titanic-survival-prediction-with-ml.ipynb

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   "source": [
    "### Titanic | ML Classification Prediction Algorithms with accuracy (79.2%)"
   ]
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   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import r2_score\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.linear_model import Perceptron\n",
    "from sklearn import svm\n",
    "import warnings"
   ]
  },
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   "source": [
    "warnings.filterwarnings('ignore')"
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   "source": [
    "## EDA"
   ]
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       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
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       "      <td>PC 17599</td>\n",
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       "      <td>C85</td>\n",
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       "      <th>3</th>\n",
       "      <td>1</td>\n",
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       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
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       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
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       "      <td>0</td>\n",
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       "      <td>Allen, Mr. William Henry</td>\n",
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       "      <td>35.0</td>\n",
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       "             Survived  Pclass  \\\n",
       "PassengerId                     \n",
       "1                   0       3   \n",
       "2                   1       1   \n",
       "3                   1       3   \n",
       "4                   1       1   \n",
       "5                   0       3   \n",
       "\n",
       "                                                          Name     Sex   Age  \\\n",
       "PassengerId                                                                    \n",
       "1                                      Braund, Mr. Owen Harris    male  22.0   \n",
       "2            Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0   \n",
       "3                                       Heikkinen, Miss. Laina  female  26.0   \n",
       "4                 Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0   \n",
       "5                                     Allen, Mr. William Henry    male  35.0   \n",
       "\n",
       "             SibSp  Parch            Ticket     Fare Cabin Embarked  \n",
       "PassengerId                                                          \n",
       "1                1      0         A/5 21171   7.2500   NaN        S  \n",
       "2                1      0          PC 17599  71.2833   C85        C  \n",
       "3                0      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "4                1      0            113803  53.1000  C123        S  \n",
       "5                0      0            373450   8.0500   NaN        S  "
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   "source": [
    "df_train = pd.read_csv(\"../input/titanic/train.csv\",index_col=\"PassengerId\")\n",
    "df_train.head()"
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       "             Pclass                                          Name     Sex  \\\n",
       "PassengerId                                                                 \n",
       "892               3                              Kelly, Mr. James    male   \n",
       "893               3              Wilkes, Mrs. James (Ellen Needs)  female   \n",
       "894               2                     Myles, Mr. Thomas Francis    male   \n",
       "895               3                              Wirz, Mr. Albert    male   \n",
       "896               3  Hirvonen, Mrs. Alexander (Helga E Lindqvist)  female   \n",
       "\n",
       "              Age  SibSp  Parch   Ticket     Fare Cabin Embarked  \n",
       "PassengerId                                                       \n",
       "892          34.5      0      0   330911   7.8292   NaN        Q  \n",
       "893          47.0      1      0   363272   7.0000   NaN        S  \n",
       "894          62.0      0      0   240276   9.6875   NaN        Q  \n",
       "895          27.0      0      0   315154   8.6625   NaN        S  \n",
       "896          22.0      1      1  3101298  12.2875   NaN        S  "
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   "source": [
    "df_test = pd.read_csv(\"../input/titanic/test.csv\", index_col=\"PassengerId\")\n",
    "df_test.head()"
   ]
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      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 418 entries, 892 to 1309\n",
      "Data columns (total 10 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   Pclass    418 non-null    int64  \n",
      " 1   Name      418 non-null    object \n",
      " 2   Sex       418 non-null    object \n",
      " 3   Age       332 non-null    float64\n",
      " 4   SibSp     418 non-null    int64  \n",
      " 5   Parch     418 non-null    int64  \n",
      " 6   Ticket    418 non-null    object \n",
      " 7   Fare      417 non-null    float64\n",
      " 8   Cabin     91 non-null     object \n",
      " 9   Embarked  418 non-null    object \n",
      "dtypes: float64(2), int64(3), object(5)\n",
      "memory usage: 35.9+ KB\n"
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       "    <tr>\n",
       "      <th>896</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Survived\n",
       "PassengerId          \n",
       "892                 0\n",
       "893                 1\n",
       "894                 0\n",
       "895                 0\n",
       "896                 1"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_test = pd.read_csv(\"../input/titanic/gender_submission.csv\", index_col=\"PassengerId\")\n",
    "Y_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "745c99bf",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-07-31T00:08:32.555750Z",
     "iopub.status.busy": "2021-07-31T00:08:32.555047Z",
     "iopub.status.idle": "2021-07-31T00:08:32.557749Z",
     "shell.execute_reply": "2021-07-31T00:08:32.558271Z",
     "shell.execute_reply.started": "2021-07-31T00:03:46.212631Z"
    },
    "papermill": {
     "duration": 0.064309,
     "end_time": "2021-07-31T00:08:32.558440",
     "exception": false,
     "start_time": "2021-07-31T00:08:32.494131",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 891 entries, 1 to 891\n",
      "Data columns (total 11 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   Survived  891 non-null    int64  \n",
      " 1   Pclass    891 non-null    int64  \n",
      " 2   Name      891 non-null    object \n",
      " 3   Sex       891 non-null    object \n",
      " 4   Age       714 non-null    float64\n",
      " 5   SibSp     891 non-null    int64  \n",
      " 6   Parch     891 non-null    int64  \n",
      " 7   Ticket    891 non-null    object \n",
      " 8   Fare      891 non-null    float64\n",
      " 9   Cabin     204 non-null    object \n",
      " 10  Embarked  889 non-null    object \n",
      "dtypes: float64(2), int64(4), object(5)\n",
      "memory usage: 83.5+ KB\n"
     ]
    }
   ],
   "source": [
    "df_train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1afd4828",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-07-31T00:08:32.656303Z",
     "iopub.status.busy": "2021-07-31T00:08:32.655637Z",
     "iopub.status.idle": "2021-07-31T00:08:32.663705Z",
     "shell.execute_reply": "2021-07-31T00:08:32.664234Z",
     "shell.execute_reply.started": "2021-07-31T00:03:46.230862Z"
    },
    "papermill": {
     "duration": 0.058512,
     "end_time": "2021-07-31T00:08:32.664406",
     "exception": false,
     "start_time": "2021-07-31T00:08:32.605894",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Survived      0\n",
       "Pclass        0\n",
       "Name          0\n",
       "Sex           0\n",
       "Age         177\n",
       "SibSp         0\n",
       "Parch         0\n",
       "Ticket        0\n",
       "Fare          0\n",
       "Cabin       687\n",
       "Embarked      2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ea412de",
   "metadata": {
    "papermill": {
     "duration": 0.050721,
     "end_time": "2021-07-31T00:08:32.763267",
     "exception": false,
     "start_time": "2021-07-31T00:08:32.712546",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Data Cleaning & Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "73dbb862",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-07-31T00:08:32.865125Z",
     "iopub.status.busy": "2021-07-31T00:08:32.864455Z",
     "iopub.status.idle": "2021-07-31T00:08:32.877039Z",
     "shell.execute_reply": "2021-07-31T00:08:32.876499Z",
     "shell.execute_reply.started": "2021-07-31T00:03:46.242682Z"
    },
    "papermill": {
     "duration": 0.064494,
     "end_time": "2021-07-31T00:08:32.877221",
     "exception": false,
     "start_time": "2021-07-31T00:08:32.812727",
