feature_eng_encoding.ipynb

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      "source": [
        "# 3. Encoding (Label Encoding, One-Hot Encoding, Rare Encoding)\n",
        "encoding : değişkenlerin temsil şekilleri ile ilgili değişiklikler yapmaktır.\n",
        "## Label Encoding & Binary Encoding\n",
        "**label encoding :** bir kategorik değişkenin sınıfları labellardır. Label encoding Labelları yeniden kodlamak demektir. iki sınıf varsa 0-1, üç sınıf varsa 0-1-2 şeklinde string bir ifadeyi makinenin anlamlandırabileceği bir dile döndürür. *Dikkat edilmesi gereken nokta kategorik değişkenin ordinal ve sıralı olmalıdır.* Sınıf farkı yoksa bu yöntem uygulanmamalıdır. Çünkü verilen 0-1 değerleri büyüklük küçüklüğü temsil etmektedir. Nominal değişkenlerde uygulaman gereken yöntem *ONE HOT ENCODING* tir. "
      ],
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        "id": "Ezap_h7-erVu"
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/gdrive; to attempt to forcibly remount, call drive.mount(\"/content/gdrive\", force_remount=True).\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount(\"/content/gdrive\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "from matplotlib import pyplot as plt\n",
        "\n",
        "import missingno as msno\n",
        "from datetime import date\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.neighbors import LocalOutlierFactor\n",
        "from sklearn.preprocessing import MinMaxScaler, LabelEncoder, StandardScaler, RobustScaler\n",
        "\n",
        "pd.set_option(\"display.max_columns\", None)\n",
        "pd.set_option(\"display.max_rows\", None)\n",
        "pd.set_option(\"display.float_format\", lambda x: \"%.3f\" % x)\n",
        "pd.set_option(\"display.width\", 500)"
      ],
      "metadata": {
        "id": "wucgdocQ9AeJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('/content/gdrive/My Drive/titanic.csv')\n",
        "df.head()"
      ],
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              "   PassengerId  Survived  Pclass                                               Name     Sex    Age  SibSp  Parch            Ticket   Fare Cabin Embarked\n",
              "0            1         0       3                            Braund, Mr. Owen Harris    male 22.000      1      0         A/5 21171  7.250   NaN        S\n",
              "1            2         1       1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female 38.000      1      0          PC 17599 71.283   C85        C\n",
              "2            3         1       3                             Heikkinen, Miss. Laina  female 26.000      0      0  STON/O2. 3101282  7.925   NaN        S\n",
              "3            4         1       1       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female 35.000      1      0            113803 53.100  C123        S\n",
              "4            5         0       3                           Allen, Mr. William Henry    male 35.000      0      0            373450  8.050   NaN        S"
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "1. kullanacağımız modelleme tekniklerinin bizden beklediği bir standart var ona uydurmak istiyoruz. \n",
        "2. bu standartla birlikte bizim model tahmin performansını iyileştirebileceğimiz geliştirebileceğimiz bazı noktalar var\n",
        "\n",
        "o yüzden bu işlemleri yapıyoruz."
      ],
      "metadata": {
        "id": "1toxswnn-b91"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"Sex\"].head()\n",
        "\n",
        "le = LabelEncoder()\n",
        "le.fit_transform(df[\"Sex\"])[0:5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oVLh-uZp9KiD",
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          "data": {
            "text/plain": [
              "array([1, 0, 0, 0, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 0-1 in hangi değer olduğunu öğrenmek istersen uygula.\n",
        "le.inverse_transform([0, 1])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j_Mz8W3u--uV",
        "outputId": "af702058-f9ad-4aa9-8bb0-ff4ef5bd0388"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['female', 'male'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
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      "cell_type": "code",
      "source": [
        "def label_encoder(dataframe, binary_col):\n",
        "    labelencoder = LabelEncoder()\n",
        "    dataframe[binary_col] = labelencoder.fit_transform(dataframe[binary_col])\n",
        "    return dataframe"
      ],
      "metadata": {
        "id": "nt_2DmH2_29M"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "binary_cols = [col for col in df.columns if df[col].dtype not in [int, float]\n",
        "               and df[col].nunique() == 2]\n",
        "binary_cols"
      ],
      "metadata": {
        "colab": {
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        },
        "id": "wHBEpemj_3Bv",
        "outputId": "772d129d-e239-41f9-d3e0-dd6e05b22c4a"
      },
      "execution_count": null,
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Sex']"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
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      "cell_type": "code",
      "source": [
        "for col in binary_cols:\n",
        "    label_encoder(df, col)\n",
        "\n",
        "df.head()"
      ],
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              "   PassengerId  Survived  Pclass                                               Name  Sex    Age  SibSp  Parch            Ticket   Fare Cabin Embarked\n",
              "0            1         0       3                            Braund, Mr. Owen Harris    1 22.000      1      0         A/5 21171  7.250   NaN        S\n",
              "1            2         1       1  Cumings, Mrs. John Bradley (Florence Briggs Th...    0 38.000      1      0          PC 17599 71.283   C85        C\n",
              "2            3         1       3                             Heikkinen, Miss. Laina    0 26.000      0      0  STON/O2. 3101282  7.925   NaN        S\n",
              "3            4         1       1       Futrelle, Mrs. Jacques Heath (Lily May Peel)    0 35.000      1      0            113803 53.100  C123        S\n",
              "4            5         0       3                           Allen, Mr. William Henry    1 35.000      0      0            373450  8.050   NaN        S"
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              "    [theme=dark] .colab-df-convert:hover {\n",
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              "\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-75ff7690-1535-403c-b97d-062e9a5d76bd');\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",
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              "          dataTable['output_type'] = 'display_data';\n",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = load_application_train()\n",
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OuIsr4ANAAx7",
        "outputId": "79d8f82e-bc40-49c4-9072-fb346f296826"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(891, 12)"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "binary_cols = [col for col in df.columns if df[col].dtype not in [int, float]\n",
        "               and df[col].nunique() == 2]\n",
        "binary_cols\n",
        "\n",
        "#unique methodu kullanırsan eksik değerleride sınıf olarak görür. dikkat et!\n",
        "#nunique eksik değeri bir sınıf olarak görmez."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TpFUXNVvADLn",
        "outputId": "e8d502d3-0f1a-4483-d1ce-172f136a8401"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Sex']"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for col in binary_cols:\n",
        "    label_encoder(df, col)"
      ],
      "metadata": {
        "id": "SOXcEOHuAKIX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 548
        },
        "id": "5toPoKrfBW3a",
        "outputId": "6f57a7b7-00ec-4614-a01c-c7e2d51dcbad"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   PassengerId  Survived  Pclass                                               Name  Sex    Age  SibSp  Parch            Ticket   Fare Cabin Embarked\n",
              "0            1         0       3                            Braund, Mr. Owen Harris    1 22.000      1      0         A/5 21171  7.250   NaN        S\n",
              "1            2         1       1  Cumings, Mrs. John Bradley (Florence Briggs Th...    0 38.000      1      0          PC 17599 71.283   C85        C\n",
              "2            3         1       3                             Heikkinen, Miss. Laina    0 26.000      0      0  STON/O2. 3101282  7.925   NaN        S\n",
              "3            4         1       1       Futrelle, Mrs. Jacques Heath (Lily May Peel)    0 35.000      1      0            113803 53.100  C123        S\n",
              "4            5         0       3                           Allen, Mr. William Henry    1 35.000      0      0            373450  8.050   NaN        S"
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              "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
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              "          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",
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              "    </div>\n",
              "  </div>\n",
              "  "
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          },
          "metadata": {},
          "execution_count": 25
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      ]
    },
    {
      "cell_type": "code",
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        "df[binary_cols].head()"
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      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "GLxLTh9LAHXK",
        "outputId": "2943a727-8d7f-468e-cae1-a5a50e651329"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Sex\n",
              "0    male\n",
              "1  female\n",
              "2  female\n",
              "3  female\n",
              "4    male"
            ],
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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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              "          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": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"Embarked\"].value_counts()"
      ],
      "metadata": {
        "id": "gSkcr-nKCIj5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"Embarked\"].nunique()"
      ],
      "metadata": {
        "id": "5kHmlk_-CJXx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(df[\"Embarked\"].unique())"
      ],
      "metadata": {
        "id": "CVswwDyACMKp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "daha büyük veri seti"
      ],
      "metadata": {
        "id": "MuZ_lu-RCQKt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def load_application_train():\n",
        "    data = pd.read_csv(\"/content/gdrive/My Drive/application_train.csv\")\n",
        "    return data\n",
        "\n",
        "df = load_application_train()\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        },
        "id": "hmfmRUKsBoXf",
        "outputId": "5c71b4e5-d055-460e-c299-8e2813fde15a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   SK_ID_CURR  TARGET NAME_CONTRACT_TYPE CODE_GENDER FLAG_OWN_CAR FLAG_OWN_REALTY  CNT_CHILDREN  AMT_INCOME_TOTAL  AMT_CREDIT  AMT_ANNUITY  AMT_GOODS_PRICE NAME_TYPE_SUITE NAME_INCOME_TYPE            NAME_EDUCATION_TYPE    NAME_FAMILY_STATUS  NAME_HOUSING_TYPE  REGION_POPULATION_RELATIVE  DAYS_BIRTH  DAYS_EMPLOYED  DAYS_REGISTRATION  DAYS_ID_PUBLISH  OWN_CAR_AGE  FLAG_MOBIL  FLAG_EMP_PHONE  FLAG_WORK_PHONE  FLAG_CONT_MOBILE  FLAG_PHONE  FLAG_EMAIL OCCUPATION_TYPE  CNT_FAM_MEMBERS  \\\n",
              "0      100002       1         Cash loans           M            N               Y             0        202500.000  406597.500    24700.500       351000.000   Unaccompanied          Working  Secondary / secondary special  Single / not married  House / apartment                       0.019       -9461           -637          -3648.000            -2120          NaN           1               1                0                 1           1           0        Laborers            1.000   \n",
              "1      100003       0         Cash loans           F            N               N             0        270000.000 1293502.500    35698.500      1129500.000          Family    State servant               Higher education               Married  House / apartment                       0.004      -16765          -1188          -1186.000             -291          NaN           1               1                0                 1           1           0      Core staff            2.000   \n",
              "2      100004       0    Revolving loans           M            Y               Y             0         67500.000  135000.000     6750.000       135000.000   Unaccompanied          Working  Secondary / secondary special  Single / not married  House / apartment                       0.010      -19046           -225          -4260.000            -2531       26.000           1               1                1                 1           1           0        Laborers            1.000   \n",
              "3      100006       0         Cash loans           F            N               Y             0        135000.000  312682.500    29686.500       297000.000   Unaccompanied          Working  Secondary / secondary special        Civil marriage  House / apartment                       0.008      -19005          -3039          -9833.000            -2437          NaN           1               1                0                 1           0           0        Laborers            2.000   \n",
              "4      100007       0         Cash loans           M            N               Y             0        121500.000  513000.000    21865.500       513000.000   Unaccompanied          Working  Secondary / secondary special  Single / not married  House / apartment                       0.029      -19932          -3038          -4311.000            -3458          NaN           1               1                0                 1           0           0      Core staff            1.000   \n",
              "\n",
              "   REGION_RATING_CLIENT  REGION_RATING_CLIENT_W_CITY WEEKDAY_APPR_PROCESS_START  HOUR_APPR_PROCESS_START  REG_REGION_NOT_LIVE_REGION  REG_REGION_NOT_WORK_REGION  LIVE_REGION_NOT_WORK_REGION  REG_CITY_NOT_LIVE_CITY  REG_CITY_NOT_WORK_CITY  LIVE_CITY_NOT_WORK_CITY       ORGANIZATION_TYPE  EXT_SOURCE_1  EXT_SOURCE_2  EXT_SOURCE_3  APARTMENTS_AVG  BASEMENTAREA_AVG  YEARS_BEGINEXPLUATATION_AVG  YEARS_BUILD_AVG  COMMONAREA_AVG  ELEVATORS_AVG  ENTRANCES_AVG  FLOORSMAX_AVG  FLOORSMIN_AVG  \\\n",
              "0                     2                            2                  WEDNESDAY                       10                           0                           0                            0                       0                       0                        0  Business Entity Type 3         0.083         0.263         0.139           0.025             0.037                        0.972            0.619           0.014          0.000          0.069          0.083          0.125   \n",
              "1                     1                            1                     MONDAY                       11                           0                           0                            0                       0                       0                        0                  School         0.311         0.622           NaN           0.096             0.053                        0.985            0.796           0.060          0.080          0.035          0.292          0.333   \n",
              "2                     2                            2                     MONDAY                        9                           0                           0                            0                       0                       0                        0              Government           NaN         0.556         0.730             NaN               NaN                          NaN              NaN             NaN            NaN            NaN            NaN            NaN   \n",
              "3                     2                            2                  WEDNESDAY                       17                           0                           0                            0                       0                       0                        0  Business Entity Type 3           NaN         0.650           NaN             NaN               NaN                          NaN              NaN             NaN            NaN            NaN            NaN            NaN   \n",
              "4                     2                            2                   THURSDAY                       11                           0                           0                            0                       0                       1                        1                Religion           NaN         0.323           NaN             NaN               NaN                          NaN              NaN             NaN            NaN            NaN            NaN            NaN   \n",
              "\n",
              "   LANDAREA_AVG  LIVINGAPARTMENTS_AVG  LIVINGAREA_AVG  NONLIVINGAPARTMENTS_AVG  NONLIVINGAREA_AVG  APARTMENTS_MODE  BASEMENTAREA_MODE  YEARS_BEGINEXPLUATATION_MODE  YEARS_BUILD_MODE  COMMONAREA_MODE  ELEVATORS_MODE  ENTRANCES_MODE  FLOORSMAX_MODE  FLOORSMIN_MODE  LANDAREA_MODE  LIVINGAPARTMENTS_MODE  LIVINGAREA_MODE  NONLIVINGAPARTMENTS_MODE  NONLIVINGAREA_MODE  APARTMENTS_MEDI  BASEMENTAREA_MEDI  YEARS_BEGINEXPLUATATION_MEDI  YEARS_BUILD_MEDI  COMMONAREA_MEDI  ELEVATORS_MEDI  ENTRANCES_MEDI  \\\n",
              "0         0.037                 0.020           0.019                    0.000              0.000            0.025              0.038                         0.972             0.634            0.014           0.000           0.069           0.083           0.125          0.038                  0.022            0.020                     0.000               0.000            0.025              0.037                         0.972             0.624            0.014           0.000           0.069   \n",
              "1         0.013                 0.077           0.055                    0.004              0.010            0.092              0.054                         0.985             0.804            0.050           0.081           0.035           0.292           0.333          0.013                  0.079            0.055                     0.000               0.000            0.097              0.053                         0.985             0.799            0.061           0.080           0.035   \n",
              "2           NaN                   NaN             NaN                      NaN                NaN              NaN                NaN                           NaN               NaN              NaN             NaN             NaN             NaN             NaN            NaN                    NaN              NaN                       NaN                 NaN              NaN                NaN                           NaN               NaN              NaN             NaN             NaN   \n",
              "3           NaN                   NaN             NaN                      NaN                NaN              NaN                NaN                           NaN               NaN              NaN             NaN             NaN             NaN             NaN            NaN                    NaN              NaN                       NaN                 NaN              NaN                NaN                           NaN               NaN              NaN             NaN             NaN   \n",
              "4           NaN                   NaN             NaN                      NaN                NaN              NaN                NaN                           NaN               NaN              NaN             NaN             NaN             NaN             NaN            NaN                    NaN              NaN                       NaN                 NaN              NaN                NaN                           NaN               NaN              NaN             NaN             NaN   \n",
              "\n",
              "   FLOORSMAX_MEDI  FLOORSMIN_MEDI  LANDAREA_MEDI  LIVINGAPARTMENTS_MEDI  LIVINGAREA_MEDI  NONLIVINGAPARTMENTS_MEDI  NONLIVINGAREA_MEDI FONDKAPREMONT_MODE  HOUSETYPE_MODE  TOTALAREA_MODE WALLSMATERIAL_MODE EMERGENCYSTATE_MODE  OBS_30_CNT_SOCIAL_CIRCLE  DEF_30_CNT_SOCIAL_CIRCLE  OBS_60_CNT_SOCIAL_CIRCLE  DEF_60_CNT_SOCIAL_CIRCLE  DAYS_LAST_PHONE_CHANGE  FLAG_DOCUMENT_2  FLAG_DOCUMENT_3  FLAG_DOCUMENT_4  FLAG_DOCUMENT_5  FLAG_DOCUMENT_6  FLAG_DOCUMENT_7  FLAG_DOCUMENT_8  FLAG_DOCUMENT_9  \\\n",
              "0           0.083           0.125          0.037                  0.021            0.019                     0.000               0.000   reg oper account  block of flats           0.015       Stone, brick                  No                     2.000                     2.000                     2.000                     2.000               -1134.000                0                1                0                0                0                0                0                0   \n",
              "1           0.292           0.333          0.013                  0.079            0.056                     0.004               0.010   reg oper account  block of flats           0.071              Block                  No                     1.000                     0.000                     1.000                     0.000                -828.000                0                1                0                0                0                0                0                0   \n",
              "2             NaN             NaN            NaN                    NaN              NaN                       NaN                 NaN                NaN             NaN             NaN                NaN                 NaN                     0.000                     0.000                     0.000                     0.000                -815.000                0                0                0                0                0                0                0                0   \n",
              "3             NaN             NaN            NaN                    NaN              NaN                       NaN                 NaN                NaN             NaN             NaN                NaN                 NaN                     2.000                     0.000                     2.000                     0.000                -617.000                0                1                0                0                0                0                0                0   \n",
              "4             NaN             NaN            NaN                    NaN              NaN                       NaN                 NaN                NaN             NaN             NaN                NaN                 NaN                     0.000                     0.000                     0.000                     0.000               -1106.000                0                0                0                0                0                0                1                0   \n",
              "\n",
              "   FLAG_DOCUMENT_10  FLAG_DOCUMENT_11  FLAG_DOCUMENT_12  FLAG_DOCUMENT_13  FLAG_DOCUMENT_14  FLAG_DOCUMENT_15  FLAG_DOCUMENT_16  FLAG_DOCUMENT_17  FLAG_DOCUMENT_18  FLAG_DOCUMENT_19  FLAG_DOCUMENT_20  FLAG_DOCUMENT_21  AMT_REQ_CREDIT_BUREAU_HOUR  AMT_REQ_CREDIT_BUREAU_DAY  AMT_REQ_CREDIT_BUREAU_WEEK  AMT_REQ_CREDIT_BUREAU_MON  AMT_REQ_CREDIT_BUREAU_QRT  AMT_REQ_CREDIT_BUREAU_YEAR  \n",
              "0                 0                 0                 0                 0                 0                 0                 0                 0                 0                 0                 0                 0                       0.000                      0.000                       0.000                      0.000                      0.000                       1.000  \n",
              "1                 0                 0                 0                 0                 0                 0                 0                 0                 0                 0                 0                 0                       0.000                      0.000                       0.000                      0.000                      0.000                       0.000  \n",
              "2                 0                 0                 0                 0                 0                 0                 0                 0                 0                 0                 0                 0                       0.000                      0.000                       0.000                      0.000                      0.000                       0.000  \n",
              "3                 0                 0                 0                 0                 0                 0                 0                 0                 0                 0                 0                 0                         NaN                        NaN                         NaN                        NaN                        NaN                         NaN  \n",
              "4                 0                 0                 0                 0                 0                 0                 0                 0                 0                 0                 0                 0                       0.000                      0.000                       0.000                      0.000                      0.000                       0.000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-42f704c6-375e-46b5-9b78-7a331b676214\">\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>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>AMT_GOODS_PRICE</th>\n",
              "      <th>NAME_TYPE_SUITE</th>\n",
              "      <th>NAME_INCOME_TYPE</th>\n",
              "      <th>NAME_EDUCATION_TYPE</th>\n",
              "      <th>NAME_FAMILY_STATUS</th>\n",
              "      <th>NAME_HOUSING_TYPE</th>\n",
