Titanic-Revisited.ipynb

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   "source": [
    "<img src=\"images/logo.png\">\n",
    "\n",
    "# Revisiting the 'Titanic Survivors' - ODSC 2017, Boston, MA\n",
    "\n",
    "At the Open Data Science Conference in Boston held on May 3rd 2017, we presented an introductory workshop on Data Science with Python. This involved teaching Python, Libraries needed for Data Science followed by Logistic Regression with Titanic. This was done with the help of our online self-learning platform : http://refactored.ai\n",
    "\n",
    "You can still sign up, revisit the workshop and provide feedback on our platform.\n",
    "\n",
    "Here we are presenting the example problem with more detail as we could only cover as much in the 4 hours duration. \n",
    "\n",
    "## Titanic Survivors Problem\n",
    "\n",
    "Kaggle posted a famous dataset from Titanic. It was about the survivors amongst the people aboard. Here is the description from Kaggle:\n",
    "\n",
    "### Competition Description\n",
    "\n",
    "The sinking of the RMS Titanic is one of the most infamous shipwrecks in history.  On April 15, 1912, during her maiden voyage, the Titanic sank after colliding with an iceberg, killing 1502 out of 2224 passengers and crew. This sensational tragedy shocked the international community and led to better safety regulations for ships.\n",
    "\n",
    "One of the reasons that the shipwreck led to such loss of life was that there were not enough lifeboats for the passengers and crew. Although there was some element of luck involved in surviving the sinking, some groups of people were more likely to survive than others, such as women, children, and the upper-class.\n",
    "\n",
    "In this challenge, we ask you to complete the analysis of what sorts of people were likely to survive. In particular, we ask you to apply the tools of machine learning to predict which passengers survived the tragedy.\n",
    "\n",
    "source: https://www.kaggle.com/c/titanic\n",
    "\n",
    "#### Loading the Data\n",
    "\n",
    "* The data has been split into train_data, test_data and can be loaded from kaggle with read_csv command:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
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   "source": [
    "# Import libraries\n",
    "%matplotlib inline\n",
    "\n",
    "from sklearn.metrics import roc_curve, roc_auc_score\n",
    "from sklearn.cross_validation import train_test_split\n",
    "\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import statsmodels.formula.api as sm\n",
    "sns.set()\n",
    "sns.set_style(\"whitegrid\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_data = pd.read_csv(\"https://raw.githubusercontent.com/agconti/kaggle-titanic/master/data/train.csv\")\n",
    "test_data = pd.read_csv(\"https://raw.githubusercontent.com/agconti/kaggle-titanic/master/data/test.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Titanic Survivors - Data Selection & Preparation\n",
    "Prior to fitting a logistic regression model for classifying who would likely survive, we have to examine the dataset with information from EDA as well as using other statistical methods. The logistic regression algorithm is also a supervised learning technique. \n",
    "\n",
    "The dataset from training and testing has data that cannot be directly used due to many issues including but not limited to:\n",
    "\n",
    "Sparse column entries in certain columns such as Cabin.\n",
    "NaN entries in columns.\n",
    "Categorical variables with string entries.\n",
    "Selection of right columns.\n",
    "Sparsity\n",
    "Let us examine sparse columns by counting the ratio of NaNs to all the values. describe() function on dataframe provides information about mean, median and the number of values ignoring NaNs only for float/integer columns. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Number of Survivors\n",
    "\n",
    "Let us look at how many people survived the titanic."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/harishkashyap/anaconda/lib/python3.5/site-packages/statsmodels/nonparametric/kdetools.py:20: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
      "  y = X[:m/2+1] + np.r_[0,X[m/2+1:],0]*1j\n"
     ]
    },
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     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1199685f8>"
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     "execution_count": 118,
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AAGAJBFMAAABYAsEUAAAAlkAwBQAAgCUQTAEAAGAJBFMAAABYAsEUAAAAlkAwBQAAgCUQ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\nqacYM2YMCxcuZNWqVcyYMYMffvgBrbb+a4Qu+fo4py+VUl5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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11996c3c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(train_data['Survived'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the above distribution, it can be observed that those who survived are approximately 2/3rd in the training set.\n",
    "\n",
    "## Features Available\n",
    "### Age\n",
    "We can see that Age column has 714 entries with missing (891 - 714 = 177) values. This would be 177/891 = 0.19 or approximately 20% of missing values. If this percentage was small, we could choose to ignore those rows for fitting a logistic regression model. There are various methods to fill the missing values. But before we discuss the ways to fix the issue of Age column sparsity, let us examine other columns as well.\n",
    "\n",
    "### PassengerId, Name, Ticket\n",
    "We can see that PassengerId, Name and Ticket are all unique to each person and hence will not serve as columns for modeling. Logistic Regression or any supervised or unsupervised learning methods need to understand patterns in the dataset. This is a necessary condition, so that algorithms can make sense of the data available by mathematically recording these patterns. Hence, Ids, Names are usually candidates that aren't useful for modeling. They are needed for identifying the person post recommendation, prediction or classification. They are also going to be useful later for other columns, thereby improving the overall dataset. \n",
    "\n",
    "### Cabin\n",
    "The Cabin column is really sparse with just (148/891 = 0.16) 16% data available. You can use len(train_data.Cabin.unique()) to determine the total length. When data is very sparse, we can ignore it for modeling in the first iteration. Later for improving the fit, this column can be investigated deeper to extract more information.\n",
    "\n",
    "### Embarked\n",
    "This data shows the point where passengers Embarked. It has very less sparsity with train_data[train_data.Embarked.notnull()] = 889 which is nearly all the data (891). Hence, it can be useful for modeling.\n",
    "\n",
    "### Person\n",
    "This is a column we created ourselves by splitting up age into different bands of Child, Adult, Senior and Unknown. We have to determine how any Unknown people are there so that we can build better models. Since, this variable depends directly on age, if we can fix the sparsity of age, this will be fixed as well."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sparsity in the dataset.\n",
    "\n",
    "Let us analyze the sparsity in the columns of the dataset. This will give us an insight into what feature engineering needs to be done to resolve the sparsity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PassengerId 1.0\n",
      "Survived 1.0\n",
      "Pclass 1.0\n",
      "Name 1.0\n",
      "Sex 1.0\n",
      "Age 0.8013468013468014\n",
      "SibSp 1.0\n",
      "Parch 1.0\n",
      "Ticket 1.0\n",
      "Fare 1.0\n",
      "Cabin 0.22895622895622897\n",
      "Embarked 0.9977553310886644\n"
     ]
    }
   ],
   "source": [
    "raw_features = train_data.columns\n",
    "\n",
    "for feature in raw_features:\n",
    "    content_prop = len(train_data[train_data[feature].notnull() == True].index)/len(train_data.index)\n",
