Titanic_Survival_Prediction.ipynb

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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import accuracy_score\n"
      ]
    },
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        "df_train = pd.read_csv('train.csv')\n",
        "df_test = pd.read_csv('test.csv')"
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              "   PassengerId  Survived  Pclass  ...     Fare Cabin  Embarked\n",
              "0            1         0       3  ...   7.2500   NaN         S\n",
              "1            2         1       1  ...  71.2833   C85         C\n",
              "2            3         1       3  ...   7.9250   NaN         S\n",
              "3            4         1       1  ...  53.1000  C123         S\n",
              "4            5         0       3  ...   8.0500   NaN         S\n",
              "\n",
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              "summary": "{\n  \"name\": \"df_train\",\n  \"rows\": 891,\n  \"fields\": [\n    {\n      \"column\": \"PassengerId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 257,\n        \"min\": 1,\n        \"max\": 891,\n        \"num_unique_values\": 891,\n        \"samples\": [\n          710,\n          440,\n          841\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Survived\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Pclass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          3,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 891,\n        \"samples\": [\n          \"Moubarek, Master. Halim Gonios (\\\"William George\\\")\",\n          \"Kvillner, Mr. Johan Henrik Johannesson\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"female\",\n          \"male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14.526497332334044,\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\": 49.693428597180905,\n        \"min\": 0.0,\n        \"max\": 512.3292,\n        \"num_unique_values\": 248,\n        \"samples\": [\n          11.2417,\n          51.8625\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cabin\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 147,\n        \"samples\": [\n          \"D45\",\n          \"B49\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Embarked\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"S\",\n          \"C\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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      "source": [
        "# Hence, we can say that 11 features and 1 target variable('Survived')\n",
        "\n",
        "# number of missing values in each column\n",
        "df_train.isnull().sum()"
      ],
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              "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"
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              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sex</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Age</th>\n",
              "      <td>177</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SibSp</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Parch</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Ticket</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fare</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Cabin</th>\n",
              "      <td>687</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Embarked</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "_KIFXxwnksd7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# since most of the values in 'Cabin' column are missing, we prefer to drop the column\n",
        "df_train = df_train.drop('Cabin', axis = 1)"
      ],
      "metadata": {
        "id": "NJSruwzyknUg"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# we can replace missing values in 'Age' column with mean\n",
        "df_train['Age'].fillna(df_train['Age'].mean(), inplace=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FEF6oN6FlEuw",
        "outputId": "9dbfe36a-3a76-4ab8-dcf0-137269fdb142"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-35-5092a5b9d614>:2: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
            "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
            "\n",
            "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
            "\n",
            "\n",
            "  df_train['Age'].fillna(df_train['Age'].mean(), inplace=True)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(df_train['Embarked'].mode()[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HOP7-5lJoRCo",
        "outputId": "1251fa53-5a3f-4eae-f916-c004b55b2cdf"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "S\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_train['Embarked'].fillna(df_train['Embarked'].mode()[0], inplace = True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NmqWeU7Po9J5",
        "outputId": "ca20f11a-38d3-4c23-a639-8a0f68e16b12"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-39-083dad81dc60>:1: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
            "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
            "\n",
            "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
            "\n",
            "\n",
            "  df_train['Embarked'].fillna(df_train['Embarked'].mode()[0], inplace = True)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_train.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 429
        },
        "id": "uBq3RLsIprad",
        "outputId": "ef176471-e564-4618-80f5-ad25c5fbbaf9"
      },
      "execution_count": 40,
      "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"
            ],
            "text/html": [
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "        vertical-align: top;\n",
              "    }\n",
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              "    .dataframe thead th {\n",
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      "source": [
        "# categorical data do not need to be determined on the basis of mean, median\n",
        "# only contiguos data is required i.e. 'Age' and 'Fare'\n",
        "df_train.describe()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "id": "u2vLA7BhqHlf",
        "outputId": "cb7e2a6d-9b0e-4bc8-9462-0dd4ade26885"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       PassengerId    Survived      Pclass         Age       SibSp       Parch        Fare\n",
              "count   891.000000  891.000000  891.000000  891.000000  891.000000  891.000000  891.000000\n",
              "mean    446.000000    0.383838    2.308642   29.699118    0.523008    0.381594   32.204208\n",
              "std     257.353842    0.486592    0.836071   13.002015    1.102743    0.806057   49.693429\n",
              "min       1.000000    0.000000    1.000000    0.420000    0.000000    0.000000    0.000000\n",
              "25%     223.500000    0.000000    2.000000   22.000000    0.000000    0.000000    7.910400\n",
              "50%     446.000000    0.000000    3.000000   29.699118    0.000000    0.000000   14.454200\n",
              "75%     668.500000    1.000000    3.000000   35.000000    1.000000    0.000000   31.000000\n",
              "max     891.000000    1.000000    3.000000   80.000000    8.000000    6.000000  512.329200"
            ],
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              "      <th>count</th>\n",
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              "      <td>891.000000</td>\n",
              "      <td>891.000000</td>\n",
              "      <td>891.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>446.000000</td>\n",
              "      <td>0.383838</td>\n",
              "      <td>2.308642</td>\n",
              "      <td>29.699118</td>\n",
              "      <td>0.523008</td>\n",
              "      <td>0.381594</td>\n",
              "      <td>32.204208</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>257.353842</td>\n",
              "      <td>0.486592</td>\n",
