Titanic_Logistic+regression.ipynb

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    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy as sp\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline"
   ]
  },
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    "## ETL of Titanic data\n",
    "\n",
    "Firstly, we carried out the Extraction, Transformation and Loading (ETL) of the Titanic data. Then, we carried out data wrangling (cleaning). Finally, we conduct logistic regression analysis to predict the survival of passengers in Titanic. More importanatly, the precision and accuracy of the predictive model were established. "
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   "source": [
    "Tb = pd.read_excel(r'C:\\Users\\PC\\Downloads\\titanic3.xls')"
   ]
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pclass</th>\n",
       "      <th>survived</th>\n",
       "      <th>name</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
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       "      <th>fare</th>\n",
       "      <th>cabin</th>\n",
       "      <th>embarked</th>\n",
       "      <th>boat</th>\n",
       "      <th>body</th>\n",
       "      <th>home.dest</th>\n",
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       "      <td>Allen, Miss. Elisabeth Walton</td>\n",
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       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Allison, Mrs. Hudson J C (Bessie Waldo Daniels)</td>\n",
       "      <td>female</td>\n",
       "      <td>25.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>C22 C26</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
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       "   pclass  survived                                             name     sex  \\\n",
       "0       1         1                    Allen, Miss. Elisabeth Walton  female   \n",
       "1       1         1                   Allison, Master. Hudson Trevor    male   \n",
       "2       1         0                     Allison, Miss. Helen Loraine  female   \n",
       "3       1         0             Allison, Mr. Hudson Joshua Creighton    male   \n",
       "4       1         0  Allison, Mrs. Hudson J C (Bessie Waldo Daniels)  female   \n",
       "\n",
       "       age  sibsp  parch  ticket      fare    cabin embarked boat   body  \\\n",
       "0  29.0000      0      0   24160  211.3375       B5        S    2    NaN   \n",
       "1   0.9167      1      2  113781  151.5500  C22 C26        S   11    NaN   \n",
       "2   2.0000      1      2  113781  151.5500  C22 C26        S  NaN    NaN   \n",
       "3  30.0000      1      2  113781  151.5500  C22 C26        S  NaN  135.0   \n",
       "4  25.0000      1      2  113781  151.5500  C22 C26        S  NaN    NaN   \n",
       "\n",
       "                         home.dest  \n",
       "0                     St Louis, MO  \n",
       "1  Montreal, PQ / Chesterville, ON  \n",
       "2  Montreal, PQ / Chesterville, ON  \n",
       "3  Montreal, PQ / Chesterville, ON  \n",
       "4  Montreal, PQ / Chesterville, ON  "
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   "source": [
    "# To view the first few rows.\n",
    "\n",
    "Tb.head(5)"
   ]
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1309 entries, 0 to 1308\n",
      "Data columns (total 14 columns):\n",
      "pclass       1309 non-null int64\n",
      "survived     1309 non-null int64\n",
      "name         1309 non-null object\n",
      "sex          1309 non-null object\n",
      "age          1046 non-null float64\n",
      "sibsp        1309 non-null int64\n",
      "parch        1309 non-null int64\n",
      "ticket       1309 non-null object\n",
      "fare         1308 non-null float64\n",
      "cabin        295 non-null object\n",
      "embarked     1307 non-null object\n",
      "boat         486 non-null object\n",
      "body         121 non-null float64\n",
      "home.dest    745 non-null object\n",
      "dtypes: float64(3), int64(4), object(7)\n",
      "memory usage: 143.2+ KB\n"
     ]
    }
   ],
   "source": [
    "# To determine the dimension of the Titanic dataset\n",
    "\n",
    "Tb.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
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    "deletable": true,
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#  of passengers in the Titanic data:1309\n"
     ]
    }
   ],
   "source": [
    "# To dertermine the number of passegers that boarded the titanic ship\n",
    "\n",
    "print('#  of passengers in the Titanic data:' +str(len(Tb.index)))\n",
    "\n"
   ]
  },
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   "cell_type": "code",
   "execution_count": 6,
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   "source": [
    "# To determine the categorical variables/attributtes of the Titanic dataset\n",
    "\n",
    "Categorical_attributes = Tb.select_dtypes(include= ['object'])\n",
    "\n",
    "\n",
    "# We observe that there are 7 categorical attributes in the data; namely; name, sex, ticket, cabin, embarked, boat, home dest."
   ]
  },
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   "cell_type": "code",
   "execution_count": 7,
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       "      <td>A36</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Belfast, NI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Appleton, Mrs. Edward Dale (Charlotte Lamson)</td>\n",
       "      <td>female</td>\n",
       "      <td>11769</td>\n",
       "      <td>C101</td>\n",
       "      <td>S</td>\n",
       "      <td>D</td>\n",
       "      <td>Bayside, Queens, NY</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Artagaveytia, Mr. Ramon</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Montevideo, Uruguay</td>\n",
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       "      <th>10</th>\n",
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       "      <td>male</td>\n",
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       "      <td>C62 C64</td>\n",
       "      <td>C</td>\n",
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       "      <td>New York, NY</td>\n",
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       "      <td>Astor, Mrs. John Jacob (Madeleine Talmadge Force)</td>\n",
       "      <td>female</td>\n",
       "      <td>PC 17757</td>\n",
       "      <td>C62 C64</td>\n",
       "      <td>C</td>\n",
       "      <td>4</td>\n",
       "      <td>New York, NY</td>\n",
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       "      <td>Aubart, Mme. Leontine Pauline</td>\n",
       "      <td>female</td>\n",
       "      <td>PC 17477</td>\n",
       "      <td>B35</td>\n",
       "      <td>C</td>\n",
       "      <td>9</td>\n",
       "      <td>Paris, France</td>\n",
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       "      <th>13</th>\n",
       "      <td>Barber, Miss. Ellen \"Nellie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>19877</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>27042</td>\n",
       "      <td>A23</td>\n",
       "      <td>S</td>\n",
       "      <td>B</td>\n",
       "      <td>Hessle, Yorks</td>\n",
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       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Baumann, Mr. John D</td>\n",
       "      <td>male</td>\n",
       "      <td>PC 17318</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>New York, NY</td>\n",
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       "      <td>Baxter, Mr. Quigg Edmond</td>\n",
       "      <td>male</td>\n",
       "      <td>PC 17558</td>\n",
       "      <td>B58 B60</td>\n",
       "      <td>C</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Montreal, PQ</td>\n",
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       "      <th>17</th>\n",
       "      <td>Baxter, Mrs. James (Helene DeLaudeniere Chaput)</td>\n",
       "      <td>female</td>\n",
       "      <td>PC 17558</td>\n",
       "      <td>B58 B60</td>\n",
       "      <td>C</td>\n",
       "      <td>6</td>\n",
       "      <td>Montreal, PQ</td>\n",
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       "      <th>18</th>\n",
       "      <td>Bazzani, Miss. Albina</td>\n",
       "      <td>female</td>\n",
       "      <td>11813</td>\n",
       "      <td>D15</td>\n",
       "      <td>C</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Beattie, Mr. Thomson</td>\n",
       "      <td>male</td>\n",
       "      <td>13050</td>\n",
       "      <td>C6</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>Winnipeg, MN</td>\n",
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       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Beckwith, Mr. Richard Leonard</td>\n",
       "      <td>male</td>\n",
       "      <td>11751</td>\n",
       "      <td>D35</td>\n",
       "      <td>S</td>\n",
       "      <td>5</td>\n",
       "      <td>New York, NY</td>\n",
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       "      <td>Beckwith, Mrs. Richard Leonard (Sallie Monypeny)</td>\n",
       "      <td>female</td>\n",
       "      <td>11751</td>\n",
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       "      <td>S</td>\n",
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       "      <td>New York, NY</td>\n",
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       "      <td>Behr, Mr. Karl Howell</td>\n",
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       "      <td>111369</td>\n",
       "      <td>C148</td>\n",
       "      <td>C</td>\n",
       "      <td>5</td>\n",
       "      <td>New York, NY</td>\n",
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       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Bidois, Miss. Rosalie</td>\n",
       "      <td>female</td>\n",
       "      <td>PC 17757</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Bird, Miss. Ellen</td>\n",
       "      <td>female</td>\n",
       "      <td>PC 17483</td>\n",
       "      <td>C97</td>\n",
       "      <td>S</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Birnbaum, Mr. Jakob</td>\n",
       "      <td>male</td>\n",
       "      <td>13905</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>NaN</td>\n",
       "      <td>San Francisco, CA</td>\n",
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       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Bishop, Mr. Dickinson H</td>\n",
       "      <td>male</td>\n",
       "      <td>11967</td>\n",
       "      <td>B49</td>\n",
       "      <td>C</td>\n",
       "      <td>7</td>\n",
       "      <td>Dowagiac, MI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Bishop, Mrs. Dickinson H (Helen Walton)</td>\n",
       "      <td>female</td>\n",
       "      <td>11967</td>\n",
       "      <td>B49</td>\n",
       "      <td>C</td>\n",
       "      <td>7</td>\n",
       "      <td>Dowagiac, MI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Bissette, Miss. Amelia</td>\n",
       "      <td>female</td>\n",
       "      <td>PC 17760</td>\n",
       "      <td>C99</td>\n",
       "      <td>S</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Bjornstrom-Steffansson, Mr. Mauritz Hakan</td>\n",
       "      <td>male</td>\n",
       "      <td>110564</td>\n",
       "      <td>C52</td>\n",
       "      <td>S</td>\n",
       "      <td>D</td>\n",
       "      <td>Stockholm, Sweden / Washington, DC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1279</th>\n",
       "      <td>Vestrom, Miss. Hulda Amanda Adolfina</td>\n",
       "      <td>female</td>\n",
       "      <td>350406</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1280</th>\n",
       "      <td>Vovk, Mr. Janko</td>\n",
       "      <td>male</td>\n",
       "      <td>349252</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1281</th>\n",
       "      <td>Waelens, Mr. Achille</td>\n",
       "      <td>male</td>\n",
       "      <td>345767</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Antwerp, Belgium / Stanton, OH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1282</th>\n",
       "      <td>Ware, Mr. Frederick</td>\n",
       "      <td>male</td>\n",
       "      <td>359309</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1283</th>\n",
       "      <td>Warren, Mr. Charles William</td>\n",
       "      <td>male</td>\n",
       "      <td>C.A. 49867</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1284</th>\n",
       "      <td>Webber, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>SOTON/OQ 3101316</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1285</th>\n",
       "      <td>Wenzel, Mr. Linhart</td>\n",
       "      <td>male</td>\n",
       "      <td>345775</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1286</th>\n",
       "      <td>Whabee, Mrs. George Joseph (Shawneene Abi-Saab)</td>\n",
       "      <td>female</td>\n",
       "      <td>2688</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>C</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1287</th>\n",
       "      <td>Widegren, Mr. Carl/Charles Peter</td>\n",
       "      <td>male</td>\n",
       "      <td>347064</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1288</th>\n",
       "      <td>Wiklund, Mr. Jakob Alfred</td>\n",
       "      <td>male</td>\n",
       "      <td>3101267</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1289</th>\n",
       "      <td>Wiklund, Mr. Karl Johan</td>\n",
       "      <td>male</td>\n",
       "      <td>3101266</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1290</th>\n",
