titanic-machine-learning.ipynb

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     "text": [
      "/kaggle/input/titanic/train.csv\n",
      "/kaggle/input/titanic/test.csv\n",
      "/kaggle/input/titanic/gender_submission.csv\n"
     ]
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   "source": [
    "# This Python 3 environment comes with many helpful analytics libraries installed\n",
    "# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
    "# For example, here's several helpful packages to load\n",
    "\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "\n",
    "# Input data files are available in the read-only \"../input/\" directory\n",
    "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
    "\n",
    "import os\n",
    "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
    "    for filename in filenames:\n",
    "        print(os.path.join(dirname, filename))\n",
    "\n",
    "# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n",
    "# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"
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     "text": [
      "['train.csv', 'test.csv', 'gender_submission.csv']\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(418, 2)"
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   "source": [
    "print(os.listdir('/kaggle/input/titanic'))\n",
    "base_dir = '/kaggle/input/titanic'\n",
    "\n",
    "data_gender = pd.read_csv(base_dir + '/gender_submission.csv')\n",
    "data_gender.head()\n",
    "data_gender.shape"
   ]
  },
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       "1            2         1       1   \n",
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       "3            4         1       1   \n",
       "4            5         0       3   \n",
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       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
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   "source": [
    "data = pd.read_csv(base_dir + '/train.csv')\n",
    "data.head()"
   ]
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       "PassengerId      0\n",
       "Survived         0\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "Age            177\n",
       "SibSp            0\n",
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       "Cabin          687\n",
       "Embarked         2\n",
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     "execution_count": 4,
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   "source": [
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "# checking for null values in the dataset\n",
    "data.isna().sum()"
   ]
  },
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       "      <td>3</td>\n",
       "      <td>Razi, Mr. Raihed</td>\n",
       "      <td>male</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
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       "      <td>864</td>\n",
       "      <td>0</td>\n",
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       "      <td>Sage, Miss. Dorothy Edith \"Dolly\"</td>\n",
       "      <td>female</td>\n",
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       "      <td>8</td>\n",
       "      <td>2</td>\n",
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       "      <td>69.5500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
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       "      <th>868</th>\n",
       "      <td>869</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>van Melkebeke, Mr. Philemon</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
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       "      <td>879</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Laleff, Mr. Kristo</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>NaN</td>\n",
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       "      <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",
       "  </tbody>\n",
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       "     PassengerId  Survived  Pclass                                      Name  \\\n",
       "5              6         0       3                          Moran, Mr. James   \n",
       "17            18         1       2              Williams, Mr. Charles Eugene   \n",
       "19            20         1       3                   Masselmani, Mrs. Fatima   \n",
       "26            27         0       3                   Emir, Mr. Farred Chehab   \n",
       "28            29         1       3             O'Dwyer, Miss. Ellen \"Nellie\"   \n",
       "..           ...       ...     ...                                       ...   \n",
       "859          860         0       3                          Razi, Mr. Raihed   \n",
       "863          864         0       3         Sage, Miss. Dorothy Edith \"Dolly\"   \n",
       "868          869         0       3               van Melkebeke, Mr. Philemon   \n",
       "878          879         0       3                        Laleff, Mr. Kristo   \n",
       "888          889         0       3  Johnston, Miss. Catherine Helen \"Carrie\"   \n",
       "\n",
       "        Sex  Age  SibSp  Parch      Ticket     Fare Cabin Embarked  \n",
       "5      male  NaN      0      0      330877   8.4583   NaN        Q  \n",
       "17     male  NaN      0      0      244373  13.0000   NaN        S  \n",
       "19   female  NaN      0      0        2649   7.2250   NaN        C  \n",
       "26     male  NaN      0      0        2631   7.2250   NaN        C  \n",
       "28   female  NaN      0      0      330959   7.8792   NaN        Q  \n",
       "..      ...  ...    ...    ...         ...      ...   ...      ...  \n",
       "859    male  NaN      0      0        2629   7.2292   NaN        C  \n",
       "863  female  NaN      8      2    CA. 2343  69.5500   NaN        S  \n",
       "868    male  NaN      0      0      345777   9.5000   NaN        S  \n",
       "878    male  NaN      0      0      349217   7.8958   NaN        S  \n",
       "888  female  NaN      1      2  W./C. 6607  23.4500   NaN        S  \n",
       "\n",
       "[177 rows x 12 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
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   "source": [
    "# Performing EDA\n",
    "\n",
    "\n",
    "\n",
    "# Checking data where age is nan\n",
    "data[data['Age'].isna()]"
   ]
  },
  {
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       "      <td>0</td>\n",
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       "      <td>349909</td>\n",
       "      <td>21.0750</td>\n",
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       "      <td>Rice, Mrs. William (Margaret Norton)</td>\n",
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       "      <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",
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       "      <th>886</th>\n",
       "      <td>887</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Montvila, Rev. Juozas</td>\n",
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       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>211536</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>370376</td>\n",
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       "     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",
       "[687 rows x 12 columns]"
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     "execution_count": 6,
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       "     PassengerId  Survived  Pclass                                      Name  \\\n",
       "5              6         0       3                          Moran, Mr. James   \n",
       "17            18         1       2              Williams, Mr. Charles Eugene   \n",
       "19            20         1       3                   Masselmani, Mrs. Fatima   \n",
       "26            27         0       3                   Emir, Mr. Farred Chehab   \n",
       "28            29         1       3             O'Dwyer, Miss. Ellen \"Nellie\"   \n",
       "..           ...       ...     ...                                       ...   \n",
       "859          860         0       3                          Razi, Mr. Raihed   \n",
       "863          864         0       3         Sage, Miss. Dorothy Edith \"Dolly\"   \n",
       "868          869         0       3               van Melkebeke, Mr. Philemon   \n",
       "878          879         0       3                        Laleff, Mr. Kristo   \n",
       "888          889         0       3  Johnston, Miss. Catherine Helen \"Carrie\"   \n",
       "\n",
       "        Sex  Age  SibSp  Parch      Ticket     Fare Cabin Embarked  \n",
       "5      male  NaN      0      0      330877   8.4583   NaN        Q  \n",
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       "26     male  NaN      0      0        2631   7.2250   NaN        C  \n",
       "28   female  NaN      0      0      330959   7.8792   NaN        Q  \n",
       "..      ...  ...    ...    ...         ...      ...   ...      ...  \n",
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       "\n",
       "[158 rows x 12 columns]"
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    },
    "tags": []
   },
   "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>Survived</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>female</th>\n",
       "      <td>81</td>\n",
       "      <td>233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>468</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Survived    0    1\n",
       "Sex               \n",
       "female     81  233\n",
       "male      468  109"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's try to plot male vs female survival ratio\n",
    "\n",
    "data_survival_gender = pd.crosstab(data['Sex'], data['Survived'])\n",
    "data_survival_gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "372d4ce3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-17T10:37:01.058428Z",
     "iopub.status.busy": "2024-11-17T10:37:01.057997Z",
     "iopub.status.idle": "2024-11-17T10:37:01.310786Z",
     "shell.execute_reply": "2024-11-17T10:37:01.309683Z"
    },
    "papermill": {
     "duration": 0.277422,
     "end_time": "2024-11-17T10:37:01.313465",
     "exception": false,
     "start_time": "2024-11-17T10:37:01.036043",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Sex', ylabel='count'>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data=data, x='Sex', hue='Survived')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4c5de1e9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-17T10:37:01.358580Z",
     "iopub.status.busy": "2024-11-17T10:37:01.357473Z",
     "iopub.status.idle": "2024-11-17T10:37:01.390575Z",
     "shell.execute_reply": "2024-11-17T10:37:01.389425Z"
    },
    "papermill": {
     "duration": 0.058557,
     "end_time": "2024-11-17T10:37:01.393264",
     "exception": false,
     "start_time": "2024-11-17T10:37:01.334707",
     "status": "completed"
    },
    "tags": []
   },
   "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>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>32.204208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>257.353842</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>49.693429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>223.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.910400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>668.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
       "count   891.000000  891.000000  891.000000  714.000000  891.000000   \n",
       "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
       "std     257.353842    0.486592    0.836071   14.526497    1.102743   \n",
       "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
       "25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n",
       "50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n",
       "75%     668.500000    1.000000    3.000000   38.000000    1.000000   \n",
       "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
       "\n",
       "            Parch        Fare  \n",
       "count  891.000000  891.000000  \n",
       "mean     0.381594   32.204208  \n",
       "std      0.806057   49.693429  \n",
       "min      0.000000    0.000000  \n",
       "25%      0.000000    7.910400  \n",
       "50%      0.000000   14.454200  \n",
       "75%      0.000000   31.000000  \n",
       "max      6.000000  512.329200  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "22a93246",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-17T10:37:01.438688Z",
     "iopub.status.busy": "2024-11-17T10:37:01.438228Z",
     "iopub.status.idle": "2024-11-17T10:37:03.725900Z",
     "shell.execute_reply": "2024-11-17T10:37:03.724573Z"
    },
    "papermill": {
     "duration": 2.313836,
     "end_time": "2024-11-17T10:37:03.729299",
     "exception": false,
     "start_time": "2024-11-17T10:37:01.415463",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 2000x1500 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.hist(bins=50, figsize=(20, 15))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "461ea5d8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-17T10:37:03.776500Z",
