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   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Preparation and Basic Statistics\n",
      "\n",
      "Author: [Pili Hu](http://hupili.net/)\n",
      "\n",
      "Data preparation, data cleansing, and getting basic statistics are the first things to do.\n",
      "The Titanic competition on Kaggle already gives us a well-formatted dataset.\n",
      "We'll try to get some basic statistics in this notebook.\n",
      "\n",
      "Competition link: [http://www.kaggle.com/c/titanic-gettingStarted](http://www.kaggle.com/c/titanic-gettingStarted)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Load data"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data = pd.read_csv('train.csv')\n",
      "data[:5]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\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</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</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</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</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</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",
        "<p>5 rows \u00d7 12 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "   PassengerId  Survived  Pclass  \\\n",
        "0            1         0       3   \n",
        "1            2         1       1   \n",
        "2            3         1       3   \n",
        "3            4         1       1   \n",
        "4            5         0       3   \n",
        "\n",
        "                                                Name     Sex  Age  SibSp  \\\n",
        "0                            Braund, Mr. Owen Harris    male   22      1   \n",
        "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female   38      1   \n",
        "2                             Heikkinen, Miss. Laina  female   26      0   \n",
        "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female   35      1   \n",
        "4                           Allen, Mr. William Henry    male   35      0   \n",
        "\n",
        "   Parch            Ticket     Fare Cabin Embarked  \n",
        "0      0         A/5 21171   7.2500   NaN        S  \n",
        "1      0          PC 17599  71.2833   C85        C  \n",
        "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
        "3      0            113803  53.1000  C123        S  \n",
        "4      0            373450   8.0500   NaN        S  \n",
        "\n",
        "[5 rows x 12 columns]"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We already have a view of the data set.\n",
      "Some of the columns are self-explainary.\n",
      "Some needs further elaboration.\n",
      "Not it's time to check the data description: [http://www.kaggle.com/c/titanic-gettingStarted/data](http://www.kaggle.com/c/titanic-gettingStarted/data)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Basic statistics"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data.describe()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>PassengerId</th>\n",
        "      <th>Survived</th>\n",
        "      <th>Pclass</th>\n",
        "      <th>Age</th>\n",
        "      <th>SibSp</th>\n",
        "      <th>Parch</th>\n",
        "      <th>Fare</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>count</th>\n",
        "      <td> 891.000000</td>\n",
        "      <td> 891.000000</td>\n",
        "      <td> 891.000000</td>\n",
        "      <td> 714.000000</td>\n",
        "      <td> 891.000000</td>\n",
        "      <td> 891.000000</td>\n",
        "      <td> 891.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>mean</th>\n",
        "      <td> 446.000000</td>\n",
        "      <td>   0.383838</td>\n",
        "      <td>   2.308642</td>\n",
        "      <td>  29.699118</td>\n",
        "      <td>   0.523008</td>\n",
        "      <td>   0.381594</td>\n",
        "      <td>  32.204208</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>std</th>\n",
        "      <td> 257.353842</td>\n",
        "      <td>   0.486592</td>\n",
        "      <td>   0.836071</td>\n",
        "      <td>  14.526497</td>\n",
        "      <td>   1.102743</td>\n",
        "      <td>   0.806057</td>\n",
        "      <td>  49.693429</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>min</th>\n",
        "      <td>   1.000000</td>\n",
        "      <td>   0.000000</td>\n",
        "      <td>   1.000000</td>\n",
        "      <td>   0.420000</td>\n",
        "      <td>   0.000000</td>\n",
        "      <td>   0.000000</td>\n",
        "      <td>   0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25%</th>\n",
        "      <td> 223.500000</td>\n",
        "      <td>   0.000000</td>\n",
        "      <td>   2.000000</td>\n",
        "      <td>  20.125000</td>\n",
        "      <td>   0.000000</td>\n",
        "      <td>   0.000000</td>\n",
        "      <td>   7.910400</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>50%</th>\n",
        "      <td> 446.000000</td>\n",
        "      <td>   0.000000</td>\n",
        "      <td>   3.000000</td>\n",
        "      <td>  28.000000</td>\n",
        "      <td>   0.000000</td>\n",
        "      <td>   0.000000</td>\n",
        "      <td>  14.454200</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>75%</th>\n",
        "      <td> 668.500000</td>\n",
        "      <td>   1.000000</td>\n",
        "      <td>   3.000000</td>\n",
        "      <td>  38.000000</td>\n",
        "      <td>   1.000000</td>\n",
        "      <td>   0.000000</td>\n",
        "      <td>  31.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>max</th>\n",
        "      <td> 891.000000</td>\n",
        "      <td>   1.000000</td>\n",
        "      <td>   3.000000</td>\n",
        "      <td>  80.000000</td>\n",
        "      <td>   8.000000</td>\n",
        "      <td>   6.000000</td>\n",
        "      <td> 512.329200</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>8 rows \u00d7 7 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
        "count   891.000000  891.000000  891.000000  714.000000  891.000000   \n",
        "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
        "std     257.353842    0.486592    0.836071   14.526497    1.102743   \n",
        "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
        "25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n",
        "50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n",
        "75%     668.500000    1.000000    3.000000   38.000000    1.000000   \n",
        "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
        "\n",
        "            Parch        Fare  \n",
        "count  891.000000  891.000000  \n",
        "mean     0.381594   32.204208  \n",
        "std      0.806057   49.693429  \n",
        "min      0.000000    0.000000  \n",
        "25%      0.000000    7.910400  \n",
        "50%      0.000000   14.454200  \n",
        "75%      0.000000   31.000000  \n",
