Titanic-For_Blog.ipynb

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     "metadata": {},
     "source": [
      "Kaggle Competition | Titanic Machine Learning from Disaster - Part II"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This is the second post on this argument. I wasn't able to achieve a better result with respect to this post (0.81340) http://elenacuoco.altervista.org/blog/archives/847 but I decided to write a post using the ipython notebook style.\n",
      "I discovered a lot of post and ipython notebook about the same competition.\n",
      " \n",
      "Here some of them which I used to take useful suggestions for the ipython notebook:\n",
      "    \n",
      "1.https://github.com/agconti/kaggle-titanic\n",
      "\n",
      "2.http://nbviewer.ipython.org/gist/mwaskom/8224591\n",
      "\n",
      "3.https://www.kaggle.com/c/titanic-gettingStarted/details/getting-started-with-random-forests\n"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "##This is the starting description of the competition\n",
      "\n",
      "###Kaggle Competition | Titanic Machine Learning from Disaster\n",
      "\n",
      ">The sinking of the RMS Titanic is one of the most infamous shipwrecks in history.  On April 15, 1912, during her maiden voyage, the Titanic sank after colliding with an iceberg, killing 1502 out of 2224 passengers and crew.  This sensational tragedy shocked the international community and led to better safety regulations for ships.\n",
      "\n",
      ">One of the reasons that the shipwreck led to such loss of life was that there were not enough lifeboats for the passengers and crew.  Although there was some element of luck involved in surviving the sinking, some groups of people were more likely to survive than others, such as women, children, and the upper-class.\n",
      "\n",
      ">In this contest, we ask you to complete the analysis of what sorts of people were likely to survive.  In particular, we ask you to apply the tools of machine learning to predict which passengers survived the tragedy.\n",
      "\n",
      ">This Kaggle Getting Started Competition provides an ideal starting place for people who may not have a lot of experience in data science and machine learning.\"\n",
      "\n",
      "From the competition [homepage](http://www.kaggle.com/c/titanic-gettingStarted).\n"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now, let's go to the code! I started using pandas, patsy and DataFrame containers"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#import library to read and plot the data\n",
      "\n",
      "import matplotlib.pyplot as plt\n",
      "%matplotlib inline\n",
      "import numpy as np\n",
      "import pandas as pd\n",
      "from pandas import Series, DataFrame\n",
      "from patsy import dmatrices,dmatrix\n",
      " \n",
      " "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#read data using pandas library\n",
      "df=pd.read_csv('./data/train.csv') \n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df"
     ],
     "language": "python",
     "metadata": {},
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        "      <td>           349216</td>\n",
        "      <td>   7.8958</td>\n",
        "      <td>         NaN</td>\n",
        "      <td> S</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>861</th>\n",
        "      <td> 862</td>\n",
        "      <td> 0</td>\n",
        "      <td> 2</td>\n",
        "      <td>                       Giles, Mr. Frederick Edward</td>\n",
        "      <td>   male</td>\n",
        "      <td> 21</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>            28134</td>\n",
        "      <td>  11.5000</td>\n",
        "      <td>         NaN</td>\n",
        "      <td> S</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>862</th>\n",
        "      <td> 863</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> Swift, Mrs. Frederick Joel (Margaret Welles Ba...</td>\n",
        "      <td> female</td>\n",
        "      <td> 48</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>            17466</td>\n",
        "      <td>  25.9292</td>\n",
        "      <td>         D17</td>\n",
        "      <td> S</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>863</th>\n",
        "      <td> 864</td>\n",
        "      <td> 0</td>\n",
        "      <td> 3</td>\n",
        "      <td>                 Sage, Miss. Dorothy Edith \"Dolly\"</td>\n",
        "      <td> female</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 8</td>\n",
        "      <td> 2</td>\n",
        "      <td>         CA. 2343</td>\n",
        "      <td>  69.5500</td>\n",
        "      <td>         NaN</td>\n",
        "      <td> S</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>864</th>\n",
        "      <td> 865</td>\n",
        "      <td> 0</td>\n",
        "      <td> 2</td>\n",
        "      <td>                            Gill, Mr. John William</td>\n",
        "      <td>   male</td>\n",
        "      <td> 24</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>           233866</td>\n",
        "      <td>  13.0000</td>\n",
        "      <td>         NaN</td>\n",
        "      <td> S</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>865</th>\n",
        "      <td> 866</td>\n",
        "      <td> 1</td>\n",
        "      <td> 2</td>\n",
        "      <td>                          Bystrom, Mrs. (Karolina)</td>\n",
        "      <td> female</td>\n",
        "      <td> 42</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>           236852</td>\n",
        "      <td>  13.0000</td>\n",
        "      <td>         NaN</td>\n",
        "      <td> S</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>866</th>\n",
        "      <td> 867</td>\n",
        "      <td> 1</td>\n",
        "      <td> 2</td>\n",
        "      <td>                      Duran y More, Miss. Asuncion</td>\n",
        "      <td> female</td>\n",
        "      <td> 27</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>    SC/PARIS 2149</td>\n",
        "      <td>  13.8583</td>\n",
        "      <td>         NaN</td>\n",
        "      <td> C</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>867</th>\n",
        "      <td> 868</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td>              Roebling, Mr. Washington Augustus II</td>\n",
        "      <td>   male</td>\n",
        "      <td> 31</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>         PC 17590</td>\n",
        "      <td>  50.4958</td>\n",
        "      <td>         A24</td>\n",
        "      <td> S</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>868</th>\n",
        "      <td> 869</td>\n",
        "      <td> 0</td>\n",
        "      <td> 3</td>\n",
        "      <td>                       van Melkebeke, Mr. Philemon</td>\n",
        "      <td>   male</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>           345777</td>\n",
        "      <td>   9.5000</td>\n",
        "      <td>         NaN</td>\n",
        "      <td> S</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>869</th>\n",
        "      <td> 870</td>\n",
        "      <td> 1</td>\n",
        "      <td> 3</td>\n",
        "      <td>                   Johnson, Master. Harold Theodor</td>\n",
        "      <td>   male</td>\n",
        "      <td>  4</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td>           347742</td>\n",
        "      <td>  11.1333</td>\n",
        "      <td>         NaN</td>\n",
        "      <td> S</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>870</th>\n",
        "      <td> 871</td>\n",
        "      <td> 0</td>\n",
        "      <td> 3</td>\n",
        "      <td>                                 Balkic, Mr. Cerin</td>\n",
        "      <td>   male</td>\n",
        "      <td> 26</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>           349248</td>\n",
        "      <td>   7.8958</td>\n",
        "      <td>         NaN</td>\n",
        "      <td> S</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>871</th>\n",
        "      <td> 872</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td>  Beckwith, Mrs. Richard Leonard (Sallie Monypeny)</td>\n",
        "      <td> female</td>\n",
        "      <td> 47</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td>            11751</td>\n",
