Titanic - Support Vector Classifier by Radha Jayaraman.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5"
   },
   "outputs": [],
   "source": [
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
    "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a"
   },
   "outputs": [],
   "source": [
    "train_data=pd.read_csv('train.csv')\n",
    "test_data=pd.read_csv('test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Survived         0\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "Age            177\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             0\n",
       "Cabin          687\n",
       "Embarked         2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "TRAINING SET \n",
    ">In training set we have missing values in Age,Cabin,Embarked with Age having the maximum number of missing values out of all. We will replace missing values in Age by median age grouped by PClass instead of median of the whole dataset.A reason for finding median grouped by PClass is that it is most related to Age which can be seen from the heat map below.And as age is right skewed, it will be better to replace with median than mean."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x192cb9ca4c8>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZkAAAEvCAYAAACXNrymAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAgAElEQVR4nOydd3hURduH79lNJwkkkJDQS0CRlhBKqCZgAoSqgIIIIiryKVgAfWkqKiCvgigiIgoKFlBAETBAAtJFWkioAiEgLY2EkN7n++MsqRvIpsDqO/d1nevanfOcM799ZvY8Z8qZI6SUKBQKhUJRFejutwCFQqFQ/HtRQUahUCgUVYYKMgqFQqGoMlSQUSgUCkWVoYKMQqFQKKoMFWQUCoVCUWWoIKNQKBT/AwghVgghYoUQJ0vZL4QQi4QQEUKI40KIdpWRrwoyCoVC8b/BN0CfO+zvCzQzbOOAzysjUxVkFAqF4n8AKeUeIOEOJoOAVVLjT6CGEMK9ovlaVPQE/0tk34g0y+URPmn31v2WYJR0YZbuwiVP3G8JpXJdb54+q26mPjPnC9grl7+rsNNMueZYuTR9Aa0FcptlUsplJmRXF7hS6PtVQ1qUCecogTmXkUKhUCjKiCGgmBJUimMsKFb4rkcFGYVCoTBX8nLvZW5XgfqFvtcDrlf0pGpMRqFQKMyV3JyybxVnIzDaMMvMB7glpaxQVxmoloxCoVCYLVLmVdq5hBCrAV+glhDiKvA2YKnlI5cCQUAgEAGkAc9URr4qyCgUCoW5kld5QUZKOeIu+yXwUqVlaEAFGYVCoTBXKrElc79QQUahUCjMlXs78F8lqCCjUCgU5opqySgUCoWiqpCVM2vsvqKCjEKhUJgrlTjwf79QQUahUCjMFdVdpjCVmXM/Ys/+Qzg71WDDd0vvSZ493xlFYz9PctIz2TJ5GbEnL5Wwqd26EX0WvICFjRUXd4bx+9vfAmBTvRr9l0ygej0Xbl2NY9OLn5J5K436Pi0Y/NVr3LoSB8D5rYc58MkGk3T1njUaD7+2ZKdnsXHKF0Qb0eXWqhGDFozHwsaSiJ3hbJu1CgDfyUNp7u+NzJOkxiexcfJSUmITaejTgse/nESiQddfWw+zd9EvJumq79uGrrNGIfQ6zqzeRdiSTUX266ws6PnxeFxaNybjZjLbX1xM8tUbuHo2oce8ZzUjAUcW/sKlrUeo3sQd/yUT8o93bODK4QXrOLF8m0m6AALfHk0zg89+mfIFUaculbBxb9WIx+ZrPju/M5ygdzSf+b36GN7D/UhNSAZg+wc/cn5XODoLPYP++xx1WjZGZ6Ej7Od97F2y8a5a/ArVq62l1CvXYvVqZ7F65VjPhaRC9cq5qTu954/DtVUj9n+4liPLggBwauJO/88KfFi9gSt/fLSO43fwYcOH2/CwoRxPrdnFkWLlqLeyIGDheFwN5Rj0klaONjXsCVz6MrXbNuHM2j3semtV/jGDVr1BNdfq6Cz0XD90lp0zv0HmVeF6c/+Cgf+7PvEvhMgVQoQJIU4KIdYKIezuhbCqQAjhK4TYXMq+S0KIWlWtYXCgP0s/ml3V2eTT2K8tTo3cWN5jMsFTl+M/Z4xRu0fmPEPw1OUs7zEZp0ZuNPZtA0DHlwZwef9plj88hcv7T9PpxQH5x1w9fJZVfWewqu8MkwOMh19bnBu78dnDk/lt2nICZxt/7itwzlg2T/uKzx6ejHNjN5r6tgXgjy9+Y1mfaXwZOJ3zO47R45XH8o+5fPgsXwZO58vA6SYHGKETdJv9NL+N/oAfe76BxyAfnJrVKWLTYrgvmYmprO4+meNfbaXT9OEAJPx1lfX93mRdnxkEjfqQh99/BqHXcSsyinV9ZrCuzwzWB84kJz2Ti1uPmKQLoJlvW2o2duMT38lsnL6cAXOM+2zA7LFsnP4Vn/hOpmZjN5oZfAZwYPkWPg+czueB0zm/KxyAloGdsLCy5LM+U1nafybtn+xJjXp3/ivcrlcrekwmZOpyHrlDvQqZupwVhnrVqFi9WmGoVx0N9So9MZXf3/42P7jc5mZkFN/2ncG3fWfwXT/Nh+fv4EOhE/jOfpoNT3/At73eoPlAH5yLlWPLJ3zJvJXKyh6TOfbVVrpN08oxJzObPxesY9+cH0qcd8uLn/JDnxl898hUbJ0daNav0x39VGFkXtk3M6Usy8qkSyk9pZStgCxgfBVrqhKEEGbRamvv2Zrqjg73LD+PAG9Ord8HQNSxC1g7VqOaa40iNtVca2Blb0tUaAQAp9bvw6N3e+14f29Ordurpa/bi0dA+0rR1dzfm+PrtfNeOxaBjaMd9sV02bvWwNrelmsGXcfX7+WBAG8AslLS8+2s7KzRniOrOK6eTUm6FEPy5TjysnO5sPFPGhnyvE2jgHacM/gk8rdD1O3aEoCcjCxkrvZn11tbYkxS3W4tSfo7lpRr8SZrezDAm7CftXyvHovAxsEOe5diPnOpgbWDLVcMPgv7eS8PFtNfEomVrTU6vQ4LGytys3LITE6/4xFNA7w5XYZ6ZV2oXp0uVK+allKv0uOTiDkeSV5O6XfwDbq2JPFyLMl38GFtz6bcuhRDkqEcz236kybF/NAkoB2nDRrOBx2i/u1yTM/k+uFz5GRklzjv7Xqns9Cjs7JAVnz9yDtzb5eVqRJMvfDuBdoACCE2oC2mZgN8IqVcJoTQA8uB9mird66QUi4UQryMFpxygNNSyuFCiGrAp0Brg45ZUspfhRBjgIGAHdAU+EVK+YYhz2eB/6At2nYeyJRSThBCuABLgQYGna9KKfcLIWYBdYBGwA0KrVAqhKgJrAZcgEMYX4H0H4+9mxPJUQV/xuToBOzdnEiNTSxikxKdUMIGwK6WY75tamwidrUc8+3qtPNg9NY5pMYksmvOD8Sfu1ZmXQ5uziRdL9CVFJ2AQ20nUgrpcqjtRFIhXUlRCTi4Oed/93t9GK0f605mchrfDp+Tn16vnQfjtswlOTaR7bO/J+582XVVc3Mi5XpBnilRCdT2alqqjczNIys5DRsnezJupuDq2RTf+c/jUK8WO15dmh90buMxsDPnfz1QZj2FcaztzK1iPnN0cyIlrsBnjm5OJEUV9Zlj7QKfdXw6gLaPdef6iUi2zv6ejKQ0TgUd4kF/b14/9BmWtlZsee870m+l3lFLWetVcjnq1d14cGBn/rqLD+3dnEguVo5unncux8xC5XgnBn/7BrU9m/L3znAifjtUZt3l4l8w8F/mBTINLYG+wAlD0lgppTdaQHnZcNH2BOpKKVtJKVsDXxtspwJeUso2FLSEZgC/Syk7AH7Ah4bAg+E8T6AFoCeEEPWFEHWANwEfwB94sJC8T4CFhnMNAb4qtM8bGCSlfLLYT3ob2Cel9EJbGK4BRhBCjBNCHBFCHPlq1eoyeMq8EMZiZ7Fb7LLYFCfm5CWWdX6VVX1mEPpNMIO/fM00XUazlGUxyv+488O1LOr8Mic3/EGHpwMAiDp5iUVdXmFZ3+kc/mYbw76cZJIuY3mWdEXpNrFhF/jpkams7/8W7V4agN7aMt9GZ6mnoX87In87aJqm0qWZ5LND323n4x6v8XngdJJjE+kzcyQA9do2JS83jw87TWBh99fo+lwgTvVd7qzFqA+KO8r0enU3dJZ6mvq349zdfFiGchRGfXV3DRtGfcBX7Segt7LIb/1UFVLmlnkzV8rSkrEVQoQZPu9Fa6mAFlgeNXyuj/bKzrNAEyHEp8BvQLBh/3Hge0Pr53bnfQAwUAgxxfDdhoIL/Q4p5S0AIcRpoCFQC9gtpUwwpK8FmhvsHwEeKlRpHIUQt/ukNkopjbX9ewCPAUgpfxNC3DT24wu/o8FcX1pWHM/Rj9BmhB8A0ccjcXCvmb/Pwc2ZlJjEIvbaHaazUZu0G0lUc61Bamwi1VxrkHYjCSjaXXVxZzi62WOwdbIn/Q53ge1H++M1XNN1/XgkjnUKdDm6ORdpxdzW5VhIl6O7M8kxJYvp5K9/MPzrKexeuL6Iroid4fR9T39XXYVJjUrAvk5BnvbuzqQVyzM1WrNJjU5A6HVYOdiRmVj0/IkR18lOy8T5gXrEHb8IQAO/ttw4eYl0gw/LQsdR/ngbyvJaeCTVi/ksuVhZJkUl4Ohe1GdJsZr+1EL5Hl2zk5HLtb9e60FdiNh9nLycXFLjk7h89Bx12jThpmHyxG08Rz9C6zvUq9RiWlKii7Y8y1Kv7kZj37bEnLx0V/uUqAQcipVjauzNEjb2dZxJMZSjtYMdGYllqye5mdlEbj9GE/92XN57skzHlAszHmspK6aMyXhKKSdKKbOEEL5oF/bOUsq2wDHARkp5E2gL7EJbaO12i6If8Blaq+KooVUkgCGFzt1ASnnGYJ9ZKP9ctGB4p+4snUHL7XPVlVImG/bdqd3/jwgaphK2anv+gHzEtqO0HNINAHevpmQmpxXp0gCtuyI7NQN3Q7dQyyHdiAg+CsCFkFBaDu2upQ/tTkSIlm7nUj3/eLe2TRA6cdcL+ZFVIfkD8meDj9BmiHbeul4eZCSnlwgyKbGJZKWmU9fLA4A2Q7pzzpC/c6Pa+XbN/dsRf0FbkbxaIV11yqirMLHhkVRv5IZDfRftrnmgD5dCQovYXAoJpbnBJ036deT6/tMAONR3Qei1v5R93ZrUaOpOcqELtcegzkSY2FV26NuQ/IH6v4KP4PmYlm+92z6LK+azuESyUtKpZ/CZ52Pd+ctQloXHb1r0bk/suasA3Lp+g8ZdHgLA0taael7NuHGh5GtEwlZtzx98j9h2lIfKUK+yCtWrh4Z040Ip9eqCoVzvxoOD7t5VBhATHkmNxm44Gsqx+QAfIouVY2RIKA8ZNDQL7MiVP07f8ZyWdtbYGcadhF5HI7+2JFyo8Er4dyYvr+ybmVLewfDqwE0pZZoQ4kG0LiwMs7OypJTrhRAXgG+EEDqgvpRypxBiH/AkYA9sAyYKISZKKaUQwktKeewOeR4CFgohnIBktG6x2113wcAE4EODDk8pZZjRsxSwBxgJzBZC9AWcTPZCOXj97XkcPnacxMQkeg1+ihefHcWQAb2rLL/I38No7NeW5/YuIDs9i61TCl6cN3rLHFb1nQFAyIyv6btgnGGqaTgXd2ozjw4u2cSAzyfS+omHSboez6bxiwB4ILAjbUf1Ii8nl5yMbDZP+MwkXRG/h+Hh58lLez4ixzCF+TbPB83ly8DpAATN+JqBhimwF3aFE2HQ1XPqcGo2cUfmSW5du0HQ9BUAtAjsSPunHiEvJ5fsjGx+nrjYJF0yN499b66k33dvIPQ6zv64m5vnrtF+8hDijl/k75BQ/lqzm54fj2fE3gVkJqYQ8pKWh1uH5ni9OIC8nFxknmTvjG/y+/ctbKyo170Ve6auMElPYc7tDKOZnyev7v5Im8L8eoHP/i9oLp8bfLZp5tc8Ov8FLG2sOL8rPH8WWcC0Ebg/1BApJYlX49ho8NmhVSEM/vAFJgT/F4Tg2NrdxPx1paSAQlz8PYwmfm151lCvthWqV6O2zOFbQ73aPuNr+hipV4eWbKL/5xNpZahXmw31ys6lOk9tfg8re1tkXh7tnu3DN73+Q1ZKOhY2VjTs3oqQaXf3oczNY9ebKxn8rVaOp3/cTcK5a/hMGkLMiYtcDAnl1I+76f3xeJ7es4CMxBS2TCioK8/sX4iVgy06Swua9G7PhqfmkXEzhYHLJ6G3skDodVzZf5oT3+24q5YK8S9oyYi7zcoRQqRIKe2LpVmjdXvVResicwFmATfRxmFut5CmAduBnWiBSQDfSSnnCSFsgY+BLob0S1LK/oaB//ZSygmGvDYD86WUu4QQ44ApaAP/Z4AEKeUMQ3D7DGiBFjj3SCnHGwb+U6SU8w3n8gWmGPK5PfBfC9iN1nXmLaW8UZovzLW77JN2b91vCUZJF2bpLlzM9H31ANf15umz6mbqM7OYMloKr1z+rsJOyzi0tswVwqbjMLMspLsGGXNCCGEvpUwxdLf9gjZ7zbQHISqACjKmoYKM6aggYxr/+iDz549lDzI+T5hlIZlzGRljlhDiEbRJAsEUTCJQKBSKfx//gu6yf1SQkVJOubuVQqFQ/Esw4wH9svKPCjIKhULxP8W/IMiU+WFMhUKhUNxbZG52mbe7IYToI4Q4K4SIEEJMNbK/gRBipxDimBDiuBAisDJ+gwoyCoVCYa5U0gKZhiW/PkNbteUhYIQQ4qFiZjOBnwyroAwHllTGT1DdZQqFQmGuVF53WUcgQkoZCSCEWAMMAgo/gSqB24vIVUd7VKTCqCCjUCgU5ooJs8sMzxGOK5S0zLAsFmjPNBZ+wvYqUPw9BbOAYCHERKAa2qouFUYFGYVCoTBXTGjJFF5n0QjGnqEp/gzOCOAbKeUCIURn4FshRCspKzaPWgUZhUKhMFcq7zmZq2gLGd+mHiW7w54F+gBIKQ8IIWzQVkSJrUjGKsiYgLk+Wf9K6Lv3W4JROrd++n5LMIq/df27G90n7KRZPrSNm5m+E6uN9a37LaFqyak0xx8GmgkhGgPX0Ab2i7/+5DLQC23NyRZoD73HUUFUkFEoFApzpZJaMlLKHCHEBLSFifVoS3KdEkK8CxyRUm4EJgNfCiFeQ+tKGyMrYd0xFWQUCoXCXKnEhzGllEFAULG0twp9Pg10rbQMDaggo1AoFOaKWrtMoVAoFFXGv2BZGRVkFAqFwlxRLRmFQqFQVBmVN7vsvqGCjEKhUJgr/6CXSpaGCjIKhUJhrqgxGYVCoVBUGSrIKBQKhaLKUAP/CoVCoagycnPvt4IKo4JMJdLznVE09vMkJz2TLZOXEXvyUgmb2q0b0WfBC1jYWHFxZxi/v/0tADbVq9F/yQSq13Ph1tU4Nr34KZm30qjv04LBX73GrSvaEkLntx7mwCcbqkT/zLkfsWf/IZydarDhu6VVksedmPLeK3Tt5UNGeiazXp3L2RPnStgs+mE+tVxrorfQE3YwnP9OW0heoS6Fp8YP59W3X6JXy/7cSij/ulYD3h7NA36eZKVnsW7KUq6fulTCpk6rxgyb/wKWNlac3RnGpndWATBi8URqNXEHwNaxGulJqXwaOB3PQV3p/kK//OPdHmzA4v4ziDr9d5l19Z41Gg+/tmSnZ7FxyhdEG6ljbq0aMWjBeCxsLInYGc62WZou38lDae7vjcyTpMYnsXHyUlJiE2no04LHv5xEoqGO/bX1MHsX/VJmTe6+bWj/3iiETkfE6l2cXrypyH6dlQVdFo3HuXVjMm8ms2/8YlKv3kBY6PGZ/xzOrRshLHRcXLuPU4s3YVfHmc6fjMfWtToyTxLx3U7OLt9WZj3GsO/RjjpvPw86HTd/DCFu6boi+2s9OwinJwKQubnkxidx9T+fkH1N84dlHRfqzpuIpXstkJJLz7xD9rUKrRlZdlR3WdkRQsxAW5AtF8gDXpBSHqzgOQcCD0kp51WCvhQppX15j2/s1xanRm4s7zEZd6+m+M8Zw/eDZpWwe2TOMwRPXU5UaARDVr5OY982XNx1nI4vDeDy/tMcWrKJji8OoNOLA9jz/o8AXD18ll+eWVDu31ZWBgf68+SQgUx/b36V51Wcrj19qN+kHo92GUGrdg8xbd5kxvR7oYTdtHFvkZqSBsAHX73HIwP8CP51BwC167jS6eEORF2NrpCWB3w9qdnYjfm+k6jv5cHgOWNZMrjk4qiDZ4/ll+nLuRx6njHfvEFz37ac2xXO6gmf5tsEzhhJRrKmN+zX/YT9ul/T+kB9Rn852aQA4+HXFufGbnz28GTqenkQOPsZVgx+u4Rd4JyxbJ72FddCIxix8g2a+rblwq5w/vjiN3Yt0C6uHcb0pscrjxE0YwUAlw+f5cexppe70Ak6zH2a34fPIy0qgT5B73J121GSzhcs8Nt0hC9Zials7DqZhoN88Jo5nH3jF9NwQEd01hb81msaelsr+u/6L5c2HCA3K4fQd3/g5olLWFSzoe/W94jac6LIOU1Cp6POu+O5OOpNcqLjafrrRyRtP0hmRMHrVdJPRRI/cBIyIxPnkX1xm/oMVyZ+AEC9Ba8R99lPpOwLQ2dng8y7hzO+/gVB5p68ftnwboL+QDspZRu0l+FcufNR+ceWGgillBsrI8BUBh4B3pxavw+AqGMXsHasRjXXGkVsqrnWwMrelqjQCABOrd+HR+/22vH+3pxat1dLX7cXj4D291C9RnvP1lR3dLjn+QI83KcbQWu3AnAy9DQOjvbUdK1Zwu52gNFb6LGwtKTw+n2T3pnIoveWUNE1/VoEeHPsZ60srhyLwMbBDgeXomXp4FIDawdbLoeeB+DYz3t5yEiZte7nQ/jGAyXS2w7sQvjGP0zS1dzfm+PrNV3XjkVg42iHfbE6Zu9aA2t7W64Z6tjx9Xt5IMAbgKyU9Hw7KzvrCvsJoKZXU5IvxZByOY687Fz+/vVP6vf2LmJTr3c7Itdqui9vPkTtbi0BbXauhZ01Qq9Db2NFXlYO2SnpZMQmcvPEJQByUjO4FXEdO3fncmu0a9uMrL+jyL4Sg8zO4damPTj6F31fV+qfJ5AZmQCkHTuLpZtW96w96iP0elL2hQGQl5aRb3dPqKTXL99P7kmQAdyBG1LKTAAp5Q0p5XUhxCUhRC0AIUR7IcQuw+dZQohlQohgYJUQ4qAQouXtkwkhdgkhvIUQY4QQi4UQ1Q3n0hn22wkhrgghLIUQTYUQW4UQR4UQe4UQDxpsGgshDgghDgsh3qvoD7R3cyI5Kj7/e3J0AvZuTiVsUqITjNrY1XIkNTYRgNTYROxqOebb1Wnnweitcxiy8nVqNq9bUalmiYubC9HXC7ogYqLicHWvZdT209ULCDmxibSUNHZs3gVAj4CuxEbHcf70hQprqV7bicTrBeV0KzoBx2Jl6ejmRFJUIZuoBKrXLmrTqOODpNy4Rfylki2rNv19TA4yDm7OJF0vqGNJ0Qk4FMvTobYTSYXqWFJUAg5uBRdov9eH8fKBRbQa3IXdHxV0GdVr58G4LXMZsfINXJqVvY7ZujmRVshXaVEJ2LoX1WTn5kSqwUbm5pGdlIa1sz2XNx8iJy2Tx8IW8+jhjzmzNIisxNQix1arVwvnVg25EVr+crVwq0l21I3879nR8flBxBjOT/iTvPsoANaN65KblEqDz6fhsflj3KY9A7p7ddkEmSfLvJkr98pbwUB9IcQ5IcQSIcTDZTjGGxgkpXwSWAM8DiCEcAfqSCmP3jaUUt4CwoHb5x0AbJNSZqO9KW6ilNIbmAIsMdh8AnwupewAlNq/IoQYJ4Q4IoQ48mfK+VLFCmMvnit2p1gWm+LEnLzEss6vsqrPDEK/CWbwl6/d0f6fihAlfVPanfbEEZPp4zkYK2tLOnRrh7WtNWNfGc3SD5ZXlhgjWoqb3N2mtNZKfc+mZKdnEnPuakVllfSRcaP8jzs/XMuizi9zcsMfdHg6AICok5dY1OUVlvWdzuFvtjHsy0kmaDKW392FSwm1vJogc/P42WsiGzpNosX4QOwbuOTbWNhZ0/2rVzj61nfkFGqFmYwJdavGYF9sW3twY9nPBhE6qnV4iKi5K4gYNAmr+m44De1Vfi2mkpdX9s1MuSdBRkqZghY0xqG9BOdHIcSYuxy2UUp5u2b9BAwzfH4cWGvE/kfgCcPn4YY87IEuwFohRBjwBVqrCrQlrVcbPn97B+3LpJTtpZTtfeybFdnnOfoRRm+Zw+gtc0iJvYmDe8HdkYObMykxiUXstZaLs1GbtBtJ+d1r1VxrkHYjCdC6OLLTtOb5xZ3h6Cz02DqVe+jIrBg25lG+D1nB9yEriIu5gVsd1/x9td1diIuOL/XYrMwsdm/bz8O9u1GvYV3qNHBn9Y6v2XjoJ1zdXfg+eDk1XcrexeIzyp+JQXOZGDSXpJib1KhTcGx1N2eSY24Wsb8VlYBjoS6c6u7OJMUW2Oj0Olr27sDxzX+WyKvNgM5Gu9CM0X60P88HzeX5oLkkxyTiWKegjjm6OZMSW7KOORaqY47uJbUDnPz1Dx7s2wEoWscidoajN6GOpUUlYFfIV3buzqRH3yxhU81gI/Q6LB3tyLqZQqNHuxC18zgyJ5fM+CTiDp/DuW0Tzc5CT/evXuHSz39wZcuRMmkpjZyoG9qgvQFLt5rkxCSUsKvWtS0uLz3OpednI7O05Vyyo+JJPx1J9pUYyM0jKeRPbFs1rZAek8jNLftmptyzdp+UMldKuUtK+TYwARgC5BTSYFPskNRCx14D4oUQbdACyRojWWwE+gohnNEC2u+GcydKKT0LbS0Ky6rIbwpbtZ1VfWewqu8MIrYdpeWQbgC4ezUlMzktv/sr/wfFJpKdmoG7l1ZJWw7pRkSw1iC7EBJKy6HdtfSh3YkI0dLtXKrnH+/WtglCJ0i/mVIR2WbD2m9+YaT/WEb6j2XXlr0EDusDQKt2D5GSnEJ8bNEgY2tnmz9Oo9fr6drLh0sRl7nwVyQBrQcysOPjDOz4OLFRcYwMeJb4uJIXktL489sQPg2czqeB0zkdfASvx7SyqO/lQUZyOslxxS7mcYlkpaRT38sDAK/HunMmOL9xjUe3VsRFXi/SdQXanX/rwE6EbypbkDmyKoQvA6fzZeB0zgYfoc0QTVddg67iQSYlNpGs1HTqGnS1GdKdc4a65Nyodr5dc/92xF+IAqBaoTpWx8Q6Fh8WiUNjN6rVd0FnqafhIB+uBocWsbkWHEqTYZruBv07ErPvNACp1+Lzx2f0ttbUaudBUoQ2uO+z4DmSzl/nr2VbyqTjTqQdP491ozpY1quNsLSg+oAeJG0/VMTG5qEm1J3zEn8//x658QWzEtOPn0df3R69s9Z9Xa1zGzLOX66wpjLzL2jJ3JPZZUKIB4A8KeXt/iZP4G/AFi0gbEELOndiDfAGUF1KeaL4TillihDiEFo32GYpZS6QJIS4KIQYJqVcK7S2fRspZTiwH63F8x0wsqK/MfL3MBr7teW5vQvITs9i65Rl+ftGb5nDqr4zAAiZ8TV9F4wzTGEO5+LOcAAOLtnEgM8n0vqJh0m6Hs+m8YsAeCCwI21H9SIvJ5ecjGw2T/isolJL5fW353H42HESE5PoNfgpXnx2FEMG9K6y/Aqzf5KcJmUAACAASURBVMcBuvbyYcOBNWSkZ/DOa+/n7/s+ZAUj/cdia2fDRyvfx8rKCp1ex5F9oaxf9Wulazm7M4wH/DyZsnsh2emZrHv9i/x9E4Pm8mngdAA2zFzB0PnjsbSx4tyucM7uCsu301orJbvKGnV6kFvRCdy8YvoU2Ijfw/Dw8+SlPR+RY5jCfJvng+bypUFX0IyvGWiYJn9hVzgRhjrWc+pwajZxR+ZJbl27QdB0bWZZi8COtH/qEfJycsnOyObniYvLrEnm5nFkxkp6/vAGQq/jwprd3Dp3jTavDyE+/CLXgkOJWL2bLovGM3D/AjITU9j/f9r5z30dgs/CcfTbOQ8hBBd+3EPimSu4dGxOk2HduXn6Mn1D5gAQ/v5PXP893GSfAZCbx/W3l9J41TvaFOa128k8fxnX10aSfuI8ydsP4T7tGXTVbGjw2VQAsq/H8ffzsyEvj+i5K2j8/WwEgvSTF7i5Jrh8OsqDGQePsiIqY4bJXTMRwhv4FKiB1nqJQOs6awEsB2KAg0B7KaWvEGIWkCKlnF/oHLXR3k39npTyHUPaGMMxEwzfh6J1pflKKXcb0hoDn6N1k1kCa6SU7xrSf0ALtOuBmXebwjy/wVNmObr2Sui791uCUTq3fvp+SzCKv3X9+y2hVOykkTEOM6BJtnnqamNd/mehqprWFzdV2GlpH79Q5muO3atfmGUh3ZOWjGGQvouRXXuB5kbsZxlJi6GYXinlN8A3hb6vg6Kj61LKi0AfI+e7CHQulGQWU6EVCoUin39BS+bezcVTKBQKhWnkybJvd0EI0UcIcVYIESGEmFqKzeNCiNNCiFNCiB8q4yeoZWUUCoXCXKmkWWNCCD3wGeAPXAUOCyE2SilPF7JpBkwDukopbwohXI2fzTRUkFEoFAozRVZed1lHIEJKGQkghFgDDAJOF7J5HvhMSnkTQEpZKQu0qe4yhUKhMFdM6C4r/OC4YRtX6Ex1KbqU11VDWmGaA82FEPuFEH8KIUqMZZcH1ZJRKBQKc8WENcmklMvQVjgxhrGZZ8UHciyAZoAvUA/YK4RoJaVMLH6gKaiWjEKhUJgrlTfwfxUoPHe/HlB8WeurwK9SymzD7NuzaEGnQqggo1AoFOZKTm7ZtztzGGhmWBjYCu1B9I3FbDYAfgCGhYubA5EV/Qmqu0yhUCjMlUpawl9KmSOEmABsA/TACinlKSHEu8ARKeVGw74AIcRptPd+vS6lLH0BwTKigoxCoVCYK5W4hL+UMggIKpb2VqHPEphk2CoNFWRMIF2Y5aoyZrt8y4ETK++3BKN4tXzyfksolWl6j/stwSgJ+vutwDhe10LvbnSfyKmEc1TiFOb7hgoyCoVCYa6Y8cvIyooKMgqFQmGuqCCjUCgUiirDjF9GVlZUkFEoFAozRaqWjEKhUCiqDBVkFAqFQlFlqNllCoVCoagyVEtGoVAoFFWGCjIKhUKhqCpkruouUygUCkVVoVoyCoVCoagq1BRmRRF6zxqNh19bstOz2DjlC6JPXiph49aqEYMWjMfCxpKIneFsm7UKAN/JQ2nu743Mk6TGJ7Fx8lJSYhNp6NOCx7+cROKVOAD+2nqYvYt+KbfGKe+9QtdePmSkZzLr1bmcPXGuhM2iH+ZTy7Umegs9YQfD+e+0heQVmuXy1PjhvPr2S/Rq2Z9bCbfKraWszJz7EXv2H8LZqQYbvlta5fkVZ9qcSXTv1ZmM9ExmvPweZ06cLWGzdPVCXGrXQq/XE3owjNlT55OXl8f8ZbNp1LQBAA6ODiQnJTO01+hy6XD3bUP790YhdDoiVu/i9OJNRfbrrCzosmg8zq0bk3kzmX3jF5N69QbCQo/P/Odwbt0IYaHj4tp9nFq8CZ21Jf4/z0RvZYGw0HP5t0OcmP+zyboa+Lahx6xRCL2O06t3cXRJSV0BH4/HpXVjMm4ms/XFxSRfvYFNDXv6fvEyrm2b8NfaPex+c1X+Mc0Gdab9hIEgJakxiQS/vISMmynl8tttFn70Ln379CQtPZ1nn32NY2EnS9jsCFmLm3tt0tMzAOgbOIK4uHhGj3qc/86bybXr0QAsWfI1K75eXSE9ZUIFmapDCJELnEDTeAZ4WkqZVortLCBFSjn/3iksiodfW5wbu/HZw5Op6+VB4OxnWDH47RJ2gXPGsnnaV1wLjWDEyjdo6tuWC7vC+eOL39i1YB0AHcb0pscrjxE0YwUAlw+f5cexFf9pXXv6UL9JPR7tMoJW7R5i2rzJjOn3Qgm7aePeIjVFc/UHX73HIwP8CP51BwC167jS6eEORF2NrrCesjI40J8nhwxk+nv3vni79+pMg8b1CfQZRhvvlrz5wRs82ffZEnaTn5+R77OFy9+n98CebNmwnSnjZubbTJn1MilJ5btQCp2gw9yn+X34PNKiEugT9C5Xtx0l6XzBe6eajvAlKzGVjV0n03CQD14zh7Nv/GIaDuiIztqC33pNQ29rRf9d/+XShgOkXr3BjmFzyUnLRFjoCdjwJtd/Dyc+9IJJunxnP82GJ+eREpXAE5vfJTLkKDcL6Wo53JeMxFS+7T6ZZgN96Dp9OFtfXExOZjZ/zl9HzQfqUfOBegXn1OvoMespvu/5HzJuptBl+nDajAng0ELTA+Bt+vbpSTOPxjz4UDc6dWzHZ4vfp0u3AUZtR4+ewNHQ4yXSf1q7kVdenWnkiCrknz8kY9YvLUuXUnpKKVsBWcD4+y3oTjT39+b4+r0AXDsWgY2jHfauNYrY2LvWwNrelmuhEQAcX7+XBwK8AchKSc+3s7KzRlt1u3J5uE83gtZuBeBk6GkcHO2p6VqzhN3ti6XeQo+FpWURLZPemcii95ZUib7SaO/ZmuqODvcsv8L49enBxrXa6ujHj57CwdGeWnfwmYWFHksrS4y5p8/AXgT9ElIuHTW9mpJ8KYaUy3HkZefy969/Ur+3dxGber3bEblWq4OXNx+idreWAEgJFnbWCL0OvY0VeVk5ZBvqW05aJgA6Sz06S4uSL+S9C7U9m5J4KYYkg65zG/+kSUBRXY0D2vHXOk1XxG+HqNdV05WTnknU4XPkZGYXsRdCIITA0s4aACt7W1JjbpomrBgDBvTm2++1m7iDh0KpXqM6bm6uFTrnvUDm5JV5M1fMOcgUZi/gASCEGC2EOC6ECBdCfFvcUAjxvBDisGH/eiGEnSF9mBDipCF9jyGtpRDikBAizHDOcr9q1MHNmaTrBe/3SYpOwKG2U1Gb2k4kRScU2EQl4ODmnP/d7/VhvHxgEa0Gd2H3R+vy0+u182DclrmMWPkGLs3qllciLm4uRF+Pzf8eExWHq3sto7afrl5AyIlNpKWksWPzLgB6BHQlNjqO86fLfqf7T6e2uwvR1wr7LJba7i5Gbb9Y8zG7T20hNSWV4E2/F9nn7eNJfFwCly9eKZcOWzcn0q4X1J20qARs3YvWLzs3J1INNjI3j+ykNKyd7bm8+RA5aZk8FraYRw9/zJmlQWQlpgJaS6RvyByGHF9C1J4TxB8zrWyruTmRUkhXSlQC9m5Fddm7OZFcSFdWcho2TvalnjMvJ5ed07/myZB5jD2yGOfmdTm9ZpdJuopTt44bV68UtK6uXY2ibh03o7ZfffURRw4HM2P6q0XSH3s0kNCjIfy4Zhn16tWpkJ4yk2fCZqaYfZARQlgAfYETQoiWwAygp5SyLfCKkUN+llJ2MOw/A9zu23gL6G1IH2hIGw98IqX0BNqjveO6eP7jhBBHhBBHjqRE3EFnybQSd/vGjfI/7vxwLYs6v8zJDX/Q4ekAAKJOXmJRl1dY1nc6h7/ZxrAvy/8+IWEk/9JaJBNHTKaP52CsrC3p0K0d1rbWjH1lNEs/WF7u/P+JCMrusxeGv4pfm/5YWVnRqVv7IvsCHw0odysGjJddiVaH0fKFWl5NkLl5/Ow1kQ2dJtFifCD2DbRAKfMkW/xn8Iv3y9T0bEr1Qt1W5dVV0j1l0F4InYWe1qMeYXXfGaxoP4H4M5fxnjCw9APKrbOkiFFPT8Sr3SP4+j1Kt64deeqpoQBs/i2Eps18aOftz44de/l6+ccV0lNWZJ4s82aumHOQsRVChAFHgMvAcqAnsE5KeQNASplg5LhWQoi9QogTwEigpSF9P/CNEOJ5tNePAhwApgsh/gM0lFKmFz+ZlHKZlLK9lLJ9e/uiL5RqP9qf54Pm8nzQXJJjEnGsU9CN4ujmTEpsYhH75OgEHAu1XBzdnUk20g1w8tc/eLBvB0DrRss2dGlE7AxHb6HH9g53gcUZNuZRvg9ZwfchK4iLuYFbnYIugtruLsRFl/521azMLHZv28/DvbtRr2Fd6jRwZ/WOr9l46Cdc3V34Png5NV2cSz3+n8rwZ4awbscq1u1YRWzMDdzqFvaZK7HRN0o9Niszi53b9uLXp3t+ml6v55F+vmz9tfxBJi0qAbs6Bb62c3cmPfpmCZtqBhuh12HpaEfWzRQaPdqFqJ3HkTm5ZMYnEXf4HM5tmxQ5NjspjdgDZ6jj18YkXSlRCdgX0mXv7lyiayslOgGHQrqsHOzISCx9bKpWy4YAJP2ttSDPbz6Iu7fpnQz/N/5pjhwO5sjhYK5HRVOvfkHro249d65HxZQ45rphYD8lJZXVazbQob0nAAkJN8nKygLgq+Xf065da5P1lAvVkqlSbo/JeEopJ0ops9Buie4Wsr8BJkgpWwPvADYAUsrxwEygPhAmhKgppfwBrVWTDmwTQvQ0ReCRVSF8GTidLwOnczb4CG2GaBeWul4eZCSnlwgyKbGJZKWmU9dLC1ZthnTnXMhRAJwb1c63a+7fjvgLUQBUc6men16nbROETpBuwiybtd/8wkj/sYz0H8uuLXsJHNYHgFbtHiIlOYX42KJBxtbONn+cRq/X07WXD5ciLnPhr0gCWg9kYMfHGdjxcWKj4hgZ8Czxccbi/D+bNV+vZ2iv0QztNZrft+xm4LBAANp4tyQlOYUbRnxWq5DPejzShYsRf+fv9+nRgcjzl4iJiiu3pviwSBwau1Gtvgs6Sz0NB/lwNbjoWyGvBYfSZJhWBxv070jMvtMApF6Lzx+f0dtaU6udB0kR17F2dsDS0U5Lt7HErXsrkiKuYwox4ZHUaOSGo0FX84E+XAwpqutiSCgPDtV0efTryNX9p+94ztToBJyb1cXGWRuHq9+9NTdN1AXw+dKVtO8QQPsOAWzcuI1RI7VWSaeO7Ui6lUR0dGwRe71eT82aWlefhYUF/fo9wqlT2kzCwuM3AwYE8NdfpfdqVCb/hpaM2c4uK4UdwC9CiIVSynghhLOR1owDECWEsERryVwDEEI0lVIeBA4KIQYA9YUQ1YFIKeUiIUQToA3wO+Ug4vcwPPw8eWnPR+QYpjDf5vmguXwZOB2AoBlfM3DBC1jYWHFhVzgRO8MB6Dl1ODWbuCPzJLeu3SBoujazrEVgR9o/9Qh5OblkZ2Tz88TF5ZEHwP4dB+jay4cNB9aQkZ7BO6+9n7/v+5AVjPQfi62dDR+tfB8rKyt0eh1H9oWyftWv5c6zMnj97XkcPnacxMQkeg1+ihefHcWQAb3vSd57tv9B915d2HJwHenpGbz5yuz8fet2rGJor9HYVbNl8aoPsbK2QqfTcXD/UX5aWTDNvO9gf7ZUoKsMtLGMIzNW0vOHNxB6HRfW7ObWuWu0eX0I8eEXuRYcSsTq3XRZNJ6B+xeQmZjC/v/T6sq5r0PwWTiOfjvnIYTgwo97SDxzhRot6tP5kxcQOh1CJ/h700GubQ8zWdfuN1cy8Ls30Ol1nP5xNwnnrtFp8hBij1/kYkgop9fsxv/j8Yzaq+na+lJBHX76j4VYOdiis7SgSe/2bBg5j5vnr3Po458Zsm4meTm5JF+9wfZJyyrkv6AtO+jTpydnz+wnLT2d554r6HY+cjiY9h0CsLa2Iui3H7C0tECv17Njx16+Wv49ABMnjKV//wBycnK5mZDI2OdeLS2ryqUSWyhCiD7AJ2g9OV9JKeeVYjcUWAt0kFIeqXC+93KWkCkIIVKklCX6hYQQTwOvA7nAMSnlmMJTmIUQ/we8AfyNNgXawWDzM9AMrTW0A3gVmAo8BWQD0cCTpXTBAfBew5Fm6axfsy7fbwlGOXBi5f2WYBSvlk/ebwmlMk3vcXej+0CC/u4294PXYnbebwmlkpN1zchglGnE93u4zNecmr/tLjU/IYQeOAf4o409HwZGSClPF7NzAH4DrNB6hCocZMy2JWMswBjSVwIri6XNKvT5c+BzI8c9ZuR07xs2hUKhMDtk5bVkOgIRUspIACHEGmAQULzv8j3gA2BKZWVszmMyCoVC8b9N5Q381wUKz5+/akjLRwjhBdSXUm6uBOX5mG1LRqFQKP7XMaUlI4QYB4wrlLRMSnl7MMtYV1p+V5wQQgcsBMaYLPIuqCCjUCgUZoopQcYQUEqbIXEVbWbtbeoBhafsOQCtgF2GZ4rcgI1CiIEVHZdRQUahUCjMFJlb4bkDtzkMNBNCNEabcTscyJ8BI6W8BeQv/yGE2AVM+VcP/CsUCsX/OpU18C+lzBFCTAC2oU1hXiGlPCWEeBc4IqXcWDk5lUQFGYVCoTBTZF6ltWSQUgYBQcXS3irF1rey8lVBRqFQKMyUSpzCfN9QQUahUCjMFCkrryVzv1BBRqFQKMwU1ZL5H8OlEvtHKxN/6/p3N7oPmOvyLcdO/XC/JZSKb9vn7rcEozTUVb+70X3g+5q+91tClZJXebPL7hsqyCgUCoWZUpkD//cLFWQUCoXCTFFBRqFQKBRVhpkukm8SKsgoFAqFmaJaMgqFQqGoMtQUZoVCoVBUGblqdplCoVAoqgrVklEoFApFlaHGZBQKhUJRZajZZQqFQqGoMlRLRqFQKBRVRm6e7n5LqDAqyFQS9X3b0HXWKIRex5nVuwhbsqnIfp2VBT0/Ho9L68Zk3Exm+4uLSb56A1fPJvSY96xmJODIwl+4tPUI1Zu4479kQv7xjg1cObxgHSeWbzNZ24C3R/OAnydZ6Vmsm7KU66culbCp06oxw+a/gKWNFWd3hrHpnVUAjFg8kVpN3AGwdaxGelIqnwZOx3NQV7q/0C//eLcHG7C4/wyiTv9tsj6AaXMm0b1XZzLSM5nx8nucOXG2hM3S1QtxqV0LvV5P6MEwZk+dT15eHvOXzaZR0wYAODg6kJyUzNBeo8ulwxRmzv2IPfsP4exUgw3fLa3y/Irz6rsT6NyzExnpGcx57QPOnTxfwmbBd/OoWbsmFno94YeOs2D6IvLy8vDr/zDPTnqahs0a8Hy/F/nr+LlK0zV61rN4+nmTlZ7J0imfculkZAmbx18fSffHfKlWvRpjHypY465WXRfGfTgBR2dHUhJTWPLqxyREx5dLh5tfG7ze1f6TkT/s4q/FJf+TnRb9H05tGpF1M4U/XviUtKs30Fnqaf/Bszi1bQJ5eYS++S1xB85ox1jqaTd3DK6dWyCl5MS8n7j62+Fy6SsLqrvMzBBCPAr8DLSQUv51z/LVCbrNfprNT84jNSqBxza/y98hR7l5vuAV2i2G+5KZmMrq7pNpOtCHTtOHs/3FxST8dZX1/d5E5uZh51qDYdvm8HdIKLcio1jXZ0b++Ucd/pSLW01/E+oDvp7UbOzGfN9J1PfyYPCcsSwZXPI9RYNnj+WX6cu5HHqeMd+8QXPftpzbFc7qCZ/m2wTOGElGchoAYb/uJ+zX/QDUfqA+o7+cXO4A071XZxo0rk+gzzDaeLfkzQ/e4Mm+z5awm/z8DFJTtPwXLn+f3gN7smXDdqaMm5lvM2XWy6QkpZRLh6kMDvTnySEDmf7e/HuSX2E69+xEvcZ1eaLbKFq2a8GU919l3ICXSti9Of5d0gw+m7NsFn79H2bHxp1E/nWR6c+/zevzXqtUXZ5+7XBrXIdJD7+Ih1dzxs5+gbcG/6eEXej2wwSvDOKjXZ8VSR85Ywx71+9i7/qdPNSlNU/85yk+f+0Tk3UIncB77hh2PfE+6VEJ+G95j+vBoSSdu5Zv02SEL1m3UgnqMpn6g3xoO3MEB8Z/SpORPQHY1nMq1jUd6fHDG4T0eROkpMUrg8m4kURQtykgBFZO1UzWZgp5/4LZZf/8tlhRRgD70N5ffc9w9WxK0qUYki/HkZedy4WNf9IowLuITaOAdpxbtxeAyN8OUbdrSwByMrKQudp63nprS6N3LnW7tSTp71hSrpl+R9ciwJtjP2v5XjkWgY2DHQ4uNYrYOLjUwNrBlsuh2p3wsZ/38lBA+xLnat3Ph/CNB0qktx3YhfCNf5is7TZ+fXqwca32wr7jR0/h4GhPLdeaJexuBxgLCz2WVsZ91WdgL4J+CSm3FlNo79ma6o4O9ySv4nTr3YWt67TfeSr0DA7V7anp6lzC7naA0VvosbCyBDSn/R1xmcsXrlS6Lm//juxdvxOAiGPnsHOsRg1XpxJ2EcfOkRh7s0R63Wb1OLX/OACn/ziBt3/Hculw9mpK8qUYUg3/ycu//knd3kX/k3X6eHPppz0AXN18iNrdtf+kY/O6xOw7BUBmfBLZt1JxbtsYgCbDH+bMIsObiqUkK6Fqb2ikFGXezJV/TZARQtgDXYFnMQQZIYROCLFECHFKCLFZCBEkhBhq2OcthNgthDgqhNgmhHAvb97V3JxIuZ6Q/z0lKoFqbk6l2sjcPLKS07Bxsge0IPX49nk8HvI+e6Z/nR90buMxsDPnfy15cS8L1Ws7kVhI263oBByLaXN0cyIpqpBNVALVaxe1adTxQVJu3CL+UnSJPNr096lQkKnt7kL0tdj87zFRsdR2dzFq+8Waj9l9agupKakEb/q9yD5vH0/i4xK4fLHyL57mhotbLWKvF/gsNioOF7daRm0/+v6/bA7/mbSUNHZu3lOlupzcapJwveBmKCE6HqfaJYNfafx95hId+3YGoEMfH+wc7LCvYXogt3VzJr3QTVlaVAK2xeq9nZsTaYX+k9lJaVg525N4+m/q9vZG6HVUq++CU5vG2NWtiaWjHQCt/zOUgODZdFn2Mta1HE3WZgpSln0zV/41QQYYDGyVUp4DEoQQ7YDHgEZAa+A5oDOAEMIS+BQYKqX0BlYAc4ydVAgxTghxRAhxZG9KyT5vg1GJpJKFXrpNbNgFfnpkKuv7v0W7lwagt7bMt9FZ6mno347I3w6W8rPvQhm0iTLYlNZaqe/ZlOz0TGLOXS2fPkAY9Y3xf80Lw1/Fr01/rKys6NStaGsr8NGAe9aKud8YLzPjPps08j8MajcUKytLvLt6VbEuI4kmXAG/n/0ND/q0ZG7QAlp0akl81A1yc3PLIaQMOoyJlXBx9W7SohLw3zobr3dHcePIefJy8hAWOuzq1uTG4XMEB8zkxtHzeL490nRtJpAnRZm3uyGE6COEOCuEiBBCTDWyf5IQ4rQQ4rgQYocQomFl/IZ/05jMCOBjw+c1hu+WwFopZR4QLYTYadj/ANAKCDH8WfVAlLGTSimXAcsAltZ/yui/JTUqAfs6BXdr9u7OpMUU7QpIjdZsUqMTEHodVg52ZCYWbWonRlwnOy0T5wfqEXf8IgAN/Npy4+Ql0m8kldEN4DPKnw4j/AC4Gh5JjTrO3B4tqe7mTHIxbbeiEnB0L9Bf3d2ZpEJdGTq9jpa9O7B4wIwSebUZ0NloF9rdGP7MEIY+NQiAk2FncKvrmr+vtrsrsdE3Sj02KzOLndv24tenOwf2HAJAr9fzSD9fHvd/2mQt/xQee3oQA0dqky3OhJ3FtU6Bz1zdXbgRU3p3alZmNvtC/qB7764c3nu0UnX5j+6L33B/ACKPR+Bcp6Cr09mtJjeNdIuVRmLsTT5+4b8AWNvZ0KGvD+mGcUBTSI9KwLZugQ47d2fSYxKL2KRFJWBXx5n0KO0/aeloR9ZN7T8Z9vZ3+Xa9Nr5NysVoshJSyEnL4GqQNjZ6ZdNBmozwNVmbKVTW7DIhhB74DPAHrgKHhRAbpZSnC5kdA9pLKdOEEP8HfAA8UdG8/xUtGSFETaAn8JUQ4hLwOppzSgvvAjglpfQ0bK2llAHlzT82PJLqjdxwqO+CzlJP04E+XAoJLWJzKSSU5kO7A9CkX0eu79fK1qG+C0KvFYN93ZrUaOpO8pW4/OM8BnUmwsSusj+/DeHTwOl8Gjid08FH8HpMy7e+lwcZyekkxxX9syXHJZKVkk59Lw8AvB7rzpnggguRR7dWxEVeJyk6ochxQghaB3YifJPpQWbN1+sZ2ms0Q3uN5vctuxk4LBCANt4tSUlO4UZs0QumrZ1t/jiNXq+nxyNduBhRMNHAp0cHIs9fIiYqjn8rP6/8lTEB4xgTMI492/bRZ6h2YW/ZrgUpSanExxYtH1s7m/xxGr1eR+eenfg74nKl6wpZtYXpgZOYHjiJI8EH6T5Eu8Hx8GpOenKa0bGX0nBwcshvpQ16aQi7f/r9LkcYJyEsEofGblQz/CcbDPLh2raiwfX6tlAaPd4DgHr9O+aPw+htrdDbWgNQu0cr8nLz8icMXA8+hmuXFtq+bq2KTCSoCqQJ213oCERIKSOllFloN+KDiuQl5U4p5e2I/idQrzJ+w7+lJTMUWCWlfOF2ghBiN3ADGCKEWAm4AL7AD8BZwEUI0VlKecDQfdZcSnmqPJnL3Dz2vbmSft+9gdDrOPvjbm6eu0b7yUOIO36Rv0NC+WvNbnp+PJ4RexeQmZhCyEuLAXDr0ByvFweQl5OLzJPsnfENGYa7KQsbK+p1b8WeqSvK7ZizO8N4wM+TKbsXkp2eybrXv8jfNzFoLp8GTgdgw8wVDJ0/HksbK87tCufsrrB8O621UrKrrFGnB7kVncDNK7El9pnCnu1/0L1XF7YcXEd6egZvvjI7f9+6HasY2ms0dtVsWbzqQ6ysrdDpdBzcf5SfVv6Sb9d3sD9b7nFX2etvz+PwseMkJibRa/BTvPjsKIYM6H1P8j6w4yCde3biFNacsQAAIABJREFUp/3fkZGewdxJH+Tv+yZ4GWMCxmHz/+3dd3gU5drH8e+9m0IKgSSU0DsI0kGadKUq4hELCojHglhRbEewIiL6igVQFEWP4hEUKyJKUYqCSi+CdJCSUJIQQnrZ+/1jJiEVEkJ2N/h8vPZid+bZmZ+7m332KTMTGMDLH07E188Xp9PJ+lUb+Wa2NWjdvX9XHp74ABXDKvB/H09i97a9jB2WfxZYcW36eT2te7Xj9ZUzSE1O5d1Hz8xOnLTwNcYNHAvAzU/eSpfB3fAL8Gfa7++xfO5SvnzjM5p2bs7Qx4ejCjvWbOPDp2eeVw7NdLFh3H/pMecJawrz3BXE7zpC88eGELt5P5GLN7BvznI6TbuHgaunkBaXyG+jraz+4SH0mPMEqJIUdZI/HpiRvd3NL86l47R7aDNhBKkx8ax5+PzyFdUFnF1WA8g5WHkY6HiW8ncAP1yIHUth/bhliYgsByar6o85lj0INMVqtXQHdgH+wGuqukREWgNTgQpYle0bqvre2fZTWHeZp/3tdJ27kAd8l5z/+AhvsHHbp56OUKiere70dIQC1fGp4OkIBRqcFujpCIW6Kep/Ja4hVkVcX+TvnK7HvrwbGJVj0Uy7ux8RuQHop6p32o9HAB1U9YG82xGR4cD9QA9VTS1JfrhIWjKq2rOAZVPBmnWmqgl2l9oaYKu9fhNW5WMYhuGVivPzMef4cQEOA7VyPK4JROYtJCJXAuO5QBUMXCSVzDksEJGKgB/wgqrmn4NrGIbhhbTQYeViWws0EpF6wBGswzxuyVlARNoA7wL9VbVkfeA5XPSVTEGtHMMwjLIg4wKNyahqhojcDyzCmk37gapuE5EJwDpVnQ/8HxAMzLMnXxxU1WtKuu+LvpIxDMMoqy5gSwZVXQgszLPsmRz3r7xgO8vBVDKGYRheyjun9BSPqWQMwzC81IVsyXiKqWQMwzC8lGnJGIZhGKUm07RkDMMwjNJyEVx92VQyhmEY3splWjL/LJFOrzyrDIFeesGiJ50NPR2hQN566haA5Zvf93SEAsWP/LenIxRowzo/T0coVd75jVM8ppIxDMPwUmbg3zAMwyg1rgKvAle2mErGMAzDS53HNUG9jqlkDMMwvJSZXWYYhmGUGjO7zDAMwyg1ZnaZYRiGUWpMd5lhGIZRaswUZsMwDKPUZJqWjGEYhlFaTEvGMAzDKDWmkjFyGfjsrTTq1Yr05DS+fvRdorYdyFemWvO6XPfqaHzK+bJ72WYWPv8xAL0euo52Q3uRGHsagKWvfMbu5Ztx+DgZ/PKdVL+0Hg4fB5u++pVf3p5frFz9nruVhnau+Y++y9E/8+eKaF6XwVOsXHuWbWbRc1auno9cT+M+7VCXkhgTz/xH3iHheBx1OjXlxvfGEnfoBAA7flzLL1O/LnKmaj1b0v6FEYjDwZ45y9k+/btc6x1+PnSZOpqwFvVIPXmaX0dPJ/FwNOLjpNOrdxLWoi7i42D/vF/ZNv07HP6+9PnqKZx+PoiPk4Pfr2Hrq18V63UqzEMT7qdz746kJKfw4sOvsOvP3fnKTPlkMuFVw/FxOtm8ZgtTxk3F5XLR6+oe3DF2JHUa1eauq+5lx5ZdFyTT2Tw16TVWrlpDWGhFvvnknVLfX06+7ToQNOoBcDhIWfw9KfM+LbCc3+U9KD9uAnFjRpG5ZydSPoTy4ybg06gJqUt/JPGdNy9orvBerWgy8TbE6eDI/37mwLRvc62v2KkpTV4YSXCz2my9+02OL/gje125GuE0e+1u/KtXAlU2DptMiv25L21eelrCYnF4OkBRich4EdkmIltEZJOIdBSR90Wkmb0+oZDndRKRP+zn/CUiz5VGvkY9WxFeL4I3ez7C/HGzGPRiwScUHDTxduaPe583ez5CeL0IGvVslb3ut1k/MGPgOGYMHMfu5ZsBuHRgR3z8fHmr/3945+qnaH9LbyrWrFTkXA17tSKsXgRv9XiE75+cxcCJBeca+OLtLHjyfd7q8Qhh9SJoYOda/e73zOz/JO8NHMfunzbSfcx12c85uHYn7w0cx3sDxxWrghGHcNmkkSwb9goLej5O3cGdCGlUPVeZBjf3JC0ukfmXP8KO936kzVNDAagzqAMOfx++v+JJfuj/NA1H9CaoZiVcqen8dMMkFvYZz8I+46nesyXhbRsUOVNhOvfuSM16Nbip6wheeeI1Hn3poQLLPT16Arf1uYvhvW+nYlhFel3dA4B9O/Yz7q5n2fT7lhJnKaprB/bhndcmum1/2RwOgu55iPhnHyfunpH4d78CZ606+csFBFDumiGk79iWvUjT0kiaPYvEWTNKIZdwyeTb2XjLS6zuNpaIf11OUOMauYqkHIlm25i3OfrVqnxPv3TafRx46zt+6zaWNf3HkRZ96sJnLISrGDdvVSYqGRHpDFwNtFXVlsCVwCFVvVNVt5/j6R8Bo1S1NdAc+Lw0Ml7Stx2bvvoFgMMb91CufCDBlSvmKhNcuSL+5QM4tGEPAJu++oVL+rY7x5YVvwB/HE4HPuX8yEzLIPV0cpFzNe7Tji1fWrmObNxDuZBAgqvkyVWlIv7BARyxc2358hea2LnSEs7syy/QH9WSz9wPb9OA0weOkXDwBK70TP7+9ndq9cv9OtTs15Z986zcBxesoWrXSwFQBZ9Af8TpwFnOD1daBul2xoykVAAcvk4cvj4X5CCDrv268OMXSwDYtuEvylcIJrxKWL5ySQlJADh9nPj4+ZK187/3HOTg3kMlD1IM7Vu3oEJIebfuE8CncVMyI4/gOhoFGRmkrvwZ305d85ULHH4HyV/MgbS0MwtTU8jYvhXS0/KVL6kKbRuStP8YyX8fR9MzOfrNair3vyxXmZRDJ0jYfhBcub+ugxrXQHycxK7cCkBmUiqu5AufsTCZxbidi4j0F5GdIrJHRP5TwHp/EfnMXv+HiNS9EP8PZaKSAaoB0aqaCqCq0aoaKSLLRaR9ViERmSIiG0TkJxGpbC+uAkTZz8vMqpRE5DkRmS0iP4vIbhG5qyQBQ6qGcSoyJvtx/NFYQiJCc5eJCCU+KvZMmahYQqqe+cLqMLIv9/7wEte+chflQgIB2LZwDWnJqTy25i0eWf0mq977nuRTiUXOVT4ijPg8ucpXzZ2rfNVQ4o/mzlU+4kyuXo/dwIO/TaX5tV1Y8doX2ctrtm3IqB8mcfNHj1O5Ue5fhmcTEBFKUuSZ/SVFxRJQLXemwIhQEu0ymukiPT4J/7BgDi5YQ0ZSKtdtms6/1r7BX+8sJC3Oej3EIQxY8iJDtrxN1MqtxGzcW+RMhakcUYnjkcezHx+POkHliIJbkq/972UWbP6KpIQkli1YWeJ9lzWO8Eq4os+8Vq7oEzjDc79WzvqNcFSuQvra39yWyz8ijNQcfwOpkTH45/nbLExgg2pkxCfS8oNH6Lh0Mo2eGQYO9/VhuaTot7MRESfwFjAAaAbcnNULlMMdwElVbQi8Drx8If4fykolsxioJSK7RORtEelRQJkgYIOqtgVWAM/ay18HdorI1yJyt4iUy/GclsBVQGfgGRHJ3WcDiMgoEVknIus2nN5TaMCCTpaa71d/wYUAWPPJUt7o/jAzBo7j9PE4+j81DICarRrgynTxfx3v5/VuD3P5nQMJrVU5/3ZKKRfAsv+bx9TOD/LnN6u5bGRfAKL+PMDULmOYOWAca/+7iBveG1uMTAXt79zBVaFSm/popouv2jzANx3H0nT0QIJrW6+HupQf+ozn63YPEt66ARWa1CxypuJkLaw1N3bYEwxuez1+fr60u7xNifdd5hT0WuVZH3TXfSS9/7bbImXt97yf6nRSsWNTdj8/mzX9xhFQpyrVh/a8cNnO4QJ2l3UA9qjqPlVNA+YCg/OUGYzV8wPwBXCFFPjHWjxlopJR1QSgHTAKOAF8JiK35SnmAj6z738CdLWfOwFoj1VR3QL8mOM536pqsqpGA8uw3oi8+56pqu1VtX3b8rkvwtVhRB/uWTiJexZOIv5YHBWqh2evC4kI4/SxuFzl46NiCal2poUQUi2M+OMnAUiMjkddiqqyfu4yarSyxhNaDO7CnhVbcGVkkhgTz8H1u6jesv5ZX6/2t/bhroWTuGvhJE4fiyMkT66E47lznT4aS0hE7lynj53Mt90/v13NJQOsboa0hGTS7e6pPcs24/RxEhAafNZcWZKiYgmsfmZ/gdXCSD56Ml+ZILuMOB34hgSSdjKBuv/qQtSyLWhGJqkx8ZxYu4uwVrlfj/T4JI7/9hfVe7UsUp68rhs5mP8unsl/F88k+mgMVapXyV5XpVploo/FFPrctNR0fl2ymm79Lj+vfZdlrugTOCqdea0clSrjionOfiwBgTjr1CNk8htU/GAuPpc0I+SZSTgbNinVXKlRMfjn+Bvwrx5O6tH8n++CnxvL6a37ra62TBcnflhLSIt6pRU1n+JUMjl/ENu3UTk2VQPI2W972F5GQWVUNQM4BYRTQmWikoHsrq7lqvoscD8w5FxPyfHcvao6A7gCaCUi4XnLFPL4rNbMXpI9UL9j8TpaX9cNgJptGpJyOpmEE7m/zBNOxJGWkEzNNlZl1fq6buxYvB4g1/hN037tOb7rMACnIqOp18Vq1foG+FOzTSOi90aeNde6j5dkD8jvXLyOlkOsXDWycuWpZBKOx5GWmEwNO1fLId3YtcTKFVa3ana5xn3aErM3CoCgyhWyl1dvVR9xCMknC5x7kU/Mpn2UrxdBUK3KOHyd1BncicOLN+Qqc2TxBurfYOWufXUHjv1qDb0lHonJHp9xBvhTqW1D4vdE4h9WHl+7i9FZzpeIbs2J33P216kwX330Lbf1HcVtfUexctGv9L++DwCXtm1KQnwiMcdjc5UPCCyXPU7jdDro3Lsjf+85eF77Lssydu3AWaMmjqoR4OODf/fepP9xZiBdkxI5ectg4m4fStztQ8nYsZ34CePI3LOzVHPFb9xLYP0IytWujPg6ibi2CycWrSvSc09t3INvxWB8w60xrtCuzUmw/zbdQYtzy/GD2L7NzLGpglok+foPilCm2MrEFGYRaQK4VDVr7mhr4G+sgfwsDuB6rGbgLcCv9nOvAhaq1cfRCGuMLOtbdrCIvITV1dYTyDcYVlS7lm2iUa/WPLTiNWsK82PvZq+7Z+EkZgwcB8B3T33Iv169G99yfuxevjl7FlnfJ2+mWrM6qCpxh08wf9wHAKz5eAnX/t/d3L/4ZRBh47wVHNtR9IHkPT9vomGv1ty38jUy7CnMWe5aOIn37FwLx3/INVPuxqecH3uXb2bPMitX7/8MJbx+NdSlnDoSzUI7V9OBHWg//EpcGZmkp6Tz1QPTi5xJM12sG/8RvT99HHE62Dt3Bad2HaHlY0OI2byfI4s3sGfOCrpMHc01q6aQGpfAqnus7e/6cAmdXh/FVcsmIyLs/WwlcX8domLTWnR+827E4UAcwt/f/cGRpZuKnKkwv/30B517d+TzVZ+QkpzCpLGvZK/77+KZ3NZ3FOUCA3j5w4n4+vnidDpZv2oj38y2ppl379+Vhyc+QMWwCvzfx5PYvW0vY4c9UeJcZ/PYs5NZu3ELcXHxXHHtcO69YwRDBvUr1X0C4MokccYbhLzwKjgcpC5ZSObBAwQMv52M3TtI/2P1WZ9e8YO5SGAQ4uODb+eunH7qUTIP/V3iWJrpYueTH9B27jjE6SByznISdx6mweM3EL95HycWrSekdQNaffgIvhWDqNS3HQ0eu4HfejwKLmXXc7Np98XTIMLpzfs48slPJc5UVBfw3GWHgVo5HtcE8v4KyypzWER8gApALCUkF2K2UGkTkXbANKAikAHsweo6+wJ4VFXX2VOYXwcGYjXzblLVEyIyF2gLJNnPHa+qi+ypzNWBBkBt4BVVfe9sOZ6pO8wrXyxfr0wF9dO9c5L/23J+LRx3WL75fU9HKFD8yIKnvnvahnURno5QqD7HPivxH8BLdYYX+a/7yb8/KXR/dqWxC6s35wiwFrhFVbflKHMf0EJVR4vIUOA6Vb3xvMPbykRLRlXXA10KWNUzR5msAYGn8zx36Fk2vUtVR51lvWEYhse4LtDJ/lU1Q0TuBxYBTuADVd0mIhOAdao6H5gFzBaRPVgtmLN9dxZZmahkDMMw/oku5EGWqroQWJhn2TM57qcAN1zAXQL/4EpGVZ/zdAbDMIyz8dKe8GL5x1YyhmEY3s6bTxdTVKaSMQzD8FIZUvbbMqaSMQzD8FJlv4oxlYxhGIbXMt1lhmEYRqm5UFOYPclUMoZhGF6q7FcxppIxDMPwWqa77B+mwgU8kdCFFJHh6QQFi3V6OkHBhlKd3xxFvyaPO3nr6VtCPvrQ0xEKFNLyUU9HKFWZF0FbxlQyxj+Ot1YwhpGXackYhmEYpUZNS8YwDMMoLaYlYxiGYZQaM4XZMAzDKDVlv4oxlYxhGIbXyrgIqhlTyRiGYXgpM/BvGIZhlBoz8G8YhmGUGtOSMQzDMErNxdCScXg6gGEYhlGwTNUi30pCRMJEZImI7Lb/DS2gTGsR+U1EtonIFhG5qSjbNi2ZEur1/Ajq9WpNRnIqPz4yk+N/HshXpkqLuvSfcjc+5fzYv2wTy56dDUC5CkFc/fb9hNSsTPzhE3x37zRSTyUR1qAa/V4dRZXmdVn1f/NYN3MhAKH1q3H1W/dnb7dC7Sqsfu0LIt9dVGi+aj1b0v6FEYjDwZ45y9k+/btc6x1+PnSZOpqwFvVIPXmaX0dPJ/FwNOLjpNOrdxLWoi7i42D/vF/ZNv07AquH0fnN0QRUqYC6lD2fLGPnrML3X5jaPVvS/bkRiNPB9jnLWf92/lx93xhN5Rb1SDl5mh/vnc7pw9GUqxjMgHcfpEqr+uyYt5IVT3+c/ZxGgzvT/v5rQJXEY3EsfvBtUk4mFDtbXrc+dwete7UjLTmVdx6dxoE/9+Urc+Njw+h2XU+CKgRxe7NbspdXqlGZUf93PyFhISTEJfD2Q28QezSmxJl823UgaNQD4HCQsvh7UuZ9WmA5v8t7UH7cBOLGjCJzz06kfAjlx03Ap1ETUpf+SOI7b5Y4S3E8Nek1Vq5aQ1hoRb755B237bdCzzbUeeF2xOHg+JylRE3/Otf6iFGDqHLLlWhGJukx8ewb+xZpR04QeGld6r50N87yAZDp4sjUL4mdv8ptud14nMx/gJ9UdbKI/Md+/ESeMknAraq6W0SqA+tFZJGqxp1tw2WiJSMimSKySUT+FJF5IhJ4AbZ5m4hML8k26vVqRWjdCD7o/ghL/jOLK1+8rcByV774b5b8ZxYfdH+E0LoR1O3ZEoAO9w3i4KrtfNDjUQ6u2k6HewcBkByXyM/Pzs6uXLKc3BfF7AHjmT1gPJ9c9RQZyans/nFd4f+PDuGySSNZNuwVFvR8nLqDOxHSqHquMg1u7klaXCLzL3+EHe/9SJunhgJQZ1AHHP4+fH/Fk/zQ/2kajuhNUM1KuDJcbJjwKQt6PMGiq5+j8W1X5tvmuYhD6DlxJPNvfYX/9X6cxoM7EZpnG5cO7UlKXCKzuz3Cpvd/5PJxVq6M1HR+f/ULVk3M/aUqTgfdnxvO1ze+yJy+44j+6yAtb+tbrFwFad2rLRH1qjO2x728/+QMbp94d4HlNixdy9ODH8+3fNj42/jly+X8p//DfDX1c256YniJM+FwEHTPQ8Q/+zhx94zEv/sVOGvVyV8uIIBy1wwhfce27EWalkbS7FkkzppR8hzn4dqBfXjntYnu3anDQd1Jd7Fz2ES29BxD+OBuBDSqmatI0p/7+XPAY2y9ciyx3/9G7advBcCVnMreMVPZ2ushdgx7gTrP344zpMRfP0WmxfivhAYDH9n3PwKuzZdFdZeq7rbvRwLHgcrn2nCZqGSAZFVtrarNgTRgdFGfKCKldi7gBn3bsf3LXwGI2rgX/5AggqpUzFUmqEpF/IMDiNqwB4DtX/5Kw37tref3ace2L34BYNsXv9Cwr7U8OSaeY1v24crILHTftS+/lLiDxzl9pPBfxeFtGnD6wDESDp7AlZ7J39/+Tq1+7XKVqdmvLfvmWRkOLlhD1a6XAqAKPoH+iNOBs5wfrrQM0hOSSTkex8mtBwDISEzh1J5IAquFFen1ylK1dQPiDhwj3s61a/7v1O+bO1e9vm3ZYb82e75fQ83LrVwZyalErd1FRmp6rvIigojgG+gPgF9wAInHThYrV0Ha9enAL18us3Js3EVgSBAVq+TrSWDPxl3EHc+/vxqNarJt1RYAtq/eSrs+HUqcyadxUzIjj+A6GgUZGaSu/BnfTl3zlQscfgfJX8yBtLQzC1NTyNi+FdLT8pV3h/atW1AhpLxb9xncpiEpB6JIPXgMTc8g9ttfCe2X+32IX/0nrmTrNUnYsAu/auEApOyLInV/FADpx06SHn0Kn/AKbsvuKsZNREaJyLoct1HF2FVVVY0CsP+tcrbCItIB8AP2nmvDZaWSyekXoCGAiHwjIuvtPsLsF1REEkRkgoj8AXQWkctEZLWIbBaRNSKS9SmvLiI/2v2QrxQ3SHBEKKejznzJnz4aS3BEaP4yR2MLLBNYKYTE41ZLM/F4HIGVQoq870uu6cyOb387a5mAiFCSIs/sOykqloBqufMFRoSSaJfRTBfp8Un4hwVzcMEaMpJSuW7TdP619g3+emchaXG5z14cVLMSYc3rEL3hnJ+z3M+LCCUhR66EqEJetxy50k4nUS40uNBtujIyWTbuQ25ZMpnb100nrHENts9dXqxcBQmNCCc28sx7HHs0htCqRa9U//7rAB0GdAbgsv6dCCwfSHDFkn3JOsIr4Yo+nv3YFX0CZ3ilXGWc9RvhqFyF9LVn/4z8E/hFhJOW4z1Mi4rB9yw/jCrffAVxP2/ItzyodUMcfj6kHjhaKjkL4kKLfFPVmaraPsdtZs5tichSuzco721wcTKJSDVgNvBvVT3n3IQyVcmIiA8wANhqL7pdVdsB7YEHRSTcXh4E/KmqHYE1wGfAGFVtBVwJJNvlWgM3AS2Am0SkVgH7zP518HvC7tzryH99Gc03AFfANWhKOEjn8HXSoE9bdn3/x1nLiRS073yF8hdRqNSmPprp4qs2D/BNx7E0HT2Q4NpnWsY+gf50e38M65/5hIyE5HzbKG6u/C9JEbLn4PBx0mLElcwZMJ4P2t9PzF8HaXf/NcXKVZCCXsLivH//m/hfLul0KZMWTqFpx0uJiYomM7PwFur5htI864Puuo+k998u2X4uFgW+hwUXDb+uO8EtGxI145tcy32rhNJg2hj2PTy9xH+/xXEhu8tU9UpVbV7A7VvgmF15ZFUixwvahoiEAN8DT6nq70X5fygrA/8BIrLJvv8LMMu+/6CI/Mu+XwtoBMQAmcCX9vImQJSqrgVQ1XjI/qL7SVVP2Y+3A3WAQzl3bP8amAkwpfZwbX3rlbS4uRcAR7fso3y18Oyy5SPCSDyWewws4Wgs5SPCcpVJsMskRccTVKUiicfjCKpSkaTo+CK9GPV6tuLYnwfOWT4pKpbA6mf2HVgtjOSjJ/OVCaoeRnJULOJ04BsSSNrJBOr+qwtRy7agGZmkxsRzYu0uwlrVJ+HgCcTHSbf3x3Dgq9Uc+qHwMaHCJETFEpwjV3C1sHxdWwlHYylfPYzEo1Yuv/KBpMQVPohf6VJrTCL+b+tvY/eCP2hnj3EVV59bB9BraB8A9m3ZQ1j1M+9xWEQ4JwvoFitM3PGTvHH3ywD4B5bjsgGdSD6ddF65sriiT+CodKY3w1GpMq6Y6OzHEhCIs049Qia/Ya0PDSPkmUnETxhH5p6dJdp3WZQWFYNfjvfQr1o46Tl6F7KEdGtJjTHXs/26p9G0M1cCdAYH0GT2eA6//CkJG3a5JXOWks4aK4b5wEhgsv3vt3kLiIgf8DXwsarOK+qGy0pLJmtMprWqPqCqaSLSE6tV0tluoWwEytnlU1Q16+eiUPhv4NQc9zMpQqW76eOl2YPvexatp9kQqy+8WpsGpJ5Oyu7+ypJ4PI60xBSqtWkAQLMhXdm7eD0Ae5ds4NLruwFw6fXd2Ltk/bl2D8Alg8/dVQYQs2kf5etFEFSrMg5fJ3UGd+Lw4tzdAEcWb6D+DVaG2ld34Niv263cR2Kyx2ecAf5UatuQ+D2RAHSacifxuyPZMfOHIuXN69jmfVSsG0GInavxNZ3YvyR3rv1LNnCJ/do0vKoDh1dtP+s2E4/GEtaoBuXCrK6oWt1acNLOW1xLPv6BcQPHMm7gWNYt/oNuQ6wfFQ3bNCb5dFKBYy+FKR9aPrvlNvi+Iaz4/OfzypRTxq4dOGvUxFE1Anx88O/em/Q/zsx40qRETt4ymLjbhxJ3+1Aydmz/x1YwAAmb9lCuXjX8a1VBfH0IG9yVk4vX5ioT2Lwe9V4ezc7bXiIj5lT2cvH1odGsJ4iet5zYBe7veixOd1kJTQb6iMhuoI/9GBFpLyLv22VuBLoDt9kTsTaJSOtzbbistGQKUgE4qapJInIJ0KmQcjuwxl4uU9W19nhM8fp3CrH/503U79WKO36ZQnpyGosePdMFOuKHF5k9YDwAS8d/SP8po+wpzJvZv2wzAGve/o6rZzxA85t6EB8Zw4LRUwEIrFyB4QtewC84AHW5aHtHf/57xROkJSTjU86POt2as+TJD86ZTzNdrBv/Eb0/fRxxOtg7dwWndh2h5WNDiNm8nyOLN7Bnzgq6TB3NNaumkBqXwKp7rAl3uz5cQqfXR3HVssmICHs/W0ncX4eo3KEx9W/oxsntBxmw5EUANr/0OZE/by7y66aZLlY8/RHXfPI4DqeD7Z+tIHbXETo+MoTjW/azf8kGts9dQZ83RjPiFyvXj/edmQg4cvXr+JUPwOHrQ/1+7flm2GRO7o5kzRtfMeSLp3BlZHL6cDRLx848S4qi2fTzelr3asfrK2eQmpzKu49Oy143aeFrjBs4FoCbn7yhCjwWAAAfBUlEQVSVLoO74Rfgz7Tf32P53KV8+cZnNO3cnKGPD0cVdqzZxodPlzwTrkwSZ7xByAuvgsNB6pKFZB48QMDw28nYvYP0P1af9ekVP5iLBAYhPj74du7K6aceJfPQ3yXPVQSPPTuZtRu3EBcXzxXXDufeO0YwZFC/0t1pposD49+nyafPIE4HJ+b+RPKuQ9R4bCiJm/cSt3gttZ++FWdQORrNtC7nnHYkml23vUTYoC6U79QMn7DyVLrJ+rGx76FpJG07ULqZbe46GFNVY4ArCli+DrjTvv8J8Elxty35xxC8j4gkqGpwnmX+wDdADWAn1lS651R1ed7yInIZMA0IwKpgrgSuB9qr6v12mQXAq6q6vLAcU2oP98oXKyLj3GU8IbbU5vWVjDdffnla8/zdON4g5KMPPR2hQBtaPurpCIXqGPlVQaNBxXJ17auK/J2z4OD3Jd5faSgTLZm8FYy9LBVrEsA5y9vjMXlbOv+1b1llri5pTsMwjAvJXLTMMAzDKDVloafpXEwlYxiG4aUyTUvGMAzDKC2mu8wwDMMoNaa7zDAMwyg1piVjGIZhlBpzZUzDMAyj1LjxtDKlxlQyhmEYXsp0lxmGYRilxlQy/zDe+mK19D917kIe0OZI/mtyeIv/hff0dIQCbVjn5+kIBQrx0tO3tN3yqqcjlCozu8wwyiBvrWAMIy/TkjEMwzBKjZldZhiGYZSazHNf3djrmUrGMAzDS5kxGcMwDKPUmDEZwzAMo9SYMRnDMAyj1Lgugu4yh6cDGIZhGAXTYvxXEiISJiJLRGS3/W/oWcqGiMgREZlelG2bSsYwDMNLZaqryLcS+g/wk6o2An6yHxfmBWBFUTdsKhnDMAwv5VIt8q2EBgMf2fc/Aq4tqJCItAOqAouLumFTyRiGYXip4nSXicgoEVmX4zaqGLuqqqpRAPa/VfIWEBEHMAV4rDj/D2bg/wKp06MlPZ4bgTgdbJu7nHVvf5drvdPPh76vj6ZKi3qknDzNwvumc/pwNOUqBjPwnQep2qo+f81byfJnPs5+zuCPHyeoSgUcPk4i1+xk2VP/RV3n/4sluHtbqj97FzgcnPxsCSfe+SLX+kp3DCb0pr5oZiaZMfEcfuJN0o+cAMC3emVqTH4A32qVQJUD/36e9CPHzztLXq+/NoEB/XuTlJzMHXc8zMZNf+Yr89OSeURUq0pycgoAAwbezIkTMdw64kZenvwURyKPAvD22x/ywYdzzitHRK+WtJlgvY/7Pl3Ojum530eHnw8dp95DaMu6pJ1MYPXd00g6HI3D10n7V+4gtFV9cLnY8PRsTvz2l/UcXydtJ91Glc5NUVW2Tv6cw9+vPa98WcJ7taLJxNsQp4Mj//uZA9O+zbW+YqemNHlhJMHNarP17jc5vuCP7HXlaoTT7LW78a9uvZcbh00m5dCJEuXJUqFnG+q8cDvicHB8zlKipn+da33EqEFUueVKNCOT9Jh49o19i7QjJwi8tC51X7obZ/kAyHRxZOqXxM5fdUEyFcVTk15j5ao1hIVW5JtP3nHbfs+lOC0UVZ0JzCxsvYgsBSIKWDW+iLu4F1ioqodEpMi5ynQlIyKZwNYci65V1QNuz+EQek4cydfDJpMQFcvQ7yawb8l6YndHZpe59KaepJ5K5KPuj9B4UCe6PjmUH+6bTkZqOr9P+YLwJjUJb1wz13Z/uHcaaQnJAFz1zoM0uqoju777/fxCOhxUnzCa/SOeJuNoDA2+fY34pX+QuudQdpHkbfuIuWYsmpJK2LABRPzn3xx64BUAak55mBNvfU7Cr5twBJYrUWWX14D+vWnUsB6XNOtKxw5teWv6S3TpOqjAsrfeej/rN2zJt/zzefMZ89BTJcohDqHdpNtYftNLJEfF0ueHF4hcvIH4XUeyy9S/uSdppxJZ2OURag3uRKunbua30dOoP6w3AIt6/wf/8BC6f/o4S/o/Dao0HXMtKdHxLOz6KIjgFxpUopw4hEsm386GG18kJTKGjote4sSidSTmyJlyJJptY96mzj35X8dLp93H/je+JnblVpyB/hfugD+Hg7qT7mLH0OdJi4rh0oWvELdoLcm7D2cXSfpzP38OeAxXchpVbu1H7advZc/oKbiSU9k7Ziqp+6PwrRpK8x9f5dTyjWTGJ12YbOdw7cA+3DLkGsa94F0n3LyQU5hV9crC1onIMRGppqpRIlINKOgXZGegm4jcCwQDfiKSoKpnG78p891lyaraOsftQFGeJCLOCxmiausGnDpwjPiDJ3ClZ7Lru9+p37ddrjL1+7Zl+xe/ALB74RpqXX4pABnJqUSu3UVGSnq+7WZVMA4fJw4/nxJ94AJbNSLt7yjSDx1D0zM49d1KQvp0zFUm8fetaEoqAEkbd+IbEQ6Af8NaiNNJwq+bAHAlpWSXuxAGDerH7P9Zrao/1mygQsUKRETka62XurA2DTh94BiJ9vt48NvfqdEv9/tYvX87Dny+EoDDC9ZQtZv1PoY0rsGxX7cBkBoTT/qpRMJa1QOg/tAe/DV1vrUBVdJiE0qUs0LbhiTtP0by38fR9EyOfrOayv0vy1Um5dAJErYfBFfuAeGgxjUQHyexK63fZplJqbiS00qUJ0twm4akHIgi9aD1GYv99ldC+3XIVSZ+9Z/Z+0vYsAu/atZnLGVfFKn7owBIP3aS9OhT+IRXuCC5iqJ96xZUCCnvtv0VVaZmFvlWQvOBkfb9kcC3eQuo6jBVra2qdYFHgY/PVcFA2a9k8hGRuiLyi4hssG9d7OU9RWSZiHyK3foRkeEiskZENonIu+db+QRHhHI6Mjb7cUJULMFVc88ADIoIJcEuo5kuUk8nUS40+Jzbvnb249y18W3SE1LY8/2a84kHgE9EOOlR0dmP04/GZFciBQm7qQ+nV6wHwL9eDTLjE6k940kaLniDiCf/DY4L99GpUT2Cw4fOtPqOHI6iRvWCWvXw/vuvsW7tYsaPeyjX8uv+NZAN65fw2dyZ1KxZ/bxyBESEkXwkJvtxUlQsARG538fAiFCScryP6fFJ+IUFE7f9b2r0a4c4HQTVqkxoy3oE1gjHNyQQgBZPXE/fxRPpMvNB/CuFnFe+LP4RYaRGnsmZGhmDf0ShM05z529QjYz4RFp+8Agdl06m0TPDwFH0ro+z8YsIJy1HrrSoGHyrhRVavvLNVxD3c/7LQQS1bojDz4fUA0cvSK6yTFWLfCuhyUAfEdkN9LEfIyLtReT9kmy4rFcyAXYFsUlEsjp/jwN9VLUtcBMwNUf5DsB4VW0mIk3t9ZeramsgExiWdwc5B9NWJ+wuOEUB/ZN53/MC+zCL8Ln4ZsQrvN/+fpx+Ptmtn/NSYMaCA1S8ticBLRoSPfMra4GPg6DLmhE16QP2DB6LX60IQq+/4vyz5ItWtGwjRj5Am7ZX0rPXv+h6eQeGD78egAXfL6FBo060bdeHn376hQ9nvXGeQQpYlv+NLKAM7J+zgqSoWPr8OJE2E0YQvW43rgwX4uMgsEY40Wt3sbjvU0Sv303rZ/N9zIqZ8/wrBXE6qdixKbufn82afuMIqFOV6kN7lixP9sYLWFbIZzz8uu4Et2xI1Ixvci33rRJKg2lj2Pfw9Pyv/T+QCy3yrSRUNUZVr1DVRva/sfbydap6ZwHl/6uq9xdl22W9ksnZXfYve5kv8J6IbAXmAc1ylF+jqvvt+1cA7YC1IrLJflw/7w5UdaaqtlfV9l2CGxUYIiEqlvLVz/xiC64WRuLxk/nKBNtlxOnAv3wgKXFF6zbJTE1n39KN1O/TtkjlC5IRFW0N2tt8I8LJOBabr1zQ5a2ofN+NHLhrIpqWAUB6VAzJ2/eRfugYZLqIX/I7Ac0bnHcWgHtGj2Td2sWsW7uYyKij1Kx1pvVRo2Y1IqOO5XtOpD2wn5CQyJy533BZ+9YAxMaeJC3N6oJ5f9b/aNu2xXllSo6KJaDGmdZdYLUwko/F5SqTFBVLYI730TckkLSTCWimi03PfsLiPuP49d+v4RcSSML+o6TFJpCRlMLhhesAOPTdH4S2qHte+bKkRsXgX/1MTv/q4aQePXmWZ+R8biynt+63utoyXZz4YS0hLeqVKE+WtKgY/HLk8qsWTvrR/J+xkG4tqTHmenbe9lL2ZwzAGRxAk9njOfzypyRs2HVBMpV1bmzJlJqyXskU5GHgGNAKaA/kvNRgYo77AnyUo5JqoqrPnc8Oj23eR8V6EYTUqozD10njQZ3YtyR3N8C+JRtodn03ABoN7MCh1dvPuk3fQH8Cq1S0gjod1O3Viti9UecTD4CkLbvxr1sd35pVEV8fKgzqTvzS3N1v5ZrVp8aL9/H3XS+QGXPmapvJW3bjrBCMM8zq5gnq3JKU3QfPOwvAjHc+ov1lfWl/WV/mz1/EiGFWq6Rjh7bEn4rn6NHc445Op5PwcKtLyMfHh6uuupJt23YC5Bq/GTSoLzt27DmvTLGb9lG+XgRB9vtYe3Anjixan6tM5KIN1L2xOwA1r+6QPQ7jDPDDGeAPQNXuzXFlurInDEQu3kiVLk2tdV2b55pIcD7iN+4lsH4E5WpXRnydRFzbhROL1hXpuac27sG3YjC+4db4Q2jX5iTsOnyOZxVNwqY9lKtXDf9aVRBfH8IGd+Xk4tyz6AKb16Pey6PZedtLZOT4jImvD41mPUH0vOXELvjtguS5GLjxOJlSU6ZnlxWiAnBYVV0iMhIobJzlJ+BbEXldVY+LSBhQXlX/Lu4ONdPF8qc/4trZjyNOB9s/W0HsriN0GjuEY1v3s3/JBrZ9toJ+b4xm5MoppMQl8MP9Z87I8O9Vr+NXPgCHrw/1+7Xnm+GTSTmZwDWzxuL080GcDg6t2s7WT346n9fDkuki8tl3qPfx89YU5nlLSd19kCoPDyN5625OL11DtSf/jSOoHLXfssby0iNP8PddE8Hl4uikD6j3v4kIQvKfezk5t8jHYp3Twh9+on//3uz8axVJycnceefY7HXr1i6m/WV98ff3Y+H3n+Lr64PT6eSnn37h/Vn/A+CB+2/n6qv7kpGRycnYOG6/86HCdnVWmuliw7j/0mPOE9YU5rkriN91hOaPDSF2834iF29g35zldJp2DwNXTyEtLpHfRk8DwD88hB5zngBVkqJO8scDM7K3u/nFuXScdg9tJowgNSaeNQ8XOsu0yDl3PvkBbeeOQ5wOIucsJ3HnYRo8fgPxm/dxYtF6Qlo3oNWHj+BbMYhKfdvR4LEb+K3Ho+BSdj03m3ZfPA0inN68jyMl+VzllOniwPj3afLpM4jTwYm5P5G86xA1HhtK4ua9xC1eS+2nb8UZVI5GM63LOacdiWbXbS8RNqgL5Ts1wyesPJVu6gXAvoemkbTtwIXJdg6PPTuZtRu3EBcXzxXXDufeO0YwZFA/t+z7bC6GE2SKNzezzsWePhecZ1kj4EsgCVgGPKCqwSLSE3hUVa/OUfYm4EmsFl06cJ+qFjpH+M3aw73yxertPHXuQh7Q5kj+QV1v4M2XXw5zZZy7kAeEOPPPfvQGbbd415TjnHwr1S/xjIrKFZoU+TvnxKmdF2YGxwVWplsyeSsYe9luoGWORU/ay5cDy/OU/Qz4rPQSGoZhnL+y3AjIUqYrGcMwjIuZN4+1FJWpZAzDMLyUackYhmEYpcZcftkwDMMoNaYlYxiGYZSaC3AxMo8zlYxhGIaXMgP/hmEYRqkx3WWGYRhGqbkYjvg3lYxhGIaXMi0ZwzAMo9RcDGMyZfrcZWWZiIyyr8ntdbw1m8lVPN6aC7w3m7fmKssuxlP9lxWjPB3gLLw1m8lVPN6aC7w3m7fmKrNMJWMYhmGUGlPJGIZhGKXGVDKe4839vt6azeQqHm/NBd6bzVtzlVlm4N8wDMMoNaYlYxiGYZQaU8kYhmEYpcZUMoZhGEapMZWMYRiGUWrMaWXcQETCzrZeVWPdlaWsEZEGwGFVTRWRnkBL4GNVjfNgpqrAJKC6qg4QkWZAZ1Wd5alMOYlIBNABUGCtqh71cKRsIlIDqEOO7x5VXem5RCAiAgwD6qvqBBGpDUSo6hpP5rpYmNllbiAi+7H+4AWoDZy071cEDqpqPQ/lOm3nKpCqhrgxToFEZBPQHqgLLALmA01UdaAHM/0AfAiMV9VWIuIDbFTVFp7KlEVE7gSeAX7G