ab-demo1-TitanicDisaster.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python 3.7.4\n"
     ]
    }
   ],
   "source": [
    "!python --version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#read titanic_train data set as csv\n",
    "#download link: https://www.kaggle.com/c/titanic/data\n",
    "titanic_data=pd.read_csv('titanic_train.csv', quotechar='\"')\n",
    "titanic_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "891"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(titanic_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891</td>\n",
       "      <td>891</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>204</td>\n",
       "      <td>889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>unique</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>891</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>681</td>\n",
       "      <td>NaN</td>\n",
       "      <td>147</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>top</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Andersson, Master. Sigvard Harald Elias</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CA. 2343</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C23 C25 C27</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>freq</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>577</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32.204208</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>257.353842</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>NaN</td>\n",
       "      <td>49.693429</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>223.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.910400</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.454200</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>668.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>512.329200</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        PassengerId    Survived      Pclass  \\\n",
       "count    891.000000  891.000000  891.000000   \n",
       "unique          NaN         NaN         NaN   \n",
       "top             NaN         NaN         NaN   \n",
       "freq            NaN         NaN         NaN   \n",
       "mean     446.000000    0.383838    2.308642   \n",
       "std      257.353842    0.486592    0.836071   \n",
       "min        1.000000    0.000000    1.000000   \n",
       "25%      223.500000    0.000000    2.000000   \n",
       "50%      446.000000    0.000000    3.000000   \n",
       "75%      668.500000    1.000000    3.000000   \n",
       "max      891.000000    1.000000    3.000000   \n",
       "\n",
       "                                           Name   Sex         Age       SibSp  \\\n",
       "count                                       891   891  714.000000  891.000000   \n",
       "unique                                      891     2         NaN         NaN   \n",
       "top     Andersson, Master. Sigvard Harald Elias  male         NaN         NaN   \n",
       "freq                                          1   577         NaN         NaN   \n",
       "mean                                        NaN   NaN   29.699118    0.523008   \n",
       "std                                         NaN   NaN   14.526497    1.102743   \n",
       "min                                         NaN   NaN    0.420000    0.000000   \n",
       "25%                                         NaN   NaN   20.125000    0.000000   \n",
       "50%                                         NaN   NaN   28.000000    0.000000   \n",
       "75%                                         NaN   NaN   38.000000    1.000000   \n",
       "max                                         NaN   NaN   80.000000    8.000000   \n",
       "\n",
       "             Parch    Ticket        Fare        Cabin Embarked  \n",
       "count   891.000000       891  891.000000          204      889  \n",
       "unique         NaN       681         NaN          147        3  \n",
       "top            NaN  CA. 2343         NaN  C23 C25 C27        S  \n",
       "freq           NaN         7         NaN            4      644  \n",
       "mean      0.381594       NaN   32.204208          NaN      NaN  \n",
       "std       0.806057       NaN   49.693429          NaN      NaN  \n",
       "min       0.000000       NaN    0.000000          NaN      NaN  \n",
       "25%       0.000000       NaN    7.910400          NaN      NaN  \n",
       "50%       0.000000       NaN   14.454200          NaN      NaN  \n",
       "75%       0.000000       NaN   31.000000          NaN      NaN  \n",
       "max       6.000000       NaN  512.329200          NaN      NaN  "
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#describe the data\n",
    "titanic_data.describe(include=\"all\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
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       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
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       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Moran, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330877</td>\n",
       "      <td>8.4583</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
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       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Palsson, Master. Gosta Leonard</td>\n",
       "      <td>male</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>349909</td>\n",
       "      <td>21.0750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>884</td>\n",
       "      <td>885</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Sutehall, Mr. Henry Jr</td>\n",
       "      <td>male</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>SOTON/OQ 392076</td>\n",
       "      <td>7.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>885</td>\n",
       "      <td>886</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Rice, Mrs. William (Margaret Norton)</td>\n",
       "      <td>female</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>382652</td>\n",
       "      <td>29.1250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>886</td>\n",
       "      <td>887</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Montvila, Rev. Juozas</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>211536</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>888</td>\n",
       "      <td>889</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>W./C. 6607</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>890</td>\n",
       "      <td>891</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Dooley, Mr. Patrick</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370376</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>708 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass                                      Name  \\\n",
       "0              1         0       3                   Braund, Mr. Owen Harris   \n",
       "2              3         1       3                    Heikkinen, Miss. Laina   \n",
       "4              5         0       3                  Allen, Mr. William Henry   \n",
       "5              6         0       3                          Moran, Mr. James   \n",
       "7              8         0       3            Palsson, Master. Gosta Leonard   \n",
       "..           ...       ...     ...                                       ...   \n",
       "884          885         0       3                    Sutehall, Mr. Henry Jr   \n",
       "885          886         0       3      Rice, Mrs. William (Margaret Norton)   \n",
       "886          887         0       2                     Montvila, Rev. Juozas   \n",
       "888          889         0       3  Johnston, Miss. Catherine Helen \"Carrie\"   \n",
       "890          891         0       3                       Dooley, Mr. Patrick   \n",
       "\n",
       "        Sex   Age  SibSp  Parch            Ticket     Fare Cabin Embarked  \n",
       "0      male  22.0      1      0         A/5 21171   7.2500   NaN        S  \n",
       "2    female  26.0      0      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "4      male  35.0      0      0            373450   8.0500   NaN        S  \n",
       "5      male   NaN      0      0            330877   8.4583   NaN        Q  \n",
       "7      male   2.0      3      1            349909  21.0750   NaN        S  \n",
       "..      ...   ...    ...    ...               ...      ...   ...      ...  \n",
       "884    male  25.0      0      0   SOTON/OQ 392076   7.0500   NaN        S  \n",
       "885  female  39.0      0      5            382652  29.1250   NaN        Q  \n",
       "886    male  27.0      0      0            211536  13.0000   NaN        S  \n",
       "888  female   NaN      1      2        W./C. 6607  23.4500   NaN        S  \n",
       "890    male  32.0      0      0            370376   7.7500   NaN        Q  \n",
       "\n",
       "[708 rows x 12 columns]"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check for missing data\n",
    "titanic_data[titanic_data.isnull().any(axis=1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>McCarthy, Mr. Timothy J</td>\n",
       "      <td>male</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17463</td>\n",
       "      <td>51.8625</td>\n",
       "      <td>E46</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Sandstrom, Miss. Marguerite Rut</td>\n",
       "      <td>female</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>PP 9549</td>\n",
       "      <td>16.7000</td>\n",
       "      <td>G6</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Bonnell, Miss. Elizabeth</td>\n",
       "      <td>female</td>\n",
       "      <td>58.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113783</td>\n",
       "      <td>26.5500</td>\n",
       "      <td>C103</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>871</td>\n",
       "      <td>872</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Beckwith, Mrs. Richard Leonard (Sallie Monypeny)</td>\n",
       "      <td>female</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>11751</td>\n",
       "      <td>52.5542</td>\n",
       "      <td>D35</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>872</td>\n",
       "      <td>873</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Carlsson, Mr. Frans Olof</td>\n",
       "      <td>male</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>695</td>\n",
       "      <td>5.0000</td>\n",
       "      <td>B51 B53 B55</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>879</td>\n",
       "      <td>880</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)</td>\n",
       "      <td>female</td>\n",
       "      <td>56.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>11767</td>\n",
       "      <td>83.1583</td>\n",
       "      <td>C50</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>887</td>\n",
       "      <td>888</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Graham, Miss. Margaret Edith</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112053</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>B42</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>889</td>\n",
       "      <td>890</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Behr, Mr. Karl Howell</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111369</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>C148</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>183 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass  \\\n",
       "1              2         1       1   \n",
       "3              4         1       1   \n",
       "6              7         0       1   \n",
       "10            11         1       3   \n",
       "11            12         1       1   \n",
       "..           ...       ...     ...   \n",
       "871          872         1       1   \n",
       "872          873         0       1   \n",
       "879          880         1       1   \n",
       "887          888         1       1   \n",
       "889          890         1       1   \n",
       "\n",
       "                                                  Name     Sex   Age  SibSp  \\\n",
       "1    Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "6                              McCarthy, Mr. Timothy J    male  54.0      0   \n",
       "10                     Sandstrom, Miss. Marguerite Rut  female   4.0      1   \n",
       "11                            Bonnell, Miss. Elizabeth  female  58.0      0   \n",
