{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "87253e06-ec78-41ce-85e5-ed2fffc0c801",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5a5f4e58-c43c-4c95-baa8-ab096bf93a2e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('Titanic-Dataset.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9aa15c23-6911-4f78-9470-12c6714d7472",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      int64\n",
       "Survived         int64\n",
       "Pclass           int64\n",
       "Name            object\n",
       "Sex             object\n",
       "Age            float64\n",
       "SibSp            int64\n",
       "Parch            int64\n",
       "Ticket          object\n",
       "Fare           float64\n",
       "Cabin           object\n",
       "Embarked        object\n",
       "dtype: object"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "608c8dbe-58c0-4fba-983f-ff8bec8cfff5",
   "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>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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "10571e55-c6c3-4b30-aa2e-cdfc0c732820",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Survived         0\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "Age            177\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             0\n",
       "Cabin          687\n",
       "Embarked         2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3b13ed14-b20b-49cf-87e5-60fb89b87e61",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "891"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "37efb582-a97a-4113-95ab-eb7a6b429e86",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "columns with more than 30% missing values:\n",
      "Series([], dtype: float64)\n"
     ]
    }
   ],
   "source": [
    "missing_percentage = df.isnull().mean() * 100\n",
    "columns_with_missing_30 = missing_percentage[missing_percentage > 30]\n",
    "print(\"columns with more than 30% missing values:\")\n",
    "print(columns_with_missing_30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e525ea43-ad3a-4446-8b19-c4d0991edce1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Columns dropped: []\n",
      "Remaining columns: Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n",
      "       'Parch', 'Ticket', 'Fare', 'Embarked'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "missing_percentage = df.isnull().mean() * 100\n",
    "threshold = 30\n",
    "columns_to_drop = missing_percentage[missing_percentage > threshold].index\n",
    "df = df.drop(columns=columns_to_drop)\n",
    "print(f\"Columns dropped: {list(columns_to_drop)}\")\n",
    "print(f\"Remaining columns: {df.columns}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "d492caa8-4c52-44f0-b8dd-29485c74a734",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>108</td>\n",
       "      <td>1</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>523</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>190</td>\n",
       "      <td>0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>596</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>353</td>\n",
       "      <td>0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>669</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>272</td>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>49</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>472</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  Name  Sex   Age  SibSp  Parch  Ticket  \\\n",
       "0            1         0       3   108    1  22.0      1      0     523   \n",
       "1            2         1       1   190    0  38.0      1      0     596   \n",
       "2            3         1       3   353    0  26.0      0      0     669   \n",
       "3            4         1       1   272    0  35.0      1      0      49   \n",
       "4            5         0       3    15    1  35.0      0      0     472   \n",
       "\n",
       "      Fare  Embarked  \n",
       "0   7.2500         2  \n",
       "1  71.2833         0  \n",
       "2   7.9250         2  \n",
       "3  53.1000         2  \n",