       "      <td>Wilkes, Mrs. James (Ellen Needs)</td>\n",
       "      <td>female</td>\n",
       "      <td>363272</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1291</th>\n",
       "      <td>Willer, Mr. Aaron (\"Abi Weller\")</td>\n",
       "      <td>male</td>\n",
       "      <td>3410</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1292</th>\n",
       "      <td>Willey, Mr. Edward</td>\n",
       "      <td>male</td>\n",
       "      <td>S.O./P.P. 751</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1293</th>\n",
       "      <td>Williams, Mr. Howard Hugh \"Harry\"</td>\n",
       "      <td>male</td>\n",
       "      <td>A/5 2466</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1294</th>\n",
       "      <td>Williams, Mr. Leslie</td>\n",
       "      <td>male</td>\n",
       "      <td>54636</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1295</th>\n",
       "      <td>Windelov, Mr. Einar</td>\n",
       "      <td>male</td>\n",
       "      <td>SOTON/OQ 3101317</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1296</th>\n",
       "      <td>Wirz, Mr. Albert</td>\n",
       "      <td>male</td>\n",
       "      <td>315154</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1297</th>\n",
       "      <td>Wiseman, Mr. Phillippe</td>\n",
       "      <td>male</td>\n",
       "      <td>A/4. 34244</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1298</th>\n",
       "      <td>Wittevrongel, Mr. Camille</td>\n",
       "      <td>male</td>\n",
       "      <td>345771</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1299</th>\n",
       "      <td>Yasbeck, Mr. Antoni</td>\n",
       "      <td>male</td>\n",
       "      <td>2659</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>C</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1300</th>\n",
       "      <td>Yasbeck, Mrs. Antoni (Selini Alexander)</td>\n",
       "      <td>female</td>\n",
       "      <td>2659</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1301</th>\n",
       "      <td>Youseff, Mr. Gerious</td>\n",
       "      <td>male</td>\n",
       "      <td>2628</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1302</th>\n",
       "      <td>Yousif, Mr. Wazli</td>\n",
       "      <td>male</td>\n",
       "      <td>2647</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1303</th>\n",
       "      <td>Yousseff, Mr. Gerious</td>\n",
       "      <td>male</td>\n",
       "      <td>2627</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1304</th>\n",
       "      <td>Zabour, Miss. Hileni</td>\n",
       "      <td>female</td>\n",
       "      <td>2665</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1305</th>\n",
       "      <td>Zabour, Miss. Thamine</td>\n",
       "      <td>female</td>\n",
       "      <td>2665</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1306</th>\n",
       "      <td>Zakarian, Mr. Mapriededer</td>\n",
       "      <td>male</td>\n",
       "      <td>2656</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1307</th>\n",
       "      <td>Zakarian, Mr. Ortin</td>\n",
       "      <td>male</td>\n",
       "      <td>2670</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1308</th>\n",
       "      <td>Zimmerman, Mr. Leo</td>\n",
       "      <td>male</td>\n",
       "      <td>315082</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1309 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   name     sex  \\\n",
       "0                         Allen, Miss. Elisabeth Walton  female   \n",
       "1                        Allison, Master. Hudson Trevor    male   \n",
       "2                          Allison, Miss. Helen Loraine  female   \n",
       "3                  Allison, Mr. Hudson Joshua Creighton    male   \n",
       "4       Allison, Mrs. Hudson J C (Bessie Waldo Daniels)  female   \n",
       "5                                   Anderson, Mr. Harry    male   \n",
       "6                     Andrews, Miss. Kornelia Theodosia  female   \n",
       "7                                Andrews, Mr. Thomas Jr    male   \n",
       "8         Appleton, Mrs. Edward Dale (Charlotte Lamson)  female   \n",
       "9                               Artagaveytia, Mr. Ramon    male   \n",
       "10                               Astor, Col. John Jacob    male   \n",
       "11    Astor, Mrs. John Jacob (Madeleine Talmadge Force)  female   \n",
       "12                        Aubart, Mme. Leontine Pauline  female   \n",
       "13                         Barber, Miss. Ellen \"Nellie\"  female   \n",
       "14                 Barkworth, Mr. Algernon Henry Wilson    male   \n",
       "15                                  Baumann, Mr. John D    male   \n",
       "16                             Baxter, Mr. Quigg Edmond    male   \n",
       "17      Baxter, Mrs. James (Helene DeLaudeniere Chaput)  female   \n",
       "18                                Bazzani, Miss. Albina  female   \n",
       "19                                 Beattie, Mr. Thomson    male   \n",
       "20                        Beckwith, Mr. Richard Leonard    male   \n",
       "21     Beckwith, Mrs. Richard Leonard (Sallie Monypeny)  female   \n",
       "22                                Behr, Mr. Karl Howell    male   \n",
       "23                                Bidois, Miss. Rosalie  female   \n",
       "24                                    Bird, Miss. Ellen  female   \n",
       "25                                  Birnbaum, Mr. Jakob    male   \n",
       "26                              Bishop, Mr. Dickinson H    male   \n",
       "27              Bishop, Mrs. Dickinson H (Helen Walton)  female   \n",
       "28                               Bissette, Miss. Amelia  female   \n",
       "29            Bjornstrom-Steffansson, Mr. Mauritz Hakan    male   \n",
       "...                                                 ...     ...   \n",
       "1279               Vestrom, Miss. Hulda Amanda Adolfina  female   \n",
       "1280                                    Vovk, Mr. Janko    male   \n",
       "1281                               Waelens, Mr. Achille    male   \n",
       "1282                                Ware, Mr. Frederick    male   \n",
       "1283                        Warren, Mr. Charles William    male   \n",
       "1284                                  Webber, Mr. James    male   \n",
       "1285                                Wenzel, Mr. Linhart    male   \n",
       "1286    Whabee, Mrs. George Joseph (Shawneene Abi-Saab)  female   \n",
       "1287                   Widegren, Mr. Carl/Charles Peter    male   \n",
       "1288                          Wiklund, Mr. Jakob Alfred    male   \n",
       "1289                            Wiklund, Mr. Karl Johan    male   \n",
       "1290                   Wilkes, Mrs. James (Ellen Needs)  female   \n",
       "1291                   Willer, Mr. Aaron (\"Abi Weller\")    male   \n",
       "1292                                 Willey, Mr. Edward    male   \n",
       "1293                  Williams, Mr. Howard Hugh \"Harry\"    male   \n",
       "1294                               Williams, Mr. Leslie    male   \n",
       "1295                                Windelov, Mr. Einar    male   \n",
       "1296                                   Wirz, Mr. Albert    male   \n",
       "1297                             Wiseman, Mr. Phillippe    male   \n",
       "1298                          Wittevrongel, Mr. Camille    male   \n",
       "1299                                Yasbeck, Mr. Antoni    male   \n",
       "1300            Yasbeck, Mrs. Antoni (Selini Alexander)  female   \n",
       "1301                               Youseff, Mr. Gerious    male   \n",
       "1302                                  Yousif, Mr. Wazli    male   \n",
       "1303                              Yousseff, Mr. Gerious    male   \n",
       "1304                               Zabour, Miss. Hileni  female   \n",
       "1305                              Zabour, Miss. Thamine  female   \n",
       "1306                          Zakarian, Mr. Mapriededer    male   \n",
       "1307                                Zakarian, Mr. Ortin    male   \n",
       "1308                                 Zimmerman, Mr. Leo    male   \n",
       "\n",
       "                ticket    cabin embarked boat  \\\n",
       "0                24160       B5        S    2   \n",
       "1               113781  C22 C26        S   11   \n",
       "2               113781  C22 C26        S  NaN   \n",
       "3               113781  C22 C26        S  NaN   \n",
       "4               113781  C22 C26        S  NaN   \n",
       "5                19952      E12        S    3   \n",
       "6                13502       D7        S   10   \n",
       "7               112050      A36        S  NaN   \n",
       "8                11769     C101        S    D   \n",
       "9             PC 17609      NaN        C  NaN   \n",
       "10            PC 17757  C62 C64        C  NaN   \n",
       "11            PC 17757  C62 C64        C    4   \n",
       "12            PC 17477      B35        C    9   \n",
       "13               19877      NaN        S    6   \n",
       "14               27042      A23        S    B   \n",
       "15            PC 17318      NaN        S  NaN   \n",
       "16            PC 17558  B58 B60        C  NaN   \n",
       "17            PC 17558  B58 B60        C    6   \n",
       "18               11813      D15        C    8   \n",
       "19               13050       C6        C    A   \n",
       "20               11751      D35        S    5   \n",
       "21               11751      D35        S    5   \n",
       "22              111369     C148        C    5   \n",
       "23            PC 17757      NaN        C    4   \n",
       "24            PC 17483      C97        S    8   \n",
       "25               13905      NaN        C  NaN   \n",
       "26               11967      B49        C    7   \n",
       "27               11967      B49        C    7   \n",
       "28            PC 17760      C99        S    8   \n",
       "29              110564      C52        S    D   \n",
       "...                ...      ...      ...  ...   \n",
       "1279            350406      NaN        S  NaN   \n",
       "1280            349252      NaN        S  NaN   \n",
       "1281            345767      NaN        S  NaN   \n",
       "1282            359309      NaN        S  NaN   \n",
       "1283        C.A. 49867      NaN        S  NaN   \n",
       "1284  SOTON/OQ 3101316      NaN        S  NaN   \n",
       "1285            345775      NaN        S  NaN   \n",
       "1286              2688      NaN        C    C   \n",
       "1287            347064      NaN        S  NaN   \n",
       "1288           3101267      NaN        S  NaN   \n",
       "1289           3101266      NaN        S  NaN   \n",
       "1290            363272      NaN        S  NaN   \n",
       "1291              3410      NaN        S  NaN   \n",
       "1292     S.O./P.P. 751      NaN        S  NaN   \n",
       "1293          A/5 2466      NaN        S  NaN   \n",
       "1294             54636      NaN        S  NaN   \n",
       "1295  SOTON/OQ 3101317      NaN        S  NaN   \n",
       "1296            315154      NaN        S  NaN   \n",
       "1297        A/4. 34244      NaN        S  NaN   \n",
       "1298            345771      NaN        S  NaN   \n",
       "1299              2659      NaN        C    C   \n",
       "1300              2659      NaN        C  NaN   \n",
       "1301              2628      NaN        C  NaN   \n",
       "1302              2647      NaN        C  NaN   \n",
       "1303              2627      NaN        C  NaN   \n",
       "1304              2665      NaN        C  NaN   \n",
       "1305              2665      NaN        C  NaN   \n",
       "1306              2656      NaN        C  NaN   \n",
       "1307              2670      NaN        C  NaN   \n",
       "1308            315082      NaN        S  NaN   \n",
       "\n",
       "                               home.dest  \n",
       "0                           St Louis, MO  \n",
       "1        Montreal, PQ / Chesterville, ON  \n",
       "2        Montreal, PQ / Chesterville, ON  \n",
       "3        Montreal, PQ / Chesterville, ON  \n",
       "4        Montreal, PQ / Chesterville, ON  \n",
       "5                           New York, NY  \n",
       "6                             Hudson, NY  \n",
       "7                            Belfast, NI  \n",
       "8                    Bayside, Queens, NY  \n",
       "9                    Montevideo, Uruguay  \n",
       "10                          New York, NY  \n",
       "11                          New York, NY  \n",
       "12                         Paris, France  \n",
       "13                                   NaN  \n",
       "14                         Hessle, Yorks  \n",
       "15                          New York, NY  \n",
       "16                          Montreal, PQ  \n",
       "17                          Montreal, PQ  \n",
       "18                                   NaN  \n",
       "19                          Winnipeg, MN  \n",
       "20                          New York, NY  \n",
       "21                          New York, NY  \n",
       "22                          New York, NY  \n",
       "23                                   NaN  \n",
       "24                                   NaN  \n",
       "25                     San Francisco, CA  \n",
       "26                          Dowagiac, MI  \n",
       "27                          Dowagiac, MI  \n",
