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     "cell_type": "markdown",
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     "source": [
      "###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",
      "\n",
      "\n",
      "###Goal for this Notebook:\n",
      "Show a simple example of an analysis of the Titanic disaster in Python using a full complement of PyData utilities. This is aimed for those looking to get into the field or those who are already in the field and looking to see an example of an analysis done with Python.\n",
      "\n",
      "####This Notebook will show basic examples of: \n",
      "####Data Handling\n",
      "*   Importing Data with Pandas\n",
      "*   Cleaning Data\n",
      "*   Exploring Data through Visualizations with Matplotlib\n",
      "\n",
      "####Data Analysis\n",
      "*    Supervised Machine learning Techniques:\n",
      "    +   Logit Regression Model \n",
      "    +   Plotting results\n",
      "    +   Support Vector Machine (SVM) using 3 kernels\n",
      "    +   Basic Random Forest\n",
      "    +   Plotting results\n",
      "\n",
      "####Valuation of the Analysis\n",
      "*   K-folds cross validation to valuate results locally\n",
      "*   Output the results from the IPython Notebook to Kaggle\n",
      "\n",
      "\n",
      "\n",
      "####Required Libraries:\n",
      "* [NumPy](http://www.numpy.org/)\n",
      "* [IPython](http://ipython.org/)\n",
      "* [Pandas](http://pandas.pydata.org/)\n",
      "* [SciKit-Learn](http://scikit-learn.org/stable/)\n",
      "* [SciPy](http://www.scipy.org/)\n",
      "* [StatsModels](http://statsmodels.sourceforge.net/)\n",
      "* [Patsy](http://patsy.readthedocs.org/en/latest/)\n",
      "* [Matplotlib](http://matplotlib.org/)\n",
      "\n",
      "***To run this notebook interactively, get it from my Github [here](https://github.com/agconti/kaggle-titanic). The competition's website is located on [Kaggle.com](http://www.kaggle.com/c/titanic-gettingStarted).***"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import matplotlib.pyplot as plt\n",
      "%matplotlib inline\n",
      "import numpy as np\n",
      "import pandas as pd\n",
      "import statsmodels.api as sm\n",
      "from statsmodels.nonparametric import smoothers_lowess\n",
      "from statsmodels.nonparametric.kde import KDEUnivariate\n",
      "from pandas import Series, DataFrame\n",
      "from patsy import dmatrices\n",
      "from sklearn import datasets, svm\n",
      "from KaggleAux import predict as ka # see github.com/agconti/kaggleaux for more details"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 145
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Data Handling\n",
      "####Let's read our data in using pandas:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df = pd.read_csv(\"data/train.csv\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 146
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Show an overview of our data: "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
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        "      <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> 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> 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> 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> 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> 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> 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> 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> 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> 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> 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> 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> 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> 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> 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> 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> 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> 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> 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> 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> 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> 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> 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> 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> 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> 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> 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> 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> 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> 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> 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 11 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 147,
       "text": [
        "     survived  pclass                                               name  \\\n",
        "0           0       3                            Braund, Mr. Owen Harris   \n",
        "1           1       1  Cumings, Mrs. John Bradley (Florence Briggs Th...   \n",
        "2           1       3                             Heikkinen, Miss. Laina   \n",
        "3           1       1       Futrelle, Mrs. Jacques Heath (Lily May Peel)   \n",
        "4           0       3                           Allen, Mr. William Henry   \n",
        "5           0       3                                   Moran, Mr. James   \n",
        "6           0       1                            McCarthy, Mr. Timothy J   \n",
        "7           0       3                     Palsson, Master. Gosta Leonard   \n",
        "8           1       3  Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)   \n",
        "9           1       2                Nasser, Mrs. Nicholas (Adele Achem)   \n",
        "10          1       3                    Sandstrom, Miss. Marguerite Rut   \n",
        "11          1       1                           Bonnell, Miss. Elizabeth   \n",
        "12          0       3                     Saundercock, Mr. William Henry   \n",
        "13          0       3                        Andersson, Mr. Anders Johan   \n",
        "14          0       3               Vestrom, Miss. Hulda Amanda Adolfina   \n",
        "15          1       2                   Hewlett, Mrs. (Mary D Kingcome)    \n",
        "16          0       3                               Rice, Master. Eugene   \n",
        "17          1       2                       Williams, Mr. Charles Eugene   \n",
        "18          0       3  Vander Planke, Mrs. Julius (Emelia Maria Vande...   \n",
        "19          1       3                            Masselmani, Mrs. Fatima   \n",
        "20          0       2                               Fynney, Mr. Joseph J   \n",
        "21          1       2                              Beesley, Mr. Lawrence   \n",
        "22          1       3                        McGowan, Miss. Anna \"Annie\"   \n",
        "23          1       1                       Sloper, Mr. William Thompson   \n",
        "24          0       3                      Palsson, Miss. Torborg Danira   \n",
        "25          1       3  Asplund, Mrs. Carl Oscar (Selma Augusta Emilia...   \n",
        "26          0       3                            Emir, Mr. Farred Chehab   \n",
        "27          0       1                     Fortune, Mr. Charles Alexander   \n",
        "28          1       3                      O'Dwyer, Miss. Ellen \"Nellie\"   \n",
        "29          0       3                                Todoroff, Mr. Lalio   \n",
        "..        ...     ...                                                ...   \n",
        "861         0       2                        Giles, Mr. Frederick Edward   \n",
        "862         1       1  Swift, Mrs. Frederick Joel (Margaret Welles Ba...   \n",
        "863         0       3                  Sage, Miss. Dorothy Edith \"Dolly\"   \n",
        "864         0       2                             Gill, Mr. John William   \n",
        "865         1       2                           Bystrom, Mrs. (Karolina)   \n",
        "866         1       2                       Duran y More, Miss. Asuncion   \n",
        "867         0       1               Roebling, Mr. Washington Augustus II   \n",
        "868         0       3                        van Melkebeke, Mr. Philemon   \n",
        "869         1       3                    Johnson, Master. Harold Theodor   \n",
        "870         0       3                                  Balkic, Mr. Cerin   \n",
        "871         1       1   Beckwith, Mrs. Richard Leonard (Sallie Monypeny)   \n",
        "872         0       1                           Carlsson, Mr. Frans Olof   \n",
        "873         0       3                        Vander Cruyssen, Mr. Victor   \n",
        "874         1       2              Abelson, Mrs. Samuel (Hannah Wizosky)   \n",
        "875         1       3                   Najib, Miss. Adele Kiamie \"Jane\"   \n",
        "876         0       3                      Gustafsson, Mr. Alfred Ossian   \n",
        "877         0       3                               Petroff, Mr. Nedelio   \n",
        "878         0       3                                 Laleff, Mr. Kristo   \n",
        "879         1       1      Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)   \n",
        "880         1       2       Shelley, Mrs. William (Imanita Parrish Hall)   \n",
        "881         0       3                                 Markun, Mr. Johann   \n",
        "882         0       3                       Dahlberg, Miss. Gerda Ulrika   \n",
        "883         0       2                      Banfield, Mr. Frederick James   \n",
        "884         0       3                             Sutehall, Mr. Henry Jr   \n",
        "885         0       3               Rice, Mrs. William (Margaret Norton)   \n",
        "886         0       2                              Montvila, Rev. Juozas   \n",
        "887         1       1                       Graham, Miss. Margaret Edith   \n",
        "888         0       3           Johnston, Miss. Catherine Helen \"Carrie\"   \n",
        "889         1       1                              Behr, Mr. Karl Howell   \n",
        "890         0       3                                Dooley, Mr. Patrick   \n",
        "\n",
        "        sex  age  sibsp  parch            ticket      fare        cabin  \\\n",
        "0      male   22      1      0         A/5 21171    7.2500          NaN   \n",
        "1    female   38      1      0          PC 17599   71.2833          C85   \n",
        "2    female   26      0      0  STON/O2. 3101282    7.9250          NaN   \n",
        "3    female   35      1      0            113803   53.1000         C123   \n",
        "4      male   35      0      0            373450    8.0500          NaN   \n",
        "5      male  NaN      0      0            330877    8.4583          NaN   \n",
        "6      male   54      0      0             17463   51.8625          E46   \n",
        "7      male    2      3      1            349909   21.0750          NaN   \n",
        "8    female   27      0      2            347742   11.1333          NaN   \n",
        "9    female   14      1      0            237736   30.0708          NaN   \n",
        "10   female    4      1      1           PP 9549   16.7000           G6   \n",
        "11   female   58      0      0            113783   26.5500         C103   \n",
        "12     male   20      0      0         A/5. 2151    8.0500          NaN   \n",
        "13     male   39      1      5            347082   31.2750          NaN   \n",
        "14   female   14      0      0            350406    7.8542          NaN   \n",
        "15   female   55      0      0            248706   16.0000          NaN   \n",
        "16     male    2      4      1            382652   29.1250          NaN   \n",
        "17     male  NaN      0      0            244373   13.0000          NaN   \n",
        "18   female   31      1      0            345763   18.0000          NaN   \n",
        "19   female  NaN      0      0              2649    7.2250          NaN   \n",
        "20     male   35      0      0            239865   26.0000          NaN   \n",
        "21     male   34      0      0            248698   13.0000          D56   \n",
        "22   female   15      0      0            330923    8.0292          NaN   \n",
        "23     male   28      0      0            113788   35.5000           A6   \n",
