Titanic_survival_pred_classifier_TASK2.ipynb

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      "cell_type": "markdown",
      "source": [
        "IMPORT NECESSARY LIBRARIES\n"
      ],
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        "id": "VMSLBoWbwThk"
      }
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      "cell_type": "code",
      "execution_count": 1,
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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.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, LOADING AND PROCESSING\n"
      ],
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        "id": "WPG0YenFw3Ty"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "titanic_data=pd.read_csv('/content/titanic_dataset.csv')"
      ],
      "metadata": {
        "id": "16iUNi8Dw1J5"
      },
      "execution_count": 2,
      "outputs": []
    },
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      "cell_type": "code",
      "source": [
        "titanic_data.head()"
      ],
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      "execution_count": 3,
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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",
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              "variable_name": "titanic_data",
              "summary": "{\n  \"name\": \"titanic_data\",\n  \"rows\": 891,\n  \"fields\": [\n    {\n      \"column\": \"PassengerId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 257,\n        \"min\": 1,\n        \"max\": 891,\n        \"num_unique_values\": 891,\n        \"samples\": [\n          710,\n          440,\n          841\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Survived\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Pclass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          3,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 891,\n        \"samples\": [\n          \"Moubarek, Master. Halim Gonios (\\\"William George\\\")\",\n          \"Kvillner, Mr. Johan Henrik Johannesson\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"female\",\n          \"male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14.526497332334042,\n        \"min\": 0.42,\n        \"max\": 80.0,\n        \"num_unique_values\": 88,\n        \"samples\": [\n          0.75,\n          22.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SibSp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 8,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Parch\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 6,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ticket\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 681,\n        \"samples\": [\n          \"11774\",\n          \"248740\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 49.6934285971809,\n        \"min\": 0.0,\n        \"max\": 512.3292,\n        \"num_unique_values\": 248,\n        \"samples\": [\n          11.2417,\n          51.8625\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cabin\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 147,\n        \"samples\": [\n          \"D45\",\n          \"B49\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Embarked\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"S\",\n          \"C\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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              "(891, 12)"
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      "execution_count": 5,
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          "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": [
        "titanic_data.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "as6x_OdUBe6E",
        "outputId": "28b29c2f-8f8d-4acc-ce3f-72a1786b9c22"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PassengerId      0\n",
              "Survived         0\n",
              "Pclass           0\n",
              "Name             0\n",
              "Sex              0\n",
              "Age            177\n",
              "SibSp            0\n",
              "Parch            0\n",
              "Ticket           0\n",
              "Fare             0\n",
              "Cabin          687\n",
              "Embarked         2\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#remove missing/null values\n",
        "titanic_data=titanic_data.drop(columns='Cabin',axis=1)"
      ],
      "metadata": {
        "id": "cxu88136CHpC"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#replacing missing values with mean number\n",
        "titanic_data['Age'].fillna(titanic_data['Age'].mean(),inplace=True)"
      ],
      "metadata": {
        "id": "jVZm-hA-ChNG"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "titanic_data.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vchqWJU8DDiz",
        "outputId": "4f7794fb-b879-4102-a43f-afdb91a0f56b"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 891 entries, 0 to 890\n",
            "Data columns (total 11 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          891 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  Embarked     889 non-null    object \n",
            "dtypes: float64(2), int64(5), object(4)\n",
            "memory usage: 76.7+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "titanic_data.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5JY1eAm0DJ4a",
        "outputId": "d68ac380-576f-4dec-f7c8-81248ba66b0f"
      },
      "execution_count": 10,
      "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       2\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#lets fix embarked, as it contains categorical data, we cannot fix it with mean value, so we can do so using mode\n",
        "print(titanic_data['Embarked'].mode())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xz25nbnrDZux",
        "outputId": "ca674508-3534-47b5-9909-193cf6851d81"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0    S\n",
            "Name: Embarked, dtype: object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(titanic_data['Embarked'].mode()[0])\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d0brJzDPDyQc",
