IOD_Lab 3.2.1-Copy3.ipynb

{
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  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "7wcSELnGVSPN"
   },
   "source": [
    "<div>\n",
    "<img src=https://www.institutedata.com/wp-content/uploads/2019/10/iod_h_tp_primary_c.svg width=\"300\">\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "MK0PFPnmVQQs"
   },
   "source": [
    "# Data\n",
    "\n",
    "> The sinking of the RMS Titanic is one of the most infamous shipwrecks in history.  On April 15, 1912, during her maiden voyage, the Titanic sank after colliding with an iceberg, killing 1502 out of 2224 passengers and crew. This sensational tragedy shocked the international community and led to better safety regulations for ships.\n",
    "\n",
    "> One of the reasons that the shipwreck led to such loss of life was that there were not enough lifeboats for the passengers and crew. Although there was some element of luck involved in surviving the sinking, some groups of people were more likely to survive than others, such as women, children, and the upper-class.\n",
    "\n",
    "In this lab, we'll explore this dataset to find insight.\n",
    "\n",
    "[Titanic Dataset](https://www.kaggle.com/c/titanic/data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Te9iSnCNVQQv"
   },
   "source": [
    "# Data Dictionary\n",
    "\n",
    "| Variable |                                 Definition | Key                                            |\n",
    "|----------|-------------------------------------------:|------------------------------------------------|\n",
    "| Survival | Survival                                   | 0 = No, 1 = Yes                                |\n",
    "| Pclass   | Ticket class                               | 1 = 1st, 2 = 2nd, 3 = 3rd                      |\n",
    "| Sex      | Sex                                        |                                                |\n",
    "| Age      | Age in years                               |                                                |\n",
    "| SibSp    | # of siblings / spouses aboard the Titanic |                                                |\n",
    "| Parch    | # of parents / children aboard the Titanic |                                                |\n",
    "| Ticket   | Ticket number                              |                                                |\n",
    "| Fare     | Passenger fare                             |                                                |\n",
    "| Cabin    | Cabin number                               |                                                |\n",
    "| Embarked | Port of Embarkation                        | C = Cherbourg, Q = Queenstown, S = Southampton |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "8CM9meP1VQQw"
   },
   "source": [
    "# Loading Modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "GHFxoWnSVQQx"
   },
   "outputs": [],
   "source": [
    "# Load necessary libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize']=(12,8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "XcpBXT93VQQ0"
   },
   "source": [
    "# Loading Dataset\n",
    "\n",
    "Read titanic dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-30T02:38:12.074784Z",
     "start_time": "2019-09-30T02:38:12.069797Z"
    },
    "colab": {},
    "colab_type": "code",
    "id": "34iS-SdQVQQ1"
   },
   "outputs": [],
   "source": [
    "# Read Titanic Dataset\n",
    "titanic_csv = 'titanic.csv'\n",
    "df = pd.read_csv(titanic_csv)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "miTc8lnzVQQ3"
   },
   "source": [
    "# Explore Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "dM51XXicVQQ4"
   },
   "source": [
    "## Head"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "VrkXE3uOVQQ4",
    "outputId": "4d473d33-3f40-4c0c-e32e-70e4805f5ff0"
   },
   "outputs": [
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       "  <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",
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       "  </thead>\n",
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       "      <td>1</td>\n",
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       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
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       "      <td>7.2500</td>\n",
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       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
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       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
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       "      <th>2</th>\n",
       "      <td>3</td>\n",
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       "      <td>Heikkinen, Miss. Laina</td>\n",
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       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
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       "      <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",
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       "      <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",
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       "  </tbody>\n",
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      "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  "
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     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check Head\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "      <td>male</td>\n",
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       "      <td>0</td>\n",
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       "      <td>276</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>63.0</td>\n",
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       "      <td>Duane, Mr. Frank</td>\n",
       "      <td>male</td>\n",
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       "      <td>0</td>\n",
       "      <td>336439</td>\n",
