ML_Project/3조/CHIC_ML_project_송명섭.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "n9U1jQ97YpfV"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "gender_submission = pd.read_csv(\"/content/drive/MyDrive/titanic/gender_submission.csv\")\n",
        "train = pd.read_csv(\"/content/drive/MyDrive/titanic/train.csv\")\n",
        "test = pd.read_csv(\"/content/drive/MyDrive/titanic/test.csv\")\n",
        "\n",
        "print(train.shape, test.shape) # train은 label 포함 12열, test는 label이 없으니 11열로 구성"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MacjrJ9fbOxA",
        "outputId": "e3c792d2-d56e-4952-e9f2-32ca0ab434ee"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(891, 12) (418, 11)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train.head(3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 213
        },
        "id": "PIiI0UjTSTV5",
        "outputId": "693c253b-3f72-4310-b955-781e4eae7ab5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   PassengerId  Survived  Pclass  \\\n",
              "0            1         0       3   \n",
              "1            2         1       1   \n",
              "2            3         1       3   \n",
              "\n",
              "                                                Name     Sex   Age  SibSp  \\\n",
              "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
              "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
              "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
              "\n",
              "   Parch            Ticket     Fare Cabin Embarked  \n",
              "0      0         A/5 21171   7.2500   NaN        S  \n",
              "1      0          PC 17599  71.2833   C85        C  \n",
              "2      0  STON/O2. 3101282   7.9250   NaN        S  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-374695fc-64f1-4ab0-b571-829beca60579\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Braund, Mr. Owen Harris</td>\n",
              "      <td>male</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>A/5 21171</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
              "      <td>female</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>PC 17599</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>C85</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Heikkinen, Miss. Laina</td>\n",
              "      <td>female</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>STON/O2. 3101282</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-374695fc-64f1-4ab0-b571-829beca60579')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-374695fc-64f1-4ab0-b571-829beca60579 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-374695fc-64f1-4ab0-b571-829beca60579');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-e874d01a-7153-49dc-b2a2-66564b6884d6\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e874d01a-7153-49dc-b2a2-66564b6884d6')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-e874d01a-7153-49dc-b2a2-66564b6884d6 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "train",
              "summary": "{\n  \"name\": \"train\",\n  \"rows\": 891,\n  \"fields\": [\n    {\n      \"column\": \"PassengerId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 257,\n        \"min\": 1,\n        \"max\": 891,\n        \"num_unique_values\": 891,\n        \"samples\": [\n          710,\n          440,\n          841\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Survived\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Pclass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          3,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 891,\n        \"samples\": [\n          \"Moubarek, Master. Halim Gonios (\\\"William George\\\")\",\n          \"Kvillner, Mr. Johan Henrik Johannesson\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"female\",\n          \"male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14.526497332334042,\n        \"min\": 0.42,\n        \"max\": 80.0,\n        \"num_unique_values\": 88,\n        \"samples\": [\n          0.75,\n          22.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SibSp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 8,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Parch\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 6,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ticket\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 681,\n        \"samples\": [\n          \"11774\",\n          \"248740\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 49.6934285971809,\n        \"min\": 0.0,\n        \"max\": 512.3292,\n        \"num_unique_values\": 248,\n        \"samples\": [\n          11.2417,\n          51.8625\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cabin\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 147,\n        \"samples\": [\n          \"D45\",\n          \"B49\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Embarked\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"S\",\n          \"C\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test.head(3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "K5KgfuWlSofX",
        "outputId": "47f6dc61-d43e-4cf8-ed02-0327a5d2491b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   PassengerId  Pclass                              Name     Sex   Age  SibSp  \\\n",
              "0          892       3                  Kelly, Mr. James    male  34.5      0   \n",
              "1          893       3  Wilkes, Mrs. James (Ellen Needs)  female  47.0      1   \n",
              "2          894       2         Myles, Mr. Thomas Francis    male  62.0      0   \n",
              "\n",
              "   Parch  Ticket    Fare Cabin Embarked  \n",
              "0      0  330911  7.8292   NaN        Q  \n",
              "1      0  363272  7.0000   NaN        S  \n",
              "2      0  240276  9.6875   NaN        Q  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-270abee9-2d8b-4526-b804-16c1a13308c3\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>892</td>\n",
              "      <td>3</td>\n",
              "      <td>Kelly, Mr. James</td>\n",
              "      <td>male</td>\n",
              "      <td>34.5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>330911</td>\n",
              "      <td>7.8292</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Q</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>893</td>\n",
              "      <td>3</td>\n",
              "      <td>Wilkes, Mrs. James (Ellen Needs)</td>\n",
              "      <td>female</td>\n",
              "      <td>47.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>363272</td>\n",
              "      <td>7.0000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>894</td>\n",
              "      <td>2</td>\n",
              "      <td>Myles, Mr. Thomas Francis</td>\n",
              "      <td>male</td>\n",
              "      <td>62.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>240276</td>\n",
              "      <td>9.6875</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Q</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-270abee9-2d8b-4526-b804-16c1a13308c3')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-270abee9-2d8b-4526-b804-16c1a13308c3 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-270abee9-2d8b-4526-b804-16c1a13308c3');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-50cc6c52-ac1c-45eb-aa7a-1d90e61b6ace\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-50cc6c52-ac1c-45eb-aa7a-1d90e61b6ace')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-50cc6c52-ac1c-45eb-aa7a-1d90e61b6ace button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "test",
