Titanic Survivor Prediction  (Top 7%).ipynb

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   "source": [
    "## Introduction\n",
    "\n",
    "<p align = \"justify\">\n",
    "    <br></br>\n",
    "&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; In this time I will share my work which is a prediction titanic survivor from kaggle. This is beginner competition from kaggle, that is, using machine learning to create a model that predicts which passengers survived the Titanic shipwreck (Source : https://www.kaggle.com/c/titanic).\n",
    "    <br></br>\n",
    " "
   ]
  },
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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",
    "sns.set(style = 'whitegrid')\n",
    "sns.distributions._has_statsmodels = False # To handle RuntimeError: Selected KDE bandwidth is 0."
   ]
  },
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     "status": "completed"
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    "scrolled": true,
    "tags": []
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   "outputs": [],
   "source": [
    "data_train = pd.read_csv('data/train.csv')\n",
    "data_test = pd.read_csv('data/test.csv')"
   ]
  },
  {
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    "tags": []
   },
   "source": [
    "# Exploratory Data Analysis"
   ]
  },
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    "tags": []
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   "outputs": [],
   "source": [
    "train = data_train.copy()\n",
    "test = data_test.copy()"
   ]
  },
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       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
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       "                                                Name     Sex   Age  SibSp  \\\n",
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       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
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   "source": [
    "train.head()"
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       "0          892       3                              Kelly, Mr. James    male   \n",
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       "2          894       2                     Myles, Mr. Thomas Francis    male   \n",
       "3          895       3                              Wirz, Mr. Albert    male   \n",
       "4          896       3  Hirvonen, Mrs. Alexander (Helga E Lindqvist)  female   \n",
       "\n",
       "    Age  SibSp  Parch   Ticket     Fare Cabin Embarked  \n",
       "0  34.5      0      0   330911   7.8292   NaN        Q  \n",
       "1  47.0      1      0   363272   7.0000   NaN        S  \n",
       "2  62.0      0      0   240276   9.6875   NaN        Q  \n",
       "3  27.0      0      0   315154   8.6625   NaN        S  \n",
       "4  22.0      1      1  3101298  12.2875   NaN        S  "
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     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
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   },
   "outputs": [
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     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      "PassengerId    891 non-null int64\n",
      "Survived       891 non-null int64\n",
      "Pclass         891 non-null int64\n",
      "Name           891 non-null object\n",
      "Sex            891 non-null object\n",
      "Age            714 non-null float64\n",
      "SibSp          891 non-null int64\n",
      "Parch          891 non-null int64\n",
      "Ticket         891 non-null object\n",
      "Fare           891 non-null float64\n",
      "Cabin          204 non-null object\n",
      "Embarked       889 non-null object\n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.7+ KB\n",
      "None\n",
      "\n",
      "\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 418 entries, 0 to 417\n",
      "Data columns (total 11 columns):\n",
      "PassengerId    418 non-null int64\n",
      "Pclass         418 non-null int64\n",
      "Name           418 non-null object\n",
      "Sex            418 non-null object\n",
      "Age            332 non-null float64\n",
      "SibSp          418 non-null int64\n",
      "Parch          418 non-null int64\n",
      "Ticket         418 non-null object\n",
      "Fare           417 non-null float64\n",
      "Cabin          91 non-null object\n",
      "Embarked       418 non-null object\n",
      "dtypes: float64(2), int64(4), object(5)\n",
      "memory usage: 36.0+ KB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(train.info())\n",
    "print('\\n')\n",
    "print(test.info())"
   ]
  },
  {
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    "tags": []
   },
   "source": [
    "## Analysis Data Numeric and Category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# get letter on column 'cabin' value to categorize column 'cabin'\n",
    "train['Cabin'] = train['Cabin'].str.get(0)\n",
    "test['Cabin'] = test['Cabin'].str.get(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
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   },
   "outputs": [],
   "source": [
    "# split into numeric and categorical data for analysis purpose\n",
    "num_data = train[['Age', 'SibSp', 'Parch', 'Fare']]\n",
    "cat_data = train[['Survived', 'Pclass', 'Sex', 'Cabin', 'Embarked']]"
   ]
  },
  {
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     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Analysis Data Numeric"
   ]
  },
  {
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   "outputs": [
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\n",
      "text/plain": [
       "<Figure size 864x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import statsmodels\n",
    "\n",
    "fig, ax = plt.subplots(2, 2 ,figsize = (12,8))\n",
    "fig.tight_layout(pad=5.0)\n",
    "# can use for loop, if to much columns\n",
    "sns.distplot(ax = ax[0, 0], a = num_data['Age'].dropna())\n",
    "ax[0, 0].set_title('Age', fontsize = 18)\n",
    "\n",
    "sns.distplot(ax = ax[0, 1], a = num_data['SibSp'].dropna())\n",
    "ax[0, 1].set_title('SibSp', fontsize = 18)\n",
    "\n",
