "source": [
        "from sklearn.ensemble import IsolationForest\n",
        "ano = IsolationForest(n_estimators=100,random_state=0).fit(X_inliers)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "X90v1kZAiI-v",
        "outputId": "86e65a26-c2d7-4df5-d29e-862d1df65db0"
      },
      "source": [
        "ano.predict(X_outliers)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([-1,  1,  1,  1,  1,  1,  1, -1,  1,  1,  1,  1,  1,  1,  1,  1,  1,\n",
              "        1,  1, -1, -1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,\n",
              "       -1,  1,  1,  1,  1,  1,  1, -1,  1,  1,  1,  1,  1,  1,  1,  1,  1,\n",
              "        1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,\n",
              "        1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,\n",
              "        1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,\n",
              "        1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 695
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "bmcda38PiI-w"
      },
      "source": [
        "from sklearn.ensemble import GradientBoostingClassifier\n",
        "\n",
        "clf_final = GradientBoostingClassifier(n_estimators=500, learning_rate=0.08, max_depth=5, subsample=0.95)\n",
        "clf_final = clf_final.fit(train_x,train_y)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "Bja90f27iI-w"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "biP7NnzViI-w",
        "outputId": "bd92bf4e-a15f-4f8c-ea40-d87aa451f67b"
      },
      "source": [
        "from optuna import Trial\n",
        "import optuna\n",
        "from sklearn.ensemble import AdaBoostClassifier, RandomForestClassifier\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.metrics import f1_score\n",
        "\n",
        "from sklearn.model_selection import StratifiedKFold\n",
        "def objective(trial):\n",
        "    \n",
        "    n_estimators = trial.suggest_int('n_estimators', 5, 500)\n",
        "      #criterion = trial.suggest_categorical('criterion', [\"gini\", \"entropy\"])\n",
        "    max_depth = trial.suggest_int('max_depth',2, 10)\n",
        "      #min_samples_split = trial.suggest_int('min_samples_split', 2, 20)\n",
        "      #min_child_weight = trial.suggest_int('min_child_weight',2,128)\n",
        "      #learning_rate = trial.suggest_loguniform('learning_rate', 0.1, 1)\n",
        "      #gamma = trial.suggest_int('gamma',0,10)\n",
        "      #subsample = trial.suggest_loguniform('subsample', 0.5,1.0)\n",
        "    est = DecisionTreeClassifier(max_depth = max_depth)\n",
        "    clf = AdaBoostClassifier(base_estimator = est, n_estimators=n_estimators)\n",
        "      #clf = RandomForestClassifier(n_estimators=n_estimators, criterion=criterion, max_depth=max_depth, min_samples_split = min_samples_split)\n",
        "    skf = StratifiedKFold(n_splits=4, shuffle=True)\n",
        "    errors = []\n",
        "    for train_index, test_index in skf.split(train_x, train_y):\n",
        "        x_train, x_test = train_x[train_index], train_x[test_index]\n",
        "        y_train, y_test = train_y[train_index], train_y[test_index]\n",
        "        clf.fit(x_train, y_train)\n",
        "        preds = clf.predict(x_test)\n",
        "        errors.append(f1_score(y_test, preds))\n",
        "    return np.array(errors).mean()\n",
        "study = optuna.create_study(direction='maximize')\n",
        "study.optimize(objective, n_trials=100)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[32m[I 2021-04-12 06:30:49,543]\u001b[0m A new study created in memory with name: no-name-093c5d51-82f0-4197-8a5e-da46ff86f541\u001b[0m\n",
            "\u001b[32m[I 2021-04-12 06:30:53,015]\u001b[0m Trial 0 finished with value: 0.3556806370346881 and parameters: {'n_estimators': 75, 'max_depth': 6}. Best is trial 0 with value: 0.3556806370346881.\u001b[0m\n",
            "\u001b[32m[I 2021-04-12 06:30:59,877]\u001b[0m Trial 1 finished with value: 0.37433695432985264 and parameters: {'n_estimators': 293, 'max_depth': 2}. Best is trial 1 with value: 0.37433695432985264.\u001b[0m\n",
            "\u001b[32m[I 2021-04-12 06:31:12,640]\u001b[0m Trial 2 finished with value: 0.40434292802713856 and parameters: {'n_estimators': 420, 'max_depth': 3}. Best is trial 2 with value: 0.40434292802713856.\u001b[0m\n",
            "\u001b[32m[I 2021-04-12 06:31:14,637]\u001b[0m Trial 3 finished with value: 0.3686920370962584 and parameters: {'n_estimators': 44, 'max_depth': 6}. Best is trial 2 with value: 0.40434292802713856.\u001b[0m\n",
            "\u001b[32m[I 2021-04-12 06:31:16,317]\u001b[0m Trial 4 finished with value: 0.3926970203285993 and parameters: {'n_estimators': 48, 'max_depth': 4}. Best is trial 2 with value: 0.40434292802713856.\u001b[0m\n",
            "\u001b[32m[I 2021-04-12 06:31:33,152]\u001b[0m Trial 5 finished with value: 0.336575992561687 and parameters: {'n_estimators': 346, 'max_depth': 7}. Best is trial 2 with value: 0.40434292802713856.\u001b[0m\n",
            "\u001b[32m[I 2021-04-12 06:31:44,623]\u001b[0m Trial 6 finished with value: 0.39035060177917325 and parameters: {'n_estimators': 213, 'max_depth': 9}. Best is trial 2 with value: 0.40434292802713856.\u001b[0m\n",
            "\u001b[32m[I 2021-04-12 06:31:46,097]\u001b[0m Trial 7 finished with value: 0.39609788359788356 and parameters: {'n_estimators': 30, 'max_depth': 7}. Best is trial 2 with value: 0.40434292802713856.\u001b[0m\n",