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       "      <th>326</th>\n",
       "      <td>327</td>\n",
       "      <td>0</td>\n",
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       "      <td>Nysveen, Mr. Johan Hansen</td>\n",
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       "      <td>61.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>345364</td>\n",
       "      <td>6.2375</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>Fortune, Mr. Mark</td>\n",
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       "      <td>64.0</td>\n",
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       "      <td>4</td>\n",
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       "      <td>C23 C25 C27</td>\n",
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       "      <td>457</td>\n",
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       "      <td>Millet, Mr. Francis Davis</td>\n",
       "      <td>male</td>\n",
       "      <td>65.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13509</td>\n",
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       "      <td>1</td>\n",
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       "      <td>63.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4134</td>\n",
       "      <td>9.5875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
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       "      <th>493</th>\n",
       "      <td>494</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Artagaveytia, Mr. Ramon</td>\n",
       "      <td>male</td>\n",
       "      <td>71.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17609</td>\n",
       "      <td>49.5042</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
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       "      <th>545</th>\n",
       "      <td>546</td>\n",
       "      <td>0</td>\n",
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       "      <td>Nicholson, Mr. Arthur Ernest</td>\n",
       "      <td>male</td>\n",
       "      <td>64.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>693</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
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       "      <td>0</td>\n",
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       "      <td>Wright, Mr. George</td>\n",
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       "      <td>62.0</td>\n",
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       "      <td>571</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Harris, Mr. George</td>\n",
       "      <td>male</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>S.W./PP 752</td>\n",
       "      <td>10.5000</td>\n",
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       "      <th>625</th>\n",
       "      <td>626</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Sutton, Mr. Frederick</td>\n",
       "      <td>male</td>\n",
       "      <td>61.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>36963</td>\n",
       "      <td>32.3208</td>\n",
       "      <td>D50</td>\n",
       "      <td>S</td>\n",
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       "      <th>630</th>\n",
       "      <td>631</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Barkworth, Mr. Algernon Henry Wilson</td>\n",
       "      <td>male</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>27042</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>A23</td>\n",
       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>672</th>\n",
       "      <td>673</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Mitchell, Mr. Henry Michael</td>\n",
       "      <td>male</td>\n",
       "      <td>70.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>C.A. 24580</td>\n",
       "      <td>10.5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>745</th>\n",
       "      <td>746</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Crosby, Capt. Edward Gifford</td>\n",
       "      <td>male</td>\n",
       "      <td>70.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>WE/P 5735</td>\n",
       "      <td>71.0000</td>\n",
       "      <td>B22</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>829</th>\n",
       "      <td>830</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Stone, Mrs. George Nelson (Martha Evelyn)</td>\n",
       "      <td>female</td>\n",
       "      <td>62.0</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>851</th>\n",
       "      <td>852</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Svensson, Mr. Johan</td>\n",
       "      <td>male</td>\n",
       "      <td>74.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>347060</td>\n",
       "      <td>7.7750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass                                       Name  \\\n",
       "33            34         0       2                      Wheadon, Mr. Edward H   \n",
       "54            55         0       1             Ostby, Mr. Engelhart Cornelius   \n",
       "96            97         0       1                  Goldschmidt, Mr. George B   \n",
       "116          117         0       3                       Connors, Mr. Patrick   \n",
       "170          171         0       1                  Van der hoef, Mr. Wyckoff   \n",
       "252          253         0       1                  Stead, Mr. William Thomas   \n",
       "275          276         1       1          Andrews, Miss. Kornelia Theodosia   \n",
       "280          281         0       3                           Duane, Mr. Frank   \n",
       "326          327         0       3                  Nysveen, Mr. Johan Hansen   \n",
       "438          439         0       1                          Fortune, Mr. Mark   \n",
       "456          457         0       1                  Millet, Mr. Francis Davis   \n",
       "483          484         1       3                     Turkula, Mrs. (Hedwig)   \n",
       "493          494         0       1                    Artagaveytia, Mr. Ramon   \n",