        "24   female    8      3      1            349909   21.0750          NaN   \n",
        "25   female   38      1      5            347077   31.3875          NaN   \n",
        "26     male  NaN      0      0              2631    7.2250          NaN   \n",
        "27     male   19      3      2             19950  263.0000  C23 C25 C27   \n",
        "28   female  NaN      0      0            330959    7.8792          NaN   \n",
        "29     male  NaN      0      0            349216    7.8958          NaN   \n",
        "..      ...  ...    ...    ...               ...       ...          ...   \n",
        "861    male   21      1      0             28134   11.5000          NaN   \n",
        "862  female   48      0      0             17466   25.9292          D17   \n",
        "863  female  NaN      8      2          CA. 2343   69.5500          NaN   \n",
        "864    male   24      0      0            233866   13.0000          NaN   \n",
        "865  female   42      0      0            236852   13.0000          NaN   \n",
        "866  female   27      1      0     SC/PARIS 2149   13.8583          NaN   \n",
        "867    male   31      0      0          PC 17590   50.4958          A24   \n",
        "868    male  NaN      0      0            345777    9.5000          NaN   \n",
        "869    male    4      1      1            347742   11.1333          NaN   \n",
        "870    male   26      0      0            349248    7.8958          NaN   \n",
        "871  female   47      1      1             11751   52.5542          D35   \n",
        "872    male   33      0      0               695    5.0000  B51 B53 B55   \n",
        "873    male   47      0      0            345765    9.0000          NaN   \n",
        "874  female   28      1      0         P/PP 3381   24.0000          NaN   \n",
        "875  female   15      0      0              2667    7.2250          NaN   \n",
        "876    male   20      0      0              7534    9.8458          NaN   \n",
        "877    male   19      0      0            349212    7.8958          NaN   \n",
        "878    male  NaN      0      0            349217    7.8958          NaN   \n",
        "879  female   56      0      1             11767   83.1583          C50   \n",
        "880  female   25      0      1            230433   26.0000          NaN   \n",
        "881    male   33      0      0            349257    7.8958          NaN   \n",
        "882  female   22      0      0              7552   10.5167          NaN   \n",
        "883    male   28      0      0  C.A./SOTON 34068   10.5000          NaN   \n",
        "884    male   25      0      0   SOTON/OQ 392076    7.0500          NaN   \n",
        "885  female   39      0      5            382652   29.1250          NaN   \n",
        "886    male   27      0      0            211536   13.0000          NaN   \n",
        "887  female   19      0      0            112053   30.0000          B42   \n",
        "888  female  NaN      1      2        W./C. 6607   23.4500          NaN   \n",
        "889    male   26      0      0            111369   30.0000         C148   \n",
        "890    male   32      0      0            370376    7.7500          NaN   \n",
        "\n",
        "    embarked  \n",
        "0          S  \n",
        "1          C  \n",
        "2          S  \n",
        "3          S  \n",
        "4          S  \n",
        "5          Q  \n",
        "6          S  \n",
        "7          S  \n",
        "8          S  \n",
        "9          C  \n",
        "10         S  \n",
        "11         S  \n",
        "12         S  \n",
        "13         S  \n",
        "14         S  \n",
        "15         S  \n",
        "16         Q  \n",
        "17         S  \n",
        "18         S  \n",
        "19         C  \n",
        "20         S  \n",
        "21         S  \n",
        "22         Q  \n",
        "23         S  \n",
        "24         S  \n",
        "25         S  \n",
        "26         C  \n",
        "27         S  \n",
        "28         Q  \n",
        "29         S  \n",
        "..       ...  \n",
        "861        S  \n",
        "862        S  \n",
        "863        S  \n",
        "864        S  \n",
        "865        S  \n",
        "866        C  \n",
        "867        S  \n",
        "868        S  \n",
        "869        S  \n",
        "870        S  \n",
        "871        S  \n",
        "872        S  \n",
        "873        S  \n",
        "874        C  \n",
        "875        C  \n",
        "876        S  \n",
        "877        S  \n",
        "878        S  \n",
        "879        C  \n",
        "880        S  \n",
        "881        S  \n",
        "882        S  \n",
        "883        S  \n",
        "884        S  \n",
        "885        Q  \n",
        "886        S  \n",
        "887        S  \n",
        "888        S  \n",
        "889        C  \n",
        "890        Q  \n",
        "\n",
        "[891 rows x 11 columns]"
       ]
      }
     ],
     "prompt_number": 147
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Let's take a look:\n",
      "\n",
      "Above is a summary of our data contained in a `Pandas` `DataFrame`. Think of a `DataFrame` as a Python's super charged version of the workflow in an Excel table. As you can see the summary holds quite a bit of information. First, it lets us know we have 891 observations, or passengers, to analyze here:\n",
      "    \n",
      "    Int64Index: 891 entries, 0 to 890\n",
      "\n",
      "Next it shows us all of the columns in `DataFrame`. Each column tells us something about each of our observations, like their `name`, `sex` or `age`. These colunms  are called a features of our dataset. You can think of the meaning of the words column and feature as interchangeable for this notebook. \n",
      "\n",
      "After each feature it lets us know how many values it contains. While most of our features have complete data on every observation, like the `survived` feature here: \n",
      "\n",
      "    survived    891  non-null values \n",
      "\n",
      "some are missing information, like the `age` feature: \n",
      "\n",
      "    age         714  non-null values \n",
      "\n",
      "These missing values are represented as `NaN`s.\n",
      "\n",
      "###Take care of missing values:\n",
      "The features `ticket` and `cabin` have many missing values and so can\u2019t add much value to our analysis. To handle this we will drop them from the dataframe to preserve the integrity of our dataset.\n",
      "\n",
      "To do that we'll use this line of code to drop the features entirely:\n",
      "\n",
      "    df = df.drop(['ticket','cabin'], axis=1) \n",
      "\n",
      "\n",
      "While this line of code removes the `NaN` values from every remaining column / feature:\n",
      "   \n",
      "    df = df.dropna()\n",
      "     \n",
      "Now we have a clean and tidy dataset that is ready for analysis. Because `.dropna()` removes an observation from our data even if it only has 1 `NaN` in one of the features, it would have removed most of our dataset if we had not dropped the `ticket` and `cabin`  features first.\n",
      "\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df = df.drop(['ticket','cabin'], axis=1)\n",
      "# Remove NaN values\n",
      "df = df.dropna() "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 148
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "For a detailed look at how to use pandas for data analysis, the best resource is Wes Mckinney's [book](http://shop.oreilly.com/product/0636920023784.do). Additional interactive tutorials that cover all of the basics can be found [here](https://bitbucket.org/hrojas/learn-pandas) (they're free).  If you still need to be convinced about the power of pandas check out this wirlwhind [look](http://wesmckinney.com/blog/?p=647) at all that pandas can do. "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Let's take a Look at our data graphically:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# specifies the parameters of our graphs\n",
      "fig = plt.figure(figsize=(18,6), dpi=1600) \n",
      "alpha=alpha_scatterplot = 0.2 \n",
      "alpha_bar_chart = 0.55\n",
      "\n",
      "# lets us plot many diffrent shaped graphs together \n",
      "ax1 = plt.subplot2grid((2,3),(0,0))\n",
      "# plots a bar graph of those who surived vs those who did not.               \n",
      "df.survived.value_counts().plot(kind='bar', alpha=alpha_bar_chart) \n",
      "ax1.set_xlim(-1, 2)\n",
      "# puts a title on our graph\n",
      "plt.title(\"Distribution of Survival, (1 = Survived)\")    \n",
      "\n",
      "plt.subplot2grid((2,3),(0,1))\n",
      "plt.scatter(df.survived, df.age, alpha=alpha_scatterplot)\n",
      "# sets the y axis lable\n",
      "plt.ylabel(\"Age\")\n",
      "# formats the grid line style of our graphs                          \n",
      "plt.grid(b=True, which='major', axis='y')  \n",
      "plt.title(\"Survial by Age,  (1 = Survived)\")\n",
      "\n",
      "ax3 = plt.subplot2grid((2,3),(0,2))\n",
      "df.pclass.value_counts().plot(kind=\"barh\", alpha=alpha_bar_chart)\n",
      "ax3.set_ylim(-1, len(df.pclass.value_counts()))\n",
      "plt.title(\"Class Distribution\")\n",
      "\n",
      "plt.subplot2grid((2,3),(1,0), colspan=2)\n",
      "# plots a kernel desnsity estimate of the subset of the 1st class passanges's age\n",
      "df.age[df.pclass == 1].plot(kind='kde')    \n",
      "df.age[df.pclass == 2].plot(kind='kde')\n",
      "df.age[df.pclass == 3].plot(kind='kde')\n",
      " # plots an axis lable\n",
      "plt.xlabel(\"Age\")    \n",
      "plt.title(\"Age Distribution within classes\")\n",
      "# sets our legend for our graph.\n",
      "plt.legend(('1st Class', '2nd Class','3rd Class'),loc='best') \n",
      "\n",
      "ax5 = plt.subplot2grid((2,3),(1,2))\n",
      "df.embarked.value_counts().plot(kind='bar', alpha=alpha_bar_chart)\n",
      "ax5.set_xlim(-1, len(df.embarked.value_counts()))\n",
      "plt.title(\"Passengers per boarding location\")# specifies the parameters of our graphs"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 170,
       "text": [
        "<matplotlib.text.Text at 0x11dcd5590>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Sbi7M2DwF6AMOAOOwHhnfxIaX7PSecwVwCnAxNsnn7di8GLHJPhdxeK8MZ2fL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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x11c679450>"
       ]
      }
     ],
     "prompt_number": 170
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Exploratory Visualization:\n",
      "\n",
      "The point of this competition is to predict if an individual will survive based on the features in the data like:\n",
      " \n",
      " * Traveling Class (called pclass in the data)\n",
      " * Sex \n",
      " * Age\n",
      " * Fare Price\n",
      "\n",
      "Let\u2019s see if we can gain a better understanding of who survived and died. \n",
      "\n",
      "\n",
      "First let\u2019s plot a bar graph of those who Survived Vs. Those who did not.\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.figure(figsize=(6,4))\n",
      "fig, ax = plt.subplots()\n",
      "df.survived.value_counts().plot(kind='barh', color=\"blue\", alpha=.65)\n",
      "ax.set_ylim(-1, len(df.survived.value_counts()))\n",
      "plt.title(\"Survival Breakdown (1 = Survived, 0 = Died)\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 150,
       "text": [
        "<matplotlib.text.Text at 0x11cce0490>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<matplotlib.figure.Figure at 0x11c46c150>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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lZM+7ft7nLUkzqMrinbE/CTlzmalMxkxZ+7gZc2XM1FWVxVuSamfPW0rInnf97HlL0gyq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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x11c46c790>"
       ]
      }
     ],
     "prompt_number": 150
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Now let\u2019s tease more structure out of the data,\n",
      "###Let\u2019s break the previous graph down by gender\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = plt.figure(figsize=(18,6))\n",
      "\n",
      "# create a plot of two subsets, male and female, of the survived variable.\n",
      "# After we do that we call value_counts() so it can be easily plotted as a bar graph. \n",
      "# 'barh' is just a horizontal bar graph\n",
      "ax1 = fig.add_subplot(121)\n",
      "df.survived[df.sex == 'male'].value_counts().plot(kind='barh',label='Male')\n",
      "df.survived[df.sex == 'female'].value_counts().plot(kind='barh', color='#FA2379',label='Female')\n",
