LSTM_best+holiday.ipynb

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  "metadata": {
    "colab": {
      "name": "LSTM_best+holiday.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "Li0vASmhIIhr",
        "HbonwFz84uV5"
      ]
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MEAXOVLFqTeD",
        "outputId": "57b4def0-5719-4ca8-d707-42d060a0f7ef"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WHrGQAT_3z6X"
      },
      "source": [
        "import numpy as np\r\n",
        "import pandas as pd\r\n",
        "\r\n",
        "from datetime import datetime\r\n",
        "from datetime import date, timedelta\r\n",
        "from sklearn.model_selection import train_test_split\r\n",
        "\r\n",
        "from matplotlib import pyplot\r\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 268,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-OIOaFMW_55S"
      },
      "source": [
        "#### train"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fH1TWRdi4eLu",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        },
        "outputId": "1a0bc18b-a437-4e34-b01f-f4ecc0477180"
      },
      "source": [
        "path = '/content/drive/MyDrive/Colab Notebooks/DACON CUP'\r\n",
        "\r\n",
        "train_df = pd.read_csv(path + '/open_data/train.csv', encoding = 'euc-kr')\r\n",
        "train_df.head()"
      ],
      "execution_count": 151,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "\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>DateTime</th>\n",
              "      <th>사용자</th>\n",
              "      <th>세션</th>\n",
              "      <th>신규방문자</th>\n",
              "      <th>페이지뷰</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2018-09-09 00:00:00</td>\n",
              "      <td>19</td>\n",
              "      <td>19</td>\n",
              "      <td>8</td>\n",
              "      <td>206</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2018-09-09 01:00:00</td>\n",
              "      <td>20</td>\n",
              "      <td>19</td>\n",
              "      <td>9</td>\n",
              "      <td>259</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2018-09-09 02:00:00</td>\n",
              "      <td>12</td>\n",
              "      <td>9</td>\n",
              "      <td>1</td>\n",
              "      <td>48</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2018-09-09 03:00:00</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "      <td>2</td>\n",
              "      <td>102</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2018-09-09 04:00:00</td>\n",
              "      <td>6</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              DateTime  사용자  세션  신규방문자  페이지뷰\n",
              "0  2018-09-09 00:00:00   19  19      8   206\n",
              "1  2018-09-09 01:00:00   20  19      9   259\n",
              "2  2018-09-09 02:00:00   12   9      1    48\n",
              "3  2018-09-09 03:00:00   10  10      2   102\n",
              "4  2018-09-09 04:00:00    6   5      3    18"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 151
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YsCktWIeAIWq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d31ad69e-1ae8-449e-a314-5241f4ba1de5"
      },
      "source": [
        "train_df.info()"
      ],
      "execution_count": 152,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 19008 entries, 0 to 19007\n",
            "Data columns (total 5 columns):\n",
            " #   Column    Non-Null Count  Dtype \n",
            "---  ------    --------------  ----- \n",
            " 0   DateTime  19008 non-null  object\n",
            " 1   사용자       19008 non-null  int64 \n",
            " 2   세션        19008 non-null  int64 \n",
            " 3   신규방문자     19008 non-null  int64 \n",
            " 4   페이지뷰      19008 non-null  int64 \n",
            "dtypes: int64(4), object(1)\n",
            "memory usage: 742.6+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eOujb_mO_676",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        },
        "outputId": "258739e6-b3c9-41d1-fbab-15f7d35b216e"
      },
      "source": [
        "# date 기준으로.\r\n",
        "\r\n",
        "train_df['DateTime'] = pd.to_datetime(train_df.DateTime) #dtype: datetime으로 변경\r\n",
        "train_df['date'] = train_df.DateTime.dt.date\r\n",
        "train_df  = train_df.groupby('date').sum().reset_index()\r\n",
        "train_df.head()"
      ],
      "execution_count": 153,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>사용자</th>\n",
              "      <th>세션</th>\n",
              "      <th>신규방문자</th>\n",
              "      <th>페이지뷰</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2018-09-09</td>\n",
              "      <td>281</td>\n",
              "      <td>266</td>\n",
              "      <td>73</td>\n",
              "      <td>1826</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2018-09-10</td>\n",
              "      <td>264</td>\n",
              "      <td>247</td>\n",
              "      <td>51</td>\n",
              "      <td>2092</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2018-09-11</td>\n",
              "      <td>329</td>\n",
              "      <td>310</td>\n",
              "      <td>58</td>\n",
              "      <td>1998</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2018-09-12</td>\n",
              "      <td>300</td>\n",
              "      <td>287</td>\n",
              "      <td>45</td>\n",
              "      <td>2595</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2018-09-13</td>\n",
              "      <td>378</td>\n",
              "      <td>344</td>\n",
              "      <td>50</td>\n",
              "      <td>3845</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         date  사용자   세션  신규방문자  페이지뷰\n",
              "0  2018-09-09  281  266     73  1826\n",
              "1  2018-09-10  264  247     51  2092\n",
              "2  2018-09-11  329  310     58  1998\n",
              "3  2018-09-12  300  287     45  2595\n",
              "4  2018-09-13  378  344     50  3845"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 153
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CM7Hluyg9nrO"
      },
      "source": [
        "# date 기준 변수 만들기\r\n",
        "\r\n",
        "\r\n",
        "\r\n",
        "def create_features(df):\r\n",
        "    \"\"\"\r\n",
        "    Creates time series features from datetime index\r\n",
        "    \"\"\"\r\n",
        "    # df['DateTime'] = df.index\r\n",
        "    # df['hour'] = df['date'].dt.hour\r\n",
        "    df['dayofweek'] = pd.to_datetime(df['date']).dt.dayofweek #요일\r\n",
        "    df['quarter'] = pd.to_datetime(df['date']).dt.quarter \r\n",
        "    df['month'] = pd.to_datetime(df['date']).dt.month\r\n",
        "    df['year'] = pd.to_datetime(df['date']).dt.year # 날짜의 해당 연도 시작에서부터의 일 수를 반환 \r\n",
        "    df['dayofyear'] = pd.to_datetime(df['date']).dt.dayofyear\r\n",
        "    df['dayofmonth'] = pd.to_datetime(df['date']).dt.day\r\n",
        "    df['weekofyear'] = pd.to_datetime(df['date']).dt.weekofyear\r\n",
        "    \r\n",
        "    X = df[['사용자','세션','신규방문자','페이지뷰','date','dayofweek','quarter','month','year' #]]\r\n",
        "           ,'dayofyear','dayofmonth','weekofyear']]\r\n",
        "    X.set_index('date', inplace = True)\r\n",
        "    X.index = pd.to_datetime(X.index)\r\n",
        "    \r\n",
        "    #X['date'] = pd.to_datetime(X['date']).dt.date\r\n",
        "    \r\n",
        "    return X"
      ],
      "execution_count": 354,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 245
        },
        "id": "am_esN9k-CjW",
        "outputId": "374e7c36-d57d-402f-e29b-4808f8285d40"
      },
      "source": [
        "train = create_features(train_df)\r\n",
        "train.head()"
      ],
      "execution_count": 615,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:17: FutureWarning: Series.dt.weekofyear and Series.dt.week have been deprecated.  Please use Series.dt.isocalendar().week instead.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\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>사용자</th>\n",
              "      <th>세션</th>\n",
              "      <th>신규방문자</th>\n",
              "      <th>페이지뷰</th>\n",
              "      <th>dayofweek</th>\n",
              "      <th>quarter</th>\n",
              "      <th>month</th>\n",
              "      <th>year</th>\n",
              "      <th>dayofyear</th>\n",
              "      <th>dayofmonth</th>\n",
              "      <th>weekofyear</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2018-09-09</th>\n",
              "      <td>281</td>\n",
              "      <td>266</td>\n",
              "      <td>73</td>\n",
              "      <td>1826</td>\n",
              "      <td>6</td>\n",
              "      <td>3</td>\n",
              "      <td>9</td>\n",
              "      <td>2018</td>\n",
              "      <td>252</td>\n",
              "      <td>9</td>\n",
              "      <td>36</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-09-10</th>\n",
              "      <td>264</td>\n",
              "      <td>247</td>\n",
              "      <td>51</td>\n",
              "      <td>2092</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>9</td>\n",
              "      <td>2018</td>\n",
              "      <td>253</td>\n",
              "      <td>10</td>\n",
              "      <td>37</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-09-11</th>\n",
              "      <td>329</td>\n",
              "      <td>310</td>\n",
              "      <td>58</td>\n",
              "      <td>1998</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>9</td>\n",
              "      <td>2018</td>\n",
              "      <td>254</td>\n",
              "      <td>11</td>\n",
              "      <td>37</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-09-12</th>\n",
              "      <td>300</td>\n",
              "      <td>287</td>\n",
              "      <td>45</td>\n",
              "      <td>2595</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>9</td>\n",
              "      <td>2018</td>\n",
              "      <td>255</td>\n",
              "      <td>12</td>\n",
              "      <td>37</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-09-13</th>\n",
              "      <td>378</td>\n",
              "      <td>344</td>\n",
              "      <td>50</td>\n",
              "      <td>3845</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>9</td>\n",
              "      <td>2018</td>\n",
              "      <td>256</td>\n",
              "      <td>13</td>\n",
              "      <td>37</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            사용자   세션  신규방문자  페이지뷰  ...  year  dayofyear  dayofmonth  weekofyear\n",
              "date                               ...                                         \n",
              "2018-09-09  281  266     73  1826  ...  2018        252           9          36\n",
              "2018-09-10  264  247     51  2092  ...  2018        253          10          37\n",
              "2018-09-11  329  310     58  1998  ...  2018        254          11          37\n",
              "2018-09-12  300  287     45  2595  ...  2018        255          12          37\n",
