월간데이콘_14/신용카드_사용자_연체_예측_AI_경진대회(1).ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "신용카드 사용자 연체 예측 AI 경진대회",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3KXop95rgunZ"
      },
      "source": [
        "https://dacon.io/competitions/official/235713/leaderboard"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AHv79_1CpPjH",
        "outputId": "168fae07-65b5-4931-d710-5590c181771e"
      },
      "source": [
        "!pip uninstall scikit-learn -y\n",
        "\n",
        "!pip install -U scikit-learn"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Uninstalling scikit-learn-0.22.2.post1:\n",
            "  Successfully uninstalled scikit-learn-0.22.2.post1\n",
            "Collecting scikit-learn\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/a8/eb/a48f25c967526b66d5f1fa7a984594f0bf0a5afafa94a8c4dbc317744620/scikit_learn-0.24.2-cp37-cp37m-manylinux2010_x86_64.whl (22.3MB)\n",
            "\u001b[K     |████████████████████████████████| 22.3MB 2.2MB/s \n",
            "\u001b[?25hRequirement already satisfied, skipping upgrade: scipy>=0.19.1 in /usr/local/lib/python3.7/dist-packages (from scikit-learn) (1.4.1)\n",
            "Collecting threadpoolctl>=2.0.0\n",
            "  Downloading https://files.pythonhosted.org/packages/f7/12/ec3f2e203afa394a149911729357aa48affc59c20e2c1c8297a60f33f133/threadpoolctl-2.1.0-py3-none-any.whl\n",
            "Requirement already satisfied, skipping upgrade: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn) (1.0.1)\n",
            "Requirement already satisfied, skipping upgrade: numpy>=1.13.3 in /usr/local/lib/python3.7/dist-packages (from scikit-learn) (1.19.5)\n",
            "Installing collected packages: threadpoolctl, scikit-learn\n",
            "Successfully installed scikit-learn-0.24.2 threadpoolctl-2.1.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "LoOLd1Gppvip",
        "outputId": "ce2c2b0b-9aea-4a69-e395-2266f4f4c6e0"
      },
      "source": [
        "import sklearn\n",
        "\n",
        "sklearn.__version__"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'0.24.2'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gtBs9b-HGgXi"
      },
      "source": [
        "index\n",
        "- gender: 성별\n",
        "- car: 차량 소유 여부\n",
        "- reality: 부동산 소유 여부\n",
        "- child_num: 자녀 수\n",
        "- income_total: 연간 소득\n",
        "- income_type: 소득 분류<br>\n",
        "['Commercial associate', 'Working', 'State servant', 'Pensioner', 'Student']\n",
        "- edu_type: 교육 수준<br>\n",
        "['Higher education' ,'Secondary / secondary special', 'Incomplete higher', 'Lower secondary', 'Academic degree']\n",
        "- family_type: 결혼 여부<br>\n",
        "['Married', 'Civil marriage', 'Separated', 'Single / not married', 'Widow']\n",
        "- house_type: 생활 방식<br>\n",
        "['Municipal apartment', 'House / apartment', 'With parents',\n",
        "'Co-op apartment', 'Rented apartment', 'Office apartment']\n",
        "- DAYS_BIRTH: 출생일\n",
        "<br>데이터 수집 당시 (0)부터 역으로 셈, 즉, -1은 데이터 수집일 하루 전에 태어났음을 의미\n",
        "- DAYS_EMPLOYED: 업무 시작일\n",
        "<br>데이터 수집 당시 (0)부터 역으로 셈, 즉, -1은 데이터 수집일 하루 전부터 일을 시작함을 의미\n",
        "<br>양수 값은 고용되지 않은 상태를 의미함\n",
        "- FLAG_MOBIL: 핸드폰 소유 여부\n",
        "- work_phone: 업무용 전화 소유 여부\n",
        "- phone: 전화 소유 여부\n",
        "- email: 이메일 소유 여부\n",
        "- occyp_type: 직업 유형\n",
        "      [nan, 'Laborers', 'Managers', 'Sales staff',\n",
        "       'High skill tech staff', 'Core staff', 'Drivers', 'Medicine staff',\n",
        "       'Accountants', 'Realty agents', 'Security staff', 'Cleaning staff',\n",
        "       'Private service staff', 'Cooking staff', 'Secretaries',\n",
        "       'HR staff', 'IT staff', 'Low-skill Laborers',\n",
        "       'Waiters/barmen staff']\n",
        "- family_size: 가족 규모\n",
        "- begin_month: 신용카드 발급 월 <br>\n",
        "데이터 수집 당시 (0)부터 역으로 셈, 즉, -1은 데이터 수집일 한 달 전에 신용카드를 발급함을 의미\n",
        "<br><br>\n",
        "- credit: 사용자의 신용카드 대금 연체를 기준으로 한 신용도\n",
        "<br>\n",
        "=> 낮을 수록 높은 신용의 신용카드 사용자를 의미함"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7c7lzItmh25r",
        "outputId": "d302bff0-bcea-49d4-cd6d-96450d6e8526"
      },
      "source": [
        "!unzip open.zip"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Archive:  open.zip\n",
            "   creating: open/\n",
            "  inflating: open/train.csv          \n",
            "  inflating: open/sample_submission.csv  \n",
