Titanic_Survival_Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 475,
   "id": "5b1e8195",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "from sklearn.metrics import accuracy_score,precision_score,recall_score,f1_score,confusion_matrix,classification_report\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 476,
   "id": "5bf298a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1=pd.read_csv(\"ttrain.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 477,
   "id": "3b2049c5",
   "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>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>887</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Montvila, Rev. Juozas</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>211536</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>888</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Graham, Miss. Margaret Edith</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112053</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>B42</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>889</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>W./C. 6607</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>890</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Behr, Mr. Karl Howell</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111369</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>C148</td>\n",
       "      <td>C</td>\n",
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       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>891</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Dooley, Mr. Patrick</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370376</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass  \\\n",
       "0              1         0       3   \n",
       "1              2         1       1   \n",
       "2              3         1       3   \n",
       "3              4         1       1   \n",
       "4              5         0       3   \n",
       "..           ...       ...     ...   \n",
       "886          887         0       2   \n",
       "887          888         1       1   \n",
       "888          889         0       3   \n",
       "889          890         1       1   \n",
       "890          891         0       3   \n",
       "\n",
       "                                                  Name     Sex   Age  SibSp  \\\n",
       "0                              Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1    Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                               Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                             Allen, Mr. William Henry    male  35.0      0   \n",
       "..                                                 ...     ...   ...    ...   \n",
       "886                              Montvila, Rev. Juozas    male  27.0      0   \n",
       "887                       Graham, Miss. Margaret Edith  female  19.0      0   \n",
       "888           Johnston, Miss. Catherine Helen \"Carrie\"  female   NaN      1   \n",
       "889                              Behr, Mr. Karl Howell    male  26.0      0   \n",
       "890                                Dooley, Mr. Patrick    male  32.0      0   \n",
       "\n",
       "     Parch            Ticket     Fare Cabin Embarked  \n",
       "0        0         A/5 21171   7.2500   NaN        S  \n",
       "1        0          PC 17599  71.2833   C85        C  \n",
       "2        0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3        0            113803  53.1000  C123        S  \n",
       "4        0            373450   8.0500   NaN        S  \n",
       "..     ...               ...      ...   ...      ...  \n",
       "886      0            211536  13.0000   NaN        S  \n",
       "887      0            112053  30.0000   B42        S  \n",
       "888      2        W./C. 6607  23.4500   NaN        S  \n",
       "889      0            111369  30.0000  C148        C  \n",
       "890      0            370376   7.7500   NaN        Q  \n",
       "\n",
       "[891 rows x 12 columns]"
      ]
     },
     "execution_count": 477,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 478,
   "id": "51196451",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n",
       "       'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 478,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 479,
   "id": "21a20179",
   "metadata": {},
   "outputs": [
    {
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       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
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       "      <th>SibSp</th>\n",
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       "      <td>892</td>\n",
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       "      <td>Kelly, Mr. James</td>\n",
       "      <td>male</td>\n",
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       "      <td>Wilkes, Mrs. James (Ellen Needs)</td>\n",
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       "      <td>47.0</td>\n",
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       "      <td>363272</td>\n",
       "      <td>7.0000</td>\n",
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       "      <th>2</th>\n",
       "      <td>894</td>\n",
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       "      <td>Myles, Mr. Thomas Francis</td>\n",
       "      <td>male</td>\n",
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       "      <td>240276</td>\n",
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       "      <td>Q</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>3</td>\n",
       "      <td>Wirz, Mr. Albert</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>315154</td>\n",
       "      <td>8.6625</td>\n",
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       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>3</td>\n",
       "      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
       "      <td>female</td>\n",
       "      <td>22.0</td>\n",
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       "      <td>1</td>\n",
       "      <td>3101298</td>\n",
       "      <td>12.2875</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>413</th>\n",
       "      <td>1305</td>\n",
       "      <td>3</td>\n",
       "      <td>Spector, Mr. Woolf</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A.5. 3236</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>1306</td>\n",
       "      <td>1</td>\n",
       "      <td>Oliva y Ocana, Dona. Fermina</td>\n",
       "      <td>female</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17758</td>\n",
       "      <td>108.9000</td>\n",
       "      <td>C105</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>1307</td>\n",
       "      <td>3</td>\n",
       "      <td>Saether, Mr. Simon Sivertsen</td>\n",
       "      <td>male</td>\n",
       "      <td>38.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>SOTON/O.Q. 3101262</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>416</th>\n",
       "      <td>1308</td>\n",
       "      <td>3</td>\n",
       "      <td>Ware, Mr. Frederick</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>359309</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>417</th>\n",
       "      <td>1309</td>\n",
       "      <td>3</td>\n",
       "      <td>Peter, Master. Michael J</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2668</td>\n",
       "      <td>22.3583</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>418 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Pclass                                          Name  \\\n",
       "0            892       3                              Kelly, Mr. James   \n",
       "1            893       3              Wilkes, Mrs. James (Ellen Needs)   \n",
       "2            894       2                     Myles, Mr. Thomas Francis   \n",
       "3            895       3                              Wirz, Mr. Albert   \n",
       "4            896       3  Hirvonen, Mrs. Alexander (Helga E Lindqvist)   \n",
       "..           ...     ...                                           ...   \n",
       "413         1305       3                            Spector, Mr. Woolf   \n",
       "414         1306       1                  Oliva y Ocana, Dona. Fermina   \n",
       "415         1307       3                  Saether, Mr. Simon Sivertsen   \n",
       "416         1308       3                           Ware, Mr. Frederick   \n",
       "417         1309       3                      Peter, Master. Michael J   \n",
       "\n",
       "        Sex   Age  SibSp  Parch              Ticket      Fare Cabin Embarked  \n",
       "0      male  34.5      0      0              330911    7.8292   NaN        Q  \n",
       "1    female  47.0      1      0              363272    7.0000   NaN        S  \n",
       "2      male  62.0      0      0              240276    9.6875   NaN        Q  \n",
       "3      male  27.0      0      0              315154    8.6625   NaN        S  \n",
       "4    female  22.0      1      1             3101298   12.2875   NaN        S  \n",
       "..      ...   ...    ...    ...                 ...       ...   ...      ...  \n",
       "413    male   NaN      0      0           A.5. 3236    8.0500   NaN        S  \n",
       "414  female  39.0      0      0            PC 17758  108.9000  C105        C  \n",
       "415    male  38.5      0      0  SOTON/O.Q. 3101262    7.2500   NaN        S  \n",
       "416    male   NaN      0      0              359309    8.0500   NaN        S  \n",
       "417    male   NaN      1      1                2668   22.3583   NaN        C  \n",
       "\n",
       "[418 rows x 11 columns]"
      ]
     },
     "execution_count": 479,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2=pd.read_csv(\"ttest.csv\")\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 480,
   "id": "68da9045",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((891, 12), (418, 11))"
      ]
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     "execution_count": 480,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df1.shape , df2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 481,
   "id": "a120ede3",
   "metadata": {},
   "outputs": [
    {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>484</th>\n",
       "      <td>485</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Bishop, Mr. Dickinson H</td>\n",
       "      <td>male</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>11967</td>\n",
