Home_Credit Default_Risk/Intro/Home_Credit_Default_Risk_final'_stacking.ipynb

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    "colab": {
      "name": " Home Credit Default Risk final' stacking",
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        "<a href=\"https://colab.research.google.com/github/qsdcfd/kaggle/blob/TIL/Home_Credit%20Default_Risk/Intro/Home_Credit_Default_Risk_final'_stacking.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "cell_type": "markdown",
      "source": [
        "추가 공부: 데이터 집산, merge, iterate,"
      ],
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        "id": "DTy7yamXDANC"
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        ""
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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "plt.style.use('fivethirtyeight')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lMufbKmoAFB7",
        "outputId": "03e8303d-667c-4da7-9f44-c8db2d8d7535"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "bureau = pd.read_csv(\"/content/drive/MyDrive/home-credit-default-risk/bureau.csv\")\n",
        "bureau.head()"
      ],
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          "height": 322
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        "id": "_TYXliqAOimA",
        "outputId": "fec66e91-2cf0-41b6-f0b1-6ed1b8c71b8b"
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      "execution_count": 3,
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              "   SK_ID_CURR  SK_ID_BUREAU CREDIT_ACTIVE CREDIT_CURRENCY  DAYS_CREDIT  \\\n",
              "0      215354       5714462        Closed      currency 1         -497   \n",
              "1      215354       5714463        Active      currency 1         -208   \n",
              "2      215354       5714464        Active      currency 1         -203   \n",
              "3      215354       5714465        Active      currency 1         -203   \n",
              "4      215354       5714466        Active      currency 1         -629   \n",
              "\n",
              "   CREDIT_DAY_OVERDUE  DAYS_CREDIT_ENDDATE  DAYS_ENDDATE_FACT  \\\n",
              "0                   0               -153.0             -153.0   \n",
              "1                   0               1075.0                NaN   \n",
              "2                   0                528.0                NaN   \n",
              "3                   0                  NaN                NaN   \n",
              "4                   0               1197.0                NaN   \n",
              "\n",
              "   AMT_CREDIT_MAX_OVERDUE  CNT_CREDIT_PROLONG  AMT_CREDIT_SUM  \\\n",
              "0                     NaN                   0         91323.0   \n",
              "1                     NaN                   0        225000.0   \n",
              "2                     NaN                   0        464323.5   \n",
              "3                     NaN                   0         90000.0   \n",
              "4                 77674.5                   0       2700000.0   \n",
              "\n",
              "   AMT_CREDIT_SUM_DEBT  AMT_CREDIT_SUM_LIMIT  AMT_CREDIT_SUM_OVERDUE  \\\n",
              "0                  0.0                   NaN                     0.0   \n",
              "1             171342.0                   NaN                     0.0   \n",
              "2                  NaN                   NaN                     0.0   \n",
              "3                  NaN                   NaN                     0.0   \n",
              "4                  NaN                   NaN                     0.0   \n",
              "\n",
              "       CREDIT_TYPE  DAYS_CREDIT_UPDATE  AMT_ANNUITY  \n",
              "0  Consumer credit                -131          NaN  \n",
              "1      Credit card                 -20          NaN  \n",
              "2  Consumer credit                 -16          NaN  \n",
              "3      Credit card                 -16          NaN  \n",
              "4  Consumer credit                 -21          NaN  "
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      "source": [
        "#Groupby client id\n",
        "previous_loan_counts = bureau.groupby('SK_ID_CURR', as_index=False)['SK_ID_BUREAU'].count().rename(columns = {'SK_ID_BUREAU': 'previous_loan_counts'})\n",
        "previous_loan_counts.head()"
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              "   SK_ID_CURR  previous_loan_counts\n",
              "0      100001                     7\n",
              "1      100002                     8\n",
              "2      100003                     4\n",
              "3      100004                     2\n",
              "4      100005                     3"
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train = pd.read_csv('/content/drive/MyDrive/home-credit-default-risk/application_train.csv')\n",
        "train = train.merge(previous_loan_counts, on = 'SK_ID_CURR', how = 'left')\n",
        "\n",
        "#fill the missing values with 0\n",
        "\n",
        "train['previous_loan_counts'] = train['previous_loan_counts'].fillna(0)\n",
        "train.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "zHc9zmHvOxqb",
        "outputId": "da1b7902-193d-4189-871a-fa6bc87a161b"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   SK_ID_CURR  TARGET NAME_CONTRACT_TYPE CODE_GENDER FLAG_OWN_CAR  \\\n",
              "0      100002       1         Cash loans           M            N   \n",
              "1      100003       0         Cash loans           F            N   \n",
              "2      100004       0    Revolving loans           M            Y   \n",
              "3      100006       0         Cash loans           F            N   \n",
              "4      100007       0         Cash loans           M            N   \n",
              "\n",
              "  FLAG_OWN_REALTY  CNT_CHILDREN  AMT_INCOME_TOTAL  AMT_CREDIT  AMT_ANNUITY  \\\n",
              "0               Y             0          202500.0    406597.5      24700.5   \n",
              "1               N             0          270000.0   1293502.5      35698.5   \n",
              "2               Y             0           67500.0    135000.0       6750.0   \n",
              "3               Y             0          135000.0    312682.5      29686.5   \n",
              "4               Y             0          121500.0    513000.0      21865.5   \n",
              "\n",
              "   ...  FLAG_DOCUMENT_19 FLAG_DOCUMENT_20 FLAG_DOCUMENT_21  \\\n",
              "0  ...                 0                0                0   \n",
              "1  ...                 0                0                0   \n",
              "2  ...                 0                0                0   \n",
              "3  ...                 0                0                0   \n",
              "4  ...                 0                0                0   \n",
              "\n",
              "  AMT_REQ_CREDIT_BUREAU_HOUR AMT_REQ_CREDIT_BUREAU_DAY  \\\n",
              "0                        0.0                       0.0   \n",
              "1                        0.0                       0.0   \n",
              "2                        0.0                       0.0   \n",
              "3                        NaN                       NaN   \n",
              "4                        0.0                       0.0   \n",
              "\n",
              "  AMT_REQ_CREDIT_BUREAU_WEEK  AMT_REQ_CREDIT_BUREAU_MON  \\\n",
              "0                        0.0                        0.0   \n",
              "1                        0.0                        0.0   \n",
              "2                        0.0                        0.0   \n",
              "3                        NaN                        NaN   \n",
              "4                        0.0                        0.0   \n",
              "\n",
              "   AMT_REQ_CREDIT_BUREAU_QRT  AMT_REQ_CREDIT_BUREAU_YEAR  previous_loan_counts  \n",
              "0                        0.0                         1.0                   8.0  \n",
              "1                        0.0                         0.0                   4.0  \n",
              "2                        0.0                         0.0                   2.0  \n",
              "3                        NaN                         NaN                   0.0  \n",
              "4                        0.0                         0.0                   1.0  \n",
              "\n",
              "[5 rows x 123 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-2afe19ad-9ed8-4ef3-988e-9796649521f6\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>SK_ID_CURR</th>\n",
              "      <th>TARGET</th>\n",
              "      <th>NAME_CONTRACT_TYPE</th>\n",
              "      <th>CODE_GENDER</th>\n",
              "      <th>FLAG_OWN_CAR</th>\n",
              "      <th>FLAG_OWN_REALTY</th>\n",
              "      <th>CNT_CHILDREN</th>\n",
              "      <th>AMT_INCOME_TOTAL</th>\n",
              "      <th>AMT_CREDIT</th>\n",
              "      <th>AMT_ANNUITY</th>\n",
              "      <th>...</th>\n",
              "      <th>FLAG_DOCUMENT_19</th>\n",
              "      <th>FLAG_DOCUMENT_20</th>\n",
              "      <th>FLAG_DOCUMENT_21</th>\n",
              "      <th>AMT_REQ_CREDIT_BUREAU_HOUR</th>\n",
              "      <th>AMT_REQ_CREDIT_BUREAU_DAY</th>\n",
              "      <th>AMT_REQ_CREDIT_BUREAU_WEEK</th>\n",
              "      <th>AMT_REQ_CREDIT_BUREAU_MON</th>\n",
              "      <th>AMT_REQ_CREDIT_BUREAU_QRT</th>\n",
              "      <th>AMT_REQ_CREDIT_BUREAU_YEAR</th>\n",
              "      <th>previous_loan_counts</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>100002</td>\n",
              "      <td>1</td>\n",
              "      <td>Cash loans</td>\n",
              "      <td>M</td>\n",
              "      <td>N</td>\n",
              "      <td>Y</td>\n",
              "      <td>0</td>\n",
              "      <td>202500.0</td>\n",
              "      <td>406597.5</td>\n",
              "      <td>24700.5</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>8.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>100003</td>\n",
              "      <td>0</td>\n",
              "      <td>Cash loans</td>\n",
              "      <td>F</td>\n",
              "      <td>N</td>\n",
              "      <td>N</td>\n",
              "      <td>0</td>\n",
              "      <td>270000.0</td>\n",
              "      <td>1293502.5</td>\n",
              "      <td>35698.5</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>100004</td>\n",
              "      <td>0</td>\n",
              "      <td>Revolving loans</td>\n",
              "      <td>M</td>\n",
              "      <td>Y</td>\n",
              "      <td>Y</td>\n",
              "      <td>0</td>\n",
              "      <td>67500.0</td>\n",
              "      <td>135000.0</td>\n",
              "      <td>6750.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>100006</td>\n",
              "      <td>0</td>\n",
              "      <td>Cash loans</td>\n",
              "      <td>F</td>\n",
              "      <td>N</td>\n",
              "      <td>Y</td>\n",
              "      <td>0</td>\n",
              "      <td>135000.0</td>\n",
              "      <td>312682.5</td>\n",
              "      <td>29686.5</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>100007</td>\n",
              "      <td>0</td>\n",
              "      <td>Cash loans</td>\n",
              "      <td>M</td>\n",
              "      <td>N</td>\n",
              "      <td>Y</td>\n",
              "      <td>0</td>\n",
              "      <td>121500.0</td>\n",
              "      <td>513000.0</td>\n",
              "      <td>21865.5</td>\n",
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              "  </tbody>\n",
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              "<p>5 rows × 123 columns</p>\n",
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            ]
          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Kernel Density Estimate plots\n",
        "\n",
        "##Plots the disribution of a variable colored by value of the target\n",
        "\n",
        "# Plots the disribution of a variable colored by value of the target\n",
        "def kde_target(var_name, df):\n",
        "    \n",
        "    # Calculate the correlation coefficient between the new variable and the target\n",
        "    corr = df['TARGET'].corr(df[var_name])\n",
        "    \n",
        "    # Calculate medians for repaid vs not repaid\n",
        "    avg_repaid = df.loc[df['TARGET'] == 0, var_name].median()\n",
        "    avg_not_repaid = df.loc[df['TARGET'] == 1, var_name].median()\n",
        "    \n",
        "    plt.figure(figsize = (12, 6))\n",
        "    \n",
        "    # Plot the distribution for target == 0 and target == 1\n",
        "    sns.kdeplot(df.loc[df['TARGET'] == 0, var_name], label = 'TARGET == 0')\n",
        "    sns.kdeplot(df.loc[df['TARGET'] == 1, var_name], label = 'TARGET == 1')\n",
        "    \n",
        "    # label the plot\n",
        "    plt.xlabel(var_name); plt.ylabel('Density'); plt.title('%s Distribution' % var_name)\n",
        "    plt.legend();\n",
        "    \n",
        "    # print out the correlation\n",
        "    print('The correlation between %s and the TARGET is %0.4f' % (var_name, corr))\n",
        "    # Print out average values\n",
        "    print('Median value for loan that was not repaid = %0.4f' % avg_not_repaid)\n",
        "    print('Median value for loan that was repaid =     %0.4f' % avg_repaid)"
      ],
      "metadata": {
        "id": "9gtgE4yTOxsv"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "kde_target('EXT_SOURCE_3', train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 459
        },
        "id": "q2dqmwT5Oxuj",
        "outputId": "51b40bff-dc76-42c9-892c-ecde7bff678c"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The correlation between EXT_SOURCE_3 and the TARGET is -0.1789\n",
            "Median value for loan that was not repaid = 0.3791\n",
            "Median value for loan that was repaid =     0.5460\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ],
            "image/png": 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P+bjGWlsH4cVWQUFB038RTcCaNWvo2bNnrMOQGNH1b9l0/Vu22lx/b8Dy168L+feKohrrOAx0SAyNN8mMc1AWsOSUBckuC7KrrParTNd07G4pTnqnu+kbTlp6p7vomeYi0aVuYoeiKfz/LygoIC0tLdZhNEt1NQZlX9coLS0t4u5Cg7SgGGM6Ay8CbQndEHnKWvtIlToGeAQ4HSgBfm2t/Tq87VfA3eGq91trX2iIuEVERGT/1hX6ueKzXL7ZVX2MiQHGdopj/GEJnN4lnqwaumftKgvwXbiL13c5Pr7L9bK2MFDrGIIW1hYGWFsYYObGveUOA4Oz3IxuH8+oDnEMb+vRYHyRRq6hunj5gT9Ya782xqQAS40xH1lrV1aqMw7oGX4cA/wfcIwxJgP4C3A0oeRmqTHmXWttXgPFLiIiIjV4fW0Jf/gynyJ/9c4MJ3WM456j0zgiw73f42TFOzmxo5MTO+69U1voDbIi18d3uXuSFh8/5PmIcqoaBS0s2eljyU4fD363m9bxDi7pmcilvZLolqqe7iKNUYP8z7TWbgO2hX/ebYxZBXQEKicoZwEv2lCfs6+MMenGmPbAaOAja20ugDHmI+A04NWGiF1ERESq2+0LcsuCfF5fW1ptW8dEJ4+OTOekjofWLSTV42BEuzhGtIurKCsPWFbl+Vie6+PHfD8/5vv4ocDPpqLatbbsLAvy8PIiHl5exCmd4vjTkNolUCLScBr81oExpitwFLCwyqaOwKZKzzeHy2oqFxERkRj4dpeXyz/NZd3u6knB+C7x/HtkK1rF1c+4jzinYVCWh0FZnojyIl+QNQV+ftiTtOT7+SHfx/ooMe4xZ3M5H23O5uKeidw1OJX2iZodTKQxaNBB8saYZOAz4G/W2reqbHsf+Lu1dn74+f+A2wi1oMRba+8Pl/8JKLXWPrhn38qD5KPN6ywiIiKHLmjhv1tcPL7Bjd9GjuOIc1hu6uZjYjs/phEN8djlhcX5ThbmO/k0x0lxIHpw8Q7L7xth/FI78fHxtG7dOtZhyD7s3LmTsrKyiueVJ16IySB5AGOMG3gTeKVqchK2Behc6XmncNkWQklK5fJPazpPY59loilrCrN4SP3R9W/ZdP1btjVr1pDWqTu//TyPuVvKq23vm+5i6ugM+rVqfF2legLDwz8X+4LMWF/Kcz8Us7TKgP6yoOHvaz18603l38el0zpBrSl7NIX//wUFBQ2+2nlLUVezeKWmptK5c+f9VwQaZN698AxdU4FV1tqHaqj2LnCpCTkWKAiPXZkNnGKMaWWMaQWcEi4TERGRematZVa2k+EzsqMmJ1f0SeLjCW0aZXJSVZLbwSU9k5g7vjWvnZxBr7Tq92k/3FTGce9k89nW6q9VpCa5ubmMHDmSkSNH0qtXL/r27VvxfOfOnWRlZfHss89G7HPEEUcwYsQIRowYwemnn87GjXunn8vOzubKK69k4MCBnHDCCYwdO5b33nsPgM8//5wuXbpUHH/kyJG89dZbNZ7f6z30NYn25brrruPwww9n+PDh+69cSw3VgnIc8EtguTHm23DZnUAXAGvtk8BMQlMM/0RomuHLwttyjTF/BRaH97tvz4B5ERERqT8bi/zc/GU+c7fEAZHrlKR7DP8e2YoJhyXEJrhDYIzhtM4JnNwxnud/LOaeJYURs5BllwaZOGcXDx6bzmV9kmIYqTQVGRkZzJ8/H4ApU6aQnJzMDTfcAMDUqVMZOnQo06dP5/LLL4/Y77333iMzM5PJkyfz4IMP8uijj2Kt5eKLL+bCCy+sWJV948aNzJo1q2K/4cOH8/rrr0cca+LEiVHPX98uuugirrrqKq699to6O2ZDzeI1n9BU6PuqY4Hratj2LPBstG0iIiJStwJBy1Orirn/60KKo8zpO7yth6dGtaJzctOeptflMFzZN5mTOsZz9bxcFu/c2+0rYOGmBfn8WODj/qFpuLR2ihyk6dOnc//993PllVeyZcsWOnasPtfTsGHD+M9//gPAvHnzcLvdEclMly5duOaaaxos5gNx3HHHsWHDhjo9ZtP+ZBEREZE69X2ujxu/yKs2RgMgzgm3DUrlxgHJzeoLe7dUF7NOb80Dy3bzz293Uzkle3JlMT/vDvDC6AziXc3nNTd3yb8aXafHK3rh04Pab/PmzezYsYMhQ4Zwzjnn8NZbb0Vt2Zg7dy5nnHEGAKtWrWLgwIH7PO6CBQsYOXJkxfOXXnqJbt261Sqmyy67LOqkUtdddx0XXngh06ZN49FHH60ot9ZijKF79+68+OKLtTrHoVKCIiIiIviDlkeWFzHlm8KoCyEe187DIyPSOTyt8Y81ORguh+HOo1I5MsPN1fPyKKn0JszeVMYF/8vhlRMzSHI3yPBdaSZmzJjBOeecA4S6YF1//fURCcqECRPIy8sjKSmJu+66K+oxbrnlFhYsWIDH4+GTTz4Bonfxqq3nnntun9snTZrEpEmTKp7X1SD5A6EERUREpIX7udDPbz7PY2F29cG0qR7D9V3KuGVkBxwtYP7d8YclMOt0JxfNzWVLyd41VD7dWs55H+UwbWwmKUpSpJamT59OdnY206ZNA2D79u2sXbuWHj16AKExKGlpaVx11VVMmTKFyZMn07dvX959992KYzz44IPk5OQwevToOolJLSgiIiLSqL25roTffZEfMUh8jwmHxfPAsekUbVnXIpKTPQZmepg7oTUTZ+9iVb6/onzBDi8TZ+9ixqlZJCtJkf346aefKC4uZtWqVRVlkydPZvr06dx2220VZS6XiylTpjBixAhuvfVWRo0axX333cfUqVO54oorACgpKamzuNSCIiIiIo1SIGi5/+tCHl5eVG1bZpyDh0akc1bX0AxdLXEJ5PaJTt4fl8XZs3NYnrt3PM7inT4u/TiX107OxONsOUlbU3OwY0bq0vTp0xk/fnxE2Zlnnsnll18ekaAAtGvXjnPPPZenn36aP/7xj7zyyivceeedPPLII2RlZZGYmMi9995bUb/qGJRbb72Vs846q35fUA2uuOIK5s+fT05ODv369eP222/n0ksvPaRjNuhK8vWl8kryUn+awkJNUn90/Vs2Xf/mpcAb5OrPcpm9ufpaH6d2iuPR41rRNnHvQoUt+frnlwc5d86uapMGTOqewJOjWrWIlqWmcP0LCgpIS0uLdRjNUl21oOzrGlVdSV7tkyIiIi3I1uIA4z7YWS058TjgoeHpvHZyZkRy0tKlxzmYcWoWgzIjJweYtq6UPy0ujFFUIs2bEhQREZEWYnW+j1M+2MnKSuMqANomOHh/XBaX90nCtIAWgQOV6nHwxthMuqdEJm6Pf1/E1B+qd5ETkUOjBEVERKQFWLLTy2kzd7G5OBBRflSWm08mtGFYm7gYRdY0tE5w8tapWbRJiPzqdPvCAhZlV+8qJyIHTwmKiIhIMzd3cxlnfriL3PJgRPm4zvHMHNeaDknq0lUbXVNcvDE2k6RKCzb6gvCrT3LZURLYx54iciCUoIiIiDRjr68t4YK5ORELDwJc2iuRl07MIEGrox+QgZkenji+VUTZtpIgl32aiy+oOXtixeFw4PVWX8dHGgev14vDUfu0Q9MMi4iINEPWWh77vijqQO5bBqZw11EpGm9ykM7qmsCNA5J5dMXe8Sdf7vBy/9JC7h2qmaRiITk5maKiIkpLS2MdSrNTWFhIamrqIR3D4XCQnJxc6/pKUERERJqZEn+Qm7/M57W1kV/WDPCPY9K4ul/tvyhIdH8eksq3OT7mbds7/uTRFUWM7RzPyHYaz9PQjDGkpKTEOoxmKTs7m86dOzfoOdXFS0REpBlZv9vPKR/sqpaceBzw7OhWSk7qiMtheHZ0KzpWmpLZAr+Zl0eBN1jzjiKyX0pQREREmoFA0DL1hyJOeDebFbmRiwqmuA1vjM3inG6JMYquecqKd1Ybj7K5OMCtX+XHKCKR5kEJioiISBO3ONvLie/v5A8LCijwRg7U7p3m4n/jW3NCB3U7qg8ndIjjuv6RrVLT1pYy4+eSGEUk0vQpQREREWmCrLXM3VzGObN3MfaDnSzL8VWrc1bXeOZOaE2vdHeUI0hd+dPgVPq1ihzW+4cFBewq09TDIgdDCYqIiEgTklce5MmVRRw7I5vzPsrhk63VFwlMchmmDEvj+dEZpLj1p76+xbsMT43KwFPprc4tD3LnooLYBSXShGkWLxERkSZgyU4vT60s4p0NpZTv48b8xG4J/HVoGh21+GKDGpDh5rZBqfz1673TOk9bW8qk7mWc3Ck+hpGJND1KUERERBqpoLV8uKmMf68oYsGOfS9Cd2wbD3cclaqxJjF04xHJvPlzCSvz/BVlNy3IZ8HZbUhWS5ZIrSlBERERaYS+2F7O7QsLWJ5bfWzJHk4DZ3dN4Lf9kxnS2tOA0Uk0bofhseNacfIHO9mzqPymogD3f13I349Jj21wIk2IEhQREZFGZFORnz8vLmTG+ppXxD4s2ckveyVxcc9E2ieqK1djMri1h2v7JfP493tXmX9qVTEX9EhkUJaSSJHaUIIiIiLSCFhrefbHYv60uJASv41aZ0yHOK4fkMyYDnE4jGngCKW27jwqhfc3lLKhKDRYKGjhlq/ymXNGa103kVpQh0gREZEY21IcYOKcHP6woCBqcnJO1wQ+O7M1M07N4qSO8fqS28gluR3889jILl1Ldvp4eY3WRhGpDSUoIiIiMWKt5bWfShj+9o6o0wUPzHTz4elZPDcmg4GZ6h7UlJzSOZ4zukTO3nXPkkJytTaKyH4pQREREYmBXWUBLv0kl998nkdhldXfk12Gh4en8/H41hzbVrNyNVVTjkkjwbm3tSu3PMi9Swv3sYeIgBIUERGRBvf+hlKOnZHNexvKqm07rp2HL85uw2V9knA61JWrKeuS7OLWQSkRZS+sLuHrnfueMlqkpVOCIiIi0kDyy4P8Zl4ul3ycy66yYMS2eCdMHpbGe6dlcViK5rBpLq7vn0zPtMjrefvCAqyNPhGCiChBERERaRCfbi3juLezeW1t9emDB2e5mXdmG37bP1kD4JsZj9PwwDFpEWWLdnqZvq7maaRFWroGSVCMMc8aY7KNMStq2H6rMebb8GOFMSZgjMkIb1tvjFke3rakIeIVERGpK8W+ILcuyOfs2TlsKYkcIO0yoSlp55zRml7p7hhFKPVtTMd4xnWuPmC+2BesYQ+Rlq2hWlCeB06raaO19p/W2kHW2kHAHcBn1trcSlXGhLcfXc9xioiI1JmFO8o5/p1snv6huNq2vuku5o5vzR8HpeLSWJNm7/6habgrfevaUhLgkRVFNe8g0oI1SIJirZ0H5O63YsiFwKv1GI6IiEi9Kg9Y7l1SwLhZu1i3O7LVxAA3DkjmkwlttLJ4C9IjzcW1/ZIjyh5dvptNRf4YRSTSeDWqMSjGmERCLS1vViq2wBxjzFJjzNWxiUxERKR2luf6GPNeNg8vLyJYZRx01xQnM0/P4r6hacS71GrS0twyMIXW8Xu/epUF4G9fa9phkapMQ80iYYzpCrxvrR2wjzq/AC6x1k6oVNbRWrvFGNMG+Ai4IdwiU6GgoKDiRaxZs6auQxcREamVd3c4+ftPHny2evJxbjsfN3bzkeiMQWDSaLyz3cn9P+1d28ZgeeWoMnomaVYvaVl69uxZ8XNaWlrEh2Zjm8fwAqp077LWbgn/m22MmQEMA+ZF2ReIfLFSt9asWaP3twXT9W/ZdP33zRe03LWogKfWVB9r0iHRwb9HtuKkjvFR9mwadP3rzk09LG/uymZVfqhrl8Xw/M50pg3KinFkNdP1b9licf0bTRcvY0wacALwTqWyJGNMyp6fgVOAqDOBiYiIxEJ+eZCJs3fx1KrqycmkHgl8eXbbJp2cSN1yOgx/HpIaUTZnczmfbyuPUUQijU9DTTP8KrAA6G2M2WyMucIY8xtjzG8qVTsHmGOtrfwJ3xaYb4xZBiwCPrDWftgQMYuIiOxPXnmQs2bv4vPtkSuDJzgNT49qxVOjMkiPazT3AqWROK1zPMPbRk6QcM8SLd4oskeDdPGy1l5YizrPE5qOuHLZOmBg/UQlIiJy8HLLApw1O4flub6I8k5JTl45KYOBmZqhS6IzxnDPkAjBjy0AACAASURBVFROnbmromzpLh/vbijjrK4JMYxMpHHQbR0REZEDlFMW4MwoyckxbTx8MqG1khPZr2PaxnFGl8iuf//4ppCgWlFElKCIiIgciGJfkPM/ymFFleRkZDsPb52SSesETdMltfPnIalUnrpoZb6f9zeUxSwekcZCCYqIiEgtBYKWKz/L4+tdkcnJqPZxTBubSZJbf1al9nqnuzm3e2SXrn8u262xKNLi6ZNURESkFqy13L6ogFmbIu9wj2ofx2snZ5Do0p9UOXB/ODIlohVlea6PDzepFUVaNn2aioiI1MITK4t5uspUwv1buXj5RCUncvD6tnJzZtfIsSgPqBVFWjh9ooqIiOzHh5tKuXtRQURZh0QH08ZmkerRn1I5NLcMjFwX5ZtdPuZu0boo0nLpU1VERGQfVub5uPLTPCrfz05xG6aNzaJjkgbEy6E7IsNdbUavB5ftjlE0IrGnBEVERKQGOWUBLpybQ5F/b3riNPDCmAwGZLhjGJk0N7cOTIl4vjDby9Kd3hpqizRvSlBERESiKA9YLvk4lw1FgYjyycPSOLFjfA17iRycQVkexnSIiyh74vuiGEUjEltKUERERKoIWstvP89jwY7IO9i/7pXI1X2TYhSVNHe/7Z8c8fzt9aVsLvLHKBqR2FGCIiIiUsW9Swp58+fSiLKR7Tz8c3g6xpga9hI5NCd1jKNXmqviecBSbeY4kZZACYqIiEglT68q4pEVkV1rDk918eKYDNwOJSdSfxzGcG2/yFaU51cXU+QLxigikdhQgiIiIhL26k8l3LYwcjrh1vEOpp+SSUa8ZuyS+veLwxNoFbc3ES7wWl79qSSGEYk0PNf+q4iItCDlpZjCfExhHqakCMrLMOVl4POCwwEOJzhd2IQEbEo6NrUVNjUd4hJiHbkcohdXF/O7L/IjphNOdBmmjc2ka4r+XErDSHQ5uLx3Ev/6bm8r3pMri7iiTxIOdS+UFkKfuCLSspSX4ti5DZO9Dceu8L87t2J2bsOxa3soGTkIwfRMbLtOBNt2JtipG4HufQh2ORw8cfvfWWLu6VVF3PpVZMuJ08BzozM4KssTo6ikpbqybzKPrihiT8+utYUBPt9WzgkdNHuctAxKUESkeQkGMLk7Q0nIzm04ssPJx57nhXn1clpHfg7k5+D8YVlFmXU6CXbqQaDvIAL9hxDofaRaWhoZf9By39JCHq0y5sTtgKknZHBqZ30hlIbXPtHJmYclREzU8NyPJUpQpMVQgiIiTU95GY7tmzA7tuDYuXVvi8jOrZicbEygcUzLaQIBnBtW49ywGj6chnW6CB7eH3//IQT6DyHYrTc49TEcKztLA1z+aS6fb4+cStjjgBdPzOC0zkomJXZ+3TspIkF5f0Mp2aUB2iRoLJQ0f/rLKCKNV1kJji3rcWzZgGPrehxbN+DYugGzazvG2v3vfxCsy41NTcemtMImp0BcPDYuAdweCAYhGICAH1NSHBqrsjsfU5CDCQT2e2wT8OP8cRnOH5fBW89iE5II9BlEoN9g/P2HYDscBupj3iA+2VLG9fPz2VISed3infDKSZmcpIUYJcZGtvPQM83FmoLQDRe/hVfWlHDTkSn72VOk6VOCIiKNhsneinPVNzjXrsSxdhWOLesxtm6n17ROJzajLcE27bGtOxBs3S70b5v2BFu3h6TUA08S/H7Mru04tm/EsXUjjp9/xLluFY5d2/e5myktxvXNF7i++YI4QuNYAv2GEOg/mEDfo7AZbZSw1LHs0gB3LSrgjXWl1bZ1SHTwwphMhrbRmBOJPWMMv+qVyN2LCyvKXlhdzO+OSNZgeWn2lKCISOx4y3GuWILru4U4VyzBsXNrnRw2mJKObdOeYFZ7bJsOBFu3x7YOJSA2o3Xdd6tyubDtOhFo14nAoBF7ywvzcf74La7vl+Jc+TWOHVv2eRhHfg6OL+fg/nJO6HWkZxLs3jc04L5Td4Ltu2Bbt1O3sINQ5rdM/bGYB74tpMBbvfVtZDsPz47OUPcZaVQuOjyRv35dSHm4oW/97gCfbi3nRLXwSTOnv3Ii0rD8PpzLvsK1+DNc33yJKTvw+f2tMaGEo30Xgm06YLPa720RyWoHCYn1EPhBSE0nMHQ0gaGjATC7tuNc+TXOPQnLfgbsO/JzcHw9H9fX8yvKrMuNzWyLTc8kmJ6BTc2A+ARsXDzExQMm1A1tT3e0YBACgVBLlN8Hfj/4fRi/HwLh54EAOEPTJ+N0hrq0JSVjk1KwSakEM9viLiqFQLcmlxz5gpb/rinhgW93V+vOBWCAGwYk8+chqbi0CKM0MhnxTs46LIFp6yoPli9WgiLNXtP6SyMiTZbZsRn3px/g+nwWjt35td4v2C40bW+ww2EEO3Ql2KELwfZdmuT0vTarHf5Rp+MfdTpYi2Pzz+FkZSnOH76t1RTHxu/D7NgMOzbTkPf6BwDW4cC26Uiwc3cCnboT7NKDYI9+2LSMBoykdop9QV5cXcLj3xexuTj6+KABGW4eGZHOkNbq0iWN1697J0UkKDM3lrGjJEDbRLX2SfOlBEVE6o+1OFcuxf3Ba7i+X7L/6i43gZ4DCPQZRLBHPwLd+0BSMx0QagzBzt0Jdu6O77Tzwe/DsXZVuDvYUhzrV2N83v0fpwGZYBCzfROO7ZtwLf6sojzYpgOBnkeEBvwPGBIaOxMjOWUB/rOqmKdXFZFXHn0ihWSX4bajUri2X7JaTaTRG97WQ590Fz/khwbLByxMW1vCDUc0089GEZSgiEh9CAZwLZ6He+arONev3nfVtAz8R48iMHA4gT4teJ0Ql5tg7yPx9j4SJl4Gfj+OzetwrFuFc8NPOLZvxGzdWG/ruBwKR/ZWHNlbcX8xG4BAh64EBhyNf8jxBHsNAEf93+ndsNvPY98X8fLqEkoD0ROTOCdc2SeZm45MJited5+laTDGcEnPyMHy//2phOsHJGM0WF6aKSUoIlJ3rMX59Xw8b07FuWV9zdUSk/AfezK+Y05ssC+wTY7LRbBrL4JdexGxqkvxbhx5OzH5uZj8HMzufCgvC3UPKy8NzfrlcITeU4cz9LPTiTUOcLnB5QKXG+tygTP83OkMjUMJhMajmLISTPHu0LkKcjE5Owju2Iq7ZHetQnduXY9z63o8c6YTTGtFYPDx+IeeQKDPwDofw7I638dD3+3mjXWl1JCXEOeES3omcfORKXRM0u+aND2/6JHIPUsK8Yd/x1fl+/k2x8dRWeqeKM2TEhQRqRPOVd/gmfYUznWraqwTOLw/vjET8A8dHR7QLQcsKYVgUgp06t6gp12zZg09O3cMrUuzaR2OTWtxrl2FY+Oafa4B4yjIw/HJu7g/eRebnIp/8MhQstJ/yCElK8tyvDz03W7eXV9GTSvipHkMV/VJ5up+SZqdS5q01glOxnaKZ9amvePUXllTogRFmi0lKCJySMyOzcS99n+4vv4i6nZrDIHBI/GecSHBHv0aODqpU/GJBHv0i7yO5WU4fv4B56pvca1YgmPdSkww+to1pqgQ97yZuOfNJJiehX/kqfiOH4dt16nWISzcUc6/vtvNnM3lNdbpkOjgt/2T+VXvJFLcjlofW6Qxu6hnYkSCMn1dCfcPTSPepW5e0vwoQRGRg1NajOedF3HPeRMT8FfbbI3BP/xkvBMuCa2QLs1TXDzBPoMI9hmE75xfQ/FunCuX4lo6f5/TSDvyd+F5/xU8779CoNeR+EaNwz/0BIiPPkX0z4V+7lpcwMyNNc901jPNxe+OSGZS90Q8Tn1pk+bl1E7xZMY5yCkP3QDI91o+3FTG2d1a6Lg9adaUoIjIgbEW16JP8fz3cRz5u6JW8Q85Hu/Eywl26tbAwUnMJaVUrP1S7vPi/H4JrsXzcH3zRWhcSxTO1d/hXP0d9uVH8R9zIr6TJxLs0gOA3b4g/1q2mye+L8IbvWGGARlubjkyhQmHxePUrFzSTHmchvN7JPDkyuKKsv/+VKwERZqlBklQjDHPAuOBbGvtgCjbRwPvAD+Hi96y1t4X3nYa8AjgBJ6x1v69IWIWkerM9k3EvfhIjVMGB3r0o/yi6wge3r+BI5NGye0hMGgEgUEjKPf7cf7wLa6FH+Na9AmmrLRadVNWivuzD3B/9gGBPgNZMfRMzsvty8/F0UeZDGvt4Q8DUzilU5xmM5IW4aLDEyMSlLlbytlWEqC91kSRZqahWlCeBx4DXtxHnc+tteMrFxhjnMDjwFhgM7DYGPOutXZlfQUqIlF4y2n36TskfjUb4/dV2xzMaI130jX4jz0pNIuUSFUuF4EBRxMYcDTlF1+Pa/FnuOfNwrn6u6jVnT8sY+APy5gV35YHu4znpbYjKXeGBgQPbe3mz0PSGNnOo8REWpQjMz0MyHCzIjf0ORy08MbaEm7UmijSzDRIgmKtnWeM6XoQuw4DfrLWrgMwxrwGnAUoQRFpIM5lC4l76RGSd26tts06XfhOvwDvhItb7volcuDiE/EfPw7/8eMw2zfh/vxDXPM/xJGfU63q4WU7eHL1VP6y/k2mdj+Dzuecy8TerZSYSIt14eGJ3LWooOL5tHWlSlCk2WlM05sMN8YsM8bMMsbs6R/SEdhUqc7mcJmI1DOzcxvxj9xNwkO34YiSnPj7HkXJ/VPxnnelkhM5aLZdZ7znX0XJQ6+z4ld/5suM6DO9tffmc/cPr/DLJ67AM3cG+LwNHKlI43BetwQqD7VaketjZV71lm2RpsxYW9MM8nV8olALyvs1jEFJBYLW2iJjzOnAI9bansaY84DTrLVXhuv9EjjGWnt95f0LCgoqXsSaNWvq8VWINH/G56Xtgtm0/XIWjijduXxJqWwZO4m8/sPUnUvqzMxsJ39b48FrDUcUbeTGzR9y8Y75eGz0NVa8qRlsO+Esco88FkxjutcmUv9uWBHHV/l7x538qpOP67sqSZGmpWfPnhU/p6WlRXyhaBQJSpS664GjgZ7APdbaU8PldwBYa6dUrl85QZH6s2bNmohfJmlmrMX5zZfE/fcxHDu3Vd9sDL6Tz8E78XJITI5BgBJL9fn///HviyK6rOxxUUYxjxfOIfnz9zHl0acXDnTtRfmF1xHsM7BeYpMQff43Lq/9VMJvPs+reN4pycl357fFUU83jXT9W7aGuP5VE5RGMc2wMaYdsMNaa40xwwh1PcsB8oGexphuwBbgAuCi2EUq0jyZ7ZuJe+XfuL5bGHV7oEc/1oyeSKdRJzdwZNLc/WvZbv76dWG18puOSObuwR0wjl4Un/NLPDNfx/3RWxhvZKLiXL+axCm/wz/0BMovug6b0aahQheJmfGHxZO4wFDiD92f3Vwc4MsdXka2i4txZCJ1o6GmGX4VGA1kGWM2A38B3ADW2ieB84BrjTF+oBS4wIaadvzGmOuB2YSmGX7WWvt9Q8Qs0hKYwjzc776E++N3oy62GExthXfS1fiPO5XStWtjEKE0V9ZaJn+zm38ui1wbxeOAf49sxS96VFqwMTkN76Sr8Z16Hu73Xwn9vlbpfuha/BnO5YvwnnMZvrETwdko7r+J1Itkt4MzusTzxrq903VPW1uiBEWajYaaxevC/Wx/jNA0xNG2zQRm1kdcIi1WaTHu2dPxzHot6noU1uEIdec6+9eQpNlhpG5Za/nzkkL+vaIoojzBaXj15AxGd4iPvl9aBt6Lb8B3ynl4Xv8P7sWfRmw3ZaXEvfoErvmzKb/iVoLd+tTXSxCJufO7J0YkKG+vL+WBY9KJd2lsoDR9usUk0pIU78Y95008H71Z46regd4DKb/kxoqVvEXqUtBabltYwNOriiPKk12GaWMzGVGLO8C2dXvKr78H3+rlxP33MZw//xix3blpLQn3/Rbf+IvxnnUpuNx1+hpEGoMxHePIinewqywIQKHXMmdzGWd21ayK0vQpQRFpAUxONu65M3B//A6mrCRqnWBWO7znXYX/2BM1O5fUi0DQctOCfF5cHfk7mOYxvHlKFke39hzQ8YK9jqD0z0/g+uR94qY/hSnZm/SYYBDPuy/h/OZLyq+6neBhGuArzYvbYZjYLYGnKiX709aWKEGRZkEJikhzZS2ONStwz3kT19J5mGAwerXkVLxnXYpvzJngPrAviCK15Q9afjs/j2lrI7sUZsQ5mHFqJgMzD/J3z+HEf9JZBI4+Hs9r/4f7y48iNjs3rSXh3t/gPfNSfOMvBpf+7Enz8YseiREJypzNZeSVB2kVp6m3pWnTJ7VIM2Pyc3B9MQf3/A9xbN1QYz2bmIz3lPPwnXqepg2WeuULWq76LI+310cmJ20SHLx9ahb9Wh16FyyblkH5NXfhP+ZE4p57MGJVehMIEDfjOVxff0H51bcT7NT9kM8n0hgMznLTPcXJut2h9YK8QXhnfSm/7p0U48hEDo0SFJHmwOfF+e2XuD//EOd3izA2emsJgE1Jw3vaJHwnnQ0J+iMm9as8YPn1J7nM2hQ5PXCHRAfvnJZFz7S6HR8SGDScksnPE/fyo9VbUzasJuEv1+A99wp8p50PDmcNRxFpGowxTOqRyN+/3TumcNraEiUo0uQpQRFpwhwbf8I1bybuL+diiquvJVFZoENXfKeci3/EWIiLPkuSSF0q8Aa59ONcPttWHlHeOdnJe6dl0TWlnv4EJaWEWlOOHkXc8w/hKNy7oJ3x+4h7/UlcyxZQdtUd2Kx29RODSAOpmqB8ucPLxiI/XZL1FU+aLv32ijQ1xbtxL5iLa94snBtW77OqdToJDByO7+SzCfQbosHv0mA2F/mZ9FEOK/Mj19fpnuLkndOy6NwAX54CQ46npNcRxL34CO5Fn0Rsc/6wjMS7r6D80t/jH36y/m9Ik9U91cXRrd0s2bl3baDp60q5+UhNES9NlxIUkaYgGMS56htc82aGBrz7fPusHujUHf/x4/CPOBmb2qqBghQJWZbj5Rcf5bC9NLKrYa80F++clkX7xAbsWpWSTvl1fyFw9CjiXngoYnptU1pM/H/+hu/bLyn/1c1a80earEndE1mys6Di+es/lXDTEckYJd7SRClBEWnMvOW4vpiNZ+ZrOLK37rOqTUrFN/wk/MePC02pqj9M0sCstTz/Ywl3LMqnLBC5bWhrN6+enElWfGzGffiPGUOg5wDinvkHru+XRGxzL/wE5+rllF91B4H+Q2ISn8ihmNg9gTsWFRCwoec/Fvj5Ltd38LPjicSYEhSRxqisBPf/3sY9+w0cBXk1VrPGEBhwNP7jT8c/+DhNEywxk18e5Pdf5lebqQtgwmHxPDUqg4QYr3BtM1pTdssDuOfOwDPtPxift2KbI28XCQ/8Ae+p5+M970rw7H/BSJHGIiveyckd45i9ee94r2lrS5WgSJOlBEWkMQkGcc2fjWf60zgKcmuu1ro9vuPH4R95GjazTQMGKFLdBxtKuW1hAZuLA9W2/bZ/En89Og2no5G06Dkcocki+g8h/sn7cW78KWKzZ/YbOL9fQvk1dxHscniMghQ5cOf3SIxIUN76uYT7jk5tPP/3RA6AEhSRRsLx43fEvfJvnBvWRN1unS78w0bjH3U6gT6DwKGFuCS2Nuz2c9vCAj6sMoUwQJLL8K/h6VxweGIMIts/27ErpX/5PzxvPYd75qsYayu2OTf/TMK91+I959f4xv0CnPpTKY3f6V3iSXIZiv2h3+VtJUG+2OFlVHu1BkrTo09dkVjzluN542k8c6ZH3WzjE/CNORPfKedhM1o3cHAi1eWXB3lk+W7+b2VRtbEmAEdkuHludCsOr+M1Tuqcy4130tX4Bx5L/FN/w7FrR8Um4/cR98bTuBZ9RvmVf1RrijR6iS4HZxwWz7S1e7tZTl9XogRFmiTdghWJIceGNST85ZqoyYl1e/BOuITih6bhveBaJScSc+UBy+PfF3HUm9t5eHn15MRp4IYBycwd37rxJyeVBHsfScn9z+IbeWq1bc4Nq0m45xo8058Bb3mUvUUaj/O6RbZYvrO+lPKAraG2SOOlFhSRGHF/9BaeV5/ABPzVtvmOORHvpKu1iJw0CkFrmZXt5Jlvd7CxKEqTCXBMGw//Gp7OgIymk5hESEii/Ko78A8aTvzzD2GK9i58agIBPO+9jOur/1F+yY0EBg2PYaAiNRvTMY6MOAe55aEpvgu8lrmbyzjjsIQYRyZyYJSgiDS0gB/PK4/h+d/b1TYFs9pRdtXtBPsMikFgItXN317OnQsL+C43DqienLRLcHDX4FQu7pmIoxlMbR0YOpqS3gPxvPwo7oWRizs6dm4j4eE78A8+jvILfott2zFGUYpE53YYzumWwNQfiivK3vy5VAmKNDlKUEQaUvFu4p+4D9eKxdU2+Y4fR/nF10NCUgwCE4m0rSTAnxYXMH1d9WmDAVLcht8dkcK1/ZJIcjev3sI2tRXlv/0L/mNPIu6Fh3Hk50Rsd339Bc5lC/GddDbes34JyWkxilSkuvO6RyYoszaWsdsXJKWZ/T+V5k0JikgDMfk5xP/jDzi3ro8ot/EJlF15O4GhJ8QmMJFKgtbyn5XF/O3rQor81fuuux1wWe8k/jgoJWaLLjaUwOCRlPQZFJrpa+4MjA1WbDMBP54503HP/xDv+IvwnXQ2xDfOGcukZTmmjYdOSc6Kab9LA5aZG8v4RQ/9fkrToXRapAGY/BwS/v77aslJMKstpXc/ruREGoV1hX7OmLWLOxYVRE1OJnZLYNE5bXng2PRmn5xUSEzGe8kNlN77HwKH96+22ZQUETftKZJuuRD3+69AaUkMghTZy2EM53WP7NI1fa1+L6VpUYIiUs9M3i4Spvwex7ZNEeWBHv0o/fP/EezcPUaRiYRYa3lqZRHHvZ3Ngh3eatsHZLh55sgynh2dQbfUltnwHjysJ6V3/Zuya+4imNm22nazu4C4N54m6Q8X4Jn+DKZKtzCRhnRe98jWko+3lrMr2pzgIo2UEhSRemTydpHw95twbI9MTvyDhlN6+8PYtIwYRSYSklsW4KL/5fLHhQWUVpmONNVj+OexaXw6oTUDU4M1HKEFcTjwjxhLyd9fpHzSNdjE6uPFTHEhnvdeJvHmXxD31BQca1eB1TSv0rD6t3LRN33vzYSAhbd/jj6eTKQxUoIiUl9Ki4n/121RkpMRlF1/L3i0eJbE1oId5Rz/zk5mRVkJfmzHOL46uy1X9U3G5Wj6s3PVKU8cvjMupPifr+I961JslIktTMCP+4vZJN53LQl3X4579huwOz8GwUpLZIyp1opS04QXIo2REhSR+uD3E//4PTg3rY0sHjSCshvuBbcnRoGJQCBoeXDZbsbP2sWWkshuH6luw2Mj05k2NpMOSS1knMnBSk7FO/Fyiv/1Gt6zf4VNSolazbn5Z+L++zhJvz+fuMfuwbl8EQTV3Ubq17lVxqF8le1lw+7q626JNEYtszOxSH2ylrgX/x+u5ZFTCfsHHhtKTlxNdCE7aRZ2lAS4el4en22rvir6kCw3U0dn0DVFfxoOSFIK3nMuw3v6Bbjmz8Yz+w0cO7ZUq2b8PtyLP8W9+FOCmW3xjxiLb8RYbIfDYhC0NHddU1wMa+1h0c6948re+rmUm46MnkiLNCb6KyRSx9wzX8X92fsRZYFuvSm77i9KTiSmPt5SxjXz8thZVn08yQ0DkvnT4FQ8TnXnOmhxCfhPOhv/mAk4ly3E/en7OL/7ChOs/n47cnbgee9lPO+9TKBrL/zHnYL/mBM1Lk3q1HndEyISlDfWlShBkSZBCYpIHXJ+txDPG09HlAWz2lL2+8kQp5V8JTZ8Qcvkrwt5eHlRtW2ZcQ6eHNWKsZ3iYxBZM+VwEjhqBIGjRmDyc3DN/xD3vFk4dmyOWt25fjXO9avxvPoEgf5Hh5KVwcfpM0MO2dndErhjUQF75r9YmednZZ6Pfq10s0waNyUoInXE7NpO/JN/w1SasccmJlF68z+w6ZkxjExass1Ffq74LI+F2dWnDx7ZzsPTJ2TQPlFjTeqLTc/EN/5ifGdchOPH73DPm4lr8acYb/UudiYYxLV8Ea7li7DxCfiHjMI/YiyBfkeBQ9dIDlybBCcntI/j4617f9/eXFdCvyFpMYxKZP+UoIjUBZ+X+MfuwRQXVhRZ46Ds+vuwHbvGLi5p0T7dWsYVn+aRUx7Zxchh4I8DU7h1YApOzdDVMIwh2Gcg5X0GUv7LG3EtmYfry49wrvom4qZGRfWyUtxfzMb9xWyC6Zn4jz0J/4ixBLscDkbXTGrvvO4JEQnKG+tKuXtwKka/R9KIKUERqQOeV5/A+fMPEWXe864g0H9IjCKSlixoLf9veRH3f11IsMp33/aJDp4+IYOR7TTNdcwkJOE/fhz+48dhcrNxffVxKFmpMuvfHo78HDwfTsPz4TQCHbviHzEW//Cx2Mw2DRy4NEXjD0vgpgX5lIcnjttYFGDxTi/D2ugzQBqvBklQjDHPAuOBbGvtgCjbLwZuAwywG7jWWrssvG19uCwA+K21RzdEzCK15Vr4MZ7/vR1R5h80At/pF8YoImnJCrxBrv08j5kbo69t8n+jWpEVr+5CjYXNaIPv9AvwnX4Bjo1rcS34CNeXc3Hk74pa37llPc43nsYz/RkCvQfiH3kq/qEnQHxi1PoiqR4Hp3WO5531ez8T3lhXqgRFGrWGakF5HngMeLGG7T8DJ1hr84wx44CngGMqbR9jrY3+aS0SQyYnm7jnH4ooC7ZuT9nVd4BDywxJw1qZ5+OS/+WwbnfkGhsGuGtwKjcfmYxD3ToarWCXHni79MB7/lU4V32L68uPcC35DFNWfYE9Yy2uH77F9cO32JcewT/0BHzHjyPY60h99kg153VPjEhQZvxcypRhaVqEVRqtBklQrLXzjDFd97H9y0pPvwI61XdMIocsGCTumb9jSvbOjGTd7tAq8TUs2CZSX95cV8INX+RT4o/s09UqzjD1hAxO7KhZupoMh5NA/yEE+g+h/NLf4/rmi1AXsOWLAVUIJAAAIABJREFUok5ZbMrLcM+fjXv+bIKt2+MbeRr+407Btm4fg+ClMRrbMZ5Uj6HQG/p82FUW5LNt5ZykzwVppBrjGJQrgFmVnltgjjHGAv+x1j4Vm7BEIrk/ehPXyq8jyrznX02wa68YRSQtkbWWh5cXcd/SwmrbBmW6eWFMBodp4cWmKy4+NED+2JMwhXm4Fn6C64s51ca87eHYuY24Gc8RN+M5/P0G4x95Gv6jj9eUxS1cvMtw5mEJvLympKLsjbUlSlCk0TI2yuwh9XKiUAvK+9HGoFSqMwZ4Ahhprc0Jl3W01m4xxrQBPgJusNbOq7xfQUFBxYtYs2ZNPUQvEil+5xZ6P3M/joC/omx31z78dPFNYNS9QhqG38KDa928ub36mgZntfVzaw8vcfp1bJbicraTsexLMpYvwLM7f591A5548vsOIa//MHZ37a0pi1uoRfkOrluxNyH5/+zdd3hUZdoG8Ps9U9M7HemIdKRX6U2qvWBb197Wtp8FdcW66uraltVV17aCgoooXaRIU6QLqIHQEUjvU8/7/UFM8s4kkEBmzkzm/l3XXOY8Z07yyCQz88xbnmiTxJI+peCSNDJKu3btyr9OSEhQ5huGTIEihOgK4EsA46SUv1Vzn78BKJJSvlQ5XrlAocBJT09XfpkilseDqBm3wXSgohiW0TEoefq/9XpXHT7+oaXYrePGVblYfEhdDG/VgJf6J+La9jF1+vP4+Ico3QvTzk0wf78I5s1rINzuU989Pgme3hfA02849Lada7xehY9/+PPqEh0/O4bjpRXTBN8fmowprU4/usbHP7IF4/H3LVBCYtxfCHEOgC8AXFO5OBFCxADQpJSFZV+PBjDDoDSJAACWxZ8qxQkAOK+9t14XJxRaMku9uOLbbGzKUt+MJlgF/jcihVsIRxLNBG+XPvB26QNncSHMP3wHy/eLYcrYXfXdC3JhXT4P1uXzoCc3gKfvMHj6DoPe8lz2V6nnTJrARa2iMHNXcXlsbkZJjQoUomAL1jbDswAMBZAqhDgM4AkAFgCQUv4bwOMAUgD8q6xx0B/bCTcE8GVZzAzgEynl4mDkTFQVcewwrPPeV2LuPsPg6TfCmIQo4mQUeHDx0izs89mpq1mMCXNGpeC8JP/pXhQhYuLgGT4ZnuGTIY7sh2XNYpjXLoWWn1Pl3bWcE7Au+hTWRZ9CT2t8cmSl91DorVis1FeXto5WCpSlhx3Ic+pI5FxQCjHB2sXrlA0hpJR/BvDnKuIZALoFKi+iWtF12P/7ojKFQsbGw3nNPXwxp6D4KdOFy5dl+3WG75RkxtzRqWgczcnkdJJs2hKuy2+F65I/w7RrC8wblsO86XuI0uIq769l/g7rwtmwLpwNPbVRWbFyAfTW5/H5rR7pkWpB6zhT+VbkLh34+kAprqnjKaFEZyskpngRhQPz6oUw/bJNiTmvuhOITzQoI4okCw+W4saVuSj1qkvuhjax4cNhyYi38hNQqoLJDG+X3vB26Q3ndffCtONHmH/4DuYt6yBcziov0bKOVYyspDSEp/cFiG7UGmjblsVKmBNC4JI20Xhha2F5bG4GCxQKPSxQiGpA5GbB9ulMJebp3BueAaMMyogiyXu/FOOBDXnQfbYDubxNFF4fmASriW8aqQasNnh7Doa352A4HSUwb1kP848rYNrxQ7WL67Xs47Au/gznAtDnv3dyZKXP0JMjK2wIGZYuaRWlFCirf3fiWIkXjTgCSyGkxgWKEGIygAVSSs9p70xUz1hnz4QoqZgaIa12OK+/j58mUkBJKfHU5gK8vL3I79wDXePw6PlxEPwdpDNhj4an/wh4+o8ASktg3roe5o0rYdr+A4TbVeUlWs4JWJfMgXXJHOjJafD0Hwn34HGQjc8JcvJ0NtonWtA12YLtOSeLUgngi32luL1TrLGJEVVSmxGUGQDeEUJ8CuAjKeUPAcqJKKSYdm+BZcNyJea6+EZ2aaaAcnkl7lqbi0/3lipxTQD/6JeIGzpwSgbVkahKxYqjBOZtG2D+saxYqW4aWE4mrAtmwbpgFrxtO8N9wYXw9BsOWLmDXDi4tHVUeYECnNzNiwUKhZIaFyhSym5CiG4ApgH4XAhRDOAjAB9LKfcHKD8iY3k8sH70qhLyntMW7tEXGZQQRYJ8l45rv8vBqt/VN4dRJoH3hiZh3DncFpQCxB4NT9/h8PQdXlas/ADzxpXQtqyH5ql6ZMW052eY9vwMOXsm3EPGwT18MmSDJkFOnGrjotbRePynAvwxa3Rzlht78z1ok8CZ/xQaajWBVEq5TUr5IIDmAO4AcCmAvUKI1UKIq4VgC22qXyzffgnTkf1KzHntX9iJmQLmaLEX4xdm+hUnqXYN34xLZXFCwWOPhqfvMDjufBI77nsZpXf8De7eQyGt9irvLooLYF30KaL/ejXsrz8Obe+uICdMNdU0xoQBjaxKbO6+EoOyIfJX61JZCNEGJ0dRpgHQcbKHyUEAdwK4GAA/WqZ6QeRlw/rlf5WYe9AY6O06G5QR1Xe7c924ZGk2jpSoPU5ax5kwd3QqWsfz000yhm61wdtpKLx9hsLpLIV54ypYVi+C6ddtfvcVUsL802qYf1oNb/uucE24Ct6ufblmL8Rc2joaa49VjIrNzSjFX7txXRuFhtoskr8DwDUA2gH4FCe7vm+odP5zACfqPEMig1g/fQvCUfGJkoyOgeuyWwzMiOqzNcecuGp5Ngpc6lZdvdIsmD0yBal2jtpRiLBFwTNoLDyDxkIcPwzLym9gWbUQorjA766m37Yj6uXt8LbqANfU61mohJBJLex4cAPgLmurlJ7vwfYcN7qlWE99IVEQ1GZK1jgA/wDQREp5e+XiBACklCXg6AnVE9re3bCsW6rEXFP/BJmQbFBGVJ8tOFCKi5dm+RUnY5vbMX9sKosTClmyYTO4Lr8Vxf+cA8eN/wfvOW2rvJ9p3y+IevkhRM24HabdW4KcJVUl2W7CiKbqdL1P93KaF4WG2hQoK6WUc6SUysRoIcR9f3wtpVzqfxlRmJEStllvKiFvs9Zwj5hsUEJUn322twTXrsiBU53VhRs7xODj4cmINnNpH4UBqw2eIeNQOuM/KP3rS/B06lXl3UwZuxH1/L2wv/IwxNEDQU6SfF3WWl3TNmdvKdy+DZeIDFCbV77Hq4lPr4tEiEKF6afVMKX/rMRcV90OmDj/n+rWO7uLcMvqXPg0h8fjPePxUr8EmDVOhaEwIwS8nXrB8deXUPLk2/CcP6jKu5m3rkf0ozfA9v4/IPJzgpwk/WHcOVGIt1Y8z2Q6dHx3pOqtpYmC6bTvuIQQw/+4rxBiGIDKr5itART6X0UUptwu2D59Swl5uveHt5pPA4nO1MvbCzFjkzpnXwB4dWAirm3PHicU/vSW7eG452loB9JhnfcBzJvXKOeFrsOy4muY138L1/gr4R57GWCreocwCowos8DUllH44LeKqV2z9pRgTHM+DmSsmnwk/G7Zf20A3qsUlwCOAbirrpMiMorl2y+hZR4tP5aaBufltxqYEdU3Uko8uakA/9yhdoc3C+DtIUm4qHW0QZkRBYbeot3JQmXPTthmz/QboRaOUti+eA+WFfPhuvJ2ePoM40L6ILqybbRSoCw8WIo8p45EG6eXknFO+9snpWwlpWwF4H9/fF12ay2lHCClnB+EPIkCrzAP1vkfKiH3sEmQTVoYlBDVN15d4r71eX7Fid0EfDIihcUJ1Wt6204offR1lN41A3rDpn7ntdws2P81A/YXH4Q4dsiADCNT3wZWtI6r2IjDpQNf7Cs1MCOiWqxBkVJeG8hEiIxm/eYTiJLi8mMZHQPXlOuNS4jqFbcucev3ufjvr+ouOXEWgc9Hp2I0p1RQJBAC3l5DUPLs+3BOuxsyNt7vLuadPyH60T/B+sV7gIvrIQJNCIEr2qofjszaU1zNvYmC45QFihBid6WvDwkhDlZ1C3yaRIElsk/AsvxLJeaaeA0Qn2hQRlSflHokpn2XgzkZ6qeSyTYNX49NxcBGNoMyIzKI2QL3qItQ/ML/4Bp/JaTPJiTC44b1qw8R/cgNMG37waAkI8flbdQCZWOmG+n5boOyITr9GpSbKn09LZCJEBnJOu99CHfFk7GenAb3yKkGZkT1RaFbx5XfZmNNpY7NANA4WsOXY1LRIdFiUGZEISAmDq7Lb4F78FjYPnoV5l2bldNa5lFEvfx/8PQaAuc190AmphiUaP3WIs6MQY2syvPU7D0leKxngoFZUSQ75QiKlHJNpa9XVXcLfJpEgSOOHoD5+8VKzDXlesDKT7Xp7OQ4vJi8OMuvOGkVZ8Ki8WksTojKyCYt4PjrP+C49THoVTTENf+0GtGPXA/zumWAZJ+OQLjSb5pXCbzsiUIGqfEaFCHEfUKI7mVf9yub3rVPCNE/cOkRBZ7ti/cgpF5+rDduDs+gMQZmRPXB7yVeXLgoC5uz1GkSHRPNWDQ+DS3j2FeHSCEEPP1HoOT5D+EadTGkUN+iiOJC2N96BvZXp0PkZRuUZP01uWUUYswVu6cdLdGxnD1RyCC12UPuXgD7yr5+DsDLAJ4G8M+6ToooWLR9v8C8UR0EdF58I5sy0lnZX+jBuIWZ2J3nUeI9Uy1YMD4NjaJN1VxJRIiOhWvaXSh98i14W5/nd9q8ZS1HUwIg1qJhaiu1s/xH6VwsT8aoTYGSIKXMF0LEAegG4HUp5bsAzg1MakSBZ537rnLsbdke3l4XGJQN1Qe/5LkxbmEm9hd6lfjgRlbMG5uKJPYWIKoRvUU7lD72BpyX3wppUadDlo+mvMbRlLp0TTt1mteigw5klnqruTdR4NTmlfKQEGIAgCsArJZSeoUQ8QD4m0thSduzE+afNyox16U3s0EYnbEtWS6MX5iF30t0JT62uR1zRqUizsLihKhWNBPc469AyYx34G3T0e+0efPakzt9+XSppzPTp4EV7RMqZhB4JPDp3pJTXEEUGLV5tXwQwFwAjwJ4qiw2AcCPdZ0UUTBY572vHHvP7QZvp57GJENhb80xJyYtzkKOUy1OLmsdhY+GJ8NuZuFLdKZkkxYonf56NaMpBYh6dTqsH/6TfVPOkhAC03xGUT5OL+FMOgq62jRqXCilbCKlbCml3FQWngNgUmBSIwocbc9OmHf4jJ5MvZ6jJ3RGlhxy4JKlWSh0q6/iN3aIwb+HJMGi8feK6Kwpoyn+a1Osy+ch6slbIY7sD35u9cgVbaNR+fOUX/I8+LmQo78UXLX6jRNCJAgh+gghhgshhgMYXHYjCitVjp506G5MMhTWPs8owdXLs+Hwmex6b5dYvNQvARqLXqI6dXI05Q04L70J0qRuOGE6vA/RT94K8w/fGZRd+GsQZcKY5nYl9tVxbhxDwVXj3zghxPUA3gRQBKDyhEQJoHXdpkUUOBw9obry/q/FuHddHnxnPzzRMx73do0zJCeiiKCZ4J5wNbznnQ/7zKegZR4tPyWcDtj/NQOuPTvhuvxWwMx+Q7V1TftoLDjoKD9elmVCkVtHLNfRUZDU5jftGQCXSCkbSilbVbqxOKGwYp33gXLM0ROqLSklXtxagL/4FCcCwMv9E1mcEAWJ3uY8lDz1H7j7j/Q7Z136OaKevxciP8eAzMLbyKZ2NIqqeItY4hWYt7/UwIwo0tSmQDEDWBqoRIiCQcv4BeYd6r4OrinXcfSEasyrS9y/Ph/PbClU4iYBvD0kCX/qEGNQZkQRKioGzlseheP6+yF9RktM6T8j6snboB3cY1By4cmsCb/O8h//xt28KHhqU6D8HcB0IQTH9yhsWRd8ohx723eF97weBmVD4abUI3Hdihy896vavMxmAj4enoxL20RXcyURBZQQ8AybiNJHX4ee0lA5pWUfR9TTd3Ir4lqa1k79sGXDCRfS890GZUORprad5KcDKBRCHKx8q8nFQoj3hBAnhBA/V3NeCCFeE0LsEUJsF0KcX+ncdUKI9LLbdbXImaicOHoApk3fKzHXpGs4ekI1kuvUMXVJFr6pNC8bABKsAvPGpGLcOVHVXElEwaK37oCSGW/D0/F8JS6cDthfewyWxZ8ZlFn4aZNgxoCGViXGURQKltoUKNMAjAQwHsA1PreaeB/A2FOcHwegXdntZgAzAUAIkQzgCQB9AfQB8IQQIqkWeRMBAKwLZ0NU2szd26I9vJ17GZgRhYvDRR6MW5iJDSdcSrxptAmLx6ehf0ObQZkRkZ/YBDjufwGuEVOUsJAStln/gnXWvwBdr+Ziquya9uooyqy9JXDrbIpCgVebPiirqrvV8PrVAE61Um0ygA/lSRsAJAohGgMYA2CZlDJHSpkLYBlOXegQ+RHZJ2Bepy6hck24iqMndFo7c9wYsyALv+R5lPh5iWYsnZCG85K4QxBRyDGb4br2L3Bc+xdITX2rY138GWxvPQN4OF3pdCa1sCPOUvE6eaJUx9JDjlNcQVQ3alygCCFsQohnhBAZQoj8sthoIcSddZRLUwCHKh0fLotVFyeqMcvizyC8FY0q9IbN4O3FFj50assOOzB2YSaOlKhNTvo3tGLR+DQ0jTFVcyURhQLPiClw3P8CpF2dgmnZsBz2lx8CnNyZ6lRiLBoubqX+232UzmleFHi16bzzCk4WBlcDWFQW21kWf6OO8zpj6enpRqdQr4Xjv6+ppAidVsxXYod7DUf23gyDMgpf4fj4n6nPjprxjwwLdKijbMNSPHiqTQkyD+Yh06DcjBJJjz/5C9vH35aAqGkPoM2sV2EpLigPm3duAp66C3uvuBu6jWvIqjM0SsP7qGjcuOxQKdbvTEeq9RQXUb0TiL//du3aVXuuNgXKVABtpZTFQggdAKSUR4QQdTWacQRA80rHzcpiRwAM9YmvrO6bnOp/ls5Oenp6WP77Wr58HyZ3xdoBPTEVyVOnIdnCZ9faCNfHv7Y8usQjP+bj7Yxiv3M3dYjB830TYNIib2pgpDz+VLWwf/zbtYOrQ0eYXnwQ2vHD5eHYQ3vQee6bKH3gRSA23sAEQ1dbKdEm/TD2lpycdOOFwAZvI9zbjv2eIoURf/+1WSTvgk9BI4RIA5BdR7nMB3Bt2W5e/QDkSyl/B7AEwGghRFLZ4vjRZTGi03M5YVk+Twm5x14KsDihKhS6dVy1PBtv71aLE00Af++bgBf7J0ZkcUJUH8i0xiiZ/ga8LdorcdO+X082dCzINSiz0CaEwKSG6hq8j9OLISUXy1Pg1KZAmQPgAyFEKwAoW8D+BoDZNblYCDELwHoA5wohDgshbhRC3CqEuLXsLgsBZADYA+A/AG4HACllDoCnAGwsu80oixGdlnndMmiFeeXHMjoG7qETDcyIQtWhIg/GLMjE0sNOJR5rFpg1IgW3dIw1KDMiqjPxiSj9v3/A27aTEjYd2gv73+8HivINSiy0jW/ggaXSO8a9BV6sP+6q/gKis1SbAuURnCwgdgBIBJAO4HcAT9bkYinllVLKxlJKi5SymZTyXSnlv6WU/y47L6WUd0gp20gpu0gpf6p07XtSyrZlt//WImeKZFLCsmSuEnJfMAGIYjM9Uq075sTwrzOxK1f9lLBptAmLLkzDmOb2aq4korATE4fSB1+Ep0N3JWw6nIGoFx4AigsNSix0JVqA8eeoz4NcLE+BVJsCpS2AXwE8i5N9SfpLKf8ipWQJTSHJ9PNGmI7uLz+Wmgb3qIuMS4hCjpQS7/5ShEmLs5DpUPsi9Ei1YPnENHRJ5jbCRPWOPRqO+56Hp3NvJWw6kI6oFx8ASooMSix0XePTWf6r/aUocLGfDAXGaQuUsjUh7+HkyMkjACYCuAnAFiHEf4VgIwkKTZbFc5RjT+8LIFMaGpQNhRqnV+Iv6/Jw//p8eHymUk9sYceCcaloFM1thInqLZsdjnue9us6b9r3K6L+8X+Ak/0+KhvWxIZmlbZWL/FIfLGP2zRTYNRkBOVmnNxFq5+UsoWUsr+U8hwA/QEMBnBLAPMjOiPa4QyYf96oxNxjLjMoGwo1x0q8mLQ4Cx/85j9F4f+6x+GDYcmINtdmgJmIwpLVBsdfnoH33G5K2LRnJ+xv/g3weKq+LgKZNIEr26pTpD/6zX+3Q6K6UJNX4GsA3C2lVN7tlR3/pew8UUjxXXvibdsZepvzDMqGQsmmTBeGfX0CP5xQZ6fGmgU+Gp6Mh3vEQ+PAMFHksEWh9N7n4G3bWQmbt22A7b0XAe5WVe7qdmqBsinLjd25boOyofqsJgVKRwCrqjm3quw8UegoyIN5/TIl5Bp7qUHJUCj5JL0Y4xdl4vcSdd50qzgTlk1Iw8QWbNZGFJGiolF6//PwntNGCVvWLoH1s7cMSir0tIwzY0hjmxKbk8HF8lT3alKgmKSUVW5pURbnPAgKKZbVCyDcFZ/o6KmN4O05yMCMyGhuXeL/NuTh9jV5cHrVc8Ob2LBiYgOcl8TF8EQRLToWjvtfgJ7WWAlbF86GZdGnBiUVeq5oo36QMyejlD1RqM7VpLiwCCGGCSGGV3VD7brREwWW7oXlu/lKyD1yKqBxsXOkynZ4cdGSLLy123+u9N2dYzFnVAoSbfychYgAmZiC0gdehB6XqMRts2fCvHapQVmFlgktomCv9JJ6qMjrN2WW6GzVpLg4AeC905wnCgmmreuhZR8vP5YWK9yDxxmYERlpR44bVy/PxsEiddjEbgJeH5iES9uwJw4RqWSjZnDc/3dEPf8XCEfFLlW2d/8OGZsAb7e+BmZnvHirhrHNozBvf8W/zZyMUvRraDvFVUS1c9qPDaWULaWUrU51C0aiRDVh+fZL5djTfyQQG29QNmSkL/eVYMyCTL/ipFmMCYvHp7E4IaJq6a3OhePupyHNFVM/hdcL+xuPQ9uz08DMQsOlrdVpXl/uK4Vb5zQvqjuc10D1hvj9IMw7Nykx94gpBmVDRvHqEjM25eOGlbko8WlwMqChFSsmpqF7qtWg7IgoXHg79YTz5kcgK+3qJ1xORP3zEYjjRwzMzHgjm9mRYK34d8lx6vjuiNPAjKi+YYFC9YZl+VfKsbdtJ+gt2xuUDRkh36XjquXZeHm7fxfomzrE4KuxqUiL4nokIqoZT99hcE27W4mJwnxEvfIwUFzl/kERwWYSmNJSHUWZy928qA6xQKH6wVECy5rFSoijJ5FlT74bI7/JxJLD6qd4Fg14bWAiXuyfCIvG/iZEVDvukVPhmjhNiWm/H4T9jScAT+T2APGdJrvgoANFbr2aexPVDgsUqhfM65ZBlFbs0qTHJ8HT+wIDM6JgWn/ciZHfZCI9X+363DBKw4Jxqbi2fYxBmRFRfeC6+Ea4+41QYuZdm2H74JWIbeQ4oKEVTaMrRqRLPBKLDjoMzIjqExYoFP6khGWFurWw54ILAQvXGUSCeftKMWVJFvJc6puE81MtWDGxAfo04M4yRHSWhIDzxr/6dZu3rF4Iy8JZBiVlLE0IXOyzWH7+gdJq7k1UOyxQKOxp+36F6eDe8mMpNLiHTTQwIwoGKSXe+LkQN6zM8Wu+eEWbKCwcl4YmMVxvQkR1xGpD6T1PQ09rooRtn70N08aVxuRkMN91KN8edqLEw2ledPZYoFDYs6z8Wjn2du0DmdLQoGwoGLy6xEM/5GP6xgL4Tq54tEccZg5Ogt3M9SZEVMfiE1F633OQ0eq0Uftbz0Lbu9ugpIzTI9WiTPMq9Uos525eVAdYoFB4Ky2GecNyJeQeytGT+qzUI3Hdihy/zvBmAcwcnIQHu8dDCBYnRBQYskkLOO56CtJU8cZcuF2wv/oIRNYxAzMLPiEEJrSwK7GvOc2L6gALFApr5vXfQjgrFuXpSakR3+W3Pst16pi8OAvf+CzEjLMIzBmVgivbsvkiEQWet+P5cF5/vxLT8nNhf/khoMR/m/P6bKLPNK/FhxxweSNz4wCqOyxQKHxJCcsKdXqXZ8iFgMlsUEIUSMdKvLhwYSZ+zHQp8SbRGhaNT8OwpvZqriQiqnueIePhuvAqJWY6sh/2fz0JeD3VXFX/9G9gRaq94u1kgUvi+2Oc5kVnhwUKhS1t/68wHdxTfiyFgPuC8QZmRIGyv9CDsQszsStPfdHvmGjGsgkN0DnZYlBmRBTJXJf82W9Le/OOjbB99FrEbD9s0gTGn6N+QDR/P6d50dlhgUJhy7LiG+XY27UvF8fXQ7tz3Ri7IBP7C9WtugY2smLRhWloyp26iMgomgbHzY/A2/o8JWxZMR+WJXMNSir4JrZQp3ktOOiAV4+MAo0CgwUKhafSEpg3fKuE3EMnGJQMBcqmTBfGL8rEsVJ128oxze2YOyoVCVY+hRGRwaw2OO55GrrPB2TW2f+Cacs6g5IKriGNbYi3VGxOkuXQseGE6xRXEJ0aX90pLJk3rlQXxyemwNutn4EZUV1bddSJyYuzkOtUP4W7rHUUPh6ejChuI0xEIUImpsBx33OQURXbDwspYZ85A1qlqcj1lc0kMLY5d/OiusMChcKSZfVC5dgzaCwXx9cjCw6U4tJlWSjyqMXJTR1i8O8hSbBoLE6IKLTozVrDccffILWKt1bC6YD9lUcg8rINzCw4LvSZ5rX0kKOaexKdHgsUCjvi94Mwpf+sxNxDxhmUDdW1/6UX45oVOXD5NCN+oGscXuiXAI09TogoRHm79IZz2t1KTMs5Afur0wFX/d7ZanhTGyyV3lVmFHqxNz9ydjOjusUChcKOZfUi5dh7bjfIhs0Myobq0hs/F+KONXnwXVv5VO94TO/JBoxEFPo8I6bANXKqEjNl7Ibtnefr9c5ecRYNAxralNjSwxxFoTPDAoXCi8cD89rFSoijJ+FPSomnNxVg+sYCJa4J4PWBibirc5xBmRER1Z7rqjvg6dJbiVl+WAHrvPeNSShIRjVjgUJ1gwUKhRXTjh+h5eeWH0t7tN8e9BRevLrE/evz8dL2QiVu1YD3hybjmvYx1VxJRBSiTGY4bn8C3ibf/CpxAAAgAElEQVQtlbB13gcwr19uTE5BMMZnofzaY04UufVq7k1UPRYoFFYsqxcox56+wwFbVDX3plDn8krctDoX7/1arMRjzAJzRqVgUks+tkQUpqJj4bj3Wci4BCVse/d5aHt2GpRUYLWNN6NlXEVvKpd+ckdGotoKWoEihBgrhPhVCLFHCPFQFedfEUJsLbv9JoTIq3TOW+nc/GDlTKFF5GXDtHW9EuP0rvBV7NZx1fJsfLFP3YoyySYwf2wqLmhir+ZKIqLwIBs0QendT0GaLeUx4XbD/up0iKxjBmYWGEIIjG6mPncv4zQvOgNBKVCEECYAbwIYB6AjgCuFEB0r30dKea+UsruUsjuA1wF8Uel06R/npJSTgpEzhR7z+m8h9IqhYr1JC+htOp7iCgpVeU4dFy3NxrdH1E/WmkRrWDQ+DT3TrAZlRkRUt/T2XeG84QElphXkwv7KI0BpiUFZBY7vNK9lh52Q9XhzAAqMYI2g9AGwR0qZIaV0AZgNYPIp7n8lgFlByYzCg5Qwr/FZHD94HMBdncLOsRIvxi/KxA8+XYbbxJuw+MI0dEi0VHMlEVF48gwaA9fEaUrMdDgD9pkzAN1rUFaBMbChDdGVGukeKfFiZy63G6baCVaB0hTAoUrHh8tifoQQLQC0AvBdpbBdCPGTEGKDEGJK4NKkUKUdSIfp8L7yY6lp8AwYZWBGdCbS890YuzATu3xerLokW7BofBrOiWWzTSKqn1wX/QmeXkOUmHnbBlg/fcugjALDbhYY0pi7edHZCcV3A1cAmCulrPyRQgsp5REhRGsA3wkhdkgp91Z1cXp6elCSjFRG/fs2XTIb0ZWOC1p3QkZmDpCZY0g+kepsHv/N+Roe3G1DgUcd9eoR78XL7UuQfzgf+WebIAUUn18jGx//s6cNvwztjhxA9O8HymPWxZ/hmGZD9vlDTnGl8Wrz+He3mrEYFVN156fnYmJU/VtzE0kC8fffrl27as8Fq0A5AqB5peNmZbGqXAHgjsoBKeWRsv9mCCFWAugBoMoC5VT/s3R20tPTjfn39bgRs/snJWQdczEf6yA7m8d/zt4S3LUz1687/JhmNvx3WDKizdxQMNQZ9vdPIYGPf92Rf30J+pO3QcvLKo81X/IJ0rr0gLfj+QZmVr3aPv5XN/bg+b3Hy493FJrQsEUbxFv5XB+OjPj7D9ZvykYA7YQQrYQQVpwsQvx24xJCdACQBGB9pViSEMJW9nUqgIEAdgUlawoJpm0bIIoqGvjJmDh4u/c3MCOqKSkl/rGtEDet9i9Orm4XjY9HpLA4IaKIIpPTTm4/bK1YTC68Xthffxzi2KFTXBk+msea0S6h4jNwrwTWHON2w1RzQXlnIKX0ALgTwBIAuwF8JqXcKYSYIYSovCvXFQBmS3W7h/MA/CSE2AZgBYDnpZQsUCKIxWdxvKfvcMBqq+beFCrcusTda/Pw1OYCv3OP9ojDGwMTYdG4yQERRR69ZXs4bnlEiYmSIkS9/DBQ5P+cGY6GNlFfp1ewHwrVQtDWoEgpFwJY6BN73Of4b1Vctw5Al4AmR6GrIA+mbRuUkHvQWIOSoZrKc+r408ocfOfzgmTRgDcGJeHyNtHVXElEFBm8vYbAeelNsM35T3lMO34Y9jeegOOBFwBzeO9oOKyJDf/ZXdGEdyULFKoFzq2gkGbZsBzCW7Ffgt74HOitOxiYEZ3OL3lujPjmhF9xkmAV+HJMKosTIqIy7guvgnvgGCVm3r0Ftg9eAcK8d8igRjaYKg2Sp+d7cLiI2w1TzbBAoZDm1/tk0Bj2Pglh3xwoxcivM7G3QN3Xv0WsCUsvTMOgRpyaR0RUTgg4b7gf3vbqRBHL6oWwLP7MoKTqRrxVQ2+fpruc5kU1xQKFQpZ2KAOmAxXb2kmhwTNgtIEZUXU8usSTP+Vj2nc5KPKon/r1TLVg2YQ0nMsGjERE/ixWlN79FPS0JkrY+um/YdqyzqCk6obvOhRO86KaYoFCIct39MTbqSdkcppB2VB1jhZ7MXFxFl7ZUeR37qq20VgwLg0NokwGZEZEFCbiElF677OQUTHlISEl7DNnQDu4x8DEzs7wpv4Fih7mU9coOFigUGjyemBev0wJebg4PuQsPeTA4K9OYP1xlxI3C+DFfgl4c1Ai7GZOySMiOh3ZtCUcdzwBqVW8NRNOB+yvPAKRl21gZmfu/FQr4q0VrwHZTh07ctwGZkThggUKhSTTjo3Q8nPLj2VUDDw9BxmYEVVW4tHxwPo8XPZtNrKdaoOTxtEa5o9NxU3nxUJwvRARUY15u/SB6+q7lJiWcwL2V6cDrvCbHmXWBAY34jQvqj0WKBSSzGuWKMeePkPZ+yREbM1yYej8TLzzS7HfueFNbFg9qQEGcDE8EdEZcY+cCtfIqUrMlLEbtneeD8udvYaxHwqdARYoFHqKCmDeslYJsfeJ8bwSeGV7IUZ+k4nf8tWtIk0CmH5+POaOTkEa15sQEZ0V11V3wNOltxKz/LAC1nnvG5PQWRjWxK4crz/uRKkn/AotCi4WKBRyzD+sgPBUzFHVGzaF3q6zgRnRgUIPbtthw5ObCuD7utIqzoTF49PwQLc4aJzSRUR09kxmOG5/At4mLZWwdd4HMK9fbkxOZ6h1vAnNYys+uHJ6gR9PuE5xBRELFApBlrU+vU8GsveJkebsLcHgr05gS4H/yMg17aLx/eQG6N3AWsWVRER0xqJj4bj3Wci4BCVse/d5aHt2GpRU7QkhMKSxOs3r+985zYtOjQUKhRRx9ABMe3crMc9A9j4xQr5Lx82rcnDT6lwUuNVhk2Sbho+GJ+P1QUmItfBphIgoEGSDJii9+ylIc0UfKeF2w/7qdIisYwZmVju+BcpqFih0GnxnQSHF8r06euI5rwdkaiODsolc6487MeirE/gso9Tv3IimNqyb0gATW0QZkBkRUWTR23eF84YHlJhWkAv7Kw8DpSUGZVU7vgXKpiwXClx6NfcmYoFCocTrgXmtz+5dXBwfVG5d4pnNBbhwURYOFXmVc1Yh8XzfBMwZlYJG0VwIT0QULJ5BY+CacLUSMx3eB/vMGYDureaq0NE42oT2CebyY6+EX/8sospYoFDIONn7JKf8WNqj4ek9xMCMIsu+Ag/GLczEi9sKofsshO+YZMYH3R24tWMsF8ITERnAdfGN8PRSXxPN2zbAOvvfBmVUO5zmRbXBAoVChuX7Rcqxp+8wwMZpRIEmpcT/0osx+KsT+CnTv8Pv7Z1i8N2EBmgbw20hiYgMo2lw3PwwvC3aK2Hrkjkwr/jaoKRqjgUK1QYLFAoNhXkwbVmnhNyDxxmUTOTIc+q4YWUu7liThyKf/YMbRmn4fHQKnu2TCLuZoyZERIazRcHxl2egJ6aq4Y/+CdOuzQYlVTODGllR+ZVkR44b2Y7Qn55GxmCBQiHBsn45hLei+Z/euDn0tp0MzKj+W3HEgQHzjmPefv+F8OOa27F2SgOMaGqv4koiIjKKTE47uf2wteL5WXi9sL/5t5De2SvZbkKXZIsSW3OM61CoaixQKCSYfaZ3uQeNZe+TACnx6HhwQx6mLs3G0RJ1F5Uok8Ar/RPxyYhkpNq5EJ6IKBTpLdvDccsjSkwUFcD+2mOAK3SnTnGaF9UUCxQynHYgHaaDe8qPpdDgGTjGwIzqr82ZLlwwPxP/2V3sd65rsgWrJqXhhg4xECwOiYhCmrfXEDgv+pMSMx1Ih+39lwEZmmsGL2jCAoVqhgUKGc539MTbpTdkUmo196Yz4dYlnt9SgFELMpGe71HOaQK4r2ssvp2QhvaJlmq+AxERhRr3xGnwnD9QiVnWLoHl2y8NyujU+jW0ovKSxvR8D44Wcx0K+WOBQsZyu2BZ960a4uL4OrUt24XRCzLx/NZCeH0+VGsZZ8LCcal4vGcCrCaOmhARhRVNg+PmR6A3bq6ErbPehPbLNoOSql6cRUPPNKsS4ygKVYUFChnKtHUdRHFB+bGMiYe3xwADM6o/itw6Hv0xH8O+zsSWLP/tg69rH43vJzdAv4a2Kq4mIqKwEBWD0rufhrRHl4fKF83nnDAwsaoN5joUqgEWKGQoy/eLlWN3/xGAxVrNvakmpJSYm1GCfl+ewJs7i/yaLqbZNcwemYxXByYhzsKnACKicCebtIDj5oeVmFaQC/vrTwDu0Nop64IqChQZomtmyDh8d0KGEblZMG3/UYl5OL3rrGw8cXI6159X5eJwFfN6J7e0Y/3UBhjbnA0wiYjqE2/PwXBNvlaJmTJ2w/bRqwZlVLXeaVZU3iTycLEX+wq5DoVULFDIMOZ1SyFkxTa33uZtoLdoZ2BG4etgkQd/XpWDUQsysbGKbvDNYkyYPTIZHwxL4fbBRET1lGvK9fB066fELKsWwLzyG4My8mc3C/RtwGledGosUMgYUsLis3uXZzB7n9RWkVvH05sK0OeL45ib4d9w0aIBd3aKxQaOmhAR1X+aBsctj0Jv2FQJ2z5+Fdr+3wxKyh/7odDpsEAhQ2h7d0H7/VD5sTSZ4e4/ysCMwosuJT5OL0bPz4/jpe2FcFQxOj7+HDs2TGmIp/skIJZrTYiIIkNMHBx3PwVpq9Rp3u2G/Y0ngOJCAxOrUFU/FJ3rUKgSvmshQ1hWL1SOvT0GAPGJBmUTXtYcc2Lo/EzcuSYPx0t1v/Odky2YPzYVn4xIQZsEswEZEhGRkfRmreG84QElpmX+DvvbzwG6/+tGsHVPsSDOUjFjIsuhY3eu5xRXUKRhgULB53TA/MMKJeQePNagZMLHvgIPrvkuGxMWZWF7jv86kwZRGl4bmIhVE9P8hs+JiCiyePqPhGvEFCVm3roOlkWzDcqoUh6awIBGnOZF1QtagSKEGCuE+FUIsUcI8VAV568XQmQKIbaW3f5c6dx1Qoj0stt1wcqZAsP8wwoIR0n5sZ6QDG+XPgZmFNpKPRIzNuWj75fH8fUBh995mwm4v2ssNl3cENe2j4FJ4zoeIiICXFfeDm+rDkrMOucdmHZvMSijClVtN0z0h6AUKEIIE4A3AYwD0BHAlUKIjlXc9VMpZfey2ztl1yYDeAJAXwB9ADwhhEgKRt4UGJaV85Vjz6AxgIlTkaqy8qgDA+Ydx8vbi+CqYlT+olZR2HhRQzzWM4E9TYiISGWxwnHn3yBj4spDQuqwzZwBkZdtYGL+C+XXHnPC49u4iyJWsN7R9AGwR0qZIaV0AZgNYHINrx0DYJmUMkdKmQtgGQDOBwpT2sE9MO3drcTcF0wwKJvQle/Scfv3uZiyJLvK/eF7plqwZHwq3huajHNiWdwREVHVZGojOG55VIlp+bmw/2sG4DVu3UfHJDNSbBVvQwvcEluz/acvU2QKVoHSFMChSseHy2K+LhZCbBdCzBVCNK/ltRQGLCu+Vo49nXpCNuTDWdmqo04MnHcCn+wp8TvXOFrDW0OSsGxCGvo25DoTIiI6PW+3fnBNukaJmX7dBuvcdw3KCNCEwGCfUZRVRznNi04KpY9evwYwS0rpFELcAuADAMNr+03S09PrPDGqcDb/vprLgc5rliixQx16I4+PGQDAqQNv7Ldg9lGL3zkBiUsae3B7Czdi9SLs3WNAguDfV6Tj4x/Z+PiHuc6D0Hb7T4jbXzGLwbpwFg7FpqCgfbfTXh6Ix/88swnzUFGkLNybi0nRx+r859DZC8Tj365d9c25g1WgHAHQvNJxs7JYOSll5cmQ7wB4odK1Q32uXVndDzrV/yydnfT09LP69zWv/AYmV8Uibz0hCWnjL0GaOZTqZGPszffgtpU52FHF7lwdEs14bWAi+jQwdsTkbB9/Cm98/CMbH//6Qdz/LPTHboKWl1Uea73gA5T0ewcypWG11wXq8b+8kQfP7Tlefry90IQmLdsghmsqQ4oRf//B+g3YCKCdEKKVEMIK4AoAykppIUTjSoeTAPxR4i8BMFoIkVS2OH50WYzCjGWlz/SuweMBFif4IqMEQ78+4VecCAB3dY7FyokNDC9OiIgo/Mn4JDjueAJSq3j7J4oLT65H8QR/PUrLODNaxZnKj906sPaYK+h5UOgJSoEipfQAuBMnC4vdAD6TUu4UQswQQkwqu9vdQoidQohtAO4GcH3ZtTkAnsLJImcjgBllMQoj2v7fYNr3a/mxFALuoZG9ON6tS/x1Qx7+tCoXhW5155JzYk34ZlwqnuqdALuZ2wYTEVHd0Nt3geuSm5SYac9OWD83Zj3K8KZ25XjFUf/t9CnyBO3jaynlQgALfWKPV/r6YQAPV3PtewDeC2iCFFCW79Sthb2de0GmNa7m3vVfZqkX163Iwbrj/p8UTW5px2sDk5Bg5RA3ERHVPfe4y2H6ZSvM238oj1kXzoL33K7wdu8f1FyGNrHh3V+Ky49XcqE8gZ3kKRiKCmBev0wJuYdOqubO9d/WLBeGzs/0K06sGvBSvwS8PzSZxQkREQWOpsFx88PQk1KVsP0/z0HknAhqKoMb2VC5v/DuPA9+L/HfXp8iC98FUcBZVi+EcFV8IqInN4C3R3A/oQkVCw+WYvyiLBzxefJtFmPC0gvT8OfzYiEEp3QREVGAxSXCcfvj6nqUogLYZz4V1P4oiTYNPVPV3Ss5ikIsUCiwdC8sy79UQu4RkyOyc/xbu4pw9fIclHjU9SZDGtuwalIauqdaDcqMiIgikd6+K1wX3ajETL/tgPWL/wY1j6FNfNahHOE6lEjHAoUCyrRlHbSsii0EpcUC9wUXGphR8Hl1iYd/yMP//ZAP6XPu9k4x+GJ0ClLspiqvJSIiCiT3hVfC06W3ErN+8z+YdvwYtByGN1V3qlz5uxNS+r5iUiRhgUIBZflWHT3x9B8FxCUalE3wlXh0XLsiBzN3FStxkwBe6Z+IZ/skwqxxShcRERlE0+C8+RHoiSlK2PbWsxC5WdVcVLd6pVkRZ6l4LTxRqmNnbvC3PabQwQKFAkY7nAHzrs1KzD1yqkHZBN+JUi8mLsrCgoPqUHWsWWD2yBTc0CHGoMyIiIgqyPgkOG57DFJUvC3UCvNg/3dw1qNYNIGBjdRRFE7zimwsUChgfEdPvO27QG8RGZ2IMwo8GPVNJjZlqc0XG0drWDg+FaOa2au5koiIKPj0Dt3hmnq9EjP9sg3Wrz4Mys8f3kQtUJYd4UL5SMYChQKjKB/mterWwq5RFxuUTHBtz3ZhzIJMHChSd+rqlGTGtxMaoGsKF8MTEVHocU+8Gp6O5ysxy/yPEJexK+A/2/eDu3XHnChw6QH/uRSaWKBQQFiWfwXhqhie1ZNS4T1/kIEZBce6Y05MWJSFTIf6pDq8iQ2LxqehaQwXwxMRUYjSTHDeOh16QlJ5SEiJFl+9C5GXHdAf3SrejHYJFTt8eiSwgtsNRywWKFT3XE5Yln2hhNyjLwHM9Xtr4cWHSnHR0iwUuNWdR65oE4VPR6Ugns0XiYgoxMmEZDhvmQ5ZqSeXpbgAtreeAfTANlAc4zOKsuQQ16FEKr5jojpnXrMYWmFe+bG0R8M9dIKBGQXe7D0luHp5Dhw+z923d4rBvwYnwcKduoiIKEx4O/WEe9K1Ssy8azMs8z8O6M8d3VwtUJYddkDndsMRiQUK1S3dC+viz5SQe/gkIDrWoIQCb+bOItz6fS68Ps+hj/eMxzO9E6CxMzwREYUZ15Rr4enQXYlZ530A0+4tAfuZ/RtaEV9pu+FMh44tPpvNUGRggUJ1yrR5LbTjR8qPpckMdz1dHC+lxNObC/Dwj/lKXAD454BE3Nc1DoLFCRERhSPNBOdtj0GPr7weRYdt5lMQ+TkB+ZEWTWCYT9PGJYc5zSsSsUChuiMlrAtnKSFP/5GQyWkGJRQ4Xl3i3nV5eGlboRK3aMB/hybj+nPZ44SIiMKbTEyB85ZHIVHxYZuWnwPbW88CemB22PJdh7KU61AiEgsUqjPar9th2rtbibnHXW5QNoHj8EhcvzIH7/9WosRjzAKfjUzBlFZRBmVGRERUt7yde+H4wHFKzLzzJ1i+Dsx6lFHN7Kg892BrthvHSgK7OJ9CDwsUqjPW+WozJ0+3ftCbtTIom8DId+m4ZFkWvj6gfqKTbNPw1dhUDGvKBoxERFS//H7BJHjbd1Vi1i/fD8h6lLQoE85PtSixpZzmFXFYoFCd0H7bAfPOTUrMdeFVBmUTGMdLvJiwKAtrjrmUeLMYExaNT0WvNDZgJCKiekgzwXHbY9DjEstDJ9ejzAhIf5QxPrt5LeY0r4jDAoXqhHXeB8qx57we0M/tWs29w8/+Qg/GLszEjhx1N5FzE8xYPD4V5yZaqrmSiIgo/MnktJPrUUTl9Si5sP376Trvj+JboHx3xIEiN7vKRxIWKHTWtD07Yd75kxJzTbnOoGzq3o8nnBj5TSb2FapPwL3TLFg0PhXNYut3A0oiIiIA8HbpDfeka5SYefcWvw8pz1bXZAvOiTWVHzu8bNoYaVig0FmzzntfOfZ26AbdZ+/0cPVFRgkmLs5ClkP95GZUUxvmjUlFst1UzZVERET1j2vKdfCc10OJWeZ/BNOOjXX2M4QQmNxS3XDmq/2ldfb9KfSxQKGzou3ZCbPPk5JryvXGJFOHpJR4aVsh/rQqF06fkevL2kThk5EpiLHwz4eIiCKMZoLz1unQEyr3R5Gwv/U0RE5mnf2YKT4FyrLDThRzmlfE4DssOit+oyfndoPX55OVcOPySty+Jg9Pby7wO/dAtzj8e3ASLBobMBIRUWSSiSlw3vY4pKh4GykK82GfOQPweurkZ5yfakGzmIpZCqVeyd28IggLFDpjpl2bqxg9Ce+1J7lOHVOXZmHWHrXHiUUD/jUoEdPPj4fG7vBERBThvOf1gGvq9UrM9NsOWD9/t06+f9XTvFigRAoWKHRmdB3WT/+thMJ99GRvvgcjvzmBtT7bCCdaBb4ck4qr2rE7PBER0R/cE6fB07m3ErMumAXT1nV18v19p3ktPexAiYfTvCIBCxQ6I+aNK2Ha/5sSc15+CxCmowvrjjkxcsEJ7C1QF5y0jjPh2wlpGNTIZlBmREREIUrT4LjlUehJqUrY/vZzEFnHzvrb90yzoGl0xTSvEo/EssPOs/6+FPpYoFDtedywzn1HDfUaAr1NR4MSOjuz95Rg8pIs5DqlEu/f0IpvJ6ShbQJ7nBAREVUpPhGO2x+H1CqtRykuhP1fTwIe9ykuPD1NCExqqfZE4W5ekYEFCtWaZcXX0E4cLT+WmgbnpTcZmNGZkVLimc0FuPX7XPhuDHJ5myhuI0xERFQDevuucF2ivg8w7d0N62dvn/X39l2HsuigAwUuTvOq71igUO2UlsDy1YdKyDN0ImSj5gYldGYcHombVufixW2Ffuce7XFypy6bKTynqxEREQWbe9zl8HTrp8SsS+bAtHHVWX3fPg2sfrt5zeMoSr3HAoVqxTr/Q2iFeeXH0maHa/K1BmZUe1kOLyYvycLcDPUJzmYC3rkgCQ92j4cI07U0REREhtA0OG5+GHpKQyVs/89z0A7vO/NvKwSuaBOtxP6XXlLNvam+CFqBIoQYK4T4VQixRwjxUBXn7xNC7BJCbBdCLBdCtKh0ziuE2Fp2mx+snEllyz4Gy5K5Ssw99nLIxBSDMqq9/YUejP4mEz+cUHfqSrFpmD8mFZe0jq7mSiIiIjql2AQ47ngC0mQuDwmnA/bXHgOK/Wcs1NRV7dTX5h9OuJCef3brWyi0BaVAEUKYALwJYByAjgCuFEL4rqjeAqCXlLIrgLkAXqh0rlRK2b3sNikYOZMPKdFsyWyISg2Y9OQ0uC68wsCkamdrlgujF2Qio1Ddqat9ghnLJ6ahb0Pu1EVERHQ29DYd4Zx2lxLTjh+G/a1nAP3M1o60jjdjQEOrEvuEoyj1WrBGUPoA2COlzJBSugDMBjC58h2klCuklH/8tm0A0CxIuVENmLasRXzGTiXmuuI2wBZVzRWhZeVRByYsysKJUvXJcUhjG5ZemIaWceZqriQiIqLa8AybBPeQ8UrMvG0DrPM+OOPvebXPKMrsvSXw6rKae1O4C1aB0hTAoUrHh8ti1bkRwKJKx3YhxE9CiA1CiCmBSJBOweWE7ZM3lZCnQ3d4+gwzKKHambO3BJcuy0aRR30iu6x1FOaOSkGijUuxiIiI6owQcF5zD7ytOihh61cfwLR57Rl9y8ktoxBjrlgf+nuJju+OsidKfSWkDHz1KYS4BMBYKeWfy46vAdBXSnlnFfedBuBOABdIKZ1lsaZSyiNCiNYAvgMwQkq5949r8vPzy/8n0tPTA/s/E4Earf4ajVdXLP2RQsMvNz0GR4PQH+T63xEz/rnP6hef1tSNu1q6oXEtPBERUUBY8nNw7ntPw1Jp/YnXasevf3oEztTGtf5+T6VbMf94xYyHkakePNfBdYorKJS1a9eu/OuEhATlHVmw5rUcAVB5H9pmZTGFEGIkgEdRqTgBACnlkbL/ZgghVgLoAWCv7/WA+j9LZ08cPYDodYuUmHvkFDQfGNqjJ1JKPPFTAV7bV+R37une8bizc5wBWYW39PR0/n1FMD7+kY2Pf2Q7m8ffHfs0zC/cB+E9uf7T5HKgw1fvoOSJmUBUTK2+123xTsxfmFV+vDrHjMRmzZAWxZ5lgWTE33+w5rZsBNBOCNFKCGEFcAUAZTcuIUQPAG8BmCSlPFEpniSEsJV9nQpgIIBdQco7suk67O+9CFGpE6welwjX1BsMTOr0youTn9XixKKd3EaYxQkREVFw6B26wXXF7UpM+/0g7G8/V+tF8/0aWNEmvqIYcenAe78W10meFFqCUqBIKT04OW1rCYDdAD6TUu4UQswQQvyxK9eLAGIBzPHZTvg8AD8JIbYBWAHgeSklC5QgMK+YD1P6z0rMNe0uICa03+A/t7XQrziJNQvMGZXCbYSJiIiCzD3qIrgHjFZi5s1rYP3yv7X6PkII3NghVom9+0sxnF4ulq9vgrZ1kZRyIQfcq8kAABhUSURBVICFPrHHK309sprr1gHoEtjsyJfIPgHbZ28rsfx2XWHqO9ygjGrmH9sK8cJWda/1ZJuGL0anoHuq/1oUIiIiCjAh4LzhfmhH9sF0oGKtsHX+R9AbnwPPgFE1/lbT2kXjuS0FKHSfLEpOlOr4PKMEV7Wr3XQxCm3cvoj8SQnbh69AOCr2GJf2aBwaezUQwh3W3/i5EE9tLlBi8VbB4oSIiMhoVhscdz8FPS5RCdvefQGaz2yNU4m3an5bDs/cVYxgbPpEwcMChfyYv18M89b1Ssx52c1wJyQblNHp/Wd3EaZvVIuTWLPAF6NTWZwQERGFAJnaCI57noY0W8pjwuOG/dXpEJm/1/j73NoxFpU/Lt2R48aaY9zNqz5hgUIKceIobP97TYl523WGZ9ikaq4w3oe/FePBDflKLNos8NmoFPRKY3FCREQUKvR2neG88a9KTCvMQ9TLDwFFBdVcpWoZZ8b4c+xKbOYu/107KXyxQKEKXg/sbz0L4SgtD0mrHY4//x+gheavymd7S3DP2jwlZjcBs0akYEAjm0FZERERUXU8A0bBNekaJaYdPYCoV6cDrpo1X7ytk7pYftFBB/bku6u5N4Wb0HzXSYawfPMJTHvUeaDOq26HbNS8miuMNW9fKW79PheVZ51aNODj4Sm4oAmLEyIiolDlmnoD3H3Unmqm37bD/vazNdp+eGBDK7okV0wVkwBe3FZY/QUUVligEABAy/gF1q8+UGKe7v3hGTrRoIxObeHBUvx5VQ70StWJWQDvD03GyGb26i8kIiIi42kanDc9BO+53ZSweeMqWD95EzjNonchBO7poo6izMkoxW95HEWpD1igEFBUAPubT5R3eQVONmR0/unBkNy169vDDly/IgeeSs9dmgD+c0ESLmwRZVxiREREVHNWG0rveRreJi3V8LLPYfnqw9NePrVlFDokVnTM0CXwAkdR6gUWKJFO12F/+1loWceVsPNPD0KG4K5dq446Me27bLgqjf4KADMHJ2FqKzZhJCIiCisxcXA88HfoialK2Pblf2FZPOeUl5o0gYe6xyuxzzNKsTuXoyjhjgVKhLMs+ATmbRuUmGvURfCeP9CgjKq3/rgTVy7PhsOrxl8dmIjL27A4ISIiCkcypSEc9z8PGa02W7TNehPmld+c8tpJLe3omFQxiiIB/H0rR1HCHQuUCGbauQnWz99TYt42HeG64jaDMqrepkwXLluWjRKPOif1xX4JuLY9u8cSERGFM/2ctii9/wVIm7qO1Pb+P2Beu7Ta6zQh8HAPdRRl3v5S7MjhKEo4Y4ESocSJo7DPnAEhK+ZKybgEOO74G1CpgVIo2JbtwkVLs1DoVouTp3rH46bzYqu5ioiIiMKJ3raTfyNHKWH7z3Mwr15U7XUTzrGja7L63uXhH/LYXT6MsUCJRMWFiHr5IYjCiuaGUgg4bn0MMqWBgYn525XrxtQl2ch3qU8y08+Px12d4wzKioiIiALB26kXHHc8AVmp/5qQEvZ3/w7ziq+rvEYIgUfOV98TrDnmwrz9pVXen0IfC5RI4/HA/sYT0H4/qIRdU2+At3Mvg5KqWnq+G5MXZyHHqe6H/kC3ODzQjcUJERFRfeQ9fxActz2uFCkAYH//H7As+6LKa8Y0s2O4Tw+06T8WoNh9+p4qFHpYoEQSKWH78J8w79qshN39RsDt09HVaL/muTFxURYyHeoTy12dY/FoDxYnRERE9Zm3z1A47ngS0mRW4raPX4N17jt+fVKEEHi+bwLMlbojHCnx4pXtRcFIl+oYC5QIYv3yfVhWqbtheNt2hvPGv4ZUv5MdOW5cuCgLx0rV4uSm82Iwo1c8RAjlSkRERIHh7TUYjrtmKGtSAMD69cewvfM84PEo8faJFtzWSV2b+trPhdhXoN6PQh8LlAhhWTDLr1O8ntYYpfc8DVht1VwVfJsyXZiwKBNZPiMn17aPxt/7JrA4ISIiiiDeHgNOLpz3ea9iWbME9n8+DJSoIyQPdotDw6iKt7cuHbh7bS50LpgPKyxQIoDl2y9h++wtJSajY1F673NAfKJBWfn75kApJi/O8lsQf8O50fjngERoLE6IiIgijrdrX5Q+9ApkXIISN+/YiOgZ/9/encdZVdd/HH997r2zssywyiIIAoJgUmghhYpJikbiz1TUh5kGmPYjl6yf5Yoa5fIL08Ty51pYFpD6IBVJA5UQDAIXQEUEBBQCZBZg9ns/vz/OGbyzABdh7iy8n4/HfXDP+X7POZ87Z2aYz/luV2KffLR7X9vMCLcdX7Pe/M0VPLhCXb2aEyUoLVzslefImnZfjX2enUPpj+/Gu/dqnKBqSbhz57JiLp67nZ211jn5waBWTBmm5ERERORQlugzkJKbHiDRqWuN/ZFNG8i97UqiSxfs3je2Tw6n1Bowf/u/i1mpFeabDSUoLZU7Gc/9kezH/7fm7oxMSq/9JYk+A1M8jbOpJM6LG0p5ZH2MWxcX8Zt3dvDU6hJe/aSMHQc4O8Z/SuJcPHc7d9az6uuPj23D5C+rW5eIiIiAd+lB6c1TiffuX2O/lZWQc9+NZM54GKqqMDOmDm9HfuZnfz9UJODy1wooj6urV3MQ23cVaXYSCTL//Fsy58yosdujMcquuoPEgC/u8xQF5QkeWrmTaatK+LgkHu7NBGo2kWZEYGjnTEZ2z+b0HtkMyI+llFBUJpz/e3cXdy4rrrMAY8zgzqF5jNcijCIiIpLE89pTesP9ZD0xhYwFc2qUZT73R6IrllB2xU1069KDKcPy+d6rBbvLl2+v5NYlRdw5tOl0b5f6KUFpaSrKyXrsHjIWvlxjt2dkUPaDScSPHbrXw7eWxrl/+U4ee28Xu6r2/ZShMhEshvTPzRVM+ncxfdpGGd0zh693z2Jwh0zys2o20r1XWMmsdaVM/7CU1fXMqtEhK8Lvv96e4V2azsB9ERERaUIysyif8FMSvfuT+acHsMRnvTmia98n9+YJlF94JeeM+BazN+QwY81nCzb+buUuBuRncGn/Vo0RuaRICUoLYls+IfuBW4l+9EGN/Z7TitJrJu+15cTdmb6mlOsXFVJY8fmbPz8sjnPf8p3ctzxoaenZOkq7rAilVU5xRaLO1MHJBnfIYNrX29Oztb4tRUREZC/MqPzGOcR7HEn2b39OpHDbZ0UVZWT//l7iC17i1xddzeub2yT1BoHrFhbSq02UEd2yGyNySYH+Emwhom8uJPuhyVit6fYSee0ou+5uEkf02+Oxm0viXPt6IbM3lNVbnh2FL7TP4IhYCf27tOfT8gRbSxO8+WkFHxbH6z2m2vqdcdbv3HudthnGDUPaMn5AK2IRjTcRERGR1CQGfJGSyY+S/fiviC15rUZZdPVyOv/8+yw48RxOsFFsJgeAuMMl87bz9292YkB+Rn2nlUamBKW5Ky0hc+bDZL78TJ2iRJcelF53F9652x4Pf/6jUiYuKKCgvG6rSeecCBMHtebS/q1omxnhgw8+oF+/tjXqrNtRxcsby3hxQxmvbipnf8bMRwwu7JvLrce1pXNONPUDRURERKq1zqNs4m3E5s8m68n7sfLPHrhaIsHhr87kw5w53NJ1NFO7n0ZZNJPiCufsF7fx9OkdGdhOSUpTowSlGYu+tYisJ6YQ2b6lTlnV8SdRNv56yKm/j2VplXPz4iIeeW9XnbLsKPzsS235/tGtyY7tvUWjV5sY449uzfijW1NUkeCljWXM/bict7dX8l5BJbWHscQMTuqaxVm9cvhmz2w6KTERERGRA2VG1UlnEh84hKwnf0Ns2YIaxVmlO7hrzVNctfFF7uk5mse7jGBzaTZnvLCV6SM7MPQwjX1tSpSgNEORjWvJfObxOk2ZAB6JUHHe5VSeMRb2MJvWm9squHJ+Ae8W1h2k/uVOGTx4Yjv65e3/04S8zAjnHpnLuUfmAlBW5awqqqQqAbkZRm7M6JgdITem2a1FRETk4POOXSi7ZjLRpf8ka9r9dR7idq8o4NerpzFp7V95tNspPND9NM6eA4+f0o5RPXIaKWqpTQlKM2KbN5L57BPEFv0D87pdshKdu1E27noSAwbXe3xZlXP3W8Xc985Oak8DHjO4cUhbrjqmNdGDNA4kO2Yc2yHzoJxLREREJFXxIcMpGXQcGS/OIHP2X7DSmj1G8uMlXLfhea7d8AJz2w1i2sfDmf+1k7lh2GG0ytCD1MamBKWpq6oi+ubrZMz7G9EVS+pNTDwSoXLUWCrO/i5k1Z2Rwt2Zs7GMWxYXs6qobqtJ7zZRHjm5Pcd1UjIhIiIiLURWDpVjLqHy1DFkPv8UGS89jVVW1KgSwRlZsJyRBcvZuepxFjw/mC7DT6TvycPxtu0aKXBRgtIUVZQTfXcZsWWvE106n0hRwR6rxnv3p/zS60j0OqpOmbvz6qZyJi8tZvHWynqPv6BPDvcMy6eNnhaIiIhIS9Q6j4qxV1B5+nlkzJ1FxtxnsR1Fdaslyjl9879g5r9IzJxCUbc+tBo0mHj/Y0kc9QU8r30jBH9oSluCYmajgPuAKPCIu99ZqzwL+ANwHPApMNbd14VlPwPGAXHgKnevuXRoc+aOFRcQWfs+0TXvElm9kugHy7GK+qf8rRY/vDcV53yP+JDhdcaarCmuYsaaEqZ/WLLHaYC75ka496v56m8pIiIihwTP70DFOZdRMfoiYq+/RMbcWUQ/WlVv3QhOu09Wwyer4aW/ApDI70jiiL4kevYl0bUnicO6k+hyOLTOS+fHOCSkJUExsygwFfgGsBFYbGaz3H1lUrVxQIG79zWzC4C7gLFmNhC4ABgEdANeNrOj3H3vi2s0tqoqKNuFlZZgpSVQVoLtKMQKPyVSsA3bvpXI5vVENm2os3bJ3sR7D6Dy9POo/MoIdiaMDYVVbNgZZ3VxFUu2VLB4awUbd+39S/Odfrnc8eW8Oqu8i4iIiLR4mVlUjRhN1YjRRDauxefPoWL+S7Tb9eleD4sUbgsWhHxrUY39iezcoHUlvwOJvPZ4Xns8P/y3VVvIycWzc/DsXMjODf7NyoaI/g7bk3S1oHwFWO3uawDM7M/AGCA5QRkDTArfzwQeMDML9//Z3cuBtWa2OjzfwjTFvl9y7phI5KNVdfo4HgjPzqVq2EgqT/kWo1bk8/7aKopXbd6vNUcAzuiRzY1D2nJMe833LSIiIpI4vDdceAUZYy/njaUrWPXKawxcu5jjdq5L+RyRsuBBNP/ZyP4snlARiRGPxKiMxIhHosQtSqvsDLKyMvFoFKKx4BWJhL1lDIyk9+E24Lu3wx3JdSCsF2zHB59A5cj/2o9I08+8nkHXB/0iZucCo9x9fLj9HWCou09MqrM8rLMx3P4QGEqQtCxy9yfD/Y8Cs919ZvWxRUVFDf8hRERERETkoMvLy6sxXkFtSyIiIiIi0mSkK0H5GOiRtH14uK/eOmYWA/IIBsuncqyIiIiIiLQA6RqDshjoZ2a9CZKLC4CLatWZBXyXYGzJucBcd3czmwX8ycymEAyS7wf8K/nA2s1CIiIiIiLSPKUlQXH3KjObCMwhmGb4MXdfYWa3A0vcfRbwKDAtHAS/nSCJIaw3nWBAfRXw301+Bi8REREREflc0jYGxd1fcPej3L2Pu08O990SJie4e5m7n+fufd39K9UzfoVlk8Pj+rv77HTFfKgys1Fm9r6ZrTazn9ZTnmVmfwnL3zCzXumPUhpKCvf/R2a20szeNrN/mNkRjRGnNIx93f+ket82Mzez49MZnzSsVO6/mZ0f/g5YYWZ/SneM0nBS+P3f08zmmdmy8P+AMxsjTjn4zOwxM9sSTlpVX7mZ2f3h98bbZjakIePRIHmpIWnNmjOAgcCF4Vo0yXavWQPcS7BmjbQAKd7/ZcDx7n4swZTgd6c3SmkoKd5/zKwNcDXwRnojlIaUyv03s37Az4Cvufsg4Jq0ByoNIsWf/5uA6e7+JYKeLg+mN0ppQE8Ao/ZSfgbBMIt+wOXAbxsyGCUoUtvuNWvcvQKoXrMm2Rjg9+H7mcCp4Zo10vzt8/67+zx3Lwk3FxFMXCEtQyo//wB3EDyYKEtncNLgUrn/E4Cp7l4A4O5b0hyjNJxU7r8DbcP3ecAnaYxPGpC7v0YwxGJPxgB/8MAiIN/MujZUPEpQpLbuwIak7Y3hvnrruHsVUAR0SEt00tBSuf/JxgHqdtly7PP+h836Pdz9+XQGJmmRys//UcBRZrbAzBaZ2d6euErzksr9nwRcbGYbgReAH6YnNGkC9vfvgwOSrlm8RKSFMbOLgeOBkxs7FkkPM4sAU4BLGzkUaTwxgi4eIwhaT18zsy+4e2GjRiXpciHwhLv/ysyGEUxudIy7Jxo7MGlZ1IIitR3ImjXS/KW07pCZjQRuBM5y9/I0xSYNb1/3vw1wDPCKma0DTgBmaaB8i5HKz/9GYJa7V7r7WmAVQcIizV8q938cMB3A3RcC2UDHtEQnjS2t6xIqQZHadq9ZY2aZBIPgZtWqU71mDSStWZPGGKXh7PP+m9mXgIcIkhP1P29Z9nr/3b3I3Tu6ey9370UwBuksd1/SOOHKQZbK7/9nCVpPMLOOBF2+1iAtQSr3fz1wKoCZHU2QoGxNa5TSWGYBl4SzeZ0AFLn7poa6mLp4SQ0HsmaNNH8p3v97gNbAjHBuhPXuflajBS0HTYr3X1qoFO//HOA0M1sJxIGfuLta0FuAFO//dcDDZnYtwYD5S/WAsmUws6cIHj50DMcY3QpkALj77wjGHJ0JrAZKgMsaNB59X4mIiIiISFOhLl4iIiIiItJkKEEREREREZEmQwmKiIiIiIg0GUpQRERERESkyVCCIiIiIiIiTYYSFBERERERaTKUoIiIiIiISJOhBEVE5BBiZuvMrNTMdia9HjCzH5rZ8nAF6eq615jZMjO7JalumZnFk7ZX7ON6Y8zsTTMrNrNtZjbXzHonlQ80s1lmVmRmO8xsnpl9Nal8RLhoWO3zvmJm48P3k8ysMoyn0MxeN7NhtepfZGZLwjqbzGy2mQ2v5/jqV2EKX8t5ZrY1/GxvmdmYfR0jIiL7pgRFROTQ8y13b530mghMBQqBGwHM7EjgNmCcu99eXRe4AliYdOygPV3EzPoCfyBYfToP6B1eJx6W9wEWAO+EZd2AZ4C/104wUvCXML6OwDxgRlIcPwJ+DfwCOAzoCTwIjKl9fNIrP4VrXg10dfe2wOXAk2bWdT/jFhGRWmKNHYCIiDQ+d0+Y2ThgsZnNJPiD/kF3X3oAp/0isNbd/xFu7wD+mlQ+iSDZuTFp3/1mdjRwF3DS/l7Q3avM7I/ADWbWCagAbgcuc/enk6r+LXx9bu7+dvImkAH0ADYdyHlFRA51akEREREA3P194JcELRCHE7SgHIilwAAzu9fMTjGz1rXKv0FSS0eS6cDXzCxnfy8YdlG7BPgUKACGAdkELTMHnZk9Z2ZlwBvAK8CShriOiMihRAmKiMih59lwrEb1a0JS2XygAzDT3csO5CLuvgYYAXQnSDq2mdkTSYlKR+pvbdhE8P9T+/243PnhuJFSYAJwrrtXEXyWbeH7fR6f9JqXykXdfTTQBjgT+Lu7J/YjZhERqYcSFBGRQ8/Z7p6f9HoYdrc+PAT8BpgYjkM5IO6+yN3Pd/dOwIkE3baqu3RtA+obs9EVSBC0gFQRdJ2qLQOoTNqeHo4bOQxYDhwX7v8U6Ghm++rSPL3W1+SUFD4eAO5e6e6zgdPM7KxUjxMRkfopQRERkWo3A1sIBn//jiBZOWjcfTHwNHBMuOtl4Lx6qp5PMDalBFhPkGDs7h5mZgYcAXxUzzW2EQxYnxQOWF8IlANnH8SPsicxoE8ariMi0qIpQREREcxsMHAVMMHdnWAAey8zu+wAzjnczCaYWedwewBwFrAorHIb8FUzm2xm7c2sjZn9kGAMyfUA7r6eYHzHXWbW2syygJ8QtJ4soh7hWJo5wP+4exFwCzDVzM42s1wzyzCzM8zs7gP4bAPCc+SE57uYoHXo1c97ThERCShBERE59Pyt1pofzwCPApPdfTWAu1eP5bjHzA77nNcpJEhI3jGzncCLBIPV7w6v8QEwHBgMrCMYe/Jt4HR3X5B0nrFAZ2A18DFwKvDNfYyRuQe43Mw6u/uvgB8BNwFbgQ3ARODZ5GvU+prsrE6s9sAIkrgt4TmvBsYe4KxnIiICWPCgTEREREREpPGpBUVERERERJoMLdQoIiKfm5mdCMyuryxc2b3ZasmfTUSkKVMXLxERERERaTLUxUtERERERJoMJSgiIiIiItJkKEEREREREZEmQwmKiIiIiIg0GUpQRERERESkyfh/vBZ6E5Jkp3oAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "kde_target('previous_loan_counts', train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 459
        },
        "id": "sy6STFIUOxwY",
        "outputId": "9ef771e3-5113-42ef-932a-a63e97fc554d"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The correlation between previous_loan_counts and the TARGET is -0.0100\n",
            "Median value for loan that was not repaid = 3.0000\n",
            "Median value for loan that was repaid =     4.0000\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "데이터 집산: aggreation statistics 공부가 필요하다"
      ],
      "metadata": {
        "id": "VyTRReiX-DSH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#group by the client id, cacluate aggreation statistics\n",
        "bureau_agg = bureau.drop(columns = ['SK_ID_BUREAU']).groupby('SK_ID_CURR', as_index = False).agg(['count', 'mean','max','min','sum']).reset_index()\n",
        "bureau_agg.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 267
        },
        "id": "oAFasPyOrLVc",
        "outputId": "4d8a36ae-ab12-41b9-c326-d3f6ed03ba9f"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  SK_ID_CURR DAYS_CREDIT                               CREDIT_DAY_OVERDUE  \\\n",
              "                   count         mean  max   min   sum              count   \n",
              "0     100001           7  -735.000000  -49 -1572 -5145                  7   \n",
              "1     100002           8  -874.000000 -103 -1437 -6992                  8   \n",
              "2     100003           4 -1400.750000 -606 -2586 -5603                  4   \n",
              "3     100004           2  -867.000000 -408 -1326 -1734                  2   \n",
              "4     100005           3  -190.666667  -62  -373  -572                  3   \n",
              "\n",
              "                ... DAYS_CREDIT_UPDATE                               \\\n",
              "  mean max min  ...              count        mean  max   min   sum   \n",
              "0  0.0   0   0  ...                  7  -93.142857   -6  -155  -652   \n",
              "1  0.0   0   0  ...                  8 -499.875000   -7 -1185 -3999   \n",
              "2  0.0   0   0  ...                  4 -816.000000  -43 -2131 -3264   \n",
              "3  0.0   0   0  ...                  2 -532.000000 -382  -682 -1064   \n",
              "4  0.0   0   0  ...                  3  -54.333333  -11  -121  -163   \n",
              "\n",
              "  AMT_ANNUITY                                      \n",
              "        count         mean      max  min      sum  \n",
              "0           7  3545.357143  10822.5  0.0  24817.5  \n",
              "1           7     0.000000      0.0  0.0      0.0  \n",
              "2           0          NaN      NaN  NaN      0.0  \n",
              "3           0          NaN      NaN  NaN      0.0  \n",
              "4           3  1420.500000   4261.5  0.0   4261.5  \n",
              "\n",
              "[5 rows x 61 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-8a0f8372-4ae7-47ed-9170-47a6a284e965\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr th {\n",
              "        text-align: left;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th>SK_ID_CURR</th>\n",
              "      <th colspan=\"5\" halign=\"left\">DAYS_CREDIT</th>\n",
              "      <th colspan=\"4\" halign=\"left\">CREDIT_DAY_OVERDUE</th>\n",
              "      <th>...</th>\n",
              "      <th colspan=\"5\" halign=\"left\">DAYS_CREDIT_UPDATE</th>\n",
              "      <th colspan=\"5\" halign=\"left\">AMT_ANNUITY</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>max</th>\n",
              "      <th>min</th>\n",
              "      <th>sum</th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>max</th>\n",
              "      <th>min</th>\n",
              "      <th>...</th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>max</th>\n",
              "      <th>min</th>\n",
              "      <th>sum</th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>max</th>\n",
              "      <th>min</th>\n",
              "      <th>sum</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>100001</td>\n",
              "      <td>7</td>\n",
              "      <td>-735.000000</td>\n",
              "      <td>-49</td>\n",
              "      <td>-1572</td>\n",
              "      <td>-5145</td>\n",
              "      <td>7</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>7</td>\n",
              "      <td>-93.142857</td>\n",
              "      <td>-6</td>\n",
              "      <td>-155</td>\n",
              "      <td>-652</td>\n",
              "      <td>7</td>\n",
              "      <td>3545.357143</td>\n",
              "      <td>10822.5</td>\n",
              "      <td>0.0</td>\n",
              "      <td>24817.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>100002</td>\n",
              "      <td>8</td>\n",
              "      <td>-874.000000</td>\n",
              "      <td>-103</td>\n",
              "      <td>-1437</td>\n",
              "      <td>-6992</td>\n",
              "      <td>8</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>8</td>\n",
              "      <td>-499.875000</td>\n",
              "      <td>-7</td>\n",
              "      <td>-1185</td>\n",
              "      <td>-3999</td>\n",
              "      <td>7</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>100003</td>\n",
              "      <td>4</td>\n",
              "      <td>-1400.750000</td>\n",
              "      <td>-606</td>\n",
              "      <td>-2586</td>\n",
              "      <td>-5603</td>\n",
              "      <td>4</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>4</td>\n",
              "      <td>-816.000000</td>\n",
              "      <td>-43</td>\n",
              "      <td>-2131</td>\n",
              "      <td>-3264</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>100004</td>\n",
              "      <td>2</td>\n",
              "      <td>-867.000000</td>\n",
              "      <td>-408</td>\n",
              "      <td>-1326</td>\n",
              "      <td>-1734</td>\n",
              "      <td>2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>2</td>\n",
              "      <td>-532.000000</td>\n",
              "      <td>-382</td>\n",
              "      <td>-682</td>\n",
              "      <td>-1064</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>100005</td>\n",
              "      <td>3</td>\n",
              "      <td>-190.666667</td>\n",
              "      <td>-62</td>\n",
              "      <td>-373</td>\n",
              "      <td>-572</td>\n",
              "      <td>3</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>3</td>\n",
              "      <td>-54.333333</td>\n",
              "      <td>-11</td>\n",
              "      <td>-121</td>\n",
              "      <td>-163</td>\n",
              "      <td>3</td>\n",
              "      <td>1420.500000</td>\n",
              "      <td>4261.5</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4261.5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 61 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8a0f8372-4ae7-47ed-9170-47a6a284e965')\"\n",
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              "                                                     [key], {});\n",
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              "\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#list of column names\n",
        "columns = ['SK_ID_CURR']\n",
        "\n",
        "for var in bureau_agg.columns.levels[0]:\n",
        "    #Skip the id name\n",
        "\n",
        "    if var != 'SK_ID_CURR':\n",
        "\n",
        "        #Iterate through the stat names\n",