       "545          546         0       1               Nicholson, Mr. Arthur Ernest   \n",
       "555          556         0       1                         Wright, Mr. George   \n",
       "570          571         1       2                         Harris, Mr. George   \n",
       "625          626         0       1                      Sutton, Mr. Frederick   \n",
       "630          631         1       1       Barkworth, Mr. Algernon Henry Wilson   \n",
       "672          673         0       2                Mitchell, Mr. Henry Michael   \n",
       "745          746         0       1               Crosby, Capt. Edward Gifford   \n",
       "829          830         1       1  Stone, Mrs. George Nelson (Martha Evelyn)   \n",
       "851          852         0       3                        Svensson, Mr. Johan   \n",
       "\n",
       "        Sex   Age  SibSp  Parch       Ticket      Fare        Cabin Embarked  \n",
       "33     male  66.0      0      0   C.A. 24579   10.5000          NaN        S  \n",
       "54     male  65.0      0      1       113509   61.9792          B30        C  \n",
       "96     male  71.0      0      0     PC 17754   34.6542           A5        C  \n",
       "116    male  70.5      0      0       370369    7.7500          NaN        Q  \n",
       "170    male  61.0      0      0       111240   33.5000          B19        S  \n",
       "252    male  62.0      0      0       113514   26.5500          C87        S  \n",
       "275  female  63.0      1      0        13502   77.9583           D7        S  \n",
       "280    male  65.0      0      0       336439    7.7500          NaN        Q  \n",
       "326    male  61.0      0      0       345364    6.2375          NaN        S  \n",
       "438    male  64.0      1      4        19950  263.0000  C23 C25 C27        S  \n",
       "456    male  65.0      0      0        13509   26.5500          E38        S  \n",
       "483  female  63.0      0      0         4134    9.5875          NaN        S  \n",
       "493    male  71.0      0      0     PC 17609   49.5042          NaN        C  \n",
       "545    male  64.0      0      0          693   26.0000          NaN        S  \n",
       "555    male  62.0      0      0       113807   26.5500          NaN        S  \n",
       "570    male  62.0      0      0  S.W./PP 752   10.5000          NaN        S  \n",
       "625    male  61.0      0      0        36963   32.3208          D50        S  \n",
       "630    male  80.0      0      0        27042   30.0000          A23        S  \n",
       "672    male  70.0      0      0   C.A. 24580   10.5000          NaN        S  \n",
       "745    male  70.0      1      1    WE/P 5735   71.0000          B22        S  \n",
       "829  female  62.0      0      0       113572   80.0000          B28      NaN  \n",
       "851    male  74.0      0      0       347060    7.7750          NaN        S  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df['Age']>60]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "zIMXv8K8VQQ7"
   },
   "source": [
    "## Tail"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "7CiYeEZ9VQQ8",
    "outputId": "0e68469d-5cc7-4754-df65-7f717023f40c"
   },
   "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>886</th>\n",
       "      <td>887</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Montvila, Rev. Juozas</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>211536</td>\n",
       "      <td>13.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>888</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Graham, Miss. Margaret Edith</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112053</td>\n",
       "      <td>30.00</td>\n",
       "      <td>B42</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>889</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>W./C. 6607</td>\n",
       "      <td>23.45</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>890</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Behr, Mr. Karl Howell</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111369</td>\n",
       "      <td>30.00</td>\n",
       "      <td>C148</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>891</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Dooley, Mr. Patrick</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370376</td>\n",
       "      <td>7.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass                                      Name  \\\n",
       "886          887         0       2                     Montvila, Rev. Juozas   \n",
       "887          888         1       1              Graham, Miss. Margaret Edith   \n",
       "888          889         0       3  Johnston, Miss. Catherine Helen \"Carrie\"   \n",
       "889          890         1       1                     Behr, Mr. Karl Howell   \n",
       "890          891         0       3                       Dooley, Mr. Patrick   \n",
       "\n",
       "        Sex   Age  SibSp  Parch      Ticket   Fare Cabin Embarked  \n",
       "886    male  27.0      0      0      211536  13.00   NaN        S  \n",
       "887  female  19.0      0      0      112053  30.00   B42        S  \n",
       "888  female   NaN      1      2  W./C. 6607  23.45   NaN        S  \n",
       "889    male  26.0      0      0      111369  30.00  C148        C  \n",
       "890    male  32.0      0      0      370376   7.75   NaN        Q  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check Tail\n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "sw6UIewHVQQ-"
   },
   "source": [
    "## Shape\n",
    "\n",
    "Find shape of dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-30T02:38:43.687396Z",
     "start_time": "2019-09-30T02:38:43.684402Z"
    },
    "colab": {},
    "colab_type": "code",
    "id": "yhPOlXcEVQQ_"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(891, 12)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ANSWER\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "QyohdWIWVQRB"
   },
   "source": [
    "## Check Types of Data\n",