       "      <td>91.0792</td>\n",
       "      <td>B49</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass                     Name   Sex   Age  \\\n",
       "484          485         1       1  Bishop, Mr. Dickinson H  male  25.0   \n",
       "\n",
       "     SibSp  Parch Ticket     Fare Cabin Embarked  \n",
       "484      1      0  11967  91.0792   B49        C  "
      ]
     },
     "execution_count": 481,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 482,
   "id": "860f2106",
   "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>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>385</th>\n",
       "      <td>1277</td>\n",
       "      <td>2</td>\n",
       "      <td>Herman, Miss. Kate</td>\n",
       "      <td>female</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>220845</td>\n",
       "      <td>65.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Pclass                Name     Sex   Age  SibSp  Parch  \\\n",
       "385         1277       2  Herman, Miss. Kate  female  24.0      1      2   \n",
       "\n",
       "     Ticket  Fare Cabin Embarked  \n",
       "385  220845  65.0   NaN        S  "
      ]
     },
     "execution_count": 482,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 483,
   "id": "cf41144d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df1.append(df2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 484,
   "id": "15a99878",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1309, 12)"
      ]
     },
     "execution_count": 484,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 485,
   "id": "5b5cfa79",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1309 entries, 0 to 417\n",
      "Data columns (total 12 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  1309 non-null   int64  \n",
      " 1   Survived     891 non-null    float64\n",
      " 2   Pclass       1309 non-null   int64  \n",
      " 3   Name         1309 non-null   object \n",
      " 4   Sex          1309 non-null   object \n",
      " 5   Age          1046 non-null   float64\n",
      " 6   SibSp        1309 non-null   int64  \n",
      " 7   Parch        1309 non-null   int64  \n",
      " 8   Ticket       1309 non-null   object \n",
      " 9   Fare         1308 non-null   float64\n",
      " 10  Cabin        295 non-null    object \n",
      " 11  Embarked     1307 non-null   object \n",
      "dtypes: float64(3), int64(4), object(5)\n",
      "memory usage: 132.9+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 486,
   "id": "06000267",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId       0\n",
       "Survived        418\n",
       "Pclass            0\n",
       "Name              0\n",
       "Sex               0\n",
       "Age             263\n",
       "SibSp             0\n",
       "Parch             0\n",
       "Ticket            0\n",
       "Fare              1\n",
       "Cabin          1014\n",
       "Embarked          2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 486,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 487,
   "id": "3215a9d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "33.29547928134557"
      ]
     },
     "execution_count": 487,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Fare\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 488,
   "id": "a8588c03",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Fare\"] = df[\"Fare\"].fillna(df[\"Fare\"].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 489,
   "id": "b924d92f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId       0\n",
       "Survived        418\n",
       "Pclass            0\n",
       "Name              0\n",
       "Sex               0\n",
       "Age             263\n",
       "SibSp             0\n",
       "Parch             0\n",
       "Ticket            0\n",
       "Fare              0\n",
       "Cabin          1014\n",
       "Embarked          2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 489,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 490,
   "id": "f5f56a90",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "S    914\n",
       "C    270\n",
       "Q    123\n",
       "Name: Embarked, dtype: int64"
      ]
     },
     "execution_count": 490,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Embarked\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 491,
   "id": "f4352667",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    S\n",
       "Name: Embarked, dtype: object"
      ]
     },
     "execution_count": 491,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Embarked\"].mode()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 492,
   "id": "b839446f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Embarked\"]=df[\"Embarked\"].fillna(\"S\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 493,
   "id": "63eeed26",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId       0\n",
       "Survived        418\n",
       "Pclass            0\n",
       "Name              0\n",
       "Sex               0\n",
       "Age             263\n",
       "SibSp             0\n",
       "Parch             0\n",
       "Ticket            0\n",
       "Fare              0\n",
       "Cabin          1014\n",
       "Embarked          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 493,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 494,
   "id": "2cfa3355",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Embarked', ylabel='count'>"
      ]
     },
     "execution_count": 494,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='Embarked',hue=\"Survived\",data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 495,
   "id": "ad9288d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>712</th>\n",
       "      <td>713</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Taylor, Mr. Elmer Zebley</td>\n",
       "      <td>male</td>\n",
       "      <td>48.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>19996</td>\n",
       "      <td>52.0000</td>\n",
       "      <td>C126</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>898</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>Connolly, Miss. Kate</td>\n",
       "      <td>female</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330972</td>\n",
       "      <td>7.6292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>344</th>\n",
       "      <td>345</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>Fox, Mr. Stanley Hubert</td>\n",
       "      <td>male</td>\n",
       "      <td>36.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>229236</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>804</th>\n",
       "      <td>805</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>Hedman, Mr. Oskar Arvid</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>347089</td>\n",
       "      <td>6.9750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>55</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Ostby, Mr. Engelhart Cornelius</td>\n",
       "      <td>male</td>\n",
       "      <td>65.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>113509</td>\n",
       "      <td>61.9792</td>\n",
       "      <td>B30</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass                            Name     Sex  \\\n",
       "712          713       1.0       1        Taylor, Mr. Elmer Zebley    male   \n",
       "6            898       NaN       3            Connolly, Miss. Kate  female   \n",
       "344          345       0.0       2         Fox, Mr. Stanley Hubert    male   \n",
       "804          805       1.0       3         Hedman, Mr. Oskar Arvid    male   \n",
       "54            55       0.0       1  Ostby, Mr. Engelhart Cornelius    male   \n",
       "\n",
       "      Age  SibSp  Parch  Ticket     Fare Cabin Embarked  \n",
       "712  48.0      1      0   19996  52.0000  C126        S  \n",
       "6    30.0      0      0  330972   7.6292   NaN        Q  \n",
       "344  36.0      0      0  229236  13.0000   NaN        S  \n",
       "804  27.0      0      0  347089   6.9750   NaN        S  \n",
       "54   65.0      0      1  113509  61.9792   B30        C  "
      ]
     },
     "execution_count": 495,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 496,
   "id": "11b1954d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Title\"]=df[\"Name\"].str.extract(\" ([A-Za-z]+)\\.\",expand=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 497,
   "id": "667685f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Mr', 'Mrs', 'Miss', 'Master', 'Don', 'Rev', 'Dr', 'Mme', 'Ms',\n",
       "       'Major', 'Lady', 'Sir', 'Mlle', 'Col', 'Capt', 'Countess',\n",
       "       'Jonkheer', 'Dona'], dtype=object)"
      ]
     },
     "execution_count": 497,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Title\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 498,
   "id": "1048d9d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Mr          757\n",
       "Miss        260\n",
       "Mrs         197\n",
       "Master       61\n",
       "Rev           8\n",
       "Dr            8\n",
       "Col           4\n",
       "Mlle          2\n",
       "Major         2\n",
       "Ms            2\n",
       "Lady          1\n",
       "Sir           1\n",
       "Mme           1\n",
       "Don           1\n",
       "Capt          1\n",
       "Countess      1\n",
       "Jonkheer      1\n",
       "Dona          1\n",
       "Name: Title, dtype: int64"
      ]
     },
     "execution_count": 498,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Title\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 499,
   "id": "3241472c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Title\"]=df[\"Title\"].replace([\"Ms\",\"Mlle\"],\"Miss\")\n",
    "df[\"Title\"]=df[\"Title\"].replace([\"Mme\",\"Countess\",\"Lady\",\"Dona\"],\"Mrs\")\n",
    "df[\"Title\"]=df[\"Title\"].replace([\"Ms\",\"Mlle\"],\"Miss\")\n",
    "df[\"Title\"]=df[\"Title\"].replace([\"Rev\",\"Johkheer\",\"Don\",\"Sir\",\"Col\",\"Major\",\"Dr\",\"Capt\"],\"Mr\")\n",
    "                                "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 500,
   "id": "389cbce8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Mr          782\n",
       "Miss        264\n",
       "Mrs         201\n",
       "Master       61\n",
       "Jonkheer      1\n",
       "Name: Title, dtype: int64"
      ]
     },
     "execution_count": 500,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Title\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 501,
   "id": "75835230",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId       0\n",
