WHY_LEO_DIED?.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "WHY LEO DIED?.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyNd9VzuT2zo1HwaPM1IUROA",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/yxt1998/WHY-LEO-DIED-/blob/main/WHY_LEO_DIED%3F.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IuwMHno1qqJB"
      },
      "source": [
        "#Based on the given data we have to predict whether the passanger survived or not. \r\n",
        "#You can see on the training data that there is an extra column of survived. 1 mean survived and 0 means dead.\r\n",
        "# Importing the passanger data from GITHUB\r\n",
        "import pandas as pd\r\n",
        "url1 = 'https://raw.githubusercontent.com/yxt1998/WHY-LEO-DIED-/main/test.csv'\r\n",
        "test = pd.read_csv(url1)\r\n",
        "url2 = 'https://raw.githubusercontent.com/yxt1998/WHY-LEO-DIED-/main/train.csv'\r\n",
        "train = pd.read_csv(url2)"
      ],
      "execution_count": 209,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "VRU8c0UWudl8",
        "outputId": "ab33a5b5-529b-4b8e-db2e-038cd3d42827"
      },
      "source": [
        "test.head()"
      ],
      "execution_count": 210,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>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>892</td>\n",
              "      <td>3</td>\n",
              "      <td>Kelly, Mr. James</td>\n",
              "      <td>male</td>\n",
              "      <td>34.5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>330911</td>\n",
              "      <td>7.8292</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Q</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>893</td>\n",
              "      <td>3</td>\n",
              "      <td>Wilkes, Mrs. James (Ellen Needs)</td>\n",
              "      <td>female</td>\n",
              "      <td>47.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>363272</td>\n",
              "      <td>7.0000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>894</td>\n",
              "      <td>2</td>\n",
              "      <td>Myles, Mr. Thomas Francis</td>\n",
              "      <td>male</td>\n",
              "      <td>62.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>240276</td>\n",
              "      <td>9.6875</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Q</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>895</td>\n",
              "      <td>3</td>\n",
              "      <td>Wirz, Mr. Albert</td>\n",
              "      <td>male</td>\n",
              "      <td>27.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>315154</td>\n",
              "      <td>8.6625</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>896</td>\n",
              "      <td>3</td>\n",
              "      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
              "      <td>female</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3101298</td>\n",
              "      <td>12.2875</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   PassengerId  Pclass  ... Cabin Embarked\n",
              "0          892       3  ...   NaN        Q\n",
              "1          893       3  ...   NaN        S\n",
              "2          894       2  ...   NaN        Q\n",
              "3          895       3  ...   NaN        S\n",
              "4          896       3  ...   NaN        S\n",
              "\n",
              "[5 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 210
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "OmE2ZluUuinm",
        "outputId": "e955628e-351c-4f6a-b624-e208ba81f41f"
      },
      "source": [
        "train.head()"
      ],
      "execution_count": 211,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Braund, Mr. Owen Harris</td>\n",
              "      <td>male</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>A/5 21171</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
              "      <td>female</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>PC 17599</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>C85</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Heikkinen, Miss. Laina</td>\n",
              "      <td>female</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>STON/O2. 3101282</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
              "      <td>female</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>113803</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>C123</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Allen, Mr. William Henry</td>\n",
              "      <td>male</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>373450</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   PassengerId  Survived  Pclass  ...     Fare Cabin  Embarked\n",
              "0            1         0       3  ...   7.2500   NaN         S\n",
              "1            2         1       1  ...  71.2833   C85         C\n",
              "2            3         1       3  ...   7.9250   NaN         S\n",
              "3            4         1       1  ...  53.1000  C123         S\n",
              "4            5         0       3  ...   8.0500   NaN         S\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 211
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vPiXqutAZl1o"
      },
      "source": [
        "# DATA PREPROSCESSING \r\n",
        "import numpy as np\r\n",
        "import seaborn as sns\r\n",
        "import matplotlib.pyplot as plt\r\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 212,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 764
        },
        "id": "hOL_G8ATethd",
        "outputId": "0d75290f-7eec-47a0-eded-25f742804050"
      },
      "source": [
        "#looking at the datas to see which attributes corelate with the labels \r\n",
        "sns.heatmap(train.corr(),   \r\n",
        "            annot=True,\r\n",
        "            cmap='RdYlGn',\r\n",
        "            linewidths=0.2)\r\n",
        "fig=plt.gcf()\r\n",
        "fig.set_size_inches(20,12)\r\n",
        "plt.show()"
      ],
      "execution_count": 213,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1440x864 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MGBqoUPZl6WS"
      },
      "source": [
        "The above corealtion matrix shows strong corelartion between sex and survival. Beside that, Fare, Age and Sibbling. However, the corelation matrix is for only numerical data. There may be the corealtion between caragorical data like Pclass, Embarked, etc."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 293
        },
        "id": "huV5H-huaOJQ",
        "outputId": "f31adb3e-38a9-4f13-8557-b0b5f9183592"
      },
      "source": [
        "#looking at the percentage survived \r\n",
        "\r\n",
        "\r\n",
        "lab=train[\"Survived\"].value_counts().keys().tolist()\r\n",
        "val=train[\"Survived\"].value_counts().values.tolist()\r\n",
        "title = \"pie chart\"\r\n",
        "plt.pie(val, labels=lab, autopct='%1.1f%%')\r\n",
        "plt.axis('equal')\r\n",
        "plt.title(title)\r\n",
        "plt.tight_layout()\r\n",
        "plt.show()"
      ],
      "execution_count": 214,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 679
        },
        "id": "vdBucrGKgarO",
        "outputId": "676d6337-574f-4ce9-cdbf-9245a0626840"
      },
      "source": [
        "plt.subplots(figsize=(10,10))\r\n",
        "sns.countplot('Sex',hue='Survived',data=train, palette='RdBu_r')\r\n",
        "plt.show()"
      ],
      "execution_count": 215,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/_decorators.py:43: FutureWarning:\n",
            "\n",
            "Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "emUHaczXkpAX"
      },
      "source": [
        "From the above graph graph we can say that, most of the female survived and male perished.This is a good explanation to why Leo died "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "id": "S0Hawuc0lC-i",
        "outputId": "2b07c5df-128f-4293-898f-a11022d2f3a4"
      },
      "source": [
        "sns.countplot(x=\"Pclass\",hue=\"Survived\",data=train)"
      ],
      "execution_count": 216,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7efe25219e48>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 216
