Task_02.ipynb

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        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.impute import SimpleImputer\n",
        "\n",
        "sns.set_theme(style=\"darkgrid\", palette= 'muted')\n",
        "%matplotlib inline"
      ]
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      "source": [
        "**Exploratory Data Analysis**\n",
        "\n",
        "Data overview\n",
        "- PassengerId: Unique id for each passenger. No effect on the target feature.\n",
        "- Survived: Whether or not the passenger survived. This is the target feature.\n",
        "  - 0 = No, 1 = Yes\n",
        "- Pclass: Reflects the socio-economic status of the passenger.\n",
        "  - 1 = 1st, Upper Class\n",
        "  - 2 = 2nd, Middle Class\n",
        "  - 3 = 3rd, Lower Class\n",
        "- Name: The name of the passenger. Includes the title of the passenger, such as \"Mr.\", \"Mrs.\", and \"Master.\".\n",
        "- Sex: Gender of the passenger, either \"male\" or \"female\".\n",
        "- Age: The age of the passenger in years.\n",
        "- SibSp: # of siblings / spouses aboard the Titanic.\n",
        "- Parch: # of parents / children aboard the Titanic.\n",
        "- Ticket: The Ticket number.\n",
        "- Fare: Passenger fare.\n",
        "- Cabin: Cabin number of the passenger.\n",
        "- Embarked: Which port the passenger embarked from.\n",
        "  - C = Cherbourg\n",
        "  - Q = Queenstown\n",
        "  - S = Southampton"
      ],
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        "path1 = '/content/drive/MyDrive/Datasets/Internship/Task-02/train.csv'\n",
        "path2 = '/content/drive/MyDrive/Datasets/Internship/Task-02/test.csv'\n",
        "\n",
        "train_data = pd.read_csv(path1)\n",
        "test_data = pd.read_csv(path2)"
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        "train_data.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",
              "4      0            373450   8.0500   NaN        S  "
            ],
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              "summary": "{\n  \"name\": \"train_data\",\n  \"rows\": 891,\n  \"fields\": [\n    {\n      \"column\": \"PassengerId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 257,\n        \"min\": 1,\n        \"max\": 891,\n        \"num_unique_values\": 891,\n        \"samples\": [\n          710,\n          440,\n          841\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Survived\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Pclass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          3,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 891,\n        \"samples\": [\n          \"Moubarek, Master. Halim Gonios (\\\"William George\\\")\",\n          \"Kvillner, Mr. Johan Henrik Johannesson\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"female\",\n          \"male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14.526497332334042,\n        \"min\": 0.42,\n        \"max\": 80.0,\n        \"num_unique_values\": 88,\n        \"samples\": [\n          0.75,\n          22.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SibSp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 8,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Parch\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 6,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ticket\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 681,\n        \"samples\": [\n          \"11774\",\n          \"248740\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 49.6934285971809,\n        \"min\": 0.0,\n        \"max\": 512.3292,\n        \"num_unique_values\": 248,\n        \"samples\": [\n          11.2417,\n          51.8625\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cabin\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 147,\n        \"samples\": [\n          \"D45\",\n          \"B49\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Embarked\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"S\",\n          \"C\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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          "metadata": {},
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          "data": {
            "text/plain": [
              "   PassengerId  Pclass                                          Name     Sex  \\\n",
              "0          892       3                              Kelly, Mr. James    male   \n",
              "1          893       3              Wilkes, Mrs. James (Ellen Needs)  female   \n",
              "2          894       2                     Myles, Mr. Thomas Francis    male   \n",
