Encoding_Scaling.ipynb

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      "cell_type": "markdown",
      "source": [
        "# Encoding Scaling\n",
        "\n",
        "* Label Encoder\n",
        "* One Hot Encoding\n",
        "* Rare Encoding"
      ],
      "metadata": {
        "id": "nh4gvIjRDdzV"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Import Necessary Libraries"
      ],
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        "id": "H-2pxEiPElv0"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "GHoWNh4aDX1-"
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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "pd.set_option(\"display.max_columns\", None)\n",
        "pd.set_option(\"display.max_rows\", None)\n",
        "pd.set_option(\"display.width\", 500)\n",
        "pd.set_option(\"display.float_format\", lambda x: \"%.3f\" % x)\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Import Dataset"
      ],
      "metadata": {
        "id": "lD_mx_lPE2Rg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "titanic = sns.load_dataset(\"titanic\")\n",
        "df = titanic.copy()\n",
        "df.head()"
      ],
      "metadata": {
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          "height": 226
        },
        "id": "2kj23d-yE0u7",
        "outputId": "86d3c428-cdd5-4001-8495-78ed2b2a84ba"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   survived  pclass     sex    age  sibsp  parch   fare embarked  class    who  adult_male deck  embark_town alive  alone\n",
              "0         0       3    male 22.000      1      0  7.250        S  Third    man        True  NaN  Southampton    no  False\n",
              "1         1       1  female 38.000      1      0 71.283        C  First  woman       False    C    Cherbourg   yes  False\n",
              "2         1       3  female 26.000      0      0  7.925        S  Third  woman       False  NaN  Southampton   yes   True\n",
              "3         1       1  female 35.000      1      0 53.100        S  First  woman       False    C  Southampton   yes  False\n",
              "4         0       3    male 35.000      0      0  8.050        S  Third    man        True  NaN  Southampton    no   True"
            ],
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              "      <td>1</td>\n",
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              "      <td>female</td>\n",
              "      <td>26.000</td>\n",
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              "      <td>7.925</td>\n",
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              "      <th>4</th>\n",
              "      <td>0</td>\n",
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              "      <td>male</td>\n",
              "      <td>35.000</td>\n",
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              "      <td>0</td>\n",
              "      <td>8.050</td>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 891,\n  \"fields\": [\n    {\n      \"column\": \"survived\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pclass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          3,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"female\",\n          \"male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14.526497332334042,\n        \"min\": 0.42,\n        \"max\": 80.0,\n        \"num_unique_values\": 88,\n        \"samples\": [\n          0.75,\n          22.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sibsp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 8,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"parch\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 6,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 49.6934285971809,\n        \"min\": 0.0,\n        \"max\": 512.3292,\n        \"num_unique_values\": 248,\n        \"samples\": [\n          11.2417,\n          51.8625\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"embarked\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"S\",\n          \"C\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"class\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Third\",\n          \"First\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"who\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"man\",\n          \"woman\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"adult_male\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          false,\n          true\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"deck\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"C\",\n          \"E\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"embark_town\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Southampton\",\n          \"Cherbourg\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"alive\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"yes\",\n          \"no\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"alone\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Label Encoding"
      ],
      "metadata": {
        "id": "DLWnNv86E9iC"
      }
    },
    {
      "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": "jR4NPqzmGBUz"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "new_df = label_encoder(df, \"sex\")"
      ],
      "metadata": {
        "id": "sPPTtAotFv74"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "new_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "KDgvOLVOGbbl",
        "outputId": "06c4844d-51e4-49b3-c214-961cc220a9d6"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   survived  pclass  sex    age  sibsp  parch   fare embarked  class    who  adult_male deck  embark_town alive  alone\n",
              "0         0       3    1 22.000      1      0  7.250        S  Third    man        True  NaN  Southampton    no  False\n",
              "1         1       1    0 38.000      1      0 71.283        C  First  woman       False    C    Cherbourg   yes  False\n",
              "2         1       3    0 26.000      0      0  7.925        S  Third  woman       False  NaN  Southampton   yes   True\n",
              "3         1       1    0 35.000      1      0 53.100        S  First  woman       False    C  Southampton   yes  False\n",
              "4         0       3    1 35.000      0      0  8.050        S  Third    man        True  NaN  Southampton    no   True"
            ],
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "new_df",
              "summary": "{\n  \"name\": \"new_df\",\n  \"rows\": 891,\n  \"fields\": [\n    {\n      \"column\": \"survived\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pclass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          3,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sex\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14.526497332334042,\n        \"min\": 0.42,\n        \"max\": 80.0,\n        \"num_unique_values\": 88,\n        \"samples\": [\n          0.75,\n          22.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sibsp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 8,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"parch\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 6,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 49.6934285971809,\n        \"min\": 0.0,\n        \"max\": 512.3292,\n        \"num_unique_values\": 248,\n        \"samples\": [\n          11.2417,\n          51.8625\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"embarked\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"S\",\n          \"C\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"class\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Third\",\n          \"First\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"who\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"man\",\n          \"woman\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"adult_male\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          false,\n          true\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"deck\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"C\",\n          \"E\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"embark_town\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Southampton\",\n          \"Cherbourg\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"alive\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"yes\",\n          \"no\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"alone\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def label_encoder_dataframe(dataframe):\n",