       "28                                   NaN  \n",
       "29    Stockholm, Sweden / Washington, DC  \n",
       "...                                  ...  \n",
       "1279                                 NaN  \n",
       "1280                                 NaN  \n",
       "1281      Antwerp, Belgium / Stanton, OH  \n",
       "1282                                 NaN  \n",
       "1283                                 NaN  \n",
       "1284                                 NaN  \n",
       "1285                                 NaN  \n",
       "1286                                 NaN  \n",
       "1287                                 NaN  \n",
       "1288                                 NaN  \n",
       "1289                                 NaN  \n",
       "1290                                 NaN  \n",
       "1291                                 NaN  \n",
       "1292                                 NaN  \n",
       "1293                                 NaN  \n",
       "1294                                 NaN  \n",
       "1295                                 NaN  \n",
       "1296                                 NaN  \n",
       "1297                                 NaN  \n",
       "1298                                 NaN  \n",
       "1299                                 NaN  \n",
       "1300                                 NaN  \n",
       "1301                                 NaN  \n",
       "1302                                 NaN  \n",
       "1303                                 NaN  \n",
       "1304                                 NaN  \n",
       "1305                                 NaN  \n",
       "1306                                 NaN  \n",
       "1307                                 NaN  \n",
       "1308                                 NaN  \n",
       "\n",
       "[1309 rows x 7 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Categorical_attributes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# To determine the Numerical attributtes of the Titanic dataset\n",
    "\n",
    "Numerical_attributes = Tb.select_dtypes(include= ['int', 'float'])\n",
    "\n",
    "# We observe that there are 3 numerical attributes/variable. namely; Age, fare, body"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>fare</th>\n",
       "      <th>body</th>\n",
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       "  </thead>\n",
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       "      <td>29.0000</td>\n",
       "      <td>211.3375</td>\n",
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       "    <tr>\n",
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       "      <td>0.9167</td>\n",
       "      <td>151.5500</td>\n",
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       "      <td>26.5500</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>77.9583</td>\n",
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       "      <td>0.0000</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>53.0000</td>\n",
       "      <td>51.4792</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>71.0000</td>\n",
       "      <td>49.5042</td>\n",
       "      <td>22.0</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>47.0000</td>\n",
       "      <td>227.5250</td>\n",
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       "      <td>80.0000</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>NaN</td>\n",
       "      <td>25.9250</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>24.0000</td>\n",
       "      <td>247.5208</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>50.0000</td>\n",
       "      <td>247.5208</td>\n",
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       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>32.0000</td>\n",
       "      <td>76.2917</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>36.0000</td>\n",
       "      <td>75.2417</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>37.0000</td>\n",
       "      <td>52.5542</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>21</th>\n",
       "      <td>47.0000</td>\n",
       "      <td>52.5542</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>26.0000</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>42.0000</td>\n",
       "      <td>227.5250</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>29.0000</td>\n",
       "      <td>221.7792</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>25</th>\n",
       "      <td>25.0000</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>148.0</td>\n",
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       "      <th>26</th>\n",
       "      <td>25.0000</td>\n",
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       "      <th>27</th>\n",
       "      <td>19.0000</td>\n",
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       "      <td>NaN</td>\n",
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       "      <th>28</th>\n",
       "      <td>35.0000</td>\n",
       "      <td>135.6333</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>29</th>\n",
       "      <td>28.0000</td>\n",
       "      <td>26.5500</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
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       "      <td>NaN</td>\n",
       "      <td>7.5500</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1284</th>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
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       "      <th>1287</th>\n",
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       "    <tr>\n",
       "      <th>1291</th>\n",
       "      <td>NaN</td>\n",
       "      <td>8.7125</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1292</th>\n",
       "      <td>NaN</td>\n",
       "      <td>7.5500</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1293</th>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1294</th>\n",
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       "    <tr>\n",
       "      <th>1295</th>\n",
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       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1296</th>\n",
       "      <td>27.0000</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>131.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1297</th>\n",
       "      <td>NaN</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1298</th>\n",
       "      <td>36.0000</td>\n",
       "      <td>9.5000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1299</th>\n",
       "      <td>27.0000</td>\n",
       "      <td>14.4542</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1300</th>\n",
       "      <td>15.0000</td>\n",
       "      <td>14.4542</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1301</th>\n",
       "      <td>45.5000</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>312.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1302</th>\n",
       "      <td>NaN</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1303</th>\n",
       "      <td>NaN</td>\n",
       "      <td>14.4583</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1304</th>\n",
       "      <td>14.5000</td>\n",
       "      <td>14.4542</td>\n",
       "      <td>328.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1305</th>\n",
       "      <td>NaN</td>\n",
       "      <td>14.4542</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1306</th>\n",
       "      <td>26.5000</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>304.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1307</th>\n",
       "      <td>27.0000</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1308</th>\n",
       "      <td>29.0000</td>\n",
       "      <td>7.8750</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1309 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          age      fare   body\n",
       "0     29.0000  211.3375    NaN\n",
       "1      0.9167  151.5500    NaN\n",
       "2      2.0000  151.5500    NaN\n",
       "3     30.0000  151.5500  135.0\n",
       "4     25.0000  151.5500    NaN\n",
       "5     48.0000   26.5500    NaN\n",
       "6     63.0000   77.9583    NaN\n",
       "7     39.0000    0.0000    NaN\n",
       "8     53.0000   51.4792    NaN\n",
       "9     71.0000   49.5042   22.0\n",
       "10    47.0000  227.5250  124.0\n",
       "11    18.0000  227.5250    NaN\n",
       "12    24.0000   69.3000    NaN\n",
       "13    26.0000   78.8500    NaN\n",
       "14    80.0000   30.0000    NaN\n",
       "15        NaN   25.9250    NaN\n",
       "16    24.0000  247.5208    NaN\n",
       "17    50.0000  247.5208    NaN\n",
       "18    32.0000   76.2917    NaN\n",
       "19    36.0000   75.2417    NaN\n",
       "20    37.0000   52.5542    NaN\n",
       "21    47.0000   52.5542    NaN\n",
       "22    26.0000   30.0000    NaN\n",
       "23    42.0000  227.5250    NaN\n",
       "24    29.0000  221.7792    NaN\n",
       "25    25.0000   26.0000  148.0\n",
       "26    25.0000   91.0792    NaN\n",
       "27    19.0000   91.0792    NaN\n",
       "28    35.0000  135.6333    NaN\n",
       "29    28.0000   26.5500    NaN\n",
       "...       ...       ...    ...\n",
       "1279  14.0000    7.8542    NaN\n",
       "1280  22.0000    7.8958    NaN\n",
       "1281  22.0000    9.0000    NaN\n",
       "1282      NaN    8.0500    NaN\n",
       "1283      NaN    7.5500    NaN\n",
       "1284      NaN    8.0500    NaN\n",
       "1285  32.5000    9.5000  298.0\n",
       "1286  38.0000    7.2292    NaN\n",
       "1287  51.0000    7.7500    NaN\n",
       "1288  18.0000    6.4958  314.0\n",
       "1289  21.0000    6.4958    NaN\n",
       "1290  47.0000    7.0000    NaN\n",
       "1291      NaN    8.7125    NaN\n",
       "1292      NaN    7.5500    NaN\n",
       "1293      NaN    8.0500    NaN\n",
       "1294  28.5000   16.1000   14.0\n",
       "1295  21.0000    7.2500    NaN\n",
       "1296  27.0000    8.6625  131.0\n",
       "1297      NaN    7.2500    NaN\n",
       "1298  36.0000    9.5000    NaN\n",
       "1299  27.0000   14.4542    NaN\n",
       "1300  15.0000   14.4542    NaN\n",
       "1301  45.5000    7.2250  312.0\n",
       "1302      NaN    7.2250    NaN\n",
       "1303      NaN   14.4583    NaN\n",
       "1304  14.5000   14.4542  328.0\n",
       "1305      NaN   14.4542    NaN\n",
       "1306  26.5000    7.2250  304.0\n",
       "1307  27.0000    7.2250    NaN\n",
       "1308  29.0000    7.8750    NaN\n",
       "\n",
       "[1309 rows x 3 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Numerical_attributes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "source": [
    "#We have been able to import the data and observe important features that can help in creating insights for intelligence reporting. In the next section, we shall look into the data cleaning which the most crucial part of the job."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Data Wrangling\n",
    "\n",
    "In this section, we dealt with  data wrangling (cleaning) which involves; removing missing values, duplicates, columns/row etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pclass          0\n",
       "survived        0\n",
       "name            0\n",
       "sex             0\n",
       "age           263\n",
       "sibsp           0\n",
       "parch           0\n",
       "ticket          0\n",
       "fare            1\n",
       "cabin        1014\n",
       "embarked        2\n",
       "boat          823\n",
       "body         1188\n",
       "home.dest     564\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To look for missing values\n",
    "\n",
    "Tb.isnull().sum()\n",
    "\n",
    "# The output shows that age, cabin, embark, body and home.dest have missing values with most occuring from body column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x663ee2c518>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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QaUoW1/SEUETWjEj5C1oRKX9tUgenPYIi8iPAkcBbgE3yLLr5MW8gVbI60vax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\no2wLnAf8v4K2asMgL0S+lFO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      "text/plain": [
       "<matplotlib.figure.Figure at 0x663ee83470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(Tb.isnull(), cmap = 'viridis')\n",
    "\n",
    "#The plot gives overview of the number of missing values in each columns.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\PC\\AppData\\Local\\Enthought\\Canopy\\edm\\envs\\User\\lib\\site-packages\\ipykernel\\__main__.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  app.launch_new_instance()\n"
     ]
    }
   ],
   "source": [
    "# To remove irrelevant columns with missing values.\n",
    "\n",
    "Categorical_attributes.drop([\"cabin\"], axis =1, inplace = True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sex</th>\n",
