Exploratory_Data_Analysis.ipynb

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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt"
      ]
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      "source": [
        "df = pd.read_csv('train.csv')"
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        "df.head()"
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              "   PassengerId  Survived  Pclass  \\\n",
              "0            1         0       3   \n",
              "1            2         1       1   \n",
              "2            3         1       3   \n",
              "3            4         1       1   \n",
              "4            5         0       3   \n",
              "\n",
              "                                                Name     Sex   Age  SibSp  \\\n",
              "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
              "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
              "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
              "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
              "4                           Allen, Mr. William Henry    male  35.0      0   \n",
              "\n",
              "   Parch            Ticket     Fare Cabin Embarked  \n",
              "0      0         A/5 21171   7.2500   NaN        S  \n",
              "1      0          PC 17599  71.2833   C85        C  \n",
              "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
              "3      0            113803  53.1000  C123        S  \n",
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          "metadata": {},
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    {
      "cell_type": "markdown",
      "source": [
        "### Why do EDA\n",
        "\n",
        "- Model building\n",
        "- Analysis and reporting\n",
        "- Validate assumptions\n",
        "- Handling missing values\n",
        "- feature engineering\n",
        "- detecting outliers"
      ],
      "metadata": {
        "id": "EPZR9pEJL7n8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Remember it is an iterative process"
      ],
      "metadata": {
        "id": "VlNQAIxrL2zw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Column Types\n",
        "\n",
        "- **Numerical** - Age,Fare,PassengerId\n",
        "- **Categorical** - Survived, Pclass, Sex, SibSp, Parch,Embarked\n",
        "- **Mixed** - Name, Ticket, Cabin"
      ],
      "metadata": {
        "id": "HRmn6Gx-MBFQ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Univariate Analysis\n",
        "\n",
        "Univariate analysis focuses on analyzing each feature in the dataset independently.\n",
        "\n",
        "- **Distribution analysis**: The distribution of each feature is examined to identify its shape, central tendency, and dispersion.\n",
        "\n",
        "- **Identifying potential issues**: Univariate analysis helps in identifying potential problems with the data such as outliers, skewness, and missing values"
      ],
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      "source": [
        "#### The shape of a data distribution refers to its overall pattern or form as it is represented on a graph. Some common shapes of data distributions include:\n",
        "\n",
        "- **Normal Distribution**: A symmetrical and bell-shaped distribution where the mean, median, and mode are equal and the majority of the data falls in the middle of the distribution with gradually decreasing frequencies towards the tails.\n",
        "\n",
        "- **Skewed Distribution**: A distribution that is not symmetrical, with one tail being longer than the other. It can be either positively skewed (right-skewed) or negatively skewed (left-skewed).\n",
        "\n",
        "- **Bimodal Distribution**: A distribution with two peaks or modes.\n",
        "\n",
        "- **Uniform Distribution**: A distribution where all values have an equal chance of occurring.\n",
        "\n",
        "The shape of the data distribution is important in identifying the presence of outliers, skewness, and the type of statistical tests and models that can be used for further analysis."
      ],
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        "#### **Dispersion** is a statistical term used to describe the spread or variability of a set of data. It measures how far the values in a data set are spread out from the central tendency (mean, median, or mode) of the data.\n",
        "\n",
        "There are several measures of dispersion, including:\n",
        "\n",
        "- **Range**: The difference between the largest and smallest values in a data set.\n",
        "\n",
        "- **Variance**: The average of the squared deviations of each value from the mean of the data set.\n",
        "\n",
        "- **Standard Deviation**: The square root of the variance. It provides a measure of the spread of the data that is in the same units as the original data.\n",
        "\n",
        "- **Interquartile range (IQR)**: The range between the first quartile (25th percentile) and the third quartile (75th percentile) of the data.\n",
        "\n",
        "Dispersion helps to describe the spread of the data, which can help to identify the presence of outliers and skewness in the data."
      ],
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        "### Steps of doing Univariate Analysis on Numerical columns\n",
        "\n",
        "- **Descriptive Statistics**: Compute basic summary statistics for the column, such as mean, median, mode, standard deviation, range, and quartiles. These statistics give a general understanding of the distribution of the data and can help identify skewness or outliers.\n",
        "\n",
        "- **Visualizations**: Create visualizations to explore the distribution of the data. Some common visualizations for numerical data include histograms, box plots, and density plots. These visualizations provide a visual representation of the distribution of the data and can help identify skewness an outliers.\n",
        "\n",
        "- **Identifying Outliers**: Identify and examine any outliers in the data. Outliers can be identified using visualizations. It is important to determine whether the outliers are due to measurement errors, data entry errors, or legitimate differences in the data, and to decide whether to include or exclude them from the analysis.\n",
        "\n",
        "- **Skewness**: Check for skewness in the data and consider transforming the data or using robust statistical methods that are less sensitive to skewness, if necessary.\n",
        "\n",
        "- **Conclusion**: Summarize the findings of the EDA and make decisions about how to proceed with further analysis.\n"
      ],
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      "source": [
        "### Age\n",
        "\n",
        "**conclusions**\n",
        "\n",
        "- Age is normally(almost) distributed\n",
        "- 20% of the values are missing\n",
        "- There are some outliers"
      ],
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      "source": [
        "df['Age'].describe()"
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            "text/plain": [
              "count    714.000000\n",
              "mean      29.699118\n",
              "std       14.526497\n",
              "min        0.420000\n",
              "25%       20.125000\n",
              "50%       28.000000\n",
              "75%       38.000000\n",
              "max       80.000000\n",
              "Name: Age, dtype: float64"
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        "df['Age'].plot(kind='hist',bins=20)"
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            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Age'].plot(kind='kde')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "zYY9XXtwgdCX",
        "outputId": "fa6ea780-aaf9-45b6-b596-bd4a2b56e94c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f0039fee1f0>"
            ]
          },
          "metadata": {},