     "iopub.status.busy": "2024-11-17T10:37:03.776093Z",
     "iopub.status.idle": "2024-11-17T10:37:03.796435Z",
     "shell.execute_reply": "2024-11-17T10:37:03.795311Z"
    },
    "papermill": {
     "duration": 0.046996,
     "end_time": "2024-11-17T10:37:03.799044",
     "exception": false,
     "start_time": "2024-11-17T10:37:03.752048",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  891 non-null    int64  \n",
      " 1   Survived     891 non-null    int64  \n",
      " 2   Pclass       891 non-null    int64  \n",
      " 3   Name         891 non-null    object \n",
      " 4   Sex          891 non-null    object \n",
      " 5   Age          714 non-null    float64\n",
      " 6   SibSp        891 non-null    int64  \n",
      " 7   Parch        891 non-null    int64  \n",
      " 8   Ticket       891 non-null    object \n",
      " 9   Fare         891 non-null    float64\n",
      " 10  Cabin        204 non-null    object \n",
      " 11  Embarked     889 non-null    object \n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.7+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "483a050d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-17T10:37:03.846928Z",
     "iopub.status.busy": "2024-11-17T10:37:03.846420Z",
     "iopub.status.idle": "2024-11-17T10:37:03.856664Z",
     "shell.execute_reply": "2024-11-17T10:37:03.855628Z"
    },
    "papermill": {
     "duration": 0.036571,
     "end_time": "2024-11-17T10:37:03.859071",
     "exception": false,
     "start_time": "2024-11-17T10:37:03.822500",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Survived         0\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "Age            177\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             0\n",
       "Cabin          687\n",
       "Embarked         2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
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       "   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",
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       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
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      "text/plain": [
       "   PassengerId  Pclass                                               Name  \\\n",
       "0            1       3                            Braund, Mr. Owen Harris   \n",
       "1            2       1  Cumings, Mrs. John Bradley (Florence Briggs Th...   \n",
       "2            3       3                             Heikkinen, Miss. Laina   \n",
       "3            4       1       Futrelle, Mrs. Jacques Heath (Lily May Peel)   \n",
       "4            5       3                           Allen, Mr. William Henry   \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",
       "1  female  38.0      1      0          PC 17599  71.2833   C85        C  \n",
       "2  female  26.0      0      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3  female  35.0      1      0            113803  53.1000  C123        S  \n",
       "4    male  35.0      0      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train = data['Survived']\n",
    "X_train = data.drop(columns=['Survived'])\n",
    "X_train.head()"
   ]
  },
  {
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Embarked', ylabel='count'>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# survival rate based on embarking port\n",
    "sns.countplot(data=data, x='Embarked', hue='Survived')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ad57a820",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-17T10:37:04.518568Z",
     "iopub.status.busy": "2024-11-17T10:37:04.518128Z",
     "iopub.status.idle": "2024-11-17T10:37:04.525625Z",
     "shell.execute_reply": "2024-11-17T10:37:04.524364Z"
    },
    "papermill": {
     "duration": 0.035628,
     "end_time": "2024-11-17T10:37:04.528195",
     "exception": false,
     "start_time": "2024-11-17T10:37:04.492567",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(891, 12)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "76a0fd9e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-17T10:37:04.578347Z",
     "iopub.status.busy": "2024-11-17T10:37:04.577948Z",
     "iopub.status.idle": "2024-11-17T10:37:04.927766Z",
     "shell.execute_reply": "2024-11-17T10:37:04.926584Z"
    },
    "papermill": {
     "duration": 0.378213,
     "end_time": "2024-11-17T10:37:04.930490",
     "exception": false,
     "start_time": "2024-11-17T10:37:04.552277",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Survived', ylabel='Age'>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# checking survival rate based on age\n",
    "sns.boxplot(data=data, x='Survived', y='Age', hue='Sex')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "91e6a71f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-17T10:37:04.981844Z",
     "iopub.status.busy": "2024-11-17T10:37:04.981371Z",
     "iopub.status.idle": "2024-11-17T10:37:05.005129Z",
     "shell.execute_reply": "2024-11-17T10:37:05.003869Z"
    },
    "papermill": {
     "duration": 0.052356,
     "end_time": "2024-11-17T10:37:05.007744",
     "exception": false,
     "start_time": "2024-11-17T10:37:04.955388",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)</td>\n",
       "      <td>female</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>347742</td>\n",
       "      <td>11.1333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Nasser, Mrs. Nicholas (Adele Achem)</td>\n",
       "      <td>female</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>237736</td>\n",
       "      <td>30.0708</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>875</th>\n",
       "      <td>876</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Najib, Miss. Adele Kiamie \"Jane\"</td>\n",
       "      <td>female</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2667</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
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       "    <tr>\n",
       "      <th>879</th>\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",
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       "    <tr>\n",
       "      <th>880</th>\n",
       "      <td>881</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Shelley, Mrs. William (Imanita Parrish Hall)</td>\n",
       "      <td>female</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>230433</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>888</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Graham, Miss. Margaret Edith</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112053</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>B42</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>890</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Behr, Mr. Karl Howell</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111369</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>C148</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>342 rows × 12 columns</p>\n",
       "</div>"
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      "text/plain": [
       "     PassengerId  Survived  Pclass  \\\n",
       "1              2         1       1   \n",
       "2              3         1       3   \n",
       "3              4         1       1   \n",
       "8              9         1       3   \n",
       "9             10         1       2   \n",
       "..           ...       ...     ...   \n",
       "875          876         1       3   \n",
       "879          880         1       1   \n",
       "880          881         1       2   \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",
       "2                               Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "8    Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)  female  27.0      0   \n",
       "9                  Nasser, Mrs. Nicholas (Adele Achem)  female  14.0      1   \n",
       "..                                                 ...     ...   ...    ...   \n",
       "875                   Najib, Miss. Adele Kiamie \"Jane\"  female  15.0      0   \n",
       "879      Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)  female  56.0      0   \n",
       "880       Shelley, Mrs. William (Imanita Parrish Hall)  female  25.0      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",
       "2        0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3        0            113803  53.1000  C123        S  \n",
       "8        2            347742  11.1333   NaN        S  \n",
       "9        0            237736  30.0708   NaN        C  \n",
       "..     ...               ...      ...   ...      ...  \n",
       "875      0              2667   7.2250   NaN        C  \n",
       "879      1             11767  83.1583   C50        C  \n",
       "880      1            230433  26.0000   NaN        S  \n",
       "887      0            112053  30.0000   B42        S  \n",
       "889      0            111369  30.0000  C148        C  \n",
       "\n",
       "[342 rows x 12 columns]"
      ]
     },
     "execution_count": 19,
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       "      <td>Montvila, Rev. Juozas</td>\n",
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       "<p>714 rows × 12 columns</p>\n",
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       "     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",
       "885          886         0       3   \n",
       "886          887         0       2   \n",
       "887          888         1       1   \n",
       "889          890         1       1   \n",
       "890          891         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",
       "885               Rice, Mrs. William (Margaret Norton)  female  39.0      0   \n",
       "886                              Montvila, Rev. Juozas    male  27.0      0   \n",
       "887                       Graham, Miss. Margaret Edith  female  19.0      0   \n",
       "889                              Behr, Mr. Karl Howell    male  26.0      0   \n",
       "890                                Dooley, Mr. Patrick    male  32.0      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  \n",
       "..     ...               ...      ...   ...      ...  \n",
       "885      5            382652  29.1250   NaN        Q  \n",
       "886      0            211536  13.0000   NaN        S  \n",
       "887      0            112053  30.0000   B42        S  \n",
       "889      0            111369  30.0000  C148        C  \n",
       "890      0            370376   7.7500   NaN        Q  \n",
       "\n",
       "[714 rows x 12 columns]"
      ]
     },
     "execution_count": 20,
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   ],
   "source": [
    "data[data['Age'].isna() == False]"
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Age', ylabel='Density'>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
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    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(data=data[data['Age'].isna() == False], x='Age',)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "7e87a064",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-17T10:37:05.521306Z",
     "iopub.status.busy": "2024-11-17T10:37:05.520459Z",
     "iopub.status.idle": "2024-11-17T10:37:05.527751Z",
     "shell.execute_reply": "2024-11-17T10:37:05.526604Z"
    },
    "papermill": {
     "duration": 0.037738,
     "end_time": "2024-11-17T10:37:05.530232",
     "exception": false,
     "start_time": "2024-11-17T10:37:05.492494",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "data_young = data[(data['Age'] >= 20) & (data['Age'] <= 30)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "69e0583c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-17T10:37:05.585635Z",
     "iopub.status.busy": "2024-11-17T10:37:05.585119Z",
     "iopub.status.idle": "2024-11-17T10:37:06.023059Z",
     "shell.execute_reply": "2024-11-17T10:37:06.021813Z"
    },
    "papermill": {
     "duration": 0.469489,
     "end_time": "2024-11-17T10:37:06.026632",
     "exception": false,
     "start_time": "2024-11-17T10:37:05.557143",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Survived', ylabel='Age'>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data=data_young, x='Survived', y='Age', hue='Sex')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "8e0a6b71",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-17T10:37:06.087825Z",
     "iopub.status.busy": "2024-11-17T10:37:06.086951Z",
     "iopub.status.idle": "2024-11-17T10:37:06.096913Z",
     "shell.execute_reply": "2024-11-17T10:37:06.095610Z"
    },
    "papermill": {
     "duration": 0.042308,
     "end_time": "2024-11-17T10:37:06.099571",
     "exception": false,
     "start_time": "2024-11-17T10:37:06.057263",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150, 12)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_old = data[data['Age'] > 40]\n",