    "    print(feature, content_prop)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Understanding Age of People Aboard\n",
    "\n",
    "About 20% of age is missing which means we need to find ways to impute it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/harishkashyap/anaconda/lib/python3.5/site-packages/statsmodels/nonparametric/kdetools.py:20: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
      "  y = X[:m/2+1] + np.r_[0,X[m/2+1:],0]*1j\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x119b51be0>"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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45JFH8Hq93HrrrQCsX7+e6upqHnzwQfr6+nC5XPHH19bWUlNTw49+9COOHDlC\nc3Mz69ev58tf/jIFBQVJ/l8gMn30nHWDmVZQuLAFs6OrKYyGwnzUMWhwNSIiMlEJb/Swdu1aFi1a\nxKpVq/jpT3/Kfffdx4oVKwBYtmwZr776KgD5+fk8//zzNDc3c+edd7J//342bdqEzWbD5XKxb98+\njh49yk033cTy5cvj/1599VVMJhPPPfcc+fn5fPWrX+W73/0u119/PQ8++GByP3uRaSa2gkJ2lpmC\n3OTNfGaqGY5c8nOjvbdHT2o1BRGRdJFQiwJEZ3Efe+wxHnvssXEfO3To0Jj3r7rqKrZt2zZuXFlZ\nGQcPHvzE56msrOTv//7vEy1PRD5BLOCWFdnja0/L+ZlMJubPLGbvkR6Odw4RCIbIzkqsTUFERKZe\nwjO4IpKeIpFIvEVB7QkTN29WdAfFUDhCu1NtCiIi6UABV2SaGPEG8QVCgDZ4SERl6Zk2hbaTAwZX\nIyIiE6GAKzJNuPp98bcVcCfOZDIxb2Z0CbZjnYMERkMGVyQiIheigCsyTcTaE8xmEyWFtguMlrPN\nP6tN4ZjaFEREUp4Crsg0EbvBzFFou6htZ6ezytJc8u1qUxARSRcKuCLThGvg9AoKak9IWLRNITqL\ne6xzkKDaFEREUpoCrsg04A+EGBwJAFpB4WLNmxXtwx0NRWh3DhlcjYiIfBIFXJFpwHXWDmaawb04\nMxy55MXbFLTpg4hIKlPAFZkGYu0JAI5i3WB2MdSmICKSPhRwRaaB2AoKxfk5ZFu1E9fFim36MBqK\ncKxTbQoiIqlKAVdkGohv0avZ20tS5cgjzxbd4VxtCiIiqUsBVyTDhcIR3IN+ABxF6r+9FCaTidrT\nN5u1O4cIjoYNrkhERM5FAVckww0M+wlHIgA4ijSDe6nmz4y1KYQ53qlNH0REUpECrkiG6x04s0Vv\nqXYwu2RVZXnknm5TOKpNH0REUpICrkiG6xuMBlyrxUxhXrbB1aS/s1dTaHcOMhpSm4KISKpRwBXJ\ncLGAW1qYg8mkLXqT4cymD2GOqU1BRCTlKOCKZLi+gVjAVXtCslSV5WHPia2moDYFEZFUo4ArksFG\nQ2EGhqMrKJTqBrOkMZtM8TVx1aYgIpJ6FHBFMph70Efk9NsOzeAm1fyZ0TaF4GiY49r0QUQkpSjg\nimSwWP8tQKnWwE2qqvIzbQpHtemDiEhKUcAVyWCxgJudZY7vwCXJYdZqCiIiKUsBVySDxdbAdRTa\ntILCJIitNtnXAAAgAElEQVStphAcDXO8S20KIiKpQgFXJIPFlwhTe8KkqB6zmoLaFEREUoUCrkiG\nCgRDDHmCgJYImyxms4naWJtCxyAhtSmIiKQEBVyRDHX2DWZaQWHyxPpwA6NhTnQPG1yNiIiAAq5I\nxhq7goIC7mSZWZ6PLdsCqE1BRCRVKOCKZKjYDmb2HGu8T1SSz2w2cVl1dBb3o45BQuHIBR4hIiKT\nTQFXJEP1DmqL3qkS29XMHwxxSm0KIiKGU8AVyVCxFgWH2hMm3ayKfLKzot9O206pTUFExGgKuCIZ\nyOcfxeMbBTSDOxUsZnO8TeHDUwOE1aYgImIoBVyRDDTmBjMF3CkRW03BFwjR4VKbgoiIkRRwRTJQ\nr1ZQmHKzKwvIssbaFAYMrkZEZHpTwBXJQLEVFPLtWeRkWQyuZnqwWszUVBUCp9sUImpTEBExigKu\nSAaKr6Cg2dspFVtNweMbpdM1YnA1IiLTlwKuSIaJRCJnVlBQ/+2UmlNZiNWiNgUREaMp4IpkGI9/\nFH8gBECJAu6UyrKamVtVAETbFCJqUxARMYQCrkiGcQ/6429rBYWpF1tNYdgbxDUQMLgaEZHpSQFX\nJMO4h86soFBSkGNgJdPT3BmFWMwmAI51ewyuRkRkelLAFckw7tP9t3n2LLK1gsKUy86yMGdGtE3h\nWJdHbQoiIgZQwBXJMH2nWxQ0e2uceTOLARjxhTjWqdUURESmmgKuSIaJtSio/9Y4NdWFmE3RNoX3\nDrkMrkZEZPpRwBXJIP5gCI9vFNAMrpFysizMrswHoPmQS20KIiJTTAFXJIO4z9qiV0uEGWverGib\nQpfbR7tz0OBqRESmFwVckQziHjqzRJhmcI11WXUhp7sUeLulw9hiRESmGQVckQwSm8HNybZgz7Ea\nXM30Zsu2MqM0Oov+jgKuiMiUUsAVySCxLXpLC3IwxaYPxTBzK3IBONE1zPFOtSmIiEwVBVyRDBJr\nUVD/bWqYU2GPtym8s99pbDEiItOIAq5IhhgNhRkciW4NW1KggJsKbNkW6uZEt+59e5/aFEREpooC\nrkiG6D/7BrNC3WCWKq6pKwOg3TnIqZ5hg6sREZkeEg64gUCAhx56iKamJpYvX87mzZvPO7a1tZW7\n7rqLhoYGVq5cyYEDB8Z8/Fe/+hWf//znaWxs5Gtf+xptbW1jPv7EE0+wdOlSrrvuOn7+858nWqrI\ntBLb4AE0g5tKGq9wnGlT0M1mIiJTIuGA+/jjj9Pa2sqWLVvYsGEDTz/9NK+//vq4cV6vlzVr1tDU\n1MS2bdtoaGjgnnvuweeL/hB+6aWX+Md//EfWr1/Ptm3bmDlzJn/7t3+L3x+dhfr1r3/NK6+8wrPP\nPstTTz3F9u3bPzFMi0x3sS16rRYzBblZBlcjMcX52dTXlAIKuCIiUyWhgOv1enn55ZdZt24ddXV1\nrFixgtWrV/Piiy+OG7tjxw7sdjv3338/tbW1PPzww+Tl5fHaa68B8Lvf/Y5vfOMb3HjjjcydO5cf\n//jHuN1udu/eDcCWLVu49957ufrqq7n22mv5wQ9+cM7nEZGo2AxuiVZQSDmf+VQ1AEdPDtDZO2Jw\nNSIimS+hgHvo0CFCoRANDQ3xY42NjbS0tIwb29LSQmNj45hjS5YsYc+ePQA88MADfOELX4h/LPYD\neWhoiO7ubpxOJ9dcc82Y5+no6MDl0r7uIufiHoytoKD+21Sz9Krq+NvvtGg1BRGRyZZQwO3p6aG4\nuBir9cwC8g6HA7/fj9vtHjO2u7ubioqKMcccDgddXV1ANOxWVlbGP7Z161ZCoRCNjY309PRgMpnG\nPL6srIxIJEJnZ2ciJYtMC+FwJH6TmfpvU095iZ0r5pYAalMQEZkKCW115PV6yc7OHnMs9n4gEBhz\n3OfznXPsx8cB7Nu3j5/97GesXr0ah8PBRx99NObcn/Q8F+L3+/F4PAk9RiaX1+sd81+5NB6Ph74h\nH+FIBIACuyXh10lMMBgEYDQ4itkSvOjzfFwgGMQcTvz1m+7nDASDeDwe7HY7TXVlfHDMzQfH3Rx3\n9lFWlPgvInrtpC5dm9Sla5PaYvdeJVtCATcnJ2fcN/7Y+3a7fUJjbbax39T37NnDmjVruPHGG7n3\n3nvjj42N/3iw/fjzXIjT6cTp1J8EU1F7e7vRJWSEgYEBjjvPLD8V9A/i7Ly0Pk/3gBvz8DBhktPL\n2+PqwWzJStr50uWcI8ODHDkyTHd3N46c0fjx7X9o4dN1BRd9Xr12UpeuTerStZleEgq4lZWV9Pf3\nEw6HMZuj3Q0ulwubzUZhYeG4sT09PWOOuVwuysvL4+/v2rWLb37zmyxfvpwnn3xyzGNj46uro71r\nsbaFsx8/EVVVVRQXFyf0GJlcXq+X9vZ2ampqEv6FRcbr7e3l31qPAEOYTDB/7kzM5osLaMFgEFev\ni5KiErJtdipnVCWnyFAAsyUreedLk3MODdq5/PIqHA4HANubR/iwY4j2XhNfq69P+Hx67aQuXZvU\npWuT2vr7+ydlIjKhgFtfX4/VamXv3r0sWbIEgObmZhYtWjRu7OLFi9m0adOYY7t37+Zb3/oWAIcP\nH+bb3/42N910E08++WQ8MANUVFRQVVXF+++/Hw+4zc3NVFVVUVZWltAnmJOTQ25ubkKPkalht9t1\nbZLA6/Uy7AsDUJyfg8126TeZWbOsZGVljWszuljZWVmYLMk7X7qcMzsri9zc3PjX+bKGWXzYcZAP\njvfjGzVTepFbKuu1k7p0bVKXrk1qmqzWkYRuMrPZbNx+++1s2LCB/fv3s3PnTjZv3syqVauA6Ixr\nrJfi5ptvZmhoiEcffZS2tjYeeeQRvF4vt956KwDr16+nurqaBx98kL6+Plwu15jHf+UrX+GJJ57g\n3XffZdeuXfziF7+IP4+IjDUwEu2dLbnIwCRT4/rTy4VFIvDn/WqdEhGZLAnN4AKsXbuWn/zkJ6xa\ntYqCggLuu+8+VqxYAcCyZcvYuHEjd9xxB/n5+Tz//PNs2LCBrVu3csUVV7Bp0yZsNhsul4t9+/YB\ncNNNN405/2OPPcYdd9zB6tWrcbvdfO9738NisbBy5UoFXJFziEQiZwJugZYIS2Uzy/OpqSqk3TnI\nOy0d/PVnLjO6JBGRjJRwwLXZbDz22GM89thj4z526NChMe9fddVVbNu2bdy4srIyDh48+InPYzab\neeCBB3jggQcSLVFkWukbCjAaiq6goBnc1PeZxdW0Owf5zzYXA8N+ivL1S4mISLIlvFWviKQWp+vM\nMnilmsFNeddfFb2BLRzRmrgiIpNFAVckzTl7zzToFyvgprw5MwqZMyO6RNhbexVwRUQmgwKuSJrr\nOD2DW5CbRZbVYnA1MhE3NMwE4D8/dNE36DO4GhGRzKOAK5LmOnqjAVdb9KaP5acDbiQCf9p3yuBq\nREQyjwKuSJpzuqItClpBIX1Ul+czf1YRAG/tUcAVEUk2BVyRNDbkCTDo0Rq46Sg2i3vomJvuPs8F\nRouISCIUcEXS2Mmu4fjbmsFNL8sWz4y/rTYFEZHkUsAVSWMnuofib2sGN71UlOZSX1MKwH/sVcAV\nEUkmBVyRNHaiKxpwc7LM2HMS3rdFDBZrU2g7OUBHz/AFRouIyEQp4IqksZPd0VBUlJdlcCVyMT6z\nuBqTKfr2W5rFFRFJGgVckTR28nSLQlGeZm/TUWmhjavmlQFqUxARSSYFXJE05Q+G6Dp9971mcNNX\nrE3heOcQx5yDBlcjIpIZFHBF0lRHzzCRSPRtBdz0tfSqKizmaJ/CH/ecNLgaEZHMoIArkqZiN5iB\nAm46K8rP4eorKgD4455ThMMRgysSEUl/CrgiaSp2g1l2lpk8m8XgauRS3LRkFgDdfR4OtvcZXI2I\nSPpTwBVJU7EZ3KpSO6bYrfiSlq5bNAN7TvSXlH9//4TB1YiIpD8FXJE0FZvBrSrLNbgSuVS2bCtL\nr6oG4E/7OgiOhgyuSEQkvSngiqShUDjCqdMbA1Q57AZXI8kQa1MY8QZpPthlcDUiIulNAVckDXX3\neQiOhgGodmgGNxN86vJySgpyAPj397WagojIpVDAFUlDJ7rPrKBQXaYZ3ExgMZu48fQs7nutXQx7\nAgZXJCKSvhRwRdLQydM3mJnNJipLFXAzRaxNYTQU5u2WDoOrERFJXwq4ImnoRFes/zYXq0Uv40xR\nO7OI2ZUFgNoUREQuhX4yiqShWIvCrIoCgyuRZDKZTHy2MTqLe+DDXrpPb8UsIiKJUcAVSTORSCS+\nRNisinyDq5Fku/HqWfG339ytWVwRkYuhgCuSZvqH/Ix4gwDxP2dL5qgozeXKWgcAf2g+TiSirXtF\nRBKlgCuSZs5eQUEBNzOtaJoDwKmeEW3dKyJyERRwRdJM7AYzgJnlalHIRJ9ZXB3funfnu8cNrkZE\nJP0o4IqkmZOnZ3BLC23k2bMMrkYmgz3HyrLFMwH4075TeP2jBlckIpJeFHBF0szJ0zO4sys1e5vJ\nVlwbbVPw+kO8vU9r4oqIJEIBVyTNxHpwZ2uJsIxWX1PKzPI8AHa+pzYFEZFEKOCKpBGPL0jvgA+A\nWbrBLKOZTCZWXDsXiK6Je6pn+AKPEBGRGAVckTQSW/8WtAbudPC5a2ZjNpsA+DfN4oqITJgCrkga\nOaklwqaV0kIbjXUVAPzbeycIh7UmrojIRCjgiqSR2BJheTYrJQU5BlcjUyG2Jm7foI99R3sNrkZE\nJD0o4IqkkRNd0RncWZUFmEwmg6uRqdC0cAaFedkA/Pv7pwyuRkQkPSjgiqSRWIuC+m+njyyrmc82\nzgag+VAPQ96QwRWJiKQ+BVyRNBEcDePs9QBaImy6uWVpdDWFUDjC7qMjBlcjIpL6FHBF0oTTNRy/\nyUg3mE0vsyoKWHx5GQDvt40QCoUNrkhEJLUp4IqkiRNnLxGmXcymnVuvvwyAQU+I3YddBlcjIpLa\nFHBF0sTJ0zeYWS1mKkvzDK5Gptp1V86Ir5zx+rsnDa5GRCS1KeCKpInYJg8zy/OwmLWCwnRjtZj5\n/DUzAWg52kuHSzubiYicjwKuSJo40X1miTCZnj5/zUxiq8O99udjxhYjIpLCFHBF0kA4HInP4GoF\nhemrtNBG3Sw7ADvfPYY/qCXDRETOxWp0ASJyYa5+L/5ANMzM1g1maSMcDtPX15e083k8Huqr4eAJ\nGPIEeXvfKT53zZyknV9EJFMo4IqkgZNnraCgJcLSh2dkiJ27XJSVJadfNhAM0tHZR0VJMd1uP6+8\n3a6AKyJyDgq4Imkg1n9rMkF1uWZw04k9N5+CopKknCsQCGDPy+OGueW8/B8n+eC4m0PH+qibW5qU\n84uIZAr14IqkgROnlwirKMklJ8ticDVitKVXOrDnROcnfvdmm8HViIikHgVckTQQv8FM7QkC2HMs\n3Pzp6Pa9f97fQWevtu8VETmbAq5IGojN4M6qUHuCRN22vBaz2UQ4Av/y1odGlyMiklISDriBQICH\nHnqIpqYmli9fzubNm887trW1lbvuuouGhgZWrlzJgQMHzjnuueeeY+3atWOOHTx4kLq6Ourr66mr\nq6Ouro4vfelLiZYrkvYGhv0MjgQAmKUlwuS0ipJcln2qGoA3dh1j2BMwuCIRkdSRcMB9/PHHaW1t\nZcuWLWzYsIGnn36a119/fdw4r9fLmjVraGpqYtu2bTQ0NHDPPffg8/nGjPv973/P008/Pe7xR48e\nZeHChbz99tvxfy+88EKi5YqkvbErKGgGV86446Z5APgCIV77izZ+EBGJSSjger1eXn75ZdatW0dd\nXR0rVqxg9erVvPjii+PG7tixA7vdzv33309tbS0PP/wweXl5vPbaawCEQiE2bNjAunXrmDNn/DI3\nbW1t1NbWUlpaisPhwOFwUFRUdJGfpkj6Onl6BQVQD66MdfnsEq6sdQCw/a0PCY6GDa5IRCQ1JBRw\nDx06RCgUoqGhIX6ssbGRlpaWcWNbWlpobGwcc2zJkiXs2bMHiC5YfuTIEbZu3TrmfDFtbW3U1NQk\nUp5IRjp+uv+2OD+Hgtxsg6uRVHPHjdFZ3L5BH2/tPWVwNSIiqSGhgNvT00NxcTFW65nlcx0OB36/\nH7fbPWZsd3c3FRUVY445HA66uroAKCgo4De/+Q0LFiw453O1tbVx8OBBbrvtNj772c+yfv16hoeT\ns1i6SDo57owG3DkzNHsr4127cAbVZXkA/O6PR4lEIgZXJCJivIQ2evB6vWRnj51Bir0fCIy9wcHn\n851z7MfHncvo6CjHjx9nzpw5bNy4kcHBQR599FEeeOABnnnmmURKxu/34/F4EnqMTC6v1zvmv/LJ\n2p0DAMwss5/za9nj8RAIBif02rqQYDAIwGhwFLMlOeeE6A5c5vD47xOZfs5kny92fXw+35ivhVuX\nzuaF7Yf4qGOQd/ad4OoFZUl5Ppk4fV9LXbo2qc3v90/KeRMKuDk5OeO+Ucfet9vtExprs9kuXJTV\nyq5du7DZbFgs0UXtN27cyJ133klPTw/l5eUTrtnpdOJ0Oic8XqZOe3u70SWkvBFfiP7h6OvIGh7m\n4MGD48YMDAzQ1T3CsCd537zdA27Mw8OEMSXlfD2uHsyWrKSdL13OORk1Apw4cYLBwcH4+zPsEfJt\nZoZ9Yba8coCc0XJMpuQ+p0yMvq+lLl2b6SWhgFtZWUl/fz/hcBizOdrd4HK5sNlsFBYWjhvb09Mz\n5pjL5ZpwOM3Lyxvz/rx50T6zrq6uhAJuVVUVxcXFEx4vk8/r9dLe3k5NTc24X4xkrAMf9QHRX9Cu\nbZjPgtnjv5Z7e3txjjgpKLz0r/NgMIir10VJUQnZNjuVM6ou+ZwAhAKYLVnJO1+6nDPJ5wsGg4wM\nDzJ79mxmzpw55mP/fSiPf3r1MKd6AwSzK1k835GU55SJ0fe11KVrk9r6+/snZSIyoYBbX1+P1Wpl\n7969LFmyBIDm5mYWLVo0buzixYvZtGnTmGO7d+/mW9/61gWfp62tjZUrV7J9+/b4N/HW1lasVitz\n585NpGRycnLIzc1N6DEyNex2u67NBXS5O+NvL5hbTq4ta9wYr9dLdlbWuJagS2HNspKVxHNmZ2Vh\nsiS3xnQ452TUCGCz2ca9dm674XL+5a1j9A/7+ec/tvPpq2ZpFtcA+r6WunRtUtNktY4kdJOZzWbj\n9ttvZ8OGDezfv5+dO3eyefNmVq1aBURnaGO9FDfffDNDQ0M8+uijtLW18cgjj+D1ern11lsv+Dy1\ntbXU1NTwox/9iCNHjtDc3Mz69ev58pe/TEGBbrSR6eNYZ/QGs4oS+znDrUiMLdvKF2+aD8DB9j5a\njrgMrkhExDgJb/Swdu1aFi1axKpVq/jpT3/Kfffdx4oVKwBYtmwZr776KgD5+fk8//zzNDc3c+ed\nd7J//342bdo0oR5ck8nEc889R35+Pl/96lf57ne/y/XXX8+DDz6YaLkiae2YM9pnObeq8AIjReC/\nXl9DYV50tvilNz4wuBoREeMk1KIA0Vncxx57jMcee2zcxw4dOjTm/auuuopt27Zd8JznOldlZSV/\n//d/n2h5IhkjEolwvPN0wJ2hgCsXZsuJzuL+vztaOfBhL/uPurhqvlZUEJHpJ+EZXBGZGq5+HyO+\nUQDmag1cmaD/en0NBbnRdpb/qVlcEZmmFHBFUtSxzjPLQKlFQSYq15bFHTdGe3FbjrpoOdpzgUeI\niGQeBVyRFBVrTzCbTcyqyDe4GkknX1h2WXxb582/byUc1u5mIjK9KOCKpKjYCgozy/PIsloMrkbS\nSa4ti6/8VXQb9KMn+vnTvlMGVyQiMrUSvslMJFnC4TCHDrcRiSRvdslut1EzZ1bSzmek9tMrKMzR\nDWZyEW5dehm/f+sjnL0j/NMrB1l6VZV+URKRaUMBVwzj8/nY80En+YXJu8t71NOeEQE3FI5wois6\ng6sVFORiZFnN/J9/Xc/j/9RMV5+HHW+3c8eN84wuS0RkSijgiqEsFitZ2TlJO1/YlxkzVJ29IwRH\nw4BWUJCL95lPVbNgTjGHj/fzv974gBVNs8nPTe6uaiIiqUg9uCIpKLbBA0CNVlCQi2Qymfj6bdGt\n1Ie9QV7+wxGDKxIRmRoKuCIpKHaDWbbVTKUjz+BqJJ1dWevguitnAPAvb31Id5/H4IpERCafAq5I\nCorN4M6eUYDFbDK4Gkl3q/56IWazieBomBe2/6fR5YiITDoFXJEUdExb9EoSza4s4K8/cxkA77Q4\nef9Ql8EViYhMLgVckRQTCIbocI0AusFMkufum+soKYje0PnLbfsJBEMGVyQiMnkUcEVSzKme4fjO\nU9qiV5Ilz57F1/9b9IYzZ+8I/1s3nIlIBlPAFUkx7WetoKAWBUmmG6+eyafmR9ed/u0fjuA8/ZcC\nEZFMo4ArkmJiN5jl2aw4imwGVyOZxGQy8c3//imslugNZ8//c0tSdxIUEUkVCrgiKebsLXpNJq2g\nIMk1u7KAO26cD8DuQ9280+I0uCIRkeRTwBVJMR+eGgBg3swigyuRTPXlFQsoL7ED8Py2FgaG/QZX\nJCKSXAq4Iimkb9CHeygaNubNUsCVyWHLsfLdLzUA0D/s51f/vN/gikREkksBVySFtJ3sj789b1ax\ngZVIpltSV8FfXTcXgP/Ye4q3WzoMrkhEJHkUcEVSSKw9wWoxM7tSa+DK5PrGf7uSsuJoq8Jz/3uf\nWhVEJGMo4IqkkLbTAbemqgCrRS9PmVy5tizuvSvaqjAwHOC5bS0GVyQikhz6CSqSQmItCmpPkKly\n9RUV3LK0BoC393Xw1t5TxhYkIpIECrgiKWLIE6Db7QWgVisoyBT62hcWUnF6VYVnXt5Ht9tjcEUi\nIpdGAVckRXx4ciD+tpYIk6mUa8vi+//HEkwmGPEG+cVvdhMKhY0uS0TkoingiqSIWP+t2WyiploB\nV6bWVfPKuGvFAgAOfNjL1p2HDa5IROTiWY0uQESi2k5F+29nVeSTk2UxuBpJVZFwGLfbTW5ublLO\nFw5HZ2rNZjN/1VjO+wedHD05xEtvfEBNZTYLZl/cL1slJSWYzZpDERFjKOCKpIi2k9rBTC7M6x3h\nzebjVHWEknK+7s5TmK1ZlJVVAPCpmgKOdQ4THI3w//y2lds+XUVOVmJBdWRkiNtuuhKHw5GUGkVE\nEqWAK5ICvP5ROlzDgFZQkAuz5+VTUFSSlHMNDw1gsmTFz1dQBJ+7Jot//csxPL4Q7x0e4palczGZ\nTEl5PhGRqaC/H4mkgI86BohEom9rBQUx2vxZxSy8rBSADzsGaDnqMrgiEZHEKOCKpIDYDmYAtbrB\nTFLAssUzcRTZAHinpQOna8TgikREJk4BVyQFxPpvq8ryyLNnGVyNCGRZzdzy6RqyrGbCEfjXXcfw\n+keNLktEZEIUcEVSQGwFBbUnSCopLsjhc9fMBqLr476x6xjhWC+NiEgKU8AVMVhwNMTxziFAKyhI\n6pk/q5hPzS8D4ET3MM2tXQZXJCJyYQq4IgY75hwiFI7Ois2bqRUUJPVc/6kqKkuj6+6+d7Ar/guZ\niEiqUsAVMVisPQHUoiCpyWI2c/On52LLjm5A8sa7xxjyBAyuSkTk/BRwRQwW26K3rMhGcUGOwdWI\nnFtBbjb/5do5APgCIV7/yzFCp3dBExFJNQq4IgZrOxm7wUztCZLa5swo5Jr6SgA6+zz8ucVpcEUi\nIuemgCtiIH8wFF8D9/I5CriS+poWVjKrIh+AfUddHD3Zf4FHiIhMPQVcEQMdPdHPaCh6g1l9TanB\n1YhcmNlk4q+umxtfr/kPzSfoH/IbXJWIyFgKuCIGav2oFwCz2cQVc0oMrkZkYuw5Vm6+bi5mEwRH\nw7z2l3aCo+rHFZHUoYArYqBD7W4AaqsLseVYDa5GZOKqyvJYelU1AL0DPv5jz0ki2gRCRFKEAq6I\nQSKRCAfb+wCov8xhcDUiiVt8eVl8abtDx9zxr2cREaNpykhkioXDYdxuNx0uT3wt0VmOLHp7ey/q\nfH19fZo5E0OYTCY+d81sXP1eBkcC/MeeU5SX5GIzGV2ZiEx3CrgiU8ztdrP9zQN09J9JAd29Q/zh\nPe9Fna+78xR5hSUUahEGMUBOloVbltbwv/9whFA4wmt/bue/NlUYXZaITHMKuCIGyMsrwN0R3e40\nPzeLGZXlF32u4aGBZJUlclHKi+3ccPVM/v39kwyOBHintZebl841uiwRmcbUgytikM5eDwBVjjyD\nKxG5dAsvc1BXE10J5Hi3l399t8PgikRkOlPAFTGALxCKrx06QwFXMsQNDbNwFNkA2PqHjzjw4cX1\nlYuIXCoFXBED9PSfWRi/ypFrYCUiyZNlNXPLp2vIspgIR+BnW5q1CYSIGCLhgBsIBHjooYdoampi\n+fLlbN68+bxjW1tbueuuu2hoaGDlypUcOHDgnOOee+451q5dO+74E088wdKlS7nuuuv4+c9/nmip\nIimreyD6Qz/LasZRZDe4GpHkKS7I4foro8ve9Q36eOJ/NBMKa5UPEZlaCQfcxx9/nNbWVrZs2cKG\nDRt4+umnef3118eN83q9rFmzhqamJrZt20ZDQwP33HMPPp9vzLjf//73PP300+Me/+tf/5pXXnmF\nZ599lqeeeort27d/YpgWSSfd/dHlwSpLczGbtaaSZJa5lbn8VVN0E4h9R1xsfeMDgysSkekmoYDr\n9Xp5+eWXWbduHXV1daxYsYLVq1fz4osvjhu7Y8cO7HY7999/P7W1tTz88MPk5eXx2muvARAKhdiw\nYQPr1q1jzpw54x6/ZcsW7r33Xq6++mquvfZafvCDH5zzeUTSTXA0TO+g+m8ls931uRqumBu96ex/\n7jzMoWPaBEJEpk5CAffQoUOEQiEaGhrixxobG2lpaRk3tqWlhcbGxjHHlixZwp49ewDweDwcOXKE\nrVu3jjkfQHd3N06nk2uuuWbM83R0dOByuRIpWSTlHOscJhyOvq0VFCQThcNhBgf6Wf3X87BlWwiH\nIwTGJn0AACAASURBVPzsn97jZEcXvb29F/UvHHvRiIhMQELr4Pb09FBcXIzVeuZhDocDv9+P2+2m\npKQkfry7u5sFCxaMebzD4eDo0aMAFBQU8Jvf/Oa8z2MymaioOLNYeFlZGZFIhM7OTsrKyhIpWySl\nHDk5GH97hm4wkwzkGRli5y4XZWUVNF5exNsH+ujp9/HES/v5zJWJb0s9MjLEbTddicOhLa1FZGIS\nCrher5fs7Owxx2LvBwKBMcd9Pt85x3583Pme5+xzf9LziKSbWMB1FNnIzrIYXI3I5LDn5lNQVMLi\nwmK6BkIcPTlAW8cIl88pY94sbbsnIpMroYCbk5MzLmDG3rfb7RMaa7PZJvQ8sfEfD7Yff54L8fv9\neDyehB4jkyv2C4zX6yUYCCb1lxZ/IJDS1zsSiXD4RDTgVpTYkvK5B4JBzOHk/PIXDAYBGA2OYrYk\n79oks8Z0Omeyzxe7PsFgel2b6xdV4nSNMOIb5d/fP0FpQRZ59qyEzufxeBL+/j+Vzv6+JqlF1ya1\n+f2Ts5RgQgG3srKS/v5+wuEwZnO0fdflcmGz2SgsLBw3tqenZ8wxl8tFefmFtyStrKyMj6+ujt6J\nG2tbmMjjz+Z0OnE6nQk9RqbG8ePH6ezqYtgfSto5fYNODh48mLTzJZuzL8CwdxQAuyWAs/PSvzZ7\nXD2YLVmESd5qDO4BN+bh4aSdczJqTIdzTkaNAP0D/Viysi88cAKm6v/jVTU5/OXQKP5gmDd2tXPt\nFXmYTBN7zpHhQY4cGaa7uztpNU6W9vZ2o0uQ89C1mV4SCrj19fVYrVb27t3LkiVLAGhubmbRokXj\nxi5evJhNmzaNObZ7926+9a1vXfB5KioqqKqq4v33348H3ObmZqqqqhLuv62qqqK4WH8OSyVer5f2\n9nbmzJlDe0+EgpKKCz9ogny50a/TVHXwzQ+BbkzAostnkZOdhBaFUACzJYvKGVWXfKpgMIir10VJ\nUQnZNntSzsn/396dh0dVnn0c/85kkkz2fSOEHQlrAmGRXRAXrAotWmtr61p3a+2rBcWKQhV8sVat\ndS3S1hXFXVQsrwuKiIYtEQyQIAFC9n2ZzCSZ8/4xMJoiQmDCyfL7XNdcMOeceeY+eXJm7jznWcCn\nMXaqMn1cXlNTE6XFBURGRJLUQWM8UplJidDQVExWXgWlNc1UO4MY3CfqRwr5Tm1NEAMHJnXoPriH\nPtf69OnToVuauyPVTcdWVVXVLg2RbUpw7XY7s2bNYsGCBdx3330UFxezfPlylixZAnhaXMPCwggM\nDOSss87iwQcf5L777uOiiy7ixRdfxOFwMHPmzGN6r1/84hc88MADJCQkYBgGDz74IFdeeWWbTzAw\nMJDgYA3k6YiCgoLwD/A/rK/2iTACAjp0fWflVQIQGxlIWKhvPmgD/P2x+Pn252jzt+Hv77sy2yPG\nzlBme8QIdNq6GT8imX2l9VTWONmwvYS+yVGEhxz9fQP8/QkODu7Q1/YhQUFBnSLO7kh10zG1V9eR\nNi/0cPvttzNs2DAuvfRSFi1axM0338yMGTMAmDRpEu+99x4AoaGhPPHEE2RmZjJnzhyys7N5+umn\nj6kPLsBVV13FOeecw0033cQtt9zCT3/6Uy699NK2hivSYdQ2uNhxcC7QnrHHdh2IdCU2PyszRvfC\nYvHMB/1h5l4MQ6uciYjvtakFFzytuIsXL2bx4sWH7cvJyWn1fPjw4bz22mtHLfOHyrJarcydO5e5\nc+e2NUSRDmnzjhIOrViaHKvbZNI9xUcHkzEonsycEgpK68nOK2fEAE39KCK+1eYWXBE5PpnfFAMQ\nGRpAVOixjyAX6WpGD0kgJsJzF2N9diFVde0zilpEui8luCIngdttsGmHZwT4iP5Rxzx6XKQr8rNa\nmTGmF1YLNLe4+fCrfeqqICI+pQRX5CTI3V9FdZ1nTtDh/Y9t5LhIVxYbGcTowYkAFJbX83VeuckR\niUhXogRX5CTYeLB7gp/VwtA+mrZOBGBUavx3XRW+LqSmXitViohvKMEVOQkyczwJ7pC+MQTb2zy2\nU6RL8rNamD46xTurwieb9qurgoj4hBJckXZWVetk174qAEYP9t2iFiJdQXxUMCNP8axQube4lh17\nK02OSES6AiW4Iu1s044SDjVKZQxOMDcYkQ5ozJBEIkI9Cz58tuUADY1NJkckIp2dElyRdrbxYPeE\nuKggeiWEmRyNSMdj87MyPSMFAGdTC2u3FJgckYh0dkpwRdpRi9tg88HpwTJSEzQ9mMgR9IgLZVi/\nGADy9lezu6Da5IhEpDNTgivSjrbuLKW2wXO7dXSq+t+K/Jjxw5MIDfIsgvLJ5v00uppNjkhEOisl\nuCLt6IMN+QCEhwQwSgmuyI8K8Pdj6qieADQ0NvN5VqHJEYlIZ6UEV6SdVNc52bDN8wU9LSMFf5uf\nyRGJdHx9ksI5pZdnMZRv9lSwr7jW5IhEpDNSgivSTj7auI/mFs/0CWeM62VyNCKdx6S0HgQFeuaL\n/mjjfppa3CZHJCKdjRJckXZgGIa3e0Jq7yh6J4abHJFI5xEUaGNyejIAtQ0utuRqwJmItI0SXJF2\nkLOnkn3FdQCcMa63ydGIdD4DekbQt4fnD8Nv9taSu7/G5IhEpDNRgivSDv7zpaf1NijQz9sSJSLH\nzmKxMHVkTwL8PV9Tz6zaRVNzi8lRiUhnoQRXxMcaGpv49OBE9ZPTe3r7EopI24QE+TNxRA8ADpQ7\nWLFmp8kRiUhnoQRXxMc+3XKARpenpUmDy0ROzOA+0SRGBwKw8v928e0B9ccVkaNTgiviY/85OLis\nV2IYgw5OdyQix8disTB+cAwBNistboNHVmymRbMqiMhRKMEV8aHc/VXs2FsJwBlje2tpXhEfCAu2\nMec0z2DN3P3VvLk2z+SIRKSjU4Ir4iOGYbDsra8BCAzwY1pGT5MjEuk6zhjdg1N6RQLw/Ps5HCit\nMzkiEenIlOCK+Mi6rAN8nVcOwIXTBxIRGmhyRCJdh9Vq4XcXjcTmZ8HV7OZvr2zB7TbMDktEOigl\nuCI+0Ohq5pm3twEQHx3M7NMGmByRSNfTOzGcn88YBMDXeeWs/mKPuQGJSIelBFfEB17/KJfSSgcA\nV543lEB/P5MjEumaLpg+kN6JYQAsf2e797oTEfk+TdApnVZFTSO79lXR0NhEQ2Mzjc5mHE4n2QUb\nSD8ljrSBcfSMD233gV4llQ2s/CgXgBEDYhk/PKld30+kO/O3WfndRSO57ZG1OJzNPPbqVu66cpwG\ndIpIK0pwpdOpbXDx5fYiduyp5Id64G3YVsSGbUUAxETYmTCiB7On9Cc+Orhd4ln+9jZcTS1YrRau\nnj1cX7Qi7eyUXlGcP6U/b3ySR+Y3xXyyuYDTRmlQp4h8RwmudBqNrmYyvykmO6/cO7jEarUQERpA\nUICNILsNP7eTRrc/e4tqASivbuTtT3fz7rpvmTqqJxdMH0hKQpjPYlqffYDPth4A4JwJfeidFO6z\nskXkyH51diobvi6isLyep17PJn1gHJFhGtgpIh5KcKVTqHc08fonuVTXuQCwAIP7RjNmcAKhwQHe\n45w1hcw6azwVNY1s3VXKhm1FrM86QIvb4MPMfXy0cR/jhyfxy7NS6Z14YsnouqwDLH02E4DwkAB+\ndVbqCZUnIsfOHmDjxp+nMf/xz6ltcPH3lVu447KxuoMiIoASXOkEGhqbeHNtnje57ZccwalDE4kK\ntx/xNdHhdqZlpDAtI4XCsnpe+ziXNV/upbnFzedZhazPLuS0UT355VmpJMaEtDmmjzft568vbsLt\nNgi22/jTFeNaJdoi0v5GDIhj5oQ+vPf5Hr74uogPNuzlrFN7mx2WiHQAmkVBOjSHs5k31+6mstYJ\nwNghCcwc3+dHk9v/lhQbwg0XpLHszjOYPbU/ATYrhgEfbdzPtUv+j0df2UJ+Yc0xl/efDfk8+MJG\n3G6D0CB//nztBFL7RLf53ETkxF1x3lCS40IBePrNbC0AISKAElzpwJyuFt76dDcVNY0AjBoUz+jB\nCcddXnS4nSvPH8ZTd8xg5oQ++FkttLgNVn+Rz40PfMRtj6zl/77aS6Or+bDXuppaWJd1gPv++SWP\nvLwFw4CI0ADuu34iA1OijjsmETkx9gAbt/4qAz+rBaerhb+8sJHmFrfZYYmIydRFQTokt9vgnXW7\nKavyzHGZNjCWU4cl+qR/XUxEENfPSeNnpw3gxQ92sHZzAc0tbnLyK8nJr+SxV7OIiwwiJsJOTIQd\nA/hyWxENjd8lvlFhgfz52gn0OsF+vCJy4gakRPKrs1P597vfsHNvFS/9ZweXnD3Y7LBExERKcKVD\n2ryzhKLyBgCG9Y9h4ogePh88khgTwi0Xj+KK84byYeY+Vn+xh4LSelxNLRSU1lHwA7c6o8PtTBmZ\nzPmT+xMXFeTTeETk+P1s2kA25pSwbXc5r6zZyahB8QzpG2N2WCJiEiW40uFU1DTy5fZiABKjg5mc\nntyuI6MjQgP56WkDmD21P1/vLic7t4zy6kbKqx2UVzficDYzYkAsp2X0ZGi/WPysGqUt0tH4WS38\n4eJR/O4vH1Hf2MzSZzN56A+nERGqqcNEuiMluNKhuA3PdF5ut4Gf1cL00SlYT9K0PxaLheH9Yxne\nP/akvJ+I+FZ8dDA3XJjO/z6bSVl1I395fiMLfjtef5SKdEMaZCYdytadpRRXeLomjD3KVGAiIv9t\ncnoy503uB8DmnaWs+M8OkyMSETMowZUOo7K20bvEbnxUEOkD40yOSEQ6o8vPHcqg3p7ZTV76zw42\n5hSbHJGInGxKcKVDMAyDjzL30eI2sFotTB/dC6tuK4rIcfC3WZn76zGEhwRgGPCX5zdRUtlgdlgi\nchIpwZUOIXd/FYUHZ00YMziBmAh1TRCR4xcXFcStv8rAYoHaBheL//kljc7D57gWka5JCa6Yzu02\n+HKb5xZieEgAIwfFmxyRiHQFIwfF88uzUgHI3V/NgweX1xaRrk8JrpguJ7+CqrqDS/EOTdSIZxHx\nmYtmnMKU9GQA1mcX8u93t5sckYicDEpwxVQtboOvvvG03kaH2xmYEmlyRCLSlVgsFm7+xUhSDw46\ne/WjXFZ/kW9yVCLS3pTgiql2FTRQ19AEwNihCSdtzlsR6T4C/P2Yf/k44qODAXj81a1s3VlqclQi\n0p6U4IppnE0tfL2nHoC4yCD69YgwOSIR6aoiwwK568pxBNtttLgN7vvXl+TuqzI7LBFpJ0pwxTQf\nbizE4XIDMG5oYrsuxysi0jsxnHm/GYPNz0JDYzN3PfU5+YU1ZoclIu1ACa6YorHJzar1BQAkxQTT\nKzHM5IhEpDsYOSieW381GqsFahua+NOTn3OgtM7ssETEx5Tgiikyd9VT3+iZk3LcsCS13orISTMx\nrQc3/2IkAJW1TuY/8TklFVoIQqQrsZkdgHQ/zc1uNuzwtJjER/iTHBdqckQi0pG53W4qKip8WmZa\n3xCu/elwnng9m7IqB3c+8Tl/vnaCdyCaiHRuSnDlpPv86yJqHS0ADOkdYnI0ItLRNdTXsmZDGbGx\nvutKUF9fy3mnDcXZNJTl72yjsLyeuY9+ysJrJpCSoC5TIp1dm7souFwu7rjjDsaMGcPkyZNZvnz5\nEY/dvn07P//5z0lPT+fCCy9k27Ztrfa/8847nHHGGaSnp3PjjTdSWVnp3ffNN9+QmprK4MGDSU1N\nJTU1lQsuuKCt4UoHYxgG76zzzEGZEGWnZ2ygyRGJSGcQFBxKWESUzx4hIZ4k9mfTBnD5uUMBKKtu\nZN7fPyN3v2ZXEOns2pzg3n///Wzfvp1nn32WBQsW8Oijj/LBBx8cdpzD4eDqq69mzJgxvPbaa6Sn\np3PNNdfQ2NgIQFZWFnfeeSc33XQTL7/8MtXV1dx+++3e1+fm5jJkyBDWrVvnfSxbtuwETlU6gqxd\nZeQXeVphzhrbQ31vRcR0P5s2gBsvTMNigZp6F/MfX8e23eVmhyUiJ6BNXRQcDgcrV65k2bJl3lbV\nq666iueee44zzzyz1bGrVq0iKCiI2267DYD58+ezdu1a3n//fWbPns3zzz/PzJkzOf/88wFYunQp\n06ZNo6CggOTkZPLy8ujXrx/R0dE+OlXpCF77JBeAoEArE4bHs27zHnMDOgq3293qzoIvVFRUYBiG\nT8sUkRNz1ql9CA705y8vbDw4hdh6/ueXo5gwoofZoYnIcWhTgpuTk0NLSwvp6enebRkZGTz55JOH\nHZuVlUVGRkarbaNGjWLz5s3Mnj2bLVu2cM0113j3JSYmkpSUxNatW70J7qBBg9p6PtKB5RfVsCmn\nBICxA0MI9PczOaKjq6ys5O2Pt3lvZ/pCSVEBIeFRhGtVYpEOZfLIZILsNhb/80tcTS0s/tdXXPaT\nIfxs2gDdbRLpZNqU4JaWlhIZGYnN9t3LYmJicDqdVFZWEhUV5d1eUlLCKaec0ur1MTEx5ObmesuK\nj49vtT82NpaioiIA8vLycLvdnHfeedTV1TF58mT++Mc/EhqqEfed1Zuf5AHgb7My5pTOU48hIWGE\nRUQd/cBjVFdb7bOyRMS3Rg9O4N7rJ3LvM19SVefkn6u2U1Bax3Vz0vC3aWZNkc6izV0UAgICWm07\n9NzlcrXa3tjY+IPHHjrux/Y3Nzezd+9eevXqxZIlS6ipqeG+++5j7ty5/P3vf29LyDidThoaNL+h\n2apqnXy0cR8A44fFEWr3w+Fw0ORqOux350Q4XS6f1ndDQwOuJt/G6Gpqwuo+/JrpCOU1NTUB0NzU\njNXPd+ft63PuLGX6urxD9dPkw9/J7vhzPFRmQ0MDQUFBh+3rFWdn0dVjuP+5zewvqec/X+7lQGkt\nt/xiBGHBAT9Qmuf78fv/SsehuunYnE5nu5TbpgQ3MDDwsA+YQ8//+0PiSMfa7faj7rfZbGzYsAG7\n3Y6fn+c29pIlS5gzZw6lpaXExcUdc8yFhYUUFhYe8/HSPj7Mqqa5xdPvdHiyZ9vevXspKi6mztni\ns/dxVBXwxRe+K6+mpoaiUit1Db77YCwtK8Xq548b39zy9HV5AJXVlVjr6jp0jJ2hzPaIEaCqugo/\n/x9OtNqqu/4c6+tq2LWrjpKSkiMec8mUCF75rJm8Iifbvq3k1kc+4+eTY+gRfeSf/Z49e3wWo/iW\n6qZ7aVOCm5CQQFVVFW63G6vVc6umrKwMu91OeHj4YceWlpa22lZWVuZNTuPj4ykrKzts/6FuCyEh\nredH7d+/PwDFxcVtSnCTkpKIjFRnRzM1Nbv565ufApA+MIYxaQPZs2cPvXr1Yk+pQVhU/FFKOHZl\nLTXklRgE+6jPbGlZNeHh4SQkJvmkPABaXFj9/H1Xpg/La2pqoqy8jKiIKALsQR0yxk5Vpo/La2pq\norS4gMiISJI6aIztUmY7xFhbE8TAgUnExMT86HEjhrn513s7Wb1hH1X1LSxfU8aV56UybVRyq+Mc\nDgd79uyhT58+P9gqLOZR3XRsVVVV7dIQ2aYEd/DgwdhsNrZs2cKoUaMAyMzMZNiwYYcdm5aWxtNP\nP91q26ZNm7j++usBSE9PZ+PGjcyePRvwtLQWFRWRlpZGXl4eF154IW+//TbJyZ4Pke3bt2Oz2ejd\nu3ebTjAwMJDgYK1MY6YPM/dRXe9prZ81daD3AyYoKAj/AP/DuqqciAB/fwLDon3WZ9bV2IDFz/cx\n+rJMX5cHYPO34e/fsWPsDGW2R4xAt6ub9ooxODj4mL4fbvz5KIb0i+Xvr2zF1ezmide3821hPdf8\ndDj+ttaDZYOCgvSd00Gpbjqm9uo60qYe83a7nVmzZrFgwQKys7NZs2YNy5cv59JLLwU8LbCH+lKc\nddZZ1NbWct9995GXl8ef//xnHA4HZ599NgAXX3wxb775JitXriQnJ4e5c+cybdo0kpOT6devH336\n9OFPf/oTu3btIjMzk7vuuouLLrqIsDCtMNPZvPPZbgB6xIYwapDvWmtFRE6W6aN7sfR3U0g4uJTv\n6i/ymfvoZ5RWql+nSEfU5iGht99+O8OGDePSSy9l0aJF3HzzzcyYMQOASZMm8d577wEQGhrKE088\nQWZmJnPmzCE7O5unn37a2wc3PT2dhQsX8ve//51f/vKXREZGct999wFgsVh4/PHHCQ0N5ZJLLuHG\nG29kwoQJzJs3z1fnLSfJjvwKdu3zrAr0k4l9sVo11Y6IdE79kiN46JapjB6cAMCufVX8/q8fs3Vn\n6VFeKSInW5u6KICnFXfx4sUsXrz4sH05OTmtng8fPpzXXnvtiGXNnj3b20XhvyUkJPDII4+0NTzp\nYN757FsA7AF+nD6ml8nRiIh4uN1uKioqjuu1188ewFuxgbz56V5q6l386anPmTWhB8N7agEXkY6i\nzQmuyLGqrGnks60FAEwfnUJIkL/JEYmIeDTU17JmQxmxsXXH9fpwO0xLj+Ozr8twNRu8se4AXyfa\nmD+gGXXzFDGfElxpN+9/ke+dGuzcSf1MjkZEpLWg4NATGpA6OCKKHokxvP/FHsqqGsktamb+k19y\n5xXjSEnQeBERM2lZFmkXTc1u3l/v6Z6QPjBOH/Yi0iVFhAYyZ9pA+iR4Zoc5UFbP/zz8CZ9nHTA5\nMpHuTQmutIv12QeoqPHMqHHupL4mRyMi0n5sflbGpUYwbWggflYLDmcLi//1Ff9atZ0Wt/rliphB\nCa60i0ODy+Kjgxk9JNHkaERE2pfFYiG9TwB3XTGa6PBAAFZ+uIu7n1pPdV37LEUqIkemBFd8Lnd/\nFd/s8YxO/smEvvhpajAR6SZSe0fy11tOY0jfaAC27CrlDw99Qu7+KpMjE+lelOCKzx1a2CHA348z\nxmlqMBHpXqLD7dx73UTOm+wZXFtS6eCPf/uUNV/uNTkyke5DsyiIT1XXOVm72TM12LSMnoQF+3aJ\nUhGRjsjtdlNTU0N5ebl36dE5k3uQFGXjn+/m4mp28/CKzezKL+GCaX2wWo7tzlZUVBRWq9qiRNpK\nCa741Acb8mlqdgOaGkxEuo+G+lp2Haii3q+QAP/Wc36fOTqej7eUUtfYwrtfFPD17gomDovB3+/H\nE9f6+lrOO20oMTEx7Rm6SJekBFd8pqXFzbuf7wFgWP8Y+iSFmxuQiMhJZA8KISw8koCA1neuwiLg\nwtho3vt8D0UVDewtceDYXM5PJvTVAjgi7UT3PcRnNmwroqzKc2tOrbciIt8Jtvsza2p/BqZEAlBa\n6eCVD3dRWtlgcmQiXZMSXPGZQ1ODxUYGcepQTQ0mIvJ9Nj8rZ4ztxejBCQDUO5p47eM8vj1QbXJk\nIl2PElzxiT2FNWTnlQFwzoQ++B2lb5mISHdksVgYNzSRGWN7YbVaaD7YtWvzzhIMQ4tCiPiKshDx\niUNTg/nbrJw5rrfJ0YiIdGyDekUxe0p/7AF+AHyeVcjHm/Zr5TMRH1GCKyesqtbJR5n7AJgyMpmI\n0ECTIxIR6fiSYkO4YPpAosI8n5nbv63g3XXf4mpqMTkykc5PCa6csFXrvsV1cGqw2VMHmByNiEjn\nEREayJxpA+kZHwrA3uJaXvs4lzpHk8mRiXRuSnDlhDQ6m1m1ztM9ISM1XlODiYi0UWCAH+dO6kdq\n7ygAyqsbefXDXVTWukyOTKTzUoIrJ2TNV3upbfC0NPxsmlpvRUSOh5/VwvTRKYwd4plhoc7RxPuZ\nxWz7tsrkyEQ6JyW4ctxaWty88UkeAAN6RjC8f6zJEYmIdF4Wi4UxQxI5fXQKVgs0NRs8uGIba77c\na3ZoIp2OElw5bp9nF1Jc4Zmk/GfTBmI5xrXVRUTkyFL7RHPupH742yy0uA0eXrGZF1fnaBoxkTZQ\ngivHxTAMXvtoFwAJ0cFMGJ5kckQiIl1HSkIYZ49OIDrMs+zvCx/s4OEVm2k6OKBXRH6cElw5Ltl5\nZeTu96y+M3tqfy3sICLiY1FhAfzpsjT69YgA4P++2sfdT6+npl6Dz0SORlmJHJfXPsoFICw4gBlj\nepkcjYhI1xQVFsjiGyYyKjUegKzcMm59eC37imtNjkykY1OCK222I7+CjTklAPxkYl/sgTaTIxIR\n6bqC7f7cdcU4zp3UF4DC8npufWQtG3OKTY5MpONSgitt9tx7OQAE222cP6WfydGIiHR9fn5Wrvnp\nCK6fMwKr1UJDYzML//EFb3ySq8FnIj9ACa60SXZuGVt2lQKeVcvCggNMjkhEpPuYOaEvC68eT2iQ\nP24Dlr21jfv/nUlDo1Y+E/k+JbhyzAzD4Nn3vgE8fW9nqfVWROSkSxsYx19+P4XeiWEArMs6wB8e\n+oT8whqTIxPpOJTgyjHbmFPCN3sqALhg+kCC7f4mRyQi0j31iA3lgd9NYVpGTwAKSuv5n0fW8mHm\nXnVZEEEJrhwjt/u71tvo8EB+cnCwg4iImMMeaOOWi0dxwwVp2PysOF0t/PXFzdz/70xNJSbdnhJc\nOSbrswvZXeCZ9/bnMwYR6O9nckQiImKxWDh7fB+W3jSZxJhgwNNl4calH5L5jWZZkO5LCa4cVUuL\nm+dXe1pv46ODOXNcb5MjEhGR7xuQEskj/zONs071fD5X1jq55x9f8OgrW6hrUGuudD9KcOWo3v7s\nW/YV1wFw8RmD8Lfp10ZEpKMJCrRx44Xp3HXlOCLDAgFY/UU+197/f6z5ci9ut/rmSvehTEV+VGml\ng+ff97TeDkiJZNroFJMjEhGRHzNmSCKP3jqNKenJAFTXuXh4xWbm/f0zvj1QbXJ0IieHlqCSH/XU\nG1k0ulqwWuCGC9Lws1rMDklERI4iIjSQ2349mjPH9eaJ17PYX1LHN3sq+P2DHzN+WDyzJ/ciLtLu\nk/eKiorCalV7mXQsSnDliDZ8XcgXXxcBcO7kfgzoGWlyRCIi0hZpp8TxyP9M441PclmxZidOVwvr\nsktY/3UJA5NDGd4vguDA4x80XF9fy3mnDSUmJsaHUYucOCW48oMczmaeeD0bgNgIO786K9XkG7Sz\nAgAAGb1JREFUiERE5Hj426xcePopTMtI4V/vZPHJliLcBuzYX0fugXoG94km7ZQ4IkMDzQ5VxGeU\n4MoPemF1DmVVDgCu/ulwLeogItLJxUYGcdnMAUQFW9i2r5GdeytpcRt8vbucr3eX0z85gvRT4kiM\nCTE7VJETpgRXDpOTX8Fbn+4GYOyQRE4dlmRyRCIi3Y/b7aaiosKnZVZUVBAaZOOMsb0YNSiezTtK\n2LWvErcBeQXV5BVUEx8VxLD+sQzoGalZc6TTUoIrrVTVOlnyr69wuw3sAX5c89PhWCwaWCYicrI1\n1NeyZkMZsbF1PiuzpKiAkPAowiMhJsLOjLG9GDcskaxdZWz7tpymZjcllQ4+zNzHZ1sLSO0dzZC+\n0cREBPksBpGTQQmueLW0uPnfZzMpr24E4OZfjCQ+OtjkqEREuq+g4FDCIqJ8Vl5d7eHThIUFBzAx\nrQejhySQs6eCbbvLqax14mpyk5VbRlZuGbGRdlJ7RzMwJVJd1qRTUIIrXv9+9xuy88oAmD21P5PS\nkk2OSERETpZAfz/SBsYxYkAsB8rq2ba7nLz91bgNg7KqRj6rOsC6rAP0TgxnUO8o+iSFmx2yyBEp\nwRUA1m09wGsf5wIwvH8sl/1kiMkRiYiIGSwWC8lxoSTHhdKY3syufVXk5FdSUtmAYcCewhr2FNYQ\n6O9H7/ggUhIjiYqKxqp50qUDUYIr7C6o5uEVmwBPn6zbfp2Bn58GFoiIdHf2QBvDB8QyfEAsFTWN\n7MivZOfeSuocTTibWthZUMe9z2YRF7WLKenJTBnZk749wjV2Q0ynBLeb27m3kgVPrcfhbMHmZ2He\npWOICvPN6jYiItJ1RIfbGT88iXHDEikoqWNHfiV5+6todhuUVjp49aNcXv0ol57xoUwZ2ZOpI5Pp\nERdqdtjSTSnB7ca27S7nnn98gcPZjNVq4ZaLR5HaO9rssEREpAOzWiykJISRkhDGqP4hRISHsGlX\nNZt2FNPcYrC/pI4XVufwwuoc+veMYEp6MuOH9yApVvPrysmjBLeb2rqrlEXPbMDp8rTc3nbJaCaM\n6GF2WCIi0on426ycOjSOn0xJpbbBxfrsQtZu3k92bplnbt391eTtr2b5O9vpnRjGqcM8LcADekaq\nG4O0KyW43YxhGHyyaT+PvLyFpmY3/jYrd1w2ltGDE8wOTUREOrGw4ADOHNebM8f1prKmkU+3FrB2\ncwE78isByC+qJb+olhVrdhIbYWfcsCTGDU1kWP9YLSghPqcEtxupbXDx2MqtfLb1AACBAX786fJx\npJ0SZ3JkIiLSlUSF2zl/cn/On9yf8moHG7YV8UV2Idl5ZTS3GJRVN7Jq3besWvctIXYbIwbGkTYw\njrSBsSTHhap1V06YEtxuYlNOCQ+v2ERFjROAhOhgbr0kQ31uRUSkXcVEBHHOhL6cM6Ev9Y4mNuYU\n88XXRWR+U4zD2Ux9YzPrswtZn10IQGyEnSH9YkjtHU1qnyj69ojAppl9pI3anOC6XC7uvvtu/vOf\n/2C327niiiu4/PLLf/DY7du3c/fdd7Nz504GDhzI3XffzdChQ73733nnHR5++GFKS0uZNGkSixYt\nIirquxVbHnjgAV599VXcbjcXXHABt91223GcYve2u6CalR/u4tMtBd5tZ4ztxVWzhmk1GhEROalC\ngvyZMrInU0b2pKm5hezccr7aXsTW3FL2FXuWJC6rbmTtZk/3BoAAfz/69QinT48I+vYIp29SBL0S\nwwgJ0neYHFmbE9z777+f7du38+yzz7J//37mzp1LcnIyZ555ZqvjHA4HV199NbNmzWLJkiW8+OKL\nXHPNNaxZswa73U5WVhZ33nknCxcuJDU1lUWLFnH77bfzxBNPAPDMM8/w7rvv8thjj9HU1MStt95K\nbGzsEZNpaW3b7nJWfriLzG+KvdsiQwO58cI0xg1LMjEyERHpKtxuNxUVFcf9+t5xfvSemswFU5Op\nrHXyTX4127+tIreglqIKBwCuphZy8ivJOdiX95DwEH8So4JIiAkiPtJOdHgg0WEBRIUHEhUWQKC/\nHwANDQ243e7jP0nplNqU4DocDlauXMmyZctITU0lNTWVq666iueee+6wBHfVqlUEBQV5W13nz5/P\n2rVref/995k9ezbPP/88M2fO5Pzzzwdg6dKlTJs2jYKCApKTk3n22We5+eabGTlyJAC33norDz/8\nsBLcIzAMg9z9VWz4uogN24rYU1jj3Rfg78dZp/bmohmnEBEaaGKUIiLSlTTU17JmQxmxsXU+KzOU\nckb19idsRDKlVS7Kqp1U1LqorGuivrHFe1xNfRM19U3s3F/zg+UE+FsJCfTD389NQoSVrwtziYkK\nISIkkMjQQMJDA4gIDSQ8JEBdILqgNiW4OTk5tLS0kJ6e7t2WkZHBk08+edixWVlZZGRktNo2atQo\nNm/ezOzZs9myZQvXXHONd19iYiJJSUls3boVf39/CgsLGT16dKv3OXDgAGVlZcTGxrYl7C6pobGJ\nbw/UkFdQxe6CarbuLKWsurHVMSFB/pw7sS/nTe6nxFZERNpFUHAoYRFRRz/wGNXVVmPx8yc2Npb/\n/rpvdDVTXt1IZa2Tqlon1XVOquqc1NS7cLuNVse6mty4mjwttyXVLWTv/faI7xkS5E9kaADhIYFE\nfC/xDQ3yJzTY829YcAChwf6EHPy/PcBPg+E6sDYluKWlpURGRmKzffeymJgYnE4nlZWVrfrPlpSU\ncMopp7R6fUxMDLm5ud6y4uPjW+2PjY2lqKiI0tJSLBZLq/2xsbEYhkFRUVGXT3BLKhsoqWig3tFE\nfWMTdQ1NVNQ0UlrloKzKQWmVg9JKxw++1s9qYXj/WE4dnsS0jJ7qZysiIl2GPcBGclwoyf+1Qpph\nGDS6WqhrcFHnaKLe0URtg+ffypp6mpqacLX4Ud/Y/IPl1h98TUFp/THH4me1EBYccDDh9SS+QYE2\n78MeaMMe4NfqeYDNir/Nir/N7+C/nkfAwed+flasFrBYLFi+96+11XOL95hD597iNnC7DQzwds3o\n7trcRSEgIKDVtkPPXS5Xq+2NjY0/eOyh435sv8PhaFX2j73PkRzqb1NX57vbJifD9m8reXb1TjCO\nfIwNSIr6LnGNiQikZ1wog/tEMqhXJPZAT7U66mtwHPu1etI4nZ6ZHGpqarAazRjNP5ysHw8/Kzgd\n1Vho8kl5za46rFYbddUlPimvPcr0ZXnNzc1YWhy4m520OFs6ZIydqUyfl9fcjN1m4HbVddgY26PM\nzhCj0dxAoF8LDbVluGy+maCoM5x3R4sxyApBIRAXAuAH+FFf56J3bBCnnHIKNv8AGhqbqXc0HUyE\nm6lvbKK+sfm7544m6hzNOJxNOJwth7UMt+bG3eykusZJ9Q/3lGhfFg7LFyaOSOTcib1NCOb4HMrT\nfN1Puk1XYWBg4GEJ5qHnQUFBx3Ss3W4/6v7AwEDv8/9ObP/7fY7kUBJVVlZGWVnZMb2mIwi2wDVn\nH++iC3UUFXaehL6yspK+iXagwWdlxvfx7Zy+feN8PyDP12X6tjw/SPL9vMid4efYHmX6PkY/Bial\n+rTE7vlzbI8Ye/q0PE+ZneG8O36MxHnuLhcWFrbabAMi/D0PwsCTLfoffHRmBnv27DE7iDZzOp2E\nhoYe/cBj1KYENyEhgaqqKtxuN1arp0N2WVkZdrud8PDww44tLS1tta2srIy4OM+XZ3x8/GGJZ1lZ\nGfHx8SQkJGAYBmVlZfTo4Vk+9lC3hUOvP5qIiAj69OlDYGCgN1YRERER6TjcbjdOp5OIiAifltum\nBHfw4MHYbDa2bNnCqFGjAMjMzGTYsGGHHZuWlsbTTz/datumTZu4/vrrAUhPT2fjxo3Mnj0b8Pxl\nVVRURHp6OvHx8fTo0YONGzd6E9zMzEySkpKOuf+tzWYjJiamLacnIiIiIieZL1tuD/G7++677z7W\ng202G4WFhbz44osMHz6c7OxsHnjgAW699Vb69etHWVkZfn5+2Gw2evXqxbJlyyguLqZHjx489thj\n5OTksHDhQmw2G3FxcSxZsoS4uDisVisLFixg0KBB/OIXvwA8TdVPPvkkQ4cOZf/+/SxcuJDLL7+8\n1QwOIiIiIiL/zWIYxo/1nj5MY2Mj99xzD6tXryYsLIyrrrqKX//61wCkpqayZMkSb6tsdnY2CxYs\nYPfu3QwaNIh77rmH1NTv+o+98cYbPPzww1RXV3tXMjvURO12u1m6dCmvvfYafn5+XHjhhdxyyy2+\nOm8RERER6aLanOCKiIiIiHRkGn0lIiIiIl2KElwRERER6VKU4IqIiIhIl6IEV0RERES6FCW4IiIi\nItKldLkEt7a2lvnz5zNx4kTGjx/P7bffTm1trXd/VVUVN910E6NGjWLGjBm89dZbJkbb/bhcLu64\n4w7GjBnD5MmTWb58udkhdVvFxcX87ne/Y9y4cUydOpUlS5Z4l8Tev38/l19+OSNHjuTcc89l3bp1\nJkfbfV199dXcfvvt3ueqG/O5XC7uuecexo4dy6RJk/jrX//q3af6MVdRURHXXnstGRkZnH766fzr\nX//y7lPdmMflcnHeeefx1VdfebcdrT4+//xzzjvvPNLT07nsssvYt29fm96zyyW4d911Fzt37uQf\n//gHzzzzDHl5edx5553e/fPmzaO+vp5XXnmFa6+9ljvvvJPs7GwTI+5e7r//frZv386zzz7LggUL\nePTRR/nggw/MDqtb+t3vfofT6eSFF17gwQcf5KOPPuLhhx8G4Prrryc+Pp5XX32V888/nxtvvJGi\noiKTI+5+Vq1axdq1a1ttu+GGG1Q3Jvvzn//M+vXreeaZZ3jggQd4+eWXefnllwFdO2a7+eabCQkJ\n4fXXX+eOO+7goYceYs2aNYDqxiwul4s//OEP5Obmttr+Y59lhYWF3HDDDcyZM4dXX32VqKgobrjh\nhra9sdGFNDQ0GEOHDjWysrK82zZv3mwMHTrUcDqdRn5+vjFo0CDjwIED3v3z58835s2bZ0a43U5D\nQ4MxYsQI46uvvvJue+yxx4xf//rXJkbVPeXl5RmpqalGeXm5d9s777xjTJkyxVi/fr0xcuRIo7Gx\n0bvvsssuM/72t7+ZEWq3VVVVZUydOtW48MILvZ9Rn3/+uerGZFVVVcbQoUNbfY499dRTxh133KFr\nx2TV1dXGoEGDjF27dnm33XTTTcaiRYtUNybJzc01Zs2aZcyaNctITU01vvzyS8Mwjv5Z9tBDD7XK\nDRwOhzFq1Cjv649Fl2rBtVqtPPHEE61WSzMMg5aWFhoaGsjKyqJHjx4kJSV592dkZLBlyxYzwu12\ncnJyaGlpabXcckZGBllZWSZG1T3FxcXxj3/8g+jo6Fbba2tr2bp1K0OHDiUwMNC7XdfJyXf//fcz\na9Ys+vfv792WlZWlujHZxo0bCQsLY/To0d5tv/3tb7n33nt17ZjMbrcTFBTEq6++SnNzM7t372bT\npk0MHjxYdWOSL7/8kvHjx7NixQqM760rdrTPsqysLMaMGePdZ7fbGTJkCJs3bz7m9+5SCW5gYCCT\nJk3C39/fu+3f//43gwYNIjIyktLSUuLj41u9JiYmRrcoTpLS0lIiIyOx2WzebTExMTidTiorK02M\nrPsJCwtj4sSJ3ueGYfDcc88xfvz4I14nxcXFJzvMbmv9+vVs3LjxsFtyqhvz7du3j+TkZN544w1m\nzpzJjBkzeOyxxzAMQ/VjsoCAAO666y5eeukl0tLSOOecc5gyZQpz5sxR3Zjk4osvZu7cua0SWTj6\nZ1lJSclh+2NjY9tUX7ajH9KxOJ3OI55gXFwcQUFB3ufPPfccq1evZtmyZQA4HI5WyS94Loimpqb2\nC1i8HA4HAQEBrbYden5ocJOY43//93/55ptvWLlyJcuXL//BelIdnRwul4u7776bBQsWHFYPR7qG\nVDcnT0NDA3v27OHll19myZIllJaWctdddxEUFKT66QDy8vKYPn06V155JTt37mTRokWMHz9eddPB\nHK0+GhsbT7i+Ol2Cu3XrVn7zm99gsVgO2/foo49y+umnA/D8889z7733Mn/+fMaPHw94Wnj/O5l1\nuVzY7fb2D1wIDAw87Jfz0PPv/2EiJ9fSpUt59tlneeihhxgwYACBgYFUV1e3OkbXycnzt7/9jWHD\nhjFhwoTD9qluzOfn50d9fT0PPvggiYmJABQUFPDCCy8wadIkqqqqWh2v+jl51q9fz8qVK1m7di0B\nAQEMGTKEoqIiHn/8ccaPH6+66UCO9ll2pHwhPDz8mN+j0yW4Y8eOJScn50ePWbZsGUuXLmXevHlc\ncskl3u0JCQmUlpa2OrasrIy4uLh2iVVaS0hIoKqqCrfbjdXq6R1TVlaG3W5v0y+t+M6iRYtYsWIF\nS5cuZcaMGYCnnv57tKuuk5Pn3Xffpby8nJEjRwJ4/yhfvXo11157rerGZPHx8QQGBnqTW4C+fftS\nXFxMQkICu3btanW86ufk2bZtG3369GnV8jd48GCefPJJ1U0Hc7TvmSPla4MHDz7m9+hSfXABXn/9\ndR544AHmz5/PZZdd1mpfWloaBw4caNXFYePGja0GPUn7GTx4MDabrVWn/szMTIYNG2ZiVN3Xo48+\nyooVK/jrX//KzJkzvdvT0tLYvn17q7+edZ2cPM899xxvv/02b731Fm+99RbTp09n+vTpvPnmm4wY\nMUJ1Y7K0tDScTif5+fnebXl5eSQnJ5OWlsa2bdtUPyaJj48nPz+f5uZm77bdu3fTs2dP1U0Hc7Tv\nmbS0NDZt2uTd53A42L59e5vqq0sluNXV1SxatIjZs2czc+ZMysrKvA/DMEhJSWHSpEncdttt7Nix\ng1deeYVVq1bxq1/9yuzQuwW73c6sWbNYsGAB2dnZrFmzhuXLl3PppZeaHVq3k5eXx+OPP87VV1/N\nyJEjW10rY8eOJSkpiXnz5pGbm8tTTz1FdnY2F1xwgdlhdwtJSUmkpKR4HyEhIYSEhJCSkqK66QD6\n9u3L1KlTmTdvHjk5OXz66ac8/fTT/PKXv2TMmDGqHxNNnz4dm83GnXfeyZ49e/jwww958skn+c1v\nfqO66WCO9lk2Z84cNm3axNNPP01ubi633347vXr1YuzYscf+Jic+y1nHsWrVKiM1NbXVY9CgQUZq\naqpRUFBgGIZhlJeXG9ddd52RlpZmzJgxw1i1apXJUXcvDofDmDdvnjFy5EhjypQpxr///W+zQ+qW\nnnzyySNeK4ZhGPn5+cYll1xijBgxwjj33HON9evXmxxx9zVv3rxWc3Xv3btXdWOy2tpaY+7cucao\nUaOMiRMnGo899ph3n+rHXLm5ucYVV1xhjB492jjzzDNbfceobsz1/XlwDePo9bF27VrjrLPOMtLT\n040rrrjC2L9/f5vez2IY35uYTERERESkk+tSXRRERERERJTgioiIiEiXogRXRERERLoUJbgiIiIi\n0qUowRURERGRLkUJroiIiIh0KUpwRURERKRLUYIrIiIiIl2KElwRERER6VKU4IqIdAB1dXWkpaUx\nadIkWlpazA5HRKRTU4IrItIBvPvuu8TExFBbW8sHH3xgdjgiIp2axTAMw+wgRES6u4suuoghQ4Zw\n4MABnE4n//znP80OSUSk07KZHYCISHeXl5fH1q1b+e1vf0tVVRV/+tOfyM/Pp3fv3gA0NjayePFi\nVq9eTXNzM2effTaNjY34+/uzePFiADZt2sSDDz5IdnY20dHRTJs2jT/84Q