4.Titanic/Titanic.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4JbuuLISIHvh"
      },
      "source": [
        "[Download Dataset](https://www.kaggle.com/competitions/titanic/data?select=train.csv)\n",
        "\n",
        "    "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "UbmOfCnAIHvk"
      },
      "outputs": [],
      "source": [
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "_poC9UBnIHvl"
      },
      "outputs": [],
      "source": [
        "data = pd.read_csv('train.csv')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I8baCb1QIHvm"
      },
      "source": [
        "### 1. Display Top 5 Rows of The Dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
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        "id": "Bl8iZQRdIHvm",
        "outputId": "0946ede3-99d1-448d-8992-6a47ddcc61d8"
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      "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  "
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              "      <td>26.0</td>\n",
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              "      <th>3</th>\n",
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              "      <td>1</td>\n",
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              "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
              "      <td>female</td>\n",
              "      <td>35.0</td>\n",
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              "      <td>53.1000</td>\n",
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              "      <td>Allen, Mr. William Henry</td>\n",
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              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "data.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KdiawhuBIHvn"
      },
      "source": [
        "### 2. Check Last 3 Rows of The Dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     PassengerId  Survived  Pclass                                      Name  \\\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",
              "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>PassengerId</th>\n",
              "      <th>Survived</th>\n",
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              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
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              "    <tr>\n",
              "      <th>888</th>\n",
              "      <td>889</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
              "      <td>female</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>W./C. 6607</td>\n",
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              "      <td>890</td>\n",
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              "      <td>Behr, Mr. Karl Howell</td>\n",
              "      <td>male</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
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              "      <td>111369</td>\n",
              "      <td>30.00</td>\n",
              "      <td>C148</td>\n",
              "      <td>C</td>\n",
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              "    <tr>\n",
              "      <th>890</th>\n",
              "      <td>891</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Dooley, Mr. Patrick</td>\n",
              "      <td>male</td>\n",
              "      <td>32.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>370376</td>\n",
              "      <td>7.75</td>\n",
              "      <td>NaN</td>\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",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
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          "metadata": {},
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      ],
      "source": [
        "data.tail(3)"
      ]
    },
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        "id": "C3_lLbVFIHvo"
      },
      "source": [
        "### 3. Find Shape of Our Dataset (Number of Rows & Number of Columns)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wgiwZ-pEIHvp",
        "outputId": "8e1f5fae-40b9-4a84-dee4-504c9b98305a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(891, 12)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "data.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NnIrEOjuIHvq",
        "outputId": "12b9a2f5-0ee2-4548-9375-b0e90abd1861"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of Rows 891\n",
            "Number of Columns 12\n"
          ]
        }
      ],
      "source": [
        "print(\"Number of Rows\",data.shape[0])\n",
        "print(\"Number of Columns\",data.shape[1])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aJcG_BNqIHvr"
      },
      "source": [
        "### 4. Get Information About Our Dataset Like Total Number Rows, Total Number of Columns, Datatypes of Each Column And Memory Requirement"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hWiRZNNeIHvr",
        "outputId": "23cbe816-938d-4bd6-84c5-cffc33097b06"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 891 entries, 0 to 890\n",
            "Data columns (total 12 columns):\n",
            " #   Column       Non-Null Count  Dtype  \n",
            "---  ------       --------------  -----  \n",
            " 0   PassengerId  891 non-null    int64  \n",
            " 1   Survived     891 non-null    int64  \n",
            " 2   Pclass       891 non-null    int64  \n",
            " 3   Name         891 non-null    object \n",
            " 4   Sex          891 non-null    object \n",
            " 5   Age          714 non-null    float64\n",
            " 6   SibSp        891 non-null    int64  \n",
            " 7   Parch        891 non-null    int64  \n",
            " 8   Ticket       891 non-null    object \n",
            " 9   Fare         891 non-null    float64\n",
            " 10  Cabin        204 non-null    object \n",
            " 11  Embarked     889 non-null    object \n",
            "dtypes: float64(2), int64(5), object(5)\n",
            "memory usage: 83.7+ KB\n"
          ]
        }
      ],
      "source": [
        "data.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vqzcTcTzIHvr"
      },
      "source": [
        "### 5. Get Overall Statistics About The Dataframe"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "tfCGbPnQIHvr",
