sophia-s-started-titanic.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"
     ]
    }
   ],
   "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",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "import sklearn\n",
    "warnings.filterwarnings('ignore')\n",
    "%matplotlib inline\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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       "<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>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892</td>\n",
       "      <td>3</td>\n",
       "      <td>Kelly, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>34.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330911</td>\n",
       "      <td>7.8292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>3</td>\n",
       "      <td>Wilkes, Mrs. James (Ellen Needs)</td>\n",
       "      <td>female</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>363272</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</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>Q</td>\n",
       "    </tr>\n",
       "    <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>S</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>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</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  \n",
       "0  34.5      0      0   330911   7.8292   NaN        Q  \n",
       "1  47.0      1      0   363272   7.0000   NaN        S  \n",
       "2  62.0      0      0   240276   9.6875   NaN        Q  \n",
       "3  27.0      0      0   315154   8.6625   NaN        S  \n",
       "4  22.0      1      1  3101298  12.2875   NaN        S  "
      ]
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   ],
   "source": [
    "# load the data\n",
    "train_data = pd.read_csv('/kaggle/input/titanic/train.csv')\n",
    "test_data = pd.read_csv('/kaggle/input/titanic/test.csv')\n",
    "train_data.head()\n",
    "test_data.head()"
   ]
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "% of women who survived: 0.7420382165605095\n"
     ]
    }
   ],
   "source": [
    "women = train_data.loc[train_data.Sex == 'female'][\"Survived\"]\n",
    "rate_women = sum(women)/len(women)\n",
    "print(\"% of women who survived:\", rate_women)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a413fb8b",
   "metadata": {
    "execution": {
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "% of men who survived: 0.18890814558058924\n"
     ]
    }
   ],
   "source": [
    "men = train_data.loc[train_data.Sex == 'male'][\"Survived\"]\n",
    "rate_men = sum(men)/len(men)\n",
    "print(\"% of men who survived:\", rate_men)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd9fad09",
   "metadata": {
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    "tags": []
   },
   "source": [
    "# missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7dd60f60",
   "metadata": {
    "execution": {
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    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "Age             86\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             1\n",
       "Cabin          327\n",
       "Embarked         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.isnull().sum()\n",
    "test_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ce186f8",
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     "status": "completed"
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    "tags": []
   },
   "source": [
    "# Age - Missing Values\n",
    "# data conduct"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "fc8406d0",
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    {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = train_data['Age'].hist(bins=15, density=True, stacked=True, alpha=0.7)\n",
    "\n",
    "train_data['Age'].plot(kind='density')\n",
    "\n",
    "ax.set(xlabel='Age')\n",
    "plt.xlim(0, 90)\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4d8eb32e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-17T03:32:28.206536Z",
     "iopub.status.busy": "2022-11-17T03:32:28.205596Z",
     "iopub.status.idle": "2022-11-17T03:32:28.216440Z",
     "shell.execute_reply": "2022-11-17T03:32:28.215564Z"
    },
    "papermill": {
     "duration": 0.023349,
     "end_time": "2022-11-17T03:32:28.218580",
     "exception": false,
     "start_time": "2022-11-17T03:32:28.195231",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "27.0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data['Age'].mean(skipna=True)\n",
    "test_data['Age'].mean(skipna=True)\n",
    "train_data['Age'].median(skipna=True)\n",
    "test_data['Age'].median(skipna=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84c2165d",
   "metadata": {
    "papermill": {
     "duration": 0.008941,
     "end_time": "2022-11-17T03:32:28.236774",
     "exception": false,
     "start_time": "2022-11-17T03:32:28.227833",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# port description"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ea2585c8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-17T03:32:28.257415Z",
     "iopub.status.busy": "2022-11-17T03:32:28.256676Z",
