Compute_task.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyP4k6xw+jD572MUxqHxvQkt",
      "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/savioratharv/Compute_tasks/blob/main/Compute_task.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Importing the required libraries"
      ],
      "metadata": {
        "id": "aHFRJJYGPJa1"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xElqpulCNoYP"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Reading the csv file"
      ],
      "metadata": {
        "id": "6LbMTsSZPLOV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset=pd.read_csv('/content/compute_dataset.csv')"
      ],
      "metadata": {
        "id": "hssJm8ndOKrl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(dataset)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e2hma-xxOSap",
        "outputId": "d9dc2ed4-c3d6-45e5-c20e-c4946597cfcd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "     PassengerId  Survived  Pclass  \\\n",
            "0              1         0       3   \n",
            "1              2         1       1   \n",
            "2              3         1       3   \n",
            "3              4         1       1   \n",
            "4              5         0       3   \n",
            "..           ...       ...     ...   \n",
            "886          887         0       2   \n",
            "887          888         1       1   \n",
            "888          889         0       3   \n",
            "889          890         1       1   \n",
            "890          891         0       3   \n",
            "\n",
            "                                                  Name     Sex   Age  SibSp  \\\n",
            "0                              Braund, Mr. Owen Harris    male  22.0      1   \n",
            "1    Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
            "2                               Heikkinen, Miss. Laina  female  26.0      0   \n",
            "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
            "4                             Allen, Mr. William Henry    male  35.0      0   \n",
            "..                                                 ...     ...   ...    ...   \n",
            "886                              Montvila, Rev. Juozas    male  27.0      0   \n",
            "887                       Graham, Miss. Margaret Edith  female  19.0      0   \n",
            "888           Johnston, Miss. Catherine Helen \"Carrie\"  female   NaN      1   \n",
            "889                              Behr, Mr. Karl Howell    male  26.0      0   \n",
            "890                                Dooley, Mr. Patrick    male  32.0      0   \n",
            "\n",
            "     Parch            Ticket     Fare Cabin Embarked  \n",
            "0        0         A/5 21171   7.2500   NaN        S  \n",
            "1        0          PC 17599  71.2833   C85        C  \n",
            "2        0  STON/O2. 3101282   7.9250   NaN        S  \n",
            "3        0            113803  53.1000  C123        S  \n",
            "4        0            373450   8.0500   NaN        S  \n",
            "..     ...               ...      ...   ...      ...  \n",
            "886      0            211536  13.0000   NaN        S  \n",
            "887      0            112053  30.0000   B42        S  \n",
            "888      2        W./C. 6607  23.4500   NaN        S  \n",
            "889      0            111369  30.0000  C148        C  \n",
            "890      0            370376   7.7500   NaN        Q  \n",
            "\n",
            "[891 rows x 12 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Finding all the null values in each column"
      ],
      "metadata": {
        "id": "8lsIL_foPNAW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nbfBblVnOXcT",
        "outputId": "767b6f21-6a94-4cbd-9b92-41a24c3db502"
      },
