titanic_survival.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# **IMPORTING REQUIRED LIBRARIES**"
      ],
      "metadata": {
        "id": "DlzyFbZGcsIX"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ELF03LOxQbu6"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# importing file"
      ],
      "metadata": {
        "id": "UO19KVTbeqv9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "#uploading the files\n",
        "uploaded=files.upload()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 106
        },
        "id": "qLZOaEy1bIpO",
        "outputId": "525641b5-7eac-40ea-cda0-4dd90b912406"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-420ca32b-1ade-48f4-87cd-ba8a75601364\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-420ca32b-1ade-48f4-87cd-ba8a75601364\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script>// Copyright 2017 Google LLC\n",
              "//\n",
              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
              " */\n",
              "(function(scope) {\n",
              "function span(text, styleAttributes = {}) {\n",
              "  const element = document.createElement('span');\n",
              "  element.textContent = text;\n",
              "  for (const key of Object.keys(styleAttributes)) {\n",
              "    element.style[key] = styleAttributes[key];\n",
              "  }\n",
              "  return element;\n",
              "}\n",
              "\n",
              "// Max number of bytes which will be uploaded at a time.\n",
              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
              "\n",
              "function _uploadFiles(inputId, outputId) {\n",
              "  const steps = uploadFilesStep(inputId, outputId);\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  // Cache steps on the outputElement to make it available for the next call\n",
              "  // to uploadFilesContinue from Python.\n",
              "  outputElement.steps = steps;\n",
              "\n",
              "  return _uploadFilesContinue(outputId);\n",
              "}\n",
              "\n",
              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
              "function _uploadFilesContinue(outputId) {\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  const steps = outputElement.steps;\n",
              "\n",
              "  const next = steps.next(outputElement.lastPromiseValue);\n",
              "  return Promise.resolve(next.value.promise).then((value) => {\n",
              "    // Cache the last promise value to make it available to the next\n",
              "    // step of the generator.\n",
              "    outputElement.lastPromiseValue = value;\n",
              "    return next.value.response;\n",
              "  });\n",
              "}\n",
              "\n",
              "/**\n",
              " * Generator function which is called between each async step of the upload\n",
              " * process.\n",
              " * @param {string} inputId Element ID of the input file picker element.\n",
              " * @param {string} outputId Element ID of the output display.\n",
              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
              " */\n",
              "function* uploadFilesStep(inputId, outputId) {\n",
              "  const inputElement = document.getElementById(inputId);\n",
              "  inputElement.disabled = false;\n",
              "\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  outputElement.innerHTML = '';\n",
              "\n",
              "  const pickedPromise = new Promise((resolve) => {\n",
              "    inputElement.addEventListener('change', (e) => {\n",
              "      resolve(e.target.files);\n",
              "    });\n",
              "  });\n",
              "\n",
              "  const cancel = document.createElement('button');\n",
              "  inputElement.parentElement.appendChild(cancel);\n",
              "  cancel.textContent = 'Cancel upload';\n",
              "  const cancelPromise = new Promise((resolve) => {\n",
              "    cancel.onclick = () => {\n",
              "      resolve(null);\n",
              "    };\n",
              "  });\n",
              "\n",
              "  // Wait for the user to pick the files.\n",
              "  const files = yield {\n",
              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
              "    response: {\n",
              "      action: 'starting',\n",
              "    }\n",
              "  };\n",
              "\n",
              "  cancel.remove();\n",
              "\n",
              "  // Disable the input element since further picks are not allowed.\n",
              "  inputElement.disabled = true;\n",
              "\n",
              "  if (!files) {\n",
              "    return {\n",
              "      response: {\n",
              "        action: 'complete',\n",
              "      }\n",
              "    };\n",
              "  }\n",
              "\n",
              "  for (const file of files) {\n",
              "    const li = document.createElement('li');\n",
              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
              "    li.append(span(\n",
              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
              "        `last modified: ${\n",
              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
              "                                    'n/a'} - `));\n",
              "    const percent = span('0% done');\n",
              "    li.appendChild(percent);\n",
              "\n",
              "    outputElement.appendChild(li);\n",
              "\n",
              "    const fileDataPromise = new Promise((resolve) => {\n",
              "      const reader = new FileReader();\n",
              "      reader.onload = (e) => {\n",
              "        resolve(e.target.result);\n",
              "      };\n",
              "      reader.readAsArrayBuffer(file);\n",
              "    });\n",
              "    // Wait for the data to be ready.\n",
              "    let fileData = yield {\n",
              "      promise: fileDataPromise,\n",
              "      response: {\n",
              "        action: 'continue',\n",
              "      }\n",
              "    };\n",
              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
              "    let position = 0;\n",
              "    do {\n",
              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
              "      const chunk = new Uint8Array(fileData, position, length);\n",
              "      position += length;\n",
              "\n",
              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
              "      yield {\n",
              "        response: {\n",
              "          action: 'append',\n",
              "          file: file.name,\n",
              "          data: base64,\n",
              "        },\n",
              "      };\n",
              "\n",
              "      let percentDone = fileData.byteLength === 0 ?\n",
              "          100 :\n",
              "          Math.round((position / fileData.byteLength) * 100);\n",
              "      percent.textContent = `${percentDone}% done`;\n",
              "\n",
              "    } while (position < fileData.byteLength);\n",
              "  }\n",
              "\n",
              "  // All done.\n",
              "  yield {\n",
              "    response: {\n",
              "      action: 'complete',\n",
              "    }\n",
              "  };\n",
              "}\n",
              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
              "scope.google.colab._files = {\n",
              "  _uploadFiles,\n",
              "  _uploadFilesContinue,\n",
              "};\n",
              "})(self);\n",
              "</script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving test.csv to test.csv\n",
            "Saving train.csv to train.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Reading the uploaded files and creating training and testing data\n",
        "train_data=pd.read_csv(\"train.csv\")\n",
        "test=pd.read_csv(\"test.csv\")"
      ],
      "metadata": {
        "id": "mtBclUo-fUFe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **EDA(Exploratory Data analysis)**"
      ],
      "metadata": {
        "id": "bGq42zlakKPE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#used to quickly view first few rows of the data frame, by default it takes 5 rows\n",
        "train_data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 345
        },
        "id": "T8t_r6SkgmtF",
        "outputId": "b294b95f-ce45-487e-e952-4a401f8d5322"
      },
      "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  \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  \n",
              "3      0            113803  53.1000  C123        S  \n",
              "4      0            373450   8.0500   NaN        S  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-18b81f61-1a1c-4ec4-8155-be99ade64330\" 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",
              "    <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",
              "    </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",
              "    </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-18b81f61-1a1c-4ec4-8155-be99ade64330')\"\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-18b81f61-1a1c-4ec4-8155-be99ade64330 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-18b81f61-1a1c-4ec4-8155-be99ade64330');\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-90335c2e-edad-4364-93e1-647e1587ffcd\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-90335c2e-edad-4364-93e1-647e1587ffcd')\"\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-90335c2e-edad-4364-93e1-647e1587ffcd 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_data",
              "summary": "{\n  \"name\": \"train_data\",\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\": 14.526497332334044,\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\": 49.693428597180905,\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": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "1.   PassengerId: Unique identifier for each passenger.\n",
        "2.  Survived: Survival status (1 = survived, 0 = did not survive).\n",
        "3. Pclass: Passenger’s class (1 = 1st class, 2 = 2nd class, 3 = 3rd class).\n",
        "4. Name: Full name of the passenger.\n",
        "5. Sex: Gender of the passenger (male or female).\n",
        "6. Age: Age of the passenger in years (some entries are missing).\n",
        "7. SibSp: Number of siblings/spouses aboard the Titanic.\n",
        "8. Parch: Number of parents/children aboard the Titanic\n",
        "9. Ticket: Ticket number of the passenger.\n",
        "10. Fare: Fare paid for the ticket (in British pounds).\n",
        "11. Cabin: Cabin number assigned to the passenger.\n",
        "12. Embarked: Port of embarkation (C = Cherbourg, Q = Queenstown, S = Southampton).\n",
        "\n"
      ],
      "metadata": {
        "id": "YYrUjDIIoEjs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#provides a concise summary of the dataframe, it also shows the memory uses of the dataframes\n",
