4_Citrus__Prediction.ipynb

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   "source": [
    "## Citrus Prediction (Regression)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data\n",
    "Source: https://dacon.io/competitions/official/236038/data\n",
    "#### Train Dataset\n",
    "* Columns:\n",
    "    * `ID`: unique citrus tree ID\n",
    "    * `착과량` : actual amount of citrus fruit drop; target variable\n",
    "    * 5 features related to the growth each tree (height and the maximum/minimum/average width of its crown)\n",
    "        * `수고(m)`, `수관폭1(min)`, `수관폭2(max)`, `수관폭평균`\n",
    "    * 89 features representing tree bud measured from 2022/09/01 to 2022/11/28.\n",
    "        * `2022-09-01 새순` , ... , `2022-11-28 새순`\n",
    "    * 89 features representing chlorophyl measured from 2022/09/01 to 2022/11/28.\n",
    "        * `2022-09-01 엽록소` , ... , `2022-11-28 엽록소`\n",
    "    \n",
    "\n",
    "#### Test Dataset\n",
    "* Columns:\n",
    "    * `ID`: unique citrus tree ID\n",
    "    * 5 features related to the growth each tree (height and the maximum/minimum/average width of its crown)\n",
    "        * `수고(m)`, `수관폭1(min)`, `수관폭2(max)`, `수관폭평균`\n",
    "    * 89 features representing tree bud measured from 2022/09/01 to 2022/11/28.\n",
    "        * `2022-09-01 새순` , ... , `2022-11-28 새순`\n",
    "    * 89 features representing chlorophyl measured from 2022/09/01 to 2022/11/28.\n",
    "        * `2022-09-01 엽록소` , ... , `2022-11-28 엽록소`\n",
    "    \n",
    "### Score Measures\n",
    "* Evaluated with NMAE\n",
    "* Public score: measured with 30% of the test data\n",
    "* Private score: measured with the remaining 70% of the test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "DG4kIYY-KJEt"
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   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import font_manager, rc\n",
    "\n",
    "import sklearn\n",
    "import sklearn.linear_model \n",
    "import sklearn.ensemble \n",
    "\n",
    "from sklearn.ensemble import AdaBoostRegressor, GradientBoostingRegressor, RandomForestRegressor\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "from xgboost import XGBRegressor\n",
    "from lightgbm import LGBMRegressor\n",
    "from catboost import CatBoostRegressor\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "\n",
    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
    "import time\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore') \n",
    "\n",
    "pd.set_option('mode.chained_assignment',  None)\n",
    "plt.rc('font', family='NanumBarunGothic')  "
   ]
  },
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    "colab": {
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    "id": "WPqiaaqcyQBZ",
    "outputId": "873e647f-da43-49f9-efb0-516e884dc11f"
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pandas Version : 1.3.5\n",
      "numpy Version : 1.21.6\n",
      "sklearn Version : 1.0.2\n",
      "seaborn Version : 0.11.2\n"
     ]
    }
   ],
   "source": [
    "print('pandas Version :', pd.__version__)\n",
    "print('numpy Version :', np.__version__)\n",
    "print('sklearn Version :', sklearn.__version__)\n",
    "print('seaborn Version :', sns.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Loading & EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "LInSs9c4KNWX"
   },
   "outputs": [],
   "source": [
    "train = pd.read_csv('train.csv')\n",
    "test = pd.read_csv('test.csv')"
   ]
  },
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       "           ID  착과량(int)  수고(m)  수관폭1(min)  수관폭2(max)  수관폭평균  2022-09-01 새순  \\\n",
       "0  TRAIN_0000       692  275.0      287.0      292.0  289.5            2.8   \n",
       "1  TRAIN_0001       534  293.0      284.0      336.0  310.0            3.3   \n",
       "2  TRAIN_0002       634  300.0      392.0      450.0  421.0            3.0   \n",
       "3  TRAIN_0003       639  289.0      368.0      379.0  373.5            3.1   \n",
       "4  TRAIN_0004       496  306.0      353.0      358.0  355.5            3.7   \n",
       "\n",
       "   2022-09-02 새순  2022-09-03 새순  2022-09-04 새순  ...  2022-11-19 엽록소  \\\n",
       "0            2.8            2.7            2.7  ...       70.978249   \n",
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       "0       70.876794       70.705253       70.559603       70.427356   \n",
       "1       71.382303       71.253604       71.092665       70.955608   \n",
       "2       71.199570       71.144020       71.026740       70.920038   \n",
       "3       69.884124       69.845683       69.794682       69.779813   \n",
       "4       68.285364       68.209860       68.209458       68.040083   \n",
       "\n",
       "   2022-11-24 엽록소  2022-11-25 엽록소  2022-11-26 엽록소  2022-11-27 엽록소  \\\n",
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       "1       70.427600  \n",
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       "3       69.133970  \n",
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       "      <th>2022-11-22 엽록소</th>\n",
       "      <th>2022-11-23 엽록소</th>\n",
       "      <th>2022-11-24 엽록소</th>\n",
       "      <th>2022-11-25 엽록소</th>\n",
       "      <th>2022-11-26 엽록소</th>\n",
       "      <th>2022-11-27 엽록소</th>\n",
       "      <th>2022-11-28 엽록소</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TEST_0000</td>\n",
       "      <td>231.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>302.0</td>\n",
       "      <td>260.5</td>\n",
       "      <td>4.3</td>\n",
       "      <td>4.2</td>\n",
       "      <td>4.2</td>\n",
       "      <td>4.1</td>\n",
       "      <td>4.1</td>\n",
       "      <td>...</td>\n",
       "      <td>72.091497</td>\n",
       "      <td>71.906176</td>\n",
       "      <td>71.886799</td>\n",
       "      <td>71.858720</td>\n",
       "      <td>71.725540</td>\n",
       "      <td>71.531618</td>\n",
       "      <td>71.476935</td>\n",
       "      <td>71.321237</td>\n",
       "      <td>71.184295</td>\n",
       "      <td>71.000223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>TEST_0001</td>\n",
       "      <td>206.0</td>\n",
       "      <td>324.0</td>\n",
       "      <td>346.0</td>\n",
       "      <td>335.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>...</td>\n",
       "      <td>68.423776</td>\n",
       "      <td>68.353200</td>\n",
       "      <td>68.346777</td>\n",
       "      <td>68.188159</td>\n",
       "      <td>67.995233</td>\n",
       "      <td>67.879958</td>\n",
       "      <td>67.744820</td>\n",
       "      <td>67.673522</td>\n",
       "      <td>67.539973</td>\n",
       "      <td>67.447849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>TEST_0002</td>\n",
       "      <td>282.0</td>\n",
       "      <td>373.0</td>\n",
       "      <td>404.0</td>\n",
       "      <td>388.5</td>\n",
       "      <td>4.7</td>\n",
       "      <td>4.7</td>\n",
       "      <td>4.6</td>\n",
       "      <td>4.6</td>\n",
       "      <td>4.5</td>\n",
       "      <td>...</td>\n",
       "      <td>74.435783</td>\n",
       "      <td>74.247468</td>\n",
       "      <td>74.142664</td>\n",
       "      <td>74.070401</td>\n",
       "      <td>73.892408</td>\n",
       "      <td>73.796143</td>\n",
       "      <td>73.615838</td>\n",
       "      <td>73.440136</td>\n",
       "      <td>73.387051</td>\n",
       "      <td>73.228319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>TEST_0003</td>\n",
       "      <td>234.0</td>\n",
       "      <td>337.0</td>\n",
       "      <td>357.0</td>\n",
       "      <td>347.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>3.6</td>\n",
       "      <td>3.6</td>\n",
       "      <td>3.6</td>\n",
       "      <td>3.5</td>\n",
       "      <td>...</td>\n",
       "      <td>73.570735</td>\n",
       "      <td>73.420243</td>\n",
       "      <td>73.313538</td>\n",
       "      <td>73.149298</td>\n",
       "      <td>73.109599</td>\n",
       "      <td>72.924511</td>\n",
       "      <td>72.847411</td>\n",
       "      <td>72.838263</td>\n",
       "      <td>72.672156</td>\n",
       "      <td>72.538171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>TEST_0004</td>\n",
       "      <td>222.0</td>\n",
       "      <td>329.0</td>\n",
       "      <td>347.0</td>\n",
       "      <td>338.0</td>\n",
       "      <td>2.7</td>\n",
       "      <td>2.7</td>\n",
       "      <td>2.7</td>\n",
       "      <td>2.7</td>\n",
       "      <td>2.6</td>\n",
       "      <td>...</td>\n",
       "      <td>72.216758</td>\n",
       "      <td>72.163153</td>\n",
       "      <td>72.052952</td>\n",
       "      <td>71.905982</td>\n",
       "      <td>71.766141</td>\n",
       "      <td>71.746813</td>\n",
       "      <td>71.668847</td>\n",