      "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": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Filling the null values in 'Embarked' with the mode(most occuring element)"
      ],
      "metadata": {
        "id": "WmaSfSFpPOVg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset['Embarked'].fillna(dataset['Embarked'].mode()[0],inplace=True)"
      ],
      "metadata": {
        "id": "OUNGz8ZCO1o1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Making sure there are no null values left in the 'Embarked' Column"
      ],
      "metadata": {
        "id": "DMMM47TGPYST"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pdzD6HkeP4x0",
        "outputId": "8adf2a49-4bd3-4d91-bee7-921298c7ce22"
      },
      "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         0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Looking at a known null value to make sure its filled with the mode only"
      ],
      "metadata": {
        "id": "JV56QCNPPaG0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(dataset['Embarked'][60:65])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sOg-QEP3Q3za",
        "outputId": "d1cabb20-d6dc-43be-83e1-b0bfc7f9ab70"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "60    C\n",
            "61    S\n",
            "62    S\n",
            "63    S\n",
            "64    C\n",
            "Name: Embarked, dtype: object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Dropping the 'Cabin' column as it has more than 50% of its values as null"
      ],
      "metadata": {
        "id": "sp8mWE76Pa86"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.drop('Cabin',axis=1,inplace=True)"
      ],
      "metadata": {
        "id": "uVmB8GdOQ-Hf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(dataset)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wi8FXOjQRXpQ",
        "outputId": "b6ca7618-0299-4447-bcc1-2db024cd427c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "     PassengerId  Survived  Pclass  \\\n",
            "0              1         0       3   \n",
            "1              2         1       1   \n",
            "2              3         1       3   \n",
            "3              4         1       1   \n",
            "4              5         0       3   \n",
            "..           ...       ...     ...   \n",
            "886          887         0       2   \n",
            "887          888         1       1   \n",
            "888          889         0       3   \n",
            "889          890         1       1   \n",
            "890          891         0       3   \n",
            "\n",
            "                                                  Name     Sex   Age  SibSp  \\\n",
            "0                              Braund, Mr. Owen Harris    male  22.0      1   \n",
            "1    Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
            "2                               Heikkinen, Miss. Laina  female  26.0      0   \n",
            "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
            "4                             Allen, Mr. William Henry    male  35.0      0   \n",
            "..                                                 ...     ...   ...    ...   \n",
            "886                              Montvila, Rev. Juozas    male  27.0      0   \n",
            "887                       Graham, Miss. Margaret Edith  female  19.0      0   \n",
            "888           Johnston, Miss. Catherine Helen \"Carrie\"  female   NaN      1   \n",
            "889                              Behr, Mr. Karl Howell    male  26.0      0   \n",
            "890                                Dooley, Mr. Patrick    male  32.0      0   \n",
            "\n",
            "     Parch            Ticket     Fare Embarked  \n",
            "0        0         A/5 21171   7.2500        S  \n",
            "1        0          PC 17599  71.2833        C  \n",
            "2        0  STON/O2. 3101282   7.9250        S  \n",
            "3        0            113803  53.1000        S  \n",
