Titanic Survive Prediction_demo2.0.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1201,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "from statistics import mode\n",
    "\n",
    "import re\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "import tensorflow as tf\n",
    "tf.random.set_seed(123)\n",
    "\n",
    "import asyncio\n",
    "\n",
    "# Input data files are available in the \"../input/\" directory.\n",
    "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
    "\n",
    "#C:\\Users\\huai\\.homejupyter\n",
    "\n",
    "#import os\n",
    "#for dirname, _, filenames in os.walk('C:/Users/huai/.homejupyter'):\n",
    "#    for filename in filenames:\n",
    "#        print(os.path.join(dirname, filename))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 1.瀏覽資料"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 931,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 943 entries, 0 to 942\n",
      "Data columns (total 12 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   ID        943 non-null    int64  \n",
      " 1   pclass    943 non-null    int64  \n",
      " 2   survived  943 non-null    int64  \n",
      " 3   name      943 non-null    object \n",
      " 4   sex       943 non-null    object \n",
      " 5   age       750 non-null    float64\n",
      " 6   sibsp     943 non-null    int64  \n",
      " 7   parch     943 non-null    int64  \n",
      " 8   ticket    943 non-null    object \n",
      " 9   fare      942 non-null    float64\n",
      " 10  cabin     212 non-null    object \n",
      " 11  embarked  942 non-null    object \n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 88.5+ KB\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 366 entries, 0 to 365\n",
      "Data columns (total 11 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   ID        366 non-null    int64  \n",
      " 1   pclass    366 non-null    int64  \n",
      " 2   name      366 non-null    object \n",
      " 3   sex       366 non-null    object \n",
      " 4   age       296 non-null    float64\n",
      " 5   sibsp     366 non-null    int64  \n",
      " 6   parch     366 non-null    int64  \n",
      " 7   ticket    366 non-null    object \n",
      " 8   fare      366 non-null    float64\n",
      " 9   cabin     83 non-null     object \n",
      " 10  embarked  365 non-null    object \n",
      "dtypes: float64(2), int64(4), object(5)\n",
      "memory usage: 31.6+ KB\n"
     ]
    }
   ],
   "source": [
    "train = pd.read_csv(\"Titanic_train.csv\" )\n",
    "tit1 = train.select_dtypes(include=['float64','int64','object'])\n",
    "train.info()\n",
    "\n",
    "test = pd.read_csv(\"Titanic_test.csv\")\n",
    "tit2 = test.select_dtypes(include=['float64','int64','object'])\n",
    "test.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 2 資料整理"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.1 填空"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 932,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "        ID  pclass  survived   name    sex    age  sibsp  parch  ticket  \\\n",
       "0    False   False     False  False  False  False  False  False   False   \n",
       "1    False   False     False  False  False  False  False  False   False   \n",
       "2    False   False     False  False  False  False  False  False   False   \n",
       "3    False   False     False  False  False  False  False  False   False   \n",
       "4    False   False     False  False  False  False  False  False   False   \n",
       "..     ...     ...       ...    ...    ...    ...    ...    ...     ...   \n",
       "938  False   False     False  False  False   True  False  False   False   \n",
       "939  False   False     False  False  False   True  False  False   False   \n",
       "940  False   False     False  False  False   True  False  False   False   \n",
       "941  False   False     False  False  False  False  False  False   False   \n",
       "942  False   False     False  False  False  False  False  False   False   \n",
       "\n",
       "      fare  cabin  embarked  \n",
       "0    False  False     False  \n",
       "1    False  False     False  \n",
       "2    False  False     False  \n",
       "3    False  False     False  \n",
