Data_analysis_train.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import the necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as mpt\n",
    "import seaborn as sb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reading the data and storing it in a variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"train.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fetching columns of the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n",
       "       'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "sb.set()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x286b9c3c940>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZwAAAE0CAYAAAAYDoW6AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzsnXdYFNf/tu9dmiAgglJFQbFFQRB7QwEVFazEXr4aa2LsHXuJvUVjN7bYYkVs2H62YDRq7GIFAUFQioCgwO6+f6ArnV1UIr7nvq65LmbPM3OeOXvYz5wyZyQKhUKBQCAQCARfGOl/bUAgEAgE/38gAo5AIBAICgURcAQCgUBQKIiAIxAIBIJCQQQcgUAgEBQKIuAIBAKBoFAQAUcgEAgEhYIIOAKBQCAoFETAEQgEAkGhIAKOQCAQCAoFEXAEAoFAUCho/tcGBAKBQJA/qa+eqqzVKlX+CzopOCLgfALqVIDC4kNFW1S253/sJDNjQv4AYFa5Hv+xk8xMebYdgDXWX1d5DQ5NL6+pNl9XeQHMDE4vs8VfWR0b/b6OLf/KfA1/70sgAo5AIBAUDWSp/7WDT0YEHIFAICgKyOX/tYNPRgQcgUAgKAIoFCLgCAQCgaAwEC0cgUAgEBQKooUjEAgEgkJBLvuvHXwyIuAIBAJBUUCW9l87+GREwBEIBIIigJg0IBAIBILC4RuYNCDWUhMIBIKigEKu+qYGfn5+tG7dmhYtWrB9+/Zs6efOncPLywsvLy9Gjx7NmzdvCnwJooXzH6NQKPCZvZiKFWzo2937i+ZV3tWRxuM7o6GtxcvAEPzHbiAlMVllnUQqoemUHti4OCDV1ODquiPc/ONM+jHuTrRaMoiE59HK8+z0nkXqm7f5+rJzdcR1XBc0tTWJDAzFb9z6HH3lptMx0MVrwUBMKlggkUq5tfc8AWsOA1DRzYl2SwbzOoOvLd/PJEUFX2VdHak7Ib0cou+HcHbsBlKz+MpNo22gi8vCAZS0swCJlId7L3Bj9WFKVrTEbcVPyuMlGlJMqljjP2AZQcev5uvpA5WaOeL+vixeBIbiO34973Ios7x046+vIf5FjFL719rD3PINwMDUiA6LBqFfugQSiZQLa/y4dfCvXL3YZqkvJ3KpV7npJFIJLlN6YOvigOR9vbr1vl59oHrnJth51OJgvyUA1PnRi8pe9ZTpuiYGeZaXjasjDd/n/SowhFM5eMxPo29hTBff6WxvOYm3sYkAlKlflUY+3ZBqapD2NpVz07YSefMLLXn1BSYNREZGsnTpUvbv34+2tjZdu3albt262NnZARAfH8+ECRPYtm0bdnZ2rF+/nqVLlzJ58uQC5ZdvCycsLIzq1avTrl072rdvT5s2bejbty8vXrwoUIb/NStWrGDFihXZPg8LC8PV1bVQvTwJDuGHYRM5efbiF89L19gAj0UD8B20nN+bjeV1SBRNJnRRS1ejhxslbc3Z3HwCf3hNoWY/D8xrpK/dZuVckatrj7K1lY9yUyXY6Bkb0HbhQPYOXsYq17HEhUThloOvvHRNR39PfEQ0a1tMYKPXFJx7umNVM/0fxtq5EpfWHWF960nKTZVgU8zYgGaLB3Bi4HJ2NR1LfEgU9SZ2UVlTe4w3byJi+NN9Ivs9p1KtlxtmNe2IfRTOXg8f5RZ2/jaPDgaoFWz0jA1ov3Agu4Ys41e3scSGRtF8fM5llpvOpLwFyXGJrG49Sbnd8g0AwH1cF8L+fcyqVpPY2mc+XrP7ol+6RI5ePtSXQ4OWs+l9fWmcR73KSeeQoV5t95qCc4Z6VaxEcdx/6Uuz6b1AIlGe78oqP7a18mFbKx/+7DKH1KR3uZaXrrEBzRcN4Mig5Wx9n3fDLB7z01Tp1AjvPZPRNzdWfibV0qDVb0M5PX4jOzx8+GfFQVouG5Krj09GlqbyFh8fT1hYWLYtPj4+0ykDAgKoV68eRkZG6Onp0bJlS44fP65MDw4OxtLSUhmAmjVrxqlTpwp8CSp1qZmamuLr68vBgwc5cuQIlStXZsGCBQXOVJDOrn2H6eTVkhbNGn/xvGya2PPiZhBxwZEA3Nh2mqrtG6ils/Nw5s6e8yhkct69TuKB399817EhAJbOFSnb8Dt6H59D171TKFOnskq+yjexJ/zWU2Le53f1j1NUb9dQLZ3/9K2cnLMDAH1TIzR0NHmXkH5nWsa5IjYNqjHw2C/02TOFsnWqqOTLuok9UTeDeP0+v3vbTmOXpbzy0vw1bRuXZqd70jMzQqqtRUpCUqbjzetUpnzrOpyfuEklTx+wa5y5LP754xQOOZRZXrqyzhVRyOX88OcUfjw2l6bDOiCRpv+gS6RSdAz1ANDS1UYuk6OQK3L0Ui5LfbmZS73KS1fRw5m7GepVoN/fVH1fryp51SUxMpZzc3bmWh4uPt0JPnsr1/SyTeyJzJD3rW2nqZzFY16a4mZGVGjhzMFemX/z5KkyNtYZxsu7zwAwLGtKcmxCrj4+GTW61LZs2YKbm1u2bcuWLZlOGRUVRenSpZX7pqamREZGKvdtbGx48eIFgYGBABw7doxXr14V+BIK1KVWt25dlixZwrFjx9i0aRNv374lJSWFX375hZo1a7Jp0yYOHDiAVCrFwcGBmTNnEhgYyNSpU0lLS0NHR4e5c+diY2PD+fPn+fXXX0lLS6NMmTLMmjWLkiVL4urqStu2bbl48SLJycnMnz+f6tWr8/DhQyZMmIBMJqNWrVqcP3+ekydP8urVK6ZOncqLFy+QSCSMHj2aBg0asGLFCm7cuEFERAQ9e2ZeRfbevXv4+PgAUKWKaj9EnxOf0T8CEHDl+hfPy8DShPiIj91KCREx6Bjqoa2vm6nbIC+dgYUJCeGZ00pVsQYgOTaRQN8AHh79B6valWi/YSRbWvqQmKHLJicMLUyID/+oiY+IoVgOvvLTKWRy2i8bQtVWdQj0v0r0k/B0X3GJ3PEN4P7RK1jXqkTnDaNY5zGJhHx8Fbc0ITHDtSa+LwctfV1lt1p+GoVMjuvyIZRvXZsg/2vEPYnIlEd9n25cWbAnWzddfpSwNOF1RPay0NHXzdStlpdOqqHBk4t3ODl/NxpaGvT8fSzvEpO59PtxTi3YzQ97plCtdV2KGxtwfM4O3kRnvjP+gKGlCQkq1Ku8dFnrVWJEDKXf16sPXWvVvHO+KTOuaIldS2c2NB5FzX4tc9QYWJqQGJH9e8roMS/Nm8g4jgxanuO55Wky9EoZ0u3obIqVNODYTytz1H0W1Jg00KdPHzp06JDtc0NDwyynlCPJ0HJUKBSZ9g0NDZk/fz5TpkxBLpfTuXNntLS0CmA+HbUnDaSmpuLv74+joyO7du1izZo1HDp0iP79+7Nu3TpkMhlr165l37597N+/n9TUVCIjI9myZQt9+/Zl//79dO7cmRs3bhATE8PixYvZuHEjBw8epFGjRixatEiZl5GREXv37qVr166sXbsWgAkTJjB8+HB8fX2xtrZGJkvv15wzZw6dOnVi//79rF69mqlTp5KYmN7PmpKSwtGjR+nevXumaxk/fjxjxozhwIEDlClTpsCFWBSQSCSgyH6XqpDJVdZJpJLMSRJQvP8nODRoOQ+P/gPA838eEn7tETaNq+fvSypBoYovFXQHR6xmkdNgdI30aTK8IwB7Bi3j/tErAIRefUjYtUeUV9EX5J2fKpozw1ezucYQihkVx3nExx8AM+eKFDMx4NHBgHy9ZPMmybks5Dl8l7npru36P45O30pq8jvexicRsOEoVVvWAsB7+Y9cXHuYRXWHsqL5OBoP9sSqRi7vV1HRS566HOqVXMUfV+cfPPh3y0lSEnIP2qqUl6plmhNJr+LZWGcYf3aYQfPFAzGyNVfJu7ooFDKVN0NDQ8qUKZNtyxpwzM3NefnypXL/5cuXmJqaKvdlMhnm5ubs2bOHffv2UbVqVaytrQt8DSq1cKKiomjXrh2Q/uPt4ODA6NGj0dTU5MyZMwQFBXHlyhWkUikaGho4OTnh7e2Nm5sbffv2xczMDBcXF2bOnMmFCxdwdXWlWbNmnD9/noiICHr37g2kV7ISJT72FTdunH5XU7FiRU6cOEFcXBzPnz/HxcUFgE6dOrF161YgvS/y6dOn/PrrrwCkpaURGhoKgIODQ7ZriomJISoqioYN05vuHTt2ZN++feqX4FdMw1GdqNC8JgDaBrq8CgxVphmYlyQ5LpHU5Mx93/Hh0Vg4VchRlxAejb5ZSWWavllJ5Z2qYy93Lv926OOJJBJkaTkPcrqM6kQld2cAdAx0icrgy9DcOFdfVo52OerKN7EnKjCUxKg4UpPecefQJaq2qo2OoR61ernzVxZf8lx8ZSTxeTSmGcqhuHlJ3sYlkpbBV16aMi72xASGkhQZR1rSOx77XsK2VW2l1s6rHg/3XswxuOeE68hOVG7+vsz0dYl8kPG7NCYphzKLC4/GyskuR12NDo14cf8ZkR/KXiJBlipDr6Q+ZWtVZnP3XwCICY7kycU72NSpwvMsg+G9js3JVq/039eXtCxeErLUq4y6nOpVYkTeLVBID/gVW9XmjzZT8tTFh0djliXvrN+lKpqsaBvoYt2gGk/808ffXt4J5tW9EEpVsSYu6AuMcX+B53A+9ALFxMSgq6vLiRMnmDVrljJdIpHQr18/9uzZg6mpKZs3b6Z169YFzk+tMRxfX1+OHTvG/Pnz0dLSwtvbm7CwMGrXrk2vXr2U+lWrVjF9+nQUCgX9+/fnypUreHh4cODAARwcHNi8eTPTpk1DJpNRs2ZN5bn37t2rDBgAOjo6yosG0NDQyPEuBNKD1ZYtW5Tn+vPPP6lUqRIAxYoVy6bPekejoaGhSlEUKf5ask85gL+j3XQsnOwwsjEDoEZPN56cyN6V9+z87Vx1j09cw75LEyQa6X38Vbzq8dj/GimJyTj2cafi+x9U02rlsKhRnuCzN3P0dW7JPuUA/u/tp2HlZIfx+/yce7jx4MS1bMc8OX87V913nvVoMiK9RaOhrcl3nnUJDrhLSmIytXo3p8p7X+bVymFVozyPc/GVkdDztzFzsqPE+/y+6+lGcJbyyktTwbMutd57kmprUsGzLuEB95THWtSrwvO/7ubr4wNnlu5TDu6v7zANa8ePZVG7hxuBJ3Moswu3c9WZVi6D6yhvJFIJmjpa1O3TnDuH/yYpNpH4iBi+a10HAL2S+pSrU4WwG0+ynX+bGvUqOI969eTENapnqFeV39er/ChVxZq3r5OID8t7TCEkS972Pd14msWjKpqsKGRy3BcNwKJWRQCMK1lRsoIFL/7NXlafBblc9U1FzMzMGDlyJL1796Z9+/Z4enri4ODAgAEDuH37NlKplJkzZ9K/f388PDwwNDTkhx9+KPAlFHhadHBwMBKJhMGDB6NQKBg3bhwymYyYmBh69OjB3r17cXJy4sWLFzx48IAdO3bg6elJ165dqVChAnPnzmX48OFMnjyZoKAgbG1tWbVqFZGRkcybNy/HPA0MDLC2tubcuXO4uLjg5+enTKtXrx47duzgxx9/5PHjx/To0YPTp0/n6r9kyZJYWlpy9uxZmjZtyuHDhwtaFEWCpOh4jo9ZR9s1w9DQ0iQuJIpjI9YAYOZgS8v5/dnayidP3Y1tpzEqZ0Yf/1/Q0NLk5vYzhF1OH0w82H8pbjN603BUR+Rpcvx+Wkny+6mj+fnyG7sW79XD0dDWJOZZFL4jVwNgYW+L5/wBrG89KU/dydnbaTOnH4NOpNebB/5Xufy7PygU/Nl/CR4z++AyshPyNDn7hqrm6210PGdHr6P52vRyiH8WxZmRayjtYIvLgv7s9fDJVQNwadYOmsztS+dTcwEIOn6NWxv9lecvYWtGQmjBBl/fRMdzYOxauq4ejoZWelnsH5VeFpb2trSbP4DVrSflqTu7bD9tZvbhJ//5aGhqcPfoZa7t+j8Atg9YTJvpfWj6cwcUcgUXVh3i2T8PcvSSHB2P/5h1eGWoL8cz1KsW8/uzrZVPnrob205TopwZvd/Xq1sZ6lVelLQ1Jz7sZb665Oh4To5ZR+v3eb8OicJ/xBpMHWxxn9+fHe/95aTJi9SkdxzuvxSXab2QamogS0nl+LBV+Y5bFpgv9AK2D8/YZGT9+vXKv5s2bUrTpk0/S14SRW5NhveEhYXRu3dvzpzJPC9eJpMxduxY7t69i0QioVGjRpw6dYqzZ8+yefNmdu/eja6uLra2tsyaNYuQkBB8fHyQy+VoaWkxefJkHBwcOHPmDMuXL0cul2NmZsbChQuVkwa2bt1KmTJluHz5MitXrmTbtm08efKESZMmkZKSQuXKlbl16xZHjx4lMjKSqVOnEh6ePlg8ZswYXFxclFOgf/75Z4BM+48ePWLixImkpaXh6OjI+fPns11nXohXTKuOeMW0eohXTKvPt/6K6bdX9qisLVbn+8+S5+cm34DztbFy5Uo6d+6MqakpJ06cwM/PL8fnagoDEXBURwQc9RABR32++YDz926VtcXqZX8W6mugyK00YGlpSb9+/dDU1MTQ0JA5c+b815YEAoHgyyMW7yx8OnbsSMeOHf9rGwKBQFC4fAOLdxa5gCMQCAT/XyICjkAgEAgKA8UXmqVWmIiAIxAIBEUBMYYjEAgEgkJBdKkJBAKBoFAQLRyBQCAQFArfQAunyD34KRAIBP8/knzs1/xF79FtNewLOik4ooUjEAgERYFvoIUjAs4n8LUtHwMfl5D52pbd+bDkTi2LL/92U3W4GnEBgIk23fNRFi5zg9PfFvq1LQUEH5cD2m75ddX/HuHpdf+2rVc+ysLFPsgvf5EqiDEcgUAgEBQKooUjEAgEgkJBtHAEAoFAUCiIFo5AIBAICgVZ2n/t4JMRAUcgEAiKAqKFIxAIBIJC4Rt4ZFIEHIFAICgKiBaOQCAQCAoFEXAEAoFAUCiIadECgUAgKBRksv/awScjAs4XoLyrI43Hd0ZDW4uXgSH4j91ASmKyyjqJVELTKT2wcXFAqqnB1XVHuPnHmfRj3J1otWQQCc+jlefZ6T2L1DdvP/t1KBQKfGYvpmIFG/p29/7s58+Nhm71GTppENraWjy6/4RZo+bxJjEpm65z34506tMeFArCgp8ze8wCYqPjMmkWbJzNqxevWOCzrEBeKjdzpOW4rmhqa/IiMJR949fxLofvMjdd91XDMbExU+qMy5jy9PJ9/OfvosuvPyk/l0qlmFcpyx+DlnLX/598fdm5OuI6rgua2ppEBobiN259jnUsN52OgS5eCwZiUsECiVTKrb3nCVhzGICKbk60WzKY1xnq2JbvZ5KSTx2zdHPEcWJnNHS0iL0Xwt+jN5CWxVNuGolUQq05fTCrXxWA52du8O/MnellVqM8tWb2RENPB6lUyt3fDhO8/698yyg3DJrVwmxcb6TaWrwNDCZs/K/Is/g0at+UUgM7gkKBPPkdETPWkXz7MQCGHg0w/fF7JNpapDyPImz0UmRxCQX2ozLfQJeatDAzO378OB07dqRt27Z4eXmxYcOGTz7nzp072blz5yefp1evXly+fPmTz6NrbIDHogH4DlrO783G8jokiiYTuqilq9HDjZK25mxuPoE/vKZQs58H5jXS1yKzcq7I1bVH2drKR7l9iWDzJDiEH4ZN5OTZi5/93HlhZGLEtGUTGdd/Mp0a9+D5s3CG+gzOpqviUImeQ7rSz2sIXZr1ISQojCHj+2fS9P6xO051axTYS3FjA7wXDmL7kGUscRtDTGgkHuO7qqXb8eNyVrSexIrWkzgwYQPJ8W84NHUTUY+fKz9f0XoSjy7c5obvXyoFGz1jA9ouHMjewctY5TqWuJAo3HKoY3npmo7+nviIaNa2mMBGryk493THqqYdANbOlbi07gjrW09SbvkFGx1jA+ovHcCFAcvxazyWxJAonCZ1UVlj690IwwoWHHGdwBH3SZjVq0pZzzoANNkwjFuL9nGsuQ9nei7EeXp3DGzNsnlQBQ1jQ8osGE7IkLk8dBtCSsgLzMf9L5NGu7wV5hP7EtxnGo/bDOflyt2UXT0JAF17OyxnDOLZkLk88hhKSlA4ZmN6FciL2sjlqm9fKYUWcCIjI5k/fz4bN27k0KFD7Nq1i6NHj3L69OlPOm+3bt3o1q3bZ3L56dg0sefFzSDigiMBuLHtNFXbN1BLZ+fhzJ0951HI5Lx7ncQDv7/5rmNDACydK1K24Xf0Pj6HrnunUKZO5S9yHbv2HaaTV0taNCvcxTbrudTm3o1AQoPCANi75SCtOjbPpgu89ZAODbrxJuEN2jramJqXJi42XpnuXN+R+s3qsG/rwQJ7qdjYgbBbT4kOfgHA33+cwrFdwwLpNLQ08F48mMOztvE6IiZTmk3tylRvVYeDPr+r5Kt8E3vCbz0l5n3dufrHKarn4Csvnf/0rZyck75AqL6pERo6mrxLSL/LL+NcEZsG1Rh47Bf67JlC2TpV8vVk4WJP9I0gEoLS83q05TQ2HRuorJFoSNHU00Gqo4WGtiZSLQ1k71KR6mhxe8kBXly4C0ByRAxvoxPQszBWqayyYtDYiaRbj0gJjgAg+o9jGLVzyaRRvEvl+YQVpL2MBSDp9mM0Sxsh0dLEqH1TYv88SerzKAAil+/g5dp9BfKiNgq56ttXSqF1qcXGxpKamsrbt+l3SsWLF2fevHno6Ojg6urK1q1bKVOmDJcvX2blypVs27aNXr16UaJECR49eoSXlxexsbFMmTIFgHnz5mFubk5CQnpTtkSJEjx79ixb+vfff8/MmTN59OgRMpmMAQMG4OnpSUpKCj4+Pty5cwcrKytiY2M/y3UaWJoQH/GxKyIhIgYdQz209XUzdXnkpTOwMCEhPHNaqSrWACTHJhLoG8DDo/9gVbsS7TeMZEtLHxJfZP4R+1R8Rv8IQMCV65/1vPlhZmlKZHikcj8q4iX6hvoU19fL1q0mS5Ph4tGYKYvHkfIulTULNwJQysyE0bOG83P3MXTq1bbAXkpYGvM6w3cUHxFDMUM9dPR1M3WrqaKr1aUZCZFx3PO/mi2fVpO6c2LRnzl21eWEoYUJ8eEfv+8P+WWtY/npFDI57ZcNoWqrOgT6XyX6STgAyXGJ3PEN4P7RK1jXqkTnDaNY5zGJhDzqmJ6VCUkZ6mxSRAzahnpo6usqu9Xy0jzdfZ6ynnXpcG0FUk0pEedu8/zkvwA82XlOeYxdj2Zo6Rfj1fXHKpVVVrQsSpMa8Uq5n/riFRqGxZHq6yq71VKfRykDCoDF5B9IOH0FRWoaOrZWJAcGU26dD1plzHj7IJiIWZ/eU6MKCnnRfw6n0Fo4VapUwc3NDXd3d7y9vVm4cCFyuZxy5crleVzlypXx9/ene/funDx5EplMhkKh4MSJE7Rp00ap8/T0zDF99erVVKtWjf3797N9+3bWrFlDaGgo27ZtA+DYsWNMnjyZkJCQz3KdEokkxwe0FDK5yjqJVJI5SQKK983kQ4OW8/BoerfL838eEn7tETaNq38W718DUqk0x+fbZLKc79rOHb+AezUv1i3exIqdi9HS1mLO6uksmbaC6KjoHI9RFYkkZy/ybN9l/rpG/VpxZuWBbJqyNStS3NiQm74BqvuSSsjpvYnZ6pgKuoMjVrPIaTC6Rvo0Gd4RgD2DlnH/6BUAQq8+JOzaI8rnU8ckkvzzyktjP7oj76Lj2V/jRw44D0PbSJ8qg1pl0n031AuHMZ0422cJsrepefrJFalq/58AEl0dyv42Hp1yFoSNX5H+oZYGhm51eD7pNx63GU7ayzis5v5cMC/qIktTfftKKdRJAzNmzODHH3/k4sWLXLx4kc6dO7No0aI8j3FwcADA2NiYKlWqcPnyZbS0tLC1taV06dJKXW7pAQEBvH37ln370pu9SUlJPHr0iCtXrtClS3r/sY2NDU5OTgW+roajOlGheU0AtA10eRUYqkwzMC9JclwiqcnvMh0THx6NhVOFHHUJ4dHom5VUpumblVS2gBx7uXP5t0MfTySRIEsr2rNXBo39gSYt0rt6ihsU58n9J8q00haleB0bz9vkzGMIZWysMDE15uaV2wAc2nmEifNH812NKliVtWDk9KEAmJgao6GhgbaODrPHzM/Xi/tIb6q+/y6L6evx4sHHGxFDc2OScvgu48JfYZ3hu8yqs6hWDqmmBkF/38+Wn4NXff7dfyHHH+KMuIzqRCV3ZwB0DHSJylDHDM2Nc61jVo52OerKN7EnKjCUxKg4UpPecefQJaq2qo2OoR61ernzV5Y6Js+njr15Ho1JzY9loGdeknexicgyeMpLY92qFlcnb0WeKkOemkzQnguUbVOHwLXHkGprUn/ZIEpUtMTfazpvwl5RUFLDX6LnWEm5r2VuQlpcAoosZadlWZpyG6bw7nEoT7v5oHiXAkBaZAxvA4NJe5U+OSV2zylsd8wusB+1EC0c1Tl79ixHjx7FzMyMTp06sXTpUiZPnszevXsBlP9waWmZo3OxYsWUf7dr146jR49y9OhRvLyyv2Qpp3S5XM7ChQvx9fXF19eXP//8k8aNG2e729LULHjs/WvJPuUA/o5207FwssPo/cykGj3deHIie7fUs/O3c9U9PnEN+y5NkGhI0THUo4pXPR77XyMlMRnHPu5UbFUbANNq5bCoUZ7gszcL7P1rYO3CjfRo3o8ezfvRt80gqjtXw9q2DACderfnnH/2iQulzEz4ZfV0ShiXAKBVp+Y8CQzi5j+38azlrTzf/q2+nPQ9rVKwATi1dK9yIH9Vh6lYO1bExMYcgLo93Lh38lq2Yx5duJ2nrnzdqjwJuJtjfrZ1q/D4r5zTMnJuyT7lAP7v7adh5WSH8fu649zDjQcnsvt6cv52rrrvPOvRZER6i0ZDW5PvPOsSHHCXlMRkavVuTpX3dcy8WjmsapTncT51LOLcbUrVtFMO5lfs7UZYlnqflybmdjBlveoCINHUwKpFTWW3WYMVQ9DS18W/7cxPCjYACRf+RdepMto2FgAYd29F/MnMk4WkxXUpv/MX4o8HEDpsoTLYALw+9heGrrXRMDIAoIRHfZJvFqx7T22+gUkDhdbCKVasGLNmzcLBwYEyZcqgUCi4f/8+VatWJTo6msePH2NtbZ3nJAI3NzfqOWleAAAgAElEQVR+/fVXFAoFY8aMUSm9Xr167Ny5k9mzZxMVFUX79u3ZtWsX9evXx8/Pj2bNmhEREcH1659nrCIpOp7jY9bRds0wNLQ0iQuJ4tiINQCYOdjScn5/trbyyVN3Y9tpjMqZ0cf/FzS0NLm5/QxhlwMBONh/KW4zetNwVEfkaXL8flpJcmziZ/H+NRAbHcfMEXOZv34WWtqahAWHM21Y+h1k1RqVmbxoPD2a9+PG5Vv8vnwb6/b9SlqajFeRrxjTd9Jn9fImOp59Y9fSY/VwNLQ0iXkWyZ+jVgNgZW9Lx/kDWNF6Up46ABMbc2LDXuaYR6k80nIjKToev7Fr8V49HA1tTWKeReE7Mj0/C3tbPOcPYH3rSXnqTs7eTps5/Rh0Yh4AD/yvcvl3f1Ao+LP/Ejxm9sFlZCfkaXL2Dc2/jr2LjufvketovG4YUm1NEoOjCBi+BmMHW+ou7s+x5j65agCuTd9O7Tl98Dy/AIVMzouLd7m36jClnO0o51WX+CcRtDg0VZnfjdm7iDh3W61yA5BFv+b52OWUXTURiZYmKc9eEDZ6Cbr2dljN+5nHbYZj0rsNWlalMWxZH8OW9ZXHBvWYTMLpf9AyL0X5XXNBKiH1+UvCxv+qto8C8RUHElWRKPJry39GDhw4wMaNG0lNTe9/bdy4MePGjePSpUvMmjWLEiVK0KhRI65fv66cNDB06FDq1q2rPMfEiRNJSUlh8eLFAKxYkd63+vPPP+eYnpiYyPTp0wkMDEQmkzFw4EA6dOhAamoqM2bM4J9//sHKyoo3b94watSoTHnlh3jFtOqIV0yrh3jFtPp866+YTlo2SGWt3oi1nyXPz02hjuF06NCBDh06ZPvcxcUFFxeXbJ9/GNjPyNy5czPtfwg0uaXr6+vnOE6kpaXF7NmF1PcqEAgEn8o30MIp1Ac/BQKBQFBAZDLVNzXw8/OjdevWtGjRgu3bt2dLf/r0Kb169aJt27b88MMPvH79usCXIAKOQCAQFAXkCtU3FYmMjGTp0qXs2LGDgwcPsnv3bh4//jgJQqFQMGTIEAYMGMChQ4eoWrUq69atK/AliLXUBAKBoAigUKNLLT4+nvj4+GyfGxoaYmhoqNwPCAigXr16GBkZAdCyZUuOHz/O0KHpjxXcvXsXPT09mjRpAsDgwYNzPK+qiIAjEAgERQE1Wi5btmxh5cqV2T4fOnRopnHvqKioTM8zmpqacuvWLeV+SEgIpUqVYtKkSdy/f5/y5csrV3MpCCLgCAQCQVFAjTXS+vTpk+MErYytG0h/TlEikXzMQqHItJ+WlsaVK1f4448/sLe3Z9myZcybN4958+YV4AJEwBEIBIKigRorimTtOssNc3Nzrl79uL7fy5cvMTU1Ve6XLl2acuXKYW9vD6QvITZs2DA1TGdGTBoQCASCosAXmDTQoEEDLl26RExMDMnJyZw4cUI5XgPg5ORETEwMgYHpD56fOXOGatWqFfgSRAtHIBAIigJf4LUDZmZmjBw5kt69e5Oamoq3tzcODg4MGDCAYcOGYW9vz2+//cbkyZNJTk7G3NycBQsWFDi/Ql1pQCAQCAQF443P9ypri8/Z8wWdFBzRwhEIBIIigDrTor9WRMD5BL7mda6+1jXLvtY13qqb1fuPnWTmTuTfwNe3Xhl8XLNshfXX5e3n0HRfmtpW/7GTzKSlPP88J/oGXk8gAo5AIBAUBdRcsuZrRAQcgUAgKAqIFo5AIBAICgOFCDgCgUAgKBREwBEIBAJBoSBmqQkEAoGgUBAtHIFAIBAUBgqZaOEIBAKBoDAQLRyBQCAQFAoi4AgEAoGgMBDTogU5YufqiOu4LmhqaxIZGIrfuPWkJCarrNMx0MVrwUBMKlggkUq5tfc8AWsOA1DRzYl2Swbz+nm08jxbvp9Jypu3anls6FafoZMGoa2txaP7T5g1ah5vEpOy6Tr37UinPu1BoSAs+DmzxywgNjouk2bBxtm8evGKBT7L1PJQUBQKBT6zF1Oxgg19u3sXSp4ATdwbMMLnR7S0tXh47zFTR87Jscy69fOmS5+OKFAQGvyc6aPnEvMqliUbfqGsbRmlzqqsJVcv/cvPvceq5cPSzRHHiZ3R0NEi9l4If4/eQFqW+pWbRiKVUGtOH8zqVwXg+Zkb/DtzJwBmDariNKUbUk0NZG9TuTplK9E3VF+KyMbVkfoTOqOhrcWr+yGcHruB1Cy+8tPoWxjz/aHp7GwxibexiQCUrGiJ67wf0CpeDBQKAubtJuTcbbXKLCutW7kxe/YEdHR0uH37PgMGjiYhITGbbuH8qXTq5ElMbHqdf/jwCd17DAHg8t/H0NUtRkpKKgA7d+5n8ZI1n+QrT76BgPNVvg8nLCyM6tWr065dO9q3b0+bNm3o27cvL168yFG/f/9+JkyYUMguc0bP2IC2Cweyd/AyVrmOJS4kCrcJXdTSNR39PfER0axtMYGNXlNw7umOVU07AKydK3Fp3RHWt56k3NQNNkYmRkxbNpFx/SfTqXEPnj8LZ6jP4Gy6Kg6V6DmkK/28htClWR9CgsIYMr5/Jk3vH7vjVLeGWvl/Ck+CQ/hh2EROnr1YaHkClDQxYtbyyYzoNxGvhl0IexbOyMk/ZdN951CZ/w3pQU/PAXRw6UHI01CGjh8IwKj+k/B26423W2+mj55LwusE5kxYqJYPHWMD6i8dwIUBy/FrPJbEkCicJnVRWWPr3QjDChYccZ3AEfdJmNWrSlnPOki1NGi0ZiiXx27kaHMf7iw/SIMVQ1T2VczYALfFAzg6cDl/NB1LfEgUDSZ2UUtTpVMjOu6djL65cabjms7py70/z7HLw4dTY9bjsepnJBoF/+kqVcqYDeuX0LnLQKpVb0JQ0DN+mTMpR239+rXo0fNHatVuQa3aLZTBRk9Plwrly1HTubky7YsGG0CRplB5+1r5KgMOpL9b29fXl4MHD3LkyBEqV678Se9hKCzKN7En/NZTYoIjAbj6xymqt2uols5/+lZOztkBgL6pERo6mrxLSL8LLONcEZsG1Rh47Bf67JlC2TpV1PZYz6U2924EEhoUBsDeLQdp1bF5Nl3grYd0aNCNNwlv0NbRxtS8NHGx8cp05/qO1G9Wh31bD6rtoaDs2neYTl4tadGscBcnbdC0Lnf/vU9IUCgAu7fsp02nltl09249oE19bxI/lJlFaV7Hvs6k0dTSZM6vU5k/ZRkvwqPU8mHhYk/0jSASgtLrzaMtp7Hp2EBljURDiqaeDlIdLTS0NZFqaSB7l4o8Vcb+msOIvfMMAP1ypryLTVDZV9km9kTdDOL1+/p8e9tpKrdvoLKmuJkR5Vs6c6hn9v9xqYaUYiWKA6CtXwzZu1SVfeVE8+YuXL16k8ePgwBYs3Yr3btlfx2ztrY2jo7VGDNmCP9eP8Wfu9dhbW0JQJ3aTiQmJnH08Hb+vX6KxQunU6xYsU/ylS9f4AVshc1XG3CyUrduXR49ekRAQABt27bFy8uLQYMGkZiYuRl87NgxOnfuTNu2bfHw8OD69esAbNq0ibZt29K+fXumTp0KQGBgIJ07d6Zjx45069aN4ODgT/ZpaGFCfHiMcj8+IoZihnpo6+uqpVPI5LRfNoTBJ+bx7NJ9op+EA5Acl8j17adZ12oSZ+bv5vt1IzDIckeYH2aWpkSGRyr3oyJeom+oT3F9vWxaWZoMF4/GHL2+D6d6NfDbdRSAUmYmjJ41nMk/zUJeiNM1fUb/SJsWzQotvw+YW5ryIkOZRYZHYZBLmaWlyXBt1YTT/x7CuZ4jB3YeyZTeqXtboiJfcfrYObV96FmZkBT+sTs1KSIGbUM9NDPUr7w0T3efJ+V1Eh2uraDjjZUkBEfy/OS/ACjSZBQrZUiHa7/iNLkb91Zl9p0XBpYmJGTIMzEiBh1DPbQy+MpL8yYyjqMDlxMXlL0X46zPZpx/akvfK7/SfsdEzk7a9ElThK3LWBIaFq7cDwuLoEQJQwwM9DPpLC3N+L//+4up0xbgVNOdy5evs3/fJgD0DYpz9lwAXboNol79NliXtWTO7IkF9qQScjW2r5QiEXBSU1Px9/enevXqjBkzhvnz5+Pn50elSpU4cOCAUieXy9m1axdr1qzh0KFD9O/fn3Xr1iGTyVi7di379u1j//79pKamEhkZyZYtW+jbty/79++nc+fO3Lhx45O9SqQScnqnXdZ/EFV0B0esZpHTYHSN9GkyvCMAewYt4/7RKwCEXn1I2LVHlG9cXS2PUqmUnF67J8vln/jc8Qu4V/Ni3eJNrNi5GC1tLeasns6SaSuIjorO8ZhvjdzKTJ7L099njp2n8XcerFq0kbW7lyGRSJRpvQZ1Zd3STQXyIZHkX2/y0tiP7si76Hj21/iRA87D0DbSp8qgVkrN21fxHHAehn/bGdRbMhCD8uaq+ZJKgHx8qaDJioaOFh6rhnJq1Fo21RnGPu9ZNJvXD30L9W6yMpL+XWb3IcuyGnNwcChe7Xpz9+4DABYvWUOF8uWwsbHm8OGT/K/vMGJj43j37h3z5q2gfTuPAntSBYVcofL2tfLVThqIioqiXbt2AKSkpODg4ED37t0JDAykatX0Ac/Ro0cD6WM4kF6RfvvtN86cOUNQUBBXrlxBKpWioaGBk5MT3t7euLm50bdvX8zMzHBxcWHmzJlcuHABV1dXmjUr2J2zy6hOVHJ3BkDHQJeowFBlmqG5MclxiaQmv8t0THx4NFaOdjnqyjexJyowlMSoOFKT3nHn0CWqtqqNjqEetXq589dvhz6eSCJBnpb/suWDxv5AkxbpXXbFDYrz5P4TZVppi1K8jo3nbXLmsaAyNlaYmBpz80r6AO2hnUeYOH8039WoglVZC0ZOHwqAiakxGhoaaOvoMHvMfFWKrEjw07gBNGuZ3nVX3KA4jzKU2YeusuSkzGVmbVOGUqYm/HvlJgAHdvgxdcE4DI0MeB0bT5XqldDQ1OCfgOsF8vTmeTQmNSso9/XMS/IuNhFZhvqVl8a6VS2uTt6KPFWGPDWZoD0XKNumDk92nMWsYTXCjl8FIPZ2MHH3QjCqYk3C05zHTjOS8DwaM6ePeeqbl+RtXCJpGXyposmKSeUyaOrqEHw6/WYw8t8nRD98jplTBRIjYnI9LivTp43B07MFAIYG+ty5G6hMs7IyJyYmlqSkzBMc7O2r4uDwHdu371N+JpFISE1Nw7NNc16/jufCxcsZPv+0rr58+YpbLqry1bZwPozh+Pr6cuzYMebPn4+2tnamO8WEhIRMEwnevHmDt7c3YWFh1K5dm169einTVq1axfTp01EoFPTv358rV67g4eHBgQMHcHBwYPPmzUybNq1AXs8t2accwP+9/TSsnOwwtjEDwLmHGw9OXMt2zJPzt3PVfedZjyYj0ls0GtqafOdZl+CAu6QkJlOrd3OqtKoNgHm1cljVKM/jszfz9bh24UZ6NO9Hj+b96NtmENWdq2H9fsZUp97tOeeffRC+lJkJv6yeTgnjEgC06tScJ4FB3PznNp61vJXn27/Vl5O+p7+pYAPw24L1ykH+Hq37U8O5OmVtrQHo0qcDZ45fyHZMaTMTFq6dhdH7MvPs1JLHgU95/X7sq1YDJ65czF4fVCXi3G1K1bTDwDa93lTs7UbYiesqa2JuB1PWqy4AEk0NrFrU5NX1xyhkcuotGUDp2hUBKFHJCkM7C179+wRVCDl/G3MnO0q8r8/Ve7rxNIsvVTRZiQuORMdAF3PndF+G5UwxrmjJy7vPVPL1gekzFikH9xs29qJunZrY2dkCMGhgLw75nch2jFwuZ9mSmdjYpH/ngwf14fbt+zx/HoGVlQUL5k+hWLFiSKVSRowYyJ69fmp5UhfRwilkbG1tiY6O5vHjx9jZ2bFhwwYAypUrB0BwcDASiYTBgwejUCgYN24cMpmMmJgYevTowd69e3FycuLFixc8ePCAHTt24OnpSdeuXalQoQJz5879ZI9J0fH4jV2L9+rhaGhrEvMsCt+RqwGwsLfFc/4A1reelKfu5OzttJnTj0En5gHwwP8ql3/3B4WCP/svwWNmH1xGdkKeJmff0JUkx2afzpkXsdFxzBwxl/nrZ6GlrUlYcDjThs0GoGqNykxeNJ4ezftx4/Itfl++jXX7fiUtTcaryFeM6ZvzbJ5vnZhXsUwePoulG39BS0uL0GdhTBw6E4BqNaowY0n6DLTrl2+yftlmNh1YhSxNRtSLVwz73zjlecrZWvM8NKLAPt5Fx/P3yHU0XjcMqbYmicFRBAxfg7GDLXUX9+dYc59cNQDXpm+n9pw+eJ5fgEIm58XFu9xbdRh5qozz/ZbiPKMXEi0N5O9S+eunVSSr2IpIjo7n1Oh1tF47DKmWJq+fRXFy5BpMHWxxXdCfXR4+uWryIiU+iSMDltFkRi80dLRQpMn4vwm/E/9MvckWGXn5Mpr+A0axe9c6tLW1ePrkGf/rNxwA55oOrF2bHpzu3n3A8JFTOHhgMxoaGjwPi6BHrx8BWLd+G+Vty/LPleNoamhy9txfzJq9tMCeVEGR9kVPXyhIFDl1Zv7HhIWF0bt3b86cOZMt7cqVK8ybN4/U1FTKli3LggUL8Pf358qVK8yZM4exY8dy9+5dJBIJjRo14tSpU5w9e5bNmzeze/dudHV1sbW1ZdasWYSEhODj44NcLkdLS4vJkyfj4OCgsk/ximnVEa+YVg/ximn1+dZfMR3dxkVlrckR9SekFAZfZcApKoiAozoi4KiHCDjq860HnFetVA84pQowA7IwKFJdagKBQPD/Ld/ApAERcAQCgaAIoBABRyAQCASFgQg4AoFAICgUFDJJ/qKvHBFwBAKBoAggWjgCgUAgKBQUctHCEQgEAkEhIFo4AoFAICgUFIqi38IRD34KBAJBESC0tpvKWut/Tn9BJwVHtHAEAoGgCPAtNA1EwPkE1nxlS3sADH6/vMdEm+7/sZPMzA1Of4Pp17qEzNe65E5DK9f/2El2/nqevsZh93LZ35L5X7LjWfq7sXZbfF1LTnWJ2P5ZzvMtTBr4al9PIBAIBIKPKOQSlTd18PPzo3Xr1rRo0YLt27MHx5MnT+Ll5UWbNm2YMGECKSkpBb4GEXAEAoGgCKBQqL6pSmRkJEuXLmXHjh0cPHiQ3bt38/jxY2V6UlISM2fOZNOmTRw5coR3795lesuyuoguNYFAICgCqNNyiY+PJz4+PtvnhoaGGBoaKvcDAgKoV68eRkZGALRs2ZLjx48zdGj623z19PQ4c+YMWlpaJCcnEx0dnel4dREBRyAQCIoAcjWWttmyZQsrV67M9vnQoUP5+eeflftRUVGULl1auW9qasqtW7cyHaOlpcW5c+cYN24cpqamNGrUqADu0xEBRyAQCIoAcjWew+nTpw8dOmSf1JG1dSKXy5FIPp5XoVBk2v+Ai4sLly9fZsmSJUyfPp3Fixer4fwjIuAIBAJBEUCdBz+zdp3lhrm5OVevXlXuv3z5ElNTU+V+XFwcd+7cUbZqvLy8GDlypBquMyMmDQgEAkER4EvMUmvQoAGXLl0iJiaG5ORkTpw4QZMmTT7mqVAwduxYwsPDATh+/Dg1a9Ys8DWIFo5AIBAUAb7Eg59mZmaMHDmS3r17k5qaire3Nw4ODgwYMIBhw4Zhb2/PrFmzGDRoEBKJBDs7O2bMmFHg/ETAEQgEgiLAl3rw08vLCy8vr0yfrV+/Xvm3u7s77u7unyUvEXAEAoGgCCCTF/0REBFwPjNlXR2pO6EzGtpaRN8P4ezYDaQmJquk0TbQxWXhAEraWYBEysO9F7ix+jAlK1rituIn5fESDSkmVazxH7CMoONXs1rIlcrNHGk5riua2pq8CAxl3/h1vMviLS9d91XDMbExU+qMy5jy9PJ9/OfvosuvH/1JpVLMq5Tlj0FLuev/jzrFB0AT9waM8PkRLW0tHt57zNSRc3iTmJRN162fN136dESBgtDg50wfPZeYV7Es2fALZW3LKHVWZS25eulffu49Vm0v6qBQKPCZvZiKFWzo2937i+aVkfpudRk8oT/aOto8vv+UuaMXkpRDeXX6X3s69G6LQqHg+bNw5o1dTFx0nDLd1LI06w79Rp/m/Xkdm/0ZjoLg6OpM13E90dTWIjTwGevGrSQ5hzr3gcGLhxH64BlH1vkCULyEPv3mDKLcd7a8S37LuT1nOLH5qNo+LNwccZjUBam2Jq/vh3Jl1HrSsvjITaNtVBznef0wqlYWWfI7gnad59HvJwDQNipOzTl9MKxohYauNveW+/Js70W1/anCt7CWWtEPmVl4+PAhlStXxt/fv9DzLmZsQLPFAzgxcDm7mo4lPiSKehO7qKypPcabNxEx/Ok+kf2eU6nWyw2zmnbEPgpnr4ePcgs7f5tHBwPUCjbFjQ3wXjiI7UOWscRtDDGhkXiM76qWbsePy1nRehIrWk/iwIQNJMe/4dDUTUQ9fq78fEXrSTy6cJsbvn8VKNiUNDFi1vLJjOg3Ea+GXQh7Fs7IyT9l033nUJn/DelBT88BdHDpQcjTUIaOHwjAqP6T8Hbrjbdbb6aPnkvC6wTmTFiothd1eBIcwg/DJnLy7Jf5sckNI+MS+CwZh8/A6XRr0ofwZ+EMmTQgm66yfUW6De7MoHY/08vtB0KDnjNgXF9luod3c37bt4zSFqU+mzcDY0MGLfyZZYMXMMZ1KJEhL+g6oVeOWku7MvjsnEmd1vUzfd5raj/eJr1lrPswprafgGPTmji51lLLh46JAXWWDeSv/ss41ngsic+iqOHTRWWN44yepL15y3GXcZxqMw1z1xpYuDsBUGfZIJLCYzjRwoeznedSc1ZvdC2M1fKnKnKFROXta+WbCzj79u3Dw8OD3bt3F3re1k3siboZxOvgSADubTuNXfsGKmv+mraNS7PTF7nUMzNCqq1FSkLmO1XzOpUp37oO5yduUstbxcYOhN16SnTwCwD+/uMUju0aFkinoaWB9+LBHJ61jdcRMZnSbGpXpnqrOhz0+V0tfx9o0LQud/+9T0hQKAC7t+ynTaeW2XT3bj2gTX1vEhPeoK2jjalFaV7Hvs6k0dTSZM6vU5k/ZRkvwqMK5EdVdu07TCevlrRo1viL5pOVOi61uH/zAWFBzwE4sPUQLTpkX8b+we1HdGnUizcJb9DW0aK0eSni37diSpmZ0KRlI0b1GP9ZvTk0ceTprUe8CI4A4NQfx2nYrkmO2ha9W/F/u05y+UhAps9t7Stwcf9ZFHI5stQ0/j1zLVtQyg9zF3tibjwlMSj9f+7xllOU7dhQZY2xgy3Bey+ikCuQp8qIOHUDa886aBsVx6yJPXeX7AcgOSKGk22mkhKXqJY/VVEoJCpvXyvfVMBJTU3Fz8+PESNGcPfuXUJCQgC4fPkyXl5etG/fnunTp9OrV/pd1rNnz+jbty8dOnSgW7du3Lt375PyL25pQmJ4tHI/MSIGHUM9tPR1VdYoZHJclw+h88m5hP99n7gnEZnyqO/TjSsL9mTrpsuPEpbGvI74mG98RAzFDPXQyeBNVV2tLs1IiIzjnn/2FlarSd05sejPHLvqVMHc0pQX4ZHK/cjwKAwM9Smur5dNm5Ymw7VVE07/ewjneo4c2HkkU3qn7m2JinzF6WPnCuRFHXxG/0ibFs2+eD5ZMbU0JSpDMH0Z8RJ9Q330cigvWZqMxi0bcuDqnzjWdeDI7uMAvIqMZtKAaYQ+Dfus3owtShGdoa7HRESjZ1gc3Sx1DmDz1PUE+F7I9vnjGw9p1LEpGpoa6OgVo06r+pQ0LamWD11LE5LCP94YJUfEoG2oh2YGH3lpoq8/wca7ERJNDTT1dCjTpjbFzIzQtzHjbVQclQe1xs13Gs2Pz6KkvQ2y5IIvbpkXX2IttcLmmwo4586dw9LSEltbW9zd3dm9ezepqamMGzeOhQsXcvDgQTQ1Pw5bjR8/nrFjx3LgwAFmzZr1SQ80AUikEiD7t62QydXSnBm+ms01hlDMqDjOIz4+LWzmXJFiJgY8OhiQ7fh8vUmkOVZEuUyutq5Rv1acWZl9Ab+yNStS3NiQm77q+/uAVJpL/vKc36975th5Gn/nwapFG1m7e1mmp6R7DerKuqXqtQSLGlKpRKXv9QMX/P+ijX0HNi7ZwpLt83N8qvxzesvJXG7ecmL77E2gUPDL0SWMXj+B2xdukJaappYPSS4+sv1f5qK5MWM7KBS0PDmHhptGEXn+DvKUNKRamuiXMyU1IZnT7WZwafBKnGb0pKSDjVr+VEUml6q8fa18U5MG9u3bh6enJwCtW7dmzJgxtGzZEhMTE6pUqQKAt7c3c+bM4c2bN9y5c4eJEycqj09KSiI2NpaSJdW7g/pA4vNoTJ0qKPeLm5fkbVwiacnvVNKUcbEnJjCUpMg40pLe8dj3Erataiu1dl71eLj3osq3MO4jvanaPP0hrWL6erx4EKJMMzQ3JikukdQM3gDiwl9hncFfVp1FtXJINTUI+vt+tvwcvOrz7/4LqPsS2Z/GDaBZy/SuqOIGxXl0/4ky7UNXWXLS20zHWNuUoZSpCf9euQnAgR1+TF0wDkMjA17HxlOleiU0NDX4J+C6Wl6KAv3H/I9GLdK7YfX09XgaGKRMK2VemvjYeN4mZy4vKxtLTEobc+ufOwAc2XWMsfNGYGBkoOxa+xx4j+pGTff0OqtnoEtI4Mc6Z2xuQmJcAu+y1Lm80NXXY8cvW3nzOr2bqt1PnZRddKqS9DwaEye7j+e0MOZdbCKyDD7y0ugYm3Bz9k5S4t4AUHVYWxKDI0l+EQtA0K70FnRicCSvrjzE2LECsbeC1fKoCl/z2IyqfL2hUE2io6O5cOECv//+O66urkyePJn4+HjOnz+f492xXC5HW1sbX19f5bZnzx7lqqkFIfT8bcyc7CjxfibXdz3dCD5xXWVNBc+61BrREQCptiYVPOsSHvCxm8+iXhWe/3VXZT+nlvdTcD4AACAASURBVO5VDuSv6jAVa8eKmNiYA1C3hxv3Tl7LdsyjC7fz1JWvW5UnATl7sK1bhcdq+PvAbwvWKwf5e7TuTw3n6pS1tQagS58OnDmevaultJkJC9fOwsi4BACenVryOPCpcnZVrQZOXLmY/fq+BTYs2sz/Wgzkfy0GMtBrKNVqVqWMrRUAHXp5ceFE9hZmKVMTZqyeQomS6cudtOjoxtMHwZ812ADsXbKTSa1HMan1KKa2n0BFp0qY21gA4NajJddOXFHrfO49WvL96G4AGJYqQbOu7jl2veXFi7O3MXG2Q982/X+uQm83wv2vqayp0NuN6mPTZx3qlDKkfPdmhOwP4E3oS2JuBWHTubEyzaRWRWJvBvElUKixfa18My0cX19f6tWrx4YNG5SfrVixgosXLxIfH8+DBw+oXLkyfn5+ABgYGPD/2LvvsKaux4/j7yQkDFmCDMW9tYJb67biVtx771VHcQvuWRdqtc7aOuqqe2/r/lardeBeIDIEZYrsJL8/+BGNIIJVbqDn1YfnMfee5H56CTn3nHtyTuHChdm/fz+tW7fm0qVLTJ06lVOnTn12hrjQKM6OWUujNSNRKI2Ieh7CGffV2LkUod6CAexq6vnRMgD/m7WVuvP60unUPAB8jl3n9vp3o+2sijjw5sXrz8r2NjSK3ePW0H3VKBRKI8KeB/PH6FUAODkXod38gSxv7pFuOQDbwo6E+79K8xh50tmXUWGvw5k8ahZL1s9FqVTy4rk/k4bPBOCb8qWZ4ZU8Au2fK7dYt3QDv+1diTpJTcjL14zsM173OoWKFCDgReauhLOjiNAI5o5eyOy101EqjQh4HsisUT8CUNqlJBMXjaVP40HcuurNxp+2sGLXEtRqNa9fhjKp35Svmi0qNJI145YzatU4jFRKgp+/ZJX7MiB5MMDA+d/j0Xx0uq+xf+Vuhi35gfknliGTwU6v7Ty7/STd53woPjSKqz+soda6UchVRkT7hnBl5Cpyly9C1UUDOdHI46NlAO7/dIDqK4bS9M8fQSbjzsJdhN1KXiH2Ur8lVJrbh+K9GyKTy7i3ZK9u35eWE1o4Mm1m+z8MVMqkcg0avFuSNywsjO+++47169cze/Zs5HI5RYoUISoqinXr1vH06VOmT59OREQESqWS6dOn4+LikuFjiiWmM04sMZ05YonpzMvpS0xfcsz4d7tqvdz1RY75peWYFk5Ky+V9NjY23Lhxg0WLFrF161bMzMz47bffCA5OHgVVrFgxNm/enNVRBUEQMi3jQy0MV46pcD5GLpdjbW1Nhw4dUCqVODk5MWfOHKljCYIgZIo6B3Sp5fgKB2DQoEEMGjRI6hiCIAifTYOocARBEIQsoBUVjiAIgpAVxD0cQRAEIUuIFo4gCIKQJTI3oY9hEhWOIAhCNiBaOIIgCEKW+EorTGepHDPTgCAIQk623zHjs4e0frn1Kyb5fKKFIwiCkA3khJaBqHD+hamFDWvOJoCZvsnzNs0qZFjZpjxPzrUln2HNP9c9MHnuOUObsyxlvjJDm+MN3s3zFtqinsRJ9NkeTl4m4KRD50+UzFqNgr/M6sNiWLQgCIKQJdRfcbG8rCIqHEEQhGxAtHAEQRCELJETRqmJCkcQBCEbEJN3CoIgCFlCjFITBEEQsoToUhMEQRCyhFrqAF+AqHAEQRCyAdHCEQRBELKEGBYtCIIgZAlR4QhpKvldBRqO74yRyoiXD16wf8I64qNjM1Vuwj+riXoZpit7ac0hbu+/jIW9NW0XDcbczgqZTM6F1Qe5ve9ShnIVb1CBBv9/vOAHLzg4fh0JaeT6WDljC1PcFgzCtlheZHI5t3ed5/LqQwCUcK1Ia68hRAaE6l5nY8eZJLyN+2SufK4VqDCpEwpjJeH3/PhrzC8kfZDrY2VkchlV5vTGoUYZAALO3OTGzG0AONQsQ8UpXZEbKVDHJXJtyiZCb37+VDE1XKszZOIAVMYqntx/xrwxC4mJjklVrn2fNrTt1QqtVkvA80B+HLeYiNAI3X77fHasPfAzvRsNIDI86rPzZJRWq8Vz9mJKFCtM324dvvrxUiirfotZ70HIlEqSfJ/xdul8tLGpzxeA8tvaWIzxJKxjM73tslzmWM7/ieil81E/efhFcuVpWJHinl2Rq5RE3/Pjrvtq1Gn8HQB889Mwou/78XzVId22/H0a49S9AXITFW9uP+Ou+2q0CV9/tRptDuhSk0sdICOOHTtGu3btaNWqFW5ubvzyyy8ADBw4kODgYPbs2cPEiRPTfO6VK1fo3LkzrVq1okWLFixYsAC1+uvdfjOzsaDNwkFsH7qUn1zHEf4ihEYTUs/tlF4526J5iY2IZlVzD93P7f2XAWg4vjP+N56wspkHm3rPx212X8ztrDKUq9XCQewaspSVDcYR4ReC68S0c32sXP0xHYkKCmVN44msd5tC5R4NcapUHIAClUvyv7WHWdfcQ/eTkcrG2MaCGksGcmHgMg7WGUe0XwgVPTpnuEyRDrWxLJaXww0mcrihBw7flqFgy2rIlQpqrx7OlXHrOdLIkzvL9lFz+dBP5vkYaxsrPL3G4zloOl3r9ibweSBDPQamKlfKuQRdh3RicOsR9HTtzwufAAaO76vb37RDI37evRS7vHk+O0tmPPX1o//ISZw8ezFLjpdCZmmF+Q8TeTN3ChGDe6J5GYhZ38FplpXncyJX/6F8+DUTZZXqWHmtQpG/wBfLpbS14JtlQ7ndz4vLtdyJeR5MicmpZ2HOVcKJyrun4OBWXW+7ffNqFOjflOsdZ/G/umOQm6goNLjFF8uXnqRM/Bgqg69wgoODmT9/PuvXr+fAgQNs376dI0eOcPr0adatW4eDg8NHn5uQkMCYMWNYtGgRBw4cYO/evTx79owtW7Z8tbzF6zgTePsZYb7BAPz9+ylcWtfKVLmClUug1Wjo/8cUhh2dR/2RbZHJk/8aZXI5xpZmAChNVWjUGrSaT4/QL1pX/3jXfj9FuTRypVfu+PRNnJyTPO25ub01CmMj4t8kXxnmr1yCwjW/YdDRufTeOYWC1Upn6HzlredM6E0f3vgkH+/xxtMUblczw2VkCjlGZsbIjZUoVEbIlQrU8YloEtXsqTSS8DvPk/MWsic+/E2GMqWlWr0q3L/1EH+fAAD2bjpA47auqco99H5M59o9efvmLSpjJXaOeYj6/1ZMHgdb6japzejuEz47R2Zt332I9m5NaPxdnSw7JoCyUlWSHj9AE5h8vuIO70dVv2HqgsbGmI+dzNtffk61y6RVe94smoMmLDT18z6Tbf3yRN54SozPSwD8N57EsX3tVOXy921MwJYzBB/4S2973k518Vt9iKSIt6DVcn/8OoJ2nv9i+dKjzcRPZhw8eJDmzZvTuHHjND8b79+/T7t27WjSpAmenp4kJX1+lWbwXWrh4eEkJiYSF5d8tZwrVy5+/PFHjI2NadCgAZs2bQLg+fPndO/encjISOrXr8+YMWOIjY0lOjqa2NjkD0WVSoWnpydv374FoGfPnpQuXZpr164RHx+Ph4cHtWunfvNlhlU+WyKD3nWFRQWFYWJphrG5qV63Wnrl5AoFTy/e4eT8HSiUCnr8Oo746Fj+9+sxTi3YQf+dU/imeXVy2VhwbM5W3oZ+ulvGMq8tUYGpj6cyN9XrVvtUOa1aQ5ulQynTrBoPjl8j9GkgALER0dzZf5n7R65SoEpJOv0ymrVNPXjzXrdgWsycbIkJfPeBEhMUhsrSDCNzU123Wnplnu04T8GW1Wl7fTlyIzlB57wJOHkDAG2SGpM8ljQ7PhtjGwsuDl3xyfP0Mfb57AkJDNE9fhX0CnNLc8zMzVJ1q6mT1NRpUouJi8aSGJ/IL4t+A+B1cCgeA6d9dobP4TlmGACXr/6TpceV29