     "iopub.status.idle": "2022-11-17T03:32:28.394728Z",
     "shell.execute_reply": "2022-11-17T03:32:28.393279Z"
    },
    "papermill": {
     "duration": 0.151693,
     "end_time": "2022-11-17T03:32:28.397685",
     "exception": false,
     "start_time": "2022-11-17T03:32:28.245992",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Most boarded port:  S\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "sns.countplot(x='Embarked',data=train_data,palette='Set1')\n",
    "print('Most boarded port: ', train_data['Embarked'].value_counts().idxmax())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a79a9978",
   "metadata": {
    "papermill": {
     "duration": 0.009196,
     "end_time": "2022-11-17T03:32:28.416765",
     "exception": false,
     "start_time": "2022-11-17T03:32:28.407569",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Final Description and Implementation\n",
    "* age fill the null with medain numbers\n",
    "* port fill the null with S\n",
    "* cabin drop it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "656b3576",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-17T03:32:28.438015Z",
     "iopub.status.busy": "2022-11-17T03:32:28.437254Z",
     "iopub.status.idle": "2022-11-17T03:32:28.463533Z",
     "shell.execute_reply": "2022-11-17T03:32:28.462353Z"
    },
    "papermill": {
     "duration": 0.039839,
     "end_time": "2022-11-17T03:32:28.466171",
     "exception": false,
     "start_time": "2022-11-17T03:32:28.426332",
     "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>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>Embarked</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330911</td>\n",
       "      <td>7.8292</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>3</td>\n",
       "      <td>Wilkes, Mrs. James (Ellen Needs)</td>\n",
       "      <td>female</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>363272</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>S</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>Q</td>\n",
       "    </tr>\n",
       "    <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>S</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>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</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 Embarked  \n",
       "0  34.5      0      0   330911   7.8292        Q  \n",
       "1  47.0      1      0   363272   7.0000        S  \n",
       "2  62.0      0      0   240276   9.6875        Q  \n",
       "3  27.0      0      0   315154   8.6625        S  \n",
       "4  22.0      1      1  3101298  12.2875        S  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df=train_data.copy()\n",
    "test_df=test_data.copy()\n",
    "\n",
    "train_data['Age'].fillna(train_df['Age'].median(skipna=True),inplace=True)\n",
    "test_data['Age'].fillna(test_df['Age'].median(skipna=True),inplace=True)\n",
    "train_data['Embarked'].fillna(train_df['Embarked'].value_counts().idxmax(),inplace=True)\n",
    "test_data['Embarked'].fillna(test_df['Embarked'].value_counts().idxmax(),inplace=True)\n",
    "train_data.drop('Cabin', axis=1, inplace=True)\n",
    "test_data.drop('Cabin', axis=1, inplace=True)\n",
    "test_data['Fare'].fillna(test_data['Fare'].median(skipna=True),inplace=True)\n",
    "test_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6280aa30",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-17T03:32:28.487895Z",
     "iopub.status.busy": "2022-11-17T03:32:28.487130Z",
     "iopub.status.idle": "2022-11-17T03:32:28.498021Z",
     "shell.execute_reply": "2022-11-17T03:32:28.496950Z"
    },
    "papermill": {
     "duration": 0.024389,
     "end_time": "2022-11-17T03:32:28.500274",
     "exception": false,
     "start_time": "2022-11-17T03:32:28.475885",
     "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",
       "Embarked       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.isnull().sum()\n",
    "test_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1db5cc55",
   "metadata": {
    "papermill": {
     "duration": 0.009279,
     "end_time": "2022-11-17T03:32:28.519290",
     "exception": false,
     "start_time": "2022-11-17T03:32:28.510011",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# unnecessary variable analysis\n",
    "* sibsp:#of siblings/spouses \n",
    "* parch:# of parents/kids \n",
    "  * both variables are highly related\n",
    "  * trun then in one variable solotravel : 0/1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9b6a1bbc",
   "metadata": {
    "execution": {
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     "start_time": "2022-11-17T03:32:28.528900",
     "status": "completed"
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    "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",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>SoloTravel</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330911</td>\n",
       "      <td>7.8292</td>\n",
       "      <td>Q</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>3</td>\n",
       "      <td>Wilkes, Mrs. James (Ellen Needs)</td>\n",