    "sns.distplot(ax = ax[1, 0], a = num_data['Parch'].dropna())\n",
    "ax[1, 0].set_title('Parch', fontsize = 18)\n",
    "\n",
    "sns.distplot(ax = ax[1, 1], a = num_data['Fare'].dropna())\n",
    "ax[1, 1].set_title('Fare', fontsize = 18)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-31T13:02:07.942928Z",
     "iopub.status.busy": "2020-08-31T13:02:07.941794Z",
     "iopub.status.idle": "2020-08-31T13:02:08.223532Z",
     "shell.execute_reply": "2020-08-31T13:02:08.224134Z"
    },
    "papermill": {
     "duration": 0.310638,
     "end_time": "2020-08-31T13:02:08.224336",
     "exception": false,
     "start_time": "2020-08-31T13:02:07.913698",
     "status": "completed"
    },
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# heatmap data numeric\n",
    "heatmapdata = train[['Survived', 'Age', 'SibSp', 'Parch', 'Fare']]\n",
    "\n",
    "cormat = heatmapdata.corr()\n",
    "fig, ax = plt.subplots(figsize = (8,4))\n",
    "sns.heatmap(data = cormat)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.018218,
     "end_time": "2020-08-31T13:02:08.261383",
     "exception": false,
     "start_time": "2020-08-31T13:02:08.243165",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Analysis Data Category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-31T13:02:08.454270Z",
     "iopub.status.busy": "2020-08-31T13:02:08.450925Z",
     "iopub.status.idle": "2020-08-31T13:02:09.306075Z",
     "shell.execute_reply": "2020-08-31T13:02:09.305267Z"
    },
    "papermill": {
     "duration": 1.025483,
     "end_time": "2020-08-31T13:02:09.306250",
     "exception": false,
     "start_time": "2020-08-31T13:02:08.280767",
     "status": "completed"
    },
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x1152 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(cat_data.shape[1], 1, figsize = (8,16))\n",
    "fig.tight_layout(pad=5.0)\n",
    "\n",
    "for i, n in enumerate(cat_data):\n",
    "        sns.barplot(ax = ax[i], x = cat_data[n].fillna('NaN').value_counts().index, y = cat_data[n].fillna('NaN').value_counts())\n",
    "        ax[i].set_title(n)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.01933,
     "end_time": "2020-08-31T13:02:09.345920",
     "exception": false,
     "start_time": "2020-08-31T13:02:09.326590",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Preprocessing Data (handle missing data, cleaning data, feature engineering, etc.) on Each Columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.018816,
     "end_time": "2020-08-31T13:02:09.384086",
     "exception": false,
     "start_time": "2020-08-31T13:02:09.365270",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Handle missing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-31T13:02:09.442522Z",
     "iopub.status.busy": "2020-08-31T13:02:09.441183Z",
     "iopub.status.idle": "2020-08-31T13:02:09.452294Z",
     "shell.execute_reply": "2020-08-31T13:02:09.451559Z"
    },
    "papermill": {
     "duration": 0.048958,
     "end_time": "2020-08-31T13:02:09.452506",
     "exception": false,
     "start_time": "2020-08-31T13:02:09.403548",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 418 entries, 0 to 417\n",
      "Data columns (total 12 columns):\n",
      "PassengerId    418 non-null int64\n",
      "Survived       418 non-null int64\n",
      "Pclass         418 non-null int64\n",
      "Name           418 non-null object\n",
      "Sex            418 non-null object\n",
      "Age            332 non-null float64\n",
      "SibSp          418 non-null int64\n",
      "Parch          418 non-null int64\n",
      "Ticket         418 non-null object\n",
      "Fare           417 non-null float64\n",
      "Cabin          91 non-null object\n",
      "Embarked       418 non-null object\n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 39.3+ KB\n"
     ]
    }
   ],
   "source": [
    "# create columns survived so that same shape with training data\n",
    "test.insert(1, 'Survived', -1)\n",
    "test.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-31T13:02:09.504517Z",
     "iopub.status.busy": "2020-08-31T13:02:09.503506Z",
     "iopub.status.idle": "2020-08-31T13:02:09.515191Z",
     "shell.execute_reply": "2020-08-31T13:02:09.514469Z"
    },
    "papermill": {
     "duration": 0.042965,
     "end_time": "2020-08-31T13:02:09.515352",
     "exception": false,
     "start_time": "2020-08-31T13:02:09.472387",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train :\n",
      " PassengerId      0\n",
      "Survived         0\n",
      "Pclass           0\n",
      "Name             0\n",
      "Sex              0\n",
      "Age            177\n",
      "SibSp            0\n",
      "Parch            0\n",
      "Ticket           0\n",
      "Fare             0\n",
      "Cabin          687\n",
      "Embarked         2\n",
      "dtype: int64\n",
      "\n",
      "\n",
      "Test :\n",
      " PassengerId      0\n",
      "Survived         0\n",
      "Pclass           0\n",
      "Name             0\n",
      "Sex              0\n",
      "Age             86\n",
      "SibSp            0\n",
      "Parch            0\n",
      "Ticket           0\n",
      "Fare             1\n",
      "Cabin          327\n",
      "Embarked         0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print('Train :\\n',train.isnull().sum())\n",
    "print('\\n')\n",
    "print('Test :\\n', test.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-31T13:02:09.572101Z",
     "iopub.status.busy": "2020-08-31T13:02:09.568986Z",
     "iopub.status.idle": "2020-08-31T13:02:09.599534Z",
     "shell.execute_reply": "2020-08-31T13:02:09.598784Z"
    },
    "papermill": {
     "duration": 0.063154,
     "end_time": "2020-08-31T13:02:09.599696",
     "exception": false,
     "start_time": "2020-08-31T13:02:09.536542",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# handle missing data on column age (do the same on data test, but with median of data train)\n",
    "train['Age'].fillna(train['Age'].median(), inplace = True)\n",
    "test['Age'].fillna(train['Age'].median(), inplace = True)\n",
    "\n",
    "# we know test data have nan values on fare (do the same with train data, for better understanding)\n",
    "train['Fare'].fillna(train['Fare'].median(), inplace = True)\n",
