Categorical_Encoding.ipynb

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  "metadata": {
    "colab": {
      "name": "Categorical Encoding.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyPxp4DnWs7o6MmCborBmiSx",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/kochlisGit/Advanced-ML/blob/main/Categorical_Encoding.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install tensorflow_addons\n",
        "!pip install category_encoders"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dV7sBRyA0laj",
        "outputId": "e75f1c7f-ddc6-4c36-a2d0-41ac2cd8c0e7"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: tensorflow_addons in /usr/local/lib/python3.7/dist-packages (0.16.1)\n",
            "Requirement already satisfied: typeguard>=2.7 in /usr/local/lib/python3.7/dist-packages (from tensorflow_addons) (2.7.1)\n",
            "Requirement already satisfied: category_encoders in /usr/local/lib/python3.7/dist-packages (2.4.0)\n",
            "Requirement already satisfied: patsy>=0.5.1 in /usr/local/lib/python3.7/dist-packages (from category_encoders) (0.5.2)\n",
            "Requirement already satisfied: statsmodels>=0.9.0 in /usr/local/lib/python3.7/dist-packages (from category_encoders) (0.10.2)\n",
            "Requirement already satisfied: scipy>=1.0.0 in /usr/local/lib/python3.7/dist-packages (from category_encoders) (1.4.1)\n",
            "Requirement already satisfied: scikit-learn>=0.20.0 in /usr/local/lib/python3.7/dist-packages (from category_encoders) (1.0.2)\n",
            "Requirement already satisfied: pandas>=0.21.1 in /usr/local/lib/python3.7/dist-packages (from category_encoders) (1.3.5)\n",
            "Requirement already satisfied: numpy>=1.14.0 in /usr/local/lib/python3.7/dist-packages (from category_encoders) (1.21.6)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.21.1->category_encoders) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.21.1->category_encoders) (2022.1)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from patsy>=0.5.1->category_encoders) (1.15.0)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.20.0->category_encoders) (1.1.0)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.20.0->category_encoders) (3.1.0)\n"
          ]
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    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "2INjkFXMDzMg"
      },
      "outputs": [],
      "source": [
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import requests\n",
        "from zipfile import ZipFile\n",
        "\n",
        "dataset_url = 'https://storage.googleapis.com/kaggle-data-sets/225/498/bundle/archive.zip?X-Goog-Algorithm=GOOG4-RSA-SHA256&X-Goog-Credential=gcp-kaggle-com%40kaggle-161607.iam.gserviceaccount.com%2F20220430%2Fauto%2Fstorage%2Fgoog4_request&X-Goog-Date=20220430T104007Z&X-Goog-Expires=259199&X-Goog-SignedHeaders=host&X-Goog-Signature=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'\n",
        "download_filename = 'download.zip'\n",
        "content_filename = 'adult.csv'\n",
        "\n",
        "req = requests.get(dataset_url)\n",
        "with open(download_filename, 'wb') as output_file:\n",
        "  output_file.write(req.content)\n",
        "print('Download completed!\\n')\n",
        "\n",
        "zf = ZipFile(download_filename)\n",
        "data = pd.read_csv(zf.open(content_filename)).dropna()\n",
        "data['occupation'] = data['occupation'].replace({'?': 'Unknown'})\n",
        "data['workclass'] = data['workclass'].replace({'?': 'Unknown'})\n",
        "data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "id": "GttbPkK5EDQf",
        "outputId": "3c819a95-cd2c-410e-cd11-5affbd49822c"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Download completed!\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       age workclass  fnlwgt     education  education.num      marital.status  \\\n",
              "0       90   Unknown   77053       HS-grad              9             Widowed   \n",
              "1       82   Private  132870       HS-grad              9             Widowed   \n",
              "2       66   Unknown  186061  Some-college             10             Widowed   \n",
              "3       54   Private  140359       7th-8th              4            Divorced   \n",
              "4       41   Private  264663  Some-college             10           Separated   \n",
              "...    ...       ...     ...           ...            ...                 ...   \n",
              "32556   22   Private  310152  Some-college             10       Never-married   \n",
              "32557   27   Private  257302    Assoc-acdm             12  Married-civ-spouse   \n",
              "32558   40   Private  154374       HS-grad              9  Married-civ-spouse   \n",
              "32559   58   Private  151910       HS-grad              9             Widowed   \n",
              "32560   22   Private  201490       HS-grad              9       Never-married   \n",
              "\n",
              "              occupation   relationship   race     sex  capital.gain  \\\n",
              "0                Unknown  Not-in-family  White  Female             0   \n",
              "1        Exec-managerial  Not-in-family  White  Female             0   \n",
              "2                Unknown      Unmarried  Black  Female             0   \n",
              "3      Machine-op-inspct      Unmarried  White  Female             0   \n",
              "4         Prof-specialty      Own-child  White  Female             0   \n",
              "...                  ...            ...    ...     ...           ...   \n",
              "32556    Protective-serv  Not-in-family  White    Male             0   \n",
              "32557       Tech-support           Wife  White  Female             0   \n",
              "32558  Machine-op-inspct        Husband  White    Male             0   \n",
              "32559       Adm-clerical      Unmarried  White  Female             0   \n",
              "32560       Adm-clerical      Own-child  White    Male             0   \n",
              "\n",
              "       capital.loss  hours.per.week native.country income  \n",
              "0              4356              40  United-States  <=50K  \n",
              "1              4356              18  United-States  <=50K  \n",
              "2              4356              40  United-States  <=50K  \n",
              "3              3900              40  United-States  <=50K  \n",
              "4              3900              40  United-States  <=50K  \n",
              "...             ...             ...            ...    ...  \n",
              "32556             0              40  United-States  <=50K  \n",
              "32557             0              38  United-States  <=50K  \n",
              "32558             0              40  United-States   >50K  \n",
              "32559             0              40  United-States  <=50K  \n",
              "32560             0              20  United-States  <=50K  \n",
              "\n",
              "[32561 rows x 15 columns]"
            ],
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              "      <th>2</th>\n",
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              "      <td>186061</td>\n",
              "      <td>Some-college</td>\n",
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              "      <td>7th-8th</td>\n",
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              "      <td>Unmarried</td>\n",
              "      <td>White</td>\n",
              "      <td>Female</td>\n",
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              "      <td>3900</td>\n",
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              "      <th>4</th>\n",
              "      <td>41</td>\n",
              "      <td>Private</td>\n",
              "      <td>264663</td>\n",
              "      <td>Some-college</td>\n",
              "      <td>10</td>\n",
              "      <td>Separated</td>\n",
              "      <td>Prof-specialty</td>\n",
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              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "To better understand the categorical encoding techniques, let's first print the unique values for each column."
      ],
      "metadata": {
        "id": "w8R9g5_MHPsP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "categorical_columns = ['workclass', 'education', 'marital.status', 'occupation', 'relationship', 'race', 'sex', 'native.country']\n",
        "\n",
        "for column in categorical_columns:\n",
        "  print('column: {},\\tunique values: {}'.format(column, data[column].unique().shape[0]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zWTbaFc3HPNx",
        "outputId": "ea358583-284f-4038-9f6c-12635e9013d5"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "column: workclass,\tunique values: 9\n",
            "column: education,\tunique values: 16\n",
            "column: marital.status,\tunique values: 7\n",
            "column: occupation,\tunique values: 15\n",
            "column: relationship,\tunique values: 6\n",
            "column: race,\tunique values: 5\n",
            "column: sex,\tunique values: 2\n",
            "column: native.country,\tunique values: 42\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Categorical Column  | Unique Values\n",
        "-------------------|------------------\n",
        "workclass       | 9\n",
        "education       | 16\n",
        "marital.status       |7 \n",
        "occupation       | 15\n",
        "relationship       | 6 \n",
        "race       | 5\n",
        "sex       | 2\n",
        "native.country       | 42\n",
        "income      | 2\n"
      ],
      "metadata": {
        "id": "nvKLZDS3IVl-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Binary Encoding of Targets\n",
        "\n",
        "Since there are 2 categories for the **income**: *{<=50K, >50K}*, we can use binary encoding."
      ],
      "metadata": {
        "id": "0wd0vZhmGG79"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "targets = data['income'].replace({'<=50K': 0, '>50K': 1}).to_numpy()\n",
        "data = data.drop(columns=['income'])\n",
        "targets"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I8J6nQ5aF4Qt",
        "outputId": "475f7e02-c7f5-4226-f905-2520a1a9ade4"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, ..., 1, 0, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Splitting dataset to train-test samples."
      ],
      "metadata": {
        "id": "-MMD1nms1wdx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "test_size = 0.1\n",
        "random_state=0\n",
        "\n",
        "x_train, x_test, y_train, y_test = train_test_split(\n",
        "    data,\n",
        "    targets,\n",
        "    test_size=test_size,\n",
        "    random_state=random_state\n",
        ")\n",
        "x_train.shape, x_test.shape, y_train.shape, y_test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "leINNlh91tWH",
        "outputId": "d8ccd756-491a-439c-b766-4a387b17cc4a"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((29304, 14), (3257, 14), (29304,), (3257,))"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Ordinal Encoding\n",
        "\n",
        "In ordinal encoding, we replace each categorical variable with an integer value (e.g. 0, 1, 2, etc.). In this case, retaining the order is important."
      ],
      "metadata": {
        "id": "dsyxoZWoFHlU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import OrdinalEncoder\n",
        "\n",
        "ordinal_encoder = OrdinalEncoder()\n",
        "ordinal_encoder.fit(data)\n",
        "x_train = ordinal_encoder.transform(x_train)\n",
        "x_test = ordinal_encoder.transform(x_test)\n",
        "x_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jYqcsrIiFsK_",
        "outputId": "e7790dba-7634-40ad-c259-dcea745f69ea"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1.8000e+01, 3.0000e+00, 1.5732e+04, ..., 0.0000e+00, 3.9000e+01,\n",
              "        3.9000e+01],\n",
              "       [1.1000e+01, 3.0000e+00, 3.2850e+03, ..., 0.0000e+00, 2.2000e+01,\n",
              "        3.9000e+01],\n",
              "       [3.8000e+01, 3.0000e+00, 2.3630e+03, ..., 0.0000e+00, 3.9000e+01,\n",
              "        3.9000e+01],\n",
              "       ...,\n",
              "       [9.0000e+00, 3.0000e+00, 1.6120e+03, ..., 0.0000e+00, 5.0000e+01,\n",
              "        3.9000e+01],\n",
              "       [2.7000e+01, 3.0000e+00, 5.1460e+03, ..., 0.0000e+00, 3.9000e+01,\n",
              "        3.9000e+01],\n",
              "       [2.2000e+01, 3.0000e+00, 1.4687e+04, ..., 0.0000e+00, 3.9000e+01,\n",
              "        3.9000e+01]])"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Building an MLP (Multi-Layer Perceptron) classifier."
