Predict_Passengers_Survival_using_Logistic_Regression(titanic_data_set).ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Predict Passengers Survival using Logistic Regression(titanic_data-set).ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyNZGrVg0I6bfZ5MmdWr1pS5",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Augusta02/Machine-Learning-Projects/blob/main/Predict_Passengers_Survival_using_Logistic_Regression(titanic_data_set).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 144,
      "metadata": {
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": "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",
              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 73
        },
        "id": "jO4OFwGYCpoY",
        "outputId": "20db3f71-1f9c-483d-b133-bdc4725455a3"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-c9eaf518-b9af-4a6a-a165-0a415821e29f\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-c9eaf518-b9af-4a6a-a165-0a415821e29f\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving train.xlsx to train (4).xlsx\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "\n",
        "from google.colab import files\n",
        "uploaded =files.upload()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import io\n",
        "train_data =pd.read_excel(io.BytesIO(uploaded[\"train.xlsx\"]))\n",
        "\n",
        "train_data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "XZk1x_l9EkHp",
        "outputId": "cf7d2f1c-4f49-4451-8ab8-cc363fa138ff"
      },
      "execution_count": 210,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Braund, Mr. Owen Harris</td>\n",
              "      <td>male</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>A/5 21171</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
              "      <td>female</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>PC 17599</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>C85</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Heikkinen, Miss. Laina</td>\n",
              "      <td>female</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>STON/O2. 3101282</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
              "      <td>female</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>113803</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>C123</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Allen, Mr. William Henry</td>\n",
              "      <td>male</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>373450</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   PassengerId  Survived  Pclass  ...     Fare Cabin  Embarked\n",
              "0            1         0       3  ...   7.2500   NaN         S\n",
              "1            2         1       1  ...  71.2833   C85         C\n",
              "2            3         1       3  ...   7.9250   NaN         S\n",
              "3            4         1       1  ...  53.1000  C123         S\n",
              "4            5         0       3  ...   8.0500   NaN         S\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 210
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_data.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xQWjehnsTc1U",
        "outputId": "ea8db926-f432-485d-e2f7-b0f8e3f5324b"
      },
      "execution_count": 211,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n",
              "       'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 211
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_data.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NogmpjnQE9jn",
        "outputId": "94775b4a-f19f-4932-a46d-02f58263bfbb"
      },
      "execution_count": 212,
      "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": 212
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_data[\"Age\"].mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "prfCCKUvQ-I0",
        "outputId": "f7012026-40e6-47c5-8fc5-e6c790ed8568"
      },
      "execution_count": 213,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "29.69911764705882"
            ]
          },
          "metadata": {},
          "execution_count": 213
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "(train_data[\"Age\"].isnull().sum()/ len(train_data[\"Age\"]))*100"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "109X0dS1S47Q",
        "outputId": "a64d6428-c64a-45c6-829a-5e4f983d6489"
      },
      "execution_count": 214,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "19.865319865319865"
            ]
          },
          "metadata": {},
          "execution_count": 214
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "(train_data[\"Cabin\"].isnull().sum()/ len(train_data[\"Cabin\"]))*100"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SKqds5trS4sx",
        "outputId": "09f51f58-216d-4228-a7d4-6751a036ab41"
      },
      "execution_count": 215,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "77.10437710437711"
            ]
          },
          "metadata": {},
          "execution_count": 215
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_data.drop([\"Cabin\"],axis=1,inplace=True)"
      ],
      "metadata": {
        "id": "Y23Y3B8vTzkP"
      },
      "execution_count": 216,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        ""
      ],
      "metadata": {
        "id": "y4-uyJzwRKiu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.displot(x=\"Age\",data=train_data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386
        },
        "id": "sMVBGRVlVhpE",
        "outputId": "8433b7ed-7e75-4919-ddd5-694d05897b7e"
      },
      "execution_count": 217,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7f367b06dad0>"
            ]
          },
          "metadata": {},
          "execution_count": 217
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_data[\"Age\"].fillna(train_data[\"Age\"].mean(), inplace=True)\n",