       "4    False  False     False  \n",
       "..     ...    ...       ...  \n",
       "938  False   True     False  \n",
       "939  False   True     False  \n",
       "940  False   True     False  \n",
       "941  False   True     False  \n",
       "942  False   True     False  \n",
       "\n",
       "[943 rows x 12 columns]"
      ]
     },
     "execution_count": 932,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 933,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID            0\n",
       "pclass        0\n",
       "survived      0\n",
       "name          0\n",
       "sex           0\n",
       "age         193\n",
       "sibsp         0\n",
       "parch         0\n",
       "ticket        0\n",
       "fare          1\n",
       "cabin       731\n",
       "embarked      1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 933,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 934,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['cabin'] = train['cabin'].fillna('X')\n",
    "test['cabin'] = test['cabin'].fillna('X')\n",
    "#cabin     客艙編號\n",
    "#.fillna: 填滿單column表格元素"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 935,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'AGE')"
      ]
     },
     "execution_count": 935,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train['age'].hist(bins=50,color='salmon')\n",
    "plt.title(\"AGE\",size=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 936,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pclass\n",
      "1    38.0\n",
      "2    28.5\n",
      "3    24.0\n",
      "Name: age, dtype: float64\n",
      "pclass\n",
      "1    39.058703\n",
      "2    29.464286\n",
      "3    25.056849\n",
      "Name: age, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "age_group = train.groupby(\"pclass\")[\"age\"]\n",
    "print(age_group.median())\n",
    "print(age_group.mean())\n",
    "#groupby contains info of frame "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 937,
   "metadata": {},
   "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>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0      29.0000\n",
       "1       0.9167\n",
       "2      30.0000\n",
       "3...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>232    28.0\n",
       "233    30.0\n",
       "234    25.0\n",
       "235    34....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>426    42.0\n",
       "427    20.0\n",
       "428    18.0\n",
       "429    30....</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   0                                                  1\n",
       "0  1  0      29.0000\n",
       "1       0.9167\n",
       "2      30.0000\n",
       "3...\n",
       "1  2  232    28.0\n",
       "233    30.0\n",
       "234    25.0\n",
       "235    34....\n",
       "2  3  426    42.0\n",
       "427    20.0\n",
       "428    18.0\n",
       "429    30...."
      ]
     },
     "execution_count": 937,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(age_group)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 938,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    }
   ],
   "source": [
    "train.loc[train.age.isnull(),'age'] = train.groupby(\"pclass\").age.transform('median')\n",
    "test.loc[test.age.isnull(),'age'] = test.groupby(\"pclass\").age.transform('median')\n",
    "print(train['age'].isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 939,
   "metadata": {},
   "outputs": [],
   "source": [
    "train[\"embarked\"] = train[\"embarked\"].fillna(mode(train[\"embarked\"]))\n",
    "test[\"embarked\"] = test[\"embarked\"].fillna(mode(test[\"embarked\"]))\n",
    "train[\"fare\"] = train[\"fare\"].fillna(mode(train[\"fare\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "mode() 离散的或标称的数据的单模（最常见的值）。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 940,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ID          0\n",
      "pclass      0\n",
      "survived    0\n",
      "name        0\n",
      "sex         0\n",
      "age         0\n",
      "sibsp       0\n",
      "parch       0\n",
      "ticket      0\n",
      "fare        0\n",
      "cabin       0\n",
      "embarked    0\n",
      "dtype: int64\n",
      "\n",
