{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "42ef793a-fb90-42c5-afb5-5fda4c41e2e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay, accuracy_score\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "import matplotlib.pyplot as plt\n",
    "#设置中文编码和负号的正常显示\n",
    "plt.rcParams['font.family']='Microsoft YaHei'\n",
    "plt.rcParams['axes.unicode_minus'] = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "8e521012-8e1e-48ad-a37d-dc25d13ed94d",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('D:/Dataset for Machine learning/titanic.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "dfb85eac-543d-48b5-9d1c-822bf9bdb198",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "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"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "20df9014-2772-4e7f-a2eb-33bff2a33dd4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Embarked</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>0.553571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q</th>\n",
       "      <td>0.389610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S</th>\n",
       "      <td>0.336957</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Survived\n",
       "Embarked          \n",
       "C         0.553571\n",
       "Q         0.389610\n",
       "S         0.336957"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[['Embarked','Survived']].groupby('Embarked').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "149834d6-00eb-4eed-9cc1-f1e60d8218cf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           男性   女性\n",
      "Survived          \n",
      "0         468   81\n",
      "1         109  233\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "fig.set(alpha=0.2)\n",
    "\n",
    "Survived_0 = data.Survived[data.Sex == 'male'].value_counts()\n",
    "Survived_1 = data.Survived[data.Sex == 'female'].value_counts()\n",
    "df = pd.DataFrame({u'男性':Survived_0, u'女性':Survived_1})\n",
    "print(df)\n",
    "df.plot(kind='bar', stacked=True)\n",
    "plt.title(u'不同性别乘客获救情况')\n",
    "plt.xlabel(u'获救与否')\n",
    "plt.ylabel(u'人数')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "86258abe-30e3-4fa0-b698-c02f4ba48d7d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x1500 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10,15))\n",
    "fig.set(alpha=0.2)\n",
    "\n",
    "Survived_0 = data.Embarked[data.Survived == 0].value_counts()\n",
    "Survived_1 = data.Embarked[data.Survived == 1].value_counts()\n",
    "df = pd.DataFrame({u'未获救':Survived_0, u'获救':Survived_1})\n",
    "df.plot(kind='bar', stacked=True)\n",
    "plt.title(u'不同登船港口的乘客获救情况')\n",
    "plt.xlabel(u'登船港口')\n",
    "plt.ylabel(u'人数')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "d195c8e7-ef5f-4634-a23b-eb8b19391498",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x1500 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10,15))\n",
    "fig.set(alpha=0.2)\n",
    "\n",
    "Survived_0 = data.SibSp[data.Survived == 0].value_counts()\n",
    "Survived_1 = data.SibSp[data.Survived == 1].value_counts()\n",
    "df = pd.DataFrame({u'未获救':Survived_0, u'获救':Survived_1})\n",
    "df.plot(kind='bar', stacked=True)\n",
    "plt.title(u'不同兄弟姐妹数的乘客获救情况')\n",
    "plt.xlabel(u'兄弟姐妹数')\n",
    "plt.ylabel(u'人数')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "d7f37cd7-7742-4945-92d5-a2f7d53a8e35",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x1500 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10,15))\n",
    "fig.set(alpha=0.2)\n",
    "\n",
    "Survived_0 = data.Parch[data.Survived == 0].value_counts()\n",
    "Survived_1 = data.Parch[data.Survived == 1].value_counts()\n",
    "df = pd.DataFrame({u'未获救':Survived_0, u'获救':Survived_1})\n",
    "df.plot(kind='bar', stacked=True)\n",
    "plt.title(u'不同父母小孩数的乘客获救情况')\n",
    "plt.xlabel(u'父母小孩数')\n",
    "plt.ylabel(u'人数')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "37291165-c960-4780-90b6-b82d6fd7f2ca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "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"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e308ff54-6ccf-4171-b83a-8d080b4f0813",
   "metadata": {},
   "source": [
    "处理缺失值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "75dc4c78-991d-4d0d-bb7e-4805f5d26943",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "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     891 non-null    object \n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.7+ KB\n"
     ]
    }
   ],
   "source": [
    "#使用众数填补Embarked缺失值\n",
    "data['Embarked'] = data['Embarked'].fillna(data['Embarked'].mode()[0])\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "962690f0-eddc-4b53-bd7a-643313277947",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "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          891 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     891 non-null    object \n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.7+ KB\n"
     ]
    }
   ],
   "source": [
    "#使用随机森林预测Age的缺失值\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "cols = ['Age', 'Survived', 'Pclass', 'SibSp', 'Parch', 'Fare']\n",
    "age_data = data[cols]\n",
    "X_train_age = age_data[age_data.Age.notnull()][cols[1:]]\n",
    "y_train_age = age_data[age_data.Age.notnull()][cols[0]]\n",
    "X_test_age = age_data[age_data.Age.isnull()][cols[1:]]\n",
    "\n",
    "rfr = RandomForestRegressor(n_estimators=200, n_jobs=-1) #n_estimators控制随机森林决策树的数量；n_jobs=-1会使用CPU的全部内核，大幅度提升速度\n",