+oz1ACao6gceDQaIyMvATcB2INNerKp6jedSgYjMAFxAb1VtKiKhwGJVvcyTuS4WpiXjBlmViIi8A8xX1YX24wHAlR7MVd7OMQE4CszG+mIaBpT3VK48XKqaISL/At5Q1WkistHDmSqp6uci8iSAnS/zXE9yk8eANqoaAyAi4cBqwOOVDHAt1g+EVE8HyaOjqrbN+lyp6kkR8fN0qIuFGZNxr8uyKhgAVf0B65emp/VT1bdV9bSqxqvqDGCIp0PZ0kXkZmAksMBe5uvBPACJ9pe3AohIJ+CUZyNlOwyczvH4NHDIQ1ny2ofn37uCpIuIkzPvZ2Wslo1xAZiWjHtFi8hTwCdYH+jhQIxnIwGQKSLDgLlYuW7mTHeGp/0bGA28qKr7RaQe1uvnSWOxuu0aiMgqoDJwvWcjZTsC/CEi32K9l4OBNSIyFkBVX3N3IBGZZmdJAjaJyE9AdmtGVR90d6Y8pgJfA1VE5EWs9/Ipz0a6eJgxGTeyJwA8C3S3F60Envf0wL+I1AXeBC7H+jJYBTykqgc8lyo/u6+8lqpu8YIsPkATrO7Fnaqa7uFIAIjIs2dbr6rPuytLFhEZebb1qvqRu7IURkQuAa7Aej9/UtW/PBzpomEqGcOrichy4BqsVvcm4ASwQlXHejDTdQUsPgVsVdXj7s5TGLtSjlMv+SMXkSAgRVUz7cdOwF9VkzyYyQFsUdXmnspwsTPdZW4gIt9x9llcnp5d0xiYAVRV1eYi0hK4RlUnejKXrYKqxtuzpj5U1WdFxNMtmTuAzsAy+3FP4HegsYhMUNXZ7g4kIs8An6vqDhHxB34AWgMZInKLqi51d6YC/IQ10SXBfhwALAa6eCqQqrpEZLOI1FbVg57KcTEzlYx7vOrpAOfwHtaspHcBVHWLiHwKeEMl4yMi1YAbgfGeDmNzAU1V9RhkHzczA+iI1QXq9koGa2rwC/b9kViTeioDjYGPAG+oZMqpalYFg6omiEigJwPZqgHbRGQNkJi10NM//i4WppJxA1VdYXcNfKSqwz2dpwCBqrrGOiYtW4anwuQxAev4mF9Vda2I1Ad2ezhT3awKxnYcaKyqsSLiqbGZtBzdYv2AOXa31F/2+JE3SBSRtqq6AUBE2gHJHs4E4PZxqn8Sb/nwXfRUNVNEKouIn6qmeTpPHtH2kfVZUzivB6I8G8miqvOAeTke78Pz06t/EZEFnMk1BFhpjzl46kwEqSLSHDgG9AIezbHOG1oLAGOAeSISaT+uhtUC8yhVXeHpDBczU8m41wFglYjMJ3ez3O3TSvO4D+tiTZeIyBFgP9YBmR4nIuWwxkAuBcplLVfV2z0Wynq9rgO62o/XANVUNRHrC94TxgBfYHWRva6q+wFEZCDg6YNXswbY/YBLODMrb4c3zMqzj3OaBjTFyugEEr3hjBcXA1PJuFekfXPgPUfUA/ytqlfav8Qdqnr6nM9wn9nADqwuoAlYlZ9Hp5eqqorIXqwxmBuxKuUvPZzpD6wv8LzLFwIL8z/DvewB9imq2hn409N58pgODMVqmbYHbgUaeTTRRcRMYfYAEQmyf/V6BRE5CPwIfAb87C1TXgFEZKOqthGRLaraUkR8gUWq2tsDWRpjfRndjHUQ7WfAo6pax91ZCmOfieBZrFaWAr9inbvM4wf9isjzwBbgKy/7jK1T1fZZnzF72WpV9dist4uJOa2MG4lIZxHZjv1LXERaicjbHo4FVvfFUqxuoP0iMl1Eup7jOe6S1Z0SZ485VMA6WaYn7MA6YG+QqnZV1Wl4z5kRsszFOpZoCNaR6yewKkNvMBartZAqIvEiclpE4j0dCkiyz1W2SUReEZGHgSBPh7pYmErGvd7A6vaJAVDVzZw5+t9jVDVZVT9X1euANkAI4C2DoTPtgwqfxjqVy3bgFQ9lGYJ1ItFlIvKeiGQdIe5NwlT1BVXdb98mYp3t2+NUtbyqOlTVT1VD7MfeMO4xAuu78H6ssdJaeH5yyUXDdJe5kYj8oaods7qA7GWbVbWVF2TrgTXTZwCwFvhMVT06zuCt7LGra7G6zXpjHYfytaou9mgwQEReBdYBn9uLrgcuVdWznm7GXewfDI3IPYnDI9eTMQdguoepZNxIRL4AXsMaaOwEPAi0V9WhHs61H+uULZ9jXYrA4+NFWSd0LIwXzMgDss9HdwNwkyfGiXLkyLo2kGB19WR14zmBBG9oMdhnbRgD1MT6vHUCfvPU6yYiG1S1rX3/S1U1rZdSYGaXuddorBNR1sA6JftirHEQT2ulqt7QN56TN82+K5R9ctN37Zsnc5SF12sMcBnwu6r2sk9K6ckDIXN2ddb3WIqLnKlk3EhVo/GS408ARORxVX0FeFFE8jVpPXkKdk+cLbgsE5FL7POWtS1ofdZR9h6WoqopIoKI+Nt5m3gwjxZy37iATCXjRiIytYDFp4B1qvqtu/Nw5niTdR7Yd5GIyEfAGFWNsx+HAlM8fDCmNxoLjAKm5FiW84vTY115ORwWkYrAN8ASETmJddyYp7SyZ7cJEJBjpptgHQ7l8S7Gi4EZk3EjEZmJdcBcztORbMOazbJPVR/yUK42qurxo8ILknOSxNmW/dOJSAfgoKoetR+PxPp8HQCeUw9fsygve6JJBeBHLzzNknEBmUrGjUTkZ6CvqmbYj32wxmX6YF2LpJmHci3DOo/UPGCuqm7zRI6CiMhmoKeqnrQfh2FdT6aFZ5N5FxHZAFxpn6SzO9bxMg9gne6/qap67Mqd9qmBRgMNga3ArKy/AePiZ7rL3KsG1syfrOvBBwHV7ZNnphb+tNJlD8JGYJ0iZaaIhGBNYfaGU/1PAX4TkXlY3T83Ai96NpJXcuZordwEzLSnoH8pIps8mAusKd7pwC9YU+SbYU0CMP4BTCXjXq9gHVW8HKvftzswyT7uwqPX+7C7WabarZrHgWfwguvJqOrHIrIOa0xBgOtUdbuHY3kjp4j42C2EK7DGZ7J4+u+8WVbLU0RmYZ1Q1PiH8PSH7x9FVWeJyEKgA9YX5jhVzRr4fMxTuUSkKdav3+uxzkYwF3jEU3nsTHm7WN4xXSxnNQdYISLRWNdo+QVARBpypuXsKdlnWlbVjDzXLTIucmZMxs1EpAZQhxwVvKeOeM4iIr9jfUnNy1HpeZSIfEbuLpYDnpoYUVbYp6yvBizOOqDWPqlnsCenMItIJmcubSFYl11Owszi+kcwlYwbicjLWC2GbViX8AXrj8xjl3m1r9j5sap6zfE7ACKyNUcXiw+wJuvobMMwyg7TXeZe1wJNVNVjg/x52ZMOwr3wip2mi8UwLgKmknGvfYAv4DWVjO1vvO+KnVkHykHug+VMF4thlCGmknGvJKzZZT+Ro6Lx5OlbbF53xU5VdXo6g2EYJWfGZNzIPgo7H1X9yN1ZDMMw3MFUMm4mIgFAbVXd6eksWexjYwo6QaY3nO/KMIwyzHSXuZGIDAJeBfyAeiLSGuv66x6bXWZ7NMf9cljnvDLHpBiGUWKmJeNGIrIe68j15TmujJk9VdebiMgKVe3h6RyGYZRtpiXjXhmqeirPdFyP1/L2SSezOID2QISH4hiGcRExlYx7/Skit2CdZ6oR1uWXV3s4E8B6zlR2GVinh7/DY2kMw7hoODwd4B/mAeBSrOnLc4B4wGOnShGRy0QkQlXrqWp9rEvh7rBv5iSUhmGUmBmT8RD7dC5Bqhp/zsKll8Frr0FiGMbFwbRk3EhEPhWREPvU/tuAnSLisbMvU8g1SFT1aayzHxuGYZSIqWTcq5ndcrkWWAjUBkZ4MI/TPvkkWNcg+TnHOjNeZxhGiZkvEvfyFRFfrEpmuqqmi4gn+yu9+RokhmFcBEwl417vYs3c2gysFJE6WIP/HqGqL9rnUcu6BklWhefAGpsxDMMoETPw72E5LplrGIZx0TFjMm4kImPsgX8RkVn27C5zfjDDMC5appJxr9vtgf++QGXg38Bkz0YyDMMoPaaSca+s88kMBD5U1c05lhmGYVx0TCXjXutFZDFWJbNIRMoDLg9nMgzDKDVm4N+NRMSBdTT9PlWNE5FwoIaqbvFwNMMwjFJhpjC7kaq6RGQ/0FhEynk6j2EYRmkzlYwbicidwBigJrAJ6AT8hplhZhjGRcqMybjXGOAy4G9V7QW0AU54NpJhGEbpMZWMe6WoagqAiPir6g6giYczGYZhlBrTXeZeh0WkIvANsERETgKRHs5kGIZRaszsMg8RkR5ABeBHVU3zdB7DMIzSYCoZN7Bnko3GukbLVmCWOV+ZYRj/BKaScQMR+QxIxzqV/gCsgf8xnk1lGIZR+kwl4wYislVVW9j3fYA1qtrWw7EMwzBKnZld5h7pWXdMN5lhGP8kpiXjBiKSCSRmPQQCgCT7vqpqiKeyGYZhlCZTyRiGYRilxnSXGYZhGKXGVDKGYRhGqTGVjGEYhlFqTCVjGIZhlJr/B+uPnN0QddBPAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(train_data.corr(),annot=True)\n",
    "#Age is correlated to PCLASS the most with corr value of -0.37."
   ]
  },
  {
   "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>Pclass</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>37.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pclass   Age\n",
       "0       1  37.0\n",
       "1       2  29.0\n",
       "2       3  24.0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=train_data.groupby('Pclass',as_index=False)['Age'].median()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\RADHA JAYARAMAN\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:10: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  # Remove the CWD from sys.path while we load stuff.\n",
      "C:\\Users\\RADHA JAYARAMAN\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n",
      "C:\\Users\\RADHA JAYARAMAN\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "#Replacing missing vales in training set\n",
    "#AGE\n",
    "for i,row in train_data.iterrows():\n",
    "    if(np.isnan(train_data['Age'][i])==True):\n",
    "        if(train_data['Pclass'][i]==1):\n",
    "            train_data['Age'][i]=37.0\n",
    "        elif(train_data['Pclass'][i]==2):\n",
    "            train_data['Age'][i]=29.0\n",
    "        else:\n",
    "            train_data['Age'][i]=24.0\n",
    "#CABIN (It has maximum number of missing values and hence it will NOT be worth a feature to work on. Still we will replace\n",
    "#the missing values with its mode)\n",
    "train_data['Cabin']=train_data['Cabin'].fillna(train_data['Cabin'].mode()[0])\n",
    "#EMBARKED\n",
    "#For this we have two missing values each having equivalent Fare of $80 and same PClass,same ticketnumber this\n",
    "#means that they had to board from the same station.And on google search, I got to know that Amelie Icard boarded from SouthHampton.\n",
    "#This means that both of them boarded from SouthHampton.\n",
    "train_data['Embarked']=train_data['Embarked'].fillna('S')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  891 non-null    int64  \n",
      " 1   Survived     891 non-null    int64  \n",
      " 2   Pclass       891 non-null    int64  \n",