              "2018-09-13  378  344     50  3845  ...  2018        256          13          37\n",
              "\n",
              "[5 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 615
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VsQqMW3Eo-dR"
      },
      "source": [
        "# len(train['dayofyear'].unique())"
      ],
      "execution_count": 356,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3JD9OY69UYVw"
      },
      "source": [
        "##### EDA "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "Hck4wCd6UcXf",
        "outputId": "86bcdcc2-53cd-479f-87db-dba4758ab1fd"
      },
      "source": [
        "# quarter간에 차이가 있는지 확인\r\n",
        "\r\n",
        "fig, axes = plt.subplots(4, 1, figsize=(8, 20))\r\n",
        "axes[0].bar(train['quarter'], train['사용자'], color='r')\r\n",
        "axes[1].bar(train['quarter'], train['세션'], color='g')\r\n",
        "axes[2].bar(train['quarter'], train['신규방문자'], color='b')\r\n",
        "axes[3].bar(train['quarter'], train['페이지뷰'], color='c')\r\n",
        "for i, title in enumerate(['사용자','세션','신규방문자','페이지뷰']):\r\n",
        "    axes[i].set_title(title)\r\n",
        "plt.show()"
      ],
      "execution_count": 357,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 49324 missing from current font.\n",
            "  font.set_text(s, 0.0, flags=flags)\n",
            "/usr/local/lib/python3.6/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 50857 missing from current font.\n",
            "  font.set_text(s, 0.0, flags=flags)\n",
            "/usr/local/lib/python3.6/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 51088 missing from current font.\n",
            "  font.set_text(s, 0.0, flags=flags)\n",
            "/usr/local/lib/python3.6/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 49464 missing from current font.\n",
            "  font.set_text(s, 0.0, flags=flags)\n",
            "/usr/local/lib/python3.6/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 49496 missing from current font.\n",
            "  font.set_text(s, 0.0, flags=flags)\n",
            "/usr/local/lib/python3.6/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 49888 missing from current font.\n",
            "  font.set_text(s, 0.0, flags=flags)\n",
            "/usr/local/lib/python3.6/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 44508 missing from current font.\n",
            "  font.set_text(s, 0.0, flags=flags)\n",
            "/usr/local/lib/python3.6/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 48169 missing from current font.\n",
            "  font.set_text(s, 0.0, flags=flags)\n",
            "/usr/local/lib/python3.6/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 47928 missing from current font.\n",
            "  font.set_text(s, 0.0, flags=flags)\n",
            "/usr/local/lib/python3.6/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 54168 missing from current font.\n",
            "  font.set_text(s, 0.0, flags=flags)\n",
            "/usr/local/lib/python3.6/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 51060 missing from current font.\n",
            "  font.set_text(s, 0.0, flags=flags)\n",
            "/usr/local/lib/python3.6/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 51648 missing from current font.\n",
            "  font.set_text(s, 0.0, flags=flags)\n",
            "/usr/local/lib/python3.6/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 48624 missing from current font.\n",
            "  font.set_text(s, 0.0, flags=flags)\n",
            "/usr/local/lib/python3.6/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 49324 missing from current font.\n",
            "  font.set_text(s, 0, flags=flags)\n",
            "/usr/local/lib/python3.6/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 50857 missing from current font.\n",
            "  font.set_text(s, 0, flags=flags)\n",
            "/usr/local/lib/python3.6/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 51088 missing from current font.\n",
            "  font.set_text(s, 0, flags=flags)\n",
            "/usr/local/lib/python3.6/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 49464 missing from current font.\n",
            "  font.set_text(s, 0, flags=flags)\n",
            "/usr/local/lib/python3.6/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 49496 missing from current font.\n",
            "  font.set_text(s, 0, flags=flags)\n",
            "/usr/local/lib/python3.6/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 49888 missing from current font.\n",
            "  font.set_text(s, 0, flags=flags)\n",
            "/usr/local/lib/python3.6/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 44508 missing from current font.\n",
            "  font.set_text(s, 0, flags=flags)\n",
            "/usr/local/lib/python3.6/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 48169 missing from current font.\n",
            "  font.set_text(s, 0, flags=flags)\n",
            "/usr/local/lib/python3.6/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 47928 missing from current font.\n",
            "  font.set_text(s, 0, flags=flags)\n",
            "/usr/local/lib/python3.6/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 54168 missing from current font.\n",
            "  font.set_text(s, 0, flags=flags)\n",
            "/usr/local/lib/python3.6/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 51060 missing from current font.\n",
            "  font.set_text(s, 0, flags=flags)\n",
            "/usr/local/lib/python3.6/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 51648 missing from current font.\n",
            "  font.set_text(s, 0, flags=flags)\n",
            "/usr/local/lib/python3.6/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 48624 missing from current font.\n",
            "  font.set_text(s, 0, flags=flags)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x1440 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "bRhcBsaxU-oJ",
        "outputId": "d91fd6b9-0106-4580-93b0-14aab9206b46"
      },
      "source": [
        "fig, axes = plt.subplots(4, 1, figsize=(8, 20))\r\n",
        "axes[0].bar(train['dayofmonth'], train['사용자'], color='r')\r\n",
        "axes[1].bar(train['dayofmonth'], train['세션'], color='g')\r\n",
        "axes[2].bar(train['dayofmonth'], train['신규방문자'], color='b')\r\n",
        "axes[3].bar(train['dayofmonth'], train['페이지뷰'], color='c')\r\n",
        "for i, title in enumerate(['사용자','세션','신규방문자','페이지뷰']):\r\n",
        "    axes[i].set_title(title)\r\n",
        "plt.show()"
      ],
      "execution_count": 358,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 49324 missing from current font.\n",
            "  font.set_text(s, 0.0, flags=flags)\n",
            "/usr/local/lib/python3.6/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 50857 missing from current font.\n",
            "  font.set_text(s, 0.0, flags=flags)\n",
            "/usr/local/lib/python3.6/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 51088 missing from current font.\n",
            "  font.set_text(s, 0.0, flags=flags)\n",
            "/usr/local/lib/python3.6/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 49464 missing from current font.\n",
            "  font.set_text(s, 0.0, flags=flags)\n",
            "/usr/local/lib/python3.6/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 49496 missing from current font.\n",
            "  font.set_text(s, 0.0, flags=flags)\n",
            "/usr/local/lib/python3.6/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 49888 missing from current font.\n",
            "  font.set_text(s, 0.0, flags=flags)\n",
            "/usr/local/lib/python3.6/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 44508 missing from current font.\n",
            "  font.set_text(s, 0.0, flags=flags)\n",
            "/usr/local/lib/python3.6/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 48169 missing from current font.\n",
            "  font.set_text(s, 0.0, flags=flags)\n",
            "/usr/local/lib/python3.6/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 47928 missing from current font.\n",
            "  font.set_text(s, 0.0, flags=flags)\n",
            "/usr/local/lib/python3.6/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 54168 missing from current font.\n",
            "  font.set_text(s, 0.0, flags=flags)\n",
            "/usr/local/lib/python3.6/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 51060 missing from current font.\n",
            "  font.set_text(s, 0.0, flags=flags)\n",
            "/usr/local/lib/python3.6/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 51648 missing from current font.\n",
            "  font.set_text(s, 0.0, flags=flags)\n",
            "/usr/local/lib/python3.6/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 48624 missing from current font.\n",
            "  font.set_text(s, 0.0, flags=flags)\n",
            "/usr/local/lib/python3.6/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 49324 missing from current font.\n",
            "  font.set_text(s, 0, flags=flags)\n",
            "/usr/local/lib/python3.6/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 50857 missing from current font.\n",
            "  font.set_text(s, 0, flags=flags)\n",
            "/usr/local/lib/python3.6/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 51088 missing from current font.\n",
            "  font.set_text(s, 0, flags=flags)\n",
            "/usr/local/lib/python3.6/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 49464 missing from current font.\n",
            "  font.set_text(s, 0, flags=flags)\n",
            "/usr/local/lib/python3.6/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 49496 missing from current font.\n",
            "  font.set_text(s, 0, flags=flags)\n",
            "/usr/local/lib/python3.6/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 49888 missing from current font.\n",
            "  font.set_text(s, 0, flags=flags)\n",
            "/usr/local/lib/python3.6/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 44508 missing from current font.\n",
            "  font.set_text(s, 0, flags=flags)\n",
            "/usr/local/lib/python3.6/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 48169 missing from current font.\n",
            "  font.set_text(s, 0, flags=flags)\n",
            "/usr/local/lib/python3.6/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 47928 missing from current font.\n",
            "  font.set_text(s, 0, flags=flags)\n",
            "/usr/local/lib/python3.6/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 54168 missing from current font.\n",
            "  font.set_text(s, 0, flags=flags)\n",
            "/usr/local/lib/python3.6/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 51060 missing from current font.\n",
            "  font.set_text(s, 0, flags=flags)\n",
            "/usr/local/lib/python3.6/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 51648 missing from current font.\n",
            "  font.set_text(s, 0, flags=flags)\n",
            "/usr/local/lib/python3.6/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 48624 missing from current font.\n",
            "  font.set_text(s, 0, flags=flags)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x1440 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WtkCVVsY_1va"
      },
      "source": [
        "#### 공휴일"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 227
        },
        "id": "acfb-bLs_5At",
        "outputId": "10e6b6b4-bf6f-4cc0-9a0a-89df68be8961"
      },
      "source": [
        "path = '/content/drive/MyDrive/Colab Notebooks/DACON CUP'\r\n",
        "\r\n",
        "holidays = pd.read_csv(path + '/open_data/holidays.csv', encoding = 'euc-kr', index_col=0)\r\n",
        "holidays.set_index('date', inplace = True)\r\n",
        "holidays.index = pd.to_datetime(holidays.index)\r\n",
        "holidays.head()"
      ],
      "execution_count": 510,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>name</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>date</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2018-01-01</th>\n",
              "      <td>1월1일</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-02-15</th>\n",
              "      <td>설날</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-02-16</th>\n",
              "      <td>설날</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-02-17</th>\n",
              "      <td>설날</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-03-01</th>\n",
              "      <td>삼일절</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            name\n",
              "date            \n",
              "2018-01-01  1월1일\n",
              "2018-02-15    설날\n",
              "2018-02-16    설날\n",
              "2018-02-17    설날\n",
              "2018-03-01   삼일절"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 510
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tGZ6AjkKOmRr"
      },
      "source": [
        "- 공휴일에 1 or 0을 넣어서 피쳐추가.\r\n",
        "    --> 결과가 좋지 않음"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 227
        },
        "id": "d9JIm_rlEy3Q",
        "outputId": "d00a4ff0-9562-4459-facb-0800bb2b731e"
      },
      "source": [
        "holidays['holiday']=1\r\n",
        "holidays.head()"
      ],
      "execution_count": 511,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>name</th>\n",
              "      <th>holiday</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2018-01-01</th>\n",
              "      <td>1월1일</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-02-15</th>\n",
              "      <td>설날</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-02-16</th>\n",
              "      <td>설날</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-02-17</th>\n",
              "      <td>설날</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-03-01</th>\n",
              "      <td>삼일절</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            name  holiday\n",
              "date                     \n",
              "2018-01-01  1월1일        1\n",
              "2018-02-15    설날        1\n",
              "2018-02-16    설날        1\n",
              "2018-02-17    설날        1\n",
              "2018-03-01   삼일절        1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 511
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DV77cI9vAl6A",
        "outputId": "f574da77-9587-4677-a51a-201455267d40"
      },
      "source": [
        "\r\n",
        "train.insert(4, 'holiday', holidays['holiday'])\r\n",
        "train = train.fillna(0)\r\n",
        "len(train[train['holiday']==1])"
      ],
      "execution_count": 616,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "42"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 616
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 227
        },
        "id": "kj-pJ4fnXYS_",
        "outputId": "bdcee77f-5961-4fa0-8365-4a83187fcb95"
      },
      "source": [
        "train.head()"
      ],
      "execution_count": 617,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>사용자</th>\n",
              "      <th>세션</th>\n",
              "      <th>신규방문자</th>\n",
              "      <th>페이지뷰</th>\n",
              "      <th>holiday</th>\n",
              "      <th>dayofweek</th>\n",
              "      <th>quarter</th>\n",
              "      <th>month</th>\n",
              "      <th>year</th>\n",
              "      <th>dayofyear</th>\n",
              "      <th>dayofmonth</th>\n",
              "      <th>weekofyear</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2018-09-09</th>\n",
              "      <td>281</td>\n",
              "      <td>266</td>\n",
              "      <td>73</td>\n",
              "      <td>1826</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6</td>\n",
              "      <td>3</td>\n",
              "      <td>9</td>\n",
              "      <td>2018</td>\n",
              "      <td>252</td>\n",
              "      <td>9</td>\n",
              "      <td>36</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-09-10</th>\n",
              "      <td>264</td>\n",
              "      <td>247</td>\n",
              "      <td>51</td>\n",
              "      <td>2092</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>9</td>\n",
              "      <td>2018</td>\n",
              "      <td>253</td>\n",
              "      <td>10</td>\n",
              "      <td>37</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-09-11</th>\n",
              "      <td>329</td>\n",
              "      <td>310</td>\n",
              "      <td>58</td>\n",
              "      <td>1998</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>9</td>\n",
              "      <td>2018</td>\n",
              "      <td>254</td>\n",
              "      <td>11</td>\n",
              "      <td>37</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-09-12</th>\n",
              "      <td>300</td>\n",
              "      <td>287</td>\n",
              "      <td>45</td>\n",
              "      <td>2595</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>9</td>\n",
              "      <td>2018</td>\n",
              "      <td>255</td>\n",
              "      <td>12</td>\n",
              "      <td>37</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-09-13</th>\n",
              "      <td>378</td>\n",
              "      <td>344</td>\n",
              "      <td>50</td>\n",
              "      <td>3845</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>9</td>\n",
              "      <td>2018</td>\n",
              "      <td>256</td>\n",
              "      <td>13</td>\n",
              "      <td>37</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            사용자   세션  신규방문자  페이지뷰  ...  year  dayofyear  dayofmonth  weekofyear\n",
              "date                               ...                                         \n",
              "2018-09-09  281  266     73  1826  ...  2018        252           9          36\n",
              "2018-09-10  264  247     51  2092  ...  2018        253          10          37\n",
              "2018-09-11  329  310     58  1998  ...  2018        254          11          37\n",
              "2018-09-12  300  287     45  2595  ...  2018        255          12          37\n",
              "2018-09-13  378  344     50  3845  ...  2018        256          13          37\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 617
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 315
        },
        "id": "_Z5LKCU-S8_m",
        "outputId": "0d7c4a3e-7b58-46b5-e4ba-88418cbc23a0"
      },
      "source": [
        "for i, value in enumerate(train['holiday']):\r\n",
        "  if value==1:\r\n",
        "    #train['사용자'][i]= train['사용자'][i] - (train['사용자'][i]*0.05)\r\n",
        "    #train['세션'][i]= train['세션'][i] - (train['세션'][i]*0.05)\r\n",
        "    #train['신규방문자'][i]= train['신규방문자'][i] - (train['신규방문자'][i]*0.05)\r\n",
        "    train['페이지뷰'][i]= train['페이지뷰'][i] - (train['페이지뷰'][i]*0.1)\r\n",
        "\r\n",
        "train = train.drop('holiday',axis=1)\r\n",
        "train.head()"
      ],
      "execution_count": 618,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:6: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>사용자</th>\n",
              "      <th>세션</th>\n",
              "      <th>신규방문자</th>\n",
              "      <th>페이지뷰</th>\n",
              "      <th>dayofweek</th>\n",
              "      <th>quarter</th>\n",
              "      <th>month</th>\n",
              "      <th>year</th>\n",
              "      <th>dayofyear</th>\n",
              "      <th>dayofmonth</th>\n",
              "      <th>weekofyear</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2018-09-09</th>\n",
              "      <td>281</td>\n",
              "      <td>266</td>\n",
              "      <td>73</td>\n",
              "      <td>1826</td>\n",
              "      <td>6</td>\n",
              "      <td>3</td>\n",
              "      <td>9</td>\n",
              "      <td>2018</td>\n",
              "      <td>252</td>\n",
              "      <td>9</td>\n",
              "      <td>36</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-09-10</th>\n",
              "      <td>264</td>\n",
              "      <td>247</td>\n",
              "      <td>51</td>\n",
              "      <td>2092</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>9</td>\n",
              "      <td>2018</td>\n",
              "      <td>253</td>\n",
              "      <td>10</td>\n",
              "      <td>37</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-09-11</th>\n",
              "      <td>329</td>\n",
              "      <td>310</td>\n",
              "      <td>58</td>\n",
              "      <td>1998</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>9</td>\n",
              "      <td>2018</td>\n",
              "      <td>254</td>\n",
              "      <td>11</td>\n",
              "      <td>37</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-09-12</th>\n",
              "      <td>300</td>\n",
              "      <td>287</td>\n",
              "      <td>45</td>\n",
              "      <td>2595</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>9</td>\n",
              "      <td>2018</td>\n",
              "      <td>255</td>\n",
              "      <td>12</td>\n",
              "      <td>37</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-09-13</th>\n",
              "      <td>378</td>\n",
              "      <td>344</td>\n",
              "      <td>50</td>\n",
              "      <td>3845</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>9</td>\n",
              "      <td>2018</td>\n",
              "      <td>256</td>\n",
              "      <td>13</td>\n",
              "      <td>37</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            사용자   세션  신규방문자  페이지뷰  ...  year  dayofyear  dayofmonth  weekofyear\n",
              "date                               ...                                         \n",
              "2018-09-09  281  266     73  1826  ...  2018        252           9          36\n",
              "2018-09-10  264  247     51  2092  ...  2018        253          10          37\n",
              "2018-09-11  329  310     58  1998  ...  2018        254          11          37\n",
              "2018-09-12  300  287     45  2595  ...  2018        255          12          37\n",
