titanic_answer1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data analysis and wrangling\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import random as rnd\n",
    "\n",
    "# visualization\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "# machine learning\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC, LinearSVC\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.linear_model import Perceptron\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['PassengerId' 'Survived' 'Pclass' 'Name' 'Sex' 'Age' 'SibSp' 'Parch'\n",
      " 'Ticket' 'Fare' 'Cabin' 'Embarked']\n",
      "<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",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 418 entries, 0 to 417\n",
      "Data columns (total 11 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  418 non-null    int64  \n",
      " 1   Pclass       418 non-null    int64  \n",
      " 2   Name         418 non-null    object \n",
      " 3   Sex          418 non-null    object \n",
      " 4   Age          332 non-null    float64\n",
      " 5   SibSp        418 non-null    int64  \n",
      " 6   Parch        418 non-null    int64  \n",
      " 7   Ticket       418 non-null    object \n",
      " 8   Fare         417 non-null    float64\n",
      " 9   Cabin        91 non-null     object \n",
      " 10  Embarked     418 non-null    object \n",
      "dtypes: float64(2), int64(4), object(5)\n",
      "memory usage: 36.0+ KB\n"
     ]
    }
   ],
   "source": [
    "train_df = pd.read_csv(r'D:\\competition\\titanic\\train.csv')\n",
    "test_df = pd.read_csv(r'D:\\competition\\titanic\\test.csv')\n",
    "combine = [train_df, test_df]\n",
    "print(train_df.columns.values)\n",
    "#对应的列的价值\n",
    "train_df.info()\n",
    "test_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "哪些特征可以进行被分类:Survived,Sex,Embarked\n",
    "哪些特征为数值类型的:Age,Fare,Discrete:SibSp,Parch\n",
    "Numerical(数值),alphanumeric(字母) data within same\n",
    "featurn.数值型、字母型的数据有相同的特征。\n",
    "比如TIcket为字母型和数值类型的数据类型。Cabin是一个\n",
    "字母类型数值。\n",
    "PassengerId=>乘客ID\n",
    "Pclass=>客舱等级(1/2/3等舱位)\n",
    "Name=>乘客姓名\n",
    "Sex=>性别\n",
    "Age=>年龄\n",
    "SibSp=>兄弟姐妹数/配偶数\n",
    "Parch=>父母数/子女数\n",
    "Ticket=>船票号\n",
    "Fare=>船票价格\n",
    "Cabin=>客舱号\n",
    "Embarked=>登船港口\n",
    "'''\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\n特征为blank,null或者空白的值\\nCabin>Age>Embarked 特征有null的对应值\\nCabin>Age两者并不匹配\\n数值特征的分布(distribution of numerical teature)\\n对应的参数见上面:\\n数据分布的特征\\n1.总人数为891人\\n2.Survived是一个0或1的分类特征\\n3.大概有38%的样例能够得以生存\\n(看Survived对应的mean的参数)\\n(代表实际生存率的32%???)\\n4.(看Parch一行)大多数乘客(大于75%)\\n没有与父母或者孩子同行，因为Parch\\n中超过75%的值为0.0000\\n(看SibSp一行)50%时的对应值为0.0000\\n75%时的对应值为1.0000\\nSibSp:堂兄弟/兄妹个数\\nParch:父母与小孩的个数\\n5.接近30%的乘客在海外有配偶或者子女\\n6.船费的差距非常大，只有很少的乘客花费512美元\\n7.很少有老年乘客的年龄在65-80之间\\n(看Age在75%-max之间)\\n'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "特征为blank,null或者空白的值\n",
    "Cabin>Age>Embarked 特征有null的对应值\n",
    "Cabin>Age两者并不匹配\n",
    "数值特征的分布(distribution of numerical teature)\n",
    "对应的参数见上面:\n",
    "数据分布的特征\n",
    "1.总人数为891人\n",
    "2.Survived是一个0或1的分类特征\n",
    "3.大概有38%的样例能够得以生存\n",
    "(看Survived对应的mean的参数)\n",
    "(代表实际生存率的32%???)\n",
    "4.(看Parch一行)大多数乘客(大于75%)\n",
    "没有与父母或者孩子同行，因为Parch\n",
    "中超过75%的值为0.0000\n",
    "(看SibSp一行)50%时的对应值为0.0000\n",
    "75%时的对应值为1.0000\n",
    "SibSp:堂兄弟/兄妹个数\n",
    "Parch:父母与小孩的个数\n",
    "5.接近30%的乘客在海外有配偶或者子女\n",
    "6.船费的差距非常大，只有很少的乘客花费512美元\n",
    "7.很少有老年乘客的年龄在65-80之间\n",
    "(看Age在75%-max之间)\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>32.204208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>257.353842</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>49.693429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>223.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.910400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>668.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
       "count   891.000000  891.000000  891.000000  714.000000  891.000000   \n",
       "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
       "std     257.353842    0.486592    0.836071   14.526497    1.102743   \n",
       "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
       "25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n",
       "50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n",
       "75%     668.500000    1.000000    3.000000   38.000000    1.000000   \n",
       "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
       "\n",
       "            Parch        Fare  \n",
       "count  891.000000  891.000000  \n",
       "mean     0.381594   32.204208  \n",
       "std      0.806057   49.693429  \n",
       "min      0.000000    0.000000  \n",
       "25%      0.000000    7.910400  \n",
       "50%      0.000000   14.454200  \n",
       "75%      0.000000   31.000000  \n",
       "max      6.000000  512.329200  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.describe()\n",
    "#这里面describe()里面有些重要的参数缺失\n",
    "#因为python中如果字段稍微多一些,则会将一些列自动省略掉"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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",
      "________________________________________\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 418 entries, 0 to 417\n",
      "Data columns (total 11 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  418 non-null    int64  \n",
      " 1   Pclass       418 non-null    int64  \n",
      " 2   Name         418 non-null    object \n",
      " 3   Sex          418 non-null    object \n",
      " 4   Age          332 non-null    float64\n",
      " 5   SibSp        418 non-null    int64  \n",
      " 6   Parch        418 non-null    int64  \n",
      " 7   Ticket       418 non-null    object \n",
      " 8   Fare         417 non-null    float64\n",
      " 9   Cabin        91 non-null     object \n",
      " 10  Embarked     418 non-null    object \n",
      "dtypes: float64(2), int64(4), object(5)\n",
      "memory usage: 36.0+ KB\n"
     ]
    }
   ],
   "source": [
    "train_df.tail()\n",
    "train_df.info()\n",
    "print('_'*40)\n",
    "#中间有40个对应的'_'\n",
    "test_df.info()\n",
    "#!!!从这个表格能够直观地看出来每一项的属性\n",
    "#以及每一项的对应的数值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "#接下来需要提出猜想：哪些假设会对数据分析造成影响\n",
    "#哪些假设会与Survival这一特征相关\n",
    "#1.完整数据，将对应的年龄特征以及Embarked的特征(与\n",
    "#生存有关的)完善出来\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891</td>\n",
       "      <td>891</td>\n",
       "      <td>891</td>\n",
       "      <td>204</td>\n",
       "      <td>889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>891</td>\n",
       "      <td>2</td>\n",
       "      <td>681</td>\n",
       "      <td>147</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>Strom, Miss. Telma Matilda</td>\n",
       "      <td>male</td>\n",
       "      <td>347082</td>\n",
       "      <td>B96 B98</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>1</td>\n",