    "data_old.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "8df45df1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-17T10:37:06.163552Z",
     "iopub.status.busy": "2024-11-17T10:37:06.162302Z",
     "iopub.status.idle": "2024-11-17T10:37:06.517709Z",
     "shell.execute_reply": "2024-11-17T10:37:06.516550Z"
    },
    "papermill": {
     "duration": 0.393049,
     "end_time": "2024-11-17T10:37:06.520726",
     "exception": false,
     "start_time": "2024-11-17T10:37:06.127677",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Survived', ylabel='Age'>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data=data, x='Survived', y='Age', hue='Sex')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "d3162b39",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-17T10:37:06.584159Z",
     "iopub.status.busy": "2024-11-17T10:37:06.583618Z",
     "iopub.status.idle": "2024-11-17T10:37:06.600892Z",
     "shell.execute_reply": "2024-11-17T10:37:06.599583Z"
    },
    "papermill": {
     "duration": 0.049663,
     "end_time": "2024-11-17T10:37:06.603597",
     "exception": false,
     "start_time": "2024-11-17T10:37:06.553934",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(418, 11)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get the test files\n",
    "data_test = pd.read_csv(base_dir + '/test.csv')\n",
    "data_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "fbd717ed",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-17T10:37:06.664500Z",
     "iopub.status.busy": "2024-11-17T10:37:06.663334Z",
     "iopub.status.idle": "2024-11-17T10:37:06.678524Z",
     "shell.execute_reply": "2024-11-17T10:37:06.676926Z"
    },
    "papermill": {
     "duration": 0.048414,
     "end_time": "2024-11-17T10:37:06.681625",
     "exception": false,
     "start_time": "2024-11-17T10:37:06.633211",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_17/2179221984.py:1: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
      "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
      "\n",
      "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
      "\n",
      "\n",
      "  data['Age'].fillna(data['Age'].mean(), inplace=True)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Survived         0\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "Age              0\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             0\n",
       "Cabin          687\n",
       "Embarked         2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "\n",
    "data['Age'].fillna(data['Age'].mean(), inplace=True)\n",
    "\n",
    "data.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "40ce07db",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-17T10:37:06.742932Z",
     "iopub.status.busy": "2024-11-17T10:37:06.742349Z",
     "iopub.status.idle": "2024-11-17T10:37:06.776362Z",
     "shell.execute_reply": "2024-11-17T10:37:06.775128Z"
    },
    "papermill": {
     "duration": 0.067269,
     "end_time": "2024-11-17T10:37:06.778969",
     "exception": false,
     "start_time": "2024-11-17T10:37:06.711700",
     "status": "completed"
    },
    "tags": []
   },
   "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",
       "      <th>AgeClass</th>\n",
       "      <th>family_size</th>\n",
       "      <th>Fare_per_person</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.000000</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",
       "      <td>66.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>3.62500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.000000</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",
       "      <td>38.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>35.64165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.000000</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",
       "      <td>78.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>7.92500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>26.55000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.000000</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",
       "      <td>105.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>8.05000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <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",
       "      <th>886</th>\n",
       "      <td>887</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Montvila, Rev. Juozas</td>\n",
       "      <td>male</td>\n",
       "      <td>27.000000</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",
       "      <td>54.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>13.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>888</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Graham, Miss. Margaret Edith</td>\n",
       "      <td>female</td>\n",
       "      <td>19.000000</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",
       "      <td>19.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>30.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>889</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>29.699118</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",
       "      <td>89.097353</td>\n",
       "      <td>3</td>\n",
       "      <td>5.86250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>890</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Behr, Mr. Karl Howell</td>\n",
       "      <td>male</td>\n",
       "      <td>26.000000</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",
       "      <td>26.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>30.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>891</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Dooley, Mr. Patrick</td>\n",
       "      <td>male</td>\n",
       "      <td>32.000000</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",
       "      <td>96.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>7.75000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 15 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",
       "886          887         0       2   \n",
       "887          888         1       1   \n",
       "888          889         0       3   \n",
       "889          890         1       1   \n",
       "890          891         0       3   \n",
       "\n",
       "                                                  Name     Sex        Age  \\\n",
       "0                              Braund, Mr. Owen Harris    male  22.000000   \n",
       "1    Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.000000   \n",
       "2                               Heikkinen, Miss. Laina  female  26.000000   \n",
       "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.000000   \n",
       "4                             Allen, Mr. William Henry    male  35.000000   \n",
       "..                                                 ...     ...        ...   \n",
       "886                              Montvila, Rev. Juozas    male  27.000000   \n",
       "887                       Graham, Miss. Margaret Edith  female  19.000000   \n",
       "888           Johnston, Miss. Catherine Helen \"Carrie\"  female  29.699118   \n",
       "889                              Behr, Mr. Karl Howell    male  26.000000   \n",
       "890                                Dooley, Mr. Patrick    male  32.000000   \n",
       "\n",
       "     SibSp  Parch            Ticket     Fare Cabin Embarked    AgeClass  \\\n",
       "0        1      0         A/5 21171   7.2500   NaN        S   66.000000   \n",
       "1        1      0          PC 17599  71.2833   C85        C   38.000000   \n",
       "2        0      0  STON/O2. 3101282   7.9250   NaN        S   78.000000   \n",
       "3        1      0            113803  53.1000  C123        S   35.000000   \n",
       "4        0      0            373450   8.0500   NaN        S  105.000000   \n",
       "..     ...    ...               ...      ...   ...      ...         ...   \n",
       "886      0      0            211536  13.0000   NaN        S   54.000000   \n",
       "887      0      0            112053  30.0000   B42        S   19.000000   \n",
       "888      1      2        W./C. 6607  23.4500   NaN        S   89.097353   \n",
       "889      0      0            111369  30.0000  C148        C   26.000000   \n",
       "890      0      0            370376   7.7500   NaN        Q   96.000000   \n",
       "\n",
       "     family_size  Fare_per_person  \n",
       "0              1          3.62500  \n",
       "1              1         35.64165  \n",
       "2              0          7.92500  \n",
       "3              1         26.55000  \n",
       "4              0          8.05000  \n",
       "..           ...              ...  \n",
       "886            0         13.00000  \n",
       "887            0         30.00000  \n",
       "888            3          5.86250  \n",
       "889            0         30.00000  \n",
       "890            0          7.75000  \n",
       "\n",
       "[891 rows x 15 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# feature engineering \n",
    "data['AgeClass'] = data['Age']*data['Pclass']\n",
    "data['family_size'] = data['SibSp'] + data['Parch']\n",
    "data['Fare_per_person'] = data['Fare']/(data['family_size'] + 1)\n",
    "\n",
    "data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "0f92dff9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-17T10:37:06.840386Z",
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     "shell.execute_reply": "2024-11-17T10:37:06.850489Z"
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId          0\n",
       "Survived             0\n",
       "Pclass               0\n",
       "Name                 0\n",
       "Sex                  0\n",
       "Age                  0\n",
       "SibSp                0\n",
       "Parch                0\n",
       "Ticket               0\n",
       "Fare                 0\n",
       "Cabin              687\n",
       "Embarked             2\n",
       "AgeClass             0\n",
       "family_size          0\n",
       "Fare_per_person      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "82db1938",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-17T10:37:06.914621Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_17/2004768351.py:1: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
      "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
      "\n",
      "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
      "\n",
      "\n",
      "  data_test['Age'].fillna(data_test['Age'].mean(), inplace=True)\n",
      "/tmp/ipykernel_17/2004768351.py:4: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
      "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
      "\n",
      "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
      "\n",
      "\n",
      "  data_test['Fare'].fillna(data_test['Fare'].mean(), inplace=True)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "Age              0\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             0\n",
       "Cabin          327\n",
       "Embarked         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "data_test['Age'].fillna(data_test['Age'].mean(), inplace=True)\n",
    "\n",
    "\n",
    "data_test['Fare'].fillna(data_test['Fare'].mean(), inplace=True)\n",
    "\n",
    "data_test.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "64522d24",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-17T10:37:06.991304Z",
     "iopub.status.busy": "2024-11-17T10:37:06.990764Z",
     "iopub.status.idle": "2024-11-17T10:37:07.008195Z",
     "shell.execute_reply": "2024-11-17T10:37:07.007077Z"
    },
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     "duration": 0.05105,
     "end_time": "2024-11-17T10:37:07.010993",
     "exception": false,
     "start_time": "2024-11-17T10:37:06.959943",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId          0\n",
       "Pclass               0\n",
       "Name                 0\n",
       "Sex                  0\n",
       "Age                  0\n",
       "SibSp                0\n",
       "Parch                0\n",
       "Ticket               0\n",
       "Fare                 0\n",
       "Cabin              327\n",
       "Embarked             0\n",
       "AgeClass             0\n",
       "family_size          0\n",
       "Fare_per_person      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "data_test['AgeClass'] = data_test['Age']*data_test['Pclass']\n",
    "data_test['family_size'] = data_test['SibSp'] + data_test['Parch']\n",
    "data_test['Fare_per_person'] = data_test['Fare']/(data_test['family_size'] + 1)\n",
    "\n",
    "data_test\n",
    "data_test.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "1073d3fa",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-17T10:37:07.076227Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "\n",
    "def substrings_in_string(big_string, substrings):\n",
    "    for substring in substrings:\n",
    "        if str.find(big_string, substring) != -1:\n",
    "            return substring\n",
    "    print(big_string)\n",
    "    return np.nan\n",
    "\n",
    "\n",