        "data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "Z9kwafXQFDxX",
        "outputId": "998e9352-6bef-4028-aa63-eda61b2babff"
      },
      "execution_count": 218,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Embarked</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Braund, Mr. Owen Harris</td>\n",
              "      <td>male</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>A/5 21171</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
              "      <td>female</td>\n",
              "      <td>38.000000</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>PC 17599</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Heikkinen, Miss. Laina</td>\n",
              "      <td>female</td>\n",
              "      <td>26.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>STON/O2. 3101282</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
              "      <td>female</td>\n",
              "      <td>35.000000</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>113803</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Allen, Mr. William Henry</td>\n",
              "      <td>male</td>\n",
              "      <td>35.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>373450</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>886</th>\n",
              "      <td>887</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>Montvila, Rev. Juozas</td>\n",
              "      <td>male</td>\n",
              "      <td>27.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>211536</td>\n",
              "      <td>13.0000</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>887</th>\n",
              "      <td>888</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Graham, Miss. Margaret Edith</td>\n",
              "      <td>female</td>\n",
              "      <td>19.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>112053</td>\n",
              "      <td>30.0000</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>888</th>\n",
              "      <td>889</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
              "      <td>female</td>\n",
              "      <td>29.699118</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>W./C. 6607</td>\n",
              "      <td>23.4500</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>889</th>\n",
              "      <td>890</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Behr, Mr. Karl Howell</td>\n",
              "      <td>male</td>\n",
              "      <td>26.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>111369</td>\n",
              "      <td>30.0000</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>890</th>\n",
              "      <td>891</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Dooley, Mr. Patrick</td>\n",
              "      <td>male</td>\n",
              "      <td>32.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>370376</td>\n",
              "      <td>7.7500</td>\n",
              "      <td>Q</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>891 rows × 11 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     PassengerId  Survived  Pclass  ...            Ticket     Fare  Embarked\n",
              "0              1         0       3  ...         A/5 21171   7.2500         S\n",
              "1              2         1       1  ...          PC 17599  71.2833         C\n",
              "2              3         1       3  ...  STON/O2. 3101282   7.9250         S\n",
              "3              4         1       1  ...            113803  53.1000         S\n",
              "4              5         0       3  ...            373450   8.0500         S\n",
              "..           ...       ...     ...  ...               ...      ...       ...\n",
              "886          887         0       2  ...            211536  13.0000         S\n",
              "887          888         1       1  ...            112053  30.0000         S\n",
              "888          889         0       3  ...        W./C. 6607  23.4500         S\n",
              "889          890         1       1  ...            111369  30.0000         C\n",
              "890          891         0       3  ...            370376   7.7500         Q\n",
              "\n",
              "[891 rows x 11 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 218
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_data.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YxfaAys8RK8A",
        "outputId": "f228b49c-ec80-484f-cd9c-ed0e9b68914b"
      },
      "execution_count": 219,
      "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": 219
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_data.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FeKSxkx5Xo4S",
        "outputId": "9ecf98a4-63e6-46a7-e272-41882f8773f8"
      },
      "execution_count": 220,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PassengerId      int64\n",
              "Survived         int64\n",
              "Pclass           int64\n",
              "Name            object\n",
              "Sex             object\n",
              "Age            float64\n",
              "SibSp            int64\n",
              "Parch            int64\n",
              "Ticket          object\n",
              "Fare           float64\n",
              "Embarked        object\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 220
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "jeNz5544XyH2",
        "outputId": "f4cb3e66-d67e-49f1-f282-97d8adcfa7cb"
      },
      "execution_count": 221,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Embarked</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Braund, Mr. Owen Harris</td>\n",
              "      <td>male</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>A/5 21171</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