            "4        0            373450   8.0500        S  \n",
            "..     ...               ...      ...      ...  \n",
            "886      0            211536  13.0000        S  \n",
            "887      0            112053  30.0000        S  \n",
            "888      2        W./C. 6607  23.4500        S  \n",
            "889      0            111369  30.0000        C  \n",
            "890      0            370376   7.7500        Q  \n",
            "\n",
            "[891 rows x 11 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Now only the Age column has missing values and the missing values are less than 50% of the total values so as per the question, dropping all the rows with missing values"
      ],
      "metadata": {
        "id": "-NYJ7HxSPdo3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.dropna(axis=0,inplace=True)"
      ],
      "metadata": {
        "id": "4KHKPqkoRe4L"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Making sure that all 177 null value rows are dropped, the final dataframe now has 714 rows instead of the original 891"
      ],
      "metadata": {
        "id": "sAZ7KBv2PhWY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(dataset)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7WJiYcn0SoG8",
        "outputId": "3a4e969a-622a-4bc6-e6fd-1a33ef689617"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "     PassengerId  Survived  Pclass  \\\n",
            "0              1         0       3   \n",
            "1              2         1       1   \n",
            "2              3         1       3   \n",
            "3              4         1       1   \n",
            "4              5         0       3   \n",
            "..           ...       ...     ...   \n",
            "885          886         0       3   \n",
            "886          887         0       2   \n",
            "887          888         1       1   \n",
            "889          890         1       1   \n",
            "890          891         0       3   \n",
            "\n",
            "                                                  Name     Sex   Age  SibSp  \\\n",
            "0                              Braund, Mr. Owen Harris    male  22.0      1   \n",
            "1    Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
            "2                               Heikkinen, Miss. Laina  female  26.0      0   \n",
            "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
            "4                             Allen, Mr. William Henry    male  35.0      0   \n",
            "..                                                 ...     ...   ...    ...   \n",
            "885               Rice, Mrs. William (Margaret Norton)  female  39.0      0   \n",
            "886                              Montvila, Rev. Juozas    male  27.0      0   \n",
            "887                       Graham, Miss. Margaret Edith  female  19.0      0   \n",
            "889                              Behr, Mr. Karl Howell    male  26.0      0   \n",
            "890                                Dooley, Mr. Patrick    male  32.0      0   \n",
            "\n",
            "     Parch            Ticket     Fare Embarked  \n",
            "0        0         A/5 21171   7.2500        S  \n",
            "1        0          PC 17599  71.2833        C  \n",
            "2        0  STON/O2. 3101282   7.9250        S  \n",
            "3        0            113803  53.1000        S  \n",
            "4        0            373450   8.0500        S  \n",
            "..     ...               ...      ...      ...  \n",
            "885      5            382652  29.1250        Q  \n",
            "886      0            211536  13.0000        S  \n",
            "887      0            112053  30.0000        S  \n",
            "889      0            111369  30.0000        C  \n",
            "890      0            370376   7.7500        Q  \n",
            "\n",