mjefXufGlev0KeyxyZqZlet5r58LHEHz2I2id1V+ebqeO/eC6TfLbEv/deig8MRWlphsLcVK9b7aFH8u/Mtp6L3vPNiuZFmceSitsmYeyYm4i/HvBo1te7gH3f1xilFhwczJIlS9izZw8qlYouXbpQvXp1ihcvriszbtw4Zs+eTYUKFfDw8OCPP/6gW7eMr83zPoNv4ZQuXRpXV1caNmxIhw4dWLhwIRqNhkKFCumV8/f3Z/ny5ezZs4fr169z+vRprKysGDx4MO3atcPNzY3Zs2cTHBxM6dLvrr6jo6PZu3cvixcvZuLEiSQkJPyrvDKZjLTWtNOoNRkud337nxyZvonE2HjiomK4/MsRyjSpAkCHZcO4uOYQi6oPZ3mj8dQZ0hKn8kU/nUue9vG0H+bKQLl9P6xiUcUhmFqbU3dUOwB2Dl7K/SNXAXhx7RH+1x9TtE65T+f6yHl4/3jplXEe04740Cj2lB/G3sojUVmbU3rwu/sAca+j2Ft5JMdbzeBbr0FYFHX8ZKa0yOUy0lqq8MPfa4oLxy/Rwrkt67024rVlPrIcMNNvZshk8jQvtbWad+fLuEUbtGo18SePZF2wj72/NRm7JS9XKrCt58LtgUu50ngSytzmFJ/U5UunTJMmEz9RUVH4+/un+omK0r84vXz5Mt9++y3W1taYmZnRpEkTjh07ptsfEBBAXFwcFSpUAKBdu3Z6+zPL4CscgBkzZnDmzBm6du1KYGAgnTp14sSJE3plGjRogI2NDSqVimbNmnH1avKH39ChQzl//jyDBw/m7du3DBw4kA0bNuie16lTJwDKlCmDnZ0dDx9m/sZkA/f2DD0yl6FH5lKpy3dYOOTW7bNwtCEmIprE2Hi950QEhn60XPm2tXEo/V6/tUyGOlGNWW5zClYpxfVtfwIQ5hvM04t3KPyR7qt6o9sz8MhcBh6ZS8UPclk62hCbRq6oD3K9X65oXWfM7a0BSIyJ586B/+FYrjDGlmbU+r6V/sFlMjRJn75X9jYgFFPHd8czc8xNfHg06vdypVemQLMqPN1+Dk2imsQ3sfjsvIBjzbIoLUzJ37SK7jnh3r5E3PPDunTG7wcMGNuHDSfWsuHEWlp2bU4eB1vdvjyOdkSFRxEXq3+fyqlwPlyqvqtoD28/imN+ByysLTJ83JxA/SoYue278yW3zYPmTRTEvztfJq5NMSpRCqvlv2AxYz6ojLFa/gsyG9u0XvKLiPN/jfF77yXjvDYkhkejiYlP51nvxL8MJ+TwVdTRsWgT1QTtuoB1lRJfK66ezFQ4GzduxNXVNdXPxo0b9V4zJCQEOzs73WN7e3uCg4M/ut/Ozk5vf2YZfIVz9uxZjhw5goODA+3bt2fJkiVMnjyZXbt26ZUzMnrXO6jRaDAyMuLmzZts2bIFGxsbWrZsybx581ixYgU7d+7UlVUoFKmel1lnluzW3dxf13YaBSoUx6Zw8r2lqt1deXDyeqrnPL3g/dFy9qXy02B0B2RyGUbGSqr3bsSdQ38REx5NVFAYZZtXA8AstzmFqpXG/+bTNHOd89qtu4H/a5tpOFV8d7zK3V15eCKNXOe9P1qubMtvqftDcotGoTKibMvq+F6+S0J0LFV6NaJ0s6oAOH5TCKfyRXly9tYnz13QOW/yVCqORZHk45Xo5Yr/iX8yXCbM25eC/39jV2akwKlxJV7/8wStWsO3XgOxq5r8YWBV0gnL4nl5fSPtc5WWXxZtoE/jQfRpPIhBbsP5plIZ8hdxAqBtTzcunLic6jl57G2ZsWoKVrktAWjczpVnD31193H+KxL/+RujUmWR50s+XybNW5Hwl/5oysjRQ4j8vi+RIwbwZtoESIgncsQAtF/wns2HQs/dxqpyCcyKJLd08/duRMixaxl+fvChKzi0+ha5iRIAu2ZViczEe+rfyMw9nN69e3P69OlUP71799Z7TY1Go9f61mq1eo8/tT+zDP4ejomJCbNmzcLFxYX8+fOj1Wq5f/8+ZcqU4cmTJ7py586dY9SoURgbG3PkyBFGjhyJlZUVK1asoHLlyrputLt371KmTBnd844cOYKLiwve3t5ERUVRsmTJf5X3bWgUe8etocuqUSiURoQ9D2HP6FUA5HMuQuv5A1nV3CPdcmeX7qHFzN58f3w+CiMFd49c4fr25FbNloGLaTG9N/VHtEWr0XJh5QGe//3pVllMaBQHx62hw6pRKFTJx9vvnny8vM5FaDl/IOuae6Rb7uTsLbSY04/BJ34E4OHxa1z59ThotfwxwIumM3tTz709miQNu4evIDY8+pO54kOj+Mt9LXXWjkSuMiLaN4TLo1Zj41KE6osHcLSR50fLAFyfvoWqc3rT8vwCtGoNLy/e5d7KQ2gS1Zzvt4TKM3oiUyrQxCdy6fuVxAalf0/pYyJCI5g7eiGz105HqTQi4Hkgs0Yln4fSLiWZuGgsfRoP4tZVbzb+tIUVu5agVqt5/TKUSf2mfNYxszNtZATRS3/EYtJMUCrRBAUQvXguiuKlMB81jsgRAyTJlfg6inujVuGyfjQypRGxz19yZ/jPWJYvSlmvwfzlmv6Ajhe/HUdpbU71Ez8iU8h54+3DvWmbsyR7UiY+5y0tLbG0tPxkOUdHR65de1fhvnr1Cnt7e739r1690j1+/fq13v7MkmnT6tA0MHv37mX9+vUkJiYCUKdOHcaPH0/Tpk3ZtGkTV69e5dixY0RFRREVFUXLli0ZNiz5Zum5c+dYtmwZ0dHRyGQyXFxcmDJlCpaWlvTs2RNLS0sCA5NvfE+bNk3XV5kRYonpjBNLTGeOWGI683L6EtPzCmX8b2fS898zVC44OJiuXbuya9cuTE1N6dKli+4CP0XLli2ZMWMGlStXZsqUKRQqVIgBAz7vgsHgWzgAbdu2pW3btqm2nzmT/EeZP39+2rVrl+Zz69WrR716H//D6NWrF9WrV//ofkEQBEOg+QoLFDg4OODu7k6vXr1ITEykQ4cOuLi4MHDgQEaOHImzszOLFi1i8uTJREdH880339CrV6/PPl62qHAEQRD+677W1DZubm64ubnpbVu3bp3u36VLl051z/xz/acrnM2bs6bvVRAE4d8y+HsfGfCfrnAEQRCyCzF5pyAIgpAlkmTZv40jKhxBEIRsIPtXN6LCEQRByBZEl5ogCIKQJb7GsOisJiocQRCEbCD7VzeiwhEEQcgWknJAlZMtprYRBEH4r3MvnPFlEJb4bv+KST6faOEIgiBkA2LQwH/c4oKGNRElwBi/5En7DHWSzOUFDCvXiBfJuboVSj1Xn5S2Pt8LGN4EmfBukkxDm1g0ZVLRK/nSnldRKtUD93yR19HmgC41UeEIgiBkA6KFIwiCIGQJMSxaEARByBJqUeEIgiAIWUF0qQmCIAhZQgwaEARBELKEaOEIgiAIWUK0cARBEIQsIVo4giAIQpZQ54BZyESF84UUaVCBOhM6oVApefXAjxPjfiEhOjbD5WRyGfWmdKdIPRdkRgqurT3M7d/P6D23XKe6FG9ahX39vACoNsyNUm7f6vab2lp8NF8+1wpUmNQJhbGS8Ht+/DXmF5I+yPexMjK5jCpzeuNQowwAAWducmPmNgBsyhelysweKMyMkcvl3P35EL57LmXq3BVuUIEaE5PPyev7fpwe9wuJH2T7VBnzvDZ0PDCdbY09iAuPBiB3iXw0+LE/ylwmoNVy+ccd+J3zzlS291VoUJku43tgpFLy4sFz1o5fQWwav+MUQxaP5MXD5xxeux+AXFbm9JszmEJlixAfG8e5nWc4seHIZ+cBUFb9FrPeg5AplST5PuPt0vloY2PSLvttbSzGeBLWsZnedlkucyzn/0T00vmonzz8V3kyS6vV4jl7MSWKFaZvtw5Zckxr18oUmNQdmbGSmHvP8RnzM+oPfo+27eqSd2gbQIsmNp7nk9fz9vZTkMko4NkDa9fKoNES5xOEz/jVJIVFffXcOeF7OHKpA2SEv78/5cqVo3Xr1rRp04YWLVrQt29fXr58+a9ed/ny5Sxfvvxf5zO1saDpooEcGLyM374bR6RfCHUmds5UOZfuruQu4siGRhPZ4jaFyv2a4lg+eaoOE6tcNJzbl++m9wSZTPd6V1ceZHMzTzY38+SPznNIjIlPM5+xjQU1lgzkwsBlHKwzjmi/ECp6dM5wmSIdamNZLC+HG0zkcEMPHL4tQ8GW1QCo+8tIbi/azdFGnpzpsZDK07thUcQhw+fOxMYC18UDOTJoGb/XH0eUXwg1J3XOVJnS7WvTbtdkzB1t9J5Xf05f7v1xju1NPTk1dh1NV45Apvi8t7yFjSWDF45g6ZAFjG0wnGC/l3SZ2DPNsvmK58dz20yqNa+ht73n1H7ExcQxruFIpraZSIX6lajYoMpn5QGQWVph/sNE3sydQsTgnmheBmLWd3CaZeX5nMjVfyjI9Lcrq1THymsVivwFPjvH53rq60f/kZM4efZilh3TyMaSokuG82jgQm7XGUG8XzAFPPR/jybF8lFwSm8edp/FnUZjCFi6ixLrxwNg19WVXM7FuNNkLN6u7sT5BlFwWp8sya7NxH+GKltUOAD29vbs37+fffv2cfjwYUqVKsWCBQukjgVAobrOvLzlQ4RvMAC3Np+mTJuamSpXomll7u48j1atIT4yhgcH/6JMu1oAlHSrTnRwOOfmbPtohnqe3fA9ezvNfXnrORN604c3PsnHfbzxNIXb1cxwGZlCjpGZMXJjJQqVEXKlAnV8InJjJd5ee3l54S4AsUFhxIW+wSyv/gd/egrWdSbklg+R/39OvDefptQH5y69MrkcrCnapDIHeqR+L8gVckyscgGgMjdBHZ+Y4VwfcqlbgWe3H/PSNwiAU78fo1brummWbdyrGX9uP8mVw5f1thdxLsbFPWfRajSoE5O4ceZ6qkopM5SVqpL0+AGawAAA4g7vR1W/YeqCxsaYj53M219+TrXLpFV73iyagyYs9LNzfK7tuw/R3q0Jjb+rk2XHtKpXgeibT4j3Sf49Bm88hm07/eNr4hPxGbuSxJBwAN7eeorSzhqZ0ojYh374zd6ENiFJt884v12WZNdk4sdQZdsuterVq+Pl5cXRo0f57bffiIuLIyEhgblz51KpUiV69uyJlZUVjx8/ZunSpTx58oRVq1Yhk8lwdnZm1qxZANy+fZsuXboQHBxMu3btGDFiRKazWOaz5U3Quz/YN0FhGFuaoTI31etWS6+cRV5b3gS+2xcdFIZd6eSrzpSutW86pP2HaVMiH8WbVOaXOqOp1K9Jqv1mTrbEvPfaMUFhqCzNMDI31XWrpVfm2Y7zFGxZnbbXlyM3khN0zpuAkzcAeLrtnO45xbt/h9LchNf/PMnAWUtmkS/1/7expRlKc1Ndl1l6Zd4GR3Bk0LI0X/us5wba7vCgwoBmmNpacvz7FWjVn/fnaJM3D6HvZQgLCsXMMhem5qaputU2TF0HgHOdCnrbn9x8RO129Xl07QFGKiXVmtVAnZj0WXkA5Hb2aF6F6B5rXr9CnsscmamZXrea+fCxxB89iNon9WSbb6aO/+zj/1ueY4YBcPnqP1l2TJWTLQmBr3WPE4JCMbLMhcLcVNetluD/igT/V7oyhab3IeLENbSJSURff6TbrrDKhZN7J0I2H8+S7KJLTSKJiYkcP36cChUqsH37dlavXs2BAwcYMGAAa9eu1ZUrVaoUx48fx8bGhnnz5vHrr79y+PBh1Go1584lf1CGhoayadMmdu/ezfr164mOjs58IJmMtJYV0nz44ZZeObkMvV0y0Ggy9uFYuX9Tbmw8ScKbtO8nyD5y3Pc/fNMr4zymHfGhUewpP4y9lUeisjan9GD9+wBlh7vhMrY9Z3t7oY7LeEtCJpeR1lqGetkyUOZDCmMlTVcO59ToNfxWbSS7O8ziux/7YZ6J1tf75HIZZOR3nI4ts38DrZa5R7wYs24i3hdukvQvKhyZTJ7mMpDa9943xi3aoFWriT/57+4V5RTJ5yxj7yW5qTHF14zFpHBeno3Vbx0aF3Kg7J7ZvLl6n+Dfjn61vO9To83wj6HKNi2ckJAQWrduDUBCQgIuLi6MGTMGIyMjzpw5g4+PD1evXkUuf1eHuri4AHDjxg0qVaqEo6MjAAsXLgTg/v371KlTB5VKhY2NDblz5yYyMhJzc/MMZep5dA4AKgtTXj94odtu7pib2IhokmL176m8CQwlb8ViaZZ7ExiKuUPud/scchMdFPbJDDK5jBLNqvJ7iykfLfM2IBTbSu+Oa+aYm/jwaNTv5UuvTIFmVbg2eROaRDWaxFh8dl6gYItqPFhzFLnKiBpLB2NVIh/H3abz1v81mfEmIBSHD85J3AfnLiNlPmRbKj9Gpsb4nr4JQPCNp4Q+CsChYrEMnVeADqO7Uqlh1eTzYWGK3wM/3T4bR1uiI94Qn06GD5mam7F17ibeRiZf1LT+vr2ui+5zqF8FY1SqjO6x3DYPmjdREB+n22bi2hSMjbFa/gsYKUGV/O+oaRPQStCNJrX4gFeYVyqhe6xytCUp/A2aD36PKqc8lNzgQdwTf+51nIo2LkG3z7JmOYqvHkPgyn28XL0/y7LnhLUys00LJ+Uezv79+zl69Cjz589HqVTSoUMH/P39qVq1Kj17fnDzz8QEACMjI2Tv3WwPCwsjLCxMty/Fx67yPyblhv3W1tPJW7E41oWTb5aX7+HK0xOpuwl8z3t/tNzTE9cp17kuMoUcY0szSrl9y5Pj1z+ZIU/pAsRFxhCVzgd90Dlv8lQqrruZX6KXK/4f5EuvTJi3LwXdqgMgM1Lg1LiSrtus5vKhKM1NOd5qZqYrGwC/8944ViyO1f+fk3I9XHn2QbaMlPlQhG8wxhamOFZO/nCxLGSPTYl8vLr7PMPZdnltw6P5aDyaj2Zqm4mUqFgSx8J5AXDt3oTrJ65m+LUAGnZvQscxXZPz5LHiuy4Nubz/QqZe432J//yNUamyyPM5AWDSvBUJf+mPEIwcPYTI7/sSOWIAb6ZNgIR4IkcM+E9WNgCR525hXqkkxkWSf48OSWeWnAAAIABJREFUvRoTfuJvvTLyXCaU2TWT8KN/8WSol15lY+ZclBLrJ/B05E9ZWtlAcpdaRn8MVbZp4aTF19cXmUzGkCFD0Gq1jB8/HrVanaqcs7MzM2bM4NWrV9jZ2TF37lyqV6/+xXLEhkZxfOxa3FaPRKE0IsIvhGM/rAbAwaUIjecPYHMzz3TL3dx8GqtCDvQ6PheF0ojbW87gf+XBJ4+du4gjUe/1N6clPjSKv9zXUmftSOQqI6J9Q7g8ajU2LkWovngARxt5frQMwPXpW6g6pzctzy9Aq9bw8uJd7q08RJ7KxSnkVp2op0E0PjBVd7ybs7cTlMHhx7GhUZwas5bma0YiVxoR+TyEk+6rsXcpQoMFA9je1POjZdKTEBXD4YFLqTujJwpjJdokNX9O/JWo5yHpPu9jokIjWTNuOaNWjcNIpST4+UtWuSffOyriXIyB87/Ho/nodF9j/8rdDFvyA/NPLEMmg51e23l2O+P3uz6kjYwgeumPWEyaCUolmqAAohfPRVG8FOajxhE5YsBnv3ZOlRQayVP3FZRYOw65yog435c8HfUTuVyKUWTxMO40GoNj3+YY57cjd7Pq5G727nPiQadpFJjUHWRQwLMHBTyTFxOM9wvhcf/5Xz27IQ8GyCiZNhu00/z9/enVqxdnzuh/L0WtVjNu3Dju3r2LTCajdu3anDp1irNnz9KzZ0+GDx+uq1iOHTvGypUr0Wg0VKhQgRkzZrBy5UoA3UCBBg0asGnTJvLnz5+hXGLFz4wTK35mjljxM/Ny+oqfLQu2yHDZQ36Hv8gxv7Rs0cLJnz9/qsoGQKFQ4OXlpbdt8uTJAGzevFlve9OmTWnatKnetg9HpKV1DEEQBENgyF1lGZUtKhxBEIT/OjG1jSAIgpAlDHkGgYwSFY4gCEI2kBO61LLNsGhBEIT/Mq1Wm+GffyswMJDu3bvTtGlThg4dytu3b1OVCQkJoU+fPrRq1YqOHTty//79T76uqHAEQRCygaz8Hs6MGTPo1q0bx44do1y5croRve9bsmQJTZo04cCBA4wYMYIZM2Z88nVFl5ogCEI2oNZm/Js4UVFRREWlXjLB0tISS0vLdJ+bmJjI33//zc8/J0/n065dO3r06MG4ceP0ys2ZM0f3b39//0++LogKRxAEIVvITLtl48aNrFixItX24cOHf3KC4vDwcMzNzXWzsNjZ2REcHJyqXMo0Yk2bNiUgICDNVtCHRIUjCIKQDWSmq6x37960bZv6y8wftkKOHj3KvHnz9LYVKlRIbyowINXj9x07doz79+/Tr18/jh49irW19UfLigpHEAQhG8hMhZORrjOAZs2a0ayZ/szviYmJVK9eHbVajUKh4NWrV9jb26d67tmzZ6latSq5cuWiTJky5MuXjxcvXogK52tJmUbGEKVMJWNoUqaSMTQpU8kYmpRpZAxRylQyhuZLTSVjaLJqFjKlUkmVKlU4cuQIbm5u7Nu3j7p1Uy82uHfvXnx8fOjbty9Pnjzh9evXFC2a/nsiW8ylJgiC8F9XLV/G59W7GvjvLlQCAgKYOHEioaGh5M2bFy8vL6ysrNi2bRshISGMGjWK4OBgPDw8ePXqFcbGxkyYMIEqVdJfMl1UOP/CMgOcvHPU/7e6vIu4SZxEn7PPQQCMVE4SJ9GXlJC8PPOOvN0lTqKvc9AWAE46dJY4SWqNgncAhjtJpqFOKvpvVcmb8aW4rwV9/rIXX5PoUhMEQcgGcsJMA6LCEQRByAZyQmeUqHAEQRCyAdHCEQRBELKEmC1aEARByBIa0