            "\u001b[32m[I 2021-04-12 06:31:56,251]\u001b[0m Trial 8 finished with value: 0.3492993848257006 and parameters: {'n_estimators': 436, 'max_depth': 2}. Best is trial 2 with value: 0.40434292802713856.\u001b[0m\n",
            "\u001b[32m[I 2021-04-12 06:32:19,469]\u001b[0m Trial 9 finished with value: 0.37206349206349204 and parameters: {'n_estimators': 433, 'max_depth': 9}. Best is trial 2 with value: 0.40434292802713856.\u001b[0m\n",
            "\u001b[32m[I 2021-04-12 06:32:36,887]\u001b[0m Trial 10 finished with value: 0.39798147472778433 and parameters: {'n_estimators': 486, 'max_depth': 4}. Best is trial 2 with value: 0.40434292802713856.\u001b[0m\n",
            "\u001b[32m[I 2021-04-12 06:32:53,816]\u001b[0m Trial 11 finished with value: 0.35746606334841624 and parameters: {'n_estimators': 477, 'max_depth': 4}. Best is trial 2 with value: 0.40434292802713856.\u001b[0m\n",
            "\u001b[32m[I 2021-04-12 06:33:12,302]\u001b[0m Trial 12 finished with value: 0.405982905982906 and parameters: {'n_estimators': 492, 'max_depth': 4}. Best is trial 12 with value: 0.405982905982906.\u001b[0m\n",
            "\u001b[32m[I 2021-04-12 06:33:23,419]\u001b[0m Trial 13 finished with value: 0.46960172595765814 and parameters: {'n_estimators': 360, 'max_depth': 3}. Best is trial 13 with value: 0.46960172595765814.\u001b[0m\n",
            "\u001b[32m[I 2021-04-12 06:33:28,736]\u001b[0m Trial 14 finished with value: 0.41164288927446824 and parameters: {'n_estimators': 177, 'max_depth': 3}. Best is trial 13 with value: 0.46960172595765814.\u001b[0m\n",
            "\u001b[32m[I 2021-04-12 06:33:32,864]\u001b[0m Trial 15 finished with value: 0.3720138030388591 and parameters: {'n_estimators': 177, 'max_depth': 2}. Best is trial 13 with value: 0.46960172595765814.\u001b[0m\n",
            "\u001b[32m[I 2021-04-12 06:33:36,663]\u001b[0m Trial 16 finished with value: 0.3391595700125646 and parameters: {'n_estimators': 127, 'max_depth': 3}. Best is trial 13 with value: 0.46960172595765814.\u001b[0m\n",
            "\u001b[32m[I 2021-04-12 06:33:49,597]\u001b[0m Trial 17 finished with value: 0.35253789459046037 and parameters: {'n_estimators': 309, 'max_depth': 5}. Best is trial 13 with value: 0.46960172595765814.\u001b[0m\n",
            "\u001b[32m[I 2021-04-12 06:34:00,575]\u001b[0m Trial 18 finished with value: 0.4007018440130395 and parameters: {'n_estimators': 363, 'max_depth': 3}. Best is trial 13 with value: 0.46960172595765814.\u001b[0m\n",
            "\u001b[32m[I 2021-04-12 06:34:10,116]\u001b[0m Trial 19 finished with value: 0.40526665225581326 and parameters: {'n_estimators': 228, 'max_depth': 5}. Best is trial 13 with value: 0.46960172595765814.\u001b[0m\n",
            "\u001b[32m[I 2021-04-12 06:34:14,187]\u001b[0m Trial 20 finished with value: 0.3957897386772752 and parameters: {'n_estimators': 169, 'max_depth': 2}. Best is trial 13 with value: 0.46960172595765814.\u001b[0m\n",
            "\u001b[32m[I 2021-04-12 06:34:22,250]\u001b[0m Trial 21 finished with value: 0.4101158427487084 and parameters: {'n_estimators': 268, 'max_depth': 3}. Best is trial 13 with value: 0.46960172595765814.\u001b[0m\n",
            "\u001b[32m[I 2021-04-12 06:34:30,376]\u001b[0m Trial 22 finished with value: 0.408487590840532 and parameters: {'n_estimators': 268, 'max_depth': 3}. Best is trial 13 with value: 0.46960172595765814.\u001b[0m\n",
            "\u001b[32m[I 2021-04-12 06:34:45,437]\u001b[0m Trial 23 finished with value: 0.39311813790074657 and parameters: {'n_estimators': 363, 'max_depth': 5}. Best is trial 13 with value: 0.46960172595765814.\u001b[0m\n",
            "\u001b[32m[I 2021-04-12 06:34:49,290]\u001b[0m Trial 24 finished with value: 0.3470406509199613 and parameters: {'n_estimators': 127, 'max_depth': 3}. Best is trial 13 with value: 0.46960172595765814.\u001b[0m\n",
            "\u001b[32m[I 2021-04-12 06:34:55,141]\u001b[0m Trial 25 finished with value: 0.35397933291937844 and parameters: {'n_estimators': 250, 'max_depth': 2}. Best is trial 13 with value: 0.46960172595765814.\u001b[0m\n",
            "\u001b[32m[I 2021-04-12 06:35:05,866]\u001b[0m Trial 26 finished with value: 0.4048470174572821 and parameters: {'n_estimators': 304, 'max_depth': 4}. Best is trial 13 with value: 0.46960172595765814.\u001b[0m\n",
            "\u001b[32m[I 2021-04-12 06:35:13,869]\u001b[0m Trial 27 finished with value: 0.42699579831932777 and parameters: {'n_estimators': 192, 'max_depth': 5}. Best is trial 13 with value: 0.46960172595765814.\u001b[0m\n",
            "\u001b[32m[I 2021-04-12 06:35:20,318]\u001b[0m Trial 28 finished with value: 0.4408338047862823 and parameters: {'n_estimators': 155, 'max_depth': 5}. Best is trial 13 with value: 0.46960172595765814.\u001b[0m\n",
            "\u001b[32m[I 2021-04-12 06:35:25,063]\u001b[0m Trial 29 finished with value: 0.3615514184397163 and parameters: {'n_estimators': 96, 'max_depth': 7}. Best is trial 13 with value: 0.46960172595765814.\u001b[0m\n",
            "\u001b[32m[I 2021-04-12 06:35:31,544]\u001b[0m Trial 30 finished with value: 0.38538622303266473 and parameters: {'n_estimators': 139, 'max_depth': 6}. Best is trial 13 with value: 0.46960172595765814.\u001b[0m\n",
            "\u001b[32m[I 2021-04-12 06:35:39,650]\u001b[0m Trial 31 finished with value: 0.38179428831493917 and parameters: {'n_estimators': 197, 'max_depth': 5}. Best is trial 13 with value: 0.46960172595765814.\u001b[0m\n",
            "\u001b[32m[I 2021-04-12 06:35:43,343]\u001b[0m Trial 32 finished with value: 0.3685685938183163 and parameters: {'n_estimators': 90, 'max_depth': 5}. Best is trial 13 with value: 0.46960172595765814.\u001b[0m\n",