        "  binary_cols = [col for col in dataframe.columns if dataframe[col].dtype not in [\"int\", \"float\"] and dataframe[col].nunique() == 2]\n",
        "  for col in binary_cols:\n",
        "    label_encoder(dataframe, col)"
      ],
      "metadata": {
        "id": "GA-EuAdwGh1h"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "label_encoder_dataframe(df)"
      ],
      "metadata": {
        "id": "n_h0Xdr8IM_N"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "2iQB4qKsIOrf",
        "outputId": "1b4ba4e0-9a42-46e3-a3f0-2e7ca5d9d993"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   survived  pclass  sex    age  sibsp  parch   fare embarked  class    who  adult_male deck  embark_town  alive  alone\n",
              "0         0       3    1 22.000      1      0  7.250        S  Third    man           1  NaN  Southampton      0      0\n",
              "1         1       1    0 38.000      1      0 71.283        C  First  woman           0    C    Cherbourg      1      0\n",
              "2         1       3    0 26.000      0      0  7.925        S  Third  woman           0  NaN  Southampton      1      1\n",
              "3         1       1    0 35.000      1      0 53.100        S  First  woman           0    C  Southampton      1      0\n",
              "4         0       3    1 35.000      0      0  8.050        S  Third    man           1  NaN  Southampton      0      1"
            ],
            "text/html": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>survived</th>\n",
              "      <th>pclass</th>\n",
              "      <th>sex</th>\n",
              "      <th>age</th>\n",
              "      <th>sibsp</th>\n",
              "      <th>parch</th>\n",
              "      <th>fare</th>\n",
              "      <th>embarked</th>\n",
              "      <th>class</th>\n",
              "      <th>who</th>\n",
              "      <th>adult_male</th>\n",
              "      <th>deck</th>\n",
              "      <th>embark_town</th>\n",
              "      <th>alive</th>\n",
              "      <th>alone</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>22.000</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>7.250</td>\n",
              "      <td>S</td>\n",
              "      <td>Third</td>\n",
              "      <td>man</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Southampton</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>38.000</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>71.283</td>\n",
              "      <td>C</td>\n",
              "      <td>First</td>\n",
              "      <td>woman</td>\n",
              "      <td>0</td>\n",
              "      <td>C</td>\n",
              "      <td>Cherbourg</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>26.000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.925</td>\n",
              "      <td>S</td>\n",
              "      <td>Third</td>\n",
              "      <td>woman</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Southampton</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>35.000</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>53.100</td>\n",
              "      <td>S</td>\n",
              "      <td>First</td>\n",
              "      <td>woman</td>\n",
              "      <td>0</td>\n",
              "      <td>C</td>\n",
              "      <td>Southampton</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>35.000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8.050</td>\n",
              "      <td>S</td>\n",
              "      <td>Third</td>\n",
              "      <td>man</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Southampton</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
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              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-03e41fc5-e664-49e6-9f20-2e3bdd75c752 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-03e41fc5-e664-49e6-9f20-2e3bdd75c752');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-3f13792a-e4f3-49d1-a112-b29adc910a47')\"\n",
              "            title=\"Suggest charts\"\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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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-3f13792a-e4f3-49d1-a112-b29adc910a47 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 891,\n  \"fields\": [\n    {\n      \"column\": \"survived\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pclass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          3,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sex\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14.526497332334042,\n        \"min\": 0.42,\n        \"max\": 80.0,\n        \"num_unique_values\": 88,\n        \"samples\": [\n          0.75,\n          22.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sibsp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 8,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"parch\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 6,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 49.6934285971809,\n        \"min\": 0.0,\n        \"max\": 512.3292,\n        \"num_unique_values\": 248,\n        \"samples\": [\n          11.2417,\n          51.8625\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"embarked\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"S\",\n          \"C\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"class\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Third\",\n          \"First\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"who\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"man\",\n          \"woman\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"adult_male\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"deck\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"C\",\n          \"E\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"embark_town\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Southampton\",\n          \"Cherbourg\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"alive\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"alone\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# One Hot Encoding"
      ],
      "metadata": {
        "id": "_pfBGmUXKeph"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def one_hot_encoding(dataframe, drop_first=True):\n",
        "  label_encoder_dataframe(dataframe)\n",
        "  cat_cols = [col for col in dataframe.columns if 10 >= dataframe[col].nunique() > 2]\n",
        "  dataframe = pd.get_dummies(dataframe, columns=cat_cols, drop_first=drop_first)\n",
        "  return dataframe"
      ],
      "metadata": {
        "id": "SsrQ4F4aIQ3C"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "new_df = one_hot_encoding(df)"
      ],
      "metadata": {
        "id": "HdktXM3nR2gK"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "new_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "_rToqbKlR6PO",