            "  inflating: open/test.csv           \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mjC_2o1vj_X7"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "from tqdm.notebook import tqdm\n",
        "import pandas as pd\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "import warnings\n",
        "\n",
        "warnings.filterwarnings('ignore')"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T_CJ33DxmVHo"
      },
      "source": [
        "train = pd.read_csv(\"open/train.csv\")\n",
        "test = pd.read_csv(\"open/test.csv\")\n",
        "submission = pd.read_csv(\"open/sample_submission.csv\")"
      ],
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vgm87K_NJYH-"
      },
      "source": [
        "unique_len = train.apply(lambda x : len(x.unique()))"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DIO1bjLLJaCL",
        "outputId": "e346ad33-3726-4146-a621-53dfa8a065b0"
      },
      "source": [
        "unique_len"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "index            26457\n",
              "gender               2\n",
              "car                  2\n",
              "reality              2\n",
              "child_num            9\n",
              "income_total       249\n",
              "income_type          5\n",
              "edu_type             5\n",
              "family_type          5\n",
              "house_type           6\n",
              "DAYS_BIRTH        6621\n",
              "DAYS_EMPLOYED     3470\n",
              "FLAG_MOBIL           1\n",
              "work_phone           2\n",
              "phone                2\n",
              "email                2\n",
              "occyp_type          19\n",
              "family_size         10\n",
              "begin_month         61\n",
              "credit               3\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 278
        },
        "id": "EL5YwZ8yKwvK",
        "outputId": "d58b3e91-e6b6-47c5-8e5e-34ed0e17d115"
      },
      "source": [
        "train[\"FLAG_MOBIL\"].value_counts().plot.bar()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc5b39ea450>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 294
        },
        "id": "oz-wUkUYX9Ut",
        "outputId": "1a5a027c-a862-40b0-ad59-a768844b3f35"
      },
      "source": [
        "train[\"family_size\"].value_counts().plot.bar()"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc5af05ea10>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qmr1R422YFo2"
      },
      "source": [
        "train[\"family_size\"] = train[\"family_size\"].apply(lambda x: 5 if x > 4 else x)\n",
        "test[\"family_size\"] = test[\"family_size\"].apply(lambda x: 5 if x > 4 else x)"
      ],
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IXXbxe9OK03q"
      },
      "source": [
        "del train[\"FLAG_MOBIL\"]\n",
        "del test[\"FLAG_MOBIL\"]\n",
        "\n",
        "del train[\"occyp_type\"]\n",
        "del test[\"occyp_type\"]"
      ],
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        },
        "id": "NiRsyu-uLVvI",
        "outputId": "d87c4cd3-baff-4ef7-f886-9aeecf32a8b4"
      },
      "source": [
        "train[\"child_num\"].value_counts().plot.bar()"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc5b32fed50>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tiIr9GfXMDRF"
      },
      "source": [
        "train[\"child_num\"] = train[\"child_num\"].apply(lambda x: 1 if x > 0 else 0)\n",
        "test[\"child_num\"] = test[\"child_num\"].apply(lambda x: 1 if x > 0 else 0)"
      ],
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QfFBj9P8ipit",
        "outputId": "cf75f51c-3993-4c0d-c589-0096132c0790"
      },
      "source": [
        "train.info()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 26457 entries, 0 to 26456\n",
            "Data columns (total 20 columns):\n",
            " #   Column         Non-Null Count  Dtype  \n",
            "---  ------         --------------  -----  \n",
            " 0   index          26457 non-null  int64  \n",
            " 1   gender         26457 non-null  object \n",
            " 2   car            26457 non-null  object \n",
            " 3   reality        26457 non-null  object \n",
            " 4   child_num      26457 non-null  int64  \n",
            " 5   income_total   26457 non-null  float64\n",
            " 6   income_type    26457 non-null  object \n",
            " 7   edu_type       26457 non-null  object \n",
            " 8   family_type    26457 non-null  object \n",
