titanic-solution.ipynb

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   "source": [
    "# Titanic: Machine Learning from Disaster\n",
    "### Predict survival on the Titanic\n",
    "- Defining the problem statement\n",
    "- Collecting the data\n",
    "- Exploratory data analysis\n",
    "- Feature engineering\n",
    "- Modelling\n",
    "- Testing"
   ]
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    "## 1. Defining the problem statement\n",
    "Complete the analysis of what sorts of people were likely to survive.  \n",
    "In particular, we ask you to apply the tools of machine learning to predict which passengers survived the Titanic tragedy."
   ]
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  {
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    "from IPython.display import Image\n",
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   "source": [
    "## 2. Collecting the data\n",
    "\n",
    "training data set and testing data set are given by Kaggle\n",
    "you can download from  \n",
    "my github [https://github.com/minsuk-heo/kaggle-titanic/tree/master](https://github.com/minsuk-heo/kaggle-titanic)  \n",
    "or you can download from kaggle directly [kaggle](https://www.kaggle.com/c/titanic/data)  \n",
    "\n",
    "### load train, test dataset using Pandas"
   ]
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  {
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   "source": [
    "import pandas as pd\n",
    "\n",
    "train = pd.read_csv('input/train.csv')\n",
    "test = pd.read_csv('input/test.csv')"
   ]
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   },
   "source": [
    "## 3. Exploratory data analysis\n",
    "Printing first 5 rows of the train dataset."
   ]
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       "      <td>0</td>\n",
       "      <td>330959</td>\n",
       "      <td>7.8792</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Todoroff, Mr. Lalio</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349216</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>51</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Panula, Master. Juha Niilo</td>\n",
       "      <td>male</td>\n",
       "      <td>7.00</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>3101295</td>\n",
       "      <td>39.6875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>52</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Nosworthy, Mr. Richard Cater</td>\n",
       "      <td>male</td>\n",
       "      <td>21.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A/4. 39886</td>\n",
       "      <td>7.8000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Harper, Mrs. Henry Sleeper (Myna Haxtun)</td>\n",
       "      <td>female</td>\n",
       "      <td>49.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17572</td>\n",
       "      <td>76.7292</td>\n",
       "      <td>D33</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>54</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Faunthorpe, Mrs. Lizzie (Elizabeth Anne Wilkin...</td>\n",
       "      <td>female</td>\n",
       "      <td>29.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2926</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>55</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Ostby, Mr. Engelhart Cornelius</td>\n",
       "      <td>male</td>\n",
       "      <td>65.00</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>113509</td>\n",
       "      <td>61.9792</td>\n",
       "      <td>B30</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Woolner, Mr. Hugh</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>19947</td>\n",
       "      <td>35.5000</td>\n",
       "      <td>C52</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>57</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Rugg, Miss. Emily</td>\n",
       "      <td>female</td>\n",
       "      <td>21.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>C.A. 31026</td>\n",
       "      <td>10.5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>58</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Novel, Mr. Mansouer</td>\n",
       "      <td>male</td>\n",
       "      <td>28.50</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2697</td>\n",
       "      <td>7.2292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>59</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>West, Miss. Constance Mirium</td>\n",
       "      <td>female</td>\n",
       "      <td>5.00</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>C.A. 34651</td>\n",
       "      <td>27.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Goodwin, Master. William Frederick</td>\n",
       "      <td>male</td>\n",
       "      <td>11.00</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>CA 2144</td>\n",
       "      <td>46.9000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>61</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Sirayanian, Mr. Orsen</td>\n",
       "      <td>male</td>\n",
       "      <td>22.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2669</td>\n",
       "      <td>7.2292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>62</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Icard, Miss. Amelie</td>\n",
       "      <td>female</td>\n",
       "      <td>38.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113572</td>\n",
       "      <td>80.0000</td>\n",
       "      <td>B28</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>63</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Harris, Mr. Henry Birkhardt</td>\n",
       "      <td>male</td>\n",
       "      <td>45.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>36973</td>\n",
       "      <td>83.4750</td>\n",
       "      <td>C83</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Skoog, Master. Harald</td>\n",
       "      <td>male</td>\n",
       "      <td>4.00</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>347088</td>\n",
       "      <td>27.9000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>65</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Stewart, Mr. Albert A</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17605</td>\n",
       "      <td>27.7208</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>66</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Moubarek, Master. Gerios</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2661</td>\n",
       "      <td>15.2458</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>67</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Nye, Mrs. (Elizabeth Ramell)</td>\n",
       "      <td>female</td>\n",
       "      <td>29.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>C.A. 29395</td>\n",
       "      <td>10.5000</td>\n",
       "      <td>F33</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>68</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Crease, Mr. Ernest James</td>\n",
       "      <td>male</td>\n",
       "      <td>19.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>S.P. 3464</td>\n",
       "      <td>8.1583</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>69</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Andersson, Miss. Erna Alexandra</td>\n",
       "      <td>female</td>\n",
       "      <td>17.00</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3101281</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>70</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Kink, Mr. Vincenz</td>\n",
       "      <td>male</td>\n",
       "      <td>26.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>315151</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>71</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Jenkin, Mr. Stephen Curnow</td>\n",
       "      <td>male</td>\n",
       "      <td>32.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>C.A. 33111</td>\n",
       "      <td>10.5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>72</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Goodwin, Miss. Lillian Amy</td>\n",
       "      <td>female</td>\n",
       "      <td>16.00</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>CA 2144</td>\n",
       "      <td>46.9000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>73</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Hood, Mr. Ambrose Jr</td>\n",
       "      <td>male</td>\n",
       "      <td>21.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>S.O.C. 14879</td>\n",
       "      <td>73.5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>74</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Chronopoulos, Mr. Apostolos</td>\n",
       "      <td>male</td>\n",
       "      <td>26.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2680</td>\n",
       "      <td>14.4542</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>75</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Bing, Mr. Lee</td>\n",
       "      <td>male</td>\n",
       "      <td>32.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1601</td>\n",
       "      <td>56.4958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>76</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Moen, Mr. Sigurd Hansen</td>\n",
       "      <td>male</td>\n",
       "      <td>25.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>348123</td>\n",
       "      <td>7.6500</td>\n",
       "      <td>F G73</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>77</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Staneff, Mr. Ivan</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349208</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>78</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Moutal, Mr. Rahamin Haim</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>374746</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>79</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Caldwell, Master. Alden Gates</td>\n",
       "      <td>male</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>248738</td>\n",
       "      <td>29.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Dowdell, Miss. Elizabeth</td>\n",
       "      <td>female</td>\n",
       "      <td>30.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>364516</td>\n",
       "      <td>12.4750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>80 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    PassengerId  Survived  Pclass  \\\n",
       "0             1         0       3   \n",
       "1             2         1       1   \n",
       "2             3         1       3   \n",
       "3             4         1       1   \n",
       "4             5         0       3   \n",
       "5             6         0       3   \n",
       "6             7         0       1   \n",
       "7             8         0       3   \n",
       "8             9         1       3   \n",
       "9            10         1       2   \n",
       "10           11         1       3   \n",
       "11           12         1       1   \n",
       "12           13         0       3   \n",
       "13           14         0       3   \n",
       "14           15         0       3   \n",
       "15           16         1       2   \n",
       "16           17         0       3   \n",
       "17           18         1       2   \n",
       "18           19         0       3   \n",
       "19           20         1       3   \n",
       "20           21         0       2   \n",
       "21           22         1       2   \n",
       "22           23         1       3   \n",
       "23           24         1       1   \n",
       "24           25         0       3   \n",
       "25           26         1       3   \n",
       "26           27         0       3   \n",
       "27           28         0       1   \n",
       "28           29         1       3   \n",
       "29           30         0       3   \n",
       "..          ...       ...     ...   \n",
       "50           51         0       3   \n",
       "51           52         0       3   \n",
       "52           53         1       1   \n",
       "53           54         1       2   \n",
       "54           55         0       1   \n",
       "55           56         1       1   \n",
       "56           57         1       2   \n",
       "57           58         0       3   \n",
       "58           59         1       2   \n",
       "59           60         0       3   \n",
       "60           61         0       3   \n",
       "61           62         1       1   \n",
       "62           63         0       1   \n",
       "63           64         0       3   \n",
       "64           65         0       1   \n",
       "65           66         1       3   \n",
       "66           67         1       2   \n",
       "67           68         0       3   \n",
       "68           69         1       3   \n",
       "69           70         0       3   \n",
       "70           71         0       2   \n",
       "71           72         0       3   \n",
       "72           73         0       2   \n",
       "73           74         0       3   \n",
       "74           75         1       3   \n",
       "75           76         0       3   \n",
       "76           77         0       3   \n",
       "77           78         0       3   \n",
       "78           79         1       2   \n",
       "79           80         1       3   \n",
       "\n",
       "                                                 Name     Sex    Age  SibSp  \\\n",
       "0                             Braund, Mr. Owen Harris    male  22.00      1   \n",
       "1   Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.00      1   \n",
       "2                              Heikkinen, Miss. Laina  female  26.00      0   \n",
       "3        Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.00      1   \n",
       "4                            Allen, Mr. William Henry    male  35.00      0   \n",
       "5                                    Moran, Mr. James    male    NaN      0   \n",
