Titanic_challenge.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Solving Titanic Challenge (Kaggle)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"https://cdn-images-1.medium.com/max/1200/1*bgFmpts-9boWUG1H2Bu6zA.jpeg\" alt=\"Titanic\" title=\"Titanic\" />"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Goal (binary classification)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "the goal is to predict the survival or the death of a given passenger based on 12 feature such as sex, age ,etc..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## what will you read in this tutorial?\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 1 Define Problem\n",
    "- 2 Installation\n",
    "- 3 Import Machine learning libraries\n",
    "- 4 EDA\n",
    "- 5 Feature Engineering\n",
    "- 6 Modeing\n",
    "- 7 Feature Selection\n",
    "- 8 Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1- Define Problem"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1-1 introduction\n",
    "RMS Titanic was a British passenger liner that sank in the North Atlantic Ocean in the early hours of 15 April 1912, after colliding with an iceberg during its maiden voyage from Southampton to New York City. There were an estimated 2,224 passengers and crew aboard, and more than 1,500 died, making it one of the deadliest commercial peacetime maritime disasters in modern history. RMS Titanic was the largest ship afloat at the time it entered service and was the second of three Olympic-class ocean liners operated by the White Star Line. It was built by the Harland and Wolff shipyard in Belfast. Thomas Andrews, her architect, died in the disaster.\n",
    "### 1-2 dataset\n",
    "Titanic dataset is an open dataset where you can reach from many different repositories and GitHub accounts. However, downloading from Kaggle will be definitely the best choice as the other sources may have slightly different versions and may not offer separate train and test files. So, please visit this <a href=\"https://www.kaggle.com/c/titanic/data\">link</a> to download the datasets (Train.csv and Test.csv) to get started."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2- installation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "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)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3- Import"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "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 and ingestion\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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "warnings.filterwarnings('ignore', category=DeprecationWarning)\n",
    "\n",
    "import pandas as pd\n",
    "pd.options.display.max_columns = 100\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "import seaborn as sns\n",
    "\n",
    "import pylab as plot\n",
    "params = { \n",
    "    'axes.labelsize': \"large\",\n",
    "    'xtick.labelsize': 'x-large',\n",
    "    'legend.fontsize': 20,\n",
    "    'figure.dpi': 150,\n",
    "    'figure.figsize': [25, 7]\n",
    "}\n",
    "plot.rcParams.update(params)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4 - Exploratory data analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As in different data projects, we'll first start diving into the data and build up our first intuitions.\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": [
    "> I am assuming that you have your Python3 environment installed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4-1 Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data=pd.read_csv('./data/train.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4-2 shape of data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(891, 12)\n"
     ]
    }
   ],
   "source": [
    "print (data.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have:\n",
    "\n",
    "891 rows\n",
    "12 columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4-3 Head of data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <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": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4-4 Features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Survived column is the ** target variable**. If Suvival = 1 the passenger survived, otherwise he's dead. The is the variable we're going to predict.\n",
    "\n",
    "The other variables describe the passengers. They are the **features**.\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 (first, second and third class)\n",
    "- The Name of the passeger\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. This describe three possible areas of the Titanic from which the people embark. Three possible values S,C,Q"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4-5 Data describe\n",
    "Pandas allows you to a have a high-level simple statistical description of the numerical features. This can be done using the describe method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>32.204208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>257.353842</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>49.693429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>223.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.910400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>668.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
       "count   891.000000  891.000000  891.000000  714.000000  891.000000   \n",
       "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
       "std     257.353842    0.486592    0.836071   14.526497    1.102743   \n",
       "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
       "25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n",
       "50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n",
       "75%     668.500000    1.000000    3.000000   38.000000    1.000000   \n",
       "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
       "\n",
       "            Parch        Fare  \n",
       "count  891.000000  891.000000  \n",
       "mean     0.381594   32.204208  \n",
       "std      0.806057   49.693429  \n",
       "min      0.000000    0.000000  \n",
       "25%      0.000000    7.910400  \n",
       "50%      0.000000   14.454200  \n",
       "75%      0.000000   31.000000  \n",
       "max      6.000000  512.329200  "
      ]
     },
     "execution_count": 135,
     "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",
    "One solution is to fill in the null values with the median age. We could also impute with the mean age but the median is more robust to outliers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4-6 Charts\n",
    "let's now make some charts.\n",
    "\n",