       "      <td>female</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>363272</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>S</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>Q</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <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>S</td>\n",
       "      <td>1</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>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>897</td>\n",
       "      <td>3</td>\n",
       "      <td>Svensson, Mr. Johan Cervin</td>\n",
       "      <td>male</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7538</td>\n",
       "      <td>9.2250</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>898</td>\n",
       "      <td>3</td>\n",
       "      <td>Connolly, Miss. Kate</td>\n",
       "      <td>female</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330972</td>\n",
       "      <td>7.6292</td>\n",
       "      <td>Q</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>899</td>\n",
       "      <td>2</td>\n",
       "      <td>Caldwell, Mr. Albert Francis</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>248738</td>\n",
       "      <td>29.0000</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>900</td>\n",
       "      <td>3</td>\n",
       "      <td>Abrahim, Mrs. Joseph (Sophie Halaut Easu)</td>\n",
       "      <td>female</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2657</td>\n",
       "      <td>7.2292</td>\n",
       "      <td>C</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>901</td>\n",
       "      <td>3</td>\n",
       "      <td>Davies, Mr. John Samuel</td>\n",
       "      <td>male</td>\n",
       "      <td>21.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>A/4 48871</td>\n",
       "      <td>24.1500</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</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",
       "5          897       3                    Svensson, Mr. Johan Cervin    male   \n",
       "6          898       3                          Connolly, Miss. Kate  female   \n",
       "7          899       2                  Caldwell, Mr. Albert Francis    male   \n",
       "8          900       3     Abrahim, Mrs. Joseph (Sophie Halaut Easu)  female   \n",
       "9          901       3                       Davies, Mr. John Samuel    male   \n",
       "\n",
       "    Age  SibSp  Parch     Ticket     Fare Embarked  SoloTravel  \n",
       "0  34.5      0      0     330911   7.8292        Q           1  \n",
       "1  47.0      1      0     363272   7.0000        S           0  \n",
       "2  62.0      0      0     240276   9.6875        Q           1  \n",
       "3  27.0      0      0     315154   8.6625        S           1  \n",
       "4  22.0      1      1    3101298  12.2875        S           0  \n",
       "5  14.0      0      0       7538   9.2250        S           1  \n",
       "6  30.0      0      0     330972   7.6292        Q           1  \n",
       "7  26.0      1      1     248738  29.0000        S           0  \n",
       "8  18.0      0      0       2657   7.2292        C           1  \n",
       "9  21.0      2      0  A/4 48871  24.1500        S           0  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data['SoloTravel'] = np.where((train_data['SibSp']+train_data['Parch'])>0,0,1)\n",
    "test_data['SoloTravel']=np.where((test_data['SibSp']+test_data['Parch'])>0,0,1)\n",
    "test_data.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53452c1b",
   "metadata": {
    "papermill": {
     "duration": 0.009632,
     "end_time": "2022-11-17T03:32:28.582421",
     "exception": false,
     "start_time": "2022-11-17T03:32:28.572789",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# remove helpless info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "63f8b18f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-17T03:32:28.604398Z",
     "iopub.status.busy": "2022-11-17T03:32:28.603622Z",
     "iopub.status.idle": "2022-11-17T03:32:28.623935Z",
     "shell.execute_reply": "2022-11-17T03:32:28.622890Z"
    },
    "papermill": {
     "duration": 0.033857,
     "end_time": "2022-11-17T03:32:28.626171",
     "exception": false,
     "start_time": "2022-11-17T03:32:28.592314",
     "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>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>SoloTravel</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",
       "      <td>330911</td>\n",
       "      <td>7.8292</td>\n",
       "      <td>Q</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>3</td>\n",
       "      <td>Wilkes, Mrs. James (Ellen Needs)</td>\n",
       "      <td>female</td>\n",
       "      <td>47.0</td>\n",
       "      <td>363272</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>S</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>240276</td>\n",
       "      <td>9.6875</td>\n",
       "      <td>Q</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <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>315154</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>S</td>\n",
       "      <td>1</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>3101298</td>\n",
       "      <td>12.2875</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</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   Ticket     Fare Embarked  SoloTravel  \n",