       "      <th>embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>female</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>male</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>female</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>male</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>female</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>male</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>female</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>male</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>female</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>male</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>male</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>female</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>female</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>female</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>male</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>male</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>male</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>female</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>female</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>male</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>male</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>female</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>male</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>female</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>female</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>male</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>male</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>female</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>female</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>male</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1279</th>\n",
       "      <td>female</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1280</th>\n",
       "      <td>male</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1281</th>\n",
       "      <td>male</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1282</th>\n",
       "      <td>male</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1283</th>\n",
       "      <td>male</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1284</th>\n",
       "      <td>male</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1285</th>\n",
       "      <td>male</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1286</th>\n",
       "      <td>female</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1287</th>\n",
       "      <td>male</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1288</th>\n",
       "      <td>male</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1289</th>\n",
       "      <td>male</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1290</th>\n",
       "      <td>female</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1291</th>\n",
       "      <td>male</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1292</th>\n",
       "      <td>male</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1293</th>\n",
       "      <td>male</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1294</th>\n",
       "      <td>male</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1295</th>\n",
       "      <td>male</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1296</th>\n",
       "      <td>male</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1297</th>\n",
       "      <td>male</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1298</th>\n",
       "      <td>male</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1299</th>\n",
       "      <td>male</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1300</th>\n",
       "      <td>female</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1301</th>\n",
       "      <td>male</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1302</th>\n",
       "      <td>male</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1303</th>\n",
       "      <td>male</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1304</th>\n",
       "      <td>female</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1305</th>\n",
       "      <td>female</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1306</th>\n",
       "      <td>male</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1307</th>\n",
       "      <td>male</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1308</th>\n",
       "      <td>male</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1309 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         sex embarked\n",
       "0     female        S\n",
       "1       male        S\n",
       "2     female        S\n",
       "3       male        S\n",
       "4     female        S\n",
       "5       male        S\n",
       "6     female        S\n",
       "7       male        S\n",
       "8     female        S\n",
       "9       male        C\n",
       "10      male        C\n",
       "11    female        C\n",
       "12    female        C\n",
       "13    female        S\n",
       "14      male        S\n",
       "15      male        S\n",
       "16      male        C\n",
       "17    female        C\n",
       "18    female        C\n",
       "19      male        C\n",
       "20      male        S\n",
       "21    female        S\n",
       "22      male        C\n",
       "23    female        C\n",
       "24    female        S\n",
       "25      male        C\n",
       "26      male        C\n",
       "27    female        C\n",
       "28    female        S\n",
       "29      male        S\n",
       "...      ...      ...\n",
       "1279  female        S\n",
       "1280    male        S\n",
       "1281    male        S\n",
       "1282    male        S\n",
       "1283    male        S\n",
       "1284    male        S\n",
       "1285    male        S\n",
       "1286  female        C\n",
       "1287    male        S\n",
       "1288    male        S\n",
       "1289    male        S\n",
       "1290  female        S\n",
       "1291    male        S\n",
       "1292    male        S\n",
       "1293    male        S\n",
       "1294    male        S\n",
       "1295    male        S\n",
       "1296    male        S\n",
       "1297    male        S\n",
       "1298    male        S\n",
       "1299    male        C\n",
       "1300  female        C\n",
       "1301    male        C\n",
       "1302    male        C\n",
       "1303    male        C\n",
       "1304  female        C\n",
       "1305  female        C\n",
       "1306    male        C\n",
       "1307    male        C\n",
       "1308    male        S\n",
       "\n",
       "[1309 rows x 2 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Categorical_attributes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x663f22e7b8>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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a1zjunkmam4dDmv2R/7/onpG0N5Lmkp6Om+a3/T0MtJLXwW2XGWy1r0EbbXeP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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x663f0c0ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#To validate that there is no more missing values in Categorical atrributes.\n",
    "\n",
    "sns.heatmap(Categorical_attributes.isnull(), cmap = 'viridis')\n",
    "\n",
    "#The output shows that categorical attributes is cleaned"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x663f457390>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x663ee04eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot( x = \"embarked\", hue = \"sex\", data = Categorical_attributes )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The countplot chart shows that population of male passengers that embark on S is more than twice the population of the female of S. Consequently, the number of male that embarked on C outnumbered that of female. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\PC\\AppData\\Local\\Enthought\\Canopy\\edm\\envs\\User\\lib\\site-packages\\ipykernel\\__main__.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  if __name__ == '__main__':\n"
     ]
    }
   ],
   "source": [
    "Numerical_attributes.drop(\"body\", axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>fare</th>\n",
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       "      <th>0</th>\n",
       "      <td>29.0000</td>\n",
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       "      <td>151.5500</td>\n",
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       "      <td>69.3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>26.0000</td>\n",
       "      <td>78.8500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>80.0000</td>\n",
       "      <td>30.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>NaN</td>\n",
       "      <td>25.9250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>24.0000</td>\n",
       "      <td>247.5208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>50.0000</td>\n",
       "      <td>247.5208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>32.0000</td>\n",
       "      <td>76.2917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>36.0000</td>\n",
       "      <td>75.2417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>37.0000</td>\n",
       "      <td>52.5542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>47.0000</td>\n",
       "      <td>52.5542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>26.0000</td>\n",
       "      <td>30.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>42.0000</td>\n",
       "      <td>227.5250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>29.0000</td>\n",
       "      <td>221.7792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>25.0000</td>\n",
       "      <td>26.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>25.0000</td>\n",
       "      <td>91.0792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>19.0000</td>\n",
       "      <td>91.0792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>35.0000</td>\n",
       "      <td>135.6333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>28.0000</td>\n",
       "      <td>26.5500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1279</th>\n",
       "      <td>14.0000</td>\n",
       "      <td>7.8542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1280</th>\n",
       "      <td>22.0000</td>\n",
       "      <td>7.8958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1281</th>\n",
       "      <td>22.0000</td>\n",
       "      <td>9.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1282</th>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1283</th>\n",
       "      <td>NaN</td>\n",
       "      <td>7.5500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1284</th>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1285</th>\n",
       "      <td>32.5000</td>\n",
       "      <td>9.5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1286</th>\n",
       "      <td>38.0000</td>\n",
       "      <td>7.2292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1287</th>\n",
       "      <td>51.0000</td>\n",
       "      <td>7.7500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1288</th>\n",
       "      <td>18.0000</td>\n",
       "      <td>6.4958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1289</th>\n",
       "      <td>21.0000</td>\n",
       "      <td>6.4958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1290</th>\n",
       "      <td>47.0000</td>\n",
       "      <td>7.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1291</th>\n",
       "      <td>NaN</td>\n",
       "      <td>8.7125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1292</th>\n",
       "      <td>NaN</td>\n",
       "      <td>7.5500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1293</th>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1294</th>\n",
       "      <td>28.5000</td>\n",
       "      <td>16.1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1295</th>\n",
       "      <td>21.0000</td>\n",
       "      <td>7.2500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1296</th>\n",
       "      <td>27.0000</td>\n",
       "      <td>8.6625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1297</th>\n",
       "      <td>NaN</td>\n",
       "      <td>7.2500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1298</th>\n",
       "      <td>36.0000</td>\n",
       "      <td>9.5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1299</th>\n",
       "      <td>27.0000</td>\n",
       "      <td>14.4542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1300</th>\n",
       "      <td>15.0000</td>\n",
       "      <td>14.4542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1301</th>\n",
       "      <td>45.5000</td>\n",
       "      <td>7.2250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1302</th>\n",
       "      <td>NaN</td>\n",
       "      <td>7.2250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1303</th>\n",
       "      <td>NaN</td>\n",
       "      <td>14.4583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1304</th>\n",
       "      <td>14.5000</td>\n",
       "      <td>14.4542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1305</th>\n",
       "      <td>NaN</td>\n",
       "      <td>14.4542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1306</th>\n",
       "      <td>26.5000</td>\n",
       "      <td>7.2250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1307</th>\n",
       "      <td>27.0000</td>\n",
       "      <td>7.2250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1308</th>\n",
       "      <td>29.0000</td>\n",
       "      <td>7.8750</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1309 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          age      fare\n",
       "0     29.0000  211.3375\n",
       "1      0.9167  151.5500\n",
       "2      2.0000  151.5500\n",
       "3     30.0000  151.5500\n",
       "4     25.0000  151.5500\n",
       "5     48.0000   26.5500\n",
       "6     63.0000   77.9583\n",
       "7     39.0000    0.0000\n",
       "8     53.0000   51.4792\n",
       "9     71.0000   49.5042\n",
       "10    47.0000  227.5250\n",
       "11    18.0000  227.5250\n",
       "12    24.0000   69.3000\n",
       "13    26.0000   78.8500\n",
