Titanic_Survival_Prediction.ipynb

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      "source": [
        "Importing the Dependencies"
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      "source": [
        "import numpy\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import accuracy_score"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "data collection and Processing"
      ],
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        "id": "pLeFaogp4fz7"
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      "cell_type": "code",
      "source": [
        "titanic_dataset = pd.read_csv('/content/Titanic-Dataset.csv')"
      ],
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        "id": "1KnHm14M4b9W"
      },
      "execution_count": 2,
      "outputs": []
    },
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      "source": [
        "# printing 1st five rows and columns of dataset\n",
        "titanic_dataset.head()"
      ],
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      "execution_count": 3,
      "outputs": [
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              "   PassengerId  Survived  Pclass  \\\n",
              "0            1         0       3   \n",
              "1            2         1       1   \n",
              "2            3         1       3   \n",
              "3            4         1       1   \n",
              "4            5         0       3   \n",
              "\n",
              "                                                Name     Sex   Age  SibSp  \\\n",
              "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
              "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
              "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
              "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
              "4                           Allen, Mr. William Henry    male  35.0      0   \n",
              "\n",
              "   Parch            Ticket     Fare Cabin Embarked  \n",
              "0      0         A/5 21171   7.2500   NaN        S  \n",
              "1      0          PC 17599  71.2833   C85        C  \n",
              "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
              "3      0            113803  53.1000  C123        S  \n",
              "4      0            373450   8.0500   NaN        S  "
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              "variable_name": "titanic_dataset",
              "summary": "{\n  \"name\": \"titanic_dataset\",\n  \"rows\": 891,\n  \"fields\": [\n    {\n      \"column\": \"PassengerId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 257,\n        \"min\": 1,\n        \"max\": 891,\n        \"num_unique_values\": 891,\n        \"samples\": [\n          710,\n          440,\n          841\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Survived\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Pclass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          3,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 891,\n        \"samples\": [\n          \"Moubarek, Master. Halim Gonios (\\\"William George\\\")\",\n          \"Kvillner, Mr. Johan Henrik Johannesson\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"female\",\n          \"male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14.526497332334042,\n        \"min\": 0.42,\n        \"max\": 80.0,\n        \"num_unique_values\": 88,\n        \"samples\": [\n          0.75,\n          22.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SibSp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 8,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Parch\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 6,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ticket\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 681,\n        \"samples\": [\n          \"11774\",\n          \"248740\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 49.6934285971809,\n        \"min\": 0.0,\n        \"max\": 512.3292,\n        \"num_unique_values\": 248,\n        \"samples\": [\n          11.2417,\n          51.8625\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cabin\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 147,\n        \"samples\": [\n          \"D45\",\n          \"B49\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Embarked\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"S\",\n          \"C\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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          "metadata": {},
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    {
      "cell_type": "code",
      "source": [
        "# printing 1st five rows and columns of dataset\n",
        "titanic_dataset.tail()"
      ],
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          "height": 206
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        "id": "bgaW8FR05RzU",
        "outputId": "970cb4b0-c5a6-45df-9934-5fa7d14bb3dd"
      },
      "execution_count": 4,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "     PassengerId  Survived  Pclass                                      Name  \\\n",
              "886          887         0       2                     Montvila, Rev. Juozas   \n",