     "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>Name</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Embarked</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>S</th>\n",
       "      <td>644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q</th>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Name\n",
       "Embarked      \n",
       "S          644\n",
       "C          168\n",
       "Q           77"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train[[\"Embarked\",\"Name\"]].groupby(by=[\"Embarked\"],as_index=True).count().sort_values(\"Name\",ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3feac66e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-07-31T00:08:32.983673Z",
     "iopub.status.busy": "2021-07-31T00:08:32.982925Z",
     "iopub.status.idle": "2021-07-31T00:08:32.986390Z",
     "shell.execute_reply": "2021-07-31T00:08:32.985767Z",
     "shell.execute_reply.started": "2021-07-31T00:03:46.26233Z"
    },
    "papermill": {
     "duration": 0.060667,
     "end_time": "2021-07-31T00:08:32.986527",
     "exception": false,
     "start_time": "2021-07-31T00:08:32.925860",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the number of null value in Embarked Column = 0\n"
     ]
    }
   ],
   "source": [
    "# so we notes that Most repeated Embarked is S, so that we can replace null value in Embarked column with it.\n",
    "most_repeated = \"S\"\n",
    "df_train.Embarked.replace(np.nan, most_repeated, inplace=True)\n",
    "df_test.Embarked.replace(np.nan, most_repeated, inplace=True)\n",
    "print(\"the number of null value in Embarked Column =\",df_train.Embarked.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "478730b6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-07-31T00:08:33.105689Z",
     "iopub.status.busy": "2021-07-31T00:08:33.098793Z",
     "iopub.status.idle": "2021-07-31T00:08:33.110573Z",
     "shell.execute_reply": "2021-07-31T00:08:33.109862Z",
     "shell.execute_reply.started": "2021-07-31T00:03:46.278708Z"
    },
    "papermill": {
     "duration": 0.075527,
     "end_time": "2021-07-31T00:08:33.110719",
     "exception": false,
     "start_time": "2021-07-31T00:08:33.035192",
     "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>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",
       "    <tr>\n",
       "      <th>PassengerId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Survived  Pclass  \\\n",
       "PassengerId                     \n",
       "1                   0       3   \n",
       "2                   1       1   \n",
       "3                   1       3   \n",
       "4                   1       1   \n",
       "5                   0       3   \n",
       "\n",
       "                                                          Name     Sex   Age  \\\n",
       "PassengerId                                                                    \n",
       "1                                      Braund, Mr. Owen Harris    male  22.0   \n",
       "2            Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0   \n",
       "3                                       Heikkinen, Miss. Laina  female  26.0   \n",
       "4                 Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0   \n",
       "5                                     Allen, Mr. William Henry    male  35.0   \n",
       "\n",
       "             SibSp  Parch            Ticket     Fare Cabin  Embarked  \n",
       "PassengerId                                                           \n",
       "1                1      0         A/5 21171   7.2500   NaN         1  \n",
       "2                1      0          PC 17599  71.2833   C85         2  \n",
       "3                0      0  STON/O2. 3101282   7.9250   NaN         1  \n",
       "4                1      0            113803  53.1000  C123         1  \n",
       "5                0      0            373450   8.0500   NaN         1  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# transform Embarked Column to numeric.\n",
    "Embarked_transform_dict = {\"S\":1, \"C\":2, \"Q\":3}\n",
    "for value in Embarked_transform_dict:\n",
    "    df_train.Embarked.replace(value, Embarked_transform_dict.get(value), inplace=True)\n",
    "    df_test.Embarked.replace(value, Embarked_transform_dict.get(value), inplace=True)\n",
    "df_train.head(5)\n",
    "# we now finish cleaning and transform column Emvarked to numeric."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ec957eb5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-07-31T00:08:33.216354Z",
     "iopub.status.busy": "2021-07-31T00:08:33.215492Z",
     "iopub.status.idle": "2021-07-31T00:08:33.219046Z",
     "shell.execute_reply": "2021-07-31T00:08:33.219582Z",
     "shell.execute_reply.started": "2021-07-31T00:03:46.310306Z"
    },
    "papermill": {
     "duration": 0.059087,
     "end_time": "2021-07-31T00:08:33.219766",
     "exception": false,
     "start_time": "2021-07-31T00:08:33.160679",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the number of null value in Cabin Column = 687\n"
     ]
    }
   ],
   "source": [
    "print(\"the number of null value in Cabin Column =\", df_train.Cabin.isnull().sum())\n",
    "# notes that null values is 687 from 891 (77%) of Cabin column is null, so i will droped it from data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5fb8682b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-07-31T00:08:33.325514Z",
     "iopub.status.busy": "2021-07-31T00:08:33.324813Z",
     "iopub.status.idle": "2021-07-31T00:08:33.351957Z",
     "shell.execute_reply": "2021-07-31T00:08:33.351296Z",
     "shell.execute_reply.started": "2021-07-31T00:03:46.327261Z"
    },
    "papermill": {
     "duration": 0.082561,
     "end_time": "2021-07-31T00:08:33.352112",
     "exception": false,
     "start_time": "2021-07-31T00:08:33.269551",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></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>Embarked</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PassengerId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>1</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Montvila, Rev. Juozas</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>211536</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Graham, Miss. Margaret Edith</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112053</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>W./C. 6607</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Behr, Mr. Karl Howell</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111369</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Dooley, Mr. Patrick</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370376</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Survived  Pclass  \\\n",
       "PassengerId                     \n",
       "1                   0       3   \n",
       "2                   1       1   \n",
       "3                   1       3   \n",
       "4                   1       1   \n",
       "5                   0       3   \n",
       "...               ...     ...   \n",
       "887                 0       2   \n",
       "888                 1       1   \n",
       "889                 0       3   \n",
       "890                 1       1   \n",
       "891                 0       3   \n",
       "\n",
       "                                                          Name     Sex   Age  \\\n",
       "PassengerId                                                                    \n",
       "1                                      Braund, Mr. Owen Harris    male  22.0   \n",
       "2            Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0   \n",
       "3                                       Heikkinen, Miss. Laina  female  26.0   \n",
       "4                 Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0   \n",
       "5                                     Allen, Mr. William Henry    male  35.0   \n",
       "...                                                        ...     ...   ...   \n",
       "887                                      Montvila, Rev. Juozas    male  27.0   \n",
       "888                               Graham, Miss. Margaret Edith  female  19.0   \n",
       "889                   Johnston, Miss. Catherine Helen \"Carrie\"  female   NaN   \n",
       "890                                      Behr, Mr. Karl Howell    male  26.0   \n",
       "891                                        Dooley, Mr. Patrick    male  32.0   \n",
       "\n",
       "             SibSp  Parch            Ticket     Fare  Embarked  \n",
       "PassengerId                                                     \n",
       "1                1      0         A/5 21171   7.2500         1  \n",
       "2                1      0          PC 17599  71.2833         2  \n",
       "3                0      0  STON/O2. 3101282   7.9250         1  \n",
       "4                1      0            113803  53.1000         1  \n",
       "5                0      0            373450   8.0500         1  \n",
       "...            ...    ...               ...      ...       ...  \n",
       "887              0      0            211536  13.0000         1  \n",
       "888              0      0            112053  30.0000         1  \n",
