EDA_on_Titanic_dataset.ipynb

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        "<a href=\"https://colab.research.google.com/github/atharvad38/Atharva3_Projects/blob/main/EDA_on_Titanic_dataset.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ]
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    {
      "cell_type": "markdown",
      "source": [
        "### Reading the titanic dataset file"
      ],
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        "id": "ekFMwhZq3LSj"
      }
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      "source": [
        "train = pd.read_csv('titanic_train.csv')"
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      "execution_count": 11,
      "outputs": []
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        "train.head()"
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      "execution_count": 12,
      "outputs": [
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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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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "train",
              "summary": "{\n  \"name\": \"train\",\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}"
            }
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Lets check how many values are missing"
      ],
      "metadata": {
        "id": "RE6Hoy476NNN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train.isnull()"
      ],
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          "height": 443
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        "id": "dYB_GGY_4ykH",
        "outputId": "a9520dbe-aa0f-4f5e-e701-f26e703f0c56"
      },
      "execution_count": 13,
      "outputs": [
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          "data": {
            "text/plain": [
              "     PassengerId  Survived  Pclass   Name    Sex    Age  SibSp  Parch  Ticket  \\\n",
              "0          False     False   False  False  False  False  False  False   False   \n",
              "1          False     False   False  False  False  False  False  False   False   \n",
              "2          False     False   False  False  False  False  False  False   False   \n",
              "3          False     False   False  False  False  False  False  False   False   \n",
              "4          False     False   False  False  False  False  False  False   False   \n",
              "..           ...       ...     ...    ...    ...    ...    ...    ...     ...   \n",
              "886        False     False   False  False  False  False  False  False   False   \n",
              "887        False     False   False  False  False  False  False  False   False   \n",
              "888        False     False   False  False  False   True  False  False   False   \n",
              "889        False     False   False  False  False  False  False  False   False   \n",
              "890        False     False   False  False  False  False  False  False   False   \n",
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              "type": "dataframe",
              "summary": "{\n  \"name\": \"train\",\n  \"rows\": 891,\n  \"fields\": [\n    {\n      \"column\": \"PassengerId\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Survived\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Pclass\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Name\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sex\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SibSp\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Parch\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ticket\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fare\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cabin\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Embarked\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lVHq7_TI6TVU",
        "outputId": "967975b0-2d26-437b-a484-d9df7485723e"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PassengerId      0\n",
              "Survived         0\n",
              "Pclass           0\n",
              "Name             0\n",
              "Sex              0\n",
              "Age            177\n",
              "SibSp            0\n",
              "Parch            0\n",
              "Ticket           0\n",
              "Fare             0\n",
              "Cabin          687\n",
              "Embarked         2\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Total 177 values from age and 687 values from cabin are missing majorly"
      ],
      "metadata": {
        "id": "bBkbGgZA6YA8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.heatmap(train.isnull())"
      ],
      "metadata": {
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          "height": 524
        },
        "id": "9PUBbKjV6WnF",
        "outputId": "a18103da-3ad3-4885-a7c7-9b81c21d79e1"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now lets make a countplot to see how many people have survived"
      ],
      "metadata": {
        "id": "Bk6Wt-xd9J54"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(x='Survived',data=train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "V2q2476Z6mcO",
        "outputId": "7ad522d0-93ec-4bf0-f5b0-8ff9b1badecf"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Survived', ylabel='count'>"
            ]
          },
          "metadata": {},
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now lets see out of how many survived , how many were men and women"
      ],
      "metadata": {
        "id": "Mikgzs_Z9i5I"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(x='Survived',hue='Sex',data=train,palette ='RdBu_r')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "wSqEorTn9U22",
        "outputId": "c5293f18-3ce0-47ef-92c1-762a6c68eebb"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Survived', ylabel='count'>"
            ]
          },
          "metadata": {},
          "execution_count": 24
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The graph above helps us understand that the majority of survivors were female, while the majority of those who lost their lives were male."
      ],
      "metadata": {
        "id": "aJr6OZ9o93Yp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(x='Survived',hue='Pclass',data=train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "aWCUoFYJ9zDO",
        "outputId": "a16d5ac5-ca87-4ec7-b9f7-9c0998d26947"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Survived', ylabel='count'>"
            ]
          },
          "metadata": {},
          "execution_count": 26
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The graph above shows that the majority of survivors were from the first class, while the majority of those who lost their lives were from the third class."
