titanic-article.ipynb

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   "source": [
    "The <a href=\"https://www.kaggle.com/c/titanic/\"> Titanic challenge</a>  on Kaggle is a competition in which the goal is to predict the survival or the death of a given passenger based on a set of variables describing him such as his age, his sex, or his passenger class on the boat.\n",
    "\n",
    "I have been playing with the Titanic dataset for a while, and I have recently achieved an accuracy score of 0.8134 on the public leaderboard. As I'm writing this post, I am ranked among the top 9% of all Kagglers. (More than 4540 teams are currently competing)\n",
    "\n",
    "This post is the opportunity to share my solution with you.\n",
    "\n",
    "To make this tutorial more \"academic\" so that everyone could benefit, I will first start with an exploratory data analysis (EDA) then I'll follow with feature engineering and finally present the predictive model I set up.\n",
    "\n",
    "Throughout this jupyter notebook, I will be using Python at each level of the pipeline.\n",
    "\n",
    "The main libraries involved in this tutorial are: \n",
    "\n",
    "* <b>Pandas</b> for data manipulation\n",
    "* <b>Matplotlib</b> and <b> seaborn</b> for data visualization\n",
    "* <b>Numpy</b> for multidimensional array computing\n",
    "* <b>sklearn</b> for machine learning and predictive modeling\n",
    "\n",
    "### Installation procedure \n",
    "\n",
    "A very easy way to install these packages is to download and install the <a href =\"http://conda.pydata.org/docs/install/quick.html#os-x-miniconda-install\">Conda</a> distribution that encapsulates them all. This distribution is available on all platforms (Windows, Linux and Mac OSX).\n",
    "\n",
    "### Nota Bene\n",
    "\n",
    "This is my first attempt as a blogger and as a machine learning practitioner as well. So if you have any advice or suggestion shoot me an email at ahmed.besbes@hotmail.com.\n",
    "If you also have a question about the code or the hypotheses I made, do not hesitate to post a comment in the comment section below.\n",
    "\n",
    "Hope you've got everything set on your computer. Let's get started."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# I -  Exploratory data analysis\n",
    "\n",
    "In this section, we'll be doing four things. \n",
    "\n",
    "- Data extraction : we'll load the dataset and have a first look at it. \n",
    "- Cleaning : we'll fill in missing values.\n",
    "- Plotting : we'll create some interesting charts that'll (hopefully) spot correlations and hidden insights out of the data.\n",
    "- Assumptions : we'll formulate hypotheses from the charts."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We import the useful libraries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# remove warnings\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "# ---\n",
    "\n",
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "pd.options.display.max_columns = 100\n",
    "from matplotlib import pyplot as plt\n",
    "import matplotlib\n",
    "matplotlib.style.use('ggplot')\n",
    "import numpy as np\n",
    "\n",
    "pd.options.display.max_rows = 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Two datasets are available: a training set and a test set.\n",
    "We'll be using the training set to build our predictive model and the testing set to score it and generate an output file to submit on the Kaggle evaluation system.\n",
    "\n",
    "We'll see how this procedure is done at the end of this post.\n",
    "\n",
    "Now let's start by loading the training set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = pd.read_csv('../data/train.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas allow you to have a sneak peak at your data."
   ]
  },
  {
   "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>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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Survived column is the target variable. If Suvival = 1 the passenger survived, otherwise he's dead.\n",
    "\n",
    "The other variables that describe the passengers are:\n",
    "\n",
    "- PassengerId: and id given to each traveler on the boat.\n",
    "- Pclass: the passenger class. It has three possible values: 1,2,3.\n",
    "- The Name\n",
    "- The Sex\n",
    "- The Age\n",
    "- SibSp: number of siblings and spouses traveling with the passenger \n",
    "- Parch: number of parents and children traveling with the passenger\n",
    "- The ticket number\n",
    "- The ticket Fare\n",
    "- The cabin number \n",
    "- The embarkation. It has three possible values S,C,Q"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas allows you to statistically describe numerical features using the describe method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>32.204208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>257.353842</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>49.693429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>223.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.910400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>668.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
       "count   891.000000  891.000000  891.000000  714.000000  891.000000   \n",
       "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
       "std     257.353842    0.486592    0.836071   14.526497    1.102743   \n",
       "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
       "25%     223.500000    0.000000    2.000000         NaN    0.000000   \n",
       "50%     446.000000    0.000000    3.000000         NaN    0.000000   \n",
       "75%     668.500000    1.000000    3.000000         NaN    1.000000   \n",
       "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
       "\n",
       "            Parch        Fare  \n",
       "count  891.000000  891.000000  \n",
       "mean     0.381594   32.204208  \n",
       "std      0.806057   49.693429  \n",
       "min      0.000000    0.000000  \n",
       "25%      0.000000    7.910400  \n",
       "50%      0.000000   14.454200  \n",
       "75%      0.000000   31.000000  \n",
       "max      6.000000  512.329200  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The count variable shows that 177 values are missing in the Age column.\n",
    "\n",
    "On solution is to replace the null values with the median age which is more robust to outliers than the mean."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data['Age'].fillna(data['Age'].median(), inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check that again."
   ]
  },
  {
   "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>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>891.000000</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>29.361582</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>13.019697</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>22.000000</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>35.000000</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",
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      ],
      "text/plain": [
       "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
       "count   891.000000  891.000000  891.000000  891.000000  891.000000   \n",
       "mean    446.000000    0.383838    2.308642   29.361582    0.523008   \n",
       "std     257.353842    0.486592    0.836071   13.019697    1.102743   \n",
       "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
       "25%     223.500000    0.000000    2.000000   22.000000    0.000000   \n",
       "50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n",
       "75%     668.500000    1.000000    3.000000   35.000000    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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Perfect.\n",
    "\n",
    "Let's now make some charts.\n",
    "\n",
    "Let's visualize survival based on the gender."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x113640f90>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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s2JCpU6emo6MjSTJjxowsXLgwHR0dqa+vz/Llyy0DAgCAY8ARw/+d73znYbdf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\nAQCgAMIfAAAKIPwBAKAAwh8AAArw/wAfhmbjUjV3JwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11366a310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "survived_sex = data[data['Survived']==1]['Sex'].value_counts()\n",
    "dead_sex = data[data['Survived']==0]['Sex'].value_counts()\n",
    "df = pd.DataFrame([survived_sex,dead_sex])\n",
    "df.index = ['Survived','Dead']\n",
    "df.plot(kind='bar',stacked=True, figsize=(13,8))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Sex variable seems to be a decisive feature. Women are more likely to survivre."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's now correlate the survival with the age variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x113832dd0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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L0tJS1dfXS5LWrVvnWEEAAAAAQtMpzxaVmZmpmTNnKjExUfX19U2uZ/GfuLYF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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1137c5450>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure = plt.figure(figsize=(13,8))\n",
