Titanic-Dataset-Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Titanic Dataset Analyst"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 842,
   "metadata": {},
   "outputs": [],
   "source": [
    "#%pip install matplotlib\n",
    "#%pip install seaborn\n",
    "#%pip install pandas\n",
    "#%pip install sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 843,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading Data\n",
    "Reading the Titanic data file Pandas DataFrame called data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 844,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_file_path = './Data/train.csv'\n",
    "test_file_path = './Data/test.csv'\n",
    "\n",
    "train = pd.read_csv(train_file_path)\n",
    "test = pd.read_csv(test_file_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reviewing Data\n",
    "Visualization and exploration of the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 845,
   "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>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>32.204208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>257.353842</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>49.693429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>223.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.910400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>668.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
       "count   891.000000  891.000000  891.000000  714.000000  891.000000   \n",
       "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
       "std     257.353842    0.486592    0.836071   14.526497    1.102743   \n",
       "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
       "25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n",
       "50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n",
       "75%     668.500000    1.000000    3.000000   38.000000    1.000000   \n",
       "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
       "\n",
       "            Parch        Fare  \n",
       "count  891.000000  891.000000  \n",
       "mean     0.381594   32.204208  \n",
       "std      0.806057   49.693429  \n",
       "min      0.000000    0.000000  \n",
       "25%      0.000000    7.910400  \n",
       "50%      0.000000   14.454200  \n",
       "75%      0.000000   31.000000  \n",
       "max      6.000000  512.329200  "
      ]
     },
     "execution_count": 845,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 846,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
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       "      <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",
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       "      <th>1</th>\n",
       "      <td>2</td>\n",
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       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
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       "      <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",
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       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
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       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
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       "    <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",
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       "    <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": 846,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 847,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
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       "      <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",
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       "      <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",
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       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 847,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Cleaning and Preprocessing\n",
    " \n",
    "1- Counting missing values per column  \n",
    "2- Handling the missing values with imputation  \n",
    "3- Encoding categorical variables "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 848,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(891, 12)\n",
      "(418, 11)\n",
      "Missing values in train :\n",
      "Age         177\n",
      "Cabin       687\n",
      "Embarked      2\n",
      "dtype: int64\n",
      "Missing values in test :\n",
      "Age       86\n",
      "Fare       1\n",
      "Cabin    327\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Shape of training data (num_rows, num_columns)\n",
    "print(train.shape)\n",
    "print(test.shape)\n",
    "\n",
    "# Number of missing values in each column of training data\n",
    "missing_val_train = (train.isnull().sum())\n",
    "missing_val_test = (test.isnull().sum())\n",
    "\n",
    "print(\"Missing values in train :\")\n",
    "print(missing_val_train[missing_val_train > 0])\n",
    "print(\"Missing values in test :\")\n",