+EhoaaeWoiIqZQFwUR\nEZOtXLmSkJAQpkyZwhlnnIGfnx8vvfSSd/8f//hH1q9fz0MPPcRLL71EbW0tq1at8u7Pycnhiiuu\nYMqUKbzzzjv85S9/Yfv27Vx11VVmnI6IiOmU4IqImKilpYW3336b008/nYCAACIiIpg0aRKvv/46\nLpeLffv28cEHH3D33Xdz6qmnMmDAAJYuXUpsbKy3jGeeeYZJkyZx9dVXk5KSwqhRo1i6dClbtmzh\nq6++MvHsRETMoS4KIiIm+vjjjykrK+Occ87xbvvJT37Cxx9/zPvvv4/dbsdisZCWlubdHxAQwIgR\nI7zPt2/fTn5+PiNHjmxVtsViIS8vjzFjxrT/iYiIdCBKcEVETPT6669jsVi48cYbOTTm12KxYLFY\neOmll7jyyisB+LHxwG63m/POO4/rrrvusH1RUVHtE7iISAemLgoiIiapqKjg448/Zs6cObzxxhu8\n+eabvPnmm7zxxhv87Gc/Y/PmzaSkpACwZcsW7+uamprYtm2b9/nAgQPJy8sjJSXF+3C5XNx7770U\nFRWd9PMSETGbElwREZO8+eabuN1ufvvb3zJgwIBWj2uvvRaLxcKKFSs455xzWLhwIevXryc3N5c7\n7riD4uJiLBYLAFdccQXbtm1j4cKF5OXlsXnzZm699Vb27dtHnz59zD1JERETKMEVETHJa6+9xoQJ\nE7zTgX1fSkoKM2bM4O233+aee+4hIyODm2++mYsvvpiwsDDS0tLw9/cHIC0tjWXLlpGTk8OcOXO4\n4YYb6NevH8888ww2m3qiiUj3o4UeREQ6MJfLxdq1a5kwYQLBwcHe7WeffTazZs36wX63IiLdnf60\nFxHpwAICAli4cCFjx47luuuuw8/Pj5UrV1JYWMjZZ59tdngiIh2SWnBFRDq4nJwcli5dSnZ2Ns3N\nzQwdOpTf//73ZGRkmB2aiEiHpARXRERERLoUDTITERERkS5FCa6IiIiIdClKcEVERESkS1GCKyIi\nIiJdihJcEREREelSlOCKiIiISJeiBFdEREREuhQluCIiIiLSpfw/S1j+LvKJzFwAAAAASUVORK5C\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11d6d37f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(train_data[train_data['Age'].notnull()].Age)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We do not have any prior information about how the age for child was defined in those years. The above information is helpful as we can categorize the people as child, adults and seniors by looking the peaks.\n",
    "\n",
    "Let us define functions to categorize the age into these three categories with age of 16 and 48 as the defining separators from the graph."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def person_type(x):\n",
    "  if x <=16:\n",
    "    return 'C'\n",
    "  elif x <= 48:\n",
    "    return 'A'\n",
    "  elif x <= 90:\n",
    "    return 'S'\n",
    "  else:\n",
    "    return 'U'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above function categorizes the continous variable 'Age' into a categorized variable. We can use apply function to transform each entry in the Age column and assign it to Person."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_data['Person'] = train_data['Age'].apply(person_type)\n",
    "test_data['Person'] = test_data['Age'].apply(person_type)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now look at who is likely to survive depending on the type of Person with a factor plot. A factor plot can consider another factor such as Sex along with the Age. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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mTEkXz/4EBATIbrfrvvvu09KlS5WWlqZBgwbp3Xff1eHDhzVo0KAq7dPf379enHp0OBxW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\nMQg+AADAGAQfAABgDIIPAAAwxv8BgnND7SrcF0AAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1199e5dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.factorplot(x=\"Person\", y=\"Survived\", hue=\"Sex\", data=train_data,\n",
    "                             size=5, kind=\"bar\", palette=\"muted\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the above plot you can see that senior women were most likely to survive with highest probability than anyone else. Now we can ignore the Sex type and visualize who was likely to survive."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Chances of Survival\n",
    "\n",
    "Let us now look at the class from which people had better chances of survival:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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NDQ1yOByKiYnx2V9XV6f58+crPDxcZWVlGj58eJ+PWV1dHdD9MEd1dbUiIiKC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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11f28e198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.factorplot(x=\"Pclass\", y=\"Survived\", data=train_data, size=5,\n",
    "                   kind=\"bar\", palette=\"muted\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that 1st class passengers had much better chances of survival. We can also visualize the distribution of Male and Female using a violin plot. This is very useful to measure the mean and variance of passengers as to their likelihood of survival in each class grouped by Male and Female. The distribution graphs are shown on either side of the line for each class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1204ef438>"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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EDIkeyg/kPqayVr4rCsrBrpLv0NjciIWZixCiChFdDvnI0VP1okvoUYWlHA6n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\nvU4td0kBY2D4IAhcrlESDLokmb4xRslaN3FEl6QQYtTi4bmp0GkD51c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      "text/plain": [
       "<matplotlib.figure.Figure at 0x120893f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(style=\"whitegrid\", palette=\"pastel\", color_codes=True)\n",
    "sns.violinplot(x=\"Pclass\", y=\"Survived\", hue=\"Sex\", data=train_data, split=True,\n",
    "               inner=\"quart\", palette=\"muted\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Let us look at the distribution of Fare"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/harishkashyap/anaconda/lib/python3.5/site-packages/statsmodels/nonparametric/kdetools.py:20: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
      "  y = X[:m/2+1] + np.r_[0,X[m/2+1:],0]*1j\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12010f470>"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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O5ebmGtoHExsailRls3OyZR+IfIzsdptycpwJ4+wuV2z+XcNPhWWTy5074UVp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\nQgghLgoS4Irz6gtj4DbvDnAuHela0FUSw/XZ0mw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ORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11ff76a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(train_data['Fare'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/harishkashyap/anaconda/lib/python3.5/site-packages/numpy/lib/function_base.py:3834: RuntimeWarning: Invalid value encountered in percentile\n",
      "  RuntimeWarning)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\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>NaN</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>NaN</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>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
       "count   891.000000  891.000000  891.000000  714.000000  891.000000   \n",
       "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
       "std     257.353842    0.486592    0.836071   14.526497    1.102743   \n",
       "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
       "25%     223.500000    0.000000    2.000000         NaN    0.000000   \n",
       "50%     446.000000    0.000000    3.000000         NaN    0.000000   \n",
       "75%     668.500000    1.000000    3.000000         NaN    1.000000   \n",
       "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
       "\n",
       "            Parch        Fare  \n",
       "count  891.000000  891.000000  \n",
       "mean     0.381594   32.204208  \n",
       "std      0.806057   49.693429  \n",
       "min      0.000000    0.000000  \n",
       "25%      0.000000    7.910400  \n",
       "50%      0.000000   14.454200  \n",
       "75%      0.000000   31.000000  \n",
       "max      6.000000  512.329200  "
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Statistical Imputations\n",
    "\n",
    "#### Sex Column\n",
    "The Sex column has entries male or female, a categorical variable.\n",
    "\n",
    "Imputation refers to methods of substituting estimates for missing values in the data. This is an important step that can train the model better as more data becomes available post imputations. There are many known methods of imputations. Sometime, by analysis and EDA, we can design custom imputation methods that provide best statistical estimates for the missing value. This reduces sparsity in the dataset. In the Titanic dataset, let us start investigating various methods to impute sparse columns.\n",