    "plt.hist([data[data['Survived']==1]['Age'],data[data['Survived']==0]['Age']], stacked=True, color = ['g','r'],\n",
    "         bins = 30,label = ['Survived','Dead'])\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Number of passengers')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you follow the chart bin by bin, you will notice that passengers who are less than 10 are more likely to survive than older ones who are more than 12 and less than 50. Older passengers seem to be rescued too.\n",
    "\n",
    "These two first charts confirm that one old code of conduct that sailors and captains follow in case of threatening situations: <b>\"Women and children first !\"</b>."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/jpeg": 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GJU8Ae9c131NJOLVokJ6minMpVsMrKevIxTcUGYZozz2/KiigAooooAtaeSLs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\nQG4JHvzRRQxourIQoCgKo4CjoAOwooorMZ//2Q==\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 9,
     "metadata": {
      "image/jpeg": {
       "height": 1000,
       "width": 1000
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(\"../data/women-children.jpg\",height=1000,width=1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Right?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's now focus on the Fare ticket of each passenger and correlate it with the survival. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1167f8090>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AAAD6O4IGjshdU6O43NxuX99QViYRNAAAAI4pLJ0CAAAAYBwzGn3M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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1162f4990>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure = plt.figure(figsize=(13,8))\n",
    "plt.hist([data[data['Survived']==1]['Fare'],data[data['Survived']==0]['Fare']], stacked=True, color = ['g','r'],\n",
    "         bins = 30,label = ['Survived','Dead'])\n",
    "plt.xlabel('Fare')\n",
    "plt.ylabel('Number of passengers')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Passengers with cheaper ticket fares are more likely to die. \n",
    "Put differently, passengers with more expensive tickets, and therefore a more important social status, seem to be rescued first."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ok this is nice. Let's now combine the age, the fare and the survival on a single chart."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x117264f90>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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RSpJNUpLXG9NxjRxtPRpyu7fVa/gaAAyEWL4XgaGs30nG008/rUOHDmn16tXq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\n/w+klpm3dURc4AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116e92ad0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(13,8))\n",
    "ax = plt.subplot()\n",
    "ax.scatter(data[data['Survived']==1]['Age'],data[data['Survived']==1]['Fare'],c='green',s=40)\n",
    "ax.scatter(data[data['Survived']==0]['Age'],data[data['Survived']==0]['Fare'],c='red',s=40)\n",
    "ax.set_xlabel('Age')\n",
    "ax.set_ylabel('Fare')\n",
    "ax.legend(('survived','dead'),scatterpoints=1,loc='upper right',fontsize=15,)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A distinct cluster of dead passengers appears on the chart. Those people are adults (age between 15 and 50) of lower class (lowest ticket fares)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In fact, the ticket fare correlates with the class as we see it in the chart below. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x116e14590>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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jRtXPajjQ2IdHh5UrVw5ebmlpSUtLS5JhhkJfX19+8IMf5KGHHsr27dtfdNs//uM/DnuI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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115b1c090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.subplot()\n",
    "ax.set_ylabel('Average fare')\n",
    "data.groupby('Pclass').mean()['Fare'].plot(kind='bar',figsize=(13,8), ax = ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's now see how the embarkation site affects the survival."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x117539e90>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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HAIACCH8AACiA8AcAgAIIfwAAKIDwBwCAAgh/AAAogPAHAIACCH8AACiA8AcA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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115b1c590>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "survived_embark = data[data['Survived']==1]['Embarked'].value_counts()\n",
    "dead_embark = data[data['Survived']==0]['Embarked'].value_counts()\n",
    "df = pd.DataFrame([survived_embark,dead_embark])\n",
    "df.index = ['Survived','Dead']\n",
    "df.plot(kind='bar',stacked=True, figsize=(13,8))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The seams to be no distinct correlation here."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# II - Feature engineering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the previous part, we flirted with the data and spotted some interesting correlations. However, we couldn't manage to analyze more complicated features like the names or the tickets because these required further processing.  \n",
    "\n",
    "In this part, we'll focus on the ways to transform these specific features in such a way they become easily fed to machine learning algorithms.\n",
    "\n",
    "We'll also create, or \"engineer\" some other features that'll be useful in building the model.\n",
    "\n",
    "We will break our code in separate functions for more clarity."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But first let's define a print function that asserts whether or not a feature has been processed. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def status(feature):\n",
    "\n",
    "    print 'Processing',feature,': ok'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Loading the data\n",
    "\n",
    "One trick when starting a machine learning problem is to combine the training set and the test set together. \n",
    "This is useful especially when your test set appears to have a feature that doesn't exist in the training set. Therefore, if we don't combine the two sets, testing our model on the test set will fail.\n",
    "\n",
    "Besides, combining the two sets will save the same work to do later on when testing.\n",
    "\n",
    "The procedure is quite simple. \n",
    "\n",
    "We start by loading the train set and the test set.\n",
    "We create an empty dataframe called <b>combined</b>. \n",
    "Then we append test to train and affect the result to <b>combined</b>."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_combined_data():\n",
    "    # reading train data\n",
    "    train = pd.read_csv('../data/train.csv')\n",
    "    \n",
    "    # reading test data\n",
    "    test = pd.read_csv('../data/test.csv')\n",
    "\n",
    "    # extracting and then removing the targets from the training data \n",
    "    targets = train.Survived\n",
    "    train.drop('Survived',1,inplace=True)\n",
    "    \n",
    "\n",
    "    # merging train data and test data for future feature engineering\n",
    "    combined = train.append(test)\n",
    "    combined.reset_index(inplace=True)\n",
    "    combined.drop('index',inplace=True,axis=1)\n",
    "    \n",
    "    return combined"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "combined = get_combined_data()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's have a look at the shape :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1309, 11)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "train and test sets are combined.\n",
    "\n",
    "You may notice that the total number of rows (1309) is the exact summation of the number of rows in the train set and the test set."
   ]
  },
  {
   "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>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>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>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>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>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>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  Pclass                                               Name  \\\n",
       "0            1       3                            Braund, Mr. Owen Harris   \n",
       "1            2       1  Cumings, Mrs. John Bradley (Florence Briggs Th...   \n",
       "2            3       3                             Heikkinen, Miss. Laina   \n",
       "3            4       1       Futrelle, Mrs. Jacques Heath (Lily May Peel)   \n",
       "4            5       3                           Allen, Mr. William Henry   \n",
       "\n",
       "      Sex   Age  SibSp  Parch            Ticket     Fare Cabin Embarked  \n",
       "0    male  22.0      1      0         A/5 21171   7.2500   NaN        S  \n",
       "1  female  38.0      1      0          PC 17599  71.2833   C85        C  \n",
       "2  female  26.0      0      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3  female  35.0      1      0            113803  53.1000  C123        S  \n",