              "2018-09-13  378  344     50  3845  ...  2018        256          13          37\n",
              "\n",
              "[5 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 618
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0Mn3-RqzjnDe"
      },
      "source": [
        "#### 범주형 인코딩\r\n",
        "\r\n",
        "- one-hot 인코딩: 카테고리가 별로 없는거\r\n",
        "- binary 인코딩: 카테고리가 많은 거"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ziFLhds4kax1",
        "outputId": "9719da30-a5bd-4719-b4f2-a7f2691d4b97"
      },
      "source": [
        "#pip install category_encoders"
      ],
      "execution_count": 416,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: category_encoders in /usr/local/lib/python3.6/dist-packages (2.2.2)\n",
            "Requirement already satisfied: statsmodels>=0.9.0 in /usr/local/lib/python3.6/dist-packages (from category_encoders) (0.10.2)\n",
            "Requirement already satisfied: numpy>=1.14.0 in /usr/local/lib/python3.6/dist-packages (from category_encoders) (1.19.5)\n",
            "Requirement already satisfied: scipy>=1.0.0 in /usr/local/lib/python3.6/dist-packages (from category_encoders) (1.4.1)\n",
            "Requirement already satisfied: patsy>=0.5.1 in /usr/local/lib/python3.6/dist-packages (from category_encoders) (0.5.1)\n",
            "Requirement already satisfied: scikit-learn>=0.20.0 in /usr/local/lib/python3.6/dist-packages (from category_encoders) (0.22.2.post1)\n",
            "Requirement already satisfied: pandas>=0.21.1 in /usr/local/lib/python3.6/dist-packages (from category_encoders) (1.1.5)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from patsy>=0.5.1->category_encoders) (1.15.0)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.6/dist-packages (from scikit-learn>=0.20.0->category_encoders) (1.0.0)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas>=0.21.1->category_encoders) (2018.9)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.6/dist-packages (from pandas>=0.21.1->category_encoders) (2.8.1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BHx-tnrbj1cc"
      },
      "source": [
        "import category_encoders as ce"
      ],
      "execution_count": 417,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4nzCaqAJd531",
        "outputId": "04f90e04-27f9-48f8-9198-aa81cba26764"
      },
      "source": [
        "# bianry encoding\r\n",
        "\r\n",
        "# dayofyear\tdayofmonth\tweekofyear\r\n",
        "\r\n",
        "encoder = ce.BinaryEncoder(cols=['dayofyear'])\r\n",
        "train = encoder.fit_transform(train)\r\n",
        "\r\n",
        "encoder = ce.BinaryEncoder(cols=['dayofmonth'])\r\n",
        "train = encoder.fit_transform(train)\r\n",
        "\r\n",
        "encoder = ce.BinaryEncoder(cols=['weekofyear'])\r\n",
        "train = encoder.fit_transform(train)\r\n",
        "\r\n",
        "print(train.shape)\r\n",
        "# train.head(3)"
      ],
      "execution_count": 619,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(792, 31)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/category_encoders/utils.py:21: FutureWarning: is_categorical is deprecated and will be removed in a future version.  Use is_categorical_dtype instead\n",
            "  elif pd.api.types.is_categorical(cols):\n",
            "/usr/local/lib/python3.6/dist-packages/category_encoders/utils.py:21: FutureWarning: is_categorical is deprecated and will be removed in a future version.  Use is_categorical_dtype instead\n",
            "  elif pd.api.types.is_categorical(cols):\n",
            "/usr/local/lib/python3.6/dist-packages/category_encoders/utils.py:21: FutureWarning: is_categorical is deprecated and will be removed in a future version.  Use is_categorical_dtype instead\n",
            "  elif pd.api.types.is_categorical(cols):\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 322
        },
        "id": "ZFEkYwX-kxU9",
        "outputId": "14fb27a4-440d-4531-c2a0-e93fa3cf909e"
      },
      "source": [
        "# one-hot encoding\r\n",
        "\r\n",
        "def dummy_data(data, columns):\r\n",
        "    for column in columns:\r\n",
        "        data = pd.concat([data, pd.get_dummies(data[column], prefix = column)], axis=1)\r\n",
        "        data = data.drop(column, axis=1)\r\n",
        "    return data\r\n",
        "\r\n",
        "\r\n",
        "dummy_columns = [\"dayofweek\", \"quarter\", \"month\", \"year\"]\r\n",
        "train = dummy_data(train,dummy_columns)\r\n",
        "\r\n",
        "print(train.shape)\r\n",
        "train.head(3)"
      ],
      "execution_count": 620,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(792, 53)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>사용자</th>\n",
              "      <th>세션</th>\n",
              "      <th>신규방문자</th>\n",
              "      <th>페이지뷰</th>\n",
              "      <th>dayofyear_0</th>\n",
              "      <th>dayofyear_1</th>\n",
              "      <th>dayofyear_2</th>\n",
              "      <th>dayofyear_3</th>\n",
              "      <th>dayofyear_4</th>\n",
              "      <th>dayofyear_5</th>\n",
              "      <th>dayofyear_6</th>\n",
              "      <th>dayofyear_7</th>\n",
              "      <th>dayofyear_8</th>\n",
              "      <th>dayofyear_9</th>\n",
              "      <th>dayofmonth_0</th>\n",
              "      <th>dayofmonth_1</th>\n",
              "      <th>dayofmonth_2</th>\n",
              "      <th>dayofmonth_3</th>\n",
              "      <th>dayofmonth_4</th>\n",
              "      <th>dayofmonth_5</th>\n",
              "      <th>weekofyear_0</th>\n",
              "      <th>weekofyear_1</th>\n",
              "      <th>weekofyear_2</th>\n",
              "      <th>weekofyear_3</th>\n",
              "      <th>weekofyear_4</th>\n",
              "      <th>weekofyear_5</th>\n",
              "      <th>weekofyear_6</th>\n",
              "      <th>dayofweek_0</th>\n",
              "      <th>dayofweek_1</th>\n",
              "      <th>dayofweek_2</th>\n",
              "      <th>dayofweek_3</th>\n",
              "      <th>dayofweek_4</th>\n",
              "      <th>dayofweek_5</th>\n",
              "      <th>dayofweek_6</th>\n",
              "      <th>quarter_1</th>\n",
              "      <th>quarter_2</th>\n",
              "      <th>quarter_3</th>\n",
              "      <th>quarter_4</th>\n",
              "      <th>month_1</th>\n",
              "      <th>month_2</th>\n",
              "      <th>month_3</th>\n",
              "      <th>month_4</th>\n",
              "      <th>month_5</th>\n",
              "      <th>month_6</th>\n",
              "      <th>month_7</th>\n",
              "      <th>month_8</th>\n",
              "      <th>month_9</th>\n",
              "      <th>month_10</th>\n",
              "      <th>month_11</th>\n",
              "      <th>month_12</th>\n",
              "      <th>year_2018</th>\n",
              "      <th>year_2019</th>\n",
              "      <th>year_2020</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2018-09-09</th>\n",
              "      <td>281</td>\n",
              "      <td>266</td>\n",
              "      <td>73</td>\n",
              "      <td>1826</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-09-10</th>\n",
              "      <td>264</td>\n",
              "      <td>247</td>\n",
              "      <td>51</td>\n",
              "      <td>2092</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-09-11</th>\n",
              "      <td>329</td>\n",
              "      <td>310</td>\n",
              "      <td>58</td>\n",
              "      <td>1998</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            사용자   세션  신규방문자  페이지뷰  ...  month_12  year_2018  year_2019  year_2020\n",
              "date                               ...                                           \n",
              "2018-09-09  281  266     73  1826  ...         0          1          0          0\n",
              "2018-09-10  264  247     51  2092  ...         0          1          0          0\n",
              "2018-09-11  329  310     58  1998  ...         0          1          0          0\n",
              "\n",
              "[3 rows x 53 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 620
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UP7gIbLQ9zmM"
      },
      "source": [
        "- 추가데이터 가공"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "id": "cJGuiHK99ucr",
        "outputId": "88520037-55b5-484f-d907-55806d60ee5d"
      },
      "source": [
        "'''\r\n",
        "user =  pd.read_csv(path + '/open_data/info_user.csv', encoding = 'euc-kr')\r\n",
        "login = pd.read_csv(path +'/open_data/info_login.csv', encoding = 'euc-kr')\r\n",
        "competition = pd.read_csv(path +'/open_data/info_competition.csv', encoding = 'euc-kr')\r\n",
        "info_submission =  pd.read_csv(path +'/open_data/info_submission.csv', encoding = 'euc-kr')\r\n",
        "'''"
      ],
      "execution_count": 553,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "\"\\nuser =  pd.read_csv(path + '/open_data/info_user.csv', encoding = 'euc-kr')\\nlogin = pd.read_csv(path +'/open_data/info_login.csv', encoding = 'euc-kr')\\ncompetition = pd.read_csv(path +'/open_data/info_competition.csv', encoding = 'euc-kr')\\ninfo_submission =  pd.read_csv(path +'/open_data/info_submission.csv', encoding = 'euc-kr')\\n\""
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 553
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M-nqMth6966w"
      },
      "source": [
        "- 데이터 전처리"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 640
        },
        "id": "MB-KiRHF92gN",
        "outputId": "abe19fed-3499-430c-da7c-6c56cb80343e"
      },
      "source": [
        "# scaling\r\n",
        "\r\n",
        "mini = train.iloc[:,:4].min()\r\n",
        "size = train.iloc[:,:4].max() - train.iloc[:,:4].min()\r\n",
        "train.iloc[:,:4] = (train.iloc[:,:4] -  mini) / size\r\n",
        "train"
      ],
      "execution_count": 621,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>사용자</th>\n",
              "      <th>세션</th>\n",
              "      <th>신규방문자</th>\n",
              "      <th>페이지뷰</th>\n",
              "      <th>dayofyear_0</th>\n",
              "      <th>dayofyear_1</th>\n",
              "      <th>dayofyear_2</th>\n",
              "      <th>dayofyear_3</th>\n",
              "      <th>dayofyear_4</th>\n",
              "      <th>dayofyear_5</th>\n",
              "      <th>dayofyear_6</th>\n",
              "      <th>dayofyear_7</th>\n",
              "      <th>dayofyear_8</th>\n",
              "      <th>dayofyear_9</th>\n",
              "      <th>dayofmonth_0</th>\n",
              "      <th>dayofmonth_1</th>\n",
              "      <th>dayofmonth_2</th>\n",
              "      <th>dayofmonth_3</th>\n",
              "      <th>dayofmonth_4</th>\n",
              "      <th>dayofmonth_5</th>\n",
              "      <th>weekofyear_0</th>\n",
              "      <th>weekofyear_1</th>\n",
              "      <th>weekofyear_2</th>\n",
              "      <th>weekofyear_3</th>\n",
              "      <th>weekofyear_4</th>\n",
              "      <th>weekofyear_5</th>\n",
              "      <th>weekofyear_6</th>\n",
              "      <th>dayofweek_0</th>\n",