    "rfr.fit(X_train_age, y_train_age)\n",
    "y_predict_age = rfr.predict(X_test_age)\n",
    "data.loc[data.Age.isnull(),'Age'] = y_predict_age # 缺失值填充\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "16daa8bf-8aa7-4633-8283-010cb9b03cb2",
   "metadata": {},
   "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>Survived</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",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>24.7985</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Survived  Pclass     Sex      Age  SibSp  Parch     Fare Embarked\n",
       "0           0       3    male  22.0000      1      0   7.2500        S\n",
       "1           1       1  female  38.0000      1      0  71.2833        C\n",
       "2           1       3  female  26.0000      0      0   7.9250        S\n",
       "3           1       1  female  35.0000      1      0  53.1000        S\n",
       "4           0       3    male  35.0000      0      0   8.0500        S\n",
       "..        ...     ...     ...      ...    ...    ...      ...      ...\n",
       "886         0       2    male  27.0000      0      0  13.0000        S\n",
       "887         1       1  female  19.0000      0      0  30.0000        S\n",
       "888         0       3  female  24.7985      1      2  23.4500        S\n",
       "889         1       1    male  26.0000      0      0  30.0000        C\n",
       "890         0       3    male  32.0000      0      0   7.7500        Q\n",
       "\n",
       "[891 rows x 8 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = data.iloc[:, [1,2,4,5,6,7,9,11]]\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "d82d684d-df9e-4079-a9c4-d40a9a7e183f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        male\n",
       "1      female\n",
       "2      female\n",
       "3      female\n",
       "4        male\n",
       "        ...  \n",
       "886      male\n",
       "887    female\n",
       "888    female\n",
       "889      male\n",
       "890      male\n",
       "Name: Sex, Length: 891, dtype: object"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1['Sex']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "7e68ee1b-25f4-4431-b926-4ac008774bb4",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
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       "      <th>3</th>\n",
       "      <td>1</td>\n",
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       "      <td>S</td>\n",
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       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <th>887</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>19.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>24.7985</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>32.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Survived  Pclass  Sex      Age  SibSp  Parch     Fare Embarked\n",
       "0           0       3    1  22.0000      1      0   7.2500        S\n",
       "1           1       1    0  38.0000      1      0  71.2833        C\n",
       "2           1       3    0  26.0000      0      0   7.9250        S\n",
       "3           1       1    0  35.0000      1      0  53.1000        S\n",
       "4           0       3    1  35.0000      0      0   8.0500        S\n",
       "..        ...     ...  ...      ...    ...    ...      ...      ...\n",
       "886         0       2    1  27.0000      0      0  13.0000        S\n",
       "887         1       1    0  19.0000      0      0  30.0000        S\n",
       "888         0       3    0  24.7985      1      2  23.4500        S\n",
       "889         1       1    1  26.0000      0      0  30.0000        C\n",
       "890         0       3    1  32.0000      0      0   7.7500        Q\n",
       "\n",
       "[891 rows x 8 columns]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "le_sex = LabelEncoder()\n",
    "encoded = le_sex.fit_transform(df1['Sex'])\n",
    "df1['Sex'] = encoded\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "3c0659c0-338e-47e5-9fc3-12de0a3c8ef2",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
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       "      <td>1</td>\n",
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       "      <th>3</th>\n",
       "      <td>1</td>\n",
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       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <th>886</th>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>2</td>\n",
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       "      <th>887</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>19.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>24.7985</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>32.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Survived  Pclass  Sex      Age  SibSp  Parch     Fare  Embarked\n",
       "0           0       3    1  22.0000      1      0   7.2500         2\n",
       "1           1       1    0  38.0000      1      0  71.2833         0\n",
       "2           1       3    0  26.0000      0      0   7.9250         2\n",
       "3           1       1    0  35.0000      1      0  53.1000         2\n",
       "4           0       3    1  35.0000      0      0   8.0500         2\n",
       "..        ...     ...  ...      ...    ...    ...      ...       ...\n",
       "886         0       2    1  27.0000      0      0  13.0000         2\n",
       "887         1       1    0  19.0000      0      0  30.0000         2\n",
