Lesson18-如何对Data Set数据集进行清洗，转换，汇总及建模之完整步骤.ipynb

{
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# 第十八课 如何对Data Set数据集进行清洗，转换，汇总及建模之完整步骤\n",
    "\n",
    "\n",
    "Python零基础学习建模：https://www.ixigua.com/home/77346806707?utm_source=xiguastudio\n",
    "\n",
    "Python源文件及数据下载链接: https://pan.baidu.com/s/1yuNlG6u9_C31fzhzbzqASA 提取码: ebrh\n",
    "\n",
    "Titanic Data Set from Kaggle (https://www.kaggle.com/c/titanic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 第一步 读取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('./titanic/titanic.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <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",
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       "    <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",
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       "      <td>S</td>\n",
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       "      <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",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <th>886</th>\n",
       "      <td>887</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Montvila, Rev. Juozas</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>211536</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>888</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Graham, Miss. Margaret Edith</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112053</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>B42</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>889</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>W./C. 6607</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>890</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Behr, Mr. Karl Howell</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111369</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>C148</td>\n",
       "      <td>C</td>\n",
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       "      <th>890</th>\n",
       "      <td>891</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Dooley, Mr. Patrick</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370376</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 12 columns</p>\n",
       "</div>"
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       "     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",
       "886          887         0       2   \n",
       "887          888         1       1   \n",
       "888          889         0       3   \n",
       "889          890         1       1   \n",
       "890          891         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",
       "886                              Montvila, Rev. Juozas    male  27.0      0   \n",
       "887                       Graham, Miss. Margaret Edith  female  19.0      0   \n",
       "888           Johnston, Miss. Catherine Helen \"Carrie\"  female   NaN      1   \n",
       "889                              Behr, Mr. Karl Howell    male  26.0      0   \n",
       "890                                Dooley, Mr. Patrick    male  32.0      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  \n",
       "..     ...               ...      ...   ...      ...  \n",
       "886      0            211536  13.0000   NaN        S  \n",
       "887      0            112053  30.0000   B42        S  \n",
       "888      2        W./C. 6607  23.4500   NaN        S  \n",
       "889      0            111369  30.0000  C148        C  \n",
       "890      0            370376   7.7500   NaN        Q  \n",
       "\n",
       "[891 rows x 12 columns]"
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   ],
   "source": [
    "data #C = Cherbourg, Q = Queenstown, S = Southampton"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 第二步 缺失数据检查，数据分析工作"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {},
   "outputs": [
    {
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       "      <td>False</td>\n",
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      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass   Name    Sex    Age  SibSp  Parch  Ticket  \\\n",
       "0          False     False   False  False  False  False  False  False   False   \n",
       "1          False     False   False  False  False  False  False  False   False   \n",
       "2          False     False   False  False  False  False  False  False   False   \n",
       "3          False     False   False  False  False  False  False  False   False   \n",
       "4          False     False   False  False  False  False  False  False   False   \n",
       "..           ...       ...     ...    ...    ...    ...    ...    ...     ...   \n",
       "886        False     False   False  False  False  False  False  False   False   \n",
       "887        False     False   False  False  False  False  False  False   False   \n",
       "888        False     False   False  False  False   True  False  False   False   \n",
       "889        False     False   False  False  False  False  False  False   False   \n",
       "890        False     False   False  False  False  False  False  False   False   \n",
       "\n",
       "      Fare  Cabin  Embarked  \n",
       "0    False   True     False  \n",
       "1    False  False     False  \n",
       "2    False   True     False  \n",
       "3    False  False     False  \n",
       "4    False   True     False  \n",
       "..     ...    ...       ...  \n",
       "886  False   True     False  \n",
       "887  False  False     False  \n",
       "888  False   True     False  \n",
       "889  False  False     False  \n",
       "890  False   True     False  \n",
       "\n",
       "[891 rows x 12 columns]"
      ]
     },