       "4    male  35.0      0      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extracting the passenger titles\n",
    "\n",
    "When looking at the passenger names one could wonder how to process them to extract an easily interpretable information.\n",
    "\n",
    "If you look closely at these first examples: \n",
    "\n",
    "- Braund, <b> Mr.</b> Owen Harris\t\n",
    "- Heikkinen, <b>Miss.</b> Laina\n",
    "- Oliva y Ocana, <b>Dona.</b> Fermina\n",
    "- Peter, <b>Master.</b> Michael J\n",
    "\n",
    "You will notice that each name has a title in it ! This can be a simple Miss. or Mrs. but it can be sometimes something more sophisticated like Master, Sir or Dona. In that case, we might introduce additional information about the social status by simply parsing the name and extracting the title.\n",
    "\n",
    "Let's see how we'll do that in the function below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_titles():\n",
    "\n",
    "    global combined\n",
    "    \n",
    "    # we extract the title from each name\n",
    "    combined['Title'] = combined['Name'].map(lambda name:name.split(',')[1].split('.')[0].strip())\n",
    "    \n",
    "    # a map of more aggregated titles\n",
    "    Title_Dictionary = {\n",
    "                        \"Capt\":       \"Officer\",\n",
    "                        \"Col\":        \"Officer\",\n",
    "                        \"Major\":      \"Officer\",\n",
    "                        \"Jonkheer\":   \"Royalty\",\n",
    "                        \"Don\":        \"Royalty\",\n",
    "                        \"Sir\" :       \"Royalty\",\n",
    "                        \"Dr\":         \"Officer\",\n",
    "                        \"Rev\":        \"Officer\",\n",
    "                        \"the Countess\":\"Royalty\",\n",
    "                        \"Dona\":       \"Royalty\",\n",
    "                        \"Mme\":        \"Mrs\",\n",
    "                        \"Mlle\":       \"Miss\",\n",
    "                        \"Ms\":         \"Mrs\",\n",
    "                        \"Mr\" :        \"Mr\",\n",
    "                        \"Mrs\" :       \"Mrs\",\n",
    "                        \"Miss\" :      \"Miss\",\n",
    "                        \"Master\" :    \"Master\",\n",
    "                        \"Lady\" :      \"Royalty\"\n",
    "\n",
    "                        }\n",
    "    \n",
    "    # we map each title\n",
    "    combined['Title'] = combined.Title.map(Title_Dictionary)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This function parses the names and extract the titles. Then, it maps the titles to categories of titles. \n",
    "We selected : \n",
    "\n",
    "- Officer\n",
    "- Royalty \n",
    "- Mr\n",
    "- Mrs\n",
    "- Miss\n",
    "- Master\n",
    "\n",
    "Let's run it !"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "get_titles()"
   ]
  },
  {
   "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>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>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>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>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>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>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  Pclass                                               Name  \\\n",
       "0            1       3                            Braund, Mr. Owen Harris   \n",
       "1            2       1  Cumings, Mrs. John Bradley (Florence Briggs Th...   \n",
       "2            3       3                             Heikkinen, Miss. Laina   \n",
       "3            4       1       Futrelle, Mrs. Jacques Heath (Lily May Peel)   \n",
       "4            5       3                           Allen, Mr. William Henry   \n",
       "\n",
       "      Sex   Age  SibSp  Parch            Ticket     Fare Cabin Embarked Title  \n",
       "0    male  22.0      1      0         A/5 21171   7.2500   NaN        S    Mr  \n",
       "1  female  38.0      1      0          PC 17599  71.2833   C85        C   Mrs  \n",
       "2  female  26.0      0      0  STON/O2. 3101282   7.9250   NaN        S  Miss  \n",
       "3  female  35.0      1      0            113803  53.1000  C123        S   Mrs  \n",
       "4    male  35.0      0      0            373450   8.0500   NaN        S    Mr  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Perfect. Now we have an additional column called <b>Title</b> that contains the information."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Processing the ages\n",
    "\n",
    "We have seen in the first part that the Age variable was missing 177 values. This is a large number ( ~ 13% of the dataset). Simply replacing them with the mean or the median age might not be the best solution since the age may differ by groups and categories of passengers. \n",
    "\n",
    "To understand why, let's group our dataset by sex, Title and passenger class."
   ]
  },
  {
   "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></th>\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Title</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=\"8\" valign=\"top\">female</th>\n",
       "      <th rowspan=\"4\" valign=\"top\">1</th>\n",
       "      <th>Miss</th>\n",
       "      <td>529.5</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99.9625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mrs</th>\n",
       "      <td>853.5</td>\n",
       "      <td>45.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>78.1125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Officer</th>\n",
       "      <td>797.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.9292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Royalty</th>\n",
       "      <td>760.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>86.5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2</th>\n",
       "      <th>Miss</th>\n",
       "      <td>606.5</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.2500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mrs</th>\n",
       "      <td>533.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">3</th>\n",
       "      <th>Miss</th>\n",
       "      <td>603.5</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mrs</th>\n",
       "      <td>668.5</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>15.5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"9\" valign=\"top\">male</th>\n",
       "      <th rowspan=\"4\" valign=\"top\">1</th>\n",
       "      <th>Master</th>\n",
       "      <td>803.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>134.5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mr</th>\n",
       "      <td>634.0</td>\n",
       "      <td>41.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>47.1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Officer</th>\n",
       "      <td>678.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.5500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Royalty</th>\n",
       "      <td>600.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.7208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2</th>\n",
       "      <th>Master</th>\n",
       "      <td>550.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>26.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mr</th>\n",
       "      <td>723.5</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Officer</th>\n",
       "      <td>513.0</td>\n",
       "      <td>41.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">3</th>\n",
       "      <th>Master</th>\n",
       "      <td>789.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>22.3583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mr</th>\n",
       "      <td>640.5</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.8958</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       PassengerId   Age  SibSp  Parch      Fare\n",
       "Sex    Pclass Title                                             \n",
       "female 1      Miss           529.5  30.0    0.0    0.0   99.9625\n",
       "              Mrs            853.5  45.0    1.0    0.0   78.1125\n",
       "              Officer        797.0  49.0    0.0    0.0   25.9292\n",
       "              Royalty        760.0  39.0    0.0    0.0   86.5000\n",
       "       2      Miss           606.5  20.0    0.0    0.0   20.2500\n",
       "              Mrs            533.0  30.0    1.0    0.0   26.0000\n",
       "       3      Miss           603.5  18.0    0.0    0.0    8.0500\n",
       "              Mrs            668.5  31.0    1.0    1.0   15.5000\n",
       "male   1      Master         803.0   6.0    1.0    2.0  134.5000\n",
       "              Mr             634.0  41.5    0.0    0.0   47.1000\n",
       "              Officer        678.0  52.0    0.0    0.0   37.5500\n",
       "              Royalty        600.0  40.0    0.0    0.0   27.7208\n",
       "       2      Master         550.0   2.0    1.0    1.0   26.0000\n",
       "              Mr             723.5  30.0    0.0    0.0   13.0000\n",
       "              Officer        513.0  41.5    0.0    0.0   13.0000\n",
       "       3      Master         789.0   6.0    3.0    1.0   22.3583\n",
