{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "ca385c14-bb36-4099-ae6d-3514f3d60e29",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>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": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# imports\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "# load data\n",
    "df = pd.read_csv(\"Titanic-Dataset.csv\")\n",
    "\n",
    "#preview first 5 \n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "b1a9c24f-d548-4985-8d61-8b9a79a89e2f",
   "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": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get data type \n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "fb8fa071-98db-4976-a5cd-ada44b19585f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>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": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#data summery \n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "322f32f9-b969-4173-bb10-7a8084997a19",
   "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": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# count of missing values for each catagoriee \n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "db56298c-8c45-4b33-9e8e-eec5b45e6b1d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " to drop: ['Cabin']\n",
      " New : ['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp', 'Parch', 'Ticket', 'Fare', 'Embarked']\n"
     ]
    }
   ],
   "source": [
    "# first make a copy\n",
    "\n",
    "# check colomns with 30% misssing values, and drop them \n",
    "missing_percentage = df.isnull().mean() * 100\n",
    "\n",
    "th = 30\n",
    "\n",
    "#filter with more than th % \n",
    "culumsToDrop = missing_percentage[missing_percentage >th].index\n",
    "\n",
    "print(f\" to drop: {list(culumsToDrop)}\")\n",
    "\n",
    "#dropping\n",
    "df = df.drop(columns = culumsToDrop)  \n",
    "\n",
    "print(f\" New : {list(df.columns)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "e9e33d88-e61f-40d1-8fee-50892f9cdc96",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\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": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# preprocessing the data \n",
    "# converting object data to numarical data \n",
    "\n",
    "label_incoder = LabelEncoder()\n",
    "for column in df.select_dtypes(include = ['object']).columns:\n",
    "    df[column] = label_incoder.fit_transform(df[column])\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "db81ad24-791f-448e-bcf2-474f33cfd380",
   "metadata": {},
   "outputs": [],
   "source": [
    "# handeling missing values for numarical data using mean\n",
    "for column in df.select_dtypes(include = ['float64']).columns:\n",
    "    fillValue = df[column].mean() \n",
    "    df[column] = df[column].fillna(fillValue)\n",
    "for column in df.select_dtypes(include = ['int64']).columns:\n",
    "    fillValue = df[column].mean() \n",
    "    df[column] = df[column].fillna(fillValue)\n",
    "\n",
    "# handeling missing values for catagorical data using mode\n",
    "for column in df.select_dtypes(include = ['object']).columns:\n",
    "    fillValue = df[column].mode()[0]\n",
    "    df[column] = df[column].fillna(fillValue)\n",
    "\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "96e04299-de38-472e-a2cf-51028f78c55e",
   "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": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "56467830-6a15-4209-84fa-11a6a46ed85b",
   "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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       "<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>22.000000</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>38.000000</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>26.000000</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>35.000000</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>35.000000</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",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>887</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>548</td>\n",
       "      <td>1</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>101</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>888</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>303</td>\n",
       "      <td>0</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>889</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>413</td>\n",
       "      <td>0</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>675</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>890</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>81</td>\n",
       "      <td>1</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>891</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>220</td>\n",
       "      <td>1</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>466</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass  Name  Sex        Age  SibSp  Parch  \\\n",
       "0              1         0       3   108    1  22.000000      1      0   \n",
       "1              2         1       1   190    0  38.000000      1      0   \n",
       "2              3         1       3   353    0  26.000000      0      0   \n",
       "3              4         1       1   272    0  35.000000      1      0   \n",
       "4              5         0       3    15    1  35.000000      0      0   \n",
       "..           ...       ...     ...   ...  ...        ...    ...    ...   \n",
       "886          887         0       2   548    1  27.000000      0      0   \n",
       "887          888         1       1   303    0  19.000000      0      0   \n",
       "888          889         0       3   413    0  29.699118      1      2   \n",
       "889          890         1       1    81    1  26.000000      0      0   \n",
       "890          891         0       3   220    1  32.000000      0      0   \n",
       "\n",
       "     Ticket     Fare  Embarked  \n",
       "0       523   7.2500         2  \n",
       "1       596  71.2833         0  \n",
       "2       669   7.9250         2  \n",
       "3        49  53.1000         2  \n",
       "4       472   8.0500         2  \n",