        "outputId": "573e07ca-f261-4564-b4a8-eb7642611a55"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        PassengerId    Survived      Pclass                     Name   Sex  \\\n",
              "count    891.000000  891.000000  891.000000                      891   891   \n",
              "unique          NaN         NaN         NaN                      891     2   \n",
              "top             NaN         NaN         NaN  Braund, Mr. Owen Harris  male   \n",
              "freq            NaN         NaN         NaN                        1   577   \n",
              "mean     446.000000    0.383838    2.308642                      NaN   NaN   \n",
              "std      257.353842    0.486592    0.836071                      NaN   NaN   \n",
              "min        1.000000    0.000000    1.000000                      NaN   NaN   \n",
              "25%      223.500000    0.000000    2.000000                      NaN   NaN   \n",
              "50%      446.000000    0.000000    3.000000                      NaN   NaN   \n",
              "75%      668.500000    1.000000    3.000000                      NaN   NaN   \n",
              "max      891.000000    1.000000    3.000000                      NaN   NaN   \n",
              "\n",
              "               Age       SibSp       Parch  Ticket        Fare    Cabin  \\\n",
              "count   714.000000  891.000000  891.000000     891  891.000000      204   \n",
              "unique         NaN         NaN         NaN     681         NaN      147   \n",
              "top            NaN         NaN         NaN  347082         NaN  B96 B98   \n",
              "freq           NaN         NaN         NaN       7         NaN        4   \n",
              "mean     29.699118    0.523008    0.381594     NaN   32.204208      NaN   \n",
              "std      14.526497    1.102743    0.806057     NaN   49.693429      NaN   \n",
              "min       0.420000    0.000000    0.000000     NaN    0.000000      NaN   \n",
              "25%      20.125000    0.000000    0.000000     NaN    7.910400      NaN   \n",
              "50%      28.000000    0.000000    0.000000     NaN   14.454200      NaN   \n",
              "75%      38.000000    1.000000    0.000000     NaN   31.000000      NaN   \n",
              "max      80.000000    8.000000    6.000000     NaN  512.329200      NaN   \n",
              "\n",
              "       Embarked  \n",
              "count       889  \n",
              "unique        3  \n",
              "top           S  \n",
              "freq        644  \n",
              "mean        NaN  \n",
              "std         NaN  \n",
              "min         NaN  \n",
              "25%         NaN  \n",
              "50%         NaN  \n",
              "75%         NaN  \n",
              "max         NaN  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-dfb0f0c8-ef07-4342-b5f8-a55f8ba22291\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>891.000000</td>\n",
              "      <td>891.000000</td>\n",
              "      <td>891.000000</td>\n",
              "      <td>891</td>\n",
              "      <td>891</td>\n",
              "      <td>714.000000</td>\n",
              "      <td>891.000000</td>\n",
              "      <td>891.000000</td>\n",
              "      <td>891</td>\n",
              "      <td>891.000000</td>\n",
              "      <td>204</td>\n",
              "      <td>889</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>unique</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>891</td>\n",
              "      <td>2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>681</td>\n",
              "      <td>NaN</td>\n",
              "      <td>147</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>top</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Braund, Mr. Owen Harris</td>\n",
              "      <td>male</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>347082</td>\n",
              "      <td>NaN</td>\n",
              "      <td>B96 B98</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>freq</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>577</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>7</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4</td>\n",
              "      <td>644</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>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>29.699118</td>\n",
              "      <td>0.523008</td>\n",
              "      <td>0.381594</td>\n",
              "      <td>NaN</td>\n",
              "      <td>32.204208</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</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>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>14.526497</td>\n",
              "      <td>1.102743</td>\n",
              "      <td>0.806057</td>\n",
              "      <td>NaN</td>\n",
              "      <td>49.693429</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</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>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.420000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</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>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>20.125000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>7.910400</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</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>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>14.454200</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</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>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>38.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>31.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</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>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>80.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>512.329200</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-dfb0f0c8-ef07-4342-b5f8-a55f8ba22291')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-dfb0f0c8-ef07-4342-b5f8-a55f8ba22291 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-dfb0f0c8-ef07-4342-b5f8-a55f8ba22291');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-f1d256f7-ebf2-442c-8146-b1f55581cc4a\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f1d256f7-ebf2-442c-8146-b1f55581cc4a')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    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-quickchart: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-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "    async function quickchart(key) {\n",