        "train_data.info()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uyckjZetjTYz",
        "outputId": "4f17b873-c6ae-4767-aad3-3759109046eb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 891 entries, 0 to 890\n",
            "Data columns (total 12 columns):\n",
            " #   Column       Non-Null Count  Dtype  \n",
            "---  ------       --------------  -----  \n",
            " 0   PassengerId  891 non-null    int64  \n",
            " 1   Survived     891 non-null    int64  \n",
            " 2   Pclass       891 non-null    int64  \n",
            " 3   Name         891 non-null    object \n",
            " 4   Sex          891 non-null    object \n",
            " 5   Age          714 non-null    float64\n",
            " 6   SibSp        891 non-null    int64  \n",
            " 7   Parch        891 non-null    int64  \n",
            " 8   Ticket       891 non-null    object \n",
            " 9   Fare         891 non-null    float64\n",
            " 10  Cabin        204 non-null    object \n",
            " 11  Embarked     889 non-null    object \n",
            "dtypes: float64(2), int64(5), object(5)\n",
            "memory usage: 83.7+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#It provides summary statistics for the numerical columns in  the dataframe, include mean ,std,min etc.\n",
        "train_data.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "qMJ7-ZR2jyme",
        "outputId": "f5033b16-8fc1-45c6-d75b-ed33540f6fb7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
              "count   891.000000  891.000000  891.000000  714.000000  891.000000   \n",
              "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
              "std     257.353842    0.486592    0.836071   14.526497    1.102743   \n",
              "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
              "25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n",
              "50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n",
              "75%     668.500000    1.000000    3.000000   38.000000    1.000000   \n",
              "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
              "\n",
              "            Parch        Fare  \n",
              "count  891.000000  891.000000  \n",
              "mean     0.381594   32.204208  \n",
              "std      0.806057   49.693429  \n",
              "min      0.000000    0.000000  \n",
              "25%      0.000000    7.910400  \n",
              "50%      0.000000   14.454200  \n",
              "75%      0.000000   31.000000  \n",
              "max      6.000000  512.329200  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ddaf8aac-8c24-4e72-b3e4-a2729bd78cff\" 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>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Fare</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>891.000000</td>\n",
              "      <td>891.000000</td>\n",
              "      <td>891.000000</td>\n",
              "      <td>714.000000</td>\n",
              "      <td>891.000000</td>\n",
              "      <td>891.000000</td>\n",
              "      <td>891.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>446.000000</td>\n",
              "      <td>0.383838</td>\n",
              "      <td>2.308642</td>\n",
              "      <td>29.699118</td>\n",
              "      <td>0.523008</td>\n",
              "      <td>0.381594</td>\n",
              "      <td>32.204208</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>257.353842</td>\n",
              "      <td>0.486592</td>\n",
              "      <td>0.836071</td>\n",
              "      <td>14.526497</td>\n",
              "      <td>1.102743</td>\n",
              "      <td>0.806057</td>\n",
              "      <td>49.693429</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.420000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>223.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>20.125000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>7.910400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>446.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>14.454200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>668.500000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>38.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>31.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>891.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>80.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>512.329200</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-ddaf8aac-8c24-4e72-b3e4-a2729bd78cff')\"\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-ddaf8aac-8c24-4e72-b3e4-a2729bd78cff 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-ddaf8aac-8c24-4e72-b3e4-a2729bd78cff');\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-eacfc315-6263-44fb-a7f9-ab5f76c54896\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-eacfc315-6263-44fb-a7f9-ab5f76c54896')\"\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-eacfc315-6263-44fb-a7f9-ab5f76c54896 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",
              "summary": "{\n  \"name\": \"train_data\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"PassengerId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 320.8159711429856,\n        \"min\": 1.0,\n        \"max\": 891.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          891.0,\n          446.0,\n          668.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Survived\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 314.8713661874558,\n        \"min\": 0.0,\n        \"max\": 891.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.3838383838383838,\n          1.0,\n          0.4865924542648585\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Pclass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 314.2523437079693,\n        \"min\": 0.8360712409770513,\n        \"max\": 891.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          891.0,\n          2.308641975308642,\n          3.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 242.9056731818781,\n        \"min\": 0.42,\n        \"max\": 714.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          29.69911764705882,\n          28.0,\n          714.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SibSp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 314.4908277465442,\n        \"min\": 0.0,\n        \"max\": 891.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          891.0,\n          0.5230078563411896,\n          8.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Parch\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 314.65971717879,\n        \"min\": 0.0,\n        \"max\": 891.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.38159371492704824,\n          6.0,\n          0.8060572211299559\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 330.6256632228577,\n        \"min\": 0.0,\n        \"max\": 891.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          32.204207968574636,\n          14.4542,\n          891.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#handling Numerical and Categorical data Seperately\n",
        "#The categorical data will be encoded into numerical data later on !!\n",
        "df_num=train_data[[\"Age\",\"SibSp\",\"Parch\",\"Fare\"]]\n",
        "df_cat=train_data[[\"Survived\",\"Sex\",\"Cabin\",\"Embarked\",\"Ticket\"]]"
      ],
      "metadata": {
        "id": "q_m_NSFuj4rx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#plotting the histograms for nmerical data\n",
        "df_num.hist(bins=20, figsize=(10, 10))\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 853
        },
        "id": "xx_RBf4ps5Sd",
        "outputId": "0786b24d-55c7-4765-ebfc-622d54a058a9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 4 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.barplot(data=train_data,x='Pclass',y=\"Fare\",hue=\"Survived\")\n",
        "plt.title(\"Average Fare based on Passenger Class and Survival status\")\n",
        "plt.xlabel(\"Passenger class\")\n",
        "plt.ylabel(\"Fare\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "id": "XXk962UMtTY4",
        "outputId": "24cd7631-8cff-42c0-fc61-a693399b4aea"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Fare')"
            ]
          },
          "metadata": {},
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjsAAAHHCAYAAABZbpmkAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAABM/ElEQVR4nO3dd1QU198G8Gdpy9JBpSkgERuKGjF2UYGIxgKKCsYoGKMmYo+akMRubEnUaGwxEUsw9h5Fjb1HsfeGLQq2AAJS975/+DI/1wUERHcdn885ew47c2fmu8Ps7rN37uwqhBACRERERDJloOsCiIiIiF4nhh0iIiKSNYYdIiIikjWGHSIiIpI1hh0iIiKSNYYdIiIikjWGHSIiIpI1hh0iIiKSNYYdIiIikjWGHZKl8uXLo02bNrouo0h2794NhUKB3bt367oU0mMLFy6EQqHAjRs3dF2KTrztj79Zs2Zo1qzZa1v/jRs3oFAosHDhwte2jbcRw04BZs+eDYVCgXr16um6FL1Tvnx5KBSKPG/p6em6Lo9KwOjRozX+r2ZmZvD09MR3332H5ORkXZcnOzk5OYiKikKzZs1gZ2cHpVKJ8uXLo0ePHjh27Jiuy3sr7d+/H61atULZsmVhamoKV1dXtG3bFkuXLtV1aW+FzZs3Y/To0a+0jgkTJmDdunUlUs+rMNJ1AfosOjoa5cuXxz///IOrV6/Cw8ND1yXplVq1auHLL7/Umm5iYqKDauh1mTNnDiwsLJCSkoJt27bh+++/x86dO3HgwAEoFApdlycLT58+RYcOHRATEwMfHx988803sLOzw40bN7BixQosWrQIt27dQrly5XRd6ltj5cqVCAkJQa1atTBw4EDY2toiLi4Oe/fuxfz58/Hxxx/rpK5t27bpZLvFsXnzZsyaNeuVAs+ECRPQsWNHBAUFlVhdxcGwk4+4uDgcPHgQa9asQZ8+fRAdHY1Ro0a90RrUajUyMzNhamr6RrdbWGXLlsUnn3xSousUQiA9PR0qlapE10vF17FjR5QuXRoA8PnnnyM4OBhr1qzB4cOH0aBBAx1X93bIzs6GWq3O94PAsGHDEBMTg2nTpmHQoEEa80aNGoVp06a9gSrlZfTo0fD09MThw4e19vv9+/dLbDupqakwNzcvdHt+GNQNnsbKR3R0NGxtbdG6dWt07NgR0dHR0rysrCzY2dmhR48eWsslJyfD1NQUQ4cOlaZlZGRg1KhR8PDwgFKphIuLC4YPH46MjAyNZRUKBfr164fo6GhUq1YNSqUSMTExAIAff/wRDRs2RKlSpaBSqeDt7Y1Vq1Zpbf/p06cYMGAASpcuDUtLS7Rr1w7//vsvFAqFVjr/999/8emnn8LBwQFKpRLVqlXDggULXmW3SaKiouDr6wt7e3solUp4enpizpw5Wu1yx9Zs3boVderUgUqlwrx58wAAiYmJGDRoEFxcXKBUKuHh4YHJkydDrVYXuo5t27ahVq1aMDU1haenJ9asWaMx//Hjxxg6dCi8vLxgYWEBKysrtGrVCqdOndJa18yZM1GtWjWYmZnB1tYWderU0eoOL+w+vXPnDoKCgmBubg57e3sMHjxY63goyIkTJ9CqVStYWVnBwsICfn5+OHz4sEab3LENBw4cwJAhQ1CmTBmYm5ujffv2ePDgQaG39SJfX18Azz4QZGZmYuTIkfD29oa1tTXMzc3RpEkT7Nq1S2u5ZcuWwdvbG5aWlrCysoKXlxd+/vlnaX5WVhbGjBmDihUrwtTUFKVKlULjxo2xfft2jfVcvHgRHTt2hJ2dHUxNTVGnTh1s2LCh2I9drVZj9OjRcHZ2hpmZGZo3b47z58+jfPnyCA8P12hbmGMyd8zEjz/+iOnTp6NChQpQKpU4f/58nvvzzp07mDdvHj788EOtoAMAhoaGGDp0aIG9OuvXr0fr1q3h7OwMpVKJChUqYNy4ccjJydFod+XKFQQHB8PR0RGmpqYoV64cQkNDkZSUJLXZvn07GjduDBsbG1hYWKBy5cr45ptv8t12rqI+5/fv34+6devC1NQU7733HhYvXqzV9ty5c/D19YVKpUK5cuUwfvz4Qj//r127hg8++CDPcGFvby/9nd9YubzGvoSHh8PCwgLXrl3DRx99BEtLS3Tt2hX9+vWDhYUF0tLStLbVpUsXODo6Sv+L58fsJCQkwMjICGPGjNFa7tKlS1AoFPjll18AFO21qjBe9nwLDw/HrFmzAEDjdHauwrwnKRQKpKamYtGiRdLyuc+p8PBwlC9fXquu3NPnzyvuMfk89uzkIzo6Gh06dICJiQm6dOmCOXPm4OjRo/jggw9gbGyM9u3bY82aNZg3b57Gk2ndunXIyMhAaGgogGcvpO3atcP+/fvRu3dvVK1aFWfOnMG0adNw+fJlrXOZO3fuxIoVK9CvXz+ULl1aOhh+/vlntGvXDl27dkVmZiaWLVuGTp06YdOmTWjdurW0fHh4OFasWIFu3bqhfv362LNnj8b8XAkJCahfv74UsMqUKYMtW7agZ8+eSE5OzvNF90VZWVl4+PChxjQzMzOYmZlhzpw5qFatGtq1awcjIyNs3LgRffv2hVqtRkREhMYyly5dQpcuXdCnTx/06tULlStXRlpaGpo2bYp///0Xffr0gaurKw4ePIjIyEjcu3cP06dPf2l9V65cQUhICD7//HOEhYUhKioKnTp1QkxMDD788EMAwPXr17Fu3Tp06tQJ7u7uSEhIwLx589C0aVOcP38ezs7OAID58+djwIAB6NixIwYOHIj09HScPn0aR44ckbrDC7tPnz59Cj8/P9y6dQsDBgyAs7MzlixZgp07d770MQHP3gCaNGkCKysrDB8+HMbGxpg3bx6aNWuGPXv2aI0x69+/P2xtbTFq1CjcuHED06dPR79+/bB8+fJCbe9F165dAwCUKlUKycnJ+O2339ClSxf06tULT548we+//46AgAD8888/qFWrFoBnL1ZdunSBn58fJk+eDAC4cOECDhw4gIEDBwJ49iI3ceJEfPbZZ6hbty6Sk5Nx7NgxHD9+XPp/nTt3Do0aNULZsmXx9ddfw9zcHCtWrEBQUBBWr16N9u3bF/mxR0ZGYsqUKWjbti0CAgJw6tQpBAQEaI09K+oxGRUVhfT0dPTu3RtKpRJ2dnZ57s8tW7YgOzsb3bp1K9b/A3gW7iwsLDBkyBBYWFhg586dGDlyJJKTk/HDDz8AADIzMxEQEICMjAz0798fjo6O+Pfff7Fp0yYkJibC2toa586dQ5s2bVCjRg2MHTsWSqUSV69exYEDB15aQ1Ge81evXkXHjh3Rs2dPhIWFYcGCBQgPD4e3tzeqVasGAIiPj0fz5s2RnZ0t/a9//fXXQvf6urm5YceOHbhz506Jnv7Lzs5GQEAAGjdujB9//BFmZmYoX748Zs2ahb/++gudOnWS2qalpWHjxo0IDw+HoaGh1rocHBzQtGlTrFixQuvMwfLly2FoaCitr7CvVYX1sudbnz59cPfuXWzfvh1LlizRWr4w70lLliyR1t+7d28AQIUKFYpU56sckxoEaTl27JgAILZv3y6EEEKtVoty5cqJgQMHSm22bt0qAIiNGzdqLPvRRx+J9957T7q/ZMkSYWBgIPbt26fRbu7cuQKAOHDggDQNgDAwMBDnzp3TqiktLU3jfmZmpqhevbrw9fWVpsXGxgoAYtCgQRptw8PDBQAxatQoaVrPnj2Fk5OTePjwoUbb0NBQYW1trbW9F7m5uQkAWrfcbeS1fEBAgMa+eX49MTExGtPHjRsnzM3NxeXLlzWmf/3118LQ0FDcunWrUPWtXr1ampaUlCScnJzE+++/L01LT08XOTk5GsvGxcUJpVIpxo4dK00LDAwU1apVK3Cbhd2n06dPFwDEihUrpDapqanCw8NDABC7du0qcDtBQUHCxMREXLt2TZp29+5dYWlpKXx8fKRpUVFRAoDw9/cXarVamj548GBhaGgoEhMTC9zOqFGjBABx6dIl8eDBAxEXFyfmzZsnlEqlcHBwEKmpqSI7O1tkZGRoLPfff/8JBwcH8emnn0rTBg4cKKysrER2dna+26tZs6Zo3bp1gTX5+fkJLy8vkZ6eLk1Tq9WiYcOGomLFikV+7PHx8cLIyEgEBQVpbGf06NECgAgLC5OmFfaYjIuLEwCElZWVuH//foGPJ7cmAOLEiRMvbfv8Y4uLi5Om5fV869OnjzAzM5P21YkTJwQAsXLlynzXPW3aNAFAPHjwoFC1PK+oz/m9e/dK0+7fvy+USqX48ssvpWmDBg0SAMSRI0c02llbW2s9/rz8/vvvAoAwMTERzZs3FyNGjBD79u3Ter7v2rUrz+dd7v8xKipKmhYWFiYAiK+//lqjrVqtFmXLlhXBwcEa01esWKH1WJs2bSqaNm0q3Z83b54AIM6cOaOxrKenp8bre2Ffq/KqOy+Feb5FRESI/GJCYd6ThBDC3Nxc43mUKywsTLi5uWlNz33dyfUqx+TzeBorD9HR0XBwcEDz5s0BPOuKCwkJwbJly6SuSF9fX5QuXVrjE+J///2H7du3IyQkRJq2cuVKVK1aFVWqVMHDhw+lW+6pgBe7+5s2bQpPT0+tmp7/NPPff/8hKSkJTZo0wfHjx6Xpuae8+vbtq7Fs//79Ne4LIbB69Wq0bdsWQgiNugICApCUlKSx3vzUq1cP27dv17h1795dq96kpCQ8fPgQTZs2xfXr1zW6zAHA3d0dAQEBGtNWrlyJJk2awNbWVqM+f39/5OTkYO/evS+tz9nZWeOTvpWVFbp3744TJ04gPj4eAKBUKmFg8OxpkJOTg0ePHkndpM/vAxsbG9y5cwdHjx7Nc1tF2aebN2+Gk5MTOnbsKC1vZmYmffIpSE5ODrZt24agoCC899570nQnJyd8/PHH2L9/v9aVUr1799boFm7SpAlycnJw8+bNl24PACpXrowyZcrA3d0dffr0gYeHB/766y+YmZnB0NBQ6tlUq9V4/PgxsrOzUadOHa39l5qaqnVK6nk2NjY4d+4crly5kuf8x48fY+fOnejcuTOePHki7d9Hjx4hICAAV65cwb///lukx75jxw5kZ2e/9DkDFP2YDA4ORpkyZfJ9vLly/1+WlpYvbZuf559vufumSZMmSEtLw8WLFwEA1tbWAICtW7fmeboFePY/AJ6dFivK6eIXa3jZc97T0xNNmjSR7pcpUwaVK1fG9evXpWmbN29G/fr1UbduXY12Xbt2LVQ9n376KWJiYtCsWTPs378f48aNQ5MmTVCxYkUcPHiwSI/tRV988YXGfYVCgU6dOmHz5s1ISUmRpi9fvhxly5ZF48aN811Xhw4dYGRkpPFecvbsWZw/f17jvaSwr1WF9bLn28sU5j2pJLzKManhlaKSDGVnZwsnJycRGhoqrly5It1yE/rWrVultn369BGWlpbSJ6fffvtNABAnT56U2lStWjXPHpDc24ABA6S2ADQ+DT9v48aNol69ekKpVGosr1AopDa9e/cWBgYGIisrS2PZpKQkjV6XhISEAmsCINasWVPgfnJzcyvwU8H+/fuFn5+fMDMz01r3zZs3Ndbz4icBIYRQqVQF1jd16tSX1vd8L0eu3E97hw4dEkIIkZOTI6ZOnSo8PDyEoaGhxjaaN28uLXf+/HlRtmxZAUB4eHiIvn37iv3790vzi7JPK1euLJo0aaJV2/r161/as3Pv3j0BQIwYMUJrXm6P0dmzZ4UQ/+sBOHz4sEa73E+yu3fvLmAP/u8T1urVq8X27dvF7t27xdWrV7XaLVy4UHh5eQljY2ONx+vu7q6xf3KfC2XLlhU9evQQW7Zs0VjPnj17hI2NjQAgqlevLoYOHSpOnTolzT9y5MhL9/Hx48eL9NgnTJggAIjr169rPS5bW1uNT6SFPSZzP1k//2m7ICXRs3P27FkRFBQkrKystOras2eP1G7IkCECgFCpVKJFixbil19+0ejhS0tLE40aNRIAROnSpUVISIhYvny5Vo9CXorynG/ZsqXW8k2bNhXNmjWT7iuVStGtWzetdj///HOhenael5qaKvbu3SsiIiKEoaGhsLW1FQkJCUKIovfsGBkZ5bk/Dh06JACI6OhoIYQQT548ESqVSqun/cWeHSGe9YBVqlRJuv/dd98JIyMjjd6Mwr5WFbZn52XPNyEK7tkpzHuSEK/es/Mqx+TzOGbnBTt37sS9e/ewbNkyLFu2TGt+dHQ0WrRoAQAIDQ3FvHnzsGXLFgQFBWHFihWoUqUKatasKbVXq9Xw8vLC1KlT89yei4uLxv28zkfv27cP7dq1g4+PD2bPng0nJycYGxsjKiqqWN8XkZuOP/nkE4SFheXZpkaNGkVeb65r167Bz88PVapUwdSpU+Hi4gITExNs3rwZ06ZN00rneT1mtVqNDz/8EMOHD89zG5UqVSp2fc+bMGECRowYgU8//RTjxo2DnZ0dDAwMMGjQII06q1atikuXLmHTpk2IiYnB6tWrMXv2bIwcORJjxox57fv0VeQ1VgB41htVGD4+PtLVWC/6448/EB4ejqCgIAwbNgz29vYwNDTExIkTpbE9wLMBoSdPnsTWrVuxZcsWbNmyBVFRUejevTsWLVokbefatWtYv349tm3bht9++w3Tpk3D3Llz8dlnn0n7eOjQoVo9gble/HqIV33szyvqMVnYsSVVqlQBAJw5c0Ya41QUiYmJaNq0KaysrDB27FhUqFABpqamOH78OL766iuN4/inn35CeHi4tI8HDBiAiRMn4vDhwyhXrhxUKhX27t2LXbt24a+//kJMTAyWL18OX19fbNu2Ld/9WdTnfEn+XwrDzMwMTZo0QZMmTVC6dGmMGTMGW7ZsQVhYWL5fn/Di4O5cz/ewPK9+/fooX748VqxYgY8//hgbN27E06dPNXpn8hMaGooePXrg5MmTqFWrFlasWAE/Pz+N511hX6sK62XPt4KUxHtSYfd7cY/JFzHsvCA6Ohr29vbSKPTnrVmzBmvXrsXcuXOhUqng4+MDJycnLF++HI0bN8bOnTvx7bffaixToUIFnDp1Cn5+fsX+TpLVq1fD1NQUW7duhVKplKZHRUVptHNzc4NarUZcXBwqVqwoTb969apGuzJlysDS0hI5OTnw9/cvVk0F2bhxIzIyMrBhwwa4urpK0/O6Qic/FSpUQEpKyivVd/XqVQghNPb75cuXAUAa+L1q1So0b94cv//+u8ayiYmJWm/w5ubmCAkJQUhICDIzM9GhQwd8//33iIyMLNI+dXNzw9mzZ7Vqu3Tp0ksfU5kyZWBmZpZn24sXL8LAwEArQL9Oq1atwnvvvYc1a9ZoPJa8vqbBxMQEbdu2Rdu2baFWq9G3b1/MmzcPI0aMkEJK7lWOPXr0QEpKCnx8fDB69Gh89tln0mk7Y2PjEjtu3dzcADw7Vtzd3aXpjx49wn///afRtiSOyby0atUKhoaG+OOPP4o1SHn37t149OgR1qxZAx8fH2l6XFxcnu29vLzg5eWF7777DgcPHkSjRo0wd+5cjB8/HgBgYGAAPz8/+Pn5YerUqZgwYQK+/fZb7Nq1K9/HXhLP+Re5ubnleYqlMM+TgtSpUwcAcO/ePQCAra0tgGfP+ecV9jTv8zp37oyff/4ZycnJWL58OcqXL4/69eu/dLmgoCD06dNHOpV1+fJlREZGarQpymtVYRX0fAPyDySFfU8qaB22trZa+xzIe78X55jUWkehWr0jnj59ijVr1qBNmzbo2LGj1q1fv3548uSJdJmrgYEBOnbsiI0bN2LJkiXIzs7WSvGdO3fGv//+i/nz5+e5vdTU1JfWZWhoCIVCoZF4b9y4oXUlV+6n3dmzZ2tMnzlzptb6goODsXr1apw9e1Zre69yWXLu+gHNT2lJSUl5PhHy07lzZxw6dAhbt27VmpeYmIjs7OyXruPu3btYu3atdD85ORmLFy9GrVq14OjoKNX64qfJlStXao39ePTokcZ9ExMTeHp6QgiBrKysIu3Tjz76CHfv3tW4TDMtLQ2//vrrSx+ToaEhWrRogfXr12t8XX5CQgKWLl2Kxo0bw8rK6qXrKSl5/a+PHDmCQ4cOabR7cf8ZGBhIPV25l9y/2MbCwgIeHh7SfHt7ezRr1gzz5s2T3qieV5zj1s/PD0ZGRlqXSOde7vu8kjgm8+Li4oJevXph27ZtWs9V4FmP0k8//YQ7d+7kuXxe/4PMzEyt14Hk5GStGr28vGBgYCDt48ePH2utP7e3qaCvRiiJ5/yLPvroIxw+fBj//POPNO3BgwcaXwNSkB07duQ5ffPmzQCejUUDnoUqQ0NDrTFXL+6/wggJCUFGRgYWLVqEmJgYdO7cuVDL2djYICAgACtWrMCyZctgYmKi9SV8hX2tKqyXPd8ASN8f9GIoKex7Uu468go1FSpUQFJSEk6fPi1Nu3fvnsZrNlD8Y/JF7Nl5zoYNG/DkyRO0a9cuz/n169dHmTJlEB0dLYWakJAQzJw5E6NGjYKXlxeqVq2qsUy3bt2wYsUKfP7559i1axcaNWqEnJwcXLx4EStWrJC+X6YgrVu3xtSpU9GyZUt8/PHHuH//PmbNmgUPDw+NA8Xb2xvBwcGYPn06Hj16JF16ntub8XzCnjRpEnbt2oV69eqhV69e8PT0xOPHj3H8+HH8/fffeR5ghdWiRQvpU3yfPn2QkpKC+fPnw97ePs83qbwMGzYMGzZsQJs2baRLUlNTU3HmzBmsWrUKN27ceOmnmUqVKqFnz544evQoHBwcsGDBAiQkJGi8ALdp0wZjx45Fjx490LBhQ5w5cwbR0dEag39zH5OjoyMaNWoEBwcHXLhwAb/88gtat24tDSwt7D7t1asXfvnlF3Tv3h2xsbFwcnLCkiVLYGZmVqh9M378eOl7J/r27QsjIyPMmzcPGRkZmDJlSqHWUVLatGmDNWvWoH379mjdujXi4uIwd+5ceHp6agzU/Oyzz/D48WP4+vqiXLlyuHnzJmbOnIlatWpJzxlPT080a9YM3t7esLOzw7Fjx7Bq1Sr069dPWs+sWbPQuHFjeHl5oVevXnjvvfeQkJCAQ4cO4c6dO0X+zhEHBwcMHDgQP/30E9q1a4eWLVvi1KlT2LJlC0qXLq3xnCmJYzI/P/30E65du4YBAwZIH7hsbW1x69YtrFy5EhcvXpS+zuJFDRs2hK2tLcLCwjBgwAAoFAosWbJE641x586d6NevHzp16oRKlSohOzsbS5YskYI6AIwdOxZ79+5F69at4ebmhvv372P27NkoV65cgYNsS+I5/6Lhw4djyZIlaNmyJQYOHChdeu7m5qbxupefwMBAuLu7o23btqhQoQJSU1Px999/Y+PGjfjggw/Qtm1bAM8Gbnfq1AkzZ86EQqFAhQoVsGnTpmJ98WDt2rXh4eGBb7/9FhkZGYU6hZUrJCQEn3zyCWbPno2AgABpYG6uwr5WFVZhnm/e3t4AgAEDBiAgIACGhoYIDQ0t9HtS7jr+/vtvTJ06Fc7OznB3d0e9evUQGhqKr776Cu3bt8eAAQOQlpaGOXPmoFKlShqDnIt7TGop0ggfmWvbtq0wNTUVqamp+bYJDw8XxsbG0uXFarVauLi4CABi/PjxeS6TmZkpJk+eLKpVqyaUSqWwtbUV3t7eYsyYMSIpKUlqB0BERETkuY7ff/9dVKxYUSiVSlGlShURFRWlNZBLiGcD8SIiIoSdnZ2wsLAQQUFB4tKlSwKAmDRpkkbbhIQEERERIVxcXISxsbFwdHQUfn5+4tdff33pvnrZAOUNGzaIGjVqCFNTU1G+fHkxefJksWDBAq2BhQWt58mTJyIyMlJ4eHgIExMTUbp0adGwYUPx448/iszMzELVt3XrVlGjRg1pv7142W16err48ssvhZOTk1CpVKJRo0bi0KFDeV4e6uPjI0qVKiWUSqWoUKGCGDZsmMb/T4jC79ObN2+Kdu3aCTMzM1G6dGkxcOBAERMT89IByrmOHz8uAgIChIWFhTAzMxPNmzcXBw8e1GiTO5D16NGjGtPzG5D5otzjq6BLPtVqtZgwYYJwc3MTSqVSvP/++2LTpk1agw9XrVolWrRoIezt7YWJiYlwdXUVffr0Effu3ZPajB8/XtStW1fY2NgIlUolqlSpIr7//nut//W1a9dE9+7dhaOjozA2NhZly5YVbdq0EatWrSrWY8/OzhYjRowQjo6OQqVSCV9fX3HhwgVRqlQp8fnnn2ssX5hjMneA6A8//FDg/n1Rdna2+O2330STJk2EtbW1MDY2Fm5ubqJHjx4ag5fzGqB84MABUb9+faFSqYSzs7MYPny49PUYuY/1+vXr4tNPPxUVKlQQpqamws7OTjRv3lz8/fff0np27NghAgMDhbOzszAxMRHOzs6iS5cuWpfb5+VVn/N5Ddw9ffq0aNq0qTA1NRVly5YV48aNky4yeNkA5T///FOEhoaKChUqCJVKJUxNTYWnp6f49ttvRXJyskbbBw8eiODgYGFmZiZsbW1Fnz59xNmzZ/McoGxubl7gdr/99lvpQoa85PU4hRAiOTlZGgT/xx9/aM0v7GtVYQcoF+b5lp2dLfr37y/KlCkjFAqFxvtNYd+TLl68KHx8fKTH9vxg5W3btonq1asLExMTUblyZfHHH39oreNVjsnnKYR4TSPCSG+cPHkS77//Pv74449CX7ZJ9C5LTEyEra0txo8frzUOj4jePhyzIzNPnz7VmjZ9+nQYGBhoDF4komfye84AkL7Wn4jebhyzIzNTpkxBbGwsmjdvDiMjI+ky3969e7/Rq3SI3hbLly/HwoUL8dFHH8HCwgL79+/Hn3/+iRYtWqBRo0a6Lo+ISgBPY8nM9u3bMWbMGJw/fx4pKSlwdXVFt27d8O2338LIiNmW6EXHjx/H8OHDcfLkSSQnJ8PBwQHBwcEYP348LCwsdF0eEZUAhh0iIiKSNY7ZISIiIllj2CEiIiJZ4yAOPPuG0rt378LS0rLYP+lAREREb5YQAk+ePIGzs3Oev1mWi2EHz35WgFcqERERvZ1u376NcuXK5TufYQeQvu7/9u3bb/R3hYiIiKj4kpOT4eLiIr2P54dhB//7zSgrKyuGHSIiorfMy4agcIAyERERyRrDDhEREckaww4RERHJGsfsEBER6QG1Wo3MzExdl6FXjI2NYWho+MrrYdghIiLSsczMTMTFxUGtVuu6FL1jY2MDR0fHV/oePIYdIiIiHRJC4N69ezA0NISLi0uBX473LhFCIC0tDffv3wcAODk5FXtdDDtEREQ6lJ2djbS0NDg7O8PMzEzX5egVlUoFALh//z7s7e2LfUqL8ZGIiEiHcnJyAAAmJiY6rkQ/5QbArKysYq+DYYeIiEgP8LcZ81YS+4Vhh4iIiGSNYYeIiIi07N69GwqFAomJia91O+Hh4QgKCnqt22DYISIi0mMPHjzAF198AVdXVyiVSjg6OiIgIAAHDhx4rdtt2LAh7t27B2tr69e6nTeBV2MRERHpseDgYGRmZmLRokV47733kJCQgB07duDRo0fFWp8QAjk5OTAyKjgCmJiYwNHRsVjb0Dfs2SHZEEIgJSVFugkhdF0SEdErSUxMxL59+zB58mQ0b94cbm5uqFu3LiIjI9GuXTvcuHEDCoUCJ0+e1FhGoVBg9+7dAP53OmrLli3w9vaGUqnEggULoFAocPHiRY3tTZs2DRUqVNBYLjExEcnJyVCpVNiyZYtG+7Vr18LS0hJpaWkAgNu3b6Nz586wsbGBnZ0dAgMDcePGDal9Tk4OhgwZAhsbG5QqVQrDhw9/I6/VDDskG6mpqQgMDJRuqampui6JiOiVWFhYwMLCAuvWrUNGRsYrrevrr7/GpEmTcOHCBXTs2BF16tRBdHS0Rpvo6Gh8/PHHWstaWVmhTZs2WLp0qVb7oKAgmJmZISsrCwEBAbC0tMS+fftw4MABWFhYoGXLltLPYPz0009YuHAhFixYgP379+Px48dYu3btKz2uwmDYISIi0lNGRkZYuHAhFi1aBBsbGzRq1AjffPMNTp8+XeR1jR07Fh9++CEqVKgAOzs7dO3aFX/++ac0//Lly4iNjUXXrl3zXL5r165Yt26d1IuTnJyMv/76S2q/fPlyqNVq/Pbbb/Dy8kLVqlURFRWFW7duSb1M06dPR2RkJDp06ICqVati7ty5b2RMEMMOERGRHgsODsbdu3exYcMGtGzZErt370bt2rWxcOHCIq2nTp06GvdDQ0Nx48YNHD58GMCzXpratWujSpUqeS7/0UcfwdjYGBs2bAAArF69GlZWVvD39wcAnDp1ClevXoWlpaXUI2VnZ4f09HRcu3YNSUlJuHfvHurVqyet08jISKuu14Fhh4iISM+Zmpriww8/xIgRI3Dw4EGEh4dj1KhR0u9oPT/uJb9vGjY3N9e47+joCF9fX+nU1NKlS/Pt1QGeDVju2LGjRvuQkBBpoHNKSgq8vb1x8uRJjdvly5fzPDX2JjHsEBERvWU8PT2RmpqKMmXKAADu3bsnzXt+sPLLdO3aFcuXL8ehQ4dw/fp1hIaGvrR9TEwMzp07h507d2qEo9q1a+PKlSuwt7eHh4eHxs3a2hrW1tZwcnLCkSNHpGWys7MRGxtb6HqLi2GHiIhITz169Ai+vr74448/cPr0acTFxWHlypWYMmUKAgMDoVKpUL9+fWng8Z49e/Ddd98Vev0dOnTAkydP8MUXX6B58+ZwdnYusL2Pjw8cHR3RtWtXuLu7a5yS6tq1K0qXLo3AwEDs27cPcXFx2L17NwYMGIA7d+4AAAYOHIhJkyZh3bp1uHjxIvr27fvav7QQYNghIiLSWxYWFqhXrx6mTZsGHx8fVK9eHSNGjECvXr3wyy+/AAAWLFiA7OxseHt7Y9CgQRg/fnyh129paYm2bdvi1KlTBZ7CyqVQKNClS5c825uZmWHv3r1wdXWVBiD37NkT6enpsLKyAgB8+eWX6NatG8LCwtCgQQNYWlqiffv2RdgjxaMQ/DISJCcnw9raGklJSdI/hN4+KSkpCAwMlO6vX78eFhYWOqyIiOjl0tPTERcXB3d3d5iamuq6HL1T0P4p7Ps3e3aIiIhI1hh2iIiISNYYdoiIiEjWGHaIiIhI1hh2iIiISNYYdoiIiEjWGHaIiIhI1hh2iIiISNYYdoiIiEjWGHaIiIhI1ox0XQAREREVjfewxW90e7E/dC/WcrNmzcIPP/yA+Ph41KxZEzNnzkTdunVLuLqXY88OERERlbjly5djyJAhGDVqFI4fP46aNWsiICAA9+/ff+O1MOwQERFRiZs6dSp69eqFHj16wNPTE3PnzoWZmRkWLFjwxmth2CEiIqISlZmZidjYWPj7+0vTDAwM4O/vj0OHDr3xehh2iIiIqEQ9fPgQOTk5cHBw0Jju4OCA+Pj4N14Pww4RERHJGsMOERERlajSpUvD0NAQCQkJGtMTEhLg6Oj4xuth2CEiIqISZWJiAm9vb+zYsUOaplarsWPHDjRo0OCN16PTsLN37160bdsWzs7OUCgUWLduncZ8IQRGjhwJJycnqFQq+Pv748qVKxptHj9+jK5du8LKygo2Njbo2bMnUlJS3uCjICIiohcNGTIE8+fPx6JFi3DhwgV88cUXSE1NRY8ePd54LToNO6mpqahZsyZmzZqV5/wpU6ZgxowZmDt3Lo4cOQJzc3MEBAQgPT1datO1a1ecO3cO27dvx6ZNm7B371707t37TT0EIiIiykNISAh+/PFHjBw5ErVq1cLJkycRExOjNWj5TVAIIcQb32oeFAoF1q5di6CgIADPenWcnZ3x5ZdfYujQoQCApKQkODg4YOHChQgNDcWFCxfg6emJo0ePok6dOgCAmJgYfPTRR7hz5w6cnZ0Lte3k5GRYW1sjKSkJVlZWr+Xx0euXkpKCwMBA6f769ethYWGhw4qIiF4uPT0dcXFxcHd3h6mpqa7L0TsF7Z/Cvn/r7ZiduLg4xMfHa1yjb21tjXr16knX6B86dAg2NjZS0AEAf39/GBgY4MiRI/muOyMjA8nJyRo3IiIikie9DTu51+EXdI1+fHw87O3tNeYbGRnBzs6uwOv4J06cCGtra+nm4uJSwtUTERGRvtDbsPM6RUZGIikpSbrdvn1b1yURERHRa6K3YSf3OvyCrtF3dHTU+kGx7OxsPH78uMDr+JVKJaysrDRuREREJE96G3bc3d3h6OiocY1+cnIyjhw5Il2j36BBAyQmJiI2NlZqs3PnTqjVatSrV++N10xERET6x0iXG09JScHVq1el+3FxcTh58iTs7Ozg6uqKQYMGYfz48ahYsSLc3d0xYsQIODs7S