              "      <td>0.836071</td>\n",
              "      <td>13.002015</td>\n",
              "      <td>1.102743</td>\n",
              "      <td>0.806057</td>\n",
              "      <td>49.693429</td>\n",
              "    </tr>\n",
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              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.420000</td>\n",
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              "      <th>25%</th>\n",
              "      <td>223.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>7.910400</td>\n",
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              "    <tr>\n",
              "      <th>50%</th>\n",
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              "      <td>0.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>29.699118</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>14.454200</td>\n",
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              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>35.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>31.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>891.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>80.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>512.329200</td>\n",
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              "</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-52c7128d-e272-4f3c-8293-37dc52a9d952 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df_train\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"PassengerId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 320.8159711429856,\n        \"min\": 1.0,\n        \"max\": 891.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          891.0,\n          446.0,\n          668.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Survived\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 314.8713661874558,\n        \"min\": 0.0,\n        \"max\": 891.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.3838383838383838,\n          1.0,\n          0.4865924542648585\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Pclass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 314.2523437079693,\n        \"min\": 0.8360712409770513,\n        \"max\": 891.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          891.0,\n          2.308641975308642,\n          3.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 305.2978992449289,\n        \"min\": 0.42,\n        \"max\": 891.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          891.0,\n          29.69911764705882,\n          35.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SibSp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 314.4908277465442,\n        \"min\": 0.0,\n        \"max\": 891.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          891.0,\n          0.5230078563411896,\n          8.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Parch\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 314.65971717879,\n        \"min\": 0.0,\n        \"max\": 891.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.38159371492704824,\n          6.0,\n          0.8060572211299559\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 330.6256632228577,\n        \"min\": 0.0,\n        \"max\": 891.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          32.204207968574636,\n          14.4542,\n          891.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.set()"
      ],
      "metadata": {
        "id": "z0uHR5a_s16-"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#No. of people survived\n",
        "df_train['Survived'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 178
        },
        "id": "wACFd76Atzkf",
        "outputId": "692c843b-6619-4cc5-d631-73594e3156eb"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Survived\n",
              "0    549\n",
              "1    342\n",
              "Name: count, dtype: int64"
            ],
            "text/html": [
              "<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>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Survived</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>549</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>342</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# making a bar graph for count of no. of survival\n",
        "sns.countplot(x = 'Survived', data = df_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 476
        },
        "id": "74R8hsKIuCKN",
        "outputId": "fe80cea4-af57-4d3e-e2b3-ceac1b39bbff"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Survived', ylabel='count'>"
            ]
          },
          "metadata": {},
          "execution_count": 55
        },
        {
          "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_train['Sex'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 178
        },
        "id": "BCgUbeVoua6a",
        "outputId": "bfc76450-3dee-4aee-dc25-cded70acc16c"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Sex\n",
              "male      577\n",
              "female    314\n",
              "Name: count, dtype: int64"
            ],
            "text/html": [
              "<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>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sex</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>male</th>\n",
              "      <td>577</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>female</th>\n",
              "      <td>314</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(x='Sex', data=df_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 478
        },
        "id": "Y62Jv69yvalx",
        "outputId": "5bea2df2-3298-405c-8cda-b2b6c967376c"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Sex', ylabel='count'>"
            ]
          },
          "metadata": {},
          "execution_count": 58
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(x='Sex', hue='Survived', data = df_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 476
        },
        "id": "nohRJC4EvjTY",
        "outputId": "f7187955-f738-49de-d88e-6376d33e4f9e"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Sex', ylabel='count'>"
            ]
          },
          "metadata": {},
          "execution_count": 59
        },
        {
          "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_train['Pclass'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 209
        },
        "id": "V1Jv5gQpv3QB",
        "outputId": "74079cfe-84a0-40ee-f99b-e99e965626e3"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pclass\n",
              "3    491\n",
              "1    216\n",
              "2    184\n",
              "Name: count, dtype: int64"
            ],
            "text/html": [
              "<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>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Pclass</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>491</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>216</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>184</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot( x = 'Pclass', data = df_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 476
        },
        "id": "0jZIM3DTwEbz",
        "outputId": "603dec49-8d0e-422b-8687-9ddbf4a5620d"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Pclass', ylabel='count'>"
            ]
          },
          "metadata": {},
          "execution_count": 62
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(x='Pclass', hue = 'Survived', data = df_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 476
        },
        "id": "zkp6VcFlwW1l",
        "outputId": "bf3fa68b-c180-4185-b67f-95ca8fb93f50"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Pclass', ylabel='count'>"
            ]
          },
          "metadata": {},
          "execution_count": 63
        },
        {
          "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_train['SibSp'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 335