     "execution_count": 247,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull() #isna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 248,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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v/QApWIcQSqpbc8R9veUMxMPuI5b0IUmHFw/rh4c+RtrQDPAQsHzx3mWBpWz/rQNzDSGEttRqPU3fRlKrh4c+BpwvaSEwH9i3eO/awP35pxlCCO2rbBvMIUqctxzgvTeRuhKFMS5KnEMV9Y3wSrdZUVkXQugaI51yaFYE4tCSWLWGgTz/0JxS/9uo9K4JSS8HbgZ2IF3gm0Gqr/4T6fDQXkn7AvuRqu2OtX3RYOOFEMamMgdhqPauiSWBM1l0LNKXgSOKw0SXBt4jaWXgYFLeeEfgOElLdWbKIYTQmr5aT9O3kdTMivirpL4S9S1s7y1WwBOAlUmVdm8CrrU9D5gn6R5S74mbOjDnEEKbyr5y7ZSy5oiHXBFL2hP4l+3L6s8VQXh1Un/NFQHz4m71zwDLZp9tCCG0oVZr/jaShktN7E3quXkVsBFwrqSVbT9gey3SSvlkXtytfgrwZPbZhhBCGyqZmrD9n8Y9RTDeH5gh6ZO27yatfPuA3wJfkjQRWApYF7i1U5MOIbSnW0ucy5qaaGX72vHALEnzSSXP+9h+WNKpwBzSKvuzccBoCKFsequ8fQ3A9nYNDweqqjuL1EA+hBBKaSytiEOIEudQSVHiHMaUCJahikp6QEcE4hBC96j0irhfifNRpEIOgDWAG2zvVjSI35P0S+eYKHEOIZRNb1UDcf8SZ9u7Fc8vD1wJHCppReBjpL3GE4HbJf3Cdlk/CYQQulCNigZiXlziXHc0cJrtfwBI2rA4224N4MkIwmNbXKyrtm79M+4raVQaMhA3ljg3HJdUT1W8FTi0/lwRhA8kBehTOzPdUBbd+oM8VnRrQUdfSVfELZU4kw4H/Z7t3sY32/4GsAqwjaQ3559uCCG0rkZP07eRtNglzkUV3fbAsQ2vCTgOeC+wAJhHKn0OIZRQ2VeunVLWoNTq9jUB99Uf2LakPwHXk3ZNXGL7NxnmF0LogG5NTfSWNDXRUxvpfm8Nxk9YraSp8zCcuFhXbVUMxAvnP9h2FL14pd2ajjnveOT8EYvaUdARWhLBMlRRlbevhRDGmG79RVrS5mtNV9b9gUUncPzF9l7F86eQUsTTi8dTgS8U7/s9cEDsJw6hfKqYmsihrNvXmqmsmwgvbIMp6WXAucDawFeK56YU97ez/aikT5OOUvpX/mmHENpR9oDZKb3Dv2VUNLMi3hBYWtLlxfuPIB0YehQwteF9WwC3ACdJWhM423YE4RBCafT1lHNFPFxBB6RTOL4K7Eg6Kuk84G+2b+z3vhWBNwOHkQL0IZLWzjjXEEJoS20xbiOpmRXxXcA9Ra73LkmPkarn/tbvfY8BN9l+GEDS1aRqvLvyTTeURWxfq7buzRGXUzOBeG9gA+BjklYFlgH+McD7bgbWLzqxPQlsRhydNGaV/QcuhIHk2jUhaRxwOil1O490duc9A7xvBvC47c8MNV4zqYlzgOUkXQP8ANjb9sL+byrywYcDlwE3Aj+1HSc5hxBKo4+epm/D2BmYaHtz4DPASf3fIGk/0iJ2WMOuiG3PB/YY5LWj+j0+Hzi/mW8cQhg93fqJpncxVsSSpgHTGp6aYXtGcX8r4FIA2zdI2qTf125OygqcCawz3PeKgo4QQtdYnBxxEXRnDPLyMiyqrQDolTS+aAe8CmlX2X8D72vme0UgDqELdevFuoy7IZ4GpjQ8HteQst2VtIvsYtKxcktLutP2rMEGi0AcQugaGUucrwXeDfxQ0makGgoAbJ9KcThGcbjGOkMFYWixxBk4DbgQuLt47gzbP5B0GLA76bfFiXGAaAihTDJuX5tNOjTjOqAH2EvSHsDkhjxy01otcd4HONn2SQ3PbUC6qLdp8dR1kq6w/dziTiqUX+wjDlWUKxDb7iMVuDW6c4D3zWpmvFZLnN9AOphjJ9Kq+BBgXeAq23NJL94NvA64oZmJhGqJYBmqaHF2TYykVkucfw/8X3GU0n2kjmu3kM6qmyLppaTeE5M6MusQQmhB32LcRlIzgfgu4Lu2a7bvIpUyX2r75uL12cDrbd8BfAO4hLS5+Ubg0Q7MOYQQWlLlXhMDlTj/TNJHbf8WeCtwc9Eac0XbW0laFrgciMq6MSpyxKGKqtwY/hxgVlHiXCMF5rnANyTNBx4mVZ88A6wp6SZgPil1Udb2n6FNESyrrVv//irb9GeIEuctBnhuv7ZnFEIIHVLWlWEUdITQhbo1tVTl1EQIITSlzEEYKpyaAJB0OPAeYAKpB+dvgFmknPGtpENC+yQdCuxWfNnFto/OPuMQQmhRWU8ybqaybjtSPnhLYGngU8DJwJG2r5I0HdhJ0p+AD5Aq62rAHEmzbf+5U5MPo6dbP9qGausraShuZh/xjqRijdmk/hIXkSrrflO8fgmwPenopLfb7i3K/5Yk7a4IIYRSKGtBRzOpiRWB1YF3Aa8CLiC1fKv/ankGWNb2AuBRST3AV4A/FAUgYQyKVWuooirvmngMuLPYxmZJc4FXNrw+hXRGXb1B0ExScP5Y3qmGEEJ7yrpropnUxDXA2yX1FJV1k4BfF7ljgKmkfHAP8HPgT7b3i2KOEELZ9FFr+jaSminouEjSNsBvSYH7AFJP4rMkTQDuAH5MOkxvW2ApSVOLLz/c9vWdmHgIoXXdmloq56W6Jrev2f70AE9v2+/xbGBi2zMKIXRctx6VVOl9xCGEMBaUdftaBOIQQtco64WrCMQhhK5R6RVx/xJn2+cUz+8BHGR78+LxqaQKvGeKL93J9lMDDBkqLirrQhWVMwy3XuKMpI2Aj5BOMK3bGNjRdpzMEUIonSpfrGsscV4G+L/iTLrjSYeGngUgaRywFjBD0krAObZndmLSYfTFqrXauvXvr1bSNXErJc4XAbcDhwLPN7xvEnAaqSHQEsCVkn4XTX9CKJ9u3b62sMKBuH+J8yuAhcAZpH3Dr5X0NeCTwNdtPwcg6QpgQyACcQihFMoZhpsLxNcAH5d0MrAK8CCwvu1eSWsA59s+RNK6wPmSNiZV4G0FfLtD8w4htKHsK9dOqeyuiYFKnAfqI2H7DknnATcAC4Bzbd+We8IhhPZ1a2qirBfremq10fsNMX7CauX89RSGFdvXqq2KgXjh/Afb7p22zxq7NB1zzr7/xyPWqy0KOkJLIliGKirrijgCcQiha/RWNUcMAx4eejMwnbR74i5gn+Lw0E8Bu5N+8XzZ9uyOzDqMukhNhCrqG8VU7FBarax7F3CM7YuLC3TvlDQHOBh4DWlP8R9JRSBhDIpgGaqonGG4xco60op3heJUjimkXRLPAg+QgvAkypuOCSF0qcpuX2Pgw0OPAr4JHAk8BVxVvPdvpKq7JYDj8k41lEmkJkIVVbnEeaDDQ88DNrB9m6QDgJOAy0gFH68qvu4ySdfa/m0nJh5GVwTLUEVl/ZjeSmXdJOBe4Oni9YdI+eMnSL0n5tmuSXoSWC73hEMI7evWX6S9JQ3FrR4e+iypnHkhMB/Y1/b9krYHbpDURwrgv+zc1EMIrapiQUcOucJw0W3ydFI/nXmknWP3NLy+O6k7ZS+p387HbA/67ds5PHTLAd73BeALzYwZQggjLWMl8c7ARNubS9qMlJ7dCUDSS4BjSenb5yR9n3SN7YLBBhuXa1YhhFB2fdSavg1jK+BSANs3AJs0vDYP2KLeiZK04J071GBRWRdCFyp7CqFTFic1IWkaMK3hqRm2ZxT3lyHtGKvrlTTe9sIiBfFIMcZBwGSGSdNGIA6hC3VrjnhxLtYVQXfGIC8/TaqhqBtne2H9QZFDPhFYG3iv7SGX2M1U1u0J7Fk8nAhsBLyl+CY9wJ9IB4j2Fu9/GXAdKT8y5HI8hBBGUsYc8bXAu4EfFjniW/q9fiYpRbHzUBfp6prZNTELmAUg6ZvATODTwBG2r5Y0i9SHYrakHUln2a3U5P9MCCGMmIyb12YDO0i6jrQg3as41X4y8DvSwcpzgCskQTq9aNCWD02nJiRtAqxn+wBJ5xQndEwAVqbIh5D+P7cnNQUKIYRSyVVZV6xy9+/39J0N9xdrI8Ti5IiPAI4uJtEraXXgV6SEtYvnfwlQ/AYIIZRU2XO5nVLWXhNNRW1JywHr2L6y/pztB2yvRWqHeXJnphdCCPnUarWmbyOp2eXzNqTVLwCSLpC0VvHwGcpbwh1CCP/RS1/Tt5HUbGpCwH0Nj48HZkmaDzwH7JN7YiGEzunW7WtlbQwfh4eG0IWq2MY0x+GhW6/21qZjzpwHfx2Hh4YQqqf0K+KSXqyLQBxC6BqVDcSDVNZdVdwHWAO4wfZuxfvHAb8Afm57es7JhvKo4kfbEHpr5dxX0FJlXb3xhaTlgSuBQxu+5FhghdwTDeUSwTJUUZWPSgJeWFnX8PTRwGm2/1G8ZxfSVrZLss4yhBAyGM3NCUNpqbIOQNLLgbdSrIYlrQ/sAewCfD7jHEMJRWoiVFFlc8QwcGUdKeB+r951DfgwsBpwBSlvPF/S/bYvzTfdUBYRLKutW//+qr4ifkFlXWF7Uj4YeOFxSpKOAh6OIBxCOXVtQUeVV8S8uLJusOdCl4jURKiisu6aiMq6ELpQFVfEOSrr1ltp06Zjzm2P3BiVdSGEkFtZe01EIA4hdI3K7yMOIYSqq+yKWNKSwLdJW9J6gX1t31m8dgpg29MlbQR8reFLNyMdnBc7J0IIpVDlFfE7gPG2t5C0A/AlSfsD55KOiv4KgO0/AtsBSNoVeCiC8NgVuyaqrVv/jMu6a6KZQHwXML5o5rMMsIB0UulRwNT+b5Y0iVSBt02+aYay6dYf5LGiirsmcqhVOBD/m5SWuBNYEXiX7b8Af5H0okBMOkb6R7YfzTbLEEJWZQ+YnVLlgo5DgctsHy7plcAVkjawPXeQ93+AVP4cQiip7l0RVzcQP0FKRwA8DiwJLDHQGyUtCyxl+295phdCCPlUeUV8CjBT0hxgAnCE7WcHee/awP2Z5hZC6JCyr1w7pbevnDniKHEOoQtVMTWRo8R55eXWbTrmPPzkHVHiHEIIuVU5RxxCCGNCZXPEA1XWkS7anQUsT7pw92Hb90raF9gPWAgca/uiDs07hBAWW1lXxOOaeM9/KuuAY4AvAScC59neBjgSWEfSysDBwJbAjsBxkpbqzLRDCGHx9dVqTd9GUjOBeKDKui2BV0j6FWnf8FXAm4Brbc+z/RRwD/C6jsw6hBBa0Fvra/o2kpoJxI2VdWcBpxaPn7C9PfBX4DBSkH6q4eueAZbNONcQQmhLrVZr+jaSWqqsAx4DLihev5CUrvgdMKXh66YAT+abaiiTaPoTqqiybTAZuLLuelLu+Duk5j63Ab8ldWabCCwFrAvcmnvCoRwiWFZbt/79lbUN5rAFHZImAzOBVUiVdV8HrgXOBiaR0hF72H6i2DUxjZTy+LLtnww1dhR0hDA6urWg4yUvWb3pmPP88w+MWEFHVNaF0IW6NRBPnPhfTcecuXP/GpV1IYSQW1+m3RDFLrLTgQ2BecA+tu9peP3dwOdJNRUzbZ811HgRiEPoQl2bI86XAdgZmGh7c0mbAScBO8F/iuBOAd4IPAtcK+lC2w8PNtioBuIcHzVCCKFZCxYj5kiaRrrmVTfD9ozi/lbApQC2b5C0ScP71gXusf1EMc41wNbAjwb7XrEiDiGEARRBd8YgL/evm+iVNN72wgFeG7amopmCjhBCCC/0NC+smxhXBOGBXhu2piICcQghLL5rSbUUFDniWxpeuwNYS9IKkiaQai2uH2qwUd2+FkIIVdSwa+J1QA+wF7AxMNn2jIZdE+NIuya+OdR4EYhDCGGURWoihBBGWQTiEEIYZRGIQwhhlMU+4tAxktYCXkO6ovyg7bggEcIAui4QS9pmsNdsX53pe4wjXUndArjR9vxM42YPbJKOB46w3SdpWeBs27tmGPdA4L+BFUhnHr4GOLDdcRvGHwe8DPhnzgAvaQ1gF2Dp+nO2j8kw7its/73hsWy73XGLsZYBVgfus/1spjGnAFOBifXnbJ+bY+zwYqUMxJK+BQM3DrW9d5vDf7T476tJbT1vAl5POolkuzbHRtIJwH2kH4yNgUeA/80wbqcC2zzgV5JOBY4GTs4wJsBupLLOK2x/TdJNmcZF0v+Q5vkEMEXSR23/MtPw3yeVrg7aF2BxSFofWA04QdKni6eXAI4DNsow/i7AZ0k/yz+UVLN9bLvjAj8HHgL+VjzO8Uv/L/3GWUDqbz7P9rrtjl9lpQzEwPnFfz8KXEfaPP1G0rl4bbG9O4CkXwA72V4oaQngF+2OXdjK9mGSrrT9Zkm/zjRupwLbUaTA/kPg47a/nWnc+vWH+g/evEzjAnwOeJPtf0paiXRKTK5A/JztozONBemk892AlYDdi+f6SHtQczgU2Iz0y+NY0kk5OQLxONsfzDBOo3VInxS/CZxp+7eSXg98LPP3qZxSBmLblwFI+qTtE4unr5WU64cNUqP7uvHAyzONu4SkNwH3F1