    "\n",
    "Check types of data you have"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-30T02:58:17.575475Z",
     "start_time": "2019-09-30T02:58:17.571485Z"
    },
    "colab": {},
    "colab_type": "code",
    "id": "z-3o2IuaVQRC"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      int64\n",
       "Survived         int64\n",
       "Pclass           int64\n",
       "Name            object\n",
       "Sex             object\n",
       "Age            float64\n",
       "SibSp            int64\n",
       "Parch            int64\n",
       "Ticket          object\n",
       "Fare           float64\n",
       "Cabin           object\n",
       "Embarked        object\n",
       "dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ANSWER\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "xGJfiKgVVQRE"
   },
   "source": [
    "## Check Null Values\n",
    "\n",
    "Check whether dataset have any null values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-30T02:58:37.576712Z",
     "start_time": "2019-09-30T02:58:37.573730Z"
    },
    "colab": {},
    "colab_type": "code",
    "id": "BooTb2vBVQRF"
   },
   "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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ANSWER\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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",
       "  </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": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check titanic data again\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "sIjPhOwTVQRH"
   },
   "source": [
    "## Fill Null Values\n",
    "\n",
    "Is there any null values in any columns? \n",
    "\n",
    "- Identify those columns\n",
    "- Fill those null values using your own logic\n",
    "    - State your logic behind every steps"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "z9w1AEp_VQRJ"
   },
   "source": [
    "### Age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-30T02:59:01.672353Z",
     "start_time": "2019-09-30T02:59:01.668361Z"
    },
    "colab": {},
    "colab_type": "code",
    "id": "p3Ojk9ZgVQRK"
   },
   "outputs": [],
   "source": [
    "# feel NaN of Age my mean\n",
    "m = df.Age.mean()\n",
    "df.Age=df.Age.fillna(m)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "k-xDz63vVQRM"
   },
   "source": [
    "### Cabin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "df.Cabin.replace([np.nan, 'C85'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
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       "      <th>Name</th>\n",
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       "      <td>Heikkinen, Miss. Laina</td>\n",
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       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
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       "      <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",
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       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>C123</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   NaN        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  C123        S  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[0:2,'Cabin']='C123'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>PassengerId</th>\n",
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       "      <td>22.0</td>\n",
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       "      <td>7.2500</td>\n",
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       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
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       "      <td>C123</td>\n",
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       "</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  C123        S  \n",
       "1      0          PC 17599  71.2833  C123        C  \n",
       "2      0  STON/O2. 3101282   7.9250  C123        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500  C123        S  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "YswR0xl1VQRO"
   },
   "source": [
    "### Embarked"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-30T03:00:37.272457Z",
     "start_time": "2019-09-30T03:00:37.268471Z"
    },
    "colab": {},
    "colab_type": "code",
    "id": "IkLCG8RrVQRP"
   },
   "outputs": [],
   "source": [
    "# ANSWER\n",
    "df.Embarked=df.Embarked.fillna('S')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId    0\n",
       "Survived       0\n",
       "Pclass         0\n",
       "Name           0\n",
       "Sex            0\n",
       "Age            0\n",
       "SibSp          0\n",
       "Parch          0\n",
       "Ticket         0\n",
       "Fare           0\n",
       "Cabin          0\n",
       "Embarked       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Zw-M5KfRVQRR"
   },
   "source": [
    "# Describe\n",
    "\n",
    "Describe your dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-30T03:00:55.748437Z",
     "start_time": "2019-09-30T03:00:55.744449Z"
    },
    "colab": {},
    "colab_type": "code",
    "id": "axUd1m0ZVQRS"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>32.204208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>257.353842</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>13.002015</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>49.693429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>223.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.910400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>668.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
       "count   891.000000  891.000000  891.000000  891.000000  891.000000   \n",
       "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
       "std     257.353842    0.486592    0.836071   13.002015    1.102743   \n",
       "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
       "25%     223.500000    0.000000    2.000000   22.000000    0.000000   \n",
       "50%     446.000000    0.000000    3.000000   29.699118    0.000000   \n",