    "test['Fare'].fillna(train['Fare'].median(), inplace = True)\n",
    "\n",
    "# handle missing data on embarked columns\n",
    "train.dropna(subset=['Embarked'] , inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-31T13:02:09.648629Z",
     "iopub.status.busy": "2020-08-31T13:02:09.647808Z",
     "iopub.status.idle": "2020-08-31T13:02:09.651654Z",
     "shell.execute_reply": "2020-08-31T13:02:09.650888Z"
    },
    "papermill": {
     "duration": 0.032841,
     "end_time": "2020-08-31T13:02:09.651795",
     "exception": false,
     "start_time": "2020-08-31T13:02:09.618954",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Drop cabin because that's have many null/nan values\n",
    "train.drop(['Cabin'], axis = 1, inplace = True)\n",
    "test.drop(['Cabin'], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-31T13:02:09.702354Z",
     "iopub.status.busy": "2020-08-31T13:02:09.698475Z",
     "iopub.status.idle": "2020-08-31T13:02:09.707247Z",
     "shell.execute_reply": "2020-08-31T13:02:09.707815Z"
    },
    "papermill": {
     "duration": 0.036986,
     "end_time": "2020-08-31T13:02:09.708017",
     "exception": false,
     "start_time": "2020-08-31T13:02:09.671031",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train :\n",
      " PassengerId    0\n",
      "Survived       0\n",
      "Pclass         0\n",
      "Name           0\n",
      "Sex            0\n",
      "Age            0\n",
      "SibSp          0\n",
      "Parch          0\n",
      "Ticket         0\n",
      "Fare           0\n",
      "Embarked       0\n",
      "dtype: int64\n",
      "\n",
      "\n",
      "Test :\n",
      " PassengerId    0\n",
      "Survived       0\n",
      "Pclass         0\n",
      "Name           0\n",
      "Sex            0\n",
      "Age            0\n",
      "SibSp          0\n",
      "Parch          0\n",
      "Ticket         0\n",
      "Fare           0\n",
      "Embarked       0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print('Train :\\n',train.isnull().sum())\n",
    "print('\\n')\n",
    "print('Test :\\n', test.isnull().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.018859,
     "end_time": "2020-08-31T13:02:09.746056",
     "exception": false,
     "start_time": "2020-08-31T13:02:09.727197",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Create Column 'FamilySurvived' & 'FamilyDied'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-31T13:02:09.795964Z",
     "iopub.status.busy": "2020-08-31T13:02:09.795005Z",
     "iopub.status.idle": "2020-08-31T13:02:09.806599Z",
     "shell.execute_reply": "2020-08-31T13:02:09.807222Z"
    },
    "papermill": {
     "duration": 0.042145,
     "end_time": "2020-08-31T13:02:09.807456",
     "exception": false,
     "start_time": "2020-08-31T13:02:09.765311",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Create column family survived & died from column 'Name' (LastName)\n",
    "train['LastName'] = train['Name'].str.split(',', expand=True)[0]\n",
    "test['LastName'] = test['Name'].str.split(',', expand=True)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-31T13:02:09.867227Z",
     "iopub.status.busy": "2020-08-31T13:02:09.866082Z",
     "iopub.status.idle": "2020-08-31T13:02:09.871965Z",
     "shell.execute_reply": "2020-08-31T13:02:09.871153Z"
    },
    "papermill": {
     "duration": 0.045165,
     "end_time": "2020-08-31T13:02:09.872124",
     "exception": false,
     "start_time": "2020-08-31T13:02:09.826959",
     "status": "completed"
    },
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>LastName</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>Braund</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>Cumings</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>Heikkinen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>Futrelle</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>Allen</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Embarked   LastName  \n",
       "0      0         A/5 21171   7.2500        S     Braund  \n",
       "1      0          PC 17599  71.2833        C    Cumings  \n",
       "2      0  STON/O2. 3101282   7.9250        S  Heikkinen  \n",
       "3      0            113803  53.1000        S   Futrelle  \n",
       "4      0            373450   8.0500        S      Allen  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-31T13:02:09.928342Z",
     "iopub.status.busy": "2020-08-31T13:02:09.927021Z",
     "iopub.status.idle": "2020-08-31T13:02:13.016209Z",
     "shell.execute_reply": "2020-08-31T13:02:13.015481Z"
    },
    "papermill": {
     "duration": 3.124203,
     "end_time": "2020-08-31T13:02:13.016363",
     "exception": false,
     "start_time": "2020-08-31T13:02:09.892160",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Create Column 'FamilySurvived' & 'FamilyDied' on Data Train\n",
    "\n",
    "sur_data = []\n",
    "died_data = []\n",
    "for index, row in train.iterrows():\n",
    "    s = train[(train['LastName']==row['LastName']) & (train['Survived']==1)]\n",
    "    d = train[(train['LastName']==row['LastName']) & (train['Survived']==0)]\n",
    "    \n",
    "    s=len(s)\n",
    "    if row['Survived'] == 1:\n",
    "        s-=1\n",
    "\n",
    "    d=len(d)\n",
    "    if row['Survived'] == 0:\n",
    "        d-=1\n",
    "        \n",
    "    sur_data.append(s)\n",
    "    died_data.append(d)\n",
    "    \n",
    "train['FamilySurvived'] = sur_data\n",
    "train['FamilyDied'] = died_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\application\\Anaconda\\lib\\site-packages\\pandas\\core\\frame.py:4102: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  errors=errors,\n"
     ]
    }
   ],
   "source": [
    "# Create Column 'FamilySurvived' & 'FamilyDied' on Data Test\n",
    "\n",
    "train_source_data = train.copy()\n",
    "train_source_data['Train'] = 1\n",
    "test['Train'] = 0\n",
    "alldata = pd.concat((train_source_data, test), sort = False).reset_index(drop = True)\n",
    "sur_data = []\n",
    "died_data = []\n",
    "for index, row in alldata.iterrows():\n",
    "    s = alldata[(alldata['LastName']==row['LastName']) & (alldata['Survived']==1)]\n",
