titanic-machine-learning-key.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Titanic: Machine Learning from Disaster\n",
    "\n",
    "Description of the challenge [here](https://www.kaggle.com/c/titanic)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our data is in .csv format. Each row represents a passenger on the titanic, and some information about them. Let's take a look at the columns:\n",
    "\n",
    " - PassengerId -- A numerical id assigned to each passenger.\n",
    " - Survived -- Whether the passenger survived (1), or didn't (0). We'll be making predictions for this column.\n",
    " - Pclass -- The class the passenger was in -- first class (1), second class (2), or third class (3).\n",
    " - Name -- the name of the passenger.\n",
    " - Sex -- The gender of the passenger -- male or female.\n",
    " - Age -- The age of the passenger. Fractional.\n",
    " - SibSp -- The number of siblings and spouses the passenger had on board.\n",
    " - Parch -- The number of parents and children the passenger had on board.\n",
    " - Ticket -- The ticket number of the passenger.\n",
    " - Fare -- How much the passenger paid for the ticker.\n",
    " - Cabin -- Which cabin the passenger was in.\n",
    " - Embarked -- Where the passenger boarded the Titanic.\n",
    " \n",
    "A good first step is to think logically about the columns and what we're trying to predict. What variables might logically affect the outcome of survived? (reading more about the titanic might help here).\n",
    "\n",
    "**Exercise.** Discuss which variables are more likely to have had an impact on the survival odds of the passengers. \n",
    "\n",
    "## Looking at the data\n",
    "\n",
    "Using your recent knowledge of pandas, load the train data into a dataframe and perform some first basic exploratory operations on it to make yourself aquainted with its features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</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      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female   38      1   \n",
       "2                             Heikkinen, Miss. Laina  female   26      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female   35      1   \n",
       "4                           Allen, Mr. William Henry    male   35      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic = pd.read_csv('data/titanic_train.csv')\n",
    "\n",
    "titanic.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>32.204208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>257.353842</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>49.693429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>223.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.910400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>668.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
       "count   891.000000  891.000000  891.000000  714.000000  891.000000   \n",
       "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
       "std     257.353842    0.486592    0.836071   14.526497    1.102743   \n",
       "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
       "25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n",
       "50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n",
       "75%     668.500000    1.000000    3.000000   38.000000    1.000000   \n",
       "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
       "\n",
       "            Parch        Fare  \n",
       "count  891.000000  891.000000  \n",
       "mean     0.381594   32.204208  \n",
       "std      0.806057   49.693429  \n",
       "min      0.000000    0.000000  \n",
       "25%      0.000000    7.910400  \n",
       "50%      0.000000   14.454200  \n",
       "75%      0.000000   31.000000  \n",
       "max      6.000000  512.329200  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x104ea2410>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAEACAYAAACwB81wAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nDnxJmhMOfEmaEw58SZoT/x8dm0Uq8Bq4NQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1050a4410>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "titanic.Survived.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x104f94e10>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAEACAYAAAC9Gb03AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nFnoRkQmnhV5EZMJpoRcRmXD/H2wOWRSNtNcCAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x104fa3ad0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "titanic.Age.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>female</th>\n",
       "      <td>314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>577</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Survived\n",
       "Sex             \n",
       "female       314\n",
       "male         577"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.groupby('Sex')[['Survived']].agg('count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</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",
       "    <tr>\n",
       "      <th>Sex</th>\n",
       "      <th>Survived</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">female</th>\n",
       "      <th>0</th>\n",
       "      <td>81</td>\n",
       "      <td>81</td>\n",
       "      <td>81</td>\n",
