pranjal_tiwari_DA__Titanic__survivors (2).ipynb

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  "metadata": {
    "colab": {
      "name": "pranjal_tiwari_DA _Titanic _survivors.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "_Le-_srxAokC"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iMiDGZ57CYKT"
      },
      "source": [
        "Reading Data using Pandas\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "id": "oSTCWFZfCg3U",
        "outputId": "7dff66f9-b6ae-4069-8a43-f6a365f30f61"
      },
      "source": [
        "df = pd.DataFrame(pd.read_csv('train.csv'))\n",
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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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>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": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j1OqSXBrDRkW",
        "outputId": "f0b33ec3-8dfe-4150-9548-e049baa8fc1d"
      },
      "source": [
        "df.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(891, 12)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GwERi4-uDgeO"
      },
      "source": [
        "Handling the null values\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y16GzFE4DjtK",
        "outputId": "b5d113f0-0a1e-4b3b-bc98-a7f86376d383"
      },
      "source": [
        "df.isnull().sum()"
      ],
      "execution_count": null,
      "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": 85
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sJfN-tabEIaS"
      },
      "source": [
        "Separating out the columns that have more than 35% of the values missing in the dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2eHyep7REROc",
        "outputId": "bb4bfea1-6ca9-402c-d851-5446d08b2c56"
      },
      "source": [
        "drop_col = df.isnull().sum()[df.isnull().sum()>(35/100 * df.shape[0])]\n",
        "drop_col"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Cabin    687\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aO7E7gXWFUYS",
        "outputId": "423c31d5-3d31-4370-858c-86c0e5871388"
      },
      "source": [
        "drop_col.index"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Cabin'], dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C3PaLkeaFbUA",
        "outputId": "f3a3e10d-45ad-4a1d-d469-4bb8e44d8628"
      },
      "source": [
        "df.drop(drop_col.index, axis=1, inplace=True)\n",
        "df.isnull().sum()"
      ],
      "execution_count": null,
      "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": 88
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PVvpuwM_GD4t",
        "outputId": "a667c152-c5ed-4b49-81ad-25205b6da30f"
      },
      "source": [
        "df.fillna(df.mean(), inplace = True)\n",
        "df.isnull().sum()"
      ],
      "execution_count": null,
      "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": 89
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8l1hg6_6GcME",
        "outputId": "711af50d-b59d-4d63-d467-c7b46e891707"
      },
      "source": [
        "df['Embarked'].describe()"
      ],
      "execution_count": null,
      "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": 90
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GPJIilOIGrVE"
      },
      "source": [
        "df['Embarked'].fillna('S', inplace = True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UACI5ZGrGzNV",
        "outputId": "01357877-c1b2-41ce-95ca-a6e9d1a2a925"
      },
      "source": [
        "df.isnull().sum() ## We have now filled all the null values"
      ],
      "execution_count": null,
      "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": 92
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
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          "base_uri": "https://localhost:8080/",
          "height": 254
        },
        "id": "uXsx4PQHHD42",
        "outputId": "c21e54d2-7c28-4202-d9e4-777c7720e8f4"
      },
      "source": [
        "df.corr()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Age</th>\n",
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              "    <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",
              "    </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",
              "    </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",
              "    </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",
              "    </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",
              "    </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",
              "    </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",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             PassengerId  Survived    Pclass  ...     SibSp     Parch      Fare\n",
              "PassengerId     1.000000 -0.005007 -0.035144  ... -0.057527 -0.001652  0.012658\n",
              "Survived       -0.005007  1.000000 -0.338481  ... -0.035322  0.081629  0.257307\n",
              "Pclass         -0.035144 -0.338481  1.000000  ...  0.083081  0.018443 -0.549500\n",
              "Age             0.033207 -0.069809 -0.331339  ... -0.232625 -0.179191  0.091566\n",
              "SibSp          -0.057527 -0.035322  0.083081  ...  1.000000  0.414838  0.159651\n",
              "Parch          -0.001652  0.081629  0.018443  ...  0.414838  1.000000  0.216225\n",
              "Fare            0.012658  0.257307 -0.549500  ...  0.159651  0.216225  1.000000\n",
              "\n",
              "[7 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        },
        "id": "5zLJQEc6HRXh",
        "outputId": "691512e1-ccce-49fb-981c-dde843e92ab9"
      },
      "source": [
        "df['FamilySize'] = df['SibSp'] + df['Parch']\n",
        "df.drop(['SibSp' , 'Parch'], axis = 1, inplace = True)\n",
        "df.corr()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>Survived</th>\n",
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              "      <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",
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              "      <td>0.065997</td>\n",
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              "    <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": 94
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "id": "ahikOCfpH1OA",
        "outputId": "6c16f1b3-8138-4512-a589-865653161562"
      },
      "source": [
        "df['Alone'] = [0 if df['FamilySize'] [i]>0 else 1 for i in df.index]\n",
        "df.head()"
      ],
      "execution_count": null,
      "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",
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              "\n",
              "    .dataframe tbody tr th {\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>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": 95
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4gfSAq02IHCQ",
        "outputId": "cf51acf6-26db-4f97-c123-0ff9c3a58bd0"
      },
      "source": [
        "df.groupby(['Alone'])['Survived'].mean()"
      ],
      "execution_count": null,
      "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": 96
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 106
        },
        "id": "a0lsVFQaIsj6",
        "outputId": "07eef09f-34c9-4799-ce6f-df7baf9c8449"
      },
      "source": [
        "df[['Alone','Fare']].corr()"
      ],
      "execution_count": null,
      "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": 97
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BuSJzFsZI6jA",
        "outputId": "3e3be413-9e33-4a19-8e33-af89cb032ca0"
      },
      "source": [
        "df['Sex'] = [0 if df['Sex'][i] == 'male' else 1 for i in df.index] ## where 1 is female 0 is male\n",
        "df.groupby(['Sex'])['Survived'].mean()"
      ],
      "execution_count": null,
      "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": 98
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T86W5lP2JhE9",
        "outputId": "a23f8622-fafb-4f4d-f13b-bc84b8928a49"
      },
      "source": [
        "df.groupby(['Embarked'])['Survived'].mean()"
      ],
      "execution_count": null,
      "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": 99
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y1eEMRZlJucx"
      },
      "source": [
        "Conclusion:\n",
        "*   People with high class or rich people have higher survival rate than others. The hierarichy might have played a role while saving the passengers.\n",
        "*   Passengers who boarded the ship at Cherbourg survived more in proportion than the others.\n",
        "*   Passengers travelling with their families have higher survival rate.\n",
        "\n",
        "\n",
        "\n"
      ]
    }
  ]
}