    "title_list=['Mrs', 'Mr', 'Master', 'Miss', 'Major', 'Rev',\n",
    "                    'Dr', 'Ms', 'Mlle','Col', 'Capt', 'Mme', 'Countess',\n",
    "                    'Don', 'Jonkheer']\n",
    "\n",
    "#replacing all titles with mr, mrs, miss, master\n",
    "def replace_titles(x):\n",
    "    title=x['Title']\n",
    "    if title in ['Don', 'Major', 'Capt', 'Jonkheer', 'Rev', 'Col']:\n",
    "        return 'Mr'\n",
    "    elif title in ['Countess', 'Mme']:\n",
    "        return 'Mrs'\n",
    "    elif title in ['Mlle', 'Ms']:\n",
    "        return 'Miss'\n",
    "    elif title =='Dr':\n",
    "        if x['Sex']=='Male':\n",
    "            return 'Mr'\n",
    "        else:\n",
    "            return 'Mrs'\n",
    "    else:\n",
    "        return title"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "88b045ff",
   "metadata": {
    "execution": {
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    },
    "tags": []
   },
   "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",
       "      <th>AgeClass</th>\n",
       "      <th>family_size</th>\n",
       "      <th>Fare_per_person</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.000000</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>2</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>3.62500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.000000</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>0</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>35.64165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.000000</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>2</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>7.92500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>2</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>26.55000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>105.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>8.05000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <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",
       "      <th>886</th>\n",
       "      <td>887</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Montvila, Rev. Juozas</td>\n",
       "      <td>male</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>211536</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>13.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>888</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Graham, Miss. Margaret Edith</td>\n",
       "      <td>female</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112053</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>B42</td>\n",
       "      <td>2</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>30.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>889</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>29.699118</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>2</td>\n",
       "      <td>89.097353</td>\n",
       "      <td>3</td>\n",
       "      <td>5.86250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>890</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Behr, Mr. Karl Howell</td>\n",
       "      <td>male</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111369</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>C148</td>\n",
       "      <td>0</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>30.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>891</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Dooley, Mr. Patrick</td>\n",
       "      <td>male</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370376</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>7.75000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 15 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",
       "886          887         0       2   \n",
       "887          888         1       1   \n",
       "888          889         0       3   \n",
       "889          890         1       1   \n",
       "890          891         0       3   \n",
       "\n",
       "                                                  Name     Sex        Age  \\\n",
       "0                              Braund, Mr. Owen Harris    male  22.000000   \n",
       "1    Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.000000   \n",
       "2                               Heikkinen, Miss. Laina  female  26.000000   \n",
       "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.000000   \n",
       "4                             Allen, Mr. William Henry    male  35.000000   \n",
       "..                                                 ...     ...        ...   \n",
       "886                              Montvila, Rev. Juozas    male  27.000000   \n",
       "887                       Graham, Miss. Margaret Edith  female  19.000000   \n",
       "888           Johnston, Miss. Catherine Helen \"Carrie\"  female  29.699118   \n",
       "889                              Behr, Mr. Karl Howell    male  26.000000   \n",
       "890                                Dooley, Mr. Patrick    male  32.000000   \n",
       "\n",
       "     SibSp  Parch            Ticket     Fare Cabin  Embarked    AgeClass  \\\n",
       "0        1      0         A/5 21171   7.2500   NaN         2   66.000000   \n",
       "1        1      0          PC 17599  71.2833   C85         0   38.000000   \n",
       "2        0      0  STON/O2. 3101282   7.9250   NaN         2   78.000000   \n",
       "3        1      0            113803  53.1000  C123         2   35.000000   \n",
       "4        0      0            373450   8.0500   NaN         2  105.000000   \n",
       "..     ...    ...               ...      ...   ...       ...         ...   \n",
       "886      0      0            211536  13.0000   NaN         2   54.000000   \n",
       "887      0      0            112053  30.0000   B42         2   19.000000   \n",
       "888      1      2        W./C. 6607  23.4500   NaN         2   89.097353   \n",
       "889      0      0            111369  30.0000  C148         0   26.000000   \n",
       "890      0      0            370376   7.7500   NaN         1   96.000000   \n",
       "\n",
       "     family_size  Fare_per_person  \n",
       "0              1          3.62500  \n",
       "1              1         35.64165  \n",
       "2              0          7.92500  \n",
       "3              1         26.55000  \n",
       "4              0          8.05000  \n",
       "..           ...              ...  \n",
       "886            0         13.00000  \n",
       "887            0         30.00000  \n",
       "888            3          5.86250  \n",
       "889            0         30.00000  \n",
       "890            0          7.75000  \n",
       "\n",
       "[891 rows x 15 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "le = LabelEncoder()\n",
    "data['Embarked'] = le.fit_transform(data['Embarked'])\n",
    "\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "15c3240b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-17T10:37:07.418808Z",
     "iopub.status.busy": "2024-11-17T10:37:07.418330Z",
     "iopub.status.idle": "2024-11-17T10:37:07.444015Z",
     "shell.execute_reply": "2024-11-17T10:37:07.442720Z"
    },
    "papermill": {
     "duration": 0.059955,
     "end_time": "2024-11-17T10:37:07.446748",
     "exception": false,
     "start_time": "2024-11-17T10:37:07.386793",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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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>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
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       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>AgeClass</th>\n",
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       "      <th>Fare_per_person</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892</td>\n",
       "      <td>3</td>\n",
       "      <td>Kelly, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>34.50000</td>\n",
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       "      <td>Wilkes, Mrs. James (Ellen Needs)</td>\n",
       "      <td>female</td>\n",
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       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>2</td>\n",
       "      <td>Myles, Mr. Thomas Francis</td>\n",
       "      <td>male</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>3</td>\n",
       "      <td>Wirz, Mr. Albert</td>\n",
       "      <td>male</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>8.662500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>3</td>\n",
       "      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
       "      <td>female</td>\n",
       "      <td>22.00000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3101298</td>\n",
       "      <td>12.2875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>4.095833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>413</th>\n",
       "      <td>1305</td>\n",
       "      <td>3</td>\n",
       "      <td>Spector, Mr. Woolf</td>\n",
       "      <td>male</td>\n",
       "      <td>30.27259</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A.5. 3236</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>90.817771</td>\n",
       "      <td>0</td>\n",
       "      <td>8.050000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>1306</td>\n",
       "      <td>1</td>\n",
       "      <td>Oliva y Ocana, Dona. Fermina</td>\n",
       "      <td>female</td>\n",
       "      <td>39.00000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17758</td>\n",
       "      <td>108.9000</td>\n",
       "      <td>C105</td>\n",
       "      <td>0</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>108.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>1307</td>\n",
       "      <td>3</td>\n",
       "      <td>Saether, Mr. Simon Sivertsen</td>\n",
       "      <td>male</td>\n",
       "      <td>38.50000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>SOTON/O.Q. 3101262</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>115.500000</td>\n",
       "      <td>0</td>\n",
       "      <td>7.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>416</th>\n",
       "      <td>1308</td>\n",
       "      <td>3</td>\n",
       "      <td>Ware, Mr. Frederick</td>\n",
       "      <td>male</td>\n",
       "      <td>30.27259</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>359309</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>90.817771</td>\n",
       "      <td>0</td>\n",
       "      <td>8.050000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>417</th>\n",
       "      <td>1309</td>\n",
       "      <td>3</td>\n",
       "      <td>Peter, Master. Michael J</td>\n",
       "      <td>male</td>\n",
       "      <td>30.27259</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2668</td>\n",
       "      <td>22.3583</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>90.817771</td>\n",
       "      <td>2</td>\n",
       "      <td>7.452767</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>418 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Pclass                                          Name  \\\n",
       "0            892       3                              Kelly, Mr. James   \n",
       "1            893       3              Wilkes, Mrs. James (Ellen Needs)   \n",
       "2            894       2                     Myles, Mr. Thomas Francis   \n",
       "3            895       3                              Wirz, Mr. Albert   \n",
       "4            896       3  Hirvonen, Mrs. Alexander (Helga E Lindqvist)   \n",
       "..           ...     ...                                           ...   \n",
       "413         1305       3                            Spector, Mr. Woolf   \n",
       "414         1306       1                  Oliva y Ocana, Dona. Fermina   \n",
       "415         1307       3                  Saether, Mr. Simon Sivertsen   \n",
       "416         1308       3                           Ware, Mr. Frederick   \n",
       "417         1309       3                      Peter, Master. Michael J   \n",
       "\n",
       "        Sex       Age  SibSp  Parch              Ticket      Fare Cabin  \\\n",
       "0      male  34.50000      0      0              330911    7.8292   NaN   \n",
       "1    female  47.00000      1      0              363272    7.0000   NaN   \n",
       "2      male  62.00000      0      0              240276    9.6875   NaN   \n",
       "3      male  27.00000      0      0              315154    8.6625   NaN   \n",
       "4    female  22.00000      1      1             3101298   12.2875   NaN   \n",
       "..      ...       ...    ...    ...                 ...       ...   ...   \n",
       "413    male  30.27259      0      0           A.5. 3236    8.0500   NaN   \n",
       "414  female  39.00000      0      0            PC 17758  108.9000  C105   \n",
       "415    male  38.50000      0      0  SOTON/O.Q. 3101262    7.2500   NaN   \n",
       "416    male  30.27259      0      0              359309    8.0500   NaN   \n",
       "417    male  30.27259      1      1                2668   22.3583   NaN   \n",