       "75%     668.500000    1.000000    3.000000   35.000000    1.000000   \n",
       "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
       "\n",
       "            Parch        Fare  \n",
       "count  891.000000  891.000000  \n",
       "mean     0.381594   32.204208  \n",
       "std      0.806057   49.693429  \n",
       "min      0.000000    0.000000  \n",
       "25%      0.000000    7.910400  \n",
       "50%      0.000000   14.454200  \n",
       "75%      0.000000   31.000000  \n",
       "max      6.000000  512.329200  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ANSWER There are in total 891 passengers, average age is 30 years, survival rate is <50%\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>-0.338481</td>\n",
       "      <td>-0.069809</td>\n",
       "      <td>-0.035322</td>\n",
       "      <td>0.081629</td>\n",
       "      <td>0.257307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pclass</th>\n",
       "      <td>-0.035144</td>\n",
       "      <td>-0.338481</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.331339</td>\n",
       "      <td>0.083081</td>\n",
       "      <td>0.018443</td>\n",
       "      <td>-0.549500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>0.033207</td>\n",
       "      <td>-0.069809</td>\n",
       "      <td>-0.331339</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.232625</td>\n",
       "      <td>-0.179191</td>\n",
       "      <td>0.091566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SibSp</th>\n",
       "      <td>-0.057527</td>\n",
       "      <td>-0.035322</td>\n",
       "      <td>0.083081</td>\n",
       "      <td>-0.232625</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.414838</td>\n",
       "      <td>0.159651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Parch</th>\n",
       "      <td>-0.001652</td>\n",
       "      <td>0.081629</td>\n",
       "      <td>0.018443</td>\n",
       "      <td>-0.179191</td>\n",
       "      <td>0.414838</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.216225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fare</th>\n",
       "      <td>0.012658</td>\n",
       "      <td>0.257307</td>\n",
       "      <td>-0.549500</td>\n",
       "      <td>0.091566</td>\n",
       "      <td>0.159651</td>\n",
       "      <td>0.216225</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             PassengerId  Survived    Pclass       Age     SibSp     Parch  \\\n",
       "PassengerId     1.000000 -0.005007 -0.035144  0.033207 -0.057527 -0.001652   \n",
       "Survived       -0.005007  1.000000 -0.338481 -0.069809 -0.035322  0.081629   \n",
       "Pclass         -0.035144 -0.338481  1.000000 -0.331339  0.083081  0.018443   \n",
       "Age             0.033207 -0.069809 -0.331339  1.000000 -0.232625 -0.179191   \n",
       "SibSp          -0.057527 -0.035322  0.083081 -0.232625  1.000000  0.414838   \n",
       "Parch          -0.001652  0.081629  0.018443 -0.179191  0.414838  1.000000   \n",
       "Fare            0.012658  0.257307 -0.549500  0.091566  0.159651  0.216225   \n",
       "\n",
       "                 Fare  \n",
       "PassengerId  0.012658  \n",
       "Survived     0.257307  \n",
       "Pclass      -0.549500  \n",
       "Age          0.091566  \n",
       "SibSp        0.159651  \n",
       "Parch        0.216225  \n",
       "Fare         1.000000  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "-RQ9hmVIVQRU"
   },
   "source": [
    "# Relationship between Features and Survival\n",
    "\n",
    "Find relationship between categorical features and survived.\n",
    "\n",
    "**Describe your findings.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "-LhNetuZVQRV"
   },
   "outputs": [],
   "source": [
    "def bar_charts(df, feature):\n",
    "    '''\n",
    "    Inputs:\n",
    "    df: Dataset\n",
    "    feature: Name of Feature to Check With Survived\n",
    "    '''\n",
    "    \n",
    "    _agg = {\n",
    "        'PassengerId': 'count'\n",
    "    }\n",
    "    _groupby = ['Survived', feature]\n",
    "\n",
    "    df_feature = df.groupby('survived')['']  .agg(_agg)\n",
    "    \n",
    "    ax = df_feature.unstack().plot(kind='bar', figsize=(15,6))\n",
    "    plt.legend(list(df_feature.index.levels[1].unique()))\n",
    "    plt.xlabel('Survived')\n",
    "    plt.xticks(np.arange(2), ('No', 'Yes'))\n",
    "    plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def bar_charts(df, feature):\n",
    "    df.groupby(feature)['Survived'].count()\n",
    "    sns.countplot(data=df, x=feature, hue='Survived')\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "vd9d2cn9VQRX"
   },
   "source": [
    "## Pclass\n",
    "\n",
    "Use barchart to find relationship between survived and pclass.  Note your findings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-30T03:02:04.043957Z",
     "start_time": "2019-09-30T03:02:04.039969Z"
    },
    "colab": {},
    "colab_type": "code",
    "id": "YcvUi3y1VQRY"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot survival rate by Pclass\n",
    "\n",
    "df=df\n",
    "feature = ('Pclass') \n",
    "bar_charts(df,feature )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "zJLXH0HNVQRa"
   },
   "source": [
    "## Sex\n",
    "\n",
    "Use barchart to find relationship between survived and sex.  Note your findings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-30T03:03:23.420605Z",
     "start_time": "2019-09-30T03:03:23.416645Z"
    },
    "colab": {},
    "colab_type": "code",
    "id": "ass51dkPVQRa"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot survival rate based on sex\n",
    "\n",
    "df=df\n",
    "feature = ('Sex') \n",
    "bar_charts(df,feature )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "xGlcY9f2VQRc"
   },
   "source": [
    "## Parch\n",
    "\n",
    "Parch = Number of parents of children travelling with each passenger."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-30T03:03:43.588783Z",
