Lec2_EDA.ipynb

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        "<a href=\"https://colab.research.google.com/github/baselhusam/Data-Preparation-Course-IEEE-CIS/blob/main/Lec2_EDA.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
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    {
      "cell_type": "markdown",
      "source": [
        "## Import Packages"
      ],
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        "id": "csHyGxF5v_K3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "1V9bUtvJwCM_"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Load the Titanic Dataset"
      ],
      "metadata": {
        "id": "2_2A0NPCwJQe"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "jblstiaEhdm_"
      },
      "outputs": [],
      "source": [
        "df = pd.read_csv(\"https://raw.githubusercontent.com/datasciencedojo/datasets/master/titanic.csv\")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## EDA Process"
      ],
      "metadata": {
        "id": "tGbNj6XQwMgN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Check the shape of the dataset\n",
        "print('Number of rows and columns:', df.shape)"
      ],
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        "outputId": "ff374a55-ef97-4e5f-a8f4-49b2eebbc303"
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      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of rows and columns: (891, 12)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check the data types of each column\n",
        "print('\\nData types of each column:\\n', df.dtypes)"
      ],
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          "base_uri": "https://localhost:8080/"
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      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Data types of each column:\n",
            " PassengerId      int64\n",
            "Survived         int64\n",
            "Pclass           int64\n",
            "Name            object\n",
            "Sex             object\n",
            "Age            float64\n",
            "SibSp            int64\n",
            "Parch            int64\n",
            "Ticket          object\n",
            "Fare           float64\n",
            "Cabin           object\n",
            "Embarked        object\n",
            "dtype: object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check the first five rows of the dataset\n",
        "print('\\nFirst five rows of the dataset:\\n')\n",
        "df.head()"
      ],
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      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "First five rows of the dataset:\n",
            "\n"
          ]
        },
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          "data": {
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              "   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  "
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      "source": [
        "# Check the last five rows of the dataset\n",
        "print('\\nLast five rows of the dataset:\\n')\n",
        "df.tail()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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      },
      "execution_count": 6,
      "outputs": [
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          "name": "stdout",
          "text": [
            "\n",
            "Last five rows of the dataset:\n",
            "\n"
          ]
        },
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          "data": {
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              "     PassengerId  Survived  Pclass                                      Name  \\\n",
              "886          887         0       2                     Montvila, Rev. Juozas   \n",
              "887          888         1       1              Graham, Miss. Margaret Edith   \n",
              "888          889         0       3  Johnston, Miss. Catherine Helen \"Carrie\"   \n",
              "889          890         1       1                     Behr, Mr. Karl Howell   \n",
              "890          891         0       3                       Dooley, Mr. Patrick   \n",
              "\n",
              "        Sex   Age  SibSp  Parch      Ticket   Fare Cabin Embarked  \n",
              "886    male  27.0      0      0      211536  13.00   NaN        S  \n",
              "887  female  19.0      0      0      112053  30.00   B42        S  \n",
              "888  female   NaN      1      2  W./C. 6607  23.45   NaN        S  \n",
              "889    male  26.0      0      0      111369  30.00  C148        C  \n",
              "890    male  32.0      0      0      370376   7.75   NaN        Q  "
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              "      <th>889</th>\n",
              "      <td>890</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Behr, Mr. Karl Howell</td>\n",
              "      <td>male</td>\n",
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              "      <td>Dooley, Mr. Patrick</td>\n",
              "      <td>male</td>\n",
              "      <td>32.0</td>\n",
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              "      <td>0</td>\n",
              "      <td>370376</td>\n",
              "      <td>7.75</td>\n",
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              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-c7b65b9c-cd07-43d6-9651-b0ab48189d8c 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-c7b65b9c-cd07-43d6-9651-b0ab48189d8c');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check the basic summary statistics of numerical columns\n",
        "print('\\nSummary statistics of numerical columns:\\n')\n",
        "df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 352
        },
        "id": "RNWrj8GAwkAm",
        "outputId": "73ef8d7f-0300-48fa-a38c-7923e4a36e31"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Summary statistics of numerical columns:\n",
            "\n"
          ]
        },
        {