        "        for stat in bureau_agg.columns.levels[1][:-1]:\n",
        "            columns.append('bureau_%s_%s' % (var, stat)) #like처럼 특정 연산자만 잡네"
      ],
      "metadata": {
        "id": "qvT2IpzQ-trC"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "bureau_agg.columns = columns\n",
        "bureau_agg.head() "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "1XJ6psQu-JsD",
        "outputId": "16b62abf-9020-40ef-be18-c7cd2b7828c7"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   SK_ID_CURR  bureau_DAYS_CREDIT_count  bureau_DAYS_CREDIT_mean  \\\n",
              "0      100001                         7              -735.000000   \n",
              "1      100002                         8              -874.000000   \n",
              "2      100003                         4             -1400.750000   \n",
              "3      100004                         2              -867.000000   \n",
              "4      100005                         3              -190.666667   \n",
              "\n",
              "   bureau_DAYS_CREDIT_max  bureau_DAYS_CREDIT_min  bureau_DAYS_CREDIT_sum  \\\n",
              "0                     -49                   -1572                   -5145   \n",
              "1                    -103                   -1437                   -6992   \n",
              "2                    -606                   -2586                   -5603   \n",
              "3                    -408                   -1326                   -1734   \n",
              "4                     -62                    -373                    -572   \n",
              "\n",
              "   bureau_CREDIT_DAY_OVERDUE_count  bureau_CREDIT_DAY_OVERDUE_mean  \\\n",
              "0                                7                             0.0   \n",
              "1                                8                             0.0   \n",
              "2                                4                             0.0   \n",
              "3                                2                             0.0   \n",
              "4                                3                             0.0   \n",
              "\n",
              "   bureau_CREDIT_DAY_OVERDUE_max  bureau_CREDIT_DAY_OVERDUE_min  ...  \\\n",
              "0                              0                              0  ...   \n",
              "1                              0                              0  ...   \n",
              "2                              0                              0  ...   \n",
              "3                              0                              0  ...   \n",
              "4                              0                              0  ...   \n",
              "\n",
              "   bureau_DAYS_CREDIT_UPDATE_count  bureau_DAYS_CREDIT_UPDATE_mean  \\\n",
              "0                                7                      -93.142857   \n",
              "1                                8                     -499.875000   \n",
              "2                                4                     -816.000000   \n",
              "3                                2                     -532.000000   \n",
              "4                                3                      -54.333333   \n",
              "\n",
              "   bureau_DAYS_CREDIT_UPDATE_max  bureau_DAYS_CREDIT_UPDATE_min  \\\n",
              "0                             -6                           -155   \n",
              "1                             -7                          -1185   \n",
              "2                            -43                          -2131   \n",
              "3                           -382                           -682   \n",
              "4                            -11                           -121   \n",
              "\n",
              "   bureau_DAYS_CREDIT_UPDATE_sum  bureau_AMT_ANNUITY_count  \\\n",
              "0                           -652                         7   \n",
              "1                          -3999                         7   \n",
              "2                          -3264                         0   \n",
              "3                          -1064                         0   \n",
              "4                           -163                         3   \n",
              "\n",
              "   bureau_AMT_ANNUITY_mean  bureau_AMT_ANNUITY_max  bureau_AMT_ANNUITY_min  \\\n",
              "0              3545.357143                 10822.5                     0.0   \n",
              "1                 0.000000                     0.0                     0.0   \n",
              "2                      NaN                     NaN                     NaN   \n",
              "3                      NaN                     NaN                     NaN   \n",
              "4              1420.500000                  4261.5                     0.0   \n",
              "\n",
              "   bureau_AMT_ANNUITY_sum  \n",
              "0                 24817.5  \n",
              "1                     0.0  \n",
              "2                     0.0  \n",
              "3                     0.0  \n",
              "4                  4261.5  \n",
              "\n",
              "[5 rows x 61 columns]"
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>100003</td>\n",
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              "      <td>-1400.750000</td>\n",
              "      <td>-606</td>\n",
              "      <td>-2586</td>\n",
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              "      <td>-816.000000</td>\n",
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              "      <th>3</th>\n",
              "      <td>100004</td>\n",
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              "      <td>-867.000000</td>\n",
              "      <td>-408</td>\n",
              "      <td>-1326</td>\n",
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              "<p>5 rows × 61 columns</p>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3b619ae2-45db-4805-b96b-71e20e9288c2')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "      </button>\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-3b619ae2-45db-4805-b96b-71e20e9288c2 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-3b619ae2-45db-4805-b96b-71e20e9288c2');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#merge with training data\n",
        "train = train.merge(bureau_agg, on = 'SK_ID_CURR', how='left')\n",
        "train.head()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "-vER8Qy8AX38",
        "outputId": "db08f14c-5926-4bcb-fcb5-2a1e1493f9bb"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   SK_ID_CURR  TARGET NAME_CONTRACT_TYPE CODE_GENDER FLAG_OWN_CAR  \\\n",
              "0      100002       1         Cash loans           M            N   \n",
              "1      100003       0         Cash loans           F            N   \n",
              "2      100004       0    Revolving loans           M            Y   \n",
              "3      100006       0         Cash loans           F            N   \n",
              "4      100007       0         Cash loans           M            N   \n",
              "\n",
              "  FLAG_OWN_REALTY  CNT_CHILDREN  AMT_INCOME_TOTAL  AMT_CREDIT  AMT_ANNUITY  \\\n",
              "0               Y             0          202500.0    406597.5      24700.5   \n",
              "1               N             0          270000.0   1293502.5      35698.5   \n",
              "2               Y             0           67500.0    135000.0       6750.0   \n",
              "3               Y             0          135000.0    312682.5      29686.5   \n",
              "4               Y             0          121500.0    513000.0      21865.5   \n",
              "\n",
              "   ...  bureau_DAYS_CREDIT_UPDATE_count bureau_DAYS_CREDIT_UPDATE_mean  \\\n",
              "0  ...                              8.0                       -499.875   \n",
              "1  ...                              4.0                       -816.000   \n",
              "2  ...                              2.0                       -532.000   \n",
              "3  ...                              NaN                            NaN   \n",
              "4  ...                              1.0                       -783.000   \n",
              "\n",
              "  bureau_DAYS_CREDIT_UPDATE_max bureau_DAYS_CREDIT_UPDATE_min  \\\n",
              "0                          -7.0                       -1185.0   \n",
              "1                         -43.0                       -2131.0   \n",
              "2                        -382.0                        -682.0   \n",
              "3                           NaN                           NaN   \n",
              "4                        -783.0                        -783.0   \n",
              "\n",
              "  bureau_DAYS_CREDIT_UPDATE_sum bureau_AMT_ANNUITY_count  \\\n",
              "0                       -3999.0                      7.0   \n",
              "1                       -3264.0                      0.0   \n",
              "2                       -1064.0                      0.0   \n",
              "3                           NaN                      NaN   \n",
              "4                        -783.0                      0.0   \n",
              "\n",
              "   bureau_AMT_ANNUITY_mean  bureau_AMT_ANNUITY_max  bureau_AMT_ANNUITY_min  \\\n",
              "0                      0.0                     0.0                     0.0   \n",
              "1                      NaN                     NaN                     NaN   \n",
              "2                      NaN                     NaN                     NaN   \n",
              "3                      NaN                     NaN                     NaN   \n",
              "4                      NaN                     NaN                     NaN   \n",
              "\n",
              "   bureau_AMT_ANNUITY_sum  \n",
              "0                     0.0  \n",
              "1                     0.0  \n",
              "2                     0.0  \n",
              "3                     NaN  \n",
              "4                     0.0  \n",
              "\n",
              "[5 rows x 183 columns]"
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              "      <th>SK_ID_CURR</th>\n",
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              "      <th>bureau_AMT_ANNUITY_sum</th>\n",
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              "      <th>0</th>\n",
              "      <td>100002</td>\n",
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              "      <td>Cash loans</td>\n",
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              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>100003</td>\n",
              "      <td>0</td>\n",
              "      <td>Cash loans</td>\n",
              "      <td>F</td>\n",
              "      <td>N</td>\n",
              "      <td>N</td>\n",
              "      <td>0</td>\n",
              "      <td>270000.0</td>\n",
              "      <td>1293502.5</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>100004</td>\n",
              "      <td>0</td>\n",
              "      <td>Revolving loans</td>\n",
              "      <td>M</td>\n",
              "      <td>Y</td>\n",
              "      <td>Y</td>\n",
              "      <td>0</td>\n",
              "      <td>67500.0</td>\n",
              "      <td>135000.0</td>\n",
              "      <td>6750.0</td>\n",
              "      <td>...</td>\n",
              "      <td>2.0</td>\n",
              "      <td>-532.000</td>\n",
              "      <td>-382.0</td>\n",
              "      <td>-682.0</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>100006</td>\n",
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              "      <td>Cash loans</td>\n",
              "      <td>F</td>\n",
              "      <td>N</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#list of new correlations\n",
        "new_corrs = []\n",
        "\n",
        "#iterate through the columns\n",
        "\n",
        "for col in columns:\n",
        "    #Calculate correlation with the target\n",
        "    corr = train[\"TARGET\"].corr(train[col])\n",
        "\n",
        "    #Append the list as a tuple\n",
        "    new_corrs.append((col,corr))"
      ],
      "metadata": {
        "id": "5JAN84p1At27"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#sort the correlations by the absolute value\n",
        "#Make sure to reverse to put the largest values at the front of list\n",
        "new_corrs = sorted(new_corrs, key = lambda x: abs(x[1]), reverse = True)\n",
        "new_corrs[:10]\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tMH6NLaIBD2Z",
        "outputId": "b262fbed-1114-48f5-83c9-a947ffba93ca"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('bureau_DAYS_CREDIT_mean', 0.08972896721998114),\n",
              " ('bureau_DAYS_CREDIT_min', 0.0752482510301036),\n",
              " ('bureau_DAYS_CREDIT_UPDATE_mean', 0.06892735266968673),\n",
              " ('bureau_DAYS_ENDDATE_FACT_min', 0.05588737984392077),\n",
              " ('bureau_DAYS_CREDIT_ENDDATE_sum', 0.0537348956010205),\n",
              " ('bureau_DAYS_ENDDATE_FACT_mean', 0.05319962585758616),\n",
              " ('bureau_DAYS_CREDIT_max', 0.04978205463997299),\n",
              " ('bureau_DAYS_ENDDATE_FACT_sum', 0.048853502611115894),\n",
              " ('bureau_DAYS_CREDIT_ENDDATE_mean', 0.046982754334835494),\n",
              " ('bureau_DAYS_CREDIT_UPDATE_min', 0.042863922470730155)]"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "kde_target('bureau_DAYS_CREDIT_mean', train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 459
        },
        "id": "FdeYC_ojBUiE",
        "outputId": "a5f18311-88c7-40d5-f13e-e5ccf4832f0b"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The correlation between bureau_DAYS_CREDIT_mean and the TARGET is 0.0897\n",
            "Median value for loan that was not repaid = -835.3333\n",
            "Median value for loan that was repaid =     -1067.0000\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Numeric Aggregations 를 위한 함수\n",
        "\n",
        "def agg_numeric(df, group_var, df_name):\n",
        "\n",
        "    \"\"\"Aggregates the numeric values in a dataframe. This can\n",
        "    be used to create features for each instance of the grouping variable.\n",
        "    \n",
        "    Parameters\n",
        "    --------\n",
        "        df (dataframe): \n",
        "            the dataframe to calculate the statistics on\n",
        "        group_var (string): \n",
        "            the variable by which to group df\n",
        "        df_name (string): \n",
        "            the variable used to rename the columns\n",
        "        \n",
        "    Return\n",
        "    --------\n",
        "        agg (dataframe): \n",
        "            a dataframe with the statistics aggregated for \n",
        "            all numeric columns. Each instance of the grouping variable will have \n",
        "            the statistics (mean, min, max, sum; currently supported) calculated. \n",
        "            The columns are also renamed to keep track of features created.\n",
        "    \n",
        "    \"\"\"\n",
        "\n",
        "    #Remove id variables other than grouping variable\n",
        "    for col in df:\n",
        "        if col != group_var and 'SK_ID' in col:\n",
        "            df = df.drop(columns = col)\n",
        "\n",
        "    group_ids = df[group_var]\n",
        "    numeric_df = df.select_dtypes('number')\n",
        "    numeric_df[group_var] = group_ids\n",
        "\n",
        "    #Group by the specified variable and calculate the statistics\n",
        "    agg = numeric_df.groupby(group_var).agg(['count', 'mean', 'max', 'min', 'sum']).reset_index()\n",
        "    #Need to create new column names\n",
        "    columns = [group_var]\n",
        "\n",
        "    #Interate through the variables names\n",
        "    for var in agg.columns.levels[0]:\n",
        "        #Skip the grouping variable\n",
        "        if var != group_var:\n",
        "            #Iterate through the stat names\n",
        "            for stat in agg.columns.levels[1][:-1]:\n",
        "                #make a new colum name for the variable and stat\n",
        "                columns.append('%s_%s_%s' % (df_name, var, stat))\n",
        "\n",
        "    agg.columns = columns\n",
        "    return agg   "
      ],
      "metadata": {
        "id": "NVYTm7yUBbXT"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "bureau_agg_new = agg_numeric(bureau.drop(columns = ['SK_ID_BUREAU']), group_var = 'SK_ID_CURR', df_name = 'bureau')\n",
        "bureau_agg_new.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "16r09lnPEDi6",
        "outputId": "db96bd80-ab9f-4a1e-8574-fd9cef6a3492"
      },
      "execution_count": 17,
      "outputs": [
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          "data": {
            "text/plain": [
              "   SK_ID_CURR  bureau_DAYS_CREDIT_count  bureau_DAYS_CREDIT_mean  \\\n",
              "0      100001                         7              -735.000000   \n",
              "1      100002                         8              -874.000000   \n",
              "2      100003                         4             -1400.750000   \n",
              "3      100004                         2              -867.000000   \n",
              "4      100005                         3              -190.666667   \n",
              "\n",
              "   bureau_DAYS_CREDIT_max  bureau_DAYS_CREDIT_min  bureau_DAYS_CREDIT_sum  \\\n",
              "0                     -49                   -1572                   -5145   \n",
              "1                    -103                   -1437                   -6992   \n",
              "2                    -606                   -2586                   -5603   \n",
              "3                    -408                   -1326                   -1734   \n",
              "4                     -62                    -373                    -572   \n",
              "\n",
              "   bureau_CREDIT_DAY_OVERDUE_count  bureau_CREDIT_DAY_OVERDUE_mean  \\\n",
              "0                                7                             0.0   \n",
              "1                                8                             0.0   \n",
              "2                                4                             0.0   \n",
              "3                                2                             0.0   \n",
              "4                                3                             0.0   \n",
              "\n",
              "   bureau_CREDIT_DAY_OVERDUE_max  bureau_CREDIT_DAY_OVERDUE_min  ...  \\\n",
              "0                              0                              0  ...   \n",
              "1                              0                              0  ...   \n",
              "2                              0                              0  ...   \n",
              "3                              0                              0  ...   \n",
              "4                              0                              0  ...   \n",
              "\n",
              "   bureau_DAYS_CREDIT_UPDATE_count  bureau_DAYS_CREDIT_UPDATE_mean  \\\n",
              "0                                7                      -93.142857   \n",
              "1                                8                     -499.875000   \n",
              "2                                4                     -816.000000   \n",
              "3                                2                     -532.000000   \n",
              "4                                3                      -54.333333   \n",
              "\n",
              "   bureau_DAYS_CREDIT_UPDATE_max  bureau_DAYS_CREDIT_UPDATE_min  \\\n",
              "0                             -6                           -155   \n",
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              "2                            -43                          -2131   \n",
              "3                           -382                           -682   \n",
              "4                            -11                           -121   \n",
              "\n",
              "   bureau_DAYS_CREDIT_UPDATE_sum  bureau_AMT_ANNUITY_count  \\\n",
              "0                           -652                         7   \n",
              "1                          -3999                         7   \n",
              "2                          -3264                         0   \n",
              "3                          -1064                         0   \n",
              "4                           -163                         3   \n",
              "\n",
              "   bureau_AMT_ANNUITY_mean  bureau_AMT_ANNUITY_max  bureau_AMT_ANNUITY_min  \\\n",
              "0              3545.357143                 10822.5                     0.0   \n",
              "1                 0.000000                     0.0                     0.0   \n",
              "2                      NaN                     NaN                     NaN   \n",
              "3                      NaN                     NaN                     NaN   \n",
              "4              1420.500000                  4261.5                     0.0   \n",
              "\n",
              "   bureau_AMT_ANNUITY_sum  \n",
              "0                 24817.5  \n",
              "1                     0.0  \n",
              "2                     0.0  \n",
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    {
      "cell_type": "code",
      "source": [
        "bureau_agg.head()"
      ],
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          "height": 300
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        "id": "9wNldI79EPIo",
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      "execution_count": 18,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "   SK_ID_CURR  bureau_DAYS_CREDIT_count  bureau_DAYS_CREDIT_mean  \\\n",
              "0      100001                         7              -735.000000   \n",
              "1      100002                         8              -874.000000   \n",
              "2      100003                         4             -1400.750000   \n",
              "3      100004                         2              -867.000000   \n",
              "4      100005                         3              -190.666667   \n",
              "\n",
              "   bureau_DAYS_CREDIT_max  bureau_DAYS_CREDIT_min  bureau_DAYS_CREDIT_sum  \\\n",
              "0                     -49                   -1572                   -5145   \n",
              "1                    -103                   -1437                   -6992   \n",
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              "4                     -62                    -373                    -572   \n",
              "\n",
              "   bureau_CREDIT_DAY_OVERDUE_count  bureau_CREDIT_DAY_OVERDUE_mean  \\\n",
              "0                                7                             0.0   \n",
              "1                                8                             0.0   \n",
              "2                                4                             0.0   \n",
              "3                                2                             0.0   \n",
              "4                                3                             0.0   \n",
              "\n",
              "   bureau_CREDIT_DAY_OVERDUE_max  bureau_CREDIT_DAY_OVERDUE_min  ...  \\\n",
              "0                              0                              0  ...   \n",
              "1                              0                              0  ...   \n",
              "2                              0                              0  ...   \n",
              "3                              0                              0  ...   \n",
              "4                              0                              0  ...   \n",
              "\n",
              "   bureau_DAYS_CREDIT_UPDATE_count  bureau_DAYS_CREDIT_UPDATE_mean  \\\n",
              "0                                7                      -93.142857   \n",
              "1                                8                     -499.875000   \n",
              "2                                4                     -816.000000   \n",
              "3                                2                     -532.000000   \n",
              "4                                3                      -54.333333   \n",
              "\n",
              "   bureau_DAYS_CREDIT_UPDATE_max  bureau_DAYS_CREDIT_UPDATE_min  \\\n",
              "0                             -6                           -155   \n",
              "1                             -7                          -1185   \n",
              "2                            -43                          -2131   \n",
              "3                           -382                           -682   \n",
              "4                            -11                           -121   \n",
              "\n",
              "   bureau_DAYS_CREDIT_UPDATE_sum  bureau_AMT_ANNUITY_count  \\\n",
              "0                           -652                         7   \n",
              "1                          -3999                         7   \n",
              "2                          -3264                         0   \n",
              "3                          -1064                         0   \n",
              "4                           -163                         3   \n",
              "\n",
              "   bureau_AMT_ANNUITY_mean  bureau_AMT_ANNUITY_max  bureau_AMT_ANNUITY_min  \\\n",
              "0              3545.357143                 10822.5                     0.0   \n",
              "1                 0.000000                     0.0                     0.0   \n",
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              "      <th>...</th>\n",
              "      <th>bureau_DAYS_CREDIT_UPDATE_count</th>\n",
              "      <th>bureau_DAYS_CREDIT_UPDATE_mean</th>\n",
              "      <th>bureau_DAYS_CREDIT_UPDATE_max</th>\n",
              "      <th>bureau_DAYS_CREDIT_UPDATE_min</th>\n",
              "      <th>bureau_DAYS_CREDIT_UPDATE_sum</th>\n",
              "      <th>bureau_AMT_ANNUITY_count</th>\n",
              "      <th>bureau_AMT_ANNUITY_mean</th>\n",
              "      <th>bureau_AMT_ANNUITY_max</th>\n",
              "      <th>bureau_AMT_ANNUITY_min</th>\n",
              "      <th>bureau_AMT_ANNUITY_sum</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>100001</td>\n",
              "      <td>7</td>\n",
              "      <td>-735.000000</td>\n",
              "      <td>-49</td>\n",
              "      <td>-1572</td>\n",
              "      <td>-5145</td>\n",
              "      <td>7</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>7</td>\n",
              "      <td>-93.142857</td>\n",
              "      <td>-6</td>\n",
              "      <td>-155</td>\n",
              "      <td>-652</td>\n",
              "      <td>7</td>\n",
              "      <td>3545.357143</td>\n",
              "      <td>10822.5</td>\n",
              "      <td>0.0</td>\n",
              "      <td>24817.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>100002</td>\n",
              "      <td>8</td>\n",
              "      <td>-874.000000</td>\n",
              "      <td>-103</td>\n",
              "      <td>-1437</td>\n",
              "      <td>-6992</td>\n",
              "      <td>8</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>8</td>\n",
              "      <td>-499.875000</td>\n",
              "      <td>-7</td>\n",
              "      <td>-1185</td>\n",
              "      <td>-3999</td>\n",
              "      <td>7</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>100003</td>\n",
              "      <td>4</td>\n",
              "      <td>-1400.750000</td>\n",
              "      <td>-606</td>\n",
              "      <td>-2586</td>\n",
              "      <td>-5603</td>\n",
              "      <td>4</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>4</td>\n",
              "      <td>-816.000000</td>\n",
              "      <td>-43</td>\n",
              "      <td>-2131</td>\n",
              "      <td>-3264</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>100004</td>\n",
              "      <td>2</td>\n",
              "      <td>-867.000000</td>\n",
              "      <td>-408</td>\n",
              "      <td>-1326</td>\n",
              "      <td>-1734</td>\n",
              "      <td>2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>2</td>\n",
              "      <td>-532.000000</td>\n",
              "      <td>-382</td>\n",
              "      <td>-682</td>\n",
              "      <td>-1064</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>100005</td>\n",
              "      <td>3</td>\n",
              "      <td>-190.666667</td>\n",
              "      <td>-62</td>\n",
              "      <td>-373</td>\n",
              "      <td>-572</td>\n",
              "      <td>3</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>3</td>\n",
              "      <td>-54.333333</td>\n",
              "      <td>-11</td>\n",
              "      <td>-121</td>\n",
              "      <td>-163</td>\n",
              "      <td>3</td>\n",
              "      <td>1420.500000</td>\n",
              "      <td>4261.5</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4261.5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 61 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-67d7686c-04ef-4382-acbb-85e2b59ec575')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#correlation Function\n",
        "\n",
        "#Function to calculate correlations with the target for a dataframe\n",
        "\n",
        "def target_corrs(df):\n",
        "\n",
        "    #list of columns\n",
        "    for col in df.columns:\n",
        "        print(col)\n",
        "        #Skip the target column\n",
        "\n",
        "        if col != 'TARGET':\n",
        "            corr = df[TARGET].corr(df[col])\n",
        "\n",
        "            corrs.append((col,corr))\n",
        "    #sort by absolute magnitude of correlations\n",
        "\n",
        "    corrs = sorted(corrs, key = lambda x: abs(x[1]), reverse = True)\n",
        "\n",
        "    return corrs"
      ],
      "metadata": {
        "id": "JGb-16eSEYn_"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Categorical Variables\n",
        "\n"
      ],
      "metadata": {
        "id": "j4xXIN6RnLwB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "categorical = pd.get_dummies(bureau.select_dtypes('object'))\n",
        "categorical['SK_ID_CURR'] = bureau['SK_ID_CURR']\n",
        "categorical.head()"
      ],
      "metadata": {
        "id": "huYq67h_FJU2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "5fabec90-31c1-4691-82d5-41e9adf679b5"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   CREDIT_ACTIVE_Active  CREDIT_ACTIVE_Bad debt  CREDIT_ACTIVE_Closed  \\\n",
              "0                     0                       0                     1   \n",
              "1                     1                       0                     0   \n",
              "2                     1                       0                     0   \n",
              "3                     1                       0                     0   \n",
              "4                     1                       0                     0   \n",
              "\n",
              "   CREDIT_ACTIVE_Sold  CREDIT_CURRENCY_currency 1  CREDIT_CURRENCY_currency 2  \\\n",
              "0                   0                           1                           0   \n",
              "1                   0                           1                           0   \n",
              "2                   0                           1                           0   \n",
              "3                   0                           1                           0   \n",
              "4                   0                           1                           0   \n",
              "\n",
              "   CREDIT_CURRENCY_currency 3  CREDIT_CURRENCY_currency 4  \\\n",
              "0                           0                           0   \n",
              "1                           0                           0   \n",
              "2                           0                           0   \n",
              "3                           0                           0   \n",
              "4                           0                           0   \n",
              "\n",
              "   CREDIT_TYPE_Another type of loan  CREDIT_TYPE_Car loan  ...  \\\n",
              "0                                 0                     0  ...   \n",
              "1                                 0                     0  ...   \n",
              "2                                 0                     0  ...   \n",
              "3                                 0                     0  ...   \n",
              "4                                 0                     0  ...   \n",
              "\n",
              "   CREDIT_TYPE_Loan for business development  \\\n",
              "0                                          0   \n",
              "1                                          0   \n",
              "2                                          0   \n",
              "3                                          0   \n",
              "4                                          0   \n",
              "\n",
              "   CREDIT_TYPE_Loan for purchase of shares (margin lending)  \\\n",
              "0                                                  0          \n",
              "1                                                  0          \n",
              "2                                                  0          \n",
              "3                                                  0          \n",
              "4                                                  0          \n",
              "\n",
              "   CREDIT_TYPE_Loan for the purchase of equipment  \\\n",
