TitanicPassengerSurvival.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## History\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."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Problem definition\n",
    "\n",
    "Knowing from a training set of samples listing passengers who survived or did not survive the Titanic disaster, can our model determine based on a given test dataset not containing the survival information, if these passengers in the test dataset survived or not."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Assumptions\n",
    "\n",
    "- #### More survivals are women and children\n",
    "- #### More survivals are 1st class passengers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import Python libs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Data analysis\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# Utils\n",
    "import string\n",
    "\n",
    "# ML \n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "from sklearn.linear_model import LogisticRegression, Perceptron\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import AdaBoostClassifier, RandomForestClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.metrics import precision_score, recall_score, f1_score, confusion_matrix, roc_curve\n",
    "\n",
    "# Visualization\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read,  analyse and clean data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(891, 12)\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>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": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# read and display data\n",
    "titanic = pd.read_csv(\"titanic.csv\")\n",
    "\n",
    "print(titanic.shape)\n",
    "titanic.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- ### Show distributions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Survived: 342\n",
      "Died: 549\n",
      "Survival rate: 0.3838383838383838\n",
      "\n",
      "\n",
      "Males: 577\n",
      "Females: 314\n",
      "Average age: 30\n",
      "Max age: 80.0\n",
      "Min age: 0.42\n",
      "First Class: 216\n",
      "Second Class: 184\n",
      "Third Class: 491\n"
     ]
    }
   ],
   "source": [
    "survived = titanic[titanic.Survived == 1]\n",
    "died = titanic[titanic.Survived == 0]\n",
    "\n",
    "males = titanic[titanic.Sex == 'male']\n",
    "females = titanic[titanic.Sex == 'female']\n",
    "\n",
    "fclass = titanic[titanic.Pclass == 1]\n",
    "sclass = titanic[titanic.Pclass == 2]\n",
    "tclass = titanic[titanic.Pclass == 3]\n",
    "\n",
    "print(\"Survived:\", len(survived))\n",
    "print(\"Died:\", len(died))\n",
    "print(\"Survival rate:\", titanic.Survived.mean())\n",
    "\n",
    "print(\"\\n\")\n",
    "\n",
    "print(\"Males:\", len(males))\n",
    "print(\"Females:\", len(females))\n",
    "print(\"Average age:\", round(titanic.Age.mean()))\n",
    "print(\"Max age:\", titanic.Age.max())\n",
    "print(\"Min age:\", titanic.Age.min())\n",
    "print(\"First Class:\", len(fclass))\n",
    "print(\"Second Class:\", len(sclass))\n",
    "print(\"Third Class:\", len(tclass))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891</td>\n",
       "      <td>891</td>\n",
       "      <td>891</td>\n",
       "      <td>204</td>\n",
       "      <td>889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>891</td>\n",
       "      <td>2</td>\n",
       "      <td>681</td>\n",
       "      <td>147</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>Lindblom, Miss. Augusta Charlotta</td>\n",
       "      <td>male</td>\n",
       "      <td>CA. 2343</td>\n",
       "      <td>G6</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>1</td>\n",
       "      <td>577</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>644</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     Name   Sex    Ticket Cabin Embarked\n",
       "count                                 891   891       891   204      889\n",
       "unique                                891     2       681   147        3\n",
       "top     Lindblom, Miss. Augusta Charlotta  male  CA. 2343    G6        S\n",
       "freq                                    1   577         7     4      644"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# View data excluding NaN values\n",
    "titanic.describe(include=['O'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x10e907198>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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j5AAAGIyQAwBgMEIOAIDBCDkAAAYj5AAAGIyQAwBgMEIOAIDBCDkAAAYj5AAAGIyQAwBg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\n9v+cytfDsbFepgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e991978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(titanic.Survived)\n",
    "plt.title(\"Not survived | Survived\")\n",
    "plt.xlabel(\"Survived\")\n",
    "plt.ylabel(\"Count\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x111cffac8>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AFiPIAQCwGEEOAIDFCHIAACxGkAMAYDGCHAAAi7kDteFTp05p0qRJ+uqrr+RwOPTYY4+p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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e991ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(titanic.Pclass)\n",
    "plt.title(\"1st | 2nd | 3rd classes\")\n",
    "plt.xlabel(\"Pclass\")\n",
    "plt.ylabel(\"Count\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x10e8d3c50>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x111d63f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels = LabelEncoder().fit_transform(titanic.Sex)\n",
    "plt.hist(labels)\n",
    "plt.title(\"Female | Male\")\n",
    "plt.xlabel(\"Sex\")\n",
    "plt.ylabel(\"Count\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Sex</th>\n",
       "      <th>female</th>\n",
       "      <th>male</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Survived</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>81</td>\n",
       "      <td>468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>233</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Sex       female  male\n",
       "Survived              \n",
       "0             81   468\n",
       "1            233   109"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# more than 68% of the survivals are women\n",
    "pd.crosstab(titanic['Survived'], titanic['Sex'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Pclass</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Survived</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>80</td>\n",
       "      <td>97</td>\n",
       "      <td>372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>136</td>\n",
       "      <td>87</td>\n",
       "      <td>119</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Pclass      1   2    3\n",
       "Survived              \n",
       "0          80  97  372\n",
       "1         136  87  119"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# about 39% of survivals are 1st class passengers\n",
    "pd.crosstab(titanic['Survived'], titanic['Pclass'])"
   ]
  },
  {
   "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>Survived</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>30.626179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>28.343690</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived        Age\n",
       "0         0  30.626179\n",
       "1         1  28.343690"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Average age survived/not survived\n",
    "titanic[['Survived', 'Age']].groupby(['Survived'], as_index=False).mean().sort_values(by='Age', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- ### Show correlations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Survived  Sex   \n",
      "0         female     81\n",
      "          male      468\n",
      "1         female    233\n",
      "          male      109\n",
      "dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sex</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>female</td>\n",
       "      <td>0.742038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>male</td>\n",
       "      <td>0.188908</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Sex  Survived\n",
       "0  female  0.742038\n",
       "1    male  0.188908"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# assume that the most of survivals are women\n",
    "print(titanic.groupby([\"Survived\", \"Sex\"]).size())\n",