          "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-8fadd4ad-ffe7-4ae2-bb4b-661b6e6c1854\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\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",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8fadd4ad-ffe7-4ae2-bb4b-661b6e6c1854')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
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              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-8fadd4ad-ffe7-4ae2-bb4b-661b6e6c1854 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-8fadd4ad-ffe7-4ae2-bb4b-661b6e6c1854');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Extract meaningful information from the data"
      ],
      "metadata": {
        "id": "Z7J7hge6wnYv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Check the number of survivors and non-survivors\n",
        "print('\\nNumber of survivors and non-survivors:\\n', df['Survived'].value_counts())\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W6tyRhRjwl__",
        "outputId": "71c0bf1a-2c05-4719-d230-adf21620cf8d"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Number of survivors and non-survivors:\n",
            " 0    549\n",
            "1    342\n",
            "Name: Survived, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check the number of male and female passengers\n",
        "print('\\nNumber of male and female passengers:\\n', df['Sex'].value_counts())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nyN5m9LEwvOB",
        "outputId": "b51864c1-ff1e-45aa-af25-7c430535239a"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Number of male and female passengers:\n",
            " male      577\n",
            "female    314\n",
            "Name: Sex, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check the number of passengers in each class\n",
        "print('\\nNumber of passengers in each class:\\n', df['Pclass'].value_counts())\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XSmUG0fwwzRW",
        "outputId": "6f753762-a211-479f-e81a-d077c178dbd2"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Number of passengers in each class:\n",
            " 3    491\n",
            "1    216\n",
            "2    184\n",
            "Name: Pclass, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Correlation Analysis"
      ],
      "metadata": {
        "id": "dbMfDoshxJZg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a correlation matrix\n",
        "corr_matrix = df.corr()\n",
        "\n",
        "corr_matrix"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "edbjlG2Lw1rp",
        "outputId": "c5823381-670b-4b41-a814-9afdcec9b8a1"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             PassengerId  Survived    Pclass       Age     SibSp     Parch  \\\n",
              "PassengerId     1.000000 -0.005007 -0.035144  0.036847 -0.057527 -0.001652   \n",
              "Survived       -0.005007  1.000000 -0.338481 -0.077221 -0.035322  0.081629   \n",
              "Pclass         -0.035144 -0.338481  1.000000 -0.369226  0.083081  0.018443   \n",
              "Age             0.036847 -0.077221 -0.369226  1.000000 -0.308247 -0.189119   \n",
              "SibSp          -0.057527 -0.035322  0.083081 -0.308247  1.000000  0.414838   \n",
              "Parch          -0.001652  0.081629  0.018443 -0.189119  0.414838  1.000000   \n",
              "Fare            0.012658  0.257307 -0.549500  0.096067  0.159651  0.216225   \n",
              "\n",
              "                 Fare  \n",
              "PassengerId  0.012658  \n",
              "Survived     0.257307  \n",
              "Pclass      -0.549500  \n",
              "Age          0.096067  \n",
              "SibSp        0.159651  \n",
              "Parch        0.216225  \n",
              "Fare         1.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-2f67ee56-17a4-461c-bc11-e0ef034732f8\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\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>PassengerId</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.005007</td>\n",
              "      <td>-0.035144</td>\n",
              "      <td>0.036847</td>\n",
              "      <td>-0.057527</td>\n",
              "      <td>-0.001652</td>\n",
              "      <td>0.012658</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Survived</th>\n",
              "      <td>-0.005007</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.338481</td>\n",
              "      <td>-0.077221</td>\n",
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              "      <td>0.081629</td>\n",
              "      <td>0.257307</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Pclass</th>\n",
              "      <td>-0.035144</td>\n",
              "      <td>-0.338481</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.369226</td>\n",
              "      <td>0.083081</td>\n",
              "      <td>0.018443</td>\n",
              "      <td>-0.549500</td>\n",
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              "    <tr>\n",
              "      <th>Age</th>\n",
              "      <td>0.036847</td>\n",
              "      <td>-0.077221</td>\n",
              "      <td>-0.369226</td>\n",
              "      <td>1.000000</td>\n",
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              "      <td>-0.189119</td>\n",
              "      <td>0.096067</td>\n",
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              "    <tr>\n",
              "      <th>SibSp</th>\n",
              "      <td>-0.057527</td>\n",
              "      <td>-0.035322</td>\n",
              "      <td>0.083081</td>\n",
              "      <td>-0.308247</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.414838</td>\n",
              "      <td>0.159651</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Parch</th>\n",
              "      <td>-0.001652</td>\n",
              "      <td>0.081629</td>\n",
              "      <td>0.018443</td>\n",
              "      <td>-0.189119</td>\n",
              "      <td>0.414838</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.216225</td>\n",
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              "    <tr>\n",