aUmCIIgZIXMzKVmqESFIwiCkA2ILjVBEAQhS4guNUEQBCFLiBaOkErhBhWoNaETCpWS1w/8ODXuFxKiYzNVxjyvDZ33T2dLEw/iwqMByF+jDLU9uyI3UpAUl8i5aZsIvvV536i2+K4KDuN7IVcpiXvgi/+En9B8kNG6TX3yDGoHWi2a2HiCZqwl1vsJAJZNa2I/rCMylZKEgBD8xyxBHfHms7KkpXkzV2bPnoixsTHe3vcZOGgMb95Epyq3cP5U2rdvSVh4BACPHj2lW/ehAFz56yimpiYkJCQCsG3bHhZ7rc5UjryuFXDx6IxcZUTk/RdcHb2OpA/O08fKqKxzUfnHflh/UxB1bDw+28/z+NcTAKisc1FpTm8sSzihMFVxb9l+nu+6mOnzBJCnYUWKe3ZFrlISfc+Pu+6rUX+QMcU3Pw0j+r4fz1cd0m3L36cxTt0bIDdR8eb2M+66r0abkPRZWT5k7VqZApO6IzNWEnPvOT5jfk6VzbZdXfIObQMkv8+eT17P29tPQSajgGcPrF0rg0ZLnE8QPuNXkxSWeo2Xr0Gr1eI5ezElihWmb7cOWXLMT8kJLZxsveKnv78/5cqVo3Xr1no/QUFBkuQxtbGg0aKBHB68jE3fjSPSL4RaEztnqkzp9rXpsHMy5o42um1ypYJmPw/n9IT1bG3qyd/L99Fk6dDPyqiwsST/glH4DZ3HI9ehJPi9xHF8H70yqqJOOE7qi2/vaTxpMYpXK3ZQcJVHcn7n4uSbMZjnQ+fxuOlwEnwCcRjb87OypCVPHht+WedFp86D+KZcXXx8njN3jkeaZWvUqEL3HsOoUrUxVao21lU2ZmamFCtaiEqVG+n2ZbayMba1oNrSQVwasJSjdcYR/TyE8p6dM1ymwoweJL2N41i98ZxqMQ3HBuXJ27AiANWWDiYmMIwTjT0522kelWb1wjSvTaoMn6K0teCbZUO53c+Ly7XciXkeTInJ3VKVy1XCicq7p+DgVl1vu33zahTo35TrHWfxv7pjkJuoKDS4RaZzpMXIxpKiS4bzaOBCbtcZQbxfMAU89N8nJsXyUXBKbx52n8WdRmMIWLqLEuvHA2DX1ZVczsW402Qs3q7uxPkGUXBany+S7VOe+vrRf+QkTp79vIuAr0WjVWf4x1Bl6woHwN7env379+v95M2bV5IsBes6E3zLhwjf5MWKbm8+Tak2NTNcJpeDNcUaV2ZfzwV6z9EkqllfbSSv7j4HwLKgPbHhn9eisKhTkZjbj0nwTa6UQ38/inVr/UkBtfGJBExcTtKrcABivJ9gZGeNTGmEdZv6hP9xksSAEACCl23l1Zrdn5UlLY0a1ePatVs8eeIDwOo1m+jWNfW6HiqVigoVvmHs2KHc+OcUf+xYS4EC+QCoVrUi0dExHDm0hRv/nGLxwumYmJhkKodjPWfCbj4j2if59/Rk4ykKtquV4TI2LkXw3XURrUaLJlFN0KmbFGhZDZV1LhzqOnPXK3ner9igME62mEpCROoW3KfY1i9P5I2nxPi8BMB/40kc29dOVS5/38YEbDlD8IG/9Lbn7VQXv9WHSIp4C1ot98evI2jn+UznSItVvQpE33xCvE/y+yx44zFs2+nPBaaJT8Rn7EoSQ5LfZ29vPUX5/++z2Id++M3epGttvb31FOP8dl8k26ds332I9m5NaPxdxucuywpZucT015Iju9QePXrErFmziImJISwsjEGDBtG1a1eWL1/OzZs3CQoKokePHtSqVYvp06cTERGBiYkJU6ZMoWzZsp99XIt8tkQHheoeRweFYWxphsrcVNdlll6Zt8ERHB68LM3X1iSpMctjSdcjszHJbcHR71Ov5pcRyrx2JAa91j1OfPkahWUu5Oamum61xIAQXYUCkHdyf96cvoo2MQnjIk7EPvCl0FpPlPkdiHvoS9CsXz4rS1oK5M/HC/9A3WN//yCsrCyxsDDX61bLl8+BP/+8xNRpC7h79yFjRg9hz+7fqFqtCeYWuTh77jLuo6cSExPL5k3LmTN7EmPGTstwDtN8tsQEhukexwaFobI0w8jcVNetll6Z0H+eUrhDbV7//QiFyoj8LaqiSVJjXtiBuJAISg1uTt7vyiM3NuLBqsNEP3uZ6XNlks+W+MB376X4wFCUlmYozE31uq4eevwGgG09F73nmxXNizKPJRW3TcLYMTcRfz3g0awtmc6RFpWTLQmB795nCUGhGFnm0suW4P+KBP9XujKFpvch4sQ1tIlJRF9/pNuusMqFk3snQjYf/yLZPsVzzDAALl/9J0uOl1FiahsDEBISQuvWrXWP3dzcCA4OZtiwYdSoUYMXL17QqlUrunbtCkBCQgJHjhwBoEuXLkydOpWyZcvy5MkTvv/+e44f//w3tUwmS/NNoVFrMlXmY2JeR7G+2kjsyhWm3bZJ7Gg1jQifTH5QyWWQxvG1aRxfZmpMgUU/oMybB5/e05M3KhVYulbDp7snSaGROE7qi9O8EfgNnpPq+Z9DLpeneX7Uav1uAl/fF7i17qV7vNhrNZ4eP1C4cAEOHTrJoUMndft+/HE5O//4JVMVjiwD5ym9MjdnbKHC1G40OTmH2JBIgs/fwbZKCeRKI8wL2ZP4JpbTrWdgXtiBBvumEO3zkvDbvhnOB4A87feSVpOx72vIlQps67lws9dCNPEJlFv+PcUndeHRlI2Zy5EGmUye4feZ3NSYoktHYJwvDw+6z9TbZ1zIgZK/TuTN1fsE/3b0X+fKzgy55ZJR2b7CSelSe59arebChQusWbOGR48eERMTo9vn4pJ8lff27Vvu3LnDpEmTdPtiYmIIDw8nd+7cn5UlKjAUh4rFdI/NHXMTFxFNUmx8psp8SGVhSoGa3/D0+DUAXt3x5fU9P/KULpDpCicx8BVmFUrqHisdbUmKeIP2g+Mr89lR6JcpxD95wbOunmjjEwBICg4j7oEvSa+Tb9SH7zxFka2zM5XhQ9OnjaVly8YAWFqYc+fuA90+JydHwsLCiYnRv9ns7FwGF5eybNnyrjtPJpORmJhEyxaNiIyM4sLFK+9tT8xUppiAUGwrFtc9Ns1rQ3x4NOr3zlN6ZYxtbLk1exsJEW8BKDOyFdG+wcS+TO4+8tmevF5JtG8wr68+wqZCsUxXOHH+r7Gq9O74xnltSAyPRhPz8ffS++JfhhNy+KquxRG06wJFx7TPVIaPvnbAK8wrldA9VjnakhT+Bs0H7zOVUx5KbvAg7ok/9zpORRuXoNtnWbMcxVePIXDlPl6u1v8b/y/KCS2cbH8PJy0//PADJ0+epFixYvzwww96+1L68jUaDSqVSu/ez86dO9NdHvVT/M57k7dicawLOwDg3MOVZyf+yXSZD2nVGhouGkjeKsl/wDYlnchdLC8vbzzNdMY3F25gWrEUqsLJ97lsujUj6uQVvTLyXKYU3TaXqGOXeTFyoa6yAYg8egnLBlVRWFsAYNW0BrG3nmQ6x/umz1iku7lfq44b1atVonjxIgAMHtSTAwdPpHqORqNhqddMChcuAMCQwb3x9r5PQEAQTk55WTB/CiYmJsjlcn74YRA7dx3MVKaXZ72xrVwc8yLJv6divVwJPH49w2WK9XKl3Ljk0U3GeSwp2u07/PZc5u2LV4Td9qFwpzq6fbZVShB+yydT+QBCz93GqnIJzIo4ApC/dyNCjl3L8PODD13BodW3yE2UANg1q0rkZ7yn0hJ57hbmlUpiXCT5febQqzHhJ/7WKyPPZUKZXTMJP/oXT4Z66VU2Zs5FKbF+Ak9H/iQqm/+n0Woz/GOosn0LJy2XLl3i6NGjODg4sGVLcp/0h10yFhYWFC5cmP3799O6dWsuXbrE1KlTOXXq1GcfNzY0ipNj19J89UgUSiMi/UI4/sNq7F2K0HD+ALY28/xomfQkxsRzaMAS6k3ridxIgTohkWMjVxL9Mizd56VFHRpJwLhlFFw5CZnSiITnL/Ef44Wpc3GcfhzBkxajsO3VAqWTHZZNamDZpIbuuT7dJ/Pm9N8oHfNQdPs8kMtIDHiF/4SfMp3jY169CmXAwNHs2L4WlUrJs6fP6dNvFACVK7mwZk1y5XT37kNGuU9h394NKBQKAvyD6N4zue997brNFC1SkL+vHsNIYcTZc5eYNXtJpnLEh0Zx9Yc11Fo3CrnKiGjfEK6MXEXu8kWoumggJxp5fLQMwP2fDlB9xVCa/vkjyGTcWbiLsP8fxn6p3xIqze1D8d4Nkcll3FuyV7cvMxJfR3Fv1Cpc1o9OvtH+/CV3hv+MZfmilPUazF+uE9J9/ovfjqO0Nqf6iR+RKeS88fbh3rTNmc6RlqTQSJ66r6DE2nHIVUbE+b7k6aifyOVSjCKLh3Gn0Rgc+zbHOL8duZtVJ3ezdyPoHnSaRoFJ3UEGBTx7UMAzeaHDeL8QHvef/0XyZUeaHDC1TbZe8dPf359evXpx5swZve2//fYbv//+O8bGxpQuXZqbN2/y22+/ceDAAQBGjBgBwNOnT3WDBpRKJdOnT9d1uWWEWPEz48SKn5kjVvzMvJy+4qedVakMl30V+fCLHPNLy9YtnPz586eqbAD69u1L3759U21PqWhSFCtWjM2bv8wVnSAIwteUjdsGOtm6whEEQfivMOR7MxklKhxBEIRsQLRwBEEQhCwhvocjCIIgZAl1Br/Qa8hEhSMIgpANiOUJBEEQhCwhBg0IgiAIWUIMGhAEQRCyhOhSEwRBELKEJgcMGsjWU9sIgiD8V2RmWqiUKZsMjahwBEEQhCyRI5cnEARBEAyPqHAEQRCELCEqHEEQBCFLiApHEARByBKiwhEEQRCyhKhwBEEQhCwhKhxBEAQhS4gKRxAEQcgSosIRBEEQsoSocARBEIQsISocQfgCIiMjpY4gCAZPzBYtAPD333+nu79q1apZlOTj/Pz8uHnzJm5ubkydOpV79+4xffp0nJ2dJct0//593N3diYuLY8eOHfTo0YOlS5fyzTffSJbpfUlJSTx8+BCFQkGpUqWQyWRSR9KJiYkhMjJSb52XfPnySZjoncjISKysrKSOkeOIyTuzWIMGDdL9oz99+nQWpnmnZ8+eAERERODn50elSpWQy+XcuHGDkiVLsn37dklyva979+507NgRc3NzNm7cyKhRo1i0aJGk2bp3787MmTMZM2YM+/bt49KlSyxZsoRdu3ZJlinFpUuXmDBhAvb29mg0GqKioli6dCkuLi5SR2PFihWsX7+e3Llz67bJZDLJ3v8pDP0CItvTClnK399f++LFC+3EiRO1P//8szYoKEgbEhKiXb9+vXbGjBlSx9MOGDBA6+vrq3vs7++v7devn4SJ3mnfvr1Wq9VqPTw8tDt27NBqtVpt27ZtpYykO37r1q1129zc3KSKo6dFixba+/fv6x7fvn1b8vOV4rvvvtOGhYVJHSOVbt26aZ88eaL7fV68eFH3vhP+PXEPJ4s5OTmRP39+Hj58yLBhw3B0dMTOzo5+/fpx8+ZNqeMRGBhIoUKFdI/z5ctHYGCghIneUSgUHD9+nLNnz1K/fn1OnTqFXC7tW9ja2poHDx7oWq0HDhwwmK4YlUpF6dKldY+l7Hr8kL29PRYWFlLHSCU2NpZixYrpHteqVYuEhAQJE+Us4h6OhP73v/9Ro0YNAM6dO4dCoZA4EXzzzTdMmDCBZs2aodVqOXjwIFWqVJE6FgAzZ85kw4YNTJ06FXt7ew4fPszs2bMlzTR9+nQmTJjA48ePqVKlCoUKFWLhwoWSZkpRpUoVPD096dSpEwqFgsOHD+Pk5KS7XyfFfbkVK1YAYGlpSefOnalbt67e+3748OFZnul9hnwBkROIezgSuXfvHhMmTODVq1dotVqcnJxYsGABxYsXlzRXQkICv//+O1evXgWgZs2adOvWDSMjw7g2CQkJwd7enmvXrvHw4UPat2+PiYmJ1LGIiYlBo9Fgbm4udRSdlPtyaZHJZGzatCkL0yRLqXA+RuoKx8/PjwkTJuDt7Y2JiQmFChVi0aJFFClSRNJcOYWocCQWHh6OTCbD2tpa6ig6/v7+PHnyhNq1axMUFESBAgWkjgTAtGnTSExMpF+/fvTv31/X3bFo0SLJMvXs2VNvEIhMJsPExISiRYsyZMgQcXX8EUlJSZw7dw5XV1fCwsI4c+YM7du3l3wU3fbt2+nSpYtBXkDkBIZx2fofYuhXeEeOHGHVqlXExcXp/vjGjx9P69atJc0F4O3tze7du1mxYgUdOnRgxIgRtG/fXtJMxYsXx8jISJfj0KFDvHz5EgcHBzw9PT/5+/4aNBoNW7dupVq1apQsWZJNmzaxc+dOypYty5QpUwziQ3TKlCloNBpcXV0BuHLlCrdv32bmzJmS5vr999/p0qULZmZmkubIqUSFI+hZt24d27Zto0ePHtja2rJ371769u1rEBWOWq1Go9Fw+vRpZsyYQWxsLLGxsZJmunXrFnv27NE9Ll26NO3bt2fRokXs27dPkkyLFy/m2bNn1K9fn+vXr7Ns2TKWL1/O3bt3mTVrFvPnz5ck1/vu3LnDwYMHAbCxsWHhwoW4ublJnAocHR3p1asX5cuXx9jYWLdd6gvBnEJUOFks5Y2b0nowNHK5XO8K2N7eXvKRYCnatGlD7dq1qVSpEuXLl6d58+Z06tRJ0kyJiYk8fvyYEiVKAPDo0SM0Gg1xcXEkJiZKkun8+fPs3bsXIyMjNm7cSJMmTahZsyY1a9akWbNmkmT6kEaj0d2PAwgNDTWI91mFChWkjpCjiQpHIilNd0NTokQJfv/9d5KSkrh//z5bt27VG1orpb59+9K7d2/dB9Pvv/+OjY2NpJkmT57MwIEDsbW1RavVEhkZycKFC1m+fLlkrUK5XK4b5HH16lUGDx6s26fRaCTJ9KEhQ4bQtm1bKleuDCS3FD09PSVOlbolo9Vq8ff3lyhNziMGDUhkwIABJCQkGFzTPSYmhlWrVnH58mU0Gg3ffvst33//vUH0+9+8eZM1a9YQExODVqtFo9EQGBjImTNnJM2VlJTEvXv3OH/+PBcvXuThw4fcuHFDsjxdunTBy8uLt2/f0rZtWy5evKgb7uvp6cnu3bsly5biwYMH5M6dm5s3b2JkZISzs7OutSOlHTt2MH/+fL2u2vz583Py5EkJU+UcooUjEUNtuu/cuZM+ffowZswYqaOk4uHhQf/+/dm7dy89e/bkxIkTlC1bVtJML1684I8//mD37t1ERUUxZMgQVq5cKWkmd3d3OnfuTHR0NMOHD8fa2pqtW7fy888/M2/ePEmzpXB3d+fo0aM0adJE6ih61qxZw/79+1m6dCnu7u6cO3eOf/75R+pYOYaocCQyfPhwYmJi8PPzo2TJksTFxRnEyJiXL1/SsWNHihYtSqtWrWjUqBGmpqZSxwKSvznfvn17AgICsLS0ZMGCBZLdaD558iTbt2/n7t27NGrUiIULFzJlyhTJW6gA1atX5/Tp08TFxWFpaQkkf6F3y5YtFC5cWNpw/6948eKsWLGC8uXL632PSupJYm1tbSlQoAClSpXi0aNHdO/enW3btkmaKScRFY5E/ve//zF16lTUajU7duygZcuWLF68mNq1a0uaa8KECUyYMIFr165x5MgRfv75Z8qXL8+CBQskzQVgbGxMREQERYoU4datW9SoUQO1Wi1JlhEjRtCsWTN27NihmwpI6u+QvE+lUqFSqThz5gxXr17FyMiImjVrGkyFExERwZUrV7hy5Ypum1RfRn2fqakpf/31F6VKleLUqVM4OzsTFxcnaaacRFQ4EvHy8mLr1q0MHDgQOzs7tmzZwujRoyWvcCD5RmliYiKJiYnIZDKUSqXUkQDo06cP7u7uLF++nI4dO3Lw4EHKlSsnSZYDBw6wZ88eunXrhpOTEy1atJCs8vuYxYsXc/36dZo1a4ZGo2HZsmV4e3vrDSKQyubNm6WOoCc4OBgHBwemTJnCrl27mDBhArt27aJZs2YG0WrNKcSgAYm0b9+e3bt306ZNG933NVq1asWBAwckzTV79mxOnjxJmTJlaNWqFa6urnqDGqSm1WqRyWTExMTg6+tLmTJlJG1ZJCUlcfbsWfbs2cP58+epWbMm3bt3p169epJlSuHm5saePXt0Fwzx8fG0b9+eQ4cOSZzM8AaAtG3blr179wLw66+/0q9fP0ly5HSihSMRR0dH/vzzT2QyGVFRUWzZssUgFp8qVKgQe/fulXy48fsmTZqU7n4pb4QbGRnRsGFDGjZsSFhYGPv27WPx4sUGUeFYWVnx9u1b3bRJiYmJBjHaEAxvAMj7190HDx4UFc5XIiocicycOZM5c+YQFBREw4YN+fbbbyWd1mPHjh107tyZyMhItm7dmmq/lN0K1apVk+zYmWFjY0O/fv0k/7BKqaA1Gg2tW7emQYMGKBQKzp8/T9GiRSXNlsKQBoCA/v030enz9YgKRyK2trZ4eXlJHUPHkP/I2rZtC0B0dDT79++ne/fuBAcHs337dgYNGiRxOsOTUkF/WFEb0qqVhjQA5EOGNPgjpxEVjkQaN26s9wf2/izDEyZMwMnJKUvzpMx6YGFhQcuWLbG1tc3S42fE2LFjKVWqFAC5cuVCo9Ewfvx4li9fLnEyw1K7dm3s7OwMZuG8tPTt29dgBoAAPH78WDeRaHBwsO7fKfcMpV76OqcQFY5E6tatS/78+enQoQOQPOrJ29ubBg0a4OnpyYYNGyTJZcjfwwkMDGT16tUAmJub4+7ubhCTihqayZMns2bNGnr06JHm1bqUH57BwcEsWLCAx48fU6FCBTQaDbt378bX11fSKZSOHz8u2bH/S8QoNYm8PyomRbt27dizZ0+a+7JayvdwLl26