          "execution_count": 28
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Age'].skew()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G1dEEEIfgdEy",
        "outputId": "e7c13f30-b659-4cc2-e58d-aea15ceaa116"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.38910778230082704"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Age'].plot(kind='box')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "bSj1owIqgdHR",
        "outputId": "84adf124-4953-4991-8435-538dd7ac5d3b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f0039fd0a60>"
            ]
          },
          "metadata": {},
          "execution_count": 30
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[df['Age'] > 65]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "VgO8OESKgdJo",
        "outputId": "507cc466-85d3-46af-ed92-df32553dbd6f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     PassengerId  Survived  Pclass                                  Name  \\\n",
              "33            34         0       2                 Wheadon, Mr. Edward H   \n",
              "96            97         0       1             Goldschmidt, Mr. George B   \n",
              "116          117         0       3                  Connors, Mr. Patrick   \n",
              "493          494         0       1               Artagaveytia, Mr. Ramon   \n",
              "630          631         1       1  Barkworth, Mr. Algernon Henry Wilson   \n",
              "672          673         0       2           Mitchell, Mr. Henry Michael   \n",
              "745          746         0       1          Crosby, Capt. Edward Gifford   \n",
              "851          852         0       3                   Svensson, Mr. Johan   \n",
              "\n",
              "      Sex   Age  SibSp  Parch      Ticket     Fare Cabin Embarked  \n",
              "33   male  66.0      0      0  C.A. 24579  10.5000   NaN        S  \n",
              "96   male  71.0      0      0    PC 17754  34.6542    A5        C  \n",
              "116  male  70.5      0      0      370369   7.7500   NaN        Q  \n",
              "493  male  71.0      0      0    PC 17609  49.5042   NaN        C  \n",
              "630  male  80.0      0      0       27042  30.0000   A23        S  \n",
              "672  male  70.0      0      0  C.A. 24580  10.5000   NaN        S  \n",
              "745  male  70.0      1      1   WE/P 5735  71.0000   B22        S  \n",
              "851  male  74.0      0      0      347060   7.7750   NaN        S  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-cd080cef-6004-490e-b515-07f4c5fede37\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>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>33</th>\n",
              "      <td>34</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>Wheadon, Mr. Edward H</td>\n",
              "      <td>male</td>\n",
              "      <td>66.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>C.A. 24579</td>\n",
              "      <td>10.5000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>96</th>\n",
              "      <td>97</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>Goldschmidt, Mr. George B</td>\n",
              "      <td>male</td>\n",
              "      <td>71.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>PC 17754</td>\n",
              "      <td>34.6542</td>\n",
              "      <td>A5</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>116</th>\n",
              "      <td>117</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Connors, Mr. Patrick</td>\n",
              "      <td>male</td>\n",
              "      <td>70.5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>370369</td>\n",
              "      <td>7.7500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Q</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>493</th>\n",
              "      <td>494</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>Artagaveytia, Mr. Ramon</td>\n",
              "      <td>male</td>\n",
              "      <td>71.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>PC 17609</td>\n",
              "      <td>49.5042</td>\n",
              "      <td>NaN</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>630</th>\n",
              "      <td>631</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Barkworth, Mr. Algernon Henry Wilson</td>\n",
              "      <td>male</td>\n",
              "      <td>80.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>27042</td>\n",
              "      <td>30.0000</td>\n",
              "      <td>A23</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>672</th>\n",
              "      <td>673</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>Mitchell, Mr. Henry Michael</td>\n",
              "      <td>male</td>\n",
              "      <td>70.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>C.A. 24580</td>\n",
              "      <td>10.5000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>745</th>\n",
              "      <td>746</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>Crosby, Capt. Edward Gifford</td>\n",
              "      <td>male</td>\n",
              "      <td>70.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>WE/P 5735</td>\n",
              "      <td>71.0000</td>\n",
              "      <td>B22</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>851</th>\n",
              "      <td>852</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Svensson, Mr. Johan</td>\n",
              "      <td>male</td>\n",
              "      <td>74.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>347060</td>\n",
              "      <td>7.7750</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-cd080cef-6004-490e-b515-07f4c5fede37')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-cd080cef-6004-490e-b515-07f4c5fede37 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-cd080cef-6004-490e-b515-07f4c5fede37');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Age'].isnull().sum()/len(df['Age'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x80x87rjgdMN",
        "outputId": "e8b77501-d923-42b7-95ab-543be9cc594a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.19865319865319866"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Fare\n",
        "\n",
        "**conclusions**\n",
        "\n",
        "- The data is highly(positively) skewed\n",
        "- Fare col actually contains the group fare and not the individual fare(This migth be and issue)\n",
        "- We need to create a new col called individual fare"
      ],
      "metadata": {
        "id": "YlyzjTZrimiZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['Fare'].describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8HXYGv5ZgdPT",
        "outputId": "88d9e76a-043c-43ba-8170-1d79472c53db"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    891.000000\n",
              "mean      32.204208\n",
              "std       49.693429\n",
              "min        0.000000\n",
              "25%        7.910400\n",
              "50%       14.454200\n",
              "75%       31.000000\n",
              "max      512.329200\n",
              "Name: Fare, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Fare'].plot(kind='hist')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "oxQItXskilFY",
        "outputId": "f7a89bea-fa20-4446-ea83-c8bb0b8f0f32"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f0037efa910>"
            ]
          },
          "metadata": {},
          "execution_count": 35
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Fare'].plot(kind='kde')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "yQ97TC9vilII",
        "outputId": "df4eb340-fe1e-4ca0-bd55-4bfd8f6a592d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f0037f48be0>"
            ]
          },
          "metadata": {},
          "execution_count": 36
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Fare'].skew()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hRtvV1BgilK-",