              "      <th>Fare</th>\n",
              "      <td>0.012658</td>\n",
              "      <td>0.257307</td>\n",
              "      <td>-0.549500</td>\n",
              "      <td>0.096067</td>\n",
              "      <td>0.159651</td>\n",
              "      <td>0.216225</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2f67ee56-17a4-461c-bc11-e0ef034732f8')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-2f67ee56-17a4-461c-bc11-e0ef034732f8 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-2f67ee56-17a4-461c-bc11-e0ef034732f8');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot the correlation matrix using heatmap\n",
        "fig, ax = plt.subplots(figsize=(15,10))\n",
        "sns.heatmap(corr_matrix, ax=ax, annot=True, cmap='coolwarm')\n",
        "plt.title('Correlation Matrix')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "id": "pbHERMHAxRYi",
        "outputId": "d7c0a470-a470-40ee-c55a-138e43d0e385"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x720 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Print the correlation values between 'Survived' and other features\n",
        "print('\\nCorrelation values between \"Survived\" and other features:\\n', corr_matrix['Survived'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Hl1xB4kExUOX",
        "outputId": "66f7b4e2-64f6-4b06-f011-733b24ce1dbf"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Correlation values between \"Survived\" and other features:\n",
            " PassengerId   -0.005007\n",
            "Survived       1.000000\n",
            "Pclass        -0.338481\n",
            "Age           -0.077221\n",
            "SibSp         -0.035322\n",
            "Parch          0.081629\n",
            "Fare           0.257307\n",
            "Name: Survived, dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "corr_matrix['Survived'].sort_values(ascending=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4c5DfWTM2JlL",
        "outputId": "ab745c12-8d74-47ca-dcc8-9eb2f186ca4b"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Survived       1.000000\n",
              "Fare           0.257307\n",
              "Parch          0.081629\n",
              "PassengerId   -0.005007\n",
              "SibSp         -0.035322\n",
              "Age           -0.077221\n",
              "Pclass        -0.338481\n",
              "Name: Survived, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Statistical Analysis"
      ],
      "metadata": {
        "id": "6bR8RNgAy5Pu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Check the mean, median and mode of 'Age'\n",
        "print('\\nMean, median and mode of \"Age\":\\n', df['Age'].mean(), df['Age'].median(), df['Age'].mode()[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9Bdti6BQxgsh",
        "outputId": "05d26d38-0c8d-482a-c00e-0ec15dc16761"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Mean, median and mode of \"Age\":\n",
            " 29.69911764705882 28.0 24.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check the range, variance and standard deviation of 'Fare'\n",
        "print('\\nRange, variance and standard deviation of \"Fare\":\\n', df['Fare'].max()-df['Fare'].min(), df['Fare'].var(), df['Fare'].std())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P8nMotUazCWs",
        "outputId": "8638ee34-fd85-4330-da19-b07aa36c565b"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Range, variance and standard deviation of \"Fare\":\n",
            " 512.3292 2469.436845743116 49.6934285971809\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Skewness"
      ],
      "metadata": {
        "id": "bo97UV3OzMg4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Check the skewness of 'Age' and 'Fare'\n",
        "print('\\nSkewness of \"Age\" and \"Fare\":\\n', df['Age'].skew(), df['Fare'].skew())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2sB-vlP9zGZA",
        "outputId": "9f22588e-37d2-472d-8d31-b64b5b04a7b4"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Skewness of \"Age\" and \"Fare\":\n",
            " 0.38910778230082704 4.787316519674893\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Age'].hist()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "3uDOW0hczOuc",
        "outputId": "eb9c9686-ea8c-492b-e245-862d507125bf"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<AxesSubplot:>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Fare'].hist()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "3_dEo5HKzR5R",
        "outputId": "f40b65db-cf20-463a-9025-82bfbb8b798e"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<AxesSubplot:>"
            ]
          },
          "metadata": {},
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot histograms to visualize the skewness\n",
        "plt.subplot(1,2,1)\n",
        "sns.histplot(df['Age'], kde=True)\n",
        "plt.title('Histogram of Age')\n",
        "\n",
        "plt.subplot(1,2,2)\n",
        "sns.histplot(df['Fare'], kde=True)\n",
        "plt.title('Histogram of Fare')\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "dVkNMqCUzT0l",
        "outputId": "5071c176-c432-4def-e045-1e7ede7bfde5"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Extract Information"
      ],
      "metadata": {
        "id": "GFPVnpMl2mf7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Survival rates by gender and class\n",
        "grouped_data = df.groupby(['Sex', 'Pclass']).mean()['Survived']\n",
        "\n",
        "grouped_data.plot(kind='bar')\n",
        "plt.title('Survival Rates by Gender and Class')\n",
        "plt.xlabel('Gender')\n",
        "plt.ylabel('Survival Rate')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        },
        "id": "OVdughhIzc7i",
        "outputId": "7b6030bc-67ab-4ee6-9757-eef571fee1ef"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Age distribution among survivors and non-survivors\n",
        "survivors = df[df['Survived'] == 1]\n",
        "non_survivors = df[df['Survived'] == 0]\n",
        "\n",
        "plt.hist([survivors['Age'], non_survivors['Age']], bins=20, label=['Survived', 'Not Survived'])\n",