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "em4Kih8ElgL2"
      },
      "source": [
        "The above figure shows the number of people of different passanger class. Leo was male and from 3rd class so it was probable for him to perish "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 679
        },
        "id": "JLBGXd8Vl2Io",
        "outputId": "5faa880b-440e-472a-89dd-437f13b1fced"
      },
      "source": [
        "train['Age_Category'] = pd.cut(train['Age'],\r\n",
        "                        bins=[0,16,32,48,64,81])\r\n",
        "plt.subplots(figsize=(10,10))\r\n",
        "sns.countplot('Age_Category',hue='Survived',data=train, palette='RdBu_r')\r\n",
        "plt.show()"
      ],
      "execution_count": 217,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/_decorators.py:43: FutureWarning:\n",
            "\n",
            "Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "X-UPVDjBot1E",
        "outputId": "253fc98c-38a2-45ea-a609-f51a2b19a90e"
      },
      "source": [
        "#changing the age into age catagory for better prediction\r\n",
        "train.loc[ train['Age'] <= 16, 'Age'] = 0\r\n",
        "train.loc[(train['Age'] > 16) & (train['Age'] <= 32), 'Age'] = 1\r\n",
        "train.loc[(train['Age'] > 32) & (train['Age'] <= 48), 'Age'] = 2\r\n",
        "train.loc[(train['Age'] > 48) & (train['Age'] <= 64), 'Age'] = 3\r\n",
        "train.loc[ train['Age'] > 64, 'Age'] = 4\r\n",
        "    \r\n",
        "train.head()"
      ],
      "execution_count": 218,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\n",
              "      <th>Age_Category</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>1.0</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",
              "      <td>(16, 32]</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>2.0</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",
              "      <td>(32, 48]</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>1.0</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",
              "      <td>(16, 32]</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>2.0</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",
              "      <td>(32, 48]</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>2.0</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",
              "      <td>(32, 48]</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   PassengerId  Survived  Pclass  ... Cabin Embarked  Age_Category\n",
              "0            1         0       3  ...   NaN        S      (16, 32]\n",
              "1            2         1       1  ...   C85        C      (32, 48]\n",
              "2            3         1       3  ...   NaN        S      (16, 32]\n",
              "3            4         1       1  ...  C123        S      (32, 48]\n",
              "4            5         0       3  ...   NaN        S      (32, 48]\n",
              "\n",
              "[5 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 218
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "Xx9e1Sfvo8Qo",
        "outputId": "d085c685-bf97-4b3c-954b-993a672a0f81"
      },
      "source": [
        "#looking at the number of family\r\n",
        "train['Family'] = train['SibSp'] + train['Parch'] + 1\r\n",
        "train['Alone'] = 0\r\n",
        "train.loc[train['Family'] == 1, 'Alone'] = 1\r\n",
        "train.head()"
      ],
      "execution_count": 219,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\n",
              "      <th>Age_Category</th>\n",
              "      <th>Family</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>1.0</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",
              "      <td>(16, 32]</td>\n",
              "      <td>2</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>2.0</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",
              "      <td>(32, 48]</td>\n",
              "      <td>2</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>1.0</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",
              "      <td>(16, 32]</td>\n",
              "      <td>1</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>2.0</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",
              "      <td>(32, 48]</td>\n",
              "      <td>2</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>2.0</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",
              "      <td>(32, 48]</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   PassengerId  Survived  Pclass  ... Age_Category Family  Alone\n",
              "0            1         0       3  ...     (16, 32]      2      0\n",
              "1            2         1       1  ...     (32, 48]      2      0\n",
              "2            3         1       3  ...     (16, 32]      1      1\n",
              "3            4         1       1  ...     (32, 48]      2      0\n",
              "4            5         0       3  ...     (32, 48]      1      1\n",
              "\n",
              "[5 rows x 15 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 219
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "mfD4wY-PpKI7",
        "outputId": "66c281dd-14f2-4c66-9317-4d981da38d88"
      },
      "source": [
        "#replacing sex catagory into number \r\n",
        "train['Sex'].replace(\"male\", 0, inplace=True)\r\n",
        "train['Sex'].replace(\"female\", 1, inplace=True)\r\n",
        "train.head()"
      ],
      "execution_count": 220,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\n",
              "      <th>Age_Category</th>\n",
              "      <th>Family</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>0</td>\n",
              "      <td>1.0</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",
              "      <td>(16, 32]</td>\n",
              "      <td>2</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>1</td>\n",
              "      <td>2.0</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",
              "      <td>(32, 48]</td>\n",
              "      <td>2</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>1</td>\n",
              "      <td>1.0</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",
              "      <td>(16, 32]</td>\n",
              "      <td>1</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>1</td>\n",
              "      <td>2.0</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",
              "      <td>(32, 48]</td>\n",
              "      <td>2</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>0</td>\n",
              "      <td>2.0</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",
              "      <td>(32, 48]</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   PassengerId  Survived  Pclass  ... Age_Category  Family  Alone\n",
              "0            1         0       3  ...     (16, 32]       2      0\n",
              "1            2         1       1  ...     (32, 48]       2      0\n",
              "2            3         1       3  ...     (16, 32]       1      1\n",
              "3            4         1       1  ...     (32, 48]       2      0\n",
              "4            5         0       3  ...     (32, 48]       1      1\n",
              "\n",
              "[5 rows x 15 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 220
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "itX022bCpx06",
        "outputId": "b446493d-2b4d-4d0f-819b-7bfb5a8bed5b"
      },
      "source": [
        "train['Fare'] = train['Fare'].fillna(train['Fare'].median())\r\n",
        "train['FareBand'] = pd.qcut(train['Fare'], 10)\r\n",
        "print (train[['FareBand', 'Survived']].groupby(['FareBand'], as_index=False).mean())"
      ],
      "execution_count": 221,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "            FareBand  Survived\n",
            "0     (-0.001, 7.55]  0.141304\n",
            "1      (7.55, 7.854]  0.298851\n",
            "2      (7.854, 8.05]  0.179245\n",
            "3       (8.05, 10.5]  0.230769\n",
            "4     (10.5, 14.454]  0.428571\n",
            "5   (14.454, 21.679]  0.420455\n",
            "6     (21.679, 27.0]  0.516854\n",
            "7     (27.0, 39.688]  0.373626\n",
            "8   (39.688, 77.958]  0.528090\n",
            "9  (77.958, 512.329]  0.758621\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 669