        "max      6.000000  512.329200  \n",
        "\n",
        "[8 rows x 7 columns]"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Some observations:\n",
      "\n",
      "   * There are 891 passengers in the training set.\n",
      "   * `Age` column has some missing values. (see the `count` row)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Prior distribution of survival\n",
      "data['Survived'].value_counts().plot(kind='bar')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 18,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x109e1b2d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x10a871d10>"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Distribution of class\n",
      "# 1st ~ Upper; 2nd ~ Middle; 3rd ~ Lower\n",
      "data['Pclass'].value_counts().sort_index().plot(kind='bar')\n",
      "\n",
      "# sort_index() makes sure the bars come in same order as:\n",
      "#     1st ~ Upper; 2nd ~ Middle; 3rd ~ Lower\n",
      "#\n",
      "# Without sort_index(), the order is [3, 1]."
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 31,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x10af51bd0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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2A8CTsR3AWSxzAAgAOnMswGzcxWzcRWcOAPABy9waYzsAPBnbAeDJ2A7gLJY5AAQAnTkW\nYDbuYjbuojMHAPiAZW6NsR0AnoztAPBkbAdwFsscAAKAzhwLMBt3MRt30ZkDAHzAMrfG2A4AT8Z2\nAHgytgM4i2UOAAFAZ44FmI27mI276MwBAD5gmVtjbAeAJ2M7ADwZ2wGcxTIHgACgM8cCzMZdzMZd\ndOYAAB8sucx/+MMfKhKJaMeOHaXLpqamlEwm1djYqPb2dk1PT5eu6+npUUNDg5qamjQ8PLwyqQPB\n2A4AT8Z2AHgytgM4a8ll/vjjj2toaKjsskwmo2QyqZGREbW1tSmTyUiScrmcTpw4oVwup6GhIR04\ncEBzc3MrkxwAULLkMv/617+uTZs2lV02ODiodDotSUqn0+rv75ckDQwMqLu7W+FwWLFYTPX19cpm\nsysQOwgStgPAU8J2AHhK2A7grIo680KhoEgkIkmKRCIqFAqSpImJCUWj0dLnRaNR5fN5H2ICABYT\nutMvUFVVdePRb+/rb2f//v2KxWKSpOrqajU3NyuRSEiSjDGSdMfHN80fJxw6PivpSYfy3Dz26///\n6p3PS5KaHcozf3zjaIXnc/M+/c7vx/H8n13Jc+vxjSMf52GM0bFjxySptC89FZdhdHS0uH379tLx\n1q1bi5OTk8VisVicmJgobt26tVgsFos9PT3Fnp6e0ud1dHQUT58+/Ymvt8y7vWOSilLR0Y9TDmS4\n3QezYTbMxuXZeKmoZuns7FRvb68kqbe3V11dXaXLjx8/rpmZGY2Ojur8+fNqbW2t5C7WgITtAPCU\nsB0AnhK2AzhryZqlu7tbb775pj744ANt2bJFv/jFL3Tw4EGlUikdPXpUsVhMfX19kqR4PK5UKqV4\nPK5QKKQjR44sWsEAAPzBK0CtMXLzLIPZMBtm8+nxClAAgA84M8cCzMZdzMZdnJkDAHzAMrfG2A4A\nT8Z2AHgytgM4i2UOAAFAZ44FmI27mI276MwBAD5gmVtjbAeAJ2M7ADwZ2wGcxTIHgACgM8cCzMZd\nzMZddOYAAB+wzK0xtgPAk7EdAJ6M7QDOYpkDQADQmWMBZuMuZuMuOnMAgA9Y5tYY2wHgydgOAE/G\ndgBnscwBIADozLEAs3EXs3EXnTkAwAcsc2uM7QDwZGwHgCdjO4CzWOYAEAB05liA2biL2biLzhwA\n4AOWuTXGdgB4MrYDwJOxHcBZLHMACAA6cyzAbNzFbNxFZw4A8AHL3BpjOwA8GdsB4MnYDuAsljkA\nBACdORZgNu5iNu6iMwcA+IBlbo2xHQCejO0A8GRsB3AWyxwAAoDOHAswG3cxG3fRmQMAfLAiy3xo\naEhNTU1qaGjQ888/vxJ3EQDGdgB4MrYDwJOxHcBZvi/z2dlZ/eQnP9HQ0JByuZxee+01nTt3zu+7\nCYCztgPAE7NxF7Px4vsyz2azqq+vVywWUzgc1ve+9z0NDAz4fTcBMG07ADwxG3cxGy++L/N8Pq8t\nW7aUjqPRqPL5vN93AwC4he/L/Poj4VjaBdsB4OmC7QDwdMF2AGeF/P6CdXV1GhsbKx2PjY0pGo2W\nfc7OnTvv4tJ3+ZdLr+0At8VsJGbDbD6tuzGbnTt3et+/388zv3btmrZu3aqTJ0/qS1/6klpbW/Xa\na69p27Ztft4NAOAWvp+Zh0Ih/epXv1JHR4dmZ2f1xBNPsMgBYIVZeQUoAMBfvAIUa9q5c+d08uRJ\nXblypezyoaEhS4kw76233lIul5MkGWP0wgsv6OTJk5ZTuYszc4t++9vf6vHHH7cdY816+eWX9cor\nr2jbtm06c+aMDh8+rK6uLklSS0uLzpw5Yznh2vXMM8/o1KlTmp2d1e7du/WnP/1Je/fu1R/+8Ac9\n/PDD+tnPfmY7onNY5hZt2bKl7Jk/uLu2b9+u06dPa8OGDbpw4YK++93v6gc/+IGefPJJlrll8Xhc\n77zzjmZmZhSJRDQ+Pq6NGzfqo48+0q5du/TOO+/Yjugc3x8ARbkdO3Z4Xvf+++/fxSRYqFgsasOG\nDZKkWCwmY4z27dunf/7zn3flHfDg7TOf+YxCoZBCoZC+8pWvaOPGjZKkz3/+81q3jnb4dljmK+z9\n99/X0NCQNm3a9InrvvrVr1pIhHk1NTU6e/asmpubJUkbNmzQ66+/rieeeIIzP8s++9nP6urVq1q/\nfr3++te/li6fnp5mmXtgma+wvXv36sqVK2ppafnEdd/85jctJMK83/3udwqHw2WXhcNh9fb26sc/\n/rGlVJCkN998U5/73OckqWx5X7t2Tb29br5oyDY6cwAIAP69AgABwDIHgABgmQNAALDMASAAWOYA\nEAD/B2CfLzuF9YfjAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10aeb6d90>"
       ]
      }
     ],
     "prompt_number": 31
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data['Age'].dropna().plot(kind='kde')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 42,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x10c52c310>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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fSqaGnN6eN92OobS2tnrdWJlMJphMJqfP2uYEa5Obm+ty3fz8fJeft13RMP/W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\nUXCDwhhjTBTcoDDGGBPF/wdnELAxxnzWWAAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10c422610>"
       ]
      }
     ],
     "prompt_number": 42
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data['SibSp'].value_counts().plot(kind='bar')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 46,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x10c738350>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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//2lz3Y0dOnSIl156ieeff54XX3yRiooKbr31VjZt2mR32g195Stf4fTp0yxa\ntIj3338/dfr09LSjh/n3v/991qxZQ2FhIb/4xS/YuHEjDz74IH/961+JRqN2583ps88+Y8OGDcDF\n94RPTExw1113ceLECZvLZvPku1nOnz/P4OAgiUSCvLw8Vq1axQMPPOCoZ9Gr7dy5kyeeeIKNGzde\nc15zczN79+61oWp+R48epbCwkOLi4lmnm6bJO++8w0MPPWRTWfqOHTvGM888w8qVK+nr65v1R29O\ndObMGW699dZrTv/kk0+YmJi44W95djty5AhLliyhqKiIjz76iEOHDlFeXn7DP4ZxqtOnT5NMJrn7\n7rvtTgE8OsxFrHjjjTd49913eeGFF+xOEcmYhrmIiAc4d8EmIiJp0zAXEfEADXMREQ/QMBcR8YD/\nB8GvIzg95Ts6AAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10c53e510>"
       ]
      }
     ],
     "prompt_number": 46
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data['Parch'].value_counts().plot(kind='bar')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 48,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x10c8768d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Zy2dmZlw9zD/44ANeffVVXnrpJV555RWqq6u57bbb2Lx5s9NpN/Tmm2/y7LPP\n8vOf/5zVq1fz0EMPEQwGWbduHW+++abTeRm+fDXLhQsXGBoaIplMUlBQwNq1a3nggQdc/Wxr9+7d\nPPXUUzzyyCPXXdfa2sr+/fsdqFrc8ePHKS4uprS0dM7l6XSa999/n4cfftihMuvOnTtHKpXinnvu\ncTplXl9++SW33XbbdZd/9tlnTE5O3vC3PDc4ceIEzz//PGvWrKG/v3/OHxm62eeff874+Djnz58n\nGAxe92/eab4c5iLifu+88w5//vOfefnll51O8QUNcxERH3Dvgk1ERLKmYS4i4gMa5iIiPqBhLiLi\nA/8PZ9EQyANjJAwAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10c461b10>"
       ]
      }
     ],
     "prompt_number": 48
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data['Fare'].dropna().plot(kind='kde')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 53,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x10ce2dd90>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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NnD3rvXapSab+c4Xx+S8mFPKplhZg5EggIGBo7zN9OlBaCnR1eaddRDRwrKGQ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\nAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10ce86e10>"
       ]
      }
     ],
     "prompt_number": 53
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data['Embarked'].value_counts().plot(kind='bar') "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 54,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x10cdb72d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x10cb3ac90>"
       ]
      }
     ],
     "prompt_number": 54
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print data['Cabin'].dropna().values\n",
