Titanic train.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "24f80b2b-b23e-4178-a544-b05abb836753",
   "metadata": {},
   "outputs": [],
   "source": [
    "# First we add the libraries we will use.\n",
    "# Ilk olaraq istifadə edəcəyimiz kitabxanaları əlavə edirik.\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1e0ebed0-c57e-4781-824f-5bd2873ba0b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>494</th>\n",
       "      <td>495</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Stanley, Mr. Edward Roland</td>\n",
       "      <td>male</td>\n",
       "      <td>21.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A/4 45380</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>840</th>\n",
       "      <td>841</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Alhomaki, Mr. Ilmari Rudolf</td>\n",
       "      <td>male</td>\n",
       "      <td>20.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>SOTON/O2 3101287</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>542</th>\n",
       "      <td>543</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Andersson, Miss. Sigrid Elisabeth</td>\n",
       "      <td>female</td>\n",
       "      <td>11.00</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>347082</td>\n",
       "      <td>31.2750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>746</th>\n",
       "      <td>747</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Abbott, Mr. Rossmore Edward</td>\n",
       "      <td>male</td>\n",
       "      <td>16.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>C.A. 2673</td>\n",
       "      <td>20.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>701</th>\n",
       "      <td>702</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Silverthorne, Mr. Spencer Victor</td>\n",
       "      <td>male</td>\n",
       "      <td>35.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17475</td>\n",
       "      <td>26.2875</td>\n",
       "      <td>E24</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>603</th>\n",
       "      <td>604</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Torber, Mr. Ernst William</td>\n",
       "      <td>male</td>\n",
       "      <td>44.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>364511</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>765</th>\n",
       "      <td>766</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Hogeboom, Mrs. John C (Anna Andrews)</td>\n",
       "      <td>female</td>\n",
       "      <td>51.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>13502</td>\n",
       "      <td>77.9583</td>\n",
       "      <td>D11</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>441</th>\n",
       "      <td>442</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Hampe, Mr. Leon</td>\n",
       "      <td>male</td>\n",
       "      <td>20.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>345769</td>\n",
       "      <td>9.5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>269</th>\n",
       "      <td>270</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Bissette, Miss. Amelia</td>\n",
       "      <td>female</td>\n",
       "      <td>35.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17760</td>\n",
       "      <td>135.6333</td>\n",
       "      <td>C99</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>803</th>\n",
       "      <td>804</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Thomas, Master. Assad Alexander</td>\n",
       "      <td>male</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2625</td>\n",
       "      <td>8.5167</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass                                  Name  \\\n",
       "494          495         0       3            Stanley, Mr. Edward Roland   \n",
       "840          841         0       3           Alhomaki, Mr. Ilmari Rudolf   \n",
       "542          543         0       3     Andersson, Miss. Sigrid Elisabeth   \n",
       "746          747         0       3           Abbott, Mr. Rossmore Edward   \n",
       "701          702         1       1      Silverthorne, Mr. Spencer Victor   \n",
       "603          604         0       3             Torber, Mr. Ernst William   \n",
       "765          766         1       1  Hogeboom, Mrs. John C (Anna Andrews)   \n",
       "441          442         0       3                       Hampe, Mr. Leon   \n",
       "269          270         1       1                Bissette, Miss. Amelia   \n",
       "803          804         1       3       Thomas, Master. Assad Alexander   \n",
       "\n",
       "        Sex    Age  SibSp  Parch            Ticket      Fare Cabin Embarked  \n",
       "494    male  21.00      0      0         A/4 45380    8.0500   NaN        S  \n",
       "840    male  20.00      0      0  SOTON/O2 3101287    7.9250   NaN        S  \n",
       "542  female  11.00      4      2            347082   31.2750   NaN        S  \n",
       "746    male  16.00      1      1         C.A. 2673   20.2500   NaN        S  \n",
       "701    male  35.00      0      0          PC 17475   26.2875   E24        S  \n",
       "603    male  44.00      0      0            364511    8.0500   NaN        S  \n",
       "765  female  51.00      1      0             13502   77.9583   D11        S  \n",
       "441    male  20.00      0      0            345769    9.5000   NaN        S  \n",
       "269  female  35.00      0      0          PC 17760  135.6333   C99        S  \n",
       "803    male   0.42      0      1              2625    8.5167   NaN        C  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Then with the help of \"Pandas\" library we get the data from internal memory in CSV format.\n",
    "# Daha sonra isə \"Pandas\" kitabxanasının köməyi ilə daxili yaddaşdan CSV formatında olan datanı əldə edirik.\n",
    "train_dataset = pd.read_csv(r\"D:\\Downloads\\Titanic train dataset.csv\")\n",
    "train_dataset.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "75c09cce-fb32-49ec-955f-a74d638938ff",
   "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>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>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>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>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>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>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>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>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>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": [
       "         Survived      Pclass         Age       SibSp       Parch        Fare\n",
       "count  891.000000  891.000000  714.000000  891.000000  891.000000  891.000000\n",
       "mean     0.383838    2.308642   29.699118    0.523008    0.381594   32.204208\n",
