ttianic.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.preprocessing import Imputer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      int64\n",
       "Pclass           int64\n",
       "Name            object\n",
       "Sex             object\n",
       "Age            float64\n",
       "SibSp            int64\n",
       "Parch            int64\n",
       "Ticket          object\n",
       "Fare           float64\n",
       "Cabin           object\n",
       "Embarked        object\n",
       "Survived         int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('train.csv')\n",
    "\n",
    "df.columns.str.match('Unnamed')\n",
    "\n",
    "df=df.loc[:, ~df.columns.str.match('Unnamed')]\n",
    "# df['Age'] = pd.to_numeric(df['Age'])\n",
    "df.head()\n",
    "# df['Age'] = df['Age'].apply(pd.to_numeric)\n",
    "# df['Age'] = df['Age'].astype('int') Can't convert NaN to int\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "16      2.000000\n",
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      "19     29.699118\n",
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      "888    29.699118\n",
      "889    26.000000\n",
      "890    32.000000\n",
      "Name: Age, Length: 891, dtype: float64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Darsb\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "Age = df.iloc[:,4:5]\n",
    "imputer = Imputer(missing_values='NaN',strategy='mean',axis=0)\n",
    "imputer = imputer.fit(Age.iloc[:,:])\n",
    "Age.iloc[:,:] = imputer.transform(Age.iloc[:,:])\n",
    "# df['Age'].fillna(df['Age'].mean)\n",
    "df['Age'] = Age\n",
    "print(df['Age'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   PassengerId  Pclass                                               Name  \\\n",
      "0            1       3                            Braund, Mr. Owen Harris   \n",
      "1            2       1  Cumings, Mrs. John Bradley (Florence Briggs Th...   \n",
      "2            3       3                             Heikkinen, Miss. Laina   \n",
      "3            4       1       Futrelle, Mrs. Jacques Heath (Lily May Peel)   \n",
      "4            5       3                           Allen, Mr. William Henry   \n",
      "\n",
      "   Sex   Age  SibSp  Parch            Ticket     Fare Cabin Embarked  Survived  \n",
      "0    1  22.0      1      0         A/5 21171   7.2500   NaN        S         0  \n",
      "1    0  38.0      1      0          PC 17599  71.2833   C85        C         1  \n",
      "2    0  26.0      0      0  STON/O2. 3101282   7.9250   NaN        S         1  \n",
      "3    0  35.0      1      0            113803  53.1000  C123        S         1  \n",
      "4    1  35.0      0      0            373450   8.0500   NaN        S         0  \n"
     ]
    }
   ],
   "source": [
    "df['Sex'] = df['Sex'].astype('str')\n",
    "for i, gender in df.iterrows():\n",
    "    if df.loc[i,'Sex'] == 'male':\n",
    "        df.loc[i,'Sex']=1\n",
    "    else:\n",
    "        df.loc[i,'Sex'] = 0\n",
    "# print(df['Sex'])\n",
    "print(df.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
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       "      <td>A/5 21171</td>\n",
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       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
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       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
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       "      <td>STON/O2. 3101282</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>0</td>\n",
       "      <td>35</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",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>1</td>\n",
       "      <td>35</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",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Pclass                                               Name  \\\n",
       "0            1       3                            Braund, Mr. Owen Harris   \n",
       "1            2       1  Cumings, Mrs. John Bradley (Florence Briggs Th...   \n",
       "2            3       3                             Heikkinen, Miss. Laina   \n",
       "3            4       1       Futrelle, Mrs. Jacques Heath (Lily May Peel)   \n",
       "4            5       3                           Allen, Mr. William Henry   \n",
       "\n",
       "   Sex  Age  SibSp  Parch            Ticket     Fare Cabin Embarked  Survived  \n",
       "0    1   22      1      0         A/5 21171   7.2500   NaN        S         0  \n",
       "1    0   38      1      0          PC 17599  71.2833   C85        C         1  \n",
       "2    0   26      0      0  STON/O2. 3101282   7.9250   NaN        S         1  \n",
       "3    0   35      1      0            113803  53.1000  C123        S         1  \n",
       "4    1   35      0      0            373450   8.0500   NaN        S         0  "
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Age'] = df['Age'].astype('int64')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "687"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Cabin'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "512.3292"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Fare'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "80"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Age'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import preprocessing\n",
    "Ages = df.iloc[:,4:5].values\n",
    "minmaxscalar_as = preprocessing.MinMaxScaler(feature_range=(0,1))\n",
    "f_scale =  minmaxscalar_as.fit_transform(Ages)\n",
    "df['Age'] = f_scale\n",
    "\n",
    "Fares = df.iloc[:,8:9].values\n",
    "minmaxscalar_as = preprocessing.MinMaxScaler(feature_range=(0,1))\n",
    "fa_scale =  minmaxscalar_as.fit_transform(Fares)\n",
    "df['Fare'] = fa_scale\n",
    "print(df.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.rename(columns={\"Sex\":\"Male\"},inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Male</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
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       "      <th>Embarked</th>\n",
       "      <th>Survived</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
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       "      <td>0.2750</td>\n",
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       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>0.139136</td>\n",
       "      <td>C85</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>0</td>\n",
       "      <td>0.3250</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>0.015469</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>0</td>\n",
       "      <td>0.4375</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>0.103644</td>\n",
       "      <td>C123</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>1</td>\n",
       "      <td>0.4375</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>0.015713</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Pclass                                               Name  \\\n",
       "0            1       2                            Braund, Mr. Owen Harris   \n",
       "1            2       0  Cumings, Mrs. John Bradley (Florence Briggs Th...   \n",
       "2            3       2                             Heikkinen, Miss. Laina   \n",
       "3            4       0       Futrelle, Mrs. Jacques Heath (Lily May Peel)   \n",
       "4            5       2                           Allen, Mr. William Henry   \n",
       "\n",
       "   Male     Age  SibSp  Parch            Ticket      Fare Cabin  Embarked  \\\n",
       "0     1  0.2750      1      0         A/5 21171  0.014151   NaN         2   \n",
       "1     0  0.4750      1      0          PC 17599  0.139136   C85         0   \n",
       "2     0  0.3250      0      0  STON/O2. 3101282  0.015469   NaN         2   \n",
       "3     0  0.4375      1      0            113803  0.103644  C123         2   \n",
       "4     1  0.4375      0      0            373450  0.015713   NaN         2   \n",
       "\n",
       "   Survived  \n",
       "0         0  \n",
       "1         1  \n",
       "2         1  \n",
       "3         1  \n",
       "4         0  "
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Embarked'] = df['Embarked'].astype('str')\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "dfle = df\n",
    "le = LabelEncoder()\n",
    "dfle.Embarked = le.fit_transform(dfle.Embarked)\n",
    "dfle.Pclass = le.fit_transform(dfle.Pclass)\n",
    "dfle.Male = le.fit_transform(dfle.Male)\n",
    "dfle.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = dfle[['PassengerId','Pclass','Name','Male','Age','SibSp','Parch','Ticket','Fare','Cabin','Embarked']]\n",
    "Y = dfle['Survived']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
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       "      <th>0</th>\n",
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       "      <td>Braund, Mr. Owen Harris</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.4750</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>0.139136</td>\n",
       "      <td>C85</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>0</td>\n",
       "      <td>0.3250</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>0.015469</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>0</td>\n",
       "      <td>0.4375</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>0.103644</td>\n",
       "      <td>C123</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>1</td>\n",
       "      <td>0.4375</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>0.015713</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>Moran, Mr. James</td>\n",
       "      <td>1</td>\n",
