Prodigy_DS_02.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "294c79bc-61bf-4313-977e-14f9acee49de",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8b17fccf-380a-469c-8400-a31c16dab9be",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     PassengerId  Survived  Pclass  \\\n",
      "0              1         0       3   \n",
      "1              2         1       1   \n",
      "2              3         1       3   \n",
      "3              4         1       1   \n",
      "4              5         0       3   \n",
      "..           ...       ...     ...   \n",
      "886          887         0       2   \n",
      "887          888         1       1   \n",
      "888          889         0       3   \n",
      "889          890         1       1   \n",
      "890          891         0       3   \n",
      "\n",
      "                                                  Name     Sex   Age  SibSp  \\\n",
      "0                              Braund, Mr. Owen Harris    male  22.0      1   \n",
      "1    Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
      "2                               Heikkinen, Miss. Laina  female  26.0      0   \n",
      "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
      "4                             Allen, Mr. William Henry    male  35.0      0   \n",
      "..                                                 ...     ...   ...    ...   \n",
      "886                              Montvila, Rev. Juozas    male  27.0      0   \n",
      "887                       Graham, Miss. Margaret Edith  female  19.0      0   \n",
      "888           Johnston, Miss. Catherine Helen \"Carrie\"  female   NaN      1   \n",
      "889                              Behr, Mr. Karl Howell    male  26.0      0   \n",
      "890                                Dooley, Mr. Patrick    male  32.0      0   \n",
      "\n",
      "     Parch            Ticket     Fare Cabin Embarked  \n",
      "0        0         A/5 21171   7.2500   NaN        S  \n",
      "1        0          PC 17599  71.2833   C85        C  \n",
      "2        0  STON/O2. 3101282   7.9250   NaN        S  \n",
      "3        0            113803  53.1000  C123        S  \n",
      "4        0            373450   8.0500   NaN        S  \n",
      "..     ...               ...      ...   ...      ...  \n",
      "886      0            211536  13.0000   NaN        S  \n",
      "887      0            112053  30.0000   B42        S  \n",
      "888      2        W./C. 6607  23.4500   NaN        S  \n",
      "889      0            111369  30.0000  C148        C  \n",
      "890      0            370376   7.7500   NaN        Q  \n",
      "\n",
      "[891 rows x 12 columns]\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv(r\"C:\\Users\\DELL\\train.csv\")\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b25fc50e-55d1-443d-888c-a80adcdf1f35",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e7c8df6-a951-4baf-8782-c8724bc1b661",
   "metadata": {},
   "source": [
    "## Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9013ef36-c1c9-48d9-8410-ddea516ffff7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Survived         0\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "Age            177\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             0\n",
       "Cabin          687\n",
       "Embarked         2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1d793044-1308-45a1-9a5e-a6ed4a23ca05",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Dropping Cabin asit is not needed\n",
    "df.drop(\"Cabin\", axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1c7edff7-c2bf-4a04-b367-ab10333faa6a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>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>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>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>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>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>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>887</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Montvila, Rev. Juozas</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>211536</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>888</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Graham, Miss. Margaret Edith</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112053</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>889</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>W./C. 6607</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>890</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Behr, Mr. Karl Howell</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111369</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>891</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Dooley, Mr. Patrick</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370376</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass  \\\n",
