Day_022_HW-特徵工程.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": {
    "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",
    "\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)"
      ]
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     "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",
    "df_train = pd.read_csv(r\"C:\\Users\\user\\100 days\\data\\titanic_train.csv\")\n",
    "df_test = pd.read_csv(r\"C:\\Users\\user\\100 days\\data\\titanic_test.csv\")\n",
    "df_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "      <th>SibSp</th>\n",
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       "      <th>Embarked</th>\n",
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       "      <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",
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       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
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       "      <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",
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       "      <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",
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       "    <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",
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       "   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()"
   ]
  },
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   "execution_count": 3,
   "metadata": {},
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      "D:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\validation.py:595: DataConversionWarning: Data with input dtype int64 was converted to float64 by MinMaxScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n",
      "D:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\validation.py:595: DataConversionWarning: Data with input dtype int64 was converted to float64 by MinMaxScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n",
      "D:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\validation.py:595: DataConversionWarning: Data with input dtype int64 was converted to float64 by MinMaxScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n",
      "D:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\validation.py:595: DataConversionWarning: Data with input dtype int64 was converted to float64 by MinMaxScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n",
      "D:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\validation.py:595: DataConversionWarning: Data with input dtype int64 was converted to float64 by MinMaxScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n",
      "D:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\validation.py:595: DataConversionWarning: Data with input dtype int64 was converted to float64 by MinMaxScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n",
      "D:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\validation.py:595: DataConversionWarning: Data with input dtype int64 was converted to float64 by MinMaxScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n",
      "D:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\validation.py:595: DataConversionWarning: Data with input dtype int64 was converted to float64 by MinMaxScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n"
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       "   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",
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       "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",
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     "execution_count": 3,
     "metadata": {},
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   ],
   "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": 6,
   "metadata": {},
   "outputs": [
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     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
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       "   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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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)\n",
    "df.head()"
   ]
  },
  {
   "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": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#2019/10/02: \n",
    "# 1. 程式區塊 C 把英文、數字都改為 <1 的數字\n",
    "# 2. 程式區塊 D = C ?   E ?"
   ]
  }
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