              "0                                               0   \n",
              "1                                               0   \n",
              "2                                               0   \n",
              "3                                               0   \n",
              "4                                               0   \n",
              "\n",
              "   CREDIT_TYPE_Loan for working capital replenishment  CREDIT_TYPE_Microloan  \\\n",
              "0                                                  0                       0   \n",
              "1                                                  0                       0   \n",
              "2                                                  0                       0   \n",
              "3                                                  0                       0   \n",
              "4                                                  0                       0   \n",
              "\n",
              "   CREDIT_TYPE_Mobile operator loan  CREDIT_TYPE_Mortgage  \\\n",
              "0                                 0                     0   \n",
              "1                                 0                     0   \n",
              "2                                 0                     0   \n",
              "3                                 0                     0   \n",
              "4                                 0                     0   \n",
              "\n",
              "   CREDIT_TYPE_Real estate loan  CREDIT_TYPE_Unknown type of loan  SK_ID_CURR  \n",
              "0                             0                                 0      215354  \n",
              "1                             0                                 0      215354  \n",
              "2                             0                                 0      215354  \n",
              "3                             0                                 0      215354  \n",
              "4                             0                                 0      215354  \n",
              "\n",
              "[5 rows x 24 columns]"
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              "      <th></th>\n",
              "      <th>CREDIT_ACTIVE_Active</th>\n",
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              "      <th>CREDIT_TYPE_Car loan</th>\n",
              "      <th>...</th>\n",
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              "      <th>CREDIT_TYPE_Loan for purchase of shares (margin lending)</th>\n",
              "      <th>CREDIT_TYPE_Loan for the purchase of equipment</th>\n",
              "      <th>CREDIT_TYPE_Loan for working capital replenishment</th>\n",
              "      <th>CREDIT_TYPE_Microloan</th>\n",
              "      <th>CREDIT_TYPE_Mobile operator loan</th>\n",
              "      <th>CREDIT_TYPE_Mortgage</th>\n",
              "      <th>CREDIT_TYPE_Real estate loan</th>\n",
              "      <th>CREDIT_TYPE_Unknown type of loan</th>\n",
              "      <th>SK_ID_CURR</th>\n",
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              "  <tbody>\n",
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              "      <th>0</th>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>215354</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>215354</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>215354</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>215354</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>215354</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 24 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-cf598e9c-46e0-4a07-8276-8e15bf10f887')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "          const element = document.querySelector('#df-cf598e9c-46e0-4a07-8276-8e15bf10f887');\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "categorical_grouped = categorical.groupby('SK_ID_CURR').agg(['sum','mean'])\n",
        "categorical_grouped.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "4TEC_uLMnvpr",
        "outputId": "23f054ff-eb3b-4545-d806-eb2e75ded06c"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           CREDIT_ACTIVE_Active           CREDIT_ACTIVE_Bad debt       \\\n",
              "                            sum      mean                    sum mean   \n",
              "SK_ID_CURR                                                              \n",
              "100001                        3  0.428571                      0  0.0   \n",
              "100002                        2  0.250000                      0  0.0   \n",
              "100003                        1  0.250000                      0  0.0   \n",
              "100004                        0  0.000000                      0  0.0   \n",
              "100005                        2  0.666667                      0  0.0   \n",
              "\n",
              "           CREDIT_ACTIVE_Closed           CREDIT_ACTIVE_Sold       \\\n",
              "                            sum      mean                sum mean   \n",
              "SK_ID_CURR                                                          \n",
              "100001                        4  0.571429                  0  0.0   \n",
              "100002                        6  0.750000                  0  0.0   \n",
              "100003                        3  0.750000                  0  0.0   \n",
              "100004                        2  1.000000                  0  0.0   \n",
              "100005                        1  0.333333                  0  0.0   \n",
              "\n",
              "           CREDIT_CURRENCY_currency 1       ... CREDIT_TYPE_Microloan       \\\n",
              "                                  sum mean  ...                   sum mean   \n",
              "SK_ID_CURR                                  ...                              \n",
              "100001                              7  1.0  ...                     0  0.0   \n",
              "100002                              8  1.0  ...                     0  0.0   \n",
              "100003                              4  1.0  ...                     0  0.0   \n",
              "100004                              2  1.0  ...                     0  0.0   \n",
              "100005                              3  1.0  ...                     0  0.0   \n",
              "\n",
              "           CREDIT_TYPE_Mobile operator loan      CREDIT_TYPE_Mortgage       \\\n",
              "                                        sum mean                  sum mean   \n",
              "SK_ID_CURR                                                                   \n",
              "100001                                    0  0.0                    0  0.0   \n",
              "100002                                    0  0.0                    0  0.0   \n",
              "100003                                    0  0.0                    0  0.0   \n",
              "100004                                    0  0.0                    0  0.0   \n",
              "100005                                    0  0.0                    0  0.0   \n",
              "\n",
              "           CREDIT_TYPE_Real estate loan      CREDIT_TYPE_Unknown type of loan  \\\n",
              "                                    sum mean                              sum   \n",
              "SK_ID_CURR                                                                      \n",
              "100001                                0  0.0                                0   \n",
              "100002                                0  0.0                                0   \n",
              "100003                                0  0.0                                0   \n",
              "100004                                0  0.0                                0   \n",
              "100005                                0  0.0                                0   \n",
              "\n",
              "                 \n",
              "           mean  \n",
              "SK_ID_CURR       \n",
              "100001      0.0  \n",
              "100002      0.0  \n",
              "100003      0.0  \n",
              "100004      0.0  \n",
              "100005      0.0  \n",
              "\n",
              "[5 rows x 46 columns]"
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              "\n",
              "  <div id=\"df-541a8c85-0201-4ca7-9279-5d6dfb5de130\">\n",
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              "      <th colspan=\"2\" halign=\"left\">CREDIT_ACTIVE_Active</th>\n",
              "      <th colspan=\"2\" halign=\"left\">CREDIT_ACTIVE_Bad debt</th>\n",
              "      <th colspan=\"2\" halign=\"left\">CREDIT_ACTIVE_Closed</th>\n",
              "      <th colspan=\"2\" halign=\"left\">CREDIT_ACTIVE_Sold</th>\n",
              "      <th colspan=\"2\" halign=\"left\">CREDIT_CURRENCY_currency 1</th>\n",
              "      <th>...</th>\n",
              "      <th colspan=\"2\" halign=\"left\">CREDIT_TYPE_Microloan</th>\n",
              "      <th colspan=\"2\" halign=\"left\">CREDIT_TYPE_Mobile operator loan</th>\n",
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              "      <th colspan=\"2\" halign=\"left\">CREDIT_TYPE_Real estate loan</th>\n",
              "      <th colspan=\"2\" halign=\"left\">CREDIT_TYPE_Unknown type of loan</th>\n",
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              "    <tr>\n",
              "      <th></th>\n",
              "      <th>sum</th>\n",
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              "      <th>sum</th>\n",
              "      <th>mean</th>\n",
              "      <th>sum</th>\n",
              "      <th>mean</th>\n",
              "      <th>sum</th>\n",
              "      <th>mean</th>\n",
              "      <th>sum</th>\n",
              "      <th>mean</th>\n",
              "      <th>...</th>\n",
              "      <th>sum</th>\n",
              "      <th>mean</th>\n",
              "      <th>sum</th>\n",
              "      <th>mean</th>\n",
              "      <th>sum</th>\n",
              "      <th>mean</th>\n",
              "      <th>sum</th>\n",
              "      <th>mean</th>\n",
              "      <th>sum</th>\n",
              "      <th>mean</th>\n",
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              "    <tr>\n",
              "      <th>SK_ID_CURR</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>100001</th>\n",
              "      <td>3</td>\n",
              "      <td>0.428571</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4</td>\n",
              "      <td>0.571429</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7</td>\n",
              "      <td>1.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>100002</th>\n",
              "      <td>2</td>\n",
              "      <td>0.250000</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6</td>\n",
              "      <td>0.750000</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8</td>\n",
              "      <td>1.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>100003</th>\n",
              "      <td>1</td>\n",
              "      <td>0.250000</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3</td>\n",
              "      <td>0.750000</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4</td>\n",
              "      <td>1.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>100004</th>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>1.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 46 columns</p>\n",
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              "                                                     [key], {});\n",
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              "\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "categorical_grouped.columns.levels[0][:10]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nfDK1Q8An9H5",
        "outputId": "e9c13912-69f6-46d9-f36b-449e7947baed"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['CREDIT_ACTIVE_Active', 'CREDIT_ACTIVE_Bad debt',\n",
              "       'CREDIT_ACTIVE_Closed', 'CREDIT_ACTIVE_Sold',\n",
              "       'CREDIT_CURRENCY_currency 1', 'CREDIT_CURRENCY_currency 2',\n",
              "       'CREDIT_CURRENCY_currency 3', 'CREDIT_CURRENCY_currency 4',\n",
              "       'CREDIT_TYPE_Another type of loan', 'CREDIT_TYPE_Car loan'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "categorical_grouped.columns.levels[1]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IIhCe7R8oYPF",
        "outputId": "db97ed17-3dbd-4c32-b311-20198891785e"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['sum', 'mean'], dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "group_var = 'SK_ID_CURR'\n",
        "\n",
        "# Need to create new column names\n",
        "columns = []\n",
        "\n",
        "# Iterate through the variables names\n",
        "for var in categorical_grouped.columns.levels[0]:\n",
        "    # Skip the grouping variable\n",
        "    if var != group_var:\n",
        "        # Iterate through the stat names\n",
        "        for stat in ['count', 'count_norm']:\n",
        "            # Make a new column name for the variable and stat\n",
        "            columns.append('%s_%s' % (var, stat))\n",
        "\n",
        "#  Rename the columns\n",
        "categorical_grouped.columns = columns\n",
        "\n",
        "categorical_grouped.head()"
      ],
      "metadata": {
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          "base_uri": "https://localhost:8080/",
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        "id": "jDXJwBVAoapp",
        "outputId": "f101d2f1-ddb3-47e6-d021-3e7186b9b566"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            CREDIT_ACTIVE_Active_count  CREDIT_ACTIVE_Active_count_norm  \\\n",
              "SK_ID_CURR                                                                \n",
              "100001                               3                         0.428571   \n",
              "100002                               2                         0.250000   \n",
              "100003                               1                         0.250000   \n",
              "100004                               0                         0.000000   \n",
              "100005                               2                         0.666667   \n",
              "\n",
              "            CREDIT_ACTIVE_Bad debt_count  CREDIT_ACTIVE_Bad debt_count_norm  \\\n",
              "SK_ID_CURR                                                                    \n",
              "100001                                 0                                0.0   \n",
              "100002                                 0                                0.0   \n",
              "100003                                 0                                0.0   \n",
              "100004                                 0                                0.0   \n",
              "100005                                 0                                0.0   \n",
              "\n",
              "            CREDIT_ACTIVE_Closed_count  CREDIT_ACTIVE_Closed_count_norm  \\\n",
              "SK_ID_CURR                                                                \n",
              "100001                               4                         0.571429   \n",
              "100002                               6                         0.750000   \n",
              "100003                               3                         0.750000   \n",
              "100004                               2                         1.000000   \n",
              "100005                               1                         0.333333   \n",
              "\n",
              "            CREDIT_ACTIVE_Sold_count  CREDIT_ACTIVE_Sold_count_norm  \\\n",
              "SK_ID_CURR                                                            \n",
              "100001                             0                            0.0   \n",
              "100002                             0                            0.0   \n",
              "100003                             0                            0.0   \n",
              "100004                             0                            0.0   \n",
              "100005                             0                            0.0   \n",
              "\n",
              "            CREDIT_CURRENCY_currency 1_count  \\\n",
              "SK_ID_CURR                                     \n",
              "100001                                     7   \n",
              "100002                                     8   \n",
              "100003                                     4   \n",
              "100004                                     2   \n",
              "100005                                     3   \n",
              "\n",
              "            CREDIT_CURRENCY_currency 1_count_norm  ...  \\\n",
              "SK_ID_CURR                                         ...   \n",
              "100001                                        1.0  ...   \n",
              "100002                                        1.0  ...   \n",
              "100003                                        1.0  ...   \n",
              "100004                                        1.0  ...   \n",
              "100005                                        1.0  ...   \n",
              "\n",
              "            CREDIT_TYPE_Microloan_count  CREDIT_TYPE_Microloan_count_norm  \\\n",
              "SK_ID_CURR                                                                  \n",
              "100001                                0                               0.0   \n",
              "100002                                0                               0.0   \n",
              "100003                                0                               0.0   \n",
              "100004                                0                               0.0   \n",
              "100005                                0                               0.0   \n",
              "\n",
              "            CREDIT_TYPE_Mobile operator loan_count  \\\n",
              "SK_ID_CURR                                           \n",
              "100001                                           0   \n",
              "100002                                           0   \n",
              "100003                                           0   \n",
              "100004                                           0   \n",
              "100005                                           0   \n",
              "\n",
              "            CREDIT_TYPE_Mobile operator loan_count_norm  \\\n",
              "SK_ID_CURR                                                \n",
              "100001                                              0.0   \n",
              "100002                                              0.0   \n",
              "100003                                              0.0   \n",
              "100004                                              0.0   \n",
              "100005                                              0.0   \n",
              "\n",
              "            CREDIT_TYPE_Mortgage_count  CREDIT_TYPE_Mortgage_count_norm  \\\n",
              "SK_ID_CURR                                                                \n",
              "100001                               0                              0.0   \n",
              "100002                               0                              0.0   \n",
              "100003                               0                              0.0   \n",
              "100004                               0                              0.0   \n",
              "100005                               0                              0.0   \n",
              "\n",
              "            CREDIT_TYPE_Real estate loan_count  \\\n",
              "SK_ID_CURR                                       \n",
              "100001                                       0   \n",
              "100002                                       0   \n",
              "100003                                       0   \n",
              "100004                                       0   \n",
              "100005                                       0   \n",
              "\n",
              "            CREDIT_TYPE_Real estate loan_count_norm  \\\n",
              "SK_ID_CURR                                            \n",
              "100001                                          0.0   \n",
              "100002                                          0.0   \n",
              "100003                                          0.0   \n",
              "100004                                          0.0   \n",
              "100005                                          0.0   \n",
              "\n",
              "            CREDIT_TYPE_Unknown type of loan_count  \\\n",
              "SK_ID_CURR                                           \n",
              "100001                                           0   \n",
              "100002                                           0   \n",
              "100003                                           0   \n",
              "100004                                           0   \n",
              "100005                                           0   \n",
              "\n",
              "            CREDIT_TYPE_Unknown type of loan_count_norm  \n",
              "SK_ID_CURR                                               \n",
              "100001                                              0.0  \n",
              "100002                                              0.0  \n",
              "100003                                              0.0  \n",
              "100004                                              0.0  \n",
              "100005                                              0.0  \n",
              "\n",
              "[5 rows x 46 columns]"
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              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "    <tr>\n",
              "      <th>100001</th>\n",
              "      <td>3</td>\n",
              "      <td>0.428571</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4</td>\n",
              "      <td>0.571429</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7</td>\n",
              "      <td>1.0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0.0</td>\n",
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              "      <th>100002</th>\n",
              "      <td>2</td>\n",
              "      <td>0.250000</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6</td>\n",
              "      <td>0.750000</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8</td>\n",
              "      <td>1.0</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>100003</th>\n",
              "      <td>1</td>\n",
              "      <td>0.250000</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
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              "      <th>100004</th>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
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          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train = train.merge(categorical_grouped, left_on = 'SK_ID_CURR', right_index = True, how = 'left') #how는 조인이네 left, right, outer\n",
        "train.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "E06IbLdAoasQ",
        "outputId": "9c0ccae4-7813-4ded-b6c6-489c2965bafa"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   SK_ID_CURR  TARGET NAME_CONTRACT_TYPE CODE_GENDER FLAG_OWN_CAR  \\\n",
              "0      100002       1         Cash loans           M            N   \n",
              "1      100003       0         Cash loans           F            N   \n",
              "2      100004       0    Revolving loans           M            Y   \n",
              "3      100006       0         Cash loans           F            N   \n",
              "4      100007       0         Cash loans           M            N   \n",
              "\n",
              "  FLAG_OWN_REALTY  CNT_CHILDREN  AMT_INCOME_TOTAL  AMT_CREDIT  AMT_ANNUITY  \\\n",
              "0               Y             0          202500.0    406597.5      24700.5   \n",
              "1               N             0          270000.0   1293502.5      35698.5   \n",
              "2               Y             0           67500.0    135000.0       6750.0   \n",
              "3               Y             0          135000.0    312682.5      29686.5   \n",
              "4               Y             0          121500.0    513000.0      21865.5   \n",
              "\n",
              "   ...  CREDIT_TYPE_Microloan_count CREDIT_TYPE_Microloan_count_norm  \\\n",
              "0  ...                          0.0                              0.0   \n",
              "1  ...                          0.0                              0.0   \n",
              "2  ...                          0.0                              0.0   \n",
              "3  ...                          NaN                              NaN   \n",
              "4  ...                          0.0                              0.0   \n",
              "\n",
              "  CREDIT_TYPE_Mobile operator loan_count  \\\n",
              "0                                    0.0   \n",
              "1                                    0.0   \n",
              "2                                    0.0   \n",
              "3                                    NaN   \n",
              "4                                    0.0   \n",
              "\n",
              "  CREDIT_TYPE_Mobile operator loan_count_norm CREDIT_TYPE_Mortgage_count  \\\n",
              "0                                         0.0                        0.0   \n",
              "1                                         0.0                        0.0   \n",
              "2                                         0.0                        0.0   \n",
              "3                                         NaN                        NaN   \n",
              "4                                         0.0                        0.0   \n",
              "\n",
              "  CREDIT_TYPE_Mortgage_count_norm  CREDIT_TYPE_Real estate loan_count  \\\n",
              "0                             0.0                                 0.0   \n",
              "1                             0.0                                 0.0   \n",
              "2                             0.0                                 0.0   \n",
              "3                             NaN                                 NaN   \n",
              "4                             0.0                                 0.0   \n",
              "\n",
              "   CREDIT_TYPE_Real estate loan_count_norm  \\\n",
              "0                                      0.0   \n",
              "1                                      0.0   \n",
              "2                                      0.0   \n",
              "3                                      NaN   \n",
              "4                                      0.0   \n",
              "\n",
              "   CREDIT_TYPE_Unknown type of loan_count  \\\n",
              "0                                     0.0   \n",
              "1                                     0.0   \n",
              "2                                     0.0   \n",
              "3                                     NaN   \n",
              "4                                     0.0   \n",
              "\n",
              "   CREDIT_TYPE_Unknown type of loan_count_norm  \n",
              "0                                          0.0  \n",
              "1                                          0.0  \n",
              "2                                          0.0  \n",
              "3                                          NaN  \n",
              "4                                          0.0  \n",
              "\n",
              "[5 rows x 229 columns]"
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              "      <th>TARGET</th>\n",
              "      <th>NAME_CONTRACT_TYPE</th>\n",
              "      <th>CODE_GENDER</th>\n",
              "      <th>FLAG_OWN_CAR</th>\n",
              "      <th>FLAG_OWN_REALTY</th>\n",
              "      <th>CNT_CHILDREN</th>\n",
              "      <th>AMT_INCOME_TOTAL</th>\n",
              "      <th>AMT_CREDIT</th>\n",
              "      <th>AMT_ANNUITY</th>\n",
              "      <th>...</th>\n",
              "      <th>CREDIT_TYPE_Microloan_count</th>\n",
              "      <th>CREDIT_TYPE_Microloan_count_norm</th>\n",
              "      <th>CREDIT_TYPE_Mobile operator loan_count</th>\n",
              "      <th>CREDIT_TYPE_Mobile operator loan_count_norm</th>\n",
              "      <th>CREDIT_TYPE_Mortgage_count</th>\n",
              "      <th>CREDIT_TYPE_Mortgage_count_norm</th>\n",
              "      <th>CREDIT_TYPE_Real estate loan_count</th>\n",
              "      <th>CREDIT_TYPE_Real estate loan_count_norm</th>\n",
              "      <th>CREDIT_TYPE_Unknown type of loan_count</th>\n",
              "      <th>CREDIT_TYPE_Unknown type of loan_count_norm</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>100002</td>\n",
              "      <td>1</td>\n",
              "      <td>Cash loans</td>\n",
              "      <td>M</td>\n",
              "      <td>N</td>\n",
              "      <td>Y</td>\n",
              "      <td>0</td>\n",
              "      <td>202500.0</td>\n",
              "      <td>406597.5</td>\n",
              "      <td>24700.5</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>100003</td>\n",
              "      <td>0</td>\n",
              "      <td>Cash loans</td>\n",
              "      <td>F</td>\n",
              "      <td>N</td>\n",
              "      <td>N</td>\n",
              "      <td>0</td>\n",
              "      <td>270000.0</td>\n",
              "      <td>1293502.5</td>\n",
              "      <td>35698.5</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>100004</td>\n",
              "      <td>0</td>\n",
              "      <td>Revolving loans</td>\n",
              "      <td>M</td>\n",
              "      <td>Y</td>\n",
              "      <td>Y</td>\n",
              "      <td>0</td>\n",
              "      <td>67500.0</td>\n",
              "      <td>135000.0</td>\n",
              "      <td>6750.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>100006</td>\n",
              "      <td>0</td>\n",
              "      <td>Cash loans</td>\n",
              "      <td>F</td>\n",
              "      <td>N</td>\n",
              "      <td>Y</td>\n",
              "      <td>0</td>\n",
              "      <td>135000.0</td>\n",
              "      <td>312682.5</td>\n",
              "      <td>29686.5</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>100007</td>\n",
              "      <td>0</td>\n",
              "      <td>Cash loans</td>\n",
              "      <td>M</td>\n",
              "      <td>N</td>\n",
              "      <td>Y</td>\n",
              "      <td>0</td>\n",
              "      <td>121500.0</td>\n",
              "      <td>513000.0</td>\n",
              "      <td>21865.5</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 229 columns</p>\n",
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          "metadata": {},
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            "text/plain": [
              "(307511, 229)"
            ]
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          "metadata": {},
          "execution_count": 26
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      "execution_count": 27,
      "outputs": [
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   bureau_DAYS_CREDIT_count  bureau_DAYS_CREDIT_mean  bureau_DAYS_CREDIT_max  \\\n",
              "0                       8.0              -874.000000                  -103.0   \n",
              "1                       4.0             -1400.750000                  -606.0   \n",
              "2                       2.0              -867.000000                  -408.0   \n",
              "3                       NaN                      NaN                     NaN   \n",
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              "5                       3.0              -757.333333                   -78.0   \n",
              "6                      18.0             -1271.500000                  -239.0   \n",
              "7                       2.0             -1939.500000                 -1138.0   \n",
              "8                       4.0             -1773.000000                 -1309.0   \n",
              "9                       NaN                      NaN                     NaN   \n",
              "\n",
              "   bureau_DAYS_CREDIT_min  bureau_DAYS_CREDIT_sum  \\\n",
              "0                 -1437.0                 -6992.0   \n",
              "1                 -2586.0                 -5603.0   \n",
              "2                 -1326.0                 -1734.0   \n",
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              "7                 -2741.0                 -3879.0   \n",
              "8                 -2508.0                 -7092.0   \n",
              "9                     NaN                     NaN   \n",
              "\n",
              "   bureau_CREDIT_DAY_OVERDUE_count  bureau_CREDIT_DAY_OVERDUE_mean  \\\n",
              "0                              8.0                             0.0   \n",
              "1                              4.0                             0.0   \n",
              "2                              2.0                             0.0   \n",
              "3                              NaN                             NaN   \n",
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              "5                              3.0                             0.0   \n",
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              "7                              2.0                             0.0   \n",