       "0              1         0       3   \n",
       "1              2         1       1   \n",
       "2              3         1       3   \n",
       "3              4         1       1   \n",
       "4              5         0       3   \n",
       "..           ...       ...     ...   \n",
       "886          887         0       2   \n",
       "887          888         1       1   \n",
       "888          889         0       3   \n",
       "889          890         1       1   \n",
       "890          891         0       3   \n",
       "\n",
       "                                                  Name     Sex   Age  SibSp  \\\n",
       "0                              Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1    Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                               Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                             Allen, Mr. William Henry    male  35.0      0   \n",
       "..                                                 ...     ...   ...    ...   \n",
       "886                              Montvila, Rev. Juozas    male  27.0      0   \n",
       "887                       Graham, Miss. Margaret Edith  female  19.0      0   \n",
       "888           Johnston, Miss. Catherine Helen \"Carrie\"  female   NaN      1   \n",
       "889                              Behr, Mr. Karl Howell    male  26.0      0   \n",
       "890                                Dooley, Mr. Patrick    male  32.0      0   \n",
       "\n",
       "     Parch            Ticket     Fare Embarked  \n",
       "0        0         A/5 21171   7.2500        S  \n",
       "1        0          PC 17599  71.2833        C  \n",
       "2        0  STON/O2. 3101282   7.9250        S  \n",
       "3        0            113803  53.1000        S  \n",
       "4        0            373450   8.0500        S  \n",
       "..     ...               ...      ...      ...  \n",
       "886      0            211536  13.0000        S  \n",
       "887      0            112053  30.0000        S  \n",
       "888      2        W./C. 6607  23.4500        S  \n",
       "889      0            111369  30.0000        C  \n",
       "890      0            370376   7.7500        Q  \n",
       "\n",
       "[891 rows x 11 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "eb8da0aa-e54c-4f7f-ac3e-8569a9e0162d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Age\"].fillna(df[\"Age\"].median(), inplace=True)\n",
    "\n",
    "mode_value = df[\"Embarked\"].mode()\n",
    "if not mode_value.empty:\n",
    "    df[\"Embarked\"] = df[\"Embarked\"].fillna(mode_value[0])\n",
    "else:\n",
    "    df[\"Embarked\"] = df[\"Embarked\"].fillna(\"S\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b7aad78e-ff90-45bd-8cc1-5bbc4d9c88fc",
   "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": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a5ce5d5-3413-4d89-8923-a1f4c3abdc69",
   "metadata": {},
   "source": [
    "## EDA"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4cb0e4db-2386-4e20-bded-82d546b397db",
   "metadata": {},
   "source": [
    "#### Distribution of Survived vs Not Survived"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "5279e8cc-f417-48a2-809b-a910ef35bf63",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Calculate the counts for survived and non-survived\n",
    "non_survived = df[df['Survived'] == 0].shape[0]\n",
    "survived = df[df['Survived'] == 1].shape[0]\n",
    "\n",
    "# Data and labels\n",
    "data = [non_survived, survived]\n",
    "labels = [\"Non-Survived\", \"Survived\"]\n",
    "\n",
    "# Colors for the pie chart\n",
    "colors = ['red', 'green']  # Red for Non-Survived, Green for Survived\n",
    "\n",
    "# Plot the pie chart\n",
    "plt.figure(figsize=(8, 8))\n",
    "plt.pie(\n",
    "    data, \n",
    "    labels=labels, \n",
    "    colors=colors, \n",
    "    autopct='%1.1f%%', \n",
