titanic-with-decisiontreeclassifier.ipynb

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     "text": [
      "/kaggle/input/titanic/train.csv\n",
      "/kaggle/input/titanic/test.csv\n",
      "/kaggle/input/titanic/gender_submission.csv\n"
     ]
    }
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   "source": [
    "# This Python 3 environment comes with many helpful analytics libraries installed\n",
    "# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
    "# For example, here's several helpful packages to load\n",
    "\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "\n",
    "# Input data files are available in the read-only \"../input/\" directory\n",
    "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
    "\n",
    "import os\n",
    "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
    "    for filename in filenames:\n",
    "        print(os.path.join(dirname, filename))\n",
    "\n",
    "# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n",
    "# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"
   ]
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       "890          891         0       3   \n",
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       "0                              Braund, Mr. Owen Harris    male  22.0      1   \n",
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       "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                             Allen, Mr. William Henry    male  35.0      0   \n",
       "..                                                 ...     ...   ...    ...   \n",
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       "887                       Graham, Miss. Margaret Edith  female  19.0      0   \n",
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       "889                              Behr, Mr. Karl Howell    male  26.0      0   \n",
       "890                                Dooley, Mr. Patrick    male  32.0      0   \n",
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     "execution_count": 2,
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   ],
   "source": [
    "df = pd.read_csv(\"../input/titanic/train.csv\")\n",
    "df"
   ]
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    "y = df.Survived"
   ]
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    {
     "data": {
      "text/plain": [
       "PassengerId    False\n",
       "Survived       False\n",
       "Pclass         False\n",
       "Name           False\n",
       "Sex            False\n",
       "Age             True\n",
       "SibSp          False\n",
       "Parch          False\n",
       "Ticket         False\n",
       "Fare           False\n",
       "Cabin           True\n",
       "Embarked        True\n",
       "dtype: bool"
      ]
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     "execution_count": 4,
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   ],
   "source": [
    "df.isnull().any()"
   ]
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   "source": [
    "df[\"Age\"].fillna(value=df[\"Age\"].mean(),inplace=True)"
   ]
  },
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    {
     "data": {
      "text/plain": [
       "S    644\n",
       "C    168\n",
       "Q     77\n",
       "Name: Embarked, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
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    }
   ],
   "source": [
    "df[\"Embarked\"].value_counts()"
   ]
  },
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       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>887</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Montvila, Rev. Juozas</td>\n",
       "      <td>male</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>211536</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>888</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Graham, Miss. Margaret Edith</td>\n",
       "      <td>female</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112053</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>B42</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>889</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>W./C. 6607</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>890</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Behr, Mr. Karl Howell</td>\n",
       "      <td>male</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111369</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>C148</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>891</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Dooley, Mr. Patrick</td>\n",
       "      <td>male</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370376</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass  \\\n",
       "0              1         0       3   \n",
       "1              2         1       1   \n",
       "2              3         1       3   \n",
       "3              4         1       1   \n",
       "4              5         0       3   \n",
       "..           ...       ...     ...   \n",
       "886          887         0       2   \n",
       "887          888         1       1   \n",
       "888          889         0       3   \n",
       "889          890         1       1   \n",
       "890          891         0       3   \n",
       "\n",
       "                                                  Name     Sex        Age  \\\n",
       "0                              Braund, Mr. Owen Harris    male  22.000000   \n",
       "1    Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.000000   \n",
       "2                               Heikkinen, Miss. Laina  female  26.000000   \n",
       "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.000000   \n",
       "4                             Allen, Mr. William Henry    male  35.000000   \n",
       "..                                                 ...     ...        ...   \n",
       "886                              Montvila, Rev. Juozas    male  27.000000   \n",
       "887                       Graham, Miss. Margaret Edith  female  19.000000   \n",
       "888           Johnston, Miss. Catherine Helen \"Carrie\"  female  29.699118   \n",
       "889                              Behr, Mr. Karl Howell    male  26.000000   \n",
       "890                                Dooley, Mr. Patrick    male  32.000000   \n",
       "\n",
       "     SibSp  Parch            Ticket     Fare Cabin Embarked  \n",
       "0        1      0         A/5 21171   7.2500   NaN        S  \n",
       "1        1      0          PC 17599  71.2833   C85        C  \n",
       "2        0      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3        1      0            113803  53.1000  C123        S  \n",