       "      <td>71.501043</td>\n",
       "      <td>71.419715</td>\n",
       "      <td>71.328224</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 183 columns</p>\n",
       "</div>\n",
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       "          const element = document.querySelector('#df-acb6e8b0-ef9c-4275-aa09-6fe97ecaf651');\n",
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       "                                                     [key], {});\n",
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       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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       "  "
      ],
      "text/plain": [
       "          ID  수고(m)  수관폭1(min)  수관폭2(max)  수관폭평균  2022-09-01 새순  \\\n",
       "0  TEST_0000  231.0      219.0      302.0  260.5            4.3   \n",
       "1  TEST_0001  206.0      324.0      346.0  335.0            2.5   \n",
       "2  TEST_0002  282.0      373.0      404.0  388.5            4.7   \n",
       "3  TEST_0003  234.0      337.0      357.0  347.0            3.6   \n",
       "4  TEST_0004  222.0      329.0      347.0  338.0            2.7   \n",
       "\n",
       "   2022-09-02 새순  2022-09-03 새순  2022-09-04 새순  2022-09-05 새순  ...  \\\n",
       "0            4.2            4.2            4.1            4.1  ...   \n",
       "1            2.5            2.5            2.5            2.5  ...   \n",
       "2            4.7            4.6            4.6            4.5  ...   \n",
       "3            3.6            3.6            3.6            3.5  ...   \n",
       "4            2.7            2.7            2.7            2.6  ...   \n",
       "\n",
       "   2022-11-19 엽록소  2022-11-20 엽록소  2022-11-21 엽록소  2022-11-22 엽록소  \\\n",
       "0       72.091497       71.906176       71.886799       71.858720   \n",
       "1       68.423776       68.353200       68.346777       68.188159   \n",
       "2       74.435783       74.247468       74.142664       74.070401   \n",
       "3       73.570735       73.420243       73.313538       73.149298   \n",
       "4       72.216758       72.163153       72.052952       71.905982   \n",
       "\n",
       "   2022-11-23 엽록소  2022-11-24 엽록소  2022-11-25 엽록소  2022-11-26 엽록소  \\\n",
       "0       71.725540       71.531618       71.476935       71.321237   \n",
       "1       67.995233       67.879958       67.744820       67.673522   \n",
       "2       73.892408       73.796143       73.615838       73.440136   \n",
       "3       73.109599       72.924511       72.847411       72.838263   \n",
       "4       71.766141       71.746813       71.668847       71.501043   \n",
       "\n",
       "   2022-11-27 엽록소  2022-11-28 엽록소  \n",
       "0       71.184295       71.000223  \n",
       "1       67.539973       67.447849  \n",
       "2       73.387051       73.228319  \n",
       "3       72.672156       72.538171  \n",
       "4       71.419715       71.328224  \n",
       "\n",
       "[5 rows x 183 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "UtGkqygDKURE",
    "outputId": "f359691f-9ed1-4c47-ef9d-db0ec629acc4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2207 entries, 0 to 2206\n",
      "Columns: 184 entries, ID to 2022-11-28 엽록소\n",
      "dtypes: float64(182), int64(1), object(1)\n",
      "memory usage: 3.1+ MB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 283
    },
    "id": "4Cr-XKZH-bKC",
    "outputId": "973711c6-5a23-48ab-b558-3235763e8936"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f522bc769a0>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "train.rename(columns={'착과량(int)':'착과량'}, inplace=True)\n",
    "plt.scatter(train.index, np.sort(train['착과량'].values))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "fvU_IVqvKWGB",
    "outputId": "76cb7463-fbaa-4e91-beae-6aa5ee5d2c7b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "184"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train.columns.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "DX5yZLvyHswt",
    "outputId": "9bef3fc5-1a59-4122-b4d6-b74128e07675"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "89\n",
      "89\n"
     ]
    }
   ],
   "source": [
    "cols_1 = [x for x in train.columns if '새순' in x]\n",
    "cols_2 = [x for x in train.columns if '엽록소' in x]\n",
    "print(len(cols_1))\n",
    "print(len(cols_2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "zECqdptB_hal"
   },
   "outputs": [],
   "source": [
    "#새순\n",
    "# plt.figure(figsize = (120, 120))\n",
    "# colormap = plt.cm.PuBu\n",
    "# sns.heatmap(train[cols_1].corr(), annot = True, cmap = colormap)\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "ioBybRwJVbcz"
   },
   "outputs": [],
   "source": [
    "#엽록소\n",
    "# plt.figure(figsize = (120, 120))\n",
    "# colormap = plt.cm.PuBu\n",
    "# sns.heatmap(train[cols_2].corr(), annot = True, cmap = colormap)\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Since there are a lot of columns in the data, I will only keep the features that show some correlation with the target variable (above 0.5 or below -0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "2nl1v8dL8JbM",
    "outputId": "bf83a8dc-2349-4ebc-e04d-cc4f64c97f8f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dropped 100 columns\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-33-93966c32a7bb>:3: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  upper = corr_matrix.where(np.triu(np.ones(corr_matrix.shape), k=1).astype(np.bool))[:1]\n"
     ]
    }
   ],
   "source": [
    "corr_matrix = train.corr().abs()\n",
    "upper = corr_matrix.where(np.triu(np.ones(corr_matrix.shape), k=1).astype(np.bool))[:1]\n",
    "to_drop = [column for column in upper.columns if any(upper[column] < 0.5)]\n",
    "print(\"dropped %d columns\" %(len(to_drop)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
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       "      <td>2.8</td>\n",
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       "           ID  착과량  2022-09-01 새순  2022-09-02 새순  2022-09-03 새순  \\\n",
       "0  TRAIN_0000  692            2.8            2.8            2.7   \n",
       "1  TRAIN_0001  534            3.3            3.3            3.3   \n",
       "2  TRAIN_0002  634            3.0            2.9            2.9   \n",
       "3  TRAIN_0003  639            3.1            3.0            3.0   \n",
       "4  TRAIN_0004  496            3.7            3.6            3.6   \n",
       "\n",
       "   2022-09-04 새순  2022-09-05 새순  2022-09-06 새순  2022-09-07 새순  2022-09-08 새순  \\\n",
       "0            2.7            2.7            2.7            2.6            2.6   \n",
       "1            3.2            3.2            3.1            3.1            3.1   \n",
       "2            2.9            2.9            2.8            2.8            2.8   \n",
       "3            3.0            3.0            2.9            2.9            2.9   \n",
       "4            3.6            3.5            3.5            3.5            3.4   \n",
       "\n",
       "   ...  2022-11-12 새순  2022-11-13 새순  2022-11-14 새순  2022-11-15 새순  \\\n",
       "0  ...            1.0            1.0            1.0            0.9   \n",
       "1  ...            1.0            0.9            0.9            0.8   \n",
       "2  ...            1.0            1.0            0.9            0.9   \n",
       "3  ...            1.1            1.1            1.1            1.0   \n",
       "4  ...            1.2            1.1            1.1            1.0   \n",
       "\n",
       "   2022-11-23 새순  2022-11-24 새순  2022-11-25 새순  2022-11-26 새순  2022-11-27 새순  \\\n",
       "0            0.7            0.7            0.7            0.7            0.7   \n",
       "1            0.6            0.5            0.0            0.0            0.0   \n",
       "2            0.7            0.7            0.6            0.6            0.6   \n",
       "3            0.8            0.8            0.8            0.7            0.7   \n",
       "4            0.8            0.7            0.7            0.7            0.6   \n",
       "\n",
       "   2022-11-28 새순  \n",
       "0            0.6  \n",
       "1            0.0  \n",
       "2            0.5  \n",
       "3            0.7  \n",
       "4            0.6  \n",
       "\n",
       "[5 rows x 84 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.drop(to_drop, axis=1, inplace = True)\n",
    "test.drop(to_drop, axis=1, inplace=True)\n",
    "train.head()"
   ]
  },
  {
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    "outputId": "626f0b8d-31ac-4ca6-b1b9-bf82530d126e"
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      " 82  2022-11-27 새순  2207 non-null   float64\n",
      " 83  2022-11-28 새순  2207 non-null   float64\n",