        },
        "id": "h3LDYaFwwgh3",
        "outputId": "415bf1a3-f159-4e3c-ead7-e8b6d664256b"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SibSp\n",
              "0    608\n",
              "1    209\n",
              "2     28\n",
              "4     18\n",
              "3     16\n",
              "8      7\n",
              "5      5\n",
              "Name: count, dtype: int64"
            ],
            "text/html": [
              "<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>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SibSp</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>608</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>209</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(x='SibSp', data = df_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 476
        },
        "id": "PnKyenHJxN_n",
        "outputId": "a2dae63c-6873-4613-882c-6a0563f3240b"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='SibSp', ylabel='count'>"
            ]
          },
          "metadata": {},
          "execution_count": 65
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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aG5+dnU1ubi5FRUXcfvvtNGvWjBkzZpCVlcVll112Ro5HkiTVLwkTio7XkCFDOHjwIAsWLGD+/PlkZGQwe/ZssrOza4ybOXMm06ZNY/LkyVRWVpKbm8vEiRNJTq53hyxJks6AQDQajca7iPqkqqqazz8vr9WenBykRYsUiu4r4cMde+JQ2enXqV0Lpt5awJ495e4pkiTVGy1bphzXRmsfOCNJkoShSJIkCTAUSZIkAYYiSZIkwFAkSZIEGIokSZIAQ5EkSRJgKJIkSQIMRZIkSYChSJIkCTAUSZIkAYYiSZIkwFAkSZIEGIokSZIAQ5EkSRJgKJIkSQIMRZIkSYChSJIkCTiJUPS73/2O7du3f2X/9u3b+d3vflfX6SVJks6oOoein//856xfv/4r+zds2MDPf/7zuk4vSZJ0RtU5FEWj0a/tP3DgAElJSXWdXpIk6YxKPpHB77//Pu+//37s9bp166iqqqo1LhKJsGTJEjIyMk6+QkmSpDPghELRCy+8wOzZswEIBAI88cQTPPHEE0cdGwqF+NWvfnXyFUqSJJ0BJxSKvv/97/Ptb3+baDTKsGHDuOWWW8jLy6sxJhAIcPbZZ9OxY0eSk09oekmSpLg5odTSpk0b2rRpA8CiRYvo3LkzrVq1Oi2FSZIknUl1Xsrp06fPqaxDkiQprk7q+tYrr7zC0qVL2bZtG5FIpNYdaYFAgBdeeOGkCpQkSToT6hyKFi5cyPTp02nVqhU9evQgKyvrVNYlSZJ0RtU5FC1atIi+ffsyf/58mjRpciprkiRJOuPq/PDGSCTC5ZdfbiCSJEkNQp1DUffu3SkrKzuVtUiSJMVNnUPRlClTeP7553n22WdPZT2SJElxUec9RePGjaOyspLbbruNKVOmcN555xEM1sxYgUCAZcuWnXSRkiRJp1udQ1FaWhppaWmcf/75p7IeSZKkuKhzKPrtb397KuuQJEmKqzrvKZIkSWpI6rxS9Je//OW4xv3jP/5jXX+EJEnSGVPnUDRixAgCgcAxx23cuLGuP0KSJOmMOaknWv9fVVVV7NixgyeffJLq6mp+9rOfnVRxkiRJZ0qdQ1GfPn2+su+qq67i2muv5c9//jP9+vWr64+QJEk6Y07LRutgMMjgwYN56qmnTsf0kiRJp9xpu/ssHA6zb9++0zW9JEnSKVXny2d/+9vfjtoeiURYt24dDz30EBdffHGdC5MkSTqT6hyKBg4c+JV3n0WjUXr16sWdd95Z58IkSZLOpDqHoqlTp9YKRYFAgFAoRMeOHbngggtOujhJkqQzpc6h6KqrrjqVdQCwatUqFixYwJYtW9i/fz/p6elceumljBkzhubNm8fGvfTSS8ycOZOysjLatm3LDTfcwNVXX11jrkOHDjFjxgyWLVtGeXk52dnZTJo0iczMzFNetyRJqv/qHIq+bMuWLezYsQOAdu3a1XmVaO/evfTo0YMRI0aQlpbG5s2bmTVrFps3b+bhhx8GYN26dYwZM4ZrrrmGoqIiXnvtNX7xi1+QkpLCd7/73dhcxcXFlJSUMGHCBNLT03nggQf48Y9/zPLly2sELEmSJDjJUPTCCy9w9913xwLREe3bt2fChAkMGjTohOYbOnRojdc5OTk0bdqUSZMmsWvXLtLT05k3bx49evTgrrvuAqBv375s27aN+++/PxaKdu7cydKlS7njjju45pprAOjevTuXXHIJS5YsYdSoUXU9ZEmS1EDV+Zb8VatWccsttwBQWFjI7NmzmT17NoWFhUSjUcaOHcvq1atPusC0tDQAKioqOHToEK+//nqNFSGAgoICtm7dyvbt2wFYs2YN1dXVNcalpaXRv3//U1KTJElqeOq8UjR37lyysrJYvHgx55xzTqx90KBB/OhHP+Laa69lzpw55OXlnfDcVVVVVFZWsmXLFubMmcPAgQNp3749W7ZsoaKiota+oM6dOwNQWlpK+/btKS0tpVWrVqSmptYat3Tp0jocrSRJaujqHIo++OADCgsLawSiI8455xz++Z//mRkzZtRp7ksuuYRdu3YBMGDAAKZPnw4cfiAkQCgUqjH+yOsj/ZFI5Kj7hkKhUGzMyUhOrr3AlpR02p6DmXAa07FKkhqPOoeiZs2afW3ACIfDNGvWrE5zz58/n4MHD7JlyxbmzZvH6NGjeeSRR+pa6ikVDAZo0SIl3mXEVSh0drxLkCTplKtzKMrJyWHRokUMGDCA7OzsGn1vvfUWv/3tb+nfv3+d5v7GN74BQHZ2Nt27d2fo0KE8//zzsbva/u/Xh0QiEYDY5bJQKMT+/ftrzRuJRGpdUjtR1dVRIpEDtdqTkoKNJixEIgepqqqOdxmSJB2XUOjs47rKUedQ9B//8R8MHz6ca6+9lh49epCRkQFAWVkZGzZsoFWrVowfP76u08dkZWXRpEkTPv74YwYOHEiTJk0oLS1lwIABsTGlpaUAsb1GmZmZfPrpp4TD4RohqLS09JQ8p6iysnEHgqqq6kZ/DiRJDU+dN4d06NCBZcuWMWLECMLhMCUlJZSUlBAOhxk5ciS///3vad++/UkX+NZbb1FRUUH79u1p2rQpOTk5/PGPf6wxpqSkhM6dO8d+Xm5uLsFgkJUrV8bGhMNh1qxZU6eN35IkqeGr80pRZWUlzZo1o6ioiKKiolr9+/fvp7KykuTk4/8RY8aM4aKLLiIrK4uzzjqL999/n4ceeoisrCwuvfRSAG666SZGjhzJlClTyM/P5/XXX+e5556rsan7vPPO45prruGee+4hGAySnp7Ogw8+SPPmzRk+fHhdD1mSJDVgdQ5FxcXFrFu3jueee+6o/T/84Q/Jyclh4sSJxz1njx49KCkpYf78+USjUdq1a8ewYcO4/vrradq0KQAXX3wxs2bNYubMmSxdupS2bdtSXFxMfn5+jbkmTpxISkoK06dPp7y8nN69e/PII4/4NGtJknRUgWg0Gq3LGwcNGsSVV17J2LFjj9o/e/Zsli1bVuMSVkNQVVXN55+X12pPTg7SokUKRfeV8OGOPXGo7PTr1K4FU28tYM+ecvcUSZLqjZYtU45ro3Wd9xTt3r2b9PT0r+xv06ZN7FlDkiRJia7OoSgtLY2ysrKv7N+6dSvnnntuXaeXJEk6o+ocigYMGMCSJUt47733avW9++67PPnkk97pJUmS6o06b7S+9dZbeeWVVxg2bBgDBw6MPVhx8+bNvPzyy7Rs2ZJbb731lBUqSZJ0OtU5FKWnp/P0008zffp0XnzxRZ5//nkAzj33XK644goKCwu/ds+RJElSIqlzKILDm6l/9atfEY1G+fzzzwFo2bIlgUDglBQnSZJ0ppxUKDoiEAjQqlWrUzGVJElSXNR5o7UkSVJDYiiSJEnCUCRJkgQYiiRJkgBDkSRJEmAokiRJAgxFkiRJgKFIkiQJMBRJkiQBhiJJkiTAUCRJkgQYiiRJkgBDkSRJEmAokiRJAgxFkiRJgKFIkiQJMBRJkiQBhiJJkiTAUCRJkgQYiiRJkgBDkSRJEmAokiRJAgxFkiRJgKFIkiQJMBRJkiQBhiJJkiTAUCRJkgQYiiRJkgBDkSRJEmAokiRJAgxFkiRJgKFIkiQJMBRJkiQBhiJJkiTAUCRJkgQYiiRJkgBDkSRJEgDJ8S5AjUMwGCAYDMS7jNOmujpKdXU03mVIkk5CQoWiP/zhDyxbtox3332XSCTC+eefz4gRI7j66qsJBP7fB+pTTz3FwoUL+dvf/kZGRgaFhYVccsklNebat28f06ZN44UXXqCiooIBAwYwceJE2rRpc6YPq9ELBgOkpZ1DUlLDXZisqqpm794DBiNJqscSKhT9+te/pl27dkyYMIEWLVrwpz/9iUmTJrFz507GjBkDwPLly5k0aRKjR4+mb9++lJSUMGbMGBYvXkyvXr1ic40bN44tW7YwZcoUmjVrxsyZMxk1ahRPP/00yckJddgNXjAYICkpyJzHX2XH7nC8yznl2rVJ5eYf9icYDBiKJKkeS6h0MG/ePFq2bBl73a9fP/bu3csjjzzCT3/6U4LBIPfffz+DBw9m3LhxAPTt25dNmzYxZ84cFixYAMD69etZs2YNDz30ELm5uQBkZGRQUFDAypUrKSgoOOPHJtixO8yHO/bEuwxJko4qoa5nfDkQHdG1a1f279/PgQMH2LZtGx9++CH5+fk1xhQUFLB27VoOHToEwOrVqwmFQvTv3z82JjMzk65du7J69erTexCSJKleSqiVoqN54403SE9P59xzz+WNN94ADq/6fFnnzp2pqKhg27ZtdO7cmdLSUjIyMmrsQ4LDwai0tPSka0pOrp0lG/J+mf/rRI+1sZybxnKcktRQJXQoWrduHSUlJdx+++0AhMOH96OEQqEa4468PtIfiURo3rx5rflSU1N55513TqqmYDBAixYpJzVHfRcKnR3vEhKS50WS6reEDUU7d+6ksLCQnJwcRo4cGe9yYqqro0QiB2q1JyUFG82HYiRykKqq6uMe31jOzYmeF0nSmREKnX1cq/kJGYoikQijRo0iLS2NWbNmEQwePpDU1FTg8O32rVu3rjH+y/2hUIidO3fWmjccDsfGnIzKysb9wVdVVd3oz8HReF4kqX5LuE0QX3zxBTfeeCP79u1j4cKFNS6DZWZmAtTaF1RaWkqTJk3o0KFDbFxZWRnRaM3bo8vKymJzSJIkfVlChaLKykrGjRtHaWkpCxcuJD09vUZ/hw4d6NSpEytWrKjRXlJSQr9+/WjatCkAeXl5hMNh1q5dGxtTVlbGe++9R15e3uk/EEmSVO8k1OWzO++8k5dffpkJEyawf/9+3nzzzVhft27daNq0KWPHjmX8+PF07NiRnJwcSkpK2LBhA48++mhsbHZ2Nrm5uRQVFXH77bfTrFkzZsyYQVZWFpdddlkcjkySJCW6hApFr776KgB33313rb4XX3yR9u3bM2TIEA4ePMiCBQuYP38+GRkZzJ49m+zs7BrjZ86cybRp05g8eTKVlZXk5uYyceJEn2YtSZKOKqESwksvvXRc44YNG8awYcO+dkzz5s2ZOnUqU6dOPRWlSZKkBi6h9hRJkiTFi6FIkiQJQ5EkSRJgKJIkSQIMRZIkSYChSJIkCTAUSZIkAYYiSZIkwFAkSZIEGIokSZIAQ5EkSRJgKJIkSQIMRZIkSYChSJIkCTAUSZIkAYYiSZIkwFAkSZIEGIokSZIAQ5EkSRJgKJIkSQIMRZIkSYChSJIkCTAUSZIkAYYiSZIkwFAkSZIEGIokSZIAQ5EkSRJgKJIkSQIMRZIkSYChSJIkCTAUSZIkAYYiSZIkwFAkSZIEGIokSZIAQ5EkSRJgKJIkSQIMRZIkSYChSJIkCTAUSZIkAYYiSZIkwFAkSZIEGIokSZIAQ5EkSRJgKJIkSQIMRZIkSUCChaKPPvqIyZMnM3ToULp168aQIUOOOu6pp57i8ssvp3v37nzve9/j5ZdfrjVm3759FBUV0adPH7Kzs7nlllvYvXv36T4ESZJUTyVUKNq8eTOrVq3i/PPPp3Pnzkcds3z5ciZNmkR+fj4LFiygV69ejBkzhjfffLPGuHHjxvHqq68yZcoU7r33XsrKyhg1ahSVlZVn4EgkSVJ9kxzvAr5s4MCBXHrppQBMmDCBd955p9aY+++/n8GDBzNu3DgA+vbty6ZNm5gzZw4LFiwAYP369axZs4aHHnqI3NxcADIyMigoKGDlypUUFBScmQOSJEn1RkKtFAWDX1/Otm3b+PDDD8nPz6/RXlBQwNq1azl06BAAq1evJhQK0b9//9iYzMxMunbtyurVq0994ZIkqd5LqJWiYyktLQUOr/p8WefOnamoqGDbtm107tyZ0tJSMjIyCAQCNcZlZmbG5jgZycm1w1tSUkLly9PqRI+1sZybxnKcktRQ1atQFA6HAQiFQjXaj7w+0h+JRGjevHmt96emph71ktyJCAYDtGiRclJz1Heh0NnxLiEheV4kqX6rV6EoEVRXR4lEDtRqT0oKNpoPxUjkIFVV1cc9vrGcmxM9L5KkMyMUOvu4VvPrVShKTU0FDt9u37p161h7JBKp0R8Khdi5c2et94fD4diYk1FZ2bg/+Kqqqhv9OTgaz4sk1W/1ahNEZmYmQK19QaWlpTRp0oQOHTrExpWVlRGNRmuMKysri80hSZL0ZfUqFHXo0IFOnTqxYsWKGu0lJSX069ePpk2bApCXl0c4HGbt2rWxMWVlZbz33nvk5eWd0ZolSVL9kFCXzw4ePMiqVasA2LFjB/v3748FoD59+tCyZUvGjh3L+PHj6dixIzk5OZSUlLBhwwYeffTR2DzZ2dnk5uZSVFTE7bffTrNmzZgxYwZZWVlcdtllcTk2SZKU2BIqFH322WfceuutNdqOvF60aBE5OTkMGTKEgwcPsmDBAubPn09GRgazZ88mOzu7xvtmzpzJtGnTmDx5MpWVleTm5jJx4kSSkxPqkCVJUoJIqITQvn17Pvjgg2OOGzZsGMOGDfvaMc2bN2fq1KlMnTr1VJUnSZIasHq1p0iSJOl0MRRJkiRhKJIkSQIMRZIkSYChSJIkCTAUSZIkAYYiSZIkwFAkSZIEGIokSZIAQ5EkSRJgKJIkSQIMRZIkSYChSJIkCTAUSZIkAYYiSZIkwFAkSZIEGIokSZIAQ5EkSRJgKJIkSQIMRZIkSYChSJIkCTAUSZIkAYYiSZIkwFAkSZIEGIokSZIAQ5EkSRJgKJIkSQIMRZIkSYChSJIkCTAUSZIkAYYiSZIkwFAkSZIEGIokSZIASI53AVJjFwwGCAYD8S7jtKmujlJdHY13GZJ0TIYiKY6CwQBpaeeQlNRwF22rqqrZu/eAwUhSwjMUSXEUDAZISgoy5/FX2bE7HO9yTrl2bVK5+Yf9CQYDhiJJCc9QJCWAHbvDfLhjT7zLkKRGreGu2UuSJJ0AQ5EkSRJePpOkeqeh37EI3rWo+DAUSUpIfvAfXWO4YxG8a1HxYSiSlHD84P9qDf2ORfCuRcWPoUhSwvGD/9i8Y1E69QxFkhKWH/ySzqSGvTYtSZJ0nBr0StHWrVspLi5m/fr1pKSkMHToUMaNG0fTpk3jXZokSWeUNy8cW4MNReFwmOuuu45OnToxa9Ysdu3axd13380XX3zB5MmT412eJOk08IP/6Lx54fg02FC0ZMkSysvLmT17NmlpaQBUVVVx5513cuONN5Kenh7fAiVJp5Qf/F/NmxeOT4MNRatXr6Zfv36xQASQn5/PHXfcwauvvspVV10Vv+IkSaecH/zH5s0LXy8QjUYb5EMg+vXrx9VXX8348eNrtA8YMIChQ4fWaj9e0ejRly0DAQgGg4T3f0FVVXWd5k50SUlBUs89i+rqak7kv5qGfm7qel7Ac/NVGvp5Ac/N1/HcfDXPzVf7unMTDAYIBI59WbXBrhRFIhFCoVCt9tTUVMLhuv8fRCAQICnpq09s6rln1Xnu+iIYrNvSdEM/N3U9L+C5+SoN/byA5+breG6+mufmq53M38UN+8KrJEnScWqwoSgUCrFv375a7eFwmNTU1DhUJEmSElmDDUWZmZmUlpbWaNu3bx+ffPIJmZmZcapKkiQlqgYbivLy8vjTn/5EJBKJta1YsYJgMEj//v3jWJkkSUpEDfbus3A4zODBg8nIyODGG2+MPbzxiiuu8OGNkiSplgYbiuDw13z88pe/rPE1H4WFhX7NhyRJqqVBhyJJkqTj1WD3FEmSJJ0IQ5EkSRKGIkmSJMBQJEmSBBiKJEmSAEORJEkSYCiqd7Zu3cq//uu/0qtXL/r3788999zDoUOH4l1WQvjoo4+YPHkyQ4cOpVu3bgwZMiTeJSWEP/zhD9x0003k5eXRq1cvhg4dytKlS/FpHLBq1Sp+9KMf0bdvXy666CIGDRrEtGnTjvq9iY1deXk5eXl5ZGVl8fbbb8e7nLh65plnyMrKqvXPvffeG+/SEsKLL77IsGHDyM7OJjc3l1tvvZVt27bFu6zjkhzvAnT8wuEw1113HZ06dWLWrFmxp3R/8cUXPqUb2Lx5M6tWraJnz55UV1f7of//+/Wvf027du2YMGECLVq04E9/+hOTJk1i586djBkzJt7lxdXevXvp0aMHI0aMIC0tjc2bNzNr1iw2b97Mww8/HO/yEsrcuXOpqqqKdxkJZeHChTRv3jz2Oj09PY7VJIbXX3+dMWPGcOWVV1JYWMjevXu57777+MlPfsKzzz7LWWedFe8Sv15U9cYDDzwQ7dWrV3TPnj2xtiVLlkS7du0a3blzZ/wKSxBVVVWxP99+++3RwYMHx7GaxPHZZ5/Vaps4cWK0d+/eNc6ZDnviiSeiXbp08XfqS7Zs2RLt1atX9PHHH4926dIlumHDhniXFFdPP/10tEuXLkf93WrsJk2aFB04cGC0uro61rZ27dpoly5don/5y1/iWNnx8fJZPbJ69Wr69etHWlparC0/P5/q6mpeffXV+BWWIIJB/3M+mpYtW9Zq69q1K/v37+fAgQNxqCixHfn9qqioiG8hCaS4uJjhw4eTkZER71KU4CorK0lJSSEQCMTajqymRevB6r2fIvVIaWkpmZmZNdpCoRCtW7emtLQ0TlWpPnrjjTdIT0/n3HPPjXcpCaGqqoq///3vvPvuu8yZM4eBAwfSvn37eJeVEFasWMGmTZu4+eab411KwhkyZAhdu3Zl0KBBPPjgg15eBK666iq2bt3K4sWL2bdvH9u2beO///u/6datG7179453ecfknqJ6JBKJEAqFarWnpqYSDofjUJHqo3Xr1lFSUsLtt98e71ISxiWXXMKuXbsAGDBgANOnT49zRYnh4MGD3H333RQWFhqgv6R169aMHTuWnj17EggEeOmll5g5cya7du1q9Ps7L774YmbPns3PfvYz7rrrLuDwyvTChQtJSkqKc3XHZiiSGpGdO3dSWFhITk4OI0eOjHc5CWP+/PkcPHiQLVu2MG/ePEaPHs0jjzxSL/4SP53mzZtHq1atuPrqq+NdSkIZMGAAAwYMiL3Ozc2lWbNm/OY3v2H06NG0adMmjtXF11//+lduu+02vv/97/Ptb3+bvXv3MnfuXG644QYee+yxhN9obSiqR0Kh0FFvFQ6Hw6SmpsahItUnkUiEUaNGkZaWxqxZs9yD9SXf+MY3AMjOzqZ79+4MHTqU559/nu9+97txrix+duzYwcMPP8ycOXNif+8c2YN24MABysvLSUlJiWeJCSU/P5+HH36YjRs3NupQVFxcTN++fZkwYUKsrVevXnz729/m97//PT/4wQ/iWN2xGYrqkczMzFp7h/bt28cnn3xSa6+R9GVffPEFN954I/v27eOJJ56ocRuxasrKyqJJkyZ8/PHH8S4lrrZv305FRQU33HBDrb6RI0fSs2dPnnzyyThUpkS2detWBg0aVKPtvPPOo0WLFvXid8pQVI/k5eXxwAMP1NhbtGLFCoLBIP37949zdUpUlZWVjBs3jtLSUhYvXuyzVI7hrbfeoqKiotFvtO7atSuLFi2q0bZx40amTZvGnXfeSffu3eNUWWIqKSkhKSmJbt26xbuUuGrbti3vvfdejbYdO3awZ88e2rVrF6eqjp+hqB4ZPnw4v/3tb7n55pu58cYb2bVrF/fccw/Dhw/3g47Dm0JXrVoFHP4l3L9/PytWrACgT58+R701vTG48847efnll5kwYQL79+/nzTffjPV169aNpk2bxq+4OBszZgwXXXQRWVlZnHXWWbz//vs89NBDZGVlcemll8a7vLgKhULk5OQcte/CCy/kwgsvPMMVJY7rr7+enJwcsrKygMNPcH7yyScZOXIkrVu3jnN18TV8+HCmTp1KcXExAwcOZO/evbG9afn5+fEu75gC0frw4ADFbN26lV/+8pesX7+elJQUhg4dSmFhYaP+YDti+/bttZZtj1i0aNFX/gXf0A0cOJAdO3Ycte/FF19s1Csi8+fPp6SkhI8//phoNEq7du34zne+w/XXX+/dVkfx+uuvM3LkSJYuXdqoV4qKi4t55ZVX2LlzJ9XV1XTq1Ilhw4YxYsSIGs/naYyi0ShLlizh8ccfZ9u2baSkpNCrVy8KCwvp3LlzvMs7JkORJEkSPrxRkiQJMBRJkiQBhiJJkiTAUCRJkgQYiiRJkgBDkSRJEmAokiRJAgxFkhqwgQMH1vhiymeeeYasrCzefvvtOFYlKVH5NR+S6qUPPviAOXPm8Pbbb/Ppp5+SlpbGBRdcwMCBAxkxYsRJz19dXc2yZctYvHgxH330ERUVFbRp04aePXty7bXX0qtXr5M/CEkJxVAkqd7561//ysiRI2nbti3Dhg2jdevW/O///i9vvfUWixYtioWiFStW1PlrF4qLi1m8eDGDBg3iiiuuICkpibKyMl555RU6dOhgKJIaIEORpHrngQceoHnz5ixdupRQKFSj77PPPov9ua7fCfjpp5/y2GOP8f3vf59f/vKXNfqi0Siff/55neaVlNjcUySp3vn444+54IILagUigFatWsX+/H/3FB3xxRdfMHnyZHJycujduze33XYb4XA41r99+3ai0Si9e/eu9d5AIFDjZxzZp/SXv/zla+eUlPgMRZLqnXbt2vHuu++yadOmOr3/rrvuYuvWrYwZM4Yrr7ySZ599lptvvpkj34/dtm1b4PDlt4MHD56SOSUlPi+fSap3fvKTnzBq1CiuvPJKevTowTe/+U369etHTk4OTZo0Oeb7mzRpwq9//evY2LZt2/Jf//VfvPTSSwwaNIg2bdpw5ZVX8rvf/Y5vfetb9OnTh969e/Otb32Lzp0712lOSYnPlSJJ9U7//v1ZsmQJAwcO5P3332fhwoVcf/315OXl8eKLLx7z/T/4wQ9qhKcf/vCHJCcns2rVqljbtGnTmDx5Mu3bt+f555/nV7/6FQUFBVx33XXs2rWrTnNKSmyGIkn1Uo8ePZg9ezZ//vOfeeqpp7jxxhspLy/n1ltvZcuWLV/73vPPP7/G65SUFFq3bs2OHTtibcFgkH/5l3/hmWee4bXXXmPu3Lnk5eXx2muvUVhYWKc5JSU2Q5Gkeq1p06b06NGDf//3f2fKlClUVFSwYsWKU/ozWrRowaBBg1iwYAF9+vThjTfeMOxIDZChSFKDcdFFFwGwe/furx330Ucf1XhdXl7OJ598Qrt27Y77Z3zyySenbE5JicFQJKneee211456V9eR/TuZmZlf+/4nnniCioqK2OvHH3+cyspK8vLygMOB52iX4A4dOsTatWsJBoN07NjxhOaUlPi8+0xSvVNcXMzBgwf5zne+Q2ZmJhUVFfz1r3/lD3/4A+3ateOqq6762vdXVFTw4x//mPz8fMrKynjsscf45je/GbtLbOfOnQwbNoy+ffvSr18//uEf/oHPPvuM5cuX8/7773PdddfRsmXLE5pTUuIzFEmqd2677TZWrFjBqlWrYis0bdu25dprr+Wmm2466kMdv2zy5Mk8++yz3H///VRUVDB48GAmTpwY+0qQjIwMioqKWLVqFY899hifffYZTZs2pUuXLhQXF3PNNdec8JySEl8g6pPFJKnOnnnmGX7+85+zdOlSunfvHu9yJJ0E9xRJkiRhKJIkSQIMRZIkSYB7iiRJkgBXiiRJkgBDkSRJEmAokiRJAgxFkiRJgKFIkiQJMBRJkiQBhiJJkiTAUCRJkgQYiiRJkgD4/wCRRQ35ZLRpMgAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(x='SibSp', hue = 'Survived', data = df_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 476
        },
        "id": "aoSZ681uxSDn",
        "outputId": "902e95ff-74e7-4553-bb72-49c92eff70f5"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='SibSp', ylabel='count'>"
            ]
          },
          "metadata": {},
          "execution_count": 66
        },
        {
          "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_train['Parch'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 335
        },
        "id": "VUT4cLnCxcpO",
        "outputId": "4dbdea48-33e2-4f84-a286-1133860d5eec"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Parch\n",
              "0    678\n",
              "1    118\n",
              "2     80\n",
              "5      5\n",
              "3      5\n",
              "4      4\n",
              "6      1\n",
              "Name: count, dtype: int64"
            ],
            "text/html": [
              "<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>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Parch</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>678</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>118</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>80</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(x='Parch', data = df_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 476
        },
        "id": "BnWCkKMax0CL",
        "outputId": "699822f3-60df-4eb8-8807-b313bb22608d"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Parch', ylabel='count'>"
            ]
          },
          "metadata": {},
          "execution_count": 68
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(x='Parch', hue = 'Survived',data=df_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 476
        },
        "id": "GNLnRpXCx9tL",
        "outputId": "3b588ade-b38c-428d-cca8-edacf4fe4a76"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Parch', ylabel='count'>"
            ]
          },
          "metadata": {},
          "execution_count": 69
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_train['Embarked'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 209
        },
        "id": "iNlr_SMKyIEa",
        "outputId": "0b58be26-d2cc-4412-cb8a-bf505fd8c71e"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Embarked\n",
              "S    646\n",
              "C    168\n",
              "Q     77\n",
              "Name: count, dtype: int64"
            ],
            "text/html": [
              "<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>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Embarked</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>S</th>\n",
              "      <td>646</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C</th>\n",
              "      <td>168</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Q</th>\n",
              "      <td>77</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(x='Embarked', data = df_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 476
        },
        "id": "KCK24RdHyUbC",
        "outputId": "022d65b4-c08e-45cd-bbd9-e87ae5e88b87"
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Embarked', ylabel='count'>"
