Titanic_Survivors.ipynb

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  "metadata": {
    "colab": {
      "name": "Titanic Survivors.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "authorship_tag": "ABX9TyMJy8YQ1pBQEhRvM2QXu+kn",
      "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/rayviswajeet/DataAnalysis.project/blob/main/Titanic_Survivors.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pnHxDSdyUtai"
      },
      "source": [
        "# **TITANIC SURVIVORS ANALYSIS**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YFMTARtnClkC"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd"
      ],
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Nd8xpthjGKHy"
      },
      "source": [
        "**Reading Data Using Pandas**\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "y3f3qIFBExDy",
        "outputId": "21f5eb60-aa06-428d-e492-8c67590d2ca1"
      },
      "source": [
        "df= pd.DataFrame(pd.read_csv('/content/train (1).csv'))\n",
        "df.head()"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    }\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": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GnbCLr0NHCE4",
        "outputId": "73394393-990d-47f0-a20e-0430dfcf1f4d"
      },
      "source": [
        "df.shape"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(891, 12)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5Zvvl5IuHMJ3"
      },
      "source": [
        "**Handeling Null Values**\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QavM6KAwHSDX",
        "outputId": "2d91d3ac-b253-4803-f9e6-22b736339758"
      },
      "source": [
        "df.isnull().sum()\n"
      ],
      "execution_count": 50,
      "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": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qXGmBJhZHfGp"
      },
      "source": [
        "**Separating out the columns which have more than 35% of the values missing in the data base**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q8-vYxcnHsg5",
        "outputId": "aee87edc-72f9-430f-c7bd-6f6ed8555cac"
      },
      "source": [
        "drop_col = df.isnull().sum()[df.isnull().sum()>(35/100 *df.shape[0])]\n",
        "drop_col "
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Series([], dtype: int64)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
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        },
        "id": "8dQvqnxQJN8M",
        "outputId": "0c4431ee-ccc5-45ce-b39b-998b4e15f5bb"
      },
      "source": [
        "drop_col.index"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Cabin'], dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R9OcaeYbJSxK",
        "outputId": "a0555e8e-2641-45ca-b0b1-88faaf853afd"
      },
      "source": [
        "df.drop(drop_col.index, axis=1, inplace=True)\n",
        "df.isnull().sum()"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PassengerId      0\n",
              "Survived         0\n",
              "Pclass           0\n",
              "Name             0\n",
              "Sex              0\n",
              "Age            177\n",
              "SibSp            0\n",
              "Parch            0\n",
              "Ticket           0\n",
              "Fare             0\n",
              "Embarked         2\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ywv6FVP0KWYp",
        "outputId": "e52b9820-97fa-42b0-c62b-8106eb14bee6"
      },
      "source": [
        "df.fillna(df.mean(), inplace = True)\n",
        "df.isnull().sum()"
      ],
      "execution_count": 19,
      "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": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dOBepB_nLlp6",
        "outputId": "ef2d52e1-033e-4405-e612-af470d8f583f"
      },
      "source": [
        "df['Embarked'].describe()"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count     889\n",
              "unique      3\n",
              "top         S\n",
              "freq      644\n",
              "Name: Embarked, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f5W5PheVL5Dp"
      },
      "source": [
        "df['Embarked'].fillna('S',inplace= True)\n"
      ],
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q0vUi2TDMGot",
        "outputId": "7877b9a7-367e-4f82-d6a7-0bfb83240d95"
      },
      "source": [
        "df.isnull().sum()"
      ],
      "execution_count": 25,
      "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": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "6q6XI7T5MTo8",
        "outputId": "fabc32d5-c4b9-480e-c525-75711df64ffe"
      },
      "source": [
        "df.corr()\n"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<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>FamilySize</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>PassengerId</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.005007</td>\n",
              "      <td>-0.035144</td>\n",
              "      <td>0.033207</td>\n",
              "      <td>-0.057527</td>\n",
              "      <td>-0.001652</td>\n",
              "      <td>0.012658</td>\n",
              "      <td>-0.040143</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Survived</th>\n",
              "      <td>-0.005007</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.338481</td>\n",
              "      <td>-0.069809</td>\n",
              "      <td>-0.035322</td>\n",
              "      <td>0.081629</td>\n",
              "      <td>0.257307</td>\n",
              "      <td>0.016639</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Pclass</th>\n",
              "      <td>-0.035144</td>\n",
              "      <td>-0.338481</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.331339</td>\n",
              "      <td>0.083081</td>\n",