      ],
      "metadata": {
        "id": "IelLAr4B-9AY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.distplot(train['Age'].dropna(),kde=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 667
        },
        "id": "YzAH2Arl-78q",
        "outputId": "fd0cbc62-0c00-4cd8-f4a4-5cbf3dbcd8a0"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-27-96d5b0357822>:1: UserWarning: \n",
            "\n",
            "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
            "\n",
            "Please adapt your code to use either `displot` (a figure-level function with\n",
            "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "\n",
            "For a guide to updating your code to use the new functions, please see\n",
            "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
            "\n",
            "  sns.distplot(train['Age'].dropna(),kde=False)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Age'>"
            ]
          },
          "metadata": {},
          "execution_count": 27
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The distribution of age for passengers on the Titanic shows that the majority of passengers were between the ages of 20 and 40. There were also a significant number of passengers under the age of 20 and over the age of 40. The distribution is slightly skewed to the right, indicating that there were more passengers over the age of 40 than under the age of 20."
      ],
      "metadata": {
        "id": "5y5Kilbo_toj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train['Age'].hist()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "id": "2CgeqQNx_bWk",
        "outputId": "9fa18254-b02d-402c-c3ff-33b5beb1b8c6"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 30
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(x='SibSp',data=train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "UTdYWMKv_-uP",
        "outputId": "352fb676-2f52-4e33-dcc0-3177ad228a84"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='SibSp', ylabel='count'>"
            ]
          },
          "metadata": {},
          "execution_count": 31
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# The graph above shows that the majority of passengers on the Titanic were traveling alone or with one other person. There were also a significant number of passengers traveling with two or three other people. The number of passengers traveling with four or more other people was relatively small.\n",
        "\n",
        "# This information could be useful for understanding the social dynamics on the Titanic. For example, passengers who were traveling alone may have been more likely to be isolated and vulnerable, while passengers who were traveling with family or friends may have had more support."
      ],
      "metadata": {
        "id": "OSu_NZbrAYDi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train['Fare'].hist()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "id": "3XC3GIVEAZiM",
        "outputId": "d46ce2d8-fedf-4812-e63c-5e0d078ecfd7"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 32
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        " The distribution of fares paid by passengers on the Titanic shows that the majority of passengers paid between 0 and 50 pounds. There were also a significant number of passengers who paid between 50 and 100 pounds. The number of passengers who paid over 100 pounds was relatively small.\n",
        "\n",
        "This information could be useful for understanding the economic status of passengers on the Titanic. For example, passengers who paid higher fares may have been more likely to be from wealthier families, while passengers who paid lower fares may have been more likely to be from poorer families."
      ],
      "metadata": {
        "id": "0b94VGvlAwUt"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Data Cleaning"
      ],
      "metadata": {
        "id": "isP26D29A34J"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "As we saw, many values in age column were missing, using Pclas , we will fill those missing values"
      ],
      "metadata": {
        "id": "a9Jv-TzGBMhD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.boxplot(x='Pclass',y='Age',data=train,palette='winter')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 578
        },
        "id": "6uM7Tgx1Ayqi",
        "outputId": "8924f295-d7d5-4abc-87bd-6570ccc18d65"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-34-83ae0cce430d>:1: FutureWarning: \n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "  sns.boxplot(x='Pclass',y='Age',data=train,palette='winter')\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Pclass', ylabel='Age'>"
            ]
          },
          "metadata": {},
          "execution_count": 34
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "  Pclass 1 Avg age = 37\n",
        "\n",
        "  Pclass 2 Avg age = 29\n",
        "  \n",
        "  Pclass 3 Avg age = 24\n"
      ],
      "metadata": {
        "id": "wCKonhAWB3KP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def impute(cols):\n",
        "  age = cols[0]\n",
        "  pclass = cols[1]\n",
        "  if pd.isnull(age):\n",
        "    if pclass == 1:\n",
        "      return 37\n",
        "    elif pclass == 2:\n",
        "      return 29\n",
        "    else:\n",
        "      return 24\n",
        "  else:\n",
        "    return age"
      ],
      "metadata": {
        "id": "9KQAFeMhCnAf"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now apply above function"
      ],
      "metadata": {
        "id": "YT8yiXxHDFqd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train['Age'] = train[['Age','Pclass']].apply(impute,axis=1)"
      ],
      "metadata": {
        "id": "fP8jVdxaC9ke"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now lets check the heatmap again"
      ],
      "metadata": {
        "id": "uLGTGY36DTMP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.heatmap(train.isnull())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 524
        },
        "id": "nQ1iP9hKDNlm",
        "outputId": "8eefae48-97b8-4367-dee5-6cc01368e401"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 37
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "deleting cabin column as it is of no use"
      ],
      "metadata": {
        "id": "ajzujdUJDvtV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train.drop('Cabin',axis=1,inplace=True)"
      ],
      "metadata": {
        "id": "WAdExFkbDZR1"
      },
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sns.heatmap(train.isnull())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 524
        },
        "id": "Z2gZ2UkPDzQb",
        "outputId": "a67b10b9-6bf9-41da-9fbf-9cff3d225797"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 39
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "  Now we will convert categorical features into some numerical values\n",
        "  #`pd.get_dummies()` converts categorical variables into a form that can be provided to ML algorithms to do a better job in prediction, by converting categorical variable(s) into dummy/indicator variables."