              "summary": "{\n  \"name\": \"test\",\n  \"rows\": 418,\n  \"fields\": [\n    {\n      \"column\": \"PassengerId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 120,\n        \"min\": 892,\n        \"max\": 1309,\n        \"num_unique_values\": 418,\n        \"samples\": [\n          1213,\n          1216,\n          1280\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Pclass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          3,\n          2,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 418,\n        \"samples\": [\n          \"Krekorian, Mr. Neshan\",\n          \"Kreuchen, Miss. Emilie\",\n          \"Canavan, Mr. Patrick\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"female\",\n          \"male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14.181209235624422,\n        \"min\": 0.17,\n        \"max\": 76.0,\n        \"num_unique_values\": 79,\n        \"samples\": [\n          10.0,\n          34.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SibSp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 8,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Parch\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 9,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1,\n          6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ticket\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 363,\n        \"samples\": [\n          \"2673\",\n          \"W./C. 6607\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 55.907576179973844,\n        \"min\": 0.0,\n        \"max\": 512.3292,\n        \"num_unique_values\": 169,\n        \"samples\": [\n          41.5792,\n          57.75\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cabin\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 76,\n        \"samples\": [\n          \"A21\",\n          \"E45\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Embarked\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Q\",\n          \"S\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train.isnull().sum() # train 결측치 Age 177, Cabin 687, Embarked 2\n",
        "test.isnull().sum() # test 결측치 Age 86, Fare 1, Cabin 327"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VT0NpTdzSvaM",
        "outputId": "9c30ac17-8038-47b0-90e1-2e4f14229040"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PassengerId      0\n",
              "Pclass           0\n",
              "Name             0\n",
              "Sex              0\n",
              "Age             86\n",
              "SibSp            0\n",
              "Parch            0\n",
              "Ticket           0\n",
              "Fare             1\n",
              "Cabin          327\n",
              "Embarked         0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Name에서 Title(호칭)을 따오자. 정규표현식으로 따온 후, 빈도를 파악한다.\n",
        "train['Title'] = train['Name'].str.extract('([A-Za-z]+)\\.', expand = False)\n",
        "test['Title'] =   test['Name'].str.extract('([A-Za-z]+)\\.', expand = False)\n",
        "\n",
        "train['Title'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V4v6FmL5l_vX",
        "outputId": "88f09b83-732c-453f-9249-7d23c1924c7f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Mr          517\n",
              "Miss        182\n",
              "Mrs         125\n",
              "Master       40\n",
              "Dr            7\n",
              "Rev           6\n",
              "Mlle          2\n",
              "Major         2\n",
              "Col           2\n",
              "Countess      1\n",
              "Capt          1\n",
              "Ms            1\n",
              "Sir           1\n",
              "Lady          1\n",
              "Mme           1\n",
              "Don           1\n",
              "Jonkheer      1\n",
              "Name: Title, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train['Title'] = train['Title'].replace(['Mlle','Ms'], 'Miss')\n",
        "train['Title'] = train['Title'].replace('Mme', 'Mrs')\n",
        "train['Title'] = train['Title'].replace(['Dr','Rev','Major','Jonkheer','Countess','Capt','Sir','Lady','Don','Col'],'Others')\n",
        "train['Title'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WAtE5twemAQ2",
        "outputId": "15cd9d5f-74a8-4559-f32d-b6758f4a4408"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PassengerId      0\n",
              "Survived         0\n",
              "Pclass           0\n",
              "Name             0\n",
              "Sex              0\n",
              "Age            177\n",
              "SibSp            0\n",
              "Parch            0\n",
              "Ticket           0\n",
              "Fare             0\n",
              "Cabin          687\n",
              "Embarked         2\n",
              "Title            0\n",
              "family           0\n",
              "ismale           0\n",
              "title            0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test['Title'] = test['Title'].replace(['Mlle','Ms'], 'Miss')\n",
        "test['Title'] = test['Title'].replace('Mme', 'Mrs')\n",
        "test['Title'] = test['Title'].replace(['Dr','Rev','Major','Jonkheer','Countess','Capt','Sir','Lady','Don','Col'],'Others')\n",
        "test['Title'].value_counts()\n",
        "test.head()"
      ],
      "metadata": {
        "id": "ceJPSDAYMPx2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 451
        },
        "outputId": "161251c7-fd06-494e-ffe7-51bb54ef2388"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   PassengerId  Pclass                                          Name     Sex  \\\n",
              "0          892       3                              Kelly, Mr. James    male   \n",
              "1          893       3              Wilkes, Mrs. James (Ellen Needs)  female   \n",
              "2          894       2                     Myles, Mr. Thomas Francis    male   \n",
              "3          895       3                              Wirz, Mr. Albert    male   \n",
              "4          896       3  Hirvonen, Mrs. Alexander (Helga E Lindqvist)  female   \n",
              "\n",
              "    Age  SibSp  Parch   Ticket     Fare Cabin Embarked Title  family  ismale  \\\n",
              "0  34.5      0      0   330911   7.8292   NaN        Q    Mr       1       1   \n",
              "1  47.0      1      0   363272   7.0000   NaN        S   Mrs       2       0   \n",
              "2  62.0      0      0   240276   9.6875   NaN        Q    Mr       1       1   \n",
              "3  27.0      0      0   315154   8.6625   NaN        S    Mr       1       1   \n",
              "4  22.0      1      1  3101298  12.2875   NaN        S   Mrs       3       0   \n",
              "\n",
              "   title  embarked  \n",
              "0    0.0         1  \n",
              "1    2.0         2  \n",
              "2    0.0         1  \n",
              "3    0.0         2  \n",
              "4    2.0         2  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-106bea98-3cd7-4b08-9b14-ee758ff98c64\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\n",
              "      <th>Title</th>\n",
              "      <th>family</th>\n",
              "      <th>ismale</th>\n",
              "      <th>title</th>\n",
              "      <th>embarked</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>892</td>\n",
              "      <td>3</td>\n",
              "      <td>Kelly, Mr. James</td>\n",
              "      <td>male</td>\n",
              "      <td>34.5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>330911</td>\n",
              "      <td>7.8292</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Q</td>\n",
              "      <td>Mr</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>893</td>\n",
              "      <td>3</td>\n",
              "      <td>Wilkes, Mrs. James (Ellen Needs)</td>\n",
              "      <td>female</td>\n",
              "      <td>47.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>363272</td>\n",
              "      <td>7.0000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>Mrs</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>894</td>\n",
              "      <td>2</td>\n",
              "      <td>Myles, Mr. Thomas Francis</td>\n",
              "      <td>male</td>\n",
              "      <td>62.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>240276</td>\n",
              "      <td>9.6875</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Q</td>\n",
              "      <td>Mr</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>895</td>\n",
              "      <td>3</td>\n",
              "      <td>Wirz, Mr. Albert</td>\n",
              "      <td>male</td>\n",
              "      <td>27.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>315154</td>\n",
              "      <td>8.6625</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>Mr</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>896</td>\n",
              "      <td>3</td>\n",
              "      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
              "      <td>female</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3101298</td>\n",
              "      <td>12.2875</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>Mrs</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-106bea98-3cd7-4b08-9b14-ee758ff98c64')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-106bea98-3cd7-4b08-9b14-ee758ff98c64 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-106bea98-3cd7-4b08-9b14-ee758ff98c64');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-fccef563-5670-4305-9fb5-e7678126c7d5\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-fccef563-5670-4305-9fb5-e7678126c7d5')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-fccef563-5670-4305-9fb5-e7678126c7d5 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "test",
