Titanic_dataset_complete_classification.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts=pd.read_csv('titanic_train.csv')\n",
    "ts.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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": [
    "ts.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      int64\n",
       "Survived         int64\n",
       "Pclass           int64\n",
       "Name            object\n",
       "Sex             object\n",
       "Age            float64\n",
       "SibSp            int64\n",
       "Parch            int64\n",
       "Ticket          object\n",
       "Fare           float64\n",
       "Cabin           object\n",
       "Embarked        object\n",
       "dtype: object"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n",
       "       'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "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": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.isnull().sum()\n",
    "#observation\n",
    "# we can ee there is null value present in the dataset  in the column Age,Cabin,Embarked"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Boarded passengers grouped by port of embarkation (C = Cherbourg, Q = Queenstown, S = Southampton):\n",
      "S    644\n",
      "C    168\n",
      "Q     77\n",
      "Name: Embarked, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "print('Boarded passengers grouped by port of embarkation (C = Cherbourg, Q = Queenstown, S = Southampton):')\n",
    "print(ts['Embarked'].value_counts())\n",
    "sns.countplot(x='Embarked', data=ts, palette='rainbow')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>32.204208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>257.353842</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>49.693429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>223.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.910400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>668.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
       "count   891.000000  891.000000  891.000000  714.000000  891.000000   \n",
       "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
       "std     257.353842    0.486592    0.836071   14.526497    1.102743   \n",
       "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
       "25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n",
       "50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n",
       "75%     668.500000    1.000000    3.000000   38.000000    1.000000   \n",
       "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
       "\n",
       "            Parch        Fare  \n",
       "count  891.000000  891.000000  \n",
       "mean     0.381594   32.204208  \n",
       "std      0.806057   49.693429  \n",
       "min      0.000000    0.000000  \n",
       "25%      0.000000    7.910400  \n",
       "50%      0.000000   14.454200  \n",
       "75%      0.000000   31.000000  \n",
       "max      6.000000  512.329200  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.describe()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "#  observation\n",
    "***we can oberve that there is high differences  between min and max, so therefore high standard deviation present in the data set.\n",
    "***we can also observe that minmum value is lesser than the median value so the data must be left skewed data.\n",
    "***there are some differences present between 75% and maximum so we can also observe some outliers in the data set.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Just by observing the graph, it can be approximated that the survival rate of men is around 20% and that of women is around 75%\n",
      "Therefore, whether a passenger is a male or a female plays an important role in determining if one is going to survive.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x154ebd6a2b0>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 411.875x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#count plot\n",
    "print('Just by observing the graph, it can be approximated that the survival rate of men is around 20% and that of women is around 75%') \n",
    "print('Therefore, whether a passenger is a male or a female plays an important role in determining if one is going to survive.')\n",
    "      \n",
    "sns.catplot(x='Sex', hue='Survived',kind= 'count',data=ts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x154f20be1f0>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#group the dataset by pclass and survived and then unstack them.\n",
    "#lets do the bivariate analysis between Pclass and survived\n",
    "\n",
    "group=ts.groupby(['Pclass','Survived'])\n",
    "pclass_survived=group.size().unstack()\n",
    "#Heat map- color encoded 2D representation of data.\n",
    "sns.heatmap(pclass_survived,annot=True,fmt='d')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It helps in determining if higher-class passengers had more survival rate than the lower class ones or vice versa. \n",
    "Class 1 passengers have a higher survival chance compared to classes 2 and 3.\n",
    "It implies that Pclass contributes a lot to a passenger’s survival rate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PassengerId</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.005007</td>\n",
       "      <td>-0.035144</td>\n",
       "      <td>0.036847</td>\n",
       "      <td>-0.057527</td>\n",
       "      <td>-0.001652</td>\n",
       "      <td>0.012658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Survived</th>\n",
       "      <td>-0.005007</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.338481</td>\n",
       "      <td>-0.077221</td>\n",
       "      <td>-0.035322</td>\n",
       "      <td>0.081629</td>\n",
       "      <td>0.257307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pclass</th>\n",
       "      <td>-0.035144</td>\n",
       "      <td>-0.338481</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.369226</td>\n",
       "      <td>0.083081</td>\n",
       "      <td>0.018443</td>\n",
       "      <td>-0.549500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>0.036847</td>\n",
       "      <td>-0.077221</td>\n",
       "      <td>-0.369226</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.308247</td>\n",
       "      <td>-0.189119</td>\n",
       "      <td>0.096067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SibSp</th>\n",
       "      <td>-0.057527</td>\n",
       "      <td>-0.035322</td>\n",
       "      <td>0.083081</td>\n",
       "      <td>-0.308247</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.414838</td>\n",
       "      <td>0.159651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Parch</th>\n",
       "      <td>-0.001652</td>\n",
       "      <td>0.081629</td>\n",
       "      <td>0.018443</td>\n",
       "      <td>-0.189119</td>\n",
       "      <td>0.414838</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.216225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fare</th>\n",
       "      <td>0.012658</td>\n",
       "      <td>0.257307</td>\n",
       "      <td>-0.549500</td>\n",
       "      <td>0.096067</td>\n",
       "      <td>0.159651</td>\n",
       "      <td>0.216225</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             PassengerId  Survived    Pclass       Age     SibSp     Parch  \\\n",
       "PassengerId     1.000000 -0.005007 -0.035144  0.036847 -0.057527 -0.001652   \n",
       "Survived       -0.005007  1.000000 -0.338481 -0.077221 -0.035322  0.081629   \n",
       "Pclass         -0.035144 -0.338481  1.000000 -0.369226  0.083081  0.018443   \n",
       "Age             0.036847 -0.077221 -0.369226  1.000000 -0.308247 -0.189119   \n",
       "SibSp          -0.057527 -0.035322  0.083081 -0.308247  1.000000  0.414838   \n",
       "Parch          -0.001652  0.081629  0.018443 -0.189119  0.414838  1.000000   \n",
       "Fare            0.012658  0.257307 -0.549500  0.096067  0.159651  0.216225   \n",
       "\n",
       "                 Fare  \n",
       "PassengerId  0.012658  \n",
       "Survived     0.257307  \n",
       "Pclass      -0.549500  \n",
       "Age          0.096067  \n",
       "SibSp        0.159651  \n",
       "Parch        0.216225  \n",
       "Fare         1.000000  "
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tscor=ts.corr()\n",
    "tscor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x154ec25cf40>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(tscor,annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#observation\n",
    "\n",
    "*** we can observe that parch is positively correlated with fare of about 22%\n",
    "*** fare is negatively(-55%) corelated with paseenger class.\n",
    "*** fare are positively related with survived paseenger we can see on heat map.\n",
    "*** we can notice on heat map that Parch is highly positively corelated with SibSp with 41%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x154f126e4c0>"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x='Pclass', y='Survived', data=ts)\n",
    "# distribution of passenger according to class (class 1 passenger has survived more compare to other class)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x154f05a6130>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#voilinplot Displays distrbution of data\n",
    "#across all the level of category.\n",
    "#let do the bivariate analysis taking x as sex and y as age.\n",
    "sns.violinplot(x='Sex',y='Age',hue='Survived',data=ts,split=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#observation\n",
    "This graph gives a summary of the age range of men, women and children who were saved. The survival rate is –\n",
    "\n",
    "Good for children.\n",
    "High for women in the age range 20-50.\n",
    "Less for men as the age increases."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x154f07916a0>"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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gglFhXgqdPbZamAkNlgDMkLR39VDX0klmgE8BcSY5qe7VwuyZABMKLAGYIaloPNX/H5wJ4NQEcSV1rRRX25PBJrhZAjBDUt7gjAAKoiGg/c3JdU8Q97vNpb4OxZgRZQnADElFYzvxUeEkRkf4OpQREx8VzvkZiby47bitFmaCmiUAMyQVjW1kBvHV/ynz8lKobenkzX3Vvg7FmBHjUQIQkaUisl9EikXkrgGOi4jc7xzfKSJznf05IvKWiOwVkSIR+Vafc1JF5DUROej8TPFetcxI6O7ppaqpPWj7//sqGJvAuMQontti3UAmeA2aAEQkDHgAWAZMA1aKyLR+xZYBBc5rFfCgs78b+K6qng8sAlb3Ofcu4A1VLQDecLaNH6s+2UGvBt8DYAMJcwmfn5fN2/urqXRufBsTbDxpASwAilX1sKp2As8Cy/uVWQ48qW4bgGQRyVDVClXdBqCqJ4G9QFafc55w3j8BXHOOdTEj7NQN4EBfBMZTXyzMoVfhhW1lvg7FmBHhSQLIAvq2g8v46Evc4zIikg/MATY6u8apagWA83PsQB8uIqtEZIuIbKmpqfEgXDNSyhvbiQx3kRofmIvAD1XemDgWTUzluS2l9NpqYSYIeZIABlrxu/+/hrOWEZF44AXg26ra5Hl4oKoPq2qhqhamp6cP5VTjZRWNbWQkBu4i8MPxpfk5HKttZeOROl+HYozXeZIAyoCcPtvZQLmnZUQkAveX/1Oq+mKfMlUikuGUyQBsuIUf6+1V9xQQITACqK+l0zNIiArnebsZbIKQJwlgM1AgIhNEJBJYAazpV2YNcLMzGmgR0KiqFSIiwK+Bvap67wDn3OK8vwV4adi1MCOupK6Vzu7ekOn/PyUmMozPzs5k3e4Kmmy1MBNkBk0AqtoN3Am8gvsm7nOqWiQid4jIHU6xdcBhoBh4BPiGs38xcBNwhYh86Lyuco7dDVwpIgeBK51t46eKyt09dxlBOgfQ2Xxpfg7tXb2s+bB/w9eYwBbuSSFVXYf7S77vvof6vFdg9QDnvcfA9wdQ1VrgE0MJ1vhOUXkjLoFxCYG/CPxQzcxK4rzxCTy3pZQbF+X5OhxjvMaeBDYe2VPRxNiEaMLDQu9PRkT40vwcdpY1srdiSGMYjPFrofev2QxLUXlTSDwAdibXzM4iMsxlTwaboGIJwAyq+mQ7NSc7gnYNAE+kxEVy5fRx/GH7cTq6bbUwExwsAZhB7Tl9Azh0WwAAK+fn0tDaxbpdFb4OxRivsARgBnV6BFBi6LYAABZPHsPE9DieeP+Yr0MxxissAZhB7SlvIic1hpjI4FoEfqhEhJsX5fFhaQM7yxp8HY4x58wSgBnUnoompmck+ToMv/C5ednERobx5AfWCjCBzxKAOavmjm6OnGhhWmair0PxC4nREXxubhZrdpRT39Lp63CMOSeWAMxZ7XPGvU+3BHDazRfm09ndy7O2ZrAJcJYAzFmdugFsLYCPTBmXwOLJY3ji/aN09diawSZwWQIwZ1VU3khqXCTjE0N7CGh/X7t4IpVN7fx5pw0JNYHLEoA5qz0VTUzPTERCaA0AT1w6JZ3JY+N59L3DuKfCMibwWAIwZ9TV08uBymamZVj3T38ul/DViyew+3iTLRZjApYlAHNGB6ua6ezptf7/M7h2ThZj4iJ59N3Dvg7FmGGxBGDOaM/pEUD2DMBAoiPCuOnCPF7fW82+Spsl1AQeSwDmjIrKG4mJCGNCWpyvQ/FbX74on7jIMH751iFfh2LMkFkCMGe0p7yJ8zISCHPZDeAzSY6N5MZFeazdWc7REy2+DseYIbEEYAakquypaLIbwB746pIJRIS5ePBtawWYwGIJwAyotK6Nk+3d1v/vgbEJ0ayYn8ML28o43tDm63CM8ZhHawKb0LOnohEI7Skgnt5Y4nHZ2y+dxDObSvnFGwe5+7pZIxiVMd5jLQAzoKLyJsJcwtTxCb4OJSBkJsdw/cJcnt9axuGaZl+HY4xHPEoAIrJURPaLSLGI3DXAcRGR+53jO0Vkbp9jj4lItYjs7nfOD0TkuIh86LyuOvfqGG8pKm9iUnoc0RGhvQbAUKy+fDJR4S7ue/2gr0MxxiODJgARCQMeAJYB04CVIjKtX7FlQIHzWgU82OfYb4ClZ/j196nqbOe1boixmxFUVN5oN4CHKD0hiq8snsCfdpRTVN7o63CMGZQnLYAFQLGqHlbVTuBZYHm/MsuBJ9VtA5AsIhkAqvoOYM/KB5DqpnaqmjqYmZ3s61ACzm2XTCQpJoKf/GW/r0MxZlCeJIAsoO/E52XOvqGWGcidTpfRYyKSMlABEVklIltEZEtNTY0Hv9Kcq13H3VevM7NsBNBQJcVE8M0rJvPOgRre2lft63CMOStPEsBATwH1n/7QkzL9PQhMAmYDFcBPByqkqg+raqGqFqanpw8Wq/GCXccbEQntEUDn4uYL85mYHseP1u6hs9vWCzD+y5MEUAbk9NnOBsqHUeZjVLVKVXtUtRd4BHdXk/EDu8oamZweT1yUjRIejshwF9+/ehqHT7Tw5AdHfR2OMWfkSQLYDBSIyAQRiQRWAGv6lVkD3OyMBloENKrqWVfKOHWPwHEtsPtMZc3o2nm80bp/ztHlU8dy+dR0fv76Qaqb2n0djjEDGjQBqGo3cCfwCrAXeE5Vi0TkDhG5wym2DjgMFOO+mv/GqfNF5BngA2CqiJSJyFedQ/eIyC4R2QlcDnzHW5Uyw1fV1E7NyQ5mZlsCOFf/+pnpdPT08m9rinwdijED8qiN7wzRXNdv30N93iuw+gznrjzD/ps8DzN4DOXpUoDrF+aOUCQD21nmvgE8yxLAOZuQFse3P1nAPX/Zz192V7J0xnhfh2TMx9iTwOZjdpU14BKYlmEJwBtuWzKR8zMS+deXdtPY1uXrcIz5GEsA5mN2HW+kYGwCMZH2BLA3RIS5+Ml1MznR3MG//2mPr8Mx5mMsAZjTVJVdxxut/9/LZmUnc+flk3lhWxlrd551cJwxo8oSgDmtorGdE82dNgJoBHzzEwXMzknmey/uotymjDZ+whKAOW1nWQOAtQBGQESYi5+vmE1Pr/Lt331Id489IGZ8zxKAOW3rsXoiw132BPAIyRsTx4+vncGmI3Xc84rNFWR8zxKAOW3rsXpmZSURFW43gEfKtXOyuWlRHg+/c5g/7zzrs5LGjDhLAAaA9q4edh9vYl7egHPyGS/6/tXTmJubzD/9fgf7Kpt8HY4JYZYADOCe/7+zp5e5lgBGXGS4i1/eMI+E6HC+8vhmqmyqCOMjlgAM4O7+AZibawlgNIxPiuaxL8+nsa2LWx/fTHNHt69DMiHIpns0gDsB5I2JJT0hytehBKShTvFxyhcKc3jyg6N87pfruWlRPmGugWZWH/0pQUxosBaAQVXZeqyBeXb1P+qmjEtg+QVZHKhqZs2OctzTahkzOqwFYCita+NEc4f1//vI/Amp1LV28tcDNaTGRnDp1LG+DsmECEsAhq0l7iWbbQSQ71w5bRwNrZ28sqeKxJgI5lhrzIwCSwCGrcfqiY8KZ8q4BF+HErJcIlw3N5vmjm5e2FZGbGQ4U8fb/w8zsuwegOGDQ7XMzUs54w1IMzrCw1zcsDCP8UnRPL3pGCV1rb4OyQQ5SwAhrqKxjUM1LSyZnObrUAwQHRHGLRfmkxAdwRPvH7XlJM2IsgQQ4t49eAKAiwssAfiLhOgIvrJ4AmEu4fH3j9pCMmbE2D2AEPb0xhJ+t7mE+Khwth2rZ3tJg69DMo7UuEi+fFE+j7x7mMfXH+GGRbkkRkf4OiwTZKwFEMJ6VSmuaWHy2HhErP/f32Qmx3DjojxONHdw59PbbQpp43WWAEJYVVM7LR3dTEqP93Uo5gwmpcezfHYW7xyo4d/X2pKSxrs8SgAislRE9otIsYjcNcBxEZH7neM7RWRun2OPiUi1iOzud06qiLwmIgednzbweZQVVzcDMHmsJQB/Nj8/ldsvmciTHxzjN+uP+DocE0QGTQAiEgY8ACwDpgErRWRav2LLgALntQp4sM+x3wBLB/jVdwFvqGoB8IazbUZRcXUz6QlRJMVY37K/+5el5/GpaeP497V7eHNfla/DMUHCk5vAC4BiVT0MICLPAsuBvu3R5cCT6p7IZIOIJItIhqpWqOo7IpI/wO9dDlzmvH8CeBv4l+FUwgxde1cPR2tbKMxL9XUoxgPPbi7loklp7C5v5I7fbuP2SyaSkRRzxvI2eZzxhCddQFlAaZ/tMmffUMv0N05VKwCcnwNOgCIiq0Rki4hsqamp8SBc44l3DtTQ1aP2tGkAiQx3cdOifKLDXfx2wzHaOnt8HZIJcJ4kgIGGh/SfstCTMsOiqg+raqGqFqanp3vjVxrgz7sqiIkIsxvAASYpJoLrF+bR1NbN81tL6bXZQ8058CQBlAE5fbazgfJhlOmvSkQyAJyf1R7EYrygvauH1/dUMT0z0aZ/CEC5qbFcNSuDfZUneeeAtYrN8HmSADYDBSIyQUQigRXAmn5l1gA3O6OBFgGNp7p3zmINcIvz/hbgpSHEbc7B2/traOnsYVZ2sq9DMcO0aEIqF2Qn8dqeqtOjuYwZqkETgKp2A3cCrwB7gedUtUhE7hCRO5xi64DDQDHwCPCNU+eLyDPAB8BUESkTka86h+4GrhSRg8CVzrYZBWt3lpMaF8mEtDhfh2KGSUS4dk426QlRPLu5hIbWTl+HZAKQR1NBqOo63F/yffc91Oe9AqvPcO7KM+yvBT7hcaTGK9o6e3hjbzXXzs2y7p8AFxnunj30gbeLeWZTCbddMpFwlz3baTxnfy0h5s191bR19XD1zAxfh2K8ID0hiuvmZlNa38bLuyp9HY4JMJYAQsyTHxwlMymahRPH+DoU4yUzs5JYPGkMHxyuZdfxRl+HYwKIJYAQsqO0gY1H6vjKxROs+yfI/N2M8eSkxPDitjJONHf4OhwTICwBhJCH3z1MQnQ4KxbYU6LBJtzlYuWCXFwiPL2xhPYue0jMDM4SQIgoqW3l5V0VXL8wl/goWwYiGCXHRvLFwhwqm9r5wZoiX4djAoAlgBDx2PojhLmEWy+a4OtQzAiaOj6By6ak8+zmUl7YWubrcIyfs0vBELCvsomnN5ZwzewsxidF+zocM8I+cf442rp6+D9/3M3M7CSmjLP5nszArAUQ5Dq7e/mH3+0gMSacu5ad5+twzCgIcwm/WDmHuKgwvvHUNlo6un0dkvFT1gLwIz29SlF5I/sqT9LS0U17Vw+bjtQyLy+FxZPTmDiMidv+582D7Klo4lc3zWNMfNQIRG380djEaO5fMYcbf72R7/1hFz/70mxb9tP8DUsAfmLbsXre2FdFfWsXCdHhJMVEEBnuYv2hWv74oXtevQX5qaxcmMOyGRlER4QN+jv/sruSB94+xOfmZvF308ePdBWMn7lochrf+eQUfvraAebnp3Ljojxfh2T8jCUAH+tV5S+7K3mv+AQ5KTF8emYG52Uk4nKu1lYuyKGsvo11uyp4ZlMJ3/ndDn6wZg+fm5vFygW5A/bvqioPvFXMf796gAtykvm3z0wf7WoZP7H68slsLannB2uKKBgbbw8Amo+xBOBD3b29PL+ljF3HG1k0cQxXz8o4/cV/ioiQkxrL7ZdO4rYlE9lwpJZnNpXy2w3HeHz9UeblpXDN7EyyUmJIjI5gy7F6Xt5dyY7SBq6Zncnd183yqLVggpPLJfx8xRyu/eV6vv7UNl5avZic1Fhfh2X8hCUAH1q3q5JdxxtZOn08SwrSBu2jdbmEiyalcdGkNGqbp/HituM8s6mE77/08THf0zMT+ffl07lpUZ71+xqSYiL49S3zWf4/7/G1J7bw/NcvJDHa1oE2lgB8ZkdpAxsO13Lx5DQumTL0lc7GxEdx2yUT+dqSCZTVt3GiuYP61k7OG59IZvKZ14o1oWlCWhwP3jiPWx7bxKont/DEVxYQFW4tw1Bnw0B9oKqpnRe3l5E3Jvacb86e6iKak5vCFeeNsy9/c0aLJ6fxf78wiw2H6/iH53bQ22vLSYY6awGMsl5Vfr+1jMjwMFbOz7VJ2cyounZONjUnO/jPdftIjI7gP66Zgcv+BkOWJYBRtulIHccb2vjS/CuQ7t8AAA+kSURBVBwSY6wf1oy+25ZMpKG1i1++fQgR+PFySwKhyhLAKDrR3MGreyqZmB7HrKwkX4djQpSI8E9/NxUFHnz7EKrKj6+Zaa3REGQJYBT917p9dHUrn70g00bnGJ8SEf7576biEnjgrUPUt3TxsxWzbchwiLGbwKNk9/FGXthWxuLJaYxNsAnZjO+5WwLn8a9XT+OVPZXc/OtN1LfY4vKhxFoAo+Rnrx8kMTqcS4cx5NOYoXp6Y4nHZaMjwrh/xRy++/wOPvM/7/HwTYVMy0wcweiMv/CoBSAiS0Vkv4gUi8hdAxwXEbnfOb5TROYOdq6I/EBEjovIh87rKu9Uyf/sLGvg9b1V3LZkIjGR1sQ2/uczF2Ty3O0X0t2jfO7B9by4rQxVGyYa7AZtAYhIGPAAcCVQBmwWkTWquqdPsWVAgfNaCDwILPTg3PtU9b+9Vhs/9bPXD5IcG8GXF+fzpx0VI/pZQ7nyM6av2TnJrPnmYu58ajv/8NwO3tpfw4+XzyAp1karBStPWgALgGJVPayqncCzwPJ+ZZYDT6rbBiBZRDI8PDeobS+p58191dy2ZCIJ9vi98XNjE6J5+raF/OOnpvDyrgqW/fwd3j90wtdhmRHiyT2ALKC0z3YZ7qv8wcpkeXDunSJyM7AF+K6q1vf/cBFZBawCyM0NvMXM73v9IKlxkdxyUf6wzrcrejPawsNc3HlFAUsK0vnO7z7khkc3smrJRP7hU1Ns+ogg40kLYKDxiv07B89U5mznPghMAmYDFcBPB/pwVX1YVQtVtTA9PbBuoG45Wsc7B2q4/ZKJthC7CTgX5CSz9u8v5voFufzqncN89hfr2VXW6OuwjBd58q1UBuT02c4Gyj0sE3mmc1W16tROEXkEWOtx1AHivtcPkBYfyU0X2kIcxr+draU5PTOJWy7M5w/by1j+wHtcOiWdX91USGS4jSIPdJ78H9wMFIjIBBGJBFYAa/qVWQPc7IwGWgQ0qmrF2c517hGcci2w+xzr4lc2Hq5lfXEtd1w6idhIu/o3gW3q+AS+9YkpzM5J4a39NXz2f95j93FrDQS6QROAqnYDdwKvAHuB51S1SETuEJE7nGLrgMNAMfAI8I2zneucc4+I7BKRncDlwHe8Vy3fu+/1A6QnRNkyfCZoxESG8fl52dx8YR51LZ0sf2A99766n87uXl+HZobJo0tTVV2H+0u+776H+rxXYLWn5zr7bxpSpAHk/UMn2HC4jn/7zDR7tN4EnfPGJ/LdK6fywz8Vcf+bxby6p4p7vzjbHh4LQNaJ52Wqyn2vHWBcYhQrFwTeqCVjPJEUG8G9X5rNozcXUtvSyTUPrOfx9Ufs4bEAY53TXvZe8Qk2H63nR8un29W/CVp9bxqvWjKRF7aV8cM/7eHZTaVcNy/7Y6Perl9oF0L+yloAXnTq6j8zKZovzs8Z/ARjgkBcVDg3LcrjM7MyOFTTzC/ePEhxdbOvwzIesATgRW/tr2ZbSQOrr5hsD8yYkCIiXDgpja9fNonoiDAeX3+EV4oq6bFlJ/2aJQAv6elV7vnLfvLGxPLFQrv6N6EpIymG1ZdNpjA/hb8eqOHhdw5RWtfq67DMGVgC8JKXPjzOvsqT/OOnphIRZv9ZTeiKDHdx7ZxsVszPofpkB5++/11e3jWykyCa4bFvKi/o6O7hp68eYEZWIp+emTH4CcaEgFnZyXzzigImpMXx9ae28X/+uIv2rh5fh2X6sATgBb/dUMLxhjb+Zel5tri2MX2kxkXy/B0XseqSifx2QwnXPLCe4uqTvg7LOCwBnKPqk+387LUDLClIY0lBYE1WZ8xoiAx38b2rzufxW+dTc7KDz/xiPU9tPGbPDPgBSwDn6L/W7aOju5cffna6r0Mxxq9dPnUs6761hLl5yfzvP+zmhkc32g1iH7MEcA42HK7lD9uPs+qSiUxMj/d1OMb4vXGJ0fz2qwv5z2tnsrOskSvv+yu/eOOg3RvwEUsAw9Te1cP3/7ib7JQYVl8+2dfhGBMwRITrF+by6ncu4fKpY/npawf41H3v8Kcd5fTacwOjyhLAMN398j4OVjfzo2tm2ELvxgxDZnIMD944j6e+tpCYiDC++cx2rrr/Xf6yu8IeIBslNhfQMLy+p4rfvH+UWxfnc/nUsb4Ox5iAtnhyGuu+tYS1O8v52esHueO320iNi+SiSWOYk5My6AWWzTU0fJYAhqiisY1/+v0OpmUkctey83wdjjFBIcwlLJ+dxadnZvD9l4pYX3yCtTsreHl3JeePT2BObgpTxiUQZsOsvcoSwBA0tnZx6+Ob6eju5f6Vc2y+H2M8cLblJgcyMyuJmVlJHG9oY3tJPTtKG9hd3kRcZBizspOZlplI/pg4SwZeYAnAQ22dPXz1ic0cqmnm8S8vYPJYG/VjzEjKSo4hKzmGZTMyOFB1km0l9Ww+WscHh2uJiQjjvPEJnJ+RSGtnty27Okz2X80DJ9u7+MZT29haUs8vVs7h4oI0X4dkTMgIcwnnZyRyfkYiHd09HKxqZm9FE/sqT7K9tIEXtpWxpCCNK6eN47KpYxmXGO3rkAOGJYBBHD3Rwtee3MKREy385HOzuHpWpq9DMiZkRYWHMSMriRlZSfT0KkdrW+jpVV7bU8Xre6sBOD8jkUunpHPZ1HTm5aXY5IxnIYH0OHZhYaFu2bJlVD6rt1f5/dYy/mPdXkTglzfM5aJJ537lP9T+UGPM2V2/MBdVZV/lSd7eX8Pb+6vZeqye7l4lLjKMuXkpLMhPZf6EVGbnJIfkSn0islVVC/vvtxZAP6rK+4dqueeV/ewobWBeXgr3fvEC8sbE+To0Y8wZiHzUTfT1yyZxsr2L9cW1rC8+weajddz7+gFUITLMxfkZCUwZl8DU8c5rXALpCVGIhN5NZY8SgIgsBX4OhAGPqurd/Y6Lc/wqoBX4sqpuO9u5IpIK/A7IB44CX1TV+nOv0tD19ip7K5v464Eant9SxpETLaQnRHHvFy/g2jlZIfmHYUwgS4iOYOmM8SydMR5wj+DbcqyOTUfq2F3eyFv7a3h+a9np8tERLjKTYshIjiYjKYbMpGhS4yJJiYskOTaSlNgIUmIjSY6NID4qPGi+EwbtAhKRMOAAcCVQBmwGVqrqnj5lrgK+iTsBLAR+rqoLz3auiNwD1Knq3SJyF5Ciqv9ytliG2wXU2tlNbXMn9a2d1LV00tDaRV1LJ6X1rRRXN1NU3kRdSycA8/JSuGFhLlfNzBiRpqJ1ARnjH5o7uqluaqeqqZ361i4a2rpobO2ksa2Lk+3dnOmbMSJMSIpxJ4Xk2AgSot1JIT46nISo8NPv46PCSYgOJz4qgugIF1HhYUSGu06/opyf4f2GswofbffNMxFhrmEPfT2XLqAFQLGqHnZ+0bPAcmBPnzLLgSfVnU02iEiyiGTgvro/07nLgcuc858A3gbOmgCG60dr9/LMpr/94o2JCGPy2HiuOG8sF04cw+LJaYxPshEExoSC+Khw4tPjB5zIsadXaevqobWzm4snp1Hf2kV9aycNrZ3uZNHaSX1LFw1tndSc7ODIiRZOtnfT3NFFe1fviMT7m1vnc5mXZx7wJAFkAaV9tstwX+UPViZrkHPHqWoFgKpWiMiANRORVcAqZ7NZRPZ7ELPH9gFrvfkLzy4NODF6H+dToVRXCK36hlJdwU/qe/lPzun0vIF2epIABmpz9G8dnamMJ+eelao+DDw8lHP8lYhsGagZFoxCqa4QWvUNpbpCcNfXkwGyZUBOn+1soNzDMmc7t8rpJsL5We152MYYY86VJwlgM1AgIhNEJBJYAazpV2YNcLO4LQIane6ds527BrjFeX8L8NI51sUYY8wQDNoFpKrdInIn8AruoZyPqWqRiNzhHH8IWId7BFAx7mGgt57tXOdX3w08JyJfBUqAL3i1Zv4pKLqyPBRKdYXQqm8o1RWCuL4B9SSwMcYY77FJMowxJkRZAjDGmBBlCWAUiMhSEdkvIsXOU89BRURyROQtEdkrIkUi8i1nf6qIvCYiB52fKb6O1VtEJExEtovIWmc7mOuaLCK/F5F9zv/jC4O1viLyHedveLeIPCMi0cFaV7AEMOKc6TAeAJYB04CVIjLNt1F5XTfwXVU9H1gErHbqeBfwhqoWAG8428HiW8DePtvBXNefA39R1fOAC3DXO+jqKyJZwN8Dhao6A/fAlRUEYV1PsQQw8k5PpaGqncCp6TCChqpWnJr8T1VP4v6CyMJdzyecYk8A1/gmQu8SkWzg08CjfXYHa10TgUuAXwOoaqeqNhCk9cU9MjJGRMKBWNzPLQVrXS0BjIIzTZMRlEQkH5gDbKTfdB+Adycy8Z2fAf8M9J30JVjrOhGoAR53urweFZE4grC+qnoc+G/cw9IrcD/P9CpBWNdTLAGMvHOeDiNQiEg88ALwbVVt8nU8I0FErgaqVXWrr2MZJeHAXOBBVZ0DtBBEXSB9OX37y4EJQCYQJyI3+jaqkWUJYOR5MpVGwBORCNxf/k+p6ovO7mCc7mMx8FkROYq7O+8KEfktwVlXcP/9lqnqRmf797gTQjDW95PAEVWtUdUu4EXgIoKzroAlgNHgyVQaAc1ZEOjXwF5VvbfPoaCb7kNV/5eqZqtqPu7/l2+q6o0EYV0BVLUSKBWRqc6uT+Cezj0Y61sCLBKRWOdv+hO472cFY10BexJ4VDgL5vyMj6bD+A8fh+RVInIx8C6wi4/6xb+H+z7Ac0AuznQfqlrnkyBHgIhcBvyjql4tImMI0rqKyGzcN7wjgcO4p3pxEYT1FZEfAl/CPbJtO/A1IJ4grCtYAjDGmJBlXUDGGBOiLAEYY0yIsgRgjDEhyhKAMcaEKEsAxhgToiwBGOMhEblWRFREzvN1LMZ4gyUAYzy3EngP9wNgxgQ8SwDGeMCZ52gx8FWcBCAiLhH5pTN//FoRWScin3eOzRORv4rIVhF55dRUAsb4E0sAxnjmGtxz4h8A6kRkLvA5IB+YifuJ0Qvh9LxIvwA+r6rzgMeAoHr62wSHcF8HYEyAWIl7Og9wTwK3EogAnlfVXqBSRN5yjk8FZgCvuaeUIQz39MLG+BVLAMYMwpnn5wpghogo7i90Bf5wplOAIlW9cJRCNGZYrAvImMF9HnhSVfNUNV9Vc4AjwAngOudewDjgMqf8fiBdRE53CYnIdF8EbszZWAIwZnAr+dur/RdwLxpSBuwGfoV79tNGZ+nPzwM/EZEdwIe455U3xq/YbKDGnAMRiVfVZqebaBOw2JlD3xi/Z/cAjDk3a0UkGfdc+T+yL38TSKwFYIwxIcruARhjTIiyBGCMMSHKEoAxxoQoSwDGGBOiLAEYY0yI+v86EggWFZ62ugAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(ts['Age'])\n",
    "# observation \n",
    "# we can say Age is almost normally distributed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x154efb901c0>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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67lOWOUwC1G2vf/8I1s6vxvKdB1DisuH+WRUYX+wEB4cs86g4L9kc4NnEgDXzqrFi15nYgWvmVcOUHcUnGaLUZcPzd1+GoMghMEDmgMXMDM/y5rAKmvt1WCkQNSGxYtuqQocFmxZNiYtllmp7qmeCKSVLbaIyKX/rq10mxrOZBTz/o0sRlHDm+msKPZ6Kvt5zQozgsArYvPgSNH/nRZ7VhJ6AhLIRDl37D6Ioo9Xtj0pEp2eijGz+/kEIyX69DupxzunWXBZQEiw8+V+HsHJOFYqcVpTm2zB6uEO3BkW5exs5sNfm9mNUgV3XDHaxCTJWzb0Q44udyHeYsPu+6TjZ6Y8bRFQCOGd7gGcmMGz58ChWzqlCgcOCTm8QWz48in+9/nvpLhrJMl1eMS7g9Oh8Y/dpErT3W+CgZoSQSInaqooS14DaU70TTPWVpbZyZD5evW8Gvu30JWyXyeCwmIGuLin++puXWpIBykxM0oPB7ROxcvfnav196ubJug0mi6KMgy3dWBrx+VhfW4OJI/N1GdjL9u8fhJDsR1MocoCyNPbthlbUbavH/PUf4Zbf/gUd3qBu+1Du3irT45UGq8BhRUm+DWMK81CSbxtw4xW5zHd/UycWP/dX1P7uL5BlBllm6hcHID6Ac7YHeDYLDItnjMeq1xpw08a9WPVaAxbPGE/LD0hK0pUow+2TNffr9smG7peQbJOorerwBgfUnhqRYErJUqtVJkFgkGT02i6TwaHn9be395wQI4gyx49f3B9Vf3/84n7dYuq1uv3qgJ6y/aU69ouy/fsHIST76ZMnXANjrBzAVgBnAZABbOScP8kYGwHgZQDjABwDsIBz3hF+zc8B3AVAAnA/5/wto8qXSwYjsPFg3b0dSGDubA/w7A1IePTNxqiZeo++2Yinb5kMONNdOpItKFEGIZnNqLYqHQmmsr3dzRV0/SXZLCjKmvU3KOpzUzAoaW9flPTZPl0HCSHpZuRMPRHAzzjn5wOYCuBexlgVgIcAvMs5rwDwbvh3hP+2EMAFAK4CsI4xRuu2kjBYgY0H4+7tQAJzZ3uAZ6vZhDa3H3Xb6nHTxr2o21aPNrc/a8pPMgMlyiAksxnVVqUjwVS2t7u5gq6/JJsZfR2xmATtz4dJn6/BdB0khKSbYYN6nPNvOeefhH/uBvAlgDEA5gLYEn7aFgD/GP55LoCXOOd+zvlRAF8BuNSo8uWSREtjszGwcW/H0tdxZvt5yPbyk8xQ6rLhmdqaqHr0TG2N4YkySpxWzf2WUP0lJIpR1/rI7SoJpoxuT6jdygx0/SXZzOjrSKnLhvUxn4/1OvaL6DpICEk3xrnxU/MZY+MAfADgQgBfc84LIv7WwTkvZIw9DWAv53x7+PHfAXiDc74zZltLACwBgLPPPrvm+PHjhpc/G+RS1qXejqWv4xzk85Dyhvuqv7n0PpL0CQYltLr9EGUOsxDKfGuxxN0x1r3+BgIi2jwBdb8lTiusVsOiPJChTff6O5iMutbrmf22P/ukditpdP0l2Szr+r9K9ltRkmGm7LdDmaF9h3EPvd6vQh1bfU2/XkeGnIT11/DWnjHmArALwP/mnHcxlrAsWn+IG3HknG8EsBEApkyZQsFCwpSlsbmgt2Pp6zgz/Tz0VX8zvfwkO1gsJowpzNN9u33VX6vVjDH0JZJkqEzqPxh1rU9HG0Lt1uCg6y/JZunu/5rNAkYXOPp+Yj/RdTB3ZVLfgZBEDM1+yxizIDSg9zzn/A/hh1sYY6PCfx8FoDX8eDOA8oiXlwH4xsjyEUIIIYQQQgghhBCSjQwb1GOhKXm/A/Al5/zfIv70KoDbwz/fDmB3xOMLGWM2xth4ABUAPjaqfIQQQgghhBBCCCGEZCsj5+nPAHAbgP9hjH0afuyfAKwGsIMxdheArwHcCACc8y8YYzsANCCUOfdezjnlAieEEEIIIYQQQgghJIZhg3qc8/+LxMH8ZiV4zSMAHjGqTIQQQgghhBBCCCGE5AJDY+oRQgghhBBCCCGEEEL0R4N6hBBCCCGEEEIIIYRkGSNj6hGdyDJHuyeAgCjBajahyGmFICRa2UxSQeeW5CJRlNHq9iMoybCYBJS6bDCbjb+Hk679EkL6NljtHbWr6aHXeaf3j+Qio+s1fW4IIelEg3oZTpY5Glu6cffWfWju8KKs0IFNi6agcmQ+NRYDROeW5CJRlHGwpRtLt9er9Xp9bQ0mjsw3dIAtXfslhPRtsNo7alfTQ6/zTu8fyUVG12v63BBC0o2+aWW4dk9AbSQAoLnDi7u37kO7J5DmkmU/OrckF7W6/erAGhCq10u316PV7c/J/RJC+jZY7R21q+mh13mn94/kIqPrNX1uCCHpRjP10kxrujYAdHoD8AYkiDJXG4nJ5QVYOnMCChwWBEQJsswNuwOUKcs4jJzOHhAl9dwqmju8CIiSLtvvj2BQQqvbD1HmMAsMpS4bLBZT2spDsk9QkjXrtSjJOblfQvQU2eYwxmBigCAIGbGUKlF/IZk2sr/tXeQ+HVYTRJkjKMoJ92V0u0pL3LQFRAk31ZRh7sVlkDmHwBh2f9Kc8nnPxH4RGRqMDN9hdL0OiBJKXDasnFOFAocFnd4g1r9/hD43hJBBQ4N6aaQ1XXvrnZcCAFq6fFi+8wBWzqlCWaEDJS4bHriyEit2HTB8anemLOMwejq71WxCWaEjqqEvK3TAak7PIFowKOFgqxv3RCxffKa2BhNLXTSwR5JmNQma9dpiMnZits2svV8rLb0lWUKrzVkzrxpbPjyKn/6wMq1LqRK1hzazgEXPftxnG9mf9i5ynyUuGx68qhLLd/beBzGyXaUlbom57AJmnj8St2zaG9V/cNlTu/5a0tR+kKHN6PAdRtdru0WIuz6unV8Nu4U+N4SQwTFkrjayzNHW7ceJjh60dfshyzzt+9Garn28vQfH23vUhmH9+0ewZl417p9VoQ7oKc81amp3pizjMHo6e5HTik2LpqCs0AEA6hcEZfbDYGt1+9UBPSB0vPfQ8kWSIoEBa+dXR9XrtfOrYfR3Xgbt/Q7tr9okm2i1OSt2HcC8mvK0L6VK1B4eb+9Jqo3sT3sXuc+lMyeo/ZLe9mVku0pL3BJz+2TN/oPbl9pMac655nWcc2P6zIQAgxO+Q6te68Uv8rjr4/KdB+AX6XNDCBkcQ2KmXqYGiNaaDp5nDd3NVh7f39SJx95qxNobqwdtSYRe09QHuh2jp8sLAkPlyHy8smxGRizliVxqrWju8EI0aACa5CafKOPRNxujloE8+mYj/n3hRTm5X0L0kqjNKXBY0r4EMVHZlD5D5GNa5exPexe5T+Uc9LUvI9tVWhqamF79B7qOk3QwOnyHNyhp1usndarXicofpPAjhJBBMiQG9RLd3X1l2QyU5NvSth+tZSo9gVDnNPLx/U2daPrOO2hLRfVaPjPQ7QzG8lhBYLrWgYEwC0zzeM1DfFkRSY1ZYGhz+1G3rV59bDDqUbr2S4heErU5nd5gWkMz9FY2pc8Q+Viicqba3kXuUzkHybTHRrWrjGm3kYzRNUav/gNdx0k6JKq/Jp3qncUkaNdrnZbfUv+dEJJuQ2L57WDd3U11P8oyldlVpdhwWw12Lp2GiWe5MHZEXtw08XNLndhwW43mkha9lxbrtXxmoNvJtOWxRit12bC+Nvo9Xl9bg1JXZgw6kuyQZxXwTEw9eqa2BnlWYy/3wxza+x3mGBLNDMkBWm3OmnnV2FXflPa2R7M9vG0Kxhbl6dJGKv2IltNefNPpxYmOHpgEqPtc//6RuH7JYJ8TEwPWzKuOe39M9L0ZTpv29ddpS+3660jQfjgMbj/I0GYxMTxz68XR9e7Wi2HR6cNtdP+61GXT/NxQ/50QMlhYNsfJmDJlCt+3b1+fz2vr9uP6dXvi7qDoPVOvP/sRRRmNrd2o23YmOOzWOy/F8DwzfAEZEgfy7QK+6fTj1f3NuGXqOAiMQeYcDosJI/KsONzm1n1p8WBlvx3o39NsQAWJrb+yzHHslAfHv+tBntWEnoCEsSPyMK7YmUnHTDJcu9sHMMAXkNUsynarAHCgyGWPfKqu9bfD40NQ5giIHJLMYRIYrGYGi8BQ6LT3siVC+kXX+qvI1Oy3ssxxrN2D4+0R7UNRHs4uzEOHNzigNlIJHfLEO424ffr4qIRcW++8FC67GUFRht0qIBDkCEoyTAKDWWD9Pjf9advbuv34xSuhGIfKErpd9U145PrqjJlxnwJd6+9prx+nvSICIofAAJkDVjPDcIcZwx3Jn5u2bj++/KYDE0qHqe3HkdYunD+6MOVznOH9NzIwuvcffKIMUYKavdlsAuxmQbf+QzAoodXtV+t1qcumaxI6o7dPdGNI30Ex7qHX+7XdY6uv6W+RyNCSsP4OieW3yh3u2IEvve8w92c/Hd6gOqAHhGb2LXr2Y7yybAbGFOZBljmaO3rw5H8dwrLLz8U3nd6o7Eov/OgyQ5YW67V8prftJBODMJOWxxqt3RPAos0fGz74THKbwIDmTn9cFuWyAmPrUFACjrR54rK/TShxGbpfQvSUqW1OuyegZrlV6NU+KKFDVs6pikvIpfRHRg13JBz4S/VGYn/jHBc5rfjpDysN78tlI49fxi2b/hJXP3bUTcNwR/LbKbCbUehy4KaN0Vl0C+ypfV2gTMUkFUEJOHaqx7D+gyxzfHXKY2h9tFhMGFOYp8u2CCEkVUNiPn1k4OY9Ky7HK8tmGNKx6M9++lqy2+4JoLXbj3k15ejwBOOyK7V2+7M2cDRlsotGQcCJHnoC2lkQewLGBmwOSLJm9rcABYomZMCMbB+UbfeWDENpr+fVlMcN/KXabve37R+svlw20itQf5snoNl+tKXYL6P+HUmF0f0Hqo+EkFw3JAb1gDN338cU5qEk32ZYJzDV/SiBqCNFBp9WOtNFTivyrKa4Tlu7J9Dr6zMZDWJF66suEJKMdGVRlhLsV6LszYQMmJHtg7JtJRmG1j6SGfhL1kDa/sHqy2UbUzhQf6T+JBrQKwsp9e9IKozuP1B9JITkuiGx/DaT9bVk12o2YVd9Ex66+nwcb++Jy660q74JG26rUZfwzq4qxT9fU4WAKKGt25/RMer0ym7b2zFl0vH2ZbCWiZPcZhYY6v7XOMyfcjZMAoMkc+zc97XhWdgsCfZrydDPGyFGStT29LdNMrJ9ULb9xDuN+O3tNTALJjUum83MUOS0qjcQU8mCm8hgZLYfahwWAWvnV8ctX3RYUrt3bzEJeGphNSaPLVJjo+4/3p5yllB6j0kqLCZBs75YdMpOOxj1MRAQ0eYJqDH1SpxWWK30NZsQMjiGRKKMTNfXoFRjSzf++EkTbqgpx3eeQFSnbdOiKagocaHDG4QsyzjlCUQl3YiMGZFpMU70KE9v2wBg9PHqHmw1mwYhSWby+UQcbvfExdSrKHLCHh0XSdf6m8J+CdGDocGuByJRu1RR4hpQYisj2wdZ5ujyBdDU4Yv6DK+vrcHEcPnSHVMvx2Rkoi29ruP0Huc8XetvMCihsdWNpTHXnspSly7JJoyuj4GAiMa2+M9NZYmTBvYyDyXKINksYf2lQT2DDaQTrrxWlkNZcE0MYIwhKIV+t1sEFDvPLD9Rsu+WuGxYOnMCChwW9AQkTCofjhFOm65ZgLWOC0DKxyqKcihblCTDbBJQ6rLBbBaSPne9HRMAo7Me694wKOcjKMmwxJwPQpJxoqNHDXKuKCt04OUlU2ODOOtaf1PYLyF6yNhBvUTt0o66aViw4aO4x1+9bwYkGUm1nXr0KRK99ptOr2b5dtRNw1nD7Djl8cMXlGBmDGaTgKAkD6gM/ckunD74h6gAACAASURBVEM3vnSvv3rMFNLzOp5D7xWJp3v99flEtHvP1N8ih1XXG4JGZqel/k9WoUE9ks2GdvbbdBnInSFZ5jjW7sHx9oi7rkV5GFeU+K5rQJRQ4rLhgSsro+6ib6itQYHDmnJMid6WD8Ue19Y7L4VflFM6VlnmCWctAMnNsuvrmLIphoYoyjjY0h13p3LiyHwa2CNJS1dMvXTtl5BMk6hdEjXilZW4bPi204e6iOv+htoajCqwo8ARHz5jIH2Kvl7bWzw1PWa5DHS2DM3+SiwYlDRnCk1McaaTntfxTM0kTTJPMCihucuLpu+86nee8hEOjDXl6TLwJoqy5kxAvfrX1P8hhKQbjRQYaCDZljq9AbR0+bBy9+e4aeNerNz9OVq6fOj0Jn6t1WzC/bMq4jLT1W2vR7snkFKgbaXzfP26PZix5k+4ft0eNLZ0QxRltHT54o7reHtPysfa2/lJ9tz1dkzZlnii1e1XOxxA6JiXbq9Hq9uf5pKRbGJOEDDd6Jh66dovIZkmUdtjDseNinT/rAp1QA8402Z/1nQajS3dkCO+FPa3TyHLHCe7fPD4RaycU4XJ5QWar7VolE9JtqBH5shE5T/Z5Ys6zlRfTxksQ/0Hray1qfYf6DpO0qHDG4QkyygfEUqAUz4iD5Iso8Mb1GX7Rvev6XNDCEk3wwb1GGPPMsZaGWOfRzw2gjH2DmPscPj/woi//Zwx9hVjrJExdqVR5RpMA8m25A1ImundvYHEry1yWjG+2Jlwn0owbKXh6S3Qtlbn+Yl3GnHsOw96gvHHpZWZt69j7e38JHvuejumVI43EySaJRFMMescGdosJoZ1t14cVe/X3XoxLCaDE2Wkab+EZJpEbU+pyxb3+LjiPM3rfp7VFDdg1Z8+hXKDbsGGjzB//UdY9VoDHriyUh3Yi3xtqcuG9bU1UeVbX1sDs8B0mfWeqPzfdHrjBjBTeX2mzr4fTHrNFKLrOEkHxgC/yHHH5o9xxeN/xh2bP4Zf5GA6VTu9sjonQp8bQki6Gbn89jkATwPYGvHYQwDe5ZyvZow9FP59BWOsCsBCABcAGA3gvxhj53HOs7qnNpBsSxJPkN69l/6ZIDDk2RLvUxAYKkfm45VlM/qMcaLVeZ5XU46m77zqNiP/3hOQUj7Wvs5PMtvr65iSPd5MoNzpiz1mutNHUhGUOF7/7AQ233FJVBbaRdPH5+R+Cck0vbVLsY8HRe22s9MbjBuw6k+fQusG3YpdB7ByThVWvdYQ9VqzWcDEkfnYUTctKs5thw4Zb3srf7sngP/98qd9xruljKqJ6dV/oOs4SYeAKGPZ859EXaeWPf8JXl4yVZftJ8qum2pW50REGZqfm9tnnKPL9gkhpC+GzdTjnH8A4LuYh+cC2BL+eQuAf4x4/CXOuZ9zfhTAVwAuNapsg2UgM8XsFu3lO3ZL729ZsTN+JkDkPpUYJ2MKQ1PcEw1waS0fKnJakWc14TfvHsaaedVR+ygf4Uj5WPWaZdfbMSV7vJnAYk5wp4/i6ZEUWMwM10wag8XP/RVXPP5nLH7ur7hm0hhYzMbW/TyrgDkXlUXtd85FZcizUv0lQ0+itif2cbvVhLXzo9vTNfOqsf79I3EDVv3pUySa3aZsK/a1ZrOA0QUOnF3kxOgCB8xmQbdZ71rbUY41mRl32Tb7fjDZzIJm/8GWYv/BYkrQftCMI2Igo2PSJZqFXOrSJ+ZjqcuGa2P6P9deVKbb9gkhpC+DnShjJOf8WwDgnH/LGCsNPz4GwN6I5zWHH8tqqcyMUyjJKbjMseG2GtRtOxPUddOiKSh29t5A9GefWvuXZTlq/7OrSnHWcDsOt7jR5vbjsbcasXJOlZphd4TTigKHNaX9DuYsu+zIwsY07/Td9f0J6S4YySoMnxxrxwt3T4XMOQTG8F7Dt/iHScZeUr1Bjg63Fy8vmapmlzvS2oUipxWFfb+ckCGpwGHFyGF2PHbjJJTk2/B1ew8ee6sRbW4/NtTWwCSE2i9BYJptZqHD0mvblmh22+gCB84aZk+qHRxovyJ2OzvqpuGbTi/aPQE89lYjAGDzHZdA4hxt3f6E29arHLmIJ+g//Oj756a4pQTtR3XWd8lJBks0k86i00w6s1lAZakrqn9S6rLploQu0SxnSnJHCBksmZL9VqtHpnl7hjG2BMASADj77LONLJMuBIGhyGlVO93tngAKHRZ0eIN9ZpWdXVWKF350GUwCS6nzGplxrLfBrNi/FTos+LqjR824KzCGF++eCquJ4ZQngF//5xdYdvm5WDu/Gst3HkDdtnp1sFHJ0pdqprPeXqNX5rREGfMqSlya74ORequ/xU4b/nFyORY/99cz5byt74FcQiINt5ox47xSHGl1q1nkZpxXiuHWgV/ue6u/I+wWjCp04lDLmf2WjXBihN0y4P0SoodM7D8IAsO4Iify7RbIsozKs/Lx7wsvwt/aPPjnP36ONrc/KsNrbPveVzbYQocF62tr4rI+KjNI2rr9CIgSHFYTRJkjKMpx/ZLIfsKo4Y5eM9onc/PMbhHgsJiw6rUGlLhsePCqSjWGcF8ZbYdyRtXe6m+hw6LOFIp8nwsdqV1/C+za7UeBPfX2IztuppLB0lv9LXJYsHnxJWiOyH5bNsKBohTrbyKyzPHVKY+hmbOVWc4k92Ri34GQWIxz49JtM8bGAXiNc35h+PdGADPDs/RGAXifc17JGPs5AHDO///w894C8EvO+Ue9bX/KlCl83759hpVfD1oDdffPOg9Lt9ejxGXD/bMqML7YiTxbaInNDes+jLtT1VecmWT3HdmIAYj72+/rpuJYe4/auZ5dVYqHrj4fVrOAhRv3ornDi8nlBXjwqkqcNdwOE2OwW00odqZ3WWtfHce2bj+uX7cn6rzOrirFT/7+vLiZkCk28AM66Nj6K8scx9o96qBqT0DC2KI8jCtyUkeYJK3ltBd/O+WJ+pK8dn41zil2YuTwqA6nrvU3hf0Sogdd62+6xLZfJgG47uk9SfUDtNq22Oe2dfvxi1cOYF5NOQocFnR6g9hV34R/vf57cPtEHG/vQUGeBcMdFqx+40u83dAadePrcJs7qS/CyQwwRj5H6f9UlLqwcNNe3fo9WUTX+tvW7cdvP/gK86ecHTdTL5Xz2Nrlw5E2d9x1fEKJC6XD7ElvJ5n6QLKarvW3tcuHE51e/PjF/Wp9eermyRhT4Eip3iWSzLWSDBmG9h3GPfR6v7Z7bPU1/S0SGVoS1t/Bnqn3KoDbAawO/7874vEXGGP/hlCijAoAH+u5Y62BHwCG30VUglSXuGxYOacK55W6cNuzH6PEZcMDV1Zixa4zHaftd10W1eBMLi/A0pkT0BMQ0daNXsundXxaAbLv3roPr943A76gHPc3nyirHbnJ5QW4ffp4LHr2Yzx+4yT1efubOnHzpr8AAPasuByl+QNvbAcimY5jbEyhyeUFWH7lRPWONhB9biQZabmz3O4JYNGzH1OngwxIUObYvOeoujy+0xvE5j1H8S/XXpCT+yVkMBgx60ir/dpQW4MSly2qHdCKNyfLHN6gmDAbrFJeb0DE2w2teLuhNep5v5oro6XLh5W7P1f3vWZeNdq6A9jf1Im7t+7DjrppatmU/ojHL+Jkly9u6W6i/saOumnqcyOf09zhxeLn/oqdS6f1mdGWZnz1TZJlXF09Ws2c3BOQcHX1aMhyatk9A9KZfiAQei+W7zyAl1JMWJCoPlB/hmgJSrI6oAeE6suPX9yvW6KMgChh+jlFuPv756iD3ps++JuumbPpOkUISSfDBvUYYy8CmAmgmDHWDOBhhAbzdjDG7gLwNYAbAYBz/gVjbAeABgAigHv1zHyr1XHeeuel8Adl3L1NexZb7LLUDm8QsixD4gDnPOkLdkCUogbwlAGy1Td8D1s+DH0BHj3cDrslOuPr5PKCuEG/VO+QD7Ob4zrLJS4bvu30wRuMD54tRQSq/dns89R9d/aS+S62ESuwm0NLWiUZkszhsJhQ7LJpLuPRK0Ze5KBpgcMCt09ES5cPcvh9spjPxOpYUFOGW6eOxelwZkGtc1O3fUCz9/rNL0q4qaYMcy8uU2PZ7P6kWddOB8l9JgbcPn181LVjzbxqGB3nPF37JcRoRs06ih34KHHZ0Nrtx9obJ+FImxvr3z+C/U2dmF0VCj98vN0Di0lAidOKr055cPK0T7NtZozhWLsHi579GJvvuETzObKMuMGbFbsOYPUN34MnIKHAYYHMOUpcNpS4bHjo6on42e8/S+nm2dKZE+ALSmju6MHo4Q7NpB2+oHb2X+V4HRYT2t0Bzb6aVr+i0GHBd94AfEEJJsbgsJrU8CC5jCHUh4scpH1y4UUpb0dOkLBATjFhQaIELdSfIVoSJcqQdEqU4bSZUDttbNTy9HW3XgynTZ/M2bTShhCSbkZmv72Zcz6Kc27hnJdxzn/HOW/nnM/inFeE//8u4vmPcM4ncM4rOedv6FkWrY4zY1A7icCZu4gdXj8aW7px/bo9mLHmT/jFKwdwsKUbv3jlAL5q82DBho8wY82fcP26PWhs6e6zo2MxC7h/VkXUANnsqlKMLcrD7dPHY1d9E7p8IhY/91es+9NXavaypTMnqK+JLF+7JxC1fVnmONnl07wjyhiLy2B7/6wK1G2vR7snEPe3U+7QY5PLCzCq4Ewne/37R+Ky3W5aNAWFDkvUufrtB1/hWEcPjrS5sXDjXvxg7fu44ZkP0XiyG6Iox53X5o4enOjoQVu3Xz2PshwKlB37eOwxK9tq7vCqg6arXmvA6jcOQpRl3BjxPrl9IjYtmoLZVaVYOnMC7n3hE83jV85NX+fcKE6rgJnnj8Qtm/Zi5tr3Q/+fP5Kyh5KUyBxx144Vuw5Ap75xxu2XEKMlmnU00LYhcuBDuZG3cvfn+Pt/+zNWvdaAB66sRN3/GocfzzoPN4Xb1AUbPsKhVjeeeKdRMxP907dMxtff9YADWH3D9wDwuOesmVcddRNP0dzhxagCB1a91oCbNu7Fwo178eBVlfjFNeerA3qRx3/K41dfqyTkiDyWVa814IrH/4xbfvsXNLZ2w2ISsHPpNGy4rQaTywswubwAhXmWuOy/62trsPXDo/jB2vfxxTddmn21UEIvrtlfu2Hdh/j+o+/jpo170XiyG8faPSkPSmUbiXP85KVPo87TT176FFKKIXbMQny/sazQAXOKAxOR9SFyO5HZnAlRWMOJMiLpmSijxy9j2fOfRH0+lj3/CXr8qc1kTaTTG1BnPt+0cS9W7v4cLV0+dHoH5/sDIYRkSqIMXcXeudXqOEsyopaUKMvFfIHoZamLpo3D0u31WDmnSnOQrbelBLLM4faJOLsoL2qA7PEFk+AXOVbsOhC13VlVI/H0e4exck4VKkpdSS1JaWzphscvah6L1cSwadGUqNkF44udaO7wqgN1kbNqSvNt2HTbFJzs8uHr9h717vn+pk489lYjVs29EBNKXXDZQgG1W7qjBxPnTzkbzd951TvF6nnaFr+M5/bp43HLb/+CEpcN//QP58MflGAyAd95gn3GuWv3BPDEO6Hsu6X5tqhB05VzquJmHyx69mO8et8M/PK6C/FNpzfh8SvnprdzbqSegIx7YgYV79lej5eXTEWhc1CKQHKA0Xe8M22/2cbvF3GqJ6Bm4CvOs8Jmy8mmOGcYNesoMjOt1o28FbsO4OUlU3HTxr1Rj9dtr8fa+dXo8onIs5qw+Y5L4AtKKMyz4ESnDw9EzKj7j1suxgeNLVHL4j9obEHttPGaM+S+bu+JW3oZGxpE+ZsveOYLcZHTqvY3tI6lbls9Vs29EIuf++uZWbwCQ932T6Jm2vcEJHgDEi4eVwT89zHkWU0Jz/0pjz+qDzKvplxNCBJZ/lVzL0S+3dLrss9sXzoXlLSvv6KU4vWXIa5vtGZeNViKp6LIacWLd18Gv8ghsNBNH5uZqaFvCIkkMKhJ+JR6t3Z+NfT6CAZlWfPzEUxxeXoi3oCkuWz95SVTAeq/E0IGQc59k9BaJvPCjy6L6zhvu/NSzK4qjVsutu2uS6MGAJUZawUOi+ZyzYAo4URHT1TmuMifFz37MVbf8L2oAbLT3iAAxG23wGFRY99suK0mainu0pkTUOS0grHQcpPI+DSrb/ie5rFsuK0GlaX5eGXZDLWjysFRVuhQB+pWzqlCkdOK0QUOnBUORuu0m/CTFz+N6ti1uf0ozbdh9DC7Gjg7MtYeAJgElrADHpTONKjK0t4Slw0PX1cFb0DCwk17sXJOFVa91tDnwKksy+qxlrhseGzBpKhzqDXAGRRlcM7VGXr7mzqxe/8JbL7jEpgEBqfVBIlD80vOYN1ZlmSuufyWBkVIKpSZFrH12GTwF9R07Teb+P0iDp3yqIP3ZYUOPFNbg/OKnTSwF8HnE9HuPTPwWeSwwt6P7Jt6iRx8U+jRNkQOhGn1MZo7vJqD5SUuG/LtlqgvwE8uvAgBicfNqLv3hU+w9c5L1Xits6tKcd8VFVj12hdxgzfra2uw8o+fx5XBbErw2Y74aAsCQ+XIfOyomxZecht/LHlWk/rzil1nBguVQT/Fy0umoiCc9bK38B+xMQUTncM8q6nXAdhcSOpgSnD9TbX8MocaGkbpP235MPXYqJIko9Mrxl3rzsqXIQg0Wy9Tpeva6xNlPPpmY1S9e/TNRvx7P5aQa0nUP0l1BmoiMtfuv1P3nRAyWHJuXZ/WMpl/fb1BHSRTOn0nu3x46Orz4+4mHzvVo04BXzpzgjpjTelYAqHBvhfvvgy/nnsBbtq4F/e9sB+NJ0NLPiJ/Vjqrj799KGr5S2dPEJ09wbjtRv6szCSbXVWqLmN55PUv0XiyG19/14PWbp8ar89lN2seS922enR4gyjJt2FMYR5K8m0odtqwadEUdWBr1WsNcNrMaiBrQWBwWMxoc/vVQb+Xl0zFqrkXYlSBHR3eoHp+I8sLhAalegJS3BT62VWlaoMaOVC6dOYEdHiC6heTRB3y2M64FLPMjwFR5zDynN20cS9WvdaAU54ALGYBu+qb1PM6d/IYLH7ur/jZjs/wt1Me/PLVzzWXGQ/WnWWX3aS5/NZlpw4wSZ7NLKjL+AGosWNsZmMv9+nabzY51RPAU+8eUq+rK+dU4al3D+FUDy3RUfh8Ig63e9Tlpjdt3IvD7R74fGLayqQMvundNigDYa8sm4ExhY6Eyx5jH19x9cS4GWk/eelTMAbNNpQx4KUlU/GXn1+Bh6+9AMue/wRvN7SqbfzOpdPw0pKpGOYItf2xZTAJLG6J7Nr51XBYo9smQWA4a5g94RLOEU4rXl4yFRtuCyUD4ZxrPq8nIKEzfPNz/ftH4vatnHtTTIiR2D5J5PZ6G4A1ann1YLIkeI8sKQ5aWASGxTPGR/WfFs8Yn/J2Wt1+zZUHrTH1i2SOdF57zQJDm9uPum31uGnjXtRtq0eb26/boJvR/ROXLUH/3Ub9H0LI4Mi5qQFay2TebmjFqrkXqjPWlKDTAmNxz/28uRMbamtQt70eBQ4LVr9xEGvmVWPLh0fV/2+fPh6MMdwTjs8QueQz8melgxk7K+7sEXlw+0WsnV+NzXuOqnfLlc7r8p0HsL+pU707unDjXs1suS/86DLcP6sC972wH0/dPDmpAbHILxGJlplEzh6o21avdqILHFZ8e9qr7id2CevOfV9j/iVnR02hn11VivtnnYdf/WdoVkBAlNWBUuVOvLK93u7IR+Kcq7PxHriyEqvf+DLqHD6+YFJUFlllgPPV+2bgpz+sxBPvNEZlv418z9q6A3GzFwfrTr23l+W3NH2fJEsQgOF5Fjy3+FJ12ZPFzCAY3LdkTHu/qS7bymUsQTIROkdntHsDCa+DY9I0Wy+ZdnMg2y7Jt6HltFdz2aPFxOIeHzXcrtneW0yCZht6qMWNVa81YNOiKXDZzsym39/Uqc6Q++8HL9fc15p51fD4RTisJqyae6EaBH7kMDsKHPGDmoLAMHp4aKVAZCiNdbdejLVvHcTbDa3qgFOXL4inbp6sZr1UHi/Ot2HtmwcBAG1uP0rybfjDPdMRlOSoc++wmqL6G7vqm7Du1ovV2FnK9kYOs8MkACc6ejTfu1xI6mASGIpc1qj3qMhlTXmmNBOguR2WYvuRKByDSFOXMlY6r71Om4BnamviZnY6dRoU8wVlbP/ouLo6R8l+e/+sCl22T+FzCCHplnODeomWyQiCgJJ8G0RRxv2zzsPS7fVRy2IBYEFNGX4wsRRPhmdSlA6zqTPWls6cgGF2M1bOuQCrXvsCK64+P27JZ+zPkYNeyqy4TYumoNhlQ7HLhoI8Cx6+9gIIAsOOumngnMNhNeEPy6ary3jd4Xh5q2/4XtxMvH99vQE//4dQOVq7/UkvD1K+RCQS+QVGyfhrYsDJLp+6XWUpcWSsPYcllP12uN2CnUunqZ23heF4QG3dAay9sRrLf38Aa+ZVoycgRW1POV9bPjyKeTXlKHJaUZpvQ2F48C/2PY6M26MMxikzG7Q6k96AhMqR+Xjk+mr0BETN9yzyS86eFZcP6tIb6gQTPfiCMv7UcBJXVI0C5xxmxvBew7eYfeEoQ/frF2Uc+Po7TB5bBEnmsAoM+4+3o2ZckaH7zSY8QTKRl5dMTXPJMkemXgf7ajf7S5Y5OsPL3Vw2sxof75vTPmz58CgevvYCbPnwKDbfcQlOe4No9wQSLrU0C0y9KRk5KPfYW41o7vDiiXca8fC1F2Dn0mlo9wTU7LplhQ7YLAIYmObSy+VXTlRnxfUEJJSPyNO84RUZl25EngWP3TgJDEBJvg2r3/gSbze0