       "..                                                 ...     ...   ...    ...   \n",
       "871   Beckwith, Mrs. Richard Leonard (Sallie Monypeny)  female  47.0      1   \n",
       "872                           Carlsson, Mr. Frans Olof    male  33.0      0   \n",
       "879      Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)  female  56.0      0   \n",
       "887                       Graham, Miss. Margaret Edith  female  19.0      0   \n",
       "889                              Behr, Mr. Karl Howell    male  26.0      0   \n",
       "\n",
       "     Parch    Ticket     Fare        Cabin Embarked  \n",
       "1        0  PC 17599  71.2833          C85        C  \n",
       "3        0    113803  53.1000         C123        S  \n",
       "6        0     17463  51.8625          E46        S  \n",
       "10       1   PP 9549  16.7000           G6        S  \n",
       "11       0    113783  26.5500         C103        S  \n",
       "..     ...       ...      ...          ...      ...  \n",
       "871      1     11751  52.5542          D35        S  \n",
       "872      0       695   5.0000  B51 B53 B55        S  \n",
       "879      1     11767  83.1583          C50        C  \n",
       "887      0    112053  30.0000          B42        S  \n",
       "889      0    111369  30.0000         C148        C  \n",
       "\n",
       "[183 rows x 12 columns]"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#remove missing data columns\n",
    "titanic_data=titanic_data.dropna()\n",
    "titanic_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 4], dtype=int64)"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check again for missing values \n",
    "#titanic_data[titanic_data.isnull().any(axis=1)]\n",
    "\n",
    "titanic_data['Parch'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import sklearn preprocessing api for feature incoding\n",
    "from sklearn import preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Encode the Sex column in numeric form \n",
    "le = preprocessing.LabelEncoder()\n",
    "titanic_data['Sex'] = le.fit_transform(titanic_data['Sex'].astype(str))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['female' 'male']\n"
     ]
    }
   ],
   "source": [
    "print(le.classes_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1      0\n",
       "3      0\n",
       "6      1\n",
       "10     0\n",
       "11     0\n",
       "      ..\n",
       "871    0\n",
       "872    1\n",
       "879    0\n",
       "887    0\n",
       "889    1\n",
       "Name: Sex, Length: 183, dtype: int32"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#validate gender\n",
    "titanic_data['Sex']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "#now standerdize the different numeric columns if required\n",
    "#titanic_data[['math score']]= preprocessing.scale(titanic_data[['math score']])\n",
    "#titanic_data[['reading score']]= preprocessing.scale(titanic_data[['reading score']])\n",
    "#titanic_data[['writing score']]= preprocessing.scale(titanic_data[['writing score']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['C', 'S', 'Q'], dtype=object)"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check fro unique values in Embarked column\n",
    "titanic_data.Embarked.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1      0\n",
       "3      0\n",
       "6      1\n",
       "10     0\n",
       "11     0\n",
       "      ..\n",
       "871    0\n",
       "872    1\n",
       "879    0\n",
       "887    0\n",
       "889    1\n",
       "Name: Sex, Length: 183, dtype: int32"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_data['Sex']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>McCarthy, Mr. Timothy J</td>\n",
       "      <td>1</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17463</td>\n",
       "      <td>51.8625</td>\n",
       "      <td>E46</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Sandstrom, Miss. Marguerite Rut</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>PP 9549</td>\n",
       "      <td>16.7000</td>\n",
       "      <td>G6</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Bonnell, Miss. Elizabeth</td>\n",
       "      <td>0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113783</td>\n",
       "      <td>26.5500</td>\n",
       "      <td>C103</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>871</td>\n",
       "      <td>872</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Beckwith, Mrs. Richard Leonard (Sallie Monypeny)</td>\n",
       "      <td>0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>11751</td>\n",
       "      <td>52.5542</td>\n",
       "      <td>D35</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>872</td>\n",
       "      <td>873</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Carlsson, Mr. Frans Olof</td>\n",
       "      <td>1</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>695</td>\n",
       "      <td>5.0000</td>\n",
       "      <td>B51 B53 B55</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>879</td>\n",
       "      <td>880</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)</td>\n",
       "      <td>0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>11767</td>\n",
       "      <td>83.1583</td>\n",
       "      <td>C50</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>887</td>\n",
       "      <td>888</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Graham, Miss. Margaret Edith</td>\n",
       "      <td>0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112053</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>B42</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>889</td>\n",
       "      <td>890</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Behr, Mr. Karl Howell</td>\n",
       "      <td>1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111369</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>C148</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>183 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass  \\\n",
       "1              2         1       1   \n",
       "3              4         1       1   \n",
       "6              7         0       1   \n",
