titanic_machine_learning_from_disaster.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sklearn\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic_df = pd.read_csv('datasets/train.csv')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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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",
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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",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
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       "    <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",
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       "      <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",
       "    <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>95</td>\n",
       "      <td>96</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Shorney, Mr. Charles Joseph</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>374910</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <td>96</td>\n",
       "      <td>97</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Goldschmidt, Mr. George B</td>\n",
       "      <td>male</td>\n",
       "      <td>71.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17754</td>\n",
       "      <td>34.6542</td>\n",
       "      <td>A5</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>97</td>\n",
       "      <td>98</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Greenfield, Mr. William Bertram</td>\n",
       "      <td>male</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>PC 17759</td>\n",
       "      <td>63.3583</td>\n",
       "      <td>D10 D12</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>98</td>\n",
       "      <td>99</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Doling, Mrs. John T (Ada Julia Bone)</td>\n",
       "      <td>female</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>231919</td>\n",
       "      <td>23.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
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       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Kantor, Mr. Sinai</td>\n",
       "      <td>male</td>\n",
       "      <td>34.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>244367</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    PassengerId  Survived  Pclass  \\\n",
       "0             1         0       3   \n",
       "1             2         1       1   \n",
       "2             3         1       3   \n",
       "3             4         1       1   \n",
       "4             5         0       3   \n",
       "..          ...       ...     ...   \n",
       "95           96         0       3   \n",
       "96           97         0       1   \n",
       "97           98         1       1   \n",
       "98           99         1       2   \n",
       "99          100         0       2   \n",
       "\n",
       "                                                 Name     Sex   Age  SibSp  \\\n",
       "0                             Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1   Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                              Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3        Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                            Allen, Mr. William Henry    male  35.0      0   \n",
       "..                                                ...     ...   ...    ...   \n",
       "95                        Shorney, Mr. Charles Joseph    male   NaN      0   \n",
       "96                          Goldschmidt, Mr. George B    male  71.0      0   \n",
       "97                    Greenfield, Mr. William Bertram    male  23.0      0   \n",
       "98               Doling, Mrs. John T (Ada Julia Bone)  female  34.0      0   \n",
       "99                                  Kantor, Mr. Sinai    male  34.0      1   \n",
       "\n",
       "    Parch            Ticket     Fare    Cabin Embarked  \n",
       "0       0         A/5 21171   7.2500      NaN        S  \n",
       "1       0          PC 17599  71.2833      C85        C  \n",
       "2       0  STON/O2. 3101282   7.9250      NaN        S  \n",
       "3       0            113803  53.1000     C123        S  \n",
       "4       0            373450   8.0500      NaN        S  \n",
       "..    ...               ...      ...      ...      ...  \n",
       "95      0            374910   8.0500      NaN        S  \n",
       "96      0          PC 17754  34.6542       A5        C  \n",
       "97      1          PC 17759  63.3583  D10 D12        C  \n",
       "98      1            231919  23.0000      NaN        S  \n",
       "99      0            244367  26.0000      NaN        S  \n",
       "\n",
       "[100 rows x 12 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_df.head(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(891, 12)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "    titanic_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic_df.drop(['PassengerId','Name','Ticket','Cabin'], 'columns', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
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       "      <td>7.2500</td>\n",
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       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
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       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
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       "      <td>3</td>\n",
       "      <td>1</td>\n",
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       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
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       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   Survived  Pclass     Sex   Age  SibSp  Parch     Fare Embarked\n",
       "0         0       3    male  22.0      1      0   7.2500        S\n",
       "1         1       1  female  38.0      1      0  71.2833        C\n",
       "2         1       3  female  26.0      0      0   7.9250        S\n",
       "3         1       1  female  35.0      1      0  53.1000        S\n",
       "4         0       3    male  35.0      0      0   8.0500        S"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(titanic_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Survived    179\n",