       "              Mr             640.5  26.0    0.0    0.0    7.8958"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped = combined.groupby(['Sex','Pclass','Title'])\n",
    "grouped.median()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Look at the median age column and see how this value can be different based on the Sex, Pclass and Title put together.\n",
    "\n",
    "For example: \n",
    "\n",
    "- If the passenger is female, from Pclass 1, and from royalty the median age is 39.\n",
    "- If the passenger is male, from Pclass 3, with a Mr title, the median age is 26.\n",
    "\n",
    "Let's create a function that fills in the missing age in <b>combined</b> based on these different attributes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def process_age():\n",
    "    \n",
    "    global combined\n",
    "    \n",
    "    # a function that fills the missing values of the Age variable\n",
    "    \n",
    "    def fillAges(row):\n",
    "        if row['Sex']=='female' and row['Pclass'] == 1:\n",
    "            if row['Title'] == 'Miss':\n",
    "                return 30\n",
    "            elif row['Title'] == 'Mrs':\n",
    "                return 45\n",
    "            elif row['Title'] == 'Officer':\n",
    "                return 49\n",
    "            elif row['Title'] == 'Royalty':\n",
    "                return 39\n",
    "\n",
    "        elif row['Sex']=='female' and row['Pclass'] == 2:\n",
    "            if row['Title'] == 'Miss':\n",
    "                return 20\n",
    "            elif row['Title'] == 'Mrs':\n",
    "                return 30\n",
    "\n",
    "        elif row['Sex']=='female' and row['Pclass'] == 3:\n",
    "            if row['Title'] == 'Miss':\n",
    "                return 18\n",
    "            elif row['Title'] == 'Mrs':\n",
    "                return 31\n",
    "\n",
    "        elif row['Sex']=='male' and row['Pclass'] == 1:\n",
    "            if row['Title'] == 'Master':\n",
    "                return 6\n",
    "            elif row['Title'] == 'Mr':\n",
    "                return 41.5\n",
    "            elif row['Title'] == 'Officer':\n",
    "                return 52\n",
    "            elif row['Title'] == 'Royalty':\n",
    "                return 40\n",
    "\n",
    "        elif row['Sex']=='male' and row['Pclass'] == 2:\n",
    "            if row['Title'] == 'Master':\n",
    "                return 2\n",
    "            elif row['Title'] == 'Mr':\n",
    "                return 30\n",
    "            elif row['Title'] == 'Officer':\n",
    "                return 41.5\n",
    "\n",
    "        elif row['Sex']=='male' and row['Pclass'] == 3:\n",
    "            if row['Title'] == 'Master':\n",
    "                return 6\n",
    "            elif row['Title'] == 'Mr':\n",
    "                return 26\n",
    "    \n",
    "    combined.Age = combined.apply(lambda r : fillAges(r) if np.isnan(r['Age']) else r['Age'], axis=1)\n",
    "    \n",
    "    status('age')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing age : ok\n"
     ]
    }
   ],
   "source": [
    "process_age()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1309 entries, 0 to 1308\n",
      "Data columns (total 12 columns):\n",
      "PassengerId    1309 non-null int64\n",
      "Pclass         1309 non-null int64\n",
      "Name           1309 non-null object\n",
      "Sex            1309 non-null object\n",
      "Age            1309 non-null float64\n",
      "SibSp          1309 non-null int64\n",
      "Parch          1309 non-null int64\n",
      "Ticket         1309 non-null object\n",
      "Fare           1308 non-null float64\n",
      "Cabin          295 non-null object\n",
      "Embarked       1307 non-null object\n",
      "Title          1309 non-null object\n",
      "dtypes: float64(2), int64(4), object(6)\n",
      "memory usage: 122.8+ KB\n"
     ]
    }
   ],
   "source": [
    "combined.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Perfect. The missing ages have been replaced. \n",
    "\n",
    "However, we notice a missing value in Fare, two missing values in Embarked and a lot of missing values in Cabin. We'll come back to these variables later.\n",
    "\n",
    "Let's now process the names."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def process_names():\n",
    "    \n",
    "    global combined\n",
    "    # we clean the Name variable\n",
    "    combined.drop('Name',axis=1,inplace=True)\n",
    "    \n",
    "    # encoding in dummy variable\n",
    "    titles_dummies = pd.get_dummies(combined['Title'],prefix='Title')\n",
    "    combined = pd.concat([combined,titles_dummies],axis=1)\n",
    "    \n",
    "    # removing the title variable\n",
    "    combined.drop('Title',axis=1,inplace=True)\n",
    "    \n",
    "    status('names')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This function drops the Name column since we won't be using it anymore because we created a Title column.\n",
    "\n",
    "Then we encode the title values using a dummy encoding."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing names : ok\n"
     ]
    }
   ],
   "source": [
    "process_names()"
   ]
  },
  {
   "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",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</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_Master</th>\n",
       "      <th>Title_Miss</th>\n",
       "      <th>Title_Mr</th>\n",
       "      <th>Title_Mrs</th>\n",
       "      <th>Title_Officer</th>\n",
       "      <th>Title_Royalty</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>3</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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>3</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>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>3</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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Pclass     Sex   Age  SibSp  Parch            Ticket     Fare  \\\n",
       "0            1       3    male  22.0      1      0         A/5 21171   7.2500   \n",
       "1            2       1  female  38.0      1      0          PC 17599  71.2833   \n",
       "2            3       3  female  26.0      0      0  STON/O2. 3101282   7.9250   \n",
       "3            4       1  female  35.0      1      0            113803  53.1000   \n",
       "4            5       3    male  35.0      0      0            373450   8.0500   \n",
       "\n",
       "  Cabin Embarked  Title_Master  Title_Miss  Title_Mr  Title_Mrs  \\\n",
       "0   NaN        S           0.0         0.0       1.0        0.0   \n",
       "1   C85        C           0.0         0.0       0.0        1.0   \n",
       "2   NaN        S           0.0         1.0       0.0        0.0   \n",
       "3  C123        S           0.0         0.0       0.0        1.0   \n",
       "4   NaN        S           0.0         0.0       1.0        0.0   \n",
       "\n",
       "   Title_Officer  Title_Royalty  \n",
       "0            0.0            0.0  \n",
       "1            0.0            0.0  \n",
       "2            0.0            0.0  \n",
       "3            0.0            0.0  \n",
       "4            0.0            0.0  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see : \n",
    "- there is no longer a name feature. \n",
    "- new variables (Title_X) appeared. These features are binary. \n",
    "    - For example, If Title_Mr = 1, the corresponding Title is Mr."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Processing Fare"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def process_fares():\n",
    "    \n",
    "    global combined\n",
    "    # there's one missing fare value - replacing it with the mean.\n",
    "    combined.Fare.fillna(combined.Fare.mean(),inplace=True)\n",
    "    \n",
    "    status('fare')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This function simply replaces one missing Fare value by the mean."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing fare : ok\n"
     ]
    }
   ],
   "source": [
    "process_fares()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Processing Embarked"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def process_embarked():\n",
    "    \n",
    "    global combined\n",
    "    # two missing embarked values - filling them with the most frequent one (S)\n",
    "    combined.Embarked.fillna('S',inplace=True)\n",
    "    \n",
    "    # dummy encoding \n",
    "    embarked_dummies = pd.get_dummies(combined['Embarked'],prefix='Embarked')\n",
    "    combined = pd.concat([combined,embarked_dummies],axis=1)\n",
    "    combined.drop('Embarked',axis=1,inplace=True)\n",
    "    \n",
    "    status('embarked')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This functions replaces the two missing values of Embarked with the most frequent Embarked value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing embarked : ok\n"
     ]
    }
   ],
   "source": [
    "process_embarked()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Processing Cabin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def process_cabin():\n",
    "    \n",
    "    global combined\n",
    "    \n",
    "    # replacing missing cabins with U (for Uknown)\n",