              "887          888         1       1              Graham, Miss. Margaret Edith   \n",
              "888          889         0       3  Johnston, Miss. Catherine Helen \"Carrie\"   \n",
              "889          890         1       1                     Behr, Mr. Karl Howell   \n",
              "890          891         0       3                       Dooley, Mr. Patrick   \n",
              "\n",
              "        Sex   Age  SibSp  Parch      Ticket   Fare Cabin Embarked  \n",
              "886    male  27.0      0      0      211536  13.00   NaN        S  \n",
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              "889    male  26.0      0      0      111369  30.00  C148        C  \n",
              "890    male  32.0      0      0      370376   7.75   NaN        Q  "
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              "type": "dataframe",
              "summary": "{\n  \"name\": \"titanic_dataset\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"PassengerId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 887,\n        \"max\": 891,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          888,\n          891,\n          889\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\": 1,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\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\": 5,\n        \"samples\": [\n          \"Graham, Miss. Margaret Edith\",\n          \"Dooley, 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\": 5.354126134736337,\n        \"min\": 19.0,\n        \"max\": 32.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          19.0,\n          32.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SibSp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          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\": 2,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          2,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ticket\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"112053\",\n          \"370376\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10.09253436952285,\n        \"min\": 7.75,\n        \"max\": 30.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          30.0,\n          7.75\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cabin\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"C148\",\n          \"B42\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Embarked\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"S\",\n          \"C\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#shape of the dataset row*columns\n",
        "titanic_dataset.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AOW_Cf2F6Awo",
        "outputId": "4e1e94b3-0dd0-4122-c785-75db031002a8"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(891, 12)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#getting the info about data\n",
        "titanic_dataset.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xsfbDtzV6kdB",
        "outputId": "35212ea4-cf88-4073-eb53-91fe4383e9e0"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 891 entries, 0 to 890\n",
            "Data columns (total 12 columns):\n",
            " #   Column       Non-Null Count  Dtype  \n",
            "---  ------       --------------  -----  \n",
            " 0   PassengerId  891 non-null    int64  \n",
            " 1   Survived     891 non-null    int64  \n",
            " 2   Pclass       891 non-null    int64  \n",
            " 3   Name         891 non-null    object \n",
            " 4   Sex          891 non-null    object \n",
            " 5   Age          714 non-null    float64\n",
            " 6   SibSp        891 non-null    int64  \n",
            " 7   Parch        891 non-null    int64  \n",
            " 8   Ticket       891 non-null    object \n",
            " 9   Fare         891 non-null    float64\n",
            " 10  Cabin        204 non-null    object \n",
            " 11  Embarked     889 non-null    object \n",
            "dtypes: float64(2), int64(5), object(5)\n",
            "memory usage: 83.7+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# check the number of missing values in each columns\n",
        "titanic_dataset.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 460
        },
        "id": "J8q7z3i-6UXo",
        "outputId": "ae7c0948-e882-4750-8fea-531c967387f8"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PassengerId      0\n",
              "Survived         0\n",
              "Pclass           0\n",
              "Name             0\n",
              "Sex              0\n",
              "Age            177\n",
              "SibSp            0\n",
              "Parch            0\n",
              "Ticket           0\n",
              "Fare             0\n",
              "Cabin          687\n",
              "Embarked         2\n",
              "dtype: int64"
            ],
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              "      <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",
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              "      <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": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "handeling the missing values\n"
