titanic-dataset.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",
    "\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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   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n"
   ]
  },
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   "execution_count": 3,
   "id": "9ed8f9fd",
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   "outputs": [],
   "source": [
    "train=pd.read_csv('/kaggle/input/titanic/train.csv')\n",
    "test=pd.read_csv('/kaggle/input/titanic/test.csv')"
   ]
  },
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    {
     "data": {
      "text/plain": [
       "(891, 12)"
      ]
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    "train.shape\n"
   ]
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     "data": {
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       "  <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",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <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>22.0</td>\n",
       "      <td>1</td>\n",
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       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
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       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
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       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \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",
       "\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  "
      ]
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    "train.head(2)\n"
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     "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"
     ]
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   "source": [
    "train.info()\n"
   ]
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 418 entries, 0 to 417\n",
      "Data columns (total 11 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  418 non-null    int64  \n",
      " 1   Pclass       418 non-null    int64  \n",
      " 2   Name         418 non-null    object \n",
      " 3   Sex          418 non-null    object \n",
      " 4   Age          332 non-null    float64\n",
      " 5   SibSp        418 non-null    int64  \n",
      " 6   Parch        418 non-null    int64  \n",
      " 7   Ticket       418 non-null    object \n",
      " 8   Fare         417 non-null    float64\n",
      " 9   Cabin        91 non-null     object \n",
      " 10  Embarked     418 non-null    object \n",
      "dtypes: float64(2), int64(4), object(5)\n",
      "memory usage: 36.0+ KB\n"
     ]
    }
   ],
   "source": [
    "test.info()\n"
   ]
  },
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   "cell_type": "code",
   "execution_count": 8,
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   "source": [
    "train.drop(columns=['Cabin'],inplace=True)\n",
    "test.drop(columns=['Cabin'],inplace=True)"
   ]
  },
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     "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",
       "Embarked         2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
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   ],
   "source": [
    "train.isnull().sum()\n"
   ]
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     "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",
       "Embarked        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
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   ],
   "source": [
    "test.isnull().sum()\n"
   ]
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "Embarked\n",
       "S    644\n",
       "C    168\n",
       "Q     77\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
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    }
   ],
   "source": [
    "train['Embarked'].value_counts()\n"
   ]
  },
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   "execution_count": 12,
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   "source": [
    "train['Embarked'].fillna('S',inplace=True)\n"
   ]
  },
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    {
     "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",
       "Embarked         0\n",
       "dtype: int64"
      ]
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     "execution_count": 13,
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    "train.isnull().sum()\n"
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    "test['Fare'].fillna(test['Fare'].mean(),inplace=True)\n"
   ]
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       "PassengerId     0\n",
       "Pclass          0\n",
       "Name            0\n",
       "Sex             0\n",
       "Age            86\n",
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       "Fare            0\n",
       "Embarked        0\n",
       "dtype: int64"
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     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.isnull().sum()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "eaa9d577",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-09-08T10:25:22.012476Z",
     "iopub.status.busy": "2023-09-08T10:25:22.012088Z",
     "iopub.status.idle": "2023-09-08T10:25:22.021766Z",
     "shell.execute_reply": "2023-09-08T10:25:22.019766Z"
    },
    "papermill": {
     "duration": 0.035827,
     "end_time": "2023-09-08T10:25:22.024812",
     "exception": false,