              "      <th>REGION_POPULATION_RELATIVE</th>\n",
              "      <th>DAYS_BIRTH</th>\n",
              "      <th>DAYS_EMPLOYED</th>\n",
              "      <th>DAYS_REGISTRATION</th>\n",
              "      <th>DAYS_ID_PUBLISH</th>\n",
              "      <th>OWN_CAR_AGE</th>\n",
              "      <th>FLAG_MOBIL</th>\n",
              "      <th>FLAG_EMP_PHONE</th>\n",
              "      <th>FLAG_WORK_PHONE</th>\n",
              "      <th>FLAG_CONT_MOBILE</th>\n",
              "      <th>FLAG_PHONE</th>\n",
              "      <th>FLAG_EMAIL</th>\n",
              "      <th>OCCUPATION_TYPE</th>\n",
              "      <th>CNT_FAM_MEMBERS</th>\n",
              "      <th>REGION_RATING_CLIENT</th>\n",
              "      <th>REGION_RATING_CLIENT_W_CITY</th>\n",
              "      <th>WEEKDAY_APPR_PROCESS_START</th>\n",
              "      <th>HOUR_APPR_PROCESS_START</th>\n",
              "      <th>REG_REGION_NOT_LIVE_REGION</th>\n",
              "      <th>REG_REGION_NOT_WORK_REGION</th>\n",
              "      <th>LIVE_REGION_NOT_WORK_REGION</th>\n",
              "      <th>REG_CITY_NOT_LIVE_CITY</th>\n",
              "      <th>REG_CITY_NOT_WORK_CITY</th>\n",
              "      <th>LIVE_CITY_NOT_WORK_CITY</th>\n",
              "      <th>ORGANIZATION_TYPE</th>\n",
              "      <th>EXT_SOURCE_1</th>\n",
              "      <th>EXT_SOURCE_2</th>\n",
              "      <th>EXT_SOURCE_3</th>\n",
              "      <th>APARTMENTS_AVG</th>\n",
              "      <th>BASEMENTAREA_AVG</th>\n",
              "      <th>YEARS_BEGINEXPLUATATION_AVG</th>\n",
              "      <th>YEARS_BUILD_AVG</th>\n",
              "      <th>COMMONAREA_AVG</th>\n",
              "      <th>ELEVATORS_AVG</th>\n",
              "      <th>ENTRANCES_AVG</th>\n",
              "      <th>FLOORSMAX_AVG</th>\n",
              "      <th>FLOORSMIN_AVG</th>\n",
              "      <th>LANDAREA_AVG</th>\n",
              "      <th>LIVINGAPARTMENTS_AVG</th>\n",
              "      <th>LIVINGAREA_AVG</th>\n",
              "      <th>NONLIVINGAPARTMENTS_AVG</th>\n",
              "      <th>NONLIVINGAREA_AVG</th>\n",
              "      <th>APARTMENTS_MODE</th>\n",
              "      <th>BASEMENTAREA_MODE</th>\n",
              "      <th>YEARS_BEGINEXPLUATATION_MODE</th>\n",
              "      <th>YEARS_BUILD_MODE</th>\n",
              "      <th>COMMONAREA_MODE</th>\n",
              "      <th>ELEVATORS_MODE</th>\n",
              "      <th>ENTRANCES_MODE</th>\n",
              "      <th>FLOORSMAX_MODE</th>\n",
              "      <th>FLOORSMIN_MODE</th>\n",
              "      <th>LANDAREA_MODE</th>\n",
              "      <th>LIVINGAPARTMENTS_MODE</th>\n",
              "      <th>LIVINGAREA_MODE</th>\n",
              "      <th>NONLIVINGAPARTMENTS_MODE</th>\n",
              "      <th>NONLIVINGAREA_MODE</th>\n",
              "      <th>APARTMENTS_MEDI</th>\n",
              "      <th>BASEMENTAREA_MEDI</th>\n",
              "      <th>YEARS_BEGINEXPLUATATION_MEDI</th>\n",
              "      <th>YEARS_BUILD_MEDI</th>\n",
              "      <th>COMMONAREA_MEDI</th>\n",
              "      <th>ELEVATORS_MEDI</th>\n",
              "      <th>ENTRANCES_MEDI</th>\n",
              "      <th>FLOORSMAX_MEDI</th>\n",
              "      <th>FLOORSMIN_MEDI</th>\n",
              "      <th>LANDAREA_MEDI</th>\n",
              "      <th>LIVINGAPARTMENTS_MEDI</th>\n",
              "      <th>LIVINGAREA_MEDI</th>\n",
              "      <th>NONLIVINGAPARTMENTS_MEDI</th>\n",
              "      <th>NONLIVINGAREA_MEDI</th>\n",
              "      <th>FONDKAPREMONT_MODE</th>\n",
              "      <th>HOUSETYPE_MODE</th>\n",
              "      <th>TOTALAREA_MODE</th>\n",
              "      <th>WALLSMATERIAL_MODE</th>\n",
              "      <th>EMERGENCYSTATE_MODE</th>\n",
              "      <th>OBS_30_CNT_SOCIAL_CIRCLE</th>\n",
              "      <th>DEF_30_CNT_SOCIAL_CIRCLE</th>\n",
              "      <th>OBS_60_CNT_SOCIAL_CIRCLE</th>\n",
              "      <th>DEF_60_CNT_SOCIAL_CIRCLE</th>\n",
              "      <th>DAYS_LAST_PHONE_CHANGE</th>\n",
              "      <th>FLAG_DOCUMENT_2</th>\n",
              "      <th>FLAG_DOCUMENT_3</th>\n",
              "      <th>FLAG_DOCUMENT_4</th>\n",
              "      <th>FLAG_DOCUMENT_5</th>\n",
              "      <th>FLAG_DOCUMENT_6</th>\n",
              "      <th>FLAG_DOCUMENT_7</th>\n",
              "      <th>FLAG_DOCUMENT_8</th>\n",
              "      <th>FLAG_DOCUMENT_9</th>\n",
              "      <th>FLAG_DOCUMENT_10</th>\n",
              "      <th>FLAG_DOCUMENT_11</th>\n",
              "      <th>FLAG_DOCUMENT_12</th>\n",
              "      <th>FLAG_DOCUMENT_13</th>\n",
              "      <th>FLAG_DOCUMENT_14</th>\n",
              "      <th>FLAG_DOCUMENT_15</th>\n",
              "      <th>FLAG_DOCUMENT_16</th>\n",
              "      <th>FLAG_DOCUMENT_17</th>\n",
              "      <th>FLAG_DOCUMENT_18</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",
              "    </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>202500.000</td>\n",
              "      <td>406597.500</td>\n",
              "      <td>24700.500</td>\n",
              "      <td>351000.000</td>\n",
              "      <td>Unaccompanied</td>\n",
              "      <td>Working</td>\n",
              "      <td>Secondary / secondary special</td>\n",
              "      <td>Single / not married</td>\n",
              "      <td>House / apartment</td>\n",
              "      <td>0.019</td>\n",
              "      <td>-9461</td>\n",
              "      <td>-637</td>\n",
              "      <td>-3648.000</td>\n",
              "      <td>-2120</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>Laborers</td>\n",
              "      <td>1.000</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>WEDNESDAY</td>\n",
              "      <td>10</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>Business Entity Type 3</td>\n",
              "      <td>0.083</td>\n",
              "      <td>0.263</td>\n",
              "      <td>0.139</td>\n",
              "      <td>0.025</td>\n",
              "      <td>0.037</td>\n",
              "      <td>0.972</td>\n",
              "      <td>0.619</td>\n",
              "      <td>0.014</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.069</td>\n",
              "      <td>0.083</td>\n",
              "      <td>0.125</td>\n",
              "      <td>0.037</td>\n",
              "      <td>0.020</td>\n",
              "      <td>0.019</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.025</td>\n",
              "      <td>0.038</td>\n",
              "      <td>0.972</td>\n",
              "      <td>0.634</td>\n",
              "      <td>0.014</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.069</td>\n",
              "      <td>0.083</td>\n",
              "      <td>0.125</td>\n",
              "      <td>0.038</td>\n",
              "      <td>0.022</td>\n",
              "      <td>0.020</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.025</td>\n",
              "      <td>0.037</td>\n",
              "      <td>0.972</td>\n",
              "      <td>0.624</td>\n",
              "      <td>0.014</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.069</td>\n",
              "      <td>0.083</td>\n",
              "      <td>0.125</td>\n",
              "      <td>0.037</td>\n",
              "      <td>0.021</td>\n",
              "      <td>0.019</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>reg oper account</td>\n",
              "      <td>block of flats</td>\n",
              "      <td>0.015</td>\n",
              "      <td>Stone, brick</td>\n",
              "      <td>No</td>\n",
              "      <td>2.000</td>\n",
              "      <td>2.000</td>\n",
              "      <td>2.000</td>\n",
              "      <td>2.000</td>\n",
              "      <td>-1134.000</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",
              "      <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>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1.000</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>270000.000</td>\n",
              "      <td>1293502.500</td>\n",
              "      <td>35698.500</td>\n",
              "      <td>1129500.000</td>\n",
              "      <td>Family</td>\n",
              "      <td>State servant</td>\n",
              "      <td>Higher education</td>\n",
              "      <td>Married</td>\n",
              "      <td>House / apartment</td>\n",
              "      <td>0.004</td>\n",
              "      <td>-16765</td>\n",
              "      <td>-1188</td>\n",
              "      <td>-1186.000</td>\n",
              "      <td>-291</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>Core staff</td>\n",
              "      <td>2.000</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>MONDAY</td>\n",
              "      <td>11</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>School</td>\n",
              "      <td>0.311</td>\n",
              "      <td>0.622</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.096</td>\n",
              "      <td>0.053</td>\n",
              "      <td>0.985</td>\n",
              "      <td>0.796</td>\n",
              "      <td>0.060</td>\n",
              "      <td>0.080</td>\n",
              "      <td>0.035</td>\n",
              "      <td>0.292</td>\n",
              "      <td>0.333</td>\n",
              "      <td>0.013</td>\n",
              "      <td>0.077</td>\n",
              "      <td>0.055</td>\n",
              "      <td>0.004</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.092</td>\n",
              "      <td>0.054</td>\n",
              "      <td>0.985</td>\n",
              "      <td>0.804</td>\n",
              "      <td>0.050</td>\n",
              "      <td>0.081</td>\n",
              "      <td>0.035</td>\n",
              "      <td>0.292</td>\n",
              "      <td>0.333</td>\n",
              "      <td>0.013</td>\n",
              "      <td>0.079</td>\n",
              "      <td>0.055</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.097</td>\n",
              "      <td>0.053</td>\n",
              "      <td>0.985</td>\n",
              "      <td>0.799</td>\n",
              "      <td>0.061</td>\n",
              "      <td>0.080</td>\n",
              "      <td>0.035</td>\n",
              "      <td>0.292</td>\n",
              "      <td>0.333</td>\n",
              "      <td>0.013</td>\n",
              "      <td>0.079</td>\n",
              "      <td>0.056</td>\n",
              "      <td>0.004</td>\n",
              "      <td>0.010</td>\n",
              "      <td>reg oper account</td>\n",
              "      <td>block of flats</td>\n",
              "      <td>0.071</td>\n",
              "      <td>Block</td>\n",
              "      <td>No</td>\n",
              "      <td>1.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>-828.000</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",
              "      <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>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</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>67500.000</td>\n",
              "      <td>135000.000</td>\n",
              "      <td>6750.000</td>\n",
              "      <td>135000.000</td>\n",
              "      <td>Unaccompanied</td>\n",
              "      <td>Working</td>\n",
              "      <td>Secondary / secondary special</td>\n",
              "      <td>Single / not married</td>\n",
              "      <td>House / apartment</td>\n",
              "      <td>0.010</td>\n",
              "      <td>-19046</td>\n",
              "      <td>-225</td>\n",
              "      <td>-4260.000</td>\n",
              "      <td>-2531</td>\n",
              "      <td>26.000</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>Laborers</td>\n",
              "      <td>1.000</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>MONDAY</td>\n",
              "      <td>9</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>Government</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.556</td>\n",
              "      <td>0.730</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",
              "      <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>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>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>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>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>-815.000</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>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>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</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>135000.000</td>\n",
              "      <td>312682.500</td>\n",
              "      <td>29686.500</td>\n",
              "      <td>297000.000</td>\n",
              "      <td>Unaccompanied</td>\n",
              "      <td>Working</td>\n",
              "      <td>Secondary / secondary special</td>\n",
              "      <td>Civil marriage</td>\n",
              "      <td>House / apartment</td>\n",
              "      <td>0.008</td>\n",
              "      <td>-19005</td>\n",
              "      <td>-3039</td>\n",
              "      <td>-9833.000</td>\n",
              "      <td>-2437</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Laborers</td>\n",
              "      <td>2.000</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>WEDNESDAY</td>\n",
              "      <td>17</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>Business Entity Type 3</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.650</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",
              "      <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>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>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>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>2.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>2.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>-617.000</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",
              "      <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>0</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",
              "    </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>121500.000</td>\n",
              "      <td>513000.000</td>\n",
              "      <td>21865.500</td>\n",
              "      <td>513000.000</td>\n",
              "      <td>Unaccompanied</td>\n",
              "      <td>Working</td>\n",
              "      <td>Secondary / secondary special</td>\n",
              "      <td>Single / not married</td>\n",
              "      <td>House / apartment</td>\n",
              "      <td>0.029</td>\n",
              "      <td>-19932</td>\n",
              "      <td>-3038</td>\n",
              "      <td>-4311.000</td>\n",
              "      <td>-3458</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Core staff</td>\n",
              "      <td>1.000</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>THURSDAY</td>\n",
              "      <td>11</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Religion</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.323</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",
              "      <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>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>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>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>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>-1106.000</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>1</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>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-42f704c6-375e-46b5-9b78-7a331b676214')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
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    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H7ktAiMIBpuS",
        "outputId": "814e11f6-34d2-4371-e49b-49bc9ccb0bbe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(307511, 122)"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "binary_cols = [col for col in df.columns if df[col].dtype not in [int, float]\n",
        "               and df[col].nunique() == 2]\n",
        "binary_cols"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SJvXHg9VByHM",
        "outputId": "a8eee505-3ecb-411a-8c63-61d763996a7b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['NAME_CONTRACT_TYPE',\n",
              " 'FLAG_OWN_CAR',\n",
              " 'FLAG_OWN_REALTY',\n",
              " 'EMERGENCYSTATE_MODE']"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[binary_cols].head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "ZJoTL7ALCVeN",
        "outputId": "49dc5be5-02be-4a87-886e-c78745241cbe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  NAME_CONTRACT_TYPE FLAG_OWN_CAR FLAG_OWN_REALTY EMERGENCYSTATE_MODE\n",
              "0         Cash loans            N               Y                  No\n",
              "1         Cash loans            N               N                  No\n",
              "2    Revolving loans            Y               Y                 NaN\n",
              "3         Cash loans            N               Y                 NaN\n",
              "4         Cash loans            N               Y                 NaN"
            ],
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "                                                     [key], {});\n",
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              "\n",
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    {
      "cell_type": "code",
      "source": [
        "for col in binary_cols:\n",
        "    label_encoder(df, col)"
      ],
      "metadata": {
        "id": "03nKBauZCnIN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## One-Hot Encoding\n",
        "\n",
        "Aşağıdaki şekilde görüldüğü gibi sınıfları değişkenlere dönüştürüyorsun. Dikkat edilmesi gereken dummy(kukla) değişken tuzağıdır. Kukla değişken tuzağı birbiri üzerinden oluşturula değişkinler yüksek korelasyona sebep olacaktır.   Bunu da drop_first diyerek bir sınıfı drop ederek önleyebilirsin. Böylece birbiri üzerinden oluşturulmaz.\n",
        "\n",
        "\n",
        "![1.png](data:image/png;base64,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)\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "4oxcRh16AX-p"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def load_application_train():\n",
        "    data = pd.read_csv('/content/gdrive/My Drive/titanic.csv')\n",
        "    return data\n",
        "\n",
        "df = load_application_train()\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 548
        },
        "id": "q9Z8fqcDQno7",
        "outputId": "5f4174eb-ee83-4346-ade0-4ddce43e4135"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   PassengerId  Survived  Pclass                                               Name     Sex    Age  SibSp  Parch            Ticket   Fare Cabin Embarked\n",
              "0            1         0       3                            Braund, Mr. Owen Harris    male 22.000      1      0         A/5 21171  7.250   NaN        S\n",
              "1            2         1       1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female 38.000      1      0          PC 17599 71.283   C85        C\n",
              "2            3         1       3                             Heikkinen, Miss. Laina  female 26.000      0      0  STON/O2. 3101282  7.925   NaN        S\n",
              "3            4         1       1       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female 35.000      1      0            113803 53.100  C123        S\n",
              "4            5         0       3                           Allen, Mr. William Henry    male 35.000      0      0            373450  8.050   NaN        S"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-5815852b-d94d-4d00-9226-090b51d04d35\">\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>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Braund, Mr. Owen Harris</td>\n",
              "      <td>male</td>\n",
              "      <td>22.000</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>A/5 21171</td>\n",
              "      <td>7.250</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
              "      <td>female</td>\n",
              "      <td>38.000</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>PC 17599</td>\n",
              "      <td>71.283</td>\n",
              "      <td>C85</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Heikkinen, Miss. Laina</td>\n",
              "      <td>female</td>\n",
              "      <td>26.000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>STON/O2. 3101282</td>\n",
              "      <td>7.925</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
              "      <td>female</td>\n",
              "      <td>35.000</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>113803</td>\n",
              "      <td>53.100</td>\n",
              "      <td>C123</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Allen, Mr. William Henry</td>\n",
              "      <td>male</td>\n",
              "      <td>35.000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>373450</td>\n",
              "      <td>8.050</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5815852b-d94d-4d00-9226-090b51d04d35')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "            + ' to learn more about interactive tables.';\n",
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            ]
          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"Embarked\"].value_counts()\n",
        "# sınıflar arası farklılık yok, nominal ölçek türüyle ölçülmüş bir değişkendir. Label Encoder yapmak sorun çıkaracağından\n",
        "# one hot encoder uygulanabilir."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SzO0n-nJAVPE",
        "outputId": "dddbf5ad-bcb2-42fb-a1fe-277029d646a4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "S    644\n",
              "C    168\n",
              "Q     77\n",
              "Name: Embarked, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.get_dummies(df, columns=[\"Embarked\"]).head()"
      ],
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              "   PassengerId  Survived  Pclass                                               Name     Sex    Age  SibSp  Parch            Ticket   Fare Cabin  Embarked_C  Embarked_Q  Embarked_S\n",
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              "3            4         1       1       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female 35.000      1      0            113803 53.100  C123           0           0           1\n",
              "4            5         0       3                           Allen, Mr. William Henry    male 35.000      0      0            373450  8.050   NaN           0           0           1"
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    {
      "cell_type": "code",
      "source": [
        "pd.get_dummies(df, columns=[\"Embarked\"], drop_first=True).head()\n",
        "\n",
        "#değişkenler birbirleri üzerinden üretilebilir olmasın diye drop_first=True diyoruz.\n",
        "# göndereceği ilk sınıfı alfabetik sıraya göre seçer. Embarked_C değişkeni uçtu :)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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        },
        "id": "g4R2S0cdAfYF",
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          "data": {
            "text/plain": [
              "   PassengerId  Survived  Pclass                                               Name     Sex    Age  SibSp  Parch            Ticket   Fare Cabin  Embarked_Q  Embarked_S\n",
              "0            1         0       3                            Braund, Mr. Owen Harris    male 22.000      1      0         A/5 21171  7.250   NaN           0           1\n",
              "1            2         1       1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female 38.000      1      0          PC 17599 71.283   C85           0           0\n",
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              "3            4         1       1       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female 35.000      1      0            113803 53.100  C123           0           1\n",
              "4            5         0       3                           Allen, Mr. William Henry    male 35.000      0      0            373450  8.050   NaN           0           1"
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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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          "metadata": {},
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    {
      "cell_type": "code",
      "source": [
        "pd.get_dummies(df, columns=[\"Embarked\"], dummy_na=True).head()\n",
        "\n",
        "#embarked değişkenindeki nan değerler içinde bir sınıf oluşturulmak istenirse bu kod kullanılır."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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        "outputId": "b008bd45-8f0b-4e92-e361-8325ca9abaaf"
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              "   PassengerId  Survived  Pclass                                               Name     Sex    Age  SibSp  Parch            Ticket   Fare Cabin  Embarked_C  Embarked_Q  Embarked_S  Embarked_nan\n",
              "0            1         0       3                            Braund, Mr. Owen Harris    male 22.000      1      0         A/5 21171  7.250   NaN           0           0           1             0\n",
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              "\n",
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          "metadata": {},
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    },
    {
      "cell_type": "code",
      "source": [
        "pd.get_dummies(df, columns=[\"Sex\", \"Embarked\"], drop_first=True).head()\n",
        "\n",
        "# get dummies methodunu kullanarak hem label encoding de binary encoding işlemini hem de\n",
        "# onehot encoding işlemini yapabiliyoruz."
