Random_forest.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "view-in-github"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/gan0606/deproy_test/blob/main/Random_forest.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jHkYklgWjTES"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "sns.set_theme(context=\"talk\", style=\"darkgrid\", font=\"MS GOTHIC\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Tt9zS3mejTEU"
      },
      "source": [
        "### データの読み込み"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# ローカル環境またはGoogleColab環境で実行いただけます  \n",
        "* ご自分に適した環境で実行してください  \n",
        "* ローカル環境で実行される場合は1つ下のcodeセルのコメントアウトを外して、2つ下のcodeセルのをコメントアウトしてください"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## ローカル環境で実行する場合"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BARjz7BkjTEV"
      },
      "outputs": [],
      "source": [
        "# train_data = pd.read_csv(\"datasets/train.csv\")\n",
        "# test_data = pd.read_csv(\"datasets/test.csv\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## GoogleColabで実行する場合"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# googleドライブをマウント\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "\n",
        "# データの読み込み\n",
        "train_data = pd.read_csv(\"/content/drive/MyDrive/deproy_test-main/datasets/train.csv\")\n",
        "test_data = pd.read_csv(\"/content/drive/MyDrive/deproy_test-main/datasets/test.csv\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "oZry_IgXjTEV",
        "outputId": "05c2cb7d-8af8-499d-b304-2598b2320435"
      },
      "outputs": [
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              "      <th>PassengerId</th>\n",
              "      <th>Survived</th>\n",
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              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
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              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
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              "      <td>Heikkinen, Miss. Laina</td>\n",
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              "      <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",
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              "    <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",
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            "text/plain": [
              "   PassengerId  Survived  Pclass  \\\n",
              "0            1         0       3   \n",
              "1            2         1       1   \n",
              "2            3         1       3   \n",
              "3            4         1       1   \n",
              "4            5         0       3   \n",
              "\n",
              "                                                Name     Sex   Age  SibSp  \\\n",
              "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
              "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
              "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
              "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
              "4                           Allen, Mr. William Henry    male  35.0      0   \n",
              "\n",
              "   Parch            Ticket     Fare Cabin Embarked  \n",
              "0      0         A/5 21171   7.2500   NaN        S  \n",
              "1      0          PC 17599  71.2833   C85        C  \n",
              "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
              "3      0            113803  53.1000  C123        S  \n",
              "4      0            373450   8.0500   NaN        S  "
            ]
          },
          "execution_count": 30,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "train_data.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iTLvdRmtjTEW",
        "outputId": "0834854e-0a24-42ed-a2e5-7c5d24c6f89f"
      },
      "outputs": [
        {
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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",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
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              "      <td>Q</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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              "      <th>2</th>\n",
              "      <td>894</td>\n",
              "      <td>2</td>\n",
              "      <td>Myles, Mr. Thomas Francis</td>\n",
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              "      <td>62.0</td>\n",
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              "      <td>0</td>\n",
              "      <td>240276</td>\n",
              "      <td>9.6875</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Q</td>\n",
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              "      <th>3</th>\n",
              "      <td>895</td>\n",
              "      <td>3</td>\n",
              "      <td>Wirz, Mr. Albert</td>\n",
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              "      <td>27.0</td>\n",
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              "      <td>0</td>\n",
              "      <td>315154</td>\n",
              "      <td>8.6625</td>\n",
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              "      <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",
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            "text/plain": [
              "   PassengerId  Pclass                                          Name     Sex  \\\n",
