logistic_reg_on_titanic_MS.ipynb

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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ]
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    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('/content/titanic_dataset.csv')"
      ],
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\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": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gHlpyUhMTFks",
        "outputId": "5db985d9-f8d4-4a39-f3c5-a9c30092a1ac"
      },
      "execution_count": 39,
      "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": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.Cabin.nunique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d2HdLoY9TJq6",
        "outputId": "b9fa29f7-0f53-4eb3-b384-63d917fd2bae"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "147"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.drop('Cabin',axis=1)"
      ],
      "metadata": {
        "id": "FCPDiljCTaXj"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 258
        },
        "id": "cI6ZcrK7ThaC",
        "outputId": "517da94d-c983-4038-dc96-6b7cf59eb820"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   PassengerId  Survived  Pclass  \\\n",
              "0            1         0       3   \n",
              "1            2         1       1   \n",
              "2            3         1       3   \n",
              "3            4         1       1   \n",
              "4            5         0       3   \n",
              "\n",
              "                                                Name     Sex   Age  SibSp  \\\n",
              "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
              "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
              "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
              "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
              "4                           Allen, Mr. William Henry    male  35.0      0   \n",
              "\n",
              "   Parch            Ticket     Fare Embarked  \n",
              "0      0         A/5 21171   7.2500        S  \n",
              "1      0          PC 17599  71.2833        C  \n",
              "2      0  STON/O2. 3101282   7.9250        S  \n",
              "3      0            113803  53.1000        S  \n",
              "4      0            373450   8.0500        S  "
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              "        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": "df",
              "summary": "{\n  \"name\": \"df\",\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\": \"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": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WeyZY8LtTmMz",
        "outputId": "f86e5df6-6b56-473a-f60e-d613fa410fff"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PassengerId      0\n",
              "Survived         0\n",
              "Pclass           0\n",
              "Name             0\n",
              "Sex              0\n",
              "Age            177\n",
              "SibSp            0\n",
              "Parch            0\n",
              "Ticket           0\n",
              "Fare             0\n",
              "Embarked         2\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "If_rE-a_TrKu",
        "outputId": "0af80d84-4b1a-4656-8445-b59797eca55e"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PassengerId      int64\n",
              "Survived         int64\n",
              "Pclass           int64\n",
              "Name            object\n",
              "Sex             object\n",
              "Age            float64\n",
              "SibSp            int64\n",
              "Parch            int64\n",
              "Ticket          object\n",
              "Fare           float64\n",
              "Embarked        object\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.hist(df['Age'],color='m')\n",
        "plt.title('histogram of age')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 470
        },
        "id": "gaZhJOvfTq31",
        "outputId": "a4f662a0-833f-45d3-f60b-94b5e5252eaa"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'histogram of age')"
            ]
          },
          "metadata": {},
          "execution_count": 45
        },
        {
          "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": [
        "df['Age']=df['Age'].fillna(df['Age'].median())"
      ],
      "metadata": {
        "id": "tPfWxaWIUMhS"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['Embarked']=df['Embarked'].fillna(df['Embarked'].mode()[0])"
      ],
      "metadata": {
        "id": "C9mnjCi1UU62"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_41jLbGjUvW0",
        "outputId": "0ed4faa0-c795-4432-e853-dde2801d819c"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PassengerId    0\n",
              "Survived       0\n",
              "Pclass         0\n",
              "Name           0\n",
              "Sex            0\n",
              "Age            0\n",
              "SibSp          0\n",
              "Parch          0\n",
              "Ticket         0\n",
              "Fare           0\n",
              "Embarked       0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# label encoding"
      ],
      "metadata": {
        "id": "NzXd1VoNUy2e"
      },
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 258
        },
        "id": "Ls2uyBAZU4sb",
        "outputId": "92a5493e-4e99-46b0-cf60-9c5b7229f44e"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   PassengerId  Survived  Pclass  \\\n",
              "0            1         0       3   \n",
              "1            2         1       1   \n",
              "2            3         1       3   \n",
              "3            4         1       1   \n",
              "4            5         0       3   \n",
              "\n",
              "                                                Name     Sex   Age  SibSp  \\\n",
              "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
              "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
              "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
              "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
              "4                           Allen, Mr. William Henry    male  35.0      0   \n",
              "\n",
              "   Parch            Ticket     Fare Embarked  \n",
              "0      0         A/5 21171   7.2500        S  \n",
              "1      0          PC 17599  71.2833        C  \n",