            ]
          },
          "metadata": {},
          "execution_count": 71
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(x='Embarked', hue = 'Survived', data = df_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 476
        },
        "id": "rvkshGhnyb_J",
        "outputId": "8a3d8d3e-c730-4d21-d620-e3d87ac9deff"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Embarked', ylabel='count'>"
            ]
          },
          "metadata": {},
          "execution_count": 72
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Encoding categorical Columns\n",
        "\n",
        "df_train.replace({'Sex': {'male': 0, 'female':1}}, inplace = True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kUsNc0_myhyD",
        "outputId": "33359427-480d-480a-e507-f8e81ab23066"
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-73-adc6861a7ccd>:3: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
            "  df_train.replace({'Sex': {'male': 0, 'female':1}}, inplace = True)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Separate features and Target variable\n",
        "\n",
        "X = df_train.drop(columns = ['PassengerId','Survived','Name','Ticket'])\n",
        "Y = df_train['Survived']"
      ],
      "metadata": {
        "id": "lZfub3dE0Hlg"
      },
      "execution_count": 82,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "Ucras5rv1F1j",
        "outputId": "58dc310f-13b0-4e89-9cfc-edd88ffa243c"
      },
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Pclass  Sex        Age  SibSp  Parch     Fare  Embarked\n",
              "0         3    0  22.000000      1      0   7.2500         0\n",
              "1         1    1  38.000000      1      0  71.2833         1\n",
              "2         3    1  26.000000      0      0   7.9250         0\n",
              "3         1    1  35.000000      1      0  53.1000         0\n",
              "4         3    0  35.000000      0      0   8.0500         0\n",
              "..      ...  ...        ...    ...    ...      ...       ...\n",
              "886       2    0  27.000000      0      0  13.0000         0\n",
              "887       1    1  19.000000      0      0  30.0000         0\n",
              "888       3    1  29.699118      1      2  23.4500         0\n",
              "889       1    0  26.000000      0      0  30.0000         1\n",
              "890       3    0  32.000000      0      0   7.7500         2\n",
              "\n",
              "[891 rows x 7 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-5e140705-9b0a-4056-a54a-a00f55055f28\" 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>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>3</td>\n",
              "      <td>0</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>38.000000</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>26.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>35.000000</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>35.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>886</th>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>27.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>13.0000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>887</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>19.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>30.0000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>888</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>29.699118</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>23.4500</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>889</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>26.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>30.0000</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>890</th>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>32.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.7500</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>891 rows × 7 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5e140705-9b0a-4056-a54a-a00f55055f28')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
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          "metadata": {},
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        "id": "3tlth5M51HF4",
        "outputId": "b39d62e0-405c-489f-9083-244e5816c121"
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      "execution_count": 76,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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    {
      "cell_type": "code",
      "source": [
        "# MODEL TRAINING\n",
        "\n",
        "model = LogisticRegression()"
      ],
      "metadata": {
        "id": "TCwDiM6w1Imm"
      },
      "execution_count": 84,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_train.replace({'Embarked': {'S': 0, 'C':1, 'Q':2}}, inplace = True)\n"
      ],
      "metadata": {
        "id": "dGDe_kg43wfA"
      },
      "execution_count": 85,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(X, Y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "id": "e3m_S3Kg35nt",
        "outputId": "22326018-2a35-4349-fa64-a6624230bb4d"
      },
      "execution_count": 86,
      "outputs": [
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              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-1 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: block;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-1 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-1 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-1 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-1 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 1ex;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-1 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression()</pre></div> </div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Y_train_prediction = model.predict(X)"
      ],
      "metadata": {
        "id": "ZhtXIYxy4cxq"
      },
      "execution_count": 88,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "training_data_accuracy = accuracy_score(Y, Y_train_prediction)"
      ],
      "metadata": {
        "id": "F6kyuXVN5AfM"
      },
      "execution_count": 89,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "training_data_accuracy"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DorksFv85h_Q",
        "outputId": "930ee4af-5fdd-4ab7-8ccb-a98e6beb5111"
      },
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8047138047138047"
            ]
          },
          "metadata": {},
          "execution_count": 90
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 842
        },
        "id": "P616hC_O5kkf",
        "outputId": "37a7d233-0bc1-4330-b1c4-b4a3ae6085c7"
      },
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     PassengerId  Pclass                                          Name  ...      Fare  Cabin  Embarked\n",
              "0            892       3                              Kelly, Mr. James  ...    7.8292    NaN         Q\n",
              "1            893       3              Wilkes, Mrs. James (Ellen Needs)  ...    7.0000    NaN         S\n",
              "2            894       2                     Myles, Mr. Thomas Francis  ...    9.6875    NaN         Q\n",
              "3            895       3                              Wirz, Mr. Albert  ...    8.6625    NaN         S\n",
              "4            896       3  Hirvonen, Mrs. Alexander (Helga E Lindqvist)  ...   12.2875    NaN         S\n",
              "..           ...     ...                                           ...  ...       ...    ...       ...\n",
              "413         1305       3                            Spector, Mr. Woolf  ...    8.0500    NaN         S\n",
              "414         1306       1                  Oliva y Ocana, Dona. Fermina  ...  108.9000   C105         C\n",
              "415         1307       3                  Saether, Mr. Simon Sivertsen  ...    7.2500    NaN         S\n",
              "416         1308       3                           Ware, Mr. Frederick  ...    8.0500    NaN         S\n",