        },
        "id": "slThfTGUqD30",
        "outputId": "b9dcf3f6-18ac-4caa-a40c-24306ba322aa"
      },
      "source": [
        "train.loc[ train['Fare'] <= 7.55, 'Fare'] = 0\r\n",
        "train.loc[(train['Fare'] > 7.55) & (train['Fare'] <= 7.854), 'Fare'] = 1\r\n",
        "train.loc[(train['Fare'] > 7.854) & (train['Fare'] <= 8.05), 'Fare']   = 2\r\n",
        "train.loc[(train['Fare'] > 8.05) & (train['Fare'] <= 10.5), 'Fare']   = 3\r\n",
        "train.loc[(train['Fare'] > 10.5) & (train['Fare'] <= 14.454), 'Fare']   = 4\r\n",
        "train.loc[(train['Fare'] > 14.454) & (train['Fare'] <= 21.679), 'Fare']   = 5\r\n",
        "train.loc[(train['Fare'] > 21.679) & (train['Fare'] <= 27), 'Fare']   = 6\r\n",
        "train.loc[(train['Fare'] > 27) & (train['Fare'] <= 39.688), 'Fare']   = 7\r\n",
        "train.loc[(train['Fare'] > 39.688) & (train['Fare'] <= 77.958), 'Fare']   = 8\r\n",
        "train.loc[ train['Fare'] > 77.958, 'Fare'] = 9\r\n",
        "train['Fare'] = train['Fare'].astype(int)\r\n",
        "\r\n",
        "train.head(20)"
      ],
      "execution_count": 222,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\n",
              "      <th>Age_Category</th>\n",
              "      <th>Family</th>\n",
              "      <th>Alone</th>\n",
              "      <th>FareBand</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>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>A/5 21171</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>(16.0, 32.0]</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>(-0.001, 7.55]</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>1</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>PC 17599</td>\n",
              "      <td>8</td>\n",
              "      <td>C85</td>\n",
              "      <td>C</td>\n",
              "      <td>(32.0, 48.0]</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>(39.688, 77.958]</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>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>STON/O2. 3101282</td>\n",
              "      <td>2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>(16.0, 32.0]</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>(7.854, 8.05]</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>1</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>113803</td>\n",
              "      <td>8</td>\n",
              "      <td>C123</td>\n",
              "      <td>S</td>\n",
              "      <td>(32.0, 48.0]</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>(39.688, 77.958]</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>0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>373450</td>\n",
              "      <td>2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>(32.0, 48.0]</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>(7.854, 8.05]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Moran, Mr. James</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>330877</td>\n",
              "      <td>3</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Q</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>(8.05, 10.5]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>McCarthy, Mr. Timothy J</td>\n",
              "      <td>0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>17463</td>\n",
              "      <td>8</td>\n",
              "      <td>E46</td>\n",
              "      <td>S</td>\n",
              "      <td>(48.0, 64.0]</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>(39.688, 77.958]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Palsson, Master. Gosta Leonard</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>349909</td>\n",
              "      <td>5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>(0.0, 16.0]</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>(14.454, 21.679]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>9</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>347742</td>\n",
              "      <td>4</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>(16.0, 32.0]</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>(10.5, 14.454]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>10</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>Nasser, Mrs. Nicholas (Adele Achem)</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>237736</td>\n",
              "      <td>7</td>\n",
              "      <td>NaN</td>\n",
              "      <td>C</td>\n",
              "      <td>(0.0, 16.0]</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>(27.0, 39.688]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>11</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Sandstrom, Miss. Marguerite Rut</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>PP 9549</td>\n",
              "      <td>5</td>\n",
              "      <td>G6</td>\n",
              "      <td>S</td>\n",
              "      <td>(0.0, 16.0]</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>(14.454, 21.679]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>12</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Bonnell, Miss. Elizabeth</td>\n",
              "      <td>1</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>113783</td>\n",
              "      <td>6</td>\n",
              "      <td>C103</td>\n",
              "      <td>S</td>\n",
              "      <td>(48.0, 64.0]</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>(21.679, 27.0]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>13</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Saundercock, Mr. William Henry</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>A/5. 2151</td>\n",
              "      <td>2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>(16.0, 32.0]</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>(7.854, 8.05]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>14</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Andersson, Mr. Anders Johan</td>\n",
              "      <td>0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>347082</td>\n",
              "      <td>7</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>(32.0, 48.0]</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>(27.0, 39.688]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>15</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Vestrom, Miss. Hulda Amanda Adolfina</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>350406</td>\n",
              "      <td>2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>(0.0, 16.0]</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>(7.55, 7.854]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>16</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>Hewlett, Mrs. (Mary D Kingcome)</td>\n",
              "      <td>1</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>248706</td>\n",
              "      <td>5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>(48.0, 64.0]</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>(14.454, 21.679]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>17</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Rice, Master. Eugene</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>382652</td>\n",
              "      <td>7</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Q</td>\n",
              "      <td>(0.0, 16.0]</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>(27.0, 39.688]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>18</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>Williams, Mr. Charles Eugene</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>244373</td>\n",
              "      <td>4</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>(10.5, 14.454]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>19</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Vander Planke, Mrs. Julius (Emelia Maria Vande...</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>345763</td>\n",
              "      <td>5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>(16.0, 32.0]</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>(14.454, 21.679]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>20</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Masselmani, Mrs. Fatima</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2649</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>C</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>(-0.001, 7.55]</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    PassengerId  Survived  Pclass  ... Family  Alone          FareBand\n",