    "    combined.Cabin.fillna('U',inplace=True)\n",
    "    \n",
    "    # mapping each Cabin value with the cabin letter\n",
    "    combined['Cabin'] = combined['Cabin'].map(lambda c : c[0])\n",
    "    \n",
    "    # dummy encoding ...\n",
    "    cabin_dummies = pd.get_dummies(combined['Cabin'],prefix='Cabin')\n",
    "    \n",
    "    combined = pd.concat([combined,cabin_dummies],axis=1)\n",
    "    \n",
    "    combined.drop('Cabin',axis=1,inplace=True)\n",
    "    \n",
    "    status('cabin')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This function replaces NaN values with U (for <i>Unknow</i>). It then maps each Cabin value to the first letter.\n",
    "Then it encodes the cabin values using dummy encoding."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing cabin : ok\n"
     ]
    }
   ],
   "source": [
    "process_cabin()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1309 entries, 0 to 1308\n",
      "Data columns (total 26 columns):\n",
      "PassengerId      1309 non-null int64\n",
      "Pclass           1309 non-null int64\n",
      "Sex              1309 non-null object\n",
      "Age              1309 non-null float64\n",
      "SibSp            1309 non-null int64\n",
      "Parch            1309 non-null int64\n",
      "Ticket           1309 non-null object\n",
      "Fare             1309 non-null float64\n",
      "Title_Master     1309 non-null float64\n",
      "Title_Miss       1309 non-null float64\n",
      "Title_Mr         1309 non-null float64\n",
      "Title_Mrs        1309 non-null float64\n",
      "Title_Officer    1309 non-null float64\n",
      "Title_Royalty    1309 non-null float64\n",
      "Embarked_C       1309 non-null float64\n",
      "Embarked_Q       1309 non-null float64\n",
      "Embarked_S       1309 non-null float64\n",
      "Cabin_A          1309 non-null float64\n",
      "Cabin_B          1309 non-null float64\n",
      "Cabin_C          1309 non-null float64\n",
      "Cabin_D          1309 non-null float64\n",
      "Cabin_E          1309 non-null float64\n",
      "Cabin_F          1309 non-null float64\n",
      "Cabin_G          1309 non-null float64\n",
      "Cabin_T          1309 non-null float64\n",
      "Cabin_U          1309 non-null float64\n",
      "dtypes: float64(20), int64(4), object(2)\n",
      "memory usage: 266.0+ KB\n"
     ]
    }
   ],
   "source": [
    "combined.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ok no missing values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>Pclass</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>Title_Master</th>\n",
       "      <th>Title_Miss</th>\n",
       "      <th>Title_Mr</th>\n",
       "      <th>Title_Mrs</th>\n",
       "      <th>Title_Officer</th>\n",
       "      <th>Title_Royalty</th>\n",
       "      <th>Embarked_C</th>\n",
       "      <th>Embarked_Q</th>\n",
       "      <th>Embarked_S</th>\n",
       "      <th>Cabin_A</th>\n",
       "      <th>Cabin_B</th>\n",
       "      <th>Cabin_C</th>\n",
       "      <th>Cabin_D</th>\n",
       "      <th>Cabin_E</th>\n",
       "      <th>Cabin_F</th>\n",
       "      <th>Cabin_G</th>\n",
       "      <th>Cabin_T</th>\n",
       "      <th>Cabin_U</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>3</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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>3</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>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>3</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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Pclass     Sex   Age  SibSp  Parch            Ticket     Fare  \\\n",
       "0            1       3    male  22.0      1      0         A/5 21171   7.2500   \n",
       "1            2       1  female  38.0      1      0          PC 17599  71.2833   \n",
       "2            3       3  female  26.0      0      0  STON/O2. 3101282   7.9250   \n",
       "3            4       1  female  35.0      1      0            113803  53.1000   \n",
       "4            5       3    male  35.0      0      0            373450   8.0500   \n",
       "\n",
       "   Title_Master  Title_Miss  Title_Mr  Title_Mrs  Title_Officer  \\\n",
       "0           0.0         0.0       1.0        0.0            0.0   \n",
       "1           0.0         0.0       0.0        1.0            0.0   \n",
       "2           0.0         1.0       0.0        0.0            0.0   \n",
       "3           0.0         0.0       0.0        1.0            0.0   \n",
       "4           0.0         0.0       1.0        0.0            0.0   \n",
       "\n",
       "   Title_Royalty  Embarked_C  Embarked_Q  Embarked_S  Cabin_A  Cabin_B  \\\n",
       "0            0.0         0.0         0.0         1.0      0.0      0.0   \n",
       "1            0.0         1.0         0.0         0.0      0.0      0.0   \n",
       "2            0.0         0.0         0.0         1.0      0.0      0.0   \n",
       "3            0.0         0.0         0.0         1.0      0.0      0.0   \n",
       "4            0.0         0.0         0.0         1.0      0.0      0.0   \n",
       "\n",
       "   Cabin_C  Cabin_D  Cabin_E  Cabin_F  Cabin_G  Cabin_T  Cabin_U  \n",
       "0      0.0      0.0      0.0      0.0      0.0      0.0      1.0  \n",
       "1      1.0      0.0      0.0      0.0      0.0      0.0      0.0  \n",
       "2      0.0      0.0      0.0      0.0      0.0      0.0      1.0  \n",
       "3      1.0      0.0      0.0      0.0      0.0      0.0      0.0  \n",
       "4      0.0      0.0      0.0      0.0      0.0      0.0      1.0  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Processing Sex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def process_sex():\n",
    "    \n",
    "    global combined\n",
    "    # mapping string values to numerical one \n",
    "    combined['Sex'] = combined['Sex'].map({'male':1,'female':0})\n",
    "    \n",
    "    status('sex')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This function maps the string values male and female to 1 and 0 respectively. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing sex : ok\n"
     ]
    }
   ],
   "source": [
    "process_sex()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Processing Pclass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def process_pclass():\n",
    "    \n",
    "    global combined\n",
    "    # encoding into 3 categories:\n",
    "    pclass_dummies = pd.get_dummies(combined['Pclass'],prefix=\"Pclass\")\n",
    "    \n",
    "    # adding dummy variables\n",
    "    combined = pd.concat([combined,pclass_dummies],axis=1)\n",
    "    \n",
    "    # removing \"Pclass\"\n",
    "    \n",
    "    combined.drop('Pclass',axis=1,inplace=True)\n",
    "    \n",
    "    status('pclass')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This function encode the values of Pclass (1,2,3) using a dummy encoding."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing pclass : ok\n"
     ]
    }
   ],
   "source": [
    "process_pclass()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Processing Ticket"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def process_ticket():\n",
    "    \n",
    "    global combined\n",
    "    \n",
    "    # a function that extracts each prefix of the ticket, returns 'XXX' if no prefix (i.e the ticket is a digit)\n",
    "    def cleanTicket(ticket):\n",
    "        ticket = ticket.replace('.','')\n",
    "        ticket = ticket.replace('/','')\n",
    "        ticket = ticket.split()\n",
    "        ticket = map(lambda t : t.strip() , ticket)\n",
    "        ticket = filter(lambda t : not t.isdigit(), ticket)\n",
    "        if len(ticket) > 0:\n",
    "            return ticket[0]\n",
    "        else: \n",
    "            return 'XXX'\n",
    "    \n",
    "\n",
    "    # Extracting dummy variables from tickets:\n",
    "\n",
    "    combined['Ticket'] = combined['Ticket'].map(cleanTicket)\n",
    "    tickets_dummies = pd.get_dummies(combined['Ticket'],prefix='Ticket')\n",
    "    combined = pd.concat([combined, tickets_dummies],axis=1)\n",
    "    combined.drop('Ticket',inplace=True,axis=1)\n",
    "\n",
    "    status('ticket')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "* This functions preprocess the tikets first by extracting the ticket prefix. When it fails in extracting a prefix it returns XXX. \n",
    "* Then it encodes prefixes using dummy encoding."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing ticket : ok\n"
     ]
    }
   ],
   "source": [
    "process_ticket()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Processing Family"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This part includes creating new variables based on the size of the family (the size is by the way, another variable we create)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def process_family():\n",
    "    \n",
    "    global combined\n",
    "    # introducing a new feature : the size of families (including the passenger)\n",
    "    combined['FamilySize'] = combined['Parch'] + combined['SibSp'] + 1\n",
    "    \n",
    "    # introducing other features based on the family size\n",
    "    combined['Singleton'] = combined['FamilySize'].map(lambda s : 1 if s == 1 else 0)\n",