       "Pclass      179\n",
       "Sex         179\n",
       "Age           2\n",
       "SibSp       179\n",
       "Parch       179\n",
       "Fare        179\n",
       "Embarked    177\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_df [titanic_df.isnull().any(axis=1)].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic_df = titanic_df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Survived    0\n",
       "Pclass      0\n",
       "Sex         0\n",
       "Age         0\n",
       "SibSp       0\n",
       "Parch       0\n",
       "Fare        0\n",
       "Embarked    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_df [titanic_df.isnull().any(axis=1)].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>712.000000</td>\n",
       "      <td>712.000000</td>\n",
       "      <td>712.000000</td>\n",
       "      <td>712.000000</td>\n",
       "      <td>712.000000</td>\n",
       "      <td>712.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>0.404494</td>\n",
       "      <td>2.240169</td>\n",
       "      <td>29.642093</td>\n",
       "      <td>0.514045</td>\n",
       "      <td>0.432584</td>\n",
       "      <td>34.567251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>0.491139</td>\n",
       "      <td>0.836854</td>\n",
       "      <td>14.492933</td>\n",
       "      <td>0.930692</td>\n",
       "      <td>0.854181</td>\n",
       "      <td>52.938648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.050000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>15.645850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>33.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Survived      Pclass         Age       SibSp       Parch        Fare\n",
       "count  712.000000  712.000000  712.000000  712.000000  712.000000  712.000000\n",
       "mean     0.404494    2.240169   29.642093    0.514045    0.432584   34.567251\n",
       "std      0.491139    0.836854   14.492933    0.930692    0.854181   52.938648\n",
       "min      0.000000    1.000000    0.420000    0.000000    0.000000    0.000000\n",
       "25%      0.000000    1.000000   20.000000    0.000000    0.000000    8.050000\n",
       "50%      0.000000    2.000000   28.000000    0.000000    0.000000   15.645850\n",
       "75%      1.000000    3.000000   38.000000    1.000000    1.000000   33.000000\n",
       "max      1.000000    3.000000   80.000000    5.000000    6.000000  512.329200"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Survived')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12,8))\n",
    "plt.scatter(titanic_df['Age'], titanic_df['Survived'])\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Survived')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-22-c7f8959a86ca>, line 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-22-c7f8959a86ca>\"\u001b[1;36m, line \u001b[1;32m1\u001b[0m\n\u001b[1;33m    pd.crosstab(titanic_df['Sex'], titanic_df['Survived'])A\u001b[0m\n\u001b[1;37m                                                          ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "pd.crosstab(titanic_df['Sex'], titanic_df['Survived'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20a30d4d848>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(titanic_data_corr,annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Survived</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.356462</td>\n",
       "      <td>-0.082446</td>\n",
       "      <td>-0.015523</td>\n",
       "      <td>0.095265</td>\n",
       "      <td>0.266100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Pclass</td>\n",
       "      <td>-0.356462</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.365902</td>\n",
       "      <td>0.065187</td>\n",
       "      <td>0.023666</td>\n",
       "      <td>-0.552893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Age</td>\n",
       "      <td>-0.082446</td>\n",
       "      <td>-0.365902</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.307351</td>\n",
       "      <td>-0.187896</td>\n",
       "      <td>0.093143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>SibSp</td>\n",
       "      <td>-0.015523</td>\n",
       "      <td>0.065187</td>\n",
       "      <td>-0.307351</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.383338</td>\n",
       "      <td>0.139860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Parch</td>\n",
       "      <td>0.095265</td>\n",
       "      <td>0.023666</td>\n",
       "      <td>-0.187896</td>\n",
       "      <td>0.383338</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.206624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Fare</td>\n",
       "      <td>0.266100</td>\n",
       "      <td>-0.552893</td>\n",
       "      <td>0.093143</td>\n",
       "      <td>0.139860</td>\n",
       "      <td>0.206624</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Survived    Pclass       Age     SibSp     Parch      Fare\n",
       "Survived  1.000000 -0.356462 -0.082446 -0.015523  0.095265  0.266100\n",
       "Pclass   -0.356462  1.000000 -0.365902  0.065187  0.023666 -0.552893\n",
       "Age      -0.082446 -0.365902  1.000000 -0.307351 -0.187896  0.093143\n",
       "SibSp    -0.015523  0.065187 -0.307351  1.000000  0.383338  0.139860\n",
       "Parch     0.095265  0.023666 -0.187896  0.383338  1.000000  0.206624\n",
       "Fare      0.266100 -0.552893  0.093143  0.139860  0.206624  1.000000"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_data_corr = titanic_df.corr()\n",
    "titanic_data_corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex   Age  SibSp  Parch     Fare Embarked\n",
       "0         0       3    1  22.0      1      0   7.2500        S\n",
       "1         1       1    0  38.0      1      0  71.2833        C\n",
       "2         1       3    0  26.0      0      0   7.9250        S\n",
       "3         1       1    0  35.0      1      0  53.1000        S\n",
       "4         0       3    1  35.0      0      0   8.0500        S"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import preprocessing\n",
    "\n",
    "label_encoding = preprocessing.LabelEncoder()\n",
    "titanic_df['Sex'] = label_encoding.fit_transform(titanic_df['Sex'].astype(str))\n",