U1L8s0bqcC22+Am0nnEJ4h6fW2pw39JU35PnA1sLqki4GfZRiz7jHb/wSw/Yikp9sdUNLaxd1HJO1B+jOpFd/jrlbHtT0HmCNpY9u/l7Q88GTGdEqf7XnFSrgmKUtqAvizpE2BP7Loz6GtFJvteQCSXm37t8Vzf5CkNudaeaUMxA0mS3oLKX2wBSmVkMs5wG2SbgVeC3wh07jnAqcBewMnkk40yeF7dCawnWC7/mlgJ0kH5xjU9mnFKd/rA3favmW4r1kMT0u6jPRLZBNgaUlfLr7vES2OeWbD/X2LG6Qg9JZWJ9pgSvFvbQngR5IesH1OhnHnSPoe6VT16aSflRy2Bd7d8LgGrJlp7CclfZF0vNoWwP2Zxq2sUlfWSVoXOAZYj1S/fcBQPT1bGH8Z0sel+2w/mmvchvFfaftvw7+z6fHWJXNgKy7KfBpYlZSe+XNjg+s2xp3Z76kFpHzjN+vtAdsYe9Cce7upleLMxXWLldrOwC9sLxjmy5oZ92pSD9ufkC6CXWv7DRnGXRbYHNiA9O/iwnbH7DRJk4A9SQugO4HTbfeO6qRGWalXxLbvANq+gj8QSesB04HlgPMk3Wr7ogzjHgw8X4y7l6RLbX8iw7hvIuUaJwJvloTtHLm1mcAlpAuV5xS3bTOM+xLgXmAOKYf5RuCfpHz0e1odVNKGtr9dpH32JaVoZtrOc/QCfBf4FfAHYG3gfcAeGcbts/14kUKYK+mZDGNC+kXxn9647ZL0DdsHSrqefhfobG+R43sAc0l/b4+SdgAtX9zvWqUMxJL+QfpH0MOL/zGsmunbnEpq1HEWKfhcArQdiEkXZLYl/WCsB+S6WPdt4ATSToGcXmp7pqQP2r5OUq5m/S+rXxgFLpN0ue3PFSvDlkj6BPB+SVsCXyHtTHmAdBrCx9uecbKa7ekAtk+UdGWmce+RdBzwUkmfIc07h8clfRww6SIgti9vY7wvFv/drd2JDeFM0o6MHUgXF8+l6GTWrUoZiG2vAiBpOdtPdvD73FOsUP6VcYVSI10IfKS4eLJCpnHvtj0r01gvIGmd4r+vAHJ9RFxG0jq27yxSKpMlvRSY3MaYU0k5xRpplbp2cXr4dRnm+x+S1rZ9l6RXk3K6OewP7ANcQzo+Z59M4z5G2ga3UfG4BrQciG0/UtxdgvTLbm3gVlL6KpdX295H0ta2Lyx+MXW1UgbiBheRjiTphMcl7QdMkrQbwzRuXgxXki6q7S7pFFJOMIefSDofuL3+RI5CA+Bg4FukfN3PWHSRql0HklI+q5BSNbOA9wNfamPMPtu9kjYm5fXrnw5yHrn1cdJ+3JeTVm37ZRp36/pKW9LSpAu6+7c7qO29Gh8Xf945nEO62HwdqZ/uTNIKNofxklYEasU1ilxppcoqeyDO/bGr0UeAI0i5qU2Kx22z/VnSBnsk3ZTjQk/hY8BPyfQLowhm55D2Zn8FOIN0xMsrSfnRthR7RD9KCshvA1ay/cVhvmxYxTazvYALisfrkW8VD7CN7Y0yjlf3RUmHkFaa5wDfyTGopKNJ/zYmkKoB7yKlxNrVa/uS4v6FxdxzOZJUG7AKcAP50kqVVfZAnPVjVz9HA2fZvn3Ydy4GSe8BDiBVDPVIWtH2BhmGftz2CRnGqfsS8L+2F0g6lvSx/x5SrvyCVgctLqLtTvozmEcK7q+y/Xz7U+ZIUgC7Hzhc0raki2s5L+i+Q9IpHbiKvzPpz3UCsIvtOzONOxV4BSlPfjJtFopIeltx99miEvBq0i/rRwb/qsV2nW1JehlpIZRrW1xllToQ296rWAG9mnR19aGMw18LnFh8NPoW8INMweLzwEGkj51XAttnGBPgUUlnAr9n0Qb7wQ42bMY423+WtCowyfbvASS1+zHxflIxxwds3y3pkkx/rti+Cdi0/ri4sr9mxk8dkApwHmoox621s1uguEBXv+B8J/B24MPFrpdW9zw3eqwo6JhSXPNYevgvGVL9AuvjpNOI66XHOSsjv0/6ZfSvIj34SVIuumuVOhDrhf0VZgFrkalxjO0fAz8ucmqnAF8jbTlr12O2r5e0v+1ZkvYa/kuaUt/bu3Lx33Y3gNcr9d5O2q6FpKV44aGHrfg66ULaGpLOJm/+FgClo8vPJJUN/1XSfhkLRt6VaZy6xpWvSUUoOf1d0t6kFexxpE8gLeufc67LmHuG1NvkO6Sftydp+OXarUodiHlhf4WvK2/jmP8iNeN5L2mVOTXT0POUOrwtKWlHXlhK3TLbRxc/DEuSglu72/h+JelaUk74PcUOgTOAH7Q5zxNIDW62Je0MeKNSI6Tv2L61zTnXnQp8yPbtkjYgfRzfOtPYS5JSHY1/zi1fsKsXmCgdMPkm26dKOg84qZ1JSjqyaO6zHymQ/YhUJJFl21kncs9F2grShb/JwFvJdG2m6sreGL6TjWN+Qiow2Nr23raHPGV1MXyU9EN8LDCNlKpom6RzSHuS55DKWE9pZ7wiYO4DvN72H4unz7B9XDvjNoz/G9sfIqWV/k6mi1OF5+u5/WIlnKXNaKHe6nEr4FXASzONeyrFJw9S06KvtTneWwCKQpYv2X7G9mkZr3nUc8/nkdITD2YY06RPCHeSriGs0/BcVyv7ijh74xgt6gv7QYo9v/WPXe00d9GipjGwqHXgEWRoH1hYl7QiObMY98ftDlhULtbv30uqhMuq2Ad+WnFri6R6M6IFkk5n0YWktpv+NHjO9nGS1rK9t6Q5mcZd2PDL474MufieQe7nkjv3jO1XARTFQ99tf4pjR6kDcYcax3yiuE3v93y7zV0am8b0rwrM0TTmmaJAZJLtRxs+5nWTepqn/ulFwFOkDmG59EhamVSAMol0fSKHB5QaE11P+uXR7gqzNsj9XBpzz8fTZu65n31Ju11CoexNfzrZOGZn4IKMPQrq43aqacyXSVeyVyLldV9lu6suctQ/zfT79AG092mmYfxlSFslX0vaoXM2cK7tT2UYeyJpJ41IRTkzXLSFbHG8p4DbSL/wX9twv61dHsXY00gXx1cB/gvYEPh14yeoNse/AViKF9YH5OjnUVmlXhHTocYxhe1Jm+wvAM6xfV+b49VlbRoj6cPF3TtJq5I7gb+SLjB2m/qnmTNJq8AVSMUcT9Hmp45ih84ngYXAQbYvJUOPakmb2P4dqTrtdhZVRm5Le3viX9fu3AYi6SjSJ9Dv2n5AqffIoaTGPG0X5BQOyzTOmFH2QJy9cUxd0WFqArAT8A1JE2zn2PObu2lM/yNkekiVZc+RSlC7yXcl/YG0S+BdpF0eT5GKc9q1B2m1ugzpwmKWbmak/epLsWh/bl27PSFyNQ3qbyqwmYvG9bbvl/R+UqlzrkB8C7AjL9yZkntbX6WUPRA3No5Zh9Rcu93GMY3eRPoHsRIZLn7V5WwaY/vwhnFfQ/rIeBFwSDvjVlS9GnB+zmrAwlynEyhy59+vg8H355bQv93v9JCi+jJXUyxIP2t3kXoozyUtKrpa2QNxvXHMqqSP4wfQfuMYACTdDvyJdGpxlk5YRY7xMDrQNEbSAaTge6gz9E2uqMGqAXNf6Mi5C2HNIr//Ipkq63J7XtKajak6SWuS+YKg7f2La0D7kHa/dLVSB2Knc636n2Lwu0zDf8v2VzKNNViOMce4q5FKsB8nFQTk7kdcJYNVA+b4hLSe0pFDPQ33gbYvJD1HuihVFYcBP1M69PY+0sW6HclwEnmj4uLlJFKAz/UJt7JKHYiLC1WfIZ1KAYDtXA1Cpko6OWNzl07lGG8lFSxcAXxTDecsduGV5o5UAxbe13C//9bGdjzsfCdjd5zt2yRtTbp2sirpovAxtnOmJr5JugB4OWkX1DUZx66kUgdi0m/n97CoQCKnrM1d6FyOceeMY1Wa7ROKXS7/tP1YPRDbnp1h7E5dLLq5Q+N2jO2nWFRh2InxfwJQXO/5ke2cBTmVVPZAfJ8zHGQ5iNzNXRplyzF2MEBU0khUA+aUYw/yWCPpHcA3SA1/JkuaZvuqUZ3UKCt7IH5O0iWkyqn6dppcFzgGynm1c+JFp3KMIYw1XwA2LdpgrkxqXbDZ6E5pdJU9EF/cwbHrja57gI1pvwFSp3KMIYw1z9j+F4DthyU9O9oTGm1lD8TnkVr7vZLUZD1XG0VsN/aGoFh5tzNepBBCGELDNr7xki4iXaR7E3m7KlZS2QPxdDp07Ha/fgWrkrbphBA6x/3+C/Dz0ZhI2ZQ9EHfy2O3GfgWPkfYAhxA6pKFJ/rKkXhsTh/6K7lH2QJz92G0tOr24sV/B0qSTCEIInXc5qfnRk8XjGvDDUZtNCZQ9EPc/dvuQDGN2sl9BCGF4T1Wo98aIKHUgLi6ASenY7SdsL8wwbKdOLw4hNOcySfuzqCUotru630SpA7GkXUndy5YCTpT0FdtfbXPYTp1eHEJoztakn+lti8c1urzxT6kDMfAp0i6J80m7Gi4H2g3EnexXEEIY3uRMvb/HjLKf4jy3+O8zxbEyba9aO316cQhhWLdK2l3J2gMdfdVtyr4i/gtp//BBkr4A3Jhj0Kr1KwhhjNmQdNRTvSfLRGDz0ZvO6Cv1itj2nsAGRSP06bY/OspTCiG0SNIPAGy/GbjY9puL+3OH/sqxr9SBWNI2wFZFt6brJEXznBCqq/Ew1sYK2fIeJT9CSh2ISYdj3g0cDGxJOo48hFB9OY+jqryyB+LnSV3SFtp+mLTlJYRQTbVB7ne9sl+se5q01/f04vDMv47yfEIIrRuoZ3cP8NrRndboK3sgfh+p8c/tktYHzhrtCYUQWjZYz+6u79/dU6uV9xOCpNcAuwJLkn5zrmo7y/H0IYRQFmXPEdcPMNwKeBXw0lGcSwghdETZA/FzRcXb34s9xSuN8nxCCCG7sgfinuJwwcmSJpGauIcQwphS2kAsaRngaGBn4Lukcue2zpULIYQyKuXFOkkHko4uWggcZPvSUZ5SCCF0TFlXxHsAIjUC+fgozyWEEDqqrIF4ru35th8lzpILIYxxZQ3EjaImPYQwppU1R/wI8GtSEH5LcR8A29GBLYQwppS1xHmwUsgQQhhzSrkiDiGEblKFHHEIIYxpEYhDCGGURSAOIYRRFoE4hBBG2f8DkH7VLPhYaegAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(data.isnull())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 249,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(data.isnull(),yticklabels=False,cbar=False,cmap='rainbow')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "177"
      ]
     },
     "execution_count": 250,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Age'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "687"
      ]
     },
     "execution_count": 251,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Cabin'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 252,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Embarked'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(891, 12)"
      ]
     },
     "execution_count": 253,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Survived', ylabel='count'>"
      ]
     },
     "execution_count": 254,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('whitegrid')\n",
    "sns.countplot(x='Survived',data=data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    549\n",