       "0  34.5   330911   7.8292        Q           1  \n",
       "1  47.0   363272   7.0000        S           0  \n",
       "2  62.0   240276   9.6875        Q           1  \n",
       "3  27.0   315154   8.6625        S           1  \n",
       "4  22.0  3101298  12.2875        S           0  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.drop('SibSp',axis=1,inplace=True)\n",
    "train_data.drop('Parch',axis=1,inplace=True)\n",
    "\n",
    "test_data.drop('SibSp',axis=1,inplace=True)\n",
    "test_data.drop('Parch',axis=1,inplace=True)\n",
    "\n",
    "test_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2238c3c",
   "metadata": {
    "papermill": {
     "duration": 0.012461,
     "end_time": "2022-11-17T03:32:28.648868",
     "exception": false,
     "start_time": "2022-11-17T03:32:28.636407",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# categorical variables\n",
    " * deal with the catogrical info: 'Pclass','Embarked','Sex'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "38ca372f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-17T03:32:28.675167Z",
     "iopub.status.busy": "2022-11-17T03:32:28.674179Z",
     "iopub.status.idle": "2022-11-17T03:32:28.718159Z",
     "shell.execute_reply": "2022-11-17T03:32:28.717262Z"
    },
    "papermill": {
     "duration": 0.060585,
     "end_time": "2022-11-17T03:32:28.720607",
     "exception": false,
     "start_time": "2022-11-17T03:32:28.660022",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>SoloTravel</th>\n",
       "      <th>Pclass_2</th>\n",
       "      <th>Pclass_3</th>\n",
       "      <th>Embarked_Q</th>\n",
       "      <th>Embarked_S</th>\n",
       "      <th>Sex_male</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>34.5</td>\n",
       "      <td>7.8292</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>47.0</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>62.0</td>\n",
       "      <td>9.6875</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>27.0</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>22.0</td>\n",
       "      <td>12.2875</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>14.0</td>\n",
       "      <td>9.2250</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>30.0</td>\n",
       "      <td>7.6292</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>26.0</td>\n",
       "      <td>29.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>18.0</td>\n",
       "      <td>7.2292</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>21.0</td>\n",
       "      <td>24.1500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Age     Fare  SoloTravel  Pclass_2  Pclass_3  Embarked_Q  Embarked_S  \\\n",
       "0  34.5   7.8292           1         0         1           1           0   \n",
       "1  47.0   7.0000           0         0         1           0           1   \n",
       "2  62.0   9.6875           1         1         0           1           0   \n",
       "3  27.0   8.6625           1         0         1           0           1   \n",
       "4  22.0  12.2875           0         0         1           0           1   \n",
       "5  14.0   9.2250           1         0         1           0           1   \n",
       "6  30.0   7.6292           1         0         1           1           0   \n",
       "7  26.0  29.0000           0         1         0           0           1   \n",
       "8  18.0   7.2292           1         0         1           0           0   \n",
       "9  21.0  24.1500           0         0         1           0           1   \n",
       "\n",
       "   Sex_male  \n",
       "0         1  \n",
       "1         0  \n",
       "2         1  \n",
       "3         1  \n",
       "4         0  \n",
       "5         1  \n",
       "6         0  \n",
       "7         1  \n",
       "8         0  \n",
       "9         1  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data = pd.get_dummies(train_data,columns=[\"Pclass\",'Embarked','Sex'],drop_first=True)\n",
    "test_data = pd.get_dummies(test_data,columns=[\"Pclass\",'Embarked','Sex'],drop_first=True)\n",
    "train_data.drop('PassengerId',axis=1,inplace=True)\n",
    "train_data.drop('Name',axis=1,inplace=True)\n",
    "train_data.drop('Ticket',axis=1,inplace=True)\n",
    "\n",
    "test_data.drop('Name',axis=1,inplace=True)\n",
    "test_data.drop('Ticket',axis=1,inplace=True)\n",
    "test_data.drop('PassengerId',axis=1,inplace=True)\n",
    "\n",
    "test_data.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e39fd97",
   "metadata": {
    "papermill": {
     "duration": 0.010249,
     "end_time": "2022-11-17T03:32:28.746725",
     "exception": false,
     "start_time": "2022-11-17T03:32:28.736476",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# exploratory data analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b53f6809",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-17T03:32:28.769248Z",
     "iopub.status.busy": "2022-11-17T03:32:28.768800Z",
     "iopub.status.idle": "2022-11-17T03:32:28.806202Z",
     "shell.execute_reply": "2022-11-17T03:32:28.805127Z"
    },
    "papermill": {