      "ax1.set_ylim(-1, 2) # this nicely sets the margins in matplotlib to deal with a recent bug 1.3.1\n",
      "plt.title(\"Who Survived? with respect to Gender, (raw value counts) \"); plt.legend(loc='best')\n",
      "\n",
      "\n",
      "# adjust graph to display the proportions of survival by gender\n",
      "ax2 = fig.add_subplot(122)\n",
      "(df.survived[df.sex == 'male'].value_counts()/float(df.sex[df.sex == 'male'].size)).plot(kind='barh',label='Male')  \n",
      "(df.survived[df.sex == 'female'].value_counts()/float(df.sex[df.sex == 'female'].size)).plot(kind='barh', color='#FA2379',label='Female')\n",
      "ax2.set_ylim(-1, 2)\n",
      "plt.title(\"Who Survived proportionally? with respect to Gender\"); plt.legend(loc='best')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 151,
       "text": [
        "<matplotlib.legend.Legend at 0x11ccdf690>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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ue/TRRwG4+eabOeecc/jud7/LU089xZNPPskpp5yyWzvuvvtunn766RnfVxqF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\nrjpVybgLeBnhvPZ1wlVk46RKxucSrrr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       "text": [
        "<matplotlib.figure.Figure at 0x11cce7290>"
       ]
      }
     ],
     "prompt_number": 151
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Here it\u2019s clear that although more men died and survived in raw value counts, females had a greater survival rate proportionally(~25%), than men (~20%)\n",
      "\n",
      "####Great! But let\u2019s go down even further:\n",
      "Can we capture more of the structure by using Pclass? Here we will bucket classes as lowest class or any of the high classes (classes 1 - 2). 3 is lowest class. Let\u2019s break it down by Gender and what Class they were traveling in.\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = plt.figure(figsize=(18,4), dpi=1600)\n",
      "alpha_level = 0.65\n",
      "\n",
      "# building on the previous code, here we create an additional subset with in the gender subset \n",
      "# we created for the survived variable. I know, thats a lot of subsets. After we do that we call \n",
      "# value_counts() so it it can be easily plotted as a bar graph. this is repeated for each gender \n",
      "# class pair.\n",
      "ax1=fig.add_subplot(141)\n",
      "female_highclass = df.survived[df.sex == 'female'][df.pclass != 3].value_counts()\n",
      "female_highclass.plot(kind='bar', label='female highclass', color='#FA2479', alpha=alpha_level)\n",
      "ax1.set_xticklabels([\"Survived\", \"Died\"], rotation=0)\n",
      "ax1.set_xlim(-1, len(female_highclass))\n",
      "plt.title(\"Who Survived? with respect to Gender and Class\"); plt.legend(loc='best')\n",
      "\n",
      "ax2=fig.add_subplot(142, sharey=ax1)\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=alpha_level)\n",
      "ax2.set_xticklabels([\"Died\",\"Survived\"], rotation=0)\n",
      "ax2.set_xlim(-1, len(female_lowclass))\n",
      "plt.legend(loc='best')\n",
      "\n",
      "ax3=fig.add_subplot(143, sharey=ax1)\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=alpha_level)\n",
      "ax3.set_xticklabels([\"Died\",\"Survived\"], rotation=0)\n",
      "ax3.set_xlim(-1, len(male_lowclass))\n",
      "plt.legend(loc='best')\n",
      "\n",
      "ax4=fig.add_subplot(144, sharey=ax1)\n",
      "male_highclass = df.survived[df.sex == 'male'][df.pclass != 3].value_counts()\n",
      "male_highclass.plot(kind='bar', label='male highclass', alpha=alpha_level, color='steelblue')\n",
      "ax4.set_xticklabels([\"Died\",\"Survived\"], rotation=0)\n",
      "ax4.set_xlim(-1, len(male_highclass))\n",
      "plt.legend(loc='best')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 152,
       "text": [
        "<matplotlib.legend.Legend at 0x11c537310>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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168eiRYvqtTV1O8yAAQOorq6uV/+pMQsWLOCss87illtuobq6mpUrV7L77rvX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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x11bd00e90>"
       ]
      }
     ],
     "prompt_number": 152
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Awesome! Now we have a lot more information on who survived and died in the tragedy. With this deeper understanding, we are better equipped to create better more insightful models. This is a typical process in interactive data analysis. First you start small and understand the most basic relationships and slowly increment the complexity of your analysis as you discover more and more about the data you\u2019re working with. Below is the progression of process laid out together:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = plt.figure(figsize=(18,12), dpi=1600)\n",
      "a = 0.65\n",
      "# Step 1\n",
      "ax1 = fig.add_subplot(341)\n",
      "df.survived.value_counts().plot(kind='bar', color=\"blue\", alpha=a)\n",
      "ax1.set_xlim(-1, len(df.survived.value_counts()))\n",
      "plt.title(\"Step. 1\")\n",
      "\n",
      "# Step 2\n",
      "ax2 = fig.add_subplot(345)\n",
      "df.survived[df.sex == 'male'].value_counts().plot(kind='bar',label='Male')\n",
      "df.survived[df.sex == 'female'].value_counts().plot(kind='bar', color='#FA2379',label='Female')\n",
      "ax2.set_xlim(-1, 2)\n",
      "plt.title(\"Step. 2 \\nWho Survied? with respect to Gender.\"); plt.legend(loc='best')\n",
      "\n",
      "ax3 = fig.add_subplot(346)\n",
      "(df.survived[df.sex == 'male'].value_counts()/float(df.sex[df.sex == 'male'].size)).plot(kind='bar',label='Male')\n",
      "(df.survived[df.sex == 'female'].value_counts()/float(df.sex[df.sex == 'female'].size)).plot(kind='bar', color='#FA2379',label='Female')\n",
      "ax3.set_xlim(-1,2)\n",
      "plt.title(\"Who Survied proportionally?\"); plt.legend(loc='best')\n",
      "\n",
      "\n",
      "# Step 3\n",
      "ax4 = fig.add_subplot(349)\n",
      "female_highclass = df.survived[df.sex == 'female'][df.pclass != 3].value_counts()\n",
      "female_highclass.plot(kind='bar', label='female highclass', color='#FA2479', alpha=a)\n",
      "ax4.set_xticklabels([\"Survived\", \"Died\"], rotation=0)\n",
      "ax4.set_xlim(-1, len(female_highclass))\n",
      "plt.title(\"Who Survived? with respect to Gender and Class\"); plt.legend(loc='best')\n",
      "\n",
      "ax5 = fig.add_subplot(3,4,10, sharey=ax1)\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",
      "ax5.set_xticklabels([\"Died\",\"Survived\"], rotation=0)\n",
      "ax5.set_xlim(-1, len(female_lowclass))\n",
      "plt.legend(loc='best')\n",
      "\n",
      "ax6 = fig.add_subplot(3,4,11, sharey=ax1)\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",
      "ax6.set_xticklabels([\"Died\",\"Survived\"], rotation=0)\n",
      "ax6.set_xlim(-1, len(male_lowclass))\n",
      "plt.legend(loc='best')\n",
      "\n",
      "ax7 = fig.add_subplot(3,4,12, sharey=ax1)\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='steelblue')\n",
      "ax7.set_xticklabels([\"Died\",\"Survived\"], rotation=0)\n",
      "ax7.set_xlim(-1, len(male_highclass))\n",
      "plt.legend(loc='best')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 153,
       "text": [
        "<matplotlib.legend.Legend at 0x11d133990>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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7c2weCCwgtB/rgf9T60JJkiTVWqNcQN4HvAsoxfnJwCiglZ7fMBk4HLi/xt/9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       "text": [
        "<matplotlib.figure.Figure at 0x11c4e4450>"
       ]
      }
     ],
     "prompt_number": 153
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "I've done my best to make the plotting code readable and intuitive, but if you\u2019re looking for a more detailed look on how to start plotting in matplotlib, check out this beautiful notebook [here](http://nbviewer.ipython.org/urls/raw.github.com/jrjohansson/scientific-python-lectures/master/Lecture-4-Matplotlib.ipynb). \n",
      "\n",
      "Now that we have a basic understanding of what we are trying to predict, let\u2019s predict it.\n",
      "##Supervised Machine Learning\n",
      "####Logistic Regression:\n",
      "\n",
      "As explained by Wikipedia:\n",
      ">In statistics, logistic regression or logit regression is a type of regression analysis used for predicting the outcome of a categorical dependent variable (a dependent variable that can take on a limited number of values, whose magnitudes are not meaningful but whose ordering of magnitudes may or may not be meaningful) based on one or more predictor variables. That is, it is used in estimating empirical values of the parameters in a qualitative response model. The probabilities describing the possible outcomes of a single trial are modeled, as a function of the explanatory (predictor) variables, using a logistic function. Frequently (and subsequently in this article) \"logistic regression\" is used to refer specifically to the problem in which the dependent variable is binary\u2014that is, the number of available categories is two\u2014and problems with more than two categories are referred to as multinomial logistic regression or, if the multiple categories are ordered, as ordered logistic regression.\n",
      "Logistic regression measures the relationship between a categorical dependent variable and one or more independent variables, which are usually (but not necessarily) continuous, by using probability scores as the predicted values of the dependent variable.[1] As such it treats the same set of problems as does probit regression using similar techniques.\n",
      "\n",
      "#### The skinny, as explained by yours truly:\n",
      "Our competition wants us to predict a binary outcome. That is, it wants to know whether some will die, (represented as a 0), or survive, (represented as 1). A good place to start is to calculate the probability that an individual observation, or person, is likely to be a 0 or 1. That way we would know the chance that someone survives, and could start making somewhat informed perdictions. If we did, we'd get results like this:: \n",
      "\n",
      "![pred](https://raw.github.com/agconti/kaggle-titanic/master/calc_prob.png) \n",
      "\n",
      "(*Y axis is the probability that someone survives, X axis is the passenger\u2019s number from 1 to 891.*)\n",
      "\n",
      "While that information is useful it doesn\u2019t let us know whether someone ended up alive or dead. It just lets us know the chance that they will survive or die. We still need to translate these probabilities into the binary decision we\u2019re looking for. But how? We could arbitrarily say that our survival cutoff is anyone with a probability of survival over 50%. In fact, this tactic would actually perform pretty well for our data and would allow you to make decently accurate predictions. Graphically it would look something like this:\n",
      "\n",
      "![predwline](https://raw.github.com/agconti/kaggle-titanic/master/calc_prob_wline.png)\n",
      "\n",
      "If you\u2019re a betting man like me, you don\u2019t like to leave everything to chance. What are the odds that setting that cutoff at 50% works? Maybe 20% or 80% would work better. Clearly we need a more exact way to make that cutoff. What can save the day? In steps the **Logistic Regression**. \n",
      "\n",
      "A logistic regression follows the all steps we took above but mathematically calculates the cutoff, or decision boundary (as stats nerds call it), for you. This way it can figure out the best cut off to choose, perhaps 50% or 51.84%, that most accurately represents the training data.\n",
      "\n",
      "The three cells below show the process of creating our Logitist regression model, training it on the data, and examining its performance. \n",
      "\n",
      "First, we define our formula for our Logit regression. In the next cell we create a regression friendly dataframe that sets up boolean values for the categorical variables in our formula and lets our regression model know the types of inputs we're giving it. The model is then instantiated and fitted before a summary of the model's performance is printed. In the last cell we graphically compare the predictions of our model to the actual values we are trying to predict, as well as the residual errors from our model to check for any structure we may have missed."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# model formula\n",
      "# here the ~ sign is an = sign, and the features of our dataset\n",