            "\u001b[32m[I 2021-04-12 06:35:51,327]\u001b[0m Trial 33 finished with value: 0.4091836734693878 and parameters: {'n_estimators': 174, 'max_depth': 6}. Best is trial 13 with value: 0.46960172595765814.\u001b[0m\n",
            "\u001b[32m[I 2021-04-12 06:35:56,799]\u001b[0m Trial 34 finished with value: 0.42900506638714186 and parameters: {'n_estimators': 150, 'max_depth': 4}. Best is trial 13 with value: 0.46960172595765814.\u001b[0m\n",
            "\u001b[32m[I 2021-04-12 06:36:01,936]\u001b[0m Trial 35 finished with value: 0.41547820429407933 and parameters: {'n_estimators': 141, 'max_depth': 4}. Best is trial 13 with value: 0.46960172595765814.\u001b[0m\n",
            "\u001b[32m[I 2021-04-12 06:36:02,290]\u001b[0m Trial 36 finished with value: 0.3478227781086626 and parameters: {'n_estimators': 8, 'max_depth': 5}. Best is trial 13 with value: 0.46960172595765814.\u001b[0m\n",
            "\u001b[32m[I 2021-04-12 06:36:07,921]\u001b[0m Trial 37 finished with value: 0.36252231389009504 and parameters: {'n_estimators': 106, 'max_depth': 8}. Best is trial 13 with value: 0.46960172595765814.\u001b[0m\n",
            "\u001b[32m[I 2021-04-12 06:36:11,107]\u001b[0m Trial 38 finished with value: 0.37416012558869705 and parameters: {'n_estimators': 69, 'max_depth': 6}. Best is trial 13 with value: 0.46960172595765814.\u001b[0m\n",
            "\u001b[32m[I 2021-04-12 06:36:19,098]\u001b[0m Trial 39 finished with value: 0.339440417332689 and parameters: {'n_estimators': 217, 'max_depth': 4}. Best is trial 13 with value: 0.46960172595765814.\u001b[0m\n",
            "\u001b[32m[I 2021-04-12 06:36:38,561]\u001b[0m Trial 40 finished with value: 0.32583942667976284 and parameters: {'n_estimators': 397, 'max_depth': 7}. Best is trial 13 with value: 0.46960172595765814.\u001b[0m\n",
            "\u001b[32m[I 2021-04-12 06:36:43,839]\u001b[0m Trial 41 finished with value: 0.37384352186389525 and parameters: {'n_estimators': 148, 'max_depth': 4}. Best is trial 13 with value: 0.46960172595765814.\u001b[0m\n",
            "\u001b[32m[I 2021-04-12 06:36:52,107]\u001b[0m Trial 42 finished with value: 0.4021825396825397 and parameters: {'n_estimators': 198, 'max_depth': 5}. Best is trial 13 with value: 0.46960172595765814.\u001b[0m\n",
            "\u001b[32m[I 2021-04-12 06:36:57,706]\u001b[0m Trial 43 finished with value: 0.421269127998594 and parameters: {'n_estimators': 155, 'max_depth': 4}. Best is trial 13 with value: 0.46960172595765814.\u001b[0m\n",
            "\u001b[32m[I 2021-04-12 06:37:06,399]\u001b[0m Trial 44 finished with value: 0.4483721454683568 and parameters: {'n_estimators': 242, 'max_depth': 4}. Best is trial 13 with value: 0.46960172595765814.\u001b[0m\n",
            "\u001b[32m[I 2021-04-12 06:37:16,321]\u001b[0m Trial 45 finished with value: 0.3501064153238066 and parameters: {'n_estimators': 242, 'max_depth': 5}. Best is trial 13 with value: 0.46960172595765814.\u001b[0m\n",
            "\u001b[32m[I 2021-04-12 06:37:26,444]\u001b[0m Trial 46 finished with value: 0.4025593990216632 and parameters: {'n_estimators': 282, 'max_depth': 4}. Best is trial 13 with value: 0.46960172595765814.\u001b[0m\n",
            "\u001b[32m[I 2021-04-12 06:37:33,153]\u001b[0m Trial 47 finished with value: 0.38838763575605684 and parameters: {'n_estimators': 226, 'max_depth': 3}. Best is trial 13 with value: 0.46960172595765814.\u001b[0m\n",
            "\u001b[32m[I 2021-04-12 06:37:42,239]\u001b[0m Trial 48 finished with value: 0.33164133738601825 and parameters: {'n_estimators': 202, 'max_depth': 6}. Best is trial 13 with value: 0.46960172595765814.\u001b[0m\n",
            "\u001b[32m[I 2021-04-12 06:37:54,286]\u001b[0m Trial 49 finished with value: 0.4108295306388527 and parameters: {'n_estimators': 330, 'max_depth': 4}. Best is trial 13 with value: 0.46960172595765814.\u001b[0m\n",
            "\u001b[32m[I 2021-04-12 06:38:04,738]\u001b[0m Trial 50 finished with value: 0.3868678692568609 and parameters: {'n_estimators': 189, 'max_depth': 10}. Best is trial 13 with value: 0.46960172595765814.\u001b[0m\n",
            "\u001b[32m[I 2021-04-12 06:38:10,269]\u001b[0m Trial 51 finished with value: 0.41341337234194386 and parameters: {'n_estimators': 158, 'max_depth': 4}. Best is trial 13 with value: 0.46960172595765814.\u001b[0m\n",
            "\u001b[32m[I 2021-04-12 06:38:14,957]\u001b[0m Trial 52 finished with value: 0.3877619561139055 and parameters: {'n_estimators': 115, 'max_depth': 5}. Best is trial 13 with value: 0.46960172595765814.\u001b[0m\n",
            "\u001b[32m[I 2021-04-12 06:38:17,563]\u001b[0m Trial 53 finished with value: 0.4259098863630824 and parameters: {'n_estimators': 72, 'max_depth': 4}. Best is trial 13 with value: 0.46960172595765814.\u001b[0m\n",
            "\u001b[32m[I 2021-04-12 06:38:19,934]\u001b[0m Trial 54 finished with value: 0.3377814432581167 and parameters: {'n_estimators': 78, 'max_depth': 3}. Best is trial 13 with value: 0.46960172595765814.\u001b[0m\n",
            "\u001b[32m[I 2021-04-12 06:38:21,755]\u001b[0m Trial 55 finished with value: 0.35552254349424167 and parameters: {'n_estimators': 43, 'max_depth': 5}. Best is trial 13 with value: 0.46960172595765814.\u001b[0m\n",
            "\u001b[32m[I 2021-04-12 06:38:35,573]\u001b[0m Trial 56 finished with value: 0.4564710387078808 and parameters: {'n_estimators': 465, 'max_depth': 3}. Best is trial 13 with value: 0.46960172595765814.\u001b[0m\n",