        "outputId": "48d68603-4147-480c-80e6-aa2be6cab908"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   survived  sex    age   fare  adult_male  alive  alone  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  class_Second  class_Third  who_man  who_woman  deck_B  deck_C  deck_D  deck_E  deck_F  deck_G  embark_town_Queenstown  embark_town_Southampton\n",
              "0         0    1 22.000  7.250           1      0      0         0         1        1        0        0        0        0        0        0        0        0        0        0        0           0           1             0            1        1          0       0       0       0       0       0       0                       0                        1\n",
              "1         1    0 38.000 71.283           0      1      0         0         0        1        0        0        0        0        0        0        0        0        0        0        0           0           0             0            0        0          1       0       1       0       0       0       0                       0                        0\n",
              "2         1    0 26.000  7.925           0      1      1         0         1        0        0        0        0        0        0        0        0        0        0        0        0           0           1             0            1        0          1       0       0       0       0       0       0                       0                        1\n",
              "3         1    0 35.000 53.100           0      1      0         0         0        1        0        0        0        0        0        0        0        0        0        0        0           0           1             0            0        0          1       0       1       0       0       0       0                       0                        1\n",
              "4         0    1 35.000  8.050           1      0      1         0         1        0        0        0        0        0        0        0        0        0        0        0        0           0           1             0            1        1          0       0       0       0       0       0       0                       0                        1"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c792b775-e7ba-4139-99a8-a09adff5b2be\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>survived</th>\n",
              "      <th>sex</th>\n",
              "      <th>age</th>\n",
              "      <th>fare</th>\n",
              "      <th>adult_male</th>\n",
              "      <th>alive</th>\n",
              "      <th>alone</th>\n",
              "      <th>pclass_2</th>\n",
              "      <th>pclass_3</th>\n",
              "      <th>sibsp_1</th>\n",
              "      <th>sibsp_2</th>\n",
              "      <th>sibsp_3</th>\n",
              "      <th>sibsp_4</th>\n",
              "      <th>sibsp_5</th>\n",
              "      <th>sibsp_8</th>\n",
              "      <th>parch_1</th>\n",
              "      <th>parch_2</th>\n",
              "      <th>parch_3</th>\n",
              "      <th>parch_4</th>\n",
              "      <th>parch_5</th>\n",
              "      <th>parch_6</th>\n",
              "      <th>embarked_Q</th>\n",
              "      <th>embarked_S</th>\n",
              "      <th>class_Second</th>\n",
              "      <th>class_Third</th>\n",
              "      <th>who_man</th>\n",
              "      <th>who_woman</th>\n",
              "      <th>deck_B</th>\n",
              "      <th>deck_C</th>\n",
              "      <th>deck_D</th>\n",
              "      <th>deck_E</th>\n",
              "      <th>deck_F</th>\n",
              "      <th>deck_G</th>\n",
              "      <th>embark_town_Queenstown</th>\n",
              "      <th>embark_town_Southampton</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>22.000</td>\n",
              "      <td>7.250</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</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>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</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>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>38.000</td>\n",
              "      <td>71.283</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <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>1</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>26.000</td>\n",
              "      <td>7.925</td>\n",
              "      <td>0</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>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>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</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>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>35.000</td>\n",
              "      <td>53.100</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <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>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</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>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>35.000</td>\n",
              "      <td>8.050</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</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>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</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>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c792b775-e7ba-4139-99a8-a09adff5b2be')\"\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 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-c792b775-e7ba-4139-99a8-a09adff5b2be 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-c792b775-e7ba-4139-99a8-a09adff5b2be');\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",
              "\n",
              "\n",
              "<div id=\"df-d88db500-0ca8-49ed-8206-7341cc8c581e\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d88db500-0ca8-49ed-8206-7341cc8c581e')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-d88db500-0ca8-49ed-8206-7341cc8c581e button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "new_df"
            }
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Rare Encoding"
      ],
      "metadata": {
        "id": "AKTIy6BfTQI_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def grab_col_names(dataframe, cat_th=10, car_th=20):\n",
        "    cat_cols = [col for col in dataframe.columns if str(dataframe[col].dtypes) in [\"category\", \"object\", \"bool\"]]\n",
        "    num_but_cat = [col for col in dataframe.columns if dataframe[col].nunique() < cat_th and dataframe[col].dtypes in [\"uint8\", \"int64\", \"float64\"]]\n",
        "    cat_but_car = [col for col in dataframe.columns if dataframe[col].nunique() > car_th and str(dataframe[col].dtypes) in [\"category\", \"object\"]]\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 = [col for col in dataframe.columns if dataframe[col].dtypes in [\"uint8\", \"int64\", \"float64\"]]\n",
        "    num_cols = [col for col in num_cols if col not in cat_cols]\n",
        "\n",
        "    return cat_cols, num_cols, cat_but_car, num_but_cat"
      ],
      "metadata": {
        "id": "hdP5lfGISMk3"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cat_cols, num_cols, cat_but_car, num_but_cat = grab_col_names(df)\n",
        "print(f\"observations: {df.shape[0]}\")\n",
        "print(f\"variables: {df.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)}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zXzvVgl_TTRt",
        "outputId": "8833b8fc-7eaf-4505-dfd0-7749c6ae197d"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "observations: 891\n",
            "variables: 15\n",
            "cat_cols: 13\n",
            "num_cols: 2\n",
            "cat_but_car: 0\n",