       "1    342\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 255,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Survived'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Survived', ylabel='count'>"
      ]
     },
     "execution_count": 256,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('whitegrid')\n",
    "sns.countplot(x='Survived',hue='Sex',data=data,palette='rainbow')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Survived', ylabel='count'>"
      ]
     },
     "execution_count": 257,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('whitegrid')\n",
    "sns.countplot(x='Survived',hue='Pclass',data=data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1aba87638c8>"
      ]
     },
     "execution_count": 258,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(data['Age'].dropna(),kde=False,color='darkred',bins=40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1aba889b0c8>"
      ]
     },
     "execution_count": 259,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(data['Age'].dropna(),kde=True,color='darkred',bins=40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 262,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['Age'].hist(bins=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='SibSp', ylabel='count'>"
      ]
     },
     "execution_count": 263,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='SibSp',data=data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Parch', ylabel='count'>"
      ]
     },
     "execution_count": 266,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='Parch',data=data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 268,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['Fare'].hist(color='blue',bins=30,figsize=(10,5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "## 第三步 数据清洗\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Pclass', ylabel='Age'>"
      ]
     },
     "execution_count": 269,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 7))\n",
    "sns.boxplot(x='Pclass',y='Age',data=data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1年龄按舱位求平均值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "37.0"
      ]
     },
     "execution_count": 270,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data['Pclass']==1]['Age'].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "29.0"
      ]
     },
     "execution_count": 271,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data['Pclass']==2]['Age'].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24.0"
      ]
     },
     "execution_count": 272,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data['Pclass']==3]['Age'].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {},
   "outputs": [],
   "source": [
    "def addage(x):\n",
    "    Age = x[0]\n",
    "    Pclass = x[1]\n",
    "    \n",
    "    if pd.isnull(Age):\n",
    "\n",
    "        if Pclass == 1:\n",
    "            return 37\n",
    "\n",
    "        elif Pclass == 2:\n",
    "            return 29\n",
    "\n",
    "        else:\n",
    "            return 24\n",
    "\n",
    "    else:\n",
    "        return Age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Age'] = data[['Age','Pclass']].apply(addage,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 277,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(data.isnull(),yticklabels=False,cbar=False,cmap='rainbow')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2船仓房间号因687缺失数据删除，总891"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "687"
      ]
     },
     "execution_count": 278,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Cabin'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop('Cabin',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "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>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>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>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>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>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>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>887</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Montvila, Rev. Juozas</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>211536</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>888</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Graham, Miss. Margaret Edith</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112053</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>889</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>W./C. 6607</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>890</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Behr, Mr. Karl Howell</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111369</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>891</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Dooley, Mr. Patrick</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370376</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 11 columns</p>\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",
       "886          887         0       2   \n",
       "887          888         1       1   \n",
       "888          889         0       3   \n",
       "889          890         1       1   \n",
       "890          891         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",
       "886                              Montvila, Rev. Juozas    male  27.0      0   \n",
       "887                       Graham, Miss. Margaret Edith  female  19.0      0   \n",
       "888           Johnston, Miss. Catherine Helen \"Carrie\"  female  24.0      1   \n",
       "889                              Behr, Mr. Karl Howell    male  26.0      0   \n",
       "890                                Dooley, Mr. Patrick    male  32.0      0   \n",
       "\n",
       "     Parch            Ticket     Fare Embarked  \n",
       "0        0         A/5 21171   7.2500        S  \n",
       "1        0          PC 17599  71.2833        C  \n",
       "2        0  STON/O2. 3101282   7.9250        S  \n",
       "3        0            113803  53.1000        S  \n",
       "4        0            373450   8.0500        S  \n",