      ],
      "metadata": {
        "id": "oytKKrnnD5P-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pd.get_dummies(train['Embarked'],drop_first=True).head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "ip-ICoMnD2oH",
        "outputId": "090d9cfe-cfd9-4632-a849-b924667d2a80"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Q      S\n",
              "0  False   True\n",
              "1  False  False\n",
              "2  False   True\n",
              "3  False   True\n",
              "4  False   True"
            ],
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              "    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-fbe676c1-8742-4736-a092-00cb67310900 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"pd\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"Q\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"S\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\"\"\"Dropping the first category is done to avoid the dummy variable trap, which occurs when the variables are highly correlated, leading to multicollinearity. This can cause issues in regression models.\n",
        "\n",
        "Example:\n",
        "Consider a column Color with categories Red, Green, and Blue.\n",
        "\n",
        "Without dropping a category:\n",
        "\n",
        "Red is represented as [1, 0, 0]\n",
        "Green is [0, 1, 0]\n",
        "Blue is [0, 0, 1]\n",
        "Using all three dummies, if you know the values of two dummies, you can infer the third. For instance, if Red=0 and Green=0, then it must be Blue=1. This perfect collinearity is problematic in regression models.\n",
        "\n",
        "With dropping the first category:\n",
        "\n",
        "Red is implied when both Green and Blue are 0\n",
        "Green is [1, 0]\n",
        "Blue is [0, 1]\n",
        "Now, the absence of Green and Blue implicitly means Red. This setup avoids multicollinearity by removing redundant information.\"\"\""
      ],
      "metadata": {
        "id": "evFNk-mXEYjr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sex = pd.get_dummies(train['Sex'],drop_first = True)\n",
        "embarked = pd.get_dummies(train['Embarked'],drop_first = True)"
      ],
      "metadata": {
        "id": "JjCZkqe1EPuS"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train.drop(['Sex','Embarked','Name','Ticket'],axis=1,inplace=True)"
      ],
      "metadata": {
        "id": "6Dt9GWpQEgjw"
      },
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "NnJsDOwAEjG6",
        "outputId": "9aa04f7f-0487-4d16-ba24-3846c72ab656"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   PassengerId  Survived  Pclass   Age  SibSp  Parch     Fare\n",
              "0            1         0       3  22.0      1      0   7.2500\n",
              "1            2         1       1  38.0      1      0  71.2833\n",
              "2            3         1       3  26.0      0      0   7.9250\n",
              "3            4         1       1  35.0      1      0  53.1000\n",
              "4            5         0       3  35.0      0      0   8.0500"
            ],
            "text/html": [
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              "      <td>8.0500</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-58e89215-86f1-4cfb-b28e-af660f9d81c5')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "      display:flex;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      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",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-58e89215-86f1-4cfb-b28e-af660f9d81c5 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-58e89215-86f1-4cfb-b28e-af660f9d81c5');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
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              "\n",
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              "            title=\"Suggest charts\"\n",
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              "    </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-5baa06db-388a-421c-8813-920e381c2caf button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "train",
              "summary": "{\n  \"name\": \"train\",\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\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13.24453184208721,\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\": \"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}"
            }
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train = pd.concat([train,sex,embarked],axis=1)"
      ],
      "metadata": {
        "id": "tv4fT9zBElih"
      },
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "_1sVpulTErpF",
        "outputId": "75819563-d2c7-467e-d869-5cc84d56be84"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   PassengerId  Survived  Pclass   Age  SibSp  Parch     Fare   male      Q  \\\n",
              "0            1         0       3  22.0      1      0   7.2500   True  False   \n",
              "1            2         1       1  38.0      1      0  71.2833  False  False   \n",
              "2            3         1       3  26.0      0      0   7.9250  False  False   \n",
              "3            4         1       1  35.0      1      0  53.1000  False  False   \n",
              "4            5         0       3  35.0      0      0   8.0500   True  False   \n",
              "\n",
              "       S  \n",
              "0   True  \n",
              "1  False  \n",
              "2   True  \n",
              "3   True  \n",
              "4   True  "
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              "    async function quickchart(key) {\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "train",
              "summary": "{\n  \"name\": \"train\",\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\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13.24453184208721,\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\": \"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\": \"male\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          false,\n          true\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Q\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"S\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          false,\n          true\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.heatmap(train.isnull(),cmap='viridis')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 524
        },
        "id": "kpxaCrEDEt9Y",