              "      <td>female</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>PC 17599</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Heikkinen, Miss. Laina</td>\n",
              "      <td>female</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>STON/O2. 3101282</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
              "      <td>female</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>113803</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Allen, Mr. William Henry</td>\n",
              "      <td>male</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>373450</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   PassengerId  Survived  Pclass  ...            Ticket     Fare  Embarked\n",
              "0            1         0       3  ...         A/5 21171   7.2500         S\n",
              "1            2         1       1  ...          PC 17599  71.2833         C\n",
              "2            3         1       3  ...  STON/O2. 3101282   7.9250         S\n",
              "3            4         1       1  ...            113803  53.1000         S\n",
              "4            5         0       3  ...            373450   8.0500         S\n",
              "\n",
              "[5 rows x 11 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 221
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        " gender= pd.get_dummies(train_data[\"Sex\"],drop_first=\"first\")\n"
      ],
      "metadata": {
        "id": "qeu1IzSbX4_D"
      },
      "execution_count": 222,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_data[\"Gender\"]= gender\n",
        "train_data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 476
        },
        "id": "vutGMQdUZCcL",
        "outputId": "4a8672c6-80b3-4ea4-dedb-2355922cf31e"
      },
      "execution_count": 223,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Embarked</th>\n",
              "      <th>Gender</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Braund, Mr. Owen Harris</td>\n",
              "      <td>male</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>A/5 21171</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>S</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
              "      <td>female</td>\n",
              "      <td>38.000000</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>PC 17599</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>C</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Heikkinen, Miss. Laina</td>\n",
              "      <td>female</td>\n",
              "      <td>26.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>STON/O2. 3101282</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>S</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
              "      <td>female</td>\n",
              "      <td>35.000000</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>113803</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>S</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Allen, Mr. William Henry</td>\n",
              "      <td>male</td>\n",
              "      <td>35.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>373450</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>S</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>886</th>\n",
              "      <td>887</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>Montvila, Rev. Juozas</td>\n",
              "      <td>male</td>\n",
              "      <td>27.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>211536</td>\n",
              "      <td>13.0000</td>\n",
              "      <td>S</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>887</th>\n",
              "      <td>888</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Graham, Miss. Margaret Edith</td>\n",
              "      <td>female</td>\n",
              "      <td>19.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>112053</td>\n",
              "      <td>30.0000</td>\n",
              "      <td>S</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>888</th>\n",
              "      <td>889</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
              "      <td>female</td>\n",
              "      <td>29.699118</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>W./C. 6607</td>\n",
              "      <td>23.4500</td>\n",
              "      <td>S</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>889</th>\n",
              "      <td>890</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Behr, Mr. Karl Howell</td>\n",
              "      <td>male</td>\n",
              "      <td>26.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>111369</td>\n",
              "      <td>30.0000</td>\n",
              "      <td>C</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>890</th>\n",
              "      <td>891</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Dooley, Mr. Patrick</td>\n",
              "      <td>male</td>\n",
              "      <td>32.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>370376</td>\n",
              "      <td>7.7500</td>\n",
              "      <td>Q</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>891 rows × 12 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     PassengerId  Survived  Pclass  ...     Fare Embarked  Gender\n",
              "0              1         0       3  ...   7.2500        S       1\n",
              "1              2         1       1  ...  71.2833        C       0\n",
              "2              3         1       3  ...   7.9250        S       0\n",
              "3              4         1       1  ...  53.1000        S       0\n",
              "4              5         0       3  ...   8.0500        S       1\n",
              "..           ...       ...     ...  ...      ...      ...     ...\n",