    "    startangle=90, \n",
    "    explode=(0, 0.1),  # Slightly separate the 'Survived' slice\n",
    "    shadow=True,       # Add shadow for an interactive feel\n",
    "    wedgeprops={'edgecolor': 'black'}  # Add border to the wedges\n",
    ")\n",
    "\n",
    "# Add a title\n",
    "plt.title(\"Survived vs Non-Survived Distribution\", fontsize=14, fontweight='bold')\n",
    "\n",
    "# Show the pie chart\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5d6dab64-d3a5-4aca-b433-ef735ef9544c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#!pip install mplcursors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "4eecdfb7-9c2a-46fb-83df-a99fee6300c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import mplcursors  # This is a library for interactive cursor effects\n",
    "\n",
    "# Separating the data for each category\n",
    "non_survived = df[df['Survived'] == 0]\n",
    "survived = df[df['Survived'] == 1]\n",
    "\n",
    "# Counting the number of non-survived and survived\n",
    "count_non_survived = len(non_survived)\n",
    "count_survived = len(survived)\n",
    "\n",
    "# Creating bar positions\n",
    "categories = ['Non-Survived', 'Survived']\n",
    "counts = [count_non_survived, count_survived]\n",
    "x_positions = range(len(categories))\n",
    "\n",
    "# Creating the bar plot\n",
    "plt.figure(figsize=(8, 5))\n",
    "bars = plt.bar(\n",
    "    x_positions, \n",
    "    counts, \n",
    "    color=['red', 'green'], \n",
    "    alpha=0.8, \n",
    "    edgecolor='black', \n",
    "    linewidth=1.5\n",
    ")\n",
    "\n",
    "# Adding value labels on top of each bar\n",
    "for bar in bars:\n",
    "    height = bar.get_height()\n",
    "    plt.text(\n",
    "        bar.get_x() + bar.get_width() / 2, \n",
    "        height + 1,  # Adjusting position slightly above the bar\n",
    "        f'{height}', \n",
    "        ha='center', \n",
    "        va='bottom', \n",
    "        fontsize=10, \n",
    "        fontweight='bold'\n",
    "    )\n",
    "\n",
    "# Adding grid lines for better readability\n",
    "plt.grid(axis='y', linestyle='--', alpha=0.5)\n",
    "\n",
    "# Add title, labels, and xticks\n",
    "plt.title('Survived vs Non-Survived Distribution', fontsize=14, fontweight='bold')\n",
    "plt.xlabel('Category', fontsize=12)\n",
    "plt.ylabel('Count', fontsize=12)\n",
    "plt.xticks(x_positions, categories, fontsize=11)\n",
    "\n",
    "# Add a cursor for interactivity\n",
    "cursor = mplcursors.cursor(bars, hover=True)\n",
    "cursor.connect(\"add\", lambda sel: sel.annotation.set_text(f\"Count: {counts[sel.index]}\"))\n",
    "\n",
    "# Show the plot\n",
    "plt.tight_layout()  # Adjusting layout for better spacing\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d620282-77bf-4a15-a263-3022eecf2d56",
   "metadata": {},
   "source": [
    "## Distriboution by Gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "bdf6a134-cb03-4911-9a57-a3de1e9b0605",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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7cRtwBIQKoJh5eXlp8uTJOnnypPbt26dFixYpMjLS7o+5U6dO5XjsbBbjmstGUlNT87XdsmXLys/PL9e+3r17m08Yio6O1h9//KFVq1bpypUrkq4+keWBBx7I13aCg4PVqlUrc37ZsmVKSkqyCwfPP/+8+e/sfwzfSNYfCufPn7dr9/f3t5sv7OUBTz/9tPlz+PPPP/Xtt9/qq6++Mrfr5+dn90fI008/rWHDhsnd3V0ZGRmKjo7W4sWLNXLkSLVs2VLVq1fX/v37C1WLJPXv39+85GTatGk59jvLta/htft/7Xz2gJDdtV/clT2oZWZmmv+eO3euhg8fnmM6fPiwOcZms6lv377at2+fjh8/rpUrV2rw4MEKDg42xxiGoenTp+daS1Z/dvl9r9eqVSvX9kceeUT33HOPpKuX8HzyySf67bff9NNPP0m6GvqzLm27kWvDeNYlT9kvfcr+B3ZB3udS7u/1a9/nzs7Oef5O55efn1+Oy/Syv1+yb79cuXLq3r27Ob9w4UKdPHnSvPTJ1dU134G+YsWKdvPZPySQpHHjxumtt95SvXr1cl2+MMeNa13v/S79//d8YY83N6PG6x23geLGI2UBB2Gz2VSnTh3VqVNHzz//vMaPH6/q1aub/yPL+oRfkt0jRbPucciSfdz1ZF1PnpvKlSurVatW+uqrr2QYhlasWKENGzaY/QX99PG5554zrzNevny5KleubP5BWL9+fYWGhppjy5QpY7fsxIkT7a5tzo2Pj4/dfPb7QCQV+jrzkiVLqmvXrlq4cKFZe/bX+5lnnpGbm5vdMm+99ZbGjBmj7du367ffftPBgwf12Wef6cSJEzp27Jj69+9vXpdeUB4eHho7dqyioqJ07tw5zZkzJ9dx176G1+7/tfN5nT259lPvgn56nJdKlSqpc+fO6ty5s6ZOnaoGDRrowIEDkvJ+n0v27/Xk5OR8/1yv917v3bu3+cnv8uXL7a5lb9++fYECaWRkpMaNG6fMzEx9++23+v77781P711cXOz+wL72ZxQZGam6devmue6sP3h9fX3Ntmvf5xkZGZYftXrmzBllZGTYBYvsr3P27UvSoEGDtGTJEklXv8Qz+zHr8ccfV7ly5fK13datW2vBggXm/JIlS+wCZr9+/SRd/QLF7J/oZ8n+epYvX15Dhw7Nc1tBQUG5tuf3/X7ta5CYmGi3/bzel9nHeHh4aNKkSXnWeO0xLcv13stAsSvWi6+Au9ySJUuMefPmGUlJSTn6EhMTDRcXF/M62pEjR5p92a8flmQ+NSolJcVo3rx5nvcP3OhRh9ktW7bMbmzWU4BcXV1zPPnnevdUGIZhXL582fDx8bG7fj7r39c+uWTPnj1263r33XdzrW/