        "      <td>  52.5542</td>\n",
        "      <td>         D35</td>\n",
        "      <td> S</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>872</th>\n",
        "      <td> 873</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td>                          Carlsson, Mr. Frans Olof</td>\n",
        "      <td>   male</td>\n",
        "      <td> 33</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>              695</td>\n",
        "      <td>   5.0000</td>\n",
        "      <td> B51 B53 B55</td>\n",
        "      <td> S</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>873</th>\n",
        "      <td> 874</td>\n",
        "      <td> 0</td>\n",
        "      <td> 3</td>\n",
        "      <td>                       Vander Cruyssen, Mr. Victor</td>\n",
        "      <td>   male</td>\n",
        "      <td> 47</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>           345765</td>\n",
        "      <td>   9.0000</td>\n",
        "      <td>         NaN</td>\n",
        "      <td> S</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>874</th>\n",
        "      <td> 875</td>\n",
        "      <td> 1</td>\n",
        "      <td> 2</td>\n",
        "      <td>             Abelson, Mrs. Samuel (Hannah Wizosky)</td>\n",
        "      <td> female</td>\n",
        "      <td> 28</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>        P/PP 3381</td>\n",
        "      <td>  24.0000</td>\n",
        "      <td>         NaN</td>\n",
        "      <td> C</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>875</th>\n",
        "      <td> 876</td>\n",
        "      <td> 1</td>\n",
        "      <td> 3</td>\n",
        "      <td>                  Najib, Miss. Adele Kiamie \"Jane\"</td>\n",
        "      <td> female</td>\n",
        "      <td> 15</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>             2667</td>\n",
        "      <td>   7.2250</td>\n",
        "      <td>         NaN</td>\n",
        "      <td> C</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>876</th>\n",
        "      <td> 877</td>\n",
        "      <td> 0</td>\n",
        "      <td> 3</td>\n",
        "      <td>                     Gustafsson, Mr. Alfred Ossian</td>\n",
        "      <td>   male</td>\n",
        "      <td> 20</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>             7534</td>\n",
        "      <td>   9.8458</td>\n",
        "      <td>         NaN</td>\n",
        "      <td> S</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>877</th>\n",
        "      <td> 878</td>\n",
        "      <td> 0</td>\n",
        "      <td> 3</td>\n",
        "      <td>                              Petroff, Mr. Nedelio</td>\n",
        "      <td>   male</td>\n",
        "      <td> 19</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>           349212</td>\n",
        "      <td>   7.8958</td>\n",
        "      <td>         NaN</td>\n",
        "      <td> S</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>878</th>\n",
        "      <td> 879</td>\n",
        "      <td> 0</td>\n",
        "      <td> 3</td>\n",
        "      <td>                                Laleff, Mr. Kristo</td>\n",
        "      <td>   male</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>           349217</td>\n",
        "      <td>   7.8958</td>\n",
        "      <td>         NaN</td>\n",
        "      <td> S</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>879</th>\n",
        "      <td> 880</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td>     Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)</td>\n",
        "      <td> female</td>\n",
        "      <td> 56</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td>            11767</td>\n",
        "      <td>  83.1583</td>\n",
        "      <td>         C50</td>\n",
        "      <td> C</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>880</th>\n",
        "      <td> 881</td>\n",
        "      <td> 1</td>\n",
        "      <td> 2</td>\n",
        "      <td>      Shelley, Mrs. William (Imanita Parrish Hall)</td>\n",
        "      <td> female</td>\n",
        "      <td> 25</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td>           230433</td>\n",
        "      <td>  26.0000</td>\n",
        "      <td>         NaN</td>\n",
        "      <td> S</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>881</th>\n",
        "      <td> 882</td>\n",
        "      <td> 0</td>\n",
        "      <td> 3</td>\n",
        "      <td>                                Markun, Mr. Johann</td>\n",
        "      <td>   male</td>\n",
        "      <td> 33</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>           349257</td>\n",
        "      <td>   7.8958</td>\n",
        "      <td>         NaN</td>\n",
        "      <td> S</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>882</th>\n",
        "      <td> 883</td>\n",
        "      <td> 0</td>\n",
        "      <td> 3</td>\n",
        "      <td>                      Dahlberg, Miss. Gerda Ulrika</td>\n",
        "      <td> female</td>\n",
        "      <td> 22</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>             7552</td>\n",
        "      <td>  10.5167</td>\n",
        "      <td>         NaN</td>\n",
        "      <td> S</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>883</th>\n",
        "      <td> 884</td>\n",
        "      <td> 0</td>\n",
        "      <td> 2</td>\n",
        "      <td>                     Banfield, Mr. Frederick James</td>\n",
        "      <td>   male</td>\n",
        "      <td> 28</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> C.A./SOTON 34068</td>\n",
        "      <td>  10.5000</td>\n",
        "      <td>         NaN</td>\n",
        "      <td> S</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>884</th>\n",
        "      <td> 885</td>\n",
        "      <td> 0</td>\n",
        "      <td> 3</td>\n",
        "      <td>                            Sutehall, Mr. Henry Jr</td>\n",
        "      <td>   male</td>\n",
        "      <td> 25</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>  SOTON/OQ 392076</td>\n",
        "      <td>   7.0500</td>\n",
        "      <td>         NaN</td>\n",
        "      <td> S</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>885</th>\n",
        "      <td> 886</td>\n",
        "      <td> 0</td>\n",
        "      <td> 3</td>\n",
        "      <td>              Rice, Mrs. William (Margaret Norton)</td>\n",
        "      <td> female</td>\n",
        "      <td> 39</td>\n",
        "      <td> 0</td>\n",
        "      <td> 5</td>\n",
        "      <td>           382652</td>\n",
        "      <td>  29.1250</td>\n",
        "      <td>         NaN</td>\n",
        "      <td> Q</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>886</th>\n",
        "      <td> 887</td>\n",
        "      <td> 0</td>\n",
        "      <td> 2</td>\n",
        "      <td>                             Montvila, Rev. Juozas</td>\n",
        "      <td>   male</td>\n",
        "      <td> 27</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>           211536</td>\n",
        "      <td>  13.0000</td>\n",
        "      <td>         NaN</td>\n",
        "      <td> S</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>887</th>\n",
        "      <td> 888</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td>                      Graham, Miss. Margaret Edith</td>\n",
        "      <td> female</td>\n",
        "      <td> 19</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>           112053</td>\n",
        "      <td>  30.0000</td>\n",
        "      <td>         B42</td>\n",
        "      <td> S</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>888</th>\n",
        "      <td> 889</td>\n",
        "      <td> 0</td>\n",
        "      <td> 3</td>\n",
        "      <td>          Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
        "      <td> female</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 1</td>\n",
        "      <td> 2</td>\n",
        "      <td>       W./C. 6607</td>\n",
        "      <td>  23.4500</td>\n",
        "      <td>         NaN</td>\n",
        "      <td> S</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>889</th>\n",
