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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   "source": [
    "## 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."
   ]
  },
  {
   "cell_type": "code",
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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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  {
   "cell_type": "code",
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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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  {
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   "source": [
    "## 3. Exploratory data analysis\n",
    "Printing first 5 rows of the train dataset."
   ]
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       "      <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 scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\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"
   },
   "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": {},
   "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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xkiRJKsTiJUmSVIjFS5IkqRCLlyRJUiEWL0mSpEIsXpIkSYVYvCRJkgqxeEmSJBVi8ZIkSSrE4iVJklSIxUuSJKkQi5ckSVIhFi9JkqRCLF6SJEmFWLwkSZIKsXhJkiQVYvGSJEkqxOIlSZJUiMVLkiSpEIuXJElSIRYvSZKkQixekiRJhVi8JEmSCumrOoB2bGjzJmZfsrTqGKqBoc2bqo4gSdoOi1cN9I6fwIlv/VrVMVQDy648CdhYdQxJ0tPwUqMkSVIhFi9JkqRCLF6SJEmFWLwkSZIKsXhJkiQVYvGSJEkqxOIlSZJUiMVLkiSpEIuXJElSIRYvSZKkQixekiRJhbT1WY0R8V7g1a2nN2bmf4+I44CrgEnA9Zl5aWvuXGARMBVYAVyQmQMdTy5JklQzO1zxahWsPwbmAXOB+RHxGuBa4CTgecCREfHK1iGLgTdm5hygBzh3LIJLkiTVTTuXGlcBb83MTZm5GfgXYA5wf2Y+2FrNWgycGhGzgEmZeUfr2M8Cp45BbkmSpNrZ4aXGzPzZlscRcSjNS44fo1nItlgFHAQc+DTjbZs+ffJopkvaSn//lKojSOpCnls6o609XgAR8XzgRuBtwADNVa8teoAhmitojW2Mt23NmvUMDTV2PHEP4l92jcbq1euqjqCa8Nyi0fDcMlJvb89OLRa19a7GiFgI3Ay8IzM/BzwCzBw2ZQbw6HbGJUmS9njtbK5/FvBV4PTM/FJr+M7ml+KQiBgHnA4sz8yHgA2togZwFrB8DHJLkiTVTjuXGi8G9gKuiogtY58EzgaWtr72TWBJ62tnAJ+OiKnAj4GPdjCvJElSbbWzuf6vgL96mi8fto359wBH7WIuSZKkruOd6yVJkgqxeEmSJBVi8ZIkSSrE4iVJklSIxUuSJKkQi5ckSVIhFi9JkqRCLF6SJEmFWLwkSZIKsXhJkiQVYvGSJEkqxOIlSZJUiMVLkiSpEIuXJElSIRYvSZKkQixekiRJhVi8JEmSCrF4SZIkFWLxkiRJKsTiJUmSVIjFS5IkqRCLlyRJUiEWL0mSpEIsXpIkSYVYvCRJkgqxeEmSJBVi8ZIkSSrE4iVJklSIxUuSJKkQi5ckSVIhFi9JkqRCLF6SJEmFWLwkSZIKsXhJkiQVYvGSJEkqxOIlSZJUiMVLkiSpEIuXJElSIX3tToyIqcDtwAmZ+cuIOA64CpgEXJ+Zl7bmzQUWAVOBFcAFmTnQ8eSSJEk109aKV0S8EPgeMKf1fBJwLXAS8DzgyIh4ZWv6YuCNmTkH6AHO7XRoSZKkOmr3UuO5wBuAR1vPjwLuz8wHW6tZi4FTI2IWMCkz72jN+yxwagfzSpIk1VZblxoz8/UAEbFl6EBg1bApq4CDtjPetunTJ49muqSt9PdPqTqCpC7kuaUz2t7jtZVeoDHseQ8wtJ3xtq1Zs56hocaOJ+5B/Muu0Vi9el3VEVQTnls0Gp5bRurt7dmpxaKdfVfjI8DMYc9n0LwM+XTjkiRJe7ydLV53AhERh0TEOOB0YHlmPgRsiIiFrXlnAcs7kFOSJKn2dqp4ZeYG4GxgKXAvcB+wpPXlM4APR8R9wGTgo7seU5Ikqf5GtccrMw8e9vhm4LBtzLmH5rseJUmSNIx3rpckSSrE4iVJklSIxUuSJKmQnb2PlySp5oY2b2L2JUurjqEaGNq8qeoIXcPiJUl7qN7xEzjxrV+rOoZqYNmVJwEbq47RFbzUKEmSVIjFS5IkqRCLlyRJUiEWL0mSpEIsXpIkSYVYvCRJkgqxeEmSJBVi8ZIkSSrE4iVJklSIxUuSJKkQi5ckSVIhFi9JkqRCLF6SJEmFWLwkSZIKsXhJkiQVYvGSJEkqxOIlSZJUiMVLkiSpEIuXJElSIRYvSZKkQixekiRJhVi8JEmSCrF4SZIkFWLxkiRJKsTiJUmSVIjFS5IkqRCLlyRJUiEWL0mSpEIsXpIkSYVYvCRJkgqxeEmSJBVi8ZIkSSrE4iVJklRI31i8aEScDlwKjAf+ITM/MRbfR5IkqU46vuIVEc8ELgdeBMwFzouI/9Lp7yNJklQ3Y7HidRzwncz8FUBELAFOAd6/g+PGAfT29oxBpPrbf9qkqiOoJvxvSKPhuUXt8twy0rA/j3GjOW4siteBwKphz1cBR7Vx3EyAadP2HoNI9feZS/+46giqienTJ1cdQTXiuUXt8tzytGYCv2h38lgUr16gMex5DzDUxnE/Ao6hWdQGxyCXJElSp4yjWbp+NJqDxqJ4PUKzQG0xA3i0jeM2At8bgzySJEljoe2Vri3GonjdBFwWEf3Ab4CTgfPG4PtIkiTVSsff1ZiZ/xe4BLgF+Anwhcz8Yae/jyRJUt30NBqNHc+SJEnSLvPO9ZIkSYVYvCRJkgqxeEmSJBVi8ZIkSSrE4iVJklSIxUuSJKkQi5ckSVIhY3HneqmjIuLF2/t6Zq4olUVSd4mI/8nIzxceITP/smAc7QEsXqqD97V+nw4cAnyf5gepHw38M7CwolyS6u+7rd9PAKYAi4EB4DTg1xVlUhfzzvWqjYj4JnBRZv689XwWcE1m/km1ySTVXUTcCfxhZg61nvcCd2TmUdUmU7dxj5fqZNaW0tXyb8CsqsJI6ir7APsNe34AMLmiLOpiXmpUndwdEZ8DbgB6gDOA26qNJKlLXA78NCK+T3NRYgFwUbWR1I281KjaiIgJwJuAl9DcDHsTcHVmDlSZS1J3iIiZNPeONoDvZeZjFUdSF7J4qVYi4mDg+cA/Ac/KzAerTSSpG0REP3AmzcuLPcA44DmZ+ReVBlPXcY+XaiMiTgOWAR+huRfjBxFxZrWpJHWJ64G5NMvX3sApwFClidSVLF6qk7fTvAywrnUJYB7wzmojSeoSB2bma2n+z91XgBfTPMdIHWXxUp0MZua6LU8ycxX+H6mkzljb+j2BwzJzTZVh1L18V6Pq5GcR8UZgfETMBS4EflJxJknd4TsR8WXgYuDbEXE48FTFmdSFXPFSnbwBeCbNk+G1wBM0y5ck7ZLMvAR4R2Y+BLyG5srXf602lbqRK16qk9cDH85M93VJGgsvjIi/pHlPrz/IzEerDqTu44qX6uRZwJ0RsTwizoiI36s6kKTuEBF/BxxPc5WrDzgnIq6sNpW6kcVLtZGZF2fmc4APAH8IrIyIz1ccS1J3eAVwFrAhM58AXg68stpI6kYWL9VKRPQA44EJNO8uvanaRJK6xNbvkJ64jTFpl1m8VBsR8VGaH4z9FuBmYG5mvr7aVJK6xA00b6I6LSLeTPNzYL9QbSR1IzfXq07uB+Zl5n9UHURS17kReBSYDRwDvDszb6w2krqRn9Wo3V5EnJeZn4qI99K8vDhCZr6/gliSukBE7A8sofkZsPdvGQZ+ALwmM39dVTZ1Jy81qg56tnq89S9J2lkfBL4HzMjMBZm5ANgfuIfm58JKHeWlRu32MvOa1sPHgS+2PqdRkjrh6Mx83vCBzNwcEe/CT8bQGHDFS3XifbwkddqGbQ1mZgPf1agxYPFSbXgfL0ljYHsbnd0ErY7zUqNqxft4Seqw50fEA9sY7wFmlg6j7mfxUm207uP15zT3XVwHXJSZ27xMIEltmlN1AO1ZLF6qk8fwPl6SOigzH6o6g/Ys7vFSnZxh6ZIk1Zk3UFVtRMRSmvfWuRN4ast4Zq6oLJQkSaPgpUbVyX7AS1u/tmgAL6smjiRJo+OKlyRJUiGueKk2IuIWtv1Zja54SZJqweKlOrls2OPxwEnA2mqiSJI0el5qVK1FxJ2Z+cKqc0iS1A5XvFQbEfHsYU97gOcD0yuKI0nSqFm8VCe38p97vBrAfwBvqi6OJEmj4w1UVQsRcQJwXGbOBt4K/AvwT8BNlQaTJGkULF7a7UXExcB7gYkR8QJgMfBVmvf1+h9VZpMkaTQsXqqDs4BjM/Ne4HTg65m5iOZlxldUmkySpFGweKkOGpn5ZOvxS4FvAWSmb8mVJNWKm+tVBwMRsS8wGZgHfBsgImYBA1UGkyRpNFzxUh38HfAT4A5gUWauiohXAzcDf19pMkmSRsEbqKoWIuJA4BmZ+dPW8+OBJzPzu5UGkyRpFCxekiRJhXipUZIkqRCLlyRJUiEWL0mSpEIsXpIkSYX8f2mULBNgIOkkAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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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Fopbe1RgRxwLLgU9m5p8DLwFzx0w5AFi5i3FJkqRJr5XN9QcBfw2cmZlfbQ4/0vhRHBIRU4AzgWWZ+QKwsVnUAM4BlnUgtyRJUu20cqnxCmA6cF1EbB37U+BcYGnzZ98EljR/dhbwpYiYDfxP4IY25pUkSaqtVjbXfxz4+E5+fNgO5j8JHLWHuSRJkrqOd66XJEkqxOIlSZJUiMVLkiSpEIuXJElSIRYvSZKkQlq+c70kqbsMDQ9x1xk3VR1DNTA0PFR1hK5h8ZKkSaq/r5/nrj6l6hiqgQWLlgKbqo7RFbzUKEmSVIjFS5IkqRCLlyRJUiEWL0mSpEIsXpIkSYVYvCRJkgqxeEmSJBVi8ZIkSSrE4iVJklSIxUuSJKkQi5ckSVIhFi9JkqRCLF6SJEmFWLwkSZIKsXhJkiQVYvGSJEkqxOIlSZJUSF/VATS+kc1DLFi0tOoYqoGRzUNVR5Ak7YLFqwZ6p/bzvsu/VnUM1cDd154MbKo6hiRpJ7zUKEmSVIjFS5IkqRCLlyRJUiEWL0mSpEIsXpIkSYVYvCRJkgqxeEmSJBVi8ZIkSSrE4iVJklSIxUuSJKkQi5ckSVIhFi9JkqRCWv6Q7IiYDXwPOCkzfxgRJwDXATOAOzPzyua8hcAtwGzgQeCizBxue3JJkqSaaWnFKyLeBjwEHNp8PAO4DTgZeBNwZESc2Jy+GLg0Mw8FeoAL2h1akiSpjlq91HgBcAmwsvn4KGBFZj7fXM1aDJwWEfOBGZn5cHPe7cBpbcwrSZJUWy1daszMDwFExNahecCqMVNWAQfuYrxlAwMzJzJd0nYGB2dVHUFSF/Lc0h4t7/HaTi8wOuZxDzCyi/GWrVmznpGR0fEnTiL+smsiVq9eV3UE1YTnFk2E55Zt9fb27NZi0e6+q/ElYO6YxwfQuAy5s3FJkqRJb3eL1yNARMQhETEFOBNYlpkvABsj4tjmvHOAZW3IKUmSVHu7VbwycyNwLrAUeAZ4FljS/PFZwPUR8SwwE7hhz2NKkiTV34T2eGXmG8d8vxw4bAdznqTxrkdJkiSN4Z3rJUmSCrF4SZIkFWLxkiRJKsTiJUmSVMju3kBVklRzI5uHWLBoadUxVAMjm4eqjtA1LF6SNEn1Tu3nfZd/reoYqoG7rz0Z2FR1jK7gpUZJkqRCLF6SJEmFWLwkSZIKsXhJkiQVYvGSJEkqxOIlSZJUiMVLkiSpEIuXJElSIRYvSZKkQixekiRJhVi8JEmSCrF4SZIkFWLxkiRJKsTiJUmSVIjFS5IkqRCLlyRJUiEWL0mSpEIsXpIkSYVYvCRJkgqxeEmSJBVi8ZIkSSrE4iVJklSIxUuSJKkQi5ckSVIhFi9JkqRCLF6SJEmFWLwkSZIKsXhJkiQVYvGSJEkqxOIlSZJUiMVLkiSpEIuXJElSIX2deNGIOBO4EpgKfCEz/6QTx5EkSaqTtq94RcQbgKuBdwALgQsj4hfbfRxJkqS66cSK1wnAfZn5TwARsQQ4FfjcOM+bAtDb29OBSPW3/5wZVUdQTfjfkCbCc4ta5bllW2P+PqZM5HmdKF7zgFVjHq8CjmrheXMB5szZpwOR6u/WK3+16giqiYGBmVVHUI14blGrPLfs1FzgB61O7kTx6gVGxzzuAUZaeN6jwDtpFLUtHcglSZLULlNolK5HJ/KkThSvl2gUqK0OAFa28LxNwEMdyCNJktQJLa90bdWJ4nUv8JmIGAQ2AKcAF3bgOJIkSbXS9nc1Zub/BRYB9wNPAHdk5t+1+ziSJEl10zM6Ojr+LEmSJO0x71wvSZJUiMVLkiSpEIuXJElSIRYvSZKkQixekiRJhVi8JEmSCrF4SZIkFdKJO9dLbRURx+3q55n5YKkskrpLRHyZbT9feBuZeV7BOJoELF6qg882vw4AhwDfpfFB6scA/ws