      ],
      "metadata": {
        "id": "PwpwN11Vxqzj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "import tensorflow_addons as tfa\n",
        "\n",
        "\n",
        "def build_model(input_size):\n",
        "  model = tf.keras.models.Sequential()\n",
        "  model.add(tf.keras.layers.Input(input_size))\n",
        "  model.add(tf.keras.layers.GaussianNoise(stddev=0.1))\n",
        "  model.add(tf.keras.layers.Dense(units=128, activation='gelu', use_bias=False))\n",
        "  model.add(tf.keras.layers.BatchNormalization())\n",
        "  model.add(tf.keras.layers.Dropout(rate=0.2))\n",
        "  model.add(tf.keras.layers.Dense(units=64, kernel_regularizer='l1', use_bias=False))\n",
        "  model.add(tf.keras.layers.BatchNormalization())\n",
        "  model.add(tf.keras.layers.Dense(units=1, activation='sigmoid'))\n",
        "\n",
        "  model.compile(\n",
        "      optimizer=tfa.optimizers.Yogi(learning_rate=0.001),\n",
        "      loss=tf.keras.losses.BinaryCrossentropy(label_smoothing=0.1),\n",
        "      metrics=['accuracy']\n",
        "  )\n",
        "  return model\n",
        "\n",
        "\n",
        "model = build_model(data.shape[1:])\n",
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "udaLpISGx5IT",
        "outputId": "38ed75a1-ac74-4e3a-ad38-cc896f12fdd4"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " gaussian_noise (GaussianNoi  (None, 14)               0         \n",
            " se)                                                             \n",
            "                                                                 \n",
            " dense (Dense)               (None, 128)               1792      \n",
            "                                                                 \n",
            " batch_normalization (BatchN  (None, 128)              512       \n",
            " ormalization)                                                   \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 128)               0         \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 64)                8192      \n",
            "                                                                 \n",
            " batch_normalization_1 (Batc  (None, 64)               256       \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 1)                 65        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 10,817\n",
            "Trainable params: 10,433\n",
            "Non-trainable params: 384\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Training the neural network with the ordinal variables."
      ],
      "metadata": {
        "id": "Wviiljaj3zoa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "batch_size = 32\n",
        "epochs = 20\n",
        "shuffle=True\n",
        "\n",
        "model.fit(x=x_train, y=y_train, batch_size=batch_size, epochs=epochs, shuffle=shuffle, validation_data=(x_test, y_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4wrdfcFl3ylx",
        "outputId": "2b73ad1d-b4bd-44e1-e55e-d3bab9b6b859"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "916/916 [==============================] - 22s 13ms/step - loss: 1.6441 - accuracy: 0.7641 - val_loss: 0.6491 - val_accuracy: 0.7710\n",
            "Epoch 2/20\n",
            "916/916 [==============================] - 7s 8ms/step - loss: 0.6042 - accuracy: 0.7877 - val_loss: 0.6521 - val_accuracy: 0.7731\n",
            "Epoch 3/20\n",
            "916/916 [==============================] - 7s 8ms/step - loss: 0.5706 - accuracy: 0.7942 - val_loss: 0.9231 - val_accuracy: 0.5388\n",
            "Epoch 4/20\n",
            "916/916 [==============================] - 7s 8ms/step - loss: 0.5486 - accuracy: 0.8001 - val_loss: 0.6819 - val_accuracy: 0.6460\n",
            "Epoch 5/20\n",
            "916/916 [==============================] - 7s 7ms/step - loss: 0.5369 - accuracy: 0.8031 - val_loss: 0.5629 - val_accuracy: 0.7928\n",
            "Epoch 6/20\n",
            "916/916 [==============================] - 7s 8ms/step - loss: 0.5313 - accuracy: 0.8046 - val_loss: 0.5119 - val_accuracy: 0.8158\n",
            "Epoch 7/20\n",
            "916/916 [==============================] - 7s 8ms/step - loss: 0.5266 - accuracy: 0.8032 - val_loss: 0.6646 - val_accuracy: 0.6736\n",
            "Epoch 8/20\n",
            "916/916 [==============================] - 7s 8ms/step - loss: 0.5189 - accuracy: 0.8042 - val_loss: 0.4970 - val_accuracy: 0.8262\n",
            "Epoch 9/20\n",
            "916/916 [==============================] - 7s 8ms/step - loss: 0.5140 - accuracy: 0.8041 - val_loss: 0.4953 - val_accuracy: 0.8155\n",
            "Epoch 10/20\n",
            "916/916 [==============================] - 7s 8ms/step - loss: 0.5113 - accuracy: 0.8051 - val_loss: 0.5406 - val_accuracy: 0.7839\n",
            "Epoch 11/20\n",
            "916/916 [==============================] - 7s 8ms/step - loss: 0.5072 - accuracy: 0.8070 - val_loss: 0.4805 - val_accuracy: 0.8268\n",
            "Epoch 12/20\n",
            "916/916 [==============================] - 8s 9ms/step - loss: 0.5071 - accuracy: 0.8057 - val_loss: 0.4838 - val_accuracy: 0.8253\n",
            "Epoch 13/20\n",
            "916/916 [==============================] - 7s 8ms/step - loss: 0.5051 - accuracy: 0.8053 - val_loss: 0.5125 - val_accuracy: 0.8161\n",
            "Epoch 14/20\n",
            "916/916 [==============================] - 7s 8ms/step - loss: 0.5037 - accuracy: 0.8069 - val_loss: 0.5076 - val_accuracy: 0.8072\n",
            "Epoch 15/20\n",
            "916/916 [==============================] - 7s 8ms/step - loss: 0.5039 - accuracy: 0.8063 - val_loss: 0.4861 - val_accuracy: 0.8161\n",
            "Epoch 16/20\n",
            "916/916 [==============================] - 7s 8ms/step - loss: 0.5009 - accuracy: 0.8072 - val_loss: 0.5607 - val_accuracy: 0.7989\n",
            "Epoch 17/20\n",
            "916/916 [==============================] - 7s 8ms/step - loss: 0.5018 - accuracy: 0.8067 - val_loss: 0.4826 - val_accuracy: 0.8314\n",
            "Epoch 18/20\n",
            "916/916 [==============================] - 7s 8ms/step - loss: 0.4979 - accuracy: 0.8073 - val_loss: 0.4835 - val_accuracy: 0.8296\n",
            "Epoch 19/20\n",
            "916/916 [==============================] - 7s 7ms/step - loss: 0.4992 - accuracy: 0.8084 - val_loss: 0.4754 - val_accuracy: 0.8330\n",
            "Epoch 20/20\n",
            "916/916 [==============================] - 7s 8ms/step - loss: 0.5012 - accuracy: 0.8067 - val_loss: 0.4785 - val_accuracy: 0.8207\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f786f050310>"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Best Validation Accuracy: **0.8330**"
      ],
      "metadata": {
        "id": "9CBpyAhU9MHa"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#One-Hot Encoding\n",
        "This cons of the ordinal encoding is that categorical variables become an **ordered** set of numbers. However, this could potentially trick the classifier into thinking that close numbers are similar (e.g. `Married and Divorced` values have the opposite meaning. However, `if Married = 0 and Divorced = 1` is assigned, then it might confuse the classifier into thinking that they are similar.\n",
        "\n",
        "One-Hot encoding takes care of the above disadvantage by separating the categorical columns into multiple binary columns (e.g. `Sex: {Male, Female}` becomes `s1, s2, where (s1, s2) = (1, 0) if Male and (0, 1) if Female`). This might work for a few unique variables. **However, if the number of unique values is large, then the dimensions of the dataset would become a very large sparsed matrix.**"
      ],
      "metadata": {
        "id": "W2Z7hTb3-H-S"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Dummy Encoding\n",
        "This is an improvement over One-Hot Encoding. Dummy encoding uses less N-1 features to represent N categories. For example, `Marital Status: {Married, Divorced, Engaged} becomes s1, s2, where (s1, s2) = (0, 0) if Married, (0, 1) if Divorced and (1, 1) if engaged).` However, in this dataset "
      ],
      "metadata": {
        "id": "WXuYfWwMGbow"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dummy_data = pd.get_dummies(data)\n",
        "dummy_data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 505
        },
        "id": "2dctwghYRQ35",
        "outputId": "bda43daa-5bd8-4e55-a469-d4f4d2d9a9d4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       age  fnlwgt  education.num  capital.gain  capital.loss  hours.per.week  \\\n",
              "0       90   77053              9             0          4356              40   \n",
              "1       82  132870              9             0          4356              18   \n",
              "2       66  186061             10             0          4356              40   \n",
              "3       54  140359              4             0          3900              40   \n",
              "4       41  264663             10             0          3900              40   \n",
              "...    ...     ...            ...           ...           ...             ...   \n",
              "32556   22  310152             10             0             0              40   \n",
              "32557   27  257302             12             0             0              38   \n",
              "32558   40  154374              9             0             0              40   \n",
              "32559   58  151910              9             0             0              40   \n",
              "32560   22  201490              9             0             0              20   \n",
              "\n",
              "       workclass_Federal-gov  workclass_Local-gov  workclass_Never-worked  \\\n",
              "0                          0                    0                       0   \n",
              "1                          0                    0                       0   \n",
              "2                          0                    0                       0   \n",
              "3                          0                    0                       0   \n",
              "4                          0                    0                       0   \n",
              "...                      ...                  ...                     ...   \n",
              "32556                      0                    0                       0   \n",
              "32557                      0                    0                       0   \n",
              "32558                      0                    0                       0   \n",
              "32559                      0                    0                       0   \n",
              "32560                      0                    0                       0   \n",
              "\n",
              "       workclass_Private  ...  native.country_Portugal  \\\n",
              "0                      0  ...                        0   \n",
              "1                      1  ...                        0   \n",
              "2                      0  ...                        0   \n",
              "3                      1  ...                        0   \n",
              "4                      1  ...                        0   \n",
              "...                  ...  ...                      ...   \n",
              "32556                  1  ...                        0   \n",
              "32557                  1  ...                        0   \n",
              "32558                  1  ...                        0   \n",
              "32559                  1  ...                        0   \n",
              "32560                  1  ...                        0   \n",
              "\n",
              "       native.country_Puerto-Rico  native.country_Scotland  \\\n",
              "0                               0                        0   \n",
              "1                               0                        0   \n",
              "2                               0                        0   \n",
              "3                               0                        0   \n",
              "4                               0                        0   \n",
              "...                           ...                      ...   \n",
              "32556                           0                        0   \n",
              "32557                           0                        0   \n",
              "32558                           0                        0   \n",
              "32559                           0                        0   \n",
              "32560                           0                        0   \n",
              "\n",
              "       native.country_South  native.country_Taiwan  native.country_Thailand  \\\n",
              "0                         0                      0                        0   \n",
              "1                         0                      0                        0   \n",
              "2                         0                      0                        0   \n",
              "3                         0                      0                        0   \n",
              "4                         0                      0                        0   \n",
              "...                     ...                    ...                      ...   \n",
              "32556                     0                      0                        0   \n",
              "32557                     0                      0                        0   \n",
              "32558                     0                      0                        0   \n",
              "32559                     0                      0                        0   \n",
              "32560                     0                      0                        0   \n",
              "\n",
              "       native.country_Trinadad&Tobago  native.country_United-States  \\\n",
              "0                                   0                             1   \n",
              "1                                   0                             1   \n",
              "2                                   0                             1   \n",
              "3                                   0                             1   \n",
              "4                                   0                             1   \n",
              "...                               ...                           ...   \n",
              "32556                               0                             1   \n",
              "32557                               0                             1   \n",
              "32558                               0                             1   \n",
              "32559                               0                             1   \n",
              "32560                               0                             1   \n",
              "\n",
              "       native.country_Vietnam  native.country_Yugoslavia  \n",
              "0                           0                          0  \n",
              "1                           0                          0  \n",
              "2                           0                          0  \n",
              "3                           0                          0  \n",
              "4                           0                          0  \n",
              "...                       ...                        ...  \n",
              "32556                       0                          0  \n",
              "32557                       0                          0  \n",