            " 9   house_type     26457 non-null  object \n",
            " 10  DAYS_BIRTH     26457 non-null  int64  \n",
            " 11  DAYS_EMPLOYED  26457 non-null  int64  \n",
            " 12  FLAG_MOBIL     26457 non-null  int64  \n",
            " 13  work_phone     26457 non-null  int64  \n",
            " 14  phone          26457 non-null  int64  \n",
            " 15  email          26457 non-null  int64  \n",
            " 16  occyp_type     18286 non-null  object \n",
            " 17  family_size    26457 non-null  float64\n",
            " 18  begin_month    26457 non-null  float64\n",
            " 19  credit         26457 non-null  float64\n",
            "dtypes: float64(4), int64(8), object(8)\n",
            "memory usage: 4.0+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q4ffP-qhlWtI",
        "outputId": "8d033f41-fe39-4222-f1a4-9ff9817e6410"
      },
      "source": [
        "print(train.shape)\n",
        "print(test.shape)\n",
        "print(submission.shape)"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(26457, 18)\n",
            "(10000, 17)\n",
            "(10000, 4)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 368
        },
        "id": "jSI9xEvKgy_M",
        "outputId": "8c0bb778-8026-43c2-aab0-007fc97de2d7"
      },
      "source": [
        "import missingno as msno\n",
        "msno.matrix(train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fef6bf658d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1800x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GOg9lo7zjKef"
      },
      "source": [
        "#Nan을 missing으로 변경\n",
        "train['occyp_type'] = train['occyp_type'].fillna('missing')\n",
        "test['occyp_type'] = test['occyp_type'].fillna('missing')"
      ],
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 759
        },
        "id": "Kv0OQDnpf519",
        "outputId": "da3d2dd9-8270-49a2-e452-6fa1af72b9fc"
      },
      "source": [
        "train "
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>index</th>\n",
              "      <th>gender</th>\n",
              "      <th>car</th>\n",
              "      <th>reality</th>\n",
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              "      <th>house_type</th>\n",
              "      <th>DAYS_BIRTH</th>\n",
              "      <th>DAYS_EMPLOYED</th>\n",
              "      <th>FLAG_MOBIL</th>\n",
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              "      <th>phone</th>\n",
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              "      <th>occyp_type</th>\n",
              "      <th>family_size</th>\n",
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              "      <th>credit</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <td>247500.0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
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              "      <td>-22.0</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>F</td>\n",
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              "      <th>...</th>\n",
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              "      <th>26452</th>\n",
              "      <td>26452</td>\n",
              "      <td>F</td>\n",
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              "    <tr>\n",
              "      <th>26453</th>\n",
              "      <td>26453</td>\n",
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              "      <td>180000.0</td>\n",
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              "      <td>House / apartment</td>\n",
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              "      <td>1</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>26454</th>\n",
              "      <td>26454</td>\n",
              "      <td>F</td>\n",
              "      <td>Y</td>\n",
              "      <td>N</td>\n",
              "      <td>0</td>\n",
              "      <td>292500.0</td>\n",
              "      <td>Working</td>\n",
              "      <td>Secondary / secondary special</td>\n",
              "      <td>Civil marriage</td>\n",
              "      <td>With parents</td>\n",
              "      <td>-10082</td>\n",
              "      <td>-2015</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Core staff</td>\n",
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              "      <td>-25.0</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26455</th>\n",
              "      <td>26455</td>\n",
              "      <td>M</td>\n",
