ex06]나의 첫 캐클 도전기.sehyun.ipynb

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"
    }
   },
   "cell_type": "markdown",
   "id": "interested-evening",
   "metadata": {},
   "source": [
    "# 첫 캐클 도전기\n",
    "\n",
    "캐클이라는 것을 오늘 처음 듣게 되어서 많이 부끄럽지만 데이터를 자주 다루는 데이터사이언스나 AI 개발자들에겐 공부하고 좋은 사이트 입니다.\n",
    "\n",
    "1. 회귀문제이다.\n",
    "\n",
    "2. 데이터 파일\n",
    "  \n",
    "  - train.csv\n",
    "  \n",
    "  - test.csv\n",
    "  \n",
    "  - sample_submission.csv\n",
    " \n",
    "3. 데이터 필드\n",
    "\n",
    "![image.png](attachment:image.png)\n",
    "\n",
    "\n",
    "4. 필요한 라이브러리\n",
    "\n",
    "- xgboost = 1.3.3\n",
    "- lightgbm =  3.1.1\n",
    "- missingno = 0.4.2\n",
    "- scikit-learn\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "prescribed-yugoslavia",
   "metadata": {},
   "source": [
    "### Baseline\n",
    "\n",
    "  1. Baseline셋팅하기\n",
    "  \n",
    "  2. 라이브러리, 데이터 가져오기\n",
    "  \n",
    "   i) 필요한 라이브러리 import 하기\n",
    "   \n",
    "   ii) 데이터 경로 지정하기\n",
    "   \n",
    "  \n",
    "  3. 데이터 이해하기\n",
    "  \n",
    "   i) 데이터 살펴보기\n",
    "   \n",
    "   ii) 데이터를 통해 변수 부르기\n",
    "   \n",
    "   iii) 학습데이터에서 라벨 제거하기\n",
    "   \n",
    "   iv) 학습데이터와 테스트 데이터 합치기\n",
    "   \n",
    "   v) 간단한 전처리"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "logical-width",
   "metadata": {},
   "source": [
    "# 데이터 가져오기!!\n",
    "## 평가문항 2번의 시작!!!!\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "invisible-saudi",
   "metadata": {},
   "source": [
    "#### 필요한 라이브러리 import하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "applicable-matrix",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "얍💢\n"
     ]
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "import os\n",
    "from os.path import join\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import missingno as msno\n",
    "\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "from sklearn.model_selection import KFold, cross_val_score\n",
    "import xgboost as xgb\n",
    "import lightgbm as lgb\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "print('얍💢')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "passive-escape",
   "metadata": {},
   "source": [
    "#### 데이터 경로 지정"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "nominated-black",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/aiffel/aiffel/kaggle_kakr_housing/data/train.csv\n",
      "/aiffel/aiffel/kaggle_kakr_housing/data/test.csv\n"
     ]
    }
   ],
   "source": [
    "data_dir = os.getenv('HOME')+'/aiffel/kaggle_kakr_housing/data'\n",
    "\n",
    "train_data_path = join(data_dir, 'train.csv')\n",
    "test_data_path = join(data_dir, 'test.csv')      # 테스트, 즉 submission 시 사용할 데이터 경로\n",
    "\n",
    "print(train_data_path)\n",
    "print(test_data_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "nominated-circus",
   "metadata": {},
   "source": [
    "# 데이터 이해하기"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "sealed-romantic",
   "metadata": {},
   "source": [
    "### 간단하게 데이터 확인"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "palestinian-thailand",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train:(15035, 21)\n",
      "test:(6468, 20)\n"
     ]
    }
   ],
   "source": [
    "train = pd.read_csv(train_data_path)\n",
    "test = pd.read_csv(test_data_path)\n",
    "\n",
    "print(\"train:{}\" .format(train.shape))\n",
    "print(\"test:{}\". format(test.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "false-february",
   "metadata": {},
   "source": [
    "### 데이터가 잘 형성이 되어있나 상위 5개만 보고 확인하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "waiting-couple",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
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       "      <th>bathrooms</th>\n",
       "      <th>sqft_living</th>\n",
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       "      <th>floors</th>\n",
       "      <th>waterfront</th>\n",
       "      <th>view</th>\n",
       "      <th>...</th>\n",
       "      <th>grade</th>\n",
       "      <th>sqft_above</th>\n",
       "      <th>sqft_basement</th>\n",
       "      <th>yr_built</th>\n",
       "      <th>yr_renovated</th>\n",
       "      <th>zipcode</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "      <th>sqft_living15</th>\n",
       "      <th>sqft_lot15</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>0</td>\n",
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       "      <td>-122.257</td>\n",
       "      <td>1340</td>\n",
       "      <td>5650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>20150225T000000</td>\n",
       "      <td>180000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.00</td>\n",
       "      <td>770</td>\n",
       "      <td>10000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>770</td>\n",
       "      <td>0</td>\n",
       "      <td>1933</td>\n",
       "      <td>0</td>\n",
       "      <td>98028</td>\n",
       "      <td>47.7379</td>\n",
       "      <td>-122.233</td>\n",
       "      <td>2720</td>\n",
       "      <td>8062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>20150218T000000</td>\n",
       "      <td>510000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1680</td>\n",
       "      <td>8080</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>1680</td>\n",
       "      <td>0</td>\n",
       "      <td>1987</td>\n",
       "      <td>0</td>\n",
       "      <td>98074</td>\n",
       "      <td>47.6168</td>\n",
       "      <td>-122.045</td>\n",
       "      <td>1800</td>\n",
       "      <td>7503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>20140627T000000</td>\n",
       "      <td>257500.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.25</td>\n",
       "      <td>1715</td>\n",
       "      <td>6819</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>1715</td>\n",
       "      <td>0</td>\n",
       "      <td>1995</td>\n",
       "      <td>0</td>\n",
       "      <td>98003</td>\n",
       "      <td>47.3097</td>\n",
       "      <td>-122.327</td>\n",
       "      <td>2238</td>\n",
       "      <td>6819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>20150115T000000</td>\n",
       "      <td>291850.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.50</td>\n",
       "      <td>1060</td>\n",
       "      <td>9711</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>1060</td>\n",
       "      <td>0</td>\n",
       "      <td>1963</td>\n",
       "      <td>0</td>\n",
       "      <td>98198</td>\n",
       "      <td>47.4095</td>\n",
       "      <td>-122.315</td>\n",
       "      <td>1650</td>\n",
       "      <td>9711</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   id             date     price  bedrooms  bathrooms  sqft_living  sqft_lot  \\\n",
       "0   0  20141013T000000  221900.0         3       1.00         1180      5650   \n",
       "1   1  20150225T000000  180000.0         2       1.00          770     10000   \n",
       "2   2  20150218T000000  510000.0         3       2.00         1680      8080   \n",
       "3   3  20140627T000000  257500.0         3       2.25         1715      6819   \n",
       "4   4  20150115T000000  291850.0         3       1.50         1060      9711   \n",
       "\n",
       "   floors  waterfront  view  ...  grade  sqft_above  sqft_basement  yr_built  \\\n",
       "0     1.0           0     0  ...      7        1180              0      1955   \n",
       "1     1.0           0     0  ...      6         770              0      1933   \n",
       "2     1.0           0     0  ...      8        1680              0      1987   \n",
       "3     2.0           0     0  ...      7        1715              0      1995   \n",
       "4     1.0           0     0  ...      7        1060              0      1963   \n",
       "\n",
       "   yr_renovated  zipcode      lat     long  sqft_living15  sqft_lot15  \n",
       "0             0    98178  47.5112 -122.257           1340        5650  \n",
       "1             0    98028  47.7379 -122.233           2720        8062  \n",
       "2             0    98074  47.6168 -122.045           1800        7503  \n",
       "3             0    98003  47.3097 -122.327           2238        6819  \n",
       "4             0    98198  47.4095 -122.315           1650        9711  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "assigned-garlic",
   "metadata": {},
   "source": [
    "### 데이터를 확인해보니 date안에 있는 데이터들이 거슬려서 조정을 하겠습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "excess-eugene",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>price</th>\n",
       "      <th>bedrooms</th>\n",
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       "      <th>sqft_living</th>\n",
       "      <th>sqft_lot</th>\n",
       "      <th>floors</th>\n",
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       "      <th>...</th>\n",
       "      <th>grade</th>\n",
       "      <th>sqft_above</th>\n",
       "      <th>sqft_basement</th>\n",
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       "      <th>zipcode</th>\n",
       "      <th>lat</th>\n",
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       "      <th>0</th>\n",
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       "      <td>0</td>\n",
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       "      <th>1</th>\n",
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       "      <td>180000.0</td>\n",
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       "      <td>10000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>770</td>\n",
       "      <td>0</td>\n",
       "      <td>1933</td>\n",
       "      <td>0</td>\n",
       "      <td>98028</td>\n",
       "      <td>47.7379</td>\n",
       "      <td>-122.233</td>\n",
       "      <td>2720</td>\n",
       "      <td>8062</td>\n",
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       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>20150218</td>\n",
       "      <td>510000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1680</td>\n",
       "      <td>8080</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>1680</td>\n",
       "      <td>0</td>\n",
       "      <td>1987</td>\n",
       "      <td>0</td>\n",
       "      <td>98074</td>\n",
       "      <td>47.6168</td>\n",
       "      <td>-122.045</td>\n",
       "      <td>1800</td>\n",
       "      <td>7503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>20140627</td>\n",
       "      <td>257500.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.25</td>\n",
       "      <td>1715</td>\n",
       "      <td>6819</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>1715</td>\n",
       "      <td>0</td>\n",
       "      <td>1995</td>\n",
       "      <td>0</td>\n",
       "      <td>98003</td>\n",
       "      <td>47.3097</td>\n",
       "      <td>-122.327</td>\n",
       "      <td>2238</td>\n",
       "      <td>6819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>20150115</td>\n",
       "      <td>291850.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.50</td>\n",
       "      <td>1060</td>\n",
       "      <td>9711</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>1060</td>\n",
       "      <td>0</td>\n",
       "      <td>1963</td>\n",
       "      <td>0</td>\n",
       "      <td>98198</td>\n",
       "      <td>47.4095</td>\n",
       "      <td>-122.315</td>\n",
       "      <td>1650</td>\n",