     "start_time": "2019-09-30T03:03:43.584794Z"
    },
    "colab": {},
    "colab_type": "code",
    "id": "ZvGfci3vVQRd"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot survival rate based on parch\n",
    "\n",
    "df=df\n",
    "feature = ('Parch') \n",
    "bar_charts(df,feature )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "hjI-7kutVQRh"
   },
   "source": [
    "## SibSp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot survival rate based on sibsp\n",
    "\n",
    "df=df\n",
    "feature = ('SibSp') \n",
    "bar_charts(df,feature )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Fq63WZRaVQRi"
   },
   "source": [
    "## Embarked"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot survival rate based on embarked\n",
    "\n",
    "df=df\n",
    "feature = ('Embarked') \n",
    "bar_charts(df,feature )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "clJS-gHBVQRk"
   },
   "source": [
    "# Feature Engineering\n",
    "\n",
    "Create some new features from existing feature."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "EsyXiJDJVQRl"
   },
   "source": [
    "## Fare Class\n",
    "\n",
    "Create a new class based on their fare. Is there any relationship between fare and survival? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "snFAHJadVQRl"
   },
   "outputs": [],
   "source": [
    "def create_fare_class(x):\n",
    "    if x > 30:\n",
    "        fare_class = 1\n",
    "    elif x > 20 and x <= 30:\n",
    "        fare_class = 2\n",
    "    elif x > 10 and x <= 20:\n",
    "        fare_class = 3\n",
    "    else:\n",
    "        fare_class = 4\n",
    "    return fare_class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-30T03:05:50.199011Z",
     "start_time": "2019-09-30T03:05:50.195015Z"
    },
    "colab": {},
    "colab_type": "code",
    "id": "LLGlreIuVQRn"
   },
   "outputs": [],
   "source": [
    "# ANSWER\n",
    "fare=[]\n",
    "for i in df.Fare:\n",
    "    t=create_fare_class(i)\n",
    "    fare.append(t)\n",
    "df['New_fare']=fare"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature = ('New_fare') \n",
    "bar_charts(df,feature )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Xb6Rdtw9VQRp"
   },
   "source": [
    "## Age Class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-30T03:18:04.132415Z",
     "start_time": "2019-09-30T03:18:04.128391Z"
    },
    "colab": {},
    "colab_type": "code",
    "id": "2uBfHL0bVQRp"
   },
   "outputs": [],
   "source": [
    "# ANSWER"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "irdrZGC0VQRs"
   },
   "outputs": [],
   "source": [
    "def create_age_class(x):\n",
    "    if x > 60:\n",
    "        age_class = 5\n",
    "    elif x > 35 and x <= 60:\n",
    "        age_class = 4\n",
    "    elif x > 25 and x <= 35:\n",
    "        age_class = 3\n",
    "    elif x > 16 and x <= 25:\n",
    "        age_class = 2\n",
    "    else:\n",
    "        age_class = 1\n",
    "    return age_class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-30T03:18:13.115637Z",
     "start_time": "2019-09-30T03:18:13.112646Z"
    },
    "colab": {},
    "colab_type": "code",
    "id": "nSysIPDYVQRu"
   },
   "outputs": [],
   "source": [
    "# ANSWER\n",
    "age=[]\n",
    "for i in df.Age:\n",
    "    t=create_age_class(i)\n",
    "    age.append(t)\n",
    "df['New_age']=age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot survival rate based age group\n",
    "df=df\n",
    "feature = ('New_age') \n",
    "bar_charts(df,feature )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    373\n",
       "2    201\n",
       "4    195\n",
       "1    100\n",
       "5     22\n",
       "Name: New_age, dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['New_age'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "bHzvfjfAVQRv"
   },
   "source": [
    "# Statistical Overview"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "FEwE4HgHVQRw"
   },
   "source": [
    "## Correlation\n",
    "\n",
    "Find correlation between `survived` and other features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "rdx8aVFNVQRw",
    "outputId": "82c9c672-79e2-4640-ad50-ab64c639eacf"
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PassengerId</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.005007</td>\n",
       "      <td>-0.035144</td>\n",
       "      <td>0.033207</td>\n",
       "      <td>-0.057527</td>\n",
       "      <td>-0.001652</td>\n",
       "      <td>0.012658</td>\n",
       "      <td>0.003258</td>\n",
       "      <td>0.026653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Survived</th>\n",
       "      <td>-0.005007</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.338481</td>\n",
       "      <td>-0.069809</td>\n",
       "      <td>-0.035322</td>\n",
       "      <td>0.081629</td>\n",
       "      <td>0.257307</td>\n",
       "      <td>-0.307600</td>\n",
       "      <td>-0.067911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pclass</th>\n",
       "      <td>-0.035144</td>\n",
       "      <td>-0.338481</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.331339</td>\n",
       "      <td>0.083081</td>\n",
       "      <td>0.018443</td>\n",
       "      <td>-0.549500</td>\n",
       "      <td>0.719670</td>\n",
       "      <td>-0.292128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>0.033207</td>\n",
       "      <td>-0.069809</td>\n",
       "      <td>-0.331339</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.232625</td>\n",
       "      <td>-0.179191</td>\n",
       "      <td>0.091566</td>\n",
       "      <td>-0.108947</td>\n",
       "      <td>0.933632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SibSp</th>\n",
       "      <td>-0.057527</td>\n",
       "      <td>-0.035322</td>\n",
       "      <td>0.083081</td>\n",
       "      <td>-0.232625</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.414838</td>\n",
       "      <td>0.159651</td>\n",
       "      <td>-0.380692</td>\n",