ZDDfw2ndujULFizQtXKePn3K+PHjDWKqFkPz559/UqxYMQoWLMjJkyfZtWsXZcuWZejQoahUKsly9e/fn5IlS1K9enXdh7yhzH4gfH2ihSMRuVzOhQsXqFOnDgAXLlxApVLx+vVrkpKSJM1mqN/DmThxIv369cPBwQFIXrzOUJZzNiS//vorhw8fZv78+Tx48IBx48bh6enJ/fv3WbRoER4eHpJlCw4OZv369QDUqlWLNm3aSJZFyHqiwpHIvHnzmDhxImPHjgWgYMGC/Pjjj+zYsUPSUU4ffg9n8uTJkn8P5/1umHr16tGpqz/KAAAFnklEQVSlSxdUKhVFixaV9GrdUO3bt48dO3ZgamrKokWLaNCgAR07dkSr1dK8eXNJs71/8aJUKg3mYkbIGqLCkUjJkiXZs2cPkZGRKBQK3fcjvv/+e0lz2djYGNz3cDw8PChZsiRubm4cP36cbdu2iW6YdMhkMt19tytXrtCtWzfddkNjiJmEr0dUOBK5d+8eq1evJjIyUm9IstRzSR08eJBhw4ZJmuFDohsmcxQKBVFRUcTExHD//n1q1aoFQEBAgG6dHKm8PxoM3o0IE6PB/htEhSORCRMm0LlzZ0qUKGFQV3mGOIuv6IbJnEGDBtGmTRuSkpLo0KED9vb2HDlyhCVLlkjeghajwf7bRIUjERMTE3r06CF1jFQMdRbf9xlSBW2ImjZtSsWKFQkPD9cNNc6VKxezZ8+mevXqkmbL6u+XCYZFDIuWyLJly7CxsaF27dp6N+UNYT41Q1OuXDndyDR4N7Ov6IYRhOxFVDgSadCgQapthvDh2bNnzzRbEFK2cAICAtLdL66aBSF7EBWOoOfq1au6fyclJXH69GksLS0ZNWqUhKkEQcgJRIUjkcjISBYuXIifnx8//fQT8+fPZ9KkSbolgQ1Jx44d2blzp9QxBEHI5uRSB/ivmjJlCs7OzkRERGBmZoa9vb3uS6BSCgwM1P0EBARw7tw5IiIipI4lCEIOIEapScTf35/OnTuzbds2VCoV7u7utGrVSupYehM+ymQycufOzeTJkyVOJQhCTiAqHIkoFArevHmj+3D39fVFLpe2wfnnn3+yYcOGVBM+1qxZU9JcgiDkDKJLTSIjR46kZ8+eBAYGMmzYMLp168YPP/wgWZ7169ezYsUKEhISdBM+NmzYkMjISIOYKVoQhOxPDBqQUFhYGLdv30atVlOhQgVJFz1r1aqV3oSPgYGBeHl56SZ8PHr0qGTZBEHIGUQLRyJ+fn5cvHiRunXrcvbsWQYNGsSdO3cky/PhhI8pyyaIb/ULgvCliApHIpMmTUKj0XDmzBl8fX2ZNGkSs2fPlixPyoSPL1++NLgJHwVByBlEhSOR+Ph42rRpw59//ombmxtVqlQhISFBsjwpEz526tRJb8LHPn360L9/f8lyCYKQc4hLV4koFAqOHz/O2bNnGTVqFKdOnZJ0lJohT/goCELOIAYNSOThw4ds2LCB+vXr06RJE9zd3Rk8eLDuw14QBCGnERWOhEJCQrC3t+fatWs8fPiQ9u3b661BIwiCkJOICkci06ZNIzExkX79+tG/f39q1apFQkICixYtkjqaIAjCVyEGDUjE29ubOXPmcPToUTp06MDcuXPx8fGROpYgCMJXIyociajVajQaDadPn6Zu3brExsbyf+3coarCUBzH8d8QtRksC0sW00CbQYuwJsJgYDGJT2D1BYS9gcFHMBgN+gyaLZYJK2LStO22y73ltnMOXL6ftMPKr/0453+2z+fjOhYAGEPhOBLHsUajkYIgUK/XU5Ikms1mrmMBgDHMcBwqy/L7KvTz+VS73XacCADM4TscRy6Xi7bbrd7vt6qqUlmWejweOp/PrqMBgBEcqTmyXq8VRZGKotB8Ppfv+4qiyHUsADCGHY4jjUZDSZIoyzK1Wi2laarpdOo6FgAYww7HkWazqdfrpU6no+v1qlqtpqIoXMcCAGMoHEcWi4VWq5XG47EOh4Mmk4nCMHQdCwCM4UjNsjzPlaapbreb+v2+yrLUfr/X/X7nP2oA/jWuRVu2XC7V7XY1GAx0PB4lSZvNxnEqADCPHY5leZ5rt9tJkobDoeI4dpwIAOxghmNZvV7/9fxzDQD/GYXjmOd5riMAgBXMcCwLw1C+73+v8zyX7/uqqkqe5+l0OjlMBwDmUDiWZVn25/sgCCwlAQC7KBwAgBXMcAAAVlA4AAArKBwAgBUUDgDAii/QBT5d47Q2kwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.heatmap(data.corr(), annot=True, linewidth = 1, linecolor='w')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(891, 12)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    549\n",
       "1    342\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Survived'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "class_survival = data.groupby('Pclass')['Survived'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pclass  Survived\n",
       "1       1           136\n",
       "        0            80\n",
       "2       0            97\n",
       "        1            87\n",
       "3       0           372\n",
       "        1           119\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class_survival"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x286be21f2e8>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "class_survival.unstack().plot(kind='bar', stacked=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using crossby in Pclass vs survival "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "pclass_data = pd.crosstab(data['Pclass'], data['Survived'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>Survived</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pclass</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>80</td>\n",
       "      <td>136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>97</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>372</td>\n",
       "      <td>119</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Survived    0    1\n",
       "Pclass            \n",
       "1          80  136\n",
       "2          97   87\n",
       "3         372  119"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pclass_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x286be460240>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pclass_data.plot(kind = 'bar', stacked=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "pclass_data['total'] = pclass_data.sum(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "('total', 'occurred at index 0')",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   2656\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2657\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2658\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 'total'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-41-22b67eb7d89c>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mpclass_data\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpclass_data\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mround\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m100\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m/\u001b[0m\u001b[0mpclass_data\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'total'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36mapply\u001b[1;34m(self, func, axis, broadcast, raw, reduce, result_type, args, **kwds)\u001b[0m\n\u001b[0;32m   6485\u001b[0m                          \u001b[0margs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   6486\u001b[0m                          kwds=kwds)\n\u001b[1;32m-> 6487\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mop\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_result\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   6488\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   6489\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mapplymap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\apply.py\u001b[0m in \u001b[0;36mget_result\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    149\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply_raw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    150\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 151\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply_standard\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    152\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    153\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mapply_empty_result\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\apply.py\u001b[0m in \u001b[0;36mapply_standard\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    255\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    256\u001b[0m         \u001b[1;31m# compute the result using the series generator\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 257\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply_series_generator\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    258\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    259\u001b[0m         \u001b[1;31m# wrap results\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\apply.py\u001b[0m in \u001b[0;36mapply_series_generator\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    284\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    285\u001b[0m                 \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mv\u001b[0m \u001b[1;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mseries_gen\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 286\u001b[1;33m                     \u001b[0mresults\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mv\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    287\u001b[0m                     \u001b[0mkeys\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mv\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    288\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-41-22b67eb7d89c>\u001b[0m in \u001b[0;36m<lambda>\u001b[1;34m(x)\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mpclass_data\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpclass_data\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mround\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m100\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m/\u001b[0m\u001b[0mpclass_data\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'total'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   2925\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2926\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2927\u001b[1;33m             \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2928\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2929\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   2657\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2658\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2659\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2660\u001b[0m         \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2661\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msize\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: ('total', 'occurred at index 0')"