        "      <td> 890</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td>                             Behr, Mr. Karl Howell</td>\n",
        "      <td>   male</td>\n",
        "      <td> 26</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>           111369</td>\n",
        "      <td>  30.0000</td>\n",
        "      <td>        C148</td>\n",
        "      <td> C</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>890</th>\n",
        "      <td> 891</td>\n",
        "      <td> 0</td>\n",
        "      <td> 3</td>\n",
        "      <td>                               Dooley, Mr. Patrick</td>\n",
        "      <td>   male</td>\n",
        "      <td> 32</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>           370376</td>\n",
        "      <td>   7.7500</td>\n",
        "      <td>         NaN</td>\n",
        "      <td> Q</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>891 rows \u00d7 12 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "     PassengerId  Survived  Pclass  \\\n",
        "0              1         0       3   \n",
        "1              2         1       1   \n",
        "2              3         1       3   \n",
        "3              4         1       1   \n",
        "4              5         0       3   \n",
        "5              6         0       3   \n",
        "6              7         0       1   \n",
        "7              8         0       3   \n",
        "8              9         1       3   \n",
        "9             10         1       2   \n",
        "10            11         1       3   \n",
        "11            12         1       1   \n",
        "12            13         0       3   \n",
        "13            14         0       3   \n",
        "14            15         0       3   \n",
        "15            16         1       2   \n",
        "16            17         0       3   \n",
        "17            18         1       2   \n",
        "18            19         0       3   \n",
        "19            20         1       3   \n",
        "20            21         0       2   \n",
        "21            22         1       2   \n",
        "22            23         1       3   \n",
        "23            24         1       1   \n",
        "24            25         0       3   \n",
        "25            26         1       3   \n",
        "26            27         0       3   \n",
        "27            28         0       1   \n",
        "28            29         1       3   \n",
        "29            30         0       3   \n",
        "..           ...       ...     ...   \n",
        "861          862         0       2   \n",
        "862          863         1       1   \n",
        "863          864         0       3   \n",
        "864          865         0       2   \n",
        "865          866         1       2   \n",
        "866          867         1       2   \n",
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        "868          869         0       3   \n",
        "869          870         1       3   \n",
        "870          871         0       3   \n",
        "871          872         1       1   \n",
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        "873          874         0       3   \n",
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        "875          876         1       3   \n",
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        "879          880         1       1   \n",
        "880          881         1       2   \n",
        "881          882         0       3   \n",
        "882          883         0       3   \n",
        "883          884         0       2   \n",
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        "889          890         1       1   \n",
        "890          891         0       3   \n",
        "\n",
        "                                                  Name     Sex  Age  SibSp  \\\n",
        "0                              Braund, Mr. Owen Harris    male   22      1   \n",
        "1    Cumings, Mrs. John Bradley (Florence Briggs Th...  female   38      1   \n",
        "2                               Heikkinen, Miss. Laina  female   26      0   \n",
        "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female   35      1   \n",
        "4                             Allen, Mr. William Henry    male   35      0   \n",
        "5                                     Moran, Mr. James    male  NaN      0   \n",
        "6                              McCarthy, Mr. Timothy J    male   54      0   \n",
        "7                       Palsson, Master. Gosta Leonard    male    2      3   \n",
        "8    Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)  female   27      0   \n",
        "9                  Nasser, Mrs. Nicholas (Adele Achem)  female   14      1   \n",
        "10                     Sandstrom, Miss. Marguerite Rut  female    4      1   \n",
        "11                            Bonnell, Miss. Elizabeth  female   58      0   \n",
        "12                      Saundercock, Mr. William Henry    male   20      0   \n",
        "13                         Andersson, Mr. Anders Johan    male   39      1   \n",
        "14                Vestrom, Miss. Hulda Amanda Adolfina  female   14      0   \n",
        "15                    Hewlett, Mrs. (Mary D Kingcome)   female   55      0   \n",
        "16                                Rice, Master. Eugene    male    2      4   \n",
        "17                        Williams, Mr. Charles Eugene    male  NaN      0   \n",
        "18   Vander Planke, Mrs. Julius (Emelia Maria Vande...  female   31      1   \n",
        "19                             Masselmani, Mrs. Fatima  female  NaN      0   \n",
        "20                                Fynney, Mr. Joseph J    male   35      0   \n",
        "21                               Beesley, Mr. Lawrence    male   34      0   \n",
        "22                         McGowan, Miss. Anna \"Annie\"  female   15      0   \n",
        "23                        Sloper, Mr. William Thompson    male   28      0   \n",
        "24                       Palsson, Miss. Torborg Danira  female    8      3   \n",
        "25   Asplund, Mrs. Carl Oscar (Selma Augusta Emilia...  female   38      1   \n",
        "26                             Emir, Mr. Farred Chehab    male  NaN      0   \n",
        "27                      Fortune, Mr. Charles Alexander    male   19      3   \n",
        "28                       O'Dwyer, Miss. Ellen \"Nellie\"  female  NaN      0   \n",
        "29                                 Todoroff, Mr. Lalio    male  NaN      0   \n",
        "..                                                 ...     ...  ...    ...   \n",
        "861                        Giles, Mr. Frederick Edward    male   21      1   \n",
        "862  Swift, Mrs. Frederick Joel (Margaret Welles Ba...  female   48      0   \n",
        "863                  Sage, Miss. Dorothy Edith \"Dolly\"  female  NaN      8   \n",
        "864                             Gill, Mr. John William    male   24      0   \n",
        "865                           Bystrom, Mrs. (Karolina)  female   42      0   \n",
        "866                       Duran y More, Miss. Asuncion  female   27      1   \n",
        "867               Roebling, Mr. Washington Augustus II    male   31      0   \n",
        "868                        van Melkebeke, Mr. Philemon    male  NaN      0   \n",
        "869                    Johnson, Master. Harold Theodor    male    4      1   \n",
        "870                                  Balkic, Mr. Cerin    male   26      0   \n",
        "871   Beckwith, Mrs. Richard Leonard (Sallie Monypeny)  female   47      1   \n",
        "872                           Carlsson, Mr. Frans Olof    male   33      0   \n",
        "873                        Vander Cruyssen, Mr. Victor    male   47      0   \n",
        "874              Abelson, Mrs. Samuel (Hannah Wizosky)  female   28      1   \n",
        "875                   Najib, Miss. Adele Kiamie \"Jane\"  female   15      0   \n",
        "876                      Gustafsson, Mr. Alfred Ossian    male   20      0   \n",
        "877                               Petroff, Mr. Nedelio    male   19      0   \n",
        "878                                 Laleff, Mr. Kristo    male  NaN      0   \n",