        "outputId": "ed12bfec-4eea-4fae-8d80-bd330367d160"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4.787316519674893"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Fare'].plot(kind='box')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "-P__Ql4vilN6",
        "outputId": "dfaa99e8-6a45-4347-89f0-1cde52687819"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f0037dd50a0>"
            ]
          },
          "metadata": {},
          "execution_count": 38
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[df['Fare'] > 250]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 375
        },
        "id": "y22YBRp8ilSZ",
        "outputId": "8ae11b1f-b461-49dd-bede-94c4305eff57"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     PassengerId  Survived  Pclass                                   Name  \\\n",
              "27            28         0       1         Fortune, Mr. Charles Alexander   \n",
              "88            89         1       1             Fortune, Miss. Mabel Helen   \n",
              "258          259         1       1                       Ward, Miss. Anna   \n",
              "311          312         1       1             Ryerson, Miss. Emily Borie   \n",
              "341          342         1       1         Fortune, Miss. Alice Elizabeth   \n",
              "438          439         0       1                      Fortune, Mr. Mark   \n",
              "679          680         1       1     Cardeza, Mr. Thomas Drake Martinez   \n",
              "737          738         1       1                 Lesurer, Mr. Gustave J   \n",
              "742          743         1       1  Ryerson, Miss. Susan Parker \"Suzette\"   \n",
              "\n",
              "        Sex   Age  SibSp  Parch    Ticket      Fare            Cabin Embarked  \n",
              "27     male  19.0      3      2     19950  263.0000      C23 C25 C27        S  \n",
              "88   female  23.0      3      2     19950  263.0000      C23 C25 C27        S  \n",
              "258  female  35.0      0      0  PC 17755  512.3292              NaN        C  \n",
              "311  female  18.0      2      2  PC 17608  262.3750  B57 B59 B63 B66        C  \n",
              "341  female  24.0      3      2     19950  263.0000      C23 C25 C27        S  \n",
              "438    male  64.0      1      4     19950  263.0000      C23 C25 C27        S  \n",
              "679    male  36.0      0      1  PC 17755  512.3292      B51 B53 B55        C  \n",
              "737    male  35.0      0      0  PC 17755  512.3292             B101        C  \n",
              "742  female  21.0      2      2  PC 17608  262.3750  B57 B59 B63 B66        C  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-2ca404b6-778f-44be-9a7b-3b601e964e61\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>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>27</th>\n",
              "      <td>28</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>Fortune, Mr. Charles Alexander</td>\n",
              "      <td>male</td>\n",
              "      <td>19.0</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>19950</td>\n",
              "      <td>263.0000</td>\n",
              "      <td>C23 C25 C27</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>88</th>\n",
              "      <td>89</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Fortune, Miss. Mabel Helen</td>\n",
              "      <td>female</td>\n",
              "      <td>23.0</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>19950</td>\n",
              "      <td>263.0000</td>\n",
              "      <td>C23 C25 C27</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>258</th>\n",
              "      <td>259</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Ward, Miss. Anna</td>\n",
              "      <td>female</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>PC 17755</td>\n",
              "      <td>512.3292</td>\n",
              "      <td>NaN</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>311</th>\n",
              "      <td>312</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Ryerson, Miss. Emily Borie</td>\n",
              "      <td>female</td>\n",
              "      <td>18.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>PC 17608</td>\n",
              "      <td>262.3750</td>\n",
              "      <td>B57 B59 B63 B66</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>341</th>\n",
              "      <td>342</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Fortune, Miss. Alice Elizabeth</td>\n",
              "      <td>female</td>\n",
              "      <td>24.0</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>19950</td>\n",
              "      <td>263.0000</td>\n",
              "      <td>C23 C25 C27</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>438</th>\n",
              "      <td>439</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>Fortune, Mr. Mark</td>\n",
              "      <td>male</td>\n",
              "      <td>64.0</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>19950</td>\n",
              "      <td>263.0000</td>\n",
              "      <td>C23 C25 C27</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>679</th>\n",
              "      <td>680</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Cardeza, Mr. Thomas Drake Martinez</td>\n",
              "      <td>male</td>\n",
              "      <td>36.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>PC 17755</td>\n",
              "      <td>512.3292</td>\n",
              "      <td>B51 B53 B55</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>737</th>\n",
              "      <td>738</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Lesurer, Mr. Gustave J</td>\n",
              "      <td>male</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>PC 17755</td>\n",
              "      <td>512.3292</td>\n",
              "      <td>B101</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>742</th>\n",
              "      <td>743</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Ryerson, Miss. Susan Parker \"Suzette\"</td>\n",
              "      <td>female</td>\n",
              "      <td>21.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>PC 17608</td>\n",
              "      <td>262.3750</td>\n",
              "      <td>B57 B59 B63 B66</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2ca404b6-778f-44be-9a7b-3b601e964e61')\"\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",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-2ca404b6-778f-44be-9a7b-3b601e964e61 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-2ca404b6-778f-44be-9a7b-3b601e964e61');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Fare'].isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KMWNtuUfilVP",
        "outputId": "4287151f-20ce-40f4-8abf-b7fce938670f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "77tsAEuiilX7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "92Z0vOROgdSf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Steps of doing Univariate Analysis on Categorical columns\n",
        "\n",
        "**Descriptive Statistics**: Compute the frequency distribution of the categories in the column. This will give a general understanding of the distribution of the categories and their relative frequencies.\n",
        "\n",
        "**Visualizations**: Create visualizations to explore the distribution of the categories. Some common visualizations for categorical data include count plots and pie charts. These visualizations provide a visual representation of the distribution of the categories and can help identify any patterns or anomalies in the data.\n",
        "\n",
        "**Missing Values**: Check for missing values in the data and decide how to handle them. Missing values can be imputed or excluded from the analysis, depending on the research question and the data set.\n",
        "\n",
        "**Conclusion**: Summarize the findings of the EDA and make decisions about how to proceed with further analysis."