       "      <td>0.3625</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330877</td>\n",
       "      <td>0.016510</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>McCarthy, Mr. Timothy J</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6750</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17463</td>\n",
       "      <td>0.101229</td>\n",
       "      <td>E46</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>Palsson, Master. Gosta Leonard</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0250</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>349909</td>\n",
       "      <td>0.041136</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)</td>\n",
       "      <td>0</td>\n",
       "      <td>0.3375</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>347742</td>\n",
       "      <td>0.021731</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>Nasser, Mrs. Nicholas (Adele Achem)</td>\n",
       "      <td>0</td>\n",
       "      <td>0.1750</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>237736</td>\n",
       "      <td>0.058694</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>Sandstrom, Miss. Marguerite Rut</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0500</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>PP 9549</td>\n",
       "      <td>0.032596</td>\n",
       "      <td>G6</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>Bonnell, Miss. Elizabeth</td>\n",
       "      <td>0</td>\n",
       "      <td>0.7250</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113783</td>\n",
       "      <td>0.051822</td>\n",
       "      <td>C103</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>Saundercock, Mr. William Henry</td>\n",
       "      <td>1</td>\n",
       "      <td>0.2500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5. 2151</td>\n",
       "      <td>0.015713</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>Andersson, Mr. Anders Johan</td>\n",
       "      <td>1</td>\n",
       "      <td>0.4875</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>347082</td>\n",
       "      <td>0.061045</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>Vestrom, Miss. Hulda Amanda Adolfina</td>\n",
       "      <td>0</td>\n",
       "      <td>0.1750</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>350406</td>\n",
       "      <td>0.015330</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>Hewlett, Mrs. (Mary D Kingcome)</td>\n",
       "      <td>0</td>\n",
       "      <td>0.6875</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>248706</td>\n",
       "      <td>0.031230</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>Rice, Master. Eugene</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0250</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>382652</td>\n",
       "      <td>0.056848</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>Williams, Mr. Charles Eugene</td>\n",
       "      <td>1</td>\n",
       "      <td>0.3625</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>244373</td>\n",
       "      <td>0.025374</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>Vander Planke, Mrs. Julius (Emelia Maria Vande...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.3875</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>345763</td>\n",
       "      <td>0.035134</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>Masselmani, Mrs. Fatima</td>\n",
       "      <td>0</td>\n",
       "      <td>0.3625</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2649</td>\n",
       "      <td>0.014102</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>Fynney, Mr. Joseph J</td>\n",
       "      <td>1</td>\n",
       "      <td>0.4375</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>239865</td>\n",
       "      <td>0.050749</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>Beesley, Mr. Lawrence</td>\n",
       "      <td>1</td>\n",
       "      <td>0.4250</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>248698</td>\n",
       "      <td>0.025374</td>\n",
       "      <td>D56</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23</td>\n",
       "      <td>McGowan, Miss. Anna \"Annie\"</td>\n",
       "      <td>0</td>\n",
       "      <td>0.1875</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330923</td>\n",
       "      <td>0.015672</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24</td>\n",
       "      <td>Sloper, Mr. William Thompson</td>\n",
       "      <td>1</td>\n",
       "      <td>0.3500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113788</td>\n",
       "      <td>0.069291</td>\n",
       "      <td>A6</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25</td>\n",
       "      <td>Palsson, Miss. Torborg Danira</td>\n",
       "      <td>0</td>\n",
       "      <td>0.1000</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>349909</td>\n",
       "      <td>0.041136</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>26</td>\n",
       "      <td>Asplund, Mrs. Carl Oscar (Selma Augusta Emilia...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.4750</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>347077</td>\n",
       "      <td>0.061264</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27</td>\n",
       "      <td>Emir, Mr. Farred Chehab</td>\n",
       "      <td>1</td>\n",
       "      <td>0.3625</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2631</td>\n",
       "      <td>0.014102</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>Fortune, Mr. Charles Alexander</td>\n",
       "      <td>1</td>\n",
       "      <td>0.2375</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>19950</td>\n",
       "      <td>0.513342</td>\n",
       "      <td>C23 C25 C27</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29</td>\n",
       "      <td>O'Dwyer, Miss. Ellen \"Nellie\"</td>\n",
       "      <td>0</td>\n",
       "      <td>0.3625</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330959</td>\n",
       "      <td>0.015379</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30</td>\n",
       "      <td>Todoroff, Mr. Lalio</td>\n",
       "      <td>1</td>\n",
       "      <td>0.3625</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349216</td>\n",
       "      <td>0.015412</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>0</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>861</th>\n",
       "      <td>862</td>\n",
       "      <td>Giles, Mr. Frederick Edward</td>\n",
       "      <td>1</td>\n",
       "      <td>0.2625</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>28134</td>\n",
       "      <td>0.022447</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>862</th>\n",
       "      <td>863</td>\n",
       "      <td>Swift, Mrs. Frederick Joel (Margaret Welles Ba...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.6000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17466</td>\n",
       "      <td>0.050610</td>\n",
       "      <td>D17</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>863</th>\n",
       "      <td>864</td>\n",
       "      <td>Sage, Miss. Dorothy Edith \"Dolly\"</td>\n",
       "      <td>0</td>\n",
       "      <td>0.3625</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>CA. 2343</td>\n",
       "      <td>0.135753</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>864</th>\n",
       "      <td>865</td>\n",
       "      <td>Gill, Mr. John William</td>\n",
       "      <td>1</td>\n",
       "      <td>0.3000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>233866</td>\n",
       "      <td>0.025374</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>865</th>\n",
       "      <td>866</td>\n",
       "      <td>Bystrom, Mrs. (Karolina)</td>\n",
       "      <td>0</td>\n",
       "      <td>0.5250</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>236852</td>\n",
       "      <td>0.025374</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>866</th>\n",
       "      <td>867</td>\n",
       "      <td>Duran y More, Miss. Asuncion</td>\n",
       "      <td>0</td>\n",
       "      <td>0.3375</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>SC/PARIS 2149</td>\n",
       "      <td>0.027050</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>867</th>\n",
       "      <td>868</td>\n",
       "      <td>Roebling, Mr. Washington Augustus II</td>\n",
       "      <td>1</td>\n",
       "      <td>0.3875</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17590</td>\n",
       "      <td>0.098561</td>\n",
       "      <td>A24</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868</th>\n",
       "      <td>869</td>\n",
       "      <td>van Melkebeke, Mr. Philemon</td>\n",
       "      <td>1</td>\n",
       "      <td>0.3625</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>345777</td>\n",
       "      <td>0.018543</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>869</th>\n",
       "      <td>870</td>\n",
       "      <td>Johnson, Master. Harold Theodor</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0500</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>347742</td>\n",
       "      <td>0.021731</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>870</th>\n",
       "      <td>871</td>\n",
       "      <td>Balkic, Mr. Cerin</td>\n",
       "      <td>1</td>\n",
       "      <td>0.3250</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349248</td>\n",
       "      <td>0.015412</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>871</th>\n",
       "      <td>872</td>\n",
       "      <td>Beckwith, Mrs. Richard Leonard (Sallie Monypeny)</td>\n",
       "      <td>0</td>\n",
       "      <td>0.5875</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>11751</td>\n",
       "      <td>0.102579</td>\n",