      "# are written as a formula to predict survived. The C() lets our \n",
      "# regression know that those variables are categorical.\n",
      "formula = 'survived ~ C(pclass) + C(sex) + age + sibsp  + C(embarked)' \n",
      "# create a results dictionary to hold our regression results for easy analysis later        \n",
      "results = {} "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 154
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# create a regression freindly dataframe using patsy's dmatrices function\n",
      "y,x = dmatrices(formula, data=df, return_type='dataframe')\n",
      "\n",
      "# instantiate our model\n",
      "model = sm.Logit(y,x)\n",
      "\n",
      "# fit our model to the training data\n",
      "res = model.fit()\n",
      "\n",
      "# save the result for outputing predictions later\n",
      "results['Logit'] = [res, formula]\n",
      "res.summary()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Optimization terminated successfully.\n",
        "         Current function value: 0.444388\n",
        "         Iterations 6\n"
       ]
      },
      {
       "html": [
        "<table class=\"simpletable\">\n",
        "<caption>Logit Regression Results</caption>\n",
        "<tr>\n",
        "  <th>Dep. Variable:</th>     <td>survived</td>     <th>  No. Observations:  </th>  <td>   712</td>  \n",
        "</tr>\n",
        "<tr>\n",
        "  <th>Model:</th>               <td>Logit</td>      <th>  Df Residuals:      </th>  <td>   704</td>  \n",
        "</tr>\n",
        "<tr>\n",
        "  <th>Method:</th>               <td>MLE</td>       <th>  Df Model:          </th>  <td>     7</td>  \n",
        "</tr>\n",
        "<tr>\n",
        "  <th>Date:</th>          <td>Sun, 06 Jul 2014</td> <th>  Pseudo R-squ.:     </th>  <td>0.3414</td>  \n",
        "</tr>\n",
        "<tr>\n",
        "  <th>Time:</th>              <td>20:10:54</td>     <th>  Log-Likelihood:    </th> <td> -316.40</td> \n",
        "</tr>\n",
        "<tr>\n",
        "  <th>converged:</th>           <td>True</td>       <th>  LL-Null:           </th> <td> -480.45</td> \n",
        "</tr>\n",
        "<tr>\n",
        "  <th> </th>                      <td> </td>        <th>  LLR p-value:       </th> <td>5.992e-67</td>\n",
        "</tr>\n",
        "</table>\n",
        "<table class=\"simpletable\">\n",
        "<tr>\n",
        "          <td></td>            <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th> <th>[95.0% Conf. Int.]</th> \n",
        "</tr>\n",
        "<tr>\n",
        "  <th>Intercept</th>        <td>    4.5423</td> <td>    0.474</td> <td>    9.583</td> <td> 0.000</td> <td>    3.613     5.471</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>C(pclass)[T.2]</th>   <td>   -1.2673</td> <td>    0.299</td> <td>   -4.245</td> <td> 0.000</td> <td>   -1.852    -0.682</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>C(pclass)[T.3]</th>   <td>   -2.4966</td> <td>    0.296</td> <td>   -8.422</td> <td> 0.000</td> <td>   -3.078    -1.916</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>C(sex)[T.male]</th>   <td>   -2.6239</td> <td>    0.218</td> <td>  -12.060</td> <td> 0.000</td> <td>   -3.050    -2.197</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>C(embarked)[T.Q]</th> <td>   -0.8351</td> <td>    0.597</td> <td>   -1.398</td> <td> 0.162</td> <td>   -2.006     0.335</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>C(embarked)[T.S]</th> <td>   -0.4254</td> <td>    0.271</td> <td>   -1.572</td> <td> 0.116</td> <td>   -0.956     0.105</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>age</th>              <td>   -0.0436</td> <td>    0.008</td> <td>   -5.264</td> <td> 0.000</td> <td>   -0.060    -0.027</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>sibsp</th>            <td>   -0.3697</td> <td>    0.123</td> <td>   -3.004</td> <td> 0.003</td> <td>   -0.611    -0.129</td>\n",
        "</tr>\n",
        "</table>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 155,
       "text": [
        "<class 'statsmodels.iolib.summary.Summary'>\n",
        "\"\"\"\n",
        "                           Logit Regression Results                           \n",
        "==============================================================================\n",
        "Dep. Variable:               survived   No. Observations:                  712\n",
        "Model:                          Logit   Df Residuals:                      704\n",
        "Method:                           MLE   Df Model:                            7\n",
        "Date:                Sun, 06 Jul 2014   Pseudo R-squ.:                  0.3414\n",
        "Time:                        20:10:54   Log-Likelihood:                -316.40\n",
        "converged:                       True   LL-Null:                       -480.45\n",
        "                                        LLR p-value:                 5.992e-67\n",
        "====================================================================================\n",
        "                       coef    std err          z      P>|z|      [95.0% Conf. Int.]\n",
        "------------------------------------------------------------------------------------\n",
        "Intercept            4.5423      0.474      9.583      0.000         3.613     5.471\n",
        "C(pclass)[T.2]      -1.2673      0.299     -4.245      0.000        -1.852    -0.682\n",
        "C(pclass)[T.3]      -2.4966      0.296     -8.422      0.000        -3.078    -1.916\n",
        "C(sex)[T.male]      -2.6239      0.218    -12.060      0.000        -3.050    -2.197\n",
        "C(embarked)[T.Q]    -0.8351      0.597     -1.398      0.162        -2.006     0.335\n",
        "C(embarked)[T.S]    -0.4254      0.271     -1.572      0.116        -0.956     0.105\n",
        "age                 -0.0436      0.008     -5.264      0.000        -0.060    -0.027\n",
        "sibsp               -0.3697      0.123     -3.004      0.003        -0.611    -0.129\n",
        "====================================================================================\n",
        "\"\"\""
       ]
      }
     ],
     "prompt_number": 155
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Plot Predictions Vs Actual\n",
      "plt.figure(figsize=(18,4));\n",
      "plt.subplot(121, axisbg=\"#DBDBDB\")\n",
      "# generate predictions from our fitted model\n",
      "ypred = res.predict(x)\n",
      "plt.plot(x.index, ypred, 'bo', x.index, y, 'mo', alpha=.25);\n",
      "plt.grid(color='white', linestyle='dashed')\n",
      "plt.title('Logit predictions, Blue: \\nFitted/predicted values: Red');\n",
      "\n",
      "# Residuals\n",
      "ax2 = plt.subplot(122, axisbg=\"#DBDBDB\")\n",
      "plt.plot(res.resid_dev, 'r-')\n",
      "plt.grid(color='white', linestyle='dashed')\n",
      "ax2.set_xlim(-1, len(res.resid_dev))\n",
      "plt.title('Logit Residuals');"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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XkjAOPSDafOmpK7wQu8YzsRdxB90Mnuimb6CHjrUOrjx3BXvaTs6Ww/ZyG86s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KeqKrElX7bpAxDiTGO1knaqXZFCB3MkgYDEq/b1Ll1RgHjclJVJeXOz9UD+NAq4Et1zjQGpkX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\nAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x11c4d4e90>"
       ]
      }
     ],
     "prompt_number": 156
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "##So how well did this work?\n",
      "Lets Look at the predictions we generated graphically:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = plt.figure(figsize=(18,9), dpi=1600)\n",
      "a = .2\n",
      "\n",
      "# Below are examples of more advanced plotting. \n",
      "# It it looks strange check out the tutorial above.\n",
      "fig.add_subplot(221, axisbg=\"#DBDBDB\")\n",
      "kde_res = KDEUnivariate(res.predict())\n",
      "kde_res.fit()\n",
      "plt.plot(kde_res.support,kde_res.density)\n",
      "plt.fill_between(kde_res.support,kde_res.density, alpha=a)\n",
      "plt.title(\"Distribution of our Predictions\")\n",
      "\n",
      "fig.add_subplot(222, axisbg=\"#DBDBDB\")\n",
      "plt.scatter(res.predict(),x['C(sex)[T.male]'] , alpha=a)\n",
      "plt.grid(b=True, which='major', axis='x')\n",
      "plt.xlabel(\"Predicted chance of survival\")\n",
      "plt.ylabel(\"Gender Bool\")\n",
      "plt.title(\"The Change of Survival Probability by Gender (1 = Male)\")\n",
      "\n",
      "fig.add_subplot(223, axisbg=\"#DBDBDB\")\n",
      "plt.scatter(res.predict(),x['C(pclass)[T.3]'] , alpha=a)\n",
      "plt.xlabel(\"Predicted chance of survival\")\n",
      "plt.ylabel(\"Class Bool\")\n",
      "plt.grid(b=True, which='major', axis='x')\n",
      "plt.title(\"The Change of Survival Probability by Lower Class (1 = 3rd Class)\")\n",
      "\n",
      "fig.add_subplot(224, axisbg=\"#DBDBDB\")\n",
      "plt.scatter(res.predict(),x.age , alpha=a)\n",
      "plt.grid(True, linewidth=0.15)\n",
      "plt.title(\"The Change of Survival Probability by Age\")\n",
      "plt.xlabel(\"Predicted chance of survival\")\n",
      "plt.ylabel(\"Age\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 157,
       "text": [
        "<matplotlib.text.Text at 0x11c643d90>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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6Sw8BTUAlZrPuA3oxd7sPA0HMsGY+YA6m9/B2IALMBg4Chc5ysp3vrgfKUpbR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\nIiIiIiIiIiIiIiIiIiI3qv8PtODp9uGGCEcAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x11d1b7a90>"
       ]
      }
     ],
     "prompt_number": 157
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Now Lets Use our Model to predict the test set values and then save the results so they can be outputed to Kaggle\n",
      "### Read the test data"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "test_data = pd.read_csv(\"data/test.csv\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 158
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Examine our dataframe"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "test_data"
     ],
     "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>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> 3</td>\n",
        "      <td>                                  Kelly, Mr. James</td>\n",
        "      <td>   male</td>\n",
        "      <td> 34.5</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>             330911</td>\n",
        "      <td>   7.8292</td>\n",
        "      <td>             NaN</td>\n",
        "      <td> Q</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1  </th>\n",
        "      <td> 3</td>\n",
        "      <td>                  Wilkes, Mrs. James (Ellen Needs)</td>\n",
        "      <td> female</td>\n",
        "      <td> 47.0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>             363272</td>\n",
        "      <td>   7.0000</td>\n",
        "      <td>             NaN</td>\n",
        "      <td> S</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2  </th>\n",
        "      <td> 2</td>\n",
        "      <td>                         Myles, Mr. Thomas Francis</td>\n",
        "      <td>   male</td>\n",
        "      <td> 62.0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>             240276</td>\n",
        "      <td>   9.6875</td>\n",
        "      <td>             NaN</td>\n",
        "      <td> Q</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3  </th>\n",
        "      <td> 3</td>\n",
        "      <td>                                  Wirz, Mr. Albert</td>\n",
        "      <td>   male</td>\n",
        "      <td> 27.0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>             315154</td>\n",
        "      <td>   8.6625</td>\n",
        "      <td>             NaN</td>\n",
        "      <td> S</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4  </th>\n",
        "      <td> 3</td>\n",
        "      <td>      Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
        "      <td> female</td>\n",
        "      <td> 22.0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td>            3101298</td>\n",
        "      <td>  12.2875</td>\n",
        "      <td>             NaN</td>\n",
        "      <td> S</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5  </th>\n",
        "      <td> 3</td>\n",
        "      <td>                        Svensson, Mr. Johan Cervin</td>\n",
        "      <td>   male</td>\n",
        "      <td> 14.0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>               7538</td>\n",
        "      <td>   9.2250</td>\n",
        "      <td>             NaN</td>\n",
        "      <td> S</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6  </th>\n",