       "Survived        418\n",
       "Pclass            0\n",
       "Name              0\n",
       "Sex               0\n",
       "Age             263\n",
       "SibSp             0\n",
       "Parch             0\n",
       "Ticket            0\n",
       "Fare              0\n",
       "Cabin          1014\n",
       "Embarked          0\n",
       "Title             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 501,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 502,
   "id": "84f357f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.774637127578304"
      ]
     },
     "execution_count": 502,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1014/1309"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 503,
   "id": "fbd13c17",
   "metadata": {},
   "outputs": [],
   "source": [
    "del df[\"Cabin\"]  #yüzde 20sinden fazlası boşsa silebiliriz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 504,
   "id": "c5cfd52d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(x=df.Age);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 505,
   "id": "78c8d9e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1046.000000\n",
       "mean       29.881138\n",
       "std        14.413493\n",
       "min         0.170000\n",
       "25%        21.000000\n",
       "50%        28.000000\n",
       "75%        39.000000\n",
       "max        80.000000\n",
       "Name: Age, dtype: float64"
      ]
     },
     "execution_count": 505,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Age\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 506,
   "id": "f944dca6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sex\n",
       "female    28.687088\n",
       "male      30.585228\n",
       "Name: Age, dtype: float64"
      ]
     },
     "execution_count": 506,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\"Sex\")[\"Age\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 507,
   "id": "dd3337f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Age\"]=df[\"Age\"].fillna(df.groupby(\"Title\")[\"Age\"].transform(\"median\")) #orta değer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 508,
   "id": "f4e318fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Family\"]=df[\"SibSp\"]+df[\"Parch\"]+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 509,
   "id": "ebe41a9d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEGCAYAAACKB4k+AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAAsTAAALEwEAmpwYAAAVj0lEQVR4nO3df7BfdX3n8eerRBRRCT/uZjHBhtEMruOugHfYWBxbiXaBWkJdYHGrpGx20j/QarXb0nZ2tZ21Y2e1FHWXmQyooWVRRFlS66hMoDp1FE0Q+RVdIookC+SKgFaKir73j+/nHq7JDXwD99xz4T4fM9/5fs7nfL7nvBPIfd3zOed7TqoKSZIAfmnoAiRJC4ehIEnqGAqSpI6hIEnqGAqSpM6SoQt4Mo444ohauXLl0GVI0lPKtm3bvldVE7Ote0qHwsqVK9m6devQZUjSU0qSO/e1zukjSVLHUJAkdQwFSVKn11BI8vtJbk1yS5LLkzwrydFJrk+yI8nHkhzYxj6zLe9o61f2WZskaW+9hUKS5cDvAZNV9VLgAOBs4C+BC6rqRcD9wPr2kfXA/a3/gjZOkjSP+p4+WgIclGQJ8GzgbuAk4Mq2fhNwemuvbcu09WuSpOf6JEkz9BYKVbULeC/wXUZh8CCwDXigqh5pw3YCy1t7OXBX++wjbfzhe243yYYkW5NsnZqa6qt8SVqU+pw+OpTRb/9HA88HDgZOfrLbraqNVTVZVZMTE7N+90KS9AT1OX30GuDbVTVVVT8FPgmcCCxt00kAK4Bdrb0LOAqgrT8EuK/H+iRJe+jzG83fBVYneTbwz8AaYCtwHXAG8FFgHXB1G7+5LX+prb+29vMJQC//L5fOTeX7adv/OGeQ/UrSXOvznML1jE4Y3wDc3Pa1Efgj4O1JdjA6Z3BJ+8glwOGt/+3A+X3VJkmaXa/3PqqqdwLv3KP7DuCEWcY+DJzZZz2SpMfmN5olSR1DQZLUMRQkSR1DQZLUMRQkSR1DQZLUMRQkSR1DQZLUMRQkSR1DQZLUMRQkSR1DQZLUMRQkSR1DQZLUMRQkSR1DQZLUMRQkSZ3eQiHJMUlunPH6QZK3JTksyTVJbm/vh7bxSfL+JDuS3JTk+L5qkyTNrs9nNH+zqo6tqmOBlwMPAVcxevbylqpaBWzh0WcxnwKsaq8NwEV91SZJmt18TR+tAb5VVXcCa4FNrX8TcHprrwUurZEvA0uTHDlP9UmSmL9QOBu4vLWXVdXdrX0PsKy1lwN3zfjMztb3C5JsSLI1ydapqam+6pWkRan3UEhyIHAa8PE911VVAbU/26uqjVU1WVWTExMTc1SlJAnm50jhFOCGqrq3Ld87PS3U3ne3/l3AUTM+t6L1SZLmyXyEwht4dOoIYDOwrrXXAVfP6D+nXYW0GnhwxjSTJGkeLOlz40kOBl4L/O6M7vcAVyRZD9wJnNX6Pw2cCuxgdKXSuX3WJknaW6+hUFU/Ag7fo+8+Rlcj7Tm2gPP6rEeS9Nj8RrMkqWMoSJI6hoIkqWMoSJI6hoIkqWMoSJI6hoIkqWMoSJI6hoIkqWMoSJI6hoIkqWMoSJI6hoIkqWMoSJI6hoIkqWMoSJI6hoIkqdNrKCRZmuTKJN9Isj3JK5IcluSaJLe390Pb2CR5f5IdSW5KcnyftUmS9tb3kcKFwGeq6sXAy4DtwPnAlqpaBWxpywCnAKvaawNwUc+1SZL20FsoJDkEeBVwCUBV/aSqHgDWApvasE3A6a29Fri0Rr4MLE1yZF/1SZL21ueRwtHAFPDhJF9LcnGSg4FlVXV3G3MPsKy1lwN3zfj8ztYnSZonfYbCEuB44KKqOg74EY9OFQFQVQXU/mw0yYYkW5NsnZqamrNiJUn9hsJOYGdVXd+Wr2QUEvdOTwu1991t/S7gqBmfX9H6fkFVbayqyaqanJiY6K14SVqMeguFqroHuCvJMa1rDXAbsBlY1/rWAVe39mbgnHYV0mrgwRnTTJKkebCk5+2/BbgsyYHAHcC5jILoiiTrgTuBs9rYTwOnAjuAh9pYSdI86jUUqupGYHKWVWtmGVvAeX3WI0l6bH6jWZLUMRQkSR1DQZLUMRQkSR1DQZLUMRQkSR1DQZLUMRQkSR1DQZLUMRQkSR1DQZLUMRQkSR1DQZLUMRQkSR1DQZLUMRQkSR1DQZLUMRQkSZ1eQyHJd5LcnOTGJFtb32FJrklye3s/tPUnyfuT7EhyU5Lj+6xNkrS3+ThSeHVVHVtV089qPh/YUlWrgC1tGeAUYFV7bQAumofaJEkzDDF9tBbY1NqbgNNn9F9aI18GliY5coD6JGnR6jsUCvhckm1JNrS+ZVV1d2vfAyxr7eXAXTM+u7P1/YIkG5JsTbJ1amqqr7olaVFa0vP2X1lVu5L8C+CaJN+YubKqKkntzwaraiOwEWBycnK/PitJemy9HilU1a72vhu4CjgBuHd6Wqi9727DdwFHzfj4itYnSZonvYVCkoOTPHe6Dfw6cAuwGVjXhq0Drm7tzcA57Sqk1cCDM6aZJEnzoM/po2XAVUmm9/O/q+ozSb4KXJFkPXAncFYb/2ngVGAH8BBwbo+1SZJm0VsoVNUdwMtm6b8PWDNLfwHn9VWPJOnx+Y1mSVLHUJAkdQwFSVLHUJAkdQwFSVJnrFBIsmWcPknSU9tjXpKa5FnAs4Ej2i2u01Y9j1nuSyRJemp7vO8p/C7wNuD5wDYeDYUfAB/sryxJ0hAeMxSq6kLgwiRvqaoPzFNNkqSBjPWN5qr6QJJfAVbO/ExVXdpTXZKkAYwVCkn+BnghcCPws9ZdgKEgSU8j4977aBJ4Sbs/kSTpaWrc7yncAvzLPguRJA1v3COFI4DbknwF+PF0Z1Wd1ktVkqRBjBsK7+qzCEnSwjDu1Uef77sQSdLwxr366IeMrjYCOBB4BvCjqnpeX4VJkubfWCeaq+q5VfW8FgIHAf8e+F/jfDbJAUm+luRTbfnoJNcn2ZHkY0kObP3PbMs72vqVT+yPJEl6ovb7Lqk18n+AfzfmR94KbJ+x/JfABVX1IuB+YH3rXw/c3/ovaOMkSfNo3Lukvn7G64wk7wEeHuNzK4DfAC5uywFOAq5sQzYBp7f22rZMW7+mjZckzZNxrz76zRntR4DvMPoh/nj+GvhD4Llt+XDggap6pC3v5NG7rS4H7gKoqkeSPNjGf2/mBpNsADYAvOAFLxizfEnSOMa9+ujc/d1wktcBu6tqW5Jf29/PP0YtG4GNAJOTk37DWpLm0LjTRyuSXJVkd3t9ok0NPZYTgdOSfAf4KKNpowuBpUmmw2gFsKu1dwFHtf0tAQ4B7tuvP40k6UkZ90Tzh4HNjJ6r8Hzg71rfPlXVH1fViqpaCZwNXFtVvw1cB5zRhq0Drm7tzW2Ztv5a77UkSfNr3FCYqKoPV9Uj7fURYOIJ7vOPgLcn2cHonMElrf8S4PDW/3bg/Ce4fUnSEzTuieb7krwRuLwtv4H9mNqpqn8A/qG17wBOmGXMw8CZ425TkjT3xj1S+E/AWcA9wN2Mpnd+p6eaJEkDGfdI4c+BdVV1P0CSw4D3MgoLSdLTxLhHCv9mOhAAqur7wHH9lCRJGsq4ofBLSQ6dXmhHCuMeZUiSniLG/cH+PuBLST7els8E3t1PSZKkoYz7jeZLk2xl9AU0gNdX1W39lSVJGsLYU0AtBAwCSXoa2+9bZ0uSnr4MBUlSx1CQJHUMBUlSx1CQJHUMBUlSx1CQJHUMBUlSx1CQJHUMBUlSp7dQSPKsJF9J8vUktyb5s9Z/dJLrk+xI8rEkB7b+Z7blHW39yr5qkyTNrs8jhR8DJ1XVy4BjgZOTrAb+Erigql4E3A+sb+PXA/e3/gvaOEnSPOotFGrkn9riM9qrGN1p9crWvwk4vbXXtmXa+jVJ0ld9kqS99XpOIckBSW4EdgPXAN8CHqiqR9qQncDy1l4O3AXQ1j8IHD7LNjck2Zpk69TUVJ/lS9Ki02soVNXPqupYYAVwAvDiOdjmxqqarKrJiYmJJ7s5SdIM83L1UVU9AFwHvAJYmmT6OQ4rgF2tvQs4CqCtPwS4bz7qkySN9Hn10USSpa19EPBaYDujcDijDVsHXN3am9sybf21VVV91SdJ2tvYT157Ao4ENiU5gFH4XFFVn0pyG/DRJP8d+BpwSRt/CfA3SXYA3wfO7rE2SdIseguFqroJOG6W/jsYnV/Ys/9h4My+6pEkPT6/0SxJ6hgKkqSOoSBJ6hgKkqSOoSBJ6hgKkqSOoSBJ6hgKkqSOoSBJ6hgKkqSOoSBJ6vR5QzwB3/3zfz3Ifl/w324eZL+Snto8UpAkdQwFSVLHUJAkdQwFSVLHUJAkdfp8RvNRSa5LcluSW5O8tfUfluSaJLe390Nbf5K8P8mOJDclOb6v2iRJs+vzSOER4B1V9RJgNXBekpcA5wNbqmoVsKUtA5wCrGqvDcBFPdYmSZpFb6FQVXdX1Q2t/UNgO7AcWAtsasM2Aae39lrg0hr5MrA0yZF91SdJ2tu8nFNIshI4DrgeWFZVd7dV9wDLWns5cNeMj+1sfXtua0OSrUm2Tk1N9Ve0JC1CvYdCkucAnwDeVlU/mLmuqgqo/dleVW2sqsmqmpyYmJjDSiVJvYZCkmcwCoTLquqTrfve6Wmh9r679e8Cjprx8RWtT5I0T/q8+ijAJcD2qvqrGas2A+taex1w9Yz+c9pVSKuBB2dMM0mS5kGfN8Q7EXgTcHOSG1vfnwDvAa5Ish64Ezirrfs0cCqwA3gIOLfH2iRJs+gtFKrqH4HsY/WaWcYXcF5f9UiSHp/faJYkdQwFSVLHUJAkdQwFSVLHUJAkdXxG8yJ04gdOHGS/X3zLFwfZr6TxeaQgSeoYCpKkjqEgSeoYCpKkjqEgSeoYCpKkjqEgSeoYCpKkjqEgSeoYCpKkjqEgSer0+YzmDyXZneSWGX2HJbkmye3t/dDWnyTvT7IjyU1Jju+rLknSvvV5pPAR4OQ9+s4HtlTVKmBLWwY4BVjVXhuAi3qsS5K0D72FQlV9Afj+Ht1rgU2tvQk4fUb/pTXyZWBpkiP7qk2SNLv5PqewrKrubu17gGWtvRy4a8a4na1PkjSPBjvRXFUF1P5+LsmGJFuTbJ2amuqhMklavOY7FO6dnhZq77tb/y7gqBnjVrS+vVTVxqqarKrJiYmJXouVpMVmvkNhM7CutdcBV8/oP6ddhbQaeHDGNJMkaZ709jjOJJcDvwYckWQn8E7gPcAVSdYDdwJnteGfBk4FdgAPAef2VZckad96C4WqesM+Vq2ZZWwB5/VViyRpPH6jWZLUMRQkSR1DQZLUMRQkSR1DQZLUMRQkSR1DQZLUMRQkSR1DQZLUMRQkSZ3ebnMh7Y/Pv+pXB9nvr37h84PsV1qoPFKQJHUMBUlSx1CQJHU8pyA9xbz7jWcMst8//dsrB9mv5pehID2GD77j7+Z9n29+32/O+z6laU4fSZI6HilIetK2v/vaQfb7r/70pEH2+3S2oEIhycnAhcABwMVV9Z6BS5KkOXPFx08YZL9nnfmVsccumFBIcgDwP4HXAjuBrybZXFW3DVuZpKeid73rXYtqv3NlIZ1TOAHYUVV3VNVPgI8CaweuSZIWlVTV0DUAkOQM4OSq+s9t+U3Av62qN+8xbgOwoS0eA3xzjko4AvjeHG1rrljTeKxpfAuxLmsaz1zW9MtVNTHbigUzfTSuqtoIbJzr7SbZWlWTc73dJ8OaxmNN41uIdVnTeOarpoU0fbQLOGrG8orWJ0maJwspFL4KrEpydJIDgbOBzQPXJEmLyoKZPqqqR5K8Gfgso0tSP1RVt85jCXM+JTUHrGk81jS+hViXNY1nXmpaMCeaJUnDW0jTR5KkgRkKkqTOog+FJB9KsjvJLUPXApDkqCTXJbktya1J3jp0TQBJnpXkK0m+3ur6s6FrmpbkgCRfS/KpoWsBSPKdJDcnuTHJ1qHrAUiyNMmVSb6RZHuSVwxczzHt72f69YMkbxuypmlJfr/9P35LksuTPGuAGvb6uZTkzFbXz5P0dmnqog8F4CPAyUMXMcMjwDuq6iXAauC8JC8ZuCaAHwMnVdXLgGOBk5OsHrakzluB7UMXsYdXV9WxC+ha9wuBz1TVi4GXMfDfV1V9s/39HAu8HHgIuGrImgCSLAd+D5isqpcyuujl7AFK+Qh7/1y6BXg98IU+d7zoQ6GqvgB8f+g6plXV3VV1Q2v/kNE/3uXDVgU18k9t8RntNfhVCklWAL8BXDx0LQtVkkOAVwGXAFTVT6rqgUGL+kVrgG9V1Z1DF9IsAQ5KsgR4NvD/5ruA2X4uVdX2qpqrOzjs06IPhYUsyUrgOOD6gUsBummaG4HdwDVVtRDq+mvgD4GfD1zHTAV8Lsm2dluWoR0NTAEfbtNsFyc5eOiiZjgbuHzoIgCqahfwXuC7wN3Ag1X1uWGrml+GwgKV5DnAJ4C3VdUPhq4HoKp+1g73VwAnJHnpkPUkeR2wu6q2DVnHLF5ZVccDpzCa/nvVwPUsAY4HLqqq44AfAecPW9JI+6LqacDHh64FIMmhjG7EeTTwfODgJG8ctqr5ZSgsQEmewSgQLquqTw5dz57a1MN1DH8u5kTgtCTfYXRX3ZOS/O2wJXW/bVJVuxnNkw9zE/1H7QR2zjiyu5JRSCwEpwA3VNW9QxfSvAb4dlVNVdVPgU8CvzJwTfPKUFhgkoTR3O/2qvqroeuZlmQiydLWPojRcy++MWRNVfXHVbWiqlYymoK4tqoG/a0uycFJnjvdBn6d0QnCwVTVPcBdSY5pXWuAhfKckjewQKaOmu8Cq5M8u/1bXMPCu4ihV4s+FJJcDnwJOCbJziTrBy7pROBNjH7rnb5c79SBawI4ErguyU2M7lN1TVUtiEtAF5hlwD8m+TrwFeDvq+ozA9cE8Bbgsvbf71jgL4YtpwvN1zL6bXxBaEdTVwI3ADcz+hk577e8mO3nUpLfSrITeAXw90k+28u+vc2FJGnaoj9SkCQ9ylCQJHUMBUlSx1CQJHUMBUlSx1CQ9iHJz/a4k+fKJ7m905Kc39rvSvIHc1KoNIcWzOM4pQXon9ttPeZEVW3G545rgfNIQRpTkuck2ZLkhva8hLWtf2V7TsFHkvzfJJcleU2SLya5PckJbdzvJPngHtt8YZIbZiyvmrkszTdDQdq3g2ZMHV0FPAz8VrvZ3auB97VbIQC8CHgf8OL2+o/AK4E/AP5kXzuoqm8BDyY5tnWdC3y4jz+MNA6nj6R9+4Xpo3ajwr9odz39OaPnXCxrq79dVTe3cbcCW6qqktwMrHyc/VwMnJvk7cB/YPgb6GkR80hBGt9vAxPAy1tY3AtMP6rxxzPG/XzG8s95/F++PsHobqGvA7ZV1X1zVbC0vwwFaXyHMHp+w0+TvBr45bnYaFU9DHwWuAinjjQwQ0Ea32XAZJsSOoe5vXX4ZYyOKhbVU7608HiXVGkBaN9ZOKSq/uvQtWhx80SzNLB2ZdMLgZOGrkXySEGS1PGcgiSpYyhIkjqGgiSpYyhIkjqGgiSp8/8Bly8L8Xq97+8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(df['Family']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 510,
   "id": "3c5dd2ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Survived       418\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "Age              0\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             0\n",
       "Embarked         0\n",
       "Title            0\n",
       "Family           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 510,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 511,
   "id": "2a2f661a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop([\"PassengerId\",\"Name\",\"Ticket\"],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 512,