       "      <td>64</td>\n",
       "      <td>81</td>\n",
       "      <td>81</td>\n",
       "      <td>81</td>\n",
       "      <td>81</td>\n",
       "      <td>6</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>233</td>\n",
       "      <td>233</td>\n",
       "      <td>233</td>\n",
       "      <td>197</td>\n",
       "      <td>233</td>\n",
       "      <td>233</td>\n",
       "      <td>233</td>\n",
       "      <td>233</td>\n",
       "      <td>91</td>\n",
       "      <td>231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">male</th>\n",
       "      <th>0</th>\n",
       "      <td>468</td>\n",
       "      <td>468</td>\n",
       "      <td>468</td>\n",
       "      <td>360</td>\n",
       "      <td>468</td>\n",
       "      <td>468</td>\n",
       "      <td>468</td>\n",
       "      <td>468</td>\n",
       "      <td>62</td>\n",
       "      <td>468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>109</td>\n",
       "      <td>109</td>\n",
       "      <td>109</td>\n",
       "      <td>93</td>\n",
       "      <td>109</td>\n",
       "      <td>109</td>\n",
       "      <td>109</td>\n",
       "      <td>109</td>\n",
       "      <td>45</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 PassengerId  Pclass  Name  Age  SibSp  Parch  Ticket  Fare  \\\n",
       "Sex    Survived                                                               \n",
       "female 0                  81      81    81   64     81     81      81    81   \n",
       "       1                 233     233   233  197    233    233     233   233   \n",
       "male   0                 468     468   468  360    468    468     468   468   \n",
       "       1                 109     109   109   93    109    109     109   109   \n",
       "\n",
       "                 Cabin  Embarked  \n",
       "Sex    Survived                   \n",
       "female 0             6        81  \n",
       "       1            91       231  \n",
       "male   0            62       468  \n",
       "       1            45       109  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.groupby(['Sex', 'Survived']).agg('count')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Handling missing data\n",
    "\n",
    "You might have noticed that not every field for all of the records contains data. This means that we will need to deal with handling missing data. There are many strategies for cleaning up missing data, but a simple one is to just fill in all the missing values with the median of all the values in the column. (Question: why the median and not the average?)\n",
    "\n",
    "Implement a solution to handle missing data on the Titanic dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# This fills all missing values with the median\n",
    "#titanic['Age'] = titanic['Age'].fillna(titanic['Age'].median())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# This is a bit more elaborated and fills with the median per group, defined as Title\n",
    "titanic['Title'] = titanic.apply(lambda x: x['Name'].split(',')[1].strip().split('.')[0], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Title\n",
       "Capt              1\n",
       "Col               2\n",
       "Don               1\n",
       "Dr                7\n",
       "Jonkheer          1\n",
       "Lady              1\n",
       "Major             2\n",
       "Master           40\n",
       "Miss            182\n",
       "Mlle              2\n",
       "Mme               1\n",
       "Mr              517\n",
       "Mrs             125\n",
       "Ms                1\n",
       "Rev               6\n",
       "Sir               1\n",
       "the Countess      1\n",
       "Name: Title, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.groupby('Title')['Title'].agg('count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.5"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic[(~titanic.Age.isnull()) & (titanic.Title=='Master')].Age.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "44.5"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "often_titles = ['Master', 'Miss', 'Mr', 'Mrs']\n",
    "titanic[(~titanic.Age.isnull()) & (~titanic.Title.isin(often_titles))].Age.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "28.0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.Age.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def assign_age(title):\n",
    "    often_titles = ['Master', 'Miss', 'Mr', 'Mrs']\n",
    "    if title in often_titles:\n",
    "        median_age_title = titanic[(~titanic.Age.isnull()) & (titanic.Title==title)].Age.median()\n",
    "    else:\n",
    "        median_age_title = titanic[(~titanic.Age.isnull()) & (~titanic.Title.isin(often_titles))].Age.median()\n",
    "    return median_age_title"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "titanic['Age'] = titanic['Age'].fillna(titanic.apply(lambda x: assign_age(x['Title']), axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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</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</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</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</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</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",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Moran, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330877</td>\n",