       "6                             McCarthy, Mr. Timothy J    male  54.00      0   \n",
       "7                      Palsson, Master. Gosta Leonard    male   2.00      3   \n",
       "8   Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)  female  27.00      0   \n",
       "9                 Nasser, Mrs. Nicholas (Adele Achem)  female  14.00      1   \n",
       "10                    Sandstrom, Miss. Marguerite Rut  female   4.00      1   \n",
       "11                           Bonnell, Miss. Elizabeth  female  58.00      0   \n",
       "12                     Saundercock, Mr. William Henry    male  20.00      0   \n",
       "13                        Andersson, Mr. Anders Johan    male  39.00      1   \n",
       "14               Vestrom, Miss. Hulda Amanda Adolfina  female  14.00      0   \n",
       "15                   Hewlett, Mrs. (Mary D Kingcome)   female  55.00      0   \n",
       "16                               Rice, Master. Eugene    male   2.00      4   \n",
       "17                       Williams, Mr. Charles Eugene    male    NaN      0   \n",
       "18  Vander Planke, Mrs. Julius (Emelia Maria Vande...  female  31.00      1   \n",
       "19                            Masselmani, Mrs. Fatima  female    NaN      0   \n",
       "20                               Fynney, Mr. Joseph J    male  35.00      0   \n",
       "21                              Beesley, Mr. Lawrence    male  34.00      0   \n",
       "22                        McGowan, Miss. Anna \"Annie\"  female  15.00      0   \n",
       "23                       Sloper, Mr. William Thompson    male  28.00      0   \n",
       "24                      Palsson, Miss. Torborg Danira  female   8.00      3   \n",
       "25  Asplund, Mrs. Carl Oscar (Selma Augusta Emilia...  female  38.00      1   \n",
       "26                            Emir, Mr. Farred Chehab    male    NaN      0   \n",
       "27                     Fortune, Mr. Charles Alexander    male  19.00      3   \n",
       "28                      O'Dwyer, Miss. Ellen \"Nellie\"  female    NaN      0   \n",
       "29                                Todoroff, Mr. Lalio    male    NaN      0   \n",
       "..                                                ...     ...    ...    ...   \n",
       "50                         Panula, Master. Juha Niilo    male   7.00      4   \n",
       "51                       Nosworthy, Mr. Richard Cater    male  21.00      0   \n",
       "52           Harper, Mrs. Henry Sleeper (Myna Haxtun)  female  49.00      1   \n",
       "53  Faunthorpe, Mrs. Lizzie (Elizabeth Anne Wilkin...  female  29.00      1   \n",
       "54                     Ostby, Mr. Engelhart Cornelius    male  65.00      0   \n",
       "55                                  Woolner, Mr. Hugh    male    NaN      0   \n",
       "56                                  Rugg, Miss. Emily  female  21.00      0   \n",
       "57                                Novel, Mr. Mansouer    male  28.50      0   \n",
       "58                       West, Miss. Constance Mirium  female   5.00      1   \n",
       "59                 Goodwin, Master. William Frederick    male  11.00      5   \n",
       "60                              Sirayanian, Mr. Orsen    male  22.00      0   \n",
       "61                                Icard, Miss. Amelie  female  38.00      0   \n",
       "62                        Harris, Mr. Henry Birkhardt    male  45.00      1   \n",
       "63                              Skoog, Master. Harald    male   4.00      3   \n",
       "64                              Stewart, Mr. Albert A    male    NaN      0   \n",
       "65                           Moubarek, Master. Gerios    male    NaN      1   \n",
       "66                       Nye, Mrs. (Elizabeth Ramell)  female  29.00      0   \n",
       "67                           Crease, Mr. Ernest James    male  19.00      0   \n",
       "68                    Andersson, Miss. Erna Alexandra  female  17.00      4   \n",
       "69                                  Kink, Mr. Vincenz    male  26.00      2   \n",
       "70                         Jenkin, Mr. Stephen Curnow    male  32.00      0   \n",
       "71                         Goodwin, Miss. Lillian Amy  female  16.00      5   \n",
       "72                               Hood, Mr. Ambrose Jr    male  21.00      0   \n",
       "73                        Chronopoulos, Mr. Apostolos    male  26.00      1   \n",
       "74                                      Bing, Mr. Lee    male  32.00      0   \n",
       "75                            Moen, Mr. Sigurd Hansen    male  25.00      0   \n",
       "76                                  Staneff, Mr. Ivan    male    NaN      0   \n",
       "77                           Moutal, Mr. Rahamin Haim    male    NaN      0   \n",
       "78                      Caldwell, Master. Alden Gates    male   0.83      0   \n",
       "79                           Dowdell, Miss. Elizabeth  female  30.00      0   \n",
       "\n",
       "    Parch            Ticket      Fare        Cabin Embarked  \n",
       "0       0         A/5 21171    7.2500          NaN        S  \n",
       "1       0          PC 17599   71.2833          C85        C  \n",
       "2       0  STON/O2. 3101282    7.9250          NaN        S  \n",
       "3       0            113803   53.1000         C123        S  \n",
       "4       0            373450    8.0500          NaN        S  \n",
       "5       0            330877    8.4583          NaN        Q  \n",
       "6       0             17463   51.8625          E46        S  \n",
       "7       1            349909   21.0750          NaN        S  \n",
       "8       2            347742   11.1333          NaN        S  \n",
       "9       0            237736   30.0708          NaN        C  \n",
       "10      1           PP 9549   16.7000           G6        S  \n",
       "11      0            113783   26.5500         C103        S  \n",
       "12      0         A/5. 2151    8.0500          NaN        S  \n",
       "13      5            347082   31.2750          NaN        S  \n",
       "14      0            350406    7.8542          NaN        S  \n",
       "15      0            248706   16.0000          NaN        S  \n",
       "16      1            382652   29.1250          NaN        Q  \n",
       "17      0            244373   13.0000          NaN        S  \n",
       "18      0            345763   18.0000          NaN        S  \n",
       "19      0              2649    7.2250          NaN        C  \n",
       "20      0            239865   26.0000          NaN        S  \n",
       "21      0            248698   13.0000          D56        S  \n",
       "22      0            330923    8.0292          NaN        Q  \n",
       "23      0            113788   35.5000           A6        S  \n",
       "24      1            349909   21.0750          NaN        S  \n",
       "25      5            347077   31.3875          NaN        S  \n",
       "26      0              2631    7.2250          NaN        C  \n",
       "27      2             19950  263.0000  C23 C25 C27        S  \n",
       "28      0            330959    7.8792          NaN        Q  \n",
       "29      0            349216    7.8958          NaN        S  \n",
       "..    ...               ...       ...          ...      ...  \n",
       "50      1           3101295   39.6875          NaN        S  \n",
       "51      0        A/4. 39886    7.8000          NaN        S  \n",
       "52      0          PC 17572   76.7292          D33        C  \n",
       "53      0              2926   26.0000          NaN        S  \n",
       "54      1            113509   61.9792          B30        C  \n",
       "55      0             19947   35.5000          C52        S  \n",
       "56      0        C.A. 31026   10.5000          NaN        S  \n",
       "57      0              2697    7.2292          NaN        C  \n",
       "58      2        C.A. 34651   27.7500          NaN        S  \n",
       "59      2           CA 2144   46.9000          NaN        S  \n",
       "60      0              2669    7.2292          NaN        C  \n",
       "61      0            113572   80.0000          B28      NaN  \n",
       "62      0             36973   83.4750          C83        S  \n",
       "63      2            347088   27.9000          NaN        S  \n",
       "64      0          PC 17605   27.7208          NaN        C  \n",
       "65      1              2661   15.2458          NaN        C  \n",
       "66      0        C.A. 29395   10.5000          F33        S  \n",
       "67      0         S.P. 3464    8.1583          NaN        S  \n",
       "68      2           3101281    7.9250          NaN        S  \n",
       "69      0            315151    8.6625          NaN        S  \n",
       "70      0        C.A. 33111   10.5000          NaN        S  \n",
       "71      2           CA 2144   46.9000          NaN        S  \n",
       "72      0      S.O.C. 14879   73.5000          NaN        S  \n",
       "73      0              2680   14.4542          NaN        C  \n",
       "74      0              1601   56.4958          NaN        S  \n",
       "75      0            348123    7.6500        F G73        S  \n",
       "76      0            349208    7.8958          NaN        S  \n",
       "77      0            374746    8.0500          NaN        S  \n",
       "78      2            248738   29.0000          NaN        S  \n",
       "79      0            364516   12.4750          NaN        S  \n",
       "\n",
       "[80 rows x 12 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head(80)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Dictionary\n",
    "- Survived: \t0 = No, 1 = Yes  \n",
    "- pclass: \tTicket class\t1 = 1st, 2 = 2nd, 3 = 3rd  \t\n",
    "- sibsp:\t# of siblings / spouses aboard the Titanic  \t\n",
    "- parch:\t# of parents / children aboard the Titanic  \t\n",
    "- ticket:\tTicket number\t\n",
    "- cabin:\tCabin number\t\n",
    "- embarked:\tPort of Embarkation\tC = Cherbourg, Q = Queenstown, S = Southampton  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "5ebc1e0e-2b5a-4d92-98e0-defa019d4439",
    "_uuid": "1892fbb34b26d775d1c428fdb7b6254449286b28"
   },
   "source": [
    "**Total rows and columns**\n",
    "\n",
    "We can see that there are 891 rows and 12 columns in our training dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>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>892</td>\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>893</td>\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>894</td>\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>895</td>\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>896</td>\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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Pclass                                          Name     Sex  \\\n",
       "0          892       3                              Kelly, Mr. James    male   \n",
       "1          893       3              Wilkes, Mrs. James (Ellen Needs)  female   \n",
       "2          894       2                     Myles, Mr. Thomas Francis    male   \n",
       "3          895       3                              Wirz, Mr. Albert    male   \n",
       "4          896       3  Hirvonen, Mrs. Alexander (Helga E Lindqvist)  female   \n",
       "\n",
       "    Age  SibSp  Parch   Ticket     Fare Cabin Embarked  \n",
       "0  34.5      0      0   330911   7.8292   NaN        Q  \n",
       "1  47.0      1      0   363272   7.0000   NaN        S  \n",
       "2  62.0      0      0   240276   9.6875   NaN        Q  \n",
       "3  27.0      0      0   315154   8.6625   NaN        S  \n",
       "4  22.0      1      1  3101298  12.2875   NaN        S  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "_cell_guid": "ed1e7849-d1b6-490d-b86b-9ca71dfafc7d",
    "_uuid": "5a641beccf0e555dfd7b9a53a17188ea6edef95b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(891, 12)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(418, 11)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "_cell_guid": "418b8a69-f2aa-442d-8f45-fa8887190938",
    "_uuid": "4ee2591110660a4a16b3da7a7530f0945e121b46"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      "PassengerId    891 non-null int64\n",
      "Survived       891 non-null int64\n",
      "Pclass         891 non-null int64\n",
      "Name           891 non-null object\n",
      "Sex            891 non-null object\n",
      "Age            714 non-null float64\n",
      "SibSp          891 non-null int64\n",
      "Parch          891 non-null int64\n",
      "Ticket         891 non-null object\n",
      "Fare           891 non-null float64\n",
      "Cabin          204 non-null object\n",
      "Embarked       889 non-null object\n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.6+ KB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 418 entries, 0 to 417\n",
      "Data columns (total 11 columns):\n",
      "PassengerId    418 non-null int64\n",
      "Pclass         418 non-null int64\n",
      "Name           418 non-null object\n",
      "Sex            418 non-null object\n",
      "Age            332 non-null float64\n",
      "SibSp          418 non-null int64\n",
      "Parch          418 non-null int64\n",
      "Ticket         418 non-null object\n",
      "Fare           417 non-null float64\n",
      "Cabin          91 non-null object\n",
      "Embarked       418 non-null object\n",
      "dtypes: float64(2), int64(4), object(5)\n",
      "memory usage: 36.0+ KB\n"
     ]
    }
   ],
   "source": [
    "test.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "abc3c4fc-6419-405f-927a-4214d2c73eec",
    "_uuid": "622d4d4b2ba8f77cc537af97fc343d4cd6de26b2"
   },
   "source": [
    "We can see that *Age* value is missing for many rows. \n",
    "\n",
    "Out of 891 rows, the *Age* value is present only in 714 rows.\n",
    "\n",