      "print len(data['Cabin'].dropna().values)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "['C85' 'C123' 'E46' 'G6' 'C103' 'D56' 'A6' 'C23 C25 C27' 'B78' 'D33' 'B30'\n",
        " 'C52' 'B28' 'C83' 'F33' 'F G73' 'C23 C25 C27' 'E31' 'A5' 'D10 D12' 'D26'\n",
        " 'C110' 'B58 B60' 'E101' 'D26' 'F E69' 'D47' 'C123' 'B86' 'F2' 'C2' 'E33'\n",
        " 'B19' 'A7' 'C49' 'F4' 'A32' 'F2' 'B4' 'B80' 'G6' 'A31' 'D36' 'D15' 'C93'\n",
        " 'C83' 'C78' 'D35' 'G6' 'C87' 'B77' 'E67' 'B94' 'C125' 'C99' 'C118' 'D7'\n",
        " 'A19' 'B49' 'D' 'C22 C26' 'C106' 'B58 B60' 'E101' 'C22 C26' 'C65' 'E36'\n",
        " 'C54' 'B57 B59 B63 B66' 'C7' 'E34' 'C32' 'D' 'B18' 'C124' 'C91' 'C2' 'E40'\n",
        " 'T' 'F2' 'C23 C25 C27' 'F33' 'C128' 'E33' 'D37' 'B35' 'E50' 'C82'\n",
        " 'B96 B98' 'D36' 'G6' 'C78' 'E10' 'C52' 'E44' 'B96 B98' 'C23 C25 C27' 'A34'\n",
        " 'C104' 'C111' 'C92' 'E38' 'D21' 'E12' 'E63' 'D' 'A14' 'B49' 'C93' 'B37'\n",
        " 'C30' 'D20' 'C22 C26' 'B79' 'C65' 'E25' 'D46' 'F33' 'B73' 'B18' 'C95'\n",
        " 'B38' 'B39' 'B22' 'C86' 'C70' 'A16' 'E67' 'C101' 'E25' 'E44' 'C68' 'A10'\n",
        " 'E68' 'B41' 'D20' 'A20' 'C125' 'F4' 'D19' 'D50' 'D9' 'A23' 'B50' 'B35'\n",
        " 'D33' 'A26' 'D48' 'E58' 'C126' 'B71' 'B51 B53 B55' 'D49' 'B5' 'B20' 'C68'\n",
        " 'F G63' 'C62 C64' 'E24' 'E24' 'C90' 'C124' 'C126' 'F G73' 'C45' 'E101'\n",
        " 'E8' 'B5' 'B101' 'D45' 'C46' 'B57 B59 B63 B66' 'B22' 'D30' 'E121' 'B77'\n",
        " 'B96 B98' 'D11' 'E77' 'F38' 'B3' 'B20' 'D6' 'B82 B84' 'D17' 'B96 B98'\n",
        " 'A36' 'E8' 'B102' 'B69' 'E121' 'B28' 'E49' 'C47' 'C92' 'D28' 'E17' 'D17'\n",
        " 'A24' 'D35' 'B51 B53 B55' 'C50' 'B42' 'C148']\n",
        "204\n"
       ]
      }
     ],
     "prompt_number": 60
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Correlation\n",
      "\n",
      "The `Age` and `Pclass` columns are categorical data.\n",
      "It's good to visualize them using pie charts.\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "figure(figsize=(12, 6))\n",
      "\n",
      "subplot(1, 2, 1)\n",
      "survived_male = data[data['Sex'] == 'male']['Survived'].value_counts().sort_index()\n",
      "pie(survived_male.values)\n",
      "legend(survived_male.index)\n",
      "\n",
      "subplot(1, 2, 2)\n",
      "survived_male = data[data['Sex'] == 'female']['Survived'].value_counts().sort_index()\n",
      "pie(survived_male.values)\n",
      "legend(survived_male.index)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 92,
       "text": [
        "<matplotlib.legend.Legend at 0x10e475310>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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HCT1YccwK6GW6RgCIAg8CKV+OCT6zifz84/n881W+PC/btyur48f/Adu+HA2q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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10e49d610>"
       ]
      }
     ],
     "prompt_number": 92
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "figure(figsize=(18, 6))\n",
      "\n",
      "for i in range(1, 4):\n",
      "    subplot(1, 3, i)\n",
      "    survived_male = data[data['Pclass'] == i]['Survived'].value_counts().sort_index()\n",
      "    pie(survived_male.values)\n",
      "    legend(survived_male.index)\n",
      "    title('Pclass=%d' % i)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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GjhxFr17/xO1+H+hsOkfCWpCEhKc566wpzJ07VSdBixSSrKwsWrdux08/nY/H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\nPNNRRFwl+nGUs047y3QMyRKHHnoo1tcWrDWdRMRFPoPO7Tu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       "text": [
        "<matplotlib.figure.Figure at 0x10e471090>"
       ]
      }
     ],
     "prompt_number": 91
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "`Age` and `Fare` are real-valued data.\n",
      "Normally, we can use scatter plots to explore the relationships.\n",
      "Since `Survived` itself is categorical, the scatter plot looks very cluttered and does not give too much information.\n",
      "Instead, we plot the KDE of different categories to find any trend."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data.plot(kind='scatter', x='Age', y='Survived', alpha=0.2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 103,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x10e62a610>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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OJ3PmzOH1118nJydHuwNSGIQQ4iO7qTe4VVVVsWzZMqZNmwZARUUF3/ve9256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\nGfwFa+28A5fj4l0AAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10d15f0d0>"
       ]
      }
     ],
     "prompt_number": 103
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "figure(figsize=(12,4))\n",
      "data['Age'].dropna().plot(kind='kde')\n",
      "data[data['Survived']==1]['Age'].dropna().plot(kind='kde')\n",
      "data[data['Survived']==0]['Age'].dropna().plot(kind='kde')\n",
      "legend(['Whole', 'Live', 'Dead'])\n",
      "axis([0, 80, axis()[2], axis()[3]])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 118,
       "text": [
        "[0, 80, 0.0, 0.035000000000000003]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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GTJ06ldTUVA4ePMi2bdvy/sM9QQpwkSXpu3uGq1dh+XL4zw85qK0nfn4weTLU\nrv3sQ7XO58O0h0zYPYFOP3Ti/cbvs2fAHur8r46m75Gf5Tif5uZqER4ToyvCW7aEQ4dg3jwjzm/w\n5dS7p7kYd5GGSxsWqdFw+VnXluRTW5LPws/T05OyZctSvXp1Zs2axZgxY1ixYgUAc+bMwdbWlmbN\nmlGuXDnc3d05e/YsAKNGjeLBgwdUqFCBFi1a4OGRearZNWvWcOTIEcqXL8/HH3+Mr6+vQT5fJooe\nbd++XbG3t1dsbW2V2bNnP3Of4cOHK7a2tkr9+vWV48ePK4qiKJcvX1bc3NwUJycn5dVXX1UWLFig\n23/q1KlKlSpVFBcXF8XFxUXZvn37M8+r549WpOzdu9fQIeQ/Q4YoyvjxTz29aJGiNG+uKGlpzz9U\ny3yeunFKqfd1PaXb2m7Kjfs3NDtvQZLrfP7zj6J06KAob72l+wO7eVNRGjZUlLffVpSUlAxl9anV\nSsXPKipT905VUtJStAs6n5KfdW1JPrUl+dSG1Ee597zc5SanepsHPD09HXt7e3bv3k2VKlVo3Lgx\nQUFBODo66vYJCQkhICCAkJAQjhw5wsiRIwkPD+fGjRvcuHEDFxcXEhMTadiwIcHBwTg4ODB9+nTK\nlCnD6NGjX/j++XWeS1EInDsHzZvD2bNQvrzu6UuXoGFDOHAA/nOZ60WGksH8w/OZfWg2n732GQNd\nBhq8n61ASkpS24isrGDlSjAxITERevSAEiUgKAji068xOHgwCQ8TWNtjLTUsDH/zjhBC5JbUR7lX\nIOYBj4iIwNbWFhsbG0xNTfH29iY4ODjTPlu2bNF9DdC0aVPi4+O5efMmlSpVwsXFBYDSpUvj6OhI\nTEyM7ji5cIRBTZ0Ko0ZlKr4VBYYMURfd0XfxfSPxBu6r3Nn812Yi3o5gkOsgKb5zy9wctmyBmzfB\n1xfS0yldGrZuVSdOcXeHEqnWhPQPoYdjD5p824TgP4OzPq8QQgjxAnorwGNiYqhWrZpuu2rVqpmK\n6Oftc/Xq1Uz7REdHc+LECZo2bap7buHChTg7O+Pn50d8fLyePoF4TPru/uPkSdizRy3A/+P77yE2\nFsaOzfoUL5PPsOgwGi5tSKtqrQjzDaOmZc1cn6uweOnrs2RJtQi/dQsGDIC0NMzM1AHxFi2gVSu4\nesWYsS3GEuwdzKgdoxi5fSQP0x5qEn9+Ij/r2pJ8akvyKQqTYvo6cXZH5J4czf7vcYmJifTs2ZMF\nCxZQurS/3kl0AAAgAElEQVQ6m8PQoUOZMmUKAJMnT2bMmDEsX778meceOHAgNjY2AFhYWODi4qKb\nxujxD7Jsy3aOtufNg4kTCTt2TPf67dswenQYn30Gpqb6ef89e/ew5vQafk79mZXdVmJ2xYwD+w8Y\nPh+FZfvIERgzBre5c2HAAML8/MDEhM8/d6NyZWjYUP3zHTTIjeNDjuM5y5MGexqwa8ourMtYGz5+\njbYfyy/xFPTtx/JLPAV9+7H8Ek9B3RYvJywsjKioKN0AcHR0dK7Oo7ce8PDwcKZNm0ZoaCgAs2bN\nwtjYmPHjx+v2effdd3Fzc8Pb2xsABwcH9u3bh5WVFampqXTt2hUPDw9GPTHa+Fh0dDSenp6cPn36\nqdekx0lo7vBh6NNH7f0uUUL39DvvqLPbffmlft42ITmB/hv7E58cz7qe66hStop+3kjAgwfQrZva\nXrRqFRRTxyh++EH9duPnn9U+/wwlg1kHZvHNsW/4sdePtKjWwsCBCyFE9kh9lHsFoge8UaNGnDt3\njujoaFJSUli3bh1eXl6Z9vHy8iIwMBBQC3YLCwusrKxQFAU/Pz+cnJyeKr6vX/93uehNmzZRr149\nfX0EITKbNg0mTcpUfIeHq0XZE7MRaubsnbM0/bYpNS1qstd3rxTf+layJGzerC6y5OMDaWkAvPWW\nuuiphwccPAjGRsZ81OYjlnRdQre13VhybImBAxdCCFGg5HjelBwICQlR6tSpo9SuXVuZOXOmoiiK\nsnjxYmXx4sW6fd5//32ldu3aSv369ZXIyEhFURTlwIEDipGRkeLs7PzUdIM+Pj5KvXr1lPr16ytv\nvPGGcuPGs6de0/NHK1Jk6idFUQ4fVpTq1RXl4UPdU2lpitKggaKsWpWzU2U3nzvO71Be+fwVZemx\npTl7gyJGL9fngweK0qmTovTpoyipqbqnd+5UlIoV1f8+dvb2WcUxwFF5/+f3ldT01GecrOCQn3Vt\nST61JfnUhqWlpQLIIxcPS0vLZ+Y0NzWn3lpQDE2+YtFOWFiY9I55eKjT1b37ru6pr7+GdesgLAxy\nMglJdvL51ZGvmHVwFj/2/JHWNVrnLuYiQm/XZ3IydO8O5cqpPSiP2lEOHoQ334QlS9SXQW0T6rm+\nJ8VNirO259oCuwKp/KxrS/KpLcmndiSX2spNzSkFuBBZiYiAnj3V+b+LFwfUCTNefRX27n1qJfqX\nkqFkMGbHGHZe3MnP/X7GxsJGu5OLnHtchJctC6tX64rw48ehSxf4/HO1PQUgNT2VoT8P5fj142zr\ntw3rMtYGDFwIIURekQL8P6QAF5rp0gW6doWhQ3VPDR4MlpYwd652b/Mg9QE+m3y4nXSbTX02YVnS\nUruTi9xLTlaHvEuXVotwU1MAzpyBjh3V2wIefzGiKAqzD87mm2PfsLXvVpwrORswcCGEEHkhX92E\nKQqPJ6eAKlKOHoVTp9SK+5HISNi+XV2PJzeelc87SXdwX+WOqYkpO97aIcV3Duj9+ixRAjZuhH/+\ngX79IDUVACcn2L8fPvtMHQkH9S/hia0n8pn7Z7y26jV2nN+h39g0VqR/1vVA8qktyad2JJeGJwW4\nEC/y8ccwYYKu9URR1NUup09XuxK0EHMvhtYrWtO8anNWv7ma4sWKa3NioZ3HRXhSEvTtqyvCa9VS\ni/Dly9Vr4vEAiHddbzb32cyAzQMIOh1kwMCFEELkR9KCIsTzREaqN16eP6+benDTJpgyBU6c0LUD\nv5S/4/7mtVWvMaTBEMa3Gp/1AcKwHj6EHj3U6yEoSNeOcvMmdOigTiE+Y8a/N+Wevnkaj9UefNT6\nI4Y2HvqCEwshhCiopAf8P6QAFy/tjTfgtddg+HAAUlLUtoNvvgF395c//Z+3/6Tjqo5MaDWB9xq/\n9/InFHnj4UP1ptzixTMV4bGx6uXy+uswe/a/RfjFuIt0XNURX2dfJrWZlO1VgoUQQhQM0gMu9KJI\n9opFRcGxY+oyl48EBIC9/csX32FhYUTdiKLdynbMaDdDiu+XlOfXZ/Hi8NNPaiHu7a1rR6lYEfbs\ngV271Dalx38X17KsxcHBB/npj5/4YMcHZCgZeRtvDhTJn3U9knxqS/KpHcml4UkBLsSzzJ6tVlGP\nWk9u34ZZs+CLL17+1Gdiz9Dph04s9FiIr4vvy59Q5L3HRXhqKvTpo349Avzvf/DLL3DgAIwc+W8R\nXql0JfYN3Mexa8cYFDyItIw0AwYvhBDC0KQFRYgnnT8PzZvDxYtQpgwAI0ZARoY6Cv4yDl0+RPd1\n3fm+2/d0tuusQbDCoB4+hF69wMREXZXJzAyA+Hi1FcXVFRYtAuNHQx1JqUm8ue5NypUoxw/df8DU\nxNSAwQshhNCC9ID/hxTgItfefRdeeUWdAQW1Dm/cGP78U20zyK0jV4/gGeTJD2/+QMfaHTUKVhhc\nSopahBsbZyrC792Dzp3B0VFdNfNxEZ6clkzPH3tiZmLG2p5rMTMxM2DwQgghXpb0gAu9KFK9Ytev\nw48/6m68BHXWkxEjXq74jrwWiddaL1a8sQKzK1Jwacng16eZGaxfr/ab9O6ta0cpWxZCQ+HsWXUa\n+fR0dfcSxUqwsc9GFBR6/tiTh2kPDRh8ZgbPZSEj+dSW5FM7kkvDkwJciP9asEBdW/xRtX3yJOze\nDaNH5/6UJ2+cpMuaLiztupQudbpoFKjIV8zM1F/cIFMRXro0hITAlSvg6wtpj1q/zUzM+LHnjxQv\nVpxu67rxIPWBgQIXQghhCNKCIsRjCQnqyirHj0ONGoC6Cn2nTuoIeG78dus33Fe5s9BjIT2demoY\nrMiXUlLUmzIzMtRR8UftKElJ6hzh5cvDqlW6mQtJy0hjwKYBxCbFEuwdjLmpuQGDF0IIkRvSgiLE\ny/jmG7XiflR8798Pv/8O/v65O91ft/+i46qOzO04V4rvosLMTO0DNzFR5wp/qLaXmJvDli1qX3jf\nvroBcooZF2NV91VULl0ZryAvGQkXQogiQgpwkaUi0Sv24IHafjJuHKC2806cqN6HWTwXK8NfvXeV\njj905NP2n9KvXr9MrxWJfOahfJfPx0V48eLqL3SJiYA6o+WmTWrx3bu3rjbHxNiEFW+s4JVSr9B9\nXXeS05INFnq+y2UBJ/nUluRTO5JLw5MCXAiAlSvVqU7q1gVg2zZ1tLJ//5yf6k7SHTqu6sjwJsMZ\n5DpI40BFgWBqCmvXgo2NujzmnTvAv9OHGxvDm29C8qNa28TYhMDugZQtXpaeP/YkJT3FcLELIYTQ\nO+kBFyItTV3ictUqaNGC9HRwcYGZM8HTM2en+iflH15b9Rqtq7fmM/fP9BOvKDgUBcaPV+/E3LkT\nrK0Bdf2e/v3V2w42b4aSJdXdU9NT6f1TbwB+7PmjzBMuhBAFgPSAC5EbP/0EVapAixYArF6tTiHX\ntWvOTpOankrP9T1xqODAnNfm6CFQUeAYGcFnn4GPD7RqBRcuAOoA+Zo16sqZnp7qTZoApiamrOu5\njrSMNPpt7CcrZgohRCElBbjIUqHuFVMUddn5CRMAtS93yhT1KSOj7J8mQ8lgUPAgzEzMWOa5DKMX\nHFyo82kABSKf48er11ibNnDqFADFiqlfulhbqwv2PGoVx8zEjJ96/URiSiIDNg0gPSM9z8IsELks\nQCSf2pJ8akdyaXhSgIuibccOdco4Dw8AvvsOHBygdeucnWbszrFcSrjE2h5rKWZcTA+BigJvyBCY\nNw/c3eHgQUCdLGXFCqhdW70E799Xdy1erDgbe28kNimWQcGD8rQIF0IIoX/SAy6KNjc3tTDq14+H\nD8HWFjZsgCZNsn+KhUcWsjhyMQcHHcSypKXeQhWFxK5dagP411+rUxWi/g743nvqwk+hoVCunLpr\nUmoSXdZ0wdbSlqWeS1/4zYoQQgjDkB5wIXLi8GG4dEmdEw749luoXz9nxffWv7Yy+9Bsfu73sxTf\nInvc3dUbMkeNgi+/BNRZUb75Bho2VF+Oi1N3NTc1Z2vfrfwe+zsjQ0fKoIIQQhQSUoCLLBXaXrE5\nc+D//g+KFSM5GWbNgmnTsn945LVIBm8ZzKY+m7CxsMn2cYU2nwZSIPPp4gKHDsHSpTB6NGRkYGQE\nCxdCy5aZZi6ktFlptvffzq9XfmXC7gl6LcILZC7zMcmntiSf2pFcGp5eC/DQ0FAcHByws7Njzpxn\nzwoxYsQI7OzscHZ25sSJEwBcuXKFdu3a8eqrr1K3bl2++uor3f53797F3d2dOnXq0LFjR+Lj4/X5\nEURhdeYMhIfDIHWe7mXLwNVVnQo8Oy4nXOaNtW+wtOtSmlTJwZC5EI/VqKEW4ZGR4O0NyckYGalt\n4h06qI/YWHXXciXKseOtHWw/v53p+6YbNm4hhBAvTW894Onp6djb27N7926qVKlC48aNCQoKwtHR\nUbdPSEgIAQEBhISEcOTIEUaOHEl4eDg3btzgxo0buLi4kJiYSMOGDQkODsbBwYFx48ZRoUIFxo0b\nx5w5c4iLi2P27NlPfzDpARcvMnAg1KkDH37IgwfqTXDbtkGDBlkfmpCcQKsVrRjkMojRzUfrPVRR\nyCUng68vXLsGwcFQvjyKApMmqcvX794NVlbqrjcTb+K20o2BzgMZ32q8YeMWQggB5LMe8IiICGxt\nbbGxscHU1BRvb2+Cg4Mz7bNlyxZ8fX0BaNq0KfHx8dy8eZNKlSrh4uICQOnSpXF0dCQmJuapY3x9\nfdm8ebO+PoIorC5fhq1b1bvegCVL1L7v7BTfqemp9FrfizbV2/BBsw/0HKgoEkqUgKAgaNYMmjaF\nP//EyAg++US9R9PNDa5fV3e1Km3Fbp/dLDu+jK+OfPXC0wohhMi/sizA33zzTX7++WcyMjJydOKY\nmBiqVaum265ataquiH7RPlevXs20T3R0NCdOnKBp06YA3Lx5E6tHw0FWVlbcvHkzR3GJnCt0vWLz\n5oGfH1hYkJSktoJnp/dbURTe+/k9TE1MWeCxINczUhS6fBpYocinsTF8/jlMnKjOFb5zJ0ZGMHUq\nvPWWWoQ//uuzStkq/DLgF+YensvSyKWahlEocpmPSD61JfnUjuTS8LKcsHjo0KGsWLGC4cOH07t3\nbwYNGoS9vX2WJ85ucfLkkP1/j0tMTKRnz54sWLCA0qVLP/M9XvQ+AwcOxMbGBgALCwtcXFxwc3MD\n/r34ZLuIbdetC4GBhC1dCmFhHD/uRvPmEB8fRljYi49fe3otx8yOcWDQAQ7uP5g/Po9sF67twYPB\nzo6wN96At97CbcECPvrIiMuXw2jSBA4fdqN6dfg76m9m1prJ+H3jKVmsJNXiqmny/o/lm3wU8O3H\n8ks8BX37sfwST0HejoqKylfxFLTtqKgo3T2I0dHR5IqSTXFxcco333yjVKlSRWnevLny3XffKSkp\nKc/d//Dhw0qnTp102zNnzlRmz56daR9/f38lKChIt21vb6/cuHFDURRFSUlJUTp27KjMnz8/0zH2\n9vbK9evXFUVRlGvXrin29vbPfP8cfDRRlEydqijvvKMoiqIkJipKpUqKcvJk1odt+2ubYj3XWrkc\nf1m/8QmhKIpy8aKivPqqovj7K8qjv2fnzVOUmjUV5e+//93tzK0zSqUvKik//vajYeIUQgiRq5rT\nODtF+p07d/j+++/59ttvadCgASNGjCAyMhJ3d/fnHtOoUSPOnTtHdHQ0KSkprFu3Di8vr0z7eHl5\nERgYCEB4eDgWFhZYWVmhKAp+fn44OTkxatSop45ZuXIlACtXrqRbt245+HVDFGmJieriJ//3f4A6\n73KrVurc3y/yR+wfDAoexE+9fqJauWov3lkILdSsCb/+qvaddOoEd+7wwQfwwQfg5gbnzqm7OVZ0\nJLR/KMO3D2fLX1sMGrIQQogcyKpC79atm+Lg4KB8+umnyrVr1zK91qBBgxceGxISotSpU0epXbu2\nMnPmTEVRFGXx4sXK4sWLdfu8//77Su3atZX69esrkZGRiqIoyoEDBxQjIyPF2dlZcXFxUVxcXJTt\n27criqIod+7cUTp06KDY2dkp7u7uSlxc3DPfOxsfTWTT3r17DR2CNubPV5RevRRFUZT79xXllVcU\n5fTpFx9yJ+mOYvuVrbLixArNwig0+cwnCnU+09IUZdw4RbGxUZRHfz9++62iWFsrSlTUv7sdjTmq\nVPysohJ6LvSl3q5Q59IAJJ/aknxqR3KprdzUnFn2gL/zzjt07tw503MPHz6kePHiREZGvvBYDw8P\nPDw8Mj3n7++faTsgIOCp41q1avXcmz7Lly/P7t27swpbiMxSUmDuXHg0a86iRepIYt26zz8kLSON\n3ut741XHi4EuA/MkTCEyMTFR7xJu3Bhefx3mzMHPbxBlykDHjrBpE7RoAY2sG7Gpzya6revG+l7r\ncbNxM3TkQgghXiDLecBdXV11C+Q81qBBA44fP67XwF6WzAMuMvn+e1izBnbu5P59sLWFvXvByen5\nh4zcPpK/7vzFtn7bKGac5e+qQujXH3/Am29C69bw1Vds31uCAQPUy/pxN+Dev/fS56c+BHsH07xa\nc8PGK4QQRYSm84Bfv36dyMhIHjx4wPHjx4mMjOT48eOEhYWRlJT00sEKkWcyMtRRxAkTAAgIUFcZ\nfFHx/e3xbwm9EMranmul+Bb5g6MjRERAXBy0bo2H0yU2boT+/dWRcIB2NdsR2D2Qbuu6EXntxd9Q\nCiGEMJznFuA7duxg7NixxMTEMGbMGMaOHcuYMWOYN28eM2fOzMsYhYE9OQVUgbNlC5QpA+3ace8e\nzJ8PU6Y8f/cDlw7w4S8fssV7CxYlLDQPp8DnM58pUvksUwZ+/FFdur5pU1on7SA0VF1T6tG96bxu\n+zpLuy6ly5ounLp5KkenL1K5zAOST21JPrUjuTS85w7tDRw4kIEDB7JhwwZ69OiRlzEJoR1FgVmz\n1NFvIyMWLlR7Zx0cnr37pfhL9P6pN6u6r8K+Qtbz3QuR54yMYMwYaNQI+venQf/+7N0xg45dzUhI\ngBEj4A2HN0hOS+b1H15nj+8eHCo854IXQghhEM/tAV+1ahU+Pj7MnTs302I3iqJgZGTE6NGj8yzI\n3JAecAHAnj3w/vvw++8k3DfG1hYOHYI6dZ7eNTElkVbftWKA8wBGN8/f17cQANy+DYMGwa1bXP08\niHZ+tfDxgcmT1Tp9ZdRKJu2dRJhvGLXL1zZ0tEIIUSjlpuZ87gj44z7v+/fvP7MAF6JAmDkTxo8H\nY2MWLIDOnZ9dfCuKwuDgwbhUcuGDZh/kfZxC5EaFCmqL1VdfUbVnM45+/BVui725eRO++gp8XXxJ\nTkumQ2AH9g/aT/Vy1Q0dsRBCCLIxC0pBJSPg2gkLC9MtwVqgRERAr15w/jzx/5hiZweHD6szoDxp\n9sHZbPxjI/sH7adEsRJ6DavA5jOfknw+cuIEeHuT0rgl3a8uxMyyFGvWQMmS8GX4lwREBLB/0H6s\ny1g/9xSSS21JPrUl+dSO5FJbms6C8ti4ceO4d+8eqampdOjQgQoVKrBq1apcBylEnpk1C8aOBVNT\nvvwSunZ9dvG9/dx2FkYsZGOfjXovvoXQG1dXiIzEzFRhW4wr9ZPCee01uHsXRjUbhZ+rHx0CO3Dr\nn1uGjlQIIYq8LEfAnZ2dOXnyJJs2bWLbtm3MmzeP1q1bc+pUzu6uz2syAl7EnTkD7dvDxYvEPTTH\nzg6OHIHaT7TBnrtzjpbftWRjn420qt7KMLEKobUNG1Def5+9NoMZFTeVrTuLU6MGTNk7heC/gtnr\nu5fyJcsbOkohhCgU9DICnpaWBsC2bdvo2bMn5cqVkx5wkf/Nnq1OB2Fuzrx50K3b08X3/Yf36bau\nGx+3+1iKb1G49OiB0cmTtLf6nV8Sm/B2k5OcPAnT3abTsVZHOv3QiYTkBENHKYQQRVaWBbinpycO\nDg5ERkbSoUMHbt26RYkS8jV9UVLg5guNjoaff4b33uPOHfj6a5g0KfMuGUoGAzYPoGW1lvg39M/T\n8ApcPvM5yedzWFnB5s1UnDmarQ9eY1uLmezdlc5n7p/RtEpTOq/pTGJKYqZDJJfaknxqS/KpHcml\n4WVZgM+ePZtDhw4RGRmJmZkZpUqVIjg4OC9iEyJ3Pv8chgwBCwvmzoWePcHGJvMun+7/lJuJN1no\nsVC+0RGFl5ER+PpS4rdI3nPYg0WXlvw07Xe+8vgKxwqOeAV58SD1gaGjFEKIIidbs6AcOnSIS5cu\nkZqaqh5kZMSAAQP0HtzLkB7wIurGDXWN+T/+INbYCgcHdXKI6v+ZfW3rX1sZ+vNQIt6JeOGMEEIU\nKhkZ3Px0GabTJ3Gs0VDcdk9g0M53uPvgLpv7bKZ4seKGjlAIIQqk3NScWRbgb731FhcvXsTFxQUT\nExPd8wsXLsxdlHlECvAiasIEuH8fFi1i/Hj1f7/++t+X/7z9J21WtGFL3y00q9rMcHEKYSDxv8fw\nu9v7VH/wF+XWf8Pg+wGkZaSxvtd6TE1MDR2eEEIUOHopwB0dHTlz5kyB+5peCnDtFJj5QuPj1Tst\nIyO5ZW6DgwOcOgVVq6ovJyQn0PTbpoxtMZa3G7xtsDALTD4LCMlnzqWmKHzXdSPd9o6geO+u+Le/\nzMNSxXmv4nt07NDR0OEVGnJtakvyqR3Jpbb0MgtK3bp1uX79eq6DEiLPLFoEXbqAjQ2ffQb9+/9b\nfGcoGfhs8qF9zfYGLb6FyA9MzYwYsqMHmz7+jW2bFAInnqZN+HUm/fIRSalJhg5PCCEKvSxHwN3c\n3IiKiqJJkyYUL672CBoZGbFly5Y8CTC3ZAS8iElMVEe/9+7luqUTr74Kv/0G1o9avKfuncqe6D38\nMuAXzEzMDBurEPnI9u3wdb+DfF/6PW5YxjLHuyqLxuyhTPEyhg5NCCEKBL20oDyequa/JzcyMqJt\n27a5izKPSAFexHz+ORw9Cj/+yKhR6lNffqn+d9MfmxgZOpKj7xzFqrSV4WIUIp86cwZ6dktj6iuL\n6Hx6AptbWOK5KgKLClUNHZoQQuR7emlBcXNzw8bGhtTUVNzc3GjSpAmurq65DlIUPPl+vtB//oG5\nc2HyZK5dg8BAGD9efelM7BmGbBvCht4b8k3xne/zWcBIPl+ekxOEHyvGQpzpY3sR2yQLkuvU4t4P\ny0EGMnJNrk1tST61I7k0vCwL8KVLl9KrVy/8/dXFSq5evUr37t31HpgQ2bZ4MbRqBfXqMWsWDBoE\nlStDfHI8b6x9g8/dP6dxlcaGjlKIfK1sWfj4Y2jdszJvnvudZf7eXB//Pg/at1GHyIUQQmgmyxYU\nZ2dnIiIiaNasGSdOnACgXr16nD59Ok8CzC1pQSkikpLU3u8dO7havj7168Mff0CFiul4BnliV96O\nBR4LDB2lEAXKzp0wYAA0HzaX+pGfMPmAMcV694Hp06FiRUOHJ4QQ+YpeWlCKFy+uu/kSIC0trcBN\nSSgKsSVLoHlzqF+fGTPg7bfVFbgn751MUmoSX3T8wtARClHgdOwI4eFwfdsYtpRZhNMwY2KSY9Ve\nlc8/h4cPDR2iEEIUaFkW4G3btuXTTz8lKSmJXbt20atXLzw9PfMiNpFP5NtesQcP1GJgyhTOnYMN\nG9Te79WnVhP0W1C+XVgk3+azgJJ8aue/ubSxgQMHwL1SP+JCV/OqbRi7A6fDwYPg6Ag//ST94VmQ\na1Nbkk/tSC4NL8sCfPbs2VSsWJF69eqxZMkSOnfuzCeffJKtk4eGhuLg4ICdnR1z5sx55j4jRozA\nzs4OZ2dnXYsLwODBg7GysqJevXqZ9p82bRpVq1bF1dUVV1dXQkNDsxWLKISWLoUmTcDFhSlT4IMP\n4PyDI4zaMYot3luoWEq+KhfiZZiawmefQeDUjhRbu53uETNYPKkLfPstfPIJtGmjzj4khBAiR7Ls\nAQe4desWAK+88kq2T5yeno69vT27d++mSpUqNG7cmKCgIBwdHXX7hISEEBAQQEhICEeOHGHkyJGE\nh4cDcODAAUqXLs2AAQMy9ZtPnz6dMmXKMHr06Bd/MOkBL9ySk9Xe761bOWHUgM6dYW/kFTqsbcaS\nrkvoWqeroSMUolC5dg26v32OU/U9GNi4B4ve+ATjwFUwaRK0b68W5DY2hg5TCCHynKY94IqiMG3a\nNCpUqIC9vT329vZUqFCB6dOnZ+tNIiIisLW1xcbGBlNTU7y9vQkODs60z5YtW/D19QWgadOmxMfH\nc+PGDQBat26NpaXlc2MTRdyyZdCwITRowEcfwZiJ/9B3qxejmo6S4lsIPbC2hl+32jG6TDjf7gin\n8fxe3O/fG86eBVtb9edx1CiIjTV0qEIIke89twCfP38+hw4d4ujRo8TFxREXF0dERASHDh1i/vz5\nWZ44JiaGatWq6barVq1KTExMjvd5loULF+Ls7Iyfnx/x8fFZ7i9eTr7rFUtKgtmzYepU9u+HM39k\ncKjiAJytnBnbYqyho8tSvstnASf51E5WuTQxgU8/qsCv7+7i4u/lqTa1NSdj42HaNHWqwvR0tT/8\n44/V1WmLOLk2tSX51I7k0vCKPe+FwMBAdu3aRcX/TDlVq1YtVq9ejbu7e7ZaQLLjydHsrI4bOnQo\nU6ZMAWDy5MmMGTOG5cuXP3PfgQMHYvPoK1ELCwtcXFxwc3MD/r34ZLsAbi9aRJitLcq9+0yaBM6j\npnL+xFnmdpqru37yVbyyLdsFZPuxrPb/596vrOvnw8x9jjT4uhlDSkyiTwcH3BYuhFGjCBsyBL78\nErcZM+Cddwj79dd88fnyaz5lO3vbj+WXeArydlRUVL6Kp6BtR0VF6QaAo6OjyY3n9oDXrVuX3377\n7ZkHvei1x8LDw5k2bZruJslZs2ZhbGzM+MdLFALvvvsubm5ueHt7A+Dg4MC+ffuwslJXLIyOjsbT\n0/O5c46/6HXpAS+kEhLAzg7Cwth20Ymhi4Io1ulDjrxzhFdKZf8eBSGENmZuDGby0bdpFr+AzR/3\n+8IK2FAAACAASURBVHea8BMnYOJEOHdO7Q/v0weMjQ0aqxBC6IOmPeCmps+fvu1Frz3WqFEjzp07\nR3R0NCkpKaxbtw4vL69M+3h5eREYGAioBbuFhYWu+H6e69ev6/5/06ZNT82SIgq5efOgc2cyHJz4\n4IsI7rcaSXDfYCm+hTCQD998g1/9f+HMK1Op8e5wvluZos5O6OoKoaHq/Rrz50OjRuoKPzIwIoQQ\nzy/AT506RZkyZZ75yM4qmMWKFSMgIIBOnTrh5OREnz59cHR0ZMmSJSxZsgSAzp07U6tWLWxtbfH3\n9+frr7/WHd+3b19atGjB2bNnqVatGitWrABg/Pjx1K9fH2dnZ/bt25etfnTxcp78+s9gYmMhIACm\nTmVB4AUuNe/Gqp7fUd+qvqEjy5F8k89CQvKpndzmsqlNfaInHqOp+1WGH29DW88rXLz46MX27eHI\nEfjwQxg+HF57rchMXSjXprYkn9qRXBrec3vA09PTX/rkHh4eeHh4ZHrO398/03ZAQMAzjw0KCnrm\n849HzEURNGcO9O1LjEUZxp1qwfBGU/G0lxlPhMgPypUoxx7/jcw5+Dmf7mmMS49VfNjHnQ8+gOLF\njaBnT3jjDfjuO+jWDVq0UFtT7O0NHboQQuS5bM0DXhBJD3ghExMD9euTdCICx5VvUfxaO85+M9PQ\nUQkhniEsOow+P/bD4rw/Gfs+4qsvi5FpLCYpCRYuhC++UIvxqVOhalWDxSuEEC8jNzWnFOCiYHj7\nbTLKW+JZ9zy/hJYiasoqHByyN9OOECLvXbt/DZ9NPtyIfUjSDz9Qt6oN8+erU4brxMWp32wtWwZ+\nfjBhApQvb7CYhRAiNzS9CVOIxwzeK3bqFMrWrUxolMCpv+7hV+G7Al18GzyfhYzkUzta5tK6jDW7\nfHYxqHk3Evs2plTz1TRrpk6Mcu/eo50sLdU5/U+dUmc4sreHWbPgn380i8OQ5NrUluRTO5JLw5MC\nXOR/48bxS7/mbL52mKTvNzJ9ipmhIxJCZIOxkTFjW4xlh88OosrOoNWXfbl44zZ16qgdKCkpj3as\nUgWWLIFDh9TpC+vUgcWLITXVoPELIYS+SAuKyN927iThnQE0Gl6cGr8exKNlNcaMMXRQQoicSkpN\nYtKeSQT9FsQYx4X8srAn587BzJn8f3v3Hpfz+T9w/HUXhUSSikI5pKPKynG2HGJsDnNabE6zE/PF\n2GZjR7ZhB8PMfmbY2JaZbdhGzCEMaaaQci4UcqioSHX3+f1xrdQcRj51V97Px+N63Pfn7nPf9/V5\n77beXff7ui7694cie7Dt3q1WTYmPVxM1+/eXNcSFEGWW1IAXIgl4BWA0kubZiPFt0nm4XwRTxroR\nGwuWlqbumBCiuHac2sHTq57G296bEOvPmfaGg9ri/n21QmGRRHzjRlUXnpenSlOCg/91ghBCmJ7U\ngIsSYapasX0fvcyh7DOMfm8zMye7MX16xUi+pfZOXxJP/ZRGLNvWb0v0C9E0sW3CqBgfnv7iC8aO\nM/K//0H79rBhQ6G9ejp1gshIVTj+v/+p4127SryPepHPpr4knvqRWJqeJOCiTPozbh120+dgNecL\n/vzZFzs7tYywEKL8q1KpCtM7T2fjkI0sO/A9szJb8/UffzFqFIwe/a9E3PDPGuIHDsDAgdC3r2oH\nD5r6MoQQotikBEWUOX+e/JPdw7oQYt0G5m/Exwe2bgUPD1P3TAihN03TWLJ3Ca9tfI0ebj14+6Ep\nbPndkSlTwM5OLRFepDTl6lU1g/Ojj9TGPm+/DfXrm/QahBD3NylBEeXe1hNbmTC3J6OiK+P4xVIm\nToThwyX5FqKiMhgMDPUbSuyoWGpY1qD5fC+O1HuXXVEZvPgijB0LgYGwfDnk5gJVq8Krr8Lhw2Bv\nD35+8MorcPGiqS9FCCHumCTg4j+VVq1YeEI4/X7oy++RTbF4/Q3+PF6PTZvgzTdL5e1LjdTe6Uvi\nqR9TxrJW1Vp83OVjdj+7m0MXD+Exz410ty+J3pfL22/DnDlqmfB589RGmtSqpZZQ2b8fMjLUD99/\n/58flg3y2dSXxFM/EkvTkwRclAkbj29kwI8D2Gj7EnZnL5H74lhefFHtVG1tbereCSFKi2stV77v\n+z2rB64mNCYU3/k+ZLiEsmWrkSVLYP16cHWFKVP+GfSuVw+++AJ27oS9e1Ui/s03auUUIYQoo6QG\nXJjcT7E/MfL3kazo+S0PdR8JX3zBnINdWLVKTcSSVceEuD9pmsa6Y+uYunUqF65cYNKDk3iy+ZMc\nOVSJjz+Gn39WS4SPGQPe3v88aedOGD8erl2DTz6BDh1Meg1CiIpP1gEvRBLw8uGLv77gvW3v8dvA\n3/D/6jeIiuLkrJ954AGZeCmEUDRNY3PCZqZuncqJtBNMaj+JIb5DSL1gwfz5agDc01PViz/6KJib\nafDjj2oNcW9v+PBDcHc39WUIISoomYQpSkRJ1IppmsZbm99iZsRMtg3fhn+6FcyejTbzU557DsaN\nq7jJt9Te6UviqZ+yGkuDwUBH145sHrqZb3p/w4rYFbjMcuHLg1N5YcI5TpxQk7WnTlW72M+abeDy\nIwMgLg4eekitazh6NJw/X6r9LqvxLK8knvqRWJqeJOCi1GUbs3lm9TOsObKG7U9vp1FNF3j2WXjz\nTZZsaUhyslrkQAgh/q19w/aEPRXG+sHrOXnpJM3mNmPk2hE0D95HZCQsXQoREeDiAmNeseRIr5dV\nIm5urobJP/wQsrJMfRlCiPuclKCIUnU24yx9l/fF3sqeJb2XYG1pDV9+CYsWcWbFdnxbmLNuHfj7\nm7qnQojy4Hzmeeb/PZ95f82jmV0znm3xLH08+nDhbBXmzYMFC6BlS1UnHtzwMGavT4ToaLW1/RNP\nyCQTIcQ9kxrwQiQBL3v+SvqLPsv78Iz/M7z58JuYGczg9Gnw9UXbtJk+b3nj5QXvvWfqngohypts\nYzYrD67kqz1fsefMHp70eZJnH3iWxtbehIaqZQyzslQlytONt1DtzQlqVPzTT6FtW1N3XwhRjkkN\nuCgR91orpmkaX0d/zaPfP8rcbnN5O+htlXxrGrz4IowcyY9x3hw6VPHW/L4Zqb3Tl8RTP+U5lhbm\nFgzwGsD6wev569m/qGFZg67fdqXj923Q/BayNSKdL7+ELVvA+cmHealdJGcH/A9CQtRIeHy87n0q\nz/EsiySe+pFYmp4k4KJEXb52mad+eYoPt3/I5qGb6eXe6/oPV6yAgwc5PXwyY8bAokVgaWm6vgoh\nKgbXWq5M7TiVE+NOMLn9ZH49/Cv1Zzkz71wIQ977lci/s6lSzYzmM56ij+dBjlh6owUEqFVTLl82\ndfeFEPcBKUERJWb7ye0MWTmE4EbBzOw6k2qVq13/YVIStGhB3srVPPJ2K9q1g7ffNl1fhRAV24Ur\nF/jxwI98t/87Dl08RD+PfvRr9iTxW9sy9zMzrNNPM9/+Ddzj12L27jswYgRUqmTqbgshygGpAS9E\nEnDTycjOYNLGSayIXcG8R+fR27130RPy8uCRR+DBB/nU+i1WrFBfC8vvOiFEaUhIS+D7/d/z3f7v\nyMzOZKD3INxzBvHbIm/OrYviqxrjaVDtPJaffQJdu5q6u0KIMk5qwEWJuNNaMU3TWHlwJT5f+HDp\n2iViRsXcmHwDfPYZpKcT3X0S06bBt9/eX8m31N7pS+Kpn/slli42LkxqP4mYkTGsDFlJbl4Obxzs\nxsGOPrT6bi0zB3/Fs8nvk9RnNOdbdicvJrZY73O/xLO0SDz1I7E0vRJNwMPCwnB3d6dp06bMmDHj\npueMGTOGpk2b4uvrS1RUVMHjTz/9NA4ODvj4+BQ5PyUlheDgYNzc3OjSpQtpaWkleQniDu1P3k/w\n0mAmb5rMgh4L+Kb3N9hWtb3xxJgYmDqV9C++pf/ASsyeDa6upd9fIYQwGAz4OfrxUZePODHuBPO6\nzyPDLJGfbNsQ99EHTJzyPAuTW5PqF8S+h14kI750N/IRQlRcJVaCYjQaadasGRs2bMDJyYnAwEBC\nQ0PxKLS94Zo1a5g7dy5r1qxh165djB07loiICAC2bdtG9erVGTJkCPv37y94zquvvoqdnR2vvvoq\nM2bMIDU1lenTp994YVKCUirizscxZesUNsVv4o32bzAycCSVzG4xnJ2VBa1bo43+H31+H4GTE8yd\nW7r9FUKI/5JjzGFj/EaWxSxj1aFV+Bm9ee57C4Lj9rLroddosfh/1HWRGeNCCKVM1YDv3LmTd999\nl7CwMICCJPm1114rOOeFF16gQ4cOPPHEEwC4u7sTHh6Oo6MjAAkJCfTo0aNIAu7u7s6WLVtwcHDg\n7NmzBAUFcfDgwRsvrBQS8GxjNocvHubQhUMcvniYMxlnOH/lPBeuXODClQtkG7PRNA0N1Q+rylbY\nVLHBpooNtarUoq51XerXqE+Dmg1oULMBrrVcqVKpSon2WQ+apvHnyT+ZvWs2W09sZXyb8YxuOZrq\nFtVv/8TnnoO0ND4O/IEfVxjYulVWPRFClG1ZuVmsObKG0JhQju1Yy4zfrWh8En5rN5Pgzwbh4Skb\n+QhxvytOzllilbdJSUnUr1+/4NjZ2Zldu3b95zlJSUkFCfjNJCcn4+DgAICDgwPJyck69/zWLl+7\nTHhCOJviN7EraRf7kvdRv0Z93O3ccavtRuNajWnt3Bq7anbYVbPD0lxll4Z/dlrLzM4kLSuNtKw0\nUrNSOZ1+mojECJbHLufkpZOcSDuBUw0n3O3cca/tjrudOx51PPCs43nzco5SEh4eTlBQEMkZyfxw\n4AcWRy/mSs4VxrQcw+Jei9Vulv9l8WLYto3NMyL5+DkDkZH3b/KdH0+hD4mnfiSWN6pSqQp9PPrQ\nx6MP6T3TWTV8FX98+xlPLRnCxQ7jGPLARIZOHE3Hh6rcsKmmxFNfEk/9SCxNr8QScMMdbu/7778Y\n7vR5+efe7vxhw4bh4uICgI2NDX5+fgUfuPwJCP917B7gzk+xP7Hg5wUcuniIBx96kM6unQmxCuGt\nwLfoFtzt+vlZENT69q8XHBRccOyGG0G9rv8815hLA78GHLxwkN/X/87Pf/1MqmMqcRfiMD9hTsOa\nDWn3UDs863iSfSybhjUb8ni3x+/qeu722MnHiRWxK3h5/cscvHCQPt36MKPzDCqdrITZFbOC5Pu2\nrxcVRfi4cZx+bTYvPWvNjz/C8ePhHD+uf3/lWI7luPjH+cpKf8ri8VPNnyK8uzPxQSlU37aSjz6f\nzOy413muUTueHDKJyYM6snP7nxRWlvpfno/zlZX+lOfj6OjoMtWf8nYcHR1dMAcxISGB4iixEpSI\niAjeeeedghKUadOmYWZmxsSJEwvOeeGFFwgKCiIkJAQoWl4Cty5BCQ9XZSpnzpyhQ4cOupeg5Bhz\nWH1oNV/u+ZLIpEgec3uM/p796dyoc9G1rEuJpmmcTj9N7PnY6+1CLAfOHcDczBzPOp542nniWceT\nJrZNcK7hjFMNJ2pVqXXHf9Dk5uWSkJbAoQuHiD0fy66kXUQkRqCh0b1Jd7o17UbXxl2xsrC6u86n\npkJAAOmvf4D/9CeYPBmGDy9GEIQQoixKT+fSO5Mxn7+Qz7xtmNY2h0CnAbz1+EDau7ZRu/4KISq0\nMlUDnpubS7Nmzdi4cSP16tWjZcuWt52EGRERwbhx4womYcLNE/BXX32V2rVrM3HiRKZPn05aWppu\nkzAvZV1i3l/zmBM5B7fabjzb4ln6evSlauWqxYhAydM0jXOZ54ok5kdTj5J0OYnEy4lkG7Oxt7LH\n2tKa6hbVsbawxsLcgpy8HLKN2WQbs0m/lk5yZjIpV1NwsnaimV0zPOw8aOnUktbOrXG1cb2rbyWK\nyM2Fnj3JbeRGh72zaNMGPvxQ3xgIIUSZcOIE2qRJXFm3kXfd2zH7gYNY211hVNvhPBMwjAY1G5i6\nh0KIElKmEnCAtWvXMm7cOIxGIyNGjOD1119n/vz5ADz//PMAjB49mrCwMKysrFi8eDEtWrQAYODA\ngWzZsoWLFy9ib2/PlClTGD58OCkpKQwYMICTJ0/i4uLC8uXLsbGxufHC7iIY5zLPMStiFvP/nk/3\npt2Z2G4i3vbeOkXBdDKyMzifeZ707HQysjNIv5ZOtjEbC3MLKptXxsLcguoW1XGwcqCOVZ1brl4S\nHh5e8NXLHdM0GD2avMNH6GPxO1Y2lVm6FMxkMKh48RS3JPHUj8RSBxERMH48GSnXGFylA3+4ZaF5\nh9KmYQDPPvA0vdx7lYvJ9mWRfD71I7HUV5mahAnQrVs3unXrVuSx/MQ739xbrEMXGhp608dtbW3Z\nsGGDLv1Lv5bOjO0zmPfXPEK8Q9j97G5ca1WcRamrW1T/75VJSsqnn6Jt28YLHtvIzazM119L8i2E\nuA+0bg3bt1N9+XLGjh3LItu2vLZ9C8vW7+fEyQWMsniRJ5sP4mn/p/Fz9DN1b4UQJnJfbkVvzDOy\nKGoRb4W/RZfGXXi/4/s413Au5R5WYD/9hDZ2LK8/vJPtJ+uzbh1UK/3SeSGEMK2sLJg1Cz7+mAvd\nh/D6lTf5ac8lfId+zVHrxThUr8OowFGEeIeYZH6REEIfZa4ExZRuFYztJ7cz8veR2Fa15ZMun/BA\nvQdM0LsKbMcOtF69eLvVOrakt+C338D6DlYpFEKICis5Gd56C1au5NyI15l86gV+WluZLi+sJ7Xp\nPP5O3sng5oMZGTgSt9pupu6tEOIuFScBv2+KAi5fu8yo30cxYMUA3nzoTTYP3SzJ9x369xJQt/TX\nX2i9ezPdcyk7r7Vg7VpJvm/mjuMp7ojEUz8SS30VxNPBAebPhw0bsD+wmQVb3Dj08mJszwWze/yv\n9EvZjTG7Cu0Xtyd4aTC/xP1Cbl6uSfteFsnnUz8SS9O7LxLw1YdW4zXPi9y8XGJGxtDfq3/xV/YQ\nNxcVRd6jj/GG40J21XqEX3+VshMhhCjCxwdWrYLly6nzRyjzwj05NGUZldMb8O2waYScOUnvhsP4\nZOcnuMxy4b2t73E+87ypey2EKAEVugTlctZlxoSNYduJbSzosYAOrh1M3a2Kad8+cjt1YbzlPCoN\n6MNHH4G5uak7JYQQZdzGjTBpEmRlcXH8+0zd8yhLlhoYPBgeG7GXHxI+46e4n+jn0Y9xrcfhZe9l\n6h4LIW5CSlD+xX++P+YGc6JfiJbku6RERZHV4RFG5czBfVIfZs6U5FsIIe5Ip05q2cIpU6j98evM\n2tWGo5+toXIljSeCfLFc9xUbex+ifs36dF7amS5Lu7D2yFrytDxT91wIcY8qdAL+YfCHfNXzK9Mt\nxVdB3KpWLDdsAxntujLWbC7D1gxg1KjS7Vd5JbV3+pJ46kdiqa87iqfBAL16QXQ0jB+P7YzX+HhL\nIMdnrqRG9TyC29qT9P1bhPdOYJDPIF7b+Bpe87yYv3s+V3KulPg1lCXy+dSPxNL0KnQC3sejj6m7\nUGEdfz+USz2e5D2/FXxwsA9t25q6R0IIUY6Zm8OAASoRnzwZmzlTmLbWj4QPl+NgZ6RdK0u2fTaM\nFZ2imdd9Hr8f+Z2GsxoyeeNkzqSfMXXvhRB3qULXgFfQSzOp9MsaW3rNxH/rLPZMXcNjr/sg81mF\nEEJnmgZr1sDUqXDpEpnjJvNJUghz5lXi0UdV6biZ3RFm75rNd/u/o1ezXoxvM57mDs1N3XMh7juy\nDnghkoDrKzsbFn2WSZ03niOgaixWG1Zh16KBqbslhBAVm6bBhg3w3nuQkMDV58cxN+sZPvo/a4KD\nYfJkcHRN4f92/x9zI+fibe/NhDYT6NK4i6z2JUQpkUmYQnfp6TB6dDjBjY7R5Z02dOhciYaJ2yX5\nvgdSe6cviad+JJb60iWeBgMEB8OWLbBiBVX37eKVz11IHPQq7Rom0qEDjBxmSw+bScSPjWeQzyBe\n+eMVmv9fcxZHLeZa7rV770MZIZ9P/UgsTa+SqTtQnmkaJCXBkSPXW1ISpKVdb1evqv9/mpmp2ypV\nwMbmeqtTB5ydr7f69aFuXahkwv8ymqYm5n/3HYSGQv+62/jjylwsZrwNI0ciNSdCCGECgYGwbBkk\nJGAxezaj/q85z3V9lGX2Y+jSJZBWrSx55ZVh7H1hKH8c/4NPdn7C5E2TeTHwRV4IeIHa1Wqb+gqE\nEP+QEpS7kJOjEtNNm9RtZCRUrgxubtC0KTRpohLoWrVUcl2rlkq4NU21vDzIyoJLlyA1VSXo586p\npP3UKUhMVO3cOZWEu7qCi4tq+fddXcHJSf+l/i5fVgMs69fD77+DhQU83SeNUQfHUH3/Tli6FFq3\n1vdNhRBCFF9qKixYAPPmYbSzJ9xjFGO2P0FNx6q8/LJaXCX2wn5mRsxk5cGVDPIexEttXqKJbRNT\n91yICkVqwAvRKwG/ehV++00NOmzcCI0bQ+fO0LYttGypEmW95eSoRDw+HhISbrw9f14l4YWTchcX\nsLcvOrpuYaFez2BQLSNDJf0XL6rXSUiAuDjYs0f9AdC6NXTpAl27gm/yegzPjFD/B58xA6ys9L9Q\nIYQQ985ohLVrYd48tL/+4kjboUw6OZK9GY0ZPx6GDoVLxjN8/tfnzP97Pg82eJAJbSbQrn47qRMX\nQgeSgBdyLwl4/pyXpUvh118hIAAGDoQePVTJiKlduwYnTxZNyhMS4MKF6yPrqamQm3t99F3ToHp1\nlZjb2kLDhippd3ODBx4Ad3c1mk9iIrz6KmzfDgsXQufOhIeHExQUZMpLrlAknvqSeOpHYqkvk8Tz\n2DGYPx9t8WIuNfRlkTacWSf7MHxUVV58EaxsMvlm7zd8GvEptlVtmdBmAn08+lDJrOxXpMrnUz8S\nS30VJ+cs+//iSlFmpkq658xRyeiIEfDhh+DoaOqeFWVpqUpemjbV8UWzsuCjmfDJJzBqlPpaU0a9\nhRCifGncGD78EMOUKdisWsX4xYsZe+x/bF/enydnDsexVytGvTiKuFHP89uRX/lk5ydM3DCRsa3G\nMsJ/BNaW1qa+AiHuCzICDqSkqLzzyy+hXTsYNw4efvg+mWuYk6P+6pg6FXx9YeZMaNTI1L0SQgih\nl8REWLoU48LFpKVXYnHuYLY4PkHv8Y0YOBD2p+zik52fsDF+IyP8RzCm1RicazibutdClBtSglLI\nnQTj8mWYNUuNePfpAxMnqsGD+0JurlrmZMoUVY/y7rvQvr2peyWEEKKkaBrs2IH23fdkh64gARe+\nzX6Cyk8O4IkJzljYxzN712yW7F1C96bdmdBmAv51/U3dayHKPFkH/A5lZ6sR7yZN4OhR2LVLjX7f\nF8l3aip89JG62MWLYdEitazLbZJvWS9UXxJPfUk89SOx1FeZi6fBAO3aYZj3OZbnk2i2/D1effQA\nE5b6kuLzECtb/ErrvS8RPfw4zR2a0yO0Bx2+6cBvh38jT8szde/LXjzLMYml6d13CXhYGPj4qJwz\nPByWLLkPEm9NU0udjBypykv274eff1YBePhhU/dOCCFEaatUCYKDsV6+EKtLZwj84RX6No6i21sB\nXKobhNvIDFZU+ZHhzZ/mzc1v4jXPiy///pKrOVdN3XMhKoT7pgQlPh5eegliYlTZyWOPmbBzpeXs\nWfj+e/j6a7Wl5bBh8NxzJbN2ohBCiPLPaCR1bQTHZq6i9o7VVMnN4ETzx0jr25Av625he0oUIwNG\nMipwFPZW9qburRBlgtSAF5IfDKNR1Xi//z6MH69alSqm7l0JOnkSfvlFjXDv3QuPP64S7/bt1Xac\nQgghxB3QNIhbeYj42auxi/gV75woEht6sTsgjy8d43DvFMJL7Sbgbudu6q4KYVJSA/4vBw+qvHPl\nSrVz5aRJFTD5zs1VFzd1qtqmuEULlXi/8ooaAV+8WJWZ3EPyLbVi+pJ46kviqR+Jpb7KezwNBvB8\nvBmPhr9Cy6tbObD+NNt8JpO1pjWLFzgyY9D3xHbyY/ZzvmwLX0JenrFE+1Pe41mWSCxNr0QT8LCw\nMNzd3WnatCkzZsy46TljxoyhadOm+Pr6EhUV9Z/Pfeedd3B2dsbf3x9/f3/CwsJu+f7t28NTT8Hm\nzWrCZYWgaXDkCMybp5ZuqVNHlZWkpqodK8+eVRMrH3usAv61IYQQwhQMBmjZyZpnVvVg2KU5nAo7\nwqcD4thxehYNfrXArccIztpW4UCXFmTMnalGwCrmF+xC6KLESlCMRiPNmjVjw4YNODk5ERgYSGho\nKB4eHgXnrFmzhrlz57JmzRp27drF2LFjiYiIuO1z3333