    "print(missing_val_test[missing_val_test > 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 849,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Numerical Columns Train: Index(['PassengerId', 'Survived', 'Pclass', 'Age', 'SibSp', 'Parch', 'Fare'], dtype='object')\n",
      "Categorical Columns Train: Index(['Name', 'Sex', 'Ticket', 'Cabin', 'Embarked'], dtype='object')\n",
      "Numerical Columns Test: Index(['PassengerId', 'Pclass', 'Age', 'SibSp', 'Parch', 'Fare'], dtype='object')\n",
      "Categorical Columns Test: Index(['Name', 'Sex', 'Ticket', 'Cabin', 'Embarked'], dtype='object')\n"
     ]
    }
   ],
   "source": [
    "df = pd.DataFrame(train)\n",
    "df_test = pd.DataFrame(test)\n",
    "\n",
    "# Identifying numerical columns\n",
    "numerical_cols_train = df.select_dtypes(include=['int64', 'float64']).columns\n",
    "numerical_cols_test = df_test.select_dtypes(include=['int64', 'float64']).columns\n",
    "\n",
    "# Identifying categorical columns\n",
    "categorical_cols_train = df.select_dtypes(include=['object', 'category']).columns\n",
    "categorical_cols_test = df_test.select_dtypes(include=['object', 'category']).columns\n",
    "\n",
    "print(\"Numerical Columns Train:\", numerical_cols_train)\n",
    "print(\"Categorical Columns Train:\", categorical_cols_train)\n",
    "\n",
    "print(\"Numerical Columns Test:\", numerical_cols_test)\n",
    "print(\"Categorical Columns Test:\", categorical_cols_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 850,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     PassengerId  Survived  Pclass  \\\n",
      "0            1.0       0.0     3.0   \n",
      "1            2.0       1.0     1.0   \n",
      "2            3.0       1.0     3.0   \n",
      "3            4.0       1.0     1.0   \n",
      "4            5.0       0.0     3.0   \n",
      "..           ...       ...     ...   \n",
      "886        887.0       0.0     2.0   \n",
      "887        888.0       1.0     1.0   \n",
      "888        889.0       0.0     3.0   \n",
      "889        890.0       1.0     1.0   \n",
      "890        891.0       0.0     3.0   \n",
      "\n",
      "                                                  Name     Sex   Age  SibSp  \\\n",
      "0                              Braund, Mr. Owen Harris    male  22.0    1.0   \n",
      "1    Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0    1.0   \n",
      "2                               Heikkinen, Miss. Laina  female  26.0    0.0   \n",
      "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0    1.0   \n",
      "4                             Allen, Mr. William Henry    male  35.0    0.0   \n",
      "..                                                 ...     ...   ...    ...   \n",
      "886                              Montvila, Rev. Juozas    male  27.0    0.0   \n",
      "887                       Graham, Miss. Margaret Edith  female  19.0    0.0   \n",
      "888           Johnston, Miss. Catherine Helen \"Carrie\"  female  28.0    1.0   \n",
      "889                              Behr, Mr. Karl Howell    male  26.0    0.0   \n",
      "890                                Dooley, Mr. Patrick    male  32.0    0.0   \n",
      "\n",
      "     Parch            Ticket     Fare    Cabin Embarked  \n",
      "0      0.0         A/5 21171   7.2500  B96 B98        S  \n",
      "1      0.0          PC 17599  71.2833      C85        C  \n",
      "2      0.0  STON/O2. 3101282   7.9250  B96 B98        S  \n",
      "3      0.0            113803  53.1000     C123        S  \n",
      "4      0.0            373450   8.0500  B96 B98        S  \n",
      "..     ...               ...      ...      ...      ...  \n",
      "886    0.0            211536  13.0000  B96 B98        S  \n",
      "887    0.0            112053  30.0000      B42        S  \n",
      "888    2.0        W./C. 6607  23.4500  B96 B98        S  \n",
      "889    0.0            111369  30.0000     C148        C  \n",
      "890    0.0            370376   7.7500  B96 B98        Q  \n",
      "\n",
      "[891 rows x 12 columns]\n"
     ]
    }
   ],
   "source": [
    "# Imputer for numerical data\n",
    "num_imputer = SimpleImputer(strategy='median')\n",
    "# Imputer for categorical data\n",
    "cat_imputer = SimpleImputer(strategy='most_frequent')\n",
    "\n",
    "# Impute numerical columns\n",
    "df[numerical_cols_train] = num_imputer.fit_transform(df[numerical_cols_train])\n",
    "df_test[numerical_cols_test] = num_imputer.fit_transform(df_test[numerical_cols_test])\n",
    "\n",
    "# Impute categorical columns\n",
    "df[categorical_cols_train] = cat_imputer.fit_transform(df[categorical_cols_train])\n",
    "df_test[categorical_cols_test] = cat_imputer.fit_transform(df_test[categorical_cols_test])\n",
    "\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 851,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 10 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  891 non-null    float64\n",
      " 1   Survived     891 non-null    float64\n",
      " 2   Pclass       891 non-null    float64\n",
