Assignment4.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## HW4.0 Final Project description"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Project Proposal\n",
    "\n",
    "Intelligent Tutoring Systems are helping saving millions of hours for students and helping them learn deeper. Students imbibe concepts at varying rates of learning. By predicting student performance on problems, content can be tailored to their needs. We have trainings around the year, which are often limited by its class size. Instead, if these trainings are delivered online adaptive to the learning ability of each employee, we can create a more skilled work force and also build a knowledge repository. The aim of the project is to predict student performance from the log of online trainings for algebra (KDD Cup 2010, Educational Data Mining Challenge. \n",
    "\n",
    "##### Data Source and Description \n",
    "\n",
    "The data is from the KDD cup. Four key terms form the building blocks of the data. These are problem (tasks for the student), step (observable part of the solution), knowledge component (concept required for a step), and opportunity (chance to demonstrate learning a knowledge component).\n",
    "\n",
    "##### Steps\n",
    "\n",
    "1. Data and Problem Understanding\n",
    "2. Build baseline model on sample \n",
    "3. EDA\n",
    "4. Build model on training set \n",
    "5. Test model on a hold out set of the data\n",
    "\n",
    "##### Success metrics Primary- \n",
    "\n",
    "Prediction accuracy of student performance- (AUC for this binary classification task) Secondary- Similarity measures for knowledge components, Time saved in hours \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### HW4.1 Build a decision to predict whether you can play tennis or not"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### HW4.1.1 What is the classification accuracy of the tree on the training data?\n",
    "\n",
    "###### Ans : \n",
    "The classification accuracy of a tree on a training dataset will be 100% because an ID3 algorithm will keep on splitting the tree till it reaches the maximum number of layers or till it runs out of examples."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### HW4.1.2 Is it possible to produce some set of correct training examples that will get the algorihtm to include the attribute Temperature in the learned tree, even though the true target concept is independent of Temperature? if no, explain. If yes, give such a set.\n",
    "\n",
    "##### Ans. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### HW4.1.3 Now, build a tree using only examples D1–D7. What is the classification accuracy for the training set? what is the accuracy for the test set (examples D8–D14)? explain why you think these are the results.\n",
    "\n",
    "###### Ans. \n",
    "The accuracy in the training dataset (D1-D7) is 100%."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### HW4.1.4 In this case, and others, there are only a few labelled examples available for training (that is, no additional data is available for testing or validation). Suggest a concrete pruning strategy, that can be readily embedded in the algorithm, to avoid over fitting. Explain why you think this strategy should work.\n",
    "\n",
    "##### Ans. \n",
    "overfitting can be avoided using decision tree pruning or by limiting the depth of the tree."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Decision Trees for Classification ID3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overwriting dataset.txt\n"
     ]
    }
   ],
   "source": [
    "%%writefile dataset.txt\n",
    "d1 sunny hot high FALSE no\n",
    "d2 sunny hot high TRUE no\n",
    "d3 overcast hot high FALSE yes\n",
    "d4 rainy mild high FALSE yes\n",
    "d5 rainy cool normal FALSE yes\n",
    "d6 rainy cool normal TRUE no\n",
    "d6 overcast cool normal TRUE yes\n",
    "d7 sunny mild high FALSE no\n",
    "d8 sunny cool normal FALSE yes\n",
    "d9 rainy mild normal FALSE yes\n",
    "d10 sunny mild normal TRUE yes\n",
    "d11 overcast mild high TRUE yes\n",
    "d12 overcast hot normal FALSE yes\n",
    "d12 rainy mild high TRUE no"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "f=open(\"/Users/z00193k/Desktop/DataScienceTrainingDocs/Assignments/DecisionTree/dataset.txt\", \"r\")\n",
    "inputs=[]\n",
    "for line in f:\n",
    "    inter=[]\n",
    "    current={}\n",
    "    splitline=[]\n",
    "    splitline = line.split()\n",
    "    current[\"outlook\"]=splitline[1]\n",
    "    current[\"temperature\"]=splitline[2]\n",
    "    current[\"humidity\"]=splitline[3]\n",
    "    current[\"wind\"]=splitline[4]\n",
    "    inter.append(current)\n",
    "    inter.append(splitline[5])\n",
    "    inter=tuple(inter)\n",
    "    inputs.append(inter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#calculating the entropy of the data\n",
    "\n",
    "from __future__ import division\n",
    "from collections import Counter, defaultdict\n",
    "from functools import partial\n",
    "import math, random\n",
    "\n",
    "def entropy(class_probabilities):\n",
    "    \"\"\"given a list of class probabilities, compute the entropy\"\"\"\n",
    "    return sum(-p * math.log(p, 2) for p in class_probabilities if p)\n",
    "\n",
    "def class_probabilities(labels):\n",
    "    total_count = len(labels)\n",
    "    return [count/total_count for count in Counter(labels).values()]\n",
    "\n",
    "#This function will return the entropy of input dataset\n",
    "def data_entropy(labeled_data):        \n",
    "    labels = [label for _, label in labeled_data]\n",
    "    probabilities = class_probabilities(labels)\n",
    "    return entropy(probabilities)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9402859586706309"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_entropy(inputs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Entropy of a Partition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def partition_entropy(subsets):\n",
    "    \"\"\"find the entropy from this partition of data into subsets\"\"\"\n",
    "    total_count = sum(len(subset) for subset in subsets)    \n",
    "    return sum(data_entropy(subset)*len(subset)/total_count for subset in subsets )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def group_by(items, key_fn):\n",
    "    \"\"\"returns a defaultdict(list), where each input item \n",
    "    is in the list whose key is key_fn(item)\"\"\"\n",
    "    groups = defaultdict(list)\n",
    "    for item in items:\n",
    "        key = key_fn(item)\n",
    "        groups[key].append(item)\n",
    "    return groups\n",
    "    \n",
    "def partition_by(inputs, attribute):\n",
    "    \"\"\"returns a dict of inputs partitioned by the attribute\n",
    "    each input is a pair (attribute_dict, label)\"\"\"\n",
    "    return group_by(inputs, lambda x: x[0][attribute])\n",
    "\n",
    "def partition_entropy_by(inputs, attribute):\n",
    "    \"\"\"computes the entropy corresponding to the given partition\"\"\"        \n",
    "    partitions = partition_by(inputs, attribute)\n",
    "    return partition_entropy(partitions.values())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "outlook 0.693536138896\n",
      "temperature 0.911063393012\n",
      "humidity 0.788450457308\n",
      "wind 0.892158928262\n"
     ]
    }
   ],
   "source": [
    "for key in['outlook','temperature','humidity','wind']:\n",
    "    print key, partition_entropy_by(inputs, key)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Information Gain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "outlook 0.246749819774\n",
      "temperature 0.029222565659\n",
      "humidity 0.151835501362\n",
      "wind 0.0481270304083\n"
     ]
    }
   ],
   "source": [
    "attributes = ['outlook','temperature','humidity','wind']\n",
    "def inf_gain(inputs, keys):\n",
    "    for key in keys:\n",
    "        IG=data_entropy(inputs)-partition_entropy_by(inputs, key)\n",
    "        print key, IG\n",
    "\n",
    "inf_gain(inputs, attributes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def classify(tree, input):\n",