     ]
    }
   ],
   "source": [
    "pclass_data = pclass_data.apply(lambda x: round(100*x/pclass_data['total']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>Survived</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>total</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pclass</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>37.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>53.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>76.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Survived     0     1  total\n",
       "Pclass                     \n",
       "1         37.0  63.0  100.0\n",
       "2         53.0  47.0  100.0\n",
       "3         76.0  24.0  100.0"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pclass_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "pclass_data.drop('total', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 1, 2]), <a list of 3 Text xticklabel objects>)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pclass_data.plot(kind='bar', stacked=True)\n",
    "mpt.xticks(rotation=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predicting data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "training_data = data[['PassengerId', 'Pclass', 'Fare']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "output_data = data['Survived']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "X, x_text, Y, y_test = train_test_split(training_data, output_data, test_size=0.3, random_state=42) \n",
    "#X,Y are training i/p and o/p and x,y are evaluating i/p o/p\n",
    "#test_size is 30%, meaning 70trainig, 30 test,can be 80-20, but not half\n",
    "#random is shuffle values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\lenovo\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='warn',\n",
       "          n_jobs=None, penalty='l2', random_state=None, solver='warn',\n",
       "          tol=0.0001, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X,Y)                    #fit means training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = model.predict(x_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 1, 0, 0, 0, 1], dtype=int64)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix, accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[134,  58],\n",
       "       [ 23,  53]], dtype=int64)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(predictions, y_test)               "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "#the above cell implies that 134 and 53 predictions are correct rest are wrong"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6977611940298507"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score = accuracy_score(predictions, y_test)\n",
    "score                                               #this means approx 70% of the predicyions are correct"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Converting gender options into boolean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \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",
       "\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  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "def bool_converter(x):\n",
    "    if(x=='male'):\n",
    "        return 0\n",
    "    else:\n",
    "        return 1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Gender'] = data['Sex'].apply(bool_converter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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",
       "      <th>Gender</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",
       "      <td>0</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",
       "      <td>1</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",
       "      <td>1</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",
       "      <td>1</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",
       "      <td>0</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  Gender  \n",
       "0      0         A/5 21171   7.2500   NaN        S       0  \n",
       "1      0          PC 17599  71.2833   C85        C       1  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S       1  \n",
       "3      0            113803  53.1000  C123        S       1  \n",
       "4      0            373450   8.0500   NaN        S       0  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "#data.drop('gender', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sex</th>\n",
       "      <th>Gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Sex  Gender\n",
       "0    male       0\n",
       "1  female       1\n",
       "2  female       1\n",
       "3  female       1\n",
       "4    male       0"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[['Sex', 'Gender']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n",
       "       'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked', 'Gender'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predicting chances of survival on the basis of gender of the person"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "training_data2 = data[['PassengerId', 'Pclass', 'Fare', 'Gender']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "output_data2 = data['Survived']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "X, x_test, Y, y_test = train_test_split(training_data2, output_data2, test_size=0.3, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "model2 = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\lenovo\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='warn',\n",
       "          n_jobs=None, penalty='l2', random_state=None, solver='warn',\n",
       "          tol=0.0001, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model2.fit(X,Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions2 = model2.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions2[:15]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix, accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[134,  33],\n",
       "       [ 23,  78]], dtype=int64)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(predictions2, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "score = accuracy_score(predictions2, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7910447761194029"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## test_data file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data = pd.read_csv('test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "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>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>892</td>\n",
       "      <td>3</td>\n",
       "      <td>Kelly, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>34.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330911</td>\n",
       "      <td>7.8292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>3</td>\n",
       "      <td>Wilkes, Mrs. James (Ellen Needs)</td>\n",
       "      <td>female</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>363272</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>2</td>\n",
       "      <td>Myles, Mr. Thomas Francis</td>\n",
       "      <td>male</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>240276</td>\n",
       "      <td>9.6875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>3</td>\n",
       "      <td>Wirz, Mr. Albert</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>315154</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>3</td>\n",
       "      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
       "      <td>female</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3101298</td>\n",
       "      <td>12.2875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Pclass                                          Name     Sex  \\\n",
       "0          892       3                              Kelly, Mr. James    male   \n",
       "1          893       3              Wilkes, Mrs. James (Ellen Needs)  female   \n",
       "2          894       2                     Myles, Mr. Thomas Francis    male   \n",
       "3          895       3                              Wirz, Mr. Albert    male   \n",
       "4          896       3  Hirvonen, Mrs. Alexander (Helga E Lindqvist)  female   \n",
       "\n",
       "    Age  SibSp  Parch   Ticket     Fare Cabin Embarked  \n",
       "0  34.5      0      0   330911   7.8292   NaN        Q  \n",
       "1  47.0      1      0   363272   7.0000   NaN        S  \n",
       "2  62.0      0      0   240276   9.6875   NaN        Q  \n",
       "3  27.0      0      0   315154   8.6625   NaN        S  \n",