    "let's visualize survival based on the gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data['Died']= 1 - data['Survived']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x17369de07b8>"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x17369de0eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.groupby('Sex').agg('sum')[['Survived','Died']].plot(kind='bar',figsize=(25,7),stacked=True,colors=['g','r'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks like male passengers are more likely to succumb.\n",
    "\n",
    "Let's plot the same graph but with ratio instead."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x17369e037f0>"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x17369e91a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#data.groupby('Sex').agg('mean')[['Survived', 'Died']].plot(kind='bar', figsize=(25, 7), \n",
    " #                                                          stacked=True, colors=['g', 'r']);\n",
    "data.groupby('Sex').agg('mean')[['Survived','Died']].plot(kind='bar',figsize=(25,7),colors=['g','b'],stacked=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Sex variable seems to be a discriminative feature. Women are more likely to survive."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's now correlate the survival with the age variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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WU7PdzMm3HJ+NT/pErn94UukcAAC6xMqlAwZKT0/PRkl2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\nq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JGiqCqLR6is73ClBdNspwHAGenMpXdG712M0TsAJCbT1iazY7v8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\n/vuTzZtLJwL8zuRxk3Phoguz7sB1Gd0xunQOAACPGWqj99Zjj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      "text/plain": [
       "<matplotlib.figure.Figure at 0x17369e03908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#fig = plt.figure(figsize=(25, 7))\n",
    "sns.violinplot(x='Sex', y='Age', \n",
    "               hue='Survived', data=data, \n",
    "               split=True,\n",
    "               palette={0: \"r\", 1: \"g\"}\n",
    "              );"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we saw in the chart above and validate by the following:\n",
    "\n",
    "- Women survive more than men, as depicted by the larger female green histogram \n",
    "\n",
    "Now, we see that:\n",
    "- The age conditions the survival for male passengers:\n",
    "    - Younger male tend to survive\n",
    "    - A large number of passengers between 20 and 40 succumb\n",
    "    \n",
    "- The age doesn't seem to have a direct impact on the female survival"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">Let's now focus on the Fare ticket of each passenger and see how it could impact the survival. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x17369f3f438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure = plt.figure(figsize=(25, 7))\n",
    "plt.hist([data[data['Survived'] == 1]['Fare'], data[data['Survived'] == 0]['Fare']], \n",
    "         stacked=True, color = ['g','r'],\n",
    "         bins = 50, 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": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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HAAAg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\nYIb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9cbgm3KPxSlbpcYZIbRRUrAp2wY45G4EnDwNVrLcepxCBakWlfxpkAYMHAfsT4PFC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grhjnVQ3XZ2huSywGq0UieqPkNGwj+RoHBq/6dS/N8/VocH+8A1Da4XogEALVloodig7nQK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\nyi9YAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1736ab26f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(25, 7))\n",
    "ax = plt.subplot()\n",
    "\n",
    "ax.scatter(data[data['Survived'] == 1]['Age'], data[data['Survived'] == 1]['Fare'], \n",
    "           c='green', s=data[data['Survived'] == 1]['Fare'])\n",
    "ax.scatter(data[data['Survived'] == 0]['Age'], data[data['Survived'] == 0]['Fare'], \n",
    "           c='red', s=data[data['Survived'] == 0]['Fare']);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The size of the circles is proportional to the ticket fare.\n",
    "\n",
    "On the x-axis, we have the ages and the y-axis, we consider the ticket fare.\n",
    "\n",
    "We can observe different clusters:\n",
    "\n",
    "1. Large green dots between x=20 and x=45: adults with the largest ticket fares\n",
    "2. Small red dots between x=10 and x=45, adults from lower classes on the boat\n",
    "3. Small greed dots between x=0 and x=7: these are the children that were saved"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">As a matter of fact, the ticket fare correlates with the class as we see it in the chart below. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1736aa9df98>"
      ]
     },
     "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=(25, 7), ax = ax);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">Let's now see how the embarkation site affects the survival."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
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SAAAAwGB0Dx5M54tfaOXde2dMqgcAAAAAoD2D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\nzPA4y+auW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      "text/plain": [
       "<matplotlib.figure.Figure at 0x17366e53be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(25, 7))\n",
    "sns.violinplot(x='Embarked', y='Fare', hue='Survived', data=data, split=True, palette={0: \"r\", 1: \"g\"});"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It seems that the embarkation C have a wider range of fare tickets and therefore the passengers who pay the highest prices are those who survive.\n",
    "\n",
    "We also see this happening in embarkation S and less in embarkation Q.\n",
    "\n",
    "Let's now stop with data exploration and switch to the next part."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5 - feature engineering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the previous part, we flirted with the data and spotted some interesting correlations.\n",
    "\n",
    "In this part, we'll see how to process and transform these variables in such a way the data becomes manageable by a machine learning algorithm.\n",
    "\n",
    "We'll also create, or \"engineer\" additional features that will be useful in building the model.\n",
    "\n",
    "We'll see along the way how to process text variables like the passenger names and integrate this information in our model.\n",
    "\n",
    "We will break our code in separate functions for more clarity.\n",
    "\n",
    "But first, let's define a print function that asserts whether or not a feature has been processed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def  status(feature):\n",