        "outputId": "b8f894e9-96df-47ee-bee4-4d3ed82b7695"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "S\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Replace mode with missing value\n",
        "titanic_data['Embarked'].fillna(titanic_data['Embarked'].mode()[0],inplace=True)"
      ],
      "metadata": {
        "id": "T8bvdd0_ETxv"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "titanic_data.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Gph1LXq3EnEF",
        "outputId": "c48966c2-f0a8-44d3-802d-08b9cabeb4ee"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PassengerId    0\n",
              "Survived       0\n",
              "Pclass         0\n",
              "Name           0\n",
              "Sex            0\n",
              "Age            0\n",
              "SibSp          0\n",
              "Parch          0\n",
              "Ticket         0\n",
              "Fare           0\n",
              "Embarked       0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Analysing the data**"
      ],
      "metadata": {
        "id": "1Cmc_LVUEyHZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "titanic_data.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "cJoltkT-EuoB",
        "outputId": "52dd9242-e038-499a-a453-4aeea274d97e"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "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",
              "count  891.000000  891.000000  \n",
              "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",
              "max      6.000000  512.329200  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-15a72d31-8be9-416a-ad24-c9262f85685d\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
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              "        vertical-align: middle;\n",
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              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Fare</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>891.000000</td>\n",
              "      <td>891.000000</td>\n",
              "      <td>891.000000</td>\n",
              "      <td>891.000000</td>\n",
              "      <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",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.420000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>446.000000</td>\n",
              "      <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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>668.500000</td>\n",
              "      <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",
              "    </tr>\n",
              "  </tbody>\n",
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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "      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-81acb91d-fc3d-4438-a852-bd1f0b4824e0 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_data\",\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": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "titanic_data['Survived'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rl8Vev0GFAey",
        "outputId": "19d4397b-66bb-4fc7-a362-f7ef4c514b58"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Survived\n",
              "0    549\n",
              "1    342\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#visualising the data\n",
        "sns.set()\n",
        "sns.countplot(titanic_data['Survived'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "XtUIyCW2FSkG",
        "outputId": "0ebc5a2e-bf8f-4e53-ddd0-5063f39193e3"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: ylabel='count'>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        },
        {
          "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_data['Sex'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pYGr92nRFdqD",
        "outputId": "fd6a47c7-7dd1-4b9e-9c26-0bdcb9af83b9"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Sex\n",
              "male      577\n",
              "female    314\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#count plot for \"Sex\" column\n",
        "sns.countplot(titanic_data['Sex'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 476
        },
        "id": "ZSx-AeIwGLpm",
        "outputId": "e97c2b48-8d11-476e-b57c-55286f787ecd"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='count', ylabel='Sex'>"
            ]
          },
          "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": [
        "#Analysing gender wise survivors\n",
        "sns.countplot(x='Sex',hue='Survived',data=titanic_data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 476
        },
        "id": "3QsUqXvQGe7r",
        "outputId": "799e6423-c90b-4761-dac5-a1f03efc5058"
      },
      "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": [
        "#count plot for \"p-class\" column\n",
        "sns.countplot(x='Pclass',data=titanic_data)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 476
        },
        "id": "VbLlBPe3HFWe",
        "outputId": "881dbfa9-e335-4946-9628-50d7892a0a1a"
      },
      "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_data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 476
        },
        "id": "coVN6rxLHYCF",
        "outputId": "b6157f2e-a2e3-406f-bb20-021caa3c17ca"
      },
      "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": "markdown",
      "source": [
        "**ENCODING CATEGORICAL COLUMNS/DATA**"
      ],
      "metadata": {
        "id": "m4XcPbI5HiZ9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "titanic_data['Sex'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MlSjKBbhHejb",
        "outputId": "9fbcfae7-cb54-4d80-9098-3e8570b3fb10"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Sex\n",
              "male      577\n",
              "female    314\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "titanic_data['Embarked'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a2SpJRkXH18K",
        "outputId": "6e4f6637-f675-4239-e32e-4cffca2e7881"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Embarked\n",