    "titanic[['Survived', 'Sex']].groupby(['Sex'], as_index=False).mean().sort_values(by='Survived', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Survived  Pclass\n",
      "0         1          80\n",
      "          2          97\n",
      "          3         372\n",
      "1         1         136\n",
      "          2          87\n",
      "          3         119\n",
      "dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.629630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.472826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.242363</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pclass  Survived\n",
       "0       1  0.629630\n",
       "1       2  0.472826\n",
       "2       3  0.242363"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# assume that there are more survivals in 1st class\n",
    "print(titanic.groupby([\"Survived\", \"Pclass\"]).size())\n",
    "titanic[['Survived', 'Pclass']].groupby(['Pclass'], as_index=False).mean().sort_values(by='Survived', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>SibSp</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.535885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.464286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.345395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>8</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SibSp  Survived\n",
       "1      1  0.535885\n",
       "2      2  0.464286\n",
       "0      0  0.345395\n",
       "3      3  0.250000\n",
       "4      4  0.166667\n",
       "5      5  0.000000\n",
       "6      8  0.000000"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# more survivals are with 1, 2 sibings/spouses\n",
    "titanic[['Survived', 'SibSp']].groupby(['SibSp'], as_index=False).mean().sort_values(by='Survived', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>160</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Sage, Master. Thomas Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>CA. 2343</td>\n",
       "      <td>69.55</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>181</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Sage, Miss. Constance Gladys</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>CA. 2343</td>\n",
       "      <td>69.55</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>202</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Sage, Mr. Frederick</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>CA. 2343</td>\n",
       "      <td>69.55</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>324</th>\n",
       "      <td>325</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Sage, Mr. George John Jr</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>CA. 2343</td>\n",
       "      <td>69.55</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>792</th>\n",
       "      <td>793</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Sage, Miss. Stella Anna</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>CA. 2343</td>\n",
       "      <td>69.55</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>846</th>\n",
       "      <td>847</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Sage, Mr. Douglas Bullen</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>CA. 2343</td>\n",
       "      <td>69.55</td>\n",
       "      <td>NaN</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>69.55</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass                               Name     Sex  \\\n",
       "159          160         0       3         Sage, Master. Thomas Henry    male   \n",
       "180          181         0       3       Sage, Miss. Constance Gladys  female   \n",
       "201          202         0       3                Sage, Mr. Frederick    male   \n",
       "324          325         0       3           Sage, Mr. George John Jr    male   \n",
       "792          793         0       3            Sage, Miss. Stella Anna  female   \n",
       "846          847         0       3           Sage, Mr. Douglas Bullen    male   \n",
       "863          864         0       3  Sage, Miss. Dorothy Edith \"Dolly\"  female   \n",
       "\n",
       "     Age  SibSp  Parch    Ticket   Fare Cabin Embarked  \n",
       "159  NaN      8      2  CA. 2343  69.55   NaN        S  \n",
       "180  NaN      8      2  CA. 2343  69.55   NaN        S  \n",
       "201  NaN      8      2  CA. 2343  69.55   NaN        S  \n",
       "324  NaN      8      2  CA. 2343  69.55   NaN        S  \n",
       "792  NaN      8      2  CA. 2343  69.55   NaN        S  \n",
       "846  NaN      8      2  CA. 2343  69.55   NaN        S  \n",
       "863  NaN      8      2  CA. 2343  69.55   NaN        S  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic[titanic.SibSp == 8]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>Parch</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.550847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.343658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>0.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Parch  Survived\n",
       "3      3  0.600000\n",
       "1      1  0.550847\n",
       "2      2  0.500000\n",
       "0      0  0.343658\n",
       "5      5  0.200000\n",
       "4      4  0.000000\n",
       "6      6  0.000000"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic[['Survived', 'Parch']].groupby(['Parch'], as_index=False).mean().sort_values(by='Survived', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature engineering\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# extract title from Name and create new feature\n",
    "def substrings_in_string(big_string, substrings):\n",
    "    for substring in substrings:\n",
    "        if big_string.find(substring) != -1:\n",
    "            return substring\n",
    "    return np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "<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",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <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>Mr</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>Mrs</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>Miss</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>Mrs</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>Mr</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 Title  \n",
       "0      0         A/5 21171   7.2500   NaN        S    Mr  \n",
       "1      0          PC 17599  71.2833   C85        C   Mrs  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  Miss  \n",
       "3      0            113803  53.1000  C123        S   Mrs  \n",
       "4      0            373450   8.0500   NaN        S    Mr  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titles = ['Mrs', 'Mr', 'Master', 'Miss', 'Major', 'Rev',\n",
    "            'Dr', 'Ms', 'Mlle','Col', 'Capt', 'Mme', 'Countess',\n",
    "            'Don', 'Jonkheer', 'Dona']\n",
    "titanic['Title'] = titanic.Name.map(lambda x: substrings_in_string(x, titles))\n",
    "titanic.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# replace all titles with Mr, Mrs, Miss, Master\n",
    "def replace_titles(x):\n",
    "    title = x.Title\n",
    "    if title in ['Don', 'Major', 'Capt', 'Jonkheer', 'Rev', 'Col']:\n",
    "        return 'Mr'\n",
    "    elif title in ['Countess', 'Mme', 'Dona']:\n",
    "        return 'Mrs'\n",
    "    elif title in ['Mlle', 'Ms']:\n",
    "        return 'Miss'\n",
    "    elif title =='Dr':\n",
    "        if x.Sex == 'male':\n",
    "            return 'Mr'\n",
    "        else:\n",
    "            return 'Mrs'\n",
    "    else:\n",
    "        return title\n",
    "\n",
    "titanic.Title = titanic.apply(replace_titles, axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>Title</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Mrs</td>\n",
       "      <td>35.634783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mr</td>\n",
       "      <td>32.984412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Miss</td>\n",
       "      <td>21.835616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Master</td>\n",
       "      <td>4.574167</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Title        Age\n",
       "3     Mrs  35.634783\n",
       "2      Mr  32.984412\n",
       "1    Miss  21.835616\n",
       "0  Master   4.574167"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# confirms that with Master title are children with average age of 4\n",