    "    combined['SmallFamily'] = combined['FamilySize'].map(lambda s : 1 if 2<=s<=4 else 0)\n",
    "    combined['LargeFamily'] = combined['FamilySize'].map(lambda s : 1 if 5<=s else 0)\n",
    "    \n",
    "    status('family')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This function introduces 4 new features: \n",
    "\n",
    "- FamilySize : the total number of relatives including the passenger (him/her)self.\n",
    "- Sigleton : a boolean variable that describes families of size = 1\n",
    "- SmallFamily : a boolean variable that describes families of 2 <= size <= 4\n",
    "- LargeFamily : a boolean variable that describes families of 5 < size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing family : ok\n"
     ]
    }
   ],
   "source": [
    "process_family()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1309, 68)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We end up with a total of 68 features. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Title_Master</th>\n",
       "      <th>Title_Miss</th>\n",
       "      <th>Title_Mr</th>\n",
       "      <th>Title_Mrs</th>\n",
       "      <th>Title_Officer</th>\n",
       "      <th>Title_Royalty</th>\n",
       "      <th>Embarked_C</th>\n",
       "      <th>Embarked_Q</th>\n",
       "      <th>Embarked_S</th>\n",
       "      <th>Cabin_A</th>\n",
       "      <th>Cabin_B</th>\n",
       "      <th>Cabin_C</th>\n",
       "      <th>Cabin_D</th>\n",
       "      <th>Cabin_E</th>\n",
       "      <th>Cabin_F</th>\n",
       "      <th>Cabin_G</th>\n",
       "      <th>Cabin_T</th>\n",
       "      <th>Cabin_U</th>\n",
       "      <th>Pclass_1</th>\n",
       "      <th>Pclass_2</th>\n",
       "      <th>Pclass_3</th>\n",
       "      <th>Ticket_A</th>\n",
       "      <th>Ticket_A4</th>\n",
       "      <th>Ticket_A5</th>\n",
       "      <th>Ticket_AQ3</th>\n",
       "      <th>Ticket_AQ4</th>\n",
       "      <th>Ticket_AS</th>\n",
       "      <th>Ticket_C</th>\n",
       "      <th>Ticket_CA</th>\n",
       "      <th>Ticket_CASOTON</th>\n",
       "      <th>Ticket_FC</th>\n",
       "      <th>Ticket_FCC</th>\n",
       "      <th>Ticket_Fa</th>\n",
       "      <th>Ticket_LINE</th>\n",
       "      <th>Ticket_LP</th>\n",
       "      <th>Ticket_PC</th>\n",
       "      <th>Ticket_PP</th>\n",
       "      <th>Ticket_PPP</th>\n",
       "      <th>Ticket_SC</th>\n",
       "      <th>Ticket_SCA3</th>\n",
       "      <th>Ticket_SCA4</th>\n",
       "      <th>Ticket_SCAH</th>\n",
       "      <th>Ticket_SCOW</th>\n",
       "      <th>Ticket_SCPARIS</th>\n",
       "      <th>Ticket_SCParis</th>\n",
       "      <th>Ticket_SOC</th>\n",
       "      <th>Ticket_SOP</th>\n",
       "      <th>Ticket_SOPP</th>\n",
       "      <th>Ticket_SOTONO2</th>\n",
       "      <th>Ticket_SOTONOQ</th>\n",
       "      <th>Ticket_SP</th>\n",
       "      <th>Ticket_STONO</th>\n",
       "      <th>Ticket_STONO2</th>\n",
       "      <th>Ticket_STONOQ</th>\n",
       "      <th>Ticket_SWPP</th>\n",
       "      <th>Ticket_WC</th>\n",
       "      <th>Ticket_WEP</th>\n",
       "      <th>Ticket_XXX</th>\n",
       "      <th>FamilySize</th>\n",
       "      <th>Singleton</th>\n",
       "      <th>SmallFamily</th>\n",
       "      <th>LargeFamily</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Sex   Age  SibSp  Parch     Fare  Title_Master  Title_Miss  \\\n",
       "0            1    1  22.0      1      0   7.2500           0.0         0.0   \n",
       "1            2    0  38.0      1      0  71.2833           0.0         0.0   \n",
       "2            3    0  26.0      0      0   7.9250           0.0         1.0   \n",
       "3            4    0  35.0      1      0  53.1000           0.0         0.0   \n",
       "4            5    1  35.0      0      0   8.0500           0.0         0.0   \n",
       "\n",
       "   Title_Mr  Title_Mrs  Title_Officer  Title_Royalty  Embarked_C  Embarked_Q  \\\n",
       "0       1.0        0.0            0.0            0.0         0.0         0.0   \n",
       "1       0.0        1.0            0.0            0.0         1.0         0.0   \n",
       "2       0.0        0.0            0.0            0.0         0.0         0.0   \n",
       "3       0.0        1.0            0.0            0.0         0.0         0.0   \n",
       "4       1.0        0.0            0.0            0.0         0.0         0.0   \n",
       "\n",
       "   Embarked_S  Cabin_A  Cabin_B  Cabin_C  Cabin_D  Cabin_E  Cabin_F  Cabin_G  \\\n",
       "0         1.0      0.0      0.0      0.0      0.0      0.0      0.0      0.0   \n",
       "1         0.0      0.0      0.0      1.0      0.0      0.0      0.0      0.0   \n",
       "2         1.0      0.0      0.0      0.0      0.0      0.0      0.0      0.0   \n",
       "3         1.0      0.0      0.0      1.0      0.0      0.0      0.0      0.0   \n",
       "4         1.0      0.0      0.0      0.0      0.0      0.0      0.0      0.0   \n",
       "\n",
       "   Cabin_T  Cabin_U  Pclass_1  Pclass_2  Pclass_3  Ticket_A  Ticket_A4  \\\n",
       "0      0.0      1.0       0.0       0.0       1.0       0.0        0.0   \n",
       "1      0.0      0.0       1.0       0.0       0.0       0.0        0.0   \n",
       "2      0.0      1.0       0.0       0.0       1.0       0.0        0.0   \n",
       "3      0.0      0.0       1.0       0.0       0.0       0.0        0.0   \n",
       "4      0.0      1.0       0.0       0.0       1.0       0.0        0.0   \n",
       "\n",
       "   Ticket_A5  Ticket_AQ3  Ticket_AQ4  Ticket_AS  Ticket_C  Ticket_CA  \\\n",
       "0        1.0         0.0         0.0        0.0       0.0        0.0   \n",
       "1        0.0         0.0         0.0        0.0       0.0        0.0   \n",
       "2        0.0         0.0         0.0        0.0       0.0        0.0   \n",
       "3        0.0         0.0         0.0        0.0       0.0        0.0   \n",
       "4        0.0         0.0         0.0        0.0       0.0        0.0   \n",
       "\n",
       "   Ticket_CASOTON  Ticket_FC  Ticket_FCC  Ticket_Fa  Ticket_LINE  Ticket_LP  \\\n",
       "0             0.0        0.0         0.0        0.0          0.0        0.0   \n",
       "1             0.0        0.0         0.0        0.0          0.0        0.0   \n",
       "2             0.0        0.0         0.0        0.0          0.0        0.0   \n",
       "3             0.0        0.0         0.0        0.0          0.0        0.0   \n",
       "4             0.0        0.0         0.0        0.0          0.0        0.0   \n",
       "\n",
       "   Ticket_PC  Ticket_PP  Ticket_PPP  Ticket_SC  Ticket_SCA3  Ticket_SCA4  \\\n",
       "0        0.0        0.0         0.0        0.0          0.0          0.0   \n",
       "1        1.0        0.0         0.0        0.0          0.0          0.0   \n",
       "2        0.0        0.0         0.0        0.0          0.0          0.0   \n",
       "3        0.0        0.0         0.0        0.0          0.0          0.0   \n",
       "4        0.0        0.0         0.0        0.0          0.0          0.0   \n",
       "\n",
       "   Ticket_SCAH  Ticket_SCOW  Ticket_SCPARIS  Ticket_SCParis  Ticket_SOC  \\\n",
       "0          0.0          0.0             0.0             0.0         0.0   \n",
       "1          0.0          0.0             0.0             0.0         0.0   \n",
       "2          0.0          0.0             0.0             0.0         0.0   \n",
       "3          0.0          0.0             0.0             0.0         0.0   \n",
       "4          0.0          0.0             0.0             0.0         0.0   \n",
       "\n",
       "   Ticket_SOP  Ticket_SOPP  Ticket_SOTONO2  Ticket_SOTONOQ  Ticket_SP  \\\n",
       "0         0.0          0.0             0.0             0.0        0.0   \n",
       "1         0.0          0.0             0.0             0.0        0.0   \n",
       "2         0.0          0.0             0.0             0.0        0.0   \n",
       "3         0.0          0.0             0.0             0.0        0.0   \n",
       "4         0.0          0.0             0.0             0.0        0.0   \n",
       "\n",
       "   Ticket_STONO  Ticket_STONO2  Ticket_STONOQ  Ticket_SWPP  Ticket_WC  \\\n",
       "0           0.0            0.0            0.0          0.0        0.0   \n",
       "1           0.0            0.0            0.0          0.0        0.0   \n",
       "2           0.0            1.0            0.0          0.0        0.0   \n",
       "3           0.0            0.0            0.0          0.0        0.0   \n",
       "4           0.0            0.0            0.0          0.0        0.0   \n",
       "\n",
       "   Ticket_WEP  Ticket_XXX  FamilySize  Singleton  SmallFamily  LargeFamily  \n",
       "0         0.0         0.0           2          0            1            0  \n",
       "1         0.0         0.0           2          0            1            0  \n",
       "2         0.0         0.0           1          1            0            0  \n",
       "3         0.0         1.0           2          0            1            0  \n",
