AutoML_AutoKeras_Regression_Classification_Sample.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s3xd54BXF_Ua"
      },
      "source": [
        "## AutoKeras AutoML Sample Code\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "3PE5NzQ2F5DW"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn import metrics\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "sCCQ_WVdGFLD",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "20a09305-6bf0-487b-97b9-9f75459748ff"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Installing collected packages: kt-legacy, keras-tuner, tensorflow-text, keras-nlp, autokeras\n",
            "Successfully installed autokeras-1.1.0 keras-nlp-0.5.2 keras-tuner-1.3.5 kt-legacy-1.0.5 tensorflow-text-2.12.1\n"
          ]
        }
      ],
      "source": [
        "!pip install autokeras"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "PVVWVSycq-A5"
      },
      "outputs": [],
      "source": [
        "import autokeras as ak"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ekk94td6qZxn"
      },
      "source": [
        "### Regression"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Load Data"
      ],
      "metadata": {
        "id": "w69HTXUPkZBu"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bNnhk17Es5k6",
        "outputId": "b29871bf-8b22-484f-d2a1-2845cc8d8b74"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 38531 entries, 0 to 38530\n",
            "Data columns (total 19 columns):\n",
            " #   Column             Non-Null Count  Dtype  \n",
            "---  ------             --------------  -----  \n",
            " 0   manufacturer_name  38531 non-null  object \n",
            " 1   model_name         38531 non-null  object \n",
            " 2   transmission       38531 non-null  object \n",
            " 3   color              38531 non-null  object \n",
            " 4   odometer_value     38531 non-null  int64  \n",
            " 5   year_produced      38531 non-null  int64  \n",
            " 6   engine_fuel        38531 non-null  object \n",
            " 7   engine_has_gas     38531 non-null  bool   \n",
            " 8   engine_type        38531 non-null  object \n",
            " 9   engine_capacity    38521 non-null  float64\n",
            " 10  body_type          38531 non-null  object \n",
            " 11  has_warranty       38531 non-null  bool   \n",
            " 12  state              38531 non-null  object \n",
            " 13  drivetrain         38531 non-null  object \n",
            " 14  price_usd          38531 non-null  float64\n",
            " 15  is_exchangeable    38531 non-null  bool   \n",
            " 16  number_of_photos   38531 non-null  float64\n",
            " 17  up_counter         38531 non-null  float64\n",
            " 18  duration_listed    38531 non-null  float64\n",
            "dtypes: bool(3), float64(5), int64(2), object(9)\n",
            "memory usage: 4.8+ MB\n"
          ]
        }
      ],
      "source": [
        "# Load cars data\n",
        "cars = pd.read_csv('cars.csv')\n",
        "cars.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "YnCEqrZzs6XU"
      },
      "outputs": [],
      "source": [
        "# Define label column\n",
        "cars_label = 'price_usd'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 288
        },
        "id": "AGgNsoZes9hz",
        "outputId": "639efdec-d4c8-4ba0-d64a-a2888745de8c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      manufacturer_name      model_name transmission   color  odometer_value  \\\n",
              "37935           Peugeot             307   mechanical  silver          320000   \n",
              "28568               BMW             525    automatic  violet          400000   \n",
              "24311         Chevrolet         Captiva   mechanical    grey           79000   \n",
              "19393              Jeep  Grand Cherokee    automatic   other          500000   \n",
              "2540               Opel           Astra   mechanical  silver          280000   \n",
              "\n",
              "       year_produced engine_fuel  engine_has_gas engine_type  engine_capacity  \\\n",
              "37935           2005    gasoline           False    gasoline              1.6   \n",
              "28568           1997      diesel           False      diesel              2.5   \n",
              "24311           2015      diesel           False      diesel              2.2   \n",
              "19393           2004      diesel           False      diesel              2.7   \n",
              "2540            2007      diesel           False      diesel              1.7   \n",
              "\n",
              "       body_type  has_warranty  state drivetrain  price_usd  is_exchangeable  \\\n",
              "37935  universal         False  owned      front     7280.0            False   \n",
              "28568  universal         False  owned       rear     2380.0             True   \n",
              "24311        suv         False  owned        all    20300.0            False   \n",
              "19393        suv         False  owned        all     9100.0            False   \n",
              "2540   universal         False  owned      front     7000.0             True   \n",
              "\n",
              "       number_of_photos  up_counter  duration_listed  \n",