              "0          892       3                              Kelly, Mr. James    male   \n",
              "1          893       3              Wilkes, Mrs. James (Ellen Needs)  female   \n",
              "2          894       2                     Myles, Mr. Thomas Francis    male   \n",
              "3          895       3                              Wirz, Mr. Albert    male   \n",
              "4          896       3  Hirvonen, Mrs. Alexander (Helga E Lindqvist)  female   \n",
              "\n",
              "    Age  SibSp  Parch   Ticket     Fare Cabin Embarked  \n",
              "0  34.5      0      0   330911   7.8292   NaN        Q  \n",
              "1  47.0      1      0   363272   7.0000   NaN        S  \n",
              "2  62.0      0      0   240276   9.6875   NaN        Q  \n",
              "3  27.0      0      0   315154   8.6625   NaN        S  \n",
              "4  22.0      1      1  3101298  12.2875   NaN        S  "
            ]
          },
          "execution_count": 31,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "test_data.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OG_KkZYFjTEW"
      },
      "source": [
        "### データの前処理"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CO8MANAMjTEW",
        "outputId": "f567776e-ba8a-4585-8492-f9df3ccadbb9"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n",
              "       'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'],\n",
              "      dtype='object')"
            ]
          },
          "execution_count": 32,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "train_data.columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PiFwO0pvjTEX",
        "outputId": "08407ecb-97ae-490c-9e85-196b6f1d29be"
      },
      "outputs": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Fare</th>\n",
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              "      <td>3</td>\n",
              "      <td>male</td>\n",
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              "      <td>S</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3</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>S</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            ],
            "text/plain": [
              "   Pclass     Sex   Age  SibSp  Parch     Fare Embarked  Survived\n",
              "0       3    male  22.0      1      0   7.2500        S         0\n",
              "1       1  female  38.0      1      0  71.2833        C         1\n",
              "2       3  female  26.0      0      0   7.9250        S         1\n",
              "3       1  female  35.0      1      0  53.1000        S         1\n",
              "4       3    male  35.0      0      0   8.0500        S         0"
            ]
          },
          "execution_count": 33,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# 乗客の名前やチケットなどは学習に使わないの削除する\n",
        "# また、PassengerIdは提出データに使うのでそれは残す\n",
        "\n",
        "test_id = test_data[\"PassengerId\"]\n",
        "\n",
        "# 学習で使う特徴量\n",
        "labels = ['Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'Embarked']\n",
        "train_data = train_data[labels + [\"Survived\"]]\n",
        "test_data = test_data[labels]\n",
        "train_data.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Gh9pOgHVjTEX",
        "outputId": "2de7204d-d3e5-4f32-b40b-7c877a1c8bf7"
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      "outputs": [
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              "      <th>3</th>\n",
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              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3</td>\n",
              "      <td>female</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>12.2875</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Pclass     Sex   Age  SibSp  Parch     Fare Embarked\n",
              "0       3    male  34.5      0      0   7.8292        Q\n",
              "1       3  female  47.0      1      0   7.0000        S\n",
              "2       2    male  62.0      0      0   9.6875        Q\n",
              "3       3    male  27.0      0      0   8.6625        S\n",
              "4       3  female  22.0      1      1  12.2875        S"
            ]
          },
          "execution_count": 34,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "test_data.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gBsXHsaIjTEX"
      },
      "source": [
        "### 欠損値の処理"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "404od5kejTEX",
        "outputId": "c35d22ba-0cb9-4361-f917-3bbb2e71afa5"
      },
      "outputs": [
        {
          "name": "stdout",
          "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   Pclass    891 non-null    int64  \n",
            " 1   Sex       891 non-null    object \n",
            " 2   Age       714 non-null    float64\n",
            " 3   SibSp     891 non-null    int64  \n",
            " 4   Parch     891 non-null    int64  \n",
            " 5   Fare      891 non-null    float64\n",
            " 6   Embarked  889 non-null    object \n",
            " 7   Survived  891 non-null    int64  \n",
            "dtypes: float64(2), int64(4), object(2)\n",
            "memory usage: 55.8+ KB\n"
          ]
        }
      ],
      "source": [
        "train_data.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "eyE130w2jTEX",
        "outputId": "22cfbbea-6a3f-4343-9a24-c9e637112c53"
