Titanic.ipynb

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  "metadata": {
    "colab": {
      "name": "Titanic.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyOIUstRvePdmmbCJ8Quf5AL",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/DorotaJanosz/machine-learning-projects/blob/master/Titanic.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kjC0AJfOA_-8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np \n",
        "import pandas as pd \n",
        "import matplotlib.pyplot as plt\n",
        "import plotly.express as px\n",
        "import seaborn as sns\n",
        "from sklearn.impute import SimpleImputer\n",
        "\n",
        "import os\n",
        "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
        "    for filename in filenames:\n",
        "        print(os.path.join(dirname, filename))"
      ],
      "execution_count": 122,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k93R4CBEBEHn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "dae36796-0c40-4551-f3fa-e788447c1bbb"
      },
      "source": [
        "train_data = pd.read_csv('train.csv')\n",
        "X_train = train_data.copy()\n",
        "X_train.head()"
      ],
      "execution_count": 123,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Braund, Mr. Owen Harris</td>\n",
              "      <td>male</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>A/5 21171</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>NaN</td>\n",
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              "      <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": 123
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7obFAqs4BFA9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "468f7690-ff1e-4697-8776-3c4786ff1391"
      },
      "source": [
        "test_data = pd.read_csv('test.csv')\n",
        "X_test = test_data.copy()\n",
        "X_test.head()"
      ],
      "execution_count": 124,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <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",
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              "      <th>0</th>\n",
              "      <td>892</td>\n",
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              "      <td>Kelly, Mr. James</td>\n",
              "      <td>male</td>\n",
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              "      <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",
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              "    <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",
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              "    </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": 124
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J3K9yB5ZBHkw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "outputId": "7796d01c-3085-4de2-9393-0a7a29803279"
      },
      "source": [
        "tmp = pd.DataFrame({'Survived':['no','yes']})\n",
        "tmp['Count'] = X_train['Survived'].value_counts()\n",
        "tmp"
      ],
      "execution_count": 125,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
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              "      <th></th>\n",
              "      <th>Survived</th>\n",
              "      <th>Count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>no</td>\n",
              "      <td>549</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>yes</td>\n",
              "      <td>342</td>\n",
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            ],
            "text/plain": [
              "  Survived  Count\n",
              "0       no    549\n",
              "1      yes    342"
            ]
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          "metadata": {
            "tags": []
          },
          "execution_count": 125
        }
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        },
        "outputId": "ead7d926-9421-4559-dbf1-8c75c0196d5b"
      },
      "source": [
        "px.bar(tmp, x='Survived', y='Count', width=800, height=500)"
      ],
      "execution_count": 126,
      "outputs": [
        {
          "output_type": "display_data",
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\"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"caxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"width\": 800, \"xaxis\": {\"anchor\": \"y\", \"domain\": [0.0, 1.0], \"title\": {\"text\": \"Survived\"}}, \"yaxis\": {\"anchor\": \"x\", \"domain\": [0.0, 1.0], \"title\": {\"text\": \"Count\"}}},\n",
              "                        {\"responsive\": true}\n",
              "                    ).then(function(){\n",
              "                            \n",
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              "            console.log([gd, 'removed!']);\n",
              "            Plotly.purge(gd);\n",
              "            observer.disconnect();\n",
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              "\n",
              "// Listen for the removal of the full notebook cells\n",
              "var notebookContainer = gd.closest('#notebook-container');\n",
              "if (notebookContainer) {{\n",
              "    x.observe(notebookContainer, {childList: true});\n",
              "}}\n",
              "\n",
              "// Listen for the clearing of the current output cell\n",
              "var outputEl = gd.closest('.output');\n",
              "if (outputEl) {{\n",
              "    x.observe(outputEl, {childList: true});\n",
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              "\n",
              "                        })\n",
              "                };\n",
              "                \n",
              "            </script>\n",
              "        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hrfu-U2iBzNr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 362
        },
        "outputId": "75899094-0936-424a-d524-da490ecefa3b"
      },
      "source": [
        "X_train.info()"
      ],
      "execution_count": 127,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 891 entries, 0 to 890\n",
            "Data columns (total 12 columns):\n",
            " #   Column       Non-Null Count  Dtype  \n",
            "---  ------       --------------  -----  \n",
            " 0   PassengerId  891 non-null    int64  \n",
            " 1   Survived     891 non-null    int64  \n",
            " 2   Pclass       891 non-null    int64  \n",
            " 3   Name         891 non-null    object \n",
            " 4   Sex          891 non-null    object \n",
            " 5   Age          714 non-null    float64\n",
            " 6   SibSp        891 non-null    int64  \n",
            " 7   Parch        891 non-null    int64  \n",
            " 8   Ticket       891 non-null    object \n",
            " 9   Fare         891 non-null    float64\n",
            " 10  Cabin        204 non-null    object \n",
            " 11  Embarked     889 non-null    object \n",
            "dtypes: float64(2), int64(5), object(5)\n",
            "memory usage: 83.7+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MjS-w2azB03N",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 344
        },
        "outputId": "ababb5e9-46e1-436b-ea5c-c1e7c382d1da"
      },
      "source": [
        "X_test.info()"
      ],
      "execution_count": 128,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 418 entries, 0 to 417\n",
            "Data columns (total 11 columns):\n",
            " #   Column       Non-Null Count  Dtype  \n",
            "---  ------       --------------  -----  \n",
            " 0   PassengerId  418 non-null    int64  \n",
            " 1   Pclass       418 non-null    int64  \n",
            " 2   Name         418 non-null    object \n",
            " 3   Sex          418 non-null    object \n",
            " 4   Age          332 non-null    float64\n",
            " 5   SibSp        418 non-null    int64  \n",
            " 6   Parch        418 non-null    int64  \n",
            " 7   Ticket       418 non-null    object \n",
            " 8   Fare         417 non-null    float64\n",
            " 9   Cabin        91 non-null     object \n",
            " 10  Embarked     418 non-null    object \n",
            "dtypes: float64(2), int64(4), object(5)\n",
            "memory usage: 36.0+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bNhHxPozCxT8",
        "colab_type": "text"
      },
      "source": [
        "Sex"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HPlU1bALCx5k",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        },
        "outputId": "f87183ef-33ee-4ebd-e78e-511da0987b59"
      },
      "source": [
        "X_train['Sex'].value_counts()"
      ],
      "execution_count": 129,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "male      577\n",
              "female    314\n",
              "Name: Sex, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 129
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9jMpeMoqC1Ir",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "041ee90f-7cbd-4f0d-b271-1ef433657c19"
      },
      "source": [
        "sns.barplot(data=X_train, x='Sex', y='Survived')"
      ],
      "execution_count": 130,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f66c6470550>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 130
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lchOFAHsC7-Q",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "db507cd7-ba9d-4855-fc57-172c7c07af42"
      },
      "source": [
        "women = train_data.loc[train_data.Sex == 'female'][\"Survived\"]\n",
        "rate_women = sum(women)/len(women)\n",
        "\n",
        "print(\"% of women who survived:\", rate_women)"
      ],
      "execution_count": 131,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "% of women who survived: 0.7420382165605095\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hru8xh7yDBp_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "4252cbcb-a565-49cf-ec56-89e50457e24e"
      },
      "source": [
        "men = train_data.loc[train_data.Sex == 'male'][\"Survived\"]\n",
        "rate_men = sum(men)/len(men)\n",
        "\n",
        "print(\"% of men who survived:\", rate_men)"
      ],
      "execution_count": 132,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "% of men who survived: 0.18890814558058924\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "baz1pOTbDFyu",
        "colab_type": "text"
      },
      "source": [
        "Cabin"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lIpqW5i4DEMk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 253
        },
        "outputId": "8719d1ca-3b30-42dc-fb80-ce13a512f87e"
      },
      "source": [
        "X_train.isnull().sum()"
      ],
      "execution_count": 133,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PassengerId      0\n",
              "Survived         0\n",
              "Pclass           0\n",
              "Name             0\n",
              "Sex              0\n",
              "Age            177\n",
              "SibSp            0\n",
              "Parch            0\n",
              "Ticket           0\n",
              "Fare             0\n",
              "Cabin          687\n",
              "Embarked         2\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 133