       "..      ...      ...       ...  \n",
       "886     101  13.0000         2  \n",
       "887      14  30.0000         2  \n",
       "888     675  23.4500         2  \n",
       "889       8  30.0000         0  \n",
       "890     466   7.7500         1  \n",
       "\n",
       "[891 rows x 11 columns]"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# display data frame \n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "df218e67-02b2-4759-961b-d1fbe6ae35f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#visulization \n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "plt.figure(figsize=(12,6))\n",
    "\n",
    "for i,col in enumerate(df.columns[:3]): #only first 3 features\n",
    "    plt.subplot(1,3,i+1)\n",
    "    sns.boxplot(x = df[col])\n",
    "    plt.title(f'blot box for {col}') \n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "bfeafeea-e3b7-4d60-ac2e-98a6d5bef6fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Function to detect and replace outliers using IQR\n",
    "def detect_outliers_iqr(column):\n",
    "    q1 = column.quantile(0.25)\n",
    "    q3 = column.quantile(0.75)\n",
    "    iqr = q3 - q1\n",
    "\n",
    "\n",
    "    lower_b = q1 - (1.5 * iqr)\n",
    "    upper_b = q3 + (1.5 * iqr)\n",
    "\n",
    "    if iqr == 0:  # Skip low variance columns\n",
    "        return column\n",
    "         \n",
    "    # Replace outliers with NaN  \n",
    "    return column.where((column >= lower_b) & (column <= upper_b), np.nan)\n",
    "\n",
    "# Apply only to numerical columns\n",
    "for col in df.columns:\n",
    "    df[col] = detect_outliers_iqr(df[col])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "231192ef-c38b-4718-b35c-505a3b7fe9d3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PassengerId      0\n",
      "Survived         0\n",
      "Pclass           0\n",
      "Name             0\n",
      "Sex              0\n",
      "Age             66\n",
      "SibSp           46\n",
      "Parch            0\n",
      "Ticket           0\n",
      "Fare           116\n",
      "Embarked         0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# check if any Null value is present from outlaiers\n",
    "null_count = df.isnull().sum()\n",
    "print(null_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "256f17e6-6ae3-4114-b471-e7396c1d594b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# handeling missing values for numarical data using mean\n",
    "for column in df.select_dtypes(include = ['float64']).columns:\n",
    "    fillValue = df[column].mean() \n",
    "    df[column] = df[column].fillna(fillValue)\n",
    "for column in df.select_dtypes(include = ['int64']).columns:\n",
    "    fillValue = df[column].mean() \n",
    "    df[column] = df[column].fillna(fillValue)\n",
    "\n",
    "# handeling missing values for catagorical data using mode\n",
    "for column in df.select_dtypes(include = ['object']).columns:\n",
    "    fillValue = df[column].mode()[0]\n",
    "    df[column] = df[column].fillna(fillValue)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "624386f3-0079-426b-82ba-8d131d529304",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "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\n"
     ]
    }
   ],
   "source": [
    "# check if any Null value is present from outlaiers\n",
    "null_count = df.isnull().sum()\n",
    "print(null_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "id": "78b45712-ec77-4647-90f3-e9cab7593acb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Data visulization \n",
    "\n",
    "plt.figure(figsize=(8,6))\n",
    "sns.histplot(df['Pclass'], bins=15, kde = True, color = 'blue')\n",
    "plt.title('Passingers class count')\n",
    "plt.xlabel('Passinger class')\n",
    "plt.ylabel('Count')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "id": "de17624a-d687-48de-915f-d4541b59be18",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Administrator\\AppData\\Local\\Temp\\ipykernel_30312\\129169255.py:4: 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.countplot(x = 'Survived', data = df, palette ='Set1')\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Data visulization 2\n",
    "\n",
    "plt.figure(figsize=(8,6))\n",
    "sns.countplot(x = 'Survived', data = df, palette ='Set1')\n",
    "plt.title('Surcvival Count')\n",
    "plt.xlabel('Survived')\n",
    "plt.ylabel('Count')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "id": "d89378b0-07c0-459c-90eb-b4d018732511",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Data visulization 3\n",
    "\n",
    "plt.figure(figsize=(8,6))\n",
    "sns.scatterplot(x='Age', y='Fare', data = df ,hue='Pclass',  palette ='Set2')\n",
    "plt.title('Age Vs Fare')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Fare')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "id": "5e6e7abe-8e4a-45f1-925b-e201709f7ced",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Count')"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,6))\n",
    "sns.countplot(x='Pclass', hue='Survived', data=df, palette='Set2')\n",
    "plt.title('Survival Count by Passenger Class')\n",
    "plt.ylabel('Count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "id": "ab6babda-1c73-4276-8dbc-92d00fa2468f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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jxEcffeR/n0WLFokxY8aItLQ0odVqRWZmprjqqqvE5s2bAz7v+PHj4vbbbxe5ublCo9GIxMREMXDgQHH//feL2tpaIcQvuw08+uijjepFE1fqP//886Jz585Cq9WKLl26iJdeeklMmTJF9O/fP+A4t9stHnvsMdG3b1+h1+uFyWQS3bp1EzfffLPYs2eP/7icnBwxceLE047xqT788EMxceJEkZKSItRqtUhISBBjxowRzz33nHA6nUF/BqfTKe666y7Rrl07odfrxYABA8QHH3wgpk2bFrBjwpmM8b333isGDRokEhIS/P9O77zzTlFWVnbWPw8RtZwkxCm7ZRMRtSEff/wxfvWrX+HTTz/FxRdfrHQ5Ea+qqgpdunTBJZdcgueff17pcoioDWJ4JaI2afv27SgqKsIdd9yBuLg4bNiwocUb4ceqkpIS/N///R/GjBmDpKQkFBUV4YknnsDOnTuxbt06/04GREThxDWvRNQmTZ8+Hd9//z0GDBiARYsWMbg2QafT4cCBA5g+fToqKipgNBoxZMgQPPfccwyuRKQYzrwSERERUdTgVllEREREFDUYXomIiIgoajC8EhEREVHUiPkLtnw+H44ePQqz2cwLMoiIiIgikBACNTU1yMzMDLgJSlNiPrwePXoUWVlZSpdBRERERKdx6NCh094NMObDq9lsBtAwGBaLReFqiIiIiOhUNpsNWVlZ/tzWnJgPryeWClgsFoZXIiIiogh2Jks8ecEWEREREUUNhlciIiIiihoMr0REREQUNWJ+zSsRERFROAkh4PF44PV6lS4lYqhUKqjV6pBsW8rwSkRERBQiLpcLxcXFqKurU7qUiGM0GpGRkQGtVntO78PwSkRERBQCPp8PhYWFUKlUyMzMhFar5Q2S0DAT7XK5cPz