Day_023_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, 觀察降低偏態的影響"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# [作業重點]\n",
    "- 觀察使用log1p降偏態時, 對於分布與迴歸分數的影響 (In[6], Out[6])\n",
    "- 修正區塊中的資料問題後, 觀察以box-cox降偏態, 對於分布與迴歸分數的影響 (In[7], Out[7])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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",
       "      <td>0</td>\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",
       "      <td>1</td>\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",
       "      <td>2</td>\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",
       "      <td>3</td>\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",
       "      <td>4</td>\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>"
      ],
      "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": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.linear_model import LogisticRegression\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",
    "\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": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 Numeric Features : ['Pclass', 'Age', 'SibSp', 'Parch', 'Fare']\n",
      "\n"
     ]
    },
    {
     "data": {
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       "\n",
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       "        text-align: right;\n",
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       "</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",
       "      <td>0</td>\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",
       "      <td>1</td>\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",
       "      <td>2</td>\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",
       "      <td>3</td>\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",
       "      <td>4</td>\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",
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      "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"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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')\n",
    "\n",
    "# 削減文字型欄位, 只剩數值型欄位\n",
    "df = df[num_features]\n",
    "df = df.fillna(0)\n",
    "MMEncoder = MinMaxScaler()\n",
    "train_num = train_Y.shape[0]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 顯示 Fare 與目標值的散佈圖\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "sns.distplot(df['Fare'][:train_num])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\henry\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "c:\\users\\henry\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "c:\\users\\henry\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "c:\\users\\henry\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "c:\\users\\henry\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.7016289882888318"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 計算基礎分數\n",
    "df_mm = MMEncoder.fit_transform(df)\n",