              "S    646\n",
              "C    168\n",
              "Q     77\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "titanic_data.replace({'Sex':{'male':0,'female':1},'Embarked':{'S':0,'C':1,'G':2}}, inplace=True)"
      ],
      "metadata": {
        "id": "2_JLTc_HH7O2"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X=titanic_data.drop(columns=['PassengerId','Name','Ticket','Survived'],axis=1)\n",
        "Y=titanic_data['Survived']"
      ],
      "metadata": {
        "id": "v11k0LC_ctVi"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X=pd.get_dummies(X)#In this example, pd.get_dummies(X) converts the categorical feature into numeric columns\n",
        "print(X)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NDb8-BtxdF7n",
        "outputId": "3a243e40-8530-4663-a692-52d2aa494eba"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "     Pclass  Sex        Age  SibSp  Parch     Fare  Embarked_0  Embarked_1  \\\n",
            "0         3    0  22.000000      1      0   7.2500        True       False   \n",
            "1         1    1  38.000000      1      0  71.2833       False        True   \n",
            "2         3    1  26.000000      0      0   7.9250        True       False   \n",
            "3         1    1  35.000000      1      0  53.1000        True       False   \n",
            "4         3    0  35.000000      0      0   8.0500        True       False   \n",
            "..      ...  ...        ...    ...    ...      ...         ...         ...   \n",
            "886       2    0  27.000000      0      0  13.0000        True       False   \n",
            "887       1    1  19.000000      0      0  30.0000        True       False   \n",
            "888       3    1  29.699118      1      2  23.4500        True       False   \n",
            "889       1    0  26.000000      0      0  30.0000       False        True   \n",
            "890       3    0  32.000000      0      0   7.7500       False       False   \n",
            "\n",
            "     Embarked_Q  \n",
            "0         False  \n",
            "1         False  \n",
            "2         False  \n",
            "3         False  \n",
            "4         False  \n",
            "..          ...  \n",
            "886       False  \n",
            "887       False  \n",
            "888       False  \n",
            "889       False  \n",
            "890        True  \n",
            "\n",
            "[891 rows x 9 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(Y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_SUt4h7zdIl5",
        "outputId": "63d05eed-7284-4f03-a941-459eed68a875"
      },
      "execution_count": 46,
      "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": [
        "**Split the data into test and train data**"
      ],
      "metadata": {
        "id": "iP_kbzeUdkcp"
      }
    },
    {
      "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",
        "\n"
      ],
      "metadata": {
        "id": "SrUrRyDrdgh4"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(X.shape, X_train.shape,X_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GeGj8paReWGa",
        "outputId": "9475e505-1620-4fd8-87f9-96786b9c2cf3"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(891, 9) (712, 9) (179, 9)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = LogisticRegression()"
      ],
      "metadata": {
        "id": "GXfjInJ4fvIg"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#use the train data on logisticregression model\n",
        "model.fit(X_train, Y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 220
        },
        "id": "xvGcYULJf1tg",
        "outputId": "497c2b17-73b1-47f5-e39c-119b62ab19ca"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression()"
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            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Evaluating and testing the model**"
      ],
      "metadata": {
        "id": "yTdGK5LCl6LM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_train_pred=model.predict(X_train)"
      ],
      "metadata": {
        "id": "nX_FFDE0l-n1"
      },
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(X_train_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3o4LuPywnE32",
        "outputId": "7137e7a8-fc54-4982-8066-35348fd2864f"
      },
      "execution_count": 54,
      "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",
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            " 0 0 0 0 0 0 1 0 1 0 0 1 0 0 1 0 0 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",
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            " 0 1 0 0 1 0 0 1 0 0 0 0 1 1 0 0 0 0 1 0 0 0 0 0 0 1 1 1 0 0 1 1 0 0 0 0 0\n",
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            " 0 0 0 1 1 0 0 1 0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_accuracy=accuracy_score(Y_train,X_train_pred)\n",
        "print(train_accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CLhDh_U-nKbf",
        "outputId": "4f18b464-a1ca-4199-990e-a633f0238986"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.8089887640449438\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_test_pred=model.predict(X_test)"
      ],
      "metadata": {
        "id": "e-oG6bz-nZfl"
      },
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(X_test_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RtFPwqHunjgS",
        "outputId": "639574ef-751f-4628-87bb-02c73b9254e5"
      },
      "execution_count": 60,
      "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 1 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 1 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_accuracy=accuracy_score(Y_test,X_test_pred)\n",
        "print('Accuracy score of test data:', test_accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Exee3DoQntMQ",
        "outputId": "f32eec01-53d2-4485-c63f-0a07bca978c8"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy score of test data: 0.7821229050279329\n"
          ]
        }
      ]
    }
  ]
}