        "plt.title('Age Distribution among Survivors and Non-Survivors')\n",
        "plt.xlabel('Age')\n",
        "plt.ylabel('Count')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "c8bY_9KN2up8",
        "outputId": "154b6756-c034-4f07-97d0-5ebea4a71ea0"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(x='Survived', data=df)\n",
        "plt.title('Survival count')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "wufyRIoU9AXm",
        "outputId": "c3cf12bb-33e9-4359-bcd5-e07ccc7a4fcd"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(x='Pclass', hue='Survived', data=df)\n",
        "plt.title('Survival count by passenger class')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "bebxKg9X9fPl",
        "outputId": "ed6e3ead-e369-4a56-b7cb-ad9101039bcc"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(x='Pclass', data=df)\n",
        "plt.title('Survival count by passenger class')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "Z_dzbQb-9on1",
        "outputId": "7baec02f-5488-46e9-a5f0-4844f1ea2238"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(x='Sex', hue='Survived', data=df)\n",
        "plt.title('Survival count by gender')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "Evbdf3Wf9sQE",
        "outputId": "963a58aa-f7a4-4846-dbfa-79dd08d4e030"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.histplot(x='Age', hue='Survived', data=df, bins=20, kde=True)\n",
        "plt.title('Survival count by age')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "01ABsD85-GAc",
        "outputId": "8ea67080-c358-48ab-fcb8-1aab33820ca9"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.histplot(x='Fare', hue='Survived', data=df, bins=20, kde=True)\n",
        "plt.title('Survival count by fare')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "G50P1lAb-JGV",
        "outputId": "bbc62638-3c9d-4303-d749-3354f419c28f"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Group by passenger class and calculate survival rate\n",
        "pclass_survival = df.groupby('Pclass')['Survived'].mean()\n",
        "pclass_survival"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mRXGoQeZ-lDF",
        "outputId": "a1b2e0ba-a10e-4bd8-ca19-891afcf0fc07"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pclass\n",
              "1    0.629630\n",
              "2    0.472826\n",
              "3    0.242363\n",
              "Name: Survived, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pclass_survival.plot(kind='bar');"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 276
        },
        "id": "LWtwll2c_Cxb",
        "outputId": "4b96853f-0fac-4215-f4a8-363352cb3548"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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Wz9B1Xul9wfkHgH9I8hTwVeC6ect+FzjEXLQPMRd4gI/03vQM8DngXuAG4JeTPMncp/394bL/EtIifFNUkhrhJRdJaoRBl6RGGHRJaoRBl6RGGHRJaoRBl6RGGHRJaoRBl6RG/B8V+6HwlXQFVgAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Group by passenger class and gender, and calculate survival rate\n",
        "class_gender_survival = df.groupby(['Pclass', 'Sex'])['Survived'].mean()\n",
        "class_gender_survival"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kg9yd2eG_FvT",
        "outputId": "2bc06d7c-c0c1-423c-f221-d36ce3fa1882"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pclass  Sex   \n",
              "1       female    0.968085\n",
              "        male      0.368852\n",
              "2       female    0.921053\n",
              "        male      0.157407\n",
              "3       female    0.500000\n",
              "        male      0.135447\n",
              "Name: Survived, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[['Pclass', 'Survived']].groupby(['Pclass'], as_index=False).mean().sort_values(by='Survived', ascending=False)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 144
        },
        "id": "zC80F6xvAGvi",
        "outputId": "11fa6a4c-cdd5-44b1-94b6-fba53ab9f344"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Pclass  Survived\n",
              "0       1  0.629630\n",
              "1       2  0.472826\n",
              "2       3  0.242363"
            ],
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              "      <th>Pclass</th>\n",
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              "      <td>0.472826</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>0.242363</td>\n",
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              "          const element = document.querySelector('#df-a5a782b8-cb73-4faa-9c9c-03d154658443');\n",
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              "            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",
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          "metadata": {},
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    },
    {
      "cell_type": "code",
      "source": [
        "sns.factorplot('Pclass','Survived',hue='Sex',data=df)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "id": "wny3EpIXBLgD",
        "outputId": "93190858-5902-45c4-a6a4-1f66f130ac24"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/seaborn/categorical.py:3717: UserWarning: The `factorplot` function has been renamed to `catplot`. The original name will be removed in a future release. Please update your code. Note that the default `kind` in `factorplot` (`'point'`) has changed `'strip'` in `catplot`.\n",
            "  warnings.warn(msg)\n",
            "/usr/local/lib/python3.8/dist-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 430.5x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## EDA 2"
      ],
      "metadata": {
        "id": "7FKGMv0qGhxz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "df = pd.read_csv(\"https://raw.githubusercontent.com/datasciencedojo/datasets/master/titanic.csv\")\n",
        "\n",
        "# Show first 5 rows\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        },
        "id": "D2mjtozvBsOO",
        "outputId": "ef77ebf1-b3cd-45ae-c6ab-6b9b79f2c8ba"
      },
      "execution_count": 66,
      "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-98e37ec9-f358-48a9-a737-05ffb666df64\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