      ],
      "metadata": {
        "id": "bviDB0DwMx-1"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Survived\n",
        "\n",
        "**conclusions**\n",
        "\n",
        "- Parch and SibSp cols can be merged to form  a new col call family_size\n",
        "- Create a new col called is_alone"
      ],
      "metadata": {
        "id": "1TSQG1aUlf95"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['Embarked'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VAVjxN-6M1NX",
        "outputId": "339674ff-9274-4499-9e01-96f76da28c5c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "S    644\n",
              "C    168\n",
              "Q     77\n",
              "Name: Embarked, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Embarked'].value_counts().plot(kind='bar')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 280
        },
        "id": "v0cgYk0Mldm0",
        "outputId": "003d3f7d-e834-4ffc-8443-fd27cd39e229"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f0037a68d00>"
            ]
          },
          "metadata": {},
          "execution_count": 61
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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yzF2SOmS4S1KHDHdJ6pDhLkkdMtwlqUP/D7feYyVbX7S9AAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Embarked'].value_counts().plot(kind='pie',autopct='%0.1f%%')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "LGPxQv7wldqb",
        "outputId": "fe5c3a57-00c6-45df-a421-f14f5317b1b4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f0037a9cb50>"
            ]
          },
          "metadata": {},
          "execution_count": 62
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Sex'].isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SGpUuBI7ldua",
        "outputId": "87847620-04fa-45d9-ffe0-a1cc28d14d29"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "NxlKSTK8ldyI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "gJZsBwI6ld3S"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "mc4F76vXld9L"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "lyv1CWveleB7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "C9q7IEo6leGs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Steps of doing Bivariate Analysis\n",
        "\n",
        "- Select 2 cols\n",
        "- Understand type of relationship\n",
        "    1. **Numerical - Numerical**<br>\n",
        "        a. You can plot graphs like scatterplot(regression plots), 2D histplot, 2D KDEplots<br>\n",
        "        b. Check correlation coefficent to check linear relationship\n",
        "    2. **Numerical - Categorical** - create visualizations that compare the distribution of the numerical data across different categories of the categorical data.<br>\n",
        "        a. You can plot graphs like barplot, boxplot, kdeplot violinplot even scatterplots<br>\n",
        "    3. **Categorical - Categorical**<br>\n",
        "        a. You can create cross-tabulations or contingency tables that show the distribution of values in one categorical column, grouped by the values in the other categorical column.<br>\n",
        "        b. You can plots like heatmap, stacked barplots, treemaps\n",
        "        \n",
        "- Write your conclusions"
      ],
      "metadata": {
        "id": "Rj31HEuOM6g5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 502
        },
        "id": "a4jsZ9OoM7ES",
        "outputId": "3f15c950-9de2-4526-94f4-01d841159cd5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     PassengerId  Survived  Pclass  \\\n",
              "0              1         0       3   \n",
              "1              2         1       1   \n",
              "2              3         1       3   \n",
              "3              4         1       1   \n",
              "4              5         0       3   \n",
              "..           ...       ...     ...   \n",
              "886          887         0       2   \n",
              "887          888         1       1   \n",
              "888          889         0       3   \n",
              "889          890         1       1   \n",
              "890          891         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",
              "886                              Montvila, Rev. Juozas    male  27.0      0   \n",
              "887                       Graham, Miss. Margaret Edith  female  19.0      0   \n",
              "888           Johnston, Miss. Catherine Helen \"Carrie\"  female   NaN      1   \n",
              "889                              Behr, Mr. Karl Howell    male  26.0      0   \n",
              "890                                Dooley, Mr. Patrick    male  32.0      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  \n",
              "..     ...               ...      ...   ...      ...  \n",
              "886      0            211536  13.0000   NaN        S  \n",
              "887      0            112053  30.0000   B42        S  \n",
              "888      2        W./C. 6607  23.4500   NaN        S  \n",
              "889      0            111369  30.0000  C148        C  \n",
              "890      0            370376   7.7500   NaN        Q  \n",
              "\n",
              "[891 rows x 12 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-eb8ccea8-7ae1-43e9-a0be-242c5b7cbb8a\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Braund, Mr. Owen Harris</td>\n",
              "      <td>male</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>A/5 21171</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
              "      <td>female</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>PC 17599</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>C85</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Heikkinen, Miss. Laina</td>\n",
              "      <td>female</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>STON/O2. 3101282</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
              "      <td>female</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>113803</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>C123</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Allen, Mr. William Henry</td>\n",
              "      <td>male</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>373450</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>886</th>\n",
              "      <td>887</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>Montvila, Rev. Juozas</td>\n",
              "      <td>male</td>\n",
              "      <td>27.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>211536</td>\n",
              "      <td>13.0000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>887</th>\n",
              "      <td>888</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Graham, Miss. Margaret Edith</td>\n",
              "      <td>female</td>\n",
              "      <td>19.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>112053</td>\n",
              "      <td>30.0000</td>\n",
              "      <td>B42</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <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",
              "      <td>23.4500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>889</th>\n",
              "      <td>890</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Behr, Mr. Karl Howell</td>\n",
              "      <td>male</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>111369</td>\n",
              "      <td>30.0000</td>\n",
              "      <td>C148</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <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>7.7500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Q</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>891 rows × 12 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-eb8ccea8-7ae1-43e9-a0be-242c5b7cbb8a')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-eb8ccea8-7ae1-43e9-a0be-242c5b7cbb8a 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-eb8ccea8-7ae1-43e9-a0be-242c5b7cbb8a');\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",
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              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.heatmap(pd.crosstab(df['Survived'],df['Pclass'],normalize='columns')*100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "Ye7Y5C1FpQUb",
        "outputId": "ac36e3cc-5f64-4e68-9297-6746ac7806f1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f0037e1adc0>"
            ]
          },
          "metadata": {},
          "execution_count": 69
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.crosstab(df['Survived'],df['Sex'],normalize='columns')*100"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "EEw8Lrgep9ud",
        "outputId": "d79f6ad5-57d6-4eb6-982b-d40f967fe5c5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Sex          female       male\n",
              "Survived                      \n",
              "0         25.796178  81.109185\n",
              "1         74.203822  18.890815"
            ],
            "text/html": [
              "\n",
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        "pd.crosstab(df['Survived'],df['Embarked'],normalize='columns')*100"
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        {