              "417         1309       3                      Peter, Master. Michael J  ...   22.3583    NaN         C\n",
              "\n",
              "[418 rows x 11 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-365825d8-1e54-4acb-8d67-1a976a6b99e2\" 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>Pclass</th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>892</td>\n",
              "      <td>3</td>\n",
              "      <td>Kelly, Mr. James</td>\n",
              "      <td>male</td>\n",
              "      <td>34.5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>330911</td>\n",
              "      <td>7.8292</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Q</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>893</td>\n",
              "      <td>3</td>\n",
              "      <td>Wilkes, Mrs. James (Ellen Needs)</td>\n",
              "      <td>female</td>\n",
              "      <td>47.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>363272</td>\n",
              "      <td>7.0000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>894</td>\n",
              "      <td>2</td>\n",
              "      <td>Myles, Mr. Thomas Francis</td>\n",
              "      <td>male</td>\n",
              "      <td>62.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>240276</td>\n",
              "      <td>9.6875</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Q</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>895</td>\n",
              "      <td>3</td>\n",
              "      <td>Wirz, Mr. Albert</td>\n",
              "      <td>male</td>\n",
              "      <td>27.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>315154</td>\n",
              "      <td>8.6625</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>896</td>\n",
              "      <td>3</td>\n",
              "      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
              "      <td>female</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3101298</td>\n",
              "      <td>12.2875</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>413</th>\n",
              "      <td>1305</td>\n",
              "      <td>3</td>\n",
              "      <td>Spector, Mr. Woolf</td>\n",
              "      <td>male</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>A.5. 3236</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>414</th>\n",
              "      <td>1306</td>\n",
              "      <td>1</td>\n",
              "      <td>Oliva y Ocana, Dona. Fermina</td>\n",
              "      <td>female</td>\n",
              "      <td>39.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>PC 17758</td>\n",
              "      <td>108.9000</td>\n",
              "      <td>C105</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>415</th>\n",
              "      <td>1307</td>\n",
              "      <td>3</td>\n",
              "      <td>Saether, Mr. Simon Sivertsen</td>\n",
              "      <td>male</td>\n",
              "      <td>38.5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>SOTON/O.Q. 3101262</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>416</th>\n",
              "      <td>1308</td>\n",
              "      <td>3</td>\n",
              "      <td>Ware, Mr. Frederick</td>\n",
              "      <td>male</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>359309</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>417</th>\n",
              "      <td>1309</td>\n",
              "      <td>3</td>\n",
              "      <td>Peter, Master. Michael J</td>\n",
              "      <td>male</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2668</td>\n",
              "      <td>22.3583</td>\n",
              "      <td>NaN</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>418 rows × 11 columns</p>\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
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              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
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              "\n",
              "  <div id=\"id_739754d5-4fba-4513-a26a-957f8aff69e3\">\n",
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              "\n",
              "      .colab-df-generate:hover {\n",
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              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df_test')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
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              "        google.colab.notebook.generateWithVariable('df_test');\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_test",
              "summary": "{\n  \"name\": \"df_test\",\n  \"rows\": 418,\n  \"fields\": [\n    {\n      \"column\": \"PassengerId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 120,\n        \"min\": 892,\n        \"max\": 1309,\n        \"num_unique_values\": 418,\n        \"samples\": [\n          1213,\n          1216,\n          1280\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Pclass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          3,\n          2,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 418,\n        \"samples\": [\n          \"Krekorian, Mr. Neshan\",\n          \"Kreuchen, Miss. Emilie\",\n          \"Canavan, Mr. Patrick\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"female\",\n          \"male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14.18120923562442,\n        \"min\": 0.17,\n        \"max\": 76.0,\n        \"num_unique_values\": 79,\n        \"samples\": [\n          10.0,\n          34.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SibSp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 8,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Parch\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 9,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1,\n          6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ticket\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 363,\n        \"samples\": [\n          \"2673\",\n          \"W./C. 6607\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 55.90757617997383,\n        \"min\": 0.0,\n        \"max\": 512.3292,\n        \"num_unique_values\": 169,\n        \"samples\": [\n          41.5792,\n          57.75\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cabin\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 76,\n        \"samples\": [\n          \"A21\",\n          \"E45\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Embarked\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Q\",\n          \"S\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 91
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_test.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 429
        },
        "id": "_QYNvo828mIg",
        "outputId": "38f14c11-697d-4972-de37-410e1ab74c0a"
      },
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PassengerId      0\n",
              "Pclass           0\n",
              "Name             0\n",
              "Sex              0\n",
              "Age             86\n",
              "SibSp            0\n",
              "Parch            0\n",
              "Ticket           0\n",
              "Fare             1\n",
              "Cabin          327\n",
              "Embarked         0\n",
              "dtype: int64"
            ],
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              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 92
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_test.drop('Cabin', axis = 1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 842
        },
        "id": "0LhjaCh48zau",
        "outputId": "59766219-49aa-4a3a-9ffd-22946921380a"
      },
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     PassengerId  Pclass  ...      Fare Embarked\n",
              "0            892       3  ...    7.8292        Q\n",
              "1            893       3  ...    7.0000        S\n",