      ],
      "metadata": {
        "id": "aH7csx-B7dhB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "titanic_dataset = titanic_dataset.drop(columns='Cabin', axis=1)"
      ],
      "metadata": {
        "id": "xZVQCiVF7AZF"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# replacing the missing values in age column with mean value\n",
        "titanic_dataset['Age'].fillna(titanic_dataset['Age'].mean(), inplace=True)\n"
      ],
      "metadata": {
        "id": "X4eComWk7va1"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        " # finding the mode value of \"Embarked\" column\n",
        " print(titanic_dataset['Embarked'].mode())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6PMcNcNi8-fR",
        "outputId": "24fa067c-7271-4e58-9e91-e3d2f2560a80"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0    0\n",
            "Name: Embarked, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        " print(titanic_dataset['Embarked'].mode()[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yMDoie7S9iFl",
        "outputId": "2bc80513-6fc4-4713-e0f0-c63c1838ff6a"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# replacing the misssing value in \"Emabarked coolmumn with mode value\"\n",
        "titanic_dataset['Embarked'].fillna(titanic_dataset['Embarked'].mode()[0], inplace=True)"
      ],
      "metadata": {
        "id": "svxQF3fa9trS"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "titanic_dataset.isnull().sum()"
      ],
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          "base_uri": "https://localhost:8080/",
          "height": 429
        },
        "id": "_unbx2So91fp",
        "outputId": "4eeed37d-85f6-4719-e43a-f5a8c93973db"
      },
      "execution_count": 13,
      "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"
            ],
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    {
      "cell_type": "markdown",
      "source": [
        "Now You can see we dont have any missing value. we have 3 missing values .\n",
        "\n",
        "\n",
        "1.   Cabin --> which is drop from the dataset  because it have lots number which is mising ..\n",
        "2.   Age : We find the mean value of given value of age .. and put it by mean value ..\n",
        "3. Emarked -->  replacing the misssing value in \"Emabarked coolmumn with mode value\"\n",
        "\n"
      ],
      "metadata": {
        "id": "PID390l3-OaP"
      }
    },
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      "source": [],
      "metadata": {
        "id": "cma5cFKP-NPV"
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    {
      "cell_type": "markdown",
      "source": [
        "**Data Analysis**"
      ],
      "metadata": {
        "id": "lAu7BJ3T-7fC"
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      "source": [],
      "metadata": {
        "id": "OszgrFHy_ces"
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    },
    {
      "cell_type": "code",
      "source": [
        "# GETTING the some statistical details of the data\n",
        "# these data contain only those columns which have numarical values\n",
        "titanic_dataset.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "sU3aJGdC-Jc6",
        "outputId": "657dbe41-f95c-4868-c0b9-eac4a63c8c9d"
      },
      "execution_count": 14,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
              "count   891.000000  891.000000  891.000000  891.000000  891.000000   \n",
              "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
              "std     257.353842    0.486592    0.836071   13.002015    1.102743   \n",
              "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
              "25%     223.500000    0.000000    2.000000   22.000000    0.000000   \n",
              "50%     446.000000    0.000000    3.000000   29.699118    0.000000   \n",
              "75%     668.500000    1.000000    3.000000   35.000000    1.000000   \n",
              "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
              "\n",
              "            Parch        Fare  \n",
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              "mean     0.381594   32.204208  \n",
              "std      0.806057   49.693429  \n",
              "min      0.000000    0.000000  \n",
              "25%      0.000000    7.910400  \n",
              "50%      0.000000   14.454200  \n",
              "75%      0.000000   31.000000  \n",
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              "    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-5fcdf5f8-9207-487a-b6b7-3084c3405da6 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\": \"titanic_dataset\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"PassengerId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 320.8159711429855,\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          0.4865924542648575\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Pclass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 314.2523437079694,\n        \"min\": 0.836071240977049,\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          0.8060572211299483\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 330.6256632228578,\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": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# finding the number of people have survived and not survived\n",