    "    print('Processing',feature,':ok')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5-1 Loading The Data\n",
    "One trick when starting a machine learning problem is to append the training set to the test set together.\n",
    "\n",
    "We'll engineer new features using the train set to prevent information leakage. Then we'll add these variables to the test set.\n",
    "\n",
    "Let's load the train and test sets and append them together."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "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",
    "    # we'll also remove the PassengerID since this is not an informative feature\n",
    "    combined = train.append(test)\n",
    "    combined.reset_index(inplace=True)\n",
    "    combined.drop(['index', 'PassengerId'], inplace=True, axis=1)\n",
    "    \n",
    "    return combined"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "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": 147,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1309, 10)\n"
     ]
    }
   ],
   "source": [
    "print (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": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
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       "      <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>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>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>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>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>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": [
       "   Pclass                                               Name     Sex   Age  \\\n",
       "0       3                            Braund, Mr. Owen Harris    male  22.0   \n",
       "1       1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0   \n",
       "2       3                             Heikkinen, Miss. Laina  female  26.0   \n",
       "3       1       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0   \n",
       "4       3                           Allen, Mr. William Henry    male  35.0   \n",
       "\n",
       "   SibSp  Parch            Ticket     Fare Cabin Embarked  \n",
       "0      1      0         A/5 21171   7.2500   NaN        S  \n",
       "1      1      0          PC 17599  71.2833   C85        C  \n",
       "2      0      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      1      0            113803  53.1000  C123        S  \n",
       "4      0      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5-2 Extracting the passenger titles\n",
    "When looking at the passenger names one could wonder how to process them to extract a useful information.\n",
    "\n",
    "If you look closely at these first examples:\n",
    "\n",
    "Braund, Mr. Owen Harris\n",
    "Heikkinen, Miss. Laina\n",
    "Oliva y Ocana, Dona. Fermina\n",
    "Peter, Master. Michael J\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 an additional information about the social status by simply parsing the name and extracting the title and converting to a binary variable.\n",
    "\n",
    "Let's see how we'll do that in the function below.\n",
    "\n",
    "Let's first see what the different titles are in the train set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "titles = set()\n",
    "for name in data['Name']:\n",
    "    titles.add(name.split(',')[1].split('.')[0].strip())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Rev', 'Ms', 'Sir', 'Mlle', 'Jonkheer', 'Capt', 'Mr', 'Miss', 'Don', 'Master', 'Mrs', 'Dr', 'Lady', 'the Countess', 'Major', 'Col', 'Mme'}\n"
     ]
    }
   ],
   "source": [
    "print (titles)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "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",
    "    \"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",
    "def get_titles():\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 title\n",
    "    # we map each title\n",
    "    combined['Title'] = combined.Title.map(Title_Dictionary)\n",
    "    status('Title')\n",
    "    return combined"
   ]
  },
  {
   "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": 152,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing Title :ok\n"
     ]
    }
   ],
   "source": [
    "combined = get_titles()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></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>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>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>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>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>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": [
       "   Pclass                                               Name     Sex   Age  \\\n",
       "0       3                            Braund, Mr. Owen Harris    male  22.0   \n",
       "1       1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0   \n",
       "2       3                             Heikkinen, Miss. Laina  female  26.0   \n",
       "3       1       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0   \n",
       "4       3                           Allen, Mr. William Henry    male  35.0   \n",
       "\n",
       "   SibSp  Parch            Ticket     Fare Cabin Embarked Title  \n",
       "0      1      0         A/5 21171   7.2500   NaN        S    Mr  \n",
       "1      1      0          PC 17599  71.2833   C85        C   Mrs  \n",
       "2      0      0  STON/O2. 3101282   7.9250   NaN        S  Miss  \n",
       "3      1      0            113803  53.1000  C123        S   Mrs  \n",
       "4      0      0            373450   8.0500   NaN        S    Mr  "
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></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>1305</th>\n",
       "      <td>1</td>\n",
       "      <td>Oliva y Ocana, Dona. Fermina</td>\n",
       "      <td>female</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17758</td>\n",
       "      <td>108.9</td>\n",
       "      <td>C105</td>\n",
       "      <td>C</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Pclass                          Name     Sex   Age  SibSp  Parch  \\\n",
       "1305       1  Oliva y Ocana, Dona. Fermina  female  39.0      0      0   \n",
       "\n",
       "        Ticket   Fare Cabin Embarked Title  \n",
       "1305  PC 17758  108.9  C105        C   NaN  "
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Let's check if the titles have been filled correctly.\n",
    "combined[combined['Title'].isnull()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is indeed a NaN value in the line 1305. In fact the corresponding name is Oliva y Ocana, **Dona**. Fermina.\n",
    "\n",
    "This title was not encoutered in the train dataset."