              "32558                       0                          0  \n",
              "32559                       0                          0  \n",
              "32560                       0                          0  \n",
              "\n",
              "[32561 rows x 108 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-eca596a7-03ff-47e5-baa2-92d2fc2b79c4\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
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              "    .dataframe thead th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>fnlwgt</th>\n",
              "      <th>education.num</th>\n",
              "      <th>capital.gain</th>\n",
              "      <th>capital.loss</th>\n",
              "      <th>hours.per.week</th>\n",
              "      <th>workclass_Federal-gov</th>\n",
              "      <th>workclass_Local-gov</th>\n",
              "      <th>workclass_Never-worked</th>\n",
              "      <th>workclass_Private</th>\n",
              "      <th>...</th>\n",
              "      <th>native.country_Portugal</th>\n",
              "      <th>native.country_Puerto-Rico</th>\n",
              "      <th>native.country_Scotland</th>\n",
              "      <th>native.country_South</th>\n",
              "      <th>native.country_Taiwan</th>\n",
              "      <th>native.country_Thailand</th>\n",
              "      <th>native.country_Trinadad&amp;Tobago</th>\n",
              "      <th>native.country_United-States</th>\n",
              "      <th>native.country_Vietnam</th>\n",
              "      <th>native.country_Yugoslavia</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>90</td>\n",
              "      <td>77053</td>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "      <td>4356</td>\n",
              "      <td>40</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>82</td>\n",
              "      <td>132870</td>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "      <td>4356</td>\n",
              "      <td>18</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>...</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>66</td>\n",
              "      <td>186061</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "      <td>4356</td>\n",
              "      <td>40</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>54</td>\n",
              "      <td>140359</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>3900</td>\n",
              "      <td>40</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>...</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>41</td>\n",
              "      <td>264663</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "      <td>3900</td>\n",
              "      <td>40</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>...</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32556</th>\n",
              "      <td>22</td>\n",
              "      <td>310152</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>40</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>...</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32557</th>\n",
              "      <td>27</td>\n",
              "      <td>257302</td>\n",
              "      <td>12</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>38</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>...</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32558</th>\n",
              "      <td>40</td>\n",
              "      <td>154374</td>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>40</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>...</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32559</th>\n",
              "      <td>58</td>\n",
              "      <td>151910</td>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>40</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>...</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32560</th>\n",
              "      <td>22</td>\n",
              "      <td>201490</td>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>20</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>...</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",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>32561 rows × 108 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-eca596a7-03ff-47e5-baa2-92d2fc2b79c4')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http:https://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-eca596a7-03ff-47e5-baa2-92d2fc2b79c4 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-eca596a7-03ff-47e5-baa2-92d2fc2b79c4');\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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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_train, x_test, y_train, y_test = train_test_split(\n",
        "    dummy_data,\n",
        "    targets,\n",
        "    test_size=test_size,\n",
        "    random_state=random_state\n",
        ")\n",
        "\n",
        "model = build_model(dummy_data.shape[1:])\n",
        "model.fit(x=x_train, y=y_train, batch_size=batch_size, epochs=epochs, shuffle=shuffle, validation_data=(x_test, y_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xd7UV0zGRbNe",
        "outputId": "9c7f68ba-cad9-4ee6-fe34-5702ec6f2490"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "916/916 [==============================] - 9s 8ms/step - loss: 1.7158 - accuracy: 0.7689 - val_loss: 0.6750 - val_accuracy: 0.7749\n",
            "Epoch 2/20\n",
            "916/916 [==============================] - 7s 7ms/step - loss: 0.6385 - accuracy: 0.7785 - val_loss: 0.6733 - val_accuracy: 0.7181\n",
            "Epoch 3/20\n",
            "916/916 [==============================] - 7s 8ms/step - loss: 0.6073 - accuracy: 0.7844 - val_loss: 0.6152 - val_accuracy: 0.7706\n",
            "Epoch 4/20\n",
            "916/916 [==============================] - 7s 7ms/step - loss: 0.5902 - accuracy: 0.7875 - val_loss: 0.5793 - val_accuracy: 0.7909\n",
            "Epoch 5/20\n",
            "916/916 [==============================] - 7s 7ms/step - loss: 0.5867 - accuracy: 0.7870 - val_loss: 0.6048 - val_accuracy: 0.7771\n",
            "Epoch 6/20\n",
            "916/916 [==============================] - 7s 7ms/step - loss: 0.5743 - accuracy: 0.7913 - val_loss: 0.5732 - val_accuracy: 0.8044\n",
            "Epoch 7/20\n",
            "916/916 [==============================] - 11s 12ms/step - loss: 0.5724 - accuracy: 0.7892 - val_loss: 0.5662 - val_accuracy: 0.7872\n",
            "Epoch 8/20\n",
            "916/916 [==============================] - 13s 14ms/step - loss: 0.5685 - accuracy: 0.7911 - val_loss: 0.6454 - val_accuracy: 0.7105\n",
            "Epoch 9/20\n",
            "916/916 [==============================] - 8s 8ms/step - loss: 0.5662 - accuracy: 0.7889 - val_loss: 0.5841 - val_accuracy: 0.7676\n",
            "Epoch 10/20\n",
            "916/916 [==============================] - 7s 8ms/step - loss: 0.5653 - accuracy: 0.7877 - val_loss: 0.5618 - val_accuracy: 0.7875\n",
            "Epoch 11/20\n",
            "916/916 [==============================] - 7s 7ms/step - loss: 0.5623 - accuracy: 0.7875 - val_loss: 0.5626 - val_accuracy: 0.7848\n",
            "Epoch 12/20\n",
            "916/916 [==============================] - 7s 7ms/step - loss: 0.5605 - accuracy: 0.7895 - val_loss: 0.5613 - val_accuracy: 0.7826\n",
            "Epoch 13/20\n",
            "916/916 [==============================] - 7s 7ms/step - loss: 0.5588 - accuracy: 0.7884 - val_loss: 0.5644 - val_accuracy: 0.7872\n",
            "Epoch 14/20\n",
            "916/916 [==============================] - 7s 7ms/step - loss: 0.5600 - accuracy: 0.7890 - val_loss: 0.5581 - val_accuracy: 0.7848\n",
            "Epoch 15/20\n",
            "916/916 [==============================] - 7s 7ms/step - loss: 0.5568 - accuracy: 0.7883 - val_loss: 0.5595 - val_accuracy: 0.7842\n",
            "Epoch 16/20\n",
            "916/916 [==============================] - 7s 7ms/step - loss: 0.5546 - accuracy: 0.7913 - val_loss: 0.5514 - val_accuracy: 0.7872\n",
            "Epoch 17/20\n",
            "916/916 [==============================] - 7s 7ms/step - loss: 0.5584 - accuracy: 0.7879 - val_loss: 0.5564 - val_accuracy: 0.7866\n",
            "Epoch 18/20\n",
            "916/916 [==============================] - 7s 7ms/step - loss: 0.5579 - accuracy: 0.7887 - val_loss: 0.5578 - val_accuracy: 0.7839\n",
            "Epoch 19/20\n",
            "916/916 [==============================] - 7s 8ms/step - loss: 0.5542 - accuracy: 0.7896 - val_loss: 0.5561 - val_accuracy: 0.7860\n",
            "Epoch 20/20\n",
            "916/916 [==============================] - 7s 8ms/step - loss: 0.5551 - accuracy: 0.7884 - val_loss: 0.5586 - val_accuracy: 0.7842\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f777b63dc10>"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Best Validation Accuracy: **0.8044**"
      ],
      "metadata": {
        "id": "Wl-mpDFmSYrM"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Effect/Deviation/Sum Encoding\n",
        "\n",
        "In dummy coding, we use 0 and 1 to represent the data but in effect encoding (also known as *Deviation Encoding or Sum Encoding*), we use three values i.e. 1,0, and -1."
      ],
      "metadata": {
        "id": "NeDGR2KySnAI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from category_encoders.sum_coding import SumEncoder\n",
        "\n",
        "x_train, x_test, y_train, y_test = train_test_split(\n",
        "    data,\n",
        "    targets,\n",
        "    test_size=test_size,\n",
        "    random_state=random_state\n",
        ")\n",
        "\n",
        "sum_encoder = SumEncoder()\n",
        "sum_encoder.fit(data)\n",
        "x_train = sum_encoder.transform(x_train)\n",
        "x_test = sum_encoder.transform(x_test)\n",
        "x_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "eUcOE9qeSbph",
        "outputId": "1f1dd4da-6f10-4d6b-a72f-10ec4e11afe1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       intercept  age  workclass_0  workclass_1  workclass_2  workclass_3  \\\n",
              "9281           1   35          0.0          1.0          0.0          0.0   \n",
              "31884          1   28          0.0          1.0          0.0          0.0   \n",
              "31580          1   55          0.0          1.0          0.0          0.0   \n",
              "18489          1   33          0.0          1.0          0.0          0.0   \n",
              "21111          1   39          0.0          0.0          0.0          0.0   \n",
              "...          ...  ...          ...          ...          ...          ...   \n",
              "13123          1   90          0.0          0.0          0.0          0.0   \n",
              "19648          1   36          0.0          1.0          0.0          0.0   \n",
              "9845           1   26          0.0          1.0          0.0          0.0   \n",
              "10799          1   44          0.0          1.0          0.0          0.0   \n",
              "2732           1   39          0.0          1.0          0.0          0.0   \n",
              "\n",
              "       workclass_4  workclass_5  workclass_6  workclass_7  ...  \\\n",
              "9281           0.0          0.0          0.0          0.0  ...   \n",
              "31884          0.0          0.0          0.0          0.0  ...   \n",
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          "metadata": {},
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    {
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      "source": [
        "model = build_model(x_train.shape[1:])\n",
        "model.fit(x=x_train, y=y_train, batch_size=batch_size, epochs=epochs, shuffle=shuffle, validation_data=(x_test, y_test))"
      ],
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        "outputId": "35b841a1-c9fe-428e-993e-4bd1fb12f0ca"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "916/916 [==============================] - 10s 9ms/step - loss: 1.6068 - accuracy: 0.7683 - val_loss: 0.8291 - val_accuracy: 0.6620\n",
            "Epoch 2/20\n",
            "916/916 [==============================] - 7s 8ms/step - loss: 0.6221 - accuracy: 0.7818 - val_loss: 0.6185 - val_accuracy: 0.7651\n",
            "Epoch 3/20\n",
            "916/916 [==============================] - 7s 8ms/step - loss: 0.6026 - accuracy: 0.7852 - val_loss: 0.5778 - val_accuracy: 0.7875\n",
            "Epoch 4/20\n",
            "916/916 [==============================] - 7s 8ms/step - loss: 0.5871 - accuracy: 0.7896 - val_loss: 0.5873 - val_accuracy: 0.7734\n",
            "Epoch 5/20\n",
            "916/916 [==============================] - 7s 8ms/step - loss: 0.5833 - accuracy: 0.7884 - val_loss: 0.5877 - val_accuracy: 0.7872\n",
            "Epoch 6/20\n",
            "916/916 [==============================] - 7s 8ms/step - loss: 0.5797 - accuracy: 0.7871 - val_loss: 0.5746 - val_accuracy: 0.7746\n",
            "Epoch 7/20\n",
            "916/916 [==============================] - 7s 8ms/step - loss: 0.5740 - accuracy: 0.7880 - val_loss: 0.5663 - val_accuracy: 0.7866\n",
            "Epoch 8/20\n",
            "916/916 [==============================] - 7s 8ms/step - loss: 0.5669 - accuracy: 0.7879 - val_loss: 0.5554 - val_accuracy: 0.7974\n",
            "Epoch 9/20\n",
            "916/916 [==============================] - 7s 8ms/step - loss: 0.5626 - accuracy: 0.7894 - val_loss: 0.5625 - val_accuracy: 0.7915\n",
            "Epoch 10/20\n",
            "916/916 [==============================] - 7s 8ms/step - loss: 0.5637 - accuracy: 0.7878 - val_loss: 0.5567 - val_accuracy: 0.7869\n",
            "Epoch 11/20\n",
            "916/916 [==============================] - 7s 8ms/step - loss: 0.5605 - accuracy: 0.7898 - val_loss: 0.5573 - val_accuracy: 0.7885\n",
            "Epoch 12/20\n",
            "916/916 [==============================] - 7s 8ms/step - loss: 0.5576 - accuracy: 0.7907 - val_loss: 0.5601 - val_accuracy: 0.7872\n",
            "Epoch 13/20\n",
            "916/916 [==============================] - 7s 8ms/step - loss: 0.5559 - accuracy: 0.7901 - val_loss: 0.5555 - val_accuracy: 0.7863\n",
            "Epoch 14/20\n",
            "916/916 [==============================] - 7s 8ms/step - loss: 0.5553 - accuracy: 0.7908 - val_loss: 0.5523 - val_accuracy: 0.7866\n",
            "Epoch 15/20\n",
            "916/916 [==============================] - 7s 8ms/step - loss: 0.5551 - accuracy: 0.7896 - val_loss: 0.5567 - val_accuracy: 0.7848\n",
            "Epoch 16/20\n",
            "916/916 [==============================] - 7s 8ms/step - loss: 0.5539 - accuracy: 0.7896 - val_loss: 0.5556 - val_accuracy: 0.7869\n",
            "Epoch 17/20\n",
            "916/916 [==============================] - 7s 8ms/step - loss: 0.5571 - accuracy: 0.7890 - val_loss: 0.5609 - val_accuracy: 0.7756\n",
            "Epoch 18/20\n",
            "916/916 [==============================] - 7s 8ms/step - loss: 0.5560 - accuracy: 0.7882 - val_loss: 0.5589 - val_accuracy: 0.7845\n",
            "Epoch 19/20\n",
            "916/916 [==============================] - 7s 8ms/step - loss: 0.5547 - accuracy: 0.7889 - val_loss: 0.5525 - val_accuracy: 0.7881\n",
            "Epoch 20/20\n",
            "916/916 [==============================] - 7s 8ms/step - loss: 0.5539 - accuracy: 0.7905 - val_loss: 0.5508 - val_accuracy: 0.7872\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f7779b18290>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Best Validation Accuracy: **0.7974**, which is similar to the accuracy of dummy encoding technique."