              "      <td>N</td>\n",
              "      <td>Y</td>\n",
              "      <td>0</td>\n",
              "      <td>171000.0</td>\n",
              "      <td>Working</td>\n",
              "      <td>Incomplete higher</td>\n",
              "      <td>Single / not married</td>\n",
              "      <td>House / apartment</td>\n",
              "      <td>-10145</td>\n",
              "      <td>-107</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Laborers</td>\n",
              "      <td>1.0</td>\n",
              "      <td>-59.0</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26456</th>\n",
              "      <td>26456</td>\n",
              "      <td>F</td>\n",
              "      <td>N</td>\n",
              "      <td>N</td>\n",
              "      <td>0</td>\n",
              "      <td>81000.0</td>\n",
              "      <td>Working</td>\n",
              "      <td>Secondary / secondary special</td>\n",
              "      <td>Civil marriage</td>\n",
              "      <td>House / apartment</td>\n",
              "      <td>-19569</td>\n",
              "      <td>-1013</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Security staff</td>\n",
              "      <td>2.0</td>\n",
              "      <td>-9.0</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>26457 rows × 20 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       index gender car  ... family_size  begin_month  credit\n",
              "0          0      F   N  ...         2.0         -6.0     1.0\n",
              "1          1      F   N  ...         3.0         -5.0     1.0\n",
              "2          2      M   Y  ...         2.0        -22.0     2.0\n",
              "3          3      F   N  ...         2.0        -37.0     0.0\n",
              "4          4      F   Y  ...         2.0        -26.0     2.0\n",
              "...      ...    ...  ..  ...         ...          ...     ...\n",
              "26452  26452      F   N  ...         4.0         -2.0     1.0\n",
              "26453  26453      F   N  ...         2.0        -47.0     2.0\n",
              "26454  26454      F   Y  ...         2.0        -25.0     2.0\n",
              "26455  26455      M   N  ...         1.0        -59.0     2.0\n",
              "26456  26456      F   N  ...         2.0         -9.0     2.0\n",
              "\n",
              "[26457 rows x 20 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sVzUUmxOnMlc"
      },
      "source": [
        "#전처리"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ns8O50cEiV2k",
        "outputId": "dde2b1de-ac5c-4e3d-c99c-4f56050fa775"
      },
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "le = LabelEncoder()\n",
        "#label = ['gender', 'car', 'reality', 'income_type'\t,'edu_type'\t,'family_type'\t,'house_type', 'occyp_type']\n",
        "label = ['gender', 'car', 'reality', 'income_type'\t,'edu_type'\t,'family_type'\t,'house_type']\n",
        "\n",
        "for col in label :\n",
        "  print(col)\n",
        "  train[col] = le.fit_transform(train[col])\n",
        "  test[col] = le.transform(test[col])"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "gender\n",
            "car\n",
            "reality\n",
            "income_type\n",
            "edu_type\n",
            "family_type\n",
            "house_type\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "OOEcY-ObjjjC",
        "outputId": "1ad1bc29-911f-4f02-ec63-30930b5bb455"
      },
      "source": [
        "train"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
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              "      <td>0</td>\n",
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              "      <td>202500.0</td>\n",
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              "      <th>4</th>\n",
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              "      <th>26453</th>\n",
              "      <td>26453</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>180000.0</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>-15291</td>\n",
              "      <td>-2475</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>-47.0</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26454</th>\n",
              "      <td>26454</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>292500.0</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>-10082</td>\n",
              "      <td>-2015</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>-25.0</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26455</th>\n",