              "      const charts = await google.colab.kernel.invokeFunction(\n",
              "          'suggestCharts', [key], {});\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-f1d256f7-ebf2-442c-8146-b1f55581cc4a button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "data.describe(include='all')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TEJ6aefwIHvs"
      },
      "source": [
        "### 6. Data Filtering"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aKd_DpBUIHvs",
        "outputId": "150cab33-c59a-4658-a0e6-a8ac96d16fe5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n",
              "       'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "data.columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "T0Y8kfTfIHvs",
        "outputId": "98360247-540c-4432-c2de-598cae3d1546"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                Name   Age\n",
              "0                            Braund, Mr. Owen Harris  22.0\n",
              "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  38.0\n",
              "2                             Heikkinen, Miss. Laina  26.0\n",
              "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  35.0\n",
              "4                           Allen, Mr. William Henry  35.0"
            ],
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            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "data[['Name','Age']].head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8mKQthaIIHvs",
        "outputId": "753215b1-46fa-41d1-e5af-5ba883e859da"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       True\n",
              "1      False\n",
              "2      False\n",
              "3      False\n",
              "4       True\n",
              "       ...  \n",
              "886     True\n",
              "887    False\n",
              "888    False\n",
              "889     True\n",
              "890     True\n",
              "Name: Sex, Length: 891, dtype: bool"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "data['Sex']=='male'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tlSJBrnJIHvt",
        "outputId": "a3318a7e-aeda-445e-f5fe-29d98e763cf7"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "577"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "sum(data['Sex']=='male')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "id": "BgAZR6fyIHvt",
        "outputId": "a54fed96-5aaf-4e7d-c318-ddc0f309bd35"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   PassengerId  Survived  Pclass                            Name   Sex   Age  \\\n",
              "0            1         0       3         Braund, Mr. Owen Harris  male  22.0   \n",
              "4            5         0       3        Allen, Mr. William Henry  male  35.0   \n",
              "5            6         0       3                Moran, Mr. James  male   NaN   \n",
              "6            7         0       1         McCarthy, Mr. Timothy J  male  54.0   \n",
              "7            8         0       3  Palsson, Master. Gosta Leonard  male   2.0   \n",
              "\n",
              "   SibSp  Parch     Ticket     Fare Cabin Embarked  \n",
              "0      1      0  A/5 21171   7.2500   NaN        S  \n",
              "4      0      0     373450   8.0500   NaN        S  \n",
              "5      0      0     330877   8.4583   NaN        Q  \n",
              "6      0      0      17463  51.8625   E46        S  \n",
              "7      3      1     349909  21.0750   NaN        S  "
            ],
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-20e8381f-9b10-47d5-aee8-9749464c143e');\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",
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              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "  [theme=dark] .colab-df-quickchart:hover {\n",
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              "      const charts = await google.colab.kernel.invokeFunction(\n",
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              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-0c7ba73a-ca48-41ee-a1df-55f5e68f87e7 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "data[data['Sex']=='male'].head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XBHTNJSbIHvt",
        "outputId": "82164a4c-d695-4b4a-ebd7-99f7e5c3dbe0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n",
              "       'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "data.columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NMku-de_IHvu",
        "outputId": "ae7d6369-8dd0-4098-b1e1-d2f9cb3bb395"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "342"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "sum(data['Survived']==1)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data[data['Survived'] == 1].head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 590
        },
        "id": "4jySksEYNAiU",
        "outputId": "42ad9ecd-c9ed-48aa-f6b1-ed978324ec31"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   PassengerId  Survived  Pclass  \\\n",
              "1            2         1       1   \n",
              "2            3         1       3   \n",
              "3            4         1       1   \n",
              "8            9         1       3   \n",
              "9           10         1       2   \n",
              "\n",
              "                                                Name     Sex   Age  SibSp  \\\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",
              "8  Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)  female  27.0      0   \n",
              "9                Nasser, Mrs. Nicholas (Adele Achem)  female  14.0      1   \n",
              "\n",
              "   Parch            Ticket     Fare Cabin Embarked  \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",
              "8      2            347742  11.1333   NaN        S  \n",
              "9      0            237736  30.0708   NaN        C  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-1997f552-c221-495a-90ff-5bea3198d0bf\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
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              "      <th>Ticket</th>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