      "ID          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",
      "cabin       0\n",
      "embarked    0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(train.isnull().sum())\n",
    "print()\n",
    "print(test.isnull().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.2 數值化"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2.1 船艙 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 941,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['B5', 'C22 C26', 'C101', 'C62 C64', 'B35', 'X', 'A23', 'D15', 'C6', 'D35', 'C148', 'B49', 'C99', 'C52', 'T', 'A31', 'C7', 'C103', 'E33', 'A21', 'B10', 'B4', 'E40', 'E24', 'B51 B53 B55', 'B96 B98', 'C46', 'E8', 'B61', 'B77', 'A9', 'C89', 'E58', 'E49', 'E52', 'E45', 'B22', 'B26', 'C85', 'B71', 'B20', 'A34', 'B58 B60', 'C86', 'A20', 'C54', 'C45', 'D20', 'A29', 'C95', 'E25', 'C23 C25 C27', 'E36', 'D34', 'D40', 'B41', 'B102', 'C123', 'E63', 'C130', 'B86', 'C92', 'A5', 'C51', 'B42', 'C91', 'C125', 'B82 B84', 'E50', 'D33', 'C83', 'B94', 'D49', 'D45', 'B69', 'B11', 'E46', 'C39', 'B18', 'D11', 'C93', 'B28', 'C49', 'B52 B54 B56', 'C132', 'B37', 'D21', 'D19', 'D17', 'B101', 'D28', 'D6', 'D9', 'B79', 'C47', 'D30', 'C90', 'E38', 'C78', 'C118', 'D36', 'D48', 'C105', 'B36', 'B30', 'C124', 'C2', 'C65', 'C104', 'C110', 'B3', 'A24', 'A32', 'A11', 'B57 B59 B63 B66', 'C28', 'E44', 'A26', 'A6', 'A7', 'C31', 'B45', 'E34', 'B78', 'B50', 'C116', 'C55 C57', 'D50', 'E67', 'C126', 'C68', 'C70', 'C53', 'B19', 'D46', 'D26', 'C80', 'C82', 'E39 E41', 'C32', 'F4', 'D56', 'F33', 'D', 'E101', 'E77', 'F2', 'F', 'F G63', 'F E57', 'F E46', 'E121', 'G6', 'F38']\n",
      "\n",
      "['C22 C26', 'E12', 'D7', 'A36', 'X', 'C62 C64', 'B58 B60', 'D35', 'C97', 'D22', 'C101', 'B38', 'B51 B53 B55', 'B96 B98', 'E31', 'A14', 'B22', 'E17', 'B71', 'B20', 'A34', 'C86', 'A16', 'A18', 'C111', 'B39', 'D10 D12', 'D33', 'C54', 'E60', 'D21', 'C124', 'D28', 'B80', 'C106', 'D30', 'C78', 'C30', 'D47', 'D43', 'B24', 'B73', 'C50', 'A10', 'B57 B59 B63 B66', 'B35', 'E44', 'A19', 'B45', 'E34', 'C87', 'B28', 'E68', 'E67', 'C126', 'C68', 'D37', 'C32', 'C7', 'C128', 'D', 'F4', 'F33', 'F2', 'D38', 'F G63', 'F G73', 'F E69', 'E10', 'G6']\n"
     ]
    }
   ],
   "source": [
    "print(train['cabin'].unique().tolist())\n",
    "print()\n",
    "print(test['cabin'].unique().tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 942,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train['cabin']: ['B', 'C', 'X', 'A', 'D', 'T', 'E', 'F', 'G']\n",
      "test['cabin']: ['C', 'E', 'D', 'A', 'X', 'B', 'F', 'G']\n"
     ]
    }
   ],
   "source": [
    "train['cabin'] = train['cabin'].map(lambda x: re.compile(\"([a-zA-Z]+)\").search(x).group())\n",
    "print(\"train['cabin']:\", train['cabin'].unique().tolist())\n",
    "\n",
    "test['cabin'] = test['cabin'].map(lambda x: re.compile(\"([a-zA-Z]+)\").search(x).group())\n",
    "print(\"test['cabin']:\", test['cabin'].unique().tolist())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 943,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train['cabin']: [2, 3, 11, 1, 4, 12, 5, 6, 7]\n",
      "test['cabin']: [3, 5, 4, 1, 11, 2, 6, 7]\n"
     ]
    }
   ],
   "source": [
    "category_1 = {'A':1, 'B':2, 'C':3, 'D':4, 'E':5, 'F':6, 'G':7, 'X':11, 'T':12}\n",
    "#category = {'A':0, 'B':0, 'C':0, 'D':0, 'E':0, 'F':0, 'G':0, 'X':0, 'T':0}\n",
    "\n",
    "train['cabin'] = train['cabin'].map(category_1)\n",
    "print(\"train['cabin']:\", train['cabin'].unique().tolist())\n",
    "\n",
    "test['cabin'] = test['cabin'].map(category_1)\n",
    "print(\"test['cabin']:\", test['cabin'].unique().tolist())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2.2 性別、登船處 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 944,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 0]\n",
      "[1, 0]\n",
      "[0, 1, 2]\n",
      "[0, 1, 2]\n"
     ]
    }
   ],
   "source": [
    "category_2 = {\"male\": 0, \"female\": 1}\n",
    "category_3 = {\"S\": 0, \"C\": 1, \"Q\": 2}\n",
    "\n",
    "train[\"sex\"] = train[\"sex\"].map(category_2)\n",
    "print(train['sex'].unique().tolist())\n",
    "\n",
    "test[\"sex\"] = test[\"sex\"].map(category_2)\n",
    "print(test['sex'].unique().tolist())\n",
    "\n",
    "train[\"embarked\"] = train[\"embarked\"].map(category_3)\n",
    "print(train[\"embarked\"].unique().tolist())\n",