              "      <td>26455</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>171000.0</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>-10145</td>\n",
              "      <td>-107</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>-59.0</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26456</th>\n",
              "      <td>26456</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>81000.0</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>-19569</td>\n",
              "      <td>-1013</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>-9.0</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>26457 rows × 18 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       index  gender  car  reality  ...  email  family_size  begin_month  credit\n",
              "0          0       0    0        0  ...      0          2.0         -6.0     1.0\n",
              "1          1       0    0        1  ...      1          3.0         -5.0     1.0\n",
              "2          2       1    1        1  ...      0          2.0        -22.0     2.0\n",
              "3          3       0    0        1  ...      0          2.0        -37.0     0.0\n",
              "4          4       0    1        1  ...      0          2.0        -26.0     2.0\n",
              "...      ...     ...  ...      ...  ...    ...          ...          ...     ...\n",
              "26452  26452       0    0        0  ...      0          4.0         -2.0     1.0\n",
              "26453  26453       0    0        1  ...      0          2.0        -47.0     2.0\n",
              "26454  26454       0    1        0  ...      0          2.0        -25.0     2.0\n",
              "26455  26455       1    0        1  ...      0          1.0        -59.0     2.0\n",
              "26456  26456       0    0        0  ...      0          2.0         -9.0     2.0\n",
              "\n",
              "[26457 rows x 18 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Obtw5Y-pKPNX",
        "outputId": "1dc4905f-53c0-4f9b-fffb-54ebfbdabd46"
      },
      "source": [
        "print(train['income_total'].min())\n",
        "print(train['income_total'].max())"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "27000.0\n",
            "1575000.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 992
        },
        "id": "kByLMNrtjzdm",
        "outputId": "6547a8db-e239-4a53-e957-b6b94d0e5498"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "df = train.corr()\n",
        "# 그림 사이즈 지정\n",
        "fig, ax = plt.subplots( figsize=(16,16) )\n",
        "\n",
        "# 삼각형 마스크를 만든다(위 쪽 삼각형에 True, 아래 삼각형에 False)\n",
        "mask = np.zeros_like(df, dtype=np.bool)\n",
        "mask[np.triu_indices_from(mask)] = True\n",
        "\n",
        "# 히트맵을 그린다\n",
        "sns.heatmap(df, \n",
        "            cmap = 'RdYlBu_r', \n",
        "            annot = True,   # 실제 값을 표시한다\n",
        "            mask=mask,      # 표시하지 않을 마스크 부분을 지정한다\n",
        "            linewidths=.5,  # 경계면 실선으로 구분하기\n",
        "            cbar_kws={\"shrink\": .5},# 컬러바 크기 절반으로 줄이기\n",
        "            vmin = -1,vmax = 1   # 컬러바 범위 -1 ~ 1\n",
        "           )  \n",
        "plt.show()\n"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x1152 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4qr52XCNkZ9r"
      },
      "source": [
        "1. child_num이랑 family size 상관관계 높아서 둘 중 하나 삭제해도 괜찮지 않을까\n",
        "2. work phone, phone이 과연 영향을 많이 미칠까?\n",
        "3. DAYS들을 좀 더 간소화해서 표현하게 변환이 필요할 듯"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J5-uqYpfY1nt"
      },
      "source": [
        "del train[\"child_num\"]\n",
        "del test[\"child_num\"]"
      ],
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YwsFL0Mdn_3D",
        "outputId": "b1a92113-2d67-4722-c3f1-3d1eba669c93"
      },
      "source": [
        "train_y"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        1.0\n",
              "1        1.0\n",
              "2        2.0\n",
              "3        0.0\n",
              "4        2.0\n",
              "        ... \n",
              "26452    1.0\n",
              "26453    2.0\n",
              "26454    2.0\n",
              "26455    2.0\n",
              "26456    2.0\n",
              "Name: credit, Length: 26457, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CCTvgJ_Dbxdj"
      },
      "source": [
        "import numpy as np\n",