              "3          895       3                              Wirz, Mr. Albert    male   \n",
              "4          896       3  Hirvonen, Mrs. Alexander (Helga E Lindqvist)  female   \n",
              "\n",
              "    Age  SibSp  Parch   Ticket     Fare Cabin Embarked  \n",
              "0  34.5      0      0   330911   7.8292   NaN        Q  \n",
              "1  47.0      1      0   363272   7.0000   NaN        S  \n",
              "2  62.0      0      0   240276   9.6875   NaN        Q  \n",
              "3  27.0      0      0   315154   8.6625   NaN        S  \n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "test_data",
              "summary": "{\n  \"name\": \"test_data\",\n  \"rows\": 418,\n  \"fields\": [\n    {\n      \"column\": \"PassengerId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 120,\n        \"min\": 892,\n        \"max\": 1309,\n        \"num_unique_values\": 418,\n        \"samples\": [\n          1213,\n          1216,\n          1280\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Pclass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          3,\n          2,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 418,\n        \"samples\": [\n          \"Krekorian, Mr. Neshan\",\n          \"Kreuchen, Miss. Emilie\",\n          \"Canavan, Mr. Patrick\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"female\",\n          \"male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14.181209235624422,\n        \"min\": 0.17,\n        \"max\": 76.0,\n        \"num_unique_values\": 79,\n        \"samples\": [\n          10.0,\n          34.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SibSp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 8,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Parch\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 9,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1,\n          6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ticket\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 363,\n        \"samples\": [\n          \"2673\",\n          \"W./C. 6607\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 55.907576179973844,\n        \"min\": 0.0,\n        \"max\": 512.3292,\n        \"num_unique_values\": 169,\n        \"samples\": [\n          41.5792,\n          57.75\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cabin\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 76,\n        \"samples\": [\n          \"A21\",\n          \"E45\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Embarked\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Q\",\n          \"S\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Features and Survival"
      ],
      "metadata": {
        "id": "CFkDBEn8TA-2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(6, 4))\n",
        "\n",
        "plt.hist(train_data[train_data['Survived'] == 1]['Age'], bins=30, alpha=0.5, label='Survived', color='green', edgecolor='black')\n",
        "plt.hist(train_data[train_data['Survived'] == 0]['Age'], bins=30, alpha=0.5, label='Did Not Survive', color='red', edgecolor='black')\n",
        "\n",
        "plt.title('Age Distribution by Survival Status')\n",
        "plt.xlabel('Age')\n",
        "plt.ylabel('Frequency')\n",
        "plt.legend()\n",
        "plt.grid(True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "moObdgI0Le1Z",
        "outputId": "fd32dd07-3f60-4e97-f647-e04d4601d6af"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "- Younger passengers, especially those < 5 years old, seem to survive at a higher rate.\n",
        "- Older passengers seem to have a lower survival rate, especially around 40 - 75 years old."
      ],
      "metadata": {
        "id": "JdJ3tBsOTFIR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(6, 4))\n",
        "\n",
        "plt.hist(train_data[train_data['Survived'] == 1]['Fare'], bins=30, alpha=0.5, label='Survived', color='green', edgecolor='black')\n",
        "plt.hist(train_data[train_data['Survived'] == 0]['Fare'], bins=30, alpha=0.5, label='Did Not Survive', color='red', edgecolor='black')\n",
        "\n",
        "plt.title('Fare Distribution by Survival Status')\n",
        "plt.xlabel('Fare')\n",
        "plt.ylabel('Frequency')\n",
        "plt.legend()\n",
        "plt.grid(True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "XykL_SKqL6kQ",
        "outputId": "a67459a8-36d5-48c6-d518-e2b1004d41d0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "- Lower fares seemed to have survived less.\n",
        "- Higher fares seemed to survive more.\n",
        "- This can be correlated with the socio-economic status of the passenger."