      "dtypes: float64(82), int64(1), object(1)\n",
      "memory usage: 1.4+ MB\n"
     ]
    }
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    "train.info()"
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       "2022-09-03 새순    0\n",
       "                ..\n",
       "2022-11-24 새순    0\n",
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       "Length: 84, dtype: int64"
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       "                    착과량  2022-09-01 새순  2022-09-02 새순  2022-09-03 새순  \\\n",
       "착과량            1.000000      -0.980777      -0.980766      -0.980633   \n",
       "2022-09-01 새순 -0.980777       1.000000       0.998097       0.998532   \n",
       "2022-09-02 새순 -0.980766       0.998097       1.000000       0.998091   \n",
       "2022-09-03 새순 -0.980633       0.998532       0.998091       1.000000   \n",
       "2022-09-04 새순 -0.980772       0.998446       0.998492       0.997982   \n",
       "\n",
       "               2022-09-04 새순  2022-09-05 새순  2022-09-06 새순  2022-09-07 새순  \\\n",
       "착과량                -0.980772      -0.980526      -0.980885      -0.980784   \n",
       "2022-09-01 새순       0.998446       0.998555       0.998277       0.998358   \n",
       "2022-09-02 새순       0.998492       0.998385       0.998564       0.998295   \n",
       "2022-09-03 새순       0.997982       0.998491       0.998386       0.998607   \n",
       "2022-09-04 새순       1.000000       0.998016       0.998393       0.998342   \n",
       "\n",
       "               2022-09-08 새순  2022-09-09 새순  ...  2022-11-12 새순  \\\n",
       "착과량                -0.980261      -0.980388  ...      -0.749539   \n",
       "2022-09-01 새순       0.998242       0.998249  ...       0.771072   \n",
       "2022-09-02 새순       0.998292       0.998213  ...       0.771230   \n",
       "2022-09-03 새순       0.998280       0.998256  ...       0.772352   \n",
       "2022-09-04 새순       0.998481       0.998144  ...       0.772649   \n",
       "\n",
       "               2022-11-13 새순  2022-11-14 새순  2022-11-15 새순  2022-11-23 새순  \\\n",
       "착과량                -0.697841      -0.635049      -0.544563       0.557371   \n",
       "2022-09-01 새순       0.721664       0.655642       0.566983      -0.556788   \n",
       "2022-09-02 새순       0.720398       0.654854       0.565656      -0.557547   \n",
       "2022-09-03 새순       0.723094       0.656561       0.568190      -0.554846   \n",
       "2022-09-04 새순       0.722548       0.657230       0.567903      -0.554963   \n",
       "\n",
       "               2022-11-24 새순  2022-11-25 새순  2022-11-26 새순  2022-11-27 새순  \\\n",
       "착과량                 0.582726       0.546006       0.603445       0.704241   \n",
       "2022-09-01 새순      -0.583302      -0.543855      -0.602950      -0.705765   \n",
       "2022-09-02 새순      -0.584870      -0.544463      -0.604538      -0.706638   \n",
       "2022-09-03 새순      -0.582715      -0.543134      -0.602024      -0.704716   \n",
       "2022-09-04 새순      -0.583703      -0.543055      -0.602929      -0.705140   \n",
       "\n",
       "               2022-11-28 새순  \n",
       "착과량                 0.769204  \n",
       "2022-09-01 새순      -0.773815  \n",
       "2022-09-02 새순      -0.773221  \n",
       "2022-09-03 새순      -0.772613  \n",
       "2022-09-04 새순      -0.772696  \n",
       "\n",
       "[5 rows x 83 columns]"
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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 90 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train.hist(figsize=(15, 15), edgecolor=\"black\")\n",
    "plt.show()\n",
    "#plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 318
    },
    "id": "XmO9xTIjCfLE",
    "outputId": "78e01d5e-877e-4098-b8a2-94708cc64def"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f522e55d910>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Observe \n",
    "unique_values = train.drop('착과량', axis=1).select_dtypes(include=\"number\").nunique().sort_values()\n",
    "unique_values.plot.bar(logy=True, figsize=(15, 4), title=\"Unique values per feature\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 411
    },
    "id": "1iOq43aRBzC5",
    "outputId": "329e22c3-9f01-492d-b9f5-c1c2bb3b654b"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"df-744c6b01-5945-48f9-ba83-88de6d9147e6\">\n",
       "    <div class=\"colab-df-container\">\n",
       "      <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>착과량</th>\n",
       "      <th>2022-09-01 새순</th>\n",
       "      <th>2022-09-02 새순</th>\n",
       "      <th>2022-09-03 새순</th>\n",
       "      <th>2022-09-04 새순</th>\n",
       "      <th>2022-09-05 새순</th>\n",
       "      <th>2022-09-06 새순</th>\n",
       "      <th>2022-09-07 새순</th>\n",
       "      <th>2022-09-08 새순</th>\n",
       "      <th>2022-09-09 새순</th>\n",
       "      <th>...</th>\n",
       "      <th>2022-11-12 새순</th>\n",
       "      <th>2022-11-13 새순</th>\n",
       "      <th>2022-11-14 새순</th>\n",
       "      <th>2022-11-15 새순</th>\n",
       "      <th>2022-11-23 새순</th>\n",
       "      <th>2022-11-24 새순</th>\n",
       "      <th>2022-11-25 새순</th>\n",
       "      <th>2022-11-26 새순</th>\n",
       "      <th>2022-11-27 새순</th>\n",
       "      <th>2022-11-28 새순</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2207.000000</td>\n",
       "      <td>2207.000000</td>\n",
       "      <td>2207.000000</td>\n",
       "      <td>2207.000000</td>\n",
       "      <td>2207.000000</td>\n",
       "      <td>2207.000000</td>\n",
       "      <td>2207.000000</td>\n",
       "      <td>2207.000000</td>\n",
       "      <td>2207.000000</td>\n",
       "      <td>2207.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>2207.000000</td>\n",
       "      <td>2207.000000</td>\n",
       "      <td>2207.000000</td>\n",
       "      <td>2207.000000</td>\n",
       "      <td>2207.000000</td>\n",
       "      <td>2207.000000</td>\n",
       "      <td>2207.000000</td>\n",
       "      <td>2207.000000</td>\n",
       "      <td>2207.000000</td>\n",
       "      <td>2207.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>406.222474</td>\n",
       "      <td>3.774807</td>\n",
       "      <td>3.736928</td>\n",
       "      <td>3.701495</td>\n",
       "      <td>3.663435</td>\n",
       "      <td>3.627503</td>\n",
       "      <td>3.590122</td>\n",
       "      <td>3.553285</td>\n",
       "      <td>3.516130</td>\n",
       "      <td>3.479157</td>\n",
       "      <td>...</td>\n",
       "      <td>1.125963</td>\n",
       "      <td>1.088536</td>\n",
       "      <td>1.052288</td>\n",
       "      <td>1.015179</td>\n",
       "      <td>0.721069</td>\n",
       "      <td>0.682102</td>\n",
       "      <td>0.632170</td>\n",
       "      <td>0.559991</td>\n",
       "      <td>0.454599</td>\n",
       "      <td>0.352832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>218.978302</td>\n",
       "      <td>0.774181</td>\n",
       "      <td>0.764067</td>\n",
       "      <td>0.754751</td>\n",
       "      <td>0.745394</td>\n",
       "      <td>0.733896</td>\n",
       "      <td>0.726348</td>\n",
       "      <td>0.714718</td>\n",
       "      <td>0.705003</td>\n",
       "      <td>0.695087</td>\n",
       "      <td>...</td>\n",
       "      <td>0.087946</td>\n",
       "      <td>0.081074</td>\n",
       "      <td>0.074506</td>\n",
       "      <td>0.069258</td>\n",
       "      <td>0.071919</td>\n",
       "      <td>0.089075</td>\n",
       "      <td>0.142182</td>\n",
       "      <td>0.208367</td>\n",
       "      <td>0.277022</td>\n",
       "      <td>0.305332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.300000</td>\n",
       "      <td>2.200000</td>\n",
       "      <td>2.200000</td>\n",
       "      <td>2.200000</td>\n",
       "      <td>2.200000</td>\n",
       "      <td>2.200000</td>\n",
       "      <td>2.100000</td>\n",
       "      <td>2.100000</td>\n",
       "      <td>2.100000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>221.000000</td>\n",
       "      <td>3.100000</td>\n",
       "      <td>3.100000</td>\n",
       "      <td>3.050000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.900000</td>\n",
       "      <td>2.900000</td>\n",
       "      <td>2.900000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>404.000000</td>\n",
       "      <td>3.800000</td>\n",
       "      <td>3.700000</td>\n",
       "      <td>3.700000</td>\n",
       "      <td>3.700000</td>\n",
       "      <td>3.600000</td>\n",
       "      <td>3.600000</td>\n",
       "      <td>3.600000</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>593.500000</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>4.300000</td>\n",
       "      <td>4.300000</td>\n",