              "      <th>dayofweek_1</th>\n",
              "      <th>dayofweek_2</th>\n",
              "      <th>dayofweek_3</th>\n",
              "      <th>dayofweek_4</th>\n",
              "      <th>dayofweek_5</th>\n",
              "      <th>dayofweek_6</th>\n",
              "      <th>quarter_1</th>\n",
              "      <th>quarter_2</th>\n",
              "      <th>quarter_3</th>\n",
              "      <th>quarter_4</th>\n",
              "      <th>month_1</th>\n",
              "      <th>month_2</th>\n",
              "      <th>month_3</th>\n",
              "      <th>month_4</th>\n",
              "      <th>month_5</th>\n",
              "      <th>month_6</th>\n",
              "      <th>month_7</th>\n",
              "      <th>month_8</th>\n",
              "      <th>month_9</th>\n",
              "      <th>month_10</th>\n",
              "      <th>month_11</th>\n",
              "      <th>month_12</th>\n",
              "      <th>year_2018</th>\n",
              "      <th>year_2019</th>\n",
              "      <th>year_2020</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2018-09-09</th>\n",
              "      <td>0.051689</td>\n",
              "      <td>0.049083</td>\n",
              "      <td>0.042604</td>\n",
              "      <td>0.011741</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-09-10</th>\n",
              "      <td>0.048551</td>\n",
              "      <td>0.045564</td>\n",
              "      <td>0.029586</td>\n",
              "      <td>0.013453</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-09-11</th>\n",
              "      <td>0.060550</td>\n",
              "      <td>0.057233</td>\n",
              "      <td>0.033728</td>\n",
              "      <td>0.012848</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-09-12</th>\n",
              "      <td>0.055197</td>\n",
              "      <td>0.052973</td>\n",
              "      <td>0.026036</td>\n",
              "      <td>0.016689</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-09-13</th>\n",
              "      <td>0.069596</td>\n",
              "      <td>0.063530</td>\n",
              "      <td>0.028994</td>\n",
              "      <td>0.024731</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-11-04</th>\n",
              "      <td>0.833487</td>\n",
              "      <td>0.828116</td>\n",
              "      <td>0.707101</td>\n",
              "      <td>0.724951</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-11-05</th>\n",
              "      <td>0.766845</td>\n",
              "      <td>0.747546</td>\n",
              "      <td>0.617160</td>\n",
              "      <td>0.662017</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-11-06</th>\n",
              "      <td>0.676020</td>\n",
              "      <td>0.662160</td>\n",
              "      <td>0.487574</td>\n",
              "      <td>0.566247</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-11-07</th>\n",
              "      <td>0.456157</td>\n",
              "      <td>0.447490</td>\n",
              "      <td>0.313609</td>\n",
              "      <td>0.369192</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-11-08</th>\n",
              "      <td>0.459849</td>\n",
              "      <td>0.448046</td>\n",
              "      <td>0.308284</td>\n",
              "      <td>0.324800</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>792 rows × 53 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                 사용자        세션     신규방문자  ...  year_2018  year_2019  year_2020\n",
              "date                                      ...                                 \n",
              "2018-09-09  0.051689  0.049083  0.042604  ...          1          0          0\n",
              "2018-09-10  0.048551  0.045564  0.029586  ...          1          0          0\n",
              "2018-09-11  0.060550  0.057233  0.033728  ...          1          0          0\n",
              "2018-09-12  0.055197  0.052973  0.026036  ...          1          0          0\n",
              "2018-09-13  0.069596  0.063530  0.028994  ...          1          0          0\n",
              "...              ...       ...       ...  ...        ...        ...        ...\n",
              "2020-11-04  0.833487  0.828116  0.707101  ...          0          0          1\n",
              "2020-11-05  0.766845  0.747546  0.617160  ...          0          0          1\n",
              "2020-11-06  0.676020  0.662160  0.487574  ...          0          0          1\n",
              "2020-11-07  0.456157  0.447490  0.313609  ...          0          0          1\n",
              "2020-11-08  0.459849  0.448046  0.308284  ...          0          0          1\n",
              "\n",
              "[792 rows x 53 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 621
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c3B6jULuUW5Q",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "82dd18eb-6c65-4c97-d821-183f22f633a3"
      },
      "source": [
        "# scaling\r\n",
        "\r\n",
        "input_window = 61\r\n",
        "output_window = 61 # 7\r\n",
        "\r\n",
        "window_x = np.zeros((train.shape[0] - (input_window + output_window), input_window, 53))\r\n",
        "window_y = np.zeros((train.shape[0] - (input_window + output_window), output_window, 4))\r\n",
        "\r\n",
        "for start in range(train.shape[0] - (input_window + output_window)):\r\n",
        "    end = start + input_window    \r\n",
        "    window_x[start,:, :] = train.iloc[start : end                , : ].values\r\n",
        "    window_y[start,:, :] = train.iloc[end   : end + output_window, :4 ].values\r\n",
        "\r\n",
        "\r\n",
        "print('window_x.shape: ', window_x.shape)\r\n",
        "print('window_y.shape: ', window_y.shape)"
      ],
      "execution_count": 622,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "window_x.shape:  (670, 61, 53)\n",
            "window_y.shape:  (670, 61, 4)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wy1pDYOzVKDD"
      },
      "source": [
        "# window_x"
      ],
      "execution_count": 623,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dHc8qQZCDedw"
      },
      "source": [
        "- 시계열 데이터 임베딩 찾아서 적용해보기\r\n",
        "- 스케일링 다르게 해보기\r\n",
        "- dropout 적용해보기"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RMVmMfUBILC-"
      },
      "source": [
        "import tensorflow as tf\r\n",
        "from tensorflow import keras\r\n",
        "from tensorflow.keras import layers, Sequential\r\n",
        "\r\n",
        "from tensorflow.keras.layers import Dense, Conv1D, LSTM, Input, TimeDistributed\r\n",
        "from tensorflow.keras.models import Model\r\n"
      ],
      "execution_count": 624,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LJZU8NwSoRz5"
      },
      "source": [
        "optimizer = Adam, loss = mse, activation_function = 따로지정x"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GKj7gc585ToK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ae5bd62a-d4f9-4456-f1ea-8aa648c03231"
      },
      "source": [
        "tf.random.set_seed(40)\r\n",
        "\r\n",
        "\r\n",
        "model = Sequential()\r\n",
        "model.add(LSTM(32, input_shape=(61, 53), return_sequences=True))  # (timestep, features)\r\n",
        "model.add(LSTM(32, return_sequences=True)) \r\n",
        "model.add(LSTM(32, return_sequences=True)) \r\n",
        "#model.add(LSTM(32, return_sequences=True)) \r\n",
        "#model.add(LSTM(32, return_sequences=True)) \r\n",
        "# model.add(LSTM(32, return_sequences=True)) \r\n",
        "model.add(TimeDistributed(Dense(4)))\r\n",
        "\r\n",
        "model.summary()\r\n",
        "\r\n",
        "opt = keras.optimizers.Adam(learning_rate=0.01)\r\n",
        "model.compile(loss='mean_squared_error', optimizer=opt)\r\n",
        "\r\n",
        "model.fit(window_x, window_y, epochs=100, batch_size=50)\r\n",
        "\r\n",
        "# lstm2층 - epochs100, batch_size=10"
      ],
      "execution_count": 625,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_9\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "lstm_28 (LSTM)               (None, 61, 32)            11008     \n",
            "_________________________________________________________________\n",
            "lstm_29 (LSTM)               (None, 61, 32)            8320      \n",
            "_________________________________________________________________\n",
            "lstm_30 (LSTM)               (None, 61, 32)            8320      \n",
            "_________________________________________________________________\n",
            "time_distributed_9 (TimeDist (None, 61, 4)             132       \n",
            "=================================================================\n",
            "Total params: 27,780\n",
            "Trainable params: 27,780\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "Epoch 1/100\n",
            "14/14 [==============================] - 5s 72ms/step - loss: 0.0390\n",
            "Epoch 2/100\n",
            "14/14 [==============================] - 1s 77ms/step - loss: 0.0099\n",
            "Epoch 3/100\n",
            "14/14 [==============================] - 1s 79ms/step - loss: 0.0077\n",
            "Epoch 4/100\n",
            "14/14 [==============================] - 1s 77ms/step - loss: 0.0068\n",
            "Epoch 5/100\n",
            "14/14 [==============================] - 1s 78ms/step - loss: 0.0057\n",
            "Epoch 6/100\n",
            "14/14 [==============================] - 1s 76ms/step - loss: 0.0051\n",
            "Epoch 7/100\n",
            "14/14 [==============================] - 1s 78ms/step - loss: 0.0044\n",
            "Epoch 8/100\n",
            "14/14 [==============================] - 1s 75ms/step - loss: 0.0039\n",
            "Epoch 9/100\n",
            "14/14 [==============================] - 1s 76ms/step - loss: 0.0034\n",
            "Epoch 10/100\n",
            "14/14 [==============================] - 1s 78ms/step - loss: 0.0030\n",
            "Epoch 11/100\n",
            "14/14 [==============================] - 1s 75ms/step - loss: 0.0028\n",
            "Epoch 12/100\n",
            "14/14 [==============================] - 1s 77ms/step - loss: 0.0024\n",
            "Epoch 13/100\n",
            "14/14 [==============================] - 1s 79ms/step - loss: 0.0022\n",
            "Epoch 14/100\n",
            "14/14 [==============================] - 1s 79ms/step - loss: 0.0021\n",
            "Epoch 15/100\n",
            "14/14 [==============================] - 1s 77ms/step - loss: 0.0023\n",
            "Epoch 16/100\n",
            "14/14 [==============================] - 1s 78ms/step - loss: 0.0018\n",
            "Epoch 17/100\n",
            "14/14 [==============================] - 1s 78ms/step - loss: 0.0016\n",
            "Epoch 18/100\n",
            "14/14 [==============================] - 1s 77ms/step - loss: 0.0014\n",
            "Epoch 19/100\n",
            "14/14 [==============================] - 1s 78ms/step - loss: 0.0013\n",
            "Epoch 20/100\n",
            "14/14 [==============================] - 1s 77ms/step - loss: 0.0012\n",
            "Epoch 21/100\n",