        "titanic_dataset['Survived'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 178
        },
        "id": "fi-ojXi9_MI9",
        "outputId": "8683d9d4-2982-4994-897c-fcf94bb67205"
      },
      "execution_count": 15,
      "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": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "0 --> 549 --> Are did'nt survived\n",
        "\n",
        "1 --> 342 --> Are  survived\n",
        "\n"
      ],
      "metadata": {
        "id": "du7dLOw0_-m7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# so now we check how much peoples are male or female in the dataset by sex columns\n",
        "titanic_dataset['Sex'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 178
        },
        "id": "sp_xor6Y_2Hk",
        "outputId": "c1cf121f-ee49-4ff4-b655-40e4f85bd461"
      },
      "execution_count": 16,
      "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": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Data Visulization"
      ],
      "metadata": {
        "id": "Blv7AeiQAn-H"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.set()"
      ],
      "metadata": {
        "id": "GziHoJehAczS"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# making the count plot for \"Survived \" column\n",
        "sns.countplot(x='Survived', data=titanic_dataset)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 475
        },
        "id": "Qg-YyVfk0o2Z",
        "outputId": "dcf5dc5f-aad4-4e3d-b69d-dbc50dbea864"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Survived', ylabel='count'>"
            ]
          },
          "metadata": {},
          "execution_count": 18
        },
        {
          "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": [
        "# making the count plot for \"Gender \" column\n",
        "sns.countplot(x='Sex', data=titanic_dataset)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 477
        },
        "id": "6ovjLBap01Xd",
        "outputId": "2ae113a9-58b8-4b76-83df-2e2b5b2fb47b"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Sex', ylabel='count'>"
            ]
          },
          "metadata": {},
          "execution_count": 19
        },
        {
          "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": [
        "# number of surviovors gender wise\n",
        "sns.countplot(x = 'Sex',  hue='Survived', data=titanic_dataset)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 475
        },
        "id": "CnLOiM2d1uaM",
        "outputId": "b9c4bdaf-c474-49db-88a7-569431acefde"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Sex', ylabel='count'>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        },
        {
          "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": [
        "# making the count plot for 'Pclass \" column\n",
        "sns.countplot(x='Pclass', data=titanic_dataset)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 475
        },
        "id": "rYH1Jsn92DDd",
        "outputId": "b62cea41-df95-4789-ab0c-ee8a24219077"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Pclass', ylabel='count'>"
            ]
          },
          "metadata": {},
          "execution_count": 21
        },
        {
          "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=titanic_dataset)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 475
        },
        "id": "2-dVVsxW223r",
        "outputId": "931b54f1-8c7d-4a65-ac2b-b6ce6f04479b"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Pclass', ylabel='count'>"
            ]
          },
          "metadata": {},
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "titanic_dataset['Pclass'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 209
        },
        "id": "fvpuxeJn3NaE",
        "outputId": "f2d1f497-df89-4659-d5fc-993991b3cdf1"
      },
      "execution_count": 23,
      "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": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Encoding the categorical Columns"
      ],
      "metadata": {
        "id": "7gMOw-xrOJ29"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "titanic_dataset['Sex'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 178
        },
        "id": "O6Bmv1FtOJUk",
        "outputId": "874aac0d-8cae-49c7-eba6-9e1a9ac9757a"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Sex\n",
              "male      577\n",
              "female    314\n",
              "Name: count, dtype: int64"
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              "      <th>Sex</th>\n",
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          },
          "metadata": {},
          "execution_count": 24
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "titanic_dataset['Embarked'].value_counts()"
      ],
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        "id": "Fd5Rng_O3j3H",
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          "height": 209
        },
        "outputId": "4a104c03-8213-4b36-a46b-46b3f3cd02cb"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Embarked\n",