    "\n",
    "### Age Column\n",
    "To impute the age column, we can use the name information. How many of names with Mr., Mrs., Miss and Master exist and use the mean values for each where the ages are missing. Here are the estimates for each category:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "miss_est = train_data[train_data['Name'].str.contains('Miss. ')].Age.mean()\n",
    "master_est = train_data[train_data['Name'].str.contains('Master. ')].Age.mean()\n",
    "mrs_est = train_data[train_data['Name'].str.contains('Mrs. ')].Age.mean()\n",
    "mr_est = train_data[train_data['Name'].str.contains('Mr. ')].Age.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above estimates can be improved further by considering the Parents column as those names containing Master and Miss would have a subset of children (unmarried referring to Master & Miss). Here is a function that takes all of these rules into consideration:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "girl_child_est = train_data[train_data['Name'].str.contains('Miss. ') & (train_data['Parch'] == 1)].Age.mean()\n",
    "boy_child_est = train_data[train_data['Name'].str.contains('Master. ') & (train_data['Parch'] == 1)].Age.mean()\n",
    "woman_adult_est = train_data[train_data['Name'].str.contains('Miss. ') & (train_data['Parch'] == 0)].Age.mean()\n",
    "man_adult_est = train_data[train_data['Name'].str.contains('Master. ') & (train_data['Parch'] == 1)].Age.mean()\n",
    "woman_married_est = train_data[train_data['Name'].str.contains('Mrs. ')].Age.mean()\n",
    "man_married_est = train_data[train_data['Name'].str.contains('Mr. ')].Age.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We shall use the above estimates with an imputation function that we will build based on the same rules as above. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import math\n",
    "def impute_age(row):\n",
    "    if math.isnan(row[5]):\n",
    "        if ((('Miss. ') in row[3]) and (row[7] == 1)):\n",
    "            return girl_child_est\n",
    "        elif ((('Master. ') in row[3]) and (row[7] == 1)):\n",
    "            return boy_child_est\n",
    "        elif ((('Miss. ') in row[3]) and (row[7] == 0)):\n",
    "            return woman_adult_est\n",
    "        elif (('Mrs. ') in row[3]):\n",
    "            return woman_married_est\n",
    "        else:\n",
    "            return man_married_est\n",
    "    else:\n",
    "        return row[5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "train_data['Imputed_Age'] = train_data.apply(impute_age, axis=1)\n",
    "test_data['Imputed_Age'] = test_data.apply(impute_age, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Mapping\n",
    "\n",
    "Let us start preparing the features for prediction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Person</th>\n",
       "      <th>Imputed_Age</th>\n",
       "      <th>C</th>\n",
       "      <th>Q</th>\n",
       "      <th>S</th>\n",
       "      <th>female</th>\n",
       "      <th>male</th>\n",
       "      <th>Age_Imputed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>A</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>A</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "      <td>A</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>A</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked Person  Imputed_Age    C  \\\n",
       "0      0         A/5 21171   7.2500   NaN        S      A         22.0  0.0   \n",
       "1      0          PC 17599  71.2833   C85        C      A         38.0  1.0   \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S      A         26.0  0.0   \n",
       "3      0            113803  53.1000  C123        S      A         35.0  0.0   \n",
       "4      0            373450   8.0500   NaN        S      A         35.0  0.0   \n",
       "\n",
       "     Q    S  female  male  Age_Imputed  \n",
       "0  0.0  1.0     0.0   1.0         22.0  \n",
       "1  0.0  0.0     1.0   0.0         38.0  \n",
       "2  0.0  1.0     1.0   0.0         26.0  \n",
       "3  0.0  1.0     1.0   0.0         35.0  \n",
       "4  0.0  1.0     0.0   1.0         35.0  "
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_embarked = pd.get_dummies(train_data['Embarked'])\n",
    "train_sex = pd.get_dummies(train_data['Sex'])\n",
    "train_data = train_data.join([train_embarked, train_sex])\n",
    "test_embarked = pd.get_dummies(test_data['Embarked'])\n",
    "test_sex = pd.get_dummies(test_data['Sex'])\n",
    "test_data = test_data.join([test_embarked, test_sex])\n",
    "train_data['Age_Imputed']=train_data.apply(impute_age, axis=1)\n",
    "test_data['Age_Imputed']=test_data.apply(impute_age, axis=1)\n",
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The columns already have the appropriate features.\n"
     ]
    }
   ],
   "source": [
    "# You dont want this to join twice if you are attempting this lesson multiple times.\n",
    "\n",
    "try:\n",
    "    train_embarked = pd.get_dummies(train_data['Embarked'])\n",
    "    train_sex = pd.get_dummies(train_data['Sex'])\n",
    "    train_data = train_data.join([train_embarked, train_sex])\n",
    "\n",
    "    test_embarked = pd.get_dummies(test_data['Embarked']) \n",
    "    test_sex = pd.get_dummies(test_data['Sex'])\n",
    "    test_data = test_data.join([test_embarked, test_sex])\n",
    "except:\n",
    "    print(\"The columns already have the appropriate features.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Logistic Regression using Statsmodels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.450489\n",
      "         Iterations 6\n"
     ]
    }
   ],
   "source": [
    "features = ['Pclass', 'Imputed_Age', 'SibSp', 'Parch', 'Fare', 'C', 'Q', 'female']\n",
    "log_model = sm.Logit(train_data['Survived'], train_data[features]).fit()\n",
    "y_pred = log_model.predict(train_data[features])\n",
    "\n",
    "roc_survival = roc_curve(train_data[['Survived']], y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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8WgAAAJDGwXSg/zJLKs41uwoAAID0lrbBNDDgAtOpBebVAQAAgJC0DaaXfeHt\n3Czz6gAAAEBI2gbTi97Qq8MmOVk0CwAAwHTpG0x7Qq8FTnPrAAAAQEj6BtMvZky5Gx8AAMAaCKYE\nUwAAAEtIy2Da65e6e0PbnMoHAACwhrQMppe84W1mTAEAAKwhLYPphYHBlBlTAAAAS0jLYDpwDdO8\nbPPqAAAAQFhaBtOBbBlmVwAAAACJYAoAAACLIJgCAADAEgimAAAAsIS0ekp8t0/6uF367JLZlQAA\nAODL0iqY7vtMavg8vM99TwAAANaRVsH0Yk/oNTMjtExUxWTJxsUMAAAAlpA2wfTAGanFHdouy5O+\nXWVuPQAAAIiUNvOFzefC24U87QkAAMBy0iKYnu2S2rtC27YM6S+uMbceAAAAREuLYLqnObx9/SQp\ny2ZeLQAAABhcWgRTT294u6rMvDoAAAAwtLQIpv0WTJOuyje7CgAAAAwmZe/KDwal909Kn12UvH1m\nVwMAAICRpGwwdfdIh9sj25xZ5tQCAACAkaVsMPUHwtvlhVJpnnTDZPPqAQAAwPBSNpgGguHtmqnS\n9Inm1QIAAICRpeTNT55e6VeHza4CAAAARqTcjOlln/Tf/xjZxpOeAAAArC/lZkxPnI/cX1kl5TnM\nqQUAAACjl3LB1D/g2tK/msO6pQAAAMki5YLpQLOKza4AAAAAo5XSwRQAAADJg2AKAAAAS0ipYOrp\nlX7/qdlVAAAAYCxSKpg2fB6++cmeUt8ZAABA6kup+ObuCW9/baZ5dQAAAMC4lAmmhz+X/uQKbRc4\npHlTzK0HAAAAxqRMMP3UHd4uyTWvDgAAAIxNygTTgb452+wKAAAAYFTKBdPpEyW7zewqAAAAYFTK\nBVMAAAAkJ4IpAAAALIFgCgAAAEsgmAIAAMASCKYAAACwBIIpAAAALIFgCgAAAEsgmAIAAMASCKYA\nAACwBIIpAAAALIFgCgAAAEsgmAIAAMASUiKYNp6VTnaaXQUAAADGIyWC6Udt4e0JWebVAQAAgLEz\nHEx9Pp82bNigBQsWaPHixaqvrx/xmNbWVtXU1Gjfvn1jKnIkwWB4++tXx+UjAAAAEGd2owds3rxZ\njY2N2rp1q1pbW/WP//iPmjZtmm677bYhj3n66afV09MzrkJH44ZSaUJ23D8GAAAAcWBoxtTj8WjH\njh166qmnVFFRoSVLlmjt2rXatm3bkMfs3r1b3d3d4y4UAAAAqc1QMG1qapLf71d1dfWVttraWjU0\nNAzav7Ot6rfSAAAUdklEQVSzUz/96U/17LPPKjjwfDsAAADwJYaCaUdHhwoLC2W3h68AKCkpkdfr\nVWdn9G3xmzZt0ooVK3TttdeOv1IAAACkNEPXmHo8HmVnR17E2b/v8/ki2n//+9/r4MGDevbZZ8dV\noNfrHfFSgEDQKSlTfX196u72DdsX1uTxeCJekdoY7/TCeKcXxju9eL3emH49Q8HU4XBEBdD+/Zyc\nnCttXq9XdXV1evrpp6OCrFFtbW1qa2sbto+39wZJOXK7O3W069S4Pg/mamlpMbsEJBDjnV4Y7/TC\neGMsDAXTsrIyud1uBQIBZWaGrgJwuVxyOp0qKCi40q+hoUGtra1av359xLWlDz74oJYvX66nn356\n1J85ZcoUFRYWDtvn6DGHunukwsIiVc6YYORbgkV4PB61tLSovLw84h85SE2Md3phvNML451e3G73\niBOIRhgKppWVlbLb7Tp06JDmz58vSdq/f7+qqqoi+s2bN0979uyJaLv11lv1k5/8RAsXLjRUoMPh\nUG5u7pDvf3Ba6vxiJSq73a7cXMMrYMFCcnJyhh1vpBbGO70w3umF8U4Psb5kw1CKczqdWrZsmerq\n6vTcc8+pvb1d9fX12rRpk6TQ7Gl+fr4cDodmzJgRdXxpaamKi4tjU/kXDg0I6XmsYQoAAJC0DD/5\n6cknn1RVVZXWrFmjZ599Vo8++qiWLFkiSVq0aJHefvvtQY/LyMgYX6VD6L9SIC9bqpkal48AAABA\nAhg+7+10OrVx40Zt3Lgx6r2mpqYhjzt69KjRjzKkqkzKtsX1IwAAABBHhmdMAQAAgHggmAIAAMAS\nCKYAAACwhKQOpme7pN6A2VUAAAAgFpI2mPYFpDcaw/uZ8bnpHwAAAAmStMG01y/5/OH9WbFdHhUA\nAAAJlrTBdKDbZ0tFPPUMAAAgqaVEMAUAAEDyS9pgesptdgUAAACIpaQMppe80p7m8D73PQEAACS/\npAyml32R+zMmmlMHAAAAYicpg+lAK6skZ5bZVQAAAGC8kj6YAgAAIDUQTAEAAGAJBFMAAABYAsEU\nAAAAlkAwBQAAgCUkXTDtC0j/ccLsKgAAABBrSRdMT7klV3d4P9tmXi0AAACInaQKpsGgdPRseL92\nmlSca149AAAAiJ2kCqYtbulEZ2h75kTpazPNrQcAAACxk1TB9NwXp/AzM6RvXm9uLQAAAIitpAqm\n/TIzJIfd7CoAAAAQS0kZTAEAAJB6kiaYBoNSe5fZVQAAACBekiaYHm6XTpw3uwoAAADES9IEU9fl\n8PbMQvPqAAAAQHwkTTDtZ8uQ7uCOfAAAgJSTdMF0olPKyDC7CgAAAMRa0gVTAAAApCaCKQAAACyB\nYAoAAABLIJgCAADAEgimAAAAsISkCKaBoHT6gtlVAAAAIJ6SIpj+tkW66DW7CgAAAMRTUgTT857w\n9jVF5tUBAACA+EmKYNqvdIL0tavNrgIAAADxkFTBNDfb7AoAAAAQL0kVTAEAAJC6CKYAAACwBIIp\nAAAALIFgCgAAAEsgmAIAAMASCKYAAACwBIIpAAAALIFgCgAAAEsgmAIAAMASCKYAAACwBIIpAAAA\nLIFgCgAAAEsgmAIAAMASCKYAAACwBIIpAAAALIFgCgAAAEsgmAIAAMASCKYAAACwBIIpAAAALIFg\nCgAAAEsgmAIAAMASCKYAAACwBIIpAAAALIFgCgAAAEsgmAIAAMASCKYAAACwBIIpAAAALIFgCgAA\nAEsgmAIAAMASCKYAAACwBIIpAAAALIFgCgAAAEsgmAIAAMASCKYAAACwBMPB1OfzacOGDVqwYIEW\nL16s+vr6Ifu+9957Wr58uWpqarRs2TLt3bt3XMUCAAAgdRkOpps3b1ZjY6O2bt2quro6bdmyRXv2\n7Inq19TUpPXr12vlypXavXu3Vq1apUceeUTHjh2LSeEAAABILYaCqcfj0Y4dO/TUU0+poqJCS5Ys\n0dq1a7Vt27aovm+99ZYWLlyo1atXa8aMGVq9erW++tWv6u23345Z8QAAAEgddiOdm5qa5Pf7VV1d\nfaWttrZWL730UlTfFStWqLe3N6q9q6trDGUCAAAg1RmaMe3o6FBhYaHs9nCeLSkpkdfrVWdnZ0Tf\nWbNmac6cOVf2P/nkE/3hD3/QwoULx1kyAAAAUpGhGVOPx6Ps7OyItv59n8835HHnz5/X+vXrVVtb\nq1tuucVQgV6vV36/X5JNfr9f3d1eQ8cjOXg8nohXpDbGO70w3umF8U4vXm9sc5mhYOpwOKICaP9+\nTk7OoMe4XC79zd/8jTIyMvTCCy8YLrCtrU2Xe/MkFajr0iUdPXrc8NdA8mhpaTG7BCQQ451eGO/0\nwnhjLAwF07KyMrndbgUCAWVmhq4CcLlccjqdKigoiOrf3t6u+++/XzabTVu3blVRUZHhAqdMmaKW\ncxN0oUvKy89X5TWVhr8GrM/j8ailpUXl5eVD/iMHqYPxTi+Md3phvNOL2+1WW1tbzL6eoWBaWVkp\nu92uQ4cOaf78+ZKk/fv3q6qqKqqvx+PR2rVrlZWVpddee03FxcVjKtDhcMhms0mSbDabcnNzx/R1\nkBxycnIY4zTCeKcXxju9MN7pIdaXbBi6+cnpdGrZsmWqq6vT4cOH9e6776q+vl5r1qyRFJo97b/W\n4MUXX1Rra6s2btyoQCAgl8sll8vFXfkAAAAYlKEZU0l68skn9cwzz2jNmjXKz8/Xo48+qiVLlkiS\nFi1apE2bNmn58uXas2ePenp6tGrVqojjly9fro0bN47681yeTJ2+YLRKAAAAJBvDwdTpdGrjxo2D\nhsumpqYr27FaSP/w2awr21mGn1MFAACAZGH5qNcXDG/XTjOvDgAAAMSX5YNpv7lXSZMnmF0FAAAA\n4iVpgikAAABSG8EUAAAAlkAwBQAAgCUQTAEAAGAJBFMAAABYAsEUAAAAlkAwBQAAgCUQTAEAAGAJ\nBFMAAABYAsEUAAAAlkAwBQAAgCUQTAEAAGAJBFMAAABYAsEUAAAAlkAwBQAAgCUQTAEAAGAJBFMA\nAABYAsEUAAAAlkAwBQAAgCVYPpgGg2ZXAAAAgESwfDD19Fm+RAAAAMRA0qS+iU6zKwAAAEA8JUUw\nnZIv3VRmdhUAAACIp6QIptcWS7akqBQAAABjRdwDAACAJRBMAQAAYAkEUwAAAFgCwRQAAACWQDAF\nAACAJRBMAQAAYAkEUwAAAFgCwRQAAACWQDAFAACAJRBMAQAAYAkEUwAAAFgCwRQAAACWQDAFAACA\nJRBMAQAAYAkEUwAAAFgCwRQAAACWQDAFAACAJRBMAQAAYAlJEUxzssyuAAAAAPFm+WBa5PTrumKz\nqwAAAEC8WT6Yzi7yy24zuwoAAADEm+WDKQAAANIDwRQAAACWQDAFAACAJRBMAQAAYAkEUwAAAFgC\nwRQAAACWQDAFAACAJRBMAQAAYAkEUwAAAFgCwRQAAACWQDAFAACAJRBMAQAAYAkEUwAAAFgCwRQA\nAACWQDAFAACAJRBMAQAAYAkEUwAAAFgCwRQAAACWQDAFAACAJRBMAQAAYAkEUwAAAFgCwRQAAACW\nQDAFAACAJRBMAQAAYAkEUwAAAFgCwRQAAACWYDiY+nw+bdiwQQsWLNDixYtVX18/ZN/GxkatWrVK\n1dXVWrlypY4cOTKuYgEAAJC6DAfTzZs3q7GxUVu3blVdXZ22bNmiPXv2RPXzeDxat26dFixYoJ07\nd6q6uloPPfSQenp6YlI4AAAAUouhYOrxeLRjxw499dRTqqio0JIlS7R27Vpt27Ytqu9bb72lnJwc\nff/739esWbP0gx/8QBMmTNA777wTs+IBAACQOgwF06amJvn9flVXV19pq62tVUNDQ1TfhoYG1dbW\nRrTNnz9fBw8eHGOpAAAASGWGgmlHR4cKCwtlt9uvtJWUlMjr9aqzszOi79mzZ1VaWhrRVlJSovb2\n9nGUCwAAgFRlH7lLmMfjUXZ2dkRb/77P54to7+npGbTvl/sNJRAISJKy+i7q3Dm/kTKRhLxeryTJ\n7XbL4/GYXA3ijfFOL4x3emG800tXV5ekcG4bL0PB1OFwRAXL/v2cnJxR9XU6naP6rP7/sbvcHepy\ndxgpE0msra3N7BKQQIx3emG80wvjnV68Xq/y8vLG/XUMBdOysjK53W4FAgFlZoauAnC5XHI6nSoo\nKIjq29ERGShdLpcmT548qs+aOHGiysvL5XA4rnwWAAAArCMQCMjr9WrixIkx+XqGgmllZaXsdrsO\nHTqk+fPnS5L279+vqqqqqL7z5s3TK6+8EtF24MABfe973xtdYXa7SkpKjJQHAACABIvFTGk/Q1OR\nTqdTy5YtU11dnQ4fPqx3331X9fX1WrNmjaTQjGj/Kfjbb79dly5d0nPPPafjx4/rxz/+sTwej5Yu\nXRqz4gEAAJA6MoLBYNDIAT09PXrmmWf07//+78rPz9fatWv1ne98R5JUUVGhTZs2afny5ZKkw4cP\nq66uTidOnNCcOXP0zDPPqKKiIvbfBQAAAJKe4WAKAAAAxAN3FQEAAMASCKYAAACwBIIpAAAALIFg\nCgAAAEsgmAIAAMASTA2mPp9PGzZs0IIFC7R48WLV19cP2bexsVGrVq1SdXW1Vq5cqSNHjiSwUsSC\nkfF+7733tHz5ctXU1GjZsmXau3dvAitFLBgZ736tra2qqanRvn37ElAhYsnIeB87dkz33Xef5s2b\np7vuuksffPBBAitFLBgZ79/85je64447VFNTo9WrV6uxsTGBlSKWfD6f7rzzzmF/R483r5kaTDdv\n3qzGxkZt3bpVdXV12rJli/bs2RPVz+PxaN26dVqwYIF27typ6upqPfTQQ+rp6TGhaozVaMe7qalJ\n69ev18qVK7V7926tWrVKjzzyiI4dO2ZC1Rir0Y73QE8//TQ/10lqtOPd1dWlBx54QLNnz9abb76p\nW2+9VQ8//LDOnz9vQtUYq9GOd3Nzsx5//HF997vf1e7du1VRUaF169ZdeRgPkofP59Njjz2m5ubm\nIfvEJK8FTdLd3R2cO3ducN++fVfa/uVf/iX4ne98J6rv9u3bg0uWLIlou+2224JvvPFG3OtEbBgZ\n7+effz744IMPRrT97d/+bfCf//mf414nYsPIePfbtWtX8N577w1WVFQEP/zww0SUiRgxMt4///nP\ng7fddltE27e//e3g+++/H/c6ERtGxru+vj549913X9nv6uoKzpkzJ/jxxx8npFbERnNzc3DZsmXB\nZcuWDfs7OhZ5zbQZ06amJvn9flVXV19pq62tVUNDQ1TfhoYG1dbWRrTNnz9fBw8ejHudiA0j471i\nxQr9/d//fVR7V1dXXGtE7BgZb0nq7OzUT3/6Uz377LMK8syPpGNkvPft26dvfOMbEW3bt2/XzTff\nHPc6ERtGxruwsFDNzc06cOCAgsGgfv3rXys/P18zZ85MZMkYpw8//FALFy7Ur371q2F/R8cir9nH\nXOU4dXR0qLCwUHZ7uISSkhJ5vV51dnaqqKjoSvvZs2d1/fXXRxxfUlIy7HQyrMXIeM+aNSvi2E8+\n+UR/+MMfdN999yWsXoyPkfGWpE2bNmnFihW69tprE10qYsDIeJ8+fVo33XSTfvjDH2rv3r2aPn26\n/uEf/kHz5883o3SMgZHxvuOOO7R3717dd999stlsyszM1Msvv6z8/HwzSscY3XvvvaPqF4u8ZtqM\nqcfjUXZ2dkRb/77P54to7+npGbTvl/vBuoyM90Dnz5/X+vXrVVtbq1tuuSWuNSJ2jIz373//ex08\neFB/93d/l7D6EFtGxru7u1uvvvqqSktL9eqrr+orX/mKHnjgAbW3tyesXoyPkfF2u91yuVyqq6vT\n9u3btXz5cj3xxBNcU5yiYpHXTAumDocjqtD+/ZycnFH1dTqd8S0SMWNkvPu5XC6tWbNGGRkZeuGF\nF+JeI2JntOPt9XpVV1enurq6qF9mSB5Gfr5tNpsqKyv18MMPq6KiQo8//rjKy8u1a9euhNWL8TEy\n3s8//7zmzJmje++9VzfccIN+9KMfKScnRzt37kxYvUicWOQ104JpWVmZ3G63AoHAlTaXyyWn06mC\ngoKovh0dHRFtLpdLkydPTkitGD8j4y1J7e3tWr16tfx+v7Zu3Rp16hfWNtrxbmhoUGtrq9avX6+a\nmhrV1NRIkh588EE9/fTTiS4bY2Tk53vy5MlRl+uUl5erra0tIbVi/IyM95EjR1RRUXFlPyMjQxUV\nFTpz5kzC6kXixCKvmRZMKysrZbfbdejQoStt+/fvV1VVVVTfefPmRV04e+DAgYgLr2FtRsbb4/Fo\n7dq1ysrK0rZt2zRp0qRElooYGO14z5s3T3v27NGuXbu0e/du7d69W5L0k5/8RI888khCa8bYGfn5\nrq6uVlNTU0TbiRMnNG3atLjXidgwMt6lpaVR1xeePHlS06dPj3udSLxY5DXTgqnT6dSyZctUV1en\nw4cP691331V9fb3WrFkjKZSw+9c5u/3223Xp0iU999xzOn78uH784x/L4/Fo6dKlZpUPg4yM94sv\nvqjW1lZt3LhRgUBALpdLLpeLu/KTyGjHOzs7WzNmzIj4I4X+MisuLjbzW4ABRn6+77nnHh07dkxb\ntmzRqVOn9MILL6i1tVV33XWXmd8CDDAy3itXrtT27du1a9cunTp1Ss8//7za2tq0fPlyM78FxFDM\n89rYV7UaP4/HE3ziiSeCNTU1wZtvvjn42muvXXlvzpw5EeteNTQ0BFesWBGcN29ecNWqVcGjR4+a\nUTLGYbTj/c1vfjNYUVER9eeJJ54wq3SMgZGf74FYxzQ5GRnvAwcOBFesWBGcO3ducMWKFcH9+/eb\nUTLGwch479ixI7h06dLg/Pnzg6tXr+bv7yT35d/Rsc5rGcEgiwYCAADAfKY+khQAAADoRzAFAACA\nJRBMAQAAYAkEUwAAAFgCwRQAAACWQDAFAACAJRBMAQAAYAkEUwAAAFgCwRQAAACWQDAFAACAJRBM\nAQAAYAn/H+N/Iqk0UItIAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11ff769e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style(\"whitegrid\")\n",
    "sns.plt.plot(roc_survival[0], roc_survival[1])\n",
    "sns.plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Logistic Regression using Scikit-Learn\n",
    "\n",
    "Here we shall learn how to perform modeling using scikit-learn."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn import metrics\n",
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Instantiate a logistic regression model as:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "log_sci_model = LogisticRegression()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Train the model with 'Fare':"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.79685746352413023"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import metrics, cross_validation\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "# Modify the code below to include all possible features.\n",
    "\n",
    "features = ['Pclass', 'Imputed_Age', 'SibSp', 'Parch', 'Fare', 'C', 'Q', 'female']\n",
    "log_sci_model = LogisticRegression()\n",
    "log_sci_model = log_sci_model.fit(train_data[features], train_data['Survived'])\n",
    "log_sci_model.score(train_data[features], train_data['Survived'])"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "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": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}