    "    d = alldata[(alldata['LastName']==row['LastName']) & (alldata['Survived']==0)]\n",
    "    \n",
    "    s=len(s)\n",
    "    if row['Survived'] == 1:\n",
    "        s-=1\n",
    "\n",
    "    d=len(d)\n",
    "    if row['Survived'] == 0:\n",
    "        d-=1\n",
    "        \n",
    "    sur_data.append(s)\n",
    "    died_data.append(d)\n",
    "    \n",
    "alldata['FamilySurvived'] = sur_data\n",
    "alldata['FamilyDied'] = died_data\n",
    "\n",
    "test = alldata[alldata['Train'] == 0]\n",
    "test.drop('Train', axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.019774,
     "end_time": "2020-08-31T13:02:13.109991",
     "exception": false,
     "start_time": "2020-08-31T13:02:13.090217",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Cleaning Data on Column 'Age' and 'Fare'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-31T13:02:13.162034Z",
     "iopub.status.busy": "2020-08-31T13:02:13.161160Z",
     "iopub.status.idle": "2020-08-31T13:02:13.175649Z",
     "shell.execute_reply": "2020-08-31T13:02:13.174856Z"
    },
    "papermill": {
     "duration": 0.045982,
     "end_time": "2020-08-31T13:02:13.175824",
     "exception": false,
     "start_time": "2020-08-31T13:02:13.129842",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Remove outlier from data train\n",
    "# https://towardsdatascience.com/ways-to-detect-and-remove-the-outliers-404d16608dba\n",
    "\n",
    "q1 = train['Age'].quantile(0.25)\n",
    "q3 = train['Age'].quantile(0.75)\n",
    "iqr = q3-q1\n",
    "train = train[~((train['Age'] < (q1 - 1.5 * iqr)) | (train['Age'] > (q3+1.5*iqr)))]\n",
    "\n",
    "q1=train['Fare'].quantile(0.25)\n",
    "q3 = train['Fare'].quantile(0.75)\n",
    "iqr = q3-q1\n",
    "train = train[~ ((train['Fare'] < q1 - 1.5 * iqr) | (train['Fare'] > (q3 + 1.5 * iqr)))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-31T13:02:13.224076Z",
     "iopub.status.busy": "2020-08-31T13:02:13.223180Z",
     "iopub.status.idle": "2020-08-31T13:02:13.228166Z",
     "shell.execute_reply": "2020-08-31T13:02:13.226904Z"
    },
    "papermill": {
     "duration": 0.032745,
     "end_time": "2020-08-31T13:02:13.228378",
     "exception": false,
     "start_time": "2020-08-31T13:02:13.195633",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\application\\Anaconda\\lib\\site-packages\\ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    }
   ],
   "source": [
    "# Do log transform for column fare to make data more close into normal distribution\n",
    "train['Fare'] = np.log1p(train['Fare']) # the same as np.log(train['Fare'] + 1)\n",
    "test['Fare'] = np.log1p(test['Fare']) # the same as np.log(test['Fare'] + 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-31T13:02:13.289953Z",
     "iopub.status.busy": "2020-08-31T13:02:13.287047Z",
     "iopub.status.idle": "2020-08-31T13:02:14.010374Z",
     "shell.execute_reply": "2020-08-31T13:02:14.011045Z"
    },
    "papermill": {
     "duration": 0.76196,
     "end_time": "2020-08-31T13:02:14.011247",
     "exception": false,
     "start_time": "2020-08-31T13:02:13.249287",
     "status": "completed"
    },
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "fig, ax = plt.subplots(1, 2 ,figsize = (16,4))\n",
    "sns.distplot(ax = ax[0], a = train['Age'])\n",
    "sns.distplot(ax = ax[1], a = train['Fare'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-31T13:02:14.075819Z",
     "iopub.status.busy": "2020-08-31T13:02:14.074900Z",
     "iopub.status.idle": "2020-08-31T13:02:14.079911Z",
     "shell.execute_reply": "2020-08-31T13:02:14.079216Z"
    },
    "papermill": {
     "duration": 0.048418,
     "end_time": "2020-08-31T13:02:14.080054",
     "exception": false,
     "start_time": "2020-08-31T13:02:14.031636",
     "status": "completed"
    },
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>LastName</th>\n",
       "      <th>FamilySurvived</th>\n",
       "      <th>FamilyDied</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>2.110213</td>\n",
       "      <td>S</td>\n",
       "      <td>Braund</td>\n",
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       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>2.188856</td>\n",
       "      <td>S</td>\n",
       "      <td>Heikkinen</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>3.990834</td>\n",
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       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>2.202765</td>\n",
       "      <td>S</td>\n",
       "      <td>Allen</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Moran, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330877</td>\n",
       "      <td>2.246893</td>\n",
       "      <td>Q</td>\n",
       "      <td>Moran</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "5            6         0       3   \n",
       "\n",
       "                                           Name     Sex   Age  SibSp  Parch  \\\n",
       "0                       Braund, Mr. Owen Harris    male  22.0      1      0   \n",
       "2                        Heikkinen, Miss. Laina  female  26.0      0      0   \n",
       "3  Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1      0   \n",
       "4                      Allen, Mr. William Henry    male  35.0      0      0   \n",
       "5                              Moran, Mr. James    male  28.0      0      0   \n",
       "\n",
       "             Ticket      Fare Embarked   LastName  FamilySurvived  FamilyDied  \n",
       "0         A/5 21171  2.110213        S     Braund               0           1  \n",
       "2  STON/O2. 3101282  2.188856        S  Heikkinen               0           0  \n",
       "3            113803  3.990834        S   Futrelle               0           1  \n",
       "4            373450  2.202765        S      Allen               1           0  \n",
       "5            330877  2.246893        Q      Moran               1           1  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
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    "execution": {
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     "status": "completed"
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    "tags": []
   },
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