     "start_time": "2023-09-08T10:25:21.988985",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "train_age=np.random.randint(train['Age'].mean()-train['Age'].std(),train['Age'].mean()+train['Age'].std(),177)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c8c629dc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-09-08T10:25:22.074372Z",
     "iopub.status.busy": "2023-09-08T10:25:22.073650Z",
     "iopub.status.idle": "2023-09-08T10:25:22.082321Z",
     "shell.execute_reply": "2023-09-08T10:25:22.080497Z"
    },
    "papermill": {
     "duration": 0.037726,
     "end_time": "2023-09-08T10:25:22.085417",
     "exception": false,
     "start_time": "2023-09-08T10:25:22.047691",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "test_age=np.random.randint(test['Age'].mean()-test['Age'].std(),test['Age'].mean()+test['Age'].std(),86)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e1bc48c2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-09-08T10:25:22.134002Z",
     "iopub.status.busy": "2023-09-08T10:25:22.133550Z",
     "iopub.status.idle": "2023-09-08T10:25:22.141019Z",
     "shell.execute_reply": "2023-09-08T10:25:22.139741Z"
    },
    "papermill": {
     "duration": 0.035261,
     "end_time": "2023-09-08T10:25:22.144443",
     "exception": false,
     "start_time": "2023-09-08T10:25:22.109182",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_20/216320660.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  train['Age'][train['Age'].isnull()]=train_age\n"
     ]
    }
   ],
   "source": [
    "train['Age'][train['Age'].isnull()]=train_age\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "0cbc7a47",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-09-08T10:25:22.190801Z",
     "iopub.status.busy": "2023-09-08T10:25:22.190320Z",
     "iopub.status.idle": "2023-09-08T10:25:22.200225Z",
     "shell.execute_reply": "2023-09-08T10:25:22.199041Z"
    },
    "papermill": {
     "duration": 0.036639,
     "end_time": "2023-09-08T10:25:22.202940",
     "exception": false,
     "start_time": "2023-09-08T10:25:22.166301",
     "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",
       "Embarked       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isnull().sum()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "3e0fa9f2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-09-08T10:25:22.255562Z",
     "iopub.status.busy": "2023-09-08T10:25:22.254913Z",
     "iopub.status.idle": "2023-09-08T10:25:22.265024Z",
     "shell.execute_reply": "2023-09-08T10:25:22.263579Z"
    },
    "papermill": {
     "duration": 0.041089,
     "end_time": "2023-09-08T10:25:22.268813",
     "exception": false,
     "start_time": "2023-09-08T10:25:22.227724",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_20/1961586285.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test['Age'][test['Age'].isnull()]=test_age\n"
     ]
    }
   ],
   "source": [
    "test['Age'][test['Age'].isnull()]=test_age\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "4b048138",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-09-08T10:25:22.316429Z",
     "iopub.status.busy": "2023-09-08T10:25:22.316039Z",
     "iopub.status.idle": "2023-09-08T10:25:22.325887Z",
     "shell.execute_reply": "2023-09-08T10:25:22.324618Z"
    },
    "papermill": {
     "duration": 0.036403,
     "end_time": "2023-09-08T10:25:22.328520",
     "exception": false,
     "start_time": "2023-09-08T10:25:22.292117",
     "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": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.isnull().sum()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "a6b5c49c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-09-08T10:25:22.377627Z",
     "iopub.status.busy": "2023-09-08T10:25:22.377202Z",
     "iopub.status.idle": "2023-09-08T10:25:22.393760Z",
     "shell.execute_reply": "2023-09-08T10:25:22.392393Z"
    },
    "papermill": {
     "duration": 0.043438,
     "end_time": "2023-09-08T10:25:22.396235",
     "exception": false,
     "start_time": "2023-09-08T10:25:22.352797",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pclass\n",
       "1    0.629630\n",
       "2    0.472826\n",
       "3    0.242363\n",
       "Name: Survived, dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.groupby(['Pclass'])['Survived'].mean()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "c4209502",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-09-08T10:25:22.447222Z",
     "iopub.status.busy": "2023-09-08T10:25:22.446575Z",
     "iopub.status.idle": "2023-09-08T10:25:22.459285Z",
     "shell.execute_reply": "2023-09-08T10:25:22.457843Z"
    },
    "papermill": {
     "duration": 0.041104,
     "end_time": "2023-09-08T10:25:22.462135",
     "exception": false,
     "start_time": "2023-09-08T10:25:22.421031",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sex\n",
       "female    0.742038\n",
       "male      0.188908\n",
       "Name: Survived, dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.groupby(['Sex'])['Survived'].mean()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "ebeebb2e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-09-08T10:25:22.512094Z",
     "iopub.status.busy": "2023-09-08T10:25:22.511715Z",
     "iopub.status.idle": "2023-09-08T10:25:22.521941Z",
     "shell.execute_reply": "2023-09-08T10:25:22.520710Z"
    },
    "papermill": {
     "duration": 0.038257,
     "end_time": "2023-09-08T10:25:22.524616",
     "exception": false,
     "start_time": "2023-09-08T10:25:22.486359",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Embarked\n",
