homework/Day_022_HW.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "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"
   ]
  },
  {
   "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",
   "metadata": {
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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": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(891, 12)"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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": 2,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "   Pclass                                               Name     Sex   Age  \\\n",
       "0       3                            Braund, Mr. Owen Harris    male  22.0   \n",
       "1       1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0   \n",
       "2       3                             Heikkinen, Miss. Laina  female  26.0   \n",
       "3       1       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0   \n",
       "4       3                           Allen, Mr. William Henry    male  35.0   \n",
       "\n",
       "   SibSp  Parch            Ticket     Fare Cabin Embarked  \n",
       "0      1      0         A/5 21171   7.2500   NaN        S  \n",
       "1      1      0          PC 17599  71.2833   C85        C  \n",
       "2      0      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      1      0            113803  53.1000  C123        S  \n",
       "4      0      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.7/site-packages/sklearn/utils/validation.py:475: DataConversionWarning: Data with input dtype int64 was converted to float64 by MinMaxScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n"
     ]
    },
    {
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       "      <th></th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.118683</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.283951</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.775862</td>\n",
       "      <td>0.016072</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.218989</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.481481</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.879310</td>\n",
       "      <td>0.140813</td>\n",
       "      <td>0.575269</td>\n",
       "      <td>0.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.400459</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.984914</td>\n",
       "      <td>0.017387</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.323124</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.070043</td>\n",
       "      <td>0.105390</td>\n",
       "      <td>0.381720</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.016845</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.699353</td>\n",
       "      <td>0.017630</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   Pclass      Name  Sex       Age  SibSp  Parch    Ticket      Fare  \\\n",
       "0     1.0  0.118683  1.0  0.283951  0.125    0.0  0.775862  0.016072   \n",
       "1     0.0  0.218989  0.0  0.481481  0.125    0.0  0.879310  0.140813   \n",
       "2     1.0  0.400459  0.0  0.333333  0.000    0.0  0.984914  0.017387   \n",
       "3     0.0  0.323124  0.0  0.444444  0.125    0.0  0.070043  0.105390   \n",
       "4     1.0  0.016845  1.0  0.444444  0.000    0.0  0.699353  0.017630   \n",
       "\n",
       "      Cabin  Embarked  \n",
       "0  0.000000  1.000000  \n",
       "1  0.575269  0.333333  \n",
       "2  0.000000  1.000000  \n",
       "3  0.381720  1.000000  \n",
       "4  0.000000  1.000000  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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": 4,
   "metadata": {},
   "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": 5,
   "metadata": {},
   "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": "markdown",
   "metadata": {
    "collapsed": true
   },
   "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",
    "A: 區塊C"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "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",
    "A: 'Name', 'Sex', 'Ticket', 'Cabin' 'Embarked'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "作業3\n",
    "* 續上題，請問哪個欄位是\"目標值\"?\n",
    "A: Survived'"
   ]
  }
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