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L2MwjLsjDJt5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Drop Cabin - too many NaNs\n",
        "X_train = X_train.drop('Cabin', axis=1)\n",
        "X_test = X_test.drop('Cabin', axis=1)"
      ],
      "execution_count": 134,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y5eopoTfDOUH",
        "colab_type": "text"
      },
      "source": [
        "Ticket, PassengerId"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tFpVJdsvDMGE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "outputId": "e4cb0adc-03d4-4848-fb3d-3cc5c0374318"
      },
      "source": [
        "X_train['Ticket'].value_counts()"
      ],
      "execution_count": 135,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CA. 2343    7\n",
              "1601        7\n",
              "347082      7\n",
              "347088      6\n",
              "CA 2144     6\n",
              "           ..\n",
              "323951      1\n",
              "7598        1\n",
              "350029      1\n",
              "343276      1\n",
              "3101298     1\n",
              "Name: Ticket, Length: 681, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 135
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gsSchQfgDSGp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Drop for PassengerId, Ticket - variables with unique values\n",
        "X_train = X_train.drop(['PassengerId', 'Ticket'], axis=1)\n",
        "X_test = X_test.drop(['PassengerId', 'Ticket'], axis=1)"
      ],
      "execution_count": 136,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YEgGELVhDc_4",
        "colab_type": "text"
      },
      "source": [
        "Embarked"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YprfZKusDXkb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 199
        },
        "outputId": "0546f4b6-23af-411d-bb2b-b43f8df3d8c7"
      },
      "source": [
        "X_train.isnull().sum()"
      ],
      "execution_count": 137,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Survived      0\n",
              "Pclass        0\n",
              "Name          0\n",
              "Sex           0\n",
              "Age         177\n",
              "SibSp         0\n",
              "Parch         0\n",
              "Fare          0\n",
              "Embarked      2\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 137
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7SgasC9ADh3u",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "c1325ce1-6441-4c59-c7d9-beb8f3784849"
      },
      "source": [
        "# Filling Embarked with most frequent\n",
        "imputer = SimpleImputer(missing_values=np.nan, strategy='most_frequent') \n",
        "imputer.fit(X_train[['Embarked']])\n",
        "X_train[['Embarked']] = imputer.transform(X_train[['Embarked']])\n",
        "imputer.statistics_"
      ],
      "execution_count": 138,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['S'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 138
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2hhpnn85DmXz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 181
        },
        "outputId": "1b6d63c0-48e9-49e1-8e56-dff6b513751b"
      },
      "source": [
        "X_test.isnull().sum()"
      ],
      "execution_count": 139,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pclass       0\n",
              "Name         0\n",
              "Sex          0\n",
              "Age         86\n",
              "SibSp        0\n",
              "Parch        0\n",
              "Fare         1\n",
              "Embarked     0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 139
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mpxUR2y2DtLP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 90
        },
        "outputId": "23f22063-2231-407b-b393-4bbb61c5f20e"
      },
      "source": [
        "X_train['Embarked'].value_counts()"
      ],
      "execution_count": 140,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "S    646\n",
              "C    168\n",
              "Q     77\n",
              "Name: Embarked, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 140
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5mcr0eo9Dv47",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 90
        },
        "outputId": "8e2708c4-79b2-4f91-fa16-5135d6c3a2b0"
      },
      "source": [
        "X_train['Embarked'] = X_train['Embarked'].replace({'S': 0, 'C': 1, 'Q': 2})\n",
        "X_train['Embarked'] = X_train['Embarked'].astype(int)\n",
        "X_train['Embarked'].value_counts()"
      ],
      "execution_count": 141,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    646\n",
              "1    168\n",
              "2     77\n",
              "Name: Embarked, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 141
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YpC3jjyEDylp",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 90
        },
        "outputId": "71dee93b-04a7-44e3-82c7-c4e449bc1b55"
      },
      "source": [
        "X_test['Embarked'] = X_test['Embarked'].replace({'S': 0, 'C': 1, 'Q': 2})\n",
        "X_test['Embarked'] = X_test['Embarked'].astype(int)\n",
        "X_test['Embarked'].value_counts()"
      ],
      "execution_count": 142,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    270\n",
              "1    102\n",
              "2     46\n",
              "Name: Embarked, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 142
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_uUL6qZuD4Ws",
        "colab_type": "text"
      },
      "source": [
        "Pclass"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FZGw3yZiD1_Q",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 90
        },
        "outputId": "f417f8c7-59c5-444d-a7e3-ee182cf3aff6"
      },
      "source": [
        "X_train['Pclass'].value_counts()"
      ],
      "execution_count": 143,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3    491\n",
              "1    216\n",
              "2    184\n",
              "Name: Pclass, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 143
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zpmDA54dD8uE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "7188da5d-3f4f-4e4f-d71b-2e0956039900"
      },
      "source": [
        "sns.barplot(data=X_train, x='Pclass', y='Survived')"
      ],
      "execution_count": 144,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f66c644b860>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 144
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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MAklqnEEgSY0zCCSpcQaBJDXOIJCkxhkEktQ4g0CSGmcQSFLjDAJJapxBIEmNMwgkqXG9BkGSjUkOJzmS5KYh+/9Gkg93++9KcmWf9UiSztRbECRZAewEXgFcBVyf5Ko53V4DfKOqngv8KvDLfdUjSRquzzOCDcCRqjpaVSeB3cB1c/pcB7y/e/5R4CeTpMeaJElzXNzjsa8Ajg1sHwd+/Gx9qupUkm8CzwTuH+yUZAuwpdt8JMnhXio+P1zGnPd/vsuv/MK4SzhfLLvPjn/r310Dlt3nlzee0+f37LPt6DMIFk1V7QJ2jbuOpZBkqqomxl2Hzp2f3fLW8ufX59DQCWDtwPaarm1onyQXAz8IPNBjTZKkOfoMgn3A+iTrkqwENgOTc/pMAqfHFV4F3FZV1WNNkqQ5ehsa6sb8twJ7gRXALVV1IMnNwFRVTQK/DXwgyRHgQWbDonVNDIFdoPzslrdmP7/4B7gktc07iyWpcQaBJDXOIDhPJLklydeTfHHctejcJFmb5PYkB5McSPKmcdek0SV5SpLPJvlC9/m9Y9w1LTWvEZwnkrwYeAT43ap6wbjr0eiSXA5cXlWfS3IpsB/4mao6OObSNIJuNoNLquqRJN8H/Bnwpqq6c8ylLRnPCM4TVfUpZr85pWWmqr5WVZ/rnj8MHGL2rnktAzXrkW7z+7pHU38hGwTSIupm0H0hcNd4K9G5SLIiyd3A14GPV1VTn59BIC2SJE8DPgb8i6r61rjr0eiq6rGq+lFmZ0DYkKSp4VmDQFoE3djyx4Dfq6r/Ou569MRU1UPA7cDGcdeylAwC6UnqLjb+NnCoqt4z7np0bpKsSvL07vn3A9cCfz7eqpaWQXCeSPIh4DPA305yPMlrxl2TRnYN8HPAy5Lc3T1+atxFaWSXA7cnuYfZOdI+XlX/bcw1LSm/PipJjfOMQJIaZxBIUuMMAklqnEEgSY0zCCSpcQaBNEeSx7qvgH4xyUeSPHWevm9P8ualrE9abAaBdKbvVNWPdrPAngReN+6CpD4ZBNL8Pg08FyDJzye5p5u3/gNzOyZ5bZJ93f6PnT6TSPLq7uziC0k+1bX9SDcH/t3dMdcv6buSBnhDmTRHkkeq6mlJLmZ2/qA/AT4F/D7w96rq/iTPqKoHk7wdeKSqfiXJM6vqge4Y/w74y6p6b5J7gY1VdSLJ06vqoSTvBe6sqt9LshJYUVXfGcsbVvM8I5DO9P3dlMRTwFeZnUfoZcBHqup+gKoatnbEC5J8uvuP/wbgR7r2O4DfSfJaYEXX9hngXyV5K/BsQ0DjdPG4C5DOQ9/ppiT+rtl55Rb0O8yuTPaFJDcCLwWoqtcl+XHgp4H9SV5UVR9MclfXtifJP6+q2xbxPUgj84xAGs1twKuTPBMgyTOG9LkU+Fo3JfUNpxuTPKeq7qqq7cAMsDbJDwFHq+rXgT8Eru79HUhn4RmBNIKqOpDkXcAnkzwGfB64cU63X2J2ZbKZ7uelXfu7u4vBAT4BfAF4K/BzSR4FpoF/3/ubkM7Ci8WS1DiHhiSpcQaBJDXOIJCkxhkEktQ4g0CSGmcQSFLjDAJJatz/A6XsDGdPHaThAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WKlJ3nR4D_Gm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 308
        },
        "outputId": "f6493f17-01c8-40d1-d4ce-0a7a7a60045a"
      },
      "source": [
        "X_train.info()"
      ],
      "execution_count": 145,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 891 entries, 0 to 890\n",
            "Data columns (total 9 columns):\n",
            " #   Column    Non-Null Count  Dtype  \n",
            "---  ------    --------------  -----  \n",
            " 0   Survived  891 non-null    int64  \n",
            " 1   Pclass    891 non-null    int64  \n",
            " 2   Name      891 non-null    object \n",
            " 3   Sex       891 non-null    object \n",
            " 4   Age       714 non-null    float64\n",
            " 5   SibSp     891 non-null    int64  \n",
            " 6   Parch     891 non-null    int64  \n",
            " 7   Fare      891 non-null    float64\n",
            " 8   Embarked  891 non-null    int64  \n",
            "dtypes: float64(2), int64(5), object(2)\n",
            "memory usage: 62.8+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-JtMb1D-EF40",
        "colab_type": "text"
      },
      "source": [
        "SibSp"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fNJ9trvzECCx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 162
        },
        "outputId": "40dd23af-46f8-43e9-eade-ed643dc0dda8"
      },
      "source": [
        "X_train['SibSp'].value_counts()"
      ],
      "execution_count": 146,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    608\n",
              "1    209\n",
              "2     28\n",
              "4     18\n",
              "3     16\n",
              "8      7\n",
              "5      5\n",
              "Name: SibSp, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 146
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SUL09AIEEJnE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 517
        },
        "outputId": "a04cb310-5668-4095-e32c-e97744e360c2"
      },
      "source": [
        "px.histogram(X_train, x='SibSp', width=800, height=500)"
      ],
      "execution_count": 147,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<html>\n",
              "<head><meta charset=\"utf-8\" /></head>\n",
              "<body>\n",
              "    <div>\n",
              "            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>\n",
              "                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
              "        <script src=\"https://cdn.plot.ly/plotly-latest.min.js\"></script>    \n",
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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M5alk9e8EWU9",
        "colab_type": "text"
      },
      "source": [
        "Parch"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qBkNOIRSEP4h",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 162
        },
        "outputId": "7b6ed4d0-99c0-4a97-ae92-04ae756eab23"
      },
      "source": [
        "X_train['Parch'].value_counts()"
      ],
      "execution_count": 149,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    678\n",
              "1    118\n",