       "      <td>8.4583</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</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",
       "5            6         0       3   \n",
       "\n",
       "                                                Name     Sex  Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male   22      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female   38      1   \n",
       "2                             Heikkinen, Miss. Laina  female   26      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female   35      1   \n",
       "4                           Allen, Mr. William Henry    male   35      0   \n",
       "5                                   Moran, Mr. James    male   30      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  \n",
       "5      0            330877   8.4583   NaN        Q    Mr  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.head(6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Non-numeric columns\n",
    "\n",
    "Several of our columns are non-numeric, which is a problem when it comes time to make predictions -- we can't feed non-numeric columns into a machine learning algorithm and expect it to make sense of them.\n",
    "\n",
    "We have to either exclude our non-numeric columns when we train our algorithm (Name, Sex, Cabin, Embarked, and Ticket), or find a way to convert them to numeric columns.\n",
    "\n",
    "Decide which non-numeric columns to keep for the analysis, based on your domain knowledge of the problem. Then, turn them into numeric values (categorical variables)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Sex\n",
    "# Check that Sex column is complete\n",
    "# titanic[titanic.Sex.isnull()]\n",
    "titanic['Sex'] = titanic.apply(lambda x: 1 if x.Sex=='female' else 0, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>61</th>\n",
       "      <td>62</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Icard, Miss. Amelie</td>\n",
       "      <td>1</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113572</td>\n",
       "      <td>80</td>\n",
       "      <td>B28</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Miss</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>829</th>\n",
       "      <td>830</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Stone, Mrs. George Nelson (Martha Evelyn)</td>\n",
       "      <td>1</td>\n",
       "      <td>62</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113572</td>\n",
       "      <td>80</td>\n",
       "      <td>B28</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mrs</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass                                       Name  \\\n",
       "61            62         1       1                        Icard, Miss. Amelie   \n",
       "829          830         1       1  Stone, Mrs. George Nelson (Martha Evelyn)   \n",
       "\n",
       "     Sex  Age  SibSp  Parch  Ticket  Fare Cabin Embarked Title  \n",
       "61     1   38      0      0  113572    80   B28      NaN  Miss  \n",
       "829    1   62      0      0  113572    80   B28      NaN   Mrs  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Embarked (C = Cherbourg; Q = Queenstown; S = Southampton)\n",
    "titanic[titanic.Embarked.isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "S    644\n",
       "C    168\n",
       "Q     77\n",
       "Name: Embarked, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.Embarked.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Fill the missing values with the most common Embarked\n",
    "titanic['Embarked'] = titanic['Embarked'].fillna('S')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "dd = {'S': 0, 'C': 1, 'Q': 2}\n",
    "titanic['Embarked'] = titanic.apply(lambda x: dd[x.Embarked], axis=1)"
   ]
  },
  {
   "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>0</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</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>1</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>1</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>1</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</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>1</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>0</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>0</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</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  Parch  \\\n",
       "0                            Braund, Mr. Owen Harris    0   22      1      0   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...    1   38      1      0   \n",
       "2                             Heikkinen, Miss. Laina    1   26      0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)    1   35      1      0   \n",
       "4                           Allen, Mr. William Henry    0   35      0      0   \n",
       "\n",
       "             Ticket     Fare Cabin  Embarked Title  \n",
       "0         A/5 21171   7.2500   NaN         0    Mr  \n",
       "1          PC 17599  71.2833   C85         1   Mrs  \n",
       "2  STON/O2. 3101282   7.9250   NaN         0  Miss  \n",