    "Similarly, *Cabin* values are also missing in many rows. Only 204 out of 891 rows have *Cabin* values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "_cell_guid": "0663e2bb-dc27-4187-94b1-ff4ff78b68bc",
    "_uuid": "3bf74de7f2483d622e41608f6017f2945639e4df"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Survived         0\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "Age            177\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             0\n",
       "Cabin          687\n",
       "Embarked         2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "Age             86\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             1\n",
       "Cabin          327\n",
       "Embarked         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "176aa52d-fde8-42e6-a3ee-db31f8b0ca49",
    "_uuid": "b48a9feff6004d783960aa1b32fdfde902d87e21"
   },
   "source": [
    "There are 177 rows with missing *Age*, 687 rows with missing *Cabin* and 2 rows with missing *Embarked* information."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "c8553d48-c5e0-4947-bd13-1b38509c850c",
    "_uuid": "1a28e607e9ed63cefe0f35a4e4d72f2f36299323"
   },
   "source": [
    "### import python lib for visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "_cell_guid": "b1d8a6d2-c22d-435c-8c98-973e8f41b138",
    "_uuid": "26411c710f69b29939c815d5f5ab01d9177df7d0",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "sns.set() # setting seaborn default for plots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bar Chart for Categorical Features\n",
    "- Pclass\n",
    "- Sex\n",
    "- SibSp ( # of siblings and spouse)\n",
    "- Parch ( # of parents and children)\n",
    "- Embarked\n",
    "- Cabin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def bar_chart(feature):\n",
    "    survived = train[train['Survived']==1][feature].value_counts()\n",
    "    dead = train[train['Survived']==0][feature].value_counts()\n",
    "    df = pd.DataFrame([survived,dead])\n",
    "    df.index = ['Survived','Dead']\n",
    "    df.plot(kind='bar',stacked=True, figsize=(10,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10da75c90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bar_chart('Sex')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Chart confirms **Women** more likely survivied than **Men**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x110e2cc10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bar_chart('Pclass')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Chart confirms **1st class** more likely survivied than **other classes**  \n",
    "The Chart confirms **3rd class** more likely dead than **other classes**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1110b6b10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bar_chart('SibSp')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Chart confirms **a person aboarded with more than 2 siblings or spouse** more likely survived  \n",
    "The Chart confirms ** a person aboarded without siblings or spouse** more likely dead"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1110c1290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bar_chart('Parch')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Chart confirms **a person aboarded with more than 2 parents or children** more likely survived  \n",
    "The Chart confirms ** a person aboarded alone** more likely dead"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11109e150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bar_chart('Embarked')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Chart confirms **a person aboarded from C** slightly more likely survived  \n",
    "The Chart confirms **a person aboarded from Q** more likely dead  \n",
    "The Chart confirms **a person aboarded from S** more likely dead"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "810cd964-24eb-44fb-9e7b-18bbddd4900f",
    "_uuid": "fd86ccdf2d1248b79c68365444e96e46a50f3f5a"
   },
   "source": [
    "## 4. Feature engineering\n",
    "\n",
    "Feature engineering is the process of using domain knowledge of the data  \n",
    "to create features (**feature vectors**) that make machine learning algorithms work.  \n",
    "\n",
    "feature vector is an n-dimensional vector of numerical features that represent some object.  \n",
    "Many algorithms in machine learning require a numerical representation of objects,  \n",
    "since such representations facilitate processing and statistical analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.1 how titanic sank?\n",
    "sank from the bow of the ship where third class rooms located  \n",
    "conclusion, Pclass is key feature for classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img src=\"https://static1.squarespace.com/static/5006453fe4b09ef2252ba068/t/5090b249e4b047ba54dfd258/1351660113175/TItanic-Survival-Infographic.jpg?format=1500w\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(url= \"https://static1.squarespace.com/static/5006453fe4b09ef2252ba068/t/5090b249e4b047ba54dfd258/1351660113175/TItanic-Survival-Infographic.jpg?format=1500w\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Moran, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330877</td>\n",
       "      <td>8.4583</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>McCarthy, Mr. Timothy J</td>\n",
       "      <td>male</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17463</td>\n",
       "      <td>51.8625</td>\n",
       "      <td>E46</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Palsson, Master. Gosta Leonard</td>\n",
       "      <td>male</td>\n",
       "      <td>2.0</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>8</th>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)</td>\n",
       "      <td>female</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>347742</td>\n",
       "      <td>11.1333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Nasser, Mrs. Nicholas (Adele Achem)</td>\n",
       "      <td>female</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>237736</td>\n",
       "      <td>30.0708</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "5            6         0       3   \n",
       "6            7         0       1   \n",
       "7            8         0       3   \n",
       "8            9         1       3   \n",
       "9           10         1       2   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "5                                   Moran, Mr. James    male   NaN      0   \n",
       "6                            McCarthy, Mr. Timothy J    male  54.0      0   \n",
       "7                     Palsson, Master. Gosta Leonard    male   2.0      3   \n",
       "8  Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)  female  27.0      0   \n",
       "9                Nasser, Mrs. Nicholas (Adele Achem)  female  14.0      1   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  \n",
       "5      0            330877   8.4583   NaN        Q  \n",
       "6      0             17463  51.8625   E46        S  \n",
       "7      1            349909  21.0750   NaN        S  \n",
       "8      2            347742  11.1333   NaN        S  \n",
       "9      0            237736  30.0708   NaN        C  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.2 Name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_test_data = [train, test] # combining train and test dataset\n",
    "\n",
    "for dataset in train_test_data:\n",
    "    dataset['Title'] = dataset['Name'].str.extract(' ([A-Za-z]+)\\.', expand=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Mr          517\n",
       "Miss        182\n",
       "Mrs         125\n",
       "Master       40\n",
       "Dr            7\n",
       "Rev           6\n",
       "Col           2\n",
       "Major         2\n",
       "Mlle          2\n",
       "Countess      1\n",
       "Ms            1\n",
       "Lady          1\n",
       "Jonkheer      1\n",
       "Don           1\n",
       "Mme           1\n",
       "Capt          1\n",
       "Sir           1\n",
       "Name: Title, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Title'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Mr        240\n",
       "Miss       78\n",
       "Mrs        72\n",
       "Master     21\n",
       "Col         2\n",
       "Rev         2\n",
       "Dona        1\n",
       "Ms          1\n",
       "Dr          1\n",
       "Name: Title, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test['Title'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Title map\n",
    "Mr : 0  \n",
    "Miss : 1  \n",
    "Mrs: 2  \n",
    "Others: 3\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "title_mapping = {\"Mr\": 0, \"Miss\": 1, \"Mrs\": 2, \n",
    "                 \"Master\": 3, \"Dr\": 3, \"Rev\": 3, \"Col\": 3, \"Major\": 3, \"Mlle\": 3,\"Countess\": 3,\n",
    "                 \"Ms\": 3, \"Lady\": 3, \"Jonkheer\": 3, \"Don\": 3, \"Dona\" : 3, \"Mme\": 3,\"Capt\": 3,\"Sir\": 3 }\n",
    "for dataset in train_test_data:\n",
    "    dataset['Title'] = dataset['Title'].map(title_mapping)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "    .dataframe thead th {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  Title  \n",
       "0      0         A/5 21171   7.2500   NaN        S      0  \n",
       "1      0          PC 17599  71.2833   C85        C      2  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S      1  \n",
       "3      0            113803  53.1000  C123        S      2  \n",
       "4      0            373450   8.0500   NaN        S      0  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892</td>\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",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</td>\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",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\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",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\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",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\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",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Pclass                                          Name     Sex  \\\n",
       "0          892       3                              Kelly, Mr. James    male   \n",
       "1          893       3              Wilkes, Mrs. James (Ellen Needs)  female   \n",
       "2          894       2                     Myles, Mr. Thomas Francis    male   \n",
       "3          895       3                              Wirz, Mr. Albert    male   \n",
       "4          896       3  Hirvonen, Mrs. Alexander (Helga E Lindqvist)  female   \n",
       "\n",
       "    Age  SibSp  Parch   Ticket     Fare Cabin Embarked  Title  \n",
       "0  34.5      0      0   330911   7.8292   NaN        Q      0  \n",
       "1  47.0      1      0   363272   7.0000   NaN        S      2  \n",
       "2  62.0      0      0   240276   9.6875   NaN        Q      0  \n",
       "3  27.0      0      0   315154   8.6625   NaN        S      0  \n",
       "4  22.0      1      1  3101298  12.2875   NaN        S      2  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1112cce10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bar_chart('Title')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# delete unnecessary feature from dataset\n",
    "train.drop('Name', axis=1, inplace=True)\n",
    "test.drop('Name', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass     Sex   Age  SibSp  Parch  \\\n",
       "0            1         0       3    male  22.0      1      0   \n",
       "1            2         1       1  female  38.0      1      0   \n",
       "2            3         1       3  female  26.0      0      0   \n",
       "3            4         1       1  female  35.0      1      0   \n",
       "4            5         0       3    male  35.0      0      0   \n",
       "\n",
       "             Ticket     Fare Cabin Embarked  Title  \n",
       "0         A/5 21171   7.2500   NaN        S      0  \n",
       "1          PC 17599  71.2833   C85        C      2  \n",
       "2  STON/O2. 3101282   7.9250   NaN        S      1  \n",
       "3            113803  53.1000  C123        S      2  \n",
       "4            373450   8.0500   NaN        S      0  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892</td>\n",
       "      <td>3</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",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>3</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",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>2</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",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>3</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",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>3</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",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Pclass     Sex   Age  SibSp  Parch   Ticket     Fare Cabin  \\\n",
       "0          892       3    male  34.5      0      0   330911   7.8292   NaN   \n",
       "1          893       3  female  47.0      1      0   363272   7.0000   NaN   \n",
       "2          894       2    male  62.0      0      0   240276   9.6875   NaN   \n",
       "3          895       3    male  27.0      0      0   315154   8.6625   NaN   \n",
       "4          896       3  female  22.0      1      1  3101298  12.2875   NaN   \n",
       "\n",
       "  Embarked  Title  \n",
       "0        Q      0  \n",
       "1        S      2  \n",
       "2        Q      0  \n",
       "3        S      0  \n",
       "4        S      2  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.3 Sex\n",
    "\n",
    "male: 0\n",
    "female: 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sex_mapping = {\"male\": 0, \"female\": 1}\n",
    "for dataset in train_test_data:\n",