       "10            11         1       3   \n",
       "11            12         1       1   \n",
       "..           ...       ...     ...   \n",
       "871          872         1       1   \n",
       "872          873         0       1   \n",
       "879          880         1       1   \n",
       "887          888         1       1   \n",
       "889          890         1       1   \n",
       "\n",
       "                                                  Name  Sex   Age  SibSp  \\\n",
       "1    Cumings, Mrs. John Bradley (Florence Briggs Th...    0  38.0      1   \n",
       "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)    0  35.0      1   \n",
       "6                              McCarthy, Mr. Timothy J    1  54.0      0   \n",
       "10                     Sandstrom, Miss. Marguerite Rut    0   4.0      1   \n",
       "11                            Bonnell, Miss. Elizabeth    0  58.0      0   \n",
       "..                                                 ...  ...   ...    ...   \n",
       "871   Beckwith, Mrs. Richard Leonard (Sallie Monypeny)    0  47.0      1   \n",
       "872                           Carlsson, Mr. Frans Olof    1  33.0      0   \n",
       "879      Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)    0  56.0      0   \n",
       "887                       Graham, Miss. Margaret Edith    0  19.0      0   \n",
       "889                              Behr, Mr. Karl Howell    1  26.0      0   \n",
       "\n",
       "     Parch    Ticket     Fare        Cabin Embarked  \n",
       "1        0  PC 17599  71.2833          C85        C  \n",
       "3        0    113803  53.1000         C123        S  \n",
       "6        0     17463  51.8625          E46        S  \n",
       "10       1   PP 9549  16.7000           G6        S  \n",
       "11       0    113783  26.5500         C103        S  \n",
       "..     ...       ...      ...          ...      ...  \n",
       "871      1     11751  52.5542          D35        S  \n",
       "872      0       695   5.0000  B51 B53 B55        S  \n",
       "879      1     11767  83.1583          C50        C  \n",
       "887      0    112053  30.0000          B42        S  \n",
       "889      0    111369  30.0000         C148        C  \n",
       "\n",
       "[183 rows x 12 columns]"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#remove missing data columns\n",
    "titanic_data=titanic_data.dropna()\n",
    "titanic_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic_data = titanic_data.drop('Ticket', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic_data = titanic_data.drop('Name', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
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       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
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       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>16.7000</td>\n",
       "      <td>G6</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
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       "      <td>871</td>\n",
       "      <td>872</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>52.5542</td>\n",
       "      <td>D35</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>872</td>\n",
       "      <td>873</td>\n",
       "      <td>0</td>\n",
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       "      <td>33.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5.0000</td>\n",
       "      <td>B51 B53 B55</td>\n",
       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <td>879</td>\n",
       "      <td>880</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>83.1583</td>\n",
       "      <td>C50</td>\n",
       "      <td>C</td>\n",
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       "    <tr>\n",
       "      <td>887</td>\n",
       "      <td>888</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>B42</td>\n",
       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <td>889</td>\n",
       "      <td>890</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>C148</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>183 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass  Sex   Age  SibSp  Parch     Fare  \\\n",
       "1              2         1       1    0  38.0      1      0  71.2833   \n",
       "3              4         1       1    0  35.0      1      0  53.1000   \n",
       "6              7         0       1    1  54.0      0      0  51.8625   \n",
       "10            11         1       3    0   4.0      1      1  16.7000   \n",
       "11            12         1       1    0  58.0      0      0  26.5500   \n",
       "..           ...       ...     ...  ...   ...    ...    ...      ...   \n",
       "871          872         1       1    0  47.0      1      1  52.5542   \n",
       "872          873         0       1    1  33.0      0      0   5.0000   \n",
       "879          880         1       1    0  56.0      0      1  83.1583   \n",
       "887          888         1       1    0  19.0      0      0  30.0000   \n",
       "889          890         1       1    1  26.0      0      0  30.0000   \n",
       "\n",
       "           Cabin Embarked  \n",
       "1            C85        C  \n",
       "3           C123        S  \n",
       "6            E46        S  \n",
       "10            G6        S  \n",
       "11          C103        S  \n",
       "..           ...      ...  \n",
       "871          D35        S  \n",
       "872  B51 B53 B55        S  \n",
       "879          C50        C  \n",
       "887          B42        S  \n",
       "889         C148        C  \n",
       "\n",
       "[183 rows x 10 columns]"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
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       "  <tbody>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [PassengerId, Survived, Pclass, Sex, Age, SibSp, Parch, Fare, Cabin, Embarked]\n",
       "Index: []"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check for missing data\n",
    "titanic_data[titanic_data.isnull().any(axis=1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic_data = titanic_data.drop('Cabin', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>3</td>\n",