            "\u001b[32m[I 2021-04-12 06:38:45,087]\u001b[0m Trial 57 finished with value: 0.41529769665362887 and parameters: {'n_estimators': 405, 'max_depth': 2}. Best is trial 13 with value: 0.46960172595765814.\u001b[0m\n",
            "\u001b[32m[I 2021-04-12 06:38:59,058]\u001b[0m Trial 58 finished with value: 0.40850864087862393 and parameters: {'n_estimators': 469, 'max_depth': 3}. Best is trial 13 with value: 0.46960172595765814.\u001b[0m\n",
            "\u001b[32m[I 2021-04-12 06:39:12,516]\u001b[0m Trial 59 finished with value: 0.4245375159227587 and parameters: {'n_estimators': 451, 'max_depth': 3}. Best is trial 13 with value: 0.46960172595765814.\u001b[0m\n",
            "\u001b[32m[I 2021-04-12 06:39:22,270]\u001b[0m Trial 60 finished with value: 0.35618860399932284 and parameters: {'n_estimators': 329, 'max_depth': 3}. Best is trial 13 with value: 0.46960172595765814.\u001b[0m\n",
            "\u001b[32m[I 2021-04-12 06:39:22,595]\u001b[0m Trial 61 finished with value: 0.39274136346380545 and parameters: {'n_estimators': 9, 'max_depth': 4}. Best is trial 13 with value: 0.46960172595765814.\u001b[0m\n",
            "\u001b[32m[I 2021-04-12 06:39:31,336]\u001b[0m Trial 62 finished with value: 0.44321428571428567 and parameters: {'n_estimators': 244, 'max_depth': 4}. Best is trial 13 with value: 0.46960172595765814.\u001b[0m\n",
            "\u001b[32m[I 2021-04-12 06:39:36,987]\u001b[0m Trial 63 finished with value: 0.40696814365064105 and parameters: {'n_estimators': 240, 'max_depth': 2}. Best is trial 13 with value: 0.46960172595765814.\u001b[0m\n",
            "\u001b[32m[I 2021-04-12 06:39:46,729]\u001b[0m Trial 64 finished with value: 0.40347035040431267 and parameters: {'n_estimators': 274, 'max_depth': 4}. Best is trial 13 with value: 0.46960172595765814.\u001b[0m\n",
            "\u001b[32m[I 2021-04-12 06:39:55,286]\u001b[0m Trial 65 finished with value: 0.4579473304473305 and parameters: {'n_estimators': 208, 'max_depth': 5}. Best is trial 13 with value: 0.46960172595765814.\u001b[0m\n",
            "\u001b[32m[I 2021-04-12 06:40:02,979]\u001b[0m Trial 66 finished with value: 0.4429195098686624 and parameters: {'n_estimators': 258, 'max_depth': 3}. Best is trial 13 with value: 0.46960172595765814.\u001b[0m\n",
            "\u001b[32m[I 2021-04-12 06:40:10,589]\u001b[0m Trial 67 finished with value: 0.42155193755754533 and parameters: {'n_estimators': 257, 'max_depth': 3}. Best is trial 13 with value: 0.46960172595765814.\u001b[0m\n",
            "\u001b[32m[I 2021-04-12 06:40:17,687]\u001b[0m Trial 68 finished with value: 0.3875676943805145 and parameters: {'n_estimators': 299, 'max_depth': 2}. Best is trial 13 with value: 0.46960172595765814.\u001b[0m\n",
            "\u001b[32m[I 2021-04-12 06:40:28,871]\u001b[0m Trial 69 finished with value: 0.3574716040051888 and parameters: {'n_estimators': 378, 'max_depth': 3}. Best is trial 13 with value: 0.46960172595765814.\u001b[0m\n",
            "\u001b[32m[I 2021-04-12 06:40:38,486]\u001b[0m Trial 70 finished with value: 0.36305339745961285 and parameters: {'n_estimators': 321, 'max_depth': 3}. Best is trial 13 with value: 0.46960172595765814.\u001b[0m\n",
            "\u001b[32m[I 2021-04-12 06:40:47,234]\u001b[0m Trial 71 finished with value: 0.4123303167420815 and parameters: {'n_estimators': 214, 'max_depth': 5}. Best is trial 13 with value: 0.46960172595765814.\u001b[0m\n",
            "\u001b[32m[I 2021-04-12 06:40:57,733]\u001b[0m Trial 72 finished with value: 0.37472222222222223 and parameters: {'n_estimators': 290, 'max_depth': 4}. Best is trial 13 with value: 0.46960172595765814.\u001b[0m\n",
            "\u001b[32m[I 2021-04-12 06:41:05,357]\u001b[0m Trial 73 finished with value: 0.42707094817750557 and parameters: {'n_estimators': 254, 'max_depth': 3}. Best is trial 13 with value: 0.46960172595765814.\u001b[0m\n",
            "\u001b[32m[I 2021-04-12 06:41:13,567]\u001b[0m Trial 74 finished with value: 0.3755251458617196 and parameters: {'n_estimators': 228, 'max_depth': 4}. Best is trial 13 with value: 0.46960172595765814.\u001b[0m\n",
            "\u001b[32m[I 2021-04-12 06:41:17,574]\u001b[0m Trial 75 finished with value: 0.3808686027902446 and parameters: {'n_estimators': 170, 'max_depth': 2}. Best is trial 13 with value: 0.46960172595765814.\u001b[0m\n",
            "\u001b[32m[I 2021-04-12 06:41:23,516]\u001b[0m Trial 76 finished with value: 0.314809022522674 and parameters: {'n_estimators': 131, 'max_depth': 6}. Best is trial 13 with value: 0.46960172595765814.\u001b[0m\n",
            "\u001b[32m[I 2021-04-12 06:41:29,921]\u001b[0m Trial 77 finished with value: 0.40888761832158055 and parameters: {'n_estimators': 181, 'max_depth': 4}. Best is trial 13 with value: 0.46960172595765814.\u001b[0m\n",
            "\u001b[32m[I 2021-04-12 06:41:39,641]\u001b[0m Trial 78 finished with value: 0.41044834984539663 and parameters: {'n_estimators': 237, 'max_depth': 5}. Best is trial 13 with value: 0.46960172595765814.\u001b[0m\n",
            "\u001b[32m[I 2021-04-12 06:42:00,009]\u001b[0m Trial 79 finished with value: 0.2931635662001745 and parameters: {'n_estimators': 500, 'max_depth': 5}. Best is trial 13 with value: 0.46960172595765814.\u001b[0m\n",
            "\u001b[32m[I 2021-04-12 06:42:10,785]\u001b[0m Trial 80 finished with value: 0.37890921971110647 and parameters: {'n_estimators': 354, 'max_depth': 3}. Best is trial 13 with value: 0.46960172595765814.\u001b[0m\n",
            "\u001b[32m[I 2021-04-12 06:42:17,187]\u001b[0m Trial 81 finished with value: 0.4203093516652839 and parameters: {'n_estimators': 212, 'max_depth': 3}. Best is trial 13 with value: 0.46960172595765814.\u001b[0m\n",
            "\u001b[32m[I 2021-04-12 06:42:23,828]\u001b[0m Trial 82 finished with value: 0.3067440709170691 and parameters: {'n_estimators': 282, 'max_depth': 2}. Best is trial 13 with value: 0.46960172595765814.\u001b[0m\n",
            "\u001b[32m[I 2021-04-12 06:42:31,450]\u001b[0m Trial 83 finished with value: 0.4464168896818317 and parameters: {'n_estimators': 251, 'max_depth': 3}. Best is trial 13 with value: 0.46960172595765814.\u001b[0m\n",
            "\u001b[32m[I 2021-04-12 06:42:40,986]\u001b[0m Trial 84 finished with value: 0.4035636651077004 and parameters: {'n_estimators': 264, 'max_depth': 4}. Best is trial 13 with value: 0.46960172595765814.\u001b[0m\n",
            "\u001b[32m[I 2021-04-12 06:42:45,771]\u001b[0m Trial 85 finished with value: 0.4231910946196661 and parameters: {'n_estimators': 160, 'max_depth': 3}. Best is trial 13 with value: 0.46960172595765814.\u001b[0m\n",
            "\u001b[32m[I 2021-04-12 06:42:53,214]\u001b[0m Trial 86 finished with value: 0.49120874809434134 and parameters: {'n_estimators': 207, 'max_depth': 4}. Best is trial 86 with value: 0.49120874809434134.\u001b[0m\n",
            "\u001b[32m[I 2021-04-12 06:42:58,991]\u001b[0m Trial 87 finished with value: 0.39116174834783746 and parameters: {'n_estimators': 245, 'max_depth': 2}. Best is trial 86 with value: 0.49120874809434134.\u001b[0m\n",
            "\u001b[32m[I 2021-04-12 06:43:05,220]\u001b[0m Trial 88 finished with value: 0.3871122583717557 and parameters: {'n_estimators': 206, 'max_depth': 3}. Best is trial 86 with value: 0.49120874809434134.\u001b[0m\n",
            "\u001b[32m[I 2021-04-12 06:43:13,724]\u001b[0m Trial 89 finished with value: 0.4033670033670034 and parameters: {'n_estimators': 230, 'max_depth': 4}. Best is trial 86 with value: 0.49120874809434134.\u001b[0m\n",
            "\u001b[32m[I 2021-04-12 06:43:21,425]\u001b[0m Trial 90 finished with value: 0.4115890274668657 and parameters: {'n_estimators': 185, 'max_depth': 5}. Best is trial 86 with value: 0.49120874809434134.\u001b[0m\n",
            "\u001b[32m[I 2021-04-12 06:43:30,668]\u001b[0m Trial 91 finished with value: 0.3824235064127247 and parameters: {'n_estimators': 261, 'max_depth': 4}. Best is trial 86 with value: 0.49120874809434134.\u001b[0m\n",
            "\u001b[32m[I 2021-04-12 06:43:37,579]\u001b[0m Trial 92 finished with value: 0.42518166819350905 and parameters: {'n_estimators': 196, 'max_depth': 4}. Best is trial 86 with value: 0.49120874809434134.\u001b[0m\n",
            "\u001b[32m[I 2021-04-12 06:43:52,700]\u001b[0m Trial 93 finished with value: 0.3555729984301413 and parameters: {'n_estimators': 422, 'max_depth': 4}. Best is trial 86 with value: 0.49120874809434134.\u001b[0m\n",
            "\u001b[32m[I 2021-04-12 06:43:59,341]\u001b[0m Trial 94 finished with value: 0.3680704099821747 and parameters: {'n_estimators': 220, 'max_depth': 3}. Best is trial 86 with value: 0.49120874809434134.\u001b[0m\n",
            "\u001b[32m[I 2021-04-12 06:44:04,078]\u001b[0m Trial 95 finished with value: 0.33435109072341007 and parameters: {'n_estimators': 116, 'max_depth': 5}. Best is trial 86 with value: 0.49120874809434134.\u001b[0m\n",
            "\u001b[32m[I 2021-04-12 06:44:13,441]\u001b[0m Trial 96 finished with value: 0.34589027530204003 and parameters: {'n_estimators': 312, 'max_depth': 3}. Best is trial 86 with value: 0.49120874809434134.\u001b[0m\n",
            "\u001b[32m[I 2021-04-12 06:44:21,008]\u001b[0m Trial 97 finished with value: 0.3845826792255364 and parameters: {'n_estimators': 164, 'max_depth': 6}. Best is trial 86 with value: 0.49120874809434134.\u001b[0m\n",
            "\u001b[32m[I 2021-04-12 06:44:26,052]\u001b[0m Trial 98 finished with value: 0.3763972070305197 and parameters: {'n_estimators': 141, 'max_depth': 4}. Best is trial 86 with value: 0.49120874809434134.\u001b[0m\n",
            "\u001b[32m[I 2021-04-12 06:44:33,123]\u001b[0m Trial 99 finished with value: 0.4139920424403183 and parameters: {'n_estimators': 236, 'max_depth': 3}. Best is trial 86 with value: 0.49120874809434134.\u001b[0m\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "eEidgYUZiI-x"
      },
      "source": [
        "info = {'n_estimators': 207, 'max_depth': 4}\n",
        "est = DecisionTreeClassifier(max_depth = info[\"max_depth\"])\n",
        "clf = AdaBoostClassifier(base_estimator = est, n_estimators=info[\"n_estimators\"])\n",
        "clf = clf.fit(train_x,train_y)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bmBBKsLliI-x"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "Lh7n0-HkiI-x"
      },
      "source": [
        "Y_pred3 = clf.predict(X_test_edit)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "AIukM0e1iI-x"
      },
      "source": [
        "Y_pred = clf_final.predict_proba(X_test_edit)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "hU754AiKiI-x"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "aMCCtJTyiI-y"
      },
      "source": [
        "Y_pred1 = (Y_pred[:,1]>0.3)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "IlkLr3joiI-y"
      },
      "source": [
        "Y_pred2 = (Y_pred[:,1]>0.8)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "NUdt7HlXiI-0",
        "outputId": "b31f0e97-51a3-4c94-a838-6280e0566a78"
      },
      "source": [
        "df_test_final"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             id     Alpha      Beta     Delta      Zeta       Eta      Iota  \\\n",
              "356           1 -0.762602 -0.430836  0.898034  0.986878  0.004067  0.214634   \n",