   ]
  },
  {
   "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": [
    "### 5-3 Processing the ages\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 and for each subset compute the median age.\n",
    "\n",
    "To avoid data leakage from the test set, we fill in missing ages in the train using the train set and we fill in ages in the test set using values calculated from the train set as well.\n",
    "\n",
    "Number of missing ages in train set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "177\n"
     ]
    }
   ],
   "source": [
    "print (combined.iloc[:891].Age.isnull().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Number of missing ages in test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "86\n"
     ]
    }
   ],
   "source": [
    "print (combined.iloc[891:].Age.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "grouped_train = combined.iloc[:891].groupby(['Sex','Pclass','Title'])\n",
    "grouped_median_train = grouped_train.median()\n",
    "grouped_median_train = grouped_median_train.reset_index()[['Sex', 'Pclass', 'Title', 'Age']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sex</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Title</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>Miss</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>Officer</td>\n",
       "      <td>49.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>Royalty</td>\n",
       "      <td>40.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>female</td>\n",
       "      <td>2</td>\n",
       "      <td>Miss</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Sex  Pclass    Title   Age\n",
       "0  female       1     Miss  30.0\n",
       "1  female       1      Mrs  40.0\n",
       "2  female       1  Officer  49.0\n",
       "3  female       1  Royalty  40.5\n",
       "4  female       2     Miss  24.0"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_median_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This dataframe will help us impute missing age values based on different criteria.\n",
    "\n",
    "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 40.5.\n",
    "If the passenger is male, from Pclass 3, with a Mr title, the median age is 26.\n",
    "Let's create a function that fills in the missing age in combined based on these different attributes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def fill_age(row):\n",
    "    condition = (\n",
    "        (grouped_median_train['Sex'] == row['Sex']) & \n",
    "        (grouped_median_train['Title'] == row['Title']) & \n",
    "        (grouped_median_train['Pclass'] == row['Pclass'])\n",
    "    ) \n",
    "    return grouped_median_train[condition]['Age'].values[0]\n",
    "\n",
    "\n",
    "def process_age():\n",
    "    global combined\n",
    "    # a function that fills the missing values of the Age variable\n",
    "    combined['Age'] = combined.apply(lambda row: fill_age(row) if np.isnan(row['Age']) else row['Age'], axis=1)\n",
    "    status('age')\n",
    "    return combined"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing age :ok\n"
     ]
    }
   ],
   "source": [
    "combined = process_age()"
   ]
  },
  {
   "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": 161,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def process_names():\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')\n",
    "    return combined"
   ]
  },
  {
   "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.\n",
    "\n",
    "You can learn about dummy coding and how to easily do it in Pandas <a href=\"http://pandas.pydata.org/pandas-docs/stable/generated/pandas.get_dummies.html\">here</a>.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing names :ok\n"
     ]
    }
   ],
   "source": [
    "combined = process_names()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></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>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</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\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</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\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</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\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</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pclass     Sex   Age  SibSp  Parch            Ticket     Fare Cabin  \\\n",
       "0       3    male  22.0      1      0         A/5 21171   7.2500   NaN   \n",
       "1       1  female  38.0      1      0          PC 17599  71.2833   C85   \n",
       "2       3  female  26.0      0      0  STON/O2. 3101282   7.9250   NaN   \n",
       "3       1  female  35.0      1      0            113803  53.1000  C123   \n",
       "4       3    male  35.0      0      0            373450   8.0500   NaN   \n",
       "\n",
       "  Embarked  Title_Master  Title_Miss  Title_Mr  Title_Mrs  Title_Officer  \\\n",
       "0        S             0           0         1          0              0   \n",
       "1        C             0           0         0          1              0   \n",
       "2        S             0           1         0          0              0   \n",
       "3        S             0           0         0          1              0   \n",
       "4        S             0           0         1          0              0   \n",
       "\n",
       "   Title_Royalty  \n",
       "0              0  \n",
       "1              0  \n",
       "2              0  \n",
       "3              0  \n",
       "4              0  "
      ]
     },
     "execution_count": 163,
     "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": [
    "### 5-4 Processing Fare"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's imputed the missing fare value by the average fare computed on the train set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def process_fares():\n",
    "    global combined\n",
    "    # there's one missing fare value - replacing it with the mean.\n",
    "    combined.Fare.fillna(combined.iloc[:891].Fare.mean(), inplace=True)\n",
    "    status('fare')\n",
    "    return combined"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This function simply replaces one missing Fare value by the mean."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing fare :ok\n"