    "titanic[['Title', 'Age']].groupby(['Title'], as_index=False).mean().sort_values(by='Age', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>Title</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Mrs</td>\n",
       "      <td>0.795455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Miss</td>\n",
       "      <td>0.703297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Master</td>\n",
       "      <td>0.575000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mr</td>\n",
       "      <td>0.160149</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Title  Survived\n",
       "3     Mrs  0.795455\n",
       "1    Miss  0.703297\n",
       "0  Master  0.575000\n",
       "2      Mr  0.160149"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic[['Title', 'Survived']].groupby(['Title'], as_index=False).mean().sort_values(by='Survived', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>Title</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>2</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>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "      <td>3</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>2</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  Title  \n",
       "0      0         A/5 21171   7.2500   NaN        S      2  \n",
       "1      0          PC 17599  71.2833   C85        C      3  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S      1  \n",
       "3      0            113803  53.1000  C123        S      3  \n",
       "4      0            373450   8.0500   NaN        S      2  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# convert titles to numeric values\n",
    "titanic.Title = LabelEncoder().fit_transform(titanic.Title)\n",
    "titanic.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>Title</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>1</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>2</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>0</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>3</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>0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>0</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>3</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>1</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>2</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  Parch  \\\n",
       "0                            Braund, Mr. Owen Harris    1  22.0      1      0   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...    0  38.0      1      0   \n",
       "2                             Heikkinen, Miss. Laina    0  26.0      0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)    0  35.0      1      0   \n",
       "4                           Allen, Mr. William Henry    1  35.0      0      0   \n",
       "\n",
       "             Ticket     Fare Cabin Embarked  Title  \n",
       "0         A/5 21171   7.2500   NaN        S      2  \n",
       "1          PC 17599  71.2833   C85        C      3  \n",
       "2  STON/O2. 3101282   7.9250   NaN        S      1  \n",
       "3            113803  53.1000  C123        S      3  \n",
       "4            373450   8.0500   NaN        S      2  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# convert genders to numeric values\n",
    "titanic.Sex = LabelEncoder().fit_transform(titanic.Sex)\n",
    "titanic.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['S' 'C' 'Q' nan]\n"
     ]
    }
   ],
   "source": [
    "print(titanic.Embarked.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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",
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       "    </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>1</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>2</td>\n",
       "      <td>2</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>0</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>0</td>\n",
       "      <td>3</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>0</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>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>0</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>2</td>\n",
       "      <td>3</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>1</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>2</td>\n",
       "      <td>2</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  Parch  \\\n",
       "0                            Braund, Mr. Owen Harris    1  22.0      1      0   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...    0  38.0      1      0   \n",
       "2                             Heikkinen, Miss. Laina    0  26.0      0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)    0  35.0      1      0   \n",
       "4                           Allen, Mr. William Henry    1  35.0      0      0   \n",
       "\n",
       "             Ticket     Fare Cabin  Embarked  Title  \n",
       "0         A/5 21171   7.2500   NaN         2      2  \n",
       "1          PC 17599  71.2833   C85         0      3  \n",
       "2  STON/O2. 3101282   7.9250   NaN         2      1  \n",
       "3            113803  53.1000  C123         2      3  \n",
       "4            373450   8.0500   NaN         2      2  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# convert ports into numerics\n",
    "titanic.Embarked = titanic.Embarked.fillna(\"Unknown\") \n",
    "titanic.Embarked = LabelEncoder().fit_transform(titanic.Embarked)\n",
    "titanic.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
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       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
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       "      <th>2</th>\n",
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       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
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       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex   Age  SibSp  Parch     Fare Cabin  Embarked  Title\n",
       "0         0       3    1  22.0      1      0   7.2500   NaN         2      2\n",
       "1         1       1    0  38.0      1      0  71.2833   C85         0      3\n",
       "2         1       3    0  26.0      0      0   7.9250   NaN         2      1\n",
       "3         1       1    0  35.0      1      0  53.1000  C123         2      3\n",
       "4         0       3    1  35.0      0      0   8.0500   NaN         2      2"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# remove unnecesary attributes and null values from dataset\n",
    "filter_arr = [\"PassengerId\", \"Ticket\", \"Name\"]\n",
    "titanic = titanic.drop(filter_arr, axis = 1)\n",
    "titanic.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "      <th>FamilySize</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>35.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex   Age  SibSp  Parch     Fare Cabin  Embarked  Title  \\\n",
       "0         0       3    1  22.0      1      0   7.2500   NaN         2      2   \n",
       "1         1       1    0  38.0      1      0  71.2833   C85         0      3   \n",
       "2         1       3    0  26.0      0      0   7.9250   NaN         2      1   \n",
       "3         1       1    0  35.0      1      0  53.1000  C123         2      3   \n",
       "4         0       3    1  35.0      0      0   8.0500   NaN         2      2   \n",
       "\n",
       "   FamilySize  \n",
       "0           1  \n",
       "1           1  \n",
       "2           0  \n",
       "3           1  \n",
       "4           0  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create new feature FamilySize based on SibSp and Parch columns\n",
    "titanic[\"FamilySize\"] = titanic.SibSp + titanic.Parch\n",
    "titanic.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "      <th>HasFamily</th>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>2</td>\n",
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       "      <th>3</th>\n",
       "      <td>1</td>\n",
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       "      <td>35.0</td>\n",
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       "      <td>C123</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex   Age     Fare Cabin  Embarked  Title  HasFamily\n",
       "0         0       3    1  22.0   7.2500   NaN         2      2          1\n",
       "1         1       1    0  38.0  71.2833   C85         0      3          1\n",