              "S    646\n",
              "C    168\n",
              "Q     77\n",
              "Name: count, dtype: int64"
            ],
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              "      <th>Embarked</th>\n",
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      "cell_type": "code",
      "source": [
        "# converting the categorcal columns\n",
        "titanic_dataset.replace({'Sex':{'male':0,'female':1}, 'Embarked':{'S':0,'C':1,'Q':2}},inplace=True)"
      ],
      "metadata": {
        "id": "mAQY5crrOtPj"
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      "execution_count": 31,
      "outputs": []
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        "titanic_dataset.head()"
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          "height": 206
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        "id": "kP6hvNjBPcoY",
        "outputId": "4637ecbd-8f81-4c7f-86e2-39d69af07c4a"
      },
      "execution_count": 32,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "   PassengerId  Survived  Pclass  \\\n",
              "0            1         0       3   \n",
              "1            2         1       1   \n",
              "2            3         1       3   \n",
              "3            4         1       1   \n",
              "4            5         0       3   \n",
              "\n",
              "                                                Name  Sex   Age  SibSp  Parch  \\\n",
              "0                            Braund, Mr. Owen Harris    0  22.0      1      0   \n",
              "1  Cumings, Mrs. John Bradley (Florence Briggs Th...    1  38.0      1      0   \n",
              "2                             Heikkinen, Miss. Laina    1  26.0      0      0   \n",
              "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)    1  35.0      1      0   \n",
              "4                           Allen, Mr. William Henry    0  35.0      0      0   \n",
              "\n",
              "             Ticket     Fare  Embarked  \n",
              "0         A/5 21171   7.2500         0  \n",
              "1          PC 17599  71.2833         1  \n",
              "2  STON/O2. 3101282   7.9250         0  \n",
              "3            113803  53.1000         0  \n",
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              "type": "dataframe",
              "variable_name": "titanic_dataset",
              "summary": "{\n  \"name\": \"titanic_dataset\",\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\": \"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\": 13.002015226002882,\n        \"min\": 0.42,\n        \"max\": 80.0,\n        \"num_unique_values\": 89,\n        \"samples\": [\n          59.0,\n          36.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SibSp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 8,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Parch\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 6,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ticket\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 681,\n        \"samples\": [\n          \"11774\",\n          \"248740\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 49.6934285971809,\n        \"min\": 0.0,\n        \"max\": 512.3292,\n        \"num_unique_values\": 248,\n        \"samples\": [\n          11.2417,\n          51.8625\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Embarked\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Seprate features and targets"
      ],
      "metadata": {
        "id": "ztsa0upVQ7T3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = titanic_dataset.drop(columns= ['PassengerId', 'Name', 'Ticket', 'Survived'],axis=1)\n",
        "Y = titanic_dataset['Survived']"
      ],
      "metadata": {
        "id": "N053fotJQAzG"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(X)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sGaBuhHsR2ZI",
        "outputId": "74a9df08-2f37-4a83-f566-6de426645b8e"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "     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]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(Y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Kd_8F7vLR6Cm",
        "outputId": "6b4988ea-c930-4fbc-897d-d59605dd243f"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0      0\n",
            "1      1\n",
            "2      1\n",
            "3      1\n",
            "4      0\n",
            "      ..\n",
            "886    0\n",
            "887    1\n",
            "888    0\n",
            "889    1\n",
            "890    0\n",
            "Name: Survived, Length: 891, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Spliting the data into training and test data"
      ],
      "metadata": {
        "id": "lWkUiXsnSF1J"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, Y_train, Y_test = train_test_split(X,Y, test_size=0.2, random_state=2)\n"
      ],
      "metadata": {
        "id": "54eAKAYdSEaX"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(X.shape, X_train.shape, X_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j34yaNMqSdml",
