200968268_L3_Ex1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f5a1bde0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "78d161b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic = pd.read_csv('train.csv')\n",
    "titanic.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ac892831",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId    891\n",
       "Survived       891\n",
       "Pclass         891\n",
       "Name           891\n",
       "Sex            891\n",
       "Age            714\n",
       "SibSp          891\n",
       "Parch          891\n",
       "Ticket         891\n",
       "Fare           891\n",
       "Cabin          204\n",
       "Embarked       889\n",
       "dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "560c344f",
   "metadata": {},
   "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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.isnull().sum()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "18b177b9",
   "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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.dropna(inplace=True)\n",
    "\n",
    "titanic.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "13f11f56",
   "metadata": {},
   "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>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\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>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>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>10</th>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Sandstrom, Miss. Marguerite Rut</td>\n",
       "      <td>female</td>\n",
       "      <td>4.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Bonnell, Miss. Elizabeth</td>\n",
       "      <td>female</td>\n",
       "      <td>58.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Beesley, Mr. Lawrence</td>\n",
       "      <td>male</td>\n",
       "      <td>34.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Sloper, Mr. William Thompson</td>\n",
       "      <td>male</td>\n",
       "      <td>28.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Fortune, Mr. Charles Alexander</td>\n",
       "      <td>male</td>\n",
       "      <td>19.0</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>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>49.0</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>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>65.0</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>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>45.0</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>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>29.0</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>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>25.0</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>88</th>\n",
       "      <td>89</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Fortune, Miss. Mabel Helen</td>\n",
       "      <td>female</td>\n",
       "      <td>23.0</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>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>93</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Chaffee, Mr. Herbert Fuller</td>\n",
       "      <td>male</td>\n",
       "      <td>46.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>W.E.P. 5734</td>\n",
       "      <td>61.1750</td>\n",
       "      <td>E31</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>97</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Goldschmidt, Mr. George B</td>\n",
       "      <td>male</td>\n",
       "      <td>71.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>98</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Greenfield, Mr. William Bertram</td>\n",
       "      <td>male</td>\n",
       "      <td>23.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>103</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>White, Mr. Richard Frasar</td>\n",
       "      <td>male</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>35281</td>\n",
       "      <td>77.2875</td>\n",
       "      <td>D26</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>111</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Porter, Mr. Walter Chamberlain</td>\n",
       "      <td>male</td>\n",
       "      <td>47.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>110465</td>\n",
       "      <td>52.0000</td>\n",
       "      <td>C110</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>119</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Baxter, Mr. Quigg Edmond</td>\n",
       "      <td>male</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>PC 17558</td>\n",
       "      <td>247.5208</td>\n",
       "      <td>B58 B60</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass  \\\n",
       "1              2         1       1   \n",
       "3              4         1       1   \n",
       "6              7         0       1   \n",
       "10            11         1       3   \n",
       "11            12         1       1   \n",
       "21            22         1       2   \n",
       "23            24         1       1   \n",
       "27            28         0       1   \n",
       "52            53         1       1   \n",
       "54            55         0       1   \n",
       "62            63         0       1   \n",
       "66            67         1       2   \n",
       "75            76         0       3   \n",
       "88            89         1       1   \n",
       "92            93         0       1   \n",
       "96            97         0       1   \n",
       "97            98         1       1   \n",
       "102          103         0       1   \n",
       "110          111         0       1   \n",
       "118          119         0       1   \n",
       "\n",
       "                                                  Name     Sex   Age  SibSp  \\\n",
       "1    Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "6                              McCarthy, Mr. Timothy J    male  54.0      0   \n",
       "10                     Sandstrom, Miss. Marguerite Rut  female   4.0      1   \n",
       "11                            Bonnell, Miss. Elizabeth  female  58.0      0   \n",
       "21                               Beesley, Mr. Lawrence    male  34.0      0   \n",
       "23                        Sloper, Mr. William Thompson    male  28.0      0   \n",
       "27                      Fortune, Mr. Charles Alexander    male  19.0      3   \n",
       "52            Harper, Mrs. Henry Sleeper (Myna Haxtun)  female  49.0      1   \n",