       "889              1      2        W./C. 6607  23.4500         1  \n",
       "890              0      0            111369  30.0000         2  \n",
       "891              0      0            370376   7.7500         3  \n",
       "\n",
       "[891 rows x 10 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.drop(\"Cabin\", axis=1, inplace=True)\n",
    "df_test.drop(\"Cabin\", axis=1, inplace=True)\n",
    "\n",
    "df_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c03bdde5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-07-31T00:08:33.459671Z",
     "iopub.status.busy": "2021-07-31T00:08:33.458630Z",
     "iopub.status.idle": "2021-07-31T00:08:33.463469Z",
     "shell.execute_reply": "2021-07-31T00:08:33.462816Z",
     "shell.execute_reply.started": "2021-07-31T00:03:46.359551Z"
    },
    "papermill": {
     "duration": 0.061156,
     "end_time": "2021-07-31T00:08:33.463617",
     "exception": false,
     "start_time": "2021-07-31T00:08:33.402461",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Range of Fare column values =  512.3292\n",
      "Range of Fare column values =  512.3292\n"
     ]
    }
   ],
   "source": [
    "# Enter to Fare column\n",
    "# calculate the range of value in Fare column.\n",
    "print(\"Range of Fare column values = \", df_train.Fare.max() - df_train.Fare.min())\n",
    "# min value is 0.0 and max value = 512.3292\n",
    "# i will divied this range to 10 sections.\n",
    "df_test.Fare.replace(np.nan, df_test.Fare.mean(), inplace=True)\n",
    "print(\"Range of Fare column values = \", df_test.Fare.max() - df_test.Fare.min())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "abee0a23",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-07-31T00:08:33.588950Z",
     "iopub.status.busy": "2021-07-31T00:08:33.588236Z",
     "iopub.status.idle": "2021-07-31T00:08:33.592428Z",
     "shell.execute_reply": "2021-07-31T00:08:33.592878Z",
     "shell.execute_reply.started": "2021-07-31T00:03:46.369082Z"
    },
    "papermill": {
     "duration": 0.078808,
     "end_time": "2021-07-31T00:08:33.593046",
     "exception": false,
     "start_time": "2021-07-31T00:08:33.514238",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "    <tr>\n",
       "      <th>PassengerId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>892</th>\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</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>893</th>\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</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>894</th>\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</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>895</th>\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</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>896</th>\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</td>\n",
       "      <td>1</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1305</th>\n",
       "      <td>3</td>\n",
       "      <td>Spector, Mr. Woolf</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A.5. 3236</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1306</th>\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</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1307</th>\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</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1308</th>\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</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1309</th>\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</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>418 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Pclass                                          Name     Sex  \\\n",
       "PassengerId                                                                 \n",
       "892               3                              Kelly, Mr. James    male   \n",
       "893               3              Wilkes, Mrs. James (Ellen Needs)  female   \n",
       "894               2                     Myles, Mr. Thomas Francis    male   \n",
       "895               3                              Wirz, Mr. Albert    male   \n",
       "896               3  Hirvonen, Mrs. Alexander (Helga E Lindqvist)  female   \n",
       "...             ...                                           ...     ...   \n",
       "1305              3                            Spector, Mr. Woolf    male   \n",
       "1306              1                  Oliva y Ocana, Dona. Fermina  female   \n",
       "1307              3                  Saether, Mr. Simon Sivertsen    male   \n",
       "1308              3                           Ware, Mr. Frederick    male   \n",
       "1309              3                      Peter, Master. Michael J    male   \n",
       "\n",
       "              Age  SibSp  Parch              Ticket  Fare  Embarked  \n",
       "PassengerId                                                          \n",
       "892          34.5      0      0              330911     7         3  \n",
       "893          47.0      1      0              363272     7         1  \n",
       "894          62.0      0      0              240276     9         3  \n",
       "895          27.0      0      0              315154     8         1  \n",
       "896          22.0      1      1             3101298    12         1  \n",
       "...           ...    ...    ...                 ...   ...       ...  \n",
       "1305          NaN      0      0           A.5. 3236     8         1  \n",
       "1306         39.0      0      0            PC 17758   108         2  \n",
       "1307         38.5      0      0  SOTON/O.Q. 3101262     7         1  \n",
       "1308          NaN      0      0              359309     8         1  \n",
       "1309          NaN      1      1                2668    22         2  \n",
       "\n",
       "[418 rows x 9 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.Fare = df_train.Fare.astype(\"int64\")\n",
    "df_test.Fare = df_test.Fare.astype(\"int64\")\n",
    "\n",
    "# df_train.info()\n",
    "df_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "4c98b47d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-07-31T00:08:33.713902Z",
     "iopub.status.busy": "2021-07-31T00:08:33.704138Z",
     "iopub.status.idle": "2021-07-31T00:08:33.718510Z",
     "shell.execute_reply": "2021-07-31T00:08:33.717982Z",
     "shell.execute_reply.started": "2021-07-31T00:03:46.393853Z"
    },
    "papermill": {
     "duration": 0.073791,
     "end_time": "2021-07-31T00:08:33.718640",
     "exception": false,
     "start_time": "2021-07-31T00:08:33.644849",
     "status": "completed"
    },
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 6, 3, 5, 11, 4]\n",
       "Categories (7, int64): [1 < 2 < 3 < 4 < 5 < 6 < 11]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bins_i = [-1, 50, 100, 150, 200, 250, 300, 350, 400, 450, 500, 550]\n",
    "labels_i = [1,2,3,4,5,6,7,8,9,10,11]\n",
    "\n",
    "df_train['stage'] = 0\n",
    "df_train['stage'] = pd.cut(df_train.Fare, bins=bins_i, labels=labels_i)\n",
    "\n",
    "df_test['stage'] = 0\n",
    "df_test['stage'] = pd.cut(df_test.Fare, bins=bins_i, labels=labels_i)\n",
    "\n",
    "df_train.stage.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "84a447cd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-07-31T00:08:33.834731Z",
     "iopub.status.busy": "2021-07-31T00:08:33.831691Z",
     "iopub.status.idle": "2021-07-31T00:08:33.839500Z",
     "shell.execute_reply": "2021-07-31T00:08:33.838805Z",
     "shell.execute_reply.started": "2021-07-31T00:03:46.418464Z"
    },
    "papermill": {
     "duration": 0.068855,
     "end_time": "2021-07-31T00:08:33.839641",
     "exception": false,
     "start_time": "2021-07-31T00:08:33.770786",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df_train.Fare = df_train.stage.astype(\"int64\")\n",
    "df_test.Fare = df_test.stage.astype(\"int64\")\n",
    "df_train.drop(\"stage\", axis=1, inplace=True)\n",
    "df_test.drop(\"stage\", axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "6ba439f7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-07-31T00:08:33.958975Z",
     "iopub.status.busy": "2021-07-31T00:08:33.958033Z",
     "iopub.status.idle": "2021-07-31T00:08:33.962124Z",
     "shell.execute_reply": "2021-07-31T00:08:33.961627Z",
     "shell.execute_reply.started": "2021-07-31T00:03:46.431861Z"
    },
    "papermill": {
     "duration": 0.070026,
     "end_time": "2021-07-31T00:08:33.962265",
     "exception": false,
     "start_time": "2021-07-31T00:08:33.892239",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
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       "    <tr>\n",
       "      <th>PassengerId</th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Survived  Pclass  \\\n",
       "PassengerId                     \n",
       "1                   0       3   \n",
       "2                   1       1   \n",
       "3                   1       3   \n",
       "4                   1       1   \n",
       "5                   0       3   \n",
       "\n",
       "                                                          Name     Sex   Age  \\\n",