    "    dataset['Sex'] = dataset['Sex'].map(sex_mapping)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1113eef90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bar_chart('Sex')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.4 Age"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4.4.1 some age is missing\n",
    "Let's use Title's median age for missing Age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>22.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>26.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>35.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>35.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330877</td>\n",
       "      <td>8.4583</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>54.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17463</td>\n",
       "      <td>51.8625</td>\n",
       "      <td>E46</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2.00</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",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>27.00</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>347742</td>\n",
       "      <td>11.1333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>14.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>237736</td>\n",
       "      <td>30.0708</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>PP 9549</td>\n",
       "      <td>16.7000</td>\n",
       "      <td>G6</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>58.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113783</td>\n",
       "      <td>26.5500</td>\n",
       "      <td>C103</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>20.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5. 2151</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>39.00</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>347082</td>\n",
       "      <td>31.2750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>14.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>350406</td>\n",
       "      <td>7.8542</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>55.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>248706</td>\n",
       "      <td>16.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>382652</td>\n",
       "      <td>29.1250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>244373</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>31.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>345763</td>\n",
       "      <td>18.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2649</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>35.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>239865</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>34.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>248698</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>D56</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>15.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330923</td>\n",
       "      <td>8.0292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>28.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113788</td>\n",
       "      <td>35.5000</td>\n",
       "      <td>A6</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>8.00</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",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>38.00</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>347077</td>\n",
       "      <td>31.3875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2631</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>19.00</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>19950</td>\n",
       "      <td>263.0000</td>\n",
       "      <td>C23 C25 C27</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330959</td>\n",
       "      <td>7.8792</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349216</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>71</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>32.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>C.A. 33111</td>\n",
       "      <td>10.5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>72</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>16.00</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>CA 2144</td>\n",
       "      <td>46.9000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>73</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>21.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>S.O.C. 14879</td>\n",
       "      <td>73.5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>74</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>26.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2680</td>\n",
       "      <td>14.4542</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>75</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>32.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1601</td>\n",
       "      <td>56.4958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>76</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>25.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>348123</td>\n",
       "      <td>7.6500</td>\n",
       "      <td>F G73</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>77</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349208</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>78</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>374746</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>79</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>248738</td>\n",
       "      <td>29.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>30.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>364516</td>\n",
       "      <td>12.4750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>81</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>22.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>345767</td>\n",
       "      <td>9.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>29.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>345779</td>\n",
       "      <td>9.5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>83</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330932</td>\n",
       "      <td>7.7875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>84</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>28.00</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",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>85</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>17.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>SO/C 14885</td>\n",
       "      <td>10.5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>86</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>33.00</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3101278</td>\n",
       "      <td>15.8500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>87</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>16.00</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>W./C. 6608</td>\n",
       "      <td>34.3750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>88</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>SOTON/OQ 392086</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>89</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>23.00</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>19950</td>\n",
       "      <td>263.0000</td>\n",
       "      <td>C23 C25 C27</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>90</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>24.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>343275</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>91</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>29.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>343276</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>92</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>20.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>347466</td>\n",
       "      <td>7.8542</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>93</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>46.00</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",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>94</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>26.00</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>C.A. 2315</td>\n",
       "      <td>20.5750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>95</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>59.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>364500</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>96</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>374910</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>97</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17754</td>\n",
       "      <td>34.6542</td>\n",
       "      <td>A5</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>98</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>23.00</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>PC 17759</td>\n",
       "      <td>63.3583</td>\n",
       "      <td>D10 D12</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>99</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>34.00</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>231919</td>\n",
       "      <td>23.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>34.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>244367</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    PassengerId  Survived  Pclass  Sex    Age  SibSp  Parch            Ticket  \\\n",
       "0             1         0       3    0  22.00      1      0         A/5 21171   \n",
       "1             2         1       1    1  38.00      1      0          PC 17599   \n",
       "2             3         1       3    1  26.00      0      0  STON/O2. 3101282   \n",
       "3             4         1       1    1  35.00      1      0            113803   \n",
       "4             5         0       3    0  35.00      0      0            373450   \n",
       "5             6         0       3    0    NaN      0      0            330877   \n",
       "6             7         0       1    0  54.00      0      0             17463   \n",
       "7             8         0       3    0   2.00      3      1            349909   \n",
       "8             9         1       3    1  27.00      0      2            347742   \n",
       "9            10         1       2    1  14.00      1      0            237736   \n",
       "10           11         1       3    1   4.00      1      1           PP 9549   \n",
       "11           12         1       1    1  58.00      0      0            113783   \n",
       "12           13         0       3    0  20.00      0      0         A/5. 2151   \n",
       "13           14         0       3    0  39.00      1      5            347082   \n",
       "14           15         0       3    1  14.00      0      0            350406   \n",
       "15           16         1       2    1  55.00      0      0            248706   \n",
       "16           17         0       3    0   2.00      4      1            382652   \n",
       "17           18         1       2    0    NaN      0      0            244373   \n",
       "18           19         0       3    1  31.00      1      0            345763   \n",
       "19           20         1       3    1    NaN      0      0              2649   \n",
       "20           21         0       2    0  35.00      0      0            239865   \n",
       "21           22         1       2    0  34.00      0      0            248698   \n",
       "22           23         1       3    1  15.00      0      0            330923   \n",
       "23           24         1       1    0  28.00      0      0            113788   \n",
       "24           25         0       3    1   8.00      3      1            349909   \n",
       "25           26         1       3    1  38.00      1      5            347077   \n",
       "26           27         0       3    0    NaN      0      0              2631   \n",
       "27           28         0       1    0  19.00      3      2             19950   \n",
       "28           29         1       3    1    NaN      0      0            330959   \n",
       "29           30         0       3    0    NaN      0      0            349216   \n",
       "..          ...       ...     ...  ...    ...    ...    ...               ...   \n",
       "70           71         0       2    0  32.00      0      0        C.A. 33111   \n",
       "71           72         0       3    1  16.00      5      2           CA 2144   \n",
       "72           73         0       2    0  21.00      0      0      S.O.C. 14879   \n",
       "73           74         0       3    0  26.00      1      0              2680   \n",
       "74           75         1       3    0  32.00      0      0              1601   \n",
       "75           76         0       3    0  25.00      0      0            348123   \n",
       "76           77         0       3    0    NaN      0      0            349208   \n",
       "77           78         0       3    0    NaN      0      0            374746   \n",
       "78           79         1       2    0   0.83      0      2            248738   \n",
       "79           80         1       3    1  30.00      0      0            364516   \n",
       "80           81         0       3    0  22.00      0      0            345767   \n",
       "81           82         1       3    0  29.00      0      0            345779   \n",
       "82           83         1       3    1    NaN      0      0            330932   \n",
       "83           84         0       1    0  28.00      0      0            113059   \n",
       "84           85         1       2    1  17.00      0      0        SO/C 14885   \n",
       "85           86         1       3    1  33.00      3      0           3101278   \n",
       "86           87         0       3    0  16.00      1      3        W./C. 6608   \n",
       "87           88         0       3    0    NaN      0      0   SOTON/OQ 392086   \n",
       "88           89         1       1    1  23.00      3      2             19950   \n",