      " 3   Sex          891 non-null    int64  \n",
      " 4   Age          891 non-null    float64\n",
      " 5   SibSp        891 non-null    float64\n",
      " 6   Parch        891 non-null    float64\n",
      " 7   Fare         891 non-null    float64\n",
      " 8   Embarked     891 non-null    int64  \n",
      " 9   Status       891 non-null    int64  \n",
      "dtypes: float64(7), int64(3)\n",
      "memory usage: 69.7 KB\n"
     ]
    }
   ],
   "source": [
    "# Extract status from 'Name'\n",
    "df['Status'] = df['Name'].str.extract(' ([A-Za-z]+)\\.')\n",
    "df_test['Status'] = df_test['Name'].str.extract(\"([A-z]+)\\.\")\n",
    "\n",
    "df.loc[~df['Status'].isin(['Don', 'Rev', 'Dr', 'Mme', 'Ms',\n",
    "       'Major', 'Lady', 'Sir', 'Mlle', 'Col', 'Capt', 'Countess',\n",
    "       'Jonkheer']),'Status']=0\n",
    "df_test.loc[~df_test['Status'].isin(['Don', 'Rev', 'Dr', 'Mme', 'Ms',\n",
    "       'Major', 'Lady', 'Sir', 'Mlle', 'Col', 'Capt', 'Countess',\n",
    "       'Jonkheer']),'Status']=0\n",
    "\n",
    "df.replace({\n",
    "    'Embarked': {'S':0, 'C':1, 'Q':2},\n",
    "    'Status': {'Don':1, 'Rev':2, 'Dr':3, 'Mme':4, 'Ms':5,\n",
    "       'Major':6, 'Lady':7, 'Sir':8, 'Mlle':9, 'Col':10, 'Capt':11, 'Countess':12,\n",
    "       'Jonkheer':13, 'Mr': 14, 'Mrs': 15, 'Miss': 16, 'Master': 17},\n",
    "    'Sex':{'male':0, 'female':1}\n",
    "},inplace=True)\n",
    "\n",
    "df_test.replace({\n",
    "    'Embarked': {'S':0, 'C':1, 'Q':2},\n",
    "    'Status': {'Don':1, 'Rev':2, 'Dr':3, 'Mme':4, 'Ms':5,\n",
    "       'Major':6, 'Lady':7, 'Sir':8, 'Mlle':9, 'Col':10, 'Capt':11, 'Countess':12,\n",
    "       'Jonkheer':13, 'Mr': 14, 'Mrs': 15, 'Miss': 16, 'Master': 17},\n",
    "    'Sex':{'male':0, 'female':1}\n",
    "},inplace=True)\n",
    "\n",
    "# Drop 'Name', 'Cabin' and 'Ticket' columns\n",
    "df = df.drop(['Name', 'Ticket', 'Cabin'], axis=1)\n",
    "df_test = df_test.drop(['Name', 'Ticket', 'Cabin'], axis=1)\n",
    "\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 852,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set visual style\n",
    "sns.set(style=\"whitegrid\")\n",
    "\n",
    "# Survival by Sex\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.countplot(x='Survived', hue='Sex', data=df)\n",
    "plt.title('Survival by Sex')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 853,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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PP1h+zp9xu3jxotX5UlJSNG/evAJh9p+0atVKderUUWxsrC5evFigPyYmRjk5OeratWuBvp9//lmZmZkaOnSoatSoYVk+mR+qzGazfv/9d7Vp00b//e9/JV1fnjhkyBA99NBDOnv2rCTpySef1IwZMyRJ3t7eevTRR9WnTx+lpqZa7c4IALbEjBUA2ICjo6OmTJmi8PBw9erVS3369JG/v7/S09O1c+dOrVmzRiNHjrTMZrRr105LlizRkiVL1LBhQ3355ZeWHdMKo3Pnzpo1a5Y+++wzTZ48+R/Hh4aGqkKFClq/fr0GDx5s+YVYkmX2a+3atfL391d8fLwWLVokk8mk9PT0Ij0P586d0wsvvKB+/frp1KlTmjNnjlq2bGk1++Xu7q4uXbpo/fr1euqpp27p7ESDBg30ySefqF69evL19dV3332npUuXWl1L8+bNNXnyZL322mtq3bq1UlNTtWDBAtWsWVN169aVs7OzoqOjFR4err59+8rR0VHr1q2Ti4uL2rVrJ0dHR0VEROiVV16Ro6Oj2rVrp9TUVC1cuFBJSUk3XNr3d/r37681a9Zo0KBBlrC3cOFCZWdnW/6MAgIC1K1bN02aNEm///67goKClJCQoNjYWFWvXl01a9Ys0nPk5OSk119/Xc8884x69epl+YDglJQUffDBB9qxY4fGjBlzw81W6tWrJycnJ82ePVvPPPOMsrKy9MEHH+jrr7+WdH3WNSAgQL6+vpoxY4auXr2qGjVq6Oeff9b27ds1bNgwSdcD5fLly1WpUiU1btxYSUlJWrFihUJCQlSxYsUiXQ8A3C4EKwCwkbZt2+q9997TsmXLtHjxYqWkpMjFxUWBgYGKjY1Vhw4dLGOHDRumlJQULVu2TNnZ2Wrbtq2ioqI0fPjwQp2rYsWKatWqlXbu3HnD5Vx/5eTkpC5dumjVqlUFlg1OmDBB2dnZmjt3rrKyslS9enUNHz5cx48f15dfflmkjQ569+6tK1euKDw8XC4uLuratavGjh1rFeSk689V/gYSt9KsWbM0ffp0ywxTzZo1NXXqVH388cfav3+/pOuzJdnZ2Vq3bp3i4uLk5uamFi1aaOzYsXJ2dlbdunW1ePFivfnmmxo9erRyc3MVFBSk5cuXq1atWpKkxx57TGXKlNHbb7+t9evXy8PDQ02aNFF0dPQ/zh7+Wbly5fTuu+8qKipK48aNU5kyZdS7d2+5u7tbbQLy6quvasmSJVq3bp0SExPl7e2tzp07a9SoUXJ0dCzy8/TAAw/o/fff14oVK7R27VolJSXJw8NDAQEBevvttxUaGnrD4+677z7FxMRowYIFGj58uMqXL69GjRpp1apV6tevn+UzyRYsWKA5c+Zo3rx5unjxoqpWrarnn3/esqvhyJEj5eLiog0bNujNN9+0LFcdM2ZMka8FAG4Xk7modxoDAHCHTZ48WT/++GOBD8stbX788UddunTJ6jOlcnJy1LZtW3Xp0kUTJ060YXUAULoxYwUAsFvvvvuuTp48qffee0+zZ8+2dTk2d/bsWcvGJiEhIUpPT9f69et15coVPf7447YuDwBKNYIVAMBu7d+/Xzt27NDTTz9t2Y6+NPv3v/+tS5cuKS4uTsuWLZOzs7MaNmyo1atXy9/f39blAUCpxlJAAAAAADCI7dYBAAAAwCCCFQAAAAAYRLACAAAAAIPYvOIvvv/+e5nNZjk7O9u6FAAAAAA2lP8B7I0bN/7HsQSrvzCbzWI/DwAAAABFyQUEq7/In6mqX7++jSsBAAAAYEsHDx4s9FjusQIAAAAAgwhWAAAAAGAQwQoAAAAADCJYAQAAAIBBBCsAAAAAMIhdAQEAAIC7UG5urrKzs21dhl1zdnaWo6PjLXksghUAAABwFzGbzUpMTNSlS5dsXUqJUKFCBfn6+spkMhl6HIIVAAAAcBfJD1WVK1eWh4eH4cBwtzKbzUpLS1NycrIkqWrVqoYej2AFAAAA3CVyc3Mtocrb29vW5dg9d3d3SVJycrIqV65saFkgm1cAAAAAd4n8e6o8PDxsXEnJkf9cGb0fjWAFAAAA3GVY/ld4t+q5IlgBAAAAgEHcYwUAAABAktSvXz/t3bvXqs3Z2VmVKlVSu3btNGrUKJUvX/4fH2fChAnau3evvvzyy9tVqt0hWAEAAACwCAwM1OTJky0/Z2dn69ChQ5ozZ46OHDmitWvXstTwBghWAAAAACw8PT3VqFEjq7bg4GBdu3ZN8+fP148//ligH9xjBQAAAKAQgoKCJElnz56VJG3cuFE9e/ZUw4YN1bZtW8XExCgrK+uGx2ZkZCgmJkYdOnRQUFCQmjRpooEDB+rIkSOWMSkpKRozZoxatmyp+vXrq3v37tq4caOlPy8vT7GxsQoLC1NQUJDCwsIUExNjeDe/W4UZKwAAAAD/KCEhQZJ07733as2aNZo2bZoee+wxjR49Wr/++qtef/11Xb58WdOmTStw7Lhx47R//36NHj1aNWrU0OnTpzVv3jyNGTNGmzZtkslk0tixY3XhwgVNnTpVnp6e+uijjzR+/Hj5+vqqefPmeuutt7R27VqNHz9e9957r3788UfFxsbK2dlZI0aMuNNPRwEEKwAAAAAWZrNZOTk5lp8vX76svXv3atGiRWrcuLECAwM1bNgwtW/fXjNmzLCMS09P16ZNmwrMIGVlZenatWuKjIxU586dJUkhISG6evWqZs2apfPnz8vHx0d79+5VeHi42rdvbxlToUIFubi4SJL27t2roKAg9erVy9Lv7u6usmXL3tbno7AIVgAAAAAs9u3bp3r16lm1OTg46KGHHtK0adN06tQpXbhwQQ8//LDVmEGDBmnQoEEFHs/FxUXLli2TJCUlJSkhIUGnTp3SV199JUmW5YPNmjXTG2+8ocOHDys0NFRt2rTR+PHjLY/TrFkzxcTEqHfv3goLC1Pbtm3Vt2/fW3rtRhCsAAAAAFjUq1dPU6dOlXT9w3NdXV1VtWpVeXp6SpIOHDggSfL29i70Y+7YsUMzZ87UyZMnVaZMGdWtW1ceHh6Srs+QSVJsbKwWL16szZs3a8uWLVZhrlq1aho8eLDKlCmjDRs2KDo6WrNnz9b999+vyMhINW/e/FY+BcXC5hUASoXc3Fxbl4C/wZ8NANiXMmXKqH79+qpfv76CgoJ0//33W0KVJJUrV07S9c0m/uzixYvauXOn0tLSrNrPnDmj8PBwPfDAA/r888914MABxcXFqV27dlbjypYtq7Fjx+rLL7/U5s2bNXr0aH333XeWkOfg4KA+ffrogw8+0M6dO/Xqq68qKytLL7zwwt9umnEnMWMFoFRwdHTUhIipSjhxytal4E/8/GtqVuzkfx4IALAbtWrVkpeXl7766it1797d0v7RRx9p9uzZ+r//+z+r8T///LMyMzM1dOhQ1ahRw9K+Y8cOSddnrH7//Xf17t1bEydOVKdOnVSrVi3VqlVLP/zwg06fPi1JevLJJxUUFKTIyEh5e3vr0Ucf1ZUrVzRz5kxdvXpVFStWvANX//cIVgBKjYQTp3Tk0FFblwEAQInm6OioF154QdOmTZO3t7fCwsKUkJCg+fPnq0+fPipfvrzV+Hr16snJyUmzZ8/WM888o6ysLH3wwQf6+uuvJUlpaWkKCAiQr6+vZsyYoatXr6pGjRr6+eeftX37dg0bNkzS9c/SWr58uSpVqqTGjRsrKSlJK1asUEhIiM1DlUSwAgAAAFBEffr0kYeHh5YtW6b169fL19dXQ4YM0ZAhQwqMve+++xQTE6MFCxZo+PDhKl++vBo1aqRVq1apX79+2r9/vwICArRgwQLNmTNH8+bN08WLF1W1alU9//zzGjp0qCRp5MiRcnFx0YYNG/Tmm2+qbNmyCgsL05gxY+705d+QyZx/txgkSQcPHpQk1a9f38aVALjVnug2kBkrO/NAvTpa//EKW5cBAHeNjIwMJSQkyM/PT25ubrYup0S42XNWlGzA5hUAAAAAYBDBCgAAAAAMIlgBAAAAgEEEKwAAAAAwiGAFAAAAAAYRrAAAAADAIIIVAAAAABhEsAIAAAAAgwhWAAAAAGAQwQoAAAAADCJYAQAAAKWA2Wwu0edesmSJ+vXrd9MxFy9e1JgxYxQcHKyQkBBNnTpV6enphs9dGE535CwAAAAAbMpkMunyb6eVm5l5R8/r6Oqq8tXvM/QYa9as0dy5c9W0adObjhsxYoTS09O1cuVKpaam6uWXX1ZaWppee+01Q+cvDIIVAAAAUErkZmYqJ+POzODcCklJSZo8ebL27NmjmjVr3nTs999/r7179+qzzz6Tv7+/JGnatGkaPHiwRo8erSpVqtzWWlkKCAAAAMAuHTp0SM7Ozvr444/VsGHDm47dv3+/fHx8LKFKkkJCQmQymXTgwIHbXSozVgAAAADsU1hYmMLCwgo1NikpSVWrVrVqc3FxUYUKFfTHH3/cjvKsMGMFAAAAoMRLT0+Xi4tLgXZXV1dl3oH7ymwerC5cuKCxY8eqefPmaty4sYYOHaoTJ05Y+iMjIxUQEGD19efUmpeXp/nz5ys0NFSNGjXSkCFD9Ouvv9riUgAAAADYiJubm7Kysgq0Z2ZmysPD47af3+bBKjw8XKdPn9bSpUv1/vvvy83NTQMGDLBsi/jLL7/o2Wef1bfffmv5ev/99y3HL1y4UHFxcZo+fbrWrVunvLw8DR48+IZPKgAAAIC7k6+vr5KTk63asrKydOnSJVWuXPm2n9+mwery5cuqVq2aZsyYoQYNGsjf31/PPfeckpOTdezYMZnNZh0/flxBQUHy8fGxfFWsWFHS9Sdq+fLlGjFihNq2bau6desqNjZWiYmJ2rp1qy0vDQAAAMAdFBwcrMTERJ0+fdrStnfvXknSgw8+eNvPb9NgVb58ecXExKhOnTqSpJSUFK1cuVK+vr6qXbu2zpw5o7S0NNWqVeuGx8fHx+vatWtq0aKFpa1cuXIKDAzUvn377sg1AAAAALjzcnNzde7cOWVkZEiSGjZsqCZNmigiIkI//fSTdu/erVdeeUU9evS47VutS3a0K+CkSZP03nvvycXFRYsWLZKHh4eOHj0qSVq1apW++eYbOTg4qHXr1oqIiFDZsmWVmJgoSQV2/6hcubKlrzjMZrPS0tKKfzEA7IrJZJK7u7uty8BNpKeny2w227oMACjxMjMzlZeXp9zcXOXm5lr1OTo6ytHV9Y7XlH/Ov9ZTVGazWWaz2fI4v//+ux5++GFFRUWpZ8+ekqR58+Zp+vTp6t+/v9zc3NSxY0eNHz/+pufOzc1VXl6e0tPTlZeXV+CcJpOpUPXZTbB6+umn9cQTT2jNmjUKDw9XXFycjh49KgcHB1WuXFmLFy/WmTNn9Prrr+vYsWN65513LPdh/XX3D1dXV12+fLnYtWRnZ+vIkSOGrgeA/XB3d1dgYKCty8BNJCQkWP6fDgAwxsnJqcAuePlvMpavfp9NajKbzcrMzDT0Jtorr7wiSZYZKm9vb3333XdWbWXKlNGsWbMKnDu//0YyMzOVk5OjkydP3rD/RjsN3ojdBKvatWtLkqKiovTjjz9q9erVioqKUu/eveXl5SVJqlOnjnx8fPT444/r4MGDcnNzk3T9Xqv876XrT46Rd6ednZ0t9QAo+Qr7ThNsx8/PjxkrALgFMjMzdfbsWbm6ulr9fiypwGzMneZqg9mywnJyclKNGjUK1Hj8+PHCP8atLqooUlJStGvXLnXs2FFOTtdLcXBwUO3atZWcnCwHBwdLqMp3//33S5ISExMtSwCTk5NVo0YNy5jk5GQFBAQUuy6TyXRHtmQEAFzHUk0AuDUcHBzk4OBwfdmfo6OtyykRHB0d5eDgIHd39wJhtChvztp084rz589r9OjR2rVrl6UtOztbhw8flr+/v8aNG6cBAwZYHXPw4EFJ12e46tatK09PT+3Zs8fSn5qaqsOHDys4OPiOXAMAAAAA2DRY1alTR61bt9aMGTO0b98+HT16VBMmTFBqaqoGDBigjh07ateuXVqwYIHOnDmj7du366WXXtIjjzwif39/ubi4qG/fvoqOjtYXX3yh+Ph4RUREyNfXVx06dLDlpQEAAAAoRWx+j9WcOXMUExOjiIgIXblyRU2bNtWaNWt0zz336J577tHcuXO1dOlSvfXWWypbtqy6du2qUaNGWY4fMWKEcnJyFBkZqYyMDAUHB2vZsmVydna23UUBAAAAKFVMZu4WtpK/1LB+/fo2rgTArfZEt4E6cuiorcvAnzxQr47Wf7zC1mUAwF0jIyNDCQkJ8vPzK3C/EG7sZs9ZUbKBTZcCAgAAAMDdgGAFAAAAAAYRrAAAAADAIIIVAAAAALt06dIlvfLKK2rdurWaNGmip556Svv37//b8b/99puGDRumJk2aqFWrVpo7d65yc3PvSK0EKwAAAKAUyMvLK3HnHj16tL7//nvNmTNHGzZs0AMPPKBBgwbp5MmTBcZmZ2dr0KBBkqR169ZpypQpWrt2rd58801DtReWzbdbBwAAAHD7OTg4aMeqz3Q5KeWOnrd8lYoK7de5yMedPn1aO3fuVFxcnB588EFJ0qRJk7Rjxw598sknGjlypNX4LVu26OzZs3rvvfdUvnx51alTRxcuXNDrr7+uZ599Vi4uLrfkev4OwQoAAAAoJS4npSjlt2Rbl1EoXl5eWrp0qdVW5yaTSSaTSampqQXG79+/X/Xq1VP58uUtbc2bN9fVq1d15MgRNWzY8LbWy1JAAAAAAHanXLlyatOmjdVM05YtW3T69GmFhoYWGJ+YmChfX1+rtsqVK0uS/vjjj9tbrAhWAAAAAEqA7777ThMnTlSHDh3Utm3bAv0ZGRkFlvu5urpKkjIzM297fQQrAAAAAHZt27ZteuaZZ9SoUSNFR0ffcIybm5uysrKs2vIDlYeHx22vkWAFAAAAwG6tXr1aL7zwgtq1a6fFixdbZqH+ytfXV8nJ1veP5f9cpUqV214nwQoAAACAXYqLi9P06dPVp08fzZkz56Y7+wUHB+vw4cO6evWqpW337t0qU6aM6tate9trJVgBAAAAsDsJCQmaOXOmHn74YQ0bNkznz5/XuXPndO7cOV25ckVZWVk6d+6cZflf+/bt5ePjo1GjRik+Pl7btm3TnDlz9Mwzz9z2rdYltlsHAAAASo3yVSqWmHNu2bJF2dnZ+vzzz/X5559b9fXs2VM9e/ZU//799e6776pZs2ZydXXV22+/ralTp+rxxx9X+fLl1bt3bz333HO34jL+EcEKAAAAKAXy8vKK9UG9t+rcDg5FWyz37LPP6tlnn73pmF9++cXq5/vuu0/Lly8vcn23AksBAQAAgFKgqMHmbjn3nXL3XyEAAAAA3GYEKwAAAAAwiGAFAAAAAAYRrAAAAADAIIIVAAAAABhEsAIAAAAAgwhWAAAAAGAQwQoAAAAADCJYAQAAAIBBBCsAAACgFMjNzS1x575w4YLGjh2r5s2bq3Hjxho6dKhOnDjxt+MvXryoMWPGKDg4WCEhIZo6darS09OLW3aRON2RswAAAACwKUdHR02ImKqEE6fu6Hn9/GtqVuzkYh0bHh6uvLw8LV26VGXKlNG8efM0YMAAbd26Ve7u7gXGjxgxQunp6Vq5cqVSU1P18ssvKy0tTa+99prRy/hHBCsAAACglEg4cUpHDh21dRmFcvnyZVWrVk3Dhg1TnTp1JEnPPfecunfvrmPHjqlBgwZW47///nvt3btXn332mfz9/SVJ06ZN0+DBgzV69GhVqVLlttbLUkAAAAAAdqd8+fKKiYmxhKqUlBStXLlSvr6+ql27doHx+/fvl4+PjyVUSVJISIhMJpMOHDhw2+tlxgoAAACAXZs0aZLee+89ubi4aNGiRfLw8CgwJikpSVWrVrVqc3FxUYUKFfTHH3/c9hqZsQIAAABg155++mlt2LBBjzzyiMLDw3Xo0KECY9LT0+Xi4lKg3dXVVZmZmbe9RoIVAAAAALtWu3ZtBQUFKSoqStWqVdPq1asLjHFzc1NWVlaB9szMzBvOcN1qBCsAAAAAdiclJUWbNm1STk6Opc3BwUG1a9dWcnJygfG+vr4F2rOysnTp0iVVrlz5ttdLsAIAAABgd86fP6/Ro0dr165dlrbs7GwdPnzYaoOKfMHBwUpMTNTp06ctbXv37pUkPfjgg7e9XoIVAAAAALtTp04dtW7dWjNmzNC+fft09OhRTZgwQampqRowYIByc3N17tw5ZWRkSJIaNmyoJk2aKCIiQj/99JN2796tV155RT169LjtW61L7AoIAAAAlBp+/jVL1DnnzJmjmJgYRURE6MqVK2ratKnWrFmje+65R7/99pv+9a9/6dVXX9Wjjz4qk8mkBQsWaOrUqXr66afl6uqqTp06aeLEibfuYm6CYAUAAACUArm5uZoVO9lm53Z0dCzycWXLltWUKVM0ZcqUAn3Vq1fXL7/8YtXm7e2t+fPnF7dMQ1gKCAAAAJQCxQk2d8O57xSCFQAAAAAYRLACAAAAAIMIVgAAAABgEMEKAAAAAAwiWAEAAAB3GbPZbOsSSoxb9VwRrAAAAIC7hLOzsyQpLS3NxpWUHPnPVf5zV1x8jhUAAABwl3B0dFSFChWUnJwsSfLw8JDJZLJxVfbJbDYrLS1NycnJqlChguEt4QlWAAAAwF3E19dXkizhCjdXoUIFy3NmBMEKAAAAuIuYTCZVrVpVlStXVnZ2tq3LsWvOzs637MOLbR6sLly4oFmzZmnHjh3KzMxUcHCwxo8fL39/f0nSkSNHFBUVpZ9//lkVK1bUgAED1L9/f8vxeXl5WrBggf7f//t/unLlioKDg/XKK6/o3nvvtdUlAQAAADbn6Oh4y0ID/pnNN68IDw/X6dOntXTpUr3//vtyc3PTgAEDlJ6erosXL2rgwIGqUaOGNmzYoPDwcEVHR2vDhg2W4xcuXKi4uDhNnz5d69atU15engYPHqysrCwbXhUAAACA0sSmM1aXL19WtWrVNGzYMNWpU0eS9Nxzz6l79+46duyYdu3aJWdnZ02bNk1OTk7y9/e3hLBevXopKytLy5cv14svvqi2bdtKkmJjYxUaGqqtW7fqkUceseHVAQAAACgtbDpjVb58ecXExFhCVUpKilauXClfX1/Vrl1b+/fvV0hIiJyc/pf/mjdvrlOnTun8+fOKj4/XtWvX1KJFC0t/uXLlFBgYqH379t3x6wEAAABQOtn8Hqt8kyZN0nvvvScXFxctWrRIHh4eSkxMtISufJUrV5Yk/fHHH0pMTJQkVa1atcCY/L7iyN96EcDdwWQyyd3d3dZl4CbS09P5MEsAgN0xm82F3q7eboLV008/rSeeeEJr1qxReHi44uLilJGRIRcXF6txrq6ukqTMzEylp6dL0g3HXL58udi1ZGdn68iRI8U+HoB9cXd3V2BgoK3LwE0kJCRY/p8OAIA9+WvW+Dt2E6xq164tSYqKitKPP/6o1atXy83NrcAmFJmZmZKuf9iZm5ubJCkrK8vyff4YI+9OOzs7W+oBUPLxwYj2z8/PjxkrAIDdOX78eKHH2jRYpaSkaNeuXerYsaPlPioHBwfVrl1bycnJ8vX1LfDBZvk/V6lSRTk5OZa2GjVqWI0JCAgodl0mk0keHh7FPh4AUDQs1QQA2KOivDlr080rzp8/r9GjR2vXrl2WtuzsbB0+fFj+/v4KDg7WgQMHlJuba+nfvXu3/Pz85O3trbp168rT01N79uyx9Kempurw4cMKDg6+o9cCAAAAoPSyabCqU6eOWrdurRkzZmjfvn06evSoJkyYoNTUVA0YMEC9evXS1atX9fLLL+v48eP64IMPtHLlSg0bNkzS9fWOffv2VXR0tL744gvFx8crIiJCvr6+6tChgy0vDQAAAEApYvN7rObMmaOYmBhFREToypUratq0qdasWaN77rlHkvT2228rKipKPXv2lI+Pj8aNG6eePXtajh8xYoRycnIUGRmpjIwMBQcHa9myZXJ2drbVJQEAAAAoZUxm7ha2cvDgQUlS/fr1bVwJgFvtiW4DdeTQUVuXgT95oF4drf94ha3LAADghoqSDWy6FBAAAAAA7gYEKwAAAAAwiGAFAAAAAAYRrAAAAADAIIIVAAAAABhEsAIAAAAAgwhWAAAAAGAQwQoAAAAADCJYAQAAAIBBBCsAAAAAMIhgBQAAAAAGEawAAAAAwCCCFQAAAAAYRLACAAAAAIMIVgAAAABgEMEKAAAAAAwiWAEAAACAQQQrAAAAADCIYAUAAAAABhGsAAAAAMAgghUAAAAAGESwAgAAAACDCFYAAAAAYBDBCgAAAAAMIlgBAAAAgEEEKwAAAAAwiGAFAAAAAAYRrAAAAADAIIIVAAAAABhEsAIAAAAAgwhWAAAAAGAQwQoAAAAADCJYAQAAAIBBBCsAAAAAMIhgBQAAAAAGEawAAAAAwCCCFQAAAAAYRLACAAAAAIMIVgAAAABgEMEKAAAAAAwiWAEAAACAQQQrAAAAADCIYAUAAAAABhGsAAAAAMAgghUAAAAAGESwAgAAAACDCFYAAAAAYJDNg9WlS5f0yiuvqHXr1mrSpImeeuop7d+/39I/cOBABQQEWH3169fP0p+ZmampU6eqRYsWaty4scaMGaOUlBRbXAoAAACAUsrJ1gWMHj1a586d05w5c+Tt7a1Vq1Zp0KBB+vDDD1WrVi398ssvmjJlitq3b285xtnZ2fL9lClTtH//fr3xxhtycXHR5MmTNWLECK1evdoWlwMAAACgFLJpsDp9+rR27typuLg4Pfjgg5KkSZMmaceOHfrkk0/Ut29fXbhwQQ0bNpSPj0+B45OSkrRx40YtXrxYTZs2lSTNmTNHnTp10vfff6/GjRvf0esBAAAAUDrZdCmgl5eXli5dqvr161vaTCaTTCaTUlNT9csvv8hkMsnPz++Gxx84cECS1Lx5c0ubn5+fqlSpon379t3e4gEAAADg/2fTYFWuXDm1adNGLi4ulrYtW7bo9OnTCg0N1dGjR1W2bFlNmzZNrVu3VqdOnTR37lxlZWVJuj5j5eXlJVdXV6vHrVy5shITE+/otQAAAAAovWx+j9Wffffdd5o4caI6dOigtm3b6qWXXlJmZqYaNGiggQMH6siRI3r99dd19uxZvf7660pPT7cKZflcXV2VmZlZ7DrMZrPS0tKMXAoAO2IymeTu7m7rMnAT6enpMpvNti4DAAArZrNZJpOpUGPtJlht27ZNL774opo0aaLo6GhJ0rRp0zR+/HiVL19eklSnTh05OzsrIiJC48aNk5ubm2X26s8yMzMN/RKVnZ2tI0eOFPt4APbF3d1dgYGBti4DN5GQkKD09HRblwEAQAE3msi5EbsIVqtXr1ZUVJQ6deqk1157zVK8k5OTJVTlu//++yVJiYmJ8vX11aVLl5SVlWV1wcnJyapSpUqx63F2dlbt2rWLfTwA+1LYd5pgO35+fsxYAQDszvHjxws91ubBKi4uTtOnT1e/fv308ssvW/0C1K9fP1WvXl2vvvqqpe3gwYNydnZWzZo15ePjo7y8PB04cEAtWrSQdP1dz6SkJAUHBxe7JpPJJA8Pj+JfFACgSFiqCQCwR0V5c9amwSohIUEzZ87Uww8/rGHDhun8+fOWPjc3N3Xs2FEzZ85UgwYN1KpVKx08eFCvv/66Bg0aJE9PT3l6eqpLly6KjIzUzJkz5e7ursmTJyskJESNGjWy3YUBAAAAKFVsGqy2bNmi7Oxsff755/r888+t+nr27KlZs2bJZDJp1apVmjlzpnx8fDRgwAANHTrUMm769OmaOXOmnn/+eUlS69atFRkZeUevAwAAAEDpZjKzqN3KwYMHJcnqs7UA3B2e6DZQRw4dtXUZ+JMH6tXR+o9X2LoMAABuqCjZwKafYwUAAAAAdwOCFQAAAAAYRLACAAAAAIMIVgAAAABgEMEKAAAAAAwiWAEAAACAQQQrAAAAADCIYAUAAAAABhGsAAAAAMAgghUAAAAAGESwAgAAAACDCFYAAAAAYBDBCgAAAAAMIlgBAAAAgEEEKwAAAAAwiGAFAAAAAAYRrAAAAADAIIIVAAAAABhEsAIAAAAAgwhWAAAAAGAQwQoAAAAADCJYAQAAAIBBBCsAAAAAMIhgBQAAAAAGEawAAAAAwCCCFQAAAAAYRLACAAAAAIMIVgAAAABgEMEKAAAAAAwiWAEAAACAQQQrAAAAADCIYAUAAAAABhGsAAAAAMAgghUAAAAAGESwAgAAAACDCFYAAAAAYBDBCgAAAAAMIlgBAAAAgEEEKwAAAAAwiGAFAAAAAAYVK1jt27dP165du2FfamqqNm3aZKgoAAAAAChJihWs+vfvrxMnTtyw7/Dhw5o4caKhogAAAACgJHEq7MDx48frjz/+kCSZzWZNmTJFnp6eBcadOnVKlSpVunUVAgAAAICdK/SMVceOHWU2m2U2my1t+T/nfzk4OKhRo0Z69dVXb0uxAAAAAGCPCj1jFRYWprCwMElSv379NGXKFPn7+9+2wgAAAACgpCh0sPqzVatW3eo6AAAAAKDEKlawysjI0KJFi/TVV18pPT1deXl5Vv0mk0nbtm27JQUCAAAAgL0rVrCKiorS+++/r5CQED3wwANycCj+x2FdunRJc+bM0ddff62rV68qICBAY8aMUdOmTSVJu3bt0uzZs3XixAlVrVpVL7zwgrp06WI5PjMzU7NmzdJ///tfZWRkKCwsTC+//LIqVqxY7JoAAAAAoCiKFay2bt2qiIgIDR061HABo0eP1rlz5zRnzhx5e3tr1apVGjRokD788EOZzWYNGzZMAwcO1OzZs/X1119r3Lhxqlixolq0aCFJmjJlivbv36833nhDLi4umjx5skaMGKHVq1cbrg0AAAAACqNYwSo7O1sNGjQwfPLTp09r586diouL04MPPihJmjRpknbs2KFPPvlEFy5cUEBAgCIiIiRJ/v7+Onz4sN5++221aNFCSUlJ2rhxoxYvXmyZ4ZozZ446deqk77//Xo0bNzZcIwAAAAD8k2Kt4WvVqpW++eYbwyf38vLS0qVLVb9+fUubyWSSyWRSamqq9u/fb5mZyte8eXMdOHBAZrNZBw4csLTl8/PzU5UqVbRv3z7D9QEAAABAYRRrxqpz586aPHmyUlJS1LBhQ7m7uxcY06NHj398nHLlyqlNmzZWbVu2bNHp06f10ksv6cMPP5Svr69Vf+XKlZWenq6LFy8qKSlJXl5ecnV1LTAmMTGx6BcGAAAAAMVQrGA1atQoSdLGjRu1cePGAv0mk6lQweqvvvvuO02cOFEdOnRQ27ZtlZGRIRcXF6sx+T9nZWUpPT29QL8kubq6KjMzs8jnz2c2m5WWllbs4wHYF5PJdMM3gGA/0tPTrT6AHgAAe2A2m2UymQo1tljB6osvvijOYTe1bds2vfjii2rSpImio6MlXQ9IWVlZVuPyf3Z3d5ebm1uBfun6ToFGfonKzs7WkSNHin08APvi7u6uwMBAW5eBm0hISFB6erqtywAAoIAbTeTcSLGCVbVq1Ypz2N9avXq1oqKi1KlTJ7322muW4qtWrark5GSrscnJyfLw8FDZsmXl6+urS5cuKSsry+qCk5OTVaVKlWLX4+zsrNq1axf7eAD2pbDvNMF2/Pz8mLECANid48ePF3pssYLVggUL/nHM888/X6jHiouL0/Tp09WvXz+9/PLLVr8ANW3aVHv37rUav3v3bjVp0kQODg568MEHlZeXpwMHDlg2uUhISFBSUpKCg4OLcEXWTCaTPDw8in08AKBoWKoJALBHRXlz9pYHK09PT1WuXLlQwSohIUEzZ87Uww8/rGHDhun8+fOWPjc3N/Xr1089e/ZUdHS0evbsqe3bt+u///2v3n77bUlSlSpV1KVLF0VGRmrmzJlyd3fX5MmTFRISokaNGhXn0gAAAACgyIoVrOLj4wu0paWlaf/+/ZoyZYomTZpUqMfZsmWLsrOz9fnnn+vzzz+36uvZs6dmzZqlhQsXavbs2XrnnXdUvXp1zZ4922oL9unTp2vmzJmWINe6dWtFRkYW57IAAAAAoFhM5lu8qH3Dhg1avXq1Pvzww1v5sHfMwYMHJcnqs7UA3B2e6DZQRw4dtXUZ+JMH6tXR+o9X2LoMAABuqCjZoFgfEHwz99xzj06cOHGrHxYAAAAA7FaxlgLeiNlsVmJiot5+++1bvmsgAAAAANizYgWrunXr/u0OGWazWa+//rqhogAAAACgJClWsAoPD79hsPL09FTbtm1Vs2ZNo3UBAAAAQIlRrGD1wgsv3Oo6AAAAAKDEKvY9VikpKVq+fLn27t2r1NRUeXl5qWnTphowYIC8vb1vZY0AAAAAYNeKtStgYmKievbsqXfeeUeurq4KDAyUk5OTVqxYoR49eigpKelW1wkAAAAAdqtYM1azZ8+Wk5OTPvvsM917772W9l9//VXPPPOMYmNjNWvWrFtWJAAAAADYs2LNWH377bcaMWKEVaiSpHvvvVfh4eH65ptvbklxAAAAAFASFCtY5ebmysvL64Z9FStW1NWrVw0VBQAAAAAlSbGCVUBAgD755JMb9n300UeqU6eOoaIAAAAAoCQp1j1Wzz33nAYNGqTLly+rc+fO8vHx0blz57Rp0yZ9++23mj9//q2uEwAAAADsVrGCVcuWLTVr1ixFR0db3U/l4+OjV199VQ8//PAtKxAAAAAA7F2xP8cqOTlZgYGBGj9+vC5fvqz4+Hi98cYb3F8FAAAAoNQpVrBavny55s6dq759+8rf31+SVLVqVZ08eVKzZs2Sq6urHnvssVtaKAAAAADYq2IFq3Xr1mnUqFEaOnSopa1q1aqKjIxUpUqVtHLlSoIVAAAAgFKjWLsCJiUlqX79+jfsa9iwoX777TdDRQEAAABASVKsYFWtWjXt2rXrhn379u2Tr6+voaIAAAAAoCQp1lLAxx9/XLNnz1Z2drbat28vb29vpaSk6KuvvtKKFSs0ZsyYW10nAAAAANitYgWrAQMGKCkpSatWrdLKlSst7Y6Ojnr66ac1cODAW1UfAAAAANi9Ym+3Pn78eD333HP64YcfdOnSJZUrV04NGjSQl5fXrawPAAAAAOxesYOVJJUtW1ahoaG3qhYAAAAAKJGKtXkFAAAAAOB/CFYAAAAAYBDBCgAAAAAMIlgBAAAAgEEEKwAAAAAwiGAFAAAAAAYRrAAAAADAIIIVAAAAABhEsAIAAAAAgwhWAAAAAGAQwQoAAAAADCJYAQAAAIBBBCsAAAAAMIhgBQAAAAAGEawAAAAAwCCCFQAAAAAYRLACAAAAAIMIVgAAAABgEMEKAAAAAAwiWAEAAACAQQQrAAAAADCIYAUAAAAABhGsAAAAAMAgghUAAAAAGESwAgAAAACD7CpYLVmyRP369bNqi4yMVEBAgNVXWFiYpT8vL0/z589XaGioGjVqpCFDhujXX3+906UDAAAAKMXsJlitWbNGc+fOLdD+yy+/6Nlnn9W3335r+Xr//fct/QsXLlRcXJymT5+udevWKS8vT4MHD1ZWVtYdrB4AAABAaWbzYJWUlKRnn31W0dHRqlmzplWf2WzW8ePHFRQUJB8fH8tXxYoVJUlZWVlavny5RowYobZt26pu3bqKjY1VYmKitm7daoOrAQAAAFAa2TxYHTp0SM7Ozvr444/VsGFDq74zZ84oLS1NtWrVuuGx8fHxunbtmlq0aGFpK1eunAIDA7Vv377bWjcAAAAA5HOydQFhYWFW90z92dGjRyVJq1at0jfffCMHBwe1bt1aERERKlu2rBITEyVJVatWtTqucuXKlj4AAAAAuN1sHqxu5ujRo3JwcFDlypW1ePFinTlzRq+//rqOHTumd955R+np6ZIkFxcXq+NcXV11+fLlYp/XbDYrLS3NUO0A7IfJZJK7u7uty8BNpKeny2w227oMAACsmM1mmUymQo2162A1fPhw9e7dW15eXpKkOnXqyMfHR48//rgOHjwoNzc3Sdfvtcr/XpIyMzMN/RKVnZ2tI0eOGCsegN1wd3dXYGCgrcvATSQkJFjeLAMAwJ78dRLn79h1sHJwcLCEqnz333+/JCkxMdGyBDA5OVk1atSwjElOTlZAQECxz+vs7KzatWsX+3gA9qWw7zTBdvz8/JixAgDYnePHjxd6rF0Hq3Hjxik5OVkrV660tB08eFCSVLt2bd17773y9PTUnj17LMEqNTVVhw8fVt++fYt9XpPJJA8PD0O1AwAKj6WaAAB7VJQ3Z22+K+DNdOzYUbt27dKCBQt05swZbd++XS+99JIeeeQR+fv7y8XFRX379lV0dLS++OILxcfHKyIiQr6+vurQoYOtywcAAABQStj1jNW//vUvzZ07V0uXLtVbb72lsmXLqmvXrho1apRlzIgRI5STk6PIyEhlZGQoODhYy5Ytk7Ozs+0KBwAAAFCqmMwsareSv9Swfv36Nq4EwK32RLeBOnLoqK3LwJ88UK+O1n+8wtZlAABwQ0XJBna9FBAAAAAASgKCFQAAAAAYRLACAAAAAIMIVgAAAABgEMEKAAAAAAwiWAEAAACAQQQrAAAAADCIYAUAAAAABhGsAAAAAMAgghUAAAAAGESwAgCgBDKbzbYuAX+DPxugdHKydQEAAKDoTCaTLv92WrmZmbYuBX/i6Oqq8tXvs3UZAGyAYAUAQAmVm5mpnIx0W5cBABBLAQEAAADAMIIVAAAAABhEsAIAAAAAgwhWAAAAAGAQwQoAAAAADCJYAQAAAIBBBCsAAAAAMIhgBQAAAAAGEawAAAAAwCCCFQAAAAAYRLACAAAAAIMIVgAAAABgEMEKAAAAAAwiWAEAAACAQQQrAAAAADCIYAUAAAAABhGsAAAAAMAgghUAAABKhdzcXFuXgJso6X8+TrYuAAAAALgTHB0dNSFiqhJOnLJ1KfgLP/+amhU72dZlGEKwAgAAQKmRcOKUjhw6ausycBdiKSAAAAAAGESwAgAAAACDCFYAAAAAYBDBCgAAAAAMIlgBAAAAgEEEKwAAAAAwiGAFAAAAAAYRrAAAAADAIIIVAAAAABhEsAIAAAAAgwhWAAAAAGAQwQoAAAAADCJYAQAAAIBBBCsAAAAAMIhgBQAAAAAG2VWwWrJkifr162fVduTIEfXt21eNGjVSWFiY3n33Xav+vLw8zZ8/X6GhoWrUqJGGDBmiX3/99U6WDQAAAKCUs5tgtWbNGs2dO9eq7eLFixo4cKBq1KihDRs2KDw8XNHR0dqwYYNlzMKFCxUXF6fp06dr3bp1ysvL0+DBg5WVlXWHrwAAAABAaeVk6wKSkpI0efJk7dmzRzVr1rTqe++99+Ts7Kxp06bJyclJ/v7+On36tJYuXapevXopKytLy5cv14svvqi2bdtKkmJjYxUaGqqtW7fqkUceufMXBAAAAKDUsfmM1aFDh+Ts7KyPP/5YDRs2tOrbv3+/QkJC5OT0v/zXvHlznTp1SufPn1d8fLyuXbumFi1aWPrLlSunwMBA7du3745dAwAAAIDSzeYzVmFhYQoLC7thX2JiourUqWPVVrlyZUnSH3/8ocTERElS1apVC4zJ7ysOs9mstLS0Yh8PwL6YTCa5u7vbugzcRHp6usxms63LKDH4O23/+Dttf/h3UzLY278ds9ksk8lUqLE2D1Y3k5GRIRcXF6s2V1dXSVJmZqbS09Ml6YZjLl++XOzzZmdn68iRI8U+HoB9cXd3V2BgoK3LwE0kJCRY/p+Of8bfafvH32n7w7+bksEe/+38NWv8HbsOVm5ubgU2ocjMzJQkeXh4yM3NTZKUlZVl+T5/jJF3JJydnVW7du1iHw/AvhT2nSbYjp+fn129Q2nv+Dtt//g7bX/4d1My2Nu/nePHjxd6rF0HK19fXyUnJ1u15f9cpUoV5eTkWNpq1KhhNSYgIKDY5zWZTPLw8Cj28QCAomF5Du42/J0Gisfe/u0UJZDbfPOKmwkODtaBAweUm5tradu9e7f8/Pzk7e2tunXrytPTU3v27LH0p6am6vDhwwoODrZFyQAAAABKIbsOVr169dLVq1f18ssv6/jx4/rggw+0cuVKDRs2TNL19Y59+/ZVdHS0vvjiC8XHxysiIkK+vr7q0KGDjatHaZOXl2frEgAAAGAjdr0U0NvbW2+//baioqLUs2dP+fj4aNy4cerZs6dlzIgRI5STk6PIyEhlZGQoODhYy5Ytk7Ozsw0rR2nk4OCgHas+0+WkFFuXgr+454GaatKlla3LAAAAdzG7ClazZs0q0NagQQOtX7/+b49xdHTU2LFjNXbs2NtZGlAol5NSlPJb8j8PxB1VrnJFW5cAAADucna9FBAAAAAASgKCFQAAAAAYRLACAAAAAIMIVgAAAABgEMEKAAAAAAwiWAEAAACAQQQrAAAAADCIYAUAAAAABhGsAAAAbhEHJyfl5eXZugwANuBk6wIAAADuFiYHRzk4OGjHqs90OSnF1uXgT+55oKaadGll6zJwFyNYAQAA3GKXk1KU8luyrcvAn5SrXNHWJeAux1JAAAAAADCIYAUAAAAABhGsAAAAAMAgghUAAAAAGESwAgAAAACDCFYAAAAAYBDBqoQxm822LgEAAADAX/A5ViWMyWTS5d9OKzcz09al4E+cPcuqbJWqti4DAAAANkKwKoFyMzOVk5Fu6zLwJ44urrYuAQAAADbEUkAAAAAAMIhgBQAAAAAGEawAAAAAwCCCFQAAAAAYRLACAAAAAIMIVgAAAABgEMEKAAAAAAwiWAEAAACAQQQrAAAAADCIYAUAAAAABhGsAAAAAMAgghUAAAAAGESwAgAAAACDCFYAAAAAYBDBCgAAAAAMIlgBAAAAgEEEKwAAAAAwiGAFAAAAAAYRrAAAAADAIIIVAAAAABhEsAIAAAAAgwhWAAAAAGAQwQoAAAAADCJYAQAAAIBBBCsAAAAAMIhgBQAAAAAGEawAAAAAwKASEaySkpIUEBBQ4OuDDz6QJB05ckR9+/ZVo0aNFBYWpnfffdfGFQMAAAAoTZxsXUBhxMfHy9XVVdu2bZPJZLK0ly1bVhcvXtTAgQMVFhamqVOn6ocfftDUqVNVpkwZ9erVy4ZVAwAAACgtSkSwOnr0qGrWrKnKlSsX6HvnnXfk7OysadOmycnJSf7+/jp9+rSWLl1KsAIAAABwR5SIpYC//PKL/P39b9i3f/9+hYSEyMnpfxmxefPmOnXqlM6fP3+nSgQAAABQipWYGSsvLy/16dNHCQkJuu+++zR8+HC1bt1aiYmJqlOnjtX4/JmtP/74Q5UqVSry+cxms9LS0m5J7beSyWSSu7u7rcsAgFsuPT1dZrPZ1mWUGLweALhb2dvrgdlstroV6WbsPljl5OTo5MmTql27tiZMmCBPT09t2rRJQ4cO1YoVK5SRkSEXFxerY1xdXSVJmZmZxTpndna2jhw5Yrj2W83d3V2BgYG2LgMAbrmEhASlp6fbuowSg9cDAHcre3w9+GvW+Dt2H6ycnJy0Z88eOTo6ys3NTZIUFBSkY8eOadmyZXJzc1NWVpbVMfmBysPDo1jndHZ2Vu3atY0VfhsUNi0DQEnj5+dnV+9Q2jteDwDcrezt9eD48eOFHmv3wUqSypQpU6Dt/vvv17fffitfX18lJydb9eX/XKVKlWKdz2QyFTuUAQCKjmVtAADJ/l4PivJGlt1vXnHs2DE1adJEe/bssWr/+eefVbt2bQUHB+vAgQPKzc219O3evVt+fn7y9va+0+UCAAAAKIXsPlj5+/urVq1amjZtmvbv368TJ07o1Vdf1Q8//KDhw4erV69eunr1ql5++WUdP35cH3zwgVauXKlhw4bZunQAAAAApYTdLwV0cHDQ4sWLFRMTo1GjRik1NVWBgYFasWKFZTfAt99+W1FRUerZs6d8fHw0btw49ezZ08aVAwAAACgt7D5YSVKlSpX06quv/m1/gwYNtH79+jtYEQAAAAD8j90vBQQAAAAAe0ewAgAAAACDCFYAAAAAYBDBCgAAAAAMIlgBAAAAgEEEKwAAAAAwiGAFAAAAAAYRrAAAAADAIIIVAAAAABhEsAIAAAAAgwhWAAAAAGAQwQoAAAAADCJYAQAAAIBBBCsAAAAAMIhgBQAAAAAGEawAAAAAwCCCFQAAAAAYRLACAAAAAIMIVgAAAABgEMEKAAAAAAwiWAEAAACAQQQrAAAAADCIYAUAAAAABhGsAAAAAMAgghUAAAAAGESwAgAAAACDCFYAAAAAYBDBCgAAAAAMIlgBAAAAgEEEKwAAAAAwiGAFAAAAAAYRrAAAAADAIIIVAAAAABhEsAIAAAAAgwhWAAAAAGAQwQoAAAAADCJYAQAAAIBBBCsAAAAAMIhgBQAAAAAGEawAAAAAwCCCFQAAAAAYRLACAAAAAIMIVgAAAABgEMEKAAAAAAwiWAEAAACAQQQrAAAAADCIYAUAAAAABt0VwSovL0/z589XaGioGjVqpCFDhujXX3+1dVkAAAAASom7IlgtXLhQcXFxmj59utatW6e8vDwNHjxYWVlZti4NAAAAQClQ4oNVVlaWli9frhEjRqht27aqW7euYmNjlZiYqK1bt9q6PAAAAAClQIkPVvHx8bp27ZpatGhhaStXrpwCAwO1b98+G1YGAAAAoLQwmc1ms62LMGLr1q164YUX9OOPP8rNzc3SPnLkSGVkZGjJkiVFerzvvvtOZrNZzs7Ot7rUW8JkMikvN0cq2X9sdx2TyUEmR0dlXE1TXm6ercvBXzg5O8nFw00pFy4qJyfH1uXgT5ycnFTR20sl/KXIJng9sE+8HtgvXgvsm72+HmRnZ8tkMqlJkyb/ONbpDtRzW6Wnp0uSXFxcrNpdXV11+fLlIj+eyWSy+q89cnAs8X9sdy03Tw9bl4CbqOjtZesS8Dfs+f+59ozXA/vF64H94rXAvtnb64HJZCp0TSX+/8j5s1RZWVlWM1aZmZlyd3cv8uM1btz4ltUGAAAAoHQo8fdYVa1aVZKUnJxs1Z6cnKwqVarYoiQAAAAApUyJD1Z169aVp6en9uzZY2lLTU3V4cOHFRwcbMPKAAAAAJQWJX4poIuLi/r27avo6GhVrFhR1apV0+zZs+Xr66sOHTrYujwAAAAApUCJD1aSNGLECOXk5CgyMlIZGRkKDg7WsmXL7HZnPwAAAAB3lxK/3ToAAAAA2FqJv8cKAAAAAGyNYAUAAAAABhGsAAAAAMAgghUAAAAAGESwAgAAAACDCFYAAAAAYBDBCgAAAAAMIlgBpVBeXp7mz5+v0NBQNWrUSEOGDNGvv/76t+MvXryoMWPGKDg4WCEhIZo6darS09PvYMUAgNttyZIl6tev303H8HoA/D2CFVAKLVy4UHFxcZo+fbrWrVunvLw8DR48WFlZWTccP2LECJ0+fVorV67UvHnztH37dk2ZMuXOFg0AuG3WrFmjuXPn/uM4Xg+Av2cym81mWxcB4M7JyspS8+bN9eKLL6p3796SpNTUVIWGhioqKkqPPPKI1fjvv/9eTz75pD777DP5+/tLkr799lsNHjxY27dvV5UqVe74NQAAbo2kpCRNnjxZe/bska+vrypVqqRVq1bdcCyvB8DNMWMFlDLx8fG6du2aWrRoYWkrV66cAgMDtW/fvgLj9+/fLx8fH8uLqCSFhITIZDLpwIEDd6RmAMDtcejQITk7O+vjjz9Ww4YNbzqW1wPg5pxsXQCAOysxMVGSVLVqVav2ypUrW/r+LCkpqcBYFxcXVahQQX/88cftKxQAcNuFhYUpLCysUGN5PQBujhkroJTJv8nYxcXFqt3V1VWZmZk3HP/XsTcbDwC4O/F6ANwcwQooZdzc3CSpwEYVmZmZcnd3v+H4G21qkZmZKQ8Pj9tTJADA7vB6ANwcwQooZfKXcSQnJ1u1Jycn3/DGY19f3wJjs7KydOnSJVWuXPn2FQoAsCu8HgA3R7ACSpm6devK09NTe/bssbSlpqbq8OHDCg4OLjA+ODhYiYmJOn36tKVt7969kqQHH3zw9hcMALALvB4AN0ewAkoZFxcX9e3bV9HR0friiy8UHx+viIgI+fr6qkOHDsrNzdW5c+eUkZEhSWrYsKGaNGmiiIgI/fTTT9q9e7deeeUV9ejRg611AeAuxusBUDQEK6AUGjFihP7zn/8oMjJSTz31lBwdHbVs2TI5Ozvrjz/+UKtWrfTZZ59JkkwmkxYsWKDq1avr6aef1qhRo9S6dWs+EBIA7nK8HgBFwwcEAwAAAIBBzFgBAAAAgEEEKwAAAAAwiGAFAAAAAAYRrAAAAADAIIIVAAAAABhEsAIAAAAAgwhWAAAAAGAQwQoAYPeOHj2qiIgItWzZUkFBQWrVqpVGjRql+Pj4O3L+N954QwEBAXfkXBMmTFBYWNgdORcA4NZxsnUBAADczLFjx/TEE0+oUaNGioyMlLe3txITE7V69Wo9/vjjevfdd9WoUaPbWsNjjz2m0NDQ23oOAEDJRrACANi1FStWyMvLS2+99ZacnP73stW+fXt16tRJCxcu1NKlS29rDb6+vvL19b2t5wAAlGwsBQQA2LXz58/LbDYrLy/Pqt3Dw0MvvfSS/v3vf0uSwsLCNGHCBKsxH3zwgQICAvTbb79Jur6k7+GHH9aCBQsUEhKiVq1aKTIyUi1btlRubq7VsVFRUWrWrJmys7OtlgIuXrxYQUFBunz5stX4lStXql69erpw4YIk6ezZsxo9erRCQkLUsGFDPf300zp8+LDVMZcvX9bEiRMVEhKi4OBgzZ49u8B1AgBKBoIVAMCutW3bVmfPntWTTz6pNWvW6MSJEzKbzZKkTp06qWfPnkV6vLNnz2r79u2KjY3VxIkT1aNHD50/f1579uyxjMnLy9PmzZvVpUsXOTs7Wx3ftWtX5eTkaOvWrVbtmzZtUqtWreTt7a2UlBQ9+eSTOnTokCZNmqSYmBjl5eWpT58+OnHihOUcgwcP1vbt2zV+/HjNmjVL3333nT777LPiPE0AABtjKSAAwK717t1b586d07JlyzRt2jRJkpeXl1q1aqX+/furQYMGRXq8nJwcjR8/Xk2bNpUkmc1mVatWTZ9++qkeeughSdKePXt07tw5de/evcDx1apVU3BwsD799FM99thjkqQzZ87op59+UmxsrCTpnXfe0aVLl7R27VpVq1ZNktS6dWt17txZ8+bN0/z58/XNN9/op59+0ltvvaXWrVtLklq0aMHGFQBQQjFjBQCweyNHjtSOHTsUExOj//znP/L09NQnn3xi2byiqB544AHL9yaTSd26ddO2bduUlZUl6frsU82aNdWwYcMbHt+tWzft27dP586ds4z39PS0hKJdu3bpgQceUJUqVZSTk6OcnBw5ODiodevW+r//+z9J0v79++Xs7Gy1KYaHh4fatGlT5OsBANgewQoAUCKUL19ejzzyiKKiorRt2zZ9+OGH8vf31+zZs3Xx4sUiPVaZMmWsfu7evbsuX76sHTt2KCsrS1u3blW3bt3+9vhOnTrJyclJmzdvlnQ9WHXs2FFubm6SpEuXLumHH35QvXr1rL7WrFmjK1euKD09XZcvX1aFChVkMpmsHtvHx6dI1wIAsA8sBQQA2K2kpCT16tVLI0eOtCy7yxcYGKiIiAiFh4fr119/laQCG1CkpaUV6jx+fn5q0KCBNm/eLAcHB6Wmpt40WJUtW1ZhYWHavHmzmjdvrmPHjmnSpElW/SEhIRo3btwNj3dxcZGXl5cuXryo3NxcOTo6WvouXbpUqJoBAPaFGSsAgN2qVKmSnJycFBcXp8zMzAL9J0+elKurq+677z55enoqMTHRqv/AgQOFPlf37t21Y8cObdq0SU2aNNG99977j+N/+OEHrV27Vvfcc49CQkIsfSEhIUpISJCfn5/q169v+froo4/0/vvvy9HRUS1atFBOTo62bdtmOS4rK0s7d+4sdM0AAPtBsAIA2C1HR0dNmTJFR48eVa9evbR27Vrt3btX27dv18yZMzVv3jw9//zzKl++vNq1a6d9+/ZpyZIl2r17t2bOnKndu3cX+lydO3fWtWvX9Nlnn91w04q/Cg0NVYUKFbR+/Xp17drVaknfgAEDlJeXpwEDBuizzz7Trl27NGnSJK1atUp+fn6Srm9Ukb/de1xcnLZv367hw4crJSWl6E8UAMDmWAoIALBrbdu21Xvvvadly5Zp8eLFSklJkYuLiwIDAxUbG6sOHTpIkoYNG6aUlBQtW7ZM2dnZatu2raKiojR8+PBCnadixYpq1aqVdu7cqU6dOv3jeCcnJ3Xp0kWrVq0qsGywSpUqWrdunWJiYjRlyhRlZmaqZs2aioqK0n/+8x/LuAULFig6Olrz589XZmamOnfurMcff1xffPFFEZ4hAIA9MJnzPwwEAAAAAFAsLAUEAAAAAIMIVgAAAABgEMEKAAAAAAwiWAEAAACAQQQrAAAAADCIYAUAAAAABhGsAAAAAMAgghUAAAAAGESwAgAAAACDCFYAAAAAYBDBCgAAAAAMIlgBAAAAgEH/H5/Ijp6Hp8XMAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Survival by Pclass\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.countplot(x='Survived', hue='Pclass', data=df)\n",
    "plt.title('Survival by Passenger Class')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 854,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Age distribution by survival\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.histplot(data=df, x='Age', hue='Survived', multiple='stack', bins=30, kde=True)\n",
    "plt.title('Age Distribution by Survival')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 855,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x900 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Correlation matrix heatmap\n",
    "df_corr = train.astype(\"float64\",errors='ignore')\n",
    "df_corr = df_corr.select_dtypes(exclude=\"object\")\n",
    "plt.subplots(figsize=(12,9))\n",
    "sns.heatmap(df_corr.corr(), annot=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 856,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-27 {color: black;}#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>RandomForestClassifier(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator 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\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(random_state=42)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(random_state=42)"
      ]
     },
     "execution_count": 856,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Define features and target\n",
    "X = df.drop('Survived', axis=1)  # assuming 'Survived' is the target and all other necessary preprocessing has been done\n",
    "y = df['Survived']\n",
    "\n",
    "# Split data into training and test sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Initialize the model\n",
    "model = RandomForestClassifier(n_estimators=100, random_state=42)\n",
    "\n",
    "# Train the model\n",
    "model.fit(X_train, y_train)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Testing the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 857,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.83\n"
     ]
    }
   ],
   "source": [
    "# Predict on the test set\n",
    "y_pred = model.predict(X_test)\n",
    "\n",
    "# Calculate accuracy\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(f\"Accuracy: {accuracy:.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predicting survival"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 858,
   "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",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896.0</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>1305.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>1306.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>1307.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>416</th>\n",
       "      <td>1308.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>417</th>\n",
       "      <td>1309.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>418 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Id  Survived\n",
       "0     892.0       0.0\n",
       "1     893.0       0.0\n",
       "2     894.0       0.0\n",
       "3     895.0       0.0\n",
       "4     896.0       0.0\n",
       "..      ...       ...\n",
       "413  1305.0       0.0\n",
       "414  1306.0       1.0\n",
       "415  1307.0       0.0\n",
       "416  1308.0       0.0\n",
       "417  1309.0       0.0\n",
       "\n",
       "[418 rows x 2 columns]"
      ]
     },
     "execution_count": 858,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred = pd.DataFrame()\n",
    "pred['Id']=df_test['PassengerId']\n",
    "pred['Survived'] = model.predict(df_test)\n",
    "\n",
    "pred"
   ]
  }
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