    "    \"\"\"classify the input using the given decision tree\"\"\"\n",
    "    \n",
    "    # if this is a leaf node, return its value\n",
    "    if tree in [True, False]:\n",
    "        return tree\n",
    "   \n",
    "    # otherwise find the correct subtree\n",
    "    attribute, subtree_dict = tree\n",
    "    \n",
    "    subtree_key = input.get(attribute)  # None if input is missing attribute\n",
    "\n",
    "    if subtree_key not in subtree_dict: # if no subtree for key,\n",
    "        subtree_key = None              # we'll use the None subtree\n",
    "    \n",
    "    subtree = subtree_dict[subtree_key] # choose the appropriate subtree\n",
    "    return classify(subtree, input)     # and use it to classify the input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "building the tree\n",
      "True\n"
     ]
    }
   ],
   "source": [
    "def build_tree_id3(inputs, split_candidates=None):\n",
    "\n",
    "    # if this is our first pass, \n",
    "    # all keys of the first input are split candidates\n",
    "    if split_candidates is None:\n",
    "        split_candidates = inputs[0][0].keys()\n",
    "\n",
    "    # count Trues and Falses in the inputs\n",
    "    num_inputs = len(inputs)\n",
    "    num_trues = len([label for item, label in inputs if label])\n",
    "    num_falses = num_inputs - num_trues\n",
    "    \n",
    "    if num_trues == 0:                  # if only Falses are left\n",
    "        return False                    # return a \"False\" leaf\n",
    "        \n",
    "    if num_falses == 0:                 # if only Trues are left\n",
    "        return True                     # return a \"True\" leaf\n",
    "\n",
    "    if not split_candidates:            # if no split candidates left\n",
    "        return num_trues >= num_falses  # return the majority leaf\n",
    "                            \n",
    "    # otherwise, split on the best attribute\n",
    "    best_attribute = min(split_candidates,\n",
    "        key=partial(partition_entropy_by, inputs))\n",
    "\n",
    "    partitions = partition_by(inputs, best_attribute)\n",
    "    new_candidates = [a for a in split_candidates \n",
    "                      if a != best_attribute]\n",
    "    \n",
    "    # recursively build the subtrees\n",
    "    subtrees = { attribute : build_tree_id3(subset, new_candidates)\n",
    "                 for attribute, subset in partitions.iteritems() }\n",
    "\n",
    "    subtrees[None] = num_trues > num_falses # default case\n",
    "\n",
    "    return (best_attribute, subtrees)\n",
    "\n",
    "print \"building the tree\"\n",
    "tree = build_tree_id3(inputs)\n",
    "print tree"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### HW4.3.1 Do a Kaggle Submission to Predict survival on the Titanic using Python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "EOL while scanning string literal (<ipython-input-5-41ed82559b72>, line 2)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-5-41ed82559b72>\"\u001b[0;36m, line \u001b[0;32m2\u001b[0m\n\u001b[0;31m    sys.path.append('/Users/z00193k/Desktop/DataScienceTrainingDocs/Assignments/DecisionTree/kaggle-titanic-master’)\u001b[0m\n\u001b[0m                                                                                                                    \n^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m EOL while scanning string literal\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "sys.path.append('/Users/z00193k/Desktop/DataScienceTrainingDocs/Assignments/DecisionTree/kaggle-titanic-master’)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "ImportError",
     "evalue": "No module named KaggleAux",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-6-6403e252ef77>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpatsy\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mdmatrices\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mdatasets\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msvm\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mKaggleAux\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpredict\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mka\u001b[0m \u001b[0;31m# see github.com/agconti/kaggleaux for more details\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mImportError\u001b[0m: No module named KaggleAux"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import statsmodels.api as sm\n",
    "from statsmodels.nonparametric.kde import KDEUnivariate\n",
    "from statsmodels.nonparametric import smoothers_lowess\n",
    "from pandas import Series, DataFrame\n",
    "from patsy import dmatrices\n",
    "from sklearn import datasets, svm\n",
    "from KaggleAux import predict as ka # see github.com/agconti/kaggleaux for more details"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"/Users/z00193k/Desktop/DataScienceTrainingDocs/Assignments/DecisionTree/kaggle-titanic-master/data/train.csv\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
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   "outputs": [
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       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>347742</td>\n",
       "      <td>11.1333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Nasser, Mrs. Nicholas (Adele Achem)</td>\n",
       "      <td>female</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>237736</td>\n",
       "      <td>30.0708</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Sandstrom, Miss. Marguerite Rut</td>\n",
       "      <td>female</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>PP 9549</td>\n",
       "      <td>16.7000</td>\n",
       "      <td>G6</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Bonnell, Miss. Elizabeth</td>\n",
       "      <td>female</td>\n",
       "      <td>58</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113783</td>\n",
       "      <td>26.5500</td>\n",
       "      <td>C103</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Saundercock, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5. 2151</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Andersson, Mr. Anders Johan</td>\n",
       "      <td>male</td>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>347082</td>\n",
       "      <td>31.2750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Vestrom, Miss. Hulda Amanda Adolfina</td>\n",
       "      <td>female</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>350406</td>\n",
       "      <td>7.8542</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Hewlett, Mrs. (Mary D Kingcome)</td>\n",
       "      <td>female</td>\n",
       "      <td>55</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>248706</td>\n",
       "      <td>16.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Rice, Master. Eugene</td>\n",
       "      <td>male</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>382652</td>\n",
       "      <td>29.1250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Williams, Mr. Charles Eugene</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>244373</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Vander Planke, Mrs. Julius (Emelia Maria Vande...</td>\n",
       "      <td>female</td>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>345763</td>\n",
       "      <td>18.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Masselmani, Mrs. Fatima</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2649</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Fynney, Mr. Joseph J</td>\n",
       "      <td>male</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>239865</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Beesley, Mr. Lawrence</td>\n",
       "      <td>male</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>248698</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>D56</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>McGowan, Miss. Anna \"Annie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330923</td>\n",
       "      <td>8.0292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Sloper, Mr. William Thompson</td>\n",