     "duration": 0.051296,
     "end_time": "2022-11-17T03:32:28.808451",
     "exception": false,
     "start_time": "2022-11-17T03:32:28.757155",
     "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>Survived</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>SoloTravel</th>\n",
       "      <th>Pclass_2</th>\n",
       "      <th>Pclass_3</th>\n",
       "      <th>Embarked_Q</th>\n",
       "      <th>Embarked_S</th>\n",
       "      <th>Sex_male</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>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</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>0.383838</td>\n",
       "      <td>29.361582</td>\n",
       "      <td>32.204208</td>\n",
       "      <td>0.602694</td>\n",
       "      <td>0.206510</td>\n",
       "      <td>0.551066</td>\n",
       "      <td>0.086420</td>\n",
       "      <td>0.725028</td>\n",
       "      <td>0.647587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.486592</td>\n",
       "      <td>13.019697</td>\n",
       "      <td>49.693429</td>\n",
       "      <td>0.489615</td>\n",
       "      <td>0.405028</td>\n",
       "      <td>0.497665</td>\n",
       "      <td>0.281141</td>\n",
       "      <td>0.446751</td>\n",
       "      <td>0.477990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</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>0.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>7.910400</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>14.454200</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>512.329200</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Survived         Age        Fare  SoloTravel    Pclass_2    Pclass_3  \\\n",
       "count  891.000000  891.000000  891.000000  891.000000  891.000000  891.000000   \n",
       "mean     0.383838   29.361582   32.204208    0.602694    0.206510    0.551066   \n",
       "std      0.486592   13.019697   49.693429    0.489615    0.405028    0.497665   \n",
       "min      0.000000    0.420000    0.000000    0.000000    0.000000    0.000000   \n",
       "25%      0.000000   22.000000    7.910400    0.000000    0.000000    0.000000   \n",
       "50%      0.000000   28.000000   14.454200    1.000000    0.000000    1.000000   \n",
       "75%      1.000000   35.000000   31.000000    1.000000    0.000000    1.000000   \n",
       "max      1.000000   80.000000  512.329200    1.000000    1.000000    1.000000   \n",
       "\n",
       "       Embarked_Q  Embarked_S    Sex_male  \n",
       "count  891.000000  891.000000  891.000000  \n",
       "mean     0.086420    0.725028    0.647587  \n",
       "std      0.281141    0.446751    0.477990  \n",
       "min      0.000000    0.000000    0.000000  \n",
       "25%      0.000000    0.000000    0.000000  \n",
       "50%      0.000000    1.000000    1.000000  \n",
       "75%      0.000000    1.000000    1.000000  \n",
       "max      1.000000    1.000000    1.000000  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.shape\n",
    "test_data.shape\n",
    "train_data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02ee7512",
   "metadata": {
    "papermill": {
     "duration": 0.01034,
     "end_time": "2022-11-17T03:32:28.829554",
     "exception": false,
     "start_time": "2022-11-17T03:32:28.819214",
     "status": "completed"
    },
    "tags": []
   },
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "b6939ed1",
   "metadata": {
    "papermill": {
     "duration": 0.010253,
     "end_time": "2022-11-17T03:32:28.850511",
     "exception": false,
     "start_time": "2022-11-17T03:32:28.840258",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## eda for age variable "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a63a6df4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-17T03:32:28.874227Z",
     "iopub.status.busy": "2022-11-17T03:32:28.873061Z",
     "iopub.status.idle": "2022-11-17T03:32:29.129582Z",
     "shell.execute_reply": "2022-11-17T03:32:29.128401Z"
    },
    "papermill": {
     "duration": 0.271262,
     "end_time": "2022-11-17T03:32:29.132397",
     "exception": false,
     "start_time": "2022-11-17T03:32:28.861135",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-10.0, 85.0)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "plt.figure(figsize=(15,8))\n",
    "\n",
    "ax = sns.kdeplot(train_data['Age'][train_data.Survived ==1],color='green',shade=True)\n",
    "sns.kdeplot(train_data[\"Age\"][train_data.Survived==0],color='red',shade=True)\n",
    "\n",
    "plt.legend(['Survived','Died'])\n",
    "plt.title('survival and death density graph for age')\n",
    "ax.set(xlabel='age')\n",
    "plt.xlim(-10,85)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d905d748",
   "metadata": {
    "papermill": {
     "duration": 0.011829,
     "end_time": "2022-11-17T03:32:29.156047",
     "exception": false,
     "start_time": "2022-11-17T03:32:29.144218",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## eda for fare variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "32115881",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-17T03:32:29.181186Z",
     "iopub.status.busy": "2022-11-17T03:32:29.180713Z",