      ],
      "metadata": {
        "id": "LnS3RbtRXVpJ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Binary Encoding of Inputs\n",
        "\n",
        "Binary encoding is a combination of hash encoding and one hot encoding."
      ],
      "metadata": {
        "id": "yJ6S7V_sJfI_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from category_encoders.binary import BinaryEncoder\n",
        "\n",
        "x_train, x_test, y_train, y_test = train_test_split(\n",
        "    data,\n",
        "    targets,\n",
        "    test_size=test_size,\n",
        "    random_state=random_state\n",
        ")\n",
        "\n",
        "binary_encoder = BinaryEncoder()\n",
        "binary_encoder.fit(data)\n",
        "x_train = binary_encoder.transform(x_train)\n",
        "x_test = binary_encoder.transform(x_test)\n",
        "x_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "iniX8AEWJdaT",
        "outputId": "72701aff-073a-43bb-f8bc-686c43dd00f7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       age  workclass_0  workclass_1  workclass_2  workclass_3  fnlwgt  \\\n",
              "9281    35            0            0            1            0  241126   \n",
              "31884   28            0            0            1            0   90547   \n",
              "31580   55            0            0            1            0   70088   \n",
              "18489   33            0            0            1            0  182423   \n",
              "21111   39            0            1            1            0  163057   \n",
              "...    ...          ...          ...          ...          ...     ...   \n",
              "13123   90            0            1            0            1  282095   \n",
              "19648   36            0            0            1            0  279721   \n",
              "9845    26            0            0            1            0   51961   \n",
              "10799   44            0            0            1            0  115323   \n",
              "2732    39            0            0            1            0  224531   \n",
              "\n",
              "       education_0  education_1  education_2  education_3  ...  sex_1  \\\n",
              "9281             0            1            0            0  ...      0   \n",
              "31884            0            0            0            0  ...      1   \n",
              "31580            0            1            0            0  ...      0   \n",
              "18489            0            0            0            0  ...      0   \n",
              "21111            0            0            0            1  ...      0   \n",
              "...            ...          ...          ...          ...  ...    ...   \n",
              "13123            0            0            0            1  ...      0   \n",
              "19648            0            0            0            0  ...      0   \n",
              "9845             0            1            1            1  ...      0   \n",
              "10799            0            1            0            0  ...      0   \n",
              "2732             0            0            0            0  ...      0   \n",
              "\n",
              "       capital.gain  capital.loss  hours.per.week  native.country_0  \\\n",
              "9281              0             0              40                 0   \n",
              "31884             0             0              23                 0   \n",
              "31580             0             0              40                 0   \n",
              "18489             0             0              40                 0   \n",
              "21111             0             0              99                 0   \n",
              "...             ...           ...             ...               ...   \n",
              "13123             0             0              40                 0   \n",
              "19648             0             0              40                 0   \n",
              "9845              0             0              51                 0   \n",
              "10799             0             0              40                 0   \n",
              "2732           7298             0              40                 0   \n",
              "\n",
              "       native.country_1  native.country_2  native.country_3  native.country_4  \\\n",
              "9281                  0                 0                 0                 0   \n",
              "31884                 0                 0                 0                 0   \n",
              "31580                 0                 0                 0                 0   \n",
              "18489                 0                 0                 0                 0   \n",
              "21111                 0                 0                 0                 0   \n",
              "...                 ...               ...               ...               ...   \n",
              "13123                 0                 0                 0                 0   \n",
              "19648                 0                 0                 0                 0   \n",
              "9845                  0                 0                 0                 0   \n",
              "10799                 0                 0                 0                 0   \n",
              "2732                  0                 0                 0                 0   \n",
              "\n",
              "       native.country_5  \n",
              "9281                  1  \n",
              "31884                 1  \n",
              "31580                 1  \n",
              "18489                 1  \n",
              "21111                 1  \n",
              "...                 ...  \n",
              "13123                 1  \n",
              "19648                 1  \n",
              "9845                  1  \n",
              "10799                 1  \n",
              "2732                  1  \n",
              "\n",
              "[29304 rows x 36 columns]"
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            "text/html": [
              "\n",
              "  <div id=\"df-1d7de7a7-bd63-44bb-9514-c6f77e551afe\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
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              "    <tr style=\"text-align: right;\">\n",
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              "      <th>age</th>\n",
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              "      <th>workclass_2</th>\n",
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              "      <th>education_1</th>\n",
              "      <th>education_2</th>\n",
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              "      <th>18489</th>\n",
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              "    [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",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-1d7de7a7-bd63-44bb-9514-c6f77e551afe 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-1d7de7a7-bd63-44bb-9514-c6f77e551afe');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = build_model(x_train.shape[1:])\n",
        "model.fit(x=x_train, y=y_train, batch_size=batch_size, epochs=epochs, shuffle=shuffle, validation_data=(x_test, y_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v_ezlxufJqDJ",
        "outputId": "3115c8a6-c71d-4882-b18b-b278e7eba34e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "916/916 [==============================] - 11s 8ms/step - loss: 1.6675 - accuracy: 0.7724 - val_loss: 0.6335 - val_accuracy: 0.7906\n",
            "Epoch 2/20\n",
            "916/916 [==============================] - 7s 8ms/step - loss: 0.6336 - accuracy: 0.7812 - val_loss: 0.6054 - val_accuracy: 0.7854\n",
            "Epoch 3/20\n",
            "916/916 [==============================] - 7s 8ms/step - loss: 0.6015 - accuracy: 0.7846 - val_loss: 0.6418 - val_accuracy: 0.7307\n",
            "Epoch 4/20\n",
            "916/916 [==============================] - 7s 8ms/step - loss: 0.5899 - accuracy: 0.7870 - val_loss: 0.5834 - val_accuracy: 0.7906\n",
            "Epoch 5/20\n",
            "916/916 [==============================] - 7s 8ms/step - loss: 0.5835 - accuracy: 0.7879 - val_loss: 0.6220 - val_accuracy: 0.7467\n",
            "Epoch 6/20\n",
            "916/916 [==============================] - 7s 8ms/step - loss: 0.5784 - accuracy: 0.7860 - val_loss: 0.5688 - val_accuracy: 0.7869\n",
            "Epoch 7/20\n",
            "916/916 [==============================] - 8s 8ms/step - loss: 0.5715 - accuracy: 0.7898 - val_loss: 0.5726 - val_accuracy: 0.7866\n",
            "Epoch 8/20\n",
            "916/916 [==============================] - 7s 8ms/step - loss: 0.5693 - accuracy: 0.7877 - val_loss: 0.5846 - val_accuracy: 0.7685\n",
            "Epoch 9/20\n",
            "916/916 [==============================] - 7s 8ms/step - loss: 0.5664 - accuracy: 0.7880 - val_loss: 0.5784 - val_accuracy: 0.7878\n",
            "Epoch 10/20\n",
            "916/916 [==============================] - 7s 8ms/step - loss: 0.5668 - accuracy: 0.7887 - val_loss: 0.5604 - val_accuracy: 0.7866\n",
            "Epoch 11/20\n",
            "916/916 [==============================] - 8s 8ms/step - loss: 0.5617 - accuracy: 0.7905 - val_loss: 0.5573 - val_accuracy: 0.7885\n",
            "Epoch 12/20\n",
            "916/916 [==============================] - 7s 8ms/step - loss: 0.5595 - accuracy: 0.7899 - val_loss: 0.5572 - val_accuracy: 0.7860\n",
            "Epoch 13/20\n",
            "916/916 [==============================] - 7s 8ms/step - loss: 0.5596 - accuracy: 0.7899 - val_loss: 0.5579 - val_accuracy: 0.7875\n",
            "Epoch 14/20\n",
            "916/916 [==============================] - 7s 8ms/step - loss: 0.5581 - accuracy: 0.7889 - val_loss: 0.5606 - val_accuracy: 0.7921\n",
            "Epoch 15/20\n",
            "916/916 [==============================] - 8s 8ms/step - loss: 0.5583 - accuracy: 0.7900 - val_loss: 0.5596 - val_accuracy: 0.7894\n",
            "Epoch 16/20\n",
            "916/916 [==============================] - 7s 8ms/step - loss: 0.5556 - accuracy: 0.7904 - val_loss: 0.5557 - val_accuracy: 0.7885\n",
            "Epoch 17/20\n",
            "916/916 [==============================] - 7s 8ms/step - loss: 0.5592 - accuracy: 0.7873 - val_loss: 0.5684 - val_accuracy: 0.7835\n",
            "Epoch 18/20\n",
            "916/916 [==============================] - 8s 9ms/step - loss: 0.5608 - accuracy: 0.7854 - val_loss: 0.5581 - val_accuracy: 0.7759\n",
            "Epoch 19/20\n",
            "916/916 [==============================] - 7s 8ms/step - loss: 0.5631 - accuracy: 0.7848 - val_loss: 0.5584 - val_accuracy: 0.7740\n",
            "Epoch 20/20\n",
            "916/916 [==============================] - 7s 8ms/step - loss: 0.5573 - accuracy: 0.7876 - val_loss: 0.5592 - val_accuracy: 0.7839\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f77661bc6d0>"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Best Validation Accuracy: **0.7906**"
      ],
      "metadata": {
        "id": "aAptX4hTKhoP"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Hash Encoder\n",
        "\n",
        "Hashing involves transforming any given categorical variable into a fixed and unique numerical value using a hash function. Popular hash functions for Hash encoders include *MD, MD2, MD5*. In this example, we will use the **MD5** method. The advantage of the hash encoder over previous approaches is that it can use any number of additional columns to describe categories. 20 Additional featutures will be used to hash the categories of this dataset."