1dsVa1aFS1btkSvXr0wd+5cZGVloV+/fggNDS30lVhEREQkbzoNO8eOHUPz5s2l+0OGDAEAhIWFYeHChRg+fDhSU1PRu3dvJCYmonHjxoiJidG49Cw6Ohr9+vWDn58fDAwMEBwcjBkzZrzxx0JERET6Sadhp1mzZijoa34UCgXGjh2LsWPH5tvGzs4OS5cufR3lERERkQzo7ZgdIiIiopLAsENERESyptPTWERERFR0t8Z6vdHtuY48U6T2e/fuxQ8//IDY2Fjcu3dP4+egdIE9O0RERFSiXvZD328ae3aIiIioRLVq1QqtWrXSdRkS9uwQERGRrDHsEBERkawx7BAREZGsccwOlag3fYXA89KyFQDKSPfvTGkIM6P8v7TydSrqlQtERPT6sGeHiIiIZI09O0RERFSiXvZD328aww4RERGVqJf90PebxrBDRET0ltH3cYEv+6HvN41jdoiIiEjWGHaIiIhI1hh2iIiISNYYdoiIiEjWGHaIiIj0gD4N6NUnJbFfGHaIiIh0yNDQEACQmZmp40r0U1paGgDA2Ni42OvgpedEREQ6ZGRkBDMzMzx48ADGxsYwMGA/BPCsRyctLQ3379+HjY2NFAqLg2GHiIhIhxQKBZycnBAXF4ebN2/quhy9Y2NjA0dHx1daB8MOERGRjpmYmKBixYo8lfUCY2PjV+rRycWwQ0REpAcMDAxgamqq6zJkiScGiYiISNYYdoiIiEjWGHaIiIhI1hh2iIiISNYYdoiIiEjWGHaIiIhI1hh2iIiISNYYdoiIiEjWGHaIiIhI1hh2iIiISNYYdoiIiEjWGHaIiIhI1hh2iIiISNYYdoiIiEjWGHaIiIhI1hh2iIiISNYYdoiIiEjWGHaIiIhI1hh2iIiISNYYdoiIiEjWGHaIiIhI1hh2iIiISNYYdoiIiEjWGHaIiIhI1hh2iIiISNYYdoiIiEjWGHaIiIhI1hh2iIiISNaMdF0AUUlRGQrMavRA4z4RERHDDsmGQgGYGTHgEBGRJp7GIiIiIlnT67CTk5ODESNGwN3dHSqVChUqVMC4ceMgxP8+vQshMHLkSDg5OUGlUsHf3x9XrlzRYdVERESkT/Q67EyePBlz5szBL7/8ggsXLmDy5MmYMmUKZs6cKbWZMmUKZsyYgblz5+LIkSMwNzdHQEAA0tPTdVg5ERER6Qu9HrNz8OBBBAYGonXr1gCA8uXL488//8Q///wD4FmvzvTp0/Hdd98hMDAQALB48WI4ODhg3bp1CA0N1VntREREpB/0umenYcOG2LFjBy5fvgwAOHXqFPbv349WrVoBAOLi4hAfHw9/f39pGWtra9SrVw+HDh3Kd70ZGRlITk7WuBEREZE86XXPztdff43k5GRUqVIFhoaGyMnJwffff4+uXbsCAOLj4wEADg4OGss5ODhI8/IyceJEjBkz5vUVTkRERHpDr3t2VqxYgejoaCxduhTHjx/HokWL8OOPP2LRokWvtN7IyEgkJSVJt9u3b5dQxURERKRv9LpnZ9iwYfj666+lsTdeXl64efMmJk6ciLCwMDg6OgIAEhIS4OTkJC2XkJCAWrVq5btepVIJpVL5WmsnIiIi/aDXPTtpaWkwMNAs0dDQEGq1GgDg7u4OR0dH7NixQ5qfnJyMI0eOoEGDBm+0Vl0SQiAlJUW6PX9pPhER0btOr3t22rZti++//x6urq6oVq0aTpw4galTp+LTTz8FACgUCgwaNAjjx49HxYoV4e7ujhEjRsDZ2RlBQUG6Lf4NSk1Nla5GA4D169fDwsJChxURERHpD70OOzNnzsSIESPQt29f3L9/H87OzujTpw9GjhwptRk+fDhSU1PRu3dvJCYmonHjxoiJiYGpqakOKyciIiJ9oRA854Hk5GRYW1sjKSkJVlZWui6nyFJSUvSmZ+fWWC+dbFffuI48o+sSiIhkr7Dv33o9ZoeIiIjoVTHsEBERkawx7BAREZGsMewQERGRrDHsEBERkawx7BAREZGsMewQERGRrDHsEBERkawx7BAREZGsMewQERGRrDHsEBERkawx7BAREZGsMewQERGRrDHsEBERkawx7BAREZGsMewQERGRrDHsEBERkawx7BAREZGsMewQERGRrDHsEBERkawx7BAREZGsMewQERGRrDHsEBERkawx7BAREZGsMewQERGRrDHsEBERkawx7BAREZGsMewQERGRrDHsEBERkawx7BAREZGsMewQERGRrDHsEBERkawx7BAREZGsMewQERGRrDHsEBERkawx7BAREZGsMewQERGRrDHsEBERkawx7BAREZGsMewQERGRrDHsEBERkawx7BAREZGsGem6ALnwHrZYZ9tWZGfC+rn7zUYsgzAy0Uktay11slkiIqJ8sWeHiIiIZI1hh4iIiGSNYYeIiIhkjWGHiIiIZI1hh4iIiGSNYYeIiIhkjWGHiIiIZI1hh4iIiGSNYYeIiIhkjWGHiIiIZE3vw86///6LTz75BKVKlYJKpYKXlxeOHTsmzRdCYOTIkXBycoJKpYK/vz+uXLmiw4qJiIhIn+h12Pnvv//QqFEjGBsbY8uWLTh//jx++ukn2NraSm2mTJmCGTNmYO7cuThy5AjMzc0REBCA9PR0HVZORERE+qLYPwR67do1REVF4dq1a/j5559hb2+PLVu2wNXVFdWqVSuR4iZPngwXFxdERUVJ09zd3aW/hRCYPn06vvvuOwQGBgIAFi9eDAcHB6xbtw6hoaElUgcRERG9vYrVs7Nnzx54eXnhyJEjWLNmDVJSUgAAp06dwqhRo0qsuA0bNqBOnTro1KkT7O3t8f7772P+/PnS/Li4OMTHx8Pf31+aZm1tjXr16uHQoUP5rjcjIwPJyckaNyIiIpKnYoWdr7/+GuPHj8f27dthYmIiTff19cXhw4dLrLjr169jzpw5qFixIrZu3YovvvgCAwYMwKJFiwAA8fHxAAAHBweN5RwcHKR5eZk4cSKsra2lm4uLS4nVTERERPqlWGHnzJkzaN++vdZ0e3t7PHz48JWLyqVWq1G7dm1MmDAB77//Pnr37o1evXph7ty5r7TeyMhIJCUlSbfbt2+XUMVERESkb4oVdmxsbHDv3j2t6SdOnEDZsmVfuahcTk5O8PT01JhWtWpV3Lp1CwDg6OgIAEhISNBok5CQIM3Li1KphJWVlcaNiIiI5KlYYSc0NBRfffUV4uPjoVAooFarceDAAQwdOhTdu3cvseIaNWqES5cuaUy7fPky3NzcADwbrOzo6IgdO3ZI85OTk3HkyBE0aNCgxOogIiKit1exws6ECRNQpUoVuLi4ICUlBZ6envDx8UHDhg3x3XfflVhxgwcPxuHDhzFhwgRcvXoVS5cuxa+//oqIiAgAgEKhwKBBgzB+/Hhs2LABZ86cQffu3eHs7IygoKASq4OIiIjeXkW+9FwIgfj4eMyYMQMjR47EmTNnkJKSgvfffx8VK1Ys0eI++OADrF27FpGRkRg7dizc3d0xffp0dO3aVWozfPhwpKamonfv3khMTETjxo0RExMDU1PTEq2FiIiI3k4KIYQoygJqtRqmpqY4d+5ciYcbXUlOToa1tTWSkpKKPX7He9jiEq6q8BTZmbA+/ad0P6lGFwgjkwKWeH3WWv6gk+3qG9eRZ3RdAhGR7BX2/bvIp7EMDAxQsWJFPHr06JUKJCIiInoTijVmZ9KkSRg2bBjOnj1b0vUQERERlahi/VxE9+7dkZaWhpo1a8LExAQqlUpj/uPHj0ukOCIiIqJXVaywM3369BIug4iIiOj1KFbYCQsLK+k6iIiIiF6LYv/qea709HRkZmZqTOM3EhMREZG+KNYA5dTUVPTr1w/29vYwNzeHra2txo2IiIhIXxQr7AwfPhw7d+7EnDlzoFQq8dtvv2HMmDFwdnbG4sW6+74ZIiIiohcV6zTWxo0bsXjxYjRr1gw9evRAkyZN4OHhATc3N0RHR2t8wzERERGRLhWrZ+fx48d47733ADwbn5N7qXnjxo2xd+/ekquOiIiI6BUVK+y89957iIuLAwBUqVIFK1asAPCsx8fGxqbEiiMiIiJ6VUUKO9evX4darUaPHj1w6tQpAMDXX3+NWbNmwdTUFIMHD8awYcNeS6FERERExVGkMTsVK1bEvXv3MHjwYABASEgIZsyYgYsXLyI2NhYeHh6oUaPGaymUiIiIqDiK1LPz4g+kb968GampqXBzc0OHDh0YdIiIiEjvFGvMDhEREdHbokhhR6FQQKFQaE0jIiIi0ldFGrMjhEB4eDiUSiWAZz8V8fnnn8Pc3Fyj3Zo1a0quQiIiIqJXUKSw8+IPgH7yySclWgwRERFRSStS2ImKinpddRARERG9FhygTERERLLGsENERESyVqwfAiX9IgyNkVSji8Z9IiIieoZhRw4UCggjE11XQUREpJd4GouIiIhkjWGHiIiIZI1hh4iIiGSNYYeIiIhkjWGHiIiIZI1hh4iIiGSNYYeIiIhkjWGHiIiIZI1hh4iIiGSNYYeIiIhkjWGHiIiIZI1hh4iIiGSNYYeIiIhkjWGHiIiIZI1hh4iIiGSNYYeIiIhkjWGHiIiIZI1hh4iIiGSNYYeIiIhkjWGHiIiIZI1hh4iIiGSNYYeIiIhkjWGHiIiIZI1hh4iIiGSNYYeIiIhkjWGHiIiIZI1hh4iIiGSNYYeIiIhkjWGHiIiIZI1hh4iIiGTtrQo7kyZNgkKhwKBBg6Rp6enpiIiIQKlSpWBhYYHg4GAkJCTorkgiIiLSK29N2Dl69CjmzZuHGjVqaEwfPHgwNm7ciJUrV2LPnj24e/cuOnTooKMqiYiISN+8FWEnJSUFXbt2xfz582FraytNT0pKwu+//46pU6fC19cX3t7eiIqKwsGDB3H48GEdVkxERET64q0IOxEREWjdujX8/f01psfGxiIrK0tjepUqVeDq6opDhw696TKJiIhIDxnpuoCXWbZsGY4fP46jR49qzYuPj4eJiQlsbGw0pjs4OCA+Pj7fdWZkZCAjI0O6n5ycXGL1EhERkX7R656d27dvY+DAgYiOjoapqWmJrXfixImwtraWbi4uLiW2biIiItIveh12YmNjcf/+fdSuXRtGRkYwMjLCnj17MGPGDBgZGcHBwQGZmZlITEzUWC4hIQGOjo75rjcyMhJJSUnS7fbt26/5kRAREZGu6PVpLD8/P5w5c0ZjWo8ePVClShV89dVXcHFxgbGxMXbs2IHg4GAAwKVLl3Dr1i00aNAg3/UqlUoolcrXWjsRERHpB70OO5aWlqhevbrGNHNzc5QqVUqa3rNnTwwZMgR2dnawsrJC//790aBBA9SvX18XJRMREZGe0euwUxjTpk2DgYEBgoODkZGRgYCAAMyePVvXZREREZGeeOvCzu7duzXum5qaYtasWZg1a5ZuCiIiIiK9ptcDlImIiIheFcMOERERyRrDDhEREckaww4RERHJGsMOERERyRrDDhEREckaww4RERHJGsMOERERyRrDDhEREckaww4RERHJGsMOERERyRrDDhEREckaww4RERHJGsMOERERyRrDDhEREckaww4RERHJGsMOERERyRrDDhEREckaww4RERHJGsMOERERyRrDDhEREckaww4RERHJGsMOERERyRrDDhEREckaww4RERHJGsMOERERyRrDDhEREckaww4RERHJGsMOERERyRrDDhEREckaww4RERHJGsMOERERyRrDDhEREckaww4RERHJGsMOERERyRrDDhEREckaww4RERHJGsMOERERyRrDDhEREckaww4RERHJGsMOERERyRrDDhEREcmaka4LICKSMyEEUlNTpfvm5uZQKBQ6rIjo3cOwQ0T0GqWmpiIwMFC6v379elhYWOiwIqJ3D09jERERkawx7BAREZGsMewQERGRrDHsEBERkawx7BAREZGs8WosInoteMk1EekLhh0iei14yTUR6QuexiIiIiJZY88OEcnerbFeOtt2WrYCQBnp/p0pDWFmJHRSi+vIMzrZLpGusWeHiIiIZE2vw87EiRPxwQcfwNLSEvb29ggKCsKlS5c02qSnpyMiIgKlSpWChYUFgoODkZCQoKOKiYiISN/oddjZs2cPIiIicPjwYWzfvh1ZWVlo0aKFxhUegwcPxsaNG7Fy5Urs2bMHd+/eRYcOHXRYNREREekTvR6zExMTo3F/4cKFsLe3R2xsLHx8fJCUlITff/8dS5cuha+vLwAgKioKVatWxeHDh1G/fn1dlE1ERER6RK97dl6UlJQEALCzswMAxMbGIisrC/7+/lKbKlWqwNXVFYcOHcp3PRkZGUhOTta4ERERkTy9NWFHrVZj0KBBaNSoEapXrw4AiI+Ph4mJCWxsbDTaOjg4ID4+Pt91TZw4EdbW1tLNxcXldZZOREREOvTWhJ2IiAicPXsWy5Yte+V1RUZGIikpSbrdvn27BCokIiIifaTXY3Zy9evXD5s2bcLevXtRrlw5abqjoyMyMzORmJio0buTkJAAR0fHfNenVCqhVCpfZ8lEesF72GKdbVuRnQnr5+43G7EMwshEJ7WstdTJZolIT+h12BFCoH///li7di12794Nd3d3jfne3t4wNjbGjh07EBwcDAC4dOkSbt26hQYNGuiiZCIiDSpDgVmNHmjcJ6I3S6/DTkREBJYuXYr169fD0tJSGodjbW0NlUoFa2tr9OzZE0OGDIGdnR2srKzQv39/NGjQgFdiEZFeUCigs29MJqJn9DrszJkzBwDQrFkzjelRUVEIDw8HAEybNg0GBgYIDg5GRkYGAgICMHv27DdcKREREekrvQ47Qrz805CpqSlmzZqFWbNmvYGKiIiI6G3z1lyNRURERFQcDDtEREQkaww7REREJGsMO0RERCRrDDtEREQka3p9NRYRERGVLCEEUlNTpfvm5uZQKBQ6rOj1Y9ghotdCGBojqUYXjftEpHupqakIDAyU7q9fvx4WFhY6rOj1Y9ghotdDodDZb2ERET2PYYeIiN4J7+LpG3qGYYeIiN4J7+LpG3qGV2MRERGRrDHsEBERkawx7BAREZGsMewQERGRrHGAMhERvTHewxbrbNuK7ExYP3e/2YhlOvt6hLWWP+hkuwCQlq0AUEa6f2dKQ5gZCZ3U4jryzBvZDnt2iIiISNYYdoiIiEjWGHaIiIhI1hh2iIiISNY4QJmIiN4J/HHadxfDDhERvRv447TvLIYdIiKid4jKUGBWowca9+WOYYeIiOgdolBAZ9+royscoExERESyxrBDREREssawQ0RERLLGsENERESyxrBDREREssawQ0RERLLGsENERESyxrBDREREssawQ0RERLLGsENERESyxrBDREREssawQ0RERLLGsENERESyxrBDREREssawQ0RERLLGsENERESyxrBDREREssawQ0RERLLGsENERESyxrBDREREssawQ0RERLLGsENERESyxrBDREREssawQ0RERLLGsENERESyxrBDREREssawQ0RERLLGsENERESyxrBDREREssawQ0RERLImm7Aza9YslC9fHqampqhXrx7++ecfXZdEREREekAWYWf58uUYMmQIRo0ahePHj6NmzZoICAjA/fv3dV0aERER6Zgsws7UqVPRq1cv9OjRA56enpg7dy7MzMywYMECXZdGREREOvbWh53MzEzExsbC399fmmZgYAB/f38cOnRIh5URERGRPjDSdQGv6uHDh8jJyYGDg4PGdAcHB1y8eDHPZTIyMpCRkSHdT0pKAgAkJycXu46cjKfFXlZOnhjn6LoEvfAqx1JJ4nH5DI/LZ/ThuOQx+QyPyWde9ZjMXV4IUWC7tz7sFMfEiRMxZswYrekuLi46qEZequu6AH0x0VrXFdBzeFz+Px6XeoPH5P8roWPyyZMnsLbOf11vfdgpXbo0DA0NkZCQoDE9ISEBjo6OeS4TGRmJIUOGSPfVajUeP36MUqVKQaFQvNZ65Sw5ORkuLi64ffs2rKysdF0OEQAel6R/eEyWHCEEnjx5Amdn5wLbvfVhx8TEBN7e3tixYweCgoIAPAsvO3bsQL9+/fJcRqlUQqlUakyzsbF5zZW+O6ysrPgEJr3D45L0DY/JklFQj06utz7sAMCQIUMQFhaGOnXqoG7dupg+fTpSU1PRo0cPXZdGREREOiaLsBMSEoIHDx5g5MiRiI+PR61atRATE6M1aJmIiIjePbIIOwDQr1+/fE9b0ZuhVCoxatQorVOERLrE45L0DY/JN08hXna9FhEREdFb7K3/UkEiIiKigjDsEBERkawx7BAREZGsMewQERGRrDHs0Cvbu3cv2rZtC2dnZygUCqxbt07XJdE7buLEifjggw9gaWkJe3t7BAUF4dKlS7oui95xc+bMQY0aNaQvE2zQoAG2bNmi67LeCQw79MpSU1NRs2ZNzJo1S9elEAEA9uzZg4iICBw+fBjbt29HVlYWWrRogdTUVF2XRu+wcuXKYdKkSYiNjcWxY8fg6+uLwMBAnDt3TtelyR4vPacSpVAosHbtWumnO4j0wYMHD2Bvb489e/bAx8dH1+UQSezs7PDDDz+gZ8+eui5F1mTzpYJERPlJSkoC8OyNhUgf5OTkYOXKlUhNTUWDBg10XY7sMewQkayp1WoMGjQIjRo1QvXq1XVdDr3jzpw5gwYNGiA9PR0WFhZYu3YtPD09dV2W7DHsEJGsRURE4OzZs9i/f7+uSyFC5cqVcfLkSSQlJWHVqlUICwvDnj17GHheM4YdIpKtfv36YdOmTdi7dy/KlSun63KIYGJiAg8PDwCAt7c3jh49ip9//hnz5s3TcWXyxrBDRLIjhED//v2xdu1a7N69G+7u7rouiShParUaGRkZui5D9hh26JWlpKTg6tWr0v24uDicPHkSdnZ2cHV11WFl9K6KiIjA0qVLsX79elhaWiI+Ph4AYG1tDZVKpePq6F0VGRmJVq1awdXVFU+ePMHSpUuxe/dubN26VdelyR4vPadXtnv3bjRv3lxrelhYGBYuXPjmC6J3nkKhyHN6VFQUwsPD32wxRP+vZ8+e2LFjB+7duwdra2vUqFEDX331FT788ENdlyZ7DDtEREQka/wGZSIiIpI1hh0iIiKSNYYdIiIikjWGHSIiIpI1hh0iIiKSNYYdIiIikjWGHSIiIpI1hh0iotegWbNmGDRokK7LICIw7BDRc8LDw6FQKKBQKKQfLBw7diyys7N1XRoRUbHxt7GISEPLli0RFRWFjIwMbN68GRERETA2NkZkZKSuS9OJzMxMmJiY6LoMInoF7NkhIg1KpRKOjo5wc3PDF198AX9/f2zYsAEAMHXqVHh5ecHc3BwuLi7o27cvUlJSpGVv3ryJtm3bwtbWFubm5qhWrRo2b94MAPjvv//QtWtXlClTBiqVChUrVkRUVJS07O3bt9G5c2fY2NjAzs4OgYGBuHHjhjQ/PDwcQUFB+PHHH+Hk5IRSpUohIiICWVlZUpt79+6hdevWUKlUcHd3x9KlS1G+fHlMnz5dapOYmIjPPvsMZcqUgZWVFXx9fXHq1Clp/ujRo1GrVi389ttvcHd3h6mpab776sCBA2jWrBnMzMxga2uLgIAA/Pfff3m2XbJkCerUqQNLS0s4Ojri448/xv3796X5Be2fzMxM9OvXD05OTjA1NYWbmxsmTpxY0L+RiJ7Dnh0iKpBKpcKjR48AAAYGBpgxYwbc3d1x/fp19O3bF8OHD8fs2bMBPPu18czMTOzduxfm5uY4f/48LCwsAAAjRozA+fPnsWXLFpQuXRpXr17F06dPAQBZWVkICAhAgwYNsG/fPhgZGWH8+PFo2bIlTp8+LfWs7Nq1C05OTti1axeuXr2KkJAQ1KpVC7169QIAdO/eHQ8fPsTu3bthbGyMIUOGaAQKAOjUqRNUKhW2bNkCa2trzJs3D35+frh8+TLs7OwAAFevXsXq1auxZs0aGBoa5rlfTp48CT8/P3z66af4+eefYWRkhF27diEnJyfP9llZWRg3bhwqV66M+/fvY8iQIQgPD5fCYEH7Z8aMGdiwYQNWrFgBV1dX3L59G7dv3y7eP5ToXSSIiP5fWFiYCAwMFEIIoVarxfbt24VSqRRDhw7Ns/3KlStFqVKlpPteXl5i9OjRebZt27at6NGjR57zlixZIipXrizUarU0LSMjQ6hUKrF161apNjc3N5GdnS216dSpkwgJCRFCCHHhwgUBQBw9elSaf+XKFQFATJs2TQghxL59+4SVlZVIT0/X2H6FChXEvHnzhBBCjBo1ShgbG4v79+/nWWuuLl26iEaNGuU7v2nTpmLgwIH5zj969KgAIJ48eSKEKHj/9O/fX/j6+mrsHyIqPJ7GIiINmzZtgoWFBUxNTdGqVSuEhIRg9OjRAIC///4bfn5+KFu2LCwtLdGtWzc8evQIaWlpAIABAwZg/PjxaNSoEUaNGoXTp09L6/3iiy+wbNky1KpVC8OHD8fBgweleadOncLVq1dhaWkJCwsLWFhYwM7ODunp6bh27ZrUrlq1aho9LU5OTlLPzaVLl2BkZITatWtL8z08PGBra6uxnZSUFJQqVUrajoWFBeLi4jS24+bmhjJlyhS4n3J7dgorNjYWbdu2haurKywtLdG0aVMAwK1bt166f8LDw3Hy5ElUrlwZAwYMwLZt2wq9XSLimB0iekHz5s1x8uRJXLlyBU+fPsWiRYtgbm6OGzduoE2bNqhRowZWr16N2NhYzJo1C8CzMSUA8Nlnn+H69evo1q0bzpw5gzp16mDmzJkAgFatWuHmzZsYPHgw7t69Cz8/PwwdOhQAkJKSAm9vb5w8eVLjdvnyZXz88cdSbcbGxhq1KhQKqNXqQj+2lJQUODk5aW3n0qVLGDZsmNTO3Nz8petSqVSF3m5qaioCAgJgZWWF6OhoHD16FGvXrgXwv31X0P6pXbs24uLiMG7cODx9+hSdO3dGx44dC719oncdww4RaTA3N4eHhwdcXV1hZPS/YX2xsbFQq9X46aefUL9+fVSqVAl3797VWt7FxQWff/451qxZgy+//BLz58+X5pUpUwZhYWH4448/MH36dPz6668Anr2ZX7lyBfb29vDw8NC4WVtbF6ruypUrIzs7GydOnJCmXb16VWPAcO3atREfHw8jIyOt7ZQuXbpI+6lGjRrYsWNHodpevHgRjx49wqRJk9CkSRNUqVJFaywRkP/+AQArKyuEhIRg/vz5WL58OVavXo3Hjx8XqWaidxXDDhEVioeHB7KysjBz5kxcv34dS5Yswdy5czXaDBo0CFu3bkVcXByOHz+OXbt2oWrVqgCAkSNHYv369bh69SrOnTuHTZs2SfO6du2K0qVLIzAwEPv27UNcXBx2796NAQMG4M6dO4Wqr0qVKvD390fv3r3xzz//4MSJE+jduzdUKhUUCgUAwN/fHw0aNEBQUBC2bduGGzdu4ODBg/j2229x7NixIu2PyMhIHD16FH379sXp06dx8eJFzJkzBw8fPtRq6+rqChMTE2nfbdiwAePGjdNoU9D+mTp1Kv78809cvHgRly9fxsqVK+Ho6AgbG5si1Uz0rmLYIaJCqVmzJqZOnYrJkyejevXqiI6O1rr8OScnBxEREahatSpatmyJSpUqSVdqmZiYIDIyEjVq1ICPjw8MDQ2xbNkyAICZmRn27t0LV1dXdOjQAVWrVkXPnj2Rnp4OKyurQte4ePFiODg4wMfHB+3bt0evXr1gaWkpXT6uUCiwefNm+Pj4oEePHqhUqRJCQ0Nx8+ZNODg4FGl/VKpUCdu2bcOpU6dQt25dNGjQAOvXr9foDctVpkwZLFy4ECtXroSnpycmTZqEH3/8UaNNQfvH0tISU6ZMQZ06dfDBBx/gxo0b2Lx5MwwM+BJOVBgKIYTQdRFERK/DnTt34OLiIg2sJqJ3E8MOEcnGzp07kZKSAi8vL9y7dw/Dhw/Hv//+i8uXL2sNbiaidwe/VJCIZCMrKwvffPMNrl+/DktLSzRs2BDR0dEMOkTvOPbsEBERkaxxdBsRERHJGsMOERERyRrDDhEREckaww4RERHJGsMOERERyRrDDhEREckaww4RERHJGsMOERERyRrDDhEREcna/wFPI1OUNQscqAAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Grouping thw data by the 'survived' column\n",