    "train_X = df_mm[:train_num]\n",
    "estimator = LogisticRegression()\n",
    "cross_val_score(estimator, train_X, train_Y, cv=5).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 作業1 \n",
    "* 試著在鐵達尼的票價 (Fare) 欄位中使用對數去偏 (log1p) , 結果是否更好?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\henry\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "c:\\users\\henry\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "c:\\users\\henry\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "c:\\users\\henry\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "c:\\users\\henry\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.7106367963253961"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 將 Fare 取 log1p 後, 看散佈圖, 並計算分數\n",
    "df_fixed = copy.deepcopy(df)\n",
    "\n",
    "df_fixed['Fare'] = np.log1p(df_fixed['Fare'])\n",
    "\n",
    "sns.distplot(df_fixed['Fare'][:train_num])\n",
    "plt.show()\n",
    "\n",
    "df_fixed = MMEncoder.fit_transform(df_fixed)\n",
    "train_X = df_fixed[:train_num]\n",
    "estimator = LogisticRegression()\n",
    "cross_val_score(estimator, train_X, train_Y, cv=5).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 作業2\n",
    "* 最後的 boxcox 區塊直接執行會造成錯誤, 起因為輸入值有負值, 請問如何修正後可以使用 boxcox? (Hint : 試圖修正資料)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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31PHG4TauW1wyZbWKTEaBLp7R1BlmRUXhlL/uuuVzWbd8btztl5UVsGR2Hi/sO8HKFNQrMhGNoYsndIUH6emPUJR99slJrzEz/njlPCJDjuf3NKe6HJEzFOjiCaM3tiicBoEOUJqfxXsXF/NWXQdHT55OdTkigIZcxCNGL1lMVqBPNi7/qWuqzvuY111Swuu1J3liax3/+ZalF1qaSMKohy6eMDqpaLr00AEKsjNZFr3qJTw4NPkPiCSZAl08oakjTMAgf5wJPl52zaJiOnoHeXanluSV1FOgiyc0dvYxt2DGtLuue1FJLotKc/mXN46luhQRBbp4Q1NHmLIkr+GSDGbGPdfOZ0d9B7saOlNdjqQ5Bbp4QjJvbJFst19ZQVYwwDM19akuRdKcAl1SzjlHU2fybj2XbIXZmXz4irn8cmfju9aEEZlqCnRJudaefvojw0lfNjeZbr+ynI7eQV7a3zp5Y5EkUaBLyh1r6wWgqvjstVOmi+sXl1Can8XP3mpIdSmSxhToknKjgT5/nMWwpotgRoCPrZ7HSwdaaD89kOpyJE0p0CXl6tpOE7CROwlNZ7dfWcHgkOPZnY2pLkXSlAJdUu5Yey9lhdmEgtP747i0rIClZQX89E0Nu0hqTO9/QeILx9p6mT+Nx89jfWJtBTsbOtnb2JXqUiQNKdAl5erae5lfnJvqMhLi9ivLCQUDPLVt6m7SITIqrkA3s3VmdsDMas3swXGef7+ZvWVmETO7I/Flil91hQdpPz3gmx56UU6Im5fP5efbj9M3oGvSZWpNGuhmlgE8AtwMLAPuMrNlY5rVAfcBTyS6QPG3Oh9c4TLWhquq6A5H2LxLC3bJ1Iqnh341UOucO+ycGwCeAtbHNnDOHXXO7QSGk1Cj+JgfrkEf69pFs1hQnMPT27QUgEyteG5wUQ7EfjIbgGsu5MXM7H7gfoCqqvO/oYD4z7H2kbv9pHoMPZE3pjYz7ryqiq89t58Dzd1cNjc/YccWOZd4eujjrWfqLuTFnHOPOeeqnXPVpaWlF3II8Zm6tl6Kc0PkZfnr5lkbrqokJ5TBt16qTXUpkkbiCfQGoDJmuwLQzAlJiGNtvb4abhk1MzfEve9dwLM7Gzl0ojvV5UiaiKdbtA1YYmYLgePABuBTSa1K0kZdey9XLZiZ6jKS4s+vX8QPf3+U//PbWr5515px25xrqOdC7nMq6W3SHrpzLgJsBJ4H9gHPOOf2mNlDZnYrgJldZWYNwCeAR81sTzKLFn/ojwzR2NlHlU+uQR9rVm6Ie94zn1/ubKS2pSfV5UgaiGvg0jm3Gdg8Zt+XYx5vY2QoRiRu9e19OAcLfDjkMur+6xfxo98f4++e3cvj911FIEG32JvsJK569+lJM0UlZerOXOHi30AvzsviS7dczisHW/ne7w6nuhzxOQW6pMyRk9Fr0Gf5c8hl1D3XzucjV8zh4ecOsL3uVKrLER/z17ViMq3sa+qiJC9ESV4o1aVclMlObJoZD398Fbd88zU+/+O3eOxPqlleXjiFFUq6UA9dUmZvYxdLywowS8y4spcV