       "89           90         0       3    0  24.00      0      0            343275   \n",
       "90           91         0       3    0  29.00      0      0            343276   \n",
       "91           92         0       3    0  20.00      0      0            347466   \n",
       "92           93         0       1    0  46.00      1      0       W.E.P. 5734   \n",
       "93           94         0       3    0  26.00      1      2         C.A. 2315   \n",
       "94           95         0       3    0  59.00      0      0            364500   \n",
       "95           96         0       3    0    NaN      0      0            374910   \n",
       "96           97         0       1    0  71.00      0      0          PC 17754   \n",
       "97           98         1       1    0  23.00      0      1          PC 17759   \n",
       "98           99         1       2    1  34.00      0      1            231919   \n",
       "99          100         0       2    0  34.00      1      0            244367   \n",
       "\n",
       "        Fare        Cabin Embarked  Title  \n",
       "0     7.2500          NaN        S      0  \n",
       "1    71.2833          C85        C      2  \n",
       "2     7.9250          NaN        S      1  \n",
       "3    53.1000         C123        S      2  \n",
       "4     8.0500          NaN        S      0  \n",
       "5     8.4583          NaN        Q      0  \n",
       "6    51.8625          E46        S      0  \n",
       "7    21.0750          NaN        S      3  \n",
       "8    11.1333          NaN        S      2  \n",
       "9    30.0708          NaN        C      2  \n",
       "10   16.7000           G6        S      1  \n",
       "11   26.5500         C103        S      1  \n",
       "12    8.0500          NaN        S      0  \n",
       "13   31.2750          NaN        S      0  \n",
       "14    7.8542          NaN        S      1  \n",
       "15   16.0000          NaN        S      2  \n",
       "16   29.1250          NaN        Q      3  \n",
       "17   13.0000          NaN        S      0  \n",
       "18   18.0000          NaN        S      2  \n",
       "19    7.2250          NaN        C      2  \n",
       "20   26.0000          NaN        S      0  \n",
       "21   13.0000          D56        S      0  \n",
       "22    8.0292          NaN        Q      1  \n",
       "23   35.5000           A6        S      0  \n",
       "24   21.0750          NaN        S      1  \n",
       "25   31.3875          NaN        S      2  \n",
       "26    7.2250          NaN        C      0  \n",
       "27  263.0000  C23 C25 C27        S      0  \n",
       "28    7.8792          NaN        Q      1  \n",
       "29    7.8958          NaN        S      0  \n",
       "..       ...          ...      ...    ...  \n",
       "70   10.5000          NaN        S      0  \n",
       "71   46.9000          NaN        S      1  \n",
       "72   73.5000          NaN        S      0  \n",
       "73   14.4542          NaN        C      0  \n",
       "74   56.4958          NaN        S      0  \n",
       "75    7.6500        F G73        S      0  \n",
       "76    7.8958          NaN        S      0  \n",
       "77    8.0500          NaN        S      0  \n",
       "78   29.0000          NaN        S      3  \n",
       "79   12.4750          NaN        S      1  \n",
       "80    9.0000          NaN        S      0  \n",
       "81    9.5000          NaN        S      0  \n",
       "82    7.7875          NaN        Q      1  \n",
       "83   47.1000          NaN        S      0  \n",
       "84   10.5000          NaN        S      1  \n",
       "85   15.8500          NaN        S      2  \n",
       "86   34.3750          NaN        S      0  \n",
       "87    8.0500          NaN        S      0  \n",
       "88  263.0000  C23 C25 C27        S      1  \n",
       "89    8.0500          NaN        S      0  \n",
       "90    8.0500          NaN        S      0  \n",
       "91    7.8542          NaN        S      0  \n",
       "92   61.1750          E31        S      0  \n",
       "93   20.5750          NaN        S      0  \n",
       "94    7.2500          NaN        S      0  \n",
       "95    8.0500          NaN        S      0  \n",
       "96   34.6542           A5        C      0  \n",
       "97   63.3583      D10 D12        C      0  \n",
       "98   23.0000          NaN        S      2  \n",
       "99   26.0000          NaN        S      0  \n",
       "\n",
       "[100 rows x 12 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# fill missing age with median age for each title (Mr, Mrs, Miss, Others)\n",
    "train[\"Age\"].fillna(train.groupby(\"Title\")[\"Age\"].transform(\"median\"), inplace=True)\n",
    "test[\"Age\"].fillna(test.groupby(\"Title\")[\"Age\"].transform(\"median\"), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      30.0\n",
       "1      35.0\n",
       "2      21.0\n",
       "3      35.0\n",
       "4      30.0\n",
       "5      30.0\n",
       "6      30.0\n",
       "7       9.0\n",
       "8      35.0\n",
       "9      35.0\n",
       "10     21.0\n",
       "11     21.0\n",
       "12     30.0\n",
       "13     30.0\n",
       "14     21.0\n",
       "15     35.0\n",
       "16      9.0\n",
       "17     30.0\n",
       "18     35.0\n",
       "19     35.0\n",
       "20     30.0\n",
       "21     30.0\n",
       "22     21.0\n",
       "23     30.0\n",
       "24     21.0\n",
       "25     35.0\n",
       "26     30.0\n",
       "27     30.0\n",
       "28     21.0\n",
       "29     30.0\n",
       "       ... \n",
       "861    30.0\n",
       "862    35.0\n",
       "863    21.0\n",
       "864    30.0\n",
       "865    35.0\n",
       "866    21.0\n",
       "867    30.0\n",
       "868    30.0\n",
       "869     9.0\n",
       "870    30.0\n",
       "871    35.0\n",
       "872    30.0\n",
       "873    30.0\n",
       "874    35.0\n",
       "875    21.0\n",
       "876    30.0\n",
       "877    30.0\n",
       "878    30.0\n",
       "879    35.0\n",
       "880    35.0\n",
       "881    30.0\n",
       "882    21.0\n",
       "883    30.0\n",
       "884    30.0\n",
       "885    35.0\n",
       "886     9.0\n",
       "887    21.0\n",
       "888    21.0\n",
       "889    30.0\n",
       "890    30.0\n",
       "Name: Age, Length: 891, dtype: float64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head(30)\n",
    "train.groupby(\"Title\")[\"Age\"].transform(\"median\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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GN2nSJB544AGeffZZ/u3f/o0ZM2bwzW9+87zr+v1+fvrTn1JcXMy3vvUtamtr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\nIsD/AZK2a7RJ9MnMAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110f03650>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "facet = sns.FacetGrid(train, hue=\"Survived\",aspect=4)\n",
    "facet.map(sns.kdeplot,'Age',shade= True)\n",
    "facet.set(xlim=(0, train['Age'].max()))\n",
    "facet.add_legend()\n",
    " \n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 20)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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2ZwdgjBEANVlD3B9D3F+HuD+G+kAcjYEGNATiUOWZ+dUqly/i1BmjNETUCYQF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\niIiIiIiIAPw/aP8Q3mwhHY8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1114b3210>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "facet = sns.FacetGrid(train, hue=\"Survived\",aspect=4)\n",
    "facet.map(sns.kdeplot,'Age',shade= True)\n",
    "facet.set(xlim=(0, train['Age'].max()))\n",
    "facet.add_legend()\n",
    "plt.xlim(0, 20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20, 30)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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TJyVJb775plfXsizv8eLFi/XjH/9Yhw4d0p/+6Z8WHXP58uX693//d506dUqX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\nAABEOAQAAAAAiHAIAAAAAJD0/wNcstWFCvwzggAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110e72e90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "facet = sns.FacetGrid(train, hue=\"Survived\",aspect=4)\n",
    "facet.map(sns.kdeplot,'Age',shade= True)\n",
    "facet.set(xlim=(0, train['Age'].max()))\n",
    "facet.add_legend()\n",
    "plt.xlim(20, 30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30, 40)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ZMGlvZ8MklwohIipkGAa2bduGd999F4lEAosWLcJ3v/tdqOok140CcM899+Dh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\nHZvfdY0NAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11149d850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "facet = sns.FacetGrid(train, hue=\"Survived\",aspect=4)\n",
    "facet.map(sns.kdeplot,'Age',shade= True)\n",
    "facet.set(xlim=(0, train['Age'].max()))\n",
    "facet.add_legend()\n",
    "plt.xlim(30, 40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(40, 60)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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T64Sd8blOeDiVQFoJipXwGAxaHrYtPP76xXKvgv4jKkfl5HreU+c7nnJuXg0j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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1118d28d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "facet = sns.FacetGrid(train, hue=\"Survived\",aspect=4)\n",
    "facet.map(sns.kdeplot,'Age',shade= True)\n",
    "facet.set(xlim=(0, train['Age'].max()))\n",
    "facet.add_legend()\n",
    "plt.xlim(40, 60)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(40, 60)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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T64Sd8blOeDiVQFoJipXwGAxaHrYtPP76xXKvgv4jKkfl5HreU+c7nnJuXg0j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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111d0e650>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "facet = sns.FacetGrid(train, hue=\"Survived\",aspect=4)\n",
    "facet.map(sns.kdeplot,'Age',shade= True)\n",
    "facet.set(xlim=(0, train['Age'].max()))\n",
    "facet.add_legend()\n",
    "plt.xlim(40, 60)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60, 80.0)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1118ff150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "facet = sns.FacetGrid(train, hue=\"Survived\",aspect=4)\n",
    "facet.map(sns.kdeplot,'Age',shade= True)\n",
    "facet.set(xlim=(0, train['Age'].max()))\n",
    "facet.add_legend()\n",
    "plt.xlim(60)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      "PassengerId    891 non-null int64\n",
      "Survived       891 non-null int64\n",
      "Pclass         891 non-null int64\n",
      "Sex            891 non-null int64\n",
      "Age            891 non-null float64\n",
      "SibSp          891 non-null int64\n",
      "Parch          891 non-null int64\n",
      "Ticket         891 non-null object\n",
      "Fare           891 non-null float64\n",
      "Cabin          204 non-null object\n",
      "Embarked       889 non-null object\n",
      "Title          891 non-null int64\n",
      "dtypes: float64(2), int64(7), object(3)\n",
      "memory usage: 83.6+ KB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 418 entries, 0 to 417\n",
      "Data columns (total 11 columns):\n",
      "PassengerId    418 non-null int64\n",
      "Pclass         418 non-null int64\n",
      "Sex            418 non-null int64\n",
      "Age            418 non-null float64\n",
      "SibSp          418 non-null int64\n",
      "Parch          418 non-null int64\n",
      "Ticket         418 non-null object\n",
      "Fare           417 non-null float64\n",
      "Cabin          91 non-null object\n",
      "Embarked       418 non-null object\n",
      "Title          418 non-null int64\n",
      "dtypes: float64(2), int64(6), object(3)\n",
      "memory usage: 36.0+ KB\n"
     ]
    }
   ],
   "source": [
    "test.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4.4.2 Binning\n",
    "Binning/Converting Numerical Age to Categorical Variable  \n",
    "\n",
    "feature vector map:  \n",
    "child: 0  \n",
    "young: 1  \n",
    "adult: 2  \n",
    "mid-age: 3  \n",
    "senior: 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for dataset in train_test_data:\n",
    "    dataset.loc[ dataset['Age'] <= 16, 'Age'] = 0,\n",
    "    dataset.loc[(dataset['Age'] > 16) & (dataset['Age'] <= 26), 'Age'] = 1,\n",
    "    dataset.loc[(dataset['Age'] > 26) & (dataset['Age'] <= 36), 'Age'] = 2,\n",
    "    dataset.loc[(dataset['Age'] > 36) & (dataset['Age'] <= 62), 'Age'] = 3,\n",
    "    dataset.loc[ dataset['Age'] > 62, 'Age'] = 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  Sex  Age  SibSp  Parch            Ticket  \\\n",
       "0            1         0       3    0  1.0      1      0         A/5 21171   \n",
       "1            2         1       1    1  3.0      1      0          PC 17599   \n",
       "2            3         1       3    1  1.0      0      0  STON/O2. 3101282   \n",
       "3            4         1       1    1  2.0      1      0            113803   \n",
       "4            5         0       3    0  2.0      0      0            373450   \n",
       "\n",
       "      Fare Cabin Embarked  Title  \n",
       "0   7.2500   NaN        S      0  \n",
       "1  71.2833   C85        C      2  \n",
       "2   7.9250   NaN        S      1  \n",
       "3  53.1000  C123        S      2  \n",
       "4   8.0500   NaN        S      0  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ldrsv+IsAAACwU59BNWXKFE2cOFGSdPjwYXm9Xr3zzjsaO3asJCkzM1M7duxQTEyMMjIy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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111220150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bar_chart('Age')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.5 Embarked"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4.5.1 filling missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1113ee790>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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zsL7P0aM15zg2olFVVbXtEdBKJCUlsL8AEay1Hp+NhWCjn/I7cuSIJkyYoPvuu0+33nqr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\nAGCIoAIAADBEUAEAABj6/1rB0sKWPvsgAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1111ee050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Pclass1 = train[train['Pclass']==1]['Embarked'].value_counts()\n",