       "std      0.486592    0.836071   14.526497    1.102743    0.806057   49.693429\n",
       "min      0.000000    1.000000    0.420000    0.000000    0.000000    0.000000\n",
       "25%      0.000000    2.000000   20.125000    0.000000    0.000000    7.910400\n",
       "50%      0.000000    3.000000   28.000000    0.000000    0.000000   14.454200\n",
       "75%      1.000000    3.000000   38.000000    1.000000    0.000000   31.000000\n",
       "max      1.000000    3.000000   80.000000    8.000000    6.000000  512.329200"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The \"Explain\" function is used to get general information about the columns in the data format number.\n",
    "# Datada number formatda olan sütunlarla bağlı ümumi məlumat əldə etmək üçün \"Describe\" funksiyasından istifadə edilir.\n",
    "train_dataset[[\"Survived\",\"Pclass\",\"Age\",\"SibSp\",\"Parch\",\"Fare\"]].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "f294cc7a-4b7f-4574-ae9d-1b1b08bf62e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n",
       "       'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_dataset.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "5bf7db29-a928-4706-9064-d53a081f8d3a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  891 non-null    int64  \n",
      " 1   Survived     891 non-null    int64  \n",
      " 2   Pclass       891 non-null    int64  \n",
      " 3   Name         891 non-null    object \n",
      " 4   Sex          891 non-null    object \n",
      " 5   Age          714 non-null    float64\n",
      " 6   SibSp        891 non-null    int64  \n",
      " 7   Parch        891 non-null    int64  \n",
      " 8   Ticket       891 non-null    object \n",
      " 9   Fare         891 non-null    float64\n",
      " 10  Cabin        204 non-null    object \n",
      " 11  Embarked     889 non-null    object \n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.7+ KB\n"
     ]
    }
   ],
   "source": [
    "train_dataset.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "12708e7e-62a2-4e0d-a4df-48801dd89f08",
   "metadata": {},
   "outputs": [
    {
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+MRYuXBgREfX19TFv3ry4/vrrY/jw4XHSSSfFggUL4qmnnor77rtv/+wVAADATvYqfr785S/HWWedFcOHDy/Yvnbt2qirq4uRI0fmt5WVlcWQIUNi+fLlERGxatWq2Lp1a8FMVVVV9O7dOz+zs8bGxmhoaCi4AAAAFKOk2DssWrQonnzyyVi5cmWT2+rq6iIioqKiomB7RUVFvPzyy/mZ0tLSgjNGO2Z23H9n06dPj+uuu67YpQIAAOQVdeZn3bp18dWvfjUWLFgQbdq02e1cLpcruJ5lWZNtO9vTzOTJk6O+vj5/WbduXTHLBgAAKC5+Vq1aFevXr49+/fpFSUlJlJSUxLJly+IHP/hBlJSU5M/47HwGZ/369fnbKisrY8uWLbFhw4bdzuysrKwsOnToUHABAAAoRlHxM2zYsHjqqadizZo1+cvJJ58cF1xwQaxZsyY++tGPRmVlZSxdujR/ny1btsSyZcti0KBBERHRr1+/aN26dcFMbW1tPP300/kZAACA/a2o9/y0b98+evfuXbCtXbt20blz5/z26urqmDZtWvTo0SN69OgR06ZNi0MOOSTGjRsXERHl5eUxYcKEuOaaa6Jz587RqVOnmDhxYvTp06fJBygAAADsL0V/4MF7ufbaa2Pz5s1xxRVXxIYNG6J///6xZMmSaN++fX5m9uzZUVJSEmPHjo3NmzfHsGHDoqamJlq1arW/lwMAABAREbksy7LmXkSxGhoaory8POrr61vM+3+OmXRPcy8B4IB5acZZzb0EANilYtpgr/7ODwAAwMFG/AAAAEkQPwAAQBLEDwAAkATxAwAAJEH8AAAASRA/AABAEsQPAACQBPEDAAAkQfwAAABJED8AAEASxA8AAJAE8QMAACRB/AAAAEkQPwAAQBLEDwAAkATxAwAAJEH8AAAASRA/AABAEsQPAACQBPEDAAAkQfwAAABJED8AAEASxA8AAJAE8QMAACRB/AAAAEkQPwAAQBLEDwAAkATxAwAAJEH8AAAASRA/AABAEsQPAACQhJLmXgAAfFgdM+me5l4CwAHz0oyzmnsJRXPmBwAASIL4AQAAkiB+AACAJIgfAAAgCeIHAABIgvgBAACSIH4AAIAkiB8AACAJ4gcAAEiC+AEAAJIgfgAAgCSIHwAAIAniBwAASIL4AQAAkiB+AACAJIgfAAAgCeIHAABIgvgBAACSIH4AAIAkiB8AACAJ4gcAAEiC+AEAAJJQVPzcdNNNccIJJ0SHDh2iQ4cOMXDgwPjVr36Vvz3Lspg6dWpUVVVF27ZtY+jQofHMM88UPEZjY2NcddVV0aVLl2jXrl2MGTMmXn311f2zNwAAALtRVPwcddRRMWPGjHjiiSfiiSeeiL/+67+Os88+Ox84M2fOjFmzZsWcOXNi5cqVUVlZGSNGjIiNGzfmH6O6ujoWL14cixYtikceeSQ2bdoUo0aNim3btu3fPQMAAPgzRcXP6NGj49Of/nQce+yxceyxx8Y//uM/xqGHHhqPPfZYZFkWN9xwQ0yZMiXOPffc6N27d9x6663xxz/+MRYuXBgREfX19TFv3ry4/vrrY/jw4XHSSSfFggUL4qmnnor77rvvgOwgAABAxD6852fbtm2xaNGiePvtt2PgwIGxdu3aqKuri5EjR+ZnysrKYsiQIbF8+fKIiFi1alVs3bq1YKaqqip69+6dnwEAADgQSoq9w1NPPRUDBw6Md955Jw499NBYvHhxHHfccfl4qaioKJivqKiIl19+OSIi6urqorS0NDp27Nhkpq6ubrdfs7GxMRobG/PXGxoail02AACQuKLP/PTs2TPWrFkTjz32WHzpS1+Kiy66KJ599tn87blcrmA+y7Im23b2XjPTp0+P8vLy/KVr167FLhsAAEhc0fFTWloaf/mXfxknn3xyTJ8+PU488cT4/ve/H5WVlRERTc7grF+/Pn82qLKyMrZs2RIbNmzY7cyuTJ48Oerr6/OXdevWFbtsAAAgcfv8d36yLIvGxsbo3r17VFZWxtKlS/O3bdmyJZYtWxaDBg2KiIh+/fpF69atC2Zqa2vj6aefzs/sSllZWf7jtXdcAAAAilHUe36+8Y1vxJlnnhldu3aNjRs3xqJFi+LBBx+Me++9N3K5XFRXV8e0adOiR48e0aNHj5g2bVoccsghMW7cuIiIKC8vjwkTJsQ111wTnTt3jk6dOsXEiROjT58+MXz48AOygwAAABFFxs/rr78e48ePj9ra2igvL48TTjgh7r333hgxYkRERFx77bWxefPmuOKKK2LDhg3Rv3//WLJkSbRv3z7/GLNnz46SkpIYO3ZsbN68OYYNGxY1NTXRqlWr/btnAAAAfyaXZVnW3IsoVkNDQ5SXl0d9fX2L+RW4Yybd09xLADhgXppxVnMv4aDktQH4MGsprw3FtME+v+cHAADgYCB+AACAJIgfAAAgCeIHAABIgvgBAACSIH4AAIAkiB8AACAJ4gcAAEiC+AEAAJIgfgAAgCSIHwAAIAniBwAASIL4AQAAkiB+AACAJIgfAAAgCeIHAABIgvgBAACSIH4AAIAkiB8AACAJ4gcAAEiC+AEAAJIgfgAAgCSIHwAAIAniBwAASIL4AQAAkiB+AACAJIgfAAAgCeIHAABIgvgBAACSIH4AAIAkiB8AACAJ4gcAAEiC+AEAAJIgfgAAgCSIHwAAIAniBwAASIL4AQAAkiB+AACAJIgfAAAgCeIHAABIgvgBAACSIH4AAIAkiB8AACAJ4gcAAEiC+AEAAJIgfgAAgCSIHwAAIAniBwAASIL4AQAAkiB+AACAJIgfAAAgCeIHAABIgvgBAACSIH4AAIAkiB8AACAJ4gcAAEhCUfEzffr0OOWUU6J9+/ZxxBFHxGc+85n47W9/WzCTZVlMnTo1qqqqom3btjF06NB45plnCmYaGxvjqquuii5dukS7du1izJgx8eqrr+773gAAAOxGUfGzbNmy+PKXvxyPPfZYLF26NN59990YOXJkvP322/mZmTNnxqxZs2LOnDmxcuXKqKysjBEjRsTGjRvzM9XV1bF48eJYtGhRPPLII7Fp06YYNWpUbNu2bf/tGQAAwJ8pKWb43nvvLbg+f/78OOKII2LVqlVx+umnR5ZlccMNN8SUKVPi3HPPjYiIW2+9NSoqKmLhwoVx+eWXR319fcybNy9uu+22GD58eERELFiwILp27Rr33XdfnHHGGftp1wAAAP7PPr3np76+PiIiOnXqFBERa9eujbq6uhg5cmR+pqysLIYMGRLLly+PiIhVq1bF1q1bC2aqqqqid+/e+RkAAID9ragzP38uy7K4+uqr4xOf+ET07t07IiLq6uoiIqKioqJgtqKiIl5++eX8TGlpaXTs2LHJzI7776yxsTEaGxvz1xsaGvZ22QAAQKL2+szPlVdeGb/5zW/i9ttvb3JbLpcruJ5lWZNtO9vTzPTp06O8vDx/6dq1694uGwAASNRexc9VV10Vv/jFL+KBBx6Io446Kr+9srIyIqLJGZz169fnzwZVVlbGli1bYsOGDbud2dnkyZOjvr4+f1m3bt3eLBsAAEhYUfGTZVlceeWVcdddd8X9998f3bt3L7i9e/fuUVlZGUuXLs1v27JlSyxbtiwGDRoUERH9+vWL1q1bF8zU1tbG008/nZ/ZWVlZWXTo0KHgAgAAUIyi3vPz5S9/ORYuXBg///nPo3379vkzPOXl5dG2bdvI5XJRXV0d06ZNix49ekSPHj1i2rRpccghh8S4cePysxMmTIhrrrkmOnfuHJ06dYqJEydGnz598p/+BgAAsL8VFT833XRTREQMHTq0YPv8+fPj4osvjoiIa6+9NjZv3hxXXHFFbNiwIfr37x9LliyJ9u3b5+dnz54dJSUlMXbs2Ni8eXMMGzYsampqolWrVvu2NwAAALuRy7Isa+5FFKuhoSHKy8ujvr6+xfwK3DGT7mnuJQAcMC/NOKu5l3BQ8toAfJi1lNeGYtpgn/7ODwAAwMFC/AAAAEkQPwAAQBLEDwAAkATxAwAAJEH8AAAASRA/AABAEsQPAACQBPEDAAAkQfwAAABJED8AAEASxA8AAJAE8QMAACRB/AAAAEkQPwAAQBLEDwAAkATxAwAAJEH8AAAASRA/AABAEsQPAACQBPEDAAAkQfwAAABJED8AAEASxA8AAJAE8QMAACRB/AAAAEkQPwAAQBLEDwAAkATxAwAAJEH8AAAASRA/AABAEsQPAACQBPEDAAAkQfwAAABJED8AAEASxA8AAJAE8QMAACRB/AAAAEkQPwAAQBLEDwAAkATxAwAAJEH8AAAASRA/AABAEsQPAACQBPEDAAAkQfwAAABJED8AAEASxA8AAJAE8QMAACRB/AAAAEkQPwAAQBLEDwAAkATxAwAAJEH8AAAASRA/AABAEsQPAACQhKLj56GHHorRo0dHVVVV5HK5uPvuuwtuz7Ispk6dGlVVVdG2bdsYOnRoPPPMMwUzjY2NcdVVV0WXLl2iXbt2MWbMmHj11Vf3aUcAAAD2pOj4efvtt+PEE0+MOXPm7PL2mTNnxqxZs2LOnDmxcuXKqKysjBEjRsTGjRvzM9XV1bF48eJYtGhRPPLII7Fp06YYNWpUbNu2be/3BAAAYA9Kir3DmWeeGWeeeeYub8uyLG644YaYMmVKnHvuuRERceutt0ZFRUUsXLgwLr/88qivr4958+bFbbfdFsOHD4+IiAULFkTXrl3jvvvuizPOOGMfdgcAAGDX9ut7ftauXRt1dXUxcuTI/LaysrIYMmRILF++PCIiVq1aFVu3bi2Yqaqqit69e+dndtbY2BgNDQ0FFwAAgGLs1/ipq6uLiIiKioqC7RUVFfnb6urqorS0NDp27LjbmZ1Nnz49ysvL85euXbvuz2UDAAAJOCCf9pbL5QquZ1nWZNvO9jQzefLkqK+vz1/WrVu339YKAACkYb/GT2VlZUREkzM469evz58NqqysjC1btsSGDRt2O7OzsrKy6NChQ8EFAACgGPs1frp37x6VlZWxdOnS/LYtW7bEsmXLYtCgQRER0a9fv2jdunXBTG1tbTz99NP5GQAAgP2t6E9727RpU/z+97/PX1+7dm2sWbMmOnXqFEcffXRUV1fHtGnTokePHtGjR4+YNm1aHHLIITFu3LiIiCgvL48JEybENddcE507d45OnTrFxIkTo0+fPvlPfwMAANjfio6fJ554Ij75yU/mr1999dUREXHRRRdFTU1NXHvttbF58+a44oorYsOGDdG/f/9YsmRJtG/fPn+f2bNnR0lJSYwdOzY2b94cw4YNi5qammjVqtV+2CUAAICmclmWZc29iGI1NDREeXl51NfXt5j3/xwz6Z7mXgLAAfPSjLOaewkHJa8NwIdZS3ltKKYNDsinvQEAALQ04gcAAEiC+AEAAJIgfgAAgCSIHwAAIAniBwAASIL4AQAAkiB+AACAJIgfAAAgCeIHAABIgvgBAACSIH4AAIAkiB8AACAJ4gcAAEiC+AEAAJIgfgAAgCSIHwAAIAniBwAASIL4AQAAkiB+AACAJIgfAAAgCeIHAABIgvgBAACSIH4AAIAkiB8AACAJ4gcAAEiC+AEAAJIgfgAAgCSIHwAAIAniBwAASIL4AQAAkiB+AACAJIgfAAAgCeIHAABIgvgBAACSIH4AAIAkiB8AACAJ4gcAAEiC+AEAAJIgfgAAgCSIHwAAIAniBwAASIL4AQAAkiB+AACAJIgfAAAgCeIHAABIgvgBAACSIH4AAIAkiB8AACAJ4gcAAEiC+AEAAJIgfgAAgCSIHwAAIAniBwAASIL4AQAAkiB+AACAJDRr/MydOze6d+8ebdq0iX79+sXDDz/cnMsBAAA+xJotfu64446orq6OKVOmxOrVq+O0006LM888M1555ZXmWhIAAPAh1mzxM2vWrJgwYUJcdtll8fGPfzxuuOGG6Nq1a9x0003NtSQAAOBDrKQ5vuiWLVti1apVMWnSpILtI0eOjOXLlzeZb2xsjMbGxvz1+vr6iIhoaGg4sAstwvbGPzb3EgAOmJb039uDidcG4MOspbw27FhHlmXvOdss8fPGG2/Etm3boqKiomB7RUVF1NXVNZmfPn16XHfddU22d+3a9YCtEYD/U35Dc68AgJampb02bNy4McrLy/c40yzxs0Mulyu4nmVZk20REZMnT46rr746f3379u3xv//7v9G5c+ddznNgNDQ0RNeuXWPdunXRoUOH5l5OshyHlsFxaBkch5bBcWgZHIfm5xg0jyzLYuPGjVFVVfWes80SP126dIlWrVo1Ocuzfv36JmeDIiLKysqirKysYNthhx12IJfIHnTo0METugVwHFoGx6FlcBxaBsehZXAcmp9j8MF7rzM+OzTLBx6UlpZGv379YunSpQXbly5dGoMGDWqOJQEAAB9yzfZrb1dffXWMHz8+Tj755Bg4cGDccsst8corr8QXv/jF5loSAADwIdZs8fP5z38+3nzzzfj2t78dtbW10bt37/jlL38Z3bp1a64l8R7KysriW9/6VpNfQeSD5Ti0DI5Dy+A4tAyOQ8vgODQ/x6Dly2Xv5zPhAAAADnLN9kdOAQAAPkjiBwAASIL4AQAAkiB+AACAJIgf8jZs2BDjx4+P8vLyKC8vj/Hjx8dbb7212/mtW7fG17/+9ejTp0+0a9cuqqqq4m//9m/jtddeK5gbOnRo5HK5gst55513gPfm4DJ37tzo3r17tGnTJvr16xcPP/zwHueXLVsW/fr1izZt2sRHP/rR+NGPftRk5s4774zjjjsuysrK4rjjjovFixcfqOV/KBRzDO66664YMWJEHH744dGhQ4cYOHBg/PrXvy6YqampafLvPpfLxTvvvHOgd+WgVsxxePDBB3f5PX7++ecL5jwXilfMcbj44ot3eRyOP/74/IznQ/EeeuihGD16dFRVVUUul4u77777Pe/jtWH/KvYYeG04OIgf8saNGxdr1qyJe++9N+69995Ys2ZNjB8/frfzf/zjH+PJJ5+Mb37zm/Hkk0/GXXfdFb/73e9izJgxTWa/8IUvRG1tbf5y8803H8hdOajccccdUV1dHVOmTInVq1fHaaedFmeeeWa88soru5xfu3ZtfPrTn47TTjstVq9eHd/4xjfiK1/5Stx55535mUcffTQ+//nPx/jx4+O//uu/Yvz48TF27NhYsWLFB7VbB5Vij8FDDz0UI0aMiF/+8pexatWq+OQnPxmjR4+O1atXF8x16NCh4N99bW1ttGnT5oPYpYNSscdhh9/+9rcF3+MePXrkb/NcKF6xx+H73/9+wfd/3bp10alTp/jc5z5XMOf5UJy33347TjzxxJgzZ877mvfasP8Vewy8NhwkMsiy7Nlnn80iInvsscfy2x599NEsIrLnn3/+fT/O448/nkVE9vLLL+e3DRkyJPvqV7+6P5f7oXLqqadmX/ziFwu29erVK5s0adIu56+99tqsV69eBdsuv/zybMCAAfnrY8eOzT71qU8VzJxxxhnZeeedt59W/eFS7DHYleOOOy677rrr8tfnz5+flZeX768lJqHY4/DAAw9kEZFt2LBht4/puVC8fX0+LF68OMvlctlLL72U3+b5sG8iIlu8ePEeZ7w2HFjv5xjsiteGlseZHyLiTz8NKi8vj/79++e3DRgwIMrLy2P58uXv+3Hq6+sjl8vFYYcdVrD9Jz/5SXTp0iWOP/74mDhxYmzcuHF/Lf2gtmXLlli1alWMHDmyYPvIkSN3+31/9NFHm8yfccYZ8cQTT8TWrVv3OFPMsUzF3hyDnW3fvj02btwYnTp1Kti+adOm6NatWxx11FExatSoJj/94//sy3E46aST4sgjj4xhw4bFAw88UHCb50Jx9sfzYd68eTF8+PAmf7Tc8+HA8trQ8nhtaJnEDxERUVdXF0cccUST7UcccUTU1dW9r8d45513YtKkSTFu3Ljo0KFDfvsFF1wQt99+ezz44IPxzW9+M+68884499xz99vaD2ZvvPFGbNu2LSoqKgq2V1RU7Pb7XldXt8v5d999N9544409zrzfY5mSvTkGO7v++uvj7bffjrFjx+a39erVK2pqauIXv/hF3H777dGmTZsYPHhwvPDCC/t1/R8We3McjjzyyLjlllvizjvvjLvuuit69uwZw4YNi4ceeig/47lQnH19PtTW1savfvWruOyyywq2ez4ceF4bWh6vDS1TSXMvgANr6tSpcd111+1xZuXKlRERkcvlmtyWZdkut+9s69atcd5558X27dtj7ty5Bbd94QtfyP/v3r17R48ePeLkk0+OJ598Mvr27ft+duNDb+fv8Xt933c1v/P2Yh8zdXv7/br99ttj6tSp8fOf/7zgBwgDBgyIAQMG5K8PHjw4+vbtGzfeeGP84Ac/2H8L/5Ap5jj07Nkzevbsmb8+cODAWLduXXzve9+L008/fa8ekz/Z2+9ZTU1NHHbYYfGZz3ymYLvnwwfDa0PL4bWh5RI/H3JXXnnle36y2jHHHBO/+c1v4vXXX29y2//8z/80+SnRzrZu3Rpjx46NtWvXxv33319w1mdX+vbtG61bt44XXngh+fjp0qVLtGrVqslP3davX7/b73tlZeUu50tKSqJz5857nHmvY5mivTkGO9xxxx0xYcKE+OlPfxrDhw/f4+xHPvKROOWUU/x0bzf25Tj8uQEDBsSCBQvy1z0XirMvxyHLsvjnf/7nGD9+fJSWlu5x1vNh//Pa0HJ4bWjZ/Nrbh1yXLl2iV69ee7y0adMmBg4cGPX19fH444/n77tixYqor6+PQYMG7fbxd4TPCy+8EPfdd1/+P7B78swzz8TWrVvjyCOP3C/7eDArLS2Nfv36xdKlSwu2L126dLff94EDBzaZX7JkSZx88snRunXrPc7s6Vimam+OQcSffqp38cUXx8KFC+Oss856z6+TZVmsWbPGv/vd2NvjsLPVq1cXfI89F4qzL8dh2bJl8fvf/z4mTJjwnl/H82H/89rQMnhtOAg0x6cs0DJ96lOfyk444YTs0UcfzR599NGsT58+2ahRowpmevbsmd11111ZlmXZ1q1bszFjxmRHHXVUtmbNmqy2tjZ/aWxszLIsy37/+99n1113XbZy5cps7dq12T333JP16tUrO+mkk7J33333A9/HlmjRokVZ69ats3nz5mXPPvtsVl1dnbVr1y7/SUmTJk3Kxo8fn59/8cUXs0MOOST72te+lj377LPZvHnzstatW2c/+9nP8jP/+Z//mbVq1SqbMWNG9txzz2UzZszISkpKCj7Nj/9T7DFYuHBhVlJSkv3whz8s+Hf/1ltv5WemTp2a3Xvvvdl///d/Z6tXr84uueSSrKSkJFuxYsUHvn8Hi2KPw+zZs7PFixdnv/vd77Knn346mzRpUhYR2Z133pmf8VwoXrHHYYcLL7ww69+//y4f0/OheBs3bsxWr16drV69OouIbNasWdnq1avzn6bqteHAK/YYeG04OIgf8t58883sggsuyNq3b5+1b98+u+CCC5p8hGxEZPPnz8+yLMvWrl2bRcQuLw888ECWZVn2yiuvZKeffnrWqVOnrLS0NPvYxz6WfeUrX8nefPPND3bnWrgf/vCHWbdu3bLS0tKsb9++2bJly/K3XXTRRdmQIUMK5h988MHspJNOykpLS7Njjjkmu+mmm5o85k9/+tOsZ8+eWevWrbNevXoV/B9CmirmGAwZMmSX/+4vuuii/Ex1dXV29NFHZ6Wlpdnhhx+ejRw5Mlu+fPkHuEcHp2KOw3e/+93sYx/7WNamTZusY8eO2Sc+8YnsnnvuafKYngvFK/a/SW+99VbWtm3b7JZbbtnl43k+FG/HR7nv7r8zXhsOvGKPgdeGg0Muy/7/d8MBAAB8iHnPDwAAkATxAwAAJEH8AAAASRA/AABAEsQPAACQBPEDAAAkQfwAAABJED8AAEASxA8AAJAE8QMAACRB/AAAAEkQPwAAQBL+P4SyZUpYqSBdAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Survived Survived\n",
      "0    549\n",
      "1    342\n",
      "Name: count, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pclass Pclass\n",
      "3    491\n",
      "1    216\n",
      "2    184\n",
      "Name: count, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sex Sex\n",
      "male      577\n",
      "female    314\n",
      "Name: count, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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AABRB/AAAAEUQPwAAQBHEDwAAUATxAwAAFEH8AAAARRA/AABAEcQPAABQBPEDAAAUQfwAAABFED8AAEARxA8AAFAE8QMAABRB/AAAAEUQPwAAQBHEDwAAUATxAwAAFEH8AAAARRA/AABAEcQPAABQBPEDAAAU4aDiZ/78+amrq0tbW1t1X6VSybx589LS0pIhQ4Zk8uTJ2bJlS5/H9fT05KqrrsqoUaMybNiwnH/++XnuuecOZhQAAIBXdMDxs2HDhixbtiynnHJKn/0LFizIwoULs2TJkmzYsCHNzc2ZNm1aduzYUV3T1taWVatWZeXKlVm3bl127tyZGTNmZM+ePQd+JAAAAK/ggOJn586dufjii/O5z30uRx99dHV/pVLJ4sWLc9NNN+XCCy/M2LFjc/fdd+fnP/95VqxYkSTp6urKnXfemdtvvz1Tp07NqaeemuXLl+exxx7Lgw8+eGiOCgAAYB8HFD9XXHFFzj333EydOrXP/qeeeiqdnZ2ZPn16dV99fX3OOuusrF+/PkmycePG7N69u8+alpaWjB07trpmXz09Penu7u6zAQAA9MfA/j5g5cqV+e53v5sNGzbsd19nZ2eSpKmpqc/+pqamPPPMM9U1gwcP7nPG6Ndrfv34fc2fPz8333xzf0cFAACo6teZn61bt+aaa67J8uXLc9RRR73surq6uj63K5XKfvv29Upr5s6dm66uruq2devW/owNAADQv/jZuHFjtm3blvHjx2fgwIEZOHBg1q5dm89+9rMZOHBg9YzPvmdwtm3bVr2vubk5vb292b59+8uu2Vd9fX2GDx/eZwMAAOiPfsXPlClT8thjj2XTpk3VbcKECbn44ouzadOmvOUtb0lzc3PWrFlTfUxvb2/Wrl2bSZMmJUnGjx+fQYMG9VnT0dGRzZs3V9cAAAAcav36zk9DQ0PGjh3bZ9+wYcMycuTI6v62tra0t7dnzJgxGTNmTNrb2zN06NBcdNFFSZLGxsbMmjUr1113XUaOHJkRI0Zkzpw5GTdu3H4XUAAAADhU+n3Bg9/k+uuvz65du3L55Zdn+/btmThxYlavXp2GhobqmkWLFmXgwIGZOXNmdu3alSlTpuSuu+7KgAEDDvU4AAAASZK6SqVSqfUQ/dXd3Z3GxsZ0dXW9Zr7/c+KN99V6hJp6+pZzaz0CAAAF6k8bHNDv/AAAABxpxA8AAFAE8QMAABRB/AAAAEUQPwAAQBHEDwAAUATxAwAAFEH8AAAARRA/AABAEcQPAABQBPEDAAAUQfwAAABFED8AAEARxA8AAFAE8QMAABRB/AAAAEUQPwAAQBHEDwAAUATxAwAAFEH8AAAARRA/AABAEcQPAABQBPEDAAAUQfwAAABFED8AAEARxA8AAFAE8QMAABRB/AAAAEUQPwAAQBHEDwAAUATxAwAAFEH8AAAARRA/AABAEcQPAABQBPEDAAAUQfwAAABFED8AAEARxA8AAFAE8QMAABRB/AAAAEUQPwAAQBHEDwAAUATxAwAAFEH8AAAARRA/AABAEcQPAABQBPEDAAAUQfwAAABFED8AAEAR+hU/S5cuzSmnnJLhw4dn+PDhOeOMM3L//fdX769UKpk3b15aWloyZMiQTJ48OVu2bOnzN3p6enLVVVdl1KhRGTZsWM4///w899xzh+ZoAAAAXka/4ue4447LLbfckkcffTSPPvpo/uAP/iDve9/7qoGzYMGCLFy4MEuWLMmGDRvS3NycadOmZceOHdW/0dbWllWrVmXlypVZt25ddu7cmRkzZmTPnj2H9sgAAAD+j7pKpVI5mD8wYsSI3HbbbfnoRz+alpaWtLW15YYbbkjyq7M8TU1NufXWW3PppZemq6srb37zm3PPPffkAx/4QJLk+eefT2tra772ta/l7LPP/q2es7u7O42Njenq6srw4cMPZvxD5sQb76v1CDX19C3n1noEAAAK1J82OODv/OzZsycrV67Miy++mDPOOCNPPfVUOjs7M3369Oqa+vr6nHXWWVm/fn2SZOPGjdm9e3efNS0tLRk7dmx1zUvp6elJd3d3nw0AAKA/+h0/jz32WN74xjemvr4+l112WVatWpW3v/3t6ezsTJI0NTX1Wd/U1FS9r7OzM4MHD87RRx/9smteyvz589PY2FjdWltb+zs2AABQuH7Hz+/93u9l06ZNefjhh/MXf/EXueSSS/L4449X76+rq+uzvlKp7LdvX79pzdy5c9PV1VXdtm7d2t+xAQCAwvU7fgYPHpzf/d3fzYQJEzJ//vy84x3vyN/+7d+mubk5SfY7g7Nt27bq2aDm5ub09vZm+/btL7vmpdTX11evMPfrDQAAoD8O+nd+KpVKenp6Mnr06DQ3N2fNmjXV+3p7e7N27dpMmjQpSTJ+/PgMGjSoz5qOjo5s3ry5ugYAAOBwGNifxR//+MdzzjnnpLW1NTt27MjKlSvzzW9+Mw888EDq6urS1taW9vb2jBkzJmPGjEl7e3uGDh2aiy66KEnS2NiYWbNm5brrrsvIkSMzYsSIzJkzJ+PGjcvUqVMPywECAAAk/Yyfn/70p/nwhz+cjo6ONDY25pRTTskDDzyQadOmJUmuv/767Nq1K5dffnm2b9+eiRMnZvXq1WloaKj+jUWLFmXgwIGZOXNmdu3alSlTpuSuu+7KgAEDDu2RAQAA/B8H/Ts/teB3fl57/M4PAAC18Kr8zg8AAMCRRPwAAABFED8AAEARxA8AAFAE8QMAABRB/AAAAEUQPwAAQBHEDwAAUATxAwAAFEH8AAAARRA/AABAEcQPAABQBPEDAAAUQfwAAABFED8AAEARxA8AAFAE8QMAABRB/AAAAEUQPwAAQBHEDwAAUATxAwAAFEH8AAAARRA/AABAEcQPAABQBPEDAAAUQfwAAABFED8AAEARxA8AAFAE8QMAABRB/AAAAEUQPwAAQBHEDwAAUATxAwAAFEH8AAAARRA/AABAEcQPAABQBPEDAAAUQfwAAABFED8AAEARxA8AAFAE8QMAABRB/AAAAEUQPwAAQBHEDwAAUATxAwAAFEH8AAAARRA/AABAEcQPAABQhIG1HgCS5MQb76v1CDX19C3n1noEAIDXvX6d+Zk/f35OP/30NDQ05JhjjskFF1yQH/3oR33WVCqVzJs3Ly0tLRkyZEgmT56cLVu29FnT09OTq666KqNGjcqwYcNy/vnn57nnnjv4owEAAHgZ/YqftWvX5oorrsjDDz+cNWvW5Je//GWmT5+eF198sbpmwYIFWbhwYZYsWZINGzakubk506ZNy44dO6pr2trasmrVqqxcuTLr1q3Lzp07M2PGjOzZs+fQHRkAAMD/0a+PvT3wwAN9bn/+85/PMccck40bN+a9731vKpVKFi9enJtuuikXXnhhkuTuu+9OU1NTVqxYkUsvvTRdXV258847c88992Tq1KlJkuXLl6e1tTUPPvhgzj777EN0aAAAAP/fQV3woKurK0kyYsSIJMlTTz2Vzs7OTJ8+vbqmvr4+Z511VtavX58k2bhxY3bv3t1nTUtLS8aOHVtdAwAAcKgd8AUPKpVKZs+enXe/+90ZO3ZskqSzszNJ0tTU1GdtU1NTnnnmmeqawYMH5+ijj95vza8fv6+enp709PRUb3d3dx/o2AAAQKEO+MzPlVdemR/84Af54he/uN99dXV1fW5XKpX99u3rldbMnz8/jY2N1a21tfVAxwYAAAp1QPFz1VVX5d57782///u/57jjjqvub25uTpL9zuBs27atejaoubk5vb292b59+8uu2dfcuXPT1dVV3bZu3XogYwMAAAXrV/xUKpVceeWV+fKXv5xvfOMbGT16dJ/7R48enebm5qxZs6a6r7e3N2vXrs2kSZOSJOPHj8+gQYP6rOno6MjmzZura/ZVX1+f4cOH99kAAAD6o1/f+bniiiuyYsWKfOUrX0lDQ0P1DE9jY2OGDBmSurq6tLW1pb29PWPGjMmYMWPS3t6eoUOH5qKLLqqunTVrVq677rqMHDkyI0aMyJw5czJu3Ljq1d8AAAAOtX7Fz9KlS5MkkydP7rP/85//fD7ykY8kSa6//vrs2rUrl19+ebZv356JEydm9erVaWhoqK5ftGhRBg4cmJkzZ2bXrl2ZMmVK7rrrrgwYMODgjgYAAOBl1FUqlUqth+iv7u7uNDY2pqur6zXzEbgTb7yv1iPU1NO3nHtQj/f6HdzrBwBQqv60wUH9zg8AAMCRQvwAAABFED8AAEARxA8AAFAE8QMAABRB/AAAAEUQPwAAQBHEDwAAUATxAwAAFEH8AAAARRA/AABAEcQPAABQBPEDAAAUQfwAAABFED8AAEARxA8AAFAE8QMAABRB/AAAAEUQPwAAQBHEDwAAUATxAwAAFEH8AAAARRA/AABAEcQPAABQBPEDAAAUQfwAAABFED8AAEARxA8AAFAE8QMAABRB/AAAAEUQPwAAQBHEDwAAUATxAwAAFEH8AAAARRA/AABAEcQPAABQBPEDAAAUQfwAAABFED8AAEARxA8AAFAE8QMAABRB/AAAAEUQPwAAQBHEDwAAUATxAwAAFEH8AAAARRA/AABAEcQPAABQBPEDAAAUQfwAAABF6Hf8PPTQQznvvPPS0tKSurq6/Mu//Euf+yuVSubNm5eWlpYMGTIkkydPzpYtW/qs6enpyVVXXZVRo0Zl2LBhOf/88/Pcc88d1IEAAAC8kn7Hz4svvph3vOMdWbJkyUvev2DBgixcuDBLlizJhg0b0tzcnGnTpmXHjh3VNW1tbVm1alVWrlyZdevWZefOnZkxY0b27Nlz4EcCAADwCgb29wHnnHNOzjnnnJe8r1KpZPHixbnpppty4YUXJknuvvvuNDU1ZcWKFbn00kvT1dWVO++8M/fcc0+mTp2aJFm+fHlaW1vz4IMP5uyzzz6IwwEAAHhph/Q7P0899VQ6Ozszffr06r76+vqcddZZWb9+fZJk48aN2b17d581LS0tGTt2bHXNvnp6etLd3d1nAwAA6I9DGj+dnZ1Jkqampj77m5qaqvd1dnZm8ODBOfroo192zb7mz5+fxsbG6tba2nooxwYAAApwWK72VldX1+d2pVLZb9++XmnN3Llz09XVVd22bt16yGYFAADKcEjjp7m5OUn2O4Ozbdu26tmg5ubm9Pb2Zvv27S+7Zl/19fUZPnx4nw0AAKA/Dmn8jB49Os3NzVmzZk11X29vb9auXZtJkyYlScaPH59Bgwb1WdPR0ZHNmzdX1wAAABxq/b7a286dO/Nf//Vf1dtPPfVUNm3alBEjRuT4449PW1tb2tvbM2bMmIwZMybt7e0ZOnRoLrrooiRJY2NjZs2aleuuuy4jR47MiBEjMmfOnIwbN6569TcAAIBDrd/x8+ijj+b3f//3q7dnz56dJLnkkkty11135frrr8+uXbty+eWXZ/v27Zk4cWJWr16dhoaG6mMWLVqUgQMHZubMmdm1a1emTJmSu+66KwMGDDgEhwQAALC/ukqlUqn1EP3V3d2dxsbGdHV1vWa+/3PijffVeoSaevqWcw/q8V6/g3v9AABK1Z82OCxXewMAAHitET8AAEARxA8AAFAE8QMAABRB/AAAAEUQPwAAQBHEDwAAUATxAwAAFEH8AAAARRA/AABAEcQPAABQBPEDAAAUQfwAAABFED8AAEARxA8AAFAE8QMAABRB/AAAAEUQPwAAQBHEDwAAUATxAwAAFEH8AAAARRA/AABAEcQPAABQBPEDAAAUQfwAAABFED8AAEARxA8AAFAE8QMAABRB/AAAAEUQPwAAQBHEDwAAUATxAwAAFEH8AAAARRA/AABAEcQPAABQBPEDAAAUYWCtBwAO3ok33lfrEWrq6VvOrfUIAMARwJkfAACgCOIHAAAogvgBAACKIH4AAIAiiB8AAKAIrvYGwEFxtUFXGwQ4UjjzAwAAFMGZH6B4zlw4cwFAGZz5AQAAiiB+AACAIogfAACgCOIHAAAoQk0veHDHHXfktttuS0dHR04++eQsXrw473nPe2o5EgAA/FZcMOfIu2BOzeLnS1/6Utra2nLHHXfkzDPPzD/+4z/mnHPOyeOPP57jjz++VmMBwKvKvzwdef/yBBy5avaxt4ULF2bWrFn58z//85x00klZvHhxWltbs3Tp0lqNBAAAvI7V5MxPb29vNm7cmBtvvLHP/unTp2f9+vX7re/p6UlPT0/1dldXV5Kku7v78A7aD3t7fl7rEWrqYP+78Pp5/Q6G1+/geP0Ojtfv4LyW/rcc+ss/v6+Nf35/PUelUvmNa2sSPy+88EL27NmTpqamPvubmprS2dm53/r58+fn5ptv3m9/a2vrYZuR/mlcXOsJjmxev4Pj9Ts4Xr+D4/U7OF4/OHK91v753bFjRxobG19xTU0veFBXV9fndqVS2W9fksydOzezZ8+u3t67d2/+93//NyNHjnzJ9aXp7u5Oa2trtm7dmuHDh9d6HArj/Uctef9RS95/1JL33/9XqVSyY8eOtLS0/Ma1NYmfUaNGZcCAAfud5dm2bdt+Z4OSpL6+PvX19X32velNbzqcIx6Rhg8fXvybn9rx/qOWvP+oJe8/asn771d+0xmfX6vJBQ8GDx6c8ePHZ82aNX32r1mzJpMmTarFSAAAwOtczT72Nnv27Hz4wx/OhAkTcsYZZ2TZsmV59tlnc9lll9VqJAAA4HWsZvHzgQ98ID/72c/yqU99Kh0dHRk7dmy+9rWv5YQTTqjVSEes+vr6fPKTn9zvo4HwavD+o5a8/6gl7z9qyfvvwNRVfptrwgEAABzhavYjpwAAAK8m8QMAABRB/AAAAEUQPwAAQBHEzxHujjvuyOjRo3PUUUdl/Pjx+da3vlXrkSjA/Pnzc/rpp6ehoSHHHHNMLrjggvzoRz+q9VgUav78+amrq0tbW1utR6EgP/nJT/Inf/InGTlyZIYOHZp3vvOd2bhxY63HogC//OUv84lPfCKjR4/OkCFD8pa3vCWf+tSnsnfv3lqPdkQQP0ewL33pS2lra8tNN92U733ve3nPe96Tc845J88++2ytR+N1bu3atbniiivy8MMPZ82aNfnlL3+Z6dOn58UXX6z1aBRmw4YNWbZsWU455ZRaj0JBtm/fnjPPPDODBg3K/fffn8cffzy333573vSmN9V6NApw66235h/+4R+yZMmS/PCHP8yCBQty22235e/+7u9qPdoRwaWuj2ATJ07MaaedlqVLl1b3nXTSSbngggsyf/78Gk5Gaf7nf/4nxxxzTNauXZv3vve9tR6HQuzcuTOnnXZa7rjjjnz605/OO9/5zixevLjWY1GAG2+8Md/+9rd92oKamDFjRpqamnLnnXdW9/3xH/9xhg4dmnvuuaeGkx0ZnPk5QvX29mbjxo2ZPn16n/3Tp0/P+vXrazQVperq6kqSjBgxosaTUJIrrrgi5557bqZOnVrrUSjMvffemwkTJuT9739/jjnmmJx66qn53Oc+V+uxKMS73/3ufP3rX88TTzyRJPn+97+fdevW5Q//8A9rPNmRYWCtB+DAvPDCC9mzZ0+ampr67G9qakpnZ2eNpqJElUols2fPzrvf/e6MHTu21uNQiJUrV+a73/1uNmzYUOtRKNCPf/zjLF26NLNnz87HP/7xfOc738nVV1+d+vr6/Omf/mmtx+N17oYbbkhXV1fe9ra3ZcCAAdmzZ08+85nP5EMf+lCtRzsiiJ8jXF1dXZ/blUplv31wOF155ZX5wQ9+kHXr1tV6FAqxdevWXHPNNVm9enWOOuqoWo9Dgfbu3ZsJEyakvb09SXLqqadmy5YtWbp0qfjhsPvSl76U5cuXZ8WKFTn55JOzadOmtLW1paWlJZdcckmtx3vNEz9HqFGjRmXAgAH7neXZtm3bfmeD4HC56qqrcu+99+ahhx7KcccdV+txKMTGjRuzbdu2jB8/vrpvz549eeihh7JkyZL09PRkwIABNZyQ17tjjz02b3/72/vsO+mkk/LP//zPNZqIkvzlX/5lbrzxxnzwgx9MkowbNy7PPPNM5s+fL35+C77zc4QaPHhwxo8fnzVr1vTZv2bNmkyaNKlGU1GKSqWSK6+8Ml/+8pfzjW98I6NHj671SBRkypQpeeyxx7Jp06bqNmHChFx88cXZtGmT8OGwO/PMM/e7vP8TTzyRE044oUYTUZKf//znecMb+v4r/IABA1zq+rfkzM8RbPbs2fnwhz+cCRMm5IwzzsiyZcvy7LPP5rLLLqv1aLzOXXHFFVmxYkW+8pWvpKGhoXoGsrGxMUOGDKnxdLzeNTQ07Pf9smHDhmXkyJG+d8ar4tprr82kSZPS3t6emTNn5jvf+U6WLVuWZcuW1Xo0CnDeeeflM5/5TI4//vicfPLJ+d73vpeFCxfmox/9aK1HOyK41PUR7o477siCBQvS0dGRsWPHZtGiRS41zGH3ct8r+/znP5+PfOQjr+4wkGTy5Mkudc2r6qtf/Wrmzp2bJ598MqNHj87s2bPzsY99rNZjUYAdO3bkr/7qr7Jq1aps27YtLS0t+dCHPpS//uu/zuDBg2s93mue+AEAAIrgOz8AAEARxA8AAFAE8QMAABRB/AAAAEUQPwAAQBHEDwAAUATxAwAAFEH8AAAARRA/AABAEcQPAABQBPEDAAAUQfwAAABF+H8eZ/cgFJxiOQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SibSp SibSp\n",