            "num_but_cat: 8\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def cat_summary(dataframe, col_name, plot=False):\n",
        "    print(f\"####################### {col_name} #######################\")\n",
        "    print(pd.DataFrame({col_name: dataframe[col_name].value_counts(),\n",
        "                        'Ratio': 100 * dataframe[col_name].value_counts() / len(dataframe)}))\n",
        "    if plot:\n",
        "        sns.countplot(x=dataframe[col_name], data=dataframe)\n",
        "        plt.show(block=True)\n",
        "\n",
        "def cat_summary_df(dataframe):\n",
        "    cat_cols, num_cols, cat_but_car, num_but_cat = grab_col_names(dataframe)\n",
        "    for col in cat_cols:\n",
        "        if dataframe[col].dtypes == \"bool\":\n",
        "            new_df = pd.DataFrame()\n",
        "            new_df[col] = dataframe[col].astype(int)\n",
        "            cat_summary(new_df, col, plot=True)\n",
        "        else:\n",
        "            cat_summary(dataframe, col, plot=True)"
      ],
      "metadata": {
        "id": "WPF3sPpeTWi9"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cat_summary_df(df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "Qj3P9ERQUM0W",
        "outputId": "5f307f97-fc17-4e65-a37e-813d5e34a564"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "####################### embarked #######################\n",
            "   embarked  Ratio\n",
            "S       644 72.278\n",
            "C       168 18.855\n",
            "Q        77  8.642\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "####################### class #######################\n",
            "        class  Ratio\n",
            "Third     491 55.107\n",
            "First     216 24.242\n",
            "Second    184 20.651\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "####################### who #######################\n",
            "       who  Ratio\n",
            "man    537 60.269\n",
            "woman  271 30.415\n",
            "child   83  9.315\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "####################### deck #######################\n",
            "   deck  Ratio\n",
            "C    59  6.622\n",
            "B    47  5.275\n",
            "D    33  3.704\n",
            "E    32  3.591\n",
            "A    15  1.684\n",
            "F    13  1.459\n",
            "G     4  0.449\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "####################### embark_town #######################\n",
            "             embark_town  Ratio\n",
            "Southampton          644 72.278\n",
            "Cherbourg            168 18.855\n",
            "Queenstown            77  8.642\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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v3lwbNmxQRESE1RcQEKDIyEjVqVOnxosEAACorl8Vdpo2bSpJKi8vPy/FAAAA1LRfFXZ+LDs7W6tWrdLBgwcrhZ8JEyacc2EAAAA1oVphZ/bs2Ro8eLAuueQSud1uORwOq8/hcBB2AABArVGtsPP000/rmWee0ZgxY2q6HgAAgBpVrefsHD16VHfeeWdN1wIAAFDjqhV27rzzTn388cc1XQsAAECNq9ZlrBYtWmj8+PFat26d4uPjVbduXa/+Rx99tEaKAwAAOFfVCjuvvfaaQkNDlZGRoYyMDK8+h8NB2AEAALVGtcLO3r17a7oOAACA86Ja9+wAAABcLKp1ZufBBx/82f433nijWsUAAADUtGqFnaNHj3rNl5aW6quvvlJBQcEZPyAUAADAV6oVdt59991KbeXl5Ro8eLB+97vfnXNRAAAANaXG7tnx8/PTyJEj9dJLL9XUKgEAAM5Zjd6gnJOTo9OnT9fkKgEAAM5JtS5jjRw50mveGKO8vDwtWbJEKSkpNVIYAABATahW2Pniiy+85v38/BQREaEXXnjhF9+pBQAAcCFVK+ysWrWqpusAAAA4L6oVdiocOnRIWVlZkqQrrrhCERERNVIUAABATanWDcrHjx/Xgw8+qEaNGqlTp07q1KmToqOjNXDgQJ04caKmawQAAKi2aoWdkSNHKiMjQx9++KEKCgpUUFCg999/XxkZGRo1alRN1wgAAFBt1bqM9c477+jvf/+7EhMTrbZbbrlFQUFB+sMf/qCZM2fWVH0AAADnpFpndk6cOKGoqKhK7ZGRkVzGAgAAtUq1wo7H49HEiRN18uRJq+3777/Xk08+KY/HU2PFAQAAnKtqXcZ6+eWX1b17dzVu3Fht2rSRJG3dulWBgYH6+OOPa7RAAACAc1GtsBMfH6/s7GwtWLBAu3btkiTdc8896tevn4KCgmq0QAAAgHNRrbCTlpamqKgoDRo0yKv9jTfe0KFDhzRmzJgaKQ4AAOBcVeuenVdffVWtWrWq1H7llVdq1qxZ51wUAABATalW2MnPz1ejRo0qtUdERCgvL++ciwIAAKgp1Qo7MTExWrNmTaX2NWvWKDo6usrrmTlzplq3bi2n0ymn0ymPx6OlS5da/SdPnlRqaqoaNmyo0NBQ9e3bVwcOHPBaR25urnr27Kng4GBFRkZq9OjROn36dHV2CwAA2FC17tkZNGiQhg8frtLSUnXp0kWStHLlSj3++OO/6gnKjRs31nPPPaeWLVvKGKN58+apV69e+uKLL3TllVdqxIgRWrJkiRYtWiSXy6UhQ4aoT58+VtAqKytTz5495Xa7tXbtWuXl5WnAgAGqW7eunn322ersGgAAsBmHMcb82oWMMfrjH/+oqVOn6tSpU5KkevXqacyYMZowYcI5FRQeHq4pU6bojjvuUEREhBYuXKg77rhDkrRr1y7FxsYqMzNTHTp00NKlS3Xrrbdq//791kMOZ82apTFjxujQoUMKCAio0jaLiorkcrlUWFgop9NZrboTRs+v1nKwp01TBvi6BACwvar+/a7WZSyHw6E///nPOnTokNatW6etW7fqyJEj5xR0ysrK9Pbbb+v48ePyeDzatGmTSktLlZSUZI1p1aqVmjRposzMTElSZmam4uPjvZ7mnJycrKKiIm3fvr3atQAAAPuo1mWsCqGhobr22mvPqYBt27bJ4/Ho5MmTCg0N1bvvvqu4uDht2bJFAQEBCgsL8xofFRWl/Px8ST/cKP3Tj62omK8YcyYlJSUqKSmx5ouKis5pHwAAQO1VrTM7NemKK67Qli1btH79eg0ePFgpKSnasWPHed1mWlqaXC6XNcXExJzX7QEAAN/xedgJCAhQixYtlJCQoLS0NLVp00b/+7//K7fbrVOnTqmgoMBr/IEDB+R2uyVJbre70ruzKuYrxpzJ2LFjVVhYaE379u2r2Z0CAAC1hs/Dzk+Vl5erpKRECQkJqlu3rlauXGn1ZWVlKTc31/qwUY/Ho23btungwYPWmBUrVsjpdCouLu6s2wgMDLTe7l4xAQAAezqne3bO1dixY9WjRw81adJEx44d08KFC5Wenq7ly5fL5XJp4MCBGjlypMLDw+V0OjV06FB5PB516NBBktStWzfFxcWpf//+mjx5svLz8zVu3DilpqYqMDDQl7sGAABqCZ+GnYMHD2rAgAHKy8uTy+VS69attXz5cnXt2lWS9NJLL8nPz099+/ZVSUmJkpOTNWPGDGv5OnXqaPHixRo8eLA8Ho9CQkKUkpKiSZMm+WqXAABALVOt5+zYDc/ZQU3jOTsAcP6d1+fsAAAAXCwIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNZ8GnbS0tJ07bXXqn79+oqMjFTv3r2VlZXlNebkyZNKTU1Vw4YNFRoaqr59++rAgQNeY3Jzc9WzZ08FBwcrMjJSo0eP1unTpy/krgAAgFrKp2EnIyNDqampWrdunVasWKHS0lJ169ZNx48ft8aMGDFCH374oRYtWqSMjAzt379fffr0sfrLysrUs2dPnTp1SmvXrtW8efM0d+5cTZgwwRe7BAAAahmHMcb4uogKhw4dUmRkpDIyMtSpUycVFhYqIiJCCxcu1B133CFJ2rVrl2JjY5WZmakOHTpo6dKluvXWW7V//35FRUVJkmbNmqUxY8bo0KFDCggI+MXtFhUVyeVyqbCwUE6ns1q1J4yeX63lYE+bpgzwdQkAYHtV/ftdq+7ZKSwslCSFh4dLkjZt2qTS0lIlJSVZY1q1aqUmTZooMzNTkpSZman4+Hgr6EhScnKyioqKtH379jNup6SkREVFRV4TAACwp1oTdsrLyzV8+HB17NhRV111lSQpPz9fAQEBCgsL8xobFRWl/Px8a8yPg05Ff0XfmaSlpcnlcllTTExMDe8NAACoLWpN2ElNTdVXX32lt99++7xva+zYsSosLLSmffv2nfdtAgAA3/D3dQGSNGTIEC1evFirV69W48aNrXa3261Tp06poKDA6+zOgQMH5Ha7rTEbNmzwWl/Fu7UqxvxUYGCgAgMDa3gvAABAbeTTMzvGGA0ZMkTvvvuuPv30UzVr1syrPyEhQXXr1tXKlSuttqysLOXm5srj8UiSPB6Ptm3bpoMHD1pjVqxYIafTqbi4uAuzIwAAoNby6Zmd1NRULVy4UO+//77q169v3WPjcrkUFBQkl8ulgQMHauTIkQoPD5fT6dTQoUPl8XjUoUMHSVK3bt0UFxen/v37a/LkycrPz9e4ceOUmprK2RsAAODbsDNz5kxJUmJiolf7nDlzdP/990uSXnrpJfn5+alv374qKSlRcnKyZsyYYY2tU6eOFi9erMGDB8vj8SgkJEQpKSmaNGnShdoNAABQi9Wq5+z4Cs/ZQU3jOTsAcP5dlM/ZAQAAqGmEHQAAYGuEHQAAYGuEHQAAYGuEHQAAYGuEHQAAYGuEHQAAYGuEHQAAYGuEHQAAYGuEHQAAYGuEHQAAYGs+/SBQAOcXn9mGH+Mz2/BbxZkdAABga4QdAABga4QdAABga4QdAABga4QdAABga4QdAABga4QdAABga4QdAABga4QdAABga4QdAABga4QdAABga4QdAABga4QdAABga4QdAABga4QdAABga4QdAABga4QdAABga4QdAABga4QdAABga4QdAABga4QdAABga4QdAABga4QdAABga4QdAABga4QdAABga4QdAABga4QdAABga4QdAABga4QdAABga4QdAABga4QdAABgaz4NO6tXr9Ztt92m6OhoORwOvffee179xhhNmDBBjRo1UlBQkJKSkpSdne015siRI+rXr5+cTqfCwsI0cOBAFRcXX8C9AAAAtZlPw87x48fVpk0bTZ8+/Yz9kydP1tSpUzVr1iytX79eISEhSk5O1smTJ60x/fr10/bt27VixQotXrxYq1ev1sMPP3yhdgEAANRy/r7ceI8ePdSjR48z9hlj9PLLL2vcuHHq1auXJGn+/PmKiorSe++9p7vvvls7d+7UsmXLtHHjRrVr106SNG3aNN1yyy16/vnnFR0dfcH2BQAA1E619p6dvXv3Kj8/X0lJSVaby+VS+/btlZmZKUnKzMxUWFiYFXQkKSkpSX5+flq/fv0FrxkAANQ+Pj2z83Py8/MlSVFRUV7tUVFRVl9+fr4iIyO9+v39/RUeHm6NOZOSkhKVlJRY80VFRTVVNgAAqGVq7Zmd8yktLU0ul8uaYmJifF0SAAA4T2pt2HG73ZKkAwcOeLUfOHDA6nO73Tp48KBX/+nTp3XkyBFrzJmMHTtWhYWF1rRv374arh4AANQWtTbsNGvWTG63WytXrrTaioqKtH79enk8HkmSx+NRQUGBNm3aZI359NNPVV5ervbt25913YGBgXI6nV4TAACwJ5/es1NcXKw9e/ZY83v37tWWLVsUHh6uJk2aaPjw4Xr66afVsmVLNWvWTOPHj1d0dLR69+4tSYqNjVX37t01aNAgzZo1S6WlpRoyZIjuvvtu3okFAAAk+TjsfP7557rpppus+ZEjR0qSUlJSNHfuXD3++OM6fvy4Hn74YRUUFOiGG27QsmXLVK9ePWuZBQsWaMiQIbr55pvl5+envn37aurUqRd8XwAAQO3k07CTmJgoY8xZ+x0OhyZNmqRJkyaddUx4eLgWLlx4PsoDAAA2UGvv2QEAAKgJhB0AAGBrhB0AAGBrhB0AAGBrhB0AAGBrhB0AAGBrtfaDQAEA9pMwer6vS0AtsmnKgAuyHc7sAAAAWyPsAAAAWyPsAAAAWyPsAAAAWyPsAAAAWyPsAAAAWyPsAAAAWyPsAAAAWyPsAAAAWyPsAAAAWyPsAAAAWyPsAAAAWyPsAAAAWyPsAAAAWyPsAAAAWyPsAAAAWyPsAAAAWyPsAAAAWyPsAAAAWyPsAAAAWyPsAAAAWyPsAAAAWyPsAAAAWyPsAAAAWyPsAAAAWyPsAAAAWyPsAAAAWyPsAAAAWyPsAAAAWyPsAAAAWyPsAAAAWyPsAAAAWyPsAAAAWyPsAAAAWyPsAAAAWyPsAAAAWyPsAAAAW7NN2Jk+fbouu+wy1atXT+3bt9eGDRt8XRIAAKgFbBF2/vrXv2rkyJGaOHGiNm/erDZt2ig5OVkHDx70dWkAAMDHbBF2XnzxRQ0aNEgPPPCA4uLiNGvWLAUHB+uNN97wdWkAAMDHLvqwc+rUKW3atElJSUlWm5+fn5KSkpSZmenDygAAQG3g7+sCztV3332nsrIyRUVFebVHRUVp165dZ1ympKREJSUl1nxhYaEkqaioqNp1lJV8X+1lYT/ncizVJI5L/FhtOC45JvFj53pMVixvjPnZcRd92KmOtLQ0Pfnkk5XaY2JifFAN7Mg17RFflwBUwnGJ2qamjsljx47J5XKdtf+iDzuXXHKJ6tSpowMHDni1HzhwQG63+4zLjB07ViNHjrTmy8vLdeTIETVs2FAOh+O81mtnRUVFiomJ0b59++R0On1dDiCJ4xK1D8dkzTHG6NixY4qOjv7ZcRd92AkICFBCQoJWrlyp3r17S/ohvKxcuVJDhgw54zKBgYEKDAz0agsLCzvPlf52OJ1OfoBR63BcorbhmKwZP3dGp8JFH3YkaeTIkUpJSVG7du103XXX6eWXX9bx48f1wAMP+Lo0AADgY7YIO3fddZcOHTqkCRMmKD8/X23bttWyZcsq3bQMAAB+e2wRdiRpyJAhZ71shQsjMDBQEydOrHSJEPAljkvUNhyTF57D/NL7tQAAAC5iF/1DBQEAAH4OYQcAANgaYQeVpKeny+FwqKCgwNelwOYcDofee++9i27dAC4uhJ1a7NChQxo8eLCaNGmiwMBAud1uJScna82aNTW2jcTERA0fPrzG1nchPPHEE2rbtq2vy0AV5Ofna+jQoWrevLkCAwMVExOj2267TStXrvR1aQB+Qwg7tVjfvn31xRdfaN68edq9e7c++OADJSYm6vDhw74uDfhF33zzjRISEvTpp59qypQp2rZtm5YtW6abbrpJqamp5227p06dOm/rvhi2D2nfvn168MEHFR0drYCAADVt2lTDhg276H53cpa9BhnUSkePHjWSTHp6+lnHfPvtt+b22283ISEhpn79+ubOO+80+fn5Vn9KSorp1auX1zLDhg0znTt3tvoleU179+41q1atMpLMJ598YhISEkxQUJDxeDxm165d1nr27Nljbr/9dhMZGWlCQkJMu3btzIoVK7y21bRpU/PUU0+Z/v37m5CQENOkSRPz/vvvm4MHD1p1x8fHm40bN1rLzJkzx7hcLvPuu++aFi1amMDAQNOtWzeTm5tr9f+05jlz5lTp9Zg4caJp06aNmT9/vmnatKlxOp3mrrvuMkVFRb/qe4Oq6dGjh7n00ktNcXFxpb6jR48aY4yRZGbPnm169+5tgoKCTIsWLcz777/vNXbbtm2me/fuJiQkxERGRpr77rvPHDp0yOrv3LmzSU1NNcOGDTMNGzY0iYmJ1rpnzJhhunfvburVq2eaNWtmFi1a5LXuL7/80tx0002mXr16Jjw83AwaNMgcO3bMa93Dhg3zWqZXr14mJSXFmm/atKmZNGmS6d+/v6lfv77V99prr5nGjRuboKAg07t3b/PCCy8Yl8v1K19F/Fo5OTkmMjLS3HDDDSY9Pd18++235qOPPjJXXnmladmypTl8+LCvS6yyit/FFT8vqD7CTi1VWlpqQkNDzfDhw83Jkycr9ZeVlZm2bduaG264wXz++edm3bp1JiEhwQoyxvxy2CkoKDAej8cMGjTI5OXlmby8PHP69GnrB6x9+/YmPT3dbN++3dx4443m+uuvt9azZcsWM2vWLLNt2zaze/duM27cOFOvXj3z7bffWmOaNm1qwsPDzaxZs8zu3bvN4MGDjdPpNN27dzd/+9vfTFZWlundu7eJjY015eXlxpgfwkzdunVNu3btzNq1a83nn39urrvuOmvbJ06cMKNGjTJXXnmlVfOJEyeq9HpMnDjRhIaGmj59+pht27aZ1atXG7fbbf70pz+d43cLP3X48GHjcDjMs88++7PjJJnGjRubhQsXmuzsbPPoo4+a0NBQ6w/S0aNHTUREhBk7dqzZuXOn2bx5s+natau56aabrHV07tzZhIaGmtGjR5tdu3ZZoVySadiwoZk9e7bJysoy48aNM3Xq1DE7duwwxhhTXFxsGjVqZB0PK1euNM2aNfMKMlUNO06n0zz//PNmz549Zs+ePeazzz4zfn5+ZsqUKSYrK8tMnz7dhIeHE3YugO7du5vGjRubEydOeLXn5eWZ4OBg88gjjxhjfjg+3n33Xa8xLpfL+ufJGGNyc3PNnXfeaVwul2nQoIG5/fbbzd69e72WmT17tmnVqpUJDAw0V1xxhZk+fbrVt3fvXiPJvPPOOyYxMdEEBQWZ1q1bm7Vr11pjvvnmG3PrrbeasLAwExwcbOLi4sySJUusZX88VRx3J0+eNEOHDjUREREmMDDQdOzY0WzYsMFaZ0JCgpkyZYo136tXL+Pv728F+X379hlJJjs72xjzwzH8zDPPmAceeMCEhoaamJgY8+qrr/66F76WI+zUYn//+99NgwYNTL169cz1119vxo4da7Zu3WqMMebjjz82derUsc54GGPM9u3bjSTroP+lsGPMmX+Z//jMToUlS5YYSeb7778/a71XXnmlmTZtmjXftGlTc99991nzeXl5RpIZP3681ZaZmWkkmby8PGPMf87crFu3zhqzc+dOI8msX7/eGPOfMzQ/VpXXY+LEiSY4ONjrTM7o0aNN+/btz7pPqJ7169cbSeYf//jHz46TZMaNG2fNFxcXG0lm6dKlxhhjnnrqKdOtWzevZSp+UWdlZRljfjiGr7766jOuu+IPW4X27dubwYMHG2N+OPPSoEEDrzNPS5YsMX5+ftYZwaqGnd69e3uNueuuu0zPnj292vr160fYOc9+KWQPGjTINGjQwJSXl/9i2Dl16pSJjY01Dz74oPnyyy/Njh07zL333muuuOIKU1JSYowx5s033zSNGjUy77zzjvn666/NO++8Y8LDw83cuXONMf8JO61atTKLFy82WVlZ5o477jBNmzY1paWlxhhjevbsabp27Wq+/PJLk5OTYz788EOTkZFhTp8+bd555x3rWM/LyzMFBQXGGGMeffRREx0dbT766COzfft2k5KSYho0aGD9kzBy5Ejr+CsvLzfh4eHmkksusX6u3nzzTXPppZda+13xj+n06dNNdna2SUtLM35+fl5n8y923LNTi/Xt21f79+/XBx98oO7duys9PV3XXHON5s6dq507dyomJkYxMTHW+Li4OIWFhWnnzp01sv3WrVtbXzdq1EiSdPDgQUlScXGxHnvsMcXGxiosLEyhoaHauXOncnNzz7qOio/viI+Pr9RWsV5J8vf317XXXmvNt2rV6hf3q6qvx2WXXab69et77dePt42aYX7Fs0p/fIyEhITI6XRa35OtW7dq1apVCg0NtaZWrVpJknJycqzlEhISzrhuj8dTab7ieNi5c6fatGmjkJAQq79jx44qLy9XVlZWleuXpHbt2nnNZ2Vl6brrrvNq++k8al52draMMYqNjT1jf2xsrI4ePapDhw794rr++te/qry8XH/5y18UHx+v2NhYzZkzR7m5uUpPT5ckTZw4US+88IL69OmjZs2aqU+fPhoxYoReffVVr3U99thj6tmzpy6//HI9+eST+vbbb7Vnzx5JUm5urjp27Kj4+Hg1b95ct956qzp16qQ6deooPDxckhQZGSm32y2Xy6Xjx49r5syZmjJlinr06KG4uDjNnj1bQUFBev311yX98MaTzz77TGVlZfryyy8VEBCgfv36WXWnp6erc+fOXjXecsst+u///m+1aNFCY8aM0SWXXKJVq1ZV+bWv7WzzcRF2Va9ePXXt2lVdu3bV+PHj9dBDD2nixIkaNWrULy7r5+dX6Y9OaWlplbddt25d62uHwyHph0+Ul3744V2xYoWef/55tWjRQkFBQbrjjjsq3Zx5pnX83HrPtx9vu2L7F2rbvyUtW7aUw+HQrl27fnHsz31PiouLddttt+nPf/5zpeUqArgkr8BSk6r6M3S+to/q+aWwHRAQ8Ivr2Lp1q/bs2eP1z5EknTx5Ujk5OTp+/LhycnI0cOBADRo0yOo/ffp0pU/hPts/jq1atdKjjz6qwYMH6+OPP1ZSUpL69u3rNf6ncnJyVFpaqo4dO1ptdevW1XXXXWcF+RtvvFHHjh3TF198obVr16pz585KTEzUc889J0nKyMjQ6NGjz1qjw+GQ2+221T+CnNm5yMTFxen48eOKjY3Vvn37tG/fPqtvx44dKigoUFxcnCQpIiJCeXl5Xstv2bLFaz4gIEBlZWW/uo41a9bo/vvv1+9//3vFx8fL7Xbrm2+++dXrOZPTp0/r888/t+azsrJUUFBg/bd2ppqr8nrgwgkPD1dycrKmT5+u48ePV+qv6rtLrrnmGm3fvl2XXXaZWrRo4TVVJWCsW7eu0nzFcRQbG6utW7d61bdmzRr5+fnpiiuukFT5Z6isrExfffXVL273iiuu0MaNG73afjqPmteiRQs5HI6zngXeuXOnIiIiFBYWJofD8bNBtri4WAkJCdqyZYvXtHv3bt17770qLi6WJM2ePdur/6uvvqp03P3cP3gPPfSQvv76a/Xv31/btm1Tu3btNG3atHN6HcLCwtSmTRulp6crIyNDiYmJ6tSpk7744gvt3r1b2dnZlc7s2P0fQcJOLXX48GF16dJFb775pr788kvt3btXixYt0uTJk9WrVy8lJSUpPj5e/fr10+bNm7VhwwYNGDBAnTt3tk6pd+nSRZ9//rnmz5+v7OxsTZw4sdIv6ssuu0zr16/XN998o++++67KB3fLli31j3/8Q1u2bNHWrVt177331tgPRt26dTV06FCtX79emzZt0v33368OHTpYlwEuu+wy7d27V1u2bNF3332nkpKSKr0euLCmT5+usrIyXXfddXrnnXeUnZ2tnTt3aurUqZUuL51Namqqjhw5onvuuUcbN25UTk6Oli9frgceeKBKIX3RokV64403tHv3bk2cOFEbNmywPjC4X79+qlevnlJSUvTVV19p1apVGjp0qPr3729dXu3SpYuWLFmiJUuWaNeuXRo8eHCVgtrQoUP10Ucf6cUXX1R2drZeffVVLV261PpDh/OjYcOG6tq1q2bMmKHvv//eqy8/P18LFizQ/fffL6lykM3OztaJEyes+WuuuUbZ2dmKjIysFLRdLpeioqIUHR2tr7/+ulJ/s2bNflXdMTExeuSRR/SPf/xDo0aN0uzZsyX95wzUj4/13/3udwoICPB63lppaak2btzo9Y9d586dtWrVKq1evVqJiYkKDw9XbGysnnnmGTVq1EiXX375r6rxYkfYqaVCQ0PVvn17vfTSS+rUqZOuuuoqjR8/XoMGDdIrr7wih8Oh999/Xw0aNFCnTp2UlJSk5s2b669//au1juTkZI0fP16PP/64rr32Wh07dkwDBgzw2s5jjz2mOnXqKC4uThEREZXuuTmbF198UQ0aNND111+v2267TcnJybrmmmtqZN+Dg4M1ZswY3XvvverYsaNCQ0O99qtv377q3r27brrpJkVEROitt96q0uuBC6t58+bavHmzbrrpJo0aNUpXXXWVunbtqpUrV2rmzJlVWkd0dLTWrFmjsrIydevWTfHx8Ro+fLjCwsLk5/fLv76efPJJvf3222rdurXmz5+vt956y/qDEBwcrOXLl+vIkSO69tprdccdd+jmm2/WK6+8Yi3/4IMPKiUlxQrOzZs310033fSL2+3YsaNmzZqlF198UW3atNGyZcs0YsQI1atXr0r7jep75ZVXVFJSouTkZK1evVr79u3TsmXL1LVrV11++eWaMGGCpB+C7CuvvKIvvvhCn3/+uR555BGvsxv9+vXTJZdcol69eumf//yn9u7dq/T0dD366KP617/+JemH4ystLU1Tp07V7t27tW3bNs2ZM0cvvvhilesdPny4li9frr1792rz5s1atWqVdfaxadOmcjgcWrx4sQ4dOqTi4mKFhIRo8ODBGj16tJYtW6YdO3Zo0KBBOnHihAYOHGitNzExUcuXL5e/v791n1tiYqIWLFhQ6azOb4Iv744GfqriOTuA3Tz00EPmhhtu8HUZvwl79+41KSkpJioqyjgcDiPJ9OnTxxw/ftwa8+9//9t069bNhISEmJYtW5qPPvqo0lvP8/LyzIABA8wll1xiAgMDTfPmzc2gQYNMYWGhNWbBggWmbdu2JiAgwDRo0MB06tTJehdixbuxvvjiC2t8xTPUVq1aZYwxZsiQIeZ3v/udCQwMNBEREaZ///7mu+++s8ZPmjTJuN1u43A4rHcBfv/992bo0KFWXT9967kx/3ln2l133WW1vfvuu0aSmTVrltfYpk2bmpdeesmrrU2bNmbixIlVfclrPYcxv+JtE8B5NnfuXA0fPpwnhuKi9/zzz6tr164KCQnR0qVLNWrUKM2YMUMPPfSQr0v7zZk4caJefPFFrVixQh06dPB1OfAB3o0FAOfBhg0bNHnyZB07dkzNmzfX1KlTCTo+8uSTT+qyyy7TunXrdN1111XpEijshTM7AADA1oi3AADA1gg7AADA1gg7AADA1gg7AADA1gg7AADA1gg7AHwqMTFRw4cPPy/rfuKJJ9S2bdvzsm4AFw/CDgCcwdy5cxUWFubrMgDUAB4qCMB2jDFV+qBQAL8NnNkBUGXl5eVKS0tTs2bNFBQUpDZt2ujvf/+7JCk9PV0Oh0PLly/X1VdfraCgIHXp0kUHDx7U0qVLFRsbK6fTqXvvvdfr06Ul6fTp0xoyZIhcLpcuueQSjR8/Xj9+3un//d//qV27dqpfv77cbrfuvfdeHTx40Oqv2PbSpUuVkJCgwMBAffbZZ5Xqz8nJUfPmzTVkyBD93PNU09PT9cADD6iwsFAOh0MOh0NPPPGEJOno0aMaMGCAGjRooODgYPXo0UPZ2dmSfghZERER1msiSW3btlWjRo2s+c8++0yBgYHWa+BwOPSXv/xFv//97xUcHKyWLVvqgw8+qOq3BEBV+O5juQBcbJ5++mnTqlUrs2zZMpOTk2PmzJljAgMDTXp6ulm1apWRZDp06GA+++wzs3nzZtOiRQvTuXNn061bN7N582azevVq07BhQ/Pcc89Z6+zcubMJDQ01w4YNM7t27TJvvvmmCQ4ONq+99po15vXXXzcfffSRycnJMZmZmcbj8ZgePXpY/RXbbt26tfn444/Nnj17zOHDh83EiRNNmzZtjDHGbN261bjdbvM///M/v7ifJSUl5uWXXzZOp9Pk5eWZvLw8c+zYMWOMMbfffruJjY01q1evNlu2bDHJycmmRYsW5tSpU8YYY/r06WNSU1ONMcYcOXLEBAQEGJfLZXbu3Gm9hh07drS2Jck0btzYLFy40GRnZ5tHH33UhIaGmsOHD1fzuwTgpwg7AKrk5MmTJjg42Kxdu9arfeDAgeaee+6xAscnn3xi9aWlpRlJJicnx2r7r//6L5OcnGzNd+7c2cTGxpry8nKrbcyYMSY2NvastWzcuNFIsgJIxbbfe+89r3EVYWfNmjWmQYMG5vnnn6/y/s6ZM8e4XC6vtt27dxtJZs2aNVbbd999Z4KCgszf/vY3Y4wxU6dONVdeeaUxxpj33nvPtG/f3vTq1cvMnDnTGGNMUlKS+dOf/mQtL8mMGzfOmi8uLjaSzNKlS6tcK4Cfx2UsAFWyZ88enThxQl27dlVoaKg1zZ8/Xzk5Oda41q1bW19HRUUpODhYzZs392r78SUoSerQoYMcDoc