              "2      0  STON/O2. 3101282   7.9250        S  \n",
              "3      0            113803  53.1000        S  \n",
              "4      0            373450   8.0500        S  "
            ],
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              "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
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              "      <td>26.0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>7.9250</td>\n",
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              "      <td>4</td>\n",
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              "      <td>1</td>\n",
              "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
              "      <td>female</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>113803</td>\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "    .colab-df-buttons div {\n",
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              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
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              "  </style>\n",
              "\n",
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              "        document.querySelector('#df-5f1447fe-a3ad-4247-ae4d-7a4707ad9306 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-5f1447fe-a3ad-4247-ae4d-7a4707ad9306');\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",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-6b51ac7c-03bf-4069-adf6-5f6d9d8d7640')\"\n",
              "            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",
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              "    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",
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              "      border-color: transparent;\n",
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              "    }\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",
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              "      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": "df",
              "summary": "{\n  \"name\": \"df\",\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\": 13.019696550973201,\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\": \"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": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CQD5QB2VU_Ek",
        "outputId": "f5317c9b-ab1c-4473-b26f-560f405bef13"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n",
              "       'Parch', 'Ticket', 'Fare', 'Embarked'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for i in ['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp','Parch', 'Ticket', 'Fare', 'Embarked']:\n",
        "  print(i,df[i].nunique())\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 690
        },
        "id": "5OvcyZf7U9jO",
        "outputId": "26e4cb12-a473-4c47-8451-be682e59331c"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "PassengerId 891\n",
            "Survived 2\n",
            "Pclass 3\n",
            "Name 891\n",
            "Sex 2\n",
            "Age 88\n",
            "SibSp 7\n",
            "Parch 7\n",
            "Ticket 681\n",
            "Fare 248\n",
            "Embarked 3\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     PassengerId  Survived  Pclass  \\\n",
              "0              1         0       3   \n",
              "1              2         1       1   \n",
              "2              3         1       3   \n",
              "3              4         1       1   \n",
              "4              5         0       3   \n",
              "..           ...       ...     ...   \n",
              "886          887         0       2   \n",
              "887          888         1       1   \n",
              "888          889         0       3   \n",
              "889          890         1       1   \n",
              "890          891         0       3   \n",
              "\n",
              "                                                  Name     Sex   Age  SibSp  \\\n",
              "0                              Braund, Mr. Owen Harris    male  22.0      1   \n",
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              "..                                                 ...     ...   ...    ...   \n",
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              "summary": "{\n  \"name\": \"df\",\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\": 13.019696550973201,\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\": \"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": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.drop(['Name','Ticket'],axis=1)"
      ],
      "metadata": {
        "id": "ScWR_y2EVtO-"
      },
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "pxZjNEsFV10N",
        "outputId": "41101a77-5f36-4db7-c401-49b1258ee6a8"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   PassengerId  Survived  Pclass     Sex   Age  SibSp  Parch     Fare Embarked\n",
              "0            1         0       3    male  22.0      1      0   7.2500        S\n",
              "1            2         1       1  female  38.0      1      0  71.2833        C\n",
              "2            3         1       3  female  26.0      0      0   7.9250        S\n",
              "3            4         1       1  female  35.0      1      0  53.1000        S\n",
              "4            5         0       3    male  35.0      0      0   8.0500        S"
            ],
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              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\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",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\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\": \"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\": 13.019696550973201,\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\": \"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": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "le = LabelEncoder()"
      ],
      "metadata": {
        "id": "mELHZ93tV6tq"
      },
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.corr()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 324
        },
        "id": "L31NW9SpWGXa",
        "outputId": "9bac4d5d-ac96-48b2-bf4c-d8546b93e222"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-56-2f6f6606aa2c>:1: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
            "  df.corr()\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             PassengerId  Survived    Pclass       Age     SibSp     Parch  \\\n",