      ],
      "metadata": {
        "id": "BKjgjPwez8bZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from category_encoders.hashing import HashingEncoder\n",
        "\n",
        "x_train, x_test, y_train, y_test = train_test_split(\n",
        "    data,\n",
        "    targets,\n",
        "    test_size=test_size,\n",
        "    random_state=random_state\n",
        ")\n",
        "\n",
        "hash_encoder = HashingEncoder(n_components=20, hash_method='md5')\n",
        "hash_encoder.fit(data)\n",
        "x_train = hash_encoder.transform(x_train)\n",
        "x_test = hash_encoder.transform(x_test)\n",
        "x_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "TxqNkZ3cyemB",
        "outputId": "cc791eef-0e36-4f77-8d47-0acd83b1dc19"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       col_0  col_1  col_2  col_3  col_4  col_5  col_6  col_7  col_8  col_9  \\\n",
              "9281       0      0      0      0      0      0      0      0      1      0   \n",
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              "21111      1      0      0      0      0      1      1      0      0      0   \n",
              "...      ...    ...    ...    ...    ...    ...    ...    ...    ...    ...   \n",
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              "2732       0      0      0      0      0      0      1      0      1      0   \n",
              "\n",
              "       ...  col_16  col_17  col_18  col_19  age  fnlwgt  education.num  \\\n",
              "9281   ...       0       2       1       1   35  241126             14   \n",
              "31884  ...       0       1       3       1   28   90547              9   \n",
              "31580  ...       0       2       1       1   55   70088             14   \n",
              "18489  ...       0       2       4       0   33  182423              9   \n",
              "21111  ...       0       1       1       1   39  163057             10   \n",
              "...    ...     ...     ...     ...     ...  ...     ...            ...   \n",
              "13123  ...       0       2       1       2   90  282095             10   \n",
              "19648  ...       0       2       2       2   36  279721              9   \n",
              "9845   ...       0       2       2       0   26   51961              8   \n",
              "10799  ...       0       2       1       1   44  115323             14   \n",
              "2732   ...       0       2       2       1   39  224531              9   \n",
              "\n",
              "       capital.gain  capital.loss  hours.per.week  \n",
              "9281              0             0              40  \n",
              "31884             0             0              23  \n",
              "31580             0             0              40  \n",
              "18489             0             0              40  \n",
              "21111             0             0              99  \n",
              "...             ...           ...             ...  \n",
              "13123             0             0              40  \n",
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              "9845              0             0              51  \n",
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              "2732           7298             0              40  \n",
              "\n",
              "[29304 rows x 26 columns]"
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              "      <td>40</td>\n",
              "    </tr>\n",
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              "<p>29304 rows × 26 columns</p>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = build_model(x_train.shape[1:])\n",
        "model.fit(x=x_train, y=y_train, batch_size=batch_size, epochs=epochs, shuffle=shuffle, validation_data=(x_test, y_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AgCNf9zs0lc6",
        "outputId": "11617400-9633-4a18-f3b6-36e9edd484ac"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "916/916 [==============================] - 9s 8ms/step - loss: 1.6417 - accuracy: 0.7719 - val_loss: 0.6505 - val_accuracy: 0.7928\n",
            "Epoch 2/20\n",
            "916/916 [==============================] - 7s 8ms/step - loss: 0.6241 - accuracy: 0.7830 - val_loss: 0.6141 - val_accuracy: 0.7869\n",
            "Epoch 3/20\n",
            "916/916 [==============================] - 7s 8ms/step - loss: 0.5997 - accuracy: 0.7880 - val_loss: 0.6015 - val_accuracy: 0.7839\n",
            "Epoch 4/20\n",
            "916/916 [==============================] - 7s 8ms/step - loss: 0.5897 - accuracy: 0.7868 - val_loss: 0.5934 - val_accuracy: 0.7875\n",
            "Epoch 5/20\n",
            "916/916 [==============================] - 7s 8ms/step - loss: 0.5805 - accuracy: 0.7892 - val_loss: 0.5760 - val_accuracy: 0.7832\n",
            "Epoch 6/20\n",
            "916/916 [==============================] - 7s 8ms/step - loss: 0.5733 - accuracy: 0.7892 - val_loss: 0.5601 - val_accuracy: 0.8032\n",
            "Epoch 7/20\n",
            "916/916 [==============================] - 7s 8ms/step - loss: 0.5751 - accuracy: 0.7887 - val_loss: 0.5671 - val_accuracy: 0.7881\n",
            "Epoch 8/20\n",
            "916/916 [==============================] - 7s 8ms/step - loss: 0.5692 - accuracy: 0.7892 - val_loss: 0.5654 - val_accuracy: 0.7875\n",
            "Epoch 9/20\n",
            "916/916 [==============================] - 8s 9ms/step - loss: 0.5655 - accuracy: 0.7898 - val_loss: 0.5704 - val_accuracy: 0.7863\n",
            "Epoch 10/20\n",
            "916/916 [==============================] - 8s 9ms/step - loss: 0.5648 - accuracy: 0.7895 - val_loss: 0.5578 - val_accuracy: 0.7854\n",
            "Epoch 11/20\n",
            "916/916 [==============================] - 7s 8ms/step - loss: 0.5629 - accuracy: 0.7902 - val_loss: 0.5603 - val_accuracy: 0.8004\n",
            "Epoch 12/20\n",
            "916/916 [==============================] - 8s 8ms/step - loss: 0.5623 - accuracy: 0.7888 - val_loss: 0.5664 - val_accuracy: 0.7860\n",
            "Epoch 13/20\n",
            "916/916 [==============================] - 7s 8ms/step - loss: 0.5615 - accuracy: 0.7880 - val_loss: 0.5544 - val_accuracy: 0.7872\n",
            "Epoch 14/20\n",
            "916/916 [==============================] - 8s 8ms/step - loss: 0.5580 - accuracy: 0.7910 - val_loss: 0.5672 - val_accuracy: 0.7955\n",
            "Epoch 15/20\n",
            "916/916 [==============================] - 7s 8ms/step - loss: 0.5597 - accuracy: 0.7910 - val_loss: 0.5623 - val_accuracy: 0.7878\n",
            "Epoch 16/20\n",
            "916/916 [==============================] - 7s 8ms/step - loss: 0.5576 - accuracy: 0.7901 - val_loss: 0.5561 - val_accuracy: 0.7906\n",
            "Epoch 17/20\n",
            "916/916 [==============================] - 7s 8ms/step - loss: 0.5560 - accuracy: 0.7901 - val_loss: 0.5540 - val_accuracy: 0.7869\n",
            "Epoch 18/20\n",
            "916/916 [==============================] - 7s 8ms/step - loss: 0.5564 - accuracy: 0.7913 - val_loss: 0.5525 - val_accuracy: 0.7937\n",
            "Epoch 19/20\n",
            "916/916 [==============================] - 7s 8ms/step - loss: 0.5548 - accuracy: 0.7921 - val_loss: 0.5504 - val_accuracy: 0.7888\n",
            "Epoch 20/20\n",
            "916/916 [==============================] - 7s 8ms/step - loss: 0.5556 - accuracy: 0.7900 - val_loss: 0.5536 - val_accuracy: 0.7872\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f7764a0db90>"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Best Validation Accuracy: **0.8032**"
      ],
      "metadata": {
        "id": "Ikp7UE94Bs3P"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#BaseN Encoder\n",
        "\n",
        "Encoding the categorial variables using numbers from an arithmetic system of base \"4\"."
      ],
      "metadata": {
        "id": "xCHxxWDJL9Qp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from category_encoders.basen import BaseNEncoder\n",
        "\n",
        "x_train, x_test, y_train, y_test = train_test_split(\n",
        "    data,\n",
        "    targets,\n",
        "    test_size=test_size,\n",
        "    random_state=random_state\n",
        ")\n",
        "\n",
        "basen_encoder = BaseNEncoder(base=4)\n",
        "basen_encoder.fit(data)\n",
        "x_train = basen_encoder.transform(x_train)\n",
        "x_test = basen_encoder.transform(x_test)\n",
        "x_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "5S0_KWd6LsL9",
        "outputId": "c7221245-b1dc-4fa7-d217-0b1fd0e775a2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       age  workclass_0  workclass_1  fnlwgt  education_0  education_1  \\\n",
              "9281    35            0            2  241126            0            2   \n",
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              "31580   55            0            2   70088            0            2   \n",
              "18489   33            0            2  182423            0            0   \n",
              "21111   39            1            2  163057            0            0   \n",
              "...    ...          ...          ...     ...          ...          ...   \n",
              "13123   90            1            1  282095            0            0   \n",
              "19648   36            0            2  279721            0            0   \n",
              "9845    26            0            2   51961            0            3   \n",
              "10799   44            0            2  115323            0            2   \n",
              "2732    39            0            2  224531            0            0   \n",
              "\n",
              "       education_2  education.num  marital.status_0  marital.status_1  ...  \\\n",
              "9281             0             14                 1                 1  ...   \n",
              "31884            1              9                 1                 1  ...   \n",
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              "2732             1              9                 1                 1  ...   \n",
              "\n",
              "       relationship_1  race_0  race_1  sex_0  capital.gain  capital.loss  \\\n",
              "9281                1       0       1      2             0             0   \n",
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              "2732                1       0       1      2          7298             0   \n",
              "\n",
              "       hours.per.week  native.country_0  native.country_1  native.country_2  \n",
              "9281               40                 0                 0                 1  \n",
              "31884              23                 0                 0                 1  \n",
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              "18489              40                 0                 0                 1  \n",