              "37935              10.0         1.0             45.5  \n",
              "28568               2.5         0.5              2.5  \n",
              "24311               4.5         6.5             30.5  \n",
              "19393               6.5        39.5            151.0  \n",
              "2540                4.0         3.0             33.0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-bd8d8a29-a9c6-428d-b7fc-db38eec9521d\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>manufacturer_name</th>\n",
              "      <th>model_name</th>\n",
              "      <th>transmission</th>\n",
              "      <th>color</th>\n",
              "      <th>odometer_value</th>\n",
              "      <th>year_produced</th>\n",
              "      <th>engine_fuel</th>\n",
              "      <th>engine_has_gas</th>\n",
              "      <th>engine_type</th>\n",
              "      <th>engine_capacity</th>\n",
              "      <th>body_type</th>\n",
              "      <th>has_warranty</th>\n",
              "      <th>state</th>\n",
              "      <th>drivetrain</th>\n",
              "      <th>price_usd</th>\n",
              "      <th>is_exchangeable</th>\n",
              "      <th>number_of_photos</th>\n",
              "      <th>up_counter</th>\n",
              "      <th>duration_listed</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>37935</th>\n",
              "      <td>Peugeot</td>\n",
              "      <td>307</td>\n",
              "      <td>mechanical</td>\n",
              "      <td>silver</td>\n",
              "      <td>320000</td>\n",
              "      <td>2005</td>\n",
              "      <td>gasoline</td>\n",
              "      <td>False</td>\n",
              "      <td>gasoline</td>\n",
              "      <td>1.6</td>\n",
              "      <td>universal</td>\n",
              "      <td>False</td>\n",
              "      <td>owned</td>\n",
              "      <td>front</td>\n",
              "      <td>7280.0</td>\n",
              "      <td>False</td>\n",
              "      <td>10.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>45.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28568</th>\n",
              "      <td>BMW</td>\n",
              "      <td>525</td>\n",
              "      <td>automatic</td>\n",
              "      <td>violet</td>\n",
              "      <td>400000</td>\n",
              "      <td>1997</td>\n",
              "      <td>diesel</td>\n",
              "      <td>False</td>\n",
              "      <td>diesel</td>\n",
              "      <td>2.5</td>\n",
              "      <td>universal</td>\n",
              "      <td>False</td>\n",
              "      <td>owned</td>\n",
              "      <td>rear</td>\n",
              "      <td>2380.0</td>\n",
              "      <td>True</td>\n",
              "      <td>2.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>2.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24311</th>\n",
              "      <td>Chevrolet</td>\n",
              "      <td>Captiva</td>\n",
              "      <td>mechanical</td>\n",
              "      <td>grey</td>\n",
              "      <td>79000</td>\n",
              "      <td>2015</td>\n",
              "      <td>diesel</td>\n",
              "      <td>False</td>\n",
              "      <td>diesel</td>\n",
              "      <td>2.2</td>\n",
              "      <td>suv</td>\n",
              "      <td>False</td>\n",
              "      <td>owned</td>\n",
              "      <td>all</td>\n",
              "      <td>20300.0</td>\n",
              "      <td>False</td>\n",
              "      <td>4.5</td>\n",
              "      <td>6.5</td>\n",
              "      <td>30.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19393</th>\n",
              "      <td>Jeep</td>\n",
              "      <td>Grand Cherokee</td>\n",
              "      <td>automatic</td>\n",
              "      <td>other</td>\n",
              "      <td>500000</td>\n",
              "      <td>2004</td>\n",
              "      <td>diesel</td>\n",
              "      <td>False</td>\n",
              "      <td>diesel</td>\n",
              "      <td>2.7</td>\n",
              "      <td>suv</td>\n",
              "      <td>False</td>\n",
              "      <td>owned</td>\n",
              "      <td>all</td>\n",
              "      <td>9100.0</td>\n",
              "      <td>False</td>\n",
              "      <td>6.5</td>\n",
              "      <td>39.5</td>\n",
              "      <td>151.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2540</th>\n",
              "      <td>Opel</td>\n",
              "      <td>Astra</td>\n",
              "      <td>mechanical</td>\n",
              "      <td>silver</td>\n",
              "      <td>280000</td>\n",
              "      <td>2007</td>\n",
              "      <td>diesel</td>\n",
              "      <td>False</td>\n",
              "      <td>diesel</td>\n",
              "      <td>1.7</td>\n",
              "      <td>universal</td>\n",
              "      <td>False</td>\n",
              "      <td>owned</td>\n",
              "      <td>front</td>\n",
              "      <td>7000.0</td>\n",
              "      <td>True</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>33.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "# Split data\n",