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              "  <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",
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              "      <th>0</th>\n",
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              "      <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",
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              "    </tr>\n",
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              "      <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",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-98e37ec9-f358-48a9-a737-05ffb666df64');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# print the shape\n",
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zvOO5An4GmPE",
        "outputId": "f6dc23be-db0e-4b98-f0c7-f831c15ed375"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(891, 12)"
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# get information about the dataset\n",
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IoDpAVE9GnHE",
        "outputId": "4764e84f-3430-497b-cc71-dab1ba520fa8"
      },
      "execution_count": 68,
      "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": [
        "df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "ePmD5qvNGn4r",
        "outputId": "00941e9d-9dd7-4ce6-c769-da1dc855c70d"
      },
      "execution_count": 69,
      "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-e404539c-dd48-4332-84b6-706239d1d86f\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\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",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e404539c-dd48-4332-84b6-706239d1d86f')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
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              "          document.querySelector('#df-e404539c-dd48-4332-84b6-706239d1d86f 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-e404539c-dd48-4332-84b6-706239d1d86f');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.select_dtypes(include='object').describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "VeUo2Ei4Go0b",
        "outputId": "d6ee8531-8a41-4070-ca48-e43485c6a4a6"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                           Name   Sex  Ticket    Cabin Embarked\n",
              "count                       891   891     891      204      889\n",
              "unique                      891     2     681      147        3\n",
              "top     Braund, Mr. Owen Harris  male  347082  B96 B98        S\n",
              "freq                          1   577       7        4      644"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-361209c7-3193-4351-8cb0-9819394337ec\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "\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>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>891</td>\n",
              "      <td>891</td>\n",
              "      <td>891</td>\n",
              "      <td>204</td>\n",
              "      <td>889</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>unique</th>\n",
              "      <td>891</td>\n",
              "      <td>2</td>\n",
              "      <td>681</td>\n",
              "      <td>147</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>top</th>\n",
              "      <td>Braund, Mr. Owen Harris</td>\n",
              "      <td>male</td>\n",
              "      <td>347082</td>\n",
              "      <td>B96 B98</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>freq</th>\n",
              "      <td>1</td>\n",
              "      <td>577</td>\n",
              "      <td>7</td>\n",
              "      <td>4</td>\n",
              "      <td>644</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-361209c7-3193-4351-8cb0-9819394337ec')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
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              "      gap: 12px;\n",
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              "\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-361209c7-3193-4351-8cb0-9819394337ec button.colab-df-convert');\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-361209c7-3193-4351-8cb0-9819394337ec');\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",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Group the data by sex and calculate the survival rate for each group\n",
        "df.groupby('Sex')['Survived'].count()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BMRP7VJgGucU",
        "outputId": "7a9237a1-8d4c-445e-fdc5-8016bfcd98e8"
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Sex\n",
              "female    314\n",
              "male      577\n",
              "Name: Survived, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Group the data by sex and calculate the survival rate for each group\n",
        "df.groupby('Sex')['Survived'].mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "homeyh-yG0Rh",
        "outputId": "0f9c4714-6b38-434b-cc9c-41fed83b87cf"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Sex\n",
              "female    0.742038\n",
              "male      0.188908\n",
              "Name: Survived, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Group the data by sex and calculate the survival rate for each group\n",
        "df.groupby('Sex')['Survived'].sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cQSVWdkbG3CB",
        "outputId": "1bd509f7-800b-4ece-8be5-dd8bfb38e00c"
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Sex\n",
              "female    233\n",
              "male      109\n",
              "Name: Survived, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Group the data by Pclass and calculate the survival rate for each group\n",
        "df.groupby('Pclass')['Survived'].mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ua-UvDoPG4EB",
        "outputId": "2e4d0f3f-2327-46e4-8db8-27589593e590"
      },
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pclass\n",
              "1    0.629630\n",
              "2    0.472826\n",
              "3    0.242363\n",