              "PassengerId     1.000000 -0.005007 -0.035144  0.034212 -0.057527 -0.001652   \n",
              "Survived       -0.005007  1.000000 -0.338481 -0.064910 -0.035322  0.081629   \n",
              "Pclass         -0.035144 -0.338481  1.000000 -0.339898  0.083081  0.018443   \n",
              "Age             0.034212 -0.064910 -0.339898  1.000000 -0.233296 -0.172482   \n",
              "SibSp          -0.057527 -0.035322  0.083081 -0.233296  1.000000  0.414838   \n",
              "Parch          -0.001652  0.081629  0.018443 -0.172482  0.414838  1.000000   \n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 7,\n  \"fields\": [\n    {\n      \"column\": \"PassengerId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3824571839654439,\n        \"min\": -0.0575268337844415,\n        \"max\": 1.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          1.0,\n          -0.0050066607670665175,\n          -0.0016520124027188366\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Survived\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.42395721445618667,\n        \"min\": -0.33848103596101514,\n        \"max\": 1.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          -0.0050066607670665175,\n          1.0,\n          0.08162940708348335\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Pclass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.506096478938897,\n        \"min\": -0.5494996199439076,\n        \"max\": 1.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          -0.03514399403038102,\n          -0.33848103596101514,\n          0.018442671310748508\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4471732159013244,\n        \"min\": -0.33989832839258594,\n        \"max\": 1.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0.03421211199737627,\n          -0.06491041993052588,\n          -0.17248195403694871\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SibSp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.41032992843273136,\n        \"min\": -0.23329633125248891,\n        \"max\": 1.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          -0.0575268337844415,\n          -0.035322498885735576,\n          0.41483769862015624\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Parch\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.38928955954868316,\n        \"min\": -0.17248195403694871,\n        \"max\": 1.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          -0.0016520124027188366,\n          0.08162940708348335,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.45580984533458957,\n        \"min\": -0.5494996199439076,\n        \"max\": 1.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0.012658219287491099,\n          0.2573065223849626,\n          0.21622494477076448\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.heatmap(df.corr())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 579
        },
        "id": "5HqsaF3OWPyQ",
        "outputId": "ad13726b-26f8-4a43-c836-3e21fb5d151c"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-57-aa4f4450a243>:1: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
            "  sns.heatmap(df.corr())\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 57
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
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      },
      "execution_count": 58,
      "outputs": [
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          "data": {
            "text/plain": [
              "   PassengerId  Survived  Pclass     Sex   Age  SibSp  Parch     Fare Embarked\n",
              "0            1         0       3    male  22.0      1      0   7.2500        S\n",
              "1            2         1       1  female  38.0      1      0  71.2833        C\n",
              "2            3         1       3  female  26.0      0      0   7.9250        S\n",
              "3            4         1       1  female  35.0      1      0  53.1000        S\n",
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              "\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\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\": \"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\": 13.019696550973201,\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\": \"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": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Sex']=le.fit_transform(df['Sex'])"
      ],
      "metadata": {
        "id": "lPa1bxXWYNQO"
      },
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['Embarked']=le.fit_transform(df['Embarked'])"
      ],
      "metadata": {
        "id": "TILr0ubSYXcX"
      },
      "execution_count": 60,
      "outputs": []
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    {
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        "df.head()"
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          "base_uri": "https://localhost:8080/",
          "height": 206
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        "id": "aXpMDJ5xYfEJ",
        "outputId": "ec89d219-662e-4f09-c055-b32232eb09e4"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   PassengerId  Survived  Pclass  Sex   Age  SibSp  Parch     Fare  Embarked\n",
              "0            1         0       3    1  22.0      1      0   7.2500         2\n",
              "1            2         1       1    0  38.0      1      0  71.2833         0\n",
              "2            3         1       3    0  26.0      0      0   7.9250         2\n",
              "3            4         1       1    0  35.0      1      0  53.1000         2\n",
              "4            5         0       3    1  35.0      0      0   8.0500         2"
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              "    <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-120b37f5-0fb3-4ea1-ba76-659b43040597 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-120b37f5-0fb3-4ea1-ba76-659b43040597');\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-f3a4ff62-f0d8-4f6c-988b-e44e3da0c654\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f3a4ff62-f0d8-4f6c-988b-e44e3da0c654')\"\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-f3a4ff62-f0d8-4f6c-988b-e44e3da0c654 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": "df",
              "summary": "{\n  \"name\": \"df\",\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\": \"Sex\",\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\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13.019696550973201,\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\": \"Embarked\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          2,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# scaling"