              "summary": "{\n  \"name\": \"test\",\n  \"rows\": 418,\n  \"fields\": [\n    {\n      \"column\": \"PassengerId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 120,\n        \"min\": 892,\n        \"max\": 1309,\n        \"num_unique_values\": 418,\n        \"samples\": [\n          1213,\n          1216,\n          1280\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Pclass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          3,\n          2,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 418,\n        \"samples\": [\n          \"Krekorian, Mr. Neshan\",\n          \"Kreuchen, Miss. Emilie\",\n          \"Canavan, Mr. Patrick\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"female\",\n          \"male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14.181209235624422,\n        \"min\": 0.17,\n        \"max\": 76.0,\n        \"num_unique_values\": 79,\n        \"samples\": [\n          10.0,\n          34.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SibSp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 8,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Parch\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 9,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1,\n          6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ticket\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 363,\n        \"samples\": [\n          \"2673\",\n          \"W./C. 6607\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 55.907576179973844,\n        \"min\": 0.0,\n        \"max\": 512.3292,\n        \"num_unique_values\": 169,\n        \"samples\": [\n          41.5792,\n          57.75\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cabin\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 76,\n        \"samples\": [\n          \"A21\",\n          \"E45\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Embarked\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Q\",\n          \"S\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Title\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"Mr\",\n          \"Mrs\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"family\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 1,\n        \"max\": 11,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          7,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ismale\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"title\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.164038333808286,\n        \"min\": 0.0,\n        \"max\": 5.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          2.0,\n          5.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"embarked\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 114
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train['family'] = 1 + train['SibSp'] + train['Parch'] #  가족 구성원 수\n",
        "test['family'] = 1 + test['SibSp'] + test['Parch'] #  가족 구성원 수"
      ],
      "metadata": {
        "id": "aR2svNkzihyK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train['ismale'] = train['Sex'].map({'female':0, 'male':1}).astype(int) # 성별 숫자 매핑\n",
        "test['ismale'] = test['Sex'].map({'female':0, 'male':1}).astype(int) # 성별 숫자 매핑\n",
        "\n",
        "train['title'] = train['Title'].map({'Mr':0,'Miss':1,\"Mrs\":2,'Master':4,'Others':5}).astype(float)\n",
        "test['title'] = test['Title'].map({'Mr':0,'Miss':1,\"Mrs\":2,'Master':4,'Others':5}).astype(float)\n"
      ],
      "metadata": {
        "id": "w05pCPsXjd4V"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train['Embarked'].fillna('S',inplace=True)\n",
        "train['embarked']= train['Embarked'].map({'C':0,'Q':1,'S':2}).astype(int)\n",
        "test['Embarked'].fillna('S',inplace=True)\n",
        "test['embarked']= test['Embarked'].map({'C':0,'Q':1,'S':2}).astype(int)"
      ],
      "metadata": {
        "id": "X4Rf-1vdkVcc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def bar_chart(feature): # 피처별 생존 여부 시각화\n",
        "  survived = train[train['Survived']==1][feature].value_counts()\n",
        "  dead = train[train['Survived']==0][feature].value_counts()\n",
        "  df = pd.DataFrame([survived,dead])\n",
        "  df.index = ['Survived','Dead']\n",
        "  df.plot(kind='bar',stacked=True, figsize=(10,5))"
      ],
      "metadata": {
        "id": "5eMrGBwZS3Ls"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "bar_chart('Embarked')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 492
        },
        "id": "ZjmXTALHS9Vo",
        "outputId": "1e92ad53-1732-4c6f-fa8b-334b4677b71a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "bar_chart('Sex')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 492
        },
        "id": "DUiclzKmlKQX",
        "outputId": "2a7a091c-f1f9-48c2-97b4-461fdcfc4f53"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 504
        },
        "id": "1EoFQ4m7lpyL",
        "outputId": "9bf6abff-9c7b-4f54-f039-ec7a3b0c5ab9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   PassengerId  Survived  Pclass  \\\n",
              "0            1         0       3   \n",
              "1            2         1       1   \n",
              "2            3         1       3   \n",
              "3            4         1       1   \n",
              "4            5         0       3   \n",
              "\n",
              "                                                Name     Sex   Age  SibSp  \\\n",
              "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
              "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
              "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
              "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
              "4                           Allen, Mr. William Henry    male  35.0      0   \n",
              "\n",
              "   Parch            Ticket     Fare Cabin Embarked Title  family  ismale  \\\n",
              "0      0         A/5 21171   7.2500   NaN        S    Mr       2       1   \n",
              "1      0          PC 17599  71.2833   C85        C   Mrs       2       0   \n",
              "2      0  STON/O2. 3101282   7.9250   NaN        S  Miss       1       0   \n",
              "3      0            113803  53.1000  C123        S   Mrs       2       0   \n",
              "4      0            373450   8.0500   NaN        S    Mr       1       1   \n",
              "\n",
              "   title  embarked  \n",
              "0    0.0         2  \n",
              "1    2.0         0  \n",
              "2    1.0         2  \n",
              "3    2.0         2  \n",
              "4    0.0         2  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-22808260-4b93-4c6a-b3b7-4c42fddef0dd\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\n",
              "      <th>Title</th>\n",
              "      <th>family</th>\n",
              "      <th>ismale</th>\n",
              "      <th>title</th>\n",
              "      <th>embarked</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Braund, Mr. Owen Harris</td>\n",
              "      <td>male</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>A/5 21171</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>Mr</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
              "      <td>female</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>PC 17599</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>C85</td>\n",
              "      <td>C</td>\n",
              "      <td>Mrs</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Heikkinen, Miss. Laina</td>\n",
              "      <td>female</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>STON/O2. 3101282</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>Miss</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
              "      <td>female</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>113803</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>C123</td>\n",
              "      <td>S</td>\n",
              "      <td>Mrs</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Allen, Mr. William Henry</td>\n",