            "14/14 [==============================] - 1s 78ms/step - loss: 0.0011\n",
            "Epoch 22/100\n",
            "14/14 [==============================] - 1s 80ms/step - loss: 0.0010\n",
            "Epoch 23/100\n",
            "14/14 [==============================] - 1s 77ms/step - loss: 0.0010\n",
            "Epoch 24/100\n",
            "14/14 [==============================] - 1s 79ms/step - loss: 9.6408e-04\n",
            "Epoch 25/100\n",
            "14/14 [==============================] - 1s 77ms/step - loss: 8.6769e-04\n",
            "Epoch 26/100\n",
            "14/14 [==============================] - 1s 79ms/step - loss: 7.6462e-04\n",
            "Epoch 27/100\n",
            "14/14 [==============================] - 1s 79ms/step - loss: 6.8990e-04\n",
            "Epoch 28/100\n",
            "14/14 [==============================] - 1s 79ms/step - loss: 6.3369e-04\n",
            "Epoch 29/100\n",
            "14/14 [==============================] - 1s 80ms/step - loss: 5.9262e-04\n",
            "Epoch 30/100\n",
            "14/14 [==============================] - 1s 82ms/step - loss: 5.5424e-04\n",
            "Epoch 31/100\n",
            "14/14 [==============================] - 1s 83ms/step - loss: 5.3261e-04\n",
            "Epoch 32/100\n",
            "14/14 [==============================] - 1s 81ms/step - loss: 4.6147e-04\n",
            "Epoch 33/100\n",
            "14/14 [==============================] - 1s 80ms/step - loss: 4.5723e-04\n",
            "Epoch 34/100\n",
            "14/14 [==============================] - 1s 81ms/step - loss: 4.4116e-04\n",
            "Epoch 35/100\n",
            "14/14 [==============================] - 1s 82ms/step - loss: 4.1716e-04\n",
            "Epoch 36/100\n",
            "14/14 [==============================] - 1s 83ms/step - loss: 4.1044e-04\n",
            "Epoch 37/100\n",
            "14/14 [==============================] - 1s 86ms/step - loss: 4.5920e-04\n",
            "Epoch 38/100\n",
            "14/14 [==============================] - 1s 80ms/step - loss: 5.6325e-04\n",
            "Epoch 39/100\n",
            "14/14 [==============================] - 1s 83ms/step - loss: 6.2348e-04\n",
            "Epoch 40/100\n",
            "14/14 [==============================] - 1s 83ms/step - loss: 4.0859e-04\n",
            "Epoch 41/100\n",
            "14/14 [==============================] - 1s 80ms/step - loss: 3.6654e-04\n",
            "Epoch 42/100\n",
            "14/14 [==============================] - 1s 82ms/step - loss: 3.3251e-04\n",
            "Epoch 43/100\n",
            "14/14 [==============================] - 1s 82ms/step - loss: 3.0795e-04\n",
            "Epoch 44/100\n",
            "14/14 [==============================] - 1s 81ms/step - loss: 3.0575e-04\n",
            "Epoch 45/100\n",
            "14/14 [==============================] - 1s 81ms/step - loss: 2.8390e-04\n",
            "Epoch 46/100\n",
            "14/14 [==============================] - 1s 83ms/step - loss: 2.6608e-04\n",
            "Epoch 47/100\n",
            "14/14 [==============================] - 1s 79ms/step - loss: 2.5353e-04\n",
            "Epoch 48/100\n",
            "14/14 [==============================] - 1s 82ms/step - loss: 2.5369e-04\n",
            "Epoch 49/100\n",
            "14/14 [==============================] - 1s 82ms/step - loss: 2.3940e-04\n",
            "Epoch 50/100\n",
            "14/14 [==============================] - 1s 82ms/step - loss: 2.3746e-04\n",
            "Epoch 51/100\n",
            "14/14 [==============================] - 1s 82ms/step - loss: 2.2301e-04\n",
            "Epoch 52/100\n",
            "14/14 [==============================] - 1s 83ms/step - loss: 2.3411e-04\n",
            "Epoch 53/100\n",
            "14/14 [==============================] - 1s 84ms/step - loss: 2.2245e-04\n",
            "Epoch 54/100\n",
            "14/14 [==============================] - 1s 83ms/step - loss: 2.2165e-04\n",
            "Epoch 55/100\n",
            "14/14 [==============================] - 1s 85ms/step - loss: 2.0748e-04\n",
            "Epoch 56/100\n",
            "14/14 [==============================] - 1s 84ms/step - loss: 2.2038e-04\n",
            "Epoch 57/100\n",
            "14/14 [==============================] - 1s 86ms/step - loss: 2.1997e-04\n",
            "Epoch 58/100\n",
            "14/14 [==============================] - 1s 87ms/step - loss: 2.1238e-04\n",
            "Epoch 59/100\n",
            "14/14 [==============================] - 1s 87ms/step - loss: 1.9355e-04\n",
            "Epoch 60/100\n",
            "14/14 [==============================] - 1s 88ms/step - loss: 1.7981e-04\n",
            "Epoch 61/100\n",
            "14/14 [==============================] - 1s 88ms/step - loss: 1.7414e-04\n",
            "Epoch 62/100\n",
            "14/14 [==============================] - 1s 87ms/step - loss: 1.6748e-04\n",
            "Epoch 63/100\n",
            "14/14 [==============================] - 1s 88ms/step - loss: 1.6759e-04\n",
            "Epoch 64/100\n",
            "14/14 [==============================] - 1s 87ms/step - loss: 1.6249e-04\n",
            "Epoch 65/100\n",
            "14/14 [==============================] - 1s 87ms/step - loss: 1.5902e-04\n",
            "Epoch 66/100\n",
            "14/14 [==============================] - 1s 85ms/step - loss: 1.9163e-04\n",
            "Epoch 67/100\n",
            "14/14 [==============================] - 1s 88ms/step - loss: 1.8112e-04\n",
            "Epoch 68/100\n",
            "14/14 [==============================] - 1s 88ms/step - loss: 1.5426e-04\n",
            "Epoch 69/100\n",
            "14/14 [==============================] - 1s 91ms/step - loss: 1.4782e-04\n",
            "Epoch 70/100\n",
            "14/14 [==============================] - 1s 85ms/step - loss: 1.4959e-04\n",
            "Epoch 71/100\n",
            "14/14 [==============================] - 1s 82ms/step - loss: 1.4017e-04\n",
            "Epoch 72/100\n",
            "14/14 [==============================] - 1s 82ms/step - loss: 1.4956e-04\n",
            "Epoch 73/100\n",
            "14/14 [==============================] - 1s 80ms/step - loss: 1.6490e-04\n",
            "Epoch 74/100\n",
            "14/14 [==============================] - 1s 81ms/step - loss: 1.5952e-04\n",
            "Epoch 75/100\n",
            "14/14 [==============================] - 1s 85ms/step - loss: 1.8368e-04\n",
            "Epoch 76/100\n",
            "14/14 [==============================] - 1s 83ms/step - loss: 1.6468e-04\n",
            "Epoch 77/100\n",
            "14/14 [==============================] - 1s 87ms/step - loss: 1.3494e-04\n",
            "Epoch 78/100\n",
            "14/14 [==============================] - 1s 83ms/step - loss: 1.4277e-04\n",
            "Epoch 79/100\n",
            "14/14 [==============================] - 1s 87ms/step - loss: 1.3571e-04\n",
            "Epoch 80/100\n",
            "14/14 [==============================] - 1s 87ms/step - loss: 1.2697e-04\n",
            "Epoch 81/100\n",
            "14/14 [==============================] - 1s 85ms/step - loss: 1.1488e-04\n",
            "Epoch 82/100\n",
            "14/14 [==============================] - 1s 86ms/step - loss: 1.0395e-04\n",
            "Epoch 83/100\n",
            "14/14 [==============================] - 1s 86ms/step - loss: 1.0336e-04\n",
            "Epoch 84/100\n",
            "14/14 [==============================] - 1s 86ms/step - loss: 1.4271e-04\n",
            "Epoch 85/100\n",
            "14/14 [==============================] - 1s 88ms/step - loss: 1.5661e-04\n",
            "Epoch 86/100\n",
            "14/14 [==============================] - 1s 85ms/step - loss: 1.3811e-04\n",
            "Epoch 87/100\n",
            "14/14 [==============================] - 1s 83ms/step - loss: 1.1450e-04\n",
            "Epoch 88/100\n",
            "14/14 [==============================] - 1s 88ms/step - loss: 1.1171e-04\n",
            "Epoch 89/100\n",
            "14/14 [==============================] - 1s 85ms/step - loss: 9.9133e-05\n",
            "Epoch 90/100\n",
            "14/14 [==============================] - 1s 84ms/step - loss: 9.3459e-05\n",
            "Epoch 91/100\n",
            "14/14 [==============================] - 1s 84ms/step - loss: 9.6585e-05\n",
            "Epoch 92/100\n",
            "14/14 [==============================] - 1s 82ms/step - loss: 8.4948e-05\n",
            "Epoch 93/100\n",
            "14/14 [==============================] - 1s 82ms/step - loss: 8.5466e-05\n",
            "Epoch 94/100\n",
            "14/14 [==============================] - 1s 83ms/step - loss: 8.6062e-05\n",
            "Epoch 95/100\n",
            "14/14 [==============================] - 1s 82ms/step - loss: 8.4116e-05\n",
            "Epoch 96/100\n",
            "14/14 [==============================] - 1s 81ms/step - loss: 7.5311e-05\n",
            "Epoch 97/100\n",
            "14/14 [==============================] - 1s 83ms/step - loss: 7.8085e-05\n",
            "Epoch 98/100\n",
            "14/14 [==============================] - 1s 81ms/step - loss: 7.7086e-05\n",
            "Epoch 99/100\n",
            "14/14 [==============================] - 1s 81ms/step - loss: 7.3094e-05\n",
            "Epoch 100/100\n",
            "14/14 [==============================] - 1s 89ms/step - loss: 7.4814e-05\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7fa0044930b8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 625
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dbInac4YQ8pV"
      },
      "source": [
        "## test : last_df"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hL8wR22W18Co",
        "outputId": "0200f49c-94dc-4d46-8d78-973873fc08fe"
      },
      "source": [
        "# train은 이미 위에서 minmax scaler적용되어있음.\r\n",
        "\r\n",
        "last_df = train.iloc[-61:,:].values[np.newaxis,...] # np.newaxis: 3D로 차원을 늘려줌. \r\n",
        "last_df.shape"
      ],
      "execution_count": 626,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1, 61, 53)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 626
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qaMBTNjj2JW9",
        "outputId": "31e2a679-7889-4b5b-b45f-fc79fc7fb117"
      },
      "source": [
        "pred_last = model.predict(last_df)\r\n",
        "#pred_last"
      ],
      "execution_count": 627,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:10 out of the last 10 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fa00385c620> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HoED9_139dCn",
        "outputId": "33442539-39ff-4ac4-d505-1a6e45aea512"
      },
      "source": [
        "pred_last = pred_last.reshape(output_window,4)\r\n",
        "pred_last = pred_last * size[:4].values + mini[:4].values\r\n",
        "pred_last = pred_last.astype(int)\r\n",
        "pred_last\r\n"
      ],
      "execution_count": 628,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[  3485,   3497,    901,  95417],\n",
              "       [  4706,   4804,   1400, 134351],\n",
              "       [  3211,   3115,    743,  78177],\n",
              "       [  3633,   3499,    902,  95610],\n",
              "       [  2877,   2720,    664,  70278],\n",