       "      <td>D35</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>872</th>\n",
       "      <td>873</td>\n",
       "      <td>Carlsson, Mr. Frans Olof</td>\n",
       "      <td>1</td>\n",
       "      <td>0.4125</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>695</td>\n",
       "      <td>0.009759</td>\n",
       "      <td>B51 B53 B55</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>873</th>\n",
       "      <td>874</td>\n",
       "      <td>Vander Cruyssen, Mr. Victor</td>\n",
       "      <td>1</td>\n",
       "      <td>0.5875</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>345765</td>\n",
       "      <td>0.017567</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>874</th>\n",
       "      <td>875</td>\n",
       "      <td>Abelson, Mrs. Samuel (Hannah Wizosky)</td>\n",
       "      <td>0</td>\n",
       "      <td>0.3500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>P/PP 3381</td>\n",
       "      <td>0.046845</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>875</th>\n",
       "      <td>876</td>\n",
       "      <td>Najib, Miss. Adele Kiamie \"Jane\"</td>\n",
       "      <td>0</td>\n",
       "      <td>0.1875</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2667</td>\n",
       "      <td>0.014102</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>876</th>\n",
       "      <td>877</td>\n",
       "      <td>Gustafsson, Mr. Alfred Ossian</td>\n",
       "      <td>1</td>\n",
       "      <td>0.2500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7534</td>\n",
       "      <td>0.019218</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>877</th>\n",
       "      <td>878</td>\n",
       "      <td>Petroff, Mr. Nedelio</td>\n",
       "      <td>1</td>\n",
       "      <td>0.2375</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349212</td>\n",
       "      <td>0.015412</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>878</th>\n",
       "      <td>879</td>\n",
       "      <td>Laleff, Mr. Kristo</td>\n",
       "      <td>1</td>\n",
       "      <td>0.3625</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349217</td>\n",
       "      <td>0.015412</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>879</th>\n",
       "      <td>880</td>\n",
       "      <td>Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)</td>\n",
       "      <td>0</td>\n",
       "      <td>0.7000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>11767</td>\n",
       "      <td>0.162314</td>\n",
       "      <td>C50</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>880</th>\n",
       "      <td>881</td>\n",
       "      <td>Shelley, Mrs. William (Imanita Parrish Hall)</td>\n",
       "      <td>0</td>\n",
       "      <td>0.3125</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>230433</td>\n",
       "      <td>0.050749</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>881</th>\n",
       "      <td>882</td>\n",
       "      <td>Markun, Mr. Johann</td>\n",
       "      <td>1</td>\n",
       "      <td>0.4125</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349257</td>\n",
       "      <td>0.015412</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>882</th>\n",
       "      <td>883</td>\n",
       "      <td>Dahlberg, Miss. Gerda Ulrika</td>\n",
       "      <td>0</td>\n",
       "      <td>0.2750</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7552</td>\n",
       "      <td>0.020527</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>883</th>\n",
       "      <td>884</td>\n",
       "      <td>Banfield, Mr. Frederick James</td>\n",
       "      <td>1</td>\n",
       "      <td>0.3500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>C.A./SOTON 34068</td>\n",
       "      <td>0.020495</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>884</th>\n",
       "      <td>885</td>\n",
       "      <td>Sutehall, Mr. Henry Jr</td>\n",
       "      <td>1</td>\n",
       "      <td>0.3125</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>SOTON/OQ 392076</td>\n",
       "      <td>0.013761</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>885</th>\n",
       "      <td>886</td>\n",
       "      <td>Rice, Mrs. William (Margaret Norton)</td>\n",
       "      <td>0</td>\n",
       "      <td>0.4875</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>382652</td>\n",
       "      <td>0.056848</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>887</td>\n",
       "      <td>Montvila, Rev. Juozas</td>\n",
       "      <td>1</td>\n",
       "      <td>0.3375</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>211536</td>\n",
       "      <td>0.025374</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>888</td>\n",
       "      <td>Graham, Miss. Margaret Edith</td>\n",
       "      <td>0</td>\n",
       "      <td>0.2375</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112053</td>\n",
       "      <td>0.058556</td>\n",
       "      <td>B42</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>889</td>\n",
       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
       "      <td>0</td>\n",
       "      <td>0.3625</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>W./C. 6607</td>\n",
       "      <td>0.045771</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</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>Behr, Mr. Karl Howell</td>\n",
       "      <td>1</td>\n",
       "      <td>0.3250</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111369</td>\n",
       "      <td>0.058556</td>\n",
       "      <td>C148</td>\n",
       "      <td>0</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>Dooley, Mr. Patrick</td>\n",
       "      <td>1</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370376</td>\n",
       "      <td>0.015127</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId                                               Name  Male  \\\n",
       "0              1                            Braund, Mr. Owen Harris     1   \n",
       "1              2  Cumings, Mrs. John Bradley (Florence Briggs Th...     0   \n",
       "2              3                             Heikkinen, Miss. Laina     0   \n",
       "3              4       Futrelle, Mrs. Jacques Heath (Lily May Peel)     0   \n",
       "4              5                           Allen, Mr. William Henry     1   \n",
       "5              6                                   Moran, Mr. James     1   \n",
       "6              7                            McCarthy, Mr. Timothy J     1   \n",
       "7              8                     Palsson, Master. Gosta Leonard     1   \n",
       "8              9  Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)     0   \n",
       "9             10                Nasser, Mrs. Nicholas (Adele Achem)     0   \n",
       "10            11                    Sandstrom, Miss. Marguerite Rut     0   \n",
       "11            12                           Bonnell, Miss. Elizabeth     0   \n",
       "12            13                     Saundercock, Mr. William Henry     1   \n",
       "13            14                        Andersson, Mr. Anders Johan     1   \n",
       "14            15               Vestrom, Miss. Hulda Amanda Adolfina     0   \n",
       "15            16                   Hewlett, Mrs. (Mary D Kingcome)      0   \n",
       "16            17                               Rice, Master. Eugene     1   \n",
       "17            18                       Williams, Mr. Charles Eugene     1   \n",
       "18            19  Vander Planke, Mrs. Julius (Emelia Maria Vande...     0   \n",
       "19            20                            Masselmani, Mrs. Fatima     0   \n",
       "20            21                               Fynney, Mr. Joseph J     1   \n",
       "21            22                              Beesley, Mr. Lawrence     1   \n",
       "22            23                        McGowan, Miss. Anna \"Annie\"     0   \n",
       "23            24                       Sloper, Mr. William Thompson     1   \n",
       "24            25                      Palsson, Miss. Torborg Danira     0   \n",
       "25            26  Asplund, Mrs. Carl Oscar (Selma Augusta Emilia...     0   \n",
       "26            27                            Emir, Mr. Farred Chehab     1   \n",
       "27            28                     Fortune, Mr. Charles Alexander     1   \n",
       "28            29                      O'Dwyer, Miss. Ellen \"Nellie\"     0   \n",
       "29            30                                Todoroff, Mr. Lalio     1   \n",
       "..           ...                                                ...   ...   \n",
       "861          862                        Giles, Mr. Frederick Edward     1   \n",
       "862          863  Swift, Mrs. Frederick Joel (Margaret Welles Ba...     0   \n",
       "863          864                  Sage, Miss. Dorothy Edith \"Dolly\"     0   \n",
       "864          865                             Gill, Mr. John William     1   \n",
       "865          866                           Bystrom, Mrs. (Karolina)     0   \n",
       "866          867                       Duran y More, Miss. Asuncion     0   \n",
       "867          868               Roebling, Mr. Washington Augustus II     1   \n",
       "868          869                        van Melkebeke, Mr. Philemon     1   \n",
       "869          870                    Johnson, Master. Harold Theodor     1   \n",
       "870          871                                  Balkic, Mr. Cerin     1   \n",
       "871          872   Beckwith, Mrs. Richard Leonard (Sallie Monypeny)     0   \n",
       "872          873                           Carlsson, Mr. Frans Olof     1   \n",
       "873          874                        Vander Cruyssen, Mr. Victor     1   \n",