fvn3Gd999Z84XxT0VhnH1iTJZy/n5+Rne3t653otgGIZx5MgR49y5cznW8cknn5jLeHp65mu7195TkeXKlSvGvffem+t9CYW9pyL7PTvZ2699pPC1j9/Mr127dhmvvvqq3SODs2RkZNjdq9GkSROz79r7a7I/gefaJyBd756K3B61m+X48eOGk5OTIclwcXExgoKCzOXWrFmTY/yNHtnarl27XN/nHTp0yLHffn5+Zn+XLl1yrS8hIcH45JNPzHlHuqciS9OmTc1+Dw8P89+ff/55vrZvGFePXdlfW2dn5xyP3jUMw+jUqVOux7Ds9z+4uLgYP//8c45lMzMzjU2bNhlHjhwx2673ns7rfVTYeyo+/fRTu+XWr1+f62vxww8/2B1bCnLcBooTZyqAYnT06FFNmDBBgwcPVrNmzdSgQQOVKVNGZ86c0erVq5Wenm6OzXrykaQclx499NBDat68uXbv3n3Tnhj15JNPytfXV+fPn9exY8fM9oiIiHx/+pilRIkS6tatm/79739Lurrf0tVPBq99+tB9992ntm3bmvdvDBw4UBs2bFBoaKicnJx07Ngxbd++XQcOHNC4cePM67379OljXod+6NAhhYWF6YknntC+ffv0ySefFO5FkBQWFqZatWrpt99+s/sUuEGDBmrQoIHd2I8++kjjxo1TixYtdO+996p8+fK6dOmS3TX6137KWVAuLi6aOHGi3WUn13JyctKQIUP02muvSbp6CVuzZs3Url07/fbbb3ZPhWrZsqXuu+8+SzXdyIULF/TGG29o8uTJCg0NVePGjVWhQgWlpKRo06ZN5uVGkv37vFatWvLy8jLvdejfv7/Wrl2r+Pj4m/alkJUqVVLbtm31xRdfKD093bwPxN/fXxEREQVe33PPPacvv/xS0v9/n2e1Z+fk5KShQ4fq1VdflXT13osjR46obdu28vLyUnx8vHbt2qUdO3aoWbNm5pPWnn/+ec2fP1+GYSgjI0PNmzdXZGSkLly4UKAncl3P888/r++++06+vr764IMPzMsepdyfyjRo0CDzHoCsL64LDAy0+1neiLu7u5YsWaLw8HClpaUpIyNDPXv21KxZs9SyZUv5+voqISEhxz1XWXr37q3XX39dp0+fVnp6uh566CF17txZ99xzj1JTUxUbG6stW7YoISFB33zzjapWrVqQl8RO48aNVadOHfNSxjfeeEN//PGHqlSpotWrV9vdK5NdRESEateubZ6V69ixo5566imFhIQoMzNThw8f1rfffqtjx45p8eLFOY4vgMMr7lQD3M2u/YQwr6lv3745ls3rOxmufYpKYc9UGIZh9OvXL8f6P/vssxzjbnSmwjAMY8eOHTnWldcThBISEq77PRVZU/ZPYtPS0uw+Mc0+ZX+KU171Xc/UqVNzrHPWrFk5xk2ZMuWGNee2XG7yOlNhGFc/cb3vvvtyrPva76no3LnzdWupXbu28b///c9u3dn7b9aZims/8c1ratiwod3TpgzDMMaMGZPr2EaNGhn+/v65vhcKcqbCMAzjo48+yrH+oUOH5jr2RmcqUlJSjNKlS9utKyAgwLhy5UqOsfn5norcXuvhw4fnOq5OnTp2n5IX5kxF2bJljTp16uS6/ry+byItLc2oUKGC3djhw4fna9vX+vrrr3Os63rvl+y2bdt23e+pyO33vzBnKgzj6vGsVKlSOdbt6upqdxy69jgbGxt73e+pyO13jzMVuF1wozZQjAYPHqzVq1erf//+evDBB1W5cmWVKFFCbm5uqlixov72t7/p448/zvV7CT777DP9/e9/V7ly5eTu7q769etr4cKFeuedd25afdd+uhoQEKD27dsXal0PPvhgjif/5HVvhr+/v3bs2KG5c+eqVatWKlu2rJydnVWqVCnVqlVLPXv21Icffqjhw4eby7i6uurLL7/U8OHDVbFiRbm5ualmzZp6++23zXsiCqtXr15215i7ubnpmWeeyTGuY8eOGjt2rNq0aaMqVaqoZMmScnFxUfny5RUREaHPPvtMgwYNslSLdPVa7zfeeOO6Y5ydnbVy5UqtWrVKjz32mPz9/eXi4iIfHx81btxYb731lnbu3KkKFSpYrudGmjZtqs2bN+vVV19VixYtdM8998jb21suLi7y8/PTI488ohkzZmj79u12DyiQrt5TM3nyZFWtWlWurq4KDg7WqFGjtHXr1hvea5NfHTp0yHGPQ2GfWuTu7p7jLFLPnj3l4pLzwgAnJye9//77WrdunTp16qRKlSrJzc1N7u7uCg4O1hNPPKEZM2bYnemSrt6kP2/ePIWEhMjNzU3ly5fXgAED9N1331m+5r5UqVL6/vvvNWjQILvfo5kzZ+Z5bHF1ddWLL75o15b94QsF0bJlS/3++++aN2+eIiIiVLFiRXl4eMjNzU3+/v5q2rSpBg8erI0bN+rHH3+0W7Zp06bav3+/XnvtNYWGhsrb21vOzs7y9fVVaGioBg4cqE2bNumRRx4pVG3ZPfjgg9q2bZvat28vT09PeXp6qnXr1tqyZYvatm2b53I1atTQL7/8omnTpqlp06YqXbq0nJ2d5eXlpfr16+vvf/+71qxZk+vxBXB0NsMowm8cAgAAd7wVK1aYYapJkyY37fI0ALcP7qkAAAAFdv78ee3Zs0cJCQnmvSHS1fugANx9OFMBAAAKbMuWLWrZsqVdW5MmTbRt27YcjwMGcOfjtx4AABSazWZT+fLlFRUVpbVr1xIogLsUZyoAAAAAWMLHCQAAAAAsIVQAAAAAsISnP+UiMzNTJ06ckJeXl2w2W3GXAwAAABQLwzB04cIFVahQ4br3TBEqcnHixAkFBQUVdxkAAACAQzh+/LgqVaqUZ7/DhYr//e9/euWVV7RhwwZdvnxZ99xzjxYvXqxGjRpJupqWxo0bpwULFuj8+fN66KGHNHfuXN17773mOs6ePatBgwbp888/l5OTkzp16qSZM2fK09MzXzVkfaPr8ePH5e3tffN3EgAAALgNJCcnKygoyPz7OC8OFSrOnTunhx56SC1bttSGDRtUrlw5/f777ypdurQ5Ztq0aZo1a5aWLl2qqlWr6rXXXlN4eLh+/fVXeXh4SJJ69OihkydPatOmTbpy5Yqee+45RUVFadmyZfmqI+uSJ29vb0IFAAAA7no3uiXAoR4pO3LkSG3btk3fffddrv2GYahChQp6+eWXNWzYMElSUlKSAgICtGTJEnXr1k0HDhxQSEiIdu7caZ7d2Lhxox577DH9+eefqlChwg3rSE5Olo+Pj5KSkggVAAAAuGvl9+9ih3r602effaZGjRqpc+fO8vf31/33368FCxaY/UePHlV8fLzatGljtvn4+Khx48aKjo6WJEVHR8vX19cMFJLUpk0bOTk5aceOHbluNzU1VcnJyXYTAAAAgPxxqFBx5MgR8/6IL774Qv369dNLL72kpUuXSpLi4+MlSQEBAXbLBQQEmH3x8fHy9/e363dxcVGZMmXMMdeaMmWKfHx8zImbtAEAAID8c6hQkZmZqYYNG2ry5Mm6//77FRUVpb59+2revHlFut1Ro0YpKSnJnI4fP16k2wMAAADuJA4VKsqXL6+QkBC7ttq1aysuLk6SFBgYKElKSEiwG5OQkGD2BQYGKjEx0a4/PT1dZ8+eNcdcy93d3bwpm5uzAQAAgIJxqFDx0EMPKTY21q7t4MGDCg4OliRVrVpVgYGB2rx5s9mfnJysHTt2KCwsTJIUFham8+fPKyYmxhzz9ddfKzMzU40bN74FewEAAADcXRzqkbJDhgxR06ZNNXnyZHXp0kU//vij5s+fr/nz50u6+iirwYMH6/XXX9e9995rPlK2QoUK6tixo6SrZzYeffRR87KpK1euaODAgerWrVu+nvwEAAAAoGAc6kzFAw88oDVr1mj58uWqW7euJk2apBkzZqhHjx7mmBEjRmjQoEGKiorSAw88oIsXL2rjxo3md1RI0ocffqhatWqpdevWeuyxx9SsWTMzmAC3k/Hjx8tms+U6paena8mSJXn222w2bdmyRZLUokWLPMdUqVKlWPcRAADc/hzqTIUkPf7443r88cfz7LfZbJo4caImTpyY55gyZcrk+4vugNtB2bJlVb16dbs2m82mcuXK5bisLy4uTidPnpT0/+9DCgkJUUpKit24mJgYpaenq3z58kVYOQAAuBs4XKgAkFNERISWLFmSa3tERIRdW/369XXy5Em1bdtWtWrVkiS9++67dmNiYmLM73IZNGhQ0RQNAADuGg51+ROA3H388ccqUaKEypcvr8cff1w//fRTruM2btyovXv3SpKGDx+e5/reeustSVLlypXVpUuXm18wAAC4qxAqAAfn7OyswMBAValSRfHx8Vq3bp3CwsJyDRZZYeG+++5T27Ztc13fH3/8odWrV0uSBg8eLBcXTlgCAABrCBWAA3vmmWeUmJio33//XQcOHNDGjRslSampqZozZ47d