              "      <td>female</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>PC 17599</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>C85</td>\n",
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              "      <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",
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              "      <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>8</th>\n",
              "      <td>9</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)</td>\n",
              "      <td>female</td>\n",
              "      <td>27.0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>347742</td>\n",
              "      <td>11.1333</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>10</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>Nasser, Mrs. Nicholas (Adele Achem)</td>\n",
              "      <td>female</td>\n",
              "      <td>14.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>237736</td>\n",
              "      <td>30.0708</td>\n",
              "      <td>NaN</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1997f552-c221-495a-90ff-5bea3198d0bf')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "    .colab-df-buttons div {\n",
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              "\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-1997f552-c221-495a-90ff-5bea3198d0bf 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-1997f552-c221-495a-90ff-5bea3198d0bf');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-b2c24c9c-5f75-44f1-9b0a-940e97fdd5b3\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-b2c24c9c-5f75-44f1-9b0a-940e97fdd5b3')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    <g>\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
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              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart: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-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "    async function quickchart(key) {\n",
              "      const charts = await google.colab.kernel.invokeFunction(\n",
              "          'suggestCharts', [key], {});\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-b2c24c9c-5f75-44f1-9b0a-940e97fdd5b3 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mJ0Vf1gEIHvu"
      },
      "source": [
        "### 7.Check Null Values In The Dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1Gv9fqEgIHvu",
        "outputId": "0d9016e5-00d1-4751-d195-020e3337abd2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PassengerId      0\n",
              "Survived         0\n",
              "Pclass           0\n",
              "Name             0\n",
              "Sex              0\n",
              "Age            177\n",
              "SibSp            0\n",
              "Parch            0\n",
              "Ticket           0\n",
              "Fare             0\n",
              "Cabin          687\n",
              "Embarked         2\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "data.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 524
        },
        "id": "NwxMkOuaIHvu",
        "outputId": "dda1f1e2-38f8-43e1-8ba0-9fc77d4bdbc2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "sns.heatmap(data.isnull())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SVvqQNN8IHvv",
        "outputId": "91f08d32-e5cb-474e-979e-021fa40069f1"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PassengerId     0.000000\n",
              "Survived        0.000000\n",
              "Pclass          0.000000\n",
              "Name            0.000000\n",
              "Sex             0.000000\n",
              "Age            19.865320\n",
              "SibSp           0.000000\n",
              "Parch           0.000000\n",
              "Ticket          0.000000\n",
              "Fare            0.000000\n",
              "Cabin          77.104377\n",
              "Embarked        0.224467\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "percent_missing = data.isnull().sum() / len(data) * 100\n",
        "percent_missing"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "umrTmZTaIHvv"
      },
      "source": [
        "### 8. Drop the Column"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KaPq8MDGIHvv",
        "outputId": "12b94d5e-5df5-40b1-98e0-da5b4a6fb708"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n",
              "       'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ],
      "source": [
        "data.columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "bacfmFEvIHvv"
      },
      "outputs": [],
      "source": [
        "data.drop('Cabin',axis=1,inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1vviqLTLODqj",
        "outputId": "298a5c1e-e78f-4c63-8e5d-77af61f72e26"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PassengerId      0\n",
              "Survived         0\n",
              "Pclass           0\n",
              "Name             0\n",
              "Sex              0\n",
              "Age            177\n",
              "SibSp            0\n",
              "Parch            0\n",
              "Ticket           0\n",
              "Fare             0\n",
              "Embarked         2\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9gnSEtVJIHvv"
      },
      "source": [
        "### 9. Handle Missing Values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gCsEMPy7IHvv",
        "outputId": "72fa238f-4870-4c96-e861-83c6210ed14f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n",
              "       'Parch', 'Ticket', 'Fare', 'Embarked'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ],
      "source": [
        "data.columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uQmgXO0vIHvw",
        "outputId": "ba500a78-620e-48de-f4c3-80d1325f0625"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    S\n",
              "Name: Embarked, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ],
      "source": [
        "data['Embarked'].mode()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "pvlVBkAeIHvw"
      },
      "outputs": [],
      "source": [
        "data['Embarked'].fillna('S',inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k_Id9gVnIHvw",
        "outputId": "affa926b-07a6-4c60-a841-84eb5f14b99e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PassengerId      0\n",