    "Pclass2 = train[train['Pclass']==2]['Embarked'].value_counts()\n",
    "Pclass3 = train[train['Pclass']==3]['Embarked'].value_counts()\n",
    "df = pd.DataFrame([Pclass1, Pclass2, Pclass3])\n",
    "df.index = ['1st class','2nd class', '3rd class']\n",
    "df.plot(kind='bar',stacked=True, figsize=(10,5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "more than 50% of 1st class are from S embark  \n",
    "more than 50% of 2nd class are from S embark  \n",
    "more than 50% of 3rd class are from S embark\n",
    "\n",
    "**fill out missing embark with S embark**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for dataset in train_test_data:\n",
    "    dataset['Embarked'] = dataset['Embarked'].fillna('S')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
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       "      <td>PC 17599</td>\n",
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       "      <td>2</td>\n",
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       "      <th>2</th>\n",
       "      <td>3</td>\n",
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       "      <td>STON/O2. 3101282</td>\n",
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       "    <tr>\n",
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       "      <td>0</td>\n",
       "      <td>113803</td>\n",
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       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  Sex  Age  SibSp  Parch            Ticket  \\\n",
       "0            1         0       3    0  1.0      1      0         A/5 21171   \n",
       "1            2         1       1    1  3.0      1      0          PC 17599   \n",
       "2            3         1       3    1  1.0      0      0  STON/O2. 3101282   \n",
       "3            4         1       1    1  2.0      1      0            113803   \n",
       "4            5         0       3    0  2.0      0      0            373450   \n",
       "\n",
       "      Fare Cabin Embarked  Title  \n",
       "0   7.2500   NaN        S      0  \n",
       "1  71.2833   C85        C      2  \n",
       "2   7.9250   NaN        S      1  \n",
       "3  53.1000  C123        S      2  \n",
       "4   8.0500   NaN        S      0  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "embarked_mapping = {\"S\": 0, \"C\": 1, \"Q\": 2}\n",
    "for dataset in train_test_data:\n",
    "    dataset['Embarked'] = dataset['Embarked'].map(embarked_mapping)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.6 Fare"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
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       "      <td>2.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
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       "      <td>239865</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>248698</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>D56</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
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       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
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       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113788</td>\n",
       "      <td>35.5000</td>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>31.3875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
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       "      <td>2</td>\n",
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       "    <tr>\n",
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       "      <td>3</td>\n",
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       "      <td>0</td>\n",
       "      <td>330959</td>\n",
       "      <td>7.8792</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349216</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17601</td>\n",
       "      <td>27.7208</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17569</td>\n",
       "      <td>146.5208</td>\n",
       "      <td>B78</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>335677</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>C.A. 24579</td>\n",
       "      <td>10.5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17604</td>\n",
       "      <td>82.1708</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113789</td>\n",
       "      <td>52.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2677</td>\n",
       "      <td>7.2292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A./5. 2152</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>345764</td>\n",
       "      <td>18.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2651</td>\n",
       "      <td>11.2417</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7546</td>\n",
       "      <td>9.4750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>11668</td>\n",
       "      <td>21.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>43</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349253</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>44</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>SC/Paris 2123</td>\n",
       "      <td>41.5792</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330958</td>\n",
       "      <td>7.8792</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>46</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>S.C./A.4. 23567</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>47</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>370371</td>\n",
       "      <td>15.5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>48</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14311</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>49</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2662</td>\n",
       "      <td>21.6792</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>349237</td>\n",
       "      <td>17.8000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    PassengerId  Survived  Pclass  Sex  Age  SibSp  Parch            Ticket  \\\n",
       "0             1         0       3    0  1.0      1      0         A/5 21171   \n",
       "1             2         1       1    1  3.0      1      0          PC 17599   \n",
       "2             3         1       3    1  1.0      0      0  STON/O2. 3101282   \n",
       "3             4         1       1    1  2.0      1      0            113803   \n",
       "4             5         0       3    0  2.0      0      0            373450   \n",
       "5             6         0       3    0  2.0      0      0            330877   \n",
       "6             7         0       1    0  3.0      0      0             17463   \n",
       "7             8         0       3    0  0.0      3      1            349909   \n",
       "8             9         1       3    1  2.0      0      2            347742   \n",
       "9            10         1       2    1  0.0      1      0            237736   \n",
       "10           11         1       3    1  0.0      1      1           PP 9549   \n",
       "11           12         1       1    1  3.0      0      0            113783   \n",
       "12           13         0       3    0  1.0      0      0         A/5. 2151   \n",
       "13           14         0       3    0  3.0      1      5            347082   \n",
       "14           15         0       3    1  0.0      0      0            350406   \n",
       "15           16         1       2    1  3.0      0      0            248706   \n",
       "16           17         0       3    0  0.0      4      1            382652   \n",
       "17           18         1       2    0  2.0      0      0            244373   \n",
       "18           19         0       3    1  2.0      1      0            345763   \n",
       "19           20         1       3    1  2.0      0      0              2649   \n",
       "20           21         0       2    0  2.0      0      0            239865   \n",
       "21           22         1       2    0  2.0      0      0            248698   \n",
       "22           23         1       3    1  0.0      0      0            330923   \n",
       "23           24         1       1    0  2.0      0      0            113788   \n",
       "24           25         0       3    1  0.0      3      1            349909   \n",
       "25           26         1       3    1  3.0      1      5            347077   \n",
       "26           27         0       3    0  2.0      0      0              2631   \n",
       "27           28         0       1    0  1.0      3      2             19950   \n",
       "28           29         1       3    1  1.0      0      0            330959   \n",
       "29           30         0       3    0  2.0      0      0            349216   \n",
       "30           31         0       1    0  3.0      0      0          PC 17601   \n",
       "31           32         1       1    1  2.0      1      0          PC 17569   \n",
       "32           33         1       3    1  1.0      0      0            335677   \n",
       "33           34         0       2    0  4.0      0      0        C.A. 24579   \n",
       "34           35         0       1    0  2.0      1      0          PC 17604   \n",
       "35           36         0       1    0  3.0      1      0            113789   \n",
       "36           37         1       3    0  2.0      0      0              2677   \n",
       "37           38         0       3    0  1.0      0      0        A./5. 2152   \n",
       "38           39         0       3    1  1.0      2      0            345764   \n",
       "39           40         1       3    1  0.0      1      0              2651   \n",
       "40           41         0       3    1  3.0      1      0              7546   \n",
       "41           42         0       2    1  2.0      1      0             11668   \n",
       "42           43         0       3    0  2.0      0      0            349253   \n",
       "43           44         1       2    1  0.0      1      2     SC/Paris 2123   \n",
       "44           45         1       3    1  1.0      0      0            330958   \n",
       "45           46         0       3    0  2.0      0      0   S.C./A.4. 23567   \n",
       "46           47         0       3    0  2.0      1      0            370371   \n",
       "47           48         1       3    1  1.0      0      0             14311   \n",
       "48           49         0       3    0  2.0      2      0              2662   \n",
       "49           50         0       3    1  1.0      1      0            349237   \n",
       "\n",
       "        Fare        Cabin  Embarked  Title  \n",
       "0     7.2500          NaN         0      0  \n",
       "1    71.2833          C85         1      2  \n",
       "2     7.9250          NaN         0      1  \n",
       "3    53.1000         C123         0      2  \n",
       "4     8.0500          NaN         0      0  \n",
       "5     8.4583          NaN         2      0  \n",
       "6    51.8625          E46         0      0  \n",
       "7    21.0750          NaN         0      3  \n",
       "8    11.1333          NaN         0      2  \n",
       "9    30.0708          NaN         1      2  \n",
       "10   16.7000           G6         0      1  \n",
       "11   26.5500         C103         0      1  \n",
       "12    8.0500          NaN         0      0  \n",
       "13   31.2750          NaN         0      0  \n",
       "14    7.8542          NaN         0      1  \n",
       "15   16.0000          NaN         0      2  \n",
       "16   29.1250          NaN         2      3  \n",
       "17   13.0000          NaN         0      0  \n",
       "18   18.0000          NaN         0      2  \n",
       "19    7.2250          NaN         1      2  \n",
       "20   26.0000          NaN         0      0  \n",
       "21   13.0000          D56         0      0  \n",
       "22    8.0292          NaN         2      1  \n",
       "23   35.5000           A6         0      0  \n",
       "24   21.0750          NaN         0      1  \n",
       "25   31.3875          NaN         0      2  \n",
       "26    7.2250          NaN         1      0  \n",
       "27  263.0000  C23 C25 C27         0      0  \n",
       "28    7.8792          NaN         2      1  \n",
       "29    7.8958          NaN         0      0  \n",
       "30   27.7208          NaN         1      3  \n",
       "31  146.5208          B78         1      2  \n",
       "32    7.7500          NaN         2      1  \n",
       "33   10.5000          NaN         0      0  \n",
       "34   82.1708          NaN         1      0  \n",
       "35   52.0000          NaN         0      0  \n",
       "36    7.2292          NaN         1      0  \n",
       "37    8.0500          NaN         0      0  \n",
       "38   18.0000          NaN         0      1  \n",
       "39   11.2417          NaN         1      1  \n",
       "40    9.4750          NaN         0      2  \n",
       "41   21.0000          NaN         0      2  \n",
       "42    7.8958          NaN         1      0  \n",
       "43   41.5792          NaN         1      1  \n",
       "44    7.8792          NaN         2      1  \n",
       "45    8.0500          NaN         0      0  \n",
       "46   15.5000          NaN         2      0  \n",
       "47    7.7500          NaN         2      1  \n",
       "48   21.6792          NaN         1      0  \n",
       "49   17.8000          NaN         0      2  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fill missing Fare with median fare for each Pclass\n",
    "train[\"Fare\"].fillna(train.groupby(\"Pclass\")[\"Fare\"].transform(\"median\"), inplace=True)\n",
    "test[\"Fare\"].fillna(test.groupby(\"Pclass\")[\"Fare\"].transform(\"median\"), inplace=True)\n",