        "879      Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)  female   56      0   \n",
        "880       Shelley, Mrs. William (Imanita Parrish Hall)  female   25      0   \n",
        "881                                 Markun, Mr. Johann    male   33      0   \n",
        "882                       Dahlberg, Miss. Gerda Ulrika  female   22      0   \n",
        "883                      Banfield, Mr. Frederick James    male   28      0   \n",
        "884                             Sutehall, Mr. Henry Jr    male   25      0   \n",
        "885               Rice, Mrs. William (Margaret Norton)  female   39      0   \n",
        "886                              Montvila, Rev. Juozas    male   27      0   \n",
        "887                       Graham, Miss. Margaret Edith  female   19      0   \n",
        "888           Johnston, Miss. Catherine Helen \"Carrie\"  female  NaN      1   \n",
        "889                              Behr, Mr. Karl Howell    male   26      0   \n",
        "890                                Dooley, Mr. Patrick    male   32      0   \n",
        "\n",
        "     Parch            Ticket      Fare        Cabin Embarked  \n",
        "0        0         A/5 21171    7.2500          NaN        S  \n",
        "1        0          PC 17599   71.2833          C85        C  \n",
        "2        0  STON/O2. 3101282    7.9250          NaN        S  \n",
        "3        0            113803   53.1000         C123        S  \n",
        "4        0            373450    8.0500          NaN        S  \n",
        "5        0            330877    8.4583          NaN        Q  \n",
        "6        0             17463   51.8625          E46        S  \n",
        "7        1            349909   21.0750          NaN        S  \n",
        "8        2            347742   11.1333          NaN        S  \n",
        "9        0            237736   30.0708          NaN        C  \n",
        "10       1           PP 9549   16.7000           G6        S  \n",
        "11       0            113783   26.5500         C103        S  \n",
        "12       0         A/5. 2151    8.0500          NaN        S  \n",
        "13       5            347082   31.2750          NaN        S  \n",
        "14       0            350406    7.8542          NaN        S  \n",
        "15       0            248706   16.0000          NaN        S  \n",
        "16       1            382652   29.1250          NaN        Q  \n",
        "17       0            244373   13.0000          NaN        S  \n",
        "18       0            345763   18.0000          NaN        S  \n",
        "19       0              2649    7.2250          NaN        C  \n",
        "20       0            239865   26.0000          NaN        S  \n",
        "21       0            248698   13.0000          D56        S  \n",
        "22       0            330923    8.0292          NaN        Q  \n",
        "23       0            113788   35.5000           A6        S  \n",
        "24       1            349909   21.0750          NaN        S  \n",
        "25       5            347077   31.3875          NaN        S  \n",
        "26       0              2631    7.2250          NaN        C  \n",
        "27       2             19950  263.0000  C23 C25 C27        S  \n",
        "28       0            330959    7.8792          NaN        Q  \n",
        "29       0            349216    7.8958          NaN        S  \n",
        "..     ...               ...       ...          ...      ...  \n",
        "861      0             28134   11.5000          NaN        S  \n",
        "862      0             17466   25.9292          D17        S  \n",
        "863      2          CA. 2343   69.5500          NaN        S  \n",
        "864      0            233866   13.0000          NaN        S  \n",
        "865      0            236852   13.0000          NaN        S  \n",
        "866      0     SC/PARIS 2149   13.8583          NaN        C  \n",
        "867      0          PC 17590   50.4958          A24        S  \n",
        "868      0            345777    9.5000          NaN        S  \n",
        "869      1            347742   11.1333          NaN        S  \n",
        "870      0            349248    7.8958          NaN        S  \n",
        "871      1             11751   52.5542          D35        S  \n",
        "872      0               695    5.0000  B51 B53 B55        S  \n",
        "873      0            345765    9.0000          NaN        S  \n",
        "874      0         P/PP 3381   24.0000          NaN        C  \n",
        "875      0              2667    7.2250          NaN        C  \n",
        "876      0              7534    9.8458          NaN        S  \n",
        "877      0            349212    7.8958          NaN        S  \n",
        "878      0            349217    7.8958          NaN        S  \n",
        "879      1             11767   83.1583          C50        C  \n",
        "880      1            230433   26.0000          NaN        S  \n",
        "881      0            349257    7.8958          NaN        S  \n",
        "882      0              7552   10.5167          NaN        S  \n",
        "883      0  C.A./SOTON 34068   10.5000          NaN        S  \n",
        "884      0   SOTON/OQ 392076    7.0500          NaN        S  \n",
        "885      5            382652   29.1250          NaN        Q  \n",
        "886      0            211536   13.0000          NaN        S  \n",
        "887      0            112053   30.0000          B42        S  \n",
        "888      2        W./C. 6607   23.4500          NaN        S  \n",
        "889      0            111369   30.0000         C148        C  \n",
        "890      0            370376    7.7500          NaN        Q  \n",
        "\n",
        "[891 rows x 12 columns]"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.describe()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\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>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "       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  "
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Reading the info of the DataFrame, it is evident that the Cabin feature has a lot of null values. So, it will not be useful for our prediction. It is better to drop it down."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The Age feature has a great number of null values, but we suppose that the age of a passenger can be very relevant for his destiny in this disaster. 177 passengers have null Age values. To take advantages of a full set for this features, it will be useful to make imputation for the missing values.\n"
     ]
    },
    {
     "cell_type": "heading",
     "level": 5,
     "metadata": {},
     "source": [
      "Now let\u2019s have a deeper look at the data, trying to understand which could be the most important features for our model and let's take a quick look at our data graphically:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = plt.figure(figsize=(20,6), dpi=1600 )\n",
      "a=0.8\n",
      "ax1 = fig.add_subplot(3,2,1) \n",
      "female = df.Survived[df.Sex == 'female'][df['Age'].isnull()].value_counts()\n",
      "female.plot(kind='bar', label='female, age is null',color='red', alpha=a)\n",
      "ax1.set_xlim(-1, len(female))\n",
      "plt.legend(loc='best')\n",
      "ax2 = fig.add_subplot(3,2,2) \n",
      "male= df.Survived[df.Sex == 'male'][df['Age'].isnull()].value_counts()\n",
      "male.plot(kind='bar', label='male,age is null', alpha=a, color='green')\n",
      "ax2.set_xlim(-1, len(male))\n",
      "plt.legend(loc='best')\n",
      " \n",
      "ax3 = fig.add_subplot(3,2,3) \n",
      "df.Age[df.Sex == 'female'][df.Survived==1] .dropna().hist(bins=16, range=(0,80), alpha = .5)\n",
      "ax3.set_title('female age dist,survived')\n",
      " \n",
      "ax4 = fig.add_subplot(3,2,4)\n",
      "df.Age[df.Sex == 'male'][df.Survived==1] .dropna().hist(bins=16, range=(0,80), alpha = .5)\n",
      "ax4.set_title('male age dist,survived') \n",
      " \n",
      "subplots_adjust(bottom=0.1, right=0.8, top=2)\n",
      "ax5 = fig.add_subplot(3,2,5) \n",
      "df.Age[df.Sex == 'female'][df.Survived==0] .dropna().hist(bins=16, range=(0,80), alpha = .5)\n",
      "ax5.set_title('female age dist,died')\n",
      " \n",
      "ax6 = fig.add_subplot(3,2,6) \n",