       "      <td>4.300000</td>\n",
       "      <td>4.200000</td>\n",
       "      <td>4.200000</td>\n",
       "      <td>4.100000</td>\n",
       "      <td>4.100000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.200000</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>799.000000</td>\n",
       "      <td>5.300000</td>\n",
       "      <td>5.300000</td>\n",
       "      <td>5.200000</td>\n",
       "      <td>5.200000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.900000</td>\n",
       "      <td>4.900000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>1.300000</td>\n",
       "      <td>1.300000</td>\n",
       "      <td>1.200000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.900000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 83 columns</p>\n",
       "</div>\n",
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-744c6b01-5945-48f9-ba83-88de6d9147e6')\"\n",
       "              title=\"Convert this dataframe to an interactive table.\"\n",
       "              style=\"display:none;\">\n",
       "        \n",
       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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       "  </svg>\n",
       "      </button>\n",
       "      \n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      flex-wrap:wrap;\n",
       "      gap: 12px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
       "      border: none;\n",
       "      border-radius: 50%;\n",
       "      cursor: pointer;\n",
       "      display: none;\n",
       "      fill: #1967D2;\n",
       "      height: 32px;\n",
       "      padding: 0 0 0 0;\n",
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       "\n",
       "    .colab-df-convert:hover {\n",
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       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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       "  </style>\n",
       "\n",
       "      <script>\n",
       "        const buttonEl =\n",
       "          document.querySelector('#df-744c6b01-5945-48f9-ba83-88de6d9147e6 button.colab-df-convert');\n",
       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-744c6b01-5945-48f9-ba83-88de6d9147e6');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
       "          const docLink = document.createElement('div');\n",
       "          docLink.innerHTML = docLinkHtml;\n",
       "          element.appendChild(docLink);\n",
       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "               착과량  2022-09-01 새순  2022-09-02 새순  2022-09-03 새순  \\\n",
       "count  2207.000000    2207.000000    2207.000000    2207.000000   \n",
       "mean    406.222474       3.774807       3.736928       3.701495   \n",
       "std     218.978302       0.774181       0.764067       0.754751   \n",
       "min       1.000000       2.300000       2.200000       2.200000   \n",
       "25%     221.000000       3.100000       3.100000       3.050000   \n",
       "50%     404.000000       3.800000       3.700000       3.700000   \n",
       "75%     593.500000       4.400000       4.400000       4.300000   \n",
       "max     799.000000       5.300000       5.300000       5.200000   \n",
       "\n",
       "       2022-09-04 새순  2022-09-05 새순  2022-09-06 새순  2022-09-07 새순  \\\n",
       "count    2207.000000    2207.000000    2207.000000    2207.000000   \n",
       "mean        3.663435       3.627503       3.590122       3.553285   \n",
       "std         0.745394       0.733896       0.726348       0.714718   \n",
       "min         2.200000       2.200000       2.200000       2.100000   \n",
       "25%         3.000000       3.000000       3.000000       2.900000   \n",
       "50%         3.700000       3.600000       3.600000       3.600000   \n",
       "75%         4.300000       4.300000       4.200000       4.200000   \n",
       "max         5.200000       5.100000       5.100000       5.000000   \n",
       "\n",
       "       2022-09-08 새순  2022-09-09 새순  ...  2022-11-12 새순  2022-11-13 새순  \\\n",
       "count    2207.000000    2207.000000  ...    2207.000000    2207.000000   \n",
       "mean        3.516130       3.479157  ...       1.125963       1.088536   \n",
       "std         0.705003       0.695087  ...       0.087946       0.081074   \n",
       "min         2.100000       2.100000  ...       0.900000       0.900000   \n",
       "25%         2.900000       2.900000  ...       1.100000       1.000000   \n",
       "50%         3.500000       3.500000  ...       1.100000       1.100000   \n",
       "75%         4.100000       4.100000  ...       1.200000       1.100000   \n",
       "max         4.900000       4.900000  ...       1.400000       1.300000   \n",
       "\n",
       "       2022-11-14 새순  2022-11-15 새순  2022-11-23 새순  2022-11-24 새순  \\\n",
       "count    2207.000000    2207.000000    2207.000000    2207.000000   \n",
       "mean        1.052288       1.015179       0.721069       0.682102   \n",
       "std         0.074506       0.069258       0.071919       0.089075   \n",
       "min         0.800000       0.800000       0.500000       0.000000   \n",
       "25%         1.000000       1.000000       0.700000       0.600000   \n",
       "50%         1.100000       1.000000       0.700000       0.700000   \n",
       "75%         1.100000       1.100000       0.800000       0.700000   \n",
       "max         1.300000       1.200000       1.000000       0.900000   \n",
       "\n",
       "       2022-11-25 새순  2022-11-26 새순  2022-11-27 새순  2022-11-28 새순  \n",
       "count    2207.000000    2207.000000    2207.000000    2207.000000  \n",
       "mean        0.632170       0.559991       0.454599       0.352832  \n",
       "std         0.142182       0.208367       0.277022       0.305332  \n",
       "min         0.000000       0.000000       0.000000       0.000000  \n",
       "25%         0.600000       0.500000       0.000000       0.000000  \n",
       "50%         0.600000       0.600000       0.600000       0.500000  \n",
       "75%         0.700000       0.700000       0.600000       0.600000  \n",
       "max         0.900000       0.900000       0.900000       0.900000  \n",
       "\n",
       "[8 rows x 83 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "fMeNrzEW_38o"
   },
   "outputs": [],
   "source": [
    "# n_columns = 4\n",
    "# n_rows = 21\n",
    "# _, axes = plt.subplots(n_rows, n_columns, figsize=(5 * n_columns, 3 * n_rows))\n",
    "# for i, c in enumerate(train.select_dtypes(exclude='object')):\n",
    "#     sns.boxplot(y = c, data = train, ax = axes[i // n_columns, i % n_columns])\n",
    "# plt.tight_layout()\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Boxplots show no significant outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "YZd67ZULB_2J"
   },
   "outputs": [],
   "source": [
    "train.drop(['ID'], axis=1, inplace=True)\n",
    "test.drop(['ID'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "7kXhBt9oRhzQ",
    "outputId": "3c82ea03-73e7-4411-8f9e-84eb636ace62"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"df-ab6ffeeb-956a-46fe-9961-6de7ce573338\">\n",
       "    <div class=\"colab-df-container\">\n",
       "      <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>VIF Factor</th>\n",
       "      <th>features</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18405.779231</td>\n",
       "      <td>2022-09-07 새순</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>17902.589115</td>\n",
       "      <td>2022-09-05 새순</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>17864.917919</td>\n",
       "      <td>2022-09-08 새순</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17315.206794</td>\n",
       "      <td>2022-09-06 새순</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17134.315071</td>\n",
       "      <td>2022-09-03 새순</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>16797.308513</td>\n",
       "      <td>2022-09-09 새순</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>16500.074726</td>\n",
       "      <td>2022-09-04 새순</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>16463.627625</td>\n",
       "      <td>2022-09-11 새순</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>16395.360804</td>\n",
       "      <td>2022-09-10 새순</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>16126.937603</td>\n",
       "      <td>2022-09-02 새순</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>15956.168358</td>\n",
       "      <td>2022-09-12 새순</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>15534.126298</td>\n",
       "      <td>2022-09-01 새순</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>15270.213209</td>\n",
       "      <td>2022-09-16 새순</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>15251.316539</td>\n",