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          "data": {
            "text/plain": [
              "Embarked          C          Q          S\n",
              "Survived                                 \n",
              "0         44.642857  61.038961  66.304348\n",
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    {
      "cell_type": "code",
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        "pd.crosstab(df['Sex'],df['Embarked'],normalize='columns')*100"
      ],
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Embarked          C          Q          S\n",
              "Sex                                      \n",
              "female    43.452381  46.753247  31.521739\n",
              "male      56.547619  53.246753  68.478261"
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    },
    {
      "cell_type": "code",
      "source": [
        "pd.crosstab(df['Pclass'],df['Embarked'],normalize='columns')*100"
      ],
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          "base_uri": "https://localhost:8080/",
          "height": 174
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      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Embarked          C          Q          S\n",
              "Pclass                                   \n",
              "1         50.595238   2.597403  19.720497\n",
              "2         10.119048   3.896104  25.465839\n",
              "3         39.285714  93.506494  54.813665"
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              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f346e747-0ef8-4d69-af11-c64ee743e70f')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-f346e747-0ef8-4d69-af11-c64ee743e70f 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-f346e747-0ef8-4d69-af11-c64ee743e70f');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# survived and age\n",
        "\n",
        "df[df['Survived'] == 1]['Age'].plot(kind='kde',label='Survived')\n",
        "df[df['Survived'] == 0]['Age'].plot(kind='kde',label='Not Survived')\n",
        "\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "OFg-yf6jsD8s",
        "outputId": "4014e5fa-40d6-45b6-83a6-2894227d7c0c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[df['Pclass'] == 1]['Age'].mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EoZ80ry3s5YV",
        "outputId": "ec61d4d0-fa2e-42fb-9879-bdbe14e8f3ad"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "38.233440860215055"
            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Feature Engineering on Fare col"
      ],
      "metadata": {
        "id": "oXpbyTdmtb3Q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['SibSp'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lSz5Dq8OudIW",
        "outputId": "5be84a54-2697-4e1f-b657-f48435f8bdd8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    608\n",
              "1    209\n",
              "2     28\n",
              "4     18\n",
              "3     16\n",
              "8      7\n",
              "5      5\n",
              "Name: SibSp, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[df['Ticket'] == 'CA. 2343']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "zcNV5rH2ugm0",
        "outputId": "a1aec758-3d02-4104-b00d-18a22f566dbf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     PassengerId  Survived  Pclass                               Name     Sex  \\\n",
              "159          160         0       3         Sage, Master. Thomas Henry    male   \n",
              "180          181         0       3       Sage, Miss. Constance Gladys  female   \n",
              "201          202         0       3                Sage, Mr. Frederick    male   \n",
              "324          325         0       3           Sage, Mr. George John Jr    male   \n",
              "792          793         0       3            Sage, Miss. Stella Anna  female   \n",
              "846          847         0       3           Sage, Mr. Douglas Bullen    male   \n",
              "863          864         0       3  Sage, Miss. Dorothy Edith \"Dolly\"  female   \n",
              "\n",
              "     Age  SibSp  Parch    Ticket   Fare Cabin Embarked  \n",
              "159  NaN      8      2  CA. 2343  69.55   NaN        S  \n",
              "180  NaN      8      2  CA. 2343  69.55   NaN        S  \n",
              "201  NaN      8      2  CA. 2343  69.55   NaN        S  \n",
              "324  NaN      8      2  CA. 2343  69.55   NaN        S  \n",
              "792  NaN      8      2  CA. 2343  69.55   NaN        S  \n",
              "846  NaN      8      2  CA. 2343  69.55   NaN        S  \n",
              "863  NaN      8      2  CA. 2343  69.55   NaN        S  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c2f61179-7c1a-4641-a7d1-707679595ed1\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>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>159</th>\n",
              "      <td>160</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Sage, Master. Thomas Henry</td>\n",
              "      <td>male</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8</td>\n",
              "      <td>2</td>\n",
              "      <td>CA. 2343</td>\n",
              "      <td>69.55</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>180</th>\n",
              "      <td>181</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Sage, Miss. Constance Gladys</td>\n",
              "      <td>female</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8</td>\n",
              "      <td>2</td>\n",
              "      <td>CA. 2343</td>\n",
              "      <td>69.55</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>201</th>\n",
              "      <td>202</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Sage, Mr. Frederick</td>\n",
              "      <td>male</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8</td>\n",
              "      <td>2</td>\n",
              "      <td>CA. 2343</td>\n",
              "      <td>69.55</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>324</th>\n",
              "      <td>325</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Sage, Mr. George John Jr</td>\n",
              "      <td>male</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8</td>\n",
              "      <td>2</td>\n",
              "      <td>CA. 2343</td>\n",
              "      <td>69.55</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>792</th>\n",
              "      <td>793</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Sage, Miss. Stella Anna</td>\n",
              "      <td>female</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8</td>\n",
              "      <td>2</td>\n",
              "      <td>CA. 2343</td>\n",
              "      <td>69.55</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
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              "                                                     [key], {});\n",
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              "  "
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          "metadata": {},
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    },
    {
      "cell_type": "code",
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        "df[df['Name'].str.contains('Sage')]"
      ],
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        "id": "fssY6ebkupG6",
        "outputId": "8ab1be75-0d36-46a2-aea8-154dead81648"
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      "execution_count": null,
      "outputs": [
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              "     PassengerId  Survived  Pclass                               Name     Sex  \\\n",
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              "      Age  SibSp  Parch    Ticket   Fare Cabin Embarked  \n",
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          "metadata": {},
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    },
    {
      "cell_type": "code",
      "source": [
        "df1 = pd.read_csv('/content/test.csv')"
      ],
      "metadata": {
        "id": "0uFlt294vAba"
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      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.concat([df,df1])"
      ],
      "metadata": {
        "id": "BXgh4PPEvvfd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df[df['Ticket'] == 'CA 2144']"
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          "height": 300
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        "id": "A2tU0iPqvv7w",
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      "execution_count": null,
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     PassengerId  Survived  Pclass                                     Name  \\\n",
              "59            60       0.0       3       Goodwin, Master. William Frederick   \n",
              "71            72       0.0       3               Goodwin, Miss. Lillian Amy   \n",
              "386          387       0.0       3          Goodwin, Master. Sidney Leonard   \n",
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              "683          684       0.0       3              Goodwin, Mr. Charles Edward   \n",