       "      <td>9711</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   id      date     price  bedrooms  bathrooms  sqft_living  sqft_lot  floors  \\\n",
       "0   0  20141013  221900.0         3       1.00         1180      5650     1.0   \n",
       "1   1  20150225  180000.0         2       1.00          770     10000     1.0   \n",
       "2   2  20150218  510000.0         3       2.00         1680      8080     1.0   \n",
       "3   3  20140627  257500.0         3       2.25         1715      6819     2.0   \n",
       "4   4  20150115  291850.0         3       1.50         1060      9711     1.0   \n",
       "\n",
       "   waterfront  view  ...  grade  sqft_above  sqft_basement  yr_built  \\\n",
       "0           0     0  ...      7        1180              0      1955   \n",
       "1           0     0  ...      6         770              0      1933   \n",
       "2           0     0  ...      8        1680              0      1987   \n",
       "3           0     0  ...      7        1715              0      1995   \n",
       "4           0     0  ...      7        1060              0      1963   \n",
       "\n",
       "   yr_renovated  zipcode      lat     long  sqft_living15  sqft_lot15  \n",
       "0             0    98178  47.5112 -122.257           1340        5650  \n",
       "1             0    98028  47.7379 -122.233           2720        8062  \n",
       "2             0    98074  47.6168 -122.045           1800        7503  \n",
       "3             0    98003  47.3097 -122.327           2238        6819  \n",
       "4             0    98198  47.4095 -122.315           1650        9711  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['date'] = train['date'].apply(lambda k : k[:8]).astype(int)\n",
    "train.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "wireless-microwave",
   "metadata": {},
   "source": [
    "#### 학습 데이터에서 라벨 제거하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "occupied-stake",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['id', 'date', 'bedrooms', 'bathrooms', 'sqft_living', 'sqft_lot',\n",
      "       'floors', 'waterfront', 'view', 'condition', 'grade', 'sqft_above',\n",
      "       'sqft_basement', 'yr_built', 'yr_renovated', 'zipcode', 'lat', 'long',\n",
      "       'sqft_living15', 'sqft_lot15'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "y = train['price']\n",
    "del train['price']\n",
    "\n",
    "print(train.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "removed-recruitment",
   "metadata": {},
   "source": [
    "### 잘 되었나 확인\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "employed-order",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>date</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>sqft_living</th>\n",
       "      <th>sqft_lot</th>\n",
       "      <th>floors</th>\n",
       "      <th>waterfront</th>\n",
       "      <th>view</th>\n",
       "      <th>condition</th>\n",
       "      <th>grade</th>\n",
       "      <th>sqft_above</th>\n",
       "      <th>sqft_basement</th>\n",
       "      <th>yr_built</th>\n",
       "      <th>yr_renovated</th>\n",
       "      <th>zipcode</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "      <th>sqft_living15</th>\n",
       "      <th>sqft_lot15</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>20141013</td>\n",
       "      <td>3</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1180</td>\n",
       "      <td>5650</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>1180</td>\n",
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       "      <td>1955</td>\n",
       "      <td>0</td>\n",
       "      <td>98178</td>\n",
       "      <td>47.5112</td>\n",
       "      <td>-122.257</td>\n",
       "      <td>1340</td>\n",
       "      <td>5650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>20150225</td>\n",
       "      <td>2</td>\n",
       "      <td>1.00</td>\n",
       "      <td>770</td>\n",
       "      <td>10000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>770</td>\n",
       "      <td>0</td>\n",
       "      <td>1933</td>\n",
       "      <td>0</td>\n",
       "      <td>98028</td>\n",
       "      <td>47.7379</td>\n",
       "      <td>-122.233</td>\n",
       "      <td>2720</td>\n",
       "      <td>8062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>20150218</td>\n",
       "      <td>3</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1680</td>\n",
       "      <td>8080</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>1680</td>\n",
       "      <td>0</td>\n",
       "      <td>1987</td>\n",
       "      <td>0</td>\n",
       "      <td>98074</td>\n",
       "      <td>47.6168</td>\n",
       "      <td>-122.045</td>\n",
       "      <td>1800</td>\n",
       "      <td>7503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>20140627</td>\n",
       "      <td>3</td>\n",
       "      <td>2.25</td>\n",
       "      <td>1715</td>\n",
       "      <td>6819</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>1715</td>\n",
       "      <td>0</td>\n",
       "      <td>1995</td>\n",
       "      <td>0</td>\n",
       "      <td>98003</td>\n",
       "      <td>47.3097</td>\n",
       "      <td>-122.327</td>\n",
       "      <td>2238</td>\n",
       "      <td>6819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>20150115</td>\n",
       "      <td>3</td>\n",
       "      <td>1.50</td>\n",
       "      <td>1060</td>\n",
       "      <td>9711</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
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       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>1060</td>\n",
       "      <td>0</td>\n",
       "      <td>1963</td>\n",
       "      <td>0</td>\n",
       "      <td>98198</td>\n",
       "      <td>47.4095</td>\n",
       "      <td>-122.315</td>\n",
       "      <td>1650</td>\n",
       "      <td>9711</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id      date  bedrooms  bathrooms  sqft_living  sqft_lot  floors  \\\n",
       "0   0  20141013         3       1.00         1180      5650     1.0   \n",
       "1   1  20150225         2       1.00          770     10000     1.0   \n",
       "2   2  20150218         3       2.00         1680      8080     1.0   \n",
       "3   3  20140627         3       2.25         1715      6819     2.0   \n",
       "4   4  20150115         3       1.50         1060      9711     1.0   \n",
       "\n",
       "   waterfront  view  condition  grade  sqft_above  sqft_basement  yr_built  \\\n",
       "0           0     0          3      7        1180              0      1955   \n",
       "1           0     0          3      6         770              0      1933   \n",
       "2           0     0          3      8        1680              0      1987   \n",
       "3           0     0          3      7        1715              0      1995   \n",
       "4           0     0          3      7        1060              0      1963   \n",
       "\n",
       "   yr_renovated  zipcode      lat     long  sqft_living15  sqft_lot15  \n",
       "0             0    98178  47.5112 -122.257           1340        5650  \n",
       "1             0    98028  47.7379 -122.233           2720        8062  \n",
       "2             0    98074  47.6168 -122.045           1800        7503  \n",
       "3             0    98003  47.3097 -122.327           2238        6819  \n",
       "4             0    98198  47.4095 -122.315           1650        9711  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "standing-sleeping",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['date', 'bedrooms', 'bathrooms', 'sqft_living', 'sqft_lot', 'floors',\n",
      "       'waterfront', 'view', 'condition', 'grade', 'sqft_above',\n",
      "       'sqft_basement', 'yr_built', 'yr_renovated', 'zipcode', 'lat', 'long',\n",
      "       'sqft_living15', 'sqft_lot15'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "\n",
    "del train['id']\n",
    "\n",
    "print(train.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "silent-convert",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['date', 'bedrooms', 'bathrooms', 'sqft_living', 'sqft_lot', 'floors',\n",
      "       'waterfront', 'view', 'condition', 'grade', 'sqft_above',\n",
      "       'sqft_basement', 'yr_built', 'yr_renovated', 'zipcode', 'lat', 'long',\n",
      "       'sqft_living15', 'sqft_lot15'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "\n",
    "test['date'] = test['date'].apply(lambda i: i[:6]).astype(int)\n",
    "\n",
    "del test['id']\n",
    "\n",
    "print(test.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "quantitative-principle",
   "metadata": {},
   "outputs": [
    {
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       "      <td>0</td>\n",
       "      <td>98028</td>\n",
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       "      <td>-122.233</td>\n",
       "      <td>2720</td>\n",
       "      <td>8062</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20150218</td>\n",
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       "      <td>1680</td>\n",
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       "      <td>98074</td>\n",
       "      <td>47.6168</td>\n",
       "      <td>-122.045</td>\n",
       "      <td>1800</td>\n",
       "      <td>7503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20140627</td>\n",
       "      <td>3</td>\n",
       "      <td>2.25</td>\n",
       "      <td>1715</td>\n",
       "      <td>6819</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>1715</td>\n",
       "      <td>0</td>\n",
       "      <td>1995</td>\n",
       "      <td>0</td>\n",
       "      <td>98003</td>\n",
       "      <td>47.3097</td>\n",
       "      <td>-122.327</td>\n",
       "      <td>2238</td>\n",
       "      <td>6819</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20150115</td>\n",
       "      <td>3</td>\n",
       "      <td>1.50</td>\n",
       "      <td>1060</td>\n",
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       "      <td>1963</td>\n",
       "      <td>0</td>\n",
       "      <td>98198</td>\n",
       "      <td>47.4095</td>\n",
       "      <td>-122.315</td>\n",
       "      <td>1650</td>\n",
       "      <td>9711</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       date  bedrooms  bathrooms  sqft_living  sqft_lot  floors  waterfront  \\\n",
       "0  20141013         3       1.00         1180      5650     1.0           0   \n",
       "1  20150225         2       1.00          770     10000     1.0           0   \n",
       "2  20150218         3       2.00         1680      8080     1.0           0   \n",
       "3  20140627         3       2.25         1715      6819     2.0           0   \n",
       "4  20150115         3       1.50         1060      9711     1.0           0   \n",
       "\n",
       "   view  condition  grade  sqft_above  sqft_basement  yr_built  yr_renovated  \\\n",
       "0     0          3      7        1180              0      1955             0   \n",
       "1     0          3      6         770              0      1933             0   \n",
       "2     0          3      8        1680              0      1987             0   \n",
       "3     0          3      7        1715              0      1995             0   \n",
       "4     0          3      7        1060              0      1963             0   \n",
       "\n",
       "   zipcode      lat     long  sqft_living15  sqft_lot15  \n",
       "0    98178  47.5112 -122.257           1340        5650  \n",