    "\n",
    "test[\"embarked\"] = test[\"embarked\"].map(category_3)\n",
    "print(test[\"embarked\"].unique().tolist())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2.3 家屬 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 945,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['fam'] = train['sibsp'] + train['parch']\n",
    "test['fam'] = test['sibsp'] + test['parch']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2.4 年齡"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### way_original"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 946,
   "metadata": {},
   "outputs": [],
   "source": [
    "#train['age'] = train['age'].astype(str)\n",
    "#test['age'] = test['age'].astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 947,
   "metadata": {},
   "outputs": [],
   "source": [
    "#train['age'] = train['age'].map(lambda x: re.compile(\"[0-9]\").search(x).group())\n",
    "#train['age'].unique().tolist()\n",
    "\n",
    "#test['age'] = test['age'].map(lambda x: re.compile(\"[0-9]\").search(x).group())\n",
    "#test['age'].unique().tolist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### way_demo_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 948,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['age'] = (train['age']/5).astype(int)\n",
    "test['age'] = (test['age']/5).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 949,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 0, 2, 3]"
      ]
     },
     "execution_count": 949,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(train['age']/5).astype(int).unique().tolist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2.5 頭銜"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 950,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train['Title']: ['Miss.', 'Master.', 'Mr.', 'Mrs.', 'Col.', 'Mme.', 'Dr.', 'Sir.', 'Mlle.', 'Dona.', 'Major.', 'Countess.', 'Don.', 'Rev.', 'Ms.']\n",
      "\n",
      "test['Title']: ['Miss.', 'Mrs.', 'Mr.', 'Major.', 'Master.', 'Capt.', 'Lady.', 'Dr.', 'Jonkheer.', 'Mlle.', 'Col.', 'Rev.', 'Ms.']\n"
     ]
    }
   ],
   "source": [
    "train['Title'] = train['name'].map(lambda x: re.compile(\"([A-Za-z]+)\\.\").search(x).group())\n",
    "test['Title'] = test['name'].map(lambda x: re.compile(\"([A-Za-z]+)\\.\").search(x).group())\n",
    "print(\"train['Title']:\", train['Title'].unique().tolist())\n",
    "print()\n",
    "print(\"test['Title']:\", test['Title'].unique().tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 951,
   "metadata": {},
   "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>Title</th>\n",
       "      <th>survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Master.</td>\n",
       "      <td>0.534884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Miss.</td>\n",
       "      <td>0.687179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mr.</td>\n",
       "      <td>0.156934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Mrs.</td>\n",
       "      <td>0.781955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ms.</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Rare.</td>\n",
       "      <td>0.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Rev.</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Royal.</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Title  survived\n",
       "0  Master.  0.534884\n",
       "1    Miss.  0.687179\n",
       "2      Mr.  0.156934\n",
       "3     Mrs.  0.781955\n",
       "4      Ms.  0.000000\n",
       "5    Rare.  0.600000\n",
       "6     Rev.  0.000000\n",
       "7   Royal.  1.000000"
      ]
     },
     "execution_count": 951,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Title'] = train['Title'].replace(\n",
    "    ['Col.', 'Dr.', 'Jonkheer.', 'Capt.'], 'Rare.')\n",
    "train['Title'] = train['Title'].replace(\n",
    "    ['Countess.', 'Lady.', 'Lady', 'Sir', 'Sir.', 'Major.','Mme.',\n",
    "    'Dona.', 'Mlle.', 'Jonkheer.'],\n",
    "    'Royal.')\n",
    "train['Title'] = train['Title'].replace(['Don.'], 'Mr.')\n",
    "\n",
    "train[['Title', 'survived']].groupby(['Title'], as_index=False).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 952,
   "metadata": {},
   "outputs": [],
   "source": [