    "train.head(50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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H2yiesTUBJk8McuCQS23mQwqIIiIiIiIyIvUaDjdu3Egmk2HdunXccccdrFmz\nplCWzWZZvXo1jz32GGvXrmXdunU0NTUB8Oijj/Kd73yHdDrdp4rExjWzK/EKVYFRnB+7cIBvpzhm\nXlSGbcFz2xLMr/1kISDqHkQRERERERkpeg2H27ZtY86cOQDMmDGDHTt2FMp2795NbW0tFRUVBINB\nZs+ezZYtWwCora3lgQce6HNF2qq2YWIxu/IDmEOwbMVQKouYTKsLk0i57NjZydVTr2Jy+SRebd6l\ngCgiIiIiIiNCr+scJhIJYrFY4bFlWTiOg23bJBIJ4vF4oSwajZJI5NYHnD9/Pnv27OlzRVyzk9k1\nVzC+amx/6l8yLr8syBtvH+SF7W189ANj+MsZn2Xdjg282ryL//X6v3PHlV8iaJ189lMprpqaeO8H\niQwytTspFrU9KRa1PSkWtb2+6TUcxmIxkslk4bHnedi2fcKyZDLZIyz2R4VZwyR7KolE54DOLwXv\nmxpix65O/vD8Qf7isgrmT/oEWee3vLz/FVY9/SD/zyVLCFnBYldTjlFTE6exsb3Y1ZCzjNqdFIva\nnhSL2p4Uy2C2vTM9ZPY6fnPWrFls2rQJgPr6eqZNm1Yoq6uro6GhgdbWVjKZDFu3bmXmzJkDqsjl\no6/EGGHDSY91QV2YQMDghfo2MlkP27S5espVnFtey6stu/iHrQ/S3NFS7GqKiIiIiIgcp9c0Nm/e\nPILBIIsXL2b16tUsX76cDRs2sG7dOgKBAMuWLWPp0qUsXryYhQsXMnbswIaFjisfGbOTnkowaHJB\nXZiOTo9tO3KfTnQFxEtGT2dvcj/3b/lHXj+8u8g1FRERERER6cnwfd8vdiUANry8ZUQPKe2SyXo8\n+dsjmKbBl2+aQCjYnb//1PQq/7NnMwDXn/8Z5kz4IIZhFKuqkqdhLlIMandSLGp7Uixqe1IsGlba\ndyN7HGcJCgZMLjw/TGfa45mtrT3KLh09nc+ddzUhK8i613/Fz3f9EsdzilRTERERERGRbgqHQ+CC\nujDxqMm2He0caOy5zuOE2HgWv+9z1ERGs3nfH/nRy4/QltGnaCIiIiIiUlwKh0PAsgzePyOK78N/\nb2rB83qO3C0Pxrl+2meYVlnHW0fe4f4t/8iOptcokRG+IiIiIiJyFlI4HCLjagKcOzHIgaYML71y\nfM9gwAyw4NxP8KHxV3Ak3cZDf/oZP3r5YRra3itCbUVERERE5GyncDiEZl1SRjBgsGlLK22J4+8t\nNAyD949dN1uYAAAYTklEQVSbyU0XfJ5zy2t5o/Utvr/1AX66419pTDUXocYiIiIiInK2UjgcQuGQ\nycyLyshkfZ565vjhpV1GR6q5tu5TLDzvGsaW1fDSoT9x7x//nvWv/5r2TGKYay0iIiIiImcjhcMh\nNnVykLE1Nrvf7eCpZ1pOeV/hxPg5LJp2HZ8695PEAzH+Z89mvvv8/fx81y/5c9OrZNzMMNZcRERE\nRETOJnaxK3CmMwyDOVfE+P2z7WzfmSAYMJj7waqTrm9oGAbTquqoqziXHc2v8eKBl3h27ws8u/cF\nAqbNtKrzuHjUhVw8+gKqw1XD/G5ERERERORMpXA4DIIBk49/KM7vn21ny5/bCQVNPnx55SnPsUyL\ny2ou5pLR09mfPMjbR97lnbZ3eaV5J68072Td63BOdBznVU6hpmw0NZFRjImMZlSkGtvUj1VERERE\nRPpHKWKYhEO5gLjxmTae3XaEjrTHx/6ikoB96pG9pmEyITaeCbHxfHjCX9CWaS8ExT3te9mXPNDj\neAOD6nAVY8pGMypcRTwYJx6M5b4CuW15MEbEjpy091JERERERM4+CofDqCxiMvfKOP/f8+1s29HO\n23s6uGbuaMbXhPp8jfJgnMtqLuKymotwPIfDna20po/Qmm7Lb3P7r7W8fsrrmIZJ1C6jLFBGNBAh\nGiijzC4rbEN2kKAZIGgFCVpBQmaQgBUgZAUxMMjdOenj+z6FP37+ufy+ny/ves7AwDYDBK0Agfw2\naOb2bdNWWBURERERKSKFw2EWi1os+HgF219JseutNP/7/z3AB2dWcMWl5YRD/ZsfyDbt3JDSstHH\nlWXcDO2ZBCmngw6ng1S2g5TT0eNxp5umLdPGoVQjPiefKGc4mJhUhisYFa6iOv81KlzFqEgV1eFq\nqsOVmIbmTxIRERERGSoKh0VgWwazL40yYXyQF15K8txLR9j65zZmXRTn8kvKiZVZp/0aQSvIqEg1\no/pwrO/7ZLwMnU6aTjdNp5PG8bJkPQfHc8ges+8DuT4+o3trHPXI6C41MLoOBh8c38HxXJz89Rw/\nt59xMySySd5ofeuEdQxZQWrjE3Nf5ROZHJ/E6Ei1ehtFRERERAaJwmERjasJ8H/NreCNdzrZ+WYn\nL9S3seXPbUyZGGHS+BCTxoUZOzqIZfUMQL7v43rgOD7BgIFpnl5AMgyDkBUiZIWoOK0rnT7Hc0lk\nE7Rl2mnLJGhPt3Mk00ZjRxNvtL7VIzxG7DCT45OYXD6JuspzmVI+mbJApIi1FxEREREZuRQOiywQ\nMJh+foT3TQ3z1rtpdu3u5M2GDt5s6ADAMMAyDUwzt/V8n0y26/6+nHDIJBwyqYjZTDonxORzwpwz\nJnRcqBxKvu+Tzpx+WLVNi8pQBZWh42Nqxs3Q2NHEwVQTB1ONHEo1svPwG+w8/AY05Hoqx0fHMrXy\nXOoqzmVqxbmMCp982RAREREREemmcFgiLMvg/Clhzp8SJtXhcag5S2Ozw+EjLp7n43ng+7levnjM\nwLZy52SzPumsTzrj0rDPoWFfJ89yBNsymFobYfp5ZdTVRnqdFbW/mg5n2PV2ikPNWQ4fyXK4zSGb\nzSXWUNAgErYoj1mMGRVk7KggY0YHqakKnFZwDFpBJsTOYULsnMJznU6aA6mD7E8cZF/yAAeSh9iX\nPMCze18AchP41MYnMCk+gUnxidTGJ1AZqlBgFBERERE5huH7fnFnIsnb8PIWEonOYldjREtnPA41\nORxsynLgUJa2hAdAMGAwbUoZ08+Lcu6E8IADWtPhLDt3J9n5Voqmw9nC85YF8ahFWcTEcXJhNZPx\n6Ojs2bQCtsE5Y0NMHBti0vgQE8eHsQe5d9P1XRpTzexLHmB/Piwmsskex8QCUWrjE5kYP4fRkWrq\nxk3EToepClVimad/v6dIX9TUxGlsbC92NeQspLYnxaK2J8UymG2vpiY+KNcpVQqHZyjf92ltc2nY\nk6FhT4ZkRy4olkVMLpga5bzJEcbXBImETx6GPM+n6XCW199JsXN3dyA0TThnTIBJE4KMHR0gEjZO\n2BOXdXxa2xxaj7i0tLo0tTgcaXcL5QHboPacMFMnRairDVNZHhjk70JOKtvBoY5GDqWacl8djbRn\nEscdZ2BQFa4szJgaC0SJ2BEigTBldoSIHSZiRyizIwTMAJZpYhlW7svMbw0T0zDVMym90i9JUixq\ne1IsantSLAqHfadweBbwfZ+mFod39mR4d2+GdKb7R14Ztxk7OkgoZGKbuaGqHWmPxpYszYezOG7u\n2KMD4cRxQQKBgYWfdMajqcXhYKPDvkMZ2tq9Qll1hc3USRGm1uYm5BnsobBH63A6ae5o4UimjYzR\nyaG2lvwkOO3H9TQORHdgzAVI27QL+91B0jouYJqGiWWYGPmtaZiY5Ldmbt/Kh0/LyB1vGkZ+axX2\nLcPKH2MWyizDxMTAPOp1zPyXdcz55lH1Dpg2ATNQ2Kp3dXDolyQpFrU9KRa1PSkWhcO+Uzg8y3ie\nz8Emh0NNWZpbHVoOu2SyxzcB04SKuEVlucW4MYHTCoSnkki67D+UZd/BLAcbszj5jkXLgknjw0yZ\nGKZ2fJia6iC2PTS9cZWVZbS2pgqPu2ZM7XTSpN0MGTe3Tee3nW4a13PxfA/P93B9D893j9r3jik7\n9nHPc4u9xmR/mZgELBvbDBDsCo5WoEeADFpBwnaIiBUmbIcI22HCVs9t5JjnzrbQqV+SpFjU9qRY\n1PakWBQO+67XCWk8z2PFihXs2rWLYDDIypUrmTx5cqH86aef5sEHH8S2bRYuXMgNN9zQ6zlSPKZp\nMH5MgPFjckM4fd+no9PHcX08N7dEhm0bxKPmaS+R0RexqMX5UyzOnxLGdX0aWxz2H8yy/1CWd/Z0\n8s6e3AcGpgGjqgKMHR2kstymPGZTHrOIl9lEwrnZWgervp5r4HaU0ZkI0p50aU+6JJIO7ancfjrj\nkXV8HMfHdX1s2yAYMAgGcvUoj9rEohbxmEU8alMetfL3ZFonrKPv+4UQ6ePh+X5+P7/1fTzfz5fl\nH/co8/A49jgf3/dOcFz++WPKu6/r9Tje8V1czyXrOWRdh3TWIePm9rOOQ9rL4tGJb3j4hguGd4Lv\naN8ETJuwlQ+NdoiwFe4OkYXHoXyo7NqPFAJmyAqO2N5Nz/fy64jm1xV1u9YXzRaeK5S7PZ8vrEHq\nZgsfNviFbe6jh9y2u310PT56axgmBmZ+ddLcvm2a2JZFwOzZs2326HW2ClvL7O6lPrr3vPs5s9Bz\nbhhGYW3UY/lHtUvX8wpt/LgPXTy3xwcwhf2uY73jP7xxfZd01iXtZElnc+uudrV5A5OgHSAcyH1F\nAgFsy8Y2LIJWkGD+g5Cu/e5toPChSCD/2DSGbuSDiIgMDt/387/r5Nfh9vPrcHsuru92/z951P+Z\nNTUXFbvaQ6rXcLhx40YymQzr1q2jvr6eNWvW8NBDDwGQzWZZvXo1TzzxBJFIhBtvvJG5c+fy0ksv\nnfQcKS2GYVAWKY374yzLYFxNgHE1AWYCHZ0eBw5laTqcm7W15UiWxpbsSc8PBox8ULSIhExCIZOA\nbeS/TKyu39Xybzfr+LkZVo3DtLVn8kHQ6THs9limmZuN1bIMwqHcsh2O65N1fDo6HRpbfCB9wnMN\nIzdxTzwfHsNBE9s2sK38l929NY3c7LS5X+yN3L5v4fsWnueTdXPhtCukOm73Nut4hbJcnQ0ss2ub\nXxbFyu1bFtiWUVj2xPdzvcuuCx1pl460R0enRzLlFq53aj6YHpguhuWA5fTYWrZLKOISDHvYwe5j\nfNPBzYehdDaJQyseTh9e78QMDCzDxsLObXvsWz0fY3WHFIP8vlG4DoXS7m3Xo0IIwc3vH7118cmF\nkeOPyX05noOHg8fAQ7WUJtuwCR4VJANWgKAZzD+X2+8abt5j6HePod5mIZQfHcRNw+wRq49uvz0e\nH/Xk0c+UJyK0tXcWnjtRSD+T7psukQFSAzKUI0uG/Ltygu97vD1Me/vpjxIb+p/oUH7fh7j2Q3j5\noa67T+7D8tyH5u5RH567hQ/OvWM/BPR9PN/NBTvPwfHzW6875Dm+A4ZPZzZz3DGu7/ZesWOsrzuz\nM02v4XDbtm3MmTMHgBkzZrBjx45C2e7du6mtraWiIrcm3ezZs9myZQv19fUnPedkpoweS2sg1etx\ncnaZNr573/N8WtuzHElkaUtmaUs6JFIOnWmXzrRHZ8alM+3S0prtY5DpKRw0iZcFOKcmQDxqEy+z\nc0GuLL9fFiAcOvVkM67rk+hwaE86tKey+a2T73nMPd5/KI03xP83WGYuZBqAm18KxR3Ai5omREIW\nVeVByqM28Wgg3xua2y+P2kRCuV4g08z9QpnOuCQ7XFKdLslU7n23JfPfk2SWtgMOzZ19+cfYA+vE\nIfP4bRbDyvdc5sOpm99iOhhGuju0msP/i6LvmeAb4Jvgmfi+AZ4FXgj8CL5ngWeCZ+Hnj8Gz8ueZ\n+WPN3GPPAt/ExMr9MSwM7PxxRu5TCN846hcEI/d6kMvuhpHr4TPz96eaJpZF/gMEMC0wTR/D9HE9\nj6zr4XoujufhuC6u5+X+s3Vzz+Xys4dh+Lnvv+Hnv3L7p3z+VN8zP/c9MzEw8vfLer6B7xp4Pvie\n0f09pWs//159o8dzBgZB2yYUsAgHbUIBm3B+P2B33aMLPh5px6Ujk6Uz45BxsnRmXTKOQ8bN4uHm\n25SLYbpHtSk311aPeq662sYOeGQ9hw6nk7ZMO1kv17srIiLDwzYsLNMmYNmYmPmRIIH8vAq5D4tt\ns3vuB/uYuSFyo1w4arTLmfPB2cn0Gg4TiQSxWKzw2LIsHMfBtm0SiQTxePe422g0SiKROOU5J3Px\npFqYNNC3IdJT1nFJdGRJZ/JDyDIurts1LCB3TChoEQ5aREI20XCAcGh4lv30PJ8jiTSptEMm6+a/\nPDJObj+dzQ9xM3I9iLmtgZHftyyDkG0RDFgEAyahYG4/FOh6zsI6yfBVz/NxPB/H8XBcj6yTe92s\nk/uFNRccDGzbJF4WJBy0hqQHIZN1OdyeJtWZpSPt0JH/XnhevucyX1fPyw+F9H1O9A/yiap2otoe\nfVxueKGD6+c+PXT9XA9loXfB8Aufjvr4+QVGux51tR8fH6MwYVCuR+eoYZTkHxu5XknzhBU9/rmA\nbRIKWARsk6Cd+/kG849DAYtAwCJomwTs0pgR1/N8OjO5n1+qM7ft6HTIuvmhqz6Fn5/nkx+6mRuC\nHcy34UDAJGh3v8+A3bU1scwTz4QMuQ87HNcrtOWu9uy4Hr7PUX8fTCIhe1C+X+msSyKVIdGRJdXh\n4Hgeruvleu5dD9f1yboeAdvkiuljCdjHD292PJeMmyHjZHKBs2u4q+f22Hd9tzCk9vj97g9Xuprt\niT7R72rTpy7r8WyPsp7lJ/47OFiGujUP7d+Xoa39kNa8BP4dGaiTDUsftOvre3Piaw/pt8UoTNSX\nmyDPKnyIedxEembumK5jbSs374FtWrmtFShM4Cf90+tvw7FYjGSye/ZGz/MKIe/YsmQySTweP+U5\np6KblGWwWUCZZVAWOXn7G1URobGxneFufUEgGDAhMAj3JjluLlx29P/UABDosd6kD45Loq2D4xf8\nGDwmEAuYxAJBiAWH8JVKU79ujvc8nLSHk84ygB/xsAibEI7YVJ3i79opeR5exiOdcU4yMLt3FmB1\nfbDrumRdl2wnJAf5L3eZZVAWO/XSO62HexsJY2IQytX55IcMCU0KIsWitienzc9/nWAQhgM4+HSS\nBXrehqQJafqu1/96Zs2axaZNmwCor69n2rRphbK6ujoaGhpobW0lk8mwdetWZs6cecpzRERERERE\npPT0+hHvvHnz2Lx5M4sXL8b3fVatWsWGDRtIpVIsWrSIZcuWsXTpUnzfZ+HChYwdO/aE54iIiIiI\niEjpKpl1DkHDSqU4NMxFikHtTopFbU+KRW1PikXDSvtOCzGJiIiIiIiIwqGIiIiIiIiU2LBSERER\nERERKQ71HIqIiIiIiIjCoYiIiIiIiCgcioiIiIiICAqHIiIiIiIigsKhiIiIiIiIoHAoIiIiIiIi\ngF3MF/c8jxUrVrBr1y6CwSArV65k8uTJxaySnKG2b9/OD37wA9auXUtDQwPLli3DMAzOP/98vvvd\n72KaJuvXr+fxxx/Htm2+/OUv8/GPf7zY1ZYRLJvNctddd7F3714ymQxf/vKXOe+889T2ZMi5rst3\nvvMd3n77bQzD4Hvf+x6hUEhtT4ZFc3Mzn/vc53jsscewbVvtTobNddddRywWA2DixIncdtttan8D\n4RfRU0895d95552+7/v+yy+/7N92223FrI6coR555BH/6quv9q+//nrf933/S1/6kv/CCy/4vu/7\nd999t//b3/7WP3TokH/11Vf76XTab2trK+yLDNQTTzzhr1y50vd93z98+LD/0Y9+VG1PhsXvfvc7\nf9myZb7v+/4LL7zg33bbbWp7MiwymYz/la98xb/qqqv8N998U+1Ohk1nZ6d/7bXX9nhO7W9gijqs\ndNu2bcyZMweAGTNmsGPHjmJWR85QtbW1PPDAA4XHr7zyCldccQUAH/nIR3juuef405/+xMyZMwkG\ng8TjcWpra9m5c2exqixngAULFvA3f/M3APi+j2VZansyLD75yU9y3333AbBv3z7Ky8vV9mRY3H//\n/SxevJgxY8YA+v9Whs/OnTvp6Ojg1ltv5ZZbbqG+vl7tb4CKGg4TiUSh+xfAsiwcxylijeRMNH/+\nfGy7ewS17/sYhgFANBqlvb2dRCJBPB4vHBONRkkkEsNeVzlzRKNRYrEYiUSCr33ta9x+++1qezJs\nbNvmzjvv5L777uOaa65R25Mh98tf/pLq6urCh/6g/29l+ITDYZYuXcpPf/pTvve97/HNb35T7W+A\nihoOY7EYyWSy8NjzvB6/xIsMBdPsbvbJZJLy8vLj2mIymezxj4fIQOzfv59bbrmFa6+9lmuuuUZt\nT4bV/fffz1NPPcXdd99NOp0uPK+2J0PhF7/4Bc899xxLlizhtdde484776SlpaVQrnYnQ2nKlCl8\n5jOfwTAMpkyZQmVlJc3NzYVytb++K2o4nDVrFps2bQKgvr6eadOmFbM6cpaYPn06f/zjHwHYtGkT\nl19+OZdeeinbtm0jnU7T3t7O7t271R7ltDQ1NXHrrbfyrW99i89//vOA2p4Mj1/96lc8/PDDAEQi\nEQzD4OKLL1bbkyH1b//2b/zrv/4ra9eu5cILL+T+++/nIx/5iNqdDIsnnniCNWvWAHDw4EESiQRX\nXnml2t8AGL7v+8V68a7ZSl9//XV832fVqlXU1dUVqzpyBtuzZw/f+MY3WL9+PW+//TZ333032WyW\nqVOnsnLlSizLYv369axbtw7f9/nSl77E/Pnzi11tGcFWrlzJf/3XfzF16tTCc3/7t3/LypUr1fZk\nSKVSKZYvX05TUxOO4/DFL36Ruro6/bsnw2bJkiWsWLEC0zTV7mRYZDIZli9fzr59+zAMg29+85tU\nVVWp/Q1AUcOhiIiIiIiIlIaiDisVERERERGR0qBwKCIiIiIiIgqHIiIiIiIionAoIiIiIiIiKByK\niIiIiIgIoBXnRURkRNmzZw8LFiw4bumjf/7nf2b8+PFFqpWIiMjIp3AoIiIjzpgxY/j1r39d7GqI\niIicURQORUTkjPD6669z3333kUqlaGlp4Qtf+AK33HILDzzwAPX19ezfv5+bb76ZD3/4w6xYsYLW\n1lbC4TB3330306dPL3b1RUREik7hUERERpxDhw5x7bXXFh5fc801HDx4kK985St88IMf5L333uMz\nn/kMt9xyCwCZTIb//M//BGDx4sXcc889TJ8+nTfffJOvfvWrPPXUU0V5HyIiIqVE4VBEREacEw0r\ndV2XZ555hocffphdu3aRSqUKZZdeeikAyWSSHTt2sHz58kJZKpXi8OHDVFVVDU/lRURESpTCoYiI\nnBFuv/12ysvL+fjHP86nP/1pfvOb3xTKwuEwAJ7nEQwGewTLAwcOUFlZOez1FRERKTVaykJERM4I\nmzdv5mtf+xqf/OQn2bJlC5DrTTxaPB7n3HPPLYTDzZs3c/PNNw97XUVEREqReg5FROSM8Nd//dfc\ndNNNlJeXM2XKFCZMmMCePXuOO+7v//7vWbFiBT/5yU8IBAL88Ic/xDCMItRYRESktBi+7/vFroSI\niIiIiIgUl4aVioiIiIiIiMKhiIiIiIiIKByKiIiIiIgICociIiIiIiKCwqGIiIiIiIigcCgiIiIi\nIiIoHIqIiIiIiAgKhyIiIiIiIgL8/4XauFMKAuOVAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1112cc750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "facet = sns.FacetGrid(train, hue=\"Survived\",aspect=4)\n",
    "facet.map(sns.kdeplot,'Fare',shade= True)\n",
    "facet.set(xlim=(0, train['Fare'].max()))\n",
    "facet.add_legend()\n",
    " \n",
    "plt.show()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 20)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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rB0800REaAmBZnpsta/NZvzIHh33uD5jWt4jpSeOSnjQu6Udjkp40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\nAJL+BYFgQ6iEUtgRAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111024ed0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "facet = sns.FacetGrid(train, hue=\"Survived\",aspect=4)\n",
    "facet.map(sns.kdeplot,'Fare',shade= True)\n",
    "facet.set(xlim=(0, train['Fare'].max()))\n",
    "facet.add_legend()\n",
    "plt.xlim(0, 20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 30)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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WrVqFVatW4fPPPwcAvPvuu3jwwQfxjW98Y9ijhgBHDqOKoqjY8cUF/HnXafQ4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\nVReUAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1112ae450>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "facet = sns.FacetGrid(train, hue=\"Survived\",aspect=4)\n",
    "facet.map(sns.kdeplot,'Fare',shade= True)\n",
    "facet.set(xlim=(0, train['Fare'].max()))\n",
    "facet.add_legend()\n",
    "plt.xlim(0, 30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 512.32920000000001)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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H2yiesTUBJk8McuCQS23mQwqIIiIiIiIyIvUaDjdu3Egmk2HdunXccccdrFmz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\nIiIoHIqIiIiIiAgKhyIiIiIiIgL8/4XauFMKAuOVAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111212b50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "facet = sns.FacetGrid(train, hue=\"Survived\",aspect=4)\n",
    "facet.map(sns.kdeplot,'Fare',shade= True)\n",
    "facet.set(xlim=(0, train['Fare'].max()))\n",
    "facet.add_legend()\n",
    "plt.xlim(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for dataset in train_test_data:\n",
    "    dataset.loc[ dataset['Fare'] <= 17, 'Fare'] = 0,\n",
    "    dataset.loc[(dataset['Fare'] > 17) & (dataset['Fare'] <= 30), 'Fare'] = 1,\n",
    "    dataset.loc[(dataset['Fare'] > 30) & (dataset['Fare'] <= 100), 'Fare'] = 2,\n",
    "    dataset.loc[ dataset['Fare'] > 100, 'Fare'] = 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>2.0</td>\n",
       "      <td>C85</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>2.0</td>\n",
       "      <td>C123</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  Sex  Age  SibSp  Parch            Ticket  \\\n",
       "0            1         0       3    0  1.0      1      0         A/5 21171   \n",
       "1            2         1       1    1  3.0      1      0          PC 17599   \n",
       "2            3         1       3    1  1.0      0      0  STON/O2. 3101282   \n",
       "3            4         1       1    1  2.0      1      0            113803   \n",
       "4            5         0       3    0  2.0      0      0            373450   \n",
       "\n",
       "   Fare Cabin  Embarked  Title  \n",
       "0   0.0   NaN         0      0  \n",
       "1   2.0   C85         1      2  \n",
       "2   0.0   NaN         0      1  \n",
       "3   2.0  C123         0      2  \n",
       "4   0.0   NaN         0      0  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.7 Cabin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "C23 C25 C27        4\n",
       "G6                 4\n",
       "B96 B98            4\n",
       "D                  3\n",
       "C22 C26            3\n",
       "E101               3\n",
       "F2                 3\n",
       "F33                3\n",
       "B57 B59 B63 B66    2\n",
       "C68                2\n",
       "B58 B60            2\n",
       "E121               2\n",
       "D20                2\n",