      ],
      "metadata": {
        "id": "iz5L8J8cTL_C"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = train_data.copy()\n",
        "df['Sex_Label'] = df['Sex'].map({'male': 'Male', 'female': 'Female'})\n",
        "\n",
        "survival_counts = df.groupby(['Sex_Label', 'Survived']).size().unstack()\n",
        "survival_counts.plot(kind='bar', stacked=False, figsize=(6, 4), color=['salmon', 'skyblue'])\n",
        "\n",
        "plt.title('Survival Counts by Sex')\n",
        "plt.xlabel('Sex')\n",
        "plt.ylabel('Number of People')\n",
        "plt.xticks(rotation=0)\n",
        "plt.legend(title='Survived', labels=['Did not Survive', 'Survived'])\n",
        "plt.grid(True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "dcpU8mmrMAED",
        "outputId": "358c953d-cf4c-450b-a309-c5f9abf79c27"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "- Females have a higher rate of survival than Males."
      ],
      "metadata": {
        "id": "SgA4sLgbTTDn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "survival_counts = train_data.groupby(['Embarked', 'Survived']).size().unstack()\n",
        "\n",
        "survival_counts.plot(kind='bar', stacked=False, figsize=(6, 4), color=['salmon', 'skyblue'])\n",
        "\n",
        "plt.title('Survival Counts by Embarked')\n",
        "plt.xlabel('Embarked')\n",
        "plt.ylabel('Number of People')\n",
        "plt.xticks(rotation=0)\n",
        "plt.legend(title='Survived', labels=['Did not Survive', 'Survived'])\n",
        "plt.grid(True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "VicZTmwkMD62",
        "outputId": "a7434482-4499-4368-d78b-83ddd27f1204"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "- Those who embarked from \"S\" had the lowest rate of survival.\n",
        "- Those who embarked from \"C\" had the highest rate to survive."
      ],
      "metadata": {
        "id": "yMKlot9mTVo3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "survival_counts = train_data.groupby(['Pclass', 'Survived']).size().unstack()\n",
        "\n",
        "survival_counts.plot(kind='bar', stacked=False, figsize=(6, 4), color=['salmon', 'skyblue'])\n",
        "\n",
        "plt.title('Survival Counts by Pclass')\n",
        "plt.xlabel('Pclass')\n",
        "plt.ylabel('Number of People')\n",
        "plt.xticks(rotation=0)\n",
        "plt.legend(title='Survived', labels=['Did not Survive', 'Survived'])\n",
        "plt.grid(True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "hSHVEAS3MI0H",
        "outputId": "ebfe5828-d2ac-493c-ac6c-6a44448fe735"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "- 1st class passengers have a high chance of survival, while 3rd class passengers have a high chance of dying.\n",
        "- Socio-economic status seems to play a part for survival."
      ],
      "metadata": {
        "id": "7Bm5XnfeTb17"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Filling in missing values**"
      ],
      "metadata": {
        "id": "XW7YxjH5TjkM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "all_data = pd.concat([train_data, test_data])"
      ],
      "metadata": {
        "id": "vbfTL7j7PJu4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "all_data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "uJurd9_TPNZ1",
        "outputId": "d31736ae-bd49-44bb-8347-a09bf1d71ca6"
      },
      "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",
              "3            4       1.0       1   \n",
              "4            5       0.0       3   \n",
              "\n",
              "                                                Name     Sex   Age  SibSp  \\\n",
              "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
              "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
              "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
              "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
              "4                           Allen, Mr. William Henry    male  35.0      0   \n",
              "\n",
              "   Parch            Ticket     Fare Cabin Embarked  \n",
              "0      0         A/5 21171   7.2500   NaN        S  \n",
              "1      0          PC 17599  71.2833   C85        C  \n",
              "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
              "3      0            113803  53.1000  C123        S  \n",
              "4      0            373450   8.0500   NaN        S  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-7f537bb5-6e3f-4c8b-9fc9-ea73412bbf61\" 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",
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              "        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>0.0</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>1.0</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>1.0</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>1.0</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>0.0</td>\n",
              "      <td>3</td>\n",
              "      <td>Allen, Mr. William Henry</td>\n",