      },
      "outputs": [
        {
          "name": "stdout",
          "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   Sex       418 non-null    object \n",
            " 2   Age       332 non-null    float64\n",
            " 3   SibSp     418 non-null    int64  \n",
            " 4   Parch     418 non-null    int64  \n",
            " 5   Fare      417 non-null    float64\n",
            " 6   Embarked  418 non-null    object \n",
            "dtypes: float64(2), int64(3), object(2)\n",
            "memory usage: 23.0+ KB\n"
          ]
        }
      ],
      "source": [
        "test_data.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "AZrI82fqjTEX",
        "outputId": "06ec8b5d-9d3b-421b-9273-51f3563c5d70"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Pclass        0\n",
              "Sex           0\n",
              "Age         177\n",
              "SibSp         0\n",
              "Parch         0\n",
              "Fare          0\n",
              "Embarked      2\n",
              "Survived      0\n",
              "dtype: int64"
            ]
          },
          "execution_count": 37,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "train_data.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JT_yVetWjTEY",
        "outputId": "92274c9b-02e5-4d95-e70c-6780b78a76ed"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Pclass       0\n",
              "Sex          0\n",
              "Age         86\n",
              "SibSp        0\n",
              "Parch        0\n",
              "Fare         1\n",
              "Embarked     0\n",
              "dtype: int64"
            ]
          },
          "execution_count": 38,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "test_data.isnull().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "thFb3qT3jTEY"
      },
      "source": [
        "### Ageの欠損値を処理"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "R5BEgZdrjTEY",
        "outputId": "5df537a0-bd3f-49d0-985a-1fd527494783"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x400 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Ageの分布を確認\n",
        "fig = plt.figure(figsize=(10, 4))\n",
        "sns.histplot(data=train_data, x=\"Age\", hue=\"Survived\", bins=8, multiple=\"dodge\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rnCoClbijTEY"
      },
      "source": [
        "### 幅広く正規分布で近い形で分布しているのでAgeの欠損値にはtrain_dataの平均値 ± 2 * 標準偏差をrandomに振る"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YSl0WvEJjTEY"
      },
      "outputs": [],
      "source": [
        "std = train_data[\"Age\"].std()\n",
        "ave = train_data[\"Age\"].mean()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zlDrOEPejTEY",
        "outputId": "68d2f0ec-f752-4a69-a116-ad7f0455e3ed"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Pclass      0\n",
              "Sex         0\n",
              "Age         0\n",
              "SibSp       0\n",
              "Parch       0\n",
              "Fare        0\n",
              "Embarked    2\n",
              "Survived    0\n",
              "dtype: int64"
            ]
          },
          "execution_count": 41,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "np.random.seed(0)\n",
        "train_data[\"Age\"].fillna(np.random.randint(ave-2*std, ave+2*std), inplace=True)\n",
        "train_data.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "128xxzetjTEY",
        "outputId": "68ba3e70-e5b5-482b-b2d1-a1fc1cf0126a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Pclass      0\n",
              "Sex         0\n",
              "Age         0\n",
              "SibSp       0\n",
              "Parch       0\n",
              "Fare        1\n",
              "Embarked    0\n",
              "dtype: int64"
            ]
          },
          "execution_count": 42,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "np.random.seed(0)\n",
        "test_data[\"Age\"].fillna(np.random.randint(ave-2*std, ave+2*std), inplace=True)\n",
        "test_data.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wZTi-3aYjTEY",
        "outputId": "b68ba98b-7a03-4ef4-a175-9bad046ab096"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x400 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Ageのtrain_dataの分布を再確認\n",
        "fig = plt.figure(figsize=(10, 4))\n",
        "sns.histplot(data=train_data, x=\"Age\", hue=\"Survived\", bins=8, multiple=\"dodge\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tGlQZj50jTEY",
        "outputId": "ae3fba5f-6035-4e57-d159-ce32ef52de2b"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x400 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Ageのtest_dataの分布\n",
        "fig = plt.figure(figsize=(10, 4))\n",
        "sns.histplot(data=test_data, x=\"Age\", bins=8)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kwBNBpY4jTEY"
      },
      "source": [
        "### Fareの欠損値の補完"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "g9dlUI-XjTEY",
        "outputId": "7ee5e216-afc1-4e2e-e835-ec06be9aab63"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x400 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Fareの分布を確認\n",
        "fig = plt.figure(figsize=(10, 4))\n",
        "sns.histplot(data=train_data, x=\"Fare\", hue=\"Survived\", bins=8, multiple=\"dodge\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lnmW0HKLjTEY"
      },
      "source": [
        "### Fareについてはtest_dataに1件だけnull値があるので、それはtrain_dataの平均値で補完する"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kiLPjtNyjTEY",