              "139         1031       NaN       3           Goodwin, Mr. Charles Frederick   \n",
              "140         1032       NaN       3              Goodwin, Miss. Jessie Allis   \n",
              "\n",
              "        Sex   Age  SibSp  Parch   Ticket  Fare Cabin Embarked  \n",
              "59     male  11.0      5      2  CA 2144  46.9   NaN        S  \n",
              "71   female  16.0      5      2  CA 2144  46.9   NaN        S  \n",
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              "      <td>46.9</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>71</th>\n",
              "      <td>72</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3</td>\n",
              "      <td>Goodwin, Miss. Lillian Amy</td>\n",
              "      <td>female</td>\n",
              "      <td>16.0</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>CA 2144</td>\n",
              "      <td>46.9</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>386</th>\n",
              "      <td>387</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3</td>\n",
              "      <td>Goodwin, Master. Sidney Leonard</td>\n",
              "      <td>male</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>CA 2144</td>\n",
              "      <td>46.9</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>480</th>\n",
              "      <td>481</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3</td>\n",
              "      <td>Goodwin, Master. Harold Victor</td>\n",
              "      <td>male</td>\n",
              "      <td>9.0</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>CA 2144</td>\n",
              "      <td>46.9</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>678</th>\n",
              "      <td>679</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3</td>\n",
              "      <td>Goodwin, Mrs. Frederick (Augusta Tyler)</td>\n",
              "      <td>female</td>\n",
              "      <td>43.0</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>CA 2144</td>\n",
              "      <td>46.9</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>683</th>\n",
              "      <td>684</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3</td>\n",
              "      <td>Goodwin, Mr. Charles Edward</td>\n",
              "      <td>male</td>\n",
              "      <td>14.0</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>CA 2144</td>\n",
              "      <td>46.9</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>139</th>\n",
              "      <td>1031</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "      <td>Goodwin, Mr. Charles Frederick</td>\n",
              "      <td>male</td>\n",
              "      <td>40.0</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>CA 2144</td>\n",
              "      <td>46.9</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>140</th>\n",
              "      <td>1032</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "      <td>Goodwin, Miss. Jessie Allis</td>\n",
              "      <td>female</td>\n",
              "      <td>10.0</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>CA 2144</td>\n",
              "      <td>46.9</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f0a0fbf0-71d5-43b3-930a-e0d63178749a')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-f0a0fbf0-71d5-43b3-930a-e0d63178749a 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-f0a0fbf0-71d5-43b3-930a-e0d63178749a');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['individual_fare'] = df['Fare']/(df['SibSp'] + df['Parch'] + 1)"
      ],
      "metadata": {
        "id": "neeDFksbwAxi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['individual_fare'].plot(kind='box')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "OLFWWkMQwIz2",
        "outputId": "48169651-fd2d-4014-a3ab-27fd6e8afd10"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f00347f4c40>"
            ]
          },
          "metadata": {},
          "execution_count": 93
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[['individual_fare','Fare']].describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "1--mMowSwd7C",
        "outputId": "d9821bc9-012a-422f-e8fd-68786f86d79d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       individual_fare         Fare\n",
              "count      1308.000000  1308.000000\n",
              "mean         20.518215    33.295479\n",
              "std          35.774337    51.758668\n",
              "min           0.000000     0.000000\n",
              "25%           7.452767     7.895800\n",
              "50%           8.512483    14.454200\n",
              "75%          24.237500    31.275000\n",
              "max         512.329200   512.329200"
            ],
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              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>individual_fare</th>\n",
              "      <th>Fare</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1308.000000</td>\n",
              "      <td>1308.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>20.518215</td>\n",
              "      <td>33.295479</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>35.774337</td>\n",
              "      <td>51.758668</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>7.452767</td>\n",
              "      <td>7.895800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>8.512483</td>\n",
              "      <td>14.454200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>24.237500</td>\n",
              "      <td>31.275000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>512.329200</td>\n",
              "      <td>512.329200</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "                                                     [key], {});\n",
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              "          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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              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 97
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Fare']."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bfHBBKcPwpUu",
        "outputId": "c7e24e22-e646-4211-86c0-9b0edcfbe0a4"
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      "execution_count": null,
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          "data": {
            "text/plain": [
              "0        7.2500\n",
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              "2        7.9250\n",
              "3       53.1000\n",
              "4        8.0500\n",
              "         ...   \n",
              "413      8.0500\n",
              "414    108.9000\n",
              "415      7.2500\n",
              "416      8.0500\n",
              "417     22.3583\n",
              "Name: Fare, Length: 1309, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 95
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 571
        },
        "id": "-Imen21qwrnD",
        "outputId": "c10319f6-9262-493c-be05-64c9e4f22650"
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      "execution_count": null,
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          "data": {
            "text/plain": [
              "     PassengerId  Survived  Pclass  \\\n",
              "0              1       0.0       3   \n",
              "1              2       1.0       1   \n",
              "2              3       1.0       3   \n",
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              "4              5       0.0       3   \n",
              "..           ...       ...     ...   \n",
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              "\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",
              "413                                 Spector, Mr. Woolf    male   NaN      0   \n",
              "414                       Oliva y Ocana, Dona. Fermina  female  39.0      0   \n",
              "415                       Saether, Mr. Simon Sivertsen    male  38.5      0   \n",
              "416                                Ware, Mr. Frederick    male   NaN      0   \n",
              "417                           Peter, Master. Michael J    male   NaN      1   \n",
              "\n",
              "     Parch              Ticket      Fare Cabin Embarked  individual_fare  \n",
              "0        0           A/5 21171    7.2500   NaN        S         3.625000  \n",
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              "3        0              113803   53.1000  C123        S        26.550000  \n",
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              "..     ...                 ...       ...   ...      ...              ...  \n",
              "413      0           A.5. 3236    8.0500   NaN        S         8.050000  \n",
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              "416      0              359309    8.0500   NaN        S         8.050000  \n",
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          "metadata": {},
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    {
      "cell_type": "code",
      "source": [
        "df['family_size'] = df['SibSp'] + df['Parch'] + 1"
      ],
      "metadata": {