       "4   8.0500         2  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "label_encoder = LabelEncoder()\n",
    "for column in df.select_dtypes(include=['object']).columns:\n",
    "        df[column] = label_encoder.fit_transform(df[column])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "39e76f83-65d7-47f8-a95e-06aaba728e0c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "data = df.copy()\n",
    "def replace_outliers_with_nan(column):\n",
    "    Q1 = column.quantile(0.25)\n",
    "    Q3 = column.quantile(0.75)\n",
    "    IQR = Q3 - Q1\n",
    "    lower_bound = Q1 - 1.5 * IQR\n",
    "    upper_bound = Q3 + 1.5 * IQR\n",
    "\n",
    "    return column.where((column >= lower_bound) & (column <= upper_bound), np.nan)\n",
    "\n",
    "for col in data.columns:\n",
    "    data[col] = replace_outliers_with_nan(data[col])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "222c4732-fa19-4582-bba5-744907c980a9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sum of null values in each colums:\n",
      "PassengerId      0\n",
      "Survived         0\n",
      "Pclass           0\n",
      "Name             0\n",
      "Sex              0\n",
      "Age            188\n",
      "SibSp           46\n",
      "Parch          213\n",
      "Ticket           0\n",
      "Fare           116\n",
      "Embarked         0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "null_counts = data.isnull().sum()\n",
    "print(\"Sum of null values in each colums:\")\n",
    "print(null_counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "0a4e3722-b512-48c0-bd65-cd0868917401",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId    0\n",
       "Survived       0\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",
       "dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.impute import KNNImputer\n",
    "imputer = KNNImputer(n_neighbors=2)\n",
    "data_imputed_knn = pd.DataFrame(imputer.fit_transform(data), columns=data.columns)\n",
    "data_imputed_knn.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "b2690691-3a3c-4982-82b6-6df2216b3203",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "titanic_df = pd.read_csv('Titanic-Dataset.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "ff42b2f9-2c82-4d67-9335-3780ec261fb4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 5))\n",
    "sns.histplot(titanic_df['Age'].dropna(), bins=20, kde=True, color='blue')\n",
    "plt.title('Age Distribution')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Count')\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8f24a294-20d6-4aac-8ece-9bd16b9c89c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "df = pd.read_csv(\"Titanic-Dataset.csv\")\n",
    "\n",
    "plt.figure(figsize=(8, 5))\n",
    "sns.countplot(x=\"Pclass\", hue=\"Survived\", data=df, palette=\"Set2\")\n",
    "plt.title(\"Survival Count by Passenger Class\")\n",
    "plt.xlabel(\"Passenger Class\")\n",
    "plt.ylabel(\"Count\")\n",
    "plt.legend([\"Not Survived\", \"Survived\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "90fd1020-a092-4a32-87b1-3b4100c1e1fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"Sex\"].value_counts().plot.pie(autopct=\"%1.1f%%\", colors=[\"lightblue\", \"pink\"], startangle=90, wedgeprops={'edgecolor': 'black'})\n",
    "plt.title(\"Gender Distribution\")\n",
    "plt.ylabel(\"\")  # إزالة التسمية الجانبية\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "40f0a0a5-5e78-4fc8-b9b7-aebd4466718d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ysaad\\AppData\\Local\\Temp\\ipykernel_19072\\87803067.py:2: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  