       "3            113803  53.1000  C123         0   Mrs  \n",
       "4            373450   8.0500   NaN         0    Mr  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cross validation\n",
    "\n",
    "We want to train the algorithm on different data than we make predictions on. This is critical if we want to avoid overfitting. Overfitting is what happens when a model fits itself to \"noise\", not signal. Every dataset has its own quirks that don't exist in the full population. For example, if I asked you to predict the top speed of a car from its horsepower and other characteristics, and gave you a dataset that randomly had cars with very high top speeds, you would create a model that overstated speed. The way to figure out if your model is doing this is to evaluate its performance on data it hasn't been trained using.\n",
    "\n",
    "Every machine learning algorithm can overfit, although some are much less prone to it. If you evaluate your algorithm on the same dataset that you train it on, it's impossible to know if it's performing well because it overfit itself to the noise, or if it actually is a good algorithm.\n",
    "\n",
    "Luckily, cross validation is a simple way to avoid overfitting. To cross validate, you split your data into some number of parts (or \"folds\"). Lets use 3 as an example. You then do this:\n",
    "\n",
    " - Combine the first two parts, train a model, make predictions on the third.\n",
    " - Combine the first and third parts, train a model, make predictions on the second.\n",
    " - Combine the second and third parts, train a model, make predictions on the first.\n",
    "\n",
    "This way, we generate predictions for the whole dataset without ever evaluating accuracy on the same data we train our model using.\n",
    "\n",
    "Familiarize yourself with cross-validation in scikit-learn reading about [KFold](http://scikit-learn.org/stable/modules/generated/sklearn.cross_validation.KFold.html)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Applying scikit-learn ML algorithms to the Titanic dataset\n",
    "\n",
    " - Follow the scikit-learn API and apply the ML algorithm of your choice to a subset of the Titanic dataset.\n",
    " - Using the wrappers described [here](http://scikit-learn.org/stable/modules/cross_validation.html#cross-validation), compute cross-validated metrics for said algorithm\n",
    " - If your algorithm takes various parameters as input, consider tuning them to improve performance\n",
    " - Repeat with various algorithms and compare its performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.cross_validation import KFold\n",
    "from sklearn import cross_validation\n",
    "\n",
    "predictors = [\"Pclass\", \"Sex\", \"Age\", \"SibSp\", \"Parch\", \"Fare\", \"Embarked\"]\n",
    "\n",
    "#kf = KFold(titanic.shape[0], n_folds=5, random_state=1)\n",
    "kf = KFold(len(titanic), n_folds=5, shuffle=True)\n",
    "train, test = [k for k in kf][0]\n",
    "# The predictors we're using the train the algorithm.  Note how we only take the rows in the train folds.\n",
    "X_train = (titanic[predictors].iloc[train,:])\n",
    "# The target we're using to train the algorithm.\n",
    "y_train = titanic[\"Survived\"].iloc[train]\n",
    "X_test = (titanic[predictors].iloc[test,:])\n",
    "y_test = titanic[\"Survived\"].iloc[test]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.79329608938547491"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf = RandomForestClassifier(n_estimators=50)\n",
    "\n",
    "rf.fit(X_train, y_train)\n",
    "rf.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.801416348114\n"
     ]
    }
   ],
   "source": [
    "# Initialize our algorithm with the default paramters\n",
    "# n_estimators is the number of trees we want to make\n",
    "# min_samples_split is the minimum number of rows we need to make a split\n",
    "# min_samples_leaf is the minimum number of samples we can have at the place where a tree branch ends (the bottom points of the tree)\n",
    "alg = RandomForestClassifier(random_state=1, n_estimators=10, min_samples_split=2, min_samples_leaf=1)\n",
    "\n",
    "scores = cross_validation.cross_val_score(alg, titanic[predictors], titanic[\"Survived\"], cv=5)\n",
    "print(scores.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.827160493827\n"
     ]
    }
   ],
   "source": [
    "#The first, and easiest, thing we can do to improve the accuracy of the random forest is to increase \n",
    "# the number of trees we're using. Training more trees will take more time, but because of the fact \n",
    "# that we're averaging many predictions made on different subsets of the data, having more trees will \n",
    "#increase accuracy greatly (up to a point).\n",
    "\n",
    "#We can also tweak the min_samples_split and min_samples_leaf variables to reduce overfitting. \n",
    "#Because of how a decision tree works (as we explained in the video), having splits that go all the way down, \n",
    "#or overly deep in the tree can result in fitting to quirks in the dataset, and not true signal. \n",
    "#Thus, increasing min_samples_split and min_samples_leaf can reduce overfitting, which will actually \n",
    "#improve our score, as we're making predictions on unseen data. A model that is less overfit, \n",