              "      <td>male</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>373450</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7f537bb5-6e3f-4c8b-9fc9-ea73412bbf61')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "all_data",
              "summary": "{\n  \"name\": \"all_data\",\n  \"rows\": 1309,\n  \"fields\": [\n    {\n      \"column\": \"PassengerId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 378,\n        \"min\": 1,\n        \"max\": 1309,\n        \"num_unique_values\": 1309,\n        \"samples\": [\n          1149,\n          1050,\n          983\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Survived\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4865924542648575,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1.0,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Pclass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          3,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1307,\n        \"samples\": [\n          \"Bryhl, Miss. Dagmar Jenny Ingeborg \",\n          \"Borebank, Mr. John James\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"female\",\n          \"male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14.413493211271321,\n        \"min\": 0.17,\n        \"max\": 80.0,\n        \"num_unique_values\": 98,\n        \"samples\": [\n          45.5,\n          23.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SibSp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 8,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Parch\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 9,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1,\n          4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ticket\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 929,\n        \"samples\": [\n          \"PC 17531\",\n          \"345765\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 51.75866823917411,\n        \"min\": 0.0,\n        \"max\": 512.3292,\n        \"num_unique_values\": 281,\n        \"samples\": [\n          11.2417,\n          35.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cabin\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 186,\n        \"samples\": [\n          \"B71\",\n          \"C51\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Embarked\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"S\",\n          \"C\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "all_data.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-SKNyWEGRxUy",
        "outputId": "c204663a-d773-4517-c372-de4233920f2b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Index: 1309 entries, 0 to 417\n",
            "Data columns (total 14 columns):\n",
            " #   Column        Non-Null Count  Dtype  \n",
            "---  ------        --------------  -----  \n",
            " 0   PassengerId   1309 non-null   int64  \n",
            " 1   Survived      891 non-null    float64\n",
            " 2   Pclass        1309 non-null   int64  \n",
            " 3   Name          1309 non-null   object \n",
            " 4   Sex           1309 non-null   int64  \n",
            " 5   Age           1309 non-null   float64\n",
            " 6   SibSp         1309 non-null   int64  \n",
            " 7   Parch         1309 non-null   int64  \n",
            " 8   Ticket        1309 non-null   object \n",
            " 9   Fare          1309 non-null   float64\n",
            " 10  Cabin         295 non-null    object \n",
            " 11  Embarked      1309 non-null   object \n",
            " 12  Deck          1309 non-null   object \n",
            " 13  CabinMissing  1309 non-null   int64  \n",
            "dtypes: float64(3), int64(6), object(5)\n",
            "memory usage: 153.4+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "all_data.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 523
        },
        "id": "ELgxGzxMRzvF",
        "outputId": "ccf10bb1-875e-48a7-8ec0-74ea81b64880"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PassengerId        0\n",
              "Survived         418\n",
              "Pclass             0\n",
              "Name               0\n",
              "Sex                0\n",
              "Age                0\n",
              "SibSp              0\n",
              "Parch              0\n",
              "Ticket             0\n",
              "Fare               0\n",
              "Cabin           1014\n",
              "Embarked           0\n",
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              "CabinMissing       0\n",
              "dtype: int64"
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              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "- Missing values in all_data:\n",
        "\n",
        "  - Survived (418 missing values)\n",
        "  - Cabin (1014 missing values)"
      ],
      "metadata": {
        "id": "HegF7mwFdFG5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "all_data.loc[:,['Cabin', 'Survived']].dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 147
        },
        "id": "l_q-OBzoUdMu",
        "outputId": "056ac81f-07ea-464c-c794-832fe8977f77"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Cabin        object\n",