              "78        12615 -0.595935  0.185198  0.173161  0.986878  0.004067 -0.285366   \n",
              "71        40811 -0.095935 -0.029992 -0.046129 -0.013122  0.004067  0.214634   \n",
              "380       65668 -0.095935 -0.029992  0.004633 -0.013122  0.004067  0.214634   \n",
              "41        72717 -0.095935  0.121907 -0.086738 -0.013122  0.004067  0.214634   \n",
              "..          ...       ...       ...       ...       ...       ...       ...   \n",
              "76   1492676578 -0.262602 -0.046870  0.076714 -0.013122  0.004067 -0.285366   \n",
              "291  1492712936 -0.262602 -0.072186 -0.081662 -0.013122  0.004067 -0.035366   \n",
              "159  1492728518 -0.262602 -0.118600 -0.029885 -0.013122  0.004067 -0.285366   \n",
              "363  1492742245 -0.262602 -0.046870 -0.011611 -0.013122  0.004067  0.214634   \n",
              "151  1492771183  0.070732 -0.186110 -0.060342  0.886878  0.004067 -0.285366   \n",
              "\n",
              "        Kappa    Lambda     late1  ...     late3     p_val   no_tran  \\\n",
              "356  0.806874 -0.197382 -0.004763  ...  0.151509  0.551793  0.199774   \n",
              "78   0.317033 -0.197382 -0.009024  ... -0.007880  0.140295  0.070145   \n",
              "71  -0.193070  0.042742 -0.009024  ... -0.007880 -0.213199 -0.096522   \n",
              "380 -0.172788  0.071593 -0.009024  ... -0.007880  0.209990 -0.059485   \n",
              "41  -0.193118 -0.197382 -0.004520  ... -0.007880 -0.213199  0.051626   \n",
              "..        ...       ...       ...  ...       ...       ...       ...   \n",
              "76  -0.172788 -0.197382 -0.009024  ... -0.007880  0.145404 -0.003930   \n",
              "291 -0.172764 -0.197382 -0.009024  ... -0.007880  0.040890 -0.115041   \n",
              "159 -0.172788  0.071593 -0.009024  ... -0.007880  0.225323  0.014589   \n",
              "363 -0.193065 -0.197382 -0.009024  ... -0.007880  0.258180 -0.096522   \n",
              "151 -0.192972 -0.197382 -0.009024  ... -0.007880  0.001170 -0.078004   \n",
              "\n",
              "       no_pay   max_bal   rem_bal     dates   last_up      past  amb  \n",
              "356  0.246418  0.037970 -0.015283  0.308519 -0.073865  0.516082    1  \n",
              "78   0.277668  0.044271  0.014866  0.123548 -0.048978  0.213857    0  \n",
              "71  -0.253582 -0.019850 -0.016139 -0.039889 -0.109561 -0.107795    0  \n",
              "380 -0.066082 -0.046223 -0.031581 -0.078049 -0.029119 -0.142645    0  \n",
              "41   0.277668  0.218281  0.131830  0.003398  0.000293  0.076996    0  \n",
              "..        ...       ...       ...       ...       ...       ...  ...  \n",
              "76   0.277668 -0.018061 -0.029703  0.048117 -0.005740  0.045758    0  \n",
              "291 -0.191082 -0.040307 -0.021191 -0.018663 -0.062804 -0.185987    0  \n",
              "159  0.277668 -0.007951 -0.024964 -0.062040  0.050318  0.023892    0  \n",
              "363 -0.441082 -0.043821 -0.029114 -0.109412  0.064647 -0.155140    0  \n",
              "151 -0.003582 -0.009145 -0.009260 -0.044739  0.060876 -0.112969    0  \n",
              "\n",
              "[410 rows x 21 columns]"
            ],
            "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>Alpha</th>\n",
              "      <th>Beta</th>\n",
              "      <th>Delta</th>\n",
              "      <th>Zeta</th>\n",
              "      <th>Eta</th>\n",
              "      <th>Iota</th>\n",
              "      <th>Kappa</th>\n",
              "      <th>Lambda</th>\n",
              "      <th>late1</th>\n",
              "      <th>...</th>\n",
              "      <th>late3</th>\n",
              "      <th>p_val</th>\n",
              "      <th>no_tran</th>\n",
              "      <th>no_pay</th>\n",
              "      <th>max_bal</th>\n",
              "      <th>rem_bal</th>\n",
              "      <th>dates</th>\n",
              "      <th>last_up</th>\n",
              "      <th>past</th>\n",
              "      <th>amb</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>356</th>\n",
              "      <td>1</td>\n",
              "      <td>-0.762602</td>\n",
              "      <td>-0.430836</td>\n",
              "      <td>0.898034</td>\n",
              "      <td>0.986878</td>\n",
              "      <td>0.004067</td>\n",
              "      <td>0.214634</td>\n",
              "      <td>0.806874</td>\n",
              "      <td>-0.197382</td>\n",
              "      <td>-0.004763</td>\n",
              "      <td>...</td>\n",
              "      <td>0.151509</td>\n",
              "      <td>0.551793</td>\n",
              "      <td>0.199774</td>\n",
              "      <td>0.246418</td>\n",
              "      <td>0.037970</td>\n",
              "      <td>-0.015283</td>\n",
              "      <td>0.308519</td>\n",
              "      <td>-0.073865</td>\n",
              "      <td>0.516082</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>78</th>\n",
              "      <td>12615</td>\n",
              "      <td>-0.595935</td>\n",
              "      <td>0.185198</td>\n",
              "      <td>0.173161</td>\n",
              "      <td>0.986878</td>\n",
              "      <td>0.004067</td>\n",
              "      <td>-0.285366</td>\n",
              "      <td>0.317033</td>\n",
              "      <td>-0.197382</td>\n",
              "      <td>-0.009024</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.007880</td>\n",