            "[714 rows x 11 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.rename(columns={'Embarked':'Embark'},inplace=True)"
      ],
      "metadata": {
        "id": "k0EubyaAUIaR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(dataset)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jqaroWLyVOmc",
        "outputId": "b1cd2b66-b32a-4883-aa9a-dcf48f0326ff"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "     PassengerId  Survived  Pclass  \\\n",
            "0              1         0       3   \n",
            "1              2         1       1   \n",
            "2              3         1       3   \n",
            "3              4         1       1   \n",
            "4              5         0       3   \n",
            "..           ...       ...     ...   \n",
            "885          886         0       3   \n",
            "886          887         0       2   \n",
            "887          888         1       1   \n",
            "889          890         1       1   \n",
            "890          891         0       3   \n",
            "\n",
            "                                                  Name     Sex   Age  SibSp  \\\n",
            "0                              Braund, Mr. Owen Harris    male  22.0      1   \n",
            "1    Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
            "2                               Heikkinen, Miss. Laina  female  26.0      0   \n",
            "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
            "4                             Allen, Mr. William Henry    male  35.0      0   \n",
            "..                                                 ...     ...   ...    ...   \n",
            "885               Rice, Mrs. William (Margaret Norton)  female  39.0      0   \n",
            "886                              Montvila, Rev. Juozas    male  27.0      0   \n",
            "887                       Graham, Miss. Margaret Edith  female  19.0      0   \n",
            "889                              Behr, Mr. Karl Howell    male  26.0      0   \n",
            "890                                Dooley, Mr. Patrick    male  32.0      0   \n",
            "\n",
            "     Parch            Ticket     Fare Embark  \n",
            "0        0         A/5 21171   7.2500      S  \n",
            "1        0          PC 17599  71.2833      C  \n",
            "2        0  STON/O2. 3101282   7.9250      S  \n",
            "3        0            113803  53.1000      S  \n",
            "4        0            373450   8.0500      S  \n",
            "..     ...               ...      ...    ...  \n",
            "885      5            382652  29.1250      Q  \n",
            "886      0            211536  13.0000      S  \n",
            "887      0            112053  30.0000      S  \n",
            "889      0            111369  30.0000      C  \n",
            "890      0            370376   7.7500      Q  \n",
            "\n",
            "[714 rows x 11 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Label Encoding the 'Embark' Column such that C=0,Q=1,S=2"
      ],
      "metadata": {
        "id": "W2Ijr-KuWb30"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import preprocessing\n",
        "le=preprocessing.LabelEncoder()\n",
        "dataset['Embark']=le.fit_transform(dataset['Embark'])"
      ],
      "metadata": {
        "id": "Wyqrn9ScVSkG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(dataset)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5gB2AHy4V5hp",
        "outputId": "651c6db0-90db-47dd-f763-54cca87a4b5a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "     PassengerId  Survived  Pclass  \\\n",
            "0              1         0       3   \n",
            "1              2         1       1   \n",
            "2              3         1       3   \n",
            "3              4         1       1   \n",
            "4              5         0       3   \n",
            "..           ...       ...     ...   \n",