              "Survived    float64\n",
              "dtype: object"
            ],
            "text/html": [
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              "</div><br><label><b>dtype:</b> object</label>"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "col = all_data.loc[:, ['Cabin', 'Survived']]\n",
        "col.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "IqW_EQuXX9c6",
        "outputId": "1beb963a-a5af-46f7-c565-7fc332aa52fb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Cabin  Survived\n",
              "0   NaN       0.0\n",
              "1   C85       1.0\n",
              "2   NaN       1.0\n",
              "3  C123       1.0\n",
              "4   NaN       0.0"
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>C123</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-cfd3203a-132f-4965-bd79-8342e390b6cc')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-cfd3203a-132f-4965-bd79-8342e390b6cc 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-cfd3203a-132f-4965-bd79-8342e390b6cc');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-80133657-3c21-4570-b6ee-775400d2c8fa\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-80133657-3c21-4570-b6ee-775400d2c8fa')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-80133657-3c21-4570-b6ee-775400d2c8fa button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "col",
              "summary": "{\n  \"name\": \"col\",\n  \"rows\": 1309,\n  \"fields\": [\n    {\n      \"column\": \"Cabin\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 186,\n        \"samples\": [\n          \"B71\",\n          \"C51\",\n          \"E52\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Survived\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4865924542648575,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1.0,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.impute import SimpleImputer\n",
        "from sklearn.compose import ColumnTransformer\n",
        "\n",
        "\n",
        "# Define the imputers for different data types\n",
        "numeric_imputer = SimpleImputer(strategy='mean')\n",
        "categorical_imputer = SimpleImputer(strategy='most_frequent')\n",
        "\n",
        "# Define the column transformer\n",
        "preprocessor = ColumnTransformer(\n",
        "    transformers=[\n",
        "        ('num', numeric_imputer, ['Survived']),\n",
        "        ('cat', categorical_imputer, ['Cabin'])\n",
        "    ])\n",
        "\n",
        "# Apply the transformer to the DataFrame\n",
        "imputed_data = preprocessor.fit_transform(col)\n",
        "\n",
        "imputed_data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s4MYs8uQVAdG",
        "outputId": "3941f1b7-d092-4094-af50-11724acb8da5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.0, 'C23 C25 C27'],\n",
              "       [1.0, 'C85'],\n",
              "       [1.0, 'C23 C25 C27'],\n",
              "       ...,\n",
              "       [0.3838383838383838, 'C23 C25 C27'],\n",
              "       [0.3838383838383838, 'C23 C25 C27'],\n",
              "       [0.3838383838383838, 'C23 C25 C27']], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "all_data['Survived'] = imputed_data[:,0].astype(float)\n",
        "all_data['Cabin'] = imputed_data[:,1]"
      ],
      "metadata": {
        "id": "kPtz2QpgZ-qW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "all_data.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Qi2R6fP-aI1l",
        "outputId": "21fcf2e8-6b08-49e1-f630-636db4d2c3db"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Index: 1309 entries, 0 to 417\n",
            "Data columns (total 14 columns):\n",
            " #   Column        Non-Null Count  Dtype  \n",
            "---  ------        --------------  -----  \n",
            " 0   PassengerId   1309 non-null   int64  \n",
            " 1   Survived      1309 non-null   float64\n",
            " 2   Pclass        1309 non-null   int64  \n",
            " 3   Name          1309 non-null   object \n",
            " 4   Sex           1309 non-null   int64  \n",
            " 5   Age           1309 non-null   float64\n",
            " 6   SibSp         1309 non-null   int64  \n",
            " 7   Parch         1309 non-null   int64  \n",
            " 8   Ticket        1309 non-null   object \n",
            " 9   Fare          1309 non-null   float64\n",
            " 10  Cabin         1309 non-null   object \n",
            " 11  Embarked      1309 non-null   object \n",
            " 12  Deck          1309 non-null   object \n",
            " 13  CabinMissing  1309 non-null   int64  \n",
            "dtypes: float64(3), int64(6), object(5)\n",
            "memory usage: 153.4+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "- No Missing value"
      ],
      "metadata": {
        "id": "25Puz-ybdkQg"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "----"
      ],
      "metadata": {
        "id": "wzpkERHkddHm"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "TVIkBQfPcZGx"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}