      ],
      "metadata": {
        "colab": {
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        "id": "_QcU1pUfAj9C",
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      "execution_count": null,
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          "data": {
            "text/plain": [
              "   PassengerId  Survived  Pclass                                               Name    Age  SibSp  Parch            Ticket   Fare Cabin  Sex_male  Embarked_Q  Embarked_S\n",
              "0            1         0       3                            Braund, Mr. Owen Harris 22.000      1      0         A/5 21171  7.250   NaN         1           0           1\n",
              "1            2         1       1  Cumings, Mrs. John Bradley (Florence Briggs Th... 38.000      1      0          PC 17599 71.283   C85         0           0           0\n",
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          },
          "metadata": {},
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      ]
    },
    {
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      "source": [
        "def one_hot_encoder(dataframe, categorical_cols, drop_first=True):\n",
        "    dataframe = pd.get_dummies(dataframe, columns=categorical_cols, drop_first=drop_first)\n",
        "    return dataframe\n",
        "# cat_cols, num_cols, cat_but_car = grab_col_names(df)\n",
        "\n",
        "ohe_cols = [col for col in df.columns if 10 >= df[col].nunique() > 2]\n",
        "ohe_cols #özel şartlardaki kolonlar üzerinde get dummies yapmak için kullan. "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GT5GAJstAmjy",
        "outputId": "f9acdb7d-542b-4f06-ce64-95b829f7da16"
      },
      "execution_count": null,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "['Pclass', 'SibSp', 'Parch', 'Embarked']"
            ]
          },
          "metadata": {},
          "execution_count": 14
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "one_hot_encoder(df, ohe_cols).head()"
      ],
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        "id": "jSsI5sCqWzxj",
        "outputId": "5d300308-1658-48cd-c514-666f3c6f0f08"
      },
      "execution_count": null,
      "outputs": [
        {
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          "data": {
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              "   PassengerId  Survived                                               Name     Sex    Age            Ticket   Fare Cabin  Pclass_2  Pclass_3  SibSp_1  SibSp_2  SibSp_3  SibSp_4  SibSp_5  SibSp_8  Parch_1  Parch_2  Parch_3  Parch_4  Parch_5  Parch_6  Embarked_Q  Embarked_S\n",
              "0            1         0                            Braund, Mr. Owen Harris    male 22.000         A/5 21171  7.250   NaN         0         1        1        0        0        0        0        0        0        0        0        0        0        0           0           1\n",
              "1            2         1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female 38.000          PC 17599 71.283   C85         0         0        1        0        0        0        0        0        0        0        0        0        0        0           0           0\n",
              "2            3         1                             Heikkinen, Miss. Laina  female 26.000  STON/O2. 3101282  7.925   NaN         0         1        0        0        0        0        0        0        0        0        0        0        0        0           0           1\n",
              "3            4         1       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female 35.000            113803 53.100  C123         0         0        1        0        0        0        0        0        0        0        0        0        0        0           0           1\n",
              "4            5         0                           Allen, Mr. William Henry    male 35.000            373450  8.050   NaN         0         1        0        0        0        0        0        0        0        0        0        0        0        0           0           1"
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          "metadata": {},
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        "id": "eBEFoJfpWMar",
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      "execution_count": null,
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          "data": {
            "text/plain": [
              "   PassengerId  Survived  Pclass                                               Name     Sex    Age  SibSp  Parch            Ticket   Fare Cabin Embarked\n",
              "0            1         0       3                            Braund, Mr. Owen Harris    male 22.000      1      0         A/5 21171  7.250   NaN        S\n",
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              "3            4         1       1       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female 35.000      1      0            113803 53.100  C123        S\n",
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              "      <td>71.283</td>\n",
              "      <td>C85</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Heikkinen, Miss. Laina</td>\n",
              "      <td>female</td>\n",
              "      <td>26.000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>STON/O2. 3101282</td>\n",
              "      <td>7.925</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
              "      <td>female</td>\n",
              "      <td>35.000</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>113803</td>\n",
              "      <td>53.100</td>\n",
              "      <td>C123</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Allen, Mr. William Henry</td>\n",
              "      <td>male</td>\n",
              "      <td>35.000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>373450</td>\n",
              "      <td>8.050</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ea3ef9ff-e790-4122-b661-c5d0d406aac1')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       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-ea3ef9ff-e790-4122-b661-c5d0d406aac1 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-ea3ef9ff-e790-4122-b661-c5d0d406aac1');\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": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Rare Encoding\n",
        "\n",
        "basitlik ve genellik yaklaşımı ile büyük çoğunluğu temsil etme yaklaşımında olmalıyız. 100.000 gözlem de 2 tane gözlem değeri varsa kuracağın modele bir katkısı olmaz. One hot encoding yaptığında ölçüm kalitesine bir katkısı olsun istiyorum ayrıca gereksiz birden fazla değişken de olsun istemiyorum. Bu gereksiz değişkenlerden kurtulmak için **RARE ENCODING** kullanabiliriz.\n",
        "\n",
        "Şekilde görülğünü gibi veri setindeki bir kategorik değişkenin sınıflarındaki az değerler belirli bir eşik değere göre belirlenip ayrı bir sınıf haline getirilir.\n",
        "\n",
        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)\n",
        "\n"
      ],
      "metadata": {
        "id": "xJs-ao9BAvme"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 1. Kategorik değişkenlerin azlık çokluk durumunun analiz edilmesi.\n",
        "#### 2. Rare kategoriler ile bağımlı değişken arasındaki ilişkinin analiz edilmesi.\n",
        "#### 3. Rare encoder yazacağız."
      ],
      "metadata": {
        "id": "AUA5iZMka84s"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 1. Kategorik değişkenlerin azlık çokluk durumunun analiz edilmesi.\n",
        "\n",
        "def load_application_train():\n",
        "    data = pd.read_csv(\"/content/gdrive/My Drive/application_train.csv\")\n",
        "    return data\n",
        "\n",
        "df = load_application_train()\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        },
        "id": "D-Ylc97BWL4b",
        "outputId": "4240b9f2-c45f-4262-db1d-d3c0e48e60d1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   SK_ID_CURR  TARGET NAME_CONTRACT_TYPE CODE_GENDER FLAG_OWN_CAR FLAG_OWN_REALTY  CNT_CHILDREN  AMT_INCOME_TOTAL  AMT_CREDIT  AMT_ANNUITY  AMT_GOODS_PRICE NAME_TYPE_SUITE NAME_INCOME_TYPE            NAME_EDUCATION_TYPE    NAME_FAMILY_STATUS  NAME_HOUSING_TYPE  REGION_POPULATION_RELATIVE  DAYS_BIRTH  DAYS_EMPLOYED  DAYS_REGISTRATION  DAYS_ID_PUBLISH  OWN_CAR_AGE  FLAG_MOBIL  FLAG_EMP_PHONE  FLAG_WORK_PHONE  FLAG_CONT_MOBILE  FLAG_PHONE  FLAG_EMAIL OCCUPATION_TYPE  CNT_FAM_MEMBERS  \\\n",
              "0      100002       1         Cash loans           M            N               Y             0        202500.000  406597.500    24700.500       351000.000   Unaccompanied          Working  Secondary / secondary special  Single / not married  House / apartment                       0.019       -9461           -637          -3648.000            -2120          NaN           1               1                0                 1           1           0        Laborers            1.000   \n",
              "1      100003       0         Cash loans           F            N               N             0        270000.000 1293502.500    35698.500      1129500.000          Family    State servant               Higher education               Married  House / apartment                       0.004      -16765          -1188          -1186.000             -291          NaN           1               1                0                 1           1           0      Core staff            2.000   \n",
              "2      100004       0    Revolving loans           M            Y               Y             0         67500.000  135000.000     6750.000       135000.000   Unaccompanied          Working  Secondary / secondary special  Single / not married  House / apartment                       0.010      -19046           -225          -4260.000            -2531       26.000           1               1                1                 1           1           0        Laborers            1.000   \n",
              "3      100006       0         Cash loans           F            N               Y             0        135000.000  312682.500    29686.500       297000.000   Unaccompanied          Working  Secondary / secondary special        Civil marriage  House / apartment                       0.008      -19005          -3039          -9833.000            -2437          NaN           1               1                0                 1           0           0        Laborers            2.000   \n",
              "4      100007       0         Cash loans           M            N               Y             0        121500.000  513000.000    21865.500       513000.000   Unaccompanied          Working  Secondary / secondary special  Single / not married  House / apartment                       0.029      -19932          -3038          -4311.000            -3458          NaN           1               1                0                 1           0           0      Core staff            1.000   \n",
              "\n",
              "   REGION_RATING_CLIENT  REGION_RATING_CLIENT_W_CITY WEEKDAY_APPR_PROCESS_START  HOUR_APPR_PROCESS_START  REG_REGION_NOT_LIVE_REGION  REG_REGION_NOT_WORK_REGION  LIVE_REGION_NOT_WORK_REGION  REG_CITY_NOT_LIVE_CITY  REG_CITY_NOT_WORK_CITY  LIVE_CITY_NOT_WORK_CITY       ORGANIZATION_TYPE  EXT_SOURCE_1  EXT_SOURCE_2  EXT_SOURCE_3  APARTMENTS_AVG  BASEMENTAREA_AVG  YEARS_BEGINEXPLUATATION_AVG  YEARS_BUILD_AVG  COMMONAREA_AVG  ELEVATORS_AVG  ENTRANCES_AVG  FLOORSMAX_AVG  FLOORSMIN_AVG  \\\n",
              "0                     2                            2                  WEDNESDAY                       10                           0                           0                            0                       0                       0                        0  Business Entity Type 3         0.083         0.263         0.139           0.025             0.037                        0.972            0.619           0.014          0.000          0.069          0.083          0.125   \n",
              "1                     1                            1                     MONDAY                       11                           0                           0                            0                       0                       0                        0                  School         0.311         0.622           NaN           0.096             0.053                        0.985            0.796           0.060          0.080          0.035          0.292          0.333   \n",
              "2                     2                            2                     MONDAY                        9                           0                           0                            0                       0                       0                        0              Government           NaN         0.556         0.730             NaN               NaN                          NaN              NaN             NaN            NaN            NaN            NaN            NaN   \n",
              "3                     2                            2                  WEDNESDAY                       17                           0                           0                            0                       0                       0                        0  Business Entity Type 3           NaN         0.650           NaN             NaN               NaN                          NaN              NaN             NaN            NaN            NaN            NaN            NaN   \n",
              "4                     2                            2                   THURSDAY                       11                           0                           0                            0                       0                       1                        1                Religion           NaN         0.323           NaN             NaN               NaN                          NaN              NaN             NaN            NaN            NaN            NaN            NaN   \n",
              "\n",
              "   LANDAREA_AVG  LIVINGAPARTMENTS_AVG  LIVINGAREA_AVG  NONLIVINGAPARTMENTS_AVG  NONLIVINGAREA_AVG  APARTMENTS_MODE  BASEMENTAREA_MODE  YEARS_BEGINEXPLUATATION_MODE  YEARS_BUILD_MODE  COMMONAREA_MODE  ELEVATORS_MODE  ENTRANCES_MODE  FLOORSMAX_MODE  FLOORSMIN_MODE  LANDAREA_MODE  LIVINGAPARTMENTS_MODE  LIVINGAREA_MODE  NONLIVINGAPARTMENTS_MODE  NONLIVINGAREA_MODE  APARTMENTS_MEDI  BASEMENTAREA_MEDI  YEARS_BEGINEXPLUATATION_MEDI  YEARS_BUILD_MEDI  COMMONAREA_MEDI  ELEVATORS_MEDI  ENTRANCES_MEDI  \\\n",
              "0         0.037                 0.020           0.019                    0.000              0.000            0.025              0.038                         0.972             0.634            0.014           0.000           0.069           0.083           0.125          0.038                  0.022            0.020                     0.000               0.000            0.025              0.037                         0.972             0.624            0.014           0.000           0.069   \n",
              "1         0.013                 0.077           0.055                    0.004              0.010            0.092              0.054                         0.985             0.804            0.050           0.081           0.035           0.292           0.333          0.013                  0.079            0.055                     0.000               0.000            0.097              0.053                         0.985             0.799            0.061           0.080           0.035   \n",
              "2           NaN                   NaN             NaN                      NaN                NaN              NaN                NaN                           NaN               NaN              NaN             NaN             NaN             NaN             NaN            NaN                    NaN              NaN                       NaN                 NaN              NaN                NaN                           NaN               NaN              NaN             NaN             NaN   \n",
              "3           NaN                   NaN             NaN                      NaN                NaN              NaN                NaN                           NaN               NaN              NaN             NaN             NaN             NaN             NaN            NaN                    NaN              NaN                       NaN                 NaN              NaN                NaN                           NaN               NaN              NaN             NaN             NaN   \n",
              "4           NaN                   NaN             NaN                      NaN                NaN              NaN                NaN                           NaN               NaN              NaN             NaN             NaN             NaN             NaN            NaN                    NaN              NaN                       NaN                 NaN              NaN                NaN                           NaN               NaN              NaN             NaN             NaN   \n",
              "\n",
              "   FLOORSMAX_MEDI  FLOORSMIN_MEDI  LANDAREA_MEDI  LIVINGAPARTMENTS_MEDI  LIVINGAREA_MEDI  NONLIVINGAPARTMENTS_MEDI  NONLIVINGAREA_MEDI FONDKAPREMONT_MODE  HOUSETYPE_MODE  TOTALAREA_MODE WALLSMATERIAL_MODE EMERGENCYSTATE_MODE  OBS_30_CNT_SOCIAL_CIRCLE  DEF_30_CNT_SOCIAL_CIRCLE  OBS_60_CNT_SOCIAL_CIRCLE  DEF_60_CNT_SOCIAL_CIRCLE  DAYS_LAST_PHONE_CHANGE  FLAG_DOCUMENT_2  FLAG_DOCUMENT_3  FLAG_DOCUMENT_4  FLAG_DOCUMENT_5  FLAG_DOCUMENT_6  FLAG_DOCUMENT_7  FLAG_DOCUMENT_8  FLAG_DOCUMENT_9  \\\n",
              "0           0.083           0.125          0.037                  0.021            0.019                     0.000               0.000   reg oper account  block of flats           0.015       Stone, brick                  No                     2.000                     2.000                     2.000                     2.000               -1134.000                0                1                0                0                0                0                0                0   \n",
              "1           0.292           0.333          0.013                  0.079            0.056                     0.004               0.010   reg oper account  block of flats           0.071              Block                  No                     1.000                     0.000                     1.000                     0.000                -828.000                0                1                0                0                0                0                0                0   \n",
              "2             NaN             NaN            NaN                    NaN              NaN                       NaN                 NaN                NaN             NaN             NaN                NaN                 NaN                     0.000                     0.000                     0.000                     0.000                -815.000                0                0                0                0                0                0                0                0   \n",
              "3             NaN             NaN            NaN                    NaN              NaN                       NaN                 NaN                NaN             NaN             NaN                NaN                 NaN                     2.000                     0.000                     2.000                     0.000                -617.000                0                1                0                0                0                0                0                0   \n",
              "4             NaN             NaN            NaN                    NaN              NaN                       NaN                 NaN                NaN             NaN             NaN                NaN                 NaN                     0.000                     0.000                     0.000                     0.000               -1106.000                0                0                0                0                0                0                1                0   \n",
              "\n",
              "   FLAG_DOCUMENT_10  FLAG_DOCUMENT_11  FLAG_DOCUMENT_12  FLAG_DOCUMENT_13  FLAG_DOCUMENT_14  FLAG_DOCUMENT_15  FLAG_DOCUMENT_16  FLAG_DOCUMENT_17  FLAG_DOCUMENT_18  FLAG_DOCUMENT_19  FLAG_DOCUMENT_20  FLAG_DOCUMENT_21  AMT_REQ_CREDIT_BUREAU_HOUR  AMT_REQ_CREDIT_BUREAU_DAY  AMT_REQ_CREDIT_BUREAU_WEEK  AMT_REQ_CREDIT_BUREAU_MON  AMT_REQ_CREDIT_BUREAU_QRT  AMT_REQ_CREDIT_BUREAU_YEAR  \n",
              "0                 0                 0                 0                 0                 0                 0                 0                 0                 0                 0                 0                 0                       0.000                      0.000                       0.000                      0.000                      0.000                       1.000  \n",
              "1                 0                 0                 0                 0                 0                 0                 0                 0                 0                 0                 0                 0                       0.000                      0.000                       0.000                      0.000                      0.000                       0.000  \n",