              "      <td>0.140295</td>\n",
              "      <td>0.070145</td>\n",
              "      <td>0.277668</td>\n",
              "      <td>0.044271</td>\n",
              "      <td>0.014866</td>\n",
              "      <td>0.123548</td>\n",
              "      <td>-0.048978</td>\n",
              "      <td>0.213857</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>71</th>\n",
              "      <td>40811</td>\n",
              "      <td>-0.095935</td>\n",
              "      <td>-0.029992</td>\n",
              "      <td>-0.046129</td>\n",
              "      <td>-0.013122</td>\n",
              "      <td>0.004067</td>\n",
              "      <td>0.214634</td>\n",
              "      <td>-0.193070</td>\n",
              "      <td>0.042742</td>\n",
              "      <td>-0.009024</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.007880</td>\n",
              "      <td>-0.213199</td>\n",
              "      <td>-0.096522</td>\n",
              "      <td>-0.253582</td>\n",
              "      <td>-0.019850</td>\n",
              "      <td>-0.016139</td>\n",
              "      <td>-0.039889</td>\n",
              "      <td>-0.109561</td>\n",
              "      <td>-0.107795</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>380</th>\n",
              "      <td>65668</td>\n",
              "      <td>-0.095935</td>\n",
              "      <td>-0.029992</td>\n",
              "      <td>0.004633</td>\n",
              "      <td>-0.013122</td>\n",
              "      <td>0.004067</td>\n",
              "      <td>0.214634</td>\n",
              "      <td>-0.172788</td>\n",
              "      <td>0.071593</td>\n",
              "      <td>-0.009024</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.007880</td>\n",
              "      <td>0.209990</td>\n",
              "      <td>-0.059485</td>\n",
              "      <td>-0.066082</td>\n",
              "      <td>-0.046223</td>\n",
              "      <td>-0.031581</td>\n",
              "      <td>-0.078049</td>\n",
              "      <td>-0.029119</td>\n",
              "      <td>-0.142645</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>72717</td>\n",
              "      <td>-0.095935</td>\n",
              "      <td>0.121907</td>\n",
              "      <td>-0.086738</td>\n",
              "      <td>-0.013122</td>\n",
              "      <td>0.004067</td>\n",
              "      <td>0.214634</td>\n",
              "      <td>-0.193118</td>\n",
              "      <td>-0.197382</td>\n",
              "      <td>-0.004520</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.007880</td>\n",
              "      <td>-0.213199</td>\n",
              "      <td>0.051626</td>\n",
              "      <td>0.277668</td>\n",
              "      <td>0.218281</td>\n",
              "      <td>0.131830</td>\n",
              "      <td>0.003398</td>\n",
              "      <td>0.000293</td>\n",
              "      <td>0.076996</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>76</th>\n",
              "      <td>1492676578</td>\n",
              "      <td>-0.262602</td>\n",
              "      <td>-0.046870</td>\n",
              "      <td>0.076714</td>\n",
              "      <td>-0.013122</td>\n",
              "      <td>0.004067</td>\n",
              "      <td>-0.285366</td>\n",
              "      <td>-0.172788</td>\n",
              "      <td>-0.197382</td>\n",
              "      <td>-0.009024</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.007880</td>\n",
              "      <td>0.145404</td>\n",
              "      <td>-0.003930</td>\n",
              "      <td>0.277668</td>\n",
              "      <td>-0.018061</td>\n",
              "      <td>-0.029703</td>\n",
              "      <td>0.048117</td>\n",
              "      <td>-0.005740</td>\n",
              "      <td>0.045758</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>291</th>\n",
              "      <td>1492712936</td>\n",
              "      <td>-0.262602</td>\n",
              "      <td>-0.072186</td>\n",
              "      <td>-0.081662</td>\n",
              "      <td>-0.013122</td>\n",
              "      <td>0.004067</td>\n",
              "      <td>-0.035366</td>\n",
              "      <td>-0.172764</td>\n",
              "      <td>-0.197382</td>\n",
              "      <td>-0.009024</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.007880</td>\n",
              "      <td>0.040890</td>\n",
              "      <td>-0.115041</td>\n",
              "      <td>-0.191082</td>\n",
              "      <td>-0.040307</td>\n",
              "      <td>-0.021191</td>\n",
              "      <td>-0.018663</td>\n",
              "      <td>-0.062804</td>\n",
              "      <td>-0.185987</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>159</th>\n",
              "      <td>1492728518</td>\n",
              "      <td>-0.262602</td>\n",
              "      <td>-0.118600</td>\n",
              "      <td>-0.029885</td>\n",
              "      <td>-0.013122</td>\n",
              "      <td>0.004067</td>\n",
              "      <td>-0.285366</td>\n",
              "      <td>-0.172788</td>\n",
              "      <td>0.071593</td>\n",
              "      <td>-0.009024</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.007880</td>\n",
              "      <td>0.225323</td>\n",
              "      <td>0.014589</td>\n",
              "      <td>0.277668</td>\n",