       "4        0      0            373450   8.0500   NaN        S  \n",
       "..     ...    ...               ...      ...   ...      ...  \n",
       "886      0      0            211536  13.0000   NaN        S  \n",
       "887      0      0            112053  30.0000   B42        S  \n",
       "888      1      2        W./C. 6607  23.4500   NaN        S  \n",
       "889      0      0            111369  30.0000  C148        C  \n",
       "890      0      0            370376   7.7500   NaN        Q  \n",
       "\n",
       "[891 rows x 12 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "513b0c2d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-09T16:44:08.672239Z",
     "iopub.status.busy": "2023-01-09T16:44:08.671818Z",
     "iopub.status.idle": "2023-01-09T16:44:09.757464Z",
     "shell.execute_reply": "2023-01-09T16:44:09.756345Z"
    },
    "papermill": {
     "duration": 1.09662,
     "end_time": "2023-01-09T16:44:09.760064",
     "exception": false,
     "start_time": "2023-01-09T16:44:08.663444",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      1\n",
       "1      0\n",
       "2      0\n",
       "3      0\n",
       "4      1\n",
       "      ..\n",
       "886    1\n",
       "887    0\n",
       "888    0\n",
       "889    1\n",
       "890    1\n",
       "Name: gender, Length: 891, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "le = LabelEncoder()\n",
    "df[\"gender\"] = le.fit_transform(df[\"Sex\"])\n",
    "df[\"gender\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e25325ff",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-09T16:44:09.775769Z",
     "iopub.status.busy": "2023-01-09T16:44:09.774830Z",
     "iopub.status.idle": "2023-01-09T16:44:09.785034Z",
     "shell.execute_reply": "2023-01-09T16:44:09.783812Z"
    },
    "papermill": {
     "duration": 0.020623,
     "end_time": "2023-01-09T16:44:09.787571",
     "exception": false,
     "start_time": "2023-01-09T16:44:09.766948",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      2\n",
       "1      0\n",
       "2      2\n",
       "3      2\n",
       "4      2\n",
       "      ..\n",
       "886    2\n",
       "887    2\n",
       "888    2\n",
       "889    0\n",
       "890    1\n",
       "Name: embarked, Length: 891, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"embarked\"] = le.fit_transform(df[\"Embarked\"])\n",
    "df[\"embarked\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "61d0e50c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-09T16:44:09.802896Z",
     "iopub.status.busy": "2023-01-09T16:44:09.802505Z",
     "iopub.status.idle": "2023-01-09T16:44:09.817064Z",
     "shell.execute_reply": "2023-01-09T16:44:09.815920Z"
    },
    "papermill": {
     "duration": 0.024989,
     "end_time": "2023-01-09T16:44:09.819451",
     "exception": false,
     "start_time": "2023-01-09T16:44:09.794462",
     "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>Pclass</th>\n",
       "      <th>gender</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>22.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>38.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>26.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>35.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>35.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>27.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>19.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>29.699118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>26.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>32.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pclass  gender        Age\n",
       "0         3       1  22.000000\n",
       "1         1       0  38.000000\n",
       "2         3       0  26.000000\n",
       "3         1       0  35.000000\n",
       "4         3       1  35.000000\n",
       "..      ...     ...        ...\n",
       "886       2       1  27.000000\n",
       "887       1       0  19.000000\n",
       "888       3       0  29.699118\n",
       "889       1       1  26.000000\n",
       "890       3       1  32.000000\n",
       "\n",
       "[891 rows x 3 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features = [\"Pclass\",\"gender\",\"Age\"]\n",
    "x = df[features]\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "896e1dae",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-09T16:44:09.835621Z",
     "iopub.status.busy": "2023-01-09T16:44:09.835082Z",
     "iopub.status.idle": "2023-01-09T16:44:10.048800Z",
     "shell.execute_reply": "2023-01-09T16:44:10.047936Z"
    },
    "papermill": {
     "duration": 0.224999,
     "end_time": "2023-01-09T16:44:10.051386",
     "exception": false,
     "start_time": "2023-01-09T16:44:09.826387",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "dtc = DecisionTreeClassifier()\n",
    "clf = dtc.fit(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a7f12f21",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-09T16:44:10.066902Z",
     "iopub.status.busy": "2023-01-09T16:44:10.066503Z",
     "iopub.status.idle": "2023-01-09T16:44:10.096122Z",
     "shell.execute_reply": "2023-01-09T16:44:10.095023Z"
    },
    "papermill": {
     "duration": 0.04022,
     "end_time": "2023-01-09T16:44:10.098606",
     "exception": false,
     "start_time": "2023-01-09T16:44:10.058386",
     "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>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",
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       "      <th>1</th>\n",
       "      <td>893</td>\n",
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       "      <td>Wilkes, Mrs. James (Ellen Needs)</td>\n",
       "      <td>female</td>\n",
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       "      <td>1</td>\n",
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       "      <td>Myles, Mr. Thomas Francis</td>\n",
       "      <td>male</td>\n",
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       "      <td>3</td>\n",
       "      <td>Wirz, Mr. Albert</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
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       "      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
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       "      <th>413</th>\n",
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       "      <td>3</td>\n",
       "      <td>Spector, Mr. Woolf</td>\n",
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       "      <th>414</th>\n",
       "      <td>1306</td>\n",
       "      <td>1</td>\n",
       "      <td>Oliva y Ocana, Dona. Fermina</td>\n",
       "      <td>female</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17758</td>\n",