    "titanic_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "   Survived  Pclass  Sex   Age  SibSp  Parch     Fare  Embarked_C  Embarked_Q  \\\n",
       "0         0       3    1  22.0      1      0   7.2500           0           0   \n",
       "1         1       1    0  38.0      1      0  71.2833           1           0   \n",
       "2         1       3    0  26.0      0      0   7.9250           0           0   \n",
       "3         1       1    0  35.0      1      0  53.1000           0           0   \n",
       "4         0       3    1  35.0      0      0   8.0500           0           0   \n",
       "\n",
       "   Embarked_S  \n",
       "0           1  \n",
       "1           0  \n",
       "2           1  \n",
       "3           1  \n",
       "4           1  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_df = pd.get_dummies(titanic_df, columns=['Embarked'])\n",
    "titanic_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "   Survived  Pclass  Sex   Age  SibSp  Parch     Fare  Embarked_C  Embarked_Q  \\\n",
       "0         0       3    1  29.0      1      0   7.0458           0           0   \n",
       "1         0       3    1   4.0      4      2  31.2750           0           0   \n",
       "2         1       2    0   7.0      0      2  26.2500           0           0   \n",
       "3         1       3    0   5.0      0      0  12.4750           0           0   \n",
       "4         1       2    0  19.0      0      0  26.0000           0           0   \n",
       "\n",
       "   Embarked_S  \n",
       "0           1  \n",
       "1           1  \n",
       "2           1  \n",
       "3           1  \n",
       "4           1  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_df = titanic_df.sample(frac=1).reset_index(drop=True) \n",
    "titanic_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "   Survived  Pclass  Sex   Age  SibSp  Parch     Fare  Embarked_C  Embarked_Q  \\\n",
       "0         0       3    1  29.0      1      0   7.0458           0           0   \n",
       "1         0       3    1   4.0      4      2  31.2750           0           0   \n",
       "2         1       2    0   7.0      0      2  26.2500           0           0   \n",
       "3         1       3    0   5.0      0      0  12.4750           0           0   \n",
       "4         1       2    0  19.0      0      0  26.0000           0           0   \n",
       "\n",
       "   Embarked_S  \n",
       "0           1  \n",
       "1           1  \n",
       "2           1  \n",
       "3           1  \n",
       "4           1  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_df.to_csv('datasets/training_processed.csv')\n",
    "titanic_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
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       "      <td>7.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>26.2500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12.4750</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  Survived  Pclass  Sex   Age  SibSp  Parch     Fare  Embarked_C  \\\n",
       "0           0         0       3    1  29.0      1      0   7.0458           0   \n",
       "1           1         0       3    1   4.0      4      2  31.2750           0   \n",
       "2           2         1       2    0   7.0      0      2  26.2500           0   \n",
       "3           3         1       3    0   5.0      0      0  12.4750           0   \n",
       "4           4         1       2    0  19.0      0      0  26.0000           0   \n",
       "\n",
       "   Embarked_Q  Embarked_S  \n",
       "0           0           1  \n",
       "1           0           1  \n",
       "2           0           1  \n",
       "3           0           1  \n",
       "4           0           1  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_df = pd.read_csv('datasets/training_processed.csv')\n",
    "titanic_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X=titanic_df.drop('Survived',axis=1)\n",
    "Y=titanic_df['Survived']\n",
    "\n",
    "x_train, x_test, y_train, y_test = train_test_split(X,Y,test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "logistic_model = LogisticRegression(penalty = 'l2' , C=1.0 , solver='liblinear').fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = logistic_model.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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>y_test</th>\n",
       "      <th>y_pred</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>75</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>563</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>170</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>120</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>515</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     y_test  y_pred\n",
       "75        1       1\n",
       "563       0       0\n",
       "170       1       0\n",
       "120       0       0\n",
       "515       1       0"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_results = pd.DataFrame ( {'y_test' : y_test, 'y_pred' : y_pred})\n",
    "pred_results.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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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>y_test</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>y_pred</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>71</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "y_test   0   1\n",
       "y_pred        \n",
       "0       71  19\n",
       "1       12  41"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_crosstab = pd.crosstab(pred_results.y_pred, pred_results.y_test)\n",
    "titanic_crosstab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import precision_score\n",
    "from sklearn.metrics import recall_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy score :  0.7832167832167832\n",
      "precision score :  0.7735849056603774\n",
      "recall score :  0.6833333333333333\n"
     ]
    }
   ],
   "source": [
    "acc = accuracy_score(y_test, y_pred)\n",
    "prec = precision_score(y_test, y_pred)\n",
    "recall = recall_score(y_test, y_pred)\n",
    "\n",
    "print ( \"accuracy score : \" , acc)\n",
    "print ( \"precision score : \" , prec)\n",
    "print ( \"recall score : \" , recall)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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