              "2     80\n",
              "5      5\n",
              "3      5\n",
              "4      4\n",
              "6      1\n",
              "Name: Parch, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 149
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GXpRczmvEbBG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 517
        },
        "outputId": "4fb4db94-5068-4471-a3f0-888d26124d14"
      },
      "source": [
        "px.histogram(X_train, x='Parch', width=800, height=500)"
      ],
      "execution_count": 150,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
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              "<head><meta charset=\"utf-8\" /></head>\n",
              "<body>\n",
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              "                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
              "        <script src=\"https://cdn.plot.ly/plotly-latest.min.js\"></script>    \n",
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              "<matplotlib.axes._subplots.AxesSubplot at 0x7f66c574a780>"
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          "data": {
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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fSV7pS_yErbU",
        "colab_type": "text"
      },
      "source": [
        "Family = Parch + SibSp"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4eaPSnzoEgWZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "89a3b2fd-5802-4f62-b6ba-7b9dade8af35"
      },
      "source": [
        "X_train['Family'] = X_train['Parch'] + X_train['SibSp']\n",
        "X_test['Family'] = X_test['Parch'] + X_test['SibSp']\n",
        "\n",
        "X_train.head()"
      ],
      "execution_count": 152,
      "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>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>Fare</th>\n",
              "      <th>Embarked</th>\n",
              "      <th>Family</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\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>7.2500</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\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>71.2833</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\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>7.9250</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\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>53.1000</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\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>8.0500</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Survived  Pclass  ... Embarked Family\n",
              "0         0       3  ...        0      1\n",
              "1         1       1  ...        1      1\n",
              "2         1       3  ...        0      0\n",
              "3         1       1  ...        0      1\n",
              "4         0       3  ...        0      0\n",
              "\n",
              "[5 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 152
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eVZGHKgiEvvf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Drop for Parch, SibSp\n",
        "X_train = X_train.drop(['Parch', 'SibSp'], axis=1)\n",
        "X_test = X_test.drop(['Parch', 'SibSp'], axis=1)"
      ],
      "execution_count": 153,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8ySXEH4hEy39",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "220ded85-1d6e-48ad-84e2-b7d6c4cafc9a"
      },
      "source": [
        "X_train.head()"
      ],
      "execution_count": 154,
      "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>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Embarked</th>\n",
              "      <th>Family</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\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>7.2500</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\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>71.2833</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\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>7.9250</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\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>53.1000</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\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>8.0500</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Survived  Pclass  ... Embarked Family\n",
              "0         0       3  ...        0      1\n",
              "1         1       1  ...        1      1\n",
              "2         1       3  ...        0      0\n",
              "3         1       1  ...        0      1\n",
              "4         0       3  ...        0      0\n",
              "\n",
              "[5 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 154
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TOqV0kCAE1S2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "ed6b92b6-399d-4d01-d26a-54cc81e84e63"
      },
      "source": [
        "sns.barplot(data=X_train, x='Family', y='Survived')"
      ],
      "execution_count": 155,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f66c56c85f8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 155
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L52eZVYEE59J",
        "colab_type": "text"
      },
      "source": [
        "Name"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZpssVRqrE3eg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "name = X_train['Name'].apply(lambda x: x.split(','))\n",
        "\n",
        "list = []\n",
        "for i in range(0, X_train.shape[0]):\n",
        "    list.append(name[i][1].split('.')[0])"
      ],
      "execution_count": 156,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7tmtWw0rE82Y",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train['Name'] = list"
      ],
      "execution_count": 157,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_7OAtcFEFNtO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "8821e9c2-5d8a-455a-c959-13c1e3bf5e21"
      },
      "source": [
        "X_train.head()"
      ],
      "execution_count": 158,
      "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>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Embarked</th>\n",
              "      <th>Family</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Mr</td>\n",
              "      <td>male</td>\n",
              "      <td>22.0</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Mrs</td>\n",
              "      <td>female</td>\n",
              "      <td>38.0</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Miss</td>\n",
              "      <td>female</td>\n",
              "      <td>26.0</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Mrs</td>\n",
              "      <td>female</td>\n",
              "      <td>35.0</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Mr</td>\n",
              "      <td>male</td>\n",
              "      <td>35.0</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Survived  Pclass   Name     Sex   Age     Fare  Embarked  Family\n",
              "0         0       3     Mr    male  22.0   7.2500         0       1\n",
              "1         1       1    Mrs  female  38.0  71.2833         1       1\n",
              "2         1       3   Miss  female  26.0   7.9250         0       0\n",
              "3         1       1    Mrs  female  35.0  53.1000         0       1\n",
              "4         0       3     Mr    male  35.0   8.0500         0       0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 158
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TaviF18jFPp1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 344
        },
        "outputId": "c61336b9-d848-4e38-b287-99ca1dd55591"
      },
      "source": [
        "X_train['Name'].value_counts()"
      ],
      "execution_count": 159,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              " Mr              517\n",
              " Miss            182\n",
              " Mrs             125\n",
              " Master           40\n",
              " Dr                7\n",
              " Rev               6\n",
              " Mlle              2\n",
              " Col               2\n",
              " Major             2\n",
              " Mme               1\n",
              " Sir               1\n",
              " the Countess      1\n",
              " Ms                1\n",
              " Don               1\n",
              " Lady              1\n",
              " Jonkheer          1\n",
              " Capt              1\n",
              "Name: Name, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 159
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "47fZOcuWFR5r",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 126
        },
        "outputId": "ea48e898-5809-4350-ea4c-2c844e0926d6"
      },
      "source": [
        "rare = ['Dr', 'Rev', 'Major', 'Col', 'Mlle', 'Mme', 'Lady', 'Ms', 'Don', 'Sir', 'Jonkheer', 'Capt', 'the Countess']\n",
        "\n",
        "for i in rare:\n",
        "    X_train['Name'] = X_train['Name'].str.replace(i,'Rare')\n",
        "X_train['Name'].value_counts()"
      ],
      "execution_count": 160,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              " Mr        517\n",
              " Miss      182\n",
              " Mrs       125\n",
              " Master     40\n",
              " Rare       27\n",
              "Name: Name, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 160
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9Ga6uwYpFVG2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "name = X_test['Name'].apply(lambda x: x.split(','))\n",
        "\n",
        "list = []\n",
        "for i in range(0, X_test.shape[0]):\n",
        "    list.append(name[i][1].split('.')[0])"
      ],
      "execution_count": 161,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qVRfTBfbFbHQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_test['Name'] = list"
      ],
      "execution_count": 162,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gmqwxPWlFdk3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 199
        },
        "outputId": "048fcb63-d3d9-4f83-9eb8-a626a978576e"
      },
      "source": [
        "X_test['Name'].value_counts()"
      ],
      "execution_count": 163,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              " Mr        240\n",
              " Miss       78\n",
              " Mrs        72\n",
              " Master     21\n",
              " Rev         2\n",
              " Col         2\n",
              " Ms          1\n",
              " Dona        1\n",
              " Dr          1\n",
              "Name: Name, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 163
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ohQ_MIfeFggf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 126
        },
        "outputId": "3520b400-33df-4213-ad8b-fbf438081600"
      },
      "source": [
        "rare = ['Col', 'Rev', 'Ms', 'Dona', 'Dr']\n",
        "\n",
        "for i in rare:\n",
        "    X_test['Name'] = X_test['Name'].str.replace(i,'Rare')\n",
        "X_test['Name'].value_counts()"
      ],
      "execution_count": 164,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              " Mr        240\n",
              " Miss       78\n",
              " Mrs        72\n",