      "df.Age[df.Sex == 'male'][df.Survived==0] .dropna().hist(bins=16, range=(0,80), alpha = .5)\n",
      "ax6.set_title('male age dist,died')\n",
      "plt.show()\n",
      " \n",
      " "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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AAAAAo4/kHaXAc96RleobjwEA6gPPeUdWiCOQJ5J3AAAAAAAKjuQdpUDNO7JC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       "text": [
        "<matplotlib.figure.Figure at 0x65bfc50>"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = plt.figure(figsize=(18,12), dpi=1600)\n",
      "a=0.8\n",
      " \n",
      "##gender and class\n",
      "ax3 = fig.add_subplot(545)\n",
      "female_highclass = df.Survived[df.Sex == 'female'][df.Pclass != 3].value_counts()\n",
      "female_highclass.plot(kind='bar', label='female highclass', color='pink', alpha=a)\n",
      " \n",
      "ax3.set_xlim(-1, len(female_highclass))\n",
      "plt.legend(loc='best')\n",
      "ax4 = fig.add_subplot(546)\n",
      "female_lowclass = df.Survived[df.Sex == 'female'][df.Pclass == 3].value_counts()\n",
      "female_lowclass.plot(kind='bar', label='female, low class', color='pink', alpha=a)\n",
      "ax4.set_xlim(-1, len(female_lowclass))\n",
      "plt.legend(loc='best')\n",
      "\n",
      "ax5 = fig.add_subplot(5,4,7) \n",
      "male_lowclass = df.Survived[df.Sex == 'male'][df.Pclass == 3].value_counts()\n",
      "male_lowclass.plot(kind='bar', label='male, low class',color='lightblue', alpha=a)\n",
      "ax5.set_xlim(-1, len(male_lowclass))\n",
      "plt.legend(loc='best')\n",
      "ax6 = fig.add_subplot(5,4,8) \n",
      "male_highclass = df.Survived[df.Sex == 'male'][df.Pclass != 3].value_counts()\n",
      "male_highclass.plot(kind='bar', label='male highclass', alpha=a, color='lightblue')\n",
      " \n",
      "ax6.set_xlim(-1, len(male_highclass))\n",
      "plt.legend(loc='best')\n",
      " \n",
      "##gender and age\n",
      "#female\n",
      "ax7 = fig.add_subplot(5,4,9)\n",
      "female_aged = df.Survived[df.Sex == 'female'][df.Age >= 60].value_counts()\n",
      "female_aged.plot(kind='bar', label='female, aged', color='pink', alpha=a)\n",
      " \n",
      "ax7.set_xlim(-1, len(female_aged))\n",
      "plt.legend(loc='best')\n",
      "ax8 = fig.add_subplot(5,4,10)\n",
      "female_child = df.Survived[df.Sex == 'female'][df.Age <= 10].value_counts()\n",
      "female_child.plot(kind='bar', label='female, children', color='pink', alpha=a)\n",
      " \n",
      "ax8.set_xlim(-1, len(female_child))\n",
      "plt.legend(loc='best')\n",
      "ax9 = fig.add_subplot(5,4,11)\n",
      "female_middleage = df.Survived[df.Sex == 'female'][df.Age>10][df.Age<=30].value_counts()\n",
      "female_middleage.plot(kind='bar', label='female, middle age(10-30)', color='pink', alpha=a)\n",
      " \n",
      "ax9.set_xlim(-1, len(female_middleage))\n",
      "plt.legend(loc='best')\n",
      "ax9 = fig.add_subplot(5,4,12)\n",
      "female_middleage = df.Survived[df.Sex == 'female'][df.Age>30][df.Age<60].value_counts()\n",
      "female_middleage.plot(kind='bar', label='female, middle age (30-60)', color='pink', alpha=a)\n",
      " \n",
      "ax9.set_xlim(-1, len(female_middleage))\n",
      "plt.legend(loc='best')\n",
      " \n",
      "#male\n",
      " \n",
      "ax10 = fig.add_subplot(5,4,13)\n",
      "male_aged = df.Survived[df.Sex == 'male'][df.Age >= 60].value_counts()\n",
      "male_aged.plot(kind='bar', label='male, aged', color='blue', alpha=a)\n",
      " \n",
      "ax10.set_xlim(-1, len(male_aged))\n",
      "plt.legend(loc='best')\n",
      "ax11 = fig.add_subplot(5,4,14)\n",
      "male_child = df.Survived[df.Sex == 'male'][df.Age <= 10].value_counts()\n",
      "male_child.plot(kind='bar', label='male, children', color='blue', alpha=a)\n",
      " \n",
      "ax11.set_xlim(-1, len(male_child))\n",
      "plt.legend(loc='best')\n",
      "ax12 = fig.add_subplot(5,4,15)\n",
      "male_middleage = df.Survived[df.Sex == 'male'][df.Age>10][df.Age<=30].value_counts()\n",
      "male_middleage.plot(kind='bar', label='male, middle age (10-30)', color='blue', alpha=a)\n",
      " \n",
      "ax12.set_xlim(-1, len(male_middleage))\n",
      "plt.legend(loc='best') \n",
      "ax12 = fig.add_subplot(5,4,16)\n",
      "male_middleage = df.Survived[df.Sex == 'male'][df.Age>30][df.Age<60].value_counts()\n",
      "male_middleage.plot(kind='bar', label='male, middle age (30-60)', color='blue', alpha=a)\n",
      " \n",
      "ax12.set_xlim(-1, len(male_middleage))\n",
      "plt.legend(loc='best') \n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "<matplotlib.legend.Legend at 0x571d790>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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9gUOBS81sH6Idhr+Lz4/i7v+EbWZc7wfcbWaZ3NSQAmNm/P3vf2fy5MnMmzeP5557jhNO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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x48b6090>"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Very interesting!!! \n",
      "If you are female and aged you have Provability=1 to survive. If you are highclass, male and female, you have a high probability to survive. If you are children you have more or less the same surviving probability, gender indipendent."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = plt.figure(figsize=(18,6), dpi=1600)\n",
      "ax1 = fig.add_subplot(121)\n",
      "df.Survived[df.Pclass == 3].value_counts().plot(kind='bar',label='LowClass',color='red')\n",
      "ax1.set_xlim(-1, 2)\n",
      "plt.legend(loc='best')\n",
      "ax2 = fig.add_subplot(122) \n",
      "df.Survived[df.Pclass != 3].value_counts().plot(kind='bar',label='HighClass',color='green')\n",
      "ax2.set_xlim(-1, 2)\n",
      "plt.legend(loc='best')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "<matplotlib.legend.Legend at 0x5eb5790>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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0ug94GLhwF+fa0FmSpFmayl0YpHmljasQVC9v4yg1Q0pp28jjiPg0cGVn+CCw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\nkir9/4xhVGkkwJlRAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x403e810>"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = plt.figure(figsize=(18,6), dpi=1600)\n",
      "ax1 = fig.add_subplot(221)\n",
      "df.Survived[df.Fare <=300].value_counts().plot(kind='bar',label='Fare <=1',color='red')\n",
      "ax1.set_xlim(-1, 2)\n",
      "plt.legend(loc='best')\n",
      "ax2 = fig.add_subplot(222) \n",
      "df.Survived[df.Fare >300].value_counts().plot(kind='bar',label='Fare>300',color='green')\n",
      "ax2.set_xlim(-1, 2)\n",
      "plt.legend(loc='best')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "<matplotlib.legend.Legend at 0x602b110>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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U97cAM4v7JwKb+o3bBMw6wNeQDkij7ARUe42yE5Amt1nAM/3iweYaCZwTEWsj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\nLf1/HboGn1yq6GMAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x4aa1450>"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "heading",
     "level": 5,
     "metadata": {},
     "source": [
      "So, as we could have imagined, being women, high-class is a good passport for surviving"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for i in range(1,4):\n",
      "    print('Male:'), i, len(df[ (df['Sex'] == 'male') & (df['Pclass'] == i) ])\n",
      "    print('Female:'), i, len(df[ (df['Sex'] == 'female') & (df['Pclass'] == i) ])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "heading",
     "level": 5,
     "metadata": {},
     "source": [
      "Male, low class-->high dead probability"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Data cleaning:"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now we could clean, impute, encode the data to have a better model for our prediction. What it is important is the imputation of age. We realize now that if you are aged and female, you will survive. If you are high class and aged are more probabible to survive. We now are going to translate these conclusion in the model"
     ]
    },
    {
     "cell_type": "heading",
     "level": 6,
     "metadata": {},
     "source": [
      "After reading the Best Practices https://www.kaggle.com/wiki/ModelSubmissionBestPractices , I reorganized the code following those suggestions."