       "888         0       3    0  24.7985      1      2  23.4500         2\n",
       "889         1       1    1  26.0000      0      0  30.0000         0\n",
       "890         0       3    1  32.0000      0      0   7.7500         1\n",
       "\n",
       "[891 rows x 8 columns]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "le_embarked = LabelEncoder()\n",
    "encoded = le_embarked.fit_transform(df1['Embarked'])\n",
    "df1['Embarked'] = encoded\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "f4593780-b65e-448e-b974-a4e17e176db6",
   "metadata": {},
   "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   Survived  891 non-null    int64  \n",
      " 1   Pclass    891 non-null    int64  \n",
      " 2   Sex       891 non-null    int32  \n",
      " 3   Age       891 non-null    float64\n",
      " 4   SibSp     891 non-null    int64  \n",
      " 5   Parch     891 non-null    int64  \n",
      " 6   Fare      891 non-null    float64\n",
      " 7   Embarked  891 non-null    int32  \n",
      "dtypes: float64(2), int32(2), int64(4)\n",
      "memory usage: 48.9 KB\n"
     ]
    }
   ],
   "source": [
    "df1.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "542fd80b-5776-44d9-b598-04a362478fb9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "## 相关关系矩阵热力图\n",
    "sns.heatmap(df1.corr(), annot=True, cmap='RdBu', xticklabels=1, yticklabels=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "d7b7b054-1277-482c-ab1e-0a8aa6b0bb5c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Survived  Pclass  Sex      Age  SibSp  Parch     Fare  Embarked\n",
      "0           0       3    1  22.0000      1      0   7.2500         2\n",
      "1           1       1    0  38.0000      1      0  71.2833         0\n",
      "2           1       3    0  26.0000      0      0   7.9250         2\n",
      "3           1       1    0  35.0000      1      0  53.1000         2\n",
      "4           0       3    1  35.0000      0      0   8.0500         2\n",
      "..        ...     ...  ...      ...    ...    ...      ...       ...\n",
      "886         0       2    1  27.0000      0      0  13.0000         2\n",
      "887         1       1    0  19.0000      0      0  30.0000         2\n",
      "888         0       3    0  24.7985      1      2  23.4500         2\n",
      "889         1       1    1  26.0000      0      0  30.0000         0\n",
      "890         0       3    1  32.0000      0      0   7.7500         1\n",
      "\n",
      "[891 rows x 8 columns]\n",
      "<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   Sex       891 non-null    int32  \n",
      " 3   Age       891 non-null    float64\n",
      " 4   SibSp     891 non-null    int64  \n",
      " 5   Parch     891 non-null    int64  \n",
      " 6   Fare      891 non-null    float64\n",
      " 7   Embarked  891 non-null    int32  \n",
      "dtypes: float64(2), int32(2), int64(4)\n",
      "memory usage: 48.9 KB\n"
     ]
    }
   ],
   "source": [
    "print(df1)\n",
    "df1.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "c5580705-f47d-4419-a6ca-c0a3046b17fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = ['Pclass','Sex','Age','SibSp','Parch','Fare','Embarked']\n",
    "X = df1[cols]\n",
    "y = df1['Survived']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "7a1f09bd-e91d-4f57-9ff4-17db1165e400",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "ce89cb9f-e066-403a-9652-9cb33193f8b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(random_state=42)</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 fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;DecisionTreeClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.tree.DecisionTreeClassifier.html\">?<span>Documentation for DecisionTreeClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>DecisionTreeClassifier(random_state=42)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeClassifier(random_state=42)"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = DecisionTreeClassifier(random_state=42)\n",
    "clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "f66cfa96-2c04-4708-88c6-f6afac9fb1be",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "f10e2c07-3b3b-42e2-a9e6-60765d0e5a45",
   "metadata": {},
   "outputs": [],
   "source": [
    "cm = confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "763df1da-a14d-446e-bb74-0fd0d8ce6ee4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Display the confusion matrix\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=['0','1'])\n",
    "disp.plot(cmap=plt.cm.Blues)\n",
    "plt.title('Confusion Matrix for Decision Tree Classifier')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "925a2cd2-d91f-4532-9403-8fb11e7dec89",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.8268156424581006\n"
     ]
    }
   ],
   "source": [
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(f\"Accuracy: {accuracy}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "7bc38e94-d487-4aee-8238-fd347c430097",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "逻辑回归的预测结果： 0.8156424581005587\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "lr = LogisticRegression() # logit 逻辑回归\n",
    "lr.fit(X_train, y_train)\n",
    "pred_lr = lr.predict(X_test) \n",
    "accuracy_lr = accuracy_score(y_test, pred_lr)\n",
    "print(\"逻辑回归的预测结果：\", accuracy_lr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9206f526-c1e1-4ec2-a733-6beb258cc54c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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