              "2            894       2  ...    9.6875        Q\n",
              "3            895       3  ...    8.6625        S\n",
              "4            896       3  ...   12.2875        S\n",
              "..           ...     ...  ...       ...      ...\n",
              "413         1305       3  ...    8.0500        S\n",
              "414         1306       1  ...  108.9000        C\n",
              "415         1307       3  ...    7.2500        S\n",
              "416         1308       3  ...    8.0500        S\n",
              "417         1309       3  ...   22.3583        C\n",
              "\n",
              "[418 rows x 10 columns]"
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Embarked</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>892</td>\n",
              "      <td>3</td>\n",
              "      <td>Kelly, Mr. James</td>\n",
              "      <td>male</td>\n",
              "      <td>34.5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>330911</td>\n",
              "      <td>7.8292</td>\n",
              "      <td>Q</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>893</td>\n",
              "      <td>3</td>\n",
              "      <td>Wilkes, Mrs. James (Ellen Needs)</td>\n",
              "      <td>female</td>\n",
              "      <td>47.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>363272</td>\n",
              "      <td>7.0000</td>\n",
              "      <td>S</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>894</td>\n",
              "      <td>2</td>\n",
              "      <td>Myles, Mr. Thomas Francis</td>\n",
              "      <td>male</td>\n",
              "      <td>62.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>240276</td>\n",
              "      <td>9.6875</td>\n",
              "      <td>Q</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>895</td>\n",
              "      <td>3</td>\n",
              "      <td>Wirz, Mr. Albert</td>\n",
              "      <td>male</td>\n",
              "      <td>27.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>315154</td>\n",
              "      <td>8.6625</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>896</td>\n",
              "      <td>3</td>\n",
              "      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
              "      <td>female</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3101298</td>\n",
              "      <td>12.2875</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>413</th>\n",
              "      <td>1305</td>\n",
              "      <td>3</td>\n",
              "      <td>Spector, Mr. Woolf</td>\n",
              "      <td>male</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>A.5. 3236</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>414</th>\n",
              "      <td>1306</td>\n",
              "      <td>1</td>\n",
              "      <td>Oliva y Ocana, Dona. Fermina</td>\n",
              "      <td>female</td>\n",
              "      <td>39.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>PC 17758</td>\n",
              "      <td>108.9000</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>415</th>\n",
              "      <td>1307</td>\n",
              "      <td>3</td>\n",
              "      <td>Saether, Mr. Simon Sivertsen</td>\n",
              "      <td>male</td>\n",
              "      <td>38.5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>SOTON/O.Q. 3101262</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>416</th>\n",
              "      <td>1308</td>\n",
              "      <td>3</td>\n",
              "      <td>Ware, Mr. Frederick</td>\n",
              "      <td>male</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>359309</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>417</th>\n",
              "      <td>1309</td>\n",
              "      <td>3</td>\n",
              "      <td>Peter, Master. Michael J</td>\n",
              "      <td>male</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2668</td>\n",
              "      <td>22.3583</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>418 rows × 10 columns</p>\n",
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              "\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "  }\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",
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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",
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              "        console.error('Error during call to suggestCharts:', error);\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df_test\",\n  \"rows\": 418,\n  \"fields\": [\n    {\n      \"column\": \"PassengerId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 120,\n        \"min\": 892,\n        \"max\": 1309,\n        \"num_unique_values\": 418,\n        \"samples\": [\n          1213,\n          1216,\n          1280\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Pclass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          3,\n          2,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 418,\n        \"samples\": [\n          \"Krekorian, Mr. Neshan\",\n          \"Kreuchen, Miss. Emilie\",\n          \"Canavan, Mr. Patrick\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"female\",\n          \"male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14.18120923562442,\n        \"min\": 0.17,\n        \"max\": 76.0,\n        \"num_unique_values\": 79,\n        \"samples\": [\n          10.0,\n          34.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SibSp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 8,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Parch\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 9,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1,\n          6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ticket\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 363,\n        \"samples\": [\n          \"2673\",\n          \"W./C. 6607\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 55.90757617997383,\n        \"min\": 0.0,\n        \"max\": 512.3292,\n        \"num_unique_values\": 169,\n        \"samples\": [\n          41.5792,\n          57.75\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Embarked\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Q\",\n          \"S\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_test['Fare'].fillna(df_test['Fare'].mean(), inplace = True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kcqrnSwV9B2u",
        "outputId": "9fe28601-cb08-4964-c53e-5a9be60bf876"
      },
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-97-455d33b384ea>:1: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
            "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
            "\n",
            "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
            "\n",
            "\n",
            "  df_test['Fare'].fillna(df_test['Fare'].mean(), inplace = True)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_test['Age'].fillna(df_test['Age'].mean(), inplace = True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D4WEeHut9bTg",
        "outputId": "6092089a-4557-46c0-a2bb-6f8319c016c6"
      },
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-98-ab579095383f>:1: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
            "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
            "\n",
            "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
            "\n",
            "\n",
            "  df_test['Age'].fillna(df_test['Age'].mean(), inplace = True)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_test.replace({'Sex': {'male':0,'female':1}}, inplace = True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qDQrbQOE-ryp",
        "outputId": "53062da5-570e-475f-ddb7-e6c746a099a7"
      },
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-99-7662cb14a439>:1: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