   "id": "da002ee5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "      <th>Family</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.8542</td>\n",
       "      <td>S</td>\n",
       "      <td>Miss</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Survived  Pclass     Sex   Age  SibSp  Parch    Fare Embarked Title  \\\n",
       "192       1.0       3  female  19.0      1      0  7.8542        S  Miss   \n",
       "\n",
       "     Family  \n",
       "192       2  "
      ]
     },
     "execution_count": 512,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 513,
   "id": "93a15200",
   "metadata": {},
   "outputs": [],
   "source": [
    "data=pd.get_dummies(df,drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 514,
   "id": "4d366d40",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Family</th>\n",
       "      <th>Sex_male</th>\n",
       "      <th>Embarked_Q</th>\n",
       "      <th>Embarked_S</th>\n",
       "      <th>Title_Master</th>\n",
       "      <th>Title_Miss</th>\n",
       "      <th>Title_Mr</th>\n",
       "      <th>Title_Mrs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>2</td>\n",
       "      <td>1</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>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>1</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",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>2</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",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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>...</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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>414</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>108.9000</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",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>38.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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>416</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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>417</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>22.3583</td>\n",
       "      <td>3</td>\n",
       "      <td>1</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",
       "  </tbody>\n",
       "</table>\n",
       "<p>1309 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Survived  Pclass   Age  SibSp  Parch      Fare  Family  Sex_male  \\\n",
       "0         0.0       3  22.0      1      0    7.2500       2         1   \n",
       "1         1.0       1  38.0      1      0   71.2833       2         0   \n",
       "2         1.0       3  26.0      0      0    7.9250       1         0   \n",
       "3         1.0       1  35.0      1      0   53.1000       2         0   \n",
       "4         0.0       3  35.0      0      0    8.0500       1         1   \n",
       "..        ...     ...   ...    ...    ...       ...     ...       ...   \n",
       "413       NaN       3  30.0      0      0    8.0500       1         1   \n",
       "414       NaN       1  39.0      0      0  108.9000       1         0   \n",
       "415       NaN       3  38.5      0      0    7.2500       1         1   \n",
       "416       NaN       3  30.0      0      0    8.0500       1         1   \n",
       "417       NaN       3   4.0      1      1   22.3583       3         1   \n",
       "\n",
       "     Embarked_Q  Embarked_S  Title_Master  Title_Miss  Title_Mr  Title_Mrs  \n",
       "0             0           1             0           0         1          0  \n",
       "1             0           0             0           0         0          1  \n",
       "2             0           1             0           1         0          0  \n",
       "3             0           1             0           0         0          1  \n",
       "4             0           1             0           0         1          0  \n",
       "..          ...         ...           ...         ...       ...        ...  \n",
       "413           0           1             0           0         1          0  \n",
       "414           0           0             0           0         0          1  \n",
       "415           0           1             0           0         1          0  \n",
       "416           0           1             0           0         1          0  \n",
       "417           0           0             1           0         0          0  \n",
       "\n",
       "[1309 rows x 14 columns]"
      ]
     },
     "execution_count": 514,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 515,
   "id": "7d3b3290",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
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       "      <th>Survived</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.338481</td>\n",
       "      <td>-0.075808</td>\n",
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       "      <th>Pclass</th>\n",
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       "      <td>1.000000</td>\n",
       "      <td>-0.386723</td>\n",
       "      <td>0.060832</td>\n",
       "      <td>0.018322</td>\n",
       "      <td>-0.558477</td>\n",
       "      <td>0.050027</td>\n",
       "      <td>0.124617</td>\n",
       "      <td>0.230491</td>\n",
       "      <td>0.091320</td>\n",
       "      <td>0.095257</td>\n",
       "      <td>0.025350</td>\n",
       "      <td>0.080726</td>\n",
       "      <td>-0.190444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>-0.075808</td>\n",
       "      <td>-0.386723</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.216262</td>\n",
       "      <td>-0.132567</td>\n",
       "      <td>0.177429</td>\n",
       "      <td>-0.214705</td>\n",
       "      <td>0.067732</td>\n",
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       "      <td>-0.033856</td>\n",
       "      <td>-0.407885</td>\n",
       "      <td>-0.293929</td>\n",
       "      <td>0.242740</td>\n",
       "      <td>0.234049</td>\n",
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       "    <tr>\n",
       "      <th>SibSp</th>\n",
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       "      <td>0.060832</td>\n",
       "      <td>-0.216262</td>\n",
       "      <td>1.000000</td>\n",
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       "      <td>0.160224</td>\n",
       "      <td>0.861952</td>\n",
       "      <td>-0.109609</td>\n",
       "      <td>-0.048678</td>\n",
       "      <td>0.073709</td>\n",
       "      <td>0.329171</td>\n",
       "      <td>0.075519</td>\n",
       "      <td>-0.248492</td>\n",
       "      <td>0.062537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Parch</th>\n",
       "      <td>0.081629</td>\n",
       "      <td>0.018322</td>\n",
       "      <td>-0.132567</td>\n",
       "      <td>0.373587</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.221522</td>\n",
       "      <td>0.792296</td>\n",
       "      <td>-0.213125</td>\n",
       "      <td>-0.100943</td>\n",
       "      <td>0.071881</td>\n",
       "      <td>0.253482</td>\n",
       "      <td>0.064589</td>\n",
       "      <td>-0.317027</td>\n",
       "      <td>0.212113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fare</th>\n",
       "      <td>0.257307</td>\n",
       "      <td>-0.558477</td>\n",
       "      <td>0.177429</td>\n",
       "      <td>0.160224</td>\n",
       "      <td>0.221522</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.226465</td>\n",
       "      <td>-0.185484</td>\n",
       "      <td>-0.130054</td>\n",
       "      <td>-0.169894</td>\n",
       "      <td>0.011596</td>\n",
       "      <td>0.090101</td>\n",
       "      <td>-0.185297</td>\n",
       "      <td>0.146365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Family</th>\n",
       "      <td>0.016639</td>\n",
       "      <td>0.050027</td>\n",
       "      <td>-0.214705</td>\n",
       "      <td>0.861952</td>\n",
       "      <td>0.792296</td>\n",
       "      <td>0.226465</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.188583</td>\n",
       "      <td>-0.087190</td>\n",
       "      <td>0.087771</td>\n",
       "      <td>0.355061</td>\n",
       "      <td>0.084976</td>\n",
       "      <td>-0.336724</td>\n",
       "      <td>0.157068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex_male</th>\n",
       "      <td>-0.543351</td>\n",
       "      <td>0.124617</td>\n",
       "      <td>0.067732</td>\n",
       "      <td>-0.109609</td>\n",
       "      <td>-0.213125</td>\n",
       "      <td>-0.185484</td>\n",
       "      <td>-0.188583</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.088651</td>\n",
       "      <td>0.115193</td>\n",
       "      <td>0.164375</td>\n",
       "      <td>-0.676028</td>\n",
       "      <td>0.902432</td>\n",
       "      <td>-0.572861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Embarked_Q</th>\n",
       "      <td>0.003650</td>\n",
       "      <td>0.230491</td>\n",
       "      <td>-0.057223</td>\n",
       "      <td>-0.048678</td>\n",
       "      <td>-0.100943</td>\n",
       "      <td>-0.130054</td>\n",
       "      <td>-0.087190</td>\n",
       "      <td>-0.088651</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.491656</td>\n",
       "      <td>-0.009091</td>\n",
       "      <td>0.203538</td>\n",
       "      <td>-0.082646</td>\n",
       "      <td>-0.108114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Embarked_S</th>\n",
       "      <td>-0.149683</td>\n",
       "      <td>0.091320</td>\n",
       "      <td>-0.033856</td>\n",
       "      <td>0.073709</td>\n",
       "      <td>0.071881</td>\n",
       "      <td>-0.169894</td>\n",
       "      <td>0.087771</td>\n",
       "      <td>0.115193</td>\n",
       "      <td>-0.491656</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.018297</td>\n",
       "      <td>-0.115222</td>\n",
       "      <td>0.104603</td>\n",
       "      <td>-0.026138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Title_Master</th>\n",
       "      <td>0.085221</td>\n",
       "      <td>0.095257</td>\n",
       "      <td>-0.407885</td>\n",
       "      <td>0.329171</td>\n",
       "      <td>0.253482</td>\n",
       "      <td>0.011596</td>\n",
       "      <td>0.355061</td>\n",
       "      <td>0.164375</td>\n",
       "      <td>-0.009091</td>\n",
       "      <td>0.018297</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.111122</td>\n",
       "      <td>-0.269312</td>\n",
       "      <td>-0.094164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Title_Miss</th>\n",
       "      <td>0.335636</td>\n",
       "      <td>0.025350</td>\n",