       "14    80.0000   30.0000\n",
       "15        NaN   25.9250\n",
       "16    24.0000  247.5208\n",
       "17    50.0000  247.5208\n",
       "18    32.0000   76.2917\n",
       "19    36.0000   75.2417\n",
       "20    37.0000   52.5542\n",
       "21    47.0000   52.5542\n",
       "22    26.0000   30.0000\n",
       "23    42.0000  227.5250\n",
       "24    29.0000  221.7792\n",
       "25    25.0000   26.0000\n",
       "26    25.0000   91.0792\n",
       "27    19.0000   91.0792\n",
       "28    35.0000  135.6333\n",
       "29    28.0000   26.5500\n",
       "...       ...       ...\n",
       "1279  14.0000    7.8542\n",
       "1280  22.0000    7.8958\n",
       "1281  22.0000    9.0000\n",
       "1282      NaN    8.0500\n",
       "1283      NaN    7.5500\n",
       "1284      NaN    8.0500\n",
       "1285  32.5000    9.5000\n",
       "1286  38.0000    7.2292\n",
       "1287  51.0000    7.7500\n",
       "1288  18.0000    6.4958\n",
       "1289  21.0000    6.4958\n",
       "1290  47.0000    7.0000\n",
       "1291      NaN    8.7125\n",
       "1292      NaN    7.5500\n",
       "1293      NaN    8.0500\n",
       "1294  28.5000   16.1000\n",
       "1295  21.0000    7.2500\n",
       "1296  27.0000    8.6625\n",
       "1297      NaN    7.2500\n",
       "1298  36.0000    9.5000\n",
       "1299  27.0000   14.4542\n",
       "1300  15.0000   14.4542\n",
       "1301  45.5000    7.2250\n",
       "1302      NaN    7.2250\n",
       "1303      NaN   14.4583\n",
       "1304  14.5000   14.4542\n",
       "1305      NaN   14.4542\n",
       "1306  26.5000    7.2250\n",
       "1307  27.0000    7.2250\n",
       "1308  29.0000    7.8750\n",
       "\n",
       "[1309 rows x 2 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Numerical_attributes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x663f281fd0>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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2XQ01ydvMrJcGYXiEoRORC4AZuQbaN4AZwHRJ25HqRL6XVAjh55ImR8RqnIi0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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x663f4a0e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(Numerical_attributes.isnull(), cmap = 'viridis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age     263\n",
       "fare      1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Numerical_attributes.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The yellow patterns of the chart shows the presence of some missing value which was established with the .isnull().sum() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\PC\\AppData\\Local\\Enthought\\Canopy\\edm\\envs\\User\\lib\\site-packages\\ipykernel\\__main__.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  if __name__ == '__main__':\n"
     ]
    }
   ],
   "source": [
    "Numerical_attributes.dropna(inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>29.0000</td>\n",
       "      <td>211.3375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.9167</td>\n",
       "      <td>151.5500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.0000</td>\n",
       "      <td>151.5500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30.0000</td>\n",
       "      <td>151.5500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>25.0000</td>\n",
       "      <td>151.5500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>48.0000</td>\n",
       "      <td>26.5500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>63.0000</td>\n",
       "      <td>77.9583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>39.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>53.0000</td>\n",
       "      <td>51.4792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>71.0000</td>\n",
       "      <td>49.5042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>47.0000</td>\n",
       "      <td>227.5250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>18.0000</td>\n",
       "      <td>227.5250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>24.0000</td>\n",
       "      <td>69.3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>26.0000</td>\n",
       "      <td>78.8500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>80.0000</td>\n",
       "      <td>30.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>24.0000</td>\n",
       "      <td>247.5208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>50.0000</td>\n",
       "      <td>247.5208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>32.0000</td>\n",
       "      <td>76.2917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>36.0000</td>\n",
       "      <td>75.2417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>37.0000</td>\n",
       "      <td>52.5542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>47.0000</td>\n",
       "      <td>52.5542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>26.0000</td>\n",
       "      <td>30.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>42.0000</td>\n",
       "      <td>227.5250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>29.0000</td>\n",
       "      <td>221.7792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>25.0000</td>\n",
       "      <td>26.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>25.0000</td>\n",
       "      <td>91.0792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>19.0000</td>\n",
       "      <td>91.0792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>35.0000</td>\n",
       "      <td>135.6333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>28.0000</td>\n",
       "      <td>26.5500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>45.0000</td>\n",
       "      <td>35.5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1269</th>\n",
       "      <td>33.0000</td>\n",
       "      <td>9.5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1270</th>\n",
       "      <td>28.0000</td>\n",
       "      <td>9.5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1271</th>\n",
       "      <td>28.0000</td>\n",
       "      <td>9.5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1272</th>\n",
       "      <td>47.0000</td>\n",
       "      <td>9.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1273</th>\n",
       "      <td>18.0000</td>\n",
       "      <td>18.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1274</th>\n",
       "      <td>31.0000</td>\n",
       "      <td>18.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1275</th>\n",
       "      <td>16.0000</td>\n",
       "      <td>18.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1276</th>\n",
       "      <td>31.0000</td>\n",
       "      <td>18.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1277</th>\n",
       "      <td>22.0000</td>\n",
       "      <td>7.2250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1278</th>\n",
       "      <td>20.0000</td>\n",
       "      <td>7.8542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1279</th>\n",
       "      <td>14.0000</td>\n",
       "      <td>7.8542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1280</th>\n",
       "      <td>22.0000</td>\n",
       "      <td>7.8958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1281</th>\n",
       "      <td>22.0000</td>\n",
       "      <td>9.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1285</th>\n",
       "      <td>32.5000</td>\n",
       "      <td>9.5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1286</th>\n",
       "      <td>38.0000</td>\n",
       "      <td>7.2292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1287</th>\n",
       "      <td>51.0000</td>\n",
       "      <td>7.7500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1288</th>\n",
       "      <td>18.0000</td>\n",
       "      <td>6.4958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1289</th>\n",
       "      <td>21.0000</td>\n",
       "      <td>6.4958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1290</th>\n",
       "      <td>47.0000</td>\n",
       "      <td>7.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1294</th>\n",
       "      <td>28.5000</td>\n",
       "      <td>16.1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1295</th>\n",
       "      <td>21.0000</td>\n",
       "      <td>7.2500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1296</th>\n",
       "      <td>27.0000</td>\n",
       "      <td>8.6625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1298</th>\n",
       "      <td>36.0000</td>\n",
       "      <td>9.5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1299</th>\n",
       "      <td>27.0000</td>\n",
       "      <td>14.4542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1300</th>\n",
       "      <td>15.0000</td>\n",
       "      <td>14.4542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1301</th>\n",
       "      <td>45.5000</td>\n",
       "      <td>7.2250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1304</th>\n",
       "      <td>14.5000</td>\n",
       "      <td>14.4542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1306</th>\n",
       "      <td>26.5000</td>\n",
       "      <td>7.2250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1307</th>\n",
       "      <td>27.0000</td>\n",
       "      <td>7.2250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1308</th>\n",
       "      <td>29.0000</td>\n",
       "      <td>7.8750</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1045 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          age      fare\n",
       "0     29.0000  211.3375\n",
       "1      0.9167  151.5500\n",
       "2      2.0000  151.5500\n",
       "3     30.0000  151.5500\n",
       "4     25.0000  151.5500\n",
       "5     48.0000   26.5500\n",
       "6     63.0000   77.9583\n",
       "7     39.0000    0.0000\n",
       "8     53.0000   51.4792\n",
       "9     71.0000   49.5042\n",
       "10    47.0000  227.5250\n",
       "11    18.0000  227.5250\n",
       "12    24.0000   69.3000\n",
       "13    26.0000   78.8500\n",
       "14    80.0000   30.0000\n",
       "16    24.0000  247.5208\n",
       "17    50.0000  247.5208\n",
       "18    32.0000   76.2917\n",
       "19    36.0000   75.2417\n",
       "20    37.0000   52.5542\n",
       "21    47.0000   52.5542\n",
       "22    26.0000   30.0000\n",
       "23    42.0000  227.5250\n",
       "24    29.0000  221.7792\n",
       "25    25.0000   26.0000\n",
       "26    25.0000   91.0792\n",
       "27    19.0000   91.0792\n",
       "28    35.0000  135.6333\n",
       "29    28.0000   26.5500\n",
       "30    45.0000   35.5000\n",
       "...       ...       ...\n",
       "1269  33.0000    9.5000\n",
       "1270  28.0000    9.5000\n",
       "1271  28.0000    9.5000\n",
       "1272  47.0000    9.0000\n",
       "1273  18.0000   18.0000\n",
       "1274  31.0000   18.0000\n",
       "1275  16.0000   18.0000\n",
       "1276  31.0000   18.0000\n",
       "1277  22.0000    7.2250\n",
       "1278  20.0000    7.8542\n",
       "1279  14.0000    7.8542\n",
       "1280  22.0000    7.8958\n",
       "1281  22.0000    9.0000\n",
       "1285  32.5000    9.5000\n",
       "1286  38.0000    7.2292\n",
       "1287  51.0000    7.7500\n",
       "1288  18.0000    6.4958\n",
       "1289  21.0000    6.4958\n",
       "1290  47.0000    7.0000\n",
       "1294  28.5000   16.1000\n",
       "1295  21.0000    7.2500\n",
       "1296  27.0000    8.6625\n",
       "1298  36.0000    9.5000\n",
       "1299  27.0000   14.4542\n",
       "1300  15.0000   14.4542\n",
       "1301  45.5000    7.2250\n",
       "1304  14.5000   14.4542\n",
       "1306  26.5000    7.2250\n",
       "1307  27.0000    7.2250\n",
       "1308  29.0000    7.8750\n",
       "\n",
       "[1045 rows x 2 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Numerical_attributes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x663f5bc2b0>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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Y7ZU0tqp8HUlXpkjbBZJOrjp2U4q+nZ+uuVaeBpqZtUw/hnSqZxOm7U2dvaRb\nJM2ts41/8y0HlFPkp8CXI2JrsokyP81zUp4n/IuBc4BLq8rmAh8DLqipOwFQROwoaSQwX9KVaajn\nExHxoiQB1wJHAj/P00gzs5YoMVdtROzX6JikujMdayzlzfFKW6UyyJ7090+ff0HOSTADirSNiAUR\n8XCd6k8C66XgqnWBFcCL6ZwXU50RwFrAM3kaaGbWMtGbfysmT06RmcBoSaPSiMiEdB5kwawfTp/3\nARbmuWnhwKtqEXET8AKwDHgMODMiXv9lIWka2W+yVyNiaqPrONLWzAZFT2/+rZi6Mx0lbSFpKrwe\n13QCMA1YAFwTEfPS+ZOAMyTNBv6LN79LaKjUl7aSPgWMBLYANgb+IOmWylvmiPiIpHWAqyVNjIiL\n610njYVdCLDt2Wd1ZkibmXWeFkXQRsQz1J/p+ARwcNX+VGC1h+OImEk2i6dfSn3CBz4ITImIlWnx\ntDvIFk97XUS8ClwHvK/ke5uZFdPlSyuU3eE/RDaeRIq43R14SNJbq5KejwD+GZhV8r3NzIoZ6h1+\nvUhbSYel/LTvB25IY/OQzdpZK03hnAn8LCLmAOsB10uaAzxANp/0oiZ8HzOzgevtzb91oCKRtlPq\n1H0VOLpO+VN4CMfM2l2HPrnn5UhbM7OK1i2tMCgGGmn7fUkPpRy1UyRtVHVsJ0l3pUjcB9OsHCTt\nmvYXSTo7BWCZmbWNiN7cWycaaE7b6cAOEbET2Xr3J8PrL2QvA45LuWv3Alamc84jy3k7Om211zQz\nG1y9kX/rQAONtL05BQUAzCAL+QU4AJgTEbNTvWcioifN0NkgImakdSMu5Y08uGZm7WGoz9LJ4bPA\njenzdkBImibpfkknpfItyWbmVCxJZXU50tbMBkWXz9Ip1OFL+jawCrg8FY0A9iCbqbMHcJik1aLJ\n1sQ5bc1sUHT5E/6AZ+lImgh8FNg3DdNA9uR+e0Q8nepMBXYhG9ffqur06lXfzMzaQvR0d6rtAT3h\nSzoQOAk4JCJeqTo0Ddgx5bYdQbaa2/y00P+LknZPs3M+Tf3V4czMBk+Xv7Rd4xN+irTdC3h7iq79\nd7JZOWsD09PsyhkRcVxEPCfpB2RRtgFMjYgb0qW+SDbjZ12yMf8bMTNrJx063TKvgUbaNsyuEhGX\nkQ3h1JbfC+zQr9aZmbVQdOiTe16OtDUzqxjqT/hmZkNFt7+0VXTo9KKhSNKk2kTJZv53YXmVvR6+\nNVeuNGYBfZ7KAAACX0lEQVQ25PjfheXiDt/MbIhwh29mNkS4w+8sHqe1evzvwnLxS1szsyHCT/hm\nZkOEO3wzsyHCHb5ZG5N0oqQFki5fc22zvnkM36yNSXoI2C8iluSoO6IqE53ZavyE30Yk/UrSfSkB\n/KRU9jlJj0i6R9KPJZ2TyjeVdJ2kmWn74OC23som6XxgW+BGSd+UdJekByTdKWn7VGeipOsl/Ra4\nNZX9W/o3MUfS/xnEr2Btxk/4bUTS2yLiWUnrki0x/RHgDrIkMn8FfgvMjogTJF0B/N+I+KOkdwLT\nIuIfB63x1hSSFgNjgRXAKxGxStJ+wBci4vCUiOi7wE7p384BwBHA5wEB1wNnpNzUNsR58bT2cqKk\nw9LnrYFjgN9HxLMAkn5BljcYYD9gTMpHALCBpLdGxEutbLC1zIbAJZJGk+WaeEvVsemVfyPAAWl7\nIO2/FRgNuMM3d/jtQtJeZJ34+yPiFUm3AQ8BjZ7ahwG7R8SrrWmhDbL/BH4XEYdJ2ga4rerYy1Wf\nBXwvIi5oXdOsU3gMv31sCDyXOvt/AHYH1gM+LGnjlDLy8Kr6NwNfquxI2rmlrbVW25A38kBP7KPe\nNOCzkt4KIGlLSe9octusQ7jDbx83ASMkLQBOA2aQ/Q/+X8A9ZGP5i4EXUv0TgbHpxdx84LiWt9ha\n6Qzge5IeoI+/zCPiZuAK4C5JDwLXAuu3ponW7vzSts1VxuXTE/4U4KKImDLY7TKzzuMn/Pb3H5Jm\nAXOBPwO/GuT2mFmH8hO+mdkQ4Sd8M7Mhwh2+mdkQ4Q7fzGyIcIdvZjZEuMM3Mxsi/j/c0CzUHZ9l\ntgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x663f5c52e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(Numerical_attributes.isnull(), cmap = 'viridis')\n",