AjgTb+r5H10GxoCnb56MgrzQAJTFxFBgC/WNfnFNFcwCQ6nLBoslvi9T4LBi5DB71ABUYZ4Ff7hnOnyiDBMD7FYBp3tEXPf0noRLa41aXj2YApIMmXOUj8hTb6qIsoSglFrMsKDIsWtfE+ZPOVsd/Ni572ssmj4+pe0YvdyR6C+d115/kOOsYaGZvMrSX6uZwR/UZ99Ws4AP/9aOHfXN6mNlhQ48cGWlLtvP1HaLEDJ05NygXuQss8gOnLJMpsMbVJetKMtilUGkqlHDcPOm0ADU2w2tmFxeoN4FVmasbb/rMsyrKVdnm00/pwijChxq5yVytpnWoFfkHeIRTlvCGVhKjJeTp32YXVUalY1W0dYdgM0kYHZVKYbZzXjm1ovV2YNay4NSCQQtCKGAxo0t3XjincaoGHaRd8bb3H6cNdyOsoLQlPZ2T0CNXVe3rT4q7t7+pk4cafOgze3H7v0ncO8V5yIoyVGzEz9obMH9s87Db949hHk15Wp5Rg93wByeJq+8x0qCEGXb698/ggeurERrl/YAp8NqCmfMk6M6nMnOEDQadYKJHhiAi8aOwJFWt/pF96KxI2B0LXJYBYwrGaYO4it32h2UvVkl8QTJRFLMUJnLhtJ1UJY5jrV70NLli4qPt2ZeNXbVN+GnP6xEqcuG+2edh9PeIB55/UssnTkB5vBSy9ig8gBQ7LJiR900iJKML09247G3GrG/qVNNUHVTeOb//bMq8NiCSWjr9qPUFQrNAQA//WFlVP9pfW0N1r51EG3dAdw/qwLji50wm+LfC624dMqA4kNXT1QH9BShm3F+2MwCvEEJv3r5U+xv6sTsqlL85O/P6zNhFhDqp4wrciLfbknYr2nr9sfN3I+N1dtXvzEbMDC0nvZiQukwiDKHTWA40urGcHtqxyAw4PuVI9WVDMr7mOpM71KXTXPmValL/4Fxoo90XnvtFobTPgmBcGKVoMQhcYbhOoWfMZugWR/16uYPpXaLEJKZcm5QTxAYKkpc+MM90xGQZEgyh8NiUge0ImdoKckS7r28Qg0mHfuFCwjFopF5aHq4P8hR5LTikde/xG9vr0FA5PjXcMDpLR8ejRtcixz0UjqayQyuKTFeSlw2PL5gEo5HZKMFQnH9HryqEls+PIr7rqjAsuc/wfRzivDc4kthMTFYTAJKXbaofcZ2uLfeeSlcdnNccg+lTEpmucgMvc0dXjz6ZvxApSxzNLZ2o25bfVTCC2XArMRlw9KZEzB6uB3P3jEF7e4Abo3IfvvKsukIiDI4gF//5xdRS9RmV5XiF9dUwW4WIPHQ8tuRw2zwBy1R50SZuVfiskUtIZpdVYpfXXcBvu304cl3D+H26ePV5dTKkt3/uGUyAiJHscsKiYfib8TOEDRavkPA5sWXoPk7rzoYUzbCgXwHDYqQ5AmMxcU/sggMgsFrPHv8MuqPnsILd09VA0W/1/AtRuSNQmGeobvOGmZBe3mk2ei10VmkyGHV/PJVpLHUM9u1ewI43t4TlzE+cvZmp09EZakL3/UE8OBVlVi+8wCeuvki5MUsh82zmtD0nRf/3//5EvfPqsC44jwMj2jDls6cgC0fHsXqG76HUQWhLLcP7PgMbW4/1tfWoKPHD0kGhjvMeHnJVHCEbhAwBqyaeyFOhW/URQ56nVvsRJsngKAUulH2xDuNccfx1M0Xochl15wd2O4JYNVrDVg190I8cGUldu8/gbu/fw5Oe4NYOadKfa5WwixFXzMok1laa+Ty6sHitDGMKnTiUIs7ov/ghNOWeqKMDxpbopYp7tz3NcYVpTZTz2IxobLEGTXzqsRp1ZxxSTJDOq+9ogz4gxKaO3xn6m+hHaJVn/riC8h47dPmuHp9+/TxuoS3ybNqLx/Oo5uahJBBknODerLM8XVHT9SdbyWu29Lt9eoSTWWQaeJZ+ergkjkiHo0Sry12G7959xAeuvp8tLn9sJhM+NGW0B3gAodVHRwscdmwau6FGF/sRJ7NhGJn74NrWnehlY5oc4cXp71B/Obdw1EDVffPqsDynQewck4Vlj0f2ufcyWPw6JtfqktXRUlWZ7jFBoIucdnQ0uXDomdDg2APXlWJzXtCMxbLCh3wixKk8HRyrQQWASmUTVY5pm9OscinOQAAIABJREFUe9UOf+wS5KdvmRyV7v2lJVPVn5s7vPj9vibUThuLp987jBVXn495NeXqcU4uL8Bdf3cOHnm9IW6g78GrJkbNVihyWtVtKjEMRw+3Q2AMB0+6sXL351EDlJFLdm1mAT0BP24L39FPR/Y7XwDo8gbVL3hlhaGMhsPtFoQTExOSlC6fGFWP1s6vRrHBMyTsVgE144txy6bomXp26tSqSl02rK+tUWeLKzOhaPbKGe3egOaXr0XTxw9KTL1UZrT39lqLWYBZYPAGorcT+RwAKMjTThD17Wkf5q//SG2LSodZsXlPaGmsWRCQ7zDDabOoSy0lWcLze4/Fxd5dO78aj77ZiHFFeZrxHB97qxFLt9dj1dwLYbcIePTNRrS5/XimtgZPvXsIbze0YvMdl8QNPD7xTqPar1Jm/q24+nwsmjYOj799CPubOlHissFiMkVdE5QboPddUYHtHx1Hc0foJtbv/u/fcO/lFXEzxJSZhv2Nb5fs0lplcFB5f7497c2qwT1vgONUtz/uuj/MZsYwR9+vV1jMDNdMGhP1Pqy79WJYzCkODsocR9p7sjqjcG/8fhGnes5kiS3Os2Z9BnO73YyKouiB2MHKfitKHKfcgfj6a9fp5nqCGah6LWHIt1lR7JLxYsRNTbMp9DgZPANpvwnJdjn3bUu5860M9ADAvJpy9UuUMsj04FWV2FXfBFHmahKK5z86qmZHiozXFrmNtxtawzO7LgbDmSQPs6pG4t4XPlGX3S5+7q+o/d1fwMCiLijJZllTOqIA0Nrtj8tGWz4iL2oATbkLf/v0UNay+es/wi2//QsaW7shyzzubvXSmRPUc7R05gRs3hN67a76JnT2BLFw417IcigzXWzmXyWz7A/Wvo/r1+3BN6dDMf2U7Uc+f39TJ9w+Mer9iDxvAHD398/B0+8dxrLLzwUD1ME5APjZ7PPws99/FjXQp7wfd2z+Kx5988w5KcizRu23bls9TrkDqNtejzyrKW7AUXlOc4cXTd95o8qY6H0xUkCS8ZOXPo0qw09e+hSBFGPikKEtKPO4urx85wEEDY7t4ksQKNoXoPqrMJsFTByZjx110/DB8pnYUTcNE0fmq6EFSCg20Yb/PoYfPvEBrnj8z/jhEx9gw38fG5TYRMpNt+vX7cGMNX/C9ev2oLEl1Iam+tob1n2IxpPduO+F/ep2RFGOes5NG/diuMOimbFVaXuUtsgfkNX2/V92f4G2Lj/u2Pwxrnj8z7hj88fo7BFx3UWj42I2Lt95AP920yQ4bWbNeI5LZ05QB9aW7zzz+z3b69UQGEr7GUnpEyn9p5W7P8esx/+Mh/7wP3jgykpMLi/A/bMq4rL0rth1AMuvnIin3zuMWVUj1T7GvJpytQ8VW76yQgdYP2cap5K5eCDvf7rpdd0PilxNNKJsZ9nznyAopradXMgonIjfL+LQqegssYdOeeD3py9Dt17sdjPGFOZhbJETYwrzBmVADzC+35Ionq1ekS9EUYbHL+GrVjdOnvbhq1Y3PH4Jokj9n8GSzddvQvSQ3beVNAREKa4DGjvTbJjdgvte2I+Vc6rwdXtP6A5z+GLf0SNi8x2XwGIWEm5jR30zDre68eTNk9U7wFqz2WKXeCjlS+Z5kTFeIrPiKrH9XvjRZVEDbgUOS9zAV3NHaPbcK8tmxN2tjk3uobw2cibbyS5fXIbe2MHO5o7QgF67J6DOgFSWID/13mEsnjEeo2PiAcbGsDMJDPNqytHhCeKNA9/g5svGqX8/K5zlL/b8Kr8rxwiEBhxjg4SPL3aiuePMMmCt+HlFTqs6K7Gv98VIicogUYNEUpCuekSBopPDOQ/9i/iZnJHO2ESJBiKURFa93fnXeq0ym75uW726ndjnrH7jy7iMrcoMNYXyOVKSWJxX6lJnlSt//9nvP8P2uy7T/AzKHAhKsubfCsKDip3eIJo7vCjNt2HDbTUocFhQmm/D5PKChO1mc4c3qs+gbHPFrgNYNfdCjCvO09znd54A3m5oxZLvT1CP9RfXnK/53CKnFWvmVUMjjF9SUllam+j9T7T0N5Mkuu6n+oVWr+t4LmQUTuRUTy9ZYrN8tl666FV/U92+Xv2idm8Aj755EPNqypEHEwKSjEffPIiHr70gbVnbh5psvn4DwLiHXu/3a4+tvkbHkpBslXNXGqtZicVwpgMaGdftgSsr0e0T1c7s6jcO4rEFZ5I57Khvxo76Zmy4rSbhNpbOnIACRygwsxJDISjJSS3xiB1cU+5mS5yjrduvdjZjO6KRWXGtZhMKHRZsWjQFT7zTqGaSjZzhplA6UaOGO6ICQSvnqMRlwwhnKOtc7ODZo2824uHrqnDzpWMxzG5W4/U1d3jVmH5nDbPDbBKw8YPoZbbTzynCA1dWot0dwN/aPFHH/G5DS9SXGJmHviDYzAKuOP8svH/wpLpETcm4F/ulIvZ35TyOcFmivoBxhGYbKvt8/bMT+I9bLlZnBMyuKsVZw+043OJOe7IMS4Ivs7Hx0QjpTbrqEQWK7lswKOHbbl9UMPBvu30YlW+nWFNhdot2bCK7xfjZjAFRQonLhpVzqlCab4PLZoYvKMEvSnjmT0dw9fdGRYXVAKAmXwrKHI/fOAmd3qAaC05pU4FQWxyUZHX7SnbZdxtaUOS04vkfXQYTYwDj+PV/NmB/U6daLuVzpCyfjUxApWju8KrtXexnsK07lDxK6289AUkdWCsrdGC4w4Ifv7g/aoBx9/4T6g2+eTXlOGuYHcUuK16//+9gi7kBqpRlQqkr4bVIvRmZZ8Xy34fi+pXm2zSfO9xhwdq3DuKR66s137Nkllslm7k4mweiLKYE198UR0P1uo5bzSbMrirFvJpyta7vqm9Ke0ZhPZbN0g0s/VlMCeLNmvS57ifavkWn7TMGLLv8XHR4ggAAq0nAssvPBXV/ohm5PDabr9+E6CHnBvWKnFaMLcpTZ7aVuGwYkWfF+toatHX7seXDo3jo6vPVjmWb249vO73qAJcyUGUysahZX7vqm/Dc4kvQ1u3H5j1HsXjGeHgDEuqPteP3S6fB4xfjstFpLfGInIGn7C/2NUrMEa2OqHJBbPeEBgAfvvYCCAKDiTF4g5Jmo2U1m9RBwlfvmwFvQIIgQD2etW8djDonyjb2N3XiV6824J/+4XwUOq2QOYdZCGXbXXb5ufAGJNz27MfqcfiCsnos1100Gic6fFi5+3NMP6dIHcQrcdlw/cVj8PpnJ9SYSQV5ZlhMDDLneOq9w7h9+nj85t1DWDmnChZzKMvf+wdb1MG4yPdUWf6jfR5Dy2223nkpun1B2MKxYp5+7zBWzqnChBIn/EEZv/7PL7Ds8nOTev+MlO8QNONtUaIMkop8h/agiNH1qCDBfguo/qq6AkHNYOBdgSCKaFAPQGiZ1HBH6CaSEi/Oama6LZPqjd0ixLUla+ZV46n3DuO+Kyrw9HuH8XZDK2ZXleKfr6mCDODkaR/sFgH3vRA9EPbYW6HYdJ3e0Je8skIHHBZT1PZnV5XivisqcFNExuj1tTV48KqJaPi2O+oxk8DUGXGJMrafPO2L6vsoCTM4B/7W2hX3+VxfWwOZczy8+wu0uf1Yd+vFWP3Gl5qz7sYUOuKy0q6ZVw1TL4NAFhOLa9OeufVi+IIynlt8CYpdFjx9y+SoG5Wx2XOV/gkHx4mOnrj4hLExijfU1mBUgR0FjtS/LCYbfy8TORME6nemGNN0WILr+LAUr+MFdjN+POu8+PYgjbOWlGWzsWU6r9iZ0sAe3cDSX749/lqxvrYG+XZ9zmmh3aJZrwt1itlnYqH4qbExAfVOUCaKMlrdfgQlWU2ImC3hO5KNKd9f2Xz9JkQPLJuX/kyZMoXv27cv7nFZ5ujyBSBKHC1dfjz57iF1GejBk9345Fi7OrijZEJV4rn1RCR0ULKmCowhKHMwAL8KZ2YNiDJe/Pg4bp8+HiaB4YHffxY1i68nIGHy2cMRlKDekSh0WNDhDUKWZUihtVdYsHEvpp9ThLu/fw5MAoPMgZJ8CwIi4u5kKBfEJ95pjAt4rWSiO9TmjstQV1HiUverZLArcdnwbzdNwm2/Cy3hWVBThlunjsX/OXAC10wao86ii0wyovz+L9degMMt7qjA2ZPLC/D4gkm44vE/Y3J5AR5bMAmnuv1Y/cZBPDo/1DG/9/JzUeyyqedQKf/mOy7B3iNtuGXqOBw82a1mzlW2u3rehfCLHE+Hl/MWu6xo7vCh2GWFw2qG3SJgYfhL0eTyAjUGjzJDQhAYDn7bDQBqmX/29xX4x4vLcPOmM6978KpKnDXcDrtZgEkQEJRkSJzDbolOdhJhQK1QbP1tOe1FpzcAk2CKCn5e4LBi5PAUIl2TIe1ERw/e+eJbXFE1KioL7Q8vGIUx0Wloda2/Jzp68MnxdkweWwRJ5jAJDPuPt+PisUWx+x2yWk578bdTnqhBo7Xzq3FOsZM+42HfdHrxy1c/j5vh88vrLsTogqhzpGv9Vfa9YMNHcV8KVs6pwq76Jiy/ciLcfhGFeVa0dfshc46egIQRTgt++eqZ2XVlhQ5su+tSmASG014Rp7r9GFuUB5fNjBue+VDd/obbaqLaO+W1Ly25DN6ADJPAwAGIkgRfkGPuf+wBcCa2bWQf4OlbJsPtE+G0mTEy34b/196Zx1lRXYn/e+rtvdDdNA2CoCwiCASUNoo4UdS4RQ0xIsZdJxE1OpkxRuOM40oyoyGOiZlxnYlrEtfJaEyM+tMYExUXUFRwFwwoSgPd0Mtbq+7vj1uv+nX3e9CveVs39/v59Kffq1dV99xbp27dOvfcc7qSNmfd+WoPY1coYKEUdMRTbGiP88iytVx13HRspfCJIAJzr/tTn7Z6/pJ5BPwWJ97at22WLNAedJk6fetpzdzkjruqgj6GRQIoBZ9s6uKmZz6gpSPOjQtnMWlkDcOru5NTpMdGSilEhKBPj4la2uM9wmrcdnozI6p1pvps1yttEB7fWJ3Xy2Iq5fDuF+19DAv5xL3MwxOloPr7WWsXT2Xp94+YPpoxefS/n7Z2cc3vVva5/646bnpe/fj6tignZrk2D517AKPry9PXFapuhTIODnIKq79tXcQSSUKBgOdFGU8mCQcDjKnf8fHDZ61dXJ3l2l993PS87o9crGvt4tos57/yuOmMLdD4pxD9UzlpaY9z/M0v9OkTCrU8Ng+jYcHHDpnsyDLagWKW3+5U5NTfIfv0aY+lUMDPn3nfc4l2HEVjdZDZ4xv5/YpPudo12F07f4bbGfQ0VLW0J9jQHvcMfb84eR8v9twNJ87ihOZx3P3iav7la9NY1xr14smNrA3h8wmftsb4+TPvexllt0QTrN2sA1LXhPxUhXzMndjIaQfszpIn3/X2W7fZ9gawacNi2qv/nHte82LYNNWEWLJgJrsMC2Mr2NiZYMrInrFjGiIBPmjp4Jx7XuO6b36Je15aw5IFMxnTEAGFZ9A6fvaujBoW5MQv70ZnPMXi+TMYUROkoTroGcwAvjp1JEnb6RO3cPLIGix3qex58ybxt01djK7THnRboknqI0EiQR1n4owDxveIwVMV9HHbX9Zw8v7jvSXEaSPbuIYIlmXxbXdwePaBE/pkyLr77/fzjvnBkVN4/r0v2GXYrlzzu5WcfeAEdqkLU18VIJFyPAPmCfuOJZp0ehgCLRE+3xKjqTZES3tXTg/KYqGAWNJhc2fc8+IZXh1AmXd9Qx4EfELz+MaeWWgHkL0w73L9wvgRtV5/UapyBxO5goHfv2hOmSWrHASVdRmTNm8Vl1xx58bUhTlz7oQemRPTWWXTBqqffWtvNncmqAn5AUXAZ7GpI8HnW2M8smwtFx0+haC/5/mzxeJtqgnR1pXq8eKWNpxletH/9Mn3uOvs/WjrSpC0HeJJh8v+960exzTVhFjX6saevW8ZVxw7jcWPr+L6E2Z6S4QXHTSJcMDHqGFB4inFsxcfjO0o7nj+Yx5cto6xDRG+2BpndH3PZcO3PvcRALsMC9MWTfLAojmIaE/Lp1eu55IjpxL0W3zc0kldJMjpv3y5R10venAF9y+ag+PE6EzYtLixedNtNbmphg9adND53pl3z71X16WxOthnOfOtz31EW1eCcMBHS3uMRndCrj+GttZo0lslkD7fTc+8z4+Pn9mvl85ie6JsC3+Ofj/f5bcpR/HUqg08tWpDj+3/esy0vM6TyHEvDTTxVyKRoqWze9lsU3WQYDC/VxhLyJoBOt9LEwr52XNEzyyxQyH7bTmpCVts6oQz7szQ39OaGTGsMMaqZA69vjxPvc6FL4duDTQWaDY2dMT7JB46775lPHjuAb0nvAZMMT0B4xnhLTL763iBlsfmEz/VYBiKDMknUFtUZ9dK2spbJnvFo2/TVBPihoWzqA372b0xwsaOhOetF/JbnqFqYfNYLjh0D3yWeB5pcyc20lClEyqk49CFAz7OnzcJnyXektRkyuH0X77izeynO/lfnLw3XfGUd64LDt2Dj1s6WXTwJH7yx3c8j8HLjt6LM9wlrf91yj40VAdp6Ujw+xWf8q39d6epJsSkpmqaakJc9fVp3hLYvp554DgOX7THuPHp9/ifM5upCQU4c+4EGmuCxJM2QZ+vx1Lad9Z3AHhtde386cRTjudJWFflJ5HqjiWSfsFY2DyW7x6yB//2h1XccOIsfJbw0ocb2XPUWL5zjx58Lzp4Eq2dCcbUhxntJs5IG9N2qQsztiHC+i1RRg0LezIJsCWaJBz0e/uPbajq4V2njXFwxLSRXHb0XjzwyiecvP94/u0Pq1h00CTaYyk2tidorAnyyaYuxjZEOOegicRTDn/b1MW5XxnPN5vH8VmbXg43vDrI+72Mu+taSxdstffyDbOcw5AvSVtxfq/shef/ajkPFNlwlEyVp9zBhO0oTmoey/zZYz1vmkeXrzPZ2TKwRPpMQ4q7vdjkWlYXDvj66HZmEoyLHlzB4vkzOPuuV70ltWfftbTHy92NT7/HVcdN32ZsWCBrxthLHn6T6775JS9h1brWKC0dcTrjSeoifsIBv/dczDzmzrO+zObOhPfylDYippNiLX58FQA3PfN+D4/89Av1RUdM1l63ImzsTHpehWMbItz0rX3wWfQYf9xy6myGVwc5eOoo1mzUXnlNtUEuP2Za1liFfkv4YmtPL7zrT5jJyx+1MCzsJ+S32GNkjWecTLOuVccqTNpOn+XSSxbMJGk7/NMDb7B4/gxG1iaorwrw/hcdnpdgLkNbImVnffG/6rj+vXSWM1B7ofp9vyVcfezUPh5/vjzHIoVcoppIpHivpa9n3JSm6rwMe06ODKgDeUaFQn6TFKOAtEednMlHhoV3/Pzp97TennT56nUuHIWXyCh9/rtfXM1Vx00vyPmBrDFZb33uI1IDNJT3ptiegAFLsvbXhYz33N/4qQbDUKTy/XUHQDRhk3J04old6sI9PCNEFNUhP37Lx3d/tdzzvNvUmaArYXPuV8bznYMm8FlblM+3aEPP3ImNntFq1DDtfbbkyXdprAnSVBtmayzJZUfvRSzpcNGDK7wBZ9ojrakmRFNtmIse7F6i2xlPcdMzH+D36cyvz7/3BRccMlkvv3UNdiNqQqRsvez0W/vtzsb2BJceNYW1m6N877DJtHYmvbrtM66eK46dhqP0TPHlv32TNZu6aIsm+f4Re1IbDngDmqqgn09bY7S0x7ns6L1o7Uxy5wur2a2xyjNsXnzEnmzsSBBL2J4nYTypMzWOGtYdtzBtgNvYEaelPUEoYNFUG+LomWOIu55xtz73EX6fMKImSMLWxrQjpo3kB0dO4ZFla2ntSnhBuBO2zeXHTKO1M0k06bChPeHtf9nRU71zLmwey9Vfn87ix1dxy58+4gdHTiHgE07ab3e6EikWHTSJf3rgDeqrAsRTNtc98Q4N1QGWLNAxgGxH8cRb6znzwAls7kzwm1c+wVaKpKOor+pfJuNCI8DWmDb8nnT7Uq549G22xlI75idu2OkoVxBvEzx8+wwL+5i31yhOuWMp85Y8p//vNYrasIn5kkahw2Bk9oNdCbsEfnpQE/Zx62k6SRbgGZm6EtkDcGcmwagK6mt4QvM4L3xF+rcfPvImJzSPwxK45dTZ3vmXr9nELb3Ky5Uxtjrk56dPvscVx07j2YsPZvH8GTz/XguxpMMXW2NZj9kSTXLS7UtZ/PgqLj1qCo4bbmVdq84qu2SB9kA7oXlcH0Pi+fctozPu8MmmKCmHPi/c37v/dc/bHfS4pSOe4qTbl3LIT//MFY++zWVHT+W7h+zBhq1xLj1qCosfX8XxN7/I2Xe9ytZYirWbo55BL7OtDtlrF066fSmH3vBnTr5jKZceNYV9xtV7dUvH/7VzeL6mM11WBX2ce98y3v5sK1c8+jY/OHIKTTUhzrnnNTZ1Jvpc/3RMpkzyiclUzkDthep/a8IWzRNG9OijmieMoCac3+tCwGexZMHMHrq9ZMHMASUmaOnMnm22Jcs13Ba52qjYmeEN26fY44dIwOLCQyez+PFVXp944aGTiRQoAVPaCzTz/GfOnVDQRBnpmKyZZVx61BTCBYrHu6Ej7nkqP7BoDlccO42bnnmfDR3xgpw/laO/NmNEg6EwDEmjnq0UtqMNUJZ0Z2u9dv50lBI2dSSwpHtQnjY8ja4PcfrcCXzaGuOSh98klrSxRDhv3iQ2dsR5atUGvtga55KH3+SpVRvwSXea9FjSdmO96c4q6BfPI+28eZNI2cr7HE3a1IYDtHTEQelsdSfttzsX/Ho5PsEz2DkKNncmOOOA8WzqTCCi49Y88dZ6nYHPNcCll52mZ9HPvW8ZZxwwnosfWkHQZxH0+YinHDZ3JmiqCWE7iqqgD0cptkSTjK4LcebcCSilX6jGNkS8JavhYLfxM+U4KASfZZFIOfzkj/oFw1Y6hsz3DpvMhb9+HdBLQWxHeQM6v4gbKwdueuYD/vnovbwXnQt//To/+eN7nNA8jmRKd+5VQR8jaoJUBX3c9MwH/MvXpnHxQyto60pwxLSRnD9vkpfB9rBpo/i0NUY8pby2HeHO7NeE/IQDPp5atYFrHltFVdBHyG+xsSPB0V8aTTylk3uc0DyO1s4kSilqw4GsA3spsqdIrqV5SfPAM+RB2kMik1IE8S5XuYOJrkR2b4SuRGFm2ocC5Rz4xxIO1SGLu87ejz9dfDC/OWcOE5uqGF4dzKrbmUkw0p+zLald1xpll2Fh3v28gysfXcni+TN45uKDOWXOeH7R6yXqi63xrGWNqAlx2dFTCfosQgGLJ95az/HNYzn/V8vZ1JnIekzacJVp7Er/VhcJ8JM/vofthiXJJrMleiLQUdlfuEfUdCeSOm/epD7X7eKHVtDamSSesvv89sNH3uwxZso8b0t7vM/1/95hkz3Z08uHA77smXfTmS7boske47wfPvIm582blNPQlk5klmmIyidh1o4aBXeEQvW/HbHsfVRHLL8+Kpq0+e1ynRDt2YsP5s6zvsxvl39KLJm/gbNQxrhcbVQoby3DwCn2+CGWdPpMtnz3V8uJJQvz7M3lBVrox9adL6zu8by484XVBTy7ymqYLFToC2NUNxiKy5A06gV82mhT586ij22IeJ5n7bEUn2+N4Si9ZDNzsB70+XrEi2uoDtJUG2JzZ4JY0uaIaSP1tpoQj14wl4StjYaPLFtLbTiA39IDyYXNY2msCdHSrgfnI2tDiGg5xtTrDHhdiRTXnzCTrbEUw8IBNncmWNcapStpex5zjlLEkja71EVI2g4N1UHmTmzkwsP2QATPs/DnJ+/jeQTuOaqGda1RdnWXPXTEU1iig023x5J877DJ+C2hK2HTlbDxWUJ1KMAPH3mTaCLFuIYI/3XKPoT8PmrDAYRu46fPsuhKpBB0ptqm2iAg/G1TF8vXbGL8iCqaakIopY2dL3ywgTvP+jI//uYM4rZN0C/4LNHHuUbVMfXdWYf3HFlDJOhDKYUC/JaFAppqg4hoT4Bd6kJccex0Nrnttc+4evYYWUNV0Ed7LEnQZ7E1lsTv0wOEWNLucY1jSYd7XlzN8OoAuzdW4bhxBXdr0G3++ZYYIb9w/Qk9Z5gLHRsjG7Z54BkKQMAn3JzhDTS2IcLNp84mUGQFLle5g4mUG77httObeWDRHG47vZmmmpCZqc4gVz9YiiXKYgmfb4lz1p2vcIjrJbZmYxdBX99nwpIF2rCU+XmfcfUMrw7y8HkHcNvpzZ532diGCPVVAZ54az2vr23j7Lte5fon3sFnCd/+u4kAXPfEu5x77zKuf+LdPt6Ct5zWzOLHV3LS7Uv5ZGM7IZ/FOQdPJJbs9obvLd8tp85mWNjvyZBp7Lr+hJlc+vCbtHTECfktRteFefbig3n6ooNY2DzWO4ffZ9FUE/Iy3GaSXpb89EUH8dvvzmVPd5lsJutao4wbHqE65M/+bFMq63l7e9Gta40ysamaZy8+mPu+vT93v7ia19e2udmj+x7flbA9w1+mwTU9lsllaMuMyfTCDw/ht989MK94eDtqFNwRgn6rhxdoWgeCeS6bK5THVMhvceqc3XoYaU+ds1ve8kDhjHHhgJX1GRUukLeWYeAEfJJVfws1fij2+LoU43elshvdCpXwUhXZMBkJZJ/0KJSnocGwszMkA0JYwKhhQSIBP0nb4YYTZ9FUG2JLNElV0Mcjy9Zy+TF7cclRUxHgV+fsz9Zois2d2hCYNpZVBX20x1P4LNi1IcwlR00l7Le47oQZ3nLWzZ0JLj5iTyIBi5SjvDh47bEU4YDF/5zZTH1ELzu999tfJuDzuR2wxd0vruaHR+9FWzTJps4EVx+rZ+F9oudFfBbUunFlRITNHbqszoTNA698wpkHjmdEbYgNW2Pekt2AZXHuV8Z7g3fbDeLr2IqQ38euDUGUUjRUB6iL+PFZ2hi2rjWqM+y63o2gCPkF0IOppO0AikiRhLjuAAAgAElEQVRAGxs/3xLzvPSeeGs9Fxw6iaDf4tKjppByFH99/wuaJ4xAKUUi6bDonmVcdexezBhbz2VH78WajTqeXWN1gKu/Po1EyuHfn3hHZyluiLBLXYjakA+xwlx+zF74RPjRN2ZgO2A7DrGk7cZ92cXzMIynHCyx6UqkaKoNcfOps+lM2Cxbs4lbT2smZTtc+JvXXQ++ACfsO46U7XD1sVOpjfj5fGuMpR+1cOoB47PGxvjx8TOLqrchv5U1Bk1oEGS1MlQOSVvx+xXaQyK91Pzh1/7GGXMnFLXclEPWcs88cGJRyx1MhP0W//K1qV6YhrENEW5cOIuwucc9gr7s/eBAlu3lS8L13O7tJXb/ojk8/94X3HnWl/FbQsBnEQxY/OxbewOwNZqkqTbYJ5nG9SfM5O4XV3Pm3An8+PerOKF5HA8uW8c+4+o5c+4EvnX7UppqQnzvsMn8dOEstkYTxFOK4dUBfnPOHJTSWaRjSZtLjpzKFcdOQ0RPyvlErzoY2xDxEmdc980vMaY+wieburjy0ZW0dMS55dTZdMRT3PnCasbUR1g8fwY/fVIn+Lj1tGaibpbctBznHzKJU/bfDQR+9PhKLj1qCgFLWLJgZo9YSDcunMXGjjgX/vp1b1s6eUhmFuC1m6Ps3liV9Zp+viXW57w3nzqb/3z2gx7XJe2xk3IUCsWVx03n2vnTWbs5xg0nzuLih7rvp1tPa2ZYxM+WLj2J2VAd4JrHVnnn6UrY3HZaMz5LJ7ZIG+x6Z60dXRfJO8B6OQO1WwLDIn4v86+jIOAj7+V/hYqFZ9G9jD5TPwZyF1sWPeJJegku8jxZbdBPfVWgRxsF/UJtngk3DMVAqAn5elwbnyh2MFGphz/Hc8VfoOeKL8d9U0gv0JSjChYTMhvKPWcmvb/vCCNqQtxx+r6cc29GIqHT92VEjYmBZzAUAimUhb8c5Eor/bfNnSRtbcxavyVGIplkj5HDiKUcokkbx1GeMWxETQhHwcl3LOWOM/blwy+2sP+kESRTirWtUUbXhXGUwnagtTPB2OFh4kk9EOyKa4+wcMCPbkY9AE/aikTKYcmT77J4/gxSjuLples56ktjUCgcB6753Uq+f8SeVAX9fNamZZw4chjX/m4lVx43DVuBT4Rrf7eSa+fPoDNh09qZYJe6MJ9viQHQVBvix79fpT3XOuKEAxY1YT+CYCvF+rYYuzVG8LvLZbX/m5C0He5/+RNOmTOetq4k9VUB/vTO5xw2TS9HXbu5C584jG8axsatXTQOiyBoea753Uo9oHb/r2uNEvZb1IQDdMSSXPib17n5lL2prw5zyh1L+dV39kcBF93/Bj8/eW9aO5NYlvDcO1/wzeaxrHUfGL/868csOmgSkYCPDe1xGqoC1FcF+XxrjF2GhbEsemSnTXtUJm3FJxvbmTqmjq3RFD438HbYb3Hzcx/yo+O/xBdbYrR2xth9RC3/dP8b/OKUvXEUVAV8OOjB5+auBJGAD8utYzr7YjoL7bjhESaOqClqWvSW9hiftUXZnFHu8OqA9masLUCkYMNOwebOGJ+1xfsEOx5TH2J4dQ89Kqj+xmIpPtzU2afcPRqrCYfNSxPA+rYoJ7qZvNOMbYjw0LkHMLpA2esGO1uiMVrak16m+HT/21QboC5SPP0F+NumTg5a8lyffV+67BBaOhLe8q1Mg93ZB05g14YIPhFOysgUD/ra3nnWl7n04TcB+I+Fs9jQHmd4dZAlT77LGQeMZ3R9hJb2OJGAhSXC+b9aTlNNiB8ePZVRw0I9Ek5ceOjkHjLccOIs6qr8rG/TGdMba0KcdecrfWRIZ7OPBH34LYtwwMJxFLZSxFPaUBYJ+ABFLOmwsSPBiJogLe1x7nxhNdfOn8EXW2Ns7kwybrg21IUDVg8DaGZZZ9/1qtdGj77+Kd9sHtujLi0dca/9vnvIHnTEUoQDPuoiAe54/mOOn71rD0Pfrac1EwpYnH3nqz2Mf/e99AkfbOjQCbeGhWmqDdGVSHFWxn43LpzFv/3hXc+IOSzi5z+f+ZAXP97kJcwA+mStve30ZqaM7A4Q39voVwBjXUH19/MtUa589O0+iQCunT+DXer637e0dsZY1xbvk5RibH2Ihur+j0PWtXZ5mdDTjG2IcP+iOYxtqOr3eUD3m1c91rdu13x9Rt79ZjyeYmNXwmSt3XEqUn9zsbkzxqdZ9HrXvuOiAbFha4xPNnX2mbDbvbGakYXI9EHu59Pzl8xjt8bqHT5/S3uc429+oc89W8hEP0XoRwdCwccOmYy/7Pc7cvoBsea6Y0pepqFs5NTfIfckSyT0clNLFJbA2IYwPquKgCWuAU57wiVs5S3vdJTie4dMoq7KT/OERnyWkBKdEKIm7KMjZoOlGNsQBoTaiIVtK2JJoTYUIJZy8IleuuOo7ix9iw6aRNJRpGzFQVNG4bOERErH+2tpTzAsHKA9lmTCiAhCFSlH8f0j9sRnCWs3dtFQHWTRQZPYEk1SE/LTWKOz7w6L+KmLBLAdxZXHTcOyYJe6EOmcgbGUQzxls3uj9m5wlDZCpmzw+8ASi1MPGE8y5TC6PgQIh08fzcaOBEGfxcSmKkQsVvxtE/tOGIHjKDoTKUJ+HZvu+4fvyT9/bSqOo2iPJhgzepg2+lUFuOrYvYiEAjhKMXeibksBfvSNGfhEGF4dwO8TTp4zjs64Q104QChgseigSTy9cj2nzBlPVdAi6PeRchRj6sOgtNu5rbSLfiRg4bcEEYgELfbcpY6UrXjo1b9xzsETsSTM1liKr04dSTLlsGzNJo780hgAfnHK3gT9evlzLOmg0DPZL37QwnF770pn3KalPUE86fSYYb7t9Oai627Kdoj1KveGE2cVLLOVYecgkVIIisXzZ3hGEUGRSBV3Aicc9rNHYzUPLJrjvTA1RoLGoJdBwnayzoQnzD3ukbLxMtZ7L1+nziZlB4paruMoL6Zs75caR5E1+cUVx07jkoffZPH8GUxsqs56bTe7S0kvPWqKlyn299/7O86cO6GH59HNp87mF89+QFNNiB8cOYUfZHifXX/CTJRSfWT4n79+zIWHTvba6uHzDsgqQ1VQZ+9dPH8GNz3zQZ8MhGkD2zlfmegZwG5cOAu/ZXHm3AnEUw5XP7bKzTYvnH3XqzywaE7WsnZvrOKZiw9mfVuUR1//lPn77NqjLrecOpu6Kh3a44wDxnPNY6s8z74HFs3hwWXr+GBDB/cvmkMi5WA7inDA4uQ7Xu4TD+vOs77M4Tc+z63P6WRZmzsTfTLXX/TgCu799n7YDlzy0ArPoPjBhg4vMy3QJ2vtufcu49ff2d8zQPU2+uXKoFsu0kvzenuz5Ttx35lwePyNdX08rk+fO4GGPOwGhVxG7/dJn+zMt57WnPfSTMdRfLy5q6Kv485KofQ3F9GE48UvTRsNf/HM+zo77Y7bw/BZ0FAd7DHuaqgOUkgH82J7G6bDB/S+PwoZPsBkpzUYikfFrfkRkaNE5D0R+VBELsv3+JbOBFuiOrYaCEoJKVsRSzkkbMWGrXEStiKecqgJ+7Ed7V03d3ITXXEH29bBsnVcOCGacPBbgt+ysBX6XAmHeErx93e9RtJWJG2FQnAcPZAJ+MRN9KCTUvh94hr0HBx3qej3DpuM7cBnbXESKUjYCkeB3/KRsnVCjKqgnxE1ISwRWtrj2I5OCV4d9JN09wchnnRI2nrZXcLW9akK+kk5OoZcLKlj823uTGA7cPcLq1EKQgEfyZQinnRIuckzasJ+LxHG1NH1pGzl/qbLHNsQ4ZPNUfyWDxFhrzF1+EQv07UdmDamjnWbo/hEWHTwJLfthLoqvVz507YY0YSjvR1FqI34CfosRtSEWLDvblgiBPw+zzgq6LazRHsx1IYDfLyxi5SjvScTKW00TdqKBfvuRjShz1sfCTB3chNJW3HotNGkbOXGttDXIWVrV3Zb6fodNm000aSDZQnfO2yyt5wHugf42bLlFRJH0afcix9aUfBAu4ahTcpRnHvfcs6+61VOun0pZ9/1Kufet7zocdv0C1MnJ92+lIOXPMdJty/l482dJYmFNljIFZvMBGrvJpZ0OL+X8er8AgY0z8WmzgQ//v2qPrHpbj51ds44Y+kEDFVBX85r2xbVy0AzvdqCPqvPMqp0Qqrz5k3KusRql7pwHxl6Z9rNlTAjnTCiKujLmtAinbTqogdXcPERe3rGsBE1Qe5+cTV+S7j8mL0A+Kwt6p0zW1m2ozjzl6/QmbA5bNqoPnU5/1fLeWd9Oyff8TKdCbvHUt107LuWjjjRhM2hN/yZw298nqSdvf3T9026zdLxkHvvt2FrnE0dcV5f2+bVNzNhRq6stRva42zqTLCpM9HH6Jcrg265KFSgfttR3PaXNRx+4/Ne+9/2lzVlTkoh/M41NKaTbvzujXWoPB1uBsN13FkpdqKJlKN4atUGzr13GSfdvpRz713GU6s2FGxcFEs6/OSP73oTdAlbfy/kc2tkTahPvNVbT2tmZIGWr+5oTFGDwVBeKsqoJyI+4L+Ao4FpwMkiMi2fc6QH3z5Le+DZSrnLZ/Vf3J35/XyLHhD6XKOT7WjPvrSRJ+V+T9naKGe5mW4TKW0AS7keFylH8cSbn+EoRVs06cUdEdGzi/q7NrTp8zp81hZ1kzQo6qsCnnwp23FjSeg6CLrshO1QGwkQTaSwLDz5Urbjyi36s1vfdH3sjP2Ugs+3xrwBmyXi/Z5IOdroaDtYlq6nT/QLqOPG9LEdRdRN7vHIsrU4SgG6DBEd5y6aSJFydJ1wZU/ZDvGU4xnVJOM6xZIprHQ7uYZPRVpuh1gypctx28RRjuf555PuOtqujD5LLzsG5dUD9AygoxRJt73S11qk24c1fQ0E2K2xKrs3TZZseYXEZIYyFIJyJRpoaY+z6N6eWRMX3buMlvZ4UcsdTAR9FksW9E24ECxBvLjBQqEC9edLImXz1KoN/PTJ93pkF2yoCnhJrzLJNGx1JWwiQSvrC9f4xirGDe/pXdERT2WtY2N1MGf2XJ/0NZL0zlqbLWFG74QRuc6f3r5LXdjbpoAz507gpNuXsuDWl1j8+Cpqwn5uXDiLR5atzVpWLGlvty7p7WkPkN6JR245rZmHX/ub91s6RnDv9k+rRLodchkauxK2ZzDMlCGdMCNX1tr0MrFcRr9ijwnyoVDjh1xtnW9MS58l3LhwVg/9uHHhrAEZ9Rqrg3xj9jjOvutVDr3hz5x916t8Y/a4vD2IBsN13Fkp9vi32Nl1HZXdaOgUMMSV328xdVQtD557AM9fMo8Hzz2AqaO6QwQUgrQn3a4NVTTVhoxBz2AYRFTam8R+wIdKqY+VUgngfmB+PifwWzobbdLWmXx8Il6narkBph2luOelNZ4XWfo4x93f7xqzHKU76kRK0dIex2cJLR1x13jkzkorxYyx9azfEmNje9zrAJW7ZFRn+hI6YknXOKZTkluijVC14YBnkEo5bnBYK5251UEpHeumpT3Oxo4EVz66UsvnKFIOrheblsdRCp/omHnpunX/Bo8sW+u9GKSNdf6MOkUCPuJJB0uEjR0Jb+lu+lwbOxLc/eJqTmgeh98S4inl1enzrVo+v1uneErhOIpYSrG5M+GVp9tft/e61hiOA0nbIeAGH/+sLeYaHGFda4yOWIp4Uhvj0te0K2GzJZok4NYv3X4BN8lI3PU+9Fvaw9GfNvhltJejYMPWOD4LkrbttVVLezznC1y2bHmFpLAz24adlWCBXsryJZbjhSluXpg8IkEdC3Xx/Bk8sGgOi+fPoKk2RKT4yTEHDcV++cpF2rDz+to278Vs8eOr+GRTF7c//1Efg116gmvJgpns3lhFQyTU54VrysgaHAVrN0d71GlDjmfM8OpgzoyuGzsSfTJ3jqgJ9dj39bVt3P3iau7++/149uKDeyTFSBvNchm+0tt9bviQsQ0RFH29Zy789euMHBbmsqP3or4qwJ1nfZnffncuVxw7jbtfXM1nW2KMbYhQFwkwclhom2WNHBbm4fMO4Gcn7c2Y+gg3LJzFXWfvx+hhIb5z0B48f+k8Fs+fwf8tX9en7recOpvOeJIHFs2hzjXQ3frcR9xwYk9D0pIFMxleHeDW5z7qIUNXwvaWljVWB7nt9OzXd1tGv2KPCfKhUOOHoE+yGqeDeS51FXRG3sy+Lui3BhTMqlAeRIPhOu6sFHv8WxW0dGzIzD7ktGaqgoVKlJF93OXLN5vLdvD7LcbUR9itsZox9ZGCGvQMBsPgpqISZYjIAuAopdR33O+nA/srpS7M2GcRsAhgt912a/7kk096nCORSPFeSyfLVm/kkL12we/TWRlxDWwdcRvHcUikFGs2trP/pBGkHEXI/c1vQTjgoyOuLUgKPcjpTDj4LbAdqA5ZJGzY0pXk9ys+5Vv77873H1jBVV+fRl3ET7UbdDeadAj7BRFhSzRFyC8oxDvu7L+b4MWest1kHI5SBCyLda1Rnn3nc06es7uuV8ohmrDpSthsbI8yc7fhoHSsGUcJW6MJQgEfQZ+QdCDiZq5NOg4pRxsYt0ZTLF+zieYJI3j8jXX8/Vcm4BOL1miSSMCiI66DVQd8QpdbX8uy9LncOmxsj3PJw29yUvNYvj57V8J+bTTsjNt0xVOMGhYi5SjvXGnZLYFIwE9Lhw4I3lgTpLUr5ZUR9lmklENXwvHaKZ60iSZsqkM+Aj4LpYSb//Qh3zloAtGEzahhIbRfnh5ApjU5aSttUPVZbGiPM3pYiJQDXYkUwyJ+bKX3iSdtlFKICPVVQZK2TVfCIWk7bOlK9og5lCPuSt6jjW3pb3ssxppNfQP5jm8MURs2iTIM/aMzFmP1pr6JMiY0hqgO71iigW3pbyEDow9VPtnUyfq2LsYNr/b6/rWbOxldX8XuBQh0PRTojMX4OEs/OLHI+us4qm+yhNOaGV4dQCE0VQdpi6VIpGxEdExXx1FEgj7qI7mDfTuOoi2aYH1bjHPdOh0xbST/cNiePeq4ZMFMfrv8U07cdxyRoK/H/ZuOQxfw6ZAgIvBpa5S/vL+BY/ce2+M86fh4lxw5Bb/PQtCe8Fu6Epx7n07CkSum3tkH6gzZlzz8Jj87aW8iQR/H3PTXPnV6YNEcrnviXa6dP91bKp15njPnTtAyHDWVeNLpUZfMfdIx9zLjaN1yWjNTR9YQCPh6XJO5ExtZdPAkAj49gWc7jhdn74hpI72Ya+kkI6PrwvgtIRy02NKV4gw3nmH6uo6uD/e4bqmUw2dbupfcPrJsLRcdPiVnIo0CxGIrqP52uP1+7/tmQmOImjzGD7FYii+64iRTqjuLrl8YVRXKKz5qMmmzprWLdRkJb8YOjzC+oYpAoDxGtGz3uImpN2AqUn9zEY+n2BxLkLL1e5Ylgt8Hw8OFSZSSSjm8+0V7n3FXoT3pDAWhoLrbG5Mow1BkcupvpRn1TgSO7GXU208p9Q/Z9s+VgSaRSNHSmSDg095XlgWOG9bAstCDFTfGXThguYNznUQiZet9fBYkU93nTB8X8AvJlCIU0Ikn4ik9wF5421JvsLzHyGpvdslx8Mq3HV2uoI8DvWQtlnL40zuf8+2vTMRyk3hc7Wb6mjiiirqqYI+lcylHUR2ysF0HmIAfWtqTxJMOTcNC+N3ykrbirhc+9s6LW27ApzP0Bnzae+3Jtz/j8OmjueZ3Kzn7wAlMGFFF0G957STotnGUboOko73w6iI+knbP39JSXvno21xwyB6Mro9ggbtMFq5+7G2vjEjQIpbQbRpLKu5+4WPOPHAClghBf3eMQgVeVuGFt73E3ImNXHDoHgT9FiF/T93ObO90O1529F5sjSa5+bkPueCQPditMYJS9Dg/6BgY4YCFcrSBMOXoZbvhgI8R1Vnd0AuaQemLLVGCAaEr7ngv/FUhi0RSMaoA2b8MOwct7XGqAoq2aLce1UcsupJ9AhQXXH8/3tjZw1iwZMFMJo6oNvrr8mlrV9YMqQ8smsOuxvAJwGetXdRFrD76uyXqMKZnGxU8g10xM/NlnltEqAoKnXHHDZlhYYn2WrdEvGd0+uUzbVyxLEDp77GU47VNW1Sfx5e5b9og43lYiReCIui3cJR+5qVsHVpjY0eCccMjVAd9KHemLOXAKXf01dcrjp3G4sdX8fB5B3SHwMgYa6XLDgcslNLP4pStw2ek4/vd+txHvL62jSOmjeSq46Z78Y1H1oR6GH1yXZPe2xsiAVqjyazXrr/XdVv7VXr22y+2RAkHhY5Y931TE9ZjrHz731gsxaZod4bYgSY8SiZtNnTEvfP0vrbloEKybw4FKlZ/c1HszMeplKP13Xbw+yxG1oSMQa8yGXLZbweKMQYOSgZN9tt1wLiM72OBz/I9STDoZ9dg6armOMrLGHTyHS/3e/bPcWP8SSzJvKmj+OYtL/WZde7vbGJtKNT9wuAOVAC+MXucd95s53Ecxf6Tmrjmdys5c+6E/nin9as9Ljp8Sp/Z0MlNNVm3TxlVCxEt68Lblm5T1nQ7P7hs3XZlTMtx3RPv8N1D9uDsAydwwa9f3+b5N3UmSDp6sDeytrSDvbpQgLVbo31mtscNMwYRQ/9prA7m9EYoJvXhACPcpaVp/R1RG6I+XNyspYOJxkiQW05r7uON0GjW33qMqA7yfktnH4+HPZuK78lYzMx82c5dn8WOm+lN1FQT4nuHTWbCiGqqQn0nl3bU8yj9zIsELHapi9AQCfBBS4d3viOmjeTW05qzetotWTCTroTN+Mbqfj8nHUexNZZi8eOrvPNddPgURtdFcp4j1zXJtj3Xtevvdd3WfpWetbGQ44dw2M+uBchaHgj4Km6yotKv485KKca/oZCfXQtoxOtNemmswWAwlINK89TzA+8DhwGfAq8CpyilVmbbf3vW8lIy0Nm/9NKcZMrRHnCuZ9jwSDDnrHOh5Urv4zgOthcLcMdmMPs7u57vTHi+7ZxZN3HjDdquB0EO77t8KPhsT6FmyA07N/28Twquv8WeCR8KmHt8+6S97dNt1FQdJNh3oq6os+3lJJ/nXKE9j7blAZfPsuNC1G2IY8YPhsGM0V/DYMV46rkYT71ByeDw1FNKpUTkQuBJwAf8MpdBr9IY6OyfZQnDq7MfV4jZxP7IVYyZy3xm1/ORI19ZB9usbKFmyA07N+XS+2LPhA8FzD2+fUrtbV9p5HP/Fvpez8cDrlDnNxQG07cYBjNGfw0Gg2HgVFzvqZT6A/CHcsthMBgMBoPBYDAYDAaDwWDQDNQj0XgHFg8TwdNgMBgMBoPBYDAYDAaDwWAYZFScp57BYDAYDAaDwWAwGAwGg6HwlCP+346UOdi8/ErtzVhRiTLyRURagE9y/DwC2FhCcYwMlVl+MWXYqJQ6aqAHDwL97U2lyWTk2T7bksnob3kx8myfcujvYGuHclBp8kDlybQ9eXam/rfS5IHKk2mwybMz6W++DHb5YfDXoVxj3+2VPRQY6vWDyq5jTv0d1Ea9bSEiryml9jUylFeGcpdfKTLkSyXKXGkyGXm2T7lkMm2xfYw826ccMpl22D6VJg9UnkzllMe0xfapNJmMPJVRdiEY7PLD4K+D0d/iMdTrB4O3jiamnsFgMBgMBoPBYDAYDAaDwTDIMEY9g8FgMBgMBoPBYDAYDAaDYZAxlI16t5dbAIwMlVA+VIYM+VKJMleaTEae7VMumUxbbB8jz/Yph0ymHbZPpckDlSdTOeUxbbF9Kk0mI09llF0IBrv8MPjrYPS3eAz1+sEgreOQjalnMBgMBoPBYDAYDAaDwWAwDFWGsqeewWAwGAwGg8FgMBgMBoPBMCQZkkY9ETlKRN4TkQ9F5LIilTFORP4kIu+IyEoR+Ud3+3AReVpEPnD/N2Qc88+uTO+JyJEFlMUnIq+LyOPlkEFE6kXkYRF5122PA0opg4hc5F6Dt0XkNyISLsd1yEPebeqnaG5yf39TRGb399giyXOqK8ebIvKiiMzK+G2NiLwlIm+IyGuFkKefMs0TkS1uuW+IyJX9PbZI8lySIcvbImKLyHD3t4K3kYj8UkQ2iMjbOX4vug7tiAzFoh8y5dSbIsmT9TnRa5+StVM/5SlZG7l99SsissKV55os+5SyfYo+dthO+XmPK0ooW7/HGSWQJa8xR4lkymscUiQZyqq/WeTZbn9TDnrrcrnJps9llqePLpeo3IrS33ypVH3Pl0q7P/KlXPfTYNffNAMZh0iZ36sHQj5jmkFTP6XUkPoDfMBHwEQgCKwAphWhnNHAbPdzLfA+MA34CXCZu/0y4Hr38zRXlhAwwZXRVyBZvg/8Gnjc/V5SGYC7ge+4n4NAfalkAHYFVgMR9/uDwFnluA6F0k/ga8ATgABzgJeLpdv9lGcu0OB+Pjotj/t9DTCiDG00L63v+R5bDHl67X8c8GyR2+ggYDbwdo7fi65DA5WhmH/9kCmr3hRRnqzPiXK1Uz/lKVkbuXWucT8HgJeBOeVon2L0HYW6PuR4npVYtn6NM0okS7/HHCWSJ69xSJFkKLv+ZpFpu/1NmeTqocvl/sumz2WUJasul6DcitPfAdShIvV9APWoqPtjAPKX/H4aCvqbUZeKsW8UuZ5ltZ0U428oeurtB3yolPpYKZUA7gfmF7oQpdR6pdRy93M78A76YTgf3aHg/v+G+3k+cL9SKq6UWg186Mq6Q4jIWOAY4L8zNpdMBhEZhn6R/h8ApVRCKdVWShkAPxARET9QBXxW4vLzoT/6OR+4R2mWAvUiMrqfxxZcHqXUi0qpVvfrUmDsDpa5wzIV6dhCnfNk4Dc7WOY2UUo9D2zexi5F16EdkKFo9EOmkrKN50QmJWunfspTMtw6d7hfA+5f70C/pWqfkowdtsUAxhUlIc9xRrFlyXfMUSryGYcUg7Lrb28qrb+BnLpcNrahz+Ukmy4Xm4rT33ypRH3Pl0q7P/KljPfToNffNJVi3ygm5eBcYcEAABAeSURBVLadFIuhaNTbFVib8X0dRe5URWQ8sA/ay2CUUmo96BsDGFlkuX4GXAo4GdtKKcNEoAW403Vj/W8RqS6VDEqpT4GfAn8D1gNblFJPlar8AdCf8nPtUwzZ8z3nt9FeM2kU8JSILBORRTsoS74yHSB62d4TIjI9z2OLIQ8iUgUcBTySsbkYbbQ9SqlD+cpQbrLpTdHp9ZzIpCzttA15oIRt5C6BeAPYADytlCpX+1SUvvZzXFEq8hlnFJt8xxxFZwDjkGJQUfrbm+30N6Ukmy6Xk1z6XBa2ocvFpqL1N18qSN/zpdLuj3wp1/00pPQ3TZntG8Wk3LaTojAUjXqSZVvRUvyKSA36Bf6flFJbt7Vrlm07JJeIHAtsUEot6+8hhZYBPaM3G7hFKbUP0Il2Wy2JDO6a9/lol9gxQLWInFaq8gdAf8rPtU8xZO/3OUXkELRR74cZmw9USs1GL8u9QEQO2kF5+ivTcmB3pdQs4BfA/+VxbDHkSXMc8IJSKtNbrBhttD1KqUP5ylBOculNUdnOc6Lk7bQdeUraRkopWym1N9oDeD8RmdFb3GyHFUGUitHXPMYVpZAl33FGscl3zFF0BjAOKYoYWbaVu78FKkefK1CXocL0uYy6XLH6my+Vou/5UqH3R76U634aMvqbppz2jWJSIbaTojAUjXrrgHEZ38dSJNdxEQmgFf5XSqn/dTd/kV4a5P7fUES5DgS+LiJr0K6+h4rIfSWWYR2wLsO74mF0h1oqGb4KrFZKtSilksD/omPAlbIN8qE/5efap1jXb7vnFJGZaDfl+UqpTentSqnP3P8bgN9SGJfk7cqklNqaXranlPoDEBCREf2tT6HlyeBb9Fp6W6Q22h6l1KF8ZSgb29CbopHjOZFJSdtpe/KUo43cstqA59CerpmUqn0qQl/zHFeUgnzHGcUm3zFHKch3HFIMKkJ/e9OP/q+U5NLlcpJLn8tFLl0uNhWpv/lSYfqeL5V4f+RLue6nIaG/aSrAvlFMKsF2UhSGolHvVWCyiEwQkSD6JfuxQhciIoJes/+OUuo/Mn56DDjT/Xwm8GjG9m+JSEhEJgCTgVd2RAal1D8rpcYqpcaj6/msUuq0EsvwObBWRKa4mw4DVpVQhr8Bc0Skyr0mh6HX/5esDfKkP/r5GHCGaOaglz+s7+exBZdHRHZDD+xOV0q9n7G9WkRq05+BI4CsWUeLINMu7vVGRPZD92Wb+nNsMeRx5agDDqZb14rZRtujlDqUrwxlYxt6U6zycj0nMilZO/VHnlK2kYg0iUi9+zmCfqF8t9dupWqfUt4bWRnAuKLoDGCcUWx58h1zlIJ8xyHFoOz625t+9n8lYxu6XE6Zculzucily8Wm4vQ3XypN3/OlEu+PfCnj/TTo9TdNJdg3ikkl2E6KhqqAbB2F/kNny3sfnaHk8iKV8Xdo98s3gTfcv68BjcAzwAfu/+EZx1zuyvQecHSB5ZlHdwaXksoA7A285rbF/wENpZQBuAb9Ivg2cC86Q01ZrsNA9RM4DzjP/SzAf7m/vwXsW0zd7oc8/w20Zuj5a+72ieiMQCuAlYW81/oh04VumSvQyTvmlrON3O9noYOpZh5XlDZCewOuB5LoWaRvl1qHdkSGIt5b25Mpp94USZ5cz4mytFM/5SlZGwEzgddded4GrnS3l6t9ij52GOD1yfk8K7F88+jHOKMEcuQ15iiRTHmNQ4okQ1n1N4s8WfW53HK5snm6XO6/bPpcZnn66HKJyq0o/R2A/BWr7wOoS8XcHwOQvSz302DX34x6VJR9o8h17deYZrDUT1xhDQaDwWAwGAwGg8FgMBgMBsMgYSguvzUYDAaDwWAwGAwGg8FgMBiGNMaoZzAYDAaDwWAwGAwGg8FgMAwyjFHPYDAYDAaDwWAwGAwGg8FgGGQYo57BYDAYDAaDwWAwGAwGg8EwyDBGPYPBYDAYDAaDwWAwGAwGg2GQYYx6FYKI2CLyhoi8LSIPiUhVuWUaKCLynIjsm2X7WSLyn+WQyVB+RORyEVkpIm+6ur5/Ac75dRG5rEDydRTiPIadh3z6bRG5WkR+UEr5DIaBIiLHi4gSkanllsVg2BbZxhYi8t8iMs39PeuzXUTmiMjL7jHviMjVJRXcYKDw738iMl5E3i6UfAZDf8nQ5fTf+HLLtDNhjHqVQ1QptbdSagaQAM4rt0ADQUR85ZbBUHmIyAHAscBspdRM4KvA2n4e68/1m1LqMaXUdYWR0mDImyHRbxsMWTgZ+CvwrXILYjDkItfYQin1HaXUqu0cfjewSCm1NzADeLC40hoMWRnQOGJbY2ODoUykdTn9t2Z7B4jG2KMKgGnEyuQvwB4icpw7i/i6iPw/ERkFICIHZ1jBXxeRWhEZLSLPZ8z2fMXd9wgReUlElrszQDXu9jUico27/a30bLyINInI0+7220TkExEZ4f52moi84pZxW9qAJyIdInKtiLwMHJBZERE5W0TeF5E/AweWrAUNlcZoYKNSKg6glNqolPrM1cO0fu0rIs+5n68WkdtF5CngHvc+mJ4+mesN2pz2/hSROvdclvt7lYisFZGAiEwSkT+KyDIR+UuGrk9w741XRWRxidvDMPT4C7AHgIic4XqNrBCRe3vvKCLnuHq3QkQeSc/Mi8iJbv+9QkSed7dNz+h33xSRySWtlWGnwx0nHAh8G9eoJyKWiNws2iPqcRH5g4gscH9rFpE/u33skyIyuoziG3Yuco0teqwYEZEb3HHtMyLS5G4eCax3j7PTRkB3/HGviDwrIh+IyDklrpNh52V773+9x8ajROS37phhhYjMdc/jE5E73P76KRGJlK1Ghp0WEalx+9y0rWG+u328aO/om4HlwDgRucQdF78pIteUV/LBiTHqVRiiZ16OBt5Cz5LPUUrtA9wPXOru9gPgAnd28StAFDgFeNLdNgt4wzWW/CvwVaXUbOA14PsZxW10t9/inhPgKuBZd/tvgd1cufYCTgIOdMuwgVPdY6qBt5VS+yul/ppRl9HANeiXg8OBaQVoIsPg5Cl0p/2++2J4cD+OaQbmK6VOQev/QvD0aoxSall6R6XUFmAFkD7vcej7IQncDvyDUqoZrec3u/v8HLhFKfVl4PMdrqFhpyWz33aNz5cDhyqlZgH/mOWQ/1VKfdn9/R208QTgSuBId/vX3W3nAT93+919gXVFrIrBAPAN4I9KqfeBzSIyG/gmMB74EvAd3Ak8EQkAvwAWuH3sL4Efl0Now05Jf8YW1cByd1z7Z/Q4F+BG4D3XKHKuiIQzjpkJHIPW8ytFZEwR62Aw9Pf9D3qOjW8C/uyOGWYDK919JgP/pZSaDrQBJ5SmFoadnIh0Ox39FogBx7t97yHADSIi7r5TgHtcHZ+C1tn9gL2BZhE5qAzyD2qM627lEBGRN9zPfwH+B63kD7hGjCCw2v39BeA/RORX6JfDdSLyKvBLd4D9f0qpN9zBzTTgBfceCgIvZZT5v+7/ZegBO8DfAccDKKX+KCKt7vbD0A+SV91zRYAN7m828EiWOu0PPKeUagEQkQeAPfNrFsNQQCnVISLNaCP0IWi93l4svMeUUlH384PA0+jB+ELgoSz7P4A2PP8J7V1ys+txMhd4qPs5Qsj9fyDdA517gevzrZdhpydbv30u8LBSaiOAUmpzluNmiMiPgHqgBnjS3f4CcJeIPEh3//wScLmIjEX39x8UpyoGg8fJwM/cz/e73wPAQ0opB/hcRP7k/j4FvXTxabeP9eF6PxkMxaafYwsHPT4AuA+3b1VKXeuOo49AT4yfDMxz93vUHX9EXV3fD/i/YtbFsNOSz/sf9BwbHwqcAdrbFNgiIg3AaqVU+pzL0BMyBkOxiboT0IA36fdvroHOAXYFRrk/f6KUWup+PsL9e939XoM28j1fEqmHCMaoVzn0uBEAROQXwH8opR4TkXnA1QBKqetE5PfA14ClIvJVpdTz7k1zDHCviCwBWoGnlVIn5ygz7v636dYFybGvAHcrpf45y28x92GSDZVju2Enw9WR54DnROQt4EwgRbfHcLjXIZ0Zx34qIptEZCbacHduliIeA/5dRIajDdDPomfo23rfW5liDbA6BgNk77eF7evVXcA3lFIrROQs3BdJpdR5ohPIHIP2tt5bKfVr0aENjgGeFJHvKKWeLXA9DAYARKQR/aI4Q0QU2kin0J77WQ8BViqlDsjxu8FQVHKMLbZ5SMaxHwG3iMgdQIur/z32yfHdYCgU/X7/c+lk+8QzPttoRwyDodScCjQBzUqppIisoftdL1OPBfh3pdRtJZZvSGGW31Y2dcCn7mdvkCIik5RSbymlrkcvqZ0qIrsDG5RSd6BneWYDS4EDRSQd56lKRLbnKfdXupc5HgE0uNufARaIyEj3t+FumdviZWCeiDS61voT+1Vrw5BDRKZIz1hgewOfAGvQBjjY/vKA9BKEOqXUW71/VEp1AK+gl9U+7sbI2QqsFpETXTlERGa5h7xAdxD4U3ufz2AYIM8AC9Mvh66RuTe1wHq3X/R0z+3bX1ZKXQlsRC8rmwh8rJS6CW24nln0Ghh2Zhagl8TsrpQar5Qah/YS2QicIDq23ii6PZreA5pEJyxAdBzT6dlObDAUmm2MLTKx0HoN2iPvr+6xx2QsBZuMNn60ud/ni0jY7cfnAa8WQXyDIRdZ3/+y8AxwPuhEhSIyrNiCGQx5UIe2TSRF5BAgl93gSeDvpTvu/65pe4Oh/xijXmVzNXrZ4F/QA+o0/yRuMHV0PL0n0IOON0TkdbRx5OfustezgN+IyJtoI9/U7ZR5DXCEiCxHx3ZYD7S7AYT/FXjKPdfT6ADFOVFKrXfr8BLw/9DBMA07JzXA3SKyytWfaWjduAb4uavjubw90zyMNsJtK0PdA8BpdC+1AW00+bZ7v6wE5rvb/xG4wF26XpdfdQyG7CilVqJjiv3Z1bn/yLLbFehJj6eBdzO2LxEdTPht9LKDFWjP1Lfd5TlTgXuKKb9hp+dk+nrlPQKMQcdzfBu4Da2/W5RSCbTB5HpX399AhzwwGEpBrrFFJp3AdBFZhvZCvdbdfjo6pt4b6BAcp2asOnkF+D163LxYKfVZcathMPTgarK///XmH4FDXA/VZYCZUDFUEr8C9hWR19DvYu9m20kp9RTwa+AlV5cfRk9+G/JAlDIe5YZuRCQE2EqplDvzfss2li4aDAaDwWDYCRCRGjeGWSPa6HGgUsokGTIMKUTkaqBDKfXTcstiMBgMBkN/MDH1DL3ZDXhQRCwgAZxTZnkMBoPBYDCUn8dFpB4duH2xMegZDAaDwWAwlB/jqWcwGAwGg8FgMBgMBoPBYDAMMkxMPYPBYDAYDAaDwWAwGAwGg2GQYYx6BoPBYDAYDAaDwWAwGAwGwyDDGPUMBoPBYDAYDAaDwWAwGAyGQYYx6hkMBoPBYDAYDAaDwWAwGAyDDGPUMxgMBoPBYDAYDAaDwWAwGAYZxqhnMBgMBoPBYDAYDAaDwWAwDDL+PymA/PIyZBP9AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1260x1260 with 56 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#lets check te pair plot\n",
    "# let do multivariate analysis using pair lot by taking all column together.\n",
    "sns.pairplot(ts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <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",
       "      <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>NaN</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>B42</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>889</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>W./C. 6607</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </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>C148</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>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass  \\\n",
       "0              1         0       3   \n",
       "1              2         1       1   \n",
       "2              3         1       3   \n",
       "3              4         1       1   \n",
       "4              5         0       3   \n",
       "..           ...       ...     ...   \n",
       "886          887         0       2   \n",
       "887          888         1       1   \n",
       "888          889         0       3   \n",
       "889          890         1       1   \n",
       "890          891         0       3   \n",
       "\n",
       "                                                  Name     Sex   Age  SibSp  \\\n",
       "0                              Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1    Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                               Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                             Allen, Mr. William Henry    male  35.0      0   \n",
       "..                                                 ...     ...   ...    ...   \n",
       "886                              Montvila, Rev. Juozas    male  27.0      0   \n",
       "887                       Graham, Miss. Margaret Edith  female  19.0      0   \n",
       "888           Johnston, Miss. Catherine Helen \"Carrie\"  female   NaN      1   \n",
       "889                              Behr, Mr. Karl Howell    male  26.0      0   \n",
       "890                                Dooley, Mr. Patrick    male  32.0      0   \n",
       "\n",
       "     Parch            Ticket     Fare Cabin Embarked  \n",
       "0        0         A/5 21171   7.2500   NaN        S  \n",
       "1        0          PC 17599  71.2833   C85        C  \n",
       "2        0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3        0            113803  53.1000  C123        S  \n",
       "4        0            373450   8.0500   NaN        S  \n",
       "..     ...               ...      ...   ...      ...  \n",
       "886      0            211536  13.0000   NaN        S  \n",
       "887      0            112053  30.0000   B42        S  \n",
       "888      2        W./C. 6607  23.4500   NaN        S  \n",
       "889      0            111369  30.0000  C148        C  \n",
       "890      0            370376   7.7500   NaN        Q  \n",
       "\n",
       "[891 rows x 12 columns]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
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       "      <td>NaN</td>\n",
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       "      <td>2</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>C</td>\n",
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       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pclass     Sex   Age  SibSp  Parch     Fare Embarked\n",
       "0         3    male  22.0      1      0   7.2500        S\n",
       "1         1  female  38.0      1      0  71.2833        C\n",
       "2         3  female  26.0      0      0   7.9250        S\n",
       "3         1  female  35.0      1      0  53.1000        S\n",
       "4         3    male  35.0      0      0   8.0500        S\n",
       "..      ...     ...   ...    ...    ...      ...      ...\n",
       "886       2    male  27.0      0      0  13.0000        S\n",
       "887       1  female  19.0      0      0  30.0000        S\n",
       "888       3  female   NaN      1      2  23.4500        S\n",
       "889       1    male  26.0      0      0  30.0000        C\n",
       "890       3    male  32.0      0      0   7.7500        Q\n",
       "\n",
       "[891 rows x 7 columns]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#we can see there is no impact of Ticket,name,cabin,passengerId so we can drop that columns\n",
    "x=ts.drop(columns=['Ticket','Cabin','Name','Survived','PassengerId'])\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>C</td>\n",
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       "      <td>3</td>\n",
       "      <td>male</td>\n",
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       "      <td>0</td>\n",
       "      <td>7.7500</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pclass     Sex   Age  SibSp  Parch     Fare Embarked\n",
       "0         3    male  22.0      1      0   7.2500        S\n",
       "1         1  female  38.0      1      0  71.2833        C\n",
       "2         3  female  26.0      0      0   7.9250        S\n",
       "3         1  female  35.0      1      0  53.1000        S\n",
       "4         3    male  35.0      0      0   8.0500        S\n",
       "..      ...     ...   ...    ...    ...      ...      ...\n",
       "886       2    male  27.0      0      0  13.0000        S\n",
       "887       1  female  19.0      0      0  30.0000        S\n",
       "888       3  female   NaN      1      2  23.4500        S\n",
       "889       1    male  26.0      0      0  30.0000        C\n",
       "890       3    male  32.0      0      0   7.7500        Q\n",
       "\n",
       "[891 rows x 7 columns]"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.impute import SimpleImputer\n",
    "imp= SimpleImputer(strategy='most_frequent')\n",
    "x['Embarked']=imp.fit_transform(x['Embarked'].values.reshape(-1,1))\n",
    "x\n",
    "# we are filling the null data with the most repeted data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pclass     Sex        Age  SibSp  Parch     Fare Embarked\n",
       "0         3    male  22.000000      1      0   7.2500        S\n",
       "1         1  female  38.000000      1      0  71.2833        C\n",
       "2         3  female  26.000000      0      0   7.9250        S\n",
       "3         1  female  35.000000      1      0  53.1000        S\n",
       "4         3    male  35.000000      0      0   8.0500        S\n",
       "..      ...     ...        ...    ...    ...      ...      ...\n",
       "886       2    male  27.000000      0      0  13.0000        S\n",
       "887       1  female  19.000000      0      0  30.0000        S\n",
       "888       3  female  29.699118      1      2  23.4500        S\n",