              "      <td>0.018443</td>\n",
              "      <td>-0.549500</td>\n",
              "      <td>0.065997</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Age</th>\n",
              "      <td>0.033207</td>\n",
              "      <td>-0.069809</td>\n",
              "      <td>-0.331339</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.232625</td>\n",
              "      <td>-0.179191</td>\n",
              "      <td>0.091566</td>\n",
              "      <td>-0.248512</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SibSp</th>\n",
              "      <td>-0.057527</td>\n",
              "      <td>-0.035322</td>\n",
              "      <td>0.083081</td>\n",
              "      <td>-0.232625</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.414838</td>\n",
              "      <td>0.159651</td>\n",
              "      <td>0.890712</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Parch</th>\n",
              "      <td>-0.001652</td>\n",
              "      <td>0.081629</td>\n",
              "      <td>0.018443</td>\n",
              "      <td>-0.179191</td>\n",
              "      <td>0.414838</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.216225</td>\n",
              "      <td>0.783111</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fare</th>\n",
              "      <td>0.012658</td>\n",
              "      <td>0.257307</td>\n",
              "      <td>-0.549500</td>\n",
              "      <td>0.091566</td>\n",
              "      <td>0.159651</td>\n",
              "      <td>0.216225</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.217138</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>FamilySize</th>\n",
              "      <td>-0.040143</td>\n",
              "      <td>0.016639</td>\n",
              "      <td>0.065997</td>\n",
              "      <td>-0.248512</td>\n",
              "      <td>0.890712</td>\n",
              "      <td>0.783111</td>\n",
              "      <td>0.217138</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             PassengerId  Survived    Pclass  ...     Parch      Fare  FamilySize\n",
              "PassengerId     1.000000 -0.005007 -0.035144  ... -0.001652  0.012658   -0.040143\n",
              "Survived       -0.005007  1.000000 -0.338481  ...  0.081629  0.257307    0.016639\n",
              "Pclass         -0.035144 -0.338481  1.000000  ...  0.018443 -0.549500    0.065997\n",
              "Age             0.033207 -0.069809 -0.331339  ... -0.179191  0.091566   -0.248512\n",
              "SibSp          -0.057527 -0.035322  0.083081  ...  0.414838  0.159651    0.890712\n",
              "Parch          -0.001652  0.081629  0.018443  ...  1.000000  0.216225    0.783111\n",
              "Fare            0.012658  0.257307 -0.549500  ...  0.216225  1.000000    0.217138\n",
              "FamilySize     -0.040143  0.016639  0.065997  ...  0.783111  0.217138    1.000000\n",
              "\n",
              "[8 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z7dBX-BMMxHY"
      },
      "source": [
        "**silbp**: Number of Siblings/Spouses Abroad\n",
        "\n",
        "**parch**: Number of Parents/Children Abroad"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 234
        },
        "id": "nhqhOC55NGEg",
        "outputId": "239463d4-5302-41c4-b3a0-b6807a07ee6f"
      },
      "source": [
        "df['FamilySize']= df['SibSp']+df['Parch']\n",
        "df.drop(['SibSp','Parch'], axis=1, inplace=True)\n",
        "df.corr()"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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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>Fare</th>\n",
              "      <th>FamilySize</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>PassengerId</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.005007</td>\n",
              "      <td>-0.035144</td>\n",
              "      <td>0.033207</td>\n",
              "      <td>0.012658</td>\n",
              "      <td>-0.040143</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Survived</th>\n",
              "      <td>-0.005007</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.338481</td>\n",
              "      <td>-0.069809</td>\n",
              "      <td>0.257307</td>\n",
              "      <td>0.016639</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Pclass</th>\n",
              "      <td>-0.035144</td>\n",
              "      <td>-0.338481</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.331339</td>\n",
              "      <td>-0.549500</td>\n",
              "      <td>0.065997</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Age</th>\n",
              "      <td>0.033207</td>\n",
              "      <td>-0.069809</td>\n",
              "      <td>-0.331339</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.091566</td>\n",
              "      <td>-0.248512</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fare</th>\n",
              "      <td>0.012658</td>\n",
              "      <td>0.257307</td>\n",
              "      <td>-0.549500</td>\n",
              "      <td>0.091566</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.217138</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>FamilySize</th>\n",
              "      <td>-0.040143</td>\n",
              "      <td>0.016639</td>\n",
              "      <td>0.065997</td>\n",
              "      <td>-0.248512</td>\n",
              "      <td>0.217138</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             PassengerId  Survived    Pclass       Age      Fare  FamilySize\n",
              "PassengerId     1.000000 -0.005007 -0.035144  0.033207  0.012658   -0.040143\n",
              "Survived       -0.005007  1.000000 -0.338481 -0.069809  0.257307    0.016639\n",
              "Pclass         -0.035144 -0.338481  1.000000 -0.331339 -0.549500    0.065997\n",
              "Age             0.033207 -0.069809 -0.331339  1.000000  0.091566   -0.248512\n",
              "Fare            0.012658  0.257307 -0.549500  0.091566  1.000000    0.217138\n",
              "FamilySize     -0.040143  0.016639  0.065997 -0.248512  0.217138    1.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9p7z7GtuRAHB"
      },
      "source": [
        "**FamilySize in the ship does not have much corrrelation with survival rate**\n",
        "\n",
        "Let's check if we weather the person was alone or not can affect the survival"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "id": "vvEuvCOWRbWr",
        "outputId": "06af95dc-e69a-4f04-a89c-d12c98782d2f"
      },
      "source": [
        "df['Alone'] = [0 if df['FamilySize'][i]>0 else 1 for i in df.index]\n",
        "df.head()"
      ],
      "execution_count": 39,
      "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>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Embarked</th>\n",
              "      <th>FamilySize</th>\n",
              "      <th>Alone</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>A/5 21171</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>S</td>\n",
              "      <td>1</td>\n",
              "      <td>0</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>PC 17599</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>C</td>\n",
              "      <td>1</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>26.0</td>\n",
              "      <td>STON/O2. 3101282</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>S</td>\n",
              "      <td>0</td>\n",
              "      <td>1</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>113803</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>S</td>\n",
              "      <td>1</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>35.0</td>\n",
              "      <td>373450</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>S</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   PassengerId  Survived  Pclass  ... Embarked FamilySize  Alone\n",
              "0            1         0       3  ...        S          1      0\n",
              "1            2         1       1  ...        C          1      0\n",
              "2            3         1       3  ...        S          0      1\n",
              "3            4         1       1  ...        S          1      0\n",
              "4            5         0       3  ...        S          0      1\n",
              "\n",
              "[5 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LtO65DRtSG28",
        "outputId": "86182f31-f595-4070-c16c-77adcf092d5c"
      },
      "source": [
        "df.groupby(['Alone'])['Survived'].mean()"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Alone\n",
              "0    0.505650\n",
              "1    0.303538\n",
              "Name: Survived, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QOMG5AefSYI8"
      },
      "source": [
        "**If the person is alone he/she has less chance of surviving.**\n",
        "\n",
        "The readon might be the person who is travelling with his family might be belonging to rich class and might be prioritising over other."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 110
        },
        "id": "wwsokg9sS8pJ",
        "outputId": "fd15c89f-3a63-44fe-85d2-e81faeb76c06"
      },
      "source": [
        "df[['Alone', 'Fare']].corr()"
      ],
      "execution_count": 41,
      "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>Alone</th>\n",
              "      <th>Fare</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Alone</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.271832</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fare</th>\n",
              "      <td>-0.271832</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          Alone      Fare\n",
              "Alone  1.000000 -0.271832\n",
              "Fare  -0.271832  1.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-6vA61WZTRGC",
        "outputId": "35d85469-1ebd-4f68-9428-25c7d3f49b98"
      },
      "source": [
        "df['Sex']=[0 if df['Sex'][i]=='male' else 1 for i in df.index] #1 for female 0 for male\n",
        "df.groupby(['Sex'])['Survived'].mean()"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Sex\n",
              "0    0.188908\n",
              "1    0.742038\n",
              "Name: Survived, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aXl0WyUOT0sh"
      },
      "source": [
        "It shows female passenger has maore chance of surving than male passenger.\n",
        "\n",
        "It shows women were prioritised over men."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ihlx1RirT-s-",
        "outputId": "75aa84ba-d300-4e2a-f96a-49a9f1ef4255"
      },
      "source": [
        "df.groupby(['Embarked'])['Survived'].mean()"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Embarked\n",
              "C    0.553571\n",
              "Q    0.389610\n",
              "S    0.339009\n",
              "Name: Survived, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X85jtl9kUeF8"
      },
      "source": [
        "# Conclusion\n",
        "\n",
        "*   Female passengers were prioritised over men\n",
        "*   People with high class or rich class have higher survival rate than others. the hierachy migt have been followedwhile saving passengers\n",
        "*   Passengers travelling with their family have heigher survival rate.\n",
        "*   Passsengers who boarded the ship at Cherbourg, survived more in proportion then the others.\n",
        "\n",
        "\n",
        "\n"
      ]
    }
  ]
}