        "      <td> 3</td>\n",
        "      <td>                              Connolly, Miss. Kate</td>\n",
        "      <td> female</td>\n",
        "      <td> 30.0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>             330972</td>\n",
        "      <td>   7.6292</td>\n",
        "      <td>             NaN</td>\n",
        "      <td> Q</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7  </th>\n",
        "      <td> 2</td>\n",
        "      <td>                      Caldwell, Mr. Albert Francis</td>\n",
        "      <td>   male</td>\n",
        "      <td> 26.0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td>             248738</td>\n",
        "      <td>  29.0000</td>\n",
        "      <td>             NaN</td>\n",
        "      <td> S</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8  </th>\n",
        "      <td> 3</td>\n",
        "      <td>         Abrahim, Mrs. Joseph (Sophie Halaut Easu)</td>\n",
        "      <td> female</td>\n",
        "      <td> 18.0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>               2657</td>\n",
        "      <td>   7.2292</td>\n",
        "      <td>             NaN</td>\n",
        "      <td> C</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9  </th>\n",
        "      <td> 3</td>\n",
        "      <td>                           Davies, Mr. John Samuel</td>\n",
        "      <td>   male</td>\n",
        "      <td> 21.0</td>\n",
        "      <td> 2</td>\n",
        "      <td> 0</td>\n",
        "      <td>          A/4 48871</td>\n",
        "      <td>  24.1500</td>\n",
        "      <td>             NaN</td>\n",
        "      <td> S</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10 </th>\n",
        "      <td> 3</td>\n",
        "      <td>                                  Ilieff, Mr. Ylio</td>\n",
        "      <td>   male</td>\n",
        "      <td>  NaN</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>             349220</td>\n",
        "      <td>   7.8958</td>\n",
        "      <td>             NaN</td>\n",
        "      <td> S</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11 </th>\n",
        "      <td> 1</td>\n",
        "      <td>                        Jones, Mr. Charles Cresson</td>\n",
        "      <td>   male</td>\n",
        "      <td> 46.0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>                694</td>\n",
        "      <td>  26.0000</td>\n",
        "      <td>             NaN</td>\n",
        "      <td> S</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12 </th>\n",
        "      <td> 1</td>\n",
        "      <td>     Snyder, Mrs. John Pillsbury (Nelle Stevenson)</td>\n",
        "      <td> female</td>\n",
        "      <td> 23.0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>              21228</td>\n",
        "      <td>  82.2667</td>\n",
        "      <td>             B45</td>\n",
        "      <td> S</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>13 </th>\n",
        "      <td> 2</td>\n",
        "      <td>                              Howard, Mr. Benjamin</td>\n",
        "      <td>   male</td>\n",
        "      <td> 63.0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>              24065</td>\n",
        "      <td>  26.0000</td>\n",
        "      <td>             NaN</td>\n",
        "      <td> S</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>14 </th>\n",
        "      <td> 1</td>\n",
        "      <td> Chaffee, Mrs. Herbert Fuller (Carrie Constance...</td>\n",
        "      <td> female</td>\n",
        "      <td> 47.0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>        W.E.P. 5734</td>\n",
        "      <td>  61.1750</td>\n",
        "      <td>             E31</td>\n",
        "      <td> S</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>15 </th>\n",
        "      <td> 2</td>\n",
        "      <td>     del Carlo, Mrs. Sebastiano (Argenia Genovesi)</td>\n",
        "      <td> female</td>\n",
        "      <td> 24.0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>      SC/PARIS 2167</td>\n",
        "      <td>  27.7208</td>\n",
        "      <td>             NaN</td>\n",
        "      <td> C</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>16 </th>\n",
        "      <td> 2</td>\n",
        "      <td>                                 Keane, Mr. Daniel</td>\n",
        "      <td>   male</td>\n",
        "      <td> 35.0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>             233734</td>\n",
        "      <td>  12.3500</td>\n",
        "      <td>             NaN</td>\n",
        "      <td> Q</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>17 </th>\n",
        "      <td> 3</td>\n",
        "      <td>                                 Assaf, Mr. Gerios</td>\n",
        "      <td>   male</td>\n",
        "      <td> 21.0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>               2692</td>\n",
        "      <td>   7.2250</td>\n",
        "      <td>             NaN</td>\n",
        "      <td> C</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>18 </th>\n",
        "      <td> 3</td>\n",
        "      <td>                      Ilmakangas, Miss. Ida Livija</td>\n",
        "      <td> female</td>\n",
        "      <td> 27.0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>   STON/O2. 3101270</td>\n",
        "      <td>   7.9250</td>\n",
        "      <td>             NaN</td>\n",
        "      <td> S</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>19 </th>\n",
        "      <td> 3</td>\n",
        "      <td>             Assaf Khalil, Mrs. Mariana (Miriam\")\"</td>\n",
        "      <td> female</td>\n",
        "      <td> 45.0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>               2696</td>\n",
        "      <td>   7.2250</td>\n",
        "      <td>             NaN</td>\n",
        "      <td> C</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>20 </th>\n",
        "      <td> 1</td>\n",
        "      <td>                            Rothschild, Mr. Martin</td>\n",
        "      <td>   male</td>\n",
        "      <td> 55.0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>           PC 17603</td>\n",
        "      <td>  59.4000</td>\n",
        "      <td>             NaN</td>\n",
        "      <td> C</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>21 </th>\n",
        "      <td> 3</td>\n",
        "      <td>                         Olsen, Master. Artur Karl</td>\n",
        "      <td>   male</td>\n",
        "      <td>  9.0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td>            C 17368</td>\n",
        "      <td>   3.1708</td>\n",
        "      <td>             NaN</td>\n",
        "      <td> S</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>22 </th>\n",
        "      <td> 1</td>\n",
        "      <td>              Flegenheim, Mrs. Alfred (Antoinette)</td>\n",
        "      <td> female</td>\n",
        "      <td>  NaN</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>           PC 17598</td>\n",
        "      <td>  31.6833</td>\n",
        "      <td>             NaN</td>\n",
        "      <td> S</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>23 </th>\n",
        "      <td> 1</td>\n",
        "      <td>                   Williams, Mr. Richard Norris II</td>\n",
        "      <td>   male</td>\n",
        "      <td> 21.0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td>           PC 17597</td>\n",
        "      <td>  61.3792</td>\n",
        "      <td>             NaN</td>\n",
        "      <td> C</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>24 </th>\n",
        "      <td> 1</td>\n",
        "      <td>   Ryerson, Mrs. Arthur Larned (Emily Maria Borie)</td>\n",
        "      <td> female</td>\n",
        "      <td> 48.0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 3</td>\n",
        "      <td>           PC 17608</td>\n",
        "      <td> 262.3750</td>\n",
        "      <td> B57 B59 B63 B66</td>\n",
        "      <td> C</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25 </th>\n",
        "      <td> 3</td>\n",
        "      <td>                           Robins, Mr. Alexander A</td>\n",
        "      <td>   male</td>\n",
        "      <td> 50.0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>          A/5. 3337</td>\n",
        "      <td>  14.5000</td>\n",
        "      <td>             NaN</td>\n",
        "      <td> S</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>26 </th>\n",
        "      <td> 1</td>\n",
        "      <td>                      Ostby, Miss. Helene Ragnhild</td>\n",
        "      <td> female</td>\n",
        "      <td> 22.0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td>             113509</td>\n",
        "      <td>  61.9792</td>\n",
        "      <td>             B36</td>\n",
        "      <td> C</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>27 </th>\n",
        "      <td> 3</td>\n",
        "      <td>                                 Daher, Mr. Shedid</td>\n",
        "      <td>   male</td>\n",
        "      <td> 22.5</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>               2698</td>\n",
        "      <td>   7.2250</td>\n",
        "      <td>             NaN</td>\n",
        "      <td> C</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>28 </th>\n",
        "      <td> 1</td>\n",
        "      <td>                           Brady, Mr. John Bertram</td>\n",
        "      <td>   male</td>\n",
        "      <td> 41.0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>             113054</td>\n",
        "      <td>  30.5000</td>\n",
        "      <td>             A21</td>\n",
        "      <td> S</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>29 </th>\n",
        "      <td> 3</td>\n",
        "      <td>                                 Samaan, Mr. Elias</td>\n",
        "      <td>   male</td>\n",
        "      <td>  NaN</td>\n",
        "      <td> 2</td>\n",
        "      <td> 0</td>\n",
        "      <td>               2662</td>\n",
        "      <td>  21.6792</td>\n",
        "      <td>             NaN</td>\n",
        "      <td> C</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",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>388</th>\n",
        "      <td> 3</td>\n",
        "      <td>                              Canavan, Mr. Patrick</td>\n",
        "      <td>   male</td>\n",
        "      <td> 21.0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>             364858</td>\n",
        "      <td>   7.7500</td>\n",
        "      <td>             NaN</td>\n",
        "      <td> Q</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>389</th>\n",
        "      <td> 3</td>\n",
        "      <td>                       Palsson, Master. Paul Folke</td>\n",
        "      <td>   male</td>\n",
        "      <td>  6.0</td>\n",
        "      <td> 3</td>\n",
        "      <td> 1</td>\n",
        "      <td>             349909</td>\n",
        "      <td>  21.0750</td>\n",
        "      <td>             NaN</td>\n",
        "      <td> S</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>390</th>\n",
        "      <td> 1</td>\n",
        "      <td>                        Payne, Mr. Vivian Ponsonby</td>\n",
        "      <td>   male</td>\n",
        "      <td> 23.0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>              12749</td>\n",
        "      <td>  93.5000</td>\n",
        "      <td>             B24</td>\n",
        "      <td> S</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>391</th>\n",
        "      <td> 1</td>\n",
        "      <td>    Lines, Mrs. Ernest H (Elizabeth Lindsey James)</td>\n",
        "      <td> female</td>\n",
        "      <td> 51.0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td>           PC 17592</td>\n",
        "      <td>  39.4000</td>\n",
        "      <td>             D28</td>\n",
        "      <td> S</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>392</th>\n",
        "      <td> 3</td>\n",
        "      <td>                     Abbott, Master. Eugene Joseph</td>\n",
        "      <td>   male</td>\n",
        "      <td> 13.0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 2</td>\n",
        "      <td>          C.A. 2673</td>\n",
        "      <td>  20.2500</td>\n",
        "      <td>             NaN</td>\n",
        "      <td> S</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>393</th>\n",
        "      <td> 2</td>\n",
        "      <td>                              Gilbert, Mr. William</td>\n",