              "Survived         0\n",
              "Pclass           0\n",
              "Name             0\n",
              "Sex              0\n",
              "Age            177\n",
              "SibSp            0\n",
              "Parch            0\n",
              "Ticket           0\n",
              "Fare             0\n",
              "Embarked         0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ],
      "source": [
        "data.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "id": "bhBQ1AqXIHvw"
      },
      "outputs": [],
      "source": [
        "data['Age'].fillna(data['Age'].mean(), inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zPRnouqFIHvw",
        "outputId": "fc1a089a-48b4-45fa-99e0-fef334801d05"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PassengerId    0\n",
              "Survived       0\n",
              "Pclass         0\n",
              "Name           0\n",
              "Sex            0\n",
              "Age            0\n",
              "SibSp          0\n",
              "Parch          0\n",
              "Ticket         0\n",
              "Fare           0\n",
              "Embarked       0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ],
      "source": [
        "data.isnull().sum()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "id": "bdnYZR4sIHvw"
      },
      "outputs": [],
      "source": [
        "data.drop(['PassengerId','Name','Ticket'],axis=1,inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "-yPIjqkrIHvx",
        "outputId": "5c5c6170-bbe4-4f94-8506-d28607a85390"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Survived  Pclass   Sex   Age  SibSp  Parch  Fare Embarked\n",
              "0         0       3  male  22.0      1      0  7.25        S"
            ],
            "text/html": [
              "\n",
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              "\n",
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              "      display:flex;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
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              "\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",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\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",
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              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-9b3a5b3f-60f2-4507-8770-a862f853616b 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-9b3a5b3f-60f2-4507-8770-a862f853616b');\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",
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              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ],
      "source": [
        "data.head(1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ETJlQY_mIHvx"
      },
      "source": [
        "### 10. Categorical Data Encoding"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "MvFrYIDPIHvy",
        "outputId": "dcdf4546-2fec-4038-9646-37002ba913c4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Survived  Pclass   Sex   Age  SibSp  Parch  Fare Embarked\n",
              "0         0       3  male  22.0      1      0  7.25        S"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b5093aea-5511-4088-93bb-9d690cd0e10f\" 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",
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              "      <th>Pclass</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Fare</th>\n",
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              "      <th>0</th>\n",
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              "      <td>7.25</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b5093aea-5511-4088-93bb-9d690cd0e10f')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-b5093aea-5511-4088-93bb-9d690cd0e10f 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-b5093aea-5511-4088-93bb-9d690cd0e10f');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ],
      "source": [
        "data.head(1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1_MqZYODIHvy",
        "outputId": "573e1edb-ebb6-4b15-f4cd-4e4a291b961b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['male', 'female'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ],
      "source": [
        "data['Sex'].unique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "id": "cIXdczlbIHvy"
      },
      "outputs": [],
      "source": [
        "data['Gender']=data['Sex'].map({'male':1,'female':0})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "bbAiNPnWIHvy",
        "outputId": "7f5513dc-4824-4c49-a0b4-b428aa0ab449"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Survived  Pclass   Sex   Age  SibSp  Parch  Fare Embarked  Gender\n",
              "0         0       3  male  22.0      1      0  7.25        S       1"
            ],
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              "\n",
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              "    </button>\n",
              "\n",
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              "      gap: 12px;\n",
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              "    .colab-df-convert {\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
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              "                                                    [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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        "data.head(1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {
        "id": "BA_jmyDxIHvy"
      },
      "outputs": [],
      "source": [
        "x = data['Sex'].map({'male':1,'female':0})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "metadata": {
        "id": "Q5JcgmDlIHvz"
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      "outputs": [],
      "source": [
        "data.insert(5,'Gender_New',x)"
      ]
    },
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      "execution_count": 50,
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        "id": "Wm4qZancIHvz",
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        {
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            "text/plain": [
              "   Survived  Pclass   Sex   Age  SibSp  Gender_New  Parch  Fare Embarked  \\\n",
              "0         0       3  male  22.0      1           1      0  7.25        S   \n",
              "\n",