       "      <td>male</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113788</td>\n",
       "      <td>35.5000</td>\n",
       "      <td>A6</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Palsson, Miss. Torborg Danira</td>\n",
       "      <td>female</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>349909</td>\n",
       "      <td>21.0750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Asplund, Mrs. Carl Oscar (Selma Augusta Emilia...</td>\n",
       "      <td>female</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>347077</td>\n",
       "      <td>31.3875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Emir, Mr. Farred Chehab</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2631</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Fortune, Mr. Charles Alexander</td>\n",
       "      <td>male</td>\n",
       "      <td>19</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>19950</td>\n",
       "      <td>263.0000</td>\n",
       "      <td>C23 C25 C27</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>O'Dwyer, Miss. Ellen \"Nellie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330959</td>\n",
       "      <td>7.8792</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Todoroff, Mr. Lalio</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349216</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>861</th>\n",
       "      <td>862</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Giles, Mr. Frederick Edward</td>\n",
       "      <td>male</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>28134</td>\n",
       "      <td>11.5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>862</th>\n",
       "      <td>863</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Swift, Mrs. Frederick Joel (Margaret Welles Ba...</td>\n",
       "      <td>female</td>\n",
       "      <td>48</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17466</td>\n",
       "      <td>25.9292</td>\n",
       "      <td>D17</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>863</th>\n",
       "      <td>864</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Sage, Miss. Dorothy Edith \"Dolly\"</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>CA. 2343</td>\n",
       "      <td>69.5500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>864</th>\n",
       "      <td>865</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Gill, Mr. John William</td>\n",
       "      <td>male</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>233866</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>865</th>\n",
       "      <td>866</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Bystrom, Mrs. (Karolina)</td>\n",
       "      <td>female</td>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>236852</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>866</th>\n",
       "      <td>867</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Duran y More, Miss. Asuncion</td>\n",
       "      <td>female</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>SC/PARIS 2149</td>\n",
       "      <td>13.8583</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>867</th>\n",
       "      <td>868</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Roebling, Mr. Washington Augustus II</td>\n",
       "      <td>male</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17590</td>\n",
       "      <td>50.4958</td>\n",
       "      <td>A24</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868</th>\n",
       "      <td>869</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>van Melkebeke, Mr. Philemon</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>345777</td>\n",
       "      <td>9.5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>869</th>\n",
       "      <td>870</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnson, Master. Harold Theodor</td>\n",
       "      <td>male</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>347742</td>\n",
       "      <td>11.1333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>870</th>\n",
       "      <td>871</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Balkic, Mr. Cerin</td>\n",
       "      <td>male</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349248</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>871</th>\n",
       "      <td>872</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Beckwith, Mrs. Richard Leonard (Sallie Monypeny)</td>\n",
       "      <td>female</td>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>11751</td>\n",
       "      <td>52.5542</td>\n",
       "      <td>D35</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>872</th>\n",
       "      <td>873</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Carlsson, Mr. Frans Olof</td>\n",
       "      <td>male</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>695</td>\n",
       "      <td>5.0000</td>\n",
       "      <td>B51 B53 B55</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>873</th>\n",
       "      <td>874</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Vander Cruyssen, Mr. Victor</td>\n",
       "      <td>male</td>\n",
       "      <td>47</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>345765</td>\n",
       "      <td>9.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>874</th>\n",
       "      <td>875</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Abelson, Mrs. Samuel (Hannah Wizosky)</td>\n",
       "      <td>female</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>P/PP 3381</td>\n",
       "      <td>24.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>875</th>\n",
       "      <td>876</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Najib, Miss. Adele Kiamie \"Jane\"</td>\n",
       "      <td>female</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2667</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>876</th>\n",
       "      <td>877</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Gustafsson, Mr. Alfred Ossian</td>\n",
       "      <td>male</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7534</td>\n",
       "      <td>9.8458</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>877</th>\n",
       "      <td>878</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Petroff, Mr. Nedelio</td>\n",
       "      <td>male</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349212</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>878</th>\n",
       "      <td>879</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Laleff, Mr. Kristo</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349217</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>879</th>\n",
       "      <td>880</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)</td>\n",
       "      <td>female</td>\n",
       "      <td>56</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>11767</td>\n",
       "      <td>83.1583</td>\n",
       "      <td>C50</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>880</th>\n",
       "      <td>881</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Shelley, Mrs. William (Imanita Parrish Hall)</td>\n",
       "      <td>female</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>230433</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>881</th>\n",
       "      <td>882</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Markun, Mr. Johann</td>\n",
       "      <td>male</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349257</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>882</th>\n",
       "      <td>883</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Dahlberg, Miss. Gerda Ulrika</td>\n",
       "      <td>female</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7552</td>\n",
       "      <td>10.5167</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>883</th>\n",
       "      <td>884</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Banfield, Mr. Frederick James</td>\n",
       "      <td>male</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>C.A./SOTON 34068</td>\n",
       "      <td>10.5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>884</th>\n",