       "      <td>577</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>644</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              Name   Sex  Ticket    Cabin Embarked\n",
       "count                          891   891     891      204      889\n",
       "unique                         891     2     681      147        3\n",
       "top     Strom, Miss. Telma Matilda  male  347082  B96 B98        S\n",
       "freq                             1   577       7        4      644"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.describe(include='O')\n",
    "#注意这里的include里面的字母为'O'\n",
    "#默认只输出数值型数据的统计信息(如上),\n",
    "#设置参数为'all'则输入的所有列都在输出中,\n",
    "#设置为O则只输出离散型变量的统计信息\n",
    "#Ticket特征之中大概有22%的重复数值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>Pclass</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.629630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.472826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.242363</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pclass  Survived\n",
       "0       1  0.629630\n",
       "1       2  0.472826\n",
       "2       3  0.242363"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#!!!分析这些数值对于survival这个属性值是否会有影响!!!\n",
    "#这里为分析的重点!!!\n",
    "#Ticket可能需要从分析中剔除，因为Ticket数值有很大的重复\n",
    "#性，可能Ticket与survival之间没有必然的联系\n",
    "#Cabin特征需要剔除掉，因为Cabin当中的空值出现的比较多\n",
    "#PassengerId需要从训练集中剔除出来，因为可能与survival\n",
    "#之间没有必然的联系\n",
    "#Name特征不是特别标准，可能不能直接与survival有关，所以\n",
    "#需要被去除掉\n",
    "#1.创建一个名为Family的新特征基于Parch和SibSp的特征\n",
    "#2.创建一个姓名的特征，抽取标题作为一个新特征\n",
    "#3.创建一个年龄段的新特征\n",
    "#4.创建一个船费范围的特征帮助我们分析\n",
    "\n",
    "#!!!加入我们的一些假设\n",
    "#1.Women更容易生存\n",
    "#2.Children更有可能生存下来\n",
    "#3.上等社会人物(Pclass=1)更有可能生存下来\n",
    "#!!!关键属性:Pclass=1,Sex=1,SibSp and Parch\n",
    "train_df[['Pclass', 'Survived']].groupby(['Pclass'], as_index=False).mean().sort_values(by='Survived', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>Sex</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>female</td>\n",
       "      <td>0.742038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>male</td>\n",
       "      <td>0.188908</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Sex  Survived\n",
       "0  female  0.742038\n",
       "1    male  0.188908"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df[[\"Sex\", \"Survived\"]].groupby(['Sex'], as_index=False).mean().sort_values(by='Survived', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>SibSp</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.535885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.464286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.345395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>8</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SibSp  Survived\n",
       "1      1  0.535885\n",
       "2      2  0.464286\n",
       "0      0  0.345395\n",
       "3      3  0.250000\n",
       "4      4  0.166667\n",
       "5      5  0.000000\n",
       "6      8  0.000000"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df[[\"SibSp\", \"Survived\"]].groupby(['SibSp'], as_index=False).mean().sort_values(by='Survived', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>Parch</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.550847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.343658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>0.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Parch  Survived\n",
       "3      3  0.600000\n",
       "1      1  0.550847\n",
       "2      2  0.500000\n",
       "0      0  0.343658\n",
       "5      5  0.200000\n",
       "4      4  0.000000\n",
       "6      6  0.000000"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df[[\"Parch\", \"Survived\"]].groupby(['Parch'], as_index=False).mean().sort_values(by='Survived', ascending=False)\n",
    "#上面处理的分布按照Pclass,Sex,SibSp,Parch来进行生存率高低的排序"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\python37\\lib\\site-packages\\numpy\\lib\\histograms.py:839: RuntimeWarning: invalid value encountered in greater_equal\n",
      "  keep = (tmp_a >= first_edge)\n",
      "d:\\python37\\lib\\site-packages\\numpy\\lib\\histograms.py:840: RuntimeWarning: invalid value encountered in less_equal\n",
      "  keep &= (tmp_a <= last_edge)\n"
     ]
    },
    {
     "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",
      "None\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 418 entries, 0 to 417\n",
      "Data columns (total 11 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  418 non-null    int64  \n",
      " 1   Pclass       418 non-null    int64  \n",
      " 2   Name         418 non-null    object \n",
      " 3   Sex          418 non-null    object \n",
      " 4   Age          332 non-null    float64\n",
      " 5   SibSp        418 non-null    int64  \n",
      " 6   Parch        418 non-null    int64  \n",
      " 7   Ticket       418 non-null    object \n",
      " 8   Fare         417 non-null    float64\n",
      " 9   Cabin        91 non-null     object \n",
      " 10  Embarked     418 non-null    object \n",
      "dtypes: float64(2), int64(4), object(5)\n",
      "memory usage: 36.0+ KB\n",
      "None\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#继续计算相应的直方图与年龄之间的关系\n",
    "g = sns.FacetGrid(train_df,col='Survived')\n",
    "#FacetGrid绘制多个图表，纵坐标col的值为'Survived'\n",
    "g.map(plt.hist,'Age',bins = 20)\n",
    "#bins=20代表绘制20个对应的图像,'Age'为属性\n",
    "#plt.hist代表绘制直方图\n",
    "\n",
    "#从中分析年龄对于生存率的影响\n",
    "#1.婴儿(年龄小于等于四岁)有较高的生还率\n",
    "#2.最老的乘客(年龄为80岁)生存了下来\n",
    "#3.大量数量的15~25岁的学生并没有生存\n",
    "#4.大多数的乘客在15~35岁的年龄区间\n",
    "print(train_df.info())\n",
    "print(test_df.info())\n",
    "#这里的时候train_df的对应类型为<class 'pandas.core.series.Series'>\n",
    "#test_df的对应类型为<class 'pandas.core.series.Series'>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\python37\\lib\\site-packages\\seaborn\\axisgrid.py:316: UserWarning: The `size` parameter has been renamed to `height`; please update your code.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "d:\\python37\\lib\\site-packages\\numpy\\lib\\histograms.py:839: RuntimeWarning: invalid value encountered in greater_equal\n",
      "  keep = (tmp_a >= first_edge)\n",
      "d:\\python37\\lib\\site-packages\\numpy\\lib\\histograms.py:840: RuntimeWarning: invalid value encountered in less_equal\n",
      "  keep &= (tmp_a <= last_edge)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1524783ed08>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 514.88x475.2 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#添加入二元变量进行观察\n",
    "grid = sns.FacetGrid(train_df, col = 'Survived',row = 'Pclass',size=2.2,aspect=1.6)\n",
    "grid.map(plt.hist,'Age',alpha = .5,bins = 20)\n",
    "grid.add_legend()\n",
    "#画出来的对应的图片横坐标为年龄，纵坐标为数量\n",
    "#1.Pclass=3有最多的乘客，但是最多的乘客并没有生还。\n",
    "#证实了我们之前的归纳猜想假设2\n",