4tqJckurv282vJwGzgMXAMHAG8OOKMqmLeed61UZEfBP4WGb+n+bj+cDNmflvq00mqe4i4hHg6MwcaT7uBR7OzKOqTaZu4x4v1cn8raWr6UfA/KrCSOoqrwNeP+bxzwIzK8qiLualRtXJYxHx58BdQA9wFvCdaiNJ6hJXA09FxHdpLEq8HfhYtZHUjbzUqNqIiH7go8C7aGyGvRf4YmYOV5lLUneIiLk09o6OAg9l5isVR1IXsnipViLijcCbgb8FDsrM56tNJKkbRMQgcDaNy4s9wBTg5zLztyoNpq7jHi/VRkScAdwN/BcaezG+HxFnV5tKUpe4E1hIo3ztA5wKjFSaSF3J4qU6+V0alwHWNS8BHA78x2ojSeoS8zLzgzT+cfeXwHE0zjFSW1m8VCdbMnPd1geZuQr/RSqpPdY2vyZwWGauqTKMupfvalSdPB0RlwJTI2IhcDHwRMWZJHWH+yLiL4ArgG9FxL8GflpxJnUhV7xUJ5cAb6BxMrwNeI1G+ZKkPZKZi4BPZuYLwAdorHz9RrWp1I1c8VKdfAi4PjPd1yWpE94WEefRuKfXWzJzZdWB1H1c8VKdHAQ8EhHLIuKsiPiZqgNJ6g4R8cfAr9FY5eoDfjsirq02lbqRxUu1kZlXZObPAX8IHA08HhH/teJYkrrDe4FzgI2Z+RrwK8CJ1UZSN7J4qVYiogeYCvTTuLv0ULWJJHWJ7d8hPW0HY9Ies3ipNiLiBhofjP0JYDmwMDM/VG0qSV3iLho3UZ0TEZfR+BzYO6qNpG7k5nrVyQrg8Mz8x6qDSOo69wArgQXAO4GrMvOeaiOpG/lZjdrrRcSFmflnEfFpGpcXt5GZn6sglqQuEBH7A0tofAbsiq3DwPeBD2Tmj6vKpu7kpUbVQc9232//R5J21x8BDwEHZObbM/PtwP7AkzQ+F1ZqKy81aq+XmTc3v30V+ErzcxolqR2Oycw3jR3IzM0R8Sn8ZAx1gCteqhPv4yWp3TbuaDAzR/FdjeoAi5dqw/t4SeqAXW10dhO02s5LjaoV7+Mlqc3eHBHP7WC8B5hbOoy6n8VLtdG8j9ev09h38d+Aj2XmDi8TSFKLDq06gCYXi5fq5BW8j5ekNsrMF6rOoMnFPV6qk7MsXZKkOvMGqqqNiFhK4946jwA/3TqemQ9WFkqSpAnwUqPq5PXAu5t/thoF3lNNHEmSJsYVL0mSpEJc8VJtRMT97PizGl3xkiTVgsVLdfKZMd9PBU4G1lYTRZKkifNSo2otIh7JzLdVnUOSpFa44qXaiIiDxzzsAd4MDFQUR5KkCbN4qU4e4P/v8RoF/hH4aHVxJEmaGG+gqlqIiJOAEzJzAXA58PfA3wL3VhpMkqQJsHhprxcRVwCfBqZFxFuBxcBf07iv13+qMpskSRNh8VIdnAMcn5nPAGcCX8/MW2hcZnxvpckkSZoAi5fqYDQzf9L8/t3A3wBkpm/JlSTVipvrVQfDEbEvMBM4HPgWQETMB4arDCZJ0kS44qU6+GPgCeBh4JbMXBURpwPLgWsqTSZJ0gR4A1XVQkTMA/bLzKeaj38N+ElmfrvSYJIkTYDFS5IkqRAvNUqSJBVi8ZIkSSrE4iVJklSIxUuSJKmQ/wfPdlB87NE/gQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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": "iVBORw0KGgoAAAANSUhEUgAAAl4AAAFYCAYAAACCkPIGAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAHLZJREFUeJzt3XuUXFWdt/GnO53uZEiCTacDQSWaQbZKBoIQlITLoIjCChNHDIwEBIFADBdBUNCAF9aLjL6CDDgw3ALjZFSYZEaNGIdlEBDk/gK+kmEvXkUuJkNibJNOJN2prn7/qGqmiSFdHar2yal6Pmux0uf0rrN/q1dz8s3e++zT1N/fjyRJkmqvOesCJEmSGoXBS5IkKRGDlyRJUiIGL0mSpEQMXpIkSYkYvCRJkhIxeEmSJCVi8JIkSUrE4CVJkpSIwUuSJCkRg5ckSVIiLVkXMEgbMA1YBfRlXIskSdK2jAAmAo8CPZV+aEcKXtOAn2ddhCRJ0jAcAtxfaeMdKXitAujq2kix2J91LcqBjo4xrF27IesyJNUZ7y2qRHNzE+3tO0E5v1RqRwpefQDFYr/BSxXzd0VSLXhv0TAMa3mUi+slSZISMXhJkiQlsiNNNUqSpDrX11egq2sNhUJv1qVUrKWllfb2TkaMeOOxyeAlSZKS6epaw6hRf8FOO+1GU1NT1uUMqb+/n40b19PVtYbx4ye+4es51ShJkpIpFHrZaadxuQhdAE1NTey007iqjdAZvCRJUlJ5CV0DqlmvwUuSJCkR13hJkqTMjB03mlFt1Y8jm3oKdK9/paK2d931E7797VsoFArMnv1xjj32uKrXM8DgJUmSMjOqrYVjLvhB1a+79MpZdFfQbs2a1dx003Xccsu/MHJkK/Pmncp73nMAb3/75KrXBAYvSWpY7TuPpqXVvwa2prNzbNYl7FAKvQW61lU2epQ3jz32CO95zwGMG7czAIcf/gHuuWd5zYKXa7wkqUE152yBs7JTz78rv//9Gjo6xr963NExntWrV9esP/+pI0kNqnnkCL71+U9mXYZy4Owrbs26hJopFouveWqxv7+f5ubaBU2DlyQ1qMLm3rr+C1XVU9icn13mh2vChF156qknXj3+wx/WMn58Z836M3hJUoNqGdnKZRf8KOsylANfvHIm0JN1GTVxwAEHsnDhjXR1dTF69GjuueduPve5L9SsP4OXJDWozb2F8l+o0rZt7i3U7NqbegosvXJWTa5bic7OCcydO59zzz2TzZsLHHPMLN797ilVr2eAwUuSGlRzfzHrEpQTtfxd6V7/SkXbPtTSkUd+mCOP/HCSvgxektSgRrS1ctztn8q6DOXAHcdfT71ONabmdhKSJEmJGLwkSZISMXhJkiQlYvCSJElKxOAlSZKUiE81SpKkzLTv3EpLa1vVr1vo7aFr3Y63477BS5IkZaaltY3fXH5s1a87ecESoLLgtXHjBubNO5Wvf/1qJk7cveq1DOZUoyRJalhPP/0r5s8/nRdffCFJfwYvSZLUsJYu/Q8+85mLavpi7MGcapQkSQ3r4osvTdqfI16SJEmJGLwkSZISqWiqMYTwM2ACsLl86kzgL4FLgJHA1THGfyy3PQK4ChgN3B5jvKTaRUuSJOXRkMErhNAE7AVMijEWyufeDHwP2J/S68p/UQ5nzwELgcOAF4E7QwhHxRiX1ah+SZKUY4XenvLWD9W/7o6okhGvUP7zrhBCB3AT0A3cHWP8A0AIYTHwMeBe4NkY43Pl84uA2YDBS5Ik/ZnSJqfZb3S6ePHSJP1UssarHVgO/C3wAWAesAewalCbVcBbgN1f57wkSVLDG3LEK8b4IPDgwHEI4RZKa7j+16BmTUCRUpDr38r5inV0jBlOczW4zs6xWZcg5VZvoZc7jr8+6zKUA72F3qrdb1evbqalJX/P9jU3N1flZ1DJGq+DgbYY4/LyqSbgt8DEQc12A1YCL73O+YqtXbuBYrF/6IZqeJ2dY1mzpjvrMqTc6uwcW5NXtaj+TF6wpGr322KxSKEwrDGZHUKxWHzNz6C5uWm7BosqWeP1JuCyEMJ0Sk8wngycCCwKIXQCG4FjgTOAXwIhhLAnpYX2J1BabC9JktTwhhzrizH+CLgTeAJ4HFgYY3wAWAD8DHgS+E6M8ZEY4ybgFGAJsAJ4Blhcm9IlSZLypaJ9vGKMlwKXbnHuO8B3ttJ2ObBvVaqTJEmqI76rUZIkZWbsm9oYNbK16tfdtLmX7j8OvZfXwoU3cvfdPwVg+vQZzJ//6arXMpjBS5IkZWbUyFaOu/1TVb/uHcdfTzfbDl6PPvowjz76ELfe+q80NTVxwQXncO+9P+Owww6vej0DDF6SJKkhdXSM56yzzmfkyJEATJr0Nl5++b9r2mf+NtKQJEmqgsmT/5IpU/4KgBdffIG77/4pBx00o6Z9GrwkSVJD+81vfs3555/FWWd9mre+dY+a9mXwkiRJDeuXv3yS886bz7x5Z3PUUTNr3p9rvCRJUkN6+eX/5gtfuJCvfOUK9t9/WpI+DV6SJCkzmzbX5p2hmzb3Dtnmu99dRE9PL9de+81Xz33kIx/lIx/5WNXrGWDwkiRJmen+Y8+Q2z7UynnnXch5512YtE/XeEmSJCVi8JIkSUrE4CVJkpSIwUuSJCkRg5ckSVIiBi9JkqRE3E5CkiRlpn1sKy2j2qp+3cKmHrq6h97LKzWDlyRJykzLqDYemHVs1a874wdLoMLgdfPN/8Q99ywHmpg582/4u787ser1DDB4SZKkhvXEE4/z+OOPcttt36Wvr8CJJx7H9OkHs8ceb6tJf67xkiRJDWu//fbn2mtvoKWlha6uLvr6+hg1anTN+jN4SZKkhtbS0sItt9zAiSfOZv/9p9HZOaF2fdXsypKkHVpxcy+TFyzJugzlQLGCF07n3WmnncmcOSdz0UXn88Mf/gezZn20Jv0YvCSpQTWPbOWYC36QdRnKgaVXzoKMXmRda88//1t6e3t4xzsCo0aN4tBDD+fXv362Zv051ShJkhrWypUv8bWvXU5vby+bN2/m/vvvZZ99ptasP0e8JElSZgqbekpbP9TgupU46KCDWbHiaU49dQ7Nzc0cdtj7OeKID1W9ngEGL0mSlJmu7t6K99uqldNOO5PTTjszSV9ONUqSJCVi8JIkSUrE4CVJkpSIwUuSJCkRg5ckSVIiBi9JkqRE3E5CkiRlZudxo2ltq34c6e0psG79KxW3/9a3rmbduj+yYMGXq17LYAYvSZKUmda2Fi674EdVv+4Xr5xZcdvHHnuEn/zkRxx00MFVr2NLTjVKkqSGtX79Om688TpOOumTSfozeEmSpIb19a9/lTPOmM/YseOS9GfwkiRJDWnp0u+z6667csABBybr0zVekiSpIS1ffhdr1/6eU045gfXr1/HKK69wzTVXcu65F9SsT4OXJElqSFdffd2rX//4x0t54onHaxq6YBjBK4TwDWB8jPGUEMJU4GZgHHAfMC/GWAgh7AEsAiYAEZgTY9xQg7olSVId6O0pDOsJxOFcd0dUUfAKIXwAOBm4s3xqEXB6jPGhEMItwFzgeuA64LoY4/dCCJcClwIXVb9sSZJUD4az11YtHX30MRx99DE172fIxfUhhF2Ay4Gvlo8nAaNjjA+Vm9wGzA4hjAQOBRYPPl/leiVJknKrkqcabwAWAF3l492BVYO+vwp4CzAeWB9jLGxxXpIkSQwx1RhCOB14Mca4PIRwSvl0M9A/qFkTUNzKecrnh6WjY8xwP6IG1tk5NusSJKkhVOt+u3p1My0t+dvNqrm5uSo/g6HWeB0PTAwhPAnsAoyhFK4mDmqzG7ASWA3sHEIYEWPsK7dZOdyC1q7dQLG4ZX6T/lxn51jWrOnOugwpt/yHi4ajWvfbYrFIoTDscZnMFYvF1/wMmpubtmuwaJuRM8b4wRjjlBjjVOCLwA9jjJ8ENoUQZpSbnQQsizFuBn5OKawBfAJYNuyKJEmS6tT2jvXNAb4ZQniG0ijYNeXz84EzQggrgEOAS954iZIkSfWh4n28Yoy3UXpSkRjjU8Cf7a8fY3we+OvqlCZJkurdzuNaaW1rq/p1e3t6WLe+t+rXfaPcuV6SJGWmta2Nb33+k1W/7tlX3AoMHbzOOedMurq6aGkpRaLPfvYL7L33lKrXM8DgJUmSGlJ/fz8vvvgCixcvfTV41Vr+nueUJEmqghdeeB6Az3zmbE4++eMsWXJ7zft0xEuSJDWk7u717L//NM4//3MUCgXOOecM9thjEtOmva9mfRq8JElSQ5oyZR+mTNnn1eOZM2fx4IMP1DR4OdUoSZIa0lNPPcljjz3y6nF/f3/N13oZvCRJUkPasKGb6677B3p6evjTnzaybNmdHHro4TXt06lGSZKUmd6envLWD9W/7lBmzDiEFSt+xamnzqGvr8hHPzr7NVOPtWDwkiRJmSltcprdRqdz536KuXM/law/pxolSZISMXhJkiQlYvCSJElKxOAlSZKUiMFLkiQpEYOXJElSIm4nIUmSMtO+82haWqsfRwq9BbrWvTJku/vvv49bb72JTZteYdq093HeeRdWvZbBDF6SJCkzLa0tPPuN+6t+3XdcePCQbX73u5f4xjeu4MYbb2OXXTo499x5PPjgAxx00Iyq1zPA4CVJkhrSfffdwwc+8EEmTNgVgMsuu4LW1taa9mnwkiRJDel3v3uRlpaRXHTR+bz88stMn35wzXexd3G9JElqSH19fTz22CNcfPEXueGGhaxY8SuWLftRTfs0eEmSpIa0yy4dHHDAgbS3t9PWNopDDz2cFSuermmfBi9JktSQpk8/hEceeZDu7m76+vp46KFf8M53vrOmfbrGKwd2HtdKa1tb1mXskDo7x2Zdwg6lt6eHdet7sy5DkipW6C1U9ATi9lx3KHvvPYUTTvgE8+efRqFQYNq093L00X9T9VoGM3jlQHNzU9YlKCf8XZGUN5XstVVLM2fOYubMWcn6c6oxB5qKWVegvPB3RZJ2bI545cCItlaOu722j7eqPtxx/PVAT9ZlSJJehyNekiRJiRi8JElSUv39/VmXMCzVrNfgJUmSkmlpaWXjxvW5CV/9/f1s3LielpbqvErINV6SJCmZ9vZOurrWsGHDH7MupWItLa20t3dW51pVuYokSVIFRoxoYfz4iVmXkRmnGiVJkhIxeEmSJCVi8JIkSUrE4CVJkpSIwUuSJCkRg5ckSVIiBi9JkqREDF6SJEmJVLSBagjhMuBjQD9wS4zxqhDCEcBVwGjg9hjjJeW2U4GbgXHAfcC8GGOhFsVLkiTlyZAjXiGEw4D3A/sABwDnhBD2BRYCs4B3AdNCCEeVP7IIODvGuBfQBMytReGSJEl5M2TwijHeCxxeHrWaQGmU7E3AszHG58rnFwGzQwiTgNExxofKH78NmF2TyiVJknKmoqnGGOPmEMJXgAuBfwN2B1YNarIKeMs2zleso2PMcJpL2kJn59isS5BUh7y3VEfFL8mOMX4phPA1YCmwF6X1XgOagCKlEbStna/Y2rUbKBb7h27YQHZub+OO46/PugzlQG+hl3VdPVmXoZzwL1INx5o13VmXsENpbm7arsGiIYNXCOGdwKgY45Mxxj+FEP6d0kL7vkHNdgNWAi8BE7dyXm9Aa0srv7n82KzLUA5MXrAEMHhJ0o6qku0kJgM3hRDaQgitlBbU3wCEEMKeIYQRwAnAshjj88CmEMKM8mdPApbVonBJkqS8qWRx/Y+BO4EngMeBX8QYvwecAiwBVgDPAIvLH5kDfDOE8AwwBrim+mVLkiTlT6WL678MfHmLc8uBfbfS9ingwCrUJkmSVFfcuV6SJCkRg5ckSVIiBi9JkqREDF6SJEmJGLwkSZISMXhJkiQlYvCSJElKxOAlSZKUiMFLkiQpEYOXJElSIgYvSZKkRAxekiRJiRi8JEmSEjF4SZIkJWLwkiRJSsTgJUmSlIjBS5IkKRGDlyRJUiIGL0mSpEQMXpIkSYkYvCRJkhIxeEmSJCVi8JIkSUrE4CVJkpSIwUuSJCmRlqwL0NCKm3uZvGBJ1mUoB4qbe7MuQZK0DQavHGge2coxF/wg6zKUA0uvnAX0ZF2GJOl1ONUoSZKUiMFLkiQpEYOXJElSIgYvSZKkRAxekiRJiRi8JEmSEjF4SZIkJWLwkiRJSsTgJUmSlIjBS5IkKRGDlyRJUiIVvasxhPAl4Ljy4Z0xxs+FEI4ArgJGA7fHGC8pt50K3AyMA+4D5sUYC1WvXJIkKWeGHPEqB6wjgf2AqcD+IYSPAwuBWcC7gGkhhKPKH1kEnB1j3AtoAubWonBJkqS8qWSqcRVwQYyxN8a4GfgvYC/g2Rjjc+XRrEXA7BDCJGB0jPGh8mdvA2bXoG5JkqTcGXKqMcb49MDXIYR3UJpyvJZSIBuwCngLsPvrnK9YR8eY4TSXtIXOzrFZlyCpDnlvqY6K1ngBhBD2Bu4EPgsUKI16DWgCipRG0Pq3cr5ia9duoFjsH7phA/GXXcOxZk131iUoJ7y3aDi8t7xWc3PTdg0WVfRUYwhhBrAcuDjG+M/AS8DEQU12A1Zu47wkSVLDq2Rx/VuB7wMnxBi/Vz79cOlbYc8QwgjgBGBZjPF5YFM5qAGcBCyrQd2SJEm5U8lU44XAKOCqEMLAuX8CTgGWlL/3Y2Bx+XtzgJtCCOOA/wNcU8V6JUmScquSxfWfBj79Ot/edyvtnwIOfIN1SZIk1R13rpckSUrE4CVJkpSIwUuSJCkRg5ckSVIiBi9JkqREDF6SJEmJGLwkSZISMXhJkiQlYvCSJElKxOAlSZKUiMFLkiQpEYOXJElSIgYvSZKkRAxekiRJiRi8JEmSEjF4SZIkJWLwkiRJSsTgJUmSlIjBS5IkKRGDlyRJUiIGL0mSpEQMXpIkSYkYvCRJkhIxeEmSJCVi8JIkSUrE4CVJkpSIwUuSJCkRg5ckSVIiBi9JkqREDF6SJEmJGLwkSZISMXhJkiQlYvCSJElKxOAlSZKUiMFLkiQpEYOXJElSIgYvSZKkRAxekiRJibRU2jCEMA74BTAzxvjbEMIRwFXAaOD2GOMl5XZTgZuBccB9wLwYY6HqlUuSJOVMRSNeIYT3AvcDe5WPRwMLgVnAu4BpIYSjys0XAWfHGPcCmoC51S5akiQpjyqdapwLnAWsLB8fCDwbY3yuPJq1CJgdQpgEjI4xPlRudxswu4r1SpIk5VZFU40xxtMBQggDp3YHVg1qsgp4yzbOV6yjY8xwmkvaQmfn2KxLkFSHvLdUR8VrvLbQDPQPOm4Cits4X7G1azdQLPYP3bCB+Muu4VizpjvrEpQT3ls0HN5bXqu5uWm7Bou296nGl4CJg453ozQN+XrnJUmSGt72Bq+HgRBC2DOEMAI4AVgWY3we2BRCmFFudxKwrAp1SpIk5d52Ba8Y4ybgFGAJsAJ4Blhc/vYc4JshhGeAMcA1b7xMSZKk/BvWGq8Y49sGfb0c2HcrbZ6i9NSjJEmSBnHnekmSpEQMXpIkSYkYvCRJkhIxeEmSJCVi8JIkSUrE4CVJkpSIwUuSJCkRg5ckSVIiBi9JkqREDF6SJEmJGLwkSZISMXhJkiQlYvCSJElKxOAlSZKUiMFLkiQpEYOXJElSIgYvSZKkRAxekiRJiRi8JEmSEjF4SZIkJWLwkiRJSsTgJUmSlIjBS5IkKRGDlyRJUiIGL0mSpEQMXpIkSYkYvCRJkhIxeEmSJCVi8JIkSUrE4CVJkpSIwUuSJCkRg5ckSVIiBi9JkqREDF6SJEmJGLwkSZISMXhJkiQlYvCSJElKxOAlSZKUSEstLhpCOAG4BBgJXB1j/Mda9CNJkpQnVR/xCiG8GbgcOBiYCpwRQnh3tfuRJEnKm1qMeB0B3B1j/ANACGEx8DHgsiE+NwKgubmpBiXl34T20VmXoJzw/yENh/cWVcp7y2sN+nmMGM7nahG8dgdWDTpeBRxYwecmArS371SDkvLvlkuOzLoE5URHx5isS1COeG9Rpby3vK6JwK8rbVyL4NUM9A86bgKKFXzuUeAQSkGtrwZ1SZIkVcsISqHr0eF8qBbB6yVKAWrAbsDKCj7XA9xfg3okSZJqoeKRrgG1CF4/Bb4cQugENgLHAmfUoB9JkqRcqfpTjTHG3wELgJ8BTwLfiTE+Uu1+JEmS8qapv79/6FaSJEl6w9y5XpIkKRGDlyRJUiIGL0mSpEQMXpIkSYkYvCRJkhIxeEmSJCVi8JIkSUqkFjvXS1UVQjh0W9+PMd6XqhZJ9SWEcCuvfb/wa8QYT01YjhqAwUt58JXynx3AnsADlF6kPh34v8CMjOqSlH/3lP+cCYwFFgEF4HhgXUY1qY65c71yI4TwY+DcGOP/Kx9PAm6IMX4428ok5V0I4WHgoBhjsXzcDDwUYzww28pUb1zjpTyZNBC6yl4AJmVVjKS6sjOwy6DjXYExGdWiOuZUo/Lk8RDCPwN3AE3AHODn2ZYkqU5cDvwyhPAApUGJ9wHnZluS6pFTjcqNEEIrcA7w15QWw/4UuC7GWMiyLkn1IYQwkdLa0X7g/hjj6oxLUh0yeClXQghvA/YG/hN4a4zxuWwrklQPQgidwImUphebgBHA22OMn8i0MNUd13gpN0IIxwNLgX+gtBbjwRDCidlWJalO3A5MpRS+dgI+BhQzrUh1yeClPLmI0jRAd3kKYD/g89mWJKlO7B5jPJnSP+7+HTiU0j1GqiqDl/KkL8bYPXAQY1yF/yKVVB1d5T8jsG+McW2Wxah++VSj8uTpEMLZwMgQwlRgPvBkxjVJqg93hxD+DbgQuCuE8B7glYxrUh1yxEt5chbwZko3w4XAekrhS5LekBjjAuDiGOPzwMcpjXx9NNuqVI8c8VKenA58M8boui5JtfDeEMKplPb0mhJjXJl1Qao/jngpT94KPBxCWBZCmBNC+IusC5JUH0IIfw8cTWmUqwX4ZAjhymyrUj0yeCk3YowXxhjfDnwVOAh4IoTw7YzLklQfPgScBGyKMa4HPggclW1JqkcGL+VKCKEJGAm0UtpdujfbiiTViS2fkG7byjnpDTN4KTdCCNdQejH2+cByYGqM8fRsq5JUJ+6gtIlqewjhPErvgf1OtiWpHrm4XnnyLLBfjPH3WRciqe7cCawEJgOHAJfGGO/MtiTVI9/VqB1eCOGMGOONIYQvUZpefI0Y42UZlCWpDoQQJgCLKb0D9tmB08CDwMdjjOuyqk31yalG5UHTFl9v+Z8kba8rgPuB3WKM74sxvg+YADxF6b2wUlU51agdXozxhvKXfwS+W35PoyRVw/QY47sGn4gxbg4hfAHfjKEacMRLeeI+XpKqbdPWTsYY+/GpRtWAwUu54T5ekmpgWwudXQStqnOqUbniPl6SqmzvEMJvtnK+CZiYuhjVP4OXcqO8j9ffUlp38S/AuTHGrU4TSFKF9sq6ADUWg5fyZDXu4yWpimKMz2ddgxqLa7yUJ3MMXZKkPHMDVeVGCGEJpb11HgZeGTgfY7wvs6IkSRoGpxqVJ7sAh5f/G9APvD+bciRJGh5HvCRJkhJxxEu5EUL4GVt/V6MjXpKkXDB4KU++POjrkcAsoCubUiRJGj6nGpVrIYSHY4zvzboOSZIq4YiXciOEsMegwyZgb6Ajo3IkSRo2g5fy5F7+Z41XP/B74JzsypEkaXjcQFW5EEKYCRwRY5wMXAD8F/CfwE8zLUySpGEweGmHF0K4EPgS0BZC2AdYBHyf0r5e/zvL2iRJGg6Dl/LgJOCwGOMK4ATghzHGmylNM34o08okSRoGg5fyoD/G+Kfy14cDPwGIMfpIriQpV1xcrzwohBDeBIwB9gPuAgghTAIKWRYmSdJwOOKlPPh74EngIeDmGOOqEMJxwHLg65lWJknSMLiBqnIhhLA7MD7G+Mvy8dHAn2KM92RamCRJw2DwkiRJSsSpRkmSpEQMXpIkSYkYvCRJkhIxeEmSJCXy/wHPo8BxjA9brQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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 scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\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 scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\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": {},
   "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",
       "Ms            1\n",
       "Sir           1\n",
       "Countess      1\n",
       "Capt          1\n",
       "Mme           1\n",
       "Don           1\n",
       "Lady          1\n",
       "Jonkheer      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",
       "Dr          1\n",
       "Dona        1\n",
       "Ms          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": {},
   "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": [
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       "</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 scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\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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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bar_chart('Title')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "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 scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\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 scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\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": {},
   "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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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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 scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\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": {},
   "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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\n",
      "text/plain": [
       "<Figure size 912.425x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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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\n",
      "text/plain": [
       "<Figure size 912.425x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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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\n",
      "text/plain": [
       "<Figure size 912.425x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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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\n",
      "text/plain": [