        "counts, bin_dividers = np.histogram(train['income_total'], bins = 7)"
      ],
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y2ZvTEUHb1Lo",
        "outputId": "e52d151d-0f7c-48fd-b149-ccbbbc4b236b"
      },
      "source": [
        "np.histogram(train['income_total'], bins = 7) #연속형 변수를 7개의 구간으로 나눈다.\n",
        "#각 구간의 요소 개수 / 각 구간의 분절점을 확인할 수 있음"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([21601,  4462,   305,    71,     9,     4,     5]),\n",
              " array([  27000.        ,  248142.85714286,  469285.71428571,\n",
              "         690428.57142857,  911571.42857143, 1132714.28571429,\n",
              "        1353857.14285714, 1575000.        ]))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nVfbsY9XcSVR",
        "outputId": "3fb8f5f0-457e-459b-b0ae-be838ac71023"
      },
      "source": [
        "pd.cut(train['income_total'], bins = bin_dividers) #  include_lowest 옵션 : 구간의 시작점도 포함하여 계산한다."
      ],
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0           (27000.0, 248142.857]\n",
              "1           (27000.0, 248142.857]\n",
              "2        (248142.857, 469285.714]\n",
              "3           (27000.0, 248142.857]\n",
              "4           (27000.0, 248142.857]\n",
              "                   ...           \n",
              "26452       (27000.0, 248142.857]\n",
              "26453       (27000.0, 248142.857]\n",
              "26454    (248142.857, 469285.714]\n",
              "26455       (27000.0, 248142.857]\n",
              "26456       (27000.0, 248142.857]\n",
              "Name: income_total, Length: 26457, dtype: category\n",
              "Categories (7, interval[float64]): [(27000.0, 248142.857] < (248142.857, 469285.714] <\n",
              "                                    (469285.714, 690428.571] < (690428.571, 911571.429] <\n",
              "                                    (911571.429, 1132714.286] < (1132714.286, 1353857.143] <\n",
              "                                    (1353857.143, 1575000.0]]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IVdOxIVLczqh"
      },
      "source": [
        "train['income_total'] = pd.factorize(pd.cut(train['income_total'],\n",
        "                                        bins = bin_dividers,\n",
        "                                        include_lowest=True,\n",
        "                                        labels = [i for i in range(7)]))[0]\n",
        "                                        \n",
        "test['income_total'] = pd.factorize(pd.cut(test['income_total'],\n",
        "                                     bins = bin_dividers,\n",
        "                                     include_lowest=True,\n",
        "                                      labels = [i for i in range(7)]))[0]"
      ],
      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YpoG_ygkk07j"
      },
      "source": [
        "train_x = train.loc[:, \"gender\": \"begin_month\"]\n",
        "train_y = train[\"credit\"]\n",
        "test_x = test.loc[:, \"gender\": \"begin_month\"]"
      ],
      "execution_count": 68,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N0bZGM4ukzhQ"
      },
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "scaler = StandardScaler()\n",
        "x_train_std = scaler.fit_transform(train_x)\n",
        "x_test_std = scaler.transform(test_x)"
      ],
      "execution_count": 69,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YC3nOqJEqLoQ",
        "outputId": "902c993e-8bbb-4e9a-ea59-ea393b70a9a9"
      },
      "source": [
        "x_train_std.shape"
      ],
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(26457, 15)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wXRrercYqRNw",
        "outputId": "685f382a-0b12-45a5-f404-f8619bbd95ab"
      },
      "source": [
        "train_y.shape"
      ],
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(26457,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TCtyvCuebvYx"
      },
      "source": [
        "##모델 학습 및 평가"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2m471znLryN2",
        "outputId": "95e50a31-3495-4fb7-ccab-e90a456a23a7"
      },
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "clf = RandomForestClassifier(random_state=0)\n",
        "clf.fit(x_train_std, train_y)"
      ],
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(random_state=0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gUbeySDNqYJy",
        "outputId": "30854f44-097f-4078-a8ee-0c257710a18a"
      },
      "source": [