       "      <td>-0.293929</td>\n",
       "      <td>0.075519</td>\n",
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       "      <td>0.090101</td>\n",
       "      <td>0.084976</td>\n",
       "      <td>-0.676028</td>\n",
       "      <td>0.203538</td>\n",
       "      <td>-0.115222</td>\n",
       "      <td>-0.111122</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.612268</td>\n",
       "      <td>-0.214078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Title_Mr</th>\n",
       "      <td>-0.561796</td>\n",
       "      <td>0.080726</td>\n",
       "      <td>0.242740</td>\n",
       "      <td>-0.248492</td>\n",
       "      <td>-0.317027</td>\n",
       "      <td>-0.185297</td>\n",
       "      <td>-0.336724</td>\n",
       "      <td>0.902432</td>\n",
       "      <td>-0.082646</td>\n",
       "      <td>0.104603</td>\n",
       "      <td>-0.269312</td>\n",
       "      <td>-0.612268</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.518831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Title_Mrs</th>\n",
       "      <td>0.347864</td>\n",
       "      <td>-0.190444</td>\n",
       "      <td>0.234049</td>\n",
       "      <td>0.062537</td>\n",
       "      <td>0.212113</td>\n",
       "      <td>0.146365</td>\n",
       "      <td>0.157068</td>\n",
       "      <td>-0.572861</td>\n",
       "      <td>-0.108114</td>\n",
       "      <td>-0.026138</td>\n",
       "      <td>-0.094164</td>\n",
       "      <td>-0.214078</td>\n",
       "      <td>-0.518831</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Survived    Pclass       Age     SibSp     Parch      Fare  \\\n",
       "Survived      1.000000 -0.338481 -0.075808 -0.035322  0.081629  0.257307   \n",
       "Pclass       -0.338481  1.000000 -0.386723  0.060832  0.018322 -0.558477   \n",
       "Age          -0.075808 -0.386723  1.000000 -0.216262 -0.132567  0.177429   \n",
       "SibSp        -0.035322  0.060832 -0.216262  1.000000  0.373587  0.160224   \n",
       "Parch         0.081629  0.018322 -0.132567  0.373587  1.000000  0.221522   \n",
       "Fare          0.257307 -0.558477  0.177429  0.160224  0.221522  1.000000   \n",
       "Family        0.016639  0.050027 -0.214705  0.861952  0.792296  0.226465   \n",
       "Sex_male     -0.543351  0.124617  0.067732 -0.109609 -0.213125 -0.185484   \n",
       "Embarked_Q    0.003650  0.230491 -0.057223 -0.048678 -0.100943 -0.130054   \n",
       "Embarked_S   -0.149683  0.091320 -0.033856  0.073709  0.071881 -0.169894   \n",
       "Title_Master  0.085221  0.095257 -0.407885  0.329171  0.253482  0.011596   \n",
       "Title_Miss    0.335636  0.025350 -0.293929  0.075519  0.064589  0.090101   \n",
       "Title_Mr     -0.561796  0.080726  0.242740 -0.248492 -0.317027 -0.185297   \n",
       "Title_Mrs     0.347864 -0.190444  0.234049  0.062537  0.212113  0.146365   \n",
       "\n",
       "                Family  Sex_male  Embarked_Q  Embarked_S  Title_Master  \\\n",
       "Survived      0.016639 -0.543351    0.003650   -0.149683      0.085221   \n",
       "Pclass        0.050027  0.124617    0.230491    0.091320      0.095257   \n",
       "Age          -0.214705  0.067732   -0.057223   -0.033856     -0.407885   \n",
       "SibSp         0.861952 -0.109609   -0.048678    0.073709      0.329171   \n",
       "Parch         0.792296 -0.213125   -0.100943    0.071881      0.253482   \n",
       "Fare          0.226465 -0.185484   -0.130054   -0.169894      0.011596   \n",
       "Family        1.000000 -0.188583   -0.087190    0.087771      0.355061   \n",
       "Sex_male     -0.188583  1.000000   -0.088651    0.115193      0.164375   \n",
       "Embarked_Q   -0.087190 -0.088651    1.000000   -0.491656     -0.009091   \n",
       "Embarked_S    0.087771  0.115193   -0.491656    1.000000      0.018297   \n",
       "Title_Master  0.355061  0.164375   -0.009091    0.018297      1.000000   \n",
       "Title_Miss    0.084976 -0.676028    0.203538   -0.115222     -0.111122   \n",
       "Title_Mr     -0.336724  0.902432   -0.082646    0.104603     -0.269312   \n",
       "Title_Mrs     0.157068 -0.572861   -0.108114   -0.026138     -0.094164   \n",
       "\n",
       "              Title_Miss  Title_Mr  Title_Mrs  \n",
       "Survived        0.335636 -0.561796   0.347864  \n",
       "Pclass          0.025350  0.080726  -0.190444  \n",
       "Age            -0.293929  0.242740   0.234049  \n",
       "SibSp           0.075519 -0.248492   0.062537  \n",
       "Parch           0.064589 -0.317027   0.212113  \n",
       "Fare            0.090101 -0.185297   0.146365  \n",
       "Family          0.084976 -0.336724   0.157068  \n",
       "Sex_male       -0.676028  0.902432  -0.572861  \n",
       "Embarked_Q      0.203538 -0.082646  -0.108114  \n",
       "Embarked_S     -0.115222  0.104603  -0.026138  \n",
       "Title_Master   -0.111122 -0.269312  -0.094164  \n",
       "Title_Miss      1.000000 -0.612268  -0.214078  \n",
       "Title_Mr       -0.612268  1.000000  -0.518831  \n",
       "Title_Mrs      -0.214078 -0.518831   1.000000  "
      ]
     },
     "execution_count": 515,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 516,
   "id": "a190e4ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "train=data[:891]\n",
    "test=data[891:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 517,
   "id": "f1a0d77d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Family</th>\n",
       "      <th>Sex_male</th>\n",
       "      <th>Embarked_Q</th>\n",
       "      <th>Embarked_S</th>\n",
       "      <th>Title_Master</th>\n",
       "      <th>Title_Miss</th>\n",
       "      <th>Title_Mr</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12.2875</td>\n",
       "      <td>3</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",
       "      <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",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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>414</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>108.9000</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",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>38.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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>416</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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>417</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>22.3583</td>\n",
       "      <td>3</td>\n",
       "      <td>1</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",
       "  </tbody>\n",
       "</table>\n",
       "<p>418 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Survived  Pclass   Age  SibSp  Parch      Fare  Family  Sex_male  \\\n",
       "0         NaN       3  34.5      0      0    7.8292       1         1   \n",
       "1         NaN       3  47.0      1      0    7.0000       2         0   \n",
       "2         NaN       2  62.0      0      0    9.6875       1         1   \n",
       "3         NaN       3  27.0      0      0    8.6625       1         1   \n",
       "4         NaN       3  22.0      1      1   12.2875       3         0   \n",
       "..        ...     ...   ...    ...    ...       ...     ...       ...   \n",
       "413       NaN       3  30.0      0      0    8.0500       1         1   \n",
       "414       NaN       1  39.0      0      0  108.9000       1         0   \n",
       "415       NaN       3  38.5      0      0    7.2500       1         1   \n",
       "416       NaN       3  30.0      0      0    8.0500       1         1   \n",
       "417       NaN       3   4.0      1      1   22.3583       3         1   \n",
       "\n",
       "     Embarked_Q  Embarked_S  Title_Master  Title_Miss  Title_Mr  Title_Mrs  \n",
       "0             1           0             0           0         1          0  \n",
       "1             0           1             0           0         0          1  \n",
       "2             1           0             0           0         1          0  \n",
       "3             0           1             0           0         1          0  \n",
       "4             0           1             0           0         0          1  \n",
       "..          ...         ...           ...         ...       ...        ...  \n",
       "413           0           1             0           0         1          0  \n",
       "414           0           0             0           0         0          1  \n",
       "415           0           1             0           0         1          0  \n",
       "416           0           1             0           0         1          0  \n",
       "417           0           0             1           0         0          0  \n",
       "\n",
       "[418 rows x 14 columns]"
      ]
     },
     "execution_count": 517,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 518,
   "id": "a2c35c6e",
   "metadata": {},
   "outputs": [],
   "source": [
    "x=train.drop(\"Survived\",axis=1)\n",
    "y=train[\"Survived\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 519,
   "id": "ec9b8c86",
   "metadata": {},
   "outputs": [],
   "source": [
    "test=test.drop(\"Survived\",axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 520,
   "id": "77ab23d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.naive_bayes import BernoulliNB\n",
    "g=GaussianNB()\n",
    "b=BernoulliNB()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 521,