              "0             1         0       3  ...      2      0    (-0.001, 7.55]\n",
              "1             2         1       1  ...      2      0  (39.688, 77.958]\n",
              "2             3         1       3  ...      1      1     (7.854, 8.05]\n",
              "3             4         1       1  ...      2      0  (39.688, 77.958]\n",
              "4             5         0       3  ...      1      1     (7.854, 8.05]\n",
              "5             6         0       3  ...      1      1      (8.05, 10.5]\n",
              "6             7         0       1  ...      1      1  (39.688, 77.958]\n",
              "7             8         0       3  ...      5      0  (14.454, 21.679]\n",
              "8             9         1       3  ...      3      0    (10.5, 14.454]\n",
              "9            10         1       2  ...      2      0    (27.0, 39.688]\n",
              "10           11         1       3  ...      3      0  (14.454, 21.679]\n",
              "11           12         1       1  ...      1      1    (21.679, 27.0]\n",
              "12           13         0       3  ...      1      1     (7.854, 8.05]\n",
              "13           14         0       3  ...      7      0    (27.0, 39.688]\n",
              "14           15         0       3  ...      1      1     (7.55, 7.854]\n",
              "15           16         1       2  ...      1      1  (14.454, 21.679]\n",
              "16           17         0       3  ...      6      0    (27.0, 39.688]\n",
              "17           18         1       2  ...      1      1    (10.5, 14.454]\n",
              "18           19         0       3  ...      2      0  (14.454, 21.679]\n",
              "19           20         1       3  ...      1      1    (-0.001, 7.55]\n",
              "\n",
              "[20 rows x 16 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 222
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "id": "fJF0FLlfsBhf",
        "outputId": "d80988e2-95f2-4c73-b986-402e7a1fdbb3"
      },
      "source": [
        "sns.countplot(x=\"Embarked\",hue=\"Survived\",data=train)"
      ],
      "execution_count": 223,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7efe2571ec88>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 223
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYwAAAEMCAYAAADXiYGSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nO3dfXRUdX7H8XdmkgkQHrIJBifEBTcSHEl5ylhkFdlN9BjYAK6tSxp1z6K0gopUGjCrMGEDSBOirmLYYKHqeliodgWagMQHLD60xU006ggVjwWpyZA0BBBiMpPMTP+gTI0k4Q4mMwn5vM7hHOb+7u/e7yTn3E9+9+F3I/x+vx8REZELMIW7ABER6RsUGCIiYogCQ0REDFFgiIiIIQoMERExJDLcBfQUn89HU1MTUVFRREREhLscEZE+we/309raSkxMDCZT+zHFJRsYTU1NHDp0KNxliIj0SSkpKQwZMqTdsks2MKKiooCzX9pisYS5GhGRvsHj8XDo0KHAMfTbLtnAOHcaymKxEB0dHeZqRET6lo5O5euit4iIGKLAEBERQy7ZU1IiIsH4+uuvqa+vp7W1NdylhERMTAxJSUnn3QnVFQWGiPR7X3/9NXV1dYwcOZKBAwde8rfi+3w+ampqaGhoICEhwXA/nZISkX6vvr6ekSNHMmjQoEs+LABMJhMjRozg1KlTwfXroXpERPqM1tZWBg4cGO4yQioqKoq2trag+igwRETo+DbSS9nFfF8Fxv/xtHrDXULQ+mLNItIxh8NBSUlJt293/fr15Obmdsu2dNH7/1iizOQs2xLuMoLyh6I7wl2CyCWvsrKS4uJiPv/8c8xmMz/60Y945JFHGD9+fLfup6CgoFu31xMUGCIinThz5gwLFixg5cqVzJgxg9bWViorK4Oebsjv9+P3+4O6hbU36tvVi4j0oMOHDwOQlZWF2WxmwIAB3HDDDVx99dXnner56quvGDt2bOBC8l133cWTTz5JdnY2EyZMYNOmTdx2223ttv/888+zYMECAPLy8njyyScBmDFjBm+99VZgvba2Nq677jo+/fRTAKqrq8nOzsZutzN79mz2798fWPe///u/ufPOO5k0aRLz5s3jxIkT3fbzUGCIiHTiyiuvxGw28/DDD7Nv376gb0PduXMnq1at4oMPPuCv/uqvOHz4MEeOHAm0l5WVMWvWrPP6/exnP6O8vDzw+d133+UHP/gB48aNo66ujnvvvZeFCxfy/vvv8/DDD/Pggw/S2NgIQG5uLuPGjWP//v3cd999bN++/eK+fAcUGCIinRg8eDB/+MMfiIiIYMWKFUydOpUFCxbQ0NBgqP/Pf/5zxowZQ2RkJEOGDCEjIyMQBEeOHOG//uu/SE9PP6/frFmz2Lt3L83NzcDZYPnZz34GnA2hG2+8kenTp2Mymbj++utJTU1l37591NbW8sknn7B48WIsFgvXXntth9u/WAoMEZEuJCcn8/d///e8/fbblJWVUV9fz2OPPWaor9Vqbfd51qxZ7Nq1C4Dy8nJuuummDp//GDVqFMnJybz11ls0Nzezd+/ewEiktraWPXv2YLfbA/+qqqr4n//5H+rr6xk6dCiDBg0KbCsxMfFiv/p5dNFbRMSg5ORkbrvtNv7pn/6Ja665hpaWlkBbR6OO7z7r8OMf/5jGxkYOHjxIeXk5v/71rzvdV1ZWFuXl5fh8Pq666ipGjRoFnA2hOXPmsHr16vP61NTU8PXXX/PNN98EQqO2trbbnjHRCENEpBNffPEF//iP/8ixY8cAcLlclJeXM2HCBGw2G3/605+ora3l9OnTbNy48YLbi4qKIjMzk6KiIk6dOsX111/f6bozZ87kvffeY+vWrWRlZQWWz549m7feeot33nkHr9eL2+1m//79HDt2jJEjR5Kamsr69evxeDxUVla2u3j+fSkwREQ6MXjwYD766CNuv/12Jk6cyC9+8QtSUlLIy8vj+uuvZ+bMmcyePZvbbruNn/70p4a2OWvWLP7t3/6NzMxMIiM7P8mTkJDAxIkT+fDDD5k5c2ZgudVqZcOGDWzcuJGpU6cyffp0Nm/ejM/nA+Dxxx/no48+YsqUKZSUlHDrrbd+vx/Ct0T4/X5/t23NgGeeeYb169dTVlZGSkoK1dXVOBwO3G43I0eOZN26dcTHxwN02XYhbrcbp9NJamqq4Tfu6cE9kf7p4MGD2Gy2cJcRch19766OnSEdYXz66adUV1czcuRI4OwUu0uXLsXhcFBRUYHdbqe4uPiCbSIiEnohCwyPx0NBQQErV64MLHM6nURHR2O32wHIzs5mz549F2wTEZHQC9ldUk899RSzZ88mKSkpsMzlcrW75SsuLg6fz8fJkye7bIuNjTW8X6fTaWi9tLQ0w9vsTaqqqsJdgkifFxkZSVNTU7jLCDmPxxPUMSQkgfHhhx/idDq7bcbEYARzDaMv6qtBJ9KbHDx4kJiYmHCXEXIWi4UJEya0W3buGkZHQhIYf/rTn/jiiy/IyMgA4NixY9xzzz3cdddd1NbWBtZrbGzEZDIRGxuL1WrttE1EREIvJNcw/uZv/oZ3332XvXv3snfvXi6//HI2b97M/PnzaWlpobKyEoBt27aRmZkJnB0ZdNYmIiKhF9YnvU0mE0VFReTn57e7dfZCbSIiEnphCYy9e/cG/j958mTKyso6XK+rNhERCS096S0iEqSeej1yMNs9fPgwc+fO5ZZbbmHu3Lntpk3vKZp8UEQkSD31SudgZm/Iz88nJyeHOXPmsHPnThwOB7///e+7vaZv0whDRKSPOX78OAcOHAhMSpiVlcWBAwcCL1HqKQoMEZE+xuVyMWLECMxmMwBms5mEhARcLleP7leBISIihigwRET6GKvVSl1dHV7v2YvkXq+X+vr6897w190UGCIifUx8fDw2my3wfvDy8nJsNhtxcXE9ul/dJSUiEiRPq7dH3kfjafViiTIbWnflypXk5eWxYcMGhg4dSmFhYbfX810KDBGRIBk9qPfkdpOTk3n55Zd7pI7O6JSUiIgYosAQERFDFBgiImKIAkNERAxRYIiIiCEKDBERMSRkt9Xed999fPXVV5hMJgYNGsSKFSuw2Wykp6djsVgC793Ozc1l2rRpAFRXV+NwONq9QCk+Pj5UJYuIdMjX1oopMips2y0sLKSiooKamhrKyspISUnp9lo6ErLAKCwsZMiQIQC88cYbPPLII2zfvh2Ap59++rwv7PP5WLp0KWvXrsVut7NhwwaKi4tZu3ZtqEoWEemQKTKKqqL53b7dtGWbDK2XkZHBL3/5S+64o/sfHuxKyE5JnQsLgDNnzhAREdHl+k6nk+joaOx2OwDZ2dns2bOnR2sUEekL7HZ7j88b1ZGQPun96KOP8t577+H3+9m06f+TNDc3F7/fT1paGkuWLGHo0KG4XC4SExMD68TFxeHz+Th58iSxsbGhLFtERAhxYKxZswaAHTt2UFRUxD/8wz+wZcsWrFYrHo+HNWvWUFBQQHFxcbft0+l0GlovLS2t2/YZSlVVVeEuQaTPi4yMpKmpyfD6MTExPVZLMHX4fD6am5uD6vNtHo8nqGNIWOaSuvXWW3E4HJw4cSIwrLJYLOTk5LBw4ULg7PS9tbW1gT6NjY2YTKagRxepqamBC+qXor4adCK9ycGDB3s0BIIRTB0mk4mBAwdedO0Wi4UJEya0W+Z2uzv9Qzsk1zCampravQlq7969DBs2jOjoaE6fPg2A3+9n9+7d2Gw24OyBvqWlhcrKSgC2bdtGZmZmKMoVEZEOhGSE0dzczOLFi2lubsZkMjFs2DBKS0s5fvw4ixYtwuv14vP5SE5OJj8/HzibnEVFReTn57e7rVZEpL9bvXo1r732Gg0NDcybN4/Y2Fh27drV4/uN8Pv9/h7fSxicG1YFc0oqZ9mWHq6qe/XEfPwi/dHBgwcDZzeMCPdzGN2lo+/d1bFTT3qLiASppw7qoQyLi6HAEBERQxQYIiJiiAJDRISzzzT0Jxdz+VqBISL9XkxMDDU1NXg8nos6kPY1fr+f48ePM2DAgKD6heXBPRGR3iQpKYmGhga+/PJL2trawl1OSAwYMICkpKSg+igwRKTfM5lMJCQkkJCQEO5SejWdkhIREUMUGCIiYogCQ0REDFFgiIiIIQoMERExRIEhIiKGKDBERMQQBYaIiBgSsgf37rvvPr766itMJhODBg1ixYoV2Gw2Dh8+TF5eHidPniQ2NpbCwkJGjx4N0GWbiIiEVshGGIWFhfzLv/wLO3bs4O677+aRRx4BID8/n5ycHCoqKsjJycHhcAT6dNUmIiKhFbLAGDJkSOD/Z86cISIiguPHj3PgwAGysrIAyMrK4sCBAzQ2NnbZJiIioRfSuaQeffRR3nvvPfx+P5s2bcLlcjFixAjMZjMAZrOZhIQEXC4Xfr+/07a4uLhQli0iIoQ4MNasWQPAjh07KCoqYvHixT2+T6fTaWi9tLS0Hq6kZ1RVVYW7BBHpJ8IyW+2tt96Kw+Hg8ssvp66uDq/Xi9lsxuv1Ul9fj9Vqxe/3d9oWjI5eZH4p6atBJyK9k9vt7vQP7ZBcw2hqasLlcgU+7927l2HDhhEfH4/NZqO8vByA8vJybDYbcXFxXbaJiEjohWSE0dzczOLFi2lubsZkMjFs2DBKS0uJiIhg5cqV5OXlsWHDBoYOHUphYWGgX1dtIiISWiEJjOHDh/PSSy912JacnMzLL78cdJuIiISWnvQWERFDFBgiImKIAkNERAxRYIiIiCEKDBERMUSBISIihigwRETEEAWGiIgYosAQERFDFBgiImKIAkNERAxRYIiIiCEKDBERMUSBISIihigwRETEkJC8D+PEiRMsW7aMo0ePYrFYGDVqFAUFBcTFxTF27FhSUlIwmc5mV1FREWPHjgXOvpmvqKgIr9fLuHHjWLt2LQMHDgxFySIi8h0hGWFEREQwf/58KioqKCsr44orrqC4uDjQvm3bNnbu3MnOnTsDYdHU1MSKFSsoLS3l9ddfJyYmhs2bN4eiXBER6YDhwOjsYP3cc89dsG9sbCxTpkwJfJ44cSK1tbVd9nn77bdJTU1l9OjRAGRnZ/Pqq68aLVdERLqZ4cAoKSnpcPnvfve7oHbo8/nYunUr6enpgWV33XUXc+bM4fHHH8fj8QDgcrlITEwMrJOYmIjL5QpqXyIi0n0ueA3j3//934GzB/r/+I//wO/3B9q++uorYmJigtrhqlWrGDRoEHfeeScA//qv/4rVauXMmTMsXbqUkpISHnrooaC22RWn02lovbS0tG7bZyhVVVWFuwQR6ScuGBiPPvooAG63m0ceeSSwPCIigssuu4zly5cb3llhYSFffvklpaWlgYvcVqsVgMGDB3P77bcHTnFZrVb2798f6FtbWxtYNxipqalER0cH3a+v6KtBJyK9k9vt7vQP7QsGxt69ewFYtmwZRUVFF13EE088gdPp5Nlnn8VisQBw6tQpoqOjGTBgAG1tbVRUVGCz2QCYNm0aq1at4siRI4wePZpt27YxY8aMi96/iIh8P4Zvq/12WPh8vnZt50YLnfn888/ZuHEjo0ePJjs7G4CkpCTmz5+Pw+EgIiKCtrY2Jk2axOLFi4GzI46CggLuvfdefD4fNpstMNoREZHQMxwYn376KQUFBXz22We43W4A/H4/ERERHDx4sMu+Y8aM4bPPPuuwraysrNN+N910EzfddJPREkVEpAcZDoy8vDx++tOf8thjjzFgwICerElERHohw4FRU1PDQw89RERERE/WIyIivZTh5zBuvvlm3n333Z6sRUREejHDIwy3280DDzxAWloaw4cPb9f2fe6eEhGRvsFwYFx11VVcddVVPVmLiIj0YoYD44EHHujJOkREpJczHBjnpgjpyNSpU7ulGBER6b0MB8Z3H5o7ceIEra2tjBgxgjfffLPbCxMRkd7FcGCcmyLkHK/Xy+9+97ugJx8UEZG+6aJfoGQ2m1mwYAGbNm3qznpERKSX+l5v3Hvvvff0IJ+ISD9h+JTU9OnT24VDc3MzHo+H/Pz8HilMLszX1oopMircZRjW1+oVkfYMB8a6devafR44cCBXXnklgwcP7vaixBhTZBRVRfPDXYZhact0+lKkLzMcGH/+538OnJ3avKGhgeHDh19wWnMREbl0GD7inzlzhmXLljF+/HhuvPFGxo8fz8MPP8zp06d7sj4REeklDAfG6tWraW5upqysjI8//piysjKam5tZvXp1T9YnIiK9hOFTUu+88w5vvPEGAwcOBODKK69k7dq13HzzzRfse+LECZYtW8bRo0exWCyMGjWKgoIC4uLiqK6uxuFw4Ha7GTlyJOvWrSM+Ph6gyzYREQktwyOM6OhoGhsb2y07ceJE4P3cXYmIiGD+/PlUVFRQVlbGFVdcQXFxMT6fj6VLl+JwOKioqMBut1NcXAzQZZuIiISe4cD4y7/8S+6++262bt3Kvn372Lp1K/fccw+33377BfvGxsYyZcqUwOeJEydSW1uL0+kkOjoau90OQHZ2Nnv27AHosk1ERELP8CmphQsXMmLECMrKyqivrychIYH58+cbCoxv8/l8bN26lfT0dFwuF4mJiYG2uLg4fD4fJ0+e7LItNjbW8P6cTqeh9dLS0ox/CbloVVVV4S5BRC6S4cBYs2YNM2fO5Pnnnw8s++CDD1izZs15ExN2ZdWqVQwaNIg777yT119/PahiL0ZqairR0dE9vh8xRsEs0ru53e5O/9A2fEqqvLyc1NTUdstSU1MpLy83XEhhYSFffvklv/3tbzGZTFitVmprawPtjY2NmEwmYmNju2wTEZHQMxwYERER+Hy+dsu8Xu95yzrzxBNP4HQ6KSkpCVwoT01NpaWlhcrKSgC2bdtGZmbmBdtERCT0DJ+SstvtPPXUUyxduhSTyYTP52P9+vWBi9Jd+fzzz9m4cSOjR48mOzsbgKSkJEpKSigqKiI/P7/drbMAJpOp0zYREQm9oF6gdO+993LDDTeQmJiIy+Xisssuo7S09IJ9x4wZw2effdZh2+TJkykrKwu6TUREQstwYFx++eVs376djz/+GJfLhdVqZfz48ZpPSkSknzAcGHD2NNHEiROZOHFiT9UjIiK9lIYHIiJiiAJDREQMUWCIiIghCgwRETFEgSEiIoYoMERExBAFhoiIGKLAEBERQxQYIiJiiAJDREQMUWCIiIghCgwRETFEgSEiIoYENVvt91FYWEhFRQU1NTWUlZWRkpICQHp6OhaLJfDe7dzcXKZNmwZAdXU1Doej3QuU4uPjQ1WyiIh8S8hGGBkZGWzZsoWRI0ee1/b000+zc+dOdu7cGQgLn8/H0qVLcTgcVFRUYLfbKS4uDlW5IiLyHSELDLvdjtVqNby+0+kkOjo68ArY7Oxs9uzZ01PliYjIBYTslFRXcnNz8fv9pKWlsWTJEoYOHYrL5SIxMTGwTlxcHD6fj5MnTxIbGxvGakVE+qewB8aWLVuwWq14PB7WrFlDQUFBt556cjqdhtZLS0vrtn1K56qqqsJdgohcpLAHxrnTVBaLhZycHBYuXBhYXltbG1ivsbERk8kU9OgiNTU1cEFdwk/BLNK7ud3uTv/QDutttd988w2nT58GwO/3s3v3bmw2G3D2QN/S0kJlZSUA27ZtIzMzM2y1ioj0dyEbYaxevZrXXnuNhoYG5s2bR2xsLKWlpSxatAiv14vP5yM5OZn8/HwATCYTRUVF5Ofnt7utVkREwiNkgbF8+XKWL19+3vIdO3Z02mfy5MmUlZX1ZFkiImKQnvQWERFDFBgiImKIAkNERAxRYIiIiCEKDBERMUSBISIihigwRETEEAWGiIgYosAQERFDFBgiImKIAkNERAxRYIiIiCEKDBERMUSBISIihigwRETEkJAERmFhIenp6YwdO5ZDhw4Flh8+fJi5c+dyyy23MHfuXI4cOWKoTUREQi8kgZGRkcGWLVsYOXJku+X5+fnk5ORQUVFBTk4ODofDUJuIiIReSALDbrdjtVrbLTt+/DgHDhwgKysLgKysLA4cOEBjY2OXbSIiEh4he0Xrd7lcLkaMGIHZbAbAbDaTkJCAy+XC7/d32hYXFxeukkVE+rWwBUaoOJ1OQ+ulpaX1cCUCUFVVFe4SROQihS0wrFYrdXV1eL1ezGYzXq+X+vp6rFYrfr+/07ZgpaamEh0d3QPfQC6Gglmkd3O73Z3+oR2222rj4+Ox2WyUl5cDUF5ejs1mIy4urss2EREJj5CMMFavXs1rr71GQ0MD8+bNIzY2ll27drFy5Ury8vLYsGEDQ4cOpbCwMNCnqzaRvs7X1oopMircZQSlL9Ys3SskgbF8+XKWL19+3vLk5GRefvnlDvt01SbS15kio6gqmh/uMoKStmxTuEuQMNOT3iIiYogCQ0REDFFgiIiIIQoMERExRIEhIiKGKDBERMQQBYaIiBiiwJA+z9PqDXcJIv3CJT/5oFz6LFFmcpZtCXcZQflD0R3hLkEkaBphiIiIIQoMERExRIEhIiKGKDBERMQQBYaIiBiiwBAREUN6xW216enpWCyWwKtUc3NzmTZtGtXV1TgcDtxuNyNHjmTdunXEx8eHuVoRkf6pVwQGwNNPP01KSkrgs8/nY+nSpaxduxa73c6GDRsoLi5m7dq1YaxSRKT/6rWnpJxOJ9HR0djtdgCys7PZs2dPmKsSEem/es0IIzc3F7/fT1paGkuWLMHlcpGYmBhoj4uLw+fzcfLkSWJjY8NYqYhI/9QrAmPLli1YrVY8Hg9r1qyhoKCAm2++uVu27XQ6Da2XlpbWLfuTrlVVVXX7NvW7C52e+P1J39ErAsNqtQJgsVjIyclh4cKF/PKXv6S2tjawTmNjIyaTKejRRWpqauBiuoSfDu59m35/lz63293pH9phv4bxzTffcPr0aQD8fj+7d+/GZrORmppKS0sLlZWVAGzbto3MzMxwlioiPaAvzjbcF2vuDmEfYRw/fpxFixbh9Xrx+XwkJyeTn5+PyWSiqKiI/Pz8drfVisilRbMN9x1hD4wrrriCHTt2dNg2efJkysrKQlyRiIh0JOynpEREpG9QYIiIiCEKDBERMUSBISIihigwRETEEAWGiEiQfG2t4S4hKN1Vb9hvqxUR6WtMkVFUFc0PdxmGpS3b1C3b0QhDREQMUWCIiIghCgwRETFEgSEiIoYoMERExBAFhoiIGKLAEBERQxQYIiJiiAJDREQM6fWBcfjwYebOncstt9zC3LlzOXLkSLhLEhHpl3p9YOTn55OTk0NFRQU5OTk4HI5wlyQi0i/16rmkjh8/zoEDB3juuecAyMrKYtWqVTQ2NhIXF9dlX7/fD4DH4zG8v6GDoi6+2DBwu90wYEi4yzDM7Xb32Lb1u+t5+v39v772+wvmd3fumHnuGPptEf6OlvYSTqeThx9+mF27dgWWzZw5k3Xr1jFu3Lgu+54+fZpDhw71dIkiIpeklJQUhgxpH4q9eoTxfcTExJCSkkJUVBQRERHhLkdEpE/w+/20trYSExNzXluvDgyr1UpdXR1erxez2YzX66W+vh6r1XrBviaT6bx0FBGRCxswYECHy3v1Re/4+HhsNhvl5eUAlJeXY7PZLnj9QkREul+vvoYB8MUXX5CXl8fXX3/N0KFDKSws5Ec/+lG4yxIR6Xd6fWCIiEjv0KtPSYmISO+hwBAREUMUGCIiYogCQ0REDOnVz2FIx1599VU2btyI3+/H7XYzbtw4Hn/88XCXJQa0trayYcMGdu/ejcViwWw2c9111/F3f/d3REX1rekx+huPx8MTTzzBG2+8QWRkJNHR0SxYsIAZM2aEu7SQUWD0MfX19fzmN79h+/btWK1W/H4/Bw8eDHdZYtCvf/1r3G43f/zjHxk8eDBtbW388Y9/xOPxKDB6uZUrV/LNN9+wa9cuoqOjOXToEPfccw+xsbFMnTo13OWFhAKjj2loaCAyMpLY2FgAIiIiuOaaa8JclRhx5MgR3njjDfbt28fgwYMBiIyMZO7cuWGuTC6kpqaGV199lbfeeovo6Gjg7FxLCxcu5Jlnnuk3gaFrGH3M1Vdfzfjx4/nJT37Cgw8+yPPPP8+JEyfCXZYYcODAAUaNGsWwYcPCXYoE6dChQ/zwhz8M/KF2zsSJE/vVJKcKjD7GZDKxYcMGXnzxRaZMmcK+ffuYPXs2J0+eDHdpIpesrp5v7k+Tmyow+qiUlBTuuOMOnnvuOYYMGcL7778f7pLkAq655hq+/PJLTp06Fe5SJEgpKSkcPXr0vD/MqqurmTRpUpiqCj0FRh9TV1fHhx9+GPh87NgxGhsbSUpKCmNVYsTo0aNJT0/H4XBw5swZALxeLy+//DJNTU1hrk66kpSURGZmJitXrgy8jOjQoUO88MIL/O3f/m2YqwsdzSXVx9TU1LBixQpqamoYMGAAPp+PO+64g+zs7HCXJgZ4PB5KSkrYs2cPUVFR+Hw+pk+fzpIlS3SXVC/X0tLCE088wZtvvklERAR1dXW89NJL2Gy2cJcWMgoMEZEgtba24nA4OHbsGKWlpYE7py51CgwRETFE1zBERMQQBYaIiBiiwBAREUMUGCIiYogCQ6Sb5OXl8eSTT3bb9tavX09ubu733s7+/fu58cYbu6Ei6e80+aD0e+np6TQ0NGA2mwPLfv7zn+NwOMJYlUjvo8AQAUpLS/nxj38c7jIC2trawl2CyHl0SkqkE6+88grZ2dk89thj2O12MjIy+OCDD3jllVeYPn06U6dOZfv27e36nDhxgnnz5jFp0iTuvPNOampqAm2rV69m+vTpTJ48mdtuu43KyspA2/r163nwwQfJzc1l8uTJ5223tbWVJUuWsGjRIjweD3V1dSxatIjrrruO9PR0fv/73wfWbWlpIS8vj2uvvZaZM2fyySef9NBPSPobBYZIFz7++GPGjh3L/v37ycrKYsmSJXzyySe8/vrrrFu3joKCgnbzQJWVlXHfffexf/9+rr766nbXIP7sz/6MHTt28P7775OVlcXixYsD8xIBvPnmm2RmZlJZWcmsWbMCy1taWrj//vuxWCz89re/JTIykoULFzJ27FjefvttXnjhBV544QXeeQp9BcAAAALOSURBVOcdAJ555hmOHj3K66+/zubNm9mxY0cIflLSHygwRID7778fu90e+PfSSy8BZyed+4u/+AvMZjMzZ87E5XIFDt433HADFouFo0ePBrbzk5/8hGuvvRaLxcJDDz1EdXU1LpcLgDlz5vCDH/yAyMhI7r77bjweD4cPHw70nThxIjfddBMmk4kBAwYAcObMGebPn88Pf/hD1q5di9ls5pNPPqGxsZEHHngAi8XCFVdcwS9+8Qt2794NnH2F74IFC4iNjcVqtXLXXXeF6scolzhdwxABSkpKzruG8corrxAfHx/4fO4gPnz48MCy6OjodiOMyy+/PPD/mJgYhg0bRn19PVarlc2bN/PP//zP1NfXExERwZkzZ9q9/Orbfc/56KOPaGtr4/HHHw+8d6Gmpob6+nrsdntgPa/XG/h8bn/nJCYmBvfDEOmEAkOkGx07dizw/6amJk6dOkVCQgKVlZVs2rSJ559/njFjxmAymbj22mvbvZinoxfxXH/99YwdO5Zf/epXvPjiiwwfPhyr1UpSUhKvvfZahzVcdtlluFwuxowZAxAY4Yh8XzolJdKN9u3bR2VlJR6Ph6eeeooJEyZgtVppamrCbDYTFxdHW1sbzzzzTOCdGBfy13/912RlZfGrX/2KxsZGxo8fT0xMDM8++ywtLS14vV4OHTrExx9/DMCMGTN49tlnOXXqFMeOHePFF1/sya8s/YgCQwRYsGABkyZNCvy7//77L2o7WVlZlJSUMGXKFD799FPWrVsHwA033MC0adO45ZZbSE9PJzo6ut1powu5//77ycjIYN68eZw+fZrS0lL+8z//k4yMDK677jqWL18eCKAHHniAxMREMjIyuPvuu5kzZ85FfReR79L05iIiYohGGCIiYogCQ0REDFFgiIiIIQoMERExRIEhIiKGKDBERMQQBYaIiBiiwBAREUMUGCIiYsj/Al9WotcRcdPxAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4sVIYT15sMFc"
      },
      "source": [
        "The above figure shows that most of the people embarked from S. So we replace the NaN vlaue with and encoded this catagorical value. We can use one hot encoder fom sklearn or we can encode ourselves too. Since there are only three valuees, I encoded myselves. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EJJBCf1ItZcn",
        "outputId": "112341e7-61f9-4b25-b6e4-295b678df842"
      },
      "source": [
        "train.groupby('Embarked').Survived.value_counts()"
      ],
      "execution_count": 224,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Embarked  Survived\n",
              "C         1            93\n",
              "          0            75\n",
              "Q         0            47\n",
              "          1            30\n",
              "S         0           427\n",
              "          1           217\n",
              "Name: Survived, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 224
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LytLX7E0vE5_",
        "outputId": "44225956-f9ec-4b43-dd46-43a18a7078fd"
      },
      "source": [
        "print(train.Embarked.isnull().sum())"
      ],
      "execution_count": 225,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zHZTgJkSvOEl",
        "outputId": "7fb4e30c-ded1-47f1-c4d5-d1f447eb3d57"
      },
      "source": [
        "train['Embarked'] = train['Embarked'].fillna('S')\r\n",
        "print(train.Embarked.isnull().sum())"
      ],
      "execution_count": 226,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "6L3hElvruymo",
        "outputId": "4bff9add-32de-4609-902d-78f0ae67db33"
      },
      "source": [
        "\r\n",
        "train['Embarked'] = train['Embarked'].map( {'S': 0, 'C': 1, 'Q': 2} ).astype(int)\r\n",
        "train.head()"
      ],
      "execution_count": 227,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\n",
              "      <th>Age_Category</th>\n",
              "      <th>Family</th>\n",
              "      <th>Alone</th>\n",