       "C    0.553571\n",
       "Q    0.389610\n",
       "S    0.339009\n",
       "Name: Survived, dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.groupby(['Embarked'])['Survived'].mean()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "e42f51d4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-09-08T10:25:22.572939Z",
     "iopub.status.busy": "2023-09-08T10:25:22.572509Z",
     "iopub.status.idle": "2023-09-08T10:25:23.172045Z",
     "shell.execute_reply": "2023-09-08T10:25:23.170167Z"
    },
    "papermill": {
     "duration": 0.626615,
     "end_time": "2023-09-08T10:25:23.174696",
     "exception": false,
     "start_time": "2023-09-08T10:25:22.548081",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_20/3826302481.py:1: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(train['Age'][train['Survived']==0])\n",
      "/tmp/ipykernel_20/3826302481.py:2: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(train['Age'][train['Survived']==1])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Age', ylabel='Density'>"
      ]
     },
     "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.distplot(train['Age'][train['Survived']==0])\n",
    "sns.distplot(train['Age'][train['Survived']==1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "522609f3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-09-08T10:25:23.227443Z",
     "iopub.status.busy": "2023-09-08T10:25:23.225099Z",
     "iopub.status.idle": "2023-09-08T10:25:23.824340Z",
     "shell.execute_reply": "2023-09-08T10:25:23.822973Z"
    },
    "papermill": {
     "duration": 0.628891,
     "end_time": "2023-09-08T10:25:23.827629",
     "exception": false,
     "start_time": "2023-09-08T10:25:23.198738",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_20/2626582821.py:1: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(train['Fare'][train['Survived']==0])\n",
      "/tmp/ipykernel_20/2626582821.py:2: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(train['Fare'][train['Survived']==1])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Fare', ylabel='Density'>"
      ]
     },
     "execution_count": 26,
     "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.distplot(train['Fare'][train['Survived']==0])\n",
    "sns.distplot(train['Fare'][train['Survived']==1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "4858e80c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-09-08T10:25:23.892535Z",
     "iopub.status.busy": "2023-09-08T10:25:23.891902Z",
     "iopub.status.idle": "2023-09-08T10:25:23.907105Z",
     "shell.execute_reply": "2023-09-08T10:25:23.905052Z"
    },
    "papermill": {
     "duration": 0.055461,
     "end_time": "2023-09-08T10:25:23.910304",
     "exception": false,
     "start_time": "2023-09-08T10:25:23.854843",
     "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",
       "Embarked       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isnull().sum()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "8ff9f879",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-09-08T10:25:23.979925Z",
     "iopub.status.busy": "2023-09-08T10:25:23.977381Z",
     "iopub.status.idle": "2023-09-08T10:25:23.986912Z",
     "shell.execute_reply": "2023-09-08T10:25:23.985486Z"
    },
    "papermill": {
     "duration": 0.049747,
     "end_time": "2023-09-08T10:25:23.989870",
     "exception": false,
     "start_time": "2023-09-08T10:25:23.940123",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "train.drop(columns=['Ticket'],inplace=True)\n",
    "test.drop(columns=['Ticket'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "5b248ae6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-09-08T10:25:24.041719Z",
     "iopub.status.busy": "2023-09-08T10:25:24.040722Z",
     "iopub.status.idle": "2023-09-08T10:25:24.047901Z",
     "shell.execute_reply": "2023-09-08T10:25:24.046756Z"
    },
    "papermill": {
     "duration": 0.035752,
     "end_time": "2023-09-08T10:25:24.050443",
     "exception": false,
     "start_time": "2023-09-08T10:25:24.014691",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "train['family']=train['SibSp']+train['Parch']+1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "84e1f878",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-09-08T10:25:24.114177Z",
     "iopub.status.busy": "2023-09-08T10:25:24.113167Z",
     "iopub.status.idle": "2023-09-08T10:25:24.120685Z",
     "shell.execute_reply": "2023-09-08T10:25:24.119428Z"
    },
    "papermill": {
     "duration": 0.041781,
     "end_time": "2023-09-08T10:25:24.123392",
     "exception": false,
     "start_time": "2023-09-08T10:25:24.081611",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "test['family']=test['SibSp']+test['Parch']+1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "40c2af53",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-09-08T10:25:24.181072Z",
     "iopub.status.busy": "2023-09-08T10:25:24.180078Z",
     "iopub.status.idle": "2023-09-08T10:25:24.191526Z",
     "shell.execute_reply": "2023-09-08T10:25:24.189854Z"
    },
    "papermill": {
     "duration": 0.043713,
     "end_time": "2023-09-08T10:25:24.194575",
     "exception": false,
     "start_time": "2023-09-08T10:25:24.150862",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "family\n",
       "1     537\n",
       "2     161\n",
       "3     102\n",
       "4      29\n",
       "6      22\n",
       "5      15\n",
       "7      12\n",
       "11      7\n",