              "886          887         0       2  ...  13.0000        S       1\n",
              "887          888         1       1  ...  30.0000        S       0\n",
              "888          889         0       3  ...  23.4500        S       0\n",
              "889          890         1       1  ...  30.0000        C       1\n",
              "890          891         0       3  ...   7.7500        Q       1\n",
              "\n",
              "[891 rows x 12 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 223
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_data.drop([\"Name\",\"Sex\",\"Ticket\",\"Embarked\"],axis=1,inplace=True)\n",
        "train_data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "5KQ2pkRGZK24",
        "outputId": "1358c28a-7fef-4ea4-b2fb-fd3767410eab"
      },
      "execution_count": 224,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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>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",
              "      <th>Gender</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   PassengerId  Survived  Pclass   Age  SibSp  Parch     Fare  Gender\n",
              "0            1         0       3  22.0      1      0   7.2500       1\n",
              "1            2         1       1  38.0      1      0  71.2833       0\n",
              "2            3         1       3  26.0      0      0   7.9250       0\n",
              "3            4         1       1  35.0      1      0  53.1000       0\n",
              "4            5         0       3  35.0      0      0   8.0500       1"
            ]
          },
          "metadata": {},
          "execution_count": 224
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_data.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E3hh6Lxdd6LD",
        "outputId": "c921d533-84ad-412f-a3c3-7be330db3795"
      },
      "execution_count": 225,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PassengerId    0\n",
              "Survived       0\n",
              "Pclass         0\n",
              "Age            0\n",
              "SibSp          0\n",
              "Parch          0\n",
              "Fare           0\n",
              "Gender         0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 225
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn import metrics\n",
        "\n",
        "x= train_data[['PassengerId', 'Pclass', \"Age\", 'SibSp', \"Parch\", 'Fare', \"Gender\"]]\n",
        "y= train_data[\"Survived\"]\n"
      ],
      "metadata": {
        "id": "ebQ7BLljb7-o"
      },
      "execution_count": 226,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train,x_test,y_train,y_test= train_test_split(x,y,test_size=20,random_state=40)"
      ],
      "metadata": {
        "id": "-DPoXDI_fTmU"
      },
      "execution_count": 230,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_lg= LogisticRegression()\n",
        "train_lg.fit(x_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dXn84naFffa0",
        "outputId": "23cf8068-8217-4536-8c22-5e9b0466bd0e"
      },
      "execution_count": 231,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:818: 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",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG,\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression()"
            ]
          },
          "metadata": {},
          "execution_count": 231
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_predict= train_lg.predict(x_test)"
      ],
      "metadata": {
        "id": "mVuw_JFHhWyy"
      },
      "execution_count": 232,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Accuracy={:2f}\".format(train_lg.score(x_test,y_test)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZKLu3zxMhWVc",
        "outputId": "48f106e8-9371-41db-ea55-7d5ce3690a7a"
      },
      "execution_count": 242,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy=0.550000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_cof= pd.crosstab(y_test,train_predict,rownames=[\"Actual\"],colnames=[\"Predicted\"])\n",
        "train_cof"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "IlUnh8NOhVm-",
        "outputId": "7b7c169c-b5a8-4927-d3bd-4e46235c70f5"
      },
      "execution_count": 246,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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>Predicted</th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Actual</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>9</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Predicted  0  1\n",
              "Actual         \n",
              "0          9  5\n",
              "1          4  2"
            ]
          },
          "metadata": {},
          "execution_count": 246
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.heatmap(train_cof,annot=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "aIQOOPb4hUNu",
        "outputId": "d871d3fd-bf9e-480d-eed6-d2f3ee4e2972"
      },
      "execution_count": 247,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f367aeac1d0>"
            ]
          },
          "metadata": {},
          "execution_count": 247
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import classification_report\n",
        "print(classification_report(y_test,train_predict))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d8wNF65CnBVC",
        "outputId": "6cc657d5-8f9e-4ac1-be88-0cd1b9c1dd9b"
      },
      "execution_count": 251,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.69      0.64      0.67        14\n",
            "           1       0.29      0.33      0.31         6\n",
            "\n",
            "    accuracy                           0.55        20\n",
            "   macro avg       0.49      0.49      0.49        20\n",
            "weighted avg       0.57      0.55      0.56        20\n",
            "\n"
          ]
        }
      ]
    }
  ]
}