              "      <td>male</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>373450</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>Mr</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-22808260-4b93-4c6a-b3b7-4c42fddef0dd')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-22808260-4b93-4c6a-b3b7-4c42fddef0dd 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-22808260-4b93-4c6a-b3b7-4c42fddef0dd');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-7bbde8fd-d98d-44e8-a6c4-b5784f5e034b\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-7bbde8fd-d98d-44e8-a6c4-b5784f5e034b')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-7bbde8fd-d98d-44e8-a6c4-b5784f5e034b button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "train",
              "summary": "{\n  \"name\": \"train\",\n  \"rows\": 891,\n  \"fields\": [\n    {\n      \"column\": \"PassengerId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 257,\n        \"min\": 1,\n        \"max\": 891,\n        \"num_unique_values\": 891,\n        \"samples\": [\n          710,\n          440,\n          841\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Survived\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Pclass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          3,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 891,\n        \"samples\": [\n          \"Moubarek, Master. Halim Gonios (\\\"William George\\\")\",\n          \"Kvillner, Mr. Johan Henrik Johannesson\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"female\",\n          \"male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14.526497332334042,\n        \"min\": 0.42,\n        \"max\": 80.0,\n        \"num_unique_values\": 88,\n        \"samples\": [\n          0.75,\n          22.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SibSp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 8,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Parch\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 6,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ticket\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 681,\n        \"samples\": [\n          \"11774\",\n          \"248740\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 49.6934285971809,\n        \"min\": 0.0,\n        \"max\": 512.3292,\n        \"num_unique_values\": 248,\n        \"samples\": [\n          11.2417,\n          51.8625\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cabin\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 147,\n        \"samples\": [\n          \"D45\",\n          \"B49\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Embarked\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"S\",\n          \"C\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Title\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Mrs\",\n          \"Others\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"family\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 1,\n        \"max\": 11,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          8,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ismale\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"title\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.2247628900110652,\n        \"min\": 0.0,\n        \"max\": 5.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          2.0,\n          5.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"embarked\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          2,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 110
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 451
        },
        "id": "OYM0pgi-mAKd",
        "outputId": "0e6e08c0-04db-4ce9-9d1a-85ab202b9dfe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   PassengerId  Pclass                                          Name     Sex  \\\n",
              "0          892       3                              Kelly, Mr. James    male   \n",
              "1          893       3              Wilkes, Mrs. James (Ellen Needs)  female   \n",
              "2          894       2                     Myles, Mr. Thomas Francis    male   \n",
              "3          895       3                              Wirz, Mr. Albert    male   \n",
              "4          896       3  Hirvonen, Mrs. Alexander (Helga E Lindqvist)  female   \n",
              "\n",
              "    Age  SibSp  Parch   Ticket     Fare Cabin Embarked Title  family  ismale  \\\n",
              "0  34.5      0      0   330911   7.8292   NaN        Q    Mr       1       1   \n",
              "1  47.0      1      0   363272   7.0000   NaN        S   Mrs       2       0   \n",
              "2  62.0      0      0   240276   9.6875   NaN        Q    Mr       1       1   \n",
              "3  27.0      0      0   315154   8.6625   NaN        S    Mr       1       1   \n",
              "4  22.0      1      1  3101298  12.2875   NaN        S   Mrs       3       0   \n",
              "\n",
              "   title  embarked  \n",
              "0    0.0         1  \n",
              "1    2.0         2  \n",
              "2    0.0         1  \n",
              "3    0.0         2  \n",
              "4    2.0         2  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ee30f60e-5b63-4e8d-bc4f-87ed440e0f6e\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\n",
              "      <th>Title</th>\n",
              "      <th>family</th>\n",
              "      <th>ismale</th>\n",
              "      <th>title</th>\n",
              "      <th>embarked</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>892</td>\n",
              "      <td>3</td>\n",
              "      <td>Kelly, Mr. James</td>\n",
              "      <td>male</td>\n",
              "      <td>34.5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>330911</td>\n",
              "      <td>7.8292</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Q</td>\n",
              "      <td>Mr</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>893</td>\n",
              "      <td>3</td>\n",
              "      <td>Wilkes, Mrs. James (Ellen Needs)</td>\n",
              "      <td>female</td>\n",
              "      <td>47.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>363272</td>\n",
              "      <td>7.0000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>Mrs</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>894</td>\n",
              "      <td>2</td>\n",
              "      <td>Myles, Mr. Thomas Francis</td>\n",
              "      <td>male</td>\n",
              "      <td>62.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>240276</td>\n",
              "      <td>9.6875</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Q</td>\n",
              "      <td>Mr</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>895</td>\n",
              "      <td>3</td>\n",
              "      <td>Wirz, Mr. Albert</td>\n",
              "      <td>male</td>\n",
              "      <td>27.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>315154</td>\n",
              "      <td>8.6625</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>Mr</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>896</td>\n",
              "      <td>3</td>\n",
              "      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
              "      <td>female</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3101298</td>\n",
              "      <td>12.2875</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>Mrs</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ee30f60e-5b63-4e8d-bc4f-87ed440e0f6e')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-ee30f60e-5b63-4e8d-bc4f-87ed440e0f6e 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-ee30f60e-5b63-4e8d-bc4f-87ed440e0f6e');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-7e101000-3064-4533-bc0f-1e48fa361161\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-7e101000-3064-4533-bc0f-1e48fa361161')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-7e101000-3064-4533-bc0f-1e48fa361161 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "test",