       "874          875              Abelson, Mrs. Samuel (Hannah Wizosky)     0   \n",
       "875          876                   Najib, Miss. Adele Kiamie \"Jane\"     0   \n",
       "876          877                      Gustafsson, Mr. Alfred Ossian     1   \n",
       "877          878                               Petroff, Mr. Nedelio     1   \n",
       "878          879                                 Laleff, Mr. Kristo     1   \n",
       "879          880      Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)     0   \n",
       "880          881       Shelley, Mrs. William (Imanita Parrish Hall)     0   \n",
       "881          882                                 Markun, Mr. Johann     1   \n",
       "882          883                       Dahlberg, Miss. Gerda Ulrika     0   \n",
       "883          884                      Banfield, Mr. Frederick James     1   \n",
       "884          885                             Sutehall, Mr. Henry Jr     1   \n",
       "885          886               Rice, Mrs. William (Margaret Norton)     0   \n",
       "886          887                              Montvila, Rev. Juozas     1   \n",
       "887          888                       Graham, Miss. Margaret Edith     0   \n",
       "888          889           Johnston, Miss. Catherine Helen \"Carrie\"     0   \n",
       "889          890                              Behr, Mr. Karl Howell     1   \n",
       "890          891                                Dooley, Mr. Patrick     1   \n",
       "\n",
       "        Age  SibSp  Parch            Ticket      Fare        Cabin  Embarked  \\\n",
       "0    0.2750      1      0         A/5 21171  0.014151          NaN         2   \n",
       "1    0.4750      1      0          PC 17599  0.139136          C85         0   \n",
       "2    0.3250      0      0  STON/O2. 3101282  0.015469          NaN         2   \n",
       "3    0.4375      1      0            113803  0.103644         C123         2   \n",
       "4    0.4375      0      0            373450  0.015713          NaN         2   \n",
       "5    0.3625      0      0            330877  0.016510          NaN         1   \n",
       "6    0.6750      0      0             17463  0.101229          E46         2   \n",
       "7    0.0250      3      1            349909  0.041136          NaN         2   \n",
       "8    0.3375      0      2            347742  0.021731          NaN         2   \n",
       "9    0.1750      1      0            237736  0.058694          NaN         0   \n",
       "10   0.0500      1      1           PP 9549  0.032596           G6         2   \n",
       "11   0.7250      0      0            113783  0.051822         C103         2   \n",
       "12   0.2500      0      0         A/5. 2151  0.015713          NaN         2   \n",
       "13   0.4875      1      5            347082  0.061045          NaN         2   \n",
       "14   0.1750      0      0            350406  0.015330          NaN         2   \n",
       "15   0.6875      0      0            248706  0.031230          NaN         2   \n",
       "16   0.0250      4      1            382652  0.056848          NaN         1   \n",
       "17   0.3625      0      0            244373  0.025374          NaN         2   \n",
       "18   0.3875      1      0            345763  0.035134          NaN         2   \n",
       "19   0.3625      0      0              2649  0.014102          NaN         0   \n",
       "20   0.4375      0      0            239865  0.050749          NaN         2   \n",
       "21   0.4250      0      0            248698  0.025374          D56         2   \n",
       "22   0.1875      0      0            330923  0.015672          NaN         1   \n",
       "23   0.3500      0      0            113788  0.069291           A6         2   \n",
       "24   0.1000      3      1            349909  0.041136          NaN         2   \n",
       "25   0.4750      1      5            347077  0.061264          NaN         2   \n",
       "26   0.3625      0      0              2631  0.014102          NaN         0   \n",
       "27   0.2375      3      2             19950  0.513342  C23 C25 C27         2   \n",
       "28   0.3625      0      0            330959  0.015379          NaN         1   \n",
       "29   0.3625      0      0            349216  0.015412          NaN         2   \n",
       "..      ...    ...    ...               ...       ...          ...       ...   \n",
       "861  0.2625      1      0             28134  0.022447          NaN         2   \n",
       "862  0.6000      0      0             17466  0.050610          D17         2   \n",
       "863  0.3625      8      2          CA. 2343  0.135753          NaN         2   \n",
       "864  0.3000      0      0            233866  0.025374          NaN         2   \n",
       "865  0.5250      0      0            236852  0.025374          NaN         2   \n",
       "866  0.3375      1      0     SC/PARIS 2149  0.027050          NaN         0   \n",
       "867  0.3875      0      0          PC 17590  0.098561          A24         2   \n",
       "868  0.3625      0      0            345777  0.018543          NaN         2   \n",
       "869  0.0500      1      1            347742  0.021731          NaN         2   \n",
       "870  0.3250      0      0            349248  0.015412          NaN         2   \n",
       "871  0.5875      1      1             11751  0.102579          D35         2   \n",
       "872  0.4125      0      0               695  0.009759  B51 B53 B55         2   \n",
       "873  0.5875      0      0            345765  0.017567          NaN         2   \n",
       "874  0.3500      1      0         P/PP 3381  0.046845          NaN         0   \n",
       "875  0.1875      0      0              2667  0.014102          NaN         0   \n",
       "876  0.2500      0      0              7534  0.019218          NaN         2   \n",
       "877  0.2375      0      0            349212  0.015412          NaN         2   \n",
       "878  0.3625      0      0            349217  0.015412          NaN         2   \n",
       "879  0.7000      0      1             11767  0.162314          C50         0   \n",
       "880  0.3125      0      1            230433  0.050749          NaN         2   \n",
       "881  0.4125      0      0            349257  0.015412          NaN         2   \n",
       "882  0.2750      0      0              7552  0.020527          NaN         2   \n",
       "883  0.3500      0      0  C.A./SOTON 34068  0.020495          NaN         2   \n",
       "884  0.3125      0      0   SOTON/OQ 392076  0.013761          NaN         2   \n",
       "885  0.4875      0      5            382652  0.056848          NaN         1   \n",
       "886  0.3375      0      0            211536  0.025374          NaN         2   \n",
       "887  0.2375      0      0            112053  0.058556          B42         2   \n",
       "888  0.3625      1      2        W./C. 6607  0.045771          NaN         2   \n",
       "889  0.3250      0      0            111369  0.058556         C148         0   \n",
       "890  0.4000      0      0            370376  0.015127          NaN         1   \n",
       "\n",
       "     Survived  Pclass_1  Pclass_2  \n",
       "0           0         0         1  \n",
       "1           1         0         0  \n",
       "2           1         0         1  \n",
       "3           1         0         0  \n",
       "4           0         0         1  \n",
       "5           0         0         1  \n",
       "6           0         0         0  \n",
       "7           0         0         1  \n",
       "8           1         0         1  \n",
       "9           1         1         0  \n",
       "10          1         0         1  \n",
       "11          1         0         0  \n",
       "12          0         0         1  \n",
       "13          0         0         1  \n",
       "14          0         0         1  \n",
       "15          1         1         0  \n",
       "16          0         0         1  \n",
       "17          1         1         0  \n",
       "18          0         0         1  \n",
       "19          1         0         1  \n",
       "20          0         1         0  \n",
       "21          1         1         0  \n",
       "22          1         0         1  \n",
       "23          1         0         0  \n",
       "24          0         0         1  \n",
       "25          1         0         1  \n",
       "26          0         0         1  \n",
       "27          0         0         0  \n",
       "28          1         0         1  \n",
       "29          0         0         1  \n",
       "..        ...       ...       ...  \n",
       "861         0         1         0  \n",
       "862         1         0         0  \n",
       "863         0         0         1  \n",
       "864         0         1         0  \n",
       "865         1         1         0  \n",
       "866         1         1         0  \n",
       "867         0         0         0  \n",
       "868         0         0         1  \n",
       "869         1         0         1  \n",
       "870         0         0         1  \n",
       "871         1         0         0  \n",
       "872         0         0         0  \n",
       "873         0         0         1  \n",
       "874         1         1         0  \n",
       "875         1         0         1  \n",
       "876         0         0         1  \n",
       "877         0         0         1  \n",
       "878         0         0         1  \n",
       "879         1         0         0  \n",
       "880         1         1         0  \n",
       "881         0         0         1  \n",
       "882         0         0         1  \n",
       "883         0         1         0  \n",