    "#2.在Pclass=2和Pclass=3的乘客大多数生还了，进一步\n",
    "#证实了我们之前的归纳猜想假设2\n",
    "#3.大多数在Pclass=1的乘客生还了，证实了我们之前的\n",
    "#归纳猜想假设3\n",
    "#4.对于年龄分布不同的乘客Pclass的对应值不同"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\python37\\lib\\site-packages\\seaborn\\axisgrid.py:645: UserWarning: Using the pointplot function without specifying `order` is likely to produce an incorrect plot.\n",
      "  warnings.warn(warning)\n",
      "d:\\python37\\lib\\site-packages\\seaborn\\axisgrid.py:650: UserWarning: Using the pointplot function without specifying `hue_order` is likely to produce an incorrect plot.\n",
      "  warnings.warn(warning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x15247c1d7c8>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 323.94x475.2 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# grid = sns.FacetGrid(train_df, col='Embarked')\n",
    "grid = sns.FacetGrid(train_df, row='Embarked', height=2.2, aspect=1.6)\n",
    "grid.map(sns.pointplot, 'Pclass', 'Survived', 'Sex', palette='deep')\n",
    "grid.add_legend()\n",
    "#1.女性乘客比男性乘客的生还率高，证明了分类1\n",
    "#2.Embarked=C的类别比较特殊，男性有较高的生存率\n",
    "#这可能是Pclass和Embarked的关系反过来作用于Pclass\n",
    "#和Survived，并不是Embarked和Survived的重要的直接联系\n",
    "#3.与Pclass=2对于C和Q港口相比，男性在Pclass=3有较好的\n",
    "#生存率\n",
    "#4.男性在Pclass=3与Pclass=2有更好的生存率，相比于Pclass\n",
    "#=2对于C和Q港口，\n",
    "#5.上岸港口导致在男性乘客中，Pclass=3的生存率不同\n",
    "\n",
    "#决策：1.将性别特征加入模型训练\n",
    "#2.完成并向模型训练加入Embarked的特征"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\python37\\lib\\site-packages\\seaborn\\axisgrid.py:316: UserWarning: The `size` parameter has been renamed to `height`; please update your code.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "d:\\python37\\lib\\site-packages\\seaborn\\axisgrid.py:645: UserWarning: Using the barplot function without specifying `order` is likely to produce an incorrect plot.\n",
      "  warnings.warn(warning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x15247d44288>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 514.88x475.2 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# grid = sns.FacetGrid(train_df, col='Embarked', hue='Survived', palette={0: 'k', 1: 'w'})\n",
    "grid = sns.FacetGrid(train_df, row='Embarked', col='Survived', size=2.2, aspect=1.6)\n",
    "grid.map(sns.barplot, 'Sex', 'Fare', alpha=.5, ci=None)\n",
    "grid.add_legend()\n",
    "#考虑关联分类特征(非数值类型)以及数值特征\n",
    "#考虑Embarked(分类非数值),Sex(分类非数值),\n",
    "#Fare(数值),Survived(分类数值)\n",
    "#!!!观察船舱对于生还率的影响\n",
    "\n",
    "#更高的费用导致乘客有更高的生还率，验证\n",
    "#对应的猜想4，登录港口与生存率有关，证明\n",
    "#了假设1与假设2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before (891, 12) (418, 11) (891, 12) (418, 11)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "('After', (891, 10), (418, 9), (891, 10), (418, 9))"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#目前我们已经提出了多种假设，但是还没有改变一个单独的特征\n",
    "#或者价值去达到该假设，现在执行我们的决定和假设去修正、创\n",
    "#建以及完成目标\n",
    "#通过去除特征我们将会处理更少的数据集，加速我们的notebook\n",
    "#并且简化我们相应的分析\n",
    "\n",
    "#我们在训练和测试集上一起执行操作去保持一致性\n",
    "#wrangle data应该为数据处理的意思\n",
    "\n",
    "#下面的操作为去除'Ticket'以及'Cabin'两个属性的操作\n",
    "print(\"Before\", train_df.shape, test_df.shape, combine[0].shape, combine[1].shape)\n",
    "train_df = train_df.drop(['Ticket', 'Cabin'], axis=1)\n",
    "test_df = test_df.drop(['Ticket', 'Cabin'], axis=1)\n",
    "combine = [train_df, test_df]\n",
    "\"After\", train_df.shape, test_df.shape, combine[0].shape, combine[1].shape\n",
    "#Cabin：客舱号，因为缺失值太多，所以选择放弃Cabin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>Sex</th>\n",
       "      <th>female</th>\n",
       "      <th>male</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Title</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Capt</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Col</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Countess</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Don</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dr</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jonkheer</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lady</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Major</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Master</th>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Miss</th>\n",
       "      <td>182</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mlle</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mme</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mr</th>\n",
       "      <td>0</td>\n",
       "      <td>517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mrs</th>\n",
       "      <td>125</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ms</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rev</th>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sir</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Sex       female  male\n",
       "Title                 \n",
       "Capt           0     1\n",
       "Col            0     2\n",
       "Countess       1     0\n",
       "Don            0     1\n",
       "Dr             1     6\n",
       "Jonkheer       0     1\n",
       "Lady           1     0\n",
       "Major          0     2\n",
       "Master         0    40\n",
       "Miss         182     0\n",
       "Mlle           2     0\n",
       "Mme            1     0\n",
       "Mr             0   517\n",
       "Mrs          125     0\n",
       "Ms             1     0\n",
       "Rev            0     6\n",
       "Sir            0     1"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#分析Name特征是否可以被用来抽取标题与生存的相关性\n",
    "#正则表达式'\\w+\\.'匹配第一个单词，结尾有一个小数点\n",
    "#观察标题、姓名和生存率，可以得出以下的观察结论\n",
    "#当我们绘制Title,Age和Survived时候，我们注意到一下\n",
    "#的观察事项：\n",
    "#1.大多数头衔带年龄群较为准确，例如研究生头衔\n",
    "#平均年龄为五岁\n",
    "#2.年龄头衔段变化轻微\n",
    "#3.一些头衔生存率较高(Mme,Lady,Sir)而不是(Don,Rev,Jonkheer)\n",
    "#我们决定保存新Title的特征为了模型的训练\n",
    "for dataset in combine:\n",
    "    dataset['Title'] = dataset.Name.str.extract(' ([A-Za-z]+)\\.', expand=False)\n",
    "pd.crosstab(train_df['Title'], train_df['Sex'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>Title</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Master</td>\n",
       "      <td>0.575000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Miss</td>\n",
       "      <td>0.702703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mr</td>\n",
       "      <td>0.156673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Mrs</td>\n",
       "      <td>0.793651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Rare</td>\n",
       "      <td>0.347826</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Title  Survived\n",
       "0  Master  0.575000\n",
       "1    Miss  0.702703\n",
       "2      Mr  0.156673\n",
       "3     Mrs  0.793651\n",
       "4    Rare  0.347826"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in combine:\n",