              "      <th>FareBand</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>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>A/5 21171</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>(16, 32]</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>(-0.001, 7.55]</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>1</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>PC 17599</td>\n",
              "      <td>8</td>\n",
              "      <td>C85</td>\n",
              "      <td>1</td>\n",
              "      <td>(32, 48]</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>(39.688, 77.958]</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>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>STON/O2. 3101282</td>\n",
              "      <td>2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>(16, 32]</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>(7.854, 8.05]</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>1</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>113803</td>\n",
              "      <td>8</td>\n",
              "      <td>C123</td>\n",
              "      <td>0</td>\n",
              "      <td>(32, 48]</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>(39.688, 77.958]</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>0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>373450</td>\n",
              "      <td>2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>(32, 48]</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>(7.854, 8.05]</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   PassengerId  Survived  Pclass  ... Family  Alone          FareBand\n",
              "0            1         0       3  ...      2      0    (-0.001, 7.55]\n",
              "1            2         1       1  ...      2      0  (39.688, 77.958]\n",
              "2            3         1       3  ...      1      1     (7.854, 8.05]\n",
              "3            4         1       1  ...      2      0  (39.688, 77.958]\n",
              "4            5         0       3  ...      1      1     (7.854, 8.05]\n",
              "\n",
              "[5 rows x 16 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 227
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H74JzM9LvfHQ"
      },
      "source": [
        "#Removing the colums that does not corelate with survival. \r\n",
        "train = train.drop(['Name', 'SibSp', 'Parch', 'Ticket', 'Cabin', 'Age_Category', 'FareBand'], axis=1)"
      ],
      "execution_count": 228,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "b--qd1GpvsKj",
        "outputId": "b4dc80dd-df9b-41d4-eb32-f6e733710dc1"
      },
      "source": [
        "train['Age'] = train['Age'].fillna(2)\r\n",
        "train['Age'] = train['Age'].astype(int)\r\n",
        "train.head()"
      ],
      "execution_count": 229,
      "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>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Embarked</th>\n",
              "      <th>Family</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>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</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>1</td>\n",
              "      <td>2</td>\n",
              "      <td>8</td>\n",
              "      <td>1</td>\n",
              "      <td>2</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>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</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>1</td>\n",
              "      <td>2</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>2</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>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   PassengerId  Survived  Pclass  Sex  Age  Fare  Embarked  Family  Alone\n",
              "0            1         0       3    0    1     0         0       2      0\n",
              "1            2         1       1    1    2     8         1       2      0\n",
              "2            3         1       3    1    1     2         0       1      1\n",
              "3            4         1       1    1    2     8         0       2      0\n",
              "4            5         0       3    0    2     2         0       1      1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 229
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "Bf74_f6Wx9DW",
        "outputId": "abdb02bd-8d52-4f7d-d9ff-b83e15671441"
      },
      "source": [
        "test.loc[ test['Age'] <= 16, 'Age'] = 0\r\n",
        "test.loc[(test['Age'] > 16) & (test['Age'] <= 32), 'Age'] = 1\r\n",
        "test.loc[(test['Age'] > 32) & (test['Age'] <= 48), 'Age'] = 2\r\n",
        "test.loc[(test['Age'] > 48) & (test['Age'] <= 64), 'Age'] = 3\r\n",
        "test.loc[ test['Age'] > 64, 'Age'] = 4\r\n",
        "test.head()"
      ],
      "execution_count": 230,
      "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>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>892</td>\n",
              "      <td>3</td>\n",
              "      <td>Kelly, Mr. James</td>\n",
              "      <td>male</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>330911</td>\n",
              "      <td>7.8292</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Q</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>893</td>\n",
              "      <td>3</td>\n",
              "      <td>Wilkes, Mrs. James (Ellen Needs)</td>\n",
              "      <td>female</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>363272</td>\n",
              "      <td>7.0000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>894</td>\n",
              "      <td>2</td>\n",
              "      <td>Myles, Mr. Thomas Francis</td>\n",
              "      <td>male</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>240276</td>\n",
              "      <td>9.6875</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Q</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>895</td>\n",
              "      <td>3</td>\n",
              "      <td>Wirz, Mr. Albert</td>\n",
              "      <td>male</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>315154</td>\n",
              "      <td>8.6625</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>896</td>\n",
              "      <td>3</td>\n",
              "      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
              "      <td>female</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3101298</td>\n",
              "      <td>12.2875</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   PassengerId  Pclass  ... Cabin Embarked\n",
              "0          892       3  ...   NaN        Q\n",
              "1          893       3  ...   NaN        S\n",
              "2          894       2  ...   NaN        Q\n",
              "3          895       3  ...   NaN        S\n",
              "4          896       3  ...   NaN        S\n",
              "\n",
              "[5 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 230
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "dpSlDDHxyv1T",
        "outputId": "d2976761-b86a-42d2-c5b8-a7c8d003caee"
      },
      "source": [
        "test['Family'] = test['SibSp'] + test['Parch'] + 1\r\n",
        "test['Alone'] = 0\r\n",
        "test.loc[test['Family'] == 1, 'Alone'] = 1\r\n",
        "test.head()"
      ],
      "execution_count": 231,
      "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>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",
              "      <th>Family</th>\n",
              "      <th>Alone</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>892</td>\n",
              "      <td>3</td>\n",
              "      <td>Kelly, Mr. James</td>\n",
              "      <td>male</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>330911</td>\n",
              "      <td>7.8292</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Q</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>893</td>\n",
              "      <td>3</td>\n",
              "      <td>Wilkes, Mrs. James (Ellen Needs)</td>\n",
              "      <td>female</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>363272</td>\n",
              "      <td>7.0000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>894</td>\n",
              "      <td>2</td>\n",
              "      <td>Myles, Mr. Thomas Francis</td>\n",