              "   Gender  \n",
              "0       1  "
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        "data['Embarked'].unique()"
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      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['S', 'C', 'Q'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
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              "     Survived  Pclass     Sex        Age  SibSp  Gender_New  Parch     Fare  \\\n",
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              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
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              "\n",
              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-7a29577b-adce-4528-b483-4faa03dfbdd4')\"\n",
              "            title=\"Suggest charts.\"\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
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              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
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              "    background-color: #434B5C;\n",
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              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const charts = await google.colab.kernel.invokeFunction(\n",
              "          'suggestCharts', [key], {});\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-7a29577b-adce-4528-b483-4faa03dfbdd4 button');\n",
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              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ],
      "source": [
        "pd.get_dummies(data,columns=['Embarked'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 55,
      "metadata": {
        "id": "MD2oQQf1IHvz"
      },
      "outputs": [],
      "source": [
        "data1= pd.get_dummies(data,columns=['Embarked'],drop_first=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 56,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        },
        "id": "Q_koiVcTIHvz",
        "outputId": "e2df993a-df9f-4603-bba5-26a26ebb292f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Survived  Pclass     Sex        Age  SibSp  Gender_New  Parch     Fare  \\\n",
              "0           0       3    male  22.000000      1           1      0   7.2500   \n",
              "1           1       1  female  38.000000      1           0      0  71.2833   \n",
              "2           1       3  female  26.000000      0           0      0   7.9250   \n",
              "3           1       1  female  35.000000      1           0      0  53.1000   \n",
              "4           0       3    male  35.000000      0           1      0   8.0500   \n",
              "..        ...     ...     ...        ...    ...         ...    ...      ...   \n",
              "886         0       2    male  27.000000      0           1      0  13.0000   \n",
              "887         1       1  female  19.000000      0           0      0  30.0000   \n",
              "888         0       3  female  29.699118      1           0      2  23.4500   \n",
              "889         1       1    male  26.000000      0           1      0  30.0000   \n",
              "890         0       3    male  32.000000      0           1      0   7.7500   \n",
              "\n",
              "     Gender  Embarked_Q  Embarked_S  \n",
              "0         1           0           1  \n",
              "1         0           0           0  \n",
              "2         0           0           1  \n",
              "3         0           0           1  \n",
              "4         1           0           1  \n",
              "..      ...         ...         ...  \n",
              "886       1           0           1  \n",
              "887       0           0           1  \n",
              "888       0           0           1  \n",
              "889       1           0           0  \n",
              "890       1           1           0  \n",
              "\n",
              "[891 rows x 11 columns]"
            ],
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              "<p>891 rows × 11 columns</p>\n",
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              "\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",
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              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-de9bef6b-a0c7-41dc-86a5-e19564892b2f');\n",
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              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "\n",
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              "\n",
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              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const charts = await google.colab.kernel.invokeFunction(\n",
              "          'suggestCharts', [key], {});\n",
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              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-7f5e7ddc-bd5d-443c-9c11-cff7cd2304a0 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ],
      "source": [
        "data1"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iWxIJGXlIHv0"
      },
      "source": [
        "### 11. What is Univariate Analysis?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 57,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T6GxAm2gIHv0",
        "outputId": "529f05d2-7705-4603-8acc-eac5ebf5404d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Survived', 'Pclass', 'Sex', 'Age', 'SibSp', 'Gender_New', 'Parch',\n",
              "       'Fare', 'Embarked', 'Gender'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ],
      "source": [
        "data.columns"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ce5KhrrqIHv0"
      },
      "source": [
        "### How Many People Survived And How Many Died?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 58,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4I_2YceKIHv0",
        "outputId": "2c4b34cf-fdac-411a-c6d7-c0ed9f8c3e4e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    549\n",
              "1    342\n",
              "Name: Survived, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ],
      "source": [
        "data['Survived'].value_counts()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4pXm5tXsIHv0"
      },
      "source": [
        "### How Many Passengers Were In First Class, Second Class and Third class?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 63,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OhqUlOn8IHv1",