        "      <td>   male</td>\n",
        "      <td> 47.0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>         C.A. 30769</td>\n",
        "      <td>  10.5000</td>\n",
        "      <td>             NaN</td>\n",
        "      <td> S</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>394</th>\n",
        "      <td> 3</td>\n",
        "      <td>                          Kink-Heilmann, Mr. Anton</td>\n",
        "      <td>   male</td>\n",
        "      <td> 29.0</td>\n",
        "      <td> 3</td>\n",
        "      <td> 1</td>\n",
        "      <td>             315153</td>\n",
        "      <td>  22.0250</td>\n",
        "      <td>             NaN</td>\n",
        "      <td> S</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>395</th>\n",
        "      <td> 1</td>\n",
        "      <td>    Smith, Mrs. Lucien Philip (Mary Eloise Hughes)</td>\n",
        "      <td> female</td>\n",
        "      <td> 18.0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>              13695</td>\n",
        "      <td>  60.0000</td>\n",
        "      <td>             C31</td>\n",
        "      <td> S</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>396</th>\n",
        "      <td> 3</td>\n",
        "      <td>                              Colbert, Mr. Patrick</td>\n",
        "      <td>   male</td>\n",
        "      <td> 24.0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>             371109</td>\n",
        "      <td>   7.2500</td>\n",
        "      <td>             NaN</td>\n",
        "      <td> Q</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>397</th>\n",
        "      <td> 1</td>\n",
        "      <td> Frolicher-Stehli, Mrs. Maxmillian (Margaretha ...</td>\n",
        "      <td> female</td>\n",
        "      <td> 48.0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td>              13567</td>\n",
        "      <td>  79.2000</td>\n",
        "      <td>             B41</td>\n",
        "      <td> C</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>398</th>\n",
        "      <td> 3</td>\n",
        "      <td>                    Larsson-Rondberg, Mr. Edvard A</td>\n",
        "      <td>   male</td>\n",
        "      <td> 22.0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>             347065</td>\n",
        "      <td>   7.7750</td>\n",
        "      <td>             NaN</td>\n",
        "      <td> S</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>399</th>\n",
        "      <td> 3</td>\n",
        "      <td>                          Conlon, Mr. Thomas Henry</td>\n",
        "      <td>   male</td>\n",
        "      <td> 31.0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>              21332</td>\n",
        "      <td>   7.7333</td>\n",
        "      <td>             NaN</td>\n",
        "      <td> Q</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>400</th>\n",
        "      <td> 1</td>\n",
        "      <td>                           Bonnell, Miss. Caroline</td>\n",
        "      <td> female</td>\n",
        "      <td> 30.0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>              36928</td>\n",
        "      <td> 164.8667</td>\n",
        "      <td>              C7</td>\n",
        "      <td> S</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>401</th>\n",
        "      <td> 2</td>\n",
        "      <td>                                   Gale, Mr. Harry</td>\n",
        "      <td>   male</td>\n",
        "      <td> 38.0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>              28664</td>\n",
        "      <td>  21.0000</td>\n",
        "      <td>             NaN</td>\n",
        "      <td> S</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>402</th>\n",
        "      <td> 1</td>\n",
        "      <td>                    Gibson, Miss. Dorothy Winifred</td>\n",
        "      <td> female</td>\n",
        "      <td> 22.0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td>             112378</td>\n",
        "      <td>  59.4000</td>\n",
        "      <td>             NaN</td>\n",
        "      <td> C</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>403</th>\n",
        "      <td> 1</td>\n",
        "      <td>                            Carrau, Mr. Jose Pedro</td>\n",
        "      <td>   male</td>\n",
        "      <td> 17.0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>             113059</td>\n",
        "      <td>  47.1000</td>\n",
        "      <td>             NaN</td>\n",
        "      <td> S</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>404</th>\n",
        "      <td> 1</td>\n",
        "      <td>                      Frauenthal, Mr. Isaac Gerald</td>\n",
        "      <td>   male</td>\n",
        "      <td> 43.0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>              17765</td>\n",
        "      <td>  27.7208</td>\n",
        "      <td>             D40</td>\n",
        "      <td> C</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>405</th>\n",
        "      <td> 2</td>\n",
        "      <td>      Nourney, Mr. Alfred (Baron von Drachstedt\")\"</td>\n",
        "      <td>   male</td>\n",
        "      <td> 20.0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>      SC/PARIS 2166</td>\n",
        "      <td>  13.8625</td>\n",
        "      <td>             D38</td>\n",
        "      <td> C</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>406</th>\n",
        "      <td> 2</td>\n",
        "      <td>                         Ware, Mr. William Jeffery</td>\n",
        "      <td>   male</td>\n",
        "      <td> 23.0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>              28666</td>\n",
        "      <td>  10.5000</td>\n",
        "      <td>             NaN</td>\n",
        "      <td> S</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>407</th>\n",
        "      <td> 1</td>\n",
        "      <td>                        Widener, Mr. George Dunton</td>\n",
        "      <td>   male</td>\n",
        "      <td> 50.0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td>             113503</td>\n",
        "      <td> 211.5000</td>\n",
        "      <td>             C80</td>\n",
        "      <td> C</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>408</th>\n",
        "      <td> 3</td>\n",
        "      <td>                   Riordan, Miss. Johanna Hannah\"\"</td>\n",
        "      <td> female</td>\n",
        "      <td>  NaN</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>             334915</td>\n",
        "      <td>   7.7208</td>\n",
        "      <td>             NaN</td>\n",
        "      <td> Q</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>409</th>\n",
        "      <td> 3</td>\n",
        "      <td>                         Peacock, Miss. Treasteall</td>\n",
        "      <td> female</td>\n",
        "      <td>  3.0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> SOTON/O.Q. 3101315</td>\n",
        "      <td>  13.7750</td>\n",
        "      <td>             NaN</td>\n",
        "      <td> S</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>410</th>\n",
        "      <td> 3</td>\n",
        "      <td>                            Naughton, Miss. Hannah</td>\n",
        "      <td> female</td>\n",
        "      <td>  NaN</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>             365237</td>\n",
        "      <td>   7.7500</td>\n",
        "      <td>             NaN</td>\n",
        "      <td> Q</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>411</th>\n",
        "      <td> 1</td>\n",
        "      <td>   Minahan, Mrs. William Edward (Lillian E Thorpe)</td>\n",
        "      <td> female</td>\n",
        "      <td> 37.0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>              19928</td>\n",
        "      <td>  90.0000</td>\n",
        "      <td>             C78</td>\n",
        "      <td> Q</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>412</th>\n",
        "      <td> 3</td>\n",
        "      <td>                    Henriksson, Miss. Jenny Lovisa</td>\n",
        "      <td> female</td>\n",
        "      <td> 28.0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>             347086</td>\n",
        "      <td>   7.7750</td>\n",
        "      <td>             NaN</td>\n",
        "      <td> S</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>413</th>\n",
        "      <td> 3</td>\n",
        "      <td>                                Spector, Mr. Woolf</td>\n",
        "      <td>   male</td>\n",
        "      <td>  NaN</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>          A.5. 3236</td>\n",
        "      <td>   8.0500</td>\n",
        "      <td>             NaN</td>\n",
        "      <td> S</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>414</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> 108.9000</td>\n",
        "      <td>            C105</td>\n",
        "      <td> C</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>415</th>\n",
        "      <td> 3</td>\n",
        "      <td>                      Saether, Mr. Simon Sivertsen</td>\n",
        "      <td>   male</td>\n",
        "      <td> 38.5</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> SOTON/O.Q. 3101262</td>\n",
        "      <td>   7.2500</td>\n",
        "      <td>             NaN</td>\n",
        "      <td> S</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>416</th>\n",
        "      <td> 3</td>\n",
        "      <td>                               Ware, Mr. Frederick</td>\n",
        "      <td>   male</td>\n",
        "      <td>  NaN</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>             359309</td>\n",
        "      <td>   8.0500</td>\n",
        "      <td>             NaN</td>\n",
        "      <td> S</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>417</th>\n",
        "      <td> 3</td>\n",
        "      <td>                          Peter, Master. Michael J</td>\n",
        "      <td>   male</td>\n",
        "      <td>  NaN</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td>               2668</td>\n",
        "      <td>  22.3583</td>\n",
        "      <td>             NaN</td>\n",
        "      <td> C</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>418 rows \u00d7 10 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 159,
       "text": [
        "     pclass                                               name     sex   age  \\\n",
        "0         3                                   Kelly, Mr. James    male  34.5   \n",
        "1         3                   Wilkes, Mrs. James (Ellen Needs)  female  47.0   \n",
        "2         2                          Myles, Mr. Thomas Francis    male  62.0   \n",
        "3         3                                   Wirz, Mr. Albert    male  27.0   \n",
        "4         3       Hirvonen, Mrs. Alexander (Helga E Lindqvist)  female  22.0   \n",
        "5         3                         Svensson, Mr. Johan Cervin    male  14.0   \n",
        "6         3                               Connolly, Miss. Kate  female  30.0   \n",
        "7         2                       Caldwell, Mr. Albert Francis    male  26.0   \n",
        "8         3          Abrahim, Mrs. Joseph (Sophie Halaut Easu)  female  18.0   \n",
        "9         3                            Davies, Mr. John Samuel    male  21.0   \n",
        "10        3                                   Ilieff, Mr. Ylio    male   NaN   \n",
        "11        1                         Jones, Mr. Charles Cresson    male  46.0   \n",
        "12        1      Snyder, Mrs. John Pillsbury (Nelle Stevenson)  female  23.0   \n",
        "13        2                               Howard, Mr. Benjamin    male  63.0   \n",
        "14        1  Chaffee, Mrs. Herbert Fuller (Carrie Constance...  female  47.0   \n",
        "15        2      del Carlo, Mrs. Sebastiano (Argenia Genovesi)  female  24.0   \n",
        "16        2                                  Keane, Mr. Daniel    male  35.0   \n",
        "17        3                                  Assaf, Mr. Gerios    male  21.0   \n",
        "18        3                       Ilmakangas, Miss. Ida Livija  female  27.0   \n",
        "19        3              Assaf Khalil, Mrs. Mariana (Miriam\")\"  female  45.0   \n",
        "20        1                             Rothschild, Mr. Martin    male  55.0   \n",
        "21        3                          Olsen, Master. Artur Karl    male   9.0   \n",
        "22        1               Flegenheim, Mrs. Alfred (Antoinette)  female   NaN   \n",