    "    dataset['Title'] = dataset['Title'].replace(['Lady', 'Countess','Capt', 'Col',\n",
    "    'Don', 'Dr', 'Major', 'Rev', 'Sir', 'Jonkheer', 'Dona'], 'Rare')\n",
    "    dataset['Title'] = dataset['Title'].replace('Mlle', 'Miss')\n",
    "    dataset['Title'] = dataset['Title'].replace('Ms', 'Miss')\n",
    "    dataset['Title'] = dataset['Title'].replace('Mme', 'Mrs')\n",
    "train_df[['Title', 'Survived']].groupby(['Title'], as_index=False).mean()\n",
    "#将内容转化为对应的'Title'以及'Survived'的对应的概率"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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     Fare Embarked  Title  \n",
       "0      0   7.2500        S      1  \n",
       "1      0  71.2833        C      3  \n",
       "2      0   7.9250        S      2  \n",
       "3      0  53.1000        S      3  \n",
       "4      0   8.0500        S      1  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "title_mapping = {\"Mr\": 1, \"Miss\": 2, \"Mrs\": 3, \"Master\": 4, \"Rare\": 5}\n",
    "#!!!这一步很关键，将对应的姓名打上相应的标签,这是nlp的常规手段\n",
    "#将相应的文本打上对应的标签\n",
    "for dataset in combine:\n",
    "    dataset['Title'] = dataset['Title'].map(title_mapping)\n",
    "    dataset['Title'] = dataset['Title'].fillna(0)\n",
    "train_df.head()\n",
    "#将姓名的特征打上相应的标注，然后原本这一数据集的对应的内容"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((891, 9), (418, 9))"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#现在我们可以安全地将Name特征的参数从训练集和测试集中分离\n",
    "#同样在训练数据集中我们不需要PassengerId特征\n",
    "train_df = train_df.drop(['Name', 'PassengerId'], axis=1)\n",
    "test_df = test_df.drop(['Name'], axis=1)\n",
    "#axis=1代表删除对应的列，这里训练集删除'Name'以及'PassengerId'\n",
    "#所在的列，测试集删除'Name'对应的所在列，如果axis=0代表按行计算\n",
    "combine = [train_df, test_df]\n",
    "train_df.shape, test_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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",
       "      <th>Title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</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>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex   Age  SibSp  Parch     Fare Embarked  Title\n",
       "0         0       3    0  22.0      1      0   7.2500        S      1\n",
       "1         1       1    1  38.0      1      0  71.2833        C      3\n",
       "2         1       3    1  26.0      0      0   7.9250        S      2\n",
       "3         1       1    1  35.0      1      0  53.1000        S      3\n",
       "4         0       3    0  35.0      0      0   8.0500        S      1"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in combine:\n",
    "    dataset['Sex'] = dataset['Sex'].map( {'female': 1, 'male': 0} ).astype(int)\n",
    "train_df.head()\n",
    "#将Sex的对应的字符串进行相应的更换\n",
    "#!!!文本处理的一大特征就是需要将相应的字符串转换为对应的数值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\python37\\lib\\site-packages\\seaborn\\axisgrid.py:316: UserWarning: The `size` parameter has been renamed to `height`; please update your code.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "d:\\python37\\lib\\site-packages\\numpy\\lib\\histograms.py:839: RuntimeWarning: invalid value encountered in greater_equal\n",
      "  keep = (tmp_a >= first_edge)\n",
      "d:\\python37\\lib\\site-packages\\numpy\\lib\\histograms.py:840: RuntimeWarning: invalid value encountered in less_equal\n",
      "  keep &= (tmp_a <= last_edge)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x15247f9b588>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 514.88x475.2 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#方法1.在平均和标准误差之中产生随机的数字\n",
    "#方法2.更准确的猜测缺失值的方式是去使用\n",
    "#其他相关的特征，比如Age,Gender,Pclass.\n",
    "#猜想Age的值对于不同种类和性别的人使用\n",
    "#中位值\n",
    "#方法3.不再使用基于中位数猜测年龄，而是\n",
    "#在均值和中间误差之间产生随机数，基于\n",
    "#Pclass和Gender的混合\n",
    "#!!!我的理解是一个是从单纯的误差之中去\n",
    "#选取中位数，而另外一个是结合不同种类\n",
    "#的人的特征之后再进行选取中位数\n",
    "# grid = sns.FacetGrid(train_df, col='Pclass', hue='Gender')\n",
    "grid = sns.FacetGrid(train_df, row='Pclass', col='Sex', size=2.2, aspect=1.6)\n",
    "#python对应的size，aspect的值\n",
    "grid.map(plt.hist, 'Age', alpha=.5, bins=20)\n",
    "grid.add_legend()\n",
    "#对应参数在如下网站https://www.jb51.net/article/191933.htm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0.],\n",
       "       [0., 0., 0.]])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "guess_ages = np.zeros((2,3))\n",
    "guess_ages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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",
       "      <th>Title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex  Age  SibSp  Parch     Fare Embarked  Title\n",
       "0         0       3    0   22      1      0   7.2500        S      1\n",
       "1         1       1    1   38      1      0  71.2833        C      3\n",
       "2         1       3    1   26      0      0   7.9250        S      2\n",
       "3         1       1    1   35      1      0  53.1000        S      3\n",
       "4         0       3    0   35      0      0   8.0500        S      1"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in combine:\n",
    "    for i in range(0, 2):\n",
    "        for j in range(0, 3):\n",
    "            guess_df = dataset[(dataset['Sex'] == i) & \\\n",
    "                                  (dataset['Pclass'] == j+1)]['Age'].dropna()\n",
    "            # age_mean = guess_df.mean()\n",
    "            # age_std = guess_df.std()\n",
    "            # age_guess = rnd.uniform(age_mean - age_std, age_mean + age_std)\n",
    "            # 这里的dropna函数的含义代表删除缺失的对象\n",
    "            age_guess = guess_df.median()\n",
    "            # Convert random age float to nearest .5 age\n",
    "            guess_ages[i,j] = int( age_guess/0.5 + 0.5 ) * 0.5\n",
    "        #计算对应的'Sex'以及'Pclass'中的平均值以及中位值\n",
    "            \n",
    "    for i in range(0, 2):\n",
    "        for j in range(0, 3):\n",
    "            dataset.loc[ (dataset.Age.isnull()) & (dataset.Sex == i) & (dataset.Pclass == j+1),\\\n",
    "                    'Age'] = guess_ages[i,j]\n",
    "        #如果'Sex'和'Age'的对应值缺失的时候，将对应的值放入\n",
    "    dataset['Age'] = dataset['Age'].astype(int)\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AgeBand</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>(-0.08, 16.0]</td>\n",
       "      <td>0.550000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(16.0, 32.0]</td>\n",
       "      <td>0.337374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(32.0, 48.0]</td>\n",
       "      <td>0.412037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(48.0, 64.0]</td>\n",
       "      <td>0.434783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(64.0, 80.0]</td>\n",
       "      <td>0.090909</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         AgeBand  Survived\n",
       "0  (-0.08, 16.0]  0.550000\n",
       "1   (16.0, 32.0]  0.337374\n",
       "2   (32.0, 48.0]  0.412037\n",
       "3   (48.0, 64.0]  0.434783\n",