        "from sklearn.model_selection import train_test_split\n",
        "cars_train, cars_test = train_test_split(cars, test_size=0.2, random_state=42)\n",
        "cars_test_labels = cars_test[cars_label]\n",
        "cars_test_nolabel = cars_test.drop([cars_label], axis=1)\n",
        "cars_train_labels = cars_train[cars_label]\n",
        "cars_train_nolabel = cars_train.drop([cars_label], axis=1)\n",
        "cars_train.head()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cars_train.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dcjTAMVpTyp9",
        "outputId": "f7ad2836-b023-4b78-b006-c5562180fa4c"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 30824 entries, 37935 to 15795\n",
            "Data columns (total 19 columns):\n",
            " #   Column             Non-Null Count  Dtype  \n",
            "---  ------             --------------  -----  \n",
            " 0   manufacturer_name  30824 non-null  object \n",
            " 1   model_name         30824 non-null  object \n",
            " 2   transmission       30824 non-null  object \n",
            " 3   color              30824 non-null  object \n",
            " 4   odometer_value     30824 non-null  int64  \n",
            " 5   year_produced      30824 non-null  int64  \n",
            " 6   engine_fuel        30824 non-null  object \n",
            " 7   engine_has_gas     30824 non-null  bool   \n",
            " 8   engine_type        30824 non-null  object \n",
            " 9   engine_capacity    30814 non-null  float64\n",
            " 10  body_type          30824 non-null  object \n",
            " 11  has_warranty       30824 non-null  bool   \n",
            " 12  state              30824 non-null  object \n",
            " 13  drivetrain         30824 non-null  object \n",
            " 14  price_usd          30824 non-null  float64\n",
            " 15  is_exchangeable    30824 non-null  bool   \n",
            " 16  number_of_photos   30824 non-null  float64\n",
            " 17  up_counter         30824 non-null  float64\n",
            " 18  duration_listed    30824 non-null  float64\n",
            "dtypes: bool(3), float64(5), int64(2), object(9)\n",
            "memory usage: 4.1+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jHN1Ej5Fn8Rr"
      },
      "source": [
        "#### Train"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "R-0680LgoBvA"
      },
      "outputs": [],
      "source": [
        "regression = ak.StructuredDataRegressor(overwrite=True, max_trials=3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kizqkom5rQIm",
        "outputId": "e380d63b-3ca3-4013-9daf-0e80043fcce8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Trial 3 Complete [00h 01m 02s]\n",
            "val_loss: 32600774.0\n",
            "\n",
            "Best val_loss So Far: 32294994.0\n",
            "Total elapsed time: 00h 03m 13s\n",
            "Epoch 1/10\n",
            "964/964 [==============================] - 3s 2ms/step - loss: 132357104.0000 - mean_squared_error: 132357104.0000\n",
            "Epoch 2/10\n",
            "964/964 [==============================] - 2s 2ms/step - loss: 42785172.0000 - mean_squared_error: 42785172.0000\n",
            "Epoch 3/10\n",
            "964/964 [==============================] - 3s 3ms/step - loss: 35596416.0000 - mean_squared_error: 35596416.0000\n",
            "Epoch 4/10\n",
            "964/964 [==============================] - 3s 3ms/step - loss: 34722740.0000 - mean_squared_error: 34722740.0000\n",
            "Epoch 5/10\n",
            "964/964 [==============================] - 2s 2ms/step - loss: 34256388.0000 - mean_squared_error: 34256388.0000\n",
            "Epoch 6/10\n",
            "964/964 [==============================] - 2s 2ms/step - loss: 33908744.0000 - mean_squared_error: 33908744.0000\n",
            "Epoch 7/10\n",
            "964/964 [==============================] - 3s 3ms/step - loss: 33608388.0000 - mean_squared_error: 33608388.0000\n",
            "Epoch 8/10\n",
            "964/964 [==============================] - 3s 3ms/step - loss: 33301324.0000 - mean_squared_error: 33301324.0000\n",
            "Epoch 9/10\n",
            "964/964 [==============================] - 2s 2ms/step - loss: 33008292.0000 - mean_squared_error: 33008292.0000\n",
            "Epoch 10/10\n",
            "964/964 [==============================] - 2s 2ms/step - loss: 32747288.0000 - mean_squared_error: 32747288.0000\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:Found untraced functions such as _update_step_xla while saving (showing 1 of 1). These functions will not be directly callable after loading.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f3129b533d0>"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "# Fit model\n",
        "regression.fit(cars_train_nolabel, cars_train_labels, epochs=10)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a9LsiL_Vn9q9"
      },
      "source": [
        "#### Predict"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RYxVgbIy4IPm",
        "outputId": "a8b01e1e-4144-4619-fb9d-ca46bbfd0198"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "241/241 [==============================] - 1s 3ms/step\n",
            "241/241 [==============================] - 0s 2ms/step\n"