              "summary": "{\n  \"name\": \"test\",\n  \"rows\": 418,\n  \"fields\": [\n    {\n      \"column\": \"PassengerId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 120,\n        \"min\": 892,\n        \"max\": 1309,\n        \"num_unique_values\": 418,\n        \"samples\": [\n          1213,\n          1216,\n          1280\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Pclass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          3,\n          2,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 418,\n        \"samples\": [\n          \"Krekorian, Mr. Neshan\",\n          \"Kreuchen, Miss. Emilie\",\n          \"Canavan, Mr. Patrick\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"female\",\n          \"male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14.181209235624422,\n        \"min\": 0.17,\n        \"max\": 76.0,\n        \"num_unique_values\": 79,\n        \"samples\": [\n          10.0,\n          34.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SibSp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 8,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Parch\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 9,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1,\n          6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ticket\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 363,\n        \"samples\": [\n          \"2673\",\n          \"W./C. 6607\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 55.907576179973844,\n        \"min\": 0.0,\n        \"max\": 512.3292,\n        \"num_unique_values\": 169,\n        \"samples\": [\n          41.5792,\n          57.75\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cabin\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 76,\n        \"samples\": [\n          \"A21\",\n          \"E45\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Embarked\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Q\",\n          \"S\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Title\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"Mr\",\n          \"Mrs\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"family\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 1,\n        \"max\": 11,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          7,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ismale\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"title\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.164038333808286,\n        \"min\": 0.0,\n        \"max\": 5.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          2.0,\n          5.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"embarked\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 111
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "label_column = ['Survived']\n",
        "feature_columns = ['Pclass','Age','Fare','embarked','title','family','ismale']\n",
        "\n",
        "train_X = train[feature_columns]\n",
        "test_X = test[feature_columns]\n",
        "train_Y = train[label_column]\n",
        "print(train_X.shape, test_X.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "id": "HZD35HousTPC",
        "outputId": "b02d764e-1ce3-40d5-af2f-abb4b535501b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(891, 7) (418, 7)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Pclass   Age     Fare  embarked  title  family  ismale\n",
              "0       3  22.0   7.2500         2    0.0       2       1\n",
              "1       1  38.0  71.2833         0    2.0       2       0\n",
              "2       3  26.0   7.9250         2    1.0       1       0\n",
              "3       1  35.0  53.1000         2    2.0       2       0\n",
              "4       3  35.0   8.0500         2    0.0       1       1"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-30831479-be99-41b9-ad93-69d8c28ec9ee\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Age</th>\n",
              "      <th>Fare</th>\n",
              "      <th>embarked</th>\n",
              "      <th>title</th>\n",
              "      <th>family</th>\n",
              "      <th>ismale</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3</td>\n",
              "      <td>22.0</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>38.0</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>26.0</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>2</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>35.0</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3</td>\n",
              "      <td>35.0</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-30831479-be99-41b9-ad93-69d8c28ec9ee')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-30831479-be99-41b9-ad93-69d8c28ec9ee 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-30831479-be99-41b9-ad93-69d8c28ec9ee');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-a3b6064d-741c-46d3-9212-e88cb60eb048\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-a3b6064d-741c-46d3-9212-e88cb60eb048')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-a3b6064d-741c-46d3-9212-e88cb60eb048 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "train_X",
              "summary": "{\n  \"name\": \"train_X\",\n  \"rows\": 891,\n  \"fields\": [\n    {\n      \"column\": \"Pclass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          3,\n          1,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14.526497332334042,\n        \"min\": 0.42,\n        \"max\": 80.0,\n        \"num_unique_values\": 88,\n        \"samples\": [\n          0.75,\n          22.0,\n          49.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 49.6934285971809,\n        \"min\": 0.0,\n        \"max\": 512.3292,\n        \"num_unique_values\": 248,\n        \"samples\": [\n          11.2417,\n          51.8625,\n          7.725\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"embarked\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          2,\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"title\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.2247628900110652,\n        \"min\": 0.0,\n        \"max\": 5.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          2.0,\n          5.0,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"family\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 1,\n        \"max\": 11,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          8,\n          1,\n          6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ismale\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 113
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "sc = StandardScaler()\n",
        "\n",
        "scaled_train_X = sc.fit_transform(train_X)\n",
        "scaled_train_X = pd.DataFrame(scaled_train_X, columns=feature_columns)\n",
        "scaled_train_X.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "ty749KM5xsuY",
        "outputId": "12ec290f-8b8a-4d1f-8a3b-5fbdd397c425"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Pclass       Age      Fare  embarked     title    family    ismale\n",
              "0  0.827377 -0.530377 -0.502445  0.585954 -0.652821  0.059160  0.737695\n",
              "1 -1.566107  0.571831  0.786845 -1.942303  0.981065  0.059160 -1.355574\n",
              "2  0.827377 -0.254825 -0.488854  0.585954  0.164122 -0.560975 -1.355574\n",
              "3 -1.566107  0.365167  0.420730  0.585954  0.981065  0.059160 -1.355574\n",
              "4  0.827377  0.365167 -0.486337  0.585954 -0.652821 -0.560975  0.737695"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-41f4b5a8-f03f-4d72-a22f-870df26cd379\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Age</th>\n",
              "      <th>Fare</th>\n",
              "      <th>embarked</th>\n",
              "      <th>title</th>\n",
              "      <th>family</th>\n",
              "      <th>ismale</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.827377</td>\n",
              "      <td>-0.530377</td>\n",
              "      <td>-0.502445</td>\n",
              "      <td>0.585954</td>\n",
              "      <td>-0.652821</td>\n",
              "      <td>0.059160</td>\n",
              "      <td>0.737695</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-1.566107</td>\n",
              "      <td>0.571831</td>\n",
              "      <td>0.786845</td>\n",
              "      <td>-1.942303</td>\n",
              "      <td>0.981065</td>\n",
              "      <td>0.059160</td>\n",
              "      <td>-1.355574</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.827377</td>\n",
              "      <td>-0.254825</td>\n",
              "      <td>-0.488854</td>\n",
              "      <td>0.585954</td>\n",
              "      <td>0.164122</td>\n",
              "      <td>-0.560975</td>\n",
              "      <td>-1.355574</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-1.566107</td>\n",
              "      <td>0.365167</td>\n",
              "      <td>0.420730</td>\n",
              "      <td>0.585954</td>\n",
              "      <td>0.981065</td>\n",
              "      <td>0.059160</td>\n",
              "      <td>-1.355574</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.827377</td>\n",
              "      <td>0.365167</td>\n",
              "      <td>-0.486337</td>\n",
              "      <td>0.585954</td>\n",
              "      <td>-0.652821</td>\n",
              "      <td>-0.560975</td>\n",