       "1    98028  47.7379 -122.233           2720        8062  \n",
       "2    98074  47.6168 -122.045           1800        7503  \n",
       "3    98003  47.3097 -122.327           2238        6819  \n",
       "4    98198  47.4095 -122.315           1650        9711  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "classified-basic",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>0</th>\n",
       "      <td>201412</td>\n",
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       "      <th>1</th>\n",
       "      <td>201412</td>\n",
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       "      <td>910</td>\n",
       "      <td>1965</td>\n",
       "      <td>0</td>\n",
       "      <td>98136</td>\n",
       "      <td>47.5208</td>\n",
       "      <td>-122.393</td>\n",
       "      <td>1360</td>\n",
       "      <td>5000</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>1530</td>\n",
       "      <td>2001</td>\n",
       "      <td>0</td>\n",
       "      <td>98053</td>\n",
       "      <td>47.6561</td>\n",
       "      <td>-122.005</td>\n",
       "      <td>4760</td>\n",
       "      <td>101930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>201504</td>\n",
       "      <td>3</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1780</td>\n",
       "      <td>7470</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>1050</td>\n",
       "      <td>730</td>\n",
       "      <td>1960</td>\n",
       "      <td>0</td>\n",
       "      <td>98146</td>\n",
       "      <td>47.5123</td>\n",
       "      <td>-122.337</td>\n",
       "      <td>1780</td>\n",
       "      <td>8113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>201503</td>\n",
       "      <td>3</td>\n",
       "      <td>2.50</td>\n",
       "      <td>1890</td>\n",
       "      <td>6560</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>1890</td>\n",
       "      <td>0</td>\n",
       "      <td>2003</td>\n",
       "      <td>0</td>\n",
       "      <td>98038</td>\n",
       "      <td>47.3684</td>\n",
       "      <td>-122.031</td>\n",
       "      <td>2390</td>\n",
       "      <td>7570</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     date  bedrooms  bathrooms  sqft_living  sqft_lot  floors  waterfront  \\\n",
       "0  201412         3       2.25         2570      7242     2.0           0   \n",
       "1  201412         4       3.00         1960      5000     1.0           0   \n",
       "2  201405         4       4.50         5420    101930     1.0           0   \n",
       "3  201504         3       1.00         1780      7470     1.0           0   \n",
       "4  201503         3       2.50         1890      6560     2.0           0   \n",
       "\n",
       "   view  condition  grade  sqft_above  sqft_basement  yr_built  yr_renovated  \\\n",
       "0     0          3      7        2170            400      1951          1991   \n",
       "1     0          5      7        1050            910      1965             0   \n",
       "2     0          3     11        3890           1530      2001             0   \n",
       "3     0          3      7        1050            730      1960             0   \n",
       "4     0          3      7        1890              0      2003             0   \n",
       "\n",
       "   zipcode      lat     long  sqft_living15  sqft_lot15  \n",
       "0    98125  47.7210 -122.319           1690        7639  \n",
       "1    98136  47.5208 -122.393           1360        5000  \n",
       "2    98053  47.6561 -122.005           4760      101930  \n",
       "3    98146  47.5123 -122.337           1780        8113  \n",
       "4    98038  47.3684 -122.031           2390        7570  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "compatible-myrtle",
   "metadata": {},
   "source": [
    "#### 타겟 데이터 확인"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "single-prefix",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         221900.0\n",
       "1         180000.0\n",
       "2         510000.0\n",
       "3         257500.0\n",
       "4         291850.0\n",
       "           ...    \n",
       "15030     610685.0\n",
       "15031    1007500.0\n",
       "15032     360000.0\n",
       "15033     400000.0\n",
       "15034     325000.0\n",
       "Name: price, Length: 15035, dtype: float64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "tropical-sharp",
   "metadata": {},
   "source": [
    "### 중요하고 중요한 데이터 전처리 과정!\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "intimate-entry",
   "metadata": {},
   "source": [
    "1. 결측치 확인하기\n",
    "\n",
    "결측치가 있다면 흰색 부분이 나왔을거고 없으면 까맣게 나왔을겁니다. 이렇게 미리 결측치를 확인해야 나중에 데이터를 분류하고 판단할 때 모델이 당황하지 않습니다"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "recognized-carol",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date             0\n",
       "bedrooms         0\n",
       "bathrooms        0\n",
       "sqft_living      0\n",
       "sqft_lot         0\n",
       "floors           0\n",
       "waterfront       0\n",
       "view             0\n",
       "condition        0\n",
       "grade            0\n",
       "sqft_above       0\n",
       "sqft_basement    0\n",
       "yr_built         0\n",
       "yr_renovated     0\n",
       "zipcode          0\n",
       "lat              0\n",
       "long             0\n",
       "sqft_living15    0\n",
       "sqft_lot15       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "train.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "collect-secretariat",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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uysy3AgupUTgB7DxQ0SXNvXOpzvj7USljLgTe235/KvD8iLgYWBfYBjgwM98DSwYmJ81Y2ddusfblqPogM1/YOhd2oxY+fVtm/rk99/uIeNkorYymQyfI/nTqO7AP1aHyQOBYYM3WGfWJzPxOROwJfAPYGDg0M49u75/I4Jmk/87/0vbMzMsj4vBOqqmJPE+0EfzbA28AvtMG66wGHBQRdwZuHhEnAR/NzFOHLKuuv1wMVbqeiogDgS2poPEZmXlZm9K8/Hxo+HQvzhHxauom/5fAqZl5ctu/BnBl58J/EyqQtjHwyMycmpFYbRTa6cCvMvOprSF0ZXvutsCoU2Z34M2ZecVwpZVmz9iIvKOARwDrAKcBbwY+m5mXRsRdqGD7nagp8FcBTwbulZk/HKTwmnWTHBjVzIvFC3luCHyLuj6OcsZuADyJus9aqT3/xU7H/UQGCca1kcu7UDP9VqRGJ340M7/ann8/8Cwq6PrOzPzT2Pv9Tk2RFig6CjguM98YEZtQIza/CDwY+COwd2aeuIz3T8X3QtK1u45tz7dk5mXDlXbmtAE8b6ZmyW4FnEGN6L+UmjF7KfBo4Gxgl8z88jAl1fWZgXbpeigiVqKmcv4gM1/d9t2JxYtx/Ax4Y1twZKpFxDHAo6iR63elgu2HZOZhY6/bHHgONaXtQZn5o7kt6exrMxzunJkbtMcrdEYNvIvKI3dDYLvMPHK4kkqzY6wDbtQZ+Rbg31QgaS3qhv+4zLwkIjam0kBsBlwEbJuZZw5SeM2K7uwGaWSsQ24hsBpwOHAb4EktPcrotTcELqM67id6JN64NnL5/VSn4++ptKEHAL+lFqr7cnvdEdSC0m8F9sjMSwYormbBeEdJRNyVmtW1B7AC1Un9qcx8XkQ8BPgU1en0jsz82ABFlnQ9MV/bnhFxayq93L2Bv1Dxh5cBZ2fmlRGxFXW9/BbVUX2RHdLqMtAuXQ+1kdo/BT5J5dB8KPA64EzqRP8k4OjMfPFghZwlY43jTYB3UrnevhYR96Iuauuw5NTu7ahOiD8Cz8vMHw9S+BlwbSPHIuJhVD76z2bmVp39qwLvoHrc7ww8DLhPZp4z+yWW5l4LFDyPmuHy4bZvNeBU4ObAq4FjW7B9TSpvZGTmPwcpsGbFWAqhlwB3oAKIX83MHwxZNg0nIp5KLfT5xcz8Rmf/Y6lBDE/LzI+MB9OnbeR2RKwLnAB8HXhDZv6r7T8TuATYGvhN557rY8DXM/OQgYqsWRQR61NpKK8E1svMsyLiSCpl0vOA86hFgr9BzQT7I3D/zPztMCWWNBdsey69DiLiNlQn9UbA1i1F5dWvjYjXUKnXbj+px63ZY452aWBLG42XmRdGxF7AEdSUpfOpBYj2a+8BuFNELD9NKUKWUhdrUI2C0wEy81tRq5kfDLw6IsjM92Tm4RFxPvDtzDx37ks+M8Y6GR4I3I1Kd3FSa+h8GzgEeGlEfAF4KbAqcBfgkVSnzBXU6IIV5vwApDkQEW8FHkPlWn9X27diZl4UEfekvicHAosi4jiD69OrE2T/MJXy4ALgpsDvI+JVmfmpIcunuddS7N2fyiv7gpZH9X3UDMFPtce7R8Sp4/cL0xRkb1YDNqDSwYyC7J+hZv48JjN/HRF3iogLM/OPmbnlkIXV7Gn3lJ8ANsnM3wNnRa1pdDfg+6PvQuuw/jk12v1GBtml6Wbbs857rQ2xRBwiM8+OiFcBmwBLSzl5HjVLbPk5KqomyIKhCyDNZ2Oj8e4ZEQ+JiBXbRe99wKbU6PUndYLsN6YCCT+hLoRToY0sG9XFPhFxNHWjf0NgYTSZ+W1qyut5wC5Rix6SmR+f5CA7LLFI1bOpBtGrgP2BMyLioZl5IXWzswdwR2oUwclU6oy3ZeZpwJrU9PCJn/IuLcPpVCfcbalzJFlrWKzQ8kPeE/gdtcDbkwYrpWZNSwUy+n1zarTRU6ig4tbA34HDIuIJw5RQcyXayIORzLwsM3ekgu1vo84BHwFOiFpE/RtUkOBuS3v/lLkh1SH5J7g6yL4xFWT/YUtJuD81E+RqU14n89XPqQXBX9rZtzI1an39iFg/IlYGHk59Rr6dmUeAnwdpms33tmdEvAX4TkSsm7Wey8Lu85n5K+CErIVeF7Z9GZXmd0PgB8BFnic1ztQx0vVARBxL9QrfgEoNsz/w8TayvdvTfHsq3/BjgQdm5i+GKvNMGjvG46iRiecAq1PBtGdm5rFjr7sHNUrtCion+z8HKfwMGDuuO1CLyxwNnEg1gF9J5Yh7emaeFBErUDc1T6DyTp/dRvvfnUo3dHxmvmTOD0SaYWPfjW7H5OOovMO/A16dmV9s+1doN8MrUgu7PT8zfzlM6TXbImI34M9UYOjVoxleEfFwYE/q/PnCzPzEcKXUbBk7P9yUumdY0L03aoMTdgC2oPKzf55aFPnLmfmwuS/1zBurh5sAl7T7x+WAH1HB1Muo78kTMvOMiFieShfyQuo78q2Biq850IJC76A6orfMzJ+3/c8F9qPSyZxDjVJ9XWYeOFRZJc0+254QEbsAB1HrPJ0BPCUzz1tatoGx+lqTqod3UouhvntOC66JYKBdGlhEvBx4PrAP8C8qt/Bdgb2B93am++4OPAK4FdVQWtoUpokTSy5uuCmwL5WP/gfUiLM9gQdRF/pPjF3oNgX+NslTW8eO/z7AjYFtqEXK/tj2b0J1vmwObJWZnxz7GzeiAge7U9Pjt2j7pyrfrOaXscD6QuCGmXl+5/mnAocCvwJe3wm2r9hGtmuKRcT9gVOoEbuHZubLYslFuh4G7EUtIP6KzPzIUGXV7IqIZ1Az3W5PBQx/DrwI+HlmXhERC6jPyc7U/cSjgL0zc59hSjxzxu6JtqTuJz9KjcD7R0Q8DXgTlYf7Xpn53RYkeDI1InGvNCf71Bi/7xu7x7wnla//FZl5aOc1WwEPAVYBPp+ZH1ja35I0HWx7QusgOAr4BdUh/SxqtvyTlhVsb+97MDXg8bnAmzJz37Z/Io5bc8dAuzTHxk/cEfFa4MLMPLiz78tUL/IewHuphuO21JStQzPz13Na6DkQEW+iespvAzwuM//d9m9CjbZ5MEsJtk+qiHgO8NHMvLg9Xoe62N8A+FpmPmjs9XcGDgDuB2zTRheMFmO5D/A04MrM3Lm9folF3qRJMhZk359KA7ER8DVqauv7M3NRCxAcAvwaeE1mfnmoMmtutTQH2wC7UCkR7pWZ548F2x9KremxArAZcPGkXzu0pBZIPprKE3sGcDtgS2Al4FmZeXJELJeZV7bX3wy4QWb+rD2e+PsJuPqe4hCqLo7LtghsG9H/HCoY8icqz+wa1PfhrZm5f3vdVNSDSlSu5X9l5vfH9h8OPBR42LW1JbyHlKaPbc/FIuJR1CDHHTPztKhUtC8FzmUZwfY2S+xgavHXYzPzsLZ/Yo5bc8dAuzSHxoJHj6KmKu1EpYk5JiJWzsxL2vOjYPuu1Mj2f0/rSM2ICOBs4JbUokybjT0/CrbfH9hu0kcmRsQ21EjLJ3Qa+ytQI+0OAtajRgl8tXvhbjc8b6YaSbftjuSPiJtk5p/b717wNRUi4iPUVPcvUHm3n0KNuvsQNSpvUUQ8hbrx/Sd1w3zKMKXVbLmWkUWrAs+grg+/B+6Ti/P1j4LtmwNnZebv5rTQmlXtvuHGwGeBU4HdMvOSNnr9LtRCyWsDd8vMC5YxFXwqrpXtM/4xakbgEZl5Qdu/EnB5O09uArycytv+YyoH9yfb6ya2HpYSCJn3HQYRcUcqddrNqFlfX8rMz7TnnkBdP1+YmR+IiOWzpdySNL1sey4pIm4ArJuL02gtoALtL2MpwfbR8bXr6rqZefbofZN03Jo7BtqlAUTER4GHUaOKAI7OzOe051bKzEvb759vr3sJ8O5pbDyMbvJbaogvUhf8V1DH++/O6zamGs63A9bPzIuGKO9MaBfpW2XmL6LS3/wyM/8VlVf6flTu+T9S+aV/Mvbe/wPWy8yTlvG3J6aR6c2Jrk3UwkwHUYHUUzLzyjb65t3AfYA3jmYCRaWOeA3wiJzgVFK6prEO6vsCtwD+AfwmM38TEatQn5EDgd8C983MS6e1Y1qLRcS61Gi812TmWzoN4QVUUOAk4KDM3GvQgs6SzsjC11KLWN4vF6cDeCPwf9TI/ndm5rHL+BsTeR2OiJtl5rmdx/ej0hdcPGCxrjfaaNN7UffTK1ApYw7OzK9HxPuBTTPzzgMWcdaM3wdP0n2xNFtse5Z2f8BYZ8JyrY0RVKD9ZSwZbL8hcCfgnMz8fed9E3PcmnsLhi6ANB9EZwXriNiJSoHwPOBJwJeAbSJiX4AWIFip/f4IaoGRk6flRB7XXM37ivbzKqpT4XtUj/s2LTXA6HU/BrYH7jnhQfaFmXlpu9G5G9W58O6IWL0Fhb5OTfNeD3hv62C4Wmb+YHSjM7pZGHv+ev85iYibRMRqnYDAZhFx64GLpYGNzg3tRhfqpvYCajrrla1T7i/AC6gUCFu1BgKZ+SFgM4Ps06UFAUdB9mOo3NMfBD4HfDMintw6ZD9IrW9yK+CrrcPaIPv0Sypt0Fqjx+0auygzv0AtlnzroQo32zrX+0XAjYCHRcQWEXEm8EzgfGBl4DURseHofZ1z7BLBhknRRmzvHhF7tMfPBj5DzX6aV7r/y/Z4dE08rXVEPxTYjbqefjwivkGtB3W7iHjhXJd3trWAWUbEgohYLiJWnYT7Ymk22fZcsu3ZOuSvbnu2NsaCLG8B3krNCDqhXW+eDpwA3Lb7NyfhuDUcA+3SHOgECu5FNQiPAk7MzBOBHaggwQ4RsV97/aWjIHNmPmE0rWnSjY1M3DYiDoiI90fEIyPilu25ewK/oUYnPnss2P7TzPzDMKWfGbnk1PWfUSPu7gsc0m54LqdueJ5F3fC8u03bW9rfmsQG8u2p/+1z2uPnU8e77oDF0sC6AVXgQS3oHlTeyHWgOuWiUoKcTy2YfHdg485N/8R2wGnpOp1x76FSh72UCpw+A/g+8OGI2Dor5dqHgFdSn4vPD1JgzYrxYGLH34DjgZ0i4tGtkTy6x1iDmvlw3hwVc0jfpY71BCqN0lnAJpn5dGom4DrAJaMXT0Fw4CJqPZ8dIuJ4aiTmq6kUQvNGdzRlRDw2Ij4IfDciPhoR27Qg808z83BgQ+AN1HfmJdRMhz8PVvhZ0NoYV0bEatT14BvAryLiXVHplaR5ybbnf257dmbD0YLtbwZuCnwFeDtwWJqaUteBgXZpjkTEs4DTqGmcF4wuepn5S+D11AJ/O0TEG9r+S5b1tyZRaxCMGsAfo455S+CB1Eik/SPiHu0Cfg/gV1Sj4IWjEf7TIiKeHhE7tv/xi6jRmQ9n8Q3PFdQNwDbUArgfjoi1lv0XJ8rvqWnMh7ZG4buAVwHfGbRUGkQbdRadgOongSOpwNCPqRzMT+50PF7e3ro2NVrzz6P3TkHwSE03sBoRt6JGZb4FOCEzz8nM46hFUD8JHB4R/9dGth9HNRS3G6DYmgVjn4UNI+JeEfGgFlS7glow/hzgLRHx1Pa6mwBPBDYGvjVAsWfcWD2sGhE3bgFFMvOz1FT3JwIvyMwnZlsYmEpR+Gtq9P9Ei4g7RqWE+iM1Yv8vwBbApzPzXZl5+dJGW06rTpD92cBHqIE8P6dSBr0VOLjzGVmUmYdk5uOpjspnZebHByn4LMnKo7wqNTP2llSA7Dgq1dzxEfHEAYsnDcq2539ue3aD7dTC4j+mgvE7Z+besPQR/dLS+EGR5s7nqN7UVYD7RsSNRk9k5q+pwPPxwB4RMXX5RDsNgv2pm96tqcVe7wDsTTUQXxUR67fA2T2pEVovpUbeTIWofMJbUZ0qt2w3PC+jAkbjNzzfoBqTb87Mvw9V5pmUmZdn5jOAM6mOlhOBD+VSFjrU9GoBoFHjf3RuuA+wGrXg8XmZ+QFqVNo+wDMiYu32unWp0c1nA+bjnRIRsVpEvDMi1hrrNFmbGr368xZIGX12fkI1lhJ4QNt3SWYe0zqwNcHaDEDaKPWMiOdQqfZOBj5Njdx9RGZ+jZrJ8E/g2Ij4cXvdIcD+mXnCEOWfSWMjl59CpVD6GfCxiNgTIDO/lZlfzMyvt9fdnLp/eD1wTE54Wq2I2Jkauf/Qdh+1kJrx9DPg/zr1sCjGUhROszbq9ABgf+CpmflUajHgT1P31XtGxPLd92TmcZl5THv/1MQCWmfUvlRqnGcCu2fmztRi6mtS1xJp3rHt+d+3Pds1ZHXg+cCjgVdl5lthctc00TCm5uIqXZ8s7cY1K7fwW4FDqdEkz20jL0bP/5q6WX4PNTJl6rQL132otDlfzcy/ZuaVmbkP8FoqZ/394OqpaRsAm2fmPwcq8oxroy4/QfWQb9L2XQq8nMU3PAd3pvJ9ITOPgGudPj9R2giJG1Ipgh4LPD1q9XfNA+088L2IeHpn3+upkcjLA6d1PuuvAj4LHA58MiJOpIJMj6UC8v+Yy7JrVu0A3IROiovmbCqI+kSoBlMn2P4F4EJqkWxNiRZMPi0iXtQeP4S6f3ovlSt1W6qD5SMR8bzM/Ay17s321Gy4k4BtM3Pf9v6Jbu90guzbAEdQqWF2pOpgz4g4pPv6iHgC1UH5emCfzDy07Z/ke4gTgT9QM1se0FKI3ZMKmpxJzX7cC64e2bwAlrr2x7S5PXXdPClrYcPlshaEfQGVXusZ1ALSSzWpQaOIWKvbhmoWAHcGvp+ZZ7UOuq2o2U+vzszDW4eu1wvNK7Y9r3Pbc0XgUcABmfmm9n6D7LpOwpnW0syKJfOQ34k2Gjszf9D23YAaefICYFfgXe2mePT+5VuP8tSJiDWpG/+vZObz277lMvPK9vtXgBUz875RuZgvX/Zfu/7rjkJrj7ufjZOB1anFXUf7VgLeRI06+ArwzGn6LIyNyluHyrP6PuDxVH7VD3QDpxGxSrs51JRon/FvUP/7x2TmRa2x/FfqXPnNzLzv2HsC2Bl4CJUG4cfAW3NK1q7QYi0txGUtwPqJzDy3XTPfSaUZe31mHtZeu5AKsH8KOCQz3zFYwTWjImJ9YA8qqL4dcCnV6N0pMy9sr1memil4J+CBmfmrZfytqWgcR8QDqPV93pmZB0XEzYBfUAu+3hJ4X2a+tL322dSsn89n5kfbvomsh6jF6y5qv9+SGqm9OvBC4OR2vlifmsFwZ+DdbfDGqMPhUcCO03Qv1dU6rI8B7peZ32z7ls9a02R9KpXMczPzqCHLOZMi4g7A14DXAB8ctaGiUsydBpyRmc9tQfZjqZHtB0QtFPtqarbsu6f1M6H5zbbnkv6HtudKWWvlrdMGSU7s9VPDmugRHtL1TXQW9IuIw6mFqb4LnBq16OdGmXkBFWB/DzWCfftoORShFvwboOhz5VJqYbJ7RqV/YBRkb65ezHAKguwLOhf2NeDqUVbLtZccQzWOH99es3wbXfAKagTvF6bhszA2EmLtiFi7TVv8SwuiP50KlB0IPLONdiYingTs2jpnND0eSOVff3ULsu8B3Ai4FRUQuHdEbN8Zjbggy5uBJ7f372CQfbp0RqhfFhFbAO8A3hoR67Zr5v5UcGTXiNi3Bd/vQ6UMuQF1ztSUyEr98zoqddSRwBuBSzpB9uXa9XEbqi3z6tF7x0ffTUPjuHUqbEbdFxwUERtQKVOOoWYCngbsGBEHAbSg6iumIMj+Riq39vLtGH5PjUT8F3WOeEgLivySSjP4I2pk+5ERsQM1++l303AvdS1+Ry0cvuVodGbneNek0qudP0zRZs151LoD+1BruIxGtl9B1cf/RcQraUF26vwBsBHwSGqg4TR/JjRP2fYsPduee0bEGp0ge0zi9VPDM9AuzaBcvKDfMdTN3KFUOoSDgScAh0XE3Vpj8TVUQ+FNwHOmZWoWLJ6qO65dzF9DTXV9QzeIGpV/eRXgFxGx3KTWR0S8LCJu1/ksbAmcHBGvaSO0Rx0Lx1ONxWdANYxa8OBS4DnTMGVvbBTB06jpib8EzoyI90bEg1o9bcXiG54DI2Jv4GPA5TlFaYMEwJ+BmwOPj0oD8yJglcz8K/AgasGi3amgwYKsXInLwdX5t68a65zThIqyKizuWG0NoY9T54J7UgtX3Twzf0zlDP05NbvhH9Q59MHAIzLzrCGOQbOn/U/3Ad5Npb64bVS6iBidAzLzXOAH1D3F6H1TN1W3BT4+DZzQRuUeTl0z92hB5r2pgOoO7f6T7rVzEoME7d7nHGoWyxXA6DrwOyrY/m/gbcCDO8H2najF/B4N7AnslZn7d/7eROqWPSJWiU66g8w8jUqnswPVlli3vW5lKkXEv5iyQHub4fBIaiDT26n7hTXbeWEv6h7jQOBNmXlAu4/YsL32MqrtJU0N256LzUDb89JRpz5M5z2F5oapY6QZFhGbU9N7X0pNfR+tYP0A6gT+tcx8Unvt2tSIvPdl5s+GKvNMiiWnqL0U2JDqPf8M1VP+i4h4FfAG4FRqxNElVMPo4cC9J3W0akQ8BjgIeEZmntH2PYg61tsAV1JBg89m5g8j4llUQ/E5Lbg0/vdiGi7wEfEM4P1U+oc/UR0qu1EpQF6fmR9vN3XvBZ4K/A14R2YeNEyJNRtG54aI2I0KoF0IPCQzv9957qbAd6jcw68CPpKZOS3fBZX2fd8aWA84MjP/HBGfBc7JzO3aaw5or/k28NLM/GP7fNwUuBeVZ/OnmfnHQQ5Cs2asobwBtWjbdsAu2RYla8+tSAUOALYArpz088R/OtdFxG2Ar1LpMEYLWj4KOAz4JnWf9d45KewsGa+DiHg0tY7PY1qnLBFxKypIsgqVr/4rWdP9b0SlRlg1a8HkiR3RPy4ingq8mJoB9hng6Mz8duuwfBvwHOAU4KfAqlQg6Q2jzoZp0IKCV7ZryHrU8V5J3VOcmJkXRsQTqe/DucCXqc/IvYBFwL1acPHqtoo0yWx7Lp1tTw3NQLs0w1ov8oeonKHfHHvuRdRIisdn5qfavqloAMA1GsfHA/emFq9aCVifyif6/Mw8PSp34oHA2lTA7bfA9pl55hBlnykRcevM/G1E3A84PzN/HpUa6I7Ugkz3B1ajZjmcR+XqPzUzdx6s0LMoIm5OLUz3dSow8O+2//5Ux9NvqJu9X7b9GwKXZeZv2uOp+X7MZ2MdcEcBT6HOCwdk5u5t/yiv7CjYfjm1mN8HpuGmX0tqjb33U6MM70iNvnws8MNcvG7HKNj+LSrYfu4wpdVsWkpgdYkgWFSu6V2BZwP7Ah+nRnA/iPr8vHjSg8sjo7qIiI1oAWMqH/WVbf9m1PnxuZl5VFTapecBd6VGuJ/f/TvDHMXMap2zu1CLnj61c4zdYPsOVLD9srH3TkU9RMSTqbQPn6JSpDyaWhR398z8XHvNy6mUCDelgkmfyMz3tOcm/l6q0yG/BlUPF1Bti1tQqSdfCXw0My+JiE2o+4ebUjPpfkgF166MztpQ0jSw7bkk2566PjDQLs2wFmj/MPCwzPxKdBY3jYiNge9RjcIjhiznbIqIPalGzxbAj7JyMW9PTetdCGyZmT+Omv56O6rB/JfsLEYySSLiXsCPc/GCXRtQDeOfUp0Hv+i89n7UlNcdqKlsm1GjDW6RLR/cpGr1cEabgjjadwfgdOBVnQbfaETSo6jp8Nst7fswLQ3k+a57wxoRBwM3o0aYPIrKrXxgZu7Wnh99Nm5K5WA9C7hvZv5rmNJrJsXYItcR8WJqEcN/UwtwfbLt7143R8H2U6kRzVMTbF/WqMr51Mgb66B/JPAIYFPq2vDlzDy9Pbc+db54DtU5fzq1OPJHM/NN439rkkTEfagOptGijs+l1iW4AbA8dd94MPDJzPx3RBxL5WY/kkqp8nQqyH5Ie/9E1sO4TufrctT940upHNxPGgu2nwjcmLqvOmkajr0rItaiRu1fQaVDuTwi7gycDPwFeGVmfrq99sbtdYtG181pOp+0WSxfp2bC7gacTc2c3QO4G/U5GY1sXwgsHLvmOJJdU8G2Z7Htqesrc7RL/6Noi/UtxcnUgkyHRcTaueSiIitT05f+Ptvlm0tt6lX3902pEYjfoQIotAvdgVRj6HntZveCzPx+Zv5igoPsW1ILkT2rNQDISn3zTmqUzVvbBZ/23KmZuSeVSugEakGz/abgRmdUD9uO6qEZLVK1TmffovY5+TyVc/kR7W8s8Z2a5Bud6KxTEMtYs2A+GAuyv5cKrh+WmV+j1rB4I/DqiNgPanHkdjP8J+C2wJMNsk+Hdl44KyKe39m9NtX5ujrw6BY0G+UNXb79vitwNPBEYN9p+T61z/lVUTmXt46IvSJim4i4Q1bKuYnNkXpddILszwY+AGxAjcbdi/p/P7a97pfU+eJtVAD6dCqVyCjIvmASrxnt2nkqde1c2EbcHUTdQzwNeBw1EvFtwIva52J/Ksj+ZOD/6ATZYbKvnSNtBPcOEXHjNvr4UKoObkXlqr8xXJ2z/YlUurE1p+HYuyLiCdT34knAz1uQfWFm/oiaNboO8MbO9+T8dm89CrJP20J+d6fuDQ7JzNMy87zM/DKV+uH71DniiRFxg6w1XS7vvtkgu6aBbc9i21PXa5np5uZ2HTdqhMTo93tQucUfQOWEhBp99yeqsXgnatTVbanc7WcDNx/6GGapLm5DpYP4NvDFzv7lOr9/GPhVd98kb8BNgCOoDoUXjT4D7bndqdFXnwVuP6ovYEHnNat0fl8wF2Weo3pYsfPcV6hpercfe8/a1FTwfYcu/wzXRfc78WJgS+DGQ5drgHqIzu83pKZ6PwFYobP/ZlTQaBF10z/aPxXnB7clPg+3BF4O3KCzbw1qVtPO7TPwLuA2S/scUCMW7zD0ccxQXSxoP1dv9wm/BP7a7hvOB54wdBnnuD4eTaV32KU9XotKHfXPdj/xyM5rN2z3ETt19sVclneGj3107byESgGzPZV+cM3Oa1ag8rKfB2w29t61xz9Xk74BAXyknRNeAtyo7V8OeEW7r/pa97oKrDZ0uWepLvakRqhfNPoetPpZvv1+h3be+BWwxdDlnYP62Jxa0PRx7XH3fvrh7R70Z8ALgZWGLq+b22xs2PZcVj3M27an2/VvG7wAbm6Tto1dqI6icotf1bYvA49uz21LpT34d3vNT9vN8F2HPoZZqpfPAO+jFic6ggoabN55frn285BWFysMUc5ZOvYbU4upXP5f3vBc46aGCQ4U/Kd6AO7Z6uB04D6tsbw68FxqdscThy77DNZB9/xwTDsH7EkLFMzHrZ0Pvgf8ALjdUuppFGy/jBqlNniZ3Wb0/79w/HcqN/vbxl63CxVYezdwy7ZvRSpgssHQxzEL9bICNQPuS1TKg4VUEPnXVFDttkOXcY7qYQ1qxsKh7fHGwL+oEcxPap+J71Kj10fvucHQ5Z7hOli7nScvozpd3tl5bsXRMVOdEYcv429M/D3E2PEs384Tl1JpU8aD7b+hZk6uM431MHaN3LF9D06j04ZgcbB9faoN8syhyz0H9XI7Kjf7W8friurM/QW1qOEXp+Wz4Oa2tA3bntdaD8yjtqfb9XNbDknXSS5Og/A+4CHUDf9fqRP9O4BDWx7aIyPiVGoE562pRsHHM/PsQQo+w2LJxQ0fRR3jGzPzDxHxFipn6Ksj4pLM/FZWSoi1qAbBzwcr+CzIzPOjFuuC6kggIo7OzIszc7+WAWB74C0RsUu2xVfG/sbET1VbRj0cTgVZdwDeRI0w+AUVSNmEys994tyXdnZ0zg9HA/ej8gn/ODP/NmS5hhIRqwC/Bx5Dpc66KfCbbKkxspwbEYdSUz23iYh9MvOvAxZbM6T9/z8YEcdl5oezUqWsQzV4XhIR/8jMvQEy883tXHkQsEJEfBp4GLVo1+0GOoQZMUqhNJYreQPquvly4CetbjahOqv3zsyzOu+b2pyhWbmUjwcui4gbAR+lFivbKzMviIj9qdzcu0bESpl5fGZeANOTSzUz/xoRu1IdLDsCf4+I9TLzD5l5WUszdEFEfAO4W0urdGX32Ce1Hpb2P2yf+ysiYjuqA+qgtv9Dmfm3iHhr2787NZv0Y6P3TlE9dP+3b4uIlamc5HtHxGsz88xWR8tn5i8jYq3R92IaLCuXemb+JiLeBLwuIn6amYd1zqm3pe4vHwD8NTNzWs4R0jjbnsW2p66vDLRL/4OIuBu1gvdrgI90gms/AT4H7BURp7eL2kHDlXT2dILszwHuRfUYf6s99+OI2IpqMB8VEZ+kpjw/kOpVvk+O5U2cdK2hfG03PIuoYMGREfGYaWoQdS2lHgJ4V2Z+MiJOA15FpRj6E/CWzDwepm6xrs2p3KnbZ+Ypbd86VKfbSsAPMvPU4Uo4e8YbtVkL972bSgGxL7BTOzde1m0EZ+Z5UXna32CQfaqsSX3u12+dridl5l8i4jXUiKI923d/T7g62H45de54HDWadbNJ7qCOiLtQOYPf0oLKo3Pd+lS+6dNaMPWZ1MjuPTLzwIhYnaqfQzPzjwMewqzLzE8ARMTjqQDq26lF0qE+A/+mcpEvHHvfxAcJRtq1c3/qmrkDsFVEHJ6Vb/vKln92BWrxy2k67hpWWQM2VsrMj3c6pa6IWhg2gAOAjIgPt7p6M5Wi8PsDFn9GdK+brR4eDmwcEV8DTs3MkzPzjS2X8KvbW/bOzB+1OgpqgeCpuJeKxYsWrkp1RN6B+sx/ggqYvR3YCHh31KKwp1AxjZ2pe42/tPuLia8L6drY9iy2PXV9ZKBd+i8sZWTFDaiRE+fl4kXLFrQA8zbUVPAHUakjRn9j6kZVRC1WdSQVRD8iMy9tDQHaxe0BVEfDs6hG81nA/TLzJ0OVeTb9hxueA1qj4axpvdEZGauHtwJExJFZo7pfPf76Sb/RGTUKO7uSGrn9zzby8IlUw/By6tzx14h4WmZ+d84LO4tiyVkuy1FTUq/IzD9HxLFUKoD9gHdGxAvbc91g+5+GLP9MmfTP80xpn4dzI+K2VCfsm9p35YQ2KvEQYAGwe0TQCba/LSK+Sc0S+3FmnjPcUcyI3agUKAsi4k25eHHfn1MB5Ae0kWdHA3tm5v7tnuLBwF2pEe5THWjvWBe4PXB+C7StRHVOvgr43LScI5YlM/8UEftQs3/2B9aOiI9So/AeSAVgXzJ2vZlo7bO+MpV3/58RcUVmfqoTbL8yInahOqZeTX2Pjm0dst9vf2Oiz7mdIPtzqJmx36XuF14KPKV1Luzb7iOvor4P+7dg+/fHOrcnth7g6v/lla2j8dtUwOwyqtN2S2ohx5dTKSJ+RM0qfgH1Hfkp8FiD7JpPbHuW+db21PWfgXYt09gIi6kLEv+32rGPgkdHUivaX0nlQ9wgIj7XGgSjk/VPqJF6G3b/zjTWX2YeHxGvo0b2PzciPpqZP4qyMDO/ExGPpnKiAfwrMy9e9l+cfEu54VkUEce0G569Rq+b9u/UUm54roqID2Tmv2HJ45/0G51R0CMiPkDlXP49NUXxXVQqgI2AY6kReZtRazvciWpMT4WxIPve1OjTNSPiLGC3rFHMh7eX79ded3WwfZhSz45cPMPp3cBHM/PL8/SGPgAy87cRcXdq0c+Xt+/+8S3Y/pb22vFg++kDlXk2bEONxH4hsDAi3piZF1IjL38BHEwt6LVb1kj2BVSqnFcCfwC+M0iph3Em1SF/eES8lxrxvwOwwyjIPu3fpXau3J26x3wltRjo6dRnaLfMPBym6x4ia+bTXakFTt/U/scnjf7PrbP2TGBT6n7iq1TKxtH7J/7z0M6RBwKvBd7f7qHuSLUp7hsRN83MP2XmQa0Dfx/qHmOqtPbUcsBx1Dny2dR58HLgbcBTqMEMO2bmvhHxQeDm1PflO6P3T1NnlPSf2PYs86ntqes/A+1aqqWM4F5IBZfnlW6DLiIOAJ4GHJ2Zp0TEl6nRJl8CzuxcuNaipnCeNUSZZ8tSPhMAZObr2gib3YA92gibX0bEaDTSxSyeAj4vdC70V1ENoZUj4u3dG/9pvtEZWUY9HJqZi6bh+MeCyzsDjwLekZnfioiDgc2pUVYHZ8sF2EZonkcF4KdGpx4+RqXN+TzVGH4A8L2IeF5mfiEijmhveR2wekRsnZlXDFLoWRQRa1ALXN4jIjaf9pFE40ajEtvv76Q+75cB9wXWiIirMvPEpQTbV8nMnQcq9oyLiJUz85KIeDo1YvfptTsOzMxzIuLlVLq584E/RcSaVF76namR3NvkNXO7T63M/GYb0PAsakbg+cB+mfmBzmvmQz38NSq90oXUiN1vUNPgz4HJ7mwYD/R0gh5nRc2C/CbwxtbxdlJ7z2pUoPWx1ICNHw5Q9FnR+V9uTH3eT8jF6dP2Bc6hOlj+FBHrZuZ5WSkhPpdTkDZnGW5BzWB4R2b+qvOZeUlEBHV++Cjwqcz8LfDb0Rvbfdm8a69Oo0k+zw3BtmeZ9ranJoeBdl3DWPBod2oEyRoRcQY12uLv037hG13cO0H2DaibvufT8pBTo7jfB5zQTujfpqY27gysSK14PxWiFlu6ov2+OTXd/5zM/AVAZu7TAogvoXrRX5uVn34qLmjjN3v/zc1fu9DvCaxBLVo28Tf+M1QPU3Pu6JwnN6dyiO5Lm8qemR8CPjTWWXczYNf29m/PfYlnV0TsRAWXnwp8swUIn0w1iO8eEV/OzH9ExPupVAE7UOlBzhuqzDNlvCMyKxf3ocDe1Gyfty2to3JadT7zx1CLAu9NLQx8M2pmx/4tkDYKth8MrAZsF5Wn+vxJbxC1z8QlEXFDalT7hVQamJdQo6zenJlfi4hHUiOvDgQOA37dti2zUigstZN7ElyXa0bnvmu/iPgEcEPgksz83n967/Xd/3jt/HP7XtwC+GMnyB6TWg9NULnW70OlCVoH+CTwtxZsvw9wGnBQu2aeTHXQPY5aE+lUmPjPQ7ezYVWqQ/7WwI0y8zftNZ+hgu+PzcwzI+K+wEMi4rCske1TkTYHljrKdkXqcw9UcLAzSn1H6rPwBOBT439rUs+VWtLoPBcRKwCrZOY/hy7TXLPtWWx7alLFhLdjNMO6NzttZOI9qZE0y1OLf54L7Al8ZhpPWhGxQrZFOkeN24g4jDr2q4AntQDyKAfxfanpm/elRm3/mbpBfFxmnjHAIcyYiFiFWrj0tFw85eqDwCOpgMgVVJ7EYzPzovb8PlTw7CRg/8z82RBlnykRsVpmXjS6qEfEbTPzOs1UiIiVMvPS2SrjXLAeli0i3kA1/P4FPD8zP9/tmOq87klUAPrBwMOmaUTeSBuJenNgq8z8Z1Ru7u8CnwW2y8xLOq+9EdV+/vswpZ05Y9fNB1O5MH8bEQupc+HtgPu3G/+JDZpeVxFxb+B4YA/gqLGO669R18vXZOYJbf+tgUtzivJwR+VGPZ1agOtLVLD9+VTH3MHAQZl5QUSsR11Xb0flbj8rJzgFwv96zVhWA3opgbiJ4LWzRC30+6PRdS8inkelN7icCjT/m1qj4F2Z+fOIuA2Vi/u2wCLq/vvgzNxviPLPlojYgbpnvjewFfAWqr3xWuAuVJD9hxGxMtVZeUcqR//Ed06PdNpaC6m1rq6IiFtQMxt+DDwvM8/tvH5VaqDCqZn5wmFKrdnUOV8uoD4HvwRekZl/Hrhoc8LrRrEeNOkWDF0AXb90ggUvp0YmPgN4ZmY+Gdge2IRqCC4crJCzpPWafzEijoYlRkV8gVqga0NqZB7t+Ssz86uZeX/guVQqhNcD95n0IHtzGJX+4VERsTBqMap7AM8DtgY+Qk3J2ikibgCM8useAjwT2Dkqj+REiogNgVdFxL3bBf75wOcjYv3r8ndGF/iIWu1u0lgP/9GxwNlUgPkJAK2hePX1NSIeRy3EsxbwoGkIskfEikt5vAlwYQuyr08FGL8EbN9G9r4qIg4CyMy/TUOQHZa4bu5PHe/bIuJJ7RryPGoB3APaa6+awu/AtVkH+EcnyL5cZv6cqpc7ADtEpVUhM387ZUH2AHanUsDskLWY4duo9QtOBnYBdomINTLzD5n5s6xFIH89CjJMaJD9f75mLGsAx4QG2b12AlG5148G9o6IO0bERtT3Ym/gQdQsl+Oojuh9I+LWmXk2lUJpOypP/dNHQfbutXXSdP+HrR5eSM34uhQ4ggoofpNaBPnRLci+CpWX/LnASVMWZF+uXRNXAw6nrp2rZM3eeD21+O+LI+LmnbfdkhrQ9Ju5L7FmW/tMLIqaJX0f4B9Um/OVEbH2sKWbfV43ivWgaWDqGC3L3anV3M/Imrp8ByrwehzwnvHRmlNiTWrE/iOjcnntBJCZH4uIC4GPAdtGxBm5eMrmwsy8KjOPGqzUs+dV1GiiQ6lGwK2Bt+fiXNMnU/kk39AevyMzL8jM10TE5cDHJvVzEhFbUCktngo8PCof/25UTv7/+uZ+bBTe8tTorYlhPSwplkwDE1l+GhFbUo3lLSPiR5n5rk6gbFFmfjIi/gj8PjPPH/Qgemgjzu4PfKtz83oIcEjWlP/PA8+KiGdQ540vUiPZL24jFO8G/HEaR5i04M+l1EyfBI6KiIdTwYODgBdGxNMy87hJDBr+jy6nZnptFBEnte/CKHD8I+BvVKBttYj4ZLaZUdMiMzNqlP6/MvMnUB1SmXlZ63w7g0ojExHxphzL47+soPP12WxcM6Iz03BSeO1cLDPPiIhtgPdS54NTqJzax3aCxi+KiL8AOwFbRMQh7Vr5ke7figlPk9L5TN+VSsv5R+AdwGXtnmFXKsB8E+q8uQmVPmYHapbo+9v7J3KGR9eoIzEiVqdmvp1DDWwaBcSOAm5DfW82i4gvUdfWrak1P95yzb+qSTb2mfgKNVP0EmrNs52BVSNir0m+j742XjeK9aCpkZlu83wDVhx7vCrwQ+CI9vhOwN+pIPuqbd/rgFcOXfZZqIubAe+mprS/fey5x1LTWz8D3LWzf0Hn9xj6GGaoHha0nzcFfgD8hRpp87Cx190AeCM1rXdXYM2hyz4Dx/4+6oZ/HWoq75+pqctvvo5/Jzq/P5sajbT80MdnPfzP9bGw8/taLJ7Zs1Lbd0fgTOBX1Aju0WtXGLrsM1gHm1AN4c+2xx9t54YN2+OHA7+jgs1f6LxvbWq03q+B2w99HDP9eejsW5UKIH+YCo78CPgEcCI1uv8903CO/G/qovPc0VRA/X5j++9CBVI2Ae4w9DHMQp2MUjO+iQoq3qHz3Irt5yuozpl/Ay8YuswzcMxeM6yHazuerdu14c/AFzv7V+j8/mngzKHLOsv1cKv2ebhodC3tPLcilbLzy9RMuQupNscLO69ZMJflneW6WJ7Kz38KNbBndN6MzvPPowKtFwE/odKRLd+eX+a1x20yt/Y//xKVtnbjtu92VKD1Cmom9dpDl3MWjtvrhvXgNmXbxE6/Uz9RqUDuDZCZl7V9b4zKLXwxlT/1kRHx0Pb7l1k8MvFWwEbATWIsfcCkisq3TlYewCOBr1Oj1w8cvSYzP0X1rj6IWsjtLm3/os5rJnqECVw9emwRQNY0/kdQeRJvDzysTfGkPX8BtQDkG4H9qDqb2OlZEfE04FHAtpn5F+AyaubPP4D7RcQ92+vi2o5zbDTejtSNw8U5ISP8rYclxZILRB9KnRN/SX0vdouIW2YtDLwVNfrmlRGxHUBO2EjM/+Asalr7ZhFxDpVX9r5UXZCZX6DOA/8E1omI7aMWin4PsAXw5Mz89RAFn2mdz8N+EXH3iFirXTtfxOJ0CA+iGourUaP5t6NmBEyNse/GUyLi+RHx0Db7ASo9xC+B4yPieVGpIzYBXkaN6PxNZv5qkMLPoO55sI3KG90LnEKlOtg6FqdYu6w9txp1v3Ew1RE1sbxmFOth2TLzg1Q6yjWAzVv7gsy8PBanGTwNuFVE3GSgYs66zPwd1emwCHhg1GLqo+cuy8xvZ+ZDqPVcNgaelpnvhskf0b8U61KdrR8Bfjv6zGdmtu/AFZl5BHXdvAvVHtkyKz3fcjlP1juZZ24EbAB8KjN/DJC1QPDbqLULtgd2jYh1BivhDPO6UawHTZ2hI/1uw2zUjc23gaPb449RPcV3ao8fSQVVrqQudqP3rUM1DH8F3G7o45ihuuiOSD+KGoH4e2qk2SLgrWOvfyxwAdUguPPQ5Z+pOqBu/O/X2XcK1bkCNbL9a9To1S255iyINalZDnca+lh61sNzqSDhbamg6RXt5wuo0bjfAu79H/5Gtxd9x/Ydet7Qx2Y9zEi9HEuN2H5tq4vPtHo6Ebh1e80dge9Rs4CeM3SZZ/DYRyPMFlAjtRdRHZLLtf0rdl67FTUD6i9UeoyjJ/3csIw62aB9Hy6iUuXcve1/R/us3BBYoZ0/j6AaCxsMXe5ZqosPt+/Cxe2z8a7Od2JDahTiaBTnue2zcZehyz1Dx76w/VwOWBm45djz+7Xz3+uBO7Z9d6HuIV4y/ncmcfOaYT1chzp6MjWF/ySWnB26HBVM+wlwo6HLOQf1sGWrh4/TZoWN6uE/fS6mZQMe2K4Ld13asVNti1WW8r6pGdXvtsT/NYD1233Ejm1f997yDtQ9+CJqceTByzxDx+11w3pwm8Jt8AK4DfSPr9xXr2kXq7Opqc0bsWR6hN3a/p8CT6Rydn+UCiDdZehjmIU6eQ81XelhVD7yjagA2gVUDuLua59MBQvWG7rcM/h5eHf7PNyfChL9BbhX5zU3Bb5D5ZRcWrB94hsBwKPbxfzMVhcv6Dz3/Pbct7sXemqEzdPG66Bd4K8Cnj/0cVkPM1InjwP+1H52O+f2p9YqOBRYo+3bgApCT0WalM6xLqSm8B5FrdnxF2ra9wrt+ZXGXn9jquE0FelzWEbjngqgnklNcX1Wuz78nlrAr/u6tYY+hpn8LHR+36bdJ2wObEblE76CGqV4+87rnkiN+N+eFoSf9I3FQfbVgGOojqXzqY6V0cCFlaiZX4uAP7S6+iPwfZYRVJu0zWuG9XAd6+lpVJD5G+2c8GBq0dNLgZcNXb45rIent3Plx4CNOvsn/n76Pxz3qOP+llQH9CGdc+no5wJq0dy3T8t50m3pn4Ol/P4FaibcKEVQN9j+ufZ9WTQt50avG9aD23RugxfAbcB/PqwO/KydzLqj1rsXtG2oEd5/o9IkfJDpHJm4dquLA7o3dO0m8JhWRweOvWfVocs9w3Vwd2rxwlEOzfXGb/apBZq+TXUyPJmxwNo0bFTnymXtJu+uY8+NLvTfAZ5CNRYvBXYae90Ok36Btx6uUR87UiMtbtQed/PKfogKwq/b2TcVuQBZSnCZGnm4KjWL5S9UXt1RrvrlqTyzN+28fuKDBiwZWL4BlWd3lc6+BwBvbdeKo4BftPPkrYYu+yzXy8Op1Cfj185RAOkjdEZrTtPG4rVMVmv3DydTQcOt2ufgROAendc/igq4HwHsxeLZIFMRRPKaYT1cx3raisUzR79LDfDo5iKf+OvGf1kPo3Plh2k5qadtYxmzddq587NUx+M2nXPiAmp24FepGWLz4rMwn7bO/3rUsbJy53//+HZv+SWWvNe+K9U593DqvvPrVCqqif98eN2wHtymbxu8AG4D/vNrkdMjqZGJi4CjOs+Nj0y8FZ2F/6Zto4LKlwM7t8cLWNyIvj01qv0K4P1Dl3WW6+G49lm4Cnhkpy66vcQ3aTc6lwFPGLrMM3DMo5E1K1Cpkf5GpQT5PbU2wcZjr38uNSLxMirwutfY869udbjt0MdmPcxovbyo3dB10yuNRnLfuR3rFuPvm+SNJYPL61GNnBU658bVqdHcfwE+RQXgV6Rmx3ynvXba6uEdVGfjZVR6rb3HXrs5lRJk1Il9EFM6zR14IYtnxT2n7YvOd2YUQPrQ+PljWrb2GT+eGoF347bvOGpU+4VUIODey/oeMMFBdq8Z1sMM1N0T2rF+AbhZZ/9UnjOvpR5GnXOfZ4pmPrVjGwVUV6VmSr8d2BW4Z9t/UypV6R/bc/cEXtruIb7fef803Et021JTMRjjf6yH8ZlgX6M6XDZv+1dun5G/UIHVV1Cz8H8AnNJe88H2GZnIz4XXDevBbfq3wQvgNof/7KWPTFyeCpy+pp2Yju48txw15bk7Sm0iL2j/Rd0sBL5CLfR369G+zvNfp6aE/5zOSM1p2ahg+ortArZ1q4urgEeN10V7fFNq9N4dhi77DNbBaKTyLdrPnduF/iuM5eIH7gE8Bnhotw7bzz2AHYY+Huvhfz7+ZaUH2ax9J94DrD323OOowNrdhi7/DNZD9/x3aLuxvYRKr7UdbfQ+FWx/XTv+s6mAyYXApkMfwyzUybHtu7A78GIqgHx1JzWLGwzrUR0zX2VKc7J36uS9LA6U3Xwpz48CSEcyJYEFlkwdtRGVUu8R7fFxVHqYO1HToC+l0ivdd+hyz2J9zOtrhvXQr20APIeWj7nv3xp661kPz560//t1OLbRDOo/UClJ/0p1wr60PX9TqrP+ryzuvP04Y6OeJ3Gj2lePY8n1CN4PPHrosg1cLytT6/18t10jR2v/bNt5/rFUAP5f7bPzCaqtugpwKjV7cMUJP2fM2+uG9eA27dvgBXCbo3/0kkGTtanG4QIW9yqvSy3wt4g2arvdGH2A1ns8Ddu13axR+SH/SU2BX6+z/xZU7+qTgXWGPoY5qov7sjjY/ui2L9qNz8Pa44keccSSI0vu2z779xx7zS4s7lVf5sK3k1wX1sMS5e+eJ28JbEo1AEdTWndt34mDaaMsqNk+R1ILuN1k6GOYhTo5jhpt9rxWFz9rn4W9RsdLjUranhrZ+0k6uWanZaNGXv6eyiU8SpPzgPZ5OIaxPPRU5+2Kc13OWTz+ZX63gfe188brGOuEas8/mSlJOde5Z7oRixc2fWj7f7+QChDdv10vV6c67xcBP2RKUuh4zbAeruU4Hjv6XvSt00nerIdr3E+9jhqcs0l7vBm1YPZVtLQP7T5iXeA+wM1Z3HE9sbN+WvnXolKonUXNfjyRGoywydBlG/gz8RSqbX3b9ngjKuZwFZ3c3O2527F4DaS12j3HP5jAgQxeN6wHt/mzDV4Atzn4J19zZOIP24Xse9SovFXbc6Ng+xXU9KwvUIHnew5R7lmuhx2o9AaH0cntBRxOrXZ+IhVQeRxwdDvRT2WQHXgINYr9kXRGJFLBgq9QK3Y/gepweE+7GK4zyQ2BsQv87YCdqJGHfwP+b+y1o171L0zbjbH1sMTxdb8Th1PTVRdRU1c/weK0EK9t+89p59LvU6O57zL0McxCnbySWpToXu3xy6npmt9q54W9WDyyfYk8m5O6UbO4rjFTp93wn8Xixt767XvyQVquduA+Q5d/luqk+93YgBpNdAeWzFF/LGPB9km+RvyH+li5ff8/Ryc9UvssfIYlR7wfT6VDOIkJHpXZOR6vGdbDsupki3YOeA7/wwLY03K+sB6WOJZV233Ce7hmHuV1qHbY5cDdl/H+iQ+gUYPa7kytc/Z3ag2szYYu14D1sUq7Vh4GHDr23O2pUepXAc8b/xxQ7dQT2/X3LkMfy/9w7F43rAe3ebQNXgC3Wf4HL9ngOw74DTWl/TbA76hg0mtZHGy/CTVy8QvUQiNTMTJx7KT+MSoX4FeoKWv/pvIi3rI9/xoqRcwiqqPht4wtyDHJ29hn4n3tc/AHKnD2CVq6mPb8/algwiJqYZI/M0UpIYBtqUDit9u2iOpoucfY615GLXb5Xdr0tmnarIcljvGY9p14OfAwqoF4HhVkHQUQH0bl6v4wFVicihRKLBlQXZFqIO/bHu9EpY7Zoj3+MtVo3IPOIrCTvFGp1M4B9mYs2EGli/lb+329duzHAau1fU9u59NrpE+Z5G3sevHedg8xOj+8D3hw5/lRKp29mbLZHSyZQu/l7bq4YWff8lRQ/Tu09HJUcOU04N6d1018sL0dh9cM66F7fOtQA3leOzon/pfv696bT3xu8vlcD1QH7KPHrhmjWYCXAc9u+1Yce8/fWLw+1tR0Miylfr7E4rQ49xq6PAPWw4btPuvqGfRjz9+eSq1zGZ2UUu25TamBcrcf+jh61oHXDevBbR5sgxfAbRb+qTUi745j+17dTmb3bo9f1G5+fkhNYduLxcH20cjEVYc+llmom72ogNn9afliqd7SRXSmqgE3o6Yv3oMpCxh0jvH97YZvlFv2fe3G5ussGWzfgMrJvN+k39yMHf+DqR70l7J46uKzqFz8/2ZshA0VUHzu0OW2Hmb8+LuNwvu2m7ktWZwbdHngmVTn3NdYPKJ5dJ6cuoYh1Ti+ORVQvjk1evus9hkZpdEZLYS5iBr5PvEjz9pxPRG4Qft93c7+p1F5Qt9ABQaO63wWbtoefxBYfehjmKV6+QDVIbtd+37sTs1+O422cHZ73VHtM7HrtHwmOse2KrAP1fm2/1Ke37bVyWeoUeynt/qZiuB65zjn9TXDerhGYHgrqpPpe3QWBL+Of+NF1OjmGw59bNbD/1QPK1Cz+14/tv+WVMfDv4EvdvavODp2Kuj65qGPYZbrZ2UqB/8zqTSDP+daFsmetm3sM76ASh30LWrG6APH64Ea5XwS1R4df26ir6fz+bphPbjNt23wArjN8D+0FjD9JDVF7a5t3yrA24A3tMc7tpPbY9vrf0LlOtub6zACYxI3qgF8BItHId6GSvtwDBOe8uA61sMzW2Pgfu3xbtT0zYPaBe50OsH29pqpuiGkgoN/aJ+BbrD1UdQ0tX+xjJkM01QX87EeqM7IrTuPR9NSRws3jma3jDrjlgdeT420GKVRmchj/y/q5latDvbr7NuSCi7fubNvByrg+C6mIPc01eDvjug/mprptFFn3+db3XyDlkqs1dcRwLn0yMl7fd6oXPS/pTobug3mxwEXUVO5b9rZ/55p+Ey0Y+l+Jh7B4s6lN3f2d8+bL6Puv35OBd2WH/87k77Nx2uG9bDMOlgZeHrne/G0//J93fPIDu29zxn6eKyHXnWwZqcu7s/iTvl1qTbo1QuHd96zKTVjcJehyz/DdbHUTmYqyLwpnWD72Gfo/5iutV0Wjj0e3WsvRwXbf9rq4V7j50RqkMfo9VNxvmzHMu+vG9aD23zZBi+A2yz8U2v0+i+phWc2bfvWowICG7QT2A4sziv7AiqAdD7w6qHLP0t1shC4IZX65LVt3wbU9P8Ps3g0/2uZBz2mVJ67w9vv21GzGp7RHj+/3RB/AXjS0GWdoeO9xkUZOAT4R+fxCp3f39Dq4CLaGgXjN4yTuFkPCbXg1uepUVQvHnvuwe14n9LZNwqUrdWe23boY5jh+hhvCAXV6fp74DFt3+Opztjnt8drUyO49xm6/LNUJzeiRp8tojphRwu4rdE+OxdTHdrHUbMczl1Wg2ASN8aCBNSiZVfS8sq26+koL/kLqNlxDxi63LNQD6NjXAlYv/3+JCow9BPagshtfze1zNrAmp33T+xifl4zrIdrqZNHA6e337dpx/vN7rlwGfXWDS7vyFg+5knbrIdrnP+OogbuPIbFC4d3g+1fbteNXagZP9+f5HPkUuqi2zl7dyqt3D1pKS9YHGT+CRVofiDVPj+cSs12g6GPYSY/E9Rgv53a+fJgOvnpqU6Hn7GMYHt7zcTOjPO6YT24ze9tAZoaEREAmXkgdQK7GfDmiLhbZv4hM38H3I2a4vfZzPx3e+uNgFOom5+Pz3nBZ0FELOw+zsyrMvMf1Ejth0fEfYBTqZx5z8/MiyNifeAuwM0iYrk5L/QsGa8LgMz8HLB7RKxMBdpHi7VBjda8gLr5e0FErDpXZZ0NERGZme33+0fEo9pTnwRWiohXAWTm5RGxfHvuzLadAZwSEffIzKuWVpeTwnqAiFidmsmxHJUKZpeI2Knzkj9R+dm3j4j/A8jMK9r5YENqmuvv5rbUs6d9Jq5qv28C0D4jn6HWp3hqO0d8j2oM7RERp1AB5odRC2BOlYjYFzg5M4+iFgt/BrBrRGycmRdm5iOomT//oIKpn6eCzGcMVOQZFRELM3NR+/3ObfdFVAfM7TufmdH945eolGObtvfEHBd5VkTEgszMdq47FjihfQZOoKY6rwfsHxG3BsjMK0fnxcz8a2b+s71/QWZeOdRx9OE1o1gPy7QBcLeI2DAzPwC8hAoo7hYRG0NdT7rnhLG63BF4K7B9Zh4x56WfOfO6HiJiuXb+WzkitqQGLP2Aalc8NCJWyszzqPST76BSpryDSitzEnDf7vlzkrXr5+ie6v1UOrkPUJ0Px0TErdr14AxqsdwrqEFxn6M6cZ+WmRfMfcln1ui61+65v0PdSz2Bup/6VkS8JiLWzszvUZ1TV1IzA+8//rdG9yOTxutGsR40rw0d6XebuY0le9FvSS1Mdi7wVRaPyNuCurA/uT2+CXUj8PKhyz+D9dDtFX0csDWV+mAlarT2eVTqnE90Xncj4EhqhMFthj6GWa6LJ7N4lMntqFQx3dXdH0QF0p7AhC/yyJIjhrahgqRfpRbbWQ/4LDWi5Lmd1y1PLYj7LiqY+G1qBOv9hj4e66FXHSzXjvO77fGNgR9RN3M7dl63LTWS4pMsXr/gjtRoo7OAmw19LLNQN++mZve8hcUj+J9NjbB7XHu8IbUY5teoDtmpWCh7KXWxV7s+bNge79g+Dx8aXUendWPJe4h3UgGAF1Od8z+nRmmuO/aezajZIf91TuLr+8bikXgrUR0Ip1GdbKeMPvdUmql/tfPErYcu8yzUwby/ZlgP/7FulqfumT/e2feidr788LVdI9p59UraLKlJ3uZzPXTOlatTa35dQC0CfQvq3upsKk3pqM1xMyrI/kfgsM7fmfjUnWPXz6Oo+8XHUZ3UJ7XPw/dYnI96IXUfegDVCbH+0McwQ/Uwmsm1AjUz+qvAJu3xBlSn0iJg7049bErNqj926PLPZB203+ftdcN6cJvv2+AFcJuhf+SSJ7PjqQVEfkSN4F5ENRDvTI3A+zo1cvMzbf/fmPB8qtQiZVuM7ftwu3BfDlxCTU/cnBpd8A/gU9S0vm2pEWv/ZAoCKf9FXVxMjU59CHBbKnjyRerG+DbUqIKvMF1TOZ/ePgM7dz/rVPDwDOqG+D3tdftRnQ/btNdsTk3nfO/Qx2E99Dr29ajG8G7t8X7UiKvvtePeqfPa7YG/UiN1zwF+3c6Zdx36OGahXm5IdbZe0I71d1TH5M2oAPzvaYFEFudcnfhG8VLq4eo0H9SN/6c6z72Yuo5+kCntYBiri49QAZKnA7dr+x7bvhOnUDnbVwfuQC2ifRZwi6HLPcOfg9VHnwOqw2GUn787cGErKu3aiUzRQuFj9TFvrxnWw9XHuLRp/wvb9sZ27Ot3nhsFmU8A7rKU9z6vPT9RaVKsh2uUfxRkX6Md+y/a+XCUcm49Kvg+Hmy/OXXv9U/gPUMfR886WA14/Ni+Z1Nt7we2xy+nOlPeS3UwfJe2DlD3czT0sfSsh7uPznedfXegcnA/v7MvqNlwh1CD/u7W2b/BpNfDUupl3l43rAc3NwPtU7cBB1KN4fsAa7d9e7aL+1epYOqdqNGZ3wU+wRQEDqip/FffsFK9oWcDT6Q6GLagVjj/LTXC4DVUgO1yKp/9p6ahHq5DXZzaPiePB17RPh8XA7+iOl4mvsOhUx+3ohanew1L5pF8cKuPJ7QL/LnUCN7fAa8Y+xt3Gvo4rIfex78cFSQ8i0qT9Xcql/ItqKmtvwFe2nn9/YAXUqMqdqCNQpr0jaU0ZKg1G86hpvXu1258T6BGuH8d2JcpC64vox5GQZPXUx0MD+g8NwqafJIpXfS0Hef27bPwQJZcoGoVqgPmbGrE/5+ojqvzmLIOqPYZOKndI9yGxbM8Xt3q5hQWz3h4avtcvHHocs9CPczra4b1cI162GD8OkAN1rgM2H1s/2hhz0ct5e88jLYm0CRu1sMSQfbV2zXhy8A9qEFMT+u87hZUsP0slszZflMWj2w+ZOjj6VEPo/bWs9vjaNfQ/dvj7ai21WgW+eiYT2NKOmepe+sPduuh7X8gnTWPaDOsWx2NUjEewDUXQZ2KYLvXDevBzW3wArjN4D+zRjJ/kRq9HCw5yv2VLB6NNRqhtgJtQdRJ36iFdo5rx7g18DJqoZ1uHdykHf+PgDXbvk2o0RirDX0MA9TFye3idwPgEVRw6fXTcvPXOdb1qeDIo9v34pZUB9OfWh2dTHVMrUQ1oNbrvHf5sb81sSudz+d6YPEo1dtQ+aYvAZ7L4sbieiwOtu80VDnnuE6eBtyq8/hQqiNyFWoEydupjshFrV42GLrMs1QPjwPWGdu3HtXheMjY/p2pdCE3H7rcs1gf76Cm6i713oCaFbcz8GZqgbOpSbXWOcZ1qNHs+7XH3Q6HXVl8L3Xntu8h4+fIadjm8zXDerhGHdy1Heu3qODYDYBV23Pvo4Kptx37rtx56HJbD7NaF6tTA3a+QEsp1u4Vdmm/L2w/b0F1Wl7Wviej+7GbUTMBJrbjmiXbW9u2fcu1c8SaVHtzbxbPBrxlO5f8s11DlpvUc8K11MNz274bUoO4Ptx53SjYvgo12v3Aocs+i3Uy768b1oPbfN8GL4DbDP4z64L9LeDznX3Ld34/sV3czwD+b+jyzsLx3wT4WDtxXw7s0PZ3b3jvQ40uePX4c9O0XYe6+Ded/PzTeBFrN/MXUiN030/NYPgxlav+Aa2OXruU901VXVgPCTUy+1yqMfhLasGy0WjVbrD9JUOXdZbr4cHt//0N2siR1vD5AjXiarm2PYoaXfIvxqY6T8NGdTYsotKKvZlqGI8axHtRsx7uO/aeNYcu9yzWR1ApUr7d3Tf2mkcMXc45qIfVqNldR3T2dUdifZlKxfZ5OuuYMEXp1trxzPtrhvVw9bGszOIFsM+lZrIcCdylnUf/CTysvXZ0TR0FVKfmPtt6WKIung58ms66Ne27se/Y65YHbk2NWh0F30d1MvGjl6n21kfbeaCbJuVu1MCOx4+OmZoV9k1qwdypmCV5bfVAdTIsonVaj9XNr4AXDl3uWayPeX/dsB7c5vu2AE2TRVReuE0iYnOAzLyis0rzVdRN4WVUAGGqZOafqWmaH6BuaDZu+xd16uCH1NTGm46eG6Cos+461MXfqWldo/flHBd11mXmuVS6nI2phYdOzMyNM/N4KrD6fSo/9fj7pqourAcADqNGa29FnQc/AGwaEctn5h+oG78/A/tExHbDFXN2ZeZXqMDA34GXRcTJ1IKvX6NGlPxfZl6ZmZ+lFkdePzN/P1R5Z9Hx1GKeP6XS5pwJvC4iNqA6pi8H7gsQESu091zjOzIt2nf9x8AdI+JhnX0ARMTG1Oflke1xDFLQ2Xc5NSBh84i4H0BmXhkRCyJiJWo24A+oac9bQ9VFZl45UHlnhdeMYj1AZl6SmV+k8orfGTiaGql4OhU8XAPYPSJWycwr2nuy/Zya+2zrYQkfBbbMzHM77Yq/Up32AETErYBjgHtk5vaZeVVELOzUyVVzXuoZ1mlvHQ8cFhHPbU/9iWp3P6o9viXwUCrAfHhmnjXXZZ1NS6mHrahUhB8Edo2Ij0TEVhGxPZWS8QIqje1U8rpRrAfNa0NH+t1mdqOCJRdQKWTu3dl/I2pa1yOY4hF57VjXpUabLAJeNPbcrakFe/Zpj6e6x9S6WOJ4V6UzeqY93prqfHrk0OWzHua8Hu5OBRV/CdyLxaPPbkWNWJ2qFErLqIMbA0+hAsxnU+liLqIagYOXbw7rYVVquv8R1IyGS6kUIT+nOl7WGrqMc1gXG7bPwJeA+3T234RazO0ndEYwTuvW6uFf7V7q/p39d2j7NqACCL+hTYef1s1rhvWwjPq4AbVGwUnt+nEJbbYsUzZ623r4j3WwoP08HvhI+/3W1CzrXzBls32WUQfdEd2jNbJ2o4Ltf23Xir8zRWtgXUs9jGZUP4maHbkHlSpmEZWi8KTOPffEz2r4D/XhdcN6cJun22jqlqZIG4n2cSon1meoi9pDqcX9Ns3Ms4cr3dyIiJtQQaMnU3kUP0vl/9qaWnjjHpn5q+FKOHesi2uKiHtSCzftR01zPWDgIg1iPtdDG427GZVXdQVgG+D7WbOAlsspG516bSJiAfA2YCMqB+0awDaZ+cEhyzWEiLgzlbd9e+DmwALg1jmdI/qXKiIeRQVM/kwF3P9FfS7uCmyemT8crHBzKCIeTtXD36hFgf9KjU68JDPvFhH7U4GEu2XmxcOVdO7M52tG13yuhzZ7ozvTZU1gLeBU4KTMfOFQZZtL1sM1RcSR1Aj/rYEPUZ0Q/9fuqxbmFIxgvzZj7a1nZOZxEfEY6jpxHnB0Zv5yyDLOhVYP76RGMo/qYQ3qHvM84HeZmfPwXnveXje6rAfNFwbap1Sb4v1maqrOQmpBkudm5pmDFmwOtQv9odR0zsupm75bAK+cT/UA1kVXRNyWmsq6FvDOzDy07V+Q0ze1d5mshyWC7YdROdofmZmnjzegp1n3WFsn7eOBZwGbzacOuKUETe5EpY05JTN/PVzJhhERd6MWx96QGuH/Q+D1mfmzQQs2xyJiQ2BPKqfsldTids+iRil+mZpB+LTMvHSwQs4RrxnFeljS6Lgj4hBqUM/9gX/Ml2voyHyuh9H1MyJeDTybSs23EjV6e14NXmjtrXdQwfVnZeYxbf+8Oj+M1cN2mXnE2PPzrT68bmA9aH4x0D7FImIVKlfe6sDfM3Nqc8suS0TclFoA8bnAttRognl5IrcuSssluSk1lfW0tm/eXeCth9KC7feiOia3yczfDFykObe0kXmZ+c8Bi3S9MJ86XJam5aVfnprufWW2nMPzTUQsT9XDwsz8V0SsRZ0vnkgtlvvTIcs3V7xmFOth6SJiN+CFwN0z8y9Dl2co87keIuIpwIepDsnN5luQfaQFmQ+hUgpdHWyfbzqDvJ4CPDYzPzNwkQbjdaNYD5pPDLRr6kXEzakA80GZ+ZOhyzMk6+Ka5nswbWS+10NErDQfRqVK+t9FxCOAfah1b7aYL2l0lma+XzNGrIerRyl+Fjg9M7ceujxDme/1EBGrA08HjsxaQHreBdlHImJdYH/gjfOlM3ZpWj28BHjtfP0sLI3XjWI9aJoZaNe8MB9yA/63rAtJkv43EbEd8JX5OPtFWpqIuBm1AOQb2uN5GTyxHhabz0H2EdtbS/IzIWk+MdAuSZIkXYv5HDST/lumASjWgyRJ85eBdkmSJEmSJEmSelgwdAGGEBFbRsTbIuLrEXFhRGREzMuFSiRJkiRJkiRJ/Sw3dAEGsidwF+Ai4Bxgg2GLI0mSJEmSJEmaVPNyRDvwcmB9YA3gRQOXRZIkSZIkSZI0webliPbMPHn0e0QMWRRJkiRJkiRJ0oSbryPaJUmSJEmSJEmaEQbaJUmSJEmSJEnqYV6mjpkJD3rQg3LoMgztrW99KwAve9nLBi3H0KyHxayLYj0U66FYD8V6WMy6KNZDsR6K9VCsh2I9LGZdFOuhWA/FeijWw5JOOeWUacwNfb2PPR588MGceuqpnHDCCUMX5b8x658RR7RLkiRJkiRJktSDgXZJkiRJkiRJknow0C5JkiRJkiRJUg8G2iVJkiRJkiRJ6sFAuyRJkiRJkiRJPSw3dAGGEBFPBJ7YHt60/bx3RLy//X5+Zr5ijoslSZIkSZIkSZpA8zLQDtwVePbYvtu2DeB3gIF2SZIkSZIkSdJ/NC9Tx2TmazMzrmW79dBllCRJkiRJkiRNhnkZaJckSZIkSZIkaaYYaJckSZIkSZIkqQcD7ZIkSZIkSZIk9WCgXZIkSZIkSZKkHgy0S5IkSZIkSZLUg4F2SZIkSZIkSZJ6MNAuSZIkSZIkSVIPBtolSZIkSZIkSerBQLskSZIkSZIkST0YaJckSZIkSZIkqQcD7ZIkSZIkSZIk9WCgXZIkSZIkSZKkHgy0S5IkSZIkSZLUg4F2SZIkSZIkSZJ6MNAuSZIkSZIkSVIPBtolSZIkSZIkSerBQLskSZIkSZIkST0YaJckSZIkSZIkqQcD7ZIkSZIkSZIk9WCgXZIkSZIkSZKkHgy0S5IkSZIkSZLUg4F2SZIkSZIkSZJ6MNAuSZIkSZIkSVIPBtolSZIkSZIkSerBQLskSZIkSZIkST0YaJckSZIkSZIkqQc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      "text/plain": [