    "test['Title'] = test['Title'].replace(\n",
    "    ['Col.', 'Dr.', 'Jonkheer.', 'Capt.'], 'Rare.')\n",
    "test['Title'] = test['Title'].replace(\n",
    "    ['Countess.', 'Lady.', 'Lady', 'Sir', 'Sir.', 'Major.','Mme.',\n",
    "    'Dona.', 'Mlle.', 'Jonkheer.'],\n",
    "    'Royal.')\n",
    "test['Title'] = test['Title'].replace(['Don.'], 'Mr.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 953,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2, 4, 1, 3, 6, 5, 8, 7]\n",
      "0\n"
     ]
    }
   ],
   "source": [
    "title_mapping = {\"Mr.\": 1 , \"Miss.\": 2 , \"Mrs.\": 3 , \"Master.\": 4 , \"Royal.\": 5 , \"Rare.\": 6 , \"Ms.\": 7 , \"Rev.\": 8 }\n",
    "train['Title'] = train['Title'].map(title_mapping)\n",
    "\n",
    "#train['Title'] = train['Title'].fillna(0)\n",
    "print(train['Title'].unique().tolist())\n",
    "print(train['Title'].isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 954,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2, 3, 1, 5, 4, 6, 8, 7]\n",
      "0\n"
     ]
    }
   ],
   "source": [
    "test['Title'] = test['Title'].map(title_mapping)\n",
    "\n",
    "#test['Title'] = test['Title'].fillna(0)\n",
    "print(test['Title'].unique().tolist())\n",
    "print(train['Title'].isnull().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2.6 結果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 955,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1dbfd2d37c8>"
      ]
     },
     "execution_count": 955,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(train.corr(), annot = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 956,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1dbfd5828c8>"
      ]
     },
     "execution_count": 956,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(test.corr(), annot = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2.7 結果去留"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 957,
   "metadata": {},
   "outputs": [],
   "source": [
    "test = test.drop(['ticket'], axis = 1)\n",
    "test = test.drop(['name'], axis = 1)\n",
    "test = test.drop(['parch'], axis = 1)\n",
    "test = test.drop(['sibsp'], axis = 1)\n",
    "\n",
    "#test = test.drop(['age'], axis = 1)\n",
    "test = test.drop(['fam'], axis = 1)\n",
    "#test = test.drop(['pclass'], axis = 1)\n",
    "#test = test.drop(['Title'], axis = 1)\n",
    "#test = test.drop(['sex'], axis = 1)\n",
    "#test = test.drop(['fare'], axis = 1)\n",
    "test = test.drop(['embarked'], axis = 1)\n",
    "\n",
    "train.drop(['name', 'ticket'], axis = 1, inplace = True)\n",
    "train = train.drop(['parch'], axis = 1)\n",
    "train = train.drop(['sibsp'], axis = 1)\n",
    "\n",
    "#train = train.drop(['age'], axis = 1)\n",
    "train = train.drop(['fam'], axis = 1)\n",
    "#train = train.drop(['pclass'], axis = 1)\n",
    "#train = train.drop(['Title'], axis = 1)\n",
    "#train = train.drop(['sex'], axis = 1)\n",
    "#train = train.drop(['fare'], axis = 1)\n",
    "train = train.drop(['embarked'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 958,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1dbfd74ce88>"
      ]
     },
     "execution_count": 958,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(train.corr(), annot = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 959,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1dbfd7bda08>"
      ]
     },
     "execution_count": 959,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(test.corr(), annot = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 3.訓練模型"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.1 通用模板_sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1184,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    train.drop(['survived','ID'], axis=1), \n",
    "    train['survived'], \n",
    "    test_size = 0.4, \n",
    "    random_state = 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.2 LOGISTIC REGRESSION_sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8053097345132744\n",
      "78.31\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "log = LogisticRegression(\n",
    "    max_iter = 50000\n",
    ")\n",
    "log.fit(X_train, y_train)\n",
    "score_log = log.score(X_train, y_train)\n",
    "pred_log = log.predict(X_test)\n",