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       "      <th>Name</th>\n",
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       "      <td>62.0</td>\n",
       "      <td>0</td>\n",
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       "      <td>2.369075</td>\n",
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       "      <td>896</td>\n",
       "      <td>-1</td>\n",
       "      <td>3</td>\n",
       "      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
       "      <td>female</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3101298</td>\n",
       "      <td>2.586824</td>\n",
       "      <td>S</td>\n",
       "      <td>Hirvonen</td>\n",
       "      <td>1</td>\n",
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       "     PassengerId  Survived  Pclass  \\\n",
       "889          892        -1       3   \n",
       "890          893        -1       3   \n",
       "891          894        -1       2   \n",
       "892          895        -1       3   \n",
       "893          896        -1       3   \n",
       "\n",
       "                                             Name     Sex   Age  SibSp  Parch  \\\n",
       "889                              Kelly, Mr. James    male  34.5      0      0   \n",
       "890              Wilkes, Mrs. James (Ellen Needs)  female  47.0      1      0   \n",
       "891                     Myles, Mr. Thomas Francis    male  62.0      0      0   \n",
       "892                              Wirz, Mr. Albert    male  27.0      0      0   \n",
       "893  Hirvonen, Mrs. Alexander (Helga E Lindqvist)  female  22.0      1      1   \n",
       "\n",
       "      Ticket      Fare Embarked  LastName  FamilySurvived  FamilyDied  \n",
       "889   330911  2.178064        Q     Kelly               3           1  \n",
       "890   363272  2.079442        S    Wilkes               0           0  \n",
       "891   240276  2.369075        Q     Myles               0           0  \n",
       "892   315154  2.268252        S      Wirz               0           0  \n",
       "893  3101298  2.586824        S  Hirvonen               1           0  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.020458,
     "end_time": "2020-08-31T13:02:14.192749",
     "exception": false,
     "start_time": "2020-08-31T13:02:14.172291",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Encode Categorical Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-31T13:02:14.248971Z",
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    },
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     "start_time": "2020-08-31T13:02:14.213726",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train shape : (718, 17)\n",
      "Test shape : (418, 17)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\application\\Anaconda\\lib\\site-packages\\ipykernel_launcher.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "le = LabelEncoder()\n",
    "le.fit(train['Pclass'])\n",
    "train['Pclass'] = le.transform(train['Pclass'])\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "# By dropping one of the one-hot encoded columns from each categorical feature, we ensure there are no \"reference\" columns—the remaining columns become linearly independent.\n",
    "# https://kiwidamien.github.io/are-you-getting-burned-by-one-hot-encoding.html\n",
    "# https://www.youtube.com/watch?v=g9aLvY8BfRM\n",
    "ohe = OneHotEncoder(sparse = False, drop = 'first', categories = 'auto')\n",
    "ohe.fit(train[['Sex', 'Embarked']])\n",
    "ohecategory_train = ohe.transform(train[['Sex', 'Embarked']])\n",
    "ohecategory_test = ohe.transform(test[['Sex', 'Embarked']])\n",
    "\n",
    "for i in range(ohecategory_train.shape[1]):\n",
    "    train['dummy_variable_' + str(i)] = ohecategory_train[:,i]\n",
    "\n",
    "for i in range(ohecategory_test.shape[1]):\n",
    "    test['dummy_variable_' + str(i)] = ohecategory_test[:,i]\n",
    "\n",
    "\n",
    "print('Train shape :', train.shape)\n",
    "print('Test shape :', test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.028714,
     "end_time": "2020-08-31T13:02:14.397545",
     "exception": false,
     "start_time": "2020-08-31T13:02:14.368831",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Standardization Numerical Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-31T13:02:14.491458Z",
     "iopub.status.busy": "2020-08-31T13:02:14.490560Z",
     "iopub.status.idle": "2020-08-31T13:02:14.516823Z",
     "shell.execute_reply": "2020-08-31T13:02:14.515733Z"
    },
    "papermill": {
     "duration": 0.056355,
     "end_time": "2020-08-31T13:02:14.517036",
     "exception": false,
     "start_time": "2020-08-31T13:02:14.460681",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\application\\Anaconda\\lib\\site-packages\\ipykernel_launcher.py:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n",
      "D:\\application\\Anaconda\\lib\\site-packages\\pandas\\core\\indexing.py:494: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self.obj[item] = s\n"
     ]
    }
   ],
   "source": [
    "# https://benalexkeen.com/feature-scaling-with-scikit-learn/\n",
    "# https://stats.stackexchange.com/questions/463690/multiple-regression-with-mixed-continuous-categorical-variables-dummy-coding-s\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "sc = StandardScaler()\n",
    "sc.fit(train[['Age', 'SibSp', 'Parch', 'Fare']])\n",
    "train[['Age', 'SibSp', 'Parch', 'Fare']] = sc.transform(train[['Age', 'SibSp', 'Parch', 'Fare']])\n",
    "test[['Age', 'SibSp', 'Parch', 'Fare']] = sc.transform(test[['Age', 'SibSp', 'Parch', 'Fare']])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-31T13:02:14.587479Z",
     "iopub.status.busy": "2020-08-31T13:02:14.586573Z",
     "iopub.status.idle": "2020-08-31T13:02:14.592175Z",
     "shell.execute_reply": "2020-08-31T13:02:14.591521Z"
    },
    "papermill": {
     "duration": 0.052684,
     "end_time": "2020-08-31T13:02:14.592321",
     "exception": false,
     "start_time": "2020-08-31T13:02:14.539637",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
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       "      <td>male</td>\n",
       "      <td>0.691688</td>\n",
       "      <td>-0.484137</td>\n",