       "      <td>108.9000</td>\n",
       "      <td>C105</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>1307</td>\n",
       "      <td>3</td>\n",
       "      <td>Saether, Mr. Simon Sivertsen</td>\n",
       "      <td>male</td>\n",
       "      <td>38.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>SOTON/O.Q. 3101262</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>416</th>\n",
       "      <td>1308</td>\n",
       "      <td>3</td>\n",
       "      <td>Ware, Mr. Frederick</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>359309</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>417</th>\n",
       "      <td>1309</td>\n",
       "      <td>3</td>\n",
       "      <td>Peter, Master. Michael J</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2668</td>\n",
       "      <td>22.3583</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>418 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Pclass                                          Name  \\\n",
       "0            892       3                              Kelly, Mr. James   \n",
       "1            893       3              Wilkes, Mrs. James (Ellen Needs)   \n",
       "2            894       2                     Myles, Mr. Thomas Francis   \n",
       "3            895       3                              Wirz, Mr. Albert   \n",
       "4            896       3  Hirvonen, Mrs. Alexander (Helga E Lindqvist)   \n",
       "..           ...     ...                                           ...   \n",
       "413         1305       3                            Spector, Mr. Woolf   \n",
       "414         1306       1                  Oliva y Ocana, Dona. Fermina   \n",
       "415         1307       3                  Saether, Mr. Simon Sivertsen   \n",
       "416         1308       3                           Ware, Mr. Frederick   \n",
       "417         1309       3                      Peter, Master. Michael J   \n",
       "\n",
       "        Sex   Age  SibSp  Parch              Ticket      Fare Cabin Embarked  \n",
       "0      male  34.5      0      0              330911    7.8292   NaN        Q  \n",
       "1    female  47.0      1      0              363272    7.0000   NaN        S  \n",
       "2      male  62.0      0      0              240276    9.6875   NaN        Q  \n",
       "3      male  27.0      0      0              315154    8.6625   NaN        S  \n",
       "4    female  22.0      1      1             3101298   12.2875   NaN        S  \n",
       "..      ...   ...    ...    ...                 ...       ...   ...      ...  \n",
       "413    male   NaN      0      0           A.5. 3236    8.0500   NaN        S  \n",
       "414  female  39.0      0      0            PC 17758  108.9000  C105        C  \n",
       "415    male  38.5      0      0  SOTON/O.Q. 3101262    7.2500   NaN        S  \n",
       "416    male   NaN      0      0              359309    8.0500   NaN        S  \n",
       "417    male   NaN      1      1                2668   22.3583   NaN        C  \n",
       "\n",
       "[418 rows x 11 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dft = pd.read_csv(\"../input/titanic/test.csv\")\n",
    "dft"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b874d2e4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-09T16:44:10.115398Z",
     "iopub.status.busy": "2023-01-09T16:44:10.114643Z",
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     "shell.execute_reply": "2023-01-09T16:44:10.124558Z"
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     "end_time": "2023-01-09T16:44:10.128284",
     "exception": false,
     "start_time": "2023-01-09T16:44:10.106149",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      1\n",
       "1      0\n",
       "2      1\n",
       "3      1\n",
       "4      0\n",
       "      ..\n",
       "413    1\n",
       "414    0\n",
       "415    1\n",
       "416    1\n",
       "417    1\n",
       "Name: gender, Length: 418, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "le = LabelEncoder()\n",
    "dft[\"gender\"] = le.fit_transform(dft[\"Sex\"])\n",
    "dft[\"gender\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7f9c12bc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-09T16:44:10.145653Z",
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     "start_time": "2023-01-09T16:44:10.136143",
     "status": "completed"
    },
    "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",
       "gender           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dft.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "60e493fe",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-09T16:44:10.172927Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "dft[\"Age\"].fillna(value = int(dft[\"Age\"].mean()),inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "beada156",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-09T16:44:10.198341Z",
     "iopub.status.busy": "2023-01-09T16:44:10.197908Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "X = dft[features]\n",
    "pred = clf.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "506e8314",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-09T16:44:10.228676Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "       0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0,\n",
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       "       0, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b0e53e31",
   "metadata": {
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     "status": "completed"
    },
    "tags": []
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   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413</th>\n",
       "      <td>1305</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>1306</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>1307</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>416</th>\n",
       "      <td>1308</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>417</th>\n",
       "      <td>1309</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>418 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived\n",
       "0            892         0\n",
       "1            893         0\n",
       "2            894         1\n",
       "3            895         1\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": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "output = pd.DataFrame({\n",
    "    \"PassengerId\":dft.PassengerId,\"Survived\":pred\n",
    "})\n",
    "output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
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   "source": [
    "output.to_csv(\"Submission3\",index=False)"
   ]
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