Day_012_HW.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# 作業 : (Kaggle)鐵達尼生存預測\n",
    "https://www.kaggle.com/c/titanic"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# [作業目標]\n",
    "- 試著模仿範例寫法, 在鐵達尼生存預測中, 觀察填補缺值以及 標準化 / 最小最大化 對數值的影響"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# [作業重點]\n",
    "- 觀察替換不同補缺方式, 對於特徵的影響 (In[4]~In[6], Out[4]~Out[6])\n",
    "- 觀察替換不同特徵縮放方式, 對於特徵的影響 (In[7]~In[8], Out[7]~Out[8])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "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  "
      ],
      "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>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    </tr>\n    <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    </tr>\n    <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  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 14
    }
   ],
   "source": [
    "# 做完特徵工程前的所有準備 (與前範例相同)\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import copy\n",
    "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "data_path = 'G:\\我的雲端硬碟\\Python程式相關\\機器學習馬拉松/'\n",
    "df_train = pd.read_csv(data_path + 'titanic_train.csv')\n",
    "df_test = pd.read_csv(data_path + 'titanic_test.csv')\n",
    "\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": 15,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "5 Numeric Features : ['Pclass', 'Age', 'SibSp', 'Parch', 'Fare']\n\n"
     ]
    }
   ],
   "source": [
    "#只取 int64, float64 兩種數值型欄位, 存於 num_features 中\n",
    "num_features = []\n",
    "for dtype, feature in zip(df.dtypes, df.columns):\n",
    "    if dtype == 'float64' or dtype == 'int64':\n",
    "        num_features.append(feature)\n",
    "print(f'{len(num_features)} Numeric Features : {num_features}\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "   Pclass   Age  SibSp  Parch     Fare\n",
       "0       3  22.0      1      0   7.2500\n",
       "1       1  38.0      1      0  71.2833\n",
       "2       3  26.0      0      0   7.9250\n",
       "3       1  35.0      1      0  53.1000\n",
       "4       3  35.0      0      0   8.0500"
      ],
      "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>Pclass</th>\n      <th>Age</th>\n      <th>SibSp</th>\n      <th>Parch</th>\n      <th>Fare</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>3</td>\n      <td>22.0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>7.2500</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>38.0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>71.2833</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>26.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>7.9250</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1</td>\n      <td>35.0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>53.1000</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>3</td>\n      <td>35.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>8.0500</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 16
    }
   ],
   "source": [
    "# 削減文字型欄位, 只剩數值型欄位\n",
    "df = df[num_features]\n",
    "train_num = train_Y.shape[0]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# 作業1\n",
    "* 試著在補空值區塊, 替換並執行兩種以上填補的缺值, 看看何者比較好?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "0.6981796497395015"
      ]
     },
     "metadata": {},
     "execution_count": 8
    }
   ],
   "source": [
    "\n",
    "# 空值補 -1, 做羅吉斯迴歸\n",
    "df_m1 = df.fillna(-1)\n",
    "train_X = df_m1[:train_num]\n",
    "estimator = LogisticRegression()\n",
    "cross_val_score(estimator, train_X, train_Y, cv=5).mean()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "0.6992969681752557"
      ]
     },
     "metadata": {},
     "execution_count": 9
    }
   ],
   "source": [
    "df_m0 = df.fillna(0)\n",
    "train_X = df_m0[:train_num]\n",
    "estimator = LogisticRegression()\n",
    "cross_val_score(estimator, train_X, train_Y, cv=5).mean()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "0.6947900320130562"
      ]
     },
     "metadata": {},
     "execution_count": 10
    }
   ],
   "source": [
    "df_mn = df.fillna(df.mean())\n",
    "train_X = df_mn[:train_num]\n",
    "estimator = LogisticRegression()\n",
    "cross_val_score(estimator, train_X, train_Y, cv=5).mean()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#0效果最好"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# 作業2\n",
    "* 使用不同的標準化方式 ( 原值 / 最小最大化 / 標準化 )，搭配羅吉斯迴歸模型，何者效果最好?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "0.6970560542338836"
      ]
     },
     "metadata": {},
     "execution_count": 11
    }
   ],
   "source": [
    "\n",
    "df_temp = MinMaxScaler().fit_transform(df_m1)\n",
    "train_X = df_temp[:train_num]\n",
    "estimator = LogisticRegression()\n",
    "cross_val_score(estimator, train_X, train_Y, cv=5).mean()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "0.6981733726696377"
      ]
     },
     "metadata": {},
     "execution_count": 18
    }
   ],
   "source": [
    "df_temp = StandardScaler().fit_transform(df_m1)\n",
    "train_X = df_temp[:train_num]\n",
    "estimator = LogisticRegression()\n",
    "cross_val_score(estimator, train_X, train_Y, cv=5).mean()\n"
   ]
  }
 ],
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