              "      <td>0.737695</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-41f4b5a8-f03f-4d72-a22f-870df26cd379')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-41f4b5a8-f03f-4d72-a22f-870df26cd379 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-41f4b5a8-f03f-4d72-a22f-870df26cd379');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-0d6764bc-50a3-4449-a72a-0cd846c72423\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-0d6764bc-50a3-4449-a72a-0cd846c72423')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-0d6764bc-50a3-4449-a72a-0cd846c72423 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "scaled_train_X",
              "summary": "{\n  \"name\": \"scaled_train_X\",\n  \"rows\": 891,\n  \"fields\": [\n    {\n      \"column\": \"Pclass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0005616400330457,\n        \"min\": -1.5661069258891576,\n        \"max\": 0.8273772438659699,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.8273772438659699,\n          -1.5661069258891576,\n          -0.3693648410115938\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.000701016559981,\n        \"min\": -2.0169791879680417,\n        \"max\": 3.4651260350566906,\n        \"num_unique_values\": 88,\n        \"samples\": [\n          -1.9942461555026214,\n          -0.5303766406838785,\n          1.3295987428505243\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0005616400330457,\n        \"min\": -0.6484216535389205,\n        \"max\": 9.667166525013505,\n        \"num_unique_values\": 248,\n        \"samples\": [\n          -0.42207354059350805,\n          0.395813560675251,\n          -0.4928811950225652\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"embarked\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0005616400330457,\n        \"min\": -1.942303199253005,\n        \"max\": 0.5859541426526597,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.5859541426526597,\n          -1.942303199253005,\n          -0.6781745283001727\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"title\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0005616400330457,\n        \"min\": -0.6528210045426099,\n        \"max\": 3.4318946910154335,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.9810652736806076,\n          3.4318946910154335,\n          0.1641221345689988\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"family\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0005616400330457,\n        \"min\": -0.5609748304808925,\n        \"max\": 5.64037224096421,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          3.779968119530679,\n          -0.5609748304808925,\n          2.5396987052416584\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ismale\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0005616400330457,\n        \"min\": -1.3555735383351195,\n        \"max\": 0.7376951317802901,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          -1.3555735383351195,\n          0.7376951317802901\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 126
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "scaled_test_X = sc.fit_transform(test_X)\n",
        "scaled_test_X = pd.DataFrame(scaled_test_X, columns=feature_columns)\n",
        "scaled_test_X.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "VVKwkgeq107f",
        "outputId": "02783aee-28ce-46d0-cab1-26f7f627e29d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Pclass       Age      Fare  embarked     title    family    ismale\n",
              "0  0.873482  0.298549 -0.497811 -0.470915 -0.684788 -0.553443  0.755929\n",
              "1  0.873482  1.181328 -0.512660  0.700767  1.035432  0.105643 -1.322876\n",
              "2 -0.315819  2.240662 -0.464532 -0.470915 -0.684788 -0.553443  0.755929\n",
              "3  0.873482 -0.231118 -0.482888  0.700767 -0.684788 -0.553443  0.755929\n",
              "4  0.873482 -0.584229 -0.417971  0.700767  1.035432  0.764728 -1.322876"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-aa4623ee-7dfe-408f-ba3f-5c0e6806fcf8\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Age</th>\n",
              "      <th>Fare</th>\n",
              "      <th>embarked</th>\n",
              "      <th>title</th>\n",
              "      <th>family</th>\n",
              "      <th>ismale</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.873482</td>\n",
              "      <td>0.298549</td>\n",
              "      <td>-0.497811</td>\n",
              "      <td>-0.470915</td>\n",
              "      <td>-0.684788</td>\n",
              "      <td>-0.553443</td>\n",
              "      <td>0.755929</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.873482</td>\n",
              "      <td>1.181328</td>\n",
              "      <td>-0.512660</td>\n",
              "      <td>0.700767</td>\n",
              "      <td>1.035432</td>\n",
              "      <td>0.105643</td>\n",
              "      <td>-1.322876</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-0.315819</td>\n",
              "      <td>2.240662</td>\n",
              "      <td>-0.464532</td>\n",
              "      <td>-0.470915</td>\n",
              "      <td>-0.684788</td>\n",
              "      <td>-0.553443</td>\n",
              "      <td>0.755929</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.873482</td>\n",
              "      <td>-0.231118</td>\n",
              "      <td>-0.482888</td>\n",
              "      <td>0.700767</td>\n",
              "      <td>-0.684788</td>\n",
              "      <td>-0.553443</td>\n",
              "      <td>0.755929</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.873482</td>\n",
              "      <td>-0.584229</td>\n",
              "      <td>-0.417971</td>\n",
              "      <td>0.700767</td>\n",
              "      <td>1.035432</td>\n",
              "      <td>0.764728</td>\n",
              "      <td>-1.322876</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-aa4623ee-7dfe-408f-ba3f-5c0e6806fcf8')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-aa4623ee-7dfe-408f-ba3f-5c0e6806fcf8 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-aa4623ee-7dfe-408f-ba3f-5c0e6806fcf8');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-6f04189e-e830-4534-9176-28f176dad577\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-6f04189e-e830-4534-9176-28f176dad577')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-6f04189e-e830-4534-9176-28f176dad577 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "scaled_test_X",
              "summary": "{\n  \"name\": \"scaled_test_X\",\n  \"rows\": 418,\n  \"fields\": [\n    {\n      \"column\": \"Pclass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0011983227786452,\n        \"min\": -1.5051202859239436,\n        \"max\": 0.873481905063612,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.873481905063612,\n          -0.3158191904301659,\n          -1.5051202859239436\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0015094348213869,\n        \"min\": -2.125913789059458,\n        \"max\": 3.2293742671753187,\n        \"num_unique_values\": 79,\n        \"samples\": [\n          -1.4316967035683879,\n          0.2985493386016243,\n          0.19261590744835827\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0012012016342402,\n        \"min\": -0.6380169981152265,\n        \"max\": 8.536850626642334,\n        \"num_unique_values\": 169,\n        \"samples\": [\n          0.10658950868415079,\n          0.39617860371607466,\n          -0.14651352367814383\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"embarked\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.001198322778645,\n        \"min\": -1.6425975911461463,\n        \"max\": 0.7007668904207107,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          -0.4709153503627178,\n          0.7007668904207107,\n          -1.6425975911461463\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"title\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0012012016342398,\n        \"min\": -0.6847879479335998,\n        \"max\": 3.6157628696614466,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          1.0354323791044189,\n          3.6157628696614466,\n          0.17532221558540953\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"family\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0011983227786452,\n        \"min\": -0.5534426017272933,\n        \"max\": 6.0374123134296465,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          3.4010703473668706,\n          0.10564288978840064,\n          2.7419848558511766\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ismale\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0011983227786452,\n        \"min\": -1.3228756555322954,\n        \"max\": 0.7559289460184545,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          -1.3228756555322954,\n          0.7559289460184545\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 127