4cRQWFiIvL++0NyJoDsMrERERUQi4XC74fD5kZWXBaDQqXU5EMRgM0Gg0KCoqgsvlgl6vb/F78YItIiIiohA6l1nFWBaqceHoEhEREVHUYHglIiIioqjB8EpEREQUw5YvXw5JklBVVdWqn3PDDTfgkksuadXPABheiYiIiMKitLQUN998M7Kzs6HT6ZCeno4LL7wQq1evbtXPHTp0KIqLi2G1Wlv1c8KFuw0QERERhcHll18Ot9uNRYsWoWPHjjh27Bi+/vprVFRUtOj9hBDwer1Qq5uPc1qtFunp6S36jEjEmVcioijis1dDVBRDlB+Fr7ZK6XKI6AxVVVXhu+++w/z58zFmzBjk5OTgvPPOw6xZszBx4kQcOHAAkiRh06ZNAa+RJAnLly8H8Muf/7/44gsMGjQIOp0OL774IiRJws6dOwM+7/HHH0eHDh0ghAhYNlBdXQ2DwYAlS5YEHP/+++8jLi4OtbW1AIAjR47g6quvRkJCApKSkjBlyhQcOHDAf7zX68XMmTMRHx+PpKQk3H333RBCtMrYnYrhlYgoCvh8PojjhyA+ewG+hX+Fb9HfID59DuLYAfg8bqXLI6LTMJlMMJlM+OCDD+B0Os/pve6++27MmzcPO3bswBVXXIGBAwfitddeCzjm9ddfx9SpUxvdJMFqtWLixIlNHj9lyhSYTCbU1dVhzJgxMJlMWLlyJb777juYTCZcdNFFcLlcAIB//vOfeOmll/Diiy/iu+++Q0VFBRYvXnxOP9eZYnglIooCUlUpfG/PBw7t+KXxyB743poPqfq4coUR0RlRq9VYuHAhFi1ahPj4eAwbNgz33XcfNm/efNbv9eCDD2LcuHHo1KkTkpKScO211+L111/39+/evRvr16/Hdddd1+Trr732WnzwwQf+W9jabDZ8+umn/uPffPNNyLKM//73v+jduze6d++Ol19+GQcPHvTPAi9YsACzZs3C5Zdfju7du+O5554L25pahlciogjn87ohtn0HOB2NOz0uiI1fweeqD39hRHRWLr/8chw9ehQfffQRLrzwQixfvhwDBgzAwoULz+p9Bg0aFPD8mmuuQVFREdasWQMAeO2119CvXz/06NGjyddPnDgRarUaH330EQDgvffeg9lsxvjx4wEA69evx969e2E2m/0zxomJiaivr8e+fftQXV2N4uJiFBQU+N9TrVY3qqu1MLwSEUU6hx3i0M6g3eLwbkj1tWEsiIhaSq/XY9y4cfj73/+OVatW4YYbbsADDzzgv/vUyetG3e6mlwTFxcUFPM/IyMCYMWP8s69vvPFG0FlXoOECriuuuMJ//Ouvv46rr77af+GXz+fDwIEDsWnTpoDH7t27MXXq1Jb/8CHC8EpEFOnUGsBgDt5vMEOoNOGrh4hCpkePHrDb7UhJSQEAFBcX+/tOvnjrdK699lq89dZbWL16Nfbt24drrrnmtMcvWbIE27Ztw7Jly3Dttdf6+wYMGIA9e/YgNTUVnTt3DnhYrVZYrVZkZGT4Z3oBwOPxYP369Wdc77lgeCUiinCyPg7SgAuC9ksDLoAcFxv7NxLFqvLycpx//vn43//+h82bN6OwsBDvvPMOHnnkEUyZMgUGgwFDhgzBww8/jO3bt2PlypX461//esbvf9lll8Fms+GWW27BmDFj0K5du2aPHzVqFNLS0nDttdeiQ4cOGDJkiL/v2muvRXJyMqZMmYJvv/0WhYWFWLFiBe644w4cPnwYAHDHHXfg4YcfxuLFi7Fz505Mnz691W+CcALDKxFRNEjMhDTwwkbNUq8RkNJzFSiIiM6GyWRCfn4+nnjiCYwcORK9evXC3/72N/z+97/HU089BQB46aWX4Ha7MWjQINxxxx146KGHzvj9LRYLJk+ejJ9++ilgFjUYSZLw61//usnjjUYjVq5ciezsbFx22WXo3r07brrpJjgcDlgsFgDAn//8Z/zmN7/BDTfcgIKCApjNZlx66aVnMSItJ4lwbcqlEJvNBqvViurqav+AExFFI19tNSSHDeLANkD4IHXoCWG0QjbFK10aEQGor69HYWEhcnNzodfrlS4n4jQ3PmeT13iHLSKiKCGbrIDJCikly98mNXM8EVEs4rIBIiIiIooanHkNEeFxA/YqiNKDQL29YQ2aKQGSwaR0aUREREQxg+E1BITHDXFwB8THTwNeT0MbAHTqD3nsdZC4Ho2IiIgoJLhsIBRqKiA+esofXP32bYTY9j2Ez6dMXUREREQxhuE1BEThZsDnbbpv/ZeAvTrMFRERERHFJobXUKguC95XXwsIzrwSERERhQLDayhkdQvel5LVcGtHIiIiIjpnDK8hIKXlAJakpvtGXQ3JyJsjEBEREYUCw2sISOZEyFf+BejYD5B+3jLckgxpym2Q0jooWRoRERFRTOFWWSEiWVMgT/gd4KhpuHhLa+AWWURERBRSdrcTNW4nHB4XDGotzBod4jS6Vv/cZ555Bo8++iiKi4vRs2dPLFiwACNGjGj1z20Kw2uI1Hs8qPa6sdVegVq3Ez0SMpDqcsCqNShdGhEREcWACqcdr+7+AdurSvxtPeLTcX2XfCTq4lrtc9966y3MmDEDzzzzDIYNG4b//Oc/mDBhArZv347s7OxW+9xgJCGECPunhpHNZoPVakV1dTUsltZZe1rvcWNj+SEs2r0GJw9mrikJN/cYgQSdsVU+l4iIiCJHfX09CgsLkZubC71eH9L3trud+O/O7wOC6wk94tPxu27DWm0GNj8/HwMGDMCzzz7rb+vevTsuueQSzJs374zfp7nxOZu8xjWvIVDlcjQKrgBQWFuO5Ud3wxtkD1giIiKiM1HjdjYZXAFge1UJatzOVvlcl8uF9evXY/z48QHt48ePx6pVq1rlM0+H4TUENpUfhgCgk9Xon9Qe+akdkPHzDgPLi/fA1kpfKCIiImobHB7XOfW3VFlZGbxeL9LS0gLa09LSUFLSdJhubVzzGgI2Vx3Gt+uOjpZkbCo/DIfHheHpnZGgNeLNfesQ4ysziIiIqJUZ1Npz6j9X0ondlH4mhGjUFi4MryFwXmou1h4vwnM7vvW3/VRxBMn6OPyu2zDoVBxmIiIiajmzRoce8elB17yaW2m9a3JyMlQqVaNZ1tLS0kazseHCZQMhoJIkfHVkZ6P2sno71pYegJbhlYiIiM5BnEaH67vko0d8ekD7id0GWutiLa1Wi4EDB2Lp0qUB7UuXLsXQoUNb5TNPh6kqBDaUHQrat+b4AVyc3QuJ+tbbwoKIiIhiX6Ku4S+64d7ndebMmbj++usxaNAgFBQU4Pnnn8fBgwfxxz/+sVU/NxiG1xCo97qD9nl8XniEL4zVEBERUayKC9NNCU529dVXo7y8HA8++CCKi4vRq1cvfPbZZ8jJyQlrHScwvIZA9/h0fHN0d5N93eLToZK4OoOIiIii1/Tp0zF9+nSlywDANa8hYdHokWdNbdSukVUY3747tDKHmYiIiCgUOPMaAmatAWMyuqCbNQ2rSwvh8LjQNT4Nw9M7QxIN/URERER07hheQyBJHwen142fyg9hTEYXaFUqHLFXo7y+FgOTw3/PXyIiIqJYpfjfs48cOYLrrrsOSUlJMBqN6NevH9avX+/vF0Jg9uzZyMzMhMFgwOjRo7Ft2zYFK25aZlw8ruw4ED0TMtDBnIQL23fHsLSOYV9UTURERBTLFA2vlZWVGDZsGDQaDT7//HNs374d//znPxEfH+8/5pFHHsHjjz+Op556CmvXrkV6ejrGjRuHmpoa5QoPwqzVIyPOimxTIhL1cVDJKqVLIiIiIoopii4bmD9/PrKysvDyyy/72zp06OD/ZyEEFixYgPvvvx+XXXYZAGDRokVIS0vD66+/jptvvjncJRMRERGRghSdef3oo48waNAgXHnllUhNTUX//v3xwgsv+PsLCwtRUlKC8ePH+9t0Oh1GjRqFVatWNfmeTqcTNpst4BFOos4GUVsJ4XaF9XOJiIiI2gJFw+v+/fvx7LPPIi8vD1988QX++Mc/4vbbb8crr7wCAP776J5679y0tLRG99g9Yd68ebBarf5HVlZW6/4QPxP2Kvg2r4Dv7Ufge+0fEF//D6KiBMLnDcvnExEREbUFii4b8Pl8GDRoEObOnQsA6N+/P7Zt24Znn30Wv/nNb/zHSZIU8DohRKO2E2bNmoWZM2f6n9tstlYPsMJeDd8XLwMHtv7Stv17iN1rIU/9K5DcrlU/n4iIiKitUHTmNSMjAz169Aho6969Ow4ePAgASE9PB4BGs6ylpaWNZmNP0Ol0sFgsAY9WV308ILj6eVzwffsuhNPR+jUQERERtQGKhtdhw4Zh165dAW27d+/23ys3NzcX6enpWLp0qb/f5XJhxYoVGDp0aFhrbY7YsyF4Z+EWwFkXvmKIiIgoZol6O0RFMUTx/oblifX2Vv/MlStXYvLkycjMzIQkSfjggw9a/TObo+iygTvvvBNDhw7F3LlzcdVVV+HHH3/E888/j+effx5Aw3KBGTNmYO7cucjLy0NeXh7mzp0Lo9GIqVOnKll6II02eJ9KBQRZ4kBERER0pkRNBXxfLgSKTtrvPqcX5PHTIJkTW+1z7XY7+vbtixtvvBGXX355q33OmVI0vA4ePBiLFy/GrFmz8OCDDyI3NxcLFizAtdde6z/m7rvvhsPhwPTp01FZWYn8/Hx8+eWXMJvNClYeSMobCLHm46b7uhcABlOYKyIiIqJYIurtjYMrABRthe/LRZAn/gGSPq5VPnvChAmYMGFCq7x3Syh+e9hJkyZh0qRJQfslScLs2bMxe/bs8BV1tsyJkAZfDLH2s1PakyDlT4KkbmZmloiIiOh06myNg+sJRVsb+lspvEYaxcNrLJD0ccCgCyF17g+x6RsIRw2kLoMhZfeAZGm9aXwiIiJqI0538Xcbujic4TVEJIMJMJggkjIh+byQ28hvP0RERBQGOsO59ccQhtcQ8dVUAlWlwE/LIZx2+Dr2g5TbC1J8qtKlERERUbQzWoCcXg1LBE6V06uhv41geA0BX20VsG4JxMav/G2iaBvEukTIV/wZUkK6csURERFR1JP0cZDHT4Pvy0WBAfbEbgNt6C++DK8hINVWwXdScPWrqYBY8wnE+ddCbkPT+URERBR6kjkR8sQ/NFyc5XQ0LBUwWlo9uNbW1mLv3r3+54WFhdi0aRMSExORnZ3dqp/dFIbXEBC71zbbJxf8qk2tRSEiIqLWIenjwr6rwLp16zBmzBj/85kzZwIApk2bhoULF4a1FoDhNTQ87uB9Xi8gRPhqISIiIgqh0aNHQ0RQllH09rAxI2/AL/+sjwMsSb8879ALQmsMf01EREREMYgzryEgWVIghl4KqVNfoK4GcDkgxadCVJZCik+BFBc5dwMjIiIiimYMryEgjCbImR3he+dRoN7e0AYJUs9hEEMmQ1K4PiIiIqJYwWUDISDZKuD74N/+4NpAQGz7Dti9Dj6fT7HaiIiIiGIJw2sIiANbgl60JTYsBWzHw1wRERERKSWSLm5qCZ/HDZ/bBZ/HBZ/XE7L3DdW4cNlAKFSWBO+zV0HyRfeXmIiIiE5Po9EAAOrq6mAwRN8WmT6vB/C4INVUAJ6fQ6tOD2FOhKTWnvP719XVAfhlnFqK4TUUMjoBm1c03ZeQDsic4CYiIop1KpUK8fHxKC0tBQAYjUZIUvRc+eJzOSFVlwU2uu2AwwFhSYasblnoFEKgrq4OpaWliI+Ph0qlOqc6GV5DQMroBGG0NNzx4tS+gl9BGON50RYREVEbkJ7ecEv4EwE2WgifD3DUAMGWCeirIGn15/QZ8fHx/vE5FwyvISD0JsiXzYRv6cvAsaKGRn0cpIIpQHouZO25T7UTERFR5JMkCRkZGUhNTYXb3cxNjCKMy1YO1QeLGm6u1ASR2wfq0Ve3+P01Gs05z7iewPAaCnojfM46yBf9ruHCLY8bQmeAUKnh1ejB6EpERNS2qFSqkIW1cJDqtVDLElBb1WS/Ny4OWv25zbyGCsNrCHhtZZBf/0fDQuecHpA0OohjRQ0Xcl30W6DHUKVLJCIiIgpKZUqAZ+A4qJe90USvBKn7kLDXFAzDawj4Du+G7Gy4gg77NuHkvQVUqz+EK6sbtOZERWojIiIiOh21Wg3RqT+8R/dCtWvtLx2yCt4LbwJ0JuWKOwXDawjIxw4E76wugxTCPdKIiIiIWoPGkgTniCvhG3wxfEf3AToD5PQOgNYAbZxV6fL8GF5DwJfSPvjdHsxJEHL0rHkhIiKitktnSQIsSUBqttKlBMUNSENA1b4boGl6EbM