       "      <td>-0.40491</td>\n",
       "      <td>373450</td>\n",
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       "      <td>Allen</td>\n",
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       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Moran, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>-0.007934</td>\n",
       "      <td>-0.484137</td>\n",
       "      <td>-0.40491</td>\n",
       "      <td>330877</td>\n",
       "      <td>-0.587211</td>\n",
       "      <td>Q</td>\n",
       "      <td>Moran</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
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       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       2   \n",
       "2            3         1       2   \n",
       "3            4         1       0   \n",
       "4            5         0       2   \n",
       "5            6         0       2   \n",
       "\n",
       "                                           Name     Sex       Age     SibSp  \\\n",
       "0                       Braund, Mr. Owen Harris    male -0.607611  0.686268   \n",
       "2                        Heikkinen, Miss. Laina  female -0.207827 -0.484137   \n",
       "3  Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  0.691688  0.686268   \n",
       "4                      Allen, Mr. William Henry    male  0.691688 -0.484137   \n",
       "5                              Moran, Mr. James    male -0.007934 -0.484137   \n",
       "\n",
       "     Parch            Ticket      Fare Embarked   LastName  FamilySurvived  \\\n",
       "0 -0.40491         A/5 21171 -0.779352        S     Braund               0   \n",
       "2 -0.40491  STON/O2. 3101282 -0.668797        S  Heikkinen               0   \n",
       "3 -0.40491            113803  1.864379        S   Futrelle               0   \n",
       "4 -0.40491            373450 -0.649245        S      Allen               1   \n",
       "5 -0.40491            330877 -0.587211        Q      Moran               1   \n",
       "\n",
       "   FamilyDied  dummy_variable_0  dummy_variable_1  dummy_variable_2  \n",
       "0           1               1.0               0.0               1.0  \n",
       "2           0               0.0               0.0               1.0  \n",
       "3           1               0.0               0.0               1.0  \n",
       "4           0               1.0               0.0               1.0  \n",
       "5           1               1.0               1.0               0.0  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
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     "start_time": "2020-08-31T13:02:14.613902",
     "status": "completed"
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    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
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       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
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       "      <th>Name</th>\n",
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       "      <th>LastName</th>\n",
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       "      <td>890</td>\n",
       "      <td>893</td>\n",
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       "      <td>3</td>\n",
       "      <td>Wilkes, Mrs. James (Ellen Needs)</td>\n",
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       "    <tr>\n",
       "      <td>891</td>\n",
       "      <td>894</td>\n",
       "      <td>-1</td>\n",
       "      <td>2</td>\n",
       "      <td>Myles, Mr. Thomas Francis</td>\n",
       "      <td>male</td>\n",
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       "      <td>-0.484137</td>\n",
       "      <td>-0.404910</td>\n",
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       "    <tr>\n",
       "      <td>892</td>\n",
       "      <td>895</td>\n",
       "      <td>-1</td>\n",
       "      <td>3</td>\n",
       "      <td>Wirz, Mr. Albert</td>\n",
       "      <td>male</td>\n",
       "      <td>-0.107881</td>\n",
       "      <td>-0.484137</td>\n",
       "      <td>-0.404910</td>\n",
       "      <td>315154</td>\n",
       "      <td>-0.557184</td>\n",
       "      <td>S</td>\n",
       "      <td>Wirz</td>\n",
       "      <td>0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <td>893</td>\n",
       "      <td>896</td>\n",
       "      <td>-1</td>\n",
       "      <td>3</td>\n",
       "      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
       "      <td>female</td>\n",
       "      <td>-0.607611</td>\n",
       "      <td>0.686268</td>\n",
       "      <td>0.870202</td>\n",
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      "text/plain": [
       "     PassengerId  Survived  Pclass  \\\n",
       "889          892        -1       3   \n",
       "890          893        -1       3   \n",
       "891          894        -1       2   \n",
       "892          895        -1       3   \n",
       "893          896        -1       3   \n",
       "\n",
       "                                             Name     Sex       Age     SibSp  \\\n",
       "889                              Kelly, Mr. James    male  0.641715 -0.484137   \n",
       "890              Wilkes, Mrs. James (Ellen Needs)  female  1.891041  0.686268   \n",
       "891                     Myles, Mr. Thomas Francis    male  3.390233 -0.484137   \n",
       "892                              Wirz, Mr. Albert    male -0.107881 -0.484137   \n",
       "893  Hirvonen, Mrs. Alexander (Helga E Lindqvist)  female -0.607611  0.686268   \n",
       "\n",
       "        Parch   Ticket      Fare Embarked  LastName  FamilySurvived  \\\n",
       "889 -0.404910   330911 -0.683968        Q     Kelly               3   \n",
       "890 -0.404910   363272 -0.822610        S    Wilkes               0   \n",
       "891 -0.404910   240276 -0.415451        Q     Myles               0   \n",
       "892 -0.404910   315154 -0.557184        S      Wirz               0   \n",
       "893  0.870202  3101298 -0.109345        S  Hirvonen               1   \n",
       "\n",
       "     FamilyDied  dummy_variable_0  dummy_variable_1  dummy_variable_2  \n",