       "4         0.0         1.0           1          1            0            0  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, the features range in different intervals. Let's normalize all of them in the unit interval. All of them except the PassengerId."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def scale_all_features():\n",
    "    \n",
    "    global combined\n",
    "    \n",
    "    features = list(combined.columns)\n",
    "    features.remove('PassengerId')\n",
    "    combined[features] = combined[features].apply(lambda x: x/x.max(), axis=0)\n",
    "    \n",
    "    print 'Features scaled successfully !'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Features scaled successfully !\n"
     ]
    }
   ],
   "source": [
    "scale_all_features()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# III - Modeling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this part, we use our knowledge of the passengers based on the features we created and then build a statistical model. You can think of this model as a black box that crunches the information of any new passenger and decides whether or not he survives.\n",
    "\n",
    "There is a wide variety of models to use, from logistic regression to decision trees and more sophisticated ones such as random forests and gradient boosted trees.\n",
    "\n",
    "We'll be using Random Forests. Random Froests has proven a great efficiency in Kaggle competitions.\n",
    "\n",
    "For more details about why ensemble methods perform well, you can refer to these posts:\n",
    "\n",
    "- http://mlwave.com/kaggle-ensembling-guide/\n",
    "- http://www.overkillanalytics.net/more-is-always-better-the-power-of-simple-ensembles/\n",
    "\n",
    "Back to our problem, we now have to:\n",
    "\n",
    "1. Break the combined dataset in train set and test set.\n",
    "2. Use the train set to build a predictive model.\n",
    "3. Evaluate the model using the train set.\n",
    "4. Test the model using the test set and generate and output file for the submission.\n",
    "\n",
    "Keep in mind that we'll have to reiterate on 2. and 3. until a acceptable evaluation score is achieved."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's start by importing the useful libraries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.feature_selection import SelectKBest\n",
    "from sklearn.cross_validation import StratifiedKFold\n",
    "from sklearn.grid_search import GridSearchCV\n",
    "from sklearn.ensemble.gradient_boosting import GradientBoostingClassifier\n",
    "from sklearn.cross_validation import cross_val_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To evaluate our model we'll be using 5-fold cross validation with the Accuracy metric.\n",
    "\n",
    "To do that, we'll define a small scoring function. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def compute_score(clf, X, y,scoring='accuracy'):\n",
    "    xval = cross_val_score(clf, X, y, cv = 5,scoring=scoring)\n",
    "    return np.mean(xval)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Recovering the train set and the test set from the combined dataset is an easy task."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def recover_train_test_target():\n",
    "    global combined\n",
    "    \n",
    "    train0 = pd.read_csv('../data/train.csv')\n",
    "    \n",
    "    targets = train0.Survived\n",
    "    train = combined.ix[0:890]\n",
    "    test = combined.ix[891:]\n",
    "    \n",
    "    return train,test,targets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train,test,targets = recover_train_test_target()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature selection\n",
    "\n",
    "We've come up with 68 features so far. This number is quite large. \n",
    "\n",
    "When feature engineering is done, we usually tend to decrease the dimensionality by selecting the \"right\" number of features that capture the essential.\n",
    "\n",
    "In fact, feature selection comes with many benefits:\n",
    "\n",
    "- It decreases redundancy among the data\n",
    "- It speeds up the training process\n",
    "- It reduces overfitting\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tree-based estimators can be used to compute feature importances, which in turn can be used to discard irrelevant features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "from sklearn.feature_selection import SelectFromModel\n",
    "clf = ExtraTreesClassifier(n_estimators=200)\n",
    "clf = clf.fit(train, targets)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's have a look at the importance of each feature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "features = pd.DataFrame()\n",
    "features['feature'] = train.columns\n",
    "features['importance'] = clf.feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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>feature</th>\n",
       "      <th>importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>PassengerId</td>\n",
       "      <td>0.127014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Title_Mr</td>\n",
       "      <td>0.124101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Age</td>\n",
       "      <td>0.118723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Fare</td>\n",
       "      <td>0.114297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Sex</td>\n",
       "      <td>0.106941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Pclass_3</td>\n",
       "      <td>0.037109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Title_Mrs</td>\n",
       "      <td>0.031979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Title_Miss</td>\n",
       "      <td>0.031961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Cabin_U</td>\n",
       "      <td>0.030650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Pclass_1</td>\n",
       "      <td>0.021821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>SmallFamily</td>\n",
       "      <td>0.020487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>FamilySize</td>\n",
       "      <td>0.019546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SibSp</td>\n",
       "      <td>0.017934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>LargeFamily</td>\n",
       "      <td>0.017437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Parch</td>\n",
       "      <td>0.015855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Pclass_2</td>\n",
       "      <td>0.013728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>Ticket_XXX</td>\n",
       "      <td>0.012662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Embarked_S</td>\n",
       "      <td>0.012453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Title_Master</td>\n",
       "      <td>0.011840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Embarked_C</td>\n",
       "      <td>0.010442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>Singleton</td>\n",
       "      <td>0.010203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Cabin_E</td>\n",
       "      <td>0.009658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Embarked_Q</td>\n",
       "      <td>0.007680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Title_Officer</td>\n",
       "      <td>0.007541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Ticket_PC</td>\n",
       "      <td>0.007076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Cabin_D</td>\n",
       "      <td>0.006796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Cabin_B</td>\n",
       "      <td>0.006337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>Ticket_SWPP</td>\n",
       "      <td>0.006315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Cabin_C</td>\n",
       "      <td>0.005715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>Ticket_STONO</td>\n",
       "      <td>0.005593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Ticket_CA</td>\n",
       "      <td>0.003512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Ticket_A5</td>\n",
       "      <td>0.003184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>Ticket_WC</td>\n",
       "      <td>0.002710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Cabin_A</td>\n",
       "      <td>0.002315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>Ticket_SOTONOQ</td>\n",
       "      <td>0.001983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Ticket_C</td>\n",
       "      <td>0.001921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>Ticket_STONO2</td>\n",
       "      <td>0.001806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Cabin_F</td>\n",
       "      <td>0.001736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>Ticket_SOPP</td>\n",
       "      <td>0.001710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Cabin_G</td>\n",
       "      <td>0.001600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>Ticket_WEP</td>\n",
       "      <td>0.001060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Title_Royalty</td>\n",
       "      <td>0.001055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Ticket_FC</td>\n",
       "      <td>0.000736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Ticket_PP</td>\n",
       "      <td>0.000702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Ticket_LINE</td>\n",
       "      <td>0.000548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Ticket_SCPARIS</td>\n",