      ],
      "metadata": {
        "id": "SkB5Mhz5Yruz"
      },
      "execution_count": 62,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "minmax = MinMaxScaler()"
      ],
      "metadata": {
        "id": "p2d7XgwrYuDE"
      },
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "dPZ03Q8JY-NM",
        "outputId": "6f440e83-afd3-4084-81d3-c41ad241842a"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   PassengerId  Survived  Pclass  Sex   Age  SibSp  Parch     Fare  Embarked\n",
              "0            1         0       3    1  22.0      1      0   7.2500         2\n",
              "1            2         1       1    0  38.0      1      0  71.2833         0\n",
              "2            3         1       3    0  26.0      0      0   7.9250         2\n",
              "3            4         1       1    0  35.0      1      0  53.1000         2\n",
              "4            5         0       3    1  35.0      0      0   8.0500         2"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-17b0ee4a-a555-497a-8113-e757e349d16c\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Embarked</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>2</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-17b0ee4a-a555-497a-8113-e757e349d16c')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-17b0ee4a-a555-497a-8113-e757e349d16c 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-17b0ee4a-a555-497a-8113-e757e349d16c');\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-6031fedd-3e5e-46c8-8ace-f929b53e5c55\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-6031fedd-3e5e-46c8-8ace-f929b53e5c55')\"\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-6031fedd-3e5e-46c8-8ace-f929b53e5c55 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": "df",
              "summary": "{\n  \"name\": \"df\",\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\": \"Sex\",\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\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13.019696550973201,\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\": \"Embarked\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          2,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x = df['Fare']\n",
        "y = df.drop('Fare',axis=1)"
      ],
      "metadata": {
        "id": "3QPe-bOVZF6R"
      },
      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I8gxanTbaA13",
        "outputId": "dc387ff8-69d8-46ee-c43b-c5c917b0091e"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       7.2500\n",
              "1      71.2833\n",
              "2       7.9250\n",
              "3      53.1000\n",
              "4       8.0500\n",
              "        ...   \n",
              "886    13.0000\n",
              "887    30.0000\n",
              "888    23.4500\n",
              "889    30.0000\n",
              "890     7.7500\n",
              "Name: Fare, Length: 891, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x = pd.DataFrame(x,columns=['Fare'])"
      ],
      "metadata": {
        "id": "Sd4sTQy0aDm-"
      },
      "execution_count": 72,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x1 = minmax.fit_transform(x)"
      ],
      "metadata": {
        "id": "_K5gAjOVaPWY"
      },
      "execution_count": 73,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "type(x1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1FLzEjv5aV0d",
        "outputId": "61820dcd-0322-4b95-ae34-005dd2801559"
      },
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "numpy.ndarray"
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x = pd.DataFrame(x1,columns=['Fare'])"
      ],
      "metadata": {
        "id": "e6yzJuAsaXuE"
      },
      "execution_count": 75,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.concat([y,x],axis=1)"
      ],
      "metadata": {
        "id": "ZgzXHF9EagQS"
      },
      "execution_count": 76,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "vuVAW8RPapBC",
        "outputId": "94cfe827-7602-4b8d-90a4-4c13d18f36b9"
      },
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   PassengerId  Survived  Pclass  Sex   Age  SibSp  Parch  Embarked      Fare\n",
              "0            1         0       3    1  22.0      1      0         2  0.014151\n",
              "1            2         1       1    0  38.0      1      0         0  0.139136\n",
              "2            3         1       3    0  26.0      0      0         2  0.015469\n",
              "3            4         1       1    0  35.0      1      0         2  0.103644\n",
              "4            5         0       3    1  35.0      0      0         2  0.015713"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3534de8d-7baa-47d0-bf2c-fe9dc275a726\" class=\"colab-df-container\">\n",
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              "      <th>PassengerId</th>\n",
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              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
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              "    }\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-953a2aaf-eede-4134-9473-81aac98fb3cf 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": "df",
              "summary": "{\n  \"name\": \"df\",\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\": \"Sex\",\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\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13.019696550973201,\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\": \"Embarked\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          2,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.09699511290236998,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 248,\n        \"samples\": [\n          0.021942337075458514,\n          0.10122885832000206\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# logistic regression"
      ],
      "metadata": {
        "id": "RxVlyavMasVT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "kTRYb_Wba9bT",
        "outputId": "e0690e45-7755-4c7b-9f80-5a9dde2000a2"
      },
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   PassengerId  Survived  Pclass  Sex   Age  SibSp  Parch  Embarked      Fare\n",
              "0            1         0       3    1  22.0      1      0         2  0.014151\n",
              "1            2         1       1    0  38.0      1      0         0  0.139136\n",
              "2            3         1       3    0  26.0      0      0         2  0.015469\n",
              "3            4         1       1    0  35.0      1      0         2  0.103644\n",