    "\n",
    "# The output shows that numerical attributes is cleaned."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x663f69b780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Numerical_attributes.hist(bins= 30, figsize =(10,3) )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "source": [
    "The age distribution is skewed to the right. Majority of the passengers are in the age bracket 22 to 25 years and the oldest passenger is of age 80 years."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Alternative method\n",
    "\n",
    "We started cleaning the data by removing irrelevant columns. Then, we checked for missing values in the remaining columns and cleaned up  accordingly. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pclass</th>\n",
       "      <th>survived</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>boat</th>\n",
       "      <th>body</th>\n",
       "      <th>home.dest</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Allen, Miss. Elisabeth Walton</td>\n",
       "      <td>female</td>\n",
       "      <td>29.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>24160</td>\n",
       "      <td>211.3375</td>\n",
       "      <td>B5</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>St Louis, MO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Allison, Master. Hudson Trevor</td>\n",
       "      <td>male</td>\n",
       "      <td>0.9167</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>C22 C26</td>\n",
       "      <td>S</td>\n",
       "      <td>11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Allison, Miss. Helen Loraine</td>\n",
       "      <td>female</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>C22 C26</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Allison, Mr. Hudson Joshua Creighton</td>\n",
       "      <td>male</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>C22 C26</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>135.0</td>\n",
       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Allison, Mrs. Hudson J C (Bessie Waldo Daniels)</td>\n",
       "      <td>female</td>\n",
       "      <td>25.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>C22 C26</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pclass  survived                                             name     sex  \\\n",
       "0       1         1                    Allen, Miss. Elisabeth Walton  female   \n",
       "1       1         1                   Allison, Master. Hudson Trevor    male   \n",
       "2       1         0                     Allison, Miss. Helen Loraine  female   \n",
       "3       1         0             Allison, Mr. Hudson Joshua Creighton    male   \n",
       "4       1         0  Allison, Mrs. Hudson J C (Bessie Waldo Daniels)  female   \n",
       "\n",
       "       age  sibsp  parch  ticket      fare    cabin embarked boat   body  \\\n",
       "0  29.0000      0      0   24160  211.3375       B5        S    2    NaN   \n",
       "1   0.9167      1      2  113781  151.5500  C22 C26        S   11    NaN   \n",
       "2   2.0000      1      2  113781  151.5500  C22 C26        S  NaN    NaN   \n",
       "3  30.0000      1      2  113781  151.5500  C22 C26        S  NaN  135.0   \n",
       "4  25.0000      1      2  113781  151.5500  C22 C26        S  NaN    NaN   \n",
       "\n",
       "                         home.dest  \n",
       "0                     St Louis, MO  \n",
       "1  Montreal, PQ / Chesterville, ON  \n",
       "2  Montreal, PQ / Chesterville, ON  \n",
       "3  Montreal, PQ / Chesterville, ON  \n",
       "4  Montreal, PQ / Chesterville, ON  "
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Tb.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#To remove irrelevant columns\n",
    "\n",
    "Tb.drop([\"name\",\"sibsp\",\"parch\",\"ticket\",\"fare\",\"cabin\",\"boat\",\"body\",\"home.dest\"], axis=1, inplace = True )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pclass</th>\n",
       "      <th>survived</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>29.0000</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>0.9167</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>female</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>female</td>\n",
       "      <td>25.0000</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>48.0000</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>63.0000</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>39.0000</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>53.0000</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>71.0000</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>47.0000</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>18.0000</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>24.0000</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>80.0000</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>24.0000</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>50.0000</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>32.0000</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>36.0000</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>37.0000</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>47.0000</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>42.0000</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>29.0000</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>25.0000</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>25.0000</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0000</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0000</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>28.0000</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1279</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>female</td>\n",
       "      <td>14.0000</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1280</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0000</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1281</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0000</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1282</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1283</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1284</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1285</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>32.5000</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1286</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0000</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1287</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>51.0000</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1288</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>18.0000</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1289</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>21.0000</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1290</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>47.0000</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1291</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1292</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1293</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1294</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>28.5000</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1295</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>21.0000</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1296</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0000</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1297</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1298</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>36.0000</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1299</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0000</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1300</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>15.0000</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1301</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>45.5000</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1302</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1303</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1304</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>female</td>\n",
       "      <td>14.5000</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1305</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1306</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>26.5000</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1307</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0000</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1308</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>29.0000</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1309 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      pclass  survived     sex      age embarked\n",
       "0          1         1  female  29.0000        S\n",
       "1          1         1    male   0.9167        S\n",
       "2          1         0  female   2.0000        S\n",
       "3          1         0    male  30.0000        S\n",
       "4          1         0  female  25.0000        S\n",
       "5          1         1    male  48.0000        S\n",
       "6          1         1  female  63.0000        S\n",
       "7          1         0    male  39.0000        S\n",
       "8          1         1  female  53.0000        S\n",
       "9          1         0    male  71.0000        C\n",
       "10         1         0    male  47.0000        C\n",
       "11         1         1  female  18.0000        C\n",
       "12         1         1  female  24.0000        C\n",
       "13         1         1  female  26.0000        S\n",
       "14         1         1    male  80.0000        S\n",
       "15         1         0    male      NaN        S\n",
       "16         1         0    male  24.0000        C\n",
       "17         1         1  female  50.0000        C\n",
       "18         1         1  female  32.0000        C\n",
       "19         1         0    male  36.0000        C\n",
       "20         1         1    male  37.0000        S\n",
       "21         1         1  female  47.0000        S\n",
       "22         1         1    male  26.0000        C\n",
       "23         1         1  female  42.0000        C\n",
       "24         1         1  female  29.0000        S\n",
       "25         1         0    male  25.0000        C\n",
       "26         1         1    male  25.0000        C\n",
       "27         1         1  female  19.0000        C\n",
       "28         1         1  female  35.0000        S\n",
       "29         1         1    male  28.0000        S\n",
       "...      ...       ...     ...      ...      ...\n",
       "1279       3         0  female  14.0000        S\n",
       "1280       3         0    male  22.0000        S\n",
       "1281       3         0    male  22.0000        S\n",
       "1282       3         0    male      NaN        S\n",
       "1283       3         0    male      NaN        S\n",
       "1284       3         0    male      NaN        S\n",
       "1285       3         0    male  32.5000        S\n",
       "1286       3         1  female  38.0000        C\n",
       "1287       3         0    male  51.0000        S\n",
       "1288       3         0    male  18.0000        S\n",
       "1289       3         0    male  21.0000        S\n",
       "1290       3         1  female  47.0000        S\n",
       "1291       3         0    male      NaN        S\n",
       "1292       3         0    male      NaN        S\n",
       "1293       3         0    male      NaN        S\n",
       "1294       3         0    male  28.5000        S\n",
       "1295       3         0    male  21.0000        S\n",
       "1296       3         0    male  27.0000        S\n",
       "1297       3         0    male      NaN        S\n",
       "1298       3         0    male  36.0000        S\n",
       "1299       3         0    male  27.0000        C\n",
       "1300       3         1  female  15.0000        C\n",
       "1301       3         0    male  45.5000        C\n",
       "1302       3         0    male      NaN        C\n",
       "1303       3         0    male      NaN        C\n",
       "1304       3         0  female  14.5000        C\n",
       "1305       3         0  female      NaN        C\n",
       "1306       3         0    male  26.5000        C\n",
       "1307       3         0    male  27.0000        C\n",
       "1308       3         0    male  29.0000        S\n",
       "\n",
       "[1309 rows x 5 columns]"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Tb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x664084d978>"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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959VtnwWc1eg1m5mN/fWS3lhbJ9WuF0TEthExISclVwB7RMRqnIg0s5KLPjW8dEozNe3t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\ncaFtZlYhLrTNzCrEhbaZWYX8f4sfv2++YcqoAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x663ee83e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#To check the status of the dataset after removing irrelevant columns\n",