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       "      <td>6</td>\n",
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       "      <td>0</td>\n",
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       "      <td>10</td>\n",
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       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
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       "      <td>1</td>\n",
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       "      <td>58.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <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",
       "      <td>871</td>\n",
       "      <td>872</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>52.5542</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>872</td>\n",
       "      <td>873</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5.0000</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>879</td>\n",
       "      <td>880</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>83.1583</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>887</td>\n",
       "      <td>888</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>889</td>\n",
       "      <td>890</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>183 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass  Sex   Age  SibSp  Parch     Fare Embarked\n",
       "1              2         1       1    0  38.0      1      0  71.2833        C\n",
       "3              4         1       1    0  35.0      1      0  53.1000        S\n",
       "6              7         0       1    1  54.0      0      0  51.8625        S\n",
       "10            11         1       3    0   4.0      1      1  16.7000        S\n",
       "11            12         1       1    0  58.0      0      0  26.5500        S\n",
       "..           ...       ...     ...  ...   ...    ...    ...      ...      ...\n",
       "871          872         1       1    0  47.0      1      1  52.5542        S\n",
       "872          873         0       1    1  33.0      0      0   5.0000        S\n",
       "879          880         1       1    0  56.0      0      1  83.1583        C\n",
       "887          888         1       1    0  19.0      0      0  30.0000        S\n",
       "889          890         1       1    1  26.0      0      0  30.0000        C\n",
       "\n",
       "[183 rows x 9 columns]"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\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>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [PassengerId, Survived, Pclass, Sex, Age, SibSp, Parch, Fare, Embarked]\n",
       "Index: []"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check for missing data\n",
    "titanic_data[titanic_data.isnull().any(axis=1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic_data=pd.get_dummies(titanic_data, columns=[\n",
    "    'Embarked'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked_C</th>\n",
       "      <th>Embarked_Q</th>\n",
       "      <th>Embarked_S</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>38.0</td>\n",
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       "      <td>71.2833</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
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       "      <td>35.0</td>\n",
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       "      <td>53.1000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>51.8625</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>16.7000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>26.5500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>871</td>\n",
       "      <td>872</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>52.5542</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>872</td>\n",
       "      <td>873</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>879</td>\n",
       "      <td>880</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>83.1583</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>887</td>\n",
       "      <td>888</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>889</td>\n",
       "      <td>890</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>183 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass  Sex   Age  SibSp  Parch     Fare  \\\n",
       "1              2         1       1    0  38.0      1      0  71.2833   \n",
       "3              4         1       1    0  35.0      1      0  53.1000   \n",
       "6              7         0       1    1  54.0      0      0  51.8625   \n",
       "10            11         1       3    0   4.0      1      1  16.7000   \n",
       "11            12         1       1    0  58.0      0      0  26.5500   \n",
       "..           ...       ...     ...  ...   ...    ...    ...      ...   \n",
       "871          872         1       1    0  47.0      1      1  52.5542   \n",
       "872          873         0       1    1  33.0      0      0   5.0000   \n",
       "879          880         1       1    0  56.0      0      1  83.1583   \n",
       "887          888         1       1    0  19.0      0      0  30.0000   \n",
       "889          890         1       1    1  26.0      0      0  30.0000   \n",
       "\n",
       "     Embarked_C  Embarked_Q  Embarked_S  \n",
       "1             1           0           0  \n",
       "3             0           0           1  \n",
       "6             0           0           1  \n",
       "10            0           0           1  \n",
       "11            0           0           1  \n",
       "..          ...         ...         ...  \n",
       "871           0           0           1  \n",
       "872           0           0           1  \n",
       "879           1           0           0  \n",
       "887           0           0           1  \n",
       "889           1           0           0  \n",
       "\n",
       "[183 rows x 11 columns]"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked_C</th>\n",
       "      <th>Embarked_Q</th>\n",
       "      <th>Embarked_S</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [PassengerId, Survived, Pclass, Sex, Age, SibSp, Parch, Fare, Embarked_C, Embarked_Q, Embarked_S]\n",