       "54                      Ostby, Mr. Engelhart Cornelius    male  65.0      0   \n",
       "62                         Harris, Mr. Henry Birkhardt    male  45.0      1   \n",
       "66                        Nye, Mrs. (Elizabeth Ramell)  female  29.0      0   \n",
       "75                             Moen, Mr. Sigurd Hansen    male  25.0      0   \n",
       "88                          Fortune, Miss. Mabel Helen  female  23.0      3   \n",
       "92                         Chaffee, Mr. Herbert Fuller    male  46.0      1   \n",
       "96                           Goldschmidt, Mr. George B    male  71.0      0   \n",
       "97                     Greenfield, Mr. William Bertram    male  23.0      0   \n",
       "102                          White, Mr. Richard Frasar    male  21.0      0   \n",
       "110                     Porter, Mr. Walter Chamberlain    male  47.0      0   \n",
       "118                           Baxter, Mr. Quigg Edmond    male  24.0      0   \n",
       "\n",
       "     Parch       Ticket      Fare        Cabin Embarked  \n",
       "1        0     PC 17599   71.2833          C85        C  \n",
       "3        0       113803   53.1000         C123        S  \n",
       "6        0        17463   51.8625          E46        S  \n",
       "10       1      PP 9549   16.7000           G6        S  \n",
       "11       0       113783   26.5500         C103        S  \n",
       "21       0       248698   13.0000          D56        S  \n",
       "23       0       113788   35.5000           A6        S  \n",
       "27       2        19950  263.0000  C23 C25 C27        S  \n",
       "52       0     PC 17572   76.7292          D33        C  \n",
       "54       1       113509   61.9792          B30        C  \n",
       "62       0        36973   83.4750          C83        S  \n",
       "66       0   C.A. 29395   10.5000          F33        S  \n",
       "75       0       348123    7.6500        F G73        S  \n",
       "88       2        19950  263.0000  C23 C25 C27        S  \n",
       "92       0  W.E.P. 5734   61.1750          E31        S  \n",
       "96       0     PC 17754   34.6542           A5        C  \n",
       "97       1     PC 17759   63.3583      D10 D12        C  \n",
       "102      1        35281   77.2875          D26        S  \n",
       "110      0       110465   52.0000         C110        S  \n",
       "118      1     PC 17558  247.5208      B58 B60        C  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e15fbb67",
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic['Family'] = titanic['Parch'] + titanic['SibSp']\n",
    "titanic['has_cabin'] = np.where(titanic['Cabin'].isnull(),1,0)\n",
    "titanic =titanic.drop(columns=['Ticket', 'PassengerId', 'Name', 'Parch', 'SibSp', 'Cabin'])\n",
    "titanic['Sex'] = titanic['Sex'].map({'male':0, 'female':1})\n",
    "titanic['Embarked'] = titanic['Embarked'].map({'C':0, 'Q':1, 'S':2})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4455a17e",
   "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>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Family</th>\n",
       "      <th>has_cabin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>51.8625</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>16.7000</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>58.0</td>\n",
       "      <td>26.5500</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Survived  Pclass  Sex   Age     Fare  Embarked  Family  has_cabin\n",
       "1          1       1    1  38.0  71.2833         0       1          0\n",
       "3          1       1    1  35.0  53.1000         2       1          0\n",
       "6          0       1    0  54.0  51.8625         2       0          0\n",
       "10         1       3    1   4.0  16.7000         2       2          0\n",
       "11         1       1    1  58.0  26.5500         2       0          0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f701309f",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = titanic.loc[:, titanic.columns != 'Survived']\n",
    "y_train = titanic['Survived']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5f54904f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.pipeline import Pipeline\n",
    "clf = Pipeline([\n",
    "    ('nb',GaussianNB())\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d5239266",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('nb', GaussianNB())])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7bdf3fe6",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "score = cross_val_score(GaussianNB(),x_train, y_train, cv=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "be8c3b03",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7594594594594595"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "2648d171",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_predict\n",
    "from sklearn.metrics import confusion_matrix\n",
    "y_pred = cross_val_predict(clf, x_train, y_train, cv=10)\n",
    "cm = confusion_matrix(y_train, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d832b017",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy =  0.7650273224043715\n",
      "TPR =  0.7398373983739838\n",
      "TNR =  0.8166666666666667\n"
     ]
    }
   ],
   "source": [
    "tn = cm[0,0]\n",
    "fp = cm[0,1]\n",
    "fn = cm[1,0]\n",
    "tp = cm[1,1]\n",
    "acc = (tp+tn)/(tp+tn+fp+fn)\n",
    "tpr = tp/(fn+tp)\n",
    "tnr = tn/(tn+fp)\n",
    "\n",
    "print(\"accuracy = \", acc)\n",
    "print(\"TPR = \", tpr)\n",
    "print(\"TNR = \", tnr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a225688c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve, roc_auc_score\n",
    "falsepr, truepr, _ = roc_curve(y_train, y_pred)\n",
    "plt.plot(falsepr,truepr)\n",
    "plt.xlabel(\"FPR\")\n",
    "plt.ylabel(\"TPR\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "79217eb4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7782520325203252"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r =  roc_auc_score(y_train,y_pred)\n",
    "r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0e721621",
   "metadata": {},
   "outputs": [],
   "source": [
    "#in general, a curve moving towards the top and left of and roc curve means the classifier is better \n",
    "#area under the curve is directly proportional to how good the classifier is\n",
    "#a 45 degree line corresponds to random predictions\n",
    "#The area under the curve is 0.78, indicating it is a decently accurate model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "56db6d24",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}