       "      <td>885</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Sutehall, Mr. Henry Jr</td>\n",
       "      <td>male</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>SOTON/OQ 392076</td>\n",
       "      <td>7.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>885</th>\n",
       "      <td>886</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Rice, Mrs. William (Margaret Norton)</td>\n",
       "      <td>female</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>382652</td>\n",
       "      <td>29.1250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>887</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Montvila, Rev. Juozas</td>\n",
       "      <td>male</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>211536</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>888</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Graham, Miss. Margaret Edith</td>\n",
       "      <td>female</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112053</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>B42</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>889</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>W./C. 6607</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>890</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Behr, Mr. Karl Howell</td>\n",
       "      <td>male</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111369</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>C148</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>891</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Dooley, Mr. Patrick</td>\n",
       "      <td>male</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370376</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass  \\\n",
       "0              1         0       3   \n",
       "1              2         1       1   \n",
       "2              3         1       3   \n",
       "3              4         1       1   \n",
       "4              5         0       3   \n",
       "5              6         0       3   \n",
       "6              7         0       1   \n",
       "7              8         0       3   \n",
       "8              9         1       3   \n",
       "9             10         1       2   \n",
       "10            11         1       3   \n",
       "11            12         1       1   \n",
       "12            13         0       3   \n",
       "13            14         0       3   \n",
       "14            15         0       3   \n",
       "15            16         1       2   \n",
       "16            17         0       3   \n",
       "17            18         1       2   \n",
       "18            19         0       3   \n",
       "19            20         1       3   \n",
       "20            21         0       2   \n",
       "21            22         1       2   \n",
       "22            23         1       3   \n",
       "23            24         1       1   \n",
       "24            25         0       3   \n",
       "25            26         1       3   \n",
       "26            27         0       3   \n",
       "27            28         0       1   \n",
       "28            29         1       3   \n",
       "29            30         0       3   \n",
       "..           ...       ...     ...   \n",
       "861          862         0       2   \n",
       "862          863         1       1   \n",
       "863          864         0       3   \n",
       "864          865         0       2   \n",
       "865          866         1       2   \n",
       "866          867         1       2   \n",
       "867          868         0       1   \n",
       "868          869         0       3   \n",
       "869          870         1       3   \n",
       "870          871         0       3   \n",
       "871          872         1       1   \n",
       "872          873         0       1   \n",
       "873          874         0       3   \n",
       "874          875         1       2   \n",
       "875          876         1       3   \n",
       "876          877         0       3   \n",
       "877          878         0       3   \n",
       "878          879         0       3   \n",
       "879          880         1       1   \n",
       "880          881         1       2   \n",
       "881          882         0       3   \n",
       "882          883         0       3   \n",
       "883          884         0       2   \n",
       "884          885         0       3   \n",
       "885          886         0       3   \n",
       "886          887         0       2   \n",
       "887          888         1       1   \n",
       "888          889         0       3   \n",
       "889          890         1       1   \n",
       "890          891         0       3   \n",
       "\n",
       "                                                  Name     Sex  Age  SibSp  \\\n",
       "0                              Braund, Mr. Owen Harris    male   22      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",
       "5                                     Moran, Mr. James    male  NaN      0   \n",
       "6                              McCarthy, Mr. Timothy J    male   54      0   \n",
       "7                       Palsson, Master. Gosta Leonard    male    2      3   \n",
       "8    Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)  female   27      0   \n",
       "9                  Nasser, Mrs. Nicholas (Adele Achem)  female   14      1   \n",
       "10                     Sandstrom, Miss. Marguerite Rut  female    4      1   \n",
       "11                            Bonnell, Miss. Elizabeth  female   58      0   \n",
       "12                      Saundercock, Mr. William Henry    male   20      0   \n",
       "13                         Andersson, Mr. Anders Johan    male   39      1   \n",
       "14                Vestrom, Miss. Hulda Amanda Adolfina  female   14      0   \n",
       "15                    Hewlett, Mrs. (Mary D Kingcome)   female   55      0   \n",
       "16                                Rice, Master. Eugene    male    2      4   \n",
       "17                        Williams, Mr. Charles Eugene    male  NaN      0   \n",
       "18   Vander Planke, Mrs. Julius (Emelia Maria Vande...  female   31      1   \n",
       "19                             Masselmani, Mrs. Fatima  female  NaN      0   \n",
       "20                                Fynney, Mr. Joseph J    male   35      0   \n",
       "21                               Beesley, Mr. Lawrence    male   34      0   \n",
       "22                         McGowan, Miss. Anna \"Annie\"  female   15      0   \n",
       "23                        Sloper, Mr. William Thompson    male   28      0   \n",
       "24                       Palsson, Miss. Torborg Danira  female    8      3   \n",
       "25   Asplund, Mrs. Carl Oscar (Selma Augusta Emilia...  female   38      1   \n",
       "26                             Emir, Mr. Farred Chehab    male  NaN      0   \n",
       "27                      Fortune, Mr. Charles Alexander    male   19      3   \n",
       "28                       O'Dwyer, Miss. Ellen \"Nellie\"  female  NaN      0   \n",
       "29                                 Todoroff, Mr. Lalio    male  NaN      0   \n",
       "..                                                 ...     ...  ...    ...   \n",
       "861                        Giles, Mr. Frederick Edward    male   21      1   \n",
       "862  Swift, Mrs. Frederick Joel (Margaret Welles Ba...  female   48      0   \n",
       "863                  Sage, Miss. Dorothy Edith \"Dolly\"  female  NaN      8   \n",
       "864                             Gill, Mr. John William    male   24      0   \n",
       "865                           Bystrom, Mrs. (Karolina)  female   42      0   \n",
       "866                       Duran y More, Miss. Asuncion  female   27      1   \n",
       "867               Roebling, Mr. Washington Augustus II    male   31      0   \n",
       "868                        van Melkebeke, Mr. Philemon    male  NaN      0   \n",
       "869                    Johnson, Master. Harold Theodor    male    4      1   \n",