              "      <td>male</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>240276</td>\n",
              "      <td>9.6875</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Q</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>895</td>\n",
              "      <td>3</td>\n",
              "      <td>Wirz, Mr. Albert</td>\n",
              "      <td>male</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>315154</td>\n",
              "      <td>8.6625</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>896</td>\n",
              "      <td>3</td>\n",
              "      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
              "      <td>female</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3101298</td>\n",
              "      <td>12.2875</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   PassengerId  Pclass  ... Family Alone\n",
              "0          892       3  ...      1     1\n",
              "1          893       3  ...      2     0\n",
              "2          894       2  ...      1     1\n",
              "3          895       3  ...      1     1\n",
              "4          896       3  ...      3     0\n",
              "\n",
              "[5 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 231
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "DyVsK3tGyzh2",
        "outputId": "3f811c23-4638-44b9-d18b-f753ae042987"
      },
      "source": [
        "test['Sex'].replace(\"male\", 0, inplace=True)\r\n",
        "test['Sex'].replace(\"female\", 1, inplace=True)\r\n",
        "test.head()"
      ],
      "execution_count": 232,
      "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>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",
              "      <th>Family</th>\n",
              "      <th>Alone</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>892</td>\n",
              "      <td>3</td>\n",
              "      <td>Kelly, Mr. James</td>\n",
              "      <td>0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>330911</td>\n",
              "      <td>7.8292</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Q</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>893</td>\n",
              "      <td>3</td>\n",
              "      <td>Wilkes, Mrs. James (Ellen Needs)</td>\n",
              "      <td>1</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>363272</td>\n",
              "      <td>7.0000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>894</td>\n",
              "      <td>2</td>\n",
              "      <td>Myles, Mr. Thomas Francis</td>\n",
              "      <td>0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>240276</td>\n",
              "      <td>9.6875</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Q</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>895</td>\n",
              "      <td>3</td>\n",
              "      <td>Wirz, Mr. Albert</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>315154</td>\n",
              "      <td>8.6625</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>896</td>\n",
              "      <td>3</td>\n",
              "      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3101298</td>\n",
              "      <td>12.2875</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   PassengerId  Pclass  ... Family  Alone\n",
              "0          892       3  ...      1      1\n",
              "1          893       3  ...      2      0\n",
              "2          894       2  ...      1      1\n",
              "3          895       3  ...      1      1\n",
              "4          896       3  ...      3      0\n",
              "\n",
              "[5 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 232
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jVrF_EOOzgAm",
        "outputId": "0965a706-9520-4d94-f8eb-b11772c6ca67"
      },
      "source": [
        "print(test.Fare.isnull().sum())"
      ],
      "execution_count": 233,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "_9oqc2ZKy13a",
        "outputId": "fda2f6a1-eff3-42fc-93bd-96b3ce90010d"
      },
      "source": [
        "test['Fare'] = test['Fare'].fillna(test['Fare'].median())\r\n",
        "test.loc[ test['Fare'] <= 7.55, 'Fare'] = 0\r\n",
        "test.loc[(test['Fare'] > 7.55) & (test['Fare'] <= 7.854), 'Fare'] = 1\r\n",
        "test.loc[(test['Fare'] > 7.854) & (test['Fare'] <= 8.05), 'Fare']   = 2\r\n",
        "test.loc[(test['Fare'] > 8.05) & (test['Fare'] <= 10.5), 'Fare']   = 3\r\n",
        "test.loc[(test['Fare'] > 10.5) & (test['Fare'] <= 14.454), 'Fare']   = 4\r\n",
        "test.loc[(test['Fare'] > 14.454) & (test['Fare'] <= 21.679), 'Fare']   = 5\r\n",
        "test.loc[(test['Fare'] > 21.679) & (test['Fare'] <= 27), 'Fare']   = 6\r\n",
        "test.loc[(test['Fare'] > 27) & (test['Fare'] <= 39.688), 'Fare']   = 7\r\n",
        "test.loc[(test['Fare'] > 39.688) & (test['Fare'] <= 77.958), 'Fare']   = 8\r\n",
        "test.loc[ test['Fare'] > 77.958, 'Fare'] = 9\r\n",
        "test['Fare'] = test['Fare'].astype(int)\r\n",
        "test.head()"
      ],
      "execution_count": 234,
      "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>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",
              "      <th>Family</th>\n",
              "      <th>Alone</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>892</td>\n",
              "      <td>3</td>\n",
              "      <td>Kelly, Mr. James</td>\n",
              "      <td>0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>330911</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Q</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>893</td>\n",
              "      <td>3</td>\n",
              "      <td>Wilkes, Mrs. James (Ellen Needs)</td>\n",
              "      <td>1</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>363272</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>894</td>\n",
              "      <td>2</td>\n",
              "      <td>Myles, Mr. Thomas Francis</td>\n",
              "      <td>0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>240276</td>\n",
              "      <td>3</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Q</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>895</td>\n",
              "      <td>3</td>\n",
              "      <td>Wirz, Mr. Albert</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>315154</td>\n",
              "      <td>3</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>896</td>\n",
              "      <td>3</td>\n",
              "      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3101298</td>\n",
              "      <td>4</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   PassengerId  Pclass  ... Family  Alone\n",
              "0          892       3  ...      1      1\n",
              "1          893       3  ...      2      0\n",
              "2          894       2  ...      1      1\n",
              "3          895       3  ...      1      1\n",
              "4          896       3  ...      3      0\n",
              "\n",
              "[5 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 234
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mvpxxS9By4Yh",
        "outputId": "568b519c-da25-4b1c-9703-eeec92bceaf2"
      },
      "source": [
        "print(test.Embarked.isnull().sum())"
      ],
      "execution_count": 235,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Cbq2P6Yfz4Qm",
        "outputId": "32a517e3-8bd8-4c4b-afbf-615779417634"
      },
      "source": [
        "test['Embarked'] = test['Embarked'].fillna('S')\r\n",
        "print(test.Embarked.isnull().sum())"
      ],
      "execution_count": 236,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "g2giQo_Xy9TQ",
        "outputId": "aea50033-8aca-4ccf-82bb-adc621e7e889"
      },
      "source": [
        "test['Embarked'] = test['Embarked'].map( {'S': 0, 'C': 1, 'Q': 2} ).astype(int)\r\n",
        "test.head()"
      ],
      "execution_count": 237,
      "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>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",
              "      <th>Family</th>\n",
              "      <th>Alone</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>892</td>\n",
              "      <td>3</td>\n",