       "884         0         0         1  \n",
       "885         0         0         1  \n",
       "886         0         1         0  \n",
       "887         1         0         0  \n",
       "888         0         0         1  \n",
       "889         1         0         0  \n",
       "890         0         0         1  \n",
       "\n",
       "[891 rows x 13 columns]"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "PClass=pd.get_dummies(df['Pclass'],prefix='Pclass',drop_first=True)\n",
    "df=pd.concat([df,PClass],axis=1)\n",
    "df.drop(['Pclass'],axis=1,inplace=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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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>Name</th>\n",
       "      <th>Male</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>Survived</th>\n",
       "      <th>Pclass_1</th>\n",
       "      <th>Pclass_2</th>\n",
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       "      <th>Embarked_2</th>\n",
       "      <th>Embarked_3</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>1</td>\n",
       "      <td>0.2750</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
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       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.4750</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
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       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>0</td>\n",
       "      <td>0.3250</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>0.015469</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>0</td>\n",
       "      <td>0.4375</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>0.103644</td>\n",
       "      <td>C123</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>1</td>\n",
       "      <td>0.4375</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>0.015713</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>Moran, Mr. James</td>\n",
       "      <td>1</td>\n",
       "      <td>0.3625</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330877</td>\n",
       "      <td>0.016510</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>McCarthy, Mr. Timothy J</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6750</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17463</td>\n",
       "      <td>0.101229</td>\n",
       "      <td>E46</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>Palsson, Master. Gosta Leonard</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0250</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>349909</td>\n",
       "      <td>0.041136</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)</td>\n",
       "      <td>0</td>\n",
       "      <td>0.3375</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>347742</td>\n",
       "      <td>0.021731</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>Nasser, Mrs. Nicholas (Adele Achem)</td>\n",
       "      <td>0</td>\n",
       "      <td>0.1750</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>237736</td>\n",
       "      <td>0.058694</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>Sandstrom, Miss. Marguerite Rut</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0500</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>PP 9549</td>\n",
       "      <td>0.032596</td>\n",
       "      <td>G6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>Bonnell, Miss. Elizabeth</td>\n",
       "      <td>0</td>\n",
       "      <td>0.7250</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113783</td>\n",
       "      <td>0.051822</td>\n",
       "      <td>C103</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>Saundercock, Mr. William Henry</td>\n",
       "      <td>1</td>\n",
       "      <td>0.2500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5. 2151</td>\n",
       "      <td>0.015713</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>Andersson, Mr. Anders Johan</td>\n",
       "      <td>1</td>\n",
       "      <td>0.4875</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>347082</td>\n",
       "      <td>0.061045</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>Vestrom, Miss. Hulda Amanda Adolfina</td>\n",
       "      <td>0</td>\n",
       "      <td>0.1750</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>350406</td>\n",
       "      <td>0.015330</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>Hewlett, Mrs. (Mary D Kingcome)</td>\n",
       "      <td>0</td>\n",
       "      <td>0.6875</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>248706</td>\n",
       "      <td>0.031230</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>Rice, Master. Eugene</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0250</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>382652</td>\n",
       "      <td>0.056848</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>Williams, Mr. Charles Eugene</td>\n",
       "      <td>1</td>\n",
       "      <td>0.3625</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>244373</td>\n",
       "      <td>0.025374</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>Vander Planke, Mrs. Julius (Emelia Maria Vande...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.3875</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>345763</td>\n",
       "      <td>0.035134</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>Masselmani, Mrs. Fatima</td>\n",
       "      <td>0</td>\n",
       "      <td>0.3625</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2649</td>\n",
       "      <td>0.014102</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>Fynney, Mr. Joseph J</td>\n",
       "      <td>1</td>\n",
       "      <td>0.4375</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>239865</td>\n",
       "      <td>0.050749</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>Beesley, Mr. Lawrence</td>\n",
       "      <td>1</td>\n",
       "      <td>0.4250</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>248698</td>\n",
       "      <td>0.025374</td>\n",
       "      <td>D56</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23</td>\n",
       "      <td>McGowan, Miss. Anna \"Annie\"</td>\n",
       "      <td>0</td>\n",
       "      <td>0.1875</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330923</td>\n",
       "      <td>0.015672</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24</td>\n",
       "      <td>Sloper, Mr. William Thompson</td>\n",
       "      <td>1</td>\n",
       "      <td>0.3500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113788</td>\n",
       "      <td>0.069291</td>\n",
       "      <td>A6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25</td>\n",
       "      <td>Palsson, Miss. Torborg Danira</td>\n",
       "      <td>0</td>\n",
       "      <td>0.1000</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>349909</td>\n",
       "      <td>0.041136</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>26</td>\n",
       "      <td>Asplund, Mrs. Carl Oscar (Selma Augusta Emilia...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.4750</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>347077</td>\n",
       "      <td>0.061264</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27</td>\n",
       "      <td>Emir, Mr. Farred Chehab</td>\n",
       "      <td>1</td>\n",
       "      <td>0.3625</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2631</td>\n",
       "      <td>0.014102</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>Fortune, Mr. Charles Alexander</td>\n",
       "      <td>1</td>\n",
       "      <td>0.2375</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>19950</td>\n",
       "      <td>0.513342</td>\n",
       "      <td>C23 C25 C27</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29</td>\n",
       "      <td>O'Dwyer, Miss. Ellen \"Nellie\"</td>\n",
       "      <td>0</td>\n",
       "      <td>0.3625</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330959</td>\n",
       "      <td>0.015379</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30</td>\n",
       "      <td>Todoroff, Mr. Lalio</td>\n",
       "      <td>1</td>\n",
       "      <td>0.3625</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349216</td>\n",
       "      <td>0.015412</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>861</th>\n",
       "      <td>862</td>\n",
       "      <td>Giles, Mr. Frederick Edward</td>\n",
       "      <td>1</td>\n",
       "      <td>0.2625</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>28134</td>\n",
       "      <td>0.022447</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>862</th>\n",
       "      <td>863</td>\n",
       "      <td>Swift, Mrs. Frederick Joel (Margaret Welles Ba...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.6000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17466</td>\n",
       "      <td>0.050610</td>\n",
       "      <td>D17</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>863</th>\n",
       "      <td>864</td>\n",
       "      <td>Sage, Miss. Dorothy Edith \"Dolly\"</td>\n",
       "      <td>0</td>\n",
       "      <td>0.3625</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>CA. 2343</td>\n",
       "      <td>0.135753</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>864</th>\n",
       "      <td>865</td>\n",
       "      <td>Gill, Mr. John William</td>\n",
       "      <td>1</td>\n",
       "      <td>0.3000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>233866</td>\n",
       "      <td>0.025374</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>865</th>\n",
       "      <td>866</td>\n",
       "      <td>Bystrom, Mrs. (Karolina)</td>\n",
       "      <td>0</td>\n",