       "..     ...               ...      ...      ...  \n",
       "886      0            211536  13.0000        S  \n",
       "887      0            112053  30.0000        S  \n",
       "888      2        W./C. 6607  23.4500        S  \n",
       "889      0            111369  30.0000        C  \n",
       "890      0            370376   7.7500        Q  \n",
       "\n",
       "[891 rows x 11 columns]"
      ]
     },
     "execution_count": 280,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 281,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(data.isnull(),yticklabels=False,cbar=False,cmap='rainbow')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 282,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Embarked'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "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>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>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>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>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>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>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>887</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Montvila, Rev. Juozas</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>211536</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>888</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Graham, Miss. Margaret Edith</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112053</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>889</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>W./C. 6607</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>890</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Behr, Mr. Karl Howell</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111369</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>891</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Dooley, Mr. Patrick</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370376</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 11 columns</p>\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",
       "886          887         0       2   \n",
       "887          888         1       1   \n",
       "888          889         0       3   \n",
       "889          890         1       1   \n",
       "890          891         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",
       "886                              Montvila, Rev. Juozas    male  27.0      0   \n",
       "887                       Graham, Miss. Margaret Edith  female  19.0      0   \n",
       "888           Johnston, Miss. Catherine Helen \"Carrie\"  female  24.0      1   \n",
       "889                              Behr, Mr. Karl Howell    male  26.0      0   \n",
       "890                                Dooley, Mr. Patrick    male  32.0      0   \n",
       "\n",
       "     Parch            Ticket     Fare Embarked  \n",
       "0        0         A/5 21171   7.2500        S  \n",
       "1        0          PC 17599  71.2833        C  \n",
       "2        0  STON/O2. 3101282   7.9250        S  \n",
       "3        0            113803  53.1000        S  \n",
       "4        0            373450   8.0500        S  \n",
       "..     ...               ...      ...      ...  \n",
       "886      0            211536  13.0000        S  \n",
       "887      0            112053  30.0000        S  \n",
       "888      2        W./C. 6607  23.4500        S  \n",
       "889      0            111369  30.0000        C  \n",
       "890      0            370376   7.7500        Q  \n",
       "\n",
       "[891 rows x 11 columns]"
      ]
     },
     "execution_count": 284,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 第四步 类别数据转换"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "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   PassengerId  891 non-null    int64  \n",
      " 1   Survived     891 non-null    int64  \n",
      " 2   Pclass       891 non-null    int64  \n",
      " 3   Name         891 non-null    object \n",
      " 4   Sex          891 non-null    object \n",
      " 5   Age          891 non-null    float64\n",
      " 6   SibSp        891 non-null    int64  \n",
      " 7   Parch        891 non-null    int64  \n",
      " 8   Ticket       891 non-null    object \n",
      " 9   Fare         891 non-null    float64\n",
      " 10  Embarked     889 non-null    object \n",
      "dtypes: float64(2), int64(5), object(4)\n",
      "memory usage: 76.7+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['S', 'C', 'Q', nan], dtype=object)"
      ]
     },
     "execution_count": 286,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Embarked'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  </thead>\n",
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       "      <th>1</th>\n",
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       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <th>886</th>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Q  S\n",
       "0    0  1\n",
       "1    0  0\n",
       "2    0  1\n",
       "3    0  1\n",
       "4    0  1\n",
       "..  .. ..\n",
       "886  0  1\n",
       "887  0  1\n",
       "888  0  1\n",
       "889  0  0\n",
       "890  1  0\n",
       "\n",
       "[891 rows x 2 columns]"
      ]
     },
     "execution_count": 288,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.get_dummies(data['Embarked'],drop_first=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>male</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
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       "      <th>887</th>\n",
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       "      <th>889</th>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     male\n",
       "0       1\n",
       "1       0\n",
       "2       0\n",
       "3       0\n",
       "4       1\n",
       "..    ...\n",
       "886     1\n",
       "887     0\n",
       "888     0\n",
       "889     1\n",
       "890     1\n",
       "\n",
       "[891 rows x 1 columns]"
      ]
     },