        "pd.pivot_table(train_data, index=\"Survived\", values=[\"Age\", \"SibSp\", \"Parch\", \"Fare\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "irf8o-FUzznf",
        "outputId": "ec267f75-2dc8-46a8-b509-95b45c1aa6a8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                Age       Fare     Parch     SibSp\n",
              "Survived                                          \n",
              "0         30.626179  22.117887  0.329690  0.553734\n",
              "1         28.343690  48.395408  0.464912  0.473684"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-8ce1255d-a773-42cf-a7b1-20841b7ecc7f\" 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>Age</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Parch</th>\n",
              "      <th>SibSp</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Survived</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>30.626179</td>\n",
              "      <td>22.117887</td>\n",
              "      <td>0.329690</td>\n",
              "      <td>0.553734</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>28.343690</td>\n",
              "      <td>48.395408</td>\n",
              "      <td>0.464912</td>\n",
              "      <td>0.473684</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-8ce1255d-a773-42cf-a7b1-20841b7ecc7f')\"\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-8ce1255d-a773-42cf-a7b1-20841b7ecc7f 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-8ce1255d-a773-42cf-a7b1-20841b7ecc7f');\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-de59bc9d-52a0-4d30-a291-cb7b3f9938ff\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-de59bc9d-52a0-4d30-a291-cb7b3f9938ff')\"\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-de59bc9d-52a0-4d30-a291-cb7b3f9938ff 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",
              "summary": "{\n  \"name\": \"pd\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"Survived\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.6139638671549115,\n        \"min\": 28.343689655172415,\n        \"max\": 30.62617924528302,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          28.343689655172415,\n          30.62617924528302\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 18.581013091826648,\n        \"min\": 22.117886885245902,\n        \"max\": 48.39540760233918,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          48.39540760233918,\n          22.117886885245902\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Parch\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.09561634693352748,\n        \"min\": 0.3296903460837887,\n        \"max\": 0.4649122807017544,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.4649122807017544,\n          0.3296903460837887\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SibSp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.05660379276107442,\n        \"min\": 0.47368421052631576,\n        \"max\": 0.5537340619307832,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.47368421052631576,\n          0.5537340619307832\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Creating the barplots for each categorical column in the dataframe\n",
        "for i in df_cat.columns:\n",
        "    plt.figure(figsize=(10,6))#sets the figure size in inches\n",
        "    sns.barplot(x=df_cat[i].value_counts().index, y=df_cat[i].value_counts())\n",
        "    plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "f2NHLMBG4jWz",
        "outputId": "b5589e74-1d04-488a-8d4d-5f5585c2335f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create pivot table for 'Sex'\n",
        "x = pd.pivot_table(\n",
        "    train_data,\n",
        "    index=\"Survived\",\n",
        "    columns=\"Sex\",\n",
        "    values=\"Ticket\",\n",
        "    aggfunc=\"count\"\n",
        ")\n",
        "print(x)\n",
        "print()\n",
        "\n",
        "# Create and print pivot table for 'Pclass'\n",
        "y= pd.pivot_table(\n",
        "    train_data,\n",
        "    index=\"Survived\",\n",
        "    columns=\"Pclass\",\n",
        "    values=\"Ticket\",\n",
        "    aggfunc=\"count\"\n",
        ")\n",
        "print(y)\n",
        "print()\n",
        "\n",
        "# Create and print pivot table for 'Embarked'\n",
        "z= pd.pivot_table(\n",
        "    train_data,\n",
        "    index=\"Survived\",\n",
        "    columns=\"Embarked\",\n",
        "    values=\"Ticket\",\n",
        "    aggfunc=\"count\"\n",
        ")\n",
        "print(z)\n",
        "print()\n",
        "\n",
        "# Print the DataFrame x\n",
        "print(x)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kMoUwsLe7-Bs",
        "outputId": "f3b197ac-654b-415c-9e98-fc64eb874015"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sex       female  male\n",
            "Survived              \n",
            "0             81   468\n",
            "1            233   109\n",
            "\n",
            "Pclass      1   2    3\n",
            "Survived              \n",
            "0          80  97  372\n",
            "1         136  87  119\n",
            "\n",
            "Embarked   C   Q    S\n",
            "Survived             \n",
            "0         75  47  427\n",
            "1         93  30  217\n",
            "\n",
            "Sex       female  male\n",
            "Survived              \n",
            "0             81   468\n",
            "1            233   109\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **DATA CLEANING**"
      ],
      "metadata": {
        "id": "z4mayoHP_Xks"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#using isnull().sum() for checkng number of null values in each column\n",
        "train_data.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 460
        },
        "id": "fGdb38-9-5DC",
        "outputId": "263dfdd8-3b58-4bd5-df84-9700cdeb342f"
      },
      "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",
              "dtype: int64"
            ],
            "text/html": [
              "<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>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>PassengerId</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Survived</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Pclass</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Name</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sex</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Age</th>\n",
              "      <td>177</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SibSp</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Parch</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Ticket</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fare</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Cabin</th>\n",
              "      <td>687</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Embarked</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Dropping irrelevent datatypes/columns\n",
        "train_data=train_data.drop(columns=[\"PassengerId\",\"Cabin\",\"Ticket\",\"Name\"])\n",
        "train_data.isnull().sum()\n",
        "train_data.head()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 338
        },
        "id": "won1b-HV_wrK",
        "outputId": "ac091901-84fc-41f9-a4ed-799873515fc6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "KeyError",
          "evalue": "\"['PassengerId', 'Cabin', 'Ticket', 'Name'] not found in axis\"",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-20-ca265e841cc9>\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m#Dropping irrelevent datatypes/columns\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mtrain_data\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtrain_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"PassengerId\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\"Cabin\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\"Ticket\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\"Name\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mtrain_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misnull\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mtrain_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36mdrop\u001b[0;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[1;32m   5579\u001b[0m                 \u001b[0mweight\u001b[0m  \u001b[0;36m1.0\u001b[0m     \u001b[0;36m0.8\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5580\u001b[0m         \"\"\"\n\u001b[0;32m-> 5581\u001b[0;31m         return super().drop(\n\u001b[0m\u001b[1;32m   5582\u001b[0m             \u001b[0mlabels\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5583\u001b[0m             \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36mdrop\u001b[0;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[1;32m   4786\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;32min\u001b[0m \u001b[0maxes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4787\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4788\u001b[0;31m                 \u001b[0mobj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_drop_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4789\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4790\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0minplace\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m_drop_axis\u001b[0;34m(self, labels, axis, level, errors, only_slice)\u001b[0m\n\u001b[1;32m   4828\u001b[0m                 \u001b[0mnew_axis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4829\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4830\u001b[0;31m                 \u001b[0mnew_axis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4831\u001b[0m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_axis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4832\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mdrop\u001b[0;34m(self, labels, errors)\u001b[0m\n\u001b[1;32m   7068\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mmask\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0many\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   7069\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0merrors\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m\"ignore\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 7070\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"{labels[mask].tolist()} not found in axis\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   7071\u001b[0m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m~\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   7072\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdelete\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: \"['PassengerId', 'Cabin', 'Ticket', 'Name'] not found in axis\""