       "PassengerId                                                                    \n",
       "1                                      Braund, Mr. Owen Harris    male  22.0   \n",
       "2            Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0   \n",
       "3                                       Heikkinen, Miss. Laina  female  26.0   \n",
       "4                 Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0   \n",
       "5                                     Allen, Mr. William Henry    male  35.0   \n",
       "\n",
       "             SibSp  Parch            Ticket  Fare  Embarked  \n",
       "PassengerId                                                  \n",
       "1                1      0         A/5 21171     1         1  \n",
       "2                1      0          PC 17599     2         2  \n",
       "3                0      0  STON/O2. 3101282     1         1  \n",
       "4                1      0            113803     2         1  \n",
       "5                0      0            373450     1         1  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c76cbea6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-07-31T00:08:34.073637Z",
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     "shell.execute_reply": "2021-07-31T00:08:34.076543Z",
     "shell.execute_reply.started": "2021-07-31T00:03:46.449967Z"
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  2,  6,  5,  4,  3, 11])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.Fare.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "86af9055",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-07-31T00:08:34.191162Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['A/5 21171', 'PC 17599', 'STON/O2. 3101282', '113803', '373450',\n",
       "       '330877', '17463', '349909', '347742', '237736', 'PP 9549',\n",
       "       '113783', 'A/5. 2151', '347082', '350406', '248706', '382652',\n",
       "       '244373', '345763', '2649', '239865', '248698', '330923', '113788',\n",
       "       '347077', '2631', '19950', '330959', '349216', 'PC 17601',\n",
       "       'PC 17569', '335677', 'C.A. 24579', 'PC 17604', '113789', '2677',\n",
       "       'A./5. 2152', '345764', '2651', '7546', '11668', '349253',\n",
       "       'SC/Paris 2123', '330958', 'S.C./A.4. 23567', '370371', '14311',\n",
       "       '2662', '349237', '3101295', 'A/4. 39886', 'PC 17572', '2926',\n",
       "       '113509', '19947', 'C.A. 31026', '2697', 'C.A. 34651', 'CA 2144',\n",
       "       '2669', '113572', '36973', '347088', 'PC 17605', '2661',\n",
       "       'C.A. 29395', 'S.P. 3464', '3101281', '315151', 'C.A. 33111',\n",
       "       'S.O.C. 14879', '2680', '1601', '348123', '349208', '374746',\n",
       "       '248738', '364516', '345767', '345779', '330932', '113059',\n",
       "       'SO/C 14885', '3101278', 'W./C. 6608', 'SOTON/OQ 392086', '343275',\n",
       "       '343276', '347466', 'W.E.P. 5734', 'C.A. 2315', '364500', '374910',\n",
       "       'PC 17754', 'PC 17759', '231919', '244367', '349245', '349215',\n",
       "       '35281', '7540', '3101276', '349207', '343120', '312991', '349249',\n",
       "       '371110', '110465', '2665', '324669', '4136', '2627',\n",
       "       'STON/O 2. 3101294', '370369', 'PC 17558', 'A4. 54510', '27267',\n",
       "       '370372', 'C 17369', '2668', '347061', '349241',\n",
       "       'SOTON/O.Q. 3101307', 'A/5. 3337', '228414', 'C.A. 29178',\n",
       "       'SC/PARIS 2133', '11752', '7534', 'PC 17593', '2678', '347081',\n",
       "       'STON/O2. 3101279', '365222', '231945', 'C.A. 33112', '350043',\n",
       "       '230080', '244310', 'S.O.P. 1166', '113776', 'A.5. 11206',\n",
       "       'A/5. 851', 'Fa 265302', 'PC 17597', '35851', 'SOTON/OQ 392090',\n",
       "       '315037', 'CA. 2343', '371362', 'C.A. 33595', '347068', '315093',\n",
       "       '363291', '113505', 'PC 17318', '111240', 'STON/O 2. 3101280',\n",
       "       '17764', '350404', '4133', 'PC 17595', '250653', 'LINE',\n",
       "       'SC/PARIS 2131', '230136', '315153', '113767', '370365', '111428',\n",
       "       '364849', '349247', '234604', '28424', '350046', 'PC 17610',\n",
       "       '368703', '4579', '370370', '248747', '345770', '3101264', '2628',\n",
       "       'A/5 3540', '347054', '2699', '367231', '112277',\n",
       "       'SOTON/O.Q. 3101311', 'F.C.C. 13528', 'A/5 21174', '250646',\n",
       "       '367229', '35273', 'STON/O2. 3101283', '243847', '11813',\n",
       "       'W/C 14208', 'SOTON/OQ 392089', '220367', '21440', '349234',\n",
       "       '19943', 'PP 4348', 'SW/PP 751', 'A/5 21173', '236171', '347067',\n",
       "       '237442', 'C.A. 29566', 'W./C. 6609', '26707', 'C.A. 31921',\n",
       "       '28665', 'SCO/W 1585', '367230', 'W./C. 14263',\n",
       "       'STON/O 2. 3101275', '2694', '19928', '347071', '250649', '11751',\n",
       "       '244252', '362316', '113514', 'A/5. 3336', '370129', '2650',\n",
       "       'PC 17585', '110152', 'PC 17755', '230433', '384461', '110413',\n",
       "       '112059', '382649', 'C.A. 17248', '347083', 'PC 17582', 'PC 17760',\n",
       "       '113798', '250644', 'PC 17596', '370375', '13502', '347073',\n",
       "       '239853', 'C.A. 2673', '336439', '347464', '345778', 'A/5. 10482',\n",
       "       '113056', '349239', '345774', '349206', '237798', '370373',\n",
       "       '19877', '11967', 'SC/Paris 2163', '349236', '349233', 'PC 17612',\n",
       "       '2693', '113781', '19988', '9234', '367226', '226593', 'A/5 2466',\n",
       "       '17421', 'PC 17758', 'P/PP 3381', 'PC 17485', '11767', 'PC 17608',\n",
       "       '250651', '349243', 'F.C.C. 13529', '347470', '29011', '36928',\n",
       "       '16966', 'A/5 21172', '349219', '234818', '345364', '28551',\n",
       "       '111361', '113043', 'PC 17611', '349225', '7598', '113784',\n",
       "       '248740', '244361', '229236', '248733', '31418', '386525',\n",
       "       'C.A. 37671', '315088', '7267', '113510', '2695', '2647', '345783',\n",
       "       '237671', '330931', '330980', 'SC/PARIS 2167', '2691',\n",
       "       'SOTON/O.Q. 3101310', 'C 7076', '110813', '2626', '14313',\n",
       "       'PC 17477', '11765', '3101267', '323951', 'C 7077', '113503',\n",
       "       '2648', '347069', 'PC 17757', '2653', 'STON/O 2. 3101293',\n",
       "       '349227', '27849', '367655', 'SC 1748', '113760', '350034',\n",
       "       '3101277', '350052', '350407', '28403', '244278', '240929',\n",
       "       'STON/O 2. 3101289', '341826', '4137', '315096', '28664', '347064',\n",
       "       '29106', '312992', '349222', '394140', 'STON/O 2. 3101269',\n",
       "       '343095', '28220', '250652', '28228', '345773', '349254',\n",
       "       'A/5. 13032', '315082', '347080', 'A/4. 34244', '2003', '250655',\n",
       "       '364851', 'SOTON/O.Q. 392078', '110564', '376564', 'SC/AH 3085',\n",
       "       'STON/O 2. 3101274', '13507', 'C.A. 18723', '345769', '347076',\n",
       "       '230434', '65306', '33638', '113794', '2666', '113786', '65303',\n",
       "       '113051', '17453', 'A/5 2817', '349240', '13509', '17464',\n",
       "       'F.C.C. 13531', '371060', '19952', '364506', '111320', '234360',\n",
       "       'A/S 2816', 'SOTON/O.Q. 3101306', '113792', '36209', '323592',\n",
       "       '315089', 'SC/AH Basle 541', '7553', '31027', '3460', '350060',\n",
       "       '3101298', '239854', 'A/5 3594', '4134', '11771', 'A.5. 18509',\n",
       "       '65304', 'SOTON/OQ 3101317', '113787', 'PC 17609', 'A/4 45380',\n",
       "       '36947', 'C.A. 6212', '350035', '315086', '364846', '330909',\n",
       "       '4135', '26360', '111427', 'C 4001', '382651', 'SOTON/OQ 3101316',\n",
       "       'PC 17473', 'PC 17603', '349209', '36967', 'C.A. 34260', '226875',\n",
       "       '349242', '12749', '349252', '2624', '2700', '367232',\n",
       "       'W./C. 14258', 'PC 17483', '3101296', '29104', '2641', '2690',\n",
       "       '315084', '113050', 'PC 17761', '364498', '13568', 'WE/P 5735',\n",
       "       '2908', '693', 'SC/PARIS 2146', '244358', '330979', '2620',\n",
       "       '347085', '113807', '11755', '345572', '372622', '349251',\n",
       "       '218629', 'SOTON/OQ 392082', 'SOTON/O.Q. 392087', 'A/4 48871',\n",
       "       '349205', '2686', '350417', 'S.W./PP 752', '11769', 'PC 17474',\n",
       "       '14312', 'A/4. 20589', '358585', '243880', '2689',\n",
       "       'STON/O 2. 3101286', '237789', '13049', '3411', '237565', '13567',\n",
       "       '14973', 'A./5. 3235', 'STON/O 2. 3101273', 'A/5 3902', '364848',\n",
       "       'SC/AH 29037', '248727', '2664', '349214', '113796', '364511',\n",
       "       '111426', '349910', '349246', '113804', 'SOTON/O.Q. 3101305',\n",
       "       '370377', '364512', '220845', '31028', '2659', '11753', '350029',\n",
       "       '54636', '36963', '219533', '349224', '334912', '27042', '347743',\n",
       "       '13214', '112052', '237668', 'STON/O 2. 3101292', '350050',\n",
       "       '349231', '13213', 'S.O./P.P. 751', 'CA. 2314', '349221', '8475',\n",
       "       '330919', '365226', '349223', '29751', '2623', '5727', '349210',\n",