       "4  22.0      1      1  3101298  12.2875   NaN        S  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data = test_data[['PassengerId', 'Pclass', 'Fare']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data['Fare'] = test_data['Fare'].fillna(test_data['Fare'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId    0\n",
       "Pclass         0\n",
       "Fare           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_predictions = model.predict(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_predictions[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_submission = {\n",
    "    'PassengerId' : test_data['PassengerId'],\n",
    "    'Survived': test_predictions\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_submission = pd.DataFrame(test_submission)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_submission = test_submission.set_index('PassengerId')"
   ]
  },
  {
   "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>Survived</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PassengerId</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>892</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>893</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>894</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>895</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>896</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Survived\n",
       "PassengerId          \n",
       "892                 0\n",
       "893                 0\n",
       "894                 0\n",
       "895                 0\n",
       "896                 0"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_submission.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_submission.to_csv('submission.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Logistic Regression-Columns-PassengerId, Fare,Pclass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## based on gender- test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "def bool_converter(x):\n",
    "    if(x=='male'):\n",
    "        return 0\n",
    "    else:\n",
    "        return 1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data2 = pd.read_csv('test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "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>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>892</td>\n",
       "      <td>3</td>\n",
       "      <td>Kelly, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>34.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330911</td>\n",
       "      <td>7.8292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>3</td>\n",
       "      <td>Wilkes, Mrs. James (Ellen Needs)</td>\n",
       "      <td>female</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>363272</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>2</td>\n",
       "      <td>Myles, Mr. Thomas Francis</td>\n",
       "      <td>male</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>240276</td>\n",
       "      <td>9.6875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>3</td>\n",
       "      <td>Wirz, Mr. Albert</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>315154</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>3</td>\n",
       "      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
       "      <td>female</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3101298</td>\n",
       "      <td>12.2875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Pclass                                          Name     Sex  \\\n",
       "0          892       3                              Kelly, Mr. James    male   \n",
       "1          893       3              Wilkes, Mrs. James (Ellen Needs)  female   \n",
       "2          894       2                     Myles, Mr. Thomas Francis    male   \n",
       "3          895       3                              Wirz, Mr. Albert    male   \n",
       "4          896       3  Hirvonen, Mrs. Alexander (Helga E Lindqvist)  female   \n",
       "\n",
       "    Age  SibSp  Parch   Ticket     Fare Cabin Embarked  \n",
       "0  34.5      0      0   330911   7.8292   NaN        Q  \n",
       "1  47.0      1      0   363272   7.0000   NaN        S  \n",
       "2  62.0      0      0   240276   9.6875   NaN        Q  \n",
       "3  27.0      0      0   315154   8.6625   NaN        S  \n",
       "4  22.0      1      1  3101298  12.2875   NaN        S  "
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data2['Gender'] = test_data2['Sex'].apply(bool_converter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "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>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",
       "      <th>Gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892</td>\n",
       "      <td>3</td>\n",
       "      <td>Kelly, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>34.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330911</td>\n",
       "      <td>7.8292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>3</td>\n",
       "      <td>Wilkes, Mrs. James (Ellen Needs)</td>\n",
       "      <td>female</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>363272</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>2</td>\n",
       "      <td>Myles, Mr. Thomas Francis</td>\n",
       "      <td>male</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>240276</td>\n",
       "      <td>9.6875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>3</td>\n",
       "      <td>Wirz, Mr. Albert</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>315154</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>3</td>\n",
       "      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
       "      <td>female</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3101298</td>\n",
       "      <td>12.2875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Pclass                                          Name     Sex  \\\n",
       "0          892       3                              Kelly, Mr. James    male   \n",
       "1          893       3              Wilkes, Mrs. James (Ellen Needs)  female   \n",
       "2          894       2                     Myles, Mr. Thomas Francis    male   \n",
       "3          895       3                              Wirz, Mr. Albert    male   \n",
       "4          896       3  Hirvonen, Mrs. Alexander (Helga E Lindqvist)  female   \n",
       "\n",
       "    Age  SibSp  Parch   Ticket     Fare Cabin Embarked  Gender  \n",
       "0  34.5      0      0   330911   7.8292   NaN        Q       0  \n",
       "1  47.0      1      0   363272   7.0000   NaN        S       1  \n",
       "2  62.0      0      0   240276   9.6875   NaN        Q       0  \n",
       "3  27.0      0      0   315154   8.6625   NaN        S       0  \n",
       "4  22.0      1      1  3101298  12.2875   NaN        S       1  "
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data2 = test_data2[['PassengerId', 'Pclass', 'Fare', 'Gender']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data2['Fare'] = test_data2['Fare'].fillna(test_data2['Fare'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId    0\n",
       "Pclass         0\n",
       "Fare           0\n",
       "Gender         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data2.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_predictions2 = model2.predict(test_data2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 0, 0, 1, 0, 1, 0, 1, 0], dtype=int64)"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_predictions2[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_submission2 = {\n",
    "    'PassengerId' : test_data['PassengerId'],\n",
    "    'Survived': test_predictions2\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_submission2 = pd.DataFrame(test_submission2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_submission2 = test_submission2.set_index('PassengerId')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "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",
       "    <tr>\n",
       "      <th>PassengerId</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>892</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>893</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>894</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>895</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>896</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Survived\n",
       "PassengerId          \n",
       "892                 0\n",
       "893                 1\n",
       "894                 0\n",
       "895                 0\n",
       "896                 1"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_submission2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_submission2.to_csv('submission_with_gender.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}