              "      <td>Kelly, Mr. James</td>\n",
              "      <td>0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>330911</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>893</td>\n",
              "      <td>3</td>\n",
              "      <td>Wilkes, Mrs. James (Ellen Needs)</td>\n",
              "      <td>1</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>363272</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>894</td>\n",
              "      <td>2</td>\n",
              "      <td>Myles, Mr. Thomas Francis</td>\n",
              "      <td>0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>240276</td>\n",
              "      <td>3</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>895</td>\n",
              "      <td>3</td>\n",
              "      <td>Wirz, Mr. Albert</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>315154</td>\n",
              "      <td>3</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>896</td>\n",
              "      <td>3</td>\n",
              "      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3101298</td>\n",
              "      <td>4</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   PassengerId  Pclass  ... Family  Alone\n",
              "0          892       3  ...      1      1\n",
              "1          893       3  ...      2      0\n",
              "2          894       2  ...      1      1\n",
              "3          895       3  ...      1      1\n",
              "4          896       3  ...      3      0\n",
              "\n",
              "[5 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 237
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "-_zMCDR5y-Xc",
        "outputId": "43df7ccf-69b8-4453-8bb7-8f069c4b172a"
      },
      "source": [
        "#Removing the colums that does not corelate with survival. \r\n",
        "test = test.drop(['Name', 'SibSp', 'Parch', 'Ticket', 'Cabin'], axis=1)\r\n",
        "\r\n",
        "test['Age'] = test['Age'].fillna(2)\r\n",
        "test['Age'] = test['Age'].astype(int)\r\n",
        "test.head()\r\n"
      ],
      "execution_count": 238,
      "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>Pclass</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Embarked</th>\n",
              "      <th>Family</th>\n",
              "      <th>Alone</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>892</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>893</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>894</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>895</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>896</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   PassengerId  Pclass  Sex  Age  Fare  Embarked  Family  Alone\n",
              "0          892       3    0    2     1         2       1      1\n",
              "1          893       3    1    2     0         0       2      0\n",
              "2          894       2    0    3     3         2       1      1\n",
              "3          895       3    0    1     3         0       1      1\n",
              "4          896       3    1    1     4         0       3      0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 238
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "79N5Kmjx0nzW"
      },
      "source": [
        "all_features = train.drop(\"Survived\",axis=1)\r\n",
        "Targeted_feature = train[\"Survived\"]"
      ],
      "execution_count": 239,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eyFal-Um1cbO",
        "outputId": "5315bcd0-34a0-45be-a1e0-f80d9ff90f98"
      },
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\r\n",
        "clf = RandomForestClassifier(bootstrap=True,\r\n",
        "                                       class_weight=None,\r\n",
        "                                       criterion='gini',\r\n",
        "                                       max_depth=None,\r\n",
        "                                       max_features='auto',\r\n",
        "                                       max_leaf_nodes=None,\r\n",
        "                                       min_impurity_decrease=0.0, \r\n",
        "                                       min_impurity_split=None,\r\n",
        "                                       min_samples_leaf=1,\r\n",
        "                                       min_samples_split=2,\r\n",
        "                                       min_weight_fraction_leaf=0.0, \r\n",
        "                                       n_estimators=400,\r\n",
        "                                       n_jobs=1,\r\n",
        "                                       oob_score=False,\r\n",
        "                                       random_state=None,\r\n",
        "                                       verbose=0,\r\n",
        "                                       warm_start=False)\r\n",
        "\r\n",
        "clf.fit(all_features, Targeted_feature)\r\n",
        "y_pred_random = clf.predict(test)\r\n",
        "acc_random_forest = round(clf.score(all_features, Targeted_feature) * 100, 2)\r\n",
        "print (acc_random_forest)\r\n",
        "submission = pd.DataFrame({ \"PassengerId\": test[\"PassengerId\"], \"Survived\": y_pred_random})"
      ],
      "execution_count": 240,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "100.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 669
        },
        "id": "6pB4epKS4rdb",
        "outputId": "b489b6c5-c78b-41b1-9395-4ca5e3989db6"
      },
      "source": [
        "submission.head(20)"
      ],
      "execution_count": 242,
      "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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>892</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>893</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>894</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>895</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>896</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>897</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>898</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>899</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>900</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>901</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>902</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>903</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>904</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>905</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>906</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>907</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>908</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>909</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>910</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>911</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    PassengerId  Survived\n",
              "0           892         1\n",
              "1           893         0\n",
              "2           894         0\n",
              "3           895         0\n",
              "4           896         0\n",
              "5           897         0\n",
              "6           898         0\n",
              "7           899         0\n",
              "8           900         1\n",
              "9           901         0\n",
              "10          902         0\n",
              "11          903         0\n",
              "12          904         1\n",
              "13          905         0\n",
              "14          906         1\n",
              "15          907         1\n",
              "16          908         0\n",
              "17          909         0\n",
              "18          910         0\n",
              "19          911         1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 242
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ax47Jz9g8937",
        "outputId": "df3abc96-24c3-4d66-ca30-44f1ddb7f84d"
      },
      "source": [
        "submission.to_csv('TITANIC_SUBMISIION.csv',index=False)\r\n",
        "print(\"Your submission was successfully saved!\")"
      ],
      "execution_count": 265,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Your submission was successfully saved!\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}