       "Index: []"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check for missing data\n",
    "titanic_data[titanic_data.isnull().any(axis=1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Using linear regression\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X = titanic_data.drop('Survived', axis=1)\n",
    "\n",
    "#Taking the labels (Survived)\n",
    "Y = titanic_data['Survived']\n",
    "\n",
    "# Spliting into 80% for training set and 20% for testing set so we can see our accuracy\n",
    "\n",
    "X_train, x_test, Y_train, y_test = train_test_split(X, Y, test_size=0.2, random_state=0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "linear_model = LinearRegression()\n",
    "linear_model.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.33577325504633904"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linear_model.score(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2.65681603e-04, -9.71204709e-02, -4.30573743e-01, -6.65251627e-03,\n",
       "        6.95937108e-02, -7.15774196e-02,  3.32962009e-04,  9.24231923e-02,\n",
       "       -1.61860324e-01,  6.94371319e-02])"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linear_model.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sex           -0.430574\n",
      "Embarked_Q    -0.161860\n",
      "Pclass        -0.097120\n",
      "Parch         -0.071577\n",
      "Age           -0.006653\n",
      "PassengerId    0.000266\n",
      "Fare           0.000333\n",
      "Embarked_S     0.069437\n",
      "SibSp          0.069594\n",
      "Embarked_C     0.092423\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "predictors = X_train.columns\n",
    "coef = pd.Series(linear_model.coef_, predictors).sort_values()\n",
    "print(coef)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_predict = linear_model.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%pylab inline\n",
    "\n",
    "pylab.rcParams['figure.figsize']=(15, 6)\n",
    "\n",
    "plt.plot(y_predict, label='Predicted')\n",
    "plt.plot(y_test.values, label='Actual')\n",
    "plt.ylabel('Price')\n",
    "\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2913153385333934"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r_square = linear_model.score(x_test, y_test)\n",
    "r_square"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.10870984580568836"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "linear_model_mse = mean_squared_error(y_predict, y_test)\n",
    "linear_model_mse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.32971176170359523"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import math\n",
    "math.sqrt(linear_model_mse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Lasso(alpha=0.2, copy_X=True, fit_intercept=True, max_iter=1000, normalize=True,\n",
       "      positive=False, precompute=False, random_state=None, selection='cyclic',\n",
       "      tol=0.0001, warm_start=False)"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Lasso demo\n",
    "\n",
    "from sklearn.linear_model import Lasso\n",
    "\n",
    "lasso_model = Lasso(alpha=0.2, normalize=True)\n",
    "lasso_model.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lasso_model.score(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PassengerId    0.0\n",
      "Pclass        -0.0\n",
      "Sex           -0.0\n",
      "Age           -0.0\n",
      "SibSp          0.0\n",
      "Parch         -0.0\n",
      "Fare           0.0\n",
      "Embarked_C     0.0\n",
      "Embarked_Q    -0.0\n",
      "Embarked_S    -0.0\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "coef = pd.Series(lasso_model.coef_, predictors).sort_values()\n",
    "print(coef)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_predict=lasso_model.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pylab.rcParams['figure.figsize']=(15, 6)\n",
    "\n",
    "plt.plot(y_predict, label='Predicted')\n",
    "plt.plot(y_test.values, label='Actual')\n",
    "plt.ylabel('Price')\n",
    "\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.19697276358469804"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r_square = lasso_model.score(x_test, y_test)\n",
    "r_square"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4284992414993432"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lasso_nodel_mse = mean_squared_error(y_predict, y_test)\n",
    "math.sqrt(lasso_nodel_mse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ridge(alpha=0.1, copy_X=True, fit_intercept=True, max_iter=None, normalize=True,\n",
       "      random_state=None, solver='auto', tol=0.001)"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Ridge demo\n",
    "from sklearn.linear_model import Ridge\n",
    "\n",
    "ridge_model = Ridge(alpha=0.1, normalize=True)\n",
    "ridge_model.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.33335345747274525"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ridge_model.score(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sex           -0.363815\n",
      "Embarked_Q    -0.182862\n",
      "Pclass        -0.072773\n",
      "Parch         -0.052196\n",
      "Embarked_S    -0.008079\n",
      "Age           -0.005605\n",
      "PassengerId    0.000226\n",
      "Fare           0.000337\n",
      "Embarked_C     0.019198\n",
      "SibSp          0.060711\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "coef = pd.Series(ridge_model.coef_, predictors).sort_values()\n",
    "print(coef)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_predict = ridge_model.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pylab.rcParams['figure.figsize']=(15, 6)\n",
    "\n",
    "plt.plot(y_predict, label='Predicted')\n",
    "plt.plot(y_test.values, label='Actual')\n",
    "plt.ylabel('Price')\n",
    "\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.28652339138166105"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r_square = ridge_model.score(x_test, y_test)\n",
    "r_square"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.33082459763031774"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ridge_model_mse = mean_squared_error(y_predict, y_test)\n",
    "math.sqrt(ridge_model_mse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}