5mTy6D1rGXZw+3d+z4+3HMO5C5rOITIhBbqkxEBkmEMt3SybV5DqUqbM8vJC/t8D7+PaRcX8zc93c8Pfv8xzu5s5eKKb5s4wPf0RwoNDDA4NM6ywlwugIRdJidqWHgaHHFfMS6+hh+K8LH5w31X8bPtxNr3dyO8OtfLqofFvLP1ffrGbzIwAmQEjFAwwvziXpWX5XL1wFgOR4Wl/QxBJPAW6pMTeppEbQCwrS58e+qhAwLhjbQV3rK3ge68dpqWrn67wIKf7I0SGHcPDjiHnWFpWwOCQIzI0TN/gEIdbT/Or3c08ubWerGCAVZVFfOjy2eTPyEz1H0k8QoEuKbG3sYsZmQEWlvj7CpfJ5ISCLCgZ/5/heNeSO+fYeqSdrz23nzePnWJXQye3rJjLlVUz0+JchJybfmeTlNjb1MnlcwvISNBEm3RhZlyzqJg71lbyV3+0mNkFWfzft47z5LZ6Boe0enW6U6DLlHPOsbexK61OiCbD7PwZ/Pn1i1h3xVx2H+/k8deP6C5JaU6BLlPueEcfXeFIWo6fJ1rAjPdfWsqdV1VS397Ho6++Q09/JNVlSYpoDF2m3N7G6AnRNOihX8yNM87nZ1dVFJEbCvKjPxzl8dePcGd1JYU5OlmabtRDlym3t6kLM7hcd/JJqMWz87j72vm0dPdz7+Nb1VNPQwp0mXJ7G7tYWJJLTki/ICbapXPyueuqKnYd7+RPf7BNY+ppRoEuU2p42PFWXQcrtJZJ0iybV8A37lzNtqPt3P/PNfRHFOrpQoEuU2pPYxcne/r5wKW6p2wy3bpqHl/7+EpeO3SSv/yXtwgPKtTTgQJdptRLB1owQ4E+BT5ZXcn/uG0FLx1o4U/+aStd4cFUlyRJpkCXKfXb/S2sqiiiOC8r1aWkhU9dU8U3N6xhe/0p7nz0Derbe1NdkiSRzkrJlGnr6efthg7+3YcuTXUpaeXfrJpHQXYmG594i1v+8TX+++0ruHXVvAnb68bV05cCPYbu05hcrx5qxTn4o8tnp7qUtPOBS0vZ/MD1fOGp7Tzw5HaefbuR/7juMhbP1qWjfqIhF5kyv93fSkleFlekwYQiL6qclcMzn3sP/+Ejl/H7d9r48Dde5YtP72DrkXbdbMMn1EOXKREZGubVg618eNkcAlqQK2WCGQFm5oT4woeW8MrBVjbvauLn249TnBtiRXkhS8sKKJ+ZTUArN05LCnSZEq8dOkln3yAf1HCLJ+RmBbllRRk3Lp3D7sZO3qo7xauHWnn5YCsFM4IsLStgaVkBi0pzCQb0i/x0oUCXpHPO8b9fPER5UTY3Lp2T6nIkRigY4MqqmVxZNZPegQgHmrvZ29TF9roOthxpJysY4LK5+SyfV8hlWqrB8xToknQvH2jl7foOvnr7Ct02zcNyQkHWVM1kTdVMBoeGeae1h72NXexr7mZnQydZwQC7j3eyfnU577mkWGvZe5ACXZLKOcc3XjhI5axsPr62ItXlSJwyMwJcPreAy+cWMOwch1tP83Z9B8/tbuYnbzZQmp/FH68sY/3qclZVFOpuSR6RtoHe0x/h5QMtvHbwJIdP9nC0rZfegSGyMzMoyA6ysCSXxaV5VM7K0Qmii/DCvhZ2NnTy8MdXkpmh3vl0FDBj8ew8Fs/O4/Yry3lpfwu/2NHIj9+o4/HXj1KSl8XqykKumFdIaX4WM3NCOBx9A0N0hSO0dIVp6R6Zg9DdF6E/MkRkeOSqmpxQkLysIO9dXMyqikLWzp/FJaW5+g/iAlmqLleqrq52NTU1U/qaQ8OOVw+18tTWOl7a38rA0DBFOZlcOiefBcU5HD3ZS+/gEO09/TR1hnFAYXYma6qKWFs1k7/60JIprXe629/cxYbH3mBWTojnv/j+uAL9YtYPl+SLnYvR2TfI83uaeeNwG2/Xd/BO6+lxfyYUDDCnIIsARv6MINmhDDIChnNwemCI7vAgJ7rChAdHbqE3emnrsrICKmZmY2aaAxLDzN50zlWP91xa9NCbO8M8U1PP09vqOd7RR3FuiLuvnc/N0Zvrjo4FxoZJb3+EQ609bK87xSsHWnn5QCuvHTrJHdUVfHRFGblZafHWXbDalm4+/d0tzAhm8PhnrlLv3IcKszP5ZHUln6yuBGAgMsz3Xz9Cb/8QZiPDNjMyA2RnZkza4x52jvaeAWqj4/avHWrllYOtFGZnsrQsn8pZ2VyzsFjnYCYRVw/dzNYB/whkAN9zzn11zPNZwI+AtUAbcKdz7ui5jpnsHnpzZ5jf7DvBc7ub+MM7bQw7uH5JCRuuquKmZXPG/WBM1Dvs7Btke90pDrX0cOTkaXJCGdyyooyPrijj6oWzFO4xTvdH+PGWY3zn5XfICAR4+nPXcklpXtw/rx66t03WU07U31/vQIT9zd3sbeziUEs3g0OO/BlBbrhsNjctm8N7FhVTmn/x6wFNx9nh5+qhTxroZpYBHARuAhqAbcBdzrm9MW3+LbDSOfcXZrYBuM05d+e5jnsxgd4fGaInHKE7HKGnf+R7S3eYY2291Lb08OaxUxzv6ANgUUkut6wo45PVlVQV55zzuJP95d51dSVvHjvFT2oaeHZnI6cHhggGjJUVhVw2t4BL5+Qxryib2dFxxBmZGczIDDAjM4OsYMA344KRoWHCkWH6BoZoPz3AyZ5+Dp7o5u36Dl452Mqp3kHet7iEr9x6BYtnnx3mCm05HwORYcpnZvObvc28uK+FttMDAJQXZbO8vIAFJblUzcqhJC+LWbkhsjMzCJgRCIyM/xvQNxjNjP5INDsG6eyLsOVIG30DQ/QNDtE7MHTmsXMOMyMnNHKsjICRlxWkIDtI/oxM8mcEKYh+P7OdPbp/9LmR7axgYKQOIyEZcLFDLlcDtc65w9GDPQWsB/bGtFkPfCX6+KfAt8zMXBIG6L/z8jt87bn9Ez4/r3AGq6uK+Mx1C7h+SSmXzslLWJCaGdULZlG