              "      <td>-0.007951</td>\n",
              "      <td>-0.024964</td>\n",
              "      <td>-0.062040</td>\n",
              "      <td>0.050318</td>\n",
              "      <td>0.023892</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>363</th>\n",
              "      <td>1492742245</td>\n",
              "      <td>-0.262602</td>\n",
              "      <td>-0.046870</td>\n",
              "      <td>-0.011611</td>\n",
              "      <td>-0.013122</td>\n",
              "      <td>0.004067</td>\n",
              "      <td>0.214634</td>\n",
              "      <td>-0.193065</td>\n",
              "      <td>-0.197382</td>\n",
              "      <td>-0.009024</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.007880</td>\n",
              "      <td>0.258180</td>\n",
              "      <td>-0.096522</td>\n",
              "      <td>-0.441082</td>\n",
              "      <td>-0.043821</td>\n",
              "      <td>-0.029114</td>\n",
              "      <td>-0.109412</td>\n",
              "      <td>0.064647</td>\n",
              "      <td>-0.155140</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>151</th>\n",
              "      <td>1492771183</td>\n",
              "      <td>0.070732</td>\n",
              "      <td>-0.186110</td>\n",
              "      <td>-0.060342</td>\n",
              "      <td>0.886878</td>\n",
              "      <td>0.004067</td>\n",
              "      <td>-0.285366</td>\n",
              "      <td>-0.192972</td>\n",
              "      <td>-0.197382</td>\n",
              "      <td>-0.009024</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.007880</td>\n",
              "      <td>0.001170</td>\n",
              "      <td>-0.078004</td>\n",
              "      <td>-0.003582</td>\n",
              "      <td>-0.009145</td>\n",
              "      <td>-0.009260</td>\n",
              "      <td>-0.044739</td>\n",
              "      <td>0.060876</td>\n",
              "      <td>-0.112969</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>410 rows × 21 columns</p>\n",
              "</div>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 665
        }
      ]
    },
    {
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      "source": [
        "sample = pd.read_csv(\"/kaggle/input/cassandra-udyam21/Sample Submission.csv\")\n",
        "sample"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     label          id\n",
              "0        1  1484353935\n",
              "1        1  1490969236\n",
              "2        1  1487077817\n",
              "3        1  1485622755\n",
              "4        1  1486796599\n",
              "..     ...         ...\n",
              "405      1  1489702642\n",
              "406      1     1835338\n",
              "407      1     1236915\n",
              "408      1  1489208099\n",
              "409      1     3788282\n",
              "\n",
              "[410 rows x 2 columns]"
            ],
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              "<p>410 rows × 2 columns</p>\n",
              "</div>"
            ]
          },
          "metadata": {
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          "execution_count": 703
        }
      ]
    },
    {
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      "metadata": {
        "trusted": true,
        "id": "js1qEufdiI-1"
      },
      "source": [
        "sample[\"label\"].iloc[df_test_final.index] =  Y_pred1.astype(np.int32)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
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              "     label          id\n",
              "0        0  1484353935\n",
              "1        0  1490969236\n",
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              "..     ...         ...\n",
              "405      0  1489702642\n",
              "406      0     1835338\n",
              "407      0     1236915\n",
              "408      0  1489208099\n",
              "409      0     3788282\n",
              "\n",
              "[410 rows x 2 columns]"
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              "</table>\n",
              "<p>410 rows × 2 columns</p>\n",
              "</div>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 705
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "UfyTtk42iI-3"
      },
      "source": [
        "sample[\"label\"].iloc[74] = 0"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "12IzlYojiI-5"
      },
      "source": [
        "sample.to_csv(\"/kaggle/working/submit.csv\",index=False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "8z8_9BcEiI-5",
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      },
      "source": [
        "sample[\"label\"].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    384\n",
              "1     26\n",
              "Name: label, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 708
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "t9Z5k2kliI-6",
        "outputId": "8be69770-6cec-471f-cb58-a1b9be266d45"
      },
      "source": [
        "sample.iloc[74]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "label             0\n",
              "id       1487549729\n",
              "Name: 74, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 672
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J_PaSEYGiI-7"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LrzfSHwniI-7"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}