       "      <td>2022-09-14 새순</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15112.966297</td>\n",
       "      <td>2022-09-13 새순</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>14622.780407</td>\n",
       "      <td>2022-09-15 새순</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>14096.081415</td>\n",
       "      <td>2022-09-18 새순</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>13778.996950</td>\n",
       "      <td>2022-09-17 새순</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>13598.917531</td>\n",
       "      <td>2022-09-19 새순</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>13079.656240</td>\n",
       "      <td>2022-09-20 새순</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>12889.472196</td>\n",
       "      <td>2022-09-23 새순</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>12562.734148</td>\n",
       "      <td>2022-09-21 새순</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>12516.922827</td>\n",
       "      <td>2022-09-22 새순</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>12281.090509</td>\n",
       "      <td>2022-09-24 새순</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>12226.073937</td>\n",
       "      <td>2022-09-26 새순</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>11244.287174</td>\n",
       "      <td>2022-09-27 새순</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>10952.669711</td>\n",
       "      <td>2022-09-25 새순</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>10818.243938</td>\n",
       "      <td>2022-09-28 새순</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>10601.070954</td>\n",
       "      <td>2022-09-29 새순</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>10348.758359</td>\n",
       "      <td>2022-09-30 새순</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>10071.082693</td>\n",
       "      <td>2022-10-01 새순</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>9530.251265</td>\n",
       "      <td>2022-10-02 새순</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>9309.194419</td>\n",
       "      <td>2022-10-04 새순</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>9250.398568</td>\n",
       "      <td>2022-10-03 새순</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>8825.459115</td>\n",
       "      <td>2022-10-05 새순</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>8741.067897</td>\n",
       "      <td>2022-10-06 새순</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>8483.996059</td>\n",
       "      <td>2022-10-08 새순</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>8113.017479</td>\n",
       "      <td>2022-10-07 새순</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>7837.990114</td>\n",
       "      <td>2022-10-10 새순</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>7766.063328</td>\n",
       "      <td>2022-10-09 새순</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>7343.314431</td>\n",
       "      <td>2022-10-11 새순</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>6830.249656</td>\n",
       "      <td>2022-10-13 새순</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>6742.496450</td>\n",
       "      <td>2022-10-12 새순</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>6714.605671</td>\n",
       "      <td>2022-10-14 새순</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>6666.777315</td>\n",
       "      <td>2022-10-15 새순</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>6313.114739</td>\n",
       "      <td>2022-10-16 새순</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>5931.114751</td>\n",
       "      <td>2022-10-18 새순</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>5800.694966</td>\n",
       "      <td>2022-10-17 새순</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>5476.049826</td>\n",
       "      <td>2022-10-19 새순</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>5243.423805</td>\n",
       "      <td>2022-10-21 새순</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>5198.308703</td>\n",
       "      <td>2022-10-20 새순</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>4898.373665</td>\n",
       "      <td>2022-10-22 새순</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>4791.629378</td>\n",
       "      <td>2022-10-24 새순</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>4687.437518</td>\n",
       "      <td>2022-10-23 새순</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>4217.410620</td>\n",
       "      <td>2022-10-25 새순</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>4200.081812</td>\n",
       "      <td>2022-10-26 새순</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>3985.217587</td>\n",
       "      <td>2022-10-27 새순</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>3962.395332</td>\n",
       "      <td>2022-10-28 새순</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>3592.580195</td>\n",
       "      <td>2022-10-29 새순</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>3479.287563</td>\n",
       "      <td>2022-10-30 새순</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>3397.497297</td>\n",
       "      <td>2022-10-31 새순</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>3211.059890</td>\n",
       "      <td>2022-11-01 새순</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>3159.934059</td>\n",
       "      <td>2022-11-02 새순</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>2857.297296</td>\n",
       "      <td>2022-11-03 새순</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>2738.372181</td>\n",
       "      <td>2022-11-04 새순</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>2603.155358</td>\n",
       "      <td>2022-11-05 새순</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>2487.727816</td>\n",
       "      <td>2022-11-06 새순</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>2380.437196</td>\n",
       "      <td>2022-11-07 새순</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>2253.518094</td>\n",
       "      <td>2022-11-08 새순</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>2080.377738</td>\n",
       "      <td>2022-11-09 새순</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>1948.747626</td>\n",
       "      <td>2022-11-10 새순</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>1747.622690</td>\n",
       "      <td>2022-11-11 새순</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>1646.975296</td>\n",
       "      <td>2022-11-12 새순</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>1429.163482</td>\n",
       "      <td>2022-11-13 새순</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>1367.327252</td>\n",
       "      <td>2022-11-14 새순</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>1210.043479</td>\n",
       "      <td>2022-11-15 새순</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>557.011634</td>\n",
       "      <td>2022-11-23 새순</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>288.221826</td>\n",
       "      <td>2022-11-24 새순</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>87.661263</td>\n",
       "      <td>2022-11-25 새순</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>45.868055</td>\n",
       "      <td>2022-11-26 새순</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>30.785563</td>\n",
       "      <td>착과량</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>21.134750</td>\n",
       "      <td>2022-11-27 새순</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>12.427126</td>\n",
       "      <td>2022-11-28 새순</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "      VIF Factor       features\n",
       "0   18405.779231  2022-09-07 새순\n",
       "1   17902.589115  2022-09-05 새순\n",
       "2   17864.917919  2022-09-08 새순\n",
       "3   17315.206794  2022-09-06 새순\n",
       "4   17134.315071  2022-09-03 새순\n",
       "5   16797.308513  2022-09-09 새순\n",
       "6   16500.074726  2022-09-04 새순\n",
       "7   16463.627625  2022-09-11 새순\n",
       "8   16395.360804  2022-09-10 새순\n",
       "9   16126.937603  2022-09-02 새순\n",
       "10  15956.168358  2022-09-12 새순\n",
       "11  15534.126298  2022-09-01 새순\n",
       "12  15270.213209  2022-09-16 새순\n",
       "13  15251.316539  2022-09-14 새순\n",
       "14  15112.966297  2022-09-13 새순\n",
       "15  14622.780407  2022-09-15 새순\n",
       "16  14096.081415  2022-09-18 새순\n",
       "17  13778.996950  2022-09-17 새순\n",
       "18  13598.917531  2022-09-19 새순\n",
       "19  13079.656240  2022-09-20 새순\n",
       "20  12889.472196  2022-09-23 새순\n",
       "21  12562.734148  2022-09-21 새순\n",
       "22  12516.922827  2022-09-22 새순\n",
       "23  12281.090509  2022-09-24 새순\n",
       "24  12226.073937  2022-09-26 새순\n",
       "25  11244.287174  2022-09-27 새순\n",
       "26  10952.669711  2022-09-25 새순\n",
       "27  10818.243938  2022-09-28 새순\n",