       "      <td>0.5250</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>236852</td>\n",
       "      <td>0.025374</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>866</th>\n",
       "      <td>867</td>\n",
       "      <td>Duran y More, Miss. Asuncion</td>\n",
       "      <td>0</td>\n",
       "      <td>0.3375</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>SC/PARIS 2149</td>\n",
       "      <td>0.027050</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>867</th>\n",
       "      <td>868</td>\n",
       "      <td>Roebling, Mr. Washington Augustus II</td>\n",
       "      <td>1</td>\n",
       "      <td>0.3875</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17590</td>\n",
       "      <td>0.098561</td>\n",
       "      <td>A24</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868</th>\n",
       "      <td>869</td>\n",
       "      <td>van Melkebeke, Mr. Philemon</td>\n",
       "      <td>1</td>\n",
       "      <td>0.3625</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>345777</td>\n",
       "      <td>0.018543</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>869</th>\n",
       "      <td>870</td>\n",
       "      <td>Johnson, Master. Harold Theodor</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0500</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>347742</td>\n",
       "      <td>0.021731</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>870</th>\n",
       "      <td>871</td>\n",
       "      <td>Balkic, Mr. Cerin</td>\n",
       "      <td>1</td>\n",
       "      <td>0.3250</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349248</td>\n",
       "      <td>0.015412</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>871</th>\n",
       "      <td>872</td>\n",
       "      <td>Beckwith, Mrs. Richard Leonard (Sallie Monypeny)</td>\n",
       "      <td>0</td>\n",
       "      <td>0.5875</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>11751</td>\n",
       "      <td>0.102579</td>\n",
       "      <td>D35</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>872</th>\n",
       "      <td>873</td>\n",
       "      <td>Carlsson, Mr. Frans Olof</td>\n",
       "      <td>1</td>\n",
       "      <td>0.4125</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>695</td>\n",
       "      <td>0.009759</td>\n",
       "      <td>B51 B53 B55</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>873</th>\n",
       "      <td>874</td>\n",
       "      <td>Vander Cruyssen, Mr. Victor</td>\n",
       "      <td>1</td>\n",
       "      <td>0.5875</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>345765</td>\n",
       "      <td>0.017567</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>874</th>\n",
       "      <td>875</td>\n",
       "      <td>Abelson, Mrs. Samuel (Hannah Wizosky)</td>\n",
       "      <td>0</td>\n",
       "      <td>0.3500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>P/PP 3381</td>\n",
       "      <td>0.046845</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>875</th>\n",
       "      <td>876</td>\n",
       "      <td>Najib, Miss. Adele Kiamie \"Jane\"</td>\n",
       "      <td>0</td>\n",
       "      <td>0.1875</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2667</td>\n",
       "      <td>0.014102</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>876</th>\n",
       "      <td>877</td>\n",
       "      <td>Gustafsson, Mr. Alfred Ossian</td>\n",
       "      <td>1</td>\n",
       "      <td>0.2500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7534</td>\n",
       "      <td>0.019218</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>877</th>\n",
       "      <td>878</td>\n",
       "      <td>Petroff, Mr. Nedelio</td>\n",
       "      <td>1</td>\n",
       "      <td>0.2375</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349212</td>\n",
       "      <td>0.015412</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>878</th>\n",
       "      <td>879</td>\n",
       "      <td>Laleff, Mr. Kristo</td>\n",
       "      <td>1</td>\n",
       "      <td>0.3625</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349217</td>\n",
       "      <td>0.015412</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>879</th>\n",
       "      <td>880</td>\n",
       "      <td>Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)</td>\n",
       "      <td>0</td>\n",
       "      <td>0.7000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>11767</td>\n",
       "      <td>0.162314</td>\n",
       "      <td>C50</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>880</th>\n",
       "      <td>881</td>\n",
       "      <td>Shelley, Mrs. William (Imanita Parrish Hall)</td>\n",
       "      <td>0</td>\n",
       "      <td>0.3125</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>230433</td>\n",
       "      <td>0.050749</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>881</th>\n",
       "      <td>882</td>\n",
       "      <td>Markun, Mr. Johann</td>\n",
       "      <td>1</td>\n",
       "      <td>0.4125</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349257</td>\n",
       "      <td>0.015412</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>882</th>\n",
       "      <td>883</td>\n",
       "      <td>Dahlberg, Miss. Gerda Ulrika</td>\n",
       "      <td>0</td>\n",
       "      <td>0.2750</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7552</td>\n",
       "      <td>0.020527</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>883</th>\n",
       "      <td>884</td>\n",
       "      <td>Banfield, Mr. Frederick James</td>\n",
       "      <td>1</td>\n",
       "      <td>0.3500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>C.A./SOTON 34068</td>\n",
       "      <td>0.020495</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>884</th>\n",
       "      <td>885</td>\n",
       "      <td>Sutehall, Mr. Henry Jr</td>\n",
       "      <td>1</td>\n",
       "      <td>0.3125</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>SOTON/OQ 392076</td>\n",
       "      <td>0.013761</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>885</th>\n",
       "      <td>886</td>\n",
       "      <td>Rice, Mrs. William (Margaret Norton)</td>\n",
       "      <td>0</td>\n",
       "      <td>0.4875</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>382652</td>\n",
       "      <td>0.056848</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>887</td>\n",
       "      <td>Montvila, Rev. Juozas</td>\n",
       "      <td>1</td>\n",
       "      <td>0.3375</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>211536</td>\n",
       "      <td>0.025374</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>888</td>\n",
       "      <td>Graham, Miss. Margaret Edith</td>\n",
       "      <td>0</td>\n",
       "      <td>0.2375</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112053</td>\n",
       "      <td>0.058556</td>\n",
       "      <td>B42</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>889</td>\n",
       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
       "      <td>0</td>\n",
       "      <td>0.3625</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>W./C. 6607</td>\n",
       "      <td>0.045771</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>890</td>\n",
       "      <td>Behr, Mr. Karl Howell</td>\n",
       "      <td>1</td>\n",
       "      <td>0.3250</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111369</td>\n",
       "      <td>0.058556</td>\n",
       "      <td>C148</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>891</td>\n",
       "      <td>Dooley, Mr. Patrick</td>\n",
       "      <td>1</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370376</td>\n",
       "      <td>0.015127</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId                                               Name  Male  \\\n",
       "0              1                            Braund, Mr. Owen Harris     1   \n",
       "1              2  Cumings, Mrs. John Bradley (Florence Briggs Th...     0   \n",
       "2              3                             Heikkinen, Miss. Laina     0   \n",
       "3              4       Futrelle, Mrs. Jacques Heath (Lily May Peel)     0   \n",
       "4              5                           Allen, Mr. William Henry     1   \n",
       "5              6                                   Moran, Mr. James     1   \n",
       "6              7                            McCarthy, Mr. Timothy J     1   \n",
       "7              8                     Palsson, Master. Gosta Leonard     1   \n",
       "8              9  Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)     0   \n",
       "9             10                Nasser, Mrs. Nicholas (Adele Achem)     0   \n",
       "10            11                    Sandstrom, Miss. Marguerite Rut     0   \n",
       "11            12                           Bonnell, Miss. Elizabeth     0   \n",
       "12            13                     Saundercock, Mr. William Henry     1   \n",
       "13            14                        Andersson, Mr. Anders Johan     1   \n",
       "14            15               Vestrom, Miss. Hulda Amanda Adolfina     0   \n",
       "15            16                   Hewlett, Mrs. (Mary D Kingcome)      0   \n",
       "16            17                               Rice, Master. Eugene     1   \n",
       "17            18                       Williams, Mr. Charles Eugene     1   \n",
       "18            19  Vander Planke, Mrs. Julius (Emelia Maria Vande...     0   \n",
       "19            20                            Masselmani, Mrs. Fatima     0   \n",
       "20            21                               Fynney, Mr. Joseph J     1   \n",
       "21            22                              Beesley, Mr. Lawrence     1   \n",