       "889           1               1.0               1.0               0.0  \n",
       "890           0               0.0               0.0               1.0  \n",
       "891           0               1.0               1.0               0.0  \n",
       "892           0               1.0               0.0               1.0  \n",
       "893           0               0.0               0.0               1.0  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.023262,
     "end_time": "2020-08-31T13:02:14.711211",
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     "start_time": "2020-08-31T13:02:14.687949",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Feature Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-31T13:02:14.766936Z",
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     "iopub.status.idle": "2020-08-31T13:02:14.770713Z",
     "shell.execute_reply": "2020-08-31T13:02:14.769974Z"
    },
    "papermill": {
     "duration": 0.035994,
     "end_time": "2020-08-31T13:02:14.770878",
     "exception": false,
     "start_time": "2020-08-31T13:02:14.734884",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train columns :\n",
      " Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n",
      "       'Parch', 'Ticket', 'Fare', 'Embarked', 'LastName', 'FamilySurvived',\n",
      "       'FamilyDied', 'dummy_variable_0', 'dummy_variable_1',\n",
      "       'dummy_variable_2'],\n",
      "      dtype='object')\n",
      "Train shape :  (718, 17)\n",
      "\n",
      "\n",
      "Test columns :\n",
      " Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n",
      "       'Parch', 'Ticket', 'Fare', 'Embarked', 'LastName', 'FamilySurvived',\n",
      "       'FamilyDied', 'dummy_variable_0', 'dummy_variable_1',\n",
      "       'dummy_variable_2'],\n",
      "      dtype='object')\n",
      "Test shape :  (418, 17)\n"
     ]
    }
   ],
   "source": [
    "# See if train and test data have same shape and column position\n",
    "print('Train columns :\\n',train.columns)\n",
    "print('Train shape : ', train.shape)\n",
    "print('\\n')\n",
    "print('Test columns :\\n',test.columns)\n",
    "print('Test shape : ', test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-31T13:02:14.841751Z",
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     "shell.execute_reply": "2020-08-31T13:02:14.845377Z"
    },
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     "duration": 0.052447,
     "end_time": "2020-08-31T13:02:14.846316",
     "exception": false,
     "start_time": "2020-08-31T13:02:14.793869",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
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       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
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       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       2   \n",
       "2            3         1       2   \n",
       "3            4         1       0   \n",
       "4            5         0       2   \n",
       "5            6         0       2   \n",
       "\n",
       "                                           Name     Sex       Age     SibSp  \\\n",
       "0                       Braund, Mr. Owen Harris    male -0.607611  0.686268   \n",
       "2                        Heikkinen, Miss. Laina  female -0.207827 -0.484137   \n",
       "3  Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  0.691688  0.686268   \n",
       "4                      Allen, Mr. William Henry    male  0.691688 -0.484137   \n",
       "5                              Moran, Mr. James    male -0.007934 -0.484137   \n",
       "\n",
       "     Parch            Ticket      Fare Embarked   LastName  FamilySurvived  \\\n",
       "0 -0.40491         A/5 21171 -0.779352        S     Braund               0   \n",
       "2 -0.40491  STON/O2. 3101282 -0.668797        S  Heikkinen               0   \n",
       "3 -0.40491            113803  1.864379        S   Futrelle               0   \n",
       "4 -0.40491            373450 -0.649245        S      Allen               1   \n",
       "5 -0.40491            330877 -0.587211        Q      Moran               1   \n",
       "\n",
       "   FamilyDied  dummy_variable_0  dummy_variable_1  dummy_variable_2  \n",
       "0           1               1.0               0.0               1.0  \n",
       "2           0               0.0               0.0               1.0  \n",
       "3           1               0.0               0.0               1.0  \n",
       "4           0               1.0               0.0               1.0  \n",
       "5           1               1.0               1.0               0.0  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# See & explore the data for dropping unused columns/features\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "execution": {
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     "iopub.status.busy": "2020-08-31T13:02:14.900248Z",
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     "shell.execute_reply": "2020-08-31T13:02:14.917434Z"
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     "duration": 0.050174,
     "end_time": "2020-08-31T13:02:14.918781",
     "exception": false,
     "start_time": "2020-08-31T13:02:14.868607",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\application\\Anaconda\\lib\\site-packages\\pandas\\core\\frame.py:4102: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  errors=errors,\n"
     ]
    }
   ],
   "source": [
    "# Drop columns 'Sex' and 'Embarked' because we haved one hot encode them\n",
    "train.drop(['PassengerId', 'Name', 'Sex', 'Ticket', 'Embarked', 'LastName'], axis = 1, inplace = True)\n",
    "test.drop(['PassengerId', 'Name', 'Sex', 'Ticket', 'Embarked', 'LastName'], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-31T13:02:14.973420Z",
     "iopub.status.busy": "2020-08-31T13:02:14.972230Z",
     "iopub.status.idle": "2020-08-31T13:02:14.976532Z",
     "shell.execute_reply": "2020-08-31T13:02:14.977290Z"
    },
    "papermill": {
     "duration": 0.036238,
     "end_time": "2020-08-31T13:02:14.977519",
     "exception": false,
     "start_time": "2020-08-31T13:02:14.941281",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train columns :\n",