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              "...               ...               ...               ...               ...  \n",
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              "9845               51                 0                 0                 1  \n",
              "10799              40                 0                 0                 1  \n",
              "2732               40                 0                 0                 1  \n",
              "\n",
              "[29304 rows x 23 columns]"
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              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>40</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9845</th>\n",
              "      <td>26</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>51961</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>8</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>51</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10799</th>\n",
              "      <td>44</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>115323</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>14</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>40</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2732</th>\n",
              "      <td>39</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>224531</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>9</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>7298</td>\n",
              "      <td>0</td>\n",
              "      <td>40</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>29304 rows × 23 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4781a9b6-f929-44a5-adc3-4f8cf0da80d8')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http:https://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-4781a9b6-f929-44a5-adc3-4f8cf0da80d8 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-4781a9b6-f929-44a5-adc3-4f8cf0da80d8');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = build_model(x_train.shape[1:])\n",
        "model.fit(x=x_train, y=y_train, batch_size=batch_size, epochs=epochs, shuffle=shuffle, validation_data=(x_test, y_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I8xcyjoZMaMr",
        "outputId": "883b9205-b7cf-4d4b-a72b-6dc914d10807"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "916/916 [==============================] - 11s 8ms/step - loss: 1.5810 - accuracy: 0.7698 - val_loss: 0.8075 - val_accuracy: 0.6945\n",
            "Epoch 2/20\n",
            "916/916 [==============================] - 7s 8ms/step - loss: 0.6178 - accuracy: 0.7839 - val_loss: 0.7165 - val_accuracy: 0.6524\n",
            "Epoch 3/20\n",
            "916/916 [==============================] - 8s 8ms/step - loss: 0.6009 - accuracy: 0.7823 - val_loss: 0.5845 - val_accuracy: 0.7710\n",
            "Epoch 4/20\n",
            "916/916 [==============================] - 9s 10ms/step - loss: 0.5899 - accuracy: 0.7833 - val_loss: 0.6068 - val_accuracy: 0.7860\n",
            "Epoch 5/20\n",
            "916/916 [==============================] - 7s 8ms/step - loss: 0.5821 - accuracy: 0.7823 - val_loss: 0.6083 - val_accuracy: 0.7614\n",
            "Epoch 6/20\n",
            "916/916 [==============================] - 7s 8ms/step - loss: 0.5706 - accuracy: 0.7872 - val_loss: 0.5674 - val_accuracy: 0.7808\n",
            "Epoch 7/20\n",
            "916/916 [==============================] - 7s 8ms/step - loss: 0.5676 - accuracy: 0.7863 - val_loss: 0.5960 - val_accuracy: 0.7611\n",
            "Epoch 8/20\n",
            "916/916 [==============================] - 7s 8ms/step - loss: 0.5678 - accuracy: 0.7867 - val_loss: 0.5724 - val_accuracy: 0.7851\n",
            "Epoch 9/20\n",
            "916/916 [==============================] - 8s 8ms/step - loss: 0.5628 - accuracy: 0.7894 - val_loss: 0.5563 - val_accuracy: 0.7921\n",
            "Epoch 10/20\n",
            "916/916 [==============================] - 7s 8ms/step - loss: 0.5622 - accuracy: 0.7885 - val_loss: 0.5606 - val_accuracy: 0.7866\n",
            "Epoch 11/20\n",
            "916/916 [==============================] - 7s 8ms/step - loss: 0.5597 - accuracy: 0.7874 - val_loss: 0.5649 - val_accuracy: 0.7786\n",
            "Epoch 12/20\n",
            "916/916 [==============================] - 7s 8ms/step - loss: 0.5594 - accuracy: 0.7888 - val_loss: 0.5581 - val_accuracy: 0.7869\n",
            "Epoch 13/20\n",
            "916/916 [==============================] - 7s 8ms/step - loss: 0.5576 - accuracy: 0.7906 - val_loss: 0.5594 - val_accuracy: 0.7869\n",
            "Epoch 14/20\n",
            "916/916 [==============================] - 7s 8ms/step - loss: 0.5555 - accuracy: 0.7911 - val_loss: 0.5603 - val_accuracy: 0.7869\n",
            "Epoch 15/20\n",
            "916/916 [==============================] - 7s 8ms/step - loss: 0.5567 - accuracy: 0.7880 - val_loss: 0.5627 - val_accuracy: 0.7860\n",
            "Epoch 16/20\n",
            "916/916 [==============================] - 7s 8ms/step - loss: 0.5559 - accuracy: 0.7904 - val_loss: 0.5578 - val_accuracy: 0.7869\n",
            "Epoch 17/20\n",
            "916/916 [==============================] - 7s 8ms/step - loss: 0.5561 - accuracy: 0.7893 - val_loss: 0.5614 - val_accuracy: 0.7829\n",
            "Epoch 18/20\n",
            "916/916 [==============================] - 8s 8ms/step - loss: 0.5565 - accuracy: 0.7902 - val_loss: 0.5584 - val_accuracy: 0.7863\n",
            "Epoch 19/20\n",
            "916/916 [==============================] - 7s 8ms/step - loss: 0.5547 - accuracy: 0.7897 - val_loss: 0.5550 - val_accuracy: 0.7866\n",
            "Epoch 20/20\n",
            "916/916 [==============================] - 7s 8ms/step - loss: 0.5540 - accuracy: 0.7890 - val_loss: 0.5579 - val_accuracy: 0.7897\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f777940c3d0>"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Best Validation Accuracy: **0.7921**"
      ],
      "metadata": {
        "id": "Us--5raUNVko"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Other Categorical Encoders"
      ],
      "metadata": {
        "id": "UrN8d9Mjuliz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from category_encoders.backward_difference import BackwardDifferenceEncoder\n",
        "from category_encoders.cat_boost import CatBoostEncoder\n",
        "from category_encoders.count import CountEncoder\n",
        "from category_encoders.glmm import GLMMEncoder\n",
        "from category_encoders.target_encoder import TargetEncoder\n",
        "from category_encoders.helmert import HelmertEncoder\n",
        "from category_encoders.james_stein import JamesSteinEncoder\n",
        "\n",
        "encoders = {\n",
        "    'Backward Difference Encoder': BackwardDifferenceEncoder(),\n",
        "    'Cat Boost Encoder': CatBoostEncoder(),\n",
        "    'Count Encoder': CountEncoder(),\n",
        "    'Generalized Linear Mixed Model Encoder': GLMMEncoder(binomial_target=True),\n",
        "    'Target Encoder': TargetEncoder(),\n",
        "    'Helmert Encoder': HelmertEncoder(),\n",
        "    'James-Stein Encoder': JamesSteinEncoder()\n",
        "}\n",
        "\n",
        "for encoder_name, encoder in encoders.items():\n",
        "  print('Evaluating: {}'.format(encoder))\n",
        "  x_train, x_test, y_train, y_test = train_test_split(\n",
        "    data,\n",
        "    targets,\n",
        "    test_size=test_size,\n",
        "    random_state=random_state\n",
        "  )\n",
        "\n",
        "  encoder.fit(data, targets)\n",
        "  x_train = encoder.transform(x_train)\n",
        "  x_test = encoder.transform(x_test)\n",
        "  \n",
        "  model = build_model(x_train.shape[1:])\n",
        "  model.fit(x=x_train, y=y_train, batch_size=batch_size, epochs=epochs, shuffle=shuffle, validation_data=(x_test, y_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-PUffQk6tyco",
        "outputId": "04244e3a-cfc5-4f0d-bf94-7567d3eb6bd2"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Evaluating: BackwardDifferenceEncoder()\n",
            "Epoch 1/20\n",
            "916/916 [==============================] - 7s 4ms/step - loss: 1.6122 - accuracy: 0.7715 - val_loss: 0.9418 - val_accuracy: 0.6742\n",
            "Epoch 2/20\n",
            "916/916 [==============================] - 3s 3ms/step - loss: 0.6321 - accuracy: 0.7838 - val_loss: 0.7613 - val_accuracy: 0.6380\n",
            "Epoch 3/20\n",
            "916/916 [==============================] - 3s 3ms/step - loss: 0.6051 - accuracy: 0.7862 - val_loss: 0.6074 - val_accuracy: 0.7811\n",
            "Epoch 4/20\n",
            "916/916 [==============================] - 3s 3ms/step - loss: 0.5926 - accuracy: 0.7853 - val_loss: 0.5854 - val_accuracy: 0.7835\n",
            "Epoch 5/20\n",
            "916/916 [==============================] - 3s 3ms/step - loss: 0.5846 - accuracy: 0.7857 - val_loss: 0.5915 - val_accuracy: 0.7777\n",
            "Epoch 6/20\n",
            "916/916 [==============================] - 3s 3ms/step - loss: 0.5760 - accuracy: 0.7887 - val_loss: 0.5742 - val_accuracy: 0.7814\n",
            "Epoch 7/20\n",
            "916/916 [==============================] - 3s 3ms/step - loss: 0.5713 - accuracy: 0.7882 - val_loss: 0.5753 - val_accuracy: 0.7839\n",
            "Epoch 8/20\n",
            "916/916 [==============================] - 3s 3ms/step - loss: 0.5685 - accuracy: 0.7882 - val_loss: 0.5769 - val_accuracy: 0.7832\n",
            "Epoch 9/20\n",
            "916/916 [==============================] - 3s 4ms/step - loss: 0.5654 - accuracy: 0.7892 - val_loss: 0.5746 - val_accuracy: 0.7842\n",
            "Epoch 10/20\n",
            "916/916 [==============================] - 3s 4ms/step - loss: 0.5631 - accuracy: 0.7883 - val_loss: 0.5668 - val_accuracy: 0.7835\n",
            "Epoch 11/20\n",
            "916/916 [==============================] - 3s 4ms/step - loss: 0.5613 - accuracy: 0.7879 - val_loss: 0.5709 - val_accuracy: 0.7756\n",
            "Epoch 12/20\n",
            "916/916 [==============================] - 3s 4ms/step - loss: 0.5612 - accuracy: 0.7891 - val_loss: 0.5608 - val_accuracy: 0.7860\n",
            "Epoch 13/20\n",
            "916/916 [==============================] - 3s 3ms/step - loss: 0.5639 - accuracy: 0.7883 - val_loss: 0.5717 - val_accuracy: 0.7792\n",
            "Epoch 14/20\n",
            "916/916 [==============================] - 3s 3ms/step - loss: 0.5651 - accuracy: 0.7851 - val_loss: 0.5617 - val_accuracy: 0.7869\n",
            "Epoch 15/20\n",
            "916/916 [==============================] - 3s 4ms/step - loss: 0.5647 - accuracy: 0.7848 - val_loss: 0.5596 - val_accuracy: 0.7866\n",
            "Epoch 16/20\n",
            "916/916 [==============================] - 3s 3ms/step - loss: 0.5605 - accuracy: 0.7872 - val_loss: 0.5616 - val_accuracy: 0.7881\n",
            "Epoch 17/20\n",