       "8       6\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['family'].value_counts()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "b257de1c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-09-08T10:25:24.249862Z",
     "iopub.status.busy": "2023-09-08T10:25:24.248834Z",
     "iopub.status.idle": "2023-09-08T10:25:24.259556Z",
     "shell.execute_reply": "2023-09-08T10:25:24.258147Z"
    },
    "papermill": {
     "duration": 0.041278,
     "end_time": "2023-09-08T10:25:24.262217",
     "exception": false,
     "start_time": "2023-09-08T10:25:24.220939",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "family\n",
       "1     0.303538\n",
       "2     0.552795\n",
       "3     0.578431\n",
       "4     0.724138\n",
       "5     0.200000\n",
       "6     0.136364\n",
       "7     0.333333\n",
       "8     0.000000\n",
       "11    0.000000\n",
       "Name: Survived, dtype: float64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.groupby(['family'])['Survived'].mean()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "8866eaef",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-09-08T10:25:24.319495Z",
     "iopub.status.busy": "2023-09-08T10:25:24.318745Z",
     "iopub.status.idle": "2023-09-08T10:25:24.324151Z",
     "shell.execute_reply": "2023-09-08T10:25:24.323087Z"
    },
    "papermill": {
     "duration": 0.035706,
     "end_time": "2023-09-08T10:25:24.326705",
     "exception": false,
     "start_time": "2023-09-08T10:25:24.290999",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def cal(number):\n",
    "    if number==1:\n",
    "        return 'Alone'\n",
    "    elif number>1 and number<5:\n",
    "        return 'Medium'\n",
    "    else:\n",
    "        return 'Large'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "cb4fcf37",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-09-08T10:25:24.381494Z",
     "iopub.status.busy": "2023-09-08T10:25:24.380652Z",
     "iopub.status.idle": "2023-09-08T10:25:24.387425Z",
     "shell.execute_reply": "2023-09-08T10:25:24.386434Z"
    },
    "papermill": {
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     "end_time": "2023-09-08T10:25:24.390537",
     "exception": false,
     "start_time": "2023-09-08T10:25:24.352920",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "train['family_size']=train['family'].apply(cal)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "97d0aed2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-09-08T10:25:24.446998Z",
     "iopub.status.busy": "2023-09-08T10:25:24.446495Z",
     "iopub.status.idle": "2023-09-08T10:25:24.465552Z",
     "shell.execute_reply": "2023-09-08T10:25:24.463811Z"
    },
    "papermill": {
     "duration": 0.050775,
     "end_time": "2023-09-08T10:25:24.468476",
     "exception": false,
     "start_time": "2023-09-08T10:25:24.417701",
     "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>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>family</th>\n",
       "      <th>family_size</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>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "      <td>Medium</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>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>2</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "\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",
       "\n",
       "   Parch     Fare Embarked  family family_size  \n",
       "0      0   7.2500        S       2      Medium  \n",
       "1      0  71.2833        C       2      Medium  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head(2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "84d8c879",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-09-08T10:25:24.529348Z",
     "iopub.status.busy": "2023-09-08T10:25:24.527820Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "test['family_size']=test['family'].apply(cal)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "fb8c1f0e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-09-08T10:25:24.610325Z",
     "iopub.status.busy": "2023-09-08T10:25:24.609891Z",
     "iopub.status.idle": "2023-09-08T10:25:24.618418Z",
     "shell.execute_reply": "2023-09-08T10:25:24.617478Z"
    },
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     "exception": false,
     "start_time": "2023-09-08T10:25:24.575812",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "train.drop(columns=['SibSp','Parch','family'],inplace=True)\n",
    "test.drop(columns=['SibSp','Parch','family'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "f7b27f81",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-09-08T10:25:24.677098Z",
     "iopub.status.busy": "2023-09-08T10:25:24.676029Z",
     "iopub.status.idle": "2023-09-08T10:25:24.682067Z",
     "shell.execute_reply": "2023-09-08T10:25:24.680617Z"
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     "exception": false,
     "start_time": "2023-09-08T10:25:24.647500",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "passengerID=test['PassengerId'].values\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "591fc2e1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-09-08T10:25:24.738642Z",
     "iopub.status.busy": "2023-09-08T10:25:24.738175Z",
     "iopub.status.idle": "2023-09-08T10:25:24.747390Z",
     "shell.execute_reply": "2023-09-08T10:25:24.746030Z"
    },
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     "end_time": "2023-09-08T10:25:24.750324",
     "exception": false,
     "start_time": "2023-09-08T10:25:24.711080",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "train.drop(columns=['Name','PassengerId'],inplace=True)\n",