        "outputId": "f91aec3c-356f-4d36-8b0f-4686b8cf5e33"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "32.204207968574636"
            ]
          },
          "execution_count": 46,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "fare_mean = train_data[\"Fare\"].mean()\n",
        "fare_mean"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "y2Wgo3VEjTEY",
        "outputId": "0a676f39-f5f6-4525-d2f7-7723ff036cb1"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "execution_count": 47,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "test_data[\"Fare\"].isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-Y2nUNtcjTEZ",
        "outputId": "8634e981-cddb-4bae-c3ee-55e233fc6280"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "execution_count": 48,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "test_data[\"Fare\"].fillna(fare_mean, inplace=True)\n",
        "test_data[\"Fare\"].isnull().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w8QrJQqujTEZ"
      },
      "source": [
        "### Embarkedの欠損値を補完"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kccIqqKhjTEZ",
        "outputId": "daa94f3b-b6b5-4312-d427-23966c413a42"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x400 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# 分布の確認\n",
        "fig = plt.figure(figsize=(10, 4))\n",
        "sns.histplot(data=train_data, x=\"Embarked\", hue=\"Survived\", bins=8, multiple=\"dodge\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oQOWpj4CjTEZ"
      },
      "source": [
        "## Categoricalな特徴量は最頻値で補完することが多い"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GvblQNBVjTEZ"
      },
      "outputs": [],
      "source": [
        "train_data[\"Embarked\"].fillna(\"S\", inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "oHmEZubujTEZ",
        "outputId": "baa246de-7fff-432e-b440-4288dc09d987"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "execution_count": 51,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "train_data[\"Embarked\"].isnull().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8y9BUenNjTEZ"
      },
      "source": [
        "### get_dummies関数を使ってcategoricalな特徴量を0か1にする"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vApGwYfEjTEZ",
        "outputId": "c9c9c608-a3d9-4ed0-c4b5-b1736bd83bf6"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Index(['Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'Embarked',\n",
              "       'Survived'],\n",
              "      dtype='object')"
            ]
          },
          "execution_count": 52,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "train_data.columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CQc0Zi6UjTEZ",
        "outputId": "0445abdb-982a-46e4-e140-ea6a9911066a"
      },
      "outputs": [
        {
          "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>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Sex_female</th>\n",
              "      <th>Sex_male</th>\n",
              "      <th>Pclass_1</th>\n",
              "      <th>Pclass_2</th>\n",
              "      <th>Pclass_3</th>\n",
              "      <th>Embarked_C</th>\n",
              "      <th>Embarked_Q</th>\n",
              "      <th>Embarked_S</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>0</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>0</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Age  SibSp  Parch     Fare  Survived  Sex_female  Sex_male  Pclass_1  \\\n",
              "0  22.0      1      0   7.2500         0       False      True     False   \n",
              "1  38.0      1      0  71.2833         1        True     False      True   \n",
              "2  26.0      0      0   7.9250         1        True     False     False   \n",
              "3  35.0      1      0  53.1000         1        True     False      True   \n",
              "4  35.0      0      0   8.0500         0       False      True     False   \n",
              "\n",
              "   Pclass_2  Pclass_3  Embarked_C  Embarked_Q  Embarked_S  \n",
              "0     False      True       False       False        True  \n",
              "1     False     False        True       False       False  \n",
              "2     False      True       False       False        True  \n",
              "3     False     False       False       False        True  \n",
              "4     False      True       False       False        True  "
            ]
          },
          "execution_count": 53,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "cat_labels = [\"Sex\", \"Pclass\", \"Embarked\"]\n",
        "train_data = pd.get_dummies(train_data, columns=cat_labels)\n",
        "test_data = pd.get_dummies(test_data, columns=cat_labels)\n",
        "train_data.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jYpI7fqxjTEZ"
      },
      "source": [
        "### modelの訓練"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "akTNPIL0jTEZ",
        "outputId": "80844ede-c575-47dd-dd17-d8697fd12918"
      },
      "outputs": [
        {
          "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"
            ]
          },
          "execution_count": 58,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# 正解データの用意\n",
        "y_train = train_data[\"Survived\"]\n",
        "y_train"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jQO9xV9OjTEZ",
        "outputId": "0517040a-5948-4939-cccf-bd1eb1668a35"
      },
      "outputs": [
        {