        "outputId": "dfd4cda4-4635-4d30-85fe-b71e1faf764e"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(891, 7) (712, 7) (179, 7)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Model training"
      ],
      "metadata": {
        "id": "zoSUtkarTc-d"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Logistic regression"
      ],
      "metadata": {
        "id": "I5CvC6WjTfQt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = LogisticRegression()"
      ],
      "metadata": {
        "id": "YSK-B7GJTMUQ"
      },
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# training the logistic regression model with training data\n",
        "model.fit(X_train, Y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 213
        },
        "id": "GpvsZyl6USql",
        "outputId": "a55864cd-0f7c-4875-cc42-8150545ba6b2"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression()"
            ],
            "text/html": [
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            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "model evaluation"
      ],
      "metadata": {
        "id": "jC4quXETUl7k"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Accuracy Score\n"
      ],
      "metadata": {
        "id": "KqZgBplLUp22"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# accuracy on training data\n",
        "X_train_prediction = model.predict(X_train)\n"
      ],
      "metadata": {
        "id": "Qaj7qdQ1Ua7I"
      },
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(X_train_prediction)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x3G2BgNUVVhb",
        "outputId": "4e1a6d86-f39a-4c7f-d57e-26ff3e57c084"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0 1 0 0 0 0 0 1 0 0 0 1 0 0 1 0 1 0 0 0 0 0 1 0 0 1 0 0 1 0 1 1 0 0 1 0 1\n",
            " 0 0 0 0 0 0 1 1 0 0 1 0 1 0 1 0 0 0 0 0 0 1 0 1 0 0 1 1 0 0 1 1 0 1 0 0 1\n",
            " 0 0 0 0 0 0 1 0 0 0 1 0 0 0 1 0 1 0 0 1 0 0 0 1 1 1 0 1 0 0 0 0 0 1 0 0 0\n",
            " 1 1 0 0 1 0 0 1 0 0 1 0 0 1 0 1 0 1 0 1 0 1 1 1 1 1 1 0 0 1 1 1 0 0 1 0 0\n",
            " 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 1 0 0 1 0 1 0 1 1 1\n",
            " 0 0 0 1 0 0 0 1 0 0 1 0 0 0 1 1 0 1 0 0 0 0 0 1 1 0 1 1 1 1 0 0 0 0 0 0 0\n",
            " 0 1 0 0 1 1 1 0 0 1 0 1 1 1 0 0 1 0 0 0 0 1 0 0 0 1 0 0 0 1 0 1 0 1 0 0 0\n",
            " 0 0 0 0 0 0 1 0 1 0 0 1 0 0 1 0 1 0 1 1 0 0 0 0 1 0 1 0 0 1 0 0 0 1 0 0 0\n",
            " 0 1 1 0 0 0 0 0 0 1 0 1 0 0 0 0 0 1 1 1 0 0 0 1 0 1 0 0 0 0 0 0 1 1 0 1 1\n",
            " 0 1 1 1 0 0 0 0 0 0 0 0 0 1 0 0 1 1 1 0 1 0 0 0 0 1 1 0 0 0 1 0 1 1 1 0 0\n",
            " 0 0 1 0 0 0 1 1 0 0 1 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 1 0 1 1 1 0 1 1 0 0 0\n",
            " 0 1 0 1 0 0 1 1 0 0 0 0 1 0 0 0 0 1 1 0 1 0 1 0 0 0 0 0 1 0 0 0 0 1 1 0 0\n",
            " 1 0 1 0 0 1 0 0 0 0 0 0 0 0 1 0 0 1 1 0 0 0 1 1 0 1 0 0 1 0 0 0 1 1 0 1 0\n",
            " 0 0 0 0 1 0 0 1 0 1 1 0 0 1 0 0 1 0 0 0 1 0 1 1 0 0 1 1 0 1 0 1 1 1 0 1 0\n",
            " 0 1 0 0 1 0 0 1 0 0 0 0 1 1 0 0 1 0 1 0 0 0 0 0 0 1 1 1 0 0 1 1 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0\n",
            " 0 0 1 0 0 0 0 0 1 0 1 0 1 0 0 0 1 0 1 1 1 0 0 0 1 0 1 0 0 0 1 1 1 0 0 1 1\n",
            " 0 0 0 1 0 1 0 0 0 0 0 1 1 0 1 1 1 0 0 0 1 0 0 0 0 1 0 0 0 1 0 0 1 0 0 0 0\n",
            " 1 0 0 1 0 1 0 0 0 1 1 1 1 1 0 0 1 1 0 1 1 1 1 0 0 0 1 1 0 0 1 0 0 0 0 0 0\n",
            " 0 0 0 1 1 0 0 1 0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "0-->  person did'nt survived\n",
        "1 --> person survived"
      ],
      "metadata": {
        "id": "lzxBVyRwVeo6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "training_data_accuracy = accuracy_score(Y_train, X_train_prediction)\n",
        "print('Accuracy of the training data' , training_data_accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EWHRE-exVeLk",
        "outputId": "cf3c65c5-6d5b-4319-eda7-d889d4385195"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy of the training data 0.8075842696629213\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# accuracy on test data\n",
        "X_test_prediction = model.predict(X_test)\n"
      ],
      "metadata": {
        "id": "p9EtWHiPVX0J"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(X_test_prediction)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LME8h1oPWOG0",
        "outputId": "d9b5f8b9-9aad-4b6f-ab4e-297a6ab0b798"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0 0 1 0 0 0 0 0 0 0 0 1 1 0 0 1 0 0 1 0 1 1 0 1 0 1 1 0 0 0 0 0 0 0 0 1 1\n",
            " 0 0 0 0 0 1 0 0 1 1 0 0 1 0 0 0 0 0 0 1 0 0 0 1 0 0 0 1 0 1 0 0 0 1 0 1 0\n",
            " 1 0 0 0 1 0 1 0 0 0 1 1 0 0 1 0 0 0 0 0 0 1 0 1 0 0 1 0 1 1 0 1 1 0 0 0 0\n",
            " 0 0 0 1 1 0 1 0 0 1 0 0 0 0 0 0 1 0 0 0 0 1 1 0 0 0 0 0 0 1 1 1 1 0 1 0 0\n",
            " 0 1 0 0 0 0 1 0 0 1 1 0 1 0 0 0 1 1 0 0 1 0 0 1 1 1 0 0 0 0 0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_data_accuracy = accuracy_score(Y_test, X_test_prediction)\n",
        "print('Accuracy of the test data' , test_data_accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t3NA4U_gWPpE",
        "outputId": "ab663421-997c-4484-dc8b-6118cc13cc15"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy of the test data 0.7821229050279329\n"
          ]
        }
      ]
    }
  ]
}