    "acc_log = round(accuracy_score(pred_log, y_test) * 100, 2)\n",
    "print(score_log)\n",
    "print(acc_log)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1114,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[190  35]\n",
      " [ 47 106]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "print(confusion_matrix(y_test, pred_log))\n",
    "tn, fp, fn, tp = confusion_matrix(y_test, pred_log).ravel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy is:  79.0\n"
     ]
    }
   ],
   "source": [
    "accuracy=((tp+tn)/(tp+tn+fp+fn))\n",
    "print('accuracy is: ', (round(accuracy, 2)*100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.3 RANDOM FOREST_sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1206,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9380530973451328\n",
      "78.57\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "# Define our optimal randomForest algo\n",
    "randomforest = RandomForestClassifier(\n",
    "    criterion = 'gini',\n",
    "    max_depth = 10,\n",
    "    max_features = 'auto',\n",
    "    n_estimators = 500)\n",
    "randomforest.fit(X_train, y_train)\n",
    "score_randomforest = randomforest.score(X_train, y_train)\n",
    "pred_randomforest = randomforest.predict(X_test)\n",
    "acc_randomforest = round(accuracy_score(pred_randomforest, y_test) * 100, 2)\n",
    "print(score_randomforest)\n",
    "print(acc_randomforest)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.4 GRADIENT BOOSTING_sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1209,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8725663716814159 80.95\n",
      "0.8761061946902655 80.95\n",
      "0.8778761061946903 80.95\n",
      "0.8778761061946903 80.16\n",
      "0.8707964601769912 80.95\n",
      "0.8690265486725663 80.42\n",
      "0.8690265486725663 80.95\n",
      "0.8725663716814159 80.69\n",
      "0.8654867256637168 81.22\n",
      "0.8654867256637168 81.48\n",
      "0.8690265486725663 80.95\n",
      "0.8654867256637168 80.42\n",
      "0.8778761061946903 81.48\n",
      "0.8725663716814159 80.95\n",
      "0.8690265486725663 80.95\n",
      "0.8707964601769912 80.69\n",
      "0.8654867256637168 80.95\n",
      "0.8743362831858407 81.48\n",
      "0.8707964601769912 82.01\n",
      "0.8707964601769912 80.69\n",
      "0.8814159292035398 81.22\n",
      "0.8725663716814159 81.22\n",
      "0.8654867256637168 80.69\n",
      "0.8672566371681416 80.16\n",
      "0.8743362831858407 81.75\n",
      "0.8761061946902655 80.95\n",
      "0.8725663716814159 80.95\n",
      "0.863716814159292 81.22\n",
      "0.8743362831858407 80.69\n",
      "0.8707964601769912 80.69\n",
      "0.8778761061946903 80.16\n",
      "0.8725663716814159 81.22\n",
      "0.8707964601769912 80.69\n",
      "0.8743362831858407 80.69\n",
      "0.8690265486725663 80.95\n",
      "0.8707964601769912 80.95\n",
      "0.8672566371681416 81.22\n",
      "0.8725663716814159 81.22\n",
      "0.8654867256637168 80.69\n",
      "0.8743362831858407 80.69\n",
      "0.8690265486725663 81.75\n",
      "0.8690265486725663 81.22\n",
      "0.8672566371681416 80.69\n",
      "0.8690265486725663 81.22\n",
      "0.8672566371681416 80.95\n",
      "0.8672566371681416 81.22\n",
      "0.8690265486725663 80.69\n",
      "0.8690265486725663 80.42\n",
      "0.8672566371681416 80.69\n",
      "0.8761061946902655 80.95\n",
      "0.8690265486725663 80.69\n",
      "0.8672566371681416 80.69\n",
      "0.8707964601769912 80.69\n",
      "0.8725663716814159 80.95\n",
      "0.8778761061946903 81.48\n",
      "0.8743362831858407 80.69\n",
      "0.8725663716814159 80.69\n",
      "0.8672566371681416 81.22\n",
      "0.8707964601769912 82.01\n",
      "0.8690265486725663 82.01\n",
      "0.8814159292035398 81.48\n",
      "0.8761061946902655 81.75\n",
      "0.863716814159292 81.48\n",
      "0.8743362831858407 80.95\n",
      "0.8778761061946903 80.16\n",
      "0.8690265486725663 81.22\n",
      "0.8654867256637168 80.95\n",
      "0.8814159292035398 81.48\n",
      "0.8690265486725663 80.42\n",
      "0.8690265486725663 80.95\n",
      "0.8672566371681416 80.42\n",
      "0.8672566371681416 80.42\n",
      "0.8690265486725663 80.95\n",
      "0.8672566371681416 80.95\n",
      "0.8743362831858407 80.42\n",