9YBb/bf0V1Bw9xe9qT/LWsVNs3tXEk1snXp7UDDKidYyWM/JRg9hvY5+LLT323XS4CfZz1kY8bUf/qt69b8I/zrjmFGRx3eIS/vR9C7myaub5/bDIBELBADctm8NNy+YwNOzYUd/B9rpTbK/v4EBz95nzYRciKxggJ5RBdmYG2aEMCgpnkJ05EsDOwSWzcxkadkSG3ZlOZEt3mNqWkf8UusORMyd442E28p/MQ+uv4NPXzL+gms95/Dh66HcA65xzn41u3wNc45zbGNNmd7RNQ3T7nWibk2OOdT9wf3TzMuBAov4g56kEODlpq/Sm9yg+ep8mp/docufzHs13zo17Q4F4eujjdW/H/i8QTxucc48Bj8XxmkllZjUT/coiI/QexUfv0+T0Hk0uUe9RPKeMG4DKmO0KoHGiNmYWBAqB9ostTkRE4hdPoG8DlpjZQjMLARuATWPabALujT6+A/htMsbPRURkYpMOuTjnIma2EXiekcsWv++c22NmDwE1zrlNwD8B/2xmtYz0zDcks+gESPmwzzSg9yg+ep8mp/docgl5j1I2U1RERBJL065ERHxCgS4i4hNpG+hm9hUzO25mO6Jft6S6Jq8ws3VmdsDMas3swVTX40VmdtTMdkU/O1O7ypxHmdn3zawlOi9ldN8sM/uNmR2Kfk/7GWcTvE8JyaO0DfSobzjnVke/Nqe6GC+ILvXwCHAzsAy4y8yWpbYqz/pg9LOja6xH/ABYN2bfg8CLzrklwIvR7XT3A85+nyABeZTugS5nO7PUg3NuABhd6kHknJxzr3L2/JP1wA+jj38IfGxKi/KgCd6nhEj3QN9oZjujvwKl/a+CUeVAfcx2Q3SfvJsDfm1mb0aXtJDxzXHONQFEv+su4RO76DzydaCb2Qtmtnucr/XAd4BLgNVAE/C/Ulqsd8S1jINwnXPuSkaGpj5vZu9PdUEyrSUkj3y9kLdz7sZ42pnZd4Fnk1zOdBHPUg9pzznXGP3eYmY/Z2So6tXUVuVJJ8yszDnXZGZlQEuqC/Ii59yJ0ccXk0e+7qGfS/TDNeo2YPdEbdNMPEs9pDUzyzWz/NHHwIfR52ciscuC3Av8IoW1eFai8sjXPfRJPGxmqxkZTjgKfC615XjDREs9pLgsr5kD/Dy6zn4QeMI591xqS0o9M3sSuAEoMbMG4L8CXwWeMbM/A+qAT6SuQm+Y4H26IRF5pKn/IiI+kbZDLiIifqNAFxHxCQW6iIhPKNBFRHxCgS4i4hPpfNmipBkzGwJ2xez6mHPuaIrKEUk4XbYoacPMepxzeRfwcxnOuaFk1CSSSBpykbRmZgvM7DUzeyv69d7o/hvM7CUze4Jor97M7jazrdH1qh+NLjUs4hkacpF0km1mO6KPjzjnbmNkbZGbnHNhM1sCPAmMrm9+NbDcOXfEzJYCdzKyKNegmX0b+DTwoyn+M4hMSIEu6aTPObd6zL5M4FvRaddDwKUxz211zh2JPv4QsBbYFp3yn40WmhKPUaBLuvsicAJYxcgQZDjmudMxjw34oXPuS1NYm8h50Ri6pLtCoMk5Nwzcw8iCZON5EbjDzGbDmXtlzp+iGkXiokCXdPdt4F4ze9JkT/IAAABMSURBVIOR4ZbT4zVyzu0F/paRuxTtBH4DlI3XViRVdNmiiIhPqIcuIuITCnQREZ9QoIuI+IQCXUTEJxToIiI+oUAXEfEJBbqIiE/8f0l4U97EhMXyAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\henry\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "c:\\users\\henry\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "c:\\users\\henry\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "c:\\users\\henry\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "c:\\users\\henry\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.7128777811941116"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 將 Fare 取 boxcox 後, 看散佈圖, 並計算分數 (執行會有 error, 請試圖修正)\n",
    "from scipy import stats\n",
    "df_fixed = copy.deepcopy(df)\n",
    "# print(df_fixed.columns)\n",
    "# df_fixed = MMEncoder.fit_transform(df_fixed)\n",
    "df_fixed = df_fixed.apply(lambda x : x+abs(x.min())+1e-10)\n",
    "# 先最小值設成 0 後，外加微小值，避免log部分出錯\n",
    "df_fixed['Fare'] = stats.boxcox(df_fixed['Fare'])[0]\n",
    "sns.distplot(df_fixed['Fare'][:train_num])\n",
    "plt.show()\n",
    "\n",
    "df_fixed = MMEncoder.fit_transform(df_fixed)\n",
    "train_X = df_fixed[:train_num]\n",
    "estimator = LogisticRegression()\n",
    "cross_val_score(estimator, train_X, train_Y, cv=5).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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