              "2                 0                 0                 0                 0                 0                 0                 0                 0                 0                 0                 0                 0                       0.000                      0.000                       0.000                      0.000                      0.000                       0.000  \n",
              "3                 0                 0                 0                 0                 0                 0                 0                 0                 0                 0                 0                 0                         NaN                        NaN                         NaN                        NaN                        NaN                         NaN  \n",
              "4                 0                 0                 0                 0                 0                 0                 0                 0                 0                 0                 0                 0                       0.000                      0.000                       0.000                      0.000                      0.000                       0.000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d3423180-a20f-4fdb-8756-42783a2faf34\">\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>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>AMT_GOODS_PRICE</th>\n",
              "      <th>NAME_TYPE_SUITE</th>\n",
              "      <th>NAME_INCOME_TYPE</th>\n",
              "      <th>NAME_EDUCATION_TYPE</th>\n",
              "      <th>NAME_FAMILY_STATUS</th>\n",
              "      <th>NAME_HOUSING_TYPE</th>\n",
              "      <th>REGION_POPULATION_RELATIVE</th>\n",
              "      <th>DAYS_BIRTH</th>\n",
              "      <th>DAYS_EMPLOYED</th>\n",
              "      <th>DAYS_REGISTRATION</th>\n",
              "      <th>DAYS_ID_PUBLISH</th>\n",
              "      <th>OWN_CAR_AGE</th>\n",
              "      <th>FLAG_MOBIL</th>\n",
              "      <th>FLAG_EMP_PHONE</th>\n",
              "      <th>FLAG_WORK_PHONE</th>\n",
              "      <th>FLAG_CONT_MOBILE</th>\n",
              "      <th>FLAG_PHONE</th>\n",
              "      <th>FLAG_EMAIL</th>\n",
              "      <th>OCCUPATION_TYPE</th>\n",
              "      <th>CNT_FAM_MEMBERS</th>\n",
              "      <th>REGION_RATING_CLIENT</th>\n",
              "      <th>REGION_RATING_CLIENT_W_CITY</th>\n",
              "      <th>WEEKDAY_APPR_PROCESS_START</th>\n",
              "      <th>HOUR_APPR_PROCESS_START</th>\n",
              "      <th>REG_REGION_NOT_LIVE_REGION</th>\n",
              "      <th>REG_REGION_NOT_WORK_REGION</th>\n",
              "      <th>LIVE_REGION_NOT_WORK_REGION</th>\n",
              "      <th>REG_CITY_NOT_LIVE_CITY</th>\n",
              "      <th>REG_CITY_NOT_WORK_CITY</th>\n",
              "      <th>LIVE_CITY_NOT_WORK_CITY</th>\n",
              "      <th>ORGANIZATION_TYPE</th>\n",
              "      <th>EXT_SOURCE_1</th>\n",
              "      <th>EXT_SOURCE_2</th>\n",
              "      <th>EXT_SOURCE_3</th>\n",
              "      <th>APARTMENTS_AVG</th>\n",
              "      <th>BASEMENTAREA_AVG</th>\n",
              "      <th>YEARS_BEGINEXPLUATATION_AVG</th>\n",
              "      <th>YEARS_BUILD_AVG</th>\n",
              "      <th>COMMONAREA_AVG</th>\n",
              "      <th>ELEVATORS_AVG</th>\n",
              "      <th>ENTRANCES_AVG</th>\n",
              "      <th>FLOORSMAX_AVG</th>\n",
              "      <th>FLOORSMIN_AVG</th>\n",
              "      <th>LANDAREA_AVG</th>\n",
              "      <th>LIVINGAPARTMENTS_AVG</th>\n",
              "      <th>LIVINGAREA_AVG</th>\n",
              "      <th>NONLIVINGAPARTMENTS_AVG</th>\n",
              "      <th>NONLIVINGAREA_AVG</th>\n",
              "      <th>APARTMENTS_MODE</th>\n",
              "      <th>BASEMENTAREA_MODE</th>\n",
              "      <th>YEARS_BEGINEXPLUATATION_MODE</th>\n",
              "      <th>YEARS_BUILD_MODE</th>\n",
              "      <th>COMMONAREA_MODE</th>\n",
              "      <th>ELEVATORS_MODE</th>\n",
              "      <th>ENTRANCES_MODE</th>\n",
              "      <th>FLOORSMAX_MODE</th>\n",
              "      <th>FLOORSMIN_MODE</th>\n",
              "      <th>LANDAREA_MODE</th>\n",
              "      <th>LIVINGAPARTMENTS_MODE</th>\n",
              "      <th>LIVINGAREA_MODE</th>\n",
              "      <th>NONLIVINGAPARTMENTS_MODE</th>\n",
              "      <th>NONLIVINGAREA_MODE</th>\n",
              "      <th>APARTMENTS_MEDI</th>\n",
              "      <th>BASEMENTAREA_MEDI</th>\n",
              "      <th>YEARS_BEGINEXPLUATATION_MEDI</th>\n",
              "      <th>YEARS_BUILD_MEDI</th>\n",
              "      <th>COMMONAREA_MEDI</th>\n",
              "      <th>ELEVATORS_MEDI</th>\n",
              "      <th>ENTRANCES_MEDI</th>\n",
              "      <th>FLOORSMAX_MEDI</th>\n",
              "      <th>FLOORSMIN_MEDI</th>\n",
              "      <th>LANDAREA_MEDI</th>\n",
              "      <th>LIVINGAPARTMENTS_MEDI</th>\n",
              "      <th>LIVINGAREA_MEDI</th>\n",
              "      <th>NONLIVINGAPARTMENTS_MEDI</th>\n",
              "      <th>NONLIVINGAREA_MEDI</th>\n",
              "      <th>FONDKAPREMONT_MODE</th>\n",
              "      <th>HOUSETYPE_MODE</th>\n",
              "      <th>TOTALAREA_MODE</th>\n",
              "      <th>WALLSMATERIAL_MODE</th>\n",
              "      <th>EMERGENCYSTATE_MODE</th>\n",
              "      <th>OBS_30_CNT_SOCIAL_CIRCLE</th>\n",
              "      <th>DEF_30_CNT_SOCIAL_CIRCLE</th>\n",
              "      <th>OBS_60_CNT_SOCIAL_CIRCLE</th>\n",
              "      <th>DEF_60_CNT_SOCIAL_CIRCLE</th>\n",
              "      <th>DAYS_LAST_PHONE_CHANGE</th>\n",
              "      <th>FLAG_DOCUMENT_2</th>\n",
              "      <th>FLAG_DOCUMENT_3</th>\n",
              "      <th>FLAG_DOCUMENT_4</th>\n",
              "      <th>FLAG_DOCUMENT_5</th>\n",
              "      <th>FLAG_DOCUMENT_6</th>\n",
              "      <th>FLAG_DOCUMENT_7</th>\n",
              "      <th>FLAG_DOCUMENT_8</th>\n",
              "      <th>FLAG_DOCUMENT_9</th>\n",
              "      <th>FLAG_DOCUMENT_10</th>\n",
              "      <th>FLAG_DOCUMENT_11</th>\n",
              "      <th>FLAG_DOCUMENT_12</th>\n",
              "      <th>FLAG_DOCUMENT_13</th>\n",
              "      <th>FLAG_DOCUMENT_14</th>\n",
              "      <th>FLAG_DOCUMENT_15</th>\n",
              "      <th>FLAG_DOCUMENT_16</th>\n",
              "      <th>FLAG_DOCUMENT_17</th>\n",
              "      <th>FLAG_DOCUMENT_18</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",
              "    </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>202500.000</td>\n",
              "      <td>406597.500</td>\n",
              "      <td>24700.500</td>\n",
              "      <td>351000.000</td>\n",
              "      <td>Unaccompanied</td>\n",
              "      <td>Working</td>\n",
              "      <td>Secondary / secondary special</td>\n",
              "      <td>Single / not married</td>\n",
              "      <td>House / apartment</td>\n",
              "      <td>0.019</td>\n",
              "      <td>-9461</td>\n",
              "      <td>-637</td>\n",
              "      <td>-3648.000</td>\n",
              "      <td>-2120</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>Laborers</td>\n",
              "      <td>1.000</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>WEDNESDAY</td>\n",
              "      <td>10</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>Business Entity Type 3</td>\n",
              "      <td>0.083</td>\n",
              "      <td>0.263</td>\n",
              "      <td>0.139</td>\n",
              "      <td>0.025</td>\n",
              "      <td>0.037</td>\n",
              "      <td>0.972</td>\n",
              "      <td>0.619</td>\n",
              "      <td>0.014</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.069</td>\n",
              "      <td>0.083</td>\n",
              "      <td>0.125</td>\n",
              "      <td>0.037</td>\n",
              "      <td>0.020</td>\n",
              "      <td>0.019</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.025</td>\n",
              "      <td>0.038</td>\n",
              "      <td>0.972</td>\n",
              "      <td>0.634</td>\n",
              "      <td>0.014</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.069</td>\n",
              "      <td>0.083</td>\n",
              "      <td>0.125</td>\n",
              "      <td>0.038</td>\n",
              "      <td>0.022</td>\n",
              "      <td>0.020</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.025</td>\n",
              "      <td>0.037</td>\n",
              "      <td>0.972</td>\n",
              "      <td>0.624</td>\n",
              "      <td>0.014</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.069</td>\n",
              "      <td>0.083</td>\n",
              "      <td>0.125</td>\n",
              "      <td>0.037</td>\n",
              "      <td>0.021</td>\n",
              "      <td>0.019</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>reg oper account</td>\n",
              "      <td>block of flats</td>\n",
              "      <td>0.015</td>\n",
              "      <td>Stone, brick</td>\n",
              "      <td>No</td>\n",
              "      <td>2.000</td>\n",
              "      <td>2.000</td>\n",
              "      <td>2.000</td>\n",
              "      <td>2.000</td>\n",
              "      <td>-1134.000</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",
              "      <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>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1.000</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>270000.000</td>\n",
              "      <td>1293502.500</td>\n",
              "      <td>35698.500</td>\n",
              "      <td>1129500.000</td>\n",
              "      <td>Family</td>\n",
              "      <td>State servant</td>\n",
              "      <td>Higher education</td>\n",
              "      <td>Married</td>\n",
              "      <td>House / apartment</td>\n",
              "      <td>0.004</td>\n",
              "      <td>-16765</td>\n",
              "      <td>-1188</td>\n",
              "      <td>-1186.000</td>\n",
              "      <td>-291</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>Core staff</td>\n",
              "      <td>2.000</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>MONDAY</td>\n",
              "      <td>11</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>School</td>\n",
              "      <td>0.311</td>\n",
              "      <td>0.622</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.096</td>\n",
              "      <td>0.053</td>\n",
              "      <td>0.985</td>\n",
              "      <td>0.796</td>\n",
              "      <td>0.060</td>\n",
              "      <td>0.080</td>\n",
              "      <td>0.035</td>\n",
              "      <td>0.292</td>\n",
              "      <td>0.333</td>\n",
              "      <td>0.013</td>\n",
              "      <td>0.077</td>\n",
              "      <td>0.055</td>\n",
              "      <td>0.004</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.092</td>\n",
              "      <td>0.054</td>\n",
              "      <td>0.985</td>\n",
              "      <td>0.804</td>\n",
              "      <td>0.050</td>\n",
              "      <td>0.081</td>\n",
              "      <td>0.035</td>\n",
              "      <td>0.292</td>\n",
              "      <td>0.333</td>\n",
              "      <td>0.013</td>\n",
              "      <td>0.079</td>\n",
              "      <td>0.055</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.097</td>\n",
              "      <td>0.053</td>\n",
              "      <td>0.985</td>\n",
              "      <td>0.799</td>\n",
              "      <td>0.061</td>\n",
              "      <td>0.080</td>\n",
              "      <td>0.035</td>\n",
              "      <td>0.292</td>\n",
              "      <td>0.333</td>\n",
              "      <td>0.013</td>\n",
              "      <td>0.079</td>\n",
              "      <td>0.056</td>\n",
              "      <td>0.004</td>\n",
              "      <td>0.010</td>\n",
              "      <td>reg oper account</td>\n",
              "      <td>block of flats</td>\n",
              "      <td>0.071</td>\n",
              "      <td>Block</td>\n",
              "      <td>No</td>\n",
              "      <td>1.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>-828.000</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",
              "      <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>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</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>67500.000</td>\n",
              "      <td>135000.000</td>\n",
              "      <td>6750.000</td>\n",
              "      <td>135000.000</td>\n",
              "      <td>Unaccompanied</td>\n",
              "      <td>Working</td>\n",
              "      <td>Secondary / secondary special</td>\n",
              "      <td>Single / not married</td>\n",
              "      <td>House / apartment</td>\n",
              "      <td>0.010</td>\n",
              "      <td>-19046</td>\n",
              "      <td>-225</td>\n",
              "      <td>-4260.000</td>\n",
              "      <td>-2531</td>\n",
              "      <td>26.000</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>Laborers</td>\n",
              "      <td>1.000</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>MONDAY</td>\n",
              "      <td>9</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>Government</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.556</td>\n",
              "      <td>0.730</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",
              "      <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>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>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>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>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>-815.000</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>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>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</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>135000.000</td>\n",
              "      <td>312682.500</td>\n",
              "      <td>29686.500</td>\n",
              "      <td>297000.000</td>\n",
              "      <td>Unaccompanied</td>\n",
              "      <td>Working</td>\n",
              "      <td>Secondary / secondary special</td>\n",
              "      <td>Civil marriage</td>\n",
              "      <td>House / apartment</td>\n",
              "      <td>0.008</td>\n",
              "      <td>-19005</td>\n",
              "      <td>-3039</td>\n",
              "      <td>-9833.000</td>\n",
              "      <td>-2437</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Laborers</td>\n",
              "      <td>2.000</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>WEDNESDAY</td>\n",
              "      <td>17</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>Business Entity Type 3</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.650</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",
              "      <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>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>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>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>2.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>2.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>-617.000</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",
              "      <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>0</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",
              "    </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>121500.000</td>\n",
              "      <td>513000.000</td>\n",
              "      <td>21865.500</td>\n",
              "      <td>513000.000</td>\n",
              "      <td>Unaccompanied</td>\n",
              "      <td>Working</td>\n",
              "      <td>Secondary / secondary special</td>\n",
              "      <td>Single / not married</td>\n",
              "      <td>House / apartment</td>\n",
              "      <td>0.029</td>\n",
              "      <td>-19932</td>\n",
              "      <td>-3038</td>\n",
              "      <td>-4311.000</td>\n",
              "      <td>-3458</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Core staff</td>\n",
              "      <td>1.000</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>THURSDAY</td>\n",
              "      <td>11</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Religion</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.323</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",
              "      <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>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>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>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>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>-1106.000</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>1</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>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d3423180-a20f-4fdb-8756-42783a2faf34')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       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",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-d3423180-a20f-4fdb-8756-42783a2faf34 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-d3423180-a20f-4fdb-8756-42783a2faf34');\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": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"NAME_EDUCATION_TYPE\"].value_counts()"
      ],
      "metadata": {
        "id": "l9sZlWLLAtHE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d09128c6-b5a5-4c2d-f67f-98098305a888"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Secondary / secondary special    218391\n",
              "Higher education                  74863\n",
              "Incomplete higher                 10277\n",
              "Lower secondary                    3816\n",
              "Academic degree                     164\n",
              "Name: NAME_EDUCATION_TYPE, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def outlier_thresholds(dataframe, col_name, q1=0.25, q3=0.75):\n",
        "    quartile1 = dataframe[col_name].quantile(q1)\n",
        "    quartile3 = dataframe[col_name].quantile(q3)\n",
        "    interquantile_range = quartile3 - quartile1\n",
        "    up_limit = quartile3 + 1.5 * interquantile_range\n",
        "    low_limit = quartile1 - 1.5 * interquantile_range\n",
        "    return low_limit, up_limit\n",
        "\n",
        "def check_outlier(dataframe, col_name):\n",
        "    low_limit, up_limit = outlier_thresholds(dataframe, col_name)\n",
        "    if dataframe[(dataframe[col_name] > up_limit) | (dataframe[col_name] < low_limit)].any(axis=None):\n",
        "        return True\n",
        "    else:\n",
        "        return False\n",
        "\n",
        "\n",
        "def grab_col_names(dataframe, cat_th=10, car_th=20):\n",
        "  \n",
        "#  cat_th=10 ve  car_th=20 seçmek tamamen opsiyonel. !\n",
        "#  eğer cat_th = 10 bir değişkenin içindeki sınıf sayısı 10 dan azsa bu aslında sayısal görünümlü kategorik değişken olabilir.\n",
        "#  car_th=20 eğer bir kategorik değişkenin 20 den fazla sınıfı varsa ve tipide object ise kategorik görünümlü kardinal olabilir.\n",
        "    \"\"\"\n",
        "\n",
        "    Veri setindeki kategorik, numerik ve kategorik fakat kardinal değişkenlerin isimlerini verir.\n",
        "    Not: Kategorik değişkenlerin içerisine numerik görünümlü kategorik değişkenler de dahildir.\n",
        "\n",
        "    Parameters\n",
        "    ------\n",
        "        dataframe: dataframe\n",
        "                Değişken isimleri alınmak istenilen dataframe\n",
        "        cat_th: int, optional\n",
        "                numerik fakat kategorik olan değişkenler için sınıf eşik değeri\n",
        "        car_th: int, optinal\n",
        "                kategorik fakat kardinal değişkenler için sınıf eşik değeri\n",
        "\n",
        "    Returns\n",
        "    ------\n",
        "        cat_cols: list\n",
        "                Kategorik değişken listesi\n",
        "        num_cols: list\n",
        "                Numerik değişken listesi\n",
        "        cat_but_car: list\n",
        "                Kategorik görünümlü kardinal değişken listesi\n",
        "\n",
        "    Examples\n",
        "    ------\n",
        "        import seaborn as sns\n",
        "        df = sns.load_dataset(\"iris\")\n",
        "        print(grab_col_names(df))\n",
        "\n",
        "\n",
        "    Notes\n",
        "    ------\n",