     ]
    }
   ],
   "source": [
    "combined = process_fares()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5-4 Processing Embarked"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def process_embarked():\n",
    "    global combined\n",
    "    # two missing embarked values - filling them with the most frequent one in the train  set(S)\n",
    "    combined.Embarked.fillna('S', inplace=True)\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",
    "    status('embarked')\n",
    "    return combined"
   ]
  },
  {
   "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": 167,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing embarked :ok\n"
     ]
    }
   ],
   "source": [
    "combined = process_embarked()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></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>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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pclass     Sex   Age  SibSp  Parch            Ticket     Fare Cabin  \\\n",
       "0       3    male  22.0      1      0         A/5 21171   7.2500   NaN   \n",
       "1       1  female  38.0      1      0          PC 17599  71.2833   C85   \n",
       "2       3  female  26.0      0      0  STON/O2. 3101282   7.9250   NaN   \n",
       "3       1  female  35.0      1      0            113803  53.1000  C123   \n",
       "4       3    male  35.0      0      0            373450   8.0500   NaN   \n",
       "\n",
       "   Title_Master  Title_Miss  Title_Mr  Title_Mrs  Title_Officer  \\\n",
       "0             0           0         1          0              0   \n",
       "1             0           0         0          1              0   \n",
       "2             0           1         0          0              0   \n",
       "3             0           0         0          1              0   \n",
       "4             0           0         1          0              0   \n",
       "\n",
       "   Title_Royalty  Embarked_C  Embarked_Q  Embarked_S  \n",
       "0              0           0           0           1  \n",
       "1              0           1           0           0  \n",
       "2              0           0           0           1  \n",
       "3              0           0           0           1  \n",
       "4              0           0           0           1  "
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5-5 Processing Cabin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_cabin, test_cabin = set(), set()\n",
    "\n",
    "for c in combined.iloc[:891]['Cabin']:\n",
    "    try:\n",
    "        train_cabin.add(c[0])\n",
    "    except:\n",
    "        train_cabin.add('U')\n",
    "        \n",
    "for c in combined.iloc[891:]['Cabin']:\n",
    "    try:\n",
    "        test_cabin.add(c[0])\n",
    "    except:\n",
    "        test_cabin.add('U')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'D', 'T', 'U', 'C', 'G', 'E', 'A', 'B', 'F'}\n"
     ]
    }
   ],
   "source": [
    "print (train_cabin)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'D', 'U', 'C', 'G', 'E', 'A', 'B', 'F'}\n"
     ]
    }
   ],
   "source": [
    "print (test_cabin)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We don't have any cabin letter in the test set that is not present in the train set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def process_cabin():\n",
    "    global combined    \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",
    "    combined = pd.concat([combined, cabin_dummies], axis=1)\n",
    "\n",
    "    combined.drop('Cabin', axis=1, inplace=True)\n",
    "    status('cabin')\n",
    "    return combined"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This function replaces NaN values with U (for Unknow). It then maps each Cabin value to the first letter. Then it encodes the cabin values using dummy encoding again."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing cabin :ok\n"
     ]
    }
   ],
   "source": [
    "combined = process_cabin()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ok no missing values now."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "    .dataframe thead th {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th>Sex</th>\n",
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       "      <th>Ticket</th>\n",
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       "      <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>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</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\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</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\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</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\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</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pclass     Sex   Age  SibSp  Parch            Ticket     Fare  \\\n",
       "0       3    male  22.0      1      0         A/5 21171   7.2500   \n",
       "1       1  female  38.0      1      0          PC 17599  71.2833   \n",
       "2       3  female  26.0      0      0  STON/O2. 3101282   7.9250   \n",
       "3       1  female  35.0      1      0            113803  53.1000   \n",
       "4       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         1          0              0   \n",
       "1             0           0         0          1              0   \n",
       "2             0           1         0          0              0   \n",
       "3             0           0         0          1              0   \n",
       "4             0           0         1          0              0   \n",
       "\n",
       "   Title_Royalty  Embarked_C  Embarked_Q  Embarked_S  Cabin_A  Cabin_B  \\\n",
       "0              0           0           0           1        0        0   \n",
       "1              0           1           0           0        0        0   \n",
       "2              0           0           0           1        0        0   \n",
       "3              0           0           0           1        0        0   \n",
       "4              0           0           0           1        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        1  \n",
       "1        1        0        0        0        0        0        0  \n",