              "8                              4.0                             0.0   \n",
              "9                              NaN                             NaN   \n",
              "\n",
              "   bureau_CREDIT_DAY_OVERDUE_max  bureau_CREDIT_DAY_OVERDUE_min  \\\n",
              "0                            0.0                            0.0   \n",
              "1                            0.0                            0.0   \n",
              "2                            0.0                            0.0   \n",
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              "8                            0.0                            0.0   \n",
              "9                            NaN                            NaN   \n",
              "\n",
              "   bureau_CREDIT_DAY_OVERDUE_sum  ...  CREDIT_TYPE_Microloan_count  \\\n",
              "0                            0.0  ...                          0.0   \n",
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              "9                            NaN  ...                          NaN   \n",
              "\n",
              "   CREDIT_TYPE_Microloan_count_norm  CREDIT_TYPE_Mobile operator loan_count  \\\n",
              "0                               0.0                                     0.0   \n",
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              "8                               0.0                                     0.0   \n",
              "9                               NaN                                     NaN   \n",
              "\n",
              "   CREDIT_TYPE_Mobile operator loan_count_norm  CREDIT_TYPE_Mortgage_count  \\\n",
              "0                                          0.0                         0.0   \n",
              "1                                          0.0                         0.0   \n",
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              "9                                          NaN                         NaN   \n",
              "\n",
              "   CREDIT_TYPE_Mortgage_count_norm  CREDIT_TYPE_Real estate loan_count  \\\n",
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              "8                              0.0                                 0.0   \n",
              "9                              NaN                                 NaN   \n",
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              "   CREDIT_TYPE_Real estate loan_count_norm  \\\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2.0</td>\n",
              "      <td>-867.000000</td>\n",
              "      <td>-408.0</td>\n",
              "      <td>-1326.0</td>\n",
              "      <td>-1734.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.0</td>\n",
              "      <td>-1149.000000</td>\n",
              "      <td>-1149.0</td>\n",
              "      <td>-1149.0</td>\n",
              "      <td>-1149.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>3.0</td>\n",
              "      <td>-757.333333</td>\n",
              "      <td>-78.0</td>\n",
              "      <td>-1097.0</td>\n",
              "      <td>-2272.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>18.0</td>\n",
              "      <td>-1271.500000</td>\n",
              "      <td>-239.0</td>\n",
              "      <td>-2882.0</td>\n",
              "      <td>-22887.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>2.0</td>\n",
              "      <td>-1939.500000</td>\n",
              "      <td>-1138.0</td>\n",
              "      <td>-2741.0</td>\n",
              "      <td>-3879.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>4.0</td>\n",
              "      <td>-1773.000000</td>\n",
              "      <td>-1309.0</td>\n",
              "      <td>-2508.0</td>\n",
              "      <td>-7092.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>10 rows × 106 columns</p>\n",
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          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Function to Handle Categorical Variables\n",
        "\n",
        "agg_numeric accepts a dataframe and a grouping variable\n",
        "\n",
        "Calculate the counts and normalized counts of each category"
      ],
      "metadata": {
        "id": "W4lyS1Gor_g1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def count_categorical(df, group_var, df_name):\n",
        "    \"\"\"Computes counts and normalized counts for each observation\n",
        "    of `group_var` of each unique category in every categorical variable\n",
        "    \n",
        "    Parameters\n",
        "    --------\n",
        "    df : dataframe \n",
        "        The dataframe to calculate the value counts for.\n",
        "        \n",
        "    group_var : string\n",
        "        The variable by which to group the dataframe. For each unique\n",
        "        value of this variable, the final dataframe will have one row\n",
        "        \n",
        "    df_name : string\n",
        "        Variable added to the front of column names to keep track of columns\n",
        "\n",
        "    \n",
        "    Return\n",
        "    --------\n",
        "    categorical : dataframe\n",
        "        A dataframe with counts and normalized counts of each unique category in every categorical variable\n",
        "        with one row for every unique value of the `group_var`.\n",
        "        \n",
        "    \"\"\"\n",
        "    \n",
        "    # Select the categorical columns\n",
        "    categorical = pd.get_dummies(df.select_dtypes('object'))\n",
        "\n",
        "    # Make sure to put the identifying id on the column\n",
        "    categorical[group_var] = df[group_var]\n",
        "\n",
        "    # Groupby the group var and calculate the sum and mean\n",
        "    categorical = categorical.groupby(group_var).agg(['sum', 'mean'])\n",
        "    \n",
        "    column_names = []\n",
        "    \n",
        "    # Iterate through the columns in level 0\n",
        "    for var in categorical.columns.levels[0]:\n",
        "        # Iterate through the stats in level 1\n",
        "        for stat in ['count', 'count_norm']:\n",
        "            # Make a new column name\n",
        "            column_names.append('%s_%s_%s' % (df_name, var, stat))\n",
        "    \n",
        "    categorical.columns = column_names\n",
        "    \n",
        "    return categorical"
      ],
      "metadata": {
        "id": "QJKfKYczuXIy"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "bureau_counts = count_categorical(bureau, group_var = 'SK_ID_CURR', df_name = 'bureau')\n",
        "bureau_counts.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "5uL67eCfug2V",
        "outputId": "ddbfe973-4ee3-4b2d-93c3-f56c5e7eaee3"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            bureau_CREDIT_ACTIVE_Active_count  \\\n",
              "SK_ID_CURR                                      \n",
              "100001                                      3   \n",
              "100002                                      2   \n",
              "100003                                      1   \n",
              "100004                                      0   \n",
              "100005                                      2   \n",
              "\n",
              "            bureau_CREDIT_ACTIVE_Active_count_norm  \\\n",
              "SK_ID_CURR                                           \n",
              "100001                                    0.428571   \n",
              "100002                                    0.250000   \n",
              "100003                                    0.250000   \n",
              "100004                                    0.000000   \n",
              "100005                                    0.666667   \n",
              "\n",
              "            bureau_CREDIT_ACTIVE_Bad debt_count  \\\n",
              "SK_ID_CURR                                        \n",
              "100001                                        0   \n",
              "100002                                        0   \n",
              "100003                                        0   \n",
              "100004                                        0   \n",
              "100005                                        0   \n",
              "\n",
              "            bureau_CREDIT_ACTIVE_Bad debt_count_norm  \\\n",
              "SK_ID_CURR                                             \n",
              "100001                                           0.0   \n",
              "100002                                           0.0   \n",
              "100003                                           0.0   \n",
              "100004                                           0.0   \n",
              "100005                                           0.0   \n",
              "\n",
              "            bureau_CREDIT_ACTIVE_Closed_count  \\\n",
              "SK_ID_CURR                                      \n",
              "100001                                      4   \n",
              "100002                                      6   \n",
              "100003                                      3   \n",
              "100004                                      2   \n",
              "100005                                      1   \n",
              "\n",
              "            bureau_CREDIT_ACTIVE_Closed_count_norm  \\\n",
              "SK_ID_CURR                                           \n",
              "100001                                    0.571429   \n",
              "100002                                    0.750000   \n",
              "100003                                    0.750000   \n",
              "100004                                    1.000000   \n",
              "100005                                    0.333333   \n",
              "\n",
              "            bureau_CREDIT_ACTIVE_Sold_count  \\\n",
              "SK_ID_CURR                                    \n",
              "100001                                    0   \n",
              "100002                                    0   \n",
              "100003                                    0   \n",
              "100004                                    0   \n",
              "100005                                    0   \n",
              "\n",
              "            bureau_CREDIT_ACTIVE_Sold_count_norm  \\\n",
              "SK_ID_CURR                                         \n",
              "100001                                       0.0   \n",
              "100002                                       0.0   \n",
              "100003                                       0.0   \n",
              "100004                                       0.0   \n",
              "100005                                       0.0   \n",
              "\n",
              "            bureau_CREDIT_CURRENCY_currency 1_count  \\\n",
              "SK_ID_CURR                                            \n",
              "100001                                            7   \n",
              "100002                                            8   \n",
              "100003                                            4   \n",
              "100004                                            2   \n",
              "100005                                            3   \n",
              "\n",
              "            bureau_CREDIT_CURRENCY_currency 1_count_norm  ...  \\\n",
              "SK_ID_CURR                                                ...   \n",
              "100001                                               1.0  ...   \n",
              "100002                                               1.0  ...   \n",
              "100003                                               1.0  ...   \n",
              "100004                                               1.0  ...   \n",
              "100005                                               1.0  ...   \n",
              "\n",
              "            bureau_CREDIT_TYPE_Microloan_count  \\\n",
              "SK_ID_CURR                                       \n",
              "100001                                       0   \n",
              "100002                                       0   \n",
              "100003                                       0   \n",
              "100004                                       0   \n",
              "100005                                       0   \n",
              "\n",
              "            bureau_CREDIT_TYPE_Microloan_count_norm  \\\n",
              "SK_ID_CURR                                            \n",
              "100001                                          0.0   \n",
              "100002                                          0.0   \n",
              "100003                                          0.0   \n",
              "100004                                          0.0   \n",
              "100005                                          0.0   \n",
              "\n",
              "            bureau_CREDIT_TYPE_Mobile operator loan_count  \\\n",
              "SK_ID_CURR                                                  \n",
              "100001                                                  0   \n",
              "100002                                                  0   \n",
              "100003                                                  0   \n",
              "100004                                                  0   \n",
              "100005                                                  0   \n",
              "\n",
              "            bureau_CREDIT_TYPE_Mobile operator loan_count_norm  \\\n",
              "SK_ID_CURR                                                       \n",
              "100001                                                    0.0    \n",
              "100002                                                    0.0    \n",
              "100003                                                    0.0    \n",
              "100004                                                    0.0    \n",
              "100005                                                    0.0    \n",
              "\n",
              "            bureau_CREDIT_TYPE_Mortgage_count  \\\n",
              "SK_ID_CURR                                      \n",
              "100001                                      0   \n",
              "100002                                      0   \n",
              "100003                                      0   \n",
              "100004                                      0   \n",
              "100005                                      0   \n",
              "\n",
              "            bureau_CREDIT_TYPE_Mortgage_count_norm  \\\n",
              "SK_ID_CURR                                           \n",
              "100001                                         0.0   \n",
              "100002                                         0.0   \n",
              "100003                                         0.0   \n",
              "100004                                         0.0   \n",
              "100005                                         0.0   \n",
              "\n",
              "            bureau_CREDIT_TYPE_Real estate loan_count  \\\n",
              "SK_ID_CURR                                              \n",
              "100001                                              0   \n",
              "100002                                              0   \n",
              "100003                                              0   \n",
              "100004                                              0   \n",
              "100005                                              0   \n",
              "\n",
              "            bureau_CREDIT_TYPE_Real estate loan_count_norm  \\\n",
              "SK_ID_CURR                                                   \n",
              "100001                                                 0.0   \n",
              "100002                                                 0.0   \n",
              "100003                                                 0.0   \n",
              "100004                                                 0.0   \n",
              "100005                                                 0.0   \n",
              "\n",
              "            bureau_CREDIT_TYPE_Unknown type of loan_count  \\\n",
              "SK_ID_CURR                                                  \n",
              "100001                                                  0   \n",
              "100002                                                  0   \n",
              "100003                                                  0   \n",
              "100004                                                  0   \n",
              "100005                                                  0   \n",
              "\n",
              "            bureau_CREDIT_TYPE_Unknown type of loan_count_norm  \n",
              "SK_ID_CURR                                                      \n",
              "100001                                                    0.0   \n",
              "100002                                                    0.0   \n",
              "100003                                                    0.0   \n",
              "100004                                                    0.0   \n",
              "100005                                                    0.0   \n",
              "\n",
              "[5 rows x 46 columns]"
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>bureau_CREDIT_ACTIVE_Active_count</th>\n",
              "      <th>bureau_CREDIT_ACTIVE_Active_count_norm</th>\n",
              "      <th>bureau_CREDIT_ACTIVE_Bad debt_count</th>\n",
              "      <th>bureau_CREDIT_ACTIVE_Bad debt_count_norm</th>\n",
              "      <th>bureau_CREDIT_ACTIVE_Closed_count</th>\n",
              "      <th>bureau_CREDIT_ACTIVE_Closed_count_norm</th>\n",
              "      <th>bureau_CREDIT_ACTIVE_Sold_count</th>\n",
              "      <th>bureau_CREDIT_ACTIVE_Sold_count_norm</th>\n",
              "      <th>bureau_CREDIT_CURRENCY_currency 1_count</th>\n",
              "      <th>bureau_CREDIT_CURRENCY_currency 1_count_norm</th>\n",
              "      <th>...</th>\n",
              "      <th>bureau_CREDIT_TYPE_Microloan_count</th>\n",
              "      <th>bureau_CREDIT_TYPE_Microloan_count_norm</th>\n",
              "      <th>bureau_CREDIT_TYPE_Mobile operator loan_count</th>\n",
              "      <th>bureau_CREDIT_TYPE_Mobile operator loan_count_norm</th>\n",
              "      <th>bureau_CREDIT_TYPE_Mortgage_count</th>\n",
              "      <th>bureau_CREDIT_TYPE_Mortgage_count_norm</th>\n",
              "      <th>bureau_CREDIT_TYPE_Real estate loan_count</th>\n",
              "      <th>bureau_CREDIT_TYPE_Real estate loan_count_norm</th>\n",
              "      <th>bureau_CREDIT_TYPE_Unknown type of loan_count</th>\n",
              "      <th>bureau_CREDIT_TYPE_Unknown type of loan_count_norm</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SK_ID_CURR</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>100001</th>\n",
              "      <td>3</td>\n",
              "      <td>0.428571</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4</td>\n",
              "      <td>0.571429</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7</td>\n",
              "      <td>1.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>100002</th>\n",
              "      <td>2</td>\n",
              "      <td>0.250000</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6</td>\n",
              "      <td>0.750000</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8</td>\n",
              "      <td>1.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>100003</th>\n",
              "      <td>1</td>\n",
              "      <td>0.250000</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3</td>\n",
              "      <td>0.750000</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4</td>\n",
              "      <td>1.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>100004</th>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>1.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>100005</th>\n",
              "      <td>2</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3</td>\n",
              "      <td>1.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 46 columns</p>\n",
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              "  "
            ]
          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Applying Operations to another dataframe\n",
        "\n",
        "monthly information about each client's previous loan(s) with other financial institutions.\n",
        "\n"
      ],
      "metadata": {
        "id": "3PAVBkY_u7q-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Read in bureau balance\n",
        "bureau_balance = pd.read_csv('/content/drive/MyDrive/home-credit-default-risk/bureau_balance.csv')\n",
        "bureau_balance.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "agc-GOXhutdN",
        "outputId": "269df6e6-1db0-4819-8982-4cb8058ebd17"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   SK_ID_BUREAU  MONTHS_BALANCE STATUS\n",
              "0       5715448               0      C\n",
              "1       5715448              -1      C\n",
              "2       5715448              -2      C\n",
              "3       5715448              -3      C\n",
              "4       5715448              -4      C"
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              "\n",
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              "      <th></th>\n",
              "      <th>SK_ID_BUREAU</th>\n",
              "      <th>MONTHS_BALANCE</th>\n",
              "      <th>STATUS</th>\n",
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    {
      "cell_type": "code",
      "source": [
        "#calculate the value counts of each status for each loan.\n",
        "#counts of each type of status for each previous loan\n",
        "\n",
        "bureau_balance_counts = count_categorical(bureau_balance, group_var = 'SK_ID_BUREAU', df_name = 'bureau_balance')\n",
        "bureau_balance_counts.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "2UWdlBkwu3wT",
        "outputId": "77baf23f-5e6c-4a53-a4de-468f11408aa6"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              bureau_balance_STATUS_0_count  \\\n",
              "SK_ID_BUREAU                                  \n",
              "5001709                                   0   \n",
              "5001710                                   5   \n",
              "5001711                                   3   \n",
              "5001712                                  10   \n",
              "5001713                                   0   \n",
              "\n",
              "              bureau_balance_STATUS_0_count_norm  \\\n",
              "SK_ID_BUREAU                                       \n",
              "5001709                                 0.000000   \n",
              "5001710                                 0.060241   \n",
              "5001711                                 0.750000   \n",
              "5001712                                 0.526316   \n",
              "5001713                                 0.000000   \n",
              "\n",
              "              bureau_balance_STATUS_1_count  \\\n",
              "SK_ID_BUREAU                                  \n",
              "5001709                                   0   \n",
              "5001710                                   0   \n",
              "5001711                                   0   \n",
              "5001712                                   0   \n",
              "5001713                                   0   \n",
              "\n",
              "              bureau_balance_STATUS_1_count_norm  \\\n",
              "SK_ID_BUREAU                                       \n",
              "5001709                                      0.0   \n",
              "5001710                                      0.0   \n",
              "5001711                                      0.0   \n",
              "5001712                                      0.0   \n",
              "5001713                                      0.0   \n",
              "\n",
              "              bureau_balance_STATUS_2_count  \\\n",
              "SK_ID_BUREAU                                  \n",
              "5001709                                   0   \n",
              "5001710                                   0   \n",
              "5001711                                   0   \n",
              "5001712                                   0   \n",
              "5001713                                   0   \n",
              "\n",
              "              bureau_balance_STATUS_2_count_norm  \\\n",
              "SK_ID_BUREAU                                       \n",
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          "metadata": {},
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    {
      "cell_type": "code",
      "source": [
        "# Calculate value count statistics for each `SK_ID_CURR` \n",
        "bureau_balance_agg = agg_numeric(bureau_balance, group_var = 'SK_ID_BUREAU', df_name = 'bureau_balance')\n",
        "bureau_balance_agg.head()"
      ],
      "metadata": {
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        "id": "Uz9SwJu5u3yo",
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      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   SK_ID_BUREAU  bureau_balance_MONTHS_BALANCE_count  \\\n",
              "0       5001709                                   97   \n",
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              "2       5001711                                    4   \n",
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              "4       5001713                                   22   \n",
              "\n",
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              "0                               -48.0                                  0   \n",
              "1                               -41.0                                  0   \n",
              "2                                -1.5                                  0   \n",
              "3                                -9.0                                  0   \n",
              "4                               -10.5                                  0   \n",
              "\n",
              "   bureau_balance_MONTHS_BALANCE_min  bureau_balance_MONTHS_BALANCE_sum  \n",
              "0                                -96                              -4656  \n",
              "1                                -82                              -3403  \n",
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              "          const element = document.querySelector('#df-bec255bf-3f45-471d-b438-f1998bfe32c9');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#dataframe grouped by the loan\n",
        "\n",
        "bureau_by_loan = bureau_balance_agg.merge(bureau_balance_counts, right_index = True, left_on = 'SK_ID_BUREAU', how='outer')\n",
        "\n",
        "#Mearge to include the SK_ID_CURR\n",
        "\n",
        "bureau_by_loan = bureau_by_loan.merge(bureau[['SK_ID_BUREAU', 'SK_ID_CURR']], on = 'SK_ID_BUREAU', how='left')\n",
        "\n",
        "bureau_by_loan.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "QPZ0Sp6fu33H",
        "outputId": "e8f2ebf2-af04-4f9a-bdb5-e68ab9c2c3b1"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   SK_ID_BUREAU  bureau_balance_MONTHS_BALANCE_count  \\\n",
              "0       5001709                                   97   \n",
              "1       5001710                                   83   \n",
              "2       5001711                                    4   \n",
              "3       5001712                                   19   \n",
              "4       5001713                                   22   \n",
              "\n",
              "   bureau_balance_MONTHS_BALANCE_mean  bureau_balance_MONTHS_BALANCE_max  \\\n",
              "0                               -48.0                                  0   \n",
              "1                               -41.0                                  0   \n",
              "2                                -1.5                                  0   \n",
              "3                                -9.0                                  0   \n",
              "4                               -10.5                                  0   \n",
              "\n",
              "   bureau_balance_MONTHS_BALANCE_min  bureau_balance_MONTHS_BALANCE_sum  \\\n",
              "0                                -96                              -4656   \n",
              "1                                -82                              -3403   \n",
              "2                                 -3                                 -6   \n",
              "3                                -18                               -171   \n",
              "4                                -21                               -231   \n",
              "\n",
              "   bureau_balance_STATUS_0_count  bureau_balance_STATUS_0_count_norm  \\\n",
              "0                              0                            0.000000   \n",
              "1                              5                            0.060241   \n",
              "2                              3                            0.750000   \n",
              "3                             10                            0.526316   \n",
              "4                              0                            0.000000   \n",
              "\n",
              "   bureau_balance_STATUS_1_count  bureau_balance_STATUS_1_count_norm  ...  \\\n",
              "0                              0                                 0.0  ...   \n",
              "1                              0                                 0.0  ...   \n",
              "2                              0                                 0.0  ...   \n",
              "3                              0                                 0.0  ...   \n",
              "4                              0                                 0.0  ...   \n",
              "\n",
              "   bureau_balance_STATUS_3_count_norm  bureau_balance_STATUS_4_count  \\\n",
              "0                                 0.0                              0   \n",
              "1                                 0.0                              0   \n",
              "2                                 0.0                              0   \n",
              "3                                 0.0                              0   \n",
              "4                                 0.0                              0   \n",
              "\n",
              "   bureau_balance_STATUS_4_count_norm  bureau_balance_STATUS_5_count  \\\n",
              "0                                 0.0                              0   \n",
              "1                                 0.0                              0   \n",
              "2                                 0.0                              0   \n",
              "3                                 0.0                              0   \n",
              "4                                 0.0                              0   \n",
              "\n",
              "   bureau_balance_STATUS_5_count_norm  bureau_balance_STATUS_C_count  \\\n",
              "0                                 0.0                             86   \n",
              "1                                 0.0                             48   \n",
              "2                                 0.0                              0   \n",
              "3                                 0.0                              9   \n",
              "4                                 0.0                              0   \n",
              "\n",
              "   bureau_balance_STATUS_C_count_norm  bureau_balance_STATUS_X_count  \\\n",
              "0                            0.886598                             11   \n",
              "1                            0.578313                             30   \n",
              "2                            0.000000                              1   \n",
              "3                            0.473684                              0   \n",
              "4                            0.000000                             22   \n",
              "\n",
              "   bureau_balance_STATUS_X_count_norm  SK_ID_CURR  \n",
              "0                            0.113402         NaN  \n",
              "1                            0.361446    162368.0  \n",
              "2                            0.250000    162368.0  \n",
              "3                            0.000000    162368.0  \n",
              "4                            1.000000    150635.0  \n",
              "\n",
              "[5 rows x 23 columns]"
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              "      <th>SK_ID_BUREAU</th>\n",
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              "      <th>bureau_balance_STATUS_C_count_norm</th>\n",
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              "<p>5 rows × 23 columns</p>\n",
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          "metadata": {},
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    },
    {
      "cell_type": "code",
      "source": [
        "bureau_balance_by_client = agg_numeric(bureau_by_loan.drop(columns = ['SK_ID_BUREAU']), group_var = 'SK_ID_CURR', df_name = 'client')\n",
        "bureau_balance_by_client.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "UY8I9aUAxzNW",
        "outputId": "c029e251-d67d-41b8-d130-a2c4a2463f44"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   SK_ID_CURR  client_bureau_balance_MONTHS_BALANCE_count_count  \\\n",
              "0    100001.0                                                 7   \n",
              "1    100002.0                                                 8   \n",
              "2    100005.0                                                 3   \n",