        "        cat_cols + num_cols + cat_but_car = toplam değişken sayısı\n",
        "        num_but_cat cat_cols'un içerisinde.\n",
        "        Return olan 3 liste toplamı toplam değişken sayısına eşittir: cat_cols + num_cols + cat_but_car = değişken sayısı\n",
        "\n",
        "    \"\"\"\n",
        "\n",
        "    # cat_cols, cat_but_car\n",
        "    cat_cols = [col for col in dataframe.columns if dataframe[col].dtypes == \"O\"]\n",
        "    num_but_cat = [col for col in dataframe.columns if dataframe[col].nunique() < cat_th and\n",
        "                   dataframe[col].dtypes != \"O\"]\n",
        "    cat_but_car = [col for col in dataframe.columns if dataframe[col].nunique() > car_th and\n",
        "                   dataframe[col].dtypes == \"O\"]\n",
        "    cat_cols = cat_cols + num_but_cat\n",
        "    cat_cols = [col for col in cat_cols if col not in cat_but_car]\n",
        "\n",
        "    # num_cols\n",
        "    num_cols = [col for col in dataframe.columns if dataframe[col].dtypes != \"O\"]\n",
        "    num_cols = [col for col in num_cols if col not in num_but_cat]\n",
        "\n",
        "    print(f\"Observations: {dataframe.shape[0]}\")\n",
        "    print(f\"Variables: {dataframe.shape[1]}\")\n",
        "    print(f'cat_cols: {len(cat_cols)}')\n",
        "    print(f'num_cols: {len(num_cols)}')\n",
        "    print(f'cat_but_car: {len(cat_but_car)}')\n",
        "    print(f'num_but_cat: {len(num_but_cat)}')\n",
        "    return cat_cols, num_cols, cat_but_car"
      ],
      "metadata": {
        "id": "SBMWj1hwcagW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cat_cols, num_cols, cat_but_car = grab_col_names(df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IYcmF-EbbWfG",
        "outputId": "f892a3a3-615f-4775-c951-6aed5cc2c561"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Observations: 307511\n",
            "Variables: 122\n",
            "cat_cols: 54\n",
            "num_cols: 67\n",
            "cat_but_car: 1\n",
            "num_but_cat: 39\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def cat_summary(dataframe, col_name, plot=False):\n",
        "    print(pd.DataFrame({col_name: dataframe[col_name].value_counts(),\n",
        "                        \"Ratio\": 100 * dataframe[col_name].value_counts() / len(dataframe)}))\n",
        "    print(\"##########################################\")\n",
        "    if plot:\n",
        "        sns.countplot(x=dataframe[col_name], data=dataframe)\n",
        "        plt.show()\n",
        "\n",
        "\n",
        "for col in cat_cols:\n",
        "    cat_summary(df, col)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vkUvl_LjbYge",
        "outputId": "15de4aa9-b127-439f-bcf8-86d940b5a2fd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                 NAME_CONTRACT_TYPE  Ratio\n",
            "Cash loans                   278232 90.479\n",
            "Revolving loans               29279  9.521\n",
            "##########################################\n",
            "     CODE_GENDER  Ratio\n",
            "F         202448 65.834\n",
            "M         105059 34.164\n",
            "XNA            4  0.001\n",
            "##########################################\n",
            "   FLAG_OWN_CAR  Ratio\n",
            "N        202924 65.989\n",
            "Y        104587 34.011\n",
            "##########################################\n",
            "   FLAG_OWN_REALTY  Ratio\n",
            "Y           213312 69.367\n",
            "N            94199 30.633\n",
            "##########################################\n",
            "                 NAME_TYPE_SUITE  Ratio\n",
            "Unaccompanied             248526 80.819\n",
            "Family                     40149 13.056\n",
            "Spouse, partner            11370  3.697\n",
            "Children                    3267  1.062\n",
            "Other_B                     1770  0.576\n",
            "Other_A                      866  0.282\n",
            "Group of people              271  0.088\n",
            "##########################################\n",
            "                      NAME_INCOME_TYPE  Ratio\n",
            "Working                         158774 51.632\n",
            "Commercial associate             71617 23.289\n",
            "Pensioner                        55362 18.003\n",
            "State servant                    21703  7.058\n",
            "Unemployed                          22  0.007\n",
            "Student                             18  0.006\n",
            "Businessman                         10  0.003\n",
            "Maternity leave                      5  0.002\n",
            "##########################################\n",
            "                               NAME_EDUCATION_TYPE  Ratio\n",
            "Secondary / secondary special               218391 71.019\n",
            "Higher education                             74863 24.345\n",
            "Incomplete higher                            10277  3.342\n",
            "Lower secondary                               3816  1.241\n",
            "Academic degree                                164  0.053\n",
            "##########################################\n",
            "                      NAME_FAMILY_STATUS  Ratio\n",
            "Married                           196432 63.878\n",
            "Single / not married               45444 14.778\n",
            "Civil marriage                     29775  9.683\n",
            "Separated                          19770  6.429\n",
            "Widow                              16088  5.232\n",
            "Unknown                                2  0.001\n",
            "##########################################\n",
            "                     NAME_HOUSING_TYPE  Ratio\n",
            "House / apartment               272868 88.734\n",
            "With parents                     14840  4.826\n",
            "Municipal apartment              11183  3.637\n",
            "Rented apartment                  4881  1.587\n",
            "Office apartment                  2617  0.851\n",
            "Co-op apartment                   1122  0.365\n",
            "##########################################\n",
            "                       OCCUPATION_TYPE  Ratio\n",
            "Laborers                         55186 17.946\n",
            "Sales staff                      32102 10.439\n",
            "Core staff                       27570  8.966\n",
            "Managers                         21371  6.950\n",
            "Drivers                          18603  6.050\n",
            "High skill tech staff            11380  3.701\n",
            "Accountants                       9813  3.191\n",
            "Medicine staff                    8537  2.776\n",
            "Security staff                    6721  2.186\n",
            "Cooking staff                     5946  1.934\n",
            "Cleaning staff                    4653  1.513\n",
            "Private service staff             2652  0.862\n",
            "Low-skill Laborers                2093  0.681\n",
            "Waiters/barmen staff              1348  0.438\n",
            "Secretaries                       1305  0.424\n",
            "Realty agents                      751  0.244\n",
            "HR staff                           563  0.183\n",
            "IT staff                           526  0.171\n",
            "##########################################\n",
            "           WEEKDAY_APPR_PROCESS_START  Ratio\n",
            "TUESDAY                         53901 17.528\n",
            "WEDNESDAY                       51934 16.889\n",
            "MONDAY                          50714 16.492\n",
            "THURSDAY                        50591 16.452\n",
            "FRIDAY                          50338 16.369\n",
            "SATURDAY                        33852 11.008\n",
            "SUNDAY                          16181  5.262\n",
            "##########################################\n",
            "                       FONDKAPREMONT_MODE  Ratio\n",
            "reg oper account                    73830 24.009\n",
            "reg oper spec account               12080  3.928\n",
            "not specified                        5687  1.849\n",
            "org spec account                     5619  1.827\n",
            "##########################################\n",
            "                  HOUSETYPE_MODE  Ratio\n",
            "block of flats            150503 48.942\n",
            "specific housing            1499  0.487\n",
            "terraced house              1212  0.394\n",
            "##########################################\n",
            "              WALLSMATERIAL_MODE  Ratio\n",
            "Panel                      66040 21.476\n",
            "Stone, brick               64815 21.077\n",
            "Block                       9253  3.009\n",
            "Wooden                      5362  1.744\n",
            "Mixed                       2296  0.747\n",
            "Monolithic                  1779  0.579\n",
            "Others                      1625  0.528\n",
            "##########################################\n",
            "     EMERGENCYSTATE_MODE  Ratio\n",
            "No                159428 51.845\n",
            "Yes                 2328  0.757\n",
            "##########################################\n",
            "   TARGET  Ratio\n",
            "0  282686 91.927\n",
            "1   24825  8.073\n",
            "##########################################\n",
            "   FLAG_MOBIL   Ratio\n",
            "1      307510 100.000\n",
            "0           1   0.000\n",
            "##########################################\n",
            "   FLAG_EMP_PHONE  Ratio\n",
            "1          252125 81.989\n",
            "0           55386 18.011\n",
            "##########################################\n",
            "   FLAG_WORK_PHONE  Ratio\n",
            "0           246203 80.063\n",
            "1            61308 19.937\n",
            "##########################################\n",
            "   FLAG_CONT_MOBILE  Ratio\n",
            "1            306937 99.813\n",
            "0               574  0.187\n",
            "##########################################\n",
            "   FLAG_PHONE  Ratio\n",
            "0      221080 71.893\n",
            "1       86431 28.107\n",
            "##########################################\n",
            "   FLAG_EMAIL  Ratio\n",
            "0      290069 94.328\n",
            "1       17442  5.672\n",
            "##########################################\n",
            "   REGION_RATING_CLIENT  Ratio\n",
            "2                226984 73.813\n",
            "3                 48330 15.717\n",
            "1                 32197 10.470\n",
            "##########################################\n",
            "   REGION_RATING_CLIENT_W_CITY  Ratio\n",
            "2                       229484 74.626\n",
            "3                        43860 14.263\n",
            "1                        34167 11.111\n",
            "##########################################\n",
            "   REG_REGION_NOT_LIVE_REGION  Ratio\n",
            "0                      302854 98.486\n",
            "1                        4657  1.514\n",
            "##########################################\n",
            "   REG_REGION_NOT_WORK_REGION  Ratio\n",
            "0                      291899 94.923\n",
            "1                       15612  5.077\n",
            "##########################################\n",
            "   LIVE_REGION_NOT_WORK_REGION  Ratio\n",
            "0                       295008 95.934\n",
            "1                        12503  4.066\n",
            "##########################################\n",
            "   REG_CITY_NOT_LIVE_CITY  Ratio\n",
            "0                  283472 92.183\n",
            "1                   24039  7.817\n",
            "##########################################\n",
            "   REG_CITY_NOT_WORK_CITY  Ratio\n",
            "0                  236644 76.955\n",
            "1                   70867 23.045\n",
            "##########################################\n",
            "   LIVE_CITY_NOT_WORK_CITY  Ratio\n",
            "0                   252296 82.045\n",
            "1                    55215 17.955\n",
            "##########################################\n",
            "        DEF_60_CNT_SOCIAL_CIRCLE  Ratio\n",
            "0.000                     280721 91.288\n",
            "1.000                      21841  7.103\n",
            "2.000                       3170  1.031\n",
            "3.000                        598  0.194\n",
            "4.000                        135  0.044\n",
            "5.000                         20  0.007\n",
            "6.000                          3  0.001\n",
            "7.000                          1  0.000\n",
            "24.000                         1  0.000\n",
            "##########################################\n",
            "   FLAG_DOCUMENT_2  Ratio\n",
            "0           307498 99.996\n",
            "1               13  0.004\n",
            "##########################################\n",
            "   FLAG_DOCUMENT_3  Ratio\n",
            "1           218340 71.002\n",
            "0            89171 28.998\n",
            "##########################################\n",
            "   FLAG_DOCUMENT_4  Ratio\n",
            "0           307486 99.992\n",
            "1               25  0.008\n",
            "##########################################\n",
            "   FLAG_DOCUMENT_5  Ratio\n",
            "0           302863 98.489\n",
            "1             4648  1.511\n",
            "##########################################\n",
            "   FLAG_DOCUMENT_6  Ratio\n",
            "0           280433 91.194\n",
            "1            27078  8.806\n",
            "##########################################\n",
            "   FLAG_DOCUMENT_7  Ratio\n",
            "0           307452 99.981\n",
            "1               59  0.019\n",
            "##########################################\n",
            "   FLAG_DOCUMENT_8  Ratio\n",
            "0           282487 91.862\n",
            "1            25024  8.138\n",
            "##########################################\n",
            "   FLAG_DOCUMENT_9  Ratio\n",
            "0           306313 99.610\n",
            "1             1198  0.390\n",
            "##########################################\n",
            "   FLAG_DOCUMENT_10  Ratio\n",
            "0            307504 99.998\n",
            "1                 7  0.002\n",
            "##########################################\n",
            "   FLAG_DOCUMENT_11  Ratio\n",
            "0            306308 99.609\n",
            "1              1203  0.391\n",
            "##########################################\n",
            "   FLAG_DOCUMENT_12  Ratio\n",
            "0            307509 99.999\n",
            "1                 2  0.001\n",
            "##########################################\n",
            "   FLAG_DOCUMENT_13  Ratio\n",
            "0            306427 99.647\n",
            "1              1084  0.353\n",
            "##########################################\n",
            "   FLAG_DOCUMENT_14  Ratio\n",
            "0            306608 99.706\n",
            "1               903  0.294\n",
            "##########################################\n",
            "   FLAG_DOCUMENT_15  Ratio\n",
            "0            307139 99.879\n",
            "1               372  0.121\n",
            "##########################################\n",
            "   FLAG_DOCUMENT_16  Ratio\n",
            "0            304458 99.007\n",
            "1              3053  0.993\n",
            "##########################################\n",
            "   FLAG_DOCUMENT_17  Ratio\n",
            "0            307429 99.973\n",
            "1                82  0.027\n",
            "##########################################\n",
            "   FLAG_DOCUMENT_18  Ratio\n",
            "0            305011 99.187\n",
            "1              2500  0.813\n",
            "##########################################\n",
            "   FLAG_DOCUMENT_19  Ratio\n",
            "0            307328 99.940\n",
            "1               183  0.060\n",
            "##########################################\n",
            "   FLAG_DOCUMENT_20  Ratio\n",
            "0            307355 99.949\n",
            "1               156  0.051\n",
            "##########################################\n",
            "   FLAG_DOCUMENT_21  Ratio\n",
            "0            307408 99.967\n",
            "1               103  0.033\n",
            "##########################################\n",
            "       AMT_REQ_CREDIT_BUREAU_HOUR  Ratio\n",
            "0.000                      264366 85.970\n",
            "1.000                        1560  0.507\n",
            "2.000                          56  0.018\n",
            "3.000                           9  0.003\n",
            "4.000                           1  0.000\n",
            "##########################################\n",
            "       AMT_REQ_CREDIT_BUREAU_DAY  Ratio\n",
            "0.000                     264503 86.014\n",
            "1.000                       1292  0.420\n",
            "2.000                        106  0.034\n",
            "3.000                         45  0.015\n",
            "4.000                         26  0.008\n",
            "5.000                          9  0.003\n",
            "6.000                          8  0.003\n",
            "9.000                          2  0.001\n",
            "8.000                          1  0.000\n",
            "##########################################\n",
            "       AMT_REQ_CREDIT_BUREAU_WEEK  Ratio\n",
            "0.000                      257456 83.723\n",
            "1.000                        8208  2.669\n",
            "2.000                         199  0.065\n",
            "3.000                          58  0.019\n",
            "4.000                          34  0.011\n",
            "6.000                          20  0.007\n",
            "5.000                          10  0.003\n",
            "8.000                           5  0.002\n",
            "7.000                           2  0.001\n",
            "##########################################\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 2. Rare kategoriler ile bağımlı değişken arasındaki ilişkinin analiz edilmesi.\n",
        "\n",
        "df[\"NAME_INCOME_TYPE\"].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jNVXwVM3blSW",
        "outputId": "bddfc225-784d-488f-dadc-371f92428c80"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Working                 158774\n",
              "Commercial associate     71617\n",
              "Pensioner                55362\n",
              "State servant            21703\n",
              "Unemployed                  22\n",
              "Student                     18\n",
              "Businessman                 10\n",
              "Maternity leave              5\n",
              "Name: NAME_INCOME_TYPE, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.groupby(\"NAME_INCOME_TYPE\")[\"TARGET\"].mean()\n",
        "\n",
        "#Student               0.000\n",
        "#Businessman           0.000 kredileri ödüyor demek\n",
        "\n",
        "#Unemployed            0.364 işsizler kredileri ödemekte zorlanıyor olabilir.\n",
        "\n",
        "\"\"\"\n",
        "Unemployed                  22\n",
        "Student                     18\n",
        "Businessman                 10\n",
        "Maternity leave              5\n",
        "bu değerler düşük diye veri setinden uçurabilirsin ya da rare ile birleştirebilirsin\n",
        "ama yüzdelerine baktığında businessman ile unemployment  kredi ödemelerinde eşit olmadığını görüyorsun\n",
        "buradaki kadar sana ait ister birleştir ister sil \"\"\"\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cWzk9gY8bnl4",
        "outputId": "94cff386-d0b0-49c5-b370-54c0aefe9001"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "NAME_INCOME_TYPE\n",
              "Businessman            0.000\n",
              "Commercial associate   0.075\n",
              "Maternity leave        0.400\n",
              "Pensioner              0.054\n",
              "State servant          0.058\n",
              "Student                0.000\n",
              "Unemployed             0.364\n",
              "Working                0.096\n",
              "Name: TARGET, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def rare_analyser(dataframe, target, cat_cols):\n",
        "    for col in cat_cols:\n",
        "        print(col, \":\", len(dataframe[col].value_counts()))\n",
        "        print(pd.DataFrame({\"COUNT\": dataframe[col].value_counts(),\n",
        "                            \"RATIO\": dataframe[col].value_counts() / len(dataframe),\n",
        "                            \"TARGET_MEAN\": dataframe.groupby(col)[target].mean()}), end=\"\\n\\n\\n\")\n",
        "\n",
        "rare_analyser(df, \"TARGET\", cat_cols)\n",