       "2         1       3    0  26.0   7.9250   NaN         2      1          0\n",
       "3         1       1    0  35.0  53.1000  C123         2      3          1\n",
       "4         0       3    1  35.0   8.0500   NaN         2      2          0"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create new feature HasFamily based on FamilySize\n",
    "titanic['HasFamily'] = 0\n",
    "titanic.loc[titanic['FamilySize'] > 0, 'HasFamily'] = 1\n",
    "titanic = titanic.drop([\"SibSp\", \"Parch\", \"FamilySize\"], axis = 1)\n",
    "titanic.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
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       "      <th>HasFamily</th>\n",
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       "      <th>2</th>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
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       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex   Age     Fare  Embarked  Title  HasFamily  HasCabin\n",
       "0         0       3    1  22.0   7.2500         2      2          1         0\n",
       "1         1       1    0  38.0  71.2833         0      3          1         1\n",
       "2         1       3    0  26.0   7.9250         2      1          0         0\n",
       "3         1       1    0  35.0  53.1000         2      3          1         1\n",
       "4         0       3    1  35.0   8.0500         2      2          0         0"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# based on cabin number create new feature HasCabin\n",
    "titanic.Cabin = titanic.Cabin.fillna(\"Unknown\")\n",
    "titanic[\"HasCabin\"] = 0\n",
    "titanic.loc[titanic['Cabin'] != 'Unknown', 'HasCabin'] = 1\n",
    "titanic = titanic.drop(\"Cabin\", axis = 1)\n",
    "titanic.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "      <th>HasFamily</th>\n",
       "      <th>HasCabin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>22.0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex   Age     Fare  Embarked  Title  HasFamily  HasCabin\n",
       "0         0       3    1  22.0   7.2500         2      2          1         0\n",
       "1         1       1    0  38.0  71.2833         0      3          1         1\n",
       "2         1       3    0  26.0   7.9250         2      1          0         0\n",
       "3         1       1    0  35.0  53.1000         2      3          1         1\n",
       "4         0       3    1  35.0   8.0500         2      2          0         0"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.Fare.fillna(titanic.Fare.dropna().median(), inplace=True)\n",
    "titanic.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
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       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex   Age  Fare  Embarked  Title  HasFamily  HasCabin\n",
       "0         0       3    1  22.0     3         2      2          1         0\n",
       "1         1       1    0  38.0     1         0      3          1         1\n",
       "2         1       3    0  26.0     2         2      1          0         0\n",
       "3         1       1    0  35.0     1         2      3          1         1\n",
       "4         0       3    1  35.0     2         2      2          0         0"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create new feature FareRange and based on ranges transform Fare into numeric\n",
    "titanic['FareRange'] = pd.qcut(titanic.Fare, 4)\n",
    "titanic.Fare = LabelEncoder().fit_transform(titanic.FareRange) # 2 1 3 2 10   1 2 2 3 10\n",
    "titanic = titanic.drop(\"FareRange\", axis = 1)\n",
    "titanic.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "      <th>HasFamily</th>\n",
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       "  <tbody>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex  Age  Fare  Embarked  Title  HasFamily  HasCabin\n",
       "0         0       3    1    3     3         2      2          1         0\n",
       "1         1       1    0    2     1         0      3          1         1\n",
       "2         1       3    0    0     2         2      1          0         0\n",
       "3         1       1    0    1     1         2      3          1         1\n",
       "4         0       3    1    1     2         2      2          0         0"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.Age.fillna(titanic.Age.dropna().median(), inplace=True)\n",
    "titanic.head()\n",
    "titanic['AgeRange'] = pd.qcut(titanic.Age, 4)\n",
    "titanic.Age = LabelEncoder().fit_transform(titanic.AgeRange)\n",
    "titanic = titanic.drop(\"AgeRange\", axis = 1)\n",
    "titanic.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((891, 8), (891,))"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "attributes = titanic.drop(\"Survived\", axis = 1)\n",
    "labels = titanic.Survived\n",
    "\n",
    "attributes.shape, labels.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((623, 8), (268, 8), (623,), (268,))"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# split data into train and test sets\n",
    "attributes_train, attributes_test, labels_train, labels_test = train_test_split(attributes, labels, train_size=0.7, stratify=labels)\n",
    "\n",
    "attributes_train.shape, attributes_test.shape, labels_train.shape, labels_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create, train, predict and score models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# init and fit grid search\n",
    "def tuneModel(estimator, params):\n",
    "    grid = GridSearchCV(estimator, params, cv = 10)\n",
    "    grid.fit(attributes_train, labels_train)\n",
    "    \n",
    "    print(grid.best_estimator_)\n",
    "    return grid.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def scoreModel(estimator):\n",
    "    acc_train = estimator.score(attributes_train, labels_train)\n",
    "    acc_test = estimator.score(attributes_test, labels_test)\n",
    "    print(\"Train accuracy score:\", acc_train)\n",
    "    print(\"Test accuracy score:\", acc_test)\n",
    "    \n",
    "    predicted = estimator.predict(attributes_test)\n",
    "    print(\"Precision score\", precision_score(labels_test, predicted, average = \"weighted\"))\n",
    "    print(\"Recall score\", recall_score(labels_test, predicted, average = \"weighted\"))\n",
    "    print(\"F1 score\", f1_score(labels_test, predicted, average = \"weighted\"))\n",
    "    print(\"Confusion matrix:\\n\", confusion_matrix(labels_test, predicted))\n",
    "    \n",
    "    fpr, tpr, _ = roc_curve(labels_test, predicted)\n",
    "\n",
    "    plt.plot(fpr, tpr)\n",
    "    plt.plot([0, 1], [0, 1], linestyle = \"--\")\n",
    "    \n",
    "    return (acc_train, acc_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LogisticRegression(C=1, class_weight=None, dual=False, fit_intercept=True,\n",
      "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
      "          penalty='l1', random_state=None, solver='liblinear', tol=0.0001,\n",
      "          verbose=0, warm_start=False)\n",
      "Train accuracy score: 0.786516853933\n",
      "Test accuracy score: 0.809701492537\n",
      "Precision score 0.808514246947\n",
      "Recall score 0.809701492537\n",
      "F1 score 0.806365962131\n",
      "Confusion matrix:\n",
      " [[147  18]\n",
      " [ 33  70]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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z2Yxf/epXaG5uxk033cQAHtc94IU3EMGiOblSl0KU8PyRAF49tQd3yW5CFiy4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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112071080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Logistic Regression\n",
    "log_regr = LogisticRegression()\n",
    "params = {\"penalty\": [\"l1\", \"l2\"], \"C\": [0.001, 0.01, 0.1, 1, 10, 100, 1000], \"fit_intercept\": [True, False]}\n",
    "\n",
    "log_regr = tuneModel(log_regr, params)\n",
    "acc_log = scoreModel(log_regr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DecisionTreeClassifier(class_weight=None, criterion='entropy', max_depth=5,\n",
      "            max_features=None, max_leaf_nodes=None,\n",
      "            min_impurity_split=1e-07, min_samples_leaf=1,\n",
      "            min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
      "            presort=False, random_state=None, splitter='best')\n",
      "Train accuracy score: 0.84911717496\n",
      "Test accuracy score: 0.791044776119\n",