       "28  10601.070954  2022-09-29 새순\n",
       "29  10348.758359  2022-09-30 새순\n",
       "30  10071.082693  2022-10-01 새순\n",
       "31   9530.251265  2022-10-02 새순\n",
       "32   9309.194419  2022-10-04 새순\n",
       "33   9250.398568  2022-10-03 새순\n",
       "34   8825.459115  2022-10-05 새순\n",
       "35   8741.067897  2022-10-06 새순\n",
       "36   8483.996059  2022-10-08 새순\n",
       "37   8113.017479  2022-10-07 새순\n",
       "38   7837.990114  2022-10-10 새순\n",
       "39   7766.063328  2022-10-09 새순\n",
       "40   7343.314431  2022-10-11 새순\n",
       "41   6830.249656  2022-10-13 새순\n",
       "42   6742.496450  2022-10-12 새순\n",
       "43   6714.605671  2022-10-14 새순\n",
       "44   6666.777315  2022-10-15 새순\n",
       "45   6313.114739  2022-10-16 새순\n",
       "46   5931.114751  2022-10-18 새순\n",
       "47   5800.694966  2022-10-17 새순\n",
       "48   5476.049826  2022-10-19 새순\n",
       "49   5243.423805  2022-10-21 새순\n",
       "50   5198.308703  2022-10-20 새순\n",
       "51   4898.373665  2022-10-22 새순\n",
       "52   4791.629378  2022-10-24 새순\n",
       "53   4687.437518  2022-10-23 새순\n",
       "54   4217.410620  2022-10-25 새순\n",
       "55   4200.081812  2022-10-26 새순\n",
       "56   3985.217587  2022-10-27 새순\n",
       "57   3962.395332  2022-10-28 새순\n",
       "58   3592.580195  2022-10-29 새순\n",
       "59   3479.287563  2022-10-30 새순\n",
       "60   3397.497297  2022-10-31 새순\n",
       "61   3211.059890  2022-11-01 새순\n",
       "62   3159.934059  2022-11-02 새순\n",
       "63   2857.297296  2022-11-03 새순\n",
       "64   2738.372181  2022-11-04 새순\n",
       "65   2603.155358  2022-11-05 새순\n",
       "66   2487.727816  2022-11-06 새순\n",
       "67   2380.437196  2022-11-07 새순\n",
       "68   2253.518094  2022-11-08 새순\n",
       "69   2080.377738  2022-11-09 새순\n",
       "70   1948.747626  2022-11-10 새순\n",
       "71   1747.622690  2022-11-11 새순\n",
       "72   1646.975296  2022-11-12 새순\n",
       "73   1429.163482  2022-11-13 새순\n",
       "74   1367.327252  2022-11-14 새순\n",
       "75   1210.043479  2022-11-15 새순\n",
       "76    557.011634  2022-11-23 새순\n",
       "77    288.221826  2022-11-24 새순\n",
       "78     87.661263  2022-11-25 새순\n",
       "79     45.868055  2022-11-26 새순\n",
       "80     30.785563            착과량\n",
       "81     21.134750  2022-11-27 새순\n",
       "82     12.427126  2022-11-28 새순"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check multicollinearity\n",
    "pd.set_option('display.max_rows', None)\n",
    "vif = pd.DataFrame()\n",
    "vif[\"VIF Factor\"] = [variance_inflation_factor(\n",
    "    train.values, i) for i in range(train.shape[1])]\n",
    "vif[\"features\"] = train.columns\n",
    "vif = vif.sort_values(\"VIF Factor\", ascending=False).reset_index(drop=True)\n",
    "vif"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "PrXePkyEWq_e"
   },
   "outputs": [],
   "source": [
    "sept_cols = [x for x in train.columns if '2022-09' in x]\n",
    "oct_cols = [x for x in train.columns if '2022-10' in x]\n",
    "train2 = train.drop(sept_cols + oct_cols, axis = 1)\n",
    "train2['새순_sep_med'] = train[sept_cols].median(axis=1)\n",
    "train2['새순_oct_med'] = train[oct_cols].median(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 802
    },
    "id": "ejxyAzT5TbiY",
    "outputId": "0260ffc3-d239-4951-f2cb-9ed5b7b07638"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>VIF Factor</th>\n",
       "      <th>features</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4001.891033</td>\n",
       "      <td>새순_oct_med</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3406.557980</td>\n",
       "      <td>새순_sep_med</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2665.225332</td>\n",
       "      <td>2022-11-01 새순</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2643.795562</td>\n",
       "      <td>2022-11-02 새순</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2530.164372</td>\n",
       "      <td>2022-11-03 새순</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2459.653718</td>\n",
       "      <td>2022-11-04 새순</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2455.690768</td>\n",
       "      <td>2022-11-05 새순</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2378.107173</td>\n",
       "      <td>2022-11-06 새순</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2278.898212</td>\n",
       "      <td>2022-11-07 새순</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2163.885633</td>\n",
       "      <td>2022-11-08 새순</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2012.720232</td>\n",
       "      <td>2022-11-09 새순</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1876.439105</td>\n",
       "      <td>2022-11-10 새순</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1709.461068</td>\n",
       "      <td>2022-11-11 새순</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1604.619763</td>\n",
       "      <td>2022-11-12 새순</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1391.167612</td>\n",
       "      <td>2022-11-13 새순</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1330.516511</td>\n",
       "      <td>2022-11-14 새순</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1168.228970</td>\n",
       "      <td>2022-11-15 새순</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>540.305107</td>\n",
       "      <td>2022-11-23 새순</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>281.379133</td>\n",
       "      <td>2022-11-24 새순</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>85.056460</td>\n",
       "      <td>2022-11-25 새순</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>44.340824</td>\n",
       "      <td>2022-11-26 새순</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>29.373835</td>\n",
       "      <td>착과량</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>20.478892</td>\n",
       "      <td>2022-11-27 새순</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>12.078107</td>\n",
       "      <td>2022-11-28 새순</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-eb37c54b-3796-422e-b475-b872fd158317')\"\n",
       "              title=\"Convert this dataframe to an interactive table.\"\n",
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       "        \n",
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       "  </svg>\n",
       "      </button>\n",
       "      \n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
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       "      gap: 12px;\n",
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       "\n",
       "    .colab-df-convert {\n",
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       "    }\n",
       "\n",
       "    .colab-df-convert:hover {\n",
       "      background-color: #E2EBFA;\n",
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "      fill: #174EA6;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
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       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
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       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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       "\n",
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       "          document.querySelector('#df-eb37c54b-3796-422e-b475-b872fd158317 button.colab-df-convert');\n",
       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-eb37c54b-3796-422e-b475-b872fd158317');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
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       "          docLink.innerHTML = docLinkHtml;\n",
       "          element.appendChild(docLink);\n",
       "        }\n",
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       "    </div>\n",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "     VIF Factor       features\n",
       "0   4001.891033     새순_oct_med\n",
       "1   3406.557980     새순_sep_med\n",
       "2   2665.225332  2022-11-01 새순\n",
       "3   2643.795562  2022-11-02 새순\n",
       "4   2530.164372  2022-11-03 새순\n",
       "5   2459.653718  2022-11-04 새순\n",
       "6   2455.690768  2022-11-05 새순\n",
       "7   2378.107173  2022-11-06 새순\n",
       "8   2278.898212  2022-11-07 새순\n",
       "9   2163.885633  2022-11-08 새순\n",
       "10  2012.720232  2022-11-09 새순\n",
       "11  1876.439105  2022-11-10 새순\n",
       "12  1709.461068  2022-11-11 새순\n",
       "13  1604.619763  2022-11-12 새순\n",
       "14  1391.167612  2022-11-13 새순\n",
       "15  1330.516511  2022-11-14 새순\n",
       "16  1168.228970  2022-11-15 새순\n",
       "17   540.305107  2022-11-23 새순\n",
       "18   281.379133  2022-11-24 새순\n",
       "19    85.056460  2022-11-25 새순\n",
       "20    44.340824  2022-11-26 새순\n",
       "21    29.373835            착과량\n",