       "4   (64.0, 80.0]  0.090909"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df['AgeBand'] = pd.cut(train_df['Age'], 5)\n",
    "train_df[['AgeBand', 'Survived']].groupby(['AgeBand'], as_index=False).mean().sort_values(by='AgeBand', ascending=True)\n",
    "#创建年龄段，判断与生存的关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>(16.0, 32.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>C</td>\n",
       "      <td>3</td>\n",
       "      <td>(32.0, 48.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "      <td>(16.0, 32.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>3</td>\n",
       "      <td>(32.0, 48.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>(32.0, 48.0]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex  Age  SibSp  Parch     Fare Embarked  Title  \\\n",
       "0         0       3    0    1      1      0   7.2500        S      1   \n",
       "1         1       1    1    2      1      0  71.2833        C      3   \n",
       "2         1       3    1    1      0      0   7.9250        S      2   \n",
       "3         1       1    1    2      1      0  53.1000        S      3   \n",
       "4         0       3    0    2      0      0   8.0500        S      1   \n",
       "\n",
       "        AgeBand  \n",
       "0  (16.0, 32.0]  \n",
       "1  (32.0, 48.0]  \n",
       "2  (16.0, 32.0]  \n",
       "3  (32.0, 48.0]  \n",
       "4  (32.0, 48.0]  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in combine:    \n",
    "    dataset.loc[ dataset['Age'] <= 16, 'Age'] = 0\n",
    "    dataset.loc[(dataset['Age'] > 16) & (dataset['Age'] <= 32), 'Age'] = 1\n",
    "    dataset.loc[(dataset['Age'] > 32) & (dataset['Age'] <= 48), 'Age'] = 2\n",
    "    dataset.loc[(dataset['Age'] > 48) & (dataset['Age'] <= 64), 'Age'] = 3\n",
    "    dataset.loc[ dataset['Age'] > 64, 'Age'] = 4\n",
    "    #后面一个'Age'是对应的属性标记'Age',即改变的是哪个属性的值\n",
    "    #!!!对太过于松散的数据，比如年龄段进行分段，这一操作也很巧妙\n",
    "train_df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <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",
       "      <th>Title</th>\n",
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       "      <td>0</td>\n",
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       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>S</td>\n",
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       "      <th>3</th>\n",
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       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex  Age  SibSp  Parch     Fare Embarked  Title\n",
       "0         0       3    0    1      1      0   7.2500        S      1\n",
       "1         1       1    1    2      1      0  71.2833        C      3\n",
       "2         1       3    1    1      0      0   7.9250        S      2\n",
       "3         1       1    1    2      1      0  53.1000        S      3\n",
       "4         0       3    0    2      0      0   8.0500        S      1"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df = train_df.drop(['AgeBand'], axis=1)\n",
    "combine = [train_df, test_df]\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FamilySize</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.724138</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.578431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.552795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>0.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.303538</td>\n",
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       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.200000</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>0.136364</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>11</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   FamilySize  Survived\n",
       "3           4  0.724138\n",
       "2           3  0.578431\n",
       "1           2  0.552795\n",
       "6           7  0.333333\n",
       "0           1  0.303538\n",
       "4           5  0.200000\n",
       "5           6  0.136364\n",
       "7           8  0.000000\n",
       "8          11  0.000000"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#创建一个新的特征值FamilySize用来记录'SibSp'以及'Parch'的属性和\n",
    "for dataset in combine:\n",
    "    dataset['FamilySize'] = dataset['SibSp'] + dataset['Parch'] + 1\n",
    "train_df[['FamilySize', 'Survived']].groupby(['FamilySize'], as_index=False).mean().sort_values(by='Survived', ascending=False)\n",
    "#显示平均值关于Survived的逆序关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 11 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    int32  \n",
      " 4   SibSp       891 non-null    int64  \n",
      " 5   Parch       891 non-null    int64  \n",
      " 6   Fare        891 non-null    float64\n",
      " 7   Embarked    889 non-null    object \n",
      " 8   Title       891 non-null    int64  \n",
      " 9   FamilySize  891 non-null    int64  \n",
      " 10  IsAlone     891 non-null    int64  \n",
      "dtypes: float64(1), int32(2), int64(7), object(1)\n",
      "memory usage: 69.7+ KB\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 418 entries, 0 to 417\n",
      "Data columns (total 11 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  418 non-null    int64  \n",
      " 1   Pclass       418 non-null    int64  \n",
      " 2   Sex          418 non-null    int32  \n",
      " 3   Age          418 non-null    int32  \n",
      " 4   SibSp        418 non-null    int64  \n",
      " 5   Parch        418 non-null    int64  \n",
      " 6   Fare         417 non-null    float64\n",
      " 7   Embarked     418 non-null    object \n",
      " 8   Title        418 non-null    int64  \n",
      " 9   FamilySize   418 non-null    int64  \n",
      " 10  IsAlone      418 non-null    int64  \n",
      "dtypes: float64(1), int32(2), int64(7), object(1)\n",
      "memory usage: 32.8+ KB\n"
     ]
    }
   ],
   "source": [
    "for dataset in combine:\n",
    "    dataset['IsAlone'] = 0\n",
    "    dataset.loc[dataset['FamilySize'] == 1, 'IsAlone'] = 1\n",
    "train_df[['IsAlone', 'Survived']].groupby(['IsAlone'], as_index=False).mean()\n",
    "#IsAlone是否为独自的一人，判断与生存率的关系\n",
    "train_df.info()\n",
    "test_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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    int32  \n",
      " 4   Fare      891 non-null    float64\n",
      " 5   Embarked  889 non-null    object \n",
      " 6   Title     891 non-null    int64  \n",
      " 7   IsAlone   891 non-null    int64  \n",
      "dtypes: float64(1), int32(2), int64(4), object(1)\n",
      "memory usage: 48.9+ KB\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 418 entries, 0 to 417\n",
      "Data columns (total 8 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  418 non-null    int64  \n",
      " 1   Pclass       418 non-null    int64  \n",
      " 2   Sex          418 non-null    int32  \n",
      " 3   Age          418 non-null    int32  \n",