              "       [  2488,   2350,    582,  65769],\n",
              "       [  2228,   2151,    454,  58631],\n",
              "       [  4531,   4550,   1206, 119642],\n",
              "       [  3928,   3894,    931,  96998],\n",
              "       [  3954,   3883,    961,  95567],\n",
              "       [  3211,   3107,    696,  73381],\n",
              "       [  3041,   2948,    645,  71217],\n",
              "       [  2246,   2236,    479,  51856],\n",
              "       [  2241,   2273,    503,  52112],\n",
              "       [  3145,   3177,    710,  76780],\n",
              "       [  3920,   3974,   1061, 100852],\n",
              "       [  3285,   3296,    759,  83454],\n",
              "       [  3606,   3611,    912,  92902],\n",
              "       [  3065,   3066,    802,  74779],\n",
              "       [  2666,   2594,    535,  77473],\n",
              "       [  2455,   2393,    425,  63556],\n",
              "       [  3256,   3180,    622,  79207],\n",
              "       [  2772,   2721,    497,  62820],\n",
              "       [  3207,   3177,    686,  72752],\n",
              "       [  3075,   3078,    620,  70567],\n",
              "       [  2946,   2967,    620,  63909],\n",
              "       [  2343,   2399,    477,  47690],\n",
              "       [  2229,   2295,    450,  46015],\n",
              "       [  2711,   2755,    502,  56745],\n",
              "       [  2906,   2951,    622,  60295],\n",
              "       [  2774,   2810,    592,  62109],\n",
              "       [  2738,   2770,    612,  61624],\n",
              "       [  3643,   3604,    890, 102662],\n",
              "       [  1892,   1945,    496,  47425],\n",
              "       [  1791,   1797,    408,  48272],\n",
              "       [  2643,   2603,    587,  75904],\n",
              "       [  2654,   2597,    601,  85617],\n",
              "       [  2473,   2454,    603,  74982],\n",
              "       [  3248,   3220,    745,  95537],\n",
              "       [  3657,   3607,    825, 112303],\n",
              "       [  2424,   2392,    525,  65864],\n",
              "       [  1904,   1891,    429,  45948],\n",
              "       [  3056,   2996,    667,  80030],\n",
              "       [  2874,   2837,    684,  73842],\n",
              "       [  1950,   1955,    436,  45350],\n",
              "       [  2422,   2399,    574,  55232],\n",
              "       [  2817,   2735,    609,  78052],\n",
              "       [  1611,   1582,    299,  41981],\n",
              "       [  1488,   1450,    244,  43159],\n",
              "       [  2298,   2239,    505,  64854],\n",
              "       [  1744,   1737,    437,  46068],\n",
              "       [  1629,   1648,    432,  43378],\n",
              "       [  2113,   2145,    627,  57762],\n",
              "       [  2022,   2087,    519,  60132],\n",
              "       [  1892,   1984,    404,  57741],\n",
              "       [  2186,   2278,    399,  64697],\n",
              "       [  3612,   3674,    726, 105597],\n",
              "       [  3687,   3714,    669, 102761],\n",
              "       [  3488,   3490,    637,  83239],\n",
              "       [  3481,   3507,    636,  75443],\n",
              "       [  3526,   3538,    587,  79893]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 628
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FY7lgRivG5-L",
        "outputId": "dd1591e7-4cba-4929-9794-6da470139f2a"
      },
      "source": [
        "pred_last.shape"
      ],
      "execution_count": 629,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(61, 4)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 629
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hkxkXBgBJ4tg"
      },
      "source": [
        "## submit"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "444WoejNJ4t9",
        "outputId": "b621ff7b-7ee7-42e6-8e46-b58d1ee5cafd"
      },
      "source": [
        "submission =  pd.read_csv(path + '/open_data/submission.csv', encoding = 'euc-kr')\r\n",
        "submission.shape"
      ],
      "execution_count": 630,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(61, 5)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 630
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HAtpKeL1J4t-",
        "outputId": "3707d2dd-9c45-4f38-f6b5-ccf4462ad6b4"
      },
      "source": [
        "pred_last.shape"
      ],
      "execution_count": 631,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(61, 4)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 631
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        },
        "id": "3VUwY_duJ4uA",
        "outputId": "779a50ca-0659-4737-92e3-02545a35e36f"
      },
      "source": [
        "pred_df = pd.DataFrame(pred_last)\r\n",
        "pred_df.head()"
      ],
      "execution_count": 632,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3485</td>\n",
              "      <td>3497</td>\n",
              "      <td>901</td>\n",
              "      <td>95417</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4706</td>\n",
              "      <td>4804</td>\n",
              "      <td>1400</td>\n",
              "      <td>134351</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3211</td>\n",
              "      <td>3115</td>\n",
              "      <td>743</td>\n",
              "      <td>78177</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3633</td>\n",
              "      <td>3499</td>\n",
              "      <td>902</td>\n",
              "      <td>95610</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2877</td>\n",
              "      <td>2720</td>\n",
              "      <td>664</td>\n",
              "      <td>70278</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      0     1     2       3\n",
              "0  3485  3497   901   95417\n",
              "1  4706  4804  1400  134351\n",
              "2  3211  3115   743   78177\n",
              "3  3633  3499   902   95610\n",
              "4  2877  2720   664   70278"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 632
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LFdMfTtYJ4uA",
        "outputId": "942ccc01-15e4-42c2-db79-16bbb2e3ad7f"
      },
      "source": [
        "# 30까지 모두 채우면 다시 0부터.\r\n",
        "\r\n",
        "for i in range(len(submission)):\r\n",
        "  submission['사용자'][i] = pred_df[0][i]\r\n",
        "  submission['세션'][i] = pred_df[1][i]\r\n",
        "  submission['신규방문자'][i] = pred_df[2][i]\r\n",
        "  submission['페이지뷰'][i] = pred_df[3][i]\r\n"
      ],
      "execution_count": 633,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  after removing the cwd from sys.path.\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:5: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \"\"\"\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:6: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:7: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  import sys\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        },
        "id": "yYcUBxPKJ4uD",
        "outputId": "2beb4053-bdcd-44d7-c0d3-6a568cf318eb"
      },
      "source": [
        "submission.head()"
      ],
      "execution_count": 634,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>DateTime</th>\n",
              "      <th>사용자</th>\n",
              "      <th>세션</th>\n",
              "      <th>신규방문자</th>\n",
              "      <th>페이지뷰</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2020-11-09</td>\n",
              "      <td>3485</td>\n",
              "      <td>3497</td>\n",
              "      <td>901</td>\n",
              "      <td>95417</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2020-11-10</td>\n",
              "      <td>4706</td>\n",
              "      <td>4804</td>\n",
              "      <td>1400</td>\n",
              "      <td>134351</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2020-11-11</td>\n",
              "      <td>3211</td>\n",
              "      <td>3115</td>\n",
              "      <td>743</td>\n",
              "      <td>78177</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2020-11-12</td>\n",
              "      <td>3633</td>\n",
              "      <td>3499</td>\n",
              "      <td>902</td>\n",
              "      <td>95610</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2020-11-13</td>\n",
              "      <td>2877</td>\n",
              "      <td>2720</td>\n",
              "      <td>664</td>\n",
              "      <td>70278</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     DateTime   사용자    세션  신규방문자    페이지뷰\n",
              "0  2020-11-09  3485  3497    901   95417\n",
              "1  2020-11-10  4706  4804   1400  134351\n",
              "2  2020-11-11  3211  3115    743   78177\n",
              "3  2020-11-12  3633  3499    902   95610\n",
              "4  2020-11-13  2877  2720    664   70278"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 634
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UeUT41zKMnRF"
      },
      "source": [
        "submission.to_csv('공휴일 feature.csv', index = False, encoding = 'euc-kr')"
      ],
      "execution_count": 635,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G0ARyON_J5BK"
      },
      "source": [
        "#### 결과 제일 좋았던, 나빴던 파일이랑 predict를 비교"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yZ0zZ_iTJiyX"
      },
      "source": [
        "path2 = \"/content/drive/MyDrive/Colab Notebooks/DACONCUP 주희\""
      ],
      "execution_count": 636,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 227
        },
        "id": "kSyu8nZ2J85o",
        "outputId": "c571d1d8-1c29-434a-b126-3babf24a37fb"
      },
      "source": [
        "best = pd.read_csv(path + '/submit_data/submission0111_1.csv', encoding = 'euc-kr')\r\n",
        "best.set_index('DateTime', inplace = True)\r\n",
        "best.index = pd.to_datetime(best.index)\r\n",
        "best.head()"
      ],
      "execution_count": 637,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>사용자</th>\n",
              "      <th>세션</th>\n",
              "      <th>신규방문자</th>\n",
              "      <th>페이지뷰</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DateTime</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2020-11-09</th>\n",
              "      <td>3544</td>\n",
              "      <td>3514</td>\n",
              "      <td>862</td>\n",
              "      <td>91260</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-11-10</th>\n",
              "      <td>4455</td>\n",
              "      <td>4214</td>\n",
              "      <td>1092</td>\n",
              "      <td>117435</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-11-11</th>\n",
              "      <td>3804</td>\n",
              "      <td>3381</td>\n",
              "      <td>744</td>\n",
              "      <td>89449</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-11-12</th>\n",
              "      <td>4189</td>\n",
              "      <td>3832</td>\n",
              "      <td>926</td>\n",
              "      <td>101809</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-11-13</th>\n",
              "      <td>3326</td>\n",
              "      <td>3021</td>\n",