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "This is my Training code:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Titatic competitor usign pandas and scikit library\n",
      "import numpy as np\n",
      "import pandas as pd\n",
      "from pandas import  DataFrame\n",
      "from patsy import dmatrices\n",
      "import string\n",
      "from operator import itemgetter\n",
      "#json library for settings file\n",
      "import json\n",
      "# import the machine learning library that holds the randomforest\n",
      "from sklearn.ensemble import RandomForestClassifier\n",
      "from sklearn.cross_validation import cross_val_score\n",
      "from sklearn.pipeline import Pipeline\n",
      "from sklearn.grid_search import GridSearchCV\n",
      "from sklearn.cross_validation import train_test_split,StratifiedShuffleSplit,StratifiedKFold\n",
      "from sklearn import preprocessing\n",
      "from sklearn.metrics import classification_report\n",
      "#joblib library for serialization\n",
      "from sklearn.externals import joblib\n",
      "\n",
      "##Read configuration parameters\n",
      "\n",
      "file_dir = './titanic-SETTINGS.json'\n",
      "\n",
      "config = json.loads(open(file_dir).read())\n",
      "train_file=config[\"HOME\"]+config[\"TRAIN_DATA_PATH\"]+'train.csv'\n",
      "MODEL_PATH=config[\"HOME\"]+config[\"MODEL_PATH\"]\n",
      "test_file=config[\"HOME\"]+config[\"TEST_DATA_PATH\"]+'test.csv'\n",
      "SUBMISSION_PATH=config[\"HOME\"]+config[\"SUBMISSION_PATH\"]\n",
      "seed= int(config[\"SEED\"])\n",
      "\n",
      "print train_file,seed\n",
      "\n",
      "# Utility function to report best scores\n",
      "def report(grid_scores, n_top=3):\n",
      "    top_scores = sorted(grid_scores, key=itemgetter(1), reverse=True)[:n_top]\n",
      "    for i, score in enumerate(top_scores):\n",
      "        print(\"Model with rank: {0}\".format(i + 1))\n",
      "        print(\"Mean validation score: {0:.3f} (std: {1:.3f})\".format(\n",
      "              score.mean_validation_score,\n",
      "              np.std(score.cv_validation_scores)))\n",
      "        print(\"Parameters: {0}\".format(score.parameters))\n",
      "        print(\"\")\n",
      "\n",
      "###utility to clean and munge data\n",
      "def substrings_in_string(big_string, substrings):\n",
      "    for substring in substrings:\n",
      "        if string.find(big_string, substring) != -1:\n",
      "            return substring\n",
      "    print big_string\n",
      "    return np.nan\n",
      "\n",
      "le = preprocessing.LabelEncoder()\n",
      "enc=preprocessing.OneHotEncoder()\n",
      "\n",
      "def clean_and_munge_data(df):\n",
      "    #setting silly values to nan\n",
      "    df.Fare = df.Fare.map(lambda x: np.nan if x==0 else x)\n",
      "    #creating a title column from name\n",
      "    title_list=['Mrs', 'Mr', 'Master', 'Miss', 'Major', 'Rev',\n",
      "                'Dr', 'Ms', 'Mlle','Col', 'Capt', 'Mme', 'Countess',\n",
      "                'Don', 'Jonkheer']\n",
      "    df['Title']=df['Name'].map(lambda x: substrings_in_string(x, title_list))\n",
      "\n",
      "    #replacing all titles with mr, mrs, miss, master\n",
      "    def replace_titles(x):\n",
      "        title=x['Title']\n",
      "        if title in ['Mr','Don', 'Major', 'Capt', 'Jonkheer', 'Rev', 'Col']:\n",
      "            return 'Mr'\n",
      "        elif title in ['Master']:\n",
      "            return 'Master'\n",
      "        elif title in ['Countess', 'Mme','Mrs']:\n",
      "            return 'Mrs'\n",
      "        elif title in ['Mlle', 'Ms','Miss']:\n",
      "            return 'Miss'\n",
      "        elif title =='Dr':\n",
      "            if x['Sex']=='Male':\n",
      "                return 'Mr'\n",
      "            else:\n",
      "                return 'Mrs'\n",
      "        elif title =='':\n",
      "            if x['Sex']=='Male':\n",
      "                return 'Master'\n",
      "            else:\n",
      "                return 'Miss'\n",
      "        else:\n",
      "            return title\n",
      "\n",
      "    df['Title']=df.apply(replace_titles, axis=1)\n",
      "\n",
      "    #Creating new family_size column\n",
      "    df['Family_Size']=df['SibSp']+df['Parch']\n",
      "    df['Family']=df['SibSp']*df['Parch']\n",
      "\n",
      "\n",
      "    #imputing nan values\n",
      "    df.loc[ (df.Fare.isnull())&(df.Pclass==1),'Fare'] =np.median(df[df['Pclass'] == 1]['Fare'].dropna())\n",
      "    df.loc[ (df.Fare.isnull())&(df.Pclass==2),'Fare'] =np.median( df[df['Pclass'] == 2]['Fare'].dropna())\n",
      "    df.loc[ (df.Fare.isnull())&(df.Pclass==3),'Fare'] = np.median(df[df['Pclass'] == 3]['Fare'].dropna())\n",
      "\n",
      "    df['Gender'] = df['Sex'].map( {'female': 0, 'male': 1} ).astype(int)\n",
      "\n",
      "    df['AgeFill']=df['Age']\n",
      "    mean_ages = np.zeros(4)\n",
      "    mean_ages[0]=np.average(df[df['Title'] == 'Miss']['Age'].dropna())\n",
      "    mean_ages[1]=np.average(df[df['Title'] == 'Mrs']['Age'].dropna())\n",
      "    mean_ages[2]=np.average(df[df['Title'] == 'Mr']['Age'].dropna())\n",
      "    mean_ages[3]=np.average(df[df['Title'] == 'Master']['Age'].dropna())\n",
      "    df.loc[ (df.Age.isnull()) & (df.Title == 'Miss') ,'AgeFill'] = mean_ages[0]\n",
      "    df.loc[ (df.Age.isnull()) & (df.Title == 'Mrs') ,'AgeFill'] = mean_ages[1]\n",
      "    df.loc[ (df.Age.isnull()) & (df.Title == 'Mr') ,'AgeFill'] = mean_ages[2]\n",
      "    df.loc[ (df.Age.isnull()) & (df.Title == 'Master') ,'AgeFill'] = mean_ages[3]\n",
      "\n",
      "    df['AgeCat']=df['AgeFill']\n",
      "    df.loc[ (df.AgeFill<=10) ,'AgeCat'] = 'child'\n",
      "    df.loc[ (df.AgeFill>60),'AgeCat'] = 'aged'\n",
      "    df.loc[ (df.AgeFill>10) & (df.AgeFill <=30) ,'AgeCat'] = 'adult'\n",
      "    df.loc[ (df.AgeFill>30) & (df.AgeFill <=60) ,'AgeCat'] = 'senior'\n",
      "\n",
      "    df.Embarked = df.Embarked.fillna('S')\n",
      "\n",
      "\n",
      "    #Special case for cabins as nan may be signal\n",
      "    df.loc[ df.Cabin.isnull()==True,'Cabin'] = 0.5\n",
      "    df.loc[ df.Cabin.isnull()==False,'Cabin'] = 1.5\n",
      "   #Fare per person\n",
      "\n",
      "    df['Fare_Per_Person']=df['Fare']/(df['Family_Size']+1)\n",
      "\n",
      "    #Age times class\n",
      "\n",
      "    df['AgeClass']=df['AgeFill']*df['Pclass']\n",
      "    df['ClassFare']=df['Pclass']*df['Fare_Per_Person']\n",
      "\n",
      "\n",
      "    df['HighLow']=df['Pclass']\n",
      "    df.loc[ (df.Fare_Per_Person<8) ,'HighLow'] = 'Low'\n",
      "    df.loc[ (df.Fare_Per_Person>=8) ,'HighLow'] = 'High'\n",
      "\n",
      "\n",
      "\n",