        "id": "8JcJBVKLw7G7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# family_type\n",
        "# 1 -> alone\n",
        "# 2-4 -> small\n",
        "# >5 -> large\n",
        "\n",
        "def transform_family_size(num):\n",
        "\n",
        "  if num == 1:\n",
        "    return 'alone'\n",
        "  elif num>1 and num <5:\n",
        "    return \"small\"\n",
        "  else:\n",
        "    return \"large\""
      ],
      "metadata": {
        "id": "CnD4AZ91xJ9J"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['family_type'] = df['family_size'].apply(transform_family_size)"
      ],
      "metadata": {
        "id": "lgNxDAqQxKXo"
      },
      "execution_count": null,
      "outputs": []
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    {
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        "id": "mJA8wmrjxqmb",
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     PassengerId  Survived  Pclass  \\\n",
              "0              1       0.0       3   \n",
              "1              2       1.0       1   \n",
              "2              3       1.0       3   \n",
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              "                                                  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",
              "413                                 Spector, Mr. Woolf    male   NaN      0   \n",
              "414                       Oliva y Ocana, Dona. Fermina  female  39.0      0   \n",
              "415                       Saether, Mr. Simon Sivertsen    male  38.5      0   \n",
              "416                                Ware, Mr. Frederick    male   NaN      0   \n",
              "417                           Peter, Master. Michael J    male   NaN      1   \n",
              "\n",
              "     Parch              Ticket      Fare Cabin Embarked  individual_fare  \\\n",
              "0        0           A/5 21171    7.2500   NaN        S         3.625000   \n",
              "1        0            PC 17599   71.2833   C85        C        35.641650   \n",
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              "4        0              373450    8.0500   NaN        S         8.050000   \n",
              "..     ...                 ...       ...   ...      ...              ...   \n",
              "413      0           A.5. 3236    8.0500   NaN        S         8.050000   \n",
              "414      0            PC 17758  108.9000  C105        C       108.900000   \n",
              "415      0  SOTON/O.Q. 3101262    7.2500   NaN        S         7.250000   \n",
              "416      0              359309    8.0500   NaN        S         8.050000   \n",
              "417      1                2668   22.3583   NaN        C         7.452767   \n",
              "\n",
              "     family_size family_type  \n",
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              "1              2       small  \n",
              "2              1       alone  \n",
              "3              2       small  \n",
              "4              1       alone  \n",
              "..           ...         ...  \n",
              "413            1       alone  \n",
              "414            1       alone  \n",
              "415            1       alone  \n",
              "416            1       alone  \n",
              "417            3       small  \n",
              "\n",
              "[1309 rows x 15 columns]"
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              "      <th>PassengerId</th>\n",
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              "      <td>113803</td>\n",
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              "      <td>Allen, Mr. William Henry</td>\n",
              "      <td>male</td>\n",
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              "      <td>0</td>\n",
              "      <td>373450</td>\n",
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              "      <td>S</td>\n",
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              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <th>413</th>\n",
              "      <td>1305</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "      <td>Spector, Mr. Woolf</td>\n",
              "      <td>male</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>A.5. 3236</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>8.050000</td>\n",
              "      <td>1</td>\n",
              "      <td>alone</td>\n",
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              "    <tr>\n",
              "      <th>414</th>\n",
              "      <td>1306</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>Oliva y Ocana, Dona. Fermina</td>\n",
              "      <td>female</td>\n",
              "      <td>39.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>PC 17758</td>\n",
              "      <td>108.9000</td>\n",
              "      <td>C105</td>\n",
              "      <td>C</td>\n",
              "      <td>108.900000</td>\n",
              "      <td>1</td>\n",
              "      <td>alone</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>415</th>\n",
              "      <td>1307</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "      <td>Saether, Mr. Simon Sivertsen</td>\n",
              "      <td>male</td>\n",
              "      <td>38.5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>SOTON/O.Q. 3101262</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>7.250000</td>\n",
              "      <td>1</td>\n",
              "      <td>alone</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>416</th>\n",
              "      <td>1308</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "      <td>Ware, Mr. Frederick</td>\n",
              "      <td>male</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>359309</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>8.050000</td>\n",
              "      <td>1</td>\n",
              "      <td>alone</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>417</th>\n",
              "      <td>1309</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "      <td>Peter, Master. Michael J</td>\n",
              "      <td>male</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2668</td>\n",
              "      <td>22.3583</td>\n",
              "      <td>NaN</td>\n",
              "      <td>C</td>\n",
              "      <td>7.452767</td>\n",
              "      <td>3</td>\n",
              "      <td>small</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1309 rows × 15 columns</p>\n",
              "</div>\n",
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          "metadata": {},
          "execution_count": 104
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.crosstab(df['Survived'],df['family_type'],normalize='columns')*100"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "JG-2KE45xwUZ",
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      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "family_type      alone      large      small\n",
              "Survived                                    \n",
              "0.0          69.646182  83.870968  42.123288\n",
              "1.0          30.353818  16.129032  57.876712"
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              "                                                     [key], {});\n",
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          "metadata": {},
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    },
    {
      "cell_type": "code",
      "source": [
        "df['surname'] = df['Name'].str.split(',').str.get(0)"
      ],
      "metadata": {
        "id": "fRBKCSR4x53g"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 921
        },
        "id": "CwqamHFzyPX9",
        "outputId": "fc58e6b2-bb42-4249-b2ea-ef68548843e5"
      },
      "execution_count": null,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "     PassengerId  Survived  Pclass  \\\n",
              "0              1       0.0       3   \n",
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              "2              3       1.0       3   \n",
              "3              4       1.0       1   \n",
              "4              5       0.0       3   \n",
              "..           ...       ...     ...   \n",
              "413         1305       NaN       3   \n",
              "414         1306       NaN       1   \n",
              "415         1307       NaN       3   \n",
              "416         1308       NaN       3   \n",
              "417         1309       NaN       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",
              "413                                 Spector, Mr. Woolf    male   NaN      0   \n",
              "414                       Oliva y Ocana, Dona. Fermina  female  39.0      0   \n",
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              "\n",
              "     Parch              Ticket      Fare Cabin Embarked  individual_fare  \\\n",
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              "\n",
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['title'] = df['Name'].str.split(',').str.get(1).str.strip().str.split(' ').str.get(0)"
      ],
      "metadata": {