      "0.8814159292035398 80.42\n",
      "0.8672566371681416 80.42\n",
      "0.8778761061946903 80.69\n",
      "0.8707964601769912 80.95\n",
      "0.8654867256637168 80.16\n",
      "0.8672566371681416 81.22\n",
      "0.863716814159292 80.69\n",
      "0.8743362831858407 80.69\n",
      "0.8743362831858407 80.16\n",
      "0.8690265486725663 80.95\n",
      "0.8690265486725663 81.48\n",
      "0.8690265486725663 80.69\n",
      "0.863716814159292 80.16\n",
      "0.8672566371681416 80.95\n",
      "0.8743362831858407 81.22\n",
      "0.8690265486725663 80.95\n",
      "0.8690265486725663 80.95\n",
      "0.8761061946902655 81.22\n",
      "0.8707964601769912 80.95\n",
      "0.8672566371681416 81.22\n",
      "0.8725663716814159 81.75\n",
      "0.8690265486725663 81.22\n",
      "0.8725663716814159 81.48\n",
      "0.863716814159292 80.69\n",
      "0.8725663716814159 80.95\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "80.94"
      ]
     },
     "execution_count": 1209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "\n",
    "acc_gbk = 0\n",
    "testacc = []\n",
    "\n",
    "async def gbk_model():\n",
    "    for i in range(100):\n",
    "        gbk = GradientBoostingClassifier(\n",
    "            learning_rate = 0.05,\n",
    "            n_estimators = 100,\n",
    "            subsample = 0.9\n",
    "        )\n",
    "        gbk.fit(X_train, y_train)\n",
    "        score_gbk = gbk.score(X_train, y_train)\n",
    "        pred_gbk = gbk.predict(X_test)\n",
    "        acc_gbk = round(accuracy_score(pred_gbk, y_test) * 100, 2)\n",
    "        print(score_gbk, acc_gbk)\n",
    "        testacc.append(acc_gbk)\n",
    "    \n",
    "    return pd.Series(testacc).mean()\n",
    "\n",
    "acc_gbk = round(await asyncio.create_task(gbk_model()), 2)\n",
    "acc_gbk"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.6 DecisionTreeClassifier_sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1208,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9610619469026549\n",
      "73.28\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "dtc = DecisionTreeClassifier(\n",
    "    criterion = 'entropy'\n",
    ")\n",
    "dtc.fit(X_train, y_train)\n",
    "pred_dtc = dtc.predict(X_test)\n",
    "acc_dtc = round(accuracy_score(pred_dtc, y_test) * 100, 2)\n",
    "score_dtc = dtc.score(X_train, y_train)\n",
    "print(score_dtc)\n",
    "print(acc_dtc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.7 Other_sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1197,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Using KNeighborsClassifier Method of neighbors class to use Nearest Neighbor algorithm\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "knn = KNeighborsClassifier(\n",
    "    n_neighbors = 5,\n",
    "    metric = 'minkowski',\n",
    "    p = 2\n",
    ")\n",
    "knn.fit(X_train, y_train)\n",
    "score_knn = knn.score(X_train, y_train)\n",
    "pred_knn = knn.predict(X_test)\n",
    "acc_knn = round(accuracy_score(pred_knn, y_test) * 100, 2)\n",
    "\n",
    "#Using SVC method of svm class to use Support Vector Machine Algorithm\n",
    "from sklearn.svm import SVC\n",
    "svc_lin = SVC(\n",
    "    kernel = 'linear',\n",
    "    random_state = 0\n",
    ")\n",
    "svc_lin.fit(X_train, y_train)\n",
    "score_svc_lin = svc_lin.score(X_train, y_train)\n",
    "pred_svc_lin = svc_lin.predict(X_test)\n",
    "acc_svc_lin = round(accuracy_score(pred_svc_lin, y_test) * 100, 2)\n",
    "\n",
    "#Using SVC method of svm class to use Kernel SVM Algorithm\n",
    "from sklearn.svm import SVC\n",
    "svc_rbf = SVC(\n",
    "    kernel = 'rbf',\n",
    "    random_state = 0\n",
    ")\n",
    "svc_rbf.fit(X_train, y_train)\n",
    "score_svc_rbf = svc_rbf.score(X_train, y_train)\n",
    "pred_svc_rbf = svc_rbf.predict(X_test)\n",
    "acc_svc_rbf = round(accuracy_score(pred_svc_rbf, y_test) * 100, 2)\n",
    "\n",
    "#Using GaussianNB method of naïve_bayes class to use Naïve Bayes Algorithm\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "gauss = GaussianNB(\n",
    ")\n",
    "gauss.fit(X_train, y_train)\n",
    "score_gauss = gauss.score(X_train,y_train)\n",
    "pred_gauss = gauss.predict(X_test)\n",