          ]
        }
      ],
      "source": [
        "# Predict with best model\n",
        "cars_pred_y = regression.predict(cars_test_nolabel)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4I9-3YFVn_bu"
      },
      "source": [
        "#### Evaluate"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hm6R6bmQ4Xxe",
        "outputId": "282cdfd8-aff0-4e27-828c-ad186eeabef4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "241/241 [==============================] - 1s 2ms/step - loss: 30894864.0000 - mean_squared_error: 30894864.0000\n",
            "[30894864.0, 30894864.0]\n"
          ]
        }
      ],
      "source": [
        "# Evaluate best model with testing data\n",
        "print(regression.evaluate(cars_test_nolabel, cars_test_labels))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "069YqWoGqbqS"
      },
      "source": [
        "### Classification"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kmGJDeAgtGJv"
      },
      "source": [
        "#### Load Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E8Y1nwN_tHEG",
        "outputId": "732cd61c-8797-4fed-c327-caa943786e92"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 891 entries, 0 to 890\n",
            "Data columns (total 12 columns):\n",
            " #   Column       Non-Null Count  Dtype  \n",
            "---  ------       --------------  -----  \n",
            " 0   PassengerId  891 non-null    int64  \n",
            " 1   Survived     891 non-null    int64  \n",
            " 2   Pclass       891 non-null    int64  \n",
            " 3   Name         891 non-null    object \n",
            " 4   Sex          891 non-null    object \n",
            " 5   Age          714 non-null    float64\n",
            " 6   SibSp        891 non-null    int64  \n",
            " 7   Parch        891 non-null    int64  \n",
            " 8   Ticket       891 non-null    object \n",
            " 9   Fare         891 non-null    float64\n",
            " 10  Cabin        204 non-null    object \n",
            " 11  Embarked     889 non-null    object \n",
            "dtypes: float64(2), int64(5), object(5)\n",
            "memory usage: 83.7+ KB\n"
          ]
        }
      ],
      "source": [
        "# Load titanic data\n",
        "titanic = pd.read_csv('titanic.csv')\n",
        "titanic.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "8FyvZX1GtHSn"
      },
      "outputs": [],
      "source": [
        "# Define label column\n",
        "titanic_label = 'Survived'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 426
        },
        "id": "DBneZMZktHeZ",
        "outputId": "e83b1f6a-1e87-459c-ff7e-2b63aa42fd2a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     PassengerId  Survived  Pclass                                Name  \\\n",
              "331          332         0       1                 Partner, Mr. Austen   \n",
              "733          734         0       2          Berriman, Mr. William John   \n",
              "382          383         0       3                  Tikkanen, Mr. Juho   \n",
              "704          705         0       3             Hansen, Mr. Henrik Juul   \n",
              "813          814         0       3  Andersson, Miss. Ebba Iris Alfrida   \n",
              "\n",
              "        Sex   Age  SibSp  Parch             Ticket     Fare Cabin Embarked  \n",
              "331    male  45.5      0      0             113043  28.5000  C124        S  \n",
              "733    male  23.0      0      0              28425  13.0000   NaN        S  \n",
              "382    male  32.0      0      0  STON/O 2. 3101293   7.9250   NaN        S  \n",
              "704    male  26.0      1      0             350025   7.8542   NaN        S  \n",
              "813  female   6.0      4      2             347082  31.2750   NaN        S  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e3aeefdd-843f-4923-a1ba-8aa8199ef2d4\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Survived</th>\n",
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              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
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              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>331</th>\n",
              "      <td>332</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>Partner, Mr. Austen</td>\n",
              "      <td>male</td>\n",
              "      <td>45.5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>113043</td>\n",
              "      <td>28.5000</td>\n",
              "      <td>C124</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>733</th>\n",
              "      <td>734</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>Berriman, Mr. William John</td>\n",
              "      <td>male</td>\n",
              "      <td>23.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>28425</td>\n",
              "      <td>13.0000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
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              "      <th>382</th>\n",
              "      <td>383</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Tikkanen, Mr. Juho</td>\n",
              "      <td>male</td>\n",
              "      <td>32.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>STON/O 2. 3101293</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>704</th>\n",