    "test.drop(columns=['Name','PassengerId'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "0515a497",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-09-08T10:25:24.805822Z",
     "iopub.status.busy": "2023-09-08T10:25:24.805339Z",
     "iopub.status.idle": "2023-09-08T10:25:24.815915Z",
     "shell.execute_reply": "2023-09-08T10:25:24.814726Z"
    },
    "papermill": {
     "duration": 0.040181,
     "end_time": "2023-09-08T10:25:24.818399",
     "exception": false,
     "start_time": "2023-09-08T10:25:24.778218",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Survived       0\n",
       "Pclass         0\n",
       "Sex            0\n",
       "Age            0\n",
       "Fare           0\n",
       "Embarked       0\n",
       "family_size    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isnull().sum()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "200c27d3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-09-08T10:25:24.876463Z",
     "iopub.status.busy": "2023-09-08T10:25:24.875806Z",
     "iopub.status.idle": "2023-09-08T10:25:24.888569Z",
     "shell.execute_reply": "2023-09-08T10:25:24.887327Z"
    },
    "papermill": {
     "duration": 0.044251,
     "end_time": "2023-09-08T10:25:24.891406",
     "exception": false,
     "start_time": "2023-09-08T10:25:24.847155",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "train=pd.get_dummies(train,columns=['Pclass','Sex','Embarked','family_size'],drop_first=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "73e1e83e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-09-08T10:25:24.946186Z",
     "iopub.status.busy": "2023-09-08T10:25:24.945784Z",
     "iopub.status.idle": "2023-09-08T10:25:24.953961Z",
     "shell.execute_reply": "2023-09-08T10:25:24.952570Z"
    },
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     "duration": 0.03862,
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     "exception": false,
     "start_time": "2023-09-08T10:25:24.918180",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(891, 10)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "5dd3a16c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-09-08T10:25:25.011343Z",
     "iopub.status.busy": "2023-09-08T10:25:25.010897Z",
     "iopub.status.idle": "2023-09-08T10:25:25.022545Z",
     "shell.execute_reply": "2023-09-08T10:25:25.021296Z"
    },
    "papermill": {
     "duration": 0.042246,
     "end_time": "2023-09-08T10:25:25.025244",
     "exception": false,
     "start_time": "2023-09-08T10:25:24.982998",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "test=pd.get_dummies(test,columns=['Pclass','Sex','Embarked','family_size'],drop_first=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "9ba4bf61",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-09-08T10:25:25.081441Z",
     "iopub.status.busy": "2023-09-08T10:25:25.080526Z",
     "iopub.status.idle": "2023-09-08T10:25:25.087173Z",
     "shell.execute_reply": "2023-09-08T10:25:25.085897Z"
    },
    "papermill": {
     "duration": 0.038212,
     "end_time": "2023-09-08T10:25:25.090140",
     "exception": false,
     "start_time": "2023-09-08T10:25:25.051928",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "X=train.iloc[:,1:].values\n",
    "y=train.iloc[:,0].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "f98e41ec",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-09-08T10:25:25.145691Z",
     "iopub.status.busy": "2023-09-08T10:25:25.145134Z",
     "iopub.status.idle": "2023-09-08T10:25:25.530738Z",
     "shell.execute_reply": "2023-09-08T10:25:25.529136Z"
    },
    "papermill": {
     "duration": 0.416977,
     "end_time": "2023-09-08T10:25:25.533718",
     "exception": false,
     "start_time": "2023-09-08T10:25:25.116741",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "4f895da9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-09-08T10:25:25.589075Z",
     "iopub.status.busy": "2023-09-08T10:25:25.588606Z",
     "iopub.status.idle": "2023-09-08T10:25:26.071538Z",
     "shell.execute_reply": "2023-09-08T10:25:26.068971Z"
    },
    "papermill": {
     "duration": 0.515012,
     "end_time": "2023-09-08T10:25:26.075506",
     "exception": false,
     "start_time": "2023-09-08T10:25:25.560494",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "# Create a Random Forest Classifier\n",
    "classifier = RandomForestClassifier(n_estimators=100, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "1ed045f8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-09-08T10:25:26.133611Z",
     "iopub.status.busy": "2023-09-08T10:25:26.133155Z",
     "iopub.status.idle": "2023-09-08T10:25:26.447797Z",
     "shell.execute_reply": "2023-09-08T10:25:26.446228Z"
    },
    "papermill": {
     "duration": 0.346868,
     "end_time": "2023-09-08T10:25:26.450870",
     "exception": false,
     "start_time": "2023-09-08T10:25:26.104002",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(random_state=42)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(random_state=42)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier.fit(X_train,y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "88e62154",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-09-08T10:25:26.508962Z",
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    },
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   },
   "outputs": [],
   "source": [
    "y_pred=classifier.predict(X_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "8aa42a23",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-09-08T10:25:26.593759Z",