              "3    100010.0                                                 2   \n",
              "4    100013.0                                                 4   \n",
              "\n",
              "   client_bureau_balance_MONTHS_BALANCE_count_mean  \\\n",
              "0                                        24.571429   \n",
              "1                                        13.750000   \n",
              "2                                         7.000000   \n",
              "3                                        36.000000   \n",
              "4                                        57.500000   \n",
              "\n",
              "   client_bureau_balance_MONTHS_BALANCE_count_max  \\\n",
              "0                                              52   \n",
              "1                                              22   \n",
              "2                                              13   \n",
              "3                                              36   \n",
              "4                                              69   \n",
              "\n",
              "   client_bureau_balance_MONTHS_BALANCE_count_min  \\\n",
              "0                                               2   \n",
              "1                                               4   \n",
              "2                                               3   \n",
              "3                                              36   \n",
              "4                                              40   \n",
              "\n",
              "   client_bureau_balance_MONTHS_BALANCE_count_sum  \\\n",
              "0                                             172   \n",
              "1                                             110   \n",
              "2                                              21   \n",
              "3                                              72   \n",
              "4                                             230   \n",
              "\n",
              "   client_bureau_balance_MONTHS_BALANCE_mean_count  \\\n",
              "0                                                7   \n",
              "1                                                8   \n",
              "2                                                3   \n",
              "3                                                2   \n",
              "4                                                4   \n",
              "\n",
              "   client_bureau_balance_MONTHS_BALANCE_mean_mean  \\\n",
              "0                                      -11.785714   \n",
              "1                                      -21.875000   \n",
              "2                                       -3.000000   \n",
              "3                                      -46.000000   \n",
              "4                                      -28.250000   \n",
              "\n",
              "   client_bureau_balance_MONTHS_BALANCE_mean_max  \\\n",
              "0                                           -0.5   \n",
              "1                                           -1.5   \n",
              "2                                           -1.0   \n",
              "3                                          -19.5   \n",
              "4                                          -19.5   \n",
              "\n",
              "   client_bureau_balance_MONTHS_BALANCE_mean_min  ...  \\\n",
              "0                                          -25.5  ...   \n",
              "1                                          -39.5  ...   \n",
              "2                                           -6.0  ...   \n",
              "3                                          -72.5  ...   \n",
              "4                                          -34.0  ...   \n",
              "\n",
              "   client_bureau_balance_STATUS_X_count_count  \\\n",
              "0                                           7   \n",
              "1                                           8   \n",
              "2                                           3   \n",
              "3                                           2   \n",
              "4                                           4   \n",
              "\n",
              "   client_bureau_balance_STATUS_X_count_mean  \\\n",
              "0                                   4.285714   \n",
              "1                                   1.875000   \n",
              "2                                   0.666667   \n",
              "3                                   0.000000   \n",
              "4                                  10.250000   \n",
              "\n",
              "   client_bureau_balance_STATUS_X_count_max  \\\n",
              "0                                         9   \n",
              "1                                         3   \n",
              "2                                         1   \n",
              "3                                         0   \n",
              "4                                        40   \n",
              "\n",
              "   client_bureau_balance_STATUS_X_count_min  \\\n",
              "0                                         0   \n",
              "1                                         0   \n",
              "2                                         0   \n",
              "3                                         0   \n",
              "4                                         0   \n",
              "\n",
              "   client_bureau_balance_STATUS_X_count_sum  \\\n",
              "0                                      30.0   \n",
              "1                                      15.0   \n",
              "2                                       2.0   \n",
              "3                                       0.0   \n",
              "4                                      41.0   \n",
              "\n",
              "   client_bureau_balance_STATUS_X_count_norm_count  \\\n",
              "0                                                7   \n",
              "1                                                8   \n",
              "2                                                3   \n",
              "3                                                2   \n",
              "4                                                4   \n",
              "\n",
              "   client_bureau_balance_STATUS_X_count_norm_mean  \\\n",
              "0                                        0.214590   \n",
              "1                                        0.161932   \n",
              "2                                        0.136752   \n",
              "3                                        0.000000   \n",
              "4                                        0.254545   \n",
              "\n",
              "   client_bureau_balance_STATUS_X_count_norm_max  \\\n",
              "0                                       0.500000   \n",
              "1                                       0.500000   \n",
              "2                                       0.333333   \n",
              "3                                       0.000000   \n",
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          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Putting the Functions Together\n"
      ],
      "metadata": {
        "id": "p-5vgHzR9GXN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Free up memory by deleting old objects\n",
        "import gc\n",
        "gc.enable()\n",
        "del train, bureau, bureau_balance, bureau_agg, bureau_agg_new, bureau_balance_agg, bureau_balance_counts, bureau_by_loan, bureau_balance_by_client, bureau_counts\n",
        "gc.collect()"
      ],
      "metadata": {
        "id": "17FZ6RsTu35y",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e4604c1b-76b9-4d42-8f39-4876e832843c"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5934"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Read in new copies of all the dataframe\n",
        "train = pd.read_csv('/content/drive/MyDrive/home-credit-default-risk/application_train.csv')\n",
        "bureau = pd.read_csv('/content/drive/MyDrive/home-credit-default-risk/bureau.csv')\n",
        "bureau_balance = pd.read_csv('/content/drive/MyDrive/home-credit-default-risk/bureau_balance.csv')"
      ],
      "metadata": {
        "id": "9qGGELHqu363"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### counts of Bureau Dataframe"
      ],
      "metadata": {
        "id": "UajromUq-c2z"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "bureau_counts = count_categorical(bureau, group_var = 'SK_ID_CURR', df_name = 'bureau')\n",
        "bureau_counts.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "w3OueZnt-bdG",
        "outputId": "b0c1bc5a-0dd6-4433-9f1c-9415c06f0d9f"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            bureau_CREDIT_ACTIVE_Active_count  \\\n",
              "SK_ID_CURR                                      \n",
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              "SK_ID_CURR                                           \n",
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              "SK_ID_CURR                                        \n",
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              "100003                                        0   \n",
              "100004                                        0   \n",
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              "SK_ID_CURR                                             \n",
              "100001                                           0.0   \n",
              "100002                                           0.0   \n",
              "100003                                           0.0   \n",
              "100004                                           0.0   \n",
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              "SK_ID_CURR                                      \n",
              "100001                                      4   \n",
              "100002                                      6   \n",
              "100003                                      3   \n",
              "100004                                      2   \n",
              "100005                                      1   \n",
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              "            bureau_CREDIT_ACTIVE_Closed_count_norm  \\\n",
              "SK_ID_CURR                                           \n",
              "100001                                    0.571429   \n",
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              "SK_ID_CURR                                    \n",
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              "100004                                    0   \n",
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              "\n",
              "            bureau_CREDIT_ACTIVE_Sold_count_norm  \\\n",
              "SK_ID_CURR                                         \n",
              "100001                                       0.0   \n",
              "100002                                       0.0   \n",
              "100003                                       0.0   \n",
              "100004                                       0.0   \n",
              "100005                                       0.0   \n",
              "\n",
              "            bureau_CREDIT_CURRENCY_currency 1_count  \\\n",
              "SK_ID_CURR                                            \n",
              "100001                                            7   \n",
              "100002                                            8   \n",
              "100003                                            4   \n",
              "100004                                            2   \n",
              "100005                                            3   \n",
              "\n",
              "            bureau_CREDIT_CURRENCY_currency 1_count_norm  ...  \\\n",
              "SK_ID_CURR                                                ...   \n",
              "100001                                               1.0  ...   \n",
              "100002                                               1.0  ...   \n",
              "100003                                               1.0  ...   \n",
              "100004                                               1.0  ...   \n",
              "100005                                               1.0  ...   \n",
              "\n",
              "            bureau_CREDIT_TYPE_Microloan_count  \\\n",
              "SK_ID_CURR                                       \n",
              "100001                                       0   \n",
              "100002                                       0   \n",
              "100003                                       0   \n",
              "100004                                       0   \n",
              "100005                                       0   \n",
              "\n",
              "            bureau_CREDIT_TYPE_Microloan_count_norm  \\\n",
              "SK_ID_CURR                                            \n",
              "100001                                          0.0   \n",
              "100002                                          0.0   \n",
              "100003                                          0.0   \n",
              "100004                                          0.0   \n",
              "100005                                          0.0   \n",
              "\n",
              "            bureau_CREDIT_TYPE_Mobile operator loan_count  \\\n",
              "SK_ID_CURR                                                  \n",
              "100001                                                  0   \n",
              "100002                                                  0   \n",
              "100003                                                  0   \n",
              "100004                                                  0   \n",
              "100005                                                  0   \n",
              "\n",
              "            bureau_CREDIT_TYPE_Mobile operator loan_count_norm  \\\n",
              "SK_ID_CURR                                                       \n",
              "100001                                                    0.0    \n",
              "100002                                                    0.0    \n",
              "100003                                                    0.0    \n",
              "100004                                                    0.0    \n",
              "100005                                                    0.0    \n",
              "\n",
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              "SK_ID_CURR                                      \n",
              "100001                                      0   \n",
              "100002                                      0   \n",
              "100003                                      0   \n",
              "100004                                      0   \n",
              "100005                                      0   \n",
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              "SK_ID_CURR                                           \n",
              "100001                                         0.0   \n",
              "100002                                         0.0   \n",
              "100003                                         0.0   \n",
              "100004                                         0.0   \n",
              "100005                                         0.0   \n",
              "\n",
              "            bureau_CREDIT_TYPE_Real estate loan_count  \\\n",
              "SK_ID_CURR                                              \n",
              "100001                                              0   \n",
              "100002                                              0   \n",
              "100003                                              0   \n",
              "100004                                              0   \n",
              "100005                                              0   \n",
              "\n",
              "            bureau_CREDIT_TYPE_Real estate loan_count_norm  \\\n",
              "SK_ID_CURR                                                   \n",
              "100001                                                 0.0   \n",
              "100002                                                 0.0   \n",
              "100003                                                 0.0   \n",
              "100004                                                 0.0   \n",
              "100005                                                 0.0   \n",
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              "SK_ID_CURR                                                  \n",
              "100001                                                  0   \n",
              "100002                                                  0   \n",
              "100003                                                  0   \n",
              "100004                                                  0   \n",
              "100005                                                  0   \n",
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              "SK_ID_CURR                                                      \n",
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              "      <th>bureau_CREDIT_ACTIVE_Active_count_norm</th>\n",
              "      <th>bureau_CREDIT_ACTIVE_Bad debt_count</th>\n",
              "      <th>bureau_CREDIT_ACTIVE_Bad debt_count_norm</th>\n",
              "      <th>bureau_CREDIT_ACTIVE_Closed_count</th>\n",
              "      <th>bureau_CREDIT_ACTIVE_Closed_count_norm</th>\n",
              "      <th>bureau_CREDIT_ACTIVE_Sold_count</th>\n",
              "      <th>bureau_CREDIT_ACTIVE_Sold_count_norm</th>\n",
              "      <th>bureau_CREDIT_CURRENCY_currency 1_count</th>\n",
              "      <th>bureau_CREDIT_CURRENCY_currency 1_count_norm</th>\n",
              "      <th>...</th>\n",
              "      <th>bureau_CREDIT_TYPE_Microloan_count</th>\n",
              "      <th>bureau_CREDIT_TYPE_Microloan_count_norm</th>\n",
              "      <th>bureau_CREDIT_TYPE_Mobile operator loan_count</th>\n",
              "      <th>bureau_CREDIT_TYPE_Mobile operator loan_count_norm</th>\n",
              "      <th>bureau_CREDIT_TYPE_Mortgage_count</th>\n",
              "      <th>bureau_CREDIT_TYPE_Mortgage_count_norm</th>\n",
              "      <th>bureau_CREDIT_TYPE_Real estate loan_count</th>\n",
              "      <th>bureau_CREDIT_TYPE_Real estate loan_count_norm</th>\n",
              "      <th>bureau_CREDIT_TYPE_Unknown type of loan_count</th>\n",
              "      <th>bureau_CREDIT_TYPE_Unknown type of loan_count_norm</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SK_ID_CURR</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>100001</th>\n",
              "      <td>3</td>\n",
              "      <td>0.428571</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4</td>\n",
              "      <td>0.571429</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7</td>\n",
              "      <td>1.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>100002</th>\n",
              "      <td>2</td>\n",
              "      <td>0.250000</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6</td>\n",
              "      <td>0.750000</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8</td>\n",
              "      <td>1.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>100003</th>\n",
              "      <td>1</td>\n",
              "      <td>0.250000</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3</td>\n",
              "      <td>0.750000</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4</td>\n",
              "      <td>1.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>100004</th>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>1.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>100005</th>\n",
              "      <td>2</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3</td>\n",
              "      <td>1.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 46 columns</p>\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Aggregated Stats of bureau dataframe"
      ],
      "metadata": {
        "id": "dEvsaHOQ-vIc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "bureau_agg = agg_numeric(bureau.drop(columns=['SK_ID_BUREAU']), group_var = 'SK_ID_CURR', df_name = 'bureau')\n",
        "bureau_agg.head()   "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "SxFLqzNT-bhU",
        "outputId": "859ece0e-1ba7-44d2-c901-90ffe1670729"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   SK_ID_CURR  bureau_DAYS_CREDIT_count  bureau_DAYS_CREDIT_mean  \\\n",
              "0      100001                         7              -735.000000   \n",
              "1      100002                         8              -874.000000   \n",
              "2      100003                         4             -1400.750000   \n",
              "3      100004                         2              -867.000000   \n",
              "4      100005                         3              -190.666667   \n",
              "\n",
              "   bureau_DAYS_CREDIT_max  bureau_DAYS_CREDIT_min  bureau_DAYS_CREDIT_sum  \\\n",
              "0                     -49                   -1572                   -5145   \n",
              "1                    -103                   -1437                   -6992   \n",
              "2                    -606                   -2586                   -5603   \n",
              "3                    -408                   -1326                   -1734   \n",
              "4                     -62                    -373                    -572   \n",
              "\n",
              "   bureau_CREDIT_DAY_OVERDUE_count  bureau_CREDIT_DAY_OVERDUE_mean  \\\n",
              "0                                7                             0.0   \n",
              "1                                8                             0.0   \n",
              "2                                4                             0.0   \n",
              "3                                2                             0.0   \n",
              "4                                3                             0.0   \n",
              "\n",
              "   bureau_CREDIT_DAY_OVERDUE_max  bureau_CREDIT_DAY_OVERDUE_min  ...  \\\n",
              "0                              0                              0  ...   \n",
              "1                              0                              0  ...   \n",
              "2                              0                              0  ...   \n",
              "3                              0                              0  ...   \n",
              "4                              0                              0  ...   \n",
              "\n",
              "   bureau_DAYS_CREDIT_UPDATE_count  bureau_DAYS_CREDIT_UPDATE_mean  \\\n",
              "0                                7                      -93.142857   \n",
              "1                                8                     -499.875000   \n",
              "2                                4                     -816.000000   \n",
              "3                                2                     -532.000000   \n",
              "4                                3                      -54.333333   \n",
              "\n",
              "   bureau_DAYS_CREDIT_UPDATE_max  bureau_DAYS_CREDIT_UPDATE_min  \\\n",
              "0                             -6                           -155   \n",
              "1                             -7                          -1185   \n",
              "2                            -43                          -2131   \n",
              "3                           -382                           -682   \n",
              "4                            -11                           -121   \n",
              "\n",
              "   bureau_DAYS_CREDIT_UPDATE_sum  bureau_AMT_ANNUITY_count  \\\n",
              "0                           -652                         7   \n",
              "1                          -3999                         7   \n",
              "2                          -3264                         0   \n",
              "3                          -1064                         0   \n",
              "4                           -163                         3   \n",
              "\n",
              "   bureau_AMT_ANNUITY_mean  bureau_AMT_ANNUITY_max  bureau_AMT_ANNUITY_min  \\\n",
              "0              3545.357143                 10822.5                     0.0   \n",
              "1                 0.000000                     0.0                     0.0   \n",
              "2                      NaN                     NaN                     NaN   \n",
              "3                      NaN                     NaN                     NaN   \n",
              "4              1420.500000                  4261.5                     0.0   \n",
              "\n",
              "   bureau_AMT_ANNUITY_sum  \n",
              "0                 24817.5  \n",
              "1                     0.0  \n",
              "2                     0.0  \n",
              "3                     0.0  \n",
              "4                  4261.5  \n",
              "\n",
              "[5 rows x 61 columns]"
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              "      <td>-816.000000</td>\n",
              "      <td>-43</td>\n",
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          },
          "metadata": {},
          "execution_count": 38
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Value counts of Bureau Balance dataframe by loan"
      ],
      "metadata": {
        "id": "FqdEWMUB_Jom"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "bureau_balance_counts = count_categorical(bureau_balance, group_var = 'SK_ID_BUREAU', df_name = 'bureau_balance')\n",
        "bureau_balance_counts.head()"
      ],
      "metadata": {
        "colab": {
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        "id": "XFn1zw5m-bkQ",
        "outputId": "e7b9fe14-b578-4e93-e350-9f4d6fa0739b"
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      "execution_count": 39,
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              "SK_ID_BUREAU                                  \n",
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              "SK_ID_BUREAU                                  \n",
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          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Aggregated stats of Bureau Balance dataframe by loan"
      ],
      "metadata": {
        "id": "EQBarOcn_0DU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "bureau_balance_agg = agg_numeric(bureau_balance, group_var = 'SK_ID_BUREAU', df_name = 'bureau_balance')\n",
        "bureau_balance_agg.head()"
      ],
      "metadata": {
        "colab": {
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          "height": 0
        },
        "id": "JznNltnI_i8a",
        "outputId": "009a22f2-f282-448f-f3cc-dba454144fb6"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   SK_ID_BUREAU  bureau_balance_MONTHS_BALANCE_count  \\\n",
              "0       5001709                                   97   \n",
              "1       5001710                                   83   \n",
              "2       5001711                                    4   \n",
              "3       5001712                                   19   \n",
              "4       5001713                                   22   \n",
              "\n",
              "   bureau_balance_MONTHS_BALANCE_mean  bureau_balance_MONTHS_BALANCE_max  \\\n",
              "0                               -48.0                                  0   \n",
              "1                               -41.0                                  0   \n",
              "2                                -1.5                                  0   \n",
              "3                                -9.0                                  0   \n",
              "4                               -10.5                                  0   \n",
              "\n",
              "   bureau_balance_MONTHS_BALANCE_min  bureau_balance_MONTHS_BALANCE_sum  \n",
              "0                                -96                              -4656  \n",
              "1                                -82                              -3403  \n",
              "2                                 -3                                 -6  \n",
              "3                                -18                               -171  \n",
              "4                                -21                               -231  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f7fd40de-5d5b-4f8a-b431-00712c71044c\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>SK_ID_BUREAU</th>\n",
              "      <th>bureau_balance_MONTHS_BALANCE_count</th>\n",
              "      <th>bureau_balance_MONTHS_BALANCE_mean</th>\n",
              "      <th>bureau_balance_MONTHS_BALANCE_max</th>\n",
              "      <th>bureau_balance_MONTHS_BALANCE_min</th>\n",
              "      <th>bureau_balance_MONTHS_BALANCE_sum</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5001709</td>\n",
              "      <td>97</td>\n",
              "      <td>-48.0</td>\n",
              "      <td>0</td>\n",
              "      <td>-96</td>\n",
              "      <td>-4656</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5001710</td>\n",
              "      <td>83</td>\n",
              "      <td>-41.0</td>\n",
              "      <td>0</td>\n",
              "      <td>-82</td>\n",
              "      <td>-3403</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5001711</td>\n",
              "      <td>4</td>\n",
              "      <td>-1.5</td>\n",
              "      <td>0</td>\n",
              "      <td>-3</td>\n",
              "      <td>-6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5001712</td>\n",
              "      <td>19</td>\n",
              "      <td>-9.0</td>\n",
              "      <td>0</td>\n",
              "      <td>-18</td>\n",
              "      <td>-171</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5001713</td>\n",
              "      <td>22</td>\n",
              "      <td>-10.5</td>\n",
              "      <td>0</td>\n",
              "      <td>-21</td>\n",
              "      <td>-231</td>\n",
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              "  </tbody>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f7fd40de-5d5b-4f8a-b431-00712c71044c')\"\n",
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              "          const element = document.querySelector('#df-f7fd40de-5d5b-4f8a-b431-00712c71044c');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
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              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Aggregated Stats of Bureau Balance by Client"
      ],
      "metadata": {
        "id": "SRaolU6HASGM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Dataframe grouped by the loan\n",
        "bureau_by_loan = bureau_balance_agg.merge(bureau_balance_counts, right_index = True, left_on = 'SK_ID_BUREAU', how = 'outer')\n",
        "\n",
        "# Merge to include the SK_ID_CURR\n",
        "bureau_by_loan = bureau[['SK_ID_BUREAU', 'SK_ID_CURR']].merge(bureau_by_loan, on = 'SK_ID_BUREAU', how = 'left')\n",
        "\n",
        "# Aggregate the stats for each client\n",
        "bureau_balance_by_client = agg_numeric(bureau_by_loan.drop(columns = ['SK_ID_BUREAU']), group_var = 'SK_ID_CURR', df_name = 'client')"
      ],
      "metadata": {
        "id": "KwYWlVXV_i_M"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Insert Computed Features into Training Data"
      ],
      "metadata": {
        "id": "Xa83eQnXBYc8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "original_features = list(train.columns)\n",
        "print('Original Number of Features: ', len(original_features))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sM0ZkrpfBdFg",
        "outputId": "e0140bc3-1418-4404-84de-efc1c59d8d18"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Original Number of Features:  122\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Merge with the value counts of bureau\n",
        "train = train.merge(bureau_counts, on ='SK_ID_CURR', how='left')\n",
        "\n",
        "#Merge with the stats of bureau\n",
        "train = train.merge(bureau_agg, on ='SK_ID_CURR', how='left')\n",
        "\n",
        "#Merge with the monthly information grouped by client\n",
        "train = train.merge(bureau_balance_by_client, on ='SK_ID_CURR', how='left')"
      ],
      "metadata": {
        "id": "wIHSZGs8_jCC"
      },
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "new_features = list(train.columns)\n",
        "print('Number of features using previous loans from other institutions data: ', len(new_features))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "okGZ0GIK_jGd",
        "outputId": "085257da-e307-47ec-a5fa-6d92af89c82b"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of features using previous loans from other institutions data:  333\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Feature Engineering Outcomes"
      ],
      "metadata": {
        "id": "NjR-8NQSCzxZ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Missing Values"
      ],
      "metadata": {
        "id": "rDpk1eA0DtlC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Function to calculate missing values by column# Funct\n",
        "def missing_values_table(df):\n",
        "    #Total missing values\n",
        "    mis_val = df.isnull().sum()\n",
        "\n",
        "    #Percentage of missing values\n",
        "    mis_val_percent = 100  * df.isnull().sum() / len(df)\n",
        "\n",
        "    #Make a table with the results\n",
        "    mis_val_table = pd.concat([mis_val, mis_val_percent], axis=1)\n",
        "\n",
        "    #Rename the columns\n",
        "    mis_val_table_ren_columns = mis_val_table.rename(columns = {0 : 'Missing Values',\n",
        "                                                                1: '% of Total Values'})\n",