     "iopub.status.idle": "2022-11-17T03:32:29.419755Z",
     "shell.execute_reply": "2022-11-17T03:32:29.418644Z"
    },
    "papermill": {
     "duration": 0.25475,
     "end_time": "2022-11-17T03:32:29.422423",
     "exception": false,
     "start_time": "2022-11-17T03:32:29.167673",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "plt.figure(figsize=(15,8))\n",
    "ax = sns.kdeplot(train_data['Fare'][train_data.Survived==1],color='green',shade=True)\n",
    "\n",
    "sns.kdeplot(train_data['Fare'][train_data.Survived==0],color='red',shade=True)\n",
    "\n",
    "plt.legend(['Survived','Died'])\n",
    "plt.title('density plot of fare')\n",
    "ax.set(xlabel='Fare')\n",
    "plt.xlim(-20,200)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "009b8f0d",
   "metadata": {
    "papermill": {
     "duration": 0.011845,
     "end_time": "2022-11-17T03:32:29.446696",
     "exception": false,
     "start_time": "2022-11-17T03:32:29.434851",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# eda for passenger variable and famoly/solo travel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "6a94f911",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-17T03:32:29.473959Z",
     "iopub.status.busy": "2022-11-17T03:32:29.473567Z",
     "iopub.status.idle": "2022-11-17T03:32:29.760172Z",
     "shell.execute_reply": "2022-11-17T03:32:29.758977Z"
    },
    "papermill": {
     "duration": 0.303012,
     "end_time": "2022-11-17T03:32:29.762603",
     "exception": false,
     "start_time": "2022-11-17T03:32:29.459591",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Pclass', ylabel='Survived'>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot('Pclass','Survived',data=train_df,color='green')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "323968d8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-17T03:32:29.789929Z",
     "iopub.status.busy": "2022-11-17T03:32:29.789230Z",
     "iopub.status.idle": "2022-11-17T03:32:30.042540Z",
     "shell.execute_reply": "2022-11-17T03:32:30.041654Z"
    },
    "papermill": {
     "duration": 0.269596,
     "end_time": "2022-11-17T03:32:30.044833",
     "exception": false,
     "start_time": "2022-11-17T03:32:29.775237",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Sex', ylabel='Survived'>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot('Sex','Survived',data=train_df,color='blue')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "24a39cdf",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-17T03:32:30.072933Z",
     "iopub.status.busy": "2022-11-17T03:32:30.072311Z",
     "iopub.status.idle": "2022-11-17T03:32:30.106248Z",
     "shell.execute_reply": "2022-11-17T03:32:30.105162Z"
    },
    "papermill": {
     "duration": 0.050916,
     "end_time": "2022-11-17T03:32:30.108805",
     "exception": false,
     "start_time": "2022-11-17T03:32:30.057889",
     "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>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>SoloTravel</th>\n",
       "      <th>Pclass_2</th>\n",
       "      <th>Pclass_3</th>\n",
       "      <th>Embarked_Q</th>\n",
       "      <th>Embarked_S</th>\n",
       "      <th>Sex_male</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>891.000000</td>\n",
       "      <td>891.000000</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>29.361582</td>\n",
       "      <td>32.204208</td>\n",
       "      <td>0.602694</td>\n",
       "      <td>0.206510</td>\n",
       "      <td>0.551066</td>\n",
       "      <td>0.086420</td>\n",
       "      <td>0.725028</td>\n",
       "      <td>0.647587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>13.019697</td>\n",
       "      <td>49.693429</td>\n",
       "      <td>0.489615</td>\n",
       "      <td>0.405028</td>\n",
       "      <td>0.497665</td>\n",
       "      <td>0.281141</td>\n",
       "      <td>0.446751</td>\n",
       "      <td>0.477990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</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>22.000000</td>\n",
       "      <td>7.910400</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>28.000000</td>\n",
       "      <td>14.454200</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>35.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>80.000000</td>\n",
       "      <td>512.329200</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Age        Fare  SoloTravel    Pclass_2    Pclass_3  Embarked_Q  \\\n",
       "count  891.000000  891.000000  891.000000  891.000000  891.000000  891.000000   \n",
       "mean    29.361582   32.204208    0.602694    0.206510    0.551066    0.086420   \n",
       "std     13.019697   49.693429    0.489615    0.405028    0.497665    0.281141   \n",
       "min      0.420000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "25%     22.000000    7.910400    0.000000    0.000000    0.000000    0.000000   \n",
       "50%     28.000000   14.454200    1.000000    0.000000    1.000000    0.000000   \n",
       "75%     35.000000   31.000000    1.000000    0.000000    1.000000    0.000000   \n",
       "max     80.000000  512.329200    1.000000    1.000000    1.000000    1.000000   \n",
       "\n",
       "       Embarked_S    Sex_male  \n",