       "E8                 2\n",
       "E44                2\n",
       "B77                2\n",
       "C65                2\n",
       "D26                2\n",
       "E24                2\n",
       "E25                2\n",
       "B20                2\n",
       "C93                2\n",
       "D33                2\n",
       "E67                2\n",
       "D35                2\n",
       "D36                2\n",
       "C52                2\n",
       "F4                 2\n",
       "C125               2\n",
       "C124               2\n",
       "                  ..\n",
       "F G63              1\n",
       "A6                 1\n",
       "D45                1\n",
       "D6                 1\n",
       "D56                1\n",
       "C101               1\n",
       "C54                1\n",
       "D28                1\n",
       "D37                1\n",
       "B102               1\n",
       "D30                1\n",
       "E17                1\n",
       "E58                1\n",
       "F E69              1\n",
       "D10 D12            1\n",
       "E50                1\n",
       "A14                1\n",
       "C91                1\n",
       "A16                1\n",
       "B38                1\n",
       "B39                1\n",
       "C95                1\n",
       "B78                1\n",
       "B79                1\n",
       "C99                1\n",
       "B37                1\n",
       "A19                1\n",
       "E12                1\n",
       "A7                 1\n",
       "D15                1\n",
       "Name: Cabin, Length: 147, dtype: int64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.Cabin.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for dataset in train_test_data:\n",
    "    dataset['Cabin'] = dataset['Cabin'].str[:1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1121b2d10>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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z/16wruXcuYKbPTwcLCcn1+4IqCLCw0M4X1DtcZ5XvOsV2Jt+l1+rVq20detWSdKmTZvU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\nAAxRqAAAAAz9P7ZN3zLpSp8aAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1123f8390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Pclass1 = train[train['Pclass']==1]['Cabin'].value_counts()\n",
    "Pclass2 = train[train['Pclass']==2]['Cabin'].value_counts()\n",
    "Pclass3 = train[train['Pclass']==3]['Cabin'].value_counts()\n",
    "df = pd.DataFrame([Pclass1, Pclass2, Pclass3])\n",
    "df.index = ['1st class','2nd class', '3rd class']\n",
    "df.plot(kind='bar',stacked=True, figsize=(10,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cabin_mapping = {\"A\": 0, \"B\": 0.4, \"C\": 0.8, \"D\": 1.2, \"E\": 1.6, \"F\": 2, \"G\": 2.4, \"T\": 2.8}\n",
    "for dataset in train_test_data:\n",
    "    dataset['Cabin'] = dataset['Cabin'].map(cabin_mapping)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# fill missing Fare with median fare for each Pclass\n",
    "train[\"Cabin\"].fillna(train.groupby(\"Pclass\")[\"Cabin\"].transform(\"median\"), inplace=True)\n",
    "test[\"Cabin\"].fillna(test.groupby(\"Pclass\")[\"Cabin\"].transform(\"median\"), inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.8 FamilySize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train[\"FamilySize\"] = train[\"SibSp\"] + train[\"Parch\"] + 1\n",
    "test[\"FamilySize\"] = test[\"SibSp\"] + test[\"Parch\"] + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 11.0)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ZUdlKhTcyi6UXEREREZFSUDicgrfaD+Is2oNpu7i57g48pqfcRSqp+tpCNWg/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\nD5GO45DK5Ikns8RSWWKJLLHkqVs8mSO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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1121ef8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "facet = sns.FacetGrid(train, hue=\"Survived\",aspect=4)\n",
    "facet.map(sns.kdeplot,'FamilySize',shade= True)\n",
    "facet.set(xlim=(0, train['FamilySize'].max()))\n",
    "facet.add_legend()\n",
    "plt.xlim(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "family_mapping = {1: 0, 2: 0.4, 3: 0.8, 4: 1.2, 5: 1.6, 6: 2, 7: 2.4, 8: 2.8, 9: 3.2, 10: 3.6, 11: 4}\n",
    "for dataset in train_test_data:\n",
    "    dataset['FamilySize'] = dataset['FamilySize'].map(family_mapping)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "      <th>FamilySize</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
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       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  Sex  Age  SibSp  Parch            Ticket  \\\n",
       "0            1         0       3    0  1.0      1      0         A/5 21171   \n",
       "1            2         1       1    1  3.0      1      0          PC 17599   \n",
       "2            3         1       3    1  1.0      0      0  STON/O2. 3101282   \n",
       "3            4         1       1    1  2.0      1      0            113803   \n",
       "4            5         0       3    0  2.0      0      0            373450   \n",
       "\n",
       "   Fare  Cabin  Embarked  Title  FamilySize  \n",
       "0   0.0    2.0         0      0         0.4  \n",
       "1   2.0    0.8         1      2         0.4  \n",
       "2   0.0    2.0         0      1         0.0  \n",
       "3   2.0    0.8         0      2         0.4  \n",
       "4   0.0    2.0         0      0         0.0  "
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        text-align: right;\n",
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       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
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       "    <tr>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  Sex  Age  SibSp  Parch            Ticket  \\\n",
       "0            1         0       3    0  1.0      1      0         A/5 21171   \n",
       "1            2         1       1    1  3.0      1      0          PC 17599   \n",
       "2            3         1       3    1  1.0      0      0  STON/O2. 3101282   \n",
       "3            4         1       1    1  2.0      1      0            113803   \n",
       "4            5         0       3    0  2.0      0      0            373450   \n",
       "\n",
       "   Fare  Cabin  Embarked  Title  FamilySize  \n",
       "0   0.0    2.0         0      0         0.4  \n",
       "1   2.0    0.8         1      2         0.4  \n",
       "2   0.0    2.0         0      1         0.0  \n",
       "3   2.0    0.8         0      2         0.4  \n",
       "4   0.0    2.0         0      0         0.0  "
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "features_drop = ['Ticket', 'SibSp', 'Parch']\n",
    "train = train.drop(features_drop, axis=1)\n",
    "test = test.drop(features_drop, axis=1)\n",
    "train = train.drop(['PassengerId'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((891, 8), (891,))"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data = train.drop('Survived', axis=1)\n",
    "target = train['Survived']\n",
    "\n",
    "train_data.shape, target.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "      <th>FamilySize</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>3</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.4</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.4</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.4</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.8</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pclass  Sex  Age  Fare  Cabin  Embarked  Title  FamilySize\n",
       "0       3    0  1.0   0.0    2.0         0      0         0.4\n",
       "1       1    1  3.0   2.0    0.8         1      2         0.4\n",
       "2       3    1  1.0   0.0    2.0         0      1         0.0\n",
       "3       1    1  2.0   2.0    0.8         0      2         0.4\n",
       "4       3    0  2.0   0.0    2.0         0      0         0.0\n",
       "5       3    0  2.0   0.0    2.0         2      0         0.0\n",
       "6       1    0  3.0   2.0    1.6         0      0         0.0\n",
       "7       3    0  0.0   1.0    2.0         0      3         1.6\n",
       "8       3    1  2.0   0.0    2.0         0      2         0.8\n",
       "9       2    1  0.0   2.0    1.8         1      2         0.4"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Modelling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Importing Classifier Modules\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 9 columns):\n",
      "Survived      891 non-null int64\n",
      "Pclass        891 non-null int64\n",
      "Sex           891 non-null int64\n",
      "Age           891 non-null float64\n",
      "Fare          891 non-null float64\n",
      "Cabin         891 non-null float64\n",
      "Embarked      891 non-null int64\n",
      "Title         891 non-null int64\n",
      "FamilySize    891 non-null float64\n",
      "dtypes: float64(4), int64(5)\n",
      "memory usage: 62.7 KB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.2 Cross Validation (K-fold)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import KFold\n",
    "from sklearn.model_selection import cross_val_score\n",
    "k_fold = KFold(n_splits=10, shuffle=True, random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.2.1 kNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.82222222  0.76404494  0.80898876  0.83146067  0.87640449  0.82022472\n",
      "  0.85393258  0.79775281  0.84269663  0.84269663]\n"
     ]
    }
   ],
   "source": [
    "clf = KNeighborsClassifier(n_neighbors = 13)\n",
    "scoring = 'accuracy'\n",
    "score = cross_val_score(clf, train_data, target, cv=k_fold, n_jobs=1, scoring=scoring)\n",
    "print(score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "82.6"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# kNN Score\n",
    "round(np.mean(score)*100, 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.2.2 Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.76666667  0.82022472  0.78651685  0.76404494  0.88764045  0.76404494\n",
      "  0.82022472  0.82022472  0.74157303  0.79775281]\n"
     ]
    }
   ],
   "source": [
    "clf = DecisionTreeClassifier()\n",
    "scoring = 'accuracy'\n",
    "score = cross_val_score(clf, train_data, target, cv=k_fold, n_jobs=1, scoring=scoring)\n",
    "print(score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "79.69"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# decision tree Score\n",
    "round(np.mean(score)*100, 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.2.3 Ramdom Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.77777778  0.80898876  0.82022472  0.76404494  0.86516854  0.82022472\n",
      "  0.79775281  0.80898876  0.76404494  0.83146067]\n"
     ]
    }
   ],
   "source": [
    "clf = RandomForestClassifier(n_estimators=13)\n",
    "scoring = 'accuracy'\n",
    "score = cross_val_score(clf, train_data, target, cv=k_fold, n_jobs=1, scoring=scoring)\n",
    "print(score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "80.59"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Random Forest Score\n",
    "round(np.mean(score)*100, 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.2.4 Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.85555556  0.73033708  0.75280899  0.75280899  0.70786517  0.80898876\n",
      "  0.76404494  0.80898876  0.86516854  0.83146067]\n"
     ]
    }
   ],
   "source": [
    "clf = GaussianNB()\n",
    "scoring = 'accuracy'\n",
    "score = cross_val_score(clf, train_data, target, cv=k_fold, n_jobs=1, scoring=scoring)\n",
    "print(score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "78.78"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Naive Bayes Score\n",
    "round(np.mean(score)*100, 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.2.5 SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.83333333  0.80898876  0.83146067  0.82022472  0.84269663  0.82022472\n",
      "  0.84269663  0.85393258  0.83146067  0.86516854]\n"
     ]
    }
   ],
   "source": [
    "clf = SVC()\n",
    "scoring = 'accuracy'\n",
    "score = cross_val_score(clf, train_data, target, cv=k_fold, n_jobs=1, scoring=scoring)\n",
    "print(score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "83.5"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(np.mean(score)*100,2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7. Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "clf = SVC()\n",
    "clf.fit(train_data, target)\n",
    "\n",
    "test_data = test.drop(\"PassengerId\", axis=1).copy()\n",
    "prediction = clf.predict(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "submission = pd.DataFrame({\n",
    "        \"PassengerId\": test[\"PassengerId\"],\n",
    "        \"Survived\": prediction\n",
    "    })\n",
    "\n",
    "submission.to_csv('submission.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived\n",
       "0          892         0\n",
       "1          893         1\n",
       "2          894         0\n",
       "3          895         0\n",
       "4          896         1"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submission = pd.read_csv('submission.csv')\n",
    "submission.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## References\n",
    "\n",
    "This notebook is created by learning from the following notebooks:\n",
    "\n",
    "- [Mukesh ChapagainTitanic Solution: A Beginner's Guide](https://www.kaggle.com/chapagain/titanic-solution-a-beginner-s-guide?scriptVersionId=1473689)\n",
    "- [How to score 0.8134 in Titanic Kaggle Challenge](http://ahmedbesbes.com/how-to-score-08134-in-titanic-kaggle-challenge.html)\n",
    "- [Titanic: factors to survive](https://olegleyz.github.io/titanic_factors.html)\n",
    "- [Titanic Survivors Dataset and Data Wrangling](http://www.codeastar.com/data-wrangling/)\n"
   ]
  }
 ],
 "metadata": {
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   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
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