      " 4   Fare         417 non-null    float64\n",
      " 5   Embarked     418 non-null    object \n",
      " 6   Title        418 non-null    int64  \n",
      " 7   IsAlone      418 non-null    int64  \n",
      "dtypes: float64(1), int32(2), int64(4), object(1)\n",
      "memory usage: 23.0+ KB\n"
     ]
    }
   ],
   "source": [
    "train_df = train_df.drop(['Parch', 'SibSp', 'FamilySize'], axis=1)\n",
    "test_df = test_df.drop(['Parch', 'SibSp', 'FamilySize'], axis=1)\n",
    "combine = [train_df, test_df]\n",
    "train_df.head()\n",
    "#删除无关的行列对应值\n",
    "train_df.info()\n",
    "test_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age*Class  Age  Pclass\n",
       "0          3    1       3\n",
       "1          2    2       1\n",
       "2          3    1       3\n",
       "3          2    2       1\n",
       "4          6    2       3\n",
       "5          3    1       3\n",
       "6          3    3       1\n",
       "7          0    0       3\n",
       "8          3    1       3\n",
       "9          0    0       2"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in combine:\n",
    "    dataset['Age*Class'] = dataset.Age * dataset.Pclass\n",
    "train_df.loc[:, ['Age*Class', 'Age', 'Pclass']].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 9 columns):\n",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   Survived   891 non-null    int64  \n",
      " 1   Pclass     891 non-null    int64  \n",
      " 2   Sex        891 non-null    int32  \n",
      " 3   Age        891 non-null    int32  \n",
      " 4   Fare       891 non-null    float64\n",
      " 5   Embarked   889 non-null    object \n",
      " 6   Title      891 non-null    int64  \n",
      " 7   IsAlone    891 non-null    int64  \n",
      " 8   Age*Class  891 non-null    int64  \n",
      "dtypes: float64(1), int32(2), int64(5), object(1)\n",
      "memory usage: 55.8+ KB\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 418 entries, 0 to 417\n",
      "Data columns (total 9 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  418 non-null    int64  \n",
      " 1   Pclass       418 non-null    int64  \n",
      " 2   Sex          418 non-null    int32  \n",
      " 3   Age          418 non-null    int32  \n",
      " 4   Fare         417 non-null    float64\n",
      " 5   Embarked     418 non-null    object \n",
      " 6   Title        418 non-null    int64  \n",
      " 7   IsAlone      418 non-null    int64  \n",
      " 8   Age*Class    418 non-null    int64  \n",
      "dtypes: float64(1), int32(2), int64(5), object(1)\n",
      "memory usage: 26.2+ KB\n"
     ]
    }
   ],
   "source": [
    "freq_port = train_df.Embarked.dropna().mode()[0]\n",
    "#dropna()删除缺失值，mode()获取沿选定轴的每个元素\n",
    "#的mode(s),一组值的mode是最常出现的值，它可以是多个值\n",
    "freq_port\n",
    "#Embarked特征有S,Q,C的价值，我们选择发生最常见的\n",
    "train_df.info()\n",
    "test_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "freq_port = ***\n",
      "S\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>Embarked</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>C</td>\n",
       "      <td>0.553571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Q</td>\n",
       "      <td>0.389610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>S</td>\n",
       "      <td>0.339009</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Embarked  Survived\n",
       "0        C  0.553571\n",
       "1        Q  0.389610\n",
       "2        S  0.339009"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('freq_port = ***')\n",
    "print(freq_port)\n",
    "for dataset in combine:\n",
    "    dataset['Embarked'] = dataset['Embarked'].fillna(freq_port)\n",
    "train_df[['Embarked', 'Survived']].groupby(['Embarked'], as_index=False).mean().sort_values(by='Survived', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 9 columns):\n",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   Survived   891 non-null    int64  \n",
      " 1   Pclass     891 non-null    int64  \n",
      " 2   Sex        891 non-null    int32  \n",
      " 3   Age        891 non-null    int32  \n",
      " 4   Fare       891 non-null    float64\n",
      " 5   Embarked   891 non-null    int32  \n",
      " 6   Title      891 non-null    int64  \n",
      " 7   IsAlone    891 non-null    int64  \n",
      " 8   Age*Class  891 non-null    int64  \n",
      "dtypes: float64(1), int32(3), int64(5)\n",
      "memory usage: 52.3 KB\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 418 entries, 0 to 417\n",
      "Data columns (total 9 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  418 non-null    int64  \n",
      " 1   Pclass       418 non-null    int64  \n",
      " 2   Sex          418 non-null    int32  \n",
      " 3   Age          418 non-null    int32  \n",
      " 4   Fare         417 non-null    float64\n",
      " 5   Embarked     418 non-null    int32  \n",
      " 6   Title        418 non-null    int64  \n",
      " 7   IsAlone      418 non-null    int64  \n",
      " 8   Age*Class    418 non-null    int64  \n",
      "dtypes: float64(1), int32(3), int64(5)\n",
      "memory usage: 24.6 KB\n"
     ]
    }
   ],
   "source": [
    "for dataset in combine:\n",
    "    dataset['Embarked'] = dataset['Embarked'].map( {'S': 0, 'C': 1, 'Q': 2} ).astype(int)\n",
    "    #使用这个map字典，并且对应完字典中的值之后转为int类型的数值\n",
    "train_df.head()\n",
    "train_df.info()\n",
    "test_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 9 columns):\n",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   Survived   891 non-null    int64  \n",
      " 1   Pclass     891 non-null    int64  \n",
      " 2   Sex        891 non-null    int32  \n",
      " 3   Age        891 non-null    int32  \n",
      " 4   Fare       891 non-null    float64\n",
      " 5   Embarked   891 non-null    int32  \n",
      " 6   Title      891 non-null    int64  \n",
      " 7   IsAlone    891 non-null    int64  \n",
      " 8   Age*Class  891 non-null    int64  \n",
      "dtypes: float64(1), int32(3), int64(5)\n",
      "memory usage: 52.3 KB\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 418 entries, 0 to 417\n",
      "Data columns (total 9 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  418 non-null    int64  \n",
      " 1   Pclass       418 non-null    int64  \n",
      " 2   Sex          418 non-null    int32  \n",
      " 3   Age          418 non-null    int32  \n",
      " 4   Fare         418 non-null    float64\n",
      " 5   Embarked     418 non-null    int32  \n",
      " 6   Title        418 non-null    int64  \n",
      " 7   IsAlone      418 non-null    int64  \n",
      " 8   Age*Class    418 non-null    int64  \n",
      "dtypes: float64(1), int32(3), int64(5)\n",
      "memory usage: 24.6 KB\n"
     ]
    }
   ],
   "source": [
    "#获取车费当中出现最多的值放到车费(Fare)的缺失值之中\n",
    "test_df['Fare'].fillna(test_df['Fare'].dropna().median(),\n",
    "                      inplace=True)\n",