            "916/916 [==============================] - 3s 3ms/step - loss: 0.5602 - accuracy: 0.7870 - val_loss: 0.5587 - val_accuracy: 0.7860\n",
            "Epoch 18/20\n",
            "916/916 [==============================] - 3s 3ms/step - loss: 0.5578 - accuracy: 0.7886 - val_loss: 0.5611 - val_accuracy: 0.7915\n",
            "Epoch 19/20\n",
            "916/916 [==============================] - 3s 3ms/step - loss: 0.5547 - accuracy: 0.7901 - val_loss: 0.5511 - val_accuracy: 0.7875\n",
            "Epoch 20/20\n",
            "916/916 [==============================] - 3s 3ms/step - loss: 0.5533 - accuracy: 0.7914 - val_loss: 0.5547 - val_accuracy: 0.7866\n",
            "Evaluating: CatBoostEncoder()\n",
            "Epoch 1/20\n",
            "916/916 [==============================] - 5s 3ms/step - loss: 1.5688 - accuracy: 0.7703 - val_loss: 0.6104 - val_accuracy: 0.7952\n",
            "Epoch 2/20\n",
            "916/916 [==============================] - 3s 3ms/step - loss: 0.6173 - accuracy: 0.7827 - val_loss: 0.7149 - val_accuracy: 0.6497\n",
            "Epoch 3/20\n",
            "916/916 [==============================] - 3s 3ms/step - loss: 0.5970 - accuracy: 0.7863 - val_loss: 0.5817 - val_accuracy: 0.7869\n",
            "Epoch 4/20\n",
            "916/916 [==============================] - 3s 3ms/step - loss: 0.5852 - accuracy: 0.7890 - val_loss: 0.5812 - val_accuracy: 0.7866\n",
            "Epoch 5/20\n",
            "916/916 [==============================] - 3s 3ms/step - loss: 0.5747 - accuracy: 0.7894 - val_loss: 0.5710 - val_accuracy: 0.7848\n",
            "Epoch 6/20\n",
            "916/916 [==============================] - 3s 3ms/step - loss: 0.5725 - accuracy: 0.7900 - val_loss: 0.5883 - val_accuracy: 0.7918\n",
            "Epoch 7/20\n",
            "916/916 [==============================] - 3s 3ms/step - loss: 0.5688 - accuracy: 0.7880 - val_loss: 0.5602 - val_accuracy: 0.7872\n",
            "Epoch 8/20\n",
            "916/916 [==============================] - 3s 3ms/step - loss: 0.5680 - accuracy: 0.7876 - val_loss: 0.5939 - val_accuracy: 0.7897\n",
            "Epoch 9/20\n",
            "916/916 [==============================] - 3s 3ms/step - loss: 0.5647 - accuracy: 0.7902 - val_loss: 0.5535 - val_accuracy: 0.7918\n",
            "Epoch 10/20\n",
            "916/916 [==============================] - 3s 3ms/step - loss: 0.5619 - accuracy: 0.7896 - val_loss: 0.5604 - val_accuracy: 0.7897\n",
            "Epoch 11/20\n",
            "916/916 [==============================] - 3s 3ms/step - loss: 0.5601 - accuracy: 0.7927 - val_loss: 0.5574 - val_accuracy: 0.7891\n",
            "Epoch 12/20\n",
            "916/916 [==============================] - 3s 3ms/step - loss: 0.5594 - accuracy: 0.7913 - val_loss: 0.5559 - val_accuracy: 0.7918\n",
            "Epoch 13/20\n",
            "916/916 [==============================] - 3s 3ms/step - loss: 0.5586 - accuracy: 0.7907 - val_loss: 0.5611 - val_accuracy: 0.7878\n",
            "Epoch 14/20\n",
            "916/916 [==============================] - 3s 3ms/step - loss: 0.5592 - accuracy: 0.7895 - val_loss: 0.5601 - val_accuracy: 0.7866\n",
            "Epoch 15/20\n",
            "916/916 [==============================] - 3s 3ms/step - loss: 0.5593 - accuracy: 0.7887 - val_loss: 0.5558 - val_accuracy: 0.7866\n",
            "Epoch 16/20\n",
            "916/916 [==============================] - 3s 3ms/step - loss: 0.5573 - accuracy: 0.7904 - val_loss: 0.5589 - val_accuracy: 0.7789\n",
            "Epoch 17/20\n",
            "916/916 [==============================] - 3s 3ms/step - loss: 0.5558 - accuracy: 0.7898 - val_loss: 0.5577 - val_accuracy: 0.7796\n",
            "Epoch 18/20\n",
            "916/916 [==============================] - 3s 3ms/step - loss: 0.5563 - accuracy: 0.7915 - val_loss: 0.5563 - val_accuracy: 0.7845\n",
            "Epoch 19/20\n",
            "916/916 [==============================] - 3s 3ms/step - loss: 0.5570 - accuracy: 0.7897 - val_loss: 0.5536 - val_accuracy: 0.7866\n",
            "Epoch 20/20\n",
            "916/916 [==============================] - 3s 3ms/step - loss: 0.5548 - accuracy: 0.7912 - val_loss: 0.5570 - val_accuracy: 0.7863\n",
            "Evaluating: CountEncoder(combine_min_nan_groups=True)\n",
            "Epoch 1/20\n",
            "916/916 [==============================] - 5s 3ms/step - loss: 1.6526 - accuracy: 0.7675 - val_loss: 0.6198 - val_accuracy: 0.7928\n",
            "Epoch 2/20\n",
            "916/916 [==============================] - 4s 4ms/step - loss: 0.5854 - accuracy: 0.7898 - val_loss: 0.5651 - val_accuracy: 0.8035\n",
            "Epoch 3/20\n",
            "916/916 [==============================] - 3s 3ms/step - loss: 0.5512 - accuracy: 0.7973 - val_loss: 0.5520 - val_accuracy: 0.7992\n",
            "Epoch 4/20\n",
            "916/916 [==============================] - 3s 3ms/step - loss: 0.5382 - accuracy: 0.7985 - val_loss: 0.5161 - val_accuracy: 0.8099\n",
            "Epoch 5/20\n",
            "916/916 [==============================] - 3s 3ms/step - loss: 0.5277 - accuracy: 0.7985 - val_loss: 0.5093 - val_accuracy: 0.8056\n",
            "Epoch 6/20\n",
            "916/916 [==============================] - 3s 3ms/step - loss: 0.5174 - accuracy: 0.8022 - val_loss: 0.5070 - val_accuracy: 0.8115\n",
            "Epoch 7/20\n",
            "916/916 [==============================] - 3s 3ms/step - loss: 0.5117 - accuracy: 0.8021 - val_loss: 0.5100 - val_accuracy: 0.8112\n",
            "Epoch 8/20\n",
            "916/916 [==============================] - 3s 3ms/step - loss: 0.5049 - accuracy: 0.8035 - val_loss: 0.5102 - val_accuracy: 0.8072\n",
            "Epoch 9/20\n",
            "916/916 [==============================] - 3s 3ms/step - loss: 0.5035 - accuracy: 0.8025 - val_loss: 0.5029 - val_accuracy: 0.8096\n",
            "Epoch 10/20\n",
            "916/916 [==============================] - 2s 3ms/step - loss: 0.5013 - accuracy: 0.8047 - val_loss: 0.5038 - val_accuracy: 0.8158\n",
            "Epoch 11/20\n",
            "916/916 [==============================] - 2s 3ms/step - loss: 0.5007 - accuracy: 0.8051 - val_loss: 0.5019 - val_accuracy: 0.8050\n",
            "Epoch 12/20\n",
            "916/916 [==============================] - 2s 3ms/step - loss: 0.4982 - accuracy: 0.8044 - val_loss: 0.4882 - val_accuracy: 0.8121\n",
            "Epoch 13/20\n",
            "916/916 [==============================] - 3s 3ms/step - loss: 0.4976 - accuracy: 0.8061 - val_loss: 0.4964 - val_accuracy: 0.8136\n",
            "Epoch 14/20\n",
            "916/916 [==============================] - 3s 3ms/step - loss: 0.4968 - accuracy: 0.8037 - val_loss: 0.4917 - val_accuracy: 0.8081\n",
            "Epoch 15/20\n",
            "916/916 [==============================] - 3s 3ms/step - loss: 0.4961 - accuracy: 0.8033 - val_loss: 0.4887 - val_accuracy: 0.8124\n",
            "Epoch 16/20\n",
            "916/916 [==============================] - 3s 3ms/step - loss: 0.4983 - accuracy: 0.8029 - val_loss: 0.4979 - val_accuracy: 0.8093\n",
            "Epoch 17/20\n",
            "916/916 [==============================] - 3s 3ms/step - loss: 0.4944 - accuracy: 0.8044 - val_loss: 0.4868 - val_accuracy: 0.8087\n",
            "Epoch 18/20\n",
            "916/916 [==============================] - 3s 3ms/step - loss: 0.4950 - accuracy: 0.8042 - val_loss: 0.4937 - val_accuracy: 0.8149\n",
            "Epoch 19/20\n",
            "916/916 [==============================] - 3s 3ms/step - loss: 0.4947 - accuracy: 0.8046 - val_loss: 0.4925 - val_accuracy: 0.8112\n",
            "Epoch 20/20\n",
            "916/916 [==============================] - 3s 3ms/step - loss: 0.4957 - accuracy: 0.8032 - val_loss: 0.4891 - val_accuracy: 0.8115\n",
            "Evaluating: GLMMEncoder(binomial_target=True)\n",
            "Epoch 1/20\n",
            "916/916 [==============================] - 5s 3ms/step - loss: 1.5992 - accuracy: 0.7698 - val_loss: 0.6632 - val_accuracy: 0.7654\n",
            "Epoch 2/20\n",
            "916/916 [==============================] - 3s 3ms/step - loss: 0.6290 - accuracy: 0.7802 - val_loss: 0.5877 - val_accuracy: 0.7998\n",
            "Epoch 3/20\n",
            "916/916 [==============================] - 3s 3ms/step - loss: 0.5949 - accuracy: 0.7849 - val_loss: 0.6756 - val_accuracy: 0.6942\n",
            "Epoch 4/20\n",
            "916/916 [==============================] - 3s 3ms/step - loss: 0.5858 - accuracy: 0.7851 - val_loss: 0.5940 - val_accuracy: 0.7670\n",
            "Epoch 5/20\n",
            "916/916 [==============================] - 3s 3ms/step - loss: 0.5817 - accuracy: 0.7841 - val_loss: 0.6014 - val_accuracy: 0.7866\n",
            "Epoch 6/20\n",
            "916/916 [==============================] - 3s 3ms/step - loss: 0.5744 - accuracy: 0.7861 - val_loss: 0.5658 - val_accuracy: 0.7869\n",
            "Epoch 7/20\n",
            "916/916 [==============================] - 3s 3ms/step - loss: 0.5701 - accuracy: 0.7860 - val_loss: 0.5736 - val_accuracy: 0.7854\n",
            "Epoch 8/20\n",
            "916/916 [==============================] - 3s 3ms/step - loss: 0.5692 - accuracy: 0.7868 - val_loss: 0.5758 - val_accuracy: 0.7839\n",
            "Epoch 9/20\n",
            "916/916 [==============================] - 3s 3ms/step - loss: 0.5683 - accuracy: 0.7853 - val_loss: 0.5610 - val_accuracy: 0.7875\n",
            "Epoch 10/20\n",
            "916/916 [==============================] - 3s 3ms/step - loss: 0.5620 - accuracy: 0.7880 - val_loss: 0.5637 - val_accuracy: 0.7875\n",
            "Epoch 11/20\n",
            "916/916 [==============================] - 3s 3ms/step - loss: 0.5605 - accuracy: 0.7901 - val_loss: 0.5563 - val_accuracy: 0.7924\n",
            "Epoch 12/20\n",
            "916/916 [==============================] - 3s 3ms/step - loss: 0.5608 - accuracy: 0.7901 - val_loss: 0.5524 - val_accuracy: 0.7940\n",
            "Epoch 13/20\n",
            "916/916 [==============================] - 3s 3ms/step - loss: 0.5576 - accuracy: 0.7910 - val_loss: 0.5592 - val_accuracy: 0.7928\n",
            "Epoch 14/20\n",
            "916/916 [==============================] - 3s 3ms/step - loss: 0.5575 - accuracy: 0.7925 - val_loss: 0.5590 - val_accuracy: 0.7866\n",
            "Epoch 15/20\n",
            "916/916 [==============================] - 3s 3ms/step - loss: 0.5574 - accuracy: 0.7927 - val_loss: 0.5533 - val_accuracy: 0.7900\n",
            "Epoch 16/20\n",
            "916/916 [==============================] - 3s 3ms/step - loss: 0.5557 - accuracy: 0.7929 - val_loss: 0.5542 - val_accuracy: 0.7872\n",
            "Epoch 17/20\n",
            "916/916 [==============================] - 3s 3ms/step - loss: 0.5550 - accuracy: 0.7941 - val_loss: 0.5540 - val_accuracy: 0.7875\n",
            "Epoch 18/20\n",
            "916/916 [==============================] - 3s 3ms/step - loss: 0.5532 - accuracy: 0.7931 - val_loss: 0.5493 - val_accuracy: 0.8020\n",
            "Epoch 19/20\n",