    "acc_gauss = round(accuracy_score(pred_gauss, y_test) * 100, 2)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 4.預測結果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1210,
   "metadata": {},
   "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>TECHNIQUE</th>\n",
       "      <th>SCORE</th>\n",
       "      <th>ACCURACY</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>RANDOM FOREST</td>\n",
       "      <td>93.805310</td>\n",
       "      <td>78.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LOGISTIC REGRESSION</td>\n",
       "      <td>80.530973</td>\n",
       "      <td>78.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GRADIENT BOOSTING</td>\n",
       "      <td>87.256637</td>\n",
       "      <td>80.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DecisionTreeClassifier</td>\n",
       "      <td>96.106195</td>\n",
       "      <td>73.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>KNeighborsClassifier</td>\n",
       "      <td>79.823009</td>\n",
       "      <td>74.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Support Vector Machine Algorithm</td>\n",
       "      <td>78.584071</td>\n",
       "      <td>77.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Kernel SVM Algorithm</td>\n",
       "      <td>67.610619</td>\n",
       "      <td>67.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Naïve Bayes Algorithm</td>\n",
       "      <td>74.690265</td>\n",
       "      <td>77.25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          TECHNIQUE      SCORE  ACCURACY\n",
       "0                     RANDOM FOREST  93.805310     78.57\n",
       "1               LOGISTIC REGRESSION  80.530973     78.60\n",
       "2                 GRADIENT BOOSTING  87.256637     80.94\n",
       "3            DecisionTreeClassifier  96.106195     73.28\n",
       "4              KNeighborsClassifier  79.823009     74.60\n",
       "5  Support Vector Machine Algorithm  78.584071     77.25\n",
       "6              Kernel SVM Algorithm  67.610619     67.46\n",
       "7             Naïve Bayes Algorithm  74.690265     77.25"
      ]
     },
     "execution_count": 1210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "see = {\n",
    "    'TECHNIQUE':[\n",
    "        'RANDOM FOREST',\n",
    "        'LOGISTIC REGRESSION',\n",
    "        'GRADIENT BOOSTING',\n",
    "        'DecisionTreeClassifier',\n",
    "        'KNeighborsClassifier',\n",
    "        'Support Vector Machine Algorithm',\n",
    "        'Kernel SVM Algorithm',\n",
    "        'Naïve Bayes Algorithm'\n",
    "        \n",
    "    ],\n",
    "    'SCORE':[\n",
    "        score_randomforest* 100,\n",
    "        score_log* 100,\n",
    "        score_gbk* 100,\n",
    "        score_dtc* 100,\n",
    "        score_knn* 100,\n",
    "        score_svc_lin* 100,\n",
    "        score_svc_rbf* 100,\n",
    "        score_gauss* 100\n",
    "    ],\n",
    "    'ACCURACY':[\n",
    "        acc_randomforest,\n",
    "        acc_LOG,\n",
    "        acc_gbk,\n",
    "        acc_dtc,\n",
    "        acc_knn,\n",
    "        acc_svc_lin,\n",
    "        acc_svc_rbf,\n",
    "        acc_gauss\n",
    "    ]}\n",
    "mod = pd.DataFrame(see)\n",
    "mod"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 5.產生檔案"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "score = max(mod[\"ACCURACY\"])\n",
    "score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "inf_score = 82.84"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "current = str(time.strftime(\"%Y%m%d_%H%M%S\", time.localtime()))\n",
    "#D:\\Desktop\\soobi_practice\n",
    "#C:/Users/huai/.homejupyter\n",
    "name_for_csv = (\n",
    "    'D:\\Desktop\\soobi_practice\\demo2_' + str(score)+ '_'+ current + '.csv')\n",
    "print(name_for_csv)\n",
    "\n",
    "\n",
    "ids = test['ID']\n",
    "predictions = gbk.predict(test.drop('ID', axis=1))\n",
    "\n",
    "\n",
    "output = pd.DataFrame({ 'ID' : ids, 'survived': predictions })\n",
    "if score > inf_score:\n",
    "    output.to_csv(name_for_csv, index=False)\n",
    "else:\n",
    "    print(\"under\", inf_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#output.to_csv(name_for_csv, index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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