            "885          886         0       3   \n",
            "886          887         0       2   \n",
            "887          888         1       1   \n",
            "889          890         1       1   \n",
            "890          891         0       3   \n",
            "\n",
            "                                                  Name     Sex   Age  SibSp  \\\n",
            "0                              Braund, Mr. Owen Harris    male  22.0      1   \n",
            "1    Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
            "2                               Heikkinen, Miss. Laina  female  26.0      0   \n",
            "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
            "4                             Allen, Mr. William Henry    male  35.0      0   \n",
            "..                                                 ...     ...   ...    ...   \n",
            "885               Rice, Mrs. William (Margaret Norton)  female  39.0      0   \n",
            "886                              Montvila, Rev. Juozas    male  27.0      0   \n",
            "887                       Graham, Miss. Margaret Edith  female  19.0      0   \n",
            "889                              Behr, Mr. Karl Howell    male  26.0      0   \n",
            "890                                Dooley, Mr. Patrick    male  32.0      0   \n",
            "\n",
            "     Parch            Ticket     Fare  Embark  \n",
            "0        0         A/5 21171   7.2500       2  \n",
            "1        0          PC 17599  71.2833       0  \n",
            "2        0  STON/O2. 3101282   7.9250       2  \n",
            "3        0            113803  53.1000       2  \n",
            "4        0            373450   8.0500       2  \n",
            "..     ...               ...      ...     ...  \n",
            "885      5            382652  29.1250       1  \n",
            "886      0            211536  13.0000       2  \n",
            "887      0            112053  30.0000       2  \n",
            "889      0            111369  30.0000       0  \n",
            "890      0            370376   7.7500       1  \n",
            "\n",
            "[714 rows x 11 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##The following scatterplot shows us a rough relationship between the sex, Pclass and their survival. Females have a higher survival rate than males and in general males and females in first Pclass and females in second Pclass have a high probability of Survival"
      ],
      "metadata": {
        "id": "0oaMDEQBjZYo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "sns.scatterplot(x=dataset['Sex'],y=dataset['Pclass'],hue=dataset['Survived'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "iJTK729nWStO",
        "outputId": "3dd7c586-4050-417a-e98a-3641c94f8011"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc64360b710>"
            ]
          },
          "metadata": {},
          "execution_count": 59
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##The following is a facet grid with points being made on different Pclasses. This graph tells us the probability of survival for a male or female in any given Pclass. From this graph we can see that females of 1st Pclass have the highest probability of survival, followed by females of 2nd Pclass and so on. So here we get to see that females in general have a much higher survival rate than males."
      ],
      "metadata": {
        "id": "wXYNk6qQlcL4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "grid = sns.FacetGrid(dataset, size=2.2, aspect=1.6)\n",
        "grid.map(sns.pointplot, 'Pclass', 'Survived', 'Sex')\n",
        "grid.add_legend()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        },
        "id": "ctPXNwD-cGkU",
        "outputId": "a8548c4b-311a-4f00-c71a-93911cae0004"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/axisgrid.py:337: UserWarning: The `size` parameter has been renamed to `height`; please update your code.\n",