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "scaled_train_X = scaled_train_X.to_numpy().astype('float32')\n",
        "scaled_test_X = scaled_test_X.to_numpy().astype('float32')\n",
        "train_Y = train_Y.to_numpy().astype('float32')\n",
        "print(train_X.shape, test_X.shape, train_Y.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9GvBCZh_2Ni0",
        "outputId": "b197fa62-c3fc-4537-e8e4-280e429e2e85"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(891, 7) (418, 7) (891, 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense, Dropout\n",
        "from tensorflow.keras.optimizers import SGD\n",
        "\n",
        "titanic_model = Sequential()\n",
        "\n",
        "titanic_model.add(Dense(64,activation='sigmoid',input_shape=(scaled_train_X.shape[1],)))\n",
        "titanic_model.add(Dropout(0.25))\n",
        "titanic_model.add(Dense(2,activation='softmax'))\n"
      ],
      "metadata": {
        "id": "_AD6jj9q2SmZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "titanic_model.compile(optimizer=SGD(), loss = 'sparse_categorical_crossentropy', metrics=['accuracy'])\n",
        "final = titanic_model.fit(scaled_train_X, train_Y, epochs=200)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3HuuWg174Kss",
        "outputId": "1216f4d5-468a-4ecf-8e07-acbbac9bea3c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/200\n",
            "28/28 [==============================] - 1s 2ms/step - loss: nan - accuracy: 0.6173\n",
            "Epoch 2/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 3/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 4/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 5/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 6/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 7/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 8/200\n",
            "28/28 [==============================] - 0s 3ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 9/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 10/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 11/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 12/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 13/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 14/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 15/200\n",
            "28/28 [==============================] - 0s 8ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 16/200\n",
            "28/28 [==============================] - 0s 4ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 17/200\n",
            "28/28 [==============================] - 0s 11ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 18/200\n",
            "28/28 [==============================] - 0s 8ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 19/200\n",
            "28/28 [==============================] - 0s 7ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 20/200\n",
            "28/28 [==============================] - 0s 6ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 21/200\n",
            "28/28 [==============================] - 0s 4ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 22/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 23/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 24/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 25/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 26/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 27/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 28/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 29/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 30/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 31/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 32/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 33/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 34/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 35/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 36/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 37/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 38/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 39/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 40/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 41/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 42/200\n",
            "28/28 [==============================] - 0s 3ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 43/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 44/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 45/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 46/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 47/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 48/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 49/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 50/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 51/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 52/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 53/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 54/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 55/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 56/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 57/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 58/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 59/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 60/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 61/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 62/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 63/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 64/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 65/200\n",
            "28/28 [==============================] - 0s 3ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 66/200\n",
            "28/28 [==============================] - 0s 3ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 67/200\n",
            "28/28 [==============================] - 0s 3ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 68/200\n",
            "28/28 [==============================] - 0s 3ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 69/200\n",
            "28/28 [==============================] - 0s 3ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 70/200\n",
            "28/28 [==============================] - 0s 3ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 71/200\n",
            "28/28 [==============================] - 0s 3ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 72/200\n",
            "28/28 [==============================] - 0s 3ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 73/200\n",
            "28/28 [==============================] - 0s 3ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 74/200\n",
            "28/28 [==============================] - 0s 3ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 75/200\n",
            "28/28 [==============================] - 0s 3ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 76/200\n",
            "28/28 [==============================] - 0s 3ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 77/200\n",
            "28/28 [==============================] - 0s 3ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 78/200\n",
            "28/28 [==============================] - 0s 3ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 79/200\n",
            "28/28 [==============================] - 0s 3ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 80/200\n",
            "28/28 [==============================] - 0s 3ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 81/200\n",
            "28/28 [==============================] - 0s 4ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 82/200\n",
            "28/28 [==============================] - 0s 4ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 83/200\n",
            "28/28 [==============================] - 0s 3ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 84/200\n",
            "28/28 [==============================] - 0s 3ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 85/200\n",
            "28/28 [==============================] - 0s 3ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 86/200\n",
            "28/28 [==============================] - 0s 3ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 87/200\n",
            "28/28 [==============================] - 0s 3ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 88/200\n",