          "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>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Sex_female</th>\n",
              "      <th>Sex_male</th>\n",
              "      <th>Pclass_1</th>\n",
              "      <th>Pclass_2</th>\n",
              "      <th>Pclass_3</th>\n",
              "      <th>Embarked_C</th>\n",
              "      <th>Embarked_Q</th>\n",
              "      <th>Embarked_S</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Age  SibSp  Parch     Fare  Sex_female  Sex_male  Pclass_1  Pclass_2  \\\n",
              "0  22.0      1      0   7.2500       False      True     False     False   \n",
              "1  38.0      1      0  71.2833        True     False      True     False   \n",
              "2  26.0      0      0   7.9250        True     False     False     False   \n",
              "3  35.0      1      0  53.1000        True     False      True     False   \n",
              "4  35.0      0      0   8.0500       False      True     False     False   \n",
              "\n",
              "   Pclass_3  Embarked_C  Embarked_Q  Embarked_S  \n",
              "0      True       False       False        True  \n",
              "1     False        True       False       False  \n",
              "2      True       False       False        True  \n",
              "3     False       False       False        True  \n",
              "4      True       False       False        True  "
            ]
          },
          "execution_count": 59,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# 説明変数のtraindata\n",
        "X_train = train_data.drop(labels=[\"Survived\"], axis=1)\n",
        "X_train.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NiIC7dZdjTEc",
        "outputId": "734711ae-7e4a-4c56-888e-b8fe72f1fd76"
      },
      "outputs": [
        {
          "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>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Sex_female</th>\n",
              "      <th>Sex_male</th>\n",
              "      <th>Pclass_1</th>\n",
              "      <th>Pclass_2</th>\n",
              "      <th>Pclass_3</th>\n",
              "      <th>Embarked_C</th>\n",
              "      <th>Embarked_Q</th>\n",
              "      <th>Embarked_S</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>34.5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.8292</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>47.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>7.0000</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>62.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>9.6875</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>27.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8.6625</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>12.2875</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Age  SibSp  Parch     Fare  Sex_female  Sex_male  Pclass_1  Pclass_2  \\\n",
              "0  34.5      0      0   7.8292       False      True     False     False   \n",
              "1  47.0      1      0   7.0000        True     False     False     False   \n",
              "2  62.0      0      0   9.6875       False      True     False      True   \n",
              "3  27.0      0      0   8.6625       False      True     False     False   \n",
              "4  22.0      1      1  12.2875        True     False     False     False   \n",
              "\n",
              "   Pclass_3  Embarked_C  Embarked_Q  Embarked_S  \n",
              "0      True       False        True       False  \n",
              "1      True       False       False        True  \n",
              "2     False       False        True       False  \n",
              "3      True       False       False        True  \n",
              "4      True       False       False        True  "
            ]
          },
          "execution_count": 60,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# 説明変数のtest_data\n",
        "X_test = test_data\n",
        "X_test.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ihXKsqcOjTEc",
        "outputId": "ab51c2c5-deb2-420a-97f6-0ff148103664"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(max_depth=5, random_state=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(max_depth=5, random_state=0)</pre></div></div></div></div></div>"
            ],
            "text/plain": [
              "RandomForestClassifier(max_depth=5, random_state=0)"
            ]
          },
          "execution_count": 63,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# import\n",
        "from sklearn.ensemble import  RandomForestClassifier\n",
        "\n",
        "# インスタンスの生成\n",
        "model = RandomForestClassifier(n_estimators=100, max_depth=5, random_state=0)\n",
        "\n",
        "# modelの訓練\n",
        "model.fit(X_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fxHv2Ni9jTEc",
        "outputId": "3491beeb-b924-4027-9120-5c99a29c731b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([0, 1, 0, 0, 1], dtype=int64)"
            ]
          },
          "execution_count": 64,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i68lzjzNjTEc"
      },
      "source": [
        "### treeの可視化"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "72lQ1EfCjTEc",
        "outputId": "1f3246b6-a4c5-4a95-d85b-43fd3f43ed08"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'Embarked']"
            ]
          },
          "execution_count": 67,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "labels"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PId5Yf2XjTEc",