      "    le.fit(df['Sex'] )\n",
      "    x_sex=le.transform(df['Sex'])\n",
      "    df['Sex']=x_sex.astype(np.float)\n",
      "\n",
      "    le.fit( df['Ticket'])\n",
      "    x_Ticket=le.transform( df['Ticket'])\n",
      "    df['Ticket']=x_Ticket.astype(np.float)\n",
      "\n",
      "    le.fit(df['Title'])\n",
      "    x_title=le.transform(df['Title'])\n",
      "    df['Title'] =x_title.astype(np.float)\n",
      "\n",
      "    le.fit(df['HighLow'])\n",
      "    x_hl=le.transform(df['HighLow'])\n",
      "    df['HighLow']=x_hl.astype(np.float)\n",
      "\n",
      "\n",
      "    le.fit(df['AgeCat'])\n",
      "    x_age=le.transform(df['AgeCat'])\n",
      "    df['AgeCat'] =x_age.astype(np.float)\n",
      "\n",
      "    le.fit(df['Embarked'])\n",
      "    x_emb=le.transform(df['Embarked'])\n",
      "    df['Embarked']=x_emb.astype(np.float)\n",
      "\n",
      "    df = df.drop(['PassengerId','Name','Age','Cabin'], axis=1) #remove Name,Age and PassengerId\n",
      "\n",
      "\n",
      "    return df\n",
      "\n",
      "#read data\n",
      "traindf=pd.read_csv(train_file)\n",
      "##clean data\n",
      "df=clean_and_munge_data(traindf)\n",
      "########################################formula################################\n",
      " \n",
      "formula_ml='Survived~Pclass+C(Title)+Sex+C(AgeCat)+Fare_Per_Person+Fare+Family_Size' \n",
      "\n",
      "y_train, x_train = dmatrices(formula_ml, data=df, return_type='dataframe')\n",
      "y_train = np.asarray(y_train).ravel()\n",
      "print y_train.shape,x_train.shape\n",
      "\n",
      "##select a train and test set\n",
      "X_train, X_test, Y_train, Y_test = train_test_split(x_train, y_train, test_size=0.2,random_state=seed)\n",
      "#instantiate and fit our model\n",
      "\n",
      "clf=RandomForestClassifier(n_estimators=500, criterion='entropy', max_depth=5, min_samples_split=1,\n",
      "  min_samples_leaf=1, max_features='auto',    bootstrap=False, oob_score=False, n_jobs=1, random_state=seed,\n",
      "  verbose=0, min_density=None, compute_importances=None)\n",
      "\n",
      "###compute grid search to find best paramters for pipeline\n",
      "param_grid = dict( )\n",
      "##classify pipeline\n",
      "pipeline=Pipeline([ ('clf',clf) ])\n",
      "grid_search = GridSearchCV(pipeline, param_grid=param_grid, verbose=3,scoring='accuracy',\\\n",
      "cv=StratifiedShuffleSplit(Y_train, n_iter=10, test_size=0.2, train_size=None, indices=None, \\\n",
      "random_state=seed, n_iterations=None)).fit(X_train, Y_train)\n",
      "# Score the results\n",
      "###print result\n",
      "print(\"Best score: %0.3f\" % grid_search.best_score_)\n",
      "print(grid_search.best_estimator_)\n",
      "report(grid_search.grid_scores_)\n",
      " \n",
      "print('-----grid search end------------')\n",
      "print ('on all train set')\n",
      "scores = cross_val_score(grid_search.best_estimator_, x_train, y_train,cv=3,scoring='accuracy')\n",
      "print scores.mean(),scores\n",
      "print ('on test set')\n",
      "scores = cross_val_score(grid_search.best_estimator_, X_test, Y_test,cv=3,scoring='accuracy')\n",
      "print scores.mean(),scores\n",
      "\n",
      "# Score the results\n",
      "\n",
      "print(classification_report(Y_train, grid_search.best_estimator_.predict(X_train) ))\n",
      "print('test data')\n",
      "print(classification_report(Y_test, grid_search.best_estimator_.predict(X_test) ))\n",
      "\n",
      "#serialize training\n",
      "model_file=MODEL_PATH+'model-rf.pkl'\n",
      "joblib.dump(grid_search.best_estimator_, model_file)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "This is my Prediction code:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Titatic competitor usign pandas and scikit library\n",
      "import numpy as np\n",
      "import pandas as pd\n",
      "from pandas import  DataFrame\n",
      "from patsy import dmatrices\n",
      "import string\n",
      "from operator import itemgetter\n",
      "#json library for settings file\n",
      "import json\n",
      "# import the machine learning library that holds the randomforest\n",
      "from sklearn.ensemble import RandomForestClassifier\n",
      "from sklearn.cross_validation import cross_val_score\n",
      "from sklearn.pipeline import Pipeline\n",
      "from sklearn.grid_search import GridSearchCV\n",
      "from sklearn.cross_validation import train_test_split,StratifiedShuffleSplit,StratifiedKFold\n",
      "from sklearn import preprocessing\n",
      "#joblib library for serialization\n",
      "from sklearn.externals import joblib\n",
      "\n",
      "#Read data and configuration parameters#\n",
      "\n",
      "file_dir = './titanic-SETTINGS.json'\n",
      "\n",
      "config = json.loads(open(file_dir).read())\n",
      "train_file=config[\"HOME\"]+config[\"TRAIN_DATA_PATH\"]+'train.csv'\n",
      "MODEL_PATH=config[\"HOME\"]+config[\"MODEL_PATH\"]\n",
      "test_file=config[\"HOME\"]+config[\"TEST_DATA_PATH\"]+'test.csv'\n",
      "SUBMISSION_PATH=config[\"HOME\"]+config[\"SUBMISSION_PATH\"]\n",
      "seed= int(config[\"SEED\"])\n",
      "\n",
      "\n",
      "print test_file, seed\n",
      "# Utility function to report best scores\n",
      "def report(grid_scores, n_top=3):\n",
      "    top_scores = sorted(grid_scores, key=itemgetter(1), reverse=True)[:n_top]\n",
      "    for i, score in enumerate(top_scores):\n",
      "        print(\"Model with rank: {0}\".format(i + 1))\n",
      "        print(\"Mean validation score: {0:.3f} (std: {1:.3f})\".format(\n",
      "              score.mean_validation_score,\n",
      "              np.std(score.cv_validation_scores)))\n",
      "        print(\"Parameters: {0}\".format(score.parameters))\n",
      "        print(\"\")\n",
      "\n",
      "###utility to clean and munge data\n",
      "def substrings_in_string(big_string, substrings):\n",
      "    for substring in substrings:\n",
      "        if string.find(big_string, substring) != -1:\n",
      "            return substring\n",
      "    print big_string\n",
      "    return np.nan\n",
      "\n",
      "le = preprocessing.LabelEncoder()\n",
      "enc=preprocessing.OneHotEncoder()\n",
      "\n",
      "def clean_and_munge_data(df):\n",
      "    #setting silly values to nan\n",
      "    df.Fare = df.Fare.map(lambda x: np.nan if x==0 else x)\n",
      "    #creating a title column from name\n",
      "    title_list=['Mrs', 'Mr', 'Master', 'Miss', 'Major', 'Rev',\n",
      "                'Dr', 'Ms', 'Mlle','Col', 'Capt', 'Mme', 'Countess',\n",
      "                'Don', 'Jonkheer']\n",
      "    df['Title']=df['Name'].map(lambda x: substrings_in_string(x, title_list))\n",
      "\n",
      "    #replacing all titles with mr, mrs, miss, master\n",