       "\n",
       "     Embarked    AgeClass  family_size  Fare_per_person  \n",
       "0           1  103.500000            0         7.829200  \n",
       "1           2  141.000000            1         3.500000  \n",
       "2           1  124.000000            0         9.687500  \n",
       "3           2   81.000000            0         8.662500  \n",
       "4           2   66.000000            2         4.095833  \n",
       "..        ...         ...          ...              ...  \n",
       "413         2   90.817771            0         8.050000  \n",
       "414         0   39.000000            0       108.900000  \n",
       "415         2  115.500000            0         7.250000  \n",
       "416         2   90.817771            0         8.050000  \n",
       "417         0   90.817771            2         7.452767  \n",
       "\n",
       "[418 rows x 14 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "le = LabelEncoder()\n",
    "data_test['Embarked'] = le.fit_transform(data_test['Embarked'])\n",
    "\n",
    "data_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "a8af1deb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-17T10:37:07.510331Z",
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     "shell.execute_reply": "2024-11-17T10:37:07.539584Z"
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "data['Title']=data['Name'].map(lambda x: substrings_in_string(x, title_list))\n",
    "data['Title']=data.apply(replace_titles, axis=1)\n",
    "\n",
    "data_test['Title']=data_test['Name'].map(lambda x: substrings_in_string(x, title_list))\n",
    "data_test['Title']=data_test.apply(replace_titles, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "42e06838",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-17T10:37:07.606231Z",
     "iopub.status.busy": "2024-11-17T10:37:07.605734Z",
     "iopub.status.idle": "2024-11-17T10:37:07.631040Z",
     "shell.execute_reply": "2024-11-17T10:37:07.629924Z"
    },
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     "start_time": "2024-11-17T10:37:07.574173",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
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       "      <td>30.27259</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>418 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Pclass                                          Name  \\\n",
       "0            892       3                              Kelly, Mr. James   \n",
       "1            893       3              Wilkes, Mrs. James (Ellen Needs)   \n",
       "2            894       2                     Myles, Mr. Thomas Francis   \n",
       "3            895       3                              Wirz, Mr. Albert   \n",
       "4            896       3  Hirvonen, Mrs. Alexander (Helga E Lindqvist)   \n",
       "..           ...     ...                                           ...   \n",
       "413         1305       3                            Spector, Mr. Woolf   \n",
       "414         1306       1                  Oliva y Ocana, Dona. Fermina   \n",
       "415         1307       3                  Saether, Mr. Simon Sivertsen   \n",
       "416         1308       3                           Ware, Mr. Frederick   \n",
       "417         1309       3                      Peter, Master. Michael J   \n",
       "\n",
       "        Sex       Age  SibSp  Parch              Ticket      Fare Cabin  \\\n",
       "0      male  34.50000      0      0              330911    7.8292   NaN   \n",
       "1    female  47.00000      1      0              363272    7.0000   NaN   \n",
       "2      male  62.00000      0      0              240276    9.6875   NaN   \n",
       "3      male  27.00000      0      0              315154    8.6625   NaN   \n",
       "4    female  22.00000      1      1             3101298   12.2875   NaN   \n",
       "..      ...       ...    ...    ...                 ...       ...   ...   \n",
       "413    male  30.27259      0      0           A.5. 3236    8.0500   NaN   \n",
       "414  female  39.00000      0      0            PC 17758  108.9000  C105   \n",
       "415    male  38.50000      0      0  SOTON/O.Q. 3101262    7.2500   NaN   \n",
       "416    male  30.27259      0      0              359309    8.0500   NaN   \n",
       "417    male  30.27259      1      1                2668   22.3583   NaN   \n",
       "\n",
       "     Embarked    AgeClass  family_size  Fare_per_person   Title  \n",
       "0           1  103.500000            0         7.829200      Mr  \n",
       "1           2  141.000000            1         3.500000     Mrs  \n",
       "2           1  124.000000            0         9.687500      Mr  \n",
       "3           2   81.000000            0         8.662500      Mr  \n",
       "4           2   66.000000            2         4.095833     Mrs  \n",
       "..        ...         ...          ...              ...     ...  \n",
       "413         2   90.817771            0         8.050000      Mr  \n",
       "414         0   39.000000            0       108.900000      Mr  \n",
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       "416         2   90.817771            0         8.050000      Mr  \n",
       "417         0   90.817771            2         7.452767  Master  \n",
       "\n",
       "[418 rows x 15 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_test"
   ]
  },
  {
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   "execution_count": 37,
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       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>105.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>8.05000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <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",
       "      <th>886</th>\n",
       "      <td>887</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Montvila, Rev. Juozas</td>\n",
       "      <td>male</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>211536</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>13.00000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>888</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Graham, Miss. Margaret Edith</td>\n",
       "      <td>female</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112053</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>B42</td>\n",
       "      <td>2</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>30.00000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>889</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>29.699118</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>2</td>\n",
       "      <td>89.097353</td>\n",
       "      <td>3</td>\n",
       "      <td>5.86250</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>890</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Behr, Mr. Karl Howell</td>\n",
       "      <td>male</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111369</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>C148</td>\n",
       "      <td>0</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>30.00000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>891</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Dooley, Mr. Patrick</td>\n",
       "      <td>male</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370376</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>7.75000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 16 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",
       "886          887         0       2   \n",
       "887          888         1       1   \n",
       "888          889         0       3   \n",
       "889          890         1       1   \n",
       "890          891         0       3   \n",
       "\n",
       "                                                  Name     Sex        Age  \\\n",
       "0                              Braund, Mr. Owen Harris    male  22.000000   \n",
       "1    Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.000000   \n",
       "2                               Heikkinen, Miss. Laina  female  26.000000   \n",
       "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.000000   \n",
       "4                             Allen, Mr. William Henry    male  35.000000   \n",
       "..                                                 ...     ...        ...   \n",
       "886                              Montvila, Rev. Juozas    male  27.000000   \n",
       "887                       Graham, Miss. Margaret Edith  female  19.000000   \n",
       "888           Johnston, Miss. Catherine Helen \"Carrie\"  female  29.699118   \n",
       "889                              Behr, Mr. Karl Howell    male  26.000000   \n",
       "890                                Dooley, Mr. Patrick    male  32.000000   \n",
       "\n",
       "     SibSp  Parch            Ticket     Fare Cabin  Embarked    AgeClass  \\\n",
       "0        1      0         A/5 21171   7.2500   NaN         2   66.000000   \n",
       "1        1      0          PC 17599  71.2833   C85         0   38.000000   \n",
       "2        0      0  STON/O2. 3101282   7.9250   NaN         2   78.000000   \n",
       "3        1      0            113803  53.1000  C123         2   35.000000   \n",
       "4        0      0            373450   8.0500   NaN         2  105.000000   \n",
       "..     ...    ...               ...      ...   ...       ...         ...   \n",
       "886      0      0            211536  13.0000   NaN         2   54.000000   \n",
       "887      0      0            112053  30.0000   B42         2   19.000000   \n",
       "888      1      2        W./C. 6607  23.4500   NaN         2   89.097353   \n",
       "889      0      0            111369  30.0000  C148         0   26.000000   \n",
       "890      0      0            370376   7.7500   NaN         1   96.000000   \n",
       "\n",
       "     family_size  Fare_per_person  Title  \n",
       "0              1          3.62500      2  \n",
       "1              1         35.64165      3  \n",
       "2              0          7.92500      1  \n",
       "3              1         26.55000      3  \n",
       "4              0          8.05000      2  \n",
       "..           ...              ...    ...  \n",
       "886            0         13.00000      2  \n",
       "887            0         30.00000      1  \n",
       "888            3          5.86250      1  \n",
       "889            0         30.00000      2  \n",
       "890            0          7.75000      2  \n",
       "\n",
       "[891 rows x 16 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Title'] = le.fit_transform(data['Title'])\n",
    "data"
   ]
  },
  {
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       "      <th>PassengerId</th>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>3</td>\n",
       "      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
       "      <td>female</td>\n",
       "      <td>22.00000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3101298</td>\n",
       "      <td>12.2875</td>\n",
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       "      <th>413</th>\n",
       "      <td>1305</td>\n",
       "      <td>3</td>\n",
       "      <td>Spector, Mr. Woolf</td>\n",
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       "      <td>30.27259</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A.5. 3236</td>\n",
       "      <td>8.0500</td>\n",
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       "      <td>1306</td>\n",
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       "      <td>Oliva y Ocana, Dona. Fermina</td>\n",
       "      <td>female</td>\n",
       "      <td>39.00000</td>\n",
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       "      <td>0</td>\n",
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       "      <th>415</th>\n",
       "      <td>1307</td>\n",
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       "      <td>Saether, Mr. Simon Sivertsen</td>\n",
       "      <td>male</td>\n",
       "      <td>38.50000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>SOTON/O.Q. 3101262</td>\n",
       "      <td>7.2500</td>\n",
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       "      <td>1308</td>\n",
       "      <td>3</td>\n",
       "      <td>Ware, Mr. Frederick</td>\n",
       "      <td>male</td>\n",
       "      <td>30.27259</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>359309</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>90.817771</td>\n",
       "      <td>0</td>\n",
       "      <td>8.050000</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>417</th>\n",
       "      <td>1309</td>\n",
       "      <td>3</td>\n",
       "      <td>Peter, Master. Michael J</td>\n",
       "      <td>male</td>\n",
       "      <td>30.27259</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2668</td>\n",
       "      <td>22.3583</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>90.817771</td>\n",
       "      <td>2</td>\n",