            "916/916 [==============================] - 3s 3ms/step - loss: 0.5537 - accuracy: 0.7932 - val_loss: 0.5502 - val_accuracy: 0.7875\n",
            "Epoch 20/20\n",
            "916/916 [==============================] - 3s 3ms/step - loss: 0.5565 - accuracy: 0.7903 - val_loss: 0.5535 - val_accuracy: 0.7851\n",
            "Evaluating: TargetEncoder()\n",
            "Epoch 1/20\n",
            "916/916 [==============================] - 4s 3ms/step - loss: 1.6035 - accuracy: 0.7716 - val_loss: 0.6238 - val_accuracy: 0.8056\n",
            "Epoch 2/20\n",
            "916/916 [==============================] - 3s 3ms/step - loss: 0.6275 - accuracy: 0.7811 - val_loss: 0.6185 - val_accuracy: 0.7737\n",
            "Epoch 3/20\n",
            "916/916 [==============================] - 3s 3ms/step - loss: 0.6049 - accuracy: 0.7836 - val_loss: 0.6133 - val_accuracy: 0.7808\n",
            "Epoch 4/20\n",
            "916/916 [==============================] - 3s 3ms/step - loss: 0.5910 - accuracy: 0.7832 - val_loss: 0.6452 - val_accuracy: 0.7194\n",
            "Epoch 5/20\n",
            "916/916 [==============================] - 3s 3ms/step - loss: 0.5854 - accuracy: 0.7851 - val_loss: 0.6032 - val_accuracy: 0.7845\n",
            "Epoch 6/20\n",
            "916/916 [==============================] - 3s 3ms/step - loss: 0.5809 - accuracy: 0.7836 - val_loss: 0.5716 - val_accuracy: 0.7863\n",
            "Epoch 7/20\n",
            "916/916 [==============================] - 3s 3ms/step - loss: 0.5722 - accuracy: 0.7870 - val_loss: 0.5757 - val_accuracy: 0.7719\n",
            "Epoch 8/20\n",
            "916/916 [==============================] - 3s 3ms/step - loss: 0.5714 - accuracy: 0.7879 - val_loss: 0.5663 - val_accuracy: 0.7848\n",
            "Epoch 9/20\n",
            "916/916 [==============================] - 3s 3ms/step - loss: 0.5663 - accuracy: 0.7878 - val_loss: 0.6079 - val_accuracy: 0.7565\n",
            "Epoch 10/20\n",
            "916/916 [==============================] - 3s 3ms/step - loss: 0.5612 - accuracy: 0.7898 - val_loss: 0.5657 - val_accuracy: 0.7891\n",
            "Epoch 11/20\n",
            "916/916 [==============================] - 3s 3ms/step - loss: 0.5613 - accuracy: 0.7893 - val_loss: 0.5570 - val_accuracy: 0.8004\n",
            "Epoch 12/20\n",
            "916/916 [==============================] - 3s 3ms/step - loss: 0.5611 - accuracy: 0.7889 - val_loss: 0.5703 - val_accuracy: 0.7854\n",
            "Epoch 13/20\n",
            "916/916 [==============================] - 3s 3ms/step - loss: 0.5602 - accuracy: 0.7896 - val_loss: 0.5614 - val_accuracy: 0.7848\n",
            "Epoch 14/20\n",
            "916/916 [==============================] - 3s 3ms/step - loss: 0.5592 - accuracy: 0.7884 - val_loss: 0.5575 - val_accuracy: 0.7869\n",
            "Epoch 15/20\n",
            "916/916 [==============================] - 3s 3ms/step - loss: 0.5564 - accuracy: 0.7916 - val_loss: 0.5530 - val_accuracy: 0.7869\n",
            "Epoch 16/20\n",
            "916/916 [==============================] - 3s 3ms/step - loss: 0.5569 - accuracy: 0.7901 - val_loss: 0.5605 - val_accuracy: 0.7863\n",
            "Epoch 17/20\n",
            "916/916 [==============================] - 3s 3ms/step - loss: 0.5593 - accuracy: 0.7880 - val_loss: 0.5501 - val_accuracy: 0.7891\n",
            "Epoch 18/20\n",
            "916/916 [==============================] - 3s 3ms/step - loss: 0.5557 - accuracy: 0.7889 - val_loss: 0.5628 - val_accuracy: 0.7749\n",
            "Epoch 19/20\n",
            "916/916 [==============================] - 3s 3ms/step - loss: 0.5546 - accuracy: 0.7906 - val_loss: 0.5630 - val_accuracy: 0.7811\n",
            "Epoch 20/20\n",
            "916/916 [==============================] - 3s 3ms/step - loss: 0.5543 - accuracy: 0.7904 - val_loss: 0.5583 - val_accuracy: 0.7863\n",
            "Evaluating: HelmertEncoder()\n",
            "Epoch 1/20\n",
            "916/916 [==============================] - 5s 4ms/step - loss: 1.6688 - accuracy: 0.7675 - val_loss: 0.6207 - val_accuracy: 0.7891\n",
            "Epoch 2/20\n",
            "916/916 [==============================] - 3s 4ms/step - loss: 0.6234 - accuracy: 0.7826 - val_loss: 0.6019 - val_accuracy: 0.7983\n",
            "Epoch 3/20\n",
            "916/916 [==============================] - 3s 3ms/step - loss: 0.6009 - accuracy: 0.7873 - val_loss: 0.6878 - val_accuracy: 0.6678\n",
            "Epoch 4/20\n",
            "916/916 [==============================] - 3s 3ms/step - loss: 0.5939 - accuracy: 0.7887 - val_loss: 0.5784 - val_accuracy: 0.8017\n",
            "Epoch 5/20\n",
            "916/916 [==============================] - 3s 3ms/step - loss: 0.5812 - accuracy: 0.7913 - val_loss: 0.6017 - val_accuracy: 0.7964\n",
            "Epoch 6/20\n",
            "916/916 [==============================] - 3s 3ms/step - loss: 0.5754 - accuracy: 0.7902 - val_loss: 0.5869 - val_accuracy: 0.7989\n",
            "Epoch 7/20\n",
            "916/916 [==============================] - 3s 3ms/step - loss: 0.5732 - accuracy: 0.7902 - val_loss: 0.5659 - val_accuracy: 0.7891\n",
            "Epoch 8/20\n",
            "916/916 [==============================] - 3s 3ms/step - loss: 0.5690 - accuracy: 0.7911 - val_loss: 0.5655 - val_accuracy: 0.8017\n",
            "Epoch 9/20\n",
            "916/916 [==============================] - 3s 3ms/step - loss: 0.5657 - accuracy: 0.7917 - val_loss: 0.5529 - val_accuracy: 0.8032\n",
            "Epoch 10/20\n",
            "916/916 [==============================] - 3s 3ms/step - loss: 0.5629 - accuracy: 0.7919 - val_loss: 0.5993 - val_accuracy: 0.7706\n",
            "Epoch 11/20\n",
            "916/916 [==============================] - 3s 3ms/step - loss: 0.5624 - accuracy: 0.7922 - val_loss: 0.5545 - val_accuracy: 0.7967\n",
            "Epoch 12/20\n",
            "916/916 [==============================] - 3s 3ms/step - loss: 0.5608 - accuracy: 0.7901 - val_loss: 0.5572 - val_accuracy: 0.7869\n",
            "Epoch 13/20\n",
            "916/916 [==============================] - 3s 3ms/step - loss: 0.5616 - accuracy: 0.7907 - val_loss: 0.5609 - val_accuracy: 0.7903\n",
            "Epoch 14/20\n",
            "916/916 [==============================] - 3s 3ms/step - loss: 0.5608 - accuracy: 0.7919 - val_loss: 0.5694 - val_accuracy: 0.7860\n",
            "Epoch 15/20\n",
            "916/916 [==============================] - 3s 3ms/step - loss: 0.5591 - accuracy: 0.7926 - val_loss: 0.5520 - val_accuracy: 0.7881\n",
            "Epoch 16/20\n",
            "916/916 [==============================] - 3s 3ms/step - loss: 0.5618 - accuracy: 0.7897 - val_loss: 0.5572 - val_accuracy: 0.7866\n",
            "Epoch 17/20\n",
            "916/916 [==============================] - 3s 3ms/step - loss: 0.5584 - accuracy: 0.7906 - val_loss: 0.5578 - val_accuracy: 0.7869\n",
            "Epoch 18/20\n",
            "916/916 [==============================] - 3s 3ms/step - loss: 0.5568 - accuracy: 0.7915 - val_loss: 0.5577 - val_accuracy: 0.7851\n",
            "Epoch 19/20\n",
            "916/916 [==============================] - 3s 3ms/step - loss: 0.5561 - accuracy: 0.7919 - val_loss: 0.5566 - val_accuracy: 0.7866\n",
            "Epoch 20/20\n",
            "916/916 [==============================] - 3s 3ms/step - loss: 0.5587 - accuracy: 0.7891 - val_loss: 0.5575 - val_accuracy: 0.7866\n",
            "Evaluating: JamesSteinEncoder()\n",
            "Epoch 1/20\n",
            "916/916 [==============================] - 4s 3ms/step - loss: 1.6417 - accuracy: 0.7693 - val_loss: 0.6371 - val_accuracy: 0.7921\n",
            "Epoch 2/20\n",
            "916/916 [==============================] - 3s 3ms/step - loss: 0.6234 - accuracy: 0.7847 - val_loss: 0.5947 - val_accuracy: 0.8050\n",
            "Epoch 3/20\n",
            "916/916 [==============================] - 3s 3ms/step - loss: 0.6020 - accuracy: 0.7875 - val_loss: 0.5886 - val_accuracy: 0.7946\n",
            "Epoch 4/20\n",
            "916/916 [==============================] - 3s 3ms/step - loss: 0.5881 - accuracy: 0.7914 - val_loss: 0.6658 - val_accuracy: 0.7049\n",
            "Epoch 5/20\n",
            "916/916 [==============================] - 3s 3ms/step - loss: 0.5826 - accuracy: 0.7897 - val_loss: 0.5803 - val_accuracy: 0.7783\n",
            "Epoch 6/20\n",
            "916/916 [==============================] - 3s 3ms/step - loss: 0.5745 - accuracy: 0.7894 - val_loss: 0.5933 - val_accuracy: 0.7903\n",
            "Epoch 7/20\n",
            "916/916 [==============================] - 3s 3ms/step - loss: 0.5706 - accuracy: 0.7896 - val_loss: 0.5735 - val_accuracy: 0.7857\n",
            "Epoch 8/20\n",
            "916/916 [==============================] - 3s 3ms/step - loss: 0.5682 - accuracy: 0.7908 - val_loss: 0.5810 - val_accuracy: 0.7792\n",
            "Epoch 9/20\n",
            "916/916 [==============================] - 3s 3ms/step - loss: 0.5650 - accuracy: 0.7897 - val_loss: 0.5609 - val_accuracy: 0.7875\n",
            "Epoch 10/20\n",
            "916/916 [==============================] - 3s 3ms/step - loss: 0.5680 - accuracy: 0.7876 - val_loss: 0.5642 - val_accuracy: 0.7869\n",
            "Epoch 11/20\n",
            "916/916 [==============================] - 3s 3ms/step - loss: 0.5658 - accuracy: 0.7888 - val_loss: 0.5695 - val_accuracy: 0.7872\n",
            "Epoch 12/20\n",
            "916/916 [==============================] - 3s 3ms/step - loss: 0.5656 - accuracy: 0.7863 - val_loss: 0.5667 - val_accuracy: 0.7909\n",
            "Epoch 13/20\n",
            "916/916 [==============================] - 3s 3ms/step - loss: 0.5635 - accuracy: 0.7890 - val_loss: 0.5594 - val_accuracy: 0.7872\n",
            "Epoch 14/20\n",
            "916/916 [==============================] - 3s 3ms/step - loss: 0.5591 - accuracy: 0.7901 - val_loss: 0.5565 - val_accuracy: 0.7875\n",
            "Epoch 15/20\n",
            "916/916 [==============================] - 3s 3ms/step - loss: 0.5578 - accuracy: 0.7909 - val_loss: 0.5753 - val_accuracy: 0.7756\n",
            "Epoch 16/20\n",
            "916/916 [==============================] - 3s 3ms/step - loss: 0.5591 - accuracy: 0.7911 - val_loss: 0.5692 - val_accuracy: 0.8053\n",
            "Epoch 17/20\n",
            "916/916 [==============================] - 3s 3ms/step - loss: 0.5608 - accuracy: 0.7881 - val_loss: 0.5549 - val_accuracy: 0.7875\n",
            "Epoch 18/20\n",
            "916/916 [==============================] - 3s 3ms/step - loss: 0.5564 - accuracy: 0.7913 - val_loss: 0.5543 - val_accuracy: 0.7835\n",
            "Epoch 19/20\n",
            "916/916 [==============================] - 3s 3ms/step - loss: 0.5562 - accuracy: 0.7903 - val_loss: 0.5473 - val_accuracy: 0.7897\n",
            "Epoch 20/20\n",
            "916/916 [==============================] - 3s 3ms/step - loss: 0.5549 - accuracy: 0.7911 - val_loss: 0.5530 - val_accuracy: 0.7891\n"
          ]
        }
      ]
    }
  ]
}