            "28/28 [==============================] - 0s 3ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 89/200\n",
            "28/28 [==============================] - 0s 3ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 90/200\n",
            "28/28 [==============================] - 0s 3ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 91/200\n",
            "28/28 [==============================] - 0s 3ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 92/200\n",
            "28/28 [==============================] - 0s 3ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 93/200\n",
            "28/28 [==============================] - 0s 3ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 94/200\n",
            "28/28 [==============================] - 0s 3ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 95/200\n",
            "28/28 [==============================] - 0s 3ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 96/200\n",
            "28/28 [==============================] - 0s 3ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 97/200\n",
            "28/28 [==============================] - 0s 3ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 98/200\n",
            "28/28 [==============================] - 0s 3ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 99/200\n",
            "28/28 [==============================] - 0s 4ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 100/200\n",
            "28/28 [==============================] - 0s 3ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 101/200\n",
            "28/28 [==============================] - 0s 4ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 102/200\n",
            "28/28 [==============================] - 0s 3ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 103/200\n",
            "28/28 [==============================] - 0s 4ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 104/200\n",
            "28/28 [==============================] - 0s 4ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 105/200\n",
            "28/28 [==============================] - 0s 3ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 106/200\n",
            "28/28 [==============================] - 0s 4ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 107/200\n",
            "28/28 [==============================] - 0s 3ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 108/200\n",
            "28/28 [==============================] - 0s 3ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 109/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 110/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 111/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 112/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 113/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 114/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 115/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 116/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 117/200\n",
            "28/28 [==============================] - 0s 3ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 118/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 119/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 120/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 121/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 122/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 123/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 124/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 125/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 126/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 127/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 128/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 129/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 130/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 131/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 132/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 133/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 134/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 135/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 136/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 137/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 138/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 139/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 140/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 141/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 142/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 143/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 144/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 145/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 146/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 147/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 148/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 149/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 150/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 151/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 152/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 153/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 154/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 155/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 156/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 157/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 158/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 159/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 160/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 161/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 162/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 163/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 164/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 165/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 166/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 167/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 168/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 169/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 170/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 171/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 172/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 173/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 174/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 175/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 176/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 177/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 178/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 179/200\n",
            "28/28 [==============================] - 0s 3ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 180/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 181/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 182/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 183/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 184/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 185/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 186/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 187/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 188/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 189/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 190/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 191/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 192/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 193/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 194/200\n",
            "28/28 [==============================] - 0s 3ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 195/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 196/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 197/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 198/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 199/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n",
            "Epoch 200/200\n",
            "28/28 [==============================] - 0s 2ms/step - loss: nan - accuracy: 0.6162\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "survived_predict = titanic_model.predict(test_X)\n",
        "print(survived_predict.shape)"
      ],
      "metadata": {
        "id": "L8SNst7E5W7K"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "survived_predict = np.argmax(survived_predict, axis=0)\n",
        "gender_submission['Survived']=survived_predict\n",
        "gender_submission.to_csv('Kaggle_Titanic_Competition.csv',index=False)"
      ],
      "metadata": {
        "id": "ZuUlTdvF5Fxl"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}