    "\n",
    "sns.heatmap(Tb.isnull(), cmap ='viridis')\n",
    "\n",
    "#The output reveals that we had some missing values in age column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#To clean up the missing values in age column and anywhere else.\n",
    "\n",
    "Tb.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pclass      0\n",
       "survived    0\n",
       "sex         0\n",
       "age         0\n",
       "embarked    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#To confirm the cleanliness of the data \n",
    "\n",
    "Tb.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x6640847da0>"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ATaQ8t6vyec7Jx4zK5zmjSAXNzNqmfV06jXKKvCqPcLwAOBLYFTguj4QEOAc4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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x66407f20b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(Tb.isnull(), cmap ='viridis')\n",
    "\n",
    "#The output chart justify the claim that the data is cleaned for further analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x00000066407E4B00>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000006640C07CF8>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x0000006640C56208>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000006640C96630>]], dtype=object)"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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35/9YhJoWynTn8H6j+t4NMjYYzffuOeCtVfV64HXAJUl+7Jg+C/q+jUTQYhl+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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x6640ac81d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#To determine the distribution of the dataset. \n",
    "\n",
    "Tb.hist(bins = 30, figsize = (10,12))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the charts, we obtained the following information:\n",
    "\n",
    "1. The top left output chart tells about age distribution of the passengers, which is skewed to the right. Majority of the passengers are in the age bracket 22 to 25 years and the oldest passenger is of age 80 years.\n",
    "2. The top right output chart reveals that large proporton of the passengers belong to pclass_3 category.\n",
    "3. The down left output chart shows that majority of the passengers could not survive it.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x6640bcab70>"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x6640ae1a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot( x = \"survived\", hue = \"sex\", data = Tb )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " More than 5 times the number of female that survive it, accounts for the number of males that couldn't survive it. Thus, larger proportion of the male passenger couldn't survive it. On the contrary, the number of female that survived twice the number of male that survived it. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x66428e17b8>"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x664183d518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot( x = \"survived\", hue = \"pclass\", data = Tb )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "More of the pclass_3 category couldn not survive it while majority of the pclass_1 category survived. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x6641046630>"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x66413cd7f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot( x = \"pclass\", y = \"age\", data = Tb )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Many of the passengers that  are of older ages belongs to pclass_1 category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>female</th>\n",
       "      <th>male</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "      female  male\n",
       "0          1     0\n",
       "1          0     1\n",
       "2          1     0\n",
       "3          0     1\n",
       "4          1     0\n",
       "5          0     1\n",
       "6          1     0\n",
       "7          0     1\n",
       "8          1     0\n",
       "9          0     1\n",
       "10         0     1\n",
       "11         1     0\n",
       "12         1     0\n",
       "13         1     0\n",
       "14         0     1\n",
       "16         0     1\n",
       "17         1     0\n",
       "18         1     0\n",
       "19         0     1\n",
       "20         0     1\n",
       "21         1     0\n",
       "22         0     1\n",
       "23         1     0\n",
       "24         1     0\n",
       "25         0     1\n",
       "26         0     1\n",
       "27         1     0\n",
       "28         1     0\n",
       "29         0     1\n",
       "30         0     1\n",
       "...      ...   ...\n",
       "1269       0     1\n",
       "1270       0     1\n",
       "1271       0     1\n",
       "1272       0     1\n",
       "1273       1     0\n",
       "1274       0     1\n",
       "1275       0     1\n",
       "1276       1     0\n",
       "1277       0     1\n",
       "1278       0     1\n",
       "1279       1     0\n",
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       "1281       0     1\n",
       "1285       0     1\n",
       "1286       1     0\n",
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       "1288       0     1\n",
       "1289       0     1\n",
       "1290       1     0\n",
       "1294       0     1\n",
       "1295       0     1\n",
       "1296       0     1\n",
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       "1300       1     0\n",
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       "1304       1     0\n",
       "1306       0     1\n",
       "1307       0     1\n",
       "1308       0     1\n",
       "\n",
       "[1044 rows x 2 columns]"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The sex column (categorical variable) generated data structure that can be grouped into dummy variables\n",
    "\n",
    "pd.get_dummies(Tb[\"sex\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# To remove one of the columns since 0 = male and 1 = female under Female and same for male column, we can remove the first couln\n",
    "\n",
    "sex = pd.get_dummies(Tb[\"sex\"], drop_first = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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      "text/plain": [
       "      male\n",
       "0        0\n",
       "1        1\n",
       "2        0\n",
       "3        1\n",
       "4        0\n",
       "5        1\n",
       "6        0\n",
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       "8        0\n",
       "9        1\n",
       "10       1\n",
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       "...    ...\n",
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       "\n",
       "[1044 rows x 1 columns]"
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     "execution_count": 113,
     "metadata": {},
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    "sex"
   ]
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  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {
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   },
   "outputs": [
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1269</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1270</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1271</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1272</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1273</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1274</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1275</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1276</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1277</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1278</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1279</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1280</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1281</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1285</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1286</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1287</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1288</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1289</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1290</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1294</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1295</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1296</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1298</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1299</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1300</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1301</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1304</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1306</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1307</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1308</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1044 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      C  Q  S\n",
       "0     0  0  1\n",
       "1     0  0  1\n",
       "2     0  0  1\n",
       "3     0  0  1\n",
       "4     0  0  1\n",
       "5     0  0  1\n",
       "6     0  0  1\n",
       "7     0  0  1\n",
       "8     0  0  1\n",
       "9     1  0  0\n",
       "10    1  0  0\n",
       "11    1  0  0\n",
       "12    1  0  0\n",
       "13    0  0  1\n",
       "14    0  0  1\n",
       "16    1  0  0\n",
       "17    1  0  0\n",
       "18    1  0  0\n",
       "19    1  0  0\n",
       "20    0  0  1\n",
       "21    0  0  1\n",
       "22    1  0  0\n",
       "23    1  0  0\n",
       "24    0  0  1\n",
       "25    1  0  0\n",
       "26    1  0  0\n",
       "27    1  0  0\n",
       "28    0  0  1\n",
       "29    0  0  1\n",
       "30    0  0  1\n",
       "...  .. .. ..\n",
       "1269  0  0  1\n",
       "1270  0  0  1\n",
       "1271  0  0  1\n",
       "1272  0  0  1\n",
       "1273  0  0  1\n",
       "1274  0  0  1\n",
       "1275  0  0  1\n",
       "1276  0  0  1\n",
       "1277  1  0  0\n",
       "1278  0  0  1\n",
       "1279  0  0  1\n",
       "1280  0  0  1\n",
       "1281  0  0  1\n",
       "1285  0  0  1\n",
       "1286  1  0  0\n",
       "1287  0  0  1\n",
       "1288  0  0  1\n",
       "1289  0  0  1\n",
       "1290  0  0  1\n",
       "1294  0  0  1\n",
       "1295  0  0  1\n",
       "1296  0  0  1\n",
       "1298  0  0  1\n",
       "1299  1  0  0\n",
       "1300  1  0  0\n",
       "1301  1  0  0\n",
       "1304  1  0  0\n",
       "1306  1  0  0\n",
       "1307  1  0  0\n",
       "1308  0  0  1\n",
       "\n",
       "[1044 rows x 3 columns]"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We appply the same to embarked and pclass column as well\n",
    "\n",
    "pd.get_dummies(Tb[\"embarked\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    " embarked = pd.get_dummies(Tb[\"embarked\"], drop_first = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "      <th>13</th>\n",
       "      <td>0</td>\n",
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       "      <th>14</th>\n",
       "      <td>0</td>\n",
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       "      <th>17</th>\n",
       "      <td>0</td>\n",
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       "      <th>18</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <th>19</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <th>20</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <th>26</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0</td>\n",
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       "      <th>28</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <th>29</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <th>30</th>\n",
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       "      <td>1</td>\n",
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       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>1269</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1270</th>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1272</th>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1275</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1276</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1277</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1278</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1279</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1280</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1281</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1285</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1286</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1287</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1288</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1289</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1290</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1294</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1295</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1296</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1298</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1299</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1300</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1301</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1304</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1306</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1307</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1308</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1044 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Q  S\n",