        "\n",
        "# Vahit hocam, yüzde 1 in altındakileri bir araya getirilecek şeklinde yorumlamıştır. İstediğin gibi\n",
        "# almakta özgürsün."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xja30KYubpbN",
        "outputId": "0d325fa2-dc06-4df6-d6c0-9298686984fe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "NAME_CONTRACT_TYPE : 2\n",
            "                  COUNT  RATIO  TARGET_MEAN\n",
            "Cash loans       278232  0.905        0.083\n",
            "Revolving loans   29279  0.095        0.055\n",
            "\n",
            "\n",
            "CODE_GENDER : 3\n",
            "      COUNT  RATIO  TARGET_MEAN\n",
            "F    202448  0.658        0.070\n",
            "M    105059  0.342        0.101\n",
            "XNA       4  0.000        0.000\n",
            "\n",
            "\n",
            "FLAG_OWN_CAR : 2\n",
            "    COUNT  RATIO  TARGET_MEAN\n",
            "N  202924  0.660        0.085\n",
            "Y  104587  0.340        0.072\n",
            "\n",
            "\n",
            "FLAG_OWN_REALTY : 2\n",
            "    COUNT  RATIO  TARGET_MEAN\n",
            "N   94199  0.306        0.083\n",
            "Y  213312  0.694        0.080\n",
            "\n",
            "\n",
            "NAME_TYPE_SUITE : 7\n",
            "                  COUNT  RATIO  TARGET_MEAN\n",
            "Children           3267  0.011        0.074\n",
            "Family            40149  0.131        0.075\n",
            "Group of people     271  0.001        0.085\n",
            "Other_A             866  0.003        0.088\n",
            "Other_B            1770  0.006        0.098\n",
            "Spouse, partner   11370  0.037        0.079\n",
            "Unaccompanied    248526  0.808        0.082\n",
            "\n",
            "\n",
            "NAME_INCOME_TYPE : 8\n",
            "                       COUNT  RATIO  TARGET_MEAN\n",
            "Businessman               10  0.000        0.000\n",
            "Commercial associate   71617  0.233        0.075\n",
            "Maternity leave            5  0.000        0.400\n",
            "Pensioner              55362  0.180        0.054\n",
            "State servant          21703  0.071        0.058\n",
            "Student                   18  0.000        0.000\n",
            "Unemployed                22  0.000        0.364\n",
            "Working               158774  0.516        0.096\n",
            "\n",
            "\n",
            "NAME_EDUCATION_TYPE : 5\n",
            "                                COUNT  RATIO  TARGET_MEAN\n",
            "Academic degree                   164  0.001        0.018\n",
            "Higher education                74863  0.243        0.054\n",
            "Incomplete higher               10277  0.033        0.085\n",
            "Lower secondary                  3816  0.012        0.109\n",
            "Secondary / secondary special  218391  0.710        0.089\n",
            "\n",
            "\n",
            "NAME_FAMILY_STATUS : 6\n",
            "                       COUNT  RATIO  TARGET_MEAN\n",
            "Civil marriage         29775  0.097        0.099\n",
            "Married               196432  0.639        0.076\n",
            "Separated              19770  0.064        0.082\n",
            "Single / not married   45444  0.148        0.098\n",
            "Unknown                    2  0.000        0.000\n",
            "Widow                  16088  0.052        0.058\n",
            "\n",
            "\n",
            "NAME_HOUSING_TYPE : 6\n",
            "                      COUNT  RATIO  TARGET_MEAN\n",
            "Co-op apartment        1122  0.004        0.079\n",
            "House / apartment    272868  0.887        0.078\n",
            "Municipal apartment   11183  0.036        0.085\n",
            "Office apartment       2617  0.009        0.066\n",
            "Rented apartment       4881  0.016        0.123\n",
            "With parents          14840  0.048        0.117\n",
            "\n",
            "\n",
            "OCCUPATION_TYPE : 18\n",
            "                       COUNT  RATIO  TARGET_MEAN\n",
            "Accountants             9813  0.032        0.048\n",
            "Cleaning staff          4653  0.015        0.096\n",
            "Cooking staff           5946  0.019        0.104\n",
            "Core staff             27570  0.090        0.063\n",
            "Drivers                18603  0.060        0.113\n",
            "HR staff                 563  0.002        0.064\n",
            "High skill tech staff  11380  0.037        0.062\n",
            "IT staff                 526  0.002        0.065\n",
            "Laborers               55186  0.179        0.106\n",
            "Low-skill Laborers      2093  0.007        0.172\n",
            "Managers               21371  0.069        0.062\n",
            "Medicine staff          8537  0.028        0.067\n",
            "Private service staff   2652  0.009        0.066\n",
            "Realty agents            751  0.002        0.079\n",
            "Sales staff            32102  0.104        0.096\n",
            "Secretaries             1305  0.004        0.070\n",
            "Security staff          6721  0.022        0.107\n",
            "Waiters/barmen staff    1348  0.004        0.113\n",
            "\n",
            "\n",
            "WEEKDAY_APPR_PROCESS_START : 7\n",
            "           COUNT  RATIO  TARGET_MEAN\n",
            "FRIDAY     50338  0.164        0.081\n",
            "MONDAY     50714  0.165        0.078\n",
            "SATURDAY   33852  0.110        0.079\n",
            "SUNDAY     16181  0.053        0.079\n",
            "THURSDAY   50591  0.165        0.081\n",
            "TUESDAY    53901  0.175        0.084\n",
            "WEDNESDAY  51934  0.169        0.082\n",
            "\n",
            "\n",
            "FONDKAPREMONT_MODE : 4\n",
            "                       COUNT  RATIO  TARGET_MEAN\n",
            "not specified           5687  0.018        0.075\n",
            "org spec account        5619  0.018        0.058\n",
            "reg oper account       73830  0.240        0.070\n",
            "reg oper spec account  12080  0.039        0.066\n",
            "\n",
            "\n",
            "HOUSETYPE_MODE : 3\n",
            "                   COUNT  RATIO  TARGET_MEAN\n",
            "block of flats    150503  0.489        0.069\n",
            "specific housing    1499  0.005        0.101\n",
            "terraced house      1212  0.004        0.085\n",
            "\n",
            "\n",
            "WALLSMATERIAL_MODE : 7\n",
            "              COUNT  RATIO  TARGET_MEAN\n",
            "Block          9253  0.030        0.070\n",
            "Mixed          2296  0.007        0.075\n",
            "Monolithic     1779  0.006        0.047\n",
            "Others         1625  0.005        0.083\n",
            "Panel         66040  0.215        0.063\n",
            "Stone, brick  64815  0.211        0.074\n",
            "Wooden         5362  0.017        0.097\n",
            "\n",
            "\n",
            "EMERGENCYSTATE_MODE : 2\n",
            "      COUNT  RATIO  TARGET_MEAN\n",
            "No   159428  0.518        0.070\n",
            "Yes    2328  0.008        0.096\n",
            "\n",
            "\n",
            "TARGET : 2\n",
            "    COUNT  RATIO  TARGET_MEAN\n",
            "0  282686  0.919        0.000\n",
            "1   24825  0.081        1.000\n",
            "\n",
            "\n",
            "FLAG_MOBIL : 2\n",
            "    COUNT  RATIO  TARGET_MEAN\n",
            "0       1  0.000        0.000\n",
            "1  307510  1.000        0.081\n",
            "\n",
            "\n",
            "FLAG_EMP_PHONE : 2\n",
            "    COUNT  RATIO  TARGET_MEAN\n",
            "0   55386  0.180        0.054\n",
            "1  252125  0.820        0.087\n",
            "\n",
            "\n",
            "FLAG_WORK_PHONE : 2\n",
            "    COUNT  RATIO  TARGET_MEAN\n",
            "0  246203  0.801        0.077\n",
            "1   61308  0.199        0.096\n",
            "\n",
            "\n",
            "FLAG_CONT_MOBILE : 2\n",
            "    COUNT  RATIO  TARGET_MEAN\n",
            "0     574  0.002        0.078\n",
            "1  306937  0.998        0.081\n",
            "\n",
            "\n",
            "FLAG_PHONE : 2\n",
            "    COUNT  RATIO  TARGET_MEAN\n",
            "0  221080  0.719        0.085\n",
            "1   86431  0.281        0.070\n",
            "\n",
            "\n",
            "FLAG_EMAIL : 2\n",
            "    COUNT  RATIO  TARGET_MEAN\n",
            "0  290069  0.943        0.081\n",
            "1   17442  0.057        0.079\n",
            "\n",
            "\n",
            "REGION_RATING_CLIENT : 3\n",
            "    COUNT  RATIO  TARGET_MEAN\n",
            "1   32197  0.105        0.048\n",
            "2  226984  0.738        0.079\n",
            "3   48330  0.157        0.111\n",
            "\n",
            "\n",
            "REGION_RATING_CLIENT_W_CITY : 3\n",
            "    COUNT  RATIO  TARGET_MEAN\n",
            "1   34167  0.111        0.048\n",
            "2  229484  0.746        0.079\n",
            "3   43860  0.143        0.114\n",
            "\n",
            "\n",
            "REG_REGION_NOT_LIVE_REGION : 2\n",
            "    COUNT  RATIO  TARGET_MEAN\n",
            "0  302854  0.985        0.081\n",
            "1    4657  0.015        0.093\n",
            "\n",
            "\n",
            "REG_REGION_NOT_WORK_REGION : 2\n",
            "    COUNT  RATIO  TARGET_MEAN\n",
            "0  291899  0.949        0.080\n",
            "1   15612  0.051        0.089\n",
            "\n",
            "\n",
            "LIVE_REGION_NOT_WORK_REGION : 2\n",
            "    COUNT  RATIO  TARGET_MEAN\n",
            "0  295008  0.959        0.081\n",
            "1   12503  0.041        0.084\n",
            "\n",
            "\n",
            "REG_CITY_NOT_LIVE_CITY : 2\n",
            "    COUNT  RATIO  TARGET_MEAN\n",
            "0  283472  0.922        0.077\n",
            "1   24039  0.078        0.122\n",
            "\n",
            "\n",
            "REG_CITY_NOT_WORK_CITY : 2\n",
            "    COUNT  RATIO  TARGET_MEAN\n",
            "0  236644  0.770        0.073\n",
            "1   70867  0.230        0.106\n",
            "\n",
            "\n",
            "LIVE_CITY_NOT_WORK_CITY : 2\n",
            "    COUNT  RATIO  TARGET_MEAN\n",
            "0  252296  0.820        0.077\n",
            "1   55215  0.180        0.100\n",
            "\n",
            "\n",
            "DEF_60_CNT_SOCIAL_CIRCLE : 9\n",
            "         COUNT  RATIO  TARGET_MEAN\n",
            "0.000   280721  0.913        0.078\n",
            "1.000    21841  0.071        0.105\n",
            "2.000     3170  0.010        0.121\n",
            "3.000      598  0.002        0.159\n",
            "4.000      135  0.000        0.111\n",
            "5.000       20  0.000        0.150\n",
            "6.000        3  0.000        0.000\n",
            "7.000        1  0.000        0.000\n",
            "24.000       1  0.000        0.000\n",
            "\n",
            "\n",
            "FLAG_DOCUMENT_2 : 2\n",
            "    COUNT  RATIO  TARGET_MEAN\n",
            "0  307498  1.000        0.081\n",
            "1      13  0.000        0.308\n",
            "\n",
            "\n",
            "FLAG_DOCUMENT_3 : 2\n",
            "    COUNT  RATIO  TARGET_MEAN\n",
            "0   89171  0.290        0.062\n",
            "1  218340  0.710        0.088\n",
            "\n",
            "\n",
            "FLAG_DOCUMENT_4 : 2\n",
            "    COUNT  RATIO  TARGET_MEAN\n",
            "0  307486  1.000        0.081\n",
            "1      25  0.000        0.000\n",
            "\n",
            "\n",
            "FLAG_DOCUMENT_5 : 2\n",
            "    COUNT  RATIO  TARGET_MEAN\n",
            "0  302863  0.985        0.081\n",
            "1    4648  0.015        0.080\n",
            "\n",
            "\n",
            "FLAG_DOCUMENT_6 : 2\n",
            "    COUNT  RATIO  TARGET_MEAN\n",
            "0  280433  0.912        0.083\n",
            "1   27078  0.088        0.056\n",
            "\n",
            "\n",
            "FLAG_DOCUMENT_7 : 2\n",
            "    COUNT  RATIO  TARGET_MEAN\n",
            "0  307452  1.000        0.081\n",
            "1      59  0.000        0.051\n",
            "\n",
            "\n",
            "FLAG_DOCUMENT_8 : 2\n",
            "    COUNT  RATIO  TARGET_MEAN\n",
            "0  282487  0.919        0.081\n",
            "1   25024  0.081        0.073\n",
            "\n",
            "\n",
            "FLAG_DOCUMENT_9 : 2\n",
            "    COUNT  RATIO  TARGET_MEAN\n",
            "0  306313  0.996        0.081\n",
            "1    1198  0.004        0.062\n",
            "\n",
            "\n",
            "FLAG_DOCUMENT_10 : 2\n",
            "    COUNT  RATIO  TARGET_MEAN\n",
            "0  307504  1.000        0.081\n",
            "1       7  0.000        0.000\n",
            "\n",
            "\n",
            "FLAG_DOCUMENT_11 : 2\n",
            "    COUNT  RATIO  TARGET_MEAN\n",
            "0  306308  0.996        0.081\n",
            "1    1203  0.004        0.062\n",
            "\n",
            "\n",
            "FLAG_DOCUMENT_12 : 2\n",
            "    COUNT  RATIO  TARGET_MEAN\n",
            "0  307509  1.000        0.081\n",
            "1       2  0.000        0.000\n",
            "\n",
            "\n",
            "FLAG_DOCUMENT_13 : 2\n",
            "    COUNT  RATIO  TARGET_MEAN\n",
            "0  306427  0.996        0.081\n",
            "1    1084  0.004        0.028\n",
            "\n",
            "\n",
            "FLAG_DOCUMENT_14 : 2\n",
            "    COUNT  RATIO  TARGET_MEAN\n",
            "0  306608  0.997        0.081\n",
            "1     903  0.003        0.033\n",
            "\n",
            "\n",
            "FLAG_DOCUMENT_15 : 2\n",
            "    COUNT  RATIO  TARGET_MEAN\n",
            "0  307139  0.999        0.081\n",
            "1     372  0.001        0.030\n",
            "\n",
            "\n",
            "FLAG_DOCUMENT_16 : 2\n",
            "    COUNT  RATIO  TARGET_MEAN\n",
            "0  304458  0.990        0.081\n",
            "1    3053  0.010        0.049\n",
            "\n",
            "\n",
            "FLAG_DOCUMENT_17 : 2\n",
            "    COUNT  RATIO  TARGET_MEAN\n",
            "0  307429  1.000        0.081\n",
            "1      82  0.000        0.024\n",
            "\n",
            "\n",
            "FLAG_DOCUMENT_18 : 2\n",
            "    COUNT  RATIO  TARGET_MEAN\n",
            "0  305011  0.992        0.081\n",
            "1    2500  0.008        0.057\n",
            "\n",
            "\n",
            "FLAG_DOCUMENT_19 : 2\n",
            "    COUNT  RATIO  TARGET_MEAN\n",
            "0  307328  0.999        0.081\n",
            "1     183  0.001        0.066\n",
            "\n",
            "\n",
            "FLAG_DOCUMENT_20 : 2\n",
            "    COUNT  RATIO  TARGET_MEAN\n",
            "0  307355  0.999        0.081\n",
            "1     156  0.001        0.083\n",
            "\n",
            "\n",
            "FLAG_DOCUMENT_21 : 2\n",
            "    COUNT  RATIO  TARGET_MEAN\n",
            "0  307408  1.000        0.081\n",
            "1     103  0.000        0.136\n",
            "\n",
            "\n",
            "AMT_REQ_CREDIT_BUREAU_HOUR : 5\n",
            "        COUNT  RATIO  TARGET_MEAN\n",
            "0.000  264366  0.860        0.077\n",
            "1.000    1560  0.005        0.080\n",
            "2.000      56  0.000        0.107\n",
            "3.000       9  0.000        0.000\n",
            "4.000       1  0.000        0.000\n",
            "\n",
            "\n",
            "AMT_REQ_CREDIT_BUREAU_DAY : 9\n",
            "        COUNT  RATIO  TARGET_MEAN\n",
            "0.000  264503  0.860        0.077\n",
            "1.000    1292  0.004        0.097\n",
            "2.000     106  0.000        0.104\n",
            "3.000      45  0.000        0.044\n",
            "4.000      26  0.000        0.115\n",
            "5.000       9  0.000        0.000\n",
            "6.000       8  0.000        0.000\n",
            "8.000       1  0.000        0.000\n",
            "9.000       2  0.000        0.000\n",
            "\n",
            "\n",
            "AMT_REQ_CREDIT_BUREAU_WEEK : 9\n",
            "        COUNT  RATIO  TARGET_MEAN\n",
            "0.000  257456  0.837        0.077\n",
            "1.000    8208  0.027        0.078\n",
            "2.000     199  0.001        0.101\n",
            "3.000      58  0.000        0.069\n",
            "4.000      34  0.000        0.118\n",
            "5.000      10  0.000        0.100\n",
            "6.000      20  0.000        0.050\n",
            "7.000       2  0.000        0.000\n",
            "8.000       5  0.000        0.000\n",
            "\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 3. Rare encoder'ın yazılması.\n",
        "\n",
        "def rare_encoder(dataframe, rare_perc):\n",
        "    temp_df = dataframe.copy()\n",
        "\n",
        "    rare_columns = [col for col in temp_df.columns if temp_df[col].dtypes == 'O'\n",
        "                    and (temp_df[col].value_counts() / len(temp_df) < rare_perc).any(axis=None)]\n",
        "\n",
        "    for var in rare_columns:\n",
        "        tmp = temp_df[var].value_counts() / len(temp_df)\n",
        "        rare_labels = tmp[tmp < rare_perc].index\n",
        "        temp_df[var] = np.where(temp_df[var].isin(rare_labels), 'Rare', temp_df[var])\n",
        "\n",
        "    return temp_df\n",
        "\n",
        "new_df = rare_encoder(df, 0.01)"
      ],
      "metadata": {
        "id": "U4PdSyRKbpd3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rare_analyser(new_df, \"TARGET\", cat_cols)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uqKwU1b0bvYJ",
        "outputId": "52a23a79-b73f-4e09-b2b2-e82d105c4093"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "NAME_CONTRACT_TYPE : 2\n",
            "                  COUNT  RATIO  TARGET_MEAN\n",
            "Cash loans       278232  0.905        0.083\n",
            "Revolving loans   29279  0.095        0.055\n",
            "\n",
            "\n",
            "CODE_GENDER : 3\n",
            "       COUNT  RATIO  TARGET_MEAN\n",
            "F     202448  0.658        0.070\n",
            "M     105059  0.342        0.101\n",
            "Rare       4  0.000        0.000\n",
            "\n",
            "\n",
            "FLAG_OWN_CAR : 2\n",
            "    COUNT  RATIO  TARGET_MEAN\n",
            "N  202924  0.660        0.085\n",
            "Y  104587  0.340        0.072\n",
            "\n",
            "\n",
            "FLAG_OWN_REALTY : 2\n",
            "    COUNT  RATIO  TARGET_MEAN\n",
            "N   94199  0.306        0.083\n",
            "Y  213312  0.694        0.080\n",
            "\n",
            "\n",
            "NAME_TYPE_SUITE : 5\n",
            "                  COUNT  RATIO  TARGET_MEAN\n",
            "Children           3267  0.011        0.074\n",
            "Family            40149  0.131        0.075\n",
            "Rare               2907  0.009        0.094\n",
            "Spouse, partner   11370  0.037        0.079\n",
            "Unaccompanied    248526  0.808        0.082\n",
            "\n",
            "\n",
            "NAME_INCOME_TYPE : 5\n",
            "                       COUNT  RATIO  TARGET_MEAN\n",
            "Commercial associate   71617  0.233        0.075\n",
            "Pensioner              55362  0.180        0.054\n",
            "Rare                      55  0.000        0.182\n",
            "State servant          21703  0.071        0.058\n",
            "Working               158774  0.516        0.096\n",
            "\n",
            "\n",
            "NAME_EDUCATION_TYPE : 5\n",
            "                                COUNT  RATIO  TARGET_MEAN\n",
            "Higher education                74863  0.243        0.054\n",
            "Incomplete higher               10277  0.033        0.085\n",
            "Lower secondary                  3816  0.012        0.109\n",
            "Rare                              164  0.001        0.018\n",
            "Secondary / secondary special  218391  0.710        0.089\n",
            "\n",
            "\n",
            "NAME_FAMILY_STATUS : 6\n",
            "                       COUNT  RATIO  TARGET_MEAN\n",
            "Civil marriage         29775  0.097        0.099\n",
            "Married               196432  0.639        0.076\n",
            "Rare                       2  0.000        0.000\n",
            "Separated              19770  0.064        0.082\n",
            "Single / not married   45444  0.148        0.098\n",
            "Widow                  16088  0.052        0.058\n",
            "\n",
            "\n",
            "NAME_HOUSING_TYPE : 5\n",
            "                      COUNT  RATIO  TARGET_MEAN\n",
            "House / apartment    272868  0.887        0.078\n",
            "Municipal apartment   11183  0.036        0.085\n",