       "<Figure size 912.425x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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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3/8VsgXL5EpKlS0iXDpMsHSJptvfoK5QkSZJ0oZvrwPi7n3qIrx07fUb5UjvhQCdlefBaSIfbg2C5PNwul83GfIXMcw+Ud0G2fub7dg6QHDhMunRJ+ZiQA4fL5dIlpAfKZbJ0kCSd666XJEmSNAfmOjW8+MlLHHsoYbVXsNYtON0rX6s9ON3LWe0WHD2Z8ZUHClZ7Od1s4/daaCVjAXJ5YRAqEw60x0PnYH9rH0PmzIFy7STF2gmK9VOwfqrcXq8eI3L0yxRrJ6HIJ9+dZHFlSrCsRioHIXPhIEma7s0XLEmSJGnXxBiv3s5xcx0YVxZSOjRmrp/lBae7Bau9cjkImMOQWZU9eCrjvgcLTnUL1rNiw/drNxIOdBKW2imL7cEyZbGVsNROWGynLLQSllrl/sV2ylIrGdZpN5Ndu+ZwLFAePLJhvaIooLtaBcsyTLJ+imK93M4f+hrF1/6aYu0UMPG9qO4iW4bIMlgeO3I5vWJxGDCTA4dJOsteWylJkiRdgOY6MG5VM01YWUhYWZj9mH5eC5X1kNkdjGSWoXI9y7n/oZz1rGAtK1jrFeQbZ00A0oQySLZSlgaBsxYuy33JsGypCpqL7bL+Yuvcp9ImSQKdA+UjPQ5dvmG9Is/HgmRRrVMFzfzYPRT3fo5jd61O+UKbZbA8cJjkwMNGo5SLo6CZHjgMrUWDpSRJknQeuaAC43Y00oTlTsJyZ2vHFUVBr88wQK5nsN4r17tZUZUzWvYKVrt9jp3KqsDJWUc3B1oNaqOa6VioXGglw1enmdbWExZaaW29XJ4trCVpCosrJIsrZ23PoZU2x756P8XaCVg7RdE9WY5OVkGzf/8XydZOQG/tzIMbLZKFZZKFFdKFFZLF5XLa68IydMryZHFlWCfpLJM0Lvr/opIkSdK+8bfxbUqShHYT2s2Es0esjeVFQTejCpzlqOV6FTJHL0aBNCs4vprxtWHdgm5/xvYCnVbCQjMpl1WgXKjCZWcsbCYsNEdl9W1abXqtFdqLB88aQIusW45Sjk2DPQXd0xTdVfLTxymO30exvjo9XA60FknrIXKhWl9cGYXMQQBdWIbOEknidZeSJEnSTjAw7qM0SVholTfk2a68NtLZrQJkGSQLehl0+2VZNytYr5aD7bVuzkOrsN4fjYr2Zg2gCWOjlwutcuRzED47zYSFZoN26zALzUtoV0G1s5jQaaV0msno1chpF+u08tMk3bUyVPZOD5dFdxW6p8kf+irF179YBs/+Bnc4SpJyZHJhmWTx4ChgTgbO4fIgNNtOlZUkSZKmMDCe59IkodMsw9tOyPNR6Oz1y5HNbn8URpNGgxOrvTJ0DgJoVobO1fWcB1cz1jOqY2cPoAPtRptOq0OneclwRLTdLEc4262EzlLCwsGEhUbGSrLGYrLOEmsssM5CsUa7WKPVX6PZP01jbZXk5ANQBc4z7xZbOWOqbDV6ObipUHuJpL00sb1I0l4ybEqSJOmCNlNgDCE8H3gd0ALeGmN8+8T+a4CbgIPAbcANMcastv/ngX6M8fU71G7tkjRNWEg3HvVcXu5w8uTsUz4H13oORjp71ehnt1+Gyl617E4uq6Da6xecXMs51s+GZd3hVNwEWKheZ9dpwMFWj0ua6xxqrnOwuc5K2mUlWWMpXWcpW2Px5DqdE1+nU9xNq79GI19n0yiYpMMwSWcUJIeBckrILLcH60tlYDV0SpIkaQ5tGhhDCI8A3gg8EVgHPhZCuDXGeFet2s3AS2OMt4cQ/j3wMuBXQwiHgDcD/wT4pR1vveZe/VrPnXRGEK2FzGwikI7qtOhmS5zsFzzQh16vmro7qJOVI6aDWxElFLTpsZj2WEy6LCTlcny9x4H1Hktp+VpMj7HAV1lIerSLLi26m4bOImlQtBbKu8i2l0g7S6QLS6SDQNleJOksDvcn9bDZXiTPOhRFYeiUJEnSjptlhPFa4CMxxgcAQgjvBZ4LvKHafjSwGGO8var/LuDngF8FngV8HnjTzjZbF7vdDKL9vJpuW41wDgJlL6uNiublSOeDOXxtEE5ro6KjwFqQ9Luk/XWaxcbBc3x5lMXkb1hMy+2FpHfWNpdP0Ezop22KRvVqdqDVIW12SFod0vYCjfYCjc4CaWuBpNUpp9M2O9BaqJadatkmaQ7qdLxTrSRJ0kVslt8ErwLuq23fBzx5k/2PBIgx/iZACOH159RKaY8kSUKzAc0GLG0+IXVL6qOi9am4g5D5UAZH8/E63X5B1ssp+l3Sfo8kXyfNujSLLs28DKGtokubHu0ko01WLpOMTnKadnKCTq2sLN/ghkEbyEkpGm3yRptiEDKbHdLWAmm7Q9pepNHukLZqobPZIWktlKF0EDxbHZJmWUazRdJol9NxU+9qK0mSNK9mCYwpUH9gYALkW9i/bQcOtGk3N39WofbW8lYfWqldNRgVHd2oqLrOMys4ObwxUTG6frSX0+9n5L0uRdaj6JevpN8lyXsk/Yw075EWPVrFeNAcBNJOcop2cry2Pai3xbscUU7JpdEqw2OzTdJq0WiVATRttUma7eGyfLVq69W+RotkrM7G5cPjd3kK75Ej233gjnaT/TJ/7JP5Y5/MJ/tF+2WWwHg38LTa9hXAvRP7rzzL/m07dapLd219J95KO6S86Y19Mk+WlzusnS77pAk0UzjQBtr1Wkn1AmhQ3r9qcdP37uf1a0TL9dNZwfHhNNyCbj66BrSX9cn7GUXWg35GkfdI+j2SPCPp90jzHg0ymvRpJX1ag+VwPaNFn2bSp52coJX0aVevQb0mfZrVe5yTRhMaZYAchtVGbfSz2SKpgmz5albbzfHytFm+R9osj0lbHL70IMdP9KDZgrQ1sb9ZHdfwutM9duTICvfff2K/m6Ea+2T+2CfzyX6ZPxdTgJ8lMH4IeH0I4Qjl5VLPAa4f7IwxfimEsBZCeEqM8c+AFwIf2JXWStpTjTRhMYXFc3hW6KTBo1uG025rd85dzYoz7po7DKO1x7wMAmw/61P0e5CPB8p2LXjWg2mbjIVmzkLaZyHPWej36WR92klOOy2Pa7JKMzlBkz6Nok+DPmlRvpI8gzwj4ewzH07P9J1IqtDaKq8THS7b5bIKnzRrITVtnVl/EEYb9YA7HmqTiX9nrK7BVZIkncWmgTHGeE8I4bXArZRjFjfFGO8IIbwf+JkY4yeBFwDvCCEcBD4FvG03Gy3p/LXZo1u2ox5Cu1nt+aG1sDl4buixatmrnjc6vENuNZ13lueHpuTlSGfS50CzYKmVs9TIq2XBcgfaZHQaOQuNPp00p9PIaad5NWKa00pymoMR0ySnQZ+k6EOeQd6H3jp5vjrcLvpVeT+jyDPoZxs/W3SrNhhBHV/WQuwZ9bYTXOujuG2vZZUkaU4lRTGX1wheDXzh07d9jO7a2n63RTVOSZ0/9snOy4taoMxGYbM7DJvl+notbNafE5oVCae7eRVeB88N3VyalEG600xYaKUstBIWWgmLrZROa1AGC620rNMsWGoWLKRlMF1o5KNwmvRJinwUQPt9iiKDfh+KMniS98vwmferV7l/VHbmdtEfhNpsFGLzc5weDJCk5U2ShteztmpThutTh8vtYTAdhM5muxyBrU0lTpr1em0eduQwxx7qju0jbTrCuo+cZjd/7JP5ZL/MnyNHVi6aDw/vly9JE9IkodOEzjYf2zIZ4icD6HotXI6NjPYZPg90MOJ5ci3ngZMZ6xnDEdB8pr/zpbQaKYut9jB4DkNoM2WhnbDQTKogWpZ1WgmLtXqdVllnsZ3STNkwWBVFMQyX5BlFXhsp7U9uTwTSsVBajZwOw2i1r7tKsdafKM/K6chbGGldnVpaTg0eBtNhCJ24nrXZrkZFy5snlWWd8bJGVTasM7FstJ0CLEk67xgYJWmXnWsArRveFXcwqpkVY+vd2vTb9cFyEEx7fU6ujYLr2gzTb0dfA2OhcyxYNsdHPxeaTTqt1ljdTnsQVssgmu5gaCryfHzUsx4q8z5U17kuLTQ4dfLUMNiOjZj2J0ZNq/fJe2vVem8YUIt+D7IebHIt61SDkdRmCxqd6q69G4XMzmg0tAqcw/UpYXTsuGaLJHGaryTp3BkYJek8MnpWaFLeDfcc5cX4dNr1bBBG69d/jkZF12vB9Phqxv3Daz/L8tlGP6lC5uTIZ7k+HPUchNHmZNlouu5CM6HdTMqwRPusT09dPLzE+oPTxxm3qhxVHUzt7VVBtTc+8lndKbgMqL3adr1uFVazLnn3VO346j2zXhlot2MYKgfPQa0/E7UsL7fbw9A62E+zM3bscHvwPo2WI6WSdJEwMErSRSxNkmpkcGdGP7N8MII5upHQ6BrQevno2s/1rGCtm/PQ6iisznLzoYEEhtNnB9d6LtZGPgdh8/DyGkU/my2AbhKGkmRwl9smsHDWoHquiiKvBdBB2JwIqYPyPINsFDpHdatld5Xi9PFRWdYdBdwtScpA2poMlTME0sGIaqvD6dOX0D+Vl2Wt2vukjV35XkqSts7AKEnaEUmS0GpAq5FA59zfb6a73w6m3k7cmGi1m/Pg6nhw7fVPzfZ1sHEA7VQ3HKrv7zRG9Qb7ypHRwXpKq7HxNaCbtqeaxkrz7COo56IMpeVU21EQ7Y5Pwx2+xrfL565W6+snKVaPDcsGoXSjGyNt+AiaRpOkuQCtNklroQqUC2UYHYbT0f5hEK3vH5S3Rvu8yZEkbZ2BUZI0l3b6ESyLS22OHV+vwuP0mw/1JgNoNUV3dT3n+Gr9BkXlvlklyWga7nDks7Y9DKJjZeNTcseC6owjobO3Lx2NCO7IO44r8vzM0c6sy/Jig5PHT1TBMoN+txY0B/W7U8JolyLrlnW28niZJK1C5UI1xbYKoc2F0XTbVn0K78IZIbR+3DCYOkVX0gXMwChJuig00tE01J1QVHe/HdxoqDcx7Xawb/gc0NozQLtZQbeXc3JtYgQ1K2a+lU4CtGvBslMLkp1mWltPhvXajdH2Qmu0f7xuSvscRkSntjVNIa3CWK28c3iJ04vbv650dC1pbTSzCqP1cDoaPe2NRk8HI6O9dfK1k8MgSlbt28q1o0kyMZq5UIbLVvU1N2ujnVUZzVr4HEzrHY6mGkQlzQ8DoyRJ25AkCe1mGdp2yuA60LFpt7VrQHv9WtlEncENik6u5RzrZ2eE2K3c03UQRtsTYXL4atUDKbSnBdRq2W4kw/carJ/tMS1bUb+WNGHxnN+vbjQq2q2Nag5CZUaRd88MooNrQqt6+fqpUYDNutVo6VauF02GI5/DQNkan25Lc6G8TrRVjobS6NSCamdsOu/wLrqtjnfRlTQzA6MkSXOifh3oTtwFd2DwOJZBsKyHzt6wbLSv16/XHY2Unlovrw3tTQTWWe+OO/o6KUc7Wwmt9MxAeeY2w/VWY+MgOrbeSGicQzDdaFT0XJ0ZRAcjohsE0UFYrY2g5uur5xhEKUcvh4FzFDD/5sABekVj/LrQarryWHgdu0607fRc6QJmYJQk6QI3ehwLsMNXKRZFQb+AXjY+BbfXL8j60MvLgJkNlv2CXk55F9w05fR6fxRUs5zVbr1OQa8Kpf1tPPYyTdgwULYaVfhsJLSaDLcHgb0cCa3Va472jZfVjmkkpOkmd9jd8yDaO2N67nDK7XAKb3Xc+kl6a8fJuuujILrl60SrO+g269d5dsbWz1gORkWb7bG76I62a495SR0ZlfaagVGSJG1bkiQ0y4zA4hYj0PJyh5Mn12eq28/L6bqDUc9BAB1uVyFzuBzur+oM9vXL60dX18s6g1BbX251xLRuEFJbaUKrOR5Ax0Jqo9qfQqsKss0qfA6CanNsvZzKO3iPwXqztt1stGmcYxA9fHiJB2vPK934OtEqfOaDsmzihkXdsZsX5avHJ0ZFq+O2qtEcD5Fjo54TAbM12h7smxpIW/VA6iNdpEkGRkmSNPcaaTnFtLOD14xuJM9HoTPrQzYRRLP+aDkIslleBc6csf3ZMLyWzxs9OdiXj4/CZv2tXWe6kTQpQ2azCqr19cH2IIyOgmgVVBsJy4un6WcZrWpKbyuthdJGi0baLsNpmtBIE5otaHbKwNoY/Htp+V6Naj1Npk8NLopik0e1ZGUgHTwrdHKktJ+UNi9oAAAPYElEQVSVgTRbH10vOnkt6Va/q2ljGDhHj3Cpr3eg2RpfNlrjo6PNNjSq5bC8NdzvtF2dbwyMkiRJNWma0Elhdx4yMt1gam8/r4dR6Bej4DkIp/28qOqN9pfHjsLr4H36tfXyfXPWevWy6rj+qH62hRmos0iARhUiB4GyUQXZRjoaIS0DZodmo1OWpwmNxiCcVstGGWIbKTTbCY3O6NjG5DIpaCY5LXo0i4wGGc08o1H0aJDRyDPSotxOi4w0z0jyjCTvkYyF02w0QjoZajd4xuimGq3xgNlqkzQGI56j0DnY/8DBFbpdpofT+rIxEVK9uZF2gIFRkiRpnw2n9u5xUJ20vNzhxIm1USAdhsoynNYDaL+AfBBgq7Cb19YHIXa0Pl53uF2VdXs5p+v1Jt5jZwJto3qdXVL1RRk8pwfSZgrNpKCT9llI+7STPp00o530aSflskVGq7ZsktEio0l/FGKzjEaW0SjWaRSnyhBbhdk075HkGd1iizc1GkibVThtV6Oc9VDZGhsRLUdFW2W9xiCwtmojpq1aiG2NlQ3fPzVaXIjsVUmSJA3t5k2SdkJRFBRFGSYHoTYfBNZicnsUPPOiDLT1unle0Ke2XdXPa6E3L4oqGI+/TxlmE9byBqf6jWGdvKiF6aKo6lfH1v79rT3qpqDJIIhmtOjTqkJpm4xWUgbSNlVQHatXBtayvE876dFO1obH1Os0KZfbnTGbk5AnLfpJkzxtkietcpm2yNMWRdqiaJRl5Xo5RbdotCBtVVN2m2NhNK1GVdNmm6RVLZstklabRqtN2mjSSFPSapr0tGXqFOBzYmCUJEnSeSNJEpIEUqA1p6F2FkUxEV7P2B6F1c5im5OnusPt0f5aCK22iyoE5zmsFbA6CK2DOpPvX3uvoholpuiTFBlp0a+m7Wak9EnzjGbRL6f0VmWNohw5bdKnUWTD9fJVhdAko8V6GUwnAmyLjEay/St48yKhR4O1okFGg15RvRgts6IxaCFZUrUqKbf7SbWetsiTZvkahN2kSVEF3qJRLqlC78+8/Gk7959hzhkYJUmSpD2WJIPprsOSDesuLzc52drm9ZJzoKgF06yAbgEnaqOveZ5TVHfjpd+nKDLo98vHv1TXllLk1TWmZYhNin65XfRJa2XlVN4+i2QcGAbebi38DgJvn5SJ+c1F9Zpp2rOBUZIkSZLO2TAcj5fW1lPKWNLZ0X+3gHLK8YYV8iqQjsInw5sf9aHISAZBdRBQ835Z7yJiYJQkSZJ08UlSaLQpGjvzWJsLlffalSRJkiRNZWCUJEmSJE1lYJQkSZIkTWVglCRJkiRNZWCUJEmSJE1lYJQkSZIkTTXTYzVCCM8HXge0gLfGGN8+sf8a4CbgIHAbcEOMMQshPAq4GXg4EIEXxBhP7mD7JUmSJEm7ZNMRxhDCI4A3Ak8FrgGuDyE8bqLazcCNMcbHUj6F82VV+a8AvxJj/LvAJ4Gf3qmGS5IkSZJ21ywjjNcCH4kxPgAQQngv8FzgDdX2o4HFGOPtVf13AT8XQrgJ+IfAs2vlfwL85Az/ZgNg6cAiraazZufJwmKHouwezQn7ZP7YJ/PJfpk/9sn8sU/mk/0yl64G7gayfW7HrpslMF4F3Ffbvg948ib7HwlcBjwUY8wmymdxJUB44hNmrC5JkiRJe+YLwDcAX9znduy6WQJjChS17QTIZ9g/Wc7EcWfz34GnUYbM/ozHSJIkSdJeuXu/G7AXZgmMd1OGt4ErgHsn9l85Zf/XgEMhhEaMsV/VqR93NuvAR2esK0mSJEnaBbNcIPgh4OkhhCMhhCXgOcAHBztjjF8C1kIIT6mKXgh8IMbYA/4UuK4qfxHwgR1ruSRJkiRpV20aGGOM9wCvBW4F7gRuiTHeEUJ4fwjhSVW1FwBvCSF8DlgG3laV/xjlXVXvohylfN1OfwGSJEmSpN2RFMXkZYaSJEmSJM02JVWSJEmSdBEyMEqSJEmSpjIwSpIkSZKmMjBKkiRJkqYyMEqSJEmSpmru5z8eQvh3wGUxxpeEEK4BbgIOArcBN8QYs4n6h4HfAh4D3A/8UIzxb/a42Re0iT55FvBzQAJ8AfjhGOOxifrfCfwX4CtV0adjjD+8l22+0E30yc8CPwIM+uEdMca3T9R/FHAz8HAgAi+IMZ7cyzZfDAb9ArwVeFdt1xHgWIzxmybqe67skhDCrZT/33tV0cuB/4nyUU4t4K2T50l13KafO9q+DfrlfwF+qNp+X4zxX0w5btOfc9qeDfrkFybLYoyfmDjuWuDNwCLw7hijj0nbIVP65D8B/6RW5RuA/xhjvHHiOM+TXRRC+H7gZ4EDwH+LMb5ylvPgQv0dbN8CYwjh6cCLgfdVRTcDL40x3h5C+PfAy4BfnTjsXwF/GmP83hDCC4H/E7hur9p8oav3SQjhIOX3/1tjjPeEEN4AvB545cRhTwL+XYzx3+xpYy8SU86TJwH/a4zx42c57FeAX4kx/nYI4aeBnwZ+cndbenGp90uM8U7gmqp8CbgDuGHKYZ4ruyCEkACPBR49CHshhEcAvw08EVgHPhZCuDXGeNfE4bN87mgbNuiXa4HvAZ4AFMAHQwg/GGP83YnDZ/k5py3aoE/OKJty3CLwTuA7Kf/g9b4QwjNijB/Ym5ZfuM7y/f/Fav/fA36P8vevSZ4nuySE8Bjg14BvA74KfCSE8Azg19n8PLggfwfblympIYSHAW8E/nW1/WhgMcZ4e1XlXcDzphz6vZQjjFD+BeYZIYTW7rb24jDZJ5R/lX9FjPGeavuzwKOmHPqtwPeEED4bQvj9EMLf2v3WXhym9AmUHxA/VX2//68QwsLEMS3gHwLvrYrexfRzSdu0Qb8M/EvgT2KMH52yz3Nld4Rq+d9CCJ8JIdwIXAt8JMb4QIzxFOX58Nyxg2b/3NH2TOuX+4BXxxi7McYe8JdM/1w56885bdu0PplWNunJwOdjjF+oQs3NeK7slM2+/78K/FSM8etTjvU82T0/SDmCeHf1s+o6YJVNzoML+Xew/bqG8deB1zIaRr+K8oNk4D7gkVOOG9arOushyulfOndjfRJjPDr4q2/118XXUP6Va9KDwC/HGB8PvJ/yr/raGWN9EkJYBj4N/ATwLcBhyr9c1V0GPFT7S+VG55K2b/LnFwAhhEPA9ZTTuKfxXNkdlwAfpvyAfzrl6O6j2PwzZdbPHW3PtH65ahDQQwh/h3Jq6vvrB834c07bM61PnjdZFkL47onjPFd2zxl9Mvj+VyPyizHG35k8yPNk1/1toFH9cfdO4MeY7Ty4YH8H2/MpqSGElwJfiTF+OITwkqo4pZyeMpAA+ZTDkynb0+ppCzbok8G+Q8DvAp+JMf7G5LExxhtq678WQviFEMKhGOPx3W73hWxan1Rz4J9Zq/MmymlCr60dOnkugefIjjnbuQL8U+D3Yoxfm3as58ruqKZjDadkVVNL30x5CcPAtM+KWT93tA0b9MszgT+uptm9D/iJGOPnJ46b5eectmGDPnlUjPFFE2XPBP64dqjnyi4523lCeX3pmzc4zvNkdzUpRwq/CzgJ/D5wms3Pgwv2d7D9uIbxOuDKKrE/DFim/OZeWatzBXDvlGPvqfbdHUJoAivA0d1t7kXhjD4JIbwF+CXgj4CPAK+aPCiEkFJOwfuFGGO/tsubRpy7aX3yHyiv4X1nVSdhdJH8wNeAQyGERtUnVzL9XNL2TD1XYoyvAp7N9Gmqniu7KITwVKATY/xwVZQAX2Tzz5S7Z6ijbdqgX3ohhKcA/xn48RjjGaPs1Q0jrt3k55y2YYM++eYQwtMn+2niUM+VXXKW86RNea3cSzY4zvNkd/0N8KEY4/0AIYTfpRyNr39+TzsPLtjfwfY8MMYYh1Mdqr/Qf1eM8YdDCH8eQnhKjPHPgBcC0y6mfj/wIspfyq6j/OXZE+QcTesT4P8APgG8J8b4rzY4Lg8h/CDweeA9IYQXAZ+orhnSOdigT/4F8JfVHdW+CLyCcvS3flwvhPCnlOfHLZTnizcm2CEb/Px6VXXjgidS+0vxxHGeK7vnMPCGEMJ3UF57/WLK0d6bQwhHgFPAcyinCw/FGL8UQlib4XNH2zOtX26kvLThuhjjRzY47jTwS2f7Oadtm9Yn7wX+7UTZ5E27PgGEEMLfprxj+vMpR7N07qb1yQ3A44G/OstnhOfJ7vpD4DdC+XSGE8AzKM+V15ztPLiQfwebp+cwvgB4Swjhc5Sjjm8DCCG8IYQw+OH108A/CCH8BeV84lfsS0svDj9AOS/+uSGEO6vXTXBGn7wY+PGqT34YeOn+NPfCV/2l6+XAH1DeqjkB3gQQQrgphPADVdUfA64PIdwFPI3y0QLaXUeAboxxrV7oubL7Yox/SDm98dPA/wDeWQXA1wK3AncCt8QY7wAIIbw/hPCk6vCpnzs6d9P6hfLGQwvAm2ufKzfAqF/O9nNO52aDc+Xnp5R9HKDqn6uqn2svoRwZvgv4HKObeugcbNAnH6d8fNzdk/U9T/ZG9ViZXwI+Svl//kuUNyB6CVPOg4vhd7CkKCan2kqSJEmSNF8jjJIkSZKkOWJglCRJkiRNZWCUJEmSJE1lYJQkSZIkTWVglCRJkiRNtefPYZQkaTeEEFrAl4E7Y4zP2O/2SJJ0IXCEUZJ0ofjHlM9dfFII4Rv3uzGSJF0IHGGUJF0ofhT4beCvgVcCg4fCvwb4Z8AJ4Dbg2THGq0MIbeAXge8EGpQPz/7fYowP7UPbJUmaS44wSpLOeyGExwHfDvwO8BvAi0IIl4YQ/mfgJcC3Ak8EVmqHvQbIgCfGGL8ZuBf4hb1styRJ884RRknSheBHgT+MMR4FjoYQvgBcD1wB/E6M8UGAEMLbgadXx3wfcBj47hACQBv42l43XJKkeWZglCSd10IIB4AXAushhC9WxQeBGymnqCa16v3aegN4ZYzxA9X7LAMLu91eSZLOJ05JlSSd714AHAWuijFeHWO8GngMsAz8D+A5IYRDVd1/BhTV+h8BN4YQ2iGEFHgH8G/2tOWSJM05A6Mk6Xz3o8CbY4zD0cNqCurbgFdRBsGPhxA+CRwCVqtqPw98kfJmN3dRjkS+eu+aLUnS/EuKoti8liRJ56EQwpOA74gxvq3a/t+Bb4sxXre/LZMk6fzgNYySpAvZXwE/GUK4nnIq6pcpb4YjSZJm4AijJEmSJGkqr2GUJEmSJE1lYJQkSZIkTWVglCRJkiRNZWCUJEmSJE1lYJQkSZIkTfX/A5QiB36eNA4RAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 912.425x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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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\n",
      "text/plain": [
       "<Figure size 912.425x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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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\n",
      "text/plain": [
       "<Figure size 912.425x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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": {},
   "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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       "      <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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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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 0x1dc57587860>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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": {},
   "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",
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      "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": {},
   "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": [
    {
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       "      <td>1</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>1.0</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>0</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>1.0</td>\n",
       "      <td>0</td>\n",
       "      <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": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 912.425x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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.show()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 20)"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 912.425x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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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\n",
      "text/plain": [
       "<Figure size 912.425x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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, 512.3292)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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qgNvN7FTgMuBc4FUzW2VmD0X1vwacY2brgC8DXxlLoytTcapSsbHsMmFm1aaoqUyw9t09JTm/iIiIiIhIMRT0nEXn3L3AvSPKPpG3vJqDb3oD8BqHXpuYq78H+PSYWppndmMV2eLeCHWY53ksnlvLG5v2ks0G+L6uWxQRERERkemnkGmoU05LQwVFfmrGQRbPraV3IM2m7bowW0REREREpqeyDIuNtamSnn9xax0Ar787pnvyiIiIiIiIlI2yDIv11aW5uU1OVSrO3FlVCosiIiIiIjJtlWVYbKgu7cgiwJLWWja1d9LbP6abuIqIiIiIiJSFsgyLyWTpm714bh3ZAN54b1+pmyIiIiIiIjLhSp+6jkK2VLdCzTOvuZpkwmfN27tK3RQREREREZEJV5ZhsZR3Qs2J+R6L5tSyduMegqnQIBERERERkQlUlmFxqljSWsfergF27O0tdVNEREREREQmlMLiOCyZWwvA6+/uKXFLREREREREJpbC4jjU16SYVZtizTt6hIaIiIiIiEwv8VI3oNwtbq3j9Xd3M5TOkIjHSt0cERERERGZQszsC8D/A8SA/cAfOOdWHeWxbgT2Oud+cpT71wBrnXOLC6mvkcVxWjK3lqF0lre2dJa6KSIiIiIiMoWY2ULgj4APOedOBv4b8OOjPZ5z7h+PNigeDY0sjtPCOTXEfI/X393FisWNpW6OiIiIiIhMHTVAInrtc8792sz+0MzuAu53zv2nmV0AfNU5d4WZbQbeA94HzgeOc871m9lVwGlAN7ALWAE865y7x8xSwDrAgE8DN0Xn/Hfn3J+bWTVwL7AEeHksjdfI4jgl4zEWtFTrJjciIiIiInIQ59wbwFPAFjP7pZn9EfDcB+zSBvy+c+4q4JfAxVH55cC/5tW7D/hMtHwJ8CjQRDjd9VzgVOBkM7sQ+APgzWhk88mxtF9hcQIsnlvHtl097OseKHVTRERERERkCnHOfQlYCTwCfBZ4HkgeoXqvc259tHwf8FtmVkU4kvhiXr2ngFPMrIIDQfJs4ATgBeBV4MRov/OAB6L9fgIU/JD4gqahRsOeNxMOZ97mnLtjxPaVwJ1AXdTwG51z6bztfwVknHNfj9Y/EjX4/ajKa8656wpt9FSzpLWWJ1fD2o27Oe+keaVujoiIiIiITAFm9nEg7pz7D+ANM/sW4VTQVsCLqiXydunLW34U+DvgUuBB51xgZgA457Jm9nPg48A5wO8BnwL+wzn3xejcs6LjfTzvmFnGEBZHHVk0s/nALYSJdCXwJTNbMaLa3YTzbI8nfNM3RPvWm9n3CC/qzHcG8C3n3Mroq2yDIkBLQyW1VQle2tBR6qaIiIiIiMjU0Q/cYmZzovUWoBZ4B1gelV16uB2dc0OEU1Zv5uApqDn3AV8HnnDOZYCXgI+Z2RwzSwAPAh8iHMz7XLTPbzOG2aWFVLwYeNw5t8c51wPcD1yR22hmi4BK59zzUdFdhMOrAJcBbwF/O+KYZwKXmNkaM/tZdJegsuV5HsvaGlm/cQ89/UOlbo6IiIiIiEwBzrkngH8Cnjaz9cDPgf8X+B/A75rZa8DgBxziJ0ADB09BzXkGaCYKks65rcB/Bx4D1hBmuF8Cfw/MM7O1wIVA+jDHOqxCpqHOA9rz1tuBs0bZviBq8L8AmNnXRxxzH3Cfc+6B6FkhPya8ELNsLWtr4KUNO3n1zQ7OP1lTUUVEREREBKJL+O44zKaTD1O3ecT6z4FFeetfz1vOAvNH1L8HuGdEWR9w5VE0vaCw6HPwvFaPcK5rodsP4Zy7MW/5H83sm2ZW75zbX0B7qKurZCj9gacouvr6SmbVvccrb+7ity+yUjdHJlhLS22pmyAzjPqcFJv6nBSb+pwUm/rc2BUSFrcQPuMjZy6wbcT21g/YfhAz84E/Ab4Zza3NKXg4tLOzj4HBzOgVi+z4BfW8+MYO3tm0m7rqI93gSMpNS0stHR1dpW6GzCDqc1Js6nNSbOpzUmwT3edmSvAs5JrFx4CLzKwlum3r5YS3fQXAObcZ6Dez3DTSa4CHj3SwaLj0M9FxMLNrgRei6yHL2rK2RrIBvPLmzlI3RUREREREZFxGDYvRhZI3AU8Aq4B7nXMvmtlDZnZGVO1q4FYz2wDUALePctjPA39oZuuA64Drj/YNTCUtDRU01Vfw/PodpW6KiIiIiIjIuHhBUPBjNqaCxcDGF9dsnZLTUAGefb2d59Zu51tfOZfG2lSpmyMTQFNlpNjU56TY1Oek2NTnpNgmYRqqN3qt8lfwMzakMMsWNRIALztNRRURERERkfKlsDjBmuoqmNNYyfPrNBVVRERERERKy8yuMrP1ZvaWmX1lLPsqLE4Ca2tgY3snu/b1lbopIiIiIiIyQ5nZfOAW4DxgJfAlM1tR6P4Ki5NgWVsjAC9pKqqIiIiIiJTOxcDjzrk90dMn7geuKHTnQp6zKGPUUJOitamK59ft4ONnLyp1c0REREREpAQ+9Uf/51rgi5N0+O//x99e9i+j1JkHtOettwNnFXoCjSxOkmVtjby/s5sde3pL3RQREREREZmZfCD/8RcekC10Z40sTpJlbQ088dpWfr1uO791/tJSN0dERERERIosGvkbbfRvMm0Bzs9bnwtsK3RnjSxOktqqJEvn1fGrVdtIZwoO7yIiIiIiIhPlMeAiM2sxsyrgcuCRQnfWyGLESw9Q3f4yid6deNk0ZIbwskN42TReJnrNDkXlaTIV9fTNWUnv7JPJpmoPe8xTj2vmp0++y6tv7uKs5bOL/I5ERERERGQmc85tNbObgCeAJHCnc+7FQvef8WEx1reH2vefoer9X+Ol+wniKQI/AX6MwI9HrzHw4uFrspqsFyPWu4v69fdT98YDDDUdR8/cU+mbfRJBomr42Etb62isTfHoy+8rLIqIiIiISNE55+4F7j2afWdmWAwCkvs2UvfeUyS3rwEPhpqOp6flRNI1cws+RqxvD5X73iG1+y0a1/6YBu9fGWxZTu/clfS1nADxClYe28wTr23lvR1dtM05/AikiIiIiIjIVDOzwmI2TdWO1dRsfpLE/vcJ4hX0zzuN3uYTjjiV9Ig8j0xVE91VTXS3nkm8t4OKve+Q2vMWjTvX0uDHGZxzImfPPp1n4h6/fGUL131i+eS8LxERERERkQk2I8KiP9hDzdZfU/3eM/j9+8lUzqJ78QX0zToeYonxn8DzSFfPprt6Nt3zzyHevZ3Kfe+Q3PUmre2r+MOmxfzwjdPovvBYaion4HwiIiIiIiKTbNqHxYpdb9C45m78oV6GGhbR2fZhhuoWgudNzgk9j3RtK121rbDgQ1R1rGXe+y/wX2veY/Mv9rDi0qvw4snJObeIiIiIiMgEmb5hMchSt/Exat56hGx1M3uO/zSZqqbitsHz6Z19Mv0Nx9Cx+imW7fgVPf+6jopzf5d42ynFbYuIiIiIiMgYTMvnLHpDvTSv+h61bz3MYIuxxz5T/KCYJ5usZufCi/lfnR9jYChD3yO30veL28l27SpZm0RERERERD5IQSOLZnYVcDOQAG5zzt0xYvtK4E6gDngKuNE5l87b/ldAxjn39Wi9AbgHWAp0AFc657aP+90Aic6tNK3+Z/y+vXQv/gh9zSdM3pTTMbDZcR5187gru4CvLN/I0JvPkn7/T0md9ikSJ/8XvIm4dlJERERERGSCjDqyaGbzgVuA84CVwJfMbMWIancDX3XOHQ94wA3RvvVm9j3gj0bU/wbwtHNuOfBd4NvjeheRqm0v0fLCbXiZQfYt+wx9LSdOiaAI4Psepy1IsG57ho7m00ldcD2xOUsZeOmn9N5/M+mt60vdRBERERERmYbMrM7M1prZ4rHsV8g01IuBx51ze5xzPcD9wBV5J14EVDrnno+K7gI+Gy1fBrwF/O2IY15KOLII8CPg42Z29ENr2TSNb9xP4+v3kqltZc/yKwp/XmIRnbogQcyHR9d341fWkTztMpJnXUmQGaLvwb9h4Ll7CNKDpW6miIiIiIhME2Z2NvAMcPxY9y1kGuo8oD1vvR04a5TtCwCcc/8SNfDrRzqmcy5tZp1AC7BtDG0HINa3l6Y1PyCxbzN9806ne95Z4E3NSzGrUz6nzk/w3Nt9fOLEGppr48RmL8Fvuo60e5LBtY+Sbt9AxUdvJNY4v9TNFRERERGRcXj3lsuvBb44SYf//tKbfvovBdS7AfgK8MOxnqCQsOgDQd66B2THsP1wRs4NLWSfYXV1lQyls/g7NlDxwnchM8TgiZ+GlmOpKfQgJfKxk+Ks2rqXRzf0839/bPaBDR/6FP1txv7n/w99//YXNH3sOmpP/RjeFJlGO9O1tNSWugkyw6jPSbGpz0mxqc9Jsc3UPuecux7AzMa8byFhcQtwft76XA4eAdwCtH7A9sPZGtXbYmZxoBbYXUBbAOjs7MN//xUaV/+QbGUj+4/7NJnKRugeKPQQJRMDTluQ4MkNXVy0rILZtXn/BDULSJ73eYbWPMyuh/+JfW+8RMWHv4hXMdUj8PTW0lJLR0dXqZshM4j6nBSb+pwUm/qcFNtE97lCg2c08lfI6N+UVMh8zceAi8ysxcyqgMuBR3IbnXObgX4zOzcqugZ4eJRjPgRcGy1/jvBmN0OFNrqiYx2Nq39IpnYue5f9dhgUy8hvLE3ie/Dgmu5DtnkVNSTOvILECReRfm81Pbr5jYiIiIiIlMCoYdE5txW4CXgCWAXc65x70cweMrMzompXA7ea2QagBrh9lMN+DTjHzNYBXyacQ1uw+jf+jUztPPYeeylBLDmWXaeE2pTP6QsTPP9uHzs604ds9zyP+JIzSJ17DcTi9D34Pxl48V8JsofWFRERERERmQxeEASj15o6FgMb3/nBX7Jr9tlQxs8m7B7IcsdTPZy2uJLfO6/hiPWC9CDpN35FevNr+C1LqPzojfj1c4rYUtFUGSk29TkpNvU5KTb1OSm2SZiGWnY3FjGzTcAFzrlNhe4zNW8bOorOxReUdVAEqEn5nNGW4MWNfbTvP/KIoRdPkjjpEpKn/xbZ/dvp+emfMbThKcos5IuIiIiISAk55xaPJShCmYZFvELuyzP1nbMkScKH/1w9+qccsVYjdf4X8Btb6X/q+/Q/+vdk+zqL0EoREREREZmJyjMsThPVSZ8z2pK8vKmfbftGv7+PX1lH8qwrSaz4KOn3VtN7/82k31tdhJaKiIiIiMhMo7BYYucsTpCIwU9fKWwOted5xJeeSeq8a/GSFfQ9civ9z/wLQXrqPzZERERERETKh8JiiVUlfc4/JsXrWwdYs6W/4P38utkkP3QN8aVnMbT+cXp/+mdkOjZOYktFRERERGQmUVicAs5alKC5xudHL3YymC78xjVeLE5ixYUkz/m/CAb76P33v2LwtZ8RZDOT2FoREREREZkJFBangJjv8ZvLUuzuzvDzdd1j3795EanzryM2fzkDLz1A33/8NdnOnZPQUhERERERmSkUFqeIJU1xVsyN88jabnZ1HflRGkfiJStIrvwkiVM/RWbPVnru/xqDqx8iyIz9WCIiIiIiIgqLU8jFlsIDfvLS0T8SIz5/BakPX0esqY2BF+6j96dfI71l7cQ1UkREREREZgSFxSmkrsLnvGOSrN4ywOtbC7/ZzUh+ZR3JM3+b5JlXEAwN0PfQt+h79H+R7do1ga0VEREREZHpTGFxijl7UZLmap8fvdDJUKbwm90cTmzOMaQ+fB2JZR8m/d5qeu770/AGOOnBCWqtiIiIiIhMVwqLU0zM97hkeYpd3Rkeen3sN7sZyYvFiR/7G6QuuJ7YnGMYeOkBeu+/ifTmVRPQWhERERERma4UFqegpU1xTp4X56HXu3HbBybkmH5lHcnTPk3y7CsJgoC+n99G3yO36q6pIiIiIiJyWAqLU9RvLq+gsdLne8/so7s/O2HHjbUsIXX+F0is+CjpbW/Qc9+f0v/0XWT3bZ+wc4iIiIiISPlTWJyiUnGPz5xcQVdflh88t48gGN/1i/k8P0Z86ZlUXHA98baTGHLP0HPfn9D36N+T2fH2hJ1HRERERETKV7zUDZAja62PcdHxKX7hBnjC9fLRZdUTenyvopbEiZcQP+5c0pteJb35NdIbXyE293gKsJM2AAAY2klEQVSSp3ycWNspeJ4+TxARERERmYkKCotmdhVwM5AAbnPO3TFi+0rgTqAOeAq40TmXNrM24G5gNuCAq51z3Wb2EeAB4P3oEK85566biDc03Zy5KMHGPWnuf7mT42YnWTgrMeHn8FLVJOx84secTWbL66TffYm+n38bv2EeyVM+TvzYc/BiE39eERERERGZukYdNjKz+cAtwHnASuBLZrZiRLW7ga86544HPOCGqPw7wHecc8uAl4GvReVnAN9yzq2MvhQUj8DzPD55YgWVCY///dReBoYm7vrFQ84VTxJffDqpC24gceqnIMjQ/+T36PnRHzOw6iGyvfsn7dwiIiIiIjK1FDLH8GLgcefcHudcD3A/cEVuo5ktAiqdc89HRXcBnzWzBPDhqP5webR8JnCJma0xs5+Z2cJxv5NprDrpc9lJFezszPD9Z/eRyU7c9YuH4/kx4vNXkDzv8yTPvhKvupHBF++j554/pPfBv2HwjV8R9I//sR4iIiIiIjJ1FTINdR7QnrfeDpw1yvYFQDPQ6ZxLjygH2Afc55x7wMxuBH4MnFtoo6urkyTjkxuYppoTa1J8ctDnP9b08KOXu/nyx2bje97kn7jxBDjuBIb27aR/81r6Nq9j4Om7GHj2h1QuPomaFedSZWcTq5jY6ymnmpaW2lI3QWYY9TkpNvU5KTb1OSk29bmxKyQs+kB+MvOAbAHbR5aT2885d2OuwDn3j2b2TTOrd84VNM+xp2eQwf6Jef5gOTml1aerL8mvNnTjB1l+56w6vGIERgBqYNE5JNrOJt65k0y7o3/bG/S9uwoe+kfiC08ivvQs4otOxUtWFqlNxdHSUktHR1epmyEziPqcFJv6nBSb+pwU20T3uZkSPAsJi1uA8/PW5wLbRmxvPcz2nUC9mcWcc5mozjYz84E/Ab4ZleekkVGduyTJYBp+5XpJxj0uP622iIExvIbSq5+DXz+HuJ1PsK+dzHZHZtsG0ptXQSxOrNWIz1tObN4K/ObFeL7uqCoiIiIiUm4KCYuPAV83sxagB7gc+FJuo3Nus5n1m9m5zrlngWuAh51zQ2b2NPA54F7g2qg8a2afAd4C7jOza4EXoushZRSe53HhcUkGMwG/WNdDRcLjkyeX5pMNz/PwGufhN84jvuwCsnu3kt3+JtldmxnYsi6slKwkPm8ZsdblxOYvx2+cr8dxiIiIiIiUgVHDonNuq5ndBDwBJIE7nXMvmtlDwJ85514Grga+a2Z1wKvA7dHuXwZ+YGY3A+8BvxOVfz6q/+eEI5DXTuSbmu48z+M3l6UYysDPVnWTzcInT6kpzjWMH9Cm2KwFxGaFl6UGAz1kdr9HsPs9Mh2bSW96LaxXUUt8/gr8ecuJzT0Ov6FV4VFEREREZArygqCsbhSzGNj42lPPMdjfX+q2lFw2G/Dg+gFWbx3ihHkpvnheA7UVUzN4ZXv3k93zHsHu98ns2kzQ1xluSFQSm72E2OxjiM1eij/7GPzKutI2dgRdVyHFpj4nxaY+J8WmPifFNgnXLJZulKaICpmGKlOU73t88oQUCxp8HnljgG/8Zwe//5FGlrYkS920Q/hV9fhVJ8GCk4gHAUHPXrL7thHsaye7r53BVQ9CEN43yattJj7nGPyWpcRmH4Pf1IYXn3rvSURERERkOlNYLHOe53HqgiSttTEeWNPP3zyym8+eUcdHl1UV9cY3Y+F5Hl7NLPyaWbDgRACCzBDZ/TuGw2N66waCt1+IdvDxZy0g1rIEv3lR+DprAV4sUcJ3ISIiIiIyvSksThNz62Ncd04VD67r5ycvdfL6ln4uP72OhbPKI1B5sUR4veOsBcNlQX8X2b3tZDt3EOzfTvrdFwk2PBlu9GN5AXIxsZbF+I0L8GLq0iIiIiIiE0F/WU8jlQmPy0+p4JX30zz5zgDf+M9dnL20kstW1tBUU37/1F5FLbHWWmKtxwMQBAFBXyfBvu3DAXLo7efhjV+FO/hxYrMW4Lcsxm9aFAZIjUCKiIiIiByV8ksQ8oE8z+OMtgQntMZ5YfMgz2/s4+VNfVy4rJr/cmI1tRWxUjfxqHmeh1dVD1X1xOYZEAXI3n3hFNbOHQT7dxwcIIensOYHyIW6BlJEREREZBQKi9NUZcLjgmNTnDY/wdPvDvLY+h6e2NDDyoUVnHdcFctakyV91MZE8TwPr7oRv7oR5i0DcgFyP8H+HWS7wgCZfvdlgg1PRTv5+A2txJrb8JvawhDZ1IZXUVPCdyIiIiIiMrUoLE5zdZU+l55QwTmLEqxuT7NqywAvb+6nqSbGucdWctbiSmbXTa9uEAbIBqhuIEbeCGRfJ8H+7WS7Ogg6d5Leso7grV8f2K9mFrHmRVGAbAsDZE1Tqd6GiIiIiEhJTa+UIEfUVBPjo8fF+PDSJG/tyrB6a5qfrermZ6u