       "22            23                        McGowan, Miss. Anna \"Annie\"     0   \n",
       "23            24                       Sloper, Mr. William Thompson     1   \n",
       "24            25                      Palsson, Miss. Torborg Danira     0   \n",
       "25            26  Asplund, Mrs. Carl Oscar (Selma Augusta Emilia...     0   \n",
       "26            27                            Emir, Mr. Farred Chehab     1   \n",
       "27            28                     Fortune, Mr. Charles Alexander     1   \n",
       "28            29                      O'Dwyer, Miss. Ellen \"Nellie\"     0   \n",
       "29            30                                Todoroff, Mr. Lalio     1   \n",
       "..           ...                                                ...   ...   \n",
       "861          862                        Giles, Mr. Frederick Edward     1   \n",
       "862          863  Swift, Mrs. Frederick Joel (Margaret Welles Ba...     0   \n",
       "863          864                  Sage, Miss. Dorothy Edith \"Dolly\"     0   \n",
       "864          865                             Gill, Mr. John William     1   \n",
       "865          866                           Bystrom, Mrs. (Karolina)     0   \n",
       "866          867                       Duran y More, Miss. Asuncion     0   \n",
       "867          868               Roebling, Mr. Washington Augustus II     1   \n",
       "868          869                        van Melkebeke, Mr. Philemon     1   \n",
       "869          870                    Johnson, Master. Harold Theodor     1   \n",
       "870          871                                  Balkic, Mr. Cerin     1   \n",
       "871          872   Beckwith, Mrs. Richard Leonard (Sallie Monypeny)     0   \n",
       "872          873                           Carlsson, Mr. Frans Olof     1   \n",
       "873          874                        Vander Cruyssen, Mr. Victor     1   \n",
       "874          875              Abelson, Mrs. Samuel (Hannah Wizosky)     0   \n",
       "875          876                   Najib, Miss. Adele Kiamie \"Jane\"     0   \n",
       "876          877                      Gustafsson, Mr. Alfred Ossian     1   \n",
       "877          878                               Petroff, Mr. Nedelio     1   \n",
       "878          879                                 Laleff, Mr. Kristo     1   \n",
       "879          880      Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)     0   \n",
       "880          881       Shelley, Mrs. William (Imanita Parrish Hall)     0   \n",
       "881          882                                 Markun, Mr. Johann     1   \n",
       "882          883                       Dahlberg, Miss. Gerda Ulrika     0   \n",
       "883          884                      Banfield, Mr. Frederick James     1   \n",
       "884          885                             Sutehall, Mr. Henry Jr     1   \n",
       "885          886               Rice, Mrs. William (Margaret Norton)     0   \n",
       "886          887                              Montvila, Rev. Juozas     1   \n",
       "887          888                       Graham, Miss. Margaret Edith     0   \n",
       "888          889           Johnston, Miss. Catherine Helen \"Carrie\"     0   \n",
       "889          890                              Behr, Mr. Karl Howell     1   \n",
       "890          891                                Dooley, Mr. Patrick     1   \n",
       "\n",
       "        Age  SibSp  Parch            Ticket      Fare        Cabin  Survived  \\\n",
       "0    0.2750      1      0         A/5 21171  0.014151          NaN         0   \n",
       "1    0.4750      1      0          PC 17599  0.139136          C85         1   \n",
       "2    0.3250      0      0  STON/O2. 3101282  0.015469          NaN         1   \n",
       "3    0.4375      1      0            113803  0.103644         C123         1   \n",
       "4    0.4375      0      0            373450  0.015713          NaN         0   \n",
       "5    0.3625      0      0            330877  0.016510          NaN         0   \n",
       "6    0.6750      0      0             17463  0.101229          E46         0   \n",
       "7    0.0250      3      1            349909  0.041136          NaN         0   \n",
       "8    0.3375      0      2            347742  0.021731          NaN         1   \n",
       "9    0.1750      1      0            237736  0.058694          NaN         1   \n",
       "10   0.0500      1      1           PP 9549  0.032596           G6         1   \n",
       "11   0.7250      0      0            113783  0.051822         C103         1   \n",
       "12   0.2500      0      0         A/5. 2151  0.015713          NaN         0   \n",
       "13   0.4875      1      5            347082  0.061045          NaN         0   \n",
       "14   0.1750      0      0            350406  0.015330          NaN         0   \n",
       "15   0.6875      0      0            248706  0.031230          NaN         1   \n",
       "16   0.0250      4      1            382652  0.056848          NaN         0   \n",
       "17   0.3625      0      0            244373  0.025374          NaN         1   \n",
       "18   0.3875      1      0            345763  0.035134          NaN         0   \n",
       "19   0.3625      0      0              2649  0.014102          NaN         1   \n",
       "20   0.4375      0      0            239865  0.050749          NaN         0   \n",
       "21   0.4250      0      0            248698  0.025374          D56         1   \n",
       "22   0.1875      0      0            330923  0.015672          NaN         1   \n",
       "23   0.3500      0      0            113788  0.069291           A6         1   \n",
       "24   0.1000      3      1            349909  0.041136          NaN         0   \n",
       "25   0.4750      1      5            347077  0.061264          NaN         1   \n",
       "26   0.3625      0      0              2631  0.014102          NaN         0   \n",
       "27   0.2375      3      2             19950  0.513342  C23 C25 C27         0   \n",
       "28   0.3625      0      0            330959  0.015379          NaN         1   \n",
       "29   0.3625      0      0            349216  0.015412          NaN         0   \n",
       "..      ...    ...    ...               ...       ...          ...       ...   \n",
       "861  0.2625      1      0             28134  0.022447          NaN         0   \n",
       "862  0.6000      0      0             17466  0.050610          D17         1   \n",
       "863  0.3625      8      2          CA. 2343  0.135753          NaN         0   \n",
       "864  0.3000      0      0            233866  0.025374          NaN         0   \n",
       "865  0.5250      0      0            236852  0.025374          NaN         1   \n",
       "866  0.3375      1      0     SC/PARIS 2149  0.027050          NaN         1   \n",
       "867  0.3875      0      0          PC 17590  0.098561          A24         0   \n",
       "868  0.3625      0      0            345777  0.018543          NaN         0   \n",
       "869  0.0500      1      1            347742  0.021731          NaN         1   \n",
       "870  0.3250      0      0            349248  0.015412          NaN         0   \n",
       "871  0.5875      1      1             11751  0.102579          D35         1   \n",
       "872  0.4125      0      0               695  0.009759  B51 B53 B55         0   \n",
       "873  0.5875      0      0            345765  0.017567          NaN         0   \n",
       "874  0.3500      1      0         P/PP 3381  0.046845          NaN         1   \n",
       "875  0.1875      0      0              2667  0.014102          NaN         1   \n",
       "876  0.2500      0      0              7534  0.019218          NaN         0   \n",
       "877  0.2375      0      0            349212  0.015412          NaN         0   \n",
       "878  0.3625      0      0            349217  0.015412          NaN         0   \n",
       "879  0.7000      0      1             11767  0.162314          C50         1   \n",
       "880  0.3125      0      1            230433  0.050749          NaN         1   \n",
       "881  0.4125      0      0            349257  0.015412          NaN         0   \n",
       "882  0.2750      0      0              7552  0.020527          NaN         0   \n",
       "883  0.3500      0      0  C.A./SOTON 34068  0.020495          NaN         0   \n",
       "884  0.3125      0      0   SOTON/OQ 392076  0.013761          NaN         0   \n",
       "885  0.4875      0      5            382652  0.056848          NaN         0   \n",
       "886  0.3375      0      0            211536  0.025374          NaN         0   \n",
       "887  0.2375      0      0            112053  0.058556          B42         1   \n",
       "888  0.3625      1      2        W./C. 6607  0.045771          NaN         0   \n",
       "889  0.3250      0      0            111369  0.058556         C148         1   \n",
       "890  0.4000      0      0            370376  0.015127          NaN         0   \n",
       "\n",
       "     Pclass_1  Pclass_2  Embarked_1  Embarked_2  Embarked_3  \n",
       "0           0         1           0           1           0  \n",
       "1           0         0           0           0           0  \n",
       "2           0         1           0           1           0  \n",
       "3           0         0           0           1           0  \n",
       "4           0         1           0           1           0  \n",
       "5           0         1           1           0           0  \n",
       "6           0         0           0           1           0  \n",
       "7           0         1           0           1           0  \n",