       "      <td>7.452767</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>418 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Pclass                                          Name  \\\n",
       "0            892       3                              Kelly, Mr. James   \n",
       "1            893       3              Wilkes, Mrs. James (Ellen Needs)   \n",
       "2            894       2                     Myles, Mr. Thomas Francis   \n",
       "3            895       3                              Wirz, Mr. Albert   \n",
       "4            896       3  Hirvonen, Mrs. Alexander (Helga E Lindqvist)   \n",
       "..           ...     ...                                           ...   \n",
       "413         1305       3                            Spector, Mr. Woolf   \n",
       "414         1306       1                  Oliva y Ocana, Dona. Fermina   \n",
       "415         1307       3                  Saether, Mr. Simon Sivertsen   \n",
       "416         1308       3                           Ware, Mr. Frederick   \n",
       "417         1309       3                      Peter, Master. Michael J   \n",
       "\n",
       "        Sex       Age  SibSp  Parch              Ticket      Fare Cabin  \\\n",
       "0      male  34.50000      0      0              330911    7.8292   NaN   \n",
       "1    female  47.00000      1      0              363272    7.0000   NaN   \n",
       "2      male  62.00000      0      0              240276    9.6875   NaN   \n",
       "3      male  27.00000      0      0              315154    8.6625   NaN   \n",
       "4    female  22.00000      1      1             3101298   12.2875   NaN   \n",
       "..      ...       ...    ...    ...                 ...       ...   ...   \n",
       "413    male  30.27259      0      0           A.5. 3236    8.0500   NaN   \n",
       "414  female  39.00000      0      0            PC 17758  108.9000  C105   \n",
       "415    male  38.50000      0      0  SOTON/O.Q. 3101262    7.2500   NaN   \n",
       "416    male  30.27259      0      0              359309    8.0500   NaN   \n",
       "417    male  30.27259      1      1                2668   22.3583   NaN   \n",
       "\n",
       "     Embarked    AgeClass  family_size  Fare_per_person  Title  \n",
       "0           1  103.500000            0         7.829200      2  \n",
       "1           2  141.000000            1         3.500000      3  \n",
       "2           1  124.000000            0         9.687500      2  \n",
       "3           2   81.000000            0         8.662500      2  \n",
       "4           2   66.000000            2         4.095833      3  \n",
       "..        ...         ...          ...              ...    ...  \n",
       "413         2   90.817771            0         8.050000      2  \n",
       "414         0   39.000000            0       108.900000      2  \n",
       "415         2  115.500000            0         7.250000      2  \n",
       "416         2   90.817771            0         8.050000      2  \n",
       "417         0   90.817771            2         7.452767      0  \n",
       "\n",
       "[418 rows x 15 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_test['Title'] = le.fit_transform(data_test['Title'])\n",
    "data_test"
   ]
  },
  {
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   "outputs": [
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       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
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       "      <td>Allen, Mr. William Henry</td>\n",
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       "      <td>0</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>887</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Montvila, Rev. Juozas</td>\n",
       "      <td>male</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>211536</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
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       "      <td>13.00000</td>\n",
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       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>888</td>\n",
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       "      <td>19.000000</td>\n",
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       "      <td>0</td>\n",
       "      <td>112053</td>\n",
       "      <td>30.0000</td>\n",
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       "      <td>2</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>30.00000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>889</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>W./C. 6607</td>\n",
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       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>890</td>\n",
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       "      <td>1</td>\n",
       "      <td>Behr, Mr. Karl Howell</td>\n",
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       "      <td>26.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111369</td>\n",
       "      <td>30.0000</td>\n",
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       "      <td>0</td>\n",
       "      <td>26.000000</td>\n",
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       "      <td>30.00000</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>891</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Dooley, Mr. Patrick</td>\n",
       "      <td>male</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370376</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
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       "      <td>7.75000</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 17 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",
       "886          887         0       2   \n",
       "887          888         1       1   \n",
       "888          889         0       3   \n",
       "889          890         1       1   \n",
       "890          891         0       3   \n",
       "\n",
       "                                                  Name     Sex        Age  \\\n",
       "0                              Braund, Mr. Owen Harris    male  22.000000   \n",
       "1    Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.000000   \n",
       "2                               Heikkinen, Miss. Laina  female  26.000000   \n",
       "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.000000   \n",
       "4                             Allen, Mr. William Henry    male  35.000000   \n",
       "..                                                 ...     ...        ...   \n",
       "886                              Montvila, Rev. Juozas    male  27.000000   \n",
       "887                       Graham, Miss. Margaret Edith  female  19.000000   \n",
       "888           Johnston, Miss. Catherine Helen \"Carrie\"  female  29.699118   \n",
       "889                              Behr, Mr. Karl Howell    male  26.000000   \n",
       "890                                Dooley, Mr. Patrick    male  32.000000   \n",
       "\n",
       "     SibSp  Parch            Ticket     Fare Cabin  Embarked    AgeClass  \\\n",
       "0        1      0         A/5 21171   7.2500   NaN         2   66.000000   \n",
       "1        1      0          PC 17599  71.2833   C85         0   38.000000   \n",
       "2        0      0  STON/O2. 3101282   7.9250   NaN         2   78.000000   \n",
       "3        1      0            113803  53.1000  C123         2   35.000000   \n",
       "4        0      0            373450   8.0500   NaN         2  105.000000   \n",
       "..     ...    ...               ...      ...   ...       ...         ...   \n",
       "886      0      0            211536  13.0000   NaN         2   54.000000   \n",
       "887      0      0            112053  30.0000   B42         2   19.000000   \n",
       "888      1      2        W./C. 6607  23.4500   NaN         2   89.097353   \n",
       "889      0      0            111369  30.0000  C148         0   26.000000   \n",
       "890      0      0            370376   7.7500   NaN         1   96.000000   \n",
       "\n",
       "     family_size  Fare_per_person  Title  isMale  \n",
       "0              1          3.62500      2       1  \n",
       "1              1         35.64165      3       0  \n",
       "2              0          7.92500      1       0  \n",
       "3              1         26.55000      3       0  \n",
       "4              0          8.05000      2       1  \n",
       "..           ...              ...    ...     ...  \n",
       "886            0         13.00000      2       1  \n",
       "887            0         30.00000      1       0  \n",
       "888            3          5.86250      1       0  \n",
       "889            0         30.00000      2       1  \n",
       "890            0          7.75000      2       1  \n",
       "\n",
       "[891 rows x 17 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['isMale'] = [1 if sex == 'male' else 0 for sex in data['Sex']]\n",
    "\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "0b3050b3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-17T10:37:07.980730Z",
     "iopub.status.busy": "2024-11-17T10:37:07.980280Z",
     "iopub.status.idle": "2024-11-17T10:37:08.007214Z",
     "shell.execute_reply": "2024-11-17T10:37:08.006072Z"
    },
    "papermill": {
     "duration": 0.064051,
     "end_time": "2024-11-17T10:37:08.009806",
     "exception": false,
     "start_time": "2024-11-17T10:37:07.945755",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
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       "      <td>7.250000</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>416</th>\n",
       "      <td>1308</td>\n",
       "      <td>3</td>\n",
       "      <td>Ware, Mr. Frederick</td>\n",
       "      <td>male</td>\n",
       "      <td>30.27259</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>359309</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>90.817771</td>\n",
       "      <td>0</td>\n",
       "      <td>8.050000</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>417</th>\n",
       "      <td>1309</td>\n",
       "      <td>3</td>\n",
       "      <td>Peter, Master. Michael J</td>\n",
       "      <td>male</td>\n",
       "      <td>30.27259</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2668</td>\n",
       "      <td>22.3583</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>90.817771</td>\n",
       "      <td>2</td>\n",
       "      <td>7.452767</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>418 rows × 16 columns</p>\n",
       "</div>"
      ],
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       "     PassengerId  Pclass                                          Name  \\\n",
       "0            892       3                              Kelly, Mr. James   \n",
       "1            893       3              Wilkes, Mrs. James (Ellen Needs)   \n",
       "2            894       2                     Myles, Mr. Thomas Francis   \n",
       "3            895       3                              Wirz, Mr. Albert   \n",
       "4            896       3  Hirvonen, Mrs. Alexander (Helga E Lindqvist)   \n",
       "..           ...     ...                                           ...   \n",
       "413         1305       3                            Spector, Mr. Woolf   \n",
       "414         1306       1                  Oliva y Ocana, Dona. Fermina   \n",
       "415         1307       3                  Saether, Mr. Simon Sivertsen   \n",
       "416         1308       3                           Ware, Mr. Frederick   \n",
       "417         1309       3                      Peter, Master. Michael J   \n",
       "\n",
       "        Sex       Age  SibSp  Parch              Ticket      Fare Cabin  \\\n",
       "0      male  34.50000      0      0              330911    7.8292   NaN   \n",
       "1    female  47.00000      1      0              363272    7.0000   NaN   \n",
       "2      male  62.00000      0      0              240276    9.6875   NaN   \n",
       "3      male  27.00000      0      0              315154    8.6625   NaN   \n",
       "4    female  22.00000      1      1             3101298   12.2875   NaN   \n",
       "..      ...       ...    ...    ...                 ...       ...   ...   \n",
       "413    male  30.27259      0      0           A.5. 3236    8.0500   NaN   \n",
       "414  female  39.00000      0      0            PC 17758  108.9000  C105   \n",
       "415    male  38.50000      0      0  SOTON/O.Q. 3101262    7.2500   NaN   \n",
       "416    male  30.27259      0      0              359309    8.0500   NaN   \n",
       "417    male  30.27259      1      1                2668   22.3583   NaN   \n",
       "\n",
       "     Embarked    AgeClass  family_size  Fare_per_person  Title  isMale  \n",
       "0           1  103.500000            0         7.829200      2       1  \n",
       "1           2  141.000000            1         3.500000      3       0  \n",
       "2           1  124.000000            0         9.687500      2       1  \n",