17/F4lJ2dbd13s2nTJt12221q0qSJ6tevr86dO0uScnNzvdbTrl27SnXn5uaqa9eumjBhgkaNGnUOr4C0c+dO+fv7q3379lZbw4YNdcUVV2jnzp2SpM6dO2vHjh06dOiQMjIylJiYqMTERKWnp6u0tFRr165VYmKi13p//JqFhITI6XRWeo0AVB83KAOokuLiYknSkiVLdOmll3r1BQYGWoGnbt26VrvD4fCar2grLy+v8naPHz+u5ORkJScna8GCBYqIiFBubq6Sk5N16tQpr7EhISGVlo+IiFB0dLTeeustPfjgg3I6nVXednXEx8crPDxcGRkZysjI0DPPPCO3260///nP2rhxo0pLS3X99dd7LXOurxGAn8eZHQBVEhcXp8DAQOXm5qpFixZeU0xMzDmte/369V7z69atU8uWLVWnTh3t2rVLhw8f1nPPPacbb7xRrVq1+lVnPYKCgrR48WLVq1dPycnJOnbsWJWWCwgIqPSOrtjYWJ0+fdqr3sOHDysrK0txcXGSfggqN954o95//31t375dN9xwg1q3bq2SkhK9+uqrateu3RlDGYDzh7ADoErq16+vxx57TCNGjNC8efOUk5OjzZs3a9q0aZo3b945rTs3N1cjR45UVlaW3nrrLU2bNk3Dhg2TJDVp0kQBAQGaNm2avv76a33wwQd66qmnftX6Q0JCtGTJEvn7+6tHjx7WWaqfc9lll6m4uFgrV67Ud999pxMnTqhly5bq1auXBg0apM8++0xbt27Vfffdp0svvVS9evWylk1MTNRbb72ltm3bKjQ0VH5+furUqZMWLFhgXYIDcOEQdgBU2VNPPaXx48crLS1NsbGx6t69u5YsWaJmzZqd03oHDBig77//Xtddd51SU1M1bNgwPfzww5J+uAw1d+5cLVq0SHFxcXruuef0/PPP/+pthIaGaunSpTLGqGfPnjp+/PjPjr/++uv1yCOP6K677lJERIQmT54sSZozZ44SEhJ06623yuPxyBijjz76yOtSVOfOnVVWVuZ1b05iYmKlNgAXhsOYn3nYBAAAwEWOMzsAAMDWCDsAfpN69Ojh9Rb6H0/PPvusr8sDUIO4jAXgN+nf//63vv/++zP2hYeHKzw8/AJXBOB8IewAAABb4zIWAACwNcIOAACwNcIOAACwNcIOAACwNcIOAACwNcIOAACwNcIOAACwNcIOAACwtf8HiQnQLslK7vYAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "####################### survived #######################\n",
            "   survived  Ratio\n",
            "0       549 61.616\n",
            "1       342 38.384\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "####################### pclass #######################\n",
            "   pclass  Ratio\n",
            "3     491 55.107\n",
            "1     216 24.242\n",
            "2     184 20.651\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "####################### sex #######################\n",
            "   sex  Ratio\n",
            "1  577 64.759\n",
            "0  314 35.241\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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2li1bprFjx+raa6+Vy+WSzWYLrNlsNmIHAACEjEbFzvPPP6/Zs2dr8uTJTT0PAABAk2rU9+x88803euCBB5p6FgAAgCbXqFd2HnjgAW3evFmPP/54U89jrNRJr1s9AhCSSuaNtHoEAIZrVOzceOONmjp1qnbt2qWUlBS1adMmaP2JJ55okuEAAACuVqNi59VXX1VsbKy2bdumbdu2Ba3ZbDZiBwAAhIxGxc7x48ebeg4AAIBm0agLlAEAAFqLRr2y89hjj33v+muvvXZZj7NkyRItWbJE//rXvyRJN998s6ZNm6ZBgwZJ+u6vqE+cOFFr1qxRdXW1MjIytHjxYjmdzsBjlJaWauzYsdq6datiY2OVlZWl/Px8RUQ0+vsSAQCAQRpVBN98803Q7draWn388ceqqKi46B8IvZTrrrtOL7zwgrp16ya/369Vq1bp/vvv1/79+3XzzTdrwoQJevvtt7V27Vo5HA7l5ORo2LBh2rFjh6Tv/tL64MGD5XK5tHPnTp08eVIjR45UmzZtNGfOnMacGgAAMEyjYmf9+vUX7GtoaNDYsWP14x//+LIfZ8iQIUG3Z8+erSVLlmjXrl267rrrtHz5chUWFgYCasWKFerRo4d27dqlPn36aPPmzTp8+LDee+89OZ1O9erVS88995wmT56sGTNmKDIysjGnBwAADNJk1+yEhYUpNzdX8+fPb9T96+vrtWbNGlVVVcntdqukpES1tbVKT08PHNO9e3d17txZxcXFkqTi4mKlpKQEva2VkZEhn8+nQ4cOXfK5qqur5fP5gjYAAGCmJr1A+fPPP1ddXd0V3efgwYOKjY1VVFSUHn/8ca1fv17JycnyeDyKjIxUXFxc0PFOp1Mej0eS5PF4gkLn/Pr5tUvJz8+Xw+EIbElJSVc0MwAAaD0a9TZWbm5u0G2/36+TJ0/q7bffVlZW1hU91k9+8hMdOHBAlZWVWrdunbKysi747p6mlpeXF3QOPp+P4AEAwFCNip39+/cH3Q4LC1PHjh314osv/s9Pav23yMhI3XjjjZKk1NRU7d27V3/4wx/00EMPqaamRhUVFUGv7ni9XrlcLkmSy+XSnj17gh7P6/UG1i4lKipKUVFRVzQnAABonRoVO1u3bm3qOQIaGhpUXV2t1NRUtWnTRkVFRcrMzJQkHT16VKWlpXK73ZIkt9ut2bNnq7y8XAkJCZKkLVu2yG63Kzk5udlmBAAArcdVfRnNqVOndPToUUnfvR3VsWPHK7p/Xl6eBg0apM6dO+vMmTMqLCzUBx98oHfffVcOh0OjRo1Sbm6u4uPjZbfbNW7cOLndbvXp00eSNGDAACUnJ2vEiBGaO3euPB6PpkyZouzsbF65AQAAkhoZO1VVVRo3bpxef/11NTQ0SJLCw8M1cuRILVq0SG3btr2sxykvL9fIkSN18uRJORwO3XrrrXr33Xf1i1/8QpI0f/58hYWFKTMzM+hLBc8LDw/Xxo0bNXbsWLndbrVr105ZWVmaNWtWY04LAAAYqNEXKG/btk0bNmxQ3759JUl///vf9cQTT2jixIlasmTJZT3O8uXLv3c9OjpaBQUFKigouOQxXbp00TvvvHP5wwMAgB+URsXOn//8Z61bt0533313YN99992nmJgYPfjgg5cdOwAAAM2tUd+z8+23317w/TaSlJCQoG+//faqhwIAAGgqjYodt9ut6dOn69y5c4F9//73vzVz5szAJ6UAAABCQaPexlqwYIEGDhyo6667Tj179pQk/eMf/1BUVJQ2b97cpAMCAABcjUbFTkpKio4dO6bVq1frk08+kSQ98sgjGj58uGJiYpp0QAAAgKvRqNjJz8+X0+nU6NGjg/a/9tprOnXqlCZPntwkwwEAAFytRl2z88orr6h79+4X7L/55pu1dOnSqx4KAACgqTQqdjwejzp16nTB/o4dO+rkyZNXPRQAAEBTaVTsJCUlaceOHRfs37FjhxITE696KAAAgKbSqGt2Ro8erfHjx6u2tlb9+/eXJBUVFenpp5/WxIkTm3RAAACAq9Go2Jk0aZK+/vpr/eY3v1FNTY2k7/60w+TJk5WXl9ekAwIAAFyNRsWOzWbT7373O02dOlVHjhxRTEyMunXrxl8aBwAAIadRsXNebGys7rjjjqaaBQAAoMk16gJlAACA1oLYAQAARiN2AACA0YgdAABgNGIHAAAYjdgBAABGI3YAAIDRiB0AAGA0YgcAABiN2AEAAEYjdgAAgNGIHQAAYDRiBwAAGI3YAQAARiN2AACA0YgdAABgNGIHAAAYjdgBAABGI3YAAIDRiB0AAGA0YgcAABiN2AEAAEYjdgAAgNGIHQAAYDRiBwAAGI3YAQAARiN2AACA0YgdAABgNGIHAAAYjdgBAABGI3YAAIDRiB0AAGA0YgcAABiN2AEAAEYjdgAAgNGIHQAAYDRiBwAAGM3S2MnPz9cdd9yh9u3bKyEhQUOHDtXRo0eDjjl37pyys7PVoUMHxcbGKjMzU16vN+iY0tJSDR48WG3btlVCQoImTZqkurq6ljwVAAAQoiyNnW3btik7O1u7du3Sli1bVFtbqwEDBqiqqipwzIQJE7RhwwatXbtW27Zt04kTJzRs2LDAen19vQYPHqyamhrt3LlTq1at0sqVKzVt2jQrTgkAAISYCCuffNOmTUG3V65cqYSEBJWUlOjnP/+5KisrtXz5chUWFqp///6SpBUrVqhHjx7atWuX+vTpo82bN+vw4cN677335HQ61atXLz333HOaPHmyZsyYocjISCtODQAAhIiQumansrJSkhQfHy9JKikpUW1trdLT0wPHdO/eXZ07d1ZxcbEkqbi4WCkpKXI6nYFjMjIy5PP5dOjQoYs+T3V1tXw+X9AGAADMFDKx09DQoPHjx6tv37665ZZbJEkej0eRkZGKi4sLOtbpdMrj8QSO+c/QOb9+fu1i8vPz5XA4AltSUlITnw0AAAgVIRM72dnZ+vjjj7VmzZpmf668vDxVVlYGtrKysmZ/TgAAYA1Lr9k5LycnRxs3btT27dt13XXXBfa7XC7V1NSooqIi6NUdr9crl8sVOGbPnj1Bj3f+01rnj/lvUVFRioqKauKzAAAAocjSV3b8fr9ycnK0fv16vf/+++ratWvQempqqtq0aaOioqLAvqNHj6q0tFRut1uS5Ha7dfDgQZWXlweO2bJli+x2u5KTk1vmRAAAQMiy9JWd7OxsFRYW6i9/+Yvat28fuMbG4XAoJiZGDodDo0aNUm5uruLj42W32zVu3Di53W716dNHkjRgwAAlJydrxIgRmjt3rjwej6ZMmaLs7GxevQEAANbGzpIlSyRJd999d9D+FStW6Ne//rUkaf78+QoLC1NmZqaqq6uVkZGhxYsXB44NDw/Xxo0bNXbsWLndbrVr105ZWVmaNWtWS50GAAAIYZbGjt/v/5/HREdHq6CgQAUFBZc8pkuXLnrnnXeacjQAAGCIkPk0FgAAQHMgdgAAgNGIHQAAYDRiBwAAGI3YAQAARiN2AACA0YgdAABgNGIHAAAYjdgBAABGI3YAAIDRiB0AAGA0YgcAABiN2AEAAEYjdgAAgNGIHQAAYDRiBwAAGI3YAQAARiN2AACA0YgdAABgNGIHAAAYjdgBAABGI3YAAIDRiB0AAGA0YgcAABiN2AEAAEYjdgAAgNGIHQAAYDRiBwAAGI3YAQAARiN2AACA0YgdAABgNGIHAAAYjdgBAABGI3YAAIDRiB0AAGA0YgcAABiN2AEAAEYjdgAAgNGIHQAAYDRiBwAAGI3YAQAARiN2AACA0YgdAABgNGIHAAAYjdgBAABGI3YAAIDRiB0AAGA0YgcAABiN2AEAAEYjdgAAgNEsjZ3t27dryJAhSkxMlM1m01tvvRW07vf7NW3aNHXq1EkxMTFKT0/XsWPHgo45ffq0hg8fLrvdrri4OI0aNUpnz55twbMAAAChzNLYqaqqUs+ePVVQUHDR9blz52rhwoVaunSpdu/erXbt2ikjI0Pnzp0LHDN8+HAdOnRIW7Zs0caNG7V9+3aNGTOmpU4BAACEuAgrn3zQoEEaNGjQRdf8fr8WLFigKVOm6P7775ckvf7663I6nXrrrbf08MMP68iRI9q0aZP27t2r3r17S5IWLVqk++67T7///e+VmJjYYucCAABCU8hes3P8+HF5PB6lp6cH9jkcDqWlpam4uFiSVFxcrLi4uEDoSFJ6errCwsK0e/fuSz52dXW1fD5f0AYAAMwUsrHj8XgkSU6nM2i/0+kMrHk8HiUkJAStR0REKD4+PnDMxeTn58vhcAS2pKSkJp4eAACEipCNneaUl5enysrKwFZWVmb1SAAAoJmEbOy4XC5JktfrDdrv9XoDay6XS+Xl5UHrdXV1On36dOCYi4mKipLdbg/aAACAmUI2drp27SqXy6WioqLAPp/Pp927d8vtdkuS3G63KioqVFJSEjjm/fffV0NDg9LS0lp8ZgAAEHos/TTW2bNn9dlnnwVuHz9+XAcOHFB8fLw6d+6s8ePH6/nnn1e3bt3UtWtXTZ06VYmJiRo6dKgkqUePHho4cKBGjx6tpUuXqra2Vjk5OXr44Yf5JBYAAJBkcex89NFHuueeewK3c3NzJUlZWVlauXKlnn76aVVVVWnMmDGqqKhQv379tGnTJkVHRwfus3r1auXk5Ojee+9VWFiYMjMztXDhwhY/FwAAEJosjZ27775bfr//kus2m02zZs3SrFmzLnlMfHy8CgsLm2M8AABggJC9ZgcAAKApEDsAAMBoxA4AADAasQMAAIxG7AAAAKMROwAAwGjEDgAAMBqxAwAAjEbsAAAAoxE7AADAaMQOAAAwGrEDAACMRuwAAACjETsAAMBoxA4AADAasQMAAIxG7AAAAKMROwAAwGjEDgAAMBqxAwAAjEbsAAAAoxE7AADAaMQOAAAwGrEDAACMRuwAAACjETsAAMBoxA4AADAasQMAAIxG7AAAAKMROwAAwGjEDgAAMBqxAwAAjEbsAAAAoxE7AADAaMQOAAAwGrEDAACMRuwAAACjETsAAMBoxA4AADAasQMAAIxG7AAAAKMROwAAwGjEDgAAMBqxAwAAjEbsAAAAoxE7AADAaMQOAAAwGrEDAACMRuwAAACjETsAAMBoxsROQUGBrr/+ekVHRystLU179uyxeiQAABACjIidN998U7m5uZo+fbr27dunnj17KiMjQ+Xl5VaPBgAALGZE7Lz00ksaPXq0Hn30USUnJ2vp0qVq27atXnvtNatHAwAAFouweoCrVVNTo5KSEuXl5QX2hYWFKT09XcXFxRe9T3V1taqrqwO3KysrJUk+n6/Z5qyv/nezPTbQmjXnz11L4ecbuLjm/vk+//h+v/97j2v1sfPVV1+pvr5eTqczaL/T6dQnn3xy0fvk5+dr5syZF+xPSkpqlhkBXJpj0eNWjwCgmbTUz/eZM2fkcDguud7qY6cx8vLylJubG7jd0NCg06dPq0OHDrLZbBZOhpbg8/mUlJSksrIy2e12q8cB0IT4+f5h8fv9OnPmjBITE7/3uFYfO9dee63Cw8Pl9XqD9nu9XrlcroveJyoqSlFRUUH74uLimmtEhCi73c5/hoCh+Pn+4fi+V3TOa/UXKEdGRio1NVVFRUWBfQ0NDSoqKpLb7bZwMgAAEApa/Ss7kpSbm6usrCz17t1bd955pxYsWKCqqio9+uijVo8GAAAsZkTsPPTQQzp16pSmTZsmj8ejXr16adOmTRdctAxI372NOX369AveygTQ+vHzjYux+f/X57UAAABasVZ/zQ4AAMD3IXYAAIDRiB0AAGA0YgcAABiN2MEPSkFBga6//