       "<Figure size 1800x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "msno.matrix(train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "prepared-vertical",
   "metadata": {},
   "source": [
    " - 결측치는 없는 것으로 판명이났다.\n",
    " \n",
    " - 우리의 목적 결국 집값을 예측하는 것이다."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "assisted-climate",
   "metadata": {},
   "source": [
    "### 전체 그래프 확인"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "tender-costume",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x3600 with 18 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(9, 2, figsize=(12, 50))   \n",
    "\n",
    "count = 1\n",
    "columns = train.columns\n",
    "for row in range(9):\n",
    "    for col in range(2):\n",
    "        sns.kdeplot(train[columns[count]], ax=ax[row][col])\n",
    "        ax[row][col].set_title(columns[count], fontsize=15)\n",
    "        count += 1\n",
    "        if count == 19 :\n",
    "            break"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "spanish-electronics",
   "metadata": {},
   "source": [
    " - 너무 하나의  값에 몰려 있는 경향이 있어서 정규분포를 통해서 로그 변환을 한다면 지금보다 데이터 분석하기 쉽고 눈에 확 들어올거라고 예상이 됩니다. 모든 함수를 다 하지 않고 필요한 함수인 가격 그래프만 하겠습니다."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "welsh-juice",
   "metadata": {},
   "source": [
    "일반 그래프\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "temporal-indonesia",
   "metadata": {
    "collapsed": true
   },
   "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"
    }
   ],
   "source": [
    "sns.kdeplot(y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "meaningful-boulder",
   "metadata": {},
   "source": [
    "로그로 변환"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "unlike-reporter",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        12.309987\n",
       "1        12.100718\n",
       "2        13.142168\n",
       "3        12.458779\n",
       "4        12.583999\n",
       "           ...    \n",
       "15030    13.322338\n",
       "15031    13.822984\n",
       "15032    12.793862\n",
       "15033    12.899222\n",
       "15034    12.691584\n",
       "Name: price, Length: 15035, dtype: float64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = np.log1p(y)\n",
    "y\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "split-aggregate",
   "metadata": {},
   "source": [
    "그 값을 가지고 정규화하여 그린 그래프"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "subsequent-chorus",
   "metadata": {
    "collapsed": true
   },
   "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"
    }
   ],
   "source": [
    "sns.kdeplot(y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "republican-switzerland",
   "metadata": {},
   "source": [
    "#### 왜 로그 변환(log -scaling)을 하라는가?\n",
    "\n",
    "그건 수학적인 의미가 가장 클 것입니다. 로그는 x의 범위는 작은 반면에 상대적으로 y의 범위는 매우 큽니다.\n",
    "\n",
    "그래서 x값이 0에 가까워지면 y값은 매우 크게 되고\n",
    "\n",
    "반대로 x값이 점점 커지만 기울기가 급격히 작아져서 x와 y값의 차이가 나지 않아 비교적 작은 y값의 모여있게 되는 성질을 같게 되어 한쪽으로 치우쳐 분포된 그래프 조차도\n",
    "\n",
    "위의 그래프 상황처럼 정규분포 모양으로 퍼지게 될 수 있는 것입니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "jewish-beach",
   "metadata": {
    "collapsed": true
   },
   "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"
    }
   ],
   "source": [
    "xx = np.linspace(0, 10, 500)\n",
    "yy = np.log(xx)\n",
    "\n",
    "plt.hlines(0, 0, 10)\n",
    "plt.vlines(0, -5, 5)\n",
    "plt.plot(xx, yy, c='r')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "recreational-respondent",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 15035 entries, 0 to 15034\n",
      "Data columns (total 19 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   date           15035 non-null  int64  \n",
      " 1   bedrooms       15035 non-null  int64  \n",
      " 2   bathrooms      15035 non-null  float64\n",
      " 3   sqft_living    15035 non-null  int64  \n",
      " 4   sqft_lot       15035 non-null  int64  \n",
      " 5   floors         15035 non-null  float64\n",
      " 6   waterfront     15035 non-null  int64  \n",
      " 7   view           15035 non-null  int64  \n",
      " 8   condition      15035 non-null  int64  \n",
      " 9   grade          15035 non-null  int64  \n",
      " 10  sqft_above     15035 non-null  int64  \n",
      " 11  sqft_basement  15035 non-null  int64  \n",
      " 12  yr_built       15035 non-null  int64  \n",
      " 13  yr_renovated   15035 non-null  int64  \n",
      " 14  zipcode        15035 non-null  int64  \n",
      " 15  lat            15035 non-null  float64\n",
      " 16  long           15035 non-null  float64\n",
      " 17  sqft_living15  15035 non-null  int64  \n",
      " 18  sqft_lot15     15035 non-null  int64  \n",
      "dtypes: float64(4), int64(15)\n",
      "memory usage: 2.2 MB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dressed-cheat",
   "metadata": {},
   "source": [
    "실수, 정수 자료형만 들어있으므로 분석하는데 문제는\n",
    "없을 것으로 예측이됩니다."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "wicked-state",
   "metadata": {},
   "source": [
    "# 모델링"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "weird-robertson",
   "metadata": {},
   "source": [
    "\n",
    "### 모델링 들어가기 전 기초 개념 탐구\n",
    "\n",
    "- baseline: 모델 성능을 비교하기 위해 기준이 되는 모델\n",
    "\n",
    "\n",
    "- Ensemble: 여러개의 알고리즘을 사용하여 그 예측을 결합보다 정확한 최종 예측 도출\n",
    "\n",
    "\n",
    "- Voting: 여러 모델 분류 후 다수결로 통해서 최종 결과 선택을 하는 것으로 분류문제에 잘 쓰인다.\n",
    "\n",
    "\n",
    "- Averaging: 모델이 계산한 실수값들의 평균 혹은 가중 평균하여 사용하는 것으로 회귀 문제에 사용이 된다.그로므로 이번 익스 때 사용할 주된 개념이다.\n",
    "\n",
    "\n",
    "- 블랜딩(blending) : baseline커널에서 여러 모델 섞기\n",
    "\n",
    "\n",
    "- RMSE 와 Cross-Validation score의 비교하기\n",
    "\n",
    "- Average Blending: 산술 평균하여 모델 만든다\n",
    "\n",
    "-  Cross Validation: 교차 검증을 통해 모델 성능 평가\n",
    "   \n",
    "-  Make Submission File\n",
    "\n",
    "  cross_val_score()함수는 회귀모델을 할 경우 R^2점수를 반환하고 그 값이 1에 가까울 수록 모델이 잘 학습이 된 것입니다. 그리고 상대적 성능이기 때문에 직관적으로 파악 가능\n",
    "   \n",
    "   \n",
    "   ![](https://images.velog.io/images/qsdcfd/post/d1c3d658-0497-4e40-9189-26a8b7592205/image.png)\n",
    "   \n",
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "prostate-screening",
   "metadata": {},
   "source": [
    "### 다양한 함수를 통해 실험하기\n",
    "\n",
    "1. 훈련 데이터아 검증 데이터 나누기\n",
    "  \n",
    "  :train_test_split 함수 이용\n",
    "  \n",
    "2. RMSE 점수 계산\n",
    "  \n",
    "  - 정의 : 평균 제곱근 오차라는 뜻을 가진 것으로 추정 값 또는 모델이 예측한 값과 실제가 차이가 있을 때 사용\n",
    "  \n",
    "  - mean_squared_error 이용\n",
    "  \n",
    "  -  y_test나 y_pred위에서 np.log1p()로 변환이 되기 때문에 np.expm1() 추가해야 함\n",
    "  \n",
    "  - random_state : 특정값 고정\n",
    "  \n",
    "  \n",
    "  \n",
    "3. 여러 가지 모델 이용\n",
    "\n",
    "  - XGBRegressor\n",
    "  \n",
    "  - LGBMRegressor\n",
    "  \n",
    "  - GradientBoostingRegressor\n",
    "  \n",
    "  - RandomForestRegressor\n",
    "  \n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "banned-yemen",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE 값 준비 완료!\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "\n",
    "def rmse(y_test, y_pred):\n",
    "    return np.sqrt(mean_squared_error(np.expm1(y_test), np.expm1(y_pred)))\n",
    "\n",
    "print('RMSE 값 준비 완료!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "equal-rental",
   "metadata": {},
   "outputs": [],
   "source": [
    "from xgboost import XGBRegressor\n",
    "from lightgbm import LGBMRegressor\n",
    "from sklearn.ensemble import GradientBoostingRegressor, RandomForestRegressor"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "focused-pennsylvania",
   "metadata": {},
   "source": [
    "#### Average Blending"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "entertaining-connecticut",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "\n",
    "gboost = GradientBoostingRegressor(random_state=2021)\n",
    "xgboost = XGBRegressor(random_state=2021)\n",
    "lightgbm = LGBMRegressor(random_state=2021)\n",
    "rdforest = RandomForestRegressor(random_state=2021)\n",
    "models = [gboost, xgboost, lightgbm, rdforest]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "embedded-protein",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'GradientBoostingRegressor'"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gboost.__class__.__name__"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "marked-olive",
   "metadata": {},
   "source": [
    "#### 훈련 데이터 셋과 테스트 데이텟 분리 후 RMSE계산"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "breeding-serbia",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'GradientBoostingRegressor': 136310.55386176528,\n",
       " 'XGBRegressor': 131075.32713281445,\n",
       " 'LGBMRegressor': 128586.70033151371,\n",
       " 'RandomForestRegressor': 136973.1337892089}"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df = {}\n",
    "\n",
    "for model in models:\n",
    "    \n",
    "    model_name = model.__class__.__name__\n",
    "\n",
    "    X_train, X_test, y_train, y_test = train_test_split(train, y, random_state=2021, test_size=0.2)\n",
    "\n",
    "    model.fit(X_train, y_train)\n",
    "    \n",
    "    y_pred = model.predict(X_test)\n",
    "\n",
    "    df[model_name] = rmse(y_test, y_pred)\n",
    "    \n",
    "    score_df = pd.DataFrame(df, index=['RMSE']).T.sort_values('RMSE', ascending=False)\n",
    "    \n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "biological-nightlife",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_scores(models, train, y):\n",
    "    df = {}\n",
    "    \n",
    "    for model in models:\n",
    "        model_name = model.__class__.__name__\n",
    "        \n",
    "        X_train, X_test, y_train, y_test = train_test_split(train, y, random_state=2021, test_size=0.2)\n",
    "        model.fit(X_train, y_train)\n",
    "        y_pred = model.predict(X_test)\n",
    "        \n",
    "        df[model_name] = rmse(y_test, y_pred)\n",
    "        score_df = pd.DataFrame(df, index=['RMSE']).T.sort_values('RMSE', ascending=False)\n",
    "            \n",
    "    return score_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "stretch-capability",
   "metadata": {},
   "source": [
    "#### 눈으로 보기 쉽게  내림차순 하여 표로 시각화"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "sought-senator",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "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>RMSE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>RandomForestRegressor</th>\n",
       "      <td>136973.133789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GradientBoostingRegressor</th>\n",