       "       'STON/O 2. 3101285', '234686', '312993', 'A/5 3536', '19996',\n",
       "       '29750', 'F.C. 12750', 'C.A. 24580', '244270', '239856', '349912',\n",
       "       '342826', '4138', '330935', '6563', '349228', '350036', '24160',\n",
       "       '17474', '349256', '2672', '113800', '248731', '363592', '35852',\n",
       "       '348121', 'PC 17475', '36864', '350025', '223596', 'PC 17476',\n",
       "       'PC 17482', '113028', '7545', '250647', '348124', '34218', '36568',\n",
       "       '347062', '350048', '12233', '250643', '113806', '315094', '36866',\n",
       "       '236853', 'STON/O2. 3101271', '239855', '28425', '233639',\n",
       "       '349201', '349218', '16988', '376566', 'STON/O 2. 3101288',\n",
       "       '250648', '113773', '335097', '29103', '392096', '345780',\n",
       "       '349204', '350042', '29108', '363294', 'SOTON/O2 3101272', '2663',\n",
       "       '347074', '112379', '364850', '8471', '345781', '350047',\n",
       "       'S.O./P.P. 3', '2674', '29105', '347078', '383121', '36865',\n",
       "       '2687', '113501', 'W./C. 6607', 'SOTON/O.Q. 3101312', '374887',\n",
       "       '3101265', '12460', 'PC 17600', '349203', '28213', '17465',\n",
       "       '349244', '2685', '2625', '347089', '347063', '112050', '347087',\n",
       "       '248723', '3474', '28206', '364499', '112058', 'STON/O2. 3101290',\n",
       "       'S.C./PARIS 2079', 'C 7075', '315098', '19972', '368323', '367228',\n",
       "       '2671', '347468', '2223', 'PC 17756', '315097', '392092', '11774',\n",
       "       'SOTON/O2 3101287', '2683', '315090', 'C.A. 5547', '349213',\n",
       "       '347060', 'PC 17592', '392091', '113055', '2629', '350026',\n",
       "       '28134', '17466', '233866', '236852', 'SC/PARIS 2149', 'PC 17590',\n",
       "       '345777', '349248', '695', '345765', '2667', '349212', '349217',\n",
       "       '349257', '7552', 'C.A./SOTON 34068', 'SOTON/OQ 392076', '211536',\n",
       "       '112053', '111369', '370376'], dtype=object)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.Ticket.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "aee55ca3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-07-31T00:08:34.303354Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# i drop this column, because this is column is outlayer of data not need.\n",
    "df_train.drop(\"Ticket\", axis=1, inplace=True)\n",
    "df_test.drop(\"Ticket\", axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "3b848d35",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PassengerId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\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>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
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       "      <td>1</td>\n",
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       "      <td>Heikkinen, Miss. Laina</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
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       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
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       "      <td>Allen, Mr. William Henry</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Survived  Pclass  \\\n",
       "PassengerId                     \n",
       "1                   0       3   \n",
       "2                   1       1   \n",
       "3                   1       3   \n",
       "4                   1       1   \n",
       "5                   0       3   \n",
       "\n",
       "                                                          Name     Sex   Age  \\\n",
       "PassengerId                                                                    \n",
       "1                                      Braund, Mr. Owen Harris    male  22.0   \n",
       "2            Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0   \n",
       "3                                       Heikkinen, Miss. Laina  female  26.0   \n",
       "4                 Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0   \n",
       "5                                     Allen, Mr. William Henry    male  35.0   \n",
       "\n",
       "             SibSp  Parch  Fare  Embarked  \n",
       "PassengerId                                \n",
       "1                1      0     1         1  \n",
       "2                1      0     2         2  \n",
       "3                0      0     1         1  \n",
       "4                1      0     2         1  \n",
       "5                0      0     1         1  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.head()"
   ]
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       "             Survived  Pclass  \\\n",
       "PassengerId                     \n",
       "1                   0       3   \n",
       "2                   1       1   \n",
       "3                   1       3   \n",
       "4                   1       1   \n",
       "5                   0       3   \n",
       "\n",
       "                                                          Name  Sex   Age  \\\n",
       "PassengerId                                                                 \n",
       "1                                      Braund, Mr. Owen Harris    1  22.0   \n",
       "2            Cumings, Mrs. John Bradley (Florence Briggs Th...    2  38.0   \n",
       "3                                       Heikkinen, Miss. Laina    2  26.0   \n",
       "4                 Futrelle, Mrs. Jacques Heath (Lily May Peel)    2  35.0   \n",
       "5                                     Allen, Mr. William Henry    1  35.0   \n",
       "\n",
       "             SibSp  Parch  Fare  Embarked  \n",
       "PassengerId                                \n",
       "1                1      0     1         1  \n",
       "2                1      0     2         2  \n",
       "3                0      0     1         1  \n",
       "4                1      0     2         1  \n",
       "5                0      0     1         1  "
      ]
     },
     "execution_count": 24,
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    }
   ],
   "source": [
    "# Sex column.\n",
    "Sex_dict = {\"male\":1, \"female\":2}\n",
    "for key, value in Sex_dict.items():\n",
    "    df_train.Sex.replace(key, value, inplace=True)\n",
    "    df_test.Sex.replace(key, value, inplace=True)\n",
    "df_train.Sex = df_train.Sex.astype(\"int64\")\n",
    "df_test.Sex = df_test.Sex.astype(\"int64\")\n",
    "df_train.head()"
   ]
  },
  {
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      "text/plain": [
       "             Survived  Pclass  Sex   Age  SibSp  Parch  Fare  Embarked  Title\n",
       "PassengerId                                                                  \n",
       "1                   0       3    1  22.0      1      0     1         1      1\n",
       "2                   1       1    2  38.0      1      0     2         2      3\n",
       "3                   1       3    2  26.0      0      0     1         1      2\n",
       "4                   1       1    2  35.0      1      0     2         1      3\n",
       "5                   0       3    1  35.0      0      0     1         1      1\n",
       "...               ...     ...  ...   ...    ...    ...   ...       ...    ...\n",
       "887                 0       2    1  27.0      0      0     1         1      5\n",
       "888                 1       1    2  19.0      0      0     1         1      2\n",
       "889                 0       3    2   NaN      1      2     1         1      2\n",
       "890                 1       1    1  26.0      0      0     1         2      1\n",
       "891                 0       3    1  32.0      0      0     1         3      1\n",
       "\n",
       "[891 rows x 9 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
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    }
   ],
   "source": [
    "df_train[\"Title\"] = 0\n",
    "\n",
    "titles = {\"Mr\": 1, \"Miss\": 2, \"Mrs\": 3, \"Master\": 4, \"Rare\": 5}\n",
    "data = [df_train, df_test]\n",
    "for dataset in data:\n",
    "    # extract titles\n",
    "    dataset['Title'] = dataset.Name.str.extract(' ([A-Za-z]+)\\.', expand=False)\n",
    "    # replace titles with a more common title or as Rare\n",
    "    dataset['Title'] = dataset['Title'].replace(['Lady', 'Countess','Capt', 'Col','Don', 'Dr',\\\n",
    "                                            'Major', 'Rev', 'Sir', 'Jonkheer', 'Dona'], 'Rare')\n",
    "    dataset['Title'] = dataset['Title'].replace(['Mlle','Ms'], 'Miss')\n",
    "    dataset['Title'] = dataset['Title'].replace('Mme', 'Mrs')\n",
    "    # convert titles into numbers\n",
    "    dataset['Title'] = dataset['Title'].map(titles)\n",
    "    # filling NaN with 0, to get safe\n",
    "    dataset['Title'] = dataset['Title'].fillna(0)\n",
    "df_train = df_train.drop(['Name'], axis=1)\n",
    "df_test = df_test.drop(['Name'], axis=1)\n",
    "df_train"
   ]
  },
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    {
     "data": {
      "text/plain": [
       "Survived      0\n",
       "Pclass        0\n",
       "Sex           0\n",
       "Age         177\n",