        "outputId": "8400c3ee-a438-4b7a-b444-c87a198100d0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Survived', 'Pclass', 'Sex', 'Age', 'SibSp', 'Gender_New', 'Parch',\n",
              "       'Fare', 'Embarked', 'Gender'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ],
      "source": [
        "data.columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 64,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_qX1H2lRIHv1",
        "outputId": "2e96b7bf-1eeb-48db-a29e-4908885287c9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3    491\n",
              "1    216\n",
              "2    184\n",
              "Name: Pclass, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ],
      "source": [
        "data['Pclass'].value_counts()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZmSuzaAVIHv1"
      },
      "source": [
        "### Number of Male And Female Passengers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 66,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JaXKhR-DIHv1",
        "outputId": "c8bf12f8-9800-4f7c-8806-bbe74399bb09"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Survived', 'Pclass', 'Sex', 'Age', 'SibSp', 'Gender_New', 'Parch',\n",
              "       'Fare', 'Embarked', 'Gender'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ],
      "source": [
        "data.columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 67,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3H4Oz0gCIHv1",
        "outputId": "d73fa903-3df1-4ac6-d9f7-ecbfe621aad0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "male      577\n",
              "female    314\n",
              "Name: Sex, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ],
      "source": [
        "data['Sex'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 69,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 503
        },
        "id": "4EBkVqFiIHv2",
        "outputId": "7db126d9-8c36-4f1d-dc41-7265be46e1c0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([ 54.,  46., 177., 346., 118.,  70.,  45.,  24.,   9.,   2.]),\n",
              " array([ 0.42 ,  8.378, 16.336, 24.294, 32.252, 40.21 , 48.168, 56.126,\n",
              "        64.084, 72.042, 80.   ]),\n",
              " <BarContainer object of 10 artists>)"
            ]
          },
          "metadata": {},
          "execution_count": 69
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.hist(data['Age'],bins=10)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DMDpeo1pIHv2"
      },
      "source": [
        "### 12. Bivariate Analsis"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 70,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "r1W4uzz_IHv3",
        "outputId": "a79fbbce-a8e4-49a7-f5f1-7c1e9777f039"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Survived  Pclass   Sex   Age  SibSp  Gender_New  Parch  Fare Embarked  \\\n",
              "0         0       3  male  22.0      1           1      0  7.25        S   \n",
              "\n",
              "   Gender  \n",
              "0       1  "
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Gender_New</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Embarked</th>\n",
              "      <th>Gender</th>\n",
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              "  </thead>\n",
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              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>male</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>7.25</td>\n",
              "      <td>S</td>\n",
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              "      buttonEl.style.display =\n",
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              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-174bc598-f951-4714-b302-97a462049736');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ],
      "source": [
        "data.head(1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 71,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "BmycH4O3IHv3",
        "outputId": "f56c395b-0a02-43f9-9d35-4ddc264c536d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Sex', ylabel='Survived'>"
            ]
          },
          "metadata": {},
          "execution_count": 71
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "sns.barplot(x='Sex',y='Survived',data=data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 72,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "4LeDzYzWIHv3",
        "outputId": "f594706c-6b93-4bf4-d92a-86b48e6a4032"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Pclass', ylabel='Survived'>"
            ]
          },
          "metadata": {},
          "execution_count": 72
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "sns.barplot(x='Pclass',y='Survived',data=data)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NEDa_f_cIHv3"
      },
      "source": [
        "### Feature Engineering"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 73,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lMbUIpAzIHv3",
        "outputId": "0d4a2c48-1800-4014-ab6c-f84d5989e5ed"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Survived', 'Pclass', 'Sex', 'Age', 'SibSp', 'Gender_New', 'Parch',\n",
              "       'Fare', 'Embarked', 'Gender'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ],
      "source": [
        "data.columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 74,
      "metadata": {
        "id": "ufqZzCRwIHv4"
      },
      "outputs": [],
      "source": [
        "data[\"Family_Size\"]=data['SibSp'] + data['Parch']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 75,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "jFOw5H0bIHv4",
        "outputId": "3087ecfc-b4e8-4f5b-8296-0a3bf4553640"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Survived  Pclass     Sex   Age  SibSp  Gender_New  Parch     Fare Embarked  \\\n",
              "0         0       3    male  22.0      1           1      0   7.2500        S   \n",
              "1         1       1  female  38.0      1           0      0  71.2833        C   \n",
              "2         1       3  female  26.0      0           0      0   7.9250        S   \n",
              "3         1       1  female  35.0      1           0      0  53.1000        S   \n",
              "4         0       3    male  35.0      0           1      0   8.0500        S   \n",