    "#and that can generalize better, will actually perform better on unseen data, but worse on seen data.\n",
    "\n",
    "alg = RandomForestClassifier(random_state=1, n_estimators=150, min_samples_split=4, min_samples_leaf=2)\n",
    "\n",
    "scores = cross_validation.cross_val_score(alg, titanic[predictors], titanic[\"Survived\"], cv=3)\n",
    "print(scores.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.791245791246\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "# Initialize our algorithm\n",
    "alg = LogisticRegression(random_state=1)\n",
    "# Compute the accuracy score for all the cross validation folds.  (much simpler than what we did before!)\n",
    "scores = cross_validation.cross_val_score(alg, titanic[predictors], titanic[\"Survived\"], cv=3)\n",
    "# Take the mean of the scores (because we have one for each fold)\n",
    "print(scores.mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generating new features\n",
    "\n",
    "If your data is rich, sometimes new features can be extracted from it that can better inform your machine learning algorithm. You can try generating new features from the name of the passengers, the members of their families present, etc."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Finding the best features\n",
    "\n",
    "Feature engineering is the most important part of any machine learning task, and there are lots more features we could calculate. But we also need a way to figure out which features are the best.\n",
    "\n",
    "One way to do this is to use univariate feature selection. This essentially goes column by column, and figures out which columns correlate most closely with what we're trying to predict (Survived).\n",
    "\n",
    "As usual, scikit-learn has a function that will help us with feature selection, [SelectKBest](http://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.SelectKBest.html). This selects the best features from the data, and allows us to specify how many it selects."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAW0AAAEpCAYAAABYyHNYAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1051a0610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn.feature_selection import SelectKBest, f_classif\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "predictors = [\"Pclass\", \"Sex\", \"Age\", \"SibSp\", \"Parch\", \"Fare\", \"Embarked\"]\n",
    "\n",
    "# Perform feature selection\n",
    "selector = SelectKBest(f_classif, k=5)\n",
    "selector.fit(titanic[predictors], titanic[\"Survived\"])\n",
    "\n",
    "# Get the raw p-values for each feature, and transform from p-values into scores\n",
    "scores = -np.log10(selector.pvalues_)\n",
    "\n",
    "# Plot the scores.  See how \"Pclass\", \"Sex\", \"Title\", and \"Fare\" are the best?\n",
    "plt.bar(range(len(predictors)), scores)\n",
    "plt.xticks(range(len(predictors)), predictors, rotation='vertical')\n",
    "plt.show()\n",
    "\n",
    "# Pick only the four best features.\n",
    "predictors = [\"Pclass\", \"Sex\", \"Fare\", \"Title\"]\n",
    "\n",
    "alg = RandomForestClassifier(random_state=1, n_estimators=150, min_samples_split=8, min_samples_leaf=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.795735129068\n"
     ]
    }
   ],
   "source": [
    "#The first, and easiest, thing we can do to improve the accuracy of the random forest is to increase \n",
    "# the number of trees we're using. Training more trees will take more time, but because of the fact \n",
    "# that we're averaging many predictions made on different subsets of the data, having more trees will \n",
    "#increase accuracy greatly (up to a point).\n",
    "\n",
    "#We can also tweak the min_samples_split and min_samples_leaf variables to reduce overfitting. \n",
    "#Because of how a decision tree works (as we explained in the video), having splits that go all the way down, \n",
    "#or overly deep in the tree can result in fitting to quirks in the dataset, and not true signal. \n",
    "#Thus, increasing min_samples_split and min_samples_leaf can reduce overfitting, which will actually \n",
    "#improve our score, as we're making predictions on unseen data. A model that is less overfit, \n",
    "#and that can generalize better, will actually perform better on unseen data, but worse on seen data.\n",
    "\n",
    "alg = RandomForestClassifier(random_state=1, n_estimators=150, min_samples_split=4, min_samples_leaf=2)\n",
    "\n",
    "scores = cross_validation.cross_val_score(alg, titanic[['Pclass', 'Sex', 'Fare', 'Embarked']], titanic[\"Survived\"], cv=3)\n",
    "print(scores.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x107099b50>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAEACAYAAACznAEdAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nmQEOBDMzSxwIZmYGwP8Hl4O5McxNx/0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x106ec3310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(titanic.Pclass, titanic.Fare)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.        , -0.54949962],\n",
       "       [-0.54949962,  1.        ]])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.corrcoef(titanic.Fare, titanic.Pclass)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}