              "      <td>760</td>\n",
              "      <td>86012</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             사용자    세션  신규방문자    페이지뷰\n",
              "DateTime                             \n",
              "2020-11-09  3544  3514    862   91260\n",
              "2020-11-10  4455  4214   1092  117435\n",
              "2020-11-11  3804  3381    744   89449\n",
              "2020-11-12  4189  3832    926  101809\n",
              "2020-11-13  3326  3021    760   86012"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 637
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sCT4at_iSIP5"
      },
      "source": [
        "worst = pd.read_csv(path2+'/submit/seed40_add 날짜feature.csv', encoding = 'euc-kr')\r\n",
        "# worst = pd.read_csv(path2 + '/submit/lstm5 e150 b10.csv', encoding = 'euc-kr')\r\n",
        "worst.set_index('DateTime', inplace = True)\r\n",
        "worst.index = pd.to_datetime(best.index)\r\n",
        "#worst.head()"
      ],
      "execution_count": 638,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6EvX_gyeMV9R"
      },
      "source": [
        "submission.set_index('DateTime', inplace = True)\r\n",
        "submission.index = pd.to_datetime(submission.index)\r\n",
        "# submission.head()"
      ],
      "execution_count": 639,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qgpdgvAzKK9R",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "outputId": "13c1a7de-4889-48cd-b1be-066f6a040529"
      },
      "source": [
        "# plot expected vs preducted\r\n",
        "\r\n",
        "\r\n",
        "plt.figure(figsize = (15,7))\r\n",
        "pyplot.plot(best['사용자'], label='best')\r\n",
        "pyplot.plot(submission['사용자'], label='Predicted')\r\n",
        "pyplot.plot(worst['사용자'], label = 'worst')\r\n",
        "pyplot.legend()\r\n",
        "pyplot.show()"
      ],
      "execution_count": 644,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VBs3Ig9sLHcw",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "outputId": "bc865fd8-8ba2-491e-c23f-0f4089dafb42"
      },
      "source": [
        "\r\n",
        "plt.figure(figsize = (15,7))\r\n",
        "pyplot.plot(best['세션'], label='best')\r\n",
        "pyplot.plot(submission['세션'], label='Predicted')\r\n",
        "pyplot.plot(worst['세션'], label = 'worst')\r\n",
        "pyplot.legend()\r\n",
        "pyplot.show()"
      ],
      "execution_count": 641,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GBhRD3aLLPV4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "outputId": "4ed5b931-de6e-4efb-c29d-16ec4f584596"
      },
      "source": [
        "\r\n",
        "plt.figure(figsize = (15,7))\r\n",
        "pyplot.plot(best['신규방문자'], label='best')\r\n",
        "pyplot.plot(submission['신규방문자'], label='Predicted')\r\n",
        "pyplot.plot(worst['신규방문자'], label = 'worst')\r\n",
        "pyplot.legend()\r\n",
        "pyplot.show()"
      ],
      "execution_count": 642,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NV6qNNQoLPZT",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "outputId": "f2549f5c-8e67-4a9b-93e2-adb6e3e74966"
      },
      "source": [
        "\r\n",
        "plt.figure(figsize = (15,7))\r\n",
        "pyplot.plot(best['페이지뷰'], label='best')\r\n",
        "pyplot.plot(submission['페이지뷰'], label='Predicted')\r\n",
        "pyplot.plot(worst['페이지뷰'], label = 'worst')\r\n",
        "pyplot.legend()\r\n",
        "pyplot.show()"
      ],
      "execution_count": 643,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XLF-l5iKM46Q"
      },
      "source": [
        "- 위에 잘 예측하지 못하는 부분들은 조금 튀는 값인 것 같음\r\n",
        "- 해당 일자에 어떤 특징(개최된 대회 등)을 파악\r\n",
        "- 이후 1) 제거 또는 2) 특징 적용  (추세파악과 관련없다면 제거하기)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SmtQVQJANMUu"
      },
      "source": [
        "## 참조"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V4016MLpNRw7"
      },
      "source": [
        "# 각 변수의 크기가 다르기 때문에 가중치를 부여한 RMSE로 모델의 성능을 평가\r\n",
        "\r\n",
        "def dacon_rmse(true, pred):\r\n",
        "    # true.shape // (N,4)\r\n",
        "    # pred.shape // (N,4)\r\n",
        "    # w0, w1, w2, w3 <= train.csv의 사용자 수, 세션 수, 신규 방문자 수, 페이지 뷰 수 4가지 항목별 평균값\r\n",
        "    score = np.sqrt(np.mean(np.square(true[:,0] - pred[:,0]))) / w0 +\\\r\n",
        "            + np.sqrt(np.mean(np.square(true[:,1] - pred[:,1]))) / w1 +\\\r\n",
        "            + np.sqrt(np.mean(np.square(true[:,2] - pred[:,2]))) / w2 +\\\r\n",
        "            + np.sqrt(np.mean(np.square(true[:,3] - pred[:,3]))) / w3 +\\\r\n",
        "     return score"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gU4pGj8uJ4uD"
      },
      "source": [
        "# 스케일링\r\n",
        "from sklearn.preprocessing import MinMaxScaler\r\n",
        "\r\n",
        "sc = MinMaxScaler()\r\n",
        "\r\n",
        "train_sc = sc.fit_transform(train)\r\n",
        "test_sc = sc.transform(test)\r\n",
        "\r\n",
        "train_sc"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hl9q9I2KJ4uD"
      },
      "source": [
        "# date 기준 변수 만들기\r\n",
        "\r\n",
        "from datetime import datetime\r\n",
        "\r\n",
        "def create_features(df):\r\n",
        "    \"\"\"\r\n",
        "    Creates time series features from datetime index\r\n",
        "    \"\"\"\r\n",
        "    # df['DateTime'] = df.index\r\n",
        "    # df['hour'] = df['date'].dt.hour\r\n",
        "    df['dayofweek'] = pd.to_datetime(df['date']).dt.dayofweek\r\n",
        "    df['quarter'] = pd.to_datetime(df['date']).dt.quarter\r\n",
        "    df['month'] = pd.to_datetime(df['date']).dt.month\r\n",
        "    df['year'] = pd.to_datetime(df['date']).dt.year\r\n",
        "    df['dayofyear'] = pd.to_datetime(df['date']).dt.dayofyear\r\n",
        "    df['dayofmonth'] = pd.to_datetime(df['date']).dt.day\r\n",
        "    df['weekofyear'] = pd.to_datetime(df['date']).dt.weekofyear\r\n",
        "    \r\n",
        "    X = df[['사용자','세션','신규방문자','페이지뷰','date','dayofweek','quarter','month','year',\r\n",
        "           'dayofyear','dayofmonth','weekofyear']]\r\n",
        "    X.set_index('date', inplace = True)\r\n",
        "    X.index = pd.to_datetime(X.index)\r\n",
        "    return X"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J5Kvtw5oJ4uE"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JbEwC3YfJ4uG"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Li0vASmhIIhr"
      },
      "source": [
        "## 예측값을 x로 다시 predict  \r\n",
        "- 마이너스 값이 나와서 실패.."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5mcOvvQQH4t2"
      },
      "source": [
        "tr_month1 = pd.DataFrame(pred_month)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "adnkLDwMBGVC"
      },
      "source": [
        "# 위 예측값을 x로 사용하기.\r\n",
        "mini1 = tr_month1.iloc[:,1:].min()\r\n",
        "size1 = tr_month1.iloc[:,1:].max() - tr_month1.iloc[:,1:].min()\r\n",
        "tr_month1.iloc[:,1:] = (tr_month1.iloc[:,1:] -  mini1) / size1\r\n",
        "\r\n",
        "tr_month1 = tr_month1.iloc[:,:].values[np.newaxis,...] # np.newaxis: 3D로 차원을 늘려줌. \r\n",
        "tr_month1.shape"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ExeU6niB_gVU"
      },
      "source": [
        "pred_month1 = model.predict(tr_month1)\r\n",
        "pred_month1[:5]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bD7YTlqWHHKq"
      },
      "source": [
        "pred_month1 = pred_month1.reshape(output_window,4)\r\n",
        "pred_month1 = pred_month1 * size.values + mini.values\r\n",
        "pred_month1 = pred_month1.astype(int)\r\n",
        "pred_month1\r\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bCmWKSqRGBGV"
      },
      "source": [
        "#### 윈도우 크기 다르게 해서 train하고 test해보기"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uyOo9JpX8t7D"
      },
      "source": [
        "aa"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HbonwFz84uV5"
      },
      "source": [
        "## test = train[-61]"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KE4rgNXRQWAO"
      },
      "source": [
        "test = train[-61:]\r\n",
        "test"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nkRbon7yRGz_"
      },
      "source": [
        "# scaling\r\n",
        "\r\n",
        "input_window = 30\r\n",
        "output_window = 30\r\n",
        "\r\n",
        "test_x = np.zeros((test.shape[0] - (input_window + output_window), input_window, 4))\r\n",
        "test_y = np.zeros((test.shape[0] - (input_window + output_window), output_window, 4))\r\n",
        "\r\n",
        "for start in range(test.shape[0] - (input_window + output_window)):\r\n",
        "    end = start + input_window    \r\n",
        "    test_x[start,:, :] = test.iloc[start : end                , 1: ].values\r\n",
        "    test_y[start,:, :] = test.iloc[end   : end + output_window, 1: ].values\r\n",
        "\r\n",
        "\r\n",
        "print('test_x.shape: ', test_x.shape)\r\n",
        "print('test_y.shape: ', test_y.shape)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lhmMXuK0AYVn"
      },
      "source": [
        "pred = model.predict(test_x)\r\n",
        "pred[:5]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GbKK46ZASpkS"
      },
      "source": [
        "pred = pred.reshape(output_window,4)\r\n",
        "pred = pred * size.values + mini.values\r\n",
        "pred = pred.astype(int)\r\n",
        "pred"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q4E0hcLCFDwi"
      },
      "source": [
        "pred.shape"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VzeIVzfY6B1o"
      },
      "source": [
        "baseline = pd.read_csv(path + '/open_data/baseline.csv', encoding = 'euc-kr' )\r\n",
        "baseline.head()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ofVAKwIvT8pC"
      },
      "source": [
        "last_month = train.iloc[-30:,1:].values[np.newaxis,...].astype(float) #float() # <- 수정 후\r\n",
        "last_month"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k7ITx1KxSdyg"
      },
      "source": [
        "\r\n",
        "#last_month = torch.tensor(window_x[-1,:,:][np.newaxis,...]).float().to(device) <- 수정 전\r\n",
        "last_month = train.iloc[-30:,1:].values[np.newaxis,...] # <- 수정 후\r\n",
        "# last_month = torch.tensor(last_month).float() # <- 수정 후\r\n",
        "\r\n",
        "for start in range((len(submission) - output_window)//7 + 2):\r\n",
        "    start = start * 7\r\n",
        "    next_week = model.predict(last_month)\r\n",
        "    #last_month = torch.cat([last_month[-7:], next_week], axis = 1) <- 수정 전\r\n",
        "    last_month = torch.cat([last_month[:,7:,:], next_week], axis = 1)# <- 수정 후\r\n",
        "\r\n",
        "    pred_week = next_week.reshape(output_window,4)\r\n",
        "    pred_week = pred_week * size.values + mini.values\r\n",
        "    pred_week = pred_week.astype(int)\r\n",
        "    \r\n",
        "    if start/7 == (len(submission) - output_window)//7 + 1:\r\n",
        "        submission.iloc[start :, 1:] = pred_week[-submission.iloc[start :, 1:].shape[0]:,:]\r\n",
        "    else:\r\n",
        "        submission.iloc[start : start + output_window, 1:] = pred_week\r\n",
        "submission"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "REbMo_D3UfPJ"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}