      "0    608\n",
      "1    209\n",
      "2     28\n",
      "4     18\n",
      "3     16\n",
      "8      7\n",
      "5      5\n",
      "Name: count, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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ePXsOzCcCAAB4HdU9fkF1dbezPa8qlUq5/vrrc/nll+ezn/1skuTWW29NfX197rzzzpx77rlpb2/PzTffnNtvvz1TpkxJktxxxx0ZPnx4HnzwwZx66qlv8+MAAAC8vh6f+XnqqafS2NiYESNG5POf/3yefvrpJMnmzZvT2tqaadOmldfW1NTk5JNPztq1a5Mk69evz+7du7utaWxszKhRo8prAAAA3gk9OvMzfvz43Hbbbfnwhz+cF154IVdddVUmTpyYTZs2pbW1NUlSX1/f7TX19fV55plnkiStra3p06dPDj/88H3WvPr619PV1ZWurq7y446Ojp6MDQAA0LP4mT59evmfR48enQkTJuSDH/xgbr311px44olJkqqqqm6vKZVK++zb219a09LSkiuuuKInowIAAHTzti513b9//4wePTpPPfVU+XdAe5/B2b59e/lsUENDQ3bt2pW2trY3XPN6FixYkPb29vK2ZcuWtzM2AABQQG8rfrq6uvLEE0/kiCOOyIgRI9LQ0JBVq1aVn9+1a1dWr16diRMnJknGjh2b3r17d1uzbdu2PPbYY+U1r6empia1tbXdNgAAgJ7o0dfe5s+fn9NPPz1HHnlktm/fnquuuiodHR0555xzUlVVlaampjQ3N2fkyJEZOXJkmpub069fv8yePTtJUldXlzlz5uSiiy7KoEGDMnDgwMyfPz+jR48uX/0NAADgndCj+Nm6dWu+8IUv5MUXX8yQIUNy4okn5uGHH85RRx2VJLn44ovT2dmZuXPnpq2tLePHj8/KlSszYMCA8ntcd911qa6uzqxZs9LZ2ZnJkydn2bJl6dWr14H9ZAAAAK9RVSqVSpUeoqc6OjpSV1eX9vb2g+YrcEdf+uNKj3BI+N3Vp1V6BAAA3kN60gZv6zc/AAAA7xXiBwAAKATxAwAAFIL4AQAACkH8AAAAhSB+AACAQhA/AABAIYgfAACgEMQPAABQCOIHAAAoBPEDAAAUgvgBAAAKQfwAAACFIH4AAIBCED8AAEAhiB8AAKAQxA8AAFAI4gcAACgE8QMAABSC+AEAAApB/AAAAIUgfgAAgEIQPwAAQCGIHwAAoBDEDwAAUAjiBwAAKATxAwAAFIL4AQAACkH8AAAAhSB+AACAQhA/AABAIYgfAACgEMQPAABQCOIHAAAoBPEDAAAUgvgBAAAKQfwAAACFIH4AAIBCED8AAEAhiB8AAKAQxA8AAFAI4gcAACgE8QMAABSC+AEAAArhbcVPS0tLqqqq0tTUVN5XKpWycOHCNDY2pm/fvpk0aVI2bdrU7XVdXV2ZN29eBg8enP79+2fmzJnZunXr2xkFAADgTe13/Kxbty5Lly7NmDFjuu2/9tprs2jRoixevDjr1q1LQ0NDpk6dmh07dpTXNDU1ZcWKFVm+fHnWrFmTnTt3ZsaMGdmzZ8/+fxIAAIA3sV/xs3Pnzpx11lm56aabcvjhh5f3l0qlXH/99bn88svz2c9+NqNGjcqtt96a//3f/82dd96ZJGlvb8/NN9+c73znO5kyZUqOP/743HHHHdm4cWMefPDBA/OpAAAA9rJf8XP++efntNNOy5QpU7rt37x5c1pbWzNt2rTyvpqampx88slZu3ZtkmT9+vXZvXt3tzWNjY0ZNWpUec3eurq60tHR0W0DAADoieqevmD58uX55S9/mXXr1u3zXGtra5Kkvr6+2/76+vo888wz5TV9+vTpdsbo1TWvvn5vLS0tueKKK3o6KgAAQFmPzvxs2bIl3/zmN3PHHXfksMMOe8N1VVVV3R6XSqV99u3tzdYsWLAg7e3t5W3Lli09GRsAAKBn8bN+/fps3749Y8eOTXV1daqrq7N69ep897vfTXV1dfmMz95ncLZv315+rqGhIbt27UpbW9sbrtlbTU1Namtru20AAAA90aP4mTx5cjZu3JgNGzaUt3HjxuWss87Khg0b8td//ddpaGjIqlWryq/ZtWtXVq9enYkTJyZJxo4dm969e3dbs23btjz22GPlNQAAAAdaj37zM2DAgIwaNarbvv79+2fQoEHl/U1NTWlubs7IkSMzcuTINDc3p1+/fpk9e3aSpK6uLnPmzMlFF12UQYMGZeDAgZk/f35Gjx69zwUUAAAADpQeX/DgL7n44ovT2dmZuXPnpq2tLePHj8/KlSszYMCA8prrrrsu1dXVmTVrVjo7OzN58uQsW7YsvXr1OtDjAAAAJEmqSqVSqdJD9FRHR0fq6urS3t5+0Pz+5+hLf1zpEQ4Jv7v6tEqPAADAe0hP2mC/7vMDAADwXiN+AACAQhA/AABAIYgfAACgEMQPAABQCOIHAAAoBPEDAAAUgvgBAAAKQfwAAACFIH4AAIBCED8AAEAhiB8AAKAQxA8AAFAI4gcAACgE8QMAABSC+AEAAApB/AAAAIUgfgAAgEIQPwAAQCGIHwAAoBDEDwAAUAjiBwAAKATxAwAAFIL4AQAACkH8AAAAhSB+AACAQhA/AABAIYgfAACgEMQPAABQCOIHAAAoBPEDAAAUgvgBAAAKQfwAAACFIH4AAIBCED8AAEAhiB8AAKAQxA8AAFAI4gcAACgE8QMAABSC+AEAAApB/AAAAIUgfgAAgEIQPwAAQCH0KH6WLFmSMWPGpLa2NrW1tZkwYULuu+++8vOlUikLFy5MY2Nj+vbtm0mTJmXTpk3d3qOrqyvz5s3L4MGD079//8ycOTNbt249MJ8GAADgDfQofoYNG5arr746jz76aB599NF8+tOfzmc+85ly4Fx77bVZtGhRFi9enHXr1qWhoSFTp07Njh07yu/R1NSUFStWZPny5VmzZk127tyZGTNmZM+ePQf2kwEAALxGValUKr2dNxg4cGC+/e1v5ytf+UoaGxvT1NSUSy65JMmfzvLU19fnmmuuybnnnpv29vYMGTIkt99+e84888wkyfPPP5/hw4fn3nvvzamnnvqW/syOjo7U1dWlvb09tbW1b2f8A+boS39c6REOCb+7+rRKjwAAwHtIT9pgv3/zs2fPnixfvjwvv/xyJkyYkM2bN6e1tTXTpk0rr6mpqcnJJ5+ctWvXJknWr1+f3bt3d1vT2NiYUaNGlde8nq6urnR0dHTbAAAAeqLH8bNx48a8733vS01NTc4777ysWLEixx57bFpbW5Mk9fX13dbX19eXn2ttbU2fPn1y+OGHv+Ga19PS0pK6urryNnz48J6ODQAAFFyP4+cjH/lINmzYkIcffjh///d/n3POOSePP/54+fmqqqpu60ul0j779vaX1ixYsCDt7e3lbcuWLT0dGwAAKLgex0+fPn3yoQ99KOPGjUtLS0uOO+643HDDDWloaEiSfc7gbN++vXw2qKGhIbt27UpbW9sbrnk9NTU15SvMvboBAAD0xNu+z0+pVEpXV1dGjBiRhoaGrFq1qvzcrl27snr16kycODFJMnbs2PTu3bvbmm3btuWxxx4rrwEAAHgnVPdk8WWXXZbp06dn+PDh2bFjR5YvX56f/vSnuf/++1NVVZWmpqY0Nzdn5MiRGTlyZJqbm9OvX7/Mnj07SVJXV5c5c+bkoosuyqBBgzJw4MDMnz8/o0ePzpQpU96RDwgAAJD0MH5eeOGFnH322dm2bVvq6uoyZsyY3H///Zk6dWqS5OKLL05nZ2fmzp2btra2jB8/PitXrsyAAQPK73Hdddeluro6s2bNSmdnZyZPnpxly5alV69eB/aTAQAAvMbbvs9PJbjPz6HLfX4AAOiJd+U+PwAAAO8l4gcAACgE8QMAABSC+AEAAApB/AAAAIUgfgAAgEIQPwAAQCGIHwAAoBDEDwAAUAjiBwAAKATxAwAAFIL4AQAACkH8AAAAhSB+AACAQhA/AABAIYgfAACgEMQPAABQCOIHAAAoBPEDAAAUgvgBAAAKQfwAAACFIH4AAIBCED8AAEAhiB8AAKAQxA8AAFAI4gcAACgE8QMAABSC+AEAAApB/AAAAIUgfgAAgEIQPwAAQCGIHwAAoBDEDwAAUAjiBwAAKATxAwAAFIL4AQAACkH8AAAAhSB+AACAQhA/AABAIYgfAACgEMQPAABQCOIHAAAoBPEDAAAUQo/ip6WlJSeccEIGDBiQoUOH5owzzsiTTz7ZbU2pVMrChQvT2NiYvn37ZtKkSdm0aVO3NV1dXZk3b14GDx6c/v37Z+bMmdm6devb/zQAAABvoEfxs3r16px//vl5+OGHs2rVqvzxj3/MtGnT8vLLL5fXXHvttVm0aFEWL16cdevWpaGhIVOnTs2OHTvKa5qamrJixYosX748a9asyc6dOzNjxozs2bPnwH0yAACA16gqlUql/X3x73//+wwdOjSrV6/Opz71qZRKpTQ2NqapqSmXXHJJkj+d5amvr88111yTc889N+3t7RkyZEhuv/32nHnmmUmS559/PsOHD8+9996bU0899S/+uR0dHamrq0t7e3tqa2v3d/wD6uhLf1zpEQ4Jv7v6tEqPAADAe0hP2uBt/eanvb09STJw4MAkyebNm9Pa2ppp06aV19TU1OTkk0/O2rVrkyTr16/P7t27u61pbGzMqFGjymv21tXVlY6Ojm4bAABAT+x3/JRKpVx44YX5xCc+kVGjRiVJWltbkyT19fXd1tbX15efa21tTZ8+fXL44Ye/4Zq9tbS0pK6urrwNHz58f8cGAAAKar/j54ILLsivf/3rfP/739/nuaqqqm6PS6XSPvv29mZrFixYkPb29vK2ZcuW/R0bAAAoqP2Kn3nz5uWee+7JT37ykwwbNqy8v6GhIUn2OYOzffv28tmghoaG7Nq1K21tbW+4Zm81NTWpra3ttgEAAPREj+KnVCrlggsuyF133ZWHHnooI0aM6Pb8iBEj0tDQkFWrVpX37dq1K6tXr87EiROTJGPHjk3v3r27rdm2bVsee+yx8hoAAIADrboni88///zceeed+eEPf5gBAwaUz/DU1dWlb9++qaqqSlNTU5qbmzNy5MiMHDkyzc3N6devX2bPnl1eO2fOnFx00UUZNGhQBg4cmPnz52f06NGZMmXKgf+EAAAA6WH8LFmyJEkyadKkbvtvueWWfPnLX06SXHzxxens7MzcuXPT1taW8ePHZ+XKlRkwYEB5/XXXXZfq6urMmjUrnZ2dmTx5cpYtW5ZevXq9vU8DAADwBt7WfX4qxX1+Dl3u8wMAQE+8a/f5AQAAeK8QPwAAQCGIHwAAoBDEDwAAUAjiBwAAKATxAwAAFIL4AQAACkH8AAAAhSB+AACAQhA/AABAIYgfAACgEMQPAABQCOIHAAAoBPEDAAAUgvgBAAAKQfwAAACFIH4AAIBCED8AAEAhiB8AAKAQxA8AAFAI4gcAACgE8QMAABSC+AEAAApB/AAAAIUgfgAAgEIQPwAAQCGIHwAAoBDEDwAAUAjiBwAAKATxAwAAFIL4AQAACkH8AAAAhSB+AACAQhA/AABAIYgfAACgEMQPAABQCOIHAAAohOpKDwDvtKMv/XGlRzhk/O7q0yo9AgDAfnPmBwAAKATxAwAAFIL4AQAACkH8AAAAhSB+AACAQuhx/PzsZz/L6aefnsbGxlRVVeXuu+/u9nypVMrChQvT2NiYvn37ZtKkSdm0aVO3NV1dXZk3b14GDx6c/v37Z+bMmdm6devb+iAAAABvpsfx8/LLL+e4447L4sWLX/f5a6+9NosWLcrixYuzbt26NDQ0ZOrUqdmxY0d5TVNTU1asWJHly5dnzZo12blzZ2bMmJE9e/bs/