       "870                                  Balkic, Mr. Cerin    male   26      0   \n",
       "871   Beckwith, Mrs. Richard Leonard (Sallie Monypeny)  female   47      1   \n",
       "872                           Carlsson, Mr. Frans Olof    male   33      0   \n",
       "873                        Vander Cruyssen, Mr. Victor    male   47      0   \n",
       "874              Abelson, Mrs. Samuel (Hannah Wizosky)  female   28      1   \n",
       "875                   Najib, Miss. Adele Kiamie \"Jane\"  female   15      0   \n",
       "876                      Gustafsson, Mr. Alfred Ossian    male   20      0   \n",
       "877                               Petroff, Mr. Nedelio    male   19      0   \n",
       "878                                 Laleff, Mr. Kristo    male  NaN      0   \n",
       "879      Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)  female   56      0   \n",
       "880       Shelley, Mrs. William (Imanita Parrish Hall)  female   25      0   \n",
       "881                                 Markun, Mr. Johann    male   33      0   \n",
       "882                       Dahlberg, Miss. Gerda Ulrika  female   22      0   \n",
       "883                      Banfield, Mr. Frederick James    male   28      0   \n",
       "884                             Sutehall, Mr. Henry Jr    male   25      0   \n",
       "885               Rice, Mrs. William (Margaret Norton)  female   39      0   \n",
       "886                              Montvila, Rev. Juozas    male   27      0   \n",
       "887                       Graham, Miss. Margaret Edith  female   19      0   \n",
       "888           Johnston, Miss. Catherine Helen \"Carrie\"  female  NaN      1   \n",
       "889                              Behr, Mr. Karl Howell    male   26      0   \n",
       "890                                Dooley, Mr. Patrick    male   32      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",
       "5        0            330877    8.4583          NaN        Q  \n",
       "6        0             17463   51.8625          E46        S  \n",
       "7        1            349909   21.0750          NaN        S  \n",
       "8        2            347742   11.1333          NaN        S  \n",
       "9        0            237736   30.0708          NaN        C  \n",
       "10       1           PP 9549   16.7000           G6        S  \n",
       "11       0            113783   26.5500         C103        S  \n",
       "12       0         A/5. 2151    8.0500          NaN        S  \n",
       "13       5            347082   31.2750          NaN        S  \n",
       "14       0            350406    7.8542          NaN        S  \n",
       "15       0            248706   16.0000          NaN        S  \n",
       "16       1            382652   29.1250          NaN        Q  \n",
       "17       0            244373   13.0000          NaN        S  \n",
       "18       0            345763   18.0000          NaN        S  \n",
       "19       0              2649    7.2250          NaN        C  \n",
       "20       0            239865   26.0000          NaN        S  \n",
       "21       0            248698   13.0000          D56        S  \n",
       "22       0            330923    8.0292          NaN        Q  \n",
       "23       0            113788   35.5000           A6        S  \n",
       "24       1            349909   21.0750          NaN        S  \n",
       "25       5            347077   31.3875          NaN        S  \n",
       "26       0              2631    7.2250          NaN        C  \n",
       "27       2             19950  263.0000  C23 C25 C27        S  \n",
       "28       0            330959    7.8792          NaN        Q  \n",
       "29       0            349216    7.8958          NaN        S  \n",
       "..     ...               ...       ...          ...      ...  \n",
       "861      0             28134   11.5000          NaN        S  \n",
       "862      0             17466   25.9292          D17        S  \n",
       "863      2          CA. 2343   69.5500          NaN        S  \n",
       "864      0            233866   13.0000          NaN        S  \n",
       "865      0            236852   13.0000          NaN        S  \n",
       "866      0     SC/PARIS 2149   13.8583          NaN        C  \n",
       "867      0          PC 17590   50.4958          A24        S  \n",
       "868      0            345777    9.5000          NaN        S  \n",
       "869      1            347742   11.1333          NaN        S  \n",
       "870      0            349248    7.8958          NaN        S  \n",
       "871      1             11751   52.5542          D35        S  \n",
       "872      0               695    5.0000  B51 B53 B55        S  \n",
       "873      0            345765    9.0000          NaN        S  \n",
       "874      0         P/PP 3381   24.0000          NaN        C  \n",
       "875      0              2667    7.2250          NaN        C  \n",
       "876      0              7534    9.8458          NaN        S  \n",
       "877      0            349212    7.8958          NaN        S  \n",
       "878      0            349217    7.8958          NaN        S  \n",
       "879      1             11767   83.1583          C50        C  \n",
       "880      1            230433   26.0000          NaN        S  \n",
       "881      0            349257    7.8958          NaN        S  \n",
       "882      0              7552   10.5167          NaN        S  \n",
       "883      0  C.A./SOTON 34068   10.5000          NaN        S  \n",
       "884      0   SOTON/OQ 392076    7.0500          NaN        S  \n",
       "885      5            382652   29.1250          NaN        Q  \n",
       "886      0            211536   13.0000          NaN        S  \n",
       "887      0            112053   30.0000          B42        S  \n",
       "888      2        W./C. 6607   23.4500          NaN        S  \n",
       "889      0            111369   30.0000         C148        C  \n",
       "890      0            370376    7.7500          NaN        Q  \n",
       "\n",
       "[891 rows x 12 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>32.204208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>257.353842</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>49.693429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>223.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.910400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>668.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
       "count   891.000000  891.000000  891.000000  714.000000  891.000000   \n",
       "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
       "std     257.353842    0.486592    0.836071   14.526497    1.102743   \n",
       "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
       "25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n",
       "50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n",
       "75%     668.500000    1.000000    3.000000   38.000000    1.000000   \n",
       "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
       "\n",
       "            Parch        Fare  \n",
       "count  891.000000  891.000000  \n",
       "mean     0.381594   32.204208  \n",
       "std      0.806057   49.693429  \n",
       "min      0.000000    0.000000  \n",
       "25%      0.000000    7.910400  \n",
       "50%      0.000000   14.454200  \n",
       "75%      0.000000   31.000000  \n",
       "max      6.000000  512.329200  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Survived         0\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "Age            177\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             0\n",
       "Cabin          687\n",
       "Embarked         2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df.index)-df.count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dropping Missing Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId    0\n",
       "Survived       0\n",
       "Pclass         0\n",
       "Name           0\n",
       "Sex            0\n",