XaytrRk/fvztL8xgID5ew8WlJK6uFBmN\nsG8f7Nihdqf880/1WHCwap06lb2dgoQQQtwXNE196bpyZR471q2gkfFTHsz6m06JFtgaKmPRoROG\nhx9WI2Le3mrypxAVTJnaCTMyMpImTZrg8k8GHBISwqpVq4ok4KtXr2bo0KEAtGrVirS0NM6ePUt8\nfPxtn3unF1kuk+/0dFVSsn27apGRUK+e2iEoOBjeeUdtgXlf7BIkhBCiLDMYwM8P/PzM4J0BnDw5\ngO9XXqDj3q+5qn1G5zOR9P/6LA9+/BnVUs5gCAiANm2gdWvV7GUip7g/lVgJSlJSEvXr1y84dnZ2\nJikp6Y7OOX369G2f+9lnn+Hr68uIESNIS0srqUsoeZqmFiJfulRt/+7np1YomTJFbQ0/ZgwcPw5x\ncfDVVzB8OLi5lXryLbVi+pJ46kviqR+Jpb7ux3g2aACvjbEjbuHLxH4eT9MRC/lfd3tqPnWBekH9\nmFTpcaJiKpH1yefq91njxqpWdO5c2L1b7c58C/djPEuKxNL0SmwE3HCHSeLdDtmPHDmSt956C4A3\n33yTCRMmsHDhwpueO2zYsIJRdBsbG/z8/AgKCgKuf/hK9Tgri6Bq1WDnTsJXr4YDBwiqXh3atiX8\nn1ruoBEjwNLy+vNr1zZdf+VYjuX4vjrOV1b6U96P85WV/pjieGK/YFrZVeZ85kAiWx9iwd5Z9y2T\nigAAF+lJREFUzIk1YEzsjpPTtwz2Po/D5a9xXbWWrvPnQ3w84a6u4OFBUN++0LIl4QkJRQaeytL1\nldfj6OjoMtWf8nYcHR1dMACckJBAcZRYDXhERATvvPNOwSTJadOmYWZmxsSJEwvOeeGFFwgKCiIk\nJAQAd3d3tmzZQnx8/H8+F9RF9+jRg/379994YcWox9HduXOwZQts2wY7d0JsrKqBa9MG2rZVt4VG\n+oUQQoiKLE/LIzwhnK/2LOS3Q7/TmK5YHhhB3JpONKxvTrd2l+lV7y/8cyKpuj9SbVWdkwMtW15v\ngYFgZ2fqSxGiQJmqAQ8ICODIkSMkJCRQr149fvjhB0JDQ4uc07NnT+bOnUtISAgRERHY2Njg4OBA\n7dq1b/ncM2fOUPefjWR++eUXfHx8SuoS7l5+wh0erlpSkpp40r49fPopPPAAVK1q6l4KIYQQJmFm\nMKOja0c6unYk9WoqoTGhLLR7HZuAC7S0HwYnh/PO5k7s3NkJDw/oMBi6NU+itVkkVfZFwscfq1IV\nO7uiSbm/P1SrZurLE+KOlehGPGvXrmXcuHEYjUZGjBjB66+/zvz58wF4/vnnARg9ejRhYWFYWVmx\nePFiWrRoccvnAgwZMoTo6GgMBgOurq7Mnz8fBweHGy+sNEbAMzNVoh0Wpva2P31aJdsPPwxBQaqm\n29y8ZPtQCsLDwwu+ehH3TuKpL4mnfiSW+pJ43rnos9EsilrE9/u/x8/Rjye9hlHv8uPs3GLFpk2w\nZw+4uobTt28QHYPyaG17GIvoSLVQQWQkHDigasoLJ+WenhXid3BJkM+mvmQnzEJKJAHXNFVGEham\nWkSE+iqsa1e1Qomvb4X8xy7/UPUl8dSXxFM/Ekt9STzvXlZuFisPrmTJ3iXsOLWDXu69GNx8MC3r\ndOD/vtjGxYtBbNqklhlv21atxNuxI/h7XsM8Zu/1hDwyUn0L3aJF0dKVhg1lFTHks6k3ScAL0S0B\nz82FrVvVdpqrVqkdJbt1g0cegQ4dwNr63t9DCCGEEEUkZyQTGhPK0n1LSc5IZpDPIAY3H4yPgw+p\nqaric+NG9QX0mTPqy+f8hNzDAwyX0lS5Sn5CvmuX2kfj3/Xk/yx2IERxSQJeyD0l4FeuwPr1Kun+\n7TdwdYXHH4fevf/5Vy1/PQshhBClJfZ8LEv3LuXb/d9Su2ptBjcfzCCfQdS1VnPCzpxRu15v3Kja\ntWsqEe/USbWGDVHfYiclFR0l371brUX+73pyma8l7oIk4IXcdTCys2HdOggNhd9/h4AAlXT36nXf\nr1QiX1XpS+KpL4mnfiSW+pJ46is8PJyHHn6ILQlbWLJvCSsPrqSlU0uGNB9Cb/feWFlYFZx7/Lga\nGc8fIa9eHTp3hi5dVEJuY/PPiUYjHDpUNCmPjQV396JJuYdHhSoxlc+mvsrUKijlQl6eKi8JDYWf\nflL/wAYNgtmzoU4dU/dOCCGEEIWYGczo4NqBDq4d+Lz756w6uIql+5by4poX6da0GyFeITzS5BEa\nNbKkUSN45hk18H3gAPzxx/U97Xx81PStLl3MCQz0xNzTE4YNU2+SlQXR0SoZ37wZZsxQQ+yF68lb\ntlSDc/KNuCim+3MEfO9etfvksmUq0R44EEJC1BZeQgghhChXLly5wIrYFSyLWcb+c/vp3aw3Id4h\ndHDtQCWzomONWVlqe45161Q7fVqNiquE/BZfeqem3lhPDkUT8oAAsLUt+YsVZY6UoBRyQzBSU+H7\n72HRIjh/HoYMUaPdnp6m66QQQgghdJV4OZHlB5azLGYZJy+dpL9nf0K8Q2hTvw1mBrMbzj99Wk37\nWrdOjZLb26tkvGtXeOihWywvrmmQmFi0dOXvv8HRsWhS7ucHVaqU/EULk5IEvBCDwYCWm6uKvxYt\ngrVr1eolw4erP3UrUC1XSZNaMX1JPPUl8dSPxFJfEk99FSeeR1OOsixmGaExoWRkZxDiFUKIdwh+\njn4YblI+YjRCVNT10fGoKGjd+npC7u19m6oTo1Gtj1g4KT94UJW3BgZeT8rd3U2eg8hnU19SA/5v\njRqp3bKefho+/1y+GhJCCCHuI01sm/DGQ28wuf1kYs7FEBoTSt/lfbEwtyDEO4R+nv3wquNVkIyb\nm6tKkoAAmDwZLl9W43jr16uF0K5eVdt+BAerSZ2OjoXezNwcvLxUGz5cPXb16vV68g0b4IMPIDlZ\n7YxdeKTc2Vnqye8zFXsEPCpKff0jhBBCCAFomkZkUiTLDyxnRdwKqlaqSj/PfvTz7Ievg+9NR8bz\nHT2qylT++EPNz6xf/3pCfstylX+7eLFoPXlkpEq+/fzUhn75rVkzqFSxx0krCilBKaRUtqIXQggh\nRLmlaRq7T+/mx9gfWRG7AnMzc/p5qGS8Rd0Wt03Gc3NVHr1+vUrIo6KgVavrCbm/v9q77w46AadO\nqQUiCrfERFW+Ujgp9/WFWrX0C4DQhSTghUgCrh+pFdOXxFNfEk/9SCz1JfHUV0nHU9M0os5GsSJ2\nBT/G/khuXi79PPrR36s/gfUCb5uMA6SnQ3j49RHy8+fVlLOgILVL513v45eRATExRZPy/fvVIua+\nvmotRS8vVZjerNldTfaUz6a+pAZcCCGEEKIYDAYDLeq2oEXdFrzf8X32Je9jRewKhvwyhCs5V+jj\n0YdezXrRvmH7G5Y2BLC2hh49VAM1qL1xI2zZAh99pPLphx5SyfjDD6u8+bYj5NWrqxmgrVtffywv\nD+LjVTIeEwOrV6u68uPH1Xaf+Qm5t7e637QpVK6sb6CELmQEXAghhBDiFjRNI/Z8LD/H/cyqQ6uI\nT4une9Pu9HTrySNNHsHa0vqOXufkSZWM57eLF6F9e2jTRuXYAQEq5y6W7Gw4fFjtOBQTc/321Clo\n0uR6Qp5/26iRyVdiqUikBKUQScCFEEIIobfEy4n8euhXVh1axY5TO2jXoB093XrSs1lPnGo43fHr\nnD6tNgSKiFBt3z6VK+cPerdqpVYsvKM68lu5elUthVg4KT9wAM6dU2UrhUfLvb3VhoSyGstdkwS8\nEEnA9SO1YvqSeOpL4qkfiaW+JJ76KovxvHztMuuOrmPVoVWsObKGRrUa0b1pdx5p8gitnFphbnbn\no8zXrqnKkogItdFmRIQaJff1VQuk5DdPT7C0vLd+h69ZQ5CdnUrICyfn6enqDXx8ijY7u3t7wwpO\nasCFEEIIIUpJDcsa9PfqT3+v/uQYc9h+ajtrj6xl5O8jSbycSOdGnenWpBtdG3elrnXd276WpeX1\nZcHzXbiglhHfu1etRz5zJhw7pkq781ct9PRUEzzr17+L0fJq1W58M1C7hsfEqMme+/fD8uXqtlq1\nG5NyT0+oWvXuAiYKyAi4EEIIIYTOki4nse7YOtYeXcuG4xtoWLMhjzR5hI6uHWlXvx1WFlbFet2s\nLDVgnZ+Yx8ZCXBxcuqSqStzdVUKe3xo1uqsFUm6Uv0xiflKe344cUSUr/07M78P6cilBKUQScCGE\nEEKUBbl5uexK3EXY0TA2J2wm+mw0fo5+dHDpQAfXDrRxbkPVyvc2mnzpEhw6pJLx/HbwIJw4oSpI\nGje+eSv2JuH5Ez//nZifP39jGYu3Nzg4VNj6cknAC5EEXD9lse6uPJN46kviqR+Jpb4knvqqSPHM\nzM5k+6nthCeEszlhM/uT9xNQL4AOLh1oW78trZxbUcOyhi7vZTSqAexjx9RqhceOwc6d4aSnB3Hs\nmCpbadwYXF3VSoYNG6qB7fzbWrXuMm++fPl6GUvhchYzs+uTPr29r69jXrOmLtdpSlIDLoQQQghR\nxllZWNGlcRe6NO4CQPq1dP48+SfhCeFM3TqVPWf24FrLlTbObWhbvy1tnNvgVtvtPzcDuhlzc3Bx\nUa1TJ/VYeLjaIEjT1ETPY8fU8uInT6pR8/Xr1cj5yZMqgS+ckP/7tl49qFQ4m6xRA9q2VS2fpsHZ\ns9cnfUZGwsKFqn7G1rZoUu7trepoKnh9uYyACyGEEEKUITnGHPYm72XnqZ3sTFQtLSsNP0c//B39\nVavrj7ud+003BdLTpUsqEc9PyP99e/48ODqqZNzJSTVn56L369YFC4ubvHheHiQkFB0xj4mBo0fV\nCxZOyr291VB9GdxYSEpQCpEEXAghhBAVxfnM80SdjSLqTBRRZ6PYc2YPSelJeNt7413HG486HnjW\n8cTDzoOGNg0xM9zLAuJ3LjsbkpJUMp6UpFpiYtH7ycmqlOVmyXnh+zVqFHrRI0eKJuX796sXdHEB\nNzc149TN7fp9E9aYl7kEPCwsjHHjxmE0GnnmmWeYOHHiDeeMGTOGtWvXUq1aNb7++mv8/f1v+9yU\nlBSeeOIJTpw4gYuLC8uXL8fGxubGC5MEXDcVqe6uLJB46kviqR+Jpb4knvqSeN4o/Vo6e5P3cuDc\nAeIuxBF3IY7Y87GkXE3BrbYbHnYeNKrVCFcbV1xrueJq40r9mvX5c+ufpRpLo1Ht/fPvxPzf9w0G\nlUfb26vbws3eHhxtsnC+dow6KYeolngYw+FDaiLooUOQk3M9IW/USBW159fe1K9foiPnZaoG3Gg0\nMnr0aDZs2ICTkxOBgYH07NkTDw+PgnPWrFnD0aNHOXLkCLt27WLkyJFERETc9rnTp08nODiYV199\nlRkzZjB9+nSmT59eUpchgOjoaPmfno4knvqSeOpHYqkviae+JJ43sra05sEGD/JggweLPJ5+LZ2D\nFw5y8MJBjqceZ9vJbSzZt4T41HiSM5Ox+tuK5gnNqWddj7rV61LPup66b63u16lWh5pVauo2im5u\nrspQ6taFwMCbn6Npav5mcrJK1pOTr7f9+/PvVyE52Ytz57zIzlZJua0t2DaHBlYXcTc7jGvaYZx2\nxmO/cSu10r7B+mICFilnyLOvi+biilljF8xdG6ri9cLN3r5Ul08ssQQ8MjKSJk2a4OLiAkBISAir\nVq0qkoCvXr2aoUOHAtCqVSvS0tI4e/Ys8fHxt3zu6tWr2bJlCwBDhw4lKChIEvASlpaWZuouVCgS\nT31JPPUjsdSXxFNfEs87Z21pTaBTIIFON2a713Kv8fKkl+n9UG/OZJzhdPppEi8nEnk6kjPp6vji\n1YukX0unhmUNbKva3tCsLayxsrDCqrIV1SpXK7hvZaGOLc0tqWxemUpmlahs9s/tv47NzczRNA0N\nNXKsaRqahUZtZ43azuCOVuTneVoexjwjRs1InpZHRqaR8xeNpKQZSU0zknbZSNolS7Zd8uJSujuX\nLhtVyzCSdTkL65RkHM6dpm78WRrmHMSZHTjlXaRuTiqO2SnUzMkkpUoNLla3IcW6BpetrUmvXo1M\n66pkWlcjo0ZVMmtW5YpNFa7UrMI160qYVcrDvHJesf4blVgCnpSURP369QuOnZ2d2bVr13+ek5SU\nxOnTp2/53OTkZBwcHABwcHAgOTm5pC5BCCGEEKJCsaxkSe1qtenUqNNtzzPmGUnLSiPlakqRdvHq\nRTKyM8jMzuTClQtkZmdyJfcKmdmZZOZkkpmdSbYxm9y8XHLyctStMeeGY6NmxIABg8GAAVW7nX8/\nf7WXwj83GAyYG8wxNzPH3GCOmcGs4H7BbSVzzO3MMatz/Wc1zcyx/eecXIM5SQYzTmvmaHlW5Blr\noBmbkJdrjuEa1EjJxjb1GrYpWdRIz6Zmeho1LyZT5+Q1amZmYXMlC5usLGpdvUqV3BzSK1tyqXJV\nFhTjv0OJJeB3ulTOndTMaJp209czGAzFWpJH3J2EhARTd6FCkXjqS+KpH4mlviSe+pJ46udOYmlu\nZk7tarWpXa12yXeoPMrJwfbSJWzT0qBp07t+eokl4E5OTpw6darg+NSpUzg7O9/2nMTERJydncnJ\nybnhcScnJ0CNep89exZHR0fOnDmDvb39Td+/cePGkpzr6JtvvjF1FyoUiae+JJ76kVjqS+KpL4mn\nfiSW+mncuPFdP6fEEvCAgACOHDlCQkIC9erV44cffiA0NLTIOT179mTu3LmEhIQQERGBjY0NDg4O\n1K5d+5bP7dmzJ9988w0TJ07km2++oXfv3jd9/6NHj5bUpQkhhBBCCFFsJZaAV6pUiblz59K1a1eM\nRiMjRozAw8OD+fPnA/D888/TvXt31qxZQ5MmTbCysmLx4sW3fS7Aa6+9xoABA1i4cGHBMoRCCCGE\nEEKUFxV2Ix4hhBBCCCHKotLZJqkUhYWF4e7uTtOmTZkxY4apu1PuPP300zg4OODj41PwWEpKCsHB\nwbi5udGlSxdZCuoOnTp1ig4dOuDl5YW3tzdz5swBJJ7FlZWVRatWrfDz88PT05PXX38dkHjeK6PR\niL+/Pz169AAknsXl4uJC8+bN8ff3p2XLloDE8l6kpaXRr18/PDw88PT0ZNeuXRLPYjp06BD+/v4F\nrWbNmsyZM0fiWUzTpk3Dy8sLHx8fBg0axLVr14oVywqVgOdv4BMWFkZsbCyhoaHExcWZulvlyvDh\nwwkLCyvyWP7mR4cPH6ZTp06y7vodqly5Mp9++ikHDhwgIiKCzz//nLi4OIlnMVWpUoXNmzcTHR3N\nvn372Lx5M3/++afE8x7Nnj0bT0/PgknrEs/iMRgMhIeHExUVRWRkJCCxvBdjx46le/fuxMXFsW/f\nPtzd3SWexdSsWTOioqKIiori77//plq1ajz++OMSz2JISEhgwYIF7Nmzh/3792M0Glm2bFnxYqlV\nIDt27NC6du1acDxt2jRt2rRpJuxR+RQfH695e3sXHDdr1kw7e/aspmmadubMGa1Zs2am6lq51qtX\nL+2PP/6QeOogMzNTCwgI0GJiYiSe9+DUqVNap06dtE2bNmmPPfaYpmny7724XFxctAsXLhR5TGJZ\nPGlpaZqrq+sNj0s87926deu0Bx98UNM0iWdxXLx4UXNzc9NSUlK0nJwc7bHHHtPWr19frFhWqBHw\nW23sI+6NbH507xISEoiKiqJVq1YSz3uQl5eHn58fDg4OBeU9Es/ie+mll/joo48wM7v+q0DiWTwG\ng4HOnTsTEBDAggVqWw6JZfHEx8dTp04dhg8fTosWLXj22WfJzMyUeOpg2bJlDBw4EJDPZ3HY2toy\nYcIEGjRoQL169bCxsSE4OLhYsaxQCbis+13yZPOju5eRkUHfvn2ZPXs21tbWRX4m8bw7ZmZmREdH\nk5iYyNatW9m8eXORn0s879xvv/2Gvb09/v7+t9wQTeJ557Zv305UVBRr167l888/Z9u2bUV+LrG8\nc7m5uezZs4dRo0axZ88erKysbvhKX+J597Kzs/n111/p37//DT+TeN6ZY8eOMWvWLBISEjh9+jQZ\nGRl8++23Rc6501hWqAT8Tjb/EXcvf/Mj4LabH4kb5eTk0LdvXwYPHlywZr3E897VrFmTRx99lL//\n/lviWUw7duxg9erVuLq6MnDgQDZt2sTgwYMlnsVUt25dAOrUqcPjjz9OZGSkxLKYnJ2dcXZ2JjAw\nEIB+/fqxZ88eHB0dJZ73YO3atTzwwAPUqVMHkN9FxbF7927atm1L7dq1qVSpEn369GHnzp3F+mxW\nqAS88OY/2dnZ/PDDD/Ts2dPU3Sr38jc/Am67+ZEoStM0RowYgaenJ+PGjSt4XOJZPBcuXCiYWX71\n6lX++OMP/P39JZ7F9MEHH3Dq1Cni4+NZtmwZHTt2ZOnSpRLPYrhy5Qrp6ekAZGZmsn79enx8fCSW\nxeTo6Ej9+vU5fPgwABs2bMDLy4sePXpIPO9BaGhoQfkJyO+i4nB3dyciIoKrV6+iaRobNmzA09Oz\neJ/NEq1WN4E1a9Zobm5uWuPGjbUPPvjA1N0pd0JCQrS6detqlStX1pydnbVFixZpFy9e1Dp16qQ1\nbdpUCw4O1lJTU03dzXJh27ZtmsFg0Hx9fTU/Pz/Nz89PW7t2rcSzmPbt26f5+/trvr6+mo+Pj/bh\nhx9qmqZJPHUQHh6u9ejRQ9M0iWdxHD9+XPP19dV8fX01Ly+vgt89Esvii46O1gICArTmzZtrjz/+\nuJaWlibxvAcZGRla7dq1tcuXLxc8JvEsnhkzZmienp6at7e3NmTIEC07O7tYsZSNeIQQQgghhChF\nFaoERQghhBBCiLJOEnAhhBBCCCFKkSTgQgghhBBClCJJwIUQQgghhChFkoALIYQQQghRiiQBF0II\nIYQQohRJAi6EEEIIIUQpkgRcCCGEEEKIUvT/Uqg5tQnMxMQAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10f299c90>"
       ]
      }
     ],
     "prompt_number": 118
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "figure(figsize=(12,4))\n",
      "data['Fare'].dropna().plot(kind='kde')\n",
      "data[data['Survived']==1]['Fare'].dropna().plot(kind='kde')\n",
      "data[data['Survived']==0]['Fare'].dropna().plot(kind='kde')\n",
      "legend(['Whole', 'Live', 'Dead'])\n",
      "axis([0, 200, axis()[2], axis()[3]])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 120,
       "text": [
        "[0, 200, 0.0, 0.029999999999999999]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Lw3ffwbvvwpIlt72pUhWHUd2jmJX6ppiZ+qc4KyXjGUhOht694fnnjcV98itP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\neVBRoeVZNyIc5sLc59PSP3Z2cv/OHf5rU4T5qlX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xo0OECKJYCIKAJ598Elu2bMGnn34K\ngA6uIkoLo/k4ODiYlo6Wvk+JYvDhhx9i8+bN2L9/vxoGQHOTKBY+nw9dXV3YunXrrH53LigxTnnF\niVLjypUr6Orqwrlz5/DRRx/hhx9+SOung6uIUmKm+UhzlSgkr7zyCrxeL7q7u7F8+XK8/vrrhmNp\nbhJzTTgcxq5du/D++++joqIire9BvzsXlBjP56Ahgigky5cvBwDU1dVh586d6OzsVA+uApDz4CqC\nKARG81HvUDbl8DWCKATLli1TRc6BAwfUW/00N4lCk0gksGvXLrz44ovq+Taz+d25oMR46kFDoiji\n1KlT6OjoKPZlEYuUaDSKUCgEAIhEIrhw4QI2bdqkHlwFIOfBVQRRCIzmY0dHB77++muIogiv14vb\nt2+rGYEIohD4/X7V//bbb9VMKzQ3iULCGMP+/fvR1taGgwcPqu2z+d05Z4f+FINchwURRKEZHh7G\nzp07AQDJZBIvvPACduzYgS1bttDBVURR2Lt3Ly5duoSxsTE0Nzfj7bffNjxIra2tDbt370ZbWxss\nFgs+/vhjCgUg5ozMufnWW2/h4sWL6O7uhiAIWLVqlXpoIM1NopBcuXIFX375pZqmGOCpC2fzu5MO\n/SEIgiAIgiCIIrGgwlQIgiAIgiAIYj5BYpwgCIIgCIIgigSJcYIgCIIgCIIoEiTGCYIgCIIgCKJI\nkBgnCIIgCIIgiCJBYpwgCIIgCIIgigSJcYIgCIIgCIIoEiTGCYIgCIIgCKJI/D/0qwo9pgmVKAAA\nAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10f286bd0>"
       ]
      }
     ],
     "prompt_number": 120
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Exercise\n",
      "\n",
      "Based on those preliminary analysis, \n",
      "can you think of some simple rules to predict the survivals?"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Simple Predictor Based-on Only Sex\n",
      "\n",
      "It's apparent that females has much better survival rate than males.\n",
      "Here's a simple rule:\n",
      "\n",
      "   * Predict 1 for a test point if `Sex==female`\n",
      "   * Predict 0 for a test point if `Sex==male`"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "test_data = pd.read_csv('test.csv')\n",
      "test_data[:5]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\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",
        "<p>5 rows \u00d7 11 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 126,
       "text": [
        "   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  \n",
        "\n",
        "[5 rows x 11 columns]"
       ]
      }
     ],
     "prompt_number": 126
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "test_data['Survived'] = test_data['Sex'].apply(lambda x: {'male': 0, 'female': 1}[x])\n",
      "test_data[:5]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\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>Survived</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",
        "<p>5 rows \u00d7 12 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 127,
       "text": [
        "   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  Survived  \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  \n",
        "\n",
        "[5 rows x 12 columns]"
       ]
      }
     ],
     "prompt_number": 127
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Now it's time to read the submission format.\n",
      "test_data[['PassengerId', 'Survived']].to_csv('submission.csv', index=False)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 129
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Submit this `submission.csv` to Kaggle. \n",
      "Congratulations.\n",
      "You have at completed the whole procedure of a Kaggle competition.\n",
      "\n",
      "This simple predictor can get a score of 0.76555\n",
      "\n",
      "As explained by the [evaluation](http://www.kaggle.com/c/titanic-gettingStarted/details/evaluation) page:\n",
      "\n",
      "> Your score is the percentage of passengers you correctly predict."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Exercise\n",
      "\n",
      "Show that the above simple predictor is optimal if `Sex` is the only information you know.\n",
      "Assume infinite data points are given.\n",
      "Or in other words, you can estimate precisely the survival probability of a given category.\n",
      "\n",
      "You can formulate like this:\n",
      "\n",
      "   * Suppose the survival probability of a category is $p$.\n",
      "   * Your strategy is to predict 1 with probability $q$ and 0 with probability $1-q$.\n",
      "   * Your score, the percentage of correct prediction, is given by:\n",
      "   $score=pq + (1-p)(1-q)$\n",
      "   * How should you set $q$ according to $p$?"
     ]
    }
   ],
   "metadata": {}
  }
 ]
}