        "23        1                    Williams, Mr. Richard Norris II    male  21.0   \n",
        "24        1    Ryerson, Mrs. Arthur Larned (Emily Maria Borie)  female  48.0   \n",
        "25        3                            Robins, Mr. Alexander A    male  50.0   \n",
        "26        1                       Ostby, Miss. Helene Ragnhild  female  22.0   \n",
        "27        3                                  Daher, Mr. Shedid    male  22.5   \n",
        "28        1                            Brady, Mr. John Bertram    male  41.0   \n",
        "29        3                                  Samaan, Mr. Elias    male   NaN   \n",
        "..      ...                                                ...     ...   ...   \n",
        "388       3                               Canavan, Mr. Patrick    male  21.0   \n",
        "389       3                        Palsson, Master. Paul Folke    male   6.0   \n",
        "390       1                         Payne, Mr. Vivian Ponsonby    male  23.0   \n",
        "391       1     Lines, Mrs. Ernest H (Elizabeth Lindsey James)  female  51.0   \n",
        "392       3                      Abbott, Master. Eugene Joseph    male  13.0   \n",
        "393       2                               Gilbert, Mr. William    male  47.0   \n",
        "394       3                           Kink-Heilmann, Mr. Anton    male  29.0   \n",
        "395       1     Smith, Mrs. Lucien Philip (Mary Eloise Hughes)  female  18.0   \n",
        "396       3                               Colbert, Mr. Patrick    male  24.0   \n",
        "397       1  Frolicher-Stehli, Mrs. Maxmillian (Margaretha ...  female  48.0   \n",
        "398       3                     Larsson-Rondberg, Mr. Edvard A    male  22.0   \n",
        "399       3                           Conlon, Mr. Thomas Henry    male  31.0   \n",
        "400       1                            Bonnell, Miss. Caroline  female  30.0   \n",
        "401       2                                    Gale, Mr. Harry    male  38.0   \n",
        "402       1                     Gibson, Miss. Dorothy Winifred  female  22.0   \n",
        "403       1                             Carrau, Mr. Jose Pedro    male  17.0   \n",
        "404       1                       Frauenthal, Mr. Isaac Gerald    male  43.0   \n",
        "405       2       Nourney, Mr. Alfred (Baron von Drachstedt\")\"    male  20.0   \n",
        "406       2                          Ware, Mr. William Jeffery    male  23.0   \n",
        "407       1                         Widener, Mr. George Dunton    male  50.0   \n",
        "408       3                    Riordan, Miss. Johanna Hannah\"\"  female   NaN   \n",
        "409       3                          Peacock, Miss. Treasteall  female   3.0   \n",
        "410       3                             Naughton, Miss. Hannah  female   NaN   \n",
        "411       1    Minahan, Mrs. William Edward (Lillian E Thorpe)  female  37.0   \n",
        "412       3                     Henriksson, Miss. Jenny Lovisa  female  28.0   \n",
        "413       3                                 Spector, Mr. Woolf    male   NaN   \n",
        "414       1                       Oliva y Ocana, Dona. Fermina  female  39.0   \n",
        "415       3                       Saether, Mr. Simon Sivertsen    male  38.5   \n",
        "416       3                                Ware, Mr. Frederick    male   NaN   \n",
        "417       3                           Peter, Master. Michael J    male   NaN   \n",
        "\n",
        "     sibsp  parch              ticket      fare            cabin embarked  \n",
        "0        0      0              330911    7.8292              NaN        Q  \n",
        "1        1      0              363272    7.0000              NaN        S  \n",
        "2        0      0              240276    9.6875              NaN        Q  \n",
        "3        0      0              315154    8.6625              NaN        S  \n",
        "4        1      1             3101298   12.2875              NaN        S  \n",
        "5        0      0                7538    9.2250              NaN        S  \n",
        "6        0      0              330972    7.6292              NaN        Q  \n",
        "7        1      1              248738   29.0000              NaN        S  \n",
        "8        0      0                2657    7.2292              NaN        C  \n",
        "9        2      0           A/4 48871   24.1500              NaN        S  \n",
        "10       0      0              349220    7.8958              NaN        S  \n",
        "11       0      0                 694   26.0000              NaN        S  \n",
        "12       1      0               21228   82.2667              B45        S  \n",
        "13       1      0               24065   26.0000              NaN        S  \n",
        "14       1      0         W.E.P. 5734   61.1750              E31        S  \n",
        "15       1      0       SC/PARIS 2167   27.7208              NaN        C  \n",
        "16       0      0              233734   12.3500              NaN        Q  \n",
        "17       0      0                2692    7.2250              NaN        C  \n",
        "18       1      0    STON/O2. 3101270    7.9250              NaN        S  \n",
        "19       0      0                2696    7.2250              NaN        C  \n",
        "20       1      0            PC 17603   59.4000              NaN        C  \n",
        "21       0      1             C 17368    3.1708              NaN        S  \n",
        "22       0      0            PC 17598   31.6833              NaN        S  \n",
        "23       0      1            PC 17597   61.3792              NaN        C  \n",
        "24       1      3            PC 17608  262.3750  B57 B59 B63 B66        C  \n",
        "25       1      0           A/5. 3337   14.5000              NaN        S  \n",
        "26       0      1              113509   61.9792              B36        C  \n",
        "27       0      0                2698    7.2250              NaN        C  \n",
        "28       0      0              113054   30.5000              A21        S  \n",
        "29       2      0                2662   21.6792              NaN        C  \n",
        "..     ...    ...                 ...       ...              ...      ...  \n",
        "388      0      0              364858    7.7500              NaN        Q  \n",
        "389      3      1              349909   21.0750              NaN        S  \n",
        "390      0      0               12749   93.5000              B24        S  \n",
        "391      0      1            PC 17592   39.4000              D28        S  \n",
        "392      0      2           C.A. 2673   20.2500              NaN        S  \n",
        "393      0      0          C.A. 30769   10.5000              NaN        S  \n",
        "394      3      1              315153   22.0250              NaN        S  \n",
        "395      1      0               13695   60.0000              C31        S  \n",
        "396      0      0              371109    7.2500              NaN        Q  \n",
        "397      1      1               13567   79.2000              B41        C  \n",
        "398      0      0              347065    7.7750              NaN        S  \n",
        "399      0      0               21332    7.7333              NaN        Q  \n",
        "400      0      0               36928  164.8667               C7        S  \n",
        "401      1      0               28664   21.0000              NaN        S  \n",
        "402      0      1              112378   59.4000              NaN        C  \n",
        "403      0      0              113059   47.1000              NaN        S  \n",
        "404      1      0               17765   27.7208              D40        C  \n",
        "405      0      0       SC/PARIS 2166   13.8625              D38        C  \n",
        "406      1      0               28666   10.5000              NaN        S  \n",
        "407      1      1              113503  211.5000              C80        C  \n",
        "408      0      0              334915    7.7208              NaN        Q  \n",
        "409      1      1  SOTON/O.Q. 3101315   13.7750              NaN        S  \n",
        "410      0      0              365237    7.7500              NaN        Q  \n",
        "411      1      0               19928   90.0000              C78        Q  \n",
        "412      0      0              347086    7.7750              NaN        S  \n",
        "413      0      0           A.5. 3236    8.0500              NaN        S  \n",
        "414      0      0            PC 17758  108.9000             C105        C  \n",
        "415      0      0  SOTON/O.Q. 3101262    7.2500              NaN        S  \n",
        "416      0      0              359309    8.0500              NaN        S  \n",
        "417      1      1                2668   22.3583              NaN        C  \n",
        "\n",
        "[418 rows x 10 columns]"
       ]
      }
     ],
     "prompt_number": 159
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Add our independent variable to our test data. (Its ussually left blank by Kaggle because its the value your trying to predict.)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "test_data['survived'] = 1.23"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 160
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Our binned results data:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "results "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 161,
       "text": [
        "{'Logit': [<statsmodels.discrete.discrete_model.BinaryResultsWrapper at 0x11c344ad0>,\n",
        "  'survived ~ C(pclass) + C(sex) + age + sibsp  + C(embarked)']}"
       ]
      }
     ],
     "prompt_number": 161
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "compared_resuts = ka.predict(test_data, results, 'Logit') # Use your model to make prediction on our test set. \n",
      "compared_resuts = Series(compared_resuts)                 # convert our model to a series for easy output"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 162
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# output and submit to kaggel\n",
      "compared_resuts.to_csv(\"data/output/logitregres.csv\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 163
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Results as scored by Kaggle: RMSE = 0.77033\n",
      "\n",
      "That result is pretty good. ECT ECT ECT"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Create an acceptable formula for our machine learning algorithms\n",
      "formula_ml = 'survived ~ C(pclass) + C(sex) + age + sibsp + parch + C(embarked)'"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 164
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Support Vector Machine (SVM)\n",
      "\n",
      "*\"So uhhh, what if a straight line just doesn\u2019t cut it.\"*\n",
      "\n",
      "**Wikipeda:**\n",
      ">In machine learning, support vector machines (SVMs, also support vector networks[1]) are supervised learning models with associated learning algorithms that analyze data and recognize patterns, used for classification and regression analysis. The basic SVM takes a set of input data and predicts, for each given input, which of two possible classes forms the output, making it a non-probabilistic binary linear classifier. Given a set of training examples, each marked as belonging to one of two categories, an SVM training algorithm builds a model that assigns new examples into one category or the other. An SVM model is a representation of the examples as points in space, mapped so that the examples of the separate categories are divided by a clear gap that is as wide as possible. New examples are then mapped into that same space and predicted to belong to a category based on which side of the gap they fall on.\n",
      "In addition to performing linear classification, SVMs can efficiently perform non-linear classification using what is called the kernel trick, implicitly mapping their inputs into high-dimensional feature spaces.\n",
      "\n",
      "##From me\n",