            "  df_test.replace({'Sex': {'male':0,'female':1}}, inplace = True)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_test.replace({'Embarked': {'S': 0, 'C':1, 'Q':2}}, inplace = True)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "84uUcvi8-9JE",
        "outputId": "83e81c23-d177-4154-86d0-dcbc9caee9b4"
      },
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-100-365b3c4fdead>:1: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
            "  df_test.replace({'Embarked': {'S': 0, 'C':1, 'Q':2}}, inplace = True)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_test = df_test.drop(columns = ['PassengerId','Name','Ticket','Cabin'])"
      ],
      "metadata": {
        "id": "WyxgIfO3_Gle"
      },
      "execution_count": 104,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "TLA5z2tf_VMm",
        "outputId": "266cdc6c-f669-4001-c8ed-6eb7ee23d72e"
      },
      "execution_count": 105,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "     Pclass  Sex       Age  SibSp  Parch      Fare  Embarked\n",
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              "3         3    0  27.00000      0      0    8.6625         0\n",
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              "..      ...  ...       ...    ...    ...       ...       ...\n",
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              "414       1    1  39.00000      0      0  108.9000         1\n",
              "415       3    0  38.50000      0      0    7.2500         0\n",
              "416       3    0  30.27259      0      0    8.0500         0\n",
              "417       3    0  30.27259      1      1   22.3583         1\n",
              "\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
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              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "        width: 32px;\n",
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              "\n",
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              "\n",
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              "            title=\"Generate code using this dataframe.\"\n",
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              "\n",
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              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "X_test",
              "summary": "{\n  \"name\": \"X_test\",\n  \"rows\": 418,\n  \"fields\": [\n    {\n      \"column\": \"Pclass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          3,\n          2,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sex\",\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\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12.63453416832505,\n        \"min\": 0.17,\n        \"max\": 76.0,\n        \"num_unique_values\": 80,\n        \"samples\": [\n          28.0,\n          34.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SibSp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 8,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Parch\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 9,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1,\n          6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 55.84050047954103,\n        \"min\": 0.0,\n        \"max\": 512.3292,\n        \"num_unique_values\": 170,\n        \"samples\": [\n          41.5792,\n          57.75\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": 105
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Y_test = model.predict(X_test)"
      ],
      "metadata": {
        "id": "IwftZc4e_7eh"
      },
      "execution_count": 106,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Y_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-wNCweg7AY73",
        "outputId": "4ac5836c-302f-48ba-d884-df463ad21f30"
      },
      "execution_count": 107,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
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            ]
          },
          "metadata": {},
          "execution_count": 107
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Y_test_original = pd.read_csv('gender_submission.csv')"
      ],
      "metadata": {
        "id": "-di_fGyuAbOs"
      },
      "execution_count": 108,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy_check = accuracy_score(Y_test_original['Survived'], Y_test)"
      ],
      "metadata": {
        "id": "S5Y0FzcXApS-"
      },
      "execution_count": 109,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy_check"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YEdXQoY7Aw7K",
        "outputId": "cd595bc2-d690-4524-c8d4-381d3c4aedbe"
      },
      "execution_count": 110,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9425837320574163"
            ]
          },
          "metadata": {},
          "execution_count": 110
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a DataFrame with 'PassengerId' and 'Survived' (Y_test)\n",
        "submission = pd.DataFrame({'PassengerId': df_test['PassengerId'], 'Survived': Y_test})\n",
        "\n",
        "# Save the DataFrame to a CSV file named 'submission.csv'\n",
        "submission.to_csv('submission.csv', index=False)\n",
        "\n"
      ],
      "metadata": {
        "id": "NbBw9efAAzD4"
      },
      "execution_count": 111,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "submission"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "DogooYsHBpl-",
        "outputId": "918b4c6a-b342-44a3-8074-e218c0460468"
      },
      "execution_count": 112,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     PassengerId  Survived\n",
              "0            892         0\n",
              "1            893         0\n",
              "2            894         0\n",
              "3            895         0\n",
              "4            896         1\n",
              "..           ...       ...\n",
              "413         1305         0\n",
              "414         1306         1\n",
              "415         1307         0\n",
              "416         1308         0\n",
              "417         1309         0\n",
              "\n",
              "[418 rows x 2 columns]"
            ],
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              "      <td>1309</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>418 rows × 2 columns</p>\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",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
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              "\n",
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              "      .colab-df-generate {\n",
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              "        border: none;\n",
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              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate: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",
              "\n",
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              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('submission')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_f6663f87-bbff-4f35-8567-9a79a16d389a button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('submission');\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "submission",
              "summary": "{\n  \"name\": \"submission\",\n  \"rows\": 418,\n  \"fields\": [\n    {\n      \"column\": \"PassengerId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 120,\n        \"min\": 892,\n        \"max\": 1309,\n        \"num_unique_values\": 418,\n        \"samples\": [\n          1213,\n          1216,\n          1280\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"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}"
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          },
          "metadata": {},
          "execution_count": 112
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "z0QluriGBu1_"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}