      " Index(['Survived', 'Pclass', 'Age', 'SibSp', 'Parch', 'Fare', 'FamilySurvived',\n",
      "       'FamilyDied', 'dummy_variable_0', 'dummy_variable_1',\n",
      "       'dummy_variable_2'],\n",
      "      dtype='object')\n",
      "Train shape :  (718, 11)\n",
      "\n",
      "\n",
      "Test columns :\n",
      " Index(['Survived', 'Pclass', 'Age', 'SibSp', 'Parch', 'Fare', 'FamilySurvived',\n",
      "       'FamilyDied', 'dummy_variable_0', 'dummy_variable_1',\n",
      "       'dummy_variable_2'],\n",
      "      dtype='object')\n",
      "Test shape :  (418, 11)\n"
     ]
    }
   ],
   "source": [
    "print('Train columns :\\n',train.columns)\n",
    "print('Train shape : ', train.shape)\n",
    "print('\\n')\n",
    "print('Test columns :\\n',test.columns)\n",
    "print('Test shape : ', test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-31T13:02:15.032840Z",
     "iopub.status.busy": "2020-08-31T13:02:15.031687Z",
     "iopub.status.idle": "2020-08-31T13:02:15.040657Z",
     "shell.execute_reply": "2020-08-31T13:02:15.041453Z"
    },
    "papermill": {
     "duration": 0.041621,
     "end_time": "2020-08-31T13:02:15.041651",
     "exception": false,
     "start_time": "2020-08-31T13:02:15.000030",
     "status": "completed"
    },
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train :\n",
      " [[ 2.         -0.60761099  0.68626782 -0.40491013 -0.77935204  0.\n",
      "   1.          1.          0.          1.        ]\n",
      " [ 2.         -0.20782662 -0.48413668 -0.40491013 -0.66879745  0.\n",
      "   0.          0.          0.          1.        ]\n",
      " [ 0.          0.6916882   0.68626782 -0.40491013  1.86437908  0.\n",
      "   1.          0.          0.          1.        ]\n",
      " [ 2.          0.6916882  -0.48413668 -0.40491013 -0.64924532  1.\n",
      "   0.          1.          0.          1.        ]\n",
      " [ 2.         -0.00793444 -0.48413668 -0.40491013 -0.58721139  1.\n",
      "   1.          1.          1.          0.        ]]\n",
      "y_train :\n",
      " [0 1 1 0 0]\n"
     ]
    }
   ],
   "source": [
    "X_train = train.iloc[:, 1:].values\n",
    "y_train = train.iloc[:, 0].values\n",
    "\n",
    "X_test = test.iloc[:, 1:].values\n",
    "y_test = test.iloc[:, 0].values\n",
    "\n",
    "print('X_train :\\n', X_train[0:5])\n",
    "print('y_train :\\n', y_train[0:5])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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     "duration": 0.022299,
     "end_time": "2020-08-31T13:02:15.086540",
     "exception": false,
     "start_time": "2020-08-31T13:02:15.064241",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Model Prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.02152,
     "end_time": "2020-08-31T13:02:15.182567",
     "exception": false,
     "start_time": "2020-08-31T13:02:15.161047",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "In this section, U had done some experiment with several machine learning algorithms like Naive Bayes, Logistic Regression, XGBoost, K-Nearest Neighbors, etc. I used GridSearchCV to find best parameter and accuracy from each algorithms, after that I implemented on data test and submitted the results prediction to kaggle. The best score I have is <b>0.79425 (top 7%)</b> with K-Nearest Neighbors algorithm (parameter : leaf_size = 1, metric = 'minkowski', n_neighbors = 12, p = 1, weights = 'distance')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.021976,
     "end_time": "2020-08-31T13:02:15.226685",
     "exception": false,
     "start_time": "2020-08-31T13:02:15.204709",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### K-Nearest Neighbors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-31T13:02:15.279621Z",
     "iopub.status.busy": "2020-08-31T13:02:15.278715Z",
     "iopub.status.idle": "2020-08-31T13:02:15.563150Z",
     "shell.execute_reply": "2020-08-31T13:02:15.562378Z"
    },
    "papermill": {
     "duration": 0.314213,
     "end_time": "2020-08-31T13:02:15.563278",
     "exception": false,
     "start_time": "2020-08-31T13:02:15.249065",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracies :  [0.83333333 0.77777778 0.80555556 0.79166667 0.88888889 0.83333333\n",
      " 0.83333333 0.79166667 0.83098592 0.85915493]\n",
      "AVG Accuracies :  0.8245696400625977\n",
      "STD: 0.03212617106401504\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "clf = KNeighborsClassifier(leaf_size = 1, metric = 'minkowski', n_neighbors = 12, p = 1, weights = 'distance')\n",
    "accuracies = cross_val_score(clf, X_train, y_train, cv = 10)\n",
    "print('Accuracies : ', accuracies)\n",
    "print('AVG Accuracies : ', accuracies.mean())\n",
    "print('STD:',accuracies.std())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-31T13:02:15.616305Z",
     "iopub.status.busy": "2020-08-31T13:02:15.615562Z",
     "iopub.status.idle": "2020-08-31T13:02:15.656965Z",
     "shell.execute_reply": "2020-08-31T13:02:15.656192Z"
    },
    "papermill": {
     "duration": 0.071708,
     "end_time": "2020-08-31T13:02:15.657091",
     "exception": false,
     "start_time": "2020-08-31T13:02:15.585383",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    288\n",
       "1    130\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(X_train, y_train)\n",
    "y_pred = clf.predict(X_test)\n",
    "y_pred = y_pred.astype('int64')\n",
    "\n",
    "submission = pd.DataFrame()\n",
    "submission['PassengerId'] = data_test['PassengerId']\n",
    "submission['Survived'] = y_pred\n",
    "submission['Survived'].value_counts()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-31T13:02:15.707938Z",
     "iopub.status.busy": "2020-08-31T13:02:15.706876Z",
     "iopub.status.idle": "2020-08-31T13:02:15.816313Z",
     "shell.execute_reply": "2020-08-31T13:02:15.816931Z"
    },
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     "duration": 0.13817,
     "end_time": "2020-08-31T13:02:15.817106",
     "exception": false,
     "start_time": "2020-08-31T13:02:15.678936",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "submission.to_csv(r'Submission.csv', index = False, header = True)"
   ]
  }
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