        "    \n",
        "    #Sort the table by percentage of missing descending\n",
        "    mis_val_table_ren_columns = mis_val_table_ren_columns[\n",
        "        mis_val_table_ren_columns.iloc[:, 1] != 0].sort_values( '% of Total Values', ascending=False).round(1)\n",
        "\n",
        "    #print somesummary information\n",
        "    print('Your selected dataframe has' + str(df.shape[1]) + 'columns.\\n'\n",
        "                                                                \"There are \" + str(mis_val_table_ren_columns.shape[0]) +\n",
        "                                                                    \" columns thathave missing values\")\n",
        "    #Return the dataframe with missing information\n",
        "    return mis_val_table_ren_columns"
      ],
      "metadata": {
        "id": "dxBBW9zk_jJD"
      },
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "missing_train = missing_values_table(train)\n",
        "missing_train.head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "7QVQ1EjWFnw7",
        "outputId": "8906a128-eed0-4a26-af57-a7d451240fe2"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Your selected dataframe has333columns.\n",
            "There are 278 columns thathave missing values\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                Missing Values  \\\n",
              "bureau_AMT_ANNUITY_min                                  227502   \n",
              "bureau_AMT_ANNUITY_max                                  227502   \n",
              "bureau_AMT_ANNUITY_mean                                 227502   \n",
              "client_bureau_balance_STATUS_4_count_min                215280   \n",
              "client_bureau_balance_STATUS_3_count_norm_mean          215280   \n",
              "client_bureau_balance_MONTHS_BALANCE_count_min          215280   \n",
              "client_bureau_balance_STATUS_4_count_max                215280   \n",
              "client_bureau_balance_STATUS_4_count_mean               215280   \n",
              "client_bureau_balance_STATUS_3_count_norm_min           215280   \n",
              "client_bureau_balance_STATUS_3_count_norm_max           215280   \n",
              "\n",
              "                                                % of Total Values  \n",
              "bureau_AMT_ANNUITY_min                                       74.0  \n",
              "bureau_AMT_ANNUITY_max                                       74.0  \n",
              "bureau_AMT_ANNUITY_mean                                      74.0  \n",
              "client_bureau_balance_STATUS_4_count_min                     70.0  \n",
              "client_bureau_balance_STATUS_3_count_norm_mean               70.0  \n",
              "client_bureau_balance_MONTHS_BALANCE_count_min               70.0  \n",
              "client_bureau_balance_STATUS_4_count_max                     70.0  \n",
              "client_bureau_balance_STATUS_4_count_mean                    70.0  \n",
              "client_bureau_balance_STATUS_3_count_norm_min                70.0  \n",
              "client_bureau_balance_STATUS_3_count_norm_max                70.0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e53d3817-616b-4241-9e3b-820fce6bb29a\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "      <th>% of Total Values</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>bureau_AMT_ANNUITY_min</th>\n",
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              "  </tbody>\n",
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              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-e53d3817-616b-4241-9e3b-820fce6bb29a');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "missing_train_vars = list(missing_train.index[missing_train['% of Total Values'] > 90])\n",
        "len(missing_train_vars)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TjEJ9w-SF3x5",
        "outputId": "0736c5f5-9e6d-48f1-dcb7-6e880f208121"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Calculate Information for Testing Data"
      ],
      "metadata": {
        "id": "W1W2NPX9GHG2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Read in the test dataframe\n",
        "test = pd.read_csv('/content/drive/MyDrive/home-credit-default-risk/application_test.csv')\n",
        "\n",
        "#Merge with the value counts of bureau\n",
        "test = test.merge(bureau_counts, on ='SK_ID_CURR', how='left')\n",
        "\n",
        "#Merge with the value counts of bureau\n",
        "test = test.merge(bureau_counts, on = 'SK_ID_CURR', how='left')\n",
        "\n",
        "#Merge with the value counts of bureau balance\n",
        "test = test.merge(bureau_balance_by_client, on = 'SK_ID_CURR', how = 'left')"
      ],
      "metadata": {
        "id": "Vxg-1vjlFsw2"
      },
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('Shape of Testing Data: ', test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iPy1sF2bGTZ-",
        "outputId": "67abd2a3-fd8f-46e1-e6fe-d9310f1ebb38"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape of Testing Data:  (48744, 318)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_labels = train['TARGET']\n",
        "\n",
        "#Align the dataframes, this will remove the 'TARGET' column\n",
        "train, test = train.align(test, join = 'inner', axis =1)\n",
        "\n",
        "train['TARGET'] = train_labels"
      ],
      "metadata": {
        "id": "tCLG4RzJGTci"
      },
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('Training Data Shape:' , train.shape)\n",
        "print('Testing Data Shape:' , test.shape)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gkHgRvOEGTgA",
        "outputId": "f2025d13-5b55-4edc-95ce-1c10e28a1fb8"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training Data Shape: (307511, 227)\n",
            "Testing Data Shape: (48744, 226)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "missing_test = missing_values_table(test)\n",
        "missing_test.head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "aZ9mHA-rH9mH",
        "outputId": "338b27ab-f2ea-48a3-f7f3-ccf765033d8f"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Your selected dataframe has226columns.\n",
            "There are 169 columns thathave missing values\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                          Missing Values  % of Total Values\n",
              "COMMONAREA_MODE                    33495               68.7\n",
              "COMMONAREA_MEDI                    33495               68.7\n",
              "COMMONAREA_AVG                     33495               68.7\n",
              "NONLIVINGAPARTMENTS_MODE           33347               68.4\n",
              "NONLIVINGAPARTMENTS_AVG            33347               68.4\n",
              "NONLIVINGAPARTMENTS_MEDI           33347               68.4\n",
              "FONDKAPREMONT_MODE                 32797               67.3\n",
              "LIVINGAPARTMENTS_MEDI              32780               67.2\n",
              "LIVINGAPARTMENTS_MODE              32780               67.2\n",
              "LIVINGAPARTMENTS_AVG               32780               67.2"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-814f206b-0ea7-4d27-9c8c-42b41cc055ff\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Missing Values</th>\n",
              "      <th>% of Total Values</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>COMMONAREA_MODE</th>\n",
              "      <td>33495</td>\n",
              "      <td>68.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>COMMONAREA_MEDI</th>\n",
              "      <td>33495</td>\n",
              "      <td>68.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>COMMONAREA_AVG</th>\n",
              "      <td>33495</td>\n",
              "      <td>68.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NONLIVINGAPARTMENTS_MODE</th>\n",
              "      <td>33347</td>\n",
              "      <td>68.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NONLIVINGAPARTMENTS_AVG</th>\n",
              "      <td>33347</td>\n",
              "      <td>68.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NONLIVINGAPARTMENTS_MEDI</th>\n",
              "      <td>33347</td>\n",
              "      <td>68.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>FONDKAPREMONT_MODE</th>\n",
              "      <td>32797</td>\n",
              "      <td>67.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LIVINGAPARTMENTS_MEDI</th>\n",
              "      <td>32780</td>\n",
              "      <td>67.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LIVINGAPARTMENTS_MODE</th>\n",
              "      <td>32780</td>\n",
              "      <td>67.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LIVINGAPARTMENTS_AVG</th>\n",
              "      <td>32780</td>\n",
              "      <td>67.2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-814f206b-0ea7-4d27-9c8c-42b41cc055ff')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-814f206b-0ea7-4d27-9c8c-42b41cc055ff button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-814f206b-0ea7-4d27-9c8c-42b41cc055ff');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "missing_test_vars = list(missing_test.index[missing_test['% of Total Values'] > 90])\n",
        "len(missing_test_vars)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n5dpORtaJ1zC",
        "outputId": "3de90000-e02e-42fd-ad80-acd686d42e1e"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "missing_columns = list(set(missing_test_vars + missing_train_vars))\n",
        "print('There are %d columns with more than 90%% missing in either the training or testing data.' % len(missing_columns))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RECQChm1H9ol",
        "outputId": "d87ee870-1695-4ddc-ee2c-39ad8571754b"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "There are 0 columns with more than 90% missing in either the training or testing data.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Drop the missing columns\n",
        "train = train.drop(columns = missing_columns)\n",
        "test = test.drop(columns = missing_columns)"
      ],
      "metadata": {
        "id": "uay0e4lVH9rD"
      },
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train.to_csv('train_bureau_raw.csv', index = False)\n",
        "test.to_csv('test_bureau_raw.csv', index = False)"
      ],
      "metadata": {
        "id": "e392DaTlH9t5"
      },
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Correlations"
      ],
      "metadata": {
        "id": "yzTTBZISKTXV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Calculate all corelations in dataframe\n",
        "corrs = train.corr()"
      ],
      "metadata": {
        "id": "Hn4vA3CcGTl3"
      },
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "corrs = corrs.sort_values('TARGET', ascending= False)\n",
        "#Ten most positive correlations\n",
        "pd.DataFrame(corrs['TARGET'].head(50))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "FXLKnlx8Ka4P",
        "outputId": "00612ea1-3526-484e-9c8c-51e638b274c2"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                  TARGET\n",
              "TARGET                                          1.000000\n",
              "client_bureau_balance_MONTHS_BALANCE_min_mean   0.089038\n",
              "DAYS_BIRTH                                      0.078239\n",
              "client_bureau_balance_MONTHS_BALANCE_mean_mean  0.076424\n",
              "client_bureau_balance_MONTHS_BALANCE_min_min    0.073225\n",
              "client_bureau_balance_MONTHS_BALANCE_sum_mean   0.072606\n",
              "client_bureau_balance_MONTHS_BALANCE_sum_min    0.068072\n",
              "client_bureau_balance_STATUS_1_count_norm_mean  0.061183\n",
              "client_bureau_balance_STATUS_1_count_norm_max   0.061122\n",
              "REGION_RATING_CLIENT_W_CITY                     0.060893\n",
              "REGION_RATING_CLIENT                            0.058899\n",
              "DAYS_LAST_PHONE_CHANGE                          0.055218\n",
              "client_bureau_balance_MONTHS_BALANCE_mean_min   0.053816\n",
              "DAYS_ID_PUBLISH                                 0.051457\n",
              "REG_CITY_NOT_WORK_CITY                          0.050994\n",
              "client_bureau_balance_MONTHS_BALANCE_min_max    0.049807\n",
              "FLAG_EMP_PHONE                                  0.045982\n",
              "REG_CITY_NOT_LIVE_CITY                          0.044395\n",
              "FLAG_DOCUMENT_3                                 0.044346\n",
              "client_bureau_balance_STATUS_0_count_norm_mean  0.044198\n",
              "client_bureau_balance_MONTHS_BALANCE_mean_max   0.043956\n",
              "DAYS_REGISTRATION                               0.041975\n",
              "OWN_CAR_AGE                                     0.037612\n",
              "client_bureau_balance_STATUS_1_count_norm_min   0.037332\n",
              "client_bureau_balance_STATUS_1_count_norm_sum   0.033614\n",
              "client_bureau_balance_STATUS_1_count_mean       0.033287\n",
              "LIVE_CITY_NOT_WORK_CITY                         0.032518\n",
              "DEF_30_CNT_SOCIAL_CIRCLE                        0.032248\n",
              "client_bureau_balance_STATUS_0_count_norm_min   0.031530\n",
              "DEF_60_CNT_SOCIAL_CIRCLE                        0.031276\n",
              "client_bureau_balance_MONTHS_BALANCE_sum_max    0.030245\n",
              "client_bureau_balance_STATUS_0_count_norm_max   0.029914\n",
              "FLAG_WORK_PHONE                                 0.028524\n",
              "client_bureau_balance_STATUS_1_count_max        0.027124\n",
              "client_bureau_balance_STATUS_1_count_min        0.024622\n",
              "client_bureau_balance_MONTHS_BALANCE_max_mean   0.022710\n",
              "client_bureau_balance_STATUS_0_count_norm_sum   0.021546\n",
              "client_bureau_balance_MONTHS_BALANCE_sum_sum    0.020601\n",
              "client_bureau_balance_MONTHS_BALANCE_max_min    0.020336\n",
              "AMT_REQ_CREDIT_BUREAU_YEAR                      0.019930\n",
              "CNT_CHILDREN                                    0.019187\n",
              "client_bureau_balance_STATUS_5_count_norm_mean  0.018951\n",
              "client_bureau_balance_STATUS_5_count_norm_min   0.018919\n",
              "client_bureau_balance_STATUS_2_count_norm_mean  0.018552\n",
              "client_bureau_balance_STATUS_1_count_sum        0.017907\n",
              "client_bureau_balance_STATUS_2_count_norm_max   0.017827\n",
              "client_bureau_balance_STATUS_3_count_norm_mean  0.017464\n",
              "client_bureau_balance_STATUS_3_count_mean       0.017156\n",
              "client_bureau_balance_STATUS_3_count_norm_max   0.016544\n",
              "client_bureau_balance_STATUS_4_count_norm_max   0.016451"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b21dbb12-262c-4dd5-86de-7cd10343a052\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>TARGET</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>TARGET</th>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>client_bureau_balance_MONTHS_BALANCE_min_mean</th>\n",
              "      <td>0.089038</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DAYS_BIRTH</th>\n",
              "      <td>0.078239</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>client_bureau_balance_MONTHS_BALANCE_mean_mean</th>\n",
              "      <td>0.076424</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>client_bureau_balance_MONTHS_BALANCE_min_min</th>\n",
              "      <td>0.073225</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>client_bureau_balance_MONTHS_BALANCE_sum_mean</th>\n",
              "      <td>0.072606</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>client_bureau_balance_MONTHS_BALANCE_sum_min</th>\n",
              "      <td>0.068072</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>client_bureau_balance_STATUS_1_count_norm_mean</th>\n",
              "      <td>0.061183</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>client_bureau_balance_STATUS_1_count_norm_max</th>\n",
              "      <td>0.061122</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>REGION_RATING_CLIENT_W_CITY</th>\n",
              "      <td>0.060893</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>REGION_RATING_CLIENT</th>\n",
              "      <td>0.058899</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DAYS_LAST_PHONE_CHANGE</th>\n",
              "      <td>0.055218</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>client_bureau_balance_MONTHS_BALANCE_mean_min</th>\n",
              "      <td>0.053816</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DAYS_ID_PUBLISH</th>\n",
              "      <td>0.051457</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>REG_CITY_NOT_WORK_CITY</th>\n",
              "      <td>0.050994</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>client_bureau_balance_MONTHS_BALANCE_min_max</th>\n",
              "      <td>0.049807</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>FLAG_EMP_PHONE</th>\n",
              "      <td>0.045982</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>REG_CITY_NOT_LIVE_CITY</th>\n",
              "      <td>0.044395</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>FLAG_DOCUMENT_3</th>\n",
              "      <td>0.044346</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>client_bureau_balance_STATUS_0_count_norm_mean</th>\n",
              "      <td>0.044198</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>client_bureau_balance_MONTHS_BALANCE_mean_max</th>\n",
              "      <td>0.043956</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DAYS_REGISTRATION</th>\n",
              "      <td>0.041975</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>OWN_CAR_AGE</th>\n",
              "      <td>0.037612</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>client_bureau_balance_STATUS_1_count_norm_min</th>\n",
              "      <td>0.037332</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>client_bureau_balance_STATUS_1_count_norm_sum</th>\n",
              "      <td>0.033614</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>client_bureau_balance_STATUS_1_count_mean</th>\n",
              "      <td>0.033287</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LIVE_CITY_NOT_WORK_CITY</th>\n",
              "      <td>0.032518</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DEF_30_CNT_SOCIAL_CIRCLE</th>\n",
              "      <td>0.032248</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>client_bureau_balance_STATUS_0_count_norm_min</th>\n",
              "      <td>0.031530</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DEF_60_CNT_SOCIAL_CIRCLE</th>\n",
              "      <td>0.031276</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>client_bureau_balance_MONTHS_BALANCE_sum_max</th>\n",
              "      <td>0.030245</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>client_bureau_balance_STATUS_0_count_norm_max</th>\n",
              "      <td>0.029914</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>FLAG_WORK_PHONE</th>\n",
              "      <td>0.028524</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>client_bureau_balance_STATUS_1_count_max</th>\n",
              "      <td>0.027124</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>client_bureau_balance_STATUS_1_count_min</th>\n",
              "      <td>0.024622</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>client_bureau_balance_MONTHS_BALANCE_max_mean</th>\n",
              "      <td>0.022710</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>client_bureau_balance_STATUS_0_count_norm_sum</th>\n",
              "      <td>0.021546</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>client_bureau_balance_MONTHS_BALANCE_sum_sum</th>\n",
              "      <td>0.020601</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>client_bureau_balance_MONTHS_BALANCE_max_min</th>\n",
              "      <td>0.020336</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AMT_REQ_CREDIT_BUREAU_YEAR</th>\n",
              "      <td>0.019930</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CNT_CHILDREN</th>\n",
              "      <td>0.019187</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>client_bureau_balance_STATUS_5_count_norm_mean</th>\n",
              "      <td>0.018951</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>client_bureau_balance_STATUS_5_count_norm_min</th>\n",
              "      <td>0.018919</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>client_bureau_balance_STATUS_2_count_norm_mean</th>\n",
              "      <td>0.018552</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>client_bureau_balance_STATUS_1_count_sum</th>\n",
              "      <td>0.017907</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>client_bureau_balance_STATUS_2_count_norm_max</th>\n",
              "      <td>0.017827</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>client_bureau_balance_STATUS_3_count_norm_mean</th>\n",
              "      <td>0.017464</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>client_bureau_balance_STATUS_3_count_mean</th>\n",
              "      <td>0.017156</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>client_bureau_balance_STATUS_3_count_norm_max</th>\n",
              "      <td>0.016544</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>client_bureau_balance_STATUS_4_count_norm_max</th>\n",
              "      <td>0.016451</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "                                                     [key], {});\n",
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              "\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Ten most negative correlations\n",
        "pd.DataFrame(corrs['TARGET'].dropna().tail(10))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "qdDBYclTKa-q",
        "outputId": "709d590e-5130-45b7-9b36-3f02f136e045"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                   TARGET\n",
              "DAYS_EMPLOYED                                   -0.044932\n",
              "client_bureau_balance_MONTHS_BALANCE_count_min  -0.048224\n",
              "client_bureau_balance_STATUS_C_count_norm_mean  -0.055936\n",
              "client_bureau_balance_STATUS_C_count_max        -0.061083\n",
              "client_bureau_balance_STATUS_C_count_mean       -0.062954\n",
              "client_bureau_balance_MONTHS_BALANCE_count_max  -0.068792\n",
              "client_bureau_balance_MONTHS_BALANCE_count_mean -0.080193\n",
              "EXT_SOURCE_1                                    -0.155317\n",
              "EXT_SOURCE_2                                    -0.160472\n",
              "EXT_SOURCE_3                                    -0.178919"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-35c5e85f-c8f2-4bac-a3b1-07ef230115c3\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>TARGET</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>DAYS_EMPLOYED</th>\n",
              "      <td>-0.044932</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>client_bureau_balance_MONTHS_BALANCE_count_min</th>\n",
              "      <td>-0.048224</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>client_bureau_balance_STATUS_C_count_norm_mean</th>\n",
              "      <td>-0.055936</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>client_bureau_balance_STATUS_C_count_max</th>\n",
              "      <td>-0.061083</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>client_bureau_balance_STATUS_C_count_mean</th>\n",
              "      <td>-0.062954</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>client_bureau_balance_MONTHS_BALANCE_count_max</th>\n",
              "      <td>-0.068792</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>client_bureau_balance_MONTHS_BALANCE_count_mean</th>\n",
              "      <td>-0.080193</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>EXT_SOURCE_1</th>\n",
              "      <td>-0.155317</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>EXT_SOURCE_2</th>\n",
              "      <td>-0.160472</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>EXT_SOURCE_3</th>\n",
              "      <td>-0.178919</td>\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-35c5e85f-c8f2-4bac-a3b1-07ef230115c3');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "kde_target(var_name =  'client_bureau_balance_STATUS_C_count_mean', df= train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "yJeSAiyYKbIS",
        "outputId": "81c12cec-6305-42af-d894-1c3d501c8835"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The correlation between client_bureau_balance_STATUS_C_count_mean and the TARGET is -0.0630\n",
            "Median value for loan that was not repaid = 5.7775\n",
            "Median value for loan that was repaid =     9.5000\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "kde_target(var_name='client_bureau_balance_STATUS_1_count_norm_mean', df=train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "dp2hiL7JKbL5",
        "outputId": "8600f5f1-68d3-4afe-ec86-ccdbd1c03ded"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The correlation between client_bureau_balance_STATUS_1_count_norm_mean and the TARGET is 0.0612\n",
            "Median value for loan that was not repaid = 0.0000\n",
            "Median value for loan that was repaid =     0.0000\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Collinear Variables"
      ],
      "metadata": {
        "id": "nUGjaycHL0cN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Set the threshold\n",
        "threshold = 0.8\n",
        "\n",
        "#Empty dictionary to hold correlated variables\n",
        "above_threshold_vars = {}\n",
        "\n",
        "#For each column, record the variables that are abobo the threshold\n",
        "\n",
        "for col in corrs:\n",
        "    above_threshold_vars[col] = list(corrs.index[corrs[col] > threshold])"
      ],
      "metadata": {
        "id": "uuz_qpvbGTpG"
      },
      "execution_count": 62,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#track columns to remove and columns already examined\n",
        "cols_to_remove = []\n",
        "cols_seen = []\n",
        "cols_to_remove_pair = []\n",
        "\n",
        "#Iterate through columns and correlated columns\n",
        "for key, value in above_threshold_vars.items():\n",
        "    #Keep track of columns already examined\n",
        "    cols_seen.append(key)\n",
        "    for x in value:\n",
        "        if x == key:\n",
        "            next\n",
        "        else:\n",
        "            #Only want to remove one in a pair\n",
        "            if x not in cols_seen:\n",
        "                cols_to_remove.append(x)\n",
        "                cols_to_remove_pair.append(key)\n",
        "\n",
        "cols_to_remove = list(set(cols_to_remove))\n",
        "print('Number of columnsto remove: ', len(cols_to_remove))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JGCztYgbL27-",
        "outputId": "aaea36f2-1604-4e11-cc71-8293ef114b89"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of columnsto remove:  96\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_corrs_removed = train.drop(columns = cols_to_remove)\n",
        "test_corrs_removed = test.drop(columns = cols_to_remove)\n",
        "\n",
        "print('Training Corrs Removed Shape:', train_corrs_removed.shape)\n",
        "print('Testing Corrs Removed Shape:', test_corrs_removed.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f0BRzpV-OKU4",
        "outputId": "4a5e5159-228d-4429-b4fb-022bc1155fb3"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training Corrs Removed Shape: (307511, 131)\n",
            "Testing Corrs Removed Shape: (48744, 130)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_corrs_removed.to_csv('train_bureau_corrs_removed.csv', index=False)\n",
        "test_corrs_removed.to_csv('test_bureau_corrs_removed.csv', index = False)"
      ],
      "metadata": {
        "id": "EWkFc7EcL3Az"
      },
      "execution_count": 65,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Modeling"
      ],
      "metadata": {
        "id": "T1iK-ccfL3Q8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import lightgbm as lgb\n",
        "\n",
        "from sklearn.model_selection import KFold\n",
        "from sklearn.metrics import roc_auc_score\n",
        "from sklearn.preprocessing import LabelBinarizer\n",
        "\n",
        "import gc\n",
        "\n",
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "Sqbjyxe-L38k"
      },
      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def model(features, test_features, encoding = 'ohe', n_folds = 5):\n",
        "\n",
        " # Extract the ids\n",
        "    train_ids = features['SK_ID_CURR']\n",
        "    test_ids = test_features['SK_ID_CURR']\n",
        "    \n",
        "    # Extract the labels for training\n",
        "    labels = features['TARGET']\n",
        "    \n",
        "    # Remove the ids and target\n",
        "    features = features.drop(columns = ['SK_ID_CURR', 'TARGET'])\n",
        "    test_features = test_features.drop(columns = ['SK_ID_CURR'])\n",
        "    \n",
        "    \n",
        "    # One Hot Encoding\n",
        "    if encoding == 'ohe':\n",
        "        features = pd.get_dummies(features)\n",
        "        test_features = pd.get_dummies(test_features)\n",
        "        \n",
        "        # Align the dataframes by the columns\n",
        "        features, test_features = features.align(test_features, join = 'inner', axis = 1)\n",
        "        \n",
        "        # No categorical indices to record\n",
        "        cat_indices = 'auto'\n",
        "    \n",
        "    # Integer label encoding\n",
        "    elif encoding == 'le':\n",
        "        \n",
        "        # Create a label encoder\n",
        "        label_encoder = LabelEncoder()\n",
        "        \n",
        "        # List for storing categorical indices\n",
        "        cat_indices = []\n",
        "        \n",
        "        # Iterate through each column\n",
        "        for i, col in enumerate(features):\n",
        "            if features[col].dtype == 'object':\n",
        "                # Map the categorical features to integers\n",
        "                features[col] = label_encoder.fit_transform(np.array(features[col].astype(str)).reshape((-1,)))\n",
        "                test_features[col] = label_encoder.transform(np.array(test_features[col].astype(str)).reshape((-1,)))\n",
        "\n",
        "                # Record the categorical indices\n",
        "                cat_indices.append(i)\n",
        "    \n",
        "    # Catch error if label encoding scheme is not valid\n",
        "    else:\n",
        "        raise ValueError(\"Encoding must be either 'ohe' or 'le'\")\n",
        "        \n",
        "    print('Training Data Shape: ', features.shape)\n",
        "    print('Testing Data Shape: ', test_features.shape)\n",
        "    \n",
        "    # Extract feature names\n",
        "    feature_names = list(features.columns)\n",
        "    \n",
        "    # Convert to np arrays\n",
        "    features = np.array(features)\n",
        "    test_features = np.array(test_features)\n",
        "    \n",
        "    # Create the kfold object\n",
        "    k_fold = KFold(n_splits = n_folds, shuffle = True, random_state = 50)\n",
        "    \n",
        "    # Empty array for feature importances\n",
        "    feature_importance_values = np.zeros(len(feature_names))\n",
        "    \n",
        "    # Empty array for test predictions\n",
        "    test_predictions = np.zeros(test_features.shape[0])\n",
        "    \n",
        "    # Empty array for out of fold validation predictions\n",
        "    out_of_fold = np.zeros(features.shape[0])\n",
        "    \n",
        "    # Lists for recording validation and training scores\n",
        "    valid_scores = []\n",
        "    train_scores = []\n",
        "    \n",
        "    # Iterate through each fold\n",
        "    for train_indices, valid_indices in k_fold.split(features):\n",
        "        \n",
        "        # Training data for the fold\n",
        "        train_features, train_labels = features[train_indices], labels[train_indices]\n",
        "        # Validation data for the fold\n",
        "        valid_features, valid_labels = features[valid_indices], labels[valid_indices]\n",
        "        \n",
        "        # Create the model\n",
        "        model = lgb.LGBMClassifier(n_estimators=10000, objective = 'binary', \n",
        "                                   class_weight = 'balanced', learning_rate = 0.05, \n",
        "                                   reg_alpha = 0.1, reg_lambda = 0.1, \n",
        "                                   subsample = 0.8, n_jobs = -1, random_state = 50)\n",
        "        \n",
        "        # Train the model\n",
        "        model.fit(train_features, train_labels, eval_metric = 'auc',\n",
        "                  eval_set = [(valid_features, valid_labels), (train_features, train_labels)],\n",
        "                  eval_names = ['valid', 'train'], categorical_feature = cat_indices,\n",
        "                  early_stopping_rounds = 100, verbose = 200)\n",
        "        \n",
        "        # Record the best iteration\n",
        "        best_iteration = model.best_iteration_\n",
        "        \n",
        "        # Record the feature importances\n",
        "        feature_importance_values += model.feature_importances_ / k_fold.n_splits\n",
        "        \n",
        "        # Make predictions\n",
        "        test_predictions += model.predict_proba(test_features, num_iteration = best_iteration)[:, 1] / k_fold.n_splits\n",
        "        \n",
        "        # Record the out of fold predictions\n",
        "        out_of_fold[valid_indices] = model.predict_proba(valid_features, num_iteration = best_iteration)[:, 1]\n",
        "        \n",
        "        # Record the best score\n",
        "        valid_score = model.best_score_['valid']['auc']\n",
        "        train_score = model.best_score_['train']['auc']\n",
        "        \n",