3fyI057i1BBERERE1YHgNhaN7IS6/EzAl/NImq+AZPAFIygBc9crVRkRERBRDuGwgBGQAvtUfwzvlT5C8PsDrBgxmSEf2QLXkRYir7+VNCoiIiIhCgOE1BKSkTEgl+6B67aGfW2QAvoa+Mb+GJETQ1xIRERHRmeOygRDwHdgGecIfgPjUEy2AWgNp8MUNSwY80XOHDSIiIqJIxpnXEJAS0+Bb9hqkgRdCMiUAPi8gq+DbsRqoPAape4HSJRIRERHFBIbXEJDSOkD4BMQ3r+HUBQLSpXdAsiQpUhcRERFRrOGygRCQ4lMhX34n0KEXcOLSLHMSpIl/BFJzFK2NiIiIKJZw5jVEpMQMSBf+FlJ9LeD1ADoDJGuK0mURERERxRSG1xCS4yxAnEXpMoiIiIhiFpcNEBEREVHUYHglIiIioqjBZQMh5LRVQHY5AK8HQmeAbEqAWq1RuiwiIiKimMHwGiKe8qNQLV0E6ejehgaDGd6RV6A+qwf0lkRliyMiIiKKEVw2EALuqlJI7/7zl+AKAI4aqL54GarSIuUKIyIiIooxDK8hIEoKAXsVoNYCuX0gdRnsv1Ws/N17cFaXKVsgERERUYzgsoFQOLIHUv8LIGV1gyjcDFFfB2nABYDeDLHsdcjCq3SFRERERDGB4TUEpNw+wJHd8H30lL9N7FkHxKdBnvB7eCVOcBMRERGFAlNVCKhM8RBrP2/cUXUMvj3roNLFhb8oIiIiohjE8BoCYs+G4J071kB21ISvGCIiIqIYxvAaApKzLninxw1AhK0WIiIioljG8BoKnfsH78vuBqHVh68WIiIiohjG8BoCQmcEMjs37lCpIQ+eALfPF/6iiIiIiGIQw2sIeLevhjxgHKTzJgKmBECjBzr2hTzlVvjWfALZ61G6RCIiIqKYwK2yQkAYzfB98izQvguk8yYAah1w7AB8nzwHSDJXvBIRERGFCMNrCKg69wdWLQYO74Y4vDugz3PeRKj0JoUqIyIiIootXDYQAl5XPbwX/Q445WYEvvZdoeo9ArLLoVBlRERERLGFM68hoNm9Dp7UbIhpD8J7eBdQVws5qxuE3gj5jYchTb1P6RKJiIiIYgLDawhIeQOgenMeAEDddzRgtAJLXgSqS4GOfQGdUdkCiYiIiGIEw2soWFPh6zsGNTk9UG00w+n1IDGnO0x7NsDQczgknUHpComIiIhiAsNrCPiMJhwaNB7P7PgWNW4nAECChPPb5+EiSyIsCtdHREREFCt4wVYIVDrr8MTWZf7gCgACAl8f3Y1N5YchBDfLIiIiIgoFhtcQ2FFZApfP22Tfpwe3otpVH+aKiIiIiGKTouF19uzZkCQp4JGenu7vF0Jg9uzZyMzMhMFgwOjRo7Ft2zYFK27aEXtV0L4qlwNewdvDEhEREYWC4jOvPXv2RHFxsf+xZcsWf98jjzyCxx9/HE899RTWrl2L9PR0jBs3DjU1NQpW3FgHc1LQvlS9GRpZ8WEmIiIiigmKpyq1Wo309HT/IyUlBUDDrOuCBQtw//3347LLLkOvXr2waNEi1NXV4fXXX1e46kDt4uIRp9Y12Te+fXeoJMWHmYiIiCgmKJ6q9uzZg8zMTOTm5uKaa67B/v37AQCFhYUoKSnB+PHj/cfqdDqMGjUKq1atCvp+TqcTNpst4NHadlcfw129xqCdMd7fplepcU3uAKRoDaj3elq9BiIiIqK2QNGtsvLz8/HKK6+gS5cuOHbsGB566CEMHToU27ZtQ0lJCQAgLS0t4DVpaWkoKioK+p7z5s3DnDlzWrXuU42MS4T09Wu4o0NP2DsOglsImDxuWNZ/BVX+RNglKaz1EBEREcUqRcPrhAkT/P/cu3dvFBQUoFOnTli0aBGGDBkCAJBOCX5CiEZtJ5s1axZmzpzpf26z2ZCVlRXiygOpqo5D7N8E0/5NMJ3SJ1wOGCb8jnfZIiIiIgoBxZcNnCwuLg69e/fGnj17/LsOnJiBPaG0tLTRbOzJdDodLBZLwKO1if2bgnce2gnZ7Wr1GoiIiIjagogKr06nEzt27EBGRgZyc3ORnp6OpUuX+vtdLhdWrFiBoUOHKlhlE7T64H1qbfjqICIiIopxiobXu+66CytWrEBhYSF++OEHXHHFFbDZbJg2bRokScKMGTMwd+5cLF68GFu3bsUNN9wAo9GIqVOnKll2I1KXQcE7uw0BjLxBLBEREVEoKLrm9fDhw/j1r3+NsrIypKSkYMiQIVizZg1ycnIAAHfffTccDgemT5+OyspK5Ofn48svv4TZbFay7EZ8BjOkginA6g8DOxLSIJ03AbLOoExhRERERDFGEkIIpYtoTTabDVarFdXV1a26/tVTUwG5tgpi+ypI9XagYz8gPRdyQmqrfSYRERFRLDibvKbozGssUZsTAXMiRHouIHyQZJXSJRERERHFHIbXEJMkCZAYXImIiIhaQ0TtNkBERERE1ByGVyIiIiKKGgyvRERERBQ1GF6JiIiIKGowvBIRERFR1OBuAyHi9flQ5XKg1FEDh9eFTGM8zBo94jS8PSwRERFRqDC8hoDH58Ve23E8u30l6r0ef3tBai4uy+0Hi5Z32CIiIqLocMxhg8vrgUqSoVNpkKSPU7qkAAyvIVDprMOTW5fDI3wB7atLC9HeFI/zM7tBliSFqiMiIiI6vYp6O47UVeHt/RtQ6qiBBKBbfDqu6jgAmXHxSpfnxzWvIbCtsrhRcD3hi0M7YHM5wlwRERER0dkpd9rx9LYVKHXUAAAEgB1VJViwdRmO1dmULe4kDK8hUNLMv1Cbux5eIcJYDREREdHZKa+vxYcHfkJTiaXa5cDu6tKw1xQMw2sI5FlTg/ZlGC3QyLxdLBEREUUuj8+HwpryoP07q4+FsZrmMbyGQK45CdYgF2VdltsfFq0+zBURERERnZ1gWQYAErXGMFbSPIbXEEjUx+HPfcYiz5LibzNr9LipSwE6n9RGREREFImStEac365rk30SgPzUDmGtpzncbSBE0gwW3NJjJGrcTniFDwa1BvFaI3cZICIiooinVqvRL6k99tmOY0PZIX+7LEn4TV4+jOrI2bee4TWE4jQ6xGl0SpdBREREdNaS9SZc3qEfJmT1xJ7q4zCoNcg1J8Gg0iBeFznLBhheiYiIiAgAkGwwAwCyTYkKVxIc17wSERERUdRgeCUiIiKiqMHwSkRERERRg+GViIiIiKIGwysRERERRQ2GVyIiIiKKGgyvRERERBQ1GF6JiIiIKGowvBIRERFR1GB4JSIiIqKowfBKRERERFGD4ZWIiIiIogbDKxERERFFDYZXIiIiIooaDK9EREREFDUYXomIiIgoajC8EhEREVHUYHglIiIioqjB8EpEREREUUOtdAGxxOXzoNblhA8COlkNs1avdElEREREMYXhNUQq6u347NBWrD5WCI/wISsuAdd0GoQsUwJ0Kg4zERERUShw2UAIVDnr8OS25fi2ZB88wgcAOGSvxGObv8IRe6XC1RERERHFDobXEDhqr8bRuupG7QIC7+zfgFq3U4GqiIiIiGIPw2sIbK08ErRvf005XF5PGKshIiIiil0MryFg0RqD9ulVGkiSFMZqiCjW1dfbUV9vV7oMIiJF8EqiEOif1B4fHNgE0UTf6Iw8WDS6sNdERLGnyl6NwppyrDh+AAICI5Kz0cmSioQ4q9KlERGFDcNrCFi1RtzQpQALd68OCLAdzckYk9kFKlmlWG1EFBuq7NV4cc8P2F1T5m/bWXUMOXEJuKXrUAZYImozGF5DQK9Wo39yFnItydheWYxadz26x2cgxWCCVWtQujwiigF7bccDgusJRfZKbK08ghEMr0TURjC8hohOpUaawYw0g1npUogoxtTV1WDF8QNB+1eUFqFfQjuYGWCJqA3gBVtERFHAK5paVX+izwc0ueqeiCj2MLwSEUU4o9GMoUntg/YXJLWHQW8KY0VERMpheCUiigI9EjKRYbQ0ak/Wx2FQageoeRtqImojeLYjIooCiXFW3NF9BNaVFuG7soMQEChIykJ+ai4SjVzrSkRtB8MrEVGUSDBaMSarB/KTswEAcQYTVJxxJaI2hme9EPEJgWqXA9UuB1xeD+J1Rlg0eujVGqVLI6IYolapYTHFK10GEZFiImbN67x58yBJEmbMmOFvE0Jg9uzZyMzMhMFgwOjRo7Ft2zbligzCK3woqinH3I1LMG/TF/jnlq/xwLpP8MnBLahx1StdHhEREVHMiIjwunbtWjz//PPo06dPQPsjjzyCxx9/HE899RTWrl2L9PR0jBs3DjU1NQpV2rRKZx0e3/I1bO5fgqoPAkuP7MSm8sMQzWxxQ0RERERnTvHwWltbi2uvvRYvvPACEhIS/O1CCCxYsAD3338/LrvsMvTq1QuLFi1CXV0dXn/9dQUrbmxnZQlcPi+6xadhaufBuKFLAc7P7AqDSoNPD25FtcuhdIlEREREp3XcUYN91cfx6cGt+PrIThy1V6OivlbpsgIoHl7/9Kc/YeLEibjgggsC2gsLC1FSUoLx48f723Q6HUaNGoVVq1YFfT+n0wmbzRbwaG3FdTbc3H04OpqTsK/6OHZWFsPp9eB33YYhxWBqdnNxIiIiokhQ5qjFG/vW4ZHNS/FR0Wa8vX8DHtzwGTZXHEW5I3ICrKIXbL355pvYsGED1q5d26ivpKQEAJCWlhbQnpaWhqKioqDvOW/ePMyZMye0hZ7G4JRsHK4oxjBZB/OhzVDV16I2tzfKHbW4OKsXNLLivyMQERERBeXxeLCurAjbKosD2gUE3ti3Dp36T0CSQrWdSrHweujQIdxxxx348ssvodfrgx4nSVLAcyFEo7aTzZo1CzNnzvQ/t9lsyMrKOveCm2H0etH/yF7of/jE32besx7m+FRUTroFKuFr1c8nIiIiOhflrjosP7onaP+a0kJkmRKC9oeTYlOC69evR2lpKQYOHAi1Wg21Wo0VK1bg3//+N9RqtX/G9cQM7AmlpaWNZmNPptPpYLFYAh6tzei0BwRXv6pSxP20DMLjbfUaiKhtsNurcby2EsdrK1Brr1a6HCKKITXu4DskVUXQ9TuKzbyOHTsWW7ZsCWi78cYb0a1bN9xzzz3o2LEj0tPTsXTpUvTv3x8A4HK5sGLFCsyfP1+JkoPS7PspaJ92xxr4Bk8A0Pohmohil8fjQamjGm8XbsSO6lIAQJ45Gdd0HIA0gwkaTfC/YBERnY5aUqGzNQU7q4412d8zPj3MFQWnWHg1m83o1atXQFtcXBySkpL87TNmzMDcuXORl5eHvLw8zJ07F0ajEVOnTlWi5KDUbmfwTo8bKolrXono3FQ6a/HIlm/g8Lr9bXtqyjB/y9f4a9/xSGN4JaJzkGSIw5ScvthdtRQ+BF5onqgzorM1RaHKGovoVHX33XdjxowZmD59OgYNGoQjR47gyy+/hNlsVrq0QJ36B+/L6QFJbwxfLUQUc9weF9Yc2x8QXE9w+bz45uguOJ2R8yc9IopOiTojZvQ+H+3j4gEAMiT0T2qPO3qdj1RD5PwFWRIxvoO+zWaD1WpFdXV1q61/FfZqeD/9D6TDuwI7VGrIU/8KKaV1LxgjothWa6/Ck3t+wIGa8ib7M4wWzOw6DJYIuZiCiKJbmaMGTp8XKkmCTtYgIQyTcGeT1xTdKitW1Gp0qB19Dcz7f4Jhy0rAWQdfdnfUDLoIIs4SMVtLEFF0UstqWNS6oP0mtY7Lk4goZJINEfYX7lMwvIZAWX0tHt71HfIsKRgz7nroZTV2Oqqx8sAGdItPx41dCqBXa5Quk4iilN5gwriMPGyuPNpk/4UZeYiLs4a5KiIiZTC8hsCGskMAgD2249hjOx7Q91P5EdR5XAyvRHROMnRxmJDRBZ8X7w5oH52aiw4/r08jImoLGF5DQNXMHbTkZm6oQER0psymeIzLyEN+Wi52VByFEEC3xAxYVWqYGF6JqA1heA2BgclZ+PzQtib7Bqdkw6QJvlaNiOhMxcVZEQcgw8yV9ETUdnGFfwgk6uIwOiOvUbtFo8ek7N7Qqvg7AhEREVEoMFWFQJxGh0nZvTEgORtfH9kFu8eJAclZ6JeUhSR9nNLlEREREcUMhtcQMWv16KrVI9ecBK/wQa/SQOJ6VyIiIqKQYngNMS4RIKLW5KqphFRvByAg9HHQmhOVLomIKKyYtIiIooDH44I4VgTV0kVARXFDY3wq3Bf8BlJ6R6i1vDCUiNoGXrBFRBQNbOWQ3/vnL8EVAKpKIb//BHDK/tJERLGM4ZWIKMJ53S74Ni0DPO7GnT4vfGuXwOOqD39hREQKYHglIopwXlc9VCX7g/arjh2A11kXxoqIiJTD8EpEFOlkFXyW4DcmEKZEQFaFsSAiIuUwvBIRRTqfF+h/QdBuMfjCMBZDRKQshlciogin0hvhlWR4x0wNnGGVZHiGXQafzgCVVq9cgUREYcStsoiIIpxKpYYwmuAVPviuewCisgRC+CAnZkI6shey1gi1hltlEVHbwPBKRBQFZEsKkN0DYuU7UFUfByQJwhQPadTVkKzJSpdHRBQ2DK8h4q6vAxw18B0/BDhqIWfkQuhN0DZzkQUR0ZmSZRlycju4JvwOvno7AEDojNAazQpXRkQUXgyvIeCuswPFeyB/+hzkk/Zh9HXqB/eYqdAwwBJRiGgNJsBgUroMIiLF8IKtEJDqbZA/fqbRBuLyvk3wbV8Fb1MbixMRERHRWWN4DQHvvp8atrJpgnrDV/DWVoa5IiIiIqLYxPAaAnJNefDO+lpIEOErhoiIiCiGMbyGQla34H3J7SFkLi0mIiIiCgWG1xBwJGUC5qYvyrIPuwROfVyYKyIiIiKKTQyvIbCsuhRlk26GL7c3AKmh0ZyE2gm/xxKXHa4g62GJiIiIIonL48GxOhsKa8pxqLYCZY5apUtqhH/PDgGLzoC5hRtwQc8C9DvvYsg+L8qFDx+VHUSNux5jueaViIiIIlx5fS322o7j3f0bYXPXAwByTIm4Lu88ZOotUKsjIzZy5jUEsk0J8AofttqrsEN4sEelwsb6GpQ4bBiVkQe1xGEmIiKiyHbMUYOXdq32B1cAKKqtwIIt36DcVadgZYEiI0JHuWN1Nbin73hsrjiCb0v2weFxoYs1DTN6nQ+bywEfZ16JiIgogpU5avBh0eYm++weF7ZVFiPNaAlzVU3jlGAIdLIk4539G/BR0WaUOmpQ43ZifdlBLNj6DSxaPcwavdIlEhEREQXlhcDhZval32c7HsZqmsfwGgI2dz12VR9r1O72efHZoW1w+3wKVEVERER0ZiRISNQF3x0pxWAOYzXNa3F43bdvH/7617/i17/+NUpLSwEAS5YswbZt20JWXLT4qfxw0L7tlcVweFxhrIaIiIjo7KQazBjfvnuTfbIkYXBKTpgrCq5F4XXFihXo3bs3fvjhB7z//vuorW3YRmHz5s144IEHQlpgNNA2cxMClST7d88iIiIiilTd4tMwMr1zQGzRyWrc3G04zOrIWQLZogu27r33Xjz00EOYOXMmzOZfppHHjBmDf/3rXyErLlr0S26PTw9tbbLvvJQOMKq0Ya6IiIiI6OykGMyYkNUTYzK74EhdFfQqDVL0ZpjVWsRpozy8btmyBa+//nqj9pSUFJSXl59zUdGm2unAmMwuWHZ0d0B7ki4Og1Nz4PC6oVdrFKqOiGKFx+OCzVkHm9sJAQGLRg+LxgiNlr8gE1FoJP58V9DMuHhlC2lGi8JrfHw8iouLkZubG9C+ceNGtGvXLiSFRZPtVcXQyWr8oftw/FR2GHVeF7pa05Ckj8PC3Wtwb9/xSpdIRFGuvt6OXbbjWLhvHep+XkevV2lwXW5/9IpPgyGCLqYgImpNLVrzOnXqVNxzzz0oKSmBJEnw+Xz4/vvvcdddd+E3v/lNqGuMeL0SMrHk8Ha8snsN3MILk1qHVcf24z87vkOizgititvpEtG5KXc58Oyu1f7gCgD1Xjf+u/dHHHNGzubhREStrUXh9f/+7/+QnZ2Ndu3aoba2Fj169MDIkSMxdOhQ/PWvfw11jRGvXVw80g0W1Hs92FB2CKtLC3G0rhoSgCs7DoBJo1O6RCKKYi5XPb4+ugsiyA1PPj+8A/X19jBXRUSkjLOeEhRC4OjRo3jhhRfwj3/8Axs2bIDP50P//v2Rl5fXGjVGvHidEXf0GoM1x/ZDp9ZAI6tQ7XKge3w62kfwmhEiig4utxNHHLVB+0vqa+HyuKBH8D0aiYhiRYvCa15eHrZt24a8vDx07NixNeqKOrIkQ6fW4Juju1DncaN7fDoGpeRALamULo2IopxWo0N7gxkHapu+IDbTYIZWzYu2iKhtOOtlA7IsIy8vr03uKhBMtcuBl3etwtv7N6Cs3o46jwvryw7i/zYuQYnDpnR5RBTlNGoNzs/sAqmJTaMlABPad4eO4ZWI2ogWrXl95JFH8Je//AVbtza9t2lbc9xRg51Bbg/7XuFG3mGLiM6Nqx5J5cX4U9ehAWvojWoNfp+XjxRbBeByKFggEVH4tOgy+Ouuuw51dXXo27cvtFotDAZDQH9FRUVIiosWG09ze9g6jxsGzooQUUup1ND8tBzd03Jwf68xqPG6AQBmlQbm3esh790ATLlN4SKJiMKjReF1wYIFIS4juumb2QpLLasg8/awRHQOJI0O0qDxwNuPwLr6Q1hP7b/kDkh6oyK1ERGFW4vC67Rp00JdR1QbkJyNTw42vYRiSGouTJrIuaUaEUUnKakdcN5EiB8/DWzvOwZSem6QVxERnT1vvR1w1AKSBJgSoVJH1n7151yNw+GA2+0OaLNYLOf6tlElQWfEpOze+OTgloD2ZL0JE7J6QiNzxwEiOjeSwQQMughS9wKIg9sBnw9STg/AFA9Jzy2yiOjceT0uSB4Pynxe1Ks1UMsS9G4HEpxeyBG09WeLwqvdbsc999yDt99+u8ldB7xe7zkXFk2Mai3Oz+yCPomZ+P7Yfjg8bvROzESeNRUJOv4pj4hCQ9IbAb0RUlKG0qUQUQyqcjtxoLYS7xRuQOXPd+7rZEnG1E6DkeGwQ2WIjF+UW7TbwN13341vvvkGzzzzDHQ6Hf773/9izpw5yMzMxCuvvBLqGqOCHjIyvV5cpbPgNxojBqi0iPO4T/9CIiIiIoV5HA6UOe14fud3/uAKAPtsZViw9RuUiciZmGzRzOvHH3+MV155BaNHj8ZNN92EESNGoHPnzsjJycFrr72Ga6+9NtR1RjSPqx7i2AHIHz8D1Nt/3olRAnoNh7tgMjTmJIUrJCIiIgquGh58cGBzk301bid2VJUgzRgZy0JbNPNaUVGB3NyGCwQsFot/a6zhw4dj5cqVoasuWtirIC9eAATcW1xAtfVbiN3r29wyCiIiIoouHiFwsDb4Vqd7bMfDWE3zWhReO3bsiAMHDgAAevTogbfffhtAw4xsfHx8qGqLGr4D24AgSwRU676At7YyzBURERERnTkJQHwz1+kk6yJjvStwluF1//798Pl8uPHGG/HTTz8BAGbNmuVf+3rnnXfiL3/5S6sUGsnkypLgnfYqyMIXvmKIiIiIzlKq0YJx7bo12SdDwuDUnDBXFNxZrXnNy8tDcXEx7rzzTgDA1VdfjX//+9/YuXMn1q1bh06dOqFv376tUmgk86R3hAbfNN2ZkA6PrDr3PcmIiIiIWlGvhEwUpOZidWmhv00jq3BDlyEwRdCdQs8qUwkhAp5/9tlnmDdvHjp27Ijs7OyQFhZN7MntEG+0AJ36Q+qeD8hqoKIY4rv3UTtkEoRaA96mgIjOldvthMPthFRvByAgdHHQabTQaQ2nfS0R0ekkG0yYlN0LF7TrhqLaChhUGmTGxcOo1sASQeeZFq15DZVnn30Wffr0gcVigcViQUFBAT7//HN/vxACs2fPRmZmJgwGA0aPHo1t27YpWHHTjvkEcM0sSNZkiCUvQSxeALH/J8iXzcDxhHRIPl6wRUTnxlHvgMtWDvUPn8L49iMwvvUINKs/hMdWgbqTtrUhIjoXyQYz2psSMCy9EwakZCPdaImo4AqcZXiVJAmSJDVqa6n27dvj4Ycfxrp167Bu3Tqcf/75mDJlij+gPvLII3j88cfx1FNPYe3atUhPT8e4ceNQU1PT4s9sDV31RogvX4b47j3AVgY464C9G+B7cy46ShJMyv6OQEQxQHbYoF/8L+g2fgXU2QBHDbSbV0D/3uNQ1dmULo+IKGwkcepagGbIsowJEyZAp9MBaNhd4Pzzz0dcXOAVaO+//36LC0pMTMSjjz6Km266CZmZmZgxYwbuueceAIDT6URaWhrmz5+Pm2+++Yzez2azwWq1orq6utVuW+sr2g7x3j+b7szuATH+JqgtCa3y2UQU+9xuJzw/LYd25dtN9rvyJ0EedBF0usiaHSEiOlNnk9fOas3rtGnTAp5fd911Z19dEF6vF++88w7sdjsKCgpQWFiIkpISjB8/3n+MTqfDqFGjsGrVqqDh1el0wul0+p/bbGGYkdi3MXjfwR2QPc7g/UREp+FzOqDdsz5ov3bvRnh6jwAYXomoDTir8Pryyy+HvIAtW7agoKAA9fX1MJlMWLx4MXr06IFVq1YBANLS0gKOT0tLQ1FRUdD3mzdvHubMmRPyOpul0QXvU6uBc1haQUSkVmmA5q70VWugUnFPEyJqGxRfjNm1a1ds2rQJa9aswS233IJp06Zh+/bt/v5T19QKIZpdZztr1ixUV1f7H4cOHWq12k+Q8gYG7+uaD6ibCbdERKehMsRB9D8/aL/odz7UcfHhK4iIYladux4ldTbsrT6OAzXlKHVE1nVGwFnOvLYGrVaLzp07AwAGDRqEtWvX4l//+pd/nWtJSQkyMjL8x5eWljaajT2ZTqfzr8kNF+HzQTrvYogfPwvssKZAGnQh3D43Imd3NCKKRlJ6LtCpf+NlStndIWV3V6YoIoopZY5abCo/jI8ObobT6wEAJOvjcFPXoWhvjIdOrVG4wgaKh9dTCSHgdDqRm5uL9PR0LF26FP379wcAuFwurFixAvPnz1e4ykBerQ6qlBzIV98LsfU7CKcdUse+QGoOPPs2Qup/gdIlElGUkyAB/ccCXQYC+zcDwgfk9gFMCQ19RETn6JC9Eu8UbghoK6u3419bl+G+fhchneEVuO+++zBhwgRkZWWhpqYGb775JpYvX44lS5ZAkiTMmDEDc+fORV5eHvLy8jB37lwYjUZMnTpVybIb8Rit8BVth/rrV4GB4yFp9RC710FsWApMuR2a5tbEEhGdAanyGHzvPgYYzJCyugGSDPHd+4C9CrjkDkhm7mhCRC133FGLTw9ubbLP6fXgp/LDSDf2CHNVTVM0vB47dgzXX389iouLYbVa0adPHyxZsgTjxo0DANx9991wOByYPn06KisrkZ+fjy+//BJms1nJshvT6lGbNwCW7O7w7VoLufo4RL/z4UvKhFNvgFXp+ogoqvnq6xp+GQYARw3E7rUB/WL9F/CldYAc1zrbARJR7PPBh+K66qD9B2srwlhN8xQNry+++GKz/ZIkYfbs2Zg9e3Z4CmqhGnc9jntceP7QNnTMyIFRpcGu6lL0VKuR5jKjT5IealmldJlEFK08roYZ1mDs1ZA8rrCVQ0SxR4KEFIM5aIDNNEbOVJziuw3EArvbiZd2r8YBewW+OboLnxzaij22UnxQ9BNqPU7Yuc8rEZ0LXRykzM5Bu6X0jhAGUxgLIqJYk2ow4+Ksnk32aWQVBqRkh7mi4BheQ+CwvQq17qYD6tIjO+HyesNcERHFElmjgdR7VNN7SqvUkAaNh6zVh78wIoopueZkTMruBbX0Szw0a/S4tecoWFSRc46JuN0GotHhk/6cp5FV0Mgy6jxuAIjI/dGIKPoIazLkK/8C39f/A2oqG25+YjBBHnsdhCWF+w0Q0TlLMZgwPL0TBqfkoMrlgFpSwazRwaIxwKCJjJ0GAIbXkMiMsyLblIDx7Ruuwqv3uBGvM2J7ZTE2lh2EhutdiegcyWoN3PFpwMV/gKg+DggfpPhU+PQmaLTc0YSIzl1ZfS1WFu/F0iM7IAQgIBCn1uK3XYcix5QAkzYybkHN8BoCPRMyYVLr8L+9P6Lu54smJAD5qbn4Q/fhsEbIv2wiil719mrIhZuh+vp/wM+bh0NWwTPySji7DIbOFK9ofUQU/fZUl+KLw9sD2uweF57ZvhL3D5gQMeGVa15DwOPz4OXdq/3BFQAEgDWlhSi0lStXGBHFDFVNOVRfLvwluAKAzwv18jchVZYoVhcRxYZSRw2WHNreZJ9H+LDh+MEwVxQcw2sIbKsohtvX9EVZXxzZgTKueyWic+CqrwPWLw3aL6/9HM46WxgrIqJYIyBQVl8btL+5PWDDjeE1BEocwf9Po9rlgA8ijNUQUazxepyQq483PJFVQFoOkJ4LqBpWfkm2cvjc3OeViFpOBQkZzezlmmNOCmM1zeOa1xDINSdhZcneJvvSDBaoeB0wEZ0DlUYPb1oHqDPzIGV3gzi6FxACUv4kiOL98FYfh8StsojoHCQbzJic0xvPbF/ZqE+v0qBPYqYCVTWN4TUEOlmSYdboUeOub9Q3KbsXrFqjAlURUazQ6gzw9r8A2LAUvg/+7W8XAKTuQyAXTIGGNykgonPULi4eUzsNwgdFP/m3/EwzWHBjlyGI10ROlmF4DQGrWoc7eo3GV0e24pqOQ6CWZeypKkVxvQ2dTYnQqDnMRHRuRE0FsHl54/Yda4C8QUBieviLIqKYkqw34bzkDuganw67xwm1JMOg0iLVaFa6tABMVSGg1+qR6KzDdQkdIFYthlRXg86d+6NTajZ0RovS5RFRlHM7HZA2BL9gC+u/gCuzM7QR9n8wRBR9DFotDFqt0mU0i+E1BOpqKqHe+QPkb9/1t6l2rIZIzID70jugsaYoWB0RRTvhcUM+sWtJSnuI3iMByJC2fQccOwA4aiFO3kKLiCiGMbyGgKa+NiC4niBVFMO77gvUD7sUen2cApURUSxQ6Y3wduwHjP41UF8L1bbvAeGDN38SYEoAdq+FSh8569GIiFoTt8oKAd+e9Q3/IKuArG5Ax74N/4cCQL3te0j1dQpWR0TRTqVSA93zIW35FvD54MufDN+QXwEqFeSNSyH1HQO1hreIJaK2gTOvISA5HZB6j4TUqR9E0XbAWQcU/ApQqSGWvwmZ+7wS0TmSaqog9T8f0sq3IR3cAQAQ7boAo66GqD4OWJMVrpCIKDwkIURMJyubzQar1Yrq6mpYLK1z8ZS3pBBS4RaI1R8GdiS1g3z+tXBbk6G1RM7mvkQUXVz2Ksj1dZDenAs4HYGdai3E1L/Cq9VDx/MMEUWps8lrXDYQArJK3Ti4AkD5EYg966CSVeEviohih0oD7441jYMrAHhc8G36BlBpwl8XEZECuGwgBMTudcH7tn4Hqf8FYayGiGKNx+OC7uD2oP2qw7vg8rrDWBERxapSRw2O2quwsfwQDCotzkvtAKtGj6QIuhEKw2so1NuD93l4v3EiOkeyGsJoCX6jaYMZgn/hIaJzdNxRg+d3fIdD9kp/27Li3biwXXeMzMhDcoQEWC4bCIWOfYN2ifZd4OGf84joHBiNZvgGjAva7x04HkZTfPgKIqKY4/S68V3JvoDgesIXR3bA5q5XoKqmMbyGQL01GSKtQ+MOWQVp+BWwa7mFDRGdG09CGjyDLmrc3nsUvGk5ClRERLGkyunA98f2B+1ffawwjNU0j8sGQmCP8KLrhN9Ds+07YMu3DVtlZXUFhl2Glc5aDJT5OwIRnRujORG1/cdC7lEAb9E2wOeDqkNPePVmGM0JSpdHRDGgvpm18w5v5CyDZHgNgay4BMz+6UsMy+yI0d3yoZZkHHY78Mrhbbiy0wBYtAalSySiGGAyJwLmRGiS2/vbuCiJiEJBp1KjR3w6fqo40mT/gKSsMFcUHMNrCCTq43Bn77F4Zc8afHJ0JwDArNHhytwB6GxJUbg6IiIioubF64z4VU4fbK8qgdvnDehrZ4xHe1Pk/IWHNykIIbvbiRq3Ex7hg1GtQbzWCFkKen0wERERUcSod7lw3GXHR0Wbsb2yGDqVGgVpHTE6Iw8pBnOrfvbZ5DXOvIZQnEaHON5fnIiIiKKQXqtFllaLKzr2h8vbGypJhk6lQZI+TunSAjC8EhFFEVdtJaT6OgCA0Bmh5cVaRBQiFfV2HLFX4e3CDSh11EAC0C0+HVd1HIDMuHily/NjeCUiigJerwe+0oNQffUKcPxQQ2NSJtwXXA85tQNUGq2yBRJR1Ct32vH09hU4sZ5UANhRVYIFW5fhz73HIs3YussvzxT3cCIiigKi+jjkdx79JbgCQPlRyO/+E6L6uHKFEVFMKK+vxYcHfkJTF0JVuxzYXV0a9pqCYXglIopwXo8bvs0rmr7dtNcD37ov4HFFzt1viCj6eHw+FNaUA2jYMalnQga6WFOhkhqi4s7qY0qWF4DLBoiIIpzX6YDq6N6g/aqS/fA466DW6sNYFRHFmhS9Cee36wqdSo3d1aXQyWqMa9cd2yqPQitHTmSMnEqIiKhJsloDnzkRcknTt2cUcfGQ1FzzSkQtl6Q14sauQ/H6vrU48PMMLAB8fXQXLmzfA0NTcxWsLhCXDRARRTi1zgAMGBe03zf4ImgNpjBWRESxRsgStlQcCQiuJ3xxeDucPo8CVTWN4ZWIKAqIhHRIo64GpJNO25IEqWAKkJKtXGFEFBOqnHVYUbwnaP+qY/vDWE3zuGyAiCgKaFwOCLcL8pTbGnYXEAJSQip8JUXQuOsBRMYWNkQUnYTwweF1B+23N3XBqEIYXomIooAoOQCxanHDNjY/32Nc1FY2/K/RDCk+VbniiCjqGSChmzUNWyqPNtnfL7FdmCsKjssGiIginK+2CmLLil8aaisbHj8TW1fCV8W9Xomo5UweL36V3QtqqXE0TDdYkGO0KlBV0xheiYginc8LeIL/OQ8eDyTR1NbiRERnRrIkwCKrcXffcegenw4JgE5WY2R6Z/ypx0gYI+gUw2UDREQRThitkPIGQhTva7Jf6twfPlMCVGGui4hih1f44Km3I2Pt57ix63lw5vSG5BMw7d0Ir+0HOPMGIE7pIn/G8EpEFOFUajVEp34QPy0DTr0VrCkBUo+hkDUaZYojophQ56qHYe8GqHashmnHajTafK9dJ8CSpERpjTC8EhFFASkhDfJld0JsWQmx8wcAAlLeIEj9zoeUkKZ0eUQU5bT1dqg3rwjar9n5I5DeMYwVBcfwSkQUJaSENPjyfwW51whAAnxGC2SdUemyiCgGqAWA+trg/fbq8BVzGrxgi4goiqh0OkiJ6ZAS0qFicCWiEJFkFdCuS/ADOvULWy2nw/BKRERE1MZJkgRp0IWA3MSln9ZkSHHxYa8pGIZXIiIiojbOI8sQ+zZBnnQLRHpuQ6OsArrmQx57HTynXiyqIK55JSKKMg57NSAEDKZ4pUshohhhV2uAnJ7QVxSjdvJ01Po8UEky4nw+WL99DzX5ExEZew0wvBIRRQ2nrQw4vBvard8CQsDZYyik7O7QWlOULo2IopwkSahIzsQBgwEfbPka9d6GG6OkGsy4cdSVsLvqGV6JiOjMOW1lUH36PKSTblSgPrIHIrk9XFNug9aarGB1RBTtrFoDCmvK8ea+9QHtpY4a/GvrN5jV7yKFKmuM4ZWIKBoc3h0QXE+Qyg5D7N8E9L8g/DURUcw47qjBJ0VbYNboMT45G3kGC7xCYJWtFD9UHMHmiiNIN1qULhMAwysRUcRz1FY1LBUIQr31ezg69YchQu5+Q0TRxweBBLUG01M6wLLqI0hHdgNqDdp3y8cFfUbhaxsv2CIiojMmACGa6fYBUviqIaLYI0PCdck5ML39KODzNjR63NBs/Q5ph/fg4l9NV7bAk3CrLCKiCGcwJcDToyBov6d7ATQGaxgrIqJYkyAA05pPfwmuJ6s6BmtlSfiLCoLhlYgoCkjZPSGS2zfuiE+D3GUg1Gr+IY2IWk64HMDhnUH7pd0bwlhN8xQNr/PmzcPgwYNhNpuRmpqKSy65BLt27Qo4RgiB2bNnIzMzEwaDAaNHj8a2bdsUqpiISBlaazJ8U26DZ/SvgZQsILk9PCOugO/yO6HhVllEdI6EJAO6uKD9vrjIuFgLUDi8rlixAn/605+wZs0aLF26FB6PB+PHj4fdbvcf88gjj+Dxxx/HU089hbVr1yI9PR3jxo1DTU2NgpUTEYWf1poM3YAL4JpyG5yX3AZV/3EMrkQUEnatHo4+I4P213cZHMZqmicJ0dxVAOF1/PhxpKamYsWKFRg5ciSEEMjMzMSMGTNwzz33AACcTifS0tIwf/583HzzzY3ew+l0wul0+p/bbDZkZWWhuroaFkvk/NZARNQSPns1JGcdIASEzgiZd9kiohAorbPBXn0c7Va+A9WRPQF9jmGXYU9WHvpndmm1z7fZbLBarWeU1yJqkVR1dTUAIDExEQBQWFiIkpISjB8/3n+MTqfDqFGjsGrVqibD67x58zBnzpzwFExEFCY+nw9S+RGI5W9BHNrR0NguD2L0NRBJ7SCrNcoWSERRTSXJWO+oRsWQiejgEzCWH4VPpYE9NQsrao6jh8GsdIl+EXPBlhACM2fOxPDhw9GrVy8AQElJw5VtaWlpAcempaX5+041a9YsVFdX+x+HDh1q3cKJiMJAqiqF7+35wIngCgBH9sD31nxI1ZGz/yIRRScfgB4JGYAkAyoNfF4PBAR8koz2piS4vB6lS/SLmJnXW2+9FZs3b8Z3333XqE+SAjcwFEI0ajtBp9NBp9O1So1ERErwed3Atu8Ap6Nxp8cFsfEriJFXQdbqw18cEcWEJJ0RVmcdVBu+Bvb/5G83SBJSxl6P+g69FKwuUETMvN5222346KOPsGzZMrRv/8tWMOnp6QDQaJa1tLS00WwsEVHMctghDgXfwkYc3g2pvjaMBRFRrBE+H9R71gcE14YOAXz1Coz19qZfqABFZ16FELjtttuwePFiLF++HLm5uQH9ubm5SE9Px9KlS9G/f38AgMvlwooVKzB//nwlSiYiCj+1BjCYgaR2kAZcAEnfsJ2NcDogNn0NaPQQKg1vskVELSbXlMO36Zug/WL7Kkip2WGsKDhFw+uf/vQnvP766/jwww9hNpv9M6xWqxUGgwGSJGHGjBmYO3cu8vLykJeXh7lz58JoNGLq1KlKlk5EFDayPg7eIZMg2cohVr4LUVPe0GGKhzT8CghLEuQ43mGLiM6BEECdLXi3vTqMxTRP0fD67LPPAgBGjx4d0P7yyy/jhhtuAADcfffdcDgcmD59OiorK5Gfn48vv/wSZnPkXPVGRNTaZLUOvs//G3jrxtoqiC9ehDz1b8oVRkQxQWi0QLs84OCOpvs79g1zRcFF1D6vreFs9g0jIopEXqcD0oq3IbaubPqALoMgnX89ZKMpvIURUcyorqsFyo/A9O5jgPAFdpqTUH3p7Uhs6hbVIXI2eS0iLtgiIqLgJGcdRFkz2/6VHWm4cQERUQt5hRfvVhej5pLbgBMhVZLh7TwAZZP+iCVVxcoWeJKI2SqLiIiC0OghWVMgSgqb7rckQWh1vGCLiFpMp1Kj0uvGo2WFuHjEZcjS6OGTJKyrLcfKwnWY2qGf0iX6MbyGkK/qeMPsh9cN6E3wGS1Q6Y1Kl0VEUU42xEEMGAex68em+wdPgMQLtojoHMTp43Bph74oqq1Ex4R01HlcUEkyCuJT4ZBV6ByfrnSJfgyvISJKD0IseRGi7HBDg9YAqWAyfJ0GQI5PUbY4Iop6LnMi5It+C/mrVwGPq6FRpYZv1DXwxKeBt2YhonNl1uihkWU88tNS1P18nskwWnBDXgFUcuRExsipJIqJihL43vsn4Dhpk3CXA2LF25CMVoDhlYjO0XFJ4GNvPSZeMRMmRy0gBOxGM76oOIJx8CFH6QKJKKrVOOtxrL4G/9u7NqC9uM6GJ7Z+g1n9xitUWWMMryEgju4NDK4n963+CL70XMgJvCMYEbWM3e3EsqO7samqBJuqSqCWZEiSBPfP22b5VGpc03EgLDqDwpUSUbSq97nxUdHmpvu8bmypOIp0Y2QsT+JuA6FQciB4X9UxSLG9GxkRtTKHx43D9ir/c4/w+YMrAByxV8HhdStQGRHFCp8QONrMjQgOnLg5SgRgeA2FpGYWMZviISReA0xELadTaZCsC76Ha5I+DloV/5BGRC0nSRKS9cHPM+nGyNkrn+E1BKTsnoBa23TfwAvhMyeFuSIiiiVmrQ4XtOsatP/C9j2QoOPOJkTUcsmSChdl9WiyTy3JGJicHeaKgmN4DQGv3gL50hmA4aRb1koSpD6jgc4DoFZzRoSIzo1Va8C0vCHQyip/m0ZW4ZpOA5udlSUiOhOSux5d9BaMa9cN8km7RhvVWkzvMRLW44cVrC4Qbw8bIm63A6qaakh11RDOekjxKRBaPWRzYqt9JhG1LTWuetg9LlQ67fCJhuUCRpWGF2oR0TkTzjr4PnoGtQWTUZeQhuP1duhUKsRr9LBu+Rba+FTI3fJb7fPPJq9xSjBENBoDkGgAEtP9v69wpSsRhZJZq4dZq4+otWdEFBsknRHysEthemseTEIg1ZQIuBwND3MipCvuUrpEPy4bICIiImrjfHU18B3YCnniH4Hk9kBtBeCuBzr2hTxuGkRlidIl+nHmlYiIiKiNk5wOiHVL4Lvwt6i44s9w+ryQJQkGWQXLN69BggR07Kt0mQAYXomIooZw1QP2aohjhYBPQMroCBgtkLjmlYjOlQTYfvMgjvrceGfHShTX2SAB6JmQgSvG/BpppYeUrtCP4ZWIKAqIejvE1m8h9m6E1KEXAAm+rd9Cat8F6H8BJAN3HCCilhPmBJTXVuLJbctx4kp+AWBrZTEOb/kGM3uPRaTcK5RrXomIokFlKaT4VEjZ3SF2rYXYuQZSRkdIaR0gyo4oXR0RRbkKZz0+LNqMpragqnI5sMdWGvaaguHMKxFRhBNuJ4TbAfH9YqD86C/tFcUQ1pSGiymcDi4fIKIW80Jgv60saP+OqmMYnt45jBUFx5lXIqJI5/MBpYcCgqtf9XGIw7sgvJ7w10VEMcWi1QMA4tQ6dI9PR2dLCuSfb3GfoI2cX44580pEFOGEzwuxd8NJLSd2kW74A5/YuxFy3zFhr4uIYkeSrMYF7bpBLaugV2mwt7oUOpUaF7bvgS0VR5Cfmqt0iX4Mr0REEU6SVRCyCr6cHqgZcSXq9XEAAL2zDqZVH0BlqwBk/iGNiFrO5nOjd0I7vLx7NfbX/LJ8YOmRnZiS0wdocjWsMhheiYginKQzwDPiShw3WfD54R3YWH4YAgJ9Etth4vlTkVxTBaPBrHSZRBTFJEmF70r2BATXEz4s2oxcc7ICVTWNv6oTEUWBSmsiFmxbgXVlB+EVPviEwKbyw3hi6zJUW5OULo+IopxHeLH6WGHQ/p/KD4exmuYxvBIRRbh6jxs/lhahxl2PNIMFF6d1wqS0Tsg0WlHncWFlyT7YXU6lyySiKCYLwOF1B+23eyLnHMNlA0REEc7ucWGPrRR35A5Au8pjMG1aDgiBkV3PQ2nHznjveCHsXhfioFO6VCKKUmpJQjdrGrZUNrGrCYABCZlhrig4hlcioginl1W4IrUjMpe9Cbl4n7/ddGQPjMntcf2FN0ArqxSskIiinU5nwCU5vbGjqgQe4QvoSzOYkW1OVKiyxhheiYgiXJxWD03FsYDgeoJcdhgpR/dDmxY529gQURTyepF4dD/+1n04yiQJTq8XKkmCCkB7jxtxDjtgtCpdJQCGVyKiiOez26DaurLhiVYPtMsDIAFH9wLOOqi2fgdf5/6QI2hmhIiii3DUwld1DPv1BrxzdAfqPA3rX9MNFvy2fQ8YirYCSZGxdIAXbBERRTrRsL+iNOxSyONvhGRKgBRngTT2OkgjrwIkyX/bAiKilpAl4GC7PCw6uNkfXAGgxGHDP/evR01WdwWrC8SZVyKiCCebrPANvxxi/ZcQ3y/+pWPrd0CHXpDGTIWIi2eAJaIWc6i1WHx8f5N99V43ttXXYHR4SwqKM69ERBHO6fUADjuwZ33jzgNbAVsZXD5P+Asjopjhk1U4aq8O2r+ntiKM1TSP4ZWIKMKp6+0Qm74O2i82fQ1NXU0YKyKiWKOWZSTrTUH728clhLGa5jG8EhFFOMnrAVz1wQ9w1UPy+YL3ExGdhkVrwOTsnk32qSUZA5Pbh7mi4LjmlYgownl1BsgdegGVx+DoOxqO9l0gABiL98Ow6Rsgpyc8Wj20ShdKRFFLeNzo6qjDxWmdsOTYfvjQcKGoUa3FH7P7IMFui5itsiQhfr6MNUbZbDZYrVZUV1fDYrEoXQ4R0Vmrq62EzuvFsfoavH1sH3ZUlwIA8szJuCY9D+kGM9wSYLAkK1wpEUUrUVMJ3yt/g6vLYNT0KECFzwuNrEK8sw6WNZ9Cldwe8vhprfb5Z5PXOPNKRBTh1BoDKqR6PLJvXcC9x/fUlGG+vRJ/7XchLBJP50R0joSAdstKJG1ZiSS1FvB5Gx4AkJShbG0n4ZpXIqIIJyRgzbH9AcH1BJfPi2+O7IRPxdM5EZ0DQxykruc1/HOPYZB+9Sdg4i2/3JigxzDlajsFf1UnIopwDrcTW23Hg/bvqimD0+1EnM4YxqqIKJZIai0wZDIwcDxQXAjx0zJIWj1w/rWAwQTJYFa6RD+GVyKiCKeSVbCodUH7TWodVLIqjBURUUzyeiA+ehqoKAaAhku2dqyB1GcUxKAJEXMjFP6diYgowhkAjMvIC9o/PiMPRpl7DRBRy3md9RCbvvEH15OJzSsg2avCX1QQDK9ERBFOSDJS9SZMyOjSqG9Uai6y4xIgfI3XwxIRnSmprhpi++qg/WL7qjBW0zwuGyAiinBOnwfP7/0Bf+icj/y0XOyoOAofgO4JGYhTafGvnd9iRrfh3OeViFpMEgLC4wp+gNsZvmJOg+GViCjCSZIMlSTjng2fIgvxuGvIOADAczu/wY6qcmSbEiBFymI0IopKQq0DOvYBdq9r+oAug8NbUDMYXomIIpxeVmN0YnvkWVPQPykbu23H4BMCV+Seh62VR2H2+RDHfV6J6BxIWi2kIZPhO7C18e2o03N/2TIrAvBsR0QU4WThRQdrKuy15Xh40xfwCB8AQCXJuKRDX3RNagdZeBWukoiimaSPg7euFvI190Gs/RziwBZAo4fUa3jD/q+qyImMkVMJERE1TWdAtasOr+1dG9DsFT68V7gR2b0SkGRJUag4IooVsike4ngRMPBCyOdNhJAA2GsAnw9SQpLS5fkxvBIRRTi7242vj+wK2v/F4R1I62JBgoo3KSCilvM5aiDLaviWvwlxeCeg1kDqPhQYOB6itgqSKV7pEgEwvBIRRTyn140ypz1of4XTDpfXE8aKiCjW+NxOyI5a+N6eD5w4n3jcEFtWQBzeCfmS25Ut8CTc55WIKMIZ1BpkxyUE7W8fFw+DShPGiogo5tTVwLfqg1+C68kqj0GUFIW9pGA480pEFOGMGh1GZ3bBmmOF6J6Qjp6JDVf97qwqwebyI7iwfQ9YdAaFqySiaCb5vA1LBeKscPQ7H/VpHSAJH0y71kG96wdg/yage77SZQJgeCUiigqJWgNm9b8IK4p345OiLRAQ6JvYHvf3vwgJagZXIjo3AoCnU3+UDLgAbx/bh72HNkMtyRiS0wUX9xmBhH2blS7RTxJCCKWLaE02mw1WqxXV1dWwWCxKl0NE1CKlDhsWbFmG8lPWvlq1Bvy591ikGXl+I6KW83o8OGo7jnnbV8D783Z8J6Tqzbiz2zAkmhNb7fPPJq9xzSsRUYTzeDzYVHa4UXAFgGqXA6tLC1F/6qbiRERnweHzYPGRnY2CKwCU1teg0FGjQFVNUzS8rly5EpMnT0ZmZiYkScIHH3wQ0C+EwOzZs5GZmQmDwYDRo0dj27ZtyhRLRKSQKrcDG8sPB+3/qfwwqpu7JzkR0WnYPS7stpUG7d9YEfwcFG6Khle73Y6+ffviqaeearL/kUceweOPP46nnnoKa9euRXp6OsaNG4eamshJ/0RErU0NGVqVKmi/RlZBlqQwVkREsUaSJMSptUH7LRp9GKtpnqLhdcKECXjooYdw2WWXNeoTQmDBggW4//77cdlll6FXr15YtGgR6urq8PrrrytQLRGRMuINcRiR3jlo/8iMzkgxmMNYERHFGo2kwqiMvKD9g1JywlhN8yJ2zWthYSFKSkowfvx4f5tOp8OoUaOwatWqoK9zOp2w2WwBDyKiaJcTl4jeCZmN2rtYU9HVmqZARUQUSyQJyLOmIq+JW01Pzu6NSPrbTsRulVVSUgIASEsLPCmnpaWhqCj4Rrnz5s3DnDlzWrU2IqJwSzGacXXHARhd3wWrSwshhEB+agdkGCycdSWic+bxuPHfnd/hoqyeGJ3ZBXttx6GT1ehsTcXGsoNYdWw/ci3JSpcJIILD6wnSKeu4hBCN2k42a9YszJw50//cZrMhKyur1eojIgqXFKMFKUYLullSAQBqdcSfwokoSsiyDIfHgzf3rYdRrUG7uAS4fV58eXgHfBDIT+mgdIl+EXvmS09PB9AwA5uRkeFvLy0tbTQbezKdTgedTtfq9RERKYWhlYhCTUBCz8QMbCg7hDqPG3uqA3ce6JfUXqHKGovYNa+5ublIT0/H0qVL/W0ulwsrVqzA0KFDFayMiIiIKMb4PBjXrjv0Kk2jro7mJBia2Ykg3BT99b22thZ79+71Py8sLMSmTZuQmJiI7OxszJgxA3PnzkVeXh7y8vIwd+5cGI1GTJ06VcGqiYiIiGKLVpJR53Hi5u7D8UPpAWyrLIZOpUZ+agdkxSXArG4capWiaHhdt24dxowZ439+Yq3qtGnTsHDhQtx9991wOByYPn06KisrkZ+fjy+//BJmMy9OICIiIgoVDyR8VLQFR+xVGJicjUnZveD2ebGx/BA+PbgV13QciPbmJKXLBABIQgihdBGt6WzulUtERETUFpU5avDA+k/haeL2sAAwKDkbv+8+vNU+/2zyWsSueSUiIiKiMBFAkt4UtDvTaA1jMc1jeCUiIiJq4yQJGJPZ9B221JKMLvGRczMU7rdCRERE1Mb5hIAECTd1KUC7uHgIACpJgsPjQq3XhcO1lcizpipdJgCGVyIiIqI2Ty+r0dWaijqvG0frqqGSZaggwe5xobMlBdnGeKVL9GN4JSIiImrzBDw+H47V2fDJwa0od9oBAJ0tKUjUGZGij5ydnrjmlYiIiKiNcwug0lWHRXt+8AdXANhrO44Xd61GncelYHWBGF6JiIiI2jgPfFhyeHuTfTXueuyxlTbZpwSGVyIiIqI2TkCgqKYiaP/+mvIwVtM8hlciIiKiNk4NGfE6Y9D+VK55JSIiIqJIYZRUuLB99yb7JEgYnJId5oqCY3glIiIiauMkTz3SdSYMTskJaFdLMq7tPBhwORWqrDFulUVERETUxtklCS/sXo0habm4tecolNTZoFOpYdEasOLoHhzRGXFNQmTcZYvhlYiIiKiNE5IMh9eNLw/vwFeHdyJBZ4Tb54XNXQ8AMJ0yI6skLhsgIiIiauNkSOiVkAEA8EGg3Gn3B1cAGJTMNa9EREREFCE0ag0uzOoBjaxq1NfeGI9UA3cbICIiIqIIUedx4eMDW3BL9xHom9gOaklGnFqLsZldMaVDX3xXsk/pEv245pWIiIiIsMdWin01ZShIy8W0LkPg9nmxsfwQvtm+O6K2ymJ4JSIiImrj9Co1eiZkYmP5ISw7uhvLsDugf2AyL9giIiIiogihklWYnNMbOlXjec0cUyLSjRYFqmoaZ16JiIiI2jiTRofKejvu6TseSw5tw9bKYuhUagxP64SBydnQyJEz38nwSkRERERINpiwtaIYeZZUjMnsAq8Q2FFRDC98SNRx5jXm+IQPVU4HKpx1cHjdSDWYYNboYVRrlS6NiIiI6LQMai0GpmShyumA3eOCQZYxul1XWLR6pUsLwPAaAh6fF4U15Xhm+0rUeVwAAAnAiPTOmJzTGxatQdkCiYiIiM6ALMlI1MchEXFKlxJU5CxgiGKVTgf+tXWZP7gCgACwsmQv1h8/CCGEcsURERERxRCG1xDYUVkMt8/bZN/nh7ej2uUIc0VEREREsYnhNQSO1lUH7at2OeDlzCsRERFRSDC8hkBna0rQvjSDpcn7BBMRERHR2eMFWyHQ0ZwMs0aPGnd9o77LOvSNuKv0iIiIiIKp87hQ73EDkgSLRgd1hE3CMbyGQKI+Dn/uMxYv71qNotoKAIBRrcXluf2RZ01TuDoiIiKi0/P4vCius+G9wo3YWVUCjazCsPROGN+uOxL1kbP7gCRi/FJ4m80Gq9WK6upqWCytu8FurbseNW4nPD4f4tRaWHUGqCSuzCAiIqLId8Rehbkbl8AjfAHtaQYL7ux9PhJ0xlb77LPJa5x5DSGTRg+ThksEiIiIKLrUe1z44MBPjYIrABxz2FBUU9Gq4fVscFqQiIiIqI1zeD3YUVUStH992cEwVtM8hlciIiKiNk4CYFBpgvabNbrwFXMaDK9EREREbZxFq8eYzC5B+4emdQxjNc1jeCUiIiJq42RJxtC0juhkSW7Ud2mHfkjURc5uA7xgi4iIiIgQrzPi5m4jcKy+BpvKDsKo1mFAcjYSdAYY1Fqly/NjeCUiIiIiAIBVZ4BVZ0AXa6rSpQTFZQNEREREFDUYXomIiIgoajC8EhEREVHUYHglIiIioqjB8EpEREREUYPhlYiIiIiiBsMrEREREUUNhlciIiIiihoMr0REREQUNRheiYiIiChqMLwSERERUdRgeCUiIiKiqMHwSkRERERRg+GViIiIiKIGwysRERERRQ2GVyIiIiKKGmqlC4gl1c46OLxueIWAVlYhxWBWuiQiijHlDjtcPg8EAK1KhWS9SemSiCjGlDtq4PR6IEsS4lQamPVxSpcUICrC6zPPPINHH30UxcXF6NmzJxYsWIARI0YoXVaAUkcNPjqwGRvKD8ErfMgwWnFlbn9kGK1IjLB/6UQUfdxuN0qctXi3cAN2Vh0DAHS2pOCqjgOQYoiDUa1XuEIiina1zjoccdjw9v4NOGyvggwJfZPa4dIOfZFmtCpdnl/ELxt46623MGPGDNx///3YuHEjRowYgQkTJuDgwYNKl+ZXWmfDU9uWY21ZEbzCBwAorqvGk9uWo7S+RuHqiCgWlLvr8M/NX/mDKwDstR3HY5u/QpXTqWBlRBQrSp12LNiyDIftVQAAHwQ2lh/Ggq3LUFpnU7a4k0R8eH388cfx29/+Fr/73e/QvXt3LFiwAFlZWXj22WeVLs3vcF01jjkah1QB4P3CTShz1Ia/KCKKGfVeF1YfK4TD627U5/J58c3RXah1M8ASUctVOWrxYdFm+CAa9VU467DXdlyBqpoW0eHV5XJh/fr1GD9+fED7+PHjsWrVqiZf43Q6YbPZAh6tbUdlSdC+otoK/2wsEVFL1Lhc2Fl9LGj/Xlsp6jwMr0TUci7hxd7q4AF1W1XwrBNuER1ey8rK4PV6kZaWFtCelpaGkpKmB3HevHmwWq3+R1ZWVqvXGa8zBO0zqjWA1OolEFEM08gyzBpd0H6TWgeVpApjRUQUayQAZk3wtfPxzfSFW0SH1xMkKTD9CSEatZ0wa9YsVFdX+x+HDh1q9fr6JbWHFCShjkjPg1UdPNwSEZ1OvM6I8zO7Bu0/v11XJPHCUCI6BwnaOIzOzAvan5+aG8ZqmhfR4TU5ORkqlarRLGtpaWmj2dgTdDodLBZLwKO1GWUNbuiSD/mUANvZkoIR6Z2h12havQYiim2pehMuaNetUfvwtE7IjktUoCIiiiVqtRqDk7PRMz4joF0C8OtOg2BRa5UprAmSEKLxytwIkp+fj4EDB+KZZ57xt/Xo0QNTpkzBvHnzTvt6m80Gq9WK6urqVg2yVU47HF4PdlUdQ627Hl3j0xGvNXCvVyIKmeOOGtR7PdheWQyfEOiZmAGjSotkA/d6JaLQKHfUoNpdjx2VJdCrNegenw6TSg1LK+8pfTZ5LeL3eZ05cyauv/56DBo0CAUFBXj++edx8OBB/PGPf1S6tADxujjEA8iIoH3QiCi2nPhlOMuUoHAlRBSrkgxmJBnM6GhJUbqUoCI+vF599dUoLy/Hgw8+iOLiYvTq1QufffYZcnJylC6NiIiIiMIs4pcNnKtwLRsgIiIiopY5m7wW0RdsERERERGdjOGViIiIiKIGwysRERERRQ2GVyIiIiKKGgyvRERERBQ1GF6JiIiIKGowvBIRERFR1GB4JSIiIqKowfBKRERERFGD4ZWIiIiIogbDKxERERFFDYZXIiIiIooaDK9EREREFDXUShfQ2oQQAACbzaZwJURERETUlBM57URua07Mh9eamhoAQFZWlsKVEBEREVFzampqYLVamz1GEmcScaOYz+fD0aNHYTabIUlSq3+ezWZDVlYWDh06BIvF0uqfF004Nk3juATHsWkax6VpHJfgODZN47gEF+6xEUKgpqYGmZmZkOXmV7XG/MyrLMto37592D/XYrHwP4QgODZN47gEx7FpGselaRyX4Dg2TeO4BBfOsTndjOsJvGCLiIiIiKIGwysRERERRQ2G1xDT6XR44IEHoNPplC4l4nBsmsZxCY5j0zSOS9M4LsFxbJrGcQkukscm5i/YIiIiIqLYwZlXIiIiIooaDK9EREREFDUYXomIiIgoajC8EhEREVHUYHhtxsqVKzF58mRkZmZCkiR88MEHp33NihUrMHDgQOj1enTs2BHPPfdco2Pee+899OjRAzqdDj169MDixYtbofrWdbZj8/7772PcuHFISUmBxWJBQUEBvvjii4BjFi5cCEmSGj3q6+tb8ScJrbMdl+XLlzf5M+/cuTPguLb4nbnhhhuaHJuePXv6j4mF78y8efMwePBgmM1mpKam4pJLLsGuXbtO+7pYP9e0ZFzawnmmJePSVs4zLRmbtnCeefbZZ9GnTx//zQYKCgrw+eefN/uaSD+/MLw2w263o2/fvnjqqafO6PjCwkJcfPHFGDFiBDZu3Ij77rsPt99+O9577z3/MatXr8bVV1+N66+/Hj/99BOuv/56XHXVVfjhhx9a68doFWc7NitXrsS4cePw2WefYf369RgzZgwmT56MjRs3BhxnsVhQXFwc8NDr9a3xI7SKsx2XE3bt2hXwM+fl5fn72up35l//+lfAmBw6dAiJiYm48sorA46L9u/MihUr8Kc//Qlr1qzB0qVL4fF4MH78eNjt9qCvaQvnmpaMS1s4z7RkXE6I9fNMS8amLZxn2rdvj4cffhjr1q3DunXrcP7552PKlCnYtm1bk8dHxflF0BkBIBYvXtzsMXfffbfo1q1bQNvNN98shgwZ4n9+1VVXiYsuuijgmAsvvFBcc801Ias13M5kbJrSo0cPMWfOHP/zl19+WVit1tAVprAzGZdly5YJAKKysjLoMfzONFi8eLGQJEkcOHDA3xZr3xkhhCgtLRUAxIoVK4Ie0xbPNWcyLk2J9fPMmYxLWz3PtOQ701bOMwkJCeK///1vk33RcH7hzGsIrV69GuPHjw9ou/DCC7Fu3Tq43e5mj1m1alXY6owEPp8PNTU1SExMDGivra1FTk4O2rdvj0mTJjWaMYlV/fv3R0ZGBsaOHYtly5YF9PE70+DFF1/EBRdcgJycnID2WPvOVFdXA0Cj/zZO1hbPNWcyLqdqC+eZsxmXtnaeacl3JtbPM16vF2+++SbsdjsKCgqaPCYazi8MryFUUlKCtLS0gLa0tDR4PB6UlZU1e0xJSUnY6owE//znP2G323HVVVf527p164aFCxfio48+whtvvAG9Xo9hw4Zhz549ClbaujIyMvD888/jvffew/vvv4+uXbti7NixWLlypf8YfmeA4uJifP755/jd734X0B5r3xkhBGbOnInhw4ejV69eQY9ra+eaMx2XU8X6eeZMx6Utnmda8p2J5fPMli1bYDKZoNPp8Mc//hGLFy9Gjx49mjw2Gs4v6rB8ShsiSVLAc/HzDcxObm/qmFPbYtkbb7yB2bNn48MPP0Rqaqq/fciQIRgyZIj/+bBhwzBgwAA8+eST+Pe//61Eqa2ua9eu6Nq1q/95QUEBDh06hMceewwjR470t7f178zChQsRHx+PSy65JKA91r4zt956KzZv3ozvvvvutMe2pXPN2YzLCW3hPHOm49IWzzMt+c7E8nmma9eu2LRpE6qqqvDee+9h2rRpWLFiRdAAG+nnF868hlB6enqj3zpKS0uhVquRlJTU7DGn/gYTq9566y389re/xdtvv40LLrig2WNlWcbgwYOj6rfbUBgyZEjAz9zWvzNCCLz00ku4/vrrodVqmz02mr8zt912Gz766CMsW7YM7du3b/bYtnSuOZtxOaEtnGdaMi4ni+XzTEvGJtbPM1qtFp07d8agQYMwb9489O3bF//617+aPDYazi8MryFUUFCApUuXBrR9+eWXGDRoEDQaTbPHDB06NGx1KuWNN97ADTfcgNdffx0TJ0487fFCCGzatAkZGRlhqC5ybNy4MeBnbsvfGaDhCuK9e/fit7/97WmPjcbvjBACt956K95//3188803yM3NPe1r2sK5piXjAsT+eaal43KqWDzPnMvYxPp55lRCCDidzib7ouL8EpbLwqJUTU2N2Lhxo9i4caMAIB5//HGxceNGUVRUJIQQ4t577xXXX3+9//j9+/cLo9Eo7rzzTrF9+3bx4osvCo1GI959913/Md9//71QqVTi4YcfFjt27BAPP/ywUKvVYs2aNWH/+c7F2Y7N66+/LtRqtXj66adFcXGx/1FVVeU/Zvbs2WLJkiVi3759YuPGjeLGG28UarVa/PDDD2H/+VrqbMfliSeeEIsXLxa7d+8WW7duFffee68AIN577z3/MW31O3PCddddJ/Lz85t8z1j4ztxyyy3CarWK5cuXB/y3UVdX5z+mLZ5rWjIubeE805JxaSvnmZaMzQmxfJ6ZNWuWWLlypSgsLBSbN28W9913n5BlWXz55ZdCiOg8vzC8NuPE9iKnPqZNmyaEEGLatGli1KhRAa9Zvny56N+/v9BqtaJDhw7i2WefbfS+77zzjujatavQaDSiW7duASeQaHG2YzNq1KhmjxdCiBkzZojs7Gyh1WpFSkqKGD9+vFi1alV4f7BzdLbjMn/+fNGpUyeh1+tFQkKCGD58uPj0008bvW9b/M4IIURVVZUwGAzi+eefb/I9Y+E709SYABAvv/yy/5i2eK5pybi0hfNMS8alrZxnWvrfUqyfZ2666SaRk5Pjr3/s2LH+4CpEdJ5fJCF+XoVLRERERBThuOaViIiIiKIGwysRERERRQ2GVyIiIiKKGgyvRERERBQ1GF6JiIiIKGowvBIRERFR1GB4JSIiIqKowfBKRERERFGD4ZWIKMLdcMMNuOSSS5Qug4goIjC8EhGFwQ033ABJkiBJEjQaDTp27Ii77roLdrtd6dKIiKKKWukCiIjaiosuuggvv/wy3G43vv32W/zud7+D3W7Hs88+q3RpRERRgzOvRERhotPpkJ6ejqysLEydOhXXXnstPvjgAwDAtm3bMHHiRFgsFpjNZowYMQL79u1r8n2WLFmC4cOHIz4+HklJSZg0aVLAsS6XC7feeisyMjKg1+vRoUMHzJs3z98/e/ZsZGdnQ6fTITMzE7fffnur/txERKHEmVciIoUYDAa43W4cOXIEI0eOxOjRo/HNN9/AYrHg+++/h8fjafJ1drsdM2fORO/evWG32/H3v/8dl156KTZt2gRZlvHvf/8bH330Ed5++21kZ2fj0KFDOHToEADg3XffxRNPPIE333wTPXv2RElJCX766adw/thEROeE4ZWISAE//vgjXn/9dYwdOxZPP/00rFYr3nzzTWg0GgBAly5dgr728ssvD3j+4osvIjU1Fdu3b0evXr1w8OBB5OXlYfjw4ZAkCTk5Of5jDx48iPT0dFxwwQXQaDTIzs7Geeed1zo/JBFRK+CyASKiMPnkk09gMpmg1+tRUFCAkSNH4sknn8SmTZswYsQIf3A9nX379mHq1Kno2LEjLBYLcnNzATQEU6Dh4rBNmzaha9euuP322/Hll1/6X3vllVfC4XCgY8eO+P3vf4/FixcHneElIopEDK9ERGEyZswYbNq0Cbt27UJ9fT3ef/99pKamwmAwnNX7TJ48GeXl5XjhhRfwww8/4IcffgDQsNYVAAYMGIDCwkL84x//gMPhwFVXXYUrrrgCAJCVlYVdu3bh6aefhsFgwPTp0zFy5Ei43e7Q/rBERK2E4ZWIKEzi4uLQuXNn5OTkBMyy9unTB99+++0ZBcjy8nLs2LEDf/3rXzF27Fh0794dlZWVjY6zWCy4+uqr8cILL+Ctt97Ce++9h4qKCgANa21/9atf4d///jeWL1+O1atXY8uWLaH7QYmIWhHXvBIRKezWW2/Fk08+iWuuuQazZs2C1WrFmjVrcN5556Fr164BxyYkJCApKQnPP/88MjIycPDgQdx7770BxzzxxBPIyMhAv379IMsy3nnnHaSnpyM+Ph4LFy6E1+tFfn4+jEYjXn31VRgMhoB1sUREkYwzr0RECktKSsI333yD2tpajBo1CgMHDsQLL7zQ5BpYWZbx5ptvYv369ejVqxfuvPNOPProowHHmEwmzJ8/H4MGDcLgwYNx4MABfPbZZ5BlGfHx8XjhhRcwbNgw9OnTB19//TU+/vhjJCUlhevHJSI6J5IQQihdBBERERHRmeDMKxERERFFDYZXIiIiIooaDK9EREREFDUYXomIiIgoajC8EhEREVHUYHglIiIioqjB8EpEREREUYPhlYiIiIiiBsMrEREREUUNhlciIiIiihoMr0REREQUNf4fZPhuILLFU1YAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Data visulization 4\n",
    "\n",
    "plt.figure(figsize=(8,6))\n",
    "sns.scatterplot(x='Pclass', y='Fare', data = df ,hue='Survived',  palette ='Set2')\n",
    "plt.title('Fare by Passenger Class')\n",
    "plt.xlabel('Pclass')\n",
    "plt.ylabel('Fare')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "084dc17b-f53a-4a98-898d-46b823256953",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:base] *",
   "language": "python",
   "name": "conda-base-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