      "Precision score 0.791860468603\n",
      "Recall score 0.791044776119\n",
      "F1 score 0.784350736539\n",
      "Confusion matrix:\n",
      " [[149  16]\n",
      " [ 40  63]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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V9A4O9xzHPy16FvnGHDxQtErqsogihiE8TQRBxNbfHMP5zsHwY3np443urbls\ndE80kTO2Bvym4U04vIMoMOZCxdPOFIcmDWFBEPDiiy/i3LlzUKvVeOmll1BYWBg+furUKWzduhWi\nKCI9PR0/+MEPoNFwVtfZ58T5zkHkpSdh1cI8VJdYkJrMi0eIJiOIAl5veBOfXDoMhUyBNcUP4P7C\nu7n2S3Fp0hDev38/fD4ftm/fjhMnTmDr1q34yU9+AiB0cdF3vvMd/PCHP0RhYSF27tyJixcvoqSk\nZNoLj3ZNozPg1YvzsaKa/UqJbpRcJodKoUaeIQdbZtci15AtdUlE02bSED569ChWrFgBAJg3bx7q\n6urCx1pbW5GSkoJf/OIXaGpqwsqVKxnAoxo7HACAsjzet0g0mZGAB2+d34uHZCthRjq+an0YCpmc\ns1+Ke5OGsNPphMFgCH+tUCgQCASgVCpht9tx/PhxvPDCCygoKMA3vvENzJ07F0uXLr3u85nNeiiV\nkf0fKz3dGNHnmypRFNHcNYQUowZzyjJiZs/maBvHWMQxvHmnuuvxkyPbYHPbEZQH8PfL/kLqkmIe\n34dTN1NjOGkIGwwGuFyu8NeCIECpDP1YSkoKCgsLYbVaAQArVqxAXV3dhCFst7unWvMV0tONUdeC\nr9cxgoEhDxaVp6O/3yl1OTckGscx1nAMb44n4MFb53+HP3V9DrlMjoeL7sOmRY9xDKeI78Opm44x\nvF6oT3p57oIFC3Dw4EEAwIkTJ1BWVhY+lp+fD5fLhba2NgDAkSNHUFpaGol6Y1rT6KnoUp6KJrqu\ng52f4k9dnyMnKQv/uOhv8EjJ/VAqeMMGJZZJ3/GrV6/GoUOHsGHDBoiiiJdffhl79uyB2+1GbW0t\nvve97+G5556DKIqYP38+7r777hkoO7o1dY6uB+czhIku5wl4MeCxI8eQhXsKVkCpUOKu3KVQyhm+\nlJgmfefL5XJ897vfveKxsdPPALB06VLs2rUr8pXFsMaOQWjUCuRlJEldClHUaLSfx2v1OyGIIv6v\nJX8PnVKLVfkrpC6LSFL88zPChtw+dA+4Mac4lVtPEiE0+327eR8OXvwEMsiwuvBuznyJRvH/hAhr\n6gjdH1yWZ5K4EiLp2UYG8P8c/ylsngFk6TOwefZ6FCUXSF0WUdRgCEfY2HowL8oiAlI0JiSrDViQ\nUY1HildDpWD/a6LLMYQjrKnTAYVchuKcZKlLIZLEeUcr3m7eh/+j6mswqJPwrQV/yU03iK6DIRxB\nHl8Abd3789gqAAAgAElEQVROFGcboVHxQ4cSiy/owzst7+KjjkMAgIaBRizKms8AJpoAQziCWrqG\nIIgiSnlrEiWYlsEL2HZ2B3pH+pGhT8PmylqUmAon/0GiBMcQjiDuF02Jam/L++gbseHe/LuwpuQB\nqLn2S3RDGMIRNNY5aRavjKYE0DLYhlRtClI0JjxZ8QQGvUOwphRJXRZRTGEIR0ggKKC5axC5aUkw\n6DgLoPjlD/qxt/V9fNB+EHPTKvCN6v+FNF0q0nSpUpdGFHMYwhHS3uOEzy+glLNgimOtg+3YVr8D\nPe5epOksuK/gbqlLIoppDOEICd8fzIuyKE590X0Mvzq7HSJE3J23DI9aH4JGoZa6LKKYxhCOEF6U\nRfFKEAXIZXKUma3IN+Zg7aw1KDVbJ/9BIpoUQzgCRFFEU+cgUpM1sJi0UpdDFBF+IYB9rfvRNtSB\nv573NFI0JvzTomchk8mkLo0objCEI6B7wA3niB93FGdKXQpRRLQPdWJb/Q50ubph0Zox6B2CWZvC\nACaKMIZwBIydiuZFWRTr/EIA77bux/vtH0EQBSzPvQOPWx+GVskzPETTgSEcAWP3B/OiLIp13qAX\nhy59AZM6GZsq16EitVTqkojiGkM4Aho7HEjSKpGTliR1KUQ3LSAE8EnXYSzLuR0GVRL+qvrPka5P\ng46zX6JpxxCeIvuwF/2DHtxmtUDO9TKKMZ3DXfhV/XZcdF5CQAxgVf4KFCTnSV0WUcJgCE/R2P3B\nZTwVTTEkKATxXtsB7LvwAQRRwJ3Zt2Np9mKpyyJKOAzhKRq/KIshTLHjZ3Wv4WT/GaRoTHiq4gnM\ntpRLXRJRQmIIT1FT5yBUSjmKso1Sl0I0oaAQhAgRSrkSd+XdCb1Kj5rSNdApdVKXRpSwGMJT4Pb4\n0dnrRFl+CpQKudTlEF1Xl7Mb2+q3oyK1DI9ZH0JFaimvfCaKAgzhKTh/cQgigNJ83h9M0SkoBLG/\n/Y/4fesfEBCDyDPkQBRFbrpBFCUYwlMQviiL68EUhbpdPfjV2R1oG+6ASW3ExooaVKXNlrosIroM\nQ3gKGjsckMkAay5nwhR9RgIetA93YnHmAqwrexRJKr3UJRHRlzCEb5E/EETrpSHkZxig03AYKTp0\nu3pRP9CIe/KXo9hUiH9Z8vfISuKe5kTRiulxi1ovDSMQFHkqmqKCIAr4oP0g9ra+j4AQQEVqKbKT\nMhnARFGOIXyLxtaDuV80Sa3H1Ytt9TvROtQGo8qADXPWIpvhSxQTGMK3KNy0gZ2TSEJu/wj+/ciP\n4Al6sDDjNqwv+yoMau5hThQrGMK3QBBENHUOIiNFhxSDRupyKAEN+YaRrDZCr9JhTcn9MGmSsSCj\nWuqyiOgmcYeJW3Cx34URb4D3B9OME0QBB9oP4oVPtuJ0/1kAwD35yxnARDGKM+FbMLZfNC/KopnU\n6+7Ha/U70Dx4AQYVTzkTxQOG8C3gRVk00z6++BnebNoDv+DHvPQqbCh/HEa1QeqyiGiKGMI3SRRD\n68HJehUyzdz4nmaGX/BDrVBhc+U6LMi4jdtOEsUJhvBN6h/0wD7sxcKydH4Q0rQRRAEfX/wMRrUB\nCzKqcXfeMizOnM/ZL1GcYQjfJJ6KpunWPzKA1+p3oMnRggxdGualz4VcJmcAE8UhhvBNauwI3R9c\nxiujKcIEUcCfLn6Ot5p/B1/Qh6q02dhYvhZyGW9iIIpXDOGb1NTpgEatQH4GZyUUWWdsDdje+BZ0\nSh2+NnsDFmfO55IHUZxjCN+EYbcPl2xuzCkyQyHn7ISmThRF9Lj7kJWUgbmWSqwpvh9LcxYjRcMz\nLUSJgElyE8a3quR6ME3dgMeOH534H/zgyCuwjdghk8nwUPF9DGCiBMKZ8E3gRVkUCaIo4pOuL7D7\n/F54gl7MsVTwzApRgmII34TGjkEo5DKU5CRLXQrFKF/Qj5+e/iXqBxqhVWixqWId7shexLVfogTF\nEL5BXl8Q7T3DKMwyQqNSSF0OxSi1QgWtQoPK1DI8VfEEzFqeVSFKZAzhG9TSNYigIHK/aLppdo8D\nOxvfxmPWh5CZlIEts2uhkqs4+yUihvCNamT/YLpJoijis+6jeLPpHYwEPEjTW7B21hqoFWqpSyOi\nKMEQvkG8KItuhsM7iNcb3kSdrQFahQZPltfgzpzbpS6LiKLMpCEsCAJefPFFnDt3Dmq1Gi+99BIK\nCwuv+r7vfOc7MJlM+Id/+IdpKVRKQUFA88Uh5KQlwaBTSV0OxYD3LnyIOlsDys2z8FTFOlh0ZqlL\nIqIoNOl9Efv374fP58P27dvx3HPPYevWrVd9zxtvvIHGxsZpKTAatPc44fUHeSqaJmQfGUSPqxcA\n8Kj1ATxVsQ5/O+8vGMBEdF2ThvDRo0exYsUKAMC8efNQV1d3xfFjx47h5MmTqK2tnZ4Ko0BTR+hU\nNC/KomsRRRFfdB/D37/7XfzszK8REALQKXW4M2cxL74ioglNejra6XTCYBjfJ1mhUCAQCECpVKK3\ntxevvvoqfvSjH2Hfvn039IJmsx5KZWRv8UlPN0b0+b6src8FAFhyWy7SU/XT+lpSmu5xjEcOzxD+\n+8hvcPjiSWiUGjxYthKZGSY2XZgCvg+njmM4dTM1hpOGsMFggMvlCn8tCAKUytCPvfvuu7Db7Xjm\nmWfQ19cHj8eDkpISrF279rrPZ7e7I1D2uPR0I/r6hiP6nJcTRRF1zf0wGzWQBQLT+lpSmu5xjEdt\nQx149eTP4PK7UZpSgmeX/RnkI1rY+l2T/zBdE9+HU8cxnLrpGMPrhfqkIbxgwQJ8+OGHePjhh3Hi\nxAmUlZWFj23ZsgVbtmwBAOzevRstLS0TBnAs6h5wY9jtx5LZmTy1SABCf5jJZDJkJWUiRWPCw8Wr\ncVfuUmQaTOgb4YcfEd24SUN49erVOHToEDZs2ABRFPHyyy9jz549cLvdcb0OPKaJ9wfTZY72nMRH\nnX/C3877C2gUajy/+Js89UxEt2zSEJbL5fjud797xWNWq/Wq74u3GfAYXpRFADDsc2L7ubdwvO80\nVHIV2oY6UGq2MoCJaEq4WcckGjsd0GuUyElPkroUksix3lPYfu4tOP0ulJiKsLlyHTL06VKXRURx\ngCE8AfuwF30OD6qtFsi5HpyQgkIQ+1r3wxv0Yu2sNbgnfzlnv0QUMQzhCYxtVVnGrSoTzun+s5iV\nUgydUoc/m7MRSpkCmUkZUpdFRHGGITwBXpSVeJx+F3Y2vo0jPSewPGcJNlbUINeQLXVZRBSnGMIT\naOpwQKmQoygrWepSaAac7DuD18+9iWGfE0XJBbgnf4XUJRFRnGMIX4fbE0BHrxOleSaolFwDjHfv\nXvgAe1reg1KuxFetD+Pegru49ktE044hfB3NXYMQwdaF8S4oBKGQKzAvfS7qBxqxoXwtspMypS6L\niBIEQ/g6Gjt4UVY8c/vd2NW0BwEhgD+f+xSykjLxrQV/KXVZRJRgGMLX0dQ5CBkAaw4vyoo3df31\n+E3Dmxj0DaHAmAdPwAOtUit1WUSUgBjC1+APCGjpGkJ+hgF6LYcoXowERrCraQ8+u3QECpkCXyl5\nEKsLVkIhj2xXLyKiG8WEuYYL3UMIBAWUcqvKuOL2j+B47ynkG3OxuXI9bz0iIskxhK8hfH9wPk9F\nx7qRwAg+u3QUd+ctg0WXir+b/w3kGrI5+yWiqMAQvoaxi7I4E45t9bZGvNawEw7vIEyaZCzIqEZB\ncp7UZRERhTGEv0QQRZzvHER6ihZmo0bqcugWjAQ8eOv8Xhzq+gJymRwPF92H6rTZUpdFRHQVhvCX\ndPW54PYGML80TepS6BaIooj/PPZf6HR2IScpC1tm1yLfmCt1WURE18QQ/pKxpg3cpCO2eAJeqBUq\nyGVy3FewEj3uXjxYdC+Ucr7FiSh68RPqSxrZtCHmnBs4j9cadmJV/grck78ci7PmS10SEdENYQhf\nRhRFNHY4YNSrkJWql7ocmoQn4MXbzb/HwYufQi6Twxv0Sl0SEdFNYQhfxjbkgX3YiwVl6ZDJZFKX\nQxM472jFr85uh80zgKykTGypXI/C5HypyyIiuikM4cs0dYRORZfxVHTUc/vdGPDYcX/hPXi4eDVU\nXPslohjET67L8KKs6NZkb0GPuxfLc+9Adfoc/Osd/4R0vUXqsoiIbhlD+DKNnYPQqBQoyDRIXQpd\nxhf04Z3md/FR5yEo5Qrclj4XRrWBAUxEMY8hPMo54kdXvwuzi8xQyNnMPVqcd7Titfod6BuxIVOf\njs2V62FU848kIooPDOFR4VPR3KoyavS5bfjPY/8FALi34C6sKX4AaoVK4qqIiCKHITyKF2VFD7vH\nAbM2Bel6C9aUPIAycwlKTEVSl0VEFHE87zqqqdMBhVyGkhyGsFR8QT92N+3Fv376b7gw1A4AeLBo\nFQOYiOIWZ8IAvP4gLnQPoyDTCI2aLe6k0DrYhm31O9Dj7kO6zgJRlLoiIqLpxxAG0NI1hKAgooz9\ngyXxdvM+/KHtI4gQcU/ecjxqfRBqhVrqsoiIph1DGLwoS2qiKMKiNWNT5TqUmq1Sl0NENGMYwgCa\nOkIhPIsXZc0IvxDA71v/gApzKcpTZ+GR4tV4qPg+aDj7JaIEk/AhHBQEnO8aQrZFj2Q9Q2C6tQ11\nYFv9Dlxy9eDCUAfKU2dBxduOiChBJXwId/Q64fUFeSp6mvmFAN5t3Y/32z+CIAq4K/dOPGZ9SOqy\niIgklfAh3Dh2fzAvyppWn3Z9gXfbDiBVa8aminUoT50ldUlERJJL+BDmRVnTJyAE0DdiQ3ZSJpbl\nLIE74MHdeXdCq9RKXRoRUVRI6BAWRRFNHQ6YjRqkmRgMkdQx3IVt9dsx7HPiX5Y8hySVHg8WrZK6\nLCKiqJLQIdxrH8GQ24/bKzMgk8mkLicuBIUg3m07gHcvfABBFLAs53bIZdyYjYjoWhI6hBs7eCo6\nkoZ8w3j1xM/Q6exCisaEpyqewGxLudRlERFFrcQO4dH14LJ8hnAkGFRJ0Co1uDN7MdaWroFOqZO6\nJCKiqJbQIdzUOQidRonctCSpS4lZF52XsLtpL7bM3gCTxoi/ue3rvO+XiOgGJWwIDzq96LWPoNpq\ngVzO9eCbFRSC+EP7R/h9634ExSBO9J3Gyrw7GcBERDchYUO4qTN0f3Apt6q8aV3Obmyr34H24U6Y\n1EY8WfEE5qZVSl0WEVHMSdgQ5kVZt25vy3toH+7EkqyFeKL0K9Cr9FKXREQUkxI3hDsdUCrkKM5O\nlrqUmHDJ1QO1XA2Lzoz15V/FHdmLUJ0+R+qyiIhiWkKG8Ig3gI5eJ2blmqBS8h7WiQSFID7oOIjf\ntbyPkpRiPDvvL5CiMSElnafxiYimKiFDuPniIESRtyZNptvVi231O3BhqB1GtQF35y3jpiZERBGU\nkCHcyIuyJnW6/yz+p+41BIQAFmXOw7qyx2BQ8VYuIqJISsgQbupwQAZgVi5D+MtEUYRMJkNRcgEy\ndGl4pOR+zEufK3VZRERxadIQFgQBL774Is6dOwe1Wo2XXnoJhYWF4eN79+7FL3/5SygUCpSVleHF\nF1+EXB6966z+gICWS0PIyzBAr+U9rWMEQcCB9oM43V+Pv53/FzCqDfjn2/+O+z4TEU2jST9h9+/f\nD5/Ph+3bt+O5557D1q1bw8c8Hg/+8z//E7/61a/wxhtvwOl04sMPP5zWgqeqrWcY/oDAU9GX6XX3\n4V8//N948/xedLm60evuBwAGMBHRNJt0Jnz06FGsWLECADBv3jzU1dWFj6nVarzxxhvQ6UJ7BAcC\nAWg0mmkqNTKaOrhf9BhBFPDHzk/wdvM++AU/5qdXobb8cRjVBqlLIyJKCJOGsNPphMEw/qGsUCgQ\nCASgVCohl8uRlpYGANi2bRvcbjeWLVs24fOZzXoolYopln2l9HTjDX9vW68LAHDHbbmwmBK7wYDb\nP4IPP/sYWqUaf73wa7izYKHUJcW8m3kv0rVxDKeOYzh1MzWGk4awwWCAy+UKfy0IApRK5RVf/+AH\nP0BrayteeeWVSW9hsdvdUyj3aunpRvT1Dd/Q9wqiiDMt/UgzaSH4Ajf8c/FEEAUc7j6OhZm3QSlX\n4uk5m2DWpsCam5OQ4xFJN/NepGvjGE4dx3DqpmMMrxfqky76LViwAAcPHgQAnDhxAmVlZVccf+GF\nF+D1evHjH/84fFo6WnX1u+DyBBL2VHT/iA0/PP5T/Kp+O/7Q9hEAoDA5H8lq/tVMRCSFSWfCq1ev\nxqFDh7BhwwaIooiXX34Ze/bsgdvtxty5c7Fr1y4sWrQIX/va1wAAW7ZswerVq6e98FuRqE0bBFHA\nxxc/w2+bfw9f0IfqtDm4M2eJ1GURESW8SUNYLpfju9/97hWPWa3W8H83NDREvqppkqgXZb3esBuf\nXPoCeqUOG2dvwOLM+dz5iogoCiTUZh1NnQ4YdCpkpcZ/1x9RFBEUg1DKlbgjexGcfhc2lD8Ok4YN\nK4iIokXChLBt0APbkBfzS9PifhZoG7Hj1w07kZOUhSfKHoU1pQjWlCKpyyIioi9JmBBu7Iz/U9Gi\nKOJQ1+fYfX4vvEEf1AoVBFHgphtERFEqYUJ4/KKs+Axhu8eB1+p3osHeBJ1Si82V67Eka2Hcz/qJ\niGJZ4oRwhwNqlRwFmfG5G5TT70KjoxmzLeV4quIJpGgS6wpwIqJYlBAh7Bzx42K/C5WFZigV8XNq\n1u5x4GT/Gdydtwz5xlz806JnkWfI5uyXiChGJEQIn4+z+4NFUcRnl47gzfN7MBLwoMCYhxJTIfKN\nOVKXRkRENyEhQnjsoqzSOLgoy+EdxG8a3sQZWwO0Cg2erKhBcXKB1GUREdEtSIgQbup0QC6TwZoT\n2/fI+oJ+/NvhH2LIN4wKcymeqnwCqVqz1GUREdEtivsQ9vmDuHBpGIVZBmjVsfnruv1u6FV6qBUq\nPFh0LxQyOZblLOHaLxFRjIvNVLoJrZeGEBTEmLw1SRRFHO45jp2Nb2NjRQ0WZFRjZd6dUpdFREQR\nEvch3Di6X3SshfCgdxivn3sTp/vPQq1Qwxf0SV0SERFFWPyHcAxeGX2s9xTeaNgNV8CN0pQSbKpc\njzRdqtRlERFRhMV1CAcFAecvDiIrVY/kJLXU5dwwl98Nv+DH+rKvYkXuHdx2kogoTsV1CHf2uuD1\nBVGWH92zYFEUcbT3JERRxOKs+VieswRzLRUwa2PrFDoREd2cuA7h8P3BUbwePOxz4o1zb+FE32kk\nq42Ylz4XKoWKAUxElADiOoSbOqJ7k45jvaew/dxbcPpdsJqKsKlyPVQKldRlERHRDInbEBZFEY2d\ngzAZ1Eg3aaUu5ypN9hb8rO41qOQq1JR+BXfnLePaLxFRgonbEO51jGDI5cPiioyo2tSif2QAabpU\nzEopxkNF92Fx1nxk6tOlLouIiCQQt1OvsfuDy6LkVLTT78L/V/drvPT5/41edx9kMhnWlNzPACYi\nSmBxOxNuiqL7g0/21eH1c7sx7HOONluInpk5ERFJJ35DuMMBnUaBvHSDZDUEhSC21e/A4Z7jUMqV\neHzWI1iVv4Jrv0REBCBOQ3jQ6UWPfQRzS1Ihl0s361TIFZDL5ChMzseWyvXISsqUrBYiIoo+cRnC\nY6eiyyS4P9jtd+PNpr1YVbACuYZs1JY/DqVMAYVcMeO1EBFRdIvLEB7bpGOmL8o63X8Wrze8iUHf\nMGQyGTZVroNGETvbZRIR0cyKyxBu6hyEUiFDcbZxRl7P7R/BrqZ38Hn3UShkCjxa8iDuK1g5I69N\nRESxK+5CeMQbQHvPMKy5JqiUM3MK+A/tH+Hz7qMoMOZic2UtcgxZM/K6REQU2+IuhFu6hiCK039r\n0khgBIPeYWQlZeCBwlUwqg1YmXsn136JiOiGxV0IhzfpmMaLss7azuHXDbugUajx/OK/g1apwar8\nFdP2ekREFJ/iLoSbOh2QAZg1DTPhkcAIdjftxSeXDkMuk+Ohonuh4D2/RER0i+IqhANBAS1dQ8hN\nT0KSNrLdiLpdvfjRif+B3etAriEbmytrkW/MiehrEBFRYomrEG7rHoYvIExL60KL1gy9Soel2Yvw\nQNEqKOVxNXRERCSBuEqSSO8X3TDQhHcvfIBvVP8ZtEotvr3oWV54RUREERNXIRypi7I8AS9+2/x7\nfHzxU8hlcjQ5WlCVNpsBTEREERU3ISyIIpo6HUgzaZGarL3l52m0n8dr9Tth89iRnZSJLZW1KEjO\ni2ClREREIXETwpdsbrg8AVRbLbf8HKIo4rfN+zDgceCBwlV4qPg+qLj2S0RE0yRuEqZp9FT0rVyU\n1WRvQbYhEwZVErZUroc36ENhcn6kSyQiIrpC/ITwaNOG0ptYD/YGfXi7eR/+2HkIizPn48/mbGS7\nQSIimjFxE8KNHYMw6FTIsehv6PvPO1qxrX4H+kdsyNRnYGXesmmukIiI6EpxEcIDQx7YhjyYX5oG\nmUw26fd/fPFTbD/3WwDAfQUr8Ujx/VArIru5BxER0WTiIoQbb/BUtCAKkMvkqEwtQ54hG+vLv4oS\nU9EMVEhERHS1uAjhpo7RTTryr71Jhy/ox56Wd9E/MoBnqrYgTWfBtxd/84ZmzURERNMlPkK40wG1\nUo7CTONVx1oG27Ctfjt63f3I0KXB6XfBqDYwgImISHIxH8JOtw8X+1woL0iBUjHe0cgX9GNv63s4\n0P4xAGBV/gp8peQBqBVqqUolIiK6QsyH8NkLAxABlH3p/mB3wI1Pug7DokvF5sr1mJVSLE2BRERE\n1xH7IdxiAxC6KMsf9OOz7qNYlnM7UjQm/PVtTyPHkAUNZ79ERBSFJg1hQRDw4osv4ty5c1Cr1Xjp\npZdQWFgYPn7gwAG8+uqrUCqVqKmpwfr166e14C872zoAuUwGpXEIW4/8HN2uHihlCizNWYxiU8GM\n1kJERHQzJg3h/fv3w+fzYfv27Thx4gS2bt2Kn/zkJwAAv9+P73//+9i1axd0Oh02btyIVatWIS0t\nbdoLBwCfP4imzn6Yy9rxo9PvQRAFrMy7Ewsyb5uR1yciIpoK+WTfcPToUaxYsQIAMG/ePNTV1YWP\nNTc3o6CgACaTCWq1GgsXLsThw4enr9ovab00BLn1KNymBpg1Jnxz/jNYX/ZVnn4mIqKYMOlM2Ol0\nwmAwhL9WKBQIBAJQKpVwOp0wGsdvC0pKSoLT6Zzw+cxmPZTKyPTl7XP6IPYXYNEsK55dsRFa1a23\nMCQgPf3qW7zo5nAMp45jOHUcw6mbqTGcNIQNBgNcLlf4a0EQoFQqr3nM5XJdEcrXYre7b7XWq6Qb\n1Nj57a9jYMCFYYcfw/BH7LkTTXq6EX19w1KXEdM4hlPHMZw6juHUTccYXi/UJz0dvWDBAhw8eBAA\ncOLECZSVlYWPWa1WtLW1weFwwOfz4ciRI5g/f36ESr4xCsWkvwIREVFUmnQmvHr1ahw6dAgbNmyA\nKIp4+eWXsWfPHrjdbtTW1uL555/H008/DVEUUVNTg8xMtgIkIiK6ETJRFMWZfMHpmOLz1MvUcRyn\njmM4dRzDqeMYTl1UnY4mIiKi6cEQJiIikghDmIiISCIMYSIiIokwhImIiCTCECYiIpIIQ5iIiEgi\nDGEiIiKJMISJiIgkMuM7ZhEREVEIZ8JEREQSYQgTERFJhCFMREQkEYYwERGRRBjCREREEmEIExER\nSSRmQlgQBLzwwguora3F5s2b0dbWdsXxAwcOoKamBrW1tdixY4dEVUa3ycZw7969WLduHTZs2IAX\nXngBgiBIVGn0mmwMx3znO9/Bf/zHf8xwdbFhsjE8deoUnnzySWzcuBHPPvssvF6vRJVGt8nG8Z13\n3sHjjz+Ompoa/OY3v5Goyuh38uRJbN68+arHZyxTxBjx3nvvid/+9rdFURTF48ePi9/4xjfCx3w+\nn3jfffeJDodD9Hq94tq1a8W+vj6pSo1aE43hyMiIeO+994put1sURVH81re+Je7fv1+SOqPZRGM4\n5vXXXxfXr18v/uAHP5jp8mLCRGMoCIL46KOPihcuXBBFURR37NghNjc3S1JntJvsvbhs2TLRbreL\nXq83/PlIV/rpT38qrlmzRly3bt0Vj89kpsTMTPjo0aNYsWIFAGDevHmoq6sLH2tubkZBQQFMJhPU\najUWLlyIw4cPS1Vq1JpoDNVqNd544w3odDoAQCAQgEajkaTOaDbRGALAsWPHcPLkSdTW1kpRXkyY\naAxbW1uRkpKCX/ziF9i0aRMcDgdKSkqkKjWqTfZeLC8vx/DwMHw+H0RRhEwmk6LMqFZQUIBXXnnl\nqsdnMlNiJoSdTicMBkP4a4VCgUAgED5mNBrDx5KSkuB0Ome8xmg30RjK5XKkpaUBALZt2wa3241l\ny5ZJUmc0m2gMe3t78eqrr+KFF16QqryYMNEY2u12HD9+HJs2bcLPf/5zfPbZZ/j000+lKjWqTTSO\nAFBaWoqamho88sgjuPvuu5GcnCxFmVHtgQcegFKpvOrxmcyUmAlhg8EAl8sV/loQhPDgffmYy+W6\nYgApZKIxHPv63/7t33Do0CG88sor/Mv5GiYaw3fffRd2ux3PPPMMfvrTn2Lv3r3YvXu3VKVGrYnG\nMCUlBYWFhbBarVCpVFixYsVVMzwKmWgcGxoa8NFHH+GDDz7AgQMHMDAwgH379klVasyZyUyJmRBe\nsGABDh48CAA4ceIEysrKwsesViva2trgcDjg8/lw5MgRzJ8/X6pSo9ZEYwgAL7zwArxeL3784x+H\nT0vTlSYawy1btmD37t3Ytm0bnnnmGaxZswZr166VqtSoNdEY5ufnw+VyhS8yOnLkCEpLSyWpM9pN\nNI5GoxFarRYajQYKhQKpqakYGhqSqtSYM5OZcvU8PEqtXr0ahw4dwoYNGyCKIl5++WXs2bMHbrcb\ntbW1eP755/H0009DFEXU1NQgMzNT6pKjzkRjOHfuXOzatQuLFi3C1772NQChUFm9erXEVUeXyd6H\nNAIvpR4AAAB3SURBVLnJxvB73/sennvuOYiiiPnz5+Puu++WuuSoNNk41tbW4sknn4RKpUJBQQEe\nf/xxqUuOelJkCrsoERERSSRmTkcTERHFG4YwERGRRBjCREREEmEIExERSYQhTEREJBGGMBERkUQY\nwkRERBJhCBMREUnk/wecmLQl9fzcdgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111f0d048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# DecisionTree\n",
    "decision_tree = DecisionTreeClassifier()\n",
    "params = {\"criterion\": [\"gini\", \"entropy\"], \"max_depth\": [None, 2, 3, 5] }\n",
    "decision_tree = tuneModel(decision_tree, params)\n",
    "acc_tree = scoreModel(decision_tree)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train accuracy score: 0.890850722311\n",
      "Test accuracy score: 0.787313432836\n",
      "Precision score 0.785457296955\n",
      "Recall score 0.787313432836\n",
      "F1 score 0.782958065443\n",
      "Confusion matrix:\n",
      " [[145  20]\n",
      " [ 37  66]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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AnaWrGMCUNHgmLJHjNjtEEZhbzv1LiS7l+EgbftP2Gpz+URSbCqBi25mS0LQh\nLAgCnnrqKbS3t0OtVuOZZ55BSUlJ5Pjnn3+O5557DqIoIisrC88//zw0Gi4uMZ2WTjsAoKGc14OJ\nvkgQBbzS9ho+PnsACpkC95XdiTtKbuG1X0pK04bw3r17EQgEsHXrVhw9ehTPPfccfvrTnwKYWOP4\n+9//Pn70ox+hpKQE27dvx5kzZ1BeXh7zwhOZKIpoto3ApFehJNckdTlEcUUuk0OlUKPQmI/NdU0o\nMOZJXRJRzEwbwocOHcLKlSsBAPPnz0dLS0vkmM1mQ3p6On75y1/i1KlTuPnmmxnAV6B30IVRVwDL\n6nO4MhYRAG/Ih9dP78bdspthQRa+XnEPFDI5z34p6U0bwi6XC0bjhZWcFAoFQqEQlEolHA4Hjhw5\ngieffBLFxcX45je/iYaGBixbtuyyj2ex6KGM8gYEWVmJdTb5x+YBAMDy6wriqvZ4qiVRcQyv3ucD\nrfjpwS0Y8TgQlofw9yv+UuqSEh5fhzM3W2M4bQgbjUa43Rf2rxUEAUrlxK+lp6ejpKQEFRUVAICV\nK1eipaVlyhB2ODwzrfkiWVkmDA2NR/UxY+2z5n7IABRl6uOm9kQcx3jDMbw6vpAPr5/+HT7q/wxy\nmRz3lN6OjYsf4BjOEF+HMxeLMbxcqE87RWnhwoX44IMPAABHjx5FVVVV5FhRURHcbje6u7sBAAcP\nHkRlZWU06k1aXn8Ip/pGUZJrQppeLXU5RJL5oO8TfNT/GfINufjHxX+Le8vvgFLBCRuUWqZ9xa9e\nvRr79+/H+vXrIYoinn32WezatQsejwdNTU34wQ9+gMcffxyiKGLBggW45ZZbZqHsxNXa7UBYEDGX\nd0VTCvKF/LD7HMg35uLW4pVQKpS4qWAZlHKGL6WmaV/5crkcTz/99EXfO99+BoBly5Zhx44d0a8s\nSbWcX6qSIUwp5qTjNF5u3Q5BFPF/Lf176JRarCpaKXVZRJLix89ZJIoimjtHoNcoUZbPGycoNfhC\nfrzRsQcfnPkYMsiwuuQWnvkSTeL/CbPo7IgHI2N+LKnJhkLOFUMp+Y147fh/jvwMIz47cvXZ2FS3\nDqVpxVKXRRQ3GMKz6HwruoFLVVKKSNeYkaY2YmH2PNxbthoqBddJJ/oihvAsarZNLlVZxuvBlLxO\nO214o2MP/o+534BRbcB3F/4VF90gugyG8CzxB8No73GiKNsIi4lra1PyCYQDeLPzLbzfux8A0GY/\nicW5Cxg7DevUAAAgAElEQVTARFNgCM+S9h4HQmGBrWhKSp2jXdhyYhsGvcPI1mdiU20Tys0l0/8i\nUYpjCM+S5sldk+ayFU1JaHfnOxjyjuC2optwX/mdUPPaL9EVYQjPkubOEWjVCswpNEtdClFUdI52\nw6pNR7rGjIdrHsKofwwV6aVSl0WUUBjCs+Ccw4NBhxcLKjOhVHBqEiW2YDiI3bZ38G7PB2jIrME3\n5/0vZOqsyNTxUgvR1WIIz4KW861orpJFCc422oMtrdtwzjOITF0Gbi++ReqSiBIaQ3gWNHN+MCWB\nPw0cxksntkKEiFsKV+D+iruhUXATEqKZYAjHWDAURluPA3kZemSadVKXQ3TVBFGAXCZHlaUCRaZ8\nrJlzHyotFdP/IhFNiyEcYyf7RhEICmxFU8IJCiHsse1F91gv/mb+o0jXmPFPi78NmUwmdWlESYMh\nHGPNHWxFU+LpGevDltZt6HcPIENrwah/DBZtOgOYKMoYwjHWYrNDrZSjuihd6lKIphUUQnjLthfv\n9LwPQRRwY8ENeLDiHmiVWqlLI0pKDOEYGhn1oX/YjXkVGVApuXQfxT9/2I/9Z/8EszoNG2vXosZa\nKXVJREmNIRxDLbbJVnQZW9EUv0JCCB/3H8CK/OthVBnw1/P+HFn6TOh49ksUcwzhGIosVVnBm7Io\nPvWN9+Ol1q044zqLkBjCqqKVKE4rlLosopTBEI6RUFjAiS47stN1yLHopS6H6CJhIYy3u/dhT9e7\nEEQBy/Oux7K8JVKXRZRyGMIx0nFmFL5AGMsb2Iqm+PPzlpdxbPg40jVmPFLzEOoyqqUuiSglMYRj\npJlLVVKcCQthiBChlCtxU+Fy6FV6NFbeB52Si8gQSYUhHCMtnSNQKmSoKbZIXQoR+l0D2NK6FTXW\nKjxQcTdqrJW885koDjCEY8Dp8qNn0IW6Ugs0ak5NIumEhTD29vwRv7f9ASExjEJjPkRR5KIbRHGC\nIRwD53dNaihjK5qkM+A+h5dObEP3eC/MahM21DRibmad1GUR0RcwhGPg/PxgTk0iKXlDPvSM92FJ\nzkKsrbofBhXv0ieKNwzhKBMEEcdtdljTNMjP4Jseza4B9yBa7Sdxa9GNKDOX4H8v/XvkGnKkLouI\nLoMhHGW2s2Nw+0JYVJ3N6240awRRwLs9H2C37R2EhBBqrJXIM+QwgIniHEM4ypo7J1vRnJpEs+Sc\nexBbWrfDNtYNk8qI9fVrkMfwJUoIDOEoa+60QyGXobaEU5Mo9jxBL/7t4I/hC/uwKPs6rKv6Ooxq\ng9RlEdEVYghH0bgngK6zY6gsSodey6Gl2BkLjCNNbYJepcN95XfArEnDwux5UpdFRFeJSRFFx212\niADmlnOpSooNQRTwfu9HeLPzbTza8AjmZtbh1qIbpS6LiK4RQziKuFQlxdKgZxgvt25Dx2gXjCq2\nnImSAUM4SgRRxHHbCMwGNYqyjVKXQ0nmwzOf4rVTuxAUgpifNRfrqx+ESc3XGVGiYwhHSc+5cYx5\nglgxN5dTkyjqgkIQaoUKm2rXYmH2dXyNESUJhnCUsBVN0SSIAj488ylMaiMWZs/DLYUrsCRnAc9+\niZIMQzhKWjpHIJMBdaW8KYtmZthrx8ut23DK2YlsXSbmZzVALpMzgImSEEM4Cjy+IDrOjKE8Lw1G\nnUrqcihBCaKAj858htc7fodAOIC5mXXYUL0Gcplc6tKIKEYYwlFwossBQRTZiqYZOT7Shq0nX4dO\nqcM36tZjSc4CXvslSnIM4Sg4v1RlA0OYrpIoijjnGUKuIRsNGbW4r+wOLMtfgnSNWerSiGgWsM81\nQ6IoosVmh1GnQmmuSepyKIHYfQ78+Oj/4PmDL2DE64BMJsPdZbczgIlSCM+EZ+jMkBuOcT9uqMuB\nXM7WIU1PFEV83P8n7Dy9G76wH/UZNVDI+XmYKBUxhGeo2Xa+Fc27oml6gXAQP2v+FVrtJ6FVaLGx\nZi1uyFvMa79EKYohPEMtk/OD68t4PZimp1aooFVoUGutwiM1D8GiTZe6JCKSEEN4Brz+EE72OlGS\na4LZoJa6HIpTDp8T20++gQcq7kaOIRub65qgkqt49ktEDOGZaOtxICyI3DWJLkkURXw6cAivnXoT\n3pAPmfoMrJlzH9QKfmAjogkM4Rk434puYCuavsTpH8Urba+hZaQNWoUGD1c3Ynn+9VKXRURxZtoQ\nFgQBTz31FNrb26FWq/HMM8+gpKTkKz/3/e9/H2azGf/wD/8Qk0LjjSiKaO4cgU6jREVBmtTlUJx5\nu+s9tIy0odoyB4/UrEWGziJ1SUQUh6adF7F3714EAgFs3boVjz/+OJ577rmv/Myrr76KkydPxqTA\neDVg92B41If6UgunlxAAwOEdxTn3IADg/oo78UjNWnxr/l8ygInosqZNj0OHDmHlypUAgPnz56Ol\npeWi44cPH8axY8fQ1NQUmwrjVKQVzVWyUp4oivjTwGH8/VtP4+fHf42QEIJOqcPy/CW8+YqIpjRt\nO9rlcsFovLB7i0KhQCgUglKpxODgIF588UX8+Mc/xp49e67oCS0WPZRKxbVXfAlZWbO/UlX7mVEA\nwM2Li5GZrpv1548FKcYx0Tl9Y/jvg7/BgTPHoFFqcFfVzcjJNnPThRng63DmOIYzN1tjOG0IG41G\nuN3uyNeCIECpnPi1t956Cw6HA4899hiGhobg8/lQXl6ONWvWXPbxHA5PFMq+ICvLhKGh8ag+5nQC\nwTCaTw+jMMsAMRia9eePBSnGMdF1j/XixWM/hzvoQWV6Ob694s8g92oxMuye/pfpkvg6nDmO4czF\nYgwvF+rThvDChQvx3nvv4Z577sHRo0dRVVUVObZ582Zs3rwZALBz5050dnZOGcDJor3XiWBIYCs6\nRYmiCJlMhlxDDtI1ZtxTtho3FSxDjtGMIS/f/Ijoyk0bwqtXr8b+/fuxfv16iKKIZ599Frt27YLH\n40m568Dnnd81aW4Z5wenmkPnjuH9vo/wrfl/CY1CjSeWfIetZyK6ZtOGsFwux9NPP33R9yoqKr7y\nc6lwBnxec6cdGpUClUVccjBVjAdc2Nr+Oo4MNUMlV6F7rBeVlgoGMBHNCBfruEqDTi/O2T2YPycT\nSgXfgFPB4cHPsbX9dbiCbpSbS7Gpdi2y9VlSl0VESYAhfJWOn29Fc6nKlBAWwthj2wt/2I81c+7D\nrUU38uyXiKKGIXyVmjk/OCU0D5/AnPQy6JQ6/Fn9BihlCuQYsqUui4iSDEP4KgRDAlq7Hci16pGV\nJHOD6WKuoBvbT76Bg+eO4sb8pdhQ04gCY57UZRFRkmIIX4XTfU74g2E0sBWdlI4NHccr7a9hPOBC\naVoxbi1aKXVJRJTkGMJX4Xwreh5b0Unnra53savzbSjlSny94h7cVnwTr/0SUcwxhK9Cs20EKqUc\nVZyalDTCQhgKuQLzsxrQaj+J9dVrkGfIkbosIkoRDOErZB/z4cyQGw3lVqhV0V37mmafJ+jBjlO7\nEBJC+POGR5BryMF3F/6V1GURUYphCF+hFttEK3puGVvRia5luBW/aXsNo4ExFJsK4Qv5oFVqpS6L\niFIQQ/gKRZaqrGAIJypvyIsdp3bh07MHoZAp8LXyu7C6+GYo5OxsEJE0GMJXIBQWcKLLjkyzFjkW\nTk1KVJ6gF0cGP0eRqQCbatdx6hERSY4hfAU6+8fg9YdxQ10uN2lPMN6QF5+ePYRbClcgQ2fF3y34\nJgqMeTz7JaK4wBC+ApFWNKcmJZTWkZN4uW07nP5RmDVpWJg9D8VphVKXRUQUwRC+Ai2ddijkMtSU\ncGpSIvCGfHj99G7s7/8T5DI57im9HfMy66Qui4joKxjC0xh1B9B9bhy1JRZo1RyueCeKIv7z8H+h\nz9WPfEMuNtc1ochUIHVZRESXxFSZRgtb0QnBF/JDrVBBLpPj9uKbcc4ziLtKb4NSzpc4EcUvvkNN\n4/z8YK4XHb/a7afxctt2rCpaiVuLbsSS3AVSl0REdEUYwlMQBBHHbXZYTBoUZBqkLoe+xBfy442O\n3+ODM59ALpPDH/ZLXRIR0VVhCE/BNjAGlzeIm67L49SkOHPaacNLJ7ZixGdHriEHm2vXoSStSOqy\niIiuCkN4Ci2TuyY1cKnKuOMJemD3OXBHya24p2w1VLz2S0QJiO9cU2jpHIFcJkNdqUXqUgjAKUcn\nznkGcWPBDZiXVY9/ueGfkKXnByQiSlwM4ctweYPoPDuGOQVm6LUqqctJaYFwAG92vIX3+/ZDKVfg\nuqwGmNRGBjARJTyG8GUct9khipyaJLXTThtebt2GIe8IcvRZ2FS7Dia1UeqyiIiigiF8GZwfLL0h\nzwj+8/B/AQBuK74J95XdCbWCXQkiSh4M4UsQRBHNNjvS9CoU5fCsa7Y5fE5YtOnI0mfgvvI7UWUp\nR7m5VOqyiIiiTi51AfGo95wLY+4AGsozIOfUpFkTCAex89Ru/Msn/4qusR4AwF2lqxjARJS0eCZ8\nCS22iVY0V8maPbbRbmxp3YZzniFk6TIgilJXREQUewzhS2jutEMGoL6UITwb3ujYgz90vw8RIm4t\nvBH3V9wFtUItdVlERDHHEP4Sjy+E032jKMtPg0nPIJgNoigiQ2vBxtq1qLRUSF0OEdGsYQh/SWu3\nHYIooqGMZ8GxEhRC+L3tD6ixVKLaOgf3lq3G3WW3Q8OzXyJKMQzhL2meXKqSU5Nio3usF1tat+Gs\n+xy6xnpRbZ0DFacdEVGKYgh/gSiKaLGNwKBVoiwvTepykkpQCOEt21680/M+BFHATQXL8UDF3VKX\nRUQkKYbwF/QPu2Ef8+P62mzI5ZyaFE2f9P8Jb3Xvg1Vrwcaatai2zpG6JCIiyTGEv4Ct6OgKCSEM\neUeQZ8jBivyl8IR8uKVwObRKrdSlERHFBYbwF0TmB/OmrBnrHe/HltatGA+48L+XPg6DSo+7SldJ\nXRYRUVxhCE/yBUI42etEcY4RZqNG6nISVlgI463ufXir610IooAV+ddDLuPCbEREl8IQntTW40Qo\nLLIVPQNjgXG8ePTn6HP1I11jxiM1D6Euo1rqsoiI4hZDeNL5XZPYir52RpUBWqUGy/OWYE3lfdAp\ndVKXREQU1xjCmJia1Nw5Ap1GgYoCs9TlJJQzrrPYeWo3Nteth1ljwt9e9xec90tEdIUYwgAGHV4M\nOX1YVJUFpYLXL69EWAjjDz3v4/e2vQiLYRwdasbNhcsZwEREV4EhDKC5k7smXY1+1wC2tG5Dz3gf\nzGoTHq55CA2ZtVKXRUSUcBjC4Pzgq7W78230jPdhae4iPFT5NehVeqlLIiJKSCkfwoFgGO09DhRk\nGmBN4yISl3PWfQ5quRoZOgvWVX8dN+QtxryseqnLIiJKaCkfwif7nAiEBLaiLyMshPFu7wf4Xec7\nKE8vw7fn/yXSNWakZ/EGNiKimUr5EG5hK/qyBtyD2NK6DV1jPTCpjbilcAVkMq6pTUQULSkfws2d\nI1Cr5KgsTJe6lLjSPHwC/9PyMkJCCItz5mNt1QMwqgxSl0VElFRSOoSHnV6cHfHguooMqJScmgRM\nzJmWyWQoTStGti4T95bfgflZDVKXRUSUlKYNYUEQ8NRTT6G9vR1qtRrPPPMMSkpKIsd3796NX/3q\nV1AoFKiqqsJTTz0FuTwxAq3FNtGKbmArGoIgYF/PB2gebsW3FvwlTGoj/vn6v+O6z0REMTTtO+ze\nvXsRCASwdetWPP7443juuecix3w+H/7zP/8TL730El599VW4XC689957MS04ms7PD55bkdohPOgZ\nwr+89x947fRu9LsHMOgZBgAGMBFRjE17Jnzo0CGsXLkSADB//ny0tLREjqnVarz66qvQ6SbWCA6F\nQtBoEmMHolBYwIluB3IsOmSnp+Yax4Io4I99H+ONjj0ICkEsyJqLpuoHYVIbpS6NiCglTBvCLpcL\nRuOFN2WFQoFQKASlUgm5XI7MzEwAwJYtW+DxeLBixYopH89i0UOpVMyw7ItlZZmu+neaTw/DHwhj\nyfW51/T7ycAT9OK9Tz+EVqnG3yz6BpYXL5K6pISXqq+laOIYzhzHcOZmawynDWGj0Qi32x35WhAE\nKJXKi75+/vnnYbPZ8MILL0w7hcXh8Myg3K/KyjJhaGj8qn/vw8O9AIA5eWnX9PuJShAFHBg4gkU5\n10EpV+LR+o2waNNRUZCfUuMQC9f6WqQLOIYzxzGcuViM4eVCfdqLfgsXLsQHH3wAADh69Ciqqqou\nOv7kk0/C7/fjJz/5SaQtnQiaO+1QKuSoLk6dqUnD3hH86MjP8FLrVvyh+30AQElaEdLU/NRMRCSF\nac+EV69ejf3792P9+vUQRRHPPvssdu3aBY/Hg4aGBuzYsQOLFy/GN77xDQDA5s2bsXr16pgXPhOO\ncT/6hlyoL7NCo4puazweCaKAD898it92/B6BcADzMuuxPH+p1GUREaW8aUNYLpfj6aefvuh7FRUV\nkf9ua2uLflUx1nL+rugUmZr0SttOfHz2T9ArddhQtx5LchZw5SsiojiQkot1NNvOL1WZvOtFi6KI\nsBiGUq7EDXmL4Qq6sb76QZg1aVKXRkREk1IuhMOCgBM2OzLStMi1JucWfCNeB37dth35hlw8VHU/\nKtJLUZFeKnVZRET0JSkXwrb+cXj8IVxfl5N0LVlRFLG//zPsPL0b/nAAaoUKgihw0Q0iojiVciH8\n+fnrwWXJ1Yp2+Jx4uXU72hynoFNqsal2HZbmLkq6DxpERMkk5UK4pXMECrkMNSUWqUuJKlfQjZPO\nDtRlVOORmoeQruF+v0RE8S6lQnjMHUDXwDhqitOh0yT+n+7wOXFs+DhuKVyBIlMB/mnxt1FozOPZ\nLxFRgkj8JLoKxyN3RSf21CRRFPHp2YN47fQueEM+FJsKUW4uQZEpX+rSiIjoKqRUCDfbJq4HJ/LW\nhU7/KH7T9hqOj7RBq9Dg4ZpGlKUVS10WERFdg5QJYUEU0dJpR7pRjcIsg9TlXJNAOIh/PfAjjAXG\nUWOpxCO1D8GqTa5r20REqSRlQrh7YBwubxA3zku8a6aeoAd6lR5qhQp3ld4GhUyOFflLE+7vICKi\ni6VMCDcn4FKVoijiwLkj2H7yDWyoacTC7Hm4uXC51GUREVGUpEwIt3TaIZMBdaWJ0b4d9Y/jlfbX\n0Dx8AmqFGoFwQOqSiIgoylIihN2+IDr6R1FRYIZBq5K6nGkdHvwcr7bthDvkQWV6OTbWrkOmLrkW\nFyEiohQJ4eM2O0QxcVbJcgc9CApBrKv6OlYW3MBlJ4mIklRKhHBL58T84HidmiSKIg4NHoMoiliS\nuwA35i9FQ0YNLNp0qUsjIqIYSvoQFkURzbYRmPQqlOSapC7nK8YDLrza/jqODjUjTW3C/KwGqBQq\nBjARUQpI+hDuHXRh1BXAsvocyONsSs/hwc+xtf11uIJuVJhLsbF2HVSK+L9mTURE0ZH0Idxii89W\n9ClHJ37e8jJUchUaK7+GWwpX8NovEVGKSf4Q7hyBDEB9nNyUNey1I1NnxZz0MtxdejuW5C5Ajj5L\n6rKIiEgCSX3q5fWHcKpvFKV5JqTp1ZLW4gq68f+1/BrPfPZ/Y9AzBJlMhvvK72AAExGlsKQ+E27t\ndiAsiGgok7YVfWyoBa+078R4wDW52UJ8XZsmIiJpJHUIt0i8VGVYCGNL6zYcOHcESrkSD865F6uK\nVvLaLxERAUjiEBZFEc2dIzBolSjLl2ZqkkKugFwmR0laETbXrkOuIUeSOoiIKD4lbQifHfFgZMyP\nJTXZUMhn78zTE/TgtVO7sap4JQqMeWiqfhBKmQIKuWLWaiAiosSQtCF8vhXdUD57d0U3D5/AK22v\nYTQwDplMho21a6FRSHtDGBERxa+kDeHmyfnBs3E92BP0YsepN/HZwCEoZArcX34Xbi++OebPS0RE\niS0pQ9gfDKO9x4mibCPSjZqYP98fet7HZwOHUGwqwKbaJuQbc2P+nERElPiSMoTbexwIhYWYtqK9\nIS9G/ePINWTjzpJVMKmNuLlgOa/9EhHRFUvKEG6e3DVpXoxa0SdG2vHrth3QKNR4YsnfQavUYFXR\nypg8FxERJa8kDeERaNUKVBSYo/q43pAXO0/txsdnD0Auk+Pu0tug4JxfIiK6RkkXwuccHgw6vFhQ\nmQmlInoBOeAexI+P/g8cficKjHnYVNuEIlN+1B6fiIhST9KFcEtnbO6KztBaoFfpsCxvMe4sXQWl\nPOmGjoiIZlnSJUlzFOcHt9lP4a2ud/HNeX8GrVKL7y3+Nm+8IiKiqEmqEA6GwmjrcSAvQ49Ms+6a\nH8cX8uO3Hb/Hh2c+gVwmxylnJ+Zm1jGAiYgoqpIqhE/2jSIQFGbUij7pOI2XW7djxOdAniEHm2ub\nUJxWGMUqiYiIJiRVCDd3zGzXJFEU8duOPbD7nLizZBXuLrsdKl77JSKiGEmqhGmx2aFWylFVdHVT\nk045OpFnzIFRZcDm2nXwhwMoSSuKUZVEREQTkiaER0Z96B92Y15FBlTKK7t26w8H8EbHHvyxbz+W\n5CzAn9Vv4HaDREQ0a5ImhFtsV9eKPu20YUvrNgx7R5Cjz8bNhStiWR4REdFXJE0In1+q8kqmJn14\n5hNsbf8tAOD24ptxb9kdUCtUMa2PiIjoy5IihENhASe67MhO1yHHor/szwmiALlMjlprFQqNeVhX\n/XWUm0tnr1AiIqIvSIoQ7jgzCl8gjBUNl25FB8JB7Op8C8NeOx6buxmZugx8b8l3IJPJZrlSIiKi\nC5IihKdqRXeOdmNL61YMeoaRrcuEK+iGSW1kABMRkeSSIoRbOkegVMhQU2yJfC8QDmK37W3s6/kQ\nALCqaCW+Vn4n1Aq1VGUSERFdJOFD2D7mQ8+gC3WlFmjUF6YmeUIefNx/ABk6KzbVrsOc9DIJqyQi\nIvqqhA/hw22DACamJgXDQXw6cAgr8q9HusaMv7nuUeQbc6Hh2S8REcWhaUNYEAQ89dRTaG9vh1qt\nxjPPPIOSkpLI8X379uHFF1+EUqlEY2Mj1q1bF9OCv+xw+0QIW3N9eO7gjzDgPgelTIFl+UtQZi6e\n1VqIiIiuxrQhvHfvXgQCAWzduhVHjx7Fc889h5/+9KcAgGAwiB/+8IfYsWMHdDodNmzYgFWrViEz\nMzPmhQOAIIg4fPIsTOUdeKnjHQiigJsLl2NhznWz8vxEREQzIZ/uBw4dOoSVK1cCAObPn4+WlpbI\nsY6ODhQXF8NsNkOtVmPRokU4cOBA7Kr9EtvZMQSLPkMo8xQsGjO+s+AxrKv6OtvPRESUEKY9E3a5\nXDAajZGvFQoFQqEQlEolXC4XTCZT5JjBYIDL5Zry8SwWPZRXuLbzdIZcAYjDxVg0pwLfWbkBWpU2\nKo+bqrKyTNP/EE2JYzhzHMOZ4xjO3GyN4bQhbDQa4Xa7I18LggClUnnJY263+6JQvhSHw3OttX5F\nllGN7d/7C9jtbow7gxhHMGqPnWqyskwYGhqXuoyExjGcOY7hzHEMZy4WY3i5UJ+2Hb1w4UJ88MEH\nAICjR4+iqqoqcqyiogLd3d1wOp0IBAI4ePAgFixYEKWSr4xCMe2fQEREFJemPRNevXo19u/fj/Xr\n10MURTz77LPYtWsXPB4Pmpqa8MQTT+DRRx+FKIpobGxETg63AiQiIroSMlEUxdl8wlic4rP1MnMc\nx5njGM4cx3DmOIYzF1ftaCIiIooNhjAREZFEGMJEREQSYQgTERFJhCFMREQkEYYwERGRRBjCRERE\nEmEIExERSYQhTEREJJFZXzGLiIiIJvBMmIiISCIMYSIiIokwhImIiCTCECYiIpIIQ5iIiEgiDGEi\nIiKJJEwIC4KAJ598Ek1NTdi0aRO6u7svOr5v3z40NjaiqakJ27Ztk6jK+DbdGO7evRtr167F+vXr\n8eSTT0IQBIkqjV/TjeF53//+9/Hv//7vs1xdYphuDD///HM8/PDD2LBhA7797W/D7/dLVGl8m24c\n33zzTTz44INobGzEb37zG4mqjH/Hjh3Dpk2bvvL9WcsUMUG8/fbb4ve+9z1RFEXxyJEj4je/+c3I\nsUAgIN5+++2i0+kU/X6/uGbNGnFoaEiqUuPWVGPo9XrF2267TfR4PKIoiuJ3v/tdce/evZLUGc+m\nGsPzXnnlFXHdunXi888/P9vlJYSpxlAQBPH+++8Xu7q6RFEUxW3btokdHR2S1BnvpnstrlixQnQ4\nHKLf74+8P9LFfvazn4n33XefuHbt2ou+P5uZkjBnwocOHcLKlSsBAPPnz0dLS0vkWEdHB4qLi2E2\nm6FWq7Fo0SIcOHBAqlLj1lRjqFar8eqrr0Kn0wEAQqEQNBqNJHXGs6nGEAAOHz6MY8eOoampSYry\nEsJUY2iz2ZCeno5f/vKX2LhxI5xOJ8rLy6UqNa5N91qsrq7G+Pg4AoEARFGETCaTosy4VlxcjBde\neOEr35/NTEmYEHa5XDAajZGvFQoFQqFQ5JjJZIocMxgMcLlcs15jvJtqDOVyOTIzMwEAW7Zsgcfj\nwYoVKySpM55NNYaDg4N48cUX8eSTT0pVXkKYagwdDgeOHDmCjRs34he/+AU+/fRTfPLJJ1KVGtem\nGkcAqKysRGNjI+69917ccsstSEtLk6LMuHbnnXdCqVR+5fuzmSkJE8JGoxFutzvytSAIkcH78jG3\n233RANKEqcbw/Nf/+q//iv379+OFF17gJ+dLmGoM33rrLTgcDjz22GP42c9+ht27d2Pnzp1SlRq3\nphrD9PR0lJSUoKKiAiqVCitXrvzKGR5NmGoc29ra8P777+Pdd9/Fvn37YLfbsWfPHqlKTTizmSkJ\nE8ILFy7EBx98AAA4evQoqqqqIscqKirQ3d0Np9OJQCCAgwcPYsGCBVKVGremGkMAePLJJ+H3+/GT\nn/wk0pami001hps3b8bOnTuxZcsWPPbYY7jvvvuwZs0aqUqNW1ONYVFREdxud+Qmo4MHD6KyslKS\nOsIaYeUAAADsSURBVOPdVONoMpmg1Wqh0WigUChgtVoxNjYmVakJZzYz5avn4XFq9erV2L9/P9av\nXw9RFPHss89i165d8Hg8aGpqwhNPPIFHH30UoiiisbEROTk5Upccd6Yaw4aGBuzYsQOLFy/GN77x\nDQATobJ69WqJq44v070OaXrTjeEPfvADPP744xBFEQsWLMAtt9widclxabpxbGpqwsMPPwyVSoXi\n4mI8+OCDUpcc96TIFO6iREREJJGEaUcTERElG4YwERGRRBjCREREEmEIExERSYQhTEREJBGGMBER\nkUQYwkRERBJhCBMREUnk/weMZ7Vi0oy+EgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1122339b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# AdaBoost\n",