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_data.isnull()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "kpO4D4rDCIMG",
        "outputId": "f0915815-30fc-4a47-9a23-ad64f4754925"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Survived  Pclass    Sex    Age  SibSp  Parch   Fare  Embarked\n",
              "0       False   False  False  False  False  False  False     False\n",
              "1       False   False  False  False  False  False  False     False\n",
              "2       False   False  False  False  False  False  False     False\n",
              "3       False   False  False  False  False  False  False     False\n",
              "4       False   False  False  False  False  False  False     False\n",
              "..        ...     ...    ...    ...    ...    ...    ...       ...\n",
              "886     False   False  False  False  False  False  False     False\n",
              "887     False   False  False  False  False  False  False     False\n",
              "888     False   False  False  False  False  False  False     False\n",
              "889     False   False  False  False  False  False  False     False\n",
              "890     False   False  False  False  False  False  False     False\n",
              "\n",
              "[891 rows x 8 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-09cca96c-a543-4350-a448-1f550bf7514c\" 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>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Embarked</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>886</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>887</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>888</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>889</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>890</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>891 rows × 8 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-09cca96c-a543-4350-a448-1f550bf7514c')\"\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-09cca96c-a543-4350-a448-1f550bf7514c 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-09cca96c-a543-4350-a448-1f550bf7514c');\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-fab823af-366d-420b-b467-e33b7446f739\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-fab823af-366d-420b-b467-e33b7446f739')\"\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-fab823af-366d-420b-b467-e33b7446f739 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",
              "summary": "{\n  \"name\": \"train_data\",\n  \"rows\": 891,\n  \"fields\": [\n    {\n      \"column\": \"Survived\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Pclass\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sex\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SibSp\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Parch\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fare\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Embarked\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Filling null values with average values\n",
        "train_data[\"Age\"] = train_data[\"Age\"].fillna(train_data[\"Age\"].mean())\n",
        "train_data[\"Embarked\"]=train_data[\"Embarked\"].fillna(train_data[\"Embarked\"].mode()[0])"
      ],
      "metadata": {
        "id": "Y8lcQ9E4AY85"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(train_data.iloc[887])\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uWvyno3RBwys",
        "outputId": "d45608e5-ae64-49de-edd5-bf03b4780d6f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Survived         1\n",
            "Pclass           1\n",
            "Sex         female\n",
            "Age           19.0\n",
            "SibSp            0\n",
            "Parch            0\n",
            "Fare          30.0\n",
            "Embarked         S\n",
            "Name: 887, dtype: object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#plotting the corelation matrix to see whether the numeric data columnns are corelated\n",
        "corr = train_data.corr(numeric_only=True)\n",
        "sns.heatmap(corr, annot=True, fmt=\".2f\", cmap=\"coolwarm\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "7NxjxfWqCYEH",
        "outputId": "56091510-18db-4c46-a8fc-27d686bdd6e6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 29
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Converting the categorical variables into the numerical variables\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "cols = [\"Sex\", \"Embarked\"]\n",
        "le = LabelEncoder()\n",
        "for i in cols:\n",
        "    train_data[i] = le.fit_transform(train_data[i])\n",
        "\n",
        "train_data.head()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "Ub0CyEDaG9EX",
        "outputId": "e3dded46-3666-4b06-bad2-b3f3240f4d4d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Survived  Pclass  Sex   Age  SibSp  Parch     Fare  Embarked\n",
              "0         0       3    1  22.0      1      0   7.2500         2\n",
              "1         1       1    0  38.0      1      0  71.2833         0\n",
              "2         1       3    0  26.0      0      0   7.9250         2\n",
              "3         1       1    0  35.0      1      0  53.1000         2\n",
              "4         0       3    1  35.0      0      0   8.0500         2"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c5650dd9-0473-46b5-b7fd-7b88243f5eab\" 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>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Embarked</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8.0500</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-c5650dd9-0473-46b5-b7fd-7b88243f5eab')\"\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-c5650dd9-0473-46b5-b7fd-7b88243f5eab 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-c5650dd9-0473-46b5-b7fd-7b88243f5eab');\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-38e68901-ffc7-40e9-9eb4-3ac762871591\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-38e68901-ffc7-40e9-9eb4-3ac762871591')\"\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-38e68901-ffc7-40e9-9eb4-3ac762871591 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_data",
              "summary": "{\n  \"name\": \"train_data\",\n  \"rows\": 891,\n  \"fields\": [\n    {\n      \"column\": \"Survived\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Pclass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          3,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sex\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13.002015226002884,\n        \"min\": 0.42,\n        \"max\": 80.0,\n        \"num_unique_values\": 89,\n        \"samples\": [\n          59.0,\n          36.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SibSp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 8,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Parch\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 6,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 49.693428597180905,\n        \"min\": 0.0,\n        \"max\": 512.3292,\n        \"num_unique_values\": 248,\n        \"samples\": [\n          11.2417,\n          51.8625\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Embarked\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 3,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Seperating Inputs and OUtput\n",
        "X = train_data.drop(columns=[\"Survived\"], axis=1)\n",
        "y = train_data[\"Survived\"]\n",
        "train_data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "jjwT7W3II2qC",
        "outputId": "3a4a4f4b-aed9-4264-90ff-a93114d9f776"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Survived  Pclass  Sex        Age  SibSp  Parch     Fare  Embarked\n",
              "0           0       3    1  22.000000      1      0   7.2500         2\n",
              "1           1       1    0  38.000000      1      0  71.2833         0\n",
              "2           1       3    0  26.000000      0      0   7.9250         2\n",
              "3           1       1    0  35.000000      1      0  53.1000         2\n",
              "4           0       3    1  35.000000      0      0   8.0500         2\n",
              "..        ...     ...  ...        ...    ...    ...      ...       ...\n",
              "886         0       2    1  27.000000      0      0  13.0000         2\n",
              "887         1       1    0  19.000000      0      0  30.0000         2\n",
              "888         0       3    0  29.699118      1      2  23.4500         2\n",
              "889         1       1    1  26.000000      0      0  30.0000         0\n",
              "890         0       3    1  32.000000      0      0   7.7500         1\n",
              "\n",
              "[891 rows x 8 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-7d39f92b-b85f-450b-851b-5fcbef01da6f\" 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>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Embarked</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>38.000000</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>26.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>35.000000</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>35.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>886</th>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>27.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>13.0000</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>887</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>19.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>30.0000</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>888</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>29.699118</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>23.4500</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>889</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>26.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>30.0000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>890</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>32.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.7500</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>891 rows × 8 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7d39f92b-b85f-450b-851b-5fcbef01da6f')\"\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-7d39f92b-b85f-450b-851b-5fcbef01da6f 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-7d39f92b-b85f-450b-851b-5fcbef01da6f');\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-716dd328-47a8-434f-a48c-8289b5bcca32\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-716dd328-47a8-434f-a48c-8289b5bcca32')\"\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-716dd328-47a8-434f-a48c-8289b5bcca32 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_de837fa4-f389-4c8a-b1c9-bf5b93d55adc\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('train_data')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_de837fa4-f389-4c8a-b1c9-bf5b93d55adc button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('train_data');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "train_data",