npFR0crLS1Ne/bssXokAE1g+/btGjJkiBITE2Wz2fTWW29ZPRJCCLGDH4w333xTubm5mj59uvbt26eePXsqIyND5eXlVo8G4CpVVVWpZ8+eKigosHoUhCA+eo4fjLS0NN1xxx16+eWXJX33TdtJSUkaN26cnnnmGYunA9BUbDab1q9fr6FDh1o9CkIEr+zgB6GmpkYlJSVKT08P7AsLC1N6erqKi4stnAwA0NyIHfwgfPXVV6qvr7/gW7WdTqc8Ho9FUwEAWgKxAwAAjEbs4Afh2muvVXh4uLxeb9B+r9crl8tl0VQAgJZA7OAHITIyUqmpqSoqKgrsa2hoUFFRkdxut4WTAQCamxF/9Ry4HLm5ucrKylLv3r115513asGCBaqqqtKjjz5q9WgArtLZs2f12WefBW4fP35cBw4cUHx8vDp37mzhZAgFfPQcPygvv/yy5s2bJ4/Ho169emnhwoVKS0uzeiwAV+mDDz7QPffcc8H+rKwsrVy5suUHQkghdgAAgNG4ZgcAABiN2AEAAEYjdgAAgNGIHQAAYDRiBwAAGI3YAQAARiN2AACA0YgdAABgNGIHAAAYjdgBAABGI3YAAIDRiB0Arda6deuUkpKimJgYdejQQenp6aqqqpIk/fGPf1SPHj0UHR2t7t27a/HixYH7PfbYY7r11ltVXV0tSaqpqdFtt92mkSNHWnIeAJoXsQOgVTp58qQeeeQRPfbYYzpy5Ig++OADDRs2TH6/X6tXr9a0adM0e/ZsHTlyRHPmzNHUqVO1atUqSdLChQtVVVWlZ555RpL07LPPqqKiQi+//LKVpwSgmURYPQAANMbJkydVV1enYcOGqUuXLpKklJQUSdL06dP14osvatiwYZKkrl276vDhw3rllVeUlZWl2NhYvfHGG7rrrrvUvn17LViwQFu3bpXdbrfsfAA0H5vf7/dbPQQAXKn6+nplZGRoz549ysjI0IABA/TLX/5SkZGRio2NVUxMjMLC/v/F67q6OjkcDnm93sC+3/72t8rPz9fkyZP1wgsvWHEaAFoAr+wAaJXCw8O1ZcsW7dy5U5s3b9aiRYv07LPPasOGDZKkZcuWKS0t7YL7nNfQ0KAdO3YoPDxcn332WYvODqBlcc0OgFbLZrOpb9++mjlzpvbv36/IyEjt2LFDiYmJ+uKLL3TjjTcGbV27dg3cd968efrkk0+0bds2bdq0SStWrLDwTAA0J17ZAdAq7d69W0VFRRowYIASEhK0e/dunTp1Sj169NDMmTP1xBNPyOFwaODAgaqurtZHH32kb775Rrm5udq/f7+mTZumdevWqW/fvnrppZf05JNP6q677tINN9xg9akBaGJcswOgVTpy5IgmTJigffv2yefzqUuXLho3bpxycnIkSYWFhZo3b54OHz6sdu3aKSUlRePHj9egQYOUmpqqfv366ZVXXgk83v3336+vvvpK27dvD3q7C0DrR+wAAACjcc0OAAAwGrEDAACMRuwAAACjETsAAMBoxA4AADAasQMAAIxG7AAAAKMROwAAwGjEDgAAMBqxAwAAjEbsAAAAo/0fWeVT+bj92jYAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "####################### sibsp #######################\n",
            "   sibsp  Ratio\n",
            "0    608 68.238\n",
            "1    209 23.457\n",
            "2     28  3.143\n",
            "4     18  2.020\n",
            "3     16  1.796\n",
            "8      7  0.786\n",
            "5      5  0.561\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "####################### parch #######################\n",
            "   parch  Ratio\n",
            "0    678 76.094\n",
            "1    118 13.244\n",
            "2     80  8.979\n",
            "5      5  0.561\n",
            "3      5  0.561\n",
            "4      4  0.449\n",
            "6      1  0.112\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "####################### adult_male #######################\n",
            "   adult_male  Ratio\n",
            "1         537 60.269\n",
            "0         354 39.731\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "####################### alive #######################\n",
            "   alive  Ratio\n",
            "0    549 61.616\n",
            "1    342 38.384\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "####################### alone #######################\n",
            "   alone  Ratio\n",
            "1    537 60.269\n",
            "0    354 39.731\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def rare_analyser(dataframe, target):\n",
        "  cat_cols, num_cols, cat_but_car, num_but_cat = grab_col_names(dataframe)\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\")"
      ],
      "metadata": {
        "id": "JzKVxKvEURSN"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rare_analyser(df, \"survived\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iCRE2xIBUr39",
        "outputId": "e519ecd7-4012-4d1c-d7f5-6468102adb3e"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "embarked : 3\n",
            "   COUNT  Ratio  TARGET_MEAN\n",
            "C    168  0.189        0.554\n",
            "Q     77  0.086        0.390\n",
            "S    644  0.723        0.337\n",
            "\n",
            "class : 3\n",
            "        COUNT  Ratio  TARGET_MEAN\n",
            "First     216  0.242        0.630\n",
            "Second    184  0.207        0.473\n",
            "Third     491  0.551        0.242\n",
            "\n",
            "who : 3\n",
            "       COUNT  Ratio  TARGET_MEAN\n",
            "child     83  0.093        0.590\n",
            "man      537  0.603        0.164\n",
            "woman    271  0.304        0.756\n",
            "\n",
            "deck : 7\n",
            "   COUNT  Ratio  TARGET_MEAN\n",
            "A     15  0.017        0.467\n",
            "B     47  0.053        0.745\n",
            "C     59  0.066        0.593\n",
            "D     33  0.037        0.758\n",
            "E     32  0.036        0.750\n",
            "F     13  0.015        0.615\n",
            "G      4  0.004        0.500\n",
            "\n",
            "embark_town : 3\n",
            "             COUNT  Ratio  TARGET_MEAN\n",
            "Cherbourg      168  0.189        0.554\n",
            "Queenstown      77  0.086        0.390\n",
            "Southampton    644  0.723        0.337\n",
            "\n",
            "survived : 2\n",
            "   COUNT  Ratio  TARGET_MEAN\n",
            "0    549  0.616        0.000\n",
            "1    342  0.384        1.000\n",
            "\n",
            "pclass : 3\n",
            "   COUNT  Ratio  TARGET_MEAN\n",
            "1    216  0.242        0.630\n",
            "2    184  0.207        0.473\n",
            "3    491  0.551        0.242\n",
            "\n",
            "sex : 2\n",
            "   COUNT  Ratio  TARGET_MEAN\n",
            "0    314  0.352        0.742\n",
            "1    577  0.648        0.189\n",
            "\n",
            "sibsp : 7\n",
            "   COUNT  Ratio  TARGET_MEAN\n",
            "0    608  0.682        0.345\n",
            "1    209  0.235        0.536\n",
            "2     28  0.031        0.464\n",
            "3     16  0.018        0.250\n",
            "4     18  0.020        0.167\n",
            "5      5  0.006        0.000\n",
            "8      7  0.008        0.000\n",
            "\n",
            "parch : 7\n",
            "   COUNT  Ratio  TARGET_MEAN\n",
            "0    678  0.761        0.344\n",
            "1    118  0.132        0.551\n",
            "2     80  0.090        0.500\n",
            "3      5  0.006        0.600\n",
            "4      4  0.004        0.000\n",
            "5      5  0.006        0.200\n",
            "6      1  0.001        0.000\n",
            "\n",
            "adult_male : 2\n",
            "   COUNT  Ratio  TARGET_MEAN\n",
            "0    354  0.397        0.718\n",
            "1    537  0.603        0.164\n",
            "\n",
            "alive : 2\n",
            "   COUNT  Ratio  TARGET_MEAN\n",
            "0    549  0.616        0.000\n",
            "1    342  0.384        1.000\n",
            "\n",
            "alone : 2\n",
            "   COUNT  Ratio  TARGET_MEAN\n",
            "0    354  0.397        0.506\n",
            "1    537  0.603        0.304\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def rare_encoder(dataframe, rare_prec):\n",
        "  temp_df = dataframe.copy()\n",
        "  rare_columns = [col for col in dataframe.columns if temp_df[col].dtypes in [\"category\", \"object\"]\n",
        "                  and (temp_df[col].value_counts() / len(temp_df) < rare_prec).any(axis=None)]\n",
        "  for col in rare_columns:\n",
        "    tmp = temp_df[col].value_counts() / len(temp_df)\n",
        "    rare_labels = tmp[tmp < rare_prec].index\n",
        "    temp_df[col] = np.where(temp_df[col].isin(rare_labels), \"Rare\", temp_df[col])\n",
        "  return temp_df"
      ],
      "metadata": {
        "id": "kD9u19W3UuNK"
      },
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "new_df = rare_encoder(df, 0.01)"
      ],
      "metadata": {
        "id": "qbSpnkORXiDG"
      },
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "new_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "qTd67Co8ZZz_",