       "2        0        0        0        0        0        0        1  \n",
       "3        1        0        0        0        0        0        0  \n",
       "4        0        0        0        0        0        0        1  "
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5-6 Processing Sex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def process_sex():\n",
    "    global combined\n",
    "    # mapping string values to numerical one \n",
    "    combined['Sex'] = combined['Sex'].map({'male':1, 'female':0})\n",
    "    status('Sex')\n",
    "    return combined"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This function maps the string values male and female to 1 and 0 respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing Sex :ok\n"
     ]
    }
   ],
   "source": [
    "combined = process_sex()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5-7 Processing Pclass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "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 variable\n",
    "    combined = pd.concat([combined, pclass_dummies],axis=1)\n",
    "    \n",
    "    # removing \"Pclass\"\n",
    "    combined.drop('Pclass',axis=1,inplace=True)\n",
    "    \n",
    "    status('Pclass')\n",
    "    return combined"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This function encodes the values of Pclass (1,2,3) using a dummy encoding."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing Pclass :ok\n"
     ]
    }
   ],
   "source": [
    "combined = process_pclass()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5-8 Processing Ticket"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's first see how the different ticket prefixes we have in our dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "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 = list(filter(lambda t : not t.isdigit(), ticket))\n",
    "    if len(ticket) > 0:\n",
    "        return ticket[0]\n",
    "    else: \n",
    "        return 'XXX'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tickets = set()\n",
    "for t in combined['Ticket']:\n",
    "    tickets.add(cleanTicket(t))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "37\n"
     ]
    }
   ],
   "source": [
    "print (len(tickets))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "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 = list(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')\n",
    "    return combined"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing Ticket :ok\n"
     ]
    }
   ],
   "source": [
    "combined = process_ticket()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5-9 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).\n",
    "\n",
    "This creation of new variables is done under a realistic assumption: Large families are grouped together, hence they are more likely to get rescued than people traveling alone."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "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')\n",
    "    return combined"
   ]
  },
  {
   "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": 220,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing family :ok\n"
     ]
    }
   ],
   "source": [
    "combined = process_family()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1309, 67)\n"
     ]
    }
   ],
   "source": [
    "print (combined.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We end up with a total of 67 features. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></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>FamilySize</th>\n",
       "      <th>Singleton</th>\n",
       "      <th>SmallFamily</th>\n",
       "      <th>LargeFamily</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\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</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\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</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\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</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\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</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Sex   Age  SibSp  Parch     Fare  Title_Master  Title_Miss  Title_Mr  \\\n",
       "0    1  22.0      1      0   7.2500             0           0         1   \n",
       "1    0  38.0      1      0  71.2833             0           0         0   \n",
       "2    0  26.0      0      0   7.9250             0           1         0   \n",
       "3    0  35.0      1      0  53.1000             0           0         0   \n",
       "4    1  35.0      0      0   8.0500             0           0         1   \n",
       "\n",
       "   Title_Mrs  Title_Officer  Title_Royalty  Embarked_C  Embarked_Q  \\\n",
       "0          0              0              0           0           0   \n",
       "1          1              0              0           1           0   \n",
       "2          0              0              0           0           0   \n",
       "3          1              0              0           0           0   \n",
       "4          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   \n",
       "1           0        0        0        1        0        0        0        0   \n",
       "2           1        0        0        0        0        0        0        0   \n",
       "3           1        0        0        1        0        0        0        0   \n",
       "4           1        0        0        0        0        0        0        0   \n",
       "\n",
       "   Cabin_T  Cabin_U  Pclass_1  Pclass_2  Pclass_3  FamilySize  Singleton  \\\n",
       "0        0        1         0         0         1           2          0   \n",
       "1        0        0         1         0         0           2          0   \n",
       "2        0        1         0         0         1           1          1   \n",
       "3        0        0         1         0         0           2          0   \n",
       "4        0        1         0         0         1           1          1   \n",
       "\n",
       "   SmallFamily  LargeFamily  Ticket_A  Ticket_A4  Ticket_A5  Ticket_AQ3  \\\n",
       "0            1            0         0          0          1           0   \n",
       "1            1            0         0          0          0           0   \n",