    "#test_df['Fare'].median没打括号，导致这里面内层放置成为\n",
    "#object()对象，相当于里面没调用这个函数，放置的只是这个\n",
    "#函数的对象\n",
    "test_df.head()\n",
    "train_df.info()\n",
    "test_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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>FareBand</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>(-0.001, 7.91]</td>\n",
       "      <td>0.197309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(7.91, 14.454]</td>\n",
       "      <td>0.303571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(14.454, 31.0]</td>\n",
       "      <td>0.454955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(31.0, 512.329]</td>\n",
       "      <td>0.581081</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          FareBand  Survived\n",
       "0   (-0.001, 7.91]  0.197309\n",
       "1   (7.91, 14.454]  0.303571\n",
       "2   (14.454, 31.0]  0.454955\n",
       "3  (31.0, 512.329]  0.581081"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#!!!车费这里面的点过于的离散，所以也需要化为具体的数字\n",
    "train_df['FareBand'] = pd.qcut(train_df['Fare'], 4)\n",
    "train_df[['FareBand', 'Survived']].groupby(['FareBand'], as_index=False).mean().sort_values(by='FareBand', ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 10 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    int32   \n",
      " 4   Fare       891 non-null    float64 \n",
      " 5   Embarked   891 non-null    int32   \n",
      " 6   Title      891 non-null    int64   \n",
      " 7   IsAlone    891 non-null    int64   \n",
      " 8   Age*Class  891 non-null    int64   \n",
      " 9   FareBand   891 non-null    category\n",
      "dtypes: category(1), float64(1), int32(3), int64(5)\n",
      "memory usage: 53.4 KB\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 418 entries, 0 to 417\n",
      "Data columns (total 9 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  418 non-null    int64  \n",
      " 1   Pclass       418 non-null    int64  \n",
      " 2   Sex          418 non-null    int32  \n",
      " 3   Age          418 non-null    int32  \n",
      " 4   Fare         418 non-null    float64\n",
      " 5   Embarked     418 non-null    int32  \n",
      " 6   Title        418 non-null    int64  \n",
      " 7   IsAlone      418 non-null    int64  \n",
      " 8   Age*Class    418 non-null    int64  \n",
      "dtypes: float64(1), int32(3), int64(5)\n",
      "memory usage: 24.6 KB\n",
      "type = ***\n",
      "0       7.2500\n",
      "1      71.2833\n",
      "2       7.9250\n",
      "3      53.1000\n",
      "4       8.0500\n",
      "        ...   \n",
      "886    13.0000\n",
      "887    30.0000\n",
      "888    23.4500\n",
      "889    30.0000\n",
      "890     7.7500\n",
      "Name: Fare, Length: 891, dtype: float64\n",
      "type = ***\n",
      "0        7.8292\n",
      "1        7.0000\n",
      "2        9.6875\n",
      "3        8.6625\n",
      "4       12.2875\n",
      "         ...   \n",
      "413      8.0500\n",
      "414    108.9000\n",
      "415      7.2500\n",
      "416      8.0500\n",
      "417     22.3583\n",
      "Name: Fare, Length: 418, dtype: float64\n"
     ]
    },
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
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       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
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       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "      <th>IsAlone</th>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
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       "      <td>0</td>\n",
       "      <td>2</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex  Age  Fare  Embarked  Title  IsAlone  Age*Class\n",
       "0         0       3    0    1     0         0      1        0          3\n",
       "1         1       1    1    2     3         1      3        0          2\n",
       "2         1       3    1    1     1         0      2        1          3\n",
       "3         1       1    1    2     3         0      3        0          2\n",
       "4         0       3    0    2     1         0      1        1          6\n",
       "5         0       3    0    1     1         2      1        1          3\n",
       "6         0       1    0    3     3         0      1        1          3\n",
       "7         0       3    0    0     2         0      4        0          0\n",
       "8         1       3    1    1     1         0      3        0          3\n",
       "9         1       2    1    0     2         1      3        0          0"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.info()\n",
    "test_df.info()\n",
    "for dataset in combine:\n",
    "    print('type = ***')\n",
    "    print(dataset['Fare'])\n",
    "    dataset.loc[dataset['Fare'] <= 7.91, 'Fare'] = 0\n",
    "    dataset.loc[(dataset['Fare'] > 7.91) & (dataset['Fare'] <= 14.454), 'Fare'] = 1\n",
    "    dataset.loc[(dataset['Fare'] > 14.454) & (dataset['Fare'] <= 31), 'Fare']   = 2\n",
    "    dataset.loc[dataset['Fare'] > 31, 'Fare'] = 3\n",
    "    dataset['Fare'] = dataset['Fare'].astype(int)\n",
    "train_df = train_df.drop(['FareBand'], axis=1)\n",
    "combine = [train_df, test_df]\n",
    "train_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((891, 8), (891,), (418, 8))"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#现在需要训练和预测需要的解决方案\n",
    "#总共有60多个预测模型算法，我们需要理解问题的类型和解题\n",
    "#所需要的方法将需求缩小到我们可以预测的少数几个模型\n",
    "#我们的问题是一个分类和回归问题，需要鉴别输出(是否Survived)\n",
    "#与变量之间的关系(Gender,Age,Port...)，我们需要使用监督学习\n",
    "#当我们通过一个给定的数据集来训练我们的模型。\n",
    "#具体的模型包括：回归模型，KNN或者k-邻近邻居\n",
    "#支持向量机模型，朴素贝叶斯分类器，决策树，\n",
    "#随机森林，感知机模型，人工智能网络模型，RVM模型\n",
    "X_train = train_df.drop(\"Survived\",axis = 1)\n",
    "Y_train = train_df[\"Survived\"]\n",
    "X_test = test_df.drop(\"PassengerId\",axis=1).copy()\n",
    "X_train.shape,Y_train.shape,X_test.shape\n",
    "#X_train,Y_train使用的都是上面进行处理过的数据，便于进行\n",
    "#后续的处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "81.37"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#逻辑斯蒂回归中，假设函数h=g(z)用于计算属于某\n",
    "#样本的可能性，if P(y=1|x)>0.5用于计算给出样本\n",
    "#的边界，决策边界为一个方程，用于标识出分类函数\n",
    "#的分类边界，逻辑回归中最常用的代价函数是交叉熵\n",
    "# Logistic Regression\n",
    "logreg = LogisticRegression()\n",
    "logreg.fit(X_train, Y_train)\n",
    "Y_pred = logreg.predict(X_test)\n",
    "acc_log = round(logreg.score(X_train, Y_train) * 100, 2)\n",
    "#2表示小数点的位数，\n",
    "acc_log\n",
    "#使用回归模型先跑下X_train以及Y_train对应的数据，得出\n",
    "#相应的准确率，每次使用交叉熵进行梯度下降算法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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>Feature</th>\n",
       "      <th>Correlation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Sex</td>\n",
       "      <td>2.201057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Title</td>\n",
       "      <td>0.406027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Embarked</td>\n",