              " Master     21\n",
              " Rare        7\n",
              "Name: Name, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 164
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8QZiRjqAFle8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 290
        },
        "outputId": "bf9b845c-11f9-471e-dfb7-53614a08d36d"
      },
      "source": [
        "X_train.info()"
      ],
      "execution_count": 165,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 891 entries, 0 to 890\n",
            "Data columns (total 8 columns):\n",
            " #   Column    Non-Null Count  Dtype  \n",
            "---  ------    --------------  -----  \n",
            " 0   Survived  891 non-null    int64  \n",
            " 1   Pclass    891 non-null    int64  \n",
            " 2   Name      891 non-null    object \n",
            " 3   Sex       891 non-null    object \n",
            " 4   Age       714 non-null    float64\n",
            " 5   Fare      891 non-null    float64\n",
            " 6   Embarked  891 non-null    int64  \n",
            " 7   Family    891 non-null    int64  \n",
            "dtypes: float64(2), int64(4), object(2)\n",
            "memory usage: 55.8+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0ZXsjz-oFne5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "outputId": "9c006dc5-3704-4661-fae8-1300c88ee60e"
      },
      "source": [
        "X_train['Name'] = X_train['Name'].str.replace('Miss', '2')\n",
        "X_train['Name'] = X_train['Name'].str.replace('Master', '4')\n",
        "X_train['Name'] = X_train['Name'].str.replace('Mrs', '3')\n",
        "X_train['Name'] = X_train['Name'].str.replace('Mr', '1')\n",
        "X_train['Name'] = X_train['Name'].str.replace('Rare', '5')\n",
        "X_train['Name']"
      ],
      "execution_count": 166,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       1\n",
              "1       3\n",
              "2       2\n",
              "3       3\n",
              "4       1\n",
              "       ..\n",
              "886     5\n",
              "887     2\n",
              "888     2\n",
              "889     1\n",
              "890     1\n",
              "Name: Name, Length: 891, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 166
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S1Md4DMbFpia",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 290
        },
        "outputId": "d53b0324-01b7-4c0e-8147-d755d160eb92"
      },
      "source": [
        "X_train.info()"
      ],
      "execution_count": 167,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 891 entries, 0 to 890\n",
            "Data columns (total 8 columns):\n",
            " #   Column    Non-Null Count  Dtype  \n",
            "---  ------    --------------  -----  \n",
            " 0   Survived  891 non-null    int64  \n",
            " 1   Pclass    891 non-null    int64  \n",
            " 2   Name      891 non-null    object \n",
            " 3   Sex       891 non-null    object \n",
            " 4   Age       714 non-null    float64\n",
            " 5   Fare      891 non-null    float64\n",
            " 6   Embarked  891 non-null    int64  \n",
            " 7   Family    891 non-null    int64  \n",
            "dtypes: float64(2), int64(4), object(2)\n",
            "memory usage: 55.8+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rs9xulRPFrtG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train['Name'] = X_train['Name'].astype(int)"
      ],
      "execution_count": 168,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iU5pA0IGFuHB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 290
        },
        "outputId": "691cee99-1910-4da7-8c19-fe0f2c1c427c"
      },
      "source": [
        "X_train.info()"
      ],
      "execution_count": 169,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 891 entries, 0 to 890\n",
            "Data columns (total 8 columns):\n",
            " #   Column    Non-Null Count  Dtype  \n",
            "---  ------    --------------  -----  \n",
            " 0   Survived  891 non-null    int64  \n",
            " 1   Pclass    891 non-null    int64  \n",
            " 2   Name      891 non-null    int64  \n",
            " 3   Sex       891 non-null    object \n",
            " 4   Age       714 non-null    float64\n",
            " 5   Fare      891 non-null    float64\n",
            " 6   Embarked  891 non-null    int64  \n",
            " 7   Family    891 non-null    int64  \n",
            "dtypes: float64(2), int64(5), object(1)\n",
            "memory usage: 55.8+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JmOGoGfqFwwn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "outputId": "cb37e0d1-8198-4907-c094-ae0d10b0dc7b"
      },
      "source": [
        "X_test['Name'] = X_test['Name'].str.replace('Miss', '2')\n",
        "X_test['Name'] = X_test['Name'].str.replace('Master', '4')\n",
        "X_test['Name'] = X_test['Name'].str.replace('Mrs', '3')\n",
        "X_test['Name'] = X_test['Name'].str.replace('Mr', '1')\n",
        "X_test['Name'] = X_test['Name'].str.replace('Rare', '5')\n",
        "X_test['Name']"
      ],
      "execution_count": 170,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       1\n",
              "1       3\n",
              "2       1\n",
              "3       1\n",
              "4       3\n",
              "       ..\n",
              "413     1\n",
              "414     5\n",
              "415     1\n",
              "416     1\n",
              "417     4\n",
              "Name: Name, Length: 418, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 170
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vVZhUg7vFziI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 271
        },
        "outputId": "890a0b52-9a95-46ef-eb11-54be42320944"
      },
      "source": [
        "X_test['Name'] = X_test['Name'].astype(int)\n",
        "X_test.info()"
      ],
      "execution_count": 171,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 418 entries, 0 to 417\n",
            "Data columns (total 7 columns):\n",
            " #   Column    Non-Null Count  Dtype  \n",
            "---  ------    --------------  -----  \n",
            " 0   Pclass    418 non-null    int64  \n",
            " 1   Name      418 non-null    int64  \n",
            " 2   Sex       418 non-null    object \n",
            " 3   Age       332 non-null    float64\n",
            " 4   Fare      417 non-null    float64\n",
            " 5   Embarked  418 non-null    int64  \n",
            " 6   Family    418 non-null    int64  \n",
            "dtypes: float64(2), int64(4), object(1)\n",
            "memory usage: 23.0+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Snh0gUJnF5ui",
        "colab_type": "text"
      },
      "source": [
        "Fare"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VTtgWH0VF2pV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 162
        },
        "outputId": "a156985b-2bd5-4bdd-a371-bc21b35a9ade"
      },
      "source": [
        "X_test.isnull().sum()"
      ],
      "execution_count": 172,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pclass       0\n",
              "Name         0\n",
              "Sex          0\n",
              "Age         86\n",
              "Fare         1\n",
              "Embarked     0\n",
              "Family       0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 172
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w2_zwPxPF8BV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "c274e676-46b3-483f-c839-7d151513112d"
      },
      "source": [
        "#Fare filling with mean\n",
        "\n",
        "imputer = SimpleImputer(missing_values=np.nan, strategy='mean') \n",
        "imputer.fit(X_train[['Fare']])\n",
        "X_test[['Fare']] = imputer.transform(X_test[['Fare']])\n",
        "imputer.statistics_"
      ],
      "execution_count": 173,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([32.20420797])"
            ]
          },
          "metadata": {
            "tags": []
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          "execution_count": 173
        }
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    {
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        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "outputId": "4e5b1820-0d50-4f5c-fde5-30dc7cc87275"
      },
      "source": [
        "X_train['Fare'].value_counts()"
      ],
      "execution_count": 174,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "8.0500     43\n",
              "13.0000    42\n",
              "7.8958     38\n",
              "7.7500     34\n",
              "26.0000    31\n",
              "           ..\n",
              "8.4583      1\n",
              "9.8375      1\n",
              "8.3625      1\n",
              "14.1083     1\n",
              "17.4000     1\n",
              "Name: Fare, Length: 248, dtype: int64"
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          },
          "metadata": {
            "tags": []
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          "execution_count": 174
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    {
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      "metadata": {
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        "colab_type": "code",
        "colab": {
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          "height": 542
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        "outputId": "8d2bbcf6-5ec8-46d2-e346-781791c246c2"
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      "source": [
        "px.histogram(X_train, x='Fare', nbins=20, opacity=0.7, width=950)"
      ],
      "execution_count": 175,
      "outputs": [
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          "metadata": {
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        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ngIeLYQIGDvs",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 126
        },
        "outputId": "7acf2652-2a86-4ef5-fa8e-b06b37932b43"
      },
      "source": [
        "pd.qcut(X_train['Fare'], 5).value_counts()"
      ],
      "execution_count": 176,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(7.854, 10.5]        184\n",
              "(21.679, 39.688]     180\n",
              "(-0.001, 7.854]      179\n",
              "(39.688, 512.329]    176\n",
              "(10.5, 21.679]       172\n",
              "Name: Fare, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 176
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a9xQ2hlTGGh_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 126
        },
        "outputId": "c82a2ca9-c78a-4219-8762-df5ed01b691a"
      },
      "source": [
        "X_train['Fare'] = pd.qcut(X_train['Fare'], 5, labels=[1, 2, 3, 4, 5])\n",
        "X_train['Fare'].value_counts()"
      ],
      "execution_count": 177,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2    184\n",
              "4    180\n",
              "1    179\n",
              "5    176\n",
              "3    172\n",
              "Name: Fare, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 177
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_w3li26_GNlt",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 290
        },
        "outputId": "be923aad-c1c4-4c67-c2f8-d8d4216d5edb"
      },
      "source": [
        "X_train.info()"