       "      <td>-0.232166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Parch</th>\n",
       "      <td>-0.001652</td>\n",
       "      <td>0.081629</td>\n",
       "      <td>0.018443</td>\n",
       "      <td>-0.179191</td>\n",
       "      <td>0.414838</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.216225</td>\n",
       "      <td>-0.387100</td>\n",
       "      <td>-0.168121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fare</th>\n",
       "      <td>0.012658</td>\n",
       "      <td>0.257307</td>\n",
       "      <td>-0.549500</td>\n",
       "      <td>0.091566</td>\n",
       "      <td>0.159651</td>\n",
       "      <td>0.216225</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.599129</td>\n",
       "      <td>0.075551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New_fare</th>\n",
       "      <td>0.003258</td>\n",
       "      <td>-0.307600</td>\n",
       "      <td>0.719670</td>\n",
       "      <td>-0.108947</td>\n",
       "      <td>-0.380692</td>\n",
       "      <td>-0.387100</td>\n",
       "      <td>-0.599129</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.080506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New_age</th>\n",
       "      <td>0.026653</td>\n",
       "      <td>-0.067911</td>\n",
       "      <td>-0.292128</td>\n",
       "      <td>0.933632</td>\n",
       "      <td>-0.232166</td>\n",
       "      <td>-0.168121</td>\n",
       "      <td>0.075551</td>\n",
       "      <td>-0.080506</td>\n",
       "      <td>1.000000</td>\n",
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       "  </tbody>\n",
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       "</div>"
      ],
      "text/plain": [
       "             PassengerId  Survived    Pclass       Age     SibSp     Parch  \\\n",
       "PassengerId     1.000000 -0.005007 -0.035144  0.033207 -0.057527 -0.001652   \n",
       "Survived       -0.005007  1.000000 -0.338481 -0.069809 -0.035322  0.081629   \n",
       "Pclass         -0.035144 -0.338481  1.000000 -0.331339  0.083081  0.018443   \n",
       "Age             0.033207 -0.069809 -0.331339  1.000000 -0.232625 -0.179191   \n",
       "SibSp          -0.057527 -0.035322  0.083081 -0.232625  1.000000  0.414838   \n",
       "Parch          -0.001652  0.081629  0.018443 -0.179191  0.414838  1.000000   \n",
       "Fare            0.012658  0.257307 -0.549500  0.091566  0.159651  0.216225   \n",
       "New_fare        0.003258 -0.307600  0.719670 -0.108947 -0.380692 -0.387100   \n",
       "New_age         0.026653 -0.067911 -0.292128  0.933632 -0.232166 -0.168121   \n",
       "\n",
       "                 Fare  New_fare   New_age  \n",
       "PassengerId  0.012658  0.003258  0.026653  \n",
       "Survived     0.257307 -0.307600 -0.067911  \n",
       "Pclass      -0.549500  0.719670 -0.292128  \n",
       "Age          0.091566 -0.108947  0.933632  \n",
       "SibSp        0.159651 -0.380692 -0.232166  \n",
       "Parch        0.216225 -0.387100 -0.168121  \n",
       "Fare         1.000000 -0.599129  0.075551  \n",
       "New_fare    -0.599129  1.000000 -0.080506  \n",
       "New_age      0.075551 -0.080506  1.000000  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "MRocCdj0VQRy"
   },
   "source": [
    "# [BONUS] Hypothesis Testing\n",
    "---\n",
    "The usual process of null hypothesis testing consists of four steps.\n",
    "\n",
    "1. Formulate the null hypothesis H_0 (commonly, that the observations are the result of pure chance) and the alternative hypothesis H_a (commonly, that the observations show a real effect combined with a component of chance variation).\n",
    "\n",
    "2. Identify a test statistic that can be used to assess the truth of the null hypothesis.\n",
    "\n",
    "3. Compute the p-value, which is the probability that a test statistic at least as significant as the one observed would be obtained assuming that the null hypothesis were true. The smaller the p-value, the stronger the evidence against the null hypothesis.\n",
    "\n",
    "4. Compare the p-value to an acceptable significance value alpha (sometimes called an alpha value). If p<=alpha, that the observed effect is statistically significant, the null hypothesis is ruled out, and the alternative hypothesis is valid."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "mSyZDV_KVQRz"
   },
   "source": [
    "### Define Hypothesis\n",
    "\n",
    "> Formulate the null hypothesis H_0 (commonly, that the observations are the result of pure chance) and the alternative hypothesis H_a (commonly, that the observations show a real effect combined with a component of chance variation).\n",
    "\n",
    "    Null Hypothesis (H0): There is no difference in the survival rate between the young and old passengers.\n",
    "\n",
    "    Alternative Hypothesis (HA): There is a difference in the survival rate between the young and old passengers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "4F4R2cPoVQR0"
   },
   "source": [
    "### Collect Data\n",
    "\n",
    "Next step is to collect data for each population group. \n",
    "\n",
    "Collect two sets of data, one with the passenger greater than 35 years of age and another one with the passenger younger than 35. The sample size should ideally be the same but it can be different. Lets say that the sample sizes is 100."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\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",
       "      <th>New_fare</th>\n",
       "      <th>New_age</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>C123</td>\n",
       "      <td>S</td>\n",
       "      <td>4</td>\n",
       "      <td>2</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>C123</td>\n",
       "      <td>C</td>\n",
       "      <td>1</td>\n",
       "      <td>4</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>C123</td>\n",
       "      <td>S</td>\n",
       "      <td>4</td>\n",
       "      <td>3</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>1</td>\n",
       "      <td>3</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>C123</td>\n",