              "4            5         0       3    1  35.0      0      0         2  0.015713"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c33f9222-c3a3-4e84-b7ff-4395ee883822\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Embarked</th>\n",
              "      <th>Fare</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0.014151</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.139136</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0.015469</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0.103644</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0.015713</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-c33f9222-c3a3-4e84-b7ff-4395ee883822')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-c33f9222-c3a3-4e84-b7ff-4395ee883822 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-c33f9222-c3a3-4e84-b7ff-4395ee883822');\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-303d8ac9-f5c8-4bdd-bbed-5aa23185e330\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-303d8ac9-f5c8-4bdd-bbed-5aa23185e330')\"\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-303d8ac9-f5c8-4bdd-bbed-5aa23185e330 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": "df",
              "summary": "{\n  \"name\": \"df\",\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\": \"Sex\",\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\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13.019696550973201,\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\": \"Embarked\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          2,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.09699511290236998,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 248,\n        \"samples\": [\n          0.021942337075458514,\n          0.10122885832000206\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x = df.drop('Survived',axis=1)\n",
        "y = df['Survived']\n",
        "\n",
        "x1=pd.DataFrame(x)\n",
        "y1=pd.DataFrame(y)"
      ],
      "metadata": {
        "id": "1yqf8PgxbKEl"
      },
      "execution_count": 80,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "x1_train,x1_test,y1_train,y1_test= train_test_split(x1,y1,test_size=0.25,random_state=42)\n"
      ],
      "metadata": {
        "id": "6Uf5izV_avJ6"
      },
      "execution_count": 81,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "lr=LogisticRegression()"
      ],
      "metadata": {
        "id": "VkSC-Z90b8AW"
      },
      "execution_count": 82,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model=lr.fit(x1_train,y1_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ULWdeOxicQAX",
        "outputId": "06fac553-d015-4b33-8f10-26082f45b674"
      },
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y1_pred = model.predict(x1_test)"
      ],
      "metadata": {
        "id": "qTPj3ldlcd5M"
      },
      "execution_count": 84,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# model validation checking\n",
        "from sklearn.metrics import confusion_matrix,precision_score,recall_score,f1_score,accuracy_score,classification_report"
      ],
      "metadata": {
        "id": "4u4A83gtclmK"
      },
      "execution_count": 85,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"the accuracy score is:\",accuracy_score(y1_test,y1_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R5_1T5yqcrju",
        "outputId": "2342d101-5b5c-4a7a-a40e-e4f3d9174c19"
      },
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "the accuracy score is: 0.7937219730941704\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "confusion_matrix(y1_test,y1_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2_yW7RHmdF6K",
        "outputId": "e042b272-77da-4de6-da90-b4b41fd8dc30"
      },
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[114,  20],\n",
              "       [ 26,  63]])"
            ]
          },
          "metadata": {},
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# sample prediction\n"
      ],
      "metadata": {
        "id": "C5quNInzdbUO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "LzC9MqZCdfxB",
        "outputId": "6e9a7580-c41a-4d3c-a247-c1b169de1970"
      },
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   PassengerId  Pclass  Sex   Age  SibSp  Parch  Embarked      Fare\n",
              "0            1       3    1  22.0      1      0         2  0.014151\n",
              "1            2       1    0  38.0      1      0         0  0.139136\n",
              "2            3       3    0  26.0      0      0         2  0.015469\n",
              "3            4       1    0  35.0      1      0         2  0.103644\n",
              "4            5       3    1  35.0      0      0         2  0.015713"
            ],
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
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              "  @keyframes spin {\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "x",
              "summary": "{\n  \"name\": \"x\",\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\": \"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          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sex\",\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\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13.019696550973201,\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\": \"Embarked\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          2,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.09699511290236998,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 248,\n        \"samples\": [\n          0.021942337075458514,\n          0.10122885832000206\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 90
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.predict([[5,3,1,35.0,0,0,2,0.015713]])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T2UJ5pL5djtt",
        "outputId": "22806d8b-bf2b-41ef-eb9b-1d4263762bd3"
      },
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0])"
            ]
          },
          "metadata": {},
          "execution_count": 91
        }
      ]
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    {
      "cell_type": "code",
      "source": [
        "model.predict([[4,\t1,\t0\t,35.0,\t1,\t0,\t2,\t0.103644]])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BL2OyDlad6yS",
        "outputId": "63069807-bfd3-45e7-854b-ad6dd30079e5"
      },
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1])"
            ]
          },
          "metadata": {},
          "execution_count": 92
        }
      ]
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}