ycBAAB4Ez2+z8/06dMzffr0132uVCrl+uuvz+WXX57PfvazSZJbb7019fX1ufPOO3Puueemvb09N998c26//fZMmTIlSXLHHXdk+PDhefDBB3Pqqae+jY8DAADw+g7ob342b96c1tbWTJs2rbyvpqYmJ598ctauXZskWb9+fXbv3t1tTWNjY0aNGlVes7eurq50dHR02wAAAHrigMZPa2trkqS+vr7b/vr6+vJzra2t6dOnTw4//PA3XLO3lpaW1NXVlbfhw4cfyLEBAIACeEeu9lZVVdXtcalU2mff3t5szYIFC9Le3l7etmzZcsBmBQAAiuGAxk9DQ0OS7HMGZ/v27eWzQQ0NDdm1a1fa2trecM3eampqUltb220DAADoiQMaPyNGjEhDQ0NWrVpV3rdr166sXr06EydOTJKMHTs2vXv37rZm27Zteeyxx8prAAAADrQeX+1t586d+e1vf1t+vHnz5mzYsCEDBw7MkUcemaampjQ3N2fkyJEZOXJkmpub069fv8yePTtJUldXlzlz5uSiiy7KoEGDMnDgwMyfPz+jR48uX/0NAADgQOtx/Dz66KM55ZRTyo8vvPDCJMk555yTZcuW5eKLL05nZ2fmzp2btra2jB8/PitXrsyAAQPKr7nuuutSXV2dWbNmpbOzM5MnT86yZcvSq1evA/CRAAAA9lVVKpVKlR6ipzo6OlJXV5f29vaD5vc/R1/640qPcEj43dWnHfD3dGwOnHfi+AAAvB09aYN35GpvAAAABxvxAwAAFIL4AQAACkH8AAAAhdDjq70BHEguSHHguCAFALw5Z34AAIBCED8AAEAhiB8AAKAQxA8AAFAI4gcAACgE8QMAABSC+AEAAApB/AAAAIUgfgAAgEIQPwAAQCGIHwAAoBDEDwAAUAjiBwAAKATxAwAAFIL4AQAACkH8AAAAhSB+AACAQhA/AABAIYgfAACgEMQPAABQCOIHAAAoBPEDAAAUgvgBAAAKQfwAAACFIH4AAIBCED8AAEAhiB8AAKAQxA8AAFAI4gcAACgE8QMAABSC+AEAAApB/AAAAIUgfgAAgEIQPwAAQCGIHwAAoBDEDwAAUAjiBwAAKATxAwAAFEJF4+fGG2/MiBEjcthhh2Xs2LH5+c9/XslxAACAQ1jF4ucHP/hBmpqacvnll+dXv/pVPvnJT2b69Ol59tlnKzUSAABwCKuu1B+8aNGizJkzJ1/96leTJNdff30eeOCBLFmyJC0tLZUaC4A/O/rSH1d6hEPG764+7YC/p+Nz4Dg+B7d34vhQXBWJn127dmX9+vW59NJLu+2fNm1a1q5du8/6rq6udHV1lR+3t7cnSTo6Ot7ZQXvgla7/rfQIh4R34pg6NgeO43NwO9DHx7E5cPy7c3BzfA5uB9Pf9zg4vfr/kVKp9BfXViR+XnzxxezZsyf19fXd9tfX16e1tXWf9S0tLbniiiv22T98+PB3bEYqo+76Sk/Am3F8Dm6Oz8HLsTm4OT4HN8eHt2rHjh2pq6t70zUV+9pbklRVVXV7XCqV9tmXJAsWLMiFF15YfvzKK6/kf/7nfzJo0KDXXc++Ojo6Mnz48GzZsiW1tbWVHoe9OD4HL8fm4Ob4HLwcm4Ob43Nwc3x6plQqZceOHWlsbPyLaysSP4MHD06vXr32Ocuzffv2fc4GJUlNTU1qamq67Xv/+9//To54yKqtrfUv0UHM8Tl4OTYHN8fn4OXYHNwcn4Ob4/PW/aUzPq+qyNXe+vTpk7Fjx2bVqlXd9q9atSoTJ06sxEgAAMAhrmJfe7vwwgtz9tlnZ9y4cZkwYUKWLl2aZ599Nuedd16lRgIAAA5hFYufM888My+99FKuvPLKbNu2LaNGjcq9996bo446qlIjHdJqamryz//8z/t8fZCDg+Nz8HJsDm6Oz8HLsTm4OT4HN8fnnVNVeivXhAMAAHiPq8hvfgAAAN5t4gcAACgE8QMAABSC+AEAAApB/BTAjTfemBEjRuSwww7L2LFj8/Of/7zSI/FnP/vZz3L66aensbExVVVVufvuuys9En/W0tKSE044IQMGDMjQoUNzxhln5Mknn6z0WCRZsmRJxowZU77534QJE3LfffdVeizeQEtLS6qqqtLU1FTpUUiycOHCVFVVddsaGhoqPRZ/9txzz+WLX/xiBg0alH79+uVjH/tY1q9fX+mxDini5xD3gx/8IE1NTbn88svzq1/9Kp/85Cczffr0PPvss5UejSQvv/xyjjvuuCxevLjSo7CX1atX5/zzz8/DDz+cVatW5Y9//GOmTZuWl19+udKjFd6wYcNy9dVX59FHH82jjz6aT3/60/nMZz6TTZs2VXo09rJu3bosXbo0Y8aMqfQovMZHP/rRbNu2rbxt3Lix0iORpK2tLSeddFJ69+6d++67L48//ni+853v5P3vf3+lRzukuNT1IW78+PH5+Mc/niVLlpT3HXPMMTnjjDPS0tJSwcnYW1VVVVasWJEzzjij0qPwOn7/+99n6NChWb16dT71qU9Vehz2MnDgwHz729/OnDlzKj0Kf7Zz5858/OMfz4033pirrroqH/vYx3L99ddXeqzCW7hwYe6+++5s2LCh0qOwl0svvTS/+MUvfEPnHebMzyFs165dWb9+faZNm9Zt/7Rp07J27doKTQXvTe3t7Un+9JdsDh579uzJ8uXL8/LLL2fChAmVHofXOP/883PaaadlypQplR6FvTz11FNpbGzMiBEj8vnPfz5PP/10pUciyT333JNx48blc5/7XIYOHZrjjz8+N910U6XHOuSIn0PYiy++mD179qS+vr7b/vr6+rS2tlZoKnjvKZVKufDCC/OJT3wio0aNqvQ4JNm4cWPe9773paamJuedd15WrFiRY489ttJj8WfLly/PL3/5S98wOAiNHz8+t912Wx544IHcdNNNaW1tzcSJE/PSSy9VerTCe/rpp7NkyZKMHDkyDzzwQM4777x84xvfyG233Vbp0Q4p1ZUegHdeVVVVt8elUmmffcAbu+CCC/LrX/86a9asqfQo/NlHPvKRbNiwIX/4wx/yn//5nznnnHOyevVqAXQQ2LJlS775zW9m5cqVOeywwyo9DnuZPn16+Z9Hjx6dCRMm5IMf/GBuvfXWXHjhhRWcjFdeeSXjxo1Lc3NzkuT444/Ppk2bsmTJknzpS1+q8HSHDmd+DmGDBw9Or1699jnLs3379n3OBgGvb968ebnnnnvyk5/8JMOGDav0OPxZnz598qEPfSjjxo1LS0tLjjvuuNxwww2VHosk69evz/bt2zN27NhUV1enuro6q1evzne/+91UV1dnz549lR6R1+jfv39Gjx6dp556qtKjFN4RRxyxz3/AOeaYY1yk6gATP4ewPn36ZOzYsVm1alW3/atWrcrEiRMrNBW8N5RKpVxwwQW566678tBDD2XEiBGVHok3USqV0tXVVekxSDJ58uRs3LgxGzZsKG/jxo3LWWedlQ0bNqRXr16VHpHX6OrqyhNPPJEjjjii0qMU3kknnbTPLRV+85vf5KijjqrQRIcmX3s7xF144YU5++yzM27cuEyYMCFLly7Ns88+m/POO6/So5E/XQ3pt7/9bfnx5s2bs2HDhgwcODBHHnlkBSfj/PPPz5133pkf/vCHGTBgQPkMal1dXfr27Vvh6Yrtsssuy/Tp0zN8+PDs2LEjy5cvz09/+tPcf//9lR6NJAMGDNjnt3H9+/fPoEGD/GbuIDB//vycfvrpOfLII7N9+/ZcddVV6ejoyDnnnFPp0QrvW9/6ViZOnJjm5ubMmjUrjzzySJYuXZqlS5dWerRDivg5xJ155pl56aWXcuWVV2bbtm0ZNWpU7r33Xv8V4SDx6KOP5pRTTik/fvX71uecc06WLVtWoalIUr48/KRJk7rtv+WWW/LlL3/53R+IshdeeCFnn312tm3blrq6uowZMyb3339/pk6dWunR4KC3devWfOELX8iLL76YIUOG5MQTT8zDDz/s7wUHgRNOOCErVqzIggULcuWVV2bEiBG5/vrrc9ZZZ1V6tEOK+/wAAACF4Dc/AABAIYgfAACgEMQPAABQCOIHAAAoBPEDAAAUgvgBAAAKQfwAAACFIH4AAIBCED8AAEAhiB8AAKAQxA8AAFAI4gcAACiE/wedkuvbJM9/fQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parch Parch\n",
      "0    678\n",
      "1    118\n",
      "2     80\n",
      "5      5\n",
      "3      5\n",
      "4      4\n",
      "6      1\n",
      "Name: count, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Embarked Embarked\n",
      "S    644\n",
      "C    168\n",
      "Q     77\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "def columns(variable):\n",
    "    category = train_dataset[variable]\n",
    "    number = category.value_counts()\n",
    "    plt.figure(figsize=(10,5))\n",
    "    plt.bar(number.index, number.values) \n",
    "    plt.title(variable)\n",
    "    plt.show()\n",
    "    print(variable, number)\n",
    "\n",
    "category1 = [\"Survived\", \"Pclass\", \"Sex\", \"SibSp\", \"Parch\", \"Embarked\"]\n",
    "\n",
    "for i in category1:\n",
    "    columns(i)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "aa3fa7c8-7a78-4422-a4bb-ab3c02e7b005",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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f3bt3x6hRo2Lv3r1x2223FZwbO3Zs/t/19fXRr1+/GDRoUKxYsSJOOumkVs81efLkmDBhQn6/ubk5amtrSzV1AADgMFSS+Nm9e3d8+tOfjg0bNsQjjzxScNVnX0466aTo1KlTrFu3bp/xU1ZWFmVlZaWYKgAAkIiix8/r4bNu3bpYvHhx9OzZ800fs2bNmti9e3dUV1cXezoAAAAR8Q/Ez44dO2L9+vX5/Q0bNsTKlSujR48eUVNTE5/61KdixYoV8cADD8SePXuisbExIiJ69OgRnTt3jj/96U9x1113xbnnnhuVlZXxzDPPxNVXXx0nnnhinHbaacV7ZQAAAH+jzfHz5JNPxtChQ/P7r38WZ/To0TFlypS4//77IyLiAx/4QMHjFi9eHEOGDInOnTvHr371q/jOd74TO3bsiNra2hg+fHjccMMN0aFDhwN4KQAAAPvX5vgZMmRIZFm23/NvdC4iora2NpYuXdrWHwsAAHBASv49PwAAAAcD8QMAACRB/AAAAEkQPwAAQBLEDwAAkATxAwAAJEH8AAAASRA/AABAEtr8JacA/PMdPWl+e0/hoLVx+vD2ngIAhwhXfgAAgCSIHwAAIAniBwAASIL4AQAAkiB+AACAJIgfAAAgCeIHAABIgvgBAACSIH4AAIAkiB8AACAJ4gcAAEiC+AEAAJIgfgAAgCSIHwAAIAniBwAASIL4AQAAkiB+AACAJIgfAAAgCeIHAABIgvgBAACSIH4AAIAkiB8AACAJ4gcAAEiC+AEAAJIgfgAAgCSIHwAAIAniBwAASIL4AQAAkiB+AACAJIgfAAAgCeIHAABIgvgBAACS0Ob4WbZsWYwYMSJqamoil8vFvHnzCs5nWRZTpkyJmpqa6NKlSwwZMiTWrFlTMKalpSWuvPLKqKysjG7dusV5550Xzz///AG9EAAAgDfS5vjZuXNnDBgwIGbMmLHP89/85jfj5ptvjhkzZsQTTzwRVVVV8bGPfSy2b9+eHzN+/Pi47777Yu7cufHoo4/Gjh074uMf/3js2bPnH38lAAAAb6BjWx/Q0NAQDQ0N+zyXZVnceuutcf3118fIkSMjImL27NnRu3fvuPvuu+Pzn/98NDU1xcyZM+MnP/lJnHXWWRERMWfOnKitrY1FixbF2WeffQAvBwAAYN+K+pmfDRs2RGNjYwwbNix/rKysLM4444x47LHHIiJi+fLlsXv37oIxNTU1UV9fnx/z91paWqK5ublgAwAAaIuixk9jY2NERPTu3bvgeO/evfPnGhsbo3PnzvGOd7xjv2P+3rRp06KioiK/1dbWFnPaAABAAkpyt7dcLlewn2VZq2N/743GTJ48OZqamvLb5s2bizZXAAAgDUWNn6qqqoiIVldwtm7dmr8aVFVVFbt27YpXXnllv2P+XllZWXTv3r1gAwAAaIuixk/fvn2jqqoqFi5cmD+2a9euWLp0aQwePDgiIgYOHBidOnUqGLNly5ZYvXp1fgwAAECxtflubzt27Ij169fn9zds2BArV66MHj16RJ8+fWL8+PExderU6NevX/Tr1y+mTp0aXbt2jc985jMREVFRURGXX355XH311dGzZ8/o0aNHTJw4Mfr375+/+xsAAECxtTl+nnzyyRg6dGh+f8KECRERMXr06Ljjjjvimmuuiddeey2+9KUvxSuvvBInn3xyPPzww1FeXp5/zC233BIdO3aMT3/60/Haa6/FmWeeGXfccUd06NChCC8JAACgtVyWZVl7T6Ktmpubo6KiIpqamnz+h0PS0ZPmt/cU4LCxcfrw9p4CAO2oLW1Qkru9AQAAHGzEDwAAkATxAwAAJEH8AAAASRA/AABAEsQPAACQBPEDAAAkQfwAAABJED8AAEASxA8AAJAE8QMAACRB/AAAAEkQPwAAQBLEDwAAkATxAwAAJEH8AAAASRA/AABAEsQPAACQBPEDAAAkQfwAAABJED8AAEASxA8AAJAE8QMAACRB/AAAAEkQPwAAQBLEDwAAkATxAwAAJEH8AAAASRA/AABAEsQPAACQBPEDAAAkQfwAAABJED8AAEASxA8AAJAE8QMAACRB/AAAAEkQPwAAQBLEDwAAkATxAwAAJEH8AAAASSh6/Bx99NGRy+VabePGjYuIiDFjxrQ6d8oppxR7GgAAAAU6FvsJn3jiidizZ09+f/Xq1fGxj30sLrjggvyxc845J2bNmpXf79y5c7GnAQAAUKDo8XPUUUcV7E+fPj2OPfbYOOOMM/LHysrKoqqqqtg/GgAAYL9K+pmfXbt2xZw5c+Kyyy6LXC6XP75kyZLo1atXHHfccTF27NjYunVrKacBAABQ/Cs/f2vevHmxbdu2GDNmTP5YQ0NDXHDBBVFXVxcbNmyIr371q/HRj340li9fHmVlZft8npaWlmhpacnvNzc3l3LaAADAYaik8TNz5sxoaGiImpqa/LELL7ww/+/6+voYNGhQ1NXVxfz582PkyJH7fJ5p06bFjTfeWMqpAgAAh7mSve3tueeei0WLFsUVV1zxhuOqq6ujrq4u1q1bt98xkydPjqampvy2efPmYk8XAAA4zJXsys+sWbOiV69eMXz48Dcc9/LLL8fmzZujurp6v2PKysr2+5Y4AACAt6IkV3727t0bs2bNitGjR0fHjv+/r3bs2BETJ06M3/zmN7Fx48ZYsmRJjBgxIiorK+P8888vxVQAAAAiokRXfhYtWhSbNm2Kyy67rOB4hw4dYtWqVXHnnXfGtm3borq6OoYOHRr33HNPlJeXl2IqAAAAEVGi+Bk2bFhkWdbqeJcuXeKhhx4qxY8EAAB4QyX9nh8AAICDhfgBAACSIH4AAIAkiB8AACAJJfueHwD4Zzh60vz2nsJBaeP0N/6ePYAUufIDAAAkQfwAAABJED8AAEASxA8AAJAE8QMAACRB/AAAAEkQPwAAQBLEDwAAkATxAwAAJEH8AAAASRA/AABAEsQPAACQBPEDAAAkQfwAAABJED8AAEASxA8AAJAE8QMAACRB/AAAAEkQPwAAQBLEDwAAkATxAwAAJEH8AAAASRA/AABAEsQPAACQBPEDAAAkQfwAAABJED8AAEASxA8AAJAE8QMAACRB/AAAAEkQPwAAQBLEDwAAkISO7T2Bw8XRk+a39xQOShunD2/vKQAAQES48gMAACRC/AAAAEkQPwAAQBKKHj9TpkyJXC5XsFVVVeXPZ1kWU6ZMiZqamujSpUsMGTIk1qxZU+xpAAAAFCjJlZ8TTjghtmzZkt9WrVqVP/fNb34zbr755pgxY0Y88cQTUVVVFR/72Mdi+/btpZgKAABARJQofjp27BhVVVX57aijjoqI/7vqc+utt8b1118fI0eOjPr6+pg9e3a8+uqrcffdd5diKgAAABFRovhZt25d1NTURN++fWPUqFHx7LPPRkTEhg0borGxMYYNG5YfW1ZWFmeccUY89thjpZgKAABARJTge35OPvnkuPPOO+O4446LF198MW666aYYPHhwrFmzJhobGyMionfv3gWP6d27dzz33HP7fc6WlpZoaWnJ7zc3Nxd72gAAwGGu6PHT0NCQ/3f//v3j1FNPjWOPPTZmz54dp5xySkRE5HK5gsdkWdbq2N+aNm1a3HjjjcWeKgAAkJCS3+q6W7du0b9//1i3bl3+rm+vXwF63datW1tdDfpbkydPjqampvy2efPmks4ZAAA4/JQ8flpaWuIPf/hDVFdXR9++faOqqioWLlyYP79r165YunRpDB48eL/PUVZWFt27dy/YAAAA2qLob3ubOHFijBgxIvr06RNbt26Nm266KZqbm2P06NGRy+Vi/PjxMXXq1OjXr1/069cvpk6dGl27do3PfOYzxZ4KAABAXtHj5/nnn4+LLrooXnrppTjqqKPilFNOid/+9rdRV1cXERHXXHNNvPbaa/GlL30pXnnllTj55JPj4YcfjvLy8mJPBQAAIK/o8TN37tw3PJ/L5WLKlCkxZcqUYv9oAACA/Sr5Z34AAAAOBuIHAABIQtHf9gZ/6+hJ89t7CgAAEBGu/AAAAIlw5QcADkOuvO/fxunD23sKQDtx5QcAAEiC+AEAAJIgfgAAgCSIHwAAIAniBwAASIL4AQAAkiB+AACAJIgfAAAgCeIHAABIgvgBAACSIH4AAIAkiB8AACAJ4gcAAEiC+AEAAJIgfgAAgCSIHwAAIAniBwAASIL4AQAAkiB+AACAJIgfAAAgCeIHAABIgvgBAACSIH4AAIAkiB8AACAJ4gcAAEiC+AEAAJIgfgAAgCSIHwAAIAniBwAASIL4AQAAkiB+AACAJIgfAAAgCeIHAABIgvgBAACSIH4AAIAkiB8AACAJRY+fadOmxQc/+MEoLy+PXr16xSc+8YlYu3ZtwZgxY8ZELpcr2E455ZRiTwUAACCv6PGzdOnSGDduXPz2t7+NhQsXxl//+tcYNmxY7Ny5s2DcOeecE1u2bMlvDz74YLGnAgAAkNex2E/43//93wX7s2bNil69esXy5cvjIx/5SP54WVlZVFVVFfvHAwAA7FPJP/PT1NQUERE9evQoOL5kyZLo1atXHHfccTF27NjYunXrfp+jpaUlmpubCzYAAIC2KGn8ZFkWEyZMiNNPPz3q6+vzxxsaGuKuu+6KRx55JL797W/HE088ER/96EejpaVln88zbdq0qKioyG+1tbWlnDYAAHAYymVZlpXqyceNGxfz58+PRx99NN71rnftd9yWLVuirq4u5s6dGyNHjmx1vqWlpSCMmpubo7a2NpqamqJ79+4lmXtbHT1pfntPAQB4CzZOH97eUwCKqLm5OSoqKt5SGxT9Mz+vu/LKK+P++++PZcuWvWH4RERUV1dHXV1drFu3bp/ny8rKoqysrBTTBAAAElH0+MmyLK688sq47777YsmSJdG3b983fczLL78cmzdvjurq6mJPBwCggHdr7JsrYqSg6J/5GTduXMyZMyfuvvvuKC8vj8bGxmhsbIzXXnstIiJ27NgREydOjN/85jexcePGWLJkSYwYMSIqKyvj/PPPL/Z0AAAAIqIEV35uv/32iIgYMmRIwfFZs2bFmDFjokOHDrFq1aq48847Y9u2bVFdXR1Dhw6Ne+65J8rLy4s9HQAAgIgo0dve3kiXLl3ioYceKvaPBQAAeEMl/54fAACAg4H4AQAAkiB+AACAJIgfAAAgCeIHAABIgvgBAACSIH4AAIAkiB8AACAJ4gcAAEiC+AEAAJIgfgAAgCSIHwAAIAniBwAASIL4AQAAkiB+AACAJIgfAAAgCeIHAABIgvgBAACSIH4AAIAkiB8AACAJ4gcAAEiC+AEAAJIgfgAAgCSIHwAAIAniBwAASIL4AQAAkiB+AACAJHRs7wkAAND+jp40v72ncNDaOH14e0+BInHlBwAASIL4AQAAkiB+AACAJIgfAAAgCeIHAABIgvgBAACSIH4AAIAkiB8AACAJ4gcAAEiC+AEAAJIgfgAAgCSIHwAAIAniBwAASELH9vzht912W3zrW9+KLVu2xAknnBC33nprfPjDH27PKQEAQIGjJ81v7ykclDZOH97eU2izdrvyc88998T48ePj+uuvj6eeeio+/OEPR0NDQ2zatKm9pgQAABzG2i1+br755rj88svjiiuuiPe+971x6623Rm1tbdx+++3tNSUAAOAw1i5ve9u1a1csX748Jk2aVHB82LBh8dhjj7Ua39LSEi0tLfn9pqamiIhobm4u7UTbYG/Lq+09BQAA+Kc5WP63+OvzyLLsTce2S/y89NJLsWfPnujdu3fB8d69e0djY2Or8dOmTYsbb7yx1fHa2tqSzREAANi/ilvbewaFtm/fHhUVFW84pl1veJDL5Qr2syxrdSwiYvLkyTFhwoT8/t69e+PPf/5z9OzZc5/jS625uTlqa2tj8+bN0b1793/6zz/cWd/Ss8alZX1LzxqXnjUuLetbeta4tA6m9c2yLLZv3x41NTVvOrZd4qeysjI6dOjQ6irP1q1bW10NiogoKyuLsrKygmNHHnlkKaf4lnTv3r3d/8M+nFnf0rPGpWV9S88al541Li3rW3rWuLQOlvV9sys+r2uXGx507tw5Bg4cGAsXLiw4vnDhwhg8eHB7TAkAADjMtdvb3iZMmBCf/exnY9CgQXHqqafGD37wg9i0aVN84QtfaK8pAQAAh7F2i58LL7wwXn755fja174WW7Zsifr6+njwwQejrq6uvab0lpWVlcUNN9zQ6q14FIf1LT1rXFrWt/SscelZ49KyvqVnjUvrUF3fXPZW7gkHAABwiGu3LzkFAAD4ZxI/AABAEsQPAACQBPEDAAAkQfy00W233RZ9+/aNI444IgYOHBi//vWv23tKh6xly5bFiBEjoqamJnK5XMybN6/gfJZlMWXKlKipqYkuXbrEkCFDYs2aNe0z2UPQtGnT4oMf/GCUl5dHr1694hOf+ESsXbu2YIw1PjC33357vP/9789/wdupp54aCxYsyJ+3vsU1bdq0yOVyMX78+Pwxa3xgpkyZErlcrmCrqqrKn7e+B+5///d/45JLLomePXtG165d4wMf+EAsX748f94aH5ijjz661e9wLpeLcePGRYT1PVB//etf4ytf+Ur07ds3unTpEsccc0x87Wtfi7179+bHHHJrnPGWzZ07N+vUqVP2wx/+MHvmmWeyq666KuvWrVv23HPPtffUDkkPPvhgdv3112e/+MUvsojI7rvvvoLz06dPz8rLy7Nf/OIX2apVq7ILL7wwq66uzpqbm9tnwoeYs88+O5s1a1a2evXqbOXKldnw4cOzPn36ZDt27MiPscYH5v7778/mz5+frV27Nlu7dm123XXXZZ06dcpWr16dZZn1LabHH388O/roo7P3v//92VVXXZU/bo0PzA033JCdcMIJ2ZYtW/Lb1q1b8+et74H585//nNXV1WVjxozJfve732UbNmzIFi1alK1fvz4/xhofmK1btxb8/i5cuDCLiGzx4sVZllnfA3XTTTdlPXv2zB544IFsw4YN2c9+9rPs7W9/e3brrbfmxxxqayx+2uBDH/pQ9oUvfKHg2PHHH59NmjSpnWZ0+Pj7+Nm7d29WVVWVTZ8+PX/sL3/5S1ZRUZF973vfa4cZHvq2bt2aRUS2dOnSLMuscam84x3vyH70ox9Z3yLavn171q9fv2zhwoXZGWeckY8fa3zgbrjhhmzAgAH7PGd9D9y1116bnX766fs9b42L76qrrsqOPfbYbO/evda3CIYPH55ddtllBcdGjhyZXXLJJVmWHZq/w9729hbt2rUrli9fHsOGDSs4PmzYsHjsscfaaVaHrw0bNkRjY2PBepeVlcUZZ5xhvf9BTU1NERHRo0ePiLDGxbZnz56YO3du7Ny5M0499VTrW0Tjxo2L4cOHx1lnnVVw3BoXx7p166Kmpib69u0bo0aNimeffTYirG8x3H///TFo0KC44IILolevXnHiiSfGD3/4w/x5a1xcu3btijlz5sRll10WuVzO+hbB6aefHr/61a/ij3/8Y0REPP300/Hoo4/GueeeGxGH5u9wx/aewKHipZdeij179kTv3r0Ljvfu3TsaGxvbaVaHr9fXdF/r/dxzz7XHlA5pWZbFhAkT4vTTT4/6+vqIsMbFsmrVqjj11FPjL3/5S7z97W+P++67L973vvfl/+hb3wMzd+7cWLFiRTzxxBOtzvkdPnAnn3xy3HnnnXHcccfFiy++GDfddFMMHjw41qxZY32L4Nlnn43bb789JkyYENddd108/vjj8S//8i9RVlYWl156qTUusnnz5sW2bdtizJgxEeFvRDFce+210dTUFMcff3x06NAh9uzZE1//+tfjoosuiohDc43FTxvlcrmC/SzLWh2jeKx3cXz5y1+O3//+9/Hoo4+2OmeND8x73vOeWLlyZWzbti1+8YtfxOjRo2Pp0qX589b3H7d58+a46qqr4uGHH44jjjhiv+Os8T+uoaEh/+/+/fvHqaeeGscee2zMnj07TjnllIiwvgdi7969MWjQoJg6dWpERJx44omxZs2auP322+PSSy/Nj7PGxTFz5sxoaGiImpqaguPW9x93zz33xJw5c+Luu++OE044IVauXBnjx4+PmpqaGD16dH7cobTG3vb2FlVWVkaHDh1aXeXZunVrq9rlwL1+tyHrfeCuvPLKuP/++2Px4sXxrne9K3/cGhdH586d493vfncMGjQopk2bFgMGDIjvfOc71rcIli9fHlu3bo2BAwdGx44do2PHjrF06dL47ne/Gx07dsyvozUunm7dukX//v1j3bp1foeLoLq6Ot73vvcVHHvve98bmzZtigh/h4vpueeei0WLFsUVV1yRP2Z9D9y//du/xaRJk2LUqFHRv3//+OxnPxv/+q//GtOmTYuIQ3ONxc9b1Llz5xg4cGAsXLiw4PjChQtj8ODB7TSrw1ffvn2jqqqqYL137doVS5cutd5vUZZl8eUvfznuvffeeOSRR6Jv374F561xaWRZFi0tLda3CM4888xYtWpVrFy5Mr8NGjQoLr744li5cmUcc8wx1rjIWlpa4g9/+ENUV1f7HS6C0047rdVXDPzxj3+Murq6iPB3uJhmzZoVvXr1iuHDh+ePWd8D9+qrr8bb3laYCx06dMjf6vqQXOP2uc/Coen1W13PnDkze+aZZ7Lx48dn3bp1yzZu3NjeUzskbd++PXvqqaeyp556KouI7Oabb86eeuqp/K3Dp0+fnlVUVGT33ntvtmrVquyiiy46qG+deLD54he/mFVUVGRLliwpuA3oq6++mh9jjQ/M5MmTs2XLlmUbNmzIfv/732fXXXdd9ra3vS17+OGHsyyzvqXwt3d7yzJrfKCuvvrqbMmSJdmzzz6b/fa3v80+/vGPZ+Xl5fn/XrO+B+bxxx/POnbsmH3961/P1q1bl911111Z165dszlz5uTHWOMDt2fPnqxPnz7Ztdde2+qc9T0wo0ePzt75znfmb3V97733ZpWVldk111yTH3OorbH4aaP/+I//yOrq6rLOnTtnJ510Uv62wbTd4sWLs4hotY0ePTrLsv+7feINN9yQVVVVZWVlZdlHPvKRbNWqVe076UPIvtY2IrJZs2blx1jjA3PZZZfl/x4cddRR2ZlnnpkPnyyzvqXw9/FjjQ/M69/H0alTp6ympiYbOXJktmbNmvx563vg/uu//iurr6/PysrKsuOPPz77wQ9+UHDeGh+4hx56KIuIbO3ata3OWd8D09zcnF111VVZnz59siOOOCI75phjsuuvvz5raWnJjznU1jiXZVnWLpecAAAA/ol85gcAAEiC+AEAAJIgfgAAgCSIHwAAIAniBwAASIL4AQAAkiB+AACAJIgfAAAgCeIHAABIgvgBAACSIH4AAIAkiB8AACAJ/w+9aaor3EEuYAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def hist(variable):\n",