       "8           0         1           0           1           0  \n",
       "9           1         0           0           0           0  \n",
       "10          0         1           0           1           0  \n",
       "11          0         0           0           1           0  \n",
       "12          0         1           0           1           0  \n",
       "13          0         1           0           1           0  \n",
       "14          0         1           0           1           0  \n",
       "15          1         0           0           1           0  \n",
       "16          0         1           1           0           0  \n",
       "17          1         0           0           1           0  \n",
       "18          0         1           0           1           0  \n",
       "19          0         1           0           0           0  \n",
       "20          1         0           0           1           0  \n",
       "21          1         0           0           1           0  \n",
       "22          0         1           1           0           0  \n",
       "23          0         0           0           1           0  \n",
       "24          0         1           0           1           0  \n",
       "25          0         1           0           1           0  \n",
       "26          0         1           0           0           0  \n",
       "27          0         0           0           1           0  \n",
       "28          0         1           1           0           0  \n",
       "29          0         1           0           1           0  \n",
       "..        ...       ...         ...         ...         ...  \n",
       "861         1         0           0           1           0  \n",
       "862         0         0           0           1           0  \n",
       "863         0         1           0           1           0  \n",
       "864         1         0           0           1           0  \n",
       "865         1         0           0           1           0  \n",
       "866         1         0           0           0           0  \n",
       "867         0         0           0           1           0  \n",
       "868         0         1           0           1           0  \n",
       "869         0         1           0           1           0  \n",
       "870         0         1           0           1           0  \n",
       "871         0         0           0           1           0  \n",
       "872         0         0           0           1           0  \n",
       "873         0         1           0           1           0  \n",
       "874         1         0           0           0           0  \n",
       "875         0         1           0           0           0  \n",
       "876         0         1           0           1           0  \n",
       "877         0         1           0           1           0  \n",
       "878         0         1           0           1           0  \n",
       "879         0         0           0           0           0  \n",
       "880         1         0           0           1           0  \n",
       "881         0         1           0           1           0  \n",
       "882         0         1           0           1           0  \n",
       "883         1         0           0           1           0  \n",
       "884         0         1           0           1           0  \n",
       "885         0         1           1           0           0  \n",
       "886         1         0           0           1           0  \n",
       "887         0         0           0           1           0  \n",
       "888         0         1           0           1           0  \n",
       "889         0         0           0           0           0  \n",
       "890         0         1           1           0           0  \n",
       "\n",
       "[891 rows x 15 columns]"
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Embarked=pd.get_dummies(df['Embarked'],prefix='Embarked',drop_first=True)\n",
    "df=pd.concat([df,Embarked],axis=1)\n",
    "df.drop(['Embarked'],axis=1,inplace=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_da = df.to_csv('new_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['PassengerId', 'Name', 'Male', 'Age', 'SibSp', 'Parch', 'Ticket',\n",
       "       'Fare', 'Cabin', 'Survived', 'Pclass_1', 'Pclass_2', 'Embarked_1',\n",
       "       'Embarked_2', 'Embarked_3'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "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>Name</th>\n",
       "      <th>Male</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>Survived</th>\n",
       "      <th>Pclass_1</th>\n",
       "      <th>Pclass_2</th>\n",
       "      <th>Embarked_1</th>\n",
       "      <th>Embarked_2</th>\n",
       "      <th>Embarked_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>1</td>\n",
       "      <td>0.2750</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>0.014151</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.4750</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>0.139136</td>\n",
       "      <td>C85</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>Heikkinen, Miss. Laina</td>\n",
       "      <td>0</td>\n",
       "      <td>0.3250</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>0.015469</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>0</td>\n",
       "      <td>0.4375</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>0.103644</td>\n",
       "      <td>C123</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>1</td>\n",
       "      <td>0.4375</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>0.015713</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId                                               Name  Male  \\\n",
       "0            1                            Braund, Mr. Owen Harris     1   \n",
       "1            2  Cumings, Mrs. John Bradley (Florence Briggs Th...     0   \n",
       "2            3                             Heikkinen, Miss. Laina     0   \n",
       "3            4       Futrelle, Mrs. Jacques Heath (Lily May Peel)     0   \n",
       "4            5                           Allen, Mr. William Henry     1   \n",
       "\n",
       "      Age  SibSp  Parch            Ticket      Fare Cabin  Survived  Pclass_1  \\\n",
       "0  0.2750      1      0         A/5 21171  0.014151   NaN         0         0   \n",
       "1  0.4750      1      0          PC 17599  0.139136   C85         1         0   \n",
       "2  0.3250      0      0  STON/O2. 3101282  0.015469   NaN         1         0   \n",
       "3  0.4375      1      0            113803  0.103644  C123         1         0   \n",
       "4  0.4375      0      0            373450  0.015713   NaN         0         0   \n",
       "\n",
       "   Pclass_2  Embarked_1  Embarked_2  Embarked_3  \n",
       "0         1           0           1           0  \n",
       "1         0           0           0           0  \n",
       "2         1           0           1           0  \n",
       "3         0           0           1           0  \n",
       "4         1           0           1           0  "
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7937219730941704\n",
      "0.8053892215568862\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Darsb\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "X = df[['Male', 'Age', 'SibSp', 'Parch','Fare', 'Pclass_1', 'Pclass_2', 'Embarked_1','Embarked_2', 'Embarked_3']]\n",
    "y = df['Survived']\n",
    "logisticRegr = LogisticRegression()\n",
    "x_train, x_test, y_train, y_test = train_test_split(X, y,random_state=0)\n",
    "logisticRegr.fit(x_train, y_train)\n",
    "predictions = logisticRegr.predict(x_test)\n",
    "score = logisticRegr.score(x_test, y_test)\n",
    "score1 = logisticRegr.score(x_train, y_train)\n",
    "\n",
    "print(score)\n",
    "print(score1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[False  True  True  True]\n",
      "[2 1 1 1]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Darsb\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\Darsb\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\Darsb\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\Darsb\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import LinearSVC\n",
    "from sklearn.feature_selection import RFE\n",
    "from sklearn import datasets\n",
    "\n",
    "svm = LogisticRegression()\n",
    "# create the RFE model for the svm classifier \n",
    "# and select attributes\n",
    "rfe = RFE(svm, 3)\n",
    "rfe = rfe.fit(dataset.data, dataset.target)\n",
    "# print summaries for the selection of attributes\n",
    "print(rfe.support_)\n",
    "print(rfe.ranking_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x150d7ef7748>"
      ]
     },
     "execution_count": 244,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,8))\n",
    "sns.heatmap(df.corr(), annot=True, cmap='viridis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "EOL while scanning string literal (<ipython-input-243-c49107e1411f>, line 2)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-243-c49107e1411f>\"\u001b[1;36m, line \u001b[1;32m2\u001b[0m\n\u001b[1;33m    X = df['Male]\u001b[0m\n\u001b[1;37m                 ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m EOL while scanning string literal\n"
     ]
    }
   ],
   "source": [
    "# from scipy.stats import pearsonr\n",
    "# X = df['Male]\n",
    "# Y = df['Survived']\n",
    "\n",
    "# #plot the variables to show linearity\n",
    "# plt.scatter(X,Y)\n",
    "# plt.show()\n",
    "# pearsonr(X,Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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