       "2            0            0         0          0          0           0   \n",
       "3            1            0         0          0          0           0   \n",
       "4            0            0         0          0          0           0   \n",
       "\n",
       "   Ticket_AQ4  Ticket_AS  Ticket_C  Ticket_CA  Ticket_CASOTON  Ticket_FC  \\\n",
       "0           0          0         0          0               0          0   \n",
       "1           0          0         0          0               0          0   \n",
       "2           0          0         0          0               0          0   \n",
       "3           0          0         0          0               0          0   \n",
       "4           0          0         0          0               0          0   \n",
       "\n",
       "   Ticket_FCC  Ticket_Fa  Ticket_LINE  Ticket_LP  Ticket_PC  Ticket_PP  \\\n",
       "0           0          0            0          0          0          0   \n",
       "1           0          0            0          0          1          0   \n",
       "2           0          0            0          0          0          0   \n",
       "3           0          0            0          0          0          0   \n",
       "4           0          0            0          0          0          0   \n",
       "\n",
       "   Ticket_PPP  Ticket_SC  Ticket_SCA3  Ticket_SCA4  Ticket_SCAH  Ticket_SCOW  \\\n",
       "0           0          0            0            0            0            0   \n",
       "1           0          0            0            0            0            0   \n",
       "2           0          0            0            0            0            0   \n",
       "3           0          0            0            0            0            0   \n",
       "4           0          0            0            0            0            0   \n",
       "\n",
       "   Ticket_SCPARIS  Ticket_SCParis  Ticket_SOC  Ticket_SOP  Ticket_SOPP  \\\n",
       "0               0               0           0           0            0   \n",
       "1               0               0           0           0            0   \n",
       "2               0               0           0           0            0   \n",
       "3               0               0           0           0            0   \n",
       "4               0               0           0           0            0   \n",
       "\n",
       "   Ticket_SOTONO2  Ticket_SOTONOQ  Ticket_SP  Ticket_STONO  Ticket_STONO2  \\\n",
       "0               0               0          0             0              0   \n",
       "1               0               0          0             0              0   \n",
       "2               0               0          0             0              1   \n",
       "3               0               0          0             0              0   \n",
       "4               0               0          0             0              0   \n",
       "\n",
       "   Ticket_STONOQ  Ticket_SWPP  Ticket_WC  Ticket_WEP  Ticket_XXX  \n",
       "0              0            0          0           0           0  \n",
       "1              0            0          0           0           0  \n",
       "2              0            0          0           0           0  \n",
       "3              0            0          0           0           1  \n",
       "4              0            0          0           0           1  "
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6- 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 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 an acceptable evaluation score is achieved."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's start by importing the useful libraries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble.gradient_boosting import GradientBoostingClassifier\n",
    "from sklearn.feature_selection import SelectKBest\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.feature_selection import SelectFromModel\n",
    "from sklearn.linear_model import LogisticRegression, LogisticRegressionCV"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To evaluate our model we'll be using a 5-fold cross validation with the accuracy since it's the metric that the competition uses in the leaderboard.\n",
    "\n",
    "To do that, we'll define a small scoring function. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "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": 225,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def recover_train_test_target():\n",
    "    global combined\n",
    "    \n",
    "    targets = pd.read_csv('./data/train.csv', usecols=['Survived'])['Survived'].values\n",
    "    train = combined.iloc[:891]\n",
    "    test = combined.iloc[891:]\n",
    "    \n",
    "    return train, test, targets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train, test, targets = recover_train_test_target()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6-1 Feature selection"
   ]
  },
  {
   "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": 227,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = RandomForestClassifier(n_estimators=50, max_features='sqrt')\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": 228,
   "metadata": {},
   "outputs": [],
   "source": [
    "features = pd.DataFrame()\n",
    "features['feature'] = train.columns\n",
    "features['importance'] = clf.feature_importances_\n",
    "features.sort_values(by=['importance'], ascending=True, inplace=True)\n",
    "features.set_index('feature', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x17369fbc898>"
      ]
     },
     "execution_count": 249,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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kSZIkSZIkSZIkSZIkSZIkqeGVlJf7P+OX1paIaEsmDNM+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\nJbBkyTJat96YI444lgMO+AEXXXQ+48aNBeD555/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      "text/plain": [
       "<matplotlib.figure.Figure at 0x17369fbcc88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "features.plot(kind='barh', figsize=(25, 25))"
   ]
  },
  {
   "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": 250,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(891, 14)\n"
     ]
    }
   ],
   "source": [
    "model = SelectFromModel(clf, prefit=True)\n",