       "22    20.478892  2022-11-27 새순\n",
       "23    12.078107  2022-11-28 새순"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vif = pd.DataFrame()\n",
    "vif[\"VIF Factor\"] = [variance_inflation_factor(\n",
    "    train2.values, i) for i in range(train2.shape[1])]\n",
    "vif[\"features\"] = train2.columns\n",
    "vif = vif.sort_values(\"VIF Factor\", ascending=False).reset_index(drop=True)\n",
    "vif"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8-qAMk4TaoIC"
   },
   "source": [
    "* VIF factors are incredibly high\n",
    "* The model performance does not improve when september~october data are replaced with mean/median\n",
    "* This approach of feature selection may not be suitable for this problem\n",
    "* [Source1](https://towardsdatascience.com/how-to-remove-multicollinearity-using-python-4da8d9d8abb2), [Source2](https://datascienceschool.net/03%20machine%20learning/06.04%20%EB%8B%A4%EC%A4%91%EA%B3%B5%EC%84%A0%EC%84%B1%EA%B3%BC%20%EB%B3%80%EC%88%98%20%EC%84%A0%ED%83%9D.html#:~:text=%EB%8B%A4%EC%A4%91%20%EA%B3%B5%EC%84%A0%EC%84%B1%EC%9D%84%20%EC%97%86%EC%95%A0%EB%8A%94,%ED%95%9C%20%EC%84%B1%EB%8A%A5%EC%9D%84%20%EB%82%98%ED%83%80%EB%82%B8%20%EA%B2%83%EC%9D%B4%EB%8B%A4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XAc6QeOvRKqt"
   },
   "source": [
    "## Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 5 regression models were tested based on stratified k-fold.\n",
    "* 3 best performing models were selected for the final ensemble model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "id": "KHqgKInMeYmZ"
   },
   "outputs": [],
   "source": [
    "def NMAE(true, pred):\n",
    "    mae = np.mean(np.abs(true-pred))\n",
    "    score = mae / np.mean(np.abs(true))\n",
    "    return score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "id": "neULx5YDfA9g"
   },
   "outputs": [],
   "source": [
    "X = train.drop('착과량', axis = 1)\n",
    "y = train['착과량']\n",
    "n_splits = 7\n",
    "\n",
    "kf = StratifiedKFold(n_splits = n_splits, shuffle = True, random_state = 42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yfOMNyLmewen"
   },
   "source": [
    "### GradientBoostingRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "PEh0VL1LezGI",
    "outputId": "4579d7c1-b66b-47ac-b34a-a0ecbcf33eca"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "========== fold 1 ==========\n",
      "NMAE: 0.07882\n",
      "runtime: 1s\n",
      "========== fold 2 ==========\n",
      "NMAE: 0.07736\n",
      "runtime: 1s\n",
      "========== fold 3 ==========\n",
      "NMAE: 0.07201\n",
      "runtime: 1s\n",
      "========== fold 4 ==========\n",
      "NMAE: 0.06944\n",
      "runtime: 1s\n",
      "========== fold 5 ==========\n",
      "NMAE: 0.07523\n",
      "runtime: 1s\n",
      "========== fold 6 ==========\n",
      "NMAE: 0.07801\n",
      "runtime: 0s\n",
      "========== fold 7 ==========\n",
      "NMAE: 0.06802\n",
      "runtime: 0s\n",
      "==============================\n",
      "Average NMAE for GBR: 0.07413\n"
     ]
    }
   ],
   "source": [
    "gbm = GradientBoostingRegressor(random_state = 42)\n",
    "gb_nmae_list = []\n",
    "gb_pred = np.zeros((test.shape[0]))\n",
    "n=0\n",
    "for tr_idx, val_idx in kf.split(X, y) :\n",
    "    start_time = time.time()\n",
    "    n+=1\n",
    "    tr_x, tr_y = X.iloc[tr_idx], y.iloc[tr_idx]\n",
    "    val_x, val_y = X.iloc[val_idx], y.iloc[val_idx]\n",
    "\n",
    "    gbm.fit(tr_x, tr_y)\n",
    "    end_time = time.time()\n",
    "    \n",
    "    pred = gbm.predict(val_x)\n",
    "    sub_pred = gbm.predict(test)\n",
    "    nmae = NMAE(val_y, pred)\n",
    "    \n",
    "    gb_nmae_list.append(nmae)\n",
    "    \n",
    "    gb_pred += (sub_pred / n_splits)\n",
    "    print(\"========== fold %d ==========\" %(n))\n",
    "    print(\"NMAE: %0.5f\" %(nmae))\n",
    "    print(\"runtime: %ds\" %(end_time - start_time))\n",
    "print(\"==============================\")\n",
    "print('Average NMAE for GBR: %0.5f' %(np.mean(gb_nmae_list)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lU2iDwhWxyHp"
   },
   "source": [
    "### Random Forest Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "pwrLmJq0xyYT",
    "outputId": "990f543f-332b-4543-95fe-04128f72cf66"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "========== fold 1 ==========\n",
      "NMAE: 0.07580\n",
      "runtime: 7s\n",
      "========== fold 2 ==========\n",
      "NMAE: 0.07732\n",
      "runtime: 5s\n",
      "========== fold 3 ==========\n",
      "NMAE: 0.07169\n",
      "runtime: 4s\n",
      "========== fold 4 ==========\n",
      "NMAE: 0.06886\n",
      "runtime: 4s\n",
      "========== fold 5 ==========\n",
      "NMAE: 0.07487\n",
      "runtime: 4s\n",
      "========== fold 6 ==========\n",
      "NMAE: 0.07783\n",
      "runtime: 4s\n",
      "========== fold 7 ==========\n",
      "NMAE: 0.06696\n",
      "runtime: 4s\n",
      "==============================\n",
      "Average NMAE for RFR: 0.07333\n"
     ]
    }
   ],
   "source": [
    "rfr = RandomForestRegressor(n_estimators = 500,\n",
    "                            max_depth = 5,\n",
    "                            random_state = 42)\n",
    "rf_nmae_list = []\n",
    "rf_pred = np.zeros((test.shape[0]))\n",
    "n=0\n",
    "for tr_idx, val_idx in kf.split(X, y) :\n",
    "    start_time = time.time()\n",
    "    n+=1\n",
    "    tr_x, tr_y = X.iloc[tr_idx], y.iloc[tr_idx]\n",
    "    val_x, val_y = X.iloc[val_idx], y.iloc[val_idx]\n",
    "\n",
    "    rfr.fit(tr_x, tr_y)\n",
    "    end_time = time.time()\n",
    "    \n",
    "    pred = rfr.predict(val_x)\n",
    "    sub_pred = rfr.predict(test)\n",
    "    nmae = NMAE(val_y, pred)\n",
    "    \n",
    "    rf_nmae_list.append(nmae)\n",
    "    \n",
    "    rf_pred += (sub_pred / n_splits)\n",
    "    print(\"========== fold %d ==========\" %(n))\n",
    "    print(\"NMAE: %0.5f\" %(nmae))\n",
    "    print(\"runtime: %ds\" %(end_time - start_time))\n",
    "print(\"==============================\")\n",
    "print('Average NMAE for RFR: %0.5f' %(np.mean(rf_nmae_list)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "PBdywaGliYwr"
   },
   "source": [
    "### LGBM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "m1uSiKrMfTJo",
    "outputId": "fc580550-5216-42d0-e6a7-0e98ec83923a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "========== fold 1 ==========\n",
      "NMAE: 0.07820\n",
      "runtime: 0s\n",
      "========== fold 2 ==========\n",
      "NMAE: 0.07902\n",
      "runtime: 0s\n",
      "========== fold 3 ==========\n",
      "NMAE: 0.07257\n",
      "runtime: 0s\n",
      "========== fold 4 ==========\n",
      "NMAE: 0.06880\n",
      "runtime: 0s\n",
      "========== fold 5 ==========\n",
      "NMAE: 0.07705\n",
      "runtime: 0s\n",
      "========== fold 6 ==========\n",
      "NMAE: 0.07849\n",
      "runtime: 0s\n",
      "========== fold 7 ==========\n",
      "NMAE: 0.06890\n",
      "runtime: 0s\n",
      "==============================\n",
      "Average NMAE for LGBM: 0.07472\n"
     ]
    }
   ],
   "source": [
    "lgbm = LGBMRegressor(n_estimators = 100,\n",
    "                     learning_rate = 0.05,\n",
    "                     random_state = 518)\n",
    "lgbm_nmae_list = []\n",
    "lgb_pred = np.zeros((test.shape[0]))\n",
    "n=0\n",
    "for tr_idx, val_idx in kf.split(X, y) :\n",
    "    start_time = time.time()\n",
    "    n+=1\n",
    "    tr_x, tr_y = X.iloc[tr_idx], y.iloc[tr_idx]\n",
    "    val_x, val_y = X.iloc[val_idx], y.iloc[val_idx]\n",
    "    \n",
    "    lgbm.fit(tr_x, tr_y)\n",
    "    end_time = time.time()\n",
    "    \n",
    "    pred = lgbm.predict(val_x)\n",
    "    sub_pred = lgbm.predict(test)\n",
    "    nmae = NMAE(val_y, pred)\n",
    "    \n",
    "    lgbm_nmae_list.append(nmae)\n",
    "    \n",
    "    lgb_pred += (sub_pred / n_splits)\n",
    "    print(\"========== fold %d ==========\" %(n))\n",
    "    print(\"NMAE: %0.5f\" %(nmae))\n",
    "    print(\"runtime: %ds\" %(end_time - start_time))\n",
    "print(\"==============================\")\n",
    "print('Average NMAE for LGBM: %0.5f' %(np.mean(lgbm_nmae_list)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "peBA3RRIiaLA"
   },
   "source": [
    "### XGBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "dz1Rq6c_fyhe",
    "outputId": "04901ee8-50c1-46a1-b033-398d8983262a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "========== fold 1 ==========\n",
      "NMAE: 0.07846\n",
      "runtime: 0s\n",
      "========== fold 2 ==========\n",
      "NMAE: 0.07864\n",
      "runtime: 0s\n",
      "========== fold 3 ==========\n",
      "NMAE: 0.07223\n",
      "runtime: 0s\n",
      "========== fold 4 ==========\n",
      "NMAE: 0.07009\n",