       "3           2   81.000000            0         8.662500      2       1  \n",
       "4           2   66.000000            2         4.095833      3       0  \n",
       "..        ...         ...          ...              ...    ...     ...  \n",
       "413         2   90.817771            0         8.050000      2       1  \n",
       "414         0   39.000000            0       108.900000      2       0  \n",
       "415         2  115.500000            0         7.250000      2       1  \n",
       "416         2   90.817771            0         8.050000      2       1  \n",
       "417         0   90.817771            2         7.452767      0       1  \n",
       "\n",
       "[418 rows x 16 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_test['isMale'] = [1 if sex == 'male' else 0 for sex in data_test['Sex']]\n",
    "\n",
    "data_test"
   ]
  },
  {
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   "id": "d3f871c1",
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       "      <td>Dooley, Mr. Patrick</td>\n",
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       "      <td>370376</td>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "  </tbody>\n",
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       "<p>891 rows × 18 columns</p>\n",
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      "text/plain": [
       "     PassengerId  Survived  Pclass  \\\n",
       "0              1         0       3   \n",
       "1              2         1       1   \n",
       "2              3         1       3   \n",
       "3              4         1       1   \n",
       "4              5         0       3   \n",
       "..           ...       ...     ...   \n",
       "886          887         0       2   \n",
       "887          888         1       1   \n",
       "888          889         0       3   \n",
       "889          890         1       1   \n",
       "890          891         0       3   \n",
       "\n",
       "                                                  Name     Sex        Age  \\\n",
       "0                              Braund, Mr. Owen Harris    male  22.000000   \n",
       "1    Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.000000   \n",
       "2                               Heikkinen, Miss. Laina  female  26.000000   \n",
       "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.000000   \n",
       "4                             Allen, Mr. William Henry    male  35.000000   \n",
       "..                                                 ...     ...        ...   \n",
       "886                              Montvila, Rev. Juozas    male  27.000000   \n",
       "887                       Graham, Miss. Margaret Edith  female  19.000000   \n",
       "888           Johnston, Miss. Catherine Helen \"Carrie\"  female  29.699118   \n",
       "889                              Behr, Mr. Karl Howell    male  26.000000   \n",
       "890                                Dooley, Mr. Patrick    male  32.000000   \n",
       "\n",
       "     SibSp  Parch            Ticket     Fare Cabin  Embarked    AgeClass  \\\n",
       "0        1      0         A/5 21171   7.2500   NaN         2   66.000000   \n",
       "1        1      0          PC 17599  71.2833   C85         0   38.000000   \n",
       "2        0      0  STON/O2. 3101282   7.9250   NaN         2   78.000000   \n",
       "3        1      0            113803  53.1000  C123         2   35.000000   \n",
       "4        0      0            373450   8.0500   NaN         2  105.000000   \n",
       "..     ...    ...               ...      ...   ...       ...         ...   \n",
       "886      0      0            211536  13.0000   NaN         2   54.000000   \n",
       "887      0      0            112053  30.0000   B42         2   19.000000   \n",
       "888      1      2        W./C. 6607  23.4500   NaN         2   89.097353   \n",
       "889      0      0            111369  30.0000  C148         0   26.000000   \n",
       "890      0      0            370376   7.7500   NaN         1   96.000000   \n",
       "\n",
       "     family_size  Fare_per_person  Title  isMale  hadCabin  \n",
       "0              1          3.62500      2       1         0  \n",
       "1              1         35.64165      3       0         1  \n",
       "2              0          7.92500      1       0         0  \n",
       "3              1         26.55000      3       0         1  \n",
       "4              0          8.05000      2       1         0  \n",
       "..           ...              ...    ...     ...       ...  \n",
       "886            0         13.00000      2       1         0  \n",
       "887            0         30.00000      1       0         1  \n",
       "888            3          5.86250      1       0         0  \n",
       "889            0         30.00000      2       1         1  \n",
       "890            0          7.75000      2       1         0  \n",
       "\n",
       "[891 rows x 18 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['hadCabin'] = data['Cabin'].notna().astype(int)\n",
    "data"
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       "      <td>0</td>\n",
       "      <td>330911</td>\n",
       "      <td>7.8292</td>\n",
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       "      <td>Wilkes, Mrs. James (Ellen Needs)</td>\n",
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       "      <td>363272</td>\n",
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       "      <td>894</td>\n",
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       "      <td>Myles, Mr. Thomas Francis</td>\n",
       "      <td>male</td>\n",
       "      <td>62.00000</td>\n",
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       "      <td>0</td>\n",
       "      <td>240276</td>\n",
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       "      <th>3</th>\n",
       "      <td>895</td>\n",
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       "      <td>Wirz, Mr. Albert</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>3</td>\n",
       "      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
       "      <td>female</td>\n",
       "      <td>22.00000</td>\n",
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       "      <td>1</td>\n",
       "      <td>3101298</td>\n",
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       "    <tr>\n",
       "      <th>413</th>\n",
       "      <td>1305</td>\n",
       "      <td>3</td>\n",
       "      <td>Spector, Mr. Woolf</td>\n",
       "      <td>male</td>\n",
       "      <td>30.27259</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A.5. 3236</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>90.817771</td>\n",
       "      <td>0</td>\n",
       "      <td>8.050000</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>1306</td>\n",
       "      <td>1</td>\n",
       "      <td>Oliva y Ocana, Dona. Fermina</td>\n",
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       "      <td>0</td>\n",
       "      <td>PC 17758</td>\n",
       "      <td>108.9000</td>\n",
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       "      <td>39.000000</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>1307</td>\n",
       "      <td>3</td>\n",
       "      <td>Saether, Mr. Simon Sivertsen</td>\n",
       "      <td>male</td>\n",
       "      <td>38.50000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>SOTON/O.Q. 3101262</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>115.500000</td>\n",
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       "      <td>7.250000</td>\n",
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       "    <tr>\n",
       "      <th>416</th>\n",
       "      <td>1308</td>\n",
       "      <td>3</td>\n",
       "      <td>Ware, Mr. Frederick</td>\n",
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       "      <td>30.27259</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>359309</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
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       "      <td>8.050000</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>417</th>\n",
       "      <td>1309</td>\n",
       "      <td>3</td>\n",
       "      <td>Peter, Master. Michael J</td>\n",
       "      <td>male</td>\n",
       "      <td>30.27259</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2668</td>\n",
       "      <td>22.3583</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>90.817771</td>\n",
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       "      <td>7.452767</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>418 rows × 17 columns</p>\n",
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      ],
      "text/plain": [
       "     PassengerId  Pclass                                          Name  \\\n",
       "0            892       3                              Kelly, Mr. James   \n",
       "1            893       3              Wilkes, Mrs. James (Ellen Needs)   \n",
       "2            894       2                     Myles, Mr. Thomas Francis   \n",
       "3            895       3                              Wirz, Mr. Albert   \n",
       "4            896       3  Hirvonen, Mrs. Alexander (Helga E Lindqvist)   \n",
       "..           ...     ...                                           ...   \n",
       "413         1305       3                            Spector, Mr. Woolf   \n",
       "414         1306       1                  Oliva y Ocana, Dona. Fermina   \n",
       "415         1307       3                  Saether, Mr. Simon Sivertsen   \n",
       "416         1308       3                           Ware, Mr. Frederick   \n",
       "417         1309       3                      Peter, Master. Michael J   \n",
       "\n",
       "        Sex       Age  SibSp  Parch              Ticket      Fare Cabin  \\\n",
       "0      male  34.50000      0      0              330911    7.8292   NaN   \n",
       "1    female  47.00000      1      0              363272    7.0000   NaN   \n",
       "2      male  62.00000      0      0              240276    9.6875   NaN   \n",
       "3      male  27.00000      0      0              315154    8.6625   NaN   \n",
       "4    female  22.00000      1      1             3101298   12.2875   NaN   \n",
       "..      ...       ...    ...    ...                 ...       ...   ...   \n",
       "413    male  30.27259      0      0           A.5. 3236    8.0500   NaN   \n",
       "414  female  39.00000      0      0            PC 17758  108.9000  C105   \n",
       "415    male  38.50000      0      0  SOTON/O.Q. 3101262    7.2500   NaN   \n",
       "416    male  30.27259      0      0              359309    8.0500   NaN   \n",
       "417    male  30.27259      1      1                2668   22.3583   NaN   \n",
       "\n",
       "     Embarked    AgeClass  family_size  Fare_per_person  Title  isMale  \\\n",
       "0           1  103.500000            0         7.829200      2       1   \n",
       "1           2  141.000000            1         3.500000      3       0   \n",
       "2           1  124.000000            0         9.687500      2       1   \n",
       "3           2   81.000000            0         8.662500      2       1   \n",
       "4           2   66.000000            2         4.095833      3       0   \n",
       "..        ...         ...          ...              ...    ...     ...   \n",
       "413         2   90.817771            0         8.050000      2       1   \n",
       "414         0   39.000000            0       108.900000      2       0   \n",
       "415         2  115.500000            0         7.250000      2       1   \n",
       "416         2   90.817771            0         8.050000      2       1   \n",
       "417         0   90.817771            2         7.452767      0       1   \n",
       "\n",
       "     hadCabin  \n",
       "0           0  \n",
       "1           0  \n",
       "2           0  \n",
       "3           0  \n",
       "4           0  \n",
       "..        ...  \n",
       "413         0  \n",
       "414         1  \n",
       "415         0  \n",
       "416         0  \n",
       "417         0  \n",
       "\n",
       "[418 rows x 17 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_test['hadCabin'] = data_test['Cabin'].notna().astype(int)\n",
    "data_test"
   ]
  },
  {
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       "     Embarked    AgeClass  family_size  Fare_per_person  Title  isMale  \\\n",
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       "417         0   90.817771            2         7.452767      0       1   \n",
       "\n",
       "     hadCabin  \n",
       "0           0  \n",
       "1           0  \n",
       "2           0  \n",
       "3           0  \n",
       "4           0  \n",
       "..        ...  \n",
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       "414         1  \n",
       "415         0  \n",
       "416         0  \n",
       "417         0  \n",
       "\n",