        "outputId": "27043616-0852-4a80-b299-19776636ceb7"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Index(['Age', 'SibSp', 'Parch', 'Fare', 'Sex_female', 'Sex_male', 'Pclass_1',\n",
              "       'Pclass_2', 'Pclass_3', 'Embarked_C', 'Embarked_Q', 'Embarked_S'],\n",
              "      dtype='object')"
            ]
          },
          "execution_count": 68,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "X_train.columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "R2iU5h2kjTEd"
      },
      "outputs": [],
      "source": [
        "train_labels = ['Age', 'SibSp', 'Parch', 'Fare', 'Sex_female', 'Sex_male', 'Pclass_1', 'Pclass_2', 'Pclass_3', 'Embarked_C', 'Embarked_Q', 'Embarked_S']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8aFsZECejTEd",
        "outputId": "ce13063c-64e4-47a7-c3f7-b361ea48c587"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1800x2000 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from sklearn.tree import plot_tree\n",
        "plt.figure(figsize=(18, 20))\n",
        "plot_tree(model.estimators_[0], max_depth=2, fontsize=24, feature_names=train_labels, filled=True)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gIaitGRbjTEd"
      },
      "source": [
        "### 各特徴量が学習でどの程度重要になったかを可視化"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YJQU0nXfjTEd",
        "outputId": "442ed9d8-2a3b-4a97-f8ae-642faa7c0055"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([0.08506099, 0.03506486, 0.03031918, 0.12282632, 0.28516426,\n",
              "       0.24474112, 0.04779358, 0.0237022 , 0.08931359, 0.01469964,\n",
              "       0.00718707, 0.01412719])"
            ]
          },
          "execution_count": 71,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "importance = model.feature_importances_\n",
        "importance"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Kc_yI9FOjTEd",
        "outputId": "90c3789d-9379-4a0a-d0ff-bbea9afba724"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(10, 6))\n",
        "plt.barh(range(len(importance)), importance)\n",
        "plt.yticks(range(len(train_labels)), train_labels)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DhP2FTKKjTEd"
      },
      "source": [
        "### Sex, Fare, Pclassが予測に大きな影響を与えている  \n",
        "### Embarkedはあまり大きな影響を与えていない"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g7IiOwZ9jTEd"
      },
      "source": [
        "### 予測・提出用データの作成"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GqqOHwuSjTEd",
        "outputId": "327195bb-cce9-418d-eaea-5ff783e6e4f6"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([0, 1, 0, 0, 1], dtype=int64)"
            ]
          },
          "execution_count": 74,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# 予測\n",
        "pred = model.predict(X_test)\n",
        "pred[:5]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VrE9kSECjTEd",
        "outputId": "0643b278-b8f0-4e57-b73f-06c70cdb138b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[0, 1, 0, 0, 1]"
            ]
          },
          "execution_count": 77,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# 予測結果はint型で提出しなければならない\n",
        "# int型になっているが、一応map関数を適応しておく\n",
        "pred = list(map(int, pred))\n",
        "pred[:5]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HYPl5VSqjTEd",
        "outputId": "b522d0f0-5bb8-4a64-e672-c5f8a7dd61df"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0    0\n",
              "1    1\n",
              "2    0\n",
              "3    0\n",
              "4    1\n",
              "Name: Survived, dtype: int64"
            ]
          },
          "execution_count": 81,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "survived_pred = pd.Series(pred, name=\"Survived\")\n",
        "survived_pred[:5]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "adJHXI0OjTEd",
        "outputId": "ecef45c0-cc01-4149-c1a8-d5f059538739"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>892</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>893</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>894</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>895</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>896</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   PassengerId  Survived\n",
              "0          892         0\n",
              "1          893         1\n",
              "2          894         0\n",
              "3          895         0\n",
              "4          896         1"
            ]
          },
          "execution_count": 85,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# 提出用のdataの作成\n",
        "submit_data = pd.concat([test_id, survived_pred], axis=1)\n",
        "submit_data.iloc[:5, :]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6LVOApRrjTEd"
      },
      "outputs": [],
      "source": [
        "# 提出用のdataをcsv形式で保存\n",
        "submit_data.to_csv(\"submit_data/Kaggle_titanic_2_Randomforest.csv\", index=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3EC4LQAPjTEd"
      },
      "source": [
        "2024/01/22  \n",
        "・Kaggleのscoreは0.78468だった  \n",
        "・Categoricalな特徴量をただ置き換えるのではなく、説明変数が増えてもpd.get_dummiesを使ったほうがよさそうだ"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uPmbDvW5jTEd"
      },
      "source": []
    }
  ],
  "metadata": {
    "colab": {
      "include_colab_link": true,
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.11.5"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}