            "Rare                   3739  0.012        0.070\n",
            "Rented apartment       4881  0.016        0.123\n",
            "With parents          14840  0.048        0.117\n",
            "\n",
            "\n",
            "OCCUPATION_TYPE : 12\n",
            "                       COUNT  RATIO  TARGET_MEAN\n",
            "Accountants             9813  0.032        0.048\n",
            "Cleaning staff          4653  0.015        0.096\n",
            "Cooking staff           5946  0.019        0.104\n",
            "Core staff             27570  0.090        0.063\n",
            "Drivers                18603  0.060        0.113\n",
            "High skill tech staff  11380  0.037        0.062\n",
            "Laborers               55186  0.179        0.106\n",
            "Managers               21371  0.069        0.062\n",
            "Medicine staff          8537  0.028        0.067\n",
            "Rare                    9238  0.030        0.098\n",
            "Sales staff            32102  0.104        0.096\n",
            "Security staff          6721  0.022        0.107\n",
            "\n",
            "\n",
            "WEEKDAY_APPR_PROCESS_START : 7\n",
            "           COUNT  RATIO  TARGET_MEAN\n",
            "FRIDAY     50338  0.164        0.081\n",
            "MONDAY     50714  0.165        0.078\n",
            "SATURDAY   33852  0.110        0.079\n",
            "SUNDAY     16181  0.053        0.079\n",
            "THURSDAY   50591  0.165        0.081\n",
            "TUESDAY    53901  0.175        0.084\n",
            "WEDNESDAY  51934  0.169        0.082\n",
            "\n",
            "\n",
            "FONDKAPREMONT_MODE : 4\n",
            "                       COUNT  RATIO  TARGET_MEAN\n",
            "not specified           5687  0.018        0.075\n",
            "org spec account        5619  0.018        0.058\n",
            "reg oper account       73830  0.240        0.070\n",
            "reg oper spec account  12080  0.039        0.066\n",
            "\n",
            "\n",
            "HOUSETYPE_MODE : 2\n",
            "                 COUNT  RATIO  TARGET_MEAN\n",
            "Rare              2711  0.009        0.094\n",
            "block of flats  150503  0.489        0.069\n",
            "\n",
            "\n",
            "WALLSMATERIAL_MODE : 5\n",
            "              COUNT  RATIO  TARGET_MEAN\n",
            "Block          9253  0.030        0.070\n",
            "Panel         66040  0.215        0.063\n",
            "Rare           5700  0.019        0.069\n",
            "Stone, brick  64815  0.211        0.074\n",
            "Wooden         5362  0.017        0.097\n",
            "\n",
            "\n",
            "EMERGENCYSTATE_MODE : 2\n",
            "       COUNT  RATIO  TARGET_MEAN\n",
            "No    159428  0.518        0.070\n",
            "Rare    2328  0.008        0.096\n",
            "\n",
            "\n",
            "TARGET : 2\n",
            "    COUNT  RATIO  TARGET_MEAN\n",
            "0  282686  0.919        0.000\n",
            "1   24825  0.081        1.000\n",
            "\n",
            "\n",
            "FLAG_MOBIL : 2\n",
            "    COUNT  RATIO  TARGET_MEAN\n",
            "0       1  0.000        0.000\n",
            "1  307510  1.000        0.081\n",
            "\n",
            "\n",
            "FLAG_EMP_PHONE : 2\n",
            "    COUNT  RATIO  TARGET_MEAN\n",
            "0   55386  0.180        0.054\n",
            "1  252125  0.820        0.087\n",
            "\n",
            "\n",
            "FLAG_WORK_PHONE : 2\n",
            "    COUNT  RATIO  TARGET_MEAN\n",
            "0  246203  0.801        0.077\n",
            "1   61308  0.199        0.096\n",
            "\n",
            "\n",
            "FLAG_CONT_MOBILE : 2\n",
            "    COUNT  RATIO  TARGET_MEAN\n",
            "0     574  0.002        0.078\n",
            "1  306937  0.998        0.081\n",
            "\n",
            "\n",
            "FLAG_PHONE : 2\n",
            "    COUNT  RATIO  TARGET_MEAN\n",
            "0  221080  0.719        0.085\n",
            "1   86431  0.281        0.070\n",
            "\n",
            "\n",
            "FLAG_EMAIL : 2\n",
            "    COUNT  RATIO  TARGET_MEAN\n",
            "0  290069  0.943        0.081\n",
            "1   17442  0.057        0.079\n",
            "\n",
            "\n",
            "REGION_RATING_CLIENT : 3\n",
            "    COUNT  RATIO  TARGET_MEAN\n",
            "1   32197  0.105        0.048\n",
            "2  226984  0.738        0.079\n",
            "3   48330  0.157        0.111\n",
            "\n",
            "\n",
            "REGION_RATING_CLIENT_W_CITY : 3\n",
            "    COUNT  RATIO  TARGET_MEAN\n",
            "1   34167  0.111        0.048\n",
            "2  229484  0.746        0.079\n",
            "3   43860  0.143        0.114\n",
            "\n",
            "\n",
            "REG_REGION_NOT_LIVE_REGION : 2\n",
            "    COUNT  RATIO  TARGET_MEAN\n",
            "0  302854  0.985        0.081\n",
            "1    4657  0.015        0.093\n",
            "\n",
            "\n",
            "REG_REGION_NOT_WORK_REGION : 2\n",
            "    COUNT  RATIO  TARGET_MEAN\n",
            "0  291899  0.949        0.080\n",
            "1   15612  0.051        0.089\n",
            "\n",
            "\n",
            "LIVE_REGION_NOT_WORK_REGION : 2\n",
            "    COUNT  RATIO  TARGET_MEAN\n",
            "0  295008  0.959        0.081\n",
            "1   12503  0.041        0.084\n",
            "\n",
            "\n",
            "REG_CITY_NOT_LIVE_CITY : 2\n",
            "    COUNT  RATIO  TARGET_MEAN\n",
            "0  283472  0.922        0.077\n",
            "1   24039  0.078        0.122\n",
            "\n",
            "\n",
            "REG_CITY_NOT_WORK_CITY : 2\n",
            "    COUNT  RATIO  TARGET_MEAN\n",
            "0  236644  0.770        0.073\n",
            "1   70867  0.230        0.106\n",
            "\n",
            "\n",
            "LIVE_CITY_NOT_WORK_CITY : 2\n",
            "    COUNT  RATIO  TARGET_MEAN\n",
            "0  252296  0.820        0.077\n",
            "1   55215  0.180        0.100\n",
            "\n",
            "\n",
            "DEF_60_CNT_SOCIAL_CIRCLE : 9\n",
            "         COUNT  RATIO  TARGET_MEAN\n",
            "0.000   280721  0.913        0.078\n",
            "1.000    21841  0.071        0.105\n",
            "2.000     3170  0.010        0.121\n",
            "3.000      598  0.002        0.159\n",
            "4.000      135  0.000        0.111\n",
            "5.000       20  0.000        0.150\n",
            "6.000        3  0.000        0.000\n",
            "7.000        1  0.000        0.000\n",
            "24.000       1  0.000        0.000\n",
            "\n",
            "\n",
            "FLAG_DOCUMENT_2 : 2\n",
            "    COUNT  RATIO  TARGET_MEAN\n",
            "0  307498  1.000        0.081\n",
            "1      13  0.000        0.308\n",
            "\n",
            "\n",
            "FLAG_DOCUMENT_3 : 2\n",
            "    COUNT  RATIO  TARGET_MEAN\n",
            "0   89171  0.290        0.062\n",
            "1  218340  0.710        0.088\n",
            "\n",
            "\n",
            "FLAG_DOCUMENT_4 : 2\n",
            "    COUNT  RATIO  TARGET_MEAN\n",
            "0  307486  1.000        0.081\n",
            "1      25  0.000        0.000\n",
            "\n",
            "\n",
            "FLAG_DOCUMENT_5 : 2\n",
            "    COUNT  RATIO  TARGET_MEAN\n",
            "0  302863  0.985        0.081\n",
            "1    4648  0.015        0.080\n",
            "\n",
            "\n",
            "FLAG_DOCUMENT_6 : 2\n",
            "    COUNT  RATIO  TARGET_MEAN\n",
            "0  280433  0.912        0.083\n",
            "1   27078  0.088        0.056\n",
            "\n",
            "\n",
            "FLAG_DOCUMENT_7 : 2\n",
            "    COUNT  RATIO  TARGET_MEAN\n",
            "0  307452  1.000        0.081\n",
            "1      59  0.000        0.051\n",
            "\n",
            "\n",
            "FLAG_DOCUMENT_8 : 2\n",
            "    COUNT  RATIO  TARGET_MEAN\n",
            "0  282487  0.919        0.081\n",
            "1   25024  0.081        0.073\n",
            "\n",
            "\n",
            "FLAG_DOCUMENT_9 : 2\n",
            "    COUNT  RATIO  TARGET_MEAN\n",
            "0  306313  0.996        0.081\n",
            "1    1198  0.004        0.062\n",
            "\n",
            "\n",
            "FLAG_DOCUMENT_10 : 2\n",
            "    COUNT  RATIO  TARGET_MEAN\n",
            "0  307504  1.000        0.081\n",
            "1       7  0.000        0.000\n",
            "\n",
            "\n",
            "FLAG_DOCUMENT_11 : 2\n",
            "    COUNT  RATIO  TARGET_MEAN\n",
            "0  306308  0.996        0.081\n",
            "1    1203  0.004        0.062\n",
            "\n",
            "\n",
            "FLAG_DOCUMENT_12 : 2\n",
            "    COUNT  RATIO  TARGET_MEAN\n",
            "0  307509  1.000        0.081\n",
            "1       2  0.000        0.000\n",
            "\n",
            "\n",
            "FLAG_DOCUMENT_13 : 2\n",
            "    COUNT  RATIO  TARGET_MEAN\n",
            "0  306427  0.996        0.081\n",
            "1    1084  0.004        0.028\n",
            "\n",
            "\n",
            "FLAG_DOCUMENT_14 : 2\n",
            "    COUNT  RATIO  TARGET_MEAN\n",
            "0  306608  0.997        0.081\n",
            "1     903  0.003        0.033\n",
            "\n",
            "\n",
            "FLAG_DOCUMENT_15 : 2\n",
            "    COUNT  RATIO  TARGET_MEAN\n",
            "0  307139  0.999        0.081\n",
            "1     372  0.001        0.030\n",
            "\n",
            "\n",
            "FLAG_DOCUMENT_16 : 2\n",
            "    COUNT  RATIO  TARGET_MEAN\n",
            "0  304458  0.990        0.081\n",
            "1    3053  0.010        0.049\n",
            "\n",
            "\n",
            "FLAG_DOCUMENT_17 : 2\n",
            "    COUNT  RATIO  TARGET_MEAN\n",
            "0  307429  1.000        0.081\n",
            "1      82  0.000        0.024\n",
            "\n",
            "\n",
            "FLAG_DOCUMENT_18 : 2\n",
            "    COUNT  RATIO  TARGET_MEAN\n",
            "0  305011  0.992        0.081\n",
            "1    2500  0.008        0.057\n",
            "\n",
            "\n",
            "FLAG_DOCUMENT_19 : 2\n",
            "    COUNT  RATIO  TARGET_MEAN\n",
            "0  307328  0.999        0.081\n",
            "1     183  0.001        0.066\n",
            "\n",
            "\n",
            "FLAG_DOCUMENT_20 : 2\n",
            "    COUNT  RATIO  TARGET_MEAN\n",
            "0  307355  0.999        0.081\n",
            "1     156  0.001        0.083\n",
            "\n",
            "\n",
            "FLAG_DOCUMENT_21 : 2\n",
            "    COUNT  RATIO  TARGET_MEAN\n",
            "0  307408  1.000        0.081\n",
            "1     103  0.000        0.136\n",
            "\n",
            "\n",
            "AMT_REQ_CREDIT_BUREAU_HOUR : 5\n",
            "        COUNT  RATIO  TARGET_MEAN\n",
            "0.000  264366  0.860        0.077\n",
            "1.000    1560  0.005        0.080\n",
            "2.000      56  0.000        0.107\n",
            "3.000       9  0.000        0.000\n",
            "4.000       1  0.000        0.000\n",
            "\n",
            "\n",
            "AMT_REQ_CREDIT_BUREAU_DAY : 9\n",
            "        COUNT  RATIO  TARGET_MEAN\n",
            "0.000  264503  0.860        0.077\n",
            "1.000    1292  0.004        0.097\n",
            "2.000     106  0.000        0.104\n",
            "3.000      45  0.000        0.044\n",
            "4.000      26  0.000        0.115\n",
            "5.000       9  0.000        0.000\n",
            "6.000       8  0.000        0.000\n",
            "8.000       1  0.000        0.000\n",
            "9.000       2  0.000        0.000\n",
            "\n",
            "\n",
            "AMT_REQ_CREDIT_BUREAU_WEEK : 9\n",
            "        COUNT  RATIO  TARGET_MEAN\n",
            "0.000  257456  0.837        0.077\n",
            "1.000    8208  0.027        0.078\n",
            "2.000     199  0.001        0.101\n",
            "3.000      58  0.000        0.069\n",
            "4.000      34  0.000        0.118\n",
            "5.000      10  0.000        0.100\n",
            "6.000      20  0.000        0.050\n",
            "7.000       2  0.000        0.000\n",
            "8.000       5  0.000        0.000\n",
            "\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"OCCUPATION_TYPE\"].value_counts()\n",
        "\n",
        "# bu değişkenin 12 tane sınıfı var bir çoğunu bir araya getirmiş üstte."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-2Ete-Nrbwhq",
        "outputId": "32c2047c-3893-4bf1-bb34-059c4445dc69"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Laborers                 55186\n",
              "Sales staff              32102\n",
              "Core staff               27570\n",
              "Managers                 21371\n",
              "Drivers                  18603\n",
              "High skill tech staff    11380\n",
              "Accountants               9813\n",
              "Medicine staff            8537\n",
              "Security staff            6721\n",
              "Cooking staff             5946\n",
              "Cleaning staff            4653\n",
              "Private service staff     2652\n",
              "Low-skill Laborers        2093\n",
              "Waiters/barmen staff      1348\n",
              "Secretaries               1305\n",
              "Realty agents              751\n",
              "HR staff                   563\n",
              "IT staff                   526\n",
              "Name: OCCUPATION_TYPE, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Feature Scaling (Özellik Ölçeklendirme)\n",
        "\n",
        "Değişkenler arası ölçüm farklarını gidermek için kullanılır. \n",
        "\n",
        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)\n",
        "\n"
      ],
      "metadata": {
        "id": "qOzZTHzGbzE9"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Ağaç yöntemlerinde yine uygulayamayabilirsin. Gradient Descent (GD) kullanılan algoritmaların train süresini kısaltmak durumudur. Ölçekler farklı olduğunda ölçek farklılığından kaynaklanan errorlerin boyutları büyük olacağından dolayı daha uzun sürede minimuma ulaşacaktır.\n",
        "\n",
        "\n",
        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)\n",
        "\n",
        "İki Önemli Kullanım \n",
        "\n",
        "1. Kullanılacak olan yöntemlere değişkenleri gönderirken onlara eşit muamele yapılması gerektiğini bildirmemiz gerekiyor. Bundan dolayı bir standartlaştırma işlemi yapılır. \n",
        "\n",
        "2. Gradient Descent (GD) kullanılan algoritmaların train süresini kısaltmak durumudur. \n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "DzEn8-rvSU7X"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Uzaklık temelli yöntemlerde büyük değerlere sahip değişkenler dominantlık sergilemektedir. KNN, KMeans, PCA gibi uzaklık temelli ve benzelik benzemezlik temelli bazı yöntemler kullanıldığı zaman ölçeklerin birbirinden farklı olması durumu yapılacak olan uzaklık benzerlik benzemezlik hesaplarında yanlılığa sebep olmaktadır. Dolayısıyla yapacağın işlem değişkenleri standartlaştırma işlemidir. \n",
        "\n",
        "**Özetle;**\n",
        "\n",
        "1. Eşit şartlarda yaklaşıp,\n",
        "2. Eğitim süresini kısaltıp,\n",
        "3. Uzaklık temelli yaklaşımlarda yanlılığın önüne geçmektir. \n",
        "\n",
        "Ağaca dayalı yöntemlerin bir çoğu, eksik değerden, aykırı değerlerden, standartlaştırmadan etlilenmez. "
      ],
      "metadata": {
        "id": "hYh2bpePVEFc"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### StandardScaler: Klasik standartlaştırma. Normalleştirme.\n",
        "\n",
        "**Ortalama 0 standart sapma 1 olacak.**\n",
        "\n",
        "Bütün değerlerden Ortalamayı çıkar, standart sapmaya böl. z = (x - u) / s\n"
      ],
      "metadata": {
        "id": "tW_Jo61_R8qB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('/content/gdrive/My Drive/titanic.csv')\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 548
        },
        "id": "rpuBgzs_bxp5",
        "outputId": "6b570dc2-28c1-4d9b-fc55-6cedfe9922a9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   PassengerId  Survived  Pclass                                               Name     Sex    Age  SibSp  Parch            Ticket   Fare Cabin Embarked\n",
              "0            1         0       3                            Braund, Mr. Owen Harris    male 22.000      1      0         A/5 21171  7.250   NaN        S\n",
              "1            2         1       1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female 38.000      1      0          PC 17599 71.283   C85        C\n",
              "2            3         1       3                             Heikkinen, Miss. Laina  female 26.000      0      0  STON/O2. 3101282  7.925   NaN        S\n",
              "3            4         1       1       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female 35.000      1      0            113803 53.100  C123        S\n",
              "4            5         0       3                           Allen, Mr. William Henry    male 35.000      0      0            373450  8.050   NaN        S"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-db8ba244-bfd7-4024-b4eb-529ee360cfad\">\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>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
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              "      <td>38.000</td>\n",
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              "      <td>71.283</td>\n",
              "      <td>C85</td>\n",
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              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Heikkinen, Miss. Laina</td>\n",
              "      <td>female</td>\n",
              "      <td>26.000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>STON/O2. 3101282</td>\n",
              "      <td>7.925</td>\n",
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              "      <td>S</td>\n",
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              "      <th>3</th>\n",
              "      <td>4</td>\n",
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              "      <td>1</td>\n",
              "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
              "      <td>female</td>\n",
              "      <td>35.000</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>113803</td>\n",
              "      <td>53.100</td>\n",
              "      <td>C123</td>\n",
              "      <td>S</td>\n",
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              "      <th>4</th>\n",
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              "      <td>Allen, Mr. William Henry</td>\n",
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              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-db8ba244-bfd7-4024-b4eb-529ee360cfad');\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": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "ss = StandardScaler()\n",
        "df[\"Age_standard_scaler\"] = ss.fit_transform(df[[\"Age\"]])\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 548
        },
        "id": "eA7jEsz4XZZQ",
        "outputId": "e43653e3-76ba-4ff1-c4e1-08572a987e8e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   PassengerId  Survived  Pclass                                               Name     Sex    Age  SibSp  Parch            Ticket   Fare Cabin Embarked  Age_standard_scaler\n",
              "0            1         0       3                            Braund, Mr. Owen Harris    male 22.000      1      0         A/5 21171  7.250   NaN        S               -0.530\n",
              "1            2         1       1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female 38.000      1      0          PC 17599 71.283   C85        C                0.572\n",
              "2            3         1       3                             Heikkinen, Miss. Laina  female 26.000      0      0  STON/O2. 3101282  7.925   NaN        S               -0.255\n",
              "3            4         1       1       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female 35.000      1      0            113803 53.100  C123        S                0.365\n",
              "4            5         0       3                           Allen, Mr. William Henry    male 35.000      0      0            373450  8.050   NaN        S                0.365"
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              "            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",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### RobustScaler: Bütün değerlerden Medyanı çıkar iqr'a böl.\n",
        "\n",
        "Ortalama ve ss diğer metriklerden etkilenen bir değerdir. Standart Scaler yerine metriklerden etkilenmeyen aykırı değerlere daha dayanıklı bir yöntem olarak bu kullanılabilir."