        "outputId": "828e8eab-e8ae-43da-8667-bae882664a96"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   survived  pclass  sex    age  sibsp  parch   fare embarked  class    who  adult_male deck  embark_town  alive  alone\n",
              "0         0       3    1 22.000      1      0  7.250        S  Third    man           1  NaN  Southampton      0      0\n",
              "1         1       1    0 38.000      1      0 71.283        C  First  woman           0    C    Cherbourg      1      0\n",
              "2         1       3    0 26.000      0      0  7.925        S  Third  woman           0  NaN  Southampton      1      1\n",
              "3         1       1    0 35.000      1      0 53.100        S  First  woman           0    C  Southampton      1      0\n",
              "4         0       3    1 35.000      0      0  8.050        S  Third    man           1  NaN  Southampton      0      1"
            ],
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              "        vertical-align: middle;\n",
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              "      <th>survived</th>\n",
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              "      <td>26.000</td>\n",
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              "      <td>0</td>\n",
              "      <td>7.925</td>\n",
              "      <td>S</td>\n",
              "      <td>Third</td>\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
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              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "type": "dataframe",
              "variable_name": "new_df",
              "summary": "{\n  \"name\": \"new_df\",\n  \"rows\": 891,\n  \"fields\": [\n    {\n      \"column\": \"survived\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pclass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          3,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sex\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14.526497332334042,\n        \"min\": 0.42,\n        \"max\": 80.0,\n        \"num_unique_values\": 88,\n        \"samples\": [\n          0.75,\n          22.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sibsp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 8,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"parch\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 6,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 49.6934285971809,\n        \"min\": 0.0,\n        \"max\": 512.3292,\n        \"num_unique_values\": 248,\n        \"samples\": [\n          11.2417,\n          51.8625\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"embarked\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"S\",\n          \"C\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"class\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Third\",\n          \"First\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"who\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"man\",\n          \"woman\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"adult_male\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"deck\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"C\",\n          \"E\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"embark_town\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Southampton\",\n          \"Cherbourg\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"alive\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"alone\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "new_df[\"deck\"].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HRuCJt5RZdEk",
        "outputId": "d75a5abb-5ee9-45d7-e38f-f4a1f48ccccf"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "C       59\n",
              "B       47\n",
              "D       33\n",
              "E       32\n",
              "A       15\n",
              "F       13\n",
              "Rare     4\n",
              "Name: deck, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "5n8khzH5Zcxy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rare_analyser(df, \"survived\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U4kj6ycxXv8g",
        "outputId": "562e6270-d342-4db7-e60d-51f39110225d"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "embarked : 3\n",
            "   COUNT  Ratio  TARGET_MEAN\n",
            "C    168  0.189        0.554\n",
            "Q     77  0.086        0.390\n",
            "S    644  0.723        0.337\n",
            "\n",
            "class : 3\n",
            "        COUNT  Ratio  TARGET_MEAN\n",
            "First     216  0.242        0.630\n",
            "Second    184  0.207        0.473\n",
            "Third     491  0.551        0.242\n",
            "\n",
            "who : 3\n",
            "       COUNT  Ratio  TARGET_MEAN\n",
            "child     83  0.093        0.590\n",
            "man      537  0.603        0.164\n",
            "woman    271  0.304        0.756\n",
            "\n",
            "deck : 7\n",
            "   COUNT  Ratio  TARGET_MEAN\n",
            "A     15  0.017        0.467\n",
            "B     47  0.053        0.745\n",
            "C     59  0.066        0.593\n",
            "D     33  0.037        0.758\n",
            "E     32  0.036        0.750\n",
            "F     13  0.015        0.615\n",
            "G      4  0.004        0.500\n",
            "\n",
            "embark_town : 3\n",
            "             COUNT  Ratio  TARGET_MEAN\n",
            "Cherbourg      168  0.189        0.554\n",
            "Queenstown      77  0.086        0.390\n",
            "Southampton    644  0.723        0.337\n",
            "\n",
            "survived : 2\n",
            "   COUNT  Ratio  TARGET_MEAN\n",
            "0    549  0.616        0.000\n",
            "1    342  0.384        1.000\n",
            "\n",
            "pclass : 3\n",
            "   COUNT  Ratio  TARGET_MEAN\n",
            "1    216  0.242        0.630\n",
            "2    184  0.207        0.473\n",
            "3    491  0.551        0.242\n",
            "\n",
            "sex : 2\n",
            "   COUNT  Ratio  TARGET_MEAN\n",
            "0    314  0.352        0.742\n",
            "1    577  0.648        0.189\n",
            "\n",
            "sibsp : 7\n",
            "   COUNT  Ratio  TARGET_MEAN\n",
            "0    608  0.682        0.345\n",
            "1    209  0.235        0.536\n",
            "2     28  0.031        0.464\n",
            "3     16  0.018        0.250\n",
            "4     18  0.020        0.167\n",
            "5      5  0.006        0.000\n",
            "8      7  0.008        0.000\n",
            "\n",
            "parch : 7\n",
            "   COUNT  Ratio  TARGET_MEAN\n",
            "0    678  0.761        0.344\n",
            "1    118  0.132        0.551\n",
            "2     80  0.090        0.500\n",
            "3      5  0.006        0.600\n",
            "4      4  0.004        0.000\n",
            "5      5  0.006        0.200\n",
            "6      1  0.001        0.000\n",
            "\n",
            "adult_male : 2\n",
            "   COUNT  Ratio  TARGET_MEAN\n",
            "0    354  0.397        0.718\n",
            "1    537  0.603        0.164\n",
            "\n",
            "alive : 2\n",
            "   COUNT  Ratio  TARGET_MEAN\n",
            "0    549  0.616        0.000\n",
            "1    342  0.384        1.000\n",
            "\n",
            "alone : 2\n",
            "   COUNT  Ratio  TARGET_MEAN\n",
            "0    354  0.397        0.506\n",
            "1    537  0.603        0.304\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Rm6Pxb6BY6uD"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}