    "tree = DecisionTreeClassifier(max_depth = 1)\n",
    "ada = AdaBoostClassifier(base_estimator = decision_tree, n_estimators = 100, learning_rate = 0.1)\n",
    "ada.fit(attributes_train, labels_train)\n",
    "acc_ada = scoreModel(ada)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
      "            max_depth=5, max_features='auto', max_leaf_nodes=None,\n",
      "            min_impurity_split=1e-07, min_samples_leaf=1,\n",
      "            min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
      "            n_estimators=10, n_jobs=1, oob_score=False, random_state=None,\n",
      "            verbose=0, warm_start=False)\n",
      "Train accuracy score: 0.855537720706\n",
      "Test accuracy score: 0.817164179104\n",
      "Precision score 0.815616068593\n",
      "Recall score 0.817164179104\n",
      "F1 score 0.815414750514\n",
      "Confusion matrix:\n",
      " [[145  20]\n",
      " [ 29  74]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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LsWfPHqxatQqiKOKZZ57Btm3b4PF40NjYiJ/85Cd49NFHIYoi5s6dixtvvDEO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\napEBlmpoegAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11234a940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Random Forest\n",
    "random_forest = RandomForestClassifier()\n",
    "params = {\"criterion\": [\"gini\", \"entropy\"], \"n_estimators\": [2, 5, 10, 25], \"max_depth\": [None, 2, 3, 5]}\n",
    "random_forest = tuneModel(random_forest, params)\n",
    "acc_forest = scoreModel(random_forest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVC(C=1, cache_size=200, class_weight=None, coef0=0.0,\n",
      "  decision_function_shape=None, degree=3, gamma=0.001, kernel='linear',\n",
      "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
      "  tol=0.001, verbose=False)\n",
      "Train accuracy score: 0.775280898876\n",
      "Test accuracy score: 0.813432835821\n",
      "Precision score 0.812651829441\n",
      "Recall score 0.813432835821\n",
      "F1 score 0.809891067934\n",
      "Confusion matrix:\n",
      " [[148  17]\n",
      " [ 33  70]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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LLbekdQADnIomuhrekA+vte7CPbJbkAULvlh1LxQyOe9+KeVNGcIulwsGgyHy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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111df2438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Support Vector Machines\n",
    "svm = SVC()\n",
    "params = {\"kernel\": [\"linear\", \"rbf\"], \"C\": [1, 10, 100, 1000], \"gamma\": [1e-3, 1e-4]}\n",
    "svm = tuneModel(svm, params)\n",
    "acc_svm =scoreModel(svm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train accuracy score: 0.84911717496\n",
      "Test accuracy score: 0.805970149254\n",
      "Precision score 0.804448662469\n",
      "Recall score 0.805970149254\n",
      "F1 score 0.802844426125\n",
      "Confusion matrix:\n",
      " [[146  19]\n",
      " [ 33  70]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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0EeqsZ/Bm9TYIooj/a+GPoFNqsSxvidRlEUmKv36GUE0z+4OJXD433m3YgwNt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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1120f0748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# k-Nearest Neighbors\n",
    "knn = KNeighborsClassifier(n_neighbors = 5)\n",
    "knn.fit(attributes_train, labels_train)\n",
    "acc_knn = scoreModel(knn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Perceptron(alpha=0.0001, class_weight=None, eta0=1.0, fit_intercept=True,\n",
      "      n_iter=5, n_jobs=1, penalty=None, random_state=0, shuffle=True,\n",
      "      verbose=0, warm_start=False)\n",
      "Train accuracy score: 0.76886035313\n",
      "Test accuracy score: 0.798507462687\n",
      "Precision score 0.806070989159\n",
      "Recall score 0.798507462687\n",
      "F1 score 0.788885160784\n",
      "Confusion matrix:\n",
      " [[154  11]\n",
      " [ 43  60]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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wsHDGCw9X9t5B1DQP3btb39oTbDPH6zVYtSBjeDZzImK1bFJQdFIqlNCotMg2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\nPYeGhgZotVo8//zzyMvLCx4/fPgwXn7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      "text/plain": [
       "<matplotlib.figure.Figure at 0x112369518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Perceptron\n",
    "perceptron = Perceptron()\n",
    "params = {\"penalty\": [None, \"l1\", \"l2\"], \"alpha\": [0.0001, 0.001, 0.01, 0.1, 1, 10, 100, 1000]}\n",
    "perceptron = tuneModel(perceptron, params)\n",
    "acc_perceptron = scoreModel(perceptron)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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>Model</th>\n",
       "      <th>TestScore</th>\n",
       "      <th>TrainScore</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Random Forest</td>\n",
       "      <td>0.817164</td>\n",
       "      <td>0.855538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Support Vector Machines</td>\n",
       "      <td>0.813433</td>\n",
       "      <td>0.775281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>0.809701</td>\n",
       "      <td>0.786517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>KNN</td>\n",
       "      <td>0.805970</td>\n",
       "      <td>0.849117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Perceptron</td>\n",
       "      <td>0.798507</td>\n",
       "      <td>0.768860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Decision Tree</td>\n",
       "      <td>0.791045</td>\n",
       "      <td>0.849117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AdaBoost</td>\n",
       "      <td>0.787313</td>\n",
       "      <td>0.890851</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Model  TestScore  TrainScore\n",
       "3            Random Forest   0.817164    0.855538\n",
       "4  Support Vector Machines   0.813433    0.775281\n",
       "0      Logistic Regression   0.809701    0.786517\n",
       "5                      KNN   0.805970    0.849117\n",
       "6               Perceptron   0.798507    0.768860\n",
       "1            Decision Tree   0.791045    0.849117\n",
       "2                 AdaBoost   0.787313    0.890851"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "models = pd.DataFrame({\n",
    "    'Model': ['Logistic Regression', 'Decision Tree', 'AdaBoost', 'Random Forest', 'Support Vector Machines', 'KNN', 'Perceptron'],\n",
    "    'TrainScore': [acc_log[0], acc_tree[0], acc_ada[0], acc_forest[0], acc_svm[0], acc_knn[0], acc_perceptron[0]],\n",
    "    'TestScore': [acc_log[1], acc_tree[1], acc_ada[1], acc_forest[1], acc_svm[1], acc_knn[1], acc_perceptron[1]]})\n",
    "models.sort_values(by='TestScore', ascending=False)"
   ]
  }
 ],
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  "kernelspec": {
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   "language": "python",
   "name": "python3"
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  "language_info": {
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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