     "execution_count": 289,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.get_dummies(data['Sex'],drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>62</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Icard, Miss. Amelie</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113572</td>\n",
       "      <td>80.0</td>\n",
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       "    <tr>\n",
       "      <th>829</th>\n",
       "      <td>830</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Stone, Mrs. George Nelson (Martha Evelyn)</td>\n",
       "      <td>female</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113572</td>\n",
       "      <td>80.0</td>\n",
       "      <td>NaN</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass                                       Name  \\\n",
       "61            62         1       1                        Icard, Miss. Amelie   \n",
       "829          830         1       1  Stone, Mrs. George Nelson (Martha Evelyn)   \n",
       "\n",
       "        Sex   Age  SibSp  Parch  Ticket  Fare Embarked  \n",
       "61   female  38.0      0      0  113572  80.0      NaN  \n",
       "829  female  62.0      0      0  113572  80.0      NaN  "
      ]
     },
     "execution_count": 290,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data['Embarked'].isna()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "metadata": {},
   "outputs": [],
   "source": [
    "sex = pd.get_dummies(data['Sex'],drop_first=True)\n",
    "embark = pd.get_dummies(data['Embarked'],drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>PC 17599</td>\n",
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       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
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       "      <td>1</td>\n",
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       "      <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",
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       "      <th>4</th>\n",
       "      <td>5</td>\n",
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       "      <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>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>887</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Montvila, Rev. Juozas</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>211536</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>888</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Graham, Miss. Margaret Edith</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112053</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>889</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>W./C. 6607</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>890</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Behr, Mr. Karl Howell</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111369</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>891</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Dooley, Mr. Patrick</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370376</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 11 columns</p>\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",
       "886          887         0       2   \n",
       "887          888         1       1   \n",
       "888          889         0       3   \n",
       "889          890         1       1   \n",
       "890          891         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",
       "886                              Montvila, Rev. Juozas    male  27.0      0   \n",
       "887                       Graham, Miss. Margaret Edith  female  19.0      0   \n",
       "888           Johnston, Miss. Catherine Helen \"Carrie\"  female  24.0      1   \n",
       "889                              Behr, Mr. Karl Howell    male  26.0      0   \n",
       "890                                Dooley, Mr. Patrick    male  32.0      0   \n",
       "\n",
       "     Parch            Ticket     Fare Embarked  \n",
       "0        0         A/5 21171   7.2500        S  \n",
       "1        0          PC 17599  71.2833        C  \n",
       "2        0  STON/O2. 3101282   7.9250        S  \n",
       "3        0            113803  53.1000        S  \n",
       "4        0            373450   8.0500        S  \n",
       "..     ...               ...      ...      ...  \n",
       "886      0            211536  13.0000        S  \n",
       "887      0            112053  30.0000        S  \n",
       "888      2        W./C. 6607  23.4500        S  \n",
       "889      0            111369  30.0000        C  \n",
       "890      0            370376   7.7500        Q  \n",
       "\n",
       "[891 rows x 11 columns]"
      ]
     },
     "execution_count": 292,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.concat([data,sex,embark],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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",
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       "      <td>Braund, Mr. Owen Harris</td>\n",
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       "      <td>22.0</td>\n",
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       "      <td>A/5 21171</td>\n",
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       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <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>S</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <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",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <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>S</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <th>...</th>\n",
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       "      <td>...</td>\n",
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       "      <th>886</th>\n",
       "      <td>887</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Montvila, Rev. Juozas</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>211536</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <th>887</th>\n",
       "      <td>888</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Graham, Miss. Margaret Edith</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112053</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <th>888</th>\n",