      "The logit model we just implemented was great in that it showed exactly where to draw our decision boundary or our 'survival cut off'.  But if you\u2019re like me, you could have thought, \"So uhhh, what if a straight line just doesn\u2019t cut it\". A linear line is okay, but can we do better? Perhaps a more complex decision boundary like a wave, circle, or maybe some sort of strange polygon would describe the variance observed in our sample better than a line.  Imagine if we were predicating survival based on age. It could be a linear decision boundary, meaning  each additional time you've gone around the sun you were 1 unit more or less likely to survive. But I think it could be easy to imagine some sort of curve, where a young healthy person would have the best chance of survival, and sadly the very old and very young a like: a poor chance. Now that\u2019s a interesting question to answer. But our logit model can only evaluate a linear decision boundary. How do we get around this? With the usual answer to life the universe and everything;  $MATH$. \n",
      "\n",
      "**The answer:**\n",
      "We could transform our logit equation from expressing a linear relationship like so:\n",
      "\n",
      "$survived  = \\beta_0 + \\beta_1pclass + \\beta_2sex + \\beta_3age + \\beta_4sibsp + \\beta_5parch + \\beta_6embarked$\n",
      "\n",
      "Which we'll represent for convenience as: \n",
      "$y = x$\n",
      "\t\t\n",
      "\n",
      "to a expressing a linear expression of a non-linear relationship: \n",
      "$\\log(y) = \\log(x)$\n",
      "\n",
      "By doing this we're not breaking the rules. Logit models are *only* efficient at modeling linear relationships, so we're just giving it a linear relationship of a non-linear thing. \n",
      "\n",
      "An easy way to visualize this by looking at a graph an exponential relationship. Like the graph of $x^3$:\n",
      "\n",
      "![x3](https://raw.github.com/agconti/kaggle-titanic/master/x3.png)\n",
      "\n",
      "Here its obvious that this is not linear. If used it as an equation for our logit model, $y = x^3$; we would get bad results. But if we transformed it by taking the log  of our equation, $\\log(y) = \\log(x^3)$. We would get a graph like this:\n",
      "\n",
      "![loglogx3](https://raw.github.com/agconti/kaggle-titanic/master/loglogx3.png)\n",
      "\n",
      "That looks pretty linear to me. \n",
      "\n",
      "This process of transforming models so that they can be better expressed in a different mathematical plane is exactly what the Support Vector Machine does for us. The math behind how it does that is not trivial, so if your interested; put on your reading glasses and head over [here](http://dustwell.com/PastWork/IntroToSVM.pdf). Below is the process of implementing a SVM model and examining the results after the SVM transforms our equation into three different mathematical plains. The first is linear, and is similar to our logic model. Next is an exponential, polynomial, transformation and finally a blank transformation.\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# set plotting parameters\n",
      "plt.figure(figsize=(8,6))\n",
      "\n",
      "# create a regression friendly data frame\n",
      "y, x = dmatrices(formula_ml, data=df, return_type='matrix')\n",
      "\n",
      "# select which features we would like to analyze\n",
      "# try chaning the selection here for diffrent output.\n",
      "# Choose : [2,3] - pretty sweet DBs [3,1] --standard DBs [7,3] -very cool DBs, [3,6] -- very long complex dbs, could take over an hour to calculate! \n",
      "feature_1 = 2\n",
      "feature_2 = 3\n",
      "\n",
      "X = np.asarray(x)\n",
      "X = X[:,[feature_1, feature_2]]  \n",
      "\n",
      "\n",
      "y = np.asarray(y)\n",
      "# needs to be 1 dimenstional so we flatten. it comes out of dmatirces with a shape. \n",
      "y = y.flatten()      \n",
      "\n",
      "n_sample = len(X)\n",
      "\n",
      "np.random.seed(0)\n",
      "order = np.random.permutation(n_sample)\n",
      "\n",
      "X = X[order]\n",
      "y = y[order].astype(np.float)\n",
      "\n",
      "# do a cross validation\n",
      "nighty_precent_of_sample = int(.9 * n_sample)\n",
      "X_train = X[:nighty_precent_of_sample]\n",
      "y_train = y[:nighty_precent_of_sample]\n",
      "X_test = X[nighty_precent_of_sample:]\n",
      "y_test = y[nighty_precent_of_sample:]\n",
      "\n",
      "# create a list of the types of kerneks we will use for your analysis\n",
      "types_of_kernels = ['linear', 'rbf', 'poly']\n",
      "\n",
      "# specify our color map for plotting the results\n",
      "color_map = plt.cm.RdBu_r\n",
      "\n",
      "# fit the model\n",
      "for fig_num, kernel in enumerate(types_of_kernels):\n",
      "    clf = svm.SVC(kernel=kernel, gamma=3)\n",
      "    clf.fit(X_train, y_train)\n",
      "\n",
      "    plt.figure(fig_num)\n",
      "    plt.scatter(X[:, 0], X[:, 1], c=y, zorder=10, cmap=color_map)\n",
      "\n",
      "    # circle out the test data\n",
      "    plt.scatter(X_test[:, 0], X_test[:, 1], s=80, facecolors='none', zorder=10)\n",
      "    \n",
      "    plt.axis('tight')\n",
      "    x_min = X[:, 0].min()\n",
      "    x_max = X[:, 0].max()\n",
      "    y_min = X[:, 1].min()\n",
      "    y_max = X[:, 1].max()\n",
      "\n",
      "    XX, YY = np.mgrid[x_min:x_max:200j, y_min:y_max:200j]\n",
      "    Z = clf.decision_function(np.c_[XX.ravel(), YY.ravel()])\n",
      "\n",
      "    # put the result into a color plot\n",
      "    Z = Z.reshape(XX.shape)\n",
      "    plt.pcolormesh(XX, YY, Z > 0, cmap=color_map)\n",
      "    plt.contour(XX, YY, Z, colors=['k', 'k', 'k'], linestyles=['--', '-', '--'],\n",
      "               levels=[-.5, 0, .5])\n",
      "\n",
      "    plt.title(kernel)\n",
      "    plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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1o3p+tfB2zVuSAmR4S1KADG9JClDa8L4QeBF4Bbg24fFPA78DngceB07JpDtJUqI0pwo2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\nSZIkSZIkSZIkSZIkSZIkSZIkKWz/DwYpL7gfuCoUAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x11c310590>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<matplotlib.figure.Figure at 0x11c918cd0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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bmzl2LH9n+uYy8r4d2A7sBB6Yoc3fZZ5/DVg736K2bt3K4sWLTwd3Pfbp3zKH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\niIiIiIiIiIiIiIiIiIiIiIiIiEiw/X92EqVMKF3MFAAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x11c4e3c10>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x11c66b590>"
       ]
      }
     ],
     "prompt_number": 165
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Any value in the blue survived while anyone in the read did not. Checkout the graph for the linear transformation. It created its decision boundary right on 50%! That guess from earlier turned out to be pretty good.  As you can see, the remaining decision boundaries are much more complex than our original linear decision boundary. These more complex boundaries may be able to capture more structure in the dataset, if that structure exists, and so might create a more powerful predictive model.\n",
      "\n",
      "Pick a decision boundary that you like, adjust the code below, and submit the results to Kaggle to see how well it worked!"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Here you can output which ever result you would like by changing the Kernel and clf.predict lines\n",
      "# Change kernel here to poly, rbf or linear\n",
      "# adjusting the gamma level also changes the degree to which the model is fitted\n",
      "clf = svm.SVC(kernel='poly', gamma=3).fit(X_train, y_train)                                                            \n",
      "y,x = dmatrices(formula_ml, data=test_data, return_type='dataframe')\n",
      "\n",
      "# Change the interger values within x.ix[:,[6,3]].dropna() explore the relationships between other \n",
      "# features. the ints are column postions. ie. [6,3] 6th column and the third column are evaluated. \n",
      "res_svm = clf.predict(x.ix[:,[6,3]].dropna()) \n",
      "\n",
      "res_svm = DataFrame(res_svm,columns=['Survived'])\n",
      "res_svm.to_csv(\"data/output/svm_poly_63_g10.csv\") # saves the results for you, change the name as you please. "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 166
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Random Forest\n",
      "\n",
      "\"Well, What if this line / decision boundary thing doesn\u2019t work at all.\"\n",
      "\n",
      "**Wikipedia, crystal clear as always:**\n",
      ">Random forests are an ensemble learning method for classification (and regression) that operate by constructing a multitude of decision trees at training time and outputting the class that is the mode of the classes output by individual trees.\n",
      "\n",
      "**Once again, the skinny and why it matters to you:**\n",
      "\n",
      "There are always skeptics, and you just might be one about all the fancy lines we've created so far. Well for you, here\u2019s another option; the Random Forest. This technique is a form of non-parametric modeling that does away with all those equations we created above, and uses raw computing power and a clever statistical observation to tease the structure out of the data. \n",
      "\n",
      "An anecdote to explain how this the forest works starts with the lowly gumball jar. We've all guess how many gumballs are in that jar at one time or another, and odds are not a single one of us guessed exactly right. Interestingly though, while each of our individual guesses for probably were wrong, the average of all of the guesses, if there were enough, usually comes out to be pretty close to the actual number of gumballs in the jar. Crazy, I know.  This idea is that clever statistical observation that lets random forests work.\n",
      "\n",
      "**How do they work?** A random forest algorithm randomly generates many extremely simple models to explain the variance observed in random subsections of our data.  These models are like our gumball guesses. They are all awful individually. Really awful. But once they are averaged, they can be powerful predictive tools. The averaging step is the secret sauce. While the vast majority of those models were extremely poor; they were all as bad as each other on average. So when their predictions are averaged together, the bad ones average their effect on our model out to zero. The thing that remains, *if anything*, is one or a handful of those models have stumbled upon the true structure of the data.\n",
      "The cell below shows the process of instantiating and fitting a random forest, generating predictions form the resulting model, and then scoring the results."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# import the machine learning library that holds the randomforest\n",
      "import sklearn.ensemble as ske\n",
      "\n",
      "# Create the random forest model and fit the model to our training data\n",
      "y, x = dmatrices(formula_ml, data=df, return_type='dataframe')\n",
      "# RandomForestClassifier expects a 1 demensional NumPy array, so we convert\n",
      "y = np.asarray(y).ravel()\n",
      "#instantiate and fit our model\n",
      "results_rf = ske.RandomForestClassifier(n_estimators=100).fit(x, y)\n",
      "\n",
      "# Score the results\n",
      "score = results_rf.score(x, y)\n",
      "print \"Mean accuracy of Random Forest Predictions on the data was: {0}\".format(score)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Mean accuracy of Random Forest Predictions on the data was: 0.945224719101\n"
       ]
      }
     ],
     "prompt_number": 167
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Our random forest performed only slightly better than a thumb wave, meaning that if you randomly assigned 1s and 0s by waving your thumb up and down you would do almost as well on average. It seems that this time our random forest did not stumble on the true structure of the data. \n",
      "\n",
      "These are just a few of the machine learning techniques that you can apply. Try a few for yourself and move up the leader board!\n",
      "\n",
      "Ready to see more an example of a more advanced analysis? Check out these note books:\n",
      "\n",
      "* [Kaggle Competition | Blue Book for Bulldozers Quantitative Model](http://nbviewer.ipython.org/urls/raw.github.com/agconti/AGC_BlueBook/master/BlueBook.ipynb#)\n",
      "* [GOOG VS AAPL Correlation Arb](http://nbviewer.ipython.org/urls/raw.github.com/agconti/AGCTrading/master/GOOG%2520V.%2520AAPL%2520Correlation%2520Arb.ipynb)\n",
      "* [US Dollar as a Vehicle Currency; an analysis through Italian Trade](https://github.com/agconti/US_Dollar_Vehicle_Currency)\n",
      "        \n",
      "####Follow me on [github](http://github.com/agconti), and [twitter](http://twitter.com/agconti) for more books to come soon!\n"
     ]
    }
   ],
   "metadata": {}
  }
 ]
}