       "[418 rows x 7 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "X_test = data_test.drop(columns=['Pclass', 'Age', 'SibSp', 'Parch', 'Fare', 'PassengerId', 'Name', 'Cabin', 'Ticket', 'Sex'])\n",
    "X_test"
   ]
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       "      <th>888</th>\n",
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       "     Embarked    AgeClass  family_size  Fare_per_person  Title  isMale  \\\n",
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       "890         1   96.000000            0          7.75000      2       1   \n",
       "\n",
       "     hadCabin  \n",
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       "[891 rows x 7 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train = data.drop(columns=['Pclass', 'Survived', 'Age', 'SibSp', 'Parch', 'Fare', 'PassengerId', 'Name', 'Sex', 'Cabin', 'Ticket'])\n",
    "X_train"
   ]
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   "id": "a070ddf7",
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    {
     "data": {
      "text/plain": [
       "(891,)"
      ]
     },
     "execution_count": 45,
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     "output_type": "execute_result"
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   "source": [
    "y_train = data['Survived']\n",
    "y_train.shape"
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   "execution_count": 46,
   "id": "cff33a97",
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    {
     "data": {
      "text/plain": [
       "(891, 7)"
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     "execution_count": 46,
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   "execution_count": 47,
   "id": "1a7f993e",
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       "(418, 7)"
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     },
     "execution_count": 49,
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   "source": [
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    "X_test = X_test[X_train.columns]\n",
    "X_test"
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       "     Embarked    AgeClass  family_size  Fare_per_person  Title  isMale  \\\n",
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       "1           0   38.000000            1         35.64165      3       0   \n",
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       "\n",
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       "..        ...  \n",
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      ],
      "text/plain": [
       "SGDClassifier(random_state=42)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we will use an sgd classifier for classification purposes.\n",
    "# Let's finally train our model on the dataset\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "\n",
    "sgd_clf = SGDClassifier(random_state=42)\n",
    "sgd_clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "99127726",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-17T10:37:09.386186Z",
     "iopub.status.busy": "2024-11-17T10:37:09.385710Z",
     "iopub.status.idle": "2024-11-17T10:37:09.396350Z",
     "shell.execute_reply": "2024-11-17T10:37:09.395197Z"
    },
    "papermill": {
     "duration": 0.052804,
     "end_time": "2024-11-17T10:37:09.398777",
     "exception": false,
     "start_time": "2024-11-17T10:37:09.345973",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(418,)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's make predictions\n",
    "y_pred = sgd_clf.predict(X_test)\n",
    "y_pred.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "38a44193",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-17T10:37:09.476364Z",
     "iopub.status.busy": "2024-11-17T10:37:09.475953Z",
     "iopub.status.idle": "2024-11-17T10:37:09.497698Z",
     "shell.execute_reply": "2024-11-17T10:37:09.496559Z"
    },
    "papermill": {
     "duration": 0.063637,
     "end_time": "2024-11-17T10:37:09.500193",
     "exception": false,
     "start_time": "2024-11-17T10:37:09.436556",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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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>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
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       "      <th>Title</th>\n",
       "      <th>isMale</th>\n",
       "      <th>hadCabin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892</td>\n",
       "      <td>3</td>\n",
       "      <td>Kelly, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>34.5</td>\n",
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       "      <td>Wilkes, Mrs. James (Ellen Needs)</td>\n",
       "      <td>female</td>\n",
       "      <td>47.0</td>\n",
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       "      <td>363272</td>\n",
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       "      <td>141.0</td>\n",
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       "      <td>3.500000</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>2</td>\n",
       "      <td>Myles, Mr. Thomas Francis</td>\n",
       "      <td>male</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>240276</td>\n",
       "      <td>9.6875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>124.0</td>\n",
       "      <td>0</td>\n",
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       "      <td>2</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>3</td>\n",
       "      <td>Wirz, Mr. Albert</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>315154</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>81.0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.662500</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>3</td>\n",
       "      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
       "      <td>female</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3101298</td>\n",
       "      <td>12.2875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>66.0</td>\n",
       "      <td>2</td>\n",
       "      <td>4.095833</td>\n",
       "      <td>3</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Pclass                                          Name     Sex  \\\n",
       "0          892       3                              Kelly, Mr. James    male   \n",
       "1          893       3              Wilkes, Mrs. James (Ellen Needs)  female   \n",
       "2          894       2                     Myles, Mr. Thomas Francis    male   \n",
       "3          895       3                              Wirz, Mr. Albert    male   \n",
       "4          896       3  Hirvonen, Mrs. Alexander (Helga E Lindqvist)  female   \n",
       "\n",
       "    Age  SibSp  Parch   Ticket     Fare Cabin  Embarked  AgeClass  \\\n",
       "0  34.5      0      0   330911   7.8292   NaN         1     103.5   \n",
       "1  47.0      1      0   363272   7.0000   NaN         2     141.0   \n",
       "2  62.0      0      0   240276   9.6875   NaN         1     124.0   \n",
       "3  27.0      0      0   315154   8.6625   NaN         2      81.0   \n",
       "4  22.0      1      1  3101298  12.2875   NaN         2      66.0   \n",
       "\n",
       "   family_size  Fare_per_person  Title  isMale  hadCabin  \n",
       "0            0         7.829200      2       1         0  \n",
       "1            1         3.500000      3       0         0  \n",
       "2            0         9.687500      2       1         0  \n",
       "3            0         8.662500      2       1         0  \n",
       "4            2         4.095833      3       0         0  "
      ]
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     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "7f2bff20",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-17T10:37:09.579076Z",
     "iopub.status.busy": "2024-11-17T10:37:09.578599Z",
     "iopub.status.idle": "2024-11-17T10:37:09.588142Z",
     "shell.execute_reply": "2024-11-17T10:37:09.586998Z"
    },
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     "duration": 0.051912,
     "end_time": "2024-11-17T10:37:09.590716",
     "exception": false,
     "start_time": "2024-11-17T10:37:09.538804",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1,\n",
       "       1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0])"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "passenger_ids = data_test['PassengerId']\n",
    "passenger_ids\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "9aadbabf",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-17T10:37:09.670046Z",
     "iopub.status.busy": "2024-11-17T10:37:09.669144Z",
     "iopub.status.idle": "2024-11-17T10:37:09.675112Z",
     "shell.execute_reply": "2024-11-17T10:37:09.673774Z"
    },
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     "duration": 0.0483,
     "end_time": "2024-11-17T10:37:09.677583",
     "exception": false,
     "start_time": "2024-11-17T10:37:09.629283",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "submission = pd.DataFrame({\n",
    "    'PassengerId': passenger_ids,\n",
    "    'Survived': y_pred\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "b1aab7b6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-17T10:37:09.757684Z",
     "iopub.status.busy": "2024-11-17T10:37:09.757158Z",
     "iopub.status.idle": "2024-11-17T10:37:09.770300Z",
     "shell.execute_reply": "2024-11-17T10:37:09.768945Z"
    },
    "papermill": {
     "duration": 0.056271,
     "end_time": "2024-11-17T10:37:09.772904",
     "exception": false,
     "start_time": "2024-11-17T10:37:09.716633",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th {\n",
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       "    .dataframe thead th {\n",
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       "    }\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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <th>413</th>\n",
       "      <td>1305</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>1306</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>1307</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>416</th>\n",
       "      <td>1308</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>417</th>\n",
       "      <td>1309</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>418 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived\n",
       "0            892         1\n",
       "1            893         1\n",
       "2            894         1\n",
       "3            895         1\n",
       "4            896         1\n",
       "..           ...       ...\n",
       "413         1305         1\n",
       "414         1306         1\n",
       "415         1307         0\n",
       "416         1308         1\n",
       "417         1309         0\n",
       "\n",
       "[418 rows x 2 columns]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "bfd020d7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-17T10:37:09.854236Z",
     "iopub.status.busy": "2024-11-17T10:37:09.853800Z",
     "iopub.status.idle": "2024-11-17T10:37:09.862843Z",
     "shell.execute_reply": "2024-11-17T10:37:09.861596Z"
    },
    "papermill": {
     "duration": 0.052451,
     "end_time": "2024-11-17T10:37:09.865537",
     "exception": false,
     "start_time": "2024-11-17T10:37:09.813086",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "submission.to_csv('/kaggle/working/submission.csv', index=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "86b523ff",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-17T10:37:09.945681Z",
     "iopub.status.busy": "2024-11-17T10:37:09.945214Z",
     "iopub.status.idle": "2024-11-17T10:37:09.950248Z",
     "shell.execute_reply": "2024-11-17T10:37:09.949118Z"
    },
    "papermill": {
     "duration": 0.048037,
     "end_time": "2024-11-17T10:37:09.952787",
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     "start_time": "2024-11-17T10:37:09.904750",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# os.listdir('/kaggle/working/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "31ea74bd",
   "metadata": {
    "papermill": {
     "duration": 0.039182,
     "end_time": "2024-11-17T10:37:10.032287",
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     "start_time": "2024-11-17T10:37:09.993105",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
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     "sourceType": "competition"
    }
   ],
   "dockerImageVersionId": 30786,
   "isGpuEnabled": false,
   "isInternetEnabled": true,
   "language": "python",
   "sourceType": "notebook"
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  "kernelspec": {
   "display_name": "Python 3",
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  "language_info": {
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    "version": 3
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   "input_path": "__notebook__.ipynb",
   "output_path": "__notebook__.ipynb",
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   "version": "2.6.0"
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 },
 "nbformat": 4,
 "nbformat_minor": 5
}