     "iopub.status.busy": "2023-09-08T10:25:26.593339Z",
     "iopub.status.idle": "2023-09-08T10:25:26.603750Z",
     "shell.execute_reply": "2023-09-08T10:25:26.602266Z"
    },
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     "start_time": "2023-09-08T10:25:26.561948",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8212290502793296"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "accuracy_score(y_pred,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "26ad9812",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-09-08T10:25:26.666918Z",
     "iopub.status.busy": "2023-09-08T10:25:26.666447Z",
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     "shell.execute_reply": "2023-09-08T10:25:26.672461Z"
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     "exception": false,
     "start_time": "2023-09-08T10:25:26.636947",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "xf=test.iloc[:,:].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "69c5e0c1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-09-08T10:25:26.738268Z",
     "iopub.status.busy": "2023-09-08T10:25:26.736519Z",
     "iopub.status.idle": "2023-09-08T10:25:26.760778Z",
     "shell.execute_reply": "2023-09-08T10:25:26.758453Z"
    },
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     "end_time": "2023-09-08T10:25:26.764051",
     "exception": false,
     "start_time": "2023-09-08T10:25:26.703980",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "y_final=classifier.predict(xf)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "e641b4d2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-09-08T10:25:26.825126Z",
     "iopub.status.busy": "2023-09-08T10:25:26.824551Z",
     "iopub.status.idle": "2023-09-08T10:25:26.832583Z",
     "shell.execute_reply": "2023-09-08T10:25:26.830651Z"
    },
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     "end_time": "2023-09-08T10:25:26.836032",
     "exception": false,
     "start_time": "2023-09-08T10:25:26.793921",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "final=pd.DataFrame()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "fc699953",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-09-08T10:25:26.893409Z",
     "iopub.status.busy": "2023-09-08T10:25:26.892320Z",
     "iopub.status.idle": "2023-09-08T10:25:26.899232Z",
     "shell.execute_reply": "2023-09-08T10:25:26.898207Z"
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "final['passengerID']=passengerID\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "064feb88",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-09-08T10:25:26.960544Z",
     "iopub.status.busy": "2023-09-08T10:25:26.959768Z",
     "iopub.status.idle": "2023-09-08T10:25:26.966228Z",
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     "status": "completed"
    },
    "tags": []
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   "outputs": [],
   "source": [
    "final['Survived']=y_final\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "d47ee6fd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-09-08T10:25:27.024477Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<p>418 rows × 2 columns</p>\n",
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       "     passengerID  Survived\n",
       "0            892         0\n",
       "1            893         0\n",
       "2            894         0\n",
       "3            895         0\n",
       "4            896         1\n",
       "..           ...       ...\n",
       "413         1305         0\n",
       "414         1306         1\n",
       "415         1307         0\n",
       "416         1308         0\n",
       "417         1309         0\n",
       "\n",
       "[418 rows x 2 columns]"
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     "execution_count": 55,
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   ],
   "source": [
    "final\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "dcd94fcd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-09-08T10:25:27.106877Z",
     "iopub.status.busy": "2023-09-08T10:25:27.105349Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "final.to_csv('Submition.csv',index=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "8d0796bb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-09-08T10:25:27.175885Z",
     "iopub.status.busy": "2023-09-08T10:25:27.174929Z",
     "iopub.status.idle": "2023-09-08T10:25:27.181865Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "f=pd.read_csv('Submition.csv')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "6a0dd928",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-09-08T10:25:27.242847Z",
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    {
     "data": {
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      ],
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       "     passengerID  Survived\n",
       "0            892         0\n",
       "1            893         0\n",
       "2            894         0\n",
       "3            895         0\n",
       "4            896         1\n",
       "..           ...       ...\n",
       "413         1305         0\n",
       "414         1306         1\n",
       "415         1307         0\n",
       "416         1308         0\n",
       "417         1309         0\n",
       "\n",
       "[418 rows x 2 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f"
   ]
  },
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