       "count  891.000000  891.000000  \n",
       "mean     0.725028    0.647587  \n",
       "std      0.446751    0.477990  \n",
       "min      0.000000    0.000000  \n",
       "25%      0.000000    0.000000  \n",
       "50%      1.000000    1.000000  \n",
       "75%      1.000000    1.000000  \n",
       "max      1.000000    1.000000  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.drop('Survived',axis=1,inplace=True)\n",
    "train_data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f151934",
   "metadata": {
    "papermill": {
     "duration": 0.013281,
     "end_time": "2022-11-17T03:32:30.136135",
     "exception": false,
     "start_time": "2022-11-17T03:32:30.122854",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# feature scailing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a13f4015",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Age', 'Fare', 'SoloTravel', 'Pclass_2', 'Pclass_3', 'Embarked_Q',\n",
       "       'Embarked_S', 'Sex_male'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols = train_data.columns\n",
    "\n",
    "cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "5f0510c2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-17T03:32:30.202344Z",
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     "exception": false,
     "start_time": "2022-11-17T03:32:30.187152",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>SoloTravel</th>\n",
       "      <th>Pclass_2</th>\n",
       "      <th>Pclass_3</th>\n",
       "      <th>Embarked_Q</th>\n",
       "      <th>Embarked_S</th>\n",
       "      <th>Sex_male</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.271174</td>\n",
       "      <td>0.014151</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.472229</td>\n",
       "      <td>0.139136</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.321438</td>\n",
       "      <td>0.015469</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.434531</td>\n",
       "      <td>0.103644</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.434531</td>\n",
       "      <td>0.015713</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Age      Fare SoloTravel Pclass_2 Pclass_3 Embarked_Q Embarked_S  \\\n",
       "0  0.271174  0.014151        0.0      0.0      1.0        0.0        1.0   \n",
       "1  0.472229  0.139136        0.0      0.0      0.0        0.0        0.0   \n",
       "2  0.321438  0.015469        1.0      0.0      1.0        0.0        1.0   \n",
       "3  0.434531  0.103644        0.0      0.0      0.0        0.0        1.0   \n",
       "4  0.434531  0.015713        1.0      0.0      1.0        0.0        1.0   \n",
       "\n",
       "  Sex_male  \n",
       "0      1.0  \n",
       "1      0.0  \n",
       "2      0.0  \n",
       "3      0.0  \n",
       "4      1.0  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "scaler = MinMaxScaler()\n",
    "\n",
    "train_data = scaler.fit_transform(train_data)\n",
    "test_data = scaler.fit_transform(test_data)\n",
    "train_data = pd.DataFrame(train_data,columns=[cols])\n",
    "test_data = pd.DataFrame(test_data,columns=[cols])\n",
    "train_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58005472",
   "metadata": {
    "papermill": {
     "duration": 0.013079,
     "end_time": "2022-11-17T03:32:30.289141",
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     "start_time": "2022-11-17T03:32:30.276062",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# model and predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "a7370d1f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-17T03:32:30.318789Z",
     "iopub.status.busy": "2022-11-17T03:32:30.317507Z",
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     "shell.execute_reply": "2022-11-17T03:32:30.770435Z"
    },
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     "duration": 0.471147,
     "end_time": "2022-11-17T03:32:30.773925",
     "exception": false,
     "start_time": "2022-11-17T03:32:30.302778",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "y = train_df['Survived']\n",
    "X = train_data\n",
    "X_test = test_data\n",
    "model = RandomForestClassifier(n_estimators=100,max_depth=5,random_state=1)\n",
    "model.fit(X,y)\n",
    "predictions = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "061eafdb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-17T03:32:30.803198Z",
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    },
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     "duration": 0.026179,
     "end_time": "2022-11-17T03:32:30.813697",
     "exception": false,
     "start_time": "2022-11-17T03:32:30.787518",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "0cd67f7c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-17T03:32:30.842671Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "submission successfully saved!\n"
     ]
    }
   ],
   "source": [
    "# submission\n",
    "output = pd.DataFrame({'PassengerId':test_df.PassengerId,'Survived':predictions})\n",
    "output.to_csv('submission.csv',index=False)\n",
    "print('submission successfully saved!')\n"
   ]
  }
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