       "      <td>0.276628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>IsAlone</td>\n",
       "      <td>0.185986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Age*Class</td>\n",
       "      <td>-0.050260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Fare</td>\n",
       "      <td>-0.071665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Age</td>\n",
       "      <td>-0.469638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Pclass</td>\n",
       "      <td>-1.200309</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Feature  Correlation\n",
       "1        Sex     2.201057\n",
       "5      Title     0.406027\n",
       "4   Embarked     0.276628\n",
       "6    IsAlone     0.185986\n",
       "7  Age*Class    -0.050260\n",
       "3       Fare    -0.071665\n",
       "2        Age    -0.469638\n",
       "0     Pclass    -1.200309"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#计算决策函数中特征的系数，正系数提升反应的log几率，\n",
    "#负系数减少log的反应几率\n",
    "#1.Sex为相应的正系数，对应的参数为(male:0,female:1),\n",
    "#因为female=1时生存率较高，所以生存率为正相关\n",
    "#2.Pclass参数相反，Survived=1时Pclass参数对应的\n",
    "#可能性减少的最少(Pclass=3死亡人最多，Pclass=1\n",
    "#死亡人最少)\n",
    "#3.Age*Class为一个好的人工模型特征因为它与Survived\n",
    "#有第二稿的负联系\n",
    "coeff_df = pd.DataFrame(train_df.columns.delete(0))\n",
    "coeff_df.columns = ['Feature']\n",
    "coeff_df[\"Correlation\"] = pd.Series(logreg.coef_[0])\n",
    "coeff_df.sort_values(by='Correlation', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "82.83"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc = SVC()\n",
    "svc.fit(X_train,Y_train)\n",
    "Y_pred = svc.predict(X_test)\n",
    "acc_svc = round(svc.score(X_train,Y_train)*100,2)\n",
    "acc_svc\n",
    "#支持向量机的目的在于求得最优的即几何间隔最大的超平面，\n",
    "#在样本数据是线性可分的时候，这里的间隔最大化又叫硬间隔最大化\n",
    "#（训练数据近似可分的话就叫软间隔）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "84.06"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn = KNeighborsClassifier(n_neighbors = 3)\n",
    "knn.fit(X_train, Y_train)\n",
    "Y_pred = knn.predict(X_test)\n",
    "acc_knn = round(knn.score(X_train, Y_train) * 100, 2)\n",
    "acc_knn\n",
    "#knn：选择k个最近的邻居，相当于当预测一个新值的时候\n",
    "#根据它距离最近的k个点来判断x属于哪个类别\n",
    "#k的选择采用交叉验证的方式，距离的计算采用欧式距离\n",
    "\n",
    "#特点：简单易用，模型训练时间快"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "76.88"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Gaussian Naive Bayes\n",
    "#贝叶斯模型公式:P(A|B)=(P(B|A)P(A))//P(B)\n",
    "#通俗地讲分类算法就是把大量已知特征及类别\n",
    "#的样本对象输入计算机，让计算机根据这些已\n",
    "#知的类别与特征归纳出类别与特征之间的规律\n",
    "#（准确地说应该是分类模型），最终目的是运\n",
    "#用得到的分类模型对新输入的对象（该对象已\n",
    "#知特征，类别是不知道的）判断出该对象所属分类。   \n",
    "gaussian = GaussianNB()\n",
    "gaussian.fit(X_train, Y_train)\n",
    "Y_pred = gaussian.predict(X_test)\n",
    "acc_gaussian = round(gaussian.score(X_train, Y_train) * 100, 2)\n",
    "acc_gaussian"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "79.35"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "perceptron = Perceptron()\n",
    "perceptron.fit(X_train, Y_train)\n",
    "Y_pred = perceptron.predict(X_test)\n",
    "acc_perceptron = round(perceptron.score(X_train, Y_train) * 100, 2)\n",
    "acc_perceptron\n",
    "#感知机模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\python37\\lib\\site-packages\\sklearn\\svm\\_base.py:977: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "79.46"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linear_svc = LinearSVC()\n",
    "linear_svc.fit(X_train, Y_train)\n",
    "Y_pred = linear_svc.predict(X_test)\n",
    "acc_linear_svc = round(linear_svc.score(X_train, Y_train) * 100, 2)\n",
    "acc_linear_svc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "76.54"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Stochastic Gradient Descent\n",
    "sgd = SGDClassifier()\n",
    "sgd.fit(X_train, Y_train)\n",
    "Y_pred = sgd.predict(X_test)\n",
    "acc_sgd = round(sgd.score(X_train, Y_train) * 100, 2)\n",
    "acc_sgd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "86.64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Decision Tree\n",
    "\n",
    "decision_tree = DecisionTreeClassifier()\n",
    "decision_tree.fit(X_train, Y_train)\n",
    "Y_pred = decision_tree.predict(X_test)\n",
    "acc_decision_tree = round(decision_tree.score(X_train, Y_train) * 100, 2)\n",
    "acc_decision_tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "86.64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Random Forest\n",
    "\n",
    "random_forest = RandomForestClassifier(n_estimators=100)\n",
    "random_forest.fit(X_train, Y_train)\n",
    "Y_pred = random_forest.predict(X_test)\n",
    "random_forest.score(X_train, Y_train)\n",
    "acc_random_forest = round(random_forest.score(X_train, Y_train) * 100, 2)\n",
    "acc_random_forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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>Model</th>\n",
       "      <th>Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Random Forest</td>\n",
       "      <td>86.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Decision Tree</td>\n",
       "      <td>86.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>KNN</td>\n",
       "      <td>84.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Support Vector Machines</td>\n",
       "      <td>82.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>81.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Linear SVC</td>\n",
       "      <td>79.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Perceptron</td>\n",
       "      <td>79.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Naive Bayes</td>\n",
       "      <td>76.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Stochastic Gradient Decent</td>\n",
       "      <td>76.54</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Model  Score\n",
       "3               Random Forest  86.64\n",
       "8               Decision Tree  86.64\n",
       "1                         KNN  84.06\n",
       "0     Support Vector Machines  82.83\n",
       "2         Logistic Regression  81.37\n",
       "7                  Linear SVC  79.46\n",
       "5                  Perceptron  79.35\n",
       "4                 Naive Bayes  76.88\n",
       "6  Stochastic Gradient Decent  76.54"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "models = pd.DataFrame({\n",
    "    'Model': ['Support Vector Machines', 'KNN', 'Logistic Regression', \n",
    "              'Random Forest', 'Naive Bayes', 'Perceptron', \n",
    "              'Stochastic Gradient Decent', 'Linear SVC', \n",
    "              'Decision Tree'],\n",
    "    'Score': [acc_svc, acc_knn, acc_log, \n",
    "              acc_random_forest, acc_gaussian, acc_perceptron, \n",
    "              acc_sgd, acc_linear_svc, acc_decision_tree]})\n",
    "models.sort_values(by='Score', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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