      ],
      "metadata": {
        "id": "rZ5toD0BcE2Y"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "rs = RobustScaler()\n",
        "df[\"Age_robuts_scaler\"] = rs.fit_transform(df[[\"Age\"]])\n",
        "df.describe().T"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 332
        },
        "id": "Q6ny_5fPXY8E",
        "outputId": "2d5f62f5-fb3e-416d-b816-b848dbc1c2db"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                      count    mean     std    min     25%     50%     75%     max\n",
              "PassengerId         891.000 446.000 257.354  1.000 223.500 446.000 668.500 891.000\n",
              "Survived            891.000   0.384   0.487  0.000   0.000   0.000   1.000   1.000\n",
              "Pclass              891.000   2.309   0.836  1.000   2.000   3.000   3.000   3.000\n",
              "Age                 714.000  29.699  14.526  0.420  20.125  28.000  38.000  80.000\n",
              "SibSp               891.000   0.523   1.103  0.000   0.000   0.000   1.000   8.000\n",
              "Parch               891.000   0.382   0.806  0.000   0.000   0.000   0.000   6.000\n",
              "Fare                891.000  32.204  49.693  0.000   7.910  14.454  31.000 512.329\n",
              "Age_standard_scaler 714.000   0.000   1.001 -2.017  -0.660  -0.117   0.572   3.465\n",
              "Age_robuts_scaler   714.000   0.095   0.813 -1.543  -0.441   0.000   0.559   2.909"
            ],
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              "\n",
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              "    <tr>\n",
              "      <th>PassengerId</th>\n",
              "      <td>891.000</td>\n",
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              "      <td>257.354</td>\n",
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              "      <th>Survived</th>\n",
              "      <td>891.000</td>\n",
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              "      <td>1.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Pclass</th>\n",
              "      <td>891.000</td>\n",
              "      <td>2.309</td>\n",
              "      <td>0.836</td>\n",
              "      <td>1.000</td>\n",
              "      <td>2.000</td>\n",
              "      <td>3.000</td>\n",
              "      <td>3.000</td>\n",
              "      <td>3.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Age</th>\n",
              "      <td>714.000</td>\n",
              "      <td>29.699</td>\n",
              "      <td>14.526</td>\n",
              "      <td>0.420</td>\n",
              "      <td>20.125</td>\n",
              "      <td>28.000</td>\n",
              "      <td>38.000</td>\n",
              "      <td>80.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SibSp</th>\n",
              "      <td>891.000</td>\n",
              "      <td>0.523</td>\n",
              "      <td>1.103</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>8.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Parch</th>\n",
              "      <td>891.000</td>\n",
              "      <td>0.382</td>\n",
              "      <td>0.806</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>6.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fare</th>\n",
              "      <td>891.000</td>\n",
              "      <td>32.204</td>\n",
              "      <td>49.693</td>\n",
              "      <td>0.000</td>\n",
              "      <td>7.910</td>\n",
              "      <td>14.454</td>\n",
              "      <td>31.000</td>\n",
              "      <td>512.329</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Age_standard_scaler</th>\n",
              "      <td>714.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1.001</td>\n",
              "      <td>-2.017</td>\n",
              "      <td>-0.660</td>\n",
              "      <td>-0.117</td>\n",
              "      <td>0.572</td>\n",
              "      <td>3.465</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Age_robuts_scaler</th>\n",
              "      <td>714.000</td>\n",
              "      <td>0.095</td>\n",
              "      <td>0.813</td>\n",
              "      <td>-1.543</td>\n",
              "      <td>-0.441</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.559</td>\n",
              "      <td>2.909</td>\n",
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              "                                                     [key], {});\n",
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              "\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "ortalaması ve standart sapması standart scalera göre farklıdır bunun sebebi aykırı değerlerden etkilenmiyor oluşudur."
      ],
      "metadata": {
        "id": "m_rO-MKAayjW"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### MinMaxScaler: Verilen 2 değer arasında değişken dönüşümüdür. Oldukça yaygın olarak kullanılır. \n",
        "\n",
        "**min değer 0 max değer 1 olması beklenir ve bu aralıkta dağılması beklenir.**"
      ],
      "metadata": {
        "id": "JXUxVXAxcHtV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# X_std = (X - X.min(axis=0)) / (X.max(axis=0) - X.min(axis=0))\n",
        "# X_scaled = X_std * (max - min) + min\n",
        "\n",
        "mms = MinMaxScaler()\n",
        "df[\"Age_min_max_scaler\"] = mms.fit_transform(df[[\"Age\"]])\n",
        "df.describe().T"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "f5yvg8IMcIjh",
        "outputId": "61914f88-e6fc-4412-ae49-4a0755935d9e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                      count    mean     std    min     25%     50%     75%     max\n",
              "PassengerId         891.000 446.000 257.354  1.000 223.500 446.000 668.500 891.000\n",
              "Survived            891.000   0.384   0.487  0.000   0.000   0.000   1.000   1.000\n",
              "Pclass              891.000   2.309   0.836  1.000   2.000   3.000   3.000   3.000\n",
              "Age                 714.000  29.699  14.526  0.420  20.125  28.000  38.000  80.000\n",
              "SibSp               891.000   0.523   1.103  0.000   0.000   0.000   1.000   8.000\n",
              "Parch               891.000   0.382   0.806  0.000   0.000   0.000   0.000   6.000\n",
              "Fare                891.000  32.204  49.693  0.000   7.910  14.454  31.000 512.329\n",
              "Age_standard_scaler 714.000   0.000   1.001 -2.017  -0.660  -0.117   0.572   3.465\n",
              "Age_robuts_scaler   714.000   0.095   0.813 -1.543  -0.441   0.000   0.559   2.909\n",
              "Age_min_max_scaler  714.000   0.368   0.183  0.000   0.248   0.347   0.472   1.000"
            ],
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              "      <td>891.000</td>\n",
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              "    <tr>\n",
              "      <th>Survived</th>\n",
              "      <td>891.000</td>\n",
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              "      <td>1.000</td>\n",
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              "    <tr>\n",
              "      <th>Pclass</th>\n",
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              "      <td>3.000</td>\n",
              "      <td>3.000</td>\n",
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              "    <tr>\n",
              "      <th>Age</th>\n",
              "      <td>714.000</td>\n",
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              "      <td>38.000</td>\n",
              "      <td>80.000</td>\n",
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              "    <tr>\n",
              "      <th>SibSp</th>\n",
              "      <td>891.000</td>\n",
              "      <td>0.523</td>\n",
              "      <td>1.103</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>8.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Parch</th>\n",
              "      <td>891.000</td>\n",
              "      <td>0.382</td>\n",
              "      <td>0.806</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>6.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fare</th>\n",
              "      <td>891.000</td>\n",
              "      <td>32.204</td>\n",
              "      <td>49.693</td>\n",
              "      <td>0.000</td>\n",
              "      <td>7.910</td>\n",
              "      <td>14.454</td>\n",
              "      <td>31.000</td>\n",
              "      <td>512.329</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Age_standard_scaler</th>\n",
              "      <td>714.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1.001</td>\n",
              "      <td>-2.017</td>\n",
              "      <td>-0.660</td>\n",
              "      <td>-0.117</td>\n",
              "      <td>0.572</td>\n",
              "      <td>3.465</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Age_robuts_scaler</th>\n",
              "      <td>714.000</td>\n",
              "      <td>0.095</td>\n",
              "      <td>0.813</td>\n",
              "      <td>-1.543</td>\n",
              "      <td>-0.441</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.559</td>\n",
              "      <td>2.909</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Age_min_max_scaler</th>\n",
              "      <td>714.000</td>\n",
              "      <td>0.368</td>\n",
              "      <td>0.183</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.248</td>\n",
              "      <td>0.347</td>\n",
              "      <td>0.472</td>\n",
              "      <td>1.000</td>\n",
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              "                                                     [key], {});\n",
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              "\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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          "metadata": {},
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    {
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        "df.head()"
      ],
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          "height": 548
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        "id": "N0Iyu_qRbeYF",
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      },
      "execution_count": null,
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   PassengerId  Survived  Pclass                                               Name     Sex    Age  SibSp  Parch            Ticket   Fare Cabin Embarked  Age_standard_scaler  Age_robuts_scaler  Age_min_max_scaler\n",
              "0            1         0       3                            Braund, Mr. Owen Harris    male 22.000      1      0         A/5 21171  7.250   NaN        S               -0.530             -0.336               0.271\n",
              "1            2         1       1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female 38.000      1      0          PC 17599 71.283   C85        C                0.572              0.559               0.472\n",
              "2            3         1       3                             Heikkinen, Miss. Laina  female 26.000      0      0  STON/O2. 3101282  7.925   NaN        S               -0.255             -0.112               0.321\n",
              "3            4         1       1       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female 35.000      1      0            113803 53.100  C123        S                0.365              0.392               0.435\n",
              "4            5         0       3                           Allen, Mr. William Henry    male 35.000      0      0            373450  8.050   NaN        S                0.365              0.392               0.435"
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\n",
              "      <th>Age_standard_scaler</th>\n",
              "      <th>Age_robuts_scaler</th>\n",
              "      <th>Age_min_max_scaler</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Braund, Mr. Owen Harris</td>\n",
              "      <td>male</td>\n",
              "      <td>22.000</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>A/5 21171</td>\n",
              "      <td>7.250</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>-0.530</td>\n",
              "      <td>-0.336</td>\n",
              "      <td>0.271</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
              "      <td>female</td>\n",
              "      <td>38.000</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>PC 17599</td>\n",
              "      <td>71.283</td>\n",
              "      <td>C85</td>\n",
              "      <td>C</td>\n",
              "      <td>0.572</td>\n",
              "      <td>0.559</td>\n",
              "      <td>0.472</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Heikkinen, Miss. Laina</td>\n",
              "      <td>female</td>\n",
              "      <td>26.000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>STON/O2. 3101282</td>\n",
              "      <td>7.925</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>-0.255</td>\n",
              "      <td>-0.112</td>\n",
              "      <td>0.321</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
              "      <td>female</td>\n",
              "      <td>35.000</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>113803</td>\n",
              "      <td>53.100</td>\n",
              "      <td>C123</td>\n",
              "      <td>S</td>\n",
              "      <td>0.365</td>\n",
              "      <td>0.392</td>\n",
              "      <td>0.435</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Allen, Mr. William Henry</td>\n",
              "      <td>male</td>\n",
              "      <td>35.000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>373450</td>\n",
              "      <td>8.050</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>0.365</td>\n",
              "      <td>0.392</td>\n",
              "      <td>0.435</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ebe08cee-324a-4e58-86be-7f893de4a3f5')\"\n",
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              "          document.querySelector('#df-ebe08cee-324a-4e58-86be-7f893de4a3f5 button.colab-df-convert');\n",
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              "          const element = document.querySelector('#df-ebe08cee-324a-4e58-86be-7f893de4a3f5');\n",
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              "            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",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "age_cols = [col for col in df.columns if \"Age\" in col]"
      ],
      "metadata": {
        "id": "CqzFnVtLX6Kf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#sayısal değişkenin çeyreklik değerlerini gösterip histogramını oluşturmak.\n",
        "def num_summary(dataframe, numerical_col, plot=False):\n",
        "    quantiles = [0.05, 0.10, 0.20, 0.30, 0.40, 0.50, 0.60, 0.70, 0.80, 0.90, 0.95, 0.99]\n",
        "    print(dataframe[numerical_col].describe(quantiles).T)\n",
        "\n",
        "    if plot:\n",
        "        dataframe[numerical_col].hist(bins=20)\n",
        "        plt.xlabel(numerical_col)\n",
        "        plt.title(numerical_col)\n",
        "        plt.show(block=True)\n",
        "\n",
        "for col in age_cols:\n",
        "    num_summary(df, col, plot=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "N-rqUXuQX6zd",
        "outputId": "bcaef9c9-d5b3-4b8b-f170-b9f5c4d9472e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "count   714.000\n",
            "mean     29.699\n",
            "std      14.526\n",
            "min       0.420\n",
            "5%        4.000\n",
            "10%      14.000\n",
            "20%      19.000\n",
            "30%      22.000\n",
            "40%      25.000\n",
            "50%      28.000\n",
            "60%      31.800\n",
            "70%      36.000\n",
            "80%      41.000\n",
            "90%      50.000\n",
            "95%      56.000\n",
            "99%      65.870\n",
            "max      80.000\n",
            "Name: Age, dtype: float64\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "count   714.000\n",
            "mean      0.000\n",
            "std       1.001\n",
            "min      -2.017\n",
            "5%       -1.770\n",
            "10%      -1.081\n",
            "20%      -0.737\n",
            "30%      -0.530\n",
            "40%      -0.324\n",
            "50%      -0.117\n",
            "60%       0.145\n",
            "70%       0.434\n",
            "80%       0.778\n",
            "90%       1.398\n",
            "95%       1.812\n",
            "99%       2.492\n",
            "max       3.465\n",
            "Name: Age_standard_scaler, dtype: float64\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "count   714.000\n",
            "mean      0.095\n",
            "std       0.813\n",
            "min      -1.543\n",
            "5%       -1.343\n",
            "10%      -0.783\n",
            "20%      -0.503\n",
            "30%      -0.336\n",
            "40%      -0.168\n",
            "50%       0.000\n",
            "60%       0.213\n",
            "70%       0.448\n",
            "80%       0.727\n",
            "90%       1.231\n",
            "95%       1.566\n",
            "99%       2.119\n",
            "max       2.909\n",
            "Name: Age_robuts_scaler, dtype: float64\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "count   714.000\n",
            "mean      0.368\n",
            "std       0.183\n",
            "min       0.000\n",
            "5%        0.045\n",
            "10%       0.171\n",
            "20%       0.233\n",
            "30%       0.271\n",
            "40%       0.309\n",
            "50%       0.347\n",
            "60%       0.394\n",
            "70%       0.447\n",
            "80%       0.510\n",
            "90%       0.623\n",
            "95%       0.698\n",
            "99%       0.822\n",
            "max       1.000\n",
            "Name: Age_min_max_scaler, dtype: float64\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Görsellerdeki dağılımı bozmadan ölçekleri farklı hale getirdik."
      ],
      "metadata": {
        "id": "u6fl6uMNttZS"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Numeric to Categorical: Sayısal Değişkenleri Kategorik Değişkenlere Çevirme\n",
        "### Binning"
      ],
      "metadata": {
        "id": "M5T4RiwKcLlj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"Age_qcut\"] = pd.qcut(df['Age'], 5)\n",
        "df.head()\n",
        "\n",
        "#hangi sınıflara dönüştürmek istediğin belli ise label argümanını kullanabilirsin.\n",
        "# qcut bir değişkenin değerlerini küçükten büyüğe sıralar ve çeyrek değerlere göre 5 parçaya böler."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 548
        },
        "id": "b4p8_MuPcMjU",
        "outputId": "1a3bbdf2-0c9d-4501-cc1a-3176bb778f87"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   PassengerId  Survived  Pclass                                               Name     Sex    Age  SibSp  Parch            Ticket   Fare Cabin Embarked  Age_standard_scaler  Age_robuts_scaler  Age_min_max_scaler      Age_qcut\n",
              "0            1         0       3                            Braund, Mr. Owen Harris    male 22.000      1      0         A/5 21171  7.250   NaN        S               -0.530             -0.336               0.271  (19.0, 25.0]\n",
              "1            2         1       1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female 38.000      1      0          PC 17599 71.283   C85        C                0.572              0.559               0.472  (31.8, 41.0]\n",
              "2            3         1       3                             Heikkinen, Miss. Laina  female 26.000      0      0  STON/O2. 3101282  7.925   NaN        S               -0.255             -0.112               0.321  (25.0, 31.8]\n",
              "3            4         1       1       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female 35.000      1      0            113803 53.100  C123        S                0.365              0.392               0.435  (31.8, 41.0]\n",
              "4            5         0       3                           Allen, Mr. William Henry    male 35.000      0      0            373450  8.050   NaN        S                0.365              0.392               0.435  (31.8, 41.0]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-8d1e17ac-1d0a-42e4-947f-3ea68a8fe72f\">\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>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\n",
              "      <th>Age_standard_scaler</th>\n",
              "      <th>Age_robuts_scaler</th>\n",
              "      <th>Age_min_max_scaler</th>\n",
              "      <th>Age_qcut</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Braund, Mr. Owen Harris</td>\n",
              "      <td>male</td>\n",
              "      <td>22.000</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>A/5 21171</td>\n",
              "      <td>7.250</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>-0.530</td>\n",
              "      <td>-0.336</td>\n",
              "      <td>0.271</td>\n",
              "      <td>(19.0, 25.0]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
              "      <td>female</td>\n",
              "      <td>38.000</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>PC 17599</td>\n",
              "      <td>71.283</td>\n",
              "      <td>C85</td>\n",
              "      <td>C</td>\n",
              "      <td>0.572</td>\n",
              "      <td>0.559</td>\n",
              "      <td>0.472</td>\n",
              "      <td>(31.8, 41.0]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Heikkinen, Miss. Laina</td>\n",
              "      <td>female</td>\n",
              "      <td>26.000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>STON/O2. 3101282</td>\n",
              "      <td>7.925</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>-0.255</td>\n",
              "      <td>-0.112</td>\n",
              "      <td>0.321</td>\n",
              "      <td>(25.0, 31.8]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
              "      <td>female</td>\n",
              "      <td>35.000</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>113803</td>\n",
              "      <td>53.100</td>\n",
              "      <td>C123</td>\n",
              "      <td>S</td>\n",
              "      <td>0.365</td>\n",
              "      <td>0.392</td>\n",
              "      <td>0.435</td>\n",
              "      <td>(31.8, 41.0]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Allen, Mr. William Henry</td>\n",
              "      <td>male</td>\n",
              "      <td>35.000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>373450</td>\n",
              "      <td>8.050</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>0.365</td>\n",
              "      <td>0.392</td>\n",
              "      <td>0.435</td>\n",
              "      <td>(31.8, 41.0]</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8d1e17ac-1d0a-42e4-947f-3ea68a8fe72f')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\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",
              "      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",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-8d1e17ac-1d0a-42e4-947f-3ea68a8fe72f 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-8d1e17ac-1d0a-42e4-947f-3ea68a8fe72f');\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": 28
        }
      ]
    }
  ]
}