              "summary": "{\n  \"name\": \"train_data\",\n  \"rows\": 891,\n  \"fields\": [\n    {\n      \"column\": \"Survived\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Pclass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          3,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sex\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13.002015226002884,\n        \"min\": 0.42,\n        \"max\": 80.0,\n        \"num_unique_values\": 89,\n        \"samples\": [\n          59.0,\n          36.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SibSp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 8,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Parch\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 6,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 49.693428597180905,\n        \"min\": 0.0,\n        \"max\": 512.3292,\n        \"num_unique_values\": 248,\n        \"samples\": [\n          11.2417,\n          51.8625\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Embarked\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 3,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split, cross_val_score\n",
        "\n",
        "#Splittinng the data into test and train data\n",
        "def classify(model):\n",
        "    x_train, x_test, y_train, y_test = train_test_split(\n",
        "        X, y, test_size=0.25, random_state=40#random_state=40 ensures that the split between training and testing data is the same every time you run the code.\n",
        "    )\n",
        "    model.fit(x_train, y_train)\n",
        "    print(\"Accuracy\", model.score(x_test, y_test))#Evaluating the model\n",
        "\n",
        "    score = cross_val_score(model, X, y, cv=5)#Cross-Validation(k-fold)\n",
        "    print(\"CV SCORE :\", np.mean(score))"
      ],
      "metadata": {
        "id": "M83e2kukJ55F"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Different algorithms**"
      ],
      "metadata": {
        "id": "f3mr5czvPZhE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Decision tree Classifier\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "model = DecisionTreeClassifier()\n",
        "classify(model)"
      ],
      "metadata": {
        "id": "fAyu2nKuLbY5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cef9b994-49b4-4ee4-c21b-edc22a4f2fb9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy 0.7757847533632287\n",
            "CV SCORE : 0.7621116063021781\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Xgboost Classifier\n",
        "from xgboost import XGBClassifier\n",
        "\n",
        "model = XGBClassifier()\n",
        "classify(model)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "36siDmoPPquw",
        "outputId": "ba8b7bc3-a543-4d38-a3fe-5324b99bed26"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy 0.8161434977578476\n",
            "CV SCORE : 0.8148327160881301\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Random Forest Classifier\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "model = RandomForestClassifier()\n",
        "classify(model)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3zKF4l12QTZJ",
        "outputId": "4e267b71-35b4-4299-ceb8-a76634504809"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy 0.820627802690583\n",
            "CV SCORE : 0.8114807607808675\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Logistic Regression\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "\n",
        "model = LogisticRegression(max_iter=1000)\n",
        "classify(model)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IZuVfDx2QjdO",
        "outputId": "b75c5f71-e734-4d8d-a143-68390f2fa2fe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy 0.8071748878923767\n",
            "CV SCORE : 0.786761659657272\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = RandomForestClassifier()\n",
        "model.fit(X, y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "id": "5Pz3xw1VQuDh",
        "outputId": "b0f335bf-b28b-4969-dca7-4b3dd44f8ea0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier()"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: black;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-1 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-1 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: block;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-1 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-1 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-1 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-1 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 1ex;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-1 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RandomForestClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier()</pre></div> </div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_test = test.drop(columns=[\"PassengerId\", \"Name\", \"Cabin\", \"Ticket\"], axis=1)\n",
        "\n",
        "X_test"
      ],
      "metadata": {
        "id": "zc6cUsztSEeY",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "outputId": "c2161efb-cfb3-4f6a-b3e6-78a7eff5b593"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Pclass     Sex   Age  SibSp  Parch      Fare Embarked\n",
              "0         3    male  34.5      0      0    7.8292        Q\n",
              "1         3  female  47.0      1      0    7.0000        S\n",
              "2         2    male  62.0      0      0    9.6875        Q\n",
              "3         3    male  27.0      0      0    8.6625        S\n",
              "4         3  female  22.0      1      1   12.2875        S\n",
              "..      ...     ...   ...    ...    ...       ...      ...\n",
              "413       3    male   NaN      0      0    8.0500        S\n",
              "414       1  female  39.0      0      0  108.9000        C\n",
              "415       3    male  38.5      0      0    7.2500        S\n",
              "416       3    male   NaN      0      0    8.0500        S\n",
              "417       3    male   NaN      1      1   22.3583        C\n",
              "\n",
              "[418 rows x 7 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e5c478ae-449c-40ab-a64c-06a679e9deec\" 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>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Embarked</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3</td>\n",
              "      <td>male</td>\n",
              "      <td>34.5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.8292</td>\n",
              "      <td>Q</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3</td>\n",
              "      <td>female</td>\n",
              "      <td>47.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>7.0000</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>male</td>\n",
              "      <td>62.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>9.6875</td>\n",
              "      <td>Q</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>male</td>\n",
              "      <td>27.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8.6625</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3</td>\n",
              "      <td>female</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>12.2875</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>413</th>\n",
              "      <td>3</td>\n",
              "      <td>male</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>414</th>\n",
              "      <td>1</td>\n",
              "      <td>female</td>\n",
              "      <td>39.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>108.9000</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>415</th>\n",
              "      <td>3</td>\n",
              "      <td>male</td>\n",
              "      <td>38.5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>416</th>\n",
              "      <td>3</td>\n",
              "      <td>male</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>417</th>\n",
              "      <td>3</td>\n",
              "      <td>male</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>22.3583</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>418 rows × 7 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e5c478ae-449c-40ab-a64c-06a679e9deec')\"\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-e5c478ae-449c-40ab-a64c-06a679e9deec 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-e5c478ae-449c-40ab-a64c-06a679e9deec');\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-88b6a762-3d43-416f-b166-bc5247d23d09\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-88b6a762-3d43-416f-b166-bc5247d23d09')\"\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-88b6a762-3d43-416f-b166-bc5247d23d09 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_e57d35c1-fcc1-433c-a611-1b3480094785\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('X_test')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_e57d35c1-fcc1-433c-a611-1b3480094785 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('X_test');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "X_test",
              "summary": "{\n  \"name\": \"X_test\",\n  \"rows\": 418,\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          2,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"female\",\n          \"male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14.18120923562442,\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\": \"Fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 55.90757617997383,\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\": \"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": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "cols = [\"Sex\", \"Embarked\"]\n",
        "le = LabelEncoder()\n",
        "\n",
        "for col in cols:\n",
        "    X_test[col] = le.fit_transform(X_test[col])\n",
        "\n",
        "X_test.head()"
      ],
      "metadata": {
        "id": "kSh76DL6SR_d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "546644fb-6fdc-434d-fa06-9f259173a282"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Pclass  Sex   Age  SibSp  Parch     Fare  Embarked\n",
              "0       3    1  34.5      0      0   7.8292         1\n",
              "1       3    0  47.0      1      0   7.0000         2\n",
              "2       2    1  62.0      0      0   9.6875         1\n",
              "3       3    1  27.0      0      0   8.6625         2\n",
