homework/Day_017_HW.ipynb

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   "source": [
    "# 作業 : (Kaggle)鐵達尼生存預測精簡版 \n",
    "https://www.kaggle.com/c/titanic"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# [作業目標]\n",
    "- 試著不依賴說明, 只依照下列程式碼回答下列問題, 初步理解什麼是\"特徵工程\"的區塊"
   ]
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# [作業重點]\n",
    "- 試著不依賴註解, 以之前所學, 回答下列問題"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "# 作業1\n",
    "* 下列A~E五個程式區塊中，哪一塊是特徵工程?\n",
    "\n",
    "# 作業2\n",
    "* 對照程式區塊 B 與 C 的結果，請問那些欄位屬於\"類別型欄位\"? (回答欄位英文名稱即可) \n",
    "\n",
    "# 作業3\n",
    "* 續上題，請問哪個欄位是\"目標值\"?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
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   "outputs": [],
   "source": [
    "# 程式區塊 A\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.preprocessing import LabelEncoder, MinMaxScaler\n",
    "\n",
    "data_path = 'data/'\n",
    "df_train = pd.read_csv(data_path + 'titanic_train.csv')\n",
    "df_test = pd.read_csv(data_path + 'titanic_test.csv')\n",
    "df_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "# 程式區塊 B\n",
    "train_Y = df_train['Survived']\n",
    "ids = df_test['PassengerId']\n",
    "df_train = df_train.drop(['PassengerId', 'Survived'] , axis=1)\n",
    "df_test = df_test.drop(['PassengerId'] , axis=1)\n",
    "df = pd.concat([df_train,df_test])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
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   "outputs": [],
   "source": [
    "# 程式區塊 C\n",
    "LEncoder = LabelEncoder()\n",
    "MMEncoder = MinMaxScaler()\n",
    "for c in df.columns:\n",
    "    df[c] = df[c].fillna(-1)\n",
    "    if df[c].dtype == 'object':\n",
    "        df[c] = LEncoder.fit_transform(list(df[c].values))\n",
    "    df[c] = MMEncoder.fit_transform(df[c].values.reshape(-1, 1))\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 程式區塊 D\n",
    "train_num = train_Y.shape[0]\n",
    "train_X = df[:train_num]\n",
    "test_X = df[train_num:]\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "estimator = LogisticRegression()\n",
    "estimator.fit(train_X, train_Y)\n",
    "pred = estimator.predict(test_X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 程式區塊 E\n",
    "sub = pd.DataFrame({'PassengerId': ids, 'Survived': pred})\n",
    "sub.to_csv('titanic_baseline.csv', index=False) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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