6aamJsWJeihXzUiybm6QyOTWf1TgeB01hbbXh8mCgN7z+sbODbNdOMrvfJ71pFRA9fzSRYqiljWxdK37DfPxZ88PrICvrp+zdZkVEREREJoLC4gwTj3ksnxNn+Zw4nX1J3tmTYePuDL9+t48n3+wl5sH8xjjzGxPMbzjwWl/pT8tw5KWqiLUsgZYlw2VBepCgq4Ns5y6C7l0EfXtJb3yFYOCpvP2q8RvD4Og3zMOvn4NfPwevpgnPL9/rQkVEREREchQWZ7C6Sp9T5/ucOj9BNhuwtTPLpj1Z2jszrNs6wK/f6RuuG/ehtsKnpsKnNuVTW+GTSvj4HvheOHLnR1kyCCAgIAggG4TbEzFv+Cseg+qkT0NVjMbqGI1VPhWJqTOa6cWTeI3z8RvnA9DQUMW+fb0EAz1ku3YR9Owm6NpFtmsXQ289B0P9B3b2Y/i1zfj1c8PwWDc7XK6bjVfTjOdPnfcpIiIiIvJBFBYFAN/3WNgQY2FDDAgfNTGQztLRDR09WboGAnoHw6/ugSzbO9MMZSAbBGQDCLJhMPQ88Iheo+VsAEOZgHT2yOevTHjMqYvT1pRg0awEbU0J5jXEScSmzmiml6omlqqG5kXDZUEQwEAP2Z69BL17CXr3EfTsJbt/B+mt6yEzlHcAH6+6Ab+mCb9mFl71ga/h9cpaPE+BUkRERERKT2FRjigV91nQAAsaDg0vuUA4siwnOEz9gIBMFtJZj750QFd/QNdAQPdAQGd/lp3dWV7a2MdTb/YCEPNh0awE1ppkeWuKY1qSUyo8QjiiSkUNsYoaaFp40LYwSHaT7dkXBcn9BH2d0N9Fuv2tcDmbPviAfjwMlJX1YXCsrMOrqAuXK6L13FdFDZ6v/8IiIiIiMjn0l6YclYBwuunYeHgeJGLhtNS61GGCXxCwvx92dGfY0RWwaU+an6/t4eHXe0jE4NjZYXA8cX6K+Q3xKX0dZRgka4lV1B4SJCEKk4O9BP1dBH1dBAPRa383DPaS3bedYMc7BAM94dDt4SRSeMlqvFTuq+qgV5LRerIyXE5U4iXDLxKVmhYrIiIiIkeksChTi+dRXwn1lXGOb4HzlyYYygS8vzfL5n0Z3t2V5oFXu3jg1S4aKn1OmJ/ihHkplremqE6VV/DxPA+ikEf93CPWC4IAhgYIBnsIBnphsI9gsDcMmkMDMNRPMNRPMNhL0LMnWu47eArskcRTeMkKvGQVJCvxk5WQrAxDZaISkhUHgmUyV3YgcIbbKjR1VkRERGQaUliUKS8R81jaHGNpc4wLj03SO5Dl3b1Z3t2V4dXN/Tz7dh8esHBWnGVzU1hrkuNmJ6fUTXPGw/O8KLRVwBie+xhkM8NBkqEBgvQgpA+8kh48UDY0AOkBsr37obODID0Q7TfIoZOKD2lhGBiTlXipSrxkVfQVjWYmq/BSVQct5+qQCut5scS4vkciIiIiMvEUFqXsVKV8Tpzrc+LcONlsku1dWTbvzbJpT4bHN/Twi/U9xDxYOCvBoqYDX60NceL+1J22OtE8P3Zg5PIoBUEAmUEYGiQYDpgDeesDw+VhIB2AoQGy/TujANpPMNh/5Gm0ObHEgRCZm1KbjAJmqvLAlNrkyDAarhNLTOkpySIiIiLlqKCwaGZXATcT3ibzNufcHSO2rwTuBOqAp4AbnXNpM2sD7gZmAw642jnXbWYNwD3AUqADuNI5t32C3pPMIL7vMa8+xrz6GL+xOEE6k2JrZ5b39mbZsi/DC9HzIyG8VrK1Pk5zTZzmmhjNNTGaamM0VsWoTPhUJj0qEh7+UYSObBDevCeTDe8Om5M7ku+Fz7iMlVlY9TwP4qlwuiq1R3WMIAjCG/kM9RMMDeSNdIZh80DI7B+eVpvt6ghDZm5kdLSw6ceGA2RuSq2XrMBLVISjnvFUGC7jFeF1nolopDaewosnIZbEiycgloR4Ai/3WibTa4MggCAD2SxkMxBkw5HlaJlsuC3I1Qmyw+Xhvrn13LZM7hk4BGSj5YCw4HDLh23VB7R45N2xDrfu5b3m7ZO3zcvddhk/7xbM/sH7e9EzYj0//PL94WVveD3a7sfCvhS94kXr+iBCRERmqFHDopnNB24BTgcGgOfM7Ann3Pq8ancD1zvnnjez7wE3AP8AfAf4jnPux2b2NeBrwP8HfAN42jl3qZldA3wb+NxEvjGZmeIxj0WNMRY1Ro8ACQL29Qds78qyvStgd3eWrfuGWLOl/7CP8vCAioRHMu4NP/ojHvMJgoCAMAzmQuHw6wf9vTzy+NEzJ+M+JGMeqYRHRcKnIuFREQ/DamUyXK9M+FQmDtRJxsM6qYRHMgqeidiBEBr38x9bcuCP21zbc8+9zAa59h94D+kspDMHXoeyQfiaCR97MpQJGEoHDOaW88uj/TKZgHR0rExeYs7/3sRjPjG/irhfTdwP2577PqTiHskKj1RN7v1H35s4VMbSpBgkxSCxTN4IZ250cyg3nbY/3DbQHV6/mR48MN22kGs4R/Lj4ahlPB4ue/6BIOHHwPfxvPz12Ij+dORbBh8+pAUEQZYBH9JD6eHtQSZzIAwGmSgIZvPKRgnTMi4BPkEULAMvFvYFP3z1Yod++fFEuD0Wx/MTEIuPWI6FU6+j/pU7TrgcbYvWvVjU92LxaJ8D5w3Lw1CrQCsiM9Ghv0ujD0dzH5bm/Y4d9DvJ7O06+EPT4d+9wYHfpcGRPiQdoeXcor7XUilkZPFi4HHn3B4AM7sfuAL4y2h9EVDpnHs+qn8X8BdmdifwYeC38sqfJAyLl0bbAH4E3GFmCefcaH/NxQBSVZX4sfL4xF9Kb24NzG0+tLxvCDr7s/QOheFnMB0wkA4YTIeBJxeyYjGfoShZxrxwNNP3IOaFr74PsagsHJU89AdKGDSjYJYJA2Y6EzAYnXcwEzA4FLBvKGBHb0D/UJbsOANAbjBm7HetLeDYeaE35vthYPXC70Ms7pHMDfjktYMAMlFQ7c+E7z8zkAuhWTIFvd0kMS9JMu6RSvgkfI94PAyciZhHIg7xGPhJj7h/oE2+DzEC4qRJkCbBEPEgTYwhYkEWP0jjkyUWZPCCNDGisiAz/OUFWTwCvCALBHhko7IsXhDgZQO8/DeR940/6J8gKg+PFicIj0oQeGRz5VmfrAcZzyMTeGT98DUT+KQDj2zWIx14pPFIZ72wP2V9svhk8cgGPhnCkJMNPDL4uRYPf4Xr4fZwefhdRW0Jy8ELP2zIvQYMt/nAezt8UDlc1xtZ0yPAz+svuTIAL/o/ePC+B5/Zz7XGI/edHN7fj47lecFwvfC7FB7jQFmA72WH12Nerl6WmJfFh+HlWLR/zMsSC7LE0llimYAYWWJeBp8sCQaJeVkSXoaYFxCPtsWiY8bJHPb7NR5BNCLq+fHwh5Ifiz7IODBiemB0NRo9HV736EgkGcpkD4zMen7eV94orudH/xK5h+oeGNX18keBh38A5P5h814PCrYj63HQesEReKLDssL3pOvckiTbN1jqZkycyfhlO4ZzBEeqM7weHH49/6dsblsAkI1yUl5gGg5OuUphwAqGwxVR4ArCQJark83ViR7KHQRALtCNCHsEw7NhhmfJ5O07PEOG7Jg/LJ3wKYynn7sY2AKkR6lZ1goJi/OA9rz1duCsUbYvAJqBTudcekT5QftE01U7gRZg2yhtaQVYccapBTRbRERERKaqulI3QGR8NgJLgE0lbsekKiQs+hz8AbEHZAvYPrKcvP0O/XD54GMeyUvA+YRBc+I/mhURERERESnMllI3YLIVEha3EAa0nLkcPAK4hWjEb8T2nUC9mcWcc5moTm6/rVG9LWYWB2qB3QW0ZQB4poB6IiIiIiIiMg6FXPj3GHCRmbWYWRVwOfBIbqNzbjPQb2a5qzyvAR6Orj98mgM3rrkWeDhafihaJ9r+dAHXK4qIiIiIiEiRjBoWnXNbgZuAJ4BVwL3OuRfN7CEzOyOqdjVwq5ltAGqA26PyLwNfMrP1hKOTN0flXwPOMbN1UZ2vTNQbEhERERERkfHzgmLcvUlERERERETKip4/ISIiIiIiIodQWBQREREREZFDKCyKiIiIiIjIIRQWRURERERE5BAKiyIiIiIiInKIeKkbMBZmdhXh4zcSwG3OuTtK3CSZJsysDngO+KRzbpOZXQz8HVAJ/MQ5d3NUbyVwJ1AHPAXc6JxLl6jZUqbM7M+BK6PVB51z/019Tiabmf0lcAUQAN9zzv2d+p1MNjP7FtDsnPvCkfqVmbUBdwOzAQdc7ZzrLlmjpWyZ2ROE/Sj3/PbfB47hMPnhSD//5GBlM7JoZvOBW4DzgJWEz29cUdpWyXRgZmcDzwDHR+uVwPeBy4DlwJlm9vGo+t3AV51zxwMecEPxWyzlLPrldAlwKuHPstPN7HdQn5NJZGYfAT4KnAycAfyBmZ2C+p1MIjO7CPh8XtGR+tV3gO8455YBLxM+j1tkTMzMI/xb7hTn3Ern3EpgC4fJD6P8rSd5yiYsAhcDjzvn9jjneoD7CT8hFRmvG4CvANui9bOAt5xzG6NP0u8GPmtmi4BK59zzUb27gM8Wu7FS9tqBP3LODTrnhoA3CH+5qc/JpHHOPQlcGPWv2YQzixpQv5NJYmazCP9I/+to/bD9yswSwIcJ/64bLi9qY2W6sOj1F2a22sy+ypHzw2H/1itJq6e4cgqL8wj/yMppBxaUqC0yjTjnrnfOPZ1XdKS+pj4o4+acW5f7Y8nMjiOcjppFfU4mmXNuyMz+AlgP/BL9rJPJ9U/ATcDeaP1I/aoZ6Myb5qz+JkerkfBn22eAi4AbgTb0c25cyiks+oTXWeR4hH9giUy0I/U19UGZMGZ2AvAo8MfAu6jPSRE45/4caAEWEo5oq9/JhDOz64H3nXO/zCsu9HcrqL/JUXDO/do5d61zbr9zbhfwPeAv0c+5cSmnsLgFaM1bn8uBaYMiE+lIfU19UCaEmZ1L+Onnf3fO/QD1OZlkZrYsurkIzrle4AHgAtTvZHJ8DrjEzFYR/rH+aeB6Dt+vdgL1ZhaLyltRf5OjYGbnRdfJ5njAJvRzblzKKSw+BlxkZi1mVgVcDjxS4jbJ9PQCYGZ2bPTL6yrgYefcZqA/+kMf4Brg4VI1UsqTmS0E/h24yjn346hYfU4m21Lgu2aWMrMk4U0d/gn1O5kEzrmPOedOjG4w8mfAz5xz13GYfhVdu/00YcAEuBb1Nzk6DcD/NLMKM6slvLnS73L4/HDY37ulavhUVjZh0Tm3lXDu+xPAKuBe59yLpW2VTEfOuX7gC8BPCa/t2cCBC++vBm41sw1ADXB7KdooZe2/AhXA35nZquiT9y+gPieTyDn3EPAg8BrwCvBc9GHFF1C/k+I5Ur/6MuFdKtcD5xM+5kBkTJxz/8nBP+e+75x7lsPkh1H+1pM8XhCMnCYuIiIiIiIiM13ZjCyKiIiIiIhI8SgsioiIiIiIyCEUFkVEREREROQQCosiIiIiIiJyCIVFEREREREROUS81A0QEREZLzMLgLVAJq/4Zefc9SVqkoiISNlTWBQRkeniQufcrlI3QkREZLpQWBQRkWnNzL4I/D6QBGYB33TO/YOZfQH4PaAa2O+cu9DMfo/wAeE+sBv4qnNuQ2laLiIiUloKiyIiMl08YWb501AvAXqBG4BPOOd2m9k5wKPAP0R1TgAWO+c6zewjwOeB851zvWZ2CfBvwPLivQUREZGpQ2FRRESmi8NOQzWzTwKXmtlxwEqgJm/zGudcZ7R8KXAs8JyZ5bY3mtks59yeSWy3iIjIlKS7oYqIyLRlZguAVcAi4Bng5hFVuvOWY8APnXMrnXMrgdOAM4C9xWiriIjIVKOwKCIi09kZQAfwDeAXwCcBzCx2mLo/B37HzFqj9RuBXxajkSIiIlORwqKIiExnvwC2AA54A2gjDI/HjqzonPsF8D+AR81sDXAV8NvOuaB4zRUREZk6vCDQ70ARERERERE5mEYWRURERERE5BAKiyIiIiIiInIIhUURERERERE5hMKiiIiIiIiIHEJhUURERERERA6hsCgiIiIiIiKHUFgUERERERGRQ/z/kDbsLYFNhh4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 912.425x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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": {},
   "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 scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\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",
       "B96 B98            4\n",
       "G6                 4\n",
       "C22 C26            3\n",
       "E101               3\n",
       "D                  3\n",
       "F2                 3\n",
       "F33                3\n",
       "C93                2\n",
       "C2                 2\n",
       "E25                2\n",
       "B77                2\n",
       "C78                2\n",
       "D17                2\n",
       "C52                2\n",
       "E8                 2\n",
       "B57 B59 B63 B66    2\n",
       "C83                2\n",
       "D35                2\n",
       "B51 B53 B55        2\n",
       "C68                2\n",
       "E44                2\n",
       "F4                 2\n",
       "B58 B60            2\n",
       "C65                2\n",
       "D33                2\n",
       "D36                2\n",
       "B5                 2\n",
       "C125               2\n",
       "C123               2\n",