       "Age            0\n",
       "SibSp          0\n",
       "Parch          0\n",
       "Fare           0\n",
       "Embarked       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.drop(['Ticket','Cabin'], axis=1) \n",
    "df = df.dropna()\n",
    "len(df.index)-df.count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### EDA Graphically"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x11a6ea910>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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/AFa5+zf2tdIppywbnWhERAYwEuPKpQ56DyIiInkqk8lw8slLmDx5NZ2dDzN5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U+X9tSvD8OGQdOmsHfvde+eITADU9tNZcTyEYTtCkv6+JJZs2awdKmbkfPkk+5P1BhjjDHGpH0J\nJTQ6Ax1VdeflFar6F9AJ6OLF8WsA21R1t6peBKYArWNs0xqY6Dn2CiCniIR47i8GjsVx7PQ66NwY\n/5o+HcqVg/LlE7X7xkMbWbhzIc/UfCbe7Y4ehcaNoUMHeO21RD1V2ta5MzzzDNxzj6uWep1uyXkL\nE9tO5OFvHmbfiX0+CDB5lSoFy5bBwYPu7+bQIX9HZIwxxhhjfC2hhEYGVT0Sc6WqHgYyeHH8QkD0\nrw/3edbFt83+WLaJTW/PFJVPRSSnF9sbY26UKgwZAi95V8gzNv3D+vNSnZfIHhz3dJWzZ6FVK7j7\nbnjT6wlu6dCLL145UYlIatxd8m6erfksrb5qxekLp30QYPLKnh1mznT1VmrUgLVr/R2RMcYYY4zx\npYQSGvEN3PXnoN4PgRKqWhk4CLznx1iMST/mzXOFC5omrmzNmgNrWLp3KT2rxz1dJTISOnZ0pSLe\nfTcdFwD11qBBbkjCnXcmaljCS3VeomJIRbp8m7qLhF4WEABvv+3qpjZp4gYUGWOMMcaYtCkogccr\niciJWNYLkMmL4+8HikS7X9izLuY2tySwzVU8I0QuGwvMiWvb/v37/3s7NDSU0NDQ+A5tjInP4MGu\nMmciswyvLnyV1+u/TpYMWWJ9XNV1gj1zxtW9DPC2sXR6JuL+XTJlgtBQWLgQCha8jt2FMS3H0GRS\nE15f+DqDGg/yXazJqH17KF0a2rRxnW7797e/J2PSu7CwMMLCwvwdhjHGmCQUb0JDVQNv8PirgFIi\nUhQ4AHQAOsbYZjbQC5gqIrWA46oaHu1xIUa9DBEpoKoHPXfvAzbGFUD0hIYx5gb8/DPs3w8PPJCo\n3X/c8SM7j+2ke7XucW7z9tvw228QFgYZMyYyzvRIBAYMcEmNOnXgf/+7rhonwUHBfN3+a+qNr0fB\n7AUTrG+SWlSpAitXwv33u6TGpEmu3asxJn2K+cXWgAED/BdMHESkMK62XAgQBYxV1Q9EJDcwFSgK\n7ALaq2qEZ5++wKNAJPCsqs73R+zGGOMPPv2+SlUvAb2B+cAfwBRV3SwiPUSku2ebucBOEdkOjAH+\nHYsuIl8CS4EyIrJHRLp5HhoqIutFZC3QENeNxRjjK6quP+aAARCU0MCua0VpFC8veJlBjQaRITD2\n8jvffAPjxsHcuYnuBmv69oW33nIjNRYsuK5d82XNx/xO8xm2dBiT1k3yTXx+EBLiBq3kzetyPTt3\nJryPMcb4USTwgqpWAGoDvTwdAl8FFqjqrcAioC+AiJQH2gPlgGbAhyI2WdMYk35c/yeT6+RpqXpr\njHVjYtzvHce+D8Wx3psOK8aYpPLdd3DypCtukQhfbviSTEGZuK/cfbE+vmmTa7c5d677AGpuQOfO\nUKSIm3PRv787sV5e2xbNVZTvO33PXRPuImemnLS6tZVvY00mwcEwdqxr61q7Nnz1lSs5YowxKY1n\nBPJBz+1TIrIZNx27Ne5LPIAJQBguydEK94VhJLBLRLbhugyuSObQjTHGL2xGsTEmfpGR8PrrMHAg\nBF7/LLRzked4Y9EbvNvkXWL70igiAtq2dUUc77gjKQI2NGwIixfDhx9C166uKImXyucrz5yOc3hi\nzhN8u+Vb38WYzETg6adh8mSXl/vwQzfwyBhjUioRKQZUBpYDIZenZHuSHvk9myW2W6AxxqQJltAw\nxsTvo4/ceP17703U7qNXjqZygcrUK1LvmseiotyAgiZN3Oduk4RKl4bly11CqnZt2L7d612rF6rO\n3Ifm8uR3TzL9j7TVJqRRI1iyBEaPdoNXLvizX5cxxsRBRLIBM3A1MU4BMVOwlpI1xhiSYcqJMSYV\nO3jQ1WT4+edEdTY5dPoQg5cM5tduv8b6+Ntvw7FjMGPGjQZqYpU1K3zxhUtK1akDY8a44TBeqHZz\nNX7o9ANNJzfl9MXTdK3c1bexJqOSJWHZMujUyXW8nTED8udPeD9jjEkOIhKES2ZMUtVZntXhIhKi\nquEiUgC43Kfb626B1vnPGONvvug2JZqGx9yKiKbl388Yn3v4YShcGIYMSdTuj816jFyZcjH8nuHX\nPDZnDvTsCatWQYECNxqoSdDKlfDgg9C6tZvf42UbmS1HttB8cnMeqfQIbzZ8M9ZpQ6lVVBS8+abL\n+Xz7LVSu7O+IjDHJSURQ1RT3oiYiE4EjqvpCtHVDgKOqOkREXgFyq+qrnqKgk4GauKkmPwKlY14A\np+Vr4nbt+lCs2DB/h3GVXbv6MGNGyorJmJQoKV6HbcqJMSZ206a5Hqpvvpmo3VfuX8m87fPoF9rv\nmsf+/BMeewymT7dkRrKpUQNWr4Zdu6BePa/bfZS9qSzLHlvG/7b9j66zunLhUtqZoxEQAO+84/J1\nTZrYSCFjjP+JSF3gYeAuEVkjIqtFpCkwBGgiIluBRsBgAFXdBEwDNgFzgZ5pNnNhjDGxsISGMeZa\ne/deqaCYNet17x6lUfSe25tBjQaRIzjHVY+dPAlt2rgao7VqJVXAxiu5c8PMmfDQQ1CzphuW4IWQ\nbCGEdQ3j1IVTNBjfgL0RexPeKRV58EH44Qd48UXo18+N3DDGGH9Q1SWqGqiqlVW1iqpWVdXvVfWo\nqjZW1VtV9W5VPR5tn0GqWkpVy6nqfH/Gb4wxyc0SGsaYq50751p+PvdcotuOfL72cwIDAulcqfNV\n61Vd8c969eCJJ5IgVnP9RNy/7Zw58PzzbvGiMmaWDFmY8cAM7it3H9XHVmf+jrR1zVy1qpuVs2AB\ntGsHp075OyJjjDHGGJMQS2gYY65QhUcfhWLF4NVXE3WI8FPh9F3Yl9HNRxMgV7/EDB4M+/fDyJFJ\nEKu5MTVrwu+/w19/uQzTrl0J7iIivFz3Zaa2m0q3Wd3oM78P5yLP+T7WZBISAosWQZ48roaql7Ny\njDHGGGOMn1hCwxjjqMILL7gPuJ99lqiuJgDPfP8M3Sp3o2rBqlet//57GDUKvv4agoOTImBzw/Lk\ncdNOOnZ0NTa8nILSsFhD1j25jj0Re6g6piqr9q/ycaDJJzgYxo51I4hq14YkLsRtjDHGGGOSkCU0\njDGuaEDv3rB0KcybB5kzJ+ows7bMYs2BNfRreHUh0B074JFHYOpUKFQoKQI2SUbETTuZM8dNRXnh\nBa+moNyU5SamPTCNfg370fKrlvT8X0+Onj2aDAH7nsiVEjIPPui63hpjjDHGmJTH5wkNEWkqIltE\n5E9Pm6nYtvlARLaJyFoRqRJt/TgRCReR9TG2zy0i80Vkq4j8ICI5ff17GJNmHTkCzZvDpk3w44+u\ncGQiRJyLoNfcXoy9dyyZM1xJiJw+DW3bumYp9eolVdAmydWs6bqg7NgB9et7NQUF4MHbHmRzr80E\nSADlRpfj498+5lLUJd/GmkwaNXI5vlGj4MknvcrzGGOMMcaYZOTThIaIBACjgHuACkBHESkbY5tm\nQElVLQ30AKJ/Fzbes29MrwILVPVWYBHQ1wfhG5O2qbo+lVWrwu23w/z5kCNHwvvF4Znvn+HeMvfS\nsFjDq57iscfcU/TsmRRBG5+6PAXlwQddgmPWLO92y5yHUc1HMb/TfL7a+BUVPqzAlI1TiNLU3y6k\nZElYtgz+/tu1dg0P93dExhhjjDHmMl+P0KgBbFPV3ap6EZgCtI6xTWtgIoCqrgByikiI5/5i4Fgs\nx20NTPDcngC08UHsxqRNly65D60NG8KAATBhArz7LmTIkOhDTtk4hRX7VjDs7mFXrR8+HLZvd0P2\nE1mSwyQ3ETftZNYsePZZeP11r/uYVipQibBHwhjZbCQjlo+g4kcVmbJxCpFRkT4O2rdy5LjyX+aO\nO1yCwxhjjDHG+F+Qj49fCNgb7f4+XJIjvm32e9bF9z1YflUNB1DVgyKSPwliNSZlu3DBtQj55x+I\niHAfPAMCIDAQMmVyS+bMV25nygQXL7rtDxyAjRvdJ7Hvv3dfOz/zDDzwAATd2MvA7uO7eWbeM3zf\n6XuyZsz67/oFC1xCY8WKRJfkMP5UqxasWuXmC7VvDxMnQpYsCe4mIjQp2YTGJRozb/s8hiwZwss/\nvkzvGr15vOrj5MmcJxmCT3oBAfDWW1C9OrRuDf37w1NPWaLOGGOMMcaffJ3QSC7q7wCMSXIRES75\nMG+ea6+5fTvkzw9580LOnO6T1KVLbjl37trl7FnImNFtny8f3HYb1K0L77wDRYokSYjnI8/T4esO\nvFTnpau6muzaBZ06wVdfJdlTGX/Ilw8WLnQtPxo0cKM2vKzqKiI0L92c5qWbs/rAav5vxf9R8oOS\nPFD+AR6t8ig1C9VEUmE24N57XV2N+++H5cvh44+9yvMYY4wxxhgf8HVCYz8Q/eNMYc+6mNvcksA2\nMYWLSIiqhotIAeBQXBv279//39uhoaGEhoYmHLUx/rRhA4wYAd98A3XqQIsWbjRF+fJu1EUK8uz3\nz1IwW0FerPPiv+vOnHFf6r/yCtx5px+DM0kjONhNSxo0yI3amDXLFUW5DlULVmVCmwkcOHmAz9d+\nTpeZXQgMCKRb5W50rtiZgtkL+ih43yhVyg126tHDtXb95hs36MkYk7KFhYURZr2YjTEmTRFV3w1u\nEJFAYCvQCDgArAQ6qurmaNs0B3qpagsRqQWMUNVa0R4vBsxR1dujrRsCHFXVIZ7OKblV9dVYnl99\n+fsZk6T27oVXX3XfiD/7rKummT/lzqYa+/tY3l/+PiseX0H24OyAKwLapYsrufDFFzYcP835+mvX\n7mP8eGjZMtGHUVWW7F3C+DXj+WbLN9S9pS5dKnWh1a2tyBSUspJ28VGFDz90pWg+++yGTokxxg9E\nBFVNF+9UafmauF27PhQrNizhDZPRrl19mDEjZcVkTEqUFK/DPi0KqqqXgN7AfOAPYIqqbhaRHiLS\n3bPNXGCniGwHxgD/9kIQkS+BpUAZEdkjIt08Dw0BmojI5WTJYF/+Hsb4lKqrmlm1qvvqd9s26Ns3\nRScz5u+Yzxs/vcG3Hb79N5kBMHKkG2AydqwlM9Kk+++H775zU1A+/TTRhxER6hWpx7jW49j7/F7a\nV2jP2NVjuXn4zXSf053FexaTGi68RaBXL1cw9KmnoF8/NwPMGGOMMcYkD5+O0PC3tJyNNmnEiRPQ\nrRvs2eOG9Zcv7++IErRy/0paftmSbx78hnpF6v27ftEi6NjRDcUvUcKPARrf+/NPaNbMDcd5880k\ny17tO7GPyesnM3H9RM5FnqNzxc50rtiZknlS/nyO8HDX7TZzZpg82XXANcakbDZCI22wERrGpF4p\nfoSGMSYeW7dCzZqu8OLixakimbEhfAOtp7RmXKtxVyUzdu6Ehx6CL7+0ZEa6UKaMq4w5ezZ07w6R\nSdOWtXCOwrxS7xU2PrWRae2mcfzccep8Vod6n9Xjk98/4djZ2Lp4pwwhIa6zz223QbVqsHq1vyMy\nxhhjjEn7bISGMf6wapVrl/DOO/D44/6Oxisr9q2g1ZRW/F/T/6PDbR3+XX/qlGue8thjrnapSUdO\nnnStfzNkgKlTfdLu4+Kli/yw4wcmrpvI/B3zubvk3XSp1IV7St5DhsAMSf58SWHGDDcFZehQNwDL\nGJMy2QiNtMFGaHjnxRcHsnt3yvpioGjR3Awf/rq/wzB+lBSvw2mlbasxqcfChW5uxrhxLqmRCvyw\n/Qc6zezE560/p0WZFv+uV4WuXd030k8/7b/4jJ9kzw5z5rik3F13ufoaN92UpE+RITADLcu0pGWZ\nlhw7e4zpm6YzePFgHpv9GI9WfpSe1XtyS85bEj5QMmrXDipUgPvuc61dP/jANYsxxhhj/GX37mMp\nMvFjzI2yKSfGJKeffnLJjBkzUkUyI0qjeOeXd+g2qxszH5x5VTIDYOBA2L/f1TS1IqDpVIYM8Pnn\nLqFRty789ZfPnip35tx0r9adxY8uZnG3xZyNPEuljyvRfnp7luxZkqIKiZYrBytXwtGjPj8txhhj\njDHpliU0jEkuK1a4qoHTpkGDBv6OJkF7IvbQfHJz5m2fx2/df7uqZgbAzJkwZp1SAn4AACAASURB\nVAx88419+5zuicB//+vaDdevnywFJErnLc2IpiPY9dwu6hepT9dZXblj7B1M2TiFS1Epo9VI9uzu\nv3uXLlCrlut6a4wx8RGRcSISLiLro63LLSLzRWSriPwgIjmjPdZXRLaJyGYRuds/URtjjP/YlBNj\nksP69dCqFYwfD6GhcW6mquw9sZedx3ayJ2IPpy6c4mzkWTIEZCB7cHZyBOfglhy3UDx3cfJmzov4\nYFjE6QunGblyJMOXDee5ms/xUt2XyBiY8aptli+HHj1g3jwoWDDJQzCpVc+e7g+iaVOYNAnuucfn\nT5kjOAdP13yaXjV6MXfbXAYtHkS/sH68WvdVOlXs5Pc6GyKutkzt2i6fGRYGw4ZZEtAYE6fxwEhg\nYrR1rwILVHWoiLwC9AVeFZHyQHugHFAYWCAipdNssQxjjImFJTSM8bU9e1yLy5EjoUWLax7efnQ7\n3275lkU7F7Fi/wqCA4MpkbsERXIWIUdwDjIFZSIyKpKTF04ScS7i34THxaiLlM5TmnL5ylHuJs+S\nrxyl8pS6JgHhjb0Rexm3Zhxjfh9Dg6INWPLoEsrkLXNtvNuhbVs3y6BatcScEJOmtW0L+fO7AhLv\nvuuGJySDAAmgZZmWtCjdgp93/8w7v7zDgJ8H8HLdl3m0yqNkCsqULHHEpXp1N3Dl0UfdFJSpU6Fk\nyu9Ga4xJZqq6WESKxljdGmjouT0BCMMlOVoBU1Q1EtglItuAGsCKZArXGGP8zhIaxvjSiRPQsiX0\n6QPt219Zff4EX6z/grGrx3Lw1EFalWnF41Uf59NWn3Jz9pu9OvSxs8fYdnQbmw9vZvORzUxcP5HN\nhzezJ2IPxXIVuyrRUfamspTIXYLgoGACJZDj545z+MxhNh/ezOoDq1m0axE7ju6g420d+bHzj9yW\n/7ZYn/PwYZebGTAAmjdPkjNk0qK6dd1QhGbNYN8+6Ns32YqsiAihxUIJLRbK8n3LeeeXdxi8eDD9\nGvbjkcqPEBTgv7e9XLnctJORI92IjQ8/dAVEjTEmAflVNRxAVQ+KSH7P+kLAsmjb7fesM8aYdMMS\nGsb4SmQkdOgAderAc88BcOTMEYYvHc4nqz+hUfFGDGsyjDuL30mAXH85m9yZc1OjUA1qFKpx1frz\nkeevSnTM3T6X4cuGsztiNxcuXeBS1CVyZ85N3sx5ufWmW6lSoApDGg+hfpH68Q7PP3PGzZp54AHo\n3v26wzXpTblysHSpy3zt3+9afQQGJmsItQrX4ruHvmPZ3mW8tug1hi4dytt3vk278u0S9X8uKcSc\