       "      <td>889</td>\n",
       "      <td>0</td>\n",
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       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>W./C. 6607</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <th>889</th>\n",
       "      <td>890</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Behr, Mr. Karl Howell</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111369</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>C</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <th>890</th>\n",
       "      <td>891</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Dooley, Mr. Patrick</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370376</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>Q</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 14 columns</p>\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",
       "886          887         0       2   \n",
       "887          888         1       1   \n",
       "888          889         0       3   \n",
       "889          890         1       1   \n",
       "890          891         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",
       "886                              Montvila, Rev. Juozas    male  27.0      0   \n",
       "887                       Graham, Miss. Margaret Edith  female  19.0      0   \n",
       "888           Johnston, Miss. Catherine Helen \"Carrie\"  female  24.0      1   \n",
       "889                              Behr, Mr. Karl Howell    male  26.0      0   \n",
       "890                                Dooley, Mr. Patrick    male  32.0      0   \n",
       "\n",
       "     Parch            Ticket     Fare Embarked  male  Q  S  \n",
       "0        0         A/5 21171   7.2500        S     1  0  1  \n",
       "1        0          PC 17599  71.2833        C     0  0  0  \n",
       "2        0  STON/O2. 3101282   7.9250        S     0  0  1  \n",
       "3        0            113803  53.1000        S     0  0  1  \n",
       "4        0            373450   8.0500        S     1  0  1  \n",
       "..     ...               ...      ...      ...   ... .. ..  \n",
       "886      0            211536  13.0000        S     1  0  1  \n",
       "887      0            112053  30.0000        S     0  0  1  \n",
       "888      2        W./C. 6607  23.4500        S     0  0  1  \n",
       "889      0            111369  30.0000        C     1  0  0  \n",
       "890      0            370376   7.7500        Q     1  1  0  \n",
       "\n",
       "[891 rows x 14 columns]"
      ]
     },
     "execution_count": 294,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
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       "      <th>SibSp</th>\n",
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       "      <th>S</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>62</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Icard, Miss. Amelie</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113572</td>\n",
       "      <td>80.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>829</th>\n",
       "      <td>830</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Stone, Mrs. George Nelson (Martha Evelyn)</td>\n",
       "      <td>female</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113572</td>\n",
       "      <td>80.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass                                       Name  \\\n",
       "61            62         1       1                        Icard, Miss. Amelie   \n",
       "829          830         1       1  Stone, Mrs. George Nelson (Martha Evelyn)   \n",
       "\n",
       "        Sex   Age  SibSp  Parch  Ticket  Fare Embarked  male  Q  S  \n",
       "61   female  38.0      0      0  113572  80.0      NaN     0  0  0  \n",
       "829  female  62.0      0      0  113572  80.0      NaN     0  0  0  "
      ]
     },
     "execution_count": 295,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data['Embarked'].isna()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop(['Sex','Embarked','Name','Ticket'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "metadata": {},
   "outputs": [
    {
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       "</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",
       "      <th>male</th>\n",
       "      <th>Q</th>\n",
       "      <th>S</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>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>887</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>888</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>889</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>890</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>891</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass   Age  SibSp  Parch     Fare  male  Q  S\n",
       "0              1         0       3  22.0      1      0   7.2500     1  0  1\n",
       "1              2         1       1  38.0      1      0  71.2833     0  0  0\n",
       "2              3         1       3  26.0      0      0   7.9250     0  0  1\n",
       "3              4         1       1  35.0      1      0  53.1000     0  0  1\n",
       "4              5         0       3  35.0      0      0   8.0500     1  0  1\n",
       "..           ...       ...     ...   ...    ...    ...      ...   ... .. ..\n",
       "886          887         0       2  27.0      0      0  13.0000     1  0  1\n",
       "887          888         1       1  19.0      0      0  30.0000     0  0  1\n",
       "888          889         0       3  24.0      1      2  23.4500     0  0  1\n",
       "889          890         1       1  26.0      0      0  30.0000     1  0  0\n",
       "890          891         0       3  32.0      0      0   7.7500     1  1  0\n",
       "\n",
       "[891 rows x 10 columns]"
      ]
     },
     "execution_count": 297,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "## 第五步 数据清洗完毕，开始使用Logistic Regression建模"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=data.drop('Survived',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>male</th>\n",