sns.boxplot(x=\"Pclass\", y=\"Age\", data=df, palette=\"Set3\")\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAq4AAAHWCAYAAAC2Zgs3AAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjEsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvc2/+5QAAAAlwSFlzAAAPYQAAD2EBqD+naQAARkxJREFUeJzt3Ql4VOX5/vGHLQHZg0JACFKQRRa1gIBSF0RTfopQqHtrBFpcEMtWEasguCAqggubQiEuiFILCi27BVwAgYqCC4tSQTGAKKsNYZn/db//a6YzQwJJgJw5M9/PdQ1hzjmZeWcyk9zznOe8p1ggEAgYAAAAEOOKez0AAAAAID8IrgAAAPAFgisAAAB8geAKAAAAXyC4AgAAwBcIrgAAAPAFgisAAAB8geAKAAAAXyC4AgAAwBcIrgDy7T//+Y8VK1bMpkyZctrvS/eh+9J9Bp1zzjl27bXXWlFYvHixu399LWpF+TgRe/S6e/jhh70eBhCTCK5AERs7dqz7w9SqVSuvh+LGEbyULFnSUlJSrHnz5vanP/3JPv/881P6mIsi7Mbb2IriQ0jwUqJECUtLS7Pf/OY3tmbNGq+HF5f0vP7ud7+zWrVqWXJysnu/tW/f3iZPnmxHjhzxeniAL5T0egBAonnttddcRe2jjz6yTZs2Wb169Twdz1VXXWW33XabBQIB27Nnj33yySeWmZnpAt2IESOsX79+oW1r165t//3vf61UqVIFug/d1plnnmm33357vr/n97//vd10003uD/zplNfYLr30UvdYk5KSLJ7dfPPN9n//938uOH3xxRc2btw4mzNnji1fvtwuuOACr4cXNyZOnGh33nmnVatWzb22zz33XNu3b58tWrTIevToYd9//7098MADXg8TiHkEV6AIbd682T788EP7+9//bnfccYcLsUOGDPF0TPXr13dVoHBPPPGEdezY0fr3728NGzZ0wUZUmStduvRpHc+BAwesbNmyrgKoi1eKFy9+2h9rLPjlL38Z8fO/5JJL7LrrrnMBdsKECZ6OzU9+/vlnO+OMM3Jdpw8BCq1t2rSxf/7zn1a+fPnQuj59+tiqVats3bp1RThawL9oFQCKkIJq5cqV7ZprrrHf/va37npudu3a5aoyFSpUsEqVKllGRoarhObWX/rll1+629JuRwWtFi1a2DvvvHNS46xSpYpNmzbNtQ889thjx+1xzcrKsm7dulnNmjVddbR69erWqVOnUG+qqsufffaZLVmyJLRb+vLLL4/oY9W6u+++26pWrepuJ68e16D58+e7aqAe73nnnec+CIRTf6C+N1r0bR5vbHn1uE6fPt21U5QpU8ZVahX6vvvuu4htVL0tV66cW965c2f3/7POOssGDBhQoF3Cx3ucX3/9tRvfqFGjjvk+fTjSutdff90Kql27dqEPWfL222+712uNGjXcz7du3br2yCOPHPM4Nm7caF27drXU1FQ3Xv0cVTFXFT9owYIF1rZtW/ea1nPSoEGDY6qMBw8edB/mtCdC96fd6vfdd59bHk6P75577rGZM2dakyZN3LaNGze2uXPnHvOY9DPU+0Lj0vgVyPN6jbz66quhn6/eU3oMW7dujdhGrxHd5+rVq11lXoH1eNXSoUOHuvvS+z08tAZpbMfbG/HNN9+494eeL41L78/rr7/+mPfGoUOH3H2pmqvHqu30fOt5z+/7FYh1VFyBIqQ/XF26dHG7n7WLVlWtlStXWsuWLUPbHD161FU71Upw1113uYqnwoPCazSFLlXIzj77bLv//vtdpfLNN990Yemtt95y/YqFpX7Hyy67zP71r3/Z3r17XYjOjcKKxtG7d28XBHfs2OH+UG7ZssVdHz16tFunoPKXv/zFfY92l4bTH2UFu8GDB7uK6/EoIN14442ugqXnRP2B+iOuwKK2h4LIz9iig6/+6OvnNXz4cNu+fbs9++yz9sEHH9jHH3/sAlmQgl16errrZX766adt4cKFNnLkSBec9HM9kRM9zl/84hfuZ6/XVN++fSO+NxiQFEgK6quvvnJfFXqCj1nPj1pG9PXdd991Pye9Jp566im3TU5OjnusCpd6PhVeFdpnz55tu3fvtooVK7rXiA44a9asmQ0bNsyFJrXK6LkLf+2r2vv+++9bz549rVGjRrZ27VoXzjds2OBCajhtpzCv148e73PPPedej3rtBcevn8uvf/1rF9AU6vRz0f3r9RZNH9Ieeughu+GGG+wPf/iD7dy5055//nkXTqN/vvpw2aFDBxds9eElr9eNKrFqB9Bt6D1VGPodoQ8jui8FToVM/e5QgFYverDSqzCu16XGftFFF7mfkaq5//73v0PvjRO9X4GYFwBQJFatWhXQW27BggXu+tGjRwM1a9YM/OlPf4rY7q233nLbjR49OrTsyJEjgXbt2rnlkydPDi2/8sorA02bNg1kZ2eHlul2L7744sC55557wjHp9nr16pXneo1N23zyySfu+ubNmyPG8NNPP7nrTz311HHvp3HjxoHLLrvsmOW6HX1/27ZtA4cPH851ne4zqHbt2m6ZnqOgPXv2BKpXrx648MILQ8uGDBnitsvr/sJvM6+x/etf/3Lb6qvk5OQEqlatGmjSpEngv//9b2i72bNnu+0GDx4cWpaRkeGWDRs2LOI2NcbmzZsf97kqyOOcMGGC2+6LL74ILdM4zzzzTDeG4wn+LIcOHRrYuXNnICsrK7B48WJ3++H3/fPPPx/zvXfccUfgjDPOCL3uPv74Y/c906dPz/P+Ro0a5bbRfeXllVdeCRQvXjzw3nvvRSwfP368+94PPvggtEzXk5KSAps2bQot0+tUy59//vnQso4dO7qxfvfdd6FlGzduDJQsWTLiNfKf//wnUKJEicBjjz0Wcd9r165124Yv1+tF36txnUhwTNHv8+PR9noNB+X2M1i2bJnb7uWXXw4tO//88wPXXHNNnreb3/crEMtoFQCKiKpgqspcccUV7rp2Haqipl3y4btdVVHTwU9//OMfI/ote/XqFXF7P/74o6t+qTqkgzx++OEHd1ElSNUvVeyid2EXlCpsotvPjXZbqnqsXbE//fRToe9HjzW//azaZR1eSVYlWAeXqSKm3aCniypXqk6puhfe+6rd6KqK/+Mf/zjme1QtDferX/3K7eI/VY9TP3uNJbzlZN68ee51EN23nBftllf1UVVSVfBUcdVBedozEPwZBwVfZ3ocqiSqTUVUUQ3et5bnJlit1N4DVVZzozYMVVn1fAZfz7oE2xdU/Q+nI/JVwQ5SNVfPU/A51vtKlW7tgdDzGaQ2BFVLw6lyq3HpOQ2/bz0v2vUefd+qGKv6fiKqekpuLQL5Ff4zUDuA3uN6DHpOVU0N0nVVU/XeP53vV8BLBFegCOgPqAKqQqt6B7WLVBftRtbuZu1KDO9n027N6AM9omcf0PerOKNdmwoe4ZfgAV8KWidj//79x/2jqz/eCjk6Cl2hXLtDn3zyyQIHyDp16uR7Wz0P0b2JOsBMTmefnn4uoj7DaApawfVBCpTRu6PV35zfwJCfx6mgoraSqVOnhrZRiFXrSDDsnYh2yWtXsV6D6tnUa0Y9pUEKQgrQCqcKhXpMwVAc7F/Vz0+tBDpyXn2/+uA0ZsyYiP5WfUhTa4N2Y+u1ot3eamsJD7EKXLq/6Ndz8HFHv55z2/Ue/hxre80MkdvMHdHLdN96PymkRt+/ZluIvm89x/mZcSLYYpPXh7/80GNQe0ZwGi09xxqX2jDCn2O1QGiZnq+mTZvan//8Z/v0009P+fsV8BI9rkARUGVU090ovOoSTWHj6quvLtBtBv/g64AfBYXcnOxUWzrSWZXQ4wVLHRWt8KT+Q1XcFKTVZ6fHfOGFFxa4onQq5HbQjRTlXJlFNSOCqrCqVKoHUmFFB+apKqwqfX4oqKlymRuFIPU5K3wpFKm6qUCuKt/AgQMjQqf6d3WAkSqqOqjs3nvvda8DHVGvvkz9jJcuXeoql6pOa8/CG2+84QK2ttfzpdvTY3jmmWdyHY+CW36e4/+/t71gdN963SjU5Xa7wb0PBX3N6j2ogxzVq1tY6kdVj7Pea5qZQB8iNFaF//CfgYKoKubBn4E+SKg/ePz48e4Dw6l6vwJeIrgCRUDBVEfMqwoVTbsoZ8yY4f646I+h5krVH/fo6XVUYQ2ng3NEbQV5BY+ToYM1dLS9/lCeaDenAo2mztJFlSsdCa8goyO0jxckCyNYaQ6/TR24I8GDS1R1Cwav8ANqoquiBRmbfi6yfv36Y6qZWhZcX5SPU3Tgkapveo2pgq/XjWakOBW0S1m7pfUaVSgKCs44EE2hU5cHH3zQBWlVWPW6fvTRR916hekrr7zSXRROH3/8cXdQnF7vwd3+mj1D60/Fa0bvOQXt6PeORC/Tfev51oe0YIX3VNB7WK8XBUPNThAdvvPjb3/7mztAT++poOzsbPf6jqaZENTCoIv2mOjnpoO2gsE1P+9XIJbRKgCcZtrNpz/8OqJa01ZFXzSlj3YjBqewUvVUfWwvvfRS6DZUVYkOvfqjrJ5ETe2jam40HRFdWOqf1awHqlAGj7bPjUKS/oCG0x9FBd3w6Ys020Fuf2QLY9u2bS7oh/cQvvzyy+6Pr/oRg2MQVfiCNFuBTqwQLb9j05RFes4VxMIfmyp02pWsXtdTKT+PU1TN089Ku901A4CCo3o9T4Vg5TG8gqkZBHTShnAa2+HDhyOWaRwKqsHnSq+paMETHAS3UX+p+rLDX/vh76MTzTiR2/gViFVd1PMZHlr1cwunnl5tr5kHoiu2uq4AX1hq3dFt6ANFsP0mnFo0cntthj+O6DFptoPoPQjRY1SVWBXf4POb3/crEMuouAKnmQKpgqmm+clN69atQxUz9QHqQBJNZaNqiP7Aqn9StxH8wx9eiVKY1TyNCgk6wElVWPXMLlu2zL799ltXvToRVfFUadEfRgUQfY92PesPrKpiqugd73tVHVPg0DyjClEKWxqDdmMGaV5MTd+jypv+kCoA5rcHM5qqYTrTkKYIUp/eX//6V3d/2pUapLYL9T9qO/X56Q+/ttPzrEpyuPyOTZVt9QeqkqXd5wqLwemwVAGNnpLqZOXncYa3C2gqKFUuNcZT5eKLL3bVa1X7tOtfr71XXnnlmBClaqI+gGm6Lo1bIVbb6XnX9EuiVgN9kFDAV3VaPaMKwGoj0GtYFOwUwHVQmx6LKrYKZzoITMu1a1sfIApC1UbtNtdtaRoy3d4LL7zg5mENP7WtApxeA4MGDXI9xHofKtCpuqzXtHqB1ZZT2OdR71W1cOj9HH7mLFW19f4OVqVzow+9ej7VIqD3md7fOugsOOVXkNbpw6xe06q86oBCVWv1synI+xWIaV5PawDEO03HU7p06cCBAwfy3Ob2228PlCpVKvDDDz+465oy6JZbbgmUL18+ULFiRbdeUwHpLTtt2rSI7/3qq68Ct912WyA1NdXdxtlnnx249tprA3/7299OODbdXvCiaYgqVarkpkPS1D2fffbZMdtHT4el8Wo6rYYNGwbKli3rxtqqVavAm2++GfF9mmpJ0/To8ej7g9NPBaenWrly5TH3ldd0WLqdefPmBZo1axZITk52953bNEyrV692Y9GUSWlpaYFnnnkm19vMa2zR02EFvfHGG+450n2npKQEbr311sC3334bsY2motLzES2vabqiFeRxhk/rpZ9h9FjyEvxZnmhqJL3uWrduHShTpkygRo0agfvuu8+NK/y5+frrrwPdu3cP1K1b173W9bxcccUVgYULF4ZuZ9GiRYFOnTq529DPRF9vvvnmwIYNGyLuT9N5jRgxwj0ePe7KlSu7KcQ0bZemBDvRVG567qKnAtN962em+9UYJ06cGOjfv78bazRNA6bp2fTz00XPu+5n/fr1oW30GtH4CkqvSb2v9dj1XtVj05R2mZmZbsq7vKbD0jRW3bp1c9OclStXLpCenh748ssvj3msjz76aOCiiy5y72P9vDR2TeOl57Qg71cglhXTP16HZwAnpt2dOrpbk66regSE04E1qrKFz1CBvKmierypowDEJnpcgRikfr5w2r2pnjYd3a1zywPhtEtYu73VMoATv58UVv/5z3+GTu8LwD/ocQVikKa/0R9bHdGvgyZ0cJeO0tZR2Kd66ij4l6Yr04E9OiJcc/+qRxrHUu+3purSV80soZ5mzcEaPl8tAH8guAIxSAcHKYzoXO86ClgHDaniGjzIAhAdeKODnnRShNdffz3ijF74Hx1gqOdHE+1rEn59INSHQB0gBcBf6HEFAACAL9DjCgAAAF8guAIAAMAX4r7HVWcc0hlTNJH0qTztJAAAAE4Nda7qpBw1atRwZ91L2OCq0FqYc0MDAACgaG3dutWdUS9hg6sqrcEnQnNgAgAAILbolOMqNAZzW8IG12B7gEIrwRUAACB2naitk4OzAAAA4AsEVwAAAPgCwRUAAAC+QHAFAACALxBcAQAA4AsEVwAAAPgCwRUAAAC+QHAFAACALxBcAQAA4AsEVwAAAPiCp8H1yJEj9tBDD1mdOnWsTJkyVrduXXvkkUcsEAiEttH/Bw8ebNWrV3fbtG/f3jZu3OjlsAEAAJBowXXEiBE2btw4e+GFF+yLL75w15988kl7/vnnQ9vo+nPPPWfjx4+3FStWWNmyZS09Pd2ys7O9HDoAAACKWLFAeHmziF177bVWrVo1mzRpUmhZ165dXWX11VdfddXWGjVqWP/+/W3AgAFu/Z49e9z3TJkyxW666aYT3sfevXutYsWK7vsqVKhwWh8PAAAACi6/ea2keejiiy+2F1980TZs2GD169e3Tz75xN5//3175pln3PrNmzdbVlaWaw8I0oNq1aqVLVu2LNfgevDgQXcJfyISUU5OjnvucOqkpqZaUlKS18MAACBheRpc77//fhcsGzZsaCVKlHA9r4899pjdeuutbn0weKnCGk7X8wplw4cPt6FDh1qi0/Oj1gucOgMHDrS0tDSvhwEAQMLyNLi++eab9tprr9nUqVOtcePGtmbNGuvTp49rD8jIyCjUbQ4aNMj69esXuq5gXKtWLUvE6qCCVqyH68zMTPez1nhjnR/GCABAPPM0uP75z392VdfgLv+mTZvaN99846qm4WFm+/btblaBIF2/4IILcr3N5ORkd0l02qXtl+qgfs5+GSsAAEjQWQV+/vlnK148cghqGTh69Kj7v6bJUqhZtGhRRAVVswu0adOmyMcLAACABK24duzY0fW0qtqmVoGPP/7YHZjVvXt3t75YsWKudeDRRx+1c8891wVZzfuqVoLOnTt7OXQAAAAkUnDVfK0Konfffbft2LHDBdI77rjDnXAg6L777rMDBw5Yz549bffu3da2bVubO3eulS5d2suhAwAAIJHmcS0KzOMau7Zs2eJmPuBofQAAEtvefOY1T3tcAQAAgPwiuAIAAMAXCK4AAADwBYIrAAAAfIHgCgAAAF8guAIAAMAXCK4AAADwBYIrAAAAfIHgCgAAAF8guAIAAMAXCK4AAADwBYIrAAAAfIHgCgAAAF8guAIAAMAXCK4AAADwhZJeDwAAAOB0O3r0qG3atMn27t1rFSpUsHr16lnx4tTv/IbgCgAA4tqaNWvs73//u+3atSu0rEqVKtalSxe74IILPB0bCobgCgAA4jq0Tpw40Zo0aWLdunWz6tWr2/fff2/z5s1zy//whz8QXn2EGjkAAIjb9gBVWhVae/bsaXXq1LHSpUu7r7qu5Vqv7eAPBFcAABCX1NOq9oD09PRj+ll1/eqrr3brtR38geAKAADikg7EErUH5KZGjRoR2yH2EVwBAEBc0uwBop7W3Gzbti1iO8Q+gisAAIhLmvJKswfoQKzoPlZdnz9/vluv7eAPBFcAABCX1MeqKa/WrVtnL774on399deWnZ3tvuq6lms987n6B9NhAQCAuKWprjTllWYPGDlyZGi5Kq1MheU/BFcAABDXFE6bNWvGmbPiAMEVAADEPYXU+vXrez0MnCQ+agAAAMAXCK4AAADwBYIrAAAAfIEeVwAAEPc0bysHZ/kfwRUAAMS1NWvWuOmwdu3aFTEdluZwZTosfyG4AoCPUDUCCh5aJ06caE2aNLFu3bpZ9erV3SlgdTYtLWcuV3/xNLiec8459s033xyz/O6777YxY8a4s1v079/fpk2bZgcPHrT09HQbO3asVatWzZPxAoCXqBoBBf+gp/eMQmvPnj1DH/Lq1KnjruvsWVqvOV75AOgPnv6UVq5c6T71BC8LFixwy6+//nr3tW/fvjZr1iybPn26LVmyxLZt2+Z+QQNAolaNatSoYQMGDHBnANJXXddyrQcQSXsn9EFPha/oYKrrV199tVuv7eAPngbXs846y1JTU0OX2bNnW926de2yyy6zPXv22KRJk+yZZ56xdu3aWfPmzW3y5Mn24Ycf2vLly70cNgB4WjVStah06dKhqpGWa722A/A/aqkRtQfkRh/8wrdD7IuZunhOTo69+uqr1r17dytWrJitXr3aDh06ZO3btw9t07BhQ0tLS7Nly5bleTtqKdALMPwCAH5G1QgoHPWBi/bq5kZ7csO3Q+yLmeA6c+ZM2717t91+++3uelZWliUlJVmlSpUitlN/q9blZfjw4VaxYsXQpVatWqd97ABwOlE1AgpHBy+qD1wHYkXvkdD1+fPnu/XaDv4QM8FVbQEdOnQI/QIurEGDBrk2g+Bl69atp2yMAOAFqkZA4WiPhI6NWbdunTsQ6+uvv3YHfuurrmu51nNgln/ExHRYmllg4cKFrkcrSD2vah9QFTa86rp9+3a3Li/JycnuAgDxWDUKPzJaqBoBx6cZNzTllTKGDmoM0nuGqbD8JyaCqw66qlq1ql1zzTWhZToYq1SpUrZo0SLr2rWrW7Z+/XrbsmWLtWnTxsPRAoA3VSPNHqAqkXpatXdKlVaFVlWN9AeYqhGQO4VTTXnFHMj+53lwVbVAwTUjI8NKlvzfcNSf2qNHD+vXr5+lpKS4F1nv3r1daG3durWnYwaAokbVCDg5Cqn169f3ehjwe3BVi4CqqJpNINqoUaPcC00V1/ATEABAIqJqBCDReR5ctcsrEAjkuk7zFOoMWroAAKgaAUhsfEwHAACALxBcAQAA4AsEVwAAAPgCwRUAAAC+QHAFAACALxBcAQAA4AueT4cFACjYSVuYxxVAoiK4AoBPrFmzxp05a9euXRFnztLpYDlzFoBEQHAFAJ+E1okTJ1qTJk2sW7duVr16dfv+++9t3rx5bjmnfQWQCNi/BAA+aA9QpVWhtWfPnlanTh13ZkF91XUt13ptBwDxjOAKADFOPa1qD0hPTz+mn1XXdepsrdd2AHKnD3YbNmywVatWua980PMnWgUAIMbpQCxRe0BuatSoEbEdgEj0h8cPgisAxDjNHiDqaVV7QLRt27ZFbAfgf+gPjy+0CgBAjNOUV6oO6Q9t9O5NXZ8/f75br+0A/A/94fGH4AoAMU59rNqluW7dOnvxxRft66+/tuzsbPdV17Vc65nPFYhEf3j8oVUAAHxAuzK1S1PVoZEjR4aWq9LKrk4gd/SHxx+CKwD4hMJps2bNOHMWkE/0h8cfftsBgI8opNavX99atGjhvhJagbzRHx5/+I0HAADiEv3h8YdWAQAAEPf94W+99VZEf3hKSgr94T7ERwwAAAD4AhVXAACQECcg6N69Oycg8DkqrgAAIC5xAoL4Q3AFAB/RH9gNGzbYqlWr3Ff+4AJ54wQE8YdWAQDw0S5PVYf0hzZIU/noqGh2dQLH4gQE8YfgCgA+69Pr1q0bfXpAPnACgvhDqwAAxDj69IDC4QQE8YfgCgAxjj49oHA4AUH8oVUAAGIcfXrAqT8BgSqttNj4Dx8xAMBHfXq5oU8PKLhAIOD1EFAIBFcAiHH06QEnf2Dj2WefbQMGDHBVV33VdS3XevgHwRUAYhx9ekDhcGBj/KHHFQB83KeXkpJCnx5wggMbNYVcXgc26v2k7erXr+/ZOJF/nn88/+677+x3v/ud281VpkwZa9q0qTsjTHgPyuDBg91BCVrfvn1727hxo6djBgAAsY8DG+OPp8H1p59+sksuucRKlSplc+bMsc8//9x98qlcuXJomyeffNKee+45Gz9+vK1YscLKli3rpoTRbjIASBT06QEFx4GN8cfT4DpixAirVauWTZ482S666CLXc6Kyfd26dUPV1tGjR9uDDz5onTp1smbNmtnLL7/sXmgzZ870cugAUGTo0wMKhwMb44+nwfWdd96xFi1a2PXXX29Vq1a1Cy+80F566aXQ+s2bN1tWVpZrDwiqWLGitWrVypYtW5brbR48eNCV/MMvAOBnnIAAKBwObIw/nh6cpRfOuHHjrF+/fvbAAw/YypUr7d5777WkpCTLyMhwoVWqVasW8X26HlwXbfjw4TZ06NAiGT8AFHWfnqpECqhapt2bqhTRpwfkjRMQxBdPg6t+Aavi+vjjj7vrqrjq04/6WRVcC2PQoEEuCAfpF7naEQDAr4L9d0uWLLEPPvjAVVfD//jqWIHw7QCcGCcg8CdPg6uqB+edd17EskaNGrlPRZKamuq+bt++PeKIQF3P6xNScnKyuwBAvFBVtVy5cq69Sv2smtpHvxN1wMncuXPd8vLly9OnBxznwEa9d7p37x5676jvVcupuvqLp00dqhKsX78+YtmGDRusdu3a7v868EDhddGiRREVVM0u0KZNmyIfLwDEUqUovGJE9Qg4Fgc2xh9Pg2vfvn1t+fLlrlVAPVtTp051zdK9evVy64sVK2Z9+vSxRx991FUU1q5da7fddpvr5+rcubOXQweAIqPfj/v377frrrvOVYrUp9e/f3/3Vde1XOs5OAuIxIGN8cfTVoGWLVvajBkzXF/qsGHD3CcgTX916623hra577777MCBA+6T0e7du61t27Zu15g+MQFAIggedHXZZZfZVVdddczBWTk5Oe7DPQdnAZE4AUH88fyUr9dee6275EVVV4VaXQAg0SdR1wf86FNTMok6kL/3TjTeO/7DxGUAEOOYRB0oHN478cfziisAIH+TqOsIaE0XeNZZZ9mhQ4fc6bJ37tzpTpetI6OZRB3I+72jY2jU06r2AFVaFVo1BSfvHX8huAKAD2i6nqZNm9qnn356zDqdDpvpfIDjn4BAswdwAgL/I7gCgA/oQFaFVs3XetFFF9mZZ55pP/zwg3300Uduudb/5je/8XqYQExSONUHvOgDG6m0+g/BFQBi3OHDh+3dd991oVXTA5Ys+b9f3ZoK68EHH3TrO3bsGLEOwP8opEYf2Aj/4aMGAMS4pUuXugNJcgumuq6ZWbRe2wFAPCO4AkCMU0uA6Cw/uQkuD24HAPGK4AoAMU79rKIjoHMTXB7cDgDiFcEVAGLcpZde6vrzZs2a5fpdw+n67Nmz3XptByB3aqfZsGGDrVq1yn2NntcV/kAXPwDEOPWxtmvXzhYuXOgOxFJPq9oDVGlVaN23b5+1b9+eA7OAPKxZs8ZNh7Vr166I6bA0xyvTYfkLv+UAwAeCU11p9oDXX389tFyVVoVWpsIC8g6tOgGBPux169bNqlev7k4Bq7NpaTlzufoLwRUAfELhVDMLaPYAHYilnla1B1BpBXKndgBVWhVae/bsGZq3tU6dOu66zqal9ZrjlTld/YGfEgD4sG3ghhtucF8JrUDedMIBtQekp6cfE0x1XaeA1XptB38guAIAgLiks2SJ2gNyU6NGjYjtEPv4qA4AYXJyciwrK8vrYcSN1NRUS0pK8noYSFA6tauop1XtAdG2bdsWsR1iH8EVAMIotI4YMcLrYcSNgQMHWlpamtfDQIKqV6+emz1AB2KF97gG+1/nz5/v1ms7+APBFQCiKoQKW7EerjMzMy0jI8ONN5bF+vgQ3xRUNeWVZg/QgVjqaVV7gCqtCq2aUk6zCnBgln8QXAEgjHZr+6VCqFDol7ECXtFUVwqnmj1g5MiRoeWqtDIVlv8QXAEAQFxTONWUV5o9QAdiqadV7QFUWv2H4AoAAOKeQmr9+vW9HgZOEh81AAAA4AsEVwAAAPgCwRUAAAC+QHAFAACALxBcAQAA4AsEVwAAAPgCwRUAAAC+QHAFAACALxBcAQAA4AsEVwAAAPgCwRUAAAC+QHAFAACAL5T0egAAAMDfcnJyLCsry+thxJXU1FRLSkryehgxx9Pg+vDDD9vQoUMjljVo0MC+/PJL9//s7Gzr37+/TZs2zQ4ePGjp6ek2duxYq1atmkcjBgAA0RRaR4wY4fUw4srAgQMtLS3N62HEHM8rro0bN7aFCxeGrpcs+b8h9e3b1/7xj3/Y9OnTrWLFinbPPfdYly5d7IMPPvBotAAAILfqoIKWHwJ2ZmamZWRkuDHHslgfn1c8D64Kqrn9cPbs2WOTJk2yqVOnWrt27dyyyZMnW6NGjWz58uXWunVrD0YLAACiaZe2n6qDyh1+Gi9i6OCsjRs3Wo0aNewXv/iF3XrrrbZlyxa3fPXq1Xbo0CFr3759aNuGDRu6F9qyZcvyvD21FOzduzfiAgAAAP/zNLi2atXKpkyZYnPnzrVx48bZ5s2b7Ve/+pXt27fPlfP1Ca5SpUoR36P+1uM1gA8fPty1FQQvtWrVKoJHAgAAgLhuFejQoUPo/82aNXNBtnbt2vbmm29amTJlCnWbgwYNsn79+oWuq+JKeAUAAPA/z1sFwqm6Wr9+fdu0aZPrP9H0Grt3747YZvv27cdtWE5OTrYKFSpEXAAAAOB/MRVc9+/fb1999ZVVr17dmjdvbqVKlbJFixaF1q9fv971wLZp08bTcQIAACDBWgUGDBhgHTt2dO0B27ZtsyFDhliJEiXs5ptvdv2pPXr0cLv9U1JSXOW0d+/eLrTGwowCP/74owvaKLxgrzKTVp8a5cqVc+8VAADilafB9dtvv3UhddeuXXbWWWdZ27Zt3VRX+r+MGjXKihcvbl27do04AUEshNZhw4a5WQ9w8jSnHk6e9lAMHjyY8AoAiFueBledEet4SpcubWPGjHGXWKJKq0JrzV+1suRK9NDCewd377Vv31vhXpsEVwBAvPL8BAR+ptBapgohAQAAIOEOzgIAAADyQnAFAACALxBcAQAA4AsEVwAAAPgCwRUAAAC+QHAFAACALxBcAQAA4AsEVwAAAPgCwRUAAAC+QHAFAACALxBcAQAA4AsEVwAAAPgCwRUAAAC+QHAFAACALxBcAQAA4AsEVwAAAPgCwRUAAAC+QHAFAACALxBcAQAA4AsEVwAAAPgCwRUAAAC+QHAFAACALxBcAQAA4AsEVwAAAMR3cM3JybH169fb4cOHT+2IAAAAgFMRXH/++Wfr0aOHnXHGGda4cWPbsmWLW967d2974oknCnpzAAAAwOkJroMGDbJPPvnEFi9ebKVLlw4tb9++vb3xxhsFvTkAAAAgX0paAc2cOdMF1NatW1uxYsVCy1V9/eqrrwp6cwAAAMDpqbju3LnTqlateszyAwcORARZAAAAwNPg2qJFC/vHP/4Ruh4MqxMnTrQ2bdqc0sEBAAAAhW4VePzxx61Dhw72+eefuxkFnn32Wff/Dz/80JYsWVLQmwMAAABOT8W1bdu2tmbNGhdamzZtavPnz3etA8uWLbPmzZsX9OYAAACA0zePa926de2ll16yjz76yFVbX331VRdiT4am0lLbQZ8+fULLsrOzrVevXlalShUrV66cde3a1bZv335S9wMAAIAECa579+7N9bJv3z53UoLCWLlypU2YMMGaNWsWsbxv3742a9Ysmz59umtD2LZtm3Xp0qVQ9wEAAIAEC66VKlWyypUrH3PR8jJlyljt2rVtyJAhdvTo0Xzd3v79++3WW291FVzdTtCePXts0qRJ9swzz1i7du1cG8LkyZNdL+3y5csLOmwAAAAkWnCdMmWK1ahRwx544AE3p6su+v/ZZ59t48aNs549e9pzzz2X77NoqRXgmmuucScwCLd69Wo7dOhQxPKGDRtaWlqa66fNy8GDB4+pBgMAACABZxXIzMy0kSNH2g033BBa1rFjR9fjqt39ixYtcuHysccec4H2eKZNm2b//ve/XatAtKysLEtKSnKV3HDVqlVz6/IyfPhwGzp0aEEfFgAAAOKt4qpd9RdeeOExy7UsWAnVzANbtmw57u1s3brV/vSnP9lrr70WcerYk6VT0qrNIHjR/QAAACABg2utWrVc72k0LdM62bVrV0S/am7UCrBjxw775S9/aSVLlnQXHYClNgP9X5VVHey1e/fuiO/TrAKpqal53m5ycrJVqFAh4gIAAIAEbBV4+umn7frrr7c5c+ZYy5Yt3bJVq1bZF198YW+99Za7rl3/N95443Fv58orr7S1a9dGLOvWrZvrYx04cKALwaVKlXKtB5oGS9avX+8quZyhCwAAIPEUOLhed911LkCOHz/eNmzY4JbpTFo6SEszBMhdd911wtspX768NWnSJGJZ2bJl3ZytweU9evSwfv36WUpKiquc9u7d24XW1q1bF3TYAAAASLTgKuecc05o1gAdtf/666+7Cqsqr0eOHDllgxs1apQVL17cVVw1W0B6erqNHTv2lN0+AAAA4jy4ytKlS11fq9oDND2WTgzwwgsvnNRgFi9eHHFdB22NGTPGXQAAAJDYChRcNQ2V5nFVYFWlVVNiqRKqNoHzzjvv9I0SAAAACS/fswportYGDRrYp59+aqNHj3anX33++edP7+gAAACAglZcNYvAvffe6w68Ovfcc/P7bQAAAEDRVlzff/9927dvnzVv3txatWrl+ll/+OGHUzMKAAAA4FQFV01B9dJLL9n3339vd9xxhztdqw7KOnr0qC1YsMCFWgAAACBmZhXQXKvdu3d3F83nqgO1NDXW/fffb1dddZW98847ligO7t7r9RAAh9ciACARFHo6LNHBWk8++aQNHz7cZs2aZX/9618tkXz73gqvhwAAAJAwTiq4BpUoUcI6d+7sLomk5q9aWXKlCl4PA3AVVz5IAQDi3SkJrolKobVMlRSvhwEAAJAQ8n1wFgAAAOAlKq4AisyPP/5o+/fv93oYvqezGIZ/xckpV66cpaSw9wzwA4IrgCILrY88Msxycg55PZS4kZmZ6fUQ4kJSUil76KHBhFfABwiuAIqEKq0Krbfc0sKqVi3v9XAAZ8eOfTZ16ir3+iS4ArGP4AqgSCm01qxZ2ethAAB8iIOzAAAA4AsEVwAAAPgCwRUAAAC+QHAFAACALxBcAQAA4AsEVwAAAPgCwRUAAAC+QHAFAACALxBcAQAA4AsEVwAAAPgCwRUAAAC+QHAFAACALxBcAQAA4AslvR4AAAA4vh9//NH279/v9TB8LysrK+IrCq9cuXKWkpJiRY3gCgBAjIfWYcOG2aFDh7weStzIzMz0egi+V6pUKRs8eHCRh1eCKwAAMUyVVoXWVhddZRXKV/Z6OIDt3feTrfhogXttElwBAMAxFFpTKlf1ehiApzg4CwAAAL5AcAUAAIAveBpcx40bZ82aNbMKFSq4S5s2bWzOnDmh9dnZ2darVy+rUqWKO3qta9eutn37di+HDAAAgEQMrjVr1rQnnnjCVq9ebatWrbJ27dpZp06d7LPPPnPr+/bta7NmzbLp06fbkiVLbNu2bdalSxcvhwwAAACPeHpwVseOHSOuP/bYY64Ku3z5chdqJ02aZFOnTnWBViZPnmyNGjVy61u3bu3RqAEAAJDQPa5HjhyxadOm2YEDB1zLgKqwmv6jffv2oW0aNmxoaWlptmzZsjxv5+DBg7Z3796ICwAAAPzP8+C6du1a17+anJxsd955p82YMcPOO+88d1aLpKQkq1SpUsT21apVO+4ZL4YPH24VK1YMXWrVqlUEjwIAAABxH1wbNGhga9assRUrVthdd91lGRkZ9vnnnxf69gYNGmR79uwJXbZu3XpKxwsAAABveH4CAlVV69Wr5/7fvHlzW7lypT377LN24403Wk5Oju3evTui6qpZBVJTU/O8PVVudQEAAEB88bziGu3o0aOuT1UhVufBXbRoUWjd+vXrbcuWLa4HFgAAAInF04qrdut36NDBHXC1b98+N4PA4sWLbd68ea4/tUePHtavXz93HlzN89q7d28XWplRAPCvHTv2eT0EIITXI+AvngbXHTt22G233Wbff/+9C6o6GYFC61VXXeXWjxo1yooXL+5OPKAqbHp6uo0dO9bLIQM4SVOnrvJ6CAAAn/I0uGqe1uMpXbq0jRkzxl0AxIdbbmlhVauW93oYQKjiyocpwD88PzgLQGJRaK1Zs7LXwwAA+FDMHZwFAAAA5IbgCgAAAF8guAIAAMAXCK4AAADwBQ7OOgkHd+/1egiAw2sRAJAICK6FUK5cOXdWr2/fW+H1UIAQvSb12gQAIF4RXAtBZ/IaPHiw7d+/3+uh+FpWVpZlZmZaRkaGpaamej0c31No1WsTAIB4RXAtJAUEQsKpodCq0/4CAAAcDwdnAQAAwBcIrgAAAPAFgisAAAB8geAKAAAAXyC4AgAAwBcIrgAAAPAFgisAAAB8geAKAAAAXyC4AgAAwBcIrgAAAPAFTvkKAIAP7N37k9dDADx/LRJcAQDwgRUrF3g9BMBzBFcAAHygVcurrEKFyl4PAzBVXL36IEVwBQDABxRaUypX9XoYgKcIrgCK1I4d+7weAhDC6xHwF4IrgCJRrlw5S0oqZVOnrvJ6KEAEvS71+gQQ+wiuAIpESkqKPfTQYNu/f7/XQ/G9rKwsy8zMtIyMDEtNTfV6OL6n0KrXJ4DYR3AFUGQUDggIp45Ca1pamtfDAIAiwwkIAAAA4AsEVwAAAPgCwRUAAAC+QHAFAACALxBcAQAA4AsEVwAAAPiCp8F1+PDh1rJlSytfvrxVrVrVOnfubOvXr4/YJjs723r16mVVqlRxc+117drVtm/f7tmYAQAAkIDBdcmSJS6ULl++3BYsWGCHDh2yq6++2g4cOBDapm/fvjZr1iybPn26237btm3WpUsXL4cNAACARDsBwdy5cyOuT5kyxVVeV69ebZdeeqnt2bPHJk2aZFOnTrV27dq5bSZPnmyNGjVyYbd169YejRwAAAAJ3eOqoCrBM+sowKoK2759+9A2DRs2dGeKWbZsWa63cfDgQdu7d2/EBQAAAP4XM8H16NGj1qdPH7vkkkusSZMmofNxJyUlWaVKlSK2rVatmluXV99sxYoVQ5datWoVyfgBAACQIMFVva7r1q2zadOmndTtDBo0yFVug5etW7eesjECAAAgQXtcg+655x6bPXu2LV261GrWrBlanpqaajk5ObZ79+6IqqtmFdC63CQnJ7sLAAAA4ounFddAIOBC64wZM+zdd9+1OnXqRKxv3ry5lSpVyhYtWhRapumytmzZYm3atPFgxAAAAEjIiqvaAzRjwNtvv+3mcg32rao3tUyZMu5rjx49rF+/fu6ArQoVKljv3r1daGVGAQAAgMTiaXAdN26c+3r55ZdHLNeUV7fffrv7/6hRo6x48eLuxAOaMSA9Pd3Gjh3ryXgBAACQoMFVrQInUrp0aRszZoy7AAAAIHHFzKwCAAAAQMzPKgAAAI5v776fvB4C4PlrkeAKAEAMK1eunJthZ8VHC7weChCi16Rem0WN4AoAQAzTrDqDBw+2/fv3ez0U39PsRZmZmZaRkZHnfPDIH4VWvTaLGsEVAIAYp4DgRUiIVwqtaWlpXg8DhcDBWQAAAPAFgisAAAB8geAKAAAAXyC4AgAAwBcIrgAAAPAFgisAAAB8geAKAAAAXyC4AgAAwBcIrgAAAPAFgisAAAB8geAKAAAAXyC4AgAAwBcIrgAAAPAFgisAAAB8geAKAAAAXyC4AgAAwBcIrgAAAPAFgisAAAB8geAKAAAAXyC4AgAAwBcIrgAAAPAFgisAAAB8geAKAAAAXyC4AgAAwBcIrgAAAPAFgisAAAB8geAKAAAAXyC4AgAAwBc8Da5Lly61jh07Wo0aNaxYsWI2c+bMiPWBQMAGDx5s1atXtzJlylj79u1t48aNno0XAAAACRpcDxw4YOeff76NGTMm1/VPPvmkPffcczZ+/HhbsWKFlS1b1tLT0y07O7vIxwoAAABvlfTyzjt06OAuuVG1dfTo0fbggw9ap06d3LKXX37ZqlWr5iqzN910UxGPFgAAAF6K2R7XzZs3W1ZWlmsPCKpYsaK1atXKli1bluf3HTx40Pbu3RtxAQAAgP/FbHBVaBVVWMPpenBdboYPH+4CbvBSq1at0z5WAAAAJHBwLaxBgwbZnj17QpetW7d6PSQAAADEc3BNTU11X7dv3x6xXNeD63KTnJxsFSpUiLgAAADA/2I2uNapU8cF1EWLFoWWqV9Vswu0adPG07EBAAAgwWYV2L9/v23atCnigKw1a9ZYSkqKpaWlWZ8+fezRRx+1c8891wXZhx56yM352rlzZy+HDQAAgEQLrqtWrbIrrrgidL1fv37ua0ZGhk2ZMsXuu+8+N9drz549bffu3da2bVubO3eulS5d2sNRAwAAIOGC6+WXX+7ma82LzqY1bNgwdwEAAEBii9keVwAAACAcwRUAAAC+QHAFAACALxBcAQAA4AsEVwAAAPgCwRUAAAC+QHAFAACALxBcAQAA4AsEVwAAAPgCwRUAAAC+QHAFAACALxBcAQAA4AsEVwAAAPgCwRUAAAC+QHAFAACALxBcAQAA4AsEVwAAAPgCwRUAAAC+UNLrAQBALMnJybGsrCyLZcHxxfo4JTU11ZKSkrweBoA4QXAFgDAKgyNGjDA/yMzMtFg3cOBAS0tL83oYAOIEwRUAoiqEClux6K233rJNmzYds7xevXrWtWtXi9XnEwBOFYIrAITRbu1YrBBOmDDBhdYSJUrYlVdeaW3atLFly5bZokWL3PI5c+bYHXfc4fUwAeC0IrgCgA/6bj/99FMXWp9++ulQz2inTp2sQ4cONmDAALde29FPCiCeMasAAMS4GTNmuK+qtBYvXtzeffdde/PNN91XXW/Xrl3EdgAQr6i4AkCM27lzp/u6b98+69u3rx09ejS0TmG1VatWEdsBQLwiuAJAjDvrrLPsiy++cD2t5cuXt44dO1qTJk1s3bp1NmvWLLc8uB0AxDNaBQAgximoBg0ePNguueQSq1ixovuq67ltBwDxiOAKADFu+fLlof/ff//9NnPmTNu+fbv7quu5bQcA8YhWAQCIcT/88IP72rBhQ/vyyy9twYIF7hIUXB7cDgDiFRVXAIhxZ555pvu6Y8eOXNcHlwe3A4B4RXAFgBh36aWXuq8//vhjruuDy4PbAUC8olUAAGLc4cOHQ/8vVqyYtW/fPnTmrIULF1ogEAhtV7Ikv9YBxC9+w8UpnUEnKyvLYllwfLE+zvBzrnNWInhhypQp7mtycrJ7b4f3uCrI6nWp5druzjvv9Hi0AJDgwXXMmDH21FNPuYBz/vnn2/PPP28XXXSR18OKaXquRowYYX6QmZlpfjBw4MCYPIc94t+2bdvc1xtvvNGaN29uS5cudQdiqadV7QErV660V199NbQdAMSrmA+ub7zxhvXr18/Gjx/vzg4zevRoS09Pt/Xr11vVqlW9Hl5MVwcVtHBqn1PAC2eccYbt2rXLPvvsM/d7MHiK16DPP/88tB3gBT/s5fPbnj728uWuWCDYHBWj9Eu6ZcuW9sILL7jrOtVhrVq1rHfv3hHzF+Zl7969bqLuPXv2WIUKFYpgxABwaq1du9Z9eJeRI0da6dKlQ+uys7Otf//+7v9qE2jatKln40Ti2rJli2/28vlFou3l25vPvFYy1j/BrV692gYNGhRaVrx4cXdgQvAUh9EOHjzoLuFPBAD4WePGja1EiRJ25MgRF1L1YV5V13fffde1CYgOytJ2gBfYy3fqsZfPh8FVPVz6RV2tWrWI5bquybZzM3z4cBs6dGgRjRAATj99YO/evbu99NJL7rrCajCwBnXr1s1tB3hBu7QTqToI78TdbzlVZ1VmDl62bt3q9ZAA4KRdcMEF9sc//tEqVaoUsbxy5cpuudYDQLyL6YqrjpjV7jGdkzucrudVQtd0MboAQLxROG3WrJlt2rTJtUGpD6xevXpUWgEkjOKxvutBU78sWrQotEwHZ+m6Jt8GgESjkFq/fn1r0aKF+0poBZBIYrriKpoKKyMjw/2S1tytmg7rwIEDrp8LAAAAiSPmg6sm3N65c6cNHjzYzbumXWVz58495oAtAAAAxLeYn8f1ZDGPKwAAQHzkNZqjAAAA4AsEVwAAAPgCwRUAAAC+QHAFAACALxBcAQAA4AsEVwAAAPgCwRUAAAC+QHAFAACAL8T8mbNOVvD8CprYFgAAALEnmNNOdF6suA+u+/btc19r1arl9VAAAABwgtymM2gl7Clfjx49atu2bbPy5ctbsWLFvB4Ooj5d6QPF1q1bOR0vUAC8d4DC4b0TuxRHFVpr1KhhxYsXT9yKqx58zZo1vR4GjkO/PPgFAhQc7x2gcHjvxKbjVVqDODgLAAAAvkBwBQAAgC8QXOGZ5ORkGzJkiPsKIP947wCFw3vH/+L+4CwAAADEByquAAAA8AWCKwAAAHyB4AoAAABfILgCAADAFwiuKHJLly61jh07urNj6GxmM2fO9HpIgC8MHz7cWrZs6c4EWLVqVevcubOtX7/e62EBMW3cuHHWrFmz0EkH2rRpY3PmzPF6WCgkgiuK3IEDB+z888+3MWPGeD0UwFeWLFlivXr1suXLl9uCBQvs0KFDdvXVV7v3FIDc6eyZTzzxhK1evdpWrVpl7dq1s06dOtlnn33m9dBQCEyHBU+p4jpjxgxXOQJQMDt37nSVVwXaSy+91OvhAL6RkpJiTz31lPXo0cProaCAShb0GwAAsWHPnj2hP8IATuzIkSM2ffp0t5dCLQPwH4IrAPjQ0aNHrU+fPnbJJZdYkyZNvB4OENPWrl3rgmp2draVK1fO7ek777zzvB4WCoHgCgA+pF7XdevW2fvvv+/1UICY16BBA1uzZo3bS/G3v/3NMjIyXIsN4dV/CK4A4DP33HOPzZ49283QoQNPABxfUlKS1atXz/2/efPmtnLlSnv22WdtwoQJXg8NBURwBQCf0LG0vXv3drs5Fy9ebHXq1PF6SIBvW20OHjzo9TBQCARXFLn9+/fbpk2bQtc3b97sduHoAJO0tDRPxwbEenvA1KlT7e2333ZzuWZlZbnlFStWtDJlyng9PCAmDRo0yDp06OD+vuzbt8+9h/TBb968eV4PDYXAdFgocvqFccUVVxyzXD1HU6ZM8WRMgF+mj8vN5MmT7fbbby/y8QB+oCmvFi1aZN9//737kKeTEQwcONCuuuoqr4eGQiC4AgAAwBc4cxYAAAB8geAKAAAAXyC4AgAAwBcIrgAAAPAFgisAAAB8geAKAAAAXyC4AgAAwBcIrgAAAPAFgisAIN90hq7OnTt7PQwACYrgCiCuQ5ZOk6pLUlKS1atXz4YNG2aHDx/2emgxSSdSfPHFF61Vq1ZWrlw5q1SpkrVo0cJGjx5tP//8s9fDAwCCK4D49utf/9qdo3zjxo3Wv39/e/jhh+2pp56yRJWTk5Pnut///vfWp08f69Spk/3rX/+yNWvW2EMPPWRvv/22zZ8/v0jHCQC5IbgCiGvJycmWmppqtWvXtrvuusvat29v77zzjlv3zDPPWNOmTa1s2bJWq1Ytu/vuu23//v2h7/3mm2+sY8eOVrlyZbdN48aN7Z///Kdb99NPP9mtt95qZ511lpUpU8bOPfdcmzx5cuh7t27dajfccIOrWqakpLgw+J///OeYXe5PP/20Va9e3apUqWK9evWyQ4cOhbZR4L7mmmvc7depU8emTp1q55xzjquABu3evdv+8Ic/uHFUqFDB2rVrZ5988klovYL6BRdcYBMnTnS3Ubp06VyfpzfffNNee+01e/311+2BBx6wli1buvvSuN9991274oorcv2+uXPnWtu2bd3j1GO49tpr7auvvooIyvfcc497jLpv/RyGDx8eqvBqfGlpae7nVKNGDbv33nsL+BMGkEhKej0AAChKCoG7du1y/y9evLg999xzLtB9/fXXLrjed999NnbsWLdeQVLBa+nSpS64fv75524XuqgSqetz5syxM8880zZt2mT//e9/3TqFz/T0dGvTpo299957VrJkSXv00Udd9ffTTz91bQuiqqYCnb7q+2+88UYXMv/4xz+69bfddpv98MMPtnjxYitVqpT169fPduzYEfF4rr/+eveYNI6KFSvahAkT7Morr7QNGza4wCy67bfeesv+/ve/W4kSJXJ9XhRaGzRo4IJqNLVa6LZzc+DAATeuZs2audA/ePBg+81vfuOqtcHnVx8UFIwVUBXodRGNadSoUTZt2jT3oSArKysidAPAMQIAEKcyMjICnTp1cv8/evRoYMGCBYHk5OTAgAEDct1++vTpgSpVqoSuN23aNPDwww/num3Hjh0D3bp1y3XdK6+8EmjQoIG7z6CDBw8GypQpE5g3b15obLVr1w4cPnw4tM31118fuPHGG93/v/jii4B+Ra9cuTK0fuPGjW7ZqFGj3PX33nsvUKFChUB2dnbE/detWzcwYcIE9/8hQ4YESpUqFdixY8dxn6tGjRoFrrvuuuNuE/2c5mbnzp1ujGvXrnXXe/fuHWjXrl3EcxE0cuTIQP369QM5OTknvF8AEFoFAMS12bNnuyqpdlN36NDBVTW1e1oWLlzoqpNnn322lS9f3vV4qhobPBBJu61VKb3kkktsyJAhrloapLYDVQpVIVWV9sMPPwytU9VQVU7dpu5bF1U/s7OzI3ajq8oYXgFV9TVYUV2/fr2r1P7yl78MrdfBZWpbCL8fVTm1iz54P7ps3rw54n60e16tBMej3faFod7hm2++2X7xi1+4VgW1F8iWLVtCLRGqvqqaq+czvFdW1WJVqfW9qjLPmDGDA+cAHBfBFUBcU2+mgpMClkJSZmam2+2vflP1Y2oXt3ZZr1692saMGRNxAJN6R9VCoEC7du1ad4T9888/79YpBKsHtm/fvrZt2zYXgAcMGODWKUw2b97c3W/4Rbvvb7nlltDYtPs/epf80aNH8/3YdD8Ku9H3o9D75z//ObSdHu+J1K9f37788ksrKPUA//jjj/bSSy/ZihUr3CX8OVTwVpB+5JFH3POvvt/f/va3bp36ijVWtWao3UGtGpdeemlEny8AhCO4AohrCm2qVKq/UhXMIAVVhcSRI0da69atXXBTAI2mcHXnnXe6/lDNSqCAFqQqZkZGhr366qvugClNJRUMawrKVatWdfcdfsmrVzSaKpSqPn788cehZari6qCwIN2P+kL1uKLvR323BaFArWCtGQRyq8bu2bPnmOWqTit4Pvjggy64N2rUKGJ8QarEqtKt5+6NN95wHxQUdkWBVeFXvbDq5V22bJn7kAAAuSG4AkhICneq7KmCqqrqK6+8YuPHj4/YRlNDzZs3z1UM//3vf7uDqBTORAchKeQpTH722WeuJSG4TrMNKDjqQCcdnKXvVyjTrvJvv/02X+Nr2LChmwGhZ8+e9tFHH7kAq/8r6KkyK1qvA8A0O4F2wauKrJaFv/zlL7Zq1aoCPR+qhCpcarf/448/7r5fFWU9Lt2PHns0tS2oTUGBXc+DZh/QgVrhNHODZipQNVfBePr06W6WB81CMGXKFJs0aZKtW7fO/Qz0AUCPT60NAJAbgiuAhHT++ee7UDVixAhr0qSJO6o+OE1T0JEjR9zMAgqkmhFAVdngjAOaGWDQoEGu1UC7t9Wrqp5XOeOMM9xMBKrydunSxX1/jx49XI+rqo/59fLLL1u1atXc7etIffWBqm82OKWVAqym59L6bt26ufHddNNNLnDq+wpCt6XptvSczJw50y677DL32NQPrACuWRKiadYAPWZVr/Ucqm0ieo5cjffJJ590bRaaYkvhWmPW9yq8qgqrHmLdl3qOZ82a5cIwAOSmmI7QynUNACCmqFqr1oXgQWUAkGgIrgAQo7TrXQdg6SQJOhmBZi/47rvv3C736AO7ACARcAICAIhR6sHVWazU/6ld7hdffLFraSC0AkhUVFwBAADgCxycBQAAAF8guAIAAMAXCK4AAADwBYIrAAAAfIHgCgAAAF8guAIAAMAXCK4AAADwBYIrAAAAzA/+H9MtpwIwu5q5AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 5))\n",
    "sns.boxplot(x=\"Pclass\", y=\"Age\", data=df, palette=\"Set3\")\n",
    "plt.title(\"Age Distribution by Passenger Class\")\n",
    "plt.xlabel(\"Passenger Class\")\n",
    "plt.ylabel(\"Age\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8351280a-3b55-4fd9-a37d-b35f856742c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 5))\n",
    "sns.scatterplot(x=\"Age\", y=\"Fare\", hue=\"Survived\", data=df, alpha=0.7, palette=\"coolwarm\")\n",
    "plt.title(\"Fare vs Age (Survival)\")\n",
    "plt.xlabel(\"Age\")\n",
    "plt.ylabel(\"Fare\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7ae371d4-0861-4d4c-b4a2-b6993602cf4b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 5))\n",
    "df.groupby(\"Pclass\")[\"Fare\"].mean().plot(marker=\"o\", linestyle=\"-\", color=\"purple\")\n",
    "plt.title(\"Average Fare by Class\")\n",
    "plt.xlabel(\"Passenger Class\")\n",
    "plt.ylabel(\"Average Fare\")\n",
    "plt.grid(True)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5a63c760-b578-46d2-9167-29c6053435a9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ea0dd29d-d47c-40f8-b78f-aab2c99215f5",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}