      " 3   Name         891 non-null    object \n",
      " 4   Sex          891 non-null    object \n",
      " 5   Age          891 non-null    float64\n",
      " 6   SibSp        891 non-null    int64  \n",
      " 7   Parch        891 non-null    int64  \n",
      " 8   Ticket       891 non-null    object \n",
      " 9   Fare         891 non-null    float64\n",
      " 10  Cabin        891 non-null    object \n",
      " 11  Embarked     891 non-null    object \n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.7+ KB\n"
     ]
    }
   ],
   "source": [
    "train_data.info()\n",
    "#no missing values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "TEST SET\n",
    ">In test set we have missing values in Age,Cabin,Fare with Age having the maximum number of missing values out of all. We will do the similar process in test data also."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>42.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>26.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pclass   Age\n",
       "0       1  42.0\n",
       "1       2  26.5\n",
       "2       3  24.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=test_data.groupby('Pclass',as_index=False)['Age'].median()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\RADHA JAYARAMAN\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:10: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  # Remove the CWD from sys.path while we load stuff.\n",
      "C:\\Users\\RADHA JAYARAMAN\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n",
      "C:\\Users\\RADHA JAYARAMAN\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "#Replacing Missing Values in Test Set\n",
    "#AGE\n",
    "for i,row in test_data.iterrows():\n",
    "    if(np.isnan(test_data['Age'][i])==True):\n",
    "        if(test_data['Pclass'][i]==1):\n",
    "            test_data['Age'][i]=42.0\n",
    "        elif(test_data['Pclass'][i]==2):\n",
    "            test_data['Age'][i]=26.0\n",
    "        else:\n",
    "            test_data['Age'][i]=24.0\n",
    "#CABIN\n",
    "test_data['Cabin']=test_data['Cabin'].fillna(test_data['Cabin'].mode()[0])\n",
    "#FARE\n",
    "#We can see that Fare is most correlated to Pclass and hence it should be computed by median of fare grouped by pclass.\n",
    "#As the person belonged to class 3, we will take median of class 3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>60.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>15.7500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>7.8958</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pclass     Fare\n",
       "0       1  60.0000\n",
       "1       2  15.7500\n",
       "2       3   7.8958"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=test_data.groupby('Pclass',as_index=False)['Fare'].median()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId    0\n",
       "Pclass         0\n",
       "Name           0\n",
       "Sex            0\n",
       "Age            0\n",
       "SibSp          0\n",
       "Parch          0\n",
       "Ticket         0\n",
       "Fare           0\n",
       "Cabin          0\n",
       "Embarked       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data['Fare']=test_data['Fare'].fillna(7.89)\n",
    "test_data.isna().sum()\n",
    "#NO MISSING VALUES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x192cc21e348>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(data=train_data,x='Age',y='Survived',hue='Survived')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">Age doesnt seem to vary much with Survival and I think it should NOT be taken as a feature to build our model upon."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PREPARING DATA FOR TRAINING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass     Sex  Parch Embarked\n",
       "0         0       3    male      0        S\n",
       "1         1       1  female      0        C\n",
       "2         1       3  female      0        S\n",
       "3         1       1  female      0        S\n",
       "4         0       3    male      0        S"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "traindata=train_data.drop(['Name','Ticket','Cabin','PassengerId','Fare','SibSp','Age'],axis=1)\n",
    "testdata=test_data.drop(['Name','Ticket','Cabin','PassengerId','Fare','SibSp','Age'],axis=1)\n",
    "traindata.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "****Getting dummies for our categorical variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train=traindata.iloc[:,1:]\n",
    "y_train=traindata.iloc[:,0]\n",
    "X_train=pd.get_dummies(X_train,['Sex','Embarked'])\n",
    "X_test=testdata.iloc[:,:]\n",
    "X_test=pd.get_dummies(X_test,['Sex','Embarked'])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1.0, break_ties=False, cache_size=200, class_weight=None, coef0=0.0,\n",
       "    decision_function_shape='ovr', degree=3, gamma='scale', kernel='rbf',\n",
       "    max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "    tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "classifier=SVC(kernel='rbf')\n",
    "classifier.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_predict=classifier.predict(X_train)\n",
    "y_test=classifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy for training set is:  0.813692480359147\n",
      "AUC SCORE for training set:  0.7705397373214457\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score,confusion_matrix,roc_auc_score,classification_report\n",
    "print('Accuracy for training set is: ',accuracy_score(y_train,y_train_predict))\n",
    "print('AUC SCORE for training set: ',roc_auc_score(y_train,y_train_predict))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x192cc648e48>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm=confusion_matrix(y_train,y_train_predict)\n",
    "sns.heatmap(cm,annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.79      0.96      0.86       549\n",
      "           1       0.89      0.58      0.71       342\n",
      "\n",
      "    accuracy                           0.81       891\n",
      "   macro avg       0.84      0.77      0.79       891\n",
      "weighted avg       0.83      0.81      0.80       891\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_train,y_train_predict))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Your submission was successfully saved!\n"
     ]
    }
   ],
   "source": [
    "output = pd.DataFrame({'PassengerId': test_data.PassengerId, 'Survived': y_test})\n",
    "output.to_csv('svc-titanic.csv', index=False)\n",
    "print(\"Your submission was successfully saved!\")"
   ]
  }
 ],
 "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}