              "Name: Survived, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create histograms of Age for survivors and non-survivors\n",
        "sns.histplot(data=df, x='Age', hue='Survived')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "u35LrohxG7A5",
        "outputId": "47bcdc69-7e17-4b1c-c373-a65b02fe72cc"
      },
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a box plot of Fare for survivors and non-survivors\n",
        "sns.boxplot(data=df, x='Survived', y='Fare')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "Fd7Stw35G9Fp",
        "outputId": "74e61fd4-b82e-4a9d-bdf7-09925dab6375"
      },
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEGCAYAAACKB4k+AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/NK7nSAAAACXBIWXMAAAsTAAALEwEAmpwYAAAWuUlEQVR4nO3df4xd5Z3f8fd3ZkIWPCGAcZFjY0xrKxEt4MZTwjYrpQWTOgEMVZOUiC7eiMaKShDtNtrAFsI4saKsopA6dDfBLNEO6TbBpN3iIEKwTWjalaFcE36EsF1uWBszeI0z4dfYrMN4vv3jnjmZMeMfgM89177vlzS6z/Occ+/9ejjM5z7nnh+RmUiSBNBTdwGSpM5hKEiSSoaCJKlkKEiSSoaCJKnUV3cBb8fJJ5+c8+fPr7sMSTqibN68+ZeZOWu6ZUd0KMyfP59Go1F3GZJ0RImIrftb5u4jSVLJUJAklQwFSVLJUJAklQwFSR2t0Whw3nnnsXnz5rpL6QqGgqSONjg4yPj4ODfeeGPdpXQFQ0FSx2o0GoyOjgIwOjrqbKENDAVJHWtwcHBK39lC9QwFSR1rYpawv74OP0NBUsfq7+8/YF+HX6WhEBFbIuKJiHg0IhrF2EkRsT4ini4eTyzGIyK+ERHNiHg8It5fZW2SOt++u49WrlxZTyFdpB0zhX+emYsyc6DoXwtszMyFwMaiD/ARYGHxswL4Zhtqk9TBBgYGytlBf38/ixcvrrmio18du48uAYaK9hBw6aTx27PlQeCEiJhdQ32SOsjg4CA9PT3OEtqk6qukJnBfRCRwS2auAU7JzO3F8r8FTinac4Btk577XDG2fdIYEbGC1kyCefPmVVi6pE4wMDDA/fffX3cZXaPqUPidzByOiL8HrI+Iv5q8MDOzCIxDVgTLGoCBgYE39VxJ0oFVuvsoM4eLxxeAvwDOAXZM7BYqHl8oVh8GTp309LnFmCSpTSoLhYiYERHvmmgDHwZ+BqwDlherLQfuKtrrgCuKo5DOBV6etJtJktQGVe4+OgX4i4iYeJ//lpn3RsTDwNqIuBLYCnyiWP8e4KNAE9gNfKrC2iRJ06gsFDLzGeDsacZHgPOnGU/gqqrqkSQdnGc0S5JKhoIkqWQoSJJKhoIkqWQoSJJKhoIkqWQoSJJKhoIkqWQoSJJKhoIkqWQoSJJKhoIkqWQoSJJKhoIkqWQoSJJKhoIkqWQoSJJKhoIkqWQoSJJKhoIkqWQoSJJKhoIkqWQoSJJKhoIkqWQoSJJKhoIkqWQoSJJKhoIkqWQoSJJKlYdCRPRGxE8j4u6if3pEPBQRzYi4IyKOKcbfWfSbxfL5VdcmSZqqHTOFa4CnJvX/CPh6Zi4AXgSuLMavBF4sxr9erCdJaqNKQyEi5gIXAn9a9AM4D/h+scoQcGnRvqToUyw/v1hfktQmVc8U/jPwB8B40Z8JvJSZY0X/OWBO0Z4DbAMolr9crC9JapPKQiEiLgJeyMzNh/l1V0REIyIaO3fuPJwvLUldr8qZwgeBZRGxBfgerd1Gq4ETIqKvWGcuMFy0h4FTAYrl7wZG9n3RzFyTmQOZOTBr1qwKy5ek7lNZKGTmdZk5NzPnA5cB92fm5cCPgY8Vqy0H7ira64o+xfL7MzOrqk+S9EZ1nKfweeD3I6JJ6zuD24rx24CZxfjvA9fWUJskdbW+g6/y9mXmA8ADRfsZ4Jxp1vk74OPtqEeSND3PaJYklQwFSVLJUJAklQwFSVLJUJAklQwFSVLJUJAklQwFSVLJUJAklQwFSVLJUJAklQwFSVLJUJAklQwFSVLJUJAklQwFSVLJUJAklQwFSVLJUJAklQwFSVLJUJAklQwFSVLJUJAklQwFSVLJUJAklQwFSVLJUJAklQwFSVLJUJAklQwFSVKpslCIiN+KiP8bEY9FxJMRsbIYPz0iHoqIZkTcERHHFOPvLPrNYvn8qmqTJE2vypnCHuC8zDwbWAQsjYhzgT8Cvp6ZC4AXgSuL9a8EXizGv16sJ0lqo8pCIVtGi+47ip8EzgO+X4wPAZcW7UuKPsXy8yMiqqpPkvRGlX6nEBG9EfEo8AKwHvgF8FJmjhWrPAfMKdpzgG0AxfKXgZnTvOaKiGhERGPnzp1Vli9JXafSUMjMvZm5CJgLnAO87zC85prMHMjMgVmzZr3dl5MkTdKWo48y8yXgx8BvAydERF+xaC4wXLSHgVMBiuXvBkbaUZ8kqaXKo49mRcQJRftY4ALgKVrh8LFiteXAXUV7XdGnWH5/ZmZV9UmS3qjv4Ku8ZbOBoYjopRU+azPz7oj4OfC9iFgF/BS4rVj/NuA7EdEEfgVcVmFtkqRpVBYKmfk48I+nGX+G1vcL+47/HfDxquqRJB2cZzRLkkqGgiSpZChIkkqHHAoR8TsR8amiPSsiTq+uLElSHQ4pFCLiRuDzwHXF0DuA/1pVUWq/ZrPJhRdeSLPZrLsUaQq3zfY61JnCvwSWAbsAMvN54F1VFaX2W7VqFbt27WLVqlV1lyJNccMNN7Br1y6+8IUv1F1KVzjUUPh1cSJZAkTEjOpKUrs1m022bNkCwJYtW/xEpo7RbDbZvn07AM8//7zbZhscaiisjYhbaF2i4tPABuDW6spSO+07O3C2oE5xww03TOk7W6jeQU9eKy5ffQeti9m9ArwX+EJmrq+4NrXJxCxhf32pLhOzhAnPP/98TZV0j4OGQmZmRNyTmWfSuvy1jjLz58+fEgTz58+vrRZJ9TrU3UePRMQ/qbQS1eb6668/YF+qy+zZs6f03/Oe99RUSfc41FD4ALApIn4REY9HxBMR8XiVhal9FixYQH9/PwD9/f0sWLCg5oqkli996UtT+l/84hdrqqR7HOoF8f5FpVWoViMjI+zZsweAPXv2MDIywsyZb7jpndR2J5544gH7OvwOaaaQmVszcyvwGq3DUsvDU3XkGxoaYuLWFZnJ7bffXnNFUsvQ0BA9Pa0/Uz09PW6bbXCoZzQvi4ingb8B/hewBfhhhXWpjTZs2MDYWOu22WNjY6xf7/EE6gwbNmxgfHwcgPHxcbfNNjjU7xS+BJwL/HVmng6cDzxYWVVqqyVLltDX19qT2NfXxwUXXFBzRVKL22b7HWoovJ6ZI0BPRPRk5o+BgQrrUhstX768nKL39vZyxRVX1FyR1OK22X6HGgovRUQ/8BPgzyNiNcV1kHTkmzlzJkuXLiUiWLp0qV8yq2O4bbbfAUMhIuYVzUuA3cB/AO4FfgFcXG1paqdly5Zx3HHHcfHF/mdVZ1m0aBGZyaJFi+oupSscbKbwPwEycxdwZ2aOZeZQZn6j2J2ko8S6devYvXs3P/jBD+ouRZripptuAuBrX/tazZV0h4OFQkxq//0qC1F9RkZGuPfee8lMfvjDHzIyYt6rMzQaDUZHRwEYHR1l8+bNNVd09DtYKOR+2jqKDA0N8frrrwPw+uuveyy4Osbg4OCU/o033lhPIV3kYKFwdkS8EhGvAmcV7Vci4tWIeKUdBap669evn3Ly2n333VdzRVLLxCxhf30dfgcMhczszczjM/NdmdlXtCf6x7erSFXrlFNOOWBfqsvENbn219fhd6iHpOootmPHjgP2pbrsu/to5cqV9RTSRQwFveEs0Q9/+MM1VSJNNTAw9RzZxYsX11RJ9zAUxLJly6b0PVdBnaLRaEzpe/RR9QwFsW7dOlp3XYWI8FwFdQyPPmo/Q0Fs2LBhytFHXolSncKjj9rPUJBXolTH8uij9qssFCLi1Ij4cUT8PCKejIhrivGTImJ9RDxdPJ5YjEdEfCMimsUtP99fVW2ayitRqlN59FH7VTlTGAP+Y2aeQeteDFdFxBnAtcDGzFwIbCz6AB8BFhY/K4BvVlibJvFKlOpUAwMDU+4f7tFH1assFDJze2Y+UrRfBZ4C5tC64upQsdoQcGnRvgS4PVseBE6IiNlV1aepli9fzplnnuksQR1ncHCQnp4eZwltEhNfMFb6JhHzad2L4R8Bz2bmCcV4AC9m5gkRcTfwlcz8P8WyjcDnM7Oxz2utoDWTYN68eYu3bt1aef2SdDSJiM2ZOe2N0ir/orm4Oc9/B/59Zk65XlK2EulNpVJmrsnMgcwcmDVr1mGsVJJUaShExDtoBcKfZ+b/KIZ3TOwWKh5fKMaHgVMnPX1uMSZJapMqjz4K4Dbgqcy8adKidcDyor0cuGvS+BXFUUjnAi9n5vaq6pMkvVFfha/9QeB3gSci4tFi7A+BrwBrI+JKYCvwiWLZPcBHgSatW39+qsLaJEnTqCwUii+MYz+Lz59m/QSuqqoeSdLBeUazJKlkKEiSSoaCJKlkKEjqaM1mkwsvvJBms1l3KV3BUJDU0VatWsWuXbtYtWpV3aV0BUNBUsdqNpts2bIFgC1btjhbaANDQVLH2nd24GyheoaCpI41MUvYX1+Hn6EgoHWD9PPOO88bo6ujzJ8//4B9HX6GgoDWNevHx8e9Mbo6yvXXX3/Avg4/Q0E0Go3yhuijo6POFtQxnn322Sn9bdu21VRJ92jLTXaqMjAwkI1G4+Ar6oAuuuiiMhSgddvDu+++u8aKpJYlS5YwNjZW9vv6+tiwYUONFR0dar3Jjjrf5ECYri/VZXIgTNfX4WcoqLwx+v76krqHoSAGBwen9L1ButS9DAUxMDDAjBkzAJgxYwaLFy+uuSKpxUNS289QEABnnXXWlEepE3hIavsZCmJkZKQ8DPWRRx5hZGSk5oqkloceemhK36MNq2coiKGhIcbHxwHYu3cvt99+e80VSS233nrrlP63vvWtmirpHoaC2LBhQ3mo39jYGOvXr6+5Ikl1MRTEkiVL6OvrA1onB11wwQU1VySpLoaCWL58OT09rU2ht7eXK664ouaKpJZPf/rTU/qf+cxnaqqkexgKYubMmSxdupSIYOnSpcycObPukiQALr/88in9yy67rKZKuoehIACWLVvGcccdx8UXX1x3KdIUE7MFZwnt0Vd3AeoMa9euZdeuXdx5551cd911dZejDnHzzTfXfgvM4eFhTj75ZDZt2sSmTZtqrWXBggVcffXVtdZQNWcKYmRkpLzy5Pr16z1PQR3ltdde47XXXqu7jK7hTEHccsst5XkK4+PjrFmzxtmCADriU/E111wDwOrVq2uupDs4UxAbN26c0vd69VL3MhRUzhL215fUPSoLhYj4dkS8EBE/mzR2UkSsj4ini8cTi/GIiG9ERDMiHo+I91dVl95o37vvHcl345P09lQ5U/gzYOk+Y9cCGzNzIbCx6AN8BFhY/KwAvllhXdpHb2/vAfuSukdloZCZPwF+tc/wJcBQ0R4CLp00fnu2PAicEBGzq6pNUy1ZsuSAfUndo93fKZySmduL9t8CpxTtOcC2Ses9V4y9QUSsiIhGRDR27txZXaVdZMWKFUQEABHBihUraq5IUl1q+6I5Wzuu3/TO68xck5kDmTkwa9asCirrPjNnzmTOnFYGz50718tcSF2s3aGwY2K3UPH4QjE+DJw6ab25xZjaYGRkhB07dgCwY8cOT16Tuli7Q2EdsLxoLwfumjR+RXEU0rnAy5N2M6li3mRH0oQqD0n9LrAJeG9EPBcRVwJfAS6IiKeBJUUf4B7gGaAJ3Ar8u6rq0htt2LCBvXv3Aq1Q8CY7Uveq7DIXmfnJ/Sw6f5p1E7iqqlp0YOeccw4PPPDAlL6k7uQZzeKxxx47YF9S9zAUxIsvvnjAvqTuYShIkkqGgiSpZChIkkqGgiSpZChIkkqGgiSpZChIkkqGgiSpVNllLiS9dTfffDPNZrPuMjrCxO/hmmuuqbmSzrBgwQKuvvrqyl7fUKhZp/7PX9f/gFVv8EeKZrPJ00/+lHn9e+supXbHvN7aobFna6PmSur37Gj1t8o1FERPT0956eyJvuo3r38vf/j+V+ouQx3ky48cX/l7GAo164RPxY1Gg8997nNl/6tf/SqLFy+usSJJdfEjoRgYGChnB/39/QaC1MUMBQFw2mmnAbBy5cqaK5FUJ0NBABx//PGcffbZzhKkLmcoSJJKhoIkqWQoSJJKhoIkqWQoSJJKXX3yWqdeYqIOXl9mqrovtzE8PMyuV3vbcgarjhxbX+1lxvBwpe/R1aHQbDZ59GdPsfe4k+oupXY9v04ANj+zo+ZK6te7+1d1lyDVpqtDAWDvcSfx2vs+WncZ6iDH/tU9dZfAnDlz2DO23WsfaYovP3I875wzp9L38DsFSVLJUJAklbp+95HUqZ4d9YtmgB27W59dTzlu/CBrHv2eHe1lYcXv0dWhMDw8TO/ulztiH7I6R+/uEYaHx2qtYcGCBbW+fyf5dXFk3DtP83eykOq3ja4OBQD2jtG7e6TuKuo3Xtzhq6f6Ozt1vL31BgJ0xn02OsXEYdKrV6+uuZLu0FGhEBFLgdVAL/CnmfmVKt/vQx/6kOcpFCZ+D35CbfH3oG7VMaEQEb3AHwMXAM8BD0fEusz8eVXv6aex3/DTmCTooFAAzgGamfkMQER8D7gEqCwUOkGnnFXdKWc0130msabqhO2zU7ZN6I7ts5NCYQ6wbVL/OeAD+64UESuAFQDz5s1rT2Vd4Nhjj627BGlabpvtFZlZdw0ARMTHgKWZ+W+L/u8CH8jMz+7vOQMDA9loNNpVoiQdFSJic2YOTLesk05eGwZOndSfW4xJktqkk0LhYWBhRJweEccAlwHraq5JkrpKx3ynkJljEfFZ4Ee0Dkn9dmY+WXNZktRVOiYUADLzHsDTiyWpJp20+0iSVDNDQZJUMhQkSSVDQZJU6piT196KiNgJbK27jqPIycAv6y5Cmobb5uF1WmbOmm7BER0KOrwiorG/sxylOrltto+7jyRJJUNBklQyFDTZmroLkPbDbbNN/E5BklRypiBJKhkKkqSSoSAiYmlE/L+IaEbEtXXXI02IiG9HxAsR8bO6a+kWhkKXi4he4I+BjwBnAJ+MiDPqrUoq/RmwtO4iuomhoHOAZmY+k5m/Br4HXFJzTRIAmfkT4Fd119FNDAXNAbZN6j9XjEnqQoaCJKlkKGgYOHVSf24xJqkLGQp6GFgYEadHxDHAZcC6mmuSVBNDoctl5hjwWeBHwFPA2sx8st6qpJaI+C6wCXhvRDwXEVfWXdPRzstcSJJKzhQkSSVDQZJUMhQkSSVDQZJUMhQkSSVDQQIi4j9FxJMR8XhEPBoRHzgMr7nscF11NiJGD8frSAfjIanqehHx28BNwD/LzD0RcTJwTGY+fwjP7SvO9ai6xtHM7K/6fSRnChLMBn6ZmXsAMvOXmfl8RGwpAoKIGIiIB4r2YER8JyL+EvhORDwYEf9w4sUi4oFi/d+LiP8SEe+OiK0R0VMsnxER2yLiHRHxDyLi3ojYHBH/OyLeV6xzekRsiognImJVm38f6mKGggT3AadGxF9HxJ9ExIcO4TlnAEsy85PAHcAnACJiNjA7MxsTK2bmy8CjwMTrXgT8KDNfp3VD+qszczHwOeBPinVWA9/MzDOB7W/3HygdKkNBXS8zR4HFwApgJ3BHRPzeQZ62LjNfK9prgY8V7U8A359m/TuAf120Lyveox/4p8CdEfEocAutWQvAB4HvFu3vvJl/j/R29NVdgNQJMnMv8ADwQEQ8ASwHxvjNB6ff2ucpuyY9dzgiRiLiLFp/+D8zzVusA74cESfRCqD7gRnAS5m5aH9lvbV/jfTWOVNQ14uI90bEwklDi4CtwBZaf8AB/tVBXuYO4A+Ad2fm4/suLGYjD9PaLXR3Zu7NzFeAv4mIjxd1REScXTzlL2nNKAAuf9P/KOktMhQk6AeGIuLnEfE4re8LBoGVwOqIaAB7D/Ia36f1R3ztAda5A/g3xeOEy4ErI+Ix4El+cyvUa4CrilmLd8JT23hIqiSp5ExBklQyFCRJJUNBklQyFCRJJUNBklQyFCRJJUNBklT6/wYRwcAH2gnEAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Use a heatmap to check for correlations with the new variable\n",
        "fig, ax = plt.subplots(figsize=(15,10))\n",
        "sns.heatmap(df.corr(), cmap='Blues', annot=True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 596
        },
        "id": "ws8blAgUHAkn",
        "outputId": "87fa186d-8f2b-4578-f52c-68d60b121812"
      },
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x720 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Distribution for the Age feature\n",
        "sns.distplot(df['Age'], hist=True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        },
        "id": "Vr69LSOgHIOH",
        "outputId": "0f33d267-07e4-45d1-9cdd-b7a40730a7f8"
      },
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Skewness for the Fare feature\n",
        "sns.distplot(df['Fare'], hist=True);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        },
        "id": "7XT0-f5kHKNu",
        "outputId": "6b9697ae-59ab-49db-e811-cc309f8d15b0"
      },
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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pnpNRdPUOUpdB4ABoqC5jyJ2jPZqSKyLFY6wWxzvNrCtNugHp1wSXUbk7nb2DnH9WbUb5k896dBzvZ7peIysiReKULQ53j7l7bZqtxt3H/G+zmS0zs21m1mpmq9KcLzezR8Lz681sfpheb2Y/N7PjZva1Edf8IrznC+E2YZY+6epNMDjk1I4xMJ7UEE7ZPXS8f4ycIiL5k807x7NiZjHgHuAaYA+wwcya3X1rSrbbgCPuvtDMVgB3AzcBfcDngQvDbaRb3L0lqrJHZV9X8J7xTMY4AKrL45TFS+joVq+giBSPTJ8cPx1LgVZ33xGuc/UwsHxEnuXAg+H+o8DVZmbu3u3u/04QQCaNfZ1BdTId4zAzGqrK1OIQkaISZeBoAnanHO8J09LmcfcE0AnUZ3Dvb4fdVJ+30ZaYLUL7swwcEIxzdBxXi0NEikeUgSMqt7j7RcCV4faJdJnMbKWZtZhZS3t7caySsr+zDwNqKjIPHA3VZRztGSAxPBxdwUREshBl4GgD5qYczwnT0uYJl2qvAw6d6qbu3hZ+HgO+S9Alli7ffe6+xN2XNDY2nlYFcm1/Zx/VFXFiJZk3kuqryxl2ONKtKbkiUhyiDBwbgEVmtsDMyoAVQPOIPM3AreH+DcA6dx/1PR9mFjezhnC/FPgIsDnnJY/Ivq6+rLqpABqqNLNKRIpLZLOq3D1hZncAa4EY8IC7bzGzu4AWd28G7gdWm1krcJgguABgZjuBWqDMzK4HrgV2AWvDoBEDngC+GVUdcm1/Zy+1WXRTQcqzHJpZJSJFIrLAAeDuawje3ZGadmfKfh9w4yjXzh/ltpfmqnz5tq+zjwtn12V1zZSyGJWlMbU4RKRoTMTB8QnpeH+CY32JrLuqzIz66jI6FDhEpEgocORJcipubWX2jbyG6nIOaUquiBQJBY48eTNwZL+ocH1VGZ29gwwOaUquiBSeAkee7OsMlhupy3JwHIIWh/Pm2wNFRApJgSNPxtXiCBc71DiHiBQDBY482dfVx/SqMkpj2f+RN4RTcjXOISLFQIEjT/Z39nFW7em9wqSiNEZVWUwtDhEpCgocebK/s49Zdaf/7qv66nIOaYxDRIqAAkee7O/q46xxBI6G6nK1OESkKChw5EHf4BCHuwfG1eJoqC7jWF+C7v5EDksmIpI9BY48ONAVzKg6q67ytO+RXLNq56HunJRJROR0KXDkQfLNf+Ma4whXyd3Z0ZOTMomInC4FjjxIPsMx8zRnVcGbz3K83nE8J2USETldChx5kGxxjGdwvDweo7YizutqcYhIgSlw5MH+zl5qKuJUl49vFfv66nKNcYhIwSlw5MG+cT7DkVRfVcbODgUOESksBY48CJ7hOP0ZVUkN4UOAnb16/7iIFI4CRx7s6+xj1jgGxpMaqpMzq9TqEJHCUeCI2ODQMB3H+8c1MJ6UfJbjdQUOESkgBY6IHTzWj/v4nuFIqq8qI1ZitB7UlFwRKZxIA4eZLTOzbWbWamar0pwvN7NHwvPrzWx+mF5vZj83s+Nm9rUR11xqZpvCa75qZhZlHcZrf/gCp1y0OOKxEubVT2H7wWPjvpeIyOmKLHCYWQy4B/gQsBi42cwWj8h2G3DE3RcCXwbuDtP7gM8Df57m1t8APgUsCrdluS997rz51Pj4B8cBFs2oZrtaHCJSQFG2OJYCre6+w90HgIeB5SPyLAceDPcfBa42M3P3bnf/d4IAcoKZzQJq3f0Zd3fgIeD6COswbsmnxk/3XRwjvW1mDbsO9dCfGMrJ/UREshVl4GgCdqcc7wnT0uZx9wTQCdSPcc89Y9wTADNbaWYtZtbS3t6eZdFzZ19nH5WlMWorx/fwX9LCGdUMDbsGyEWkYCbt4Li73+fuS9x9SWNjY8HKkXyBU66GYhbNqAFg+wF1V4lIYUQZONqAuSnHc8K0tHnMLA7UAYfGuOecMe5ZVPZ19uZkYDzpnMYqSgyNc4hIwUQZODYAi8xsgZmVASuA5hF5moFbw/0bgHXh2EVa7r4P6DKzy8PZVJ8EfpT7oufO/s7xvflvpIrSGPPqq9h+QDOrRKQwctPxnoa7J8zsDmAtEAMecPctZnYX0OLuzcD9wGozawUOEwQXAMxsJ1ALlJnZ9cC17r4V+DTwT0Al8NNwK0pDw86BY/05GxhPWjijmlcVOESkQCILHADuvgZYMyLtzpT9PuDGUa6dP0p6C3Bh7koZnfZj/QwNO7On5mYqbtL5Z9Ww7pWD9A0OUVEay+m9RUTGMmkHx4tB29Hg3RlNOQ4ci2fVMjTsbNuvVoeI5J8CR4TajgbPcDRNy23guGB2HQBb93Xl9L4iIplQ4IjQ3qPBciO5WKcq1ZxpldSUx9m6V4FDRPJPgSNCe4/2UlsRp6aiNKf3LSkx3j67li17O3N6XxGRTChwRGjv0d6cD4wnLZ5Vyyv7jzE0POrsZRGRSChwRKjtaF/OB8aTFs+upWdgSO8gF5G8U+CIUJQtjgtm1wKwReMcIpJnChwROd6foLN3MLLAsWhGDeXxEl7afTSS+4uIjEaBIyL7whlVs6fmdkZVUlm8hHfMqWPjG0ciub+IyGgUOCLSFgaOqMY4AC45expb2rroG9S7OUQkfxQ4IrI3fPgvqq4qgEvmTWNgaFjTckUkrxQ4ItJ2tIdYiTEzxwscprrk7GkAbNyl7ioRyR8Fjoi8cbiXpqmVxEpy8wKndBpryjl7+hSe23U0su8QERlJgSMiuw51M69+SuTfc+m8abTsOsIpXmMiIpJTChwR2XWoJy+B4/JzptNxvJ9X9SpZEckTBY4IHO0ZoLN3kHnTqyL/rve9LXif+i9fPRj5d4mIgAJHJHYdCt7DkY8Wx6y6ShbNqOZXr3ZE/l0iIqDAEYnk+lHz6qNvcUDQ6nh252F6B/Q8h4hET4EjAm+ELY6zp0ff4oAgcAwkhnnm9UN5+T4RObNF+s5xM1sGfAWIAd9y9y+OOF8OPARcChwCbnL3neG5zwK3AUPAH7v72jB9J3AsTE+4+5Io63A6dh7qYWZtOZVluX8f+HfXv/GWtN+9pInK0hiPbz3AVefNyPl3ioikiqzFYWYx4B7gQ8Bi4GYzWzwi223AEXdfCHwZuDu8djGwArgAWAZ8Pbxf0lXufnExBg2ANw53562bCqCiNMYHF89kzaZ9DCSG8/a9InJmirKrainQ6u473H0AeBhYPiLPcuDBcP9R4GozszD9YXfvd/fXgdbwfhPCzkM9zMtTN1XS9RfP5mjPIE9tb8/r94rImSfKwNEE7E453hOmpc3j7gmgE6gf41oHfmZmG81s5WhfbmYrzazFzFra2/P3y7S7P0H7sf68zKhKdeWiRqZOKeVHL+zN6/eKyJlnIg6OX+HulxB0gd1uZu9Ll8nd73P3Je6+pLGxMW+F234weBBv4YyavH0nBMusf/iiWTy+9QCdPYN5/W4RObNEGTjagLkpx3PCtLR5zCwO1BEMko96rbsnPw8Cj1FkXVjb9gdv5DvvrPwGDoCPXXY2vYND/PP6XXn/bhE5c0QZODYAi8xsgZmVEQx2N4/I0wzcGu7fAKzzYNGlZmCFmZWb2QJgEfCsmVWZWQ2AmVUB1wKbI6xD1l7Zf4yK0pK8TcVNdcHsOq5c1MC3n96pd3SISGQim47r7gkzuwNYSzAd9wF332JmdwEt7t4M3A+sNrNW4DBBcCHM9z1gK5AAbnf3ITObCTwWjJ8TB77r7v8WVR1Ox6sHjvG2mTWRrop7Kv/1N8/lY99az2cefYnLz6k/6dzHLju7IGUSkckl0uc43H0NsGZE2p0p+33AjaNc+7fA345I2wG8M/clzZ1t+48V9FmK95xbz7vnT+OJlw/wjqY6ppRH+lcsImegiTg4XrQ6jvfTcXygIOMbSWbG31x/IX2DQ/zblv0FK4eITF4KHDm0bf8xoDAD46nOP6uWKxY20LLrCC/tOVrQsojI5KPAkUPFEjgAPvj2mcybPoVHN+7hjXDRRRGRXFDgyKGt+7qYXlVGY3V5oYtCPFbCLZfPo7aylAee3skr+7oKXSQRmSQ0cppDG3cd4ZKzpxHO+iq46vI4K993Dg/9eiern9kFBn/+W+dRW1F6Ur50CydqBpaIjEYtjhxpP9bP6x3dvHv+tEIX5SS1FaWsvPJcLj+nntXP7OKKL67jf6995cTS7yIi2VKLI0c27joMwJL50wtckrcqi5fwO++czec+/Ha+tq6Vr//iNe75+WssmTeNj17SRN/AcCRLwIvI5KTAkSPPvn6E8ngJFzXVFbooo7qwqY5//MSltB3t5YfPt/HY82187rHNxEqM88+q4V1zp3HeWYV7eFFEJgYFjhxp2XWYi+dOpSxe/L1/TVMruf2qhXz6/eeyua2Lv/vpy7y4p5Mte7toqC7j6rfP5Oalc4tmrEZEikvx/5abALr7E2zZ28W7i7Cb6lTMjIvm1PGRd8xm1bLzuXnp2cRLSnhkw25uuu8ZXu/QNF4ReSsFjhz4xbZ2hoad31hYP3bmIhUrMS5qquOODyzk+oub2Lb/GB/+6lN8b8NugnUnRUQC6qrKgTWb9tFQXcZlC4o7cKSbdjtSiRlLF0znvLNq+H7Lbj7zLy/x0DO7+OjFTdx25YI8lFJEip0Cxzj1DCR48pUD3Hjp3IINKmcSELJVV1nKH1yxgKe2d/D41v3sPtzDRXPqWLpgYnXHiUjuqatqnNa9cpC+wWE+8o5ZhS5KzpWY8Ztva+SPfvNc4iXGivt+zZcef5XE0HChiyYiBaTAMU7fa9nDjJryonx+I1fmTJvCHVct5KPvmsNXn9zOdV97mufeOFLoYolIgShwjMPGXUf41avt/MEVCyb9sw/lpTH+7396J9+45RIOdffzu1//Dz71UAsbdx3W4LnIGUZjHOPwD0+8Sn1VGZ98z7xCFyVvPnTRLK58WyP3P/U63/r3HTy+9QDnNlbxwcUzuXxBPW+fVcvM2vJxPwOi9bNEipcCx2lqfnEvT23v4HO//XamlJ1Zf4zV5XH+5IOLuO3KBfzrS3u591c7+OavdnDvL3cAMKUsRmN1OXVTSqmrfHNbfnETU1PSKkqDZU6iGNwXkeicWb/xcmTTnk4+8+iLLJk3jVt/Y36hi1Mw1eVxbnr32QwNQ//gEPs6+9jX2cu+zj4OdQ+w50gvW/Z2MTQcdGV9Z0SAKIuVUFEa9JaWxkrCzSiNl1B24jhIq6kopao8xtzpUzinoYqpU8ryXt8oqGUlE1GkgcPMlgFfAWLAt9z9iyPOlwMPAZcCh4Cb3H1neO6zwG3AEPDH7r42k3tGyd35wXNt3PmjzUyfUsY/fuLSCbHESD6Ul8aY31DF/Iaqk9KH3enuT9DVl+A959RztHeAoz2DdPYO0tU3SP/gMJvbOhkYGmZwyBkcGmZwaJiuwUEGE8Fxf2KY3sEhnnj5wIn7NlSXsXBGNQtnVLNoRs2J/Rk14+8mg+AX+kBimGN9gxzrS3CsP8HiWTX0J4ZJDDtDw05ZrISX2jopLbE3g14Y7K67eHbYonISw05iKLgmMewMJIaD+iaG2dTWydDwMMMeBNLy0hJe2H2UmbXlzKipmPRjZzIxRRY4zCwG3ANcA+wBNphZs7tvTcl2G3DE3Rea2QrgbuAmM1sMrAAuAGYDT5jZ28JrxrpnzhzvT7C/s4/dh3t4cc9Rml/cy472bpbOn86XV1xMQxG8sKnYlVjQWqipKOWKRQ1p82TSVTWQGOa9C+vZdaiHHR3HaT0YbD96YS/H+hIn8tVUxMNgUs25jdVMryqjtrKUmoo48ZIgyLs73QOJIHj1Bp9HegZoP9bPwWP9tB/rZ+/RXvoTpz/t+L6ndpz2td9+eicQPM0/o6acWXUVzJpayazaChpqyqmvKqOhupz66rITXX4V8RjlpSWUx0sm5RpjIydgpB76KPlGTtk4+RpPm/7W7x37mtG+HyAxFPys9QwM0d2foHdgiO6BIXoGEvz8lXYGh4bD/1AMkxhyzptVc6K1XRa2vMtLS6gqi1NZFmNKuFWWxt/cL4tRURojZoYZefn7j7LFsRRodfcdAGb2MLAcSP0lvxz4q3D/UeBrFtR6OfCwu/cDr5tZa3g/Mrhnznzkq0+xM3xvhRlccvY0/s+NC/nou5rOyP8JFnIsoixewqKZNSyaWQPMPJH+nWd2caw/wcGuftqP9XEw/OW/7pV2vteyJ+P7V5XFmFFbQWN1OYtn13JWXQW15XFqKkqprohTUxGnqjxOaUkJsRKjxAhaEsNvtpIGh5zEUNCaeM859fQODlFiRrzEKCkxnt7eQUmJESsJ0uLhveIlwT/4gaFh+geHWbpgOvu7+tjf2cfezl72He1j694unnz5AH2DYwezkpRfHkbws2tYcDAibeTvmGx/UY5MyPSa0X7Bn8kT9Az4jx2HGBwaHtefQ4kF/+kwM2JmPH/nNSfGE3MlysDRBOxOOd4DXDZaHndPmFknUB+mPzPi2qZwf6x7AmBmK4GV4eFxM9t2GnU4yU7gB+O9CTQAHeO/TVHIqi63jPPLxnv9WLZOrr8bmFz1mUx1gTzWp/IL47o87ZTRSTs47u73AfcVuhwjmVmLuy8pdDlyYTLVBVSfYjaZ6gITvz5Rjuy2AXNTjueEaWnzmFkcqCMYJB/t2kzuKSIiEYoycGwAFpnZAjMrIxjsbh6Rpxm4Ndy/AVjnQednM7DCzMrNbAGwCHg2w3uKiEiEIuuqCscs7gDWEkydfcDdt5jZXUCLuzcD9wOrw8HvwwSBgDDf9wgGvRPA7e4+BJDunlHVISJF1302DpOpLqD6FLPJVBeY4PUxrTMkIiLZ0NNrIiKSFQUOERHJigJHnpjZMjPbZmatZraq0OXJhJk9YGYHzWxzStp0M3vczLaHn9PCdDOzr4b1e8nMLilcyd/KzOaa2c/NbKuZbTGzPwnTJ2p9KszsWTN7MazPX4fpC8xsfVjuR8JJJIQTTR4J09eb2fyCViANM4uZ2fNm9pPweCLXZaeZbTKzF8ysJUybkD9r6Shw5EHK8isfAhYDN4fLqhS7fwKWjUhbBTzp7ouAJ8NjCOq2KNxWAt/IUxkzlQD+u7svBi4Hbg//DiZqffqBD7j7O4GLgWVmdjnBsj1fdveFwBGCZX0gZXkf4MthvmLzJ8DLKccTuS4AV7n7xSnPa0zUn7W3cndtEW/Ae4C1KcefBT5b6HJlWPb5wOaU423ArHB/FrAt3L8XuDldvmLcgB8RrHk24esDTAGeI1hFoQOIh+knfu4IZiK+J9yPh/ms0GVPqcMcgl+mHwB+QrACx4SsS1iunUDDiLQJ/7OW3NTiyI90y680jZK32M10933h/n7eXDhqwtQx7Np4F7CeCVyfsGvnBeAg8DjwGnDU3ZOrPqaW+aTlfYDk8j7F4h+AzwDJxbjqmbh1gWAJrp+Z2cZw+SOYwD9rI03aJUckeu7uZjah5nObWTXwL8B/c/eu1JVEJ1p9PHi26WIzmwo8Bpxf2BKdHjP7CHDQ3Tea2fsLXJxcucLd28xsBvC4mb2SenKi/ayNpBZHfkympVIOmNksgPDzYJhe9HU0s1KCoPEdd0+uVzlh65Pk7keBnxN050wNl++Bk8s82vI+xeC9wHVmthN4mKC76itMzLoA4O5t4edBgqC+lEnws5akwJEfk2mplNRlYm4lGCtIpn8ynCFyOdCZ0iwvOAuaFvcDL7v7l1JOTdT6NIYtDcyskmC85mWCAHJDmG1kfdIt71Nw7v5Zd5/j7vMJ/m2sc/dbmIB1ATCzKjOrSe4D1wKbmaA/a2kVepDlTNmA3wZeJeiH/lyhy5Nhmf8fsA8YJOh3vY2gL/lJYDvwBDA9zGsEM8deAzYBSwpd/hF1uYKg3/kl4IVw++0JXJ93AM+H9dkM3Bmmn0Owrlsr8H2gPEyvCI9bw/PnFLoOo9Tr/cBPJnJdwnK/GG5bkv/eJ+rPWrpNS46IiEhW1FUlIiJZUeAQEZGsKHCIiEhWFDhERCQrChwiIpIVPTkuEhEzGyKYXpl0vbvvLFBxRHJG03FFImJmx929OstrjODf5fCYmUUKRF1VInliZtVm9qSZPRe+q2F5mD7fgne1PETwMN9cM/sLM9sQvp/hrwtbcpGTqatKJDqV4eq1AK8DNwIf9WBxxQbgGTNLLj2zCLjV3Z8xs2vD46UETxU3m9n73P1XeS6/SFoKHCLR6XX3i5MH4SKL/8vM3kewfHgTby6tvcvdnwn3rw2358PjaoJAosAhRUGBQyR/bgEagUvdfTBcDbYiPNedks+Av3P3e/NcPpGMaIxDJH/qCN47MWhmVwHzRsm3FviD8N0hmFlT+F4HkaKgFodI/nwH+LGZbQJagFfSZXL3n5nZ24Ffhy+aOg58nDff3yBSUJqOKyIiWVFXlYiIZEWBQ0REsqLAISIiWVHgEBGRrChwiIhIVhQ4REQkKwocIiKSlf8PWQUO0GcjPXcAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## EDA 3 - From Scratch (With Students)"
      ],
      "metadata": {
        "id": "bpQDCxpNHQjL"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "WMOrqj9xHLZm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Data Link from [Kaggle](https://www.kaggle.com/competitions/titanic/data)"
      ],
      "metadata": {
        "id": "gEOX9v5cIdKJ"
      }
    }
  ]
}