            "  warnings.warn(msg, UserWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/seaborn/axisgrid.py:670: UserWarning: Using the pointplot function without specifying `order` is likely to produce an incorrect plot.\n",
            "  warnings.warn(warning)\n",
            "/usr/local/lib/python3.7/dist-packages/seaborn/axisgrid.py:675: UserWarning: Using the pointplot function without specifying `hue_order` is likely to produce an incorrect plot.\n",
            "  warnings.warn(warning)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7fc64369f990>"
            ]
          },
          "metadata": {},
          "execution_count": 60
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 323.94x158.4 with 1 Axes>"
            ],
            "image/png": 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0RXMRaezztQfHRk/nAle7K5vXBa52txkTMle2acTs4Z1p5OZHK84na7YxI61C/uG6GbjRT9mNwOBSnv8M4EWc97Rb4yxo1Bl4CPgTsBa4VFU74Dy1PVPMOf4PmK+q5+N0W/3DzcRTJoIGM3dUcqOqvqKqW72e2OOK5veJyBp35fL7gFvdY/cAI3ACYirwV3ebMSHVokEijeskBNxnSuqWgOVhMqSU5cFsVNVVqpoPrAE+U2fG/SqgOVAbmOquE/IScE4x57gaeFREvgM+x3ktssz6h4I2Pd08Zs1EJM4dlfRMVT8CPiqy7S8+vz+Gs7ZAcceOwWkRGlOuduw/ErD8aD9aBdMsSHlpg4bvRLx8n+/5OHFkBLBAVXuJSHOcYFWUACmq+mMp61Isr8/RG4HFIjIbn8V/VfWfoaiUMeHUuE4CW3/zH9CaBGm5hckWAge0X0J8/doc69e+1c8+c4HhIjJcVVVEOqhqWllVwGuf2QbgA3f/mj4fYyJOv6TAjZy+5wVrBIXF26UsL63ngWdFJA3/jaQRQCywUkTWuN/LjKeWmao+VZYXjQS5ubm8P3M2GRlO0r+MjG1MnTaDG3vdQHR0dJhrZ0ojpVNT5q/dwSdrjk/omHzOyaR0DDaQHxbvANdS/CDADGBcSU+sqpuAtj7fb/VTdqbPYX92yz/HfeRU1cPAXSWtRzBes2YswF2ZyZeqep4TFmrlmTUjNzeX+//4IHP/++lxZddc3Y1XXnqRmJgSj4SbCiA3L58Zaek8PnM1Wbn5VHPTcqd0LPd5Zp4v5k6/GIzT2X8qzqPl28C4qjDPzOu/uId8fo8HUqjC62a+P3N2sYEMYO5/P2XmrDn0TulVzrUyZSkmOoq+Sc147fMNbNx1iCZ1Eugb5PEz3Nb/uCYX+I/7qXK8vs60zOezWFX/B+gS2qpVXFOnTS9VuTGm7HlqmblZLI6KwslmUWXTC2RsC7w4xk/rN5CZmUn16tXLqUbGGK+Pmcs41meWC2yi9JPwKq3GJ59c0PFfnP3793PJZV3pndKLQQP7c1qY3hs1pioJ+JgpIueJyMmq2kJVWwJP4by2sBYompesyujTOyXoPgcOHOA/Y8fR7eru3DH0HhZ+8SX5+RXvnT5jIkWwPrM3gGwAEbkMeBZnCPg34M3QVq3iurHXDVxzdbdiyy679BLuGHIbdeo4T+GqyucLv2DInXdz9e+vY+w74zlwwBalNZWHiNzn5jOcEKLzPykiDwXfM8h5Ak3NEJEVqnqu+/toYKeqPul+/05V25e2AmWlvBc0yc3NZeasOTzx1AiysrKoVq0aTz3xOL169iA6OpojR44w54OPGPfuBH74YW2hY6tXT6DnDT0YNHAAZ7Y6o9zqbE5cZVrQpPmjH/pNAbTpuWtLPDVDRNYC3VQ1JJkrReRJ4KCqvlCq8wQJZquB9qqa697QUFX94miZqnrNOhty4Vqdqds13dm0aTPNm5/Gp3M/Oq5cVVm+PI3xEybyydx55OYWntFy0YUXMHjQALpe0cXmppnieApmbiB7D/+TZvtteu7aE55OJSKvA7cDP+Jkiz4dZ5JsLPCkqs4SkVuBnkAi0Ap4AYjDmfOWBXRX1T0icidO/sE4YD0wWFUzfYOZiJyOk26/IZAJ3KmqhVsDfgR7zJwELBSRWcBh4Ev3Bs/AedQ0QYgInTp15OV/vsDC+fMYPuwPNGzYoKD862+W8Idh99P1qmRef/Mt9uyxZQ5MiYQkBZCq3g1sxUnZk4j/FD5t3eucBzwNZLrpgL526wYwQ1XPc5/2fqD4QcQ3geGq2glnfuurXusaMJip6tPAg8BYoLMea8ZFAcO9XsQ4GjU6ifuH38vC+fN46cXn6dDh2FP61q0ZvPDiy3S+vCuPPPZ/rF5TZcdXTMmEOgUQBE7hs0BVD6jqTpyGTsFiSDgpggDaisiXbpLXgRRJEyQiNXASSk51r/EG4JvzMCAvKYC+KWbbOq8XMMeLi4vj+uuu5frrrmX16jWMnzCROR98RHZ2NtnZ2UyfMZPpM2bSoUN7bh40gGuuvoq4uLhwV9tUbKFOAQR+UviIyAUETxEETqOop6qucB9NuxQ5fxSwr6R98V6zZpgQadv2HP7+7NMsWjifhx58gMaNTy4oS0v7jj8++DCXd72KV0aOZvv2YMl9TRUWLGNkWaQAOprCRwBEpMMJHl8TyBCRWJyWWSGquh/YKCJ93POLiJzr9eQWzCqIevXqcvfQO1nw6VxeHfUKF114bORs585djBz1Kpd3vYr7//gQy5Ytx0uCAFOllEcKoNKm8HkcWAIsxpmrWpyBwBA3+/QavC9P6S1rRkmJSDLwChAN/FtVnytS/j/AHThvFewEblfVzW5ZHs7zNsAvqtqDAMI1mhnK1ZnW/bSedydMZOas2WRmFs5u2qZNa24eNJDrr+tOfHx8mV7XVCheRzOjgSn4H83sW5rpGZVByIKZu3bAOnxWNAf6+65oLiJXAEvc4dl7gC6q2s8tO6iqNbxeL1zBrDzs37+fGe/PYvyEiWzeXPhpoU6d2vTtncKA/jfRtKktLRqBTnSeWbEpgCI9kEFog9lFOPNQrnG/Pwagqs/62b8DMEpVL3G/WzArIj8/ny8XLWb8uxNZ+MWXhR41o6Ki6HpFFwYPGsDFF12I261hKj/7D+lRKPvMTnRV8iHAxz7f40VkqYh8IyI9Q1HByiYqKorLL7uUf7/5GvPmfshtt95MzZpO9vL8/Hw+/Ww+t9x2B8ndezB+wkQOHjwU5IzGRI5Qtsx6A8mqeof7fTBwgaoOK2bfQTjL0l1+dFFgETlFVdNFpCUwH7hSVTcUOc53RfNOmzdvDsm9VGSZmZnMmv0B4ydMZN26nwqVJSYmknJjTwYN6E/Lli3CVENTStYy8yjsj5ki0g1nlfTL/S0sLCJjgQ9UdZq/61WFx8xAVJUl36YyfsJEPv10Pnl5hbtIOne+mJsHDeTyyy61NQoqFwtmHoUymMXgDABcibMEVSowQFXX+OzTAZiG04L7yWd7XZzXIbJEpAHOKxE3+A4eFFXVg5mvrRkZTJo8hcnvTWXv3sKvRzVr1oyBA26iT0ovatc+8fyaoRy9NcWyYOZRqKdmdAdexpmaMUZVnxaRvwJLVXW2iHwK/A7IcA/5RVV7iMjFOK8y5OP0672sqgHnyVgwO15WVhYfffwJ48ZPYNXqNYXK4uPjuaHHdQweOIDWrc/yfM5gL9abMmfBzKOQBrPyZMHMP1VlxcpVjBs/gY8/+YScnMLJE847L4nBAwdwVbeuxMbGBjyXBbNyZ8HMI8s5UwWICO3PbUf7c9vxp0f/l8nvTWXS5Cls3+F0UaamLiU1dSmNGjViwE19ualfH+rXrx/mWlcM9lhdedjrTFVMgwYNGHbvPXw+/7+88tKLnJfUqaBs+/btvPTKSDpffiUP/u+jrFi5Mow1rRjS09PZtGkz6enp4a6KCcKCWRUVGxvLtd2TmTRhHLNnTqNvn5SC16JycnKYNXsOKX36c2Pvfrw/czaZmZlMnTbjuBXci46aRorc3Nwqdb+RwPrMTIF9+/Yxbfr7vDthEr8WaYnExcaSnZNz3DGRuIJ7BVux3vrMPLJgZo6Tl5fH5wu/YPyEiSxa9FXQ/Vu0aE7Dhg0L/tWJSMHrVIV+Fvzu7ocE2f/4/fzvf+zEgc/rU8eCCxTef/PmzaR9t8Lv/T73zN/Kc8V6C2YeWTAzAW3Y8DODbrmNnTt3hbsqFUanjh14b9K75XU5C2YeRc6zgQmJ009vGfSRSkRISIjn6N/Fo38gfX8e+6N5dFvx+/n+rKi2ZmQE38mUOwtmJqhgK7h37NA+ZC2V4gJiscHy2AEe9oeCI4rZ/7YhQ1m5arXfOjVp7DktvSlHFsxMUH16p7A87buA5aFStM+rPPS/qV/AYBbK+zUlZ1MzTFCBVnC/5upu9OoZMAlwpVPV7jdS2ACA8STYCu6RpgLdrw0AeGQtM+NJTEwMvVN6Fawe1bjxyfRO6RWRgQyO3W9SUieaNz+NpKROEX2/kcD6zIwJwN7HrDysZWaMiQjWMjMn5JRTTin005iKwoKZOSH22GUqKnvMNMZEBAtmxpiIEDHzzERkJxCuteYaAFXpTWy73/KzS1WTw3TtSiViglk4ichSVU0Kdz3Ki92vqYjsMdMYExEsmBljIoIFs7LxZrgrUM7sfk2FY31mxpiIYC0zY0xEsGBWCiIyRkR2iIj/TH4RRESaicgCEfleRNaIyP3hrlMoiUi8iHwrIivc+30q3HUy/tljZimIyGXAQWCcqrYNd31CTUQaA41VdbmI1ASWAT1V9fswVy0kxElvm6iqB0UkFlgE3K+q34S5aqYY1jIrBVX9AtgT7nqUF1XNUNXl7u8HgB+AiH3jXB0H3a+x7sf++ldQFsxMiYhIc6ADsCS8NQktEYkWke+AHcA8VY3o+63MLJiZEyYiNYDpwAOquj/c9QklVc1T1fZAU+B8EYn47oTKyoKZOSFu39F0YIKqzgh3fcqLqu4DFgD2nmQFZcHMeOZ2iL8N/KCq/wx3fUJNRBqKSB339wTgKmBteGtl/LFgVgoiMgn4GjhLRH4VkSHhrlOIXQIMBrqKyHfup3u4KxVCjYEFIrISSMXpM/sgzHUyftjUDGNMRLCWmTEmIlgwM8ZEBAtmxpiIYMHMGBMRLJgZYyKCBbMqTETy3OkVq0VkqohUD7DvkyLyUHnWz5gTYcGsajusqu3djB/ZwN3hrpAxJWXBzBz1JXAGgIjcLCIr3Txe44vuKCJ3ikiqWz79aItORPq4rbwVIvKFu+0cNyfYd+45W5XrXZkqwybNVmEiclBVa4hIDM77lp8AXwDvAxer6i4Rqaeqe0TkSeCgqr4gIvVVdbd7jr8B21V1pIisApJVNV1E6qjqPhEZCXyjqhNEJA6IVtXDYblhE9GsZVa1JbjpbZYCv+C8d9kVmKqquwBUtbh8bW1F5Es3eA0EznG3LwbGisidQLS77WvgTyLyCHCaBTITKjHhroAJq8NuepsCzrvkQY3FyTC7QkRuBboAqOrdInIBcC2wTEQ6qepEEVnibvtIRO5S1flleA/GANYyM8ebD/QRkfoAIlKvmH1qAhluOqCBRzeKyOmqukRV/wLsBJqJSEvgZ1X9FzALaBfyOzBVkrXMTCGqukZEngYWikgekAbcWmS3x3EyzO50f9Z0t//D7eAX4DNgBfAIMFhEcoBtwDMhvwlTJdkAgDEmIthjpjEmIlgwM8ZEBAtmxpiIYMHMGBMRLJgZYyKCBTNjTESwYGaMiQgWzIwxEeH/AaiPj86ZWVjCAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##While the previous graph showed us the probability, the following is a countplot which as the name suggests gives us the bar graph with the y axis being the total count. Here we can see that very few of the total number of males in 3rd Pclass actually survived, which explains the extremely low probability of survival in the previous graph. We also see that almost all the females in 1st Pclass survived hence the higher probability of survival in the previous graph. We also see almost all females of second Pclass survived. Many of the 2nd Pclass males weren't able to survive as depicted in the graph and the probability in the previous graph also says the same. Although, when it comes to first class males, a few were able to survive. Only some of the females in 3rd Pclass were able to survive as opposed to the general trend for females in other two classes."
      ],
      "metadata": {
        "id": "YjFkNGC4nyO5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(x=dataset['Survived'],hue=dataset[['Pclass','Sex']].apply(tuple, axis=1))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "VrYNUwKFZLO7",
        "outputId": "3b2b9557-1e24-48db-c9f6-31a56ce2a1e9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc64368ec10>"
            ]
          },
          "metadata": {},
          "execution_count": 61
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Hence we can conclude that survival depends highly on the person's gender. Females are more likely to survive than males. Lower Pclasses also have a significantly higher survival rate than higher Pclasses. But survival depends more on gender than the Pclass as we have deduced from the above graphs"
      ],
      "metadata": {
        "id": "BFdjZbzK0c7u"
      }
    }
  ]
}