       "889       1    male  26.000000      0      0  30.0000        C\n",
       "890       3    male  32.000000      0      0   7.7500        Q\n",
       "\n",
       "[891 rows x 7 columns]"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# will fill all the missing age NaN value with mean of data using simple imputer\n",
    "imp= SimpleImputer(strategy='mean')\n",
    "x['Age']=imp.fit_transform(x['Age'].values.reshape(-1,1))\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pclass      0\n",
       "Sex         0\n",
       "Age         0\n",
       "SibSp       0\n",
       "Parch       0\n",
       "Fare        0\n",
       "Embarked    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.isnull().sum()\n",
    "# we can find there is no null value present will proceed further for model preparation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#observation\n",
    "we can observe that in our data set there sex and embarked column has categorical data without converting them into numeric we cant proceed further \n",
    "so we must need to convert it numeric data\n",
    "*** we will use label encoder to convert categorical data to numeric."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "le=LabelEncoder()\n",
    "x['Sex']=le.fit_transform(x['Sex'].astype(str))\n",
    "x['Embarked']=le.fit_transform(x['Embarked'].astype(str))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\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>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pclass  Sex        Age  SibSp  Parch     Fare  Embarked\n",
       "0         3    1  22.000000      1      0   7.2500         2\n",
       "1         1    0  38.000000      1      0  71.2833         0\n",
       "2         3    0  26.000000      0      0   7.9250         2\n",
       "3         1    0  35.000000      1      0  53.1000         2\n",
       "4         3    1  35.000000      0      0   8.0500         2\n",
       "..      ...  ...        ...    ...    ...      ...       ...\n",
       "886       2    1  27.000000      0      0  13.0000         2\n",
       "887       1    0  19.000000      0      0  30.0000         2\n",
       "888       3    0  29.699118      1      2  23.4500         2\n",
       "889       1    1  26.000000      0      0  30.0000         0\n",
       "890       3    1  32.000000      0      0   7.7500         1\n",
       "\n",
       "[891 rows x 7 columns]"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "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>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Survived\n",
       "0           0\n",
       "1           1\n",
       "2           1\n",
       "3           1\n",
       "4           0\n",
       "..        ...\n",
       "886         0\n",
       "887         1\n",
       "888         0\n",
       "889         1\n",
       "890         0\n",
       "\n",
       "[891 rows x 1 columns]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# lets segrigate target varible\n",
    "y=ts['Survived']\n",
    "y=pd.DataFrame(y)\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pclass  Sex        Age  SibSp  Parch     Fare  Embarked\n",
       "0         3    1  22.000000      1      0   7.2500         2\n",
       "1         1    0  38.000000      1      0  71.2833         0\n",
       "2         3    0  26.000000      0      0   7.9250         2\n",
       "3         1    0  35.000000      1      0  53.1000         2\n",
       "4         3    1  35.000000      0      0   8.0500         2\n",
       "..      ...  ...        ...    ...    ...      ...       ...\n",
       "886       2    1  27.000000      0      0  13.0000         2\n",
       "887       1    0  19.000000      0      0  30.0000         2\n",
       "888       3    0  29.699118      1      2  23.4500         2\n",
       "889       1    1  26.000000      0      0  30.0000         0\n",
       "890       3    1  32.000000      0      0   7.7500         1\n",
       "\n",
       "[891 rows x 7 columns]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x #is my feature varible"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(891, 7)\n",
      "(820, 7)\n"
     ]
    }
   ],
   "source": [
    "# lets check the outliers present in the data sets\n",
    "from scipy.stats import zscore\n",
    "z_score=abs(zscore(x))\n",
    "print(x.shape)\n",
    "df_x=x.loc[(z_score<3).all(axis=1)]\n",
    "print(df_x.shape)\n",
    "# we can see there were outliers present in the datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pclass     -0.632242\n",
       "Sex        -0.664152\n",
       "Age         0.318314\n",
       "SibSp       1.979577\n",
       "Parch       2.122629\n",
       "Fare        2.318761\n",
       "Embarked   -1.277386\n",
       "dtype: float64"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_x.skew()\n",
    "\n",
    "# we can observe that my featue data contians skewed data so we have to reduce our skewed data to make our data more countable "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# lets treat the skewed data using \n",
    "\n",
    "\n",
    "*** log transform\n",
    "\n",
    "\n",
    "*** box cox (we can only use boxcox when data is positive)\n",
    "\n",
    "\n",
    "*** square root transform\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "# lets apply log transform\n",
    "import numpy as np\n",
    "for col in df_x.columns:\n",
    "    if df_x.skew().loc[col]>0.55:\n",
    "        df_x[col]=np.log1p(df_x[col])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pclass     -0.632242\n",
       "Sex        -0.664152\n",
       "Age         0.318314\n",
       "SibSp       1.292829\n",
       "Parch       1.832391\n",
       "Fare        0.196364\n",
       "Embarked   -1.277386\n",
       "dtype: float64"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_x.skew()\n",
    "# we can observe there i no much difference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "#lets use square root tranformation\n",
    "import numpy as np\n",
    "for col in df_x.columns:\n",
    "    if df_x.skew().loc[col]>0.55:\n",
    "        df_x[col]=np.sqrt(df_x[col])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pclass     -0.632242\n",
       "Sex        -0.664152\n",
       "Age         0.318314\n",
       "SibSp       1.012643\n",
       "Parch       1.642836\n",
       "Fare        0.196364\n",
       "Embarked   -1.277386\n",
       "dtype: float64"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " df_x.skew()\n",
    "# we can observe there is no much difference by sqrt transformation\n",
    "# but the skewed result is better than log transform"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_x=x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "# lets start classification process"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score,roc_curve,auc,mean_squared_error\n",
    "from sklearn.metrics import confusion_matrix,classification_report\n",
    "from sklearn.model_selection import train_test_split\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.2, random_state=4)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy_score =  0.8324022346368715\n",
      "confusion_matrix = [[108  11]\n",
      " [ 19  41]]\n",
      "classification_report               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.85      0.91      0.88       119\n",
      "           1       0.79      0.68      0.73        60\n",
      "\n",
      "    accuracy                           0.83       179\n",
      "   macro avg       0.82      0.80      0.81       179\n",
      "weighted avg       0.83      0.83      0.83       179\n",
      "\n"
     ]
    }
   ],
   "source": [
    "lg=LogisticRegression()\n",
    "lg.fit(x_train,y_train)\n",
    "pred=lg.predict(x_test)\n",
    "print('Accuracy_score = ',accuracy_score(y_test,pred))\n",
    "print('confusion_matrix =', confusion_matrix(y_test,pred))\n",
    "print('classification_report',classification_report(y_test,pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score of LogisticRegression() is:\n",
      "0.8324022346368715\n",
      "[[108  11]\n",
      " [ 19  41]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.85      0.91      0.88       119\n",
      "           1       0.79      0.68      0.73        60\n",
      "\n",
      "    accuracy                           0.83       179\n",
      "   macro avg       0.82      0.80      0.81       179\n",
      "weighted avg       0.83      0.83      0.83       179\n",
      "\n",
      "mean cross_val_score for LogisticRegression() is: 0.786761659657272\n",
      "standard deviation in accuracy score for LogisticRegression() is: 0.01733439181829156\n",
      "confusion_matrix for crossvalidation [[464  85]\n",
      " [105 237]]\n",
      "roc_auc_score =  0.7954481792717086\n",
      "AxesSubplot(0.125,0.808774;0.62x0.0712264)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x3600 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "\n",
      "Accuracy score of GaussianNB() is:\n",
      "0.8212290502793296\n",
      "[[104  15]\n",
      " [ 17  43]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.86      0.87      0.87       119\n",
      "           1       0.74      0.72      0.73        60\n",
      "\n",
      "    accuracy                           0.82       179\n",
      "   macro avg       0.80      0.80      0.80       179\n",
      "weighted avg       0.82      0.82      0.82       179\n",
      "\n",
      "mean cross_val_score for GaussianNB() is: 0.7912685958194715\n",
      "standard deviation in accuracy score for GaussianNB() is: 0.012075695771434674\n",
      "confusion_matrix for crossvalidation [[458  91]\n",
      " [ 95 247]]\n",
      "roc_auc_score =  0.7953081232492997\n",
      "AxesSubplot(0.125,0.808774;0.62x0.0712264)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x3600 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "\n",
      "Accuracy score of SVC() is:\n",
      "0.6983240223463687\n",
      "[[108  11]\n",
      " [ 43  17]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.72      0.91      0.80       119\n",
      "           1       0.61      0.28      0.39        60\n",
      "\n",
      "    accuracy                           0.70       179\n",
      "   macro avg       0.66      0.60      0.59       179\n",
      "weighted avg       0.68      0.70      0.66       179\n",
      "\n",
      "mean cross_val_score for SVC() is: 0.6757328479065972\n",
      "standard deviation in accuracy score for SVC() is: 0.040160168097246485\n",
      "confusion_matrix for crossvalidation [[502  47]\n",
      " [242 100]]\n",
      "roc_auc_score =  0.5954481792717086\n",
      "AxesSubplot(0.125,0.808774;0.62x0.0712264)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x3600 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "\n",
      "Accuracy score of DecisionTreeClassifier() is:\n",
      "0.8547486033519553\n",
      "[[112   7]\n",
      " [ 19  41]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.85      0.94      0.90       119\n",
      "           1       0.85      0.68      0.76        60\n",
      "\n",
      "    accuracy                           0.85       179\n",
      "   macro avg       0.85      0.81      0.83       179\n",
      "weighted avg       0.85      0.85      0.85       179\n",
      "\n",
      "mean cross_val_score for DecisionTreeClassifier() is: 0.7710752620676669\n",
      "standard deviation in accuracy score for DecisionTreeClassifier() is: 0.020853004280382868\n",
      "confusion_matrix for crossvalidation [[447 102]\n",
      " [102 240]]\n",
      "roc_auc_score =  0.8122549019607843\n",
      "AxesSubplot(0.125,0.808774;0.62x0.0712264)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x3600 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "\n",
      "Accuracy score of KNeighborsClassifier() is:\n",
      "0.7541899441340782\n",
      "[[97 22]\n",
      " [22 38]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.82      0.82      0.82       119\n",