        "train_proba = clf.predict_proba(train_x)\n",
        "train_proba"
      ],
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.29666667, 0.19333333, 0.51      ],\n",
              "       [0.25166667, 0.17357143, 0.5747619 ],\n",
              "       [0.185     , 0.19      , 0.625     ],\n",
              "       ...,\n",
              "       [0.19166667, 0.25033333, 0.558     ],\n",
              "       [0.27      , 0.16      , 0.57      ],\n",
              "       [0.15166667, 0.23833333, 0.61      ]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c8UnBIBDdRr8",
        "outputId": "896e9afb-25cd-479e-99bc-12f833ae1553"
      },
      "source": [
        "from sklearn.metrics import log_loss\n",
        "\n",
        "print(log_loss(train_y, train_proba))"
      ],
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.9402330458172518\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pfrGrOTAZ3en"
      },
      "source": [
        "submission.iloc[:,1:] =  clf.predict_proba(x_test_std)"
      ],
      "execution_count": 76,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 404
        },
        "id": "7FhaI4tLuKp5",
        "outputId": "4bee8b40-0641-42ad-d461-5a12045e1dc8"
      },
      "source": [
        "submission"
      ],
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>index</th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>26457</td>\n",
              "      <td>0.050000</td>\n",
              "      <td>0.210000</td>\n",
              "      <td>0.740000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>26458</td>\n",
              "      <td>0.430000</td>\n",
              "      <td>0.142500</td>\n",
              "      <td>0.427500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>26459</td>\n",
              "      <td>0.060000</td>\n",
              "      <td>0.110000</td>\n",
              "      <td>0.830000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>26460</td>\n",
              "      <td>0.072333</td>\n",
              "      <td>0.030000</td>\n",
              "      <td>0.897667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>26461</td>\n",
              "      <td>0.017500</td>\n",
              "      <td>0.260000</td>\n",
              "      <td>0.722500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9995</th>\n",
              "      <td>36452</td>\n",
              "      <td>0.100000</td>\n",
              "      <td>0.296667</td>\n",
              "      <td>0.603333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9996</th>\n",
              "      <td>36453</td>\n",
              "      <td>0.280000</td>\n",
              "      <td>0.550000</td>\n",
              "      <td>0.170000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9997</th>\n",
              "      <td>36454</td>\n",
              "      <td>0.098333</td>\n",
              "      <td>0.010000</td>\n",
              "      <td>0.891667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9998</th>\n",
              "      <td>36455</td>\n",
              "      <td>0.550000</td>\n",
              "      <td>0.190000</td>\n",
              "      <td>0.260000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9999</th>\n",
              "      <td>36456</td>\n",
              "      <td>0.156667</td>\n",
              "      <td>0.253333</td>\n",
              "      <td>0.590000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>10000 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      index         0         1         2\n",
              "0     26457  0.050000  0.210000  0.740000\n",
              "1     26458  0.430000  0.142500  0.427500\n",
              "2     26459  0.060000  0.110000  0.830000\n",
              "3     26460  0.072333  0.030000  0.897667\n",
              "4     26461  0.017500  0.260000  0.722500\n",
              "...     ...       ...       ...       ...\n",
              "9995  36452  0.100000  0.296667  0.603333\n",
              "9996  36453  0.280000  0.550000  0.170000\n",
              "9997  36454  0.098333  0.010000  0.891667\n",
              "9998  36455  0.550000  0.190000  0.260000\n",
              "9999  36456  0.156667  0.253333  0.590000\n",
              "\n",
              "[10000 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BTQUMX0iusnV"
      },
      "source": [
        "submission.to_csv(\"submission.csv\", index = False, header = True)\n",
        "#성능 0.99015"
      ],
      "execution_count": 78,
      "outputs": []
    }
  ]
}