              "      <td>705</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Hansen, Mr. Henrik Juul</td>\n",
              "      <td>male</td>\n",
              "      <td>26.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>350025</td>\n",
              "      <td>7.8542</td>\n",
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              "    <tr>\n",
              "      <th>813</th>\n",
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              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Andersson, Miss. Ebba Iris Alfrida</td>\n",
              "      <td>female</td>\n",
              "      <td>6.0</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>347082</td>\n",
              "      <td>31.2750</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e3aeefdd-843f-4923-a1ba-8aa8199ef2d4')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "# split the data\n",
        "from sklearn.model_selection import train_test_split\n",
        "titanic_train, titanic_test = train_test_split(titanic, test_size=0.2, random_state=42)\n",
        "titanic_test_labels = titanic_test[titanic_label]\n",
        "titanic_test_nolabel = titanic_test.drop([titanic_label], axis=1)\n",
        "titanic_train_labels = titanic_train[titanic_label]\n",
        "titanic_train_nolabel = titanic_train.drop([titanic_label], axis=1)\n",
        "titanic_train.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u1A4gT3Nqcx1"
      },
      "source": [
        "#### Train"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "9AQGIMkZt33j"
      },
      "outputs": [],
      "source": [
        "# Instantiate classifier\n",
        "classification = ak.StructuredDataClassifier(overwrite=True, max_trials=10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "NjqOAHNKt4F2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1bd6726d-95c2-417d-c001-6397d98b4a4a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Trial 10 Complete [00h 00m 05s]\n",
            "val_accuracy: 0.8557692170143127\n",
            "\n",
            "Best val_accuracy So Far: 0.8846153616905212\n",
            "Total elapsed time: 00h 00m 52s\n",
            "Epoch 1/10\n",
            "23/23 [==============================] - 1s 3ms/step - loss: 0.6948 - accuracy: 0.5449\n",
            "Epoch 2/10\n",
            "23/23 [==============================] - 0s 2ms/step - loss: 0.6656 - accuracy: 0.6348\n",
            "Epoch 3/10\n",
            "23/23 [==============================] - 0s 3ms/step - loss: 0.6405 - accuracy: 0.6376\n",
            "Epoch 4/10\n",
            "23/23 [==============================] - 0s 3ms/step - loss: 0.6048 - accuracy: 0.6826\n",
            "Epoch 5/10\n",
            "23/23 [==============================] - 0s 3ms/step - loss: 0.5651 - accuracy: 0.7303\n",
            "Epoch 6/10\n",
            "23/23 [==============================] - 0s 3ms/step - loss: 0.5329 - accuracy: 0.7458\n",
            "Epoch 7/10\n",
            "23/23 [==============================] - 0s 3ms/step - loss: 0.5166 - accuracy: 0.7795\n",
            "Epoch 8/10\n",
            "23/23 [==============================] - 0s 3ms/step - loss: 0.4716 - accuracy: 0.8118\n",
            "Epoch 9/10\n",
            "23/23 [==============================] - 0s 3ms/step - loss: 0.4602 - accuracy: 0.7935\n",
            "Epoch 10/10\n",
            "23/23 [==============================] - 0s 2ms/step - loss: 0.4511 - accuracy: 0.8104\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:Found untraced functions such as _update_step_xla while saving (showing 1 of 1). These functions will not be directly callable after loading.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f312441c520>"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "# Fit model\n",
        "classification.fit(titanic_train_nolabel, titanic_train_labels, epochs=10)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gV6GY647qeHq"
      },
      "source": [
        "#### Predict"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3-cvAHgzt8AA",
        "outputId": "f553ade1-97cd-47f3-b43f-2fe6222fffc0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "6/6 [==============================] - 0s 5ms/step\n",
            "6/6 [==============================] - 0s 2ms/step\n"
          ]
        }
      ],
      "source": [
        "# Predict with best model\n",
        "titanic_pred_y = classification.predict(titanic_test_nolabel)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "07NJZ7ZGqfqK"
      },
      "source": [
        "#### Evaluate"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FgEk8BcLugJv",
        "outputId": "99ce1956-2ad0-4c7d-f8e6-eb9d642f8773"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "6/6 [==============================] - 0s 3ms/step - loss: 0.5555 - accuracy: 0.7877\n",
            "[0.5555302500724792, 0.7877094745635986]\n"
          ]
        }
      ],
      "source": [
        "print(classification.evaluate(titanic_test_nolabel, titanic_test_labels))"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [
        "s3xd54BXF_Ua"
      ],
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
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      "name": "python"
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  "nbformat_minor": 0
}