        "outputId": "7fec1ee5-b46e-45a8-eefc-aed92e6a63c5"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 48
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "AS you can see from above graph, there are no null values"
      ],
      "metadata": {
        "id": "qYjxR8lPFBD1"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Building a Logistic Regression model"
      ],
      "metadata": {
        "id": "WJd4RhqiFgg6"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Train test split"
      ],
      "metadata": {
        "id": "Su1IaHgWFl1t"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train.drop('Survived',axis=1).head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "kDJIvO91E9SK",
        "outputId": "ab18bfff-19b4-4f1d-f917-e43996ddb2c1"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   PassengerId  Pclass   Age  SibSp  Parch     Fare   male      Q      S\n",
              "0            1       3  22.0      1      0   7.2500   True  False   True\n",
              "1            2       1  38.0      1      0  71.2833  False  False  False\n",
              "2            3       3  26.0      0      0   7.9250  False  False   True\n",
              "3            4       1  35.0      1      0  53.1000  False  False   True\n",
              "4            5       3  35.0      0      0   8.0500   True  False   True"
            ],
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              "  }\n",
              "\n",
              "  @keyframes spin {\n",
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              "    async function quickchart(key) {\n",
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              "        document.querySelector('#' + key + ' button');\n",
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              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"train\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"PassengerId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 1,\n        \"max\": 5,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          2,\n          5,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Pclass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6.833739825307955,\n        \"min\": 22.0,\n        \"max\": 38.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          38.0,\n          35.0\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\": 1,\n        \"num_unique_values\": 2,\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\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 30.5100288352535,\n        \"min\": 7.25,\n        \"max\": 71.2833,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          71.2833\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"male\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Q\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"S\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train['Survived'].head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Mzb6WNT7Fsix",
        "outputId": "e2215767-66a6-4c38-e0dc-bda0a2af6021"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0\n",
              "1    1\n",
              "2    1\n",
              "3    1\n",
              "4    0\n",
              "Name: Survived, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split"
      ],
      "metadata": {
        "id": "hMMrUR0nFwlQ"
      },
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train,x_test,y_train,y_test = train_test_split(train.drop('Survived',axis=1),train['Survived'],test_size=0.30,random_state=101)"
      ],
      "metadata": {
        "id": "gUXo4WlZF3Xk"
      },
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Training and Predicting"
      ],
      "metadata": {
        "id": "0KaQj8hbGpTh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression"
      ],
      "metadata": {
        "id": "JshmKbT_GNXP"
      },
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "logmodel = LogisticRegression()\n",
        "logmodel.fit(x_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 240
        },
        "id": "RtgPk8gMGyR8",
        "outputId": "3f25dda3-f759-4595-9ce5-684e64847b7e"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predictions = logmodel.predict(x_test)"
      ],
      "metadata": {
        "id": "_QN2qEmFHBOX"
      },
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix"
      ],
      "metadata": {
        "id": "eD20HPoFHLGV"
      },
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy = confusion_matrix(y_test,predictions)"
      ],
      "metadata": {
        "id": "UArawqF1HPat"
      },
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jIpJdlh-HXqL",
        "outputId": "2f9bf05a-1909-4618-b0aa-08ea7e21353a"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[133,  21],\n",
              "       [ 42,  72]])"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score"
      ],
      "metadata": {
        "id": "TKmOb118HY_6"
      },
      "execution_count": 60,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy = accuracy_score(y_test,predictions)\n",
        "accuracy"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "23RkeafrHixm",
        "outputId": "98cf5d28-ed7c-4285-a1ff-b018bd08ea1a"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7649253731343284"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predictions"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZI1xKtrrHpqw",
        "outputId": "fcb99769-6364-4bec-9279-e70ae8443bf9"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0,\n",
              "       1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0,\n",
              "       0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0,\n",
              "       1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0,\n",
              "       0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0,\n",
              "       0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1,\n",
              "       1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0,\n",
              "       0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0,\n",
              "       1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1,\n",
              "       0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1,\n",
              "       1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1,\n",
              "       0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0,\n",
              "       1, 0, 0, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "aPTHW-h7Htbu"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}