       "      <td>S</td>\n",
       "      <td>4</td>\n",
       "      <td>3</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  New_fare  New_age  \n",
       "0      0         A/5 21171   7.2500  C123        S         4        2  \n",
       "1      0          PC 17599  71.2833  C123        C         1        4  \n",
       "2      0  STON/O2. 3101282   7.9250  C123        S         4        3  \n",
       "3      0            113803  53.1000  C123        S         1        3  \n",
       "4      0            373450   8.0500  C123        S         4        3  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # define function to categorize age greater and less than 35\n",
    "\n",
    "# def age_compute (x):\n",
    "#     if x>35:\n",
    "#         y=1\n",
    "#     else: \n",
    "#         x<=35\n",
    "#         y=0\n",
    "#     return y\n",
    "\n",
    "# t=[]\n",
    "# for i in df.Age:\n",
    "#     t.append(age_compute(i))\n",
    "    \n",
    "# df['age_cat']=t\n",
    "\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# categorize age group into less than and greater than 35\n",
    "\n",
    "df['age_cat'] = df['Age'].apply(lambda x: 0 if x <= 35 else 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df=df\n",
    "feature = ('age_cat') \n",
    "bar_charts(df,feature )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "bhRfTi_PVQR4"
   },
   "source": [
    "### Set alpha (let alpha = 0.05)\n",
    "\n",
    "> Identify a test statistic that can be used to assess the truth of the null hypothesis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-30T03:16:00.572626Z",
     "start_time": "2019-09-30T03:16:00.568636Z"
    },
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    "colab_type": "code",
    "id": "8-y-fmZIVQR5"
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   "outputs": [],
   "source": [
    "# ANSWER"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "kwRxiB2ZVQR7"
   },
   "source": [
    "### Calculate point estimate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "pLBXpOF3VQR8"
   },
   "outputs": [],
   "source": [
    "# draw 100 samples of survival rate of young and old, each time do a resampling from the main population pool\n",
    "\n",
    "a = []\n",
    "b = []\n",
    "N=100\n",
    "\n",
    "for k in range(N):\n",
    "    df1=df.sample(100)\n",
    "    ser = df1.groupby('age_cat').Survived.value_counts().unstack()\n",
    "    sur_young = ser.loc[0,1]/ser.loc[0,0:].sum()\n",
    "    a.append(sur_young)\n",
    "    sur_old = ser.loc[1,1]/ser.loc[1,0:].sum()\n",
    "    b.append(sur_old)\n",
    "\n",
    "a = np.array(a)\n",
    "var_a = a.var(ddof=1)\n",
    "s_a = np.sqrt(var_a)\n",
    "\n",
    "b = np.array(b)\n",
    "var_b = b.var(ddof=1)\n",
    "s_b = np.sqrt(var_b)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "-jEn1ds-VQSA"
   },
   "source": [
    "### Calculate test statistic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "s3SxkRBGVQSB"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.17437274974881803"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Calculate the t-statistic\n",
    "N=100\n",
    "t = (a.mean() - b.mean()) / (np.sqrt((s_a**2/N)+(s_b**2/N)))\n",
    "t"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "w0c8ZzCUVQSC"
   },
   "source": [
    "### Find the p-value\n",
    "\n",
    "> Compute the P-value, which is the probability that a test statistic at least as significant as the one observed would be obtained assuming that the null hypothesis were true. The smaller the P-value, the stronger the evidence against the null hypothesis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Compare with the critical t-value\n",
    "## Degrees of freedom\n",
    "df = (N-2)+(N-2)\n",
    "\n",
    "#p-value after comparison with the t \n",
    "if (t > 0):\n",
    "    p = 1 - stats.t.cdf(t, df = df) \n",
    "else:\n",
    "    p = stats.t.cdf(t, df = df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "YZsL2DI6VQSG",
    "outputId": "ea730196-3e4c-45f8-c74b-c87851cfad25"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "t = -0.17437274974881803\n",
      "p = 0.4308762542474879\n"
     ]
    }
   ],
   "source": [
    "print(\"t = \" + str(t))\n",
    "print(\"p = \" + str(p))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "zYAt-GyOVQSK"
   },
   "source": [
    "### Interpret results\n",
    "\n",
    "> Compare the p-value to an acceptable significance value  alpha (sometimes called an alpha value). If p<=alpha, that the observed effect is statistically significant, the null hypothesis is ruled out, and the alternative hypothesis is valid."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "9a_HkzglVQSK"
   },
   "outputs": [],
   "source": [
    "alpha = .05\n",
    "\n",
    "def print_sig(p_value, alpha):\n",
    "    if p_value < alpha:\n",
    "        print(\"We reject our null hypothesis.\")\n",
    "    elif p_value > alpha:\n",
    "        print(\"We fail to reject our null hypothesis.\")\n",
    "    else:\n",
    "        print(\"Our test is inconclusive.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "j2QHMBE6VQSM",
    "outputId": "28e3546f-edb7-4ab6-e5f2-adf39fbbfe6f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "t = -0.174372749748818\n",
      "p = 0.8617506917056161\n",
      "We fail to reject our null hypothesis.\n"
     ]
    }
   ],
   "source": [
    "# ## Cross Checking with the internal scipy function\n",
    "t2, p2 = stats.ttest_ind(a,b)\n",
    "print(\"t = \" + str(t2))\n",
    "print(\"p = \" + str(p2))\n",
    "\n",
    "print_sig(p2, alpha)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "HdJ9hO0Emlfq"
   },
   "source": [
    "\n",
    "\n",
    "---\n",
    "\n",
    "\n",
    "\n",
    "---\n",
    "\n",
    "\n",
    "\n",
    "> > > > > > > > > © 2021 Institute of Data\n",
    "\n",
    "\n",
    "---\n",
    "\n",
    "\n",
    "\n",
    "---\n",
    "\n",
    "\n",
    "\n"
   ]
  }
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