       "0     0  1\n",
       "1     0  1\n",
       "2     0  1\n",
       "3     0  1\n",
       "4     0  1\n",
       "5     0  1\n",
       "6     0  1\n",
       "7     0  1\n",
       "8     0  1\n",
       "9     0  0\n",
       "10    0  0\n",
       "11    0  0\n",
       "12    0  0\n",
       "13    0  1\n",
       "14    0  1\n",
       "16    0  0\n",
       "17    0  0\n",
       "18    0  0\n",
       "19    0  0\n",
       "20    0  1\n",
       "21    0  1\n",
       "22    0  0\n",
       "23    0  0\n",
       "24    0  1\n",
       "25    0  0\n",
       "26    0  0\n",
       "27    0  0\n",
       "28    0  1\n",
       "29    0  1\n",
       "30    0  1\n",
       "...  .. ..\n",
       "1269  0  1\n",
       "1270  0  1\n",
       "1271  0  1\n",
       "1272  0  1\n",
       "1273  0  1\n",
       "1274  0  1\n",
       "1275  0  1\n",
       "1276  0  1\n",
       "1277  0  0\n",
       "1278  0  1\n",
       "1279  0  1\n",
       "1280  0  1\n",
       "1281  0  1\n",
       "1285  0  1\n",
       "1286  0  0\n",
       "1287  0  1\n",
       "1288  0  1\n",
       "1289  0  1\n",
       "1290  0  1\n",
       "1294  0  1\n",
       "1295  0  1\n",
       "1296  0  1\n",
       "1298  0  1\n",
       "1299  0  0\n",
       "1300  0  0\n",
       "1301  0  0\n",
       "1304  0  0\n",
       "1306  0  0\n",
       "1307  0  0\n",
       "1308  0  1\n",
       "\n",
       "[1044 rows x 2 columns]"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embarked"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <th>10</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <th>13</th>\n",
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       "      <th>14</th>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <th>16</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <th>29</th>\n",
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       "      <td>1</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>1269</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1270</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1271</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1272</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1273</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1274</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1275</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1276</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1277</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1278</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1279</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1280</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1281</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1285</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1286</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1287</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1288</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1289</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1290</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1294</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1295</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1296</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1298</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1299</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1300</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1301</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1304</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1306</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1307</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1308</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1044 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      1  2  3\n",
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       "1     1  0  0\n",
       "2     1  0  0\n",
       "3     1  0  0\n",
       "4     1  0  0\n",
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       "19    1  0  0\n",
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       "21    1  0  0\n",
       "22    1  0  0\n",
       "23    1  0  0\n",
       "24    1  0  0\n",
       "25    1  0  0\n",
       "26    1  0  0\n",
       "27    1  0  0\n",
       "28    1  0  0\n",
       "29    1  0  0\n",
       "30    1  0  0\n",
       "...  .. .. ..\n",
       "1269  0  0  1\n",
       "1270  0  0  1\n",
       "1271  0  0  1\n",
       "1272  0  0  1\n",
       "1273  0  0  1\n",
       "1274  0  0  1\n",
       "1275  0  0  1\n",
       "1276  0  0  1\n",
       "1277  0  0  1\n",
       "1278  0  0  1\n",
       "1279  0  0  1\n",
       "1280  0  0  1\n",
       "1281  0  0  1\n",
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       "1286  0  0  1\n",
       "1287  0  0  1\n",
       "1288  0  0  1\n",
       "1289  0  0  1\n",
       "1290  0  0  1\n",
       "1294  0  0  1\n",
       "1295  0  0  1\n",
       "1296  0  0  1\n",
       "1298  0  0  1\n",
       "1299  0  0  1\n",
       "1300  0  0  1\n",
       "1301  0  0  1\n",
       "1304  0  0  1\n",
       "1306  0  0  1\n",
       "1307  0  0  1\n",
       "1308  0  0  1\n",
       "\n",
       "[1044 rows x 3 columns]"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.get_dummies(Tb[\"pclass\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "pclass = pd.get_dummies(Tb[\"pclass\"], drop_first = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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      "text/plain": [
       "      2  3\n",
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       "[1044 rows x 2 columns]"
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     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pclass"
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  },
  {
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   "execution_count": 208,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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       "   pclass  survived     sex      age embarked  male  Q  S  2  3 ...  male  Q  \\\n",
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       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Tb = pd.concat([Tb,sex, embarked, pclass], axis = 1)\n",
    "Tb.drop(\"embarked\", axis =1)\n",
    "Tb.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {
    "collapsed": false
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   "outputs": [
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       "<p>5 rows × 29 columns</p>\n",
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      ],
      "text/plain": [
       "   pclass  survived     sex      age  male  Q  S  2  3  male ...  male  Q  S  \\\n",
       "0       1         1  female  29.0000     0  0  1  0  0     0 ...     0  0  1   \n",
       "1       1         1    male   0.9167     1  0  1  0  0     1 ...     1  0  1   \n",
       "2       1         0  female   2.0000     0  0  1  0  0     0 ...     0  0  1   \n",
       "3       1         0    male  30.0000     1  0  1  0  0     1 ...     1  0  1   \n",
       "4       1         0  female  25.0000     0  0  1  0  0     0 ...     0  0  1   \n",
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       "4  0  0     0  0  1  0  0  \n",
       "\n",
       "[5 rows x 29 columns]"
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Tb = Tb.drop(\"embarked\", axis =1)\n",
    "\n",
    "Tb.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {
    "collapsed": false
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   "outputs": [
    {
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       "<p>5 rows × 28 columns</p>\n",
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      ],
      "text/plain": [
       "   pclass  survived      age  male  Q  S  2  3  male  Q ...  male  Q  S  2  3  \\\n",
       "0       1         1  29.0000     0  0  1  0  0     0  0 ...     0  0  1  0  0   \n",
       "1       1         1   0.9167     1  0  1  0  0     1  0 ...     1  0  1  0  0   \n",
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       "[5 rows x 28 columns]"
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     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Tb.drop(\"sex\", axis =1, inplace = True)\n",
    "Tb.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {
    "collapsed": false
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      ],
      "text/plain": [
       "   pclass  survived      age  male  Q  S  2  3  male  Q ...  male  Q  S  2  3  \\\n",
       "0       1         1  29.0000     0  0  1  0  0     0  0 ...     0  0  1  0  0   \n",
       "1       1         1   0.9167     1  0  1  0  0     1  0 ...     1  0  1  0  0   \n",
       "2       1         0   2.0000     0  0  1  0  0     0  0 ...     0  0  1  0  0   \n",
       "3       1         0  30.0000     1  0  1  0  0     1  0 ...     1  0  1  0  0   \n",
       "4       1         0  25.0000     0  0  1  0  0     0  0 ...     0  0  1  0  0   \n",
       "\n",
       "   male  Q  S  2  3  \n",
       "0     0  0  1  0  0  \n",
       "1     1  0  1  0  0  \n",
       "2     0  0  1  0  0  \n",
       "3     1  0  1  0  0  \n",
       "4     0  0  1  0  0  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 224,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Tb.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#To remove duplicate columns\n",
    "\n",
    "Tb = Tb.loc[:,~Tb.columns.duplicated()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pclass</th>\n",
       "      <th>survived</th>\n",
       "      <th>age</th>\n",
       "      <th>male</th>\n",
       "      <th>Q</th>\n",
       "      <th>S</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>29.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.9167</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>25.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pclass  survived      age  male  Q  S  2  3\n",
       "0       1         1  29.0000     0  0  1  0  0\n",
       "1       1         1   0.9167     1  0  1  0  0\n",
       "2       1         0   2.0000     0  0  1  0  0\n",
       "3       1         0  30.0000     1  0  1  0  0\n",
       "4       1         0  25.0000     0  0  1  0  0"
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Tb.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Logistics regression analysis\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Firstly, we define the dependent and the independent variable. Then, import necessary modules and libraries. \n",
    "\n",
    "X = Tb.drop(\"survived\", axis = 1)\n",
    "y = Tb[\"survived\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.cross_validation import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "logmodel=LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 234,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logmodel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 235,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logmodel.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "predictions = logmodel.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'             precision    recall  f1-score   support\\n\\n          0       0.80      0.81      0.81       180\\n          1       0.74      0.73      0.74       134\\n\\navg / total       0.78      0.78      0.78       314\\n'"
      ]
     },
     "execution_count": 239,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classification_report(y_test, predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[146,  34],\n",
       "       [ 36,  98]], dtype=int64)"
      ]
     },
     "execution_count": 241,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test,predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.77707006369426757"
      ]
     },
     "execution_count": 243,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test,predictions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The logistics model created for predictions has approximately 78% accuracy and 80% precision. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary\n",
    "\n",
    "Titanic is well known incident in the world history. We extracted, transformed, loaded and imported the Titanic dataset into Python Jupiter Notebook. Then, we carried out Exploratory data analysis as well as logistic regression analysis to predict survivals. \n",
    "\n",
    "During the exporatory process, we observe that there are 1309 passengers in the Titanic ship with age distribution skewed to the right. majority of the passengers are in the age bracket 22 to 25 years and the oldest passenger is of age 80 years.\n",
    "\n",
    "Pclass-wise,large proporton of the passengers belong to pclass_3 category.\n",
    "\n",
    "More of the passengers could not survive.  More than 5 times the number of female that survive is the number of males that couldn't survive. Thus, larger proportion of the male passenger couldn't survive. On the contrary, the number of female that survived twice the number of male that survived.\n",
    "\n",
    "Many of the passengers that are of older ages belongs to pclass_1 category. The logistics model created for predictions has approximately 78% accuracy and 80% precision.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Thanks for reading!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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