       "                  ..\n",
       "D46                1\n",
       "B78                1\n",
       "D28                1\n",
       "B102               1\n",
       "C54                1\n",
       "B37                1\n",
       "B73                1\n",
       "A34                1\n",
       "A5                 1\n",
       "D30                1\n",
       "D37                1\n",
       "E77                1\n",
       "C62 C64            1\n",
       "C95                1\n",
       "C49                1\n",
       "C103               1\n",
       "B94                1\n",
       "E38                1\n",
       "B82 B84            1\n",
       "D15                1\n",
       "D11                1\n",
       "E40                1\n",
       "A36                1\n",
       "A24                1\n",
       "E36                1\n",
       "E50                1\n",
       "D10 D12            1\n",
       "B3                 1\n",
       "D7                 1\n",
       "F38                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": {},
   "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 0x1dc5a6c0eb8>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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": {},
   "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": {},
   "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": {},
   "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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\n",
      "text/plain": [
       "<Figure size 912.425x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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": {},
   "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 scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\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",
       "      <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>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",
       "      <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>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": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\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",
       "      <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>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",
       "      <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>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": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "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": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>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",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</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>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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</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>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",
       "    </tr>\n",
       "    <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",
       "    </tr>\n",
       "    <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",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <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",
       "    </tr>\n",
       "    <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",
       "    </tr>\n",
       "    <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": 82,
   "metadata": {},
   "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": 83,
   "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": 84,
   "metadata": {},
   "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": 85,
   "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": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "82.6"
      ]
     },
     "execution_count": 86,
     "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": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.76666667 0.82022472 0.76404494 0.7752809  0.88764045 0.76404494\n",
      " 0.83146067 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": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "79.69"
      ]
     },
     "execution_count": 88,
     "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": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.82222222 0.78651685 0.83146067 0.7752809  0.86516854 0.82022472\n",
      " 0.80898876 0.80898876 0.75280899 0.80898876]\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": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "80.81"
      ]
     },
     "execution_count": 90,
     "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": 91,
   "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": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "78.78"
      ]
     },
     "execution_count": 92,
     "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": 93,
   "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"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\RND\\AppData\\Local\\conda\\conda\\envs\\venv2\\lib\\site-packages\\sklearn\\svm\\base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "C:\\Users\\RND\\AppData\\Local\\conda\\conda\\envs\\venv2\\lib\\site-packages\\sklearn\\svm\\base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "C:\\Users\\RND\\AppData\\Local\\conda\\conda\\envs\\venv2\\lib\\site-packages\\sklearn\\svm\\base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "C:\\Users\\RND\\AppData\\Local\\conda\\conda\\envs\\venv2\\lib\\site-packages\\sklearn\\svm\\base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "C:\\Users\\RND\\AppData\\Local\\conda\\conda\\envs\\venv2\\lib\\site-packages\\sklearn\\svm\\base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "C:\\Users\\RND\\AppData\\Local\\conda\\conda\\envs\\venv2\\lib\\site-packages\\sklearn\\svm\\base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "C:\\Users\\RND\\AppData\\Local\\conda\\conda\\envs\\venv2\\lib\\site-packages\\sklearn\\svm\\base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "C:\\Users\\RND\\AppData\\Local\\conda\\conda\\envs\\venv2\\lib\\site-packages\\sklearn\\svm\\base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "C:\\Users\\RND\\AppData\\Local\\conda\\conda\\envs\\venv2\\lib\\site-packages\\sklearn\\svm\\base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "C:\\Users\\RND\\AppData\\Local\\conda\\conda\\envs\\venv2\\lib\\site-packages\\sklearn\\svm\\base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\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": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "83.5"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(np.mean(score)*100,2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7. Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\RND\\AppData\\Local\\conda\\conda\\envs\\venv2\\lib\\site-packages\\sklearn\\svm\\base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n"
     ]
    }
   ],
   "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": 96,
   "metadata": {},
   "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": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\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": 97,
     "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"
   ]
  }
 ],
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