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       "dtype: int64"
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   "source": [
    "df_train.isnull().sum()"
   ]
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       "      <td>3</td>\n",
       "      <td>2</td>\n",
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       "      <td>2</td>\n",
       "      <td>0.640270</td>\n",
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       "      <th>887</th>\n",
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       "      <th>890</th>\n",
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       "      <td>0</td>\n",
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       "<p>714 rows × 9 columns</p>\n",
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      ],
      "text/plain": [
       "             Survived  Pclass  Sex       Age  SibSp  Parch  Fare  Embarked  \\\n",
       "PassengerId                                                                  \n",
       "1                   0       3    1 -0.530005      1      0     1         1   \n",
       "2                   1       1    2  0.571430      1      0     2         2   \n",
       "3                   1       3    2 -0.254646      0      0     1         1   \n",
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       "5                   0       3    1  0.364911      0      0     1         1   \n",
       "...               ...     ...  ...       ...    ...    ...   ...       ...   \n",
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       "890                 1       1    1 -0.254646      0      0     1         2   \n",
       "891                 0       3    1  0.158392      0      0     1         3   \n",
       "\n",
       "             Title  \n",
       "PassengerId         \n",
       "1                1  \n",
       "2                3  \n",
       "3                2  \n",
       "4                3  \n",
       "5                1  \n",
       "...            ...  \n",
       "886              3  \n",
       "887              5  \n",
       "888              2  \n",
       "890              1  \n",
       "891              1  \n",
       "\n",
       "[714 rows x 9 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_Age_train = df_train.loc[pd.notna(df_train.Age)]\n",
    "df_Age_train.Age = df_Age_train.Age.astype(\"float64\")\n",
    "df_Age_train.Age = (df_Age_train.Age - df_Age_train.Age.mean()) / df_Age_train.Age.std()\n",
    "df_Age_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "0b9ca43b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-07-31T00:08:35.104215Z",
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     "iopub.status.idle": "2021-07-31T00:08:35.113035Z",
     "shell.execute_reply": "2021-07-31T00:08:35.113551Z",
     "shell.execute_reply.started": "2021-07-31T00:03:46.628768Z"
    },
    "papermill": {
     "duration": 0.068901,
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     "exception": false,
     "start_time": "2021-07-31T00:08:35.044815",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.07722109457217771"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_Age_train.Survived.corr(df_Age_train.Age)\n",
    "# so i will drop Age column, because it is correlation between Age and Survived is very small."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "d0a4a933",
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     "status": "completed"
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    "tags": []
   },
   "outputs": [
    {
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      "text/plain": [
       "             Survived  Pclass  Sex  SibSp  Parch  Fare  Embarked  Title\n",
       "PassengerId                                                            \n",
       "1                   0       3    1      1      0     1         1      1\n",
       "2                   1       1    2      1      0     2         2      3\n",
       "3                   1       3    2      0      0     1         1      2\n",
       "4                   1       1    2      1      0     2         1      3\n",
       "5                   0       3    1      0      0     1         1      1\n",
       "...               ...     ...  ...    ...    ...   ...       ...    ...\n",
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       "888                 1       1    2      0      0     1         1      2\n",
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       "890                 1       1    1      0      0     1         2      1\n",
       "891                 0       3    1      0      0     1         3      1\n",
       "\n",
       "[891 rows x 8 columns]"
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     },
     "execution_count": 29,
     "metadata": {},
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   ],
   "source": [
    "df_train.drop(\"Age\", axis=1, inplace=True)\n",
    "df_test.drop(\"Age\", axis=1, inplace=True)\n",
    "df_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
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   "outputs": [
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1308</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1309</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>418 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Pclass  Sex  SibSp  Parch  Fare  Embarked  Title\n",
       "PassengerId                                                  \n",
       "892               3    1      0      0     1         3      1\n",
       "893               3    2      1      0     1         1      3\n",
       "894               2    1      0      0     1         3      1\n",
       "895               3    1      0      0     1         1      1\n",
       "896               3    2      1      1     1         1      3\n",
       "...             ...  ...    ...    ...   ...       ...    ...\n",
       "1305              3    1      0      0     1         1      1\n",
       "1306              1    2      0      0     3         2      5\n",
       "1307              3    1      0      0     1         1      1\n",
       "1308              3    1      0      0     1         1      1\n",
       "1309              3    1      1      1     1         2      4\n",
       "\n",
       "[418 rows x 7 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "4ea4d6a1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-07-31T00:08:35.547221Z",
     "iopub.status.busy": "2021-07-31T00:08:35.546152Z",
     "iopub.status.idle": "2021-07-31T00:08:35.549325Z",
     "shell.execute_reply": "2021-07-31T00:08:35.548791Z",
     "shell.execute_reply.started": "2021-07-31T00:03:46.678094Z"
    },
    "papermill": {
     "duration": 0.070558,
     "end_time": "2021-07-31T00:08:35.549597",
     "exception": false,
     "start_time": "2021-07-31T00:08:35.479039",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "data = [df_train, df_test]\n",
    "for dataset in data:\n",
    "    dataset['FamilySize'] = dataset['SibSp'] +  dataset['Parch'] + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "ebdf364f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-07-31T00:08:35.679953Z",
     "iopub.status.busy": "2021-07-31T00:08:35.679300Z",
     "iopub.status.idle": "2021-07-31T00:08:35.692257Z",
     "shell.execute_reply": "2021-07-31T00:08:35.691178Z",
     "shell.execute_reply.started": "2021-07-31T00:03:46.691495Z"
    },
    "papermill": {
     "duration": 0.081277,
     "end_time": "2021-07-31T00:08:35.692510",
     "exception": false,
     "start_time": "2021-07-31T00:08:35.611233",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   IsAlone  Survived\n",
      "0        0  0.505650\n",
      "1        1  0.303538\n"
     ]
    }
   ],
   "source": [
    "\n",
    "for dataset in data:\n",
    "    dataset['IsAlone'] = 0\n",
    "    dataset.loc[dataset['FamilySize'] == 1, 'IsAlone'] = 1\n",
    "\n",
    "print (df_train[['IsAlone', 'Survived']].groupby(['IsAlone'], as_index=False).mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "0bc66413",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-07-31T00:08:35.821131Z",
     "iopub.status.busy": "2021-07-31T00:08:35.820485Z",
     "iopub.status.idle": "2021-07-31T00:08:36.621387Z",
     "shell.execute_reply": "2021-07-31T00:08:36.620756Z",
     "shell.execute_reply.started": "2021-07-31T00:03:46.712178Z"
    },
    "papermill": {
     "duration": 0.869934,
     "end_time": "2021-07-31T00:08:36.621548",
     "exception": false,
     "start_time": "2021-07-31T00:08:35.751614",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "colormap=plt.cm.RdBu\n",
    "figure = plt.figure(figsize=(12,12))\n",
    "sns.heatmap(df_train.corr(),linewidths=0.1,vmax=1.0, square=True, cmap=colormap, linecolor='white', annot=True)\n",
    "plt.title(\"Correlations\",size=15)\n",
    "plt.xlabel(\"Features\")\n",
    "plt.ylabel(\"Features\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "31cb4663",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-07-31T00:08:36.749343Z",
     "iopub.status.busy": "2021-07-31T00:08:36.748572Z",
     "iopub.status.idle": "2021-07-31T00:08:36.751519Z",
     "shell.execute_reply": "2021-07-31T00:08:36.752010Z",
     "shell.execute_reply.started": "2021-07-31T00:03:47.496066Z"
    },
    "papermill": {
     "duration": 0.07039,
     "end_time": "2021-07-31T00:08:36.752194",
     "exception": false,
     "start_time": "2021-07-31T00:08:36.681804",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "891"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columns = [\"Pclass\",\"Sex\", \"Fare\", \"Embarked\",\"Title\",\"IsAlone\"]\n",
    "X_train = df_train[columns]\n",
    "Y_train = df_train[\"Survived\"]\n",
    "len(Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "b72e5302",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-07-31T00:08:36.878849Z",
     "iopub.status.busy": "2021-07-31T00:08:36.878168Z",
     "iopub.status.idle": "2021-07-31T00:08:36.884434Z",
     "shell.execute_reply": "2021-07-31T00:08:36.884929Z",
     "shell.execute_reply.started": "2021-07-31T00:03:47.506176Z"
    },
    "papermill": {
     "duration": 0.071451,
     "end_time": "2021-07-31T00:08:36.885116",
     "exception": false,
     "start_time": "2021-07-31T00:08:36.813665",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "418"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test = df_test[columns]\n",
    "len(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "af02c9c7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-07-31T00:08:37.011396Z",
     "iopub.status.busy": "2021-07-31T00:08:37.010663Z",
     "iopub.status.idle": "2021-07-31T00:08:37.016453Z",
     "shell.execute_reply": "2021-07-31T00:08:37.015801Z",
     "shell.execute_reply.started": "2021-07-31T00:03:47.521431Z"
    },
    "papermill": {
     "duration": 0.070561,
     "end_time": "2021-07-31T00:08:37.016595",
     "exception": false,
     "start_time": "2021-07-31T00:08:36.946034",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "418"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(Y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3f5771c",
   "metadata": {
    "papermill": {
     "duration": 0.06049,
     "end_time": "2021-07-31T00:08:37.138192",
     "exception": false,
     "start_time": "2021-07-31T00:08:37.077702",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## ML Clasification Prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af213d37",
   "metadata": {
    "papermill": {
     "duration": 0.060868,
     "end_time": "2021-07-31T00:08:37.260232",
     "exception": false,
     "start_time": "2021-07-31T00:08:37.199364",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### 1- SGDClassifier (66%)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "133e535e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-07-31T00:08:37.390396Z",
     "iopub.status.busy": "2021-07-31T00:08:37.389692Z",
     "iopub.status.idle": "2021-07-31T00:08:37.537012Z",
     "shell.execute_reply": "2021-07-31T00:08:37.537542Z",
     "shell.execute_reply.started": "2021-07-31T00:03:47.532932Z"
    },
    "papermill": {
     "duration": 0.216234,
     "end_time": "2021-07-31T00:08:37.537713",
     "exception": false,
     "start_time": "2021-07-31T00:08:37.321479",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the train score of SGD =  70.82 %\n"
     ]
    }
   ],
   "source": [
    "sgd_clf = SGDClassifier(random_state=42, max_iter=1000, tol=1e-3)\n",
    "sgd_clf.fit(X_train, Y_train)\n",
    "Y_pred_SGD = sgd_clf.predict(X_test)\n",
    "print(\"the train score of SGD = \",round(sgd_clf.score(X_train, Y_train) *100, 2),\"%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f90cf2f",
   "metadata": {
    "papermill": {
     "duration": 0.061114,
     "end_time": "2021-07-31T00:08:37.660434",
     "exception": false,
     "start_time": "2021-07-31T00:08:37.599320",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### 2- Random Forest (78%)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "547936b6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-07-31T00:08:37.787020Z",
     "iopub.status.busy": "2021-07-31T00:08:37.786395Z",
     "iopub.status.idle": "2021-07-31T00:08:38.244696Z",
     "shell.execute_reply": "2021-07-31T00:08:38.245198Z",
     "shell.execute_reply.started": "2021-07-31T00:03:47.718447Z"
    },
    "papermill": {
     "duration": 0.523233,
     "end_time": "2021-07-31T00:08:38.245385",
     "exception": false,
     "start_time": "2021-07-31T00:08:37.722152",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the train score of random_forest =  84.18 %\n"
     ]
    }
   ],
   "source": [
    "random_forest = RandomForestClassifier(n_estimators=40, min_samples_leaf=2, max_features=0.1, n_jobs=-1)\n",
    "random_forest.fit(X_train, Y_train)\n",
    "Y_pred_Random = random_forest.predict(X_test)\n",
    "print(\"the train score of random_forest = \",round(random_forest.score(X_train, Y_train) *100, 2),\"%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2ea6bb7",
   "metadata": {
    "papermill": {
     "duration": 0.061808,
     "end_time": "2021-07-31T00:08:38.369252",
     "exception": false,
     "start_time": "2021-07-31T00:08:38.307444",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### 3- Logistic Regression (76.6%)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "8763754c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-07-31T00:08:38.502067Z",
     "iopub.status.busy": "2021-07-31T00:08:38.501063Z",
     "iopub.status.idle": "2021-07-31T00:08:38.512605Z",
     "shell.execute_reply": "2021-07-31T00:08:38.511972Z",
     "shell.execute_reply.started": "2021-07-31T00:03:48.167824Z"
    },
    "papermill": {
     "duration": 0.081085,
     "end_time": "2021-07-31T00:08:38.512735",
     "exception": false,
     "start_time": "2021-07-31T00:08:38.431650",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the train score of logistic_regression =  78.11 %\n"
     ]
    }
   ],
   "source": [
    "logistic_regression = LogisticRegression(solver='liblinear',max_iter=1000)\n",
    "logistic_regression.fit(X_train, Y_train)\n",
    "Y_pred_Logistic = logistic_regression.predict(X_test)\n",
    "print(\"the train score of logistic_regression = \",round(logistic_regression.score(X_train, Y_train) *100, 2),\"%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91571ecc",
   "metadata": {
    "papermill": {
     "duration": 0.061855,
     "end_time": "2021-07-31T00:08:38.636864",
     "exception": false,
     "start_time": "2021-07-31T00:08:38.575009",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### 4- Decision Tree (78%)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "9d6f8eb9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-07-31T00:08:38.771017Z",
     "iopub.status.busy": "2021-07-31T00:08:38.769931Z",
     "iopub.status.idle": "2021-07-31T00:08:38.779919Z",
     "shell.execute_reply": "2021-07-31T00:08:38.779375Z",
     "shell.execute_reply.started": "2021-07-31T00:03:48.185517Z"
    },
    "papermill": {
     "duration": 0.080381,
     "end_time": "2021-07-31T00:08:38.780066",
     "exception": false,
     "start_time": "2021-07-31T00:08:38.699685",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the score of prediction =  84.4 %\n"
     ]
    }
   ],
   "source": [
    "tree = DecisionTreeClassifier(random_state=25)\n",
    "tree.fit(X_train, Y_train)\n",
    "Y_pred_Tree= tree.predict(X_test)\n",
    "print(\"the score of prediction = \",round(tree.score(X_train, Y_train) * 100,2), \"%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "0594c8f0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-07-31T00:08:38.910448Z",
     "iopub.status.busy": "2021-07-31T00:08:38.909793Z",
     "iopub.status.idle": "2021-07-31T00:08:39.466157Z",
     "shell.execute_reply": "2021-07-31T00:08:39.466646Z",
     "shell.execute_reply.started": "2021-07-31T00:03:48.200716Z"
    },
    "papermill": {
     "duration": 0.62375,
     "end_time": "2021-07-31T00:08:39.466814",
     "exception": false,
     "start_time": "2021-07-31T00:08:38.843064",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8190277777777779"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores= cross_val_score(tree, X_train, Y_train, scoring=\"accuracy\", cv=100)\n",
    "scores.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9fd4b0f",
   "metadata": {
    "papermill": {
     "duration": 0.062116,
     "end_time": "2021-07-31T00:08:39.590903",
     "exception": false,
     "start_time": "2021-07-31T00:08:39.528787",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### 5- SVM (76.55%)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "49cedee5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-07-31T00:08:39.724712Z",
     "iopub.status.busy": "2021-07-31T00:08:39.723990Z",
     "iopub.status.idle": "2021-07-31T00:08:39.745561Z",
     "shell.execute_reply": "2021-07-31T00:08:39.746063Z",
     "shell.execute_reply.started": "2021-07-31T00:03:48.750413Z"
    },
    "papermill": {
     "duration": 0.091723,
     "end_time": "2021-07-31T00:08:39.746242",
     "exception": false,
     "start_time": "2021-07-31T00:08:39.654519",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the score of prediction =  78.68 %\n"
     ]
    }
   ],
   "source": [
    "\n",
    "clf = svm.SVC(kernel = 'linear')\n",
    "clf.fit(X_train, Y_train)\n",
    "Y_predict_svm = clf.predict(X_test)\n",