   "id": "6f732568",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-25 {color: black;background-color: white;}#sk-container-id-25 pre{padding: 0;}#sk-container-id-25 div.sk-toggleable {background-color: white;}#sk-container-id-25 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-25 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-25 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-25 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-25 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-25 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-25 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-25 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-25 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-25 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-25 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-25 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-25 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-25 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-25 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-25 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-25 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-25 div.sk-item {position: relative;z-index: 1;}#sk-container-id-25 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-25 div.sk-item::before, #sk-container-id-25 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-25 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-25 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-25 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-25 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-25 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-25 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-25 div.sk-label-container {text-align: center;}#sk-container-id-25 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-25 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-25\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GaussianNB()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-25\" type=\"checkbox\" checked><label for=\"sk-estimator-id-25\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GaussianNB</label><div class=\"sk-toggleable__content\"><pre>GaussianNB()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "GaussianNB()"
      ]
     },
     "execution_count": 521,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g.fit(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 522,
   "id": "a54ba420",
   "metadata": {},
   "outputs": [],
   "source": [
    "tahmin1=g.predict(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 523,
   "id": "833652c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(418,)"
      ]
     },
     "execution_count": 523,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tahmin1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 524,
   "id": "8fd85680",
   "metadata": {},
   "outputs": [],
   "source": [
    "sonuc1=df2[[\"PassengerId\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 525,
   "id": "ba101a4d",
   "metadata": {},
   "outputs": [],
   "source": [
    "sonuc1[\"Survived\"]=tahmin1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 526,
   "id": "c0dc649e",
   "metadata": {},
   "outputs": [],
   "source": [
    "sonuc1[\"Survived\"]=sonuc1[\"Survived\"].astype(\"int32\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 527,
   "id": "669a756a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 418 entries, 0 to 417\n",
      "Data columns (total 2 columns):\n",
      " #   Column       Non-Null Count  Dtype\n",
      "---  ------       --------------  -----\n",
      " 0   PassengerId  418 non-null    int64\n",
      " 1   Survived     418 non-null    int32\n",
      "dtypes: int32(1), int64(1)\n",
      "memory usage: 5.0 KB\n"
     ]
    }
   ],
   "source": [
    "sonuc1.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 528,
   "id": "b57e819e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413</th>\n",
       "      <td>1305</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>1306</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>1307</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>416</th>\n",
       "      <td>1308</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>417</th>\n",
       "      <td>1309</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>418 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived\n",
       "0            892         0\n",
       "1            893         1\n",
       "2            894         0\n",
       "3            895         0\n",
       "4            896         1\n",
       "..           ...       ...\n",
       "413         1305         0\n",
       "414         1306         1\n",
       "415         1307         0\n",
       "416         1308         0\n",
       "417         1309         1\n",
       "\n",
       "[418 rows x 2 columns]"
      ]
     },
     "execution_count": 528,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sonuc1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 529,
   "id": "25eb6fb4",
   "metadata": {},
   "outputs": [],
   "source": [
    "sonuc1.to_csv(\"titanic_tahmin1.csv\",index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 530,
   "id": "bc6d720f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-26 {color: black;background-color: white;}#sk-container-id-26 pre{padding: 0;}#sk-container-id-26 div.sk-toggleable {background-color: white;}#sk-container-id-26 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-26 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-26 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-26 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-26 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-26 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-26 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-26 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-26 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-26 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-26 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-26 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-26 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-26 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-26 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-26 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-26 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-26 div.sk-item {position: relative;z-index: 1;}#sk-container-id-26 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-26 div.sk-item::before, #sk-container-id-26 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-26 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-26 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-26 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-26 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-26 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-26 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-26 div.sk-label-container {text-align: center;}#sk-container-id-26 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-26 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-26\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>BernoulliNB()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-26\" type=\"checkbox\" checked><label for=\"sk-estimator-id-26\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">BernoulliNB</label><div class=\"sk-toggleable__content\"><pre>BernoulliNB()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "BernoulliNB()"
      ]
     },
     "execution_count": 530,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b=BernoulliNB()\n",
    "b.fit(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 531,
   "id": "7224123c",
   "metadata": {},
   "outputs": [],
   "source": [
    "tahmin2 = b.predict(test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 532,
   "id": "9df374eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "sonuc2 = df2[[\"PassengerId\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 533,
   "id": "b0903ce1",
   "metadata": {},
   "outputs": [],
   "source": [
    "sonuc2[\"Survived\"] = tahmin2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 534,
   "id": "89dfb308",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413</th>\n",
       "      <td>1305</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>1306</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>1307</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>416</th>\n",
       "      <td>1308</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>417</th>\n",
       "      <td>1309</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>418 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived\n",
       "0            892       0.0\n",
       "1            893       1.0\n",
       "2            894       0.0\n",
       "3            895       0.0\n",
       "4            896       1.0\n",
       "..           ...       ...\n",
       "413         1305       0.0\n",
       "414         1306       1.0\n",
       "415         1307       0.0\n",
       "416         1308       0.0\n",
       "417         1309       1.0\n",
       "\n",
       "[418 rows x 2 columns]"
      ]
     },
     "execution_count": 534,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sonuc2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 535,
   "id": "134e9fd2",
   "metadata": {},
   "outputs": [],
   "source": [
    "sonuc2['Survived']=sonuc2['Survived'].astype('int32')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 536,
   "id": "26dd46b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "sonuc2.to_csv(\"titanic_tahmin2.csv\",index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 537,
   "id": "0081569e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-27 {color: black;background-color: white;}#sk-container-id-27 pre{padding: 0;}#sk-container-id-27 div.sk-toggleable {background-color: white;}#sk-container-id-27 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-27 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-27 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-27 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-27 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-27 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-27 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-27 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-27 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-27 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-27 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-27 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-27 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-27 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-27 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-27 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-27 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-27 div.sk-item {position: relative;z-index: 1;}#sk-container-id-27 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-27 div.sk-item::before, #sk-container-id-27 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-27 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-27 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-27 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-27 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-27 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-27 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-27 div.sk-label-container {text-align: center;}#sk-container-id-27 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-27 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-27\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-27\" type=\"checkbox\" checked><label for=\"sk-estimator-id-27\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "KNeighborsClassifier()"