ngrJwIQwf7pOOt8aYtCtRU0r69+//7+3Q0FBC45kCa4wxvhAWFkZYWFiSHtMSGsb4ygsvuKTGyJGc\nvHCKwYsH8/HvH9O+fHvW9FhDkZxFfPK0wUHB3Jb/tjhHWSTG+fNuBkHp0q6ziTFeuflm+PlnuP9+\nNxThyy8hc+ZkD6P2LbVZ1GURC/5awGuLXmPQ4kEMvGsgzUo180kdGm9cnoLSqxdUrQqTJ9sULmNM\nnMJFJERVw0WkAHDIs34/EL1vdWHPulhFT2gYY4w/xEymDhgw4IaP6fOvqESkqYhsEZE/PYWMYtvm\nA0+F5rUiUjmhfUWkn4jsE5HVnqWpr38PY67LqFGwYAE6dSpfbplOudHl2HtiL2t6rOGjlh/5LJnh\nCxcvum+Ss2WDzz6z9qzmOuXMCXPnuiEIjRu7eUt+ICI0KdmElY+v5D8N/kOf+X1o8HkDluxZ4pd4\nwE1BmTwZ+vVzs3MGDYJLKaNBizHGv8SzXDYb6Oq5/QgwK9r6DiKSUUSKA6WAlckVpDHGpAQ+TWiI\nSAAwCrgHqAB0FJGyMbZpBpRU1dJAD+BjL/d9T1Wrepbvffl7GHNd5s6FgQM5OGUcTeY8wLClw5ja\nbioT205MVYkMcB+uunRxSY0vv4QgG9NlEiNjRtf1JDQUatSADRv8FoqIcF+5+9jw1AYeq/IYD33z\nEK2+asWGcP/F1LEj/PYbzJ/vTtHOnX4LxRjjZyLyJbAUKCMie0SkGzAYaCIiW4FGnvuo6iZgGrAJ\nmAv0tA4nxpj0xtcjNGoA21R1t6peBKbgKjVH1xpPaypVXQHkFJEQL/a174lNyrN+PXTtStjwp6k0\nvw2hxUJZ+cRK6hap6+/IrtvFiy6ZcfiwK2SY8fobpxhzRUCAm680cCDcdRfMnu3XcAIDAulauStb\ne2/lzmJ30nhSYx759hF2Hd/ll3iKFHH1NFq3dtNRRo+GqCi/hGKM8SNVfUhVb1bVYFUtoqrjVfWY\nqjZW1VtV9W5VPR5t+0GqWkpVy6nqfH/Gbowx/uDrhEYhYG+0+/u4tvpyXNsktG9vzxSVT0UkZ9KF\nbEwiHThAVKt7+aRLBR4/8hmzO8zmjQZv+LWrQmKdPetqZkREwJw5kMm/HS9NWvLQQ/C//0HPnjB4\nMPj5y8RMQZl4vvbzbHt6G8VyFqPaJ9V4dt6zHDp9KOGdk1hAgGuI9OuvbipKw4awdWuyh2GMMcYY\nk2r4p8x7/LwZefEhUEJVKwMHgfd8G5IxCTh9mtPNGvFBhVMsq1+MNT3WULNwTX9HlSgnTrj5/Dly\nwMyZfqnhaNK6GjVg+XKYMQM6dYLTp/0dETmCczDgzgFs6rkJRSk3uhz9w/pz8vzJZI+lXDmX1Gjf\n3nXAHTzYjZgyxhhjjDFX8/VXx/uB6EUDYqu+HFeF5oxx7auq0avKjQXmxBWAtagyvnYp8iLbm9Vg\nbdBf3DxkAs/d9qC/Q0q0PXtca9a6dWHkSPeNsTE+Ubgw/PILPPWUS3BMnw7ly/s7KkKyhfBBsw94\nrtZz9AvrR+mRpelbry9P3vEkwUHByRZHYCA8/TTcey/06AFffOH+T955Z7KFYEya44t2gcYYY/zL\n1wmNVUApESkKHAA6AB1jbDMb6AVMFZFawHFPW6ojce0rIgVU9aBn//uAjXEFYC2qjC/tjdjLkg51\nKHvoJHV+2cgt+Uv5O6REW7rUddbs0weef966mZhkkCULfP45jB/v5le8/74bsZEClMhdgkltJ7E+\nfD2vLXyN95e/z1t3vsXDtz9MYEBgssVRrBh8/70bLdWtm8v9DBvmam4YY66PL9oFGmOM8S+ffv+q\nqpeA3sB84A9giqpuFpEeItLds81cYKeIbAfGAD3j29dz6KEisl5E1gINged9+XsYE5tpf0xjxKPl\nafLHOW5fvC3VJjNU4aOPoE0b+PRTeOEFS2aYZCQCjz7qKmK+/TY88USKmIJyWcWQinz30HdMajuJ\nMb+PofKYykzdOJVLUcnXX1XE1bTZtMlNR6lSxZ2qU6eSLQRjjDHGmBTJ5wPKVfV7T1Xm0qp6uc3U\nGFX9JNo2vT0Vmiup6ur49vWs76KqFVW1sqq2UdVwX/8exlx28vxJun7blZ9GPMegxcHkXbSMwJvy\n+TusRDl6FO6/H8aOhcWLoXlzf0dk0q2KFV3v0vPnoVIlWLLE3xFdpX7R+izutpghjYfwwcoPKDu6\nLJ+u/pTzkeeTLYYsWWDAAHeaNm2C0qXdNJTzyReCMcYYY0yKYjPkjbkOy/ctp8qYKty+8TCjZ10k\n49wfoFTqHJkxe7b73FikCCxbBmXK+Dsik+5lzw4TJ8K777r5T6+8AufO+Tuqf4kIzUs3Z3G3xYxr\nNY4Zm2ZQ8oOSvLfsPU6cP5FscRQvDl99BXPnwrx5ULYsTJgAkZHJFoIxxhhjTIpgCQ1jvHDx0kX6\nh/Wn9ZTWjM3/OC9+sIqA6TOgWjV/h3bd/v4bHnwQXnzRfXYcMQKCk6/WoTEJa9sW1q+HHTugcmVY\nsMDfEV1FRGhQtAHfd/qe2R1ns2L/CoqNKEbvub3ZcmRLssVRpYpLakycCOPGwa23uuljZ88mWwjG\nGGOMMX5lCQ1jErD1yFbqfFaHFftXsLH659z57PtXihimIidOwH/+A7ff7gaVrF9vHRNMCpYvn2vr\n+u670L27y8Lt2+fvqK5RtWBVprabyoanNpAncx5CPw+lyaQmfLP5Gy5cupAsMdSv7xrGTJzoRmwU\nLw7vvOOmlBljjDHGpGWW0DAmDlEaxaiVo6g3vh6PVXmMuUVfJ98Dj7iCEy1a+Ds8rx05Am+95ebb\n79kDa9bAwIGQObO/IzPGC/feC3/84eZVVK4M/fpBRIS/o7pGoRyFeOvOt9j93G66VurKBys+oNB7\nheg9tzcr969EVX0eQ926birZokXw119QsqTrjLJ8uSv+a4wxxhiT1lhCw5hYbDy0kXqf1eOrjV+x\n5NElPPlPcaRtW/jiC2jVyt/hJUjVfYjp3v1KIiMszM2zt3aPJtXJnNlVw1y1Cnbtcn/U776bItt8\nBAcF83DFhwnrGsaqJ1YRkjWEh795mPIflufNn95kzYE1Pk9ulC8Pn30GW7e6riidOrlc0OjRNmrD\nGGOMMWmLJTSMiebMxTO8tvA17pxwJ49UeoRfu/1KmakL4JFHYOZMuPtuf4cYp6goWLEC3njDfaDp\n3BmKFXPdED791H2wMSZVK17cZeV++glWrnR/4H37usIwKVCxXMX4T8P/8GfvPxnfejxnL56l3fR2\nlPigBM9//zxhu8J82iUlf354+WX480947z03LaV4cWjZ0uVmT5702VMbY4wxxiSLIH8HYExKcCnq\nEpPWT+KNRW9Qr0g91j+5noIZ88DTz8DCha6FZMmS/g7zKpcuwYYNLrQlS9xnvDx53Aj9zz6DWrVA\nxN9RGuMDFSrA9OmuaOiIEe7+XXe5xGOzZpAhg78jvIqIUKtwLWoVrsXQJkPZcGgDMzfP5KUfX2Lr\nka3ULVKXJiWa0LhEY27PfzuSxP9xAwKgUSO3nDzppqVMmQK9ernT1rKla9lcsGCSPq0xxhhjjM9Z\nQsOka6rKDzt+4JUFr5AtYzamPzCd2rfUdmO1OzR3X2cuWwa5cvktxogIN2Vk50432mLjRldSYOtW\nN32kXj1o0sQVASxRwm9hGpP8SpaEkSPdH/+0aTB0KDzxhJsW1rIlNG4MWbP6O8qriAgVQypSMaQi\n/UL7cfTsUX7a+RML/lrAR799xPFzx6lVuBa1C9emduHaVC9UnWwZsyXZ82fPDg8/7JajR2HOHNcp\npU8f93LXooVLfNSsaXV2jDHGGJPySXIUKvMXEdG0/PuZxLsUdYmvN3/N4MWDuXDpAgNCB3BfufuQ\nyEj44AMYPNh9SOre3afDHKKiIDzclQW4vOzZc/USFQVFi7qlfHn3ZXSFCm4KSbak+5xjTNqwY4cb\ngvDdd25aSuXKrlpmnTpQsaLLAgak3NmW+07sY/m+5Szbu4xl+5axLnwdpfOU5o6b76BSSCUqF6hM\nxZCK5MyUM0mfNzLS5W7nznWjvTZudG1hGzRwSdNq1dwUFmPSEhFBVdPFWMa0fE3crl0fihUb5u8w\nrrJrVx9mzEhZMdl58s6LLw5k9+5j/g7jKkWL5mb48Nf9HYZPJMXrsI3QMOnKwVMHmbhuIp/8/gn5\ns+ZnQOgAWpRpQQDiWgM89xzcfLObw1GmzA0/X2wJi5jJixw5XCmAYsVc0uK229zw7yJF3P2cOW3q\niDFeK1kSnn/eLSdPuuq4S5e6iph//AHHjsGtt7rCojff7OZZFCzoRmFly+aWrFndEhQEgYFuCQi4\ncjv6/aCgJP0PWjhHYdqVb0e78u0AOB95njUH17D6wGrWHVzH5A2T2XhoI/my5qNSSCUqhVSifL7y\nlL2pLKXzliZLhiyJet6gINf+tX59d//UKXfqfv4Zhg2D1avdqala1SU3qlRxCdZixdxpMMYYY9KC\n3buPpcjEj4mbzxMaItIUGIErQDpOVYfEss0HQDPgNNBVVdfGt6+I5AamAkWBXUB7VU15ffxMinDy\n/El+2PEDX6z/grBdYdxX7j4mtp1I7cK1EYAFC1xf0/Bw18+0XTuvP6Coxp+w2L376oRFsWLuC+M2\nbdztIkVS3Ih4Y9KO7NndfKwmTa6sO3ECtmyB7dvhwAE4eBDWr4fjx+H0afdJ/tQpdzsy0hWruXTJ\nZSdju33pEmTMCMHB1/4MDnYvADlzuuXy7ejrcuW6sly+ny3bv69BwUHB/9bfuOxS1CV2HNvBuoPr\nWBe+jumbprPlyBZ2HNtBSNYQyt5U9qqlTN4yFMhWgADxfmRKtmxuxk7jxu6+qmsF+/vvLrkxapQ7\njYcOuRxS2bJuKV36yoiywoVTXDkTY9Ikb661jTEmrfJpQkNEAoBRQCPgb2CViMxS1S3RtmkGlFTV\n0iJSE/gYqJXAvq8CC1R1qIi8AvT1rDNJLCwsjNDQUH+HcV2iNIrNhzfz8+6fmfPnHJbsWULtW2rz\nQPkHmNR2EtmDs7uhEUOHuvYfmTLBK69Ahw7ua8poEpOwqFQJWre+krBYtSr1ncOUKDX+LaY0dg5x\n/2Fr1HBLIlxzDqOi4MIFt5w/f/XPs2fdKJGICJdIif5z3z73MyLCJVOiL+fOXUluxJL0CMyVizLZ\ns1MmUyYeyFQCMpWHApm4VCQD4RePs+vcQXb8/Tfbty8g7PQ4Np/aRbieJP9NRbklX0lK5C5J8dzF\nKZG7BCVyl6B4ruLudTEeIi5xUbIktG9/Zf3p07Btm0tubNkC8+e7l9fdu12uKCTkymizggWhQAE4\nejSMO+8MpUAB9/hNN9koj8Sw/88GvLvWNt7buzeMW24J9XcYKZ6dp4TZOUo+vh6hUQPYpqq7AURk\nCtAaiP4i2xqYCKCqK0Qkp4iEAMXj2bc10NCz/wQgDEto+ERKv2CKjIpkx9EdbDy0kY2HNvL7gd9Z\nsncJuTLlon6R+nSr3I2p7aaS49RFN37668FukvjevWjrNpz+aBK7Qmqyd5+w9zPYu/faJaGERUIj\nLFL6OUwt7DzeODuHN+6acxgQ4JKimTIl3ZNcvOgSHzETHdGTH3v2uMTJ+fMuAXLuHIHnznGzZ6lz\n7px77OxZOBuEnglGz/6FXNrOxeAgLmQI5ExGOBV4ib8CLnIuYyCRWTIRmTMbmjMXgXnykjFvfrLk\nu5nsBYqQq0AxsocUITBPXsid2yVXgoLImtWNOqtcOfZfY//+qxMcBw/Cjz+GsWpVKAcPuoTx8eOQ\nN6+r0ZEnj7t9+Wf02zHXZcyYdKc8NbL/z8bDm2tt4yX7EOodO08Js3OUfHyd0CgE7I12fx/uhTeh\nbQolsG+IqoYDqOpBEbFSZWmEqnIx6iKnL5zm2LljHDt77N+fR88e5e+Tf7P3xF72ROxhT8Qe9p3Y\nR4HMBaic8VZuiyzKA2er8/Lp1mT/8wwZv9tGlv3juXSwL+dOHWZH7uqsy1KbhUEfsDBTbQ5ODiJo\nKtxyy9VLgwZX37cpIcaYZJUhw5VP7klEPAuRkWQ8e5aMZ8+S7cwZ8p89i545w/Gjf3P00G5OhO/h\n1OH9nD98gNP7t3N04yoCI04QfPIM2U5Hkvd8ALnOQfazUZzPFMTZrMGcz5GFyBzZiMyRnahcOSF3\nLsidm8A8NxGU9yZuzRtCxZtDCC5/E0HZcpBNTzDgjaOujUqmTFyMFA4dgsOHXeeVf/658jM83HV3\nir7+8pIxo0s4e7tkyfLvU5I585Ul+v1MmVJ0zVhjYuPNtbYxxqRZKbEoaGKqq3ldtrl9ezhzxrNT\njL0u3++x6VkKntnx70Hl8q1/t1eiAs5xKvPGawJWFIl2XIkRmqhnB4398ehH+nf76PvHPF4s22t8\n2yd0vCu/NAD7Tpxj2SfDY3k+95zXHi/GfY07XhVFiAKiUFEgKtq5DkA0CIkKJI8GkScqkJJRgWSI\nDCTzRSVzZBRZoi6SWaPIyC5OylGOBW3nRMZ8nMycj7+z3czRPKU5UbQJZ+qURsqU5qaQQPLlg6fy\nwZv5IF8+d4FrjDHpRlCQqy2S/coUEwFyU43cCewaGRXJsbPHOHDmCBtPH+b44b2cCt/L2UN/c/5I\nOHL8OAHHIwg68RcZd5wmeO0ZMp86R5bTF8h2+iJZzkWR6aJy6hQc++h9MkdCxksQGQRZMgRQIIOQ\nPyCAS4HCpUAhSjw/A4RLAVduR+USNLegCKq4nxGCRoCqWxel7j09CvfTrXPvMBcVLgDHLr/dqHv3\nwbMf4nmb9vyM/sZ2ubxS9PUSc0UC4t0qjgdjW73j2CnmT/zIq+dMSI4zVQlQ310Sng/MzJCq03x2\n/OvVqZObZWqMMSb182nbVhGpBfRX1aae+68CGr1YkYh8DPykqlM997fgppMUj2tfEdkMhKpquIgU\n8OxfLpbnT5v9qYwxxhhjzA1L7W1bvbnW9qy3a2JjTIqU0tu2rgJKiUhR4ADQAegYY5vZQC9gqudF\n+bgnUXEknn1nA12BIcAjwKzYnjy1v0kZY4wxxhgTD2+ute2a2BiTZvk0oaGql0SkNzCfK62kNotI\nD/ewfqKqc0WkuYhsx7Vt7Rbfvp5DDwGmicijwG6gPcYYY4wxxqQjCVwvG2NMmufTKSfGGGOMMcYY\nY4wxvpBma3mLyIsiEiUieaKt6ysi20Rks4jc7c/4UjoRGeo5T2tF5GsRyRHtMTuPXhKRpiKyRUT+\nFJFX/B1PaiAihUVkkYj8ISIbROQZz/rcIjJfRLaKyA8iktPfsaZ0IhIgIqtFZLbnvp3D6+RpJT7d\n83r3h4jUtPN4fUTkeRHZKCLrRWSyiGS0cxg/ERknIuEisj7aujjPmb0vXyuOc2jXNsYYk8akyYSG\niBQGmuCmo1xeVw43NaUc0Az4UERsPmHc5gMVVLUysA3oCyAi5bHz6BURCQBGAfcAFYCOIlLWv1Gl\nCpHAC6paAagN9PKct1eBBap6K7AIz9+kidezwKZo9+0cXr//A+Z6Ck9XArZg59FrInIz8DRQVVUr\n4qa6dsTOYULG4947oov1nNn7cpxiO4d2bWMAEJHqnsYCl+93EZFZIvJB9C9D0zsRKSUidWNZX1dE\nSvojppRMRLKISEXPEuzveNKLNJnQAN4HXoqxrjUwRVUjVXUX7o3M+nTHQVUXqGqU5+5yoLDndivs\nPHqrBrBNVXer6kVgCu7v0MRDVQ+q6lrP7VPAZtzfX2tggmezCUAb/0SYOngSu82BT6OttnN4HTzf\n3tZX1fEAnte9COw8Xq9AIKuIBAGZgf3YOYyXqi4GjsVYHdc5s/flWMR2Du3axkQzBtfBGRFpAAwG\nJgIRwCd+jCulGQGciGX9Cc9jBhCRDCIyAtiHS6Z+DvwlrusQIlLZj+GleWkuoSEirYC9qrohxkOF\ngL3R7u/3rDMJexSY67lt59F7Mc/VPuxcXRcRKQZUxl14hqhqOLikB5Dff5GlCpcTu9ELJdk5vD7F\ngSMiMt4zdecTEcmCnUevqerfwHBgD+79IkJVF2DnMDHyx3HO7H05cezaJn0LVNWjntsPAp+o6teq\n+h+glB/jSmlCYvlMhWddseQPJ8UaDmQDiqpqNVWtihvxVUJEPgJm+jW6NM7XbVt9QkR+BEKir8Jd\ntL8BvIabbmISEM95fF1V53i2eR24qKpf+SFEk46JSDZgBvCsqp4SkZgVjK2icRxEpAUQrqprRSQ0\nnk3tHMYvCKgK9FLV30Tkfdywf/tb9JKI5MKNLCiK++Zzuog8jJ3DpGDnLJHs2sYAgSISpKqRQCOg\ne7THUuXnIx/JFc9jmZMtipSvOVBao3XbUNUTIvIUcAQ3lc34SKr8D6uqsSYsROQ2XLZwnWfuY2Fg\ntYjUwGXbi0TbvLBnXboV13m8TES64v6D3hVt9X7glmj30/15jIf9zSWSZ2j6DGCSqs7yrA4XkRBV\nDffMez3kvwhTvLpAKxFpjrvgyC4ik4CDdg6vyz7ciL/fPPe/xiU07G/Re42Bvy5/EyoiM4E62DlM\njLjOmb0vXwe7tjEeXwE/i8gR4CzwK7iaEbjkq3F+E5EnVHVs9JUi8jjwu59iSomioiczLvO0VT6s\nqsv9EVR6kaamnKjqRlUtoKolVLU47mK0iqoeAmYDD3qqqxfHDSdb6c94UzIRaYobrt5KVc9He2g2\n0MHOo1dWAaVEpKiIZAQ64M6fSdhnwCZV/b9o62YDXT23HwFmxdzJOKr6mqoWUdUSuL+7RaraGZiD\nnUOveYb37xWRMp5VjYA/sL/F67EHqCUimTxfNDTCFaq1c5gw8SyXxXXO7H05bledQ7u2MZep6kDg\nRVytg3rRPowG4AoZG+c5oJuIhInIcM/yM/AYrvC4cTaJSJeYK0WkE64WnPEhiSWZlGaIyF/AHdG+\nGeqL+w94ETeMfb4/40vJRGQbkBH4x7Nquar29Dxm59FLnoun/8O9QY5T1cF+DinF81TT/gXYgBtS\nrbipZCuBabhv0XYD7VX1uL/iTC1EpCHwoqq28lRut3N4HUSkEq6wagbgL6AbrsilnUcviUg/XGLt\nIrAGeBzIjp3DOInIl0AokBcIB/oB3wLTieWc2fvyteI4h69h1zbGXDcRuRO4zXP3D1Vd5M94UhoR\nKQR8gxvtc3nkyh24UbJtVdVGfPlQmk5oGGOMMcYYY4wxviYidwEVPHc3qepCf8aTXlhCwxhjjDHG\nGGOMMalOmqqhYYwxxhhjjDHG/H87d+yqVR3HcfzzIZvuUoLcLckh4eKSECR3if4BQ43SocX/pCFo\nE4LW4G4h0VKUOCg4iYgOJQiBSwRJIA5NQd+G+2zRkNfrw3l8vaYD53ngM53hzTk/Xg6CBgAAALA4\nggYAAACwOIIGAAAAsDiCBgAAALA4ggbAhmj7Qdu/27617i0AAHDYBA2AzfFxkltJLq57CAAAHDZB\nA2ADtN1KspvkclZBo/u+bPug7bW237c9t7p3uu3Ntnfa/tB2e43zAQDgfxM0ADbD2SQ/zswvSf5o\n+3aSc0nemJmdJJ8kOZMkbY8k+SLJ+Zl5J8lXST5bz2wAAHg2R9Y9AIDn4mKSK6vrr5Ncyv4z/mqS\nzMzvbW+s7p9McirJ9bbNftz+7cXOBQCAgxE0ABau7etJ3k9yqu0keSXJnMAWbAAAANZJREFUJPn2\nv/6S5KeZ2X1BEwEA4LnzyQnA8n2YZG9m3pyZEzNzPMmjJE+SnF+dpbGd5L3V7x8mOdb23WT/E5S2\nO+sYDgAAz0rQAFi+j/LvtzG+SbKd5NckPyfZS3I3ydOZ+SvJhSSft72f5F5W52sAAMBSdGbWvQGA\nQ9J2a2b+bHs0ye0kuzPzeN27AADgoJyhAbDZvmv7WpJXk3wqZgAAsCm8oQEAAAAsjjM0AAAAgMUR\nNAAAAIDFETQAAACAxRE0AAAAgMURNAAAAIDFETQAAACAxfkH2svNajaFYvAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1179b9710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# specifies the parameters of our graphs\n",
    "fig = plt.figure(figsize=(18,6), dpi=1600) \n",
    "alpha=alpha_scatterplot = 0.2 \n",
    "alpha_bar_chart = 0.55\n",
    "\n",
    "# lets us plot many diffrent shaped graphs together \n",
    "ax1 = plt.subplot2grid((2,3),(0,0))\n",
    "# plots a bar graph of those who surived vs those who did not.               \n",
    "df.Survived.value_counts().plot(kind='bar', alpha=alpha_bar_chart)\n",
    "# this nicely sets the margins in matplotlib to deal with a recent bug 1.3.1\n",
    "ax1.set_xlim(-1, 2)\n",
    "# puts a title on our graph\n",
    "plt.title(\"Distribution of Survival, (1 = Survived)\")    \n",
    "\n",
    "plt.subplot2grid((2,3),(0,1))\n",
    "plt.scatter(df.Survived, df.Age, alpha=alpha_scatterplot)\n",
    "# sets the y axis lable\n",
    "plt.ylabel(\"Age\")\n",
    "# formats the grid line style of our graphs                          \n",
    "plt.grid(b=True, which='major', axis='y')  \n",
    "plt.title(\"Survival by Age,  (1 = Survived)\")\n",
    "\n",
    "ax3 = plt.subplot2grid((2,3),(0,2))\n",
    "df.Pclass.value_counts().plot(kind=\"barh\", alpha=alpha_bar_chart)\n",
    "ax3.set_ylim(-1, len(df.Pclass.value_counts()))\n",
    "plt.title(\"Class Distribution\")\n",
    "\n",
    "plt.subplot2grid((2,3),(1,0), colspan=2)\n",
    "# plots a kernel density estimate of the subset of the 1st class passangers's age\n",
    "df.Age[df.Pclass == 1].plot(kind='kde')    \n",
    "df.Age[df.Pclass == 2].plot(kind='kde')\n",
    "df.Age[df.Pclass == 3].plot(kind='kde')\n",
    " # plots an axis lable\n",
    "plt.xlabel(\"Age\")    \n",
    "plt.title(\"Age Distribution within classes\")\n",
    "# sets our legend for our graph.\n",
    "plt.legend(('1st Class', '2nd Class','3rd Class'),loc='best') \n",
    "\n",
    "ax5 = plt.subplot2grid((2,3),(1,2))\n",
    "df.Embarked.value_counts().plot(kind='bar', alpha=alpha_bar_chart)\n",
    "ax5.set_xlim(-1, len(df.Embarked.value_counts()))\n",
    "# specifies the parameters of our graphs\n",
    "plt.title(\"Passengers per boarding location\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x11ac14c90>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a721cd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,4))\n",
    "df.Survived.value_counts().plot(kind='barh', color = 'Black', alpha=1 )\n",
    "plt.title('Survival Statistics: 1 = Survived, 0 = Died')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    79.470199\n",
      "1    20.529801\n",
      "Name: Survived, dtype: float64\n",
      "0    24.710425\n",
      "1    75.289575\n",
      "Name: Survived, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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AIE0DAAAAYJCmAQAAADBI0wAAAAAYpGkAAAAADNI0AAAAAAZpGgAAAACDNA0A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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11aa4a850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(18,4))\n",
    "df_male = df.Survived[df.Sex =='male'].value_counts().sort_index()\n",
    "df_female = df.Survived[df.Sex == 'female'].value_counts().sort_index()\n",
    "\n",
    "ax1 = fig.add_subplot(121)\n",
    "df_male.plot(kind='barh',label='Male', alpha=.5)\n",
    "df_female.plot(kind='barh', label='Female', color='red', alpha=.5)\n",
    "plt.legend(loc='best')\n",
    "plt.title('Surval by gender')\n",
    "ax1.set_ylim(-1, 2) \n",
    "\n",
    "#adjust graph to display the proportions of survival by gender\n",
    "ax2 = fig.add_subplot(122)\n",
    "(df_male/df_male.sum()).plot(kind='barh',label='Male', alpha=.5)\n",
    "(df_female/df_female.sum()).plot(kind='barh', label='Female', color='red', alpha=.5) \n",
    "plt.legend(loc='best')\n",
    "plt.title('Proportion of survival by gender')\n",
    "ax2.set_ylim(-1, 2)\n",
    "\n",
    "print (df_male/df_male.sum())*100\n",
    "print (df_female/df_female.sum())*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x11b4d9110>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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CpIZSSvPqpt/Ou+g+zyezmDU2Hphlg9QOFDUB0YTNgD0i4tF8Wr0d8vUbsPwUg3NofKpE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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b034b90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(18,4))\n",
    "\n",
    "ax1=fig.add_subplot(141)\n",
    "female_highclass = df.Survived[df.Sex == 'female'][df.Pclass != 3].value_counts()\n",
    "female_highclass.plot(kind='bar', label='female, highclass', color='red', alpha=.5)\n",
    "ax1.set_xticklabels([\"Survived\", \"Died\"], rotation=0)\n",
    "ax1.set_xlim(-1, len(female_highclass))\n",
    "plt.title(\"Who Survived? with respect to Gender and Class\"); \n",
    "plt.legend(loc='best')\n",
    "\n",
    "ax2=fig.add_subplot(142)\n",
    "female_lowclass = df.Survived[df.Sex == 'female'][df.Pclass ==3].value_counts()\n",
    "female_lowclass.plot(kind='bar', label = 'female, lowclass', color='red', alpha=0.5)\n",
    "ax2.set_xticklabels([\"Survived\", \"Died\"], rotation=0)\n",
    "ax2.set_xlim(-1, len(female_highclass))\n",
    "plt.legend(loc='best')\n",
    "\n",
    "ax3=fig.add_subplot(143)\n",
    "female_highclass = df.Survived[df.Sex == 'male'][df.Pclass != 3].value_counts()\n",
    "female_highclass.plot(kind='bar', label='male, highclass', color='red', alpha=.5)\n",
    "ax3.set_xticklabels([\"Survived\", \"Died\"], rotation=0)\n",
    "ax3.set_xlim(-1, len(female_highclass))\n",
    "plt.legend(loc='best')\n",
    "\n",
    "ax4=fig.add_subplot(144)\n",
    "female_lowclass = df.Survived[df.Sex == 'male'][df.Pclass ==3].value_counts()\n",
    "female_lowclass.plot(kind='bar', label = 'male, lowclass', color='red', alpha=0.5)\n",
    "ax4.set_xticklabels([\"Survived\", \"Died\"], rotation=0)\n",
    "ax4.set_xlim(-1, len(female_highclass))\n",
    "plt.legend(loc='best')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#formula = 'Survived ~ C(Pclass) + C(Sex) + Age + SibSp + C(Emabarked)'\n",
    "#results = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}