      "    def replace_titles(x):\n",
      "        title=x['Title']\n",
      "        if title in ['Mr','Don', 'Major', 'Capt', 'Jonkheer', 'Rev', 'Col']:\n",
      "            return 'Mr'\n",
      "        elif title in ['Master']:\n",
      "            return 'Master'\n",
      "        elif title in ['Countess', 'Mme','Mrs']:\n",
      "            return 'Mrs'\n",
      "        elif title in ['Mlle', 'Ms','Miss']:\n",
      "            return 'Miss'\n",
      "        elif title =='Dr':\n",
      "            if x['Sex']=='Male':\n",
      "                return 'Mr'\n",
      "            else:\n",
      "                return 'Mrs'\n",
      "        elif title =='':\n",
      "            if x['Sex']=='Male':\n",
      "                return 'Master'\n",
      "            else:\n",
      "                return 'Miss'\n",
      "        else:\n",
      "            return title\n",
      "\n",
      "    df['Title']=df.apply(replace_titles, axis=1)\n",
      "\n",
      "    #Creating new family_size column\n",
      "    df['Family_Size']=df['SibSp']+df['Parch']\n",
      "    df['Family']=df['SibSp']*df['Parch']\n",
      "\n",
      "\n",
      "    #imputing nan values\n",
      "    df.loc[ (df.Fare.isnull())&(df.Pclass==1),'Fare'] =np.median(df[df['Pclass'] == 1]['Fare'].dropna())\n",
      "    df.loc[ (df.Fare.isnull())&(df.Pclass==2),'Fare'] =np.median( df[df['Pclass'] == 2]['Fare'].dropna())\n",
      "    df.loc[ (df.Fare.isnull())&(df.Pclass==3),'Fare'] = np.median(df[df['Pclass'] == 3]['Fare'].dropna())\n",
      "\n",
      "    df['Gender'] = df['Sex'].map( {'female': 0, 'male': 1} ).astype(int)\n",
      "\n",
      "    df['AgeFill']=df['Age']\n",
      "    mean_ages = np.zeros(4)\n",
      "    mean_ages[0]=np.average(df[df['Title'] == 'Miss']['Age'].dropna())\n",
      "    mean_ages[1]=np.average(df[df['Title'] == 'Mrs']['Age'].dropna())\n",
      "    mean_ages[2]=np.average(df[df['Title'] == 'Mr']['Age'].dropna())\n",
      "    mean_ages[3]=np.average(df[df['Title'] == 'Master']['Age'].dropna())\n",
      "    df.loc[ (df.Age.isnull()) & (df.Title == 'Miss') ,'AgeFill'] = mean_ages[0]\n",
      "    df.loc[ (df.Age.isnull()) & (df.Title == 'Mrs') ,'AgeFill'] = mean_ages[1]\n",
      "    df.loc[ (df.Age.isnull()) & (df.Title == 'Mr') ,'AgeFill'] = mean_ages[2]\n",
      "    df.loc[ (df.Age.isnull()) & (df.Title == 'Master') ,'AgeFill'] = mean_ages[3]\n",
      "\n",
      "    df['AgeCat']=df['AgeFill']\n",
      "    df.loc[ (df.AgeFill<=10) ,'AgeCat'] = 'child'\n",
      "    df.loc[ (df.AgeFill>60),'AgeCat'] = 'aged'\n",
      "    df.loc[ (df.AgeFill>10) & (df.AgeFill <=30) ,'AgeCat'] = 'adult'\n",
      "    df.loc[ (df.AgeFill>30) & (df.AgeFill <=60) ,'AgeCat'] = 'senior'\n",
      "\n",
      "    df.Embarked = df.Embarked.fillna('S')\n",
      "\n",
      "\n",
      "    #Special case for cabins as nan may be signal\n",
      "    df.loc[ df.Cabin.isnull()==True,'Cabin'] = 0.5\n",
      "    df.loc[ df.Cabin.isnull()==False,'Cabin'] = 1.5\n",
      "   #Fare per person\n",
      "\n",
      "    df['Fare_Per_Person']=df['Fare']/(df['Family_Size']+1)\n",
      "\n",
      "    #Age times class\n",
      "\n",
      "    df['AgeClass']=df['AgeFill']*df['Pclass']\n",
      "    df['ClassFare']=df['Pclass']*df['Fare_Per_Person']\n",
      "\n",
      "\n",
      "    df['HighLow']=df['Pclass']\n",
      "    df.loc[ (df.Fare_Per_Person<8) ,'HighLow'] = 'Low'\n",
      "    df.loc[ (df.Fare_Per_Person>=8) ,'HighLow'] = 'High'\n",
      "\n",
      "\n",
      "\n",
      "    le.fit(df['Sex'] )\n",
      "    x_sex=le.transform(df['Sex'])\n",
      "    df['Sex']=x_sex.astype(np.float)\n",
      "\n",
      "    le.fit( df['Ticket'])\n",
      "    x_Ticket=le.transform( df['Ticket'])\n",
      "    df['Ticket']=x_Ticket.astype(np.float)\n",
      "\n",
      "    le.fit(df['Title'])\n",
      "    x_title=le.transform(df['Title'])\n",
      "    df['Title'] =x_title.astype(np.float)\n",
      "\n",
      "    le.fit(df['HighLow'])\n",
      "    x_hl=le.transform(df['HighLow'])\n",
      "    df['HighLow']=x_hl.astype(np.float)\n",
      "\n",
      "\n",
      "    le.fit(df['AgeCat'])\n",
      "    x_age=le.transform(df['AgeCat'])\n",
      "    df['AgeCat'] =x_age.astype(np.float)\n",
      "\n",
      "    le.fit(df['Embarked'])\n",
      "    x_emb=le.transform(df['Embarked'])\n",
      "    df['Embarked']=x_emb.astype(np.float)\n",
      "\n",
      "    df = df.drop(['PassengerId','Name','Age','Cabin'], axis=1) #remove Name,Age and PassengerId\n",
      "\n",
      "\n",
      "    return df\n",
      "\n",
      "#read data\n",
      "\n",
      "testdf=pd.read_csv(test_file)\n",
      "\n",
      "ID=testdf['PassengerId']\n",
      "##clean data\n",
      "df_test=clean_and_munge_data(testdf)\n",
      "df_test['Survived'] =  [0 for x in range(len(df_test))]\n",
      "\n",
      "print df_test.shape\n",
      "########################################formula################################\n",
      " \n",
      "formula_ml='Survived~Pclass+C(Title)+Sex+C(AgeCat)+Fare_Per_Person+Fare+Family_Size' \n",
      "\n",
      "y_p,x_test = dmatrices(formula_ml, data=df_test, return_type='dataframe')\n",
      "y_p = np.asarray(y_p).ravel()\n",
      "print  y_p.shape,x_test.shape\n",
      "#serialize training\n",
      "model_file=MODEL_PATH+'model-rf.pkl'\n",
      "clf = joblib.load(model_file)\n",
      "####estimate prediction on test data set\n",
      "y_p=clf.predict(x_test).astype(int)\n",
      "print y_p.shape\n",
      "\n",
      "outfile=SUBMISSION_PATH+'prediction-BS.csv'\n",
      "dfjo = DataFrame(dict(Survived=y_p,PassengerId=ID), columns=['Survived','PassengerId'])\n",
      "dfjo.to_csv(outfile,index_label=None,index_col=False,index=False)\n",
      "\n",
      "\n",
      "#\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Few considerations"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      ">Be aware in selecting a seed in order to reproduce your results! \n",
      "\n",
      ">Play a bit with paramaters in the gridsearch to got better results.\n",
      "\n",
      "With the above configuration I was able to reach 0.81340, but never I could obtain a better results with these algorithms and features selection in the leaderboard even with very high accuracy in my tests! "
     ]
    },
    {
     "cell_type": "heading",
     "level": 6,
     "metadata": {},
     "source": [
      "I have to thank all the people who wrote Tutorials in the Kaggle Forum, the various post in different blog which helped me in handling the python notebbok, the pandas library, matplotlib and the beautiful \n",
      "scikit-learn package (http://scikit-learn.org/stable/)"
     ]
    }
   ],
   "metadata": {}
  }
 ]
}