    "train_reduced = model.transform(train)\n",
    "print (train_reduced.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(418, 14)\n"
     ]
    }
   ],
   "source": [
    "test_reduced = model.transform(test)\n",
    "print (test_reduced.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Yay! Now we're down to a lot less features."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll see if we'll use the reduced or the full version of the train set."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6-2 Let's try different base models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "logreg = LogisticRegression()\n",
    "logreg_cv = LogisticRegressionCV()\n",
    "rf = RandomForestClassifier()\n",
    "gboost = GradientBoostingClassifier()\n",
    "\n",
    "models = [logreg, logreg_cv, rf, gboost]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross-validation of : <class 'sklearn.linear_model.logistic.LogisticRegression'>\n",
      "CV score = 0.8125769783320514\n",
      "****\n",
      "Cross-validation of : <class 'sklearn.linear_model.logistic.LogisticRegressionCV'>\n",
      "CV score = 0.8215533300919537\n",
      "****\n",
      "Cross-validation of : <class 'sklearn.ensemble.forest.RandomForestClassifier'>\n",
      "CV score = 0.8114722140360249\n",
      "****\n",
      "Cross-validation of : <class 'sklearn.ensemble.gradient_boosting.GradientBoostingClassifier'>\n",
      "CV score = 0.8249176976842577\n",
      "****\n"
     ]
    }
   ],
   "source": [
    "for model in models:\n",
    "    print ( 'Cross-validation of : {0}'.format(model.__class__))\n",
    "    score = compute_score(clf=model, X=train_reduced, y=targets, scoring='accuracy')\n",
    "    print('CV score = {0}'.format(score))\n",
    "    print ('****')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6-3 Hyperparameters tuning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As mentioned in the beginning of the Modeling part, we will be using a Random Forest model. It may not be the best model for this task but we'll show how to tune. This work can be applied to different models.\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 <a href=\"https://www.analyticsvidhya.com/blog/2015/06/tuning-random-forest-model/\">link </a>: \n",
    "\n",
    "Additionally, we'll use the full train set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# turn run_gs to True if you want to run the gridsearch again.\n",
    "run_gs = False\n",
    "\n",
    "if run_gs:\n",
    "    parameter_grid = {\n",
    "                 'max_depth' : [4, 6, 8],\n",
    "                 'n_estimators': [50, 10],\n",
    "                 'max_features': ['sqrt', 'auto', 'log2'],\n",
    "                 'min_samples_split': [2, 3, 10],\n",
    "                 'min_samples_leaf': [1, 3, 10],\n",
    "                 'bootstrap': [True, False],\n",
    "                 }\n",
    "    forest = RandomForestClassifier()\n",
    "    cross_validation = StratifiedKFold(n_splits=5)\n",
    "\n",
    "    grid_search = GridSearchCV(forest,\n",
    "                               scoring='accuracy',\n",
    "                               param_grid=parameter_grid,\n",
    "                               cv=cross_validation,\n",
    "                               verbose=1\n",
    "                              )\n",
    "\n",
    "    grid_search.fit(train, targets)\n",
    "    model = grid_search\n",
    "    parameters = grid_search.best_params_\n",
    "\n",
    "    print('Best score: {}'.format(grid_search.best_score_))\n",
    "    print('Best parameters: {}'.format(grid_search.best_params_))\n",
    "    \n",
    "else: \n",
    "    parameters = {'bootstrap': False, 'min_samples_leaf': 3, 'n_estimators': 50, \n",
    "                  'min_samples_split': 10, 'max_features': 'sqrt', 'max_depth': 6}\n",
    "    \n",
    "    model = RandomForestClassifier(**parameters)\n",
    "    model.fit(train, targets)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that the model is built by scanning several combinations of the hyperparameters, we can generate an output file to submit on Kaggle."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {},
   "outputs": [],
   "source": [
    "output = model.predict(test).astype(int)\n",
    "df_output = pd.DataFrame()\n",
    "aux = pd.read_csv('data/test.csv')\n",
    "df_output['PassengerId'] = aux['PassengerId']\n",
    "df_output['Survived'] = output\n",
    "df_output[['PassengerId','Survived']].to_csv('gridsearch_rf.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6-4 [BONUS]  Blending different models\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I haven't personally uploaded a submission based on model blending but here's how you could do it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "trained_models = []\n",
    "for model in models:\n",
    "    model.fit(train, targets)\n",
    "    trained_models.append(model)\n",
    "\n",
    "predictions = []\n",
    "for model in trained_models:\n",
    "    predictions.append(model.predict_proba(test)[:, 1])\n",
    "\n",
    "predictions_df = pd.DataFrame(predictions).T\n",
    "predictions_df['out'] = predictions_df.mean(axis=1)\n",
    "predictions_df['PassengerId'] = aux['PassengerId']\n",
    "predictions_df['out'] = predictions_df['out'].map(lambda s: 1 if s >= 0.5 else 0)\n",
    "\n",
    "predictions_df = predictions_df[['PassengerId', 'out']]\n",
    "predictions_df.columns = ['PassengerId', 'Survived']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "predictions_df.to_csv('blending_base_models.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To have a good blending submission, the base models should be different and their correlations uncorrelated."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7 - Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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 a data science 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",
    "- Blending\n",
    "\n",
    "This post can be downloaded as a notebook if you ever want to test and play with it : <a href=\"https://github.com/mjbahmani/\"> my github repo </a>\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)\n",
    "- Run auto-ML frameworks\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."
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