        "        valid_scores.append(valid_score)\n",
        "        train_scores.append(train_score)\n",
        "        \n",
        "        # Clean up memory\n",
        "        gc.enable()\n",
        "        del model, train_features, valid_features\n",
        "        gc.collect()\n",
        "        \n",
        "    # Make the submission dataframe\n",
        "    submission = pd.DataFrame({'SK_ID_CURR': test_ids, 'TARGET': test_predictions})\n",
        "    \n",
        "    # Make the feature importance dataframe\n",
        "    feature_importances = pd.DataFrame({'feature': feature_names, 'importance': feature_importance_values})\n",
        "    \n",
        "    # Overall validation score\n",
        "    valid_auc = roc_auc_score(labels, out_of_fold)\n",
        "    \n",
        "    # Add the overall scores to the metrics\n",
        "    valid_scores.append(valid_auc)\n",
        "    train_scores.append(np.mean(train_scores))\n",
        "    \n",
        "    # Needed for creating dataframe of validation scores\n",
        "    fold_names = list(range(n_folds))\n",
        "    fold_names.append('overall')\n",
        "    \n",
        "    # Dataframe of validation scores\n",
        "    metrics = pd.DataFrame({'fold': fold_names,\n",
        "                            'train': train_scores,\n",
        "                            'valid': valid_scores}) \n",
        "    \n",
        "    return submission, feature_importances, metrics"
      ],
      "metadata": {
        "id": "sWWzD_9HcvkA"
      },
      "execution_count": 67,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_feature_importances(df):\n",
        "    #Sort features according to importance\n",
        "    df = df.sort_values('importance', ascending = False).reset_index()\n",
        "\n",
        "    #Normalize the feature importances to add up to one\n",
        "    df['importance_normalized'] = df['importance'] / df['importance'].sum()\n",
        "\n",
        "    #Make a horizontal bar chart of feature importances\n",
        "    plt.figure(figsize=(10,6))\n",
        "    ax = plt.subplot()\n",
        "\n",
        "    #Need to reverse the index to plot most importanct on top\n",
        "    ax.barh(list(reversed(list(df.index[:15]))),\n",
        "            df['importance_normalized'].head(15),\n",
        "            align = 'center', edgecolor = 'k')\n",
        "    \n",
        "    #Set the yticks and labels\n",
        "    ax.set_yticks(list(reversed(list(df.index[:15]))))\n",
        "    ax.set_yticklabels(df['feature'].head(15))\n",
        "\n",
        "    #plot labeling\n",
        "    plt.xlabel('Normalized Importance');\n",
        "    plt.title('Feature Importances')\n",
        "    plt.show()\n",
        "    return df\n"
      ],
      "metadata": {
        "id": "nK8x4FmfdDRp"
      },
      "execution_count": 68,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 알고리즘 A/B Test"
      ],
      "metadata": {
        "id": "9TO0XUcSuljh"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### control"
      ],
      "metadata": {
        "id": "ossy9yD2ma11"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_control = pd.read_csv('/content/drive/MyDrive/home-credit-default-risk/application_train.csv')\n",
        "test_control = pd.read_csv('/content/drive/MyDrive/home-credit-default-risk/application_test.csv')"
      ],
      "metadata": {
        "id": "UIy6npQplj2C"
      },
      "execution_count": 69,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "submission, fi, metrics = model(train_control, test_control)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "66k-x7kMmo06",
        "outputId": "b46b7aca-1769-4fb1-ec0e-269ea76fe87a"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training Data Shape:  (307511, 241)\n",
            "Testing Data Shape:  (48744, 241)\n",
            "Training until validation scores don't improve for 100 rounds.\n",
            "[200]\ttrain's auc: 0.798882\ttrain's binary_logloss: 0.547678\tvalid's auc: 0.755525\tvalid's binary_logloss: 0.563196\n",
            "Early stopping, best iteration is:\n",
            "[234]\ttrain's auc: 0.804502\ttrain's binary_logloss: 0.542206\tvalid's auc: 0.755743\tvalid's binary_logloss: 0.559787\n",
            "Training until validation scores don't improve for 100 rounds.\n",
            "[200]\ttrain's auc: 0.798461\ttrain's binary_logloss: 0.548215\tvalid's auc: 0.758478\tvalid's binary_logloss: 0.563404\n",
            "Early stopping, best iteration is:\n",
            "[276]\ttrain's auc: 0.810258\ttrain's binary_logloss: 0.536484\tvalid's auc: 0.7587\tvalid's binary_logloss: 0.556356\n",
            "Training until validation scores don't improve for 100 rounds.\n",
            "[200]\ttrain's auc: 0.797491\ttrain's binary_logloss: 0.549643\tvalid's auc: 0.762759\tvalid's binary_logloss: 0.564665\n",
            "Early stopping, best iteration is:\n",
            "[268]\ttrain's auc: 0.808601\ttrain's binary_logloss: 0.538806\tvalid's auc: 0.763116\tvalid's binary_logloss: 0.557908\n",
            "Training until validation scores don't improve for 100 rounds.\n",
            "[200]\ttrain's auc: 0.798997\ttrain's binary_logloss: 0.547789\tvalid's auc: 0.758091\tvalid's binary_logloss: 0.562066\n",
            "Early stopping, best iteration is:\n",
            "[220]\ttrain's auc: 0.802598\ttrain's binary_logloss: 0.544216\tvalid's auc: 0.758302\tvalid's binary_logloss: 0.559926\n",
            "Training until validation scores don't improve for 100 rounds.\n",
            "[200]\ttrain's auc: 0.798393\ttrain's binary_logloss: 0.548274\tvalid's auc: 0.758041\tvalid's binary_logloss: 0.564647\n",
            "Early stopping, best iteration is:\n",
            "[217]\ttrain's auc: 0.801185\ttrain's binary_logloss: 0.545486\tvalid's auc: 0.75844\tvalid's binary_logloss: 0.562914\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "metrics"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "9jS4GhxGmo3L",
        "outputId": "3b08aadf-ae4f-4d90-c3fa-f6ef6a3d6221"
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      fold     train     valid\n",
              "0        0  0.804502  0.755743\n",
              "1        1  0.810258  0.758700\n",
              "2        2  0.808601  0.763116\n",
              "3        3  0.802598  0.758302\n",
              "4        4  0.801185  0.758440\n",
              "5  overall  0.805429  0.758870"
            ],
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              "      <th></th>\n",
              "      <th>fold</th>\n",
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              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>0.804502</td>\n",
              "      <td>0.755743</td>\n",
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              "      <th>1</th>\n",
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              "      <td>0.810258</td>\n",
              "      <td>0.758700</td>\n",
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              "      <th>2</th>\n",
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              "      <td>0.808601</td>\n",
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              "      <td>0.805429</td>\n",
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    {
      "cell_type": "code",
      "source": [
        "fi_sorted = plot_feature_importances(fi)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "7_GU3inbmo8r",
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      "execution_count": 72,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "submission.to_csv('control.csv', index = False)"
      ],
      "metadata": {
        "id": "JTLeyKsru0Jr"
      },
      "execution_count": 73,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Test one"
      ],
      "metadata": {
        "id": "e3Z0YERYu2wk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "submission_raw, fi_raw, metrics_raw = model(train, test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LvM5HCgvmo_S",
        "outputId": "a183e24e-007d-48a8-ac7b-e07ae95ed5f0"
      },
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training Data Shape:  (307511, 346)\n",
            "Testing Data Shape:  (48744, 346)\n",
            "Training until validation scores don't improve for 100 rounds.\n",
            "[200]\ttrain's auc: 0.801235\ttrain's binary_logloss: 0.54569\tvalid's auc: 0.755628\tvalid's binary_logloss: 0.561213\n",
            "Early stopping, best iteration is:\n",
            "[181]\ttrain's auc: 0.797952\ttrain's binary_logloss: 0.548931\tvalid's auc: 0.755953\tvalid's binary_logloss: 0.563121\n",
            "Training until validation scores don't improve for 100 rounds.\n",
            "[200]\ttrain's auc: 0.801144\ttrain's binary_logloss: 0.546008\tvalid's auc: 0.758814\tvalid's binary_logloss: 0.561855\n",
            "Early stopping, best iteration is:\n",
            "[275]\ttrain's auc: 0.813902\ttrain's binary_logloss: 0.533418\tvalid's auc: 0.759042\tvalid's binary_logloss: 0.554072\n",
            "Training until validation scores don't improve for 100 rounds.\n",
            "[200]\ttrain's auc: 0.800427\ttrain's binary_logloss: 0.546912\tvalid's auc: 0.763707\tvalid's binary_logloss: 0.562354\n",
            "Early stopping, best iteration is:\n",
            "[201]\ttrain's auc: 0.800605\ttrain's binary_logloss: 0.546732\tvalid's auc: 0.763736\tvalid's binary_logloss: 0.56224\n",
            "Training until validation scores don't improve for 100 rounds.\n",
            "[200]\ttrain's auc: 0.801514\ttrain's binary_logloss: 0.545869\tvalid's auc: 0.758244\tvalid's binary_logloss: 0.560778\n",
            "Early stopping, best iteration is:\n",
            "[197]\ttrain's auc: 0.800904\ttrain's binary_logloss: 0.546456\tvalid's auc: 0.758293\tvalid's binary_logloss: 0.561092\n",
            "Training until validation scores don't improve for 100 rounds.\n",
            "[200]\ttrain's auc: 0.800995\ttrain's binary_logloss: 0.546163\tvalid's auc: 0.758221\tvalid's binary_logloss: 0.562737\n",
            "Early stopping, best iteration is:\n",
            "[288]\ttrain's auc: 0.815606\ttrain's binary_logloss: 0.531798\tvalid's auc: 0.758557\tvalid's binary_logloss: 0.55398\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "metrics_raw"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "pyVbSgVBu8Vo",
        "outputId": "ca44b37e-452d-4fb5-9842-98200a921d24"
      },
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      fold     train     valid\n",
              "0        0  0.797952  0.755953\n",
              "1        1  0.813902  0.759042\n",
              "2        2  0.800605  0.763736\n",
              "3        3  0.800904  0.758293\n",
              "4        4  0.815606  0.758557\n",
              "5  overall  0.805794  0.759072"
            ],
            "text/html": [
              "\n",
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              "    <div class=\"colab-df-container\">\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>fold</th>\n",
              "      <th>train</th>\n",
              "      <th>valid</th>\n",
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              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>0.797952</td>\n",
              "      <td>0.755953</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>0.813902</td>\n",
              "      <td>0.759042</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>0.800605</td>\n",
              "      <td>0.763736</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>0.800904</td>\n",
              "      <td>0.758293</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>0.815606</td>\n",
              "      <td>0.758557</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>overall</td>\n",
              "      <td>0.805794</td>\n",
              "      <td>0.759072</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-de9a2d94-bcbe-44b6-8904-fd2fc45f2b5e')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "        \n",
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              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-de9a2d94-bcbe-44b6-8904-fd2fc45f2b5e button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-de9a2d94-bcbe-44b6-8904-fd2fc45f2b5e');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fi_raw_sorted = plot_feature_importances(fi_raw)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 405
        },
        "id": "VVsMmA_0u9_z",
        "outputId": "4b2618a8-745e-45ad-938f-0f1ad8cf840c"
      },
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "top_100 = list(fi_raw_sorted['feature'])[:100]\n",
        "new_features = [x for x in top_100 if x not in list(fi['feature'])]\n",
        "\n",
        "print('%% of Top 100 Features created from the bureau data = %d.00' % len(new_features))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kYm09lrdvBlz",
        "outputId": "4b9601a3-8177-4d29-bedf-ae3e18f40233"
      },
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "% of Top 100 Features created from the bureau data = 34.00\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "submission_raw.to_csv('test_one.csv', index = False)"
      ],
      "metadata": {
        "id": "E_Z0wNEuqCkA"
      },
      "execution_count": 78,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Test Two"
      ],
      "metadata": {
        "id": "8LdhfzU2smWX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "submission_corrs, fi_corrs, metrics_corr = model(train_corrs_removed, test_corrs_removed)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2W2P8tfhso2P",
        "outputId": "bddf2ca9-21d4-4eed-c95c-241618cfd9a6"
      },
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training Data Shape:  (307511, 250)\n",
            "Testing Data Shape:  (48744, 250)\n",
            "Training until validation scores don't improve for 100 rounds.\n",
            "[200]\ttrain's auc: 0.797407\ttrain's binary_logloss: 0.549342\tvalid's auc: 0.752745\tvalid's binary_logloss: 0.564514\n",
            "Early stopping, best iteration is:\n",
            "[261]\ttrain's auc: 0.807366\ttrain's binary_logloss: 0.539813\tvalid's auc: 0.753391\tvalid's binary_logloss: 0.558391\n",
            "Training until validation scores don't improve for 100 rounds.\n",
            "[200]\ttrain's auc: 0.797291\ttrain's binary_logloss: 0.549506\tvalid's auc: 0.755139\tvalid's binary_logloss: 0.564734\n",
            "Early stopping, best iteration is:\n",
            "[274]\ttrain's auc: 0.809385\ttrain's binary_logloss: 0.537854\tvalid's auc: 0.755358\tvalid's binary_logloss: 0.557701\n",
            "Training until validation scores don't improve for 100 rounds.\n",
            "[200]\ttrain's auc: 0.795987\ttrain's binary_logloss: 0.551051\tvalid's auc: 0.759933\tvalid's binary_logloss: 0.56606\n",
            "Early stopping, best iteration is:\n",
            "[235]\ttrain's auc: 0.802026\ttrain's binary_logloss: 0.545302\tvalid's auc: 0.760099\tvalid's binary_logloss: 0.562424\n",
            "Training until validation scores don't improve for 100 rounds.\n",
            "[200]\ttrain's auc: 0.797335\ttrain's binary_logloss: 0.549528\tvalid's auc: 0.755366\tvalid's binary_logloss: 0.564134\n",
            "Early stopping, best iteration is:\n",
            "[200]\ttrain's auc: 0.797335\ttrain's binary_logloss: 0.549528\tvalid's auc: 0.755366\tvalid's binary_logloss: 0.564134\n",
            "Training until validation scores don't improve for 100 rounds.\n",
            "[200]\ttrain's auc: 0.796932\ttrain's binary_logloss: 0.549747\tvalid's auc: 0.754741\tvalid's binary_logloss: 0.566246\n",
            "Early stopping, best iteration is:\n",
            "[258]\ttrain's auc: 0.806369\ttrain's binary_logloss: 0.540702\tvalid's auc: 0.754819\tvalid's binary_logloss: 0.560668\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "metrics_corr    "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "WM1cRqvAso4R",
        "outputId": "a4bc9dc0-b930-454b-cd6d-c9894aaba520"
      },
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      fold     train     valid\n",
              "0        0  0.807366  0.753391\n",
              "1        1  0.809385  0.755358\n",
              "2        2  0.802026  0.760099\n",
              "3        3  0.797335  0.755366\n",
              "4        4  0.806369  0.754819\n",
              "5  overall  0.804496  0.755792"
            ],
            "text/html": [
              "\n",
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              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>fold</th>\n",
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              "      <th>0</th>\n",
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              "      <td>0.807366</td>\n",
              "      <td>0.753391</td>\n",
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              "      <td>1</td>\n",
              "      <td>0.809385</td>\n",
              "      <td>0.755358</td>\n",
              "    </tr>\n",
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              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>0.802026</td>\n",
              "      <td>0.760099</td>\n",
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              "      <td>0.797335</td>\n",
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              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>0.806369</td>\n",
              "      <td>0.754819</td>\n",
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              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>overall</td>\n",
              "      <td>0.804496</td>\n",
              "      <td>0.755792</td>\n",
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              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-c34fc815-ef98-4630-801e-0ccbd00fd0df button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-c34fc815-ef98-4630-801e-0ccbd00fd0df');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fi_corrs_sorted = plot_feature_importances(fi_corrs)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 405
        },
        "id": "a4RwDvvFso6X",
        "outputId": "3e446167-84cb-4db0-f34f-ea8ffb5e09e3"
      },
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "submission_corrs.to_csv('test_two.csv', index = False)"
      ],
      "metadata": {
        "id": "5Jm8tsxovJlh"
      },
      "execution_count": 82,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 추가 모델링\n",
        "\n",
        "\n",
        "#### Stacking Test-Sklearn, XGBoost, CatBoost, LightGBM\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "ElHms-8wueBQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from scipy.stats import skew\n",
        "import xgboost as xgb\n",
        "from sklearn.model_selection import KFold\n",
        "from sklearn.ensemble import ExtraTreesClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.metrics import mean_squared_error\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from math import sqrt\n",
        "from sklearn.metrics import roc_auc_score\n",
        "from sklearn.model_selection import KFold\n",
        "from catboost import CatBoostClassifier #무슨 패키지인지 공부하기\n",
        "from lightgbm import LGBMClassifier\n",
        "import gc"
      ],
      "metadata": {
        "id": "txmt3A90AOk4"
      },
      "execution_count": 86,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install catboost"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CcWg12WTMpXe",
        "outputId": "e170f4e1-c1a5-4e5f-a0fb-56bda2b2df10"
      },
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting catboost\n",
            "  Downloading catboost-1.0.5-cp37-none-manylinux1_x86_64.whl (76.6 MB)\n",
            "\u001b[K     |████████████████████████████████| 76.6 MB 1.2 MB/s \n",
            "\u001b[?25hRequirement already satisfied: plotly in /usr/local/lib/python3.7/dist-packages (from catboost) (5.5.0)\n",
            "Requirement already satisfied: graphviz in /usr/local/lib/python3.7/dist-packages (from catboost) (0.10.1)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from catboost) (1.15.0)\n",
            "Requirement already satisfied: pandas>=0.24.0 in /usr/local/lib/python3.7/dist-packages (from catboost) (1.3.5)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from catboost) (3.2.2)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from catboost) (1.4.1)\n",
            "Requirement already satisfied: numpy>=1.16.0 in /usr/local/lib/python3.7/dist-packages (from catboost) (1.21.6)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.24.0->catboost) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.24.0->catboost) (2022.1)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->catboost) (1.4.2)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->catboost) (3.0.8)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib->catboost) (0.11.0)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from kiwisolver>=1.0.1->matplotlib->catboost) (4.2.0)\n",
            "Requirement already satisfied: tenacity>=6.2.0 in /usr/local/lib/python3.7/dist-packages (from plotly->catboost) (8.0.1)\n",
            "Installing collected packages: catboost\n",
            "Successfully installed catboost-1.0.5\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "RBfIQcaXMpco"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "NFOLDS = 3\n",
        "SEED = 0\n",
        "NROWS = None"
      ],
      "metadata": {
        "id": "WV2kr1DgAyNk"
      },
      "execution_count": 87,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data = pd.read_csv('/content/drive/MyDrive/home-credit-default-risk/application_train.csv')\n",
        "test = pd.read_csv('/content/drive/MyDrive/home-credit-default-risk/application_test.csv')\n",
        "prev = pd.read_csv('/content/drive/MyDrive/home-credit-default-risk/previous_application.csv')"
      ],
      "metadata": {
        "id": "J_r-H2upA3KA"
      },
      "execution_count": 88,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "categorical_feats = [\n",
        "    f for f in data.columns if data[f].dtype == 'object'\n",
        "]\n",
        "\n",
        "for f_ in categorical_feats:\n",
        "    data[f_], indexer = pd.factorize(data[f_])\n",
        "    test[f_] = indexer.get_indexer(test[f_])\n",
        "    \n",
        "gc.enable()\n",
        "\n",
        "y_train = data['TARGET']\n",
        "del data['TARGET']"
      ],
      "metadata": {
        "id": "WyqvjlwCA3MT"
      },
      "execution_count": 89,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "prev_cat_features = [\n",
        "    f_ for f_ in prev.columns if prev[f_].dtype == 'object'\n",
        "]\n",
        "for f_ in prev_cat_features:\n",
        "    prev[f_], _ = pd.factorize(prev[f_])\n",
        "    \n",
        "avg_prev = prev.groupby('SK_ID_CURR').mean()\n",
        "cnt_prev = prev[['SK_ID_CURR', 'SK_ID_PREV']].groupby('SK_ID_CURR').count()\n",
        "avg_prev['nb_app'] = cnt_prev['SK_ID_PREV']\n",
        "del avg_prev['SK_ID_PREV']\n",
        "\n",
        "x_train = data.merge(right=avg_prev.reset_index(), how='left', on='SK_ID_CURR')\n",
        "x_test = test.merge(right=avg_prev.reset_index(), how='left', on='SK_ID_CURR')\n",
        "\n",
        "x_train = x_train.fillna(0)\n",
        "x_test= x_test.fillna(0)\n",
        "\n",
        "ntrain = x_train.shape[0]\n",
        "ntest = x_test.shape[0]\n",
        "\n",
        "excluded_feats = ['SK_ID_CURR']\n",
        "features = [f_ for f_ in x_train.columns if f_ not in excluded_feats]\n",
        "\n",
        "x_train = x_train[features]\n",
        "x_test = x_test[features]\n",
        "\n",
        "kf = KFold(n_splits = NFOLDS, shuffle=True, random_state=SEED)\n",
        "\n",
        "class SklearnWrapper(object):\n",
        "    def __init__(self, clf, seed=0, params=None):\n",
        "        params['random_state'] = seed\n",
        "        self.clf = clf(**params)\n",
        "\n",
        "    def train(self, x_train, y_train):\n",
        "        self.clf.fit(x_train, y_train)\n",
        "\n",
        "    def predict(self, x):\n",
        "        return self.clf.predict_proba(x)[:,1]\n",
        "\n",
        "class CatboostWrapper(object):\n",
        "    def __init__(self, clf, seed=0, params=None):\n",
        "        params['random_seed'] = seed\n",
        "        self.clf = clf(**params)\n",
        "\n",
        "    def train(self, x_train, y_train):\n",
        "        self.clf.fit(x_train, y_train)\n",
        "\n",
        "    def predict(self, x):\n",
        "        return self.clf.predict_proba(x)[:,1]\n",
        "        \n",
        "class LightGBMWrapper(object):\n",
        "    def __init__(self, clf, seed=0, params=None):\n",
        "        params['feature_fraction_seed'] = seed\n",
        "        params['bagging_seed'] = seed\n",
        "        self.clf = clf(**params)\n",
        "\n",
        "    def train(self, x_train, y_train):\n",
        "        self.clf.fit(x_train, y_train)\n",
        "\n",
        "    def predict(self, x):\n",
        "        return self.clf.predict_proba(x)[:,1]\n",
        "\n",
        "\n",
        "class XgbWrapper(object):\n",
        "    def __init__(self, seed=0, params=None):\n",
        "        self.param = params\n",
        "        self.param['seed'] = seed\n",
        "        self.nrounds = params.pop('nrounds', 250)\n",
        "\n",
        "    def train(self, x_train, y_train):\n",
        "        dtrain = xgb.DMatrix(x_train, label=y_train)\n",
        "        self.gbdt = xgb.train(self.param, dtrain, self.nrounds)\n",
        "\n",
        "    def predict(self, x):\n",
        "        return self.gbdt.predict(xgb.DMatrix(x))\n",
        "\n",
        "\n",
        "def get_oof(clf):\n",
        "    oof_train = np.zeros((ntrain,))\n",
        "    oof_test = np.zeros((ntest,))\n",
        "    oof_test_skf = np.empty((NFOLDS, ntest))\n",
        "\n",
        "    for i, (train_index, test_index) in enumerate(kf.split(x_train)):\n",
        "        x_tr = x_train.loc[train_index]\n",
        "        y_tr = y_train.loc[train_index]\n",
        "        x_te = x_train.loc[test_index]\n",
        "\n",
        "        clf.train(x_tr, y_tr)\n",
        "\n",
        "        oof_train[test_index] = clf.predict(x_te)\n",
        "        oof_test_skf[i, :] = clf.predict(x_test)\n",
        "\n",
        "    oof_test[:] = oof_test_skf.mean(axis=0)\n",
        "    return oof_train.reshape(-1, 1), oof_test.reshape(-1, 1)\n"
      ],
      "metadata": {
        "id": "Rx3PcBIgA3Oa"
      },
      "execution_count": 91,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "et_params = {\n",
        "    'n_jobs': 16,\n",
        "    'n_estimators': 200,\n",
        "    'max_features': 0.5,\n",
        "    'max_depth': 12,\n",
        "    'min_samples_leaf': 2,\n",
        "}\n",
        "\n",
        "rf_params = {\n",
        "    'n_jobs': 16,\n",
        "    'n_estimators': 200,\n",
        "    'max_features': 0.2,\n",
        "    'max_depth': 12,\n",
        "    'min_samples_leaf': 2,\n",
        "}\n",
        "\n",
        "xgb_params = {\n",
        "    'seed': 0,\n",
        "    'colsample_bytree': 0.7,\n",
        "    'silent': 1,\n",
        "    'subsample': 0.7,\n",
        "    'learning_rate': 0.075,\n",
        "    'objective': 'binary:logistic',\n",
        "    'max_depth': 4,\n",
        "    'num_parallel_tree': 1,\n",
        "    'min_child_weight': 1,\n",
        "    'nrounds': 200\n",
        "}\n",
        "\n",
        "catboost_params = {\n",
        "    'iterations': 200,\n",
        "    'learning_rate': 0.5,\n",
        "    'depth': 3,\n",
        "    'l2_leaf_reg': 40,\n",
        "    'bootstrap_type': 'Bernoulli',\n",
        "    'subsample': 0.7,\n",
        "    'scale_pos_weight': 5,\n",
        "    'eval_metric': 'AUC',\n",
        "    'od_type': 'Iter',\n",
        "    'allow_writing_files': False\n",
        "}\n",
        "\n",
        "lightgbm_params = {\n",
        "    'n_estimators':200,\n",
        "    'learning_rate':0.1,\n",
        "    'num_leaves':123,\n",
        "    'colsample_bytree':0.8,\n",
        "    'subsample':0.9,\n",
        "    'max_depth':15,\n",
        "    'reg_alpha':0.1,\n",
        "    'reg_lambda':0.1,\n",
        "    'min_split_gain':0.01,\n",
        "    'min_child_weight':2    \n",
        "}\n"
      ],
      "metadata": {
        "id": "4rtUyLU4A3Sk"
      },
      "execution_count": 92,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "xg = XgbWrapper(seed=SEED, params=xgb_params)\n",
        "et = SklearnWrapper(clf=ExtraTreesClassifier, seed=SEED, params=et_params)\n",
        "rf = SklearnWrapper(clf=RandomForestClassifier, seed=SEED, params=rf_params)\n",
        "cb = CatboostWrapper(clf= CatBoostClassifier, seed = SEED, params=catboost_params)\n",
        "lg = LightGBMWrapper(clf = LGBMClassifier, seed = SEED, params = lightgbm_params)\n",
        "\n",
        "xg_oof_train, xg_oof_test = get_oof(xg)\n",
        "et_oof_train, et_oof_test = get_oof(et)\n",
        "rf_oof_train, rf_oof_test = get_oof(rf)\n",
        "cb_oof_train, cb_oof_test = get_oof(cb)\n",
        "\n",
        "print(\"XG-CV: {}\".format(sqrt(mean_squared_error(y_train, xg_oof_train))))\n",
        "print(\"ET-CV: {}\".format(sqrt(mean_squared_error(y_train, et_oof_train))))\n",
        "print(\"RF-CV: {}\".format(sqrt(mean_squared_error(y_train, rf_oof_train))))\n",
        "print(\"RF-CV: {}\".format(sqrt(mean_squared_error(y_train, cb_oof_train))))\n",
        "\n",
        "x_train = np.concatenate((xg_oof_train, et_oof_train, rf_oof_train, cb_oof_train), axis=1)\n",
        "x_test = np.concatenate((xg_oof_test, et_oof_test, rf_oof_test, cb_oof_test), axis=1)\n",
        "\n",
        "print(\"{},{}\".format(x_train.shape, x_test.shape))\n",
        "\n",
        "logistic_regression = LogisticRegression()\n",
        "logistic_regression.fit(x_train,y_train)\n",
        "\n",
        "test['TARGET'] = logistic_regression.predict_proba(x_test)[:,1]\n",
        "\n",
        "test[['SK_ID_CURR', 'TARGET']].to_csv('first_submission.csv', index=False, float_format='%.8f')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dt0-F0iDA3VL",
        "outputId": "ce25e1da-26f4-4026-fb09-6a6c7e51ccc2"
      },
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
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            "0:\ttotal: 88.4ms\tremaining: 17.6s\n",
            "1:\ttotal: 159ms\tremaining: 15.7s\n",
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            "133:\ttotal: 9.96s\tremaining: 4.91s\n",
            "134:\ttotal: 10s\tremaining: 4.83s\n",
            "135:\ttotal: 10.1s\tremaining: 4.76s\n",
            "136:\ttotal: 10.2s\tremaining: 4.68s\n",
            "137:\ttotal: 10.3s\tremaining: 4.61s\n",
            "138:\ttotal: 10.3s\tremaining: 4.54s\n",
            "139:\ttotal: 10.4s\tremaining: 4.46s\n",
            "140:\ttotal: 10.5s\tremaining: 4.38s\n",
            "141:\ttotal: 10.6s\tremaining: 4.31s\n",
            "142:\ttotal: 10.6s\tremaining: 4.23s\n",
            "143:\ttotal: 10.7s\tremaining: 4.17s\n",
            "144:\ttotal: 10.8s\tremaining: 4.09s\n",
            "145:\ttotal: 10.9s\tremaining: 4.01s\n",
            "146:\ttotal: 10.9s\tremaining: 3.94s\n",
            "147:\ttotal: 11s\tremaining: 3.86s\n",
            "148:\ttotal: 11.1s\tremaining: 3.79s\n",
            "149:\ttotal: 11.1s\tremaining: 3.71s\n",
            "150:\ttotal: 11.2s\tremaining: 3.64s\n",
            "151:\ttotal: 11.3s\tremaining: 3.57s\n",
            "152:\ttotal: 11.4s\tremaining: 3.49s\n",
            "153:\ttotal: 11.4s\tremaining: 3.42s\n",
            "154:\ttotal: 11.5s\tremaining: 3.34s\n",
            "155:\ttotal: 11.6s\tremaining: 3.27s\n",
            "156:\ttotal: 11.7s\tremaining: 3.2s\n",
            "157:\ttotal: 11.8s\tremaining: 3.13s\n",
            "158:\ttotal: 11.8s\tremaining: 3.05s\n",
            "159:\ttotal: 11.9s\tremaining: 2.97s\n",
            "160:\ttotal: 12s\tremaining: 2.9s\n",
            "161:\ttotal: 12s\tremaining: 2.83s\n",
            "162:\ttotal: 12.1s\tremaining: 2.75s\n",
            "163:\ttotal: 12.2s\tremaining: 2.67s\n",
            "164:\ttotal: 12.3s\tremaining: 2.6s\n",
            "165:\ttotal: 12.3s\tremaining: 2.53s\n",
            "166:\ttotal: 12.4s\tremaining: 2.45s\n",
            "167:\ttotal: 12.5s\tremaining: 2.38s\n",
            "168:\ttotal: 12.5s\tremaining: 2.3s\n",
            "169:\ttotal: 12.6s\tremaining: 2.23s\n",
            "170:\ttotal: 12.7s\tremaining: 2.15s\n",
            "171:\ttotal: 12.8s\tremaining: 2.08s\n",
            "172:\ttotal: 12.8s\tremaining: 2s\n",
            "173:\ttotal: 12.9s\tremaining: 1.93s\n",
            "174:\ttotal: 13s\tremaining: 1.86s\n",
            "175:\ttotal: 13.1s\tremaining: 1.78s\n",
            "176:\ttotal: 13.1s\tremaining: 1.71s\n",
            "177:\ttotal: 13.2s\tremaining: 1.63s\n",
            "178:\ttotal: 13.3s\tremaining: 1.55s\n",
            "179:\ttotal: 13.3s\tremaining: 1.48s\n",
            "180:\ttotal: 13.4s\tremaining: 1.41s\n",
            "181:\ttotal: 13.5s\tremaining: 1.33s\n",
            "182:\ttotal: 13.6s\tremaining: 1.26s\n",
            "183:\ttotal: 13.6s\tremaining: 1.19s\n",
            "184:\ttotal: 13.7s\tremaining: 1.11s\n",
            "185:\ttotal: 13.8s\tremaining: 1.04s\n",
            "186:\ttotal: 13.9s\tremaining: 963ms\n",
            "187:\ttotal: 13.9s\tremaining: 889ms\n",
            "188:\ttotal: 14s\tremaining: 815ms\n",
            "189:\ttotal: 14.1s\tremaining: 741ms\n",
            "190:\ttotal: 14.2s\tremaining: 667ms\n",
            "191:\ttotal: 14.2s\tremaining: 592ms\n",
            "192:\ttotal: 14.3s\tremaining: 518ms\n",
            "193:\ttotal: 14.3s\tremaining: 444ms\n",
            "194:\ttotal: 14.4s\tremaining: 370ms\n",
            "195:\ttotal: 14.5s\tremaining: 296ms\n",
            "196:\ttotal: 14.6s\tremaining: 222ms\n",
            "197:\ttotal: 14.6s\tremaining: 148ms\n",
            "198:\ttotal: 14.7s\tremaining: 73.9ms\n",
            "199:\ttotal: 14.8s\tremaining: 0us\n",
            "XG-CV: 0.2596818070894085\n",
            "ET-CV: 0.26296502347137407\n",
            "RF-CV: 0.2629856213006646\n",
            "RF-CV: 0.3309547098229581\n",
            "(307511, 4),(48744, 4)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "f2em5uJaNJXz"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}