2J9++klff/21JGnYsGF5rnP69OnKyMiQr6+v+vbtW3TFAwCAuwahAnBgNWrUUJkyZcz58PBw+fn5SZL5pZBZ/vnPf0qSgoKC1K1bt1zXd+7cOb333nuSpBdffFGenp5FUTYAALjLECoABzZ16lS78LBp0yadOXNGkuweBRsXF6eVK1dKkv7xj3/keUnT3LlzdfHiRbm5uemll14qusIBAMBdhVABOLC5c+eqSpUqCg4OVkhIiMLDwyVJpUqV0uDBg81xM2bMUHp6unx8fBQVFZXrutLS0jR79mxJUo8ePXiULAAAuGkIFYADGz16tFq3bq0rV67oyJEjCg4OVo8ePRQTE6OQkBBJUlJSkhYuXChJioqKkpeXV67r+uCDDxQfHy+bzaaXX375lu0DAAC489kMwzCKuwhHk5ycLB8fHyUlJcnb27u4ywEAAACKRX7/LuZMBQAAAABLCBUAAAAALOFbrxxUXFycTp8+XdxlALjLlC1bVpUrVy7uMgAAtxlChQOKi4tTzZq1lZJyubhLAXCX8fAoqdjYAwQLAECBECoc0OnTp/8vUHwgqXZxlwPgrnFAKSk9dfr0aUIFAKBACBUOrbakhsVdBAAAAHBd3KgNAAAAwBJCBQAAAABLCBUAAAAALCFUAAAAALCEUAEAAADAEkIFAAAAAEsIFQAAAAAsIVQAAAAAsIRQAQAAAMASQgUAAAAASwgVAAAAACwhVAAAAACwhFABAAAAwBJCBQAAAABLCBUAAAAALCFUAAAAALCEUAEAAADAEkIFAAAAAEsIFQAAAAAsIVQAAAAAsIRQAQAAAMASQgUAAAAASwgVAAAAACwhVAAAAACwhFABAAAAwBJCBQAAAABLCBUAAAAALCFUAAAAALCEUAEAAADAEkIFAAAAAEsIFQAAAAAsIVQAAAAAsIRQAQAAAMASQgUAAAAASwgVAAAAACwhVAAAAACwhFABAAAAwBJCBQAAAABLCBUAAAAALCFUAAAAALCEUAEAAADAEkIFAAAAAEsIFQAAAAAsIVQAAAAAsIRQAQAAAMASQgUAAAAASwgVAAAAACwhVAAAAACwhFABAAAAwBJCBQAAAABLCBUAAAAALCFUAAAAALCEUAEAAADAEkIFAAAAAEsIFQAAAAAsIVQAAAAAsMShQsX48eNls9nsplq1apn9KSkpGjBggPz8/OTp6alOnTopISHBbh1xcXGKiIhQyZIl5e/vr+HDhys9Pf1W7woAAABw13Ap7gKuVadOHX311VfmvIvL/y9xyJAhWrdunVatWiUfHx8NHDhQTz31lLZt2yZJysjIUEREhAIDA7V9+3adPHlSzz77rFxdXTV58uRbvi8AAADA3cDhQoWLi4sCAwNztCclJWnRokVatmyZWrVqJUlavHixateurR9++EFNmjTRl19+qV9//VVfffWVAgIC1KBBA02aNEmvvPKKxo8fLzc3t1u9OwAAAMAdz6Euf5Kk33//XRUqVFC1atXUo0cPxcXFSZJiYmJ05coVtWnTxhxbq1YtVa5cWdHR0ZKk6Oho1atXTwEBAeaY8PBwJScna//+/bd2RwAAAIC7hEOdqWjcuLGWLFmimjVr6uTJk5owYYIefvhh7du3T/Hx8XJzc5Ovr6/dMgEBAYqPj5ckxcfH2wWKrP6svrykpqYqNTXVnE9OTr5JewQAAADc+RwqVLRv3978d/369dW4cWMFBwdr5cqVKlGiRJFtd8qUKZowYUKRrR8AAAC4kznc5U/Z+fr6qkaNGjp06JACAwOVlpam8+fP241JSEgw78EIDAzM8TSorPnc7tPIMmrUKCUlJZnT8ePHb+6OAAAAAHcwhw4VFy9e1OHDh1W+fHmFhobK1dVVmzdvNvtjY2MVFxensLAwSVJYWJj27t2rxMREc8ymTZvk7e2tkJCQPLfj7u4ub29vuwkAAABA/jjU5U/Dhg3TE088oeDgYJ04cULjxo2Ts7OzunfvLh8fH/Xp00dDhw5VmTJl5O3trUGDBiksLExNmjSRJLVr104hISHq1auXpk2bpvj4eI0ZM0YDBgyQu7t7Me8dAAAAcGdyqFDx559/qnv37jpz5ozKlSunZs2a6YcfflC5cuUkSdOnT5eTk5M6deqk1NRUhYeH69133zWXd3Z21tq1a9WvXz+FhYWpVKlSioyM1MSJE4trlwAAAIA7ns0wDKO4i3A0ycnJ8vHxUVJSUrFcCrV7926FhoZKipHU8JZvH8DdarekUMXExKhhQ449AID8/13s0PdUAAAAAHB8hAoAAAAAlhAqAAAAAFhCqAAAAABgCaECAAAAgCWECgAAAACWECoAAAAAWEKoAAAAAGAJoQIAAACAJYQKAAAAAJYQKgAAAABYQqgAAAAAYAmhAgAAAIAlhAoAAAAAlhAqAAAAAFhCqAAAAABgCaECAAAAgCWECgAAAACWECoAAAAAWEKoAAAAAGAJoQIAAACAJYQKAAAAAJYQKgAAAABYQqgAAAAAYAmhAgAAAIAlhAoAAAAAlhAqAAAAAFhCqAAAAABgCaECAAAAgCWECgAAAACWECoAAAAAWEKoAAAAAGAJoQIAAACAJYQKAAAAAJYQKgAAAABYQqgAAAAAYAmhAgAAAIAlhAoAAAAAlhAqAAAAAFhCqAAAAABgCaECAAAAgCWECgAAAACWECoAAAAAWEKoAAAAAGAJoQIAAACAJYQKAAAAAJYQKgAAAABYQqgAAAAAYAmhAgAAAIAlhAoAAAAAlhAqAAAAAFhCqAAAAABgCaECAAAAgCWECgAAAACWECoAAAAAWEKoAAAAAGAJoQIAAACAJYQKAAAAAJYQKgAAAABYQqgAAAAAYAmhAgAAAIAlhAoAAAAAlhAqAAAAAFhCqAAAAABgCaECAAAAgCWECgAAAACWECoAAAAAWEKoAAAAAGAJoQIAAACAJYQKAAAAAJYQKgAAAABYQqgAAAAAYInDhoo333xTNptNgwcPNttSUlI0YMAA+fn5ydPTU506dVJCQoLdcnFxcYqIiFDJkiXl7++v4cOHKz09/RZXDwAAANw9HDJU7Ny5U//+979Vv359u/YhQ4bo888/16pVq7R161adOHFCTz31lNmfkZGhiIgIpaWlafv27Vq6dKmWLFmisWPH3updAAAAAO4aDhcqLl68qB49emjBggUqXbq02Z6UlKRFixbpX//6l1q1aqXQ0FAtXrxY27dv1w8//CBJ+vLLL/Xrr7/qgw8+UIMGDdS+fXtNmjRJc+bMUVpaWnHtEgAAAHBHc7hQMWDAAEVERKhNmzZ27TExMbpy5Ypde61atVS5cmVFR0dLkqKjo1WvXj0FBASYY8LDw5WcnKz9+/fnuc3U1FQlJyfbTQAAAADyx6W4C8huxYoV2r17t3bu3JmjLz4+Xm5ubvL19bVrDwgIUHx8vDkme6DI6s/qy8uUKVM0YcIEi9UDAAAAdyeHOVNx/Phx/eMf/9CHH34oDw+PW7rtUaNGKSkpyZyOHz9+S7cPAAAA3M4cJlTExMQoMTFRDRs2lIuLi1xcXLR161bNmjVLLi4uCggIUFpams6fP2+3XEJCggIDAyVJgYGBOZ4GlTWfNSY37u7u8vb2tpsAAAAA5I/DhIrWrVtr79692rNnjzk1atRIPXr0MP/t6uqqzZs3m8vExsYqLi5OYWFhkqSwsDDt3btXiYmJ5phNmzbJ29tbISEht3yfAAAAgLuBw9xT4eXlpbp169q1lSpVSn5+fmZ7nz59NHToUJUpU0be3t4aNGiQwsLC1KRJE0lSu3btFBISol69emnatGmKj4/XmDFjNGDAALm7u9/yfQIAAADuBg4TKvJj+vTpcnJyUqdOnZSamqrw8HC9++67Zr+zs7PWrl2rfv36KSwsTKVKlVJkZKQmTpxYjFUDAAAAdzabYRhGcRfhaJKTk+Xj46OkpKRiub9i9+7dCg0NlRQjqeEt3z6Au9VuSaGKiYlRw4YcewAA+f+72GHuqQAAAABweyJUAAAAALCEUAEAAADAEkIFAAAAAEsIFQAAAAAsIVQAAAAAsIRQAQAAAMASQgUAAAAASwgVAAAAACwhVAAAAACwhFABAAAAwBJCBQAAAABLCBUAAAAALCFUAAAAALCEUAEAAADAEkIFAAAAAEsIFQAAAAAsIVQAAAAAsIRQAQAAAMASQgUAAAAASwgVAAAAACwhVAAAAACwhFABAAAAwBJCBQAAAABLCBUAAAAALCFUAAAAALCEUAEAAADAEkIFAAAAAEsIFQAAAAAsIVQAAAAAsIRQAQAAAMASQgUAAAAASwgVAAAAACwhVAAAAACwhFABAAAAwBJCBQAAAABLCBUAAAAALCFUAAAAALCEUAEAAADAEkIFAAAAAEsIFQAAAAAsIVQAAAAAsIRQAQAAAMASQgUAAAAASwgVAAAAACwhVAAAAACwhFABAAAAwBJCBQAAAABLCBUAAAAALCFUAAAAALCEUAEAAFBAM2bM0H333SdfX1+5u7urUqVK6ty5s3755RdzzOuvv64HH3xQ7u7ustlsstlsSklJyXOd69atM8fdaCzgaAodKlq1aqXNmzfn2f/NN9+oVatWhV09AACAw9q6datOnTqlatWqqXr16jp58qRWr16tli1b6tKlS5Kk1atX6+DBgypXrtwN15eQkKDnn3++qMsGikyhQ8WWLVuUkJCQZ39iYqK2bt1a2NUDAAA4rOXLl+vEiRPavXu3fv31V40ePVqSdPbsWf3222+SpLVr1+rcuXP6+9//fsP1Pffcczp//rw6dOhQpHUDRcXFysI2my3PvkOHDsnLy8vK6gEAABySh4eH1qxZo6lTpyo5OVmxsbGSpHLlyqlGjRqSpEqVKuVrXbNnz9aGDRv09ttvKzk5Wf/973+LrG6gqBQoVCxdulRLly41519//XUtWLAgx7jz58/rl19+0WOPPWa9QgAAAAeUkJCgHTt2mPNVq1bV559/XqAPVffv368RI0aoXbt2GjJkiCZMmFAUpQJFrkCXP12+fFmnTp3SqVOnJEkXLlww57Om06dPy93dXS+++KIWLlxYJEUDAAAUtxdffFGZmZk6duyYunbtqqNHj6pr1666cOFCvtfxzDPPyMvLS0uXLr3uFSCAoyvQmYp+/fqpX79+kq6m8ZkzZ+pvf/tbkRQGAADg6Gw2mypXrqzRo0fro48+0v79+7V8+XJFRUXla/lffvlFLi4uuueeeyRJaWlpZl/ZsmU1bdo09e/fv0hqB26mQt9TcfTo0ZtZBwAAwG3hzJkzWr9+vbp27So3NzdJ0vr1683+rKc/5Vd6errS09NztF+6dMkuZACOzNKN2tLVS6COHTumc+fOyTCMHP2PPPKI1U0AAAA4jAsXLujZZ5/VCy+8oOrVqyspKUnHjx+XJHl5eempp56SJPXo0UM7duzQ2bNnzWXr1Kkjm82madOm6amnnsrxt9P48ePN+yr++usveXh43KK9AqwpdKg4ffq0Bg0apI8//lgZGRk5+g3DkM1my7UPAADgduXr66tu3brpxx9/1OHDh3XlyhUFBQWpefPmGj16tIKDgyVJ//vf/3T48GG7ZY8cOSJJSk5OvuV1A0Wp0KEiKipKn3/+uV566SU9/PDDKl269M2sCwAAwCH5+vpq+fLlNxy3ZcuWAq97/PjxGj9+fMGLAopZoUPFl19+qSFDhmjatGk3sx4AAAAAt5lCf6N2yZIlVaVKlZtYCgAAAIDbUaHPVPTs2VNr1qzhMWcAgLtGXFycTp8+XdxlALjLlC1bVpUrVy7uMq6r0KHi6aef1tatW/Xoo48qKipKQUFBcnZ2zjGuYcOGlgoEAMARxMXFqXbNmrqcklLcpQC4y5T08NCB2FiHDhaFDhXNmjUz/71p06Yc/Tz9CQBwJzl9+rQup6ToA0m1i7sYAHeNA5J6pqTo9OnTd2aoWLx48c2sAwCA20JtSZyDBwB7hQ4VkZGRN7MOAAAAALepQj/9CQAAAAAkC2cqnn/++RuOsdlsWrRoUWE3AQAAAOA2UOhQ8fXXX8tms9m1ZWRk6OTJk8rIyFC5cuVUqlQpywUCAAAAcGyFvvzpjz/+0NGjR+2muLg4Xb58WbNmzZKXl5c2b95coHXOnTtX9evXl7e3t7y9vRUWFqYNGzaY/SkpKRowYID8/Pzk6empTp06KSEhwW4dcXFxioiIUMmSJeXv76/hw4crPT29sLsJAAAA4AZu+j0Vrq6uGjhwoNq1a6eBAwcWaNlKlSrpzTffVExMjHbt2qVWrVqpQ4cO2r9/vyRpyJAh+vzzz7Vq1Spt3bpVJ06c0FNPPWUun5GRoYiICKWlpWn79u1aunSplixZorFjx97UfQQAAACQjVFE5s2bZ3h6elpeT+nSpY2FCxca58+fN1xdXY1Vq1aZfQcOHDAkGdHR0YZhGMb69esNJycnIz4+3hwzd+5cw9vb20hNTc33NpOSkgxJRlJSkuX6CyMmJsaQZEgxhmQwMTEx3aLp6rEnJiamWI59ji7r2BxT/D8oJiamu2iKkYziPDbn9+/iInv606ZNm1SyZMlCL5+RkaEVK1bo0qVLCgsLU0xMjK5cuaI2bdqYY2rVqqXKlSsrOjpakhQdHa169eopICDAHBMeHq7k5GTzbEduUlNTlZycbDcBAAAAyJ9C36g9ceLEXNvPnz+vb7/9Vrt379bIkSMLvN69e/cqLCxMKSkp8vT01Jo1axQSEqI9e/bIzc1Nvr6+duMDAgIUHx8vSYqPj7cLFFn9WX15mTJliiZMmFDgWgEAAABYCBXjx4/Ptb106dKqXr265s2bp759+xZ4vTVr1tSePXuUlJSk1atXKzIyUlu3bi1smfkyatQoDR061JxPTk5WUFBQkW4TAAAAuFMUOlRkZmbezDpMbm5uuueeeyRJoaGh2rlzp2bOnKmuXbsqLS1N58+ftztbkZCQoMDAQElSYGCgfvzxR7v1ZT0dKmtMbtzd3eXu7n6T9wQAAAC4Ozj8N2pnZmYqNTVVoaGhcnV1tXtMbWxsrOLi4hQWFiZJCgsL0969e5WYmGiO2bRpk7y9vRUSEnLLawcAAADuBoU+U5Fl69atWrdunY4dOyZJCg4OVkREhJo3b17gdY0aNUrt27dX5cqVdeHCBS1btkxbtmzRF198IR8fH/Xp00dDhw5VmTJl5O3trUGDBiksLExNmjSRJLVr104hISHq1auXpk2bpvj4eI0ZM0YDBgzgTAQAAABQRAodKtLS0tS9e3d9+umnMgzDvCTp/Pnzevvtt/Xkk09q+fLlcnV1zfc6ExMT9eyzz+rkyZPy8fFR/fr19cUXX6ht27aSpOnTp8vJyUmdOnVSamqqwsPD9e6775rLOzs7a+3aterXr5/CwsJUqlQpRUZG5nlTOQAAAADrCh0qJkyYoDVr1mjYsGF6+eWXzacsJSYm6u2339Zbb72liRMnatKkSfle56JFi67b7+HhoTlz5mjOnDl5jgkODtb69evzvU0AAAAA1hT6noply5YpMjJS06ZNs3uMq7+/v6ZOnapnn31W//nPf25KkQAAAAAcV6FDxcmTJ9W4ceM8+xs3bnzd74YAAAAAcGcodKioVKmStmzZkmf/1q1bValSpcKuHgAAAMBtotChIjIyUitXrtSLL76o2NhYZWRkKDMzU7GxserXr59WrVql3r1738RSAQAAADiiQt+oPXr0aB0+fFjz58/XggUL5OR0NZ9kZmbKMAxFRkZq9OjRN61QAAAAAI6p0KHC2dlZS5Ys0dChQ7V+/Xq776l47LHHVL9+/ZtWJAAAAADHVaBQkZKSosGDB6tOnToaNGiQJKl+/fo5AsSsWbM0b948zZw5s0DfUwEAAADg9lOgeyrmz5+vJUuWKCIi4rrjIiIi9N5772nhwoWWigMAAADg+AoUKlauXKlOnTqpWrVq1x1XvXp1de7cWcuXL7dUHAAAAADHV6BQsXfvXjVr1ixfY5s2bapffvmlUEUBAAAAuH0UKFSkpaXJzc0tX2Pd3NyUmppaqKIAAAAA3D4KFCoqVKigffv25Wvsvn37VKFChUIVBQAAAOD2UaBQ0aZNG73//vtKTEy87rjExES9//77atu2raXiAAAAADi+AoWKV155RSkpKWrVqpV27NiR65gdO3aodevWSklJ0fDhw29KkQAAAAAcV4G+p6JatWpauXKlunfvrqZNm6patWqqV6+evLy8dOHCBe3bt0+HDx9WyZIltWLFClWvXr2o6gYAAADgIAr8jdoRERH65ZdfNHXqVK1du1affvqp2VehQgX17dtXI0aMuOFjZwEAAADcGQocKiSpSpUqmjt3rubOnasLFy4oOTlZ3t7e8vLyutn1AQAAAHBwhQoV2Xl5eREmAAAAgLtYgW7UBgAAAIBrESoAAAAAWEKoAAAAAGAJoQIAAACAJYQKAAAAAJYQKgAAAABYQqgAAAAAYAmhAgAAAIAlhAoAAAAAlhAqAAAAAFhCqAAAAABgCaECAAAAgCWECgAAAACWECoAAAAAWEKoAAAAAGAJoQIAAACAJYQKAAAAAJYQKgAAAABYQqgAAAAAYAmhAgAAAIAlhAoAAAAAlhAqAAAAAFhCqAAAAABgCaECAAAAgCWECgAAAACWECoAAAAAWEKoAAAAAGAJoQIAAACAJYQKAAAAAJYQKgAAAABYQqgAAAAAYAmhAgAAAIAlhAoAAAAAlhAqAAAAAFhCqAAAAABgCaECAAAAgCWECgAAAACWECoAAAAAWEKoAAAAAGAJoQIAAACAJYQKAAAAAJYQKgAAAABYQqgAAAAAYAmhAgAAAIAlhAoAAAAAlhAqAAAAAFhCqAAAAABgCaECAAAAgCWECgAAAACWECoAAAAAWEKoAAAAAGAJoQIAAACAJYQKAAAAAJY4VKiYMmWKHnjgAXl5ecnf318dO3ZUbGys3ZiUlBQNGDBAfn5+8vT0VKdOnZSQkGA3Ji4uThERESpZsqT8/f01fPhwpaen38pdAQAAAO4aDhUqtm7dqgEDBuiHH37Qpk2bdOXKFbVr106XLl0yxwwZMkSff/65Vq1apa1bt+rEiRN66qmnzP6MjAxFREQoLS1N27dv19KlS7VkyRKNHTu2OHYJAAAAuOO5FHcB2W3cuNFufsmSJfL391dMTIweeeQRJSUladGiRVq2bJlatWolSVq8eLFq166tH374QU2aNNGXX36pX3/9VV999ZUCAgLUoEEDTZo0Sa+88orGjx8vNze34tg1AAAA4I7lUGcqrpWUlCRJKlOmjCQpJiZGV65cUZs2bcwxtWrVUuXKlRUdHS1Jio6OVr169RQQEGCOCQ8PV3Jysvbv338LqwcAAADuDg51piK7zMxMDR48WA899JDq1q0rSYqPj5ebm5t8fX3txgYEBCg+Pt4ckz1QZPVn9eUmNTVVqamp5nxycvLN2g0AAADgjuewZyoGDBigffv2acWKFUW+rSlTpsjHx8ecgoKCinybAAAAwJ3CIUPFwIEDtXbtWn3zzTeqVKmS2R4YGKi0tDSdP3/ebnxCQoICAwPNMdc+DSprPmvMtUaNGqWkpCRzOn78+E3cGwAAAODO5lChwjAMDRw4UGvWrNHXX3+tqlWr2vWHhobK1dVVmzdvNttiY2MVFxensLAwSVJYWJj27t2rxMREc8ymTZvk7e2tkJCQXLfr7u4ub29vuwkAAABA/jjUPRUDBgzQsmXL9N///ldeXl7mPRA+Pj4qUaKEfHx81KdPHw0dOlRlypSRt7e3Bg0apLCwMDVp0kSS1K5dO4WEhKhXr16aNm2a4uPjNWbMGA0YMEDu7u7FuXsAAADAHcmhQsXcuXMlSS1atLBrX7x4sXr37i1Jmj59upycnNSpUyelpqYqPDxc7777rjnW2dlZa9euVb9+/RQWFqZSpUopMjJSEydOvFW7AQAAANxVHCpUGIZxwzEeHh6aM2eO5syZk+eY4OBgrV+//maWBgAAACAPDnVPBQAAAIDbD6ECAAAAgCWECgAAAACWECoAAAAAWEKoAAAAAGAJoQIAAACAJYQKAAAAAJYQKgAAAABYQqgAAAAAYAmhAgAAAIAlhAoAAAAAlhAqAAAAAFhCqAAAAABgCaECAAAAgCWECgAAAACWECoAAAAAWEKoAAAAAGAJoQIAAACAJYQKAAAAAJYQKgAAAABYQqgAAAAAYAmhAgAAAIAlhAoAAAAAlhAqAAAAAFhCqAAAAABgCaECAAAAgCWECgAAAACWECoAAAAAWEKoAAAAAGAJoQIAAACAJYQKAAAAAJYQKgAAAABYQqgAAAAAYAmhAgAAAIAlhAoAAAAAlhAqAAAAAFhCqAAAAABgCaECAAAAgCWECgAAAACWECoAAAAAWEKoAAAAAGAJoQIAAACAJYQKAAAAAJYQKgAAAABYQqgAAAAAYAmhAgAAAIAlhAoAAAAAlhAqAAAAAFhCqAAAAABgCaECAAAAgCWECgAAAACWECoAAAAAWEKoAAAAAGAJoQIAAACAJYQKAAAAAJYQKgAAAABYQqgAAAAAYAmhAgAAAIAlhAoAAAAAlhAqAAAAAFhCqAAAAABgCaECAAAAgCWECgAAAACWECoAAAAAWEKoAAAAAGAJoQIAAACAJYQKAAAAAJYQKgAAAABYQqgAAAAAYAmhAgAAAIAlhAoAAAAAlhAqAAAAAFjiUKHi22+/1RNPPKEKFSrIZrPp008/tes3DENjx45V+fLlVaJECbVp00a///673ZizZ8+qR48e8vb2lq+vr/r06aOLFy/ewr0AAAAA7i4OFSouXbqk++67T3PmzMm1f9q0aZo1a5bmzZunHTt2qFSpUgoPD1dKSoo5pkePHtq/f782bdqktWvX6ttvv1VUVNSt2gUAAADgruNS3AVk1759e7Vv3z7XPsMwNGPGDI0ZM0YdOnSQJL3//vsKCAjQp59+qm7duunAgQPauHGjdu7cqUaNGkmSZs+erccee0z//Oc/VaFChVu2LwAAAMDdwqHOVFzP0aNHFR8frzZt2phtPj4+aty4saKjoyVJ0dHR8vX1NQOFJLVp00ZOTk7asWNHnutOTU1VcnKy3QQAAAAgf26bUBEfHy9JCggIsGsPCAgw++Lj4+Xv72/X7+LiojJlyphjcjNlyhT5+PiYU1BQ0E2uHgAAALhz3TahoiiNGjVKSUlJ5nT8+PHiLgkAAAC4bdw2oSIwMFCSlJCQYNeekJBg9gUGBioxMdGuPz09XWfPnjXH5Mbd3V3e3t52EwAAAID8uW1CRdWqVRUYGKjNmzebbcnJydqxY4fCwsIkSWFhYTp//rxiYmLMMV9//bUyMzPVuHHjW14zAAAAcDdwqKc/Xbx4UYcOHTLnjx49qj179qhMmTKqXLmyBg8erNdff1333nuvqlatqtdee00VKlRQx44dJUm1a9fWo48+qr59+2revHm6cuWKBg4cqG7duvHkJwAAAKCIOFSo2LVrl1q2bGnODx06VJIUGRmpJUuWaMSIEbp06ZKioqJ0/vx5NWvWTBs3bpSHh4e5zIcffqiBAweqdevWcnJyUqdOnTRr1qxbvi8AAADA3cKhQkWLFi1kGEae/TabTRMnTtTEiRPzHFOmTBktW7asKMoDAAAAkIvb5p4KAAAAAI6JUAEAAADAEkIFAAAAAEsIFQAAAAAsIVQAAAAAsIRQAQAAAMASQgUAAAAASwgVAAAAACwhVAAAAACwhFABAAAAwBJCBQAAAABLCBUAAAAALCFUAAAAALCEUAEAAADAEkIFAAAAAEsIFQAAAAAsIVQAAAAAsIRQAQAAAMASQgUAAAAASwgVAAAAACwhVAAAAACwhFABAAAAwBJCBQAAAABLCBUAAAAALCFUAAAAALCEUAEAAADAEkIFAAAAAEsIFQAAAAAsIVQAAAAAsIRQAQAAAMASQgUAAAAASwgVAAAAACwhVAAAAACwhFABAAAAwBJCBQAAAABLCBUAAAAALCFUAAAAALCEUAEAAADAEkIFAAAAAEsIFQAAAAAsIVQAAAAAsIRQAQAAAMASQgUAAAAASwgVAAAAACwhVAAAAACwhFABAAAAwBJCBQAAAABLCBUAAAAALCFUAAAAALCEUAEAAADAEkIFAAAAAEsIFQAAAAAsIVQAAAAAsIRQAQAAAMASQgUAAAAASwgVAAAAACwhVAAAAACwhFABAAAAwBJCBQAAAABLCBUAAAAALCFUAAAAALCEUAEAAADAEkIFAAAAAEsIFQAAAAAsIVQAAAAAsIRQAQAAAMASQgUAAAAASwgVAAAAACwhVAAAAACwhFABAAAAwBJCBQAAAABL7thQMWfOHFWpUkUeHh5q3Lixfvzxx+IuCQAAALgj3ZGh4qOPPtLQoUM1btw47d69W/fdd5/Cw8OVmJhY3KUBAAAAd5w7MlT861//Ut++ffXcc88pJCRE8+bNU8mSJfXee+8Vd2kAAADAHeeOCxVpaWmKiYlRmzZtzDYnJye1adNG0dHRxVgZAAAAcGdyKe4CbrbTp08rIyNDAQEBdu0BAQH67bffcl0mNTVVqamp5nxSUpIkKTk5uegKvY6LFy/+379iJF283lAAuIliJV09BhXX8c+RZR2bOTIDuJVi/++/xXVsztqmYRjXHXfHhYrCmDJliiZMmJCjPSgoqBiqyS6qmLcP4G7UvHnz4i7BoXFkBlAcivvYfOHCBfn4+OTZf8eFirJly8rZ2VkJCQl27QkJCQoMDMx1mVGjRmno0KHmfGZmps6ePSs/Pz/ZbLYirRe4mZKTkxUUFKTjx4/L29u7uMsBAIhjM25vhmHowoULqlChwnXH3XGhws3NTaGhodq8ebM6duwo6WpI2Lx5swYOHJjrMu7u7nJ3d7dr8/X1LeJKgaLj7e3N/7gAwMFwbMbt6npnKLLccaFCkoYOHarIyEg1atRIDz74oGbMmKFLly7pueeeK+7SAAAAgDvOHRkqunbtqlOnTmns2LGKj49XgwYNtHHjxhw3bwMAAACw7o4MFZI0cODAPC93Au5U7u7uGjduXI7L+QAAxYdjM+4GNuNGz4cCAAAAgOu44778DgAAAMCtRagAAAAAYAmhArgL9O7d23zEMgAgb4ZhKCoqSmXKlJHNZtOePXuKpY4//vijWLcPFNQde6M2AABAQW3cuFFLlizRli1bVK1aNZUtW7a4SwJuC4QKAACA/3P48GGVL19eTZs2Le5SgNsKlz8BDqZFixYaNGiQBg8erNKlSysgIEALFiwwv8DRy8tL99xzjzZs2CBJysjIUJ8+fVS1alWVKFFCNWvW1MyZM6+7jczMTE2ZMsVc5r777tPq1atvxe4BgMPq3bu3Bg0apLi4ONlsNlWpUuWGx8stW7bIZrPpiy++0P33368SJUqoVatWSkxM1IYNG1S7dm15e3vrmWee0eXLl83lNm7cqGbNmsnX11d+fn56/PHHdfjw4evWt2/fPrVv316enp4KCAhQr169dPr06SJ7PYCCIFQADmjp0qUqW7asfvzxRw0aNEj9+vVT586d1bRpU+3evVvt2rVTr169dPnyZWVmZqpSpUpatWqVfv31V40dO1ajR4/WypUr81z/lClT9P7772vevHnav3+/hgwZop49e2rr1q23cC8BwLHMnDlTEydOVKVKlXTy5Ent3Lkz38fL8ePH65133tH27dt1/PhxdenSRTNmzNCyZcu0bt06ffnll5o9e7Y5/tKlSxo6dKh27dqlzZs3y8nJSU8++aQyMzNzre38+fNq1aqV7r//fu3atUsbN25UQkKCunTpUqSvCZBvBgCH0rx5c6NZs2bmfHp6ulGqVCmjV69eZtvJkycNSUZ0dHSu6xgwYIDRqVMncz4yMtLo0KGDYRiGkZKSYpQsWdLYvn273TJ9+vQxunfvfhP3BABuP9OnTzeCg4MNw8jf8fKbb74xJBlfffWV2T9lyhRDknH48GGz7YUXXjDCw8Pz3O6pU6cMScbevXsNwzCMo0ePGpKMn376yTAMw5g0aZLRrl07u2WOHz9uSDJiY2MLvb/AzcI9FYADql+/vvlvZ2dn+fn5qV69emZbQECAJCkxMVGSNGfOHL333nuKi4vTX3/9pbS0NDVo0CDXdR86dEiXL19W27Zt7drT0tJ0//333+Q9AYDbV0GOl9mP2wEBASpZsqSqVatm1/bjjz+a87///rvGjh2rHTt26PTp0+YZiri4ONWtWzdHLT///LO++eYbeXp65ug7fPiwatSoUbidBG4SQgXggFxdXe3mbTabXZvNZpN09d6IFStWaNiwYXr77bcVFhYmLy8vvfXWW9qxY0eu67548aIkad26dapYsaJdn7u7+83cDQC4rRXkeHntMTq343j2S5ueeOIJBQcHa8GCBapQoYIyMzNVt25dpaWl5VnLE088oalTp+boK1++fMF2DCgChArgNrdt2zY1bdpU/fv3N9uud7NfSEiI3N3dFRcXp+bNm9+KEgHgtlRUx8szZ84oNjZWCxYs0MMPPyxJ+v7776+7TMOGDfXxxx+rSpUqcnHhzzc4Ht6VwG3u3nvv1fvvv68vvvhCVatW1X/+8x/t3LlTVatWzXW8l5eXhg0bpiFDhigzM1PNmjVTUlKStm3bJm9vb0VGRt7iPQAAx1RUx8vSpUvLz89P8+fPV/ny5RUXF6eRI0ded5kBAwZowYIF6t69u0aMGKEyZcro0KFDWrFihRYuXChnZ+dC1QLcLIQK4Db3wgsv6KefflLXrl1ls9nUvXt39e/f33zkbG4mTZqkcuXKacqUKTpy5Ih8fX3VsGFDjR49+hZWDgCOryiOl05OTlqxYoVeeukl1a1bVzVr1tSsWbPUokWLPJepUKGCtm3bpldeeUXt2rVTamqqgoOD9eijj8rJiYd5ovjZDMMwirsIAAAAALcvoi0AAAAASwgVAAAAACwhVAAAAACwhFABAAAAwBJCBQAAAABLCBUAAAAALCFUAAAAALCEUAEAAADAEkIFAAAAAEsIFQCAW2Lv3r16+umnFRwcLA8PD1WsWFFt27bV7Nmzi7s0AIBFNsMwjOIuAgBwZ9u+fbtatmypypUrKzIyUoGBgTp+/Lh++OEHHT58WIcOHSruEgEAFhAqAABFLiIiQjt37tTBgwfl6+tr15eYmCh/f//iKQwAcFNw+RMAoMgdPnxYderUyREoJOUIFB988IFCQ0NVokQJlSlTRt26ddPx48fN/sWLF8tms+m9996zW27y5Mmy2Wxav359kewDACBvnKkAABS58PBwRUdHa/v27apbt26e49544w299tpr6tKli5o3b65Tp05p9uzZ8vT01E8//WSGkieeeELfffed9u7dq6CgIO3du1eNGjVSr169tHDhwlu0VwCALIQKAECR27Rpk9q3by9JevDBB/Xwww+rdevWatmypVxdXSVJx44dU/Xq1TVx4kSNHj3aXHbfvn26//77NWHCBLM9Pj5ederUUWhoqNauXasmTZrozJkz2rt3r7y9vW/9DgLAXY5QAQC4JXbu3KkpU6boiy++0OXLlyVJ5cqV08KFC/W3v/1N06dP18svv5zrfRePPPKIKlasqE2bNpltK1asUPfu3fXggw9q586d2rRpk1q3bn0rdwkA8H8IFQCAWyotLU0///yz1qxZo+nTpysjI0N79uzRO++8o7lz5+a5XP369fXzzz/btT3++ONat26doqKi9O9//7uoSwcA5MGluAsAANxd3Nzc9MADD+iBBx5QjRo19Nxzz2nVqlXKzMyUzWbThg0b5OzsnGM5T09Pu/kzZ85o165dkqRff/1VmZmZcnLi+SMAUBwIFQCAYtOoUSNJ0smTJ1W9enUZhqGqVauqRo0aN1x2wIABunDhgqZMmaJRo0ZpxowZGjp0aFGXDADIBR/pAACK3DfffKPcrrbNevxrzZo19dRTT8nZ2VkTJkzIMdYwDJ05c8acX716tT766CO9+eabGjlypLp166YxY8bo4MGDRbsjAIBccU8FAKDI1a1bV5cvX9aTTz6pWrVqKS0tTdu3b9dHH32koKAg83Gxb775pkaNGqWmTZuqY8eO8vLy0tGjR7VmzRpFRUVp2LBhSkxMVJ06dVSvXj1t3rxZNptNZ86cUZ06dVStWjV9//33XAYFALcYoQIAUOQ2btyoVatWafv27frzzz+VlpamypUrq3379hozZozdF+B98sknmj59un766SdJUlBQkFq3bq2XXnpJNWrUUKdOnbRp0ybt3btXwcHB5nKfffaZOnTooKlTp2rEiBG3fB8B4G5GqAAAAABgCeeHAQAAAFhCqAAAAABgCaECAAAAgCWECgAAAACWECoAAAAAWEKoAAAAAGAJoQIAAACAJYQKAAAAAJYQKgAAAABYQqgAAAAAYAmhAgAAAIAlhAoAAAAAlhAqAAAAAFjy/wD/VQfs/Da4awAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Grouping the data by 'Sex' and count\n",
    "sex_counts = df['Sex'].value_counts()\n",
    "\n",
    "# Custom colors for each gender\n",
    "colors = ['red' if sex == 'female' else 'blue' for sex in sex_counts.index]\n",
    "\n",
    "# Plotting the bar chart\n",
    "plt.figure(figsize=(8, 6))\n",
    "bars = plt.bar(sex_counts.index, sex_counts.values, color=colors, edgecolor='black')\n",
    "\n",
    "# Adding value labels on top of each bar\n",
    "for bar in bars:\n",
    "    height = bar.get_height()\n",
    "    plt.text(\n",
    "        bar.get_x() + bar.get_width() / 2, \n",
    "        height + 1,  # Position slightly above the bar\n",
    "        f'{height}', \n",
    "        ha='center', \n",
    "        va='bottom', \n",
    "        fontsize=10, \n",
    "        fontweight='bold'\n",
    "    )\n",
    "\n",
    "# Adding interactive hover effect\n",
    "cursor = mplcursors.cursor(bars, hover=True)\n",
    "cursor.connect(\"add\", lambda sel: sel.annotation.set_text(f\"Count: {int(sel.target[1])}\"))\n",
    "\n",
    "# Adding title and labels\n",
    "plt.title('Survived vs Non-Survived by Gender', fontsize=14, fontweight='bold')\n",
    "plt.xlabel('Sex', fontsize=12)\n",
    "plt.ylabel('Count', fontsize=12)\n",
    "\n",
    "# Showing the plot\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ac22f46f-2968-492d-867f-839c5603a03e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}