       "      <td>136310.553862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XGBRegressor</th>\n",
       "      <td>131075.327133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LGBMRegressor</th>\n",
       "      <td>128586.700332</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    RMSE\n",
       "RandomForestRegressor      136973.133789\n",
       "GradientBoostingRegressor  136310.553862\n",
       "XGBRegressor               131075.327133\n",
       "LGBMRegressor              128586.700332"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "get_scores(models, train, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "packed-intention",
   "metadata": {},
   "source": [
    "## 하이퍼파라미터 튜닝하기\n",
    "\n",
    "  i) 파라미터란? \n",
    "     \n",
    "   모델 파라미터와 하이퍼 파라미터로 나뉘는데요 전자는 모델이 학습하면서 최적화가 되어야하는 것을 의미하고 후자는 모델이 학습하기 위해 미리 넣어주는 파라미터입니다.\n",
    "     \n",
    "  ii) 내 입맛대로 데이터 재구성하기.\n",
    "  \n",
    "   - 삭제\n",
    "   \n",
    "   - 추가\n",
    "   \n",
    "   - 기타 등등"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "economic-cleaner",
   "metadata": {},
   "source": [
    "### 그리티 탐색 (Grid Search)\n",
    "\n",
    "   : GridSearchCV를 통해서 탐색하고자 하는 하이퍼파라미터 와 시도해볼 값을 지정하기만 하면 되는 것입니다. \n",
    "   \n",
    "   그러면 교차 검증을 통해 평가 가능.  \n",
    "   \n",
    "   사람이 지정한 범주 내에서는 최적을 찾지만 그것이 무조건 최적인지는 의문이다.\n",
    "   \n",
    "   적은 수 탐색에 유리\n",
    "   \n",
    "   반복횟수로 컴퓨터 자원 제어"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "spectacular-regression",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "controversial-questionnaire",
   "metadata": {},
   "outputs": [],
   "source": [
    "param_grid = {\n",
    "    'n_estimators': [50, 150],\n",
    "    'max_depth': [1, 11],\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "soviet-tactics",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "model = LGBMRegressor(random_state=2021)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "unauthorized-swedish",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 4 candidates, totalling 20 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=5)]: Using backend LokyBackend with 5 concurrent workers.\n",
      "[Parallel(n_jobs=5)]: Done  20 out of  20 | elapsed:    4.6s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, estimator=LGBMRegressor(random_state=2021), n_jobs=5,\n",
       "             param_grid={'max_depth': [1, 11], 'n_estimators': [50, 150]},\n",
       "             scoring='neg_mean_squared_error', verbose=1)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "grid_model = GridSearchCV(model, param_grid=param_grid, \\\n",
    "                        scoring='neg_mean_squared_error', \\\n",
    "                        cv=5, verbose=1, n_jobs=5)\n",
    "\n",
    "grid_model.fit(train, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "light-destruction",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'mean_fit_time': array([0.23990455, 0.32712984, 0.46271162, 1.14252167]),\n",
       " 'std_fit_time': array([0.05658307, 0.08216713, 0.04155051, 0.15305868]),\n",
       " 'mean_score_time': array([0.01673069, 0.01952748, 0.02612367, 0.08038573]),\n",
       " 'std_score_time': array([0.01458827, 0.00342536, 0.0092715 , 0.03885585]),\n",
       " 'param_max_depth': masked_array(data=[1, 1, 11, 11],\n",
       "              mask=[False, False, False, False],\n",
       "        fill_value='?',\n",
       "             dtype=object),\n",
       " 'param_n_estimators': masked_array(data=[50, 150, 50, 150],\n",
       "              mask=[False, False, False, False],\n",
       "        fill_value='?',\n",
       "             dtype=object),\n",
       " 'params': [{'max_depth': 1, 'n_estimators': 50},\n",
       "  {'max_depth': 1, 'n_estimators': 150},\n",
       "  {'max_depth': 11, 'n_estimators': 50},\n",
       "  {'max_depth': 11, 'n_estimators': 150}],\n",
       " 'split0_test_score': array([-0.0756974 , -0.04827215, -0.02866785, -0.02582918]),\n",
       " 'split1_test_score': array([-0.07666447, -0.05106487, -0.03042507, -0.02753529]),\n",
       " 'split2_test_score': array([-0.07354904, -0.04888365, -0.03064645, -0.02800941]),\n",
       " 'split3_test_score': array([-0.07510863, -0.04900634, -0.03028773, -0.02724349]),\n",
       " 'split4_test_score': array([-0.06595281, -0.04502073, -0.02651535, -0.02393296]),\n",
       " 'mean_test_score': array([-0.07339447, -0.04844955, -0.02930849, -0.02651007]),\n",
       " 'std_test_score': array([0.00385583, 0.00195584, 0.00156255, 0.00147933]),\n",
       " 'rank_test_score': array([4, 3, 2, 1], dtype=int32)}"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "grid_model.cv_results_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "simplified-empire",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'max_depth': 1, 'n_estimators': 50},\n",
       " {'max_depth': 1, 'n_estimators': 150},\n",
       " {'max_depth': 11, 'n_estimators': 50},\n",
       " {'max_depth': 11, 'n_estimators': 150}]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "params = grid_model.cv_results_['params']\n",
    "params\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "elegant-buddy",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.07339447, -0.04844955, -0.02930849, -0.02651007])"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score = grid_model.cv_results_['mean_test_score']\n",
    "score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "public-immune",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>max_depth</th>\n",
       "      <th>n_estimators</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "      <td>-0.073394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>150</td>\n",
       "      <td>-0.048450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11</td>\n",
       "      <td>50</td>\n",
       "      <td>-0.029308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11</td>\n",
       "      <td>150</td>\n",
       "      <td>-0.026510</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   max_depth  n_estimators     score\n",
       "0          1            50 -0.073394\n",
       "1          1           150 -0.048450\n",
       "2         11            50 -0.029308\n",
       "3         11           150 -0.026510"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = pd.DataFrame(params)\n",
    "results['score'] = score\n",
    "\n",
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "organic-berlin",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>max_depth</th>\n",
       "      <th>n_estimators</th>\n",
       "      <th>score</th>\n",
       "      <th>RMSE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "      <td>-0.073394</td>\n",
       "      <td>0.270914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>150</td>\n",
       "      <td>-0.048450</td>\n",
       "      <td>0.220113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11</td>\n",
       "      <td>50</td>\n",
       "      <td>-0.029308</td>\n",
       "      <td>0.171197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11</td>\n",
       "      <td>150</td>\n",
       "      <td>-0.026510</td>\n",
       "      <td>0.162819</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   max_depth  n_estimators     score      RMSE\n",
       "0          1            50 -0.073394  0.270914\n",
       "1          1           150 -0.048450  0.220113\n",
       "2         11            50 -0.029308  0.171197\n",
       "3         11           150 -0.026510  0.162819"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results['RMSE'] = np.sqrt(-1 * results['score'])\n",
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "local-bennett",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "        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>max_depth</th>\n",
       "      <th>n_estimators</th>\n",
       "      <th>score</th>\n",
       "      <th>RMSLE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "      <td>-0.073394</td>\n",
       "      <td>0.270914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>150</td>\n",
       "      <td>-0.048450</td>\n",
       "      <td>0.220113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11</td>\n",
       "      <td>50</td>\n",
       "      <td>-0.029308</td>\n",
       "      <td>0.171197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11</td>\n",
       "      <td>150</td>\n",
       "      <td>-0.026510</td>\n",
       "      <td>0.162819</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   max_depth  n_estimators     score     RMSLE\n",
       "0          1            50 -0.073394  0.270914\n",
       "1          1           150 -0.048450  0.220113\n",
       "2         11            50 -0.029308  0.171197\n",
       "3         11           150 -0.026510  0.162819"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = results.rename(columns={'RMSE': 'RMSLE'})\n",
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "clinical-defense",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>max_depth</th>\n",
       "      <th>n_estimators</th>\n",
       "      <th>score</th>\n",
       "      <th>RMSLE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11</td>\n",
       "      <td>150</td>\n",
       "      <td>-0.026510</td>\n",
       "      <td>0.162819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11</td>\n",
       "      <td>50</td>\n",
       "      <td>-0.029308</td>\n",
       "      <td>0.171197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>150</td>\n",
       "      <td>-0.048450</td>\n",
       "      <td>0.220113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "      <td>-0.073394</td>\n",
       "      <td>0.270914</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   max_depth  n_estimators     score     RMSLE\n",
       "3         11           150 -0.026510  0.162819\n",
       "2         11            50 -0.029308  0.171197\n",
       "1          1           150 -0.048450  0.220113\n",
       "0          1            50 -0.073394  0.270914"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.sort_values('RMSLE')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "posted-excess",
   "metadata": {},
   "source": [
    "하이퍼파라미터 설정"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "piano-allergy",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([13.12560243, 13.09628793, 14.13113036, ..., 13.01704247,\n",
       "       12.75557202, 13.00145069])"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = LGBMRegressor(max_depth=11, n_estimators=150, random_state=2021)\n",
    "model.fit(train, y)\n",
    "prediction = model.predict(test)\n",
    "prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "naked-guard",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 501621.15408975,  487129.78854802, 1371107.86874033, ...,\n",
       "        450016.82364051,  346476.10333105,  443054.66217101])"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction = np.expm1(prediction)\n",
    "prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "representative-booking",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>15035</td>\n",
       "      <td>100000</td>\n",
       "    </tr>\n",
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       "      <td>15036</td>\n",
       "      <td>100000</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>100000</td>\n",
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       "      <th>3</th>\n",
       "      <td>15038</td>\n",
       "      <td>100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15039</td>\n",