       "      <td>0.000527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Ticket_FCC</td>\n",
       "      <td>0.000488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Ticket_A4</td>\n",
       "      <td>0.000446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>Ticket_SOC</td>\n",
       "      <td>0.000381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>Ticket_SCParis</td>\n",
       "      <td>0.000362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Cabin_T</td>\n",
       "      <td>0.000333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>Ticket_SOP</td>\n",
       "      <td>0.000246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>Ticket_SCAH</td>\n",
       "      <td>0.000168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Ticket_SC</td>\n",
       "      <td>0.000104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>Ticket_SOTONO2</td>\n",
       "      <td>0.000092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Ticket_PPP</td>\n",
       "      <td>0.000085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>Ticket_SP</td>\n",
       "      <td>0.000078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Ticket_Fa</td>\n",
       "      <td>0.000063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>Ticket_SCOW</td>\n",
       "      <td>0.000045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Ticket_SCA4</td>\n",
       "      <td>0.000044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Ticket_AS</td>\n",
       "      <td>0.000043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Ticket_CASOTON</td>\n",
       "      <td>0.000028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Ticket_LP</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>Ticket_SCA3</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Ticket_AQ4</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Ticket_AQ3</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Ticket_A</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>Ticket_STONOQ</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           feature  importance\n",
       "0      PassengerId    0.127014\n",
       "8         Title_Mr    0.124101\n",
       "2              Age    0.118723\n",
       "5             Fare    0.114297\n",
       "1              Sex    0.106941\n",
       "26        Pclass_3    0.037109\n",
       "9        Title_Mrs    0.031979\n",
       "7       Title_Miss    0.031961\n",
       "23         Cabin_U    0.030650\n",
       "24        Pclass_1    0.021821\n",
       "66     SmallFamily    0.020487\n",
       "64      FamilySize    0.019546\n",
       "3            SibSp    0.017934\n",
       "67     LargeFamily    0.017437\n",
       "4            Parch    0.015855\n",
       "25        Pclass_2    0.013728\n",
       "63      Ticket_XXX    0.012662\n",
       "14      Embarked_S    0.012453\n",
       "6     Title_Master    0.011840\n",
       "12      Embarked_C    0.010442\n",
       "65       Singleton    0.010203\n",
       "19         Cabin_E    0.009658\n",
       "13      Embarked_Q    0.007680\n",
       "10   Title_Officer    0.007541\n",
       "41       Ticket_PC    0.007076\n",
       "18         Cabin_D    0.006796\n",
       "16         Cabin_B    0.006337\n",
       "60     Ticket_SWPP    0.006315\n",
       "17         Cabin_C    0.005715\n",
       "57    Ticket_STONO    0.005593\n",
       "34       Ticket_CA    0.003512\n",
       "29       Ticket_A5    0.003184\n",
       "61       Ticket_WC    0.002710\n",
       "15         Cabin_A    0.002315\n",
       "55  Ticket_SOTONOQ    0.001983\n",
       "33        Ticket_C    0.001921\n",
       "58   Ticket_STONO2    0.001806\n",
       "20         Cabin_F    0.001736\n",
       "53     Ticket_SOPP    0.001710\n",
       "21         Cabin_G    0.001600\n",
       "62      Ticket_WEP    0.001060\n",
       "11   Title_Royalty    0.001055\n",
       "36       Ticket_FC    0.000736\n",
       "42       Ticket_PP    0.000702\n",
       "39     Ticket_LINE    0.000548\n",
       "49  Ticket_SCPARIS    0.000527\n",
       "37      Ticket_FCC    0.000488\n",
       "28       Ticket_A4    0.000446\n",
       "51      Ticket_SOC    0.000381\n",
       "50  Ticket_SCParis    0.000362\n",
       "22         Cabin_T    0.000333\n",
       "52      Ticket_SOP    0.000246\n",
       "47     Ticket_SCAH    0.000168\n",
       "44       Ticket_SC    0.000104\n",
       "54  Ticket_SOTONO2    0.000092\n",
       "43      Ticket_PPP    0.000085\n",
       "56       Ticket_SP    0.000078\n",
       "38       Ticket_Fa    0.000063\n",
       "48     Ticket_SCOW    0.000045\n",
       "46     Ticket_SCA4    0.000044\n",
       "32       Ticket_AS    0.000043\n",
       "35  Ticket_CASOTON    0.000028\n",
       "40       Ticket_LP    0.000000\n",
       "45     Ticket_SCA3    0.000000\n",
       "31      Ticket_AQ4    0.000000\n",
       "30      Ticket_AQ3    0.000000\n",
       "27        Ticket_A    0.000000\n",
       "59   Ticket_STONOQ    0.000000"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features.sort(['importance'],ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you may notice, there is a great importance linked to Title_Mr, Age, Fare, and Sex. \n",
    "\n",
    "There is also an important correlation with the Passenger_Id.\n",
    "\n",
    "Let's now transform our train set and test set in a more compact datasets. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(891, 15)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = SelectFromModel(clf, prefit=True)\n",
    "train_new = model.transform(train)\n",
    "train_new.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(418, 15)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_new = model.transform(test)\n",
    "test_new.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Yay! Now we're down to 15 features."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hyperparameters tuning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As mentioned in the beginning of the Modeling part, we will be using a Random Forest model.\n",
    "\n",
    "Random Forest are quite handy. They do however come with some parameters to tweak in order to get an optimal model for the prediction task.\n",
    "\n",
    "To learn more about Random Forests, you can refer to this link: \n",
    "https://www.analyticsvidhya.com/blog/2015/06/tuning-random-forest-model/\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best score: 0.833894500561\n",
      "Best parameters: {'n_estimators': 200, 'criterion': 'gini', 'max_depth': 5}\n"
     ]
    }
   ],
   "source": [
    "forest = RandomForestClassifier(max_features='sqrt')\n",
    "\n",
    "parameter_grid = {\n",
    "                 'max_depth' : [4,5,6,7,8],\n",
    "                 'n_estimators': [200,210,240,250],\n",
    "                 'criterion': ['gini','entropy']\n",
    "                 }\n",
    "\n",
    "cross_validation = StratifiedKFold(targets, n_folds=5)\n",
    "\n",
    "grid_search = GridSearchCV(forest,\n",
    "                           param_grid=parameter_grid,\n",
    "                           cv=cross_validation)\n",
    "\n",
    "grid_search.fit(train_new, targets)\n",
    "\n",
    "print('Best score: {}'.format(grid_search.best_score_))\n",
    "print('Best parameters: {}'.format(grid_search.best_params_))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that the model is built by scanning all several combinations of hyperparameters, we can generate an output file to submit on Kaggle.\n",
    "\n",
    "This solution allowed me to get an accuracy score of 0.8134 on the public leaderboard."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "output = grid_search.predict(test_new).astype(int)\n",
    "df_output = pd.DataFrame()\n",
    "df_output['PassengerId'] = test['PassengerId']\n",
    "df_output['Survived'] = output\n",
    "df_output[['PassengerId','Survived']].to_csv('../data/output.csv',index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# IV - Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "In this article, we explored an interesting dataset brought to us by <a href=\"http://kaggle.com\">Kaggle</a>.\n",
    "\n",
    "We went through the basic bricks of the pipeline:\n",
    "\n",
    "- Data exploration and visualization: an initial step to formulate hypotheses\n",
    "- Data cleaning \n",
    "- Feature engineering \n",
    "- Feature selection\n",
    "- Hyperparameters tuning\n",
    "- Submission\n",
    "\n",
    "Lots of articles have been written about this challenge, so obviously there is a room for improvement.\n",
    "\n",
    "Here is what I suggest for next steps:\n",
    "\n",
    "- Dig more in the data and eventually build new features.\n",
    "- Try different models : logistic regressions, Gradient Boosted trees, XGboost, ...\n",
    "- Try ensemble learning techniques (stacking, blending)\n",
    "- Maybe try other families of ML models (neural networks?)\n",
    "\n",
    "I would be more than happy if you could find out a way to improve my solution. This could make me update the article and definitely give you credit for that. So feel free to post a comment or shoot me an email at ahmed.besbes@hotmail.com"
   ]
  }
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