      "           1       0.63      0.63      0.63        60\n",
      "\n",
      "    accuracy                           0.75       179\n",
      "   macro avg       0.72      0.72      0.72       179\n",
      "weighted avg       0.75      0.75      0.75       179\n",
      "\n",
      "mean cross_val_score for KNeighborsClassifier() is: 0.698141987320319\n",
      "standard deviation in accuracy score for KNeighborsClassifier() is: 0.038696042787943925\n",
      "confusion_matrix for crossvalidation [[437 112]\n",
      " [157 185]]\n",
      "roc_auc_score =  0.7242296918767507\n",
      "AxesSubplot(0.125,0.808774;0.62x0.0712264)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x3600 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import sklearn\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.model_selection import cross_val_score,cross_val_predict\n",
    "\n",
    "model=[LogisticRegression(),GaussianNB(),SVC(),DecisionTreeClassifier(),KNeighborsClassifier()]\n",
    "\n",
    "for m in model:\n",
    "    m.fit(x_train,y_train)\n",
    "    m.score(x_train,y_train)\n",
    "    predm=m.predict(x_test)\n",
    "    print('Accuracy score of',m,'is:')\n",
    "    score=accuracy_score(y_test,predm)\n",
    "    print(score)\n",
    "    cm=confusion_matrix(y_test,predm)\n",
    "    print(cm)\n",
    "    print(classification_report(y_test,predm))\n",
    "    CVS=cross_val_score(m,x,y,cv=5,scoring='accuracy').mean()\n",
    "    print('mean cross_val_score for',m, 'is:',CVS)\n",
    "    print('standard deviation in accuracy score for',m,'is:',cross_val_score(m,x,y,cv=5,scoring='accuracy').std())\n",
    "    y_pred = cross_val_predict(m,x,y,cv=5)\n",
    "    conf_mat = confusion_matrix(y,y_pred)\n",
    "    print('confusion_matrix for crossvalidation',conf_mat)\n",
    "    false_positive_rate,true_positive_rate,thresholds=roc_curve(y_test,predm)\n",
    "    roc_auc= auc(false_positive_rate,true_positive_rate)\n",
    "    print('roc_auc_score = ',roc_auc)\n",
    "    plt.figure(figsize=(10,50))\n",
    "    plt.subplot(911)\n",
    "    plt.title(m)\n",
    "    print(sns.heatmap(cm,annot=True))\n",
    "    plt.subplot(912)\n",
    "    plt.title(m)\n",
    "    plt.plot(false_positive_rate,true_positive_rate, label='AUC = %0.2f'% roc_auc)\n",
    "    plt.plot([0,1],[0,1],'r--')\n",
    "    plt.legend(loc='lower right')\n",
    "    plt.xlabel('False positive rate')\n",
    "    plt.ylabel('True positive rate')\n",
    "    plt.show()\n",
    "    \n",
    "    print('\\n\\n\\n')"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# observation\n",
    "with  the above classification we can say decision tree clasifier is working quite well with an \n",
    "\n",
    "***accuracy score of 85% \n",
    "\n",
    "***cross_validation_score is almost 77% \n",
    "\n",
    "while seeing the score we can say our data is learning more with decision tree clasifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "#lets check the other algorithm for good accuracy score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'n_neighbors': 7}\n",
      "7\n",
      "0.7177425665101722\n"
     ]
    }
   ],
   "source": [
    "# lets apply grid search cv to get the best parameters\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "neighbors={'n_neighbors':range(1,30)}\n",
    "knr=KNeighborsClassifier()\n",
    "gknr=GridSearchCV(knr,neighbors,cv=10)\n",
    "gknr.fit(x_train,y_train)\n",
    "print(gknr.best_params_)\n",
    "print(gknr.best_estimator_.n_neighbors)\n",
    "print(gknr.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy for Kneighbors classifier on CV data:  0.7430167597765364\n"
     ]
    }
   ],
   "source": [
    "knr=KNeighborsClassifier(n_neighbors=7)\n",
    "knr.fit(x_train,y_train)\n",
    "pred=knr.predict(x_test)\n",
    "print(\"Accuracy for Kneighbors classifier on CV data: \",accuracy_score(y_test,pred))"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "#obervation\n",
    "\n",
    "we can see we are getting accuracy score of 74%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean croos val score for KneighborsClassifier: 0.7049149457033457\n",
      "standard deviation in accuracy score for KneighborsClassifier: 0.04757283952816299\n"
     ]
    }
   ],
   "source": [
    "print('mean cross val score for KneighborsClassifier:',cross_val_score(knr,x,y,cv=5,scoring='accuracy').mean())\n",
    "print('standard deviation in accuracy score for KneighborsClassifier:',cross_val_score(knr,x,y,cv=5,scoring='accuracy').std())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "# we are getting cross val score of almost 70% we can say our model is under fitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7808824977839062\n",
      "linear\n",
      "{'C': 1, 'kernel': 'linear'}\n"
     ]
    }
   ],
   "source": [
    "from sklearn import svm\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "parameters={'kernel':('linear','rbf'),'C':[1,10]}\n",
    "svc=svm.SVC()\n",
    "clf=GridSearchCV(svc, parameters)\n",
    "clf.fit(x_train,y_train)\n",
    "print(clf.best_score_)\n",
    "print(clf.best_estimator_.kernel)\n",
    "print(clf.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy for svc classifier on CV data:  0.8100558659217877\n"
     ]
    }
   ],
   "source": [
    "svc=svm.SVC(kernel='linear',C=1)\n",
    "svc.fit(x_train,y_train)\n",
    "pred=svc.predict(x_test)\n",
    "print(\"Accuracy for svc classifier on CV data: \",accuracy_score(y_test,pred))"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "#observation\n",
    "we can observe that we are getting the accuracy score of 81% with svc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'criterion': 'gini'}\n",
      "0.7710134935487047\n"
     ]
    }
   ],
   "source": [
    "# lets find the best parameters of decision tree classifier with grid serach cv\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "dtc=DecisionTreeClassifier()\n",
    "grid_param ={\n",
    "    'criterion':['gini','entropy']\n",
    "}\n",
    "\n",
    "gd_sr = GridSearchCV(estimator=dtc,param_grid=grid_param, scoring='accuracy',cv=5)\n",
    "\n",
    "gd_sr.fit(x_train,y_train)\n",
    "\n",
    "best_parameters=gd_sr.best_params_\n",
    "print(best_parameters)\n",
    "best_result= gd_sr.best_score_\n",
    "print(best_result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy for Decision tree classifier on CV data:  0.8547486033519553\n"
     ]
    }
   ],
   "source": [
    "dtc=DecisionTreeClassifier(criterion='gini')\n",
    "dtc.fit(x_train,y_train)\n",
    "pred=dtc.predict(x_test)\n",
    "print(\"Accuracy for Decision tree classifier on CV data: \",accuracy_score(y_test,pred))"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# observation\n",
    "\n",
    "we can observe that with decision tree classifier our ,model is working well and gives accuracy score of 85%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean croos val score for decision tree classifier: 0.7632289247379325\n",
      "standard deviation in accuracy score for decision tree clasifier: 0.02094014512765293\n"
     ]
    }
   ],
   "source": [
    "print('mean cross val score for decision tree classifier:',cross_val_score(gd_sr,x,y,cv=5,scoring='accuracy').mean())\n",
    "print('standard deviation in accuracy score for decision tree clasifier:',cross_val_score(gd_sr,x,y,cv=5,scoring='accuracy').std())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'criterion': 'entropy', 'n_estimators': 100}\n",
      "0.79915295971634\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "rfc=RandomForestClassifier(random_state=42)\n",
    "#randomforestclassifier(100)-----Default\n",
    "parameters={'n_estimators':[10,100,500,1000],'criterion' :['gini', 'entropy']}\n",
    "rfc=GridSearchCV(estimator=rfc,param_grid=parameters,cv=5)\n",
    "rfc.fit(x_train,y_train)\n",
    "print(rfc.best_params_)\n",
    "print(rfc.best_score_)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy for Random Forest on CV data:  0.8100558659217877\n"
     ]
    }
   ],
   "source": [
    "rfc1=RandomForestClassifier(random_state=42,n_estimators=100,criterion='entropy')\n",
    "rfc1.fit(x_train,y_train)\n",
    "pred=rfc1.predict(x_test)\n",
    "print(\"Accuracy for Random Forest on CV data: \",accuracy_score(y_test,pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean croos val score for random forest classifier: 0.8126169104262131\n",
      "standard deviation in accuracy score for random forest clasifier: 0.036039262961452045\n"
     ]
    }
   ],
   "source": [
    "print('mean cross val score for random forest classifier:',cross_val_score(rfc1,x,y,cv=5,scoring='accuracy').mean())\n",
    "print('standard deviation in accuracy score for random forest clasifier:',cross_val_score(rfc1,x,y,cv=5,scoring='accuracy').std())"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# observation\n",
    "\n",
    "we can obeserve that with random forest classifier we are getting accuracy score of 81%\n",
    "\n",
    "and cross validation score of 81% which is quite good."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'learning_rate': 0.01, 'n_estimators': 1000}\n",
      "0.801920614596671\n"
     ]
    }
   ],
   "source": [
    "adc=AdaBoostClassifier()\n",
    "parameters={'n_estimators':[500,1000,2000],'learning_rate':[.001,0.01,.1]}\n",
    "adc=GridSearchCV(estimator=adc,param_grid=parameters,cv=5)\n",
    "adc.fit(x_train,y_train)\n",
    "print(adc.best_params_)\n",
    "print(adc.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy for ADA on CV data:  0.8100558659217877\n"
     ]
    }
   ],
   "source": [
    "adc=AdaBoostClassifier(learning_rate=0.001,n_estimators=1000)\n",
    "adc.fit(x_train,y_train)\n",
    "pred=adc.predict(x_test)\n",
    "print(\"Accuracy for ADA on CV data: \",accuracy_score(y_test,pred))"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "#observation \n",
    "\n",
    "we can conclude that decision tree clasifier algorithm is giving best accuracy score from all."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy for Decision tree classifier on CV data:  0.8547486033519553\n",
      "\n",
      "\n",
      "mean cross val score for Decision tree classifier: 0.769945389492185\n",
      "\n",
      "\n",
      "standard deviation in accuracy score for decision tree clasifier: 0.033049228856138514\n",
      "\n",
      "\n",
      "RMSE is:  0.38111861230861543\n"
     ]
    }
   ],
   "source": [
    "# lets save the model with Decision tree classifier\n",
    "dtc=DecisionTreeClassifier(criterion='gini')\n",
    "dtc.fit(x_train,y_train)\n",
    "pred=dtc.predict(x_test)\n",
    "print(\"Accuracy for Decision tree classifier on CV data: \",accuracy_score(y_test,pred))\n",
    "print('\\n')\n",
    "print('mean cross val score for Decision tree classifier:',cross_val_score(dtc,x,y,cv=5,scoring='accuracy').mean())\n",
    "print('\\n')\n",
    "print('standard deviation in accuracy score for decision tree clasifier:',cross_val_score(dtc,x,y,cv=5,scoring='accuracy').std())\n",
    "print('\\n')\n",
    "print('RMSE is: ',np.sqrt(mean_squared_error(y_test,pred)))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Titanic dataset.pkl']"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# lets Save the model\n",
    "import joblib\n",
    "joblib.dump(dtc,'Titanic dataset.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "       1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0], dtype=int64)"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# lets test the model how its working\n",
    "op_dt=dtc.predict(df_x)\n",
    "op_dt"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "#conclusion\n",
    "\n",
    "we can observe from the dataset that DecisionTree is working quite well with accuracy score of 85% and cross validation score of 77%.\n",
    "while giving the input data we can see the survival rate titanic train is working quite well. there is no similar output so we can say that our model is working quite well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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