      ]
     },
     "execution_count": 537,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k=KNeighborsClassifier()\n",
    "k.fit(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 538,
   "id": "02d4f2d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "tahmin3=k.predict(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 539,
   "id": "4ceebcaa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413</th>\n",
       "      <td>1305</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>1306</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>1307</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>416</th>\n",
       "      <td>1308</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>417</th>\n",
       "      <td>1309</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>418 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived\n",
       "0            892       0.0\n",
       "1            893       0.0\n",
       "2            894       0.0\n",
       "3            895       0.0\n",
       "4            896       0.0\n",
       "..           ...       ...\n",
       "413         1305       0.0\n",
       "414         1306       1.0\n",
       "415         1307       0.0\n",
       "416         1308       0.0\n",
       "417         1309       1.0\n",
       "\n",
       "[418 rows x 2 columns]"
      ]
     },
     "execution_count": 539,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sonuc3=df2[[\"PassengerId\"]]\n",
    "sonuc3[\"Survived\"] = tahmin3\n",
    "sonuc3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 540,
   "id": "122542aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "sonuc3['Survived']=sonuc3['Survived'].astype('int32')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 541,
   "id": "9e3a1a17",
   "metadata": {},
   "outputs": [],
   "source": [
    "sonuc3.to_csv(\"titanic_tahmin3.csv\",index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 542,
   "id": "b3b27447",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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      ],
      "text/plain": [
       "DecisionTreeClassifier()"
      ]
     },
     "execution_count": 542,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d=DecisionTreeClassifier()\n",
    "d.fit(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 543,
   "id": "ca972185",
   "metadata": {},
   "outputs": [],
   "source": [
    "tahmin4 = d.predict(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 544,
   "id": "949e7ace",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413</th>\n",
       "      <td>1305</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>1306</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>1307</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>416</th>\n",
       "      <td>1308</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>417</th>\n",
       "      <td>1309</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>418 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived\n",
       "0            892       0.0\n",
       "1            893       1.0\n",
       "2            894       1.0\n",
       "3            895       1.0\n",
       "4            896       1.0\n",
       "..           ...       ...\n",
       "413         1305       0.0\n",
       "414         1306       1.0\n",
       "415         1307       0.0\n",
       "416         1308       0.0\n",
       "417         1309       1.0\n",
       "\n",
       "[418 rows x 2 columns]"
      ]
     },
     "execution_count": 544,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sonuc4=df2[[\"PassengerId\"]]\n",
    "sonuc4[\"Survived\"] = tahmin4\n",
    "sonuc4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 545,
   "id": "1059a449",
   "metadata": {},
   "outputs": [],
   "source": [
    "sonuc4['Survived']=sonuc4['Survived'].astype('int32')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 546,
   "id": "1a8c1d49",
   "metadata": {},
   "outputs": [],
   "source": [
    "sonuc4.to_csv(\"titanic_tahmin4.csv\",index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 547,
   "id": "e3597f9b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-29 {color: black;background-color: white;}#sk-container-id-29 pre{padding: 0;}#sk-container-id-29 div.sk-toggleable {background-color: white;}#sk-container-id-29 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-29 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-29 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-29 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-29 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-29 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-29 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-29 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-29 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-29 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-29 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-29 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-29 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-29 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-29 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-29 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-29 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-29 div.sk-item {position: relative;z-index: 1;}#sk-container-id-29 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-29 div.sk-item::before, #sk-container-id-29 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-29 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-29 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-29 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-29 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-29 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-29 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-29 div.sk-label-container {text-align: center;}#sk-container-id-29 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-29 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-29\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-29\" type=\"checkbox\" checked><label for=\"sk-estimator-id-29\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 547,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r=RandomForestClassifier()\n",
    "r.fit(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 548,
   "id": "8775de6e",
   "metadata": {},
   "outputs": [],
   "source": [
    "tahmin5=r.predict(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 549,
   "id": "1edde6a6",
   "metadata": {},
   "outputs": [
    {
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      ]
     },
     "execution_count": 549,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tahmin5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 550,
   "id": "840a47f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
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       "  </thead>\n",
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       "      <td>892</td>\n",
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       "      <th>1</th>\n",
       "      <td>893</td>\n",
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       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413</th>\n",
       "      <td>1305</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>1306</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>1307</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>416</th>\n",
       "      <td>1308</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>417</th>\n",
       "      <td>1309</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>418 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived\n",
       "0            892       0.0\n",
       "1            893       0.0\n",
       "2            894       0.0\n",
       "3            895       1.0\n",
       "4            896       0.0\n",
       "..           ...       ...\n",
       "413         1305       0.0\n",
       "414         1306       1.0\n",
       "415         1307       0.0\n",
       "416         1308       0.0\n",
       "417         1309       1.0\n",
       "\n",
       "[418 rows x 2 columns]"
      ]
     },
     "execution_count": 550,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sonuc5=df2[[\"PassengerId\"]]\n",
    "sonuc5[\"Survived\"] = tahmin5\n",
    "sonuc5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 551,
   "id": "4d6e389d",
   "metadata": {},
   "outputs": [],
   "source": [
    "sonuc5['Survived']=sonuc5['Survived'].astype('int32')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 552,
   "id": "af09d3f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "sonuc5.to_csv(\"titanic_tahmin5.csv\",index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 553,
   "id": "0af2a7eb",
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "GradientBoostingClassifier()"
      ]
     },
     "execution_count": 553,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "h=GradientBoostingClassifier()\n",
    "h.fit(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 554,
   "id": "9a6b0c76",
   "metadata": {},
   "outputs": [],
   "source": [
    "tahmin6=h.predict(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 555,
   "id": "76a02964",
   "metadata": {},
   "outputs": [
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      ]
     },
     "execution_count": 555,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tahmin6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 556,
   "id": "bb258cb7",
   "metadata": {},
   "outputs": [
    {
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       "<p>418 rows × 2 columns</p>\n",
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      ],
      "text/plain": [
       "     PassengerId  Survived\n",
       "0            892       0.0\n",
       "1            893       0.0\n",
       "2            894       0.0\n",
       "3            895       0.0\n",
       "4            896       1.0\n",
       "..           ...       ...\n",
       "413         1305       0.0\n",
       "414         1306       1.0\n",
       "415         1307       0.0\n",
       "416         1308       0.0\n",
       "417         1309       1.0\n",
       "\n",
       "[418 rows x 2 columns]"
      ]
     },
     "execution_count": 556,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sonuc6=df2[[\"PassengerId\"]]\n",
    "sonuc6[\"Survived\"] = tahmin6\n",
    "sonuc6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 557,
   "id": "1d23f124",
   "metadata": {},