    "print(\"the score of prediction = \",round(clf.score(X_train, Y_train) * 100,2), \"%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe7c0551",
   "metadata": {
    "papermill": {
     "duration": 0.064318,
     "end_time": "2021-07-31T00:08:39.875649",
     "exception": false,
     "start_time": "2021-07-31T00:08:39.811331",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### 6- KNeighbors (79.18%)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "f29ecab9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-07-31T00:08:40.014426Z",
     "iopub.status.busy": "2021-07-31T00:08:40.013470Z",
     "iopub.status.idle": "2021-07-31T00:08:40.086665Z",
     "shell.execute_reply": "2021-07-31T00:08:40.087137Z",
     "shell.execute_reply.started": "2021-07-31T00:03:48.780149Z"
    },
    "papermill": {
     "duration": 0.148581,
     "end_time": "2021-07-31T00:08:40.087311",
     "exception": false,
     "start_time": "2021-07-31T00:08:39.938730",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the score of prediction =  82.6 %\n"
     ]
    }
   ],
   "source": [
    "knn = KNeighborsClassifier(n_neighbors=5)\n",
    "knn.fit(X_train, Y_train)\n",
    "Y_pred_KNN= knn.predict(X_test)\n",
    "print(\"the score of prediction = \",round(knn.score(X_train, Y_train) * 100,2), \"%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "068fffc7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-07-31T00:08:40.222739Z",
     "iopub.status.busy": "2021-07-31T00:08:40.222044Z",
     "iopub.status.idle": "2021-07-31T00:08:40.228311Z",
     "shell.execute_reply": "2021-07-31T00:08:40.228754Z",
     "shell.execute_reply.started": "2021-07-31T00:03:48.84482Z"
    },
    "papermill": {
     "duration": 0.078601,
     "end_time": "2021-07-31T00:08:40.228918",
     "exception": false,
     "start_time": "2021-07-31T00:08:40.150317",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "output_csv = {\"PassengerId\":[*range(892,892+len(Y_pred_KNN))], \"Survived\":Y_pred_KNN}\n",
    "Y_pre = pd.DataFrame(output_csv)\n",
    "Y_pre.set_index(\"PassengerId\", drop=True, append=False, inplace=True)\n",
    "Y_pre.to_csv(\"/kaggle/working/submission.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c34caf18",
   "metadata": {
    "papermill": {
     "duration": 0.062696,
     "end_time": "2021-07-31T00:08:40.354836",
     "exception": false,
     "start_time": "2021-07-31T00:08:40.292140",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### 7- MlPClassifier (77.5%)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "a281fffd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-07-31T00:08:40.488136Z",
     "iopub.status.busy": "2021-07-31T00:08:40.487494Z",
     "iopub.status.idle": "2021-07-31T00:08:40.508937Z",
     "shell.execute_reply": "2021-07-31T00:08:40.508317Z",
     "shell.execute_reply.started": "2021-07-31T00:03:48.859067Z"
    },
    "papermill": {
     "duration": 0.091404,
     "end_time": "2021-07-31T00:08:40.509083",
     "exception": false,
     "start_time": "2021-07-31T00:08:40.417679",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the score of prediction =  61.62 %\n"
     ]
    }
   ],
   "source": [
    "clf = MLPClassifier(solver='lbfgs', alpha=1e-5, hidden_layer_sizes=(3, 2), random_state=1)\n",
    "clf.fit(X_train, Y_train)\n",
    "Y_pred_clf= clf.predict(X_test)\n",
    "print(\"the score of prediction = \",round(clf.score(X_train, Y_train) * 100,2), \"%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b484252a",
   "metadata": {
    "papermill": {
     "duration": 0.063204,
     "end_time": "2021-07-31T00:08:40.635600",
     "exception": false,
     "start_time": "2021-07-31T00:08:40.572396",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### 8- GaussianNB (74.6%)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "d6d0efd8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-07-31T00:08:40.770246Z",
     "iopub.status.busy": "2021-07-31T00:08:40.769574Z",
     "iopub.status.idle": "2021-07-31T00:08:40.778252Z",
     "shell.execute_reply": "2021-07-31T00:08:40.778740Z",
     "shell.execute_reply.started": "2021-07-31T00:03:48.894959Z"
    },
    "papermill": {
     "duration": 0.079824,
     "end_time": "2021-07-31T00:08:40.778903",
     "exception": false,
     "start_time": "2021-07-31T00:08:40.699079",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the train score for Gaussian =  77.78 %\n"
     ]
    }
   ],
   "source": [
    "gaussian = GaussianNB()\n",
    "gaussian.fit(X_train, Y_train)\n",
    "Y_pred_gaussian = gaussian.predict(X_test)\n",
    "print(\"the train score for Gaussian = \", round(gaussian.score(X_train, Y_train) * 100, 2), \"%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0ad7a94",
   "metadata": {
    "papermill": {
     "duration": 0.064504,
     "end_time": "2021-07-31T00:08:40.906764",
     "exception": false,
     "start_time": "2021-07-31T00:08:40.842260",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### 9- Perceptron (78.2%)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "011e1401",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-07-31T00:08:41.042473Z",
     "iopub.status.busy": "2021-07-31T00:08:41.041454Z",
     "iopub.status.idle": "2021-07-31T00:08:41.052353Z",
     "shell.execute_reply": "2021-07-31T00:08:41.051726Z",
     "shell.execute_reply.started": "2021-07-31T00:03:48.908432Z"
    },
    "papermill": {
     "duration": 0.081929,
     "end_time": "2021-07-31T00:08:41.052498",
     "exception": false,
     "start_time": "2021-07-31T00:08:40.970569",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the train score for Perceptron =  78.11 %\n"
     ]
    }
   ],
   "source": [
    "perceptron = Perceptron()\n",
    "perceptron.fit(X_train, Y_train)\n",
    "Y_pred_perceptron = perceptron.predict(X_test)\n",
    "print(\"the train score for Perceptron = \",round(perceptron.score(X_train, Y_train) * 100, 2), \"%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "922db0db",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-07-31T00:08:41.185592Z",
     "iopub.status.busy": "2021-07-31T00:08:41.184703Z",
     "iopub.status.idle": "2021-07-31T00:08:41.187894Z",
     "shell.execute_reply": "2021-07-31T00:08:41.187290Z",
     "shell.execute_reply.started": "2021-07-31T00:03:48.923208Z"
    },
    "papermill": {
     "duration": 0.071001,
     "end_time": "2021-07-31T00:08:41.188024",
     "exception": false,
     "start_time": "2021-07-31T00:08:41.117023",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# !kaggle competitions download -c titanic\n",
    "# !kaggle competitions submit -c titanic -f /kaggle/working/submission.csv -m \"submission\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "b6dc4b20",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-07-31T00:08:41.330859Z",
     "iopub.status.busy": "2021-07-31T00:08:41.330190Z",
     "iopub.status.idle": "2021-07-31T00:08:41.332964Z",
     "shell.execute_reply": "2021-07-31T00:08:41.333547Z",
     "shell.execute_reply.started": "2021-07-31T00:03:48.92887Z"
    },
    "papermill": {
     "duration": 0.081363,
     "end_time": "2021-07-31T00:08:41.333707",
     "exception": false,
     "start_time": "2021-07-31T00:08:41.252344",
     "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>models</th>\n",
       "      <th>test_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>KNeighbors</td>\n",
       "      <td>79.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Perceptron</td>\n",
       "      <td>78.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Random Forest</td>\n",
       "      <td>78.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Decision Tree</td>\n",
       "      <td>78.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>MlPClassifier</td>\n",
       "      <td>77.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>76.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>SVM</td>\n",
       "      <td>76.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>GaussianNB</td>\n",
       "      <td>74.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SGDClassifier</td>\n",
       "      <td>66.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                models  test_score\n",
       "6           KNeighbors       79.18\n",
       "9           Perceptron       78.20\n",
       "2        Random Forest       78.00\n",
       "4        Decision Tree       78.00\n",
       "7        MlPClassifier       77.50\n",
       "3  Logistic Regression       76.60\n",
       "5                  SVM       76.55\n",
       "8           GaussianNB       74.60\n",
       "1        SGDClassifier       66.00"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = [\"SGDClassifier\", \"Random Forest\", \"Logistic Regression\", \"Decision Tree\", \"SVM\",\n",
    "\"KNeighbors\", \"MlPClassifier\", \"GaussianNB\", \"Perceptron\"]\n",
    "score = [66, 78, 76.6, 78, 76.55, 79.18, 77.5, 74.6, 78.2]\n",
    "data_dict = {\"models\": model, \"test_score\": score}\n",
    "data_score = pd.DataFrame(data_dict)\n",
    "data_score.index = data_score.index + 1\n",
    "data_score.sort_values(\"test_score\",ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "630d4444",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": []
  }
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