      "runtime: 0s\n",
      "========== fold 5 ==========\n",
      "NMAE: 0.07584\n",
      "runtime: 0s\n",
      "========== fold 6 ==========\n",
      "NMAE: 0.07886\n",
      "runtime: 0s\n",
      "========== fold 7 ==========\n",
      "NMAE: 0.06763\n",
      "runtime: 0s\n",
      "==============================\n",
      "Average NMAE for XGB: 0.07453\n"
     ]
    }
   ],
   "source": [
    "#xgb = XGBRegressor(objective='reg:squarederror', random_state = 518)\n",
    "xgb = XGBRegressor(objective='reg:squarederror',\n",
    "             max_depth = 5,\n",
    "             n_estimators = 100,\n",
    "             random_state = 42,\n",
    "             learning_rate = 0.05)\n",
    "xgb_nmae_list = []\n",
    "xgb_pred = np.zeros((test.shape[0]))\n",
    "n=0\n",
    "for tr_idx, val_idx in kf.split(X, y) :\n",
    "    start_time = time.time()\n",
    "    n+=1\n",
    "    tr_x, tr_y = X.iloc[tr_idx], y.iloc[tr_idx]\n",
    "    val_x, val_y = X.iloc[val_idx], y.iloc[val_idx]\n",
    "    \n",
    "    xgb.fit(tr_x, tr_y)\n",
    "    end_time = time.time()\n",
    "    \n",
    "    pred = xgb.predict(val_x)\n",
    "    sub_pred = xgb.predict(test)\n",
    "    nmae = NMAE(val_y, pred)\n",
    "    \n",
    "    xgb_nmae_list.append(nmae)\n",
    "    \n",
    "    xgb_pred += (sub_pred / n_splits)\n",
    "    print(\"========== fold %d ==========\" %(n))\n",
    "    print(\"NMAE: %0.5f\" %(nmae))\n",
    "    print(\"runtime: %ds\" %(end_time - start_time))\n",
    "print(\"==============================\")\n",
    "print('Average NMAE for XGB: %0.5f' %(np.mean(xgb_nmae_list)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gY5rNMQiiq2m"
   },
   "source": [
    "### CatBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "2s1EMdN2iPXr",
    "outputId": "ed6de521-5597-437b-8a7f-b3fec0ff1083"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "========== fold 1 ==========\n",
      "NMAE: 0.07843\n",
      "runtime: 5s\n",
      "========== fold 2 ==========\n",
      "NMAE: 0.08031\n",
      "runtime: 3s\n",
      "========== fold 3 ==========\n",
      "NMAE: 0.07412\n",
      "runtime: 2s\n",
      "========== fold 4 ==========\n",
      "NMAE: 0.06825\n",
      "runtime: 2s\n",
      "========== fold 5 ==========\n",
      "NMAE: 0.07707\n",
      "runtime: 2s\n",
      "========== fold 6 ==========\n",
      "NMAE: 0.07963\n",
      "runtime: 2s\n",
      "========== fold 7 ==========\n",
      "NMAE: 0.06946\n",
      "runtime: 2s\n",
      "==============================\n",
      "Average NMAE for Catboost: 0.07532\n"
     ]
    }
   ],
   "source": [
    "cat = CatBoostRegressor(random_state = 518, silent = True)\n",
    "cat_nmae_list = []\n",
    "cat_pred = np.zeros((test.shape[0]))\n",
    "n=0\n",
    "for tr_idx, val_idx in kf.split(X, y) :\n",
    "    start_time = time.time()\n",
    "    n+=1\n",
    "    tr_x, tr_y = X.iloc[tr_idx], y.iloc[tr_idx]\n",
    "    val_x, val_y = X.iloc[val_idx], y.iloc[val_idx]\n",
    "    \n",
    "    cat.fit(tr_x, tr_y)\n",
    "    end_time = time.time()\n",
    "    \n",
    "    pred = cat.predict(val_x)\n",
    "    sub_pred = cat.predict(test)\n",
    "    nmae = NMAE(val_y, pred)\n",
    "    \n",
    "    cat_nmae_list.append(nmae)\n",
    "    \n",
    "    cat_pred += (sub_pred / n_splits)\n",
    "    print(\"========== fold %d ==========\" %(n))\n",
    "    print(\"NMAE: %0.5f\" %(nmae))\n",
    "    print(\"runtime: %ds\" %(end_time - start_time))\n",
    "print(\"==============================\")\n",
    "print('Average NMAE for Catboost: %0.5f' %(np.mean(cat_nmae_list)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {
    "id": "M-MarH2zzFpw"
   },
   "outputs": [],
   "source": [
    "# visualize the kfold results\n",
    "results = pd.DataFrame()\n",
    "results['GBR'] = gb_nmae_list\n",
    "results['RFR'] = rf_nmae_list\n",
    "results['LGBM'] = lgbm_nmae_list\n",
    "results['XGB'] = xgb_nmae_list\n",
    "results['CB'] = cat_nmae_list\n",
    "dat = [['GBR', np.mean(gb_nmae_list)],\n",
    "       ['RFR', np.mean(rf_nmae_list)],\n",
    "       ['LGBM', np.mean(lgbm_nmae_list)],\n",
    "       ['XGB', np.mean(xgb_nmae_list)],\n",
    "       ['CB', np.mean(cat_nmae_list)]]\n",
    "mean_nmae_res = pd.DataFrame(dat, columns = ['Model', 'Avg NMAE'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 283
    },
    "id": "Xy7jivqrzNIM",
    "outputId": "58080480-345a-43be-a57e-507ee064368d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f522821e640>"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "results.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 297
    },
    "id": "y5vKkj3g1zOx",
    "outputId": "64074f39-c658-4064-8803-03cf7239aea7",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f522eb4fdf0>"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize in order of average NMAE\n",
    "sns.barplot(data = mean_nmae_res.sort_values('Avg NMAE'), x='Model', y='Avg NMAE')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Predict with GBR, RF, and XGB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 424
    },
    "id": "GoIlB-Wli0aw",
    "outputId": "653a126e-0717-4277-ada3-2526ac131ea0"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
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       "\n",
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       "    }\n",
       "\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>착과량(int)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TEST_0000</td>\n",
       "      <td>245.573472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>TEST_0001</td>\n",
       "      <td>748.161133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>TEST_0002</td>\n",
       "      <td>152.640217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>TEST_0003</td>\n",
       "      <td>444.606550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>TEST_0004</td>\n",
       "      <td>684.653017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2203</th>\n",
       "      <td>TEST_2203</td>\n",
       "      <td>753.195896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2204</th>\n",
       "      <td>TEST_2204</td>\n",
       "      <td>329.753771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2205</th>\n",
       "      <td>TEST_2205</td>\n",
       "      <td>365.698152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2206</th>\n",
       "      <td>TEST_2206</td>\n",
       "      <td>233.136032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2207</th>\n",
       "      <td>TEST_2207</td>\n",
       "      <td>107.431755</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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       "      height: 32px;\n",
       "      padding: 0 0 0 0;\n",
       "      width: 32px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert:hover {\n",
       "      background-color: #E2EBFA;\n",
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "      fill: #174EA6;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
       "    }\n",
       "  </style>\n",
       "\n",
       "      <script>\n",
       "        const buttonEl =\n",
       "          document.querySelector('#df-91da8b0f-d894-40f1-827a-ae12c5000f43 button.colab-df-convert');\n",
       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-91da8b0f-d894-40f1-827a-ae12c5000f43');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
       "          const docLink = document.createElement('div');\n",
       "          docLink.innerHTML = docLinkHtml;\n",
       "          element.appendChild(docLink);\n",
       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "             ID    착과량(int)\n",
       "0     TEST_0000  245.573472\n",
       "1     TEST_0001  748.161133\n",
       "2     TEST_0002  152.640217\n",
       "3     TEST_0003  444.606550\n",
       "4     TEST_0004  684.653017\n",
       "...         ...         ...\n",
       "2203  TEST_2203  753.195896\n",
       "2204  TEST_2204  329.753771\n",
       "2205  TEST_2205  365.698152\n",
       "2206  TEST_2206  233.136032\n",
       "2207  TEST_2207  107.431755\n",
       "\n",
       "[2208 rows x 2 columns]"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('max_rows', 10)\n",
    "submission = pd.read_csv('sample_submission.csv')\n",
    "submission['착과량(int)'] = (gb_pred + rf_pred + xgb_pred) / 3\n",
    "submission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "wPg7SYVQjaSN"
   },
   "outputs": [],
   "source": [
    "submission.to_csv('sub_emsemble.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### FInal scores\n",
    "* public: 0.0726345495 / private: 0.0728141561"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "1ZM2dfMe7oCN"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}