   "outputs": [],
   "source": [
    "sonuc6['Survived']=sonuc6['Survived'].astype('int32')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 558,
   "id": "31032945",
   "metadata": {},
   "outputs": [],
   "source": [
    "sonuc6.to_csv(\"titanic_tahmin6.csv\",index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ae2c4daa",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "ad828bf9",
   "metadata": {},
   "source": [
    "### Creating y_test to getting classification reports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 559,
   "id": "dcce9c28",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"titanic.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 560,
   "id": "60bccb2f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1310 entries, 0 to 1309\n",
      "Data columns (total 14 columns):\n",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   pclass     1309 non-null   float64\n",
      " 1   survived   1309 non-null   float64\n",
      " 2   name       1309 non-null   object \n",
      " 3   sex        1309 non-null   object \n",
      " 4   age        1046 non-null   float64\n",
      " 5   sibsp      1309 non-null   float64\n",
      " 6   parch      1309 non-null   float64\n",
      " 7   ticket     1309 non-null   object \n",
      " 8   fare       1308 non-null   float64\n",
      " 9   cabin      295 non-null    object \n",
      " 10  embarked   1307 non-null   object \n",
      " 11  boat       486 non-null    object \n",
      " 12  body       121 non-null    float64\n",
      " 13  home.dest  745 non-null    object \n",
      "dtypes: float64(7), object(7)\n",
      "memory usage: 143.4+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 561,
   "id": "2a5231b3",
   "metadata": {},
   "outputs": [
    {
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       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1307</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Zakarian, Mr. Ortin</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2670</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1308</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Zimmerman, Mr. Leo</td>\n",
       "      <td>male</td>\n",
       "      <td>29.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>315082</td>\n",
       "      <td>7.8750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1309</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1310 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      pclass  survived                                             name  \\\n",
       "0        1.0       1.0                    Allen, Miss. Elisabeth Walton   \n",
       "1        1.0       1.0                   Allison, Master. Hudson Trevor   \n",
       "2        1.0       0.0                     Allison, Miss. Helen Loraine   \n",
       "3        1.0       0.0             Allison, Mr. Hudson Joshua Creighton   \n",
       "4        1.0       0.0  Allison, Mrs. Hudson J C (Bessie Waldo Daniels)   \n",
       "...      ...       ...                                              ...   \n",
       "1305     3.0       0.0                            Zabour, Miss. Thamine   \n",
       "1306     3.0       0.0                        Zakarian, Mr. Mapriededer   \n",
       "1307     3.0       0.0                              Zakarian, Mr. Ortin   \n",
       "1308     3.0       0.0                               Zimmerman, Mr. Leo   \n",
       "1309     NaN       NaN                                              NaN   \n",
       "\n",
       "         sex      age  sibsp  parch  ticket      fare    cabin embarked boat  \\\n",
       "0     female  29.0000    0.0    0.0   24160  211.3375       B5        S    2   \n",
       "1       male   0.9167    1.0    2.0  113781  151.5500  C22 C26        S   11   \n",
       "2     female   2.0000    1.0    2.0  113781  151.5500  C22 C26        S  NaN   \n",
       "3       male  30.0000    1.0    2.0  113781  151.5500  C22 C26        S  NaN   \n",
       "4     female  25.0000    1.0    2.0  113781  151.5500  C22 C26        S  NaN   \n",
       "...      ...      ...    ...    ...     ...       ...      ...      ...  ...   \n",
       "1305  female      NaN    1.0    0.0    2665   14.4542      NaN        C  NaN   \n",
       "1306    male  26.5000    0.0    0.0    2656    7.2250      NaN        C  NaN   \n",
       "1307    male  27.0000    0.0    0.0    2670    7.2250      NaN        C  NaN   \n",
       "1308    male  29.0000    0.0    0.0  315082    7.8750      NaN        S  NaN   \n",
       "1309     NaN      NaN    NaN    NaN     NaN       NaN      NaN      NaN  NaN   \n",
       "\n",
       "       body                        home.dest  \n",
       "0       NaN                     St Louis, MO  \n",
       "1       NaN  Montreal, PQ / Chesterville, ON  \n",
       "2       NaN  Montreal, PQ / Chesterville, ON  \n",
       "3     135.0  Montreal, PQ / Chesterville, ON  \n",
       "4       NaN  Montreal, PQ / Chesterville, ON  \n",
       "...     ...                              ...  \n",
       "1305    NaN                              NaN  \n",
       "1306  304.0                              NaN  \n",
       "1307    NaN                              NaN  \n",
       "1308    NaN                              NaN  \n",
       "1309    NaN                              NaN  \n",
       "\n",
       "[1310 rows x 14 columns]"
      ]
     },
     "execution_count": 561,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 562,
   "id": "490324f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.survived = df.survived.fillna(0.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 563,
   "id": "b7feec53",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "892     0.0\n",
       "893     0.0\n",
       "894     1.0\n",
       "895     1.0\n",
       "896     0.0\n",
       "       ... \n",
       "1305    0.0\n",
       "1306    0.0\n",
       "1307    0.0\n",
       "1308    0.0\n",
       "1309    0.0\n",
       "Name: survived, Length: 418, dtype: float64"
      ]
     },
     "execution_count": 563,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test = df[892:][\"survived\"]\n",
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 564,
   "id": "15a384ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test.to_csv(\"y_test.csv\",header=False,index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 565,
   "id": "d2547492",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(418,)"
      ]
     },
     "execution_count": 565,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 566,
   "id": "9e81ecea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "892     0.0\n",
       "893     0.0\n",
       "894     1.0\n",
       "895     1.0\n",
       "896     0.0\n",
       "       ... \n",
       "1305    0.0\n",
       "1306    0.0\n",
       "1307    0.0\n",
       "1308    0.0\n",
       "1309    0.0\n",
       "Name: survived, Length: 418, dtype: float64"
      ]
     },
     "execution_count": 566,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 567,
   "id": "c9cc9901",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test = pd.read_csv(\"y_test.csv\",header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 568,
   "id": "250c452c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(418,)"
      ]
     },
     "execution_count": 568,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tahmin6.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 569,
   "id": "f8f71561",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.64      0.74      0.68       270\n",
      "         1.0       0.33      0.24      0.28       148\n",
      "\n",
      "    accuracy                           0.56       418\n",
      "   macro avg       0.48      0.49      0.48       418\n",
      "weighted avg       0.53      0.56      0.54       418\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(tahmin6,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 570,
   "id": "99b462c0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.62      0.75      0.68       256\n",
      "         1.0       0.40      0.26      0.31       162\n",
      "\n",
      "    accuracy                           0.56       418\n",
      "   macro avg       0.51      0.50      0.49       418\n",
      "weighted avg       0.53      0.56      0.54       418\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(tahmin5,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 571,
   "id": "452c395e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.60      0.76      0.67       247\n",
      "         1.0       0.43      0.27      0.33       171\n",
      "\n",
      "    accuracy                           0.56       418\n",
      "   macro avg       0.52      0.51      0.50       418\n",
      "weighted avg       0.53      0.56      0.53       418\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(tahmin4,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 572,
   "id": "4bdfd284",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.62      0.75      0.67       257\n",
      "         1.0       0.39      0.25      0.31       161\n",
      "\n",
      "    accuracy                           0.56       418\n",
      "   macro avg       0.50      0.50      0.49       418\n",
      "weighted avg       0.53      0.56      0.53       418\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(tahmin3,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 573,
   "id": "09d7af46",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.57      0.73      0.64       245\n",
      "         1.0       0.38      0.23      0.29       173\n",
      "\n",
      "    accuracy                           0.52       418\n",
      "   macro avg       0.48      0.48      0.46       418\n",
      "weighted avg       0.49      0.52      0.50       418\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(tahmin2,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 574,
   "id": "0e0654e0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.57      0.73      0.64       243\n",
      "         1.0       0.38      0.23      0.28       175\n",
      "\n",
      "    accuracy                           0.52       418\n",
      "   macro avg       0.47      0.48      0.46       418\n",
      "weighted avg       0.49      0.52      0.49       418\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(tahmin1,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c17d02f2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "540b3539",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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