              "\n",
              "   Gender  Family_Size  \n",
              "0       1            1  \n",
              "1       0            1  \n",
              "2       0            0  \n",
              "3       0            1  \n",
              "4       1            0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-fe53b780-b944-41d0-a4d5-c699ac0029b4\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Gender_New</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Embarked</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Family_Size</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>male</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>S</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>female</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>C</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>female</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>S</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>female</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>S</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>male</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>S</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fe53b780-b944-41d0-a4d5-c699ac0029b4')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-fe53b780-b944-41d0-a4d5-c699ac0029b4 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-fe53b780-b944-41d0-a4d5-c699ac0029b4');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-5329906a-7f4e-4f5e-8120-44480163dcc0\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-5329906a-7f4e-4f5e-8120-44480163dcc0')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    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-quickchart: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-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "    async function quickchart(key) {\n",
              "      const charts = await google.colab.kernel.invokeFunction(\n",
              "          'suggestCharts', [key], {});\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-5329906a-7f4e-4f5e-8120-44480163dcc0 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ],
      "source": [
        "data.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 76,
      "metadata": {
        "id": "OBvUiH8bIHv4"
      },
      "outputs": [],
      "source": [
        "data['Fare_Per_Person']= data['Fare'] / (data['Family_Size']+1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 77,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "NIzMR4BeIHv4",
        "outputId": "e3b72709-969c-4a04-d1b8-471e8f397c8d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Survived  Pclass     Sex   Age  SibSp  Gender_New  Parch     Fare Embarked  \\\n",
              "0         0       3    male  22.0      1           1      0   7.2500        S   \n",
              "1         1       1  female  38.0      1           0      0  71.2833        C   \n",
              "2         1       3  female  26.0      0           0      0   7.9250        S   \n",
              "3         1       1  female  35.0      1           0      0  53.1000        S   \n",
              "4         0       3    male  35.0      0           1      0   8.0500        S   \n",
              "\n",
              "   Gender  Family_Size  Fare_Per_Person  \n",
              "0       1            1          3.62500  \n",
              "1       0            1         35.64165  \n",
              "2       0            0          7.92500  \n",
              "3       0            1         26.55000  \n",
              "4       1            0          8.05000  "
            ],
            "text/html": [
              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Gender_New</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Embarked</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Family_Size</th>\n",
              "      <th>Fare_Per_Person</th>\n",
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              "      <td>3</td>\n",
              "      <td>male</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>S</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3.62500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>female</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>C</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>35.64165</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>female</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>S</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.92500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>female</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>S</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>26.55000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>male</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>S</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>8.05000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-adf74d41-db0f-4e3f-a6d1-6da84e00e2a3')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-adf74d41-db0f-4e3f-a6d1-6da84e00e2a3 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-adf74d41-db0f-4e3f-a6d1-6da84e00e2a3');\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",
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              "\n",
              "\n",
              "<div id=\"df-b865caef-2b85-4dcd-81fc-309a110f2f4e\">\n",
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              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    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-quickchart: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-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "    async function quickchart(key) {\n",
              "      const charts = await google.colab.kernel.invokeFunction(\n",
              "          'suggestCharts', [key], {});\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-b865caef-2b85-4dcd-81fc-309a110f2f4e button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ],
      "source": [
        "data.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9sKj6UK0IHv4"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
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      "file_extension": ".py",
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      "nbconvert_exporter": "python",
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    "colab": {
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  "nbformat_minor": 0
}