              "4       3    0  22.0      1      1  12.2875         2"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-55ebbab7-a71f-416c-b8ce-e41dbcb2b8ab\" 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>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Embarked</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>34.5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.8292</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>47.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>7.0000</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>62.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>9.6875</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>27.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8.6625</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>12.2875</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-55ebbab7-a71f-416c-b8ce-e41dbcb2b8ab')\"\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-55ebbab7-a71f-416c-b8ce-e41dbcb2b8ab 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-55ebbab7-a71f-416c-b8ce-e41dbcb2b8ab');\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-44816dd2-6dee-43b4-99a3-d9ec1b4590ff\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-44816dd2-6dee-43b4-99a3-d9ec1b4590ff')\"\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-44816dd2-6dee-43b4-99a3-d9ec1b4590ff 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": "X_test",
              "summary": "{\n  \"name\": \"X_test\",\n  \"rows\": 418,\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          2,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sex\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14.18120923562442,\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\": \"Fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 55.90757617997383,\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\": \"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": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_test[\"Age\"] = X_test[\"Age\"].fillna(X_test[\"Age\"].mean())\n",
        "X_test[\"Fare\"] = X_test[\"Fare\"].fillna(X_test[\"Fare\"].mean())\n",
        "\n",
        "X_test.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 303
        },
        "id": "QPEGnCYfTCON",
        "outputId": "5a711303-2899-4838-8d40-72fdd2867c17"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pclass      0\n",
              "Sex         0\n",
              "Age         0\n",
              "SibSp       0\n",
              "Parch       0\n",
              "Fare        0\n",
              "Embarked    0\n",
              "dtype: int64"
            ],
            "text/html": [
              "<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>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Pclass</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sex</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Age</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SibSp</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Parch</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fare</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Embarked</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_test = test.drop(columns=[\"PassengerId\", \"Name\", \"Cabin\", \"Ticket\"], axis=1)\n",
        "\n",
        "X_test[\"Age\"] = X_test[\"Age\"].fillna(X_test[\"Age\"].mean())\n",
        "X_test[\"Fare\"] = X_test[\"Fare\"].fillna(X_test[\"Fare\"].mean())\n",
        "\n",
        "X_test.isnull().sum()\n",
        "\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "cols = [\"Sex\", \"Embarked\"]\n",
        "le = LabelEncoder()\n",
        "\n",
        "for col in cols:\n",
        "    X_test[col] = le.fit_transform(X_test[col])\n",
        "\n",
        "X_test.head()\n",
        "X_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "zWEnvO1lTQFZ",
        "outputId": "73e41968-1705-4e2d-9c29-d1b0bb0a9459"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Pclass  Sex       Age  SibSp  Parch      Fare  Embarked\n",
              "0         3    1  34.50000      0      0    7.8292         1\n",
              "1         3    0  47.00000      1      0    7.0000         2\n",
              "2         2    1  62.00000      0      0    9.6875         1\n",
              "3         3    1  27.00000      0      0    8.6625         2\n",
              "4         3    0  22.00000      1      1   12.2875         2\n",
              "..      ...  ...       ...    ...    ...       ...       ...\n",
              "413       3    1  30.27259      0      0    8.0500         2\n",
              "414       1    0  39.00000      0      0  108.9000         0\n",
              "415       3    1  38.50000      0      0    7.2500         2\n",
              "416       3    1  30.27259      0      0    8.0500         2\n",
              "417       3    1  30.27259      1      1   22.3583         0\n",
              "\n",
              "[418 rows x 7 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-5ebf3510-5264-4a2b-bdc8-3027f918046f\" 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>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Embarked</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>34.50000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.8292</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>47.00000</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>7.0000</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>62.00000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>9.6875</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>27.00000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8.6625</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>22.00000</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>12.2875</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>413</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>30.27259</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>414</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>39.00000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>108.9000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>415</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>38.50000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>416</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>30.27259</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>417</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>30.27259</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>22.3583</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>418 rows × 7 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5ebf3510-5264-4a2b-bdc8-3027f918046f')\"\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-5ebf3510-5264-4a2b-bdc8-3027f918046f 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-5ebf3510-5264-4a2b-bdc8-3027f918046f');\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-8a3e81bb-0625-424e-835f-949fbdabc4c4\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-8a3e81bb-0625-424e-835f-949fbdabc4c4')\"\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-8a3e81bb-0625-424e-835f-949fbdabc4c4 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_5d774d09-9d72-4175-8e65-d31e69cf25cd\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('X_test')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_5d774d09-9d72-4175-8e65-d31e69cf25cd button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('X_test');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "X_test",
              "summary": "{\n  \"name\": \"X_test\",\n  \"rows\": 418,\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          2,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sex\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12.63453416832505,\n        \"min\": 0.17,\n        \"max\": 76.0,\n        \"num_unique_values\": 80,\n        \"samples\": [\n          28.0,\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\": \"Fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 55.84050047954103,\n        \"min\": 0.0,\n        \"max\": 512.3292,\n        \"num_unique_values\": 170,\n        \"samples\": [\n          41.5792,\n          57.75\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": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pred = model.predict(X_test)\n",
        "pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bXWXrmZ_TYat",
        "outputId": "e03ba444-5af7-45b7-ae94-b14ac49322a4"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1,\n",
              "       1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1,\n",
              "       1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1,\n",
              "       1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0,\n",
              "       0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1,\n",
              "       0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1,\n",
              "       1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0,\n",
              "       0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,\n",
              "       1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1,\n",
              "       0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1,\n",
              "       0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0,\n",
              "       1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0,\n",
              "       0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0,\n",
              "       1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1,\n",
              "       0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Let's ask our model\n",
        "import pandas as pd\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "\n",
        "def get_user_input():\n",
        "    Pclass = int(input(\"Enter Pclass (1, 2, or 3): \"))\n",
        "    Sex = int(input(\"Enter Sex (0 for male, 1 for female): \"))\n",
        "    Age = float(input(\"Enter Age: \"))\n",
        "    SibSp = int(input(\"Enter number of siblings/spouses aboard: \"))\n",
        "    Parch = int(input(\"Enter number of parents/children aboard: \"))\n",
        "    Fare = float(input(\"Enter Fare: \"))\n",
        "    Embarked = int(input(\"Enter Embarked (0 for C, 1 for Q, 2 for S): \"))\n",
        "\n",
        "    user_data = {\n",
        "        'Pclass': [Pclass],\n",
        "        'Sex': [Sex],\n",
        "        'Age': [Age],\n",
        "        'SibSp': [SibSp],\n",
        "        'Parch': [Parch],\n",
        "        'Fare': [Fare],\n",
        "        'Embarked': [Embarked]\n",
        "    }\n",
        "\n",
        "    return pd.DataFrame(user_data)\n",
        "\n",
        "def predict_survival(model, user_input):\n",
        "    prediction = model.predict(user_input)\n",
        "    return prediction[0]\n",
        "\n",
        "# Get user input\n",
        "user_input = get_user_input()\n",
        "\n",
        "# Predict survival\n",
        "prediction = predict_survival(model, user_input)\n",
        "\n",
        "# Output the prediction\n",
        "if prediction == 1:\n",
        "    print(\"The model predicts that the passenger would survive.\")\n",
        "else:\n",
        "    print(\"The model predicts that the passenger would not survive.\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ET8UrCE3UMOn",
        "outputId": "5fd99ca3-15be-4af2-e4a6-9e683b83e9ae"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Enter Pclass (1, 2, or 3): 1\n",
            "Enter Sex (0 for male, 1 for female): 0\n",
            "Enter Age: 34\n",
            "Enter number of siblings/spouses aboard: 2\n",
            "Enter number of parents/children aboard: 1\n",
            "Enter Fare: 9.6875\n",
            "Enter Embarked (0 for C, 1 for Q, 2 for S): 0\n",
            "The model predicts that the passenger would survive.\n"
          ]
        }
      ]
    }
  ]
}