    "    plt.figure(figsize=(10,5))\n",
    "    plt.hist(train_dataset[variable])\n",
    "    plt.title(variable)\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "numbers = [\"Fare\",\"Age\"]\n",
    "\n",
    "for i in numbers:\n",
    "    hist(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b280c2f6-b4f8-42f9-a26e-d9281563de27",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "bc63624f-e0cc-49a4-87e1-63c23e7a15ea",
   "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>Pclass</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.629630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.472826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.242363</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pclass  Survived\n",
       "0       1  0.629630\n",
       "1       2  0.472826\n",
       "2       3  0.242363"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_dataset[[\"Pclass\",\"Survived\"]].groupby([\"Pclass\"],as_index= False).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "1048bec6-9a2f-4af0-b31c-37a1d2078326",
   "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>Sex</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>female</td>\n",
       "      <td>0.742038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>male</td>\n",
       "      <td>0.188908</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Sex  Survived\n",
       "0  female  0.742038\n",
       "1    male  0.188908"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_dataset[[\"Sex\",\"Survived\"]].groupby([\"Sex\"],as_index=False).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "72d47263-8ac1-4379-959d-f6b625ea038b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.535885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.464286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.345395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>8</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SibSp  Survived\n",
       "1      1  0.535885\n",
       "2      2  0.464286\n",
       "0      0  0.345395\n",
       "3      3  0.250000\n",
       "4      4  0.166667\n",
       "5      5  0.000000\n",
       "6      8  0.000000"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_dataset[[\"SibSp\",\"Survived\"]].groupby([\"SibSp\"],as_index=False).mean().sort_values(by=\"Survived\",ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "7fed4a0d-88aa-47db-a451-0e4adfbe6906",
   "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>Parch</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.550847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.343658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>0.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Parch  Survived\n",
       "3      3  0.600000\n",
       "1      1  0.550847\n",
       "2      2  0.500000\n",
       "0      0  0.343658\n",
       "5      5  0.200000\n",
       "4      4  0.000000\n",
       "6      6  0.000000"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_dataset[[\"Parch\",\"Survived\"]].groupby(\"Parch\",as_index=False).mean().sort_values(by=\"Survived\",ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "a01456bd-0c81-4bf2-8fa2-d441b95d537a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pclass  Sex   \n",
       "1       female    66.911765\n",
       "        male      33.088235\n",
       "2       female    80.459770\n",
       "        male      19.540230\n",
       "3       female    60.504202\n",
       "        male      39.495798\n",
       "Name: proportion, dtype: float64"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "survived = train_dataset[train_dataset[\"Survived\"] == 1]\n",
    "\n",
    "# Hər bir Pclass üçün sağ qalanların cins faizini hesablamaq\n",
    "gender_percentage_by_pclass = survived.groupby(\"Pclass\")[\"Sex\"].value_counts(normalize=True) * 100\n",
    "gender_percentage_by_pclass\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "d627b61a-8edb-4b2d-8e85-e46c0881c0dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "\n",
    "\n",
    "# Nəticələri DataFrame-ə çevir\n",
    "gender_percentage_df = gender_percentage_by_pclass.unstack()\n",
    "\n",
    "# Vizuallaşdırma\n",
    "gender_percentage_df.plot(kind='bar', stacked=True, figsize=(10, 6))\n",
    "\n",
    "plt.title('Survived Individuals Gender Percentage by Pclass')\n",
    "plt.xlabel('Pclass')\n",
    "plt.ylabel('Percentage')\n",
    "plt.legend(title='Gender', labels=['Female', 'Male'], loc='upper right')\n",
    "plt.xticks(rotation=0)\n",
    "plt.grid(axis='y')\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "a7f5e2b9-32e1-4dac-9913-fb43ffdcc15c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Embarked\n",
      "C    0.553571\n",
      "Q    0.389610\n",
      "S    0.336957\n",
      "Name: Survived, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "# Hər bir Embarked üçün həyatda qalma faizi ortalamasını hesablayın\n",
    "survival_rate_by_embarked = train_dataset.groupby(\"Embarked\")[\"Survived\"].mean() \n",
    "\n",
    "# Nəticəni çap et\n",
    "print(survival_rate_by_embarked)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "8acc0a33-c0c0-4385-885c-434d08df8340",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Embarked\n",
      "C    0.553571\n",
      "Q    0.389610\n",
      "S    0.336957\n",
      "Name: Survived, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(survival_rate_by_embarked)\n",
    "\n",
    "survival_rate_by_embarked.plot(kind='bar', color=['skyblue', 'lightgreen', 'salmon'], figsize=(8, 5))\n",
    "\n",
    "plt.title('Survival Rate by Embarked Location')\n",
    "plt.xlabel('Embarked')\n",
    "plt.ylabel('Survival Rate (%)')\n",
    "plt.xticks(rotation=0)\n",
    "plt.grid(axis='y')\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.12.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}