       "      <td>100000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      id   price\n",
       "0  15035  100000\n",
       "1  15036  100000\n",
       "2  15037  100000\n",
       "3  15038  100000\n",
       "4  15039  100000"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_dir = os.getenv('HOME')+'/aiffel/kaggle_kakr_housing/data'\n",
    "\n",
    "submission_path = join(data_dir, 'sample_submission.csv')\n",
    "submission = pd.read_csv(submission_path)\n",
    "submission.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "precise-viewer",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>id</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>15035</td>\n",
       "      <td>5.016212e+05</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15036</td>\n",
       "      <td>4.871298e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15037</td>\n",
       "      <td>1.371108e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>15038</td>\n",
       "      <td>2.816628e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15039</td>\n",
       "      <td>3.293298e+05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      id         price\n",
       "0  15035  5.016212e+05\n",
       "1  15036  4.871298e+05\n",
       "2  15037  1.371108e+06\n",
       "3  15038  2.816628e+05\n",
       "4  15039  3.293298e+05"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submission['price'] = prediction\n",
    "submission.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "invisible-treat",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/aiffel/aiffel/kaggle_kakr_housing/data/submission_lgbm_RMSLE_0.1643.csv\n"
     ]
    }
   ],
   "source": [
    "submission_csv_path = '{}/submission_{}_RMSLE_{}.csv'.format(data_dir, 'lgbm', '0.1643')\n",
    "submission.to_csv(submission_csv_path, index=False)\n",
    "print(submission_csv_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "established-thing",
   "metadata": {},
   "source": [
    "### 다양한 하이퍼 파라미터에 변화를 주기"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "western-nowhere",
   "metadata": {},
   "source": [
    "\n",
    "- param_grid : 탐색할 파리미터 종류\n",
    "\n",
    "- scoring :  성능 평가 지표\n",
    "\n",
    "- cv : cross validation을 수행\n",
    "\n",
    "- verbose :  메세지의 양\n",
    "\n",
    "- n_jobs : 사용할 CPU의 개수\n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "permanent-division",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 6 candidates, totalling 30 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=5)]: Using backend LokyBackend with 5 concurrent workers.\n",
      "[Parallel(n_jobs=5)]: Done  30 out of  30 | elapsed:   12.2s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>max_depth</th>\n",
       "      <th>n_estimators</th>\n",
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       "      <th>RMSLE</th>\n",
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       "  </thead>\n",
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       "      <th>5</th>\n",
       "      <td>20</td>\n",
       "      <td>300</td>\n",
       "      <td>-0.026028</td>\n",
       "      <td>0.161331</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20</td>\n",
       "      <td>200</td>\n",
       "      <td>-0.026128</td>\n",
       "      <td>0.161643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>15</td>\n",
       "      <td>300</td>\n",
       "      <td>-0.026160</td>\n",
       "      <td>0.161741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15</td>\n",
       "      <td>200</td>\n",
       "      <td>-0.026220</td>\n",
       "      <td>0.161925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10</td>\n",
       "      <td>300</td>\n",
       "      <td>-0.026261</td>\n",
       "      <td>0.162052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>200</td>\n",
       "      <td>-0.026338</td>\n",
       "      <td>0.162288</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   max_depth  n_estimators     score     RMSLE\n",
       "5         20           300 -0.026028  0.161331\n",
       "4         20           200 -0.026128  0.161643\n",
       "3         15           300 -0.026160  0.161741\n",
       "2         15           200 -0.026220  0.161925\n",
       "1         10           300 -0.026261  0.162052\n",
       "0         10           200 -0.026338  0.162288"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "param_grid = {\n",
    "    'n_estimators': [200, 300],\n",
    "\n",
    "    'max_depth': [10, 15, 20],\n",
    "}\n",
    "\n",
    "model = LGBMRegressor(random_state=2021)\n",
    "my_GridSearch(model, train, y, param_grid, verbose=2, n_jobs=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "touched-sense",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([13.13544777, 13.13655717, 14.1650178 , ..., 13.02067447,\n",
       "       12.75126678, 13.01138965])"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = LGBMRegressor(max_depth=20, n_estimators=300, random_state=2021)\n",
    "model.fit(train, y)\n",
    "prediction = model.predict(test)\n",
    "prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "injured-curtis",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 506584.18475054,  507146.49984578, 1418367.47049527, ...,\n",
       "        451654.25932566,  344987.64378839,  447480.12956772])"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction = np.expm1(prediction)\n",
    "prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "unlimited-spending",
   "metadata": {},
   "source": [
    "### Make Submission File\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "rubber-compound",
   "metadata": {},
   "source": [
    "#### 예측값 생성"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "sweet-excellence",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>15035</td>\n",
       "      <td>100000</td>\n",
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       "      <td>15036</td>\n",
       "      <td>100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15037</td>\n",
       "      <td>100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>15038</td>\n",
       "      <td>100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15039</td>\n",
       "      <td>100000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      id   price\n",
       "0  15035  100000\n",
       "1  15036  100000\n",
       "2  15037  100000\n",
       "3  15038  100000\n",
       "4  15039  100000"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_dir = os.getenv('HOME')+'/aiffel/kaggle_kakr_housing/data'\n",
    "\n",
    "submission_path = join(data_dir, 'sample_submission.csv')\n",
    "submission = pd.read_csv(submission_path)\n",
    "submission.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "irish-circuit",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "      <th>price</th>\n",
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       "      <td>5.065842e+05</td>\n",
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       "      <td>15036</td>\n",
       "      <td>5.071465e+05</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15037</td>\n",
       "      <td>1.418367e+06</td>\n",
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       "      <th>3</th>\n",
       "      <td>15038</td>\n",
       "      <td>2.742945e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15039</td>\n",
       "      <td>3.317907e+05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      id         price\n",
       "0  15035  5.065842e+05\n",
       "1  15036  5.071465e+05\n",
       "2  15037  1.418367e+06\n",
       "3  15038  2.742945e+05\n",
       "4  15039  3.317907e+05"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submission['price'] = prediction\n",
    "submission.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "several-marble",
   "metadata": {},
   "source": [
    "## 평가문항 1번"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "golden-hopkins",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/aiffel/aiffel/kaggle_kakr_housing/data/submission_lgbm_RMSLE_0.1615279.csv\n"
     ]
    }
   ],
   "source": [
    "\n",
    "submission_csv_path = '{}/submission_{}_RMSLE_{}.csv'.format(data_dir, 'lgbm', '0.1615279')\n",
    "submission.to_csv(submission_csv_path, index=False)\n",
    "print(submission_csv_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "covered-democracy",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def save_submission(model, train, y, test, model_name, rmsle=None):\n",
    "    model.fit(train, y)\n",
    "    prediction = model.predict(test)\n",
    "    prediction = np.expm1(prediction)\n",
    "    data_dir = os.getenv('HOME')+'/aiffel/kaggle_kakr_housing/data'\n",
    "    submission_path = join(data_dir, 'sample_submission.csv')\n",
    "    submission = pd.read_csv(submission_path)\n",
    "    submission['price'] = prediction\n",
    "    submission_csv_path = '{}/submission_{}_RMSLE_{}.csv'.format(data_dir, model_name, rmsle)\n",
    "    submission.to_csv(submission_csv_path, index=False)\n",
    "    print('{} saved!'.format(submission_csv_path))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "bottom-sucking",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n",
      "/aiffel/aiffel/kaggle_kakr_housing/data/submission_lgbm_RMSLE_0.1615279.csv saved!\n"
     ]
    }
   ],
   "source": [
    "save_submission(model, train, y, test, 'lgbm', rmsle='0.1615279')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "touched-notion",
   "metadata": {},
   "source": [
    "## 평가문항 3번\n",
    "\n",
    "과적합이 일어난 것을 알게되어. 하이퍼파라미터인 max_depth를 절반 값보단 큰 20으로 줄였을 때 11만 보다 작은 10만 언저리의 값이 나오게 되어서 다시 한 번 모든 모델엔 적합한 하이퍼파라미터가 있구나를 깨닫게 된 좋은 공부였습니다."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bound-preference",
   "metadata": {},
   "source": [
    "< max_depth = 20>\n",
    "\n",
    "![image](https://user-images.githubusercontent.com/90362666/136971580-a401c85f-0c27-40ee-85e6-8e26cd073731.png)\n",
    "\n",
    "< max_depth = 35>\n",
    "\n",
    "![image](https://user-images.githubusercontent.com/90362666/136973150-b3e45922-acd3-455e-828d-54972f2f4e25.png)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "textile-functionality",
   "metadata": {},
   "source": [
    "# 프로젝트 회고\n",
    "\n",
    "1, 느낀점 : 앞에서 언급했던 것처럼 캐클에 대해서 너무 많이 몰라서 시작부터 코드를 치지 않고 유튜브와 블로그에서  캐클을 어떻게 사용하고 어떤 방식으로 공부를 하고 쓰이는 지에 대해서 먼저 보았습니다. \n",
    "\n",
    "그러다보니 왜 사람들이 모델학습을 시키기 전에 데이터를 전처리하고 이상치나 결측치 값을 신중하게 찾는 지 알게 되어 저 또한 시간에 걸쳐서 찾은 것 같고 그로인해서 조금은 빠르게 데이터 처리를 할 수 있었습니다. \n",
    "\n",
    "다 마무리하고 제출을 뭘로 해야할지 몰라서 처음에 ipynb로 했다가 제출이 되지 않아서 상민퍼실님께 질문을 드린다음에 블로그를 찾아보니 submission_csv파일을 넣어야 한다는 글귀를 보고 찾아서 넣었더니 실행 결과가 되어서 좋았지만 11만이 넘어서 무슨 문제가 있나 하고  하이퍼파라미터를 수정했습니다.\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "2. 하이퍼파라미터 수정.\n",
    "\n",
    "  처음에는 max_depth =  35를 했습니다. 다다익선이라고 생각을 했습니다 허나 11만이 넘었고 더 늘려서 50까지 했을 때는 12만이 되었습니다. 그래서 저는 생각을 했습니다. 이건 과적합이 일어난 것이라고요.\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "3. 과적합, 도대체 넌.\n",
    "\n",
    "항상 신기하고 궁금한 왜 하이퍼파라미터를 계속 올려도 결과가 좋아지는 한계치가 있고 그것을 넘어가면 과적합 현상이 일어난다 그러면 과적합은 무엇일까요?\n",
    "\n",
    "과적합은 말 그대로 학습 데이터를 너무 과하게 한다는 뜻으로 학습데이터를 늘리면 부분 오차가 감소하지만 실제 데이터에서는 오차가 증가합니다.\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "![image](https://user-images.githubusercontent.com/90362666/136971659-b19ea5b9-943b-4a66-8d25-a112b8f8d1e5.png)\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "royal-religious",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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