        "id": "P3tWn-jlyiu5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "temp_df = df[df['title'].isin(['Mr.','Miss.','Mrs.','Master.','ootherr'])]"
      ],
      "metadata": {
        "id": "c23dUQ_xypZw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pd.crosstab(temp_df['Survived'],temp_df['title'],normalize='columns')*100"
      ],
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        },
        "id": "lEo_Ce-00tpD",
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "title     Master.     Miss.        Mr.  Mrs.    ootherr\n",
              "Survived                                               \n",
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              "    }\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",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-bb518448-17a8-451b-8354-ffd3e3db1ac2 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-bb518448-17a8-451b-8354-ffd3e3db1ac2');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 130
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['title'] = df['title'].str.replace('Rev.','other')\n",
        "df['title'] = df['title'].str.replace('Dr.','other')\n",
        "df['title'] = df['title'].str.replace('Col.','other')\n",
        "df['title'] = df['title'].str.replace('Major.','other')\n",
        "df['title'] = df['title'].str.replace('Capt.','other')\n",
        "df['title'] = df['title'].str.replace('the','other')\n",
        "df['title'] = df['title'].str.replace('Jonkheer.','other')\n",
        "# ,'Dr.','Col.','Major.','Don.','Capt.','the','Jonkheer.']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z8AxHHImy9wm",
        "outputId": "ad3043aa-4495-4fb6-a1a3-b7f27400e336"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-124-ffb23deeff2c>:1: FutureWarning: The default value of regex will change from True to False in a future version.\n",
            "  df['title'] = df['title'].str.replace('Rev.','other')\n",
            "<ipython-input-124-ffb23deeff2c>:2: FutureWarning: The default value of regex will change from True to False in a future version.\n",
            "  df['title'] = df['title'].str.replace('Dr.','other')\n",
            "<ipython-input-124-ffb23deeff2c>:3: FutureWarning: The default value of regex will change from True to False in a future version.\n",
            "  df['title'] = df['title'].str.replace('Col.','other')\n",
            "<ipython-input-124-ffb23deeff2c>:4: FutureWarning: The default value of regex will change from True to False in a future version.\n",
            "  df['title'] = df['title'].str.replace('Major.','other')\n",
            "<ipython-input-124-ffb23deeff2c>:5: FutureWarning: The default value of regex will change from True to False in a future version.\n",
            "  df['title'] = df['title'].str.replace('Capt.','other')\n",
            "<ipython-input-124-ffb23deeff2c>:7: FutureWarning: The default value of regex will change from True to False in a future version.\n",
            "  df['title'] = df['title'].str.replace('Jonkheer.','other')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Cabin'].isnull().sum()/len(df['Cabin'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LJg--91tzc3O",
        "outputId": "55f80d20-ce2c-4bd2-83ee-5a18b92d4b83"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.774637127578304"
            ]
          },
          "metadata": {},
          "execution_count": 133
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Cabin'].fillna('M',inplace=True)"
      ],
      "metadata": {
        "id": "_CeVn5ow1FsE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['Cabin'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AwlWc4fK15rb",
        "outputId": "bc2c8d72-575d-4e59-b11e-1403e68d9f7f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "M                  1014\n",
              "C23 C25 C27           6\n",
              "B57 B59 B63 B66       5\n",
              "G6                    5\n",
              "F33                   4\n",
              "                   ... \n",
              "A14                   1\n",
              "E63                   1\n",
              "E12                   1\n",
              "E38                   1\n",
              "C105                  1\n",
              "Name: Cabin, Length: 187, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 138
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['deck'] = df['Cabin'].str[0]"
      ],
      "metadata": {
        "id": "1rk_rl0b2U_O"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['deck'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H1OljPFG2bQ7",
        "outputId": "0366286c-a60d-42aa-c59a-f408a99c9083"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "M    1014\n",
              "C      94\n",
              "B      65\n",
              "D      46\n",
              "E      41\n",
              "A      22\n",
              "F      21\n",
              "G       5\n",
              "T       1\n",
              "Name: deck, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 141
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.crosstab(df['deck'],df['Pclass'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "TauTgl8J2cJq",
        "outputId": "4ed07d93-1684-45dd-d6cc-96600e75ad50"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pclass   1    2    3\n",
              "deck                \n",
              "A       22    0    0\n",
              "B       65    0    0\n",
              "C       94    0    0\n",
              "D       40    6    0\n",
              "E       34    4    3\n",
              "F        0   13    8\n",
              "G        0    0    5\n",
              "M       67  254  693\n",
              "T        1    0    0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-cfaad42f-471b-4c73-aa9e-f9ef6af7a575\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      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",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      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",
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              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-cfaad42f-471b-4c73-aa9e-f9ef6af7a575 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-cfaad42f-471b-4c73-aa9e-f9ef6af7a575');\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",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 143
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.crosstab(df['deck'],df['Survived'],normalize='index').plot(kind='bar',stacked=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "GaZlN8r32sVA",
        "outputId": "c255993e-c62e-4f5f-f182-7a052898d5d3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f00343b9af0>"
            ]
          },
          "metadata": {},
          "execution_count": 149
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.heatmap(df.corr())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "WS8GHvt43Jxi",
        "outputId": "20ddfaaf-da46-439d-d989-d36c72e373e8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f00343513d0>"
            ]
          },
          "metadata": {},
          "execution_count": 152
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.pairplot(df1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "DX58BTyo4lLS",
        "outputId": "42417851-4631-4050-f570-fe699f454fe5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.PairGrid at 0x7f003427fdc0>"
            ]
          },
          "metadata": {},
          "execution_count": 155
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x1080 with 42 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "tN8-MBVf5N6N",
        "outputId": "aec9464f-6317-4a7e-eafe-083fd4a88d67"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     PassengerId  Pclass                                          Name  \\\n",
              "0            892       3                              Kelly, Mr. James   \n",
              "1            893       3              Wilkes, Mrs. James (Ellen Needs)   \n",
              "2            894       2                     Myles, Mr. Thomas Francis   \n",
              "3            895       3                              Wirz, Mr. Albert   \n",
              "4            896       3  Hirvonen, Mrs. Alexander (Helga E Lindqvist)   \n",
              "..           ...     ...                                           ...   \n",
              "413         1305       3                            Spector, Mr. Woolf   \n",
              "414         1306       1                  Oliva y Ocana, Dona. Fermina   \n",
              "415         1307       3                  Saether, Mr. Simon Sivertsen   \n",
              "416         1308       3                           Ware, Mr. Frederick   \n",
              "417         1309       3                      Peter, Master. Michael J   \n",
              "\n",
              "        Sex   Age  SibSp  Parch              Ticket      Fare Cabin Embarked  \n",
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              "<p>418 rows × 11 columns</p>\n",
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          "metadata": {},
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    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "3xgKdcAy5Wo5"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}