       "      <th>Q</th>\n",
       "      <th>S</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
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       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>887</td>\n",
       "      <td>2</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>888</td>\n",
       "      <td>1</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>889</td>\n",
       "      <td>3</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>890</td>\n",
       "      <td>1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>891</td>\n",
       "      <td>3</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Pclass   Age  SibSp  Parch     Fare  male  Q  S\n",
       "0              1       3  22.0      1      0   7.2500     1  0  1\n",
       "1              2       1  38.0      1      0  71.2833     0  0  0\n",
       "2              3       3  26.0      0      0   7.9250     0  0  1\n",
       "3              4       1  35.0      1      0  53.1000     0  0  1\n",
       "4              5       3  35.0      0      0   8.0500     1  0  1\n",
       "..           ...     ...   ...    ...    ...      ...   ... .. ..\n",
       "886          887       2  27.0      0      0  13.0000     1  0  1\n",
       "887          888       1  19.0      0      0  30.0000     0  0  1\n",
       "888          889       3  24.0      1      2  23.4500     0  0  1\n",
       "889          890       1  26.0      0      0  30.0000     1  0  0\n",
       "890          891       3  32.0      0      0   7.7500     1  1  0\n",
       "\n",
       "[891 rows x 9 columns]"
      ]
     },
     "execution_count": 299,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "metadata": {},
   "outputs": [],
   "source": [
    "y=data['Survived']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      0\n",
       "1      1\n",
       "2      1\n",
       "3      1\n",
       "4      0\n",
       "      ..\n",
       "886    0\n",
       "887    1\n",
       "888    0\n",
       "889    1\n",
       "890    0\n",
       "Name: Survived, Length: 891, dtype: int64"
      ]
     },
     "execution_count": 301,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 第六步 训练模型和预测模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\raymond\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:940: ConvergenceWarning:\n",
      "\n",
      "lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
       "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
       "                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
       "                   warm_start=False)"
      ]
     },
     "execution_count": 304,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logitmodel = LogisticRegression()\n",
    "logitmodel.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "                   intercept_scaling=1, l1_ratio=None, max_iter=1000,\n",
       "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
       "                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
       "                   warm_start=False)"
      ]
     },
     "execution_count": 305,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
    "                   intercept_scaling=1, l1_ratio=None, max_iter=1000,\n",
    "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
    "                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
    "                   warm_start=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(268,)"
      ]
     },
     "execution_count": 306,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_predictions = logitmodel.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[144,  24],\n",
       "       [ 32,  68]], dtype=int64)"
      ]
     },
     "execution_count": 309,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test,y_predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7910447761194029"
      ]
     },
     "execution_count": 310,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy=accuracy_score(y_test,y_predictions)\n",
    "accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1,\n",
       "       0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0,\n",
       "       1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1,\n",
       "       1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1,\n",
       "       0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,\n",
       "       0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 0,\n",
       "       1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0,\n",
       "       1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1,\n",
       "       0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1,\n",
       "       0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1,\n",
       "       0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 1, 0], dtype=int64)"
      ]
     },
     "execution_count": 311,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "495    0\n",
       "648    0\n",
       "278    0\n",
       "31     1\n",
       "255    1\n",
       "      ..\n",
       "263    0\n",
       "718    0\n",
       "620    0\n",
       "786    1\n",
       "64     0\n",
       "Name: Survived, Length: 268, dtype: int64"
      ]
     },
     "execution_count": 312,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 第七步 模型评估"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.82      0.86      0.84       168\n",
      "           1       0.74      0.68      0.71       100\n",
      "\n",
      "    accuracy                           0.79       268\n",
      "   macro avg       0.78      0.77      0.77       268\n",
      "weighted avg       0.79      0.79      0.79       268\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,y_predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[144,  24],\n",
       "       [ 32,  68]], dtype=int64)"
      ]
     },
     "execution_count": 237,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test,y_predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(268,)"
      ]
     },
     "execution_count": 236,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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