      ],
      "execution_count": 178,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 891 entries, 0 to 890\n",
            "Data columns (total 8 columns):\n",
            " #   Column    Non-Null Count  Dtype   \n",
            "---  ------    --------------  -----   \n",
            " 0   Survived  891 non-null    int64   \n",
            " 1   Pclass    891 non-null    int64   \n",
            " 2   Name      891 non-null    int64   \n",
            " 3   Sex       891 non-null    object  \n",
            " 4   Age       714 non-null    float64 \n",
            " 5   Fare      891 non-null    category\n",
            " 6   Embarked  891 non-null    int64   \n",
            " 7   Family    891 non-null    int64   \n",
            "dtypes: category(1), float64(1), int64(5), object(1)\n",
            "memory usage: 49.9+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qBOrGKEMGQmF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 290
        },
        "outputId": "64fb927f-aa02-43ce-db83-a2104c1d8b5b"
      },
      "source": [
        "X_train['Fare'] = X_train['Fare'].astype(int)\n",
        "X_train.info()"
      ],
      "execution_count": 179,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 891 entries, 0 to 890\n",
            "Data columns (total 8 columns):\n",
            " #   Column    Non-Null Count  Dtype  \n",
            "---  ------    --------------  -----  \n",
            " 0   Survived  891 non-null    int64  \n",
            " 1   Pclass    891 non-null    int64  \n",
            " 2   Name      891 non-null    int64  \n",
            " 3   Sex       891 non-null    object \n",
            " 4   Age       714 non-null    float64\n",
            " 5   Fare      891 non-null    int64  \n",
            " 6   Embarked  891 non-null    int64  \n",
            " 7   Family    891 non-null    int64  \n",
            "dtypes: float64(1), int64(6), object(1)\n",
            "memory usage: 55.8+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KMLo3uDkGTiA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "8dc202fc-ba62-46e4-a7f3-0fea3a261d20"
      },
      "source": [
        "X_test.loc[X_test['Fare'] < 7.854, 'Fare'] = 1\n",
        "X_test.loc[(X_test['Fare'] > 7.854) & (X_test['Fare'] < 10.5), 'Fare'] = 2\n",
        "X_test.loc[(X_test['Fare'] > 10.5) & (X_test['Fare'] < 21.679), 'Fare'] = 3\n",
        "X_test.loc[(X_test['Fare'] > 21.679) & (X_test['Fare'] < 39.688), 'Fare'] = 4\n",
        "X_test.loc[X_test['Fare'] > 39.688, 'Fare'] = 5\n",
        "\n",
        "X_test.head()"
      ],
      "execution_count": 180,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "   Pclass  Name     Sex   Age  Fare  Embarked  Family\n",
              "0       3     1    male  34.5   1.0         2       0\n",
              "1       3     3  female  47.0   1.0         0       1\n",
              "2       2     1    male  62.0   2.0         2       0\n",
              "3       3     1    male  27.0   2.0         0       0\n",
              "4       3     3  female  22.0   3.0         0       2"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 180
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4ek7gYWeGWB_",
        "colab_type": "code",
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          "height": 271
        },
        "outputId": "6699ded5-3a63-4b5b-ffc6-7f59eeb3ca88"
      },
      "source": [
        "X_test['Fare'] = X_test['Fare'].astype(int)\n",
        "X_test.info()"
      ],
      "execution_count": 181,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 418 entries, 0 to 417\n",
            "Data columns (total 7 columns):\n",
            " #   Column    Non-Null Count  Dtype  \n",
            "---  ------    --------------  -----  \n",
            " 0   Pclass    418 non-null    int64  \n",
            " 1   Name      418 non-null    int64  \n",
            " 2   Sex       418 non-null    object \n",
            " 3   Age       332 non-null    float64\n",
            " 4   Fare      418 non-null    int64  \n",
            " 5   Embarked  418 non-null    int64  \n",
            " 6   Family    418 non-null    int64  \n",
            "dtypes: float64(1), int64(5), object(1)\n",
            "memory usage: 23.0+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ta6gvSjkGYcY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 144
        },
        "outputId": "dd6b3636-728e-4deb-be96-f60f55fd9160"
      },
      "source": [
        "X_test['Fare'].value_counts()"
      ],
      "execution_count": 182,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5     88\n",
              "1     88\n",
              "3     84\n",
              "4     80\n",
              "2     67\n",
              "10    11\n",
              "Name: Fare, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 182
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BfMOsBZdGbtA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "fe8eec3b-bf19-488e-9e80-9edcd89d6435"
      },
      "source": [
        "X_train.head()"
      ],
      "execution_count": 183,
      "outputs": [
        {
          "output_type": "execute_result",
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              "      <td>26.0</td>\n",
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            "text/plain": [
              "   Survived  Pclass  Name     Sex   Age  Fare  Embarked  Family\n",
              "0         0       3     1    male  22.0     1         0       1\n",
              "1         1       1     3  female  38.0     5         1       1\n",
              "2         1       3     2  female  26.0     2         0       0\n",
              "3         1       1     3  female  35.0     5         0       1\n",
              "4         0       3     1    male  35.0     2         0       0"
            ]
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          "execution_count": 183
        }
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    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VE5AjioUGhif",
        "colab_type": "text"
      },
      "source": [
        "Age"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HsP15C2eGee3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "outputId": "1884b20e-d3e4-401b-a484-64e5b2b48885"
      },
      "source": [
        "X_train['Age'].value_counts()"
      ],
      "execution_count": 184,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "24.00    30\n",
              "22.00    27\n",
              "18.00    26\n",
              "19.00    25\n",
              "30.00    25\n",
              "         ..\n",
              "55.50     1\n",
              "70.50     1\n",
              "66.00     1\n",
              "23.50     1\n",
              "0.42      1\n",
              "Name: Age, Length: 88, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 184
        }
      ]
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    {
      "cell_type": "code",
      "metadata": {
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        "colab_type": "code",
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          "height": 542
        },
        "outputId": "9a7c0134-8337-4026-9c42-0b7170519630"
      },
      "source": [
        "trace1 = px.histogram(X_train, x='Age', nbins=20, opacity=0.7, width=950)\n",
        "trace1"
      ],
      "execution_count": 185,
      "outputs": [
        {
          "output_type": "display_data",
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              "            Plotly.purge(gd);\n",
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              "        </div>\n",
              "</body>\n",
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            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3IixTg5rGmBm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 675
        },
        "outputId": "87a47f23-5547-4dac-9f97-4c00cf9a196b"
      },
      "source": [
        "#Age vs. Pclass\n",
        "\n",
        "grid = sns.FacetGrid(X_train, row='Pclass',col='Survived', height=3, aspect=1.3)\n",
        "grid.map(plt.hist, 'Age', alpha=.5, bins=20)\n",
        "grid.add_legend()"
      ],
      "execution_count": 186,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7f66c5330da0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 186
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 569.6x648 with 6 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mm1KNzSfGocj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 459
        },
        "outputId": "695fa0fe-1631-44fe-b69d-127d503412f7"
      },
      "source": [
        "# Age vs Sex\n",
        "\n",
        "grid = sns.FacetGrid(X_train, col='Survived', row='Sex', height=3, aspect=1.3)\n",
        "grid.map(plt.hist, 'Age', alpha=.5, bins=20)\n",
        "grid.add_legend()"
      ],
      "execution_count": 187,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7f66c51b6320>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 187
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 569.6x432 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3Ap00guPGy81",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 162
        },
        "outputId": "2d1dcb4b-a1f4-4400-a7af-82fe501458bb"
      },
      "source": [
        "median = X_train.groupby(['Pclass', 'Sex'])['Age'].median()\n",
        "median"
      ],
      "execution_count": 188,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pclass  Sex   \n",
              "1       female    35.0\n",
              "        male      40.0\n",
              "2       female    28.0\n",
              "        male      30.0\n",
              "3       female    21.5\n",
              "        male      25.0\n",
              "Name: Age, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 188
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Es_l4uJ2G9Uk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 162
        },
        "outputId": "1e53f5c9-81c3-4578-87e5-57850accf5fc"
      },
      "source": [
        "X_train.groupby(['Pclass', 'Sex'])['Age'].apply(lambda x: x.isnull().sum())"
      ],
      "execution_count": 189,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pclass  Sex   \n",
              "1       female     9\n",
              "        male      21\n",
              "2       female     2\n",
              "        male       9\n",
              "3       female    42\n",
              "        male      94\n",
              "Name: Age, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 189
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FZI2bMpDHBn3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def train_age_na(pclass, sex, na):\n",
        "    X_train.loc[(X_train['Pclass'] == pclass) & (X_train['Sex'] == sex), 'Age'] = X_train.loc[(X_train['Pclass'] == pclass) & (X_train['Sex'] == sex), 'Age'].fillna(na)"
      ],
      "execution_count": 190,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZU-xWNgqHFSf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "na_list = np.array([[35, 40], [28, 30], [21.5, 25]])\n",
        "sex = ['female', 'male']\n",
        "pclass = [1, 2, 3]\n",
        "\n",
        "for idx_p, p in enumerate(pclass):\n",
        "    for idx_s, s in enumerate(sex):\n",
        "        train_age_na(p, s, na_list[idx_p, idx_s])"
      ],
      "execution_count": 191,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qfPEUCdrHRvW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def test_age_na(pclass, sex, na):\n",
        "    X_test.loc[(X_test['Pclass'] == pclass) & (X_test['Sex'] == sex), 'Age'] = X_test.loc[(X_test['Pclass'] == pclass) & (X_test['Sex'] == sex), 'Age'].fillna(na)\n",
        "\n",
        "for idx_p, p in enumerate(pclass):\n",
        "    for idx_s, s in enumerate(sex):\n",
        "        test_age_na(p, s, na_list[idx_p, idx_s])"
      ],
      "execution_count": 192,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BJmvt5PwHsNZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "9010d460-c50a-4e06-e9ae-00233b61b38c"
      },
      "source": [
        "X_train['Age'].isnull().sum()"
      ],
      "execution_count": 193,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 193
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JSwyJbPzHw1M",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "outputId": "9f8901ee-be43-4842-8505-0bc6e28b7cda"
      },
      "source": [
        "from plotly.subplots import make_subplots\n",
        "\n",
        "fig = make_subplots(rows=1, cols=2, horizontal_spacing=0.03, shared_yaxes=True)\n",
        "\n",
        "trace1 = trace1['data'][0]\n",
        "trace2 =px.histogram(X_train, x='Age', nbins=20, opacity=0.7, width=950)['data'][0]\n",
        "\n",
        "fig.add_trace(trace1, row=1, col=1)\n",
        "fig.add_trace(trace2, row=1, col=2)\n",
        "fig.show()"
      ],
      "execution_count": 194,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<html>\n",
              "<head><meta charset=\"utf-8\" /></head>\n",
              "<body>\n",
              "    <div>\n",
              "            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>\n",
              "                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
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          "metadata": {
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    {
      "cell_type": "code",
      "metadata": {
        "id": "ZPtxNfWUHzCR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 126
        },
        "outputId": "33fb9d1d-da02-4902-f785-b9a476a285c1"
      },
      "source": [
        "pd.cut(X_train['Age'], 5).value_counts()"
      ],
      "execution_count": 195,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(16.336, 32.252]    493\n",
              "(32.252, 48.168]    218\n",
              "(0.34, 16.336]      100\n",
              "(48.168, 64.084]     69\n",
              "(64.084, 80.0]       11\n",
              "Name: Age, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 195
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1Y88v-nlH4mk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 126
        },
        "outputId": "93d1a674-c977-41b4-db57-df5f20db2955"
      },
      "source": [
        "X_train['Age'] = pd.cut(X_train['Age'], 5, labels=[1, 2, 3, 4, 5])\n",
        "X_train['Age'].value_counts()"
      ],
      "execution_count": 196,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2    493\n",
              "3    218\n",
              "1    100\n",
              "4     69\n",
              "5     11\n",
              "Name: Age, dtype: int64"
            ]
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          "metadata": {
            "tags": []
          },
          "execution_count": 196
        }
      ]
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    {
      "cell_type": "code",
      "metadata": {
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          "height": 206
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        "outputId": "783309cc-c121-482e-8536-a51389e0c6b0"
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      "source": [
        "X_train[['Age', 'Survived']].groupby(['Age'], as_index=False).mean().sort_values(by='Age', ascending=True)"
      ],
      "execution_count": 197,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th></th>\n",
              "      <th>Age</th>\n",
              "      <th>Survived</th>\n",
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            "text/plain": [
              "  Age  Survived\n",
              "0   1  0.550000\n",
              "1   2  0.336714\n",
              "2   3  0.412844\n",
              "3   4  0.434783\n",
              "4   5  0.090909"
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          "metadata": {
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          "execution_count": 197
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    {
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        "outputId": "45fe8eda-7ad2-4daa-8d36-4c78fe04f9e0"
      },
      "source": [
        "X_train['Age'] = X_train['Age'].astype(int)\n",
        "X_train.info()"
      ],
      "execution_count": 198,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 891 entries, 0 to 890\n",
            "Data columns (total 8 columns):\n",
            " #   Column    Non-Null Count  Dtype \n",
            "---  ------    --------------  ----- \n",
            " 0   Survived  891 non-null    int64 \n",
            " 1   Pclass    891 non-null    int64 \n",
            " 2   Name      891 non-null    int64 \n",
            " 3   Sex       891 non-null    object\n",
            " 4   Age       891 non-null    int64 \n",
            " 5   Fare      891 non-null    int64 \n",
            " 6   Embarked  891 non-null    int64 \n",
            " 7   Family    891 non-null    int64 \n",
            "dtypes: int64(7), object(1)\n",
            "memory usage: 55.8+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eX3yncO4IGEd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_test.loc[X_test['Age'] < 16.336, 'Age'] = 1\n",
        "X_test.loc[(X_test['Age'] > 16.336) & (X_test['Age'] < 32.252), 'Age'] = 2\n",
        "X_test.loc[(X_test['Age'] > 32.252) & (X_test['Age'] < 48.168), 'Age'] = 3\n",
        "X_test.loc[(X_test['Age'] > 48.168) & (X_test['Age'] < 64.084), 'Age'] = 4\n",
        "X_test.loc[X_test['Age'] > 64.084, 'Age'] = 5"
      ],
      "execution_count": 199,
      "outputs": []
    },
    {
      "cell_type": "code",
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        "colab_type": "code",
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        "outputId": "e5ec2f2f-bf3e-4f42-84b4-01c17c2f5d9d"
      },
      "source": [
        "X_test['Age'] = X_test['Age'].astype(int)\n",
        "X_test.info()"
      ],
      "execution_count": 200,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 418 entries, 0 to 417\n",
            "Data columns (total 7 columns):\n",
            " #   Column    Non-Null Count  Dtype \n",
            "---  ------    --------------  ----- \n",
            " 0   Pclass    418 non-null    int64 \n",
            " 1   Name      418 non-null    int64 \n",
            " 2   Sex       418 non-null    object\n",
            " 3   Age       418 non-null    int64 \n",
            " 4   Fare      418 non-null    int64 \n",
            " 5   Embarked  418 non-null    int64 \n",
            " 6   Family    418 non-null    int64 \n",
            "dtypes: int64(6), object(1)\n",
            "memory usage: 23.0+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4xPyT7gmIUhD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "7ec79cd0-990d-43dd-e5b7-3c2e46e88815"
      },
      "source": [
        "X_train.head()"
      ],
      "execution_count": 201,
      "outputs": [
        {
          "output_type": "execute_result",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Survived  Pclass  Name     Sex  Age  Fare  Embarked  Family\n",
              "0         0       3     1    male    2     1         0       1\n",
              "1         1       1     3  female    3     5         1       1\n",
              "2         1       3     2  female    2     2         0       0\n",
              "3         1       1     3  female    3     5         0       1\n",
              "4         0       3     1    male    3     2         0       0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 201
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jfVGQfvAIZ3L",
        "colab_type": "text"
      },
      "source": [
        "Age x Class"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ip7NH3V5IXJD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "ce9c145b-d975-4819-fe20-d43234e777c2"
      },
      "source": [
        "X_train['Age*Class'] = X_train['Age'] * X_train['Pclass']\n",
        "X_train.head()"
      ],
      "execution_count": 202,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "      <td>9</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            ],
            "text/plain": [
              "   Survived  Pclass  Name     Sex  Age  Fare  Embarked  Family  Age*Class\n",
              "0         0       3     1    male    2     1         0       1          6\n",
              "1         1       1     3  female    3     5         1       1          3\n",
              "2         1       3     2  female    2     2         0       0          6\n",
              "3         1       1     3  female    3     5         0       1          3\n",
              "4         0       3     1    male    3     2         0       0          9"
            ]
          },
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            "tags": []
          },
          "execution_count": 202
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iFLsDvU0Ic2l",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_test['Age*Class'] = X_test['Age'] * X_test['Pclass']"
      ],
      "execution_count": 203,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xhoLcUnIIhp3",
        "colab_type": "text"
      },
      "source": [
        "One hot encoder for categorical features"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4ZmiSIXZIeo-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "c09c8eaf-c8d3-48b8-f1e2-ef54c0648a60"
      },
      "source": [
        "X_train = pd.get_dummies(data=X_train, drop_first=True)\n",
        "X_train.head()"
      ],
      "execution_count": 204,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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            "text/plain": [
              "   Survived  Pclass  Name  Age  Fare  Embarked  Family  Age*Class  Sex_male\n",
              "0         0       3     1    2     1         0       1          6         1\n",
              "1         1       1     3    3     5         1       1          3         0\n",
              "2         1       3     2    2     2         0       0          6         0\n",
              "3         1       1     3    3     5         0       1          3         0\n",
              "4         0       3     1    3     2         0       0          9         1"
            ]
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          "metadata": {
            "tags": []
          },
          "execution_count": 204
        }
      ]
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    {
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        "colab": {
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          "height": 206
        },
        "outputId": "713f8833-f1a9-4e0c-e59e-ae1e7dbbd647"
      },
      "source": [
        "X_test = pd.get_dummies(data=X_test, drop_first=True)\n",
        "X_test.head()"
      ],
      "execution_count": 205,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    .dataframe tbody tr th:only-of-type {\n",
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            ],
            "text/plain": [
              "   Pclass  Name  Age  Fare  Embarked  Family  Age*Class  Sex_male\n",
              "0       3     1    3     1         2       0          9         1\n",
              "1       3     3    3     1         0       1          9         0\n",
              "2       2     1    4     2         2       0          8         1\n",
              "3       3     1    2     2         0       0          6         1\n",
              "4       3     3    2     3         0       2          6         0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 205
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OFToQuiWIuUL",
        "colab_type": "code",
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          "height": 308
        },
        "outputId": "8c83f469-50e4-4701-f4e6-d42920e875dc"
      },
      "source": [
        "X_train.info()"
      ],
      "execution_count": 206,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 891 entries, 0 to 890\n",
            "Data columns (total 9 columns):\n",
            " #   Column     Non-Null Count  Dtype\n",
            "---  ------     --------------  -----\n",
            " 0   Survived   891 non-null    int64\n",
            " 1   Pclass     891 non-null    int64\n",
            " 2   Name       891 non-null    int64\n",
            " 3   Age        891 non-null    int64\n",
            " 4   Fare       891 non-null    int64\n",
            " 5   Embarked   891 non-null    int64\n",
            " 6   Family     891 non-null    int64\n",
            " 7   Age*Class  891 non-null    int64\n",
            " 8   Sex_male   891 non-null    uint8\n",
            "dtypes: int64(8), uint8(1)\n",
            "memory usage: 56.7 KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UCfGbLwrIwKG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 290
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        "outputId": "2b6b4981-cb1a-41a4-db8b-bddabd07210b"
      },
      "source": [
        "X_test.info()"
      ],
      "execution_count": 207,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 418 entries, 0 to 417\n",
            "Data columns (total 8 columns):\n",
            " #   Column     Non-Null Count  Dtype\n",
            "---  ------     --------------  -----\n",
            " 0   Pclass     418 non-null    int64\n",
            " 1   Name       418 non-null    int64\n",
            " 2   Age        418 non-null    int64\n",
            " 3   Fare       418 non-null    int64\n",
            " 4   Embarked   418 non-null    int64\n",
            " 5   Family     418 non-null    int64\n",
            " 6   Age*Class  418 non-null    int64\n",
            " 7   Sex_male   418 non-null    uint8\n",
            "dtypes: int64(7), uint8(1)\n",
            "memory usage: 23.4 KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Lcmdgw7wI2iC",
        "colab_type": "text"
      },
      "source": [
        "Correlations"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "65wFrYVWI0Kq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 331
        },
        "outputId": "d94d8bfc-4f1c-4b41-a537-2cfcb6f4512e"
      },
      "source": [
        "corr = X_train.corr()\n",
        "corr"
      ],
      "execution_count": 208,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "      <td>0.016639</td>\n",
              "      <td>-0.351685</td>\n",
              "      <td>-0.543351</td>\n",
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              "    <tr>\n",
              "      <th>Pclass</th>\n",
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              "      <td>-0.184841</td>\n",
              "      <td>-0.419593</td>\n",
              "      <td>-0.705206</td>\n",
              "      <td>0.045702</td>\n",
              "      <td>0.065997</td>\n",
              "      <td>0.627019</td>\n",
              "      <td>0.131900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Name</th>\n",
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              "      <td>1.000000</td>\n",
              "      <td>-0.053459</td>\n",
              "      <td>0.331468</td>\n",
              "      <td>0.051211</td>\n",
              "      <td>0.328287</td>\n",
              "      <td>-0.252443</td>\n",
              "      <td>-0.508099</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Age</th>\n",
              "      <td>-0.036384</td>\n",
              "      <td>-0.419593</td>\n",
              "      <td>-0.053459</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.173361</td>\n",
              "      <td>-0.050496</td>\n",
              "      <td>-0.232052</td>\n",
              "      <td>0.387555</td>\n",
              "      <td>0.074045</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fare</th>\n",
              "      <td>0.317783</td>\n",
              "      <td>-0.705206</td>\n",
              "      <td>0.331468</td>\n",
              "      <td>0.173361</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.089125</td>\n",
              "      <td>0.418125</td>\n",
              "      <td>-0.564872</td>\n",
              "      <td>-0.244943</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Embarked</th>\n",
              "      <td>0.106811</td>\n",
              "      <td>0.045702</td>\n",
              "      <td>0.051211</td>\n",
              "      <td>-0.050496</td>\n",
              "      <td>-0.089125</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.080281</td>\n",
              "      <td>-0.024056</td>\n",
              "      <td>-0.116569</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Family</th>\n",
              "      <td>0.016639</td>\n",
              "      <td>0.065997</td>\n",
              "      <td>0.328287</td>\n",
              "      <td>-0.232052</td>\n",
              "      <td>0.418125</td>\n",
              "      <td>-0.080281</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.158938</td>\n",
              "      <td>-0.200988</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Age*Class</th>\n",
              "      <td>-0.351685</td>\n",
              "      <td>0.627019</td>\n",
              "      <td>-0.252443</td>\n",
              "      <td>0.387555</td>\n",
              "      <td>-0.564872</td>\n",
              "      <td>-0.024056</td>\n",
              "      <td>-0.158938</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.184177</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sex_male</th>\n",
              "      <td>-0.543351</td>\n",
              "      <td>0.131900</td>\n",
              "      <td>-0.508099</td>\n",
              "      <td>0.074045</td>\n",
              "      <td>-0.244943</td>\n",
              "      <td>-0.116569</td>\n",
              "      <td>-0.200988</td>\n",
              "      <td>0.184177</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           Survived    Pclass      Name  ...    Family  Age*Class  Sex_male\n",
              "Survived   1.000000 -0.338481  0.414088  ...  0.016639  -0.351685 -0.543351\n",
              "Pclass    -0.338481  1.000000 -0.184841  ...  0.065997   0.627019  0.131900\n",
              "Name       0.414088 -0.184841  1.000000  ...  0.328287  -0.252443 -0.508099\n",
              "Age       -0.036384 -0.419593 -0.053459  ... -0.232052   0.387555  0.074045\n",
              "Fare       0.317783 -0.705206  0.331468  ...  0.418125  -0.564872 -0.244943\n",
              "Embarked   0.106811  0.045702  0.051211  ... -0.080281  -0.024056 -0.116569\n",
              "Family     0.016639  0.065997  0.328287  ...  1.000000  -0.158938 -0.200988\n",
              "Age*Class -0.351685  0.627019 -0.252443  ... -0.158938   1.000000  0.184177\n",
              "Sex_male  -0.543351  0.131900 -0.508099  ... -0.200988   0.184177  1.000000\n",
              "\n",
              "[9 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 208
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yd8C_AG3I5_e",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 506
        },
        "outputId": "843cf525-a771-430c-dd43-2704ce4da79e"
      },
      "source": [
        "plt.figure(figsize=(10, 8)),\n",
        "sns.heatmap(corr, cmap='viridis')"
      ],
      "execution_count": 209,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f66c4a43668>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 209
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "djPRQtvwMoW8",
        "colab_type": "text"
      },
      "source": [
        "Target"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6AixsfiiMnLd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "outputId": "8cb41e44-35b2-4995-8f5d-d5ae0f09e3a0"
      },
      "source": [
        "y_train = X_train.pop('Survived')\n",
        "y_train"
      ],
      "execution_count": 210,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      0\n",
              "1      1\n",
              "2      1\n",
              "3      1\n",
              "4      0\n",
              "      ..\n",
              "886    0\n",
              "887    1\n",
              "888    0\n",
              "889    1\n",
              "890    0\n",
              "Name: Survived, Length: 891, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 210
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tMjRG7bwMTmn",
        "colab_type": "text"
      },
      "source": [
        "Feature Scaling"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PcQJJ91tMWVj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "\n",
        "columns = X_train.columns\n",
        "\n",
        "scaler = MinMaxScaler()\n",
        "scaler.fit(X_train)\n",
        "X_train = scaler.transform(X_train)\n",
        "X_test = scaler.transform(X_test)"
      ],
      "execution_count": 211,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ix6cc60YI_a4",
        "colab_type": "text"
      },
      "source": [
        "Modeling part"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bKMr9CywJFq0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "e766a4d9-d478-4e6d-99ab-4e1b5bff1e67"
      },
      "source": [
        "#Logistic Regression\n",
        "\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "\n",
        "log_reg =  LogisticRegression()\n",
        "log_reg.fit(X_train, y_train)\n",
        "\n",
        "score = log_reg.score(X_train, y_train)\n",
        "print(score)"
      ],
      "execution_count": 212,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.8103254769921436\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fqox9qOyJI9p",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "47ec40c8-ddd2-40e2-f30b-bca98c7c75b3"
      },
      "source": [
        "#SVM\n",
        "\n",
        "from sklearn.svm import SVC\n",
        "\n",
        "svc = SVC(C=1.0, kernel='poly') \n",
        "svc.fit(X_train, y_train)\n",
        "\n",
        "score = svc.score(X_train, y_train)\n",
        "print(score)"
      ],
      "execution_count": 213,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.8361391694725028\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2Za95ff3JMGg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "a6c2a39b-a6ca-4b9d-f146-781e374def3a"
      },
      "source": [
        "#KNeighbors\n",
        "\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "\n",
        "knc = KNeighborsClassifier(n_neighbors = 3)\n",
        "knc.fit(X_train, y_train)\n",
        "\n",
        "score = knc.score(X_train, y_train)\n",
        "print(score)"
      ],
      "execution_count": 214,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.8417508417508418\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gvw8l8p9JOo6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# GridSearchCV for Random Forest \n",
        "\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "random_forest = RandomForestClassifier(n_estimators=100)\n",
        "\n",
        "\n",
        "params = {'criterion': ['gini', 'entropy'],\n",
        "          'max_depth': np.arange(1, 10),\n",
        "          'min_samples_leaf': np.arange(1, 5),\n",
        "          'min_samples_split': np.arange(2, 5)}\n",
        "\n",
        "grid_search = GridSearchCV(random_forest, params, scoring='accuracy', cv=5)\n",
        "grid_search.fit(X_train, y_train)\n",
        "\n",
        "grid_search.best_params_ \n"
      ],
      "execution_count": 215,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3aXn_w9TKMlM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "0a789c23-7a5e-4ef4-8ec6-e9c31642d4af"
      },
      "source": [
        "#Random Forest \n",
        "\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "random_forest = RandomForestClassifier(criterion = 'gini', \n",
        "                                       min_samples_leaf = 3, \n",
        "                                       min_samples_split = 3,\n",
        "                                       max_depth = 5)\n",
        "random_forest.fit(X_train, y_train)\n",
        "\n",
        "score = random_forest.score(X_train, y_train)\n",
        "print(score)\n",
        "\n",
        "predictions = random_forest.predict(X_test)"
      ],
      "execution_count": 216,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.8451178451178452\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gEFADm2ML9mQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "outputId": "80314594-eb69-40fb-c370-bfc2d358c058"
      },
      "source": [
        "random_forest.feature_importances_"
      ],
      "execution_count": 217,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.10511415, 0.28716185, 0.02840225, 0.08751284, 0.0199359 ,\n",
              "       0.09243764, 0.09806857, 0.2813668 ])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 217
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UUgwOzzdMDET",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "outputId": "2a859bec-ad38-41fb-b95a-5d5071a537b6"
      },
      "source": [
        "features = pd.DataFrame(data={'feature': columns, 'feature_importance': random_forest.feature_importances_})\n",
        "features.sort_values(by='feature_importance', ascending=False)"
      ],
      "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>feature</th>\n",
              "      <th>feature_importance</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Name</td>\n",
              "      <td>0.287162</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Sex_male</td>\n",
              "      <td>0.281367</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Pclass</td>\n",
              "      <td>0.105114</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Age*Class</td>\n",
              "      <td>0.098069</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Family</td>\n",
              "      <td>0.092438</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Fare</td>\n",
              "      <td>0.087513</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Age</td>\n",
              "      <td>0.028402</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Embarked</td>\n",
              "      <td>0.019936</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     feature  feature_importance\n",
              "1       Name            0.287162\n",
              "7   Sex_male            0.281367\n",
              "0     Pclass            0.105114\n",
              "6  Age*Class            0.098069\n",
              "5     Family            0.092438\n",
              "3       Fare            0.087513\n",
              "2        Age            0.028402\n",
              "4   Embarked            0.019936"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 218
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VOdOXstUDYEC",
        "colab_type": "text"
      },
      "source": [
        "Metrics"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SUj7mbM1D4O3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_pred = random_forest.predict(X_train)"
      ],
      "execution_count": 219,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EukbhZrfMu-R",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "1236b456-d53f-4b61-ec03-e3ea8e10752c"
      },
      "source": [
        "#Accuracy score\n",
        "\n",
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "accuracy = accuracy_score(y_train, y_pred)\n",
        "print(f'Accuracy score: {round(accuracy, 3)}')"
      ],
      "execution_count": 236,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy score: 0.845\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fdTz0s2rF10S",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 315
        },
        "outputId": "af7fc89a-d617-4310-c930-ffdf96e463d1"
      },
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "cm = confusion_matrix(y_train, y_pred)\n",
        "\n",
        "from mlxtend.plotting import plot_confusion_matrix\n",
        "plot_confusion_matrix(cm)\n"
      ],
      "execution_count": 226,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(<Figure size 432x288 with 1 Axes>,\n",
              " <matplotlib.axes._subplots.AxesSubplot at 0x7f66c4923278>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 226
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DXlyMGmyKtE1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 90
        },
        "outputId": "6cc839e3-f3de-4aab-9daf-41c82e879496"
      },
      "source": [
        "tn, fp, fn, tp = cm.ravel()\n",
        "\n",
        "print(f'TN - true negative: {tn}')\n",
        "print(f'FN - false nagative: {fn}')\n",
        "print(f'fN - false positive: {fn}')\n",
        "print(f'TP - true positive: {tp}')\n"
      ],
      "execution_count": 227,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "TN - true negative: 506\n",
            "FN - false nagative: 95\n",
            "fN - false positive: 95\n",
            "TP - true positive: 247\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wY1BvRXfK_KD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "7e151936-6533-4725-9e8c-02b002dbb8b2"
      },
      "source": [
        "# False Positive Rate\n",
        "\n",
        "FPR = fp / (fp + tn)\n",
        "print(f'False positive rate: {round(FPR,3)}')"
      ],
      "execution_count": 230,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "False positive rate: 0.078\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aZENARMjLZTC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "a76b1169-700d-4904-cd9d-c221c73f8674"
      },
      "source": [
        "# False Negative Rate\n",
        "\n",
        "FNR = fn / (fn + tp)\n",
        "print(f'False negative rate: {round(FNR, 3)}')"
      ],
      "execution_count": 231,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "False negative rate: 0.278\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ui3Ay0ssLopk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "a4523bcf-f006-4f62-966c-6db132717f03"
      },
      "source": [
        "# Precision\n",
        "\n",
        "precision = tp / (tp + fp)\n",
        "print(f'Precision: {round(precision, 3)}')"
      ],
      "execution_count": 232,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Precision: 0.852\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nrjy11Q4L-da",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "82e9116b-045c-4e79-b3d2-12249a73e01d"
      },
      "source": [
        "# Recall\n",
        "\n",
        "recall = tp / (tp + fn)\n",
        "print(f'Recall: {round(recall, 3)}')"
      ],
      "execution_count": 233,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Recall: 0.722\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y4Ihj5DsMJRR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "d5a6d3f2-810a-4ebe-c10c-4c6230321121"
      },
      "source": [
        "# F1 Score\n",
        "\n",
        "from sklearn.metrics import f1_score\n",
        "\n",
        "f1 = f1_score(y_train, y_pred)\n",
        "print(f'F1 Score: {round(f1, 3)}')"
      ],
      "execution_count": 234,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "F1 Score: 0.782\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RBe3Ma9wNIAg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 181
        },
        "outputId": "fe1c2fc8-8fd3-40cf-b97a-e7e4f3dfd7de"
      },
      "source": [
        "#Classification report\n",
        "\n",
        "from sklearn.metrics import classification_report\n",
        "\n",
        "print(classification_report(y_train, y_pred))\n"
      ],
      "execution_count": 237,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.84      0.92      0.88       549\n",
            "           1       0.85      0.72      0.78       342\n",
            "\n",
            "    accuracy                           0.85       891\n",
            "   macro avg       0.85      0.82      0.83       891\n",
            "weighted avg       0.85      0.85      0.84       891\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UU-o1Xw5NYz0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "outputId": "e01265ee-c13b-4884-9f3c-45309bda9e43"
      },
      "source": [
        "#ROC Curve\n",
        "\n",
        "import plotly.graph_objects as go\n",
        "from sklearn.metrics import roc_curve\n",
        "fpr, tpr, tresh = roc_curve(y_train, y_pred, pos_label=1)\n",
        "\n",
        "fig = go.Figure(\n",
        "    data=[\n",
        "        go.Scatter(x=roc['fpr'], y=roc['tpr'],\n",
        "                line_color='red',\n",
        "                name='ROC Curve'),\n",
        "        go.Scatter(x=[0, 1], y=[0, 1],\n",
        "                mode='lines', \n",
        "                line_dash='dash', \n",
        "                line_color='navy')\n",
        "    ],\n",
        "    layout=go.Layout(xaxis_title='False Positive Rate',\n",
        "                      yaxis_title='True Positive Rate',\n",
        "                      title='ROC Curve',\n",
        "                      showlegend=False,\n",
        "                      width=700))\n",
        "fig.show()\n"
      ],
      "execution_count": 242,
      "outputs": [
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          "metadata": {
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    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YaYpI3siLzih",
        "colab_type": "text"
      },
      "source": [
        "Import to CSV"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gNmOjNZ5JWD-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "output = pd